National Science Board Natio

nal Scie

nce Fou

ndatio

n

Science & 

Engineering

Indicators

20

14
National Science Board

DAN E. ARVIZU

Chairman, Director and Chief Executive, National Renewable 

Energy Laboratory

KELVIN K. DROEGEMEIER

Vice Chairman, Vice President for Research, Regents’ Professor 

of Meteorology and Weathernews Chair Emeritus, University 

of Oklahoma

DEBORAH L. BALL

William H. Payne Collegiate Chair, Arthur F. Thurnau Professor, 

Dean of the School of Education, University of Michigan

BONNIE BASSLER

Howard Hughes Medical Institute Investigator, Squibb Professor 

of Molecular Biology, Princeton University

ARTHUR BIENENSTOCK

Professor Emeritus of Photon Science, Stanford University

RAY M. BOWEN

President Emeritus, Texas A&M University, Visiting 

Distinguished Professor, Rice University

VINTON G. CERF

Vice President and Chief Internet Evangelist for Google

FRANCE A. CÓRDOVA

President Emeritus, Purdue University

RUTH DAVID

President and CEO, Analytic Services, Inc.

INEZ FUNG

Professor of Atmospheric Science, University of 

California, Berkeley

ESIN GULARI

Dean of Engineering and Science, Clemson University

G. PETER LEPAGE

Professor of Physics, Harold Tanner Dean, College of Arts and 

Sciences, Cornell University

ALAN I. LESHNER

Chief Executive Officer and Executive Publisher, Science, 

American Association for the Advancement of Science

W. CARL LINEBERGER

Fellow of JILA, E. U. Condon Distinguished Professor of 

Chemistry, University of Colorado

STEPHEN MAYO

Bren Professor of Biology and Chemistry, Chair, Division of 

Biology, California Institute of Technology

G. P. (BUD) PETERSON

President, Georgia Institute of Technology

DOUGLAS D. RANDALL

Professor Emeritus and Thomas Jefferson Fellow, Director 

Emeritus Interdisciplinary Plant Group, University of 

Missouri-Columbia

GERALDINE RICHMOND

Richard M. and Patricia H. Noyes Professor of Chemistry, 

University of Oregon

ANNEILA I. SARGENT

Ira S. Bowen Professor of Astronomy, Vice President for Student 

Affairs, California Institute of Technology

DIANE L. SOUVAINE

Vice Provost for Research, Professor of Computer Science, 

Tufts University

ARNOLD F. STANCELL

Former Vice-President Mobil Oil, Professor Emeritus of 

Chemical Engineering and Turner Servant Leadership Chair 

Emeritus, School of Chemical and Biomolecular Engineering, 

Georgia Institute of Technology

CLAUDE M. STEELE

Dean, School of Education, Stanford University

ROBERT J. ZIMMER

President, University of Chicago

MARIA T. ZUBER

E.A. Griswold Professor of Geophysics, Vice President for 

Research, Massachusetts Institute of Technology

CORA B. MARRETT

Member ex officio, Acting Director, National Science Foundation

MICHAEL L. VAN WOERT

Executive Officer, National Science Board, and Director, 

National Science Board Office

National Science Board Committee on Science and Engineering Indicators

Ray M. Bowen, Chairman

Bonnie Bassler

Arthur Bienenstock

Ruth David

Douglas D. Randall

Claude M. Steele

Robert J. Zimmer

Executive Secretaries

Robert Bell

Nirmala Kannankutty

NSB Staff Liaison

Matthew B. Wilson
Science & 

Engineering

Indicators

20

14
Cover Image

The flower-like structure on the cover of Science and Engineering Indicators 2014 is a graph that 

illustrates potential energy surfaces in a molecule called sym-triazine. The theoretical approach 

behind the graph is part of a larger effort that helped explain how sym-triazine can simultaneously 

break into three parts. Most molecules break apart one step at a time, so the phenomenon is 

rare. Researchers at the University of Southern California used computational chemistry tools to 

produce the graph, explaining the experimental results obtained by collaborators at the University 

of California, San Diego. The researchers reported their findings in the August 8, 2008, issue of the 

journal Science. This work was supported by the National Science Foundation under the auspices 

of the iOpenShell (Center for Computational Studies of Electronic Structure and Spectroscopy of 

Open-Shell and Electronically Excited Species). (Credit: Vadim Mozhayskiy and Anna I. Krylov, 

Department of Chemistry, University of Southern California.)

Recommended Citation

National Science Board. 2014. Science and Engineering Indicators 2014. Arlington VA: National 

Science Foundation (NSB 14-01).
February 6, 2014 

MEMORANDUM FROM THE CHAIRMAN OF THE NATIONAL SCIENCE BOARD 

TO: The President and Congress of the United States 

SUBJECT: Science and Engineering Indicators 2014 

As Chairman of the National Science Board, it is my honor to transmit on behalf of the Board the 

twenty-first in the series of biennial science indicators reports, Science and Engineering 

Indicators 2014. The Board submits this report as required by 42 U.S.C. § 1863 (j) (I). 

The Indicators series was designed to provide a broad base of quantitative information about 

U.S. science, engineering, and technology for use by policymakers, researchers, and the general 

public. Science and Engineering Indicators 2014 contains analyses of key aspects of the scope, 

quality, and vitality of the Nation's science and engineering enterprise in the context of global 

science and technology. 

The report presents information on science, technology, engineering, and mathematics education 

at all levels; the scientific and engineering workforce; U.S. and international research and 

development performance; U.S. competitiveness in high technology; and public attitudes and 

understanding of science and engineering. A chapter on state-level science and engineering 

enables state comparisons on selected indicators. An Overview chapter synthesizes selected key 

themes emerging from the report. 

The Board hopes that the Administration and Congress find the new quantitative information and 

analysis in the report useful and timely for the planning of national priorities, policies, and 

programs in science and technology. 

Chairman 

National Science Board 

Dan E. Arvizu

Chairman

National Science Board
The National Science Board (NSB) extends its appreciation to 

the staff of the National Science Foundation and to the many 

others, too numerous to list individually, who contributed to 

the preparation of this report.

Primary responsibility for the production of the volume was 

assigned to Robert Bell, Director, Science and Engineering 

Indicators Program of the National Center for Science and 

Engineering Statistics (NCSES); John R. Gawalt, Director, 

NCSES; and the Directorate for Social, Behavioral and Economic Sciences under the leadership of Myron P. Gutmann 

and Joanne Tornow. The authors were:

Overview. Robert Bell, Michael Reksulak, NCSES

Chapter 1. Xianglei Chen, Robin R. Henke, Susan L. 

Rotermund, RTI International 

Chapter 2. Jaquelina C. Falkenheim, NCSES

Chapter 3. Beethika Khan, NCSES

Chapter 4. Mark Boroush, Francisco A. Moris, NCSES

Chapter 5. Michael Gibbons, Katherine Hale, Derek Hill, 

NCSES; Kim Hamilton, The Patent Board

Chapter 6. Derek Hill, NCSES

Chapter 7. John Besley, Michigan State University

Chapter 8. Nirmala Kannankutty, NCSES; 

Christina Freyman, SRI International; 

Paula C. Dunnigan, Taratec Corporation

The volume benefited from extensive contributions from 

NCSES staff. NCSES senior staff and survey managers 

assured availability of data under often stringent deadlines: 

Ronda K. Britt, Mark Fiegener, John Finamore, John 

E. Jankowski, Kelly H. Kang, Flora Lan, Lynn Milan, 

Christopher Pece, Steven Proudfoot, Emilda B. Rivers, 

Raymond M. Wolfe, and Michael Yamaner. Stephen Cohen, 

Jock Black, Wan-Ying Chang, and Darius Singpurwalla 

provided advice with statistical or data presentation issues. 

Acknowledgments

Jaquelina Falkenheim, Katherine Hale, Derek Hill, and 

Beethika Khan served administrative as well as authorship 

roles. Peter Muhlberger refined the index and helped finalize 

Chapters 1 and 7. Lawrence Burton, Daniel Foley, and Mark 

C. Regets reviewed draft material and did analytic work 

in support of the volume. Jacqueline Durham assisted in 

acquiring data from outside sources, and Jasmine Harvey 

provided clerical support.

Cheryl Roesel, Nirmala Kannankutty, and Robin Pentola 

managed production of the volume. Cheryl Roesel managed editorial, composition, and printing services; Nirmala 

Kannankutty developed, coordinated, and monitored the 

production schedule; and Robin Pentola was responsible for 

production, direction, and management of the website. Tanya 

Gore assisted with the final review of the page composition 

and appendix tables, and Rajinder Raut provided a technical 

review of the website. 

August Gering and Marceline Murawski led the editing 

team at RTI International: Mr. Gering, Margaret Smith, 

Michelle Back, Anne Gering, Justin Faerber, Claudia Clark, 

Loretta Bohn, and Roxanne Snaauw. Betty Baker, Mei Chin, 

Kanika Dutt, Heidi Hunt, Sally Leighton, Bansari Patidar, 

Jay Philoon, Christopher River, John Root, Carl Trapani, and 

Caleb Winslow of Penobscot Bay Media, LLC, performed 

Web design, programming, and review of the electronic 

publication and website. OmniStudio, Inc., Washington D.C., 

provided the design and production for the volume. Prudy 

Brown and Lori Thurgood of SRI International played major 

roles in production of Chapters 1 and 7, respectively. Others 

who provided much appreciated advice and assistance are 

listed under Contributors and Reviewers. 

The National Science Board is especially grateful to the Committee on Science and Engineering Indicators for overseeing 

preparation of the volume and to the National Science Board 

Office, under the direction of Michael L. Van Woert, which 

provided vital coordination throughout the project. Matthew 

B. Wilson served as Board Office Liaison to the committee, 

and Robert Bell and Nirmala Kannankutty were the Executive Secretaries. Jean M. Pomeroy provided helpful guidance 

throughout the publication process.

iv ♦

Dan E. Arvizu

Chairman, National Science Board

Ray M. Bowen

Chairman, Committee on Science and Engineering 

Indicators
t v

The following persons contributed to the report by 

reviewing chapters or otherwise assisting in its 

preparation. Their help is greatly appreciated. 

Laura Adolfie, U.S. Department of Defense

Jeff Allum, Council of Graduate Schools

Nick C. Allum, University of Essex

Phillip Altbach, Boston College

Amy Apon, Clemson University

Thomas Arrison, National Academy of Sciences

Cheikhou Athie, Taratec Corporation

Robert Atkinson, Information Technology and 

Innovation Foundation

John Benskin, SRI International

Rajika Bhandari, Institute for International Education

Richard Bissell, National Academy of Sciences 

Craig Boardman, Ohio State University

George R. Boggs, American Association of 

Community Colleges

Nava Brenner, Central Bureau of Statistics, Israel

Jack Buckley, National Center for Education Statistics

William S. Bush, University of Louisville

Jill Cape, Cape Designs

Chiao-Ling Chang, UNESCO Institute for Statistics

Lulu Chen, U.S. Census Bureau 

Elise M. Christopher, National Center for Education Statistics

Marta Civil, University of North Carolina at Chapel Hill

Jonathan Cole, Columbia University 

Alva Daniels, U.S. Environmental Protection Agency

James Duderstadt, University of Michigan

Anthony D. Dudo, University of Texas

Mary Good, University of Arkansas at Little Rock

Janice Earle, National Science Foundation

Dieter Ernst, East West Center

Michael Finn, Oak Ridge Institute for Science and Education

Dennis Fixler, Bureau of Economic Analysis

Carolyn L. Funk, Pew Research Center

Adam Gamoran, University of Wisconsin at Madison

Fred Gault, UNU-MERIT

Kenneth Gibbs, National Cancer Institute 

Donna Ginther, University of Kansas

Mark C. Glander, National Center for Education Statistics 

Matt Greene, National Center for Education Statistics

Martin P. Grueber, Battelle

Gary Guenther, Congressional Research Service

Edith Gummer, National Science Foundation

Susan Hackwood, California Council on Science 

and Technology

P. Sol Hart, University of Michigan

Elisabeth D. Hensley, RTI International 

Diana Hicks, Georgia Tech

Chad Holliday, East Meets West Solutions, LLC 

Matt Hourihan, American Association for the Advancement 

of Science 

Peggy Huff, Taratec Corporation

Steven Hurlburt, Delta Cost Project at AIR

Brandon Jones, U.S. Environmental Protection Agency

Michael Jones, U.S. Department of Labor

Cheryl Kerr, Bureau of Labor Statistics

Suzette M. Kimball, U.S. Geological Survey

Karen King, National Science Foundation

Rita J. Kirshstein, Delta Cost Project at AIR

Edward B. Knipling, U.S. Department of Agriculture

Kei Koizumi, Office of Science and Technology Policy

Fae L. Korsmo, National Science Foundation

Barbara E. Kridl, RTI International 

Joydip Kundu, Office of Management and Budget

Charles F. Larson, Innovation Research International

Rolf F. Lehming, National Science Foundation (retired)

Bruce V. Lewenstein, Cornell University

Joanna Lewis, Georgetown University

James H. Lightbourne, National Science Foundation

Mary Lindquist, Columbus State University

Elise S. Lipkowitz, National Science Foundation

Susan Carol Losh, Florida State University

B. Lindsay Lowell, Georgetown University

Barbara Means, SRI International

Eulus Moore, U.S. Office of Personnel Management

Ram Mudambi, Temple University

Gail Mulligan, National Center for Education Statistics

Francis Narin, CHI Research (retired)

Steve Olson, National Academy of Sciences 

Deborah Olster, National Science Foundation

Amy Patterson, National Institutes of Health

Joshua Powers, Indiana State University

Stephen Provasnik, National Center for Education Statistics 

John H. Ralph, National Center for Education Statistics 

Andrew Reamer, George Washington University

E.J. Reedy, Kauffman Foundation

Andrew Revkin, The New York Times

Carol Robbins, Bureau of Economic Analysis

Jessica L. Robles, RTI International 

Henry Sauermann, Georgia Institute of Technology

Lance A. Selfa, NORC at the University of Chicago

Meena Selvakumar, Pacific Science Center

Stephanie Shipp, Science and Technology Policy Institute

Michael Siegrist, Swiss Federal Institute of Technology

Nicole Smith, Georgetown University

Tom M. Smith, Vanderbilt University

Dixie Sommers, Bureau of Labor Statistics

Paula Stephan, Georgia State University

Laura Stubbs, U.S. Department of Defense

Contributors and Reviewers
Kathryn Sullivan, National Oceanic and 

Atmospheric Administration

Gregory Tassey, National Institute of Standards and 

Technology

Ed Ungar, Taratec Corporation

Ted Weidner, Purdue University

John A. White, University of Arkansas

Grady Wilburn, National Center for Education Statistics 

Sacha Wunsch-Vincent, World Intellectual Property 

Organization

Yu Xie, University of Michigan

Michael Zastrocky, The Leadership Board for CIOs

vi t Contributors and Reviewers
Acronyms and Abbreviations......................................................................................................x

About Science and Engineering Indicators..............................................................................xii

SEI’s Different Parts.....................................................................................................................xii

Presentation................................................................................................................................. xiii

Overview....................................................................................................................................O-1

Introduction.................................................................................................................................O-3

Science and Technology in the World Economy........................................................................O-3

The U.S. Science and Engineering Landscape .........................................................................O-13

Conclusion ................................................................................................................................O-21

Notes.........................................................................................................................................O-22

Glossary ....................................................................................................................................O-22

References.................................................................................................................................O-23

Chapter 1. Elementary and Secondary Mathematics and Science Education.................... 1-1

Highlights.................................................................................................................................... 1-4

Introduction................................................................................................................................. 1-8

Student Learning in Mathematics and Science......................................................................... 1-10

Student Coursetaking in High School Mathematics and Science............................................. 1-19

Teachers of Mathematics and Science...................................................................................... 1-26

Instructional Technology and Digital Learning........................................................................ 1-34

Transition to Higher Education................................................................................................. 1-38

Conclusion ................................................................................................................................ 1-41

Notes......................................................................................................................................... 1-42

Glossary .................................................................................................................................... 1-44

References................................................................................................................................. 1-45

Chapter 2. Higher Education in Science and Engineering ................................................... 2-1

Highlights.................................................................................................................................... 2-4

Introduction................................................................................................................................. 2-7

The U.S. Higher Education System............................................................................................ 2-7

Undergraduate Education, Enrollment, and Degrees in the United States............................... 2-20

Graduate Education, Enrollment, and Degrees in the United States ........................................ 2-27

International S&E Higher Education........................................................................................ 2-37

Conclusion ................................................................................................................................ 2-44

Notes......................................................................................................................................... 2-45

Glossary .................................................................................................................................... 2-48

References................................................................................................................................. 2-48

Chapter 3. Science and Engineering Labor Force................................................................. 3-1

Highlights.................................................................................................................................... 3-5

Introduction................................................................................................................................. 3-7

U.S. S&E Workforce: Definition, Size, and Growth.................................................................. 3-7

S&E Workers in the Economy.................................................................................................. 3-19

S&E Labor Market Conditions................................................................................................. 3-28

Age and Retirement of the S&E Workforce ............................................................................. 3-40

Women and Minorities in the S&E Workforce ........................................................................ 3-43

Immigration and the S&E Workforce....................................................................................... 3-51

Global S&E Labor Force .......................................................................................................... 3-59

Conclusion ................................................................................................................................ 3-61

Notes......................................................................................................................................... 3-62

Glossary .................................................................................................................................... 3-64

References................................................................................................................................. 3-64

Contents

t vii
Chapter 4. Research and Development: National Trends 

and International Comparisons............................................................................................... 4-1

Highlights.................................................................................................................................... 4-4

Introduction................................................................................................................................. 4-6

Trends in U.S. R&D Performance .............................................................................................. 4-6

International Comparisons of R&D Performance..................................................................... 4-16

U.S. Business R&D................................................................................................................... 4-22

R&D by Multinational Companies .......................................................................................... 4-25

Cross-National Comparisons of Business R&D....................................................................... 4-29

Federal Programs to Promote Technology Transfer and the Commercialization of 

 Federal R&D.......................................................................................................................... 4-39

Conclusion ................................................................................................................................ 4-46

Notes......................................................................................................................................... 4-47

Glossary .................................................................................................................................... 4-48

References................................................................................................................................. 4-49

Chapter 5. Academic Research and Development................................................................. 5-1

Highlights.................................................................................................................................... 5-5

Introduction................................................................................................................................. 5-8

Expenditures and Funding for Academic R&D.......................................................................... 5-8

Infrastructure for Academic R&D ........................................................................................... 5-18

Doctoral Scientists and Engineers in Academia ....................................................................... 5-23

Outputs of S&E Research: Articles and Patents....................................................................... 5-35

Conclusion ............................................................................................................................... 5-57

Notes......................................................................................................................................... 5-58

Glossary .................................................................................................................................... 5-63

References................................................................................................................................. 5-63

Chapter 6. Industry, Technology, and the Global Marketplace........................................... 6-1

Highlights.................................................................................................................................... 6-5

Introduction................................................................................................................................. 6-7

Knowledge- and Technology-Intensive Industries in the World Economy.............................. 6-10

Worldwide Distribution of Knowledge- and Technology-Intensive Industries ....................... 6-20

Trade and Other Globalization Indicators................................................................................. 6-29

Innovation-Related Indicators of the United States and Other Major Economies.................... 6-39

Investment and Innovation in Clean Energy Technologies...................................................... 6-49

Conclusion ............................................................................................................................... 6-55

Notes......................................................................................................................................... 6-56

Glossary .................................................................................................................................... 6-58

References................................................................................................................................. 6-59

Chapter 7. Science and Technology: Public Attitudes and Understanding ........................ 7-1

Highlights.................................................................................................................................... 7-4

Introduction................................................................................................................................. 7-6

Interest, Information Sources, and Involvement....................................................................... 7-10

Public Knowledge about S&T .................................................................................................. 7-20

Public Attitudes about S&T in General .................................................................................... 7-26

Public Attitudes about Specific S&T-Related Issues................................................................ 7-37

Conclusion ................................................................................................................................ 7-46

Notes......................................................................................................................................... 7-47

Glossary .................................................................................................................................... 7-49

References................................................................................................................................. 7-50

viii t Contents
Science and Engineering Indicators 2014 t ix

Chapter 8. State Indicators...................................................................................................... 8-1

Introduction................................................................................................................................. 8-7

Reference .................................................................................................................................. 8-10

Elementary and Secondary Education ...................................................................................... 8-12

Higher Education ...................................................................................................................... 8-42

Workforce ................................................................................................................................. 8-76

Financial Research and Development Inputs............................................................................ 8-90

Research and Development Outputs....................................................................................... 8-106

Science and Technology in the Economy............................................................................... 8-116

Appendix. Methodology and Statistics....................................................................................A-1

Introduction.................................................................................................................................A-1

Selection of Data Sources...........................................................................................................A-1

Data Sources ...............................................................................................................................A-2

Data Accuracy.............................................................................................................................A-2

Statistical Testing for Data From Sample Surveys.....................................................................A-3

Glossary .....................................................................................................................................A-4

List of Appendix Tables............................................................................................................B-1

Index............................................................................................................................................I-1
Acronyms and Abbreviations

AAAS American Association for the Advancement 

of Science

ACE American Council on Education

ACS American Community Survey

ADP American Diploma Project

AFT American Federation of Teachers

AID Agency for International Development

ANBERD Analytical Business Enterprise R&D

AP Advanced Placement

APL Applied Physics Laboratory

ARRA American Recovery and Reinvestment Act

ATP advanced technology products

AUTM Association of University Technology 

Managers

BBVA Banco Bilbao Vizcaya Argentaria

BEA Bureau of Economic Analysis

BLS Bureau of Labor Statistics

BRDIS Business R&D and Innovation Survey

CCSSI Common Core State Standards Initiative

CEO chief executive officer

CGS Council of Graduate Schools

CIP Classification of Instructional Programs

CIS Community Innovation Survey

CNSTAT Committee on National Statistics

CPS Current Population Survey

CRADA cooperative research and development 

agreement

CSEP Center for the Study of Education Policy, 

Illinois State University

DHS Department of Homeland Security

DOC Department of Commerce

DOD Department of Defense

DOE Department of Energy

DOI Department of the Interior

DOT Department of Transportation

EC European Community

ECLS-K Early Childhood Longitudinal StudyKindergarten

ECS Education Commission of the States

ED Department of Education

EPA Environmental Protection Agency

EP European Patent Office

EPSCoR Experimental Program to Stimulate 

Competitive Research

Esnet DOE’s Energy Sciences Network

EU European Union

FDA Food and Drug Administration

FDI foreign direct investment

FDIUS Survey of Foreign Direct Investment in the 

United States

FFRDC federally funded research and development 

center

FY fiscal year

GAO Government Accountability Office

GBAORD government budget appropriations or 

outlays for R&D

Gbps gigabits per second

GDP gross domestic product

GED General Equivalency Diploma

GM genetically modified

GSS General Social Survey 

GUF general university fund

HBCU historically black college or university

HERD Higher Education Research and 

Development Survey

HHS Department of Health and Human Services

HPC high performance computing

HSLS High School Longitudinal Study

HSTS High School Transcript Study

HT high technology

ICE Immigration and Customs Enforcement

ICT information and communications 

technologies

IDeA Institutional Development Award

IDR interdisciplinary research

IEA International Energy Agency

IOF involuntarily out-of-field

IPO initial public offering

IRC Internal Revenue Code

IRI Industrial Research Institute

IRS Internal Revenue Service

K–12 kindergarten through twelfth grade

KI knowledge intensive

KTI knowledge- and technology-intensive

LEHD Longitudinal Employer-Household 

Dynamics

LEP limited English proficient

LTT long-term trend

MEP Manufacturing Extension Partnership

MER market exchange rate

MIT Massachusetts Institute of Technology

MNC multinational company

MOFA majority-owned foreign affiliate

NAEP National Assessment of 

Educational Progress

NAGB National Assessment Governing Board

NAICS North American Industry Classification 

System

NASA National Aeronautics and Space 

Administration

NASF net assignable square feet

x t
Science and Engineering Indicators 2014 ♦ xi

NCES National Center for Education Statistics

NCLB The No Child Left Behind Act of 2001

NCRPA National Cooperative Research and 

Production Act

NCSES National Center for Science and 

Engineering Statistics

NGA National Governors Association

NIH National Institutes of Health

NIPA national income and product accounts

NIST National Institute for Standards and 

Technology

NLR National Lambda Rail

NOAA National Oceanic and Atmospheric 

Administration

NORC National Opinion Research Center

NRC National Research Council

NS&E natural sciences and engineering

NSB National Science Board

NSCG National Survey of College Graduates

NSF National Science Foundation

NSRCG National Survey of Recent College 

Graduates

OECD Organisation for Economic Co-operation 

and Development

OES Occupational Employment Statistics

OSTP Office of Science and Technology Policy

PEJ Project for Excellence in Journalism

PISA Program for International Student 

Assessment

PPP purchasing power parity

PSM Professional Science Master’s

PUMS Public Use Microdata Sample

R&D research and development

R&E research and experimentation

RA research assistantship

RD&D research, development, and demonstration

RDT research, development, and testing

S&E science and engineering

S&T science and technology

SASS Schools and Staffing Survey

SBIR Small Business Innovation Research

SCI Science Citation Index

SDR Survey of Doctorate Recipients

SED Survey of Earned Doctorates

SEH science, engineering, and health

SESTAT Scientists and Engineers Statistical 

Data System

SLDS Statewide longitudinal data systems

SOI Statistics of Income

SSCI Social Sciences Citation Index

STEM science, technology, engineering, and 

mathematics

STTR Small Business Technology Transfer

TA teaching assistant

TFA Teach for America

TIMSS Trends in International Mathematics and 

Sciences Study

TIP Technology Innovation Program

U&C universities and colleges

UK United Kingdom

UNESCO United Nations Educational, Scientific and 

Cultural Organization

USDA Department of Agriculture

USDIA Survey of U.S. Direct Investment Abroad

USGS U.S. Geological Survey

USPTO U.S. Patent and Trademark Office

VA Department of Veterans Affairs

WebCASPAR Integrated Science and Engineering 

Resources Data System

WTO World Trade Organization
xii t

About Science and Engineering Indicators

Science and Engineering Indicators (SEI) is first and 

foremost a volume of record comprising the major highquality quantitative data on the U.S. and international science 

and engineering enterprise. SEI is factual and policy neutral. 

It does not offer policy options, and it does not make policy 

recommendations. SEI employs a variety of presentation 

styles—tables, figures, narrative text, bulleted text, Webbased links, highlights, introductions, conclusions, reference 

lists—to make the data accessible to readers with different 

information needs and different information-processing 

preferences.

The data are “indicators.” Indicators are quantitative 

representations that might reasonably be thought to provide 

summary information bearing on the scope, quality, and vitality 

of the science and engineering enterprise. The indicators 

reported in SEI are intended to contribute to an understanding 

of the current environment and to inform the development 

of future policies. SEI does not model the dynamics of the 

science and engineering enterprise, and it avoids strong claims 

about the significance of the indicators it reports. SEI is used 

by readers who hold a variety of views about which indicators 

are most significant for different purposes.

SEI is prepared by the National Science Foundation’s 

National Center for Science and Engineering Statistics 

(NCSES) under the guidance of the National Science Board 

(Board). It is subject to extensive review by outside experts, 

interested federal agencies, Board members, and NSF internal 

reviewers for accuracy, coverage, and balance.

SEI includes more information about measurement 

than many readers unaccustomed to analyzing social and 

economic data may find easy to absorb. This information 

is included because readers need a good understanding of 

what the reported measures mean and how the data were 

collected in order to use the data appropriately. SEI’s data 

analyses, however, are relatively accessible. The data can 

be examined in various ways, and SEI generally emphasizes 

neutral, factual description and avoids unconventional or 

controversial analysis. As a result, SEI almost exclusively uses 

simple statistical tools that should be familiar and accessible 

to a college bound high school graduate. Readers comfortable 

with numbers and percentages and equipped with a general 

conceptual understanding of terms such as “statistical 

significance” and “margin of error” will readily understand 

the statistical material in SEI. A statistical appendix aids 

readers’ interpretation of the material presented.

SEI’s Different Parts

SEI includes an overview, seven chapters that follow a 

generally consistent pattern, and an eighth chapter, on state 

indicators, presented in a unique format. The chapter titles are 

as follows:

 Elementary and Secondary Mathematics and Science 

Education

 Higher Education in Science and Engineering

 Science and Engineering Labor Force

 Research and Development: National Trends and 

International Comparisons

 Academic Research and Development

 Industry, Technology, and the Global Marketplace

 Science and Technology: Public Attitudes and 

Understanding

 State Indicators

An appendix volume, available online at https://

www.nsf.gov/statistics/indicators/ contains detailed data 

tables keyed to each of the eight chapters. SEI includes a list 

of acronyms and abbreviations and an index.

The National Science Board authors one or more 

companion pieces, which draw on the data in SEI and offer 

recommendations on issues of concern for national science 

and engineering research or education policy, in keeping 

with the Board’s statutory responsibility to bring attention 

to such issues. In addition, the Board publishes the Science 

and Engineering Indicators Digest, a condensed version of 

SEI comprising a small selection of important indicators. The 

digest serves two purposes: (1) to draw attention to important 

trends and data points from across the chapters of SEI and (2) 

to introduce readers to the data resources available in the main 

volume of SEI 2014 and associated products.

The Overview

The overview is a selective synthesis that brings together 

patterns and trends that unite data in several of the substantive 

chapters. The overview helps readers to synthesize the 

findings in SEI as a whole and draws connections among 

separately prepared chapters that deal with related topics. It 

is intended to serve readers with varying levels of expertise. 

Because the overview relies heavily on figures, it is well 

adapted for use in developing presentations, and presentation 

graphics for the figures in the overview are available on 

the Web. Like the core chapters, the overview strives for 

a descriptive synthesis and a balanced tone, and it does not 

take or suggest policy positions.
Science and Engineering Indicators 2014 t xiii

The Seven Core Chapters

Each chapter consists of contents and lists of sidebars, 

text tables, and figures; highlights; introduction (chapter 

overview and chapter organization); a narrative synthesis of 

data and related contextual information; conclusion; notes; 

glossary; and references.

Highlights. The highlights provide an outline of major dimensions of a chapter topic. Each highlight starts with a statement 

that summarizes a key point made in the chapter. Bulleted 

points supporting the key point follow.

Introduction. The chapter overview provides a brief 

explanation of the importance of the topic. It situates the topic 

in the context of major concepts, terms, and developments 

relevant to the data reported. The introduction includes a 

brief narrative account of the logical flow of topics within 

the chapter.

Narrative. The chapter narrative is a descriptive synthesis 

that brings together significant findings. It is also a balanced 

presentation of contextual information that is useful for 

interpreting the findings. As a descriptive synthesis, the 

narrative aims (1) to enable the reader to assimilate a large 

amount of information by putting it in an order that facilitates 

comprehension and retention and (2) to order the material 

so that major points readily come to the reader’s attention. 

As a balanced presentation, the narrative aims to include 

appropriate caveats and context information such that (3) a 

nonexpert reader will understand what uses of the data may 

or may not be appropriate, and (4) an expert reader will be 

satisfied that the presentation reflects a good understanding of 

the policy and fact context in which the data are interpreted by 

users with a range of science policy views.

Figures. Figures provide visually compelling representations 

of major findings discussed in the text. Figures also enable 

readers to test narrative interpretations offered in the text by 

examining the data themselves.

Text Tables. Text tables help to illustrate and to support 

points made in the text.

Sidebars. Sidebars discuss interesting recent developments in 

the field, more speculative information than is presented in the 

regular chapter text, or other special topics. Sidebars can also 

present definitions or highlight crosscutting themes.

Appendix Tables. Appendix tables, available online 

(http://www.nsf.gov/statistics/indicators/), provide the 

most complete presentation of quantitative data, without 

contextual information or interpretive aids. According to past 

surveys of SEI users, even experienced expert readers find 

it helpful to consult the chapter text in conjunction with the 

appendix tables.

Conclusion. The conclusion summarizes important findings. 

It offers a perspective on important trends but stops short of 

definitive pronouncements about either likely futures or policy 

implications. Conclusions tend to avoid factual syntheses that 

suggest distinctive or controversial viewpoints.

Notes. Information that augments points of discussion in the 

text is presented as endnotes.

Glossary. The glossary defines terms used in the chapter.

References. SEI includes references to data sources cited in 

the text, stressing national or internationally comparable data. 

SEI does not attempt to review the analytic literature on a topic 

or summarize the social science or policy perspectives that 

might be brought to bear on it. References to that literature are 

included where they help to explain the basis for statements 

in the text.

The State Indicators Chapter

This chapter consists of data that can be used by people 

involved in state-level policy making, including journalists 

and interested citizens, to assess trends in S&T-related 

activities in their states. No interpretive narrative synthesizes 

overall patterns and trends. SEI includes state-level 

indicators to call attention to state performance in S&T and 

to foster consideration of state-level activities in this area.

Indicators are drawn from a range of variables, most of 

which are part of the subject matter of the seven core chapters. The text explains the meaning of each indicator and 

provides important caveats about how to interpret it. Approximately three to five bullets highlight significant findings covering a 10-year span, when available. Data for the 

indicators are graphically displayed in state-by-state tables, 

in United States maps that code states into quartiles, and 

in histograms that show how state values are distributed. 

A small number of appendix tables for this chapter can be 

found online. The online state data tool (http://www.nsf.gov/

statistics/seind14/c8/interactive) provides additional data on 

state S&T over the past 20 years.

Presentation

SEI is released in printed and electronic formats. The printed 

volume provides the full content except for the appendix tables. 

The complete content of SEI is posted online at https://

www.nsf.gov/statistics/indicators/ in html format and PDF, 

with text tables, appendix tables, and source data for each 

figure available in spreadsheet (MS Excel) format. In addition, 

selected figures are also available in presentation-style format 

as MS PowerPoint and JPEG files.
Overview

♦ O-1

Introduction.................................................................................................................................O-3

Science and Technology in the World Economy........................................................................O-3

Knowledge- and Technology-Intensive Economic Activity ...................................................O-3

R&D Performance ...................................................................................................................O-5

Workers with S&E Skills.........................................................................................................O-7

Research Publications............................................................................................................O-10

Innovation-Related Indicators ...............................................................................................O-10

The U.S. Science and Engineering Landscape .........................................................................O-13

Cross-Sector Collaboration....................................................................................................O-13

U.S. Higher Education...........................................................................................................O-15

Degree Production .................................................................................................................O-16

Demographics of the U.S. S&E Labor Force ........................................................................O-17

R&D Funding ........................................................................................................................O-19

Conclusion ................................................................................................................................O-21

Notes.........................................................................................................................................O-22

Glossary ....................................................................................................................................O-22

References.................................................................................................................................O-23

List of Figures

Figure O-1. KTI share of GDP, by selected country/economy: 1999, 2005, and 2012..............O-4

Figure O-2. Output of KTI industries as a share of GDP for selected developing 

economies: 2012.....................................................................................................................O-4

Figure O-3. Output of HT manufacturing industries for selected regions/countries/ 

economies: 1997–2012...........................................................................................................O-5

Figure O-4. Global share of commercial KI services value added for selected countries/

economies: 1997–2012...........................................................................................................O-5

Figure O-5. Global R&D expenditures, by region: 2011............................................................O-6

Figure O-6. Global share of expenditures on R&D, by selected country/economy: 

1996, 2005, and 2011.............................................................................................................O-6

Figure O-7. Gross expenditures on R&D as share of GDP, for the United States, EU, 

and selected other countries: 1981–2011 ...............................................................................O-7

Figure O-8. Estimated number of researchers in selected countries/regions: 1995–2011..........O-7

Figure O-9. Researchers as a share of total employment in selected countries/regions: 

1995–2011..............................................................................................................................O-8

Figure O-10. First university degrees, by location: 2001–10 .....................................................O-8

Figure O-11. S&E first university degrees as a share of all first university degrees, 

by country: 2000–10 ..............................................................................................................O-9

Figure O-12. Internationally mobile students enrolled in tertiary education, by selected 

country: 2010 .........................................................................................................................O-9

Figure O-13. S&E articles, by global share of selected region/country: 2001–11 ...................O-10

Figure O-14. Share of U.S., EU, and China S&E articles that are in the world’s top 

1% of cited articles: 2002–12...............................................................................................O-11
O-2 ♦ Overview

Figure O-15. USPTO patents granted, by location of inventor: 2003–12 ................................O-11

Figure O-16. Global triadic patent families, by selected region/country/economy: 

1998–2010............................................................................................................................O-12

Figure O-17. USPTO patents granted, by selected technology areas for selected 

country/economy of inventor: 2010–12...............................................................................O-12

Figure O-18. Global exports of royalties and fees, by selected region/country/ 

economy: 2004–11...............................................................................................................O-13

Figure O-19. U.S. academic and non-academic S&E articles: 1997–2012..............................O-13

Figure O-20. Share of articles authored at U.S. academic institutions that have authors 

from multiple U.S. institutions: Selected years, 1990–2012................................................O-14

Figure O-21. Number of authors and authors per paper for U.S. academic institutions: 

1988–2012............................................................................................................................O-14

Figure O-22. Federal awards and research expenditures at very high research activity 

institutions, by institutional control: 1987–2010 .................................................................O-15

Figure O-23. Community college attendance among recent S&E bachelor’s recipients: 

1999–2010............................................................................................................................O-16

Figure O-24. Selected average revenues and expenditures at public very high research 

universities: 1987–2010 .......................................................................................................O-16

Figure O-25. S&E degrees, by level: 2000–11.........................................................................O-16

Figure O-26. Bachelor’s degrees, by broad field of degree: 2000–11......................................O-17

Figure O-27. Share of S&E bachelor’s degrees among U.S. citizens and permanent 

residents, by race and ethnicity: 2000–11............................................................................O-17

Figure O-28. Women in the workforce and in S&E: 1993 and 2010 .......................................O-18

Figure O-29. Women in S&E occupations: 1993–2010 ...........................................................O-18

Figure O-30. Share of workers in S&E occupations, by selected race and ethnicity: 

Selected years, 1993–2010...................................................................................................O-18

Figure O-31. Foreign-born workers in S&E occupations, by education level: 2000, 

2006, and 2011.....................................................................................................................O-18

Figure O-32. Foreign-born individuals with highest degree in S&E living in the United 

States, by place of birth: 2010..............................................................................................O-19

Figure O-33. Ratio of U.S. federal-to-nonfederal funding for R&D: 1953–2011....................O-19

Figure O-34. Sources of S&E R&D funding for public and private academic institutions: 

FYs 1999 and 2012 ..............................................................................................................O-20

Figure O-35. U.S. total R&D expenditures: 1953–2011 ..........................................................O-20

Figure O-36. Year-to-year changes in U.S. R&D expenditures, by performing sector: 

2006–11................................................................................................................................O-21
Science and Engineering Indicators 2014 ♦ O-3

Introduction

This overview of the National Science Board’s Science 

and Engineering Indicators 2014 highlights some major developments in international and U.S. science and engineering 

(S&E).

The international component of the overview is focused 

primarily on relatively recent changes affecting patterns in 

the ways science and engineering are translated into innovations with commercial and economic value. It pays particular attention to describing how the global map of science and 

technology (S&T)-related economic activity in the wake of 

the severe economic downturn in 2008–09 is different from 

the patterns present in data from before the downturn.

The domestic component of the overview has a significantly different focus in two respects. First, it takes a much 

more long-term view than the international component, 

counted mostly in decades rather than in years. Second, it 

focuses primarily on the institutions that are or have been 

centrally involved in producing research outputs such as publications and patents. It summarizes continuities and changes 

in the kinds of people who staff those institutions, the practices that characterize them, and the products they make.

Especially over the long term, the international and domestic S&E trends that Science and Engineering Indicators 

describes can be understood in light of the worldwide trend 

toward more knowledge-intensive economies. In this type of 

economy, research, its commercial exploitation, and other intellectual work are of growing importance. Such economies 

rely on sustained investment in research and development 

that produces useful innovations. They also rely on higher 

education that prepares students to use S&E knowledge and 

related research skills to develop new and better ways to make 

products and perform services. As a result, data on trends in 

R&D and human resources infrastructure feature prominently in both parts of the overview and throughout Science and 

Engineering Indicators. Knowledge-intensive economies, 

however, also rely on other kinds of infrastructure, including 

reliable and modern transportation and communications and 

a broadly educated and literate population, to enable them to 

function effectively.

The overview is not intended to be comprehensive. 

Numerous important topics that are addressed in individual 

chapters, and even some that crosscut the volume, are not 

covered in the overview. Major findings on particular topics 

can be found in the “Highlights” sections that appear at the 

beginning of chapters 1–7. 

The indicators included derive from a variety of national, 

international, public, and private sources and are not always 

strictly comparable in a statistical sense. As noted in the 

text, in some cases the quality of available data is less than 

ideal, and the metrics and models relating them to each other and to economic and social outcomes need further development. Thus, the emphasis is on broad trends. Individual 

data points and findings should be interpreted with care.

Science and Technology 

in the World Economy

Knowledge- and Technology-Intensive 

Economic Activity

Knowledge- and technology-intensive (KTI) industries 

represent a growing portion of global S&T economic activity. KTI industries accounted for 27% of world gross 

domestic product (GDP) in 2012. They consist of high-technology (HT) manufacturing (e.g., aircraft and spacecraft; 

pharmaceuticals) and knowledge-intensive (KI) services 

(e.g., commercial business, financial, and communication 

services). These industries play a larger role in the United 

States than in the economy of any other large developed 

country, accounting for 40% of U.S. GDP.1 KTI concentrations were in the range of 29%–30% for other large, developed regional and national economies (European Union 

[EU; see “Glossary” for member countries], Canada, Japan, 

and South Korea). The trend since 1999 indicates that, except for Japan between 2005 and 2012, the KTI share for all 

of these economies has been rising (figure O-1).

The KTI share of the world’s developed economies grew 

from 29% to 32% between 1997 and 2012. This was due 

mostly to increases in commercial and public (education and 

health) KI services, indicating a continuing movement away 

from manufacturing and toward services in these economies. 

In recent years, regional and national shares of worldwide KTI production have been shifting. Regionally, the 

shift has produced a growing concentration of commercial 

KTI economic activity in East and Southeast Asia.2

 That region is approaching a concentration of commercial KTI activity comparable to that of the world’s established regional 

centers, North America and Western Europe.

Likewise, an increasing amount of worldwide KTI production is occurring in the developing world. To a large 

extent, this is due to China’s large modernizing economy. 

Economic growth in other Asian locations, however, has 

contributed as well, and KTI economic activity is also 

growing in countries such as Brazil, Turkey, and South 

Africa (figure O-2).

The growth of KTI activity in the developing world is 

most apparent in manufacturing and is largely due to China. 

Between 2003 and 2012, China’s HT manufacturing rose 

more than fivefold, resulting in its global share climbing 

from 8% to 24% in 2012. Even amid this shift, the United 

States remains the largest global provider of HT manufacturing (27% of the global total) (figure O-3).

KI services, despite growth in worldwide production 

attributed to developing countries, remain concentrated in 

developed countries. The United States is the world’s largest provider of commercial KI services (32%), followed by 

the EU (23%). China’s commercial KI services account for 

8% of the world total, much more than any other developing 

country. China is tied with Japan as the third-largest global 
O-4 ♦ Overview

provider of these services. The share of developed countries 

in worldwide production of commercial KI services fell 

from 90% in 2003 to 79% in 2012, due entirely to a collective 15 percentage point decline in the global shares of the 

United States, the EU, and Japan (figure O-4). Nonetheless, 

developed countries continue to dominate global trade in 

these industries.

The value added of commercial KI services in developed 

economies grew between 2003 and 2008. Due to the international economic downturn, however, these services then 

contracted before resuming growth in 2010. In the United 

States, commercial KI services’ value added rebounded after 

2009 and, in 2012, stood 12% higher than its level prior to 

the global recession. The EU fared much worse. The EU’s 

production of commercial KI services remained stagnant between 2009 and 2012 and was below its pre-recession peak at 

the end of this period. As a result, following the international 

economic downturn, the EU’s global share in these KI services industries declined considerably. In contrast, the U.S. 

global share not only remained steady, but employment in 

commercial KI services in the United States rose above levels prior to the global downturn. At the same time, commercial KI services in developing countries, and especially 

in China, grew rapidly.

As the distribution of commercial KTI production gradually shifted from developed to developing countries during the international economic downturn, parallel changes 

occurred in trade in KTI goods and services. The developed 

world generally lost market share in global KTI exports 

during this period. Japan, for example, suffered marked 

declines in global market share, as did the EU. But some 

large European economies, notably Germany and the 

United Kingdom (UK), fared better than other parts of the 

EU. The United States was more successful in maintaining 

its position in global KTI competition than most other longestablished developed economies.

Figure O-1

KTI share of GDP, by selected country/economy: 1999, 2005, and 2012

Percent 

EU = European Union; GDP = gross domestic product; KTI = knowledge and technology intensive.

NOTES: KTI industries include knowledge-intensive (KI) services and high-technology (HT) manufacturing industries classied by the Organisation for 

Economic Co-operation and Development. KI services include business, nancial, communications, education, and health. HT manufacturing industries 

include aerospace, communications and semiconductors, computers and ofce machinery, pharmaceuticals, and scientic instruments and measuring 

equipment. Data are not available for EU members Cyprus, Estonia, Latvia, Lithuania, Luxembourg, Malta, and Slovenia. 

SOURCE: IHS Global Insight, special tabulations (2013) of the World Industry Service database. See appendix table 6-18 for a full list of countries in each region.

Science and Engineering Indicators 2014

United States EU Canada Japan South Korea

0

10

20

30

40

50

1999 2005 2012

GDP = gross domestic product; HT = high technology; KI = 

knowledge intensive; KTI = knowledge and technology intensive.

NOTES: Output of KTI industries is on a value-added basis. Value 

added is the amount contributed by a country, rm, or other entity to 

the value of a good or service and excludes purchases of domestic 

and imported materials and inputs. KTI industries include KI services 

and HT manufacturing industries classied by the Organisation for 

Economic Co-operation and Development. KI services include 

business, nancial, communications, education, and health. 

Commercial KI services include business, nancial, and communications services. Public KI services include education and health. HT 

manufacturing industries include aerospace, communications and 

semiconductors, computers and ofce machinery, pharmaceuticals, 

and measuring, testing, and control instruments. Developing 

economies are classied by the World Bank as higher- and 

lower-middle income and low income. 

SOURCE: IHS Global Insight, World Industry Service database 

(2013). See appendix tables 6-3–6-7.

Science and Engineering Indicators 2014

Percent

Figure O-2

Output of KTI industries as a share of GDP for 

selected developing economies: 2012

Developing

economies

Turkey Brazil China India South

Africa

Indonesia

0

5

10

15

20

25

Commercial KI industries

Public KI industries

HT manufacturing industries
Science and Engineering Indicators 2014 ♦ O-5

R&D Performance

R&D expenditures increase human and knowledge capital, laying the groundwork for innovations, including those 

that fuel KTI industries. In 2011, the proportion of global 

R&D performance attributable to the East and Southeast 

Asia region, including China, was comparable (31.8%) to 

that in North America (32.2%) and substantially larger than 

that in Europe (24.0%) (figure O-5). 

Among individual countries, the United States is by 

far the largest investor in R&D. In absolute terms, the top 

three R&D performing countries—the United States ($429 

billion), China ($208 billion), and Japan ($147 billion)—

accounted for over half of the estimated $1.44 trillion in 

global R&D in 2011. The U.S. share was 30% of the global 

total in 2011. China (15%) and Japan (10%) were the nextlargest R&D performers. The total for the EU was 22% (figure O-6).

Despite growth in nominal measures of R&D, both the 

United States and the EU experienced substantial declines 

in the last decade in their shares of global R&D. Between 

2001 and 2011, the U.S. share declined from 37% to 30% of 

the global total, while the EU share dropped from 26% to 

22%. During the same period, the economies of East and 

Southeast Asia and South Asia—including China, India, 

Japan, Malaysia, Singapore, South Korea, and Taiwan—

saw an increase in their combined share from 25% to 34% 

of the global total. The pace of growth over the past 10 years 

in China’s overall R&D remains exceptionally high at about 

18% annually adjusted for inflation, propelling it to 14.5% 

of the global total in 2011, up from 2.2% in 2000.

Although the United States performs far more R&D than 

any other individual country, several other economies have 

greater R&D intensity—that is, a higher ratio of R&D expenditures to GDP. In 2011, R&D intensity in the United 

States was 2.8%. Most economies with higher R&D intensity—including Israel, Finland, South Korea, Sweden, 

Denmark, Taiwan, and Switzerland—tend to be much 

smaller than the United States. More apt comparisons are 

with Germany, France, the UK, and Japan, which allocated, 

respectively, 2.9%, 2.2%, 1.8%, and 3.4% of GDP to R&D. 

However, relatively high R&D investments alone are no 

guarantee of robust economic growth, as indicated by the 

experience of Japan during the last decade.

Moreover, in several countries, R&D intensity has been 

growing rapidly (figure O-7). Along with China, South 

Korea is a notable example. In 1991, gross expenditure Figure O-3

Output of HT manufacturing industries for 

selected regions/countries/economies: 1997–2012

Billions of dollars

EU = European Union; HT = high technology.

NOTES: Output of HT manufacturing industries is on a value-added 

basis. Value added is the amount contributed by a country, rm, or 

other entity to the value of a good or service and excludes purchases 

of domestic and imported materials and inputs. HT manufacturing 

industries are classied by the Organisation for Economic 

Co-operation and Development and include aircraft and spacecraft, 

communications, computers, pharmaceuticals, semiconductors, and 

testing, measuring, and control instruments. The EU excludes 

Cyprus, Estonia, Latvia, Lithuania, Luxembourg, Malta, and Slovenia. 

China includes Hong Kong. Developed countries classied as 

high-income countries by the World Bank. Developing countries 

classied as upper- and lower-middle-income countries and 

low-income countries by the World Bank.

SOURCE: IHS Global Insight, World Industry Service database 

(2013). See appendix table 6-7. 

Science and Engineering Indicators 2014

1997 2000 2002 2004 2006 2008 2010 2012

0

100

200

300

400

500

United States

EU

Japan

Other developed

China

Other developing

Figure O-4

Global share of commercial KI services value added 

for selected countries/economies: 1997–2012

Percent

EU = European Union; KI = knowledge intensive.

NOTES: Output of knowledge- and technology-intensive industries is 

on a value-added basis. Value added is the amount contributed by a 

country, rm, or other entity to the value of a good or service and 

excludes purchases of domestic and imported materials and inputs. 

The EU excludes Cyprus, Estonia, Latvia, Lithuania, Luxembourg, 

Malta, and Slovenia. China includes Hong Kong. Developed 

economies are classied by the World Bank as high income. 

Developing economies are classied by the World Bank as upperand lower-middle income and low income.

SOURCE: IHS Global Insight, World Industry Service database 

(2013).

Science and Engineering Indicators 2014

1997 2000 2002 2004 2006 2008 2010 2012

0

10

20

30

40

50

United States

EU

Japan

Other developed

China

Other developing
O-6 ♦ Overview

on R&D as a share of GDP was 1.8% for South Korea. By 

2011, that measure had increased to over 4.0%. A stated 

goal by the European Union (one of the five targets for the 

EU in 2020 [EC 2013]), along with many individual developed countries, is to achieve a 3% R&D-to-GDP ratio to 

promote innovation.

At the same time that the growth of KI economies around 

the world intensifies the competition among national economies, it also increases interdependencies. Taking advantage 

of improved worldwide capacity to perform R&D and other 

knowledge-oriented economic activities, multinational corporations (MNCs) have increasingly made R&D investments outside their home countries. To be sure, the bulk of 

R&D by U.S. MNCs is still performed in the United States 

(84% of their $252 billion in R&D globally in 2010) and 

in Europe. But rapid growth in R&D by majority-owned 

foreign affiliates (MOFAs) of U.S. MNCs in China, India, 

Brazil, and Israel is closing the gap between these emerging 

countries and traditional centers of U.S. MOFA investments 

in Europe, Canada, and Japan.

Notably, U.S. MOFA R&D performance in China 

more than doubled in current dollars from 2005 to 2008, 

with year-to-year, double-digit increases to a record $1.7 

billion in 2008. This is consistent with increases in total 

R&D performed in China in recent years and with China’s 

Figure O-5

Global R&D expenditures, by region: 2011

Billions of U.S. PPP dollars

PPP = purchasing power parity.

NOTES: Foreign currencies are converted to U.S. dollars through PPPs. Some country gures are estimated. Countries are grouped according to the 

regions described by The World Factbook, available at www.cia.gov/library/publications/the-world-factbook/index.html.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, estimates (August 2013). Based on data from the 

Organisation for Economic Co-operation and Development, Main Science and Technology Indicators (2013/1); and the United Nations Educational, 

Scientic and Cultural Organization Institute for Statistics, http://stats.uis.unesco.org/unesco/ReportFolders/ReportFolders.aspx, table 25, accessed 2 

August 2013.

Science and Engineering Indicators 2014

World total = $1,435

South America 

$36 (2.5%)

Africa

$11 (0.8%)

North America 

$462 (32.2%)

Europe

$345 (24.0%)

South Asia

$36 (2.5%)

Australia

and Oceania

$24 (1.6%)

Middle East

$31 (2.1%)

Central Asia

$35 (2.5%)

East and Southeast

Asia $456 (31.8%)

Central America 

and Caribbean 

$0.6 (< 0.1%)

Figure O-6

Global share of expenditures on R&D, by selected 

country/economy: 1996, 2005, and 2011

Percent 

EU = European Union.

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, estimates (August 2013), based on data 

from the Organisation for Economic Co-operation and Development, 

Main Science and Technology Indicators (2013/1); and the United 

Nations Educational, Scientic and Cultural Organization Institute for 

Statistics, http://stats.uis.unesco.org/unesco/ReportFolders/

ReportFolders.aspx, table 25, accessed 2 August 2013.

Science and Engineering Indicators 2014

United States China Japan EU

0

10

20

30

40

50

1996 2005 2011
Science and Engineering Indicators 2014 ♦ O-7

emergence as the second-largest R&D-performing country. 

Reported R&D activity by U.S. MOFAs tripled in India and 

more than doubled in Brazil from 2007 to 2010. U.S. MOFA 

R&D expenditures in Brazil and India are now on par with 

those in China.

Concurrently, affiliates of foreign MNCs located in the 

United States (U.S. affiliates) performed $41.3 billion of 

R&D in 2010, a slight increase after almost no change in 

2009 and 2008. R&D by these companies has accounted 

for 14%–15% of U.S. business R&D performance since 

2007. Three-fourths of R&D by U.S. affiliates of foreign 

MNCs in 2010 was performed by firms owned by parent 

companies based in five countries: Switzerland (22.0%), 

the UK (14.5%), Germany (13.8%), France (12.7%), and 

Japan (12.4%).

In addition to lowering R&D labor costs, MNCs’ overseas 

R&D investments bring development work closer to emerging 

markets and enable product designers to take advantage of 

proximity to consumers and better information about whether and how consumers are likely to use new products. These 

investments, often encouraged by governments in developing 

countries, also increase local capacity for performing further 

R&D work (Thursby and Thursby 2006).

Workers with S&E Skills

The presence of workers with S&E skills is one of the key 

indicators of national competitiveness. Comprehensive, internationally comparable data on the worldwide S&E workforce do not exist. However, the Organisation for Economic 

Co-operation and Development (OECD) reports international data on professionals engaged in research. Although 

national differences in these data may be affected by survey 

procedures and interpretations of international statistical 

standards, the data can be used to make broad comparisons 

of national trends. 

The United States continues to enjoy a distinct but decreasing advantage in the supply of human capital for 

research and other work involving S&E. In absolute numbers, the United States had one of the largest populations 

of researchers at the latest count, but China—which almost 

tripled its number since the mid-1990s—has been catching 

up (figure O-8).3

Figure O-7

Gross expenditures on R&D as share of GDP, for the 

United States, EU, and selected other countries: 

1981–2011

Percent

EU = European Union; GDP = gross domestic product.

NOTES: Data are not available for all countries in all years. The table 

includes the top seven R&D-performing countries. Figures for the 

United States reect international standards for calculating gross 

expenditures on R&D, which differ slightly from the National Science 

Foundation’s protocol for tallying U.S. total R&D. Data for Japan for 

1996 onward may not be consistent with earlier data because of 

changes in methodology. 

SOURCE: Organisation for Economic Co-operation and Development, 

Main Science and Technology Indicators (2013/1). See appendix table 

4-13.

Science and Engineering Indicators 2014

1981 1987 1991 1995 1999 2003 2007 2011

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

United States

France

China

Germany

United Kingdom

EU

Japan

South Korea

Figure O-8

Estimated number of researchers in selected 

countries/regions: 1995–2011

Thousands

EU = European Union.

NOTES: Data are not available for all countries/regions for all years. 

Researchers are full-time equivalents. Before 2009, counts for China 

were not consistent with Organisation for Economic Co-operation 

and Development (OECD) standards. 

SOURCE: OECD, Main Science and Technology Indicators (2013/1 

and earlier years), http://www.oecd.org/sti/msti.htm. 

Science and Engineering Indicators 2014

1995 1997 1999 2001 2003 2005 2007 2009 2011

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

United States

EU

Japan

South Korea

China

Russia
O-8 ♦ Overview

There is no doubt that the worldwide total of workers 

engaged in research has been growing strongly and that 

growth has been more robust in some countries than in others. The most rapid expansion has occurred in South Korea 

(which doubled its number of researchers between 1995 and 

2006 and continued to grow strongly thereafter) and China 

(which reported tripling its number of researchers between 

1995 and 2008 and likewise reported substantial growth 

in later years).4 The United States and the EU experienced 

steady growth at lower rates, with a 36% increase in the 

United States between 1995 and 2007 (OECD data for the 

United States are not available after 2007) and a 65% increase in the EU between 1995 and 2010. Exceptions to the 

worldwide trend between 1995 and 2011 were the numbers 

of researchers in Japan (which remained flat) and in Russia 

(which declined).

Researchers measured as a share of employment is another indicator of national competitiveness in an international 

knowledge economy. Several economies in Asia have shown 

a sustained increase in that statistic since 1995. Foremost 

among them is South Korea (figure O-9), but growth is also 

evident in others—for example, in Singapore, Taiwan, and 

China. Singapore, for instance, has published estimates 

suggesting that its total number of workers with S&E skills 

will increase by nearly 50% by 2030 (NPTD 2013).5

Data on recipients of higher education degrees also indicate that other countries are catching up to—and, in some 

respects, surpassing—the United States. Between 2001 and 

2010, the number of first university degrees in the United 

States increased from 1.3 million to 1.7 million. During the 

same time period, the number of first university degrees in 

China grew from 0.5 million to 2.6 million. The rates of 

growth in the EU and in Japan, South Korea, and Taiwan 

were comparable to that in the United States (figure O-10).

S&E degrees, important for an innovative knowledge 

economy, are more prevalent in some countries than others. Globally, the number of first university degrees in S&E 

reached about 5.5 million in 2010. Almost a quarter of those 

degrees were conferred in China (24%), 17% in the EU, and 

10% in the United States. In several Asian countries, these 

degrees comprise a larger proportion of all first university 

degrees than they do in the United States. Differences in engineering are especially large: whereas 5% of all bachelor’s 

degrees awarded in the United States were in engineering, 

31% of such degrees in China were in this field.

The S&E proportion of all first university degrees in 

Western countries has typically been stable in recent years. 

From 2001 to 2010, this share held steady in the United 

States (from 31.8% to 31.5%) and in Germany (from 37.3% to 

Figure O-9

Researchers as a share of total employment in 

selected countries/regions: 1995–2011

Per thousand

EU = European Union.

NOTES: Data are not available for all countries/regions for all years. 

Researchers are full-time equivalents per thousand total employment. 

Before 2009, counts for China were not consistent with Organisation 

for Economic Co-operation and Development (OECD) standards.

SOURCE: OECD, Main Science and Technology Indicators (2013/1 

and earlier years), http://www.oecd.org/sti/msti.htm. 

Science and Engineering Indicators 2014

1995 1997 1999 2001 2003 2005 2007 2009 2011

0

2

4

6

8

10

12

14

United States

EU

Japan

South Korea

Russia

China

Figure O-10

First university degrees, by location: 2001–10

Thousands

NOTES: Data for rst university degrees use International Standard 

Classication of Education, level 5A. Data not available for all 

locations in all years.

SOURCES: China—National Bureau of Statistics of China, China 

Statistical Yearbook, annual series (Beijing) (various years); 

Japan—Government of Japan, Ministry of Education, Culture, Sports, 

Science and Technology, Monbusho Survey of Education (annual 

series; various years); Taiwan—Ministry of Education, Educational 

Statistics of the Republic of China (annual series; various years); 

United Kingdom—Higher Education Statistics Agency, special 

tabulations (various years); United States—National Center for 

Education Statistics, Integrated Postsecondary Education Data 

System, Completions Survey, and National Science Foundation, 

National Center for Science and Engineering Statistics, Integrated 

Science and Engineering Resources Data System, http://webcaspar. 

nsf.gov; and other countries—Organisation for Economic Cooperation and Development, OECD Stat Extracts, http://stats. 

oecd.org/Index.aspx.

Science and Engineering Indicators 2014

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

0

500

1,000

1,500

2,000

2,500

3,000

United States

China

France/Germany/Italy/Spain/

United Kingdom

Japan/South Korea/Taiwan
Science and Engineering Indicators 2014 ♦ O-9

37.6%). In contrast, this proportion decreased considerably 

in several Asian countries, such as China (from 72.5% to 

49.8%), Japan (from 65.6% to 59.3%), and South Korea 

(from 45.2% to 40.1%) (figure O-11).

The relationship between degrees conferred in a country 

and future capabilities in its workforce is complicated by 

the fact that increasing numbers of students are receiving 

higher education outside their home countries. The United 

States remains the destination of choice for the largest 

number of internationally mobile students worldwide. In 

2012, foreign graduate students in S&E fields (163,390) 

outnumbered foreign students pursuing S&E undergraduate 

degrees (116,640) in the United States. Other popular 

destinations for internationally mobile students are the UK, 

Australia, France, and Germany (figure O-12). Yet, due to 

efforts by other countries to attract more foreign students 

as well as increased enforcement of visa requirements for 

students wanting to pursue a degree in the United States 

(among other factors), the U.S.-enrolled share of the world’s 

internationally mobile students fell from 25% in 2000 

to 19% in 2010. While a declining share of international 

students in the natural sciences and engineering opted for 

the United States, this drop in numbers was offset by an 

increase in international students coming to the United 

States to study social and behavioral sciences.

Whereas the U.S. share of internationally mobile students 

fell, the actual number of foreign undergraduate students 

entering the United States increased, rising by 18% between 

fall 2011 and fall 2012. Within the S&E fields, the largest 

increases occurred in engineering and the social sciences. 

The majority of foreign students studied in non-S&E fields. 

Foreign undergraduates in the United States predominantly 

originate from China, South Korea, and Saudi Arabia.

The number of foreign graduate students in the United 

States increased by 3% between fall 2011 and fall 2012. 

A much larger share of those students (nearly 6 out of 10) 

was enrolled in S&E fields as compared to undergraduate 

students (3 out of 10). This cohort of foreign graduate 

students chose somewhat different fields of study from 

earlier years: more studied mathematics, social sciences, 

and psychology, and fewer studied computer science, 

biological sciences, and engineering.

Figure O-11

S&E first university degrees as a share of all first 

university degrees, by country: 2000–10

Percent

NOTE: Data for rst university degrees use International Standard 

Classication of Education, level 5A.

SOURCES: China—National Bureau of Statistics of China, China 

Statistical Yearbook, annual series (Beijing) (various years); 

Japan—Government of Japan, Ministry of Education, Culture, Sports, 

Science and Technology, Monbusho Survey of Education (annual 

series; various years); Taiwan—Ministry of Education, Educational 

Statistics of the Republic of China (annual series; various years); 

United Kingdom—Higher Education Statistics Agency, special 

tabulations (various years); United States—National Center for 

Education Statistics, Integrated Postsecondary Education Data 

System, Completions Survey, and National Science Foundation, 

National Center for Science and Engineering Statistics, Integrated 

Science and Engineering Resources Data System, http://webcaspar. 

nsf.gov; and other countries—Organisation for Economic Cooperation and Development, OECD Stat Extracts, http://stats. 

oecd.org/Index.aspx.

Science and Engineering Indicators 2014

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

0

20

40

60

80

China

Germany

Japan

South Korea

United States

United Kingdom

Thousands

NOTES: Data are based on the number of students who have 

crossed a national border and moved to another country with the 

objective of studying, i.e., mobile students. Data for Canada and the 

Russian Federation correspond to 2009. Data for Germany exclude 

advanced research programs (e.g., doctorate). 

SOURCE: UNESCO Institute for Statistics, special tabulations (2013).

Science and Engineering Indicators 2014

Italy

China

Canada

Russian

Federation

Japan

Germany

France

Australia

United

Kingdom

United

States

0 100 200 300 400 500 600 700 800

Figure O-12

Internationally mobile students enrolled in tertiary 

education, by selected country: 2010
O-10 ♦ Overview

Research Publications

Refereed journal articles are a tangible and readily 

measured output of research activity. Despite the growth 

in research capability abroad, the United States continues 

to be the world leader in the publication of S&E articles 

when publications are measured at the individual country 

level. In 2011, the United States accounted for 26% of the 

world’s 828,000 articles.6 Nonetheless, the U.S. share of the 

global total of refereed journal articles has been declining, 

dropping by 4 percentage points between 2001 and 2011. 

Similarly, shares for the EU and Japan fell from 35% to 31% 

and from 9% to 6%, respectively, between 2001 and 2011. 

This was due mainly to increased output of research articles 

in East and Southeast Asia and in developing countries, such 

as Brazil and India. China’s share of refereed journal articles 

grew the fastest among larger developing economies during 

this time period, almost quadrupling from 3% to 11% of the 

world total (figure O-13).

Citations to refereed journal articles are an oft-used 

indicator of the quality and impact of research output. 

Researchers based in the United States continue to set the 

bar with respect to the production of influential research results. Between 2002 and 2012, 1.6%–1.8% of U.S.-authored 

S&E articles have been among the world’s top 1% of cited 

articles, compared with 0.7%–0.9% of articles from the EU 

(figure O-14). The share of China’s articles in the top 1% remained behind the United States and the EU but experienced 

a sixfold increase (0.1% to 0.6%) over the period. Overall, 

U.S.-authored articles represented 48% of the world’s top 1% 

of cited articles during this time period.

Citation data can also signal the extent of collaboration 

among researchers, both nationally and across borders. The 

trend toward more collaboration varies among S&E fields, 

research institutions, and countries. Citation patterns, like 

coauthorship patterns, are strongly influenced by cultural, 

geographic, and language ties. Thus, U.S. articles are disproportionately cited by Canadian and UK articles. In comparison, U.S. authors cite Chinese articles much less than 

suggested by the overall citation trends. Within Europe and 

Asia (with the exception of Japan), cross-national citation 

is common, with most country pairs in each continent surpassing the expected number of citations.7

U.S. articles are highly cited across all broad scientific 

fields. Citations for U.S. engineering articles exhibited a 

slight increase between 2002 and 2012, and citations declined slightly for chemistry and social sciences. EU articles 

are cited more than expected in physics and agriculture. 

China underperformed on this measure across all science 

fields, with the notable exceptions of computer science and 

geosciences, in which China overperformed.

Innovation-Related Indicators

In addition to the research findings in published articles, 

patents are an important output often produced by S&E 

research. Although patents do not necessarily become 

commercialized or lead to practical innovations—some are 

accumulated to provide a basis for legal action to discourage 

competitors from innovating, and others are simply deemed not 

to be commercially viable—patent grants and applications can 

sometimes lead to new or significantly improved products or 

processes or new methods of organizing productive activities.

The United States Patent and Trademark Office (USPTO) 

accepts applications from and grants patents to inventors 

worldwide. Trends in USPTO patenting activity indicate 

changes in inventive activity in different parts of the world 

(figure O-15).

The USPTO granted more than 250,000 patents in 2012, 

of which 120,000 were to U.S. inventors. This represents 

the highest number worldwide. Japan (51,000) and the EU 

(36,000) posted the next-highest numbers of successful patent 

applications to the USPTO. Although the absolute number 

Figure O-13

S&E articles, by global share of selected region/

country: 2001–11

Percent

EU = European Union.

NOTES: Article counts are from the set of journals covered by the 

Science Citation Index (SCI) and Social Sciences Citation Index 

(SSCI). Articles are classied by the year of publication, and are 

assigned to a country/economy on the basis of the institutional 

address(es) listed in the article. Articles are credited on a 

fractional-count basis (i.e., for articles with collaborating institutions 

from multiple countries/economies, each country/economy receives 

fractional credit on the basis of the proportion of its participating 

institutions). Counts for all six groups sum to the world total. Data for 

Bulgaria, Hungary, and Romania are included with the EU and not 

with developing economies.

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, and The Patent Board,TM special 

tabulations (2013) from Thomson Reuters, SCI and SSCI, http:// 

thomsonreuters.com/products_services/science/. See appendix 

table 5-26. 

Science and Engineering Indicators 2014

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

0

5

10

15

20

25

30

35

40

United States

EU

Japan

Other developed

China

Other developing
Science and Engineering Indicators 2014 ♦ O-11

of USPTO patents granted to U.S. inventors increased 

from 87,000 to 120,000 between 2003 and 2012, the U.S. 

share declined by 5 percentage points (from 53% to 48%) 

in this period. This likely signals increased technological 

capabilities abroad, which, in a globalized marketplace, 

underscore the need for patent protection in foreign countries. 

Developing countries received 9,000 patents (less than 4% of 

total patents), with China and India receiving the bulk of the 

relatively small number of patents granted to these countries.

Data on the numbers of patents granted provide no indication of patent quality. Triadic patents, in which inventors 

simultaneously seek patent protection in three of the world’s 

largest markets—the United States, the EU, and Japan—indicate patents expected to have high commercial value. In 

2010, the number of these triadic patents was estimated to be 

about 49,000. The shares of the United States, the EU, and 

Japan stayed roughly equal (at around 30% each) during the 

period from 2000 to 2010. Although South Korea still produces far fewer patented inventions than the long-standing 

global leaders, the country made rapid and notable progress 

on this indicator in the last decade, doubling its filings from 

2% to 4% of the global total (figure O-16).

Globally, there are indications that various economies receive the majority of their patent grants in certain technology 

areas (figure O-17). U.S. inventors accounted for nearly 70% 

of all U.S. patents granted in medical equipment and electronics, far higher than the overall U.S. share, indicating that 

U.S. inventors are very active in this area. In addition, the 

United States has slightly higher than average shares in information and communications technologies (ICT) and biotechnology and pharmaceuticals. EU inventors have a somewhat 

higher than average share in biotechnology and pharmaceuticals, receiving 21% of all U.S. patents in the area; an additional technology area where the EU has a slightly higher 

Figure O-14

Share of U.S., EU, and China S&E articles that are 

in the world’s top 1% of cited articles: 2002–12

Percent

EU = European Union.

NOTES: Article/citation counts are from the set of journals covered 

by the Science Citation Index (SCI) and Social Sciences Citation 

Index (SSCI). Articles are classied by the year they entered the 

database, rather than their year of publication, and are assigned to a 

country/economy on the basis of the institutional address(es) listed 

in the article. Articles are credited on a fractional-count basis (i.e., for 

articles with collaborating institutions from multiple countries/ 

regions, each country/region receives fractional credit on the basis of 

the proportion of its participating institutions). See appendix table 

5-24 for countries included in the EU, which in this gure is treated 

as a single country. Citation counts are based on a 3-year period 

with a 2-year lag (e.g., citations for 2012 are references made in 

articles in the 2012 data tape to articles in the 2008–10 data tapes). 

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, and The Patent Board,TM special 

tabulations (2013) from Thomson Reuters, SCI and SSCI, http:// 

thomsonreuters.com/products_services/science/. See appendix 

table 5-57.

Science and Engineering Indicators 2014

2002 2004 2006 2008 2010 2012

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

United States

EU

China

EU = European Union; USPTO = U.S. Patent and Trademark Ofce.

NOTES: Technologies are classied by The Patent Board.TM Patent grants are fractionally allocated among countries on the basis of the proportion of the 

residences of all named inventors. 

SOURCE: The Patent Board,TM special tabulations (2013) from Proprietary Patent database. See appendix table 6-40.

Science and Engineering Indicators 2014

Share (percent)

Figure O-15

USPTO patents granted, by location of inventor: 2003–12

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

0

20

40

60

80

100

United

States

Japan

EU

Other

developed

Developing
O-12 ♦ Overview

than average share is automation and control and measuring 

and instrumentation (17%).

KTI industries account for a large share of USPTO patent grants awarded to inventors in the United States. In 

2011, HT manufacturers garnered 29,000 of the 58,000 

patents granted to all U.S. manufacturing industries. U.S. 

commercial KI services industries accounted for 46% of 

the 43,000 patents issued to nonmanufacturing industries 

in 2011. Although HT manufacturing is a smaller part of the 

U.S. economy than KI services, the majority of inventions attributable to KTI industries occur on the manufacturing side. 

In manufacturing, five of the six HT manufacturing 

industries—aircraft and spacecraft; communications; 

computers; pharmaceuticals; and testing, measuring, and 

control instruments—reported rates of product and process 

innovation that were at least double the manufacturing sector average. In KI services industries, software firms lead in 

incidence of innovation, with 69% of companies reporting 

the introduction of a new product or service, compared to 

the 9% average for all nonmanufacturing industries. Other 

KI services industries—such as computer systems design, 

data processing and hosting, and scientific R&D services—

also report innovation at rates that are three to four times 

higher than the nonmanufacturing average.

Innovative activities and trade in intellectual property 

are strongly related. Intellectual property trade is measured 

by royalties and fees collected for licensing or franchising 

proprietary technologies. Although sometimes affected by 

different tax treatments, income from intellectual property 

broadly indicates which nations are producing intellectual 

products with commercial value. U.S. export income from 

royalties and fees has exhibited a strongly positive trend 

Figure O-16

Global triadic patent families, by selected region/

country/economy: 1998–2010

Number

EU = European Union; ROW = rest of world. 

NOTES: Triadic patent families include patents applied in the U.S. 

Patent and Trademark Ofce, European Patent Ofce, and Japan 

Patent Ofce. Patent families are fractionally allocated among 

regions/countries/economies based on the proportion of the 

residences of all named inventors.

SOURCE: Organisation for Economic Co-operation and Development, 

Patents Statistics, http://stats.oecd.org/WBOS/index.aspx, Patents by 

Region database, accessed 15 January 2011. See appendix table 6-54.

Science and Engineering Indicators 2014

1998 2000 2002 2004 2006 2008 2010

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

United States

EU

Japan

South Korea

ROW

Figure O-17

USPTO patents granted, by selected technology areas for selected country/economy of inventor: 2010–12

Share (percent)

EU = European Union; ICT = information and communications technologies; USPTO = U.S. Patent and Trademark Ofce.

NOTES: Technologies are classied by The Patent Board.TM Patents are fractionally allocated among countries on the basis of the proportion of the 

residences of all named inventors. 

SOURCE: The Patent Board,TM special tabulations (2013) from Proprietary Patent database. See appendix tables 6-40 and 6-43–6-53. 

Science and Engineering Indicators 2014

United States Japan EU

0

20

40

60

80

All technology areas ICT Biotechnology and

pharmaceuticals

Medical equipment

and electronics

Automation and control and 

measuring and instrumentation
Science and Engineering Indicators 2014 ♦ O-13

over the last decade (figure O-18). In 2011, the United States 

posted export income of $121 billion in royalties and fees. 

The EU exported intellectual property in the amount of $54 

billion while accumulating a small trade deficit in this area. 

Like the United States, Japan, which exported $29 billion in 

royalties and fees, had a substantial trade surplus in this area. 

Three economies that import more rights to production than 

they export (and are, therefore, net importers of royalties and 

fees) are among countries that the World Bank has recently 

classified as developing: China, Russia (reclassified as developed in 2012), and Brazil.

The U.S. Science and 

Engineering Landscape

Changes in the major institutions that engage in S&E 

R&D and help prepare the workforce of the future usually occur gradually, typically over a longer time scale than 

changes in economic markets. This section describes consequential changes and continuities in the major institutions 

involved in U.S. S&E activity over the last two decades, focusing on institutional features that play important roles in 

R&D and in S&E education. Attention is devoted primarily 

to higher education, industry, and government, which are 

the largest funders and performers of R&D and the biggest 

employers of workers with S&E training. However, other 

institutions that play important niche roles (e.g., nonprofit 

funders and performers of research; federally funded research and development centers [FFRDCs]) are also mentioned. Other institutions that lay important foundations for 

a knowledge economy (e.g., K–12 education) are discussed 

in the body of the report.

Cross-Sector Collaboration

Ironically, a focus on institutions highlights one of the 

most striking changes in the U.S. S&E landscape in recent 

years—the growth of cross-institution, cross-sector, and 

cross-national collaboration. Institutions and disciplines 

that formerly inhabited almost entirely separate worlds more 

frequently collaborate on projects and cross boundaries to 

enter previously unfamiliar territory.

Publication data show the clearest evidence of this trend. 

Although the distribution of S&E publication activity between academic and nonacademic institutions remained 

relatively stable between 1997 and 2012 (figure O-19), 

with academic institutions producing the large majority of 

Figure O-18

Global exports of royalties and fees, by selected 

region/country/economy: 2004–11

Billions of dollars

EU = European Union.

NOTES: EU exports do not include intra-EU exports. Developed 

countries are classied as high-income economies by the World 

Bank. Developing countries are classied as upper- and lowermiddle income and low income by the World Bank. Sum of 

regions/countries/economies does not add up to total due to 

rounding and discrepancies.

SOURCE: World Trade Organization, International trade and tariff 

data, http://www.wto.org/english/res_e/statis_e/statis_e.htm, 

accessed 8 August 2013.

Science and Engineering Indicators 2014

2004 2005 2006 2007 2008 2009 2010 2011

0

40

80

120

160

200

240

280

World 

United States 

EU 

Japan 

Other developed 

China 

Other developing

 

NOTES: Article counts are from the set of journals covered by the 

Science Citation Index (SCI) and Social Sciences Citation Index 

(SSCI). Articles are classied by the year they entered the database 

and are assigned to U.S. institution(s) based on the institutional 

address(es) listed in the article. Articles are credited on a fractional 

count basis; for articles with institutional addresses from multiple 

countries/U.S. institutions, each U.S. institution receives fractional 

credit on the basis of the proportion of its participating institutions. 

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, and The Patent Board,

TM special tabulations 

(2013) from Thomson Reuters, SCI and SSCI, http://thomsonreuters. 

com/products_services/science/. See appendix table 5-40.

Science and Engineering Indicators 2014

Figure O-19

U.S. academic and non-academic S&E articles: 

1997–2012

Thousands of articles

1997 2000 2002 2004 2006 2008 2010 2012

0

50

100

150

200

250

U.S. academic institution

U.S. non-academic institution
O-14 ♦ Overview

publications, the proportion of collaborative publications 

increased. The share of S&E articles with more than one 

named author grew, as did the percentages involving institutional and international coauthorships (figures O-20 and 

O-21).

From 1990 to 2012, the share of purely U.S. S&E articles 

with authors from multiple institutions grew from 34% to 

62%. Collaborative publication was more common in the 

U.S. academic sector than in other U.S. institutional sectors. 

The share of purely U.S. academic articles with authors from 

multiple academic institutions rose from 16% in 1990 to 

31% in 2012 (figure O-20).8 Other U.S. institutional sectors 

showed a similar trend toward collaborative publication 

among multiple institutions during this period. The average 

number of authors on papers published by authors from U.S. 

academic institutions also increased considerably, rising 

from 3 authors in 1990 to 8 authors in 2012 (figure O-21).

Between 1997 and 2012, internationally coauthored articles grew from 16% to 25% of the world’s total. In the 

United States, the trend toward more international collaboration was even stronger. The percentage of U.S. articles with coauthors from institutions in other countries 

almost doubled (from 19% to 35%) between 1997 and 2012. 

Worldwide in 2012, 59% of all S&E articles with only domestic authors were produced with coauthors at different 

institutions (43% in 1997). Collaborative research articles 

receive more citations than single-author articles, suggesting higher quality or greater impact.

Publication data reveal increased collaboration 

between U.S. authors at academic institutions and other 

organizations that perform R&D, indicating a growing 

connection between the basic research performed in the 

academic sector and the more applied work characteristic 

of other sectors. In various institutional sectors—including 

industry, federal government, FFRDCs, and private 

nonprofit—the proportion of articles with academic sector 

coauthors increased by about 12–14 percentage points 

between 1997 and 2012.

The flow of funding among institutions also illustrates 

the trend toward collaborative research. Over the past 15 

years, pass-through funding, in which funding for R&D 

at one university is shared with one or more collaborating 

institutions, has grown more rapidly than overall academic 

R&D expenditures. Between FY 2000 and FY 2009, the 

pass-through funds that universities provided to other 

universities grew by 171% (from $700 million to $1.9 

billion), while overall academic R&D expenditures grew by 

only 82% (from $30.1 billion to $54.9 billion).

Moreover, a growing proportion of patents are citing S&E literature on their cover pages. This indicates an 

Figure O-20

Share of articles authored at U.S. academic 

institutions that have authors from multiple U.S. 

institutions: Selected years, 1990–2012

Percent 

NOTES: Article counts are from the set of journals covered by the 

Science Citation Index (SCI) and Social Sciences Citation Index 

(SSCI). Articles are classied by the year they entered the database, 

rather than their year of publication, and are assigned to the U.S. 

academic sector on the basis of the institutional address(es) listed in 

the article. All article authors have U.S. academic institutional 

addresses.

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, and The Patent Board,TM special 

tabulations (2013) from Thomson Reuters, SCI and SSCI, 

http://thomsonreuters.com/products_services/science/.

Science and Engineering Indicators 2014

1990 1991 1994 1997 2000 2003 2006 2009 2012

0

10

20

30

40

Figure O-21

Number of authors and authors per paper for U.S. 

academic institutions: 1988–2012

Authors (thousands)

NOTES: Article counts are from the set of journals covered by the 

Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). 

Articles are classied by the year they entered the database, rather 

than their year of publication, and are assigned to the U.S. academic 

sector on the basis of the author institutional address(es) listed in the 

article. All articles have at least one U.S. academic institutional 

address. Authors counted are individual author names on each article, 

and an individual author name is counted each time it appears in 

the dataset. 

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, and The Patent Board,TM special 

tabulations (2013) from Thomson Reuters, SCI and SSCI, 

http://thomsonreuters.com/products_services/science/.

Science and Engineering Indicators 2014

1988 1992 1996 2000 2004 2008 2012

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

Authors per paper

0

1

2

3

4

5

6

7

8

9

Authors per paper

Authors
Science and Engineering Indicators 2014 ♦ O-15

increasing connection between higher education and the 

institutions that translate research findings into commercial 

innovations. Of patents awarded to both U.S. and foreign 

assignees, 12% cited S&E articles in 2003, and that share 

grew to 15% in 2012. 

Just as academic research is increasingly interconnected 

both nationally and globally, business R&D has also been 

developing more international and interorganizational linkages. The rise in these kinds of linkages has coincided with 

the decline of large research organizations, such as Bell 

Labs, that performed fundamental research inside major 

corporations and with a concomitant drop in research 

publications attributed to industry (from 15,614 to 11,779 

between 1990 and 2012).

U.S. Higher Education

Institutions of higher education are responsible for S&E 

education and training and perform the majority of U.S. 

basic research. In these respects, the functions of the higher 

education system have remained largely unchanged in 

recent decades.

The organization of higher education, however, has undergone significant modifications, including changes in the 

opportunity structure for research doctorate holders. Over 

the past 20 years, there has been a declining ratio of tenured 

to nontenured positions, even as the professoriate has aged 

substantially.9 Growth in the numbers of individuals in other 

positions—including academic postdoctorates and nontenured full- and part-time positions—has been substantial.

Between 1995 and 2010, the proportion of S&E faculty 

in academia reporting research as their primary job activity edged up slightly (from 33% to 36%), and the share of 

those identifying teaching as their primary activity fell from 

54% to 47%. Further evidence of the growing importance 

of research in the U.S. academic sector can be seen in the 

growth of research expenditures in general and in revenues 

from federal appropriations, grants, and contracts. 

In public very high research universities,10 inflationadjusted research expenditures grew by about 150%, and 

revenues from federal awards grew by about 190% in the 

same period. In private very high research universities, the 

corresponding growth rates were approximately 160% and 

140% (figure O-22).

Historically, the training of the next generation of 

highly skilled researchers in S&E has been concentrated 

in doctorate-granting institutions with very high research 

activity. It still is, but to a lesser extent than it once was. In 

2011, these institutions awarded 74% of doctoral degrees, 42% 

of master’s degrees, and 38% of bachelor’s degrees in S&E 

fields. That is down from 94% (doctoral), 55% (master’s), and 

45% (bachelor’s) in 1998. The change suggests a growing role 

in advanced S&E education for higher education institutions 

that are less centrally research- and S&E-oriented.

In addition, higher education institutions that are primarily oriented toward teaching, such as community colleges, 

play an important role in preparing students for advanced 

training in S&E. One-fifth of all U.S. citizens or permanent 

residents who received a doctoral degree from 2007 to 2011 

had earned some college credit from a community or 2-year 

college. Moreover, the share of bachelor’s degree recipients 

with at least some credit from community colleges increased 

from 43% in 1999 to 49% in 2010 (figure O-23).

Revenue and expenditure patterns for higher education 

institutions have also undergone significant changes over 

the last two decades. Between 1987 and 2010, state and local 

appropriations per full-time equivalent (FTE) enrolled student at public universities fell by more than 25% on average 

after adjusting for inflation. At the same time, inflationadjusted net student tuition per FTE student more than doubled at these universities, in effect replacing public sources 

of funding with private ones. Tuition and fees for public colleges and universities grew faster than median household 

income during this period (figure O-24).

To acquire revenue to support research and other operating activities, higher education institutions in the United 

States increasingly tapped sources such as higher tuition 

rates that generate revenues from students from moreaffluent families, foreign students who pay full tuition, 

and outside grant support for research activities. Increasing 

grant receipts, however, do not necessarily cover the full 

costs of grant administration, especially in S&E areas, 

such as biomedical research, for which universities must 

Figure O-22

Federal awards and research expenditures at very 

high research activity institutions, by institutional 

control: 1987–2010

Billions of constant 2005 dollars

NOTES: Gross domestic product implicit price deators are used to 

convert current dollars to constant 2005 dollars. Very high research 

activity institutions are designated by the 2005 Carnegie classication

code. See The Carnegie Classication of Institutions of Higher 

Education, http://classifications.carnegiefoundation.org/index.php. 

SOURCES: IPEDS Analytics: Delta Cost Project Database: 1987– 

2010 and National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2013) of the Higher 

Education Research and Development Survey.

Science and Engineering Indicators 2014

1987 1990 1994 1998 2002 2006 2010

0

5

10

15

20

25

Federal awards,

public institution

Research expenditures,

public institution

Federal awards,

private institution

Research expenditures,

private institution
O-16 ♦ Overview

bear the significant costs of monitoring compliance with 

research regulations.

Finally, among various long-term changes, one feature 

of the higher education research landscape shows remarkable continuity. The bulk of R&D expenditures in the United 

States are concentrated among a small number of researchintensive institutions, and the extent of this concentration has 

remained very consistent over the last two decades, even as 

the identity of the institutions in the top groups has changed. 

In FY 2012, the top 10 institutions in terms of R&D performance accounted for 18.0% (18.8% in FY 1989), the top 20 

for 30.6% (32.5%), and the top 100 for 78.8% (82.0%).

Degree Production

With the growth of a knowledge economy over recent 

decades, a larger number of U.S. students are getting S&E 

degrees and eventually finding jobs in S&E occupations. 

Between 2000 and 2011, there were sizeable increases in the 

number of earned S&E degrees at the bachelor’s (+39.1%), 

master’s (+56.6%), and doctoral levels (+35.5%) (figure 

O-25). These increases were similar to the corresponding 

increases for degrees in all fields in the same period—38.2% 

(bachelor’s), 60.1% (master’s), and 33.2% (doctoral). 

As the number of S&E bachelor’s degrees has grown 

steadily over the past 15 years (with a new peak of over 

half a million in 2011 [figure O-26]), increasing proportions 

of the graduates earning those degrees have been women 

or members of racial and ethnic minorities (figure O-27). 

Since the late 1990s, about 57% of all bachelor’s degrees 

and half of all S&E degrees have been awarded to women. 

Percentages of S&E degrees awarded to women are highest 

Figure O-23

Community college attendance among recent S&E 

bachelor’s recipients: 1999–2010

Percent 

NOTES: Recent graduates are those who earned degrees in the 

2 academic years preceding the survey year or, for the 2006 

survey year, in the 3 preceding academic years. For 2006, recent 

graduates are those who earned degrees between 1 July 2002 and 

30 June 2005. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2010) of the National 

Survey of Recent College Graduates.

Science and Engineering Indicators 2014

1999 2001 2003 2006 2008 2010

0

10

20

30

40

50

Figure O-24

Selected average revenues and expenditures at 

public very high research universities: 1987–2010

2010 dollars

NOTE: Data are per full-time equivalent student.

SOURCE: IPEDS Analytics: Delta Cost Project Database, 1987–2010, 

special tabulations (2013).

Science and Engineering Indicators 2014

1987 1990 1993 1996 2001 2004 2007 2010

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

Expenditures—Instruction

Revenues—Net tuition

Revenues—State and local appropriations

Figure O-25

S&E degrees, by level: 2000–11

Thousands

NOTES: Data are based on degree-granting institutions eligible to 

participate in Title IV federal nancial aid programs and do not match 

previously published data from Science and Engineering Indicators 

2008 and earlier years that were based on accredited higher 

education institutions. S&E doctorates exclude other health sciences 

because of changes in doctoral categories in the source data. 

SOURCES: National Center for Education Statistics, Integrated 

Postsecondary Education Data System, Completions Survey; and 

National Science Foundation, National Center for Science and 

Engineering Statistics, Integrated Science and Engineering 

Resources Data System, http://webcaspar.nsf.gov.

Science and Engineering Indicators 2014

2000 2003 2005 2007 2009 2011

0

100

200

300

400

500

600

Bachelor’s

Master’s

Doctorate
Science and Engineering Indicators 2014 ♦ O-17

in the biological, agricultural, and social sciences and in psychology. At the same time, for all racial and ethnic groups, 

the total number of bachelor’s degrees earned, the number 

of S&E bachelor’s degrees earned, and the number of bachelor’s degrees in most S&E fields (except computer science) 

have generally increased since 2000. 

For over 20 years, about one-third of U.S. bachelor’s degrees have been awarded in S&E fields. Likewise, the distribution of degrees across S&E fields remained remarkably 

similar between 2000 and 2011. Percentages of bachelor’s 

degrees in S&E were almost unchanged in engineering 

(about 14% in both years), biological and agricultural sciences (21%), and psychology (18%). Physical sciences (3.7% 

in 2000; 3.5% in 2011) and mathematics (2.9% in 2000; 

3.3% in 2011) also did not exhibit major changes. Social sciences experienced a slight increase (28.5% in 2000; 31.1% 

in 2011) and computer sciences a small decrease (9.4% in 

2000; 7.9% in 2011).

Demographics of the U.S. S&E Labor Force

Although the demographics of persons receiving S&E 

training and entering the S&E labor force remain quite 

different from those of the general U.S. population, there 

has been some general movement toward more diversity of 

participation in S&E occupations. Proportions of workers 

in minority groups have increased, while the percentage of 

whites has dropped from 84% in 1993 to 70% in 2010.

While women represent half of the college-educated 

workforce, they are underrepresented in the S&E 

workforce. In 2010, women accounted for only 37% of 

employed individuals with a highest degree in an S&E field 

and 28% of employed individuals in S&E occupations. Yet, 

these percentages represent increases since 1993, when 

the comparable figures were 31% and 23%, respectively 

(figures O-28 and O-29).

S&E participation has also risen over time among racial 

and ethnic minorities, particularly among Asians but also, to 

a lesser degree, among Hispanics and blacks (figure O-30). 

Despite this increase, participation varies substantially 

across groups. In 2010, Asians worked in S&E occupations 

at much higher rates (19%) than their representation in the 

general U.S. population (5%), whereas historically underrepresented racial and ethnic groups, particularly blacks 

and Hispanics, represented a much smaller proportion of 

the S&E workforce than their share of the U.S. population. 

In total, Hispanics, blacks, and American Indians or Alaska 

Natives account for 26% of the U.S. population age 21 and 

over but only for 10% of workers in S&E occupations and 

for 13% of S&E highest degree holders. In comparison, in 

1993, Hispanics and blacks accounted for 7% of workers in 

S&E occupations, 8% of S&E highest degree holders, and 

9% of the college-degreed workforce.

The share of workers holding a bachelor’s degree or above 

in S&E occupations who are foreign born has increased over 

the last decade. Among college-educated S&E workers, the 

SOURCES: National Center for Education Statistics, Integrated 

Postsecondary Education Data System, Completions Survey; and 

National Science Foundation, National Center for Science and 

Engineering Statistics, Integrated Science and Engineering 

Resources Data System, http://webcaspar.nsf.gov.

Science and Engineering Indicators 2014

Thousands

Figure O-26

Bachelor’s degrees, by broad field of degree: 

2000–11

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

Non-S&E elds

S&E elds

Figure O-27

Share of S&E bachelor’s degrees among U.S. 

citizens and permanent residents, by race and 

ethnicity: 2000–11

URM = underrepresented minorities (black, Hispanic, and American 

Indian or Alaska Native). 

NOTES: Hispanic may be any race. American Indian or Alaska Native, 

Asian or Pacic Islander, black or African American, and white refer 

to individuals who are not of Hispanic origin. Percentages do not sum 

to 100 because data do not include individuals who did not report 

their race and ethnicity.

SOURCES: National Center for Education Statistics, Integrated 

Postsecondary Education Data System, Completions Survey; and 

National Science Foundation, National Center for Science and 

Engineering Statistics, WebCASPAR database, 

http://webcaspar.nsf.gov.

Science and Engineering Indicators 2014

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

0

2

4

6

8

10

12

URM and Asian or 

Pacic Islander (Percent) White (Percent)

Asian or Pacic Islander

Hispanic

American Indian or Alaska Native

Black or

African

American

White

0

57

59

61

63

65

67

69

71

73

75
O-18 ♦ Overview

foreign-born share increased from 22.4% in 2000 to 26.2% 

in 2011 (figure O-31). The percentage of workers with a doctorate who are foreign born increased from 37.6% in 2000 

to 43.2% in 2011. For holders of bachelor’s and master’s 

degrees, the changes were, respectively, from 16.5% to 

19.0% and from 29.0% to 34.3% between 2000 and 2011.

Among foreign-born individuals with S&E doctorates living in the United States in 2010, slightly more than 

Figure O-28

Women in the workforce and in S&E: 1993 and 2010

Percent 

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) and National Survey of College Graduates (NSCG) 

(1993 and 2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

College-educated

workforce

S&E occupations S&E highest degree

0

10

20

30

40

50

60

1993 2010

Figure O-29

Women in S&E occupations: 1993–2010

Percent

NOTE: National estimates were not available from the Scientists and 

Engineers Statistical Data System (SESTAT) in 2001.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, SESTAT (1993–2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

1993 1995 1997 1999 2003 2006 2008 2010

0

10

20

30

40

50

60

70

80

All S&E occupations

Computer/mathematical scientists

Biological/agricultural/environmental life scientists

Physical scientists

Social scientists

Engineers

Figure O-30

Share of workers in S&E occupations, by selected 

race and ethnicity: Selected years, 1993–2010

Percent

NOTES: Before 2003, Asian included Native Hawaiians and Other 

Pacic Islanders. Hispanic may be any race. Asian and black or 

African American refer to individuals who are not of Hispanic origin.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (1993–2010), http://sestat.nsf.gov. 

Science and Engineering Indicators 2014

1993 1995 1997 1999 2003 2006 2008 2010

0

5

10

15

20

Asian

Black or 

Hispanic African American

Figure O-31

Foreign-born workers in S&E occupations, 

by education level: 2000, 2006, and 2011

Percent 

NOTES: All college educated includes professional degrees not 

broken out separately. These data include all S&E occupations except 

postsecondary teachers because these occupations are not 

separately identiable in the source data les.

SOURCES: Census Bureau, 2000 Decennial Census Public Use 

Microdata Sample (PUMS), and American Community Survey 

(2006, 2011).

Science and Engineering Indicators 2014

2000 2006 2011

0

10

20

30

40

50

All college

educated

Bachelor's Master's Doctorate
Science and Engineering Indicators 2014 ♦ O-19

one-third were born in China (23%) and India (13%) (figure O-32). After rising for most of the 2000–09 decade, 

the number of foreign recipients of U.S. S&E doctoral 

degrees declined in 2009 and 2010. Newer data indicate 

a slight increase, suggesting that the decline may have 

been temporary.

R&D Funding

Of the more than $420 billion of U.S. R&D funding, over 

90% comes from either the business sector (63% in 2011) 

or the federal government (30% in 2011). These proportions have been relatively stable over the last decade (69% 

and 25%, respectively, in 2000). Consistent with the growing commercial relevance of systematic knowledge, business sector funding as a proportion of overall R&D funding 

increased rapidly for over 30 years beginning in 1965. In 

the last two decades, however, federal funding has also increased substantially, and the ratio between U.S. federal and 

business sector R&D funding has been relatively stable, with 

U.S. federal funding being somewhat less than half the size 

of business sector spending on R&D since the mid-1990s. 

Thus, although federal funding as a proportion of national 

R&D had declined during the decades following World War 

II, the federal government has continued to fund a large and 

generally stable share of national R&D over the last decade 

(figure O-33).

During the last two decades, the division in national R&D 

among basic research, applied research, and development has 

also been fairly stable (18%, 19%, and 63%, respectively, in 

2011). Different institutions tend to perform different kinds 

of R&D projects. In 2011, the business sector was the largest 

performer of R&D activities in the United States (70%) because it performed most of U.S. applied research (57%) and 

development (88%). It executes relatively little basic research 

(17% in 2011). The academic sector, which performed only 

15% of national R&D in 2011, in contrast, accounted for most 

U.S. basic research (55%). 

In many respects, federal funding patterns show 

substantial continuity. Thus, the Department of Defense has 

continually accounted for more than half of annual federal 

R&D spending. Likewise, federal funding consistently has 

been the main source of funding for academic R&D. Over 

the last decade, the federally funded proportion of R&D 

at public academic institutions increased from 52% (1999) 

to 58% (2012). At private institutions, it remained roughly 

constant, at or around 72% (figure O-34). For all academic 

institutions, the share of academic R&D expenditures 

that is funded by the institutions themselves has increased 

substantially over the last four decades. It grew from about 

12% in 1972 to approximately 19% in 1990 and has remained 

relatively stable since then.

Federal R&D spending over the last two decades 

has changed substantially in one respect: health-related 

R&D has grown sharply, going from 12% of total federal 

R&D budget authority in FY 1980 to 22% in FY 2011. A 

corresponding major shift has occurred in the distribution 

of academic R&D expenditures among S&E fields, which 

has moved away from physical sciences and toward the life 

sciences. Data on research space at academic institutions 

and publications likewise reflect a more dominant role for 

life sciences in academic R&D. 

During the international financial crisis that started in 

late 2008, the three institutional sectors mainly responsible 

Figure O-32

Foreign-born individuals with highest degree in S&E 

living in the United States, by place of birth: 2010

Percent 

NOTE: Only countries/economies with shares of 3% or more 

are shown. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (2010), http://sestat.nsf.gov. 

Science and Engineering Indicators 2014

China India United

Kingdom

Canada Germany Taiwan South

Korea 

0

5

10

15

20

25

Figure O-33

Ratio of U.S. federal-to-nonfederal funding for 

R&D: 1953–2011

Ratio

NOTE: Federal R&D/gross domestic product ratios represent the 

federal government as a funder of R&D by all performers; the 

nonfederal ratios reect all other sources of R&D funding. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, National Patterns of R&D Resources 

(annual series). 

Science and Engineering Indicators 2014

1953 1963 1969 1975 1981 1987 1993 1999 2005 2011

0

0.5

1.0

1.5

2.0

2.5
O-20 ♦ Overview

for R&D funding and performance—business, universities 

and colleges, and the federal government—faced budgetary 

challenges. Many businesses were unable to secure credit or 

were unwilling to make investments in view of uncertainty 

about the length and the intensity of the economic downturn. 

Universities and colleges faced steep budget cuts, prompted 

by declining state appropriations or shrinking endowments. 

Along with many governments across the world, the 

federal government took on unexpected and unprecedented 

financial commitments to guarantee the integrity of the 

international and national financial systems.

Consequently, R&D investments in all three sectors 

were curtailed and broke away from their long-term growth 

trend. In the United States, for the first time in 50 years, 

R&D expenditures remained stagnant in 2009 (figure 

O-35). The main reason for this was a sharp reduction in 

business R&D. The overall national impact was tempered 

by the infusion of American Recovery and Reinvestment 

Act of 2009 (ARRA) R&D funding during the depths of 

the downturn. After ARRA funding subsided, business 

R&D growth led a rebound in overall national R&D. Figure 

O-36 illustrates the expenditures by various R&D funding 

sectors over the 5 years ending in 2011 (figure O-36).

While R&D expenditures have recovered to some 

extent, the deviation from the overall long-term trend 

Figure O-34

Sources of S&E R&D funding for public and private academic institutions: FYs 1999 and 2012

Percent 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Academic Research and Development Survey and the 

Higher Education Research and Development Survey (various years). 

Science and Engineering Indicators 2014

Federal government State and local government Business Academic institutions Other

0

20

40

60

80

Public 1999 Public 2012 Private 1999 Private 2012

Figure O-35

U.S. total R&D expenditures: 1953–2011

Billions of dollars

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, National Patterns of R&D Resources 

(annual series). See appendix table 4-2.

Science and Engineering Indicators 2014

1953 1963 1969 1975 1981 1987 1993 1999 2005 2011

0

50

100

150

200

250

300

350

400

450

Current dollars

Constant 2005 dollars

remains discernible in the data. Adjusted for inflation 

(in 2005 dollars), R&D expenditures in the United States 

for 2011 ($374.4 billion) were about the same as in 2008 

($374.5 billion).
Science and Engineering Indicators 2014 ♦ O-21

Conclusion

In recent decades, the implications of investment in science and engineering and in knowledge- and technologyintensive industries for economic prosperity have become 

increasingly important. This is indicated by the rise in 

knowledge- and technology-intensive production and trade 

and by increased investments in R&D across the world.

The global economic downturn had a significant impact 

on S&E-related trends, especially in developed economies. 

Its effects included increased funding uncertainty affecting 

R&D activities and changes in institutions of higher learning, such as more reliance on nontenured positions. During 

the downturn, economic activity involving S&E increased 

in the developing world, continuing the gradual shift in the 

world’s knowledge-based economic activity toward developing nations and away from developed ones. The increase was 

pronounced in Asia but also notable in other parts of the developing world. Knowledge-intensive services in developing 

countries grew rapidly, especially in China.

U.S. knowledge- and technology-intensive industries, as 

well as the U.S. economy generally, weathered the global 

economic downturn better than comparable industries and 

economies in the EU and Japan. Smaller, more recently developed economies in South Korea and Taiwan also withstood the downturn relatively well.

Concurrent with the downturn, several emerging economies demonstrated significant growth in scientific output, 

as measured by publications and patents. The growth in 

publication output in China was striking, and the influence 

of China’s publications also increased. In addition, rapid 

growth (6%–9% average annually) in three other Asian locations—South Korea, Taiwan, and Singapore—also reduced 

the global share of S&E publication by the United States, the 

EU, and Japan.

Recently developed economies are becoming better 

positioned to challenge the S&E leadership of developed 

economies. Economies such as South Korea, Taiwan, and 

Singapore, with their emphasis on high-quality education, 

are poised to narrow the gap. China, with a per capita income comparable to other developing countries, is unique 

among developing countries in having a global presence 

in knowledge- and technology-intensive economic activity 

and R&D performance that is comparable to or exceeds that 

of most long-standing developed countries.

As the world economy has changed, the U.S. S&E enterprise has also undergone substantial changes in the last two 

decades. The recent economic downturn disturbed the continuity of trends that had characterized the major institutions 

that fund and perform U.S. R&D and that provide advanced 

training in S&E. Such breaks in long-term patterns included 

lower R&D investments by businesses as well as slightly 

decreased stay rates of foreign recipients of advanced S&E 

degrees. However, many of those developments appear to 

have been temporary, and there are signs of a return to predownturn patterns and trends. 

Nevertheless, the ongoing economic recovery has 

brought with it indications of emerging changes in S&E 

education and R&D. Potentially disruptive developments 

include the emergence of massive open online courses as an 

Figure O-36

Year-to-year changes in U.S. R&D expenditures, by performing sector: 2006–11

Billions of current dollars

FFRDC = federally funded R&D center.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, National Patterns of R&D Resources (annual series). 

Science and Engineering Indicators 2014

All performers Business Federal

intramural

FFRDCs Universities

and colleges

Other nonprot

organizations

–15

–10

–5

0

5

10

15

20

25

30

2006–07 2007–08 2008–09 2009–10 2010–11
O-22 ♦ Overview

avenue for trying to reduce the cost of higher education and 

the continuing R&D budget uncertainty that accompanies a 

difficult fiscal environment.

As more countries around the world develop R&D and 

human capital infrastructure to sustain a knowledge-oriented economy, the United States, not surprisingly, is playing 

a less dominant role in many areas of S&E-related activity. 

However, it remains the world’s leading nation in numerous 

indicators of S&E activity, such as high-value patenting, 

that can have a large impact on innovation and economic 

growth. Moreover, the increasing interconnectedness of 

both the global economy and the international scientific 

community may provide opportunities for improvements in 

U.S. S&E and the U.S. economy and also for increased sharing of the gains of international R&D.

Notes

1. Countries classified by the World Bank as high income 

are developed countries, while those classified in the other 

income levels—upper middle income, lower middle income, 

and low income—are classified as developing. Russia, 

which the World Bank recently classified as a developed 

country, reported a substantially higher proportion (54%) 

of KTI activity in its economy in 2012 than the United 

States. However, large year-to-year fluctuations in Russian 

estimates (e.g., from 30.7% in 2005 to 38.9% in 2006) 

strongly suggest that these data are not reliable.

2. The East and Southeast Asia region includes China, 

Indonesia, Japan, Malaysia, Singapore, South Korea, 

Taiwan, and Thailand.

3. The rapid decline in this measure for China in 2008–

09 is due to a methodological change. Since 2009, China has 

collected data on researchers using the international statistical system definition of researcher in the OECD Frascati 

Manual, whereas earlier Chinese data often used a more expansive United Nations Educational, Scientific and Cultural 

Organization (UNESCO) concept (see [OECD 2012:29]).

4. Changes in data collection practices in South 

Korea and China make comparisons of recent data with 

pre-2006 data (for South Korea) and pre-2009 data (for 

China) problematic. 

5. The Population White Paper published in early 2013 

estimates that the number of Singaporeans in “Professional, 

Managerial, Executive and Technical (PMET) jobs” (NPTD 

2013:4), which are roughly equivalent to S&E occupations, 

is expected to rise by nearly 50% to about 1.25 million, 

compared to 850,000 today.

6. The article counts, coauthorships, and citations discussed here are derived from publications data recorded by 

the Science Unit of Thomson Reuters in the Science Citation 

Index and Social Sciences Citation Index (http://www.

thomsonreuters.com/business_units/scientific/). Chapter 5 

(sidebar “Bibliometric Data and Terminology”) provides 

details about how publication indicators are tabulated.

7. If a country receives 10% of the citations in the worldwide scientific literature, its expected number of citations 

by any given country would be 10% of that country’s total 

citations. Similarly, if a country is credited with authorship 

of 10% of the world’s internationally coauthored articles, 

it would be expected to coauthor 10% of the international 

articles attributed to any other country. A more detailed explanation of citation and coauthorship indexes can be found 

in chapter 5 under the sidebar “Normalizing Coauthorship 

and Citation Data.”

8. In these data, articles are attributed to different U.S. 

academic institutions only when the authors are from 

different universities or colleges, not when they come 

from different units of the same university or college (e.g., 

the engineering school and the economics department). 

In contrast, chapter 5 treats all articles whose authors 

report different institutional addresses as instances of 

interinstitutional collaboration, even when the addresses are 

part of the same university. Using the less stringent chapter 5 

collaboration indicator, the increase in the proportion of U.S. 

academic articles involving interinstitutional collaboration 

shows a similar trend, rising from 34% in 1990 to 51% in 

2012. International data in the overview use the chapter 5 

collaboration indicator; international data unifying different 

addresses that can be considered part of the same institution 

are not currently available.

9. Full-time, tenure-track faculty positions as either 

senior or junior faculty continue to be the norm in academic 

doctoral employment. Such positions constituted about 90% 

of academic doctoral positions in the early 1970s but had 

dropped to about 80% by the mid-1990s and to about 70% 

by 2010.

10. The Carnegie Classification of Institutions of Higher 

Education considers doctorate-granting universities that 

award at least 20 doctoral degrees per year to be research 

universities. The 2010 Carnegie Classification includes 

three subgroups of research universities based on the 

level of research activity: very high research activity (108 

institutions), high research activity (99 institutions), and 

doctoral/research universities (90 institutions).

Glossary

European Union (EU): As of June 2013, the EU 

comprised 27 member nations: Austria, Belgium, Bulgaria, 

Cyprus, Czech Republic, Denmark, Estonia, Finland, France, 

Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, 

Luxembourg, Malta, the Netherlands, Poland, Portugal, 

Romania, Slovakia, Slovenia, Spain, Sweden, and the 

United Kingdom. Croatia joined the EU in July 2013. Unless 

otherwise noted, Organisation for Economic Co-operation 

and Development data on the EU include all 28 members; 

data on the EU from other sources are limited to the 27 

nations that were members as of June 2013.
Science and Engineering Indicators 2014 ♦ O-23

High-technology (HT) manufacturing: Includes 

air- and spacecraft; pharmaceuticals; office, accounting, 

and computing machinery; radio, television, and communication equipment; and medical, precision, and optical 

instruments.

Knowledge- and technology-intensive (KTI) industries: They consist of high-technology manufacturing and 

knowledge-intensive service industries.

Knowledge-intensive (KI) services: Includes 

commercial business, financial, and communication services 

and largely publicly supported education and health services. 

Commercial KI services exclude education and health.

References

European Commission (EC). 2013. Europe 2020 Targets. 

Available at http://ec.europa.eu/europe2020/europe-2020-

in-a-nutshell/targets/index_en.htm. Accessed 6 November 

2013.

National Population and Talent Division (NPTD). 2013. 

A sustainable population for a dynamic Singapore: 

Population white paper. Available at http://population.sg/

whitepaper/resource-files/population-white-paper.pdf. 

Accessed 12 September 2013.

Organisation for Economic Co-operation and Development 

(OECD). 2012. Main Science and Technology Indicators.

Vol. 2011/2. Paris.

Thursby J, Thursby M. 2006. Here or there? A survey of 

factors in multinational R&D location. Report to the 

Government-University-Industry Research Roundtable. 

Washington, DC: National Academy of Sciences.
Chapter 1

Elementary and Secondary 

Mathematics and 

Science Education

♦ 1-1

Highlights.....................................................................................................................................1-4

Student Learning in Mathematics and Science.........................................................................1-4

Student Coursetaking in Mathematics and Science..................................................................1-4

Teachers of Mathematics and Science......................................................................................1-5

Instructional Technology and Digital Learning........................................................................1-6

Transition to Higher Education ................................................................................................1-6

Introduction..................................................................................................................................1-8

Chapter Overview.....................................................................................................................1-8

Chapter Organization................................................................................................................1-8

Student Learning in Mathematics and Science..........................................................................1-10

Mathematics and Science Performance During the Kindergarten Year.................................1-10

Mathematics and Science Performance in Grades 4 and 8.....................................................1-12

International Comparisons of Mathematics and Science Performance ..................................1-17

Student Coursetaking in Mathematics and Science ...................................................................1-19

High School Graduation Requirements and Curriculum Standards.......................................1-20

Ninth Grade Mathematics and Science Coursetaking ............................................................1-21

Participation and Performance in the Advanced Placement Program....................................1-23

Teachers of Mathematics and Science.......................................................................................1-26

Characteristics of High-Quality Teachers ..............................................................................1-26

Teacher Professional Development ........................................................................................1-31

Teachers’ Working Conditions...............................................................................................1-31

Mathematics and Science Teacher Attrition...........................................................................1-33

Instructional Technology and Digital Learning.........................................................................1-34

Technology as an Instructional Tool ......................................................................................1-35

Research on Instructional Technology ...................................................................................1-35

Internet Access........................................................................................................................1-36

Distance Education and Online Learning...............................................................................1-36

Research on Effectiveness of Online Learning ......................................................................1-37

Transition to Higher Education..................................................................................................1-38

Completion of High School....................................................................................................1-38

Enrollment in Postsecondary Education.................................................................................1-39

Conclusion .................................................................................................................................1-41

Notes..........................................................................................................................................1-42

Glossary .....................................................................................................................................1-44

References..................................................................................................................................1-45
1-2 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

List of Tables

Table 1-1. Indicators of elementary and secondary school mathematics and 

science education ....................................................................................................................1-9

Table 1-2. Changes in NAEP mathematics and science score gaps between 

selected groups of students in grades 4 and 8: 1990–2011 ...................................................1-16

Table 1-3. Highest-level mathematics course in which ninth graders enrolled, 

by student and family characteristics: 2009..........................................................................1-21

Table 1-4. Highest-level science course in which ninth graders enrolled, by student 

and family characteristics: 2009............................................................................................1-23

Table 1-5. Public school students who took or passed an AP exam in high school, 

by subject: Graduating classes 2002, 2007, and 2012 ..........................................................1-25

Table 1-6. Public school students who took or passed an AP exam as a proportion of 

overall student population, by subject: Graduating classes 2002, 2007, and 2012...............1-25

Table 1-7. Mathematics and science teachers, by path to certification and grade 

level: 2012.............................................................................................................................1-28

Table 1-8. Mathematics and science teachers with an undergraduate or graduate degree 

in mathematics or science, by grade level: 2012...................................................................1-29

Table 1-9. Mathematics and science teachers considering themselves very well prepared 

for various tasks associated with instruction, by grade level: 2012......................................1-31

Table 1-10. Mathematics and science teachers, by most recent participation in 

subject-focused professional development and grade level: 2012........................................1-32

Table 1-11. Mathematics and science teachers spending time in subject-focused 

professional development in the past 3 years, by grade level: 2012.....................................1-32

Table 1-12. School program representatives reporting various issues as serious problems 

for mathematics and science instruction, by school level: 2012...........................................1-33

Table 1-13. Public school teachers reporting the availability and frequency of use of 

technology devices, by school level: 2009............................................................................1-35

Table 1-14. Public school districts with students enrolled in distance education courses 

indicating how important various reasons were for having distance education courses 

in their district, by district characteristic: School year 2009–10...........................................1-37

Table 1-15. Beginning 2003–04 postsecondary students who took remedial courses 

during their enrollment, by type of first institution: 2003–09...............................................1-41

Table 1-A. Average number of AP mathematics and science courses offered in 

high schools, by school characteristic: 2012.........................................................................1-24

List of Sidebars

Race to the Top ............................................................................................................................1-8

Monitoring Progress Toward Successful K−12 STEM Education..............................................1-9

The Role of Nonschool Factors in Student Learning.................................................................1-13

TIMSS 2011 Sample Items........................................................................................................1-17

Common Core State Standards and Next Generation Science Standards..................................1-20

Access to Advanced Placement Courses in Mathematics and Science .....................................1-24

100Kin10....................................................................................................................................1-34
Science and Engineering Indicators 2014 ♦ 1-3

List of Figures

Figure 1-1. Average mathematics assessment scores of first-time kindergartners, 

by child and family characteristics: Fall 2010 and spring 2011............................................1-11

Figure 1-2. Average NAEP mathematics scores of students in grades 4 and 8: 1990–2011........1-14

Figure 1-3. Students in grades 4 and 8 scoring at or above NAEP’s proficient level 

in mathematics for their grade: 1990–2011 ..........................................................................1-14

Figure 1-4. Average NAEP science scores of students in grade 8, by student and 

school characteristics: 2009 and 2011...................................................................................1-15

Figure 1-5. Average NAEP mathematics scores of students in grade 8, by sex, race, 

and ethnicity: 2011................................................................................................................1-15

Figure 1-6. Average NAEP mathematics scores and score gaps for white and black 

students in grade 4: 1990–2011.............................................................................................1-16

Figure 1-7. Average TIMSS mathematics scores of students in grades 4 and 8, 

by country/jurisdiction: 2011 ................................................................................................1-18

Figure 1-8. Average TIMSS mathematics and science scores of U.S. students in 

grades 4 and 8: 1995–2011 ...................................................................................................1-19

Figure 1-9. Average TIMSS science scores of students in grades 4 and 8, 

by country/jurisdiction: 2011 ................................................................................................1-19

Figure 1-10. Highest-level mathematics course in which ninth graders enrolled, 

by socioeconomic quintile: 2009 ..........................................................................................1-22

Figure 1-11. Highest-level science course in which ninth graders enrolled, 

by socioeconomic quintile: 2009 ..........................................................................................1-22

Figure 1-12. Public school students in graduating class of 2012 who took AP exams 

in mathematics and science in high school, by sex...............................................................1-26

Figure 1-13. Mathematics and science teachers’ years of experience teaching 

their subject, by grade level: 2012 ........................................................................................1-27

Figure 1-14. Mathematics and science classes taught by teachers with 2 years or 

less of experience teaching their subject, by students in school eligible for 

free/reduced-price lunch: 2012 .............................................................................................1-28

Figure 1-15. Elementary teachers meeting NCTM- and NSTA-recommended 

college-level coursework in mathematics and science: 2012 ...............................................1-30

Figure 1-16. Elementary teachers’ self-assessment of their preparedness 

to teach mathematics and science: 2012 ...............................................................................1-30

Figure 1-17. Beginning public elementary and secondary teachers (2007–08) 

who had left teaching by 2009–10 ........................................................................................1-34

Figure 1-18. On-time graduation rates of U.S. public high school students, 

by race and ethnicity: 2006 and 2010 ...................................................................................1-39

Figure 1-19. Immediate college enrollment rates among high school graduates, 

by institution type: 1975–2011..............................................................................................1-39

Figure 1-20. First-time entry rates into university-level education, 

by OECD country: 2010........................................................................................................1-40

Figure 1-A. High school students with access to various AP mathematics 

and science courses: 2012 .....................................................................................................1-24
1-4 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

Student Learning in Mathematics 

and Science

U.S. fourth and eighth graders have made substantial 

gains in mathematics since 1990. Although eighth grade 

scores show a continuous upward trend, fourth grade 

scores leveled off during recent years. In science, 2011 

eighth graders performed slightly better than their counterparts tested in 2009.

♦ The average mathematics score at grade 4 rose by 27 

points from 1990 to 2007 and then remained essentially 

flat from 2007 to 2011.

♦ The average mathematics score at grade 8 increased 

steadily from 1990 to 2011 with a total gain of 21 points 

over the period.

♦ The average science score at grade 8 improved slightly, 

increasing from 150 in 2009 to 152 in 2011. (Earlier science assessment scores were not comparable with recent 

ones because of framework changes).

Despite improvement, relatively few students reached 

their grade-specific proficiency levels in mathematics and 

science on the 2011 National Assessment of Educational 

Progress.

♦ In mathematics, the percentage of students reaching the 

proficient level remained well below half in 2011: 40% 

of fourth graders and 35% of eighth graders performed at 

or above this level.

♦ In science, 32% of eighth graders performed at or above 

the proficient level for their grade in 2011.

Performance disparities in mathematics and science 

were evident among different demographic groups 

at grades K, 4, and 8. Some score gaps narrowed over 

time, however.

♦ At grades K, 4, and 8, students from low-income families or homes where the primary language used was not 

English had lower mathematics and science scores than 

their peers from more advantaged backgrounds.

♦ Black, Hispanic, and American Indian or Alaska Native 

students performed substantially lower than their white 

and Asian or Pacific Islander counterparts.

♦ Sex differences in achievement were generally small and 

favored boys in most cases. Among black students, however, girls performed better.

♦ Some gaps in mathematics narrowed over time at grade 

4. Between 1990 and 2011, the score gaps decreased between white and black students (from 32 to 25 points) and 

between low- and high-performing students (i.e., at the 

10th and 90th percentiles) (from 82 to 73 points).

♦ Some gaps in science also narrowed somewhat during 

the relatively short period of time from 2009 to 2011. 

The white-black gap decreased from 36 to 34 points. The 

white-Hispanic gap fell from 30 to 26 points. The gap between low- and high-performing students dropped from 

89 to 87 points.

Although U.S. fourth and eighth graders outperformed 

students in many other countries/jurisdictions on the 

2011 Trends in International Mathematics and Science 

Study (TIMSS) tests, they were not among the very topachieving groups in the world.

♦ The U.S. average score on the 2011 TIMSS mathematics assessment was substantially lower than those of 

seven countries/jurisdictions at grade 4 and those of 

six countries/jurisdictions at grade 8. The top performers—Singapore, Republic of Korea, and two cities (Hong 

Kong and Taipei)—each scored at least 50 points higher 

than the United States at grade 4 (591−606 versus 541) 

and at least 77 points higher than the United States at 

grade 8 (586−613 versus 509).

♦ Between 1995 and 2011, U.S. fourth and eighth graders improved both their scores and international ranking 

in mathematics. In science, U.S. eighth graders’ performance improved, but their relative international ranking 

was unchanged. U.S. fourth graders’ science performance did not change, and their relative international 

position slipped.

Student Coursetaking in Mathematics 

and Science

Algebra 1 and biology 1 were the most common subjects 

taken by ninth graders in 2009.

♦ In mathematics, 52% of ninth graders reported enrollment in algebra 1. In addition, 29% reported enrollment 

in courses above algebra 1, such as geometry.

♦ In science, 38% of ninth graders reported enrollment in 

biology 1, with 32% in earth/environmental/physical science courses and 7% in courses above biology 1.

♦ Nearly twice as many ninth graders reported no science enrollment (18%) as reported no mathematics 

enrollment (10%).

Ninth grade coursetaking in mathematics and science in 

2009 varied by parental education and socioeconomic 

status (SES).

♦ Students who had at least one parent with a master’s degree or higher were more than twice as likely to report enrollment in a mathematics course above algebra 1 (51%) 

as were their peers whose parents had less than a 4-year 

college degree (22%).

Highlights
♦ More than one-fourth of students in the lowest SES category reported no science enrollment (27%), compared 

with 11% of students in the highest SES category.

♦ About 17% of students in the lowest SES category reported no mathematics enrollment, compared with 6% of 

those in the highest SES category.

The number of students taking at least one Advanced 

Placement (AP) exam in mathematics or science has doubled in the past decade from 250,000 students in the class 

of 2002 to 500,000 students in the class of 2012.

♦ Calculus AB and biology were the most popular AP exams in mathematics and science, with 212,000 students 

in the graduating class of 2012 taking calculus AB and 

153,000 students taking biology.

♦ Although more students in the class of 2012 were taking 

AP exams, the AP program in mathematics and science 

involved a relatively small proportion of all high school 

students. Just 17% of all students took an AP mathematics 

or science exam, with 9% passing.

Although increasing numbers of students are taking AP 

exams, passing rates (a score of 3 or higher out of 5) have 

declined or remained steady in most mathematics and 

science subjects. 

♦ The overall passing rate for any AP mathematics or science exam dropped from 62% in 2002 to 54% in 2012. 

♦ The two most popular exams, calculus AB and biology, 

showed the largest decreases, with average passing rates 

dropping by 9 percentage points for calculus AB and 13 

percentage points for biology since 2002.

The proportion of male and female students in the class 

of 2012 taking mathematics and science exams varied by 

subject. Black and Hispanic students were underrepresented among AP exam takers.

♦ Male students were more likely than female students to 

take advanced AP courses, including calculus BC (59% 

versus 41%), physics B (65% versus 35%), and both 

physics C courses (about 75% versus 25%).

♦ Female students were more likely than male students to 

take AP exams in biology (59% versus 41%) and environmental science (55% versus 45%). Male students were 

four times more likely than female students to take the 

computer science A exam (81% versus 19%).

♦ Black students made up about 15% of the 2012 graduating class, but they represented less than 8% of students 

taking any AP mathematics or science exam.

♦ Hispanic students made up about 18% of the class of 

2012, but their representation among AP exam takers 

ranged from a high of 15% for environmental science 

to a low of 8% for calculus BC and 7% for physics C: 

electricity/magnetism.

Teachers of Mathematics and Science

Novice science teachers—those with 2 or fewer years 

of experience—are more prevalent at schools with 

the highest proportions of low-income and non-Asian 

minority students.

♦ In 2012, 23% of science classes at schools with the highest concentrations of students eligible for free/reducedprice lunch (i.e., 75%−100% of students) were taught by 

novice teachers, compared with 10% of science classes at 

schools with the lowest concentrations of free/reducedprice lunch-eligible students (i.e., 0%−25% of students).

♦ Similarly, 21% of science classes at schools with the 

highest concentrations of non-Asian minority students 

were taught by novice teachers, compared with 14% of 

classes at schools with the lowest concentrations of nonAsian minority students.

♦ Students in high-poverty schools were more likely to 

have novice teachers in science than in mathematics: 

23% of science classes compared with 14% of mathematics classes were taught by teachers with 2 or fewer years 

of experience.

A majority of high school mathematics and science 

teachers hold degrees in their teaching field or in science 

or mathematics education.

♦ In 2012, 73% of high school mathematics teachers had 

an undergraduate or graduate degree in mathematics or 

mathematics education, and 82% of high school science 

teachers had an undergraduate or graduate degree in science (any subject), engineering, or science education.

♦ A small percentage (4%−5%) of elementary school teachers of mathematics or science held a degree in mathematics or science.

♦ Mathematics and science classes with the highest concentrations of non-Asian minority students or the lowest-achieving students were less likely to be taught by 

teachers with a degree in their teaching field.

Elementary teachers are much more confident in their 

ability to teach mathematics than in their ability to teach 

science.

♦ In 2012, 77% of elementary teachers reported feeling very 

well prepared to teach mathematics, compared with 39% 

reporting they felt very well prepared to teach science.

♦ About half of mathematics and science teachers at most 

levels felt very well prepared to encourage the participation of female students in mathematics and science. 

Elementary teachers of science were an exception––only 

30% felt well prepared to encourage female students to 

participate in science.

Science and Engineering Indicators 2014 ♦ 1-5
1-6 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

A majority of middle and high school mathematics and 

science teachers participated in at least one professional 

development activity that focused on mathematics or science in the 3 years prior to 2012.

♦ The participation rate for middle and high school mathematics and science teachers ranged from 82% to 89%.

♦ Among elementary school teachers, 87% participated in 

at least one math professional development activity, and 

59% participated in at least one science professional development activity in the 3 years prior to 2012.

♦ In 2012, 32% of high school mathematics teachers and 

36% of high school science teachers reported that they 

had spent more than 35 hours in subject-specific professional development activities during the prior 3 years. 

Far fewer elementary school teachers of mathematics 

(11%) or science (4%) reported participating in subjectspecific professional development activities for more 

than 35 hours.

Overall, schools are more supportive of mathematics instruction than science instruction.

♦ In 2012, 82% of mathematics program representatives 

reported that the importance their school placed on mathematics teaching promoted effective instruction in mathematics, whereas 60% of science program representatives 

reported that this was the case.

♦ About 70% of mathematics program representatives, 

compared with about 50% of science program representatives, reported that school management of instructional 

resources promoted effective instruction in mathematics 

or science.

♦ Various problems were viewed as serious barriers to effective instruction. For mathematics instruction at the 

high school level, the most frequently cited problem was 

low student interest in mathematics. At the elementary 

level, low student reading abilities was the most frequently cited barrier to effective mathematics instruction.

♦ For science instruction, frequently cited problems included inadequate funds for purchasing equipment and 

lack of science facilities. At the elementary level, more 

than one-quarter of program representatives reported insufficient time to teach science as a serious problem for 

science instruction.

Secondary mathematics and science teachers had higher 3-year attrition rates than did their colleagues who 

taught at the elementary level or taught other fields at 

the secondary level.

♦ Among teachers who began teaching in 2007–08, onequarter of secondary mathematics and science teachers 

had left teaching by 2009–10, compared with 11% of 

elementary teachers and 10% of secondary teachers of 

other fields.

Instructional Technology and Digital Learning

Access to the Internet in U.S. schools is nearly universal.

♦ In 2008, 98% of U.S. public school classrooms had 

Internet access, and the ratio of students to instructional 

computers was 3:1, compared with a ratio of 7:1 in 2000.

An increasing number of students have access to 

and are enrolling in distance education, particularly 

online learning.

♦ Online learning programs range from programs that are 

fully online with all instruction occurring via the Internet 

to hybrid or “blended learning” programs that combine 

face-to-face teacher instruction with online components.

♦ More than 1 million elementary and secondary students 

were enrolled in online or blended learning courses in 

2007−08, a 47% increase from 2005−06.

♦ A recent nationally representative survey of public 

school districts found that providing courses not otherwise available at their schools and providing students 

with opportunities to recover course credits from classes 

missed or failed were the top reasons for offering online 

learning options.

Rigorous research examining the impact of instructional 

technology and online learning on student achievement 

remains limited.

♦ Three recent rigorous meta-analyses compared the mathematics achievement of students taught in classes using 

technology-assisted mathematics programs with that of 

students in control classes using standard methods. All 

three studies found small positive effects when technology 

was incorporated into classroom mathematics instruction.

Transition to Higher Education

Rates of students graduating within 4 years of entering ninth grade (“on-time” graduation) have increased 

in recent years, but differences among racial and ethnic 

groups persist.

♦ In 2010, 78% of public school students completed high 

school on time, up from 73% in 2006. All racial and ethnic groups made progress during this period, with improvement ranging from 3 percentage points for white 

students to 10 percentage points for Hispanic students.

♦ In 2010, Asian or Pacific Islander and white students graduated on time at a higher rate (94% and 83%, respectively) 

than did black, Hispanic, and American Indian or Alaska 

Native students (66%, 71%, and 69%, respectively).
The U.S. high school graduation rate lags behind those of 

many developed nations.

♦ The United States ranked 22nd out of 26 Organisation 

for Economic Co-operation and Development (OECD) 

countries for which graduation rate data were available 

in 2010, with an average graduation rate of 77% among 

the population of 18-year-olds, compared with the OECD 

average of 84%.

♦ The relative standing of U.S. high school graduation rates 

did not improve between 2006 and 2010, ranking 16th 

in both 2006 and 2008 and 17th in 2010 among the 21 

OECD countries with available data.

The majority of U.S. high school graduates enroll in a 

postsecondary institution immediately after high school 

completion, but a sizeable percentage of entering students need remedial courses to prepare themselves for 

college-level work.

♦ Close to 70% of 2011 high school graduates had enrolled 

in a postsecondary institution by the October following 

high school completion, an increase of 17 percentage 

points since 1975.

♦ Relatively more female graduates than male graduates 

enrolled immediately in postsecondary education in 2011 

(72% versus 65%).

♦ Students from high-income families enrolled at a higher 

rate (82%) than did students from middle-income (66%) 

or low-income families (53%).

♦ Internationally, the percentage of U.S. young adults enrolling in university-level education for the first time was 

74% in 2010, above the OECD average of 62%. Among 

30 OECD countries for which data were available, the 

United States ranked 9th.

♦ Half of beginning postsecondary students took some type 

of remedial course after entering college in 2003−04. The 

math remediation rate was 57% for those entering 2-year 

institutions and 29% for those entering 4-year institutions.

Science and Engineering Indicators 2014 ♦ 1-7
1-8 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

Introduction

Chapter Overview

U.S. education reform at the elementary and secondary 

levels continues to focus on improving students’ learning. 

Reform goals include increasing student achievement, reducing performance gaps between students in different demographic groups, and raising the international ranking of 

U.S. students from the middle to the top on international 

tests (The White House n.d.).1 Although policymakers have 

remained committed to these goals, strategies and efforts 

to promote them have shifted over time. Most recently, the 

federal government has given states seeking to meet these 

goals more flexibility by granting them waivers from the 

stringent standards required by the No Child Left Behind 

Act of 2001 (NCLB).2 In exchange for the waivers, the states 

agreed to undertake essential reforms to raise standards, 

improve accountability, and enhance teacher effectiveness 

(U.S. Department of Education 2012a). In addition, the federal government created the Race to the Top (RTTT) grant 

program, inviting states to voluntarily participate in this 

program designed to promote state-led reform efforts (U.S. 

Department of Education 2009, 2011). Through grant competition, RTTT encourages states and local school districts 

to design and implement their own reform plans to address 

their unique educational challenges (see sidebar, “Race to 

the Top”).

Concern about the ability of the United States to compete in the global economy has also lent urgency to calls for 

reform of science, technology, engineering, and mathematics (STEM) education (National Academy of Science 2005; 

NSB 2007). Federal and state policymakers and legislators 

have called for national efforts to develop a strong STEM 

pathway from high schools to colleges that eventually will 

expand the STEM-capable workforce in the United States 

(Kuenzi 2008; NGA 2011; President’s Council of Advisors 

on Science and Technology 2012; The White House n.d). At 

the K−12 level, reform efforts to improve mathematics and 

science learning include increasing advanced coursetaking 

in these areas, promoting early participation in gatekeeper courses such as algebra 1, recruiting and training more 

mathematics and science teachers, designing new curricular 

standards for mathematics and science learning, and expanding secondary education programs that prepare students to 

enter STEM fields in college (Engberg and Wolniak 2013). 

Recently, the National Research Council (NRC) began working with the National Science Foundation (NSF) and the 

U.S. Department of Education to develop a new set of indicators that will track national progress in K−12 mathematics 

and science teaching and learning (see sidebar, “Monitoring 

Progress Toward Successful K−12 STEM Education”).

Chapter Organization

To provide a national portrait of K−12 STEM education in the United States, this chapter compiles indicators of 

precollege mathematics and science learning based mainly 

on data from the National Center for Education Statistics 

(NCES) of the U.S. Department of Education. Table 1-1 

contains an overview of the topics covered in this chapter 

and the indicators used to address them.

This chapter is organized into five sections. The first section begins with data from a new longitudinal study of U.S. 

kindergartners conducted in 2010−11. These data provide 

Race to the Top (RTTT) is a $4.35 billion competitive 

grant program funded by the U.S. Department of Education 

as part of the American Recovery and Reinvestment Act 

of 2009 (U.S. Department of Education 2009). The program provides monetary incentives for states and school 

districts to create conditions for education innovation and 

reform that would significantly improve student achievement (particularly in mathematics and science), narrow 

learning gaps, increase high school graduation rates, and 

increase the number of students admitted to college. To 

achieve these outcomes, RTTT focuses on reform strategies in four core areas:

♦ Adopting standards and designing assessments that prepare students to succeed in college and the workplace 

and to compete in a global economy;

♦ Building data systems that measure changes in student 

achievement and informing teachers and principals 

about how they can improve instruction;

♦ Recruiting, developing, rewarding, and retaining effective teachers and principals, especially where they are 

needed most; and

♦ Improving the performance of the lowest-achieving 

schools.

Since the launch of RTTT in 2009, a total of 18 states 

and the District of Columbia have won awards. In 2012, 

the Obama Administration launched an RTTT competition at the school district level. Known as Race to the 

Top–District, this program focuses on changes within 

schools and is targeted at supporting locally developed 

plans for improving classroom practices and resources. 

As of December 2012, the program made awards to 16 

school districts across the nation. Additional information about RTTT is available at http://www2.ed.gov/programs/racetothetop/index.html.

Race to the Top
Science and Engineering Indicators 2014 ♦ 1-9

In 2011, the National Research Council (NRC 2011) 

articulated three goals for K−12 STEM education:

♦ Expand the number of students who ultimately pursue advanced degrees and careers in STEM fields and 

broaden the participation of women and minorities in 

those fields;

♦ Expand the STEM-capable workforce and broaden the 

participation of women and minorities in that workforce; and

♦ Increase science literacy for all students, including 

those who do not pursue STEM-related careers or additional study in the STEM disciplines.

The NRC concluded that realizing these goals would 

require changing the way that STEM subjects are taught. 

Accordingly, the NRC recommended that the United 

States needs to systematically monitor national progress 

toward achieving these goals and commissioned a group 

of experts to develop indicators that, taken together, could 

constitute a viable monitoring system. This system will 

be based on recommendations from national reports that 

provide evidence supporting “best practices.” The NRC 

recently released a report that identifies 14 indicators 

for monitoring progress in STEM teaching and learning 

(NRC 2012b). Once fully developed, this system of indicators will measure student knowledge, interest, and 

participation in the STEM disciplines and STEM-related 

activities; track financial, human capital, and material investments in K−12 STEM education at the federal, state, 

and local levels; provide information about the capabilities of the STEM education workforce, including teachers and principals; and facilitate strategic planning for 

federal investments in STEM education and workforce 

development when used with labor force projections.

Working closely with the U.S. Department of 

Education, NSF has also undertaken several activities to 

build the proposed system of indicators. These activities 

include the following: 

♦ Determining what data and data collection vehicles currently exist that could be used or modified to enable 

these indicators to be tabulated and reported;

♦ Fully developing operational definitions of the proposed new indicators; and

♦ Engaging stakeholders in the STEM policy community 

and experts in the collection of national statistical data 

to identify the best methods to collect these data.

Monitoring Progress Toward Successful K−12 STEM Education

Table 1-1

Indicators of elementary and secondary school mathematics and science education

Topic Indicator

Student learning in

 mathematics and science

• Mathematics and science performance of first-time kindergarten students in the 2010−11 school year

• Trends in fourth and eighth graders’ mathematics performance from 1990 to 2011

• Eighth graders’ science performance in 2009 and 2011

• International comparisons of fourth and eighth graders’ mathematics and science achievement in 2011

Student coursetaking in 

mathematics and science

• Highest level of mathematics and science coursetaking by high school freshmen in 2009

• Trends in participation and performance in Advanced Placement program from 2002 to 2012

• High school Advanced Placement mathematics and science course offerings in 2012

Teachers of mathematics 

and science

• Experience, certification, and subject-matter preparation of mathematics and science teachers in 2012

• Professional development of mathematics and science teachers in 2012

• Working conditions of mathematics and science teachers in 2012

• Attrition rates of beginning public school teachers from 2007−08 to 2009−10

Instructional technology and 

digital learning

• Term definitions and review of emerging policies, practices, and the effects of instructional 

 technology and distance education on student learning

Transitions to higher 

education

• Trends in on-time high school graduation rates from 2006 to 2010

• International comparisons of secondary school graduation rates in 2010

• Immediate college enrollment from 1975 to 2011

• Remedial coursetaking among 2003−04 beginning postsecondary students

• International comparisons of college enrollment rates in 2010

Science and Engineering Indicators 2014
1-10 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

examined (e.g., family poverty is determined by students’ 

eligibility for free/reduced-price lunch instead of being calculated directly from family income). These limitations may 

impede providing a full picture of STEM education at the 

K−12 level.

Student Learning in 

Mathematics and Science

Increasing overall student achievement, especially lifting the performance of low achievers, is an essential goal of 

education reform in the United States. Reform efforts center 

on improving student learning in mathematics and science 

because these fields are widely regarded as critical to the 

nation’s economy (Atkinson and Mayo 2010; President’s 

Council of Advisors on Science and Technology 2012). 

This section presents indicators of U.S. student performance 

in mathematics and science, beginning with a snapshot of 

the mathematics and science test scores of a recent cohort 

of U.S. kindergartners. It then presents long-term trends in 

the mathematics and science performance of U.S. fourth 

and eighth graders,4

 examining more than two decades of 

changes in overall performance and in gaps between different groups. The section ends by placing U.S. student performance in an international context, comparing U.S. fourth 

and eighth graders’ mathematics and science test scores with 

those of their peers in other nations.

Mathematics and Science Performance 

During the Kindergarten Year

The Early Childhood Longitudinal Study, Kindergarten 

Class of 2010−11 (ECLS-K:2011) is a nationally representative, longitudinal study of children’s development, 

early learning, and school progress (Mulligan, Hastedt, 

and McCarroll 2012). The study began with approximately 

18,200 children in kindergarten in fall 2010 and will follow 

and test them every year until spring 2016, when most of 

them are expected to be in fifth grade. The study gathers information from many sources, including the students themselves, their families, teachers, schools, and before- and 

after-school care providers. These data provide a wealth of 

information on children’s cognitive, social, emotional, and 

physical development; family and neighborhood environments; school conditions; and before- and after-school care. 

The longitudinal study design will enable research on how 

various family, school, community, and individual factors 

are associated with school performance over time. At the 

time this chapter was prepared, only data from the initial 

year of the study were available for analysis. This section, 

therefore, presents descriptive information on children when 

they enter school and their initial mathematics and science 

assessment results (mathematics and science assessment 

scores cannot be compared directly because scales are developed independently for each subject). This information 

will serve as a baseline for measuring students’ progress 

on future assessments as they advance through elementary 

a snapshot of kindergarten students’ status as they enter 

school, including baseline measures of their mathematics 

and science performance. This section then covers elementary and secondary students’ performance on standardized 

mathematics and science assessments, focusing on recent 

trends in student performance, changes in performance gaps 

among different groups, and the international standing of 

U.S. students vis-à-vis their peers abroad.

The second section focuses on mathematics and science 

coursetaking in high school. It begins by examining ninth 

graders’ enrollment in mathematics and science courses, providing information on what courses students take as they enter high school. The section then uses data from the College 

Board to examine trends in participation and performance 

in the STEM-related Advanced Placement (AP) programs 

among high school graduating classes. High school course 

completion data from the most recent transcript studies were 

reported in the 2012 edition of Science and Engineering 

Indicators; no new course completion data were available 

for this volume. Therefore, this section is somewhat limited 

because of fewer data.

The third section turns to U.S. elementary, middle, and 

high school mathematics and science teachers in 2012, examining their experience, licensure, subject matter preparation, professional development, and working conditions. In 

addition, this section presents new data on beginning mathematics and science teachers’ attrition in the first 3 years 

of teaching.

The fourth section examines how technology is used as 

an instructional tool in K−12 education. In the absence of 

nationally representative data, this section mainly provides 

a literature review, focusing on term definitions, emerging 

policies and practices, and the latest research findings on the 

effects of instructional technology and distance education on 

student learning in mathematics and science.

The last section presents indicators of student transitions 

from secondary to postsecondary education—the subject 

of chapter 2 in this volume. Updated indicators include ontime high school graduation rates, immediate college enrollment rates, and international comparisons of high school 

graduation rates and postsecondary enrollment. This section 

also includes data on remedial coursetaking by beginning 

postsecondary students, an indicator of the extent to which 

secondary schools prepare entering students for collegelevel work.

This chapter focuses primarily on national patterns and 

trends, but it also discusses variation in student performance 

or access to educational resources by demographic, family, 

and school characteristics.3

 Because of the unavailability of 

national data, this chapter cannot report indicators for many 

other activities that are important to understanding K−12 

STEM education, such as use of high-quality mathematics 

and science curricular materials, time spent on mathematics 

and science learning, participation in STEM-related activities outside of school, and interest in pursuing a STEM degree and career. In addition, certain measures in this chapter 

may not capture the full dimension of the construct being 
Science and Engineering Indicators 2014 ♦ 1-11

school. Findings from these assessments will be presented in 

future editions of Science and Engineering Indicators.

Demographic Profile of U.S. First-Time Kindergartners. In fall 2010, about 3.5 million U.S. children entered 

kindergarten for the first time (Mulligan, Hastedt, and 

McCarroll 2012). Students in this cohort came from diverse 

backgrounds: about two-fifths of kindergartners (38%) had 

at least one parent with a bachelor’s degree or higher, 32% 

had parents who attended some college but did not earn a 

bachelor’s degree, and 29% had parents with no more than a 

high school education (appendix table 1-1). About one-quarter of children were living in families with incomes below 

the federal poverty level (25%) or in single-parent households (22%). Fifteen percent of students came from families 

where the primary language used at home was not English. 

Nearly half (47%) were racial and ethnic minorities, with 

Hispanics being the largest minority group (24%), followed 

by blacks (13%) and Asians (4%).5

 The following analysis 

examines the size and direction of achievement differences 

among different groups at the outset of formal schooling.

Mathematics Performance.6 Even as early as kindergarten, large gaps in mathematical understanding already exist 

among different subpopulations. Initial mathematics assessment scores varied by parental education level; for example, 

children whose parents had less than a high school education scored 15 points (on a scale of 0−75) below their peers 

whose parents attended a graduate or professional school 

(figure 1-1). Students from homes with a primary language 

other than English earned an average of 24 points on the 

initial mathematics test, compared with 30 points earned by 

those with a primary home language of English. Students 

from families with incomes below the federal poverty level 

scored 9 points below their peers from families with incomes at or above 200% of the federal poverty level. Those 

from single-parent households also did not perform as well 

as those from two-parent households (26 versus 31 points). 

The gaps were further evident among different racial and 

ethnic groups: black and Hispanic students lagged behind 

Asian students by 9 to 10 points and white students by 6 to 

7 points.

By spring 2011, the overall average mathematics score of 

kindergartners had increased by 13 points, from 29 to 42, on 

the 0−75 scale (figure 1-1). All groups gained 12−13 points 

from fall 2010 to spring 2011. Although the performance gaps 

did not widen during this period, students’ initial exposure to 

formal schooling did not help narrow these gaps either.

Science Performance. Overall, kindergartners earned 

an average of 11 points (on a scale of 0−20) on their initial science assessment administered several months after 

the beginning of the school year (appendix table 1-1). Like 

in mathematics, variations in science performance among 

kindergartners with different characteristics were evident 

at this early stage of schooling, and the pattern of variations was largely similar. For example, science assessment 

Figure 1-1

Average mathematics assessment scores of 

first-time kindergartners, by child and family 

characteristics: Fall 2010 and spring 2011 

Graduate/

professional school

Parent(s) with less than

high school education

Not English 

Primary home language

English

One-parent household

Two-parent household

At or above 200% of

the federal poverty level

Family income below

the federal poverty level

or Alaska Native

American Indian

Asian

Hispanic

Black

White

Female

Male

All kindergartners

0 10 20 30 40 50 60

Mathematics assessment score

Spring 2011

Fall 2010

NOTES: Mathematics assessment scores range from 0 to 75. 

Family’s poverty level is based on 2010 U.S. Census poverty 

thresholds, which identify incomes determined to meet household 

needs given family size. For example, in 2010, a family of two was 

below the poverty threshold if its income was lower than $14,220. 

Parents’ education is the highest level of education achieved by 

either of the parents or guardians in a two-parent household or by the 

only parent or guardian in a single-parent household. Hispanic may 

be any race. American Indian or Alaska Native, Asian, black or 

African American, and white refer to individuals who are not of 

Hispanic origin.

SOURCE: Mulligan GM, Hastedt S, McCarroll JC, First-Time 

Kindergartners in 2010−11: First Findings From the Kindergarten 

Rounds of the Early Childhood Longitudinal Study, Kindergarten 

Class of 2010−11 (ECLS-K:2011), NCES 2012-049 (2012). See 

appendix table 1-1. 

Science and Engineering Indicators 2014
1-12 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

scores increased with parental education level, with children whose parents had less than a high school education 

scoring 4 points below their peers whose parents attended 

a graduate or professional school (9 versus 13 points). 

Kindergartners from homes with a primary home language 

other than English earned an average of 9 points on the initial science assessment, compared with 12 points earned by 

those with a primary home language of English. Those from 

households with incomes below the federal poverty level 

also had lower scores than their peers from households with 

incomes at or above 200% of the federal poverty level (10 

versus 13 points). Among all racial and ethnic groups, white 

children earned the highest average score (12 points), followed by American Indian or Alaska Native and Asian children (about 11 points for both groups); black and Hispanic 

children earned the lowest average score (about 10 points 

for both groups).

Large gaps in student performance at the beginning of 

formal schooling suggest that nonschool factors play a big 

role in these disparities. Although a body of research has 

attempted to identify various factors underlying students’ 

achievement gaps, efforts have mostly focused on schoolrelated factors such as teacher quality, available resources, 

principal leadership, and school climate, or such nonschool 

factors as sex, race and ethnicity, and family socioeconomic status (SES) (Coleman et al. 1966; Corcoran and Evans 

2008; Fryer and Levitt 2004; Greenwald, Hedges, and Laine 

1996; Hanushek and Rivkin 2006; Lamb and Fullarton 

2002; Leonidas et al. 2010; OECD 2005; Rivkin, Hanushek, 

and Kain 2005). Researchers are now turning their attention 

to a broader range of nonschool factors beyond students’ 

demographic and socioeconomic backgrounds, and probing 

deeper into their roles in student achievement (Henig and 

Reville 2011) (see sidebar, “The Role of Nonschool Factors 

in Student Learning”).

Mathematics and Science Performance in 

Grades 4 and 8

The National Assessment of Educational Progress 

(NAEP), a congressionally mandated study, has monitored 

changes in U.S. students’ academic performance in mathematics, science, and other subjects since 1969 (NCES 

2011a, 2012). NAEP has two assessment programs: the 

main NAEP and the NAEP Long-Term Trend (LTT).7

 The 

main NAEP assesses national samples of fourth and eighth 

graders at regular intervals, and twelfth graders on an occasional basis. These assessments are updated periodically 

to reflect changes in curriculum standards. The NAEP LTT 

assesses the performance of students ages 9, 13, and 17. Its 

content framework has remained the same since it was first 

administered in 1969 in science and in 1973 in mathematics, 

permitting analyses of trends over more than three decades. 

This section examines recent performance results using the 

main NAEP data only. The most recent available findings 

based on NAEP LTT data have been reported in previous 

editions of Science and Engineering Indicators.

8

Reporting Results for the Main NAEP

The main NAEP reports student performance in two 

ways: scale scores and achievement levels. Scale scores use 

a continuous scale to measure student learning. For mathematics assessments, scales range from 0 to 500 for grades 

4 and 8 and from 0 to 300 for grade 12. For science assessments, scales range from 0 to 300 for all grades. Scores cannot be compared across subjects because NAEP scales are 

developed independently for each subject.

In addition to scale scores, NAEP reports student results 

in terms of achievement levels. Developed by the National 

Assessment Governing Board (NAGB), achievement levels are intended to measure the extent to which students’ 

actual achievement matches the achievement expected of 

them. Based on recommendations from panels of educators, policymakers, and the general public, NAGB sets three 

achievement levels for mathematics (NAGB 2010a), science 

(NAGB 2010b), and other subjects assessed by NAEP:

♦ Basic denotes partial mastery of materials appropriate for 

the grade level.

♦ Proficient indicates solid academic performance.

♦ Advanced represents superior academic performance.

Based on their test scores, students’ performance can be 

categorized as below basic, basic, proficient, and advanced.

9 

Achievement levels cannot be compared across grade levels 

because they were developed independently at each grade 

level.10 Although the NAEP achievement levels can be helpful in understanding and interpreting student results and 

have been widely used by national and state officials, there 

is ongoing disagreement about whether they are appropriately defined (Harvey 2011). A study commissioned by the 

National Academy of Sciences judged the NAEP achievement levels to be “fundamentally flawed” (Pellegrino, Jones, 

and Mitchell 1999). In addition, the National Mathematics 

Advisory Panel concluded that NAEP scores for the two 

highest achievement categories (proficient and advanced) 

were set too high (NMAP 2008). Because of criticisms like 

these, NCES has recommended that achievement levels be 

used on a trial basis and interpreted with caution (NCES 

2011a, 2012). The following review of NAEP results reports 

both average scale scores and the percentage of students performing at or above the proficient level.

Mathematics Performance from 1990 to 2011

Average Score. The average mathematics score of U.S. 

fourth graders increased by 27 points from 1990 to 2007, 

leveled off between 2007 and 2009, and then rose by 1 point 

from 2009 to 2011 (figure 1-2). This overall trend was reflected in almost all demographic groups,11 across students 

at all performance levels (i.e., 10th to 90th percentiles12), and 

among students at both public and private schools. For example, from 1990 to 2007, the fourth grade average mathematics score increased substantially—by 28 points for white 

students, 34 points for black students, 27 points for Hispanic 

students, and 28 points for Asian or Pacific Islander students 
Science and Engineering Indicators 2014 ♦ 1-13

(appendix table 1-2). Average scores for these racial and ethnic groups remained unchanged between 2007 and 2009 and 

then increased by 1 or 2 points from 2009 to 2011.

Among U.S. eighth graders, the average mathematics 

score increased continuously from 1990 to 2011, with a total gain of 21 points over the period (figure 1-2). Although 

the scores of all demographic groups have improved 

substantially since 1990, not all groups have experienced 

this upward trend in recent years. For example, the average mathematics scores for male students, whites, Asians 

or Pacific Islanders, American Indians or Alaska Natives, 

and those attending private schools remained unchanged 

between 2009 and 2011 (appendix table 1-2). Groups that 

experienced score gains during this period included black 

The major national studies of student academic performance include only partial data on nonschool factors that 

can affect student learning. Nonschool factors often available from the major national studies used in this chapter 

include student’s demographic characteristics (e.g., sex 

and race and ethnicity) and family backgrounds (e.g., 

family income, parental education, and the primary home 

language). Other nonschool factors such as personality 

traits, health and nutrition, and neighborhood characteristics matter for learning as well, but they are relatively 

difficult to measure and therefore rarely covered in the 

national studies on education and student achievement.

Research on nonschool factors dates back to the 1966 

release of the report Equality of Educational Opportunity 

(Coleman et al. 1966), which examined the interrelationships among race and ethnicity, family characteristics, 

and student achievement. The authors of this report concluded that students’ socioeconomic background (measured by parents’ income, occupation, and education) was 

a far more influential factor than were school-related factors. Since then, this line of research has evolved, adding 

such familial factors as household structure, immigrant 

status, the primary home language, parenting style, and 

parental involvement and support as having an impact 

on student achievement. The findings of this research are 

generally consistent: students from low-income families, 

those whose parents have lower levels of educational 

attainment or are uninvolved in their children’s education, and those who live in a single-parent household or a 

home where the primary language spoken is not English 

generally do not perform as well as students from more 

advantaged backgrounds (Aud, Fox, and KewalRamani 

2010; Berliner 2009; Campbell et al. 2008; HampdenThompson and Johnston 2006; Jeynes 2005; Kreider 

and Ellis 2011; Lareau 2011; Lee and Burkham 2002; 

Mulligan, Halle, and Kinukawa 2012; Pong, Dronkers, 

and Hampden-Thompson 2003; Rothstein 2004; Schmid 

2001; Spera 2005; Stockton 2011). Research further indicates that differential access to high-quality preschool 

care and programs, which is highly related to parental income, is a contributing factor to initial academic achievement gaps (Camilli et al. 2010; Chambers et al. 2010; 

Flanagan and McPhee 2009).

To attempt to explain more of the variation in student achievement, researchers also turned to personality 

traits, exploring whether and how attributes like perseverance, motivation, self-control, self-efficacy, and social skills contribute to students’ academic achievement 

(Almlund et al. 2011; Bozick and Dempsey 2010; Dalton 

2010; Duckworth et al. 2007; Heckman and Kautz 2012; 

Lennon 2010a, 2010b; McClelland, Acock, and Morrison 

2006; Pintrich and de Groot 1990; Schunk 1981; Snyder 

2001; Tough 2012; Walls and Little 2005; WebsterStratton and Reid 2004). Though not conclusive, cumulative evidence points to persistence, motivation to learn 

and achieve, the ability to delay gratification and aim 

for long-term goals, belief in one’s ability to accomplish 

academic tasks, and the ability to self-regulate and use 

self-control as being positively associated with achievement measures such as standardized test scores, grades, 

and high school completion.

Researchers have also examined the effects of healthrelated factors on student learning (Berliner 2009; Castelli 

et al. 2007; Chernoff et al. 2007; Conti, Heckman, and 

Urzua 2010; Daniels et al. 2005; Hack et al. 2002; 

Nihiser et al. 2007; Rothstein 2010; Stockton 2011). Low 

birth weight, unhealthy eating, malnutrition, environmental pollution, inadequate medical/dental/vision care, 

and exposure to stress and discord at home can induce a 

variety of physical, sociological, and psychological problems, ranging from neurological damage and attention 

disorders to excessive absenteeism, linguistic underdevelopment, and oppositional behavior. These problems, 

in turn, can adversely affect student learning outcomes.

Finally, the effects of children’s home life on academic achievement can be influenced by neighborhood 

characteristics such as the unemployment rate, concentration of poverty, incidence of violence and gang activities, and rates of mobility and homelessness (Ainsworth 

2002; Berliner 2009; Rothstein 2010). Research indicates 

that students living in impoverished or unsafe communities have a higher frequency of developmental and 

health problems than do those from more affluent or 

safe communities, even after controlling for family conditions, and those developmental and health problems, 

in turn, are associated with such academic outcomes as 

low test scores and dropping out of school (Arneshensal 

and Sucoff 1996; Brooks-Gunn et al. 1993; Catsambis 

and Beveridge 2001; Garner and Raudenbush 1991; 

Wickrama, Noh, and Bryant 2005).

The Role of Nonschool Factors in Student Learning
1-14 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

female students (whose scores increased by 2 points), 

Hispanic male and female students (by 3 and 5 points, respectively), and low- or high-income students (by 2 and 3 

points, respectively).13

Achievement Level. Trends in the percentages of fourth 

and eighth graders reaching the proficient level parallel the 

scale score trends (figure 1-3). The percentage of fourth 

graders performing at or above the proficient level increased 

steadily through 2007 and essentially leveled off from 2009 

to 2011. Eighth graders overall showed continuous improvement from 1990 to 2011, though the improvement did not 

persist for some groups during recent years (appendix table 

1-3). Furthermore, despite overall upward trends, the actual 

percentage of students reaching the proficient level in mathematics remained well below half—in 2011, 40% of fourth 

graders and 35% of eighth graders performed at or above 

this level.

Science Performance from 2009 to 2011

In 2009, the framework for the main NAEP science assessment was significantly changed to reflect advances in 

science, curriculum standards, assessments, and research on 

science learning (NAGB 2010b). Because of these modifications, the results from the 2009 and 2011 assessments cannot be compared with those from the earlier assessments. 

Whereas the 2009 assessment included students in grades 

4, 8, and 12, the 2011 assessment targeted students only in 

grade 8. This section, therefore, discusses the 2009 and 2011 

assessment results for students in grade 8 only.14

Average Score. The average science score of eighth graders increased from 150 in 2009 to 152 in 2011 (figure 1-4).15 

With a few exceptions (Asian or Pacific Islander students, 

high-performing students [at the 90 percentile], and private 

school students), most demographic groups improved their 

science scores during this period, with score gains ranging 

from 1 point for female students and white students to 3 

Figure 1-2

Average NAEP mathematics scores of students in 

grades 4 and 8: 1990–2011 

Average score

NAEP = National Assessment of Educational Progress.

NOTES: NAEP mathematics assessment scores range from 0 to 500 

for grades 4 and 8. From 1996 on, data are for students allowed to 

use testing accommodations 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2012) of NAEP 1990, 

1992, 1996, 2000, 2003, 2005, 2007, 2009, and 2011 mathematics 

assessments, National Center for Education Statistics. See appendix 

table 1-2.

Science and Engineering Indicators 2014

1990 1992 1996 2000 2003 2005 2007 2009 2011

200

0

220

240

260

280

300

Grade 4 

Grade 8

Figure 1-3

Students in grades 4 and 8 scoring at or above NAEP’s proficient level in mathematics for their grade: 1990–2011

Percent 

1990 1992 1996 2000 2003

Grade 4 Grade 8

2005 2007 2009 2011 1990 1992 1996 2000 2003 2005 2007 2009 2011

0

10

20

30

40

50

NAEP = National Assessment of Educational Progress.

NOTE: From 1996 on, data are for students allowed to use testing accommodations.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2012) of NAEP 1990, 1992, 1996, 

2000, 2003, 2005, 2007, 2009, and 2011 mathematics assessments, National Center for Education Statistics. See appendix table 1-3. 

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 1-15

majority of students performed below the proficient level on 

the science assessment in both years. In 2011, for example, 

68% of eighth graders failed to reach the proficient level in 

science. The percentage who scored below this level was especially high among black and Hispanic students (90% and 

84%, respectively), particularly among female students in 

both groups (91% and 87%, respectively).

Changes in Performance Gaps in Mathematics 

and Science

Most performance gaps that existed in earlier years 

persisted in 2011, although none of these gaps have widened since 1990 (appendix tables 1-2 and 1-4). Overall, 

sex differences were small, with male students performing 

slightly better than female students in mathematics and science. Differences between male and female students, however, were not consistent across racial and ethnic groups. 

Although eighth grade white male students in 2011 had 

higher mathematics scores than their female counterparts 

(295 versus 292), similar sex differences were not observed 

among Hispanic, Asian or Pacific Islander, and American 

Indian or Alaska Native students (figure 1-5). Among black 

eighth graders, the gap was reversed: female students performed slightly better than male students (264 versus 261).

Large performance gaps existed among other groups. 

For both mathematics and science at grades 4 and 8, white 

and Asian or Pacific Islander students performed better than 

points for black students, 4 points for low-income students, 

and 5 points for Hispanic students (appendix table 1-4).

Achievement Level. Like scale scores, the percentage of 

eighth graders performing at or above the proficient level 

in science increased slightly from 30% in 2009 to 32% in 

2011 (appendix table 1-5). Despite this improvement, the 

Figure 1-4

Average NAEP science scores of students in 

grade 8, by student and school characteristics: 

2009 and 2011 

0 50 100 150 200

NAEP science assessment score

Private

Public school

90th

75th

50th

25th

Score at the

10th percentile

Not eligible

Eligible for free/

reduced-price lunch

American Indian

or Alaska Native

Asian or

Pacic Islander

Hispanic

Black

White

Female

Male

All students

2011

2009

NAEP = National Assessment of Educational Progress.

NOTES: NAEP science assessment scores range from 0 to 300 for 

grade 8. Scores for percentile rows are not averages but the actual 

scores that mark each percentile listed. For example, a score at the 

10th percentile indicates that 10% of students perform at or below 

this score. Hispanic may be any race. American Indian or Alaska 

Native, black or African American, Asian or Pacic Islander, and white 

refer to individuals who are not of Hispanic origin.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2012) of NAEP 2009 

and 2011 science assessments, National Center for Education 

Statistics. See appendix table 1-4.

Science and Engineering Indicators 2014

Figure 1-5

Average NAEP mathematics scores of students in 

grade 8, by sex, race, and ethnicity: 2011 

Average score

 

White Black Hispanic American

Indian or

Alaska Native

0

210

230

250

270

290

310

Male Female

Asian

or Pacic

Islander

NAEP = National Assessment of Educational Progress.

NOTES: NAEP mathematics assessment scores range from 0 to 500 

for grade 8. Hispanic may be any race. American Indian or Alaska 

Native, Asian or Pacic Islander, black or African American, and white 

refer to individuals who are not of Hispanic origin.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2012) of NAEP 2011 

mathematics assessments, National Center for Education Statistics. 

See appendix table 1-2. 

Science and Engineering Indicators 2014
1-16 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

their black, Hispanic, or American Indian or Alaska Native 

counterparts (appendix tables 1-2 and 1-4). Students from 

higher-income families also had higher scores in mathematics and science than those from lower-income families. Gaps 

were observed by school type as well, with private school 

students scoring higher than public school students.16

Some gaps in mathematics and science scores have narrowed over time (table 1-2). In mathematics, gap reductions 

occurred among fourth grade students but not among eighth 

grade students. Specifically, the 32-point white-black gap 

in mathematics performance among fourth grade students 

decreased to 25 points between 1990 and 2011 because of 

larger gains by black students (figure 1-6). The reduction 

in the white-black gap occurred among both male and female fourth graders (table 1-2; appendix table 1-2). Further, 

the fourth graders’ score at the 10th percentile rose more 

than did the score at the 90th percentile, reducing the gap 

between low- and high-performing students from 82 to 73 

points between 1990 and 2011. None of these gap reductions 

was observed among eighth grade students, however.

In science, the eighth graders’ average score increased 

more for black students (3 points) and Hispanic students 

(5 points) than for white students (1 point) between 2009 

and 2011, narrowing the white-black gap (especially among 

male students) and the white-Hispanic gap (among both 

male and female students) (table 1-2; appendix table 1-4). 

Finally, the eighth graders’ science score at the 10th percentile rose faster than that at the 90th percentile, reducing the 

gap between low- and high-performing students from 89 to 

87 points.

Table 1-2

Changes in NAEP mathematics and science score gaps between selected groups of students in grades 4 

and 8: 1990–2011

Change in score gap

Grade 4 

mathematics

Grade 8 

mathematics

Grade 8 

science

Score gap between selected groups of students 1990–2011 1990–2011 2009–11a

Males and females ................................................................................ ≈ ≈ ≈

Whites and blacks ................................................................................. ↓ ≈ ↓

White males and black males............................................................ ↓ ≈ ↓

White females and black females...................................................... ↓ ≈ ≈

Whites and Hispanicsb .......................................................................... ≈ ≈ ↓

White males and Hispanic males....................................................... ≈ ≈ ↓

White females and Hispanic females................................................. ≈ ≈ ↓

Students from low-income families and those from 

other familiesc .................................................................................... ≈ ≈ ≈

Low-performing students and high-performing studentsd .................... ↓ ≈ ↓

Public school students and private school students ............................ ≈ ≈ ≈

 = no change; = decrease. 

NAEP = National Assessment of Educational Progress.

a

 Changes in science score gaps for grade 8 are presented only for 2009–11 because prior assessments were not comparable with those in or after 2009.

b Hispanic may be any race. 

c

 Information on student eligibility for subsidized lunch program, a measure of family poverty, was first collected in 1996. Changes in mathematics score 

gaps in 1990–2011 columns cover 1996–2011. 

d Gap between students who scored at the 10th and 90th percentiles.

NOTE: From 1996 on, students were allowed to use testing accommodations.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2012) of NAEP 1990, 1996, and 2011 

mathematics assessments and of NAEP 2009 and 2011 science assessments, National Center for Education Statistics. See appendix tables 1-2 and 1-4.

Science and Engineering Indicators 2014

Figure 1-6

Average NAEP mathematics scores and score 

gaps for white and black students in grade 4: 

1990–2011

Average score

1990 1992 1996 2000 2003 2005 2007 2009 2011

0

180

200

220

240

260

White

Black

32*

34*

34*

31*

27* 26* 26 26 25

Score gap

* = Gap between white and black students is signicantly different 

from the 2011 gap.

NAEP = National Assessment of Educational Progress.

NOTES: NAEP mathematics assessment scores range from 0 to 500 

for grade 4. From 1996 on, data are for students allowed to use 

testing accommodations.

SOURCE: National Science Foundation, National Center for Science and 

Engineering Statistics, special tabulations (2012) of NAEP 1990, 1992, 

1996, 2000, 2003, 2005, 2007, 2009, and 2011 mathematics assessments, National Center for Education Statistics. See appendix table 1-2.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 1-17

countries. In 2009, the U.S. average score ranked 18th in 

mathematics and 13th in science out of 34 Organisation for 

Economic Co-operation and Development (OECD) nations 

participating in the assessment.18

First conducted in 1995, TIMSS assesses the mathematics and science performance of fourth and eighth graders 

every 4 years. TIMSS has been administered five times, 

most recently in 2011. Over 20,000 students in more than 

1,000 schools across the United States took the assessment 

in spring 2011, joining almost 500,000 other students from 

62 countries and jurisdictions (Provasnik et al. 2012).

TIMSS is designed to test students’ knowledge of specific mathematics and science topics that are closely tied to 

the curricula of the participating education systems (Mullis 

et al. 2009). The assessment framework includes two dimensions: a content domain for the subject matter to be assessed 

within mathematics and science and a cognitive domain for 

the skills (e.g., knowing, applying, and reasoning) expected 

of students as they learn the mathematics or science content. 

Specifically, the content domain for fourth and eighth grade 

mathematics and science in TIMSS 2011 includes the following topics (see sidebar, “TIMSS 2011 Sample Items”):

International Comparisons of Mathematics 

and Science Performance

Two international assessments—the Trends in International 

Mathematics and Sciences Study (TIMSS) and the Program 

for International Student Assessment (PISA)—compare U.S. 

students’ achievement in mathematics and science with that 

of students in other countries. These two assessments differ 

in several fundamental ways, including the purpose of the 

study, age of the students tested, test content, and the number of participating nations.17 Targeting students in grades 

4 and 8 regardless of their age, the TIMSS tests focus on 

students’ application of skills and knowledge to tasks akin 

to those encountered in school. The PISA tests, in contrast, 

assess the abilities of 15-year-olds to apply mathematics and 

science skills and information to solve real problems they 

may face at work or in daily life. This section compares the 

mathematics and science performance of U.S. students with 

that of their counterparts in other countries using assessment 

data from the latest administration of TIMSS (2011). No 

new data from PISA were available for this volume. The 

most recent PISA results showed that U.S. 15-year-olds 

did not perform as well as their peers in many developed 

Sample for grade 4 mathematics:

A shelf is 240 cm long. Chris is putting boxes on the shelf. 

Each box takes up 20 cm of shelf space. Which of these 

number sentences shows how many boxes Chris can fit 

on the shelf? 

A. 240 – 20 C. 240 + 20

B. 240 ÷ 20 D. 240 x 20

Answer: B.

Sample for grade 4 science:

A ribbon is tied to a pole to measure the wind strength as 

shown below.

Write the numbers 1, 2, 3, and 4 in the correct order that 

shows the wind strength from the strongest to weakest.

Answer: 3, 4, 1, 2

Sample for grade 8 mathematics:

Which of these is equal to 2(x+y) – (2x-y)?

A. 3y C. 4x + 3y

B. y D. 4x + 2y

Answer: A

Sample for grade 8 science:

The diagram below shows Earth’s water cycle.

What is the source of energy for the water cycle? 

A. The Moon C. The tides

B. The Sun D. The wind

Answer: B

TIMSS 2011 Sample Items

The above math and science sample questions come directly from http://timssandpirls.bc.edu/timss2011/downloads/

TIMSS2011_Frameworks.pdf.
1-18 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

Mathematics

♦ Number, Geometric Shapes and Measures, Data 

Display (Grade 4)

♦ Number, Algebra, Geometry, Data and Chance 

(Grade 8)

Science

♦ Life Science, Physical Science, Earth Science 

(Grade 4)

♦ Biology, Chemistry, Physics, Earth Science (Grade 8)

Within each topic in the content domain, students are assessed on several skills, including their knowledge of facts, 

concepts, and procedures; application of those facts, concepts, 

and procedures to solve problems; and reasoning (i.e., solving unfamiliar, complex, or multistep problems). Although 

the content differs for fourth and eighth graders, reflecting the 

nature and difficulty of the mathematics and science taught at 

each grade, the cognitive domain is the same for both grade 

levels and subjects. A more detailed discussion of the framework for the TIMSS 2011 mathematics and science assessments can be found at http://timssandpirls.bc.edu/timss2011/

downloads/TIMSS2011_Frameworks.pdf.

Mathematics Performance of U.S. Students in 

Grades 4 and 8 on TIMSS

Performance on the 2011 TIMSS Mathematics Tests.

The U.S. average score on the 2011 TIMSS mathematics assessment was 541 at grade 4 and 509 at grade 8 (figure 1-7). 

Both scores were higher than the international TIMSS average, which is set to 500 at both grades.19 Among 50 countries/

jurisdictions that participated in the 2011 TIMSS mathematics 

assessment at grade 4, the U.S. average mathematics score 

was among the top 13 (seven scored higher; five did not differ), outperforming 37 countries/jurisdictions (appendix table 

1-6).20 The top scorers—Singapore, Republic of Korea, and 

Hong Kong (China)—each had average scores above 600.

At grade 8, the U.S average mathematics score was below 

the scores of six countries/jurisdictions, not different from 

the scores of seven, and higher than those of 28, placing the 

United States among the top 14 in eighth grade mathematics. The average scores of students in the Republic of Korea, 

Singapore, and Taipei21 (the top three leaders) were at least 

100 points higher than the average score of U.S. eighth graders (609−613 versus 509).

Performance Trends. Over the 16 years since the first 

TIMSS mathematics administration in 1995, U.S. fourth and 

eighth graders raised their scores and international ranking.22

At grade 4, the average mathematics score of 541 in 2011 

was 23 points higher than the score of 518 in 1995 (figure 

1-8). Not only did U.S. fourth graders’ mathematics scores 

increase but also the U.S. position relative to other nations 

climbed from 1995 to 2011. Among the 17 countries that 

participated in both the 1995 and 2011 TIMSS mathematics 

assessment of fourth graders, 7 outscored the United States 

in 1995 compared with 4 in 2011 (Provasnik et al. 2012).

At grade 8, the U.S. average score of 509 in 2011 reflected a 17-point increase over the 1995 score (492) (figure 

1-8). The relative standing of U.S eighth graders’ mathematics performance has also improved over this time period: 

among the 16 countries that participated in both the 1995 

and 2011 TIMSS mathematics assessment of eighth graders, 

5 outperformed the United States in 2011, down from 8 in 

1995 (Provasnik et al. 2012).

Science Performance of U.S. Students in Grades 4 

and 8 on TIMSS

Performance on the 2011 TIMSS Science Tests. In 

2011, the average science scores of both U.S. fourth and 

eighth grade students (544 and 525, respectively) were higher than the international TIMSS scale average (500) (figure 

1-9). At grade 4, the United States was among the top seven 

countries/jurisdictions, outperforming 43 among a total of 

50 participants (appendix table 1-7). Students in Republic 

of Korea, Singapore, Finland, Japan, Russian Federation, 

Figure 1-7

Average TIMSS mathematics scores of students in 

grades 4 and 8, by country/jurisdiction: 2011

Grade 4 Grade 8

Score 

higher 

than 

United 

States

Singapore ..................606

Republic of Korea ......605

Hong Kong (China) ....602

Taipei (Taiwan) ...........591

Japan .........................585

Northern Ireland.........562

Belgium (Flemish) ......549

Republic of Korea .....613

Singapore .................611

Taipei (Taiwan) ..........609

Hong Kong (China) .....586 

Japan ........................570

Russian Federation ...539

Score not 

statistically

different 

from 

United 

States

Finland .......................545

England .....................542

Russian Federation ....542

United States ...........541

Netherlands ...............540

Denmark ....................537

Israel .........................516

Finland ......................514

United States ..........509

England ....................507

Australia ....................505

Hungary ....................505

Slovenia ....................505

Lithuania ...................502

Score 

lower 

than 

United 

States

(selected 

countries)

Lithuania ...................534

Portugal ....................532

Germany ...................528

Ireland .......................527

Australia ....................516

Serbia .......................516

Hungary ....................515

Slovenia ....................513

Czech Republic ........511

Austria ......................508

Italy ...........................498

New Zealand ............488

Kazakhstan ...............487

Sweden ....................484

Ukraine .....................479

Norway .....................475

Armenia ....................467

Romania ...................458

United Arab Emirates ...456

Turkey .......................452

TIMSS = Trends in International Mathematics and Science Study.

NOTES: Hong Kong is a Special Administrative Region of the 

People’s Republic of China. Taipei is the capital city of Taiwan. 

Countries/jurisdictions are ordered by 2011 average score. 

Countries/jurisdictions with identical rounded estimates are listed 

alphabetically.

SOURCE: Provasnik S, Kastberg D, Ferraro D, Lemanski N, Roey S, 

Jenkins F, Highlights From TIMSS 2011: Mathematics and Science 

Achievement of U.S. Fourth- and Eighth-Grade Students in an 

International Context, NCES 2013-009 (2012). See appendix table 1-6.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 1-19

and Taipei outscored students in the United States (552−587 

versus 544). At grade 8, the U.S. average science score of 

525 was lower than those of 8 countries/jurisdictions, higher 

than those of 29, and not measurably different from those of 

the remaining 4.

Performance Trends. In contrast to the mathematics trends, which showed significant improvement in both 

grades, the average scores of U.S. students on the TIMSS 

science assessment have remained flat since 1995 for fourth 

graders and improved 12 points for eighth graders (figure 

1-8). U.S. fourth and eighth graders have not improved their 

international position. Among 17 countries and jurisdictions 

that participated in both the 1995 and 2011 fourth grade 

TIMSS science assessments, 3 outscored the United States 

in 2011 compared with 2 in 1995; at grade 8, the number 

scoring higher than the United States was 6 in both years 

(Provasnik et al. 2012).

Student Coursetaking 

in Mathematics and Science

Mathematics and science coursetaking in high school 

is a strong predictor of students’ overall educational success. Students who take advanced mathematics and science 

courses in high school are more likely to earn high scores on 

academic assessments, enroll in college, pursue mathematics and science majors, and complete a bachelor’s degree 

(Bozick and Lauff 2007; Chen 2009; NCES 2010, 2011b; 

Nord et al. 2011). Advanced coursetaking in high school is 

also associated with greater labor market returns and higher 

job satisfaction, even when controlling for demographic 

characteristics and postsecondary education and attainment 

(Altonji, Blom, and Maghir 2012; NRC 2012c). Analysis 

of the NAEP High School Transcript Study (NAEP HSTS) 

Figure 1-8

Average TIMSS mathematics and science scores 

of U.S. students in grades 4 and 8: 1995–2011 

Average score

1995 1999 2003

Mathematics

Science

2007 2011

0

500

550

600

1995 1999 2003 2007 2011

0

500

550

600

Grade 4

Grade 8 

Grade 4

Grade 8 

TIMSS = Trends in International Mathematics and Science Study.

NOTES: TIMSS mathematics and science assessment scores range 

from 0 to 1,000 for grades 4 and 8. U.S. fourth graders did not 

participate in TIMSS in 1999; score is interpolated. Average 

mathematics and science scores of students in grade 4 and grade 8 

cannot be compared directly because the test items differ across 

grade levels to reect the nature, difculty, and emphasis of the 

subject matter taught in school at each grade.

SOURCES: Gonzales P, Williams T, Jocelyn L, Roey S, Kastberg D, 

Brenwald S, Highlights From TIMSS 2007: Mathematics and Science 

Achievement of U.S. Fourth- and Eighth-Grade Students in an 

International Context, NCES 2009-001 (2008); Provasnik S, Kastberg 

D, Ferraro D, Lemanski N, Roey S, Jenkins F, Highlights From TIMSS 

2011: Mathematics and Science Achievement of U.S. Fourth- and 

Eighth-Grade Students in an International Context, NCES 2013-009 

(2012).

Science and Engineering Indicators 2014

Figure 1-9

Average TIMSS science scores of students in 

grades 4 and 8, by country/jurisdiction: 2011

Grade 4 Grade 8

Score 

higher 

than 

United 

States

Republic of Korea ....587

Singapore ................583

Finland .....................570

Japan .......................559

Russian

 Federation .............552

Taipei (Taiwan) .........552

Singapore .................590

Taipei (Taiwan) ..........564

Republic of Korea .....560

Japan ........................558

Finland .....................552

Slovenia ....................543

Russian Federation ...542

Hong Kong 

 (China) ....................535

Score not 

statistically

different 

from 

United 

States

United States ........ 544 England ....................533

United States ..........525

Hungary ....................522

Australia ....................519

Israel .........................516

Score 

lower than 

United 

States

(selected 

countries)

Czech Republic ...... 536

Hong Kong 

 (China) .................. 535

Hungary .................. 534

Sweden .................. 533

Austria .................... 532

Slovak Republic ...... 532

Netherlands ............ 531

England .................. 529

Denmark ................. 528

Germany ................. 528

Lithuania ...................514

New Zealand ............512

Sweden ....................509

Italy ...........................501

Ukraine .....................501

Norway .....................494

Kazakhstan ...............490

Turkey .......................483

Islamic Republic 

 of Iran .....................474

Romania ...................465

TIMSS = Trends in International Mathematics and Science Study.

NOTES: Hong Kong is a Special Administrative Region of the People’s 

Republic of China. Taipei is the capital city of Taiwan. Countries/

jurisdictions are ordered by 2011 average score. Countries/jurisdictions 

with identical rounded estimates are listed alphabetically. 

SOURCE: Provasnik S, Kastberg D, Ferraro D, Lemanski N, Roey S, 

Jenkins F, Highlights From TIMSS 2011: Mathematics and Science 

Achievement of U.S. Fourth- and Eighth-Grade Students in an 

International Context, NCES 2013-009 (2012). See appendix table 1-7.

Science and Engineering Indicators 2014
1-20 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

showed that the percentage of students earning credits for 

mathematics and science courses has increased steadily 

since 1990, though gaps among different groups of students 

remain (NSB 2012).23 This section draws on data from the 

High School Longitudinal Study of 2009 (HSLS:09) and the 

College Board’s AP program to augment earlier findings on 

mathematics and science coursetaking in high school, advanced coursetaking, and differences in coursetaking among 

various demographic groups. The section begins with contextual information about programmatic efforts to increase 

mathematics and science coursetaking and to standardize 

the quality of these courses. This information informs the 

interpretation of ninth grade coursetaking patterns found in 

the HSLS data.

High School Graduation Requirements and 

Curriculum Standards

Government and education leaders from 35 states participate in the American Diploma Project (ADP), which seeks 

to improve student achievement by aligning high school 

academic content standards with the demands of college 

and careers and requiring all graduating students to have 

completed a college- and career-ready curriculum (Achieve 

2012). ADP encourages states and school districts to adopt 

graduation benchmarks that align high school coursework 

with the expectations of colleges and employers. The ADP 

graduation benchmarks suggest that for students to be considered ready for college and career, all students should complete 4 years of mathematics coursework at least through the 

level of pre-calculus.24 In science, students should complete 

at least 3 years of coursework, including biology, chemistry, and physics. Currently, 23 states and the District of 

Columbia have adopted these graduation requirements 

(Achieve 2012). Two reform efforts, the Common Core 

State Standards Initiative (CCSSI) and the Next Generation 

Science Standards, focus on the content of the courses that 

students take rather than the number or level of courses. 

The goal of these efforts is to ensure that academic standards across states are similar and include the rigorous content and higher-order skills necessary to prepare all students 

for college and careers (see sidebar, “Common Core State 

Standards and Next Generation Science Standards”).

To provide a clear and consistent framework of the 

skills and knowledge students must master in grades 

K−12, the National Governors Association (NGA) Center 

for Best Practices, the Council of Chief State School 

Officers (CCSSO) and Achieve Inc. coordinated a stateled effort to develop the Common Core State Standards 

(CCSS) in English language arts and mathematics (NGA/

CCSSO 2010). The standards aim to ensure that all students have “the academic knowledge and skills in literacy 

and mathematics needed to qualify for and succeed in 

entry-level, credit-bearing postsecondary coursework or 

postsecondary job training” (Achieve 2012).

The CCSS were developed through a rigorous drafting 

and review process involving three workgroups (NGA/

CCSSO 2010). One workgroup, composed of experts in 

assessment, curriculum design, cognitive development, 

and child development, drafted the standards. A second 

group, including business representatives and classroom 

educators as well as scholars, revised that draft, and a 

validation committee of education scholars, teachers, and 

other experts evaluated the final draft. Leaders of the initiative then solicited opinions from other experts who had 

not been consulted in earlier stages and released this draft 

for public comment. The standards writers reviewed the 

nearly 10,000 comments from the public and revised the 

standards before the final version was published in June 

2010. As of August 2013, 45 states and the District of 

Columbia have formally adopted the CCSS (http://www.

corestandards.org).

In a recent survey, school superintendents agreed that 

the CCSS are more rigorous than previous standards and 

will improve students’ English language arts and math 

skills (Kober and Rentner 2012). The superintendents also 

noted that implementing the CCSS will require substantial changes in curriculum and instruction. Whereas the 

majority of the participating states hoped to implement 

the standards fully by the 2014−15 school year, many superintendents expressed concern about having sufficient 

resources for such large-scale change. To assist implementation efforts, two state consortia, the Partnership for 

Assessment of Readiness for College and Careers and 

Smarter Balanced Assessment, received federal grants to 

create assessment systems based on the standards. Both 

consortia will administer these assessments in 2014−15.

In addition to the CCSS in English language arts 

and mathematics, Achieve Inc. has worked with the 

National Research Council (NRC), the National Science 

Teachers Association, the American Association for the 

Advancement of Science, and 26 states to develop K−12 

science standards (http://nextgenscience.org). The Next 

Generation Science Standards (NGSS) are based on the 

Framework for K−12 Science Education, which identifies broad ideas and practices in the natural sciences and 

engineering that all students should be familiar with by 

the time they graduate from high school (NRC 2012a). 

Following a rigorous development and review process for 

the NGSS, similar to that followed for the mathematics 

and English language arts standards, science educators 

and experts released an initial draft, which they revised 

substantially after receiving public comments. The final 

draft was released in April 2013, and states are now considering adoption of the standards.

Common Core State Standards and Next Generation Science Standards
Science and Engineering Indicators 2014 ♦ 1-21

Table 1-3

Highest-level mathematics course in which ninth graders enrolled, by student and family characteristics: 2009

(Percent distribution)

Student and family characteristic

No 

mathematics

Basic 

mathematics/

pre-algebraa Algebra 1

Above 

algebra 1b

All grade 9 students ..................................................... 10.3 9.0 52.1 28.7

Sex

Male....................................................................... 11.1 9.2 51.7 28.0

Female................................................................... 9.5 8.8 52.4 29.3

Race or ethnicity.......................................................

Asian ..................................................................... 7.3 6.6 28.1 58.0

Black ..................................................................... 14.1 11.4 56.0 18.5

Hispanicc ............................................................... 13.3 8.9 53.1 24.8

White ..................................................................... 8.4 8.6 51.6 31.4

Otherd .................................................................... 9.3 8.7 55.5 26.6

Parents’ highest educatione

Less than high school ........................................... 18.4 12.6 46.8 22.2

High school diploma or equivalent........................ 11.9 10.7 55.5 21.9

Associate’s degree................................................ 8.5 8.9 59.7 22.9

Bachelor’s degree ................................................. 7.1 5.4 46.8 40.7

Master’s degree or higher ..................................... 5.1 4.0 39.8 51.1

a

 Basic mathematics includes review/remedial mathematics.

b Above algebra 1 includes geometry 1, algebra 2, trigonometry, integrated math 2, statistics, analytic geometry, and calculus.

c

 Hispanic may be any race. American Indian or Alaska Native, Asian, black or African American, Native Hawaiian or Other Pacific Islander, white, and 

more than one race refer to individuals who are not of Hispanic origin. 

d Other includes Alaska Native, American Indian, Native Hawaiian, Pacific Islander, and more than one race.

e

 The highest level of education achieved by either parent.

NOTE: Detail may not sum to total due to rounding.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2012) of High School Longitudinal 

Study of 2009 (HSLS:09), National Center for Education Statistics. See appendix table 1-8.

Science and Engineering Indicators 2014

Ninth Grade Mathematics and Science 

Coursetaking

HSLS:09 provides detailed data about student coursetaking in mathematics and science in ninth grade.25 Based 

on a nationally representative sample of approximately 

24,000 ninth graders in 944 schools, it focuses on understanding students’ trajectories from the beginning of high 

school into higher education and the workforce (Ingels et 

al. 2011). HSLS:09 includes a heightened focus on STEM 

coursetaking and the high school and personal factors that 

lead students into and out of STEM fields of study and related careers. The data reported here are based on the base 

year of the study, conducted in fall 2009 when participants 

were in the ninth grade.26 The base year supplies data about 

the mathematics and science courses that ninth graders took 

and about variations in their coursetaking by such factors as 

race and ethnicity, parental education level, and SES. The 

data are based on students’ self-report of what mathematics and science courses they enrolled in at the beginning of 

ninth grade, not on evidence that they successfully completed the courses.

Mathematics Coursetaking

Algebra 1 is considered a “gateway” course leading to 

more advanced coursetaking in mathematics and to higher 

levels of achievement (Loveless 2008; Tierney et al. 2009). 

An expert panel convened by the Institution of Education 

Sciences to advise high schools on how to prepare students 

for college recommended that at a minimum all students 

should pass algebra 1 by the end of their ninth grade year 

(Tierny et al. 2009). The HSLS data indicate that the majority of students (81%) who were ninth graders in 2009 (the 

graduating class of 2012) were on track to meet this benchmark (table 1-3; appendix table 1-8), with 52% reporting 

enrollment in algebra 1 and 29% reporting enrollment in a 

more advanced math course than algebra 1, such as geometry 1 or algebra 2.27 About 20% of students were not on 

track to meet this benchmark, however, with 9% reporting 

enrollment in basic mathematics or pre-algebra and 10% reporting no enrollment in any mathematics course. Research 

suggests that students who do not take any mathematics in 

ninth grade may suffer long-term consequences in terms of 

their educational success in high school and their entry into 

college or the workforce (Aughinbaugh 2012; Finkelstein et 

al. 2012; Long, Conger, and Iatarola 2012).

The percentage of students taking coursework above the 

level of algebra 1 in ninth grade (29%) indicates that many 

students are taking this course before reaching high school. 

These self-reported data are in line with NAEP transcript data 

(reported in the 2012 Science and Engineering Indicators), 

which indicated that 26% of high school graduates took algebra 1 before high school in 2009, up from 20% in 2005 
1-22 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

reported no mathematics enrollment compared with 7% of 

students who had at least one parent with a bachelor’s degree 

(table 1-3). About 17% of students in the lowest SES quintile reported no mathematics enrollment compared with 6% 

of those in the highest SES quintile (figure 1-10).

Science Coursetaking

Biology is the most common science subject students 

take in ninth grade: nearly 4 in 10 students in ninth grade 

(39%) reported enrollment in biology 1 (table 1-4; appendix 

table 1-9). About 7% reported enrollment in a science course 

above the level of biology 1, such as chemistry 1 or physics 

1. A total of 18% of ninth graders reported no science enrollment, about twice the total of students reporting no mathematics enrollment (10%). Science coursetaking also varied 

by parental education level, SES, and race and ethnicity, 

showing similar patterns to those reported in mathematics.30

The largest differences were in the percentage of students 

who reported no science enrollment. More than one-fourth 

of students in the lowest SES quintile (27%) reported no 

science enrollment compared with 11% of students in the 

highest SES quintile (figure 1-11). Proportionally more students who had parents with less than a high school education 

reported no science enrollment than did students who had at 

(NSB 2012). NAEP HSTS data show that nearly two-thirds 

of graduates who completed a rigorous high school curriculum took algebra 1 before high school (Nord et al. 2011).28

The percentage of students reporting enrollment in 

courses above algebra 1 varied by parental education level, 

SES,29 and race and ethnicity. Students who had at least one 

parent with a master’s degree or higher were most likely to 

report enrollment in a mathematics course above algebra 1 

(51%), followed by students with at least one parent with a 

bachelor’s degree (41%). About 22% of students with parents at all other education levels (associate’s degree, high 

school diploma, and less than high school) reported enrolling in courses above algebra 1, with no significant difference among students with parents at these education levels. 

Nearly 50% of students in the highest SES quintile reported 

taking a course above algebra 1 compared with just 18% of 

students in the lowest SES quintile (figure 1-10). Asian students were more likely to report enrollment in courses above 

algebra 1 (58%) compared with white (31%), Hispanic 

(25%), and black (19%) students (table 1-3).

At the other end of the spectrum are students who reported no mathematics enrollment in ninth grade: 18% of 

students whose parents had less than a high school education 

Figure 1-10

Highest-level mathematics course in which ninth 

graders enrolled, by socioeconomic quintile: 2009

Percent

 

No

mathematics

Basic

mathematics/

pre-algebra

Algebra 1 Above

algebra 1

0

20

40

60

80

100

NOTES: Basic mathematics includes review/remedial mathematics. 

Above algebra 1 includes geometry 1, algebra 2, trigonometry, 

integrated math 2, statistics, analytic geometry, and calculus. 

Socioeconomic status (SES) is a composite variable derived from 

parental education level, parental occupation, and family income. The 

quintile measure divides the SES distribution into ve equal groups. 

Quintile 1 corresponds to the lowest one-fth of the population, and 

quintile 5 corresponds to the highest. For this report, the middle three 

quintiles are combined to form one category. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2012) of High School 

Longitudinal Study of 2009 (HSLS:09), National Center for Education 

Statistics. See appendix table 1-8.

Science and Engineering Indicators 2014

Figure 1-11

Highest-level science course in which ninth 

graders enrolled, by socioeconomic quintile: 2009 

Percent

 

No science General/earth/

physical science

Biology 1 Above

biology 1

0

20

40

60

80

100

NOTES: Above biology 1 includes chemistry 1, physics 1, biology 2, 

Advanced Placement/International Baccalaureate (AP/IB) biology, 

chemistry 2, AP/IB chemistry, physics 2, and AP/IB physics. 

Socioeconomic status (SES) is a composite variable derived from 

parental education level, parental occupation, and family income. The 

quintile measure divides the SES distribution into ve equal groups. 

Quintile 1 corresponds to the lowest one-fth of the population, and 

quintile 5 corresponds to the highest. For this report, the middle three 

quintiles are combined to form one category. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2012) of High School 

Longitudinal Study of 2009 (HSLS:09), National Center for Education 

Statistics. See appendix table 1-9.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 1-23

in college than their peers who do not take these courses 

(Klopfenstein and Thomas 2009; Porter and Polikoff 2012). 

Access to AP courses is an issue, however. The College 

Board, the nonprofit organization that administers the AP 

program, notes that availability and variety of AP courses 

is lower in schools with higher numbers of low-income and 

traditionally underserved minority students (College Board 

2013). Some schools, particularly small schools and schools 

in low-income and remote areas, may not offer any AP 

courses for their students (see sidebar, “Access to Advanced 

Placement Courses in Mathematics and Science”).

Calculus AB and biology are the most popular AP exams 

in mathematics and science. According to the College Board, 

212,000 students in the graduating class of 2012 took calculus AB and 153,000 students took biology (appendix table 

1-10). Statistics and chemistry were the next most popular, 

with 129,000 students taking the statistics exam and 100,000 

taking chemistry. Exam taking is lower for more advanced 

subjects, including calculus BC (71,000) and physics B 

(63,000). The least common exams are computer science A 

(19,000) and physics C: electricity/magnetism (13,000).

The number of students taking at least one AP exam in 

mathematics or science has doubled in the past decade. In 

the class of 2012, 500,000 students took an AP mathematics or science exam during high school, up from 250,000 

least one parent with a bachelor’s degree (29% versus 13%) 

(table 1-4). Asian students were twice as likely as other 

racial and ethnic groups to report enrollment in a science 

course above biology 1 (14% versus about 7% for all other 

racial and ethnic groups).

Participation and Performance in the 

Advanced Placement Program

Several programs offer high school students the opportunity to earn college credit while still in high school. The AP 

program is one of the largest and best known. Other options 

for students interested in earning college credit during high 

school include dual enrollment, with students concurrently 

enrolling in college courses while still in high school, and 

the International Baccalaureate program, which offers college credit for high school courses (Thomas et al. 2013). 

In the AP program, students take college-level courses 

at their high school. Courses are offered in 34 different subjects and students who earn a passing score (3 or higher out 

of 5) on an AP exam can earn college credits, placement into 

more advanced college courses, or both, depending on the 

policy of the postsecondary institution they attend. Research 

suggests that students who take AP or other college-level 

courses in high school are more likely to enroll and persist 

Table 1-4

Highest-level science course in which ninth graders enrolled, by student and family characteristics: 2009

(Percent distribution)

Student and family characteristic No science

General

science

Earth/

environmental/

physical 

science Biology 1

Above 

biology 1a

All grade 9 students ..................................................... 18.0 5.1 31.2 38.7 7.0

Sex

Male....................................................................... 18.9 5.1 31.3 38.0 6.7

Female................................................................... 17.0 5.2 31.1 39.4 7.3

Race or ethnicity.......................................................

Asian ..................................................................... 12.9 5.4 16.7 51.2 13.9

Black ..................................................................... 25.2 5.5 27.2 35.1 7.0

Hispanicb ............................................................... 22.1 3.9 23.1 43.9 7.1

White ..................................................................... 15.0 5.2 36.7 36.7 6.4

Otherc .................................................................... 16.2 6.9 31.2 37.9 7.8

Parents’ highest educationd

Less than high school ........................................... 29.3 5.2 23.8 38.4 3.3

High school diploma or equivalent........................ 20.7 5.8 32.7 34.9 5.8

Associate’s degree................................................ 15.2 7.2 33.8 37.3 6.6

Bachelor’s degree ................................................. 13.4 4.1 31.7 42.7 8.1

Master’s degree or higher ..................................... 10.8 3.7 27.7 47.2 10.7

a

 Above biology 1 includes chemistry 1, biology 2, Advanced Placement/International Baccalaureate (AP/IB) biology, chemistry 2, AP/IB chemistry, 

physics 2, and AP/IB physics.

b Hispanic may be any race. American Indian or Alaska Native, Asian, black or African American, Native Hawaiian or Other Pacific Islander, white, and 

more than one race refer to individuals who are not of Hispanic origin.

c

 Other includes Alaska Native, American Indian, Native Hawaiian, Pacific Islander, and more than one race.

d The highest level of education achieved by either parent.

NOTE: Detail may not sum to total due to rounding.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2012) of High School Longitudinal 

Study of 2009 (HSLS:09), National Center for Education Statistics. See appendix table 1-9.

Science and Engineering Indicators 2014
1-24 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

students in the class of 2002 (table 1-5). The AP statistics 

test stands out as experiencing especially rapid growth: In 

2002, approximately 40,000 students took the exam, rising 

to nearly 130,000 students in 2012. Environmental science 

also experienced rapid growth, rising from 18,000 exam takers in 2002 to 89,000 in 2012.

Although the number of students taking AP exams in 

mathematics and science has doubled, the AP program in 

mathematics and science involves a relatively small proportion of all high school students. For example, 17% of all students in the class of 2012 took an AP mathematics or science 

exam, with 9% passing (table 1-6).

As the number of students taking AP exams has increased, 

so has the number passing these exams. Nearly 270,000 

students in the class of 2012 passed an AP mathematics or 

science exam in 2012 compared with about 155,000 in 2002 

(table 1-5). Although increasing numbers of students are 

taking and passing AP exams, passing rates have declined 

or remained steady in most mathematics and science subjects. The overall pass rate for any AP mathematics or science exam dropped from 62% in 2002 to 54% in 2012. The 

two most popular exams, calculus AB and biology, showed 

the largest decreases, with average passing rates dropping 

by 9 percentage points for calculus AB and 13 percentage 

points for biology since 2002. In contrast, passing rates for 

exams in more advanced subjects have remained steady or 

even increased, with average passing rates remaining steady 

for calculus BC and physics B and increasing by about 7 

percentage points for both physics C exams. 

The 2012 National Survey of Science and Mathematics 

Education provides information about school AP course 

offerings (Banilower et al. 2013). In 2012, AP calculus 

AB and AP biology were the most widely accessible 

courses in high schools, available to 81% and 74% of 

high school students, respectively (figure 1-A). 

The least accessible courses were AP calculus BC in 

math and AP physics C in science, available to 47% and 

25% of high school students, respectively. The number 

of AP mathematics and science courses offered varied by 

school characteristics. For example, the largest schools 

offered an average of two AP mathematics courses and 

three AP science courses, whereas the smallest schools 

offered about one AP mathematics and one AP science 

course (table 1-A). The average number of both mathematics and science courses available at low-poverty 

schools and suburban and urban schools was about twice 

those available at high-poverty schools and rural schools.

Access to Advanced Placement Courses in Mathematics and Science

Figure 1-A

High school students with access to various 

AP mathematics and science courses: 2012

Percent

AP = Advanced Placement.

SOURCE: Banilower ER, Smith PS, Weiss IR, Malzahn KA, Campbell 

KM, Weis AM, Report of the 2012 National Survey of Science and 

Mathematics Education (2013).

Science and Engineering Indicators 2014

Physics C 

Environmental

science 

Physics B 

Chemistry 

Biology 

AP science

Calculus BC 

Statistics 

Calculus AB 

AP mathematics 

0 20 40 60 80 100

Table 1-A

Average number of AP mathematics and science 

courses offered in high schools, by school 

characteristic: 2012

School characteristic Mathematics Science

Students in school eligible for 

free/reduced-price lunch

0%−25%.............................. 1.4 2.0

25%−50%............................ 1.1 1.5

50%−75%............................ 0.8 1.1

75%−100%.......................... 0.7 1.1

School size

Smallest ............................... 0.6 0.7

Second group ...................... 0.9 1.2

Third group .......................... 1.6 2.1

Largest................................. 2.1 2.8

Community type

Rural..................................... 0.6 0.7

Suburban ............................. 1.2 1.7

Urban ................................... 1.3 1.7

AP = Advanced Placement.

SOURCE: Banilower ER, Smith PS, Weiss IR, Malzahn KA, Campbell 

KM, Weis AM, Report of the 2012 National Survey of Science and 

Mathematics Education (2013).

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 1-25

AP exams covering more advanced material, such as calculus BC and physics, are taken by fewer students, but the 

pass rates are much higher. For example, 70,000 students 

in the class of 2012 took the calculus BC exam; more than 

200,000 took the relatively less demanding calculus AB 

exam. The pass rate for calculus BC was 82%, compared 

with 57% for calculus AB (table 1-5). In science, about 

13,000 students in the class of 2012 took the physics C: electricity/magnetism exam; more than 150,000 students took 

the AP biology exam. The pass rate for physics C was 71%, 

much higher than the passing rate for AP biology (49%).

AP Exam Taking by Sex and Race and Ethnicity

The proportion of male and female students taking particular AP exams differs by test subject (figure 1-12). Male 

students are more likely than female students to take AP exams in advanced subjects, including calculus BC (59% versus 41%), physics B (65% versus 35%), and both physics C 

Table 1-5

Public school students who took or passed an AP exam in high school, by subject: Graduating classes 

2002, 2007, and 2012

Students who took 

an AP exam (number)

Students who passed 

an AP exam (number)a

Students who passed 

an AP exam (%)a

Subject 2002 2007 2012 2002 2007 2012 2002 2007 2012

Any AP exam............................................ 471,404 694,705 954,070 305,098 424,004 573,472 64.7 61.0 60.1

Any AP mathematics or science exam..... 250,465 364,732 497,924 154,450 208,515 268,251 61.7 57.2 53.9

AP mathematics exam

Calculus AB .......................................... 123,388 166,239 211,570 81,293 95,338 120,469 65.9 57.3 56.9

Calculus BC.......................................... 32,760 51,434 70,828 26,078 40,675 57,808 79.6 79.1 81.6

Statistics ............................................... 40,207 81,992 129,403 22,569 47,578 74,478 56.1 58.0 57.6

AP science exam

Biology.................................................. 73,951 109,899 152,742 45,231 64,771 74,211 61.2 58.9 48.6

Chemistry.............................................. 45,859 72,866 100,362 25,796 40,161 52,689 56.3 55.1 52.5

Environmental science.......................... 18,099 41,145 88,683 9,290 20,579 43,350 51.3 50.0 48.9

Computer science A ............................. 12,166 11,670 19,067 7,433 6,766 11,743 61.1 58.0 61.6

Physics B.............................................. 28,688 43,099 63,125 16,514 25,022 36,928 57.6 58.1 58.5

Physics C: Electricity/magnetism ......... 7,141 8,638 12,766 4,586 6,129 9,078 64.2 71.0 71.1

Physics C: Mechanics .......................... 14,717 20,672 28,923 10,065 14,570 21,892 68.4 70.5 75.7

AP = Advanced Placement.

a

 Students scoring 3, 4, or 5 on a scale of 1–5 for an AP exam.

NOTES: The College Board reports AP results by graduating class rather than by calendar year. Results include exams taken by graduates throughout 

their high school career.

SOURCE: The College Board, The 9th Annual AP® Report to the Nation—Subject Supplement. Copyright © 2013, www.collegeboard.org. Reproduced 

with permission.

Science and Engineering Indicators 2014

Table 1-6

Public school students who took or passed an AP exam as a proportion of overall student population, by 

subject: Graduating classes 2002, 2007, and 2012

(Percent)

Students who took an AP exam Students who passed an AP exama

Subject 2002 2007 2012 2002 2007 2012

Any subject............................................... 18.0 23.5 32.4 11.6 14.3 19.5

Mathematics or scienceb .......................... 9.6 12.3 16.9 5.9 7.1 9.1

AP = Advanced Placement. 

a

 Students scoring 3, 4, or 5 on a scale of 1–5 for an AP exam.

b Includes calculus AB, calculus BC, statistics, biology, chemistry, environmental science, computer science A, physics B, physics C: electricity/

magnetism, and physics C: mechanics.

NOTES: The College Board reports AP results by graduating class, rather than by calendar year. Results include exams taken by graduates throughout 

their high school career. 

SOURCE: The College Board, The 9th Annual AP® Report to the Nation—Subject Supplement. Copyright © 2013, www.collegeboard.org. Reproduced 

with permission.

Science and Engineering Indicators 2014
1-26 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

exams (about 75% versus 25%). Similar percentages of male 

and female students took AP exams in calculus AB and statistics. Female students took AP exams at higher rates than 

male students in biology (59% versus 41%) and environmental science (55% versus 45%). Computer science A showed 

the largest difference in exam taking by sex, with a distribution of 81% of male students and 19% of female students.

Black and Hispanic students are underrepresented among 

AP exam takers. Although black students made up about 

15% of the 2012 graduating class, they comprised less than 

8% of students taking any AP mathematics or science exam 

(appendix table 1-10). Black students were particularly underrepresented in the exam-taking population for AP exams 

in calculus BC and both physics C exams, accounting for 

only about 3% of the students taking those exams. Hispanic 

students, who made up about 18% of the class of 2012, were 

also underrepresented in the AP exam-taking population. 

Their representation among AP exam takers ranged from a 

high of 15% for environmental science to a low of 8% for 

calculus BC and 7% for physics C: electricity/magnetism. 

Conversely, Asian students are overrepresented among AP 

exam takers. Asian students accounted for about 6% of the 

class of 2012 but accounted for about 30% of the exam takers in physics C: electricity/magnetism, calculus BC, and 

computer science A. Their lowest representation among 

exam takers was 13% for environmental science.

Teachers of Mathematics and Science

Teacher quality is one of the most important factors 

influencing student learning. Students’ achievement in 

mathematics and science depends in part on their access to 

high-quality instruction in those subjects. Many factors affect teacher quality, including qualifications, ongoing professional development, attrition, and working conditions. 

The 2012 National Survey of Science and Mathematics 

Education (NSSME), the fifth in a series of surveys of mathematics and science teachers first administered in 1977, provides a comprehensive review of these topics (Banilower et 

al. 2013). The 2012 NSSME is a nationally representative 

survey based on a sample of 7,752 mathematics and science teachers in elementary and secondary schools across 

the United States. This section highlights the major findings of the NSSME and supplements those findings with 

national data on teacher attrition from the U.S. Department 

of Education’s Beginning Teacher Longitudinal Study 

(BTLS).31

Characteristics of High-Quality Teachers

Extensive research suggests that high-quality teaching has 

a positive effect on student achievement (Boyd et al. 2008; 

Clotfelter, Ladd, and Vigdor 2007; Goe 2008; Guarino, 

Santibanez, and Daley 2006; Hanushek 2011; Harris and 

Sass 2011), but the specific teacher characteristics that contribute to student success are less clear. Some studies have 

cast doubt on whether commonly measured indicators, such 

as teachers’ licensure scores or the selectivity of their undergraduate institutions, are related to their teaching effectiveness (Boyd et al. 2006; Buddin and Zamarro 2009a, 2009b; 

Hanushek and Rivkin 2006; Harris and Sass 2011; Sass et al. 

2012). Efforts to improve measures of teaching quality have 

proliferated in recent years. Recent efforts have focused on 

“value-added” models—strategies for measuring teacher 

effectiveness by comparing test score gains of students in 

the same school who have similar backgrounds and initial 

scores but different teachers (Baker et al. 2010; Goldhaber, 

Liddle, and Theobald 2013; Hanushek and Rivkin 2006; 

Harris and Sass 2011; Loeb, Kalogrides, and Béteille 2012). 

Following this line of research, some researchers, including 

the Measures of Effective Teaching (MET) Project and the 

Figure 1-12

Public school students in graduating class of 2012 

who took AP exams in mathematics and science 

in high school, by sex 

Percent

Physics C:

Mechanics

Physics C:

Electricity/

magnetism

Physics B 

Environmental

science

Computer

science A 

Chemistry

Biology

AP science

Statistics

Calculus BC

Calculus AB

AP mathematics

0 20 40 60 80 100

Female

Male

AP = Advanced Placement.

NOTES: The College Board reports AP results by graduating class 

rather than by calendar year. Results include exams taken by 

graduates throughout their high school career.

SOURCE: The College Board, The 9th Annual AP® Report to the 

Nation—Subject Supplement. Copyright © 2013, www.collegeboard. 

org. Reproduced with permission. 

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 1-27

National Center for Teacher Effectiveness (NCTE), have attempted to establish composite indicators for effective teaching (Kane et al. 2013; MET Project 2012; NCTE 2013).

This section reports on several indicators of teacher quality that are available from major national studies, including 

teaching experience, professional certification, in-field preparation (i.e., earning a postsecondary degree in the teaching 

field), content coursetaking, and teachers’ self-assessment of 

their preparation. Other less easily observed characteristics 

may also contribute to teacher effectiveness, including teachers’ abilities to motivate students, engage students in learning, maximize instruction time, and diagnose and overcome 

students’ learning difficulties. However, these characteristics are often difficult and costly to measure and therefore 

are rarely included in nationally representative surveys.

Teaching Experience. In general, as teachers gain more 

years of experience, they become more effective in helping students learn (Boyd et al. 2006; Harris and Sass 2011; 

Clotfelter, Ladd, and Vigdor 2007; Rice 2010). Recent studies have found that novice teachers (i.e., teachers with 2 or 

fewer years of experience) are more likely than experienced 

teachers to work in high-poverty, high-minority schools 

and teach low-achieving students (Loeb, Kalogrides, and 

Béteille 2012; LoGerfo, Christopher, and Flanagan 2012; 

Sass et al. 2012). According to data from the NSSME, in 

2012, the percentage of novice mathematics teachers ranged 

from 10% to 14% in elementary, middle, and high schools, 

whereas the percentage of novice science teachers ranged 

from 13% to 16% across the school levels (figure 1-13).

Schools with the highest proportions of low-income students were more likely than other schools to have novice 

science teachers. In schools with the highest concentrations 

of students eligible for free/reduced-price lunch (FRL) (i.e., 

75%−100% of students), 23% of science classes were taught 

by teachers with 2 or fewer years of experience, compared 

with 10% of science classes in schools with the lowest concentrations of FRL-eligible students (i.e., 0%−25% of students) (figure 1-14). In contrast, the distribution of novice 

mathematics teachers did not vary significantly depending 

on a school’s percentage of FRL students. Moreover, students in high-poverty schools were much less likely to have 

novice teachers in mathematics than in science: 14% of 

mathematics classes, compared with 23% of science classes, 

were taught by teachers with 2 or fewer years of experience.

A similar pattern was seen across mathematics and science for non-Asian minority students. Science classes with 

the highest percentages of non-Asian minority students were 

more likely to have novice science teachers (21%) than were 

classes with the lowest percentages of non-Asian minority 

students (14%), but such differences were not observed for 

mathematics teachers (appendix table 1-11).

Higher-achieving students tended to have more experienced mathematics teachers. For example, 15% of math 

classes composed of mostly low achievers had mathematics 

teachers with 2 or fewer years of experience, whereas 8% of 

math classes composed of mostly high achievers had such 

mathematics teachers (appendix table 1-11). A similar pattern appears for science, with classes of mostly low achievers 

(18%) more likely than classes of mostly high achievers (12%) 

to have science teachers with 2 or fewer years of experience.

Certification. Each state requires public school teachers 

to earn a certificate that licenses them to teach. States set 

criteria for various types of certification; usually a full certification entails a combination of passing scores on tests, a 

Figure 1-13

Mathematics and science teachers’ years of experience teaching their subject, by grade level: 2012

Percent 

NOTE: Detail may not sum to total due to rounding.

SOURCE: Banilower ER, Smith PS, Weiss IR, Malzahn KA, Campbell KM, Weis AM, Report of the 2012 National Survey of Science and Mathematics 

Education (2013).

Science and Engineering Indicators 2014

Elementary Middle 

Mathematics teachers Science teachers

High Elementary Middle High 

0

20

40

60

80

100

≤ 2 years 3–5 years 6–10 years ≥ 11 years
1-28 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

bachelor’s degree with a specified number of credits in education and in the discipline taught, and supervised practice 

teaching experience (NCTQ 2013). Criteria for certification 

vary among grade levels, with elementary teachers usually 

certified to teach multiple subjects and high school teachers 

certified within subject areas. Whether middle school teachers are certified in multiple subjects or individual subjects 

varies across states.

Fully certified teachers are distinguished from those who 

are granted alternative certificates. Alternative certificates 

are issued to persons who must complete a certification program in order to continue teaching, those who have satisfied 

all requirements except the completion of a probationary 

teaching period, and those who require some additional 

coursework or need to pass a test.

The NSSME reported four different paths to full and alternative certification: an undergraduate program leading to 

a bachelor’s degree and teaching certificate; a post-baccalaureate program leading to a certificate; a master’s program 

that also awarded a teaching certificate; and no formal teacher preparation. Elementary mathematics and science teachers were the most likely to have earned a bachelor’s degree 

and teaching certificate as part of an undergraduate program: 

about 60% of elementary teachers of mathematics and science followed this path to certification, compared with 48% 

of high school mathematics teachers and 34% of high school 

science teachers (table 1-7). In contrast, high school mathematics and science teachers were more likely than their elementary counterparts to have earned a certificate through 

a post-baccalaureate program—30% of high school science 

teachers followed this path to certification, compared with 

13% of elementary school science teachers. High school 

mathematics and science teachers were also more likely to 

report no formal teacher preparation (8% and 10%, respectively) than were their elementary school counterparts (1%).

Some studies have shown that fully certified mathematics 

and science teachers are more prevalent in low-poverty and 

low-minority schools (NSB 2012). Students from disadvantaged backgrounds (minority students, low-SES students, 

and those whose first language was not English) are more 

likely than their counterparts to be taught by mathematics 

or science teachers with alternative certification (LoGerfo, 

Christopher, and Flanagan 2012). The NSSME did not report data on this issue.

Degree in Field and Content Coursetaking. Over the 

past decade, few issues related to teaching quality have received more attention than in-field teaching in middle and 

high schools (Almy and Theokas 2010; Dee and Cohodes 

2008; Peske and Haycock 2006). In-field teaching refers to 

Figure 1-14

Mathematics and science classes taught by 

teachers with 2 years or less of experience 

teaching their subject, by students in school 

eligible for free/reduced-price lunch: 2012 

Percent

 

SOURCE: Banilower ER, Smith PS, Weiss IR, Malzahn KA, Campbell 

KM, Weis AM, Report of the 2012 National Survey of Science and 

Mathematics Education (2013). See appendix table 1-11.

Science and Engineering Indicators 2014

Mathematics classes Science classes

0

5

10

15

20

25

0–25 25–50 50–75 75–100

Students eligible for free/reduced price lunch (%)

Table 1-7

Mathematics and science teachers, by path to certification and grade level: 2012

(Percent distribution)

Grade level

An undergraduate 

program leading to a 

bachelor’s degree and 

a teaching credential 

A post-baccalaureate 

credentialing program (no 

master’s degree awarded) 

A master’s program 

that also awarded a 

teaching credential 

No formal teacher 

preparation 

Mathematics teachers

Elementary............................... 63 14 22 1

Middle...................................... 55 17 25 3

High......................................... 48 20 22 10

Science teachers

Elementary............................... 61 13 25 1

Middle...................................... 47 23 26 4

High ......................................... 34 30 28 8

SOURCE: Banilower ER, Smith PS, Weiss IR, Malzahn KA, Campbell KM, Weis AM, Report of the 2012 National Survey of Science and Mathematics 

Education (2013).

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 1-29

the assignment of teachers to teach subjects that match their 

training or education. To some extent, this emphasis can be 

traced back to the implementation of the federal No Child Left 

Behind Act (NCLB), which mandated that all students have 

teachers who demonstrate subject area competence. To determine whether teachers have subject-specific preparation for 

the fields they teach, recent research has focused on matching 

teachers’ formal preparation (as indicated by degree major, 

certification field, or both) with their teaching field (Hill and 

Gruber 2011; McGrath, Holt, and Seastrom 2005; Morton et 

al. 2008). The NSSME followed a similar approach, using 

teachers’ degree field and postsecondary coursework completed in mathematics and science as indicators of preparation 

to teach mathematics and science at the elementary, middle, 

and high school levels (Banilower et al. 2013).32

In 2012, 82% of high school science teachers and 73% of 

high school mathematics teachers held degrees in their teaching field or in science or mathematics education (table 1-8). 

High school mathematics and science teachers were twice 

as likely as their middle school counterparts to hold in-field 

degrees. Very few elementary school teachers who taught 

mathematics or science held an in-field degree (about 5%).

Many secondary science classes, especially at the high 

school level, focus on more discrete areas of science, such 

as biology or chemistry. In 2012, biology teachers were the 

most likely among high school science teachers to have a 

degree in their specific teaching field, with 53% having a 

degree in biology (appendix table 1-12). Another 37% had 

at least three college courses beyond introductory biology. 

In mathematics, 52% of high school mathematics teachers 

had a degree in mathematics (table 1-8). Almost all high 

school mathematics teachers had completed a calculus 

course (93%), and the vast majority of them had taken college coursework in advanced calculus (79%), linear algebra 

(80%), and statistics (83%) (appendix table 1-13). Other 

college courses completed by the majority of high school 

mathematics teachers included abstract algebra (67%), differential equations (62%), axiomatic geometry (55%), analytic geometry (53%), probability (56%), number theory 

(54%), and discrete mathematics (52%). About 77% of high 

school mathematics teachers had taken a course in computer 

science. Substantially fewer middle school teachers had taken college coursework in each of these subject areas.

According to the NSSME data, the likelihood of middle 

and high school classes being taught by a teacher with in-field 

preparation varied by the concentration of high or low achievers in both mathematics and science classes and by the percent of non-Asian minority students in mathematics classes. 

For example, 61% of mathematics classes and 76% of science classes composed mostly of high-achieving students 

were taught by teachers with an in-field degree, compared 

with 49% of mathematics classes and 50% of science classes 

composed mostly of low-achieving students (appendix table 

1-14). The difference by the concentration of non-Asian minority students was large for mathematics but less so for science: 44% of classes with the highest percentage of non-Asian 

minority students had a mathematics teacher with an in-field 

degree, compared with 64% of classes with the lowest percentage of such students; for science, it was 58% and 68%, 

respectively. The differences among schools with the highest 

and lowest percentages of FRL-eligible students ranged from 

58% to 68% (statistically significant) for science and from 

51% to 56% (not statistically significant) for math.

Although elementary school teachers are not generally 

expected to have degrees in mathematics or science, both the 

National Council of Teachers of Mathematics (NCTM) and 

the National Science Teachers Association (NSTA) have 

recommendations for the number and types of courses that 

elementary teachers should take to be adequately prepared 

to teach these subjects (Banilower et al. 2013). The NSTA 

suggests that elementary science teachers have one course 

each in life, earth, and physical sciences. In 2012, 36% of 

elementary school teachers met this standard, and 38% had 

taken courses in two of the three areas (figure 1-15). Six 

percent of elementary teachers had no college courses in 

science. For mathematics, the NCTM recommends that elementary school teachers take college coursework in five 

areas, including numbers and operations, algebra, geometry, 

Table 1-8

Mathematics and science teachers with an undergraduate or graduate degree in mathematics or science, 

by grade level: 2012

(Percent)

Mathematics teachers’ degree Science teachers’ degree

Grade level Mathematics

Mathematics 

education

Mathematics 

or 

mathematics 

education

None of 

these fields

Science or

engineering

Science 

education

Science,

engineering, 

or science 

education

None of 

these fields

Elementary..... 4 2 4 96 4 2 5 95

Middle............ 23 26 35 65 26 27 41 59

High............... 52 54 73 27 61 48 82 18

SOURCE: Banilower ER, Smith PS, Weiss IR, Malzahn KA, Campbell KM, Weis AM, Report of the 2012 National Survey of Science and Mathematics 

Education (2013).

Science and Engineering Indicators 2014
1-30 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

probability, and statistics. In 2012, 10% of elementary teachers met the standard of having coursework in all five of these 

areas, 57% had courses in one to two of these areas, and 1% 

had no courses in these areas.

Self-Assessment of Preparedness to Teach. Elementary 

teachers were much more confident in their ability to teach 

mathematics than in their ability to teach science: 77% of elementary teachers felt very well prepared to teach mathematics, but just 39% reported being very well prepared to teach 

science (figure 1-16). Within mathematics, elementary teachers felt most prepared to teach numbers and operations; threequarters reported that they felt very well prepared to teach this 

topic, compared with approximately half who felt very well 

prepared to teach measurement, geometry, and early algebra 

(appendix table 1-15). Within science, elementary teachers 

felt most prepared to teach life and earth science, with about 

one-fourth reporting feeling very well prepared to teach these 

topics. In contrast, just 17% reported feeling very well prepared to teach physical science, and 4% reported feeling very 

well prepared to teach engineering.

Middle and high school teachers of mathematics and science who were surveyed in the NSSME were asked about 

their perceived level of preparedness to teach subtopics within 

their major subject areas. High school chemistry teachers were 

the most likely to report feeling very well prepared to teach 

topics in their discipline, ranging from 66% for properties of 

solutions to 83% for elements, compounds, and mixtures (appendix table 1-16). Overall, high school science teachers felt 

more prepared to teach biology, chemistry, and physics than 

middle school science teachers, but no difference was found 

in levels of preparedness between grade levels for teaching 

earth or environmental science. Both middle and high school 

science teachers reported very little preparedness for teaching 

engineering, with 6% of middle school and 7% of high school 

teachers reporting they felt very well prepared.

In mathematics, high school teachers were generally 

more likely than middle school teachers to report feeling 

very well prepared to teach most topics. For example, 91% 

of high school teachers reported feeling very well prepared 

to teach algebraic thinking, compared with 76% of middle 

school teachers (appendix table 1-17).

Self-Assessment of Preparedness for Tasks Associated 

with Instruction. In the NSSME, mathematics and science 

teachers were also asked how well prepared they felt to manage tasks associated with instruction, including handling classroom discipline and encouraging underrepresented groups to 

participate in their subject. The majority of respondents felt 

very well prepared to handle classroom discipline, with elementary school teachers most likely to feel prepared (about 

70% compared with about 60% of middle and high school 

teachers) (table 1-9). About half of mathematics and science 

teachers at most levels felt very well prepared to encourage 

the participation of female students in mathematics and science. Elementary teachers of science were an exception––

only 30% felt well prepared to encourage female participation 

Figure 1-16

Elementary teachers’ self-assessment of their 

preparedness to teach mathematics and science: 

2012

Percent

 

NOTE: Detail may not sum to total due to rounding.

SOURCE: Banilower ER, Smith PS, Weiss IR, Malzahn KA, Campbell 

KM, Weis AM, Report of the 2012 National Survey of Science and 

Mathematics Education (2013). See appendix table 1-15.

Science and Engineering Indicators 2014

Mathematics Science 

0

20

40

60

80

100

Not adequately prepared Somewhat prepared 

Fairly well prepared Very well prepared 

Figure 1-15

Elementary teachers meeting NCTM- and NSTArecommended college-level coursework in 

mathematics and science: 2012 

Percent 

Mathematics courses taken Science courses taken

All 5 3−4 1−2 0 All 3 2 1 0

0

20

40

60

80

100

NCTM = National Council of Teachers of Mathematics; NSTA = 

National Science Teachers Association.

NOTES: NCTM recommended that elementary teachers take 

college-level courses in number and operations, algebra, geometry, 

probability, and statistics. NSTA recommended that elementary 

teachers take college-level courses in life science, earth science, and 

physical science. Detail may not sum to total due to rounding.

SOURCE: Banilower ER, Smith PS, Weiss IR, Malzahn KA, Campbell 

KM, Weis AM, Report of the 2012 National Survey of Science and 

Mathematics Education (2013).

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 1-31

in science. In mathematics, about half of elementary teachers 

felt very well prepared to encourage students from low-SES 

groups and racial or ethnic minorities to participate in their 

subject, compared with about 40% of high school mathematics teachers. This pattern was reversed among science teachers, with high school teachers more likely to feel very well 

prepared to encourage participation among students from 

these groups (about 45% at the high school level compared 

with about 30% at the elementary level). Teachers of science 

at the elementary level felt the least prepared overall to encourage interest in science among all students, with just 25% 

reporting feeling well prepared to do so.

Teacher Professional Development

Professional development enables teachers to update 

their knowledge, sharpen their skills, and acquire new 

teaching techniques, all of which may enhance the quality 

of teaching and learning (Davis, Petish, and Smithey 2006; 

Richardson and Placier 2001). Research indicates that teacher professional development can have measurable effects on 

student performance. For example, an analysis examining 

outcomes across 16 studies of professional development for 

mathematics and science teachers found that professional 

development had significant effects on student performance 

in mathematics (CCSSO 2009). The 2012 NSSME collected 

data on how recently mathematics and science teachers participated in subject-specific professional development and 

how many hours they spent on professional development in 

the past 3 years.

Recent Participation. A majority of middle school and 

high school mathematics and science teachers participated 

in at least one professional development activity focused on 

mathematics or science in the last 3 years. The rates for middle and high school science teachers ranged from 82% to 89% 

(table 1-10). Teachers responsible for elementary science instruction were far less likely to participate in a science-focused 

professional development activity, with 59% reporting participation in at least one such activity in the past 3 years and 15% 

reporting that they had never participated in a science-focused 

professional development activity (compared with 3%−6% of 

teachers at all other levels and subjects).

Time Spent. In the NSSME, teachers were asked to 

report the number of hours that they had spent on subjectspecific professional development in the past 3 years. About 

36% of high school science teachers and 32% of high school 

mathematics teachers reported that they had spent more than 

35 hours participating in subject-specific professional development activities in the past 3 years (table 1-11). Elementary 

science teachers were the least likely to have spent time participating in subject-specific professional development: 65% 

reported participation in less than 6 hours of professional 

development, compared with 35% of elementary mathematics teachers and 22%−30% of middle school and high school 

mathematics and science teachers.

Teachers’ Working Conditions

Teachers’ perceptions of their working conditions play 

a role in determining the supply of qualified teachers and 

influencing their decisions about remaining in the profession (Darling-Hammond and Sykes 2003; Hanushek, Kain, 

and Rivkin 2004; Ingersoll and May 2012; Ladd 2009; 

Johnson et al. 2004). Mathematics and science teachers are 

more likely than other teachers to cite job dissatisfaction as 

a reason for leaving teaching (Ingersoll and May 2012). Safe 

Table 1-9

Mathematics and science teachers considering themselves very well prepared for various tasks associated with 

instruction, by grade level: 2012

(Percent)

Grade level

Manage 

classroom 

discipline

Encourage 

students’ interest 

in mathematics 

or science

Encourage 

participation 

of females in 

mathematics 

or science

Encourage 

participation 

of low-SES 

students in 

mathematics 

or science

Encourage 

participation of 

racial or ethnic 

minorities in 

mathematics 

or science

Mathematics teachers

Elementary ..................................... 69 48 56 52 50

Middle ............................................ 61 46 56 53 48

High ............................................... 58 39 51 40 39

Science teachers

Elementary ..................................... 72 25 30 31 30

Middle ............................................ 60 39 46 36 36

High ............................................... 59 53 55 44 44

SES = socioeconomic status.

SOURCE: Banilower ER, Smith PS, Weiss IR, Malzahn KA, Campbell KM, Weis AM, Report of the 2012 National Survey of Science and Mathematics 

Education (2013).

Science and Engineering Indicators 2014
1-32 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

environments, strong administrative leadership, cooperation 

among teachers, high levels of parent involvement, and sufficient learning resources can enhance teachers’ commitment to their schools, promote job satisfaction, and improve 

teachers’ effectiveness (Berry, Smylie, and Fuller 2008; 

Brill and McCartney 2008; Guarino, Santibanez, and Daley 

2006; Ingersoll and May 2012). Among the working conditions that contribute to teachers’ dissatisfaction are lack 

of administrative support, low parent involvement, and student discipline problems (Ingersoll and May 2012; Guarino, 

Santibanez, and Daley 2006). Moreover, teacher job satisfaction and retention rates tend to be lower in schools with 

high proportions of minority, low-income, or low-achieving 

students (Berry, Smylie, and Fuller 2008; Hanushek, Kain, 

and Rivkin 2004; Ingersoll and May 2012).

The NSSME provides extensive data on working conditions that affect teachers’ perceptions of their school environments. Mathematics and science program representatives 

at each school site were asked to identify which school factors inhibited or promoted effective instruction in their subject area. Mathematics program representatives were more 

likely to report that their schools were supportive of math 

instruction than science program representatives were to report that their schools were supportive of science instruction. 

For example, 82% of mathematics program representatives 

reported that the importance their school placed on subject 

teaching promoted effective instruction in mathematics, 

whereas 60% of science program representatives reported so 

for instruction in science (appendix table 1-18). About 70% 

of mathematics program representatives and 53% of science 

program representatives agreed that school management 

of instructional resources promoted effective instruction in 

their subject. Many of the representatives (52%–65%) also 

agreed that district professional development policies and 

practices promoted effective teaching in their subject area. 

Relatively lower percentages of respondents (56% for mathematics and 44% for science) agreed that the time provided 

for teacher professional development promoted effective instruction in their subject area.

School program representatives were also asked to rate the 

extent to which several factors were problems for instruction. 

These included student factors such as high absenteeism, 

lack of student interest, low reading ability, and inappropriate behavior; teacher factors such as lack of teacher interest 

and insufficient time to share ideas; and school factors such 

as inadequate funds for equipment. Representatives were 

asked to classify issues on a scale, ranging from “not a significant problem” to “a serious problem.”

For science instruction, one of the most frequently cited problems was inadequate funds for purchasing equipment: about 30% of program representatives in elementary, 

middle, and high schools reported this as a serious problem 

for science instruction (table 1-12). At the middle and high 

Table 1-10

Mathematics and science teachers, by most recent participation in subject-focused professional development 

and grade level: 2012

(Percent distribution)

Grade level

In the past

3 years 

4−6

years ago 

7−10

years ago 

More than 

10 years ago Never 

Mathematics teachers.................................

Elementary............................................... 87 7 1 1 3

Middle...................................................... 89 4 1 2 4

High ......................................................... 88 6 2 1 4

Science teachers.........................................

Elementary............................................... 59 16 5 5 15

Middle...................................................... 82 6 3 4 6

High ......................................................... 85 7 2 1 5

NOTE: Detail may not sum to total due to rounding.

SOURCE: Banilower ER, Smith PS, Weiss IR, Malzahn KA, Campbell KM, Weis AM, Report of the 2012 National Survey of Science and Mathematics 

Education (2013).

Science and Engineering Indicators 2014

Table 1-11

Mathematics and science teachers spending time 

in subject-focused professional development in 

the past 3 years, by grade level: 2012

(Percent distribution)

Grade level

< 6 

hours 

6−15 

hours

16−35 

hours

> 35 

hours

Mathematics teachers

Elementary.................. 35 35 20 11

Middle......................... 22 24 23 31

High ............................ 23 24 22 32

Science teachers

Elementary.................. 65 22 8 4

Middle......................... 30 24 20 27

High ............................ 23 20 21 36

NOTE: Detail may not sum to total due to rounding.

SOURCE: Banilower ER, Smith PS, Weiss IR, Malzahn KA, Campbell 

KM, Weis AM, Report of the 2012 National Survey of Science and 

Mathematics Education (2013).

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 1-33

school levels, 19% of respondents cited low student reading 

abilities as a serious problem for science instruction in their 

schools; 16% of elementary program respondents cited low 

reading ability as a serious problem. Several other problems 

were reported more frequently in elementary schools than 

in high schools, including insufficient time to teach science 

(27% versus 10%) and lack of opportunities for science 

teachers to share ideas (20% versus 13%). Low student interest in science was cited as a serious problem for instruction among 5% of respondents in elementary schools and 

13% of those in high schools. For mathematics instruction at 

the elementary level, the most frequently cited problem was 

low student reading abilities (22%), which was mentioned 

substantially more often than low student interest in mathematics (14%). At the high school level, this pattern was reversed: 30% of respondents mentioned low student interest 

in math as a serious problem but only 20% mentioned low 

student reading ability. At the middle school level, percentages of respondents mentioning these two problems were 

similar (about 25%).

In the NSSME data, both mathematics and science teachers in high-poverty schools found student behavior problems 

to be a greater barrier to effective instruction than did teachers in low-poverty schools (Banilower et al. 2013). Teacher 

behavior was also more frequently seen as a problem in 

high-poverty schools compared with low-poverty schools, 

though to a far lesser extent than student behavior.

Mathematics and Science Teacher Attrition

In view of the potential for large numbers of teachers to 

retire in the next few years and the importance of improving 

students’ mathematics and science achievement, both government (The White House 2012) and advocacy organizations (see sidebar “100Kin10”) seek to prepare more new 

mathematics and science teachers to ensure that there is an 

ample supply of highly qualified teachers in these subjects. 

If, however, new teachers leave the profession within a few 

years of beginning teaching, attrition may negate efforts to 

expand the teaching force (Ingersoll and Perda 2010). A recent study found that teacher attrition varied greatly among 

schools, and that high-poverty, high-minority, and urban 

public schools had the highest mathematics and science 

teacher turnover (Ingersoll and May 2012).

Annual attrition rates among public school teachers, 

measured by the Teacher Follow-up Survey six times since 

1988–89, indicate that mathematics and science teachers 

leave the profession at about the same rates as all teachers do (NSB 2012). Eight percent of all 2007 teachers had 

left the profession by 2008, and the corresponding rates for 

mathematics and science teachers were similar (8% and 9%, 

respectively) (NSB 2012).

The Beginning Teacher Longitudinal Study (BTLS) expands the ability to measure teacher attrition from 1-year 

rates to cumulative rates for each of the first 5 years of teaching. It focuses specifically on the attrition rate of beginning 

teachers rather than yearly attrition rates for all teachers. 

Beginning teachers who entered the profession in 2007–08 

were surveyed in their first year and again in each of the next 

Table 1-12

School program representatives reporting various issues as serious problems for mathematics and science 

instruction, by school level: 2012

(Percent)

Mathematics instruction Science instruction

Issues Elementary Middle High Elementary Middle High

Student issues

High student absenteeism............................ 8 13 16 8 13 13

Inappropriate student behavior..................... 10 16 10 9 15 8

Low student interest in mathematics 

or science.................................................. 14 25 30 5 11 13

Low student reading abilities........................ 22 24 20 16 19 19

Teacher issues

Insufficient time to teach mathematics 

or science.................................................. 13 12 10 27 17 10

Lack of opportunities for teachers to 

share ideas................................................ 15 14 9 20 16 13

Lack of teacher interest in mathematics 

or science.................................................. 2 1 2 4 3 2

School issues

Inadequate funds for purchasing equipment 

and supplies.............................................. 12 18 16 30 32 28

Lack of parental support............................... 15 17 15 10 14 9

SOURCE: Banilower ER, Smith PS, Weiss IR, Malzahn KA, Campbell KM, Weis AM, Report of the 2012 National Survey of Science and Mathematics 

Education (2013).

Science and Engineering Indicators 2014
1-34 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

4 years to gather information on their early careers. This section reviews data from the first 3 years of the study.

Although rates of attrition after the first year of teaching 

in the BTLS were not significantly different among mathematics and science teachers and teachers of other subjects at 

the secondary level, the situation changed by the third year 

of teaching. At the secondary level, beginning mathematics 

and science teachers’ rates of attrition by their third year of 

teaching were higher than the rates of those who taught other 

subjects. Whereas 10% of other secondary-level teachers had 

left the profession by 2009−10 (their third year of teaching), 

25% of secondary mathematics and science teachers had departed by then (figure 1-17; appendix table 1-19). Beginning 

secondary mathematics and science teachers’ attrition rates 

as of the third year also exceeded those of beginning elementary teachers (11%). Although statistically significant, these 

results are based on a small sample of teachers and should 

be interpreted with caution. Data from years 4 and 5 of the 

study will enable more conclusive findings about the attrition rates of secondary mathematics and science teachers 

compared with secondary teachers of other subjects.

Instructional Technology 

and Digital Learning

Federal and state policies encourage greater use of instructional technology, increasingly referred to as “digital 

learning” or “digital education.” The Alliance for Excellent 

Education defines digital learning as “any instructional 

practice that is effectively using technology to strengthen 

the student learning experience” (Alliance for Excellent 

Education 2012). Digital learning encompasses a broad 

array of tools and practices, including online courses, applications of technology in the classroom, computer-based 

assessment, and adaptive software for students with special 

needs. In 2010, the U.S. Department of Education released 

a National Education Technology Plan (NETP) calling for 

the use of advanced technologies throughout the education 

system to improve student learning, accelerate implementation of effective practices, and enable schools to use data and 

information for continuous improvement (U.S. Department 

of Education 2010). Since publication of the NETP, reports 

about and initiatives involving digital education have proliferated (Alliance for Excellent Education 2011, 2012; Staker 

and Horn 2012; Watson et al. 2012; Wicks 2010). 

Figure 1-17

Beginning public elementary and secondary 

teachers (2007–08) who had left teaching by 2009–10

Percent

 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2012) of Beginning 

Teacher Longitudinal Study (BTLS), First Through Third Wave 

Preliminary Data File, 2007–08, 2008–09, 2009–10, National Center 

for Education Statistics. See appendix table 1-19.

Science and Engineering Indicators 2014

Elementary

teachers

Secondary

mathematics and

science teachers

All other

secondary

teachers

0

5

10

15

20

25

30

100Kin10

100Kin10 aims to ensure that all U.S. students have 

the STEM literacy needed to prepare them for employment and citizenship. In 2011, President Obama 

set a goal of training 100,000 well-qualified mathematics and science educators over the next 10 years. 

100Kin10 was launched to meet this goal by improving 

STEM teacher training and retention. Begun through 

the efforts of the Carnegie Corporation of New York 

and Opportunity Equation, the program brings funders 

together with partners from a variety of sectors (e.g., 

federal and state government agencies, corporations, 

universities, and nonprofits) to contribute toward the 

overall goal. 100Kin10 aims to build long-term capacity for training and retaining STEM teachers by evaluating the implementation of programs and identifying 

and disseminating best practices. The University of 

Chicago (Urban Education Institute and Center for 

Elementary Mathematics and Science Education) is 

developing methods and tools that will allow partners 

to view emerging data, measure the impact of their 

investments, and create opportunities for partners to 

work with and learn from each other.

As of August 2013, 26 funders have pledged more 

than $52 million toward the work of 100Kin10 partner 

organizations. More than 150 partner organizations 

have been selected to participate and have currently 

committed to training 40,000 STEM teachers by 2016. 

More information about 100Kin10 and current partners can be found at http://www.100kin10.org/.
Science and Engineering Indicators 2014 ♦ 1-35

The National Council of Teachers of Mathematics, for 

example, strongly endorsed the use of educational technology in mathematics education, saying that it is “essential” and “enhances student learning” (National Council of 

Teachers of Mathematics 2011). Findings from a number of 

studies have shown that the strategic use of technology tools 

in mathematics and science education, in particular, can support the learning of mathematical and scientific procedures 

and skills as well as the development of advanced proficiencies such as problem solving and reasoning (Hegedus and 

Roschelle 2013; Pierce et al. 2011; Rutten, van Joolingen, 

and van der Veen 2012). Proponents suggest that computer applications and technological tools, either alone or in 

concert with traditional instruction, may improve student 

achievement in mathematics and science by tailoring lessons and skill practice to individual students’ needs or by 

offering students additional opportunities to interact with information through computer simulations or other methods. 

In addition, computerized assessment may provide more 

precise and efficient feedback on student learning, allowing 

teachers to adapt instruction to student needs more effectively (Tucker 2009). Instruction through technology may 

also motivate students’ interest in mathematics and science.

This section focuses specifically on instructional technology, defined as technology products and tools designed to 

assist teaching and learning, in elementary and secondary 

schools. It distinguishes between the use of technology as 

an instructional tool and online learning, a special form of 

distance education. The section begins by discussing recent 

research on the effectiveness of technology as an instructional tool. It then updates national estimates of access to 

computers and the Internet and examines the current state of 

distance education, specifically online learning. This section 

ends with an overview of the research on the effectiveness 

of online learning.

Technology as an Instructional Tool

The use of instructional technology in K−12 classrooms 

has been growing at a rapid pace. Many school districts have 

invested in technology such as computers, mobile devices, 

and interactive whiteboards. In 2009, NCES surveyed a nationally representative sample of teachers to determine the 

availability and use of educational technology among teachers in public elementary and secondary schools. Teachers 

reported having the following technology devices either 

available as needed or in the classroom every day: LCD (liquid crystal display) or DLP (digital light processing) projectors (36% available as needed and 48% in the classroom 

every day), interactive whiteboards (28% and 23%, respectively), and digital cameras (64% and 14%, respectively) (table 1-13). Among teachers who reported that these devices 

were available to them, one-half or more also reported that 

they used these devices for instruction sometimes or often: 

72% of teachers used LCD or DLP projectors, 57% used interactive whiteboards, and 49% used digital cameras (Gray, 

Thomas, and Lewis 2010).

The 2012 NSSME surveyed teachers about the adequacy 

of the instructional technology (e.g., computers, calculators, 

probes/sensors) available to them (Banilower et al. 2013). 

High school mathematics teachers were the most likely to 

indicate that their instructional technology resources were 

adequate (69%), whereas elementary and middle school 

science teachers were the least likely to indicate so (35%) 

(Banilower et al. 2013).

Research on Instructional Technology

Despite the rapid growth in the use of technology in 

classrooms, a substantial base of rigorous research on the 

effectiveness of technology in improving student achievement is lacking. Few national studies are available and many 

Table 1-13

Public school teachers reporting the availability and frequency of use of technology devices, by school level: 

2009

(Percent)

Digital projector Interactive whiteboard Digital camera

Availability Used for 

instruction 

sometimes 

or oftena

Availability Used for 

instruction 

sometimes 

or oftena

Availability Used for 

instruction 

sometimes 

or oftena School level

As 

needed

In 

classroom 

every day

As

needed

In 

classroom 

every day

As

needed

In 

classroom 

every day

All public school 

teachersb............ 36 48 72 28 23 57 64 14 49

Elementary ..... 37 44 68 31 23 58 67 14 53

Secondary ...... 33 56 78 23 23 58 57 13 41

a

 Based only on teachers reporting that the device was available as needed or in the classroom every day.

b Data for teachers in combined schools (i.e., those with both elementary and secondary grades) are included in All public school teachers but are not 

shown separately.

SOURCE: Gray L, Thomas N, Lewis L., Teachers’ Use of Educational Technology in U.S. Public Schools: 2009, NCES 2010-040 (2010).

Science and Engineering Indicators 2014
1-36 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

studies that have been conducted are often of brief duration 

and are product-specific studies based on small samples and 

nonrigorous research designs. The Office of Educational 

Technology has issued a report outlining the problems 

with current research into digital education and providing 

a framework for how research evidence can be improved 

(U.S. Department of Education 2013).

Three recent meta-analyses reviewed studies that compared the mathematics achievement of students taught in 

elementary and secondary classes using technology-assisted 

mathematics programs with that of students in control classes using alternative programs or standard methods (Cheung 

and Slavin 2011; Li and Ma 2010; Rakes et al. 2010). All 

three studies found small positive effects on student achievement when technology was incorporated into classroom 

mathematics instruction.33

One recent study used a randomized control trial design to 

examine the effectiveness of a technology-based algebra curriculum in a wide variety of middle schools and high schools 

in seven states (Pane et al. 2013). Participating schools were 

matched into similar pairs and randomly assigned to either 

continue with the current algebra curriculum for 2 years or 

to adopt a technology-assisted program using a personalized, mastery-learning, blended-learning approach. Schools 

assigned to implement the program did so under conditions 

similar to schools that independently adopted it. Analysis of 

posttest outcomes on an algebra proficiency exam found no 

effects in the first year of implementation but found strong 

evidence in support of a positive effect in the second year. 

The estimated effect was statistically significant for high 

schools but not for middle schools; in both cases, the magnitude was sufficient to improve the average student’s performance by approximately 8 percentage points. 

An earlier national study of the effectiveness of instructional technology failed to find any statistically significant 

effects of several specific instructional technologies on student achievement (Dynarski et al. 2007). Researchers tested 

three grade 6 math products in 28 schools and three algebra 

products in 23 schools. Teachers in selected schools volunteered to participate and were randomly assigned to use 

or not use the educational software. Researchers compared 

students’ test results and other outcomes. No effects on 

sixth grade mathematics or algebra achievement were observed. During the second year of the evaluation, two grade 

6 math products and two algebra products were tested, and 

again researchers observed no significant effects on student 

achievement (Campuzano et al. 2009). No science products 

were tested.

Several small-scale studies of specific instructional technology applications suggest that educational computer programs and video games may promote student engagement 

and learning when they make use of proven pedagogical 

techniques (Barab et al. 2007; Ketelhut 2007; Nelson 2007; 

Neulight et al. 2007; Steinkuehler and Duncan 2008). One 

study found that the use of interactive whiteboard technology was associated with increased motivation in mathematics 

among elementary school students (Torff and Tirotta 2010). 

Another study of a popular algebra program found that 

students randomly assigned to computer-aided instruction 

using the algebra program scored higher on a test of pre-algebra and algebra skills than students assigned to traditional 

instruction (Barrow, Markman, and Rouse 2009). 

Internet Access

Access to the Internet is nearly universal in public elementary and secondary schools in the United States. In 2008, 

100% of public schools had instructional computers with 

Internet access (Gray, Thomas, and Lewis 2010). Student access to the Internet via instructional computers at school has 

increased substantially since 2000. In 2008, the average public school had 189 instructional computers compared with 

110 in 2000. There were three students per computer with 

Internet access in 2008 compared with seven students per 

computer with Internet access in 2000. Mobile devices are 

also enhancing students’ access to the Internet. Nearly 50% 

of high school students and 40% of middle school students 

now own or have access to a smartphone or tablet, marking a 

400% increase since 2007 (Project Tomorrow 2012).

Although Internet access is nearly universal, connection 

speeds and adequate bandwidth are areas of concern (Fox 

et al. 2012). A 2010 Federal Communications Commission 

survey of schools with federal funding for Internet access 

found that most had access to some form of broadband service (Federal Communications Commission 2010). Nearly 

80% of survey respondents, however, reported that their 

broadband connections were inadequate and slow Internet 

connection speeds were the primary problem. Bandwidth 

availability and connection speed affect which online content, applications, and functionality students and educators 

are able to use effectively in the classroom (Fox et al. 2012).

Distance Education and Online Learning

In addition to potentially enhancing learning in the classroom, technology can also enable students to receive instruction remotely through distance education or online learning. 

Distance education may include videoconferencing and televised or audiotaped courses, but Internet courses (hereafter 

referred to as online learning) are the most widespread and 

fastest-growing mode of delivery (Queen and Lewis 2011). 

Online learning programs range from programs that are fully 

online with all instruction occurring via the Internet to hybrid 

or “blended learning” programs that combine face-to-face 

teacher instruction with online components (Picciano and 

Seaman 2009; Staker and Horn 2012; Watson et al. 2011).

The United States is experiencing rapid growth in online 

learning at the K–12 level. The Sloan Consortium estimates 

that more than 1 million elementary and secondary students 

were enrolled in online or blended learning courses in 2007–

08, a 47% increase from the 2005–06 school year.34 These 

estimates are based on two national surveys of public school 

districts (Picciano and Seaman 2009). Based on this level 
Science and Engineering Indicators 2014 ♦ 1-37

♦ Increased access to quality educational resources and 

courses, particularly for students in rural or other 

remote locations;

♦ Differentiated instruction based on student need and preferred pace of learning;

♦ Personalized learning to build on students’ interests and 

increase motivation;

♦ Reduced costs for school facilities as students access educational resources from home or other community spaces;

♦ Access to a wider variety of courses, including AP, higherlevel math and science, and foreign languages;

♦ Credit recovery options to assist struggling students and 

those who need an additional course to graduate; 

♦ Access to international experts to increase knowledge and 

understanding of careers; and 

♦ Increased access to simulations and virtual field trips.

of growth, the International Association for Online K-12 

Learning (iNACOL) estimates that more than 1.5 million 

K–12 students participated in some form of online learning in 2010 (Wicks 2010). A nationally representative survey of public school districts conducted by NCES in 2009 

found that providing courses not otherwise available at their 

schools and giving students opportunities to recover course 

credits for classes missed or failed were the top reasons for 

offering online learning options (Queen and Lewis 2011). 

The survey found that credit recovery is especially important 

for urban schools: 81% indicated this was a very important 

reason for making online learning opportunities available 

(table 1-14).

Research on Effectiveness of Online Learning

Policymakers and researchers (Bakia et al. 2012; Watson 

et al. 2012; U.S. Department of Education 2010) cite numerous potential benefits of online learning:

Table 1-14

Public school districts with students enrolled in distance education courses indicating how important various 

reasons were for having distance education courses in their district, by district characteristic: School year 

2009–10

(Percent)

District characteristic

Provide 

courses not 

available at 

school

Provide 

opportunity 

for students 

to recover 

course credits 

from classes 

missed or 

failed 

Offer 

Advanced 

Placement or 

college-level 

course

Reduce 

student 

scheduling 

conflicts

Provide 

opportunities 

for students 

who are 

homebound or 

have special 

needs

Provide 

opportunity 

for students 

to accelerate 

credit 

accumulation 

for early 

graduation

All public school districts with 

students enrolled in distance 

education courses ................. 64 57 41 30 25 15

District enrollment size

< 2,500 ............................... 69 49 45 28 22 12

2,500–9,999........................ 53 72 33 31 30 20

≥ 10,000 ............................. 47 81 29 47 41 25

Community type

City ..................................... 37 81 23 30 41 29

Suburban............................ 52 66 30 36 35 19

Town................................... 60 60 40 26 30 18

Rural................................... 73 49 48 30 17 11

Region

Northeast............................ 75 46 39 36 24 11

Southeast........................... 74 65 51 42 25 18

Central................................ 61 59 38 27 26 11

West ................................... 56 56 42 26 25 22

Poverty concentration

< 10%................................. 64 60 36 31 24 15

10%–19% .......................... 62 55 43 29 26 14

≥ 20%................................. 65 57 42 31 24 18

NOTES: Response options in the questionnaire were “not important,” “somewhat important,” “very important,” and “don’t know.” Only the “very 

important” responses are shown in the table. Percentages are based on the 55% of public school districts with students enrolled in distance education 

courses in the 2009–10 school year. Poverty estimates for school districts were based on Title I data provided to the U.S. Department of Education by the 

U.S. Census Bureau.

SOURCE: Queen B, Lewis L, Distance Education Courses for Public Elementary and Secondary School Students: 2009–10, NCES 2012-008 (2011).

Science and Engineering Indicators 2014
1-38 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

Despite the many potential benefits of online learning 

envisioned by policymakers and researchers, few rigorous 

studies have addressed the effectiveness of online learning 

compared with that of traditional school models at the K−12 

level (Means et al. 2010). A systematic search of the research literature from 1994 through 2008 identified only five 

studies published between 1994 and 2008 that rigorously assessed online learning at the K–12 level and only one study 

(O’Dwyer, Carey, and Kleiman 2007) that assessed the impact of technology on mathematics learning in an elementary 

classroom in the United States (Means et al. 2010). O’Dwyer 

et al. (2007) used a quasi-experimental design to compare 

the learning of 231 students participating in the Louisiana 

Algebra I Online initiative with the learning of 232 students 

in comparison classrooms that had similar demographics but 

used traditional instruction. Scores on matched pretests and 

posttests showed that the online students performed as well 

as their peers in conventional classrooms. Other recent studies have found some positive effects for online learning, but 

researchers stress that teacher training and the way in which 

online components are integrated into the curriculum are important variables that could affect outcomes and need to be 

the subject of more rigorous research (Norris, Hossain, and 

Soloway 2012; Tamin et al. 2011).

Transition to Higher Education

Ensuring that students graduate from high school on time 

(i.e., within 4 years) and are ready for college or the labor 

market has been an important goal of high school education in the United States for decades.35 Increasingly, skills 

learned in high school do not guarantee access to jobs that 

support families, because most of the fastest-growing, wellpaying jobs in today’s labor market require at least some 

postsecondary education (Carnevale, Smith, and Strohl 

2010). About a quarter of U.S. public school students do not 

graduate from high school with a regular diploma within the 

expected period of 4 years (Chapman et al. 2011). Among 

those who do graduate from high school, many go to college or combine school with work, but some enter the labor 

market without pursuing additional education, at least in the 

short term (Ingels et al. 2012).

This section updates several indicators related to U.S. 

students’ transitions from high school to college, including on-time high school graduation rates, long-term trends 

in immediate college enrollment after high school, the high 

school graduation and postsecondary entry rates of U.S. 

students relative to those of students in other countries, and 

remediation rates among students entering postsecondary 

institutions across the United States. Together, these indicators present a broad picture of the transition of U.S. students 

from high school to postsecondary education, the topic of 

chapter 2.

Completion of High School

High school completion in the United States can be defined 

and measured in a variety of ways (Seastrom et al. 2006). Based 

on a relatively inclusive definition—receiving a regular high 

school diploma or earning an equivalency credential, such as 

a General Educational Development (GED) certificate—about 

83% of the U.S. population ages 18–24 had completed a high 

school education in 2009 (Snyder and Dillow 2012).

Beginning with the 2011–12 school year, the U.S. 

Department of Education required all states to use a more restricted definition, emphasizing on-time graduation and considering only recipients of diplomas (Curran and Reyna 2010; 

Chapman et al. 2011). Under this definition, the high school 

graduation rate is calculated as the percentage of students in a 

freshman class who graduate with a regular diploma 4 years 

later (Seastrom et al. 2006). This rate requires student-level 

data over time. Because not all states had these longitudinal 

data prior to the 2011−12 school year, the U.S. Department 

of Education currently uses one of the best estimates—the 

Averaged Freshman Graduation Rate (AFGR)—to measure 

on-time high school graduation rates (Seastrom et al. 2006). 

The AFGR calculation divides the aggregate count of the 

number of diplomas in a particular year by the estimated size 

of the incoming freshman class 4 years earlier.36 Starting with 

the 2011–12 school year, the U.S. Department of Education 

required all states to use a measure that is based on studentlevel data over time in order to increase the accuracy of ontime graduation rates (U.S. Department of Education 2012b). 

To facilitate state-by-state comparisons, the governors of all 

50 states agreed to work toward implementing this method to 

tabulate statistics for their public high schools (NGA 2005).

On-Time Graduation Rates from 2006 to 2010

The U.S. on-time graduation rate among public high 

school students has increased steadily since 2006 (appendix table 1-20). In 2010, 78% of public high school students 

graduated on time with a regular diploma, up from 73% in 

2006 (figure 1-18). Asian or Pacific Islander students (94%) 

graduated on time at a higher rate than did white students 

(83%) who, in turn, had a higher on-time graduation rate 

than did black, Hispanic, and American Indian or Alaska 

Native students (66%–71%). Between 2006 and 2010, 

however, on-time graduation rates improved more among 

black (from 59% to 66%), Hispanic (from 61% to 71%), 

and American Indian or Alaska Native (from 62% to 69%) 

students than among white (from 80% to 83%) and Asian 

or Pacific Islander (from 89% to 93%) students, therefore 

narrowing the gaps between black, Hispanic, and American 

Indian or Alaska Native students and their white and Asian 

or Pacific Islander counterparts.

Sex differences in on-time graduation rates persisted over 

time (appendix table 1-20). In each year between 2006 and 

2009,37 the percentage of male students who graduated from 

high school within 4 years was lower than that of female 
Science and Engineering Indicators 2014 ♦ 1-39

students. In 2009, for example, graduation rates for male students lagged behind those for female students by 8 percentage points (73% versus 81%).

High School Graduation Rates in the United 

States and Other OECD Nations

Each year, OECD estimates upper secondary graduation rates for its member countries and selected nonmember 

countries by dividing the number of graduates in a country by the number of people at the typical graduation age 

(OECD 2012).38 These estimates enable a broad comparison 

among nations and illuminate the U.S. standing internationally. U.S. graduation rates are below those of many OECD 

countries. Of the 26 OECD nations for which graduation 

rate data were available in 2010, the United States ranked 

22nd, with an average graduation rate of 77% compared 

with the OECD average of 84% (appendix table 1-21). The 

top-ranked countries include Japan, Greece, Korea, Ireland, 

Slovenia, Finland, Israel, and the United Kingdom, each of 

which had high school graduation rates above 90%.39

The relative standing of U.S. high school graduation rates 

has not improved during recent years. Among the 21 OECD 

countries for which graduation rate data were available in 

2006, 2008, and 2010,40 the United States ranked 16th in both 

2006 and 2008 and 17th in 2010 (OECD 2008, 2010, 2012).

Enrollment in Postsecondary Education

Upon completing high school, students make critical 

choices about the next stage of their lives. Today, a majority 

of U.S. high school students expect to attend college at some 

point, and many do so immediately after high school graduation. In 2010, 93% of high school seniors expected to attend 

a postsecondary institution, with 60% having definite plans 

to graduate from a 4-year college program and 24% having 

definite plans to attend graduate or professional school after 

college (Aud et al. 2012). In 2011, 68% of students enrolled 

in a postsecondary institution immediately after they graduated from high school (i.e., by the October following high 

school completion), with 27% enrolling in 2-year colleges 

and 41% enrolling in 4-year institutions (figure 1-19).

The immediate college enrollment rate increased from 

51% in 1975 to 68% in 2011, though the upward trend appeared to level off from 2009 to 2011 (figure 1-19). Overall, 

immediate college enrollment rose more for women (from 

Figure 1-18

On-time graduation rates of U.S. public high school 

students, by race and ethnicity: 2006 and 2010

Percent

 

NOTES: On-time high school graduation rate is the percentage of 

entering ninth graders who graduated 4 years later. Hispanic may be 

any race. American Indian or Alaska Native, Asian or Pacic Islander, 

black or African American, and white refer to individuals who are not 

of Hispanic origin.

SOURCES: Stillwell R, Sable J, Public School Graduates and 

Dropouts from the Common Core of Data: School Year 2009–10: 

First Look (Provisional Data), National Center for Education Statistics 

(NCES), NCES 2013-309 (2013); Common Core Data Table Library, 

https://nces.ed.gov/ccd/tables/AFGR.asp, accessed February 2013. See 

appendix table 1-20.

Science and Engineering Indicators 2014

All

students

White Black Hispanic Asian or

Pacic

Islander

American

Indian or

Alaska

Native

0

20

40

60

80

100

2006 2010 Figure 1-19

Immediate college enrollment rates among high 

school graduates, by institution type: 1975–2011

Percent

NOTES: Includes students ages 16–24 completing high school in 

survey year. Immediate college enrollment rates are dened as rates 

of high school graduates enrolled in college in October after 

completing high school. Before 1992, high school graduates referred 

to those who had completed 12 years of schooling. As of 1992, high 

school graduates are those who have received a high school diploma 

or equivalency certicate.

SOURCE: Aud S, Wilkinson-Flicker S, Kristapovich P, Rathbun A, 

Wang X, Zhang J, The Condition of Education 2013, NCES 2013-037 

(2013). See appendix table 1-22.

Science and Engineering Indicators 2014

1975 1979 1983 1987 1991 1995 1999 2003 2007 2011

0

20

40

60

80

All graduates

2-year institution

4-year institution
1-40 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

49% to 72%) than for men (from 53% to 65%); thus, the 

enrollment pattern has shifted over time to higher enrollment 

rates for women than for men (appendix table 1-22).

Large gaps persisted among students of different socioeconomic backgrounds. In each year between 1975 and 

2011, the immediate college enrollment rates were lower 

among students from low-income families than among students from middle- and high-income families (appendix table 1-22). In 2011, the immediate college enrollment rate of 

students from low-income families was about 29 percentage 

points lower than the rate of those from high-income families (53% versus 82%). Enrollment rates also varied with 

parental education, with students whose parents had only a 

high school education (54%) or some college (67%) trailing 

behind those whose parents had a bachelor’s or advanced 

degree (83%). Gaps existed among racial and ethnic groups 

as well. In each year between 1995 and 2011, for example, 

the enrollment rate of Hispanic students was lower than the 

rate for white students (e.g., 63% versus 69% in 2011). The 

immediate college enrollment rate of black students was also 

lower than the rate for white students in every year from 

1995 to 2009 (e.g., 62% versus 71% in 2009).41

Postsecondary Enrollment in an 

International Context

Participation in education beyond secondary schooling 

has been rising in many countries (Altbach, Reisberg, and 

Rumbley 2009; OECD 2012). One measure of such participation is the OECD-developed first-time entry rate into a 

university-level education program (referred to as a “tertiary-type A” program by OECD42). This measure, though 

not perfect,43 provides a broad comparison of postsecondary enrollment rates in the United States and those in other 

OECD countries.

According to OECD data, the percentage of U.S. young 

adults enrolling in university-level education for the first 

time was 74% in 2010, above the OECD average of 62% 

(figure 1-20). The United States ranked 9th out of the 30 

countries with available data. Women enroll in college at 

higher rates than men in most OECD countries, including 

the United States (appendix table 1-23). In the United States, 

women enrolled at a rate of 82% (compared with the OECD 

average of 69%), and men enrolled at a rate of 67% (compared with the OECD average of 55%).

Preparation for College

Despite the increasing numbers of U.S. students entering college, many are unprepared for college-level work 

and need remedial help to address their skill deficiencies 

(Kurlaender and Howell 2012). Nationally, half of first-time 

postsecondary students took some type of remedial course 

after they entered college, and 42% took one or more remedial math courses (table 1-15).44 The overall remediation 

rates were much higher at 2-year institutions than at 4-year 

institutions (65% versus 37%) and at minimally selective 

4-year institutions than at highly selective 4-year institutions (53% versus 22%). This variation largely reflects the 

kinds of students admitted to different types of institutions: 

4-year colleges, particularly highly selective ones, tend to 

admit students with greater academic preparation than more 

accessible 2-year colleges, and this pattern, in turn, affects 

the number of students needing remedial education at these 

institutions (Berkner and Choy 2008).

Figure 1-20

First-time entry rates into university-level education, 

by OECD country: 2010

Percent

OECD = Organisation for Economic Co-operation and Development.

NOTES: Portugal’s rate was overestimated because it included 

students who enrolled in the rst year of any postsecondary program 

instead of a university-level education program. Countries/jurisdictions 

are ordered by 2010 rst-time entry rate. Tied countries/jurisdictions 

with identical rounded estimates are listed alphabetically.

SOURCE: OECD, Education at a Glance: OECD Indicators 2012

(2012). See appendix table 1-23.

Science and Engineering Indicators 2014

Luxembourg

Mexico

Belgium

Turkey

Germany

Estonia

Switzerland

Italy

Japan

Spain

Hungary

Ireland

Israel

Czech Republic

United Kingdom

Austria

Slovak Republic

Denmark

Netherlands

Finland

Korea

United States

Sweden

Norway

Slovenia

New Zealand

Poland

Portugal

Iceland

Australia

OECD average

0 20 40 60 80 100
Science and Engineering Indicators 2014 ♦ 1-41

Conclusion

Raising student achievement, reducing performance gaps, 

and improving the international ranking of U.S. students on 

achievement tests from the middle to the top are high priorities for education reform across the United States. How well 

does this country perform in these areas? The indicators in 

this chapter present a mixed picture of the progress of elementary and secondary mathematics and science education 

in the United States. NAEP mathematics assessment results 

show that average mathematics scores for fourth and eighth 

graders have increased substantially since 1990, but this 

improvement has slowed down or halted for many groups 

in recent years. In science, eighth graders made small gains 

from 2009 to 2011. Overall, a large majority of U.S. fourth 

and eighth graders did not demonstrate proficiency in the 

knowledge and skills taught at their grade level. In particular, students from disadvantaged backgrounds lagged behind 

their more advantaged peers, with these disparities starting 

as early as kindergarten. International assessments have also 

produced mixed results. Although U.S. students have performed above the international average on the TIMSS mathematics and science tests, they have not been among the very 

top-achieving groups in the world.

Efforts to improve student achievement include raising 

high school graduation requirements, strengthening the rigor 

of curriculum standards, increasing advanced coursetaking, 

and promoting early participation in gatekeeper courses 

such as algebra 1. These efforts have brought some positive changes: increasing numbers of states adopted a common set of rigorous academic standards designed to ensure 

that students graduate from high school prepared for college 

and careers; rising proportions of students earned advanced 

mathematics and science credits before high school completion; large majorities of ninth graders took algebra 1 during 

or before their freshman year; and the number of students 

taking mathematics and science AP exams doubled in the 

recent decade. There is still room for improvement, however: the overall percentage of students taking mathematics 

and science AP tests remains very small; a sizeable number 

of students do not take any math or science in their freshman 

year; and wide gaps among students from different social 

and economic backgrounds persist.

Efforts to improve student achievement also focus on 

ensuring that all students have access to highly qualified 

teachers, although there has not yet been a consensus on 

what constitutes a “highly qualified” teacher. The majority 

of K−12 mathematics and science teachers held a teaching 

certificate and had taught their subjects for 3 or more years. 

Indicators of in-field teaching and undergraduate coursework suggest that high school mathematics and science 

teachers were generally better prepared for their teaching 

subjects than middle and elementary school teachers. Fully 

certified, well-prepared, and experienced teachers were not 

evenly distributed across schools or classes. Overall, schools 

or classes with lower concentrations of non-Asian minority and low-income students and higher concentrations of 

high-achieving students were more likely to have fully certified and better-prepared mathematics and science teachers. 

Working conditions were also not evenly distributed across 

schools: high-poverty schools were more likely to suffer 

from various problems that inhibit effective teaching (e.g., 

low student interest, high absenteeism, inadequate teacher 

preparation, and lack of materials and supplies).

The majority of middle and high school mathematics and 

science teachers participated in subject-focused professional development activities, but elementary science teachers 

were far less likely to do so. Many teachers reported that 

their professional development activities were of short duration, lasting in total from less than 6 hours to 35 hours during 

the past 3 years. About a quarter of secondary mathematics 

and science teachers left teaching within 3 years of entering 

the profession; this attrition rate was more than double the 

rate for other secondary-level teachers.

Recent federal and state policies encourage greater use of 

technology throughout the education system as a way to improve students’ learning experience. The use of instructional 

technology in K−12 classrooms has been growing at a rapid 

pace. Many school districts have invested in technology 

such as computers and mobile devices. The number of students participating in online learning courses is also rising, 

Table 1-15

Beginning 2003–04 postsecondary students who 

took remedial courses during their enrollment, by 

type of first institution: 2003–09

(Percent)

Type of first institution

One or more 

remedial 

courses

in any field

One or more 

remedial 

mathematics 

courses

All beginning postsecondary 

students ............................... 50.4 42.2

4-year institutiona ................. 37.2 29.2

Highly selective ................ 22.4 15.0

Moderately selective ........ 37.1 29.4

Minimally selective or open 

admission ..................... 53.4 44.7

2-year institution.................. 65.4 57.1

a

 A small proportion of students who first attended private, for-profit 

4-year institutions were excluded from the estimates that are based 

on institution selectivity because information on the selectivity of 

these institutions was not available. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2012) of 2003–04 

Beginning Postsecondary Students Longitudinal Study, Second 

Follow-up (BPS:04/09) and Postsecondary Education Transcript 

Study of 2009 (PETS:09), National Center for Education Statistics.

Science and Engineering Indicators 2014
1-42 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

jumping from 220,000 in 2003 to an estimated 1.8 million in 

2010. Rigorous research on the effects of instructional technology and online learning has just begun, showing some 

modest positive effects on student mathematics learning, but 

far more research is needed to determine which technologies 

are effective and under what conditions.

Ensuring that students graduate from high school and 

are ready for college or the labor market is an important 

goal of high school education in the United States. Since 

2006, the U.S. on-time high school graduation rates have 

improved steadily. In 2010, the vast majority of public high 

school students graduated with a regular diploma 4 years after entering ninth grade. Significant racial and ethnic and sex 

differences persisted, however, with white, Asian or Pacific 

Islander, and female students having higher graduation rates 

than their counterparts. In the broad international context, 

the United States ranked 22nd in graduation rates among 26 

OECD countries with available data in 2010, and its relative 

standing has not improved in recent years.

The vast majority of high school seniors expect to attend 

college after completing high school, and many do so directly after high school graduation. Immediate college enrollment rates have increased for all students as well as for 

many demographic groups since 1975, although this upward 

trend leveled off somewhat from 2009 to 2011. Wide gaps 

have persisted, with black students, Hispanic students, lowincome students, and students whose parents have less education enrolling in college at lower rates than their peers. 

Large proportions of college entrants, particularly those beginning at 2-year or minimally selective 4-year institutions, 

took remedial courses to address their skill deficiencies in 

mathematics and other areas.

Notes

1. The terms achievement and performance are used 

interchangeably in this section when discussing scores on 

mathematics and science assessments.

2. The No Child Left Behind Act of 2001 has been due 

for congressional reauthorization since 2007. President 

Obama announced in September 2011 that his administration would grant waivers from NCLB requirements to states 

in exchange for state-developed reform plans to prepare all 

students for college and career, focus aid on the neediest students, and support effective teaching and leadership. As of 

October 2012, 44 states had requested waivers from NCLB 

and 33 states (plus the District of Columbia) had been approved to implement their state-tailored reform agendas. The 

33 approved states include Arizona, Arkansas, Colorado, 

Connecticut, Delaware, Florida, Georgia, Indiana, Kansas, 

Kentucky, Louisiana, Maryland, Massachusetts, Michigan, 

Minnesota, Mississippi, Missouri, New Jersey, New Mexico, 

New York, North Carolina, Nevada, Ohio, Oklahoma, 

Oregon, Rhode Island, South Carolina, South Dakota, 

Tennessee, Utah, Virginia, Washington, and Wisconsin. 

The 11 states with outstanding requests for waivers include 

Alabama, Alaska, California, Hawaii, Idaho, Illinois, Iowa, 

Maine, New Hampshire, North Dakota, and West Virginia. 

The 6 states that have not yet requested a waiver include 

Montana, Nebraska, Pennsylvania, Texas, Vermont (request 

withdrawn), and Wyoming (http://www.ed.gov/news/pressreleases/seven-more-states-puerto-rico-and-bureau-indianeducation-request-nclb-flexibili).

3. Whenever a difference is cited in this chapter, it was 

tested using Student’s t-test statistic to minimize the chances 

of concluding that the difference exists based on the sample when no true difference exists in the population from 

which the sample was drawn. These tests were done with a 

significance level of 0.1, which means that a reported difference would occur by chance no more than once in 10 

samples when there was no actual difference between the 

population means.

4. No new assessment data on high school students were 

available at the time this chapter was prepared. The 2012 

volume of Science and Engineering Indicators (NSB 2012) 

contains recent trend data on mathematics and science performance of students in grade 12.

5. Asians and Pacific Islanders are combined into one 

category in some indicators for which the data were not collected separately for the two groups.

6. Mathematics assessments were administered in fall 

2010 and spring 2011. These assessments were designed 

to measure students’ conceptual knowledge, procedural 

knowledge, and problem-solving skills and included questions on number sense, properties, and operations; measurement; geometry and spatial sense; data analysis, statistics, 

and probability; and pre-algebra skills (Mulligan, Hastedt, 

and McCarroll 2012). Although the assessments included 

largely items related to students’ knowledge at the kindergarten level, easier and more difficult items were included 

to measure the achievement of students performing below 

or above grade level. Some students who spoke a language 

other than English or Spanish at home did not participate 

in mathematics assessments because of low English proficiency. Because the ECLS-K:2011 is a longitudinal study, 

the assessments were developed to measure the growth in 

performance of children from kindergarten entry through 

fifth grade.

7. These two NAEP assessment programs differ in many 

respects, including samples of students and assessment 

times, instruments, and contents. See http://nces.ed.gov/nationsreportcard/about/ltt_main_diff.asp.

8. The 2010 volume reviewed long-term trends in mathematics from 1973 to 2008, and the 2004 volume examined 

trends in science from 1969 to 1999. The long-term trend assessments in mathematics were administered again in 2012 

and are not yet available; no long-term trend assessments in 

science have been conducted since 1999.

9. Students in the below-basic category have scores 

that are lower than the minimum score for the basic level. Students in the basic category have scores that are at 

or above the minimum score for the basic level but lower 

than the minimum score for the proficient level. Students 

in the proficient category have scores that are at or above 
Science and Engineering Indicators 2014 ♦ 1-43

the minimum score for the proficient level but lower than 

the minimum score for the advanced level. Students in the 

advanced category have scores that are at or above the minimum score for the advanced level.

10. See NAEP’s mathematics and science achievement 

levels defined by grade at http://nces.ed.gov/nationsreportcard/mathematics/achieveall.asp and http://nces.ed.gov/nationsreportcard/science/achieveall.asp.

11. Estimates for long-term trends could not be performed 

for American Indian or Alaska Native students because of 

unavailable data in the 1990s.

12. Percentiles are scores below which the scores of a 

specified percentage of the population fall. For example, 

among fourth graders in 2011, the 10th percentile score for 

mathematics was 203. This means that 10% of fourth graders had mathematics scores at or below 203, and 90% scored 

above 203. The scores at various percentiles indicate students’ performance levels.

13. Students’ eligibility for free/reduced-price lunch is 

often used as a proxy measure of family poverty. In this 

chapter, students who are eligible for free/reduced-price 

lunch are considered to come from low-income families.

14. For fourth and twelfth graders’ science assessment results in 2009, see Science and Engineering Indicators 2012

(NSB 2012:1-13). For results from administration years 

prior to 2009, see Science and Engineering Indicators 2008

(NSB 2008:1-13–1-14).

15. The substantive implication of this small increase 

will be clearer when more assessment data are available for 

analysis in the future.

16. Differences in performance between public and private school students reflect in part different types of students 

enrolled in public and private schools and differences in the 

availability of resources, admissions policies, level of parental involvement, and school conditions.

17. For detailed comparisons between PISA and TIMSS, 

see Science and Engineering Indicators 2010 (NSB 

2010:1–16).

18. For more information about the PISA results, see Science 

and Engineering Indicators 2012 (NSB 2012:1-14–1-16).

19. The scores are reported on a scale from 0 to 1,000, 

with the TIMSS scale average set at 500 and the standard 

deviation set at 100.

20. The TIMSS results presented in this report exclude individual U.S. states, Canadian provinces, and Dubai and Abu 

Dhabi in the United Arab Emirates. These states/provinces 

participated in 2011 TIMSS as “benchmarking participants” 

in order to assess the comparative international standing of 

their students’ achievement and to view their curriculum and 

instruction in an international context.

21. Taipei is the capital city of Taiwan.

22. The TIMSS scale for each subject and grade originally was established to have a mean of 500 as the average 

of all of the countries and jurisdictions that participated in 

TIMSS 1995. TIMSS assessments since then have scaled the 

achievement data so that scores are comparable from assessment to assessment. Thus, for example, a score of 500 in 

fourth grade mathematics in 2011 is equivalent to a score 

of 500 in fourth grade mathematics in 1995, 1999, 2003, 

or 2007.

23. The transcript studies reported in 2012 have not been 

updated since then.

24. A recent NCES study of algebra and geometry curricula in the nation’s high schools found substantial variation in rigor and curriculum coverage among these courses 

(Brown et al. 2013). For more information, see http://nces.

ed.gov/pubsearch/pubsinfo.asp?pubid=2013451.

25. NCES established the Secondary Longitudinal Studies 

Program (SLSP) to study the educational, vocational, and 

personal development of young people beginning with their 

high school years and following them over time into adult 

roles and responsibilities. Thus far, the SLSP consists of five 

major studies: the National Longitudinal Study of the High 

School Class of 1972 (NLS:72); High School and Beyond 

(HS&B); the National Education Longitudinal Study of 

1988 (NELS:88); the Education Longitudinal Study of 

2002 (ELS:2002); and the High School Longitudinal Study 

of 2009 (HSLS:09). More information about each of these 

studies is available at http://nces.ed.gov/surveys/slsp.

26. The first follow-up collection of HSLS:09 was conducted in spring 2012 when most sample members were in 

the eleventh grade. Data from this collection were not available at the time of publication. Future follow-ups will include 

collection and coding of high school transcripts in 2013 and 

a second follow-up in 2016 when most sample members 

will be 3 years beyond high school graduation. Additional 

follow-ups are currently planned to at least age 26.

27. It is important to note that the data from HSLS indicate the percentage of students who enrolled in algebra in 

ninth grade but not the percentage who passed the course. 

28. NAEP HSTS identifies three curriculum levels based 

on the types of courses students take: standard, midlevel, 

and rigorous. A rigorous mathematics curriculum includes 

4 years of mathematics including up to at least pre-calculus 

(Nord et al. 2011).

29. Socioeconomic status (SES) is a composite variable 

derived from parental education level, parental occupation, 

and family income. The quintile measure divides the SES 

distribution into five equal groups. Quintile 1 corresponds to 

the lowest one-fifth of the population and quintile 5 corresponds to the highest. For this report, the middle three quintiles are combined to form one category.

30. White students were equally likely to report enrollment in biology 1 or earth/environmental/physical science in 

ninth grade (36% each), whereas students in other racial and 

ethnic groups were more likely to report enrollment in biology 1: 35% of black students and 44% of Hispanic students 

reported enrollment in biology 1 compared with 27% and 

24%, respectively, in earth/environmental/physical science. 

Asian students were the most likely to report enrollment in 

biology 1 (51%) and the least likely to report enrollment in 

environmental/physical science (17%). Research does not 

indicate why this coursetaking pattern is different for whites 

compared with other groups.
1-44 ♦ Chapter 1. Elementary and Secondary Mathematics and Science Education

31. In previous editions of Science and Engineering 

Indicators, data from the NCES Schools and Staffing Survey 

(SASS) have been used to describe teachers and teaching. 

The 2011−12 SASS data were not available for analyses at 

the time this chapter was prepared, however.

32. The NSSME reports the percentage of mathematics 

teachers who have a degree in mathematics or mathematics 

education and the percentage of science teachers who have a 

degree in science (any subject), engineering, or science education. Teachers of mathematics with related degrees, such as 

computer science or physics are not included in the percentage of mathematics teachers with degrees in their field. The 

NSSME provides further level of detail for science teachers, 

indicating the percentage of teachers of each discrete science 

subject that have a degree in that particular area.

33. Effect sizes ranged from +0.1 to +0.2, indicating a 

difference of .1 to .2 standard deviations, generally considered small effect sizes.

34. Public school enrollment in K−12 in the United States 

in 2008 was approximately 49 million students (http://nces.

ed.gov/fastfacts/display.asp?id=65). 

35. See the U.S. Education Dashboard at http://dashboard.ed.gov/about.aspx.

36. The incoming freshman class size is estimated by 

summing the enrollment in eighth grade for 1 year, ninth 

grade for the next year, and tenth grade for the year after, 

and then dividing by 3. For example, the 2009–10 on-time 

graduation rate equals the total number of diploma recipients 

in 2009–10 divided by the average membership of the eighth 

grade class in 2005–06, the ninth grade class in 2006–07, and 

the tenth grade class in 2007–08 (Stillwell and Sable 2013).

37. Gender data were not available in 2010.

38. Upper secondary education as defined by OECD corresponds to high school education in the United States. In 

the calculation of the U.S. graduation rates, OECD included 

only students who earned a regular diploma and excluded 

those who completed a GED certificate program or other 

alternative forms of upper secondary education. OECD defines the typical age as the age of the students at the beginning of the school year; students will generally be 1 year 

older than the age indicated when they graduate at the end 

of the school year. According to OECD, the typical graduation age in the United States is 17 years old. The U.S. high 

school graduation rates calculated by OECD cannot be directly compared with U.S. on-time graduation rates because 

of the different population bases and calculation methods for 

the two measures.

39. Portugal’s rate, though at the top, was not reliable and 

therefore is not listed here.

40. These countries are Czech Republic, Denmark, 

Finland, Germany, Greece, Hungary, Iceland, Ireland, Italy, 

Japan, Korea, Luxembourg, Mexico, Norway, Poland, 

Slovak Republic, Spain, Sweden, Turkey, the United 

Kingdom, and the United States.

41. The 2011 immediate college enrollment rates for 

whites and blacks were not measurably different (69% and 

65%, respectively).

42. As defined by OECD, a “tertiary-type A” program provides education that is largely theoretical and is intended to provide sufficient qualifications for gaining entry into advanced 

research programs and professions with high-skill requirements. Entry into these programs normally requires successful 

completion of upper secondary education (e.g., high school); 

admission is competitive in most cases. Minimum cumulative 

duration at this level is 3 years of full-time enrollment.

43. International comparisons are often difficult because 

of differences between education systems, types of degrees 

awarded across countries, and definitions used in different countries. Some researchers have pinpointed various 

problems and limitations of international comparisons and 

warned readers to interpret data including those published 

by OECD with caution (Adelman 2008; Wellman 2007).

44. The data are from the U.S. Department of Education’s 

2003–04 Beginning Postsecondary Longitudinal Study 

(BPS:04/09). This national, longitudinal study examines 

students who first began their postsecondary education in 

the 2003–04 academic year and follows them for 6 years 

through 2009. Students are considered to have participated in remedial education if they took a remedial course 

at some point during these 6 years according to their 

postsecondary transcripts.

Glossary

Student Learning in Mathematics and Science

Eligibility for National School Lunch Program: 

Student eligibility for this program, which provides free or 

reduced-price lunches, is a commonly used indicator for 

family poverty. Eligibility information is part of the administrative data kept by schools and is based on parent-reported 

family income and family size.

Organisation for Economic Co-operation and 

Development (OECD): An international organization of 34 

countries headquartered in Paris, France. The member countries are Australia, Austria, Belgium, Canada, Chile, Czech 

Republic, Estonia, Denmark, Finland, France, Germany, 

Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, 

Korea, Luxembourg, Mexico, the Netherlands, New Zealand, 

Norway, Poland, Portugal, Slovak Republic, Slovenia, 

Spain, Sweden, Switzerland, Turkey, United Kingdom, and 

United States. Among its many activities, the OECD compiles social, economic, and science and technology statistics 

for all member and selected non-member countries.

Repeating cross-sectional studies: This type of research 

focuses on how a specific group of students performs in a 

particular year, and then looks at the performance of a similar group of students at a later point in time. An example 

would be comparing fourth graders in 1990 to fourth graders 

in 2011 in NAEP.

Scale score: Scale scores place students on a continuous achievement scale based on their overall performance 

on the assessment. Each assessment program develops its 

own scales.
Science and Engineering Indicators 2014 ♦ 1-45

Student Coursetaking in Mathematics 

and Science

Advanced Placement (AP): Courses that teach collegelevel material and skills to high school students who can earn 

college credits by demonstrating advanced proficiency on a 

final course exam. The curricula and exams for AP courses, 

available for a wide range of academic subjects, are developed by the College Board.

Teachers of Mathematics and Science

Elementary schools: Schools that have no grades higher 

than 8.

High schools: Schools that have at least one grade higher 

than 8 and no grade in K–6.

Middle schools: Schools that have any of grades 5–8 and 

no grade lower than 5 and no grade higher than 8.

Professional development: In-service training activities designed to help teachers improve their subject matter 

knowledge, acquire new teaching skills, and stay informed 

about changing policies and practices.

Instructional Technology and Digital Learning

Blended learning: Any time a student learns at least in 

part at a supervised, traditional school location away from 

home and at least in part through online delivery with some 

element of student control over time, place, path, and/or 

pace; often used synonymously with “hybrid learning.”

Distance education: A mode of delivering education 

and instruction to students who are not physically present 

in a traditional setting such as a classroom. Also known as 

“distance learning,” it provides access to learning when the 

source of information and the learners are separated by time 

and/or distance.

Online learning: Education in which instruction and 

content are delivered primarily over the Internet.

Transition to Higher Education

GED certificate: This award is received following successful completion of the General Educational Development 

(GED) test. The GED program, sponsored by the American 

Council on Education, enables individuals to demonstrate 

that they have acquired a level of learning comparable to 

that of high school graduates.

High school completer: An individual who has been 

awarded a high school diploma or an equivalent credential, 

including a GED certificate.

High school diploma: A formal document regulated by 

the state certifying the successful completion of a prescribed 

secondary school program of studies. In some states or communities, high school diplomas are differentiated by type, 

such as an academic diploma, a general diploma, or a vocational diploma.

Postsecondary education: The provision of a formal 

instructional program with a curriculum designed primarily 

for students who have completed the requirements for a high 

school diploma or its equivalent. These programs include 

those with an academic, vocational, or continuing professional education purpose and exclude vocational and adult 

basic education programs.

Remedial courses: Courses taught within postsecondary 

education that cover content below the college level.

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Chapter 2

Higher Education in 

Science and Engineering

♦ 2-1

Highlights.....................................................................................................................................2-4

Characteristics of the U.S. Higher Education System..............................................................2-4

Undergraduate Education, Enrollment, and Degrees ...............................................................2-4

Graduate Education, Enrollment, and Degrees.........................................................................2-5

International S&E Higher Education........................................................................................2-6

 Introduction.................................................................................................................................2-7

Chapter Overview.....................................................................................................................2-7

Chapter Organization................................................................................................................2-7

The U.S. Higher Education System.............................................................................................2-7

Institutions Providing S&E Education......................................................................................2-7

Trends in Higher Education Expenditures and Revenues ......................................................2-12

Financing Higher Education...................................................................................................2-15

Undergraduate Education, Enrollment, and Degrees in the United States................................2-20

Undergraduate Enrollment in the United States.....................................................................2-20

Undergraduate Degree Awards...............................................................................................2-24

Graduate Education, Enrollment, and Degrees in the United States .........................................2-27

Graduate Enrollment by Field ................................................................................................2-27

S&E Master’s Degrees ...........................................................................................................2-29

S&E Doctoral Degrees ...........................................................................................................2-31

International S&E Higher Education.........................................................................................2-37

Higher Education Expenditures..............................................................................................2-37

Educational Attainment ..........................................................................................................2-38

First University Degrees in S&E Fields .................................................................................2-38

S&E First University Degrees by Sex ....................................................................................2-40

Global Comparison of S&E Doctoral Degrees.......................................................................2-41

Global Student Mobility ........................................................................................................2-42

Conclusion .................................................................................................................................2-44

Notes..........................................................................................................................................2-45

Glossary .....................................................................................................................................2-48

References .................................................................................................................................2-48

List of Sidebars

Carnegie Classification of Academic Institutions........................................................................2-8

Improving Measurement of Productivity in Higher Education .................................................2-16

Discipline-Based Education Research .......................................................................................2-23

Master’s Completion and Attrition in S&E ...............................................................................2-30

Professional Science Master’s Degrees.....................................................................................2-30

Mapping Mobility in European Higher Education ....................................................................2-40
2-2 ♦ Chapter 2. Higher Education in Science and Engineering

List of Tables

Table 2-1. Degree-granting institutions, by control and level of institution: 2011–12 ...............2-8

Table 2-2. U.S. citizen and permanent resident S&E doctorate recipients whose 

baccalaureate origin is a high Hispanic enrollment institution, by race and 

ethnicity: 2007–11...................................................................................................................2-9

Table 2-3. U.S. citizen and permanent resident S&E doctorate recipients whose 

baccalaureate origin is an HBCU, by race and ethnicity: 2007–11 ........................................2-9

Table 2-4. U.S. citizen and permanent resident S&E doctorate recipients who reported 

earning college credit from a community or 2-year college, by race and ethnicity: 

2007–11.................................................................................................................................2-10

Table 2-5. Community college attendance among recent recipients of S&E bachelor’s 

and master’s degrees, by degree level and degree year: 1999–2010 ....................................2-10

Table 2-6. Community college attendance among recent recipients of S&E degrees, 

by sex, race, ethnicity, and citizenship status: 2010 .............................................................2-11

Table 2-7. Title IV Institutions, by distance education status, control, and level of 

institution: 2011–12 ..............................................................................................................2-12

Table 2-8. Net tuition and fees for full-time undergraduate students by institutional 

control: 2007–08 through 2012–13.......................................................................................2-16

Table 2-9. Full-time S&E graduate students, by source and mechanism of primary 

support: 2011.........................................................................................................................2-17

Table 2-10. Primary support mechanisms for S&E doctorate recipients, by 2010 Carnegie 

classification of doctorate-granting institution: 2011 ...........................................................2-20

Table 2-11. Foreign students enrolled in U.S. higher education institutions, by broad 

field and academic level: 2008–12 .......................................................................................2-24

Table 2-12. Median number of years from entering graduate school to receipt of S&E 

doctorate, by 2010 Carnegie classification of doctorate-granting institution: 1997–2011 ...2-33

Table 2-13. Foreign recipients of U.S. S&E doctorates, by country/economy of origin: 

1991–2011.............................................................................................................................2-34

Table 2-14. Asian recipients of U.S. S&E doctorates, by field and country/economy 

of origin: 1991–2011.............................................................................................................2-35

Table 2-15. European recipients of U.S. S&E doctorates, by field and region/country 

of origin: 1991–2011.............................................................................................................2-36

Table 2-16. North American, South American, and Middle Eastern recipients of U.S. 

S&E doctorates, by field and region/country of origin: 1991–2011.....................................2-37

Table 2-17. Internationally mobile students in selected OECD countries and the United 

States: 2005 and 2010 ...........................................................................................................2-43

List of Figures

Figure 2-1. Community college attendance among recent recipients of S&E degrees, 

by field of most recent degree: 2003 and 2010.....................................................................2-11

Figure 2-2. Selected average revenues and expenditures at public very high research 

universities: 1987–2010 ........................................................................................................2-13

Figure 2-3. Average expenditures per FTE on research at public and private very high 

research universities: 1987–2010..........................................................................................2-13

Figure 2-4. Average expenditures per FTE on instruction at public and private very high 

research universities: 1987–2010..........................................................................................2-14

Figure 2-5. Selected average revenues and expenditures at public 4-year and other 

postsecondary institutions: 1987–2010.................................................................................2-14

Figure 2-6. Selected average revenues and expenditures at community colleges: 

1987–2010.............................................................................................................................2-15

Figure 2-7. Full-time S&E graduate students, by field and mechanism of primary support: 

2011.......................................................................................................................................2-18

Figure 2-8. Full-time S&E graduate students with primary support from federal 

government, by field: 2011 ...................................................................................................2-18

Figure 2-9. Full-time S&E graduate students, by source of primary support: 1997–2011........2-19

Figure 2-10. Freshmen intending S&E major, by race and ethnicity: 1997–2012 ....................2-21
Science and Engineering Indicators 2014 ♦ 2-3

Figure 2-11. Engineering: Freshmen intentions and degrees, by sex ........................................2-21

Figure 2-12. Engineering: Freshmen intentions and degrees, by race and ethnicity .................2-22

Figure 2-13. Natural sciences: Freshmen intentions and degrees, by sex .................................2-22

Figure 2-14. Natural sciences: Freshmen intentions and degrees, by race and ethnicity ..........2-22

Figure 2-15. Foreign undergraduate student enrollment in U.S. universities, by top 10 

places of origin and field: November 2012...........................................................................2-24

Figure 2-16. U.S. engineering enrollment, by level: 1991–2011...............................................2-25

Figure 2-17. S&E bachelor’s degrees, by field: 2000–11..........................................................2-25

Figure 2-18. Women’s share of S&E bachelor’s degrees, by field: 2000–11 ...........................2-26

Figure 2-19. Share of S&E bachelor’s degrees among U.S. citizens and permanent 

residents, by race and ethnicity: 2000–11.............................................................................2-26

Figure 2-20. First-time, full-time graduate enrollment in engineering and computer 

sciences and unemployment rate of all workers: 2000–11 ...................................................2-28

Figure 2-21. S&E master’s degrees, by field: 2000–11.............................................................2-29

Figure 2-22. S&E master’s degrees, by sex of recipient: 2000–11 ...........................................2-31

Figure 2-23. S&E master’s degrees, by race, ethnicity, and citizenship of recipient: 

2000–11.................................................................................................................................2-31

Figure 2-24. S&E doctoral degrees earned in U.S. universities, by field: 2000–11..................2-32

Figure 2-25. S&E doctoral degrees earned by U.S. citizen and permanent resident 

underrepresented minorities, by race and ethnicity: 2000–11 ..............................................2-33

Figure 2-26. S&E doctoral degrees, by sex, race, ethnicity, and citizenship: 2000–11 ............2-34

Figure 2-27. U.S. S&E doctoral degree recipients, by selected Asian country/economy 

of origin: 1991–2011.............................................................................................................2-35

Figure 2-28. U.S. S&E doctoral degree recipients, by selected Western European 

country: 1991–2011 ..............................................................................................................2-35

Figure 2-29. U.S. S&E doctoral degree recipients from Europe, by region: 1991–2011..........2-36

Figure 2-30. U.S. S&E doctoral degree recipients from Canada, Mexico, and Brazil: 

1991–2011.............................................................................................................................2-36

Figure 2-31. Attainment of tertiary-type A and advanced research programs, by country 

and age group: 2010..............................................................................................................2-38

Figure 2-32. First university natural sciences and engineering degrees, by selected country: 

2000–10.................................................................................................................................2-39

Figure 2-33. Natural sciences and engineering doctoral degrees, by selected country: 

2001–10.................................................................................................................................2-41

Figure 2-34. S&E doctoral degrees earned by Chinese students at home universities 

and U.S. universities: 1994–2010 .........................................................................................2-41

Figure 2-35. Internationally mobile students enrolled in tertiary education, by selected 

country: 2010 ........................................................................................................................2-42
2-4 ♦ Chapter 2. Higher Education in Science and Engineering

Characteristics of the U.S. Higher 

Education System

Doctorate-granting institutions with very high research 

activity are the leading producers of S&E degrees at 

the bachelor’s, master’s, and doctoral levels, but other 

types of institutions are also important in the education 

of S&E graduates.

♦ In 2011, doctorate-granting institutions with very high 

research activity awarded 74% of doctoral degrees, 42% 

of master’s degrees, and 38% of bachelor’s degrees in 

S&E fields. 

♦ Baccalaureate colleges are the source of relatively few 

S&E bachelor’s degrees but are a prominent source of future S&E doctorate recipients. 

♦ Master’s colleges and universities awarded close to 30% of 

all S&E bachelor’s degrees and 25% of all S&E master’s 

degrees in 2011.

♦ Nearly one in five U.S. citizens or permanent residents who 

received a doctoral degree from 2007 to 2011 had earned 

some college credit from a community or 2-year college.

Higher education spending and revenue patterns and 

trends underwent substantial changes over the last 

two decades. 

♦ Net student tuition more than doubled at public universities, whereas state and local appropriations fell by more 

than 25%. 

♦ Although tuition remained lower at public very high research universities than at their private counterparts, average revenue from student tuition increased more rapidly at 

public institutions. 

♦ In public very high research universities, revenues from 

federal appropriations, grants, and contracts per full-time 

equivalent (FTE) student nearly doubled between 1987 

and 2010, and research expenditures grew by 79% in the 

same period. In private very high research universities, 

revenues from federal appropriations, grants, and contracts 

per FTE student grew by 61%, and research expenditures 

increased by 89%.

♦ Since 2007, expanding enrollment at community colleges, 

coupled with reductions in state and local appropriations, 

contributed to an 8% reduction in instructional spending 

per FTE student.

Over the past decade in the United States, tuition and 

fees for colleges and universities have grown faster than 

median household income.

♦ Undergraduate debt varies by type of institution and state. 

However, among recent graduates with S&E bachelor’s 

degrees, the level of undergraduate debt does not vary 

by major. 

♦ Levels of debt of doctorate recipients vary by field. In S&E 

fields, high levels of graduate debt were most common 

among doctorate recipients in social sciences, psychology, 

and medical or other health sciences.

♦ At the time of doctoral degree conferral, nearly half of 

2011 S&E doctorate recipients had debt related to their undergraduate or graduate education.

Undergraduate Education, Enrollment, 

and Degrees

Undergraduate enrollment in U.S. higher education rose 

from 12.5 million to 18.3 million in the 15 years ending 

in 2011. The largest increases coincided with the two economic downturns, 2000–02 and 2008–10.

♦ Associate’s colleges enroll the largest number of students, 

followed by master’s colleges and universities and doctorate-granting institutions with very high research activity.

♦ Increased enrollment in higher education is projected to 

come mainly from minority groups, particularly Hispanics 

and Asians. 

The number of S&E bachelor’s degrees has risen steadily over the past 15 years, reaching a new peak of over 

half a million in 2011. The proportion of S&E bachelor’s degrees has remained stable at about 32% during 

this period.

♦ All S&E fields experienced increases in the numbers of 

bachelor’s degrees awarded in 2011, including computer 

sciences, which had declined sharply in the mid-2000s and 

had remained flat through 2009. 

♦ Women have earned about 57% of all bachelor’s degrees 

and half of all S&E bachelor’s degrees since the late 1990s. 

Men earn the majority of bachelor’s degrees in engineering, computer sciences, and physics. More women than 

men earn degrees in the biological, agricultural, and social 

sciences and in psychology.

♦ Between 2000 and 2011, the proportion of S&E bachelor’s 

degrees awarded to women remained flat. During this period, it declined in computer sciences, mathematics, physics, engineering, and economics. 

The racial and ethnic composition of those earning S&E 

bachelor’s degrees is changing, reflecting both population changes and increases in college attendance by 

members of minority groups.

♦ For all racial and ethnic groups, the total number of bachelor’s degrees earned, the number of S&E bachelor’s degrees earned, and the number of bachelor’s degrees in most 

S&E fields have increased since 2000.

Highlights

2-4 ♦ Chapter 2. Higher Education in Science and Engineering
The number of foreign undergraduate students in the 

United States increased substantially (18%) between fall 

2011 and fall 2012. 

♦ Most of the increase in undergraduate foreign enrollment 

was in non-S&E fields. Within S&E fields, the largest increases were in engineering and the social sciences.

♦ China, South Korea, and Saudi Arabia were the top countries sending undergraduates to the United States.

Graduate Education, Enrollment, and Degrees

Graduate enrollment in S&E increased from about 

493,000 to more than 608,000 between 2000 and 2011.

♦ Graduate enrollment grew in most S&E fields, with particularly strong growth in engineering and in the biological 

and social sciences.

♦ Women continued to enroll at disproportionately low rates 

in engineering (23%), computer sciences (25%), physical 

sciences (33%), and economics (38%).

♦ In 2011, underrepresented minority students (blacks, 

Hispanics, and American Indians and Alaska Natives) 

made up 12% of students enrolled in graduate S&E programs, with Asians and Pacific Islanders representing 6% 

and whites 47%. Temporary residents accounted for most 

of the remainder of graduate S&E enrollment.

In 2011, the federal government was the primary source 

of financial support for 19% of full-time S&E graduate 

students. In recent years, this proportion has fluctuated 

between 18% and 20%.

♦ In 2009, the federal government funded 61% of S&E graduate students on traineeships, 51% of those with research 

assistantships, and 24% of those with fellowships.

♦ Graduate students in the biological sciences, the physical 

sciences, and engineering received relatively more federal 

financial support than those in computer sciences, mathematics, medical and other health sciences, psychology, 

and social sciences.

Between fall 2011 and fall 2012, the number of foreign 

graduate students increased by 3%, with all the increase 

occurring in non-S&E fields. 

♦ Nearly 6 out of 10 foreign graduate students in the United 

States in fall 2012 were enrolled in S&E fields, compared 

with about 3 in 10 foreign undergraduates.

♦ The number of foreign graduate students enrolled in S&E 

fields between 2011 and 2012 was stable, with declines 

in the numbers of foreign students in computer sciences, 

biological sciences, and engineering offset by increases in 

mathematics, social sciences, and psychology. 

♦ In fall 2012, about 60% of the foreign S&E graduate students in the United States came from China and India. 

Master’s degrees awarded in S&E fields increased from 

about 100,000 in 2000 to about 151,000 in 2011. In this 

period, the growth of S&E degrees at the master’s level 

(57%) was higher than growth at the bachelor’s (39%) 

and doctoral levels (38%).

♦ Increases occurred in most major S&E fields, with the largest in engineering, psychology, and political sciences and 

public administration. 

♦ The number and percentage of master’s degrees awarded to women in most major S&E fields have increased 

since 2000.

♦ The number of S&E master’s degrees awarded increased 

for all racial and ethnic groups from 2000 to 2011. 

During this period, the proportion earned by blacks and 

Hispanics increased, that of Asians and Pacific Islanders 

and American Indians and Alaska Natives remained flat, 

and that of whites decreased. 

In 2011, U.S. academic institutions awarded about 38,000 

S&E doctorates. 

♦ The number of S&E doctorates conferred annually by U.S. 

universities increased steeply from 2002 to 2007, then flattened and declined slightly in 2010, but increased again 

in 2011. 

♦ Among fields that award large numbers of doctorates, the 

biggest increases in degrees awarded between 2000 and 

2011 were in engineering (58%) and in the biological sciences (52%).

Students on temporary visas continue to earn high proportions of U.S. S&E doctorates, and these students 

dominated degrees in some fields. They also earned large 

shares of the master’s degrees in S&E fields.

♦ In 2011, foreign students earned 56% of all engineering 

doctorates, 51% of all computer sciences doctorates, 44% 

of physics doctorates, and 60% of the economics doctorates. Their overall share of S&E degrees was about 

one-third.

♦ After steep growth from 2002 to 2008, the number of temporary residents earning S&E doctoral degrees declined 

through 2010, but it increased again in 2011.

♦ In 2011, temporary visa students earned 26% of S&E master’s degrees, receiving 45% of those in computer sciences, 

44% of those in economics, 42% of those in engineering, 

and 35% of those in physics.

Science and Engineering Indicators 2014 ♦ 2-5
International S&E Higher Education

In 2010, more than 5.5 million first university degrees 

were awarded in S&E worldwide. Students in China 

earned about 24%, those in the European Union (EU) 

earned about 17%, and those in the United States earned 

about 10% of these degrees.

♦ The number of S&E first university degrees awarded in 

China, Taiwan, Turkey, Germany, and Poland approximately doubled between 2000 and 2010. During this period, S&E first university degrees awarded in the United 

States and several other countries (e.g., Australia, Italy, 

the United Kingdom, Canada, and South Korea) increased between 23% and 56%, whereas those awarded in 

France, Japan, and Spain declined by 14%, 9%, and 4%, 

respectively.

♦ S&E degrees continue to account for about one-third of all 

bachelor’s degrees awarded in the United States. In Japan, 

6 out of 10 first degrees were awarded in S&E fields in 

2010; in China, half.

♦ In the United States, about 5% of all bachelor’s degrees 

awarded in 2010 were in engineering. This compares with 

about 18% throughout Asia and 31% in China specifically. 

In 2010, the United States awarded the largest number of 

S&E doctoral degrees of any individual country, followed 

by China, Russia, Germany, and the United Kingdom.

♦ The numbers of S&E doctoral degrees awarded in China 

and the United States have risen substantially in recent 

years. S&E doctorates awarded in South Korea and in 

many European countries have risen more modestly. S&E 

doctorates awarded in Japan increased fairly steadily 

through 2006 but have declined since then.

♦ In 2007, China overtook the United States as the world 

leader in the number of doctoral degrees awarded in the 

natural sciences and engineering; in 2010, this number in 

China was stable. 

♦ Women earned 41% of S&E doctoral degrees awarded 

in the United States in 2010, about the same as women’s 

percentages in Australia, Canada, the EU, and Mexico 

and a higher proportion than in Malaysia, South Korea, 

and Taiwan.

International student mobility expanded over the past 

two decades, as countries are increasingly competing for 

foreign students. 

♦ The United States remains the destination for the largest 

number of internationally mobile students worldwide (undergraduate and graduate), although its share decreased 

from 25% in 2000 to 19% in 2010. Among OECD countries, the U.S. share in natural sciences and engineering 

fields has declined during this period, but an increase in 

international students coming to the United States to study 

social and behavioral sciences has kept the overall S&E 

share stable.

♦ Some countries expanded recruitment of foreign students 

as their own populations of college-age students decreased.

♦ In addition to the United States, other countries that are 

among the top destinations for foreign students include the 

United Kingdom, Australia, Germany, and France.

2-6 ♦ Chapter 2. Higher Education in Science and Engineering
Science and Engineering Indicators 2014 ♦ 2-7

 Introduction

Chapter Overview

Higher education performs a number of societal functions, including developing human capital; building the 

knowledge base through research and knowledge development; and disseminating, using, and maintaining knowledge 

(Organisation for Economic Co-operation and Development 

[OECD] 2008). S&E higher education provides the advanced 

skills needed for a competitive workforce and, particularly 

in the case of graduate-level S&E education, the research 

capability necessary for innovation. This chapter focuses on 

the development of human capital through higher education.

Indicators presented in this chapter are discussed in the 

context of national and global developments, including 

changing demographics, increasing foreign student mobility, and global competition in higher education. The composition of the U.S. college-age population is becoming more 

diverse as the Asian and Hispanic shares of the population 

increase. During the latest economic downturn, public institutions of higher education faced unique pressures due 

to a combination of increasing enrollments and tight state 

budgets. Private institutions likewise experienced financial 

challenges stemming from declining incomes and the effects of stock market fluctuations on endowment growth. 

Technology has enabled very rapid growth in the delivery 

of online courses; the consequences of these changes remain 

to be seen.

Although the United States has historically been a world 

leader in providing broad access to higher education and in 

attracting foreign students, many other countries are providing expanded educational access to their own populations and attracting growing numbers of foreign students. 

Nevertheless, increases in foreign students contributed to 

most of the growth in overall S&E graduate enrollment in 

the United States in recent years. Following a decline in the 

number of foreign students coming to the United States after 11 September 2001, foreign student enrollment in S&E 

has recovered. 

Chapter Organization

This chapter begins with an overview of the characteristics of U.S. higher education institutions providing instruction in S&E, followed by a discussion of characteristics 

of undergraduate and graduate education.1 Trends are discussed by field and demographic group, with attention to the 

flow of foreign students into the United States by country. 

Various international higher education indicators are then 

presented, including comparative S&E degree production 

in several world regions and indicators that measure the 

growing dependence of industrialized countries on foreign 

S&E students.

The data in this chapter come from a variety of federal 

and nonfederal sources, primarily surveys conducted by the 

National Science Foundation’s (NSF’s) National Center 

for Science and Engineering Statistics (NCSES) and the 

National Center for Education Statistics (NCES) at the 

U.S. Department of Education. Data also come from international organizations, such as the OECD and the United 

Nations Educational, Scientific and Cultural Organization 

(UNESCO) Institute of Statistics, as well as individual countries. Most of the data in the chapter are from censuses of 

the population—for example, all students receiving degrees 

from U.S. academic institutions—and are not subject to 

sampling variability. 

The U.S. Higher Education System

Higher education in S&E produces an educated S&E 

workforce and an informed citizenry. It has also been receiving increased attention as an important component of U.S. 

economic competitiveness. In his 24 February 2009 address 

to a joint session of Congress, President Barack Obama 

called for every American to commit to at least 1 year of 

education or career training after completing high school. 

A 2012 report by the President’s Council of Advisors on 

Science and Technology (PCAST 2012) notes that economic forecasts point to a need to increase the proportion 

of college graduates going into the natural sciences and engineering over the next decade. This section discusses the 

characteristics of U.S. higher education institutions providing S&E education and the financing of higher education.

Institutions Providing S&E Education

The U.S. higher education system consists of a large 

number of diverse academic institutions that vary in their 

missions, learning environments, selectivity levels, religious 

affiliations, types of students served, types of degrees offered, and sectors (public, private nonprofit, or private forprofit) (Aud et al. 2010). There were approximately 4,700 

postsecondary degree-granting institutions in the United 

States in the 2011–12 academic year. Of these, 63% offered 

bachelor’s or higher degrees, 30% offered only associate’s 

degrees, and 7% offered degrees that were at least 2-year 

but less than 4-year as the highest degree awarded.2 More 

than half of the 4-year institutions are private nonprofits, 

23% are public, and 25% are private for-profits. The majority of 2-year degree-granting institutions are public (56%) or 

private for-profit (39%) (table 2-1) (NCES 2012). In 2011, 

U.S. academic institutions awarded nearly 3.5 million associate’s, bachelor’s, master’s, and doctoral degrees; 23% of 

the degrees were in S&E (appendix table 2-1).3

Doctorate-granting institutions with very high research 

activity, though few in number, are the leading producers of 

S&E degrees at the bachelor’s, master’s, and doctoral levels. 

In 2011, these research institutions awarded 74% of doctoral 

degrees, 42% of master’s degrees, and 38% of bachelor’s 

degrees in S&E fields (appendix table 2-1). (See sidebar, “Carnegie Classification of Academic Institutions.”) 

Master’s colleges and universities awarded another 29% of 

S&E bachelor’s degrees and 25% of S&E master’s degrees 

in 2011. 
2-8 ♦ Chapter 2. Higher Education in Science and Engineering

Baccalaureate colleges were the source of relatively few 

S&E bachelor’s degrees (12%) (appendix table 2-1), but 

they produce a larger proportion of future S&E doctorate 

recipients (15%) (NSF/NCSES 2013b). When adjusted by 

the number of bachelor’s degrees awarded in all fields, baccalaureate colleges as a group yield more future S&E doctorates per 100 bachelor’s degrees awarded than all other 

types of institutions except research universities. 

High Hispanic enrollment institutions (HHEs) and historically black colleges and universities (HBCUs) play an important role in training Hispanic and black U.S. citizens and 

permanent residents for doctoral-level study in S&E fields.4

Among Hispanic U.S. citizen and permanent resident S&E 

doctorate recipients who received their doctorates between 

2007 and 2011, 29% had obtained their baccalaureate credential at an HHE (table 2-2). Similarly, among black U.S. citizen and permanent resident doctorate recipients who received 

their doctorates in S&E fields during the same period, 26% 

had obtained their baccalaureate degree at an HBCU (table 

2-3). HBCUs are the second most important contributor of 

black S&E doctorate recipients after non-HBCU institutions 

with very high research activity (NSF/NCSES 2013b). 

Table 2-1

Degree-granting institutions, by control and level of institution: 2011–12

Institution level

All degree-granting 

institutions Public Private nonprofit Private for-profit

Total................................................................................... 4,706 1,649 1,653 1,404

2-year............................................................................. 1,738 967 100 671

4-year............................................................................. 2,968 682 1,553 733

SOURCE: U.S Department of Education, National Center for Education Statistics, Digest of Education Statistics, 2011, table 279, based on data from 

Integrated Postsecondary Education Data System Fall 2011 Institutional Characteristics component.

Science and Engineering Indicators 2014

The Carnegie Classification of Institutions of 

Higher Education is widely used in higher education 

research to characterize and control for differences in 

academic institutions.

The 2010 classification update retains the structure adopted in 2005. It includes 4,634 institutions, 483 of which 

were added after the 2005 update. More than three-quarters of the new institutions (77%) are from the private 

for-profit sector, 19% from the private nonprofit sector, 

and 4% from the public sector. 

The Carnegie Classification categorizes academic institutions primarily on the basis of highest degree conferred, level of degree production, and research activity.* 

In this report, several Carnegie categories have been aggregated for statistical purposes. The characteristics of 

those aggregated groups are as follows:

♦ Doctorate-granting universities include institutions 

that award at least 20 doctoral degrees per year. They 

include three subgroups based on level of research activity: very high research activity (108 institutions), 

high research activity (99 institutions), and doctoral/research universities (90 institutions). Because doctorategranting institutions with very high research activity are 

central to S&E education and research, data on these 

institutions are reported separately.

♦ Master’s colleges and universities include the 724 institutions that award at least 50 master’s degrees and 

fewer than 20 doctoral degrees per year.

♦ Baccalaureate colleges include the 810 institutions at 

which baccalaureate degrees represent at least 10% of 

all undergraduate degrees and that award fewer than 50 

master’s degrees or 20 doctoral degrees per year.

♦ Associate’s colleges include the 1,920 institutions at 

which all degrees awarded are associate’s degrees or at 

which bachelor’s degrees account for less than 10% of 

all undergraduate degrees.

♦ Special-focus institutions are the 851 institutions 

at which at least 75% of degrees are concentrated in 

a single field or a set of related fields (e.g., medical 

schools and medical centers, schools of engineering, 

and schools of business and management).

♦ Tribal colleges are the 32 colleges and universities that 

are members of the American Indian Higher Education 

Consortium.

*Research activity is based on two indexes (aggregate level of research and per capita research activity) derived from a principal components analysis of data on research and development expenditures, S&E 

research staff, and field of doctoral degree. See http://classifications.

carnegiefoundation.org for more information on the classification system and on the methodology used in defining the categories.

Carnegie Classification of Academic Institutions
Science and Engineering Indicators 2014 ♦ 2-9

Table 2-2

U.S. citizen and permanent resident S&E doctorate recipients whose baccalaureate origin is a high Hispanic 

enrollment institution, by race and ethnicity: 2007–11

Earned baccalaureate 

degree from a high Hispanic 

enrollment institution

Race and ethnicity All Yes No Yes (%)

All races and ethnicities................................................................................... 101,216 3,773 97,443 3.7

American Indian or Alaska Native................................................................ 376 21 355 5.6

Asian ............................................................................................................ 10,258 178 10,080 1.7

Black or African American ........................................................................... 4,958 181 4,777 3.7

Hispanica ...................................................................................................... 5,776 1,652 4,124 28.6

Native Hawaiian or Other Pacific Islander ................................................... 218 15 203 6.9

White............................................................................................................ 75,973 1,601 74,372 2.1

More than one race...................................................................................... 2,110 69 2,041 3.3

Unknown or unreported............................................................................... 1,547 56 1,491 3.6

a

 Hispanic may be any race. American Indian or Alaska Native, Asian, black or African American, Native Hawaiian or Other Pacific Islander, white, and 

more than one race refer to individuals who are not of Hispanic origin. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2013) of the Survey of Earned 

Doctorates, 2007–11.

Science and Engineering Indicators 2014

Table 2-3

U.S. citizen and permanent resident S&E doctorate recipients whose baccalaureate origin is an HBCU, 

by race and ethnicity: 2007–11

Earned baccalaureate 

degree from an HBCU

Race and ethnicity All Yes No Yes (%)

All races and ethnicities................................................................................... 101,216 1,480 99,736 1.5

American Indian or Alaska Native................................................................ 376 D D D

Asian ............................................................................................................ 10,258 9 10,249 0.1

Black or African American ........................................................................... 4,958 1,304 3,654 26.3

Hispanica ...................................................................................................... 5,776 21 5,755 0.4

Native Hawaiian or Other Pacific Islander ................................................... 218 D D D

White............................................................................................................ 75,973 88 75,885 0.1

More than one race...................................................................................... 2,110 40 2,070 1.9

Unknown or unreported............................................................................... 1,547 D D D

a

 Hispanic may be any race. American Indian or Alaska Native, Asian, black or African American, Native Hawaiian or Other Pacific Islander, white, and 

more than one race refer to individuals who are not of Hispanic origin.

D = suppressed to avoid disclosure of confidential information.

HBCU = historically black college or university.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2013) of the Survey of Earned 

Doctorates, 2007–11.

Science and Engineering Indicators 2014

Minority-serving academic institutions enroll a substantial fraction of minority undergraduates (NSF/NCSES 

2013a).5 In 2010, HBCUs awarded 19% of the 43,000 S&E 

bachelor’s degrees earned by black U.S. citizens and permanent residents; HHEs awarded about 30% of the 46,000 

S&E bachelor’s degrees earned by Hispanic U.S. citizens and permanent residents. However, the percentages 

of blacks earning S&E bachelor’s degrees from HBCUs 

and of Hispanics earning S&E bachelor’s degrees from 

HHEs have declined since 2001. Tribal colleges, which 

mainly offer 2-year degrees, account for about 1% of S&E 

bachelor’s degrees to American Indians; this proportion 

has been fairly stable over time.6

Community Colleges

Community colleges (also known as public 2-year colleges or associate’s colleges) play a key role in increasing 

access to higher education for all citizens. These institutions 

serve diverse groups of students and offer a more affordable means of participating in postsecondary education. 

Community colleges are important in preparing students 

to enter the workforce with certificates or associate’s 
2-10 ♦ Chapter 2. Higher Education in Science and Engineering

degrees or to transition to 4-year colleges or universities. 

Community colleges tend to be closely connected with local 

businesses, community organizations, and government, so 

they can be more responsive to local workforce needs (NRC 

and NAE 2012). 

In the 2011–12 academic year, there were nearly 1,000 

community colleges in the United States, enrolling more 

than 7 million students, or about a third of all postsecondary students (NCES 2012). Six out of 10 community college 

students were enrolled part time. With the economic recession, enrollment in community colleges increased by about 

800,000 students between 2007 and 2009 but slowed down 

in 2010 and declined slightly in 2011 as the labor market 

improved (Knapp, Kelly-Reid, and Ginder 2009, 2011).

Community colleges play a significant role in the education of individuals who go on to acquire advanced S&E 

credentials. Among U.S. citizen and permanent resident 

S&E doctorate holders who received their doctorates between 2007 and 2011, nearly 20% indicated that they had 

earned college credit from a community or 2-year college 

(table 2-4). According to data from the National Survey of 

Recent College Graduates (NSRCG), the proportion of recent bachelor’s S&E graduates who reported ever attending a community college has increased since the late 1990s 

(table 2-5). Nearly half of 2008 and 2009 S&E graduates 

said that they had attended a community college (49% of the 

bachelor’s recipients and 36% of the master’s recipients). 

Graduates in physical sciences, engineering, and computer 

and mathematical sciences were less likely than those in 

the biological and social sciences to have attended a community college. Between 2003 and 2010, the proportion of 

S&E graduates who attended community colleges remained 

stable in all broad fields (figure 2-1).

In 2010, female S&E bachelor’s and master’s degree 

recipients were more likely to have attended a community 

college than their male counterparts (table 2-6). Attendance 

was higher among U.S. citizens and permanent visa holders than among temporary visa holders. Attendance was 

Table 2-4

U.S. citizen and permanent resident S&E doctorate recipients who reported earning college credit from a 

community or 2-year college, by race and ethnicity: 2007–11

Earned college credit from a 

community or 2-year college

Race and ethnicity All Yes No Yes (%)

All races and ethnicities................................................................................... 99,029 18,484 80,545 18.7

American Indian or Alaska Native................................................................ 360 125 235 34.7

Asian ............................................................................................................ 10,197 1,279 8,918 12.5

Black or African American ........................................................................... 4,755 819 3,936 17.2

Hispanica ...................................................................................................... 5,517 1,236 4,281 22.4

Native Hawaiian or Other Pacific Islander ................................................... 200 58 142 29.0

White............................................................................................................ 74,649 14,237 60,412 19.1

More than one race...................................................................................... 2,076 461 1,615 22.2

Unknown or unreported............................................................................... 1,275 269 1,006 21.1

a

 Hispanic may be any race. American Indian or Alaska Native, Asian, black or African American, Native Hawaiian or Other Pacific Islander, white, and 

more than one race refer to individuals who are not of Hispanic origin. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2012) of the Survey of Earned 

Doctorates, 2007–11.

Science and Engineering Indicators 2014

Table 2-5

Community college attendance among recent recipients of S&E bachelor’s and master’s degrees, by degree 

level and degree year: 1999–2010

1999 2001 2003 2006 2008 2010

Degree level Number Percent Number Percent Number Percent Number Percent Number Percent Number Percent

All recent graduates .... 900,400 41 918,400 44 958,400 45 1,634,200 45 1,138,400 46 1,136,700 46

Bachelor’s................ 743,400 43 758,300 46 794,400 47 1,343,000 47 934,300 49 916,500 49

Master’s ................... 157,000 35 160,100 34 164,000 34 291,200 34 204,100 35 220,300 36

NOTES: Recent graduates are those who earned degrees in the 2 academic years preceding the survey year or, for the 2006 survey year, in the 3 

preceding academic years. For 2006, recent graduates are those who earned degrees between 1 July 2002 and 30 June 2005. Data are rounded to the 

nearest 100. Detail may not add to total because of rounding.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2013) of the National Survey of 

Recent College Graduates, 1999, 2001, 2003, 2006, 2008, and 2010. 

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 2-11

lower for Asian S&E graduates than for whites, blacks, or 

Hispanics. The likelihood of attending a community college 

before receiving an S&E bachelor’s or master’s degree was 

related to parental education level. Nearly 6 out of 10 of the 

S&E graduates who reported that their fathers or mothers 

had less than a high school diploma attended a community 

college, compared with about one-third of those whose fathers or mothers had a professional or a doctoral degree.

For-Profit Institutions

In 2011, about 3,400 higher education institutions in the 

United States operated on a for-profit basis. Nearly half of 

these institutions offer only less-than-2-year programs, and 

about 4 out of 10 are degree-granting institutions.7 Two-year, 

for-profit institutions enroll considerably fewer students than 

community colleges. Over the last 12 years, however, the 

number of for-profit institutions has grown rapidly, and the 

number of degrees they award has more than tripled (NCES 

2012; appendix table 2-2). A large part of that increase is 

accounted for by the growth of the University of Phoenix. 

In 2011, for-profit academic institutions awarded between 

2% and 6% of S&E degrees at the bachelor’s, master’s, and 

doctoral levels, as well as 33% of S&E degrees at the associate’s level (appendix tables 2-1 and 2-2). Computer sciences 

accounted for 73% of the associate’s degrees and 51% of 

the bachelor’s degrees awarded by for-profit institutions in 

S&E fields in 2011 (appendix table 2-3). For-profit institutions awarded fewer S&E master’s and doctoral degrees 

than associate’s and bachelor’s. At the master’s level, S&E 

degrees were mainly in psychology, social sciences, and 

computer sciences; at the doctoral level, they were almost 

exclusively in psychology and social sciences. In 2011, degrees in psychology represented nearly half of the master’s 

and three-quarters of the doctoral degrees awarded by forprofit institutions in S&E fields. Degrees in social science 

accounted for one-quarter of the master’s and a similar proportion of the doctoral degrees awarded in S&E fields.

Figure 2-1

Community college attendance among recent 

recipients of S&E degrees, by field of most recent 

degree: 2003 and 2010

NOTE: Recent graduates are those who earned degrees in the 2 

academic years preceding the survey year.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, National Survey of Recent College 

Graduates, 2003 and 2010. 

Science and Engineering Indicators 2014

Percent

Engineering

Social sciences

and related sciences

Physical sciences

and related sciences

Computer and

mathematical

sciences 

Biological, agricultural,

and environmental

life sciences

0 10 20 30 40 50 60

2010

2003

Table 2-6

Community college attendance among recent 

recipients of S&E degrees, by sex, race, ethnicity, 

and citizenship status: 2010

Characteristic Number Percent

All graduates ..................................... 1,136,700 46

Sex

Female ........................................... 564,600 49

Male ............................................... 572,100 43

Race, ethnicity

American Indian or Alaska Native... 1,900 39

Asian .............................................. 155,300 39

Black or African American ............. 72,000 46

Hispanica ........................................ 120,600 51

Native Hawaiian or Other 

Pacific Islander........................... 4,900 64

White.............................................. 746,400 46

More than one race........................ 35,700 52

Citizenship status

U.S. citizen..................................... 1,033,400 48

Permanent visa .............................. 27,200 42

Temporary visa............................... 76,100 15

Father’s education

Less than high school.................... 65,200 59

High school diploma 

or equivalent............................... 210,300 52

Some college, vocational, 

or trade school ........................... 221,500 50

Bachelor’s ...................................... 292,100 45

Master’s ......................................... 175,500 43

Professional degree ....................... 86,300 31

Doctorate....................................... 64,600 34

Not applicable................................ 21,100 47

Mother’s education

Less than high school.................... 63,600 58

High school diploma 

or equivalent............................... 226,600 53

Some college, vocational, 

or trade school ........................... 284,400 49

Bachelor’s ...................................... 313,400 41

Master’s ......................................... 175,200 42

Professional degree ....................... 34,700 36

Doctorate....................................... 27,900 33

Not applicable................................ 10,900 37

a

 Hispanic may be any race. American Indian or Alaska Native, Asian, 

black or African American, Native Hawaiian or Other Pacific Islander, 

white, and more than one race refer to individuals who are not of 

Hispanic origin. 

NOTES: Recent graduates are those who earned degrees between 1 

July 2007 and 30 June 2009. Data are rounded to the nearest 100.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2013) of the National 

Survey of Recent College Graduates, 2010.

Science and Engineering Indicators 2014
2-12 ♦ Chapter 2. Higher Education in Science and Engineering

Online and Distance Education

Online education and distance education enable institutions of higher education to reach a wider audience by expanding access for students in remote geographic locations 

and providing greater flexibility for students who face time 

constraints, physical impairments, responsibility to care 

for dependents, and similar challenges. Online education 

is a relatively new phenomenon, and online enrollment has 

grown substantially in recent years. In 2011–12, about 62% 

of 2- and 4-year Title IV institutions (i.e., institutions that 

participate in federal financial aid programs) offered some 

distance education opportunities to their students (table 2-7) 

(Ginder and Sykes 2013).8 The vast majority of public institutions offered some distance education to their students, as 

did more than half of the private nonprofit and about 71% of 

the private for-profit 4-year institutions. In the United States, 

30 Title IV institutions were exclusively distance education 

institutions; most of these institutions were private for-profits, and more than 90% of the degrees awarded were in nonS&E fields (Ginder and Sykes 2013). 

More recently, changes in the online education landscape 

have accelerated with the appearance of massive open online courses (MOOCs). MOOCs can provide broad access to 

higher education. Through their online platforms, they also 

have the potential to collect massive amounts of information 

that can be used to conduct experimental research on how 

people learn and to identify online practices that improve 

learning (U.S. Department of Education 2013). 

MOOCs originated when a Stanford professor, Sebastian 

Thrun, and the director of research at Google, Peter Norvig, 

opened admission to their course on artificial intelligence in 

fall 2011. Until then, enrollment was typically 200 students. 

When free online access was offered, 160,000 students from 

190 countries registered for the class, and about 23,000 completed it. Previous efforts by academic institutions, such as 

the Open Learning Initiative at Carnegie Mellon University 

and OpenCourseWare at the Massachusetts Institute of 

Technology, had included online courses for public access; 

however, the Stanford class also allowed students to take 

quizzes, submit homework, and attend virtual office hours. 

In the wake of the popular response to this class, other selective universities have collaborated in joint ventures (e.g., 

Udacity, Coursera, edX) to offer free versions of their courses online, reaching large populations of students around the 

world. These companies are growing rapidly, adding new 

courses and students, and increasing the number of university partners in the United States and abroad (Lewin 2013). 

In fall 2012, edX and Udacity gave students the option of 

paying a small fee to take a proctored final exam that will 

validate their learning (Parry 2012). In February 2013, the 

American Council on Education approved five Coursera 

courses for college credit (Kolowich 2013). It is not clear 

whether colleges will generally be willing to grant credit for 

those courses. 

Changing modes of online education are prompting questions about how the use of this technology will affect the 

higher education sector. In particular, it is not yet clear how 

many students can sustain commitment to learning in the absence of more personal contact and to what extent the growing access to higher education facilitated by MOOCs will 

translate into learning and, in the long run, to higher levels 

of educational achievement.

Trends in Higher Education Expenditures 

and Revenues

Higher education spending and revenue patterns changed 

substantially over the last two decades, in trends that intensified during the economic downturn of the late 2000s. 

Although all types of higher education institutions faced 

competing demands in a stringent budget environment, 

each type faced unique challenges. Increases in the number 

Table 2-7 

Title IV Institutions, by distance education status, control, and level of institution: 2011–12

Institutional control and level All

No distance 

education

Some distance 

education

Exclusively distance 

education

All 2- and 4-year institutions ....................................... 5,288 1,966 3,292 30

Public....................................................................... 1,755 138 1,616 1

2-year................................................................... 1,072 74 998 0

4-year................................................................... 683 64 618 1

Private nonprofit ...................................................... 1,751 823 921 7

2-year................................................................... 185 149 36 0

4-year.................................................................... 1,566 674 885 7

Private for-profit....................................................... 1,782 1,005 755 22

2-year................................................................... 1,048 811 235 2

4-year................................................................... 734 194 520 20

NOTES: Title IV institutions are those with a written agreement with the Secretary of Education that allows the institution to participate in any of the Title 

IV federal student financial assistance programs. Data are for institutions surveyed during 2011–12. 

SOURCES: U.S. Department of Education, National Center for Education Statistics (NCES), Integrated Postsecondary Education Data System, Fall 2011, 

Institutional Characteristics Component; NCES, 2013. Characteristics of Exclusively Distance Education Institutions, by State: 2011–12. NCES 2013-172. 

Washington DC. http://nces.ed.gov/pubs2013/2013172.pdf. Accessed 17 September 2013.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 2-13

of students seeking an affordable college education compounded the challenges created by tight budgets. This section shows trends in inflation-adjusted average spending and 

revenue per full-time equivalent (FTE) student from 1987 to 

2010, based on data from the Delta Cost Project.9 

Very High Research Universities— 

Public and Private Institutions

Net tuition and federal appropriations, grants, and contracts are the largest sources of revenues centrally involved 

with education for both public and private very high research 

institutions (appendix table 2-4).10 For public institutions, 

state and local appropriations are also critical, supplying a 

similar amount of revenue as either of the other two sources 

(nearly $10,000 per FTE in 2010); in contrast, they are a 

small source of revenue for their private counterparts (about 

$400 per FTE in 2010). Much more important for private institutions are private and affiliated gifts, investment returns, 

and endowment income, which are usually the largest source 

of revenue.11

State and local appropriations for public very high research universities have declined since 1987, with a particularly steep drop between 2007 and 2010 (figure 2-2). This 

decline coincided with a compensating increase in net tuition. In 1987, average state appropriations per FTE at public 

very high research institutions were more than three times 

the amount of net tuition ($13,600 versus $4,000). By 2010, 

however, appropriations had dropped to $9,800 per FTE, 

whereas net tuition had increased from $4,000 to $9,600 

per FTE (appendix table 2-4). This change represents a shift 

in tuition burden from state and local governments to individual students and their families. Starting at a higher level, 

net tuition at private very high research universities also increased during this period. But the increase, from $16,000 to 

$23,000, was proportionally much smaller. 

Revenue from federal appropriations, grants, and contracts, the source used for most research expenditures, is 

highest at the most research-intensive universities (appendix 

table 2-4). Between 1987 and 2010, these funds increased 

at both the public and the private very high research institutions. At the public very high research institutions, these 

funds per FTE almost doubled, reaching the same level as 

the state and local appropriations (about $10,000). At private 

very high research institutions, they increased somewhat 

less, by more than 60% in this 24-year period. 

Research and instruction are the two largest core education expenditures at both public and private very high 

research universities. Between 1987 and 2010, research expenditures increased substantially at both types of institutions—by 89% at the private universities and by 79% at their 

public counterparts (figure 2-3; appendix table 2-5).12

Instructional spending per FTE followed a pattern similar to that of research expenditures. It was much higher at 

private very high research institutions than at their public 

counterparts, and it also increased at a higher rate. In the late 

1980s and early 1990s, instructional spending at private very 

high research universities was slightly more than double that 

of the public ones. By the mid-2000s, it was more than triple 

(figure 2-4). 

Most other expenditures also increased at both types of 

very high research institutions; however, at the public ones, 

spending on plant operation and maintenance declined from 

2007 to 2010, with a sharp drop in 2010 (appendix table 2-5). 

Figure 2-2

Selected average revenues and expenditures at 

public very high research universities: 1987–2010

2010 dollars

NOTE: Data are per full-time equivalent student.

SOURCE: IPEDS Analytics: Delta Cost Project Database, 1987–2010, 

special tabulations (2013).

Science and Engineering Indicators 2014

1987 1990 1993 1996 2001 2004 2007 2010

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

Expenditures—Instruction

Revenues—Net tuition

Revenues—State and local appropriations

Figure 2-3

Average expenditures per FTE on research at 

public and private very high research universities: 

1987–2010

2010 dollars

FTE = full-time equivalent student.

SOURCE: IPEDS Analytics: Delta Cost Project Database, 1987–2010, 

special tabulations (2013).

Science and Engineering Indicators 2014

0

5,000

10,000

15,000

20,000

25,000

30,000

1987 1990 1993 1996 2001 2004 2007 2010

Private very high research university

Public very high research university
2-14 ♦ Chapter 2. Higher Education in Science and Engineering

Four-Year and Other Graduate Public Institutions 

From 1987 to 2010, state and local appropriations and 

net student tuition were the largest sources of revenues centrally involved with education at other public institutions offering 4-year and graduate degrees (appendix table 2-4).13

At these institutions, total revenues from these two sources 

were lower than those at public very high research universities and higher than those at community colleges. Overall, 

the percentage drop in revenue per FTE from state and local 

appropriations was similar to that experienced at the public 

very high research institutions. In 2010, net student tuition 

replaced state and local appropriations as the largest source 

of revenue in the public 4-year institutions. Average state 

appropriations per FTE in 1987 ($8,400) were three times 

higher than the corresponding amount of tuition revenue 

($2,800). By 2010, average revenues from net student tuition, at $6,600 per FTE, exceeded average revenues from 

state appropriations per FTE by more than $500 (figure 2-5).

Spending on instruction at these institutions has been at 

least three times as high as almost all the other standard expense categories. It increased from an average of $5,800 per 

FTE in 1987 to $6,800 per FTE in 2010 (appendix table 2-5). 

Other expenditures represented much smaller shares of total 

spending; most of these expenditures increased. Spending 

on plant operation and maintenance fell by 4% over the 24-

year period, with a large decline from 2007 to 2010 (18%). 

Community Colleges

Both revenues and expenditures are much lower for community colleges than for other public institutions of higher 

education.14 As in these other institutions, the main sources of 

revenue at community colleges are state and local appropriations and net student tuition (appendix table 2-4). In 2010, 

average revenues from state and local appropriations at community colleges were about $5,600 per FTE, compared with 

$9,800 at public very high research institutions; average revenues from net tuition were $3,300 per FTE, compared with 

$9,600 at public very high research institutions.

Between 1987 and 2010, revenues from state and local 

appropriations at community colleges decreased from an average of $6,800 per FTE to $5,600 per FTE, with a steep 

drop from 2007 to 2010 (figure 2-6). During this 24-year 

period, as state support declined, revenues from net tuition 

more than doubled. In 1987, revenues from state and local 

appropriations represented 64% of total revenues at community colleges, and tuition accounted for 15%. By 2010, 

state and local appropriations had dropped to 46% of total 

revenues, whereas the proportion of revenues from tuition 

nearly doubled, to 27%. 

At community colleges, instruction is by far the largest 

expenditure (appendix table 2-5). In 1987, spending on instruction was $4,700 per FTE, about 43% of total expenditures. In 2010, average instructional spending per FTE 

($4,800) was nearly identical to the 1987 level. Overall, 

these expenditures had increased somewhat through 2008 

but dropped by 10% between 2008 and 2010 (figure 2-6). 

Expenditures on student services, institutional and academic 

Figure 2-4

Average expenditures per FTE on instruction at 

public and private very high research universities: 

1987–2010

2010 dollars

FTE = full-time equivalent student.

SOURCE: IPEDS Analytics: Delta Cost Project Database, 1987–2010, 

special tabulations (2013).

Science and Engineering Indicators 2014

1987 1990 1993 1996 2001 2007 2010 2004

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

Private very high research university

Public very high research university

Figure 2-5

Selected average revenues and expenditures at 

public 4-year and other postsecondary institutions: 

1987–2010

2010 dollars

NOTES: Average expenditures and revenues are per full-time 

equivalent. Four-year and other postsecondary institutions include 

doctorate-granting universities/high research activity, 

doctoral/research universities, master’s colleges/universities, and 

baccalaureate colleges, according to the 2005 Carnegie 

Classication of Institutions.

SOURCE: IPEDS Analytics: Delta Cost Project Database, 1987–2010, 

special tabulations (2013).

Science and Engineering Indicators 2014

1987 1990 1993 1996 2001 2004 2007 2010

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

Expenditures—Instruction

Revenues—Net tuition

Revenues—State and local appropriations
Science and Engineering Indicators 2014 ♦ 2-15

support, and plant operation and maintenance also declined 

between 2007 and 2010. 

Public Institutions Comparison

Between 1987 and 2010, revenues from state and local 

appropriations and net tuition, the main two revenue sources 

at public institutions, grew less at community colleges than 

at the other two types of public institutions. In community 

colleges, these two revenue sources combined increased by 

6% during this period, lower than the comparable increases 

at the public 4-year and other graduate institutions (14%) 

and the very high research institutions (11%). However, 

trends in these individual revenue sources were substantially 

different. States and localities cut funding for all three categories of institutions, but the reduction was smaller in the 

community colleges (18%) than in the very high research 

public institutions (28%) and the 4-year and other graduate 

public institutions (27%). Unlike the community colleges, 

though, the other two types of public institutions were able 

to increase revenues from net tuition. FTE net tuition revenues increased by 143% at the public very high research 

universities and by 136% at the 4-year and other graduate 

public institutions, compared with 104% at community colleges (appendix table 2-4).

Expenditures for instruction followed a different pattern. 

They rose most rapidly at the very high research institutions 

(30%), where there was pressure to keep faculty salaries (a 

major component of instructional expenses) competitive 

with those of their private counterparts, which spent more on 

instruction to begin with and were increasing these expenses 

at an even more rapid rate (79%) (appendix table 2-5). At 

community colleges, FTE instructional expenses were essentially the same at the end of the period as they were at 

the beginning;15 in 4-year and other graduate institutions, 

they fell somewhere in between. Overall, during this period, 

community colleges had more limited resources and less 

flexibility to draw on alternate revenue sources to support 

their instructional expenses, which were growing because of 

large increases in enrollment (see section “Undergraduate 

Enrollment in the United States”).

In recent years, universities have been under pressure to 

improve the way they monitor and manage their performance 

and are attempting to contain costs without compromising 

quality or accessibility. In May 2012, the National Research 

Council released a report titled “Improving Measurement 

of Productivity in Higher Education” (NRC 2012a), which 

examined key issues regarding the measurement of productivity (for a summary of the panel’s conclusions and recommendations, see sidebar, “Improving Measurement of 

Productivity in Higher Education”).

Financing Higher Education

Cost of Higher Education

Affordability and access to U.S. higher education institutions are continuing concerns (NCPPHE 2008; NRC 2012a). 

Estimated average net tuition and fees (i.e., the published 

prices minus grant aid and tax benefits) paid by full-time undergraduate students in public 4-year colleges declined from 

2007–08 to 2009–10 and in their private counterparts from 

2007–08 to 2010–11 because of unusually large increases 

in grant aid and tax credits. However, since then, net tuition 

and fees have increased at both public and private nonprofit 

institutions. At public 2-year colleges, net tuition and fees 

followed a similar pattern, but since 2008–09, the average student enrolled full time has received enough funding 

through federal tax benefits and grant aid from all sources to 

cover other expenses, in addition to tuition and fees (–$1,220 

net tuition in 2012–13) (table 2-8) (College Board 2012a).16

For at least the past 10 years, tuition and fees for colleges and universities in the United States have grown rapidly (see section “Trends in Higher Education Expenditures 

and Revenues”), whereas real median household income 

declined 8.9% between 1999 and 2011 (DeNavas-Walt, 

Proctor, and Smith 2012). Some evidence suggests that increases in net tuition and fees, however, have fallen disproportionately on households at higher levels in the income 

distribution, where financial aid is less readily available 

(College Board 2012a).17

Undergraduate Financial Support Patterns 

and Debt

Financial Support for Undergraduate Education.

With rising tuition, students increasingly rely on financial 

aid (particularly loans) to finance their education. Financial 

aid for undergraduate students comes mainly in the form of 

Figure 2-6

Selected average revenues and expenditures at 

community colleges: 1987–2010

2010 dollars

NOTES: Revenues and expenditures are per full-time equivalent. 

Community colleges are public associate’s colleges in the 2005 

Carnegie Classication of Institutions.

SOURCE: IPEDS Analytics: Delta Cost Project Database, 1987–2010, 

special tabulations (2013).

Science and Engineering Indicators 2014

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

1987 1990 1993 1996 2001 2004 2007 2010

Expenditures—Instruction

Revenues—Net tuition

Revenues—State and local appropriations
2-16 ♦ Chapter 2. Higher Education in Science and Engineering

grants, student loans, and work-study. A financial aid package may contain one or more of these kinds of support. In 

the 2011–12 academic year, federal loans constituted 38% 

of the $185 billion in student aid that undergraduate students 

received, followed by federal grants (26%), institutional 

grants (18%), state grants (5%), private employer grants 

(4%), and federal work-study programs (1%) (College 

Board 2012b). According to the latest data available from 

the NCES National Postsecondary Student Aid Study, a 

higher proportion of undergraduates in private for-profit institutions (96%) and in private nonprofit 4-year institutions 

(85%) than those in public 4-year (71%) or public 2-year 

(48%) institutions received some type of financial aid (Wei 

et al. 2009). 

Undergraduate Debt. Among recent graduates with 

S&E bachelor’s degrees, the level of undergraduate debt 

does not vary much by undergraduate major (NSF/NCSES 

2010); however, levels of debt vary by type of institution 

and state. Levels of undergraduate debt for students from 

public colleges and universities are almost as high as those 

for students from private colleges and universities. Nearly 6 

An expert panel convened by the National Research 

Council produced a report on measuring productivity in 

higher education (NRC 2012a). The panel defined productivity as a ratio of outputs (degrees completed, credit 

hours passed, or other indicators of successful completion) to inputs (labor and nonlabor factors of production). 

The panel identified the many complexities characteristic of higher education processes that complicate the 

measurement of productivity in this sector. Among them 

are the following:

♦ The need to disentangle the joint production of outputs (e.g., educated citizens, research findings, athletic events, hospital services) and inputs (e.g., labor, 

public service)

♦ The high variability in the quality and characteristics of inputs (e.g., teachers and students) and outputs 

(e.g., degrees)

♦ The difficulty of making meaningful comparisons 

across institutions, given that the diversity of its institutions is in itself one of the system’s main strengths

♦ The need to account for the differences in students’ 

preparedness for college and to measure the academic 

value added in terms of student achievement of learning 

outcomes and competencies

The panel made several recommendations to develop 

the data infrastructure necessary to measure productivity and to improve data collection across the federal statistical system, in particular by the National Center for 

Education Statistics (NCES) and the Bureau of Labor 

Statistics (BLS). The panel noted that, at the moment, 

the graduation rates produced by the NCES Integrated 

Postsecondary Education Data System (IPEDS) survey 

restrict the denominator to first-time, full-time students, 

so graduation rates are not meaningful productivity indicators for institutions that enroll more part-time students 

or in instances in which students transfer to a different 

institution. More accurate productivity measurement will 

require the development of comprehensive longitudinal 

student databases to be able to calculate more precise 

graduation rates, follow students through their college 

years and into their careers, and compile detailed reports 

on which colleges produce the most successful graduates. 

To do that, the panel recommended that the BLS facilitate 

multistate links of unemployment insurance records and 

education data. That step will enable research on issues 

such as return on investment from postsecondary training or placement rates in different occupations. Given the 

importance of higher education, the panel also advocated 

efforts to include colleges and universities in the U.S. 

Economic Census, as was the case in 1977.

Improving Measurement of Productivity in Higher Education

Table 2-8

Net tuition and fees for full-time undergraduate students by institutional control: 2007–08 through 2012–13

(2012 U.S. dollars)

Institutional control 2007–08 2008–09 2009–10 2010–11 2011–12 2012–13a

Public 2-year ....................................................................... 10 -450 -890 -1,460 -1,350 -1,220

Public 4-year ....................................................................... 2,470 2,340 1,950 2,120 2,620 2,910

Private, nonprofit 4-year...................................................... 13,870 13,440 12,650 12,540 12,600 13,380

a

 Estimated value.

NOTES: Prices have been rounded to the nearest $10. Net tuition and fees equal published tuition and fees minus total grant aid and tax benefits.

SOURCE: The College Board, Annual Survey of Colleges, Trends in College Pricing (2012). 

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 2-17

out of 10 students who earned bachelor’s degrees in 2010–

11 from the public 4-year colleges where they began their 

studies graduated with debt, and their average total debt was 

$23,800. Among students who earned their bachelor’s from 

the private 4-year institutions where they began their studies, two-thirds graduated with debt, and their average total 

debt was $29,900. Students who attend private for-profit 

institutions are more likely to borrow, and to borrow larger 

amounts, than those who attend public and private nonprofit 

institutions (College Board 2012b).

Levels of debt varied widely by state. Average debt for 

2011 graduates of public 4-year colleges and universities 

ranged from $16,317 in Utah to $32,385 in New Hampshire. 

Average debt for graduates of private nonprofit colleges 

and universities ranged from $18,614 in Utah to $34,017 in 

Connecticut (Reid and Cochrane 2012). Cost of living may 

account for some of the differences by state.

Graduate Financial Support Patterns and Debt

Financial Support for S&E Graduate Education. More 

than one-third of all S&E graduate students are primarily 

self-supporting; that is, they rely primarily on loans, their 

own funds, or family funds for financial support. The other 

approximately two-thirds receive primary financial support 

from a variety of sources, including the federal government, 

university sources, employers, nonprofit organizations, and 

foreign governments.

Support mechanisms include research assistantships 

(RAs), teaching assistantships (TAs), fellowships, and traineeships. Sources of funding include federal agency support, 

nonfederal support, and self-support. Nonfederal support includes state funds, particularly in the large public university 

systems; these funds are affected by the condition of overall 

state budgets. Most graduate students, especially those who 

pursue doctoral degrees, are supported by more than one 

source or mechanism during their time in graduate school, 

and some receive support from several different sources and 

mechanisms in any given academic year.

Other than self-support, over time RAs have been the 

most prevalent primary mechanism of financial support for 

full-time S&E graduate students (appendix table 2-6). In 

2011, 27% of full-time S&E graduate students were supported primarily by RAs, 18% primarily through TAs, and 

12% primarily by fellowships or traineeships (table 2-9). 

Primary mechanisms of support differ widely by S&E 

field of study (figure 2-7; appendix table 2-7). For example, 

in fall 2011, full-time students in physical sciences were 

financially supported mainly through RAs (40%) and TAs 

(38%). RAs also were important in agricultural sciences 

(51%); earth, atmospheric, and ocean sciences (39%); biological sciences (38%); and engineering (38%, and in particular in materials and chemical engineering). In mathematics, 

nearly half (49%) of full-time students were supported primarily through TAs and another quarter were self-supported. Full-time students in computer sciences and the social 

and behavioral sciences were mainly self-supporting (49% 

and 48%, respectively) or received TAs (14% and 20%, respectively). Students in medical and other health sciences 

were mainly self-supporting (59%).

The federal government plays a substantial role in supporting S&E graduate students in some fields but a smaller 

role in others. Federal financial support for graduate education reaches a larger proportion of students in the biological 

sciences; the physical sciences; the earth, atmospheric, and 

ocean sciences; and engineering. Lower proportions of students in computer sciences, mathematics, medical and other 

health sciences, psychology, and social sciences receive federal support (figure 2-8). Appendix table 2-8 provides detailed information by field and mechanism. 

Table 2-9

Full-time S&E graduate students, by source and mechanism of primary support: 2011

Source All

Research 

assistantship Fellowship Traineeship

Teaching 

assistantship Other Self-supporta

All sources (number)................. 444,991 121,010 40,583 12,557 80,719 29,799 160,323

Federal.................................. 84,816 61,799 9,766 7,600 1,091 4,560 NA

Nonfederal ............................ 199,852 59,211 30,817 4,957 79,628 25,239 NA

All sources (%) ......................... 100.0 27.2 9.1 2.8 18.2 6.7 36.1

Federal.................................. 100.0 72.9 11.5 9.0 1.3 5.4 NA

Nonfederal ............................ 100.0 29.6 15.4 2.5 39.8 12.6 NA

NA = not available; self-support is not included in federal or nonfederal counts.

a

 Includes any loans (including federal) and support from personal or family financial contributions.

NOTES: S&E includes health fields (i.e., medical sciences and other health sciences). These fields are reported separately in data from the National 

Science Foundation’s Survey of Graduate Students and Postdoctorates in Science and Engineering (GSS). S&E excludes fields that are collected by the 

GSS (architecture, communication, and family and consumer sciences/human sciences) that are not included in other tables in this report from other data 

sources. Percentages may not add to 100% because of rounding.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2012) of the Survey of Graduate 

Students and Postdoctorates in Science and Engineering, 2011.

Science and Engineering Indicators 2014
2-18 ♦ Chapter 2. Higher Education in Science and Engineering

The federal government was the primary source of financial support for 19% of full-time S&E graduate students in 

2011, whereas 45% were supported by nonfederal sources 

(institutional, state or local government, other U.S. sources, 

or other non-U.S. sources) and 36% were self-supported (appendix table 2-6). The number of full-time S&E graduate 

students supported by the federal government increased between 1998 and 2004 but has been fairly stable since then, 

whereas the number of students supported by nonfederal 

sources or through self-support has gradually increased between 1997 and 2011 (figure 2-9). 

For some mechanisms of support, the federal role is fairly 

large. In 2011, the federal government funded 61% of S&E 

graduate students who were on traineeships, 51% of those 

with RAs, and 24% of those with fellowships (appendix 

table 2-8). 

Most federal financial support for graduate education is 

in the form of RAs funded through grants to universities for 

academic research. RAs are the primary mechanism of support for 73% of federally supported full-time S&E graduate 

students. Fellowships and traineeships are the means of funding for 21% of the federally funded full-time S&E graduate 

students. For students supported through nonfederal sources 

in 2011, TAs were the most prominent mechanism (40%), 

followed by RAs (30%) (table 2-9; appendix table 2-6).

The National Institutes of Health (NIH) and NSF support 

most of the full-time S&E graduate students whose primary 

support comes from the federal government, followed by the 

U.S. Department of Defense (DOD) (appendix table 2-9). 

In 2011, NIH supported about 26,000 students, NSF about 

24,000, and DOD about 9,000. Trends in federal agency 

support of graduate students show considerable increases 

from 1997 to 2011 in the proportion of students funded by 

NSF, from 21% to 29% (appendix table 2-9). NSF supported 

nearly 60% of students in computer sciences or mathematics 

whose primary support comes from the federal government; 

50% of those in earth, atmospheric, and ocean sciences; 39% 

of those in the physical sciences; and 34% of those in engineering overall (about 43% of those in chemical and electrical engineering) (appendix table 2-10). The proportion 

Figure 2-7

Full-time S&E graduate students, by field and 

mechanism of primary support: 2011

NOTE: Self-support includes any loans (including federal) and 

support from personal or family nancial contributions. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2012) of the Survey of 

Graduate Students and Postdoctorates in Science and Engineering, 

2011.

Science and Engineering Indicators 2014

Percent

0 20 40 60 80 100

Social sciences

Mathematics,

statistics

Psychology

Medical, other

health sciences

Computer

sciences

Biological

sciences

Engineering

Earth, atmospheric,

and ocean sciences

Physical

sciences

Agricultural

sciences

All S&E

Research

assistantship

Teaching

assistantship

Fellowship Traineeship

Other Self-support

Figure 2-8

Full-time S&E graduate students with primary 

support from federal government, by field: 2011

0 10 20 30 40

Social

sciences

Other health

sciences

Psychology

Mathematics/

statistics

Medical

sciences

Computer

sciences

Agricultural

sciences

Engineering

Earth, atmospheric,

and ocean sciences

Physical

sciences

Biological

sciences

All S&E

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2012) of the Survey of 

Graduate Students and Postdoctorates in Science and Engineering, 

2011.

Science and Engineering Indicators 2014

Percent
Science and Engineering Indicators 2014 ♦ 2-19

of students funded by NIH increased from 28% to 33% between 1997 and 2008 but since then has decreased to 30%. 

In 2011, NIH funded about 71% of such students in the biological sciences, 53% of those in the medical sciences, and 

43% of those in psychology. The proportion of graduate students supported by DOD decreased slightly between 1997 

and 2011. In 2011, DOD supported almost half of the S&E 

graduate students in aerospace engineering, about one-third 

of those in industrial and electrical engineering, and close to 

one-quarter of those in mechanical engineering and in computer sciences.

For doctoral degree students, notable differences exist in 

primary support mechanisms by type of doctorate-granting 

institution (table 2-10). In 2011, RAs were the primary support mechanism for S&E doctorate recipients from research 

universities (i.e., doctorate-granting institutions with very 

high research activity, which receive the most federal funding, as well as those with high research activity). For those 

from medical schools, which are heavily funded by NIH, 

fellowships or traineeships accounted for the main source 

of support. Students at less research-intensive universities 

relied mostly on personal funds. These differences by type 

of institution hold for all S&E fields (NSF/NCSES 2000; 

NSB 2010). 

Notable differences also exist in primary support mechanisms for doctoral degree students by sex, race or ethnicity, 

and citizenship (appendix table 2-11). In 2011, among U.S. 

citizens and permanent residents, men were more likely than 

women to be supported by RAs (31% compared with 22%). 

Women were more likely than men to be supported by fellowships or traineeships (29% compared with 24%) and to 

support themselves from personal sources (18% compared 

with 12%). Also, among U.S. citizens and permanent residents, whites and Asians were more likely than other racial 

or ethnic groups to receive primary support from RAs (28% 

and 32%, respectively), whereas underrepresented minorities depended more on fellowships or traineeships (35%). 

The primary source of support for doctoral degree students 

with temporary visas was an RA (50%). 

To some extent, the sex, citizenship, and racial and ethnic differences in types of support mechanisms are related 

to differences in field of study. White and Asian men, as 

well as foreign doctoral degree students, are more likely than 

white and Asian women and underrepresented minority doctoral degree students of both sexes to receive doctorates in 

engineering and physical sciences, fields largely supported 

by RAs. Women and underrepresented minorities are more 

likely than other groups to receive doctorates in social sciences and psychology, fields in which self-support is prevalent. However, differences in type of support by sex, race or 

ethnicity, or citizenship remain, even after accounting for 

doctoral field (NSF/NCSES 2000, NSB 2010). 

Graduate Debt. At the time of doctoral degree conferral, 

45% of S&E doctorate recipients have debt related to their 

undergraduate or graduate education. In 2011, 28% of S&E 

doctorate recipients reported having undergraduate debt, and 

32% reported having graduate debt. For some, debt levels 

were high, especially for graduate debt: 5% reported more 

than $40,000 of undergraduate debt, and 7% reported more 

than $70,000 of graduate debt (appendix table 2-12).

Levels of debt vary widely by doctoral field. A higher 

percentage of doctorate recipients in non-S&E fields (49%) 

than those in S&E fields (32%) reported graduate debt. In 

2011, within S&E, high levels of graduate debt were most 

common among doctorate recipients in social sciences, psychology, and medical and other health sciences. The proportion of doctorate recipients in these fields who reported 

graduate debt has increased since 2001. Psychology doctorate recipients were most likely to report having graduate debt 

and also high levels of debt.18 In 2011, 24% of psychology 

doctoral degree recipients reported graduate debt of more 

than $70,000 (appendix table 2-12). Doctorate recipients in 

mathematics and computer sciences were the least likely to 

report graduate debt. Since 2001, the proportion of doctorate 

recipients reporting graduate debt higher than $30,000 has 

increased in all broad fields except engineering and mathematics (appendix table 2-13). 

Men and women differed little in level of undergraduate debt, but women were more likely to have accumulated 

more graduate debt. U.S. citizens and permanent residents 

Figure 2-9

Full-time S&E graduate students, by source of 

primary support: 1997–2011

Number

NOTES: Self support includes any loans (including federal) and 

support from personal or family nancial contributions. In 2007, the 

survey was redesigned to improve reporting. In this gure, “2007” 

shows data as collected in 2007. Because of methodological 

changes, counts should be used with caution for trend analysis. See 

http://www.nsf.gov/statistics/nsf10307/ for more detail. S&E 

excludes elds that were collected in this survey starting in 2007 

(architecture, communication, and consumer sciences/human 

sciences) that are not included in other tables in this report.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2012) of the Survey of 

Graduate Students and Postdoctorates in Science and Engineering, 

2011.

Science and Engineering Indicators 2014

1997 1999 2001 2003 2005 2007 2009 2011

0

50,000

100,000

150,000

200,000

250,000

Federal

Nonfederal

Self-support
2-20 ♦ Chapter 2. Higher Education in Science and Engineering

accumulated more debt than temporary visa holders. Blacks, 

Hispanics, and American Indian and Alaska Natives had 

higher levels of graduate debt than whites, even accounting 

for differences in field of doctorate (NSF/NCSES 2012). 

Undergraduate Education, Enrollment, 

and Degrees in the United States

Undergraduate education in S&E courses prepares students majoring in S&E for the workforce. It also prepares 

nonmajors to become knowledgeable citizens with a basic 

understanding of science and mathematics concepts. This 

section includes indicators related to enrollment by type of 

institution, field, and demographic characteristics; intentions 

to major in S&E fields; and recent trends in the number of 

earned S&E degrees.

Undergraduate Enrollment in the 

United States

Overall Undergraduate Enrollment

Over the last 15 years, enrollment in U.S. institutions of 

higher education at all levels rose from 14.5 million students 

in fall 1996 to 21.3 million in fall 2011, with two main periods of high growth—between 2000 and 2002 and between 

2007 and 2010, the two most recent recessionary periods. 

Undergraduate enrollment typically represents about 86% of 

all postsecondary enrollment (appendix table 2-14).

In 2011, for the first time since 1996, undergraduate enrollment declined slightly. As in previous years, the types of 

institutions enrolling the largest numbers of students at the 

undergraduate level were associate’s colleges (8.2 million, 

45% of all students enrolled), master’s colleges/universities (3.8 million, 21%), and doctorate-granting universities 

with very high research activity (2.0 million, 11%). Between 

1996 and 2011, undergraduate enrollment nearly doubled 

at doctoral/research universities and increased by 56% at 

associate’s colleges, 47% at master’s colleges, and 39% at 

baccalaureate colleges (appendix table 2-14). (See sidebar, 

“Carnegie Classification of Academic Institutions,” for definitions of the types of academic institutions.) 

According to the latest Census Bureau projections, the 

number of college-age individuals (ages 20–24) is expected 

to decline from 22.6 million in 2015 to 21.6 million in 2025 

but increase in the longer term (to 25.3 million by 2060) (appendix table 2-15). The short-term decline in this segment 

of the population is mostly due to a drop in the number of 

whites who are not Hispanic, which is projected overall to 

continue to fall through 2060, and a decline in the population of blacks who are not Hispanic between 2015 and 2035. 

The populations of 20–24-year-old Hispanics and of Asians 

who are not Hispanic are expected to increase continuously 

between 2015 and 2060. The proportion of Hispanics in this 

age group is expected to grow from 22% in 2015 to 36% in 

2060, and the proportion of Asians in this age group is expected to increase from 5% to 7%. Increased enrollment in 

higher education is projected to come mainly from minority 

groups, particularly Hispanics.19

Undergraduate Enrollment in S&E

Freshmen’s Intentions to Major in S&E. Since 1971, 

the annual The American Freshman: National Norms survey, 

administered by the Higher Education Research Institute at 

Table 2-10

Primary support mechanisms for S&E doctorate recipients, by 2010 Carnegie classification of doctorategranting institution: 2011

Mechanism All institutions

Research 

universities 

(very high 

research 

activity)

Research 

universities 

(high research 

activity)

Doctoral/

research 

universities

Medical schools 

and medical 

centers

Other/not 

classified

Doctorate recipients (n) ..................... 36,654 27,641 5,773 1,219 1,197 824

All mechanisms ................................. 100.0 100.0 100.0 100.0 100.0 100.0

Fellowship or traineeship............... 20.9 22.6 13.2 11.7 35.2 12.5

Grant.............................................. 6.4 6.8 3.2 2.5 18.0 3.2

Teaching assistantship................... 16.1 16.3 21.2 7.7 1.3 7.3

Research assistantship.................. 32.7 35.8 28.6 7.9 20.2 14.1

Other assistantship........................ 0.5 0.4 1.1 1.1 D D

Personal......................................... 9.6 6.3 17.3 34.8 9.4 30.0

Other.............................................. 3.3 2.8 4.4 7.1 D D

Unknown........................................ 10.4 8.9 11.1 27.2 12.2 28.5

D = suppressed to avoid disclosure of confidential information.

NOTES: Personal support mechanisms include personal savings, other personal earnings, other family earnings or savings, and loans. Traineeships 

include internships and residencies. Other support mechanisms include employer reimbursement or assistance, foreign support, and other sources. 

Percentages may not add to 100% because of rounding. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2012), of the 2011 Survey of 

Earned Doctorates.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 2-21

the University of California–Los Angeles, has asked freshmen at a large number of universities and colleges about 

their intended majors.20 The data have proven to be a broadly accurate picture of trends in degree fields several years 

later.21 Data show that up until 2007, about one-third of all 

freshmen planned to study S&E; this proportion gradually 

rose to 39% by 2012. Increases in the proportion of freshmen planning to major in biological and agricultural sciences account for most of this growth. In 2012, about 13% of 

freshmen intended to major in the biological and agricultural 

sciences and about 10% each in the social and behavioral 

sciences and engineering. About 3% each intended to major 

in physical sciences and mathematics, statistics, or computer 

sciences (appendix table 2-16).

In 2012, more than half of Asian American or Asian 

freshmen reported that they intended to major in S&E; proportions were lower for Hispanic or Latino freshmen (42%) 

and lower still for white (37%), black (36%), and American 

Indian or Alaska Native (33%) freshmen (figure 2-10). The 

proportions planning to major in S&E were higher for men 

than for women in every racial and ethnic group (appendix 

table 2-16). For most racial and ethnic groups, about 10% 

planned to major in social and behavioral sciences; about 

8%–10% in engineering; about 12% in biological and agricultural sciences; 3% in mathematics, statistics, or computer 

sciences; and 2% in physical sciences. Higher proportions of 

Asian American or Asian freshmen than of those from other 

racial and ethnic groups planned to major in engineering; 

biological and agricultural sciences; and mathematics, statistics, or computer sciences. Higher proportions of blacks 

and Hispanics or Latinos intended to major in the social and 

behavioral sciences. The percentage of all freshmen intending to major in mathematics, statistics, or computer sciences 

has dropped since the late 1990s, whereas the percentages 

of students intending to major in biological and agricultural 

sciences, engineering, and the social and behavioral sciences 

have increased. 

Generally, the percentages of students earning bachelor’s 

degrees in specific S&E fields are similar to the percentages planning to major in those fields, with the exception 

of engineering and social and behavioral sciences (see 

“S&E Bachelor’s Degrees” section and appendix tables 

2-17 and 2-23 for trends in bachelor’s degrees; see section 

on “Persistence and Retention in Undergraduate Education 

[S&E versus Non-S&E Fields]” in NSB 2012 for a discussion of longitudinal data on undergraduate attrition in S&E). 

For both sexes and all racial and ethnic groups, the percentage of students earning bachelor’s degrees in engineering is 

smaller than the percentage planning to major in it (figures 

2-11 and 2-12). The percentage earning bachelor’s degrees 

in social and behavioral sciences in 2011 (16%) (appendix 

table 2-17) is larger than the percentage that planned to major 

in those fields as freshmen 6 years earlier (10%) (appendix 

table 2-16). For women, blacks, and Hispanics—unlike for 

men, whites, and Asians—the proportion earning bachelor’s 

degrees in the natural sciences is smaller than the proportion 

who begin college planning to major in these fields (figures 

2-13 and 2-14). 

Figure 2-10

Freshmen intending S&E major, by race and 

ethnicity: 1997–2012

Percent

NOTE: In 2001, Native Hawaiian or Pacic Islander was added as a 

category under Asian American or Asian. 

SOURCE: Higher Education Research Institute, University of 

California at Los Angeles, Survey of the American Freshman: National 

Norms, special tabulations (2013).

Science and Engineering Indicators 2014

1997 2000 2002 2004 2006 2008 2010 2012

0

10

20

30

40

50

60

Asian American or Asian

Hispanic or Latino

African American or black

White or Caucasian

American Indian or Alaska Native

 

NOTES: Degrees do not reect the same student cohort.

SOURCES: Higher Education Research Institute, University of 

California at Los Angeles, Survey of the American Freshman: National 

Norms, special tabulations (2013); National Center for Education 

Statistics, Integrated Postsecondary Education Data System, 

Completions Survey, 2011; and National Science Foundation, 

National Center for Science and Engineering Statistics, WebCASPAR 

database, http://webcaspar.nsf.gov. 

Science and Engineering Indicators 2014

Figure 2-11

Engineering: Freshmen intentions and degrees, 

by sex

Percent

Total Male Female

0

5

10

15

20

25

Intentions (2005) Degrees (2011)
2-22 ♦ Chapter 2. Higher Education in Science and Engineering

According to the 2012 PCAST report on science, technology, engineering, and mathematics (STEM) education 

(PCAST 2012), to retain the U.S. historical preeminence in 

S&E, the United States will need to increase the proportion 

of students who receive undergraduate degrees in STEM (or 

the natural sciences and engineering) fields considerably 

over current rates. Persistent historic patterns suggest that 

generating such an increase may be challenging because the 

following have been true for at least 15 years: 

♦ The proportion of freshmen intending to major in the different S&E fields changed little for most fields, except for 

biological and agricultural sciences, and even declined for 

mathematics, statistics, and computer sciences (appendix 

table 2-16). 

♦ The proportion of bachelor’s degrees in the natural sciences and engineering combined has remained 15%–17% 

(appendix table 2-17 and NSB 2010).22

♦ The patterns of net undergraduate migration into S&E 

majors and attrition out of them have been stable (see 

section on “Persistence and Retention in Undergraduate 

Education [S&E versus Non-S&E Fields]” in NSB 2008 

and NSB 2012). 

One strategy to increase retention of students in STEM 

fields, however, is to improve student learning by improving 

the quality of undergraduate education in S&E. The 2012 

 

NOTES: Degrees do not reect the same student cohort. Asian 

American or Asian includes Native Hawaiian or Pacic Islander. 

SOURCES: Higher Education Research Institute, University of 

California at Los Angeles, Survey of the American Freshman: National 

Norms, special tabulations (2013); National Center for Education 

Statistics, Integrated Postsecondary Education Data System, 

Completions Survey, 2011; and National Science Foundation, 

National Center for Science and Engineering Statistics, WebCASPAR 

database, http://webcaspar.nsf.gov.

Science and Engineering Indicators 2014

Figure 2-12

Engineering: Freshmen intentions and degrees, 

by race and ethnicity

Percent

White or

Caucasian

Asian

American 

or Asian

African

American 

or black

Hispanic 

or Latino

0

5

10

15

20

25

Intentions (2005) Degrees (2011)

 

NOTE: Degrees do not reect the same student cohort. 

SOURCES: Higher Education Research Institute, University of 

California at Los Angeles, Survey of the American Freshman: National 

Norms, special tabulations (2013); National Center for Education 

Statistics, Integrated Postsecondary Education Data System, 

Completions Survey, 2011; and National Science Foundation, 

National Center for Science and Engineering Statistics, WebCASPAR 

database, http://webcaspar.nsf.gov.

Science and Engineering Indicators 2014

Figure 2-13

Natural sciences: Freshmen intentions and 

degrees, by sex

Percent

Total Male Female

0

5

10

15

20

25

Intentions (2005) Degrees (2011)

 

NOTES: Degrees do not reect the same student cohort. Asian 

American or Asian includes Native Hawaiian or Pacic Islander. 

SOURCES: Higher Education Research Institute, University of 

California at Los Angeles, Survey of the American Freshman: National 

Norms, special tabulations (2013); National Center for Education 

Statistics, Integrated Postsecondary Education Data System, 

Completions Survey, 2011; and National Science Foundation, 

National Center for Science and Engineering Statistics, WebCASPAR 

database, http://webcaspar.nsf.gov.

Science and Engineering Indicators 2014

Figure 2-14

Natural sciences: Freshmen intentions and 

degrees, by race and ethnicity

Percent

White or

Caucasian

Asian

American 

or Asian

African

American or

black

Hispanic 

or Latino

0

5

10

15

20

25

Intentions (2005) Degrees (2011)
Science and Engineering Indicators 2014 ♦ 2-23

National Academies report, “Discipline-Based Education 

Research: Understanding and Improving Learning in 

Undergraduate Science and Engineering” (NRC 2012b), examines available research on current teaching practices that 

have been shown to be more effective than the traditional lecture (see sidebar, “Discipline-Based Education Research”). 

The demographic profile of students planning to major 

in S&E has become more diverse over time. The proportion of white students declined from about three-quarters in 

1998 to about two-thirds in 2012. On the other hand, in the 

same period, the proportion of Asian American or Asian students doubled to 16%, and the proportion of Hispanic students nearly tripled, also to 16%, in 2012. American Indian 

or Alaska Native and black students accounted for roughly 

2% and 11%, respectively, of freshmen intending to major in 

S&E in both 1998 and 2012 (appendix table 2-18).

Foreign Undergraduate Enrollment.

23 In recent years, 

foreign undergraduate enrollment has been on the rise. In 

the 2011–12 academic year, the number of foreign students 

enrolled in bachelor’s degree programs in U.S. academic institutions rose 11% from the previous year, to approximately 

245,000 (IIE 2012). This rise continues a 5-year trend following the decline seen after 9/11. The number of foreign 

undergraduates enrolled in 2011–12 was 18% above the 

peak in 2001–02. New enrollments of foreign undergraduates in 2011–12 increased by 8% over the previous year. The 

countries that accounted for the largest numbers of foreign 

undergraduates enrolled in a U.S. institution in 2011–12 

were China (75,000), South Korea (38,000), Saudi Arabia 

(14,000), India (13,000), Canada (13,000), and Vietnam 

(11,000). The numbers of Chinese and Saudi Arabian undergraduates each increased by 31% over the previous year. The 

numbers of South Korean undergraduates increased by 1%, 

whereas the numbers of Indian undergraduates decreased by 

7%. In 2011–12, among all foreign students (undergraduate and graduate), the number of those studying mathematics and computer sciences increased 11% over the preceding 

year, and the number of those studying engineering, physical 

and life sciences, and social sciences also grew, each by 4% 

(IIE 2012). 

More recent data from the Student and Exchange Visitor 

Information System (SEVIS) at the Department of Homeland 

Security show a substantial increase in foreign undergraduate enrollment in the United States between November 2011 

and November 2012 (table 2-11; appendix table 2-19).24

Most of the increase in foreign enrollment was in non-S&E 

fields, but within S&E the largest increases were in engineering and the social sciences. The top 10 countries sending 

foreign undergraduates in fall 2012 were similar to those in 

the preceding year (figure 2-15; appendix table 2-19). Onethird of all foreign students in undergraduate programs at 

U.S. institutions are enrolled in S&E fields; in 2012, the 

proportion of undergraduate students enrolled in S&E fields 

was 50% or higher among students from Malaysia, Kuwait, 

India, and Nigeria.25 Between 2008 and 2011, undergraduate foreign enrollment in S&E increased each year by about 

6%–10%, with the growth rate more than doubling in 2012 

(21%). At the undergraduate level, growth in non-S&E 

fields was between 1% and 3% each year between 2008 and 

2011 but climbed to 16% in 2012 (table 2-11). About 50% 

of the growth in foreign undergraduate enrollment in the last 

year, both in S&E and non-S&E fields, is accounted for by 

the increase in the number of students from China.

Engineering Enrollment. For the most part, students do 

not declare majors until their sophomore year. Because of 

this, undergraduate enrollment data for domestic students 

are not available by field. However, engineering is an exception. Engineering programs generally require students to 

Discipline-Based 

Education Research

The purpose of discipline-based education research 

(DBER) is to improve teaching and learning in S&E 

by bringing together general findings and perspectives 

from the science of learning and expert knowledge of 

specific S&E disciplines. DBER seeks to understand 

how people learn the concepts, practices, and thinking 

of S&E fields. It focuses on a group of related research 

fields (physics, chemistry, engineering, biology, the 

geosciences, and astronomy). 

In 2012, at the request of the National Science 

Foundation, the National Research Council (NRC) 

examined the status, contributions, and future directions of DBER in undergraduate education. It found 

that across the different disciplines, students have incorrect understandings of basic concepts, in particular 

those involving time or space scales that are very large 

or very small. The NRC also concluded that students 

find important aspects of the fields that seem easy or 

obvious to experts to be challenging and to pose barriers to further learning, especially when instructors are 

unaware of the challenges for the novice.

DBER has shown that actively involving undergraduate students in the learning process improves 

understanding more than listening to a traditional lecture. Effective instruction strategies can promote conceptual change. Such strategies include, for example, 

making lectures more interactive, having students 

work in groups, and incorporating authentic activities 

and open-ended problems into teaching (e.g., learning 

in laboratories or learning in a field setting). Students 

can be taught more expert-like problem-solving skills 

and strategies to improve their understanding of concepts by instructional practices that provide steps and 

prompts to guide them, use multiple ways to represent those concepts, and help them to make their own 

thinking visible.
2-24 ♦ Chapter 2. Higher Education in Science and Engineering

declare a major or an intent to major in the first year of college, so engineering enrollment data can serve as an early 

indicator of both future undergraduate engineering degrees 

and student interest in engineering careers. The Engineering 

Workforce Commission administers an annual fall survey 

that tracks enrollment in undergraduate and graduate engineering programs (EWC 2012).

Undergraduate engineering enrollment was flat in the 

late 1990s, increased from 2000 to 2003, declined slightly 

through 2006, and has risen steadily since then to a peak 

of 511,000 in 2011 (figure 2-16; appendix table 2-20). The 

number of undergraduate engineering students increased by 

26% between 2006 and 2011. Full-time freshman enrollment 

followed a similar pattern, reaching 122,000 in 2011—the 

highest since 1982. These trends correspond with declines 

in the college-age population through the mid-1990s, particularly the drop in white 20–24-year-olds, who account for 

the majority of engineering students (NSF/NCSES 2013a). 

Enrollment by Disability Status. According to the most 

recent available estimates, 11% of undergraduate students 

reported a disability in 2008. Nearly half of them were enrolled in 2-year institutions, 41% in 4-year institutions, 3% 

in less-than-2-year institutions, and 8% in more than one institution. About one in five undergraduates with a disability 

was in an S&E field (NSF/NCSES 2013a).

Undergraduate Degree Awards

The number of undergraduate degrees awarded by U.S. 

academic institutions has been increasing over the past two 

decades in both S&E and non-S&E fields. These trends 

are expected to continue at least through 2021 (Hussar and 

Bailey 2013). 

Table 2-11

Foreign students enrolled in U.S. higher education institutions, by broad field and academic level: 2008–12

Field and level 2008 2009 2010 2011 2012

All fields

All levels.................................................................................. 526,570 525,680 537,650 574,360 635,650

Undergraduate .................................................................... 266,320 272,980 284,770 297,950 351,030

Graduate ............................................................................. 260,260 252,710 252,890 276,400 284,620

S&E fields

All levels .............................................................................. 229,010 229,230 235,990 260,280 280,020

Undergraduate................................................................. 76,780 81,110 87,590 96,400 116,640

Graduate.......................................................................... 152,230 148,120 148,400 163,880 163,390

Non-S&E fields

All levels .............................................................................. 297,560 296,460 301,670 314,080 355,630

Undergraduate................................................................. 189,530 191,870 197,180 201,560 234,390

Graduate.......................................................................... 108,030 104,590 104,490 112,520 121,240

NOTES: Data include active foreign national students on F-1 visas and exclude those on optional practical training. Undergraduate level includes 

associate’s and bachelor’s degrees; graduate level includes master’s and doctoral degrees. Numbers are rounded to the nearest 10. Detail may not add 

to total because of rounding.

SOURCE: U.S. Department of Homeland Security, U.S. Immigration and Customs Enforcement, Student and Exchange Visitor Information System 

database, special tabulations (2013).

Science and Engineering Indicators 2014

Figure 2-15

Foreign undergraduate student enrollment in U.S. 

universities, by top 10 places of origin and field: 

November 2012 

NOTES: Data include active foreign national students on F-1 visas 

and exclude those on optional practical training. Undergraduate 

enrollment includes associate’s and bachelor’s degrees.

SOURCE: Bureau of Citizenship and Immigration Services, Student 

and Exchange Visitor Information System database, special 

tabulations (2013). 

Science and Engineering Indicators 2014

Thousands

0 20 40 60 80 100

Mexico

Nigeria

Nepal

Malaysia

Vietnam

Canada

India

Saudi Arabia

South Korea

China

All elds

S&E
Science and Engineering Indicators 2014 ♦ 2-25

S&E Associate’s Degrees

Community colleges often are an important and relatively 

inexpensive gateway for students entering higher education. 

Associate’s degrees, largely offered by 2-year programs at 

community colleges, are the terminal degree for some, but 

others continue their education at 4-year colleges or universities and subsequently earn higher degrees.26 Many who 

transfer to baccalaureate-granting institutions do not earn associate’s degrees before transferring. Combined, associate’s 

degrees in S&E and in engineering technologies accounted 

for about 12% of all associate’s degrees in 2011 (appendix 

table 2-21).

S&E associate’s degrees from all types of academic institutions have been rising continuously since 2007, after a 

steep decline between 2003 and 2007. The overall trend mirrors the pattern of computer sciences, which also peaked in 

2003, declined through 2007, and increased through 2011. 

Associate’s degrees earned in engineering technologies (not 

included in S&E degree totals because of their applied focus) declined from about 40,000 in 2000 to about 30,000 in 

2006, but they have been rising since then to about 38,000 in 

2011 (appendix table 2-21).27

In 2011, women earned 62% of all associate’s degrees, 

up from 60% in 2000, and 43% of S&E associate’s degrees, 

down from 48% in 2000. Most of the decline is attributable 

to a decrease in women’s share of computer sciences degrees, which dropped from 42% in 2000 to 23% in 2011 

(appendix table 2-21). 

Students from underrepresented minority groups (blacks, 

Hispanics, and American Indians and Alaska Natives) earn 

a higher proportion of associate’s degrees than of bachelor’s or more advanced degrees, both in S&E fields and in 

all fields.28 (See the “S&E Bachelor’s Degrees by Race and 

Ethnicity” and “Doctoral Degrees by Race and Ethnicity” 

sections.) In 2011, underrepresented minorities earned 27% 

of S&E associate’s degrees—more than one-third of all associate’s degrees in social and behavioral sciences and more 

than one-quarter of all associate’s degrees in biological sciences, physical sciences, and mathematics (appendix table 

2-22). Since 2000, the number of S&E associate’s degrees 

earned by these students grew faster than the overall national 

increase.

S&E Bachelor’s Degrees

The baccalaureate is the most prevalent S&E degree, accounting for nearly 70% of all S&E degrees awarded. S&E 

bachelor’s degrees have consistently accounted for roughly 

one-third of all bachelor’s degrees for at least the past 10 

years. The number of S&E bachelor’s degrees awarded rose 

steadily from about 400,000 in 2000 to more than 550,000 in 

2011 (appendix table 2-17).29

In the last decade, the number of bachelor’s degrees 

awarded increased fairly consistently, although to different extents, in all S&E fields. The exception was computer 

sciences, where the number increased sharply from 2000 to 

2004, dropped as sharply through 2009, but increased again 

in 2010 and 2011 (figure 2-17; appendix table 2-17).

S&E Bachelor’s Degrees by Sex. Since 1982, women 

have outnumbered men in undergraduate education. They 

have earned relatively constant fractions of all bachelor’s 

and S&E bachelor’s degrees for several years. Since the 

Figure 2-16

U.S. engineering enrollment, by level: 1991–2011

Thousands

NOTE: Enrollment data include full- and part-time students.

SOURCE: American Association of Engineering Societies, 

Engineering Workforce Commission, Engineering & Technology 

Enrollments (various years).

Science and Engineering Indicators 2014

1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011

0

100

200

300

400

500

600

Undergraduate

Graduate

Figure 2-17

S&E bachelor’s degrees, by field: 2000–11

Thousands

NOTE: Physical sciences include earth, atmospheric, and ocean 

sciences. 

SOURCES: National Center for Education Statistics, Integrated Postsecondary Education Data System, Completions Survey; and National 

Science Foundation, National Center for Science and Engineering 

Statistics, WebCASPAR database, http://webcaspar.nsf.gov.

Science and Engineering Indicators 2014

2000 2003 2005 2007 2009 2011

0

50

100

150

200

250

300

Social/behavioral sciences

Biological/agricultural sciences

Engineering

Computer sciences

Physical sciences

Mathematics
2-26 ♦ Chapter 2. Higher Education in Science and Engineering

late 1990s, women have earned about 57% of all bachelor’s degrees and about half of all S&E bachelor’s degrees. 

Among U.S. citizens and permanent residents, women 

also earn about half of all S&E bachelor’s degrees (NSF/

NCSES 2013a). 

Within S&E, men and women tend to study different 

fields; these tendencies are also observed at the master’s 

and doctoral levels, as will be seen below and in the workforce data in chapter 3. In 2011, men earned the vast majority of bachelor’s degrees awarded in engineering, computer 

sciences, and physics. Women earned half or more of the 

bachelor’s degrees in psychology, biological sciences, agricultural sciences, and all the broad fields within social sciences except for economics (appendix table 2-17).

Since 2000, changes have not followed a consistent pattern. The share of bachelor’s degrees awarded to women 

declined in computer sciences (by 10%), mathematics (by 

5%), physics (by 2%), and engineering (by 2%) (figure 2-18; 

appendix table 2-17). Fields in which the proportion of bachelor’s degrees awarded to women grew during this period 

include atmospheric sciences (by 9%), agricultural sciences 

(by 6%), astronomy (by 3%), chemistry (by 2%), anthropology (by 3%), and political science and public administration 

(by 1%) (appendix table 2-17).

The number of bachelor’s degrees awarded to men and 

women in S&E and in all fields increased in similar proportions between 2000 and 2011.30

S&E Bachelor’s Degrees by Race and Ethnicity. The 

racial and ethnic composition of the cohort of S&E bachelor’s 

degree recipients has changed over time, reflecting both population changes and increasing rates of college attendance by 

members of minority groups.31 Between 2000 and 2011, the 

share of S&E degrees awarded to white students among U.S. 

citizens and permanent residents declined from 71% to 63%, 

although the number of S&E bachelor’s degrees earned by 

white students increased during that time (figure 2-19; appendix table 2-23). The share awarded to Hispanic students 

increased from 7% to 10% and to Asians and Pacific Islanders 

from 9% to 10%. The shares to black and American Indian or 

Alaska Native students have remained flat since 2000, at 9% 

and 1%, respectively. The number of S&E bachelor’s degrees 

earned by students of unknown race or ethnicity nearly tripled 

in this period, to about 42,000.

Despite considerable progress over the past two decades 

for underrepresented minority groups earning bachelor’s degrees in any field, the gap in educational attainment between 

young minorities and whites continues to be wide. In 2011, 

the percentage of the population ages 25–29 with bachelor’s 

or higher degrees was 20% for blacks, 13% for Hispanics, 

and 39% for whites. These figures changed from the 1980 

shares of 12%, 8%, and 25%, respectively (Aud et al. 2012). 

Differences in completion of bachelor’s degrees in S&E by 

Figure 2-18

Women’s share of S&E bachelor’s degrees, 

by field: 2000–11

Percent

NOTE: Physical sciences include earth, atmospheric, and ocean sciences. 

SOURCES: National Center for Education Statistics, Integrated 

Postsecondary Education Data System, Completions Survey; and National

Science Foundation, National Center for Science and Engineering Statistics, 

WebCASPAR database, http://webcaspar.nsf.gov.

Science and Engineering Indicators 2014

2000 2003 2005 2007 2009 2011

0

20

40

60

80

100

Physical sciences

Biological/agricultural sciences

Mathematics 

Computer sciences

Psychology

Social sciences

Engineering

Figure 2-19

Share of S&E bachelor’s degrees among U.S. 

citizens and permanent residents, by race and 

ethnicity: 2000–11

URM = underrepresented minorities (black, Hispanic, and American 

Indian or Alaska Native). 

NOTES: Hispanic may be any race. American Indian or Alaska Native, 

Asian or Pacic Islander, black or African American, and white refer 

to individuals who are not of Hispanic origin. Percentages do not sum 

to 100 because data do not include individuals who did not report 

their race and ethnicity.

SOURCES: National Center for Education Statistics, Integrated 

Postsecondary Education Data System, Completions Survey; and 

National Science Foundation, National Center for Science and 

Engineering Statistics, WebCASPAR database, 

http://webcaspar.nsf.gov.

Science and Engineering Indicators 2014

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

0

2

4

6

8

10

12

URM and Asian or 

Pacic Islander (Percent) White (Percent)

Asian or Pacic Islander

Hispanic

American Indian or Alaska Native

Black or

African

American

White

0

57

59

61

63

65

67

69

71

73

75
Science and Engineering Indicators 2014 ♦ 2-27

race or ethnicity reflect differences in high school completion rates, college enrollment rates, and college persistence 

and attainment rates. In general, blacks, Hispanics, and 

American Indians and Alaska Natives are less likely than 

whites and Asians or Pacific Islanders to graduate from high 

school, to enroll in college, and to graduate from college. 

(For information on immediate post-high school college enrollment rates, see the “Transition to Higher Education” section in chapter 1.) Among those who do enroll in or graduate 

from college, blacks, Hispanics, and American Indians and 

Alaska Natives are about as likely as whites to choose S&E 

fields; Asians or Pacific Islanders are more likely than members of other racial and ethnic groups to choose these fields. 

For Asians and Pacific Islanders, almost half of all bachelor’s degrees received are in S&E, compared with close 

to one-third of all bachelor’s degrees earned by each of the 

other racial and ethnic groups. However, the proportion of 

Asians and Pacific Islanders earning degrees in the social 

sciences is similar to that of other racial and ethnic groups 

(appendix table 2-23).

The contrast in field distribution among whites, blacks, 

Hispanics, and American Indians and Alaska Natives on the 

one hand and Asians and Pacific Islanders on the other is 

apparent within S&E fields as well. White, black, Hispanic, 

and American Indian and Alaska Native S&E baccalaureate recipients share a similar distribution across broad S&E 

fields. In 2011, between 9% and 11% of all baccalaureate recipients in each of these racial and ethnic groups earned their 

degrees in the natural sciences, 2%–4% in engineering, and 

15%–18% in the social and behavioral sciences. Asian and 

Pacific Islander baccalaureate recipients earned 21% of their 

bachelor’s degrees in natural sciences and 8% in engineering 

(appendix table 2-23).

Since 2000, the total number of bachelor’s degrees and 

the number of S&E bachelor’s degrees rose for all racial and 

ethnic groups. The number of bachelor’s degrees in all broad 

S&E fields except computer sciences also rose for most racial and ethnic groups (appendix table 2-23). In all racial and 

ethnic groups, the number of degrees in computer sciences 

followed the pattern for the general population: it increased 

considerably through 2003–04 and then sharply declined 

through 2008–09. In the last 2 or 3 years, the numbers started to increase, and in the case of Hispanics, the number of 

earned bachelor’s degrees in computer sciences in 2011 was 

close to the peak reached in 2004. 

Bachelor’s Degrees by Citizenship. Students on temporary visas in the United States have consistently earned 

a small share (3%–4%) of S&E degrees at the bachelor’s 

level. In 2011, these students earned a larger share of bachelor’s degrees awarded in economics and in chemical, electrical, and industrial engineering (about 10%). The number 

of S&E bachelor’s degrees awarded to students on temporary visas increased from about 15,000 in 2000 to about 

19,000 in 2004, then declined to 17,000 by 2008, but it increased through 2011, peaking at almost 21,000 (appendix 

table 2-23). 

Graduate Education, Enrollment, 

and Degrees in the United States

Graduate education in S&E contributes to global competitiveness, producing the highly skilled workers of the future 

and the research needed for a knowledge-based economy. 

This section includes indicators related to graduate enrollment; recent trends in the number of earned degrees in S&E 

fields; and participation by women, minorities, and foreign 

students in graduate education in U.S. academic institutions.

Graduate Enrollment by Field

S&E graduate enrollment in the United States increased 

between 2000 and 2011 to more than 600,000 (appendix 

table 2-24).32 Graduate enrollment grew considerably in 

most S&E fields, particularly in engineering and in the biological and the social sciences (where most of the growth is 

accounted for by the increase of graduate enrollment in political science and public administration). Graduate enrollment in computer sciences grew rapidly in the early 2000s, 

then decreased through 2006, but it has generally increased 

since then.

Graduate enrollment in engineering grew between 2000 

and 2011. Although the rate of growth slowed somewhat in 

2011, the number of full-time engineering students reached 

a new peak in that year (appendix table 2-25). 

The number of full-time students enrolled for the first 

time in S&E graduate departments is an indicator of developing trends. Despite some drops in first-time, full-time enrollment in engineering and computer sciences in the early 

to mid-2000s, this indicator has increased fairly steadily in 

most broad S&E fields, particularly between 2008 and 2011. 

In 2011, the number of first-time, full-time S&E graduate 

students reached a new peak in most S&E fields (appendix 

table 2-26).

 First-time, full-time graduate enrollment, particularly in 

engineering and to some extent in computer sciences, often 

follows trends in employment opportunities. When employment opportunities are plentiful, recent graduates often forgo graduate school, but when employment opportunities are 

scarce, further training in graduate school may be perceived 

as a better option. Figure 2-20 shows trends in unemployment rates and trends in first-time, full-time graduate enrollment in engineering and computer sciences. Enrollment in 

S&E fields that offer fewer employment opportunities at the 

bachelor’s level (e.g., biological sciences) does not follow 

this trend. According to data from the NSRCG, the proportion of recent S&E bachelor’s recipients who were taking 

classes or enrolled full-time in a degree program after obtaining their degree increased to about 30% among those 

who graduated in 2008 and 2009, up from about 25% among 

those who graduated earlier in the decade.33
2-28 ♦ Chapter 2. Higher Education in Science and Engineering

Graduate Enrollment by Sex 

In 2011, 46% of the S&E graduate students enrolled in 

the United States were women (appendix table 2-24). The 

proportions of women graduate students enrolled in S&E 

differed considerably by field, with the lowest proportions 

in engineering, computer sciences, and physical sciences. 

Women constituted the majority of graduate students in psychology, medical and other health sciences, biological sciences, and social sciences, and they were half or close to 

half of graduate students in agricultural sciences and earth, 

atmospheric, and ocean sciences. Among the social sciences, economics has an unusually low proportion of women. 

Except for computer sciences and physical sciences, in most 

of these broad fields, the proportion of women enrolled increased between 2000 and 2005–07, but it has remained 

fairly stable since then. The proportion of women enrolled 

in graduate programs in computer sciences peaked in 2000 

and has decreased since then. In the physical sciences, the 

proportion of women increased gradually in the last two decades, from 25% in 1991 to 33% in 2011 (for earlier data, 

see NSB 2008).

Graduate Enrollment of Underrepresented Groups

In 2011, among U.S. citizens and permanent residents, 

underrepresented minority students (blacks, Hispanics, and 

American Indians and Alaska Natives) accounted for 17% of 

students enrolled in graduate S&E programs (appendix table 

2-27). The proportion of underrepresented minorities was 

highest in psychology and the social sciences (23%), medical and other health sciences (19%), and computer sciences 

(15%); it was lowest in the earth, atmospheric, and ocean 

sciences (9%) and the physical sciences (10%). Between 

2000 and 2011, the proportion of underrepresented minorities enrolled has increased in all broad S&E fields, in particular in psychology and computer sciences.

In 2011, whites accounted for about 65% of S&E graduate enrollment among U.S. citizens and permanent residents. 

They constituted a larger proportion of graduate students 

enrolled in agricultural sciences and in earth, atmospheric, 

and ocean sciences (about 80%) and a smaller proportion 

of those enrolled in computer sciences and social sciences 

(about 60%). The proportions of whites in other fields fell 

in between. Over time, however, the proportion of whites 

among graduates enrolled in S&E has declined in all broad 

S&E fields except for computer sciences, where the proportion of whites increased slightly, from 58% in 2000 to 60% 

in 2011.

Asians and Pacific Islanders accounted for 9% of S&E 

graduate enrollment among U.S. citizens and permanent residents in 2011, with larger proportions in computer sciences 

(14%), engineering (13%), the biological and medical sciences (about 12% and 11%, respectively) and a lower proportion in the agricultural sciences (3%); earth, atmospheric, 

and ocean sciences (4%); psychology (5%); and the social 

sciences (6%). Between 2000 and 2011, the proportion of 

Asians and Pacific Islanders enrolled increased slightly in 

most broad fields, but it declined in computer sciences (from 

21% in 2000 to 14% in 2011). 

About 20% of graduate students reporting a disability 

were enrolled in S&E fields. Nearly two-thirds of those in 

S&E fields were men; nearly 90% were 24 years old or older 

(NSF/NCSES 2013a). 

Foreign Graduate Enrollment

In 2011, nearly 174,000 foreign students on temporary visas were enrolled in S&E graduate programs (appendix table 

2-27). The concentration of foreign enrollment was highest 

in computer sciences, engineering, physical sciences, mathematics/statistics, chemistry, and economics.34

Following a post-9/11 decline, the numbers of first-time, 

full-time foreign graduates enrolled increased more or less 

consistently in most broad fields through 2011 (appendix 

table 2-26). Declines and subsequent increases were concentrated in engineering and computer sciences, the fields heavily favored by foreign students. However, between 2000 and 

2011, foreign students’ share of first-time, full-time S&E 

graduate enrollment dropped in other broad fields, particularly in the physical sciences (from 43% to 40%) and the 

social sciences (from 29% to 24%).

According to data collected by the Institute of 

International Education (IIE), the overall number of foreign 

graduate students in all fields increased by 1% from academic year 2010–11 to 2011–12 (IIE 2012). The number of new 

foreign graduate students increased by 3%. India, China, 

Figure 2-20

First-time, full-time graduate enrollment in 

engineering and computer sciences and 

unemployment rate of all workers: 2000–11

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2012) of the Survey of 

Graduate Students and Postdoctorates in Science and Engineering, 

2011. Unemployment rates are from the Bureau of Labor Statistics, 

Current Population Survey, table 1, employment status of the civilian 

noninstitutional population, 1942 to date, http://www.bls.gov/cps/ 

cpsaat01.htm, accessed 4 February 2013.

Science and Engineering Indicators 2014

2000 2003 2005 2007 2009 2011

0

5

10

15

20

25

30

35

Thousands Percent

Engineering

Computer sciences

0

2

4

6

8

10

12

14

16

Unemployment rate (%)
Science and Engineering Indicators 2014 ♦ 2-29

South Korea, Taiwan, and Canada were the top originating 

locations for foreign graduate students, similar to the leading 

foreign sources for undergraduate enrollment. 

More recent data from SEVIS show an overall 3% increase in foreign graduate students from November 2011 

to November 2012 in all fields (appendix table 2-28, table 

2-11).35 In 2012, nearly 60% of all foreign students in graduate programs at U.S. institutions were enrolled in S&E 

fields. Between fall 2011 and fall 2012, the number of foreign graduate students enrolled in S&E fields was stable, 

with declines in the numbers of foreign students in computer 

sciences (5%), biological sciences (4%), and engineering 

(2%) offset by increases in mathematics (11%), social sciences (7%), and psychology (4%). China and India continued to account for about 61% of the foreign S&E graduates 

in the United States in November 2012; however, between 

fall 2011 and fall 2012, the number of S&E foreign students 

from China increased, whereas the number of foreign students from India declined. South Korea, Taiwan, and Canada 

also sent large numbers of S&E graduate students, although 

these economies sent larger numbers of graduate students in 

non-S&E fields, primarily business and the humanities. 

S&E Master’s Degrees

In some fields, such as engineering and geosciences, 

a master’s degree can be a terminal degree that fully prepares students for an established career track. In other fields, 

master’s degrees primarily mark a step toward doctoral degrees. Master’s degrees awarded in S&E fields increased 

from about 96,000 in 2000 to about 151,000 in 2011, with 

growth concentrated in two periods, 2002–04 and 2007–11 

(appendix table 2-29).36 Increases occurred in all major science fields. Master’s degrees awarded in engineering and 

computer sciences declined between 2004 and 2007, but 

they have since increased. The number of master’s degrees 

awarded in engineering in 2011 was the highest in the last 

12 years; in the case of computer sciences, the number of 

master’s degrees awarded in 2011 was near its peak in 2004 

(figure 2-21). During this 12-year period, growth was particularly high in engineering, psychology, and political science and public administration (appendix table 2-29). Both 

students and institutions are concerned that success rates 

in completing master’s degrees are too low (see sidebar, 

“Master’s Completion and Attrition in S&E”).

In 2012, the Commission on Pathways through Graduate 

School and into Careers, a 14-member commission composed of industry leaders and university executives, led a 

research effort to understand the different career paths students may take and to modernize graduate education by emphasizing skills that align more closely with workforce needs 

(Wendler et al. 2012).37 Professional science master’s degree 

programs, which stress interdisciplinary training, are part of 

this relatively new direction in graduate education (for details, see sidebar, “Professional Science Master’s Degrees”). 

Master’s Degrees by Sex

The number of S&E master’s degrees earned by both men 

and women rose between 2000 and 2011 (figure 2-22). In 

2000, women earned 43% of all S&E master’s degrees; by 

2011, they earned 45% (appendix table 2-29). Among U.S. 

citizens and permanent residents, women earned nearly half 

of all S&E master’s degrees; among temporary residents, 

women earned about one-third of all S&E master’s degrees 

(NSF/NCSES 2013a). 

Women’s share of S&E master’s degrees varies by field. 

As with bachelor’s degrees, in 2011, women earned a majority of master’s degrees in psychology, biological sciences, 

agricultural sciences, and most social sciences except economics, but low proportions of master’s degrees in engineering, computer sciences, and physics. Women’s share 

of master’s degrees in engineering in 2011, however, was 

slightly higher than their share in 2000 (appendix table 2-29). 

The number of master’s degrees awarded to women in most 

major S&E fields increased fairly consistently throughout 

the last decade. In computer sciences, the numbers increased 

through 2004, then declined sharply through 2007, but they 

have increased consistently since then.

Master’s Degrees by Race and Ethnicity

The number of S&E master’s degrees awarded to U.S. 

citizens and permanent residents increased for all racial and 

ethnic groups between 2000 and 2011 (figure 2-23; appendix table 2-30).38

The proportion of master’s degrees in S&E fields earned 

by U.S. citizens and permanent residents from underrepresented racial and ethnic minorities increased slightly between 

Figure 2-21

S&E master’s degrees, by field: 2000–11

Thousands

NOTE: Physical sciences include earth, atmospheric, and ocean sciences. 

SOURCES: National Center for Education Statistics, Integrated 

Postsecondary Education Data System, Completions Survey; and 

National Science Foundation, National Center for Science and 

Engineering Statistics, WebCASPAR database, http://webcaspar.nsf.gov. 

Science and Engineering Indicators 2014

2000 2003 2005 2007 2009 2011

0

10

20

30

40

50

Engineering

Psychology

Social sciences

Computer sciences

Biological/agricultural sciences

Physical

sciences

Mathematics
2-30 ♦ Chapter 2. Higher Education in Science and Engineering

Master’s Completion 

and Attrition in S&E

Trends in master’s education have attracted considerable attention in recent years, but little is known about 

the extent to which master’s students succeed in completing their programs. A study by the Council of Graduate 

Schools (CGS 2013) collected data on master’s completion and attrition trends in master’s programs from the 

2003–04 to the 2006–07 academic years from five academic institutions in five broad S&E fields (biological 

and agricultural sciences, engineering, mathematics and 

computer sciences, physical and earth sciences, social 

and behavioral sciences) and in business. Although the 

data from this study are not nationally representative 

and cannot be generalized to S&E graduate programs 

as a whole, they come from a range of fields and institutions and are suggestive of factors affecting master’s 

degree completion. 

In surveys, graduating S&E master’s students said 

that the most important factor contributing to the successful completion of a master’s program was their motivation and determination, followed by nonfinancial family 

support, being a full-time student, quality of teaching, 

and supportive faculty. 

S&E master’s students who left their programs reported that the most important factors preventing them from 

earning a master’s degree were interference from employment, program structure, lack of adequate financial 

support, and lack of support from faculty. Among students who reported having concerns about their ability to 

complete their master’s in S&E, the most frequently reported challenge was finding the time to manage school, 

work, and family commitments.

In the institutions studied, 41% of the S&E master’s 

students completed their program within 2 years, 60% 

within 3 years, and 66% within 4 years. Completion rates 

within 4 years varied little by S&E field, but rates within 

2 years were lowest for students in physical and earth sciences. Women, Asians and Pacific Islanders, temporary 

residents, and younger cohorts of students completed 

their master’s degrees at higher rates.

About 10% of students in S&E fields left their programs within 6 months, 17% within 1 year, and 23% 

within 2 years. The median time to degree for students in 

S&E fields was 23 months, and the median time to attrition was 8 months.

Professional Science 

Master’s Degrees

Professional science master’s (PSM) degrees provide advanced training in an S&E field beyond the 

bachelor’s degree level while also developing administrative and business skills that are valued by employers, including leadership, project management, 

teamwork, and communication. Starting from a handful of PSM programs in 1997, there are now almost 

300 such programs in more than 100 institutions in 32 

states and the District of Columbia, as well as some 

international programs in Canada, Australia, and the 

United Kingdom. 

Total enrollment in PSM programs in the United 

States reached nearly 5,800 students in 2012, about 

one-third of whom were first-time enrollees (Allum, 

Gonzales, and Remington 2013). More than half of the 

enrollees were men (55%) and, among U.S. citizens 

and permanent residents, one-quarter were underrepresented minorities. The majority of the students were 

enrolled in one of four fields of study: computational 

sciences (21%), biotechnology (16%), environmental 

sciences and natural resources (14%), and mathematics and statistics (14%). 

Nearly 1,800 PSM degrees were awarded in 2012. 

More than one in five of them were in biotechnology, 

and a similar proportion was in computer or information sciences. Men earned the majority of the PSM 

degrees awarded in chemistry, geosciences and geographic information systems, bioinformatics and computational biology, and mathematics and statistics. 

Women earned the majority of the degrees granted in 

medical-related sciences and environmental sciences 

and natural resources.

PSM programs have not yet been subject to a systematic, formal evaluation. However, according to 

the Outcomes for PSM Alumni: 2010/11 survey conducted by the Council of Graduate Schools (Bell and 

Allum 2011), more than 8 in 10 PSM program graduates were working in the summer of 2011, the vast 

majority of them in jobs closely related to their fields 

of study. More than half of those working full-time reported salaries of $50,000 or higher. Similar findings 

are reported by individual PSM programs that track 

student outcomes (Carpenter 2012).

2000 and 2011. The trends are not very different from those 

found in the data on bachelor’s degree awards among racial and ethnic groups. Blacks accounted for 10% of S&E 

master’s degree recipients in 2011, up from 8% in 2000; 

Hispanics accounted for 8%, up from 5%; and American 

Indians and Alaska Natives accounted for 0.5%, similar to 

the proportion in 2000. The proportion of Asian and Pacific 

Islander S&E recipients also remained flat in this period.

The percentage of S&E master’s degrees earned by white 

students fell from 70% in 2000 to 61% in 2011, whereas 

the percentage of degrees earned by blacks, Hispanics, and 

temporary residents increased. The proportion of S&E master’s degrees recipients of other or unknown race doubled 

between 2000 and 2011, to 12% (appendix table 2-30). 
Science and Engineering Indicators 2014 ♦ 2-31

Master’s Degrees by Citizenship

Foreign students make up a much higher proportion of 

S&E master’s degree recipients than of bachelor’s or associate’s degree recipients. In 2011, foreign students earned 

more than one-quarter of S&E master’s degrees. Their degrees were heavily concentrated in computer sciences, economics, and engineering, where they received more than 4 

out of 10 of all master’s degrees awarded in 2011 (appendix 

table 2-30). Within engineering, students on temporary visas 

earned more than half of the master’s degrees in electrical 

and chemical engineering.

The number of S&E master’s degrees awarded to students on temporary visas reached its highest point in a decade in 2011 (39,000), after a sharp decline between 2004 

and 2007. Most of the drop during this period was accounted 

for by decreasing numbers of temporary residents in the 

computer sciences and engineering fields, but in both fields 

numbers rebounded in both fields by about one-third in the 

following years.

S&E Doctoral Degrees

Doctoral education in the United States generates new 

knowledge important for the society as a whole and for U.S. 

competitiveness in a global knowledge-based economy. It 

prepares a new generation of researchers in academia, industry, and government, as well as a highly skilled workforce 

for other sectors of the economy. 

The number of S&E doctorates (excluding those in other 

health sciences) conferred annually by U.S. universities increased steadily between 2002 and 2008, then decreased in 

2009 and 2010.39 The number rose by nearly 5% in 2011, to 

more than 38,000 (appendix table 2-31).40 The growth in the 

number of S&E doctorates between 2000 and 2012 occurred 

among U.S. citizens and permanent residents as well as temporary residents. The largest increases were in engineering 

and the biological sciences (figure 2-24).

Time to Doctoral Degree Completion

The time required to earn a doctoral degree and the success rates of those entering doctoral programs are concerns 

for those pursuing a degree, the universities awarding the 

degree, and the agencies and organizations funding graduate 

study. Longer times to degree mean lost earnings and a higher risk of attrition. Time to degree (as measured by time from 

graduate school entry to doctorate receipt) increased through 

the mid-1990s but has since decreased in all S&E fields from 

7.7 to 7.0 years (appendix table 2-32). The physical sciences 

and mathematics had the shortest time to degree, whereas 

the social sciences and medical and other health sciences 

had the longest. 

Between 1997 and 2011, time to degree for doctorate 

recipients decreased in each of the Carnegie types of academic institutions awarding doctoral degrees (see sidebar, 

“Carnegie Classification of Academic Institutions”). Time 

to degree was shortest at research universities with very high 

research activity (6.9 years in 2011, down from 7.2 years in 

1997). Doctorate recipients at medical schools also finished 

quickly (6.7 years in 2011). Time to degree was longer at 

universities that were less strongly oriented toward research 

(table 2-12).

Figure 2-22

S&E master’s degrees, by sex of recipient: 2000–11

Thousands

SOURCES: National Center for Education Statistics, Integrated 

Postsecondary Education Data System, Completions Survey; and 

National Science Foundation, National Center for Science and 

Engineering Statistics, WebCASPAR database, http://webcaspar.nsf.gov.

Science and Engineering Indicators 2014

2000 2003 2005 2007 2009 2011

0

20

40

60

80

100

Male

Female

Figure 2-23

S&E master’s degrees, by race, ethnicity, and 

citizenship of recipient: 2000–11

Thousands

NOTES: Data on race and ethnicity include U.S. citizens and permanent 

residents. Hispanic may be any race. American Indian or Alaska Native, 

Asian or Pacic islander, black or African American, and white refer to 

individuals who are not of Hispanic origin. 

SOURCES: National Center for Education Statistics, Integrated 

Postsecondary Education Data System, Completions Survey; and 

National Science Foundation, National Center for Science and 

Engineering Statistics, WebCASPAR database, http://webcaspar.nsf.gov.

Science and Engineering Indicators 2014

2000 2003 2005 2007 2009 2011

0

20

40

60

80

White

Asian or Pacic Islander

Black or African American

Hispanic

American Indian or

Alaska Native

Temporary resident
2-32 ♦ Chapter 2. Higher Education in Science and Engineering

The median time to degree varies somewhat by demographics, but these variations tend to reflect differences among broad 

fields of study. In 2011, across all doctorate recipients, women 

have a longer time to degree than men (7.9 versus 7.4 years, 

respectively) (NSF/NCSES 2012). However, these differences 

were very small or nonexistent when men and women were compared within broad fields.41 Time to degree for men and women 

was similar in most broad S&E fields except for engineering, 

where it was slightly shorter for women (6.5 versus 6.9 for men). 

Within broad S&E fields, time to degree was longer for temporary visa holders than for U.S. citizens and permanent residents, 

and, in most broad fields, it was shorter for whites than for any 

other racial or ethnic group. In the life sciences, time to degree of 

Hispanic doctorate recipients was as short as that of whites (6.7).

Doctoral Degrees by Sex

Among U.S. citizens and permanent residents, the proportion of S&E doctoral degrees (excluding those in other 

health sciences; see endnote 39) earned by women grew 

from 43% in 2000 to 47% in 2011 (appendix table 2-31). 

During this decade, women made gains in most major fields, 

but considerable disparities continued in certain fields. In 

2011, women earned half or more of doctorates in non-S&E 

fields, in most social and behavioral sciences except for economics, in the biological sciences, and in medical and other 

health sciences. They earned fewer than one-third of the 

doctorates awarded in physical sciences, mathematics and 

computer sciences, and engineering (appendix table 2-31). 

Although the percentages of degrees earned by women in 

physical sciences and engineering are low, they are higher 

than they were in 2000.

The number of S&E doctoral degrees earned by women 

grew faster than that earned by men. The number of U.S. 

citizen and permanent resident women earning doctorates in 

S&E increased from nearly 8,000 in 2000 to nearly 11,000 in 

2011, while the number earned by men increased from about 

10,000 to nearly 12,000 in the same time interval (appendix table 2-31). The increase in the number of S&E doctorates earned by women occurred in most major S&E fields. 

For example, the number of engineering doctorates earned 

by U.S. citizen and permanent resident women increased 

from approximately 500 in 2000 to 900 in 2011, biological 

sciences doctorates from 1,700 to 2,900, and physical sciences doctorates from 600 to nearly 900. A decrease in the 

number of doctorates earned by U.S. citizen and permanent 

resident men in the early years of the decade occurred in 

non-S&E fields and in many S&E fields. However, since 

2005, the number of doctorates earned by men has increased 

in all major S&E fields except for agricultural sciences, 

and psychology.

Doctoral Degrees by Disability Status

In 2011, 3% of doctorate recipients reported having a disability. Compared with persons without disabilities, those 

with disabilities were less likely to earn doctorates in engineering fields (9% versus 17%) and more likely to earn 

doctorates in the social and behavioral sciences (21% versus 

17%). Nearly one-third of the S&E doctorate recipients with 

disabilities reported a learning disability, 17% reported being blind or visually impaired, 13% reported a physical or 

orthopedic disability, 12% indicated being deaf or hard of 

hearing, 4% reported a vocal or speech disability, and 21% 

cited other or unspecified disabilities (NSF/NCSES 2013a). 

Doctoral Degrees by Race and Ethnicity

The number and proportion of doctoral degrees in S&E 

fields earned by underrepresented minorities increased between 2000 and 2011. In 2011, blacks earned 1,233 S&E 

doctorates, Hispanics earned 1,326, and American Indians 

and Alaska Natives earned 113—accounting for 8% of S&E 

Figure 2-24

S&E doctoral degrees earned in U.S. universities, 

by field: 2000–11

Thousands

NOTES: Physical sciences include earth, atmospheric, and ocean 

sciences. Data differ from doctoral degree data in other tables and 

gures in this report that are based on the National Science 

Foundation Survey of Earned Doctorates and that refer to research 

doctorates only. Greatest differences are in psychology and medical 

sciences. 

SOURCES: National Center for Education Statistics, Integrated 

Postsecondary Education Data System, Completions Survey; and 

National Science Foundation, National Center for Science and 

Engineering Statistics, Integrated Science and Engineering 

Resources Data System (WebCASPAR), http://webcaspar.nsf.gov.

Science and Engineering Indicators 2014

0

2

4

6

8

10

12

2000 2003 2005 2007 2009 2011

Engineering

Physical sciences

Mathematics

Computer sciences

Biological sciences

Social/behavioral sciences

Medical sciences

Agricultural sciences
Science and Engineering Indicators 2014 ♦ 2-33

doctoral degrees (excluding doctorates in other health sciences; see endnote 39) earned that year, up from 6% in 2000 

(appendix table 2-33).42 Their share of the S&E doctorates 

earned by U.S. citizens and permanent residents rose from 

9% to 12% in the same period. Gains by all groups contributed to this rise, although the number of S&E degrees 

earned by blacks and Hispanics rose considerably more than 

the number earned by American Indians and Alaska Natives 

(figure 2-25). Asian and Pacific Islander U.S. citizens and 

permanent residents earned 6% of all S&E doctorates in 

2011, similar to 2000. 

Although the number of S&E doctorates earned by white 

U.S. citizens and permanent residents increased between 

2000 and 2011 (figure 2-26), the number of S&E doctorates 

awarded to minorities and temporary residents increased at 

a faster pace. As a result, the proportion of S&E doctoral 

degrees earned by white U.S. citizens and permanent residents decreased from 53% in 2000 to 43% in 2011 (appendix 

table 2-33).

Foreign S&E Doctorate Recipients

Temporary residents earned nearly 13,000 S&E doctorates in 2011, up from about 8,000 in 2000. Foreign students 

on temporary visas earned a larger proportion of doctoral 

degrees than master’s, bachelor’s, or associate’s degrees 

(appendix tables 2-33, 2-30, 2-23, and 2-22, respectively). 

The temporary residents’ share of S&E doctorates rose from 

31% in 2000 to 36% in 2011. In some fields, these students 

earned even larger shares of doctoral degrees. In 2011, they 

earned half or more of doctoral degrees awarded in engineering, computer sciences, and economics. They earned 

considerably lower proportions of doctoral degrees in other 

S&E fields—for example, 27% in biological sciences, 26% 

Table 2-12

Median number of years from entering graduate school to receipt of S&E doctorate, by 2010 Carnegie 

classification of doctorate-granting institution: 1997–2011

Year All institutions

Research 

universities 

(very high 

research 

activity)

Research 

universities 

(high research 

activity)

Doctoral/

research 

universities

Medical schools 

and medical 

centers

Other/not 

classified

1997.................................................... 7.7 7.2 8.2 9.7 7.7 8.2

1998.................................................... 7.3 7.2 8.2 9.2 6.9 7.7

1999.................................................... 7.2 7.2 7.9 9.0 6.7 7.7

2000.................................................... 7.5 7.2 8.2 9.2 7.2 7.9

2001.................................................... 7.2 7.2 8.2 9.7 6.9 7.7

2002.................................................... 7.5 7.2 8.2 9.9 6.9 7.9

2003.................................................... 7.6 7.2 8.2 9.7 6.9 8.7

2004.................................................... 7.2 7.0 8.0 9.3 6.9 7.7

2005.................................................... 7.3 7.2 7.9 9.3 7.0 7.9

2006.................................................... 7.2 7.0 7.9 9.0 6.9 7.7

2007.................................................... 7.0 6.9 7.7 8.9 6.9 7.7

2008.................................................... 7.0 6.9 7.7 8.9 6.7 7.6

2009.................................................... 7.0 6.9 7.7 9.2 6.8 7.7

2010.................................................... 7.0 6.9 7.7 8.9 6.7 7.4

2011.................................................... 7.0 6.9 7.7 8.7 6.7 7.7

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2012) of the Survey of Earned 

Doctorates, 2011.

Science and Engineering Indicators 2014

Figure 2-25

S&E doctoral degrees earned by U.S. citizen and 

permanent resident underrepresented minorities, 

by race and ethnicity: 2000–11

Number

NOTES: Data differ from doctoral degree data in other tables and 

gures in this report that are based on the National Science 

Foundation Survey of Earned Doctorates and that refer to research 

doctorates only. Greatest differences are in psychology and 

medical/other health sciences. S&E excludes other health sciences. 

Hispanic may be any race. American Indian or Alaska Native and 

black or African American refer to individuals who are not of Hispanic 

origin.

SOURCES: National Center for Education Statistics, Integrated 

Postsecondary Education Data System, Completions Survey; and 

National Science Foundation, National Center for Science and 

Engineering Statistics, Integrated Science and Engineering 

Resources Data System (WebCASPAR), http://webcaspar.nsf.gov. 

See appendix table 2-33.

Science and Engineering Indicators 2014

2000 2003 2005 2007 2009 2011

0

200

400

600

800

1,000

1,200

1,400

Black or African American

Hispanic

American Indian or Alaska Native
2-34 ♦ Chapter 2. Higher Education in Science and Engineering

Figure 2-26

S&E doctoral degrees, by sex, race, ethnicity, and 

citizenship: 2000–11

Thousands

NOTES: Minority includes American Indian or Alaska Native, Asian or 

Pacic Islander, black or African American, and Hispanic. Data differ 

from doctoral degree data in other tables and gures in this report 

that are based on the National Science Foundation Survey of Earned 

Doctorates and that refer to research doctorates only. Greatest 

differences are in psychology and medical/other health sciences. 

S&E excludes other health sciences. 

SOURCES: National Center for Education Statistics, Integrated 

Postsecondary Education Data System, Completions Survey; and 

National Science Foundation, National Center for Science and 

Engineering Statistics, Integrated Science and Engineering 

Resources Data System (WebCASPAR), http://webcaspar.nsf.gov. 

See appendix tables 2-31 and 2-33.

Science and Engineering Indicators 2014

2000 2003 2005 2007 2009 2011

0

2

4

6

8

10

12

14

16

U.S. citizen/permanent resident 

white female

U.S. citizen/permanent

resident white male

Temporary resident

U.S. citizen/permanent resident minority

U.S. citizen/permanent resident other/unknown race/ethnicity

Table 2-13

Foreign recipients of U.S. S&E doctorates, by 

country/economy of origin: 1991–2011

Country/economy Number Percent

All foreign recipients................ 235,582 100.0

Top 10 total.......................... 160,082 68.0

China................................ 63,341 26.9

India.................................. 27,787 11.8

South Korea ..................... 22,400 9.5

Taiwan .............................. 16,997 7.2

Canada............................. 7,511 3.2

Turkey............................... 6,138 2.6

Thailand............................ 4,232 1.8

Germany........................... 3,985 1.7

Japan ............................... 3,974 1.7

Mexico.............................. 3,717 1.6

All others.............................. 75,500 32.0

NOTE: Foreign doctorate recipients include permanent and 

temporary residents. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2012) of the Survey 

of Earned Doctorates, 2011.

Science and Engineering Indicators 2014

in medical sciences, 7% in psychology, and between 11% 

and 38% in most social sciences (except economics) (appendix table 2-33).

Countries and Economies of Origin

The top 10 countries and economies of origin of foreign 

S&E doctorate recipients (both permanent and temporary 

residents) together accounted for 68% of all foreign recipients of U.S. S&E doctoral degrees from 1991 to 2011 (table 

2-13). Six out of those top 10 locations are in Asia. 

Asia. From 1991 to 2011, students from four Asian 

countries and economies (China, India, South Korea, and 

Taiwan, in descending order) earned more than half of U.S. 

S&E doctoral degrees awarded to foreign students (131,000 

of 236,000)—more than three times more than students from 

Europe (41,000). China accounted for almost half of these 

(63,000), followed by India (28,000), South Korea (22,000), 

and Taiwan (17,000). Most of these degrees were awarded 

in engineering, biological sciences, and physical sciences 

(table 2-14). About one in five of the doctorates awarded to 

South Korean and Taiwanese recipients in this period was in 

a non-S&E field.

The number of S&E doctorates earned by students from 

China declined in the late 1990s, increased through 2007, and 

dropped 16% in the following 3 years, but it rose 4% in 2011 

(figure 2-27). Over the 20-year period, however, despite these 

fluctuations, the number of S&E doctorates earned by Chinese 

nationals more than doubled. The number of S&E doctorates 

earned by students from India also declined in the late 1990s, 

but it has increased almost every year since 2002; over the last 

two decades it nearly tripled. South Korea followed a similar 

trend but with a less dramatic increase. The number of S&E 

doctoral degrees earned by South Korean students also dipped 

in the late 1990s and then rose in the mid-2000s. In contrast, 

Taiwan experienced a substantially different trajectory. In 

1991, its students earned more U.S. S&E doctoral degrees 

than those from India or South Korea.43 However, as universities in Taiwan increased their capacity for advanced S&E 

education in the 1990s, the number of students from Taiwan 

earning S&E doctorates from U.S. universities declined. Since 

2004, however, the number of Taiwanese doctorate recipients 

in the United States has been slowly going up again.

Europe. European students earned far fewer U.S. S&E 

doctorates than Asian students between 1991 and 2011, and 

they tended to focus less on engineering than did their Asian 

counterparts (tables 2-14 and 2-15). European countries 

whose students earned the largest number of U.S. S&E doctorates from 1991 to 2011 were Germany, Russia, the United 

Kingdom, Greece, Italy, Romania, and France, in that order. Trends in doctorate recipients from individual Western 

European countries vary (figure 2-28). The number of 

Central and Eastern European students earning S&E doctorates at U.S. universities quintupled between 1991 and 2011, 

to 553. Although their numbers almost reached the Western 

Europe total between 2005 and 2007, they have declined 
Science and Engineering Indicators 2014 ♦ 2-35

since then (figure 2-29). A higher proportion of doctorate 

recipients from Russia, Romania, and Greece than from the 

United Kingdom, France, Italy, and Germany earned their 

doctorates in S&E. Russian and Romanian doctorate recipients were more likely than those from Western European 

countries to earn their doctorates in mathematics and physical sciences, and Greeks were more likely to earn doctoral 

degrees in engineering (table 2-15).

The Americas. Despite the proximity of Canada and 

Mexico to the United States, the shares of U.S. S&E doctoral degrees awarded to residents of these countries were 

small compared with those awarded to students from Asia 

and Europe. The number of U.S. doctoral S&E degrees 

earned by students from Canada increased from about 320 

in 1991 to nearly 500 in 2009, but it has declined in the last 2 

years. The overall number of doctoral degree recipients from 

Table 2-14

Asian recipients of U.S. S&E doctorates, by field and country/economy of origin: 1991–2011

Field Asia China India South Korea Taiwan

All fields ....................................................................................... 174,538 68,104 30,985 28,898 21,307

S&E .......................................................................................... 150,963 63,341 27,787 22,400 16,997

Engineering .......................................................................... 54,831 20,823 12,144 8,779 7,294

Science ................................................................................ 96,132 42,518 15,643 13,621 9,703

Agricultural sciences ........................................................ 5,296 1,804 727 831 630

Biological sciences........................................................... 27,276 14,326 4,760 2,746 2,753

Computer sciences........................................................... 8,400 3,312 2,346 1,017 855

Earth, atmospheric, and ocean sciences ......................... 3,063 1,737 324 377 286

Mathematics..................................................................... 7,356 4,068 782 1,017 639

Medical/other health sciences.......................................... 5,226 1,342 1,232 654 936

Physical sciences ............................................................. 22,155 11,947 3,145 2,561 1,684

Psychology ....................................................................... 2,213 485 320 462 350

Social sciences................................................................. 15,147 3,497 2,007 3,956 1,570

Non-S&E.................................................................................. 23,575 4,763 3,198 6,498 4,310

NOTES: Data include permanent and temporary residents. Asia includes Afghanistan, Armenia, Azerbaijan, Bangladesh, Bhutan, Brunei, Burma/

Myanmar, Cambodia, China, Georgia, Hong Kong, India, Indonesia, Japan, Kazakhstan, Kyrgyzstan, Laos, Macau, Malaysia, Maldives, Mongolia, Nepal, 

Pakistan, Philippines, Singapore, South Korea, Spratly Islands, Sri Lanka, Taiwan, Tajikistan, Thailand, Turkmenistan, Uzbekistan, and Vietnam.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2012) of the Survey of Earned 

Doctorates, 2011.

Science and Engineering Indicators 2014

Figure 2-27

U.S. S&E doctoral degree recipients, by selected 

Asian country/economy of origin: 1991–2011

Number

NOTE: Degree recipients include permanent and temporary residents.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2012) of the Survey 

of Earned Doctorates, 2011.

Science and Engineering Indicators 2014

1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011

0

1,000

2,000

3,000

4,000

5,000

China

India

South Korea

Taiwan

Figure 2-28

U.S. S&E doctoral degree recipients, by selected 

Western European country: 1991–2011

Number

NOTE: Degree recipients include permanent and temporary residents. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2012) of the Survey of 

Earned Doctorates, 2011.

Science and Engineering Indicators 2014

1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011

0

50

100

150

200

250

300 Germany

United Kingdom

Greece

Italy

France
2-36 ♦ Chapter 2. Higher Education in Science and Engineering

Figure 2-29

U.S. S&E doctoral degree recipients from Europe, 

by region: 1991–2011

Number

NOTES: Degree recipients include permanent and temporary 

residents. Western Europe includes Andorra, Austria, Belgium, 

France, Germany, Greece, Ireland, Italy, Liechtenstein, Luxembourg, 

Malta, Monaco, Netherlands, Portugal, San Marino, Spain, 

Switzerland, and United Kingdom. Central and Eastern Europe 

includes Albania, Belarus, Bosnia-Herzegovina, Bulgaria, Croatia, 

Czech Republic, Estonia, Hungary, Kosovo, Latvia, Lithuania, 

Macedonia, Moldova, Montenegro, Poland, Romania, Russia, Serbia, 

Slovakia, Slovenia, and Ukraine. Scandinavia includes Denmark, 

Finland, Iceland, Norway, and Sweden.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2012) of the Survey of 

Earned Doctorates, 2011.

Science and Engineering Indicators 2014

1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011

0

200

400

600

800

1,000

Western Europe

Central and Eastern Europe

Scandinavia

Figure 2-30

U.S. S&E doctoral degree recipients from Canada, 

Mexico, and Brazil: 1991–2011

Number

NOTE: Degree recipients include permanent and temporary residents.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2012) of the Survey of 

Earned Doctorates, 2011. 

Science and Engineering Indicators 2014

1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011

0

100

200

300

400

500

600

Canada

Mexico

Brazil

Table 2-15

European recipients of U.S. S&E doctorates, by field and region/country of origin: 1991–2011

Field

All 

European 

countries Germany Russia

United 

Kingdom Greece Italy Romania France

All fields ...................................................... 40,529 5,442 3,505 4,035 2,821 2,963 2,414 2,767

S&E ......................................................... 31,479 3,986 3,115 2,669 2,479 2,245 2,144 2,056

Engineering ......................................... 6,142 610 464 281 888 465 346 646

Science ............................................... 25,337 3,376 2,651 2,388 1,591 1,780 1,798 1,410

Agricultural sciences ....................... 827 113 27 68 61 60 23 62

Biological sciences.......................... 4,912 700 445 548 264 229 264 297

Computer sciences.......................... 1,803 238 141 66 249 93 263 78

Earth, atmospheric, and ocean 

sciences ....................................... 1,154 189 113 153 40 90 43 91

Mathematics.................................... 3,015 300 407 178 168 217 398 83

Medical/other health sciences......... 752 105 16 128 62 33 21 36

Physical sciences ............................ 6,190 749 1,088 462 375 384 554 412

Psychology ...................................... 1,174 228 54 216 56 66 37 37

Social sciences................................ 5,510 754 360 569 316 608 195 314

Non-S&E................................................. 9,050 1,456 390 1,366 342 718 270 711

NOTE: Data include permanent and temporary residents. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2012) of the Survey of Earned 

Doctorates, 2011.

Science and Engineering Indicators 2014

Mexico and Brazil peaked earlier (2003 and 1996, respectively) and declined in recent years (figure 2-30). 

A higher proportion of Mexican and Brazilian students 

earned U.S. doctorates in S&E fields than the comparable 

proportion for Canadians (table 2-16). In particular, higher 

proportions of Mexican and Brazilian students than Canadian 

students received U.S. doctoral degrees in engineering and 

agricultural sciences.
Science and Engineering Indicators 2014 ♦ 2-37

The Middle East. Between 1991 and 2011, Middle 

Eastern students earned far fewer U.S. S&E doctorates 

(about 20,000) than did students from Asia, Europe, or 

the Americas (tables 2-14, 2-15, and 2-16). Students from 

Turkey earned the largest number of U.S. S&E doctorates 

in this region, followed by those from Iran and Jordan. A 

larger proportion of doctorate recipients from Iran earned 

their doctorates in engineering (55%) than recipients from 

any other country above. More than one-third of doctorate recipients from Turkey and Jordan earned their doctorates in engineering, a proportion similar to that from Asian 

countries.

International S&E Higher Education

In the 1990s, many countries expanded their higher education systems and increased access to higher education. At 

the same time, flows of students worldwide increased. More 

recently, a number of countries have adopted policies to encourage the return of students who studied abroad, to attract 

foreign students, or both. As the world becomes more interconnected, students who enroll in tertiary (post-high school) 

institutions outside their own countries have opportunities 

to expand their knowledge of other societies and languages 

and improve their employability in globalized labor markets.

Higher Education Expenditures

Increasingly, governments around the world have come 

to regard movement toward a knowledge-based economy 

as key to economic progress. Realizing that this requires 

a well-trained workforce, they have invested in upgrading 

and expanding their higher education systems and broadening participation in them. In most instances, government 

spending underwrites these initiatives. Recent investments 

by several governments to send large numbers of their 

students to study abroad are a strategy for workforce and 

economic development. Examples include the Brazilian 

Scientific Mobility Program (also known as “Science without Borders”), launched officially in July 2011, whose goal 

is to enable 75,000 Brazilian students to study in foreign 

countries (Knobel 2012). Similarly, the government of 

Saudi Arabia has invested considerably in a scholarship program launched in 2005 that has supported study abroad for 

more than 100,000 Saudi students throughout the world, at 

an estimated cost of at least $5 billion since the program’s 

inception (Knickmeyer 2012).

One indicator of the importance of higher education is the 

percentage of a nation’s resources devoted to it, as measured 

by the ratio of expenditures on tertiary education to gross domestic product (GDP). Between 2005 and 2009, this indicator declined for the United States and Canada, even though 

Table 2-16

North American, South American, and Middle Eastern recipients of U.S. S&E doctorates, by field and region/

country of origin: 1991–2011

North and South Americaa Middle Eastb

Field

All 

countries Canada Mexico Brazil

All 

countries Turkey Iran Jordan

All fields .................................................... 28,759 11,329 4,458 3,904 19,660 7,257 3,290 2,012

S&E ....................................................... 21,634 7,511 3,717 3,168 16,169 6,138 3,079 1,737

Engineering ....................................... 4,331 1,098 866 750 7,118 2,728 1,800 783

Science ............................................. 17,303 6,413 2,851 2,418 9,051 3,410 1,279 954

Agricultural sciences ..................... 2,037 263 578 422 627 256 68 84

Biological sciences........................ 4,114 1,614 593 543 1,603 511 300 173

Computer sciences........................ 733 253 112 174 924 348 120 91

Earth/atmospheric/ocean sciences... 780 254 140 133 264 109 29 14

Mathematics.................................. 1,058 342 204 164 741 299 138 83

Medical/other health sciences....... 1,030 556 98 172 563 55 66 148

Physical sciences .......................... 2,058 903 315 162 1,482 592 322 187

Psychology .................................... 1,270 897 84 80 442 123 38 8

Social sciences.............................. 4,223 1,331 727 568 2,405 1,117 198 166

Non-S&E............................................... 7,125 3,818 741 736 3,491 1,119 211 275

a

 North America includes Bermuda, Canada, and Mexico; South America includes Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Guyana, Paraguay, 

Peru, Suriname, Uruguay, and Venezuela.

b Middle East includes Bahrain, Gaza Strip, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Syria, Turkey, United Arab Emirates, 

West Bank, and Yemen.

NOTE: Data include permanent and temporary residents.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2012) of the Survey of Earned 

Doctorates, 2011.

Science and Engineering Indicators 2014
2-38 ♦ Chapter 2. Higher Education in Science and Engineering

of all OECD countries, these countries and South Korea 

spent the highest percentage of GDP on higher education. 

Between 1995 and 2005, U.S. expenditures on tertiary education as a percentage of GDP were about double the OECD 

average; by 2009, this proportion had decreased to about 

60% above the OECD average. Between 2005 and 2009, 

expenditures on tertiary education as a percentage of GDP 

rose in most other OECD countries; they remained stable in 

the United Kingdom (appendix table 2-34). As a result of the 

global recession and fiscal crisis, some European governments have cut investments on higher education. The effects 

of these cuts are not yet evident in the most recent data. 

Higher education funding data are not always comparable 

across different nations. They can vary between countries 

for reasons unrelated to actual expenditures, such as differences in measurement, prevalence of public versus private 

institutions,44 types and levels of government funding included, and types and levels of education included. 

Educational Attainment

Higher education in the United States expanded greatly 

after World War II. As a result, the U.S. population led 

the world in educational attainment for several decades. 

Because of this, the United States offered clear advantages 

for firms whose work would benefit from the availability of 

a highly educated workforce. In the 1990s, however, many 

countries in Europe and Asia began to expand their higher 

education systems. Some of them have now surpassed the 

United States in the attainment of degrees from tertiary-type 

A (see “Glossary”) and advanced research programs in their 

younger cohorts. Over time, the expansion of higher education elsewhere has substantially diminished the U.S. educational advantage and its related economic advantages.

Although the United States continues to be among those 

countries with the highest percentage of the population ages 

25–64 with a bachelor’s degree or higher, several other 

countries have surpassed the United States in the percentage 

of the younger population (ages 25–34) with a bachelor’s 

degree or higher (figure 2-31; appendix table 2-35).

 China has lower tertiary education attainment than all 

OECD countries. China’s tertiary attainment rates are also 

lower than those of Brazil and Russia, the two non-OECD, 

G20 countries for which data are available. As in most OECD 

countries, attainment among the younger population (ages 

25–34) in China is higher than in the older population.45

First University Degrees in S&E Fields

Almost 17 million students worldwide earned first university degrees in 2010, with about 5.5 million of these in S&E 

fields (appendix table 2-36). These worldwide totals include 

only countries for which relatively recent data are available 

(primarily countries in Asia, Europe, and the Americas) and 

are therefore underestimates. Asian universities accounted 

for nearly 2.5 million of the world’s S&E first university degrees in 2010, close to half of them in engineering. Students 

Figure 2-31

Attainment of tertiary-type A and advanced research 

programs, by country and age group: 2010

OECD = Organisation for Economic Co-operation and Development.

NOTES: International Standard Classication of Education (ISCED) 

tertiary-type A programs, ISCED 5A, are largely theory-based and 

designed to provide sufcient qualications for entry to advanced 

research programs and professions with high skill requirements such 

as medicine, dentistry, or architecture and have a minimum duration 

of 3 years’ full-time equivalent, although they typically last 4 years or 

longer. In the United States, they correspond to bachelor’s and 

master’s degrees. Advanced research programs are tertiary 

programs leading directly to award of an advanced research 

qualication (e.g., doctorate).

SOURCE: OECD, Education at a Glance 2012: OECD Indicators

(2012). 

Science and Engineering Indicators 2014

China

Brazil

Austria

Turkey

Slovenia

Italy

Portugal

Mexico

Germany

Greece

Belgium

Czech Republic

Slovakia

Chile 

France

Hungary

Russian Federation (2002)

Luxembourg

Spain

Estonia

OECD average

Ireland

Finland

Poland

New Zealand

Switzerland

Japan

Sweden

Canada

Denmark

Australia 

United Kingdom

South Korea

Iceland

Netherlands

Israel

United States

Norway

0 10 20

Percent

30 40 50

25–64 years old

25–34 years old
Science and Engineering Indicators 2014 ♦ 2-39

across Europe (including Eastern Europe and Russia) earned 

more than 1.5 million first university S&E degrees (nearly 

40% of them in engineering), and students in North America 

earned more than 700,000 such degrees in 2010 (22% 

in engineering).

In several countries and economies around the world, 

the proportion of first university degrees in S&E fields was 

higher than in the United States. Half or more of all first 

university degrees in Japan and China were in S&E fields, 

compared with about one-third in the United States. National 

differences in engineering degrees largely account for overall differences in the proportion of S&E degrees, given that 

the disparity was especially large in engineering. However, 

differences in the taxonomies and quality of engineering programs and level of reporting detail across countries 

make comparisons problematic. For example, according to 

Wadhwa et al. (2007), in China in the mid-2000s, the term 

“engineer” had no standard definition and did not translate 

well into different dialects, so the reports sent to the Ministry 

of Education from different Chinese provinces did not count 

degrees consistently. In the late 1990s, the Chinese government implemented top-down policy changes to increase 

enrollment in engineering. However, the total number of 

technical schools and the corresponding teachers and staff 

declined, which meant that degree awards were achieved by 

increasing class sizes and student-to-teacher ratios. 

China has traditionally awarded a large proportion of its 

first university degrees in engineering, although the percentage declined from 43% in 2000 to 31% in 2011 (appendix 

table 2-37). Other places with a high proportion of engineering degrees are Singapore, Iran, South Korea, and Taiwan 

(appendix table 2-36). In the United States, about 5% of 

all bachelor’s degrees are in engineering. About 11% of all 

bachelor’s degrees awarded in the United States and worldwide are in natural sciences (physical, biological, computer, 

and agricultural sciences, as well as mathematics). 

The number of S&E first university degrees awarded in 

China, Taiwan, Turkey, Germany, and Poland more than 

doubled or nearly doubled between 2000 and 2010. During 

this period, S&E first university degrees awarded in the 

United States and several other countries (i.e., Australia, 

Italy, the United Kingdom, Canada, and South Korea) increased between 23% and 56%, whereas those awarded in 

France, Japan, and Spain declined by 14%, 9%, and 4%, respectively (appendix table 2-37). 

Natural sciences and engineering degrees account for 

most of the increase in S&E first university degrees in 

China. The number of natural sciences and engineering first 

university degrees in China grew by more than 300% between 2000 and 2010 (figure 2-32). The number awarded in 

Germany grew by nearly 90%, and the number awarded in 

South Korea, the United States, and the United Kingdom increased between 20% and 29%; in Japan, it declined by 9%. 

In 1999, 29 European countries, through the Bologna 

Declaration, initiated a system of reforms in higher education throughout Europe. The goal of the Bologna Process 

was to harmonize certain aspects of higher education within 

participating countries so that degrees were comparable; 

credits were transferable; and students, teachers, and researchers could move freely from institution to institution 

across national borders. Ten years later, the European Higher 

Education Area was launched, and higher education reform 

in Europe was extended to 47 participating countries. In recent years, countries have made considerable changes: they 

have modified higher education structures by implementing 

three degree cycles (bachelor’s, master’s, and doctorate), developed quality assurance systems, and established mechanisms to facilitate mobility (EACEA 2012). In 2009, for the 

first time, the Bologna Process established a quantitative 

target for student mobility. By 2020, at least 20% of those 

graduating in the Area should have spent time studying 

abroad. For details on student mobility in Europe, see sidebar, “Mapping Mobility in European Higher Education.” 

Figure 2-32

First university natural sciences and engineering 

degrees, by selected country: 2000–10

Thousands

NOTE: Natural sciences include agricultural sciences; biological 

sciences; computer sciences; earth, atmospheric, and ocean 

sciences; and mathematics. 

SOURCES: China—National Bureau of Statistics of China, China 

Statistical Yearbook, annual series (Beijing) various years; Germany 

and South Korea—Organisation for Economic Co-operation and 

Development, Education Online Database, http://stats.oecd.org/ 

Index.aspx; Japan—Government of Japan, Ministry of Education, 

Culture, Sports,Science and Technology, Higher Education Bureau, 

Monbusho Survey of Education; United Kingdom—Higher Education 

Statistics Agency; and United States—National Center for Education 

Statistics, Integrated Postsecondary Education Data System, 

Completions Survey; and National Science Foundation, National 

Center for Science and Engineering Statistics, WebCASPAR 

database, http://webcaspar.nsf.gov. 

Science and Engineering Indicators 2014

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

0

200

400

600

800

1,000

1,200

China

United States

Japan

South Korea

United Kingdom

Germany
2-40 ♦ Chapter 2. Higher Education in Science and Engineering

S&E First University Degrees by Sex

Women earned half or more of first university degrees in 

S&E in many countries around the world in 2010, including 

the United States and a number of smaller countries. Most 

large countries in Europe are not far behind, with more than 

40% of first university S&E degrees earned by women. In 

the Middle East, women earned nearly half or more of the 

S&E first university degrees in most countries in the region, 

except for Iraq, Turkey, Iran, and Jordan. In several Asian 

and African countries, women generally earn about onethird or fewer of the first university degrees awarded in S&E 

fields (appendix table 2-38). 

In Canada, Japan, the United States, and many smaller countries, more than half of the S&E first university degrees earned 

by women were in the social and behavioral sciences. In South 

Korea and Singapore, nearly half of the S&E first university degrees earned by women were in engineering, a much higher proportion than in the United States or in any countries in Europe.

A 2011 study produced for the European Commission’s 

Directorate-General for Education and Culture examines 

degree mobility and credit mobility into, out of, and 

between 32 European countries (the European Union 

[EU]-27, European Free Trade Association [EFTA]-4, 

and Turkey, also called the “Europe 32 [EU 32] area”) 

(Teichler et al. 2011). 

The report distinguishes between two types of student 

mobility. Degree or diploma mobility includes students 

who travel abroad to obtain a degree, whereas credit 

mobility refers to students who study abroad on a more 

temporary basis. Data for degree mobility come from 

United Nations Educational, Scientific and Cultural 

Organization, Organisation for Economic Co-operation 

and Development, and Eurostat data. For credit mobility, however, there is no comprehensive data set, so the 

study examines data on participation in ERASMUS, an 

EU study-abroad program that enables students at higher education institutions in Europe to study in another 

participating country for a period between 3 months and 

1 year.*

 Although ERASMUS is one of the largest programs of its kind in this region, it supports only a portion of total credit mobility in Europe, so its figures are 

an underestimation. 

Average degree mobility levels in the EU 32 region 

are high by global standards and increased considerably 

between 1998–99 and 2006–07. In 2006–07, 1.5 million 

foreign students, representing 51% of the global student 

market, were enrolled in a degree program in the EU 32. 

In addition, despite growing competition worldwide, EU 

32 countries have increased their global share of foreign 

students since 1998–99. The strong growth in foreign enrollment was fueled primarily by students from non-EU 

32 nations. These students accounted for 58% of all foreign students in 2006–07, compared with 38% of nationals from EU 32 countries (in the case of 4% of foreign 

students, the nationality was unknown). 

Degree mobility levels differed considerably across 

countries. Almost two-thirds of all foreign students pursuing a degree in the EU 32 zone were enrolled in one 

of three countries: the United Kingdom, Germany, and 

France. In all other countries of the EU 32, regional mobility levels are considerably lower. The proportion of 

EU 32 students in a degree program in a foreign country 

grew by nearly 40% between 1998–99 and 2006–07, but 

growth was considerably lower than that of foreign nationals studying in the EU 32 zone.

Large differences exist between countries. At one extreme, in Cyprus, the majority of citizens are enrolled 

abroad (1,380 abroad for every 1,000 at home); at the 

other, in the United Kingdom, studying in a foreign 

country is rare (12 abroad for every 1,000 in the United 

Kingdom). The vast majority of students from the EU 32 

who are pursuing a degree in another country choose a 

country in the same region.

With regard to credit mobility, according to 

ERASMUS statistics, the number of students embarking in a study-abroad program more than doubled in the 

11-year period between 1998–99 and 2008–09, to nearly 

200,000. Despite this growth, the number of students 

participating in ERASMUS represents a very small share 

(less than 1%) of EU 32 students.

Spain, Finland, Malta, Poland, Portugal, and Slovakia 

are more attractive as study-abroad destinations than for 

degree-type studies. Compared to the other EU 32 countries in 2006–07, these countries hosted more ERASMUS 

students than foreign degree students. Although the 

United Kingdom has a large number of college students, 

it has one of the lowest numbers of study-abroad students. 

In the case of both degree and credit mobility, in 2008–

09, 21 out of the 32 countries were either net exporters or 

net importers. Eastern European countries tended to be 

net exporters (with the exception of the Czech Republic 

and Hungary), and countries from Western and Northern 

Europe tended to be net importers. Ten countries were net 

importers of degree-seeking students but net exporters of 

study-abroad students. These countries include Germany, 

France, the Czech Republic, and Hungary.

Students in the social sciences, business, and law; engineering; and humanities and arts more often embarked 

on ERASMUS study-abroad programs than students 

in mathematics, computing, sciences, agriculture, and 

teacher training and education science.†

* ERASMUS also provides opportunities for student placements in 

enterprises and for university staff teaching and training, and it also funds 

cooperation projects between higher education institutions across Europe.

† The data do not allow comparisons by degree level.

Mapping Mobility in European Higher Education
Science and Engineering Indicators 2014 ♦ 2-41

Global Comparison of S&E Doctoral Degrees

More than 200,000 S&E doctoral degrees were earned 

worldwide in 2010.46 The United States awarded the largest number of S&E doctoral degrees of any country (about 

33,000), followed by China (about 31,000), Russia (almost 

16,000), Germany (about 12,000), and the United Kingdom 

(about 11,000) (appendix table 2-39). About 58,000 S&E 

doctoral degrees were earned in the European Union (EU; 

see “Glossary” for member countries).

Women earned 41% of S&E doctoral degrees awarded in 

the United States in 2010, about the same percentage earned 

by women in Australia, Canada, the EU, and Mexico (appendix table 2-40). In the United States, women earned nearly 

half of the S&E doctoral degrees awarded to U.S. citizens and 

permanent residents in 2010 (appendix table 2-31). Women 

earned close to half of S&E doctoral degrees in Portugal and 

Italy but less than one-quarter of those in the Netherlands, 

South Korea, and Taiwan (appendix table 2-40). 

The number of S&E doctoral degrees awarded in China 

rose steeply between 2000 and 2009 and leveled off in 2010. 

Although the rise was steeper in China, the trend was similar 

to the recent trend in doctoral production in the United States 

(appendix tables 2-41 and 2-42). 

In 2007, China surpassed the United States as the world’s 

largest producer of natural sciences and engineering doctoral degrees (figure 2-33). In the United States, as well as in 

France, Germany, Italy, Spain, Switzerland, and the United 

Kingdom, the largest numbers of S&E doctoral degrees 

were awarded in the physical and biological sciences (appendix table 2-41).

In Asia, China has been the largest producer of S&E doctoral degrees since 2000 (appendix table 2-42). As China’s 

capacity for advanced S&E education increased, the number of S&E doctorates awarded rose from about 4,000 in 

1996 to more than 31,000 in 2010, a substantially faster rate 

of growth than that of the number of doctorates earned by 

Chinese citizens in the United States during the same period 

(figure 2-34). In the mid-1990s the number of “homegrown” 

Chinese doctorate recipients and the number of doctorate 

recipients of Chinese origin with U.S. degrees were very 

similar, but since then the gap has grown considerably because of the large increase of doctorates awarded in China. 

In 2007, the Chinese Ministry of Education announced that 

China would begin to limit admissions to doctoral programs 

and would focus more on quality of graduates (Mooney 

2007). The number of S&E doctorates awarded in India, 

South Korea, and Taiwan also increased from 1996 to 2010, 

but at a lower rate; in Japan the numbers rose consistently 

through 2006 but declined in the following years. In China, 

Japan, South Korea, and Taiwan, more than half of S&E 

doctorates were awarded in engineering. In India, close to 

three-quarters of the S&E doctorates were awarded in the 

physical and biological sciences (appendix table 2-42).

Figure 2-33

Natural sciences and engineering doctoral 

degrees, by selected country: 2001–10

Thousands

NOTES: Natural sciences and engineering include biological, physical, 

earth, atmospheric, ocean, and agricultural sciences; computer sciences; 

mathematics; and engineering. Data for India are not available for 

2007–10; data for Japan are not available for 2010.

SOURCES: China—National Bureau of Statistics of China; India— 

Department of Science and Technology; Japan—Government of Japan, 

Ministry of Education, Culture, Sports, Science and Technology, Higher 

Education Bureau, Monbusho Survey of Education; Russia—Institute for 

Statistical Studies and Economics of Knowledge/National Research 

University, Higher School of Economics; Germany and South Korea— 

Organisation for Economic Co-operation and Development, Education 

Online database, http://www.oecd.org/education/database/; United 

Kingdom—Higher Education Statistics Agency; and Organisation for 

Economic Co-operation and Development, Education Online database, 

http://stats.oecd. org/Index.aspx; United States—National Center for 

Education Statistics, Integrated Postsecondary Education Data System, 

Completions Survey; and National Science Foundation, National Center 

for Science and Engineering Statistics, Integrated Science and 

Engineering Resources Data System (WebCASPAR), 

http://webcaspar.nsf.gov.

Science and Engineering Indicators 2014

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

0

10

20

30

40

United States

Germany

Russia

China

United Kingdom

Japan

South Korea

India

Figure 2-34

S&E doctoral degrees earned by Chinese students at 

home universities and U.S. universities: 1994–2010

Thousands

NOTE: Degree recipients in the United States include permanent and 

temporary residents. 

SOURCES: China—National Research Center for Science and 

Technology for Development and Education Statistics Yearbook of 

China (various years); United States—National Science Foundation, 

National Center for Science and Engineering Statistics, special 

tabulations (2012) of the Survey of Earned Doctorates, 2011.

Science and Engineering Indicators 2014

1994 1996 1998 2000 2002 2004 2006 2008 2010

0

5

10

15

20

25

30

35

S&E doctorates

earned in China S&E doctorates earned 

by Chinese recipients 

in the United States
2-42 ♦ Chapter 2. Higher Education in Science and Engineering

Global Student Mobility 

Students have become more internationally mobile in the 

past two decades, and countries are increasingly competing 

for them. According to data from UNESCO, the number 

of internationally mobile students nearly doubled between 

2000 and 2010, to 3.6 million (UNESCO 2011).47 In general, students migrate from developing countries to the more 

developed countries and from Europe and Asia to the United 

States. However, a few countries have emerged as regional 

hubs in their geographic regions—for example, Australia, 

China, and South Korea for East Asia and South Africa for 

sub-Saharan Africa (UNESCO 2009). In addition, several 

countries have set targets for increasing the numbers of international students they host; among these are Jordan (which 

plans to host 100,000 students by 2020), Singapore (150,000 

by 2015), Japan (300,000 by 2025), and China (500,000 by 

2020) (Bhandari and Belyavina 2012).

Some students migrate temporarily for education, whereas others remain abroad permanently after completing their 

studies. Some factors influencing the decision to seek a degree abroad include the policies of the countries of origin 

regarding sponsoring their citizens’ study abroad, the tuition 

fee policies of the countries of destination, the financial support the countries of destination offer to international students, the cost of living and exchange rates that affect the 

cost of international education, and the perceived value of 

obtaining a foreign credential. The long-term return from 

international education also depends on how international 

degrees are recognized by the labor market in the country of 

origin (OECD 2010). For host countries, enrolling international students can help raise revenues from higher education and can also be part of a larger strategy to attract highly 

skilled workers, in particular as demographic changes in 

many developed countries cause their own populations of 

college-age students to decrease (OECD 2012) (appendix 

table 2-43).48

In recent years, many countries have expanded their provision of transnational education. One growing trend is the 

establishment of branch campuses: offshore programs established by higher education institutions in foreign countries. 

Branch campuses give students the opportunity to earn degrees from foreign universities without leaving their home 

countries. According to research by the Observatory on 

Borderless Higher Education, by the end of 2011, 200 degree-awarding international branch campuses were operating 

worldwide, and 37 new ones were planning to open in 2012 

and 2013 (Lawton and Katsomitros 2012). Collaborative 

programs, such as international joint and dual-degree programs, are another trend in transnational education. In these 

programs, students study at two or more institutions; after 

successfully completing the requirements, they receive a 

separate diploma from each institution in dual-degree programs or a single diploma representing both institutions in 

joint degree programs (CGS 2010). The most common fields 

for dual degrees at the master’s level are business, engineering, and the social sciences; at the doctoral level, engineering 

and physical sciences predominate (for additional details, see 

sidebar, “Transnational Higher Education,” in NSB 2012). 

More internationally mobile students (both undergraduate and graduate) go to the United States than to any other 

country (figure 2-35). Other top destinations for international students include the United Kingdom (11%), Australia 

(7%), France (7%), and Germany (6%). Together with the 

United States, these countries receive about half of all internationally mobile students worldwide.

Although the United States remains the destination 

for the largest number of internationally mobile students 

Figure 2-35

Internationally mobile students enrolled in tertiary 

education, by selected country: 2010

NOTES: Data are based on the number of students who have 

crossed a national border and moved to another country with the 

objective of studying (i.e., mobile students). Data for Canada and the 

Russian Federation correspond to 2009. Data for the Netherlands 

and Germany exclude advanced research programs (e.g., doctorate). 

Data for Spain exclude advanced research programs and 

tertiary-type B programs (e.g., associate’s).

SOURCE: UNESCO Institute for Statistics, special tabulations (2013).

Science and Engineering Indicators 2014

Thousands

Slovakia

Chile

Portugal

Romania

Finland

Brazil

Hungary

Norway

Denmark

Poland

Turkey

Netherlands

Sweden

Czech Republic

Belgium

New Zealand

Switzerland

Spain

South Korea

Austria

Italy

China

Canada

Russian Federation

Japan

Germany

France

Australia

United Kingdom

United States

0 200 400 600 800
Science and Engineering Indicators 2014 ♦ 2-43

worldwide, its share in all fields has declined from 25% in 

2000 to 19% in 2010 (UNESCO 2011). Between 2005 and 

2010, the U.S. share in the natural sciences and engineering declined as well, but an increase in international students 

coming to the United States to study social and behavioral 

sciences kept the overall S&E share stable (table 2-17).

In the United States, international students are a small 

proportion (about 3%) of students enrolled in higher education (including both undergraduate and graduate levels); 

this proportion is higher at the graduate level. In other countries, the proportion of international students is much higher. 

Australia, with a much smaller higher education system than 

the United States, has a higher percentage (21%) of international students but a lower share (7%) of international 

students worldwide. Other countries with relatively high 

percentages of international higher education students in 

their higher education systems include the United Kingdom 

(16%), Austria (15%), Switzerland (15%), and New Zealand 

(14%).49 In Switzerland and the United Kingdom, more 

than 4 out of 10 doctoral students are international students. A number of other countries, including New Zealand, 

Australia, the United States, Ireland, Sweden, and Canada, 

have relatively high percentages (more than 20%) of doctoral students who are internationally mobile (OECD 2012).

Since the late 1990s, the United Kingdom has been actively working to improve its position in international education, both by recruiting foreign students to study in the 

country and by expanding its provision of transnational education (British Council 2013; UK Council for International 

Student Affairs 2013). Between 1994 and 2010, foreign 

student enrollment in S&E fields in the United Kingdom 

increased, especially at the graduate level, with increasing 

flows of students from China and India (appendix table 

2-44). The overall pattern of top countries is similar to that of 

the United States. In 2010, foreign students made up 48% of 

all graduate students studying S&E in the United Kingdom 

(an increase from 29% in the mid-1990s). Foreign students 

accounted for 60% of graduate students in mathematics and 

computer sciences, as well as in engineering. Students from 

China and India accounted for most of the increase, but the 

number of graduate students from Nigeria, Pakistan, the 

United States, France, Ireland, and Germany also increased 

considerably. At the undergraduate level, the overall percentage of foreign students in S&E did not increase as much 

during this period. As a result of recent stricter student visa 

regulations that apply to those from non-EU countries, in the 

last year, foreign enrollment declined at the graduate level, 

mainly due to a decline in the number of students from India 

and Pakistan. The declines were larger in mathematics and 

computer sciences and in engineering (appendix table 2-44). 

Japan has increased its enrollment of foreign students 

in recent years (both in S&E and in all fields) and in 2008 

announced plans to triple foreign enrollment in 12 years 

(McNeil 2008, 2010). Nonetheless, growth has slowed 

considerably in the last 2 years (appendix table 2-45; appendix table 2-41 in NSB 2012), perhaps caused in part by 

the March 2011 earthquake and tsunami (McNeil 2012). In 

2012, slightly more than 70,000 foreign students were enrolled in S&E programs in Japanese universities, similar 

to 2010, and up from 57,000 in 2004. Unlike in the United 

Kingdom, foreign S&E student enrollment in Japan is concentrated at the undergraduate level, accounting for more 

than two-thirds of all foreign S&E students. Foreign nationals accounted for 3% of undergraduate and 17% of graduate 

Table 2-17

Internationally mobile students in selected OECD countries and the United States: 2005 and 2010

S&E

All fields Total

Natural sciences 

and engineering

Social and 

behavioral sciences

Host location 2005 2010 2005 2010 2005 2010 2005 2010

Worldwide ................................ 2,814,917 3,645,622 NA NA NA NA NA NA

OECD countriesa ................... 2,128,579 2,574,710 800,052 995,295 598,416 746,718 201,635 248,577 

United States..................... 590,158 684,807 242,358 301,447 202,516 245,937 39,842 55,510 

United States/OECD (%)a...... 27.7 26.6 30.3 30.3 33.8 32.9 19.8 22.3 

United States/

worldwide (%)...................... 21.0 18.8 NA NA NA NA NA NA

NA = not available.

OECD = Organisation for Economic Co-operation and Development.

a

 OECD countries include those where data on internationally mobile students by field of study were available: Austria, Australia, Belgium, Canada, Czech 

Republic, Denmark, Finland, France, Germany, Hungary, Italy, Japan, Netherlands, New Zealand, Norway, Poland, Portugal, Spain, Sweden, Switzerland, 

Turkey, United Kingdom, and United States.

NOTE: Internationally mobile students are students who have crossed a national border and moved to another country with the objective of studying 

(i.e., mobile students).

SOURCE: UNESCO Institute for Statistics, special tabulations (2013).

Science and Engineering Indicators 2014
2-44 ♦ Chapter 2. Higher Education in Science and Engineering

S&E students in Japan. The vast majority of the foreign 

students were from Asian countries. In 2012, Chinese students accounted for 70% of the foreign S&E undergraduate 

students and 59% of the foreign S&E graduate students in 

Japan. South Koreans were 18% of the foreign undergraduates and 9% of the foreign graduate students. Indonesia, 

Vietnam, Malaysia, Thailand, Mongolia, and Nepal were 

among the top 10 countries of origin for both undergraduates and graduate students (appendix table 2-45).

Foreign students constitute an increasing share of enrollment in Canadian universities at the undergraduate level. In 

2010, foreign S&E students accounted for about 7% of undergraduate S&E enrollment in Canada, up from 5% in 2000. 

At the graduate level, the proportion of foreign students in 

S&E fields was stable during that period (19%). In 2010, at 

both the undergraduate and graduate levels, the highest percentages of foreign S&E students were in mathematics and 

computer sciences and in engineering. At the undergraduate 

level, China was the top country of origin of foreign S&E 

students in Canada, accounting for 13% of foreign undergraduate students, followed by France and the United States 

(11% each). At the graduate level, the top country of origin 

of foreign S&E students was France (13%), followed by Iran 

and China (11% each) (appendix table 2-46). 

Although the United States hosts the largest number of 

international students worldwide, U.S. students constitute a 

relatively small share of foreign students worldwide. About 

57,000 U.S. students (in all fields) were reported as foreign 

students by OECD and OECD-partner countries in 2010, far 

fewer than the number of foreign students from China, India, 

South Korea, Germany, Turkey, or France. The main destinations of U.S. students were the United Kingdom (15,600), 

Canada (9,100), Germany (3,900), France (3,400), New 

Zealand (3,200), and Australia (3,000)—mostly Englishspeaking OECD countries (OECD 2012).

Nearly 275,000 U.S. university students enrolled in 

study-abroad programs in the 2010–11 academic year (credit 

mobility), a 1% increase from the preceding year but a 78% 

rise from 2000–01 (IIE 2012). Nearly 40% were enrolled in 

programs during the summer term; more than one-third enrolled in programs lasting one semester, 13% in short-term 

programs lasting up to 8 weeks, 4% for the academic or the 

calendar year, and the rest for one or two quarters or a month. 

About 9% were master’s and 1% were doctoral students; 

the rest were undergraduates, primarily juniors or seniors. 

Nearly two-thirds of the U.S. students studying abroad were 

women and more than three-quarters were white. More than 

one-third were studying in S&E fields: 23% in social sciences, 8% in physical or life sciences, 4% in engineering, 2% 

in mathematics or computer sciences, and 1% in agricultural 

sciences; these proportions have been stable since 2000–01. 

The leading destinations for study-abroad programs in the 

2010–11 academic year were the United Kingdom, Italy, 

and Spain, followed by France and China.

According to a recent study conducted by IIE and Project 

Atlas, more than 43,000 U.S. students are enrolled in 

academic degree programs in the 13 countries represented 

(degree mobility). Most students were enrolled in master’s 

degree programs (44%), followed by students in bachelor’s 

degree programs (39%) and doctoral programs (19%). 

Almost three-quarters of them studied in Anglophone 

countries; the top destination was the United Kingdom. 

Humanities, social sciences, and business and management 

were the most popular broad fields of study for students pursuing a degree abroad (Belyavina and Bhandari 2012).

Conclusion

S&E higher education in the United States is attracting 

growing numbers of students. The number of bachelor’s and 

master’s degrees awarded in all fields and in S&E fields continues to rise, having reached new peaks in 2011. Most of the 

growth in undergraduate S&E education occurred in science 

fields, in particular in the social and behavioral sciences and 

in the biological sciences. In engineering, bachelor’s degrees have increased consistently for the last 10 years but 

have not yet reached the record high levels attained in the 

1980s. After a steep decline between 2004 and 2007, computer sciences degree awards began to rebound. The number 

of master’s and doctoral degrees awarded grew in all major 

S&E fields. In the last decade, growth in doctoral degrees 

awarded occurred mostly in the natural sciences and engineering fields. 

Over the last two decades, higher education spending 

and revenue patterns and trends have undergone substantial changes, which intensified during the recent economic 

downturn. Public institutions faced competing demands in 

a tight budget environment, caught between declining state 

appropriations and the need to maintain educational quality 

and access. Community colleges, which serve diverse groups 

of students and play a key role in increasing access to higher 

education, were particularly affected, as the number of students seeking an affordable college education increased.

Foreign student enrollment in S&E has recovered since 

the decline post-9/11. In recent years, foreign student enrollment has increased considerably at the undergraduate and 

graduate levels, both in S&E and non-S&E fields.

Globalization of higher education continues to expand. 

Universities in several other countries have expanded their 

enrollment of foreign S&E students. The United States continues to attract the largest number and fraction of internationally mobile students worldwide, although its share of 

foreign students in all fields has decreased in recent years. 

The share of international students in the natural sciences 

and engineering fields declined as well, but an increase in 

international students coming to the United States to study 

social and behavioral sciences has kept the overall S&E 

share stable.

Higher education is undergoing rapid transformation. The 

growth of distance and online education through MOOCs 

and similar innovations expands access to knowledge and 

has the potential to decrease the cost of some degrees, at the 
Science and Engineering Indicators 2014 ♦ 2-45

same time as pressures have been increasing to reduce rising 

costs. However, it is too early to assess whether MOOCs will 

be widely adopted by different types of institutions, whether 

increased access will be accompanied by increased learning, 

and what consequences online and distance innovations will 

bring to the higher education landscape.

Notes

1. Data on postdoctoral scientists and engineers are included in chapters 3 and 5. Data on stay rates of doctorate 

recipients are included in chapter 3.

2. U.S. Department of Education, National Center for 

Education Statistics, Integrated Postsecondary Education 

Data System (IPEDS), fall 2011, Institutional Characteristics 

component, special tabulation.

3. For a crosswalk between the Classification of 

Instructional Programs (CIP) codes and the academic fields 

in enrollment and completion tables, see https://webcaspar.

nsf.gov/Help/dataMapHelpDisplay.jsp?subHeader=DataSo

urceBySubject&type=DS&abbr=DEGS&noHeader=1&JS=

No, accessed 23 August 2013.

4. High Hispanic enrollment institutions are those whose 

undergraduate, full-time equivalent student enrollment is 

at least 25% Hispanic, according to fall 2011 data in the 

Integrated Postsecondary Education Data System, directed 

by the National Center for Education Statistics. HBCUs are 

listed by the White House Initiative on Historically Black 

Colleges and Universities. The Higher Education Act of 

1965, as amended, defines an HBCU as “any historically 

black college or university that was established prior to 

1964, whose principal mission was, and is, the education 

of black Americans, and that is accredited by a nationally 

recognized accrediting agency or association determined 

by the Secretary [of Education] to be a reliable authority as to the quality of training offered or is, according to 

such an agency or association, making reasonable progress 

toward accreditation.”

5. Minority-serving institutions include HBCUs (see 

endnote 4), high Hispanic enrollment institutions, and 

tribal colleges. 

6. See tables 5-8, 5-9, and 5-10 in NSF/NCSES 2013a for 

additional details.

7. U.S. Department of Education, National Center for 

Education Statistics, Integrated Postsecondary Education 

Data System (IPEDS), fall 2011, Institutional Characteristics 

component, special tabulation.

8. In 2011–12 IPEDS began asking institutions whether 

they were exclusively a distance education institution, that 

is, whether all of their programs were offered via distance 

education, defined as “education that uses one or more technologies to deliver instruction to students who are separated 

from the instructor and to support regular and substantive 

interaction between the students and the instructor synchronously or asynchronously.” A distance education course is a 

course in which the instructional content is delivered exclusively via distance education. A distance education program 

is a program for which all the required coursework for program completion can be completed via distance education 

courses. Examinations, orientation, and practical experience 

components of courses or programs are not considered instructional content. For more details, see the IPEDS online 

glossary at http://nces.ed.gov/ipeds/glossary/.

9. For the definition of “net tuition revenue,” see 

“Glossary.” Definitions of standard expense categories are 

available at http://www.deltacostproject.org/resources/pdf/

Delta-Spending-Trends-Production.pdf, and an explanation 

of revenue sources is available at http://www.deltacostproject.org/pdfs/Revenue_Trends_Production.pdf.

10. Another large source of revenue for very high research institutions is “hospitals, independent operations, and 

other sources,” which includes revenue generated by hospitals operated by the institution and revenues independent of 

or unrelated to instruction, research, or public services.

11. In 2010, income from private and affiliated gifts, investment returns, and endowment income at private very 

high research institutions ($62,000 per FTE) was more than 

the income from net tuition ($23,000 per FTE) and federal appropriations ($28,000 per FTE) combined (appendix 

table 2-4).

12. Another large source of expenditures for very high 

research institutions is “auxiliary enterprises, hospitals and 

clinics, and independent and other operations.” Auxiliary enterprises include dormitories, bookstores, and meal services.

13. The 4-year and graduate institutions category includes the following 2005 Carnegie institution types: 

doctorate-granting universities/high research activity, doctoral/research universities, master’s colleges/universities, 

and baccalaureate colleges. The figures in this section correspond to the public institutions.

14. Community colleges are the public “associate’s colleges” in the 2005 Carnegie Classification.

15. The proportion of U.S.-trained doctorate holders 

employed at community colleges in adjunct positions grew 

from 12% in 1993 to 28% in 2010, according to estimates 

from the Survey of Doctorate Recipients. This suggests that 

one of the ways community colleges may have reined in 

expenses during this period was to increase their reliance 

on adjuncts.

16. In this section, data on net tuition and fees paid by 

full-time undergraduate students are based on data reported to the College Board by colleges and universities in the 

Annual Survey of Colleges. Net tuition and fees equal published tuition and fees minus total grant aid and tax benefits. Data on net tuition revenues reported in the section 

“Trends in Higher Education Revenues and Expenditures” 

are based on IPEDS data. Net tuition revenue, in this case, is 

the amount of money the institution takes in from students 

after institutional grant aid is provided. 
2-46 ♦ Chapter 2. Higher Education in Science and Engineering

17. For more details, see Figure 2009_8 at http://trends.

collegeboard.org/college-pricing/figures-tables/net-pricesby-income-over-time-public-sector and Figure 2009_9 

at http://trends.collegeboard.org/college-pricing/figurestables/net-prices-by-income-over-time-private-sector. 

Accessed 29 July 2013.

18. Clinical psychology programs and programs that 

emphasize professional practice (professional schools 

and PsyD programs) are associated with higher debt, but 

even in the more research-focused subfields of psychology, lower percentages of doctorate recipients were debtfree, and higher percentages had higher levels of debt, than 

those in other S&E fields. For information on debt levels 

of clinical versus nonclinical psychology doctorates in 

1993–96, see Psychology Doctorate Recipients: How Much 

Financial Debt at Graduation? (NSF 00-321) at http://

www.nsf.gov/statistics/issuebrf/sib00321.htm (accessed 6 

November 2013).

19. The population projections in this section and in appendix table 2-15 are based on the latest population projections published by the Census Bureau, which are in turn 

based on the 2010 Census (http://www.census.gov/population/projections/data/national/2012/downloadablefiles.html, 

accessed 15 May 2013). In its publication “Projection of 

Education Statistics,” NCES projects enrollment trends in 

postsecondary institutions. However, in the latest publication 

(Hussar and Bailey 2013), NCES used Census projections 

from 2008, which were based on the 2000 Census. Unlike 

the Census Bureau, NCES incorporates disposable income 

(a measure of ability to pay) and age-specific unemployment 

rates (a measure of opportunity costs) in its projections. 

20. These data are from sample surveys and are subject 

to sampling error. Information on estimated standard errors can be found in appendix D of the annual report The 

American Freshman: National Norms Fall 2012, published by the Cooperative Institutional Research Program 

of the Higher Education Research Institute, University of 

California–Los Angeles (http://www.heri.ucla.edu/monographs/TheAmericanFreshman2012.pdf). Data reported 

here are significant at the 0.05 level.

21. The number of S&E degrees awarded to a particular 

freshman cohort is lower than the number of students reporting intentions to major in S&E. It reflects losses of students 

from S&E, gains of undecided students and students from 

non-S&E fields after their freshman year, and general attrition from bachelor’s degree programs. 

22. The PCAST report also included associate’s degrees 

trends in the natural sciences and engineering. The proportion of associate’s degrees in these fields was also fairly 

stable at about 5%, except in the early 2000s when it increased to 8%–9% because of the rise in the number of associate’s degree awards in computer sciences, which declined 

after 2004. 

23. The data in this section come from the Institute of 

International Education (IIE) and the Student and Exchange 

Visitor Information System (SEVIS). IIE conducts an annual survey of about 3,000 accredited U.S. higher education institutions. An international student in this survey is 

defined as anyone studying at an institution of higher education in the United States on a temporary visa that allows 

academic coursework, primarily F and J visas. SEVIS collects administrative data, including the numbers of all foreign national students enrolled in colleges and universities in 

the United States. Data on exchange visitors are not included 

in this chapter; some limited data on this topic can be found 

in chapter 3.

24. The figures include active foreign national students 

on F-1 visas in the SEVIS database, excluding those participating in optional practical training (OPT). Students with 

F visas have the option of working in the United States by 

engaging in OPT, temporary employment directly related 

to the student’s major area of study, either during or after 

completion of the degree program. Students can apply for 

12 months of OPT at each level of education. Starting in 

2008, students in certain STEM fields became eligible for 

an additional 17 months of OPT. The number of students in 

OPT varies according to labor market conditions. According 

to data from SEVIS, the number of students with F1 visas in OPT declined sharply between November 2010 and 

November 2011 and rose back up steeply by November 

2012 (68,510 in November 2010; 22,820 in November 2011; 

and 80,680 in November 2012).

25. These data include foreign students pursuing both 

bachelor’s and associate’s degrees. Comparable data for 

U.S. citizen and permanent resident students do not exist. 

However, the proportion of S&E associate’s and bachelor’s 

degree awards earned by U.S. citizens and permanent residents is considerably lower. 

26. About 14% of recent S&E bachelor’s degree recipients who earned their degree between 1 July 2007 and 

30 June 2009 had previously earned an associate’s degree 

(National Science Foundation, National Center for Science 

and Engineering Statistics, National Survey of Recent 

College Graduates 2010, special tabulation). 

27. Data on degree completion from NCES were obtained from WebCASPAR (https://webcaspar.nsf.gov/). 

Data uploaded in WebCASPAR correspond to NCES provisional data, which undergo all NCES data quality control 

procedures and are imputed for nonresponding institutions. 

These data are used by NCES in their First Look (Provisional 

Data) publications. 

28. Data for racial and ethnic groups are for U.S. citizens 

and permanent residents only.

29. Data on degree completion from NCES were obtained from WebCASPAR (https://webcaspar.nsf.gov/). 

Data uploaded in WebCASPAR correspond to NCES provisional data, which undergo all NCES data quality control 

procedures and are imputed for nonresponding institutions. 

These data are used by NCES in their First Look (Provisional 

Data) publications. 
Science and Engineering Indicators 2014 ♦ 2-47

30. For longer trends in degrees, see NSB 2010. For more 

detail on enrollment and degrees by sex and by race and ethnicity, see NSF/NCSES 2013a.

31. Data for racial and ethnic groups are for U.S. citizens 

and permanent residents only. 

32. The Survey of Graduate Students and Postdoctorates 

in Science and Engineering was redesigned in 2007. Because 

of methodological changes, the data collected from 2007 

through 2010 are not strictly comparable to those collected 

before 2007. As a result, care should be used when assessing trends within the GSS data. Throughout the appendix 

tables in this chapter, “2007new” reports the data as collected in 2007, and “2007old” provides the data as they would 

have been collected in 2006. In addition, between 2008 and 

2010, the survey conducted a more rigorous follow-up with 

institutions regarding the exclusion of practitioner-oriented 

graduate degree programs. Some or all of the declines in 

psychology and other health fields in 2008–10 are likely 

due to this increased effort to exclude practitioner-oriented 

graduate degree programs rather than changes in actual enrollments. Care should therefore be used when examining 

long-term trends. Because of this methodological change, in 

this section, “S&E” excludes psychology and other health 

fields. For a detailed discussion on the survey redesign, 

please see appendix A, “Technical Notes,” in Graduate 

Students and Postdoctorates in Science and Engineering: 

Fall 2007 (http://www.nsf.gov/statistics/nsf10307/). 

33. Special tabulation NSRCG (2003, 2006, 2008, 2010). 

34. See NSF/NCSES 2013a for more detail on enrollment 

of foreign students by sex.

35. The figures include active foreign national students 

on F-1 visas in the SEVIS database, excluding those on OPT 

(temporary employment directly related to the student’s major area of study either during or after completion of the degree program). See endnote 24.

36. Data on degree completion from NCES were obtained from WebCASPAR (https://webcaspar.nsf.gov/). 

Data uploaded in WebCASPAR correspond to NCES provisional data, which undergo all NCES data quality control 

procedures and are imputed for nonresponding institutions. 

These data are used by NCES in their First Look (Provisional 

Data) publications. 

37. Chapter 3 includes a sidebar on a 2012 NIH report 

discussing how employment patterns in the biological sciences have changed in the last two decades.

38. Data for racial and ethnic groups are for U.S. citizens 

and permanent residents only.

39. In 2008, NCES allowed optional reporting in three 

new doctoral degree categories: doctor’s—research/scholarship, doctor’s—professional practice, and doctor’s—other. 

Degrees formerly classified as professional degrees (e.g., 

MDs and JDs) could then be reported as doctoral degrees, 

most often as doctor’s—professional practice. Data for 2008 

and 2009 included only those doctorates reported under 

the old category plus those reported as doctor’s—research/

scholarship. Data for 2010 and 2011 included data reported 

as doctor’s—research/scholarship, as the old category was 

eliminated. As a result of these methodological changes, 

doctor’s—research/scholarship degrees in “other health sciences” declined sharply between 2009 and 2010. To facilitate comparability over time, “S&E” excludes “other health 

sciences” throughout the sections “S&E Doctoral Degrees,” 

“Doctoral Degrees by Sex,” and “Doctoral Degrees by Race 

and Ethnicity.” 

40. Data on degree completion from NCES were obtained from WebCASPAR (https://webcaspar.nsf.gov/). 

Data uploaded in WebCASPAR correspond to NCES provisional data, which undergo all NCES data quality control 

procedures and are imputed for nonresponding institutions. 

These data are used by NCES in their First Look (Provisional 

Data) publications. 

41. See table 32 in the 2011 Doctorate Recipients from 

U.S. Universities report (NSF/NCSES 2012), where broad 

fields are aggregated as follows: life sciences includes agricultural sciences and natural resources, biological and 

biomedical sciences, and health sciences; physical sciences 

includes mathematics and computer and information sciences; and social sciences includes psychology.

42. For the corresponding proportion in the 1990s, see 

NSB 2008.

43. The number of S&E doctorate recipients from 

China surpassed that of Taiwan in 1990. Up until that year, 

Taiwanese students earned more U.S. S&E doctorates than 

Chinese, Indian, or South Korean students. (See NSB 2008 

figure 2-25 and NSB 2010 figure 2-22.)

44. According to an international database compiled by 

the Program for Research on Private Higher Education, at 

the State University of New York at Albany, the United 

States and Japan have long-standing private higher education sectors, and Western Europe has an almost completely 

public higher education sector. Eastern and Central Europe 

and several African countries have recently seen growth 

in private higher education. In most countries in Latin 

America, more than half of all higher education institutions 

are private. For more information, see http://www.albany.

edu/dept/eaps/prophe/index.html (accessed 15 May 2013). 

45. These data are based on national labor force surveys 

and are subject to sampling error; therefore, small differences between countries may not be meaningful. The standard error for the U.S. percentage of 25–64-year-olds with 

a bachelor’s or higher degree is roughly 0.1, and the standard error for the U.S. percentage of 25–34-year-olds with a 

bachelor’s or higher degree is roughly 0.4.

46. In international degree comparisons, S&E does not 

include medical or health fields. This is because international sources cannot separate the MD degrees from degrees in 

the health fields, and the MDs are professional or practitioner degrees, not research degrees.

47. Internationally mobile students are students who have 

crossed a national or territorial border for the purposes of 

education and are now enrolled outside their country of origin. This concept is different from “foreign students,” which 
2-48 ♦ Chapter 2. Higher Education in Science and Engineering

are those who are not citizens of the country where they are 

enrolled, but may, in some cases, be long-term residents or 

have been born in the country (OECD 2012).

48. The population of individuals ages 20–24 (a proxy 

for the college-age population) decreased in China, Europe, 

Japan, and the United States in the 1990s and is projected 

to continue decreasing in China, Europe (mainly Eastern 

Europe), Japan, South Korea, and South America. The U.S. 

population of 20–24-year-olds is projected to increase.

49. In Luxembourg, international students represent 41%, 

mostly due to the high level of integration with neighboring 

countries (OECD 2012).

Glossary

Baccalaureate-origin institution: The college or university from which an S&E doctorate recipient earned a 

bachelor’s degree.

Credit mobility: Short-term, for-credit foreign study and 

exchange programs that last less than a full school year.

Degree or diploma mobility: For-credit foreign study 

programs in which students pursue a higher education degree outside their usual country of residence. 

European Union (EU): As of June 2013, the European 

Union comprised 27 member nations: Austria, Belgium, 

Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, 

Finland, France, Germany, Greece, Hungary, Ireland, Italy, 

Latvia, Lithuania, Luxembourg, Malta, the Netherlands, 

Poland, Portugal, Romania, Slovakia, Slovenia, Spain, 

Sweden, and the United Kingdom. Croatia joined the EU 

in July 2013. Unless otherwise noted, Organisation for 

Economic Co-operation and Development data on the EU 

include all 28 members; data on the EU from other sources are limited to the 27 nations that were members as of 

June 2013.

First university degree: A terminal undergraduate degree program; these degrees are classified as level 5A first 

university in the International Standard Classification of 

Education, which is developed by UNESCO, although individual countries use different names for the first terminal 

degree (e.g., corso di Laurea in Italy, diplom in Germany, 

licence in France, and bachelor’s degree in the United States 

and in Asian countries).

G20: Group of Twenty brings together finance ministers and central bank governors from Argentina, Australia, 

Brazil, Canada, China, France, Germany, India, Indonesia, 

Italy, Japan, the Republic of Korea, Mexico, Russia, Saudi 

Arabia, South Africa, Turkey, the United Kingdom, the 

United States, and the EU.

Internationally mobile students: Students who have 

crossed a national or territorial border for purposes of education and are now enrolled outside their countries of origin. This term refers to degree mobility in data collected 

by UNESCO/UNESCO Institute for Statistics, OECD, and 

Eurostat and excludes students who travel for credit mobility.

Natural sciences: Include agricultural; biological; computer; earth, atmospheric, and ocean; and physical sciences 

and mathematics.

Net price: The published price of an undergraduate college education minus the average grant aid and tax benefits 

that students receive.

Net tuition revenue: Total revenue from tuition and 

fees (including grant and loan aid used by students to pay 

tuition); excludes institutional student aid that is applied to 

tuition and fees.

Tertiary-type A programs: Higher education programs 

that are largely theory based and designed to provide sufficient qualifications for entry to advanced research programs and to professions with high skill requirements, such 

as medicine, dentistry, or architecture. These programs have 

a minimum duration of 3 years, although they typically last 

4 or more years and correspond to bachelor’s or master’s 

degrees in the United States.

Tertiary-type B programs: Higher education programs 

that focus on practical, technical, or occupational skills for 

direct entry into the labor market and have a minimum duration of 2 years. These programs correspond to associate’s 

degree programs in the United States.

Underrepresented minorities: Blacks, Hispanics, and 

American Indians and Alaska Natives are considered to be 

underrepresented minorities in S&E. 

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Statistics and Board on Testing and Assessment, Division 

of Behavioral and Social Sciences and Education. 

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Learning in Undergraduate Science and Engineering. 

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Directions of Discipline-Based Education Research, 

Board on Science Education, Division of Behavioral 

and Social Sciences and Education. Washington, DC: 

The National Academies Press. Available at http://www.

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June 2013.

National Research Council (NRC) and National Academy 

of Engineering (NAE). 2012. Community Colleges in 

the Evolving STEM Education Landscape: Summary 

of a Summit. S. Olson and J.B. Labov, rapporteurs. 

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Universities. Board on Higher Education and Workforce, 

Division on Policy and Global Affairs. Board on Life 

Sciences, Division on Earth and Life Studies. Board on 

Science Education, Teacher Advisory Council, Division 

of Behavioral and Social Sciences and Education. 

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08-01; vol. 2, NSB 08-01A). Arlington, VA: National 

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National Science Foundation, National Center for Science 

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Testing Service.
Chapter 3

Science and Engineering 

Labor Force

♦ 3-1

Highlights.....................................................................................................................................3-5

U.S. S&E Workforce: Definition, Size, and Growth................................................................3-5

S&E Workers in the Economy .................................................................................................3-5

S&E Labor Market Conditions.................................................................................................3-5

Demographics of the S&E Workforce......................................................................................3-6

Global S&E Labor Force ..........................................................................................................3-6

Introduction..................................................................................................................................3-7

Chapter Overview.....................................................................................................................3-7

Chapter Organization................................................................................................................3-7

U.S. S&E Workforce: Definition, Size, and Growth...................................................................3-7

Definition of the S&E Workforce.............................................................................................3-7

Size of the S&E Workforce ....................................................................................................3-10

Growth of the S&E Workforce...............................................................................................3-11

Educational Distribution of Workers in S&E Occupations....................................................3-14

Occupational Distribution of S&E Degree Holders and Relationship between Jobs 

and Degrees........................................................................................................................3-16

S&E Workers in the Economy...................................................................................................3-19

Employment Sectors...............................................................................................................3-19

Employer Size.........................................................................................................................3-23

Industry Employment .............................................................................................................3-24

Employment by Metropolitan Area ........................................................................................3-24

Scientists and Engineers and Innovation-Related Activities..................................................3-24

S&E Labor Market Conditions..................................................................................................3-28

Unemployment .......................................................................................................................3-28

Involuntarily Working Out of One’s Field of Highest Degree...............................................3-31

Earnings..................................................................................................................................3-32

Recent S&E Graduates...........................................................................................................3-33

Age and Retirement of the S&E Workforce ..............................................................................3-40

Age Differences among Occupations.....................................................................................3-41

Age Differences among Degree Fields...................................................................................3-41

Retirement...............................................................................................................................3-42

Women and Minorities in the S&E Workforce .........................................................................3-43

Women in the S&E Workforce...............................................................................................3-43

Minorities in the S&E Workforce...........................................................................................3-45

Salary Differences for Women and Racial and Ethnic Minorities.........................................3-49

Immigration and the S&E Workforce........................................................................................3-51

Characteristics of Foreign-Born Scientists and Engineers .....................................................3-52

Source of Education ...............................................................................................................3-53

New Foreign-Born Workers ..................................................................................................3-54

High-Skill Migration Worldwide ..........................................................................................3-58
3-2 ♦ Chapter 3. Science and Engineering Labor Force

List of Tables

Table 3-1. Major sources of data on the U.S. labor force............................................................3-8

Table 3-2. Classification of degree fields and occupations .........................................................3-9

Table 3-3. Measures and size of U.S. S&E workforce: 2010, 2011, and 2012 .........................3-10

Table 3-4. Educational background of college graduates employed in S&E occupations, 

by broad S&E occupational category: 2010..........................................................................3-16

Table 3-5. Relationship of highest degree to job among S&E highest degree holders 

not in S&E occupations, by degree level: 2010 ....................................................................3-17

Table 3-6. Employment sector of scientists and engineers, by broad occupational 

category and degree field: 2010 ............................................................................................3-20

Table 3-7. Self-employed scientists and engineers, by education, occupation, and type 

of business: 2010...................................................................................................................3-22

Table 3-8. Employment in S&E occupations, by major industry sector: May 2012.................3-25

Table 3-9. Metropolitan areas with largest proportion of workers in S&E occupations: 

May 2012 ..............................................................................................................................3-26

Table 3-10. R&D activity rate of scientists and engineers employed in S&E occupations, 

by broad occupational category and level of highest degree: 2010 ......................................3-27

Table 3-11. Scientists and engineers participating in work-related training, by labor force 

status and occupation: 2010 ..................................................................................................3-28

Table 3-12. Alternative measures of labor underutilization ......................................................3-30

Table 3-13. Scientists and engineers who are working involuntarily out of field, 

by S&E degree field: 1993–2010..........................................................................................3-31

Table 3-14. Annual earnings and earnings growth in science, technology, and related 

occupations: May 2009–May 2012.......................................................................................3-32

Table 3-15. Median salaries for employed college-educated individuals, by broad field 

of highest degree and broad occupational category: 2010....................................................3-32

Table 3-16. Labor market indicators for recent S&E degree recipients up to 5 years 

after receiving degree, by level and field of highest degree: 2010 .......................................3-35

Table 3-17. Employment characteristics of recent SEH doctorate recipients up to 3 years 

after receiving doctorate, by field of degree: 2001–10 .........................................................3-36

Table 3-18. Employed SEH doctorate recipients holding tenure and tenure-track 

appointments at academic institutions, by field of and years since degree: 1993–2010.......3-36

Table 3-19. Salaries for recent SEH doctorate recipients up to 5 years after receiving 

degree at selected percentiles, by field of degree: 2010........................................................3-37

Table 3-20. Median salaries for recent SEH doctorate recipients up to 5 years after 

receiving degree, by field of degree and employment sector: 2010 .....................................3-37

Table 3-21. Median salaries for recent U.S. SEH doctorate recipients in postdoc and 

non-postdoc positions up to 5 years after receiving degree: 2010........................................3-40

Table 3-22. Racial and ethnic distribution of employed individuals in S&E occupations, 

and of S&E degree holders, college graduates, and U.S. residents: 2010 ............................3-47

List of Sidebars

NSF’s Scientists and Engineers Statistical Data System ............................................................3-9

Projected Growth of Employment in S&E Occupations ..........................................................3-12

The U.S. S&E Workforce Without a Bachelor’s Degree .........................................................3-15

Employment of Biomedical Sciences Doctorates .....................................................................3-39

S&E Credentials and the Male-Female Gap in S&E Employment ...........................................3-46

Global S&E Labor Force ...........................................................................................................3-59

Size and Growth of the Global S&E Labor Force..................................................................3-59

R&D Employment Abroad by U.S. Companies.....................................................................3-61

Conclusion .................................................................................................................................3-61

Notes..........................................................................................................................................3-62

Glossary .....................................................................................................................................3-64

References..................................................................................................................................3-64
Science and Engineering Indicators 2014 ♦ 3-3

Table 3-23. Distribution of workers in S&E occupations, by race and ethnicity: 

1993–2010.............................................................................................................................3-48

Table 3-24. Racial and ethnic distribution of employed individuals with S&E highest 

degree, by field of highest degree: 2010 ...............................................................................3-48

Table 3-25. Racial and ethnic distribution of employed individuals with S&E highest 

degree, by level of highest degree: 2010...............................................................................3-48

Table 3-26. Median annual salary among S&E highest degree holders working full time, 

by sex, race, and ethnicity: 1995, 2003, 2010.......................................................................3-49

Table 3-27. Foreign-born workers in S&E occupations, by education level: Selected 

years, 2000–11 ......................................................................................................................3-52

Table 3-28. Annual salaries for new H-1B visa recipients, by occupation: FY 2011 ...............3-55

Table 3-29. Five-year stay rates for U.S. S&E doctorate recipients with temporary 

visas at graduation, by selected country/region/economy: 2001–11 ....................................3-57

Table 3-A. Bureau of Labor Statistics projections of employment and job openings in 

S&E and other selected occupations: 2010–20.....................................................................3-13

List of Figures

Figure 3-1. Employment in S&E occupations, by broad occupational category: 2010 

and 2012................................................................................................................................3-11

Figure 3-2. S&E degrees among college graduates, by field and level of highest 

degree: 2010..........................................................................................................................3-11

Figure 3-3. Average annual growth in the number of employed individuals whose 

highest degree is in S&E, by field and level of highest degree: 2003–10 ............................3-14

Figure 3-4. Educational attainment, by type of occupation: 2011.............................................3-14

Figure 3-5. Occupational distribution of scientists and engineers, by broad field of 

highest degree: 2010 .............................................................................................................3-16

Figure 3-6. Occupational distribution of S&E highest degree holders, by field of 

highest degree: 2010 .............................................................................................................3-18

Figure 3-7. S&E degree holders working in S&E occupations, by level and field of 

S&E highest degree: 2010.....................................................................................................3-18

Figure 3-8. S&E degree holders employed in jobs related to highest degree, by level of 

and years since highest degree: 2010....................................................................................3-19

Figure 3-9. Employed scientists and engineers, by employment sector: 1993–2010................3-20

Figure 3-10. S&E highest degree holders, by degree level and employment sector: 2010 .......3-21

Figure 3-11. Broad S&E occupational categories, by employment sector: 2010......................3-22

Figure 3-12. Scientists and engineers employed in the business sector, by employer 

size: 2010 ..............................................................................................................................3-23

Figure 3-13. S&E highest degree holders employed in the business sector, by highest 

degree level and employer size: 2010 ...................................................................................3-24

Figure 3-14. Employed scientists and engineers with R&D activity, by broad field of 

highest degree and broad occupational category: 2010 ........................................................3-27

Figure 3-15. Employed SEH doctorate holders with R&D activity, by years since 

doctoral degree: 2010............................................................................................................3-27

Figure 3-16. Unemployment rates of scientists and engineers, by level of and years 

since highest degree: 2010 ....................................................................................................3-29

Figure 3-17. Unemployment rate, by occupation: 1983–2012 ..................................................3-29

Figure 3-18. Unemployment rates for workers in S&E, STEM, and all occupations: 

March 2008–April 2013........................................................................................................3-30

Figure 3-19. Measures of labor underutilization for workers in S&E and all occupations: 

March 2008–April 2013........................................................................................................3-30

Figure 3-20. Scientists and engineers who are working involuntarily out of field, 

by level of and years since highest degree: 2010..................................................................3-31

Figure 3-21. Median salaries for employed college-educated individuals, by broad 

field of highest degree and years since highest degree: 2010 ...............................................3-33

Figure 3-22. Median salaries for employed scientists and engineers, by level of and 

years since highest degree: 2010...........................................................................................3-33
3-4 ♦ Chapter 3. Science and Engineering Labor Force

Figure 3-23. Median salaries for employed scientists and engineers, by broad field 

and level of highest degree: 2010..........................................................................................3-34

Figure 3-24. Recent U.S. SEH doctorate recipients in postdoc positions, by field of 

and years since doctorate: 2010 ............................................................................................3-40

Figure 3-25. Age distribution of scientists and engineers in the labor force, by sex: 

1993 and 2010.......................................................................................................................3-41

Figure 3-26. Age distribution of employed scientists and engineers, by broad 

occupational category and broad field of highest degree: 2010............................................3-41

Figure 3-27. Older scientists and engineers who work full time, by age and highest 

degree level: 2010 .................................................................................................................3-42

Figure 3-28. Older scientists and engineers who report not working because of retirement, 

by age: 2010 ..........................................................................................................................3-42

Figure 3-29. Women in the workforce and in S&E: 1993 and 2010 .........................................3-43

Figure 3-30. Women in S&E occupations: 1993–2010.............................................................3-44

Figure 3-31. Employed women with highest degree in S&E, by degree level: 1993–2010......3-45

Figure 3-32. Highest degree holders in S&E not in the labor force, by sex and age: 2010.......3-47

Figure 3-33. Estimated salary differences between women and men with highest degree 

in S&E employed full time, controlling for selected characteristics, 

by degree level: 2010 ............................................................................................................3-50

Figure 3-34. Estimated salary differences between minorities and whites and Asians 

with highest degree in S&E employed full time, controlling for selected characteristics, 

by degree level: 2010 ............................................................................................................3-50

Figure 3-35. Foreign-born scientists and engineers employed in S&E occupations, 

by highest degree level and broad occupational category: 2010...........................................3-52

Figure 3-36. Foreign-born individuals with highest degree in S&E living in the United 

States, by place of birth: 2010...............................................................................................3-53

Figure 3-37. Temporary work visas issued in categories with many high-skilled 

workers: FYs 1991–2012 ......................................................................................................3-54

Figure 3-38. Plans of foreign recipients of U.S. S&E doctoral degrees at graduation 

to stay in the United States, by year of doctorate: 1991–2011..............................................3-55

Figure 3-39. Plans of foreign recipients of U.S. S&E doctoral degrees at graduation 

to stay in the United States, by place of origin and year of doctorate: 1998–2001 

and 2008–11..........................................................................................................................3-56

Figure 3-40. Stay rates for U.S. S&E doctorate recipients with permanent or temporary 

visas at graduation, by selected year of doctorate: 2003–11.................................................3-57

Figure 3-41. Top countries of origin of foreign-born persons residing in OECD countries 

and having at least a tertiary education, age 25 years or more, by sex: 2000.......................3-59

Figure 3-42. Estimated number of researchers in selected countries/regions: 1995–2011........3-60

Figure 3-43. Researchers as a share of total employment in selected countries/regions: 

1995–2011.............................................................................................................................3-60

Figure 3-A. Bureau of Labor Statistics projected increases in employment for S&E 

and selected other occupations: 2010–20..............................................................................3-12

Figure 3-B. Bureau of Labor Statistics projected job openings in S&E and selected other 

occupations: 2010–20............................................................................................................3-13

Figure 3-C. Estimated differences in the proportions of women and of men with S&E 

highest degree employed in S&E occupations, controlling for selected characteristics: 

2010.......................................................................................................................................3-46
U.S. S&E Workforce: Definition, Size, 

and Growth

The S&E workforce can be defined in several ways: by 

workers in S&E occupations, by holders of S&E degrees, 

and by the use of S&E technical expertise on the job. The 

estimated size of the S&E workforce varies depending on 

the criteria chosen.

♦ In 2010, estimates of the size of the U.S. S&E workforce 

ranged from approximately 5 million to more than 19 

million depending on the definition used.

♦ In 2010, there were about 5.4 million college graduates employed in S&E occupations in the United States. 

Occupations in the computer and mathematical sciences 

(2.4 million) and engineering (1.6 million) were the largest categories of S&E occupations. Occupations in the 

life sciences (597,000), social sciences (518,000), and 

physical sciences (320,000) each employed a smaller 

number of S&E workers. 

♦ In 2010, about 19.5 million college graduates in the 

United States had a bachelor’s or higher level degree in an 

S&E field of study. Almost three-fourths (74%) of these 

college graduates (14.5 million) held their highest level 

of degree (bachelor’s, master’s, professional, or doctorate) in an S&E field. Overall, the most common fields of 

S&E highest degrees were social sciences (40%) and engineering (23%). Computer and mathematical sciences, 

life sciences, and physical sciences together accounted 

for slightly more than one-third (38%) of individuals with 

S&E highest degrees. 

♦ The application of S&E knowledge and skills is widespread across the U.S. economy and not just limited 

to S&E occupations. The number of college-educated 

individuals reporting that their jobs require at least a 

bachelor’s degree level of technical expertise in one or 

more S&E fields (16.5 million) is significantly higher 

than the number in occupations with formal S&E titles 

(5.4 million).

The S&E workforce has grown steadily over time.

♦ Between 1960 and 2011, the number of workers in S&E 

occupations grew at an average annual rate of 3.3%, 

greater than the 1.5% growth rate for the total workforce.

♦ Data from more recent years indicate that trends in S&E 

employment compared favorably to overall employment 

trends during and after the 2007–09 economic downturn. Between 2006 and 2012, the number of workers 

employed in S&E occupations rose slightly, whereas the 

total workforce shrank.

S&E Workers in the Economy

Scientists and engineers work for all types of employers.

♦ By far the largest employer of scientists and engineers 

(individuals with an S&E degree or employed in an S&E 

occupation) is the business sector (70%), followed by the 

education sector (19%) and the government sector (11%). 

Within the business sector, for-profit businesses employ 

the largest number of scientists and engineers.

♦ Scientists and engineers with S&E doctorates are more 

evenly distributed between the business sector (46%) 

and the education sector (45%). Within the education 

sector, over 90% are found in 4-year academic institutions, including those in postdoctoral and other temporary positions.

♦ Small firms are important employers of those with S&E 

highest degrees (individuals who attained their highest 

level of degree in an S&E field of study). Firms with 

fewer than 100 persons employ 37% of such individuals 

in the business sector.

♦ Within the business sector, the industry with the largest 

number of workers in S&E occupations is the professional, scientific, and technical services industry.

♦ Employment in S&E occupations is geographically concentrated in the United States. The 20 metropolitan areas 

with the largest proportion of the workforce employed in 

S&E occupations accounted for 18% of nationwide S&E 

employment, compared to 8% of all employment.

S&E Labor Market Conditions

Workers with S&E degrees or in S&E occupations tend 

to earn more than other comparable workers.

♦ Half of the workers in S&E occupations earned $78,270 

or more in 2012, more than double the median earnings 

($34,750) of the total U.S. workforce.

♦ Employed college graduates with a highest degree in S&E 

earn more than those with non-S&E degrees. Moreover, 

within each broad degree field (S&E and non-S&E), 

those employed in S&E occupations earn more than those 

in non-S&E occupations.

Individuals whose work is associated with S&E are less 

often exposed to unemployment.

♦ Unemployment rates for those in S&E occupations tend 

to be lower than those for all college graduates and much 

lower than those for the overall labor force. In October 

2010, an estimated 4.3% of scientists and engineers and 

5.1% of all college-educated individuals in the labor 

force were unemployed. At the same time, the official unemployment rate for the entire U.S. labor force was 9.0%.

♦ Unemployment rates for S&E doctorate holders are generally lower than for those at other degree levels.

Highlights

Science and Engineering Indicators 2014 ♦ 3-5
Demographics of the S&E Workforce

The U.S. S&E labor force is aging. However, in 2010, a 

larger proportion of older scientists and engineers reported being in the labor force than in 1993. 

♦ The proportion of scientists and engineers in the U.S. labor force over age 50 increased from 20% in 1993 to 33% 

in 2010. The median age of such individuals was 44 years 

in 2010, compared to 41 years in 1993. 

♦ Between 1993 and 2010, increasing percentages of scientists and engineers in their 60s reported that they were 

still in the labor force. Whereas 54% of scientists and engineers between the ages of 60 and 69 were employed in 

1993, the comparable percentage rose to 63% in 2010.

Women remain underrepresented in the S&E workforce, 

although to a lesser degree than in the past.

♦ Despite accounting for half of the college-educated workforce, in 2010 women constituted 37% of employed individuals with a highest degree in an S&E field and 28% of 

employed individuals in S&E occupations. 

♦ From 1993 to 2010, growth occurred in both the proportion of workers with a highest degree in an S&E field 

who are women (increasing from 31% to 37%) and the 

proportion of women in S&E occupations (increasing 

from 23% to 28%).

♦ Women employed in S&E occupations are concentrated 

in different occupational categories than are men, with 

relatively high proportions of women in the social sciences (58%) and life sciences (48%) and relatively low 

proportions in engineering (13%) and computer and 

mathematical sciences (25%).

Historically underrepresented racial and ethnic groups, 

particularly blacks and Hispanics, continue to display 

lower S&E participation rates relative to their presence 

in the U.S. population. Conversely, Asians and foreignborn individuals display higher S&E participation rates 

relative to their overall presence in the U.S. population.

♦ Hispanics, blacks, and American Indians or Alaska 

Natives together make up 26% of the U.S. population age 

21 and older but a much smaller proportion of the S&E 

workforce: 10% of workers in S&E occupations and 13% 

of S&E highest degree holders.

♦ Asians work in S&E occupations at higher rates (19%) 

than their representation in the U.S. population age 21 and 

older (5%). Asians have a large presence in engineering 

and computer sciences occupations, particularly among 

computer software and hardware engineers, software developers, and postsecondary teachers in engineering. 

♦ About 70% of workers in S&E occupations are non-Hispanic whites, which is comparable to their overall representation in the U.S. population age 21 and older (68%).

♦ Foreign-born individuals account for slightly more than 

one-fourth of all workers in S&E occupations, which is 

higher than their representation in the entire college-educated workforce (15%). Foreign-born workers employed 

in S&E occupations tend to have higher levels of education than their U.S. native-born counterparts.

A variety of indicators point to a decline in the immigration of scientists and engineers during the 2007–09 

economic downturn. However, data since the downturn 

suggest that this decline may be temporary.

♦ After several years of growth, the number of temporary 

work visas issued to high-skill workers fell during the 

2007–09 economic downturn. It has rebounded since 

then, although data for 2012 indicate that the issuance of 

temporary work visas has not yet reached the recent highs 

seen in 2007 and 2008. 

♦ After rising for most of the decade 2000–09, the number 

of foreign recipients of U.S. S&E doctoral degrees declined in 2009 and 2010. It has risen slightly in 2011 but 

remains below the recent highs seen in 2007 and 2008. 

♦ Among foreign-born U.S. S&E doctorate recipients with 

temporary visas at graduation, the proportion that remained in the United States 5 years after receiving their 

degrees rose during the first half of the decade of the 

2000s, reaching 67% in 2005. The proportion declined 

during the economic downturn but rose to 66% in 2011. 

Global S&E Labor Force

Worldwide, the number of workers engaged in research 

has been growing.

♦ Among countries with large numbers of researchers—

defined as workers engaged in the creation and 

development of new knowledge, products, and 

processes—growth has been most rapid since the mid1990s in China and South Korea.

♦ The United States and the European Union experienced steady growth but at a lower rate than in China or 

South Korea.

♦ Japan and Russia were exceptions to the worldwide 

trend. Between 1995 and 2011, the number of researchers 

in Japan remained largely unchanged, and in Russia the 

number declined.

3-6 ♦ Chapter 3. Science and Engineering Labor Force
Science and Engineering Indicators 2014 ♦ 3-7

Introduction

Chapter Overview

Policymakers and scholars consistently emphasize innovation based on S&E research and development as a vehicle 

for a nation’s economic growth and global competitiveness. 

Workers with S&E expertise are an integral part of a nation’s innovative capacity because of their high skill level, 

their creative ideas, and their ability not only to advance basic scientific knowledge but also to transform advances in 

fundamental knowledge into tangible products and services. 

As a result, these workers make important contributions to 

improving living standards and accelerating the pace of a 

nation’s economic and productivity growth.

Chapter Organization

The U.S. workforce includes both individuals employed 

in S&E occupations and individuals educated in S&E fields 

but employed in a variety of non-S&E occupations. Many 

more individuals have S&E degrees than work in S&E occupations. Indicative of a knowledge-based economy, many 

individuals in non-S&E occupations report that their work 

nevertheless requires a bachelor’s degree level of S&E expertise. Therefore, the first section in this chapter, “U.S. 

S&E Workforce: Definition, Size, and Growth,” discusses 

the U.S. S&E workforce based on three measures: workers 

in S&E occupations, holders of S&E degrees, and use of 

S&E technical expertise on the job. This section also discusses the interplay between educational background and 

occupational choice as well as the growth in the U.S. S&E 

workforce over time. 

The second section in this chapter, “S&E Workers in 

the Economy,” examines the distribution of S&E workers 

across employment sectors. It describes the distribution of 

S&E workers across sectors (e.g., business, education, government) as well as within particular sectors (e.g., local, 

state, and federal government). This section also presents 

data on geographic distribution of S&E employment in the 

United States. Data on R&D activity and work-related training by S&E workers are also discussed. 

The third section, “S&E Labor Market Conditions,” looks 

at labor market outcomes for S&E workers. Data in this section focus on earnings and unemployment. Data on recent 

S&E graduates are also discussed, as are broader measures 

of labor underutilization that go beyond the conventional unemployment rate. 

The next three sections cover labor force demographics. 

“Age and Retirement of the S&E Workforce” presents data 

on the age distribution and retirement patterns of S&E workers. “Women and Minorities in the S&E Workforce” focuses 

on S&E participation by women and by racial and ethnic 

minorities; this section also presents data on salary differences by sex and by race and ethnicity. “Immigration and 

the S&E Workforce” presents data on S&E participation by 

foreign-born individuals in the United States as well as the 

worldwide migration patterns of high-skill workers. 

The final section in this chapter is “Global S&E Labor 

Force.” Although there are indications that the global S&E 

labor force has grown, international data on the characteristics of this broader labor force are particularly limited and 

are not always comparable with data for the United States. In 

this final section, data from the Organisation for Economic 

Co-operation and Development (OECD) are used to present 

indicators of worldwide R&D employment. 

This chapter uses a variety of data sources, including, but 

not limited to, the National Science Foundation’s (NSF’s) 

Scientists and Engineers Statistical Data System (SESTAT), 

the Census Bureau’s American Community Survey (ACS), 

the Occupational Employment Statistics (OES) survey administered by the Bureau of Labor Statistics (BLS), and the 

Current Population Survey (CPS) sponsored jointly by the 

Census Bureau and BLS. Different sources cover different 

segments of the population and different levels of detail on 

different topics. (See table 3-1 and sidebar, “NSF’s Scientists 

and Engineers Statistical Data System.”) Although data collection methods and definitions can differ across surveys in 

ways that affect estimates, combining data from different 

sources facilitates an accurate and comprehensive picture 

of the very specialized S&E workforce. A particular measure or categorization of the workforce may be better suited 

for addressing some questions than others, and a particular 

data source may not include information in every category. 

Analyses of long-term trends, international trends, and comparison of S&E and non-S&E workers are discussed whenever data are available.

U.S. S&E Workforce: 

Definition, Size, and Growth

Definition of the S&E Workforce

Because there is no standard definition of S&E workers, this section uses multiple categorizations to measure 

the U.S. S&E workforce. In general, this section defines the 

S&E workforce to include people who either work in S&E 

occupations or hold S&E degrees.1

 The application of S&E 

knowledge and skills is not limited to jobs with formal S&E 

titles; the number of college graduates reporting that their 

jobs require at least a bachelor’s degree level of knowledge 

in one or more S&E fields exceeds the number of workers 

employed in S&E occupations in the economy. Therefore, 

this section also presents data on the use of S&E technical expertise on the job to provide an estimate of the U.S. 

S&E workforce. The estimated number of scientists and 

engineers varies based on the criteria applied to define the 

S&E workforce.

U.S. federal occupation data classify workers by the activities or tasks they primarily perform in their jobs. The 

NSF and Census Bureau occupational data in this chapter 

come from federal statistical surveys in which individuals 

or household members provide information about job titles 

and work activities. This information is used to classify jobs 
3-8 ♦ Chapter 3. Science and Engineering Labor Force

into standard occupational categories based on the Standard 

Occupational Classification (SOC) system.2 In contrast, the 

BLS-administered OES survey relies on employers to classify their workers using SOC definitions. Differences between employer- and individual-provided information can 

affect the content of occupational data.

NSF has developed a widely used set of SOC categories 

that it calls S&E occupations. Very broadly, these occupations include life scientists, computer and mathematical scientists, physical scientists, social scientists, and engineers. 

NSF also includes postsecondary teachers of these fields in 

S&E occupations. A second category of occupations, S&Erelated occupations, includes health-related occupations, 

S&E managers, S&E technicians and technologists, architects, actuaries, S&E precollege teachers, and postsecondary teachers in S&E-related fields. The S&E occupations 

are generally assumed to require at least a bachelor’s degree level of education in an S&E field. The vast majority 

of S&E-related occupations also require S&E knowledge 

or training, but an S&E bachelor’s degree may not be a 

required credential for employment in some of these occupations. Examples include health technicians and computer 

network managers. Other occupations, although classified 

as non-S&E occupations, may include individuals who use 

S&E technical expertise in their work. Examples include 

technical writers who edit scientific publications and salespeople who sell specialized research equipment to chemists 

and biologists. The NSF occupational classification of S&E, 

S&E-related, and non-S&E occupations appears in table 3-2 

along with the NSF educational classification of S&E, S&Erelated, and non-S&E degree fields.

Other general terms, including science, technology, engineering, and mathematics (STEM), science and technology 

(S&T), and science, engineering, and technology (SET), are 

often used to designate the part of the labor force that works 

with S&E. These terms are broadly equivalent and have no 

standard definition.

The number of individuals who have S&E training or 

who reported applying S&E technical expertise in their 

jobs exceeds the number of individuals employed in S&E 

Table 3-1

Major sources of data on the U.S. labor force

Data source Data collection agency Data years Major topics Respondent Coverage

Occupational 

Employment 

Statistics (OES)

Department of Labor, 

Bureau of Labor Statistics

Through 2012 Employment status 

Occupation 

Salary 

Industry

Employer location 

(national, state, 

metropolitan 

statistical area)

Employing 

organizations

All full-time and part-time 

wage and salary workers in 

non-farm industries; does 

not cover self-employed, 

unincorporated firms, 

household workers, or 

unpaid family workers

Scientists and 

Engineers Statistical 

Data System—see 

sidebar “NSF’s 

Scientists and 

Engineers Statistical 

Data System”

National Science 

Foundation, National 

Center for Science and 

Engineering Statistics

Through 2010 Employment status 

Occupation 

Job characteristics 

(work activities, 

technical 

expertise)

Salary 

Detailed educational 

history 

Demographic 

characteristics

Individuals Individuals with bachelor’s 

degree or higher in S&E or 

S&E-related field or with 

non-S&E bachelor’s but 

working in S&E or S&Erelated occupation

American Community 

Survey (ACS)

Department of 

Commerce, 

Census Bureau

Through 2011 Employment status 

Occupation 

First bachelor’s 

degree field 

Educational 

attainment 

Demographic 

characteristics

Households U.S. population

Current Population 

Survey (CPS)

Department of Labor, 

Bureau of Labor Statistics

Through 2013 Employment status 

Occupation 

Educational 

attainment 

Demographic 

characteristics

Households Civilian noninstitutional 

population age 16 and over

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 3-9

NSF’s Scientists and Engineers Statistical Data System 

(SESTAT) provides detailed employment, education, and 

demographic data for scientists and engineers under age 76 

residing in the United States. The 2010 SESTAT defines 

scientists and engineers as individuals who have college 

degrees in S&E or S&E-related fields or who are working 

in S&E or S&E-related occupations.* (See table 3-2 for definitions of S&E and S&E-related occupations.) Unless otherwise noted, the term “scientists and engineers” as used in 

this chapter refers to this broad SESTAT population. Data 

available through SESTAT are collected by three large 

demographic and workforce surveys of individuals conducted by NSF: the National Survey of College Graduates 

(NSCG), the National Survey of Recent College Graduates 

(NSRCG), and the Survey of Doctorate Recipients (SDR). 

SESTAT integrates the data from the three surveys, and 

together the data provide a comprehensive picture of scientists and engineers in the United States. 

The NSCG is the central component of SESTAT, 

providing data that detail the characteristics of the entire 

college-educated population in the United States (regardless of their S&E background). Its population of college 

graduates includes individuals trained as scientists and 

engineers who hold at least a bachelor’s degree. Because 

it covers the entire college graduate population residing 

in the United States, the NSCG provides information on 

individuals educated or employed in S&E fields as well 

as those employed or educated in non-S&E fields. The 

data presented in this chapter for all college graduates 

(regardless of S&E background) are based on the NSCG. 

Whereas NSCG data cover the general college-educated population, the NSRCG supplements SESTAT by 

adding recent college graduates at the bachelor’s and 

master’s degree level. The 2010 NSRCG data represent 

almost 1.5 million recent bachelor’s and master’s graduates in science, engineering, and health (SEH) fields from 

academic years 2008 and 2009.

The SDR supplements SESTAT by adding doctoral 

scientists and engineers who earned their SEH doctorates 

from U.S. academic institutions. Data from the 2010 SDR 

were collected from doctoral graduates who received 

SEH research degrees from a U.S. academic institution 

before 1 July 2009. 

*For details on the 2010 SESTAT see http://www.nsf.gov/statistics/

sestat/ and http://www.nsf.gov/statistics/infbrief/nsf13311/.

NSF’s Scientists and Engineers Statistical Data System 

Table 3-2

Classification of degree fields and occupations

Occupation 

classification

Classification Degree field Occupation STEM S&T

S&E Biological, agricultural, and environmental 

life sciences

Biological, agricultural, and environmental 

life scientists

X X

Computer and mathematical sciences Computer and mathematical scientists X X

Physical sciences Physical scientists X X

Social sciences Social scientists X X

Engineering Engineers X X

S&E postsecondary teachers X X

S&E-related Health fields Health-related occupations

Science and math teacher education S&E managers X

Technology and technical fields S&E precollege teachers

Architecture S&E technicians and technologists X X

Actuarial science Architects

Actuaries

S&E-related postsecondary teachers

Non-S&E Management and administration Non-S&E managers

Education (except science and math 

teacher education)

Management-related occupations

Non-S&E precollege teachers

Social services and related fields Non-S&E postsecondary teachers

Sales and marketing Social services occupations

Arts and humanities Sales and marketing occupations

Other fields Arts and humanities occupations

Other occupations

S&T = science and technology; STEM = science, technology, engineering, and mathematics.

NOTES: The designations STEM and S&T refer to occupations only. For more detailed classification of occupations and degrees by S&E, S&E-related, 

and non-S&E, see National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT), http://sestat.nsf.gov.

Science and Engineering Indicators 2014
3-10 ♦ Chapter 3. Science and Engineering Labor Force

occupations. A relatively narrow definition of the S&E 

workforce consists of workers in occupations that NSF designates as S&E occupations. A much broader definition of 

an S&E worker, defined by SESTAT, includes any individual with at least a bachelor’s (or higher) degree in an S&E or 

S&E-related field of study or a college graduate in any field 

employed in an S&E or S&E-related occupation. The S&E 

workforce may also be defined by the technical expertise or 

training required to perform a job. Unlike information on 

occupational categories or educational credentials, information on the use of technical knowledge, skills, or expertise in 

a person’s job reflects that individual’s subjective opinion 

about the content and characteristics of the job.3 The next 

section provides estimates of the size of the S&E workforce 

using all three definitions. 

Size of the S&E Workforce

Defined by occupation, the U.S. S&E workforce totals 

between 5.8 million and 6.0 million people according to the 

most recent estimates (table 3-3). Those in S&E occupations 

who had at least a bachelor’s degree are estimated at between 

4.3 million and 5.4 million (table 3-3).4 By far the largest 

categories of S&E occupations are in computer and mathematical sciences and engineering, which together account 

for between three-fourths and four-fifths of all employed 

workers in S&E occupations (figure 3-1). Occupations in 

the life sciences, social sciences, and physical sciences each 

employ a smaller proportion of S&E workers.

As noted earlier, S&E degree holders greatly outnumber those currently employed in S&E occupations. In 2010, 

about 19.5 million college graduates in the United States had 

a bachelor’s or higher level degree in an S&E field of study 

(table 3-3). Almost three-fourths of these college graduates 

(14.5 million) attained their highest degree in an S&E field 

(in this chapter, these individuals are referred to as S&E 

highest degree holders). An individual’s highest degree is 

often an accurate representation of the skills and credentials 

that one employs in the labor market, which is why the data 

presented in this chapter by educational attainment are often provided for highest degree. Overall, social sciences and 

engineering were the most common degree fields among 

individuals with S&E highest degrees (figure 3-2). Of the 

14.5 million S&E highest degree holders, slightly more than 

one-fourth attained a master’s degree (3 million) or doctorate (979,000) as their highest degree.5

The majority of individuals with a highest degree in S&E 

reported that their job was either closely or somewhat related to their field of highest degree (table 3-3). This is despite 

Table 3-3

Measures and size of U.S. S&E workforce: 2010, 2011, and 2012

Measure Education coverage Data source Workers

Occupation

Employed in S&E occupations ....................................... All education levels 2012 BLS OES 5,968,000

Employed in S&E occupations ....................................... Bachelor’s and above 2010 NSF/NCSES SESTAT 5,398,000

Employed in S&E occupations ....................................... All education levels 2011 Census Bureau ACS 5,756,000

Employed in S&E occupations ....................................... Bachelor’s and above 2011 Census Bureau ACS 4,279,000

Education

At least one degree in S&E field ..................................... Bachelor’s and above 2010 NSF/NCSES SESTAT 19,493,000

Highest degree in S&E field ............................................ Bachelor’s and above 2010 NSF/NCSES SESTAT 14,457,000

Job closely related to highest degree ......................... Bachelor’s and above 2010 NSF/NCSES SESTAT 5,396,000

S&E occupation....................................................... Bachelor’s and above 2010 NSF/NCSES SESTAT 2,796,000

Other occupation..................................................... Bachelor’s and above 2010 NSF/NCSES SESTAT 2,600,000

Job somewhat related to highest degree.................... Bachelor’s and above 2010 NSF/NCSES SESTAT 3,358,000

S&E occupation....................................................... Bachelor’s and above 2010 NSF/NCSES SESTAT 966,000

Other occupation..................................................... Bachelor’s and above 2010 NSF/NCSES SESTAT 2,392,000

Job requires S&E technical expertise at bachelor’s level

In one or more S&E fields ............................................... Bachelor’s and above 2010 NSF/NCSES SESTAT NSCG 16,456,000

Engineering, computer science, mathematics, 

or natural sciences .................................................. Bachelor’s and above 2010 NSF/NCSES SESTAT NSCG 11,710,000

Social sciences ........................................................... Bachelor’s and above 2010 NSF/NCSES SESTAT NSCG 7,443,000

ACS = American Community Survey; BLS = Bureau of Labor Statistics; NSCG = National Survey of College Graduates; NSF/NCSES = National Science 

Foundation, National Center for Science and Engineering Statistics; OES = Occupational Employment Statistics Survey; SESTAT = Scientists and 

Engineers Statistical Data System.

NOTES: Estimates of the S&E workforce vary across the example surveys because of differences in the scope of the data collection (SESTAT surveys 

collect data from individuals with bachelor’s degrees and above only); because of the survey respondent (SESTAT surveys collect data from individuals, 

OES collects data from establishments, and ACS collects data from households); or because of the level of detail collected on an occupation, which 

aids in classifying a reported occupation into a standard occupational category. All of these differences can affect the estimates. For example, the 

SESTAT estimate of the number of workers in S&E occupations includes postsecondary teachers of S&E fields; however, postsecondary teachers in 

ACS are grouped under a single occupation code regardless of field and are therefore not included in the ACS estimate of the number of workers in 

S&E occupations.

SOURCES: BLS, 2012 OES; Census Bureau, 2011 ACS; NSF/NCSES, 2010 NSCG, and 2010 SESTAT integrated file. 

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 3-11

the fact that many of these individuals were employed in occupations not categorized as S&E. This suggests that the application of S&E knowledge and skills is widespread across 

the U.S. economy and not just limited to S&E occupations. 

The extensive use of S&E expertise in the workplace is 

also evident from the number of college graduates who indicate that their jobs require technical expertise at the bachelor’s degree level in S&E fields. According to the 2010 

National Survey of College Graduates (NSCG), 16.5 million college graduates reported that their jobs require at least 

this level of technical expertise in one or more S&E fields 

(table 3-3). This figure is much higher than the estimated 

number of college graduates employed in S&E occupations 

(5.4 million).

Growth of the S&E Workforce

The S&E workforce has grown faster over time than the 

overall workforce. According to Census Bureau data, employment in S&E occupations grew from about 1.1 million 

in 1960 to about 5.8 million in 2011.6 This represents an 

average annual growth rate of 3.3%, compared to the 1.5% 

growth in total employment during this period. As a proportion of all employment, S&E occupational employment 

grew from 1.6% in 1960 to 4.1% in 2011.

Data from more recent years indicate that trends in S&E 

employment compared favorably to overall employment 

trends during and after the 2007–09 economic downturn. 

OES employment estimates from BLS indicate that the size 

of the S&E workforce rose slightly from 5.4 million in May 

2006 to 5.8 million in May 2009 and then remained relatively steady through May 2012, reaching a level of 6 million. 

In contrast, the total workforce during this period declined 

from 133 million in May 2006 to 131 million in May 2009 

and then to 130 million in May 2012. The broader STEM 

aggregate (including S&E technicians, S&E managers, etc.) 

remained relatively steady at 7.9 million in May 2012, compared with 7.8 million in May 2009 and 7.4 million in May 

2006. BLS projects that between 2010 and 2020 S&E occupations—particularly computer and mathematical sciences, 

life sciences, and social sciences-related occupations—will 

grow at a faster rate than the total workforce. (See sidebar, 

“Projected Growth of Employment in S&E Occupations.”) 

The growth in the number of individuals with S&E degrees in recent years can be examined using data from NSF’s 

SESTAT. The number of S&E highest degree holders employed in the United States grew from 9.6 million to 11.4 

million between 2003 and 2010, with most broad fields exhibiting growth (figure 3-3). Similarly, employment in S&E 

occupations among college degree holders rose from 4.8 

million to 5.4 million during this timeframe. Although individuals with advanced degrees beyond the bachelor’s level 

SOURCES: Bureau of Labor Statistics, Occupational Employment 

Statistics Survey, 2012; National Science Foundation, National 

Center for Science and Engineering Statistics, Scientists and 

Engineers Statistical Data System (SESTAT) (2010), http://sestat.nsf.gov. 

Science and Engineering Indicators 2014

Percent

Figure 3-1

Employment in S&E occupations, by broad 

occupational category: 2010 and 2012

2010

(bachelor’s

education and

higher)

2012

(all education

levels)

0

20

40

60

80

100

Life scientists

Computer and 

mathematical 

scientists

Physical scientists

Social scientists

Engineers

NOTE: All degree levels include professional degrees not shown 

separately.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

Percent

Figure 3-2

S&E degrees among college graduates, by field 

and level of highest degree: 2010

All

highest

degree

levels

Highest

degree:

Bachelor’s

Highest

degree:

Master’s

Highest

degree:

Doctorate

0

20

40

60

80

100

Biological, 

agricultural, and 

environmental 

life sciences

Computer and 

mathematical 

sciences

Physical

sciences

Social

sciences

Engineering
3-12 ♦ Chapter 3. Science and Engineering Labor Force

The most recent Bureau of Labor Statistics (BLS) occupational projections, for the period 2010–20, suggest that 

total employment in occupations that NSF classifies as S&E 

will increase at a faster rate (18.7%) than employment in all 

occupations (14.3%) (figure 3-A; table 3-A). These projections are based only on the demand for narrowly defined 

S&E occupations and do not include the wider range of 

occupations in which S&E degree holders often use their 

training.

BLS also projects that, for the period 2010–20, job openings in NSF-identified S&E occupations will represent a 

slightly larger proportion of current employment than openings in all other occupations: 39.6% versus 38.3% (figure 

3-B). Job openings include both growth in total employment 

and openings caused by attrition.

Of the BLS-projected job openings in NSF-identified 

S&E occupations, 59% are in computer and mathematical 

scientist occupations, the largest sub-category of S&E occupations (table 3-A). These occupations also have the largest growth rate (23.1%). Life sciences and social sciences 

occupations, which account for a much smaller proportion 

of S&E occupations, have the next highest projected growth 

rates: 20.4% and 18.5%, respectively. Job openings in the 

social sciences are projected to be particularly high, representing half of the current employment in that field. Physical 

scientists and engineering occupations are projected to grow 

at rates slightly lower than the rate for all occupations. Total 

job openings in physical sciences, however, are expected to 

represent a larger share of current employment than openings in all occupations.

In addition to S&E occupations, table 3-A also shows 

selected other occupations that contain significant numbers 

of S&E-trained workers. Among these, the health care practitioners and technicians occupation, which employs more 

workers than all S&E occupations combined, is projected 

to grow at 25.9%, nearly double the rate of growth in all 

occupations. The postsecondary teachers occupation, which 

includes all fields of instruction, and the S&E managers occupation are projected to grow 17.4% and 14.0%, respectively, both of which are lower than the projected growth 

rate in S&E occupations but close to (S&E managers) or 

higher than (postsecondary teachers) the projected growth 

rate in all occupations. In contrast, BLS projects that computer programmers and S&E technicians will grow more 

slowly than all occupations as well as all S&E occupations. 

Employment projections are uncertain.* Many industry and government decisions that affect hiring are closely 

linked to national and global fluctuations in aggregate economic activity, which are difficult to forecast long in advance. In addition, technological and other innovations will 

influence demand for workers in specific occupations. The 

assumptions underlying projections are sensitive to fundamental empirical relationships, and, as a result, may become less accurate as overall economic conditions change.

* Although BLS does a reasonable job of projecting employment in 

many occupations, the mean absolute percentage error in the 1996 forecast of employment in detailed occupations in 2006 was 17.6% (Wyatt 

2010). The inaccuracies in the 1996 projection of 2006 employment 

were primarily driven by not projecting the housing bubble and increases in oil prices (Wyatt 2010).

Projected Growth of Employment in S&E Occupations 

Figure 3-A

Bureau of Labor Statistics projected increases in employment for S&E and selected other occupations: 2010–20

Percent 

SOURCE: Bureau of Labor Statistics, Employment Projections program, 2010–20, special tabulations (2013) of 2010–20 Employment Projections. See 

appendix table 3-2. 

Science and Engineering Indicators 2014

All

S&E

occupations

Life

scientists

Computer

and

mathematical

scientists

Social

scientists

Physical

scientists

Engineers All

occupations

Health

care

practitioners

and technicians

S&E

managers

Postsecondary

teachers

Computer Lawyers

programmers

0

5

10

15

20

25

30
Science and Engineering Indicators 2014 ♦ 3-13

Projected Growth of Employment in S&E Occupations—continued

Table 3-A

Bureau of Labor Statistics projections of employment and job openings in S&E and other selected occupations: 

2010–20

(Thousands)

Occupation

BLS National 

Employment 

Matrix 

2010 estimate

BLS 

projected 2020 

employment

Job openings 

from growth 

and net 

replacements, 

2010–20

10-year 

growth in total 

employment (%)

10-year job 

openings as 

percentage 

of 2010 

employment

All occupations............................................. 143,068 163,537 54,787 14.3 38.3

All S&E ...................................................... 5,546 6,585 2,197 18.7 39.6

Computer and mathematical 

scientists............................................ 3,157 3,886 1,290 23.1 40.9

Life scientists ........................................ 286 344 106 20.4 37.1

Physical scientists................................. 282 318 122 12.7 43.2

Social scientists and related 

occupations....................................... 302 358 152 18.5 50.4

Engineers .............................................. 1,519 1,679 526 10.6 34.6

S&E-related occupations..........................

S&E managers....................................... 534 609 186 14.0 34.8

S&E technicians .................................... 808 873 275 8.0 34.1

Computer programmers........................ 363 407 128 12.0 35.3

Health care practitioners 

and technicians ................................. 7,799 9,819 3,591 25.9 46.0

Selected other occupations......................

Postsecondary teachers ....................... 1,756 2,062 586 17.4 33.4

Lawyers................................................. 728 802 212 10.1 29.1

BLS = Bureau of Labor Statistics.

NOTES: Estimates of current and projected employment for 2010–20 are from BLS’s National Employment Matrix; data in the matrix are from the 

Occupational Employment Statistics (OES) survey and the Current Population Survey (CPS). Together, these sources cover paid workers, self-employed 

workers, and unpaid family workers in all industries, agriculture, and private households. Because data are derived from multiple sources, they can 

often differ from employment data provided by OES, CPS, or other employment surveys alone. BLS does not make projections for S&E occupations as 

a group; numbers in the table are based on the sum of BLS projections for occupations that the National Science Foundation considers as S&E. See 

appendix table 3-2.

SOURCE: BLS, Employment Projections program, 2010–20, special tabulations (2013) of 2010–20 Employment Projections.

Science and Engineering Indicators 2014

Figure 3-B

Bureau of Labor Statistics projected job openings in S&E and selected other occupations: 2010–20

Percentage of 2010 employment 

SOURCE: Bureau of Labor Statistics, Employment Projections program, 2010–20, special tabulations (2013) of 2010–20 Employment Projections. See 

appendix table 3-2. 

Science and Engineering Indicators 2014

All

S&E

occupations

Life

scientists

Computer

and

mathematical

scientists

Social

scientists 

Physical 

scientists

Engineers All

occupations

Health

care

practitioners

and technicians

S&E

managers

Postsecondary

teachers 

Computer Lawyers

programmers

0

10

20

30

40

50

60
3-14 ♦ Chapter 3. Science and Engineering Labor Force

GED = General Equivalency Diploma.

SOURCE: Census Bureau, American Community Survey (2011). 

Science and Engineering Indicators 2014

Percent

Figure 3-4

Educational attainment, by type of occupation: 2011

S&E occupations,

except

postsecondary

teachers

All other

occupations

0

20

40

60

80

100

High school or less, 

no diploma

High school 

diploma or GED

Some college, 

no degree

Associate’s

Bachelor’s

Master’s

Professional

Doctorate

account for a minority of the college graduate population, 

between 2003 and 2010 the growth in S&E degree holders 

with advanced degrees generally outpaced the growth in individuals with bachelor’s degrees in most broad fields (with 

the exception of social sciences) (figure 3-3). (See chapter 2 

for a fuller discussion of S&E degrees.) 

A number of factors likely contributed to the growth in 

the U.S. S&E labor force over time: the rising demand for 

S&E skills in a global and highly technological economic 

landscape; increases in U.S. S&E degrees earned by women, 

by racial and ethnic minority groups, and by foreign-born 

individuals; temporary and permanent migration to the 

United States of those with foreign S&E educations; and the 

relatively small proportion of scientists and engineers retiring from the S&E labor force. The demographic sections of 

this chapter provide data on aging and retirement patterns 

of scientists and engineers as well as on S&E participation 

by women, by racial and ethnic minorities, and by foreignborn individuals.

Educational Distribution of Workers in 

S&E Occupations

Workers in S&E occupations have undergone more 

formal education than the general workforce (figure 3-4). 

Data from the 2011 ACS indicate that a larger proportion 

of workers in nonacademic S&E occupations (74%) hold a 

bachelor’s or higher degree than workers in all other occupations (30%).7

 The proportion of workers with advanced 

degrees beyond the bachelor’s level is 31% in S&E occupations, compared to 11% in all other occupations. About 

7% of all S&E workers (except postsecondary teachers) 

have doctorates.

Figure 3-3

Average annual growth in the number of employed individuals whose highest degree is in S&E, by field and level 

of highest degree: 2003–10

Percent 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Science and Engineering Statistical Data System 

(SESTAT) (2003 and 2010), http://sestat.nsf.gov. 

Science and Engineering Indicators 2014

Computer and

mathematical sciences

Biological, agricultural, and

environmental life sciences

Physical sciences Social sciences Engineering

–1

0

–2

1

2

3

4

5

6

Bachelor’s Master’s Doctorate
Science and Engineering Indicators 2014 ♦ 3-15

Compared with the rest of the workforce, a very small 

minority of those employed in S&E occupations have only 

a high school degree. Many individuals enter the S&E 

workforce with marketable technical skills from technical 

or vocational schools (with or without an earned associate’s degree) or college courses, and many acquire these 

skills through workforce experience or on-the-job training. 

In information technology, and to some extent in other occupations, employers frequently use certification exams, not 

formal degrees, to judge skills. (See sidebar, “The U.S. S&E 

Workforce Without a Bachelor’s Degree” and the discussion 

in chapter 2.)

According to the 2010 SESTAT data, the vast majority (81%) of college graduates employed in S&E occupations have at least one S&E degree (table 3-4), suggesting 

that formal S&E training is the usual pathway for obtaining 

employment in these occupations. However, the importance 

of formal S&E training in the same broad field as one’s S&E 

occupation varies across occupational categories. Among 

computer and mathematical scientists, for example, less 

than half (44%) have a bachelor’s or higher level degree in 

the field of computer and mathematical sciences. The proportion is significantly higher in other broad S&E occupational categories: 73% of life scientists, 72% of physical 

scientists, 77% of social scientists, and 81% of engineers 

have a bachelor’s or higher level degree in their respective 

broad field. Slightly more than one-fourth (28%) of computer and mathematical scientists do not have any S&E degree. 

The next section presents data on the proportion of S&E degree holders who obtain employment in S&E and non-S&E 

occupational categories. 

Although the Scientists and Engineers Statistical Data 

System (SESTAT) provides detailed information on college graduate scientists and engineers, it lacks similar 

data on individuals who do not have a bachelor’s degree. 

The Census Bureau’s American Community Survey 

(ACS) provides nationally representative occupational 

data for workers at all levels of education.* In 2011, about 

one-fourth of S&E workers age 25 and older did not have 

a bachelor’s degree. This sidebar looks at the demographic, educational, and employment characteristics of these 

S&E workers without a bachelor’s degree.†

Relative to college graduate workers employed in 

S&E occupations, a disproportionate number of those 

without a bachelor’s degree employed in S&E occupations were black or Hispanic and native U.S. born. In 

2011, about 9% of S&E workers without a bachelor’s degree were black, and another 9% were Hispanic. In contrast, 6% of college-educated S&E workers were black 

and 5% were Hispanic. Asians represented only 3% of 

S&E workers without a bachelor’s degree, compared to 

19% of S&E workers with a bachelor’s degree. In 2011, 

only 8% of S&E workers without a college degree were 

foreign born, compared to about one-fourth of collegeeducated S&E workers.

S&E workers without a bachelor’s degree were 

mostly concentrated in computer occupations, with 69% 

employed in the field. In comparison, 44% of the collegeeducated S&E workers held computer jobs. Among computer occupations, computer support specialists, network 

and computer systems administrators, and other computer occupations together represented about half of the 

S&E workers without a bachelor’s degree employed in 

computer occupations. Unlike the computer field, life sciences, physical sciences, and social sciences occupations 

had much smaller proportions of workers without a bachelor’s degree. About 3% of the S&E workforce without 

a bachelor’s degree were employed in these areas combined, compared to about one-fifth of the college-educated S&E workforce. 

Relative to other occupations, S&E occupations provide stable employment with good earnings for workers 

without a college degree. In 2011, the median earnings 

among workers 25 years of age and older, without a 

bachelor’s degree, and employed in S&E occupations 

($60,000) was twice as high as the median earnings 

among comparable workers employed in other occupations ($30,000). The unemployment rate among these 

workers in S&E occupations was 6%, about half the rate 

in other occupations (11%).

Workers employed in S&E occupations had more 

formal training (even if they did not have a bachelor’s 

degree) than those employed in other occupations, so it 

is not surprising that salaries were higher in S&E jobs. 

About one-third of the workers without a bachelor’s 

degree employed in S&E occupations had an associate’s degree, compared to 14% of those employed in 

other occupations.

* For methodological reasons, estimates from ACS and SESTAT differ slightly even for the college graduate population, which both surveys cover. For example, the two surveys vary in the level of detail 

collected on work activities, which affects how workers are coded into 

standard occupational categories. In addition, ACS collects data from 

households, whereas SESTAT collects data from individuals. Finally, 

the analysis using ACS data counts postsecondary teachers of S&E as 

working in non-S&E occupations because the Census Bureau data do 

not identify them by field.

† This sidebar defines the S&E workforce by workers in S&E occupations (except postsecondary teachers in S&E fields). The ACS data do 

not allow for separate identification of postsecondary teachers by fields. 

See appendix table 3-1 for a list of S&E occupations in the 2011 ACS.

The U.S. S&E Workforce Without a Bachelor’s Degree 
3-16 ♦ Chapter 3. Science and Engineering Labor Force

Occupational Distribution of S&E Degree 

Holders and Relationship between Jobs and 

Degrees

NSF’s SESTAT provides information on the degree 

and occupational choices of scientists and engineers in the 

United States, thus enabling a comparison of the interplay 

between degree and occupation for members of the S&E 

workforce with and without a highest degree in an S&E discipline. Although an S&E degree is often necessary to obtain 

S&E employment, the data indicate that many individuals 

with S&E degrees pursue careers outside of S&E. The majority of workers with S&E training who work in non-S&E 

jobs reported that their work is nonetheless related to their 

S&E training, suggesting that the application of S&E skills 

and expertise extends well beyond the jobs NSF classifies as 

S&E. (The next section, “S&E Workers in the Economy,” 

provides data on R&D activity of scientists and engineers 

employed in S&E and non-S&E occupations.) 

Only about half of S&E highest degree holders are employed in an S&E (35%) or S&E-related (14%) occupation; 

the rest are employed in non-S&E occupations. Figure 3-5 

shows the occupational distribution of the S&E workforce 

with S&E, S&E-related, and non-S&E highest degrees. The 

largest category of non-S&E jobs for S&E highest degree 

holders is management and management-related occupations (2.1 million workers), followed by sales and marketing 

occupations (995,000 workers) (appendix table 3-3). Other 

non-S&E occupations with a large number of S&E-trained 

workers include social services occupations (400,000) and 

Table 3-4

Educational background of college graduates employed in S&E occupations, by broad S&E occupational 

category: 2010

(Percent)

Educational background

All S&E 

occupations

Biological, 

agricultural, 

and 

environmental 

life scientists

Computer and 

mathematical 

scientists

Physical 

scientists

Social 

scientists Engineers

Total (n) .................................................... 5,398,000 597,000 2,394,000 320,000 518,000 1,569,000

At least one S&E degree.......................... 81.1 86.3 72.1 96.9 81.9 89.5

At least one S&E degree in field....... 81.1 73.2 44.2 72.2 76.8 81.0

Highest degree in field ..................... 74.1 66.3 40.1 66.3 67.4 73.4

All degrees in S&E............................ 69.3 71.4 61.8 88.1 56.2 80.5

No S&E degrees but at least one 

S&E-related degree.............................. 4.7 7.4 4.6 2.5 2.1 5.1

No S&E or S&E-related degree but at 

least one non-S&E degree................... 14.2 6.5 23.4 0.6 16.0 5.3

NOTES: At least one S&E degree in field is the proportion of workers in a particular S&E occupational category with at least one degree in the same 

broad field. Highest degree in field is the proportion of workers in a particular S&E occupational category with highest degree in the same broad field. 

For example, among computer and mathematical scientists, these data refer to the proportion with at least one college-level or higher degree in the 

broad field of computer and mathematical sciences and the proportion with highest degree in the broad field of computer and mathematical sciences, 

respectively. Detail may not add to total because of rounding.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (2010), http://sestat.nsf.gov. 

Science and Engineering Indicators 2014

NOTE: Scientists and engineers include those with one or more S&E 

or S&E-related degrees at the bachelor’s level or higher or those who 

have only a non-S&E degree at the bachelor's level or higher and are 

employed in an S&E or S&E-related occupation. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

Percent

Figure 3-5

Occupational distribution of scientists and 

engineers, by broad field of highest degree: 2010

S&E eld S&E-related

eld

Non-S&E

eld 

0

20

40

60

80

100

S&E occupation

S&E-related

occupation

Non-S&E

occupation
Science and Engineering Indicators 2014 ♦ 3-17

college and precollege teaching in non-S&E areas (358,000). 

S&E highest degree holders also work in S&E-related jobs 

(14%) such as health occupations (532,000), S&E managerial positions (417,000), S&E technicians or technologists positions (405,000), and precollege teaching in S&E 

areas (196,000). 

Most individuals who have S&E highest degrees but are 

not working in S&E occupations do not see their field of 

highest degree as entirely irrelevant to their work. Rather, 

most indicate that their jobs are either closely (35%) or 

somewhat (32%) related to their highest degree field (table 

3-5). Among S&E highest degree holders in non-S&E managerial and management-related occupations, for example, 

33% indicate that their jobs are closely related, and another 

40% say that their jobs are somewhat related, to their S&E 

degree. Among those in social services and related occupations, 73% say that their jobs are closely related, and another 

21% say that their jobs are somewhat related, to their S&E 

degree. Among workers in sales and marketing, 50% characterize their jobs as closely or somewhat related to their 

S&E degree.

Unlike members of the S&E workforce with an S&E 

highest degree, half or more of the S&E workforce with 

S&E-related or non-S&E highest degrees obtain employment in their respective broad occupational category (figure 

3-5). For those with an S&E-related highest degree, the largest category of jobs is health occupations (3.2 million); for 

those with a non-S&E highest degree, the largest category 

of jobs is management and management-related occupations 

(862,000) (appendix table 3-3). Significant numbers of the 

S&E workforce with a non-S&E highest degree also work in 

health occupations (604,000), in precollege teaching in S&E 

areas (536,000), or as lawyers or judges (571,000).

The pattern of significant proportions of S&E highest degree holders obtaining employment in areas other than S&E 

occupations has been robust over time. SESTAT data from 

1993 indicate that 36% of all scientists and engineers with 

S&E highest degrees were employed in S&E occupations, 

and the rest held positions in areas other than S&E. 

The proportion of S&E highest degree holders who go 

on to work in S&E occupations varies substantially by S&E 

degree fields and levels. Individuals with social sciences 

highest degrees are the least likely to work in S&E occupations; these individuals primarily obtain non-S&E employment (figure 3-6). Only about 13% of social sciences 

highest degree holders work in S&E occupations, whereas 

80% work in non-S&E occupations. Similar proportions of 

life sciences highest degree holders work in S&E occupations (30%) and in S&E-related occupations (26%) such as 

health occupations, and less than half (44%) work in nonS&E occupations. In contrast, individuals with computer 

and mathematical sciences (54%), physical sciences (51%), 

or engineering (58%) highest degrees are much more likely 

to work in S&E occupations. Computer and mathematical 

sciences highest degree holders are the most likely to obtain employment in the broad S&E field in which they were 

trained (51%), whereas social sciences highest degree holders are the least likely to do so (8%). 

This pattern of field differences generally characterizes 

individuals whose highest degree is at either a bachelor’s or 

master’s degree level. At the doctoral level, the size of these 

field differences shrinks substantially (figure 3-7). S&E doctorate holders most often work in an S&E occupation similar 

to their doctoral field. 

Whereas figure 3-7 shows the proportion of S&E degree 

holders employed in S&E occupations, figure 3-8 shows the 

proportion of S&E degree holders (regardless of occupational categories) who reported that their work is related to their 

S&E degree. Workers with more advanced S&E training are 

more likely than those with only bachelor’s level degrees to 

work in a job that is related to their field of highest degree. 

Up to 5 years after receiving their degrees, 97% of S&E doctorate holders say that they have jobs closely or somewhat 

related to their degree field, compared with 92% of master’s 

degree holders and 73% of bachelor’s degree holders (figure 

3-8). In general, higher proportions of employed individuals with natural sciences and engineering highest degrees 

compared with those with social sciences highest degrees 

Table 3-5

Relationship of highest degree to job among S&E highest degree holders not in S&E occupations, by degree 

level: 2010

Highest degree Workers (n)

Degree related to job (%)

Closely Somewhat Not

All degree levelsa .................................................... 7,386,000 35.2 32.4 32.4

Bachelor’s ........................................................... 5,902,000 31.1 33.1 35.8

Master’s .............................................................. 1,242,000 51.8 28.7 19.5

Doctorate............................................................ 236,000 49.6 34.3 16.1

a

 Includes professional degrees not broken out separately.

NOTE: Detail may not add to total because of rounding.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014
3-18 ♦ Chapter 3. Science and Engineering Labor Force

Figure 3-7

S&E degree holders working in S&E occupations, by level and field of S&E highest degree: 2010

Percent 

NOTE: Individuals may have degrees in more than one S&E degree eld.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (2010), http://sestat.nsf.gov. 

Science and Engineering Indicators 2014

Highest degree: Doctorate Highest degree: Master’s Highest degree: Bachelor’s

0

20

40

60

80

100

Computer and Physical sciences Social sciences Engineering

mathematical sciences

Biological, agricultural, 

and environmental life sciences

NOTES: Detail may not add to total because of rounding. For each broad S&E highest degree eld, S&E occupation (in eld of highest degree) includes 

individuals who report being employed in an occupation in the same broad category. For example, for highest degree holders in computer and 

mathematical sciences, S&E occupation (in eld of highest degree) includes those who report computer or mathematical sciences as their occupation, 

and S&E occupation (not in eld of highest degree) includes those who report an S&E occupation other than computer or mathematical sciences 

occupations.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (2010), http://sestat.nsf.gov. 

Science and Engineering Indicators 2014

Percent

Figure 3-6

Occupational distribution of S&E highest degree holders, by field of highest degree: 2010

Computer and

mathematical

sciences

Biological, agricultural,

and environmental

life sciences

Physical sciences 

Field of highest degree

Social sciences Engineering

0

20

40

60

80

100

S&E occupation

(in eld of highest 

degree)

S&E occupation

(not in eld of

highest degree)

Non-S&E

occupations

S&E-related

occupations
Science and Engineering Indicators 2014 ♦ 3-19

indicate that their jobs are related to their field of highest degree. Thus, among the SESTAT population of scientists and 

engineers in 2010, 75% of life sciences highest degree holders, 77% of physical sciences highest degree holders, 87% of 

computer and mathematical sciences highest degree holders, 

and 88% of engineering highest degree holders reported that 

their jobs were either closely or somewhat related to their 

highest degree field compared with 66% of social sciences 

highest degree holders. This is not surprising given that individuals trained in the social sciences most often obtain employment in non-S&E occupations. 

The pattern of a stronger relationship between S&E jobs 

and S&E degrees at higher degree levels is robust across career stages, as seen in comparisons among groups of bachelor’s, master’s, and doctoral degree holders at comparable 

numbers of years since receiving their degrees (figure 3-8). 

For each group, the relationship between job and field of 

highest degree becomes weaker over time. Possible reasons 

for this decline include changes in career interests, development of skills in different areas, promotion to general management positions, or realization that some of the original 

training has become obsolete. Despite these potential factors, the career-cycle decline in the relevance of an S&E degree appears modest.

Figure 3-8

S&E degree holders employed in jobs related to 

highest degree, by level of and years since 

highest degree: 2010

Percent

NOTE: Data include those who report their job is either closely 

related or somewhat related to the eld of their highest degree.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2010), http://sestat.nsf.gov. 

Science and Engineering Indicators 2014

< 5 5–9 10–14 15–19

Years since degree

20–24 25–29 30–34 ≥ 35

0

60

70

80

90

100

Bachelor’s

Master’s

Doctorate

S&E Workers in the Economy

To understand the economic and scientific contributions 

of scientists and engineers, it is important to know how they 

are distributed across the economy and what kind of work 

they perform. This section examines the characteristics of 

organizations that employ scientists and engineers, including sector and size of employing organizations. This section also describes the distribution of S&E workers within 

particular sectors. The data indicate that individuals trained 

in S&E fields or working in S&E occupations are found in 

all sectors, including for-profit businesses; non-profit organizations; public and private educational institutions; and 

local, state, and federal government. This section also examines self-employed scientists and engineers, as well as 

the concentration of S&E workers by industry sectors and 

by geography. 

The S&E labor force is often seen as a major contributor 

to innovation. Work such as patenting activity, R&D activity, and work-related training are indicators of worker skill 

level, productivity, and innovative capacity. In addition to 

collecting information on formal education and employment, SESTAT gathers data on the degree to which workers 

engage in such activities. This section concludes with data 

on these activities.

Throughout this section, data are provided for the broad 

SESTAT population of scientists and engineers, including those employed in S&E or S&E-related occupations as 

well as those with S&E or S&E-related bachelor’s or higher 

level degrees. Whenever possible, the data distinguish between individuals with S&E degrees and those working in 

S&E occupations. 

Employment Sectors

The business sector is by far the largest employer of the 

broad S&E workforce covered by SESTAT, employing about 

70% of individuals trained or working in S&E in 2010 (table 

3-6). The education sector, including private and public institutions, employs 19% of the SESTAT population of scientists and engineers, and the government sector, including 

federal, state, and local government, employs another 11%. 

Within the business sector, for-profit businesses account for 

a larger number of scientists and engineers than non-profit 

organizations or the self-employed; within the education 

sector, 2-year and precollege institutions employ a larger 

number of scientists and engineers than 4-year institutions. 

The relative distribution in the business, education, and 

government sectors has remained relatively stable since the 

early 1990s (figure 3-9). Nonetheless, some minor shifts occurred between 1993 and 2010: 

♦ The proportion of scientists and engineers working in 

4-year educational institutions dropped slightly (from 

9.3% to 7.9%). 
3-20 ♦ Chapter 3. Science and Engineering Labor Force

Table 3-6

Employment sector of scientists and engineers, by broad occupational category and degree field: 2010

Employment sector

All employed 

scientists and 

engineers

Highest degree 

in S&E

S&E 

occupations

S&E-related 

occupations

Non-S&E 

occupations

Total (n) ........................................................................ 21,903,000 11,385,000 5,398,000 6,957,000 9,549,000

Business/industry (%).............................................. 69.8 71.8 70.3 68.3 70.7

For-profit businesses............................................ 52.5 58.6 62.1 45.3 52.3

Nonprofit organizations........................................ 10.7 7.0 4.6 17.6 9.1

Self-employed, unincorporated businesses ........ 6.6 6.2 3.6 5.4 9.3

Education (%) ......................................................... 19.0 15.5 17.6 23.0 16.9

4-year institutions................................................. 7.9 8.6 14.3 7.4 4.7

2-year and precollege institutions ....................... 11.1 6.9 3.3 15.7 12.2

Government (%)....................................................... 11.2 12.6 12.2 8.7 12.4

Federal ................................................................. 4.5 5.4 6.3 3.7 4.2

State/local ............................................................ 6.6 7.3 5.9 5.0 8.2

NOTE: Scientists and engineers include those with one or more S&E or S&E-related degrees at the bachelor’s level or higher or those who have only a 

non-S&E degree at the bachelor’s level or higher and are employed in an S&E or S&E-related occupation. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

NOTES: During 1993–99, scientists and engineers include those with one or more S&E degrees at the bachelor’s level or higher or those who have only a 

non-S&E degree at the bachelor’s level or higher and are employed in an S&E occupation. During 2003–10, scientists and engineers include those with 

one or more S&E or S&E-related degrees at the bachelor’s level or higher or those who have only a non-S&E degree at the bachelor’s level or higher and 

are employed in an S&E or S&E-related occupation. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (1993–2010), http://sestat.nsf.gov. 

Science and Engineering Indicators 2014

Percent

Figure 3-9

Employed scientists and engineers, by employment sector: 1993–2010

1993 1995 1997 1999 2003 2006 2008 2010

0

20

40

60

80

100

4-year institutions

2-year and precollege institutions

Federal government

State/local government

Self-employed, unincorporated

businesses

Nonprot organizations

For-prot businesses
Science and Engineering Indicators 2014 ♦ 3-21

♦ The proportion of scientists and engineers working in the 

federal government declined by almost one-third (from 

6.4% to 4.5%). 

♦ The proportion of scientists and engineers working in the 

non-profit sector nearly doubled (from 5.8% to 10.7%). 

Some differences exist in the concentration of particular 

groups of S&E workers across employment sectors. For example, academic institutions are the largest employer of the 

SESTAT population with doctorates, even though the business sector is the largest employer of the overall SESTAT 

population. Whereas individuals employed in engineering 

occupations and computer and mathematical sciences occupations are largely concentrated in the business sector, those 

employed as life scientists and social scientists are more 

evenly distributed between the business and education sectors. The following discussion provides a deeper analysis of 

the economic sectors in which scientists and engineers work.

Education Sector

Overall, the education sector employs nearly one-fifth 

of the broad S&E workforce covered by SESTAT (table 

3-6). Depending on the population, however, the proportion 

working within different parts of the education sector varies. 

For example, within the education sector, the vast majority 

of S&E highest degree holders whose highest degree is at the 

doctoral level work in 4-year institutions, but the majority of 

those whose highest degree is at the bachelor’s level work 

in 2-year and precollege institutions (figure 3-10; appendix 

table 3-4). In addition to tenure or tenure-track faculty, the 

doctorate population in the education sector includes individuals who hold postdoctoral appointments and other temporary positions, work in various other S&E teaching and 

research jobs, perform administrative functions, and are 

employed in a wide variety of non-S&E occupations. (See 

chapter 5 for additional details on academic employment of 

science, engineering, and health [SEH] doctorates.) 

Of scientists and engineers who are employed in S&E 

occupations, 18% work in the education sector (table 3-6). 

Within the education sector, the majority of those employed 

in S&E occupations are concentrated in 4-year institutions 

(81%). In comparison, the great majority of workers in 

S&E-related or non-S&E occupations in the education sector are found in 2-year and precollege institutions (68% and 

72%, respectively). These workers in these types of institutions are primarily teachers. Within S&E occupations, larger 

proportions of life, physical, and social scientists work in the 

education sector than engineers or computer and mathematical scientists (figure 3-11).

Business Sector

For-profit businesses. For-profit businesses employ the 

largest proportion of scientists and engineers (table 3-6). For 

the broad SESTAT population with doctorates, however, 

for-profit businesses are second to 4-year educational institutions (figure 3-10; appendix table 3-4). Approximately 

three-fourths of scientists and engineers working in computer and mathematical sciences occupations (73%) and in 

engineering occupations (76%) are employed by for-profit 

businesses. The proportions are much lower for those in other S&E occupations, ranging from 18% for social scientists 

to 40% for physical scientists (figure 3-11). 

Non-profit organizations. Non-profit organizations have 

shown substantial growth in the percentage of scientists and 

engineers that they employ (figure 3-9). This growth is driven primarily by those working in S&E-related occupations, 

which include health-related jobs. Among all scientists and 

engineers employed in S&E-related occupations, 18% work 

in non-profit organizations (table 3-6). Among those in S&E 

occupations, the proportion working in non-profit organizations is much smaller (5%), although the proportion varies 

significantly across S&E occupational categories: from 2% 

of engineers to 9% of social scientists are employed by these 

organizations (figure 3-11). 

Self-employment. In 2010, almost 4.2 million scientists and engineers (19%) reported being self-employed in 

either an unincorporated or incorporated business, professional practice, or farm (table 3-7).8

 Scientists and engineers 

working in S&E-related or non-S&E occupations reported 

higher levels of self-employment (18% and 24%, respectively) than those working in S&E occupations (12%). Among 

S&E highest degree holders, those with professional degrees 

NOTE: All degree levels include professional degrees not reported 

separately.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

Percent

Figure 3-10

S&E highest degree holders, by degree level and 

employment sector: 2010

All

degree

levels

Bachelor’s Master’s Doctorate

0

20

40

60

80

100

4-year

institutions

2-year and 

precollege

institutions

Federal 

government

State/local 

government

Self-employed, 

unincorporated 

businesses

Nonprot 

organizations

For-prot 

businesses
3-22 ♦ Chapter 3. Science and Engineering Labor Force

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (2010) http://sestat.nsf.gov. 

Science and Engineering Indicators 2014

Percent

Figure 3-11

Broad S&E occupational categories, by employment sector: 2010

All S&E

occupations

Computer and

mathematical

scientists

Biological,

agricultural, and

environmental

life scientists

Physical and

related

scientists

Social and

related scientists

Engineers

0

20

40

60

80

100

4-year institutions

2-year and precollege institutions

Federal government

State/local government

Self-employed,

unincorporated businesses

Nonprot organizations

For-prot businesses

Table 3-7

Self-employed scientists and engineers, by education, occupation, and type of business: 2010

(Percent)

Characteristic Total

Unincorporated 

business

Incorporated 

business

All self-employed scientists and engineers...................................... 19.0 6.6 12.4

Highest degree in S&E field.............................................................. 17.9 6.2 11.7

Biological, agricultural, and environmental life sciences .............. 17.8 6.3 11.5

Computer and mathematical sciences......................................... 14.8 4.5 10.3

Physical sciences ......................................................................... 17.3 5.9 11.4

Social sciences............................................................................. 18.9 7.8 11.1

Engineering................................................................................... 18.7 4.7 14.0

S&E highest degree level..................................................................

Bachelor’s ..................................................................................... 19.4 6.3 13.1

Master’s ........................................................................................ 14.7 5.9 8.8

Doctorate...................................................................................... 11.7 5.3 6.4

Professional .................................................................................. 47.8 39.1 8.7

Occupation

S&E occupation ............................................................................ 12.0 3.6 8.4

Biological, agricultural, and environmental life scientists ......... 6.2 2.0 4.2

Computer and mathematical scientists .................................... 11.7 3.1 8.6

Physical scientists..................................................................... 7.5 1.9 5.6

Social scientists ........................................................................ 16.4 10.4 6.0

Engineers .................................................................................. 14.1 2.9 11.2

S&E-related occupations.............................................................. 18.4 5.4 13.0

Non-S&E occupations .................................................................. 23.5 9.3 14.2

NOTE: Scientists and engineers include those with one or more S&E or S&E-related degrees at the bachelor’s level or higher or those who have only a 

non-S&E degree at the bachelor’s level or higher and are employed in an S&E or S&E-related occupation. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 3-23

reported significantly higher rates of self-employment (48%) 

than those with a bachelor’s degree (19%), master’s degree 

(15%), or doctorate (12%) as their highest degree. 

Incorporated businesses account for the majority of 

self-employed scientists and engineers, with the exception 

of those with a highest degree at the professional level or 

those in social sciences occupations, who primarily work 

in unincorporated businesses (table 3-7). The higher levels 

of unincorporated self-employment among social scientists 

and professional degree holders are largely driven by psychologists. About one-third of those working as psychologists (32%) are self-employed, mostly in unincorporated 

businesses. Nearly half of those whose highest degree at the 

professional level is in a field of psychology (48%) are selfemployed, again primarily in unincorporated businesses. 

Government Sector 

Federal government. The U.S. federal government is 

a major employer of scientists and engineers. According to 

data from the U.S. Office of Personnel Management, in 2012 

the federal government employed approximately 325,000 

persons in S&E occupations, which represents about 15% of 

the federal civilian workforce. Federal workers in S&E jobs 

are almost evenly distributed among computer and mathematical sciences occupations (33%); engineering occupations (32%); and life sciences, physical sciences, and social 

sciences occupations (36%).9

 The vast majority (80%) of the 

federal workers in S&E occupations have a bachelor’s or 

higher level degree.

The five federal agencies with the largest proportions of 

scientists and engineers in their workforce are those with 

strong scientific missions: the National Aeronautics and Space 

Administration (NASA) (65%), the Nuclear Regulatory 

Commission (NRC) (62%), the Environmental Protection 

Agency (EPA) (60%), NSF (40%), and the Department of 

Energy (33%). The Department of Defense employs the 

largest number of scientists and engineers (150,000), accounting for 46% of the federal S&E workforce.10

Among federal workers hired in 2012, about 9% were in 

S&E occupations. Nearly one-third of these newly hired workers were in occupations related to information technology. 

State and local government. In 2010, SESTAT estimated that almost 1.5 million scientists and engineers (7%) 

were working in state and local governments in the United 

States (table 3-6). Public educational institutions, which are 

included in the education sector, are not included in this statistic. The state and local government sector hires a larger 

proportion of scientists and engineers with bachelor’s or 

master’s degrees than of those with doctorates (figure 3-10). 

Approximately 6% of scientists and engineers employed in 

S&E occupations are employed by state and local governments (table 3-6). Within S&E occupations, larger proportions of life scientists, physical scientists, social scientists, 

and engineers work in state and local governments relative 

to computer and mathematical scientists (figure 3-11). 

Employer Size

The vast majority of educational institutions and government entities that employ individuals trained in S&E fields 

or working in S&E occupations are larger employers (i.e., 

having 100 or more employees). These large organizations 

employ 88% of scientists and engineers in the education sector and 92% of those in the government sector. In contrast, 

scientists and engineers working in the business sector are 

more broadly distributed across firms of many sizes (figure 

3-12; appendix table 3-5). 

Workers employed in the business sector in S&E occupations are more densely concentrated in larger firms than the 

broad SESTAT population or even than all those with S&E 

highest degrees (figure 3-12; appendix table 3-5). The largest firms (those with 5,000 or more employees) employ 42% 

of college-educated workers in S&E occupations, compared 

to 30% of the broad SESTAT population. The proportion in 

firms with 100 or more employees is 75% for S&E occupations compared with 62% for all scientists and engineers. 

Within the business sector, workers at different degree levels are distributed similarly across firms of different sizes 

(figure 3-13). 

Many scientists and engineers who are self-employed 

work in businesses with 10 or fewer employees. In all, 82% of 

self-employed individuals in unincorporated businesses and 

41% of self-employed individuals in incorporated businesses 

NOTE: Scientists and engineers include those with one or more S&E 

or S&E-related degrees at the bachelor’s level or higher, or those 

who have only a non-S&E degree at the bachelor’s level or higher 

and are employed in an S&E or S&E-related occupation. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

Percent

Figure 3-12

Scientists and engineers employed in the business 

sector, by employer size: 2010

All

scientists

and engineers

Workers with

S&E highest

degree

Workers

in S&E

occupations

0

20

40

60

80

100

< 100

100–4,999

≥ 5,000

Number of

employees
3-24 ♦ Chapter 3. Science and Engineering Labor Force

work in businesses with 10 or fewer employees. In contrast, 

only 5% of all other scientists and engineers work in businesses with 10 or fewer employees. Many of these scientists 

and engineers likely think of themselves as independent professionals rather than small business owners.

Industry Employment

The OES survey provides detailed estimates for employment in S&E occupations by type of industry; however, it excludes the self-employed and those employed in agriculture 

and in recent startups. Industries vary in their proportions of 

S&E workers (table 3-8). In 2012, the industry group with 

the largest S&E employment was professional, scientific, 

and technical services, which employed about 1.8 million 

(31%) S&E workers, followed by manufacturing, which 

employed 887,000 (15%) S&E workers (table 3-8). The 

government, which includes federal, state, and local government, employed 636,000 (11%) S&E workers; educational 

services, which includes private and public educational institutions, employed another 684,000 (12%) S&E workers. 

These four industry groups—professional, scientific, and 

technical services; manufacturing; government; and educational services—had a disproportionate concentration of 

S&E jobs. Together, these industry groups employed about 

two-thirds of all workers in S&E occupations (68%), compared with one-third of workers in all occupations (32%). 

S&E employment intensity, defined by an industry’s 

S&E employment as a proportion of its total employment, 

was highest in professional, scientific, and technical services 

(24%) followed by information (17%) and management of 

companies and enterprises (13%) (table 3-8). The broad industry groups with S&E employment intensity below the national average (4.6%) together employed 59% of all workers 

in 2012 but only 14% of workers in S&E occupations. These 

groups with S&E employment intensity below the national 

average include large employers such as health care and social 

assistance, retail trade, and accommodation and food services. 

Employment by Metropolitan Area

The availability of a skilled workforce is an important 

predictor of a region’s population, productivity, and technological growth (Carlino, Chatterjee, and Hunt 2001; Glaeser 

and Saiz 2003). The federal government uses standard definitions to describe geographical regions in the United States 

for comparative purposes. It designates very large metropolitan areas, sometimes dividing them into smaller metropolitan divisions that can also be substantial in size (Office 

of Management and Budget 2009).

This section presents the following indicators of the 

availability of S&E workers in a metropolitan area: (1) the 

number of S&E workers in the metropolitan area or division, 

(2) the proportion of the entire metropolitan area workforce 

in S&E occupations, and (3) the proportion of the nationwide S&E workforce in the metropolitan area. Data on the 

metropolitan areas with the largest proportion of workers in 

S&E occupations appear in table 3-9. These estimates are 

affected by the geographic scope of each metropolitan area, 

which can vary significantly. In particular, comparisons between areas can be strongly affected by how much territory 

outside the urban core is included in the metropolitan area. 

S&E employment in the United States is geographically 

concentrated; that is, a small number of geographic areas account for a significant proportion of S&E jobs. For example, 

the 20 metropolitan areas listed in table 3-9 account for 18% 

of nationwide employment in S&E jobs, compared to about 

8% of employment in all occupations. 

Scientists and Engineers and InnovationRelated Activities

Who Performs R&D?

Because R&D creates new types of goods and services 

that can fuel economic and productivity growth and enhance 

living standards, individuals with S&E expertise who use 

their knowledge in R&D attract special interest. This section 

uses SESTAT data to examine the R&D activity of scientists and engineers. In this section, R&D activity is defined 

as the proportion of workers who reported basic research, 

applied research, design, or development as a primary or 

secondary work activity in their principal job (i.e., activities 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

Percent

Figure 3-13

S&E highest degree holders employed in the 

business sector, by highest degree level and 

employer size: 2010

Bachelor’s Master’s Doctorate

0

20

40

60

80

100

≤ 10

11–24

25–99

100–499

500–999

1,000–4,999

5,000–24,999

≥ 25,000

Number of

employees
Science and Engineering Indicators 2014 ♦ 3-25

that rank first or second in total work hours from a list of 

14 activities).11

The SESTAT data from 2010 indicate that 27% of employed scientists and engineers reported R&D as a primary 

or secondary work activity. However, the proportion who 

do so varies substantially across occupations and degrees 

(figure 3-14). In general, SESTAT respondents employed in 

S&E occupations are the most likely to perform R&D as a 

primary or secondary work activity (57%), but a considerable proportion of those in S&E-related (21%) or non-S&E 

occupations (16%) also reported R&D as a primary or secondary activity. This indicates that R&D activity spans a 

broad range of occupations. 

Nearly half of the scientists and engineers who have a 

highest degree in a non-S&E field but are employed in an 

S&E job reported R&D activity (47%), although they did so 

less often than those who have a highest degree in an S&E 

field and are employed in an S&E job (60%). Many S&E degree holders subsequently earn degrees in other fields, such 

as medicine, law, or business. The SESTAT data from 2010 

indicate that the majority of scientists and engineers (67%) 

with a highest degree in a non-S&E field also obtained other 

degrees in S&E or S&E-related fields. 

Those with doctorates account for a disproportionate segment of R&D performers. These individuals constitute only 

5% of all SESTAT respondents but 11% of SESTAT respondents who reported R&D as a major work activity. However, 

the majority of R&D performers in the S&E workforce have 

bachelor’s (53%) or master’s (32%) degrees. 

Among the SESTAT population employed in S&E occupations, life scientists (75%) reported the highest rates of 

R&D activity, whereas social scientists (49%) and computer 

and mathematical scientists (46%) reported the lowest rates 

(table 3-10). In most occupations, those with doctorates indicated higher rates of R&D activity than those with a bachelor’s or master’s degree as their highest degree (table 3-10).12

SEH doctorate holders in later career stages reported 

lower rates of R&D activity than those in earlier career 

stages (figure 3-15). Thus, 55% of those who received their 

SEH doctorate in 1990 or earlier reported R&D activity in 

2010, compared to 67% of those who received their doctorates between 1991 and 2009. The decline in R&D activity 

over the course of individuals’ careers may reflect movement into management, growth of other career interests, or 

possession of scientific knowledge and skills that are no longer in demand. It may also reflect increased opportunity for 

more experienced scientists to perform functions involving 

Table 3-8

Employment in S&E occupations, by major industry sector: May 2012

Industry sector

U.S. total S&E 

employment in 

industry (%)

Industry 

workforce in S&E 

All occupations S&E occupations occupations (%)

U.S. total—all industries................................................................ 130,287,700 5,968,240 100 4.6

Agriculture, forestry, fishing, and hunting .................................. 393,840 1,110 0.0 0.3

Mining ........................................................................................ 783,110 62,260 1.0 8.0

Utilities ....................................................................................... 552,750 49,160 0.8 8.9

Construction .............................................................................. 5,611,950 53,070 0.9 0.9

Manufacturing............................................................................ 11,866,540 887,060 14.9 7.5

Wholesale trade ......................................................................... 5,623,510 235,120 3.9 4.2

Retail trade................................................................................. 14,982,710 50,970 0.9 0.3

Transportation and warehousing ............................................... 5,014,660 41,070 0.7 0.8

Information................................................................................. 2,688,380 446,310 7.5 16.6

Finance and insurance............................................................... 5,535,000 299,180 5.0 5.4

Real estate, rental, and leasing.................................................. 1,928,950 12,110 0.2 0.6

Professional, scientific, and technical services ......................... 7,768,610 1,831,940 30.7 23.6

Management of companies and enterprises ............................. 2,003,680 259,200 4.3 12.9

Administrative and support and waste management 

and remediation ..................................................................... 7,991,260 180,950 3.0 2.3

Educational services.................................................................. 12,683,810 683,510 11.5 5.4

Health care and social assistance ............................................. 17,720,090 187,780 3.1 1.1

Arts, entertainment, and recreation ........................................... 1,937,910 9,050 0.2 0.5

Accommodation and food services........................................... 11,675,540 2,570 0.0 0.0

Other services (except federal, state, and local government)..... 3,809,410 40,030 0.7 1.1

Federal, state, and local government (OES designation) .......... 9,716,010 635,760 10.7 6.5

OES = Occupational Employment Statistics.

NOTES: Industries are defined by the North American Industry Classification System (NAICS). The OES Survey does not cover employment among 

self-employed workers and employment in the following industries: crop production (NAICS 111); animal production (NAICS 112); fishing, hunting, and 

trapping (NAICS 114); and private households (NAICS 814). As a result, the data do not represent total U.S. employment. Differences between any two 

industry sectors may not be statistically significant.

SOURCE: Bureau of Labor Statistics, OES Survey (May 2012).

Science and Engineering Indicators 2014

Workers employed (n)
3-26 ♦ Chapter 3. Science and Engineering Labor Force

the interpretation and use of, as opposed to the creation and 

development of, scientific knowledge.

For the most part, scientists and engineers performing 

R&D activity are distributed similarly across broad employment sectors as scientists and engineers who do not perform 

R&D as a primary or secondary work activity. About 70% 

of scientists and engineers in each group are employed in 

the business sector (68% and 71%, respectively), about 20% 

are employed in the education sector (21% and 18%, respectively), and 11% are employed in the government sector. 

However, within the education sector, 4-year institutions 

employ 66% of SESTAT respondents who perform R&D as 

a primary or secondary work activity, compared to 31% of 

those who do not. 

Patenting Activity

The U.S. Patent and Trademark Office (USPTO) grants 

patents to inventions that are new, useful, and not obvious. 

Patenting is a limited but useful indicator of the inventive 

activity of scientists and engineers. Not all patent applications received by the USPTO are granted, not all granted 

patents result in commercial products, and not all R&D leads 

to patents because inventors often protect commercially useful discoveries in other ways such as copyrights and trade 

secrets. NSF data indicate that, among U.S.-trained SEH 

doctorates, 16% reported patenting activity during the period 

from 2003 to 2008 (National Science Board [NSB] 2012).13

Patenting activity varied significantly across disciplines, 

with doctorate holders in engineering and physical sciences 

reporting the highest rates and those in mathematics, statistics, and psychology reporting the lowest rates. Doctorate 

holders in engineering and physical sciences also reported 

the highest average number of patent applications per person 

and the highest average number of patents granted. For an 

in-depth analysis of the relevant data, see the NSB Science 

and Engineering Indicators 2012 (NSB 2012).

Work-Related Training

In addition to formal education, workers very often engage in work-related training. Such training can contribute 

to innovation and productivity growth by enhancing skills, 

efficiency, and knowledge. In 2010, 55% of scientists and 

engineers in the labor force reported participating in workrelated training within the past 12 months of being surveyed 

(table 3-11). Among those who were employed, workers in 

S&E-related jobs (health-related occupations, S&E managers, S&E precollege teachers, and S&E technicians and technologists) exhibited higher rates of participation (73%) than 

workers in S&E (55%) or non-S&E jobs (61%). In general, 

employed scientists and engineers reported higher rates of 

Table 3-9

Metropolitan areas with largest proportion of workers in S&E occupations: May 2012

Metropolitan area

Workers employed (n) Metropolitan 

area workforce 

in S&E 

occupations (%)

U.S. total S&E 

employment 

in metropolitan 

area (%)

All 

occupations

S&E 

occupations

U.S. total.................................................................................. 130,287,700 5,968,240 4.6 100.0 

San Jose–Sunnyvale–Santa Clara, CA................................ 898,610 142,430 15.9 2.4 

Boulder, CO ......................................................................... 159,440 21,160 13.3 0.4 

Huntsville, AL....................................................................... 203,400 26,590 13.1 0.4 

Corvallis, OR........................................................................ 33,310 4,170 12.5 0.1 

Framingham, MA, NECTA Division ...................................... 157,290 19,550 12.4 0.3 

Durham–Chapel Hill, NC...................................................... 272,250 32,690 12.0 0.5 

Washington-Arlington-Alexandria, DC-VA-MD-WV, 

Metropolitan Division........................................................ 2,343,510 265,370 11.3 4.4 

Lowell-Billerica-Chelmsford, MA-NH, NECTA Division ....... 116,620 12,830 11.0 0.2 

Seattle-Bellevue-Everett, WA, Metropolitan Division........... 1,409,500 148,670 10.5 2.5 

Bethesda-Rockville-Frederick, MD, Metropolitan Division... 560,000 54,380 9.7 0.9 

Bloomington-Normal, IL ...................................................... 86,920 8,280 9.5 0.1 

Kennewick-Pasco-Richland, WA......................................... 97,300 8,850 9.1 0.1 

Boston-Cambridge-Quincy, MA, NECTA Division ............... 1,711,350 154,470 9.0 2.6 

San Francisco–San Mateo–Redwood City, CA, 

Metropolitan Division........................................................ 1,000,430 89,480 8.9 1.5 

Ann Arbor, MI....................................................................... 193,760 16,870 8.7 0.3 

Fort Collins–Loveland, CO................................................... 132,630 11,060 8.3 0.2 

Ames, IA............................................................................... 40,270 3,280 8.1 0.1 

Olympia, WA........................................................................ 93,850 7,520 8.0 0.1 

Austin–Round Rock–San Marcos, TX.................................. 812,600 64,780 8.0 1.1 

College Station–Bryan, TX................................................... 92,990 7,370 7.9 0.1 

NECTA = New England City and Town Area.

NOTES: The data exclude metropolitan statistical areas where S&E proportions were suppressed. Larger metropolitan areas are broken into component 

metropolitan divisions. Differences between any two areas may not be statistically significant.

SOURCE: Bureau of Labor Statistics, Occupational Employment Statistics Survey (May 2012).

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 3-27

participation (63%) than unemployed scientists and engineers (30%). Women participated in work-related training 

at a higher rate than men: 58% of women compared with 

52% of men (appendix table 3-6). This difference exists 

among most groups defined by labor force status or highest 

degree level.

Figure 3-14

Employed scientists and engineers with R&D 

activity, by broad field of highest degree and broad 

occupational category: 2010

Percent

NOTES: Scientists and engineers include those with one or more S&E 

or S&E-related degrees at the bachelor’s level or higher or those who 

have only a non-S&E degree at the bachelor’s level or higher and are 

employed in an S&E or S&E-related occupation. R&D activity here refers 

to the share of workers reporting basic research, applied research, 

design, or development as a primary or secondary work activity in their 

principal job—activities ranking rst or second in work hours.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

All elds

S&E eld

S&E-related

eld

Non-S&E

eld

0 20 40 60 80 100

Non-S&E

occupations

S&E occupations

S&E-related

occupations

Table 3-10

R&D activity rate of scientists and engineers employed in S&E occupations, by broad occupational category and 

level of highest degree: 2010

(Percent)

Highest degree level

Biological, 

agricultural, and 

environmental 

life scientists

Computer and 

mathematical 

scientists

Physical 

scientists

Social 

scientists Engineers

All degree levels ...................................... 75.2 45.5 70.3 49.4 66.5

Bachelor’s ............................................ 66.9 44.0 65.6 47.6 62.9

Master’s ............................................... 74.5 46.3 65.5 46.8 70.0

Doctorate............................................. 86.8 64.1 80.0 54.2 83.9

NOTES: All degree levels include professional degrees not broken out separately. R&D activity rate is the proportion of workers who report that basic 

research, applied research, design, or development is a primary or secondary work activity in their principal job—activities ranking first or second in 

work hours.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

Figure 3-15

Employed SEH doctorate holders with R&D 

activity, by years since doctoral degree: 2010

Percent

SEH = science, engineering, and health.

NOTE: R&D activity here refers to the share of workers reporting 

basic research, applied research, design, or development as a 

primary or secondary work activity in their principal job—activities 

ranking rst or second in work hours.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Doctorate Recipients (SDR) 

(2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

Years since degree

< 5 5–9 10–14 15–19 20–24 25–29 30–34 ≥ 35 

0

50

60

70

80

90

100
3-28 ♦ Chapter 3. Science and Engineering Labor Force

Among scientists and engineers who participated in workrelated training within the 12 months before being surveyed, 

most did so to improve skills or knowledge in their current 

occupational field (52%) (appendix table 3-7).14 Others did 

so for licensure/certification in their current occupational 

field (24%) or because it was required or expected by their 

employer (15%). Relative to those who were employed or 

not in the labor force, those who were unemployed more 

often reported that they engaged in work-related training to 

facilitate a change to a different occupational field. Not surprisingly, those who were not in the labor force more often 

reported that they engaged in this activity for leisure or personal interest than those who were in the labor force. 

S&E Labor Market Conditions

This section looks at a variety of labor market indicators 

to assess the overall health of the labor market for scientists 

and engineers. Indicators of labor market participation (such 

as rates of unemployment and involuntarily working out of 

one’s degree field) and earnings provide meaningful information on economic rewards and the overall attractiveness 

of careers in S&E fields. Many labor market indicators are 

lagging indicators, which change some time after other indicators show that the economy has begun to follow a particular trend. For example, although the most recent recession 

officially began in December 2007 and ended in June 2009, 

unemployment rates continued to rise after the recession had 

officially ended.15 Rates of unemployment, rates of working 

involuntarily out of one’s field of highest degree, and earnings should all be considered in this context. 

Unemployment

In general, those who hold S&E degrees or work in S&E 

occupations have had lower rates of unemployment than the 

broader labor force. However, the S&E workforce is not exempt from unemployment due to overall business cycles or to 

specific events affecting individuals in their fields. In October 

2010, an estimated 4.3% of the broad SESTAT population 

were unemployed (appendix table 3-8). At the same time, the 

official unemployment rate reported by BLS for the entire 

U.S. labor force was about twice as high, 9.0%.16 According 

to the NSCG, the unemployment rate for all college graduates 

was 5.1% in the same period. Thus, joblessness among scientists and engineers compares favorably with the rates for the 

labor force as a whole and the college-educated labor force. 

In 2010, scientists and engineers employed in non-S&E 

occupations generally had a higher unemployment rate 

(5.6%) than those employed in S&E occupations (unemployment rates ranged from 2.3% among social scientists to 4.6% 

among engineers) (appendix table 3-8). Advanced degree 

holders are less vulnerable to unemployment than those with 

only bachelor’s degrees (appendix table 3-8). Nonetheless, 

a comparison of SESTAT data from 2006, before the onset 

of the economic downturn, and from 2010, after the downturn ended, shows clear evidence that the SESTAT population of scientists and engineers were affected by the broader 

economic conditions: unemployment rates for comparable 

groups were generally higher in 2010 than in 2006.17 For 

example, between 2006 and 2010, the unemployment rate 

among scientists and engineers with a highest degree at 

the bachelor’s level rose from 2.9% to 4.9%; among those 

with a doctorate, the rate rose from 1.6% to 2.6%. During 

the same period, unemployment rates nearly doubled among 

engineers (from 2.4% in 2006 to 4.6% in 2010) and among 

scientists and engineers employed in non-S&E occupations 

(from 3.0% in 2006 to 5.6% in 2010). 

The extent of unemployment also varies by career 

stages. Scientists and engineers in the early- to mid-stages 

of their career cycles (about 5 to 30 years after obtaining 

Table 3-11

Scientists and engineers participating in workrelated training, by labor force status and 

occupation: 2010

Labor force status and occupation Number Percent

All scientists and engineers................ 14,688,000 54.6

Employed........................................ 13,894,000 63.4

S&E occupations......................... 2,950,000 54.6

Biological, agric ultural, and 

environmental life scientists... 351,000 58.8

Computer and mathematical 

scientists .............................. 1,154,000 48.2

Physical scientists ................... 166,000 51.9

Social scientists....................... 343,000 66.2

Engineers................................. 937,000 59.7

S&E-related occupations ............ 5,085,000 73.1

Non-S&E occupations................. 5,859,000 61.4

Unemployed ................................... 297,000 29.9

S&E occupations......................... 54,000 25.0

Biological, agricultural, and 

environmental life scientists... 5,000 21.7

Computer and mathematical 

scientists .............................. 20,000 21.5

Physical and related scientists.... 4,000 36.4

Social and related scientists.... 4,000 33.3

Engineers................................. 21,000 27.6

S&E-related occupations ............ 72,000 39.6

Non-S&E occupations................. 171,000 30.1

Not in labor force ............................ 497,000 12.5

NOTES: Scientists and engineers include those with one or more 

S&E or S&E-related degrees at the bachelor’s level or higher or those 

who have only a non-S&E degree at the bachelor’s level or higher 

and are employed in an S&E or S&E-related occupation in 2010. 

Unemployed individuals are those not working but who looked for 

a job in the preceding 4 weeks. For unemployed, the last job held 

was used for classification. Detail may not add to total because 

of rounding.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 3-29

their highest degree) are less likely to be jobless than those 

at earlier points in their careers (figure 3-16). As workers 

strengthen their skills by acquiring labor market experience 

and adding on-the-job knowledge to their formal training, 

their work situations become more secure. However, among 

scientists and engineers in the later stages of their careers 

(about 35 or more years after obtaining their highest degree), 

the unemployment rates are higher than for those who are in 

the early- to mid-career stages. This suggests that over time 

scientists and engineers either become more selective about 

the work they are willing to do or find their skills becoming 

obsolete, which results in higher unemployment toward the 

later stages of their careers. 

CPS data allow for analysis of unemployment rates over 

the past three decades.18 CPS data indicate that workers employed in S&E occupations have historically experienced 

lower unemployment rates than the overall labor market (figure 3-17). CPS data for the period 1983–2012 indicate that 

the unemployment rate for college-educated individuals in 

S&E occupations ranged from a low of 1.3% to a high of 

4.3%, which contrasted favorably with rates for the entire 

college-educated labor force (ranging from 1.8% to 7.8%). 

The unemployment rate for S&E technicians and computer 

programmers ranged from 2.1% to 7.4%; in comparison, the 

unemployment rate for the entire labor force ranged from 

4.0% to 9.6%. 

Figure 3-16

Unemployment rates of scientists and engineers, 

by level of and years since highest degree: 2010

Percent

NOTES: All degree levels include professional degrees not shown 

separately. Scientists and engineers include those with one or more 

S&E or S&E-related degrees at the bachelor’s level or higher or those 

who have only a non-S&E degree degree at the bachelor's level or 

higher and are employed in an S&E or S&E-related occupation. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2010), http://sestat.nsf.gov. 

Science and Engineering Indicators 2014

Years since degree

1–4 5–9 10–14 15–19 20–24 25–29 30–34 ≥ 35

0

2

4

6

8

10

All degree levels

Bachelor’s

Master’s

Doctorate

Figure 3-17

Unemployment rate, by occupation: 1983–2012

Percent

SOURCES: National Bureau of Economic Research, Merged 

Outgoing Rotation Group les (1983–2012); Bureau of Labor 

Statistics, Current Population Survey (1983–2012). 

Science and Engineering Indicators 2014

1983 1988 1991 1994 1997 2000 2003 2006 2009 2012

0

2

4

6

8

10

12

All U.S. workers

Workers with bachelor’s degree or higher

Workers in S&E occupations 

(bachelor’s degree or higher)

S&E technicians and computer 

programmers (any education level)

During the economic downturn that began in late 2007, 

unemployment rates among workers employed in S&E occupations generally followed the historic pattern (figure 

3-18). Unemployment peaked at 5.7% in S&E occupations 

and 6.1% in the broader STEM occupations, which include 

S&E occupations as well as computer programmers, technicians, and S&E managers. In comparison, peak unemployment in all occupations was considerably higher (10.5%). 

In addition to lower rates, unemployment in S&E occupations began declining earlier than in all occupations. As of 

early 2013, however, unemployment rates among all workers (7.7%) as well as S&E workers (3.0%) were still higher 

than in the beginning of 2008 (5.4% and 2.1%, respectively). 

Broader Measures of Labor Underutilization

The most commonly cited unemployment measure is the 

percentage of people who are not working but who have 

looked for work in the preceding 4 weeks. This is the official 

unemployment rate (U3). In addition to U3, BLS reports five 

other measures (table 3-12), which provide narrower (U1 

and U2) or broader (U4–U6) measures of unemployment 

than the standard measure (U3). These additional measures, 

called “alternative measures of labor underutilization,” 

provide additional detail about differences in employment 

patterns between the S&E labor force and the overall labor 

force (appendix table 3-9). 

Trends in indicators of labor underutilization during the 

economic downturn that began at the end of 2007 consistently indicate that workers whose most recent job was in 

an S&E occupation experienced lower underutilization rates 

than the general labor force (figure 3-19). In addition to 

lower U3, workers in S&E occupations experienced lower 

long-term unemployment (U1), defined as unemployment 
3-30 ♦ Chapter 3. Science and Engineering Labor Force

Figure 3-18

Unemployment rates for workers in S&E, STEM, 

and all occupations: March 2008–April 2013

Percent

STEM = science, technology, engineering, and mathematics.

NOTES: Data for S&E, STEM, and all occupations include people at 

all education levels. Estimates are not seasonally adjusted. Estimates 

are made from pooled microrecords of the Current Population Survey 

(CPS) and, although similar, are not the same as the 3-month moving 

average.

SOURCE: Bureau of Labor Statistics, CPS, Public-Use Microdata 

Sample (PUMS), January 2008–April 2013.

Science and Engineering Indicators 2014

3/08 3/09 11/09 7/10 3/11 11/11 7/12 4/13

0

2

4

6

8

10

12

All occupations

S&E occupations

STEM occupations

lasting 15 weeks or longer, throughout the economic downturn. Although U1 in S&E occupations stabilized and began 

gradually declining in the latter part of 2009, U1 in all occupations continued to rise until the beginning of 2010. Beginning 

around the end of 2009, the rate of long-term unemployment 

in the general labor force exceeded the rate of standard unemployment for those in S&E occupations.

The most comprehensive labor underutilization indicator (U6) includes various kinds of workers who are not employed full time but would like to be. More than the standard 

unemployment rate, this indicator captures the difference 

between workers’ labor market aspirations and outcomes. 

The gap between this measure and the standard unemployment rate among workers in S&E occupations is substantially smaller than the comparable gap in the general labor 

force (appendix table 3-9). This suggests that underutilized 

workers—that is, those who work part time but would like 

to obtain full-time employment or those who would like to 

work but have stopped looking for employment—are a more 

significant factor among the general labor force than among 

those in S&E occupations. 

Table 3-12

Alternative measures of labor underutilization

Measure Definition

U1......... Percentage of the labor force unemployed 

for 15 weeks or longer

U2......... Percentage of the labor force who lost jobs 

or completed temporary work

U3......... Official unemployment rate: percentage 

of the labor force without jobs who have 

actively looked for work within the past 4 

weeks

U4......... U3 + percentage of the labor force who 

are discouraged workers (those who have 

stopped looking for work)

U5......... U4 + percentage of the labor force who are 

marginally attached workers (those who 

would like to work but have not looked for 

work recently)

U6......... U5 + percentage of the labor force who are 

part-time workers but want to work full time

SOURCE: Bureau of Labor Statistics, http://www.bls.gov/lau/stalt.htm.

Science and Engineering Indicators 2014

Figure 3-19

Measures of labor underutilization for workers in 

S&E and all occupations: March 2008–April 2013

Percent

U1 = percentage of labor force unemployed for 15 weeks or more; 

U3 = percentage of labor force without jobs who have looked for 

work in past 4 weeks (ofcial unemployment rate).

NOTES: Data for S&E and all occupations include workers at all 

education levels. Estimates are not seasonally adjusted. Estimates 

are made from the pooled microrecords of the Current Population 

Survey and, although similar, are not the same as the 3-month 

moving average.

SOURCE: Bureau of Labor Statistics, Current Population Survey, 

Public-Use Microdata Sample (PUMS), January 2008–April 2013.

Science and Engineering Indicators 2014

3/08 3/09 11/09 7/10 3/11 11/11 7/12 4/13

0

2

4

6

8

10

12

U3, all occupations

U1, all occupations

U3, S&E occupations

U1, S&E occupations
Science and Engineering Indicators 2014 ♦ 3-31

Involuntarily Working Out of One’s Field of 

Highest Degree

SESTAT data provide information on the relevance of 

individuals’ educational background for their principal 

job. SESTAT also provides data on why individuals obtain 

employment outside of their field of highest degree. The 

SESTAT population of scientists and engineers who reported that a lack of suitable jobs in their field of highest degree 

was the reason for their working out of field are identified as 

those who are working involuntarily out of field (IOF). The 

size of this group as a proportion of all employed scientists 

and engineers is considered the IOF rate.

Of the nearly 22 million employed scientists and engineers in 2010, almost 1.4 million reported working out of 

the field of their highest degree because of a lack of suitable 

jobs in their degree field, indicating an IOF rate of 6.4%. 

SESTAT respondents were allowed to report more than 

one reason for working out of field. Other reasons included 

pay and promotion opportunities (reported by 2.1 million 

individuals), change in career or professional interests (1.8 

million), working conditions (2.1 million), family-related 

reasons (1 million), job location (1.9 million), and other reasons (400,000). When asked about the single most important 

reason for working in a job not related to their field of highest degree, pay and promotion opportunities were cited by 

most, followed by change in career interests and lack of a 

suitable job in their field of highest degree. 

IOF rates vary by degree fields and levels. Scientists and 

engineers with a highest degree in engineering and computer and mathematical sciences display lower IOF rates than 

those with physical, life, or social sciences degrees (table 

3-13). Advanced degree holders are less likely to work involuntarily out of field than those with bachelor’s degrees 

only: in 2010, the IOF rate was 2.9% for the SESTAT population with doctorates, 4.0% for those with master’s degrees, 

and 8.8% for those with bachelor’s degrees only. However, 

among bachelor’s degree holders, IOF rates gradually decline across career stages up to mid- to late career points, 

and then gradually rise (figure 3-20). In comparison, among 

holders of master’s degrees and doctorates, IOF rates remain 

stable over the long term. 

Table 3-13

Scientists and engineers who are working involuntarily out of field, by S&E degree field: 1993–2010

(Percent)

S&E degree field 1993 1995 1997 1999 2003 2006 2008 2010

All scientists and engineers..................................................... 7.8 7.7 7.3 5.4 5.9 6.2 5.3 6.4

Highest degree in S&E field ................................................. 9.2 8.9 8.5 6.3 7.8 8.1 7.1 8.4

Biological, agricultural, and environmental life sciences... 10.3 10.2 10.0 8.3 10.1 9.7 10.1 10.1

Computer and mathematical sciences ............................ 5.3 4.1 4.0 2.9 4.9 5.7 4.5 5.1

Physical sciences............................................................. 9.7 10.2 10.0 7.6 8.8 8.6 7.1 8.2

Social sciences ................................................................ 13.3 12.7 12.1 8.7 10.1 10.6 9.2 11.3

Engineering ...................................................................... 4.4 4.4 3.9 2.7 4.2 4.5 3.6 4.9

NOTES: During 1993–99, scientists and engineers include those with one or more S&E degrees at the bachelor’s level or higher or those who have only 

a non-S&E degree at the bachelor’s level or higher and are employed in an S&E occupation. During 2003–10, scientists and engineers include those with 

one or more S&E or S&E-related degrees at the bachelor’s level or higher or those who have only a non-S&E degree at the bachelor’s level or higher and 

are employed in an S&E or S&E-related occupation. The involuntarily out-of-field rate is the proportion of all employed individuals who report that their job 

is not related to their field of highest degree because a job in their highest degree field was not available.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (1993–2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

Figure 3-20

Scientists and engineers who are working 

involuntarily out of field, by level of and years 

since highest degree: 2010

Percent

NOTES: Involuntarily out-of-eld rate is the proportion of all 

employed individuals who reported working in a job not related to 

their eld of highest degree because a job in that eld was not 

available. Scientists and engineers include those with one or more 

S&E or S&E-related degrees at the bachelor’s level or higher or those 

who have only a non-S&E degree at the bachelor’s level or higher 

and are employed in an S&E or S&E-related occupation. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

Years since degree

1–4 5–9 10–14 15–19 20–24 25–29 30–34 ≥ 35

0

2

4

6

8

10

12

14

Doctorate

All degree

levels

Bachelor’s

Master’s
3-32 ♦ Chapter 3. Science and Engineering Labor Force

Earnings

Based on the OES survey, the estimated annual earnings 

of individuals in S&E occupations are considerably higher 

than those of the total workforce. Median annual earnings 

in 2012 in S&E occupations (regardless of education level 

or field) was $78,270, which is more than double the median for all U.S. workers ($34,750) (table 3-14). This is not 

surprising given the level of formal education and overall 

technical skills associated with S&E occupations. The difference in average (mean) earnings was less dramatic but 

still quite wide, with individuals in S&E occupations earning considerably more on average ($82,930) than workers 

in all occupations ($45,790). Median S&E earnings ranged 

from $67,660 among social scientists to $86,500 among 

engineers. The 2009–12 annual growth in mean and median earnings for S&E occupations were generally similar to 

those for all employed U.S. workers in the OES data. 

According to SESTAT, the annual median salary for individuals trained or employed in S&E ($65,000) is higher 

than that for all college-educated individuals ($56,000). 

The 2010 NSCG data indicate that the annual median salary 

for college-educated workers with a highest degree in S&E 

($65,000) or S&E-related fields ($68,000) is more than for 

those with non-S&E degrees ($50,000) (table 3-15). Within 

each broad degree field, however, those employed in S&E 

occupations earn more than those in non-S&E occupations. 

For example, among individuals with a highest degree in a 

non-S&E field, the annual median salary for those employed 

Table 3-14

Annual earnings and earnings growth in science, technology, and related occupations: May 2009–May 2012

Occupation

2009 

annual 

earnings ($)

2012 

annual 

earnings ($)

Annual 

growth rate 

2009–12 

(%)

2009 

annual 

earnings ($)

2012 

annual 

earnings ($)

Annual 

growth rate 

2009–12 

(%)

Mean Median

All U.S. employment............................... 43,460 45,790 1.8 33,190 34,750 1.5 

STEM occupations ............................. 76,600 82,160 2.4 71,080 75,840 2.2

S&E occupations............................. 78,480 82,930 1.9 74,380 78,270 1.7

Computer and mathematical 

scientists.................................... 76,280 80,080 1.6 72,930 76,170 1.5

Life scientists............................... 77,400 79,430 0.9 68,240 69,980 0.8

Physical scientists ....................... 78,880 83,750 2.0 71,670 74,880 1.5

Social scientists........................... 69,140 73,230 1.9 63,130 67,660 2.3

Engineers..................................... 86,140 91,450 2.0 82,130 86,500 1.7

Technology occupations ................. 72,500 78,740 2.8 60,650 65,300 2.5

S&E-related occupations (not 

listed above)...................................... 70,980 74,840 1.8 58,910 61,540 1.5

Health-related occupations.............. 70,840 74,740 1.8 58,670 61,320 1.5

Other S&E-related occupations ...... 77,930 80,380 1.0 71,020 72,950 0.9

STEM = science, technology, engineering, and mathematics.

NOTES: See table 3-2 for definitions of S&E, S&E-related, and STEM occupations. Occupational Employment Statistics (OES) employment data do not 

cover employment in agriculture, private household, or among self-employed and therefore do not represent total U.S. employment.

SOURCE: Bureau of Labor Statistics, OES Survey (May 2009 and May 2012).

Science and Engineering Indicators 2014

Table 3-15

Median salaries for employed college-educated individuals, by broad field of highest degree and broad 

occupational category: 2010

(Median annual salary, dollars)

Highest degree field

All 

occupations

S&E 

occupations

S&E-related 

occupations

Non-S&E 

occupations

All degrees............................................................................. 56,000 75,000 65,000 50,000

S&E .................................................................................... 65,000 78,000 65,000 50,000

S&E-related........................................................................ 68,000 72,000 70,000 50,000

Non-S&E............................................................................ 50,000 70,000 53,000 50,000

NOTES: See table 3-2 for definitions of S&E, S&E-related, and non-S&E degrees and occupations. Salaries are rounded to the nearest $1,000.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, National Survey of College Graduates (2010).

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 3-33

in an S&E occupation ($70,000) is more than for those employed in a non-S&E occupation ($50,000); among individuals with a highest degree in an S&E or S&E-related field, 

those employed in an S&E or S&E-related occupation earn 

more than those employed in a non-S&E occupation.

The earnings premium enjoyed by college-educated individuals with an S&E or S&E-related degree is present at all 

career stages. Figure 3-21 presents data on median salaries 

for groups with S&E, S&E-related, or non-S&E highest degrees at comparable numbers of years since receiving their 

highest degrees. Although median salaries are similar in the 

beginning for S&E and non-S&E degree holders, both of 

which are lower than that for S&E-related degree holders, 

the rise in earnings associated with career progression is 

much steeper among individuals with S&E degrees. 

Earnings vary by degree levels. In 2010, the annual median salaries among scientists and engineers with bachelor’s or 

master’s as highest degree levels were $57,000 and $68,000, 

respectively. Those with doctorates ($85,000) or professional degrees ($116,000) earned significantly more. The pattern 

by degree level holds across career stages (figure 3-22).

S&E highest degree holders earn more than non-S&E 

highest degree holders at the master’s degree and doctoral 

levels (figure 3-23). Among professional degree holders, in 

contrast, non-S&E degree holders earn more than S&E degree holders. 

Among employed individuals without a bachelor’s 

degree, S&E occupations provide stable jobs with competitive salaries relative to those workers in non-S&E occupations. (See sidebar, “The U.S. S&E Workforce Without 

a Bachelor’s Degree.”)

Recent S&E Graduates

In today’s knowledge-based and globally integrated 

economy marked by rapid information flow and development of new knowledge, products, and processes, demand 

for certain skills and abilities may change fast. The employment outcomes of recent graduates are an important indicator of current changes in labor market conditions. Compared 

with experienced S&E workers, recent S&E graduates more 

often bring new ideas and newly acquired skills to the labor market. This section examines the employment outcomes of recent recipients of S&E bachelor’s, master’s, and 

doctoral degrees. 

General Labor Market Indicators for 

Recent Graduates

Table 3-16 summarizes some basic labor market statistics 

in 2010 for recent recipients of S&E degrees; recent here is 

defined as between 1 and 5 years since receiving the degree. 

Figure 3-21

Median salaries for employed college-educated 

individuals, by broad field of highest degree and 

years since highest degree: 2010

Thousands of dollars

NOTE: See table 3-2 for classication of S&E, S&E-related, and 

non-S&E degree elds.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, National Survey of College Graduates 

(2010).

Science and Engineering Indicators 2014

1–4 5–9 10–14 15–19 

Years since highest degree

20–24 25–29 30–34 ≥ 35

0

20

40

60

80

100

S&E-related eld

Non-S&E eld

S&E eld

Figure 3-22

Median salaries for employed scientists and 

engineers, by level of and years since highest 

degree: 2010

Thousands of dollars

NOTE: Scientists and engineers include those with one or more S&E 

or S&E-related degrees at the bachelor’s level or higher or those who 

have only a non-S&E degree at the bachelor’s level or higher and are 

employed in an S&E or S&E-related occupation. 

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

Years since highest degree

1–4 5–9 10–14 15–19 20–24 25–29 30–34 ≥ 35

0

20

40

60

80

100

120

140

160

Bachelor’s

Master’s

Doctorate

Professional
3-34 ♦ Chapter 3. Science and Engineering Labor Force

Among the nearly 22 million employed SESTAT respondents in October 2010, about 1.8 million are recent S&E 

degree recipients. Overall, the unemployment rate among 

these recent graduates was 6.6%, higher than the 4.3% unemployment rate seen among the entire SESTAT population of scientists and engineers. However, none of the recent 

graduating groups by S&E degree field or level exceeded the 

unemployment rate of 9.0% for the entire U.S. labor force. 

Among recent bachelor’s degree holders, the unemployment rate averaged 7.7%, ranging from 5.2% for those with 

physical sciences degrees to 8.8% for those with social sciences degrees. Overall, unemployment was generally lower 

for those with doctorates than for those with less advanced 

degrees. Early in their careers, as individuals gather labor 

market experience and on-the-job skills, they tend to have 

a higher incidence of job change and unemployment, which 

may partially explain some of the higher unemployment 

rates seen among those with a bachelor’s degree as their 

highest level degree. 

A useful but more subjective indicator of labor market 

conditions for recent graduates is the proportion who report 

that their job is unrelated to their highest degree field because a job in their degree field was not available (working 

involuntarily out of field or IOF rate). Of the 1.8 million 

employed scientists and engineers who received their highest degree in an S&E field in the previous 5 years, 10.8% 

indicated working involuntarily out of field (table 3-16). 

A larger proportion of recent S&E degree recipients reported working out of field because a suitable job was not 

available (10.8%) compared to the overall SESTAT population of scientists and engineers (6.4%). When asked about 

the single most important reason for working out of field, 

the most frequently cited reason by recent S&E degree recipients was lack of a suitable job in their degree field (cited 

by 29% of recent S&E degree recipients working out of 

field), followed by pay and promotion opportunities (20%) 

and change in career or professional interests (13%). The 

responses provided by the entire SESTAT population working out of field (regardless of graduation year) were similar, 

but the factors were ranked differently: the most commonly 

cited reason was pay and promotion opportunities (cited by 

26% of all SESTAT respondents working out of field), followed by change in career or professional interests (21%) 

and lack of a suitable job in their degree field (19%). 

Among recent bachelor’s degree holders, the IOF rate in 

2010 averaged 13.5%, but it ranged from 4.1% for recent 

engineering graduates to 18.0% for recent graduates in the 

social sciences (table 3-16). In all degree fields for which 

reliable estimates are available, the IOF rate was lower for 

advanced degree (master’s) holders than for those with 

bachelor’s degrees only.

The median salary for recent S&E bachelor’s degree recipients in 2010 was $35,000, ranging from $30,000 in life 

sciences and physical sciences to $57,000 in engineering 

(table 3-16). Recent master’s degree recipients had a median 

salary of $55,000, and recent doctorate recipients had a median salary of $60,000.

In 2010, among recent S&E degree recipients, those 

who received their degrees in 2008 or 2009, after the economic downturn began, had higher unemployment rates and 

IOF rates (7.4% and 12.6%, respectively) than those who 

received their degrees between 2005 and 2007 (6.0% and 

9.5%, respectively) (appendix table 3-10). In particular, 

among recent master’s degree holders, the unemployment 

rate was higher for the group receiving degrees between 

2008 and 2009 than the group receiving degrees between 

2005 and 2007; among recent bachelor’s degree holders, the 

IOF rate was higher for the group receiving degrees between 

2008 and 2009 than the group receiving degrees between 

2005 and 2007. The doctorate population in these two groups 

reported similar unemployment rates and IOF rates in 2010. 

Recent Doctorate Recipients

The career rewards of highly skilled individuals in general, and doctorate holders in particular, often extend beyond salary and employment to the more personal rewards 

of doing the kind of work for which they have trained. No 

single standard measure satisfactorily reflects the state of the 

Figure 3-23

Median salaries for employed scientists and 

engineers, by broad field and level of highest 

degree: 2010

Thousands of dollars

NOTES: See table 3-2 for denitions of S&E, S&E-related, and 

non-S&E degrees. Scientists and engineers include those with one or 

more S&E or S&E-related degrees at the bachelor's level or higher or 

those who have only a non-S&E degree at the bachelor's level or 

higher and are employed in an S&E or S&E-related occupation. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2010), http://sestat.nsf.gov. 

Science and Engineering Indicators 2014

0 20 40 60 80 100 120 140

S&E eld

S&E-related

eld

Non-S&E

eld

Bachelor’s

Doctorate

Professional

Master’s
Science and Engineering Indicators 2014 ♦ 3-35

doctoral S&E labor market. This section discusses a range of 

relevant labor market indicators, including unemployment 

rates, IOF employment, employment in academia compared 

with other sectors, employment in postdoctoral positions, 

and salaries. Although a doctorate opens both career and salary opportunities, these opportunities may come at the price 

of many years of lost labor market earnings. For some doctorate holders, an ensuing postdoctoral position can further 

extend this period of low earnings.

Unemployment. As of October 2010, the 2.3% unemployment rate (table 3-17) for SEH doctorate recipients up to 

3 years after receiving their doctorates was almost identical 

to the unemployment rate for all SEH doctorates (2.4%); it 

was considerably lower than the unemployment rate of the 

civilian labor force in general (9.0%) and the unemployment 

rate for the entire SESTAT population regardless of level or 

year of award of highest degree (4.3%). 

Working involuntarily out of field. About 1.8% of the 

employed recent SEH doctorate recipients reported that they 

took a job that was not related to the field of their doctorate 

because a suitable job in their field was not available (table 

3-17). This compared favorably with the IOF rate for the 

entire SESTAT population (6.4%). 

Tenure-track positions. Although many science doctorate recipients aspire to tenure-track academic appointments 

(Sauermann and Roach 2012), most end up working in other 

positions and sectors. In 2010, about 15% of those who had 

earned their SEH doctorate within the previous 3 years had a 

tenure or tenure-track faculty appointment, a proportion that 

has held broadly steady since 1993 (table 3-18). Across the 

broad SEH fields, this proportion varied significantly, from 

about 7% to 8% among recent doctorates in life sciences, 

physical sciences, and engineering to about 41% among 

those in the social sciences. 

The proportion of SEH doctorates who hold a tenure or 

tenure-track faculty appointment increases the more time 

has passed since earning their doctorate. In 2010, the proportion of SEH doctorates with tenure or tenure-track appointments who had been in the labor market for 3 to 5 years was 

higher (20%) than the rate among those who had completed 

their doctorate within 3 years (15%) (table 3-18). The extent 

of the increase varies across the broad areas of training. In 

the social sciences, for example, a relatively large percentage of individuals get into a tenure or tenure-track position 

within 3 years of obtaining their doctorate, and the increase 

associated with 3 to 5 years of labor market exposure is not 

as dramatic as in some other fields, such as physical sciences 

or mathematics and statistics. (See chapter 5 for a discussion of trends in tenure-track positions as a proportion of all 

academic positions.)

The availability of tenure-track positions may be counterbalanced by the availability of desirable nonacademic 

employment opportunities. Although the proportion of individuals who obtain tenure or tenure-track employment 

within 3 years of completing their doctorates has remained 

Table 3-16

Labor market indicators for recent S&E degree recipients up to 5 years after receiving degree, by level and field 

of highest degree: 2010

Indicator and highest degree level

All S&E 

fields

Biological, 

agricultural, and 

environmental 

life sciences

Computer and 

mathematical 

sciences

Physical 

sciences

Social 

sciences Engineering

Unemployment rate (%)

All degree levels........................................... 6.6 6.1 6.7 4.2 8.0 4.4

Bachelor’s ................................................ 7.7 7.3 8.2 5.2 8.8 5.6

Master’s.................................................... 4.0 2.3 2.6 5.3 5.9 2.9

Doctorate ................................................. 1.6 2.8 S S S 3.6

Involuntarily out-of-field (IOF) rate (%)

All degree levels........................................... 10.8 10.2 7.5 9.9 15.6 3.7

Bachelor’s ................................................ 13.5 12.4 10.6 10.9 18.0 4.1

Master’s.................................................... 4.7 4.8 1.4 S 6.3 3.0

Doctorate ................................................. 1.7 S S S S S

Median annual salary ($)

All degree levels........................................... 40,000 35,000 55,000 36,000 33,000 60,000

Bachelor’s ................................................ 35,000 30,000 50,000 30,000 31,000 57,000

Master’s.................................................... 55,000 48,000 68,000 32,000 39,000 73,000

Doctorate ................................................. 60,000 47,000 85,000 55,000 62,000 85,000

S = suppressed for reasons of confidentiality and/or reliability.

NOTES: Median annual salaries are rounded to the nearest $1,000. All degree levels includes professional degrees not broken out separately. Data include 

degrees earned from October 2005 to October 2009. The IOF rate is the proportion of all employed individuals who report that their job is not related to 

their field of highest degree because a job in their highest degree field was not available.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System (SESTAT) 

(2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014
3-36 ♦ Chapter 3. Science and Engineering Labor Force

Table 3-17

Employment characteristics of recent SEH doctorate recipients up to 3 years after receiving doctorate, 

by field of degree: 2001–10

Recent doctorates (n) Unemployment rate (%)

Involuntarily 

out-of-field rate (%)

Field of doctorate 2001 2003 2006 2008 2010 2001 2003 2006 2008 2010 2001 2003 2006 2008 2010

All recent SEH doctorates ...... 48,700 43,700 49,500 52,600 52,700 1.3 2.5 1.2 1.5 2.3 2.8 2.1 1.4 1.3 1.8

Biological, agricultural, 

and environmental 

life sciences ..................... 12,300 11,200 12,600 13,400 14,100 1.4 2.4 0.9 1.7 1.5 2.6 1.0 0.3 1.0 1.5

Computer and 

information sciences ....... 1,600 1,400 1,500 2,400 2,500 0.3 4.1 1.9 S S S S 2.6 1.4 S

Mathematics and statistics.. 2,200 1,600 2,000 2,400 2,400 0.2 3.4 S S S 1.4 3.4 2.2 1.1 S

Physical sciences .............. 7,700 6,500 7,400 7,500 7,700 1.5 1.3 1.1 3.0 2.6 5.4 4.2 2.6 2.3 1.4

Psychology ......................... 7,200 6,300 7,000 5,800 5,400 1.5 2.7 1.2 0.8 3.8 3.0 1.5 1.4 0.8 2.0

Social sciences................... 5,800 6,000 6,200 5,900 6,000 1.6 3.1 1.4 2.1 3.4 3.3 3.0 2.3 3.4 3.5

Engineering......................... 9,400 8,000 9,500 12,000 11,300 1.5 3.0 1.8 1.2 2.7 2.0 3.0 1.6 0.7 1.9

Health.................................. 2,400 2,700 3,200 3,300 3,400 0.4 0.7 0.9 1.2 S S 1.1 S S S

S = suppressed for reasons of confidentiality and/or reliability.

SEH = science, engineering, and health.

NOTES: Involuntarily out-of-field rate is the proportion of all employed individuals who report working in a job not related to their field of doctorate 

because a job in that field was not available. Data for 2001 and 2006 include graduates from 12 months to 36 months prior to the survey reference date; 

data for 2003, 2008, and 2010 include graduates from 15 months to 36 months prior to the survey reference date. Detail may not add to total because 

of rounding. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Survey of Doctorate Recipients (SDR) (2001–10), 

http://sestat.nsf.gov.

Science and Engineering Indicators 2014

Table 3-18

Employed SEH doctorate recipients holding tenure and tenure-track appointments at academic institutions, 

by field of and years since degree: 1993–2010

(Percent)

Years since doctorate and field 1993 1995 1997 1999 2001 2003 2006 2008 2010

< 3 years

All SEH fields ...................................................... 18.1 16.3 15.8 13.5 16.5 18.6 17.7 16.2 14.7

Biological, agricultural, and environmental 

life sciences ................................................. 9.0 8.5 9.3 7.7 8.6 7.8 7.2 6.5 7.6

Computer and information sciences................ 31.5 36.5 23.4 18.2 20.7 32.5 31.2 22.0 20.8

Mathematics and statistics ............................. 40.9 39.8 26.9 18.9 25.2 38.4 31.6 31.3 26.1

Physical sciences............................................ 8.8 6.9 8.5 7.8 10.0 13.3 9.8 8.8 6.8

Psychology...................................................... 12.8 13.6 14.7 16.0 15.6 14.6 17.0 18.1 16.0

Social sciences ............................................... 43.5 35.9 37.4 35.4 38.5 44.8 39.3 45.4 41.1

Engineering ..................................................... 15.0 11.5 9.4 6.4 11.3 10.8 12.4 9.3 7.5

Health.............................................................. 33.9 34.2 30.1 28.1 32.1 30.3 36.2 27.7 24.2

3–5 years

All SEH fields ...................................................... 27.0 24.6 24.2 21.0 18.5 23.8 25.9 22.9 19.7

Biological, agricultural, and environmental 

life sciences ................................................. 17.3 17.0 18.1 16.4 14.3 15.5 13.7 14.3 10.6

Computer and information sciences............... 55.7 37.4 40.7 25.9 17.3 32.2 45.7 37.8 22.2

Mathematics and statistics ............................. 54.9 45.5 48.1 41.0 28.9 45.5 50.6 40.7 41.7

Physical sciences............................................ 18.8 15.5 14.5 11.9 15.8 18.3 19.7 16.5 14.7

Psychology...................................................... 17.0 20.7 16.8 17.6 17.5 19.9 23.8 18.3 19.1

Social sciences ............................................... 54.3 52.4 50.4 46.5 38.8 46.0 50.4 48.9 46.7

Engineering ..................................................... 22.7 19.3 19.4 12.6 10.8 15.9 16.3 15.5 13.0

Health.............................................................. 47.4 40.2 41.1 39.5 25.1 40.8 43.1 34.4 33.3

SEH = science, engineering, and health.

NOTES: Proportions are calculated on the basis of all doctorates working in all sectors of the economy. Data for 1993–99, 2001, and 2006 include 

graduates from 12 months to 60 months prior to the survey reference date; data for 2003, 2008, and 2010 include graduates from 15 months to 60 

months prior to the survey reference date.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Survey of Doctorate Recipients (1993–2010), 

http://sestat.nsf.gov.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 3-37

broadly stable since 1993, the proportion of graduates with 

tenure or tenure-track positions within 3 to 5 years of receiving their doctorates has declined since 1993 in most broad 

areas of SEH training (table 3-18). One of the steepest declines occurred in computer sciences despite the high demand for computer sciences faculty.

Salaries for recent SEH doctorate recipients. For all 

SEH degree fields in 2010, the median annual salary for recent doctorate recipients within 5 years after receiving their 

degrees was $66,000. Across various SEH degree fields, 

median annual salaries ranged from a low of $50,000 in 

biological sciences to a high of $94,000 in computer and 

information sciences (table 3-19). Between 2008 and 2010, a 

period marked by the economic downturn and its immediate 

aftermath, median salaries for recent recipients of doctoral 

degrees in most SEH areas either stayed the same or declined slightly (the median salary for recent SEH doctorate 

recipients in 2008 was $67,000). 

By type of employment, salaries for recent doctorate recipients ranged from $42,000 for postdoctoral positions in 

4-year institutions to $90,000 for those employed in the business sector (table 3-20). Each sector, however, exhibited substantial variation depending on SEH fields of training.

Postdoctoral Positions

A significant number of new S&E doctorate recipients 

take a postdoctoral appointment (generally known as a postdoc) as their first position after receiving their doctorate. 

Table 3-19

Salaries for recent SEH doctorate recipients 

up to 5 years after receiving degree at selected 

percentiles, by field of degree: 2010

(Dollars)

Field of doctorate

25th 

percentile

50th 

percentile

75th 

percentile

All SEH fields............... 47,000 66,000 90,000

Biological, 

agricultural, and 

environmental 

life sciences.......... 42,000 50,000 71,000

Computer and 

information 

sciences ............... 75,000 94,000 120,000

Mathematics and 

statistics ............... 51,000 64,000 95,000

Physical sciences .... 45,000 60,000 84,000

Psychology .............. 47,000 60,000 77,000

Social sciences........ 50,000 63,000 84,000

Engineering.............. 67,000 87,000 101,000

Health....................... 57,000 75,000 92,000

SEH = science, engineering, and health.

NOTES: Salaries are rounded to the nearest $1,000. Data include 

graduates from 15 months to 60 months prior to the survey 

reference date.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Doctorate Recipients (2010), 

http://sestat.nsf.gov.

Science and Engineering Indicators 2014

Table 3-20

Median salaries for recent SEH doctorate recipients up to 5 years after receiving degree, by field of degree and 

employment sector: 2010

(Dollars)

Education

4-year institutions

Field of doctorate

All 

sectors

All 

positions

Tenured or 

tenure-track 

position Postdoc

2-year or 

precollege 

institutions Government

Business/ 

industry

All SEH fields........................................... 66,000 52,000 65,000 42,000 52,000 76,000 90,000

Biological, agricultural, and 

environmental life sciences .............. 50,000 45,000 60,000 42,000 45,000 65,000 73,000

Computer and information sciences.... 94,000 70,000 74,000 47,000 S 99,000 111,000

Mathematics and statistics.................. 64,000 56,000 62,000 51,000 58,000 S 95,000

Physical sciences ................................ 60,000 47,000 60,000 42,000 51,000 71,000 86,000

Psychology .......................................... 60,000 55,000 57,000 42,000 59,000 78,000 65,000

Social sciences.................................... 63,000 58,000 63,000 44,000 57,000 85,000 98,000

Engineering............................................ 87,000 59,000 80,000 42,000 S 86,000 95,000

Health................................................... 75,000 69,000 72,000 41,000 51,000 85,000 93,000

S = suppressed for reasons of confidentiality and/or reliability.

SEH = science, engineering, and health.

NOTES: Salaries are rounded to the nearest $1,000. Data include graduates from 15 months to 60 months prior to the survey reference date. The 2-year 

or precollege institutions include 2-year colleges and community colleges or technical institutes and also preschool, elementary, middle, or secondary 

schools. The 4-year institutions include 4-year colleges or universities, medical schools, and university-affiliated research institutes.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Survey of Doctorate Recipients (2010), 

http://sestat.nsf.gov.

Science and Engineering Indicators 2014
3-38 ♦ Chapter 3. Science and Engineering Labor Force

Postdoc positions are defined as temporary, short-term positions, primarily for acquiring additional training in an academic, government, industry, or non-profit setting.19 This 

section looks at employment characteristics of postdocs. 

The incidence of SEH doctorate holders taking postdoc 

positions during their careers has risen over time. Among 

U.S. SEH doctorate holders who received their doctorate 

before 1972, 31% reported having had a postdoc position 

earlier in their careers; this proportion rose to 46% among 

2002–05 graduates (NSB 2010). Although individuals in 

postdoc positions often perform cutting-edge research, these 

positions generally offer lower salaries than permanent positions, which essentially adds to the costs of doctoral studies and has the unintended consequence of making science 

careers less desirable to potential graduate students. The 

growing number of postdoc positions, as well as the rise in 

average postdoc tenure, has received much attention in science policy in recent years (e.g., NIH 2012). Neither the reasons for this growth nor its effects on the state of scientific 

research are well understood. However, possible contributing factors include increases in competition for tenure-track 

academic research jobs, the need for collaborative research 

in large teams, the influx of graduate students in SEH areas with strong postdoc traditions, and the need for additional specialized training. (See sidebar, “Employment of 

Biomedical Sciences Doctorates.”) 

Number of postdocs. In October 2010, NSF’s Survey 

of Doctorate Recipients (SDR) estimated that 30,800 U.S. 

SEH doctorate recipients were employed in postdoc positions. The vast majority of these postdoc positions were in 

4-year academic institutions (75%), with the remainder in 

industry (16%) and government (10%). The fall 2010 and 

fall 2011 estimates from NSF’s Survey of Graduate Students 

and Postdoctorates in Science and Engineering, which covers academic postdocs, were 63,400 and 62,900, respectively (NSF/NCSES 2013a and 2013b). These estimates cover 

different segments of the postdoc population. The Survey 

of Graduate Students and Postdoctorates in Science and 

Engineering gathers information on postdocs from U.S. academic graduate departments, regardless of where these individuals earned their doctorates. It does not cover individuals 

in nonacademic employment, at some university research 

centers, or at academic departments that lack graduate programs. In contrast, the SDR covers U.S. residents with research doctorates in SEH fields from U.S. universities, but 

not those with doctorates from non-U.S. universities. As a 

result, the SDR omits a large number of postdocs who are 

foreign trained. The two survey estimates overlap in some 

populations (U.S.-trained doctorates and those working 

in academia), but differ in others (the Survey of Graduate 

Students and Postdoctorates in Science and Engineering 

covers foreign-trained doctorates, but not those in the industry or government sectors). In addition, the titles of postdoc researchers vary across organizations and often change 

as individuals advance through their postdoc appointment; 

both of these factors further complicate the data collection 

process (NIH 2012).20

Postdocs by academic discipline. Although postdocs are 

increasingly common in SEH fields, the extent to which a 

postdoc appointment is part of an individual’s career path 

varies greatly across SEH fields. In the field of life sciences, 

for example, postdocs have historically been more common 

than in other SEH fields. According to NSF’s Survey of 

Earned Doctorates (SED), the proportion of new doctorate 

recipients in 2011 indicating that they would take a postdoc 

appointment after graduation ranged from nearly 70% in life 

sciences (including agricultural sciences/natural resources, 

biological/biomedical sciences, and health sciences) to 37% 

in the social sciences (appendix table 3-11). SDR data indicate that in 2010 about half of those who had received their 

doctorates in the previous 3 years in biological/agricultural/

environmental life sciences (53%) or physical sciences 

(47%) were employed in postdoc positions, compared to 

only 11% in the social sciences (figure 3-24). Within physical sciences, chemistry and physics have particularly strong 

postdoc traditions. 

Postdoc compensation. Low compensation for postdocs 

is frequently raised as a concern by those who are worried 

about the effect of the increasing number and length of postdoc positions on the attractiveness of science careers. In 

2010, the median salary for postdocs who had received their 

doctorate within the past 5 years was just over half (57%) 

the median salary paid to non-postdocs (table 3-21). This 

proportion ranged from about half among individuals with 

doctorates in engineering (48%) and computer and information sciences (50%) to about three-quarters among those 

with doctorates in social sciences (69%) and mathematics 

and statistics (76%).

Among recent graduates, similar proportions of postdocs 

and non-postdocs have access to certain employer-provided benefits, such as health insurance (95% of postdocs and 

92% of non-postdocs) and paid vacation, sick, or personal 

days (87% of postdocs and 86% of non-postdocs). However, 

a much smaller proportion of recent graduates in postdoc 

positions have access to employer-provided pensions or retirement plans (56% of postdocs and 84% of non-postdocs). 

Information on the quality of these benefits—for example, 

the coverage and premium of health insurance plans, number of personal days offered by employer, and type of retirement benefits—is not available. 

Reasons for taking postdoc positions. The 2010 SDR 

asked individuals in postdoc positions to report their reason for accepting these appointments. When asked about 

the primary reason, most responses were consistent with 

the traditional objective of a postdoc position as a type of 

advanced apprenticeship for career progression, such as 

“postdoc generally expected in field,” “additional training 

in PhD field,” “additional training in an area outside of PhD 
Science and Engineering Indicators 2014 ♦ 3-39

Employment patterns in the biomedical sciences have 

changed in the past two decades. The growth in the number of doctorates trained in the field has far surpassed the 

growth in academic positions, contributing to lengthy postdoc appointments, stiff competition for academic jobs, and 

an increasing proportion of doctorates going into positions that are not research-intensive (National Institutes of 

Health [NIH] 2012). According to the Survey of Doctorate 

Recipients (SDR), between 1993 and 2010, the number of 

U.S.-educated doctorate holders in the biomedical sciences 

substantially rose (from about 105,000 to nearly 180,000).*

Over this same time, the proportion employed in academia 

declined (58% to 51%) as did the proportion employed in 

tenure or tenure-track positions (35% to 26%) despite the 

fact that both increased in absolute number. The proportion 

of U.S.-educated doctorate holders who reported research 

(basic or applied) as their primary or secondary work activity also declined in the education sector (from 75% to 

70%). In contrast, the proportion of biomedical sciences 

doctorates employed in the business sector rose (from 31% 

to 39%). The majority of the increase in the business sector 

was driven by those whose jobs did not involve research as 

their primary or secondary work activity. The proportion of 

biomedical sciences doctorates reporting that they are employed in jobs closely related to their doctoral degree has 

declined over this same time (from 68% to 60%), whereas 

the proportion employed in jobs “somewhat” related to their 

doctorate has increased (from 24% to 32%). The available 

data cover the U.S.-educated doctorate holders; the data 

on foreign-trained doctorates in the field, a segment of the 

workforce that has grown significantly (NIH 2012), are not 

comprehensive. The information on postdoc researchers is 

also not comprehensive. 

Despite the persistence of generally favorable employment indicators for biomedical sciences doctorates (the unemployment rate was around 2% in 1993 and 2010, and the 

rate of working involuntarily out of field was around 3% in 

both periods), the changes in the employment patterns have 

generated significant concerns in the profession. Concerns 

center on the rising number of research doctorates unable to 

find tenure-track academic research positions, the increasing number and length of postdoc appointments, the influx 

of foreign-trained doctorates seeking academic positions, 

and the rising number of early career doctorates taking positions that are not research-intensive and for which current 

graduate programs may not provide appropriate preparation. 

In addition, the overall training period, including PhD and 

postdoc research, is longer in the biomedical sciences than 

in other comparable disciplines, such as chemistry, physics, 

and mathematics (NIH 2012). Furthermore, average starting salaries are lower among doctorates in the biomedical 

sciences than in other fields, such as chemistry, clinical and 

health fields, and economics (NIH 2012). 

In light of the changes in the profession and the resulting 

concern in the science community, NIH convened a working 

group consisting of biomedical educators and other experts on 

the biomedical workforce to develop a set of policy recommendations to support a robust and viable workforce.†

 The 

working group recently presented specific recommendations 

targeted at enhancing graduate training, postdoc research experience, and data collection and dissemination regarding the 

biomedical workforce. The following is a summary of the 

main recommendations of this working group: 

♦ To prepare early career scientists for a wide range of career options, encourage graduate programs to undertake 

innovative approaches. These may include offering alternative degree programs, such as master’s programs, 

and providing training in areas that are generally not 

covered in a research-oriented doctoral program, such 

as project management, business entrepreneurship 

skills, working in small businesses, and teaching in academic institutions that are not research-intensive. 

♦ To shorten the length of training in the field, limit the 

number of years that a graduate student may be supported by NIH funds (any combination of training grants, 

fellowships, and research project grants). 

♦ To improve the quality of training and mentoring received by graduate students and postdoc researchers, 

increase the proportion of trainees supported by NIH 

training grants and fellowships relative to the proportion supported by NIH research project grants without 

increasing the total number of graduate student and postdoc researcher positions. 

♦ Improve postdoc compensation and benefits, and facilitate the prompt transitions of postdocs and doctoral 

students into permanent positions by developing individual career development opportunities. 

♦ Encourage institutions receiving NIH funds to gather and 

share comprehensive information on career outcomes of 

their PhD trainees and postdoc researchers, such as completion rates, time to degree, time in postdoc training, and 

post-training career outcome. This will help prospective 

graduate students and postdocs contemplating careers in 

the biomedical sciences to make informed decisions in a 

changing biomedical labor market.

♦ Encourage NIH, through collaboration with other federal agencies, to undertake initiatives to enhance the 

collection, analysis, and dissemination of information 

on biomedical sciences doctorates and postdocs.

* See NIH (2012) for a discussion on the fields of science considered as 

biomedical sciences. Based on the report, the following degree categories 

from the SDR are included in the data presented in this sidebar: biochemistry and biophysics, bioengineering and biomedical engineering, cell and 

molecular biology, microbiological sciences and immunology, zoology, biology (general), botany, ecology, genetics (animal and plant), nutritional 

science, pharmacology (human and animal), physiology and pathology 

(human and animal), and other biological sciences.

† For detailed information, see the NIH report available at http://acd.od.nih.

gov/Biomedical_research_wgreport.pdf (accessed 16 November 2013).

Employment of Biomedical Sciences Doctorates 
3-40 ♦ Chapter 3. Science and Engineering Labor Force

field,” or “work with a specific person or place.” However, 

13% of those in postdoc appointments reported lack of other 

employment as the primary reason for accepting these positions. In life sciences and physical sciences, the two broad 

fields with relatively high levels of postdoc appointments, 

the proportions of those reporting lack of other employment 

as the primary reason for accepting a postdoc position were 

low (11% and 17%, respectively) compared with the proportion of those in the social sciences (30%), an area where 

postdocs are typically not as common. 

Age and Retirement 

of the S&E Workforce

This section focuses on indicators of the aging of the 

S&E workforce, for example, the retirement patterns of S&E 

workers and workforce participation levels among older individuals. The high concentration of S&E workers over age 

50 suggests that the S&E workforce will soon experience 

high levels of turnover. The age distribution and retirement 

patterns of S&E workers have important implications for the 

supply of S&E expertise in the economy. An aging S&E labor force may translate into rising output and productivity 

as S&E workers acquire additional skills, gain experience, 

and improve their judgment. Consequently, the retirement 

of experienced workers could mean loss of valuable S&E 

expertise and knowledge. However, the retirement of older 

workers also makes room for newly trained S&E workers 

who may bring updated skills and new approaches to solving 

problems (Stephan and Levin 1992). 

The aging of the S&E labor force is reflected in rising 

median ages. In 2010, the median age of scientists and engineers in the labor force was 44 years, compared to 41 years 

in 1993. Another indicator of the aging of the S&E labor 

force is the increasing percentage of individuals in this labor 

force over age 50 (between the ages of 51 and 75) (figure 

3-25). In 1993, about 1 in every 5 scientists and engineers 

Figure 3-24

Recent U.S. SEH doctorate recipients in postdoc 

positions, by field of and years since doctorate: 2010

Percent

SEH = science, engineering, and health.

NOTES: Proportions are calculated on the basis of all doctorates 

working in all sectors of the economy. Data include graduates from 

15 months to 60 months prior to the survey reference date (October 

2010). The 3–5 year estimate for Computer and information sciences 

is suppressed for reasons of condentiality and/or reliability. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Doctorate Recipients (2010), 

http://sestat.nsf.gov. 

Science and Engineering Indicators 2014

0 10 20 30 40 50 60

< 3 years since degree

3–5 years since degree

Health

Engineering

Social sciences

Psychology

Physical sciences

Mathematics

and statistics

Computer and

information sciences

Biological, agricultural,

and environmental

life sciences

All SEH elds

Table 3-21

Median salaries for recent U.S. SEH doctorate 

recipients in postdoc and non-postdoc positions 

up to 5 years after receiving degree: 2010

(Dollars)

Field of doctorate

All 

positions Postdocs

Nonpostdocs

All SEH fields............... 66,000 43,000 76,000

Biological, 

agricultural, and 

environmental 

life sciences.......... 50,000 42,000 65,000

Computer and 

information 

sciences ............... 94,000 48,000 97,000

Mathematics and 

statistics ............... 64,000 53,000 70,000

Physical sciences .... 60,000 44,000 76,000

Psychology .............. 60,000 43,000 64,000

Social sciences........ 63,000 44,000 64,000

Engineering.............. 87,000 44,000 91,000

Health....................... 75,000 47,000 77,000

SEH = science, engineering, and health.

NOTES: Salaries are rounded to the nearest $1,000. Data include 

graduates from 15 months to 60 months prior to the survey 

reference date.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Doctorate Recipients (2010), 

http://sestat.nsf.gov.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 3-41

in the labor force was in that age group (20%), whereas by 

2010 the proportion rose to 1 out of 3 (33%). 

Between 1993 and 2010, the proportion of scientists and 

engineers in the labor force over 50 years of age rose for 

both men and women; however, the female labor force continues to be younger relative to their male counterparts (figure 3-25). In 2010, 30% of female scientists and engineers 

in the labor force were between 51 and 75 years of age, compared to 36% of male scientists and engineers in the labor 

force. In 2010, the median ages in the SESTAT population 

were 42 years for women and 45 years for men, whereas in 

1993 the median ages were 38 and 42, respectively.

Age Differences among Occupations

SESTAT respondents working in S&E occupations are 

younger than those in S&E-related or non-S&E occupations 

(figure 3-26). In 2010, 26% of those in S&E occupations 

were between 51 and 75 years of age compared with 34% 

of those in S&E-related occupations and 36% of those in 

non-S&E occupations. The median age of the SESTAT population employed in S&E occupations was 42 years, compared to 44 years among those employed in S&E-related 

occupations and 45 years among those employed in nonS&E occupations. 

The age differences across S&E and non-S&E occupations were more pronounced for men than for women. 

Among male scientists and engineers, 27% of those employed in S&E occupations were between the ages of 51 and 

75 compared with 41% of those employed in non-S&E occupations. Among female scientists and engineers, 24% of 

those employed in S&E occupations were between the ages 

of 51 and 75 compared with 30% of those employed in nonS&E occupations. 

Age Differences among Degree Fields

Similar to the trend seen across broad occupational categories, S&E highest degree holders are generally younger 

than those holding highest degrees in S&E-related or nonS&E fields (figure 3-26). In 2010, 30% of S&E highest degree holders were between 51 and 75 years of age compared 

with 36% of those with highest degrees in S&E-related or 

non-S&E fields. However, degree holders in different S&E 

fields varied in their ages. S&E highest degree holders in the 

physical sciences, particularly the men in this group, were 

older than those in other S&E fields (appendix table 3-12). 

S&E highest degree holders in computer and mathematical 

NOTES: For 1993 data, scientists and engineers include those with 

one or more S&E degrees at the bachelor’s level or higher or those 

who have only a non-S&E degree at the bachelor's level or higher 

and are employed in an S&E occupation. For 2010 data, scientists 

and engineers include those with one or more S&E or S&E-related 

degrees at the bachelor’s level or higher or those who have only a 

non-S&E degree at the bachelor’s level or higher and are employed 

in an S&E or S&E-related occupation. The Scientists and Engineers 

Statistical Data System (SESTAT) does not cover scientists and 

engineers over age 75. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, SESTAT (1993, 2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

Figure 3-25

Age distribution of scientists and engineers in the 

labor force, by sex: 1993 and 2010

1993

Total Female Male

2010 1993 2010 1993 2010

0

20

40

60

80

100

< 30

30–50

51–75

Years of

age

Percent

NOTES: Scientists and engineers include those with one or more 

S&E or S&E-related degrees at the bachelor's level or higher or those 

who have only a non-S&E degree at the bachelor's level or higher 

and are employed in an S&E or S&E-related occupation. The 

Scientists and Engineers Statistical Data System (SESTAT) does not 

cover scientists and engineers over age 75.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, SESTAT (2010), http://sestat.nsf.gov. 

Science and Engineering Indicators 2014

Figure 3-26

Age distribution of employed scientists and 

engineers, by broad occupational category and 

broad field of highest degree: 2010

 S&E S&Erelated

Occupation Highest degree

NonS&E S&E S&ErelatedNonS&E

0

20

40

60

80

100

Percent

< 30

30–50

51–75

Years of

age
3-42 ♦ Chapter 3. Science and Engineering Labor Force

sciences, in social sciences, and in engineering were relatively young. 

Within broad degree areas, the age profile of different degree fields varies (appendix table 3-12). For example, within 

computer and mathematical sciences degree fields, 16% of 

highest degree holders in computer and information sciences were between 51 and 75 years of age compared with 

39% of highest degree holders in mathematics and statistics. 

In all broad S&E fields of highest degree except computer 

and mathematical sciences, women were younger than their 

male counterparts (appendix table 3-12). 

Retirement

The increasing proportion of the SESTAT labor force 

over 50 years of age raises the issue of how impending retirement will affect the supply of S&E workers. Patterns of 

labor force participation among older individuals provide 

useful information about potential retirement ages and how 

retirement ages may have changed over time. 

Recent patterns of leaving the labor force and shifting to 

part-time work among older members of the workforce suggest that after age 55 the labor force participation rate among 

scientists and engineers begins to decline and is markedly 

reduced by the time workers reach their late 60s. One indication of the relationship between age and the level of labor 

force participation is illustrated by figure 3-27, which shows 

the proportions of older scientists and engineers working full 

time. In 2010, at age 50, 80% of scientists and engineers 

worked full time (35 hours or more per week) in their principal job. Among individuals in their mid- to late-50s, this proportion dropped steeply. Among those in their mid-60s, for 

example, only about one-third worked full time. The overall 

pattern of declining full-time participation starting in individuals’ mid- to late-50s held at all degree levels, although 

doctorate holders generally worked full time at higher rates 

than bachelor’s degree holders (figure 3-27). 

Between 1993 and 2010, increasing proportions of 

SESTAT respondents in their 60s reported still being in the 

labor force. Whereas 69% of SESTAT respondents between 

the ages of 60 and 64 were in the labor force in 1993, this proportion rose to 74% in 2010. For those between the ages of 65 

and 69, the proportion rose from 39% in 1993 to 47% in 2010. 

Reasons provided by SESTAT respondents for labor 

force nonparticipation or part-time work status also shed 

light on the relationship between age and retirement. In 

2010, about 2.5 million scientists and engineers reported 

that they were out of the labor force because of retirement. 

The vast majority (87%) of retired individuals were 60–75 

years of age, and half of the retired individuals (51%) were 

between the ages of 67 and 75. Individuals with doctorates 

reported lower rates of retirement than those without doctorates (figure 3-28).

Figure 3-28

Older scientists and engineers who report not 

working because of retirement, by age: 2010

Percent

NOTES: All degree levels include professional degrees not reported 

separately. The missing data points are suppressed for reasons of 

condentiality and/or reliability. Scientists and engineers include 

those with one or more S&E or S&E-related degrees at the bachelor’s 

level or higher or those who have only a non-S&E degree at the 

bachelor’s level or higher and are employed in an S&E or S&E-related 

occupation. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2010), http://sestat.nsf.gov. 

Science and Engineering Indicators 2014

Years of age

50 53 55 57 59 61 63 65 67 69 71 73 75

0

20

40

60

80

100

All degree levels

Bachelor’s

Master’s

Doctorate

Figure 3-27

Older scientists and engineers who work full time, 

by age and highest degree level: 2010

Percent

NOTES: All degree levels include professional degrees not reported 

separately. Scientists and engineers include those with one or more 

S&E or S&E-related degrees at the bachelor’s level or higher or those 

who have only a non-S&E degree at the bachelor’s level or higher 

and are employed in an S&E or S&E-related occupation. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2010), http://sestat.nsf.gov. 

Science and Engineering Indicators 2014

Years of age

50 53 55 57 59 61 63 65 67 69 71 73 75

0

20

40

60

80

100

All degree levels

Bachelor’s

Master’s

Doctorate
Science and Engineering Indicators 2014 ♦ 3-43

Retirement, however, does not always mean that workers 

permanently leave the labor force. After nominally retiring 

from their jobs, some workers continue to work part time, 

work in a different capacity, or decide to return to the labor 

market at a later time. About 1.4 million scientists and engineers employed in 2010 reported that they had previously 

retired from a job. A total of 653,000 scientists and engineers working part time in 2010 reported their reason for 

working part time as having “previously retired or semi-retired.” Individuals who chose to stay in or return to the labor 

market following an occurrence of retirement were younger 

(median age 62) than those who were out of the labor force 

following retirement (median age 67). 

Compared to all employed scientists and engineers included in SESTAT, the 1.4 million SESTAT respondents who 

stayed in or returned to the workforce after having retired 

from a previous position were less likely to hold S&E jobs 

(18% versus 25% for all employed SESTAT respondents) or 

to work in areas closely related to their highest degree (46% 

versus 58% for all employed SESTAT respondents) and 

more likely to be self-employed in unincorporated businesses (17% versus 7% for all employed SESTAT respondents).

Women and Minorities 

in the S&E Workforce

As researchers and policymakers increasingly emphasize the need for expanding S&E capabilities in the United 

States, many view demographic groups with lower rates of 

S&E participation as an underutilized source of human capital for S&E work. Historically, in the United States, S&E 

fields have had particularly low concentrations of women 

and members of many racial and ethnic minority groups (i.e., 

blacks, Hispanics, American Indians or Alaska Natives), 

both relative to the concentrations of these groups in other 

occupational or degree areas and relative to their representation in the general population. However, women and racial and ethnic minorities increasingly have been choosing 

a wider range of degrees and occupations over time. This 

section presents data on S&E participation by women and by 

racial and ethnic minorities. It also presents data on earnings 

differentials by sex and by race and ethnicity. 

Women in the S&E Workforce

Historically, men have outnumbered women by wide 

margins with regards to both S&E employment and S&E 

training. Although the number of women in S&E occupations or with S&E degrees nearly doubled over the past two 

decades, the disparity has narrowed only modestly. The imbalance is still particularly pronounced in S&E occupations. 

In 2010, women constituted only 28% of workers in these 

occupations, even though they accounted for nearly half of 

the college-educated workforce. Among S&E degree holders, the disparity was smaller but nonetheless significant, 

with women representing 37% of employed individuals with 

a highest degree in S&E (figure 3-29). 

Women in S&E Occupations

Although women represented only 28% of individuals in 

S&E occupations in 2010, women’s presence varies widely 

across S&E occupational fields (appendix table 3-13). The 

percentage of female S&E workers is lowest in engineering, 

where women constituted 13% of the workforce in 2010. 

Among engineering occupations with large numbers of workers, the disparity between men and women is greatest among 

mechanical engineers, with women accounting for only 7% 

of the workforce. Other large engineering occupations in 

which women account for about 11% to 12% of the workforce include electrical and computer hardware engineers 

and aerospace, aeronautical, and astronautical engineers. 

Other disproportionately male S&E occupations include 

physical scientists (30% women) and computer and mathematical scientists (25% women). Within the physical sciences occupations, physicists and astronomers have the largest 

imbalance (18% women). Within the computer and mathematical sciences occupations, the largest component, computer and information scientists, has the smallest proportion 

of women (23%). The mathematical scientists component is 

much closer to parity (46% women). 

In 2010, sex parity in S&E occupations was close among 

life scientists (48% women). Within the life sciences occupations, biological and medical scientists, the largest component, had reached gender parity (52% women). The field 

of social sciences was majority female (58%). Occupations 

within the social sciences, however, varied with respect to 

the proportion of female workers. Thus, women accounted 

for slightly more than one-third of economists (37%) but 

Figure 3-29

Women in the workforce and in S&E: 1993 and 2010

Percent 

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) and National Survey of College Graduates (NSCG) 

(1993 and 2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

College-educated

workforce

S&E occupations S&E highest degree

0

10

20

30

40

50

60

1993 2010
3-44 ♦ Chapter 3. Science and Engineering Labor Force

more than two-thirds of psychologists (70%). Psychologists, 

estimated at about 171,000 total workers in SESTAT (appendix table 3-13), was an example of a large S&E occupation with substantially more women than men.

In contrast to jobs in S&E occupations, a majority of jobs 

in S&E-related occupations (56%) are held by women (appendix table 3-13). The largest component, health-related 

occupations, employed a large number of women (68% 

women), primarily as nurse practitioners, pharmacists, registered nurses, dietitians, therapists, physician assistants, and 

health technologists and technicians. 

Since the early 1990s, the number of women working 

in each broad S&E occupational category has risen significantly. The rate of growth has been strongest among life 

scientists, computer and mathematical scientists, and social 

scientists. These three broad S&E fields together employed 

80% of women in S&E occupations in 2010, compared with 

59% of men in S&E occupations. Between 1993 and 2010, 

the number of women more than doubled among life scientists (an increase of 162%) and nearly doubled among social scientists (an increase of 87%). The number of men also 

grew, but the rate of growth for women was greater than that 

for men, resulting in an increase in the proportion of female 

life scientists and female social scientists (figure 3-30). 

During the same period, the number of women in computer and mathematical sciences occupations nearly doubled 

(an increase of 97%). However, unlike the other broad S&E 

occupational categories, the rate of growth in male participation was larger (161%) than that of women, resulting in 

an overall decline in the proportion of women from 31% to 

25%. These trends made the gender disparity among computer and mathematical scientists second only to engineers. 

The declining proportion of women in the computer and 

mathematical sciences occupations reflects increasing disparities in participation among those whose highest degree 

is at the bachelor’s degree level. Among computer and mathematical scientists with a doctoral degree, the proportion of 

women increased, from 16% in 1993 to 20% in 2010. 

During the past two decades, women have also increased 

their proportion among workers in engineering (from 9% 

to 13%) and in the physical sciences (from 21% to 30%). 

In these two occupational categories, this increase was led 

by an expansion of women’s numbers in the workforce (by 

67% in engineering and 60% in physical sciences) while 

men’s numbers barely changed between 1993 and 2010. 

Women among S&E Highest Degree Holders

The sex disparity among employed S&E highest degree 

holders is less than the disparity among those in S&E occupations. In 2010, among individuals with a highest degree 

in an S&E field, women constituted 37% of those who were 

employed, up from 31% in 1993. The pattern of variation 

in the proportion of men and women among degree fields 

echoes the pattern of variation among occupations associated with those fields (appendix table 3-14). In 2010, 54% 

of S&E highest degree holders in the social sciences fields 

were women, as were 48% of those with a highest degree 

in the biological and related sciences. Men outnumbered 

women among computer sciences and mathematics highest 

degree holders (28% women) and among physical sciences 

highest degree holders (27% women). Disparities, however, 

were greatest among those with a highest degree in engineering (only 14% women). In all fields except computer 

and mathematical sciences, the proportion of women in the 

workforce with associated highest degrees has been increasing over the past two decades. In computer and mathematical sciences, this proportion has declined even as the number 

of women with a highest degree in the field has risen.

Sex differences are not limited to the field of degree, but 

also extend to the level of S&E degree. Men outnumber 

women among S&E highest degree holders at the bachelor’s, master’s, and doctoral levels. Moreover, the sex disparity is higher among S&E doctorate holders than among 

S&E bachelor’s or master’s degree holders. For example, in 

2010 women accounted for 38% of those whose highest degree in S&E was at the bachelor’s or master’s level but 30% 

of those whose highest degree in S&E was at the doctoral 

level (figure 3-31). At the doctoral level, however, the proportion of women has been steadily increasing. The trend at 

the bachelor’s and master’s levels has been somewhat different: although the proportion of women in the workforce rose 

from 1993 to 2003, it remained mostly steady from 2003 to 

2010 (figure 3-31). 

Working men and women with S&E highest degrees also 

differ in the extent to which they are employed in the same 

field as their S&E highest degree. However, this disparity is 

Figure 3-30

Women in S&E occupations: 1993–2010

Percent

NOTE: National estimates were not available from the Scientists and 

Engineers Statistical Data System (SESTAT) in 2001.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, SESTAT (1993–2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

1993 1995 1997 1999 2003 2006 2008 2010

0

10

20

30

40

50

60

70

80

All S&E occupations

Computer/mathematical scientists

Biological/agricultural/environmental life scientists

Physical scientists

Social scientists

Engineers
Science and Engineering Indicators 2014 ♦ 3-45

largely the result of women having a high concentration in the 

two degree areas—social sciences and life sciences—where 

degree holders most often work in non-S&E occupations. In 

2010, these two broad fields accounted for three-fourths of 

all employed women with S&E highest degrees, compared 

with 41% of all employed men with S&E highest degrees 

(appendix table 3-14). (See sidebar, “S&E Credentials and 

the Male-Female Gap in S&E Employment.”) 

Across all S&E degree areas, 19% of women with an S&E 

highest degree are employed in the S&E field in which they 

earned their degree compared with 32% of men (appendix 

table 3-15). However, within the majority of degree areas (life 

sciences, social sciences, and engineering), similar proportions 

of men and women are employed in the S&E field in which 

they earned their degree. Computer and mathematical sciences 

fields are exceptions, where a larger proportion of men (54%) 

than women (43%) work in an occupation that matches their 

degree field and a larger proportion of women (38%) than men 

(27%) work in non-S&E occupations. Among those with life 

sciences degrees, although a similar proportion of men (23%) 

and women (22%) work in their degree field, a larger proportion of women (35%) than men (18%) are employed in S&Erelated occupations. These sex differences in the degree fields 

of life sciences and computer and mathematical sciences are 

primarily driven by those whose highest degrees are at the 

bachelor’s or master’s levels. 

Men and women with a highest degree in an S&E field also 

differ in their labor force nonparticipation rates. Compared 

with men, women were more likely to be out of the labor 

force (22% versus 14% for men). The difference in nonparticipation was particularly pronounced between the ages of 

30 and 65 (figure 3-32). In 2010, 19% of the women in this 

age group with an S&E highest degree were out of the labor 

force compared with 7% of the men. Many women in this 

group identified family reasons as an important factor: 48% 

of women reported that family was a factor for their labor 

force nonparticipation compared with 9% of men. Within 

this age range, women were also much more likely than 

men to report that they did not need to work or did not want 

to work (41% of women versus 26% of men). Men, on the 

other hand, were much more likely than women to cite retirement as a reason for not working (28% of women versus 

71% of men). 

Minorities in the S&E Workforce

The participation of underrepresented racial and ethnic 

minorities in the S&E workforce has been a concern of policymakers who are interested in the development and employment of diverse human capital to maintain the United States’ 

global competitiveness in S&E. This section addresses the 

level of diversity in S&E by race and Hispanic ethnicity.21

Like the preceding section, this section draws on data from 

NSF’s SESTAT surveys to report on levels of S&E participation: first across occupations and then across the overall 

workforce with S&E degrees. 

Whether defined by occupation, S&E degree, or the combined criteria used in SESTAT, the majority of scientists and 

engineers in the United States are non-Hispanic whites. The 

next largest group of scientists and engineers are Asians. On 

the other hand, several racial and ethnic minority groups, including blacks, Hispanics, and American Indians or Alaska 

Natives, have low levels of participation in S&E fields both 

compared with other groups and compared with their proportion in the population (table 3-22). 

Race and Ethnicity Trends in S&E Occupations 

In 2010, among the 5.4 million workers employed in 

S&E occupations, 70% were white, which is similar to the 

proportion (68%) in the U.S. population age 21 and older 

(table 3-22). However, S&E participation by whites varied 

across the broad S&E occupational categories, from 65% of 

computer and mathematical scientists to 81% of social scientists (appendix table 3-16). The concentration of whites 

in some occupations was more pronounced: they accounted for approximately 90% of workers among forestry and 

conservation scientists, geologists and earth scientists, and 

political scientists. 

Asians, with nearly a million workers in S&E occupations, 

accounted for 19% of S&E employment. Among the overall population age 21 and older, their proportion was much 

smaller (5%). Asians had a large presence in computer and 

engineering fields, constituting 33% of computer software 

engineers, 30% of software developers, 40% of computer 

hardware engineers, 27% of bioengineers or biomedical engineers, and 35% of postsecondary teachers in engineering 

(appendix table 3-16). On the other hand, the proportion of 

Asians in social sciences occupations was much lower both 

Figure 3-31

Employed women with highest degree in S&E, 

by degree level: 1993–2010

Percent

NOTE: National estimates were not available from the Scientists and 

Engineers Statistical Data System (SESTAT) in 2001. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, SESTAT (1993–2010), http://sestat.nsf.gov. 

Science and Engineering Indicators 2014

1993 1995 1997 1999 2003 2006 2008 2010

0

5

10

15

20

25

30

35

40

45

Bachelor’s

Master’s

Doctorate
3-46 ♦ Chapter 3. Science and Engineering Labor Force

compared with their participation in other S&E fields and 

compared with whites. For example, Asians accounted for 

just 6% of workers in social sciences occupations. 

The social sciences are the one S&E occupational category in which the proportions of blacks (5%) and Hispanics 

(6%) are similar to that of Asians (6%) (appendix table 

3-16). As a result, underrepresented racial and ethnic minorities (blacks, Hispanics, and American Indians or Alaska 

Natives) collectively outnumber Asians among social scientists. In the other broad S&E occupational categories, Asians 

represent a larger segment than all underrepresented racial 

and ethnic minorities combined. 

In general, the proportions of Hispanics across the broad 

S&E occupational categories were roughly similar (between 

5% and 6%), whereas blacks had higher rates of participation among computer and mathematical scientists (6%) 

relative to life scientists (3%), physical scientists (3%), 

and engineers (4%) (appendix table 3-16). Hispanics had 

a particularly large presence among sociologists (13%); 

psychologists (7%); aeronautical, aerospace, and astronautical engineers (9%); and civil engineers (8%). Blacks had 

relatively high participation rates among computer support 

specialists (16%), information security analysts (14%), and 

sociologists (13%).

Over the past two decades, the U.S. workforce in S&E 

occupations has been becoming more diverse with increasing proportions of Asians, blacks, and Hispanics and a decreasing proportion of whites (table 3-23). In 1993, 84% of 

Among college-educated individuals, a significantly 

higher proportion of men than women are employed in 

S&E occupations. For example, among S&E highest 

degree holders working full time, 26% of women, compared to 43% of men, hold positions with formal S&E 

jobs. This gender gap in S&E employment is found in 

all racial and ethnic groups. For example, among S&E 

highest degree holders working full time, S&E jobs 

are held by 43% of Asian women compared to 58% of 

Asian men, 22% of black women compared to 32% of 

black men, 19% of Hispanic women compared to 37% 

of Hispanic men, and 24% of white women compared 

to 41% of white men. The participation gap exists despite the trend that increasing proportions of women in 

all racial and ethnic groups are graduating from college. 

In most racial and ethnic groups, for example, a higher 

percentage of women than men have college degrees.

Field of degree, level of highest degree, employment sector, and other characteristics that are typically 

believed to be associated with occupational fields vary 

between men and women. As a result, it can be misleading to directly compare S&E employment rates by sex. 

Compared with men, women tend to have many characteristics—such as degrees in the life and social sciences, 

highest degrees at the bachelor’s level, and employment 

in 2-year academic institutions and in the non-profit sector—that are associated with working outside S&E occupations. Statistical models can estimate the size of the 

male-female participation gap in S&E occupations when 

various occupation-related factors are taken into account. 

However, estimates of these differences vary somewhat 

depending on the assumptions that underlie the statistical 

model used. 

After accounting for differences between men and 

women in field of degree, level of highest degree, and 

employment sector, the participation gap in S&E occupations declines significantly (from 17 to 6 percentage 

points) but does not attenuate completely (figure 3-C). 

Adding measures of personal and family characteristics 

that may affect S&E participation to academic and employment information further reduces the estimated participation gap marginally (from 6 to 5 percentage points). 

This suggests that although measurable differences between men and women explain a significant portion of 

the male-female participation gap in S&E occupations, 

they do not entirely explain the differing propensity of 

men and women to obtain S&E employment. As such, 

boosting college attendance alone is unlikely to equalize 

male-female participation in S&E employment as long as 

men and women study different fields and attain degrees 

at different levels. 

S&E Credentials and the Male-Female Gap in S&E Employment

Figure 3-C

Estimated differences in the proportions of women 

and of men with S&E highest degree employed in 

S&E occupations, controlling for selected 

characteristics: 2010

NOTE: Coefcients are estimated in a probit regression model using 

a binary (0–1) variable indicating employment in S&E occupations as 

the dependent variable. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, SESTAT (2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

Total difference

Controlling for

academic training and

employment sector

Plus demographics and

other characteristics

0 5 10

Negative differential (percentage point)

15 20
Science and Engineering Indicators 2014 ♦ 3-47

workers in S&E occupations reported their race as white. By 

2010, this proportion declined to 70%. Most of the decline 

in the proportion of whites during this period was offset by 

an increase in the proportion of Asians and, to a lesser degree, by an increase in the proportion of some other groups, 

particularly Hispanics.

Some of the changes by race may reflect changes to the 

way NSF workforce surveys collect information on this topic. After 2000, respondents were able to report two or more 

races rather than just one. Some of those who self-reported 

as white in the 1990s may have instead reported a multiracial 

identity after 2000 once they were given the option, which 

would decrease the estimated numbers of whites. However, 

because less than 2% of S&E workers reported a multiracial 

identity in years when that option was available, it is unlikely that this change contributed much to the decline in the 

proportion of whites between 1993 and 2010. 

Racial and Ethnic Differences among S&E 

Degree Holders 

Among employed S&E highest degree holders, racial 

and ethnic groups vary with respect to their proportions in 

different degree fields (table 3-24; appendix table 3-17). 

Differences in highest degree fields largely resemble the 

differences among S&E occupations. Asians have higher 

participation rates among engineering highest degree holders and among computer and mathematical sciences highest 

degree holders relative to other broad S&E degree fields. 

Blacks have higher participation rates in computer and 

mathematical sciences and in the social sciences. Hispanics 

have higher participation rates in engineering and in the 

social sciences. Whites represent a larger segment of life, 

physical, and social sciences highest degree holders than 

engineering or computer and mathematical sciences highest 

degree holders.

The demographic groups also differ in the level of their 

highest degree (table 3-25). For example, Asians account for 

a larger proportion of those whose highest degree is at the 

master’s or doctoral level compared with those whose highest 

Figure 3-32

Highest degree holders in S&E not in the labor 

force, by sex and age: 2010

Percent

NOTE: Not in the labor force includes those not working nor looking 

for work in the 4 weeks prior to October 2010.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2010), http://sestat.nsf.gov 

Science and Engineering Indicators 2014

≤ 24 25–29 30–34 35–39 40–44 45–49

Years of age

50–54 55–59 60–64 65–69 70–75

0

20

40

60

80

100

Female

Male

Table 3-22

Racial and ethnic distribution of employed individuals in S&E occupations, and of S&E degree holders, college 

graduates, and U.S. residents: 2010

(Percent)

Race and ethnicity

S&E 

occupations

S&E highest 

degree holders

College degree 

holders

U.S. 

residential 

populationa

Total (n) ......................................................................................... 5,398,000 11,385,000 40,623,000 221,319,000

American Indian or Alaska Native............................................. 0.2 0.2 0.3 0.6

Asian ......................................................................................... 18.5 13.9 7.9 4.9

Black......................................................................................... 4.6 5.7 6.8 11.5

Hispanic.................................................................................... 5.2 6.8 7.1 13.9

Native Hawaiian or Other Pacific Islander ................................ 0.2 0.3 0.3 0.1

White......................................................................................... 69.9 71.5 76.2 67.5

More than one race................................................................... 1.4 1.5 1.4 1.5

a

 Age 21 and over.

NOTES: Hispanic may be any race. American Indian or Alaska Native, Asian, black or African American, Native Hawaiian or Other Pacific Islander, white, 

and more than one race refer to individuals who are not of Hispanic origin.

SOURCES: Census Bureau, American Community Survey (2010); National Science Foundation, National Center for Science and Engineering Statistics, 

Scientists and Engineers Statistical Data System (SESTAT), and National Survey of College Graduates (NSCG) (2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014
3-48 ♦ Chapter 3. Science and Engineering Labor Force

Table 3-23

Distribution of workers in S&E occupations, by race and ethnicity: 1993–2010

(Percent)

Race and ethnicity 1993 1995 1997 1999 2003 2006 2008 2010

American Indian or Alaska Native ........................................... 0.2 0.3 0.3 0.3 0.3 0.4 0.3 0.2

Asian........................................................................................ 9.1 9.6 10.4 11.0 14.2 16.1 16.9 18.5

Black........................................................................................ 3.6 3.4 3.4 3.4 4.3 3.9 3.9 4.6

Hispanic................................................................................... 2.9 2.8 3.1 3.4 4.4 4.6 4.9 5.2

Native Hawaiian or Other Pacific Islander............................... NA NA NA NA 0.3 0.5 0.4 0.2

White ....................................................................................... 84.1 83.9 82.9 81.8 75.2 73.2 71.8 69.9

More than one race ................................................................. NA NA NA NA 1.4 1.4 1.7 1.4

NA = not available.

NOTES: Before 2003, respondents could not classify themselves in more than one racial and ethnic category. Before 2003, Asian included Native 

Hawaiian and Other Pacific Islander. Hispanic may be any race. American Indian or Alaska Native, Asian, black or African American, Native Hawaiian or 

Other Pacific Islander, white, and more than one race refer to individuals who are not of Hispanic origin.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (1993–2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

Table 3-24

Racial and ethnic distribution of employed individuals with S&E highest degree, by field of highest degree: 2010

(Percent)

Race and ethnicity

All S&E 

fields

Biological, 

agricultural, and 

environmental 

life sciences

Computer and 

mathematical 

sciences

Physical 

sciences

Social 

sciences Engineering

Employed with highest degree in S&E (n) ...... 11,385,000 1,764,000 1,886,000 693,000 4,363,000 2,679,000 

American Indian or Alaska Native............... 0.2 0.3 0.2 0.3 0.3 0.2

Asian ........................................................... 13.9 12.0 22.7 15.2 6.5 20.6

Black........................................................... 5.7 3.6 7.7 3.6 7.6 3.2

Hispanic...................................................... 6.8 6.2 5.5 4.5 7.7 7.4

Native Hawaiian or Other Pacific Islander ... 0.3 S 0.2 0.1 0.3 0.4

White........................................................... 71.5 75.7 62.6 75.3 75.8 66.9

More than one race..................................... 1.5 1.4 1.1 1.2 1.9 1.3

S = suppressed for reasons of confidentiality and/or reliability.

NOTES: Hispanic may be any race. American Indian or Alaska Native, Asian, black or African American, Native Hawaiian or Other Pacific Islander, white, 

and more than one race refer to individuals who are not of Hispanic origin.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System (SESTAT) 

(2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

Table 3-25

Racial and ethnic distribution of employed individuals with S&E highest degree, by level of highest degree: 2010

(Percent)

Race and ethnicity Bachelor’s Master’s Doctorate

Employed with highest degree in S&E (n) ................................................... 8,160,000 2,356,000 847,000

American Indian or Alaska Native............................................................ 0.3 0.2 0.1

Asian ........................................................................................................ 11.0 20.6 23.0

Black........................................................................................................ 6.1 5.6 2.8

Hispanic................................................................................................... 7.5 5.7 3.8

Native Hawaiian or Other Pacific Islander ................................................ 0.4 0.2 0.1

White........................................................................................................ 73.1 66.3 69.1

More than one race.................................................................................. 1.6 1.4 1.1

NOTES: Hispanic may be any race. American Indian or Alaska Native, Asian, black or African American, Native Hawaiian or Other Pacific Islander, white, 

and more than one race refer to individuals who are not of Hispanic origin.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 3-49

degree is at the bachelor’s level. Conversely, non-Asians 

represent a larger proportion of those whose highest degree 

is at the bachelor’s and master’s degree level compared with 

those whose highest degree is at the doctoral level. 

Asian S&E highest degree holders are more likely than 

those in other racial and ethnic groups to work in S&E occupations and to work in the area in which they earned their 

degree (appendix table 3-15). Among blacks, Hispanics, 

and whites, about one-quarter or less of S&E highest degree 

holders work in their same broad field of highest degree. By 

comparison, nearly 40% of Asians work in the same broad 

field in which they received their highest degree. 

Salary Differences for Women and Racial and 

Ethnic Minorities

Women and racial and ethnic minority groups generally receive less pay than their male and white counterparts 

(table 3-26). In 2010, among full-time workers with a highest degree in an S&E field, the median salary for women 

($53,000) was about one-third lower than that for men 

($80,000). Among S&E highest degree holders who work 

full-time in S&E occupations, the difference in median 

salary between men ($85,000) and women ($69,000) was 

smaller (19% less) (appendix table 3-18). 

Salary differences among racial and ethnic groups were 

somewhat smaller than salary differences between men and 

women (table 3-26; appendix table 3-19). Among S&E 

highest degree holders working full time, American Indians 

or Alaska Natives earned 18% less than whites, blacks 

earned 22% less than whites, and Hispanics earned 17% 

less than whites. Relative to Asians, American Indians or 

Alaska Natives earned 21% less, blacks earned 25% less, 

and Hispanics earned 20% less. These salary differences 

were generally more modest among those who worked in 

S&E occupations (appendix table 3-19). 

Overall, salary differences between men and women and 

among racial and ethnic groups remained largely unchanged 

between 1995 and 2010 (table 3-26). 

Differences in average age, work experience, academic 

training, sector and occupation of employment, and other 

characteristics can make direct comparison of salary statistics misleading. Statistical models can estimate the size of 

the salary difference between men and women, or the salary difference between racial and ethnic groups, when various salary-related factors are taken into account. Estimates of 

these differences vary somewhat depending on the assumptions that underlie the statistical model used. The remainder 

of this section presents estimated salary differences between 

men and women among individuals who are otherwise similar in age, work experience, field of highest degree, type of 

academic institution awarding highest degree (Carnegie classification and public/private status), occupational field and 

sector, and other relevant characteristics that are likely to influence salaries. Data bearing on salary differences between 

minorities (American Indians or Alaska Natives, blacks, 

Hispanics, Native Hawaiians or Other Pacific Islanders, and 

those reporting more than one race) relative to Asians and 

whites are also included. 

Without accounting for any factors except level of degree, 

women working full time whose highest degree is at the bachelor’s level in an S&E field earned 31% less than men (figure 3-33).22 The salary difference is smaller, but nonetheless 

substantial, at both the master’s level (29%) and the doctoral 

level (22%). The salary differences for non-Asian minorities 

relative to whites and Asians are narrower (figure 3-34). On 

average, minority salary levels are 22% lower than those of 

whites and Asians at the bachelor’s level, 14% lower at the 

master’s level, and 16% lower at the doctoral level. 

Effects of Education, Employment, and 

Experience on Salary Differences

Salaries differ across degree field, occupational field and 

sector, and experience. For example, median salaries in 2010 

were generally higher among individuals with highest degrees in engineering ($86,000), physical sciences ($68,000), 

or computer and mathematical sciences ($79,000) compared 

with those with highest degrees in life sciences ($50,000) or 

social sciences ($50,000). Degree areas with lower salaries 

generally have higher concentrations of women and of racial 

and ethnic minorities. Disproportionately larger proportions 

Table 3-26

Median annual salary among S&E highest degree 

holders working full time, by sex, race, and 

ethnicity: 1995, 2003, 2010

(Dollars)

Characteristic 1995 2003 2010

All............................................ 44,000 60,000 70,000

Sex

Female............................. 34,000 45,000 53,000

Male................................. 49,000 68,000 80,000

Race and ethnicity

American Indian or 

Alaska Native............... S 48,000 59,000

Asian ............................... 45,000 64,000 75,000

Black ............................... 35,000 48,000 56,000

Hispanic .......................... 38,000 50,000 60,000

Native Hawaiian or 

Other Pacific Islander... NA 56,000 56,000

White ............................... 45,000 60,000 72,000

More than one race ......... NA 50,000 60,000

NA = not available; S = suppressed for reasons of confidentiality 

and/or reliability.

NOTES: Salaries are rounded to the nearest $1,000. Data for 1995 

include some individuals with multiple races in each category. 

Hispanic may be any race. American Indian or Alaska Native, Asian, 

black or African American, Native Hawaiian or Other Pacific Islander, 

white, and more than one race refer to individuals who are not of 

Hispanic origin.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (1995, 2003, 2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014
3-50 ♦ Chapter 3. Science and Engineering Labor Force

of degree holders in life sciences, and particularly in the social sciences, relative to other S&E degree fields, work in 

occupations not categorized as S&E, where salaries are generally lower than in S&E occupations (appendix table 3-18). 

As a result, differences in degree and occupational fields are 

likely to explain much of the salary differences by sex and 

by race and ethnicity. 

Salaries also differ across employment sector. Academic 

and non-profit employers typically pay less for similar 

skills than employers in the private sector, and government 

compensation falls somewhere between these two groups. 

These differences are salient for understanding salary variations by sex and by race and ethnicity because men, Asians, 

and whites are more highly concentrated in the private 

for-profit sector. 

Salaries also vary by indicators of experience, such as 

age or years since completing one’s degree. Because of the 

rapid increase in female participation in S&E fields in recent 

years, female S&E highest degree holders employed full 

time are younger than their male counterparts (median age 

40 years for women versus 44 years for men), which translates to fewer years of labor market experience for women 

relative to men. White S&E highest degree holders with similar characteristics are also older (44 years) compared with 

Asians (39 years) and most other racial and ethnic minorities 

(Hispanics: 39 years, blacks: 42 years, American Indians or 

Alaska Natives: 43 years, and Native Hawaiians or Other 

Pacific Islanders: 33 years). 

After controlling for differences in field of highest degree, degree-granting institution, field of occupation, employment sector, and experience,23 the estimated salary 

difference between men and women narrows by more than 

half (figure 3-33). However, among men and women in similar jobs, and with similar highest degree fields and levels of 

experience, women still earn 12% less than men among individuals whose highest degree is at the bachelor’s level, 10% 

less than men among individuals whose highest degree is at 

Figure 3-33

Estimated salary differences between women and 

men with highest degree in S&E employed full time, 

controlling for selected characteristics, by degree 

level: 2010

Negative differential (percent)

NOTES: Salary differences represent the estimated percentage 

difference in women’s average full-time salary relative to men’s 

average full-time salary. Coefcients are estimated in an ordinary 

least squares regression model using the natural log of full-time 

annual salary as dependent variable and then transformed into 

percentage difference.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, SESTAT (2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

Plus demographics

and other

characteristics

Controlling for

education and

employment

Total difference

in full-time salary

0 5 10 15 20 25 30 35

Bachelor’s

Doctorate

Master’s

Figure 3-34

Estimated salary differences between minorities and 

whites and Asians with highest degree in S&E 

employed full time, controlling for selected 

characteristics, by degree level: 2010

Negative differential (percent)

NOTES: Salary differences represent the estimated percentage 

difference in the average full-time salary of minorities relative to the 

average full-time salary of whites and Asians. Coefcients are 

estimated in an ordinary least squares regression model using the 

natural log of full-time annual salary as dependent variable and then 

transformed into percentage difference. Minorities include American 

Indian or Alaska Natives, blacks, Hispanics (of any race), Native 

Hawaiian or Other Pacic Islanders, and those reporting more than 

one race. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, SESTAT (2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

0 5 10 15 20 25

Bachelor’s

Doctorate

Master’s

Plus demographics

and other

characteristics

Controlling for

education and

employment

Total difference

in full-time salary
Science and Engineering Indicators 2014 ♦ 3-51

the master’s level, and 9% less than men among individuals 

whose highest degree is at the doctoral level. 

Compared with whites and Asians, other racial and ethnic 

groups with their highest degree at the bachelor’s level also 

earn less (15%) after controlling for education, occupation, 

and experience (figure 3-34). Although the initial salary gap 

for racial and ethnic minorities is smaller than for women, 

less of this initial salary gap is explained by differences in 

education, occupation, and experience. Among those whose 

highest degree is at the bachelor’s level, after controlling for 

education, occupation, and experience, more than half of the 

initial salary gap among racial and ethnic minorities persists, 

compared to less than half of the initial salary gap persisting 

among women. In comparison, among those with a master’s 

or doctoral degree, the salary gap across racial and ethnic 

groups is significantly attenuated: after controlling for these 

factors, the salary gap is only 5% for those at the master’s 

degree level and only 4% for those at the doctorate level. 

Effects of Demographic and Other Factors on 

Salary Differences 

Salaries vary by factors beyond education, occupation, 

and experience. For example, marital status, the presence of 

children, parental education, and other personal characteristics are often associated with salary differences. These differences reflect a wide range of issues, both voluntary and 

involuntary, including, but not limited to, factors affecting 

individual career- and education-related decisions, differences in how individuals balance family obligations and career aspirations, productivity and human capital differences 

among workers that surveys do not measure, and possible effects of employer prejudice or discrimination. Salaries also 

differ across regions, partly reflecting differences in the cost 

of living across geographic areas. 

However, adding measures of personal and family characteristics that may affect compensation24 to education, occupation, and experience results in only marginal changes 

in the estimated salary differences between men and women 

compared with estimates that account for education, occupation, and experience alone. Women who are similar to men 

along all of these dimensions receive salaries that are 11% 

(among bachelor’s degree holders) to 8% (among doctoral 

degree holders) less than their male counterparts (figure 

3-33). The salary difference among racial and ethnic groups 

largely disappears among advanced degree holders, but a 

significant amount of the difference remains among bachelor’s degree holders (figure 3-34). 

The analysis of salary differences suggests that attributes 

related to human capital (fields of education and occupation, 

employment sector, and experience) are much more important than socioeconomic and demographic attributes in explaining the salary differences observed among S&E highest 

degree holders by sex and across racial and ethnic groups. 

Nonetheless, the analysis also shows that measurable differences in human capital do not entirely explain income differences between demographic groups.25

Salary Differences among Recent Graduates

Salary differences among recent S&E graduates warrant 

particular attention. Employment metrics of recent graduates 

are important indicators of current conditions in the labor 

market, particularly for young people considering S&E careers. Salary differences among recent S&E graduates, particularly across racial and ethnic groups, are substantially 

narrower than in the population of S&E degree holders as 

a whole. This suggests that recent cohorts of S&E highest 

degree holders are much closer to earnings parity than their 

older counterparts. For example, in 2010, among recent 

graduates who attained their highest degree in or after 2005, 

minorities working full time earned 7% (among those whose 

S&E highest degree was at the bachelor’s or doctorate level) 

to 8% (among those whose S&E highest degree was at the 

master’s level) less than Asians and whites. These salary differences are substantially higher, ranging from 14% to 22%, 

among all S&E highest degree holders (regardless of graduation year) (figure 3-34). After accounting for differences 

in education, occupation, and experience, the salary differences for recently graduated minorities relative to whites 

and Asians are almost attenuated among bachelor’s degree 

holders (a 3% salary gap remains) and completely attenuated 

among advanced degree holders. In contrast, when all S&E 

highest degree holders (regardless of graduation cohort) are 

included in the analysis, a significant amount of the salary 

gap remains unexplained by these human capital attributes, 

particularly among bachelor’s degree holders (figure 3-34). 

After controlling for differences in education, employment, demographic, and socioeconomic attributes, the 

gender salary gap among recent graduates is not completely attenuated, but it is lower. After controlling for 

these factors, women earn about 5% to 9% less than men 

among recent graduates, compared with about 8% to 11% 

less among all S&E highest degree holders (regardless of 

graduation cohort). 

Immigration and the S&E Workforce

The industrialized nations of the world have long benefitted from the inflow of foreign-born scientists and engineers 

and the S&E skills and knowledge they bring. S&E skills are 

more easily transferrable across international borders than 

many other skills, and many countries have made it a national priority to attract international talent in S&E (NSB 2008). 

A large proportion of workers employed in S&E fields in the 

United States are foreign born. This section presents data on 

foreign-born scientists and engineers in the U.S. economy, 

including recent indicators of migration to the United States 

and the rate at which foreign-born recipients of U.S. doctoral degrees remain in the United States after earning their 

degree (stay rates). Data from various sources, including 

the Census Bureau, the U.S. Citizenship and Immigration 

Services (USCIS), and NSF (SESTAT and SED) are discussed to study the immigrant S&E workforce in the United 
3-52 ♦ Chapter 3. Science and Engineering Labor Force

States. This section ends with a discussion of the global migration patterns of high-skill workers.

“Foreign-born” is a broad category, ranging from longterm U.S. residents with strong roots in the United States 

to recent immigrants who compete in global job markets 

and whose main social, educational, and economic ties are 

in their countries of origin. When interpreting data on foreign-born workers, the range of individuals in this category 

should be kept in mind. Both the number and proportion of 

foreign-born workers employed in S&E occupations in the 

United States have risen over time (table 3-27). Nationally 

representative survey data, such as SESTAT and ACS, although collected in different ways, yield broadly consistent 

estimates of the number of foreign-born scientists and engineers in the United States. In 2011, foreign-born individuals 

accounted for 21% of workers employed in nonacademic 

S&E occupations in the United States, which is higher than 

their representation in the overall population (13%). Among 

college-educated workers in nonacademic S&E occupations, the proportion of foreign-born individuals is higher: 

26%, which is up from 22% in 2000 (table 3-27). 

Characteristics of Foreign-Born Scientists 

and Engineers

Compared to the entire college-educated workforce, college graduates employed in S&E occupations are disproportionately foreign born. Among SESTAT respondents 

employed in S&E occupations in 2010, 27% were foreign 

born. Among all college-educated workers (regardless of 

occupational category) in 2010, 15% were foreign born. In 

general, foreign-born workers employed in S&E occupations tend to have higher levels of education than their U.S. 

native-born counterparts. Among individuals employed in 

S&E occupations, 19% of foreign-born scientists and engineers have a doctorate, compared to 10% of U.S. nativeborn scientists and engineers in these occupations. In most 

S&E occupations, the higher the degree level, the greater 

the proportion of the workforce who are foreign born (figure 

3-35). This relationship is weakest among social scientists 

and strongest among computer and mathematical scientists 

and engineers. In 2010, at the bachelor’s degree level, the 

Figure 3-35

Foreign-born scientists and engineers employed in 

S&E occupations, by highest degree level and broad 

occupational category: 2010

Percent

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2010), http://sestat.nsf.gov. 

Science and Engineering Indicators 2014

0 10 20 30 40 50 60

Computer and

mathematical

scientists

Biological,

agricultural,

and environmental

life scientists

Physical and

related scientists

Social and

related scientists

Engineers

Bachelor’s

Doctorate

Master’s

Table 3-27

Foreign-born workers in S&E occupations, by education level: Selected years, 2000–11

(Percent)

Education

Decennial 

census SESTAT ACS SESTAT ACS SESTAT ACS ACS SESTAT ACS ACS

All college educateda ..... 22.4 22.6 24.2 23.8 25.3 24.6 24.9 25.2 27.4 26.5 26.2

Bachelor’s .................. 16.5 16.4 17.7 17.3 18.1 17.2 18.4 18.3 20.1 19.0 19.0

Master’s ..................... 29.0 29.4 32.0 31.7 33.5 32.7 32.7 33.4 34.9 35.0 34.3

Doctorate................... 37.6 36.4 37.8 36.6 41.8 37.8 40.9 41.6 41.5 44.2 43.2

ACS = American Community Survey; SESTAT = Scientists and Engineers Statistical Data System.

a Includes professional degrees not broken out separately.

NOTES: The data from the ACS and the Decennial Census include all S&E occupations except postsecondary teachers because these occupations are 

not separately identifiable in the 2000 Census or ACS data files. SESTAT 2006 and 2008 data do not include foreign workers who arrived in the United 

States after the 2000 Decennial Census and also did not earn an S&E degree in the United States.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, SESTAT (2003–10), http://sestat.nsf.gov; Census 

Bureau, 2000 Decennial Census Public Use Microdata Sample (PUMS), and ACS (2003, 2006, 2008, 2009, 2010, 2011).

Science and Engineering Indicators 2014

2003 2006

2000

2008 2009 2010 2011
Science and Engineering Indicators 2014 ♦ 3-53

proportion of foreign-born individuals in S&E occupations 

ranged from 13% (physical scientists) to 23% (computer 

and mathematical scientists). However, at the doctoral level, 

over 40% were foreign born in each S&E occupation except 

the social sciences.

Among SESTAT respondents employed in S&E occupations, foreign-born workers (median age 40 years) are 

younger than their native-born counterparts (median age 

43). The distribution by sex is largely similar across foreignborn (26% female) and native-born (28% female) workers in 

S&E jobs. Asians account for the majority (60%) of foreignborn workers in S&E occupations but only a very small segment (3%) of U.S. native-born workers in these occupations 

(appendix table 3-20). In comparison, whites represent 27% 

of foreign-born workers in S&E jobs but 86% of native-born 

workers in these jobs. Nearly 90% of all Asians employed in 

S&E occupations are foreign-born. 

 In 2010, 56% of the foreign-born S&E highest degree 

holders in the United States were from Asia; 21% were from 

Europe. The remaining foreign-born workers came from 

North America, Central America, the Caribbean, South 

America, and Africa, each of which supplied 4% to 5% of 

the foreign-born S&E highest degree holders in the United 

States. In 2010, the leading country of origin among immigrants with a highest degree in S&E was India, which accounted for 19% of the foreign-born S&E highest degree 

holders (figure 3-36). With less than half the total for India, 

China was the second leading country with 8%. Source countries for the nearly 395,000 foreign-born holders of S&E 

doctorates are somewhat more concentrated, with China providing a higher proportion (23%) than India (13%). These 

patterns by source region and country for foreign-born S&E 

highest degree holders in the United States have been stable 

since 2003. 

Source of Education 

The SESTAT surveys ask respondents to provide information on where they received their postsecondary degrees. 

They also ask foreign-born respondents to provide information on why they came to the United States. Together, this 

information is helpful for understanding the educational and 

career paths of foreign-born scientists and engineers working in the United States and possible factors that influence 

these paths. 

The majority of foreign-born scientists and engineers in 

the United States received their initial university training 

abroad. In 2010, there were about 4.3 million college-educated, foreign-born individuals employed in the United States 

with an S&E degree or in an S&E occupation; of these, 2.3 

million received their first bachelor’s degree abroad. Many 

of these individuals came to the United States for job or economic opportunities, educational opportunities, or familyrelated reasons.26 Among employed foreign-born scientists 

and engineers, 54% of those whose highest degree is at 

the bachelor’s level received their initial university degree 

from a foreign institution. The proportion is similar among 

foreign-born scientists and engineers with advanced degrees 

(53%), although SESTAT lacks information for a small proportion of individuals in this group.27

Many foreign-born scientists and engineers in the United 

States appear to come here for further higher education after 

receiving their initial university training abroad. Of the 2.1 

million foreign-born scientists and engineers who are employed in the United States and hold an advanced degree, 

Figure 3-36

Foreign-born individuals with highest degree in S&E living in the United States, by place of birth: 2010

UK = United Kingdom.

NOTE: Percentages may not add to 100% because of rounding.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (2010), http://sestat.nsf.gov.

Science and Engineering Indicators 2014

India 19%

China 8%

 Philippines

 6%

 Canada 4%

UK 6%

Taiwan 3%

Germany 3% Vietnam 3%

Mexico 3%

Other foreign

born 36%

China 23%

 India 13%

Russia 3%

Canada 4%

 Taiwan 3%

 Germany 4%

 South Korea 3%

 Iran 2%

 Japan 3%

S&E highest degree S&E doctorate

Other foreign

born 37%

South Korea 2% 

Japan 2% 

Iran 2% 

Russia 2% 

Hong Kong 2% 

Pakistan 2% 

France 2% 

UK 4%
3-54 ♦ Chapter 3. Science and Engineering Labor Force

two-thirds completed their highest degree in the United 

States, divided almost evenly between those who received 

their first bachelor’s degree abroad (671,000) and those who 

received their first bachelor’s degree in the United States 

(647,000). Almost one-fourth of foreign-born scientists and 

engineers with an advanced degree (472,000) received both 

their initial university degree and advanced (highest) degree 

abroad. In contrast, only a small number of foreign-born scientists and engineers (35,000) received their first bachelor’s 

degree in the United States and their highest degree abroad. 

The information provided by foreign-born scientists and 

engineers on factors that influenced their migration to the 

United States reinforces the patterns seen in the migration 

data. Among those who obtained their initial university degree abroad but their highest degree in the United States, the 

most commonly cited reason for coming to the United States 

was educational opportunities (27%). Family-related reasons (9%) and job/economic opportunities (7%) were cited 

by much smaller proportions. In comparison, among those 

who received both degrees abroad, the most commonly cited 

reasons for coming to the United States were job/economic 

opportunities (29%) and family-related reasons (23%), followed by scientific or professional infrastructure (11%), and 

educational opportunities (10%).

Among the foreign-born doctorate holders employed in 

the United States, 58% received this degree from a U.S. 

institution and 83% (of those for whom SESTAT contains 

information on first bachelor’s degree) received their initial 

university degree from a foreign institution. 

New Foreign-Born Workers 

During the 2007–09 economic downturn, two indicators—the number of temporary work visas issued by the U.S. 

government in visa classes for high-skill workers and the 

stay rates of foreign-born U.S. doctorate recipients—showed 

evidence that the volume of new foreign-born workers entering the U.S. S&E workforce might be declining. Recent 

data, however, indicate that this period of decline may be 

temporary. In addition to these two indicators, this section 

discusses characteristics of workers with temporary work 

visas and country profiles of new foreign-born workers. 

Temporary Visas

The number of temporary work visas issued for high-skill 

workers provides an indication of new immigrant workers 

entering the U.S. labor force.28 After several years of growth, 

the largest classes of these temporary visas declined during 

the recent economic downturn (figure 3-37). Data since the 

downturn, however, suggest that growth has resumed in recent years. Despite the increases in the issuance of temporary 

visas since fiscal year (FY) 2009, the numbers have not yet 

reached the recent highs seen in FY 2007, before the beginning of the economic downturn (figure 3-37). A decline in the 

issuance of these visas, particularly H-1B visas, also occurred 

around the more mild recession in 2001.

H-1B visas account for a significant proportion of foreign-born high-skill workers employed by U.S. firms on 

temporary visas. This type of visa is issued to individuals 

who seek temporary entry into the United States in a specialty occupation that requires professional skills. It is issued for up to 3 years with the possibility of an extension 

to 6 years. In 2012, the United States issued nearly 136,000 

H-1B visas, up 23% from the recent low in 2009 (110,000) 

but still down from the recent peak of about 154,000 issued 

in 2007 (figure 3-37).

Issuance of visas in other temporary work categories that 

usually contain large numbers of high-skill workers also 

rose since 2009; however, the H-1B visa category has shown 

continued increase since 2009, unlike the J-1 and L-1 categories (figure 3-37). 

Characteristics of H-1B Visa Recipients

Although H-1B visas are not issued exclusively for scientists and engineers, the majority of H-1B visa recipients work 

in S&E or S&E-related occupations (appendix table 3-21). 

However, precise counts of H-1B visas issued to individuals in these occupations cannot be obtained because USCIS 

does not classify occupations with the same taxonomy used 

by NSF. In 2011, workers in computer-related occupations 

as classified by USCIS were the most common recipients of 

H-1B visas, accounting for almost half (48%) of new H-1B 

visas issued. The total number of newly initiated H-1B visas for workers in computer-related fields increased significantly between 2010 and 2011, following a steep decline 

between 2008 and 2009 during the economic downturn. The 

Figure 3-37

Temporary work visas issued in categories with 

many high-skilled workers: FYs 1991–2012

Thousands

NOTE: J-1 exchange visitor visa is used for many different skill levels. 

SOURCE: U.S. Department of State, Nonimmigrant Visa Issuances 

by Visa Class and by Nationality and Nonimmigrant Visas by 

Individual Class of Admission, http://www.travel.state.gov/visa/ 

statistics/nivstats/nivstats_4582.html (accessed 12 April 2013). 

Science and Engineering Indicators 2014

1991 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

0

50

100

150

200

250

300

350

400

J-1 (exchange visitors)

H1-B

L-1 (intracompany transferee)
Science and Engineering Indicators 2014 ♦ 3-55

proportion of H-1B recipients who worked in computer sciences was considerably lower in the earlier part of the 2000s. 

For example, in 2002, only 25% of H-1B visa recipients 

worked in computer-related fields (NSB 2012).

H-1B visa recipients tend to possess advanced degrees. In 

FY 2011, 55% of new H-1B visa recipients had an advanced 

degree, including 39% with master’s degrees, 5% with professional degrees, and 12% with doctorates (DHS USCIS 

2012). The degree distribution differs by occupations. In 

FY 2009, for example, the vast majority of mathematical 

and physical scientists (83%) and life scientists (87%) with 

H-1B visas held advanced degrees; 44% of mathematical 

and physical scientists and 61% of life scientists with H-1B 

visas had doctorates (NSB 2012). 

In 2011, 53% of new H-1B visa recipients were from 

India, and another 10% were from China (DHS USCIS 

2012). H-1B visa recipients are relatively young. In 2011, 

46% of new H-1B visa recipients were between the ages of 

25 and 29, and another 25% were between the ages of 30 and 

34 (DHS USCIS 2012). 

Table 3-28 shows salaries paid to new recipients of H-1B 

visas by occupation group. These starting salaries, taken 

from final visa application forms sent to USCIS, are different from H-1B salaries that firms report on their applications 

to the Department of Labor, which are filed much earlier 

in the H-1B process. The relatively low median salaries 

for workers in life sciences may reflect the common use 

of H-1B visas to hire individuals for relatively low-paying 

postdoc positions.

Short-Term Stay Rates for U.S. S&E Doctorate 

Recipients

Among doctorate recipients, the period immediately after 

earning their doctorate is a pivotal point that can substantially affect long-term career trajectories. During this period, 

foreign-born doctorate recipients who remain in the United 

States may set themselves on a path to long-term residency. 

At the time they receive their doctorates, foreign-born 

students at U.S. universities report whether they intend to 

stay in the United States and whether they have a firm offer to work in the United States (either a postdoc or a job) 

the following year.29 These responses provide estimates of 

short-term stay rates.30

Most foreign-born noncitizen recipients of U.S. S&E 

doctorates plan to stay in the United States after graduation. At the time of doctorate receipt, 75% of foreign-born 

recipients of U.S. S&E doctorates, including those on both 

temporary and permanent visas, plan to stay in the United 

States, and 48% have either accepted an offer of postdoc 

study or employment or are continuing employment in the 

United States (figure 3-38). The proportion of foreign-born 

S&E doctorate recipients planning to stay in the United 

States has risen over time. In 1991, 68% of foreign students 

who earned S&E doctorates at U.S. universities reported that 

they planned to stay in the United States after graduation, Table 3-28

Annual salaries for new H-1B visa recipients, by 

occupation: FY 2011

(Dollars)

Occupation Median Mean

Administrative specializations ............ 55,000 67,000

Architecture, engineering, 

and surveying ................................. 72,000 79,000

Art....................................................... 45,000 54,000

Computer-related occupations .......... 64,000 70,000

Education............................................ 46,000 56,000

Entertainment and recreation............. 35,000 43,000

Law and jurisprudence....................... 85,000 106,000

Life sciences....................................... 47,000 56,000

Managers and officials nec ................ 81,000 103,000

Mathematics and physical sciences... 70,000 74,000

Medicine and health........................... 57,000 93,000

Miscellaneous professional, 

technical, and managerial............... 70,000 82,000

Museum, library, and archival 

sciences.......................................... 48,000 58,000

Religion and theology .......................... 36,000 41,000

Social sciences.................................... 65,000 78,000

Writing ................................................ 43,000 51,000

nec = not elsewhere classified.

SOURCE: Department of Homeland Security (DHS), U.S. 

Citizenship and Immigration Services; Characteristics of H-1B 

Specialty Occupation Workers, Fiscal Year 2011 Annual Report to 

Congress, http://www.uscis.gov/USCIS/Resources/Reports%20

and%20Studies/H-1B/h1b-fy-11-characteristics.pdf, accessed 20 

December 2012.

Science and Engineering Indicators 2014

Figure 3-38

Plans of foreign recipients of U.S. S&E doctoral 

degrees at graduation to stay in the United States, 

by year of doctorate: 1991–2011

Number

NOTE: Data include doctorate recipients on temporary and 

permanent visas.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2013) of the Survey of 

Earned Doctorates (SED) (1991–2011). 

Science and Engineering Indicators 2014

1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

Plans to stay

Firm plans to stay

Recipients
3-56 ♦ Chapter 3. Science and Engineering Labor Force

and 37% said that they had firm offers for postdoc study or 

employment. Throughout the 1980s, these proportions were 

about 50% and 33%, respectively (NSB 2012). 

During the latter part of the decade 2000–09, a period 

marked by the economic downturn and financial crisis, both 

the percentage of foreign-born S&E doctorate recipients reporting plans to stay in the United States and the percentage 

of those reporting firm offers to stay declined slightly (figure 3-38). The overall number of foreign-born S&E doctorate recipients also declined in 2009 and 2010. Although the 

numbers have since risen in 2011, the levels remain below 

the recent peaks seen in 2008. 

Overall, S&E short-term stay rates reflect the high shortterm stay rates in computer and mathematical sciences, the 

biological and related sciences, the physical sciences, and 

engineering (appendix table 3-22). Between 2008 and 2011, 

the short-term stay rates in these four fields ranged from 

77% to 83%, as measured by reports of intentions to stay 

in the United States. However, the short-term stay rates for 

foreign-born U.S. S&E doctorate recipients in health fields 

(71%) were somewhat lower, and those in the social sciences (57%) were substantially lower. 

Stay rates vary by place of origin. Between 2008 and 

2011, the vast majority of U.S. S&E doctorate recipients 

from China (86%) and from India (87%) reported plans to 

stay in the United States, and close to 60% of these individuals reported accepting firm offers for employment or 

postdoc research in the United States (appendix table 3-22). 

U.S. S&E doctorate recipients from Japan, South Korea, and 

Taiwan were less likely than those from China and India to 

stay in the United States (figure 3-39). About half of U.S. 

S&E doctorate recipients from Europe had firm plans to 

stay in the United States after graduation (appendix table 

3-22). In North America, the percentage of U.S. S&E doctorate recipients who had definite plans to stay in the United 

States was higher for those from Canada than for those from 

Mexico (appendix table 3-22).

Among U.S. S&E doctorate recipients from the two top 

countries of origin, China and India, the proportions reporting plans to stay in the United States have declined since the 

early part of the decade of the 2000s (appendix table 3-22). 

Long-Term Stay Rates for U.S. S&E Doctorate 

Recipients 

Long-term stay rates indicate the degree to which foreignborn recipients of U.S. S&E doctorates enter and remain in 

the U.S. labor force to pursue their careers. For a particular 

cohort of foreign-born noncitizen S&E doctorate recipients, 

the proportion of that cohort that pays federal taxes a given 

number of years after receiving their degrees is an indicator of the cohort’s long-term stay rate.31 Estimates of shortterm stay rates are derived from data on reported intentions 

to stay in the United States within the year after graduation. 

Stay rates over the short term can be compared with those 

over a longer duration to analyze how stated intentions for 

the period immediately after graduation compare with actual 

behavior some years later. 

Stay rate data include foreign-born noncitizen recipients 

of U.S. S&E doctorates who were on either a permanent or a 

temporary visa at the time they received their doctorates. For 

the 2001 and 2006 graduating cohorts, stay rate data are available separately for permanent and temporary visa holders. 

Within these cohorts, stay rates are particularly stable over 

time among individuals who received their doctorates while 

on a permanent visa (figure 3-40). Temporary residents, who 

account for the vast majority of noncitizen recipients of U.S. 

S&E doctorates, have lower stay rates than do permanent 

residents, and their stay rates decline with additional years 

Figure 3-39

Plans of foreign recipients of U.S. S&E doctoral 

degrees at graduation to stay in the United States, 

by place of origin and year of doctorate: 1998–2001 

and 2008–11

NOTES: Data reect proportions of each group reporting rm 

commitment to postgraduation employment in the United States. 

Data include doctorate recipients on temporary and permanent visas. 

Data for China include Hong Kong.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2013) of Survey of 

Earned Doctorates (SED). 

Science and Engineering Indicators 2014

Brazil

Mexico

Japan

Taiwan

Canada

South Korea

France

Italy

Germany

Turkey

China

India

Iran

1998–2001

2008–11

0 20 40

Percent

60 80
Science and Engineering Indicators 2014 ♦ 3-57

since degree. For example, among foreign-born U.S. S&E 

doctorate recipients from the 2001 cohort, those who were 

on a temporary visa at the time they earned their degree had 

a 2-year stay rate in 2003 that was 16 percentage points lower than those with a permanent visa. This difference grew 

wider over time, reaching almost 26 percentage points by 

2011, as stay rates for temporary visa holders fell while stay 

rates for permanent residents changed little. 

The stay rates within the entire 2001 and 2006 cohorts of 

foreign-born noncitizen recipients of U.S. S&E doctorates 

fell with additional years since graduation (Finn 2014); this 

was a result of the declining stay rates among temporary 

visa holders, who accounted for nearly 90% of all noncitizen 

U.S. S&E doctorate recipients in these cohorts. The 2001 cohort had a stay rate after 2 years of 73%; after 10 years, this 

rate declined by 8 percentage points. The 2006 cohort had a 

2-year stay rate of 74%, which declined to 68% after 5 years. 

In comparison, among the cohort of foreign-born U.S. S&E 

doctorate recipients who earned their degrees in 1995, stay 

rates were relatively stable as additional years passed since 

graduation. The 1995 cohort had a 2-year stay rate of 65%, 

which dropped to 61% after 16 years (Finn 2014). Stay rate 

data for the 1995 cohort, however, are not separately available for permanent and temporary residents. Data from earlier and subsequent years suggest that temporary visa holders 

accounted for the vast majority of foreign-born noncitizen 

recipients of U.S. S&E doctorates in 1995 (Finn 2012); as a 

result, temporary residents likely played an important role in 

the overall stability of the stay rate within this cohort.

In recent years, long-term stay rates have fluctuated within a fairly narrow range, neither increasing nor declining 

consistently (table 3-29). Among U.S. S&E doctorate recipients with a temporary visa at graduation, 5-year stay rates 

rose in the latter part of the decade of the 2000s after declining for several years around the 2007–09 economic downturn. While figure 3-40 shows the stay rate data annually 

for fixed cohorts (2001 and 2006 graduating cohorts), table 

3-29 presents data on 5-year stay rates during the 2001–11 

period. Data for each year reflect the stay rate in that year for 

the cohort that received their doctorates 5 years earlier. The 

5-year stay rate rose to 66% in 2011, close to the recent high 

level seen in 2005 (67%). 

The trends in the 5-year stay rates vary across source 

countries (table 3-29). Among foreign-born recipients from 

China (the largest source country) who were temporary residents at the time they received their U.S. S&E doctorates, 

Figure 3-40

Stay rates for U.S. S&E doctorate recipients with 

permanent or temporary visas at graduation, 

by selected year of doctorate: 2003–11

Percent

SOURCE: Finn M. 2014 (forthcoming). Stay Rates of Foreign Doctoral 

Recipients from U.S. Universities: 2011. Oak Ridge, TN: Oak Ridge 

Institute for Science and Education. 

Science and Engineering Indicators 2014

2003 2004 2005 2006 2007 2008 2009 2010 2011

40

50

60

70

80

90

100

2001 graduates, permanent visa

2001 graduates, temporary visa

2006 graduates,

permanent visa

2006 graduates,

temporary visa

Table 3-29

Five-year stay rates for U.S. S&E doctorate recipients with temporary visas at graduation, by selected country/

region/economy: 2001–11

(Percent)

Country/region/economy 2001 2003 2005 2007 2009 2011

All countries/regions/economies............................................. 58 64 67 63 62 66

China.................................................................................... 98 93 95 94 89 85

India ..................................................................................... 89 90 89 83 79 82

Europe ................................................................................. 53 63 67 67 60 62

Canada ................................................................................ 66 63 60 56 53 55

South Korea......................................................................... 22 36 44 42 42 42

Japan ................................................................................... 24 39 41 33 40 38

Taiwan.................................................................................. 41 48 52 43 37 38

Mexico ................................................................................. 31 22 32 33 35 39

Brazil.................................................................................... 26 26 31 32 33 37

NOTE: Data for each year reflect the stay rate in that year for the cohort that received their doctoral degrees 5 years earlier.

SOURCE: Finn M. 2014 (forthcoming). Stay Rates of Foreign Doctoral Recipients from U.S. Universities: 2011. Oak Ridge, TN: Oak Ridge Institute for 

Science and Education.

Science and Engineering Indicators 2014
3-58 ♦ Chapter 3. Science and Engineering Labor Force

the 5-year stay rate declined in 2011, continuing the trend 

since before the economic downturn. However, even with 

this decline, rates remain higher than those of other major 

locations. Foreign-born U.S. S&E doctorate recipients from 

other major source countries/economies, like India and 

Taiwan, saw slight increases in the 5-year stay rate between 

2009 and 2011, although their stay rate overall declined between 2001 and 2011. Among foreign-born recipients of 

U.S. S&E doctorates from South Korea who were on a temporary visa at the time they received their doctorate, stay 

rates remained stable between 2007 and 2011 after doubling 

during the first half of the decade. 

Data from the 2006 cohort suggest that among temporary visa holders receiving U.S. S&E doctorates, stated intentions to stay in the United States (short-term stay rates) 

are reasonable indicators of stay rates some years later (Finn 

2014). Among temporary residents who received their U.S. 

S&E doctorate in 2006 and reported definite plans to stay in 

the United States within the year after graduation, 94% were 

in the United States 1 year later and 80% remained 5 years 

later. Among the 2006 cohort of temporary residents who reported plans to stay in the United States (as opposed to firm 

employment offers), 86% were in the United States 1 year 

later and 72% remained 5 years later. A number of factors 

are likely to affect how precisely short-term intentions to 

stay in the United States predict actual behavior some years 

later. Among these are overall economic conditions and job 

opportunities in the United States, comparable conditions 

in the doctorate recipient’s country of origin, and familyrelated and other personal considerations.

High-Skill Migration Worldwide 

No worldwide or internationally comparable data exist 

on the migration of workers in S&E occupations or with 

college-level S&E degrees. Docquier and Rapoport (2012) 

compiled and analyzed data on international migration to 

OECD countries by educational attainment in 1990 and 2000 

(see also Docquier, Lowell, and Marfouk 2009; Docquier 

and Marfouk 2006). They defined high-skill migrants as the 

total number of foreign-born individuals, age 25 and over, 

with some postsecondary education living in an OECD 

country. They gathered data for nearly 200 source countries 

(which included OECD and non-OECD countries), all but a 

handful of which are independent nations. More recent and 

comprehensive data on global high-skill migration patterns 

are not currently available. However, the flow of migration 

historically has been from developing to developed nations, 

and the OECD data for the 1990 to 2000 period confirm this 

pattern. As R&D activity expands in developing countries, 

press reports suggest increased movement in the opposite 

direction; however, systematic and recent data do not exist 

to address that pattern. 

The data on migration to OECD countries indicate several patterns in international migration of individuals age 25 

and older:

♦ Between 1990 and 2000, the total number of immigrants 

(regardless of skill level) in OECD countries increased 

from about 42 million to about 59 million. 

♦ Globally, OECD countries account for the vast majority of high-skill immigrants. The migration rate among 

high-skill individuals to the OECD nations changed only 

slightly between 1990 and 2000 (rising from 5.1% to 

5.5%). Nonetheless, because worldwide education levels 

are rising, the proportion of high-skill individuals among 

those who immigrated to OECD countries rose during this 

period, from 30% to 35%. 

♦ Rates of legal emigration were much greater among highskill individuals (5.5% in 2000) than among those with less 

education (1.3% in 2000).

♦ In countries that the World Bank classifies as low income, 

the gap in emigration rates between high- and low-skill 

groups (7.6% and 0.3%, respectively, in 2000) was especially large. In comparison, the rates of high- and low-skill 

emigration rates were similar in countries that the World 

Bank classifies as high income (3.9% and 3.6%, respectively, in 2000). 

♦ Between 1990 and 2000, the proportion of women among 

high-skill migrants rose, partly because of the worldwide 

increase in the proportion of individuals with some postsecondary education who are women.

♦ In 2000, the countries estimated to have the largest number of high-skill emigrants living in OECD countries were 

the United Kingdom (1.5 million), the Philippines (1.1 

million), India (1.0 million), Mexico (0.9 million), and 

Germany (0.9 million) (figure 3-41). The proportion of 

high-skill emigrants who are women varied considerably 

across source countries (figure 3-41; see also Docquier, 

Lowell, and Marfouk 2009). 

In a more limited study covering six major destination 

countries (United States, Canada, Australia, Germany, 

United Kingdom, and France), Defoort (2008) concluded 

that worldwide emigration rates for high-skill persons between 1975 and 2000 were stable in a large number of countries. Stable rates of emigration, however, would produce an 

increase in the total number of high-skill emigrants due to 

rising levels of worldwide education and skill. 

Regarding high-skill migration to the United States, college-educated foreign-born workers in the United States are 

disproportionately found in S&E occupations and disproportionately have advanced degrees (see “Characteristics of the 

Foreign-Born Scientists and Engineers”). However, current 

international data do not enable researchers to assess whether 

and how migration rates globally or to OECD countries vary 

among different categories of high-skill workers.
Science and Engineering Indicators 2014 ♦ 3-59

Global S&E Labor Force

The rising emphasis on developing S&E expertise and 

technical capabilities has been a global phenomenon. S&E 

work is not limited to developed economies; it occurs 

throughout the world. Such work, however, is concentrated 

in developed nations, where a significant portion of R&D 

also takes place. The availability of a suitable labor force 

is an important determinant of where businesses choose to 

locate S&E work (Davis and Hart 2010), and concentrations 

of existing S&E work, in turn, spawn new employment opportunities for workers with relevant S&E knowledge and 

skills. As a result, governments in many countries have 

made increased investments in S&E-related postsecondary 

education a high priority. At the same time, high-skill workers, such as those educated or employed in S&E fields, are 

increasingly mobile, and the number that leave their native 

countries to pursue education and career goals is growing. In 

recent years, many nations, recognizing the value of highskill workers for the economy as a whole, have changed their 

laws to make it easier for such workers to immigrate. These 

changes indicate an accelerating competition for globally 

mobile talent (Shachar 2006).

Data on the global S&E workforce, however, are very 

limited, which makes it difficult to analyze the precise 

size and characteristics of this specialized workforce. 

Unfortunately, the internationally comparable data that exist 

are limited to establishment surveys that provide only basic information about workers in S&E occupations or with 

training in S&E disciplines. In contrast, SESTAT includes 

far more data on members of the U.S. S&E labor force than 

is available in other national statistical systems. In addition, 

although surveys that collect workforce data are conducted 

in many OECD member countries, they do not cover several 

countries—including Brazil, India, and Israel—that have 

high and rising levels of science and technology capability, 

and they do not provide fully comparable data for China.

This section provides information about the size and 

growth of workforce segments whose jobs involve R&D in 

nations for which relevant data exist. 

Size and Growth of the Global S&E 

Labor Force

Although comprehensive data on the worldwide S&E 

workforce do not exist, OECD data covering significant, 

internationally comparable segments of the S&E workforce 

provide strong evidence of widespread, though uneven, 

growth in the world’s developed nations. OECD countries, 

which include most of the world’s highly developed nations, 

compile data on researchers from establishment surveys in 

member and selected non-member countries. These surveys 

generally use a standardized occupational classification 

that defines researchers as “professionals engaged in the 

conception or creation of new knowledge, products, processes, methods and systems and also in the management 

of the projects concerned” (OECD 2002:93). Because this 

definition can be applied differently when different nations 

conduct surveys, international comparisons should be made 

with caution. OECD also reports data on a broader measure 

of all personnel employed directly in R&D. In addition to 

researchers, the data on total R&D personnel include those 

who provide direct services to R&D such as clerical and administrative staff employed in R&D organizations. 

OECD reports an estimated increase in the number of 

researchers in its member countries from 2.8 million in 

1995 to 4.2 million in 2007. OECD also publishes estimates for seven non-member economies, including China 

and Russia; adding these to the OECD member total for 

Figure 3-41

Top countries of origin of foreign-born persons 

residing in OECD countries and having at least 

a tertiary education, age 25 years or more, 

by sex: 2000 

OECD = Organisation for Economic Co-operation and Development; 

UK = United Kingdom. 

NOTE: Tertiary education is roughly equivalent in U.S. terms to 

individuals who have earned at least technical school or associate’s 

degrees and includes all degrees up to the doctorate.

SOURCE: Docquier F, Lowell B, Marfouk A. 2009. A gendered 

assessment of highly skilled emigration. Population and Development 

Review 35(2):297–321, http://onlinelibrary.wiley.com/doi/10.1111/ 

j.1728-4457.2009.00277.x/abstract (accessed 22 January 2013). 

Science and Engineering Indicators 2014

Thousands of emigrants

UK

Philippines

India

Mexico

Germany

China

Korea

Canada

Vietnam

Poland

United States

0 200 400 600 800 1,000 1,200 1,400 1,600

Female Male
3-60 ♦ Chapter 3. Science and Engineering Labor Force

2007 yields a worldwide estimate of 6.3 million researchers. However, numerous uncertainties affect this estimate, 

including, but not limited to, lack of coverage of countries 

with significant R&D enterprise, as well as methodological 

inconsistencies over time and across countries. For example, 

some non-member countries that engage in large and growing amounts of research (e.g., India, Brazil) are omitted entirely from these totals. In addition, for some countries and 

regions, including the United States and the European Union 

(EU; see glossary for member countries), OECD estimates 

are derived from multiple national data sources and not 

from a uniform or standardized data collection procedure. 

For example, China’s data after 2008 are collected in accordance with OECD definitions and standards; compared 

to China’s estimate for 2008, these data yield estimates of 

about 440,000, 382,000, and 274,000 fewer researchers in 

2009, 2010, and 2011, respectively.

Despite these limitations for making worldwide estimates of the number of researchers, the OECD data are a 

reasonable starting point for estimating the rate of worldwide growth. For most economies with large numbers of researchers, growth since the mid-1990s has been substantial 

(figure 3-42). China, whose pre-2009 data did not entirely 

correspond to the OECD definition, reported about triple the 

number of researchers in 2008 compared with 1995. South 

Korea doubled its number of researchers between 1995 and 

2006 and continued to grow strongly between 2007 and 2011. 

The United States and the EU experienced steady growth but 

at a lower rate; the number of researchers grew 36% in the 

United States between 1995 and 2007 and 65% in the EU 

between 1995 and 2010. Exceptions to the overall worldwide 

trend included Japan (which experienced little change) and 

Russia (which experienced a decline, especially early in the 

period; see also Gokhberg and Nekipelova 2002). Trends in 

full-time equivalent R&D personnel were generally parallel 

to those for researchers in those cases for which both kinds 

of data are available (appendix table 3-23).

OECD also estimates the proportion of researchers in the 

workforce. In OECD’s most recent estimates, small economies in Scandinavia (Denmark, Finland, Norway, Sweden) 

report that between 1% and 2% of their employed workforce 

are researchers; small economies in East Asia (Singapore, 

Taiwan) report that about 1% of their workforce are researchers (appendix table 3-24). Among economies with more than 

200,000 researchers, OECD’s latest estimates are that researchers make up the highest proportions of the workforce 

in Japan (1.0%), South Korea (1.2%), and the United States 

(0.95%). Although China reports a large number of researchers, they are a much smaller percentage of its workforce 

(0.17%) than in OECD member countries.

Several Asian economies have shown marked and continuous increases in the percentage of their workforce employed as researchers. These include China, South Korea, 

Singapore, and Taiwan (appendix table 3-24). In the United 

States and Japan, where growth occurred at all, it took place 

mostly between the mid-1990s and the early 2000s (figure 

3-43). Patterns and trends in the proportion of the workforce 

Figure 3-42

Estimated number of researchers in selected 

countries/regions: 1995–2011

Thousands

EU = European Union.

NOTES: Data are not available for all countries/regions for all years. 

Researchers are full-time equivalents. Before 2009, counts for China 

were not consistent with Organisation for Economic Co-operation 

and Development (OECD) standards. 

SOURCE: OECD, Main Science and Technology Indicators (2013/1 

and earlier years), http://www.oecd.org/sti/msti.htm. 

Science and Engineering Indicators 2014

1995 1997 1999 2001 2003 2005 2007 2009 2011

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

United States

EU

Japan

South Korea

China

Russia

Figure 3-43

Researchers as a share of total employment in 

selected countries/regions: 1995–2011

Per thousand

EU = European Union.

NOTES: Data are not available for all countries/regions for all years. 

Researchers are full-time equivalents per thousand total employment. 

Before 2009, counts for China were not consistent with Organisation 

for Economic Co-operation and Development (OECD) standards.

SOURCE: OECD, Main Science and Technology Indicators (2013/1 

and earlier years), http://www.oecd.org/sti/msti.htm. 

Science and Engineering Indicators 2014

1995 1997 1999 2001 2003 2005 2007 2009 2011

0

2

4

6

8

10

12

14

United States

EU

Japan

South Korea

Russia

China
Science and Engineering Indicators 2014 ♦ 3-61

classified as R&D personnel are generally similar to those 

for researchers. 

The proportion of female researchers varies considerably 

across OECD economies. According to the most recent estimates for the selected OECD countries for which data by 

sex are available, Japan (14% women) and South Korea (17% 

women) have a significant imbalance among researchers. By 

comparison, Turkey, Sweden, Spain, and Poland are more 

balanced with women representing between 35% and 40% 

of researchers.

R&D Employment Abroad by U.S. Companies

R&D jobs located abroad in U.S.-owned companies are 

an indicator of global engagement by U.S. companies in the 

world’s S&E workforce. Data from NSF’s Business R&D 

and Innovation Survey (BRDIS) provide an overview of 

R&D employment in the business sector and enable comparisons between domestic and foreign R&D employment 

in companies located in the United States (both U.S.- and 

foreign-owned) that have R&D activity. These data identify 

employment as either domestic or foreign on the basis of the 

job’s location and not on the basis of the company’s ownership, the employee’s citizenship, or the employee’s place of 

birth. Chapter 4 includes a detailed analysis of BRDIS data 

on R&D employment abroad by U.S. companies.

Conclusion

The S&E labor force may be defined in a variety of ways. 

At its core are individuals in S&E occupations, but those 

with S&E degrees who are employed in a variety of other jobs also play a role. Many more individuals hold S&E 

degrees than work in S&E occupations. Indicative of a 

knowledge-based economy, many of those in non-S&E occupations report that their work nonetheless requires at least 

a bachelor’s degree level of S&E knowledge and skills. This 

suggests that the application of S&E knowledge and technical expertise is widespread across the U.S. economy and not 

just limited to S&E occupations. 

In both the United States and the rest of the world, the 

S&E workforce has experienced strong growth over time. 

During the 2007–09 economic downturn, S&E employment 

remained more resilient in the United States than overall 

employment. Policymakers with otherwise divergent perspectives agree that jobs involving S&E are good for workers and good for the economy as a whole. These jobs pay 

more, even when compared to jobs requiring similar levels 

of education and comparably specialized skills. Although 

S&E workers are not totally exempt from joblessness, workers with S&E training or in S&E occupations are less often 

exposed to periods of unemployment. 

Innovation based on S&E R&D is globally recognized 

as an important vehicle for a nation’s economic growth and 

competitive advantage. As such, it is not surprising that 

growing numbers of workers worldwide are engaged in research. Growth has been especially marked in rapidly developing economies, such as China and South Korea, that have 

either recently joined the ranks of the world’s developed 

economies or are poised to do so. Mature developed economies in North America and Europe have maintained slower 

growth whereas the number of researchers in the struggling 

Japanese economy has been stagnant. 

The demographic composition of the S&E workforce in 

the United States is changing. The baby boom portion of the 

S&E workforce continues to age into retirement. However, 

increasing proportions of scientists and engineers are postponing retirement to somewhat later ages. At the same time, 

members of historically underrepresented groups (e.g., 

women, blacks, Hispanics) have played an increasing role 

in the U.S. S&E labor force, although this has been more the 

case in some fields (e.g., life sciences and social sciences) 

than in others (e.g., computer and mathematical sciences, 

physical sciences, and engineering). Despite the recent increases in S&E participation by women and by racial and 

ethnic minorities, both groups remain underrepresented in 

the U.S. S&E workforce compared to their overall labor 

force participation. For example, women account for slightly more than one-fourth of all workers employed in S&E 

occupations in the United States despite representing half of 

the college-educated workforce. 

The United States has remained an attractive destination 

for foreign students and workers with advanced S&E training. In the wake of the 2001 recession, there were increases 

in both temporary work visas and stay rates of foreign recipients of S&E doctorates. Although declines occurred during 

the 2007–09 economic downturn—a period marked by rising 

unemployment in the United States among workers in S&E 

as well as in other occupations—growth has since resumed. 

In today’s dynamic marketplace, where information 

flows rapidly and technology is always evolving, labor 

market conditions change fast. Numerous factors—such 

as global competition, demographic trends, aggregate economic activities, and S&E training pathways and career opportunities—will affect the availability of workers equipped 

with S&E expertise as well as the kinds of jobs that the U.S. 

economy generates in the future. As a result, comprehensive 

and timely analysis of current labor force and demographic 

trends will play a critical role in providing the information 

needed to understand the dynamic S&E landscape both in 

the United States and globally.
3-62 ♦ Chapter 3. Science and Engineering Labor Force

Notes

1. The standard definition of the term labor force is a 

subset of the population that includes both those who are 

employed and those who are not working but seeking work 

(unemployed); other individuals are not considered to be 

in the labor force. When data refer only to employed persons, the term workforce is used. For data on unemployment 

rates by occupation, calculations assume that unemployed 

individuals are seeking further employment in their most 

recent occupation. 

2. The SOC is used by federal statistical agencies to classify workers into occupational categories for the purpose 

of collecting, calculating, and disseminating data. Detailed 

information on the SOC is available at http://www.bls.

gov/SOC/. 

3. Despite the limitations of this subjective measure, 

variations among occupations in the proportions of workers 

who say that they need this level of S&E technical expertise 

are in accordance with common sense. For example, among 

postsecondary teachers of physics, 95% said that their job 

required at least a bachelor’s degree level of knowledge in 

engineering, computer sciences, mathematics, or the natural 

sciences. Among postsecondary teachers of business commerce or marketing, 83% said that their job required at least 

this level of expertise in other fields such as health, business, or education. Among the SESTAT population whose 

occupation is secretary/receptionist/typist, fewer than 10% 

said that their job required bachelor’s level S&E expertise 

of any kind, and 12% said that their job required at least this 

level of expertise in other fields such as health, business, 

or education. 

4. Estimates of the size of the S&E workforce vary across 

the example surveys because of differences in the scope of 

the data collection (SESTAT surveys collect data from individuals with at least a bachelor’s degree); because of the 

type of survey respondent (SESTAT surveys collect data 

from individuals, OES collects data from establishments, 

and ACS collects data from households); or because of the 

level of detail collected on an occupation, which aids in classifying a reported occupation into a standard occupational 

category. All of these differences can affect the estimates. 

For example, the SESTAT estimate of the number of workers in S&E occupations includes postsecondary teachers of 

S&E fields; however, postsecondary teachers in ACS are 

grouped under a single occupation code regardless of field 

and are therefore not included in the ACS estimate of the 

number of workers in S&E occupations. 

5. Among those with doctorates in an S&E field, life sciences and social sciences were the most common fields, followed by physical sciences, engineering, and computer and 

mathematical sciences. 

6. The data on S&E employment level for 1960 are calculated using the Census Bureau’s 1960 Decennial Census 

microdata, adjusted by the Integrated Public Use Microdata 

Series (IPUMS) from the University of Minnesota’s 

Minnesota Population Center (http://www.ipums.org). 

The data for 2011 are calculated using the 2011 American 

Community Survey (ACS) public use microdata sample 

(PUMS) files from the Census Bureau (http://www.census.gov/acs/www/data_documentation/public_use_microdata_sample/). S&E employment levels for 1960 and 2011 

include workers at all education levels and do not include 

S&E postsecondary teachers. Although the 1960 Decennial 

Census data allow for separate identification of S&E postsecondary teachers, the 2011 ACS data aggregate all postsecondary teachers into one occupation code and therefore 

do not allow for separate identification of S&E postsecondary teachers. For 1960, including S&E postsecondary teachers would increase the number of workers employed in S&E 

occupations to nearly 1.2 million. See appendix table 3-1 for 

a list of S&E occupations in the 1960 Decennial Census and 

2011 ACS. 

7. Many comparisons using Census Bureau data on occupations are limited to looking at all S&E occupations 

except postsecondary teachers (i.e., nonacademic S&E occupations) because the Census Bureau aggregates all postsecondary teachers into one occupation code. NSF surveys 

of scientists and engineers and some BLS surveys collect 

data on postsecondary teachers by field. 

8. The data on self-employment from SESTAT include 

those who report being self-employed or employed by a 

business owner in either an unincorporated or incorporated 

business, professional practice, or farm. As a result, the data 

may capture both self-employed individuals in their own 

businesses as well as those whose principal employer is a 

business owner. This is a major reason why the SESTAT 

estimate of self-employed workers in S&E occupations 

is higher than those from other surveys (e.g., the Census 

Bureau’s ACS). 

9. Employment in the federal government is largely limited to those with U.S. citizenship. In the competitive civil 

service, only U.S. citizens and nationals may be appointed; 

however, in the excepted service or the Senior Executive 

Service, certain noncitizens who meet specific employability requirements may be employed. Many federal workers 

with S&E employment are in occupations that, nationwide, 

include relatively large concentrations of foreign-born persons, some of whom are not U.S. citizens, rendering them 

ineligible for many federal jobs. 

10. This list does not include the National Institutes of 

Health, which is a part of the Department of Health and 

Human Services (DHHS). DHHS accounted for 5% of total 

federal S&E employment in 2012. 

11. The other 10 activities are used to define four additional broad categories of primary/secondary work activities, including teaching; management and administration; 

computer applications; and professional services, production workers, or other work activities not specified. 

12. Social scientists were exceptions. In 2010, the difference in R&D activity rates between social scientists with 

doctorates and social scientists with bachelor’s degrees was 

not statistically significant. 
Science and Engineering Indicators 2014 ♦ 3-63

13. The patent activity rate is the proportion who reported 

having been named as an inventor on a patent application in 

the previous 5 years. 

14. Although SESTAT respondents were allowed to provide more than one reason for participating in work-related 

training, the data presented in this section are on the most 

important reason for participating in such training. 

15. The Business Cycle Dating Committee of the National 

Bureau of Economic Research is generally the source for determining the beginning and end of recessions or expansions 

in the U.S. economy. See http://www.nber.org/cycles/recessions.html for additional information. 

16. The Bureau of Labor Statistics civilian unemployment 

rate for persons 16 years and over, not seasonally adjusted, 

is available at http://data.bls.gov/timeseries/LNU04000000 

(accessed 4 December 2012). 

17. Social scientists were exceptions. The change in the 

unemployment rate from 2006 to 2010 among social scientists was not statistically significant. 

18. The CPS is the source of the official unemployment 

rate. 

19. Although the formal job title is often postdoc fellowship or research associate, titles vary among organizations. 

This chapter generally uses the shorter, more commonly 

used, and best understood name, postdoc. A postdoc is generally considered a temporary position that individuals take 

primarily for additional training—a period of advanced professional apprenticeship—after completion of a doctorate. 

20. NSF is currently developing a data collection strategy as part of its Early Career Doctorates Project (ECDP) to 

gather in-depth information about postdoc researchers and 

other early career doctorates. The ECDP will collect information related to educational achievement, professional activities, employer demographics, professional and personal 

life balance, mentoring, training and research opportunities, 

and career paths and plans for individuals who earned their 

doctorate in the past 10 years and are employed in an academic institution or a research facility. 

21. In this chapter, American Indian or Alaska Native, 

Asian, black, Native Hawaiian or Other Pacific Islander, 

white, and more than one race refer to individuals who are 

not of Hispanic origin. Hispanics may be any race. 

22. Salary differences represent estimated percentage differences in women’s reported full-time annual salary relative to men’s reported full-time annual salary as of October 

2010. Coefficients are estimated in an ordinary least squares 

regression model using natural log of full-time annual salary 

as the dependent variable. This estimated percentage difference in earnings differs slightly from the observed difference 

in median earnings by sex because the former addresses differences in mean earnings rather than median. 

23. Included are 20 SESTAT field of degree categories 

(out of 21 S&E fields), 38 SESTAT occupational categories (out of 39 categories), 6 SESTAT employment sector 

categories (out of 7), years since highest degree, years since 

highest degree squared, Carnegie classification of school 

awarding highest degree, and private/public status of postsecondary institution awarding highest degree. 

24. In addition to the education- and employment-related 

variables, the following indicators are included: nativity and 

citizenship, marital status, disability, number of children living in the household, geographic region (classified into nine 

U.S. Census divisions), and whether either parent holds a 

bachelor’s or higher level degree. The sex regression controls for racial and ethnic minority status, and the race and 

ethnicity regression controls for sex. 

25. The regression analysis addresses major factors that 

affect differences in earnings but does not attempt to cover 

all possible sources of difference. For a more detailed discussion on the topic, see Blau and Kahn (2007), Mincer 

(1974), Polachek (2008), and Xie and Shauman (2003). 

26. When asked about the most important reason for 

coming to the United States, many foreign-born scientists 

and engineers who obtained their initial university degree 

abroad cited family-related reasons (24%), job or economic 

opportunities (23%), and educational opportunities (14%). 

27. For an additional 15% (about 321,000) of foreignborn employed SESTAT respondents who hold an advanced 

degree, SESTAT lacks information on first bachelor’s degree, including the country in which they received their 

bachelor’s degree. Nearly three-fourths of these individuals 

received their highest degree from a foreign institution. The 

vast majority of foreign-born advanced degree holders for 

whom SESTAT contains information on first bachelor’s degree and who received their advanced degree abroad also 

received their initial university education abroad. It is therefore highly likely that a significant portion of the group for 

whom SESTAT is missing first bachelor’s degree information also received this degree abroad. 

28. For all types of temporary work visas, the actual number of individuals using them is less than the number issued. 

For example, some individuals may have job offers from 

employers in more than one country and may choose not to 

foreclose any options until a visa is certain. 

29. This question is part of the Survey of Earned 

Doctorates (SED), which is administered to individuals receiving research doctoral degrees from all accredited U.S. 

institutions. For information on the SED, see http://www.

nsf.gov/statistics/srvydoctorates/. The information on plan 

to stay or definite commitment to stay reflects intentions 

within the year after graduation as reported by the doctorate 

recipient around the graduation date. As such, any changes 

in intentions after survey completion are not captured. 

30. Many foreign recipients of U.S. doctorates who report that they plan to stay in the United States the year after graduation may do so using their student (F-1) visa and 

never obtain a new visa that would permit a longer stay. 

Student visas permit an additional 12-month stay in the 

United States after graduation if a student applies for optional practical training (OPT). OPT refers to paid or unpaid 

work that is performed at least 20 hours a week and that is 

related to a student’s field of study. Starting in April 2008, 
3-64 ♦ Chapter 3. Science and Engineering Labor Force

those earning a degree in science, technology, engineering, 

and mathematical (STEM) fields could apply for an extension of their OPT to a total of 29 months. Data from the 

Department of Homeland Security’s Student and Exchange 

Visitor Information System show that 75.6% of students 

with F-1 visas completing a doctorate in any field between 

2004 and 2009 had applied for OPT. 

31. Tax data that are used for estimating stay rates are 

reported by tax authorities in aggregate forms for groups of 

individuals in order to protect confidentiality of individual 

tax payers. 

Glossary

European Union (EU): As of June 2013, the EU comprised 27 member nations: Austria, Belgium, Bulgaria, Cyprus, 

Czech Republic, Denmark, Estonia, Finland, France, Germany, 

Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, 

Malta, the Netherlands, Poland, Portugal, Romania, Slovakia, 

Slovenia, Spain, Sweden, and the United Kingdom. Croatia 

joined the EU in July 2013. Unless otherwise noted, OECD 

data on the EU include all 28 members; data on the EU from 

other sources are limited to the 27 nations that were members 

as of June 2013.

Involuntarily out of field (IOF) employment: Employment in a job not related to the field of one’s highest degree 

because a job in that field was not available. The IOF rate is 

the proportion of all employed individuals that report IOF 

employment.

Labor force: A subset of the population that includes 

both those who are employed and those who are not working but seeking work (unemployed); other individuals are 

not considered to be in the labor force.

Organisation for Economic Co-operation and 

Development (OECD): An international organization of 34 

countries headquartered in Paris, France. The member countries are Australia, Austria, Belgium, Canada, Chile, Czech 

Republic, Estonia, Denmark, Finland, France, Germany, 

Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, 

Korea, Luxembourg, Mexico, the Netherlands, New Zealand, 

Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, 

Sweden, Switzerland, Turkey, United Kingdom, and United 

States. Among its many activities, the OECD compiles social, 

economic, and science and technology statistics for all member and selected non-member countries.

Postdoc: A temporary position awarded in academia, industry, government, or a non-profit organization, primarily 

for gaining additional education and training in research after completion of a doctorate.

Scientists and Engineers Statistical Data System 

(SESTAT): A system of three surveys conducted by the 

National Science Foundation that measure the educational, occupational, and demographic characteristics of the 

S&E workforce. The three surveys are the National Survey 

of College Graduates (NSCG), the Survey of Doctorate 

Recipients (SDR), and the National Survey of Recent 

College Graduates (NSRCG). 

Stay rate: The proportion of foreign recipients of U.S. 

S&E doctoral degrees who stay in the United States after 

receiving their doctorate.

Tertiary education: Roughly equivalent in U.S. terms to 

individuals who have earned at least technical school or associate’s degrees, including all degrees up to the doctorate.

Workforce: A subset of the labor force that includes only 

employed individuals.

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and Immigration Services (USCIS). 2012. Characteristics 

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M, editors, International Migration, Remittances and 

the Brain Drain. New York: Palgrave Macmillan. pp. 

151–200.

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and development. Journal of Economic Literature 50(3): 

681–730.

Finn M. 2012. Stay Rates of Foreign Doctoral Recipients 

from U.S. Universities: 2009. Oak Ridge, TN: Oak Ridge 

Institute for Science and Education.

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Working Papers 10191, National Bureau of Economic 

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3-66 ♦ Chapter 3. Science and Engineering Labor Force
Chapter 4

Research and Development: 

National Trends and 

International Comparisons

♦ 4-1

Highlights.....................................................................................................................................4-4

Trends in U.S. R&D Performance............................................................................................4-4

International Comparisons of R&D Performance ....................................................................4-4

U.S. Business R&D ..................................................................................................................4-5

R&D by Multinational Companies...........................................................................................4-5

Federal R&D Performance and Funding ..................................................................................4-5

Federal Programs to Promote Technology Transfer and the Commercialization 

of Federal R&D....................................................................................................................4-5

Introduction..................................................................................................................................4-6

Chapter Overview.....................................................................................................................4-6

Chapter Organization................................................................................................................4-6

Trends in U.S. R&D Performance ...............................................................................................4-6

U.S. Total R&D and R&D Intensity.........................................................................................4-6

Performers of R&D...................................................................................................................4-9

Sources of R&D Funding .......................................................................................................4-12

R&D, by Character of Work...................................................................................................4-15

International Comparisons of R&D Performance......................................................................4-16

Global Pattern of R&D Expenditures.....................................................................................4-16

Comparison of Country R&D Intensities...............................................................................4-18

Comparative Composition of Country R&D Performance ....................................................4-20

U.S. Business R&D....................................................................................................................4-22

Recent Trends in Domestic Business R&D............................................................................4-23

Domestic and International Funding Sources, by Type of Source .........................................4-24

Business Activities for Domestic R&D..................................................................................4-25

R&D by Multinational Companies............................................................................................4-25

U.S. Affiliates of Foreign Companies ....................................................................................4-26

U.S. MNCs’ Parent Companies and Their Foreign Affiliates................................................4-27

Cross-National Comparisons of Business R&D........................................................................4-29

Federal R&D Performance and Funding ...................................................................................4-31

Federal R&D Budget, by National Objectives.......................................................................4-31

Federal Spending on R&D, by Agency ..................................................................................4-33

Federal Spending on Research, by Field ................................................................................4-37

Cross-National Comparisons of Government R&D Priorities...................................................4-39

Federal Programs to Promote Technology Transfer and the Commercialization of 

Federal R&D.........................................................................................................................4-39

Federal Technology Transfer..................................................................................................4-41

Small Business Innovation-Related Programs........................................................................4-43

Other Programs.......................................................................................................................4-46

Conclusion .................................................................................................................................4-46

Notes..........................................................................................................................................4-47

Glossary .....................................................................................................................................4-48

References..................................................................................................................................4-49
4-2 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

List of Tables

Table 4-1. U.S. R&D expenditures, by performing sector and source of funding: 2006–11 ......4-8

Table 4-2. Annual rates of growth in U.S. R&D expenditures, total and by performing 

sectors: 1991–2011................................................................................................................4-10

Table 4-3. U.S. R&D expenditures, by performing sector, source of funds, and character 

of work: 2011 ........................................................................................................................4-14

Table 4-4. International comparisons of gross domestic expenditures on R&D and R&D 

share of gross domestic product, by region/country/economy: 2011 or most recent year....4-19

Table 4-5. Gross expenditures on R&D for selected countries, by performing sector and 

funding sources: 2011 or most recent year ...........................................................................4-21

Table 4-6. Basic research as a share of gross expenditures on R&D, for selected 

countries: 2011......................................................................................................................4-22

Table 4-7. Funding sources for domestic business R&D performed: 2011...............................4-24

Table 4-8. Domestic business R&D performance and funding from abroad for selected 

industries: 2011 .....................................................................................................................4-26

Table 4-9. R&D performed by majority-owned affiliates of foreign companies in the 

United States, by selected industry of affiliate and investor country: 2010..........................4-27

Table 4-10. R&D performed abroad by majority-owned foreign affiliates of U.S. parent 

companies, by selected industry of affiliate and host region/country/economy: 2010.........4-28

Table 4-11. R&D performed abroad, shares, and R&D intensity of majority-owned 

foreign affiliates of U.S. parent companies, by selected host country: 2007 and 2010........4-29

Table 4-12. Share of manufacturing and nonmanufacturing in business R&D, 

by selected country: 2010 or most recent year......................................................................4-30

Table 4-13. Federal obligations for R&D and R&D plant, by agency and performer: 

FY 2011.................................................................................................................................4-33

Table 4-14. Federal obligations for R&D, by agency and character of work: FY 2011 ...........4-36

Table 4-15. Government R&D support by major socioeconomic objectives, for selected 

countries and years: 1990–2011............................................................................................4-40

Table 4-16. Federal laboratory technology transfer activity indicators, total and selected 

U.S. agencies: FYs 2006 and 2010 .......................................................................................4-44

Table 4-17. SBIR and STTR awards, number and funding, by type of award: Selected 

years, FYs 1983–2011...........................................................................................................4-45

Table 4-A. Top 10 U.S. states in R&D performance, by sector and intensity: 2010.................4-12

List of Sidebars

R&D in the U.S. National Income and Product Accounts...........................................................4-6

Measured and Unmeasured R&D................................................................................................4-7

Location of R&D Performance, by State...................................................................................4-12

Comparing International R&D Expenditures ............................................................................4-17

Federal R&E Tax Credit ............................................................................................................4-24

Federal Budgetary Concepts and Related Terms.......................................................................4-32

Tracking R&D: The Gap between Performer- and Source-Reported Expenditures..................4-35

Government Funding Mechanisms for Academic Research .....................................................4-41

Major Federal Policies Promoting Technology Transfer and Commercialization of R&D......4-42

Federal Technology Transfer: Activities and Metrics...............................................................4-43
Science and Engineering Indicators 2014 ♦ 4-3

List of Figures

Figure 4-1. U.S. total R&D expenditures: 1953–2011 ................................................................4-9

Figure 4-2. Year-to-year changes in U.S. R&D expenditures, by performing sector: 

2006–11...................................................................................................................................4-9

Figure 4-3. Ratio of U.S. R&D to gross domestic product, by federal and nonfederal 

funding for R&D: 1953–2011...............................................................................................4-10

Figure 4-4. Shares of U.S. total R&D expenditures, by performing sector and funding 

source: 2011 ..........................................................................................................................4-11

Figure 4-5. U.S. R&D, by performing and funding sectors: 1953–2011...................................4-11

Figure 4-6. U.S. total R&D expenditures, by source of funds: 1953–2011...............................4-15

Figure 4-7. U.S. R&D by character of work, basic research by performing sector, and 

basic research by source of funds: 2011 ...............................................................................4-16

Figure 4-8. Global R&D expenditures, by region: 2011 ...........................................................4-18

Figure 4-9. Gross domestic expenditures on R&D by the United States, EU, and selected 

other countries: 1981–2011...................................................................................................4-20

Figure 4-10. Gross expenditures on R&D as share of GDP, for the United States, EU, 

and selected other countries: 1981–2011 ..............................................................................4-20

Figure 4-11. U.S. business R&D, by major source of funds: 1990–2011 .................................4-22

Figure 4-12. Percentage change in U.S. domestic business R&D performance: 2008–09 

and 2010–11..........................................................................................................................4-23

Figure 4-13. Share of U.S. business R&D performance funded by the federal government: 

2010.......................................................................................................................................4-23

Figure 4-14. Domestic and international funding sources for U.S. business R&D 

performance, by type of source: 2011...................................................................................4-25

Figure 4-15. Industry share of business R&D in selected countries: 2010 or most 

recent year.............................................................................................................................4-30

Figure 4-16. Business enterprise R&D and R&D by foreign affiliates, by selected 

shares: 2009...........................................................................................................................4-31

Figure 4-17. Federal budget authority for R&D and R&D plant, by budget function: 

FYs 2000–12 .........................................................................................................................4-32

Figure 4-18. Federal obligations for R&D and R&D plant: FYs 1980–2011 ...........................4-34

Figure 4-19. Federal obligations for R&D, by agency and character of work: FY 2011 ..........4-36

Figure 4-20. Federal obligations for research, by agency and major S&E field: FY 2011 .......4-38

Figure 4-A. Differences in federal R&D support, as reported by performers and federal 

agencies: 1985–2011.............................................................................................................4-35
4-4 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

Trends in U.S. R&D Performance

The total of U.S. research and development performance 

returned to current dollar growth in 2010 and 2011. On 

a constant dollar basis, however, U.S. total R&D in 2011 

remains slightly below that for 2008, and the 2009 and 

2010 levels are noticeably below the 2008 level.

♦ Overall R&D performed in the United States totaled 

$406.7 billion (current dollars) in 2010, roughly the same 

as the 2009 level of $404.7 billion. U.S. R&D in 2011 totaled $424.4 billion, an increase of $17.7 billion.

♦ This growth in U.S. R&D expenditures in 2011 followed a 

2-year period of stagnation (2009 and 2010). This resulted 

chiefly from a drop in business R&D in the face of the 

national and international financial crisis and economic 

downturn that started in late 2008.

♦ This seeming return to growth in 2011 is less apparent, 

however, when the U.S. R&D data are adjusted for inflation. On a constant dollar basis, the U.S. total R&D in 2011 

is essentially equal to the 2008 level.

The business sector continues to account for most of U.S. 

R&D performance and U.S. R&D funding.

♦ The business sector performed $294 billion of R&D in 

2011, or 69% of the U.S. total, drawing on business, federal sources, and other sources of R&D support. The business sector itself provided $267 billion of funding for R&D 

in 2011, or 63% of the U.S. total, most all of which supported R&D performed by business.

♦ Even with the declining levels of R&D expenditures in 

both 2009 and 2010, business R&D performance has accounted for most of the nation’s R&D growth over the last 

5 years.

♦ The academic sector is the second-largest performer of 

U.S. R&D, accounting for an estimated $63 billion in 

2011, or about 15% of the national total.

♦ The federal government is the second-largest funder of 

U.S. R&D, accounting for an estimated $126 billion, or 

30% of U.S. total R&D performance in 2011.

Most of U.S. basic research is conducted at universities and colleges and funded by the federal government. 

However, the largest share of U.S. total R&D is development, which is largely performed by the business sector. The business sector also performs the majority of 

applied research.

♦ In 2011, basic research was about 18% ($75 billion) of 

total U.S. R&D performance, applied research was about 

19% ($82 billion), and development was about 63% 

($267 billion).

♦ Universities and colleges historically have been the main 

performers of U.S. basic research, and they accounted for 

about 55% of all U.S. basic research in 2011. The federal 

government remains the primary source of basic research 

funding, accounting for about 55% of all such funding 

in 2011.

♦ The business sector is the predominant performer of applied research, accounting for 57% of all U.S. applied 

research in 2011. Business is also the largest source of 

funding for applied research, providing 53% in 2011.

♦ Development is by far the largest component of U.S. R&D. 

Funding for development comes primarily from the business sector (78% in 2011); nearly all of the rest comes from 

the federal government.

International Comparisons of R&D 

Performance

The top three R&D-performing countries—United 

States, China, and Japan—accounted for over half of the 

estimated $1.435 trillion in global R&D in 2011.

♦ The United States, the largest single R&D-performing 

country, accounted for just under 30% of the 2011 global 

total, down from 37% in 2001.

♦ The economies of East/Southeast and South Asia—including China, India, Japan, Malaysia, Singapore, South 

Korea, and Taiwan—represented 25% of the global R&D 

total in 2001 but accounted for 34% in 2011. China (15%) 

and Japan (10%) were the largest R&D performers in 

this group.

♦ The pace of real growth over the past 10 years in China’s 

overall R&D remains exceptionally high at about 18% annually, adjusted for inflation.

♦ The European Union accounted for 22% total global R&D 

in 2011, down from 26% in 2001.

High-income countries, which tend to emphasize production of high-technology goods and services, devote larger 

shares of their GDP to R&D.

♦ The U.S. R&D/gross domestic product (GDP) ratio (or 

R&D intensity) was just over 2.8% in 2011 and has fluctuated between 2.6% and 2.9% during the past 10 years, 

largely reflecting changes in business R&D spending.

♦ In 2011, the United States ranked 10th in R&D intensity—

surpassed by Israel, South Korea, Finland, Japan, Sweden, 

Denmark, Taiwan, Germany, and Switzerland. However, 

all of these economies performed much less R&D annually 

than the United States.

♦ Among the top European R&D-performing countries, 

Germany reported a 2.9% R&D/GDP ratio in 2011, France 

reported 2.2%, and the United Kingdom reported 1.8%.

Highlights

4-4 ♦ Chapter 4. Research and Development: National Trends and International Comparisons
Science and Engineering Indicators 2014 ♦ 4-5

♦ South Korea’s R&D/GDP ratio moved upward to 4.0% in 

2011. Japan’s ratio was 3.4%. China’s ratio remains comparatively low, somewhat above 1.8%, but has more than 

doubled from just under 1.0% in 2001.

U.S. Business R&D

In 2011, business R&D performance reached $294 billion, a record in current dollars but still below the 2008 

peak when measured in inflation-adjusted dollars.

♦ Total U.S. business R&D performance increased from 

2010 to 2011 by 5%. However, when measured in inflation-adjusted dollars, 2011 business R&D performance of 

$259.4 billion is still below the 2008 peak of $267.7 billion, at the beginning of the most recent recession.

♦ Funding from business and other nonfederal sources increased 5.1% in constant dollars in 2011, the first such 

increase since 2008. On the other hand, federally funded 

business R&D as reported by performers dropped 10% in 

constant dollars in 2011 after a 15% decline in 2010.

R&D by Multinational Companies

The majority of R&D by U.S. multinational companies 

(MNCs) is still performed in the United States (84.1% of 

their $252 billion in R&D globally in 2010). Europe hosts 

the largest expenditures of R&D performed by majorityowned foreign affiliates (MOFAs) of U.S. MNCs, but affiliates in other regions, especially in Asia, are increasing 

their shares.

♦ Parent companies of U.S. MNCs performed $212.5 billion of R&D in the United States, according to preliminary 2010 data. Their MOFAs performed $39.5 billion, so 

that U.S. MNCs as a whole performed $252.0 billion in 

R&D globally in 2010, up 2.2% from the $246.5 billion 

performed in 2009.

♦ European host countries accounted for 62% of U.S. MOFA 

R&D in 2010. Asia-Pacific was the second-largest host region for U.S. MOFA R&D with 21.1%, including 4.8% in 

Japan and a record high of 16.3% in the rest of the region. 

The Middle East and Latin America each accounted for 

about 5% in 2010, up from 3.0% and 3.4%, respectively, 

in 2007.

♦ Europe, Canada, and Japan have long hosted the majority 

of R&D by U.S. MOFAs. Seven of 13 countries with at 

least $1 billion in U.S. MOFA R&D in 2010 are in Europe. 

However, rapid growth in reported R&D by U.S. MOFAs 

in China, India, Brazil, and Israel has put these locations in 

the billion-dollar-plus category.

♦ U.S.-owned MOFA R&D in China more than doubled 

from 2005 to 2008, with year-to-year double-digit increases to a record $1.7 billion in 2008, although it declined to 

$1.5 billion by 2010. U.S. MOFA R&D tripled in India and 

more than doubled in Brazil from 2007 to 2010, growing 

much faster than U.S. MOFA production activity in those 

countries, according to preliminary 2010 statistics. Brazil’s 

and India’s U.S. MOFA R&D expenditures are now on par 

with affiliates in China.

Federal R&D Performance and Funding

Federal spending on R&D increased annually on both 

current and constant dollar bases from the late 1990s 

through FY 2010. Funding dropped in FY 2011, which 

was a noticeable departure from the recent trend.

♦ Federal obligations for the total of R&D and R&D plant 

were $136 billion in FY 2011 ($132 billion for R&D and 

an additional $4 billion for R&D plant). The corresponding 

data for FYs 2009 and 2010 were higher: $145 billion and 

$147 billion, respectively.

♦ Defense continues to account for more than half of annual 

federal R&D spending. Health-related R&D accounts for 

the majority of federal nondefense R&D. Over the last two 

decades, the greatest change in federal R&D priorities has 

been the rise in health-related R&D.

♦ Fifteen federal departments and 12 other agencies engage 

in and/or fund R&D in the United States. Nine of these 

departments/agencies reported R&D spending in FY 2011 

in excess of $1 billion, and the nine together accounted 

for 97% of all federal obligations for R&D that year: the 

Departments of Agriculture, Commerce, Defense, Energy, 

Health and Human Services, Homeland Security, and 

Transportation; the National Science Foundation; and the 

National Aeronautics and Space Administration.

Federal Programs to Promote Technology 

Transfer and the Commercialization of 

Federal R&D

The federal government has been active since the early 

1980s in establishing policies and programs to better 

transfer and economically exploit the results of federally 

funded R&D.

♦ The latest statistics suggest that the federal departments/

agencies accounting for the largest portion of federal R&D 

continue to be active in their use of the technology transfer 

authorities provided by the Technology Innovation Act of 

1980 (Stevenson-Wydler Act) and subsequent legislation.

♦ The levels of funding going to small, entrepreneurial companies engaged in R&D with eventual commercialization objectives, through the Small Business Innovation 

Research and Small Business Technology Transfer programs, are now vastly larger than when these programs 

were first initiated in, respectively, the early 1980s and the 

mid-1990s.

Science and Engineering Indicators 2014 ♦ 4-5
4-6 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

Introduction

Chapter Overview

This chapter discusses how different economic sectors—

including business, the federal government, and universities 

and colleges—contributed to recent trends in research and 

development funding and performance. It emphasizes R&D 

in the business and federal sectors (chapter 5 covers academic R&D in detail).

The importance of these trends to national welfare is 

highlighted by the recent change in the U.S. gross domestic product (GDP) and related National Income and Product 

Accounts treating R&D as investment. The change recognizes R&D as a long-term contributor to GDP growth (see 

sidebar, “R&D in the U.S. National Income and Product 

Accounts”).

In addition to U.S. R&D trends, this chapter presents international R&D comparisons at the national and economic 

sector levels. One major trend highlighted here is the particularly rapid expansion of R&D performance in Asia. The 

chapter also details the distribution of R&D performed by 

foreign affiliates of U.S. multinational companies (MNCs).

Chapter Organization

This chapter is organized in eight sections covering national R&D totals, business activity, and government efforts 

in the United States and internationally. The first two sections cover U.S. and international comparisons in national 

R&D performance and funding.

The next three sections detail business sector R&D from the 

perspective of U.S. domestic activity, MNCs owned by U.S. 

parent companies or located in the United States, and crossnational industry R&D comparisons. The last three sections 

provide further detail on the R&D performed and/or funded by 

the U.S. federal government, compare the national government 

R&D priorities of the United States and the other major R&Dperforming countries, and discuss several U.S. federal programs to promote technology transfer and commercialization.

Trends in U.S. R&D Performance

The U.S. R&D system consists of a variety of performers and sources of funding. These include businesses, the 

federal government, universities and colleges, other government (nonfederal) agencies, and nonprofit organizations. 

Organizations that perform R&D often receive significant 

levels of outside funding; those that fund R&D may also 

be significant performers. This section discusses the current levels and notable recent trends in overall U.S. R&D 

performance and funding. (Definitions for key terms in 

this section appear in this chapter’s glossary. The sidebar 

“Measured and Unmeasured R&D” discusses the main data 

sources that provide the basis for this analysis. Appendix 

tables 4-1–4-9 provide additional core data on U.S. R&D 

funding and performance.)

U.S. Total R&D and R&D Intensity

R&D performed in the United States totaled $424.4 billion (current dollars) in 2011, an increase of $17.7 billion 

over the previous year (table 4-1). The comparable total 

in 2008 was $406.6 billion, having increased $26.9 billion over the previous year. However, 2009 and 2010 were 

more difficult years for what has, over the longer term, been 

a mainly expanding U.S. R&D enterprise (figure 4-1). In 

2009 and 2010, total U.S. R&D fluctuated narrowly around 

the 2008 level, showing little expansion ($404.7 billion in 

2009; $406.7 billion in 2010). These circumstances resulted 

chiefly from a lowered level of business R&D in the face of 

the national and international financial crisis and economic 

downturn that started in late 2008 (figure 4-2).

The challenging path for U.S. R&D performance over the 

last several years is more apparent when the R&D expenditure figures are adjusted for inflation.1

 On a constant dollar basis, U.S. total R&D in 2010 was below the 2008 level 

(table 4-1). Furthermore, the 2011 level only barely returns 

to the 2008 level. Much the same is true for R&D performance by the business sector (which accounts for around 

two-thirds of all U.S. R&D performance), although even in 

2011 this sector’s R&D remains well below the 2008 level 

in inflation-adjusted terms (table 4-1).

R&D in the U.S. National 

Income and Product Accounts

The most recent comprehensive revision of the 

U.S. GDP and related National Income and Product 

Accounts (NIPA), released July 2013 by the U.S. 

Bureau of Economic Analysis (BEA), includes a 

change to treat R&D as a fixed investment with longterm benefits. Prior to the change, NIPA considered 

R&D as an expense or as an intermediate input cost in 

the business sector and as consumption in the government and nonprofit sectors (BEA 2013). This update 

is one of several NIPA changes aimed at capturing 

the role of intangible assets in economic growth. 

Intangibles or intellectual property products include 

software, R&D, and entertainment, literary, and artistic originals. (For background on the July 2013 

release, see http://www.bea.gov/national/an1.htm; 

for full, revised NIPA statistics, see http://www.bea.

gov/national/index.htm#gdp.) The National Science 

Foundation’s surveys serve as the primary data source 

for the R&D component of these revisions. For further 

details, see the forthcoming InfoBrief on incorporating R&D as investment in GDP statistics at http://

www.nsf.gov/statistics.
Science and Engineering Indicators 2014 ♦ 4-7

U.S. total R&D grew by 4.4% in 2011, compared with a 

3.9% expansion of GDP that year (table 4-2).2 These relative 

changes better mirror what has been the “historical” pattern 

of R&D and GDP growth than the experiences of 2009 or 

2010. As a matter of longer-term averages, the growth of 

U.S. total R&D has outpaced that of the nation’s GDP—

whether the averaging period is the past 5, 10, or 20 years 

(table 4-2). But, again, 2009 and 2010 were notably different experiences. U.S. total R&D dropped by 0.5% in 2009 

mainly because of the hefty decline in R&D performed by 

the business sector (figure 4-2). GDP declined even more 

sharply that year, by 2.5%. GDP rebounded in 2010, growing by 4.2% over the 2009 level. R&D, however, did not 

match this pace, growing by only 0.5% over the 2009 level—held back by another year of decline in business sector R&D expenditures (figure 4-2). R&D’s return to a more 

familiar pace of growth in 2011 owes much to the return of 

a relatively high rate of expansion of business sector R&D 

(table 4-2; figure 4-2). (Preliminary data for 2012, available 

too late to incorporate in this chapter’s charts and tables, put 

the U.S. R&D total at $452.6 billion that year, an increase 

of 5.7% over the prior year, well ahead of the 4.0% pace of 

The statistics on U.S. R&D discussed in this section 

reflect the National Science Foundation’s (NSF’s) periodic National Patterns of R&D Resources reports and 

data series, which provide a comprehensive account of 

total U.S. R&D performance. The National Patterns data, 

in turn, derive from five major NSF surveys of the organizations that perform the bulk of U.S. R&D:

♦ Business R&D and Innovation Survey

♦ Higher Education R&D Survey

♦ Survey of Federal Funds for R&D

♦ Survey of R&D Expenditures at Federally Funded 

R&D Centers

♦ Survey of R&D Funding and Performance by Nonprofit 

Organizations

The National Patterns analysis integrates R&D spending and funding data from these separate surveys into 

U.S. R&D performance totals, which are then reported 

on a calendar-year basis and for the main performing sectors and funding sources.

Because of practical constraints in the surveys, some 

elements of R&D performance are omitted from the U.S. 

totals. In evaluating R&D performance trends over time 

and in international comparisons, it is important to be 

aware of these omissions.

The U.S. business R&D estimates are derived from 

a survey of R&D-performing companies with five or 

more employees. No estimates of R&D performance currently are available for companies with fewer than five 

employees. (NSF is in the process of designing and implementing a Microbusiness Innovation and Science and 

Technology Survey, which will collect data from companies with fewer than five employees.)

Until recently, the U.S. statistics for business R&D did 

not include social science R&D, and, likewise, R&D in 

the humanities and other non-S&E fields (such as law) 

was excluded from the U.S. academic R&D statistics. 

Other countries include both of these R&D components 

in their national statistics, making their national R&D 

expenditures relatively larger when compared with those 

of the United States. Both of these shortfalls are now addressed in the U.S. statistics. NSF’s Business R&D and 

Innovation Survey—which replaced the previous Survey 

of Industrial R&D, starting with the 2008 data year—includes social science R&D. Also, the Higher Education 

R&D Survey—which replaced the previous Survey of 

R&D Expenditures at Universities and Colleges, starting 

with the 2010 academic fiscal year—directly includes 

non-S&E R&D expenditures in the reported academic 

R&D totals. (The academic R&D totals reported by the 

National Patterns statistics have been revised back to 

2003 to include the non-S&E R&D expenditures.)

The statistics for academic R&D track research expenditures that are separately accounted for in both sponsored research and institutionally funded research. U.S. 

universities do not report funds for research that are not 

separately accounted for, such as estimates of faculty 

time spent on research. This can be a limitation in international R&D comparisons because such estimates are 

often included in the national statistics of other countries.

Likewise, the activity of individuals performing R&D 

on their own time and not under the auspices of a corporation, university, or other organization is omitted from 

official U.S. R&D statistics.

Statistics on R&D performed by state governments are 

collected in a biennial NSF/U.S. Census Bureau survey, 

but these amounts (typically totaling only several hundred million dollars annually) are not yet regularly included in the National Patterns totals. Moreover, NSF has 

not fielded a full survey on R&D performance by nonprofit organizations since 1998—the National Patterns 

performance figures for this sector in the national R&D 

totals are estimated.

The National Center for Science and Engineering 

Statistics commissioned the National Research Council’s 

Committee on National Statistics to review the methodologies used in preparing the National Patterns data. The 

review panel began work in mid-2011 and provided its 

report in early 2013.

Measured and Unmeasured R&D
4-8 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

GDP growth, and mainly again the result of increased business R&D. This continuation in 2012 of the strong pace of 

R&D growth in 2011 suggests a return to the longer-term 

trend of R&D expansion in the wake of the 2008–09 domestic and international economic downturns [Boroush 2013].)

A consequence of these shifting growth rates is that the 

R&D intensity of the national economy (the ratio of R&D 

expenditures to GDP) exhibited a noticeable decline in 2010 

and 2011, compared with the earlier years (figure 4-3). (The 

ratio of total national R&D expenditures to GDP is often 

reported as a measure of the intensity of a nation’s overall 

R&D effort and is widely used as an international benchmark for comparing countries’ R&D systems.)

U.S. expenditures on R&D totaled 2.80% of GDP in 2010 

and 2.81% in 2011. Both of these figures are lower than the 

2.90% ratio that prevailed in 2009 (figure 4-3). Over the 10-

year period from 2001 to 2011, the ratio has fluctuated to 

some degree year to year, between a low of 2.57% in 2004 

and a high of 2.90% in 2009. The ratio had been rising since 

Table 4-1

U.S. R&D expenditures, by performing sector and source of funding: 2006–11

Sector 2006 2007 2008 2009 2010 2011

Current $millions

All performing sectors ............................................ 352,567 379,681 406,610 404,697 406,708 424,413

Business ............................................................. 247,669 269,267 290,681 282,393 278,977 294,093

Federal government............................................ 41,611 44,133 45,649 47,363 48,939 49,394

Federal intramurala .......................................... 28,240 29,859 29,839 30,560 31,217 31,505

FFRDCs........................................................... 13,371 14,274 15,810 16,804 17,985 17,889

Industry administeredb................................. 3,122 5,165 6,346 6,646 7,214 7,037

U&C administeredb ...................................... 7,306 5,567 4,766 5,052 5,315 5,294

Nonprofit administered................................ 2,943 3,543 4,698 5,106 5,457 5,558

Universities and colleges.................................... 48,951 51,149 53,917 56,939 60,235 63,102

Other nonprofit organizations............................. 14,336 15,132 16,363 18,002 18,294 17,825

All funding sectors.................................................. 352,567 379,681 406,610 404,697 406,708 424,413

Business ............................................................. 227,110 246,741 258,691 247,274 249,182 267,290

Federal government............................................ 101,558 106,858 119,423 127,467 126,962 125,686

Universities and colleges.................................... 10,076 10,833 11,640 11,884 11,990 12,488

Nonfederal government...................................... 3,182 3,438 3,706 3,808 3,782 3,832

Other nonprofit organizations............................. 10,641 11,810 13,151 14,264 14,793 15,117

Constant 2005 $millions

All performing sectors ............................................ 341,532 357,426 374,472 368,815 366,434 374,394

Business ............................................................. 239,917 253,484 267,706 257,355 251,351 259,433

Federal government............................................ 40,308 41,546 42,041 43,164 44,330 43,572

Federal intramurala .......................................... 27,356 28,109 27,480 27,850 28,126 27,792

FFRDCs........................................................... 12,953 13,438 14,560 15,314 16,204 15,780

Industry administeredb................................. 3,024 4,862 5,844 6,057 6,499 6,207

U&C administeredb ...................................... 7,078 5,241 4,389 4,604 4,789 4,670

Nonprofit administered................................ 2,851 3,335 4,327 4,653 4,916 4,903

Universities and colleges.................................... 47,419 48,151 49,656 51,891 54,270 55,665

Other nonprofit organizations............................. 13,888 14,245 15,070 16,406 16,482 15,724

All funding sectors.................................................. 341,532 357,426 374,472 368,815 366,434 374,394

Business ............................................................. 220,002 232,278 238,244 225,349 224,506 235,788

Federal government............................................ 98,379 100,595 109,984 116,165 114,390 110,873

Universities and colleges.................................... 9,760 10,198 10,720 10,831 10,802 11,016

Nonfederal government...................................... 3,083 3,237 3,413 3,471 3,408 3,381

Other nonprofit organizations............................. 10,308 11,118 12,111 12,999 13,328 13,335

FFRDC = federally funded R&D center; U&C = university and college.

a

 Includes expenditures of federal intramural R&D and costs associated with administering extramural R&D.

b Los Alamos National Laboratory (some $2 billion in annual R&D expenditures in recent years) became industry administered in June 2006; previously, 

it was U&C administered. Lawrence Livermore National Laboratory (more than $1 billion in annual R&D expenditures in recent years) became industry 

administered in October 2007; previously, it was U&C administered. These shifts in administration category are a main reason for the changes apparent in 

the R&D performer figures across 2006, 2007, and 2008. 

NOTES: Data are based on annual reports by performers except for the nonprofit sector. Expenditure levels for academic and federal government 

performers are calendar-year approximations based on fiscal-year data. For federal government expenditures, the approximation is equal to 75% of 

the amount reported in same fiscal year plus 25% of the amount reported in the subsequent fiscal year. For academic expenditures, the respective 

percentages are 50% and 50%, because those fiscal years generally begin on 1 July instead of 1 October. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, National Patterns of R&D Resources (annual series).

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 4-9

2004 (figure 4-3). The lower levels in 2010 and 2011 represent a noticeable reversal.

 Most of the rise of the R&D/GDP ratio over the past 

several decades has come from the increase of nonfederal 

spending on R&D, particularly that by the business sector 

(figure 4-3). This reflects the growing role of business R&D 

in the national R&D system and, in turn, the growing prominence of R&D-derived goods and services in the national 

and global economies. By contrast, the ratio of federal R&D 

spending to GDP declined from the mid-1980s to the late 

1990s, notably from cuts in defense-related R&D. There had 

been a gradual uptick through 2009, the result of increased 

federal spending on biomedical and national security R&D 

and the one-time incremental funding for R&D provided 

by the American Recovery and Reinvestment Act of 2009 

(ARRA).

Performers of R&D

The National Science Foundation (NSF) tracks the R&D 

spending patterns of all the major performers in the overall 

U.S. R&D system: businesses, the intramural R&D activities of federal agencies, federally funded research and development centers (FFRDCs), universities and colleges, and 

other nonprofit organizations.

Business Sector

In 2011, the business sector continued to be the largest 

performer of U.S. R&D, conducting $294.1 billion, or 69%, 

of the national total (table 4-1; figure 4-4). The 2011 level of 

business R&D performance rose over the 2010 level ($279.0 

billion) and reversed apparent declines in 2009 and 2010. 

Over the 5-year period of 2006–11, business R&D performance grew an average of 3.5% annually, although somewhat behind the 3.8% rate of growth of overall U.S. R&D 

(table 4-2).

The business sector’s predominance in the composition 

of national R&D has long been the case, with its annual 

Figure 4-1

U.S. total R&D expenditures: 1953–2011

Billions of dollars

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, National Patterns of R&D Resources 

(annual series). See appendix table 4-2.

Science and Engineering Indicators 2014

1953 1963 1969 1975 1981 1987 1993 1999 2005 2011

0

50

100

150

200

250

300

350

400

450

Current dollars

Constant 2005 dollars

Figure 4-2

Year-to-year changes in U.S. R&D expenditures, by performing sector: 2006–11

Billions of current dollars

FFRDC = federally funded R&D center.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, National Patterns of R&D Resources (annual series). 

Science and Engineering Indicators 2014

All performers Business Federal

intramural

FFRDCs Universities

and colleges

Other nonprot

organizations

–15

–10

–5

0

5

10

15

20

25

30

2006–07 2007–08 2008–09 2009–10 2010–11
4-10 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

share ranging between 68% and 74% over the 20-year period of 1991–2011 (figure 4-5).

Universities and Colleges

Academia is the second-largest performer of U.S. R&D. 

Universities and colleges performed $63.1 billion,3 or 15%, 

of U.S. R&D in 2011 (table 4-1; figure 4-4). The total of 

academic R&D performance has increased by several billion 

dollars each year since 2006. Annual growth of R&D in this 

sector has averaged 5.2% over the period of 2006–11, well 

ahead of the rate of total national R&D (table 4-2).

Over the 20-year period of 1991–2011, the academic sector’s share in U.S. R&D has ranged between 11% and 15% 

annually. Furthermore, as discussed below, universities and 

colleges have a special niche in the nation’s R&D system: 

they performed more than half (55%) of the nation’s basic 

research in 2011.

Federal Agencies and FFRDCs

R&D performed by the federal government includes the 

activities of agency intramural laboratories and that of the 

FFRDCs. Federal intramural R&D performance includes the 

spending for both agency laboratory R&D and for agency 

activities to plan and administer intramural and extramural 

R&D projects. FFRDCs are R&D-performing organizations 

Table 4-2

Annual rates of growth in U.S. R&D expenditures, total and by performing sectors: 1991–2011

(Percent)

Longer-term trend Most recent years

Expenditures and gross domestic product 1991–2011 2001–11 2006–11 2008–09 2009–10 2010–11

Current dollars

Total R&D, all performers............................ 5.0 4.3 3.8 -0.5 0.5 4.4

Business ................................................. 4.8 3.8 3.5 -2.9 -1.2 5.4

Federal government................................ 3.8 4.2 3.5 3.8 3.9 0.4

Federal intramurala .............................. 3.7 3.5 2.2 2.4 2.2 0.9

FFRDCs............................................... 4.0 5.5 6.0 6.3 7.0 -0.5

Universities and colleges........................ 6.4 6.5 5.2 5.6 5.8 4.8

Other nonprofit organizations ................. 6.9 4.8 4.5 10.0 1.6 -2.6

Gross domestic product............................. 4.7 3.9 2.4 -2.5 4.2 3.9

Constant 2005 dollars

Total R&D, all performers............................ 2.8 2.0 1.9 -1.5 -0.6 2.2

Business ................................................. 2.7 1.5 1.6 -3.9 -2.3 3.2

Federal government................................ 1.7 1.9 1.6 2.7 2.7 -1.7

Federal intramurala .............................. 1.6 1.2 0.3 1.3 1.0 -1.2

FFRDCs............................................... 1.9 3.2 4.0 5.2 5.8 -2.6

Universities and colleges........................ 4.2 4.1 3.3 4.5 4.6 2.6

Other nonprofit organizations ................. 4.7 2.5 2.5 8.9 0.5 -4.6

Gross domestic product............................. 2.6 1.6 0.5 -3.5 3.0 1.7

FFRDC = federally funded R&D center.

a

 Includes expenditures of federal intramural R&D and costs associated with administering extramural R&D.

NOTE: Longer-term trend rates are calculated as compound annual growth rates. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, National Patterns of R&D Resources (annual series).

Science and Engineering Indicators 2014

Figure 4-3

Ratio of U.S. R&D to gross domestic product, 

by federal and nonfederal funding for R&D: 

1953–2011

Percent

GDP = gross domestic product.

NOTE: Federal R&D/GDP ratios represent the federal government as 

a funder of R&D by all performers; the nonfederal ratios reect all 

other sources of R&D funding. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, National Patterns of R&D Resources 

(annual series).

Science and Engineering Indicators 2014

1953 1963 1969 1975 1981 1987 19991993 2005 2011

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Total R&D/GDP

Federal R&D/GDP

Nonfederal R&D/GDP
Science and Engineering Indicators 2014 ♦ 4-11

that are exclusively or substantially financed by the federal 

government. An FFRDC is operated to provide R&D capability to serve agency mission objectives or, in some cases, 

to provide major facilities at universities for research and 

associated training purposes. (There were 40 FFRDCs in 

2011; see appendix table 4-10). Each FFRDC is administered by an industrial firm, a university, a nonprofit institution, or a consortium.

The federal government conducted $49.4 billion, or 

12%, of U.S. R&D in 2011 (table 4-1; figure 4-4). Of this 

amount, $31.5 billion (7% of the U.S. total) was intramural 

R&D performed by federal agencies in their own research 

facilities, and $17.9 billion (4%) was R&D performed by 

the 40 FFRDCs.

The federal total was up only barely in 2011 (an increase 

of $0.5 billion over the prior year). Over the 2006–11 period 

more generally, however, it has increased from $1 billion to 

$2 billion annually (table 4-1). In 1991, the federal performance share was 15%, but it gradually declined in the years 

since 2006, ranging annually between 11% and 12%.

The volume of the federal government’s R&D performance is relatively small compared with that of the U.S. 

business sector. Even so, the $49.4 billion performance total 

in 2011 exceeded the total national R&D expenditures of 

every country except China, Japan, Germany, South Korea, 

and France.4

Other Nonprofit Organizations

R&D performed in the United States by nonprofit organizations other than universities and certain FFRDCs was 

estimated at $17.8 billion in 2011 (table 4-1). This was 4% 

Figure 4-4

Shares of U.S. total R&D expenditures, by 

performing sector and funding source: 2011

NOTES: National R&D expenditures are estimated to be $424.4 

billion in 2011. Federal performing sector includes federal agencies 

and federally funded R&D centers. State and local government 

support to business is included in business support for business 

performance.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, National Patterns of R&D Resources 

(annual series). See appendix tables 4-2 and 4-6. 

Science and Engineering Indicators 2014

Funding source

Performing sector

Business 63.0%

Federal

government

29.6%

Other nonprot organizations 3.6% Universities and colleges 

2.9%

Nonfederal government 0.9%

Business 69.3%

Federal

government

11.6%

Universities and

colleges 14.9%

Other nonprot organizations 4.2%

Figure 4-5

U.S. R&D, by performing and funding sectors: 

1953–2011

NOTES: Federal performers of R&D include federal agencies and 

federally funded R&D centers. Other funding includes support from 

universities and colleges, nonfederal government, and nonprot 

organizations. State and local government funding to businesses is 

included in business support for business R&D performance.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, National Patterns of R&D Resources 

(annual series). See appendix tables 4-2 and 4-6. 

Science and Engineering Indicators 2014

Performing sector

Funding sector

Billions of current dollars 

1953 1963 1969 1975 1981 1987 1993 1999 2005 2011

0

50

100

150

200

250

300

350

400

450

Business

U.S. total

Federal government

Universities and colleges

Nonprot 

0

50

100

150

200

250

300

350

400

450

1953 1963 1969 1975 1981 1987 1993 1999 2005 2011

Business

U.S. total

Federal government

Other
4-12 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

of U.S. R&D in 2011, a share that has been largely the same 

since 2000 (figure 4-4).

Geographic Location of R&D

The sidebar “Location of R&D Performance, by State,” 

summarizes the leading geographic locations of U.S. R&D 

performance. For additional R&D indicators at the state level, see chapter 8.

Sources of R&D Funding

Funds that support the conduct of R&D in the United 

States come from a variety of sources, including businesses, 

federal and nonfederal government agencies, academic institutions, and other nonprofit organizations. The mix of funding sources varies by performer.

R&D Funding by Business

The business sector is the predominant source of funding 

for the R&D performed in the United States. In 2011, business sector funding accounted for $267.3 billion, or 63% of 

the $424.4 billion of total U.S. R&D performance (table 4-1; 

figure 4-4).

Nearly all of the business sector’s funding for R&D 

(98%) is directed toward business R&D performance (table 

4-3).5

 The small remainder goes to academic and other nonprofit performers.

The business sector’s predominant role in the nation’s 

R&D funding began in the early 1980s, when the support 

it provided started to exceed 50% of all U.S. R&D funding (figure 4-6). This business sector share moved up annually until reaching 69% in 2000. However, this share has 

declined somewhat in the years since, amid rising federal 

R&D funding, to 64% in 2006 and 63% in 2011.

Distribution of R&D expenditures among 

the U.S. states

In 2010, the 10 states with the largest R&D expenditure levels accounted for about 62% of U.S. R&D expenditures that can be allocated to the states: California, 

Massachusetts, Texas, Maryland, New Jersey, New 

York, Washington, Illinois, Michigan, and Pennsylvania 

(table 4-A).* California alone accounted for 22% of the 

U.S. total, almost 4 times as much as Massachusetts, the 

next highest state. The top 20 states accounted for 84% of 

the R&D total; the 20 lowest-ranking states accounted for 

around 5% (appendix tables 4-11 and 4-12).

The states with the biggest R&D expenditures are 

not necessarily those with the greatest intensity of 

R&D. Among those with the highest R&D/GDP ratios 

in 2010 were New Mexico, Maryland, Massachusetts, 

Location of R&D Performance, by State

Table 4-A

Top 10 U.S. states in R&D performance, by sector and intensity: 2010

All R&Da Sector ranking R&D intensity (R&D/GDP ratio)

Rank State

Amount

(current

$millions) Business

Universities 

and colleges

Federal intramural 

and FFRDCsb State

R&D/GDP 

(%)

GDP 

(current 

$billions)

1 California 81,005 California California Maryland New Mexico 8.07 77.1

2 Massachusetts 20,657 New Jersey New York California Maryland 6.28 293.3

3 Texas 19,504 Texas Texas New Mexico Massachusetts 5.47 377.8

4 Maryland 18,429 Massachusetts Maryland Virginia Washington 4.91 339.8

5 New Jersey 17,876 Washington Pennsylvania District of Columbia California 4.31 1,877.6

6 New York 17,141 Illinois Massachusetts Massachusetts Michigan 3.99 368.4

7 Washington 16,685 Michigan North Carolina Tennessee Missouri 3.80 243.4

8 Illinois 15,820 New York Illinois Alabama New Jersey 3.72 480.4

9 Michigan 14,702 Pennsylvania Ohio Washington Delaware 3.64 64.0

10 Pennsylvania 13,074 Missouri Michigan Illinois New Hampshire 3.50 61.6

FFRDC = federally funded R&D center; GDP = gross domestic product.

a

 Includes in-state total R&D performance of business sector, universities and colleges, federal agencies, FFRDCs, and federally financed nonprofit R&D.

b Includes costs associated with the administration of intramural and extramural programs by federal personnel and actual intramural R&D performance.

NOTES: Small differences in parameters for state rankings may not be significant. Rankings do not account for the margin of error of the estimates from 

sample surveys.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, National Patterns of R&D Resources (annual series). 

State GDP data are from the U.S. Bureau of Economic Analysis. See appendix tables 4-11 and 4-12.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 4-13

and Washington (table 4-A). New Mexico is the location of a number of major government research facilities. 

Maryland is the site of many government research facilities and growing research universities. Massachusetts 

benefits from both leading research universities and 

thriving high-technology industries. Washington State is 

home to government research facilities, leading research 

universities, and high-technology industries. California 

has relatively high R&D intensity and benefits from the 

presence of Silicon Valley, other high-technology industries, federal R&D, and leading research universities, but 

it is still fifth on this list.

U.S. R&D performance, by sector and state

The proportion of R&D performed by each of the 

main R&D-performing sectors (business, universities 

and colleges, federal intramural R&D facilities, and 

FFRDCs) varies across the states, but the states that 

lead in total R&D also tend to be well represented in 

each of these sectors (table 4-A).

In 2010, R&D performed by the business sector accounted for about 69% of the U.S. total R&D that could 

be allocated to specific states. Of the top 10 states in total 

R&D performance, 9 are also in the top 10 in industry 

R&D. Missouri, 10th in business sector R&D, surpasses 

Maryland in the business R&D ranking.

University-performed R&D accounts for 16% of the 

allocable U.S. total and mirrors the distribution of overall R&D performance. Only New Jersey and Washington 

fall out of the top 10 total R&D states, replaced by North 

Carolina and Ohio.

Federal R&D performance (including both intramural R&D facilities and FFRDCs)—about 13% of the 

U.S. total—is more concentrated geographically than 

that in other sectors. Only five jurisdictions—Maryland, 

California, New Mexico, Virginia, and the District of 

Columbia—account for 63% of all federal R&D performance.†

 This figure rises to 80% when the other 5 

of the top 10 performers—Massachusetts, Tennessee, 

Alabama, Washington, and Illinois—are included.

Federal R&D accounts for the bulk of total R&D 

in several states, including New Mexico (84%), 

which is home to the nation’s two largest FFRDCs 

(Los Alamos and Sandia National Laboratories), 

and Tennessee (42%), which is home to Oak Ridge 

National Laboratory. The high figures for Maryland 

(58%), the District of Columbia (72%), and Virginia 

(45%) reflect the concentration of federal facilities 

and federal R&D administrative offices in the national 

capital area.

* The latest data available on the distribution of U.S. R&D performance by state are for 2010 (appendix table 4-11). Total U.S. R&D 

expenditures that year are estimated at $406.7 billion. Of this total, 

$377.0 billion could be attributed to one of the 50 states or the District 

of Columbia. This state-attributed total differs from the U.S. total for 

a number of reasons: some business R&D expenditures cannot be allocated to any of the 50 states or the District of Columbia because 

respondents did not answer the question related to location, nonfederal sources of nonprofit R&D expenditures (an estimated $11.3 billion in 2010) could not be allocated by state, state-level university 

R&D data have not been adjusted for double-counting of R&D passed 

from one academic institution to another, and state-level university 

and federal R&D performance data are not converted from fiscal to 

calendar years.

† Federal intramural R&D includes costs associated with the administration of intramural and extramural programs by federal personnel, as well as actual intramural R&D performance. This is a main 

reason for the large amount of federal intramural R&D in the District 

of Columbia.

Location of R&D Performance, by State—continued

R&D Funding by the Federal Government

The federal government is the second-largest source of 

overall funding for U.S. R&D. It is a major source for most 

U.S. performer sectors except private businesses, where the 

federal role, while not negligible, is substantially overshadowed by the business sector’s own funds.

Funds from the federal government accounted for $125.7 

billion, or 30%, of U.S. total R&D in 2011 (table 4-1; figure 

4-4). This funding was mainly directed to federal, business, 

and academic performers, but other nonprofit organizations 

were also recipients (table 4-3).

Federal funding accounted for all of the $31.5 billion 

of federal intramural R&D performance in 2011 and nearly all of the $17.9 billion of R&D performed by FFRDCs. 

(Nonfederal support for FFRDC R&D has been around $0.4 

billion in recent years, or less than 1% of total support; see 

appendix table 4-10.)

Federal funding to the business sector accounted for $31.3 

billion of business R&D performance in 2011, or 11% of the 

sector’s R&D total that year (table 4-3). Federal funds to 

academia supported $38.7 billion (61%) of the $63.1 billion 

spent on academic R&D in 2011. For the R&D performed 

by other nonprofit organizations, $6.3 billion (about 35%) 

of this sector’s $17.8 billion of performance was supported 

by federal funds.

The federal government was once the leading sponsor of 

the nation’s R&D, funding some 67% of all U.S. R&D in 1964 

(figure 4-6). The federal share decreased in subsequent years 

to 49% in 1979, on down to a historical low of 25% in 2000. 

However, changing business conditions and expanded federal funding for health, defense, and counterterrorism R&D 

pushed the federal funding share above 30% in 2009 and 2010 

and to nearly 30% in 2011. Similarly, through the early 1960s, 

more than half of the nation’s business-performed R&D had 

been funded by the federal government. This share then declined in subsequent years to below 10% in 2000, but it increased again to 11% by 2011 (appendix table 4-2).
4-14 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

Table 4-3

U.S. R&D expenditures, by performing sector, source of funds, and character of work: 2011

Source of funds ($millions)

Performing sector and 

character of work Total Business

Federal 

government

Universities 

and colleges

Nonfederal 

government

Other 

nonprofit 

organizations

Total 

expenditures 

(% distribution)

R&D ................................................... 424,413 267,290 125,686 12,488 3,832 15,117 100.0

Business ........................................ 294,093 262,784 31,309 * * * 69.3

Federal government....................... 49,394 * 49,394 * * * 11.6

Federal intramural ...................... 31,505 * 31,505 * * * 7.4

FFRDCs...................................... 17,889 * 17,889 * * * 4.2

Industry administered.............. 7,037 * 7,037 * * * 1.7

U&C administered ................... 5,294 * 5,294 * * * 1.2

Nonprofit administered............ 5,558 * 5,558 * * * 1.3

Universities and colleges............... 63,102 3,173 38,710 12,488 3,832 4,899 14.9

Other nonprofit organizations........ 17,825 1,333 6,274 * * 10,218 4.2

Percent distribution by source....... 100.0 63.0 29.6 2.9 0.9 3.6 na

Basic research ............................... 74,961 15,072 40,913 7,828 2,402 8,744 100.0

Business..................................... 13,020 12,343 677 * * * 17.4

Federal government ................... 11,467 * 11,467 * * * 15.3

Federal intramural................. 4,875 * 4,875 * * * 6.5

FFRDCs ................................ 6,592 * 6,592 * * * 8.8

Industry administered ........ 2,761 * 2,761 * * * 3.7

U&C administered.............. 2,212 * 2,212 * * * 3.0

Nonprofit administered ...... 1,619 * 1,619 * * * 2.2

Universities and colleges ........... 40,952 1,989 25,662 7,828 2,402 3,071 54.6

Other nonprofit organizations .... 9,521 740 3,108 * * 5,673 12.7

Percent distribution by source ... 100.0 20.1 54.6 10.4 3.2 11.7 na

Applied research............................ 82,379 43,947 30,311 3,255 999 3,866 100.0

Business..................................... 47,186 42,782 4,404 * * * 57.3

Federal government ................... 12,885 * 12,885 * * * 15.6

Federal intramural................. 7,747 * 7,747 * * * 9.4

FFRDCs ................................ 5,138 * 5,138 * * * 6.2

Industry administered ........ 2,223 * 2,223 * * * 2.7

U&C administered.............. 1,314 * 1,314 * * * 1.6

Nonprofit administered ...... 1,602 * 1,602 * * * 1.9

Universities and colleges ........... 16,614 827 10,256 3,255 999 1,277 20.2

Other nonprofit organizations .... 5,693 338 2,766 * * 2,590 6.9

Percent distribution by source ... 100.0 53.3 36.8 4.0 1.2 4.7 na

Development.................................. 267,074 208,271 54,461 1,405 431 2,506 100.0

Business..................................... 233,887 207,659 26,228 * * * 87.6

Federal government ................... 25,041 * 25,041 * * * 9.4

Federal intramural................. 18,884 * 18,884 * * * 7.1

FFRDCs ................................ 6,158 * 6,158 * * * 2.3

Industry administered ........ 2,053 * 2,053 * * * 0.8

U&C administered.............. 1,768 * 1,768 * * * 0.7

Nonprofit administered ...... 2,336 * 2,336 * * * 0.9

Universities and colleges ........... 5,536 357 2,792 1,405 431 551 2.1

Other nonprofit organizations .... 2,610 255 400 * * 1,955 1.0

Percent distribution by source ... 100.0 78.0 20.4 0.5 0.2 0.9 na

* = small to negligible amount, included as part of the funding provided by other sectors; na = not applicable.

FFRDC = federally funded R&D center; U&C = university and college. 

NOTES: Funding for FFRDC performance is chiefly federal, but any nonfederal support is included in the federal figures. State and local government 

support to business is included in business support for business performance.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, National Patterns of R&D Resources (annual series).

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 4-15

R&D Funding from Other Sources

The balance of R&D funding from other sources is small: 

$31.4 billion in 2011, or about 7% of all U.S. R&D performance 

that year. Of this amount, $12.5 billion (3%) was academia’s 

own institutional funds, all of which remain in the academic 

sector; $3.8 billion (1%) was from state and local governments, 

primarily supporting academic research; and $15.1 billion (4%) 

was from other nonprofit organizations, the majority of which 

funds this sector’s own R&D. In addition, some funds from the 

nonprofit sector support academic R&D.

The share of R&D funding from these sources has been 

gradually increasing over the 2006–11 period (figure 4-6). 

In 2006, these other sources accounted for just under 7% of 

U.S. total R&D.

R&D, by Character of Work

R&D encompasses a wide range of activities: from research yielding fundamental knowledge in the physical, 

life, and social sciences; to research addressing national 

defense needs and such critical societal issues as global 

climate change, energy efficiency, and health care; to the 

development of platform or general-purpose technologies 

that can enable the creation and commercial application of 

new and improved goods and services. The most widely applied classification of these activities characterizes R&D 

as “basic research,” “applied research,” or “(experimental) 

development” (OMB 2012b; OECD 2002; NSF 2006).(For 

definitions of these terms, see this chapter’s glossary.) These 

categories have been criticized as reinforcing the idea that 

creating new knowledge and innovation is a linear process 

beginning with basic research, followed by applied research 

and development, and ending with the production and diffusion of new technology. However, alternative classifications 

that involve measureable distinctions and capture major 

differences in types of R&D have yet to emerge. Despite 

the recognized limitations of the basic research-applied 

research-development classification framework, it remains 

useful in providing indications of differences in the motivation, expected time horizons, outputs, and types of investments associated with R&D projects.

The most recent character-of-work cross-section in 

NSF’s R&D expenditures and funding data covers 2011.6

Basic research activities accounted for 18% ($75.0 billion) 

of the $424.4 billion of total U.S. R&D that year. Applied 

research was 19% ($82.4 billion); development was 63% 

($267.1 billion) (table 4-3; figure 4-7).

Basic Research

Universities and colleges remain the primary performers of U.S. basic research, accounting for 55% of the $75.0 

billion in 2011 (table 4-3). The business sector performed 

about 17%; the federal government (agency intramural labs 

and FFRDCs) performed 15%; and other nonprofit organizations performed 13%.

The federal government continues as the prime source of 

funding for basic research, accounting for about 55% of all such 

funding in 2011 (table 4-3). The business sector was the secondlargest performer at 20%, but although its $15.1 billion of funding for basic research is small compared to its $267.3 billion of 

funding for all R&D that year, the contribution is particularly 

significant to the national R&D as a whole. Universities and 

colleges themselves provide about 10% of basic research funding. Other nonprofit organizations provide 12%.

In choosing whether to perform basic research, businesses 

consider various factors, such as the extent of appropriability of results, the commercialization risks involved, and the 

uncertainties of investment returns over business-acceptable 

time horizons. Despite the risks and uncertainties involved, 

many companies believe that company engagement in basic 

research can help them develop human capital, attract and 

retain talent, absorb external knowledge, and strengthen innovation capacity. Businesses that invest most heavily in basic research tend to be in industries that are most directly tied 

to ongoing scientific and technological advances, such as the 

pharmaceuticals and scientific R&D service industries.

Applied Research

The business sector performed 57% of the $82.4 billion 

of applied research in 2011 (table 4-3). Universities and colleges accounted for 20%; the federal government (federal 

agency intramural labs and FFRDCs) accounted for 16%; 

and nonprofit organizations accounted for 7%.

Businesses provided the bulk of funding (53%) for applied 

research in 2011. The federal government provided 37%. 

Academia, nonfederal governments, and other nonprofit organizations contributed 4%, 1%, and 5%, respectively.

Industries that perform relatively large amounts of applied research include chemicals and aerospace. Federal 

funding for applied research is spread broadly across all the 

Figure 4-6

U.S. total R&D expenditures, by source of funds: 

1953–2011

Percent

NOTE: Other includes universities and colleges, state and local 

government, and other nonprot organizations. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, National Patterns of R&D Resources 

(annual series). See appendix table 4-6.

Science and Engineering Indicators 2014

0

10

20

30

40

50

60

70

80

Business

Federal government

Other

1953 1963 1969 1975 1981 1987 1993 1999 2005 2011
4-16 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

performers, with the largest amounts (in 2011) going to universities and colleges, federal intramural labs, the business 

sector, and FFRDCs (table 4-3).

Development

The business sector dominates in development, performing 88% of the $267.1 billion that the United States 

devoted to development in 2011 (table 4-3).7 The federal 

government (agency intramural labs, FFRDCs) accounted 

for another 9%—much of it was defense related, with the 

federal government being the main consumer. By contrast, 

academia and other nonprofit organizations perform very 

little development, respectively 2% and 1% of the total 

in 2011.

The business sector provided about three-quarters (78%) 

of development funding ($208.3 billion) in 2011, nearly 

all of it in support of development activities by businesses 

(table 4-3). The federal government provided 20% ($54.5 

billion) of the funding, with more than half going to the 

business sector—especially in defense-related industries—

and most of the remainder going to federal intramural labs 

and FFRDCs. Universities and colleges, other nonprofit 

organizations, and nonfederal government agencies provided small amounts of funding to support performance of 

development activities.

International Comparisons 

of R&D Performance

Data on R&D expenditures by country and region provide a broad picture of the changing distribution of R&D 

capabilities and activities around the world. R&D data 

available from the Organisation for Economic Co-operation 

and Development (OECD) cover the organization’s 34 

member countries and 7 nonmembers (OECD 2013). 

The United Nations Educational, Scientific and Cultural 

Organization’s (UNESCO’s) Institute for Statistics provides data on additional countries (UNESCO 2013). The 

discussion in this section draws on both of these data sets.

Cross-national comparisons of R&D expenditures and 

funding necessarily involve currency conversions. The 

analysis in this section uses the international convention of 

converting foreign currencies into U.S. dollars via purchasing power parity (PPP) exchange rates (for a discussion of 

this methodology, see the sidebar, “Comparing International 

R&D Expenditures”).

Global Pattern of R&D Expenditures

Worldwide R&D expenditures totaled an estimated 

$1,435 billion (current PPP dollars) in 2011.8 The corresponding estimate for 5 years earlier in 2006 is $1,051 

billion. Ten years earlier, in 2001, it was $753 billion. By 

these figures, growth in total global R&D has been rapid, 

Figure 4-7

U.S. R&D by character of work, basic research by 

performing sector, and basic research by source of 

funds: 2011

NOTES: National R&D expenditures were estimated at $424.4 billion 

in 2011. National basic research expenditures were estimated at 

$75.0 billion in 2011. Federal performers include federal agencies 

and federally funded R&D centers. State and local government 

support to industry is included in industry support for industry 

performance. State and local government support to universities 

and colleges is included in universities and colleges support of 

performance by universities and colleges.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, National Patterns of R&D Resources 

(annual series). See appendix tables 4-3–4-5 and 4-7.

Science and Engineering Indicators 2014

Basic research, by performing sector

Basic research, by source of funds

U.S. total R&D, by character of work

Basic research 19.0%

Applied

research

19.5%

Development

61.5%

Business 21.1%

Federal

government

14.6%

Other nonprot

organizations 12.1%

Business 22.5%

Other nonprot

organizations 11.1%

Universities and

colleges 52.2%

Universities

and colleges

13.0%

Federal

government 53.3%
Science and Engineering Indicators 2014 ♦ 4-17

Comparing International 

R&D Expenditures

Comparisons of international R&D statistics are hampered by the lack of R&D-specific exchange rates. Two 

approaches are commonly used: (1) express national 

R&D expenditures as a percentage of gross domestic 

product (GDP), or (2) convert all expenditures to a single 

currency. The first method is straightforward but permits 

only gross comparisons of R&D intensity. The second 

method permits absolute level-of-effort comparisons and 

finer-grain analyses but entails selecting an appropriate 

method of currency conversion. The choice is between 

market exchange rates (MERs) and purchasing power 

parities (PPPs), both of which are available for a large 

number of countries over an extended period.

MERs represent the relative value of currencies for 

cross-border trade of goods and services but may not 

accurately reflect the cost of nontraded goods and services. They are also subject to currency speculation, 

political events, wars or boycotts, and official currency intervention. PPPs were developed to overcome 

these shortcomings (Ward 1985). They take into account the cost differences of buying a similar market 

basket of goods and services covering tradables and 

nontradables. The PPP basket is assumed to be representative of total GDP across countries. PPPs are the 

preferred international standard for calculating crosscountry R&D comparisons and are used in all official 

R&D tabulations of the OECD.*

Because MERs tend to understate the domestic purchasing power of developing countries’ currencies, PPPs 

can produce substantially larger R&D estimates than 

MERs for these countries. For example, China’s R&D 

expenditures in 2010 (as reported to the OECD) are $178 

billion in PPP terms but only $104 billion using MERs.

However, PPPs for large developing countries such 

as China and India are often rough approximations 

and have other shortcomings. For example, structural 

differences and income disparities between developing and developed countries may result in PPPs based 

on markedly different sets of goods and services. In 

addition, the resulting PPPs may have very different 

relationships to the cost of R&D in different countries.

R&D performance in developing countries often is 

concentrated geographically in the most advanced cities 

and regions in terms of infrastructure and level of educated 

workforce. The costs of goods and services in these areas 

can be substantially greater than for the country as a whole.

* Recent research raises some unresolved questions about the 

use of GDP PPPs for deflating R&D expenditures. In analyzing the 

manufacturing R&D inputs and outputs of six industrialized OECD 

countries, Dougherty et al. (2007:312) concluded that “the use of 

an R&D PPP will yield comparative costs and R&D intensities that 

vary substantially from the current practice of using GDP PPPs, 

likely increasing the real R&D performance of the comparison 

countries relative to the United States.”

averaging 6.4% annually over the 5-year period and 6.7% 

annually over the 10-year period.

Overall, global R&D performance remains highly concentrated in three geographic regions: North America, Asia, 

and Europe (figure 4-8). North America (United States, 

Canada, Mexico) accounted for 32% ($462 billion) of worldwide R&D performance in 2011; the combination of East/

Southeast and South Asia (including China, Taiwan, Japan, 

India, South Korea) accounted for 34% ($492 billion); and 

Europe, including (but not limited to) European Union (EU; 

see “Glossary” for member countries) countries accounted 

for 24% ($345 billion). The remainder, around 10%, reflects 

the R&D of countries in the regions of Central and South 

America, Central Asia, the Middle East, Australia/Oceania, 

and Africa.

The geographic concentration of R&D is more apparent 

when looking at specific countries (table 4-4). Three countries account for more than half of global R&D. The United 

States is by far the largest R&D performer ($429 billion in 

2011), accounting for just under 30% of the global total, but 

down from 37% in 2001. China was the second-largest performer ($208 billion) in 2011, accounting for about 15% of 

the global total. Japan is third at 10% ($147 billion). The 

largest EU performers spend comparatively less: Germany 

($93 billion, 7%), France ($52 billion, 4%), and the United 

Kingdom ($40 billion, 3%). R&D spending by South Korea 

has also been rising in recent years and accounted for 4% 

($60 billion) of the global total in 2011. Taken together, 

these top seven countries account for about 72% of total 

global R&D. The Russian Federation, Taiwan, Brazil, Italy, 

Canada, India, Australia, and Spain make up the next tier of 

performers, with total R&D expenditures ranging from $20 

billion to $35 billion. The top seven countries, along with the 

second group of eight economies, together account for 84% 

of current global R&D.

The generally vigorous pace at which total global R&D 

continues to grow is certainly one of the prominent developments, a reflection of the growing knowledge-intensiveness 

of the economic competition among the world’s nations. The 

other major trend is the particularly rapid expansion of R&D 

performance in the regions of East/Southeast and South Asia, 

including economies such as China, India, Japan, Malaysia, 

Singapore, South Korea, Taiwan, and Thailand. The R&D 

performed in these two Asian regions represented only 25% 

of total global R&D in 2001 but increased to 34% in 2011, 

including China (15%) and Japan (10%).

China continues to exhibit the world’s most dramatic 

R&D growth pattern (figure 4-9; appendix table 4-13). The 

World Bank revised China’s PPP exchange rate in late 2007, 

significantly lowering the dollar value of its R&D expenditures. Nonetheless, the pace of growth over the past 10 years 

(2001–11) in China’s overall R&D remains exceptionally 

high at 20.7% annually (still very high, at 18.1% per year, 

when adjusted for inflation).

The rate of growth in South Korea’s R&D has also been 

quite high, averaging 10.9% annually over the same 10-year 
4-18 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

period. The growth in Japan’s R&D has been much slower, 

at an annual average rate of 3.5%.

By comparison, while the United States remains atop the 

list of the world’s R&D-performing nations, its pace of growth 

in R&D performance has averaged 4.4% over the same 2001–

11 period, and its share of global R&D has declined from 37% 

to 30%. Total R&D by EU nations has been growing over the 

same 10 years at an annual average rate of 5.0%. The pace of 

growth during the same period for Germany (5.5%), France 

(3.8%), and the United Kingdom (3.1%) has been somewhat 

slower. The EU countries accounted for 22% of total global 

R&D in 2011, down from 26% in 2001.9

Comparison of Country R&D Intensities

R&D intensity provides another basis for international 

comparisons of R&D performance. This metric does not 

require conversion of a country’s currency to a standard international benchmark (dollars), but it does provide a means 

to adjust for differences in the sizes of national economies.

The U.S. R&D/GDP ratio was somewhat over 2.8% in 

2011 (table 4-4). At this level, the United States is 10th 

among the economies tracked by the OECD and UNESCO. 

Israel continues to have the highest ratio at 4.4%. South 

Korea is now second at 4.0%, and Finland is third at 3.8%. 

Japan and Sweden are both around 3.4%. Denmark is at 

3.1%, and Taiwan is at 3.0%. Germany and Switzerland, 

both at 2.9%, are slightly ahead of the United States. By way 

of comparison, the United States was eighth in R&D intensity in the data for 2007; it has been gradually slipping in the 

world rank for this indicator in recent years.

The R&D/GDP ratio in the United States has ranged from 

1.4% in 1953 to well above 2.8% in 1963–67 to a historical 

high of 2.9% in 2009. Over the 10-year period from 2001 to 

2011, the ratio fluctuated between a low of 2.6% in 2004 to 

a high of 2.9% in 2009 (figure 4-10; appendix table 4-13). 

The ratio has generally been rising since 2004, but the drop 

in 2010 to 2.8% is a noticeable departure.

Most of the growth over time in the U.S. R&D/GDP ratio 

can be attributed to increases in nonfederal R&D spending, 

primarily that financed by business. Nonfederally financed 

R&D increased from about 0.6% of GDP in 1953 to 2.0% of 

GDP in 2011. This increase in the nonfederal R&D/GDP ratio reflects the growing role of business R&D in the national 

R&D system and, more broadly, the growing prominence 

of R&D-derived products and services in the national and 

global economies.

Figure 4-8

Global R&D expenditures, by region: 2011

Billions of U.S. PPP dollars

PPP = purchasing power parity.

NOTES: Foreign currencies are converted to U.S. dollars through PPPs. Some country gures are estimated. Countries are grouped according to the 

regions described by The World Factbook, available at www.cia.gov/library/publications/the-world-factbook/index.html.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, estimates (August 2013). Based on data from the 

Organisation for Economic Co-operation and Development, Main Science and Technology Indicators (2013/1); and the United Nations Educational, 

Scientic and Cultural Organization Institute for Statistics, http://stats.uis.unesco.org/unesco/ReportFolders/ReportFolders.aspx, table 25, accessed 2 

August 2013.

Science and Engineering Indicators 2014

World total = $1,435

South America 

$36 (2.5%)

Africa

$11 (0.8%)

North America 

$462 (32.2%)

Europe

$345 (24.0%)

South Asia

$36 (2.5%)

Australia

and Oceania

$24 (1.6%)

Middle East

$31 (2.1%)

Central Asia

$35 (2.5%)

East and Southeast

Asia $456 (31.8%)

Central America 

and Caribbean 

$0.6 (< 0.1%)
Science and Engineering Indicators 2014 ♦ 4-19

Table 4-4

International comparisons of gross domestic expenditures on R&D and R&D share of gross domestic product, 

by region/country/economy: 2011 or most recent year

Region/country/economy

GERD

(PPP $millions)

GERD/GDP

(%) Region/country/economy

GERD

(PPP $millions)

GERD/GDP

(%)

North America Middle East

United States (2011)a ................ 429,143.0 2.85 Turkey (2011)............................. 10,826.9 0.86

Canada (2011)........................... 24,289.3 1.74 Israel (2011) .............................. 9,822.7 4.38

Mexico (2011) ........................... 8,209.4 0.43 Iran (2008)................................. 6,432.2 0.79

South America Africa

Brazil (2010) .............................. 25,340.2 1.16 South Africa (2009) ................... 4,416.2 0.87

Argentina (2011)........................ 4,640.6 0.65 Egypt (2011).............................. 2,230.6 0.43

Chile (2010)............................... 1,331.4 0.42 Morocco (2010)......................... 1,115.6 0.73

Colombia (2010)........................ 856.7 0.16 Tunisia (2009)............................ 1,055.9 1.10

Europe Central Asia

Germany (2011) ........................ 93,055.5 2.88 Russian Federation (2011) ........ 35,045.1 1.09

France (2011)............................ 51,891.0 2.24

United Kingdom (2011)............. 39,627.1 1.77 South Asia

Italy (2011) ................................ 24,812.1 1.25 India (2007) ............................... 24,305.9 0.76

Spain (2011).............................. 19,763.1 1.33 Pakistan (2011) ......................... 1,618.5 0.33

Netherlands (2011).................... 14,581.5 2.04

Sweden (2011).......................... 13,216.2 3.37 East and Southeast Asia

Switzerland (2008) .................... 10,525.2 2.87 China (2011).............................. 208,171.8 1.84

Austria (2011)............................ 9,761.9 2.75 Japan (2011) ............................. 146,537.3 3.39

Belgium (2011).......................... 8,719.4 2.04 South Korea (2011) ................... 59,890.0 4.03

Finland (2011) ........................... 7,634.8 3.78 Taiwan (2011)............................ 26,493.1 3.02

Denmark (2011)......................... 7,052.4 3.09 Singapore (2011)....................... 7,060.2 2.23

Poland (2011)............................ 6,227.9 0.76 Malaysia (2011)......................... 4,953.4 1.07

Czech Republic (2011).............. 5,086.5 1.85 Thailand (2009) ......................... 1,355.8 0.25

Norway (2011)........................... 5,006.7 1.66 Indonesia (2009) ....................... 802.3 0.08

Portugal (2011).......................... 4,037.6 1.49

Ireland (2011) ............................ 3,223.0 1.70 Australia, Oceania

Hungary (2011) ......................... 2,581.9 1.21 Australia (2010) ......................... 20,578.1 2.20

Ukraine (2011)........................... 2,400.0 0.73 New Zealand (2011).................. 1,772.1 1.30

Greece (2007) ........................... 1,866.8 0.60

Romania (2011)......................... 1,648.5 0.50 Selected country groups

Slovenia (2011) ......................... 1,387.8 2.47 European Union (2011) ............. 320,455.9 1.94

Belarus (2011)........................... 1,074.1 0.76 OECD (2011)............................. 1,034,024.3 2.37

Slovak Republic (2011) ............. 882.3 0.68 G20 (2011) ................................ 1,323,147.2 2.02

Luxembourg (2011)................... 656.2 1.43

Croatia (2011) ........................... 642.9 0.75

Serbia (2011)............................. 633.9 0.73

Bulgaria (2011).......................... 632.6 0.57

G20 = Group of Twenty; GDP = gross domestic product; GERD = gross expenditures (domestic) on R&D; OECD = Organisation for Economic Cooperation and Development; PPP = purchasing power parity.

a

 Figures for the United States in this table may differ slightly from those cited earlier in the chapter. Data here reflect international standards for 

calculating GERD, which vary slightly from the National Science Foundation’s protocol for tallying U.S. total R&D.

NOTES: The table includes countries with annual GERD of $500 million or more. Year of data is listed in parentheses. Foreign currencies are converted 

to dollars through PPPs. Countries are grouped according to the regions described by The World Factbook, www.cia.gov/library/publications/the-worldfactbook/index.html. No countries in the Central American and Caribbean region had annual GERD of $500 million or more. Data for Israel are civilian 

R&D only. See sources below for GERD statistics on additional countries.

SOURCES: Organisation for Economic Co-operation and Development, Main Science and Technology Indicators (2013/1); United Nations Educational, 

Scientific and Cultural Organization Institute for Statistics, http://stats.uis.unesco.org/unesco/ReportFolders/ReportFolders.aspx, table 25, accessed 

August 2013.

Science and Engineering Indicators 2014
4-20 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

Among the other top seven R&D-performing countries, 

most had increasing R&D/GDP ratios over the 2000–11 period (figure 4-10). However, for some, the rise was modest 

at best, and for others, it was quite large. France exhibited 

only a bare increase over this period: from 2.2% in 2001 to 

somewhat over 2.2% in 2011. The United Kingdom’s ratio 

was also rather flat over the same period, around 1.8%. For 

Germany, the ratio increased from 2.5% in 2001 to 2.9% 

in 2011. Japan was also in the modest increase category: 

from 3.1% in 2001 to 3.4% in 2011. (Japan’s rising ratio 

reflects in part the confluence of declining GDP and largely 

flat R&D spending.) The high-risers were China and South 

Korea. China’s ratio doubled over the period: from just under 1.0% in 2001 to somewhat above 1.8% in 2011. South 

Korea’s ratio increased from 2.5% in 2001 to 4.0% in 2011.

In addition to the United States, countries in Nordic and 

Western Europe and the most advanced areas of Asia have 

R&D/GDP ratios above 1.5%. This pattern broadly reflects 

the global distribution of wealth and level of economic 

development. Countries with high incomes tend to emphasize the production of high-technology goods and services 

and are also those that invest heavily in R&D activities. 

Private sectors in low-income countries often have a low 

concentration of high-technology industries, resulting in low 

overall R&D spending and, therefore, low R&D/GDP ratios.

Comparative Composition of Country 

R&D Performance

The business sector is the predominant R&D performer 

for the top seven R&D-performing nations (table 4-5; appendix table 4-14). For the United States, the business sector 

accounted for 69% of gross expenditures on R&D in 2011. 

Japan’s business sector was the highest, accounting for 77% 

of the country’s overall R&D performance. China (76%) 

and South Korea (77%) were also well above the U.S. level. 

Germany, at 67%, was close to the level of the United States. 

France and the United Kingdom were somewhat lower, at, 

respectively, 63% and 62%.

Figure 4-9

Gross domestic expenditures on R&D by the 

United States, EU, and selected other countries: 

1981–2011

Billions of current PPP dollars

EU = European Union; PPP = purchasing power parity.

NOTES: Data are not available for all countries in all years. Data for 

the United States in this gure reect international standards for 

calculating gross expenditures on R&D, which vary slightly from the 

National Science Foundation’s approach to tallying U.S. total R&D. 

Data for Japan for 1996 onward may not be consistent with earlier 

data because of changes in methodology. EU data for all years are 

based on the current 27 EU member countries. 

SOURCE: Organisation for Economic Co-operation and Development, 

Main Science and Technology Indicators (2013/1). See appendix table 

4-13.

Science and Engineering Indicators 2014

1981 1987 1991 1995 1999 2003 2007 2011

0

50

100

150

200

250

300

350

400

450

500

United States

EU

France

Germany

United Kingdom

China

Japan

South Korea

Figure 4-10

Gross expenditures on R&D as share of GDP, for the 

United States, EU, and selected other countries: 

1981–2011

Percent

EU = European Union; GDP = gross domestic product.

NOTES: Data are not available for all countries in all years. The table 

includes the top seven R&D-performing countries. Figures for the 

United States reect international standards for calculating gross 

expenditures on R&D, which differ slightly from the National Science 

Foundation’s protocol for tallying U.S. total R&D. Data for Japan for 

1996 onward may not be consistent with earlier data because of 

changes in methodology. 

SOURCE: Organisation for Economic Co-operation and Development, 

Main Science and Technology Indicators (2013/1). See appendix table 

4-13.

Science and Engineering Indicators 2014

1981 1987 1991 1995 1999 2003 2007 2011

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

United States

France

China

Germany

United Kingdom

EU

Japan

South Korea
Science and Engineering Indicators 2014 ♦ 4-21

The R&D performed by the government ranges over 8%–

16% of total national R&D for the leading seven countries. 

Japan (8%) and the United Kingdom (9%) are on the lower 

end of this range. China (16%), Germany (15%), and France 

(14%) are at the high end. The United States and South 

Korea lie in between.

Academic R&D ranges from 8% to 27% of total national 

R&D performance for these countries. China has the lowest 

ratio, at 8%. The United Kingdom has the highest, at 27%. 

The United States (15%), Japan (13%), and South Korea 

(10%) have lower shares; Germany (18%) and France (21%) 

have higher shares.

With regard to the funding of R&D, the business sector 

is again the predominant source for the top seven R&Dperforming nations (table 4-5). In 2011, funding for about 

77% of Japan’s total national R&D came from the business 

sector. The corresponding figures for South Korea, China, 

and Germany are also high, in the 66%–74% range. R&D 

funding from business is lower, but still predominant, in the 

United States (59%) and France (54%). The corresponding 

figure for the United Kingdom (45%) is notably lower.

Government is the second major source of R&D funding 

for these seven countries. France is the highest, at 37%. The 

lowest is Japan, at 16%. The United States (31%), the United 

Kingdom (32%), and Germany (30%) are on the higher side. 

South Korea (25%) and China (22%) are in between.

Funding from abroad refers to funding from businesses, universities, governments, and other organizations 

located outside of the country. Among the top seven R&Dperforming countries, the United Kingdom is the most notable in this category, with 17% of R&D funding coming 

from abroad. France is also comparatively high, at nearly 

8%. Germany and the United States are both around 4%, 

and the rest are much lower. (For the United States, the funding from abroad reflects foreign funding for domestic R&D 

performance by the business and higher education sectors.)

Another dimension in which to compare countries is the 

extent of total national R&D performance directed to basic research. None of the other top seven R&D-performing 

countries come close to the United States in its $74 billion 

of support for basic research in 2011 (table 4-6). The next 

closest is Japan, at $18 billion, and then France, at $13 billion. The U.S. basic research share (17%) is also high among 

this group, although it is exceeded by France (25%). China 

has the lowest share of basic research (5%) in this group 

of countries.

Table 4-5

Gross expenditures on R&D for selected countries, by performing sector and funding sources: 2011 or most 

recent year

Country

GERD PPP 

($billions)

Share of total (%)

Business Government Higher education Private nonprofit

R&D performance

United States (2011)a................... 429.1 68.5 12.7 14.6 4.3

China (2011) ................................ 208.2 75.7 16.3 7.9 0.0

Japan (2011)................................ 146.5 77.0 8.4 13.2 1.5

Germany (2011)........................... 93.1 67.3 14.7 18.0 **

South Korea (2011)...................... 59.9 76.5 11.7 10.1 1.6

France (2011)............................... 51.9 63.4 14.1 21.2 1.2

United Kingdom (2011)................ 39.6 61.5 9.3 26.9 2.4

R&D funding sources

United States (2011)a, b ................ 429.1 58.6 31.2 6.4 3.8

China (2011) ................................ 208.2 73.9 21.7 NA 1.3

Japan (2011)................................ 146.5 76.5 16.4 6.6 0.5

Germany (2010)........................... 93.1 65.6 30.3 0.2 3.9

South Korea (2011)...................... 59.9 73.7 24.9 1.2 0.2

France (2010)............................... 51.9 53.5 37.0 1.8 7.6

United Kingdom (2011)................ 39.6 44.6 32.2 6.2 17.0

** = included in data for other performing sectors; NA = not available.

GERD = gross expenditures on R&D; PPP = purchasing power parity.

a

 Figures for the United States in this table reflect international standards for calculating GERD, which vary slightly from the National Science Foundation’s 

protocol for tallying U.S. total R&D. 

b The data for U.S. funding from abroad include foreign funding for business R&D and higher education R&D.

NOTES: The table includes the top seven R&D-performing countries. Percentages may not add to 100 due to rounding. Data years are listed in 

parentheses.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, National Patterns of R&D Resources (annual series); 

Organisation for Economic Co-operation and Development, Main Science and Technology Indicators (2013/1). 

Science and Engineering Indicators 2014
4-22 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

U.S. Business R&D

Total U.S. business R&D performance reached a record 

$294.1 billion in 2011, a 5% increase from 2010 according 

to statistics from the Business R&D and Innovation Survey 

(BRDIS). However, measured in inflation-adjusted dollars, 

the 2011 business R&D performance of $259.4 billion (up 

3% from 2010) is still below the 2008 peak of $267.7 billion, at the beginning of the most recent recession.10 Over 

the past two decades, constant dollar U.S. business R&D 

performance follows peaks and troughs timed close to business cycle changes, short-term up-and-down movements in 

constant dollar GDP (figure 4-11).11

The company size distribution of U.S. business R&D 

performance has changed little since 2008. In 2011, large 

companies (those with 25,000 domestic employees or more) 

performed 35% of U.S. business R&D. Companies with 5 to 

499 employees performed about 20% (appendix table 4-15).12

Business and other nonfederal funding sources increased 

5.1% in constant dollars in 2011, the first such increase since 

2008. On the other hand, federally funded business R&D 

as reported by performers dropped 10% in constant dollars 

in 2011 after a 15% decline in 2010, following increases in 

2008 and 2009.

The rest of this section focuses on recent industrylevel data measured in current dollars. See appendix tables 

4-15–4-22.

Figure 4-11

U.S. business R&D, by major source of funds: 1990–2011

Millions of 2005 constant dollars

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics and U.S. Census Bureau, Survey of Industrial R&D and 

Business R&D and Innovation Survey (annual series).

Science and Engineering Indicators 2014

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

0

50,000

100,000

150,000

200,000

250,000

300,000

Total

Company and other nonfederal

Federal

Recession marker

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

–30

–20

–10

0

10

20

30

40

Percent change

Federal

Company and other nonfederal

Total

Table 4-6

Basic research as a share of gross expenditures 

on R&D, for selected countries: 2011

Country

GERD PPP 

($billions)

Basic research

PPP 

($billions)

Share 

(%)

United Statesa

................. 429.1 74.3 17.3

China .............................. 208.2 9.9 4.7

Japan.............................. 146.5 18.0 12.3

Germany......................... 93.1 NA NA

South Korea.................... 59.9 10.8 18.1

France ............................ 51.9 13.1 25.3

United Kingdom ............. 39.6 4.3 10.8

NA = not available.

GERD = gross expenditures on R&D; PPP = purchasing power parity.

a

 Figures for the United States in this table reflect international 

standards for calculating gross expenditures on R&D, which vary 

slightly from the National Science Foundation’s protocol for tallying 

U.S. total R&D. 

NOTES: The table includes the top seven R&D-performing countries. 

Percentages may not add to 100 due to rounding.

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, National Patterns of R&D 

Resources (annual series); Organisation for Economic Co-operation 

and Development, Main Science and Technology Indicators (2013/1).

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 4-23

which includes aerospace (63%); in PST services (20.3%), 

which includes scientific R&D services and architectural, 

engineering, and related services; and in computer and electronic products manufacturing (9.9%) (figure 4-13).

Apart from direct funding for R&D in the form of contracts and grants to businesses, the U.S. government offers 

indirect R&D support via fiscal incentives such as tax credits (see sidebar, “Federal R&E Tax Credit”).

Recent Trends in Domestic Business R&D

Trends in U.S. business R&D performance are driven by 

five industries (called “top industries” below) that together 

accounted for $239.0 billion, or 81%, of domestic business R&D performance in 2011: computer and electronic 

product manufacturing, chemicals manufacturing (including pharmaceuticals), transportation equipment (including 

aerospace), information (including software publishers), and 

professional, scientific, and technical (PST) services.

Manufacturing industries historically account for the 

largest share of U.S. business R&D performance (68% in 

2011). However, between 2010 and 2011, nonmanufacturing industries’ R&D grew faster (12.7%) than manufacturing 

R&D (2.4%). Indeed, the largest growth in domestic R&D 

performance among the top five industries in 2011 occurred 

in information services (13.6%) and PST services (13.4%). 

Computer and electronic product manufacturing increased 

by 4.7%. The other two top industries posted drops in R&D 

expenditures: chemicals (4.7% decrease, including 7.0% 

decline in pharmaceuticals) and transportation equipment 

(4.7% decrease) (figure 4-12; appendix table 4-15).

Overall, domestic R&D performance bounced back by 

5.4% from 2010 to 2011 after declining 2.9% during the 

recession years from 2008 to 2009. Company and other 

nonfederal funding sources increased 7.4% in 2010–11 after declining 4.5% in 2008–09. In contrast, federal sources 

decreased 8.5% in 2010–11.

At the same time, federal funding accounted for only 

10.6% of domestic business R&D in 2011, down from 

12.3% in 2010. This funding source is also highly concentrated in some industries, based on 2010 detailed statistics. 

The highest shares of federal funding for domestic business 

R&D are in transportation equipment manufacturing (47%), 

Figure 4-12

Percentage change in U.S. domestic business 

R&D performance: 2008–09 and 2010–11

PST = professional, scientic, and technical.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics and U.S. Census Bureau, Business R&D 

and Innovation Survey (annual series).

Science and Engineering Indicators 2014

20

15

10

5

–5

All industries

Manufacturing

industries

Chemicals

Computer

and electronic

products

Transportation

equipment

Nonmanufacturing

industries

Information

PST

services

Scientic

R&D services

2008–09

2010–11

Figure 4-13

Share of U.S. business R&D performance funded by the federal government: 2010

Percent

 

PST = professional, scientic, and technical.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics and U.S. Census Bureau, Business R&D and Innovation 

Survey (2010).

Science and Engineering Indicators 2014

All

industries

Chemicals Computer and

electronic products

Transportation

equipment

Aerospace

products and parts

Information PST

services

0

10

20

30

40

50

60

70
4-24 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

new details available from BRDIS. Most domestic R&D is 

funded from domestic sources (regardless of ownership) and 

by company-owned units (regardless of their location). In 

2011, $238.8 billion (81.2% of $294.1 billion of domestic 

R&D performance) was funded internally (company-owned 

units regardless of location), including $3.3 billion by subsidiaries located abroad (table 4-7; see also appendix tables 

4-15–4-19).

More generally, the $294.1 billion in 2011 U.S. business 

R&D performance can be partitioned in four major funding 

and location sources (table 4-7). The largest of these four 

components, $235.4 billion (80% percent), was funded by 

U.S.-located, within-company sources. Domestic external 

sources funded another $43.1 billion (15%). The bottom left 

row in figure 4-14 shows the distribution of these external 

domestic sources, the largest of which is the federal government. Overall, $278.6 billion (95%) of domestic business 

R&D performance was funded by U.S.-located sources in 

2011, as summarized in the left panels of figure 4-14.

The remainder, $15.5 billion (5%), was funded by sources from abroad as shown in the right panels of figure 4-14. 

These sources may be classified by ownership or affiliation, 

namely, subsidiaries abroad owned by U.S.-located companies, foreign parents of U.S.-located companies, or independent foreign sources (primarily companies).

Table 4-8 provides further detail on 2011 funding from 

abroad for selected industries by affiliation and type of organization (for-profit companies, foreign governments, and 

others, including foreign universities). Virtually all of the 

$15.5 billion in funding from abroad for domestic business 

R&D performance came from other companies. About half 

(48%) came from foreign parent companies, 29% came 

from foreign independent companies, and 22% came from 

company-owned units abroad (see also appendix tables 4-17 

and 4-19).

The top five industries received $12.4 billion, or 80%, 

of total funding from abroad in 2011, about the same share 

of these industries in total domestic performance (81%). 

However, chemicals (including its pharmaceuticals and 

Domestic and International Funding Sources, 

by Type of Source

Funding for domestic business R&D may be classified 

by the geographic location of funding sources, by ownership, and by a combination of these categories according to 

Federal R&E Tax Credit

The United States and other OECD countries offer 

fiscal incentives for business R&D at the national and 

subnational levels (Thomson 2012). For businesses, tax 

credits reduce after-tax costs of R&D activities. For 

governments, tax credits are forgone revenue, known as 

tax expenditures. Public incentives for R&D are generally justified by the inability of private performers to 

fully capture benefits from these activities, given the 

intangible nature of knowledge and information.

The U.S. research and experimentation (R&E) tax 

credit was originally established by the Economic 

Recovery Tax Act of 1981 on a temporary basis. It 

has been extended and modified several times and 

was last renewed through 31 December 2013 by the 

American Taxpayer Relief Act of 2012.*

 The credit 

is designed to apply to incremental amounts beyond 

recent research activity by a business. In particular, the 

regular research tax credit applies to 20% of qualified 

research expenses beyond a base.† The efficiency of 

the credit, how much a dollar worth of credit generates 

research activities beyond what otherwise would occur, depends on the effective credit (after limitations 

in overall business credits and other adjustments to 

the statutory credit are taken into account for a given 

taxpayer) and how sensitive R&D is to business costs. 

For an overview and methodologies to estimate the 

effectiveness of the R&E credit, see Guenther (2013) 

and Hall (1995).

Research tax credit claims fell 6.4% to $7.8 billion 

in 2009 from $8.3 billion in 2008, whereas corporate 

tax returns claiming the credit dropped 3% to 12,359 

filers (appendix tables 4-21 and 4-22), based on estimates from Statistics of Income/Internal Revenue 

Service (IRS). The reported reduction in credit activity 

is consistent with the 3.3% decline in company-funded 

domestic R&D over the same period (appendix table 

4-15). R&E credit claims relative to company-funded 

domestic R&D have fluctuated rather narrowly between 3.0% and 3.5% since 2001 (3.5% in 2009).

* See Internal Revenue Code (IRC) Section 41(a)(1). P.L. 112-

240, Section 301. The 2012 Act retroactively extended the research 

tax credit from 1 January 2012 through 31 December 2013.

† For the regular credit, the base amount is a multiyear average of 

research intensity (research relative to gross receipts) up to a maximum of 50% of current research spending. Variations include the 

alternative simplified credit and the alternative incremental R&E tax 

credit (AIRC; IRC Section 41(c)(4)), in place for 1996–2008 tax 

years (Guenther 2013). See also IRS form 6765 at http://www.irs.

gov/pub/irs-pdf/f6765.pdf.

Table 4-7

Funding sources for domestic business R&D 

performed: 2011

(Millions of U.S. dollars)

Geographic source

Within 

company

Outside 

company

All 

sources

All locations ............ 238,768 55,324 294,093

United States ...... 235,427 43,123 278,550

Outside United 

States .............. 3,342 12,199 15,543

NOTE: Detail may not add to total due to rounding.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics and U.S. Census Bureau, Business R&D 

and Innovation Survey (2011).

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 4-25

will be assigned to the pharmaceuticals industry because this 

is the largest component of its R&D expense (Shackelford 

2012). In addition to collecting data by the main industry 

classification, BRDIS collects data by lines of business most 

closely related to R&D expense. Codes for line of business 

are collected at a rather fine level of detail, as indicated in 

appendix table 4-20. However, most companies performed 

R&D in only one business activity area. In 2010, 86% of 

companies reported domestic R&D performed by and paid 

for by the company related to only one business activity. See 

Shackelford (2012) for an in-depth analysis of the relationship between business codes and industry codes.

R&D by Multinational Companies

The spread of R&D by MNCs reflects a number of trends 

in international production and innovation. Among these are 

the need to strengthen or complement internal technological 

capabilities, increased complexity of global supply-chains 

in R&D-intensive sectors, and improved scientific and technological resources across the globe (Moncada-PaternὸCastello, Vivarelli, and Voigt 2011; OECD 2008). R&D 

associated with FDI, the ownership or control of a business 

(affiliate) in another country, represents another dimension 

of the international character of knowledge creation and 

exploitation. Direct investment is defined as ownership or 

control of 10% or more of the voting securities of a business 

medicines component) and PST services (including its scientific R&D services component) accounted for a larger 

share in funding from abroad compared with their share in 

total domestic R&D performance.

At the same time, sources of funding from abroad differ 

considerably for pharmaceuticals and scientific R&D services. Over half ($1.0 billion or 55%) of funding from abroad 

for scientific R&D services companies came from foreign 

independent companies, with the balance coming almost 

exclusively from foreign parents ($809 million or 43%) (table 4-8). For pharmaceuticals, affiliated sources dominated 

funding from abroad (26% from subsidiaries located abroad 

and 39% from foreign parents), based on BRDIS statistics, 

consistent with the high level of outward and inward foreign direct investment (FDI) in R&D in this industry discussed later in this chapter. Foreign independent companies 

accounted for a third (34%) of funding from abroad for 

this industry.

Business Activities for Domestic R&D

Data at the industry level presented above are obtained 

by classifying a company’s total R&D into a single industry, 

even if R&D activities occur in multiple lines of business. 

For example, if a company has $100 million in R&D expenses—$80 million in pharmaceuticals and $20 million in 

medical devices—the total R&D expense of $100 million 

Domestic and international funding sources for U.S. business R&D performance, by type of source: 2011

Figure 4-14

NOTE: Detail may not add to total due to rounding.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics and U.S. Census Bureau, Business R&D Innovation 

Survey (2010 and 2011).

Science and Engineering Indicators 2014

Domestic business R&D performance

$294,093

(100%)

(5.4%)

Funded by U.S.- 

located sources

$278,550

(94.7%)

(5.4%)

Funded by sources

located abroad 

$15,543

(5.3%)

(6.1%)

Company-owned, 

U.S.-located units

$235,427 

(80.1%)

(7.9%)

Federal 

government

$31,309 

(10.6%)

(–8.5%)

Independent

companies 

$11,124 

(3.8%)

(1.0%)

Other domestic 

sources

$690

(0.2%)

(–26.2%)

Other U.S.-located 

sources

$43,123 

(14.7%)

(–6.6%)

Afliated sources

$10,780

(3.7%) 

(1.5%)

Independent sources

$4,761

(1.6%) 

(18.3%)

Other sources 

from abroad

$192 

(0.1%) 

(73.0%)

Unafliated 

companies 

$4,569 

(1.6%) 

(16.8%)

Foreign parent 

companies

$7,438 

(2.5%) 

(4.7%)

Company-owned 

units outside US

$3,342 

(1.1%) 

(–5.0%)

LEGEND:

Current US$millions

(percent of total)

(percent change 

from 2010)
4-26 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

Table 4-8

Domestic business R&D performance and funding from abroad for selected industries: 2011

(Millions of U.S. dollars)

Funding from abroad Funding 

from abroad 

as share of 

domestic 

business 

Industry Total Percent Total Percent Subsidiaries R&D (%) a

Foreign 

parent 

companies

Unaffiliated 

companies

Foreign 

governments

All other 

funding 

from 

abroad

All industries ......... 294,093 100.0 15,543 100.0 3,342 7,438 4,569 63 129 5.3

Manufacturing 

industries .......... 201,361 68.5 11,497 74.0 2,527 5,871 2,999 46 54 5.7

Chemicals............. 55,324 18.8 5,229 33.6 1,354 2,209 1,658 0 8 9.5

Pharmaceuticals 

and medicines... 45,949 15.6 4,717 30.3 1,235 1,848 1,626 0 8 10.3

Computer and 

electronic 

products............ 62,704 21.3 3,291 21.2 521 1,735 991 D D 5.2

Transportation 

equipment......... 40,880 13.9 857 5.5 D D D D 7 2.1

Nonmanufacturing 

industries .......... 92,731 31.5 4,046 26.0 815 1,568 1,570 17 76 4.4

Information ........... 41,865 14.2 565 3.6 D D D D * 1.3

PST services......... 38,219 13.0 2,489 16.0 201 1,038 1,175 17 58 6.5

Scientific R&D 

services............. 15,301 5.2 1,862 12.0 0 809 1,024 2 27 12.2

* = less than $500,000; D = data withheld to avoid disclosing operations of individual companies.

PST = professional, scientific, and technical.

a

 In the table, subsidiaries are company-owned units located outside the United States. Although all estimates include an adjustment to the weight to 

account for unit nonresponse, the estimates for domestic R&D paid by subsidiaries abroad do not include item imputation. Caution should be used when 

comparing the subsidiaries’ estimates to other estimates presented in the table.

NOTES: Detail may not add to total due to rounding. Industry classification is based on the dominant business code for domestic R&D performance, 

where available. For companies that did not report business codes, the classification used for sampling was assigned. Statistics pertain to companies 

located in the United States that performed or funded R&D.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics and U.S. Census Bureau, Business R&D and Innovation 

Survey (2011).

Science and Engineering Indicators 2014

Domestic 

business R&D 

performance

(affiliate) in another country. This section covers statistics 

collected by BEA on R&D performed by majority-owned 

affiliates (those owned more than 50% by their parent companies) of foreign MNCs located in the United States and on 

R&D performed by U.S. MNCs and their majority-owned 

foreign affiliates.13

Between 2000 and 2010, U.S. R&D performed by members of MNCs grew faster than R&D in the U.S. business 

sector as a whole. Over this period, R&D performed by all 

U.S.-located businesses grew at an average annual rate of 

1.1% in constant dollars. R&D performed in the United 

States by affiliates of foreign MNCs grew at an average annual rate of 2.3% in constant dollars. R&D performed in the 

United States by parents of U.S. MNCs also grew at an average annual rate of 2.3% in constant dollars over the same 

2000–10 period.14

In 2010, parent companies performed $212.5 billion 

($191.5 billion in constant dollars) or 76% of U.S. business 

R&D—higher than their 68% share in 2000. U.S. affiliates 

of foreign MNCs performed about 15% of U.S. business 

R&D in 2010, compared with 11% in 2000.15 The rest of this 

section looks at changes in recent years (in current dollars) 

See appendix tables 4-23–4-30.

U.S. Affiliates of Foreign Companies

Affiliates of foreign MNCs located in the United States 

(U.S. affiliates) performed $41.3 billion of R&D in 2010, up 

2.1% after little change in 2009 and 2008 (appendix table 

4-23). R&D by these companies has accounted for 14%–15% 

of U.S. business R&D performance since 2007, according to 

BEA and NSF statistics. Year-to-year movements in U.S. 

affiliates’ R&D activity reflect a combination of changes 

in foreign ownership of existing U.S.-located firms, the establishment of new R&D-performing companies by foreign 

investors, and variations in R&D strategies and resources by 

firms that are foreign owned in consecutive years.

In 2010, three-fourths of R&D by U.S. affiliates of foreign MNCs was performed by firms owned by parent companies based in five countries: Switzerland (22.0%), the United 

Kingdom (14.5%), Germany (13.8%), France (12.7%), and 

Japan (12.4%) (table 4-9; appendix table 4-23).
Science and Engineering Indicators 2014 ♦ 4-27

Table 4-9

R&D performed by majority-owned affiliates of foreign companies in the United States, by selected industry of 

affiliate and investor country: 2010

(Millions of current U.S. dollars)

Nonmanufacturing

Country

All 

industries Total Chemicals Machinery

Computer, 

electronic 

products

Electrical 

equipment, 

appliances, 

components

Transportation 

equipment

Wholesale 

trade Information

Professional, 

scientific, 

technical 

services

All countries....... 41,272 29,894 16,638 2,509 4,731 621 2,306 6,035 1,870 2,843

Canada .......... 575 314 1 9 D 1 211 106 49 84

France............ 5,248 4,064 1,360 D 1,891 225 71 145 D 74

Germany........ 5,679 4,731 2,099 D 106 18 907 338 D 79

Japan............. 5,112 1,842 713 117 479 47 287 2,302 194 669

Netherlands... 1,910 1,592 169 D D 5 D D 3 26

Switzerland.... 9,086 7,676 7,103 40 D D 6 D 2 1,019

United 

 Kingdom...... 5,975 5,621 4,046 45 282 D 425 102 111 137

Other.............. 7,687 4,054 1,146 633 957 193 D 2,546 134 755

D = suppressed to avoid disclosure of confidential information.

NOTES: Preliminary 2010 estimates are for majority-owned (> 50%) affiliates of foreign companies by country of ultimate beneficial owner and industry of 

affiliate. Includes R&D conducted by foreign affiliates, whether for themselves or others under contract; excludes R&D conducted by others for affiliates. 

SOURCE: Bureau of Economic Analysis, Survey of Foreign Direct Investment in the United States (annual series), http://www.bea.gov/international, 

accessed January 2013.

Science and Engineering Indicators 2014

Manufacturing

Manufacturing U.S. affiliates performed 70% or more of 

U.S. affiliates R&D since 2006 (appendix tables 4-24 and 

4-25). The R&D intensity (R&D divided by value added) 

of manufacturing U.S. affiliates was 6.4% in 2010—little 

changed since 2007.16 R&D by affiliates classified in pharmaceuticals increased by 4% to $15.1 billion in 2010. This industry has accounted for at least a third of U.S. affiliates R&D 

since 2006 and has the highest R&D intensity (32.2% in 2010) 

among the largest R&D-performing industries within U.S. affiliates. Other manufacturing industries posting increases in 

R&D performance include computers and electronic products 

(8.7%) and electrical equipment, appliances, and components 

(8.9%). On the other hand, transportation equipment R&D 

was flat in 2010 after double-digit declines in 2009 and 2008, 

in part associated with changes in foreign ownership within 

the industry. Within nonmanufacturing industries, affiliates in 

information services increased R&D performance by 11.2% 

in 2010, whereas PST services R&D declined by 6.8%.

U.S. MNCs’ Parent Companies and Their 

Foreign Affiliates

Parent companies of U.S. MNCs performed $212.5 billion of R&D in the United States, based on preliminary 

2010 data from BEA (appendix table 4-30).17 Their majority-owned foreign affiliates (MOFAs) performed $39.5 billion (appendix table 4-26). (The latter was essentially flat 

after declining 6.0% in 2009, the first such decline since 

2001). Thus, U.S. MNCs (U.S. parent companies and their 

MOFAs) performed $252.0 billion in R&D globally in 2010. 

From 2000 to 2010, global R&D by U.S. MNCs grew at an 

average annual rate of 2.6% in constant dollars. R&D performed overseas by MOFAs grew at a 4.4% annual rate in 

constant dollars, compared with a 2.3% annual rate by U.S. 

parents on the same basis. However, parent companies still 

perform over 80% of U.S. MNCs R&D in the United States 

(84% in 2010 compared with 88% in 2000). The rest of this 

section focuses on recent trends in geographic and industrial 

focus of MOFA R&D in current dollars (see appendix tables 

4-26–4-28).

European host countries accounted for 62% of U.S. 

MOFA R&D in 2010, down from 66% in 2007 (table 4-10; 

appendix table 4-26). At the same time, Germany and the 

United Kingdom remain by far the largest hosts of U.S.-

owned R&D with at least $6 billion each. Another 5 of the 

13 countries with at least $1 billion in U.S. MOFA R&D in 

2010 are in Europe (table 4-11). The shares of R&D performed by U.S. MOFAs in Canada and Japan—traditional 

locations for U.S. FDI and R&D along with Europe—have 

declined from 7.9% to 7.0% and from 5.6% to 4.8%, respectively, from 2007 to 2010.

On the other hand, the shares of R&D activities by affiliates in other regions are increasing. The region of AsiaPacific, excluding Japan, accounted for a record 16.3% 

of U.S. MOFA R&D in 2010. The Middle East and Latin 

America each accounted for about 5% in 2010, up from 

3.0% and 3.4%, respectively, in 2007. Within these emerging regions for U.S.-owned R&D, China, India, Brazil, and 

Israel accounted for the largest shares.

 U.S. MOFA R&D performance in China more than 

doubled in current dollars from 2005 to 2008, with year-toyear double-digit increases to a record $1.7 billion in 2008. 
4-28 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

Table 4-10

R&D performed abroad by majority-owned foreign affiliates of U.S. parent companies, by selected industry of 

affiliate and host region/country/economy: 2010

(Millions of current U.S. dollars)

Region/country/ 

economy

All 

industries Total Chemicals Machinery

Computer, 

electronic 

products

Electrical 

equipment, 

appliances, 

components

Transportation 

equipment

Wholesale 

trade Information

Professional, 

scientific, 

technical 

services

All countries........... 39,470 27,571 8,532 1,448 6,030 703 7,584 1,975 2,018 7,759

Canada .............. 2,749 1,449 434 26 286 D 535 174 311 806

Europe ............... 24,406 18,208 6,351 963 2,997 376 5,047 1,379 865 3,855

Austria ............ 277 D 21 111 8 23 4 6 0 D

Belgium .......... 2,116 D D 15 9 D D D * 321

Czech 

Republic...... 68 D 9 6 D 0 9 D 0 2

Denmark......... 196 D D 8 63 * 0 D 3 2

Finland............ 221 D 12 D D 4 2 2 0 D

France ............ 1,984 1,783 410 96 575 D 347 83 41 73

Germany......... 6,713 5,505 341 275 1,017 190 3,162 568 48 552

Greece............ 27 26 22 0 * 0 0 1 0 *

Hungary.......... 65 30 5 2 * 2 D 3 0 31

Ireland ............ 1,431 1,045 585 * 283 0 2 3 297 D

Italy................. 589 401 187 76 29 4 52 8 2 176

Luxembourg ... D D D 0 0 0 0 1 * D

Netherlands.... 1,290 1,074 701 28 41 D D 10 52 151

Norway ........... 137 D 3 D 38 0 0 * D 2

Poland ............ 136 62 7 1 1 * 45 1 2 71

Portugal.......... 56 D 29 1 1 1 D 1 D *

Russia............. 65 D 5 0 1 0 2 6 * D

Spain .............. 607 545 146 3 D 10 92 D 0 D

Sweden .......... 520 334 52 49 D 4 D 4 D D

Switzerland..... 1,558 935 460 56 185 17 D 259 D D

Turkey............. 53 50 31 * 0 0 14 1 1 1

United 

Kingdom ..... 5,905 3,736 1,695 191 323 28 984 D 183 1,778

Other .............. D D D 2 1 1 14 2 0 28

Latin America 

and OWH ......... 1,949 1,725 356 D 96 D 1,030 D D 142

Argentina........ 115 73 47 D D 0 9 1 0 D

Brazil .............. 1,372 1,281 215 51 77 1 D 22 D 33

Mexico ........... 338 305 D 4 D D D 2 * 31

Africa.................. 88 D 23 1 * 0 9 4 0 D

South Africa ... 74 D 23 * 0 0 6 3 0 D

Middle East........ 1,965 D 50 D 640 0 0 D D D

Israel............... 1,948 D 47 D 640 0 0 D D 950

Asia and Pacific... 8,313 5,290 1,319 275 2,011 275 962 289 765 1,955

Australia ......... 767 560 162 12 D D D 28 4 170

China.............. 1,452 D 101 41 348 109 55 9 D 443

Hong Kong..... 153 104 12 0 86 5 0 6 6 37

India ............... 1,644 446 83 D 231 6 73 D D 778

Indonesia........ 28 D 2 0 0 0 * * 0 D

Japan ............. 1,885 1,576 808 152 300 D 74 57 D D

Malaysia ......... 376 337 2 * 320 * 0 2 0 37

New Zealand... 21 18 2 1 * 5 0 1 0 2

Philippines...... 55 D 4 0 18 * 1 * 0 D

Singapore....... 753 514 67 D 424 8 12 12 18 206

South Korea ... 835 780 49 19 166 0 D D D 27

Taiwan............ 235 127 21 D 82 D D 14 D D

Thailand.......... 106 D 6 4 D 0 8 3 0 D

Other .............. 2 2 * 0 2 0 0 0 0 0

* = ≤ $500,000; D = suppressed to avoid disclosure of confidential information.

OWH = other Western Hemisphere.

NOTES: Preliminary 2010 estimates are for majority-owned (> 50%) affiliates of U.S. parent companies by host country and industry of affiliate. Includes 

R&D conducted by foreign affiliates, whether for themselves or others under contract; excludes R&D conducted by others for affiliates. 

SOURCE: Bureau of Economic Analysis, Survey of U.S. Direct Investment Abroad (annual series), http://www.bea.gov/international, accessed January 2013.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 4-29

This is consistent with increases in total R&D performed 

in China in recent years and its emergence as the secondlargest R&D-performing country (see section, “International 

Comparisons of R&D Performance”). Single-digit declines 

in 2009 and 2010 put R&D performed by U.S. MOFAs in 

China at $1.5 billion in 2010 (appendix table 4-26).

Reported R&D activity by U.S. MOFAs tripled in India 

and more than doubled in Brazil from 2007 to 2010 in current dollars, growing much faster than U.S. MOFA production activity in those countries measured as value added 

(thus increasing their R&D intensity measured as the ratio 

to value added). U.S. MOFA R&D expenditures in Brazil 

and India are now on par with affiliates in China. Among 

countries with at least $1 billion in R&D performed by U.S. 

MOFAs in 2010, U.S. MOFAs located in Israel have the 

largest R&D intensity (table 4-11).

Three manufacturing industries, chemicals (which includes pharmaceuticals), transportation equipment, and 

computer and electronic products accounted for 56% of U.S. 

MOFA R&D in 2010. Overall, affiliates classified in manufacturing accounted for 70%. The largest R&D-performing 

nonmanufacturing industries were information services and 

PST services (table 4-10; appendix table 4-28).

In spite of the relative decline in the share of traditional locations such as Europe as a whole and Japan, they remain the 

top R&D hosts for U.S. MNCs in major industries, reflecting 

both strengths of host countries in certain technologies and the 

large R&D stocks by U.S. MNCs in these locations.

Germany is by far the largest location of U.S. MOFA 

R&D in transportation equipment ($3.2 billion of $7.6 billion in this industry by U.S. MOFAs globally) and in computers and electronic products manufacturing ($1.0 billion 

out of $6.0 billion by U.S. MOFAs globally). The United 

Kingdom is the top location in chemicals manufacturing 

R&D and in PST services R&D by U.S. MOFAs. Japan is 

the second-largest host for R&D performed by U.S. MOFAs 

classified in chemicals manufacturing.

On the other hand, among MOFAs classified in PST services, India has emerged as the second-largest host country 

for U.S.-owned R&D performance after the United Kingdom 

($0.8 billion compared with the United Kingdom’s $1.8 billion), based on available preliminary 2010 country-industry 

details from BEA (table 4-10).

Cross-National Comparisons 

of Business R&D

This section compares business R&D across OECD 

countries across two dimensions: the distribution of business R&D across industries and the role of affiliates of 

foreign MNCs.

Companies classified in manufacturing perform most 

business R&D in the top seven R&D-performing countries, with shares ranging from 89% in Germany to 69% in 

the United States, based on OECD’s Analytical Business 

Enterprise R&D (ANBERD) database (see table 4-12).18

These countries, however, differ in terms of the focus of 

their business R&D.

Pharmaceuticals manufacturing is the largest business R&D sector in the United Kingdom (28% of United 

Kingdom business enterprise R&D) and in the United States 

(16% of U.S. business enterprise R&D). Motor vehicles 

R&D has the largest share in Germany (33%). R&D in radio, 

television, and communication equipment manufacturing, 

Table 4-11

R&D performed abroad, shares, and R&D intensity of majority-owned foreign affiliates of U.S. parent 

companies, by selected host country: 2007 and 2010

Country

R&D performed (US$millions) R&D performed shares (%) R&D/value added ratio (%)

2007 2010 2007 2010 2007 2010

Total....................................... 34,446 39,470 100.0 100.0 3.1 3.2

Germany ............................ 6,403 6,713 18.6 17.0 7.2 8.0

United Kingdom................. 6,000 5,905 17.4 15.0 3.6 3.9

Canada .............................. 2,712 2,749 7.9 7.0 2.3 2.1

Belgium.............................. 1,191 2,116 3.5 5.4 5.1 8.6

France................................ 1,557 1,984 4.5 5.0 2.8 4.0

Israel .................................. 1,025 1,948 3.0 4.9 22.9 28.0

Japan ................................. 1,919 1,885 5.6 4.8 4.8 3.9

India ................................... 382 1,644 1.1 4.2 5.2 9.9

Switzerland ........................ 1,162 1,558 3.4 3.9 4.4 4.7

China.................................. 1,173 1,452 3.4 3.7 5.5 3.9

Ireland ................................ 1,510 1,431 4.4 3.6 2.7 2.3

Brazil.................................. 607 1,372 1.8 3.5 1.9 3.0

Netherlands ....................... 752 1,290 2.2 3.3 2.7 5.4

NOTES: Sorted by 2010 R&D performed. Data are for majority-owned (> 50%) foreign affiliates of U.S. parent companies. Data include R&D expenditures 

performed by affiliates, whether for themselves or for others under contract. Data exclude R&D expenditures by others for affiliates under contract. 

SOURCE: Bureau of Economic Analysis, Survey of U.S. Direct Investment Abroad (annual series), http://www.bea.gov/international/index.htm#omc, 

accessed 14 January 2013.

Science and Engineering Indicators 2014
4-30 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

which includes semiconductor devices, accounts for close to 

half (48%) of South Korea’s business enterprise R&D (figure 4-15).

Business R&D in other transportation equipment (appendix table 4-31), which includes commercial and defenserelated aerospace and spacecraft, has the highest shares in the 

United States (13%), France (12%), and the United Kingdom 

(11%).19 These three countries also report the largest proportion of defense R&D within government budget appropriations or outlays for R&D (GBAORD) (table 4-15) discussed 

elsewhere in this chapter. In addition, France and the United 

Kingdom host 17 of the top 25 EU R&D-performing companies classified in the related category of aerospace and 

defense, according to the 2012 EU Scoreboard (EC 2012).

R&D in services industries (the main R&D performing component in nonmanufacturing) had the largest share 

in the United States (30%) and the lowest share in China 

(7%), based on the most recent comparable industry-level 

ANBERD data. Within services, computer and related services accounted for the largest share in the United States and 

the United Kingdom (figure 4-15; appendix table 4-31).

R&D performed within a country by affiliates of foreign 

MNCs represented more than half of business enterprise 

R&D in smaller OECD countries such as Belgium, Ireland, 

Israel, and several Eastern and Central European countries 

in 2009 (figure 4-16). Japan, the second-largest business 

R&D performer among countries reporting foreign-affiliate 

R&D, had the lowest share (6%), compared with about 14% 

for the United States.

Table 4-12

Share of manufacturing and nonmanufacturing in 

business R&D, by selected country: 2010 or most 

recent year

(Percent)

Country Manufacturing Nonmanufacturing

Germany (2008).............. 89.0 11.0

South Korea (2010)......... 87.7 12.3

Japan (2010)................... 87.1 12.9

China (2009) ................... 84.0 16.0

France (2007).................. 83.6 16.4

United Kingdom (2009)... 73.9 26.1

United States (2009)....... 69.3 30.7

NOTES: Industry classifications for France and South Korea are 

based on product field. For all other countries, data are based on 

main activity.

SOURCE: Organisation for Economic Co-operation and 

Development, Analytical Business Enterprise R&D (ANBERD) 

Statistical Analysis Database (STAN), R&D Expenditures in Industry, 

http://stats.oecd.org/Index.aspx?DataSetCode=ANBERD2011_REV3, 

accessed 7 February 2013.

Science and Engineering Indicators 2014

Figure 4-15

Industry share of business R&D in selected 

countries: 2010 or most recent year

Percent

NOTES: Data for China are not available for all industries. Data are 

classied according to International Standard Industrial 

Classication, Revision 3.1, by Organisation for Economic 

Co-operation and Development source. Data for France and South 

Korea are based on product eld. For all other countries, data are 

based on main activity.

SOURCE: Organisation for Economic Co-operation and 

Development, Analytical Business Enterprise R&D (ANBERD) 

Statistical Analysis Database (STAN), R&D Expenditures in 

Industry, http://stats.oecd.org/Index.aspx?DataSetCode= 

ANBERD2011_REV3, accessed 7 February 2013. 

Science and Engineering Indicators 2014

0

10

20

30

40

50

60

0

10

20

30

40

50

60

United

States

(2009)

China

(2009)

Japan

(2010)

Germany

(2008)

South

Korea

(2010)

France

(2007)

United

Kingdom

(2009)

United

States

(2009)

China

(2009)

Japan

(2010)

Germany

(2008)

South

Korea

(2010)

France

(2007)

United

Kingdom

(2009)

United

States

(2009)

China

(2009)

Japan

(2010)

Germany

(2008)

South

Korea

(2010)

France

(2007)

United

Kingdom

(2009)

United

States

(2009)

China

(2009)

Japan

(2010)

Germany

(2008)

South

Korea

(2010)

France

(2007)

United

Kingdom

(2009)

0

10

20

30

40

50

60

Motor vehicles

Radio, TV, and communication equipment

Pharmaceuticals

Other transport equipment

0

10

20

30

40

50

60

Services Computer and

related services

R&D services
Science and Engineering Indicators 2014 ♦ 4-31

Federal R&D 

Performance and Funding

The U.S. government supports and facilitates the nation’s 

R&D system through various policy avenues. The most direct of these are the R&D activities conducted by federal 

organizations (whether agency intramural laboratories and 

facilities or FFRDCs) and the funding for R&D provided 

to other performers (such as businesses and academic institutions).20 This section provides statistical detail on these 

federally performed and funded R&D activities—in particular, how the funding has been allocated among differing 

national objectives, how current federal spending on R&D 

differs across the agencies, and how the current spending 

is allocated among differing research fields. The next section compares federal R&D spending priorities with those of 

national governments in the other major R&D-performing 

countries. (For definitions of key federal budget terms used 

in this section, see the sidebar, “Federal Budgetary Concepts 

and Related Terms.”)

Federal R&D Budget, by National Objectives

Federal support for the nation’s R&D spans a range of objectives: national defense, health, space, energy, natural resources and environment, general science, and various other 

categories. The Office of Management and Budget (OMB) 

classifies agency funding requests into 20 broad categories 

termed budget functions (OMB 2012a). Federal agency 

R&D activities appear in 15 of these 20 functional categories.21 While the authority for spending granted to the agencies (termed budget authority or appropriations) through the 

federal budget legislation enacted annually by the Congress is 

not yet actual spending, a look at how this budget authority 

divides among the various functional categories provides a 

useful picture of the present priorities and trends in federal 

support for U.S. R&D. 

Budget authority for all spending on R&D by the federal agencies totaled $144.4 billion (current dollars) in FY 

2011 (figure 4-17; appendix tables 4-32 and 4-33). In FY 

2010, the total was $149.0 billion. It was $164.3 billion in 

FY 2009—noticeably higher because of the one-time $18.7 

Figure 4-16

Business enterprise R&D and R&D by foreign affiliates, by selected shares: 2009

Percent

BERD = Business Enterprise R&D; OECD = Organisation for Economic Co-operation and Development.

SOURCE: OECD, Main Science and Technology Indicators (2012/2).

Science and Engineering Indicators 2014

0 10 20

Country BERD/OECD BERD

30 40 50 0

10

20

30

40

50

60

70

80

Foreign afliate R&D/country BERD

United States

Japan

Germany

Italy

Spain France

Australia

Canada

Sweden

United Kingdom

Czech Republic

Belgium

Hungary

Austria

Poland

Ireland

Israel

Netherlands
4-32 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

billion increase from ARRA.22 The totals in FYs 2006 and 

2001 were $136.0 billion and $91.5 billion, respectively.

Defense-Related R&D

R&D directed at national defense objectives is supported 

primarily by the Department of Defense (DOD) but also includes some R&D by the Department of Energy (DOE) and 

the Department of Justice (where some R&D by the Federal 

Bureau of Investigation comes under a defense category). 

National defense represented about 58% ($83.2 billion) of 

the total budget authority for R&D in FY 2011 (appendix 

table 4-32). It also accounted for 58% in FY 2006 and 51% 

in FY 2001.

This predominance of national defense R&D goes back 

many years. In FY 1980, there was rough equivalence between national defense and nondefense R&D. By FY 1985, 

national defense had become more than twice as large as 

nondefense, but from 1986 to 2001, nondefense R&D 

surged, with the national defense share shrinking back to just 

over half. Following September 11, 2001, however, national 

defense R&D again increased as a share, accounting for 59% 

of federal R&D budget authority in FY 2008. The drop to 

52% in FY 2009 reflects chiefly an effect of the one-time 

increase in R&D budget authority from ARRA, primarily 

targeted at health, energy, and general science research.

Nondefense R&D

Nondefense R&D spans the other 14 budget function categories, which include activities in the areas of health, space 

research and technology, energy, general science, natural resources and environment, transportation, agriculture, education, international affairs, veterans benefits, and a number of 

other small categories related to economic and governance 

matters. Budget authority for nondefense R&D accounted 

for 42% ($61.2 billion) in FY 2011 (appendix table 4-32). 

It was also 42% in FY 2006, but it was just under 50% in 

FY 2001.

Federal Budgetary Concepts 

and Related Terms

Budget authority. This refers to the funding authority conferred by federal law to incur financial obligations that will result in outlays. The basic forms of 

budget authority are appropriations, contract authority, and borrowing authority.

Obligations. Federal obligations represent the dollar amounts for orders placed, contracts and grants 

awarded, services received, and similar transactions 

during a given period, regardless of when funds were 

appropriated or payment was required.

Outlays. Federal outlays represent the dollar 

amounts for checks issued and cash payments made 

during a given period, regardless of when funds were 

appropriated or obligated.

R&D plant. In general, R&D plant refers to the 

acquisition of, construction of, major repairs to, or 

alterations in structures, works, equipment, facilities, or land for use in R&D activities. Data included 

in this section refer to obligated federal dollars for 

R&D plant.

Figure 4-17

Federal budget authority for R&D and R&D plant, 

by budget function: FYs 2000–12

NOTES: Data for FY 2012 are preliminary. Data for FY 2009 include 

the additional federal funding for R&D appropriated by the American 

Recovery and Reinvestment Act of 2009. Other includes all 

nondefense functions not separately graphed: international affairs, 

commerce and housing credit, transportation, community and 

regional development, education and training, Medicare, income 

security, veterans benets, and administration of justice. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Federal R&D Funding by Budget Function 

(FYs 2010–12). See appendix table 4-32. 

Science and Engineering Indicators 2014

Budget function

Nondefense budget function

Billions of current dollars 

2000 2002 2004 2006 2008 2010 2012

0

20

40

60

80

100

120

140

160

180

Total

 Defense

Nondefense

0

5

10

15

20

25

30

35

40

45

Energy

Natural resources and environment

Agriculture

Health

Space

General science

Other

2000 2002 2004 2006 2008 2010 2012
Science and Engineering Indicators 2014 ♦ 4-33

The most striking change in federal R&D priorities over 

the past two decades has been the considerable increase in 

health-related R&D, which now accounts for just over half 

of all nondefense R&D (figure 4-17). Health R&D was 

12% of total federal R&D budget authority in FY 1980 but 

rose to 22% in FY 2011. This rise in share jumped after FY 

1998, when national policymakers set the National Institutes 

of Health (NIH) budget on course to double by FY 2003. 

Health research was also particularly favored by the ARRA 

increment, rising to 26% of the total R&D budget authority 

in FY 2009 (appendix table 4-32).

The budget allocation for space-related R&D peaked in 

the 1960s during the height of the nation’s efforts to surpass 

the Soviet Union in space exploration. It stood at 10%–11% 

of total R&D budget authority throughout the 1990s. The 

loss of the Space Shuttle Columbia and its crew in February 

2003 prompted curtailment of manned space missions. In 

FY 2006, the space R&D share was down to about 8%; it 

was 6% in FY 2011.

Nondefense federal R&D classified as general science 

had about a 4% share of total federal R&D in the mid-1990s, 

growing to 7% in FY 2011. However, much of this change 

reflected an important reclassification: starting in FY 1998, 

several DOE programs were shifted from the energy category to general science.

Federal Spending on R&D, by Agency

Fifteen federal departments and a dozen other agencies engage in and/or fund R&D in the United States. Nine of these 

departments/agencies reported R&D spending in excess of 

$1 billion annually in FY 2011, and these nine accounted for 

97% of the total (table 4-13; appendix table 4-35). Another 

six of the departments/agencies reported spending above $100 

million annually.

(The budget figures reported in this section are in obligations. For the distribution of federal R&D across the agencies, data on spending in obligations terms provide the most 

comprehensive and consistent account. Budget authority, as 

discussed earlier, lays out the themes of the broad federal 

spending plan. Spending obligations reflect federal dollars 

as they are spent, that is, the implementation of the plan by 

federal agencies. Because planning and actual spending are 

different steps, the reported statistics on R&D in obligations 

Table 4-13

Federal obligations for R&D and R&D plant, by agency and performer: FY 2011

(Millions of dollars)

Total by performers

Agency Total R&D

R&D 

plant

Intramural 

and 

FFRDCs 

Percent 

of total

Extramural 

performers

Percent 

of total

All agencies ...................................................... 136,418.1 132,140.6 4,277.4 44,196.3 32.4 92,221.7 67.6

Department of Defense................................. 71,842.3 71,684.2 158.1 22,268.8 31.0 49,573.5 69.0

Department of Health and Human Services ... 31,766.3 31,573.7 192.6 6,200.1 19.5 25,566.2 80.5

Department of Energy................................... 9,923.2 9,136.2 786.9 7,516.7 75.7 2,406.5 24.3

National Aeronautics and 

Space Administration................................ 8,429.0 6,570.5 1,858.5 2,070.2 24.6 6,358.7 75.4

National Science Foundation........................ 5,373.3 4,924.4 448.9 350.6 6.5 5,022.6 93.5

Department of Agriculture............................. 2,634.6 2,591.3 43.3 1,657.2 62.9 977.4 37.1

Department of Commerce ............................ 1,419.7 1,135.5 284.2 1,011.3 71.2 408.3 28.8

Department of Homeland Security ............... 1,051.1 634.7 416.4 667.5 63.5 383.6 36.5

Department of Transportation....................... 1,021.2 997.0 24.2 349.2 34.2 671.9 65.8

Department of the Interior............................. 694.8 688.6 6.3 571.5 82.2 123.4 17.8

Department of Veterans Affairs..................... 579.0 579.0 0.0 579.0 100.0 0.0 0.0

Environmental Protection Agency................. 577.0 577.0 0.0 464.9 80.6 112.1 19.4

Department of Education.............................. 346.3 346.3 0.0 19.1 5.5 327.3 94.5

Smithsonian Institution ................................. 227.0 169.0 58.0 227.0 100.0 0.0 0.0

Department of Justice .................................. 101.0 101.0 0.0 27.9 27.6 73.1 72.4

All other agencies ......................................... 432.3 432.3 0.0 215.3 49.8 217.0 50.2

FFRDC = federally funded R&D center. 

NOTES: The table lists all agencies with R&D obligations greater than $100 million in FY 2011. R&D is basic research, applied research, and development 

and does not include R&D plant. Intramural activities include actual intramural R&D performance and costs associated with planning and administration 

of both intramural and extramural programs by federal personnel. Extramural performers includes federally funded R&D performed in the United States 

and U.S. territories by businesses, universities and colleges, other nonprofit institutions, state and local governments, and foreign organizations. All other 

agencies includes Department of Housing and Urban Development, Department of Labor, Department of State, Department of Treasury, Agency for 

International Development, Appalachian Regional Commission, Federal Communications Commission, Federal Trade Commission, Library of Congress, 

National Archives and Records Administration, Nuclear Regulatory Commission, and Social Security Administration.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Federal Funds for Research and Development (FYs 

2010–12). See appendix table 4-35.

Science and Engineering Indicators 2014
4-34 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

typically differ from the corresponding items in budget authority terms.)

In FY 2011, federal obligations for R&D and R&D plant 

together totaled $136.4 billion: $132.1 billion for R&D and 

an additional $4.3 billion for R&D plant (table 4-13). The 

corresponding figures for FY 2010 were $147.0 billion in total, $140.4 billion for R&D, and $6.6 billion for R&D plant; 

for FY 2009, they were $144.8 billion in total, $141.1 billion 

for R&D, and $3.7 billion for R&D plant (appendix table 

4-34). Federal obligations for R&D increased annually on 

both a current and constant dollar basis from the late 1990s 

through FY 2010 (figure 4-18; appendix table 4-34). The 

FY 2011 drop in funding was a noticeable departure from 

this trend.

(The corresponding figures for federal funding of U.S. 

R&D cited in table 4-1 earlier in this chapter are lower. The 

table 4-1 figures are based on performers’ reports of their 

R&D expenditures from federal funds. This difference between performer and source of funding reports of the level 

of R&D expenditures has been present in the U.S. data for 

more than 15 years and reflects various technical issues. For 

a discussion, see the sidebar, “Tracking R&D: The Gap between Performer- and Source-Reported Expenditures.”)

The nine departments/agencies that account presently 

for almost all federal R&D differ widely in the balance of 

R&D performed and/or funded among intramural laboratories, FFRDCs, and various extramural performers (including 

private businesses, universities and colleges, other nonprofit 

organizations, state and local governments, and foreign organizations). There are also significant differences in the 

character-of-work profiles, that is, the balances among the basic research, applied research, and development conducted.

Department of Defense

In FY 2010, DOD obligated a total of $71.8 billion for 

R&D and R&D plant (table 4-13), which represented a little 

over half (53%) of all federal spending on R&D and R&D 

plant that year. Nearly the entire DOD total was R&D spending ($71.7 billion), with the remainder spent on R&D plant.

Thirty-one percent ($22.3 billion) of the total was spending by the department’s intramural labs, related agency R&D 

program activities, and FFRDCs (table 4-13). Extramural 

performers accounted for 69% ($49.6 billion) of the obligations, with the bulk going to business firms ($46.6 billion; 

appendix table 4-35).

Considering just the R&D component, relatively small 

amounts were spent on basic research ($1.9 billion, 3%) 

and applied research ($4.7 billion, 7%) in FY 2011 (table 4-14). The vast majority of obligations, $65.1 billion (91%), went to development. Furthermore, the bulk 

of this DOD development ($59.0 billion) was allocated 

for major systems development, which includes the main 

activities in developing, testing, and evaluating combat 

systems (figure 4-19). The remaining DOD development 

($6.1 billion) was allocated for advanced technology development, which is more similar to other agencies’ development obligations.

Department of Health and Human Services

The Department of Health and Human Services (HHS) is 

the main federal source of spending for health-related R&D. 

In FY 2011, the department obligated $31.8 billion for R&D 

and R&D plant, or 23% of the total of federal obligations 

that year (appendix table 4-35). Nearly all of this was for 

R&D ($31.6 billion). Furthermore, much of the total, $29.9 

billion, represented the R&D activities of NIH.

For the department as a whole, R&D and R&D plant 

obligations for agency intramural activities and FFRDCs 

accounted for 20% ($6.2 billion) of the total. Extramural 

performers accounted for 81% ($25.6 billion). Universities 

and colleges ($18.3 billion) and other nonprofit organizations ($4.9 billion) conducted the most sizable of these extramural activities (appendix table 4-35).

Nearly all of HHS R&D funding is allocated to research: 51% for basic research and 49% for applied research 

(table 4-14).

Department of Energy

DOE obligated $9.9 billion for R&D and R&D plant in 

FY 2011, about 8% of the total of federal obligations that 

year. Of this amount, $9.1 billion was for R&D and $0.8 billion was for R&D plant.

The department’s intramural laboratories and FFRDCs 

accounted for 76% of the total obligations. Many of DOE’s 

Figure 4-18

Federal obligations for R&D and R&D plant: 

FYs 1980–2011

Billions of current dollars

NOTE: Data for FYs 2009 and 2010 include obligations from the 

additional federal R&D funding appropriated by the American 

Recovery and Reinvestment Act of 2009.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Federal Funds for Research and 

Development (annual series). See appendix table 4-34.

Science and Engineering Indicators 2014

1980 1987 1983 1991 1995 1999 2003 2007 2011

0

20

40

60

80

100

120

140

160

R&D and R&D plant

R&D

R&D plant
Science and Engineering Indicators 2014 ♦ 4-35

In the United States—and in some other Organisation 

for Economic Co-operation and Development (OECD) 

countries—the figures for total government support of 

R&D reported by government agencies differ from those 

reported by the performers of R&D. In keeping with international guidance and standards, most countries provide 

totals and time series of national R&D expenditures based 

primarily on data reported by R&D performers (OECD 

2002). Differences between the data provided by funders 

and that provided by performers can arise for numerous 

reasons, such as the different calendars for reporting government obligations (fiscal years) and performance expenditures (calendar years). In the United States, there 

has been a sizable gap between performer and funder data 

for federal R&D over the past two decades.

In the mid-1980s, performer-reported federal R&D in 

the United States exceeded federal reports of funding by 

$3 billion to $4 billion annually (5%–10% of the government total). This pattern reversed itself, however, at 

the end of the decade: in 1989, the government-reported 

R&D total exceeded performer reports by almost $1 billion. The government-reported excess increased noticeably from then to 2007, when federal agencies reported 

obligating $127 billion in total R&D to all R&D performers ($55 billion to the business sector), compared with 

$107 billion in federal funding reported by the performers of R&D ($27 billion by businesses). In other words, 

the business-reported total was some 50% less than the 

federally reported R&D support to industry in FY 2007 

(figure 4-A; appendix table 4-36). These differences in 

federal R&D totals were seen primarily in DOD funding of development activities by industry. The figures 

for 2008–11 suggest a narrowing of the federal agency 

reporting excess, but they are primarily the result of a 

manual imputation procedure for business R&D performers in these years.

Several investigations into the possible causes for the 

data gap have produced insights but no conclusive explanation. A General Accounting Office investigation made 

the following assessment:

Because the gap is the result of comparing two 

dissimilar types of financial data [federal obligations 

and performer expenditures], it does not necessarily 

reflect poor quality data, nor does it reflect whether 

performers are receiving or spending all the federal 

R&D funds obligated to them. Thus, even if the data 

collection and reporting issues were addressed, a gap 

would still exist. (GAO 2001:2)

Echoing this assessment, the National Research 

Council (NRC 2005) noted that comparing federal 

outlays for R&D (as opposed to obligations) with performer expenditures results in a smaller discrepancy. (In 

FY 2007, federal agencies reported total R&D outlays of 

$109 billion, compared with the performer-related total 

of $107 billion. In FY 2011, federal agencies reported 

R&D outlays of $131 billion, compared with the performer-reported total of $134 billion.)

Tracking R&D: The Gap between Performer- and Source-Reported Expenditures

Percent

Figure 4-A

Differences in federal R&D support, as reported by 

performers and federal agencies: 1985–2011 

NOTE: Difference is dened as the percentage of federally reported 

R&D, with a positive difference indicating that performer-reported 

R&D exceeds agency-reported R&D.

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics (NSF/NCSES), National Patterns of R&D 

Resources (annual series); and NSF/NCSES, Federal Funds for 

Research and Development (FYs 2010–12). See appendix table 4-36.

Science and Engineering Indicators 2014

All performers

Business performers

–60

–40

–20

–30

–50

–10

0

20

10

1985 1990 1993 1996 1999 2002 2005 2008 2011

–60

–40

–20

–30

–50

–10

0

20

10

1985 1990 1993 1996 1999 2002 2005 2008 2011
4-36 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

Table 4-14

Federal obligations for R&D, by agency and character of work: FY 2011

(Millions of current dollars)

Percent of total R&D

Agency Total R&D

Basic 

research

Applied 

research Development

Basic 

research 

Applied 

research Development

All agencies ................................................ 132,140.6 29,060.8 29,105.9 73,973.9 22.0 22.0 56.0

Department of Defense........................... 71,684.2 1,903.9 4,674.3 65,106.1 2.7 6.5 90.8

Department of Health and 

Human Services................................... 31,573.7 16,123.7 15,316.7 133.3 51.1 48.5 0.4

Department of Energy............................. 9,136.2 3,717.2 3,054.2 2,364.8 40.7 33.4 25.9

National Aeronautics and 

Space Administration.......................... 6,570.5 856.8 717.8 4,995.8 13.0 10.9 76.0

National Science Foundation.................. 4,924.4 4,581.2 343.2 0.0 93.0 7.0 0.0

Department of Agriculture....................... 2,591.3 1,078.8 1,293.4 219.1 41.6 49.9 8.5

Department of Commerce ...................... 1,135.5 149.8 839.3 146.4 13.2 73.9 12.9

Department of Transportation................. 997.0 8.1 704.6 284.2 0.8 70.7 28.5

Department of the Interior....................... 688.6 48.9 564.6 75.1 7.1 82.0 10.9

Department of Homeland Security ......... 634.7 91.2 208.2 335.2 14.4 32.8 52.8

Department of Veterans Affairs............... 579.0 218.0 314.0 47.0 37.7 54.2 8.1

Environmental Protection Agency........... 577.0 88.0 403.4 85.6 15.3 69.9 14.8

Department of Education........................ 346.3 7.5 205.6 133.2 2.2 59.4 38.5

Smithsonian Institution ........................... 169.0 169.0 0.0 0.0 100.0 0.0 0.0

Department of Justice ............................ 101.0 17.5 65.8 17.7 17.3 65.1 17.5

All other agencies ................................... 432.2 1.2 400.8 30.4 0.3 92.7 7.0

NOTES: The table lists all agencies with R&D obligations greater than $100 million in FY 2011. Detail may not add to total due to rounding. All other 

agencies includes Department of Housing and Urban Development, Department of Labor, Department of State, Department of the Treasury, Agency for 

International Development, Appalachian Regional Commission, Federal Communications Commission, Federal Trade Commission, Library of Congress, 

National Archives and Records Administration, Nuclear Regulatory Commission, and Social Security Administration.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Federal Funds for Research and Development (FYs 

2010–12). See appendix table 4-35.

Science and Engineering Indicators 2014

Figure 4-19

Federal obligations for R&D, by agency and character of work: FY 2011 

DOC = Department of Commerce; DOD = Department of Defense; DOE = Department of Energy; DOT= Department of Transportation; HHS = Department 

of Health and Human Services; NASA = National Aeronautics and Space Administration; NSF = National Science Foundation; USDA = U.S. Department 

of Agriculture.

NOTE: Detail may not add to total due to rounding. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Federal Funds for Research and Development 

(FYs 2010–12). See appendix table 4-35.

Science and Engineering Indicators 2014

Other than DOD DOD

Basic research 3%

Applied research 7%

Advanced

technology

development 9%

Major systems

development 82%

Basic research 45%

Applied research 40%

Development 15%

DOD 54%

HHS 24%

NASA 5%

USDA 2%

DOE 7%

NSF 4%

DOC 1%

DOT 1% Other 3%
Science and Engineering Indicators 2014 ♦ 4-37

research activities require specialized equipment and facilities available only at its intramural laboratories and 

FFRDCs, which are used by scientists and engineers from 

other agencies and sectors as well as by DOE researchers. 

Accordingly, DOE invests more resources in its intramural 

laboratories and FFRDCs than other federal agencies. The 

24% of obligations to extramural performers went chiefly to 

businesses and universities and colleges.

For the $9.1 billion obligated to R&D, basic research accounted for 41%, applied research accounted for 33%, and 

development accounted for 26%. DOE R&D activities are 

distributed among domestic energy systems, defense (much 

of it funded by the department’s National Nuclear Security 

Administration), and general science (much of which is 

funded by the department’s Office of Science).

National Aeronautics and Space Administration

The National Aeronautics and Space Administration 

(NASA) obligated $8.4 billion to R&D in FY 2011, 6% of 

the federal total. Seventy-five percent of these obligations 

were for extramural R&D, given chiefly to business sector 

performers. Agency intramural R&D and that by FFRDCs 

represented 25% of the NASA obligations total. By character of work, 76% of the NASA R&D obligations funded 

development activities, 13% funded basic research, and 11% 

funded applied research.

National Science Foundation

NSF obligated $5.4 billion for R&D and R&D plant in 

FY 2011, or 4% of the federal total. Extramural performers, 

chiefly universities and colleges ($5.0 billion), represented 

94% of this total. Basic research accounted for about 93% 

of the R&D component. NSF is the federal government’s 

primary source of funding for academic basic science and 

engineering research and the second-largest federal source 

(after HHS) of R&D funds for universities and colleges.

Department of Agriculture

The Department of Agriculture (USDA) obligated $2.6 

billion for R&D in FY 2011, with the main focus on life sciences. The agency is also one of the largest research funders 

in the social sciences, particularly agricultural economics. 

Of USDA’s total obligations for FY 2011, about 63% ($1.7 

billion) funded R&D by agency intramural performers, 

chiefly the Agricultural Research Service. Basic research 

accounts for about 42%, applied research accounts for 50%, 

and development accounts for 9%.

Department of Commerce

The Department of Commerce (DOC) obligated $1.4 billion for R&D in FY 2011, most of which represented the 

R&D and R&D plant spending of the National Oceanic and 

Atmospheric Administration and the National Institute of 

Standards and Technology. Seventy-one percent of this total 

was for agency intramural R&D; 29% went to extramural 

performers, primarily businesses and universities and colleges. For the R&D component, 13% was basic research, 

74% was applied research, and 13% was development.

Department of Homeland Security

The Department of Homeland Security (DHS) obligated 

$1.1 billion for R&D and R&D plant in FY 2011, nearly 

all of which was for activities by the department’s Science 

and Technology Directorate. Sixty-four percent of this total 

was for agency intramural and FFRDC activities. Just under 37% was conducted by extramural performers—mainly 

businesses—but also universities and colleges and other 

nonprofit organizations. Of the obligations for R&D, 14% 

was basic research, 33% was applied research, and 53% 

was development.

Department of Transportation

The Department of Transportation (DOT) obligated $1.0 

billion for R&D and R&D plant in FY 2011, most of which 

was for activities by the department’s Federal Aviation 

Administration and Federal Highway Administration. 

Thirty-four percent of this obligations total was for agency 

intramural and FFRDC activities. Sixty-six percent was conducted by extramural performers—mainly businesses—but 

also state and local governments, universities and colleges, 

and other nonprofit organizations. Of the obligations for 

R&D, barely 1% was basic research, 71% was applied research, and 29% was development.

Other Agencies

The six other departments/agencies obligating more 

than $100 million annually for R&D in FY 2011 were the 

Departments of Education (ED), the Interior (DOI), Justice, 

and Veterans Affairs; the Environmental Protection Agency 

(EPA); and the Smithsonian Institution (tables 4-13 and 

4-14). These agencies varied with respect to the character of research and the roles of intramural, FFRDC, and 

extramural performers.

Federal Spending on Research, by Field

The research conducted and/or funded by the federal government spans the full range of S&E fields. These fields vary 

widely with respect to their current funding levels and the 

history of support (appendix tables 4-37 and 4-38).

Funding for basic and applied research combined accounted for $58.2 billion (about 44%) of the $132.1 billion 

total of federal obligations for R&D in FY 2011 (table 4-14). 

Of this amount, $30.2 billion (52% of $58.2 billion) supported research in the life sciences (figure 4-20; appendix 

table 4-37). The fields with the next-largest amounts were 

engineering ($10.1 billion, 17%) and the physical sciences 

($5.5 billion, 10%), followed by mathematics and computer 

sciences ($3.3 billion, 6%) and environmental sciences ($3.1 

billion, 5%). The balance of federal obligations for research 

in FY 2011 supported psychology, the social sciences, and 

all other sciences ($5.9 billion overall, or 10% of the total 

for research).

With differing missions, the federal agencies vary significantly in the types of S&E fields emphasized. HHS accounted for the largest share (54%) of federal obligations 
4-38 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

for research in FY 2011 (appendix table 4-37). Most of this 

amount funded research in medical and related life sciences, 

primarily through NIH. The five next-largest federal agencies for research funding that year were DOE (12%), DOD 

(11%), NSF (8%), USDA (4%), and NASA (3%).

DOE’s $6.8 billion in research obligations provided 

funding for research in the physical sciences ($2.6 billion) 

and engineering ($2.3 billion), along with mathematics 

and computer sciences ($1.0 billion). DOD’s $6.6 billion 

of research funding emphasized engineering ($3.6 billion) 

but also included mathematics and computer sciences ($1.0 

billion), physical sciences ($0.8 billion), and life sciences 

($0.6 billion). NSF—not a mission agency in the traditional 

sense—is charged with “promoting the health of science.” 

Consequently, it had a comparatively diverse $4.9 billion 

research portfolio that allocated about $0.7 billion to $0.9 

billion in each of the following fields: environmental, life, 

mathematics and computer, and physical sciences and engineering. Lesser amounts were allocated to psychology 

and the social and other sciences. USDA’s $2.4 billion was 

directed primarily at the life (agricultural) sciences ($1.9 

billion). NASA’s $1.6 billion for research emphasized engineering ($0.6 billion), followed by the physical sciences 

($0.4 billion) and environmental sciences ($0.4 billion).

Growth in federal research obligations has slowed in recent years. Federal obligations for research in all S&E fields 

expanded on average at 1.7% annually (in current dollars) 

over the 2006–11 period but at a much higher 2.7% over the 

2001–11 period (appendix table 4-38).

Looking just at the recent period of FY 2006–11, the 

level of federal research obligations in the life sciences, psychology, and the social sciences experienced average annual 

growth at or just below the pace of expansion for all S&E 

(1.7%), meaning these fields essentially maintained their 

Figure 4-20

Federal obligations for research, by agency and major S&E field: FY 2011

Billions of current dollars

0 5 10 15

Total, all agencies HHS DOD

20 25 30 35

DOE NASA NSF

USDA DOC Other agencies

0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

DOC = Department of Commerce; DOD = Department of Defense; DOE = Department of Energy; HHS = Department of Health and Human Services; 

NASA = National Aeronautics and Space Administration; nec = not elsewhere classied; NSF = National Science Foundation; USDA = U.S. Department of 

Agriculture.

NOTES: The scales for Total, all agencies, and HHS differ from those of other agencies listed. Research includes basic and applied research.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Federal Funds for Research and Development 

(FYs 2010–12). See appendix table 4-37.

Science and Engineering Indicators 2014

0 5 10 15 20 25 30 35

Other sciences nec

Social sciences

Psychology

Environmental sciences

Math/computer sciences

Physical sciences

Engineering

Life sciences

Other sciences nec

Social sciences

Psychology

Environmental sciences

Math/computer sciences

Physical sciences

Engineering

Life sciences

Other sciences nec

Social sciences

Psychology

Environmental sciences

Math/computer sciences

Physical sciences

Engineering

Life sciences
Science and Engineering Indicators 2014 ♦ 4-39

shares of the total (appendix table 4-38). Obligations for the 

fields of mathematics/computer sciences and engineering, 

however, expanded at average paces well above that for all 

S&E, meaning these fields’ shares of the total were increasing. Obligations for the physical sciences grew at less than 

half the rate of all S&E, a greater level of obligations in FY 

2011 than in FY 2006, but a declining share of the whole. 

The field of environmental sciences experienced both a declining share and a lower absolute level in FY 2011 compared with that in FY 2006.

Cross-National Comparisons of 

Government R&D Priorities

Government R&D funding statistics compiled annually 

by the OECD provide insights into how national government 

priorities for R&D differ across countries. Known technically as government budget appropriations or outlays for R&D 

(GBAORD), this indicator provides data on how a country’s 

overall government funding for R&D splits among a set of 

socioeconomic categories (e.g., defense, health, space, general research).23 These GBAORD statistics for the United 

States and other top R&D-performing countries appear in 

table 4-15 (with added detail in appendix table 4-39).24

Defense is an objective for government funding of R&D 

for the top seven R&D-performing countries, but the share 

varies widely (table 4-15). Defense accounted for 57% of 

U.S. federal R&D support in 2011, but it was markedly lower elsewhere: a smaller but still sizable 16% in South Korea 

and 15% in the United Kingdom, and below 7% in France, 

Germany, and Japan. (GBAORD statistics have not yet been 

available for China.)

Defense has received more than 50% of the federal R&D 

budget in the United States for much of the past 20 years. 

It was 63% in 1990 as the long Cold War period drew to a 

close, but it dropped in subsequent years. The defense share 

of government R&D funding for the other countries over the 

past 20 years has generally declined or remained at a stable, 

low level.

The health and environment objective accounted for 

some 57% of nondefense federal R&D budget support in the 

United States in FY 2011 and 33% in the United Kingdom. 

For both countries, the share has expanded markedly over 

the share prevailing several decades ago. The health and 

environment share is currently 14% in South Korea and 

10% or less in France, Germany, and Japan. The funding 

under this objective is predominantly health (in contrast to 

the environment) in the United States and mainly health in 

the United Kingdom (appendix table 4-39). However, in 

the other countries, it is more balanced between health and 

the environment.

The economic development objective encompasses agriculture, fisheries and forestry, industry, infrastructure, and 

energy. In the United States, government R&D funding in 

this category was 20% of all nondefense federal support for 

R&D in 1990, dropping to 11% in 2011 (table 4-15).25 In the 

United Kingdom, it was 32% in 1990 but declined to 8% in 

2011. France was 33% in 1990 but dropped to 17% in 2011. 

Japan was 34% in 1990 but dropped to only 27% in 2011 

(with particular emphasis on energy and industrial production and technology). Germany was 26% in 1990 and 24% 

in 2011 (with an industrial production and technology emphasis). South Korea (50%) exhibits the largest share by far 

in this category in 2011 (with a strong emphasis on industrial 

production and technology).

The civil space objective now accounts for 14% of nondefense federal R&D funding in the United States (table 

4-15). The share has generally been declining over the last 

20 years: 21% in 2000 and 24% in 1990. The share in France 

is currently about 14% and has been around that level for 

almost 20 years. The share has been well below 10% for the 

rest of the top R&D countries.

Both the nonoriented research fund and general university fund (GUF) objectives reflect government funding for 

R&D by academic, government, and other performers that 

is directed chiefly at the general advancement of knowledge 

in the natural sciences, engineering, social sciences, humanities, and related fields. For some of the countries, the sum 

of these two objectives currently represents by far the largest part of nondefense GBAORD: Japan (59%), Germany 

(58%), and the United Kingdom (52%). France (42%) and 

South Korea (31%) were below half but still sizable. The 

corresponding 2011 share for the United States (16%) was 

substantially smaller. Nevertheless, cross-national comparisons of these particular indicators can be difficult because 

some countries (notably the United States) do not use the 

GUF mechanism to fund R&D for general advancement 

of knowledge, do not separately account for GUF funding 

(e.g., South Korea), and/or more typically direct R&D funding to project-specific grants or contracts, which are then 

assigned to the more specific socioeconomic objectives 

(see the sidebar, “Government Funding Mechanisms for 

Academic Research”).

Finally, the education and society objective represents a 

comparatively small component of nondefense government 

R&D funding for all seven of the countries. However, it 

is notably higher in Germany (4%), France (5%), and the 

United Kingdom (4%) than in Japan (1%). The United States 

(3%) and South Korea (3%) are in between.

Federal Programs to Promote 

Technology Transfer and the 

Commercialization of Federal R&D

Starting in the late 1970s, concerns by domestic policymakers about the strength of U.S. industries and their ability 

to succeed in the increasingly competitive global economy 

took on greater intensity. The issues raised included whether 

the new knowledge and technologies arising from federally 

funded R&D were being fully and effectively exploited for 

the benefit of the national economy, whether there were undue barriers in the private marketplace that worked to slow 
4-40 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

Table 4-15

Government R&D support by major socioeconomic objectives, for selected countries and years: 1990–2011

Percent of nondefense

Region/country 

and year

GBAORD 

(current US$ 

millions, PPP)

Economic 

development 

programs

Health 

and 

environment

Education 

and 

society

Civil 

space 

Non-oriented 

research

General 

university 

funds

Percent of GBAORD

Defense Nondefense

United States

1990................. 63,781.0 62.6 37.4 20.1 40.2 3.4 24.2 10.1 na

2000................. 83,612.5 51.6 48.4 13.4 49.9 1.8 20.9 13.8 na

2005................. 131,259.0 56.9 43.1 11.2 55.8 2.8 17.1 13.2 na

2011................. 144,379.0 56.8 43.2 10.5 56.8 2.9 13.9 16.0 na

EU

1990................. na na na na na na na na na

2000................. 76,388.3 12.9 87.1 22.4 11.5 3.4 6.0 15.4 34.7

2005................. 90,797.3 10.4 89.6 19.8 13.3 4.1 5.4 20.0 36.4

2011................. 111,574.9 4.6 95.4 21.0 13.7 4.7 5.9 17.6 34.1

France

1990................. 13,650.6 40.0 60.0 32.8 9.3 0.8 13.0 24.6 18.9

2000................. 14,740.2 21.4 78.6 17.7 9.7 1.1 13.2 27.4 28.5

2005................. 18,084.5 20.8 79.2 16.4 11.8 0.4 10.9 28.3 29.4

2011................. 19,422.2 6.8 93.2 17.3 9.8 5.4 13.9 17.9 24.4

Germany

1990................. 13,328.4 13.5 86.5 25.9 10.8 2.9 6.8 15.2 37.6

2000................. 16,808.7 7.8 92.2 21.6 9.4 3.9 5.1 17.5 42.4

2005................. 19,865.0 5.8 94.2 20.3 10.1 4.1 5.2 18.0 43.1

2011................. 29,234.2 4.0 96.0 24.4 9.5 3.9 4.9 17.0 41.0

United Kingdom

1990................. 8,102.3 43.5 56.5 31.9 18.1 4.0 5.5 10.3 29.8

2000................. 10,359.1 36.2 63.8 12.1 28.3 6.4 3.5 18.8 30.4

2005................. 13,228.0 23.9 76.1 7.1 25.8 7.4 3.0 25.9 30.2

2011................. 13,280.0 14.6 85.4 7.9 32.5 4.4 3.4 22.5 29.3

China

1990................. NA NA NA NA NA NA NA NA NA

2000................. NA NA NA NA NA NA NA NA NA

2005................. NA NA NA NA NA NA NA NA NA

2011................. NA NA NA NA NA NA NA NA NA

Japan

1990................. 10,133.6 5.4 94.6 34.1 4.5 1.1 6.9 8.4 45.1

2000................. 21,173.8 4.1 95.9 33.4 6.6 1.0 5.8 14.6 37.0

2005................. 27,617.8 4.0 96.0 33.2 6.8 0.8 7.0 16.9 35.3

2011................. 34,172.2 2.7 97.3 26.6 7.0 0.7 6.7 21.5 37.9

South Korea

1990................. NA NA NA NA NA NA NA NA NA

2000................. 5,024.7 20.5 79.5 53.4 14.8 3.8 3.1 24.9 **

2005................. 8,539.3 14.6 85.4 51.9 18.8 5.1 4.2 20.1 **

2011................. 15,897.8 16.3 83.7 49.9 14.1 2.7 2.4 30.9 **

** = included in other categories; na = not applicable; NA = not available.

EU = European Union; GBAORD = government budget appropriations or outlays for R&D; PPP = purchasing power parity.

NOTES: Foreign currencies are converted to dollars through PPPs. GBAORD data are not yet available for China. The socioeconomic objective categories 

are aggregates of the 14 categories identified by Eurostat’s 2007 Nomenclature for the Analysis and Comparison of Scientific Programs and Budgets. The 

figures are as reported by the Organisation for Economic Co-operation and Development.

SOURCE: Organisation for Economic Co-operation and Development, Main Science and Technology Indicators (2012/2). See appendix table 4-39.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 4-41

businesses in the creation and commercialization of innovations and new technologies, and whether better publicprivate partnerships for R&D and business innovation had 

the potential to significantly aid the nation’s economy in 

responding to these emerging challenges (Tassey 2007). 

As the reality of the global economic changes deepened 

throughout the 1980s and 1990s (and into the present), it 

became apparent that the United States’ global science and 

technology leadership needed to have a match in a dynamic 

economic system able to quickly absorb and capitalize on 

R&D advances in ways beneficial to the economic fortunes 

of U.S. consumers and businesses.

Numerous national policies and related initiatives have 

been directed at these challenges over the last 30 years, 

including how to better transfer and economically exploit 

the results of federally funded R&D. One major national 

policy thrust has been to enhance formal mechanisms for 

transferring knowledge arising from federally funded and 

performed R&D (Crow and Bozeman 1998; NRC 2003). 

Other policies have taken on strengthening the prospects for 

the development and flow of early-stage technologies into 

the commercial marketplace, accelerating the commercial 

exploitation of academic R&D, and facilitating the conduct of R&D on ideas and technologies with commercial 

potential by entrepreneurial small and/or minority-owned 

businesses. (For an overview of major federal policy initiatives in this realm since the early 1980s, see the sidebar, 

“Major Federal Policies Promoting Technology Transfer 

and Commercialization of R&D.”)

 The sections immediately below focus on this theme of 

the transfer and commercial exploitation of federally funded R&D and review the status indicators for several major 

federal policies and programs directed at these objectives. 

(Chapter 5 contains related information about the knowledge diffusion and patents arising from academic research.)

Federal Technology Transfer

Technology transfer is “the process by which technology 

or knowledge developed in one place or for one purpose is 

applied and used in another place for the same or different 

purpose” (FLC 2011:3). As applied in the federal setting, 

technology transfer refers to the various processes through 

which inventions and other intellectual assets arising from 

federal laboratory R&D are conveyed to outside parties for 

further development and commercial applications. It can 

also involve linking R&D capabilities and the resources of 

federal laboratories with outside public or private organizations for mutual benefit, including flowing know-how and 

technologies developed on the outside into federal research 

facilities to better meet mission objectives and enhance internal capabilities.

The Stevenson-Wydler Act of 1980 (P.L. 96-480) directed 

federal agencies with laboratory operations to become active 

in the technology transfer process. It also required these agencies to establish technology transfer offices (termed an Office 

of Research and Technology Applications [ORTA]) to assist 

in identifying transfer opportunities and establishing appropriate arrangements for transfer relationships with nonfederal parties. Follow-on legislation in the 1980s through 2000 

amending Stevenson-Wydler have worked to extend and refine the authorities available to the agencies and their federal 

labs to identify and manage intellectual assets created by their 

R&D and to participate in collaborative R&D relationships 

with nonfederal parties, including private businesses, universities, and nonprofit organizations (FLC 2011).

Government Funding Mechanisms 

for Academic Research

U.S. universities generally do not maintain data 

on departmental research (i.e., research that is not 

separately budgeted and accounted for). As such, U.S. 

R&D totals are understated relative to the R&D effort reported for other countries. The national totals 

for Europe, Canada, and Japan include the research 

component of general university fund (GUF) block 

grants provided by all levels of government to the academic sector. These funds can support departmental 

R&D programs that are not separately budgeted. GUF 

is not equivalent to basic research. The U.S. federal 

government does not provide research support through 

a GUF equivalent, preferring instead to support specific, separately budgeted R&D projects. However, 

some state government funding probably does support 

departmental research, not separately accounted for, at 

U.S. public universities.

The treatment of GUF is one of the major areas of 

difficulty in making international R&D comparisons. 

In many countries, governments support academic 

research primarily through large block grants that are 

used at the discretion of each higher education institution to cover administrative, teaching, and research 

costs. Only the R&D component of GUF is included 

in national R&D statistics, but problems arise in identifying the amount of the R&D component and the objective of the research. Moreover, government GUF 

support is in addition to support provided in the form 

of earmarked, directed, or project-specific grants and 

contracts (funds that can be assigned to specific socioeconomic categories).

In several large European countries (France, 

Germany, Italy, and the United Kingdom), GUF accounts for 50% or more of total government R&D funding to universities. In Canada, GUF accounts for about 

38% of government academic R&D support. Thus, 

international data on academic R&D reflect not only 

the relative international funding priorities but also the 

funding mechanisms and philosophies regarded as the 

best methods for financing academic research.
4-42 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

Technology Innovation Act of 1980 (Stevenson-Wydler 

Act) (P.L. 96-480)—Established technology transfer as a 

federal government mission by directing federal labs to 

facilitate the transfer of federally owned and originated 

technology to nonfederal parties.

University and Small Business Patent Procedures 

Act of 1980 (Bayh-Dole Act) (P.L. 96-517)—Permitted 

small businesses, universities, and nonprofits to obtain 

titles to inventions developed with federal funds. Also 

allowed government-owned and government-operated 

laboratories to grant exclusive patent rights to commercial organizations.

Small Business Innovation Development Act of 

1982 (P.L. 97-219)—Established the Small Business 

Innovation Research (SBIR) program, which required 

federal agencies to set aside funds for small businesses to 

engage in R&D connected to agency missions.

National Cooperative Research Act of 1984 (P.L. 98-

462)—Encouraged U.S. firms to collaborate in generic 

precompetitive research by establishing a rule of reason for evaluating the antitrust implications of research 

joint ventures.

Patent and Trademark Clarification Act of 1984 (P.L. 

98-620)—Provided further amendments to the StevensonWydler Act and the Bayh-Dole Act regarding the use of 

patents and licenses to implement technology transfer.

Federal Technology Transfer Act of 1986 (P.L. 99-

502)—Enabled federal laboratories to enter cooperative 

research and development agreements (CRADAs) with 

outside parties and to negotiate licenses for patented inventions made at the laboratory.

Executive Order 12591, Facilitating Access to Science 

and Technology (April 1987)—Issued by President 

Reagan, this executive order sought to ensure that the 

federal laboratories implemented technology transfer.

Omnibus Trade and Competitiveness Act of 1988 (P.L. 

100-418)—Directed attention to public-private cooperation on R&D, technology transfer, and commercialization (in addition to measures on trade and intellectual 

property protection). Also established the Manufacturing 

Extension Partnership (MEP) program at the National 

Institute of Standards and Technology.

National Competitiveness Technology Transfer Act of 

1989 (P.L. 101-189)—Amended the Federal Technology 

Transfer Act to expand the use of CRADAs to include 

government-owned, contractor-operated federal laboratories and to increase nondisclosure provisions.

Small Business Innovation Development Act of 1992 

(P.L. 102-564)—Reauthorized the existing SBIR program, increasing both the percentage of an agency’s 

budget to be devoted to SBIR and the maximum level of 

awards. Also established the Small Business Technology 

Transfer (STTR) program to enhance opportunities for 

collaborative R&D efforts between government-owned, 

contractor-operated federal laboratories and small businesses, universities, and nonprofit partners.

National Cooperative Research and Production 

Act of 1993 (P.L. 103-42)—Relaxed restrictions on 

cooperative production activities, enabling research 

joint venture participants to work together on jointly 

acquired technologies.

National Technology Transfer and Advancement 

Act of 1995 (P.L. 104-113)—Amended the StevensonWydler Act to make CRADAs more attractive to federal 

laboratories, scientists, and private industry.

Technology Transfer Commercialization Act of 2000 

(P.L. 106-404)—Broadened CRADA licensing authority to make such agreements more attractive to private 

industry and increase the transfer of federal technology. 

Established technology transfer performance reporting 

requirements for agencies with federal laboratories.

America COMPETES Act of 2007 (America Creating 

Opportunities to Meaningfully Promote Excellence in 

Technology, Education, and Sciences [COMPETES] 

Act) (P.L. 110-69)—Authorized increased investment in 

R&D; strengthened educational opportunities in science, 

technology, engineering, and mathematics from elementary through graduate school; and further promoted the 

nation’s innovation infrastructure. Among various provisions, the act created the Advanced Research Project 

Agency–Energy (ARPA-E) to promote and fund R&D 

on advanced energy technologies; it also called for a 

President’s Council on Innovation and Competitiveness.

America COMPETES Reauthorization Act of 2010 

(P.L. 111–358)—Updated the America COMPETES Act 

of 2007 and authorized additional funding to science, 

technology, and education programs over the succeeding 

3 years. Numerous provisions were intended to broadly 

strengthen the foundation of the U.S. economy, create 

new jobs, and increase U.S. competitiveness abroad.

Presidential Memorandum, Accelerating Technology 

Transfer and Commercialization of Federal Research in 

Support of High-Growth Businesses (October 2011)—

Issued by President Obama, this memorandum directed 

a variety of actions by federal departments and agencies 

to establish goals and measure performance, streamline 

administrative processes, and facilitate local and regional 

partnerships to accelerate technology transfer and support private sector commercialization.

Major Federal Policies Promoting Technology 

Transfer and Commercialization of R&D
Science and Engineering Indicators 2014 ♦ 4-43

The metrics on federal technology transfer continue to 

primarily track the number of activities, that is, invention 

disclosures, patent applications and awards, licenses to outside parties of patents and other intellectual property, and 

agreements to conduct collaborative research with outside 

parties (IDA STPI 2011). Systematic documentation of the 

downstream outcomes and impacts of transfer remains a 

challenge. Also notably missing for most agencies and their 

labs is an accounting of the technical articles published in 

professional journals, conference papers, and other kinds 

of scientific communications. Most federal laboratory scientists, engineers, and managers continue to view these 

traditional forms of new knowledge dissemination as an essential technology transfer mechanism. (For further discussion of the current mechanisms and main metrics for federal 

technology transfer, see the sidebar “Federal Technology 

Transfer: Activities and Metrics.”)

Six agencies continue to account for most of the annual 

total of federal technology transfer activities: DOD, HHS, 

DOE, NASA, USDA, and DOC. Statistics for these six 

agencies in FYs 2006 and 2010, spanning the activity areas 

of invention disclosures and patenting, intellectual property 

licensing, and collaborative relationships for R&D, appear 

in table 4-16. (Similar statistics for a larger set of agencies, 

going back to FY 2001, appear in appendix table 4-40.)

As is apparent in the distribution of the statistics across 

the activity types in table 4-16, most agencies engage in all 

of the transfer activity types to some degree, but there are differences in the emphases. Some agencies are more intensive 

in patenting and licensing activities (such as HHS, DOE, and 

NASA); some place greater emphasis on transfer through 

collaborative R&D relationships (such as DOD, USDA, and 

DOC). Some agencies have unique transfer authorities that 

can confer practical advantages. NASA, for example, can 

establish collaborative R&D relationships through special 

authorities it has under the NASA Space Act of 1958; USDA 

has a number of special authorities for establishing R&D 

collaborations other than through Cooperative Research and 

Development Agreements (CRADAs); DOE has contractoroperated national labs, with nonfederal staff, that are not 

constrained by the normal federal limitation on copyright 

by federal employees and can use copyright to protect and 

transfer computer software. In general, the mix of technology transfer activities pursued by each agency reflects a broad 

range of considerations such as agency mission priorities, 

the technologies principally targeted for development, the 

intellectual property protection tools and policies available, 

and the types of external parties through which transfer and 

collaboration are chiefly pursued.

Small Business Innovation-Related Programs

The Small Business Innovation Research (SBIR) program 

and Small Business Technology Transfer (STTR) program 

are longstanding federal programs that provide competitively awarded funding to small businesses for various purposes. These include stimulating technological innovation, 

Federal Technology Transfer: 

Activities and Metrics

Federal technology transfer can take a variety of 

forms (FLC 2011), including the following:

Commercial transfer. Movement of knowledge or 

technology developed by a federal laboratory to private organizations into the commercial marketplace.

Scientific dissemination. Publications, conference papers, and working papers, distributed through 

scientific/technical channels; other forms of data 

dissemination.

Export of resources. Federal laboratory personnel 

made available to outside organizations with R&D 

needs through collaborative agreements or other service mechanisms.

Import of resources. Outside technology or expertise brought in by a federal laboratory to enhance the 

existing internal capabilities.

Dual use. Development of technologies, products, 

or families of products with both commercial and federal applications.

Federal technology transfer metrics to date have 

typically covered activities in three main classes of intellectual asset management and transfer:

Invention disclosure and patenting. Counts of 

invention disclosures filed (typically, an inventing 

scientist or engineer filing a written notice of the invention with the laboratory’s technology transfer office), patent applications filed with the U.S. Patent and 

Trademark Office (or abroad), and patents granted.

Licensing. Licensing of intellectual property, such 

as patents or copyrights, to outside parties.

Collaborative relationships for R&D. Including, 

but not limited to, Cooperative Research and 

Development Agreements (CRADAs).

Data on technology transfer metrics such as these 

are now increasingly available. Nonetheless, it has been 

long and well recognized by the federal technology 

transfer community that counts of patent applications 

and awards, intellectual property licenses, CRADAs, 

and the like cannot, normally, by themselves provide 

a reasonable gauge of the downstream outcomes and 

impacts that result from the transfers––many of which 

involve considerable time and numerous subsequent 

developments to reach full fruition. There is a growing 

literature on federal technology transfer success stories, facilitated in part by the annual agency technology transfer performance reporting mandated by the 

Technology Transfer Commercialization Act of 2000 

and through regularly updated reports by technology 

transfer professional organizations such as the Federal 

Laboratory Consortium. Even so, the documentation 

of these downstream outcomes and impacts is well 

short of complete.
4-44 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

addressing federal R&D needs, increasing private sector 

commercialization of innovations flowing from federal 

R&D, and fostering technology transfer through cooperative 

R&D between small businesses and research institutions. 

The U.S. Small Business Administration provides overall 

coordination for both programs, with implementation by the 

federal agencies that participate (SBA 2013). The attention 

devoted to smaller and/or startup R&D-based companies by 

these programs exemplifies the promotion of innovationbased entrepreneurship via public-private partnerships that 

enable not only financing but also R&D collaboration and 

commercialization opportunities (Gilbert, Audretsch, and 

McDougall 2004; Link and Scott 2010).

The SBIR program was established by the Small Business 

Innovation Development Act of 1982 (P.L. 97-219) for the 

purpose of stimulating technological innovation by increasing the participation of small companies in federal R&D 

projects, increasing private sector commercialization of 

innovation derived from federal R&D, and fostering participation by minority and disadvantaged persons in technological innovation. The program was reauthorized by the 

Small Business Reauthorization Act of 2000 (P.L. 106-544), 

extending the program through the end of September 2008. 

Subsequently, the program has received several extensions 

from the Congress, which now carries the program through 

2017. Eleven federal agencies currently participate in the 

SBIR program: USDA, DOC, DOD, ED, DOE, HHS, DHS, 

DOT, EPA, NASA, and NSF.

The STTR program was established by the Small Business 

Technology Transfer Act of 1992 (P.L. 102-564, Title II) 

for the purpose of facilitating cooperative R&D by small 

businesses, universities, and nonprofit research organizations and encouraging the transfer of technology developed 

through such research by entrepreneurial small businesses. 

Table 4-16

Federal laboratory technology transfer activity indicators, total and selected U.S. agencies: FYs 2006 and 2010

(Number)

Technology transfer activity

 All federal

laboratories DOD HHS DOE NASA USDA DOC

FY 2006

Invention disclosures and patenting

Inventions disclosed ................................... 5,193 1,056 442 1,694 1,749 105 14

Patent applications ..................................... 1,912 691 166 726 142 83 5

Patents issued ............................................ 1,284 472 164 438 85 39 7

Licensing

All licenses, total active in the FY ............... 10,186 444 1,535 5,916 2,856 332 111

Invention licenses.................................... 4,163 438 1,213 1,420 308 332 111

Other intellectual property licenses......... 6,023 6 322 4,496 2,548 0 0

Collaborative relationships for R&D

CRADAs, total active in the FY................... 7,268 2,999 164 631 1 195 3,008

Traditional CRADAs................................. 3,666 2,424 92 631 1 163 149

Other collaborative R&D relationships........ 9,738 0 0 0 4,275 3,477 2,114

FY 2010

Invention disclosures and patenting

Inventions disclosed ................................... 4,783 698 363 1,616 1,722 164 34

Patent applications ..................................... 1,830 436 113 965 144 112 19

Patents issued ............................................ 1,143 304 153 480 129 44 11

Licensing

All licenses, total active in the FY ............... 13,542 397 1,941 6,224 3,901 343 41

Invention licenses.................................... 4,004 341 1,240 1,453 354 343 41

Other intellectual property licenses......... 9,121 56 683 4,771 3,547 0 0

Collaborative relationships for R&D

CRADAs, total active in the FY................... 8,525 3,248 447 697 1 287 2,399

Traditional CRADAs................................. 4,768 2,516 300 697 1 233 101

Other collaborative R&D relationships........ 18,667 287 0 0 4,246 11,214 2,897

CRADA = Cooperative Research and Development Agreement; DOC = Department of Commerce; DOD = Department of Defense; DOE = Department 

of Energy; NASA = National Aeronautics and Space Administration; HHS = Department of Health and Human Services; USDA = U.S. Department 

of Agriculture.

NOTES: Other federal agencies not listed but included in the All federal laboratories totals are the Department of Homeland Security, Department of the 

Interior, Department of Transportation, Department of Veterans Affairs, and Environmental Protection Agency. Invention licenses refers to inventions that are 

patented or could be patented. Other intellectual property licenses refers to intellectual property protected through mechanisms other than a patent (e.g., 

copyright). Total CRADAs refers to all agreements executed under CRADA authority (15 USC 3710a). Traditional CRADAs are collaborative R&D partnerships 

between a federal laboratory and one or more nonfederal organizations. Federal agencies have varying authorities for other kinds of collaborative R&D 

relationships. Detail may not add to total due to categories in the source data that are not displayed or other distinctions in the source data.

SOURCE: National Institute of Standards and Technology, Federal Laboratory Technology Transfer, Fiscal Year 2010 Summary Report to the President 

and the Congress, August 2012, http://www.nist.gov/tpo/publications/index.cfm. See appendix table 4-40.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 4-45

The program was reauthorized through the end of September 

2009 by the Small Business Technology Transfer Program 

Reauthorization Act of 2001 (P.L. 107-50). Congress has 

likewise provided a number of extensions since then, with 

the program now continuing through 2017. Five federal 

agencies currently participate in the STTR program: DOD, 

DOE, HHS, NASA, and NSF.

For SBIR, federal agencies with extramural R&D budgets 

exceeding $100 million annually must set aside 2.5% (since 

FY 1997) for SBIR awards to U.S.-located small businesses 

(defined as those with fewer than 500 employees, including any affiliates). Three phases of activities are recognized. 

Phase I: A small company can apply for a Phase I funding 

award (normally not exceeding $150,000) for up to 6 months 

to assess the scientific and technical feasibility of an idea 

with commercial potential. Phase II: Based on the scientific/

technical achievements in Phase I and continued expectation 

of commercial potential, the company can apply for Phase 

II funding (normally, not exceeding $1,000,000) for 2 years 

of further development. Phase III: Where the Phase I and II 

results warrant, the company pursues a course toward commercialization. The SBIR program itself does not provide 

funding for Phase III, but depending on the agency Phase III 

may involve non-SBIR-funded R&D or production contracts 

for products, processes, or services intended for use by the 

federal government. Several agencies offer bridge funding 

to Phase III and other commercialization support for startups 

(NRC 2008:208–16).

The initial round of SBIR awards was for FY 1983. This 

amounted to 789 Phase I awards, across all the participating agencies, for a total of $38.1 million of funding (table 

4-17; appendix table 4-41). By FY 2011, the program had 

expanded considerably: 5,396 awards (3,626 Phase I; 1,770 

Phase II), with total funding of $1.946 billion ($502 million 

Phase I; $1.444 billion Phase II). In FY 2011, the majority 

of the funding reflected awards by DOD (43%) and HHS 

(32%) (appendix table 4-42). NASA (9%), DOE (7%), and 

NSF (5%) accounted for smaller shares. The other six participating agencies were 1% or less of the total.

For the STTR program, federal agencies with extramural 

R&D budgets that exceed $1 billion annually must reserve 

0.3% for STTR awards to small businesses. STTR operates 

within the same three-phase framework as SBIR. Phase I 

provides awards for company efforts to establish the technical merit, feasibility, and commercial potential of proposed 

projects; the funding in this phase normally does not exceed 

$100,000 over 1 year. Phase II is for continued R&D efforts, 

but award is conditional on success in Phase I and continued 

expectation of commercial potential. Phase II funding normally does not exceed $750,000 over 2 years. Phase III is for 

the small business to pursue commercialization objectives, 

based on the Phase I and II results. The STTR program does 

Table 4-17

SBIR and STTR awards, number and funding, by type of award: Selected years, FYs 1983–2011

Number of awards Funding ($millions)

Fiscal year Total Phase I Phase II Total Phase I Phase II

SBIR

1983........................................................ 789 789 0 38.1 38.1 0.0

1985........................................................ 1,839 1,483 356 195.5 74.5 121.0

1990........................................................ 3,225 2,379 846 453.7 121.2 332.4

1995........................................................ 4,366 3,092 1,274 960.8 236.5 724.3

2000........................................................ 5,307 3,959 1,348 1,062.2 295.0 767.2

2005........................................................ 6,083 4,216 1,867 1,857.6 452.5 1,405.1

2009........................................................ 5,796 4,016 1,780 1,926.2 503.4 1,422.8

2010........................................................ 6,184 4,271 1,913 2,115.2 548.0 1,567.3

2011........................................................ 5,396 3,626 1,770 1,946.0 502.1 1,443.9

STTR

1983........................................................ na na na na na na

1985........................................................ na na na na na na

1990........................................................ na na na na na na

1995........................................................ 1 1 0 0.1 0.1 0.0

2000........................................................ 410 315 95 64.0 23.7 40.3

2005........................................................ 802 579 223 227.7 66.1 161.6

2009........................................................ 831 593 238 236.8 72.2 164.6

2010........................................................ 905 625 280 289.2 77.5 211.6

2011........................................................ 708 468 240 234.6 64.2 170.4

na = not applicable.

SBIR = Small Business Innovation Research program; STTR = Small Business Technology Transfer program.

NOTES: The first SBIR program awards were made in FY 1983. The first STTR program award was made in FY 1995. 

SOURCE: Small Business Administration, SBIR/STTR official website, http://www.sbir.gov/past-awards, accessed 25 February 2013. See appendix 

tables 4-41–4-43.

Science and Engineering Indicators 2014
4-46 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

not provide funding for Phase III activities. Furthermore, to 

pursue Phase III, companies must secure non-STTR R&D 

funding and/or production contracts for products, processes, 

or services for use by the federal government.

The STTR program started with a single Phase I award 

for $100,000 in FY 1995 (table 4-17). In FY 2011, there 

were 708 awards (468 Phase I; 240 Phase II), with funding totaling $235 million ($64 million Phase I; $170 million 

Phase II). Fewer federal agencies participate in STTR, but 

those dominant in SBIR are also dominant in STTR. STTR 

awards from DOD accounted for 44% of the $235 million 

award total in FY 2011 (appendix table 4-43). HHS accounted for 36% of the STTR awards, and the remainder was from 

NASA (9%), DOE (8%), and NSF (4%).

Other Programs

The federal policies, authorities, and incentives established by the Stevenson-Wydler Act (and the subsequent 

amending legislation) and the SBIR and STTR programs are 

far from the whole of federal efforts to promote the transfer and commercialization of federal R&D. Numerous programs for these purposes exist in the federal agencies. Given 

the specifics of agency missions, they have a narrower scope 

and smaller pools of resources. Several examples are described below.

The Hollings Manufacturing Extension Partnership 

(MEP) is a nationwide network of manufacturing extension 

centers located in all 50 U.S. states and Puerto Rico. MEP 

was created by the Omnibus Trade and Competitiveness Act 

of 1988 (P.L. 100-418) and is headed by the Department of 

Commerce’s National Institute of Standards and Technology 

(NIST 2013a). The MEP centers (nonprofit) exist as a partnership among the federal government, state and local governments, and the private sector. MEP provides technical 

expertise and other services to small and medium-sized U.S. 

manufacturers to improve their ability to develop new customers, expand into new markets, and create new products. 

The centers work directly with manufacturers to engage 

specific issues, including technology acceleration, process 

improvements, innovation strategies, workforce training, 

supply-chain development, and exporting. They also serve 

to connect manufacturers with universities and research laboratories, trade associations, and other relevant public and 

private resources. A recent MEP annual report (FY 2012) 

describes the program as operating with $300 million of annual resources: $100 million from the federal government, 

and $200 million from state and local governments and the 

private sector (NIST 2013b). The MEP report indicates that 

technical expertise and other services were provided during 

FY 2012 to 31,373 U.S. manufacturing companies and attributes impacts of $6.6 billion in increased or retained sales, 

61,139 increased or retained jobs, and $900 million in cost 

savings for these businesses.

The Department of Energy’s Advanced Research Projects 

Agency—Energy (ARPA-E) provides funding, technical assistance, and market development to advance high-potential, 

high-impact energy technologies that are too early stage for 

private sector investment (DOE 2013). The main interest 

is energy technology projects with the potential to radically improve U.S. economic security, national security, and 

environmental quality––in particular, short-term research 

that can have transformational impacts, not basic or incremental research. ARPA-E was authorized by the America 

COMPETES Act of 2007 (P.L. 110-69), and it received $400 

million of initial funding through the American Recovery 

and Investment Act of 2009 (P.L. 111-5). Federal funding 

(appropriations) for ARPA-E was $180 million in FY 2011 

and $275 million in FY 2012. The program is currently authorized through FY 2013, although the FY 2013 funding 

level remains unresolved at this time (DOE 2013). ARPA-E 

reports 190 funded projects active as of November 2012, 

with a total of 275 projects funded since 2009. The program 

currently identifies 14 project areas, with topics including 

advanced batteries, energy storage technologies, improved 

building energy efficiencies, biofuels, and solar energy.

The National Science Foundation’s Industry/University 

Cooperative Research Centers (I/UCRC) Program supports 

university/industry partnerships for the conduct of industrially relevant fundamental research, collaborative education, 

and the transfer of university-developed ideas, research results, and technology to industry (NSF 2013). NSF provides 

support to I/UCRC through partnership mechanisms where, 

according to NSF, funding is typically leveraged from 10 to 

15 times by business and other nonfederal funding. The I/

UCRC Program reports there are currently 60 such centers 

across the United States, with over 1,000 nonacademic members: 85% are industrial firms, with the remainder comprised 

of state governments, national laboratories, and other federal 

agencies. NSF funding to I/UCRC was about $15 million in 

FY 2011. Research is prioritized and executed in cooperation 

with each center’s membership organizations.

Conclusion

Worldwide R&D performance (measured as expenditures) totaled an estimated $1,435 billion (current PPP dollars) in 2011 (latest global total available). The comparable 

figure for 2001 was $753 billion, which reflected a brisk, 

6.7% average annual growth over this 10-year period.

U.S. R&D increased to $407 billion in 2010 and to $424 

billion in 2011 (table 4-1). At just under 30% of the global 

total in 2011, the United States remains, by far, the world’s 

largest R&D performer. Nonetheless, with other countries 

also expanding their investments in R&D, the U.S. share 

has declined since 2001, when it was 37%. From 2001 to 

2011, the share of total global R&D accounted for by East/

Southeast Asia and South Asia—including China, India, 

Japan, Singapore, South Korea, and Taiwan—increased 

from 25% to 34% in 2011. By contrast, the EU countries 

accounted for 22% of total global R&D in 2011, down from 

26% in 2001.
Science and Engineering Indicators 2014 ♦ 4-47

China continues to exhibit the most dramatic R&D growth 

pattern. At $208 billion of R&D expenditures in 2011, China 

is the world’s second-largest R&D performer. While this is 

less than half the U.S. level, the growth in China’s R&D 

spending has averaged an exceptionally high 20.7% annually in 2001–11 (18.1% adjusted for inflation). By comparison, the annual growth rate for U.S. R&D averaged 4.3% 

over this same period. Corresponding average annual growth 

rates for the largest R&D countries of the EU (Germany, 

France, United Kingdom) are in the 3%–6% range.

The growth in total of U.S. R&D expenditures in 2010 

and 2011 followed a shallow decline in 2009 ($1.9 billion or 

0.5%), mainly the result of a drop in business R&D in the face 

of the national and international financial crisis and economic 

downturn that started in late 2008. But while small, this was 

only the second such (current dollar) decline in U.S. R&D 

since the early 1950s. R&D’s year-over-year expansion from 

2009 to 2010 was 0.5%; for 2010 to 2011, it was 4.4%. R&D 

growth in 2010 was well behind that of GDP (4.2%) that year, 

but in 2011 R&D returned to the more normal circumstance of 

outpacing that year’s GDP growth (3.9%). The ratio of R&D 

to GDP dropped from 2.90% in 2009 to 2.81% in 2010 and 

rose slightly to 2.81% in 2011. The statistics for 2012 and 

beyond, when they are available, will be important in determining if the historic pattern whereby R&D growth matches 

or exceeds GDP growth has resumed.

Notes

1. In this chapter constant or inflation-adjusted dollars 

are based on the GDP implicit price deflator (in 2005 dollars) as published by BEA (NIPA Table 1.1.9. Implicit Price 

Deflators for Gross Domestic Product) as of May 2012. See 

appendix table 4-1. GDP deflators are calculated on an economy-wide rather than an R&D-specific basis.

2. In this chapter, GDP data are from BEA, Survey of 

Current Business, 31 May 2012.

3. The data for academic R&D described in this chapter adjust the academic fiscal year basis of the survey data 

to calendar year and net out double-counting from passthroughs of research funds from one academic institution to 

another. Accordingly, the data may differ from what is cited 

in chapter 5.

4. Furthermore, this figure does not include federal government investments in R&D infrastructure and equipment, 

which support the maintenance and operation of unique research facilities and the conduct of research activities that 

would be too costly or risky for a single company or academic institution to undertake.

5. R&D funding by business in this section refers to 

nonfederal funding for domestic business R&D plus business funding for U.S. academic R&D and nonprofit R&D 

performers.

6. It is straightforward arithmetic, based on the data in 

appendix tables 4-2–4-5, to calculate similar character-ofwork shares for years earlier than 2011. Nonetheless, care 

must be applied in describing character-of-work shares 

over time. The survey methods for collecting data on character-of-work shares have on occasion been revised, most 

notably for the academic, business, and FFRDC R&D expenditure surveys. Some differences observed in the shares 

directly calculated from the appendix table time series data 

more nearly reflect the result of these improvements in the 

character-of-work questions.

7. The OECD notes that in measuring R&D, the greatest source of error is typically the difficulty of locating the 

dividing line between experimental development and related activities needed to realize an innovation (OECD 2002, 

paragraph 111). Most definitions of R&D set the cutoff at 

the point when a particular product or process reaches “market readiness.” At this point, the defining characteristics 

of the product or process are substantially set (at least for 

manufactured goods, if not also for services), and further 

work is primarily aimed at developing markets, engaging in 

preproduction planning, and streamlining the production or 

control system.

8. The figures cited here for total global R&D in 2001, 

2006, and 2011 are NSF estimates. R&D expenditures for 

all countries are denominated in U.S. dollars, based on purchasing power parities. These estimates are based on data 

from the OECD, Main Science and Technology Indicators

(Volume 2013/1) and from R&D statistics for additional 

countries assembled by UNESCO, Institute for Statistics (as 

of early August 2013). At present, there is no database on 

R&D spending that is comprehensive and consistent for all 

nations performing R&D. The OECD and UNESCO databases together provide R&D performance statistics for 214 

countries, although the data are not current or complete for 

all. NSF’s estimate of total global R&D reflects 91 countries, with reported annual R&D expenditures of $50 million 

or more, which accounts for most all of current global R&D.

9. The figures cited for the EU in 2001 are adjusted to 

include all of the current 28 member countries.

10. The last recession was officially dated December 

2007 to June 2009. For details, see http://www.nber.org/

cycles.html.

11. See Archibugi, Filippetti, and Frenz (2013) and references therein for studies on the relationship of R&D, innovation, and business cycles. 

12. BRDIS does not collect data for companies with 

fewer than five employees. See sidebar, “Measured and 

Unmeasured R&D,” for more details including a new survey 

under development to cover these companies.

13. For forthcoming releases from a project linking and 

comparing BEA’s MNC and BRDIS foreign statistics, see 

http://www.nsf.gov/statistics/rdlink/.

14. BEA releases MNC statistics in current dollars. 

Figures in the text were deflated by the authors using the 

GDP implicit price deflator (2005 = 1.00000) published separately by BEA (see endnote 1; appendix table 4-1).

15. Some companies are both parents of U.S. MNCs and 

subsidiaries of foreign MNCs, so the latter shares overlap.
4-48 ♦ Chapter 4. Research and Development: National Trends and International Comparisons

16. For value-added and other MNCs operations data, see 

http://www.bea.gov/iTable/index_MNC.cfm.

17. See additional MNC R&D parent data by industry in 

appendix table 4-29.

18. U.S. business R&D data in ANBERD are for 2009. 

U.S. (BRDIS) 2010 statistics were used elsewhere in this 

chapter. ANBERD industry-level data presented here are 

based on International Standard Industrial Classification 

(ISIC) Revision 3.1. For ANBERD methodology, see OECD 

(2012). For additional cross-country indicators such as value 

added and trade in high-technology industries, see chapter 6.

19. Note that 2007 data for France in this section are arguably less comparable than more recent data from the other 

countries given the economic and financial crisis that started 

in 2008.

20. The analysis in this section focuses primarily on developments in federal R&D priorities and funding support 

over the course of the last decade. But there is a particularly interesting story to tell in how the comparatively minor 

federal role in the nation’s science and research system up 

until World War II was reconsidered, redirected, and greatly 

enlarged, starting shortly after the end of the war and up 

through the subsequent decades to the present. For a review 

of the essential elements of this evolving postwar federal 

role, see Jankowski (2013).

21. The 15 budget function categories in which federally performed and/or funded R&D activities typically appear are national defense (050); international affairs (150); 

general science, space, and technology (250); energy (270); 

natural resources and environment (300); agriculture (350); 

commerce and housing credit (370); transportation (400); 

community and regional development (450); education, 

training, and social services (500); health (550); Medicare 

(570); income security (600); veterans benefits and services 

(700); and administration of justice (750). The other five categories in which R&D typically does not occur are social 

security (650), general government (800), net interest (900), 

allowances (920), and undistributed offsetting receipts 

(950). Furthermore, to clarify analysis, NCSES statistics on 

federal R&D funding by budget function normally separate 

the (250) function into subfunctions: general science and basic research (251) and space flight, research, and supporting 

activities (252).

22. For more on the effect of ARRA in R&D performance, see chapter 5.

23. GBAORD parses total government funding on R&D 

into the 14 socioeconomic categories specified by the EU’s 

2007 edition of the Nomenclature for the Analysis and 

Comparison of Scientific Programs and Budgets (NABS). 

These categories are exploration and exploitation of the earth; 

environment; exploration and exploitation of space; transport, telecommunications, and other infrastructures; energy; 

industrial production and technology; health; agriculture; 

education; culture, recreation, religion, and mass media; political and social systems, structures, and processes; general 

advancement of knowledge: R&D financed from general 

university funds; general advancement of knowledge: R&D 

financed from sources other than general university funds; 

and defense. GBAORD statistics published by the OECD in 

the Main Science and Technology Indicators series report on 

clusters of these 14 NABS categories.

24. GBAORD statistics reported for the United States are 

budget authority figures.

25. Some analysts argue that the low nondefense GBAORD 

share for economic development in the United States reflects 

the expectation that businesses will finance industrial R&D 

activities with their own funds. Moreover, government R&D 

that may be useful to industry is often funded with other purposes in mind, such as defense and space, and is, therefore, 

classified under other socioeconomic objectives.

Glossary

Affiliate: A company or business enterprise located in 

one country but owned or controlled (in terms of 10% or 

more of voting securities or equivalent) by a parent company in another country; may be either incorporated or 

unincorporated.

Applied research: The objective of applied research is to 

gain knowledge or understanding to meet a specific, recognized need. In industry, applied research includes investigations to discover new scientific knowledge that has specific 

commercial objectives with respect to products, processes, 

or services.

Basic research: The objective of basic research is to gain 

more comprehensive knowledge or understanding of the 

subject under study without specific applications in mind. 

Although basic research may not have specific applications 

as its goal, it can be directed in fields of present or potential 

interest. This is often the case with basic research performed 

by industry or mission-driven federal agencies.

Development: Development is the systematic use of the 

knowledge or understanding gained from research directed 

toward the production of useful materials, devices, systems, 

or methods, including the design and development of prototypes and processes.

European Union (EU): As of June 2013, the EU comprised 27 member nations: Austria, Belgium, Bulgaria, 

Cyprus, Czech Republic, Denmark, Estonia, Finland, 

France, Germany, Greece, Hungary, Ireland, Italy, Latvia, 

Lithuania, Luxembourg, Malta, the Netherlands, Poland, 

Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, and 

the United Kingdom. Croatia joined the EU in July 2013. 

Unless otherwise noted, Organisation for Economic Cooperation and Development data on the EU include all 28 

members; data on the EU from other sources are limited to 

the 27 nations that were members as of June 2013.

Federally funded research and development center 

(FFRDC): R&D-performing organizations that are exclusively or substantially financed by the federal government 

either to meet a particular R&D objective or, in some instances, to provide major facilities at universities for research and 

associated training purposes. Each FFRDC is administered 

by an industrial firm, a university, or a nonprofit institution.

Foreign affiliate: Company located outside the United 

States but owned by a U.S. parent company.
Science and Engineering Indicators 2014 ♦ 4-49

Foreign direct investment (FDI): Ownership or control 

of 10% or more of the voting securities (or equivalent) of a 

business located outside the home country.

Gross domestic product (GDP): The market value of 

goods and services produced within a country. It is one of the 

main measures in the national income and product accounts.

G20: Group of Twenty brings together finance ministers and central bank governors from Argentina, Australia, 

Brazil, Canada, China, France, Germany, India, Indonesia, 

Italy, Japan, the Republic of Korea, Mexico, Russia, Saudi 

Arabia, South Africa, Turkey, the United Kingdom, the 

United States, and the EU.

Innovation: The introduction of new or significantly 

improved products (goods or services), processes, organizational methods, and marketing methods in internal business 

practices or in the open marketplace (OECD/Eurostat 2005).

Majority-owned affiliate: Company owned or controlled, by more than 50% of the voting securities (or equivalent), by its parent company.

Multinational company (MNC): A parent company and 

its foreign affiliates.

National income and product accounts (NIPA): The 

economic accounts of a country that display the value and 

composition of national output and the distribution of incomes generated in this production.

Organisation for Economic Co-operation and 

Development (OECD): An international organization of 34 

countries, headquartered in Paris, France. The member countries are Australia, Austria, Belgium, Canada, Chile, Czech 

Republic, Denmark, Estonia, Finland, France, Germany, 

Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, 

South Korea, Luxembourg, Mexico, the Netherlands, New 

Zealand, Norway, Poland, Portugal, the Slovak Republic, 

Slovenia, Spain, Sweden, Switzerland, Turkey, the United 

Kingdom, and the United States. Among its many activities, 

the OECD compiles social, economic, and science and technology statistics for all member and selected nonmember 

countries.

R&D: Research and development, also called research 

and experimental development; comprises creative work 

undertaken on a systematic basis to increase the stock of 

knowledge—including knowledge of man, culture, and society—and its use to devise new applications (OECD 2002).

R&D intensity: A measure of R&D expenditures relative to size, production, financial, or other characteristic for 

a given R&D-performing unit (e.g., country, sector, company). Examples include R&D-to-GDP ratio and R&D valueadded ratio.

Technology transfer: The process by which technology 

or knowledge developed in one place or for one purpose is 

applied and exploited in another place for some other purpose. In the federal setting, technology transfer is the process by which existing knowledge, facilities, or capabilities 

developed under federal research and development funding 

are utilized to fulfill public and private needs.

U.S. affiliate: Company located in the United States but 

owned by a foreign parent.

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Moncada-Paternò-Castello P, Vivarelli M, Voigt P. 2011. 

Drivers and impacts in the globalization of corporate 

R&D: An introduction based on the European experience. 

Industrial and Corporate Change, 20(2):585–603.

National Institute of Standards and Technology (NIST), 

Manufacturing Extension Partnership. 2013a. About the 

manufacturing extension partnership. http://www.nist.

gov/mep/about.cfm. Accessed 21 March 2013.

National Institute of Standards and Technology (NIST), 

Manufacturing Extension Partnership. 2013b. 

Manufacturing extension partnership: Making an impact 

on U.S. manufacturing. http://www.nist.gov/mep/upload/

MEP-PARTNERING-IMPACTS-2013-2.pdf. Accessed 

21 March 2013.

National Research Council (NRC). 2003. GovernmentIndustry Partnerships for the Development of New 

Technologies. Wessner CW, editor. Washington, DC: 

National Academies Press.

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Washington, DC: National Academies Press.

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the SBIR Program. Wessner CW, editor. Washington, 

DC: National Academies Press.

National Science Foundation (NSF). 2006. Definitions of 

Research and Development: An Annotated Compilation 

of Official Sources. Arlington, VA. Available at http://

www.nsf.gov/statistics/randdef/.

National Science Foundation (NSF), Division of Industrial 

Innovation and Partnerships. 2013. Industry & University 

Cooperative Research Program (I/UCRC). http://www.

nsf.gov/eng/iip/iucrc/index.jsp. Accessed 21 March 2013.

Office of Management and Budget (OMB), Executive Office 

of the President. 2012a. Budget of the United States 

Government, fiscal year 2013, historical tables, table 5.1. 

http://www.whitehouse.gov/omb/budget/Historicals.

Office of Management and Budget (OMB), Executive 

Office of the President. 2012b. Circular A-11, Section 

84-4. http://www.whitehouse.gov/sites/default/files/

omb/assets/a11_current_year/s84.pdf.

Organisation for Economic Co-operation and Development 

(OECD). 2002. Proposed Standard Practice for Surveys 

on Research and Experimental Development (Frascati 

Manual). Paris.

Organisation for Economic Co-operation and Development 

(OECD). 2008. The Internationalisation of Business 

R&D: Evidence, Impacts and Implications. Paris.

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(OECD). 2012. The OECD Analytical BERD 

(ANBERD) Database. http://www.oecd.org/sti/ind/

ANBERD_full_documentation.pdf.

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(OECD). 2013. Main Science and Technology 

Indicators. Vol. 2013/1. Paris.

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statistics/infbrief/nsf13306/.

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United Kingdom: Edward Elgar Publishing.

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and Social Research, University of Melbourne.

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Changing what’s possible. http://www.arpa-e.energy.

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Accessed 22 February 2013.

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Expenditures in the OECD. Paris: OECD.
Chapter 5

Academic Research 

and Development

♦ 5-1

Highlights.....................................................................................................................................5-5

Spending for Academic R&D...................................................................................................5-5

Infrastructure for Academic R&D............................................................................................5-5

Cyberinfrastructure ...................................................................................................................5-5

Doctoral Scientists and Engineers in Academia.......................................................................5-5

Outputs of Academic S&E Research: Articles and Patents......................................................5-6

Introduction..................................................................................................................................5-8

Chapter Overview.....................................................................................................................5-8

Chapter Organization................................................................................................................5-8

Expenditures and Funding for Academic R&D...........................................................................5-8

National Academic R&D Expenditures....................................................................................5-8

Sources of Support for Academic S&E R&D ..........................................................................5-9

Academic R&D Expenditures, by Field .................................................................................5-13

Academic R&D, by Institution Type......................................................................................5-16

Infrastructure for Academic R&D .............................................................................................5-18

Research Facilities..................................................................................................................5-19

Research Equipment ...............................................................................................................5-21

Cyberinfrastructure .................................................................................................................5-22

Doctoral Scientists and Engineers in Academia ........................................................................5-23

Trends in Academic Employment of Doctoral Scientists and Engineers...............................5-24

Academic Researchers............................................................................................................5-29

Academic Employment in Postdoc Positions.........................................................................5-31

Federal Support of Doctoral Researchers in Academia..........................................................5-34

Outputs of S&E Research: Articles and Patents........................................................................5-35

S&E Article Output ................................................................................................................5-35

Coauthorship and Collaboration in S&E Literature ...............................................................5-40

Trends in Citation of S&E Articles ........................................................................................5-47

Citation of S&E Articles by USPTO Patents .........................................................................5-51

Academic Patenting................................................................................................................5-53

Conclusion .................................................................................................................................5-57

Notes..........................................................................................................................................5-58

Glossary .....................................................................................................................................5-63

References..................................................................................................................................5-63
5-2 ♦ Chapter 5. Academic Research and Development

List of Tables

Table 5-1. Federally financed higher education R&D expenditures funded by the 

American Recovery and Reinvestment Act of 2009, by institution type and control: 

FYs 2010–12 .........................................................................................................................5-10

Table 5-2. Higher education R&D expenditures, by source, character of work, and 

institutional control: FYs 2010–12........................................................................................5-10

Table 5-3. R&D expenditures in non-S&E fields at universities and colleges: 

FYs 2010–12 .........................................................................................................................5-11

Table 5-4. Top six federal agencies’ shares of federally funded academic R&D 

expenditures: FYs 2003–12...................................................................................................5-13

Table 5-5. Total and federally financed higher education R&D expenditures passed 

through to subrecipients, by institutional control: FY 2012 .................................................5-18

Table 5-6. Total and federally financed higher education R&D expenditures received 

as a subrecipient, by institutional control: FY 2012 .............................................................5-18

Table 5-7. New construction of S&E research space in academic institutions, by field 

and time of construction: FYs 2002–11................................................................................5-20

Table 5-8. Bandwidth at academic institutions: FYs 2005–12..................................................5-22

Table 5-9. Tenure status by field of doctorate: 1997 and 2010 .................................................5-25

Table 5-10. Tenure status of academically employed SEH doctorate holders, by age: 

1997 and 2010.......................................................................................................................5-26

Table 5-11. Women as percentage of SEH doctorate holders employed in academia, 

by position: Selected years, 1973–2010................................................................................5-26

Table 5-12. Foreign-trained SEH doctorate holders employed in academia, by degree 

field and sex: 2010 ................................................................................................................5-27

Table 5-13. Underrepresented minorities as percentage of SEH doctorate holders 

employed in academia, by position: Selected years, 1973–2010..........................................5-28

Table 5-14. Academically employed SEH doctorate holders, by age: 1995 and 2010 .............5-29

Table 5-15. SEH faculty reporting research as primary work activity, by years since 

doctorate and degree field: 2010...........................................................................................5-31

Table 5-16. Full-time SEH graduate students and graduate research assistants at 

universities and colleges, by degree field: Selected years, 1973–2011 ................................5-32

Table 5-17. SEH doctorate holders with academic employment in postdoc position, 

by demographic group: Selected years, 1973–2010 .............................................................5-33

Table 5-18. SEH doctorate holders with academic employment in postdoc position, 

by Carnegie institution type and years since doctorate: 2010...............................................5-34

Table 5-19. Reasons for accepting postdoc position: 2008–10 .................................................5-34

Table 5-20. S&E articles in all fields, by country/economy: 2001 and 2011............................5-38

Table 5-21. S&E research portfolios of selected regions/countries, by field: 2011 ..................5-39

Table 5-22. Share of U.S. S&E articles, by sector and field: 2012 ...........................................5-40

Table 5-23. International coauthorship of S&E articles with the United States, 

by selected country/economy: 2002 and 2012......................................................................5-43

Table 5-24. Index of international collaboration on S&E articles, by selected country/ 

economy pair: 1997 and 2012...............................................................................................5-45

List of Sidebars

Data on the Financial and Infrastructure Resources for Academic R&D....................................5-9

National Research Council: Recommendations to Strengthen America’s Research Universities....5-12

Experimental Program to Stimulate Competitive Research ......................................................5-14

Postdoctoral Researchers...........................................................................................................5-32

Bibliometric Data and Terminology ..........................................................................................5-36

Normalizing Coauthorship and Citation Data............................................................................5-45

Identifying Clean Energy and Pollution Control Patents...........................................................5-54
Science and Engineering Indicators 2014 ♦ 5-3

List of Figures

Figure 5-1. Federal and nonfederal academic S&E R&D expenditures: FYs 1996–2012 ........5-13

Figure 5-2. Academic S&E R&D expenditures, by source of funding: FYs 1972–2012..........5-13

Figure 5-3. Academic R&D financed by business for selected countries: 1981–2011 .............5-15

Figure 5-4. Academic R&D expenditures, by selected S&E field: FYs 1999–2012.................5-15

Figure 5-5. Federally financed academic R&D expenditures, by agency and S&E field: 

FY 2012.................................................................................................................................5-16

Figure 5-6. Sources of S&E R&D funding for public and private academic institutions: 

FY 2012.................................................................................................................................5-16

Figure 5-7. Share of academic S&E R&D, by institution rank in R&D expenditures: 

FYs 1989–2012 .....................................................................................................................5-17

Figure 5-8. Total and federally funded academic S&E R&D pass-throughs: 

FYs 2000–09 .........................................................................................................................5-17

Figure 5-9. Change in S&E research space in academic institutions, by 2-year period: 

FYs 1988–2011 .....................................................................................................................5-19

Figure 5-10. S&E research space at academic institutions, by field: FYs 2001 and 2011 ........5-19

Figure 5-11. Current fund expenditures for S&E research equipment at academic 

institutions, by field: FYs 2002–12.......................................................................................5-21

Figure 5-12. SEH doctorate holders employed in academia, by type of position: 

1973–2010.............................................................................................................................5-24

Figure 5-13. SEH doctorate holders employed in academia, by degree field: 

1973–2010.............................................................................................................................5-25

Figure 5-14. Women as percentage of SEH doctorate holders with full-time employment 

in academia, by academic rank: Selected years, 1973–2010 ................................................5-27

Figure 5-15. SEH doctorate holders employed in academia, by birthplace: 1973–2010 ..........5-28

Figure 5-16. Full-time faculty ages 65–75 at research universities and other higher 

education institutions: 1973–2010 ........................................................................................5-30

Figure 5-17. Primary work activity of full-time doctoral SEH faculty: 1973–2010 .................5-30

Figure 5-18. SEH doctorate holders with academic employment in postdoc position, 

by degree field: Selected years, 1973–2010..........................................................................5-33

Figure 5-19. S&E articles, by global share of selected region/country: 2001–11 .....................5-37

Figure 5-20. U.S. academic and non-academic S&E articles: 1997–2012................................5-39

Figure 5-21. Share of world articles in all fields authored by multiple authors, 

institutions, and nations: 1997 and 2012...............................................................................5-40

Figure 5-22. Share of world’s S&E articles with international collaboration, 

by S&E field: 1997 and 2012................................................................................................5-41

Figure 5-23. Share of S&E articles internationally coauthored, by selected country: 

2002 and 2012.......................................................................................................................5-42

Figure 5-24. Selected country share of U.S. internationally coauthored articles: 

1997 and 2012.......................................................................................................................5-44

Figure 5-25. Share of U.S. sector articles coauthored with foreign institutions, academia, 

and other U.S. sectors: 2010 .................................................................................................5-47

Figure 5-26. Average citations per S&E article, by country of author: 1992–2012..................5-48

Figure 5-27. Share of selected region/country citations that are international: 

1992–2012.............................................................................................................................5-48

Figure 5-28. Relative citation index to the United States, by scientific field: 2012..................5-49

Figure 5-29. Share of U.S., EU, and China S&E articles that are in the world’s top 

1% of cited articles: 2002–12................................................................................................5-50

Table 5-25. U.S. sector articles coauthored with other U.S. sectors and foreign institutions: 

2002 and 2012.......................................................................................................................5-46

Table 5-26. Relative citation index, by selected country/economy pair: 2012..........................5-49

Table 5-27. Patent citations to S&E articles, by selected patent technology area and 

article field: 2003–12...............................................................................................................5-55

Table 5-A. EPSCoR and EPSCoR-like program budgets, by agency: FYs 2001–12................5-14
5-4 ♦ Chapter 5. Academic Research and Development

Figure 5-30. Index of highly cited articles, by selected S&E field and region/country: 

2002 and 2012.......................................................................................................................5-51

Figure 5-31. Within-U.S. article citations: Relative citation index, 1992–2012 .......................5-52

Figure 5-32. Citations of U.S. S&E articles in U.S. patents, by selected S&E article 

field and technology area: 2012 ............................................................................................5-52

Figure 5-33. Citation of U.S. S&E articles in U.S. patents, by selected S&E field and 

article author sector: 2012.....................................................................................................5-53

Figure 5-34. USPTO patents granted to U.S. and non-U.S. academic institutions: 

1992–2012.............................................................................................................................5-55

Figure 5-35. EPO patents granted to U.S. and non-U.S. academic institutions: 

1992–2012.............................................................................................................................5-56

Figure 5-36. U.S. academic patents, by technology area: Selected 5-year averages, 

1993–2012.............................................................................................................................5-56

Figure 5-37. U.S. university patenting activities: 2002–11 .......................................................5-57
Spending for Academic R&D

In 2012, U.S. academic institutions spent $65.8 billion on 

research and development in all fields, including $62.3 

billion on S&E R&D and an additional $3.5 billion in 

non-S&E fields. 

♦ Academic R&D expenditures rose by almost 14% from 

2009–11, with the American Recovery and Reinvestment 

Act of 2009 (ARRA) providing almost $7 billion during 

these years.

♦ In 2012, ARRA expenditures dropped to $2.5 billion. Total 

academic R&D expenditures increased by less than 1% 

from the 2011 level (and decreased by 1% after adjusting 

for inflation). 

♦ In 2012 and throughout the past four decades, expenditures 

were concentrated in a relatively small number of public 

and private research-intensive universities. 

♦ The federal government provided about 60% of total academic R&D in FY 2012 (over $40 billion), a share that 

has remained relatively constant since the late 1980s. 

Six agencies provide over 90% of federal support for 

academic R&D in S&E—the Department of Health and 

Human Services (mainly through the National Institutes 

of Health), the National Science Foundation (NSF), the 

Department of Defense, the National Aeronautics and 

Space Administration, the Department of Energy, and the 

Department of Agriculture. 

Institutions’ own funds provided nearly 20% of S&E 

R&D in FY 2012 ($12.1 billion), while state and local 

governments, nonprofit organizations, and businesses 

funded smaller shares.

♦ State and local governments funded $3.4 billion of S&E 

R&D in FY 2012 (5.5%).

♦ Nonprofit organizations funded $3.7 billion of S&E academic R&D in FY 2012 (just under 6%).

♦ Businesses funded $3.2 billion of S&E academic R&D in 

FY 2012 (just over 5%). 

Over the last 20 years, the distribution of academic R&D 

expenditures across the broad S&E fields shifted in favor 

of life sciences and away from physical sciences. 

♦ In 2012, life sciences continued to receive the largest share 

(60%) of funding in academic S&E R&D.

♦ Over the last 20 years, life sciences was the only broad 

S&E field to experience a sizable increase in share—6 percentage points—of total academic S&E R&D. 

Infrastructure for Academic R&D

Research space at academic institutions has continued to 

grow annually over the last two decades, although the 

pace of growth has slowed in the last few years.

♦ Total research space at research-performing universities 

and colleges was 3.5% greater at the end of FY 2011 than 

it was in FY 2009.

♦ Research space for the biological and biomedical sciences accounted for 26.8% of all S&E research space in FY 

2011, making it the largest of all the major fields. 

In FY 2012, about $2.0 billion was spent for academic research equipment (i.e., movable items such as computers 

or microscopes), an 11.6% decrease from FY 2011 after 

adjusting for inflation. 

♦ Equipment spending as a share of total R&D expenditures 

fell from 4.6% in FY 2001 to a three-decade low of 3.2% 

in FY 2012. 

♦ Three S&E fields accounted for 86% of equipment expenditures in FY 2012: life sciences (41%), engineering 

(28%), and physical sciences (17%).

♦ In FY 2012, the federal share of support for all academic 

research equipment funding was 57%, which was below 

the average (58.7%) for the FY 2000–09 decade preceding 

the full impact of ARRA.

Cyberinfrastructure

Academic networking infrastructure is rapidly expanding in capability and coverage. 

♦ Research-performing institutions have gained greater access to high-performance networks since FY 2005, when 

NSF began collecting these data.

♦ Due to their research demands, doctorate-granting institutions have significantly higher bandwidth access and 

high-performance computing resources than non-doctorate-granting institutions. 

Doctoral Scientists and Engineers 

in Academia

The doctoral academic S&E workforce numbered about 

360,000 in 2010. 

♦ The U.S.-trained portion of the workforce numbered about 

295,000, while the foreign-trained portion numbered 

about 64,000. 

♦ The growth from 2008–10 in the doctoral academic S&E 

workforce reflects an increase in the overall population of 

doctoral scientists and engineers across the various sectors 

of the economy. 

Highlights

Science and Engineering Indicators 2014 ♦ 5-5
♦ The share of all U.S.-trained S&E doctorate holders employed in academia dropped from 55% in 1973 to 44% 

in 2010.

Among U.S.-trained S&E doctorate holders employed 

full-time in academia, faculty positions remained the 

predominant type of employment in 2010. However, the 

number of nonfaculty positions, including postdoctorates (postdocs), grew more rapidly than the number of 

faculty, particularly in recent years. 

♦ The percentage of S&E doctorate holders employed in academia who held full-time faculty positions declined from 

about 90% in the early 1970s to less than 75% in 2010. 

♦ Compared to 1997, a smaller share of the doctoral academic S&E workforce had achieved tenure in 2010. In 

1997, tenured positions accounted for an estimated 53% 

of doctoral academic employment; this decreased to 48% 

in 2010.

The demographic profile of the U.S.-trained academic 

doctoral workforce has shifted substantially over time. 

♦ The number of women in academia grew substantially 

between 1997 and 2010, from about 60,000 to 105,000. 

Women as a share of full-time senior doctoral S&E faculty 

also increased.

♦ In 2010, underrepresented minorities (blacks, Hispanics, 

and American Indians or Alaska Natives) constituted 8.3% 

of total U.S.-trained academic S&E doctoral employment 

and of full-time faculty positions, up from about 2% in 

1973 and 7%–8% of these positions in 2003. 

♦ The foreign-born share of U.S.-trained S&E doctorate 

holders in academia increased from about 12% in 1973 to 

26% in 2010.

♦ In 2010, about one-half of all U.S.-trained postdocs and 

almost three-fourths of total academically employed postdocs were born outside of the United States. 

♦ The U.S.-trained doctoral academic S&E workforce has 

aged substantially since 1995. In 2010, 20% of this workforce was between 60 and 75 years of age. 

Since 1997, there have been modest increases in the share 

of full-time faculty who identify research as their primary work activity. 

♦ The share of full-time faculty with S&E degrees who identified research as their primary work activity rose from 

33% in 1997 to 36% in 2010, while the share identifying 

teaching as their primary activity fell from 54% to 47%.

♦ In 2010, 37% of recently degreed S&E doctoral faculty 

identified research as their primary work activity. 

A substantial pool of academic researchers exists outside 

the ranks of tenure-track faculty. 

♦ Approximately 40,000 S&E doctorate holders were employed in academic postdoc positions in 2011. Of these, 

about 23,000 were trained in the United States. 

♦ In 2010, 41% of recently degreed U.S.-trained S&E doctorate holders in academia (less than 4 years beyond the 

doctorate) held postdoc positions, exceeding the share 

(35%) employed in full-time faculty positions. Among 

U.S.-trained S&E doctorate holders 4–7 years beyond their 

doctorate degrees, 13% held postdoc positions. 

♦ Almost 500,000 graduate research assistants worked in 

academia in 2011. 

For S&E as a whole and for many fields, the share of 

U.S.-trained academic S&E doctorate holders receiving 

federal support declined since the early 1990s.

♦ In 2010, about the same percentage of S&E doctorate holders received federal support as had received support in the 

early 1970s (about 45%). 

♦ During the late 1980s and very early 1990s, a somewhat 

higher share of S&E doctorate holders received federal 

support (49%).

♦ Among full-time faculty, recent doctorate recipients were 

less likely to receive federal support than their more established colleagues.

Outputs of Academic S&E Research: 

Articles and Patents

Global shares of S&E article output of the United States, 

the European Union (EU), and Japan have declined. 

China’s global share has risen sharply.

♦ The United States, the world’s second-largest producer, accounted for 26% of the world’s total S&E articles in 2011, 

down from 30% in 2001. The share for the EU, the world’s 

largest producer, also declined, from 35% in 2001 to 31% 

in 2011. Japan’s share fell from 9% to 6%.

♦ China grew the fastest among larger developing economies, with its share rising from 3% to 11%. China has 

become the world’s third-largest producer of scientific articles, after the EU and the United States.

♦ Brazil and India also grew rapidly, with their global shares 

reaching 2% and 3%, respectively. Iran, a developing nation with a much smaller publication base in 2001, grew to 

a 1% global share by 2011.

5-6 ♦ Chapter 5. Academic Research and Development
More than two-thirds of global S&E articles had authors from different institutions or different countries in 

2012, compared with just over half of such articles 15 

years earlier. 

♦ Coauthored articles with only domestic institutional authors increased from 36% of all articles in 1997 to 44% in 

2012. Internationally coauthored articles grew from 16% 

to 25% over the same period. 

♦ In the United States, 35% of its articles were coauthored 

with institutions in other countries in 2012, compared with 

16% in 1997. The center of U.S. collaboration is the U.S. 

academic sector, which coauthored 53% of its articles with 

other U.S. sectors or foreign institutions in 2012.

Citation data suggest that the influence of U.S.-authored 

articles remains quite high but has dropped some over 

the past 10 years. 

♦ In 2012, articles with U.S. authors were among the top 1% 

most-cited articles about 74% more often than expected, 

based on the U.S. share of all articles, compared with 85% 

in 2002. 

♦ Between 2002 and 2012, EU-authored articles, on average, 

became more influential. In 2002, they were cited 21% 

less often than expected among the top 1% most-cited articles; in 2012, the EU improved to 6% less often. In 2012, 

China’s share of highly cited articles was 37% less than 

expected. 

U.S. academic patents rose sharply from 3,300 in 2009 to 

5,100 in 2012.

♦ Patents granted by the U.S. Patent and Trademark Office 

(USPTO) to U.S. academic institutions increased by more 

than 50% from 2009 to 2012, mirroring strong growth of 

all USTPO patents.

♦ Biotechnology patents made up 1% of all USPTO patents 

but 25% of U.S. university patents in 2012. 

Science and Engineering Indicators 2014 ♦ 5-7
5-8 ♦ Chapter 5. Academic Research and Development

Introduction

Chapter Overview

U.S. academic institutions prepare the next generation 

of science, engineering, and mathematics professionals and 

conduct about half of the nation’s basic research, giving 

them a central position in the nation’s research and development system. 

This chapter reports trends in academic R&D inputs—

funding, infrastructure, and personnel—and academic R&D 

outputs—journal articles, citations to these articles, and various patent-based measures. (An additional major output of 

academic R&D, educated and trained personnel, is discussed 

in chapter 2.) Throughout the chapter, two key trends are 

explored: a generally stable distribution of academic R&D 

resources across different types of institutions, and a continuous increase in collaboration in research and research 

outputs. The consistent distribution of academic resources 

is evident in the relatively stable pattern of R&D expenditures over time among the major categories of colleges and 

universities as well as the primacy of certain fields and agencies in the funding for research and research infrastructure. 

Growing research collaboration is seen in increases in the 

amount of funds that universities pass through to others and 

in articles that are authored by more than one department, 

institution, sector, or country. 

Chapter Organization

The first section of this chapter examines trends in spending and funding for academic R&D, identifies key funders 

of academic R&D, and describes the allocation of funds 

across academic institutions and S&E fields. Because the 

federal government has been the primary source of funding 

for academic R&D for more than half a century, the section 

highlights the importance of federal-agency support both 

historically and more recently, as universities have spent 

American Recovery and Reinvestment Act of 2009 (ARRA) 

funds. This section highlights new data from the Higher 

Education Research and Development Survey (HERD) covering 2010–12, including improved information on the distribution of academic R&D among basic research, applied 

research, and development. This section also includes new 

data on R&D collaboration, as evidenced by the growth of 

pass-through funding arrangements. 

The chapter’s second section summarizes data on infrastructure for academic R&D. The section reports on current 

trends in academic research facilities, research equipment, 

and cyberinfrastructure. These trends include changes, by 

field, in research space and equipment as well as data on 

universities’ access to high-performance computing (HPC) 

and networking resources. 

The third section discusses trends in the employment 

of doctoral scientists and engineers working in academia. 

Major trends examined include the numbers of doctoral scientists and engineers who are academically employed, their 

changing demographic composition, and the types of positions they hold. The section further examines employment 

patterns in the different segments of the academic workforce 

that are engaged in research, especially full-time faculty, 

postdoctorates (postdocs), and graduate research assistants. 

In addition, the section reports data on academic scientists 

and engineers receiving research support from the federal 

government. A central theme in this section is that whether 

looking across 15–20 years or across four decades, the academically employed S&E workforce, like the S&E workforce throughout the economy, has changed substantially.

The fourth and final section of this chapter analyzes trends 

in two types of research outputs: S&E articles, which are 

largely (but not exclusively) produced by the academic sector, and patents issued to U.S. universities. This section first 

compares the volume of S&E articles for selected regions, 

countries, and economies, focusing (when appropriate) on 

patterns and trends in articles by U.S. academic researchers. 

Trends in coauthored articles, both across U.S. sectors and 

internationally, are indicators of increasing collaboration in 

S&E research. Trends in production of influential articles, as 

measured by the frequency with which articles are cited, are 

examined, with emphasis on international comparisons. The 

analysis of academic patenting activities examines patents, 

licenses, and income from these as forms of academic R&D 

output. Patent citations to the S&E literature are also examined, with emphasis on citations in awarded patents for clean 

energy and related technologies. 

Expenditures and Funding for 

Academic R&D

Academic R&D is a key component of the overall U.S. 

R&D enterprise.1 Academic scientists and engineers conduct 

the bulk of the nation’s basic research and are especially important as a source of the new knowledge that basic research 

produces. Indicators tracking the status of the financial resources, research facilities, and instrumentation that are used 

in this work are discussed in this and the next section of the 

chapter (for an overview of the sources of data used, see 

sidebar, “Data on the Financial and Infrastructure Resources 

for Academic R&D”). 

National Academic R&D Expenditures

Expenditures by U.S. colleges and universities on R&D 

in all fields totaled $65.8 billion in 2012 (appendix table 

5-1).2 When adjusted for inflation, academic R&D fell 

by 1% from 2011 to 2012.3 Expenditures in life sciences, 

physical sciences, and social sciences dropped by between 

2% and 3% after adjusting for inflation. Expenditures in 

computer sciences and mathematical sciences increased 

by around 3% after adjusting for inflation; in other broad 

fields of science, expenditures remained relatively constant. 

Engineering expenditures increased by just below 1% after 

adjusting for inflation. 
Science and Engineering Indicators 2014 ♦ 5-9

One-time ARRA funding was responsible for a sizable 

amount of academic R&D expenditures from 2010 to 2012 

(over $9.3 billion). ARRA expenditures peaked in 2011 at 

$4.2 billion. In 2010 and 2012, they were similar—around 

$2.5 billion in each of these years (table 5-1). Looking 

across the period from 2009 to 2012, academic R&D expenditures would have increased by an average annual rate 

of 1.8% after adjusting for inflation if ARRA had not been 

enacted; with ARRA funds, these expenditures increased by 

an average annual rate of 3.1% after adjusting for inflation.4

ARRA expenditures are expected to appear in the academic 

R&D total through 2014, in diminishing amounts. 

A methodological change also contributed to the growth 

in reported academic R&D expenditures in recent years. 

As a result of a more extensive screening effort during the 

first year of the redesigned HERD survey to include institutions with substantial non-S&E R&D, 170 institutions were 

added to the survey population. The additional universities 

accounted for $533 million in total R&D expenditures in 

FY 2011. 

Academic R&D spending is primarily for basic research—in 2012, 64% was spent on basic research, 27% 

was spent on applied research, and 9% was spent on development (table 5-2).5

 The estimated percentage of spending 

on basic research is somewhat less than institutions had reported throughout the late 1990s and the 2000–09 decade 

(appendix table 5-2). Improvements to the survey question in 

2010 likely affected how universities reported these shares.6

Academic institutions spent a total of $3.5 billion on 

R&D in non-S&E fields in FY 2012, an increase of 7% 

(before adjusting for inflation) over the $3.3 billion spent 

in 2011 (table 5-3).7 The federal government funds a much 

smaller proportion of R&D in non-S&E than in S&E fields: 

34% of the $3.5 billion spent on non-S&E R&D in FY 2012, 

compared to 63% of the $62.3 billion spent that year on S&E 

R&D. The largest amounts reported for R&D in non-S&E 

fields were for education ($1.2 billion), business and management ($440 million), and humanities ($340 million). 

Sources of Support for Academic S&E R&D

Academic R&D relies on funding support from a variety 

of sources, including the federal government, universities’ 

and colleges’ own institutional funds, state and local government, business, and other organizations (appendix table 

5-3). The federal government has consistently provided the 

majority of funding for academic R&D in S&E. In 2012, 

the National Research Council reviewed the state of U.S. 

research universities and issued a report exploring ways 

to strengthen the partnership between government, universities, and industry in support of national goals (see the 

sidebar “National Research Council: Recommendations to 

Strengthen America’s Research Universities”). 

Recent data on the financial and infrastructure resources supporting U.S. academic R&D are drawn 

from two ongoing National Science Foundation (NSF) 

surveys, the annual Higher Education Research and 

Development Survey (HERD) and the Survey of Science 

and Engineering Research Facilities. 

Data on current operating expenditures for academic 

R&D are derived from HERD and its predecessor, NSF’s 

Survey of Research and Development Expenditures at 

Universities and Colleges, which covered the period from 

1972 to 2009. The survey population for the predecessor survey comprised academic institutions that granted 

a bachelor’s degree or a higher degree in S&E fields and 

spent at least $150,000 annually on separately budgeted 

S&E R&D. 

HERD updated data collection to reflect current accounting principles that provide more valid and reliable 

measurements of the amount of U.S. academic R&D expenditures. Data from the revised and expanded survey 

cover expenditures starting with academic FY 2010. The 

survey population is made up of academic institutions 

that grant a bachelor’s degree or a higher degree in any 

field and spend at least $150,000 annually on all separately budgeted R&D. 

Like its predecessor, HERD captures comparable information on R&D expenditures by sources of funding and 

field, which allows for continued trend analysis. It also 

includes a more comprehensive treatment of S&E and 

non-S&E fields, an expanded population of surveyed institutions, and greater detail about the sources of funding 

for R&D expenditures by field. Improvements in the redesigned survey are more fully described in Britt (2010).

As did its predecessor, HERD captures data on moveable research equipment purchased from current operating funds. Fixed equipment and capital construction 

projects are not included in the R&D expenditure totals. 

HERD data are in current-year dollars and reported on 

an academic-year basis (e.g., FY 2012 covers July 2011–

June 2012 for most institutions). 

Data on federal obligations for academic R&D are reported in chapter 4; that chapter also provides data on the 

academic sector’s share of the nation’s overall R&D. 

The data on research facilities and cyberinfrastructure come from the Survey of Science and Engineering 

Research Facilities. The facilities survey includes all universities and colleges in HERD with $1 million or more in 

R&D expenditures. Starting in 2003, the facilities survey 

included data on computing and networking capacities. 

Data on the Financial and Infrastructure Resources for Academic R&D
5-10 ♦ Chapter 5. Academic Research and Development

Table 5-1

Federally financed higher education R&D expenditures funded by the American Recovery and Reinvestment Act 

of 2009, by institution type and control: FYs 2010–12

(Thousands of dollars)

2010 2011 2012

Type of institution

All federal 

R&D 

expenditures ARRA

NonARRA

All federal 

R&D 

expenditures ARRA

NonARRA

All federal 

R&D 

expenditures ARRA

NonARRA

All institutions ........... 37,477,100 2,684,122 34,792,978 40,771,096 4,173,353 36,597,743 40,130,460 2,446,913 37,683,547

Very high 

research............. 27,641,468 1,980,718 25,660,750 30,047,688 3,113,463 26,934,225 29,845,004 1,814,405 28,030,599

High research 

and doctoral 

research............. 4,166,736 235,252 3,931,484 4,539,039 398,103 4,140,936 4,488,204 286,804 4,201,400

Special focus ........ 3,728,104 317,508 3,410,596 3,989,628 484,395 3,505,233 3,682,928 234,013 3,448,915

Other..................... 1,940,792 150,644 1,790,148 2,194,741 177,392 2,017,349 2,114,324 111,691 2,002,633

Public.................... 23,351,313 1,609,243 21,742,070 25,388,804 2,547,655 22,841,149 25,112,353 1,612,725 23,499,628

Private................... 14,125,787 1,074,879 13,050,908 15,382,292 1,625,698 13,756,594 15,018,107 834,188 14,183,919

ARRA = American Recovery and Reinvestment Act of 2009.

NOTES: Data include S&E and non-S&E federal expenditures. Data for FY 2012 include only those institutions with $1 million or more in total R&D 

expenditures. Institutions reporting less than $1 million in total R&D expenditures completed a shorter version of the FY 2012 survey form and that form 

did not request information on ARRA-funded expenditures.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2013) of the Higher Education 

Research and Development Survey.

Science and Engineering Indicators 2014

Table 5-2

Higher education R&D expenditures, by source, character of work, and institutional control: FYs 2010–12

(Thousands of dollars)

All sources Federal sources

Fiscal year and 

institution type Total

Basic 

research

Applied 

research Development Total

Basic 

research

Applied 

research Development

2010

All institutions...... 61,257,398 40,447,510 15,509,065 5,300,823 37,477,100 25,385,643 9,417,733 2,673,724

Public .............. 41,233,759 27,269,400 10,397,033 3,567,326 23,351,313 15,806,171 5,733,271 1,811,871

Private ............. 20,023,639 13,178,110 5,112,032 1,733,497 14,125,787 9,579,472 3,684,462 861,853

2011

All institutions...... 65,274,235 42,524,917 17,015,016 5,734,302 40,771,096 27,096,972 10,713,838 2,960,286

Public .............. 43,913,855 28,865,817 11,350,366 3,697,672 25,388,804 16,970,999 6,599,322 1,818,483

Private ............. 21,360,380 13,659,100 5,664,650 2,036,630 15,382,292 10,125,973 4,114,516 1,141,803

2012

All institutions...... 65,774,524 41,992,517 17,718,281 6,063,726 40,130,460 26,072,764 10,890,277 3,167,419

Public .............. 44,180,528 28,635,051 11,785,332 3,760,145 25,112,353 16,524,660 6,715,555 1,872,138

Private ............. 21,593,996 13,357,466 5,932,949 2,303,581 15,018,107 9,548,104 4,174,722 1,295,281

NOTE: Data include S&E and non-S&E R&D expenditures.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Higher Education Research and Development Survey.

Science and Engineering Indicators 2014

Federal Expenditures

The federal government provided $38.9 billion (63%) 

of the $62.3 billion of academic spending on S&E R&D 

in FY 2012 (figure 5-1).8 The federal share was somewhat 

higher in the 1970s, although the federal government has 

long contributed the majority of funds for S&E academic 

R&D (figure 5-2). For the most part, federal R&D funding 

to the academic sector is allocated through competitive 

peer review. 

Federal expenditures for S&E academic R&D increased 

more from 2009 to 2012 (4.5% inflation-adjusted annual 

growth rate) than they did from 2005 to 2008 (–0.6% inflation-adjusted annual growth rate). The higher growth rates in 

later years largely reflect ARRA expenditures. Universities 
Science and Engineering Indicators 2014 ♦ 5-11

reported $4.2 billion in expenditures funded by ARRA in FY 

2011 and an additional $2.4 billion in ARRA expenditures in 

FY 2012 (table 5-1). The distribution of ARRA funds across 

institutions—with just under three-quarters of these funds 

spent at the nation’s most research-intensive schools—generally mirrored the overall federal distribution of funds for 

academic R&D discussed below. 

Basic research activities represented 65% of federal 

expenditures for academic R&D in FY 2012 (table 5-2).9

Applied research represented 27%, and development activities accounted for the remaining 8%. The distribution in FY 

2011 was very similar. Chapter 4 provides further detail on 

federal obligations for academic R&D, by character of work.

Top Federal Agency Supporters

Six agencies are responsible for the vast majority of annual federal expenditures for academic R&D: the Department 

of Health and Human Services (HHS), in particular, the 

National Institutes of Health (NIH); the National Science 

Foundation (NSF); the Department of Defense (DOD); the 

Department of Energy (DOE); the National Aeronautics 

and Space Administration (NASA); and the Department of 

Agriculture (USDA). In federal FY 2012, these six agencies 

represented over 92% of the estimated $38.9 billion federal 

expenditures for academic S&E R&D (appendix table 5-4; 

chapter 4 provides data on these agencies’ obligations for 

academic R&D).10

Among these six agencies, HHS is by far the largest funder, 

providing about 56% of total federal academic S&E R&D 

expenditures in FY 2012. NSF and DOD follow HHS, each 

providing between 12% and 13%; DOE, NASA, and USDA 

provided smaller shares of between 3% and 5% of total federal 

academic S&E R&D expenditures in FY 2012. From 2003 to 

2012, the relative ranking of the top six funding agencies in 

terms of academic S&E R&D expenditures has remained relatively stable (table 5-4). 

The federal government’s overall support for academic 

R&D is the combined result of numerous discrete funding 

decisions made by the R&D-supporting federal agencies, 

with input from the White House and Congress. Varying 

missions, priorities, and objectives affect the level of funds 

that universities and colleges receive as well as how they 

are spent. Broad geographic distribution of academic research capability and federal funding of academic R&D is 

one such objective. The Experimental Program to Stimulate 

Competitive Research (EPSCoR) is a long-standing, multiagency federal program that seeks to increase the geographical dispersion of federal support for academic R&D. An 

overview of the program and recent statistics on its activities are presented in the sidebar “Experimental Program to 

Stimulate Competitive Research.” 

Other Sources of Funding

Notwithstanding the continuing dominant federal role in 

academic S&E R&D funding, nonfederal funding sources 

have also grown steadily over the past 15 years (figure 5-1). 

Adjusted for inflation, annual growth in nonfederal funding 

for academic R&D averaged almost 4% from 1996 to 2012. 

♦ University and college institutional funds. In FY 2012, 

institutional funds from universities and colleges comprised the second-largest source of funding for academic 

S&E R&D, accounting for over 19% ($12.1 billion) of the 

total (appendix table 5-5). The share of support represented 

by institutional funds has remained near 20% since 1990 

(appendix table 5-3). In addition to internal funding from 

general revenues, institutionally financed R&D includes 

unrecovered indirect costs and committed cost sharing.11

Table 5-3

R&D expenditures in non-S&E fields at universities and colleges: FYs 2010–12

(Millions of current dollars)

2010 2011 2012

Field

Total 

expenditures

Federal 

expenditures

Total 

expenditures

Federal 

expenditures

Total 

expenditures

Federal 

expenditures

All non-S&E fields.............................................. 2,897 967 3,278 1,118 3,508 1,195

Business and management ........................... 368 86 400 100 442 96

Communication, journalism, and 

library science ............................................ 130 41 153 53 159 53

Education....................................................... 995 536 1,115 630 1,229 686

Humanities..................................................... 263 58 313 61 341 68

Law ................................................................ 98 19 125 27 132 25

Social work .................................................... 177 94 194 105 199 109

Visual and performing arts............................. 66 5 77 7 85 10

Other non-S&E fields ..................................... 800 127 901 134 922 148

NOTE: Detail may not add to total because some respondents reporting non-S&E R&D expenditures did not break out total and federal funds by 

non-S&E fields.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Higher Education Research and Development Survey.

Science and Engineering Indicators 2014
5-12 ♦ Chapter 5. Academic Research and Development

♦ State and local government funds. State and local governments provided 5.5% ($3.4 billion) of academic S&E 

R&D funding in FY 2012. The state and local government 

funding share has declined from a peak of 10% in the early 

1970s to below 6% in recent years. However, these figures 

are likely to understate the actual contribution of state and 

local governments to academic R&D, particularly for public institutions, because they reflect only funds that these 

governments directly target to academic R&D activities.12

They exclude any general-purpose, state government, or 

local government appropriations that academic institutions designate and use to fund separately budgeted research or to pay for unrecovered indirect costs; such funds 

are categorized as institutional funds. (See chapter 8, 

“State Indicators,” for some indicators of academic R&D 

by state.) 

♦ Nonprofit funds. Nonprofit organizations provided 5.9% 

($3.7 billion) of academic S&E R&D funding in FY 2012, 

a slightly higher share than that provided by state and local governments. A relatively large share of S&E nonprofit 

In 2010, the Committee on Research Universities of the 

National Academies’ National Research Council (NRC) 

undertook a 2-year effort to examine the health and competitiveness of the nation’s research universities and assess 

their capacity to compete globally. Prompted by a request 

from a bipartisan group of senators and congressmen, 

the NRC study Research Universities and the Future of 

America: Ten Breakthrough Actions Vital to Our Nation’s 

Prosperity and Security (NRC 2012) emphasized the importance of partnerships among institutions involved in research, efficiency and productivity in research operations, 

and efforts to cultivate research talent. 

The NRC report gave the following recommendations:

♦ The federal government should adopt stable, efficient, 

and effective policies and funding for university R&D 

and for graduate education. 

♦ States should provide public research universities 

with greater autonomy to compete strategically. States 

also should strive to restore per-student funding to the 

mean inflation-adjusted level for the 15-year period 

covering 1987–2002. The federal government should 

provide incentives to strengthen state support for public research universities. 

♦ The partnership between businesses and other research-performing institutions should be strengthened 

so that new knowledge, ideas, and technology are 

transferred more rapidly into the economy.

♦ Universities, university associations, and key stakeholders should work together to increase university efficiency and provide a greater return on investment for 

research sponsors while also educating key audiences 

about the value of U.S. research universities.

♦ The federal government should create a Strategic 

Investment Program to fund education and research initiatives that advance key national priorities. This effort 

should include a program of endowed faculty chairs 

to facilitate the careers of young investigators and a 

program to strengthen universities’ research infrastructures, with an initial focus on cyberinfrastructure.

♦ The federal government and other research sponsors 

should strive to fund the full costs of research projects 

that they sponsor at research universities.

♦ Federal and state governments should eliminate regulations that increase administrative costs and impede 

research productivity without improving the research 

environment. Specifically, state and federal policymakers should review the costs and benefits of regulations and eliminate those regulations whose costs 

outweigh their benefits. Furthermore, the federal government should make regulations and reporting requirements more consistent across agencies.

♦ Research universities, federal agencies, and employers across all sectors should improve the capacity 

of graduate programs to attract talented students by 

addressing attrition rates, length of time to degree, 

funding, and alignment with both student career 

opportunities and national interests. To do so, the 

federal government should increase its support for 

graduate education, and employers should engage 

more deeply with research university programs, for 

example, by providing internships and advising on 

curriculum design.

♦ Research universities, government at all levels, and 

other stakeholders should strive to ensure that all 

Americans, including women and underrepresented 

minorities, have the opportunity to study and eventually pursue careers in science, technology, engineering, 

and mathematics (STEM). To do so, research universities should participate in efforts to improve STEM 

education at the primary- and secondary-school levels.

♦ The federal government should ensure that the United 

States continues to benefit strongly from the participation of international students and scholars in research. 

Specifically, federal agencies should recruit international scholars; make it easier for researchers to obtain 

permanent residency or U.S. citizenship; and, consistent with homeland security considerations, improve 

the efficiency of visa processing.

National Research Council: Recommendations 

to Strengthen America’s Research Universities
Science and Engineering Indicators 2014 ♦ 5-13

funding (73%) is directed toward R&D in life sciences. 

Life sciences comprise somewhat less (60%) of total federal funding for S&E academic R&D (appendix table 5-5).

♦ Business funds. At $3.2 billion in FY 2012, support from 

the business sector accounts for the smallest share of academic S&E R&D funding (5.1%). Support for academia 

has never been a major component of business-funded 

R&D in the United States, although it is in some other 

countries (figure 5-3). 

♦ Other sources of funds. In FY 2012, all other sources 

of support, such as foreign-government funding or gifts 

designated for research, accounted for less than 2% (just 

under $1 billion) of academic S&E R&D funding. 

Academic R&D Expenditures, by Field

Investment in academic S&E R&D is distributed across 

eight broad fields, including life sciences, engineering, 

physical sciences, environmental sciences, social sciences, 

computer sciences, psychology, and mathematical sciences 

(appendix table 5-5). Expenditures have long been concentrated in life sciences, which have received more than half of 

Figure 5-1

Federal and nonfederal academic S&E R&D 

expenditures: FYs 1996–2012

Billions of dollars

NOTES: Data include expenditures for S&E R&D. Gross domestic 

product implicit price deators were used to convert current dollars 

to constant 2005 dollars.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Higher Education Research and 

Development Survey. See appendix table 5-2.

Science and Engineering Indicators 2014

1996 1998 2000 2002 2004 2006 2008 2010 2012

0

5

10

15

20

25

30

35

40

45

Federal (constant

2005 dollars)

Federal (current dollars)

Nonfederal

(constant

2005 dollars)

Nonfederal

(current dollars)

Figure 5-2

Academic S&E R&D expenditures, by source of 

funding: FYs 1972–2012

Percent

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Higher Education Research and 

Development Survey.

Science and Engineering Indicators 2014

1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012

0

10

20

30

40

50

60

70

80

Federal government

State/local government

Business

Academic institutions

All other sources

Table 5-4

Top six federal agencies’ shares of federally funded academic R&D expenditures: FYs 2003–12

(Percent)

Agency 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Health and Human Services............ 44.3 51.8 55.8 56.7 56.1 56.0 55.4 57.3 57.4 55.6

National Science Foundation .......... 9.9 11.7 12.1 11.9 11.7 12.1 12.1 12.5 12.5 13.0

Department of Defense ................... 8.2 9.0 8.9 9.2 9.1 9.8 10.4 12.1 12.0 12.4

Department of Energy ..................... 3.3 3.4 3.6 3.7 3.7 3.6 3.8 4.2 4.7 5.0

National Aeronautics and 

Space Administration................... 3.8 4.0 3.9 3.5 3.5 3.4 3.4 4.0 3.6 3.4

Department of Agriculture ............... 2.6 2.8 2.8 2.9 3.0 2.9 2.8 2.6 2.5 2.8

NOTE: Health and Human Services includes primarily the National Institutes of Health. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2013) of the Higher Education 

Research and Development Survey.

Science and Engineering Indicators 2014
5-14 ♦ Chapter 5. Academic Research and Development

all academic R&D expenditures for more than three decades. 

Life sciences consist primarily of medical sciences, biological sciences, and agricultural sciences. In FY 2012, academic R&D in life sciences accounted for $37.2 billion (60%) of 

the $62.3 billion academic S&E R&D total. R&D projects in 

life sciences constituted a slightly smaller share—58%—of 

federally supported academic S&E R&D that year. 

Within life sciences, medical sciences accounted for 55% 

of the total academic R&D; biological sciences accounted 

for another 31%. Adjusted for inflation, academic R&D 

expenditures in medical sciences almost doubled from FY 

1999 to FY 2011 (figure 5-4) and then dropped slightly in 

FY 2012. The sizeable increase from FY 1999 to FY 2011 

resulted, in part, from a near-doubling of NIH’s budget from 

1998 to 2003. Academic R&D expenditures in biological 

sciences (and in life sciences as a whole) increased by about 

80% from FY 1999 to FY 2011 after adjusting for inflation. 

As with medical sciences, academic R&D expenditures in 

biological sciences dipped slightly in FY 2012. Meanwhile, 

expenditures in agricultural sciences rose slightly from FY 

2011 to FY 2012. 

The Experimental Program to Stimulate Competitive 

Research (EPSCoR) is based on the premise that universities and their S&E faculty and students are valuable 

resources that potentially can influence a state’s development in the 21st century in much the same way that 

agricultural, industrial, and natural resources did in the 

20th century. 

EPSCoR’s purposes and early history are rooted in 

the early history of the National Science Foundation 

(NSF) and federal support of R&D. In 1978, Congress 

authorized NSF to initiate EPSCoR in response to broad 

public concerns about the extent of geographical concentration of federal funding for R&D. Eligibility for 

EPSCoR participation was limited to those jurisdictions 

that historically have received lesser amounts of federal 

R&D funding and have demonstrated a commitment to 

develop their research bases and improve the quality of 

S&E research conducted at their universities and colleges. EPSCoR sought to increase the R&D competitiveness of eligible states through the development and 

utilization of the science and technology (S&T) resources 

residing in their most research-oriented universities. The 

program sought to achieve this objective by (1) stimulating sustainable S&T infrastructure improvements at the 

state and institutional levels that would significantly increase the ability of EPSCoR researchers to compete for 

federal and private sector R&D funding, and (2) accelerating the movement of EPSCoR researchers and institutions into the mainstream of federal and private-sector 

R&D support.

The experience of the NSF EPSCoR program during 

the 1980s prompted Congress to authorize the creation 

of EPSCoR and EPSCoR-like programs in six other 

federal agencies: the Departments of Energy, Defense 

(DOD), and Agriculture; the National Aeronautics and 

Space Administration; the National Institutes of Health; 

and the Environmental Protection Agency (EPA). Two 

of these, EPA and DOD, discontinued issuing separate EPSCoR program solicitations in FY 2006 and FY 

2010, respectively. 

In FY 2012, the five remaining agencies spent a total of $483.8 million on EPSCoR and EPSCoR-like programs, up from $225.3 million in 2001 (table 5-A). 

Experimental Program to Stimulate Competitive Research

Table 5-A

EPSCoR and EPSCoR-like program budgets, by agency: FYs 2001–12

(Millions of dollars)

Agency 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

All agencies ....... 225.3 288.9 358.0 353.3 367.4 367.1 363.1 418.9 437.2 460.1 436.0 483.8

DOD ............... 18.7 15.7 15.7 8.4 11.4 11.5 9.5 17.0 14.1 0.0 0.0 0.0

DOE ............... 7.7 7.7 11.7 7.7 7.6 7.3 7.3 14.7 16.8 21.6 8.5 8.5

EPA ................ 2.5 2.5 2.5 2.5 2.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0

NASA ............. 10.0 10.0 10.0 10.0 12.0 12.5 12.8 15.5 20.0 25.0 25.0 18.4

NIH................. 100.0 160.0 210.0 214.0 222.0 220.0 218.0 223.6 224.3 228.8 226.5 276.5

NSF................ 74.8 79.3 88.8 93.7 93.4 97.8 101.5 120.0 133.0 147.1 146.8 150.9

USDA ............. 11.6 13.7 19.3 17.0 18.6 18.0 14.0 28.1 29.0 37.6 29.2 29.5

DOD = Department of Defense; DOE = Department of Energy; EPA = Environmental Protection Agency; EPSCoR = Experimental Program to 

Stimulate Competitive Research; NASA = National Aeronautics and Space Administration; NIH = National Institutes of Health; NSF = National Science 

Foundation; USDA = U.S. Department of Agriculture.

NOTES: EPA and DOD discontinued issuing separate EPSCoR program solicitations in FY 2006 and FY 2010, respectively. USDA reported budget in 

FY 2012 includes $6.8 million in unobligated funds. 

SOURCE: Data are provided by agency EPSCoR representatives and are collected by the NSF Office of Integrative Activities, Office of EPSCoR, 

January 2013.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 5-15

The other broad fields of S&E experienced different rates 

of growth in recent years. Growth in inflation-adjusted academic R&D expenditures from FY 1999 to FY 2012 was 

greater in engineering (82%) than in environmental sciences 

(35%), physical sciences (37%), or social sciences (29%). 

Inflation-adjusted expenditures for computer sciences and 

mathematical sciences increased by from 50% to 60% 

from FY 1999 to FY 2012, and expenditures for psychology doubled, although the growth in these fields started at 

lower bases than the other broad fields of S&E (figure 5-4). 

Certain smaller fields within the broad fields have experienced steady growth in recent years. For example, academic 

R&D expenditures for astronomy, a field within physical 

sciences, although small relative to other fields, have increased steadily in recent years (appendix table 5-1). Even 

after adjusting for inflation, academic expenditures for astronomy grew by 34% from 2005 to 2012. Similarly, within 

the social sciences, sociology has also seen steady growth in 

recent years; from 2005 to 2012, expenditures increased by 

24% after adjusting for inflation. 

Agencies differ in the extent to which they focus funds on 

various fields of S&E (figure 5-5). HHS—primarily NIH—

supports the vast majority of federal funding in life sciences 

(84%) and is also the lead funding agency in psychology and 

the social sciences. By contrast, and while their shares of 

total academic R&D funding are much smaller, DOD, DOE, 

NASA, and NSF have more diversified funding patterns. In 

FY 2012, NSF was the lead federal funding agency for academic research in physical sciences, mathematics, computer 

sciences, and environmental sciences. DOD was the lead 

funding agency in engineering. 

Federal funding has played a larger role in overall support 

for some fields than others (appendix table 5-5). The federal 

government is the dominant funder in S&E fields such as 

atmospheric sciences (82% in FY 2012), physics (77%), and 

aeronautical and astronautical engineering (76%). It plays 

a smaller role in other S&E fields, such as agricultural sciences (34%). 

The federally financed proportion of R&D spending in 

all of the broad S&E fields has generally been stable or 

has increased since 1990.13 This reverses the trend between 

1975 and 1990, when the federal share had declined in all 

the broad fields. 

Figure 5-3

Academic R&D financed by business for selected 

countries: 1981–2011

Percent

NOTES: Data are from the top seven R&D performing countries. Data 

are not available for all countries for all years. Data for Japan for 

1996 onward may not be consistent with earlier data due to changes 

in methodology. Data for China for 2001 and 2002 are estimated by 

the National Science Foundation. Data for the United States are 

collected as part of National Patterns of R&D Resources and differ 

from Higher Education Research and Development expenditures 

data; pass-through funds are removed. 

SOURCE: Organisation for Economic Co-operation and 

Development, Main Science and Technology Indicators (2012/2).

Science and Engineering Indicators 2014

1981 1987 1991 1995 1999 2003 2007 2011

0

5

10

15

20

25

30

35

40

United States

Germany

France

United Kingdom

Japan

China

South Korea

Figure 5-4

Academic R&D expenditures, by selected S&E 

field: FYs 1999–2012

Billions of constant 2005 dollars

NOTE: See appendix table 4-1 for the gross domestic product 

implicit price deators used to convert current dollars to constant 

2005 dollars.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Higher Education Research and 

Development Survey. See appendix table 5-1.

Science and Engineering Indicators 2014

1999 2002 2004 2006 2008 2010 2012

0

2

4

6

8

10

12

14

16

18

20

Medical sciences

Biological sciences

Engineering

Physical sciences

Environmental sciences

Social sciences

Computer sciences

Psychology

Mathematical sciences
5-16 ♦ Chapter 5. Academic Research and Development

Academic R&D, by Institution Type

The prior discussion examined R&D for the academic 

sector as a whole. This section discusses some of the differences in S&E R&D conducted by public and private universities and colleges. Although public and private universities 

rely on the same major sources of S&E R&D funding, the 

importance of the different sources varies substantially 

(figure 5-6). For example, endowments generally provide 

a larger share of total revenue at private universities than 

at public universities, while state appropriations provide 

a larger share of total revenue at public universities. (See 

the section “Trends in Higher Education Expenditures and 

Revenues” in chapter 2 for a discussion of average university revenue and expenditures per student at different types 

of institutions.) 

R&D Expenditures at Public and Private 

Universities and Colleges

In FY 2012, public institutions spent $41.6 billion in academic S&E R&D, and private institutions spent $20.6 billion, 

about one-half as much (appendix table 5-3). Similarly, of the 

top 100 academic institutions in academic R&D expenditures 

in 2012, two-thirds were public universities and colleges, and 

one-third were private schools (appendix table 5-6). 

The federal government provided the majority of the 

S&E R&D funds that public and private institutions spent 

on R&D in FY 2012 (just under 60% and just over 70%, 

respectively). Public institutions received around 7% of their 

S&E R&D funds from state and local governments, while 

private institutions received a little less than 2%. 

At both public and private academic universities, institutions’ own funds were a significant source of support for 

S&E R&D expenditures. Public academic institutions supported a larger portion of their S&E R&D from their own 

sources—22%, compared to 13% at private institutions. 

This larger proportion of institutional R&D funds in public 

institutions may reflect the general-purpose state and local 

government funds that public institutions directed toward 

R&D. Private institutions, in contrast, reported a larger proportion of unrecovered indirect costs (43% of their institutional total in FY 2012 versus 31% for public institutions).14

Private institutions also reported a larger proportion of cost 

sharing (14% of their institutional total in FY 2012 versus 

8% for public institutions). 

Public and private institutions both received 5%–6% of 

their R&D support from business in FY 2012. Nonprofit 

organizations funded 5.5% of total R&D expenditures in 

public institutions and 7.4% in private institutions. Funding 

from all other sources was less than 2% in both public and 

private institutions. 

DOD = Department of Defense; DOE = Department of Energy;  

HHS = Department of Health and Human Services; NASA = National 

Aeronautics and Space Administration; NSF = National Science 

Foundation; USDA = U.S. Department of Agriculture.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Higher Education Research and 

Development Survey, FY 2012. See appendix table 5-4.

Science and Engineering Indicators 2014

Billions

Figure 5-5

Federally financed academic R&D expenditures, 

by agency and S&E field: FY 2012

DOD DOE HHS NASA NSF USDA

0

5

10

15

20

25

Computer sciences

Environmental sciences

Life sciences

Mathematical sciences

Physical sciences

Psychology

Social sciences

Engineering

 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Higher Education Research and 

Development Survey, FY 2012. See appendix table 5-3. 

Science and Engineering Indicators 2014

Figure 5-6

Sources of S&E R&D funding for public and private 

academic institutions: FY 2012

Percent

Federal

government

State/local

government

Business Academic

institutions

Other

0

10

20

30

40

50

60

70

80

Public Private
Science and Engineering Indicators 2014 ♦ 5-17

Distribution of R&D Funds across Academic 

Institutions

Academic R&D expenditures are concentrated in a relatively small number of institutions. In FY 2012, 907 out of 

a total of approximately 2,250 baccalaureate-, master’s-, 

and doctorate-granting institutions reported spending at least 

$150,000 on R&D. Of these, the top-spending 20 institutions 

accounted for 31% of total academic S&E R&D spending, 

and the top-spending 100 institutions accounted for 79% of 

this spending. Although there were slight shifts in the share 

of academic S&E R&D expenditures accounted for by the top 

20 and top 100 institutions in recent years, the relative shares 

have been remarkably stable over the past two decades (figure 

5-7). Even so, the identities of the universities in each of these 

groups have varied over time. The top 100 institutions in S&E 

R&D are listed in appendix table 5-6. 

R&D Collaboration between Academic Institutions

Research collaboration involving multiple institutions 

is a growing trend. Contributing to this growth are federal 

initiatives to encourage collaborative research and also technological advances that facilitate communication and provide opportunities to mobilize specialized skills beyond the 

capacity of an individual institution. Opportunities to share 

risk and increase research credibility have also contributed 

to the growth of collaborative R&D (Cummings and Kiesler 

2007). Academic R&D collaboration is notably evident 

in the growth of jointly authored research articles (for details, see the section “Outputs of Academic S&E Research: 

Articles and Patents” in this chapter). 

This trend is also evident in flows of funds among institutions to support collaborative research activities. One measure 

of this research collaboration is the amount of total expenditures for R&D that universities pass through to others, including academic institutions and other entities. Available data 

on pass-through funding encompass S&E R&D from 2000 

to 2009 and total R&D (including non-S&E as well as S&E 

funds) from 2010 to 2012. As with overall academic R&D 

funding, pass-through funding arrangements are heavily concentrated in the most research-intensive institutions. 

Between FY 2000 and FY 2009, pass-through funding 

for collaborative projects among universities and colleges 

grew more rapidly (although from a much lower base) than 

the decade’s growth in overall academic R&D expenditures 

(appendix table 5-7; see also Hale [2012]). In FY 2000, total 

academic S&E R&D expenditures stood at $30.1 billion; this 

grew to $54.9 billion in FY 2009, an increase of 47% after 

adjusting for inflation. In contrast, the pass-through funds that 

universities provided to other universities from FY 2000 to 

FY 2009 more than doubled over this period of time, rising 

from $700 million in FY 2000 to $1.9 billion in FY 2009.15

The federal government contributed extensively to the 

growth in pass-through funding from FY 2000 to FY 2009. 

Almost 90% of all pass-through funds that universities provided to other universities came from federal funds during 

this decade (figure 5-8), a larger share than the federal government’s share of total academic R&D expenditures. 

Figure 5-7

Share of academic S&E R&D, by institution rank in 

R&D expenditures: FYs 1989–2012

Percent

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2013) of the Higher 

Education Research and Development Survey. See appendix table 

5-6. 

Science and Engineering Indicators 2014

1989 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

0

20

40

60

80

100

Top 10 institutions

Top 20 institutions

Top 100 institutions

Figure 5-8

Total and federally funded academic S&E R&D 

pass-throughs: FYs 2000–09

Millions of dollars

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Research and Development 

Expenditures at Universities and Colleges. 

Science and Engineering Indicators 2014

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

All academic R&D passed

through to other universities

Federal academic R&D passed 

through to other universities

All academic R&D

received from other

universities

Federal academic R&D received

from other universities
5-18 ♦ Chapter 5. Academic Research and Development

From 2010 to 2012, pass-through funding continued to 

increase. The federal government continues to be the major provider of pass-through funds; in FY 2012, it was the 

source for over 85% of all pass-through funds provided or 

received (tables 5-5 and 5-6).

The growth in pass-through funding has been accompanied by changing research practices, seen particularly in 

the growth of larger research teams, including many that 

span multiple disciplines, and in increasing numbers of coauthored articles (discussed later in this chapter in the section “Outputs of Academic S&E Research: Articles and 

Patents”). Although interdisciplinary research is widely 

viewed as a growing trend in academic S&E R&D, developing a generally agreed-on concept of interdisciplinary 

research and measuring how it has grown have proven to 

be challenging. (See the sidebar “Can Bibliometric Data 

Provide Accurate Indicators of Interdisciplinary Research?”

in Science and Engineering Indicators 2010 [NSB 2010:5–

35].) Efforts have been undertaken to measure the extent to 

which interdisciplinary research involves closely related versus dissimilar fields. For example, Porter and Rafols (2009) 

suggest that article citations are mainly distributed among 

closely related disciplinary areas, reflecting relatively modest increases in interdisciplinarity over the past 30–40 years. 

Infrastructure for Academic R&D

Physical infrastructure is an essential resource for the 

conduct of R&D. Not long ago, the capital infrastructure for 

R&D consisted primarily of research space (e.g., laboratories and computer rooms) and instrumentation. Accordingly, 

the square footage of a designated research space and counts 

of instruments have been the principal indicators of the status of research infrastructure. 

Advances in information technology have brought significant changes to both the methods of scientific research 

and the infrastructure necessary to conduct R&D. The 

technologies, human interfaces, and associated processing 

Table 5-5

Total and federally financed higher education R&D expenditures passed through to subrecipients, by 

institutional control: FY 2012

(Thousands of dollars)

R&D expenditures passed through to subrecipients

R&D expenditures and type of institution

All R&D 

expenditures Total

Higher 

education 

subrecipients Businesses

Nonprofit 

organizations

Other 

subrecipients

Total R&D, all institutions............................... 65,774,524 5,538,500 3,069,428 1,059,136 831,731 578,205

Public......................................................... 44,180,528 3,508,057 1,947,649 730,506 475,926 353,976

Private........................................................ 21,593,996 2,030,443 1,121,779 328,630 355,805 224,229

Federally financed R&D, all institutions ..... 40,130,460 4,825,558 2,747,592 875,356 719,952 482,658

Public ..................................................... 25,112,353 3,073,569 1,724,890 641,078 413,454 294,147

Private .................................................... 15,018,107 1,751,989 1,022,702 234,278 306,498 188,511

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Higher Education Research and Development Survey.

Science and Engineering Indicators 2014

Table 5-6

Total and federally financed higher education R&D expenditures received as a subrecipient, by institutional 

control: FY 2012

(Thousands of dollars)

R&D expenditures received as a subrecipient

R&D expenditures and type of institution

All R&D 

expenditures Total

Higher 

education 

passthrough 

entities Businesses

Nonprofit 

organizations

Other 

passthrough 

entities

Total R&D, all institutions............................... 65,774,524 6,412,757 2,922,945 1,127,495 1,176,053 1,186,264

Public......................................................... 44,180,528 4,421,429 1,873,170 802,323 727,619 1,018,317

Private........................................................ 21,593,996 1,991,328 1,049,775 325,172 448,434 167,947

Federally financed R&D, all institutions ..... 40,130,460 5,650,745 2,687,335 938,593 1,004,832 1,019,985

Public ..................................................... 25,112,353 3,860,761 1,712,539 651,948 621,515 874,759

Private .................................................... 15,018,107 1,789,984 974,796 286,645 383,317 145,226

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Higher Education Research and Development Survey.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 5-19

capabilities resulting from these innovations are often called 

cyberinfrastructure.

Cyberinfrastructure has become an essential resource for 

science. It helps researchers process, transfer, manage, and 

store large quantities of data. Cyberinfrastructure includes 

resources such as high-capacity networks, which are used 

to transfer information, and data storage systems, which 

are used for short-term access or long-term curation. It may 

also involve HPC systems used to analyze data, create visualization environments, or facilitate remote use of scientific instrumentation (NSF 2012). Indicators for research 

facilities, research equipment, and cyberinfrastructure are 

highlighted below.

Research Facilities

Research Space

The nation’s research-performing colleges and universities had 202.9 million net assignable square feet (NASF) of 

research space available at the end of FY 2011 (appendix 

table 5-8).16 This was 3.5% above the net assignable square 

footage at the end of FY 2009 and continued more than two 

decades of expansion. However, this increase was less than 

the median growth (4.7%) for all biennial periods measured 

from FY 1988 to FY 2011 (figure 5-9).

Biological and biomedical sciences continued to account 

for the bulk of growth, increasing by 8.0% during the FY 

2009–11 period (appendix table 5-8). This field accounted 

for the largest portion of research space (26.8%), which 

totaled 54.3 million NASF.17 From FY 2001 to FY 2011, 

research space in biological and biomedical sciences grew 

64.5% (figure 5-10). The related field of health and clinical 

sciences was the second largest in FY 2011, accounting for 

36.7 million NASF and 18.1% of the total. Still sizable are 

engineering (31.7 million NASF, 15.6%); physical sciences 

(29.6 million NASF, 14.6%); and agricultural and natural 

resources (27.6 million NASF, 13.6%). Excluding biological and biomedical sciences, total S&E research space has 

grown only 1.4% since FY 2005. The growth rates have 

varied across the S&E fields (appendix table 5-8). The computer and information sciences, engineering, and psychology have all increased research space by at least 10%, while 

 

NOTES: Space is measured in net assignable square feet. The 

biennial survey cycle ran on even years from FYs 1988–98 and, 

subsequently, on odd years from FYs 1999–2011.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Science and Engineering 

Research Facilities.

Science and Engineering Indicators 2014

Figure 5-9

Change in S&E research space in academic 

institutions, by 2-year period: FYs 1988–2011

Percent

1988

–90

1990

–92

1992

–94

1994

–96

1996

–98

1999

–2001

2001

–03

2003

–05

2005

–07

2007

–09

2009

–11

0

2

4

6

8

10

12

Figure 5-10

S&E research space at academic institutions, 

by field: FYs 2001 and 2011

Net assignable millions of square feet

NOTES: Research animal space was not collected in FY 2001. S&E 

elds are those used in the National Center for Education Statistics 

(NCES) Classication of Instructional Programs (CIP). NCES updates 

the CIP every 10 years. S&E elds here reect the NCES 2010 CIP 

update.

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Science and Engineering 

Research Facilities. See appendix table 5-8.

Science and Engineering Indicators 2014

Research

animal space

Other eld of S&E

Social sciences

Psychology

Physical sciences

Mathematics

and statistics

Health and

clinical sciences

Engineering

Computer and

information sciences

Biological and

biomedical sciences

Agricultural and

natural resources

FY 2011

FY 2001

0 10 20 30 40 50 60
5-20 ♦ Chapter 5. Academic Research and Development

space devoted to the other broad science fields has declined 

or remained the same.18

New Construction

New research space is added each year through new 

construction projects and the repurposing of existing space. 

Along similar lines, some space is withdrawn from use. The 

net result has been an increase in research space for more than 

two decades. As part of this process, academic institutions 

broke ground on 8.1 million NASF of new S&E research 

space construction projects in FYs 2010–11. This total is 

50% lower than NASF constructed in FYs 2002–03 (table 

5-7). Although the growth rate of new construction projects 

has declined over the past decade, institutions initiated new 

Table 5-7

New construction of S&E research space in academic institutions, by field and time of construction: FYs 

2002–11

Field

Started in FY 2002 

or FY 2003

Started in FY 2004 

or FY 2005

Started in FY 2006 

or FY 2007

Started in FY 2008 

or FY 2009

Started in FY 2010 

or FY 2011

Net assignable square feet (millions)

All fields ........................................ 16.2 10.1 8.8 9.9 8.1

Agricultural and natural 

resources............................... 0.8 0.4 0.5 0.4 0.4

Biological and biomedical 

sciences ................................ 4.0 3.2 2.9 3.5 2.0

Computer and information 

sciences ................................ 1.0 0.3 0.6 0.3 0.1

Engineering............................... 2.2 1.5 1.3 2.1 1.3

Health and clinical sciences ..... 5.0 3.3 1.7 1.9 2.8

Mathematics and statistics....... * * * * *

Physical sciences ..................... 2.1 0.8 1.0 1.0 0.9

Earth, atmospheric, 

and ocean sciences........... 0.6 0.3 0.3 0.1 0.3

Astronomy, chemistry, 

and physics ....................... 1.5 0.5 0.7 0.9 0.6

Psychology ............................... 0.2 0.2 0.1 0.3 0.1

Social sciences......................... 0.2 0.1 0.1 0.2 0.1

Other sciences.......................... 0.7 0.3 0.7 0.3 0.3

Research animal spacea ........... 1.4 1.2 1.0 0.8 0.6

Share of total new construction square feet (%)

All fields ....................................... 100.0 100.0 100.0 100.0 100.0

Agricultural and natural 

resources............................... 4.9 3.9 5.7 4.0 4.9

Biological and biomedical 

sciences ................................ 24.7 31.4 33.0 35.4 24.7

Computer and information 

sciences ................................ 6.2 2.9 6.8 3.0 1.2

Engineering............................... 13.6 14.7 14.8 21.2 16.0

Health and clinical sciences ..... 30.9 32.4 19.3 19.2 34.6

Mathematics and statistics....... * * * * *

Physical sciences ..................... 13.0 7.8 11.4 10.1 11.1

Earth, atmospheric, 

and ocean sciences........... 3.7 2.9 3.4 1.0 3.7

Astronomy, chemistry, 

and physics ....................... 9.3 4.9 8.0 9.1 7.4

Psychology ............................... 1.2 2.0 1.1 3.0 1.2

Social sciences......................... 1.2 1.0 1.1 2.0 1.2

Other sciences.......................... 4.3 2.9 8.0 3.0 3.7

Research animal spacea ........... 8.6 11.8 11.4 8.1 7.4

* = > 0 but < 50,000 net assignable square feet.

a

 Figures for research animal space are listed separately and are also included in individual field totals.

NOTES: Detail may not add to total because of rounding. S&E fields are those used in the National Center for Education Statistics (NCES) Classification 

of Instructional Programs (CIP). NCES updates the CIP every 10 years; S&E fields here reflect the NCES 2010 CIP update. For comparison of subfields in 

the FY 2005 and FY 2007 surveys, see S&E Research Facilities: FY 2007, detailed statistical tables. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Survey of Science and Engineering Research Facilities.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 5-21

construction in all fields in this latest period. The health and 

clinical sciences and the biological and biomedical sciences 

fields both saw 2.0 million NASF or more of new construction initiated. Engineering research space construction accounted for 1.3 million NASF. No other fields added more 

than 0.9 million NASF through new construction during 

this time. 

Academic institutions draw on various sources to fund 

their capital projects, including the institutions’ own funds, 

state or local governments, and the federal government (appendix table 5-9). Institutions provide the majority of funds 

for construction of new research space, typically accounting 

for over 60.0% of the cost. For the construction of new research space initiated in FYs 2010–11, 61.9% of the funding 

came from institutions’ internal sources, 30.5% from state 

and local governments, and the remaining 7.6% from the 

federal government. The percentage of this funding from institutional sources has remained the same since FYs 2006–

07.19 The federal portion of funding has been under 10.0% 

in recent years but declined to 3.2% in FYs 2008–09 before 

this recent bounce.

Repair and Renovation

Academic institutions expended $3.5 billion on major repairs and renovations of S&E research space in FYs 

2010–11 (appendix table 5-10).20 They anticipated $3.1 billion in costs for planned repair and renovation of research 

space with start dates in FYs 2012–13. Nearly $1.0 billion 

was planned to improve space in biological and biomedical 

sciences as well as close to $1.0 billion for improvements to 

health and clinical sciences space. In addition to these slated 

improvements, academic institutions reported $4.8 billion in 

repair and renovation projects from their institutional plans 

that were not yet funded or scheduled to start in FYs 2012–

13. An additional $2.6 billion in needed improvements were 

identified that lay beyond institutional plans. The total backlog of deferred improvements was greater than all projects 

started or planned for the FY 2010–13 period. The costs 

for deferred repairs and renovations have consistently been 

greater than those started or planned for similar cycles in 

the past. 

Research Equipment

In FY 2012, about $2.0 billion in current funds were 

spent for movable S&E academic research equipment necessary for the conduct of organized research projects (appendix table 5-11).21 This spending accounted for 3.2% of 

the $62.3 billion of total academic S&E R&D expenditures. 

Spending decreased 11.6% from FY 2011 to FY 2012 when 

adjusted for inflation. Expenditures for academic research 

equipment reached the highest mark in several decades in 

FY 2004. Due in part to ARRA funding, research equipment 

expenditures approached this level again in FYs 2010–11. 

After this temporary increase, the FY 2012 expenditures 

fell to the lowest level measured in constant dollars since 

FY 2001. 

Research equipment expenditures continue to be concentrated in just a few S&E fields. In FY 2012, three fields accounted for 85.8% of the annual total: life sciences (41.0%), 

engineering (28.1%), and physical sciences (16.7%). The 

shares for these three fields have remained similarly predominant for many years (appendix table 5-11). Even so, 

when adjusted for inflation, the annual level of equipment 

spending in engineering, physical sciences, and the largest 

life sciences subfields of biological sciences and medical 

sciences declined from FY 2011 to FY 2012 to pre–FY 2010 

levels (figure 5-11). 

Some academic research equipment funding comes from 

the federal government. These federal funds are generally 

received as part of research grants or as separate equipment 

grants. In FY 2012, the federal government supported 57.0% 

of total academic S&E research equipment funding, which 

marked a 6 percentage point decline from the 25-year high 

reached in FY 2011 (appendix table 5-12). The federal share 

of funding varies significantly by S&E field, ranging from 

34% to 84% in FY 2012. Atmospheric sciences had the largest proportion of federally funded R&D equipment (83.6%), 

with astronomy (83.4%) and physics (80.8%) ranking just 

behind. Agricultural sciences (34.1%) received the smallest 

Figure 5-11

Current fund expenditures for S&E research 

equipment at academic institutions, by field: 

FYs 2002–12

Millions of constant 2005 dollars

NOTE: See appendix table 4-1 for gross domestic product implicit 

price deators used to convert current dollars to constant 2005 

dollars.

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Research and Development 

Expenditures at Universities and Colleges, and Higher Education 

Research and Development Survey. See appendix table 5-11.

Science and Engineering Indicators 2014

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

0

100

200

300

400

500

600

Engineering

Medical sciences

Physical sciences

Biological sciences

Environmental sciences

Computer sciences
5-22 ♦ Chapter 5. Academic Research and Development

share of federal research equipment funding, followed by 

civil engineering (37.2%).

Cyberinfrastructure

Academic institutions continue to enhance their cyberinfrastructure, which is an essential component to both research and instruction. The cyberinfrastructure indicators 

noted here include access to high-speed/high-capacity bandwidth, dark fiber, HPC, and the ability to store large amounts 

of data for immediate access or long-term curation.

Networking

Networking is an essential component of cyberinfrastructure. It facilitates research-related activities such as 

communication, data transfer, HPC, and remote use of instrumentation.22 Universities may have networks that are 

available to the entire campus community for both research 

and nonresearch activities. The traffic on these campus networks cannot be differentiated between administrative, instructional, research, and general student purposes. Thus, 

total bandwidth capacity cannot be treated as an indicator 

solely of research capacity, and changes in research uses 

cannot be inferred from changes in bandwidth capacity. 

Some cyberinfrastructure is dedicated primarily to research activities. For example, research-performing universities may have access to high-performance networks such 

as Internet2, an organization established in 1997 that is 

composed of research, academic, industry and government 

partners, and National LambdaRail, a university-owned organization established in 2003 that manages a 12,000-mile 

high-speed network.23 The Energy Sciences Network, a 

DOE-funded network supporting 30 major DOE sites as well 

as researchers at universities and other research institutions, 

serves a similar purpose. Regional networks or gigapops

(gigabit points of presence) facilitate access by providing 

networking resources and supplemental bandwidth to the 

national networks, which are often referred to as the network backbone. These resources are provided to universities 

as well as government agencies, federally funded research 

and development centers (FFRDCs), and other entities. The 

regional networks not only serve as network access points, 

they also provide advanced network services to ensure reliable and efficient data transfer.

By FY 2012, access to high-performance networks had 

become widespread at research universities, which is evidenced by the 63% of institutions reporting bandwidth of at 

least 1 gigabit per second (Gbps) (table 5-8). Thirty percent 

of academic institutions anticipated network connections of 

10 Gbps or greater in FY 2012, compared with 15% of institutions with such access in 2009. 

Doctorate-granting institutions have significantly higher 

bandwidth capacity than non-doctorate-granting institutions 

due to their research demands. In FY 2011, the percentage 

of doctorate-granting institutions with bandwidth of at least 

2.5 Gbps (43%) was more than 10 times greater than that 

of non-doctorate-granting institutions (4%). Furthermore, in 

FY 2012, 53% of doctorate-granting institutions estimated 

that they would have bandwidth of 2.5 Gbps or greater, compared to 5% of non-doctorate-granting institutions. 

Dark fiber is fiber-optic cable that has already been laid 

but is not yet being used. The amount of dark fiber controlled by institutions indicates the ability to expand existing 

Table 5-8

Bandwidth at academic institutions: FYs 2005–12

(Percent distribution)

Bandwidth FY 2005 FY 2007 FY 2009 FY 2011 FY 2012a

All bandwidth.............................................................................. 100 100 100 100 100

No bandwidth ......................................................................... 0 0 0 0 0

10 Mbps or less ...................................................................... 6 3 1 1 *

11 Mbps–100 Mbps................................................................ 42 33 19 9 8

101 Mbps–999 Mbps.............................................................. 30 31 35 31 27

1 Gbps–2.4 Gbps.................................................................... 15 23 25 28 26

2.5 Gbps–9 Gbps.................................................................... 4 4 5 6 7

10 Gbps .................................................................................. * 2 4 7 10

More than 10 Gbps................................................................. 2 4 11 18 20

More than 20 Gbpsb ............................................................ na na na 6 8

Number of institutions ................................................................ 449 448 495 539 538

 * = > 0 but < 0.5%. na = not applicable; category was added to FY 2011 survey.

Gbps = gigabits/second; Mbps = megabits/second. 

a

 Figures for 2012 are estimated.

b More than 20 Gbps is a subset of more than 10 Gbps. 

NOTES: Detail may not add to total because of rounding. FYs 2009, 2011, and 2012 include bandwidth to Internet1 (also termed “commodity Internet”), 

Internet2, and National LambdaRail. Data for FY 2005 and FY 2007 are limited to Internet1 and Internet2. The response categories in the FY 2005 survey 

varied slightly from those in the FYs 2007–11 surveys; in the FY 2005 survey, the categories included “1–2.5 Gbps” and “2.6–9 Gbps.” 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Survey of Science and Engineering Research Facilities.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 5-23

network capabilities, either between existing campus buildings or from the campus to an external network. The percentage of academic institutions with these unused cables 

has increased steadily in recent years. The percentage of institutions with dark fiber to their Internet service provider 

has grown from 29% in FY 2005 to 47% in FY 2011. The 

percentage of institutions with dark fiber between their own 

buildings remained high throughout this period, increasing 

slightly from 86% in FY 2005 to 90% in FY 2011.

High-Performance Computing

Many academic research institutions manage their HPC 

resources through a distinct organizational unit within the 

institution that has a separate staff and budget. A total of 192 

academic institutions reported ownership of centrally administered HPC resources in FY 2011.24 This approach enables 

faculty to focus on their primary responsibilities instead of 

being diverted by administration and fundraising to support 

their own HPC. Central HPC administration can decrease 

overall operating expenses and create wider availability of 

computing resources.25 However, many HPC resources, not 

included here, reside beyond direct institutional administration because they are supported by external funding sources. 

Forty-seven percent of doctorate-granting institutions 

provided centrally administered HPC resources, compared 

to less than 9% of non-doctorate-granting institutions. 

Similar percentages of public doctorate-granting (48%) and 

private doctorate-granting (45%) institutions provided these 

resources. Clusters are the most common centrally administered HPC architecture used by academic institutions because they provide the most flexibility and cost efficiency 

for scaling in addition to their generally lower administrative costs. Over 97% of HPC-providing institutions employ 

cluster architectures (appendix table 5-13). HPC-providing 

institutions also use architectures such as massively parallel 

processors (11% of institutions), symmetric multiprocessors 

(19%), or other types of architectures (20%), all of which can 

be used in conjunction with or as an alternative to clusters.26

Colleges and universities often share their HPC resources 

with external organizations. In FY 2011, these partnerships 

most often involved other colleges or universities (72%). 

Sharing of HPC resources with other external users was 

fairly evenly distributed among government (21%), industry 

(18%), and nonprofit organizational (17%) partners. Public 

institutions were more likely to have external users of their 

HPC than were private institutions.

Data Storage

As the collection of massive data sets has increased in 

recent years, data storage and curation have become an increasingly critical issue. Data management plans are often 

required in funding proposals where large data sets will be 

used. Of the academic institutions with centrally administered HPC in FY 2011, 56% reported usable online storage 

greater than 100 terabytes.27 A smaller share of public (21%) 

and private institutions (18%) provided greater than 500 

terabytes of online storage.

As of FY 2011, 45% of institutions with centrally administered HPC reported no archival storage. Archival storage 

includes online and offline storage for files and data that do 

not support immediate access from HPC resources. This percentage changed little from FY 2009 (43%), yet it stands 

much higher than FY 2007 (29%). 

Doctoral Scientists and 

Engineers in Academia

S&E doctorate holders employed at U.S. universities and 

colleges hold a central role in the nation’s academic R&D 

enterprise. Through the R&D they undertake, S&E doctorate holders produce new knowledge and contribute to marketplace innovation. They also teach and provide training 

opportunities for young people who may then go on to earn 

S&E doctorates and themselves train the next generation of 

scientists and engineers. 

This section examines trends in the demographic composition of the doctoral S&E academic workforce and its deployment across institutions, positions, and fields. Particular 

attention is paid to the component of the academic workforce that is more focused on research, including graduate 

assistants, those employed in postdoctoral positions, and 

researchers receiving federal support. A central message of 

this section is that, whether looking across 15–20 years or 

across four decades, the demographic composition of the 

academically employed S&E workforce, like the S&E workforce throughout the economy, has changed substantially. 

There have also been changes, although not as substantial, 

in how this workforce has been deployed across institutions, positions, and fields. Longer-term comparisons from 

1973 to 2010 are made to illustrate fluctuations over multiple decades and trends that, once started, have not stopped. 

Shorter-term comparisons (from the early to mid-1990s to 

2010) are made to illustrate what the past 15–20 years have 

brought forth.28 Comparisons over the 7-year period from 

2003 to 2010 are used in the discussion of minorities in the 

academically employed workforce because data prior to the 

early years of the 2000–09 decade are not directly comparable to data from 2003 to 2010. 

Unless specifically noted, estimates of S&E doctorate 

holders in this section come from the Survey of Doctorate 

Recipients (SDR), a biennial NSF survey that is limited to individuals, including foreign-born individuals, who received 

their research doctorate in science, engineering, or health at 

a U.S. institution. Since foreign-trained doctorate holders 

are also an important component of the academic doctoral 

workforce, this section also draws from the National Survey 

of College Graduates (NSCG) to provide estimates of foreign-trained, academically employed doctorate holders, by 

gender and field of degree. 

The SDR substantially undercounts academically employed postdocs, many of whom were trained outside the 
5-24 ♦ Chapter 5. Academic Research and Development

United States. To provide more complete postdoc counts, 

this section supplements SDR data on postdocs with data 

on postdocs from the Survey of Graduate Students and 

Postdoctorates in Science and Engineering (GSS), an annual 

survey jointly conducted by NSF and NIH. Data on graduate 

assistants are also provided from this survey. (See chapter 

3 for more information on foreign-born doctorate holders 

working in the United States.)

Owing to the complex interrelationships among faculty 

and nonfaculty positions that jointly produce R&D outcomes, much of the discussion addresses the overall academic employment of U.S.-trained S&E doctorate holders, 

regardless of position or rank. However, at various points, 

full-time faculty and those who work outside of the full-time 

faculty population are discussed separately.

Trends in Academic Employment of Doctoral 

Scientists and Engineers

Academic employment of doctoral scientists and engineers grew over the past three decades and reached an 

estimated 359,000 in 2010. Of this total, the large majority—almost 295,000—were U.S. trained. Among these, 

there was a substantial increase over the employment numbers estimated in 2008 (appendix table 5-14). The change 

from 2008 reflects an increase in the overall population of 

doctoral scientists and engineers across the various sectors 

of the economy rather than a shift toward a higher proportion 

of doctoral scientists and engineers finding employment in 

the academic sector. 

The United States is unlike many other countries in terms 

of the fraction of doctorate holders employed in academia. 

A comparison of 1990–2006 doctorate recipients in 14 

countries for which data are available found that, in most of 

these countries, more than half of the doctorate holders were 

employed in academia, compared with 47% for the United 

States. Only the United States, Austria, and Belgium had 

substantial fractions of doctorate holders employed in the 

business sector, and the United States had one of the smallest fractions employed in government (Auriol 2010). In recent decades, growth in the number of doctoral scientists and 

engineers in the academic sector has been slower than the 

rate of growth in the business and government sectors, resulting in a decline in the academic sector’s share of all S&E 

doctorates from 55% in the early 1970s to just under 50% in 

the mid-1990s to about 44% in 2010. 

Academic Employment of S&E Doctorate Holders

The doctoral academic S&E workforce includes doctorate holders in S&E who are employed at 2-year or 4-year 

colleges or universities, including medical schools and university research institutes. This workforce is employed in 

the following positions: full and associate professors (senior 

faculty); assistant professors (junior faculty); postdoctoral 

researchers (postdocs); other full-time positions, such as instructors, lecturers, adjunct faculty, research associates, and 

administrators; and part-time positions of all kinds. 

Full-time faculty positions as either senior or junior faculty continue to be the norm in academic employment, but 

S&E doctorate holders are increasingly employed in other 

full-time positions, as postdocs, and in part-time positions 

(figure 5-12). Over the past 40 years, and especially since 

the mid-1990s, average annual growth rates have been much 

higher for nonfaculty and part-time positions than for fulltime faculty positions. The share of full-time faculty among 

all U.S.-trained, academically employed S&E doctorate 

holders fell from almost 90% in the early 1970s to about 

80% by the mid-1990s and then dropped further, to about 

70% in 2010 (appendix table 5-14). From the early 1970s to 

2010, the share of other full-time positions rose from 6% to 

16%, the share of postdocs increased from 4% to 8%, and 

the share of part-time positions increased from 2% to 6% of 

all academic S&E doctorate holders. There has also been a 

Figure 5-12

SEH doctorate holders employed in academia, 

by type of position: 1973–2010

Percent

SEH = science, engineering, and health.

NOTES: Full-time faculty includes full, associate, and assistant 

professors plus instructors for 1973–95; for 1997–2010, full-time 

faculty includes full, associate, and assistant professors. Other 

full-time positions include such positions as research associates, 

adjunct appointments, lecturers, and administrative positions for all 

years plus instructors for 1997–2010. Academic employment is 

limited to U.S. doctorate holders employed at 2- or 4-year colleges or 

universities, medical schools, and university research institutes. Data 

beginning with 2008 include all U.S.-trained doctorate recipients who 

lived or worked in the United States on the survey date. These data 

correct for a slight undercount in prior years, when some U.S.-trained 

doctorate recipients who either planned to live abroad or were living 

abroad were excluded. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2013) of the 

1973–2010 Survey of Doctorate Recipients.

Science and Engineering Indicators 2014

1973 1977 1981 1985 1989 1993 1997 2001 2006 2010

0

20

40

60

80

100

Full-time faculty

Other full-time positions

Postdocs

Part-time positions
Science and Engineering Indicators 2014 ♦ 5-25

decrease in the percentage of U.S.-trained doctorate holders 

in tenured positions (discussed below). 

The proportion of full-time faculty among S&E doctorate 

holders in higher education gradually declined in all fields 

between 1973 and 2010. Growth in postdoc positions and 

other full-time and part-time positions helped to account for 

the declining share of full-time faculty positions (appendix 

table 5-14).

From the early 1980s through 2010, growth in the number 

of life scientists and psychologists with academic employment was consistently stronger than for doctorate holders in 

other S&E fields (figure 5-13). Growth in academic employment slowed in the early 1990s for social sciences, physical 

sciences, and mathematics but has increased since then in 

social sciences and mathematics (appendix table 5-14). 

Trends in Tenure Status

Among U.S.-trained S&E doctorate holders working fulltime in academia, the proportion that has achieved tenure has 

diminished since 1997, although the proportion in tenuretrack positions has not. In 1997, tenured positions accounted 

for an estimated 53% of positions held by U.S.-trained S&E 

doctorate holders in academic employment; this decreased 

to 48% in 2010 as other positions grew as a share of overall doctoral academic employment.29 The same percentage 

of positions in 1997 as in 2010 (just over 16%) was untenured but on a tenure track. Analysis of U.S. Department of 

Education data at all degree-granting institutions indicates 

larger decreases of about 10 percentage points over the past 

15–20 years in tenured positions’ share of academic employment (AAUP 2010). In addition, it is likely that a higher 

proportion of foreign-trained doctorate holders than U.S.-

trained doctorate holders working in academia are in nontenured and non-tenure-track positions. If so, the tenured 

proportion of the academic doctoral workforce (regardless 

of degree location) would be somewhat less than the 48% 

found among those who were trained in the United States 

(Stephan and Levin 2003). 

In both 1997 and 2010, the distribution of tenure status 

across the fields of S&E varied (table 5-9). For those with 

doctoral degrees in life sciences, mathematical sciences, social sciences, psychology, and engineering, the percentage 

of tenured positions by field decreased from 1997 to 2010 

by 4–9 percentage points, depending on the field. For those 

with a doctoral degree in physical sciences, there was less 

change between 1997 and 2010—about 50% were tenured in 

each year. For those with a degree in computer and information sciences, a larger percentage held tenured positions in 

2010 (53%) than in 1997 (46%). 

Tenure status also varied by age in 1997 and 2010 (table 

5-10). In 2010, lower percentages of doctorate holders at 

each age group were tenured.30 For example, 38% of those 

40–44 years of age held tenured positions in 2010, compared 

with 47% in 1997. For those 50–64 years of age, there were 

even larger differences between 1997 and 2010 in tenure status by age. For example, 70% of those 60–64 years of age 

held tenured positions in 2010, while 85% of those in this 

age range held tenured positions in 1997. There was a much 

larger presence in the doctoral academic workforce of those 

ages 65–75 years in 2010 (25,100; 9%) than in 1997 (8,500; 

 

SEH = science, engineering, and health.

NOTES: Data for computer sciences are not available before 1981. 

Academic employment is limited to U.S. doctorate holders employed 

at 2- or 4-year colleges or universities, excluding those employed 

part time who are students or retired. Physical sciences include 

earth, atmospheric, and ocean sciences; life sciences include 

biological, agricultural, environmental, and health sciences. Data 

beginning with 2008 include all U.S.-trained doctorate recipients who 

lived or worked in the United States on the survey date. These data 

correct for a slight undercount in prior years, when some U.S.-trained 

doctorate recipients who either planned to live abroad or were living 

abroad were excluded. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2013) of the 

1973–2010 Survey of Doctorate Recipients.

Science and Engineering Indicators 2014

Figure 5-13

SEH doctorate holders employed in academia, 

by degree field: 1973–2010

Thousands

1973 1981 1985 1989 1993 1997 2001 2006 2010

0

50

100

150

200

250

300

350

Life sciences

Social sciences

Physical sciences

Psychology

Engineering

Mathematics

Computer sciences

Table 5-9

Tenure status by field of doctorate: 1997 and 2010

(Percent)

Field of doctorate 1997 2010

Mathematical sciences.......................... 70.3 64.2

Social sciences...................................... 63.0 58.5

Computer and information sciences ..... 45.5 53.4

Engineering............................................ 58.6 49.7

Physical and related sciences............... 50.7 48.7

Psychology............................................ 50.4 42.4

Life sciences.......................................... 43.6 39.5

NOTES: Academic employment is limited to U.S. doctorate holders 

employed at 2- or 4-year colleges or universities, including medical 

schools and university research institutes. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2013), of the Survey 

of Doctorate Recipients. 

Science and Engineering Indicators 2014
5-26 ♦ Chapter 5. Academic Research and Development

4%), making it difficult to compare changes in tenure status 

in this age range over time. 

The reduction from 1997 to 2010 in tenured positions’ 

share of total positions occurred across most (but not all) 

Carnegie classifications (see the chapter 2 sidebar “Carnegie 

Classification of Academic Institutions” for a discussion of 

Carnegie classifications). In 1997, 47% of academically employed S&E doctorate holders at the most research-intensive 

institutions held tenured positions; this percentage decreased 

to just over 40% in 2010. Similar reductions occurred at less 

research-intensive doctorate-granting institutions and at 

master’s-granting institutions. However, at medical schools, 

similar percentages of academically employed doctorate 

holders held tenured positions in 1997 (31%) and 2010 

(29%). At baccalaureate institutions, a slightly higher share 

of academically employed doctorate holders held tenured 

positions in 2010 (60%) than in 1997 (58%). 

Women in the Academic S&E Workforce

The past 40 years have seen tremendous growth in the participation of women in the academic doctoral S&E workforce. 

In 1973, only about 11,000 U.S.-trained women were employed 

at this level. In 2010, by contrast, about 105,000 U.S.-trained 

women with S&E doctorates were employed in academia, 

nearly a 10-fold increase.31 The number of U.S.-trained women 

with S&E doctorates employed in academia almost doubled 

over the past 15 years, rising from about 60,000 in 1997 to over 

105,000 in 2010. In comparison, the number of U.S.-trained 

male S&E doctorate holders grew by just less than 10% over 

the same period and by about 80% over the four-decade period, from about 110,000 in 1973 to just under 200,000 in 2010 

(appendix table 5-15).32 An estimated 19,000 women were employed in academia as foreign-trained doctorate holders in S&E 

in 2010, along with an estimated 45,000 foreign-trained men.33

These differential rates of increase are reflected in the 

steadily rising share of women in the academic S&E workforce. 

Women constituted 36% of all U.S.-trained, academic S&E 

doctoral employment and 32% of full-time faculty in 2010, up 

from 9% and 7%, respectively, in 1973 (appendix table 5-15). 

Women’s share of academic S&E employment increased 

markedly over time in all position categories, though to a lesser 

degree in part-time positions (table 5-11). Women have held a 

larger share of junior faculty positions than positions at either 

the associate or full professor rank. However, as a result of the 

Table 5-10

Tenure status of academically employed SEH 

doctorate holders, by age: 1997 and 2010

(Percent)

Age 1997 2010

All ages.................................................. 52.6 47.8

< 30.................................................... D D

30–34 ................................................. 4.9 2.7

35–39 ................................................. 24.9 20.7

40–44 ................................................. 46.9 38.0

45–49 ................................................. 63.0 56.1

50–54 ................................................. 72.0 63.5

55–59 ................................................. 78.3 67.6

60–64 ................................................. 84.6 69.6

65–75 ................................................. 80.0 76.1

D = suppressed to avoid disclosure of confidential information.

SEH = science, engineering, and health.

NOTE: Academic employment is limited to U.S. doctorate holders 

employed at 2- or 4-year colleges or universities, medical schools, 

and university research institutes.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2013) of the Survey 

of Doctorate Recipients. 

Science and Engineering Indicators 2014

Table 5-11

Women as percentage of SEH doctorate holders employed in academia, by position: Selected years, 1973–2010

(Percent)

Position 1973 1983 1993 2003 2010a

All positions ................................................................................ 9.1 15.0 21.9 30.3 35.7

Full-time senior faculty............................................................ 5.8 9.3 14.2 22.8 28.0

Full-time junior faculty ............................................................ 11.3 23.5 32.2 39.7 44.2

Other full-time positions ......................................................... 14.5 23.1 30.2 34.8 41.7

Postdocs................................................................................. 14.3 30.1 30.8 38.0 39.0

Part-time positions ................................................................. 48.3 41.7 61.0 54.5 55.3

SEH = science, engineering, and health.

a

 Data for 2010 include all U.S.-trained doctorate recipients who lived or worked in the United States on the survey date. These data correct for a slight 

undercount in prior years, when some U.S.-trained doctorate recipients who either planned to live abroad or were living abroad were excluded.

NOTES: Academic employment is limited to U.S. doctorate holders employed at 2- or 4-year colleges or universities, medical schools, and university 

research institutes. Senior faculty includes full and associate professors; junior faculty includes assistant professors and instructors in 1973, 1983, and 

1993; in 2003 and 2010, junior faculty includes assistant professors. Other full-time positions include positions such as research associates, adjunct 

appointments, instructors (in 2003 and 2010), lecturers, and administrative positions. Part-time positions exclude those employed part time who are 

students or retired. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2013) of the 2003 and 2010 Survey of 

Doctorate Recipients.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 5-27

decades-long trend in the rising proportion of women earning 

doctoral degrees, coupled with their slightly greater propensity 

to enter academic employment, the share of women in all faculty ranks rose significantly between 1973 and 2010. In 2010, 

women constituted 22% of full professors, 37% of associate 

professors, and 44% of assistant professors (figure 5-14). 

Compared with their male counterparts in the U.S.-trained 

academic doctoral S&E workforce, women were more heavily 

concentrated in the fields of life sciences, social sciences, and 

psychology, with correspondingly lower shares in engineering, physical sciences, mathematics, and computer sciences. 

Women’s share of doctorate holders in each of these fields, 

however, grew during the 1973–2010 period (appendix table 

5-15). The field distribution of foreign-trained female doctorate holders largely mirrored this distribution (table 5-12). 

Minorities in the Academic S&E Workforce

Although the number of U.S.-trained, academically employed S&E doctorate holders who are members of underrepresented minority groups (i.e., blacks, Hispanics, and 

American Indians or Alaska Natives) has increased over 

time, they remain a small percentage of the total (appendix 

table 5-16).34 These groups constituted 8.3% of total academic employment and about the same percentage of fulltime faculty positions in 2010, up from about 2% in 1973 

and up from 7% (of full-time faculty positions) and 7.9% (of 

all positions) in 2003 (table 5-13). Underrepresented minority groups have a higher share of employment in other positions, which include part-time positions, than in the full-time 

faculty and postdoc employment categories. Compared to 

white S&E doctorate holders employed in academia, underrepresented minorities were concentrated in social sciences 

and less represented in physical sciences and life sciences 

(appendix table 5-16). 

In both 2003 and 2010, a slightly higher percentage of 

women than men who are underrepresented minorities held 

faculty positions.35 Female blacks held about 4.6% of faculty 

positions held by women in 2003 and about 5.1% of these 

positions in 2010. Male blacks were in about 2.9% of faculty positions held by men in 2003 and about 3.4% in 2010. 

Similarly, female Hispanics occupied about 4.3% of faculty 

positions held by women in 2003 and about 4.8% in 2010. 

Male Hispanics were in about 3.2% of faculty positions occupied by men in 2003 and about 3.9% in 2010. Male and 

female American Indians and Alaska Natives held about the 

same percentage of faculty positions in 2003 and 2010 (less 

than 1%). 

The share of Asians or Pacific Islanders employed in the 

S&E academic doctoral workforce grew dramatically over 

the past three decades, rising from 4% in 1973 to 16% in 

 

SEH = science, engineering, and health.

NOTES: Academic employment is limited to U.S. doctorate holders 

employed at 2- or 4-year colleges or universities, medical schools, 

and university research institutes, excluding those employed part 

time who are students or retired. Junior faculty includes assistant 

professors and instructors in 1973, 1983, and 1993; in 2003 and 

2010, junior faculty includes assistant professors. Data for 2010 

include all U.S.-trained doctorate recipients who lived or worked in 

the United States on the survey date. These data correct for a slight 

undercount in prior years,when some U.S.-trained doctorate 

recipients who either planned to live abroad or were living abroad 

were excluded. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2013) of 2003 and 

2010 Survey of Doctorate Recipients.

Science and Engineering Indicators 2014

Figure 5-14

Women as percentage of SEH doctorate holders 

with full-time employment in academia, by 

academic rank: Selected years, 1973–2010

Percent

1973 1983 1993 2003 2010

0

10

20

30

40

50

Full professor Associate professor Junior faculty

Table 5-12

Foreign-trained SEH doctorate holders employed 

in academia, by degree field and sex: 2010

Field Total Male Female

Full-time positions

All fields ............................. 61,000 43,000 18,000

Physical sciences .............. 15,000 13,000 2,000

Computer and 

mathematical sciences... 3,000 3,000 S

Life sciences ...................... 34,000 20,000 14,000

Social sciences and 

psychology..................... 4,000 3,000 1,000

Engineering........................ 5,000 4,000 1,000

Part-time positions

All fields ............................. 3,000 2,000 1,000

S = suppressed for reasons of confidentiality and/or reliability.

SEH = science, engineering, and health.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2013) of the National 

Survey of College Graduates.

Science and Engineering Indicators 2014
5-28 ♦ Chapter 5. Academic Research and Development

2010.36 Asians or Pacific Islanders were heavily represented 

among those with degrees in engineering and computer sciences, where they constituted 31% and 37%, respectively, 

of the S&E academic doctoral workforce in 2010. Among 

those with degrees in social sciences (9%) and psychology (6%), far smaller proportions were Asians or Pacific 

Islanders (appendix table 5-16). A larger share of Asians or 

Pacific Islanders than whites was employed at research universities and medical schools in 2010. 

In both 2003 and 2010, a higher percentage of male 

Asians or Pacific Islanders held faculty positions than their 

female counterparts. Male Asians or Pacific Islanders were 

in about 12.0% of faculty positions occupied by men in 2003 

and about 14.4% of these positions in 2010. Female Asians 

or Pacific Islanders held about 8.9% of faculty positions occupied by women in 2003 and about 12.1% in 2010. Both 

male and female Asians or Pacific Islanders increased their 

share of faculty positions from 2003 to 2010. 

Foreign-Born U.S. S&E Doctorate Holders in the 

Academic Workforce

Academia has long relied on foreign-born doctorate holders, many of them with doctoral degrees from U.S. universities, to staff faculty and other academic positions. The 

following discussion is limited to foreign-born individuals 

with U.S. doctorates. 

Academic employment of foreign-born, U.S.-trained 

S&E doctorate holders has increased continuously since the 

1970s at a rate that has exceeded the growth in academic employment of U.S.-born S&E doctorate holders. As a result, 

the foreign-born share of the total academic employment of 

U.S. S&E doctorate holders increased from 12% in 1973 to 

about 26% in 2010 (figure 5-15) and reached particularly 

Table 5-13

Underrepresented minorities as percentage of SEH doctorate holders employed in academia, by position: 

Selected years, 1973–2010

(Percent)

Position 1973 1983 1993 2003 2010a

All positions .......................................................................... 2.0 3.7 5.0 7.9 8.3

Full-time faculty ................................................................ 1.9 3.6 5.0 7.0 8.3

Postdocs........................................................................... 2.4 4.8 4.5 7.0 7.0

Other positions ................................................................. 2.9 4.1 5.3 7.3 8.6

SEH = science, engineering, and health.

a

 Data for 2010 include all U.S.-trained doctorate recipients who lived or worked in the United States on the survey date. These data correct for a slight 

undercount in prior years, when some U.S.-trained doctorate recipients who either planned to live abroad or were living abroad were excluded.

NOTES: Underrepresented minorities include blacks, Hispanics, and American Indians or Alaska Natives. Academic employment is limited to U.S. 

doctorate holders employed at 2- or 4-year colleges or universities, medical schools, and university research institutes. Faculty includes full, associate, 

and assistant professors plus instructors in 1973, 1983, and 1993. In 2003 and 2010, faculty includes full, associate, and assistant professors. Other 

positions include part-time positions and full-time positions such as research associates, adjunct appointments, instructors (in 2003 and 2010), lecturers, 

and administrative positions. Other positions exclude those employed part time who are students or retired.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2013) of the 2003 and 2010 Survey of 

Doctorate Recipients.

Science and Engineering Indicators 2014

 

SEH = science, engineering, and health. 

NOTES: Academic employment is limited to U.S. doctorate holders 

employed at 2- or 4-year colleges or universities, medical schools, 

and university research centers, excluding those employed part time 

who are students or retired. Data beginning with 2008 include all 

U.S.-trained doctorate recipients who lived or worked in the United 

States on the survey date. These data correct for a slight undercount 

in prior years, when some U.S.-trained doctorate recipients who 

either planned to live abroad or were living abroad were excluded. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2013) of the 2010 

Survey of Doctorate Recipients.

Science and Engineering Indicators 2014

Figure 5-15

SEH doctorate holders employed in academia, 

by birthplace: 1973–2010

Thousands

1973 1977 1981 1985 1989 1993 1997 2001 2006 2010

0

50

100

150

200

250

300

350

U.S. born

Foreign born
Science and Engineering Indicators 2014 ♦ 5-29

high proportions in engineering (49%) and computer sciences (51%) (appendix table 5-17). In all fields, foreign-born 

doctorate holders were a larger share of postdoc employment 

than of full-time faculty employment. Overall, 49% of postdoc positions were held by foreign-born U.S. S&E doctorate 

holders, compared to 24% of full-time faculty positions. 

Of the 46,000 U.S.-trained Asian or Pacific Islander S&E 

doctorate holders employed in academia in 2010, 10% were 

native-born U.S. citizens, 39% were naturalized U.S. citizens, and 51% were noncitizens. In 2010, Asians or Pacific 

Islanders represented 52% of the foreign-born S&E faculty 

employed full-time in the United States and nearly 70% of 

the foreign-born S&E doctorate holders with postdoc appointments. In contrast, only about 2% of native-born, fulltime faculty and 5% of native-born postdocs were Asians or 

Pacific Islanders. (See chapter 3 for a discussion of foreignborn individuals in the S&E workforce.) 

Age Composition of the Academic 

Doctoral Workforce

The trend toward relatively fewer full-time faculty positions and relatively more postdoc and other full-time and 

part-time positions is especially noteworthy because of the 

steady increase over the past 15–20 years in the share of fulltime faculty positions that are held by those over 65 years 

of age. 

In 1994, the Age Discrimination in Employment Act of 

1967 (ADEA) became fully applicable to universities and 

colleges, prohibiting the forced retirement of faculty at any 

age. From this point through 2010, as more individuals born 

during the period of high birth rates from 1946 to 1964 (the 

“Baby Boomers”) began to move through middle age into 

their 50s and 60s, the proportion of academically employed 

doctorate holders in the oldest age groups increased (table 

5-14). In 2010, 20% of U.S.-trained, academically employed 

doctorate holders in S&E were between 60 and 75 years of 

age, double the percentage (10%) of those in this age range 

in 1995.37 In 1995, full-time faculty ages 60–75 years held 

less than 2% of doctoral academic positions; this percentage 

increased to 7% in 2010. (See chapter 3 for a discussion of 

the age profile and retirement patterns of the S&E doctoral 

workforce in other institutional sectors.) 

Many of the older U.S.-trained, academically employed 

doctorate holders work at research-intensive universities. 

The percentage of doctorate holders working at the most 

research-intensive institutions who were between 60 and 75 

years of age increased by 8 percentage points between 1995 

and 2010, rising from just under 10% in 1995 to just under 

18% in 2010. Meanwhile, the percentage of doctorate holders working at the most research-intensive institutions who 

were between 30 and 44 years of age decreased by 6 percentage points between 1995 and 2010. In 1995, over 50% 

of doctorate holders working at the most research-intensive 

institutions were between 30 and 44 years of age; in 2010, 

this percentage had fallen to less than 44%. 

A comparison of the age distribution of full-time faculty 

positions at research universities and other universities and 

colleges shows that there has been a relatively sharp increase 

since the mid-1990s—when ADEA became applicable to 

the professoriate—in the percentage of these positions held 

by those ages 65–75 years. The data show that the share of 

those ages 65–75 years was rising well before the act became mandatory, dipped in the early 1990s at research universities (and leveled off at other institutions), and then rose 

steeply in most years from 1995 to 2010, particularly at the 

most research-intensive universities (figure 5-16; appendix 

table 5-18). 

Academic Researchers

The interconnectedness of research, teaching, and public service activities in academia makes it difficult to assess 

the precise size and characteristics of the academic research 

workforce by examining the employment trends in academic 

positions. Individuals with the same academic job titles may 

be involved in research activities to differing degrees or not 

be involved in research. Therefore, self-reported research 

involvement is a better measure than position title for gauging research activity.38 This section limits the analysis to 

academic S&E doctorate holders who reported that research 

is either their primary or secondary work activity (i.e., the 

activity that occupies the most or second-most hours of their 

work time during a typical work week). 

Doctoral S&E Researchers

Since 1973, the number of U.S.-trained, academically 

employed S&E researchers grew from just over 80,000 to 

almost 200,000 (appendix table 5-19). In 2010, of those 

identified as such researchers, over 140,000 were employed 

in full-time faculty positions.39

Looking across all doctoral academic positions and 

across the past four decades, the proportion of academically 

employed S&E doctorate holders who identified research as 

their primary or secondary activity has fluctuated between 

Table 5-14

Academically employed SEH doctorate holders, 

by age: 1995 and 2010

(Percent)

Age 1995 2010a

20–39..................................................... 29.2 26.6

40–59..................................................... 61.0 53.2

60–75..................................................... 9.8 20.1

SEH = science, engineering, and health.

a

 Data for 2010 include all U.S.-trained doctorate recipients who 

lived or worked in the United States on the survey date. These data 

correct for a slight undercount in prior years, when some U.S.-

trained doctorate recipients who either planned to live abroad or 

were living abroad were excluded. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2013) of the Survey 

of Doctorate Recipients. 

Science and Engineering Indicators 2014
5-30 ♦ Chapter 5. Academic Research and Development

about 60% and 75%. A similar pattern of fluctuation occurred for full-time faculty. In 2010, 67% of S&E doctorate 

holders in academia classified research as their primary or 

secondary activity.40

Looking across fields, the proportions of researchers 

among all academic S&E doctorate holders and all fulltime faculty were higher in life sciences, engineering, and 

computer sciences than in social sciences and psychology 

(appendix table 5-19). In most fields, the share of academic 

S&E doctorate holders who reported research as their primary or secondary responsibility declined slightly between 

1993 and 2010. 

A different picture emerges when considering those who 

report research as their primary work activity. In contrast to 

the declining share of academic employees who reported research as their primary or secondary work activity, the share 

who reported research as their primary work activity generally increased throughout the period from 1973 to 2010. 

Among full-time doctoral S&E faculty, the increased 

share of doctorate holders reporting research as their primary work activity reflects a shift in priority from teaching to 

research. Over the last four decades, the proportion of fulltime faculty identifying research as their primary work activity climbed from 19% to 36%, while the share of faculty 

with teaching as their primary activity fell from 68% to 47% 

(figure 5-17). 

The balance of emphasis between teaching and research 

varied across the disciplines. A higher share of faculty with 

doctorate degrees in life sciences identified research as their 

primary work activity, and a higher share of faculty with 

doctorate degrees in mathematics and social sciences reported teaching as their primary activity. Since 1991, the proportion of doctorate holders who reported research as a primary 

work activity declined among computer scientists and life 

scientists but grew among mathematicians, psychologists, 

engineers, and social scientists (appendix table 5-19).

S&E Full-Time Faculty

In 2010, 37% of the S&E doctoral faculty who had earned 

their degree since 2007 identified research as their primary 

work activity, a slightly lower share than that reported by 

faculty who had earned S&E doctorate degrees 4–7 years 

earlier or 8–11 years earlier (both 41%) (table 5-15). The 

Figure 5-16

Full-time faculty ages 65–75 at research universities 

and other higher education institutions: 1973–2010

Percent

NOTES: Faculty positions include full, associate, and assistant 

professors and instructors from 1973 to 1995; from 1997 to 2010, 

faculty positions include full, associate, and assistant professors. 

Data beginning with 2008 include all U.S.-trained doctorate 

recipients who lived or worked in the United States on the survey 

date. These data correct for a slight undercount in prior years, when 

some U.S.-trained doctorate recipients who either planned to live 

abroad or were living abroad were excluded. 

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, 1973–95 Survey of Doctorate 

Recipients, and special tabulations (2013) of the 1997–2010 Survey 

of Doctorate Recipients. See appendix table 5-18.

Science and Engineering Indicators 2014

1973 1977 1981 1985 1989 1993 1997 2001 2006 2010

0

2

4

6

8

10

12

Research universities

Other higher education institutions

 

SEH = science, engineering, and health.

NOTES: Academic employment is limited to U.S. doctorate holders 

employed at 2- or 4-year colleges or universities, medical schools, 

and university research institutes, excluding those employed part 

time who are students or retired. Full-time faculty includes full, 

associate, and assistant professors plus instructors for 1973, 1983, 

and 1993; for 2003 and 2010, full-time faculty includes full, associate, 

and assistant professors. Research includes basic or applied 

research, development, or design. “Other” includes a wide range of 

activities. Data for 2010 include all U.S.-trained doctorate recipients 

who lived or worked in the United States on the survey date. These 

data correct for a slight undercount in prior years, when some 

U.S.-trained doctorate recipients who either planned to live abroad or 

were living abroad were excluded. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2013) of the 2003 and 

2010 Survey of Doctorate Recipients.

Science and Engineering Indicators 2014

Figure 5-17

Primary work activity of full-time doctoral SEH 

faculty: 1973–2010

Percent

1973 1983 1993 2003 2010

0

10

20

30

40

50

60

70

80

Research Teaching Other
Science and Engineering Indicators 2014 ♦ 5-31

comparable percentage for faculty 12 or more years from 

receipt of their degree is somewhat lower (34%). The higher 

share of primary researchers within the second and third 

cohorts, 4–11 years since receiving their doctorate, coincides with the period during which many faculty would be 

preparing to apply for tenure at their university and would 

have heightened motivation to complete research projects 

and publish results. For faculty members who received their 

doctoral degree 12 or more years ago, other responsibilities—such as mentoring younger faculty, advising doctoral 

students, and accepting major committee assignments or faculty leadership roles—may become primary work activities. 

A similar pattern across career stages prevailed in most 

degree fields. Research was more frequently a primary 

work activity for faculty in engineering than for faculty in 

other fields. 

Graduate Research Assistants

The close coupling of advanced training with hands-on 

research experience is a key feature of U.S. graduate education. Many of the nearly one-half million full-time S&E 

graduate students in 2011 conduct research as part of their 

academic studies (table 5-16). 

The number of research assistants—full-time graduate 

students whose primary mechanism of financial support is 

a research assistantship—has grown faster than graduate enrollment, both overall and in most fields. Graduate research 

assistantships were the primary means of support for 27% of 

graduate students in 2011, up from 22% in the early 1970s. 

Academic Employment in Postdoc Positions

About 44,000 S&E doctorate holders were employed 

in academic postdoc positions in 2011 (see sidebar, 

“Postdoctoral Researchers”). The estimate comes from the 

GSS, which reported a total of about 63,000 postdocs in 

2011, with about two-thirds (over 44,000) holding doctorates 

in S&E and about one-third holding doctorates in non-S&E 

fields. SDR data indicate that the U.S.-trained component of 

academically employed postdocs with S&E degrees climbed 

from 4,000 in the early 1970s to 22,800 in 2010 (appendix 

table 5-14). During that time period, the share of postdocs 

increased from 4% to 8% of all U.S.-trained, academically 

employed S&E doctorate holders. Postdocs were much more 

prevalent in life sciences, physical sciences, and engineering 

than in social sciences, although there were increases across 

all fields in 2010. Growth from 2003 to 2010 was greatest in 

the proportion of U.S.-trained postdocs in physical sciences 

and engineering (figure 5-18; appendix table 5-14). 

The demographic profile of U.S.-trained individuals employed in academic postdoc positions has changed dramatically over the past 40 years. In particular, the proportions of 

postdocs held by women, racial and ethnic minorities, and 

foreign-born individuals have climbed (table 5-17).

A temporary postdoc appointment is a common stop 

along the career path of S&E doctorate holders, particularly 

during their early career stages. In 2010, 41% of recently degreed, U.S.-trained S&E doctorate holders in academia were 

employed in postdoc positions, while 35% were employed 

in full-time faculty positions (appendix table 5-20). Recently 

degreed refers to those who received their doctorate within 

1–3 years prior to the 2010 SDR. Early career refers to those 

who received their doctorate within 1–7 years prior to the 

2010 SDR. A lower share (13%) of U.S.-trained, academically employed S&E doctorate holders 4–7 years beyond 

their doctoral degree was employed in academic postdoc 

positions; 60% held full-time faculty positions (appendix 

table 5-20). 

In 2010, over three-fourths (78%) of recently degreed, 

U.S.-trained academic postdocs were employed at the most 

research-intensive universities (table 5-18). The postdoc 

Table 5-15

SEH faculty reporting research as primary work activity, by years since doctorate and degree field: 2010

(Percent)

Years since doctorate All fields

Computer and 

information 

sciences Life sciences

Mathematics 

and statistics

Physical 

sciences Psychology

Social 

sciences Engineering

All years since 

doctorate ................ 36.1 35.0 42.6 29.8 33.4 33.0 29.2 40.5

1–3.......................... 37.2 12.5 37.8 38.5 27.3 33.3 39.0 50.0

4–7.......................... 41.2 50.0 43.3 40.9 35.1 35.9 34.9 52.6

8–11........................ 40.5 25.0 45.3 25.0 42.1 37.1 34.4 51.9

≥ 12......................... 34.1 35.5 42.4 26.2 31.8 31.4 25.8 34.8

SEH = science, engineering, and health.

NOTES: Academic employment is limited to U.S. doctorate holders employed at 2- or 4-year colleges or universities, medical schools, and university 

research institutes, excluding those employed part time who are students or retired. Faculty includes full, associate, and assistant professors. Research 

includes basic or applied research, development, and design. Physical sciences include earth, atmospheric, and ocean sciences; life sciences include 

biological, agricultural, environmental, and health sciences.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2013) of the Survey of Doctorate 

Recipients.

Science and Engineering Indicators 2014
5-32 ♦ Chapter 5. Academic Research and Development

populations employed at medical schools and other universities and colleges included a larger pool of doctorate holders 

who had not recently earned their doctoral degree. The fields 

of life sciences and physical sciences have had the highest 

incidence of postdocs over the years (figure 5-18).

Recent data indicate that the economic downturn of the 

late 2000s may have influenced some early career doctorate 

holders to take academic postdoc positions when they would 

have preferred other employment. The percentages of postdocs citing “other employment not available” as a reason for 

accepting a postdoc position increased between 2008 and 

2010, while most other reasons for obtaining a postdoc decreased (table 5-19). (The percentage of postdocs citing “obtaining training outside the PhD field” also increased.) 

Table 5-16

Full-time SEH graduate students and graduate research assistants at universities and colleges, by degree field: 

Selected years, 1973–2011

1973 1983 1993 2003 2011a

Group and degree field Thousands Percent Thousands Percent Thousands Percent Thousands Percent Thousands Percent

Graduate students.............. 161.6 100 252.0 100 329.6 100 398.0 100 457.3 100

Computer sciences......... 2.9 2 10.6 4 17.4 5 30.9 8 33.8 7

Life sciences ................... 40.6 25 69.2 28 91.6 28 123.2 31 124.4 27

Mathematics ................... 10.3 6 11.0 4 14.5 4 14.6 4 18.7 4

Physical sciences ........... 28.9 18 37.2 15 41.9 13 41.9 11 49.3 11

Psychology ..................... 15.2 9 26.6 11 34.8 11 35.8 9 39.3 9

Social sciences............... 32.4 20 43.5 17 55.6 17 61.3 15 74.2 16

Engineering..................... 31.3 19 53.9 21 73.8 22 90.4 23 107.2 23

Graduate research 

assistants ...................... 35.9 100 54.9 100 90.2 100 114.3 100 122.5 100

Computer sciences......... 0.7 2 1.4 3 3.8 4 7.5 7 8.3 7

Life sciences ................... 9.4 26 16.5 30 28.0 31 35.5 31 37.7 31

Mathematics ................... 0.7 2 0.8 2 1.4 2 1.8 2 2.1 2

Physical sciences ........... 8.9 25 12.6 23 17.0 19 18.1 16 19.6 16

Psychology ..................... 1.9 5 3.0 5 4.6 5 5.6 5 5.6 5

Social sciences............... 4.0 11 5.0 9 7.4 8 8.4 7 7.6 6

Engineering..................... 10.4 29 15.6 28 28.0 31 37.4 33 40.1 33

SEH = science, engineering, and health.

a

 Total includes fields not shown separately that were added or reclassified in the 2007 survey.

NOTES: Detail may not add to total because of rounding. Graduate research assistants are full-time graduate students with research assistantships as 

their primary mechanism of support. Physical sciences include earth, atmospheric, and ocean sciences; life sciences include biological, agricultural, 

environmental, and health sciences.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2013) of the Survey of Graduate 

Students and Postdoctorates in Science and Engineering.

Science and Engineering Indicators 2014

A postdoctorate (postdoc) is a temporary position in academia, industry, a nonprofit organization, or government 

that is taken after the completion of a doctorate. It serves 

as a period of apprenticeship for the purpose of gaining 

scientific, technical, and professional skills. Ideally, the individual employed in a postdoc position gains these skills 

under the guidance of an adviser, with the administrative 

and infrastructural support of a host institution, and with 

the financial support of a funding organization. However, 

the conditions of postdoc employment vary widely between academic and non-academic settings, across disciplines, and even within institutions, and formal job titles 

are an unreliable guide to actual work roles.

Postdoctoral researchers have become indispensable 

to the S&E enterprise and perform a substantial portion of the nation’s research. Most have recently earned 

their doctoral degree, and so they bring a new set of 

techniques and perspectives that broadens their research 

teams’ experience and makes them more competitive 

for additional research funding. In addition to conducting research, postdoctoral researchers also educate, 

train, and supervise undergraduate students engaged 

in research; help write grant proposals and papers; and 

present research results at professional society meetings 

(COSEPUP 2000).

Postdoctoral Researchers
Science and Engineering Indicators 2014 ♦ 5-33

Figure 5-18

SEH doctorate holders with academic employment in postdoc position, by degree field: Selected years, 

1973–2010

Percent

SEH = science, engineering, and health.

NOTES: Some data were not available; other data were suppressed for reasons of condentiality and/or reliability. Academic employment is limited to 

U.S. doctorate holders employed at 2- or 4-year colleges or universities, medical schools, and university research institutes, excluding those employed 

part time who are students or retired. Physical sciences include earth, atmospheric, and ocean sciences; life sciences include biological, agricultural, 

environmental, and health sciences. Data for 2010 include all U.S.-trained doctorate recipients who lived or worked in the United States on the survey 

date. These data correct for a slight undercount in prior years, when some U.S.-trained doctorate recipients who either planned to live abroad or were 

living abroad were excluded.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2013) of the 2003 and 2010 Survey of 

Doctorate Recipients.

Science and Engineering Indicators 2014

Physical sciences Mathematics Computer sciences Life sciences Psychology Social sciences Engineering

0

2

4

6

8

10

12

14

1973 1983 2003 2010

Table 5-17

SEH doctorate holders with academic employment in postdoc position, by demographic group: Selected years, 

1973–2010

(Percent distribution)

Demographic group 1973 1983 1993 2003 2010a

Sex

Female .................................................................................... 16.7 30.1 30.8 37.6 39.0

Male ........................................................................................ 83.3 69.9 69.2 62.4 60.5

Race/ethnicity

White....................................................................................... 85.7 81.9 68.4 63.1 54.9

Asian or Pacific Islander ......................................................... 11.9 13.3 27.1 30.6 36.6

Underrepresented minority ..................................................... 2.4 4.8 4.5 7.0 7.1

Place of birth

United States .......................................................................... 82.5 81.7 60.9 57.0 51.0

Foreign.................................................................................... 17.5 18.3 39.1 43.0 49.0

SEH = science, engineering, and health.

a

 Data for 2010 include all U.S.-trained doctorate recipients who lived or worked in the United States on the survey date. These data correct for a slight 

undercount in prior years, when some U.S.-trained doctorate recipients who either planned to live abroad or were living abroad were excluded.

NOTES: Academic employment is limited to U.S. doctorate holders employed at 2- or 4-year colleges or universities, medical schools, and university 

research institutes, excluding those employed part time who are students or retired. Underrepresented minorities include blacks, Hispanics, and 

American Indians or Alaska Natives. Asian or Pacific Islander includes Pacific Islanders from 1973–93 but excludes them from 2003–10.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2013) of the 2003 and 2010 Survey of 

Doctorate Recipients.

Science and Engineering Indicators 2014
5-34 ♦ Chapter 5. Academic Research and Development

Federal Support of Doctoral Researchers 

in Academia

The federal government provides academic researchers 

with a substantial portion of overall research support. This 

support may include assistance in the form of fellowships, 

traineeships, and research grants. For example, faculty 

members often receive research grants while postdocs often 

are funded through fellowships. This section presents data 

from S&E doctorate holders in academia who reported on 

the presence or absence (but not magnitude or type) of federal support for their work. Comparisons are made over the 

approximately 40-year period between the early 1970s and 

2010 and between the roughly two-decade-long period between the late 1980s or very early 1990s and 2010.41

Academic Scientists and Engineers Who Receive 

Federal Support

The share of S&E doctorate holders and researchers in 

academia who receive federal support has varied over time 

according to the level of research activity and the type of 

academic position held (appendix table 5-21). In general, a 

larger share of doctorate holders and researchers received 

federal support in the late 1980s and very early 1990s than 

Table 5-18

SEH doctorate holders with academic employment in postdoc position, by Carnegie institution type and years 

since doctorate: 2010

(Percent distribution)

Institution type

Postdocs 

(thousands)

Years since doctorate

1–3 4–7 ≥ 8

All institutions ................................................. 22.8 100.0 100.0 100.0

Doctorate-granting, very high research ...... 17.0 77.9 69.7 61.8

Other doctorate-granting institutions ......... 2.4 9.4 13.8 7.7

Medical schools/medical centers ............... 2.1 7.4 10.7 23.3

Other universities and colleges................... 1.3 5.3 5.8 7.2

SEH = science, engineering, and health.

NOTES: Academic employment is limited to U.S. doctorate holders employed at 2- or 4-year colleges or universities, medical schools, and university 

research institutes, excluding those employed part time who are students or retired. Institutions are designated by the 2005 Carnegie classification 

code. For information on these institutional categories, see The Carnegie Classification of Institutions of Higher Education, http://classifications.

carnegiefoundation.org/index.php.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2013) of the Survey of Doctorate 

Recipients. 

Science and Engineering Indicators 2014

Table 5-19

Reasons for accepting postdoc position: 2008–10

2008–10

Population 

change (%)

Distribution 

Reason change (%)

2008 2010

Total Percent Total Percent

All reasons

Additional training in PhD field....................... 12,200 67.6 14,800 65.2 21.3 -3.6

Training outside of PhD field.......................... 8,100 44.9 11,000 48.2 35.8 7.3

Work with person/at place............................. 11,300 62.9 12,900 56.8 14.2 -9.7

Other employment not available.................... 3,900 21.7 7,000 30.7 79.5 41.5

Postdoc expected in this field ....................... 13,900 76.9 17,000 75.1 22.3 -2.3

Some other reason ........................................ 1,500 8.3 1,900 8.4 26.7 1.2

Most important reason

Additional training in PhD field................... 4,000 22.3 4,300 19.0 7.5 -14.8

Training outside of PhD field ...................... 2,600 14.6 4,000 17.6 53.8 20.5

Work with person/at place ......................... 2,900 16.3 3,500 15.6 20.7 -4.3

Other employment not available ................ 1,800 9.8 3,100 13.5 72.2 37.8

Postdoc expected in this field.................... 5,800 32.0 6,800 29.8 17.2 -6.9

Some other reason..................................... 900 5.0 1,100 4.7 22.2 -6.0

NOTES: Data are for academically employed, U.S. trained postdocs. Numbers are rounded to the nearest 100. Detail may not add to total because of 

rounding.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics with National Opinion Research Center, special 

tabulations (2013) of the Survey of Doctorate Recipients.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 5-35

in either the early 1970s or in 2010. In 2010, 45% of all 

U.S.-trained S&E doctorate holders in academia and 56% 

of those for whom research was a primary or secondary activity reported federal government support for their work.42

Looking across all fields, about the same percentage (45%) 

of U.S.-trained, academically employed doctorate holders 

received federal support in the early 1970s as in 2010. In the 

very early 1990s, however, a somewhat higher percentage 

(49%) received federal support. A somewhat smaller share 

of those for whom research was a primary or secondary responsibility received federal support in 1973 (52%) than in 

1991 (58%) or 2010 (56%). The share of full-time faculty 

who received federal support from 1973 to 2010 fluctuated, 

rising from 42% in 1973 to 48% in 1991 and then dipping to 

45% in 2010. A larger share of academic doctorate holders 

employed in nonfaculty positions received federal support 

in 1973 (60%) and in the very early 1990s (59%) than in 

2010 (42%). 

Federal support varied by the field in which the academically employed held their doctoral degree. Over the past 40 

years, U.S.-trained doctorate holders in engineering, life sciences, and physical sciences have been more likely to report 

receiving federal support than doctoral degree holders in 

mathematics, psychology, or social sciences (appendix table 

5-21). In mathematics, gradually larger shares of doctorate 

holders received federal support (27% in 1973; just over 

34% in the very early 1990s and in 2010). In psychology 

and social sciences, by contrast, gradually smaller shares 

received federal support. For example, in 1973, 38% of doctorate holders in psychology and 26% of doctorate holders in 

the social sciences reported federal support. This decreased 

to 33% and 20%, respectively, in 2010. 

Federal support is more prevalent in medical schools and 

in the most research-intensive universities (very high research activity institutions according to Carnegie classification) (appendix table 5-22). About 65% of S&E doctorate 

holders and full-time faculty employed in these institutions 

received federal support in 2010. The percentage with federal support was about 50% at high research activity institutions; at other universities and colleges, it ranged from about 

15%–30%. 

Federal Support of Early Career S&E 

Doctorate Holders

Federal support has been less available to early career 

S&E doctoral faculty than to more established faculty, and 

the percentage of early career S&E faculty with federal support has declined (appendix table 5-23). In 2010, less than 

28% of recent doctorate recipients in full-time faculty positions received federal support, down from 38% two decades earlier. Of recent S&E doctorate recipients employed 

in postdoc positions in 2010, 72% received federal support, 

which was a substantial decline from the early 1990s (84%). 

S&E doctorate holders employed as full-time faculty 

who had received their doctorate 4–7 years earlier were 

more likely to receive federal support than those with more 

recently earned doctorates, and the same was true of those 

employed in postdoc positions. As with recent doctorate 

recipients, the share of full-time faculty and postdocs 4–7 

years beyond their doctorate who received federal support 

also declined from the early 1990s. The shares of early career full-time faculty and postdocs with federal support were 

generally higher in some fields (life sciences, physical sciences, and engineering) than in others (mathematics and social sciences). 

Outputs of S&E Research: 

Articles and Patents

Chapter 2 of this volume discusses the human capital outputs of higher education in S&E. This section of the current 

chapter continues that theme by examining the intellectual 

output of S&E research. The section presents indicators derived from both published research articles and U.S. patents.

Researchers have traditionally published the results 

of their work in the world’s peer-reviewed S&E journals. 

These bibliometric data (see sidebar, “Bibliometric Data and 

Terminology”) are indicators of national and global scientific activity. For example, a count of the coauthorships on 

U.S. articles is an indicator of the partnerships involved in 

the U.S. scientific effort. Likewise, measures involving citations and patents can be indicators of international patterns 

of influence and of invention based on scientific research. 

Bibliometric indicators are calculated for different countries 

and—within the United States alone—for different sectors.

Overall, the indicators provide insight into five broad 

areas. The first section, “S&E Article Output,” examines 

the quantity and national origin of S&E publications. The 

second section, “Coauthorship and Collaboration in S&E 

Literature,” examines the national partnerships in these 

publications. The third section, “Trends in Citation of S&E 

Articles,” examines various patterns of national scientific 

sharing and influence. The fourth section, “Citation of S&E 

Articles by USPTO Patents,” examines the utilization of 

S&E literature by inventors. And, finally, the fifth section, 

“Academic Patenting,” examines patenting and related activities in academia. 

Discussions of regional and country indicators will examine patterns and trends in developed and developing countries, as classified by the World Bank. Countries classified 

by the World Bank as high income are considered developed; those classified as upper- and lower-middle income 

and as low income are considered developing.

43

S&E Article Output

This section begins by describing and comparing the S&E 

article output of the United States to other regions, countries, 

and economies in the world. The article output of China and 

other developing countries has increased much more rapidly 

than that of the United States and other developed countries 

over the last 15 years. Although the United States remains 
5-36 ♦ Chapter 5. Academic Research and Development

a major producer of S&E articles, its global share of article 

production has declined. This section then examines U.S. 

article output in academia, the largest producer of U.S. articles, and other institutional sectors. 

Article Output by Country

A growing number of countries produce S&E articles. 

Over the period from 1988 to 2012, a total of 199 countries were authors on at least one S&E article (appendix 

table 5-24).44

The four major producers of the world’s S&E articles 

in 2011 were the European Union (EU; see “Glossary” for 

member countries) (31%), the United States (26%), China 

(11%), and Japan (6%).45 Together, they accounted for 73% 

of the world’s S&E publications in 2011 (figure 5-19; appendix table 5-26). The EU, the United States, and Japan 

have been major producers for several decades. China 

emerged as a major producer in the mid-2000s. Overall, 

47 countries—less than a quarter of those that produced 

S&E articles in 2011 (see appendix table 5-24)—accounted 

for 98% of global output (table 5-20).

Between 2001 and 2011, the total world S&E article output grew at an average annual rate of 2.8% (table 5-20). The 

total for developing countries grew more than three times 

faster (9.9% average annual) than the world total. China propelled growth of developing countries (15.6%), resulting in 

its global share climbing from 3% to 11% (figure 5-19). The 

fifth-largest S&E article producer in 2001, China surpassed 

Japan in 2007 to become the third-largest S&E article producer, behind the EU and the United States (appendix table 

5-26). China’s growth in S&E publication is concurrent with 

its enormous growth in GDP over the last decade, which is 

consistent with findings by many researchers that there is a 

high correlation between these two measures (Price 1969; 

Narin, Stevens, and Whitlow 1991).

The article counts, coauthorships, and citations discussed in this section are derived from S&E articles, notes, 

and reviews published in a set of scientific and technical 

journals tracked by the Science Unit of Thomson Reuters 

in the Science Citation Index (SCI) and Social Sciences 

Citation Index (SSCI) (http://www.thomsonreuters.com/

business_units/scientific/). Journal items excluded are 

letters to the editor, news stories, editorials, and other 

material whose purpose is not the presentation or discussion of scientific data, theory, methods, apparatus, 

or experiments. 

Journal selection. This section uses a changing set of 

journals that reflects the current mix of journals and articles in the world. Thomson Reuters selects journals each 

year as described at http://www.thomsonreuters.com/

products_services/science/free/essays/journal_selection_

process/, and the selected journals become part of SCI and 

SSCI. The journals selected are notable for their relatively 

high citation rank within their S&E subfields; journals of 

only regional interest are excluded.

The number of journals analyzed by the National 

Science Foundation from SCI and SSCI was 4,093 in 

1988 and 5,087 in 2012, an annual growth rate slightly 

less than 1.0%. These journals give good coverage of a 

core set of internationally recognized, peer-reviewed scientific journals. The coverage includes electronic-only 

journals and print journals with electronic versions. In 

the period 1988–2012, the database contained 16 million 

S&E articles, notes, and reviews. Over the same period, 

the average number of articles, notes, and reviews per 

journal per year increased from about 111 to 168, an annual growth rate of about 1.7%.

Article data. Except where noted, author means departmental or institutional author. Articles are attributed 

to countries or sectors by the country or sector of the institutional address(es) given in the articles, not by the national origins or the citizenship of the authoring scientists 

or engineers. If no institutional affiliation is listed, the 

article is excluded from the counts in this chapter. 

Likewise, coauthorship refers to institutional coauthorship. An article is considered coauthored only if it 

shows different institutional affiliations or different departments of the same institution; multiple listings of the 

same department of an institution are considered one institutional author. The same logic applies to cross-sector 

and international collaboration. 

Two methods of counting articles are used: fractional

and whole counts. Fractional counting is used for article 

and citation counts. In fractional counting, credit for coauthored articles is divided among the collaborating institutions or countries based on the proportion of their 

participating departments or institutions. Whole counting 

is used for coauthorship data. In whole counting, each 

institution or country receives one credit for its participation in the article. 

Data in the section “Article Output by Country” are 

reported by publication year through 2011 as recorded in the SCI and SSCI data files through late January 

2013. These data are noted as “by year of publication.” 

Publication data in the remaining bibliometrics sections 

are reported through 2012. These data are noted as “by 

data file year.”

The region/country/economy breakouts are reported 

in appendix table 5-24. Data reported in this section are 

grouped into 13 broad S&E fields and 125 subfields (appendix table 5-25). 

Bibliometric Data and Terminology
Science and Engineering Indicators 2014 ♦ 5-37

Among other larger emerging economies, over the decade Brazil grew at a 6.4% average annual rate and India 

grew at a 7.6% average annual rate, resulting in their global 

shares increasing 1 percentage point to reach 2% and 3%, 

respectively (table 5-20). Rapid growth of S&E articles in 

Brazil, India, and China coincided with increased R&D 

expenditures and growth in S&E degrees awarded at the 

bachelor’s-degree and doctoral-degree levels (see chapter 2, 

“Higher Education in Science and Engineering”). 

Smaller developing countries with rapid S&E article 

growth (11%–23% annual average) included Iran, Malaysia, 

Pakistan, Thailand, and Tunisia.

Developed economies’ S&E article production grew 

more slowly (1.5%) than that of developing economies 

(9.9%) over the decade. U.S. growth in S&E article production was even slower (1.1%) than the average for all 

developed economies. The U.S. global share fell from 30% 

to 26%, mostly as a result of developing economies’ more 

rapid growth. 

The EU, the world’s largest producer, grew slightly more 

slowly (1.4%) than all developed countries. Among EU 

member countries, growth rates were slower for the three 

largest—France, Germany, and the United Kingdom—and 

generally much faster in Ireland, Portugal, and other smaller 

member countries. Although EU article production grew 

slightly faster than that of the United States, the EU’s global share fell from 35% to 31% because of far more rapid 

growth of developing countries. 

S&E article production of Japan, the fourth-largest producer, contracted (-1.7% annual average) over the decade. 

As a result, Japan’s global share dropped from 9% to 6%, 

a far greater decline (35%) compared to the declines of the 

shares of the United States and the EU (15% and 12%). The 

weakening of Japan’s position may reflect its lengthy economic stagnation despite recent increases in R&D expenditures and reform of its research universities.46 Also among 

major developed nations, Russia saw its S&E article output 

decline (-1.0% annual average) over the decade.

Publication output by developed economies outside of 

the EU, the United States, and Japan grew much faster, primarily due to rapid growth (6%–9% annual average) in three 

Asian locations—South Korea, Taiwan, and Singapore. 

The distribution of S&E article output by field provides 

an indication of the priority and emphasis of scientific research in different locations.47 The S&E article portfolios of 

the four major producers—the EU, the United States, China, 

and Japan—have distinct differences (table 5-21; appendix 

tables 5-27–5-39). The United States is focused primarily 

on biological sciences and medical sciences, more so than 

the world at large; together, these fields account for 52% of 

U.S. 2011 articles. The United States also produces a higher 

proportion of S&E articles than the rest of the world in other 

life sciences, psychology, and social sciences, although this 

may be due in part to how Thomson Reuters selects journals 

to include in its database.48

Like the United States, the EU is also focused primarily 

on biological sciences and medical sciences. However, the 

EU has placed a greater emphasis than the United States on 

physics, chemistry, and engineering. 

Japan’s articles are fairly evenly divided among biological sciences, medical sciences, chemistry, and physics. 

China’s S&E portfolio is dominated by chemistry, physics, and engineering, with a far higher concentration in these 

fields than the three other major producers and most other 

countries. These fields largely fueled China’s rapid growth 

in article output. Compared to the rest of the world, China 

and Japan put very little emphasis on publication in other life 

sciences, psychology, and social sciences.

Article Output by U.S. Sector

Six U.S. institutional sectors produce S&E articles: the 

federal government, industry, academia, FFRDCs, private 

nonprofit organizations, and state and local governments.49

Figure 5-19

S&E articles, by global share of selected region/

country: 2001–11

Percent

EU = European Union.

NOTES: Article counts are from the set of journals covered by the 

Science Citation Index (SCI) and Social Sciences Citation Index 

(SSCI). Articles are classied by the year of publication, and are 

assigned to a country/economy on the basis of the institutional 

address(es) listed in the article. Articles are credited on a 

fractional-count basis (i.e., for articles with collaborating institutions 

from multiple countries/economies, each country/economy receives 

fractional credit on the basis of the proportion of its participating 

institutions). Counts for all six groups sum to the world total. Data for 

Bulgaria, Hungary, and Romania are included with the EU and not 

with developing economies.

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, and The Patent Board,TM special 

tabulations (2013) from Thomson Reuters, SCI and SSCI, http:// 

thomsonreuters.com/products_services/science/. See appendix 

table 5-26. 

Science and Engineering Indicators 2014

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

0

5

10

15

20

25

30

35

40

United States

EU

Japan

Other developed

China

Other developing
5-38 ♦ Chapter 5. Academic Research and Development

Table 5-20

S&E articles in all fields, by country/economy: 2001 and 2011

Rank Country/economy 2001 2011

Average annual 

change (%)

2011 world

total (%)

2011 cumulative 

world total (%)

- World ..................................... 629,386 827,705 2.8 na na

1 United States......................... 190,597 212,394 1.1 25.7 25.7

2 China ..................................... 21,134 89,894 15.6 10.9 36.5

3 Japan..................................... 56,082 47,106 -1.7 5.7 42.2

4 Germany................................ 42,678 46,259 0.8 5.6 47.8

5 United Kingdom .................... 45,588 46,035 0.1 5.6 53.4

6 France ................................... 30,602 31,685 0.3 3.8 57.2

7 Canada.................................. 21,945 29,114 2.9 3.5 60.7

8 Italy........................................ 22,093 26,503 1.8 3.2 63.9

9 South Korea........................... 11,008 25,593 8.8 3.1 67.0

10 Spain ..................................... 15,324 22,910 4.1 2.8 69.8

11 India....................................... 10,801 22,480 7.6 2.7 72.5

12 Australia................................. 14,484 20,603 3.6 2.5 75.0

13 Netherlands ........................... 12,117 15,508 2.5 1.9 76.8

14 Taiwan ................................... 7,912 14,809 6.5 1.8 78.6

15 Russia.................................... 15,658 14,151 -1.0 1.7 80.3

16 Brazil...................................... 7,052 13,148 6.4 1.6 81.9

17 Switzerland............................ 7,950 10,019 2.3 1.2 83.1

18 Sweden ................................. 10,022 9,473 -0.6 1.1 84.3

19 Turkey .................................... 4,151 8,328 7.2 1.0 85.3

20 Iran ........................................ 1,035 8,176 23.0 1.0 86.3

21 Poland ................................... 5,629 7,564 3.0 0.9 87.2

22 Belgium ................................. 5,827 7,484 2.5 0.9 88.1

23 Israel...................................... 6,235 6,096 -0.2 0.7 88.8

24 Denmark ................................ 4,917 6,071 2.1 0.7 89.6

25 Austria ................................... 4,480 5,102 1.3 0.6 90.2

26 Finland................................... 4,930 4,878 -0.1 0.6 90.8

27 Norway .................................. 3,215 4,777 4.0 0.6 91.4

28 Portugal................................. 2,081 4,621 8.3 0.6 91.9

29 Singapore .............................. 2,434 4,543 6.4 0.5 92.5

30 Greece................................... 3,204 4,534 3.5 0.5 93.0

31 Mexico................................... 3,204 4,173 2.7 0.5 93.5

32 Czech Republic ..................... 2,571 4,127 4.8 0.5 94.0

33 Argentina ............................... 2,931 3,863 2.8 0.5 94.5

34 New Zealand ......................... 2,851 3,472 2.0 0.4 94.9

35 Ireland.................................... 1,588 3,186 7.2 0.4 95.3

36 South Africa........................... 2,291 3,125 3.2 0.4 95.7

37 Egypt ..................................... 1,463 2,515 5.6 0.3 96.0

38 Thailand................................. 727 2,304 12.2 0.3 96.2

39 Hungary................................. 2,398 2,289 -0.5 0.3 96.5

40 Malaysia ................................ 472 2,092 16.0 0.3 96.8

41 Chile ...................................... 1,159 1,979 5.5 0.2 97.0

42 Ukraine .................................. 2,239 1,727 -2.6 0.2 97.2

43 Romania ................................ 927 1,626 5.8 0.2 97.4

44 Saudi Arabia.......................... 565 1,491 10.2 0.2 97.6

45 Croatia................................... 696 1,289 6.3 0.2 97.8

46 Serbia .................................... NA 1,269 na 0.2 97.9

47 Pakistan................................. 279 1,268 16.3 0.2 98.1

48 Slovenia................................. 851 1,239 3.8 0.1 98.2

49 Slovakia................................. 924 1,099 1.8 0.1 98.3

50 Tunisia ................................... 352 1,016 11.2 0.1 98.5

na = not applicable; NA = not available.

NOTES: Countries/economies shown produced 1,000 articles or more in 2011. Countries/economies are ranked based on the 2011 total. Article counts 

are from the set of journals covered by the Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). Articles are classified by their year of 

publication and are assigned to a country/economy on the basis of the institutional address(es) listed in the article. Articles are credited on a fractionalcount basis (i.e., for articles with collaborating institutions from multiple countries/economies, each country/economy receives fractional credit on the 

basis of the proportion of its participating institutions). Detail does not add to total because of countries/economies not shown.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent Board,TM special tabulations (2013) from 

Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/. See appendix table 5-26. 

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 5-39

This section describes patterns and trends in the sector distributions of U.S. article output. 

The U.S. academic sector is the largest producer of S&E 

articles, accounting for three-fourths of U.S. S&E article 

output. This sector was largely responsible for the slight 

growth of U.S. S&E article output over the last 15 years. 

The number of academic S&E articles rose from 138,000 

to 163,000 between 1997 and 2012. As a result, academia’s 

share of all U.S. articles rose from 73% to 76% (figure 5-20). 

S&E publications in the non-academic sectors decreased 

slightly from 52,000 to 51,000 during this period. These sectors had divergent trends: 

♦ Articles in the private nonprofit sector grew from 15,000 to 

18,000 and at an even greater pace than the academic sector 

between 1997 and 2012 (appendix table 5-40). However, 

this sector’s much smaller size resulted in a lesser impact 

on total U.S. growth.

♦ Articles in FFRDCs fluctuated between 5,000 and 6,000.50

♦ Industry and the federal government exhibited similar 

trends, starting the period at 14,000 articles and then declining, especially over the past 10 years. However, industry articles dropped further than federal government 

articles to end the period at 12,000, compared with 13,000 

for the federal government.

Except for the FFRDCs, the research portfolios of the 

U.S. sectors are dominated by life sciences (biological sciences and medical sciences), with nearly half or more of all 

articles in these fields (table 5-22). The dominance of life 

Table 5-21

S&E research portfolios of selected regions/countries, by field: 2011

(Percent)

Field World United States EU China Japan

All articles (n) ........................................................ 827,705 212,394 254,482 89,894 47,106

Engineering....................................................... 10.7 7.1 9.0 16.9 11.0

Agricultural sciences......................................... 2.3 1.6 2.4 2.1 2.4

Astronomy......................................................... 1.3 1.4 1.6 0.6 1.0

Biological sciences ........................................... 19.5 23.3 19.3 14.8 20.7

Chemistry.......................................................... 13.9 8.2 12.8 24.9 17.3

Computer sciences........................................... 1.1 1.1 1.1 1.7 0.3

Geosciences ..................................................... 5.6 5.5 5.7 4.8 4.2

Mathematics ..................................................... 2.2 1.9 2.6 2.6 1.5

Medical sciences .............................................. 22.1 28.3 24.0 10.6 21.3

Other life sciences ............................................ 1.2 2.2 1.0 0.2 0.2

Physics ............................................................. 13.1 8.6 12.6 19.4 18.2

Psychology ....................................................... 2.8 4.8 2.9 0.4 0.8

Social sciences................................................. 4.1 6.0 4.9 0.9 1.0 

EU = European Union.

NOTES: Article counts are from the set of journals covered by the Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). Articles are 

classified by their year of publication and are assigned to the country on the basis of the institutional address(es) listed in the article. Articles are credited 

on a fractional-count basis (i.e., for articles with collaborating institutions from multiple countries, each country receives fractional credit on the basis of 

the proportion of its participating institutions). See appendix table 5-24 for countries/economies included in the EU. Percentages may not add to 100% 

because of rounding.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent Board,TM special tabulations (2013) from 

Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/. See appendix tables 5-27—5-39.

Science and Engineering Indicators 2014

 

NOTES: Article counts are from the set of journals covered by the 

Science Citation Index (SCI) and Social Sciences Citation Index 

(SSCI). Articles are classied by the year they entered the database 

and are assigned to U.S. institution(s) based on the institutional 

address(es) listed in the article. Articles are credited on a fractional 

count basis; for articles with institutional addresses from multiple 

countries/U.S. institutions, each country/U.S. institution receives 

fractional credit on the basis of the proportion of its participating 

institutions. 

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, and The Patent Board,

TM special tabulations 

(2013) from Thomson Reuters, SCI and SSCI, http://thomsonreuters. 

com/products_services/science/. See appendix table 5-40.

Science and Engineering Indicators 2014

Figure 5-20

U.S. academic and non-academic S&E articles: 

1997–2012

Thousands of articles

1997 2000 2002 2004 2006 2008 2010 2012

0

50

100

150

200

250

U.S. academic institution

U.S. non-academic institution
5-40 ♦ Chapter 5. Academic Research and Development

sciences is especially pronounced in the nonprofit sector, 

where 79% of the articles are in the biological sciences and 

medical sciences. With a much larger number of articles, academia has 49% of its S&E literature in life sciences. The research portfolio of FFRDCs is dominated by physics (36%), 

chemistry (19%), and engineering (16%), with far less concentration in life sciences (11%). This reflects the FFRDCs’ 

more specialized and mission-oriented research programs in 

these and other physical sciences.

Coauthorship and Collaboration in 

S&E Literature

Collaborative S&E research facilitates knowledge transfer and sharing among individuals, institutions, and nations. 

It can be an indicator of interconnections among researchers 

in different institutional settings and the growing capacity 

of researchers to address complex problems by drawing on 

diverse skills and perspectives. Collaboration on S&E research publications over the last 15 years has been increasing, with higher shares of scientific articles with more than 

one named author and a higher proportion of articles with 

institutional and international coauthorships (figure 5-21). 

The largest increase was in international collaboration; the 

percentage of articles with authors from different countries 

rose from 16% to 25% between 1997 and 2012. 

The following two sections explore the growth of collaborative publication.51 The first section looks at international 

collaboration. The second section examines collaboration 

across institutional sectors—including academia, the federal 

Table 5-22

Share of U.S. S&E articles, by sector and field: 2012

(Percent)

Sector

Federal 

government Industry Academic FFRDCs

Private 

nonprofit

State/local 

government

All fields combined (n) ............................. 13,075 11,779 163,137 5,690 18,322 1,728 

Engineering.......................................... 6.0 13.8 7.0 15.9 1.2 3.8

Agricultural sciences............................ 4.6 2.0 1.5 0.3 0.4 1.1

Astronomy............................................ 1.8 0.6 1.4 5.4 2.4 0.0

Biological sciences .............................. 29.7 22.7 22.7 8.9 24.6 24.9

Chemistry............................................. 4.7 12.4 8.4 18.7 2.0 0.9

Computer sciences.............................. 0.3 2.6 1.2 0.7 0.1 0.2

Geosciences ........................................ 12.3 5.3 5.2 10.3 2.9 13.7

Mathematics ........................................ 0.4 0.7 2.4 1.0 0.2 0.0

Medical sciences ................................. 26.3 24.5 25.8 2.5 54.3 43.5

Other life sciences ............................... 1.5 2.1 2.1 0.0 3.6 4.5

Physics ................................................ 6.4 10.0 8.8 35.5 1.3 0.3

Psychology .......................................... 2.6 1.5 6.0 0.0 2.7 4.0

Social sciences.................................... 3.5 1.7 7.4 0.6 4.2 3.2

NOTES: Article counts are from the set of journals covered by the Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). Articles are 

classified by the year they entered the database, rather than their year of publication, and are assigned to a sector on the basis of the institutional 

address(es) listed in the article. Articles are credited on a fractional-count basis (i.e., for articles with collaborating institutions from multiple sectors, each 

sector receives fractional credit on the basis of the proportion of its participating institutions). 

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent Board,TM special tabulations (2013) from 

Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/. See appendix table 5-40.

Science and Engineering Indicators 2014

 

NOTES: Article counts are from the set of journals covered by the 

Science Citation Index (SCI) and Social Sciences Citation Index 

(SSCI). Articles are classied by the year they entered the database. 

Articles by multiple authors are those with multiple persons authoring 

the article; articles by multiple domestic institutions and multiple 

nations have multiple institutional addresses listed on the article. 

Authors from different departments within the same institution are 

considered to be from different institutions. 

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, and The Patent Board,

TM special tabulations 

(2013) from Thomson Reuters, SCI and SSCI, http://thomsonreuters. 

com/products_services/science/. See appendix table 5-41.

Science and Engineering Indicators 2014

Figure 5-21

Share of world articles in all fields authored by 

multiple authors, institutions, and nations: 

1997 and 2012

Percent

Multiple

authors

Multiple

domestic institutions

Multiple

nations

0

20

40

60

80

100

1997 2012
Science and Engineering Indicators 2014 ♦ 5-41

government, and industry—within the United States. (Data 

on sectors for other countries are not available.)

International Collaboration

International scientific collaborations reflect wider patterns of relationships among countries. Linguistic and 

historical factors (Narin, Stevens, and Whitlow 1991), geography, and cultural relations (Glänzel and Schubert 2005) 

play a role in these relationships. In recent years, coauthorships in Europe have risen in response to EU policies actively encouraging intra-European, cross-border collaboration. 

Strong ties among science establishments in Asia, though 

without the formal framework that characterizes Europe, 

have led to similar collaboration. 

Rates of international collaboration by field. International collaboration on scientific articles, as measured 

by the shares of articles coauthored by institutional authors 

in different countries, has increased markedly over the last 

15 years. S&E articles with coauthors from more than one 

country have grown to nearly one-fourth of the world’s S&E 

articles, rising from 16% in 1997 to 25% in 2012. This is a 

slightly larger increase than the increase in purely domestic 

coauthorships during the same period (from 36% to 44%) 

(figure 5-21).

Researchers in different fields have different tendencies 

to collaborate internationally. Astronomy is the most international field, with over half of its articles internationally 

coauthored (56%) (figure 5-22). Geosciences, computer sciences, mathematics, physics, and biological sciences have 

relatively high rates of international collaboration, with 

shares in the range of 27%–34%. Fields with low rates of 

collaboration (17%–21%) include psychology, chemistry, 

social sciences, and other life sciences. Possible factors influencing variations among fields include the existence of 

formal international collaborative programs, expensive infrastructure (e.g., atomic colliders and telescopes) that results in cost sharing and collaboration among countries, the 

geographic scope (local versus international) of research 

fields, and path dependencies from earlier, relatively local 

ways of doing research. 

International collaboration has risen across all scientific 

fields over the last 15 years. The two fields with the highest 

rates of international collaboration—astronomy and geosciences—had increases of 17 and 14 percentage points, respectively, in their shares between 1997 and 2012. Physics 

and chemistry had far lower gains of just 5 and 7 percentage 

points, respectively. Psychology and other life sciences had 

strong gains yet remain among the four fields with the least 

amount of international collaboration.

Rates of international collaboration by country/region. Countries vary widely in the proportion of their S&E 

articles that are internationally coauthored, ranging from 25% 

(Iran) to as much as 80% (Saudi Arabia) for articles in 2012 

(appendix table 5-41; see also appendix tables 5-42–5-54 

for individual fields). The shares of larger countries are generally lower (from 25% to 60%) than smaller countries (from 

50% to 80%). The difference is likely because the bigger and 

more diversified scientific establishments in larger countries allow opportunities for collaborative scientific teams within their 

Figure 5-22

Share of world’s S&E articles with international collaboration, by S&E field: 1997 and 2012

Percent

NOTES: Data are from the set of journals covered by the Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). Articles are classied by the 

year they entered the database, rather than their year of publication, and are assigned to a country/economy on the basis of the institutional address(es) listed in 

the article. Articles are credited on a whole-count basis (i.e., each collaborating institution or country is credited one count). Internationally coauthored articles 

may also have multiple domestic coauthors.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent Board,

TM special tabulations (2013) from 

Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/. See appendix tables 5-42–5-54.

Science and Engineering Indicators 2014

Astronomy GeosciencesComputer

sciences

MathematicsPhysics Biological

sciences

Agricultural

sciences

Medical

sciences

EngineeringPsychologyChemistry Social

sciences

Other life

sciences

0

10

20

30

40

50

60

70

1997 2012
5-42 ♦ Chapter 5. Academic Research and Development

borders, whereas smaller countries do not have the research infrastructure or personnel to support such collaboration.

The U.S. international collaboration rate was 35% in 

2012, significantly lower than France, Germany, and the 

United Kingdom (figure 5-23). However, because the United 

States has a higher share of articles with domestic coauthors, 

its overall proportion of coauthored articles is similar to that 

of the three EU countries. 

The higher international collaboration rates of large EU 

member countries relative to the United States are likely due to 

their smaller science establishments, which increase the need 

for collaboration teams with international participation. In addition, the EU’s Framework Programmes for Research and 

Technological Development and other programs designed to 

increase collaboration among EU member countries and with 

other countries likely boost their international collaboration. 

Japan and China have even lower international collaboration shares than the United States (figure 5-23). One factor that 

may explain their low shares is that Asia does not have a formal 

framework like the EU to facilitate international collaboration. 

Another possible factor is that some Chinese and Japanese scientists may not speak English or publish their research in that 

language, which could limit their visibility in the international 

scientific community, where English is commonly used.

Rates of international collaboration have generally risen over 

the last decade, though to varying degrees (figure 5-23). The 

U.S. rate rose 10 percentage points to reach 35% between 2002 

and 2012. Canada had a similar increase (from 40% to 50%) 

over the same period. 

The increase has been even more dramatic for EU members and other European countries. The shares of France, 

Germany, and the United Kingdom increased by 12–16 

percentage points to reach over 50%. The EU’s Framework 

Programmes for Research and Technological Development, 

now in their seventh year, have likely been a major factor in 

these countries’ increases. 

China is an exception to the general trend of increasing international collaboration. China’s rate of international collaboration (27%) remained stable over the last decade during China’s 

period of very rapid article growth. In contrast, Chinese domestic collaboration increased in this period: the proportion of its 

articles that had multiple domestic institutional authors rose by 

11 percentage points, reaching 44% (appendix table 5-41).

Preferred collaboration partners. Different countries 

have different preferred partners for international scientific 

collaboration. The remainder of this section describes global 

partnership patterns, with particular emphasis on patterns of 

U.S. involvement in international collaboration. 

The nation that most often coauthors with the United 

States is China, a collaborator on 16% of U.S. internationally coauthored articles (table 5-23). 52 As shown in figure 

5-24, other countries that are important partners for the 

United States are the United Kingdom (14%), Germany 

(13%), Canada (11%), France (9%), Italy (7%), and Japan 

(7%). Canada and China are notable among these countries 

for having unusually high rates of U.S. participation in their 

own internationally coauthored articles (49% and 48%, respectively). For the other five countries, the comparable 

rates range from 29% to 37%.

For most countries, the percentage of U.S. internationally coauthored papers on which they are coauthors has 

Figure 5-23

Share of S&E articles internationally coauthored, by selected country: 2002 and 2012

Percent

NOTES: Article counts are from the set of journals covered by the Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). Articles are classied 

by the year they entered the database, rather than their year of publication, and are assigned to a country/economy on the basis of the institutional address(es) 

listed in the article. Articles are credited on a whole-count basis (i.e., each collaborating institution or country is credited one count). Internationally coauthored 

articles may also have multiple domestic coauthors.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent Board,TM special tabulations (2013) from 

Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/. See appendix table 5-41.

Science and Engineering Indicators 2014

United States Canada United Kingdom Germany France China Japan Australia

0

10

20

30

40

50

60

70

2002 2012
Science and Engineering Indicators 2014 ♦ 5-43

stayed stable over the decade. China and Japan are exceptions. China’s share of U.S. internationally authored articles 

tripled from 5% in 2002 to 16% in 2012, coinciding with its 

rapid expansion of article production. China swiftly moved 

up from the sixth-largest collaborating country in 2005 to 

the second-largest collaborating country in 2010 before becoming the largest in 2011. Japan’s share of U.S. coauthored 

articles dropped from 10% to 7%, coinciding with its decline 

in article production.

Several countries that collaborate on relatively few U.S. 

internationally coauthored articles have very high U.S. participation in their own internationally coauthored articles. 

Three economies—Israel, South Korea, and Taiwan—have 

more than 50% of their international articles coauthored 

with the United States. Other countries with relatively large 

U.S. shares of their internationally coauthored articles include Mexico, Chile, Brazil, and Turkey.

Table 5-23

International coauthorship of S&E articles with the United States, by selected country/economy: 2002 and 2012

(Percent)

U.S. share of country/economy’s 

international articles

Country/economy’s share of U.S. 

international articles

Country/economy 2002 2012 2002 2012

World ............................................................. 43.8 43.0 na na

China.......................................................... 36.8 47.5 5.1 16.2

United Kingdom......................................... 30.9 33.2 13.1 14.3

Germany .................................................... 30.3 31.0 13.8 13.3

Canada ...................................................... 53.1 48.9 11.3 11.4

France........................................................ 25.5 28.5 8.6 8.8

Italy ............................................................ 32.4 34.0 6.9 7.4

Japan ......................................................... 41.2 37.1 9.8 6.8

Australia..................................................... 36.6 32.9 4.7 6.0

South Korea............................................... 55.1 53.9 3.7 6.0

Spain.......................................................... 26.9 29.5 3.9 5.8

Netherlands ............................................... 29.6 33.7 4.4 5.6

Switzerland ................................................ 31.6 33.4 4.0 4.8

Sweden...................................................... 27.3 30.5 3.4 3.4

Brazil.......................................................... 37.0 41.5 2.5 3.2

Israel .......................................................... 52.8 55.6 3.5 2.8

India ........................................................... 34.3 34.2 1.9 2.7

Taiwan........................................................ 55.4 52.3 1.9 2.7

Belgium...................................................... 23.5 26.0 2.2 2.5

Russia ........................................................ 25.3 29.9 3.8 2.4

Denmark .................................................... 29.8 32.3 2.0 2.3

Austria........................................................ 24.8 28.9 1.5 2.0

Poland........................................................ 26.2 32.2 1.9 2.0

Mexico ....................................................... 42.5 46.3 1.6 1.7

Norway....................................................... 29.6 30.8 1.2 1.6

Finland ....................................................... 27.9 29.9 1.5 1.5

Singapore................................................... 30.0 31.7 0.7 1.5

Greece ....................................................... 27.7 37.7 0.9 1.5

South Africa ............................................... 31.0 39.3 0.8 1.4

Turkey ........................................................ 39.7 40.3 0.9 1.3

Chile........................................................... 40.4 45.1 0.8 1.3

Portugal ..................................................... 19.5 25.1 0.6 1.3

Czech Republic.......................................... 21.1 29.3 0.8 1.2

New Zealand.............................................. 37.4 34.1 1.1 1.2

Argentina.................................................... 35.2 38.2 1.1 1.2

Ireland ........................................................ 23.4 30.1 0.5 1.0

Hungary ..................................................... 29.3 33.9 1.1 1.0

na = not applicable.

NOTES: Internationally coauthored articles have at least one collaborating institution from the indicated country/economy and an institution from outside 

that country/economy. Article counts are from the set of journals covered by the Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). 

Articles are classified by the year they entered the database, rather than their year of publication, and are assigned to a country/economy on the basis 

of the institutional address(es) listed in the article. Articles are credited on a whole-count basis (i.e., each collaborating country/economy is credited one 

count). Countries/economies are ranked by the percentage of their share of the United States’ international articles in 2012; countries/economies with 

less than 1% of the United States’ 2012 international articles are omitted.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent Board,TM special tabulations (2013) from 

Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/. See appendix table 5-56.

Science and Engineering Indicators 2014
5-44 ♦ Chapter 5. Academic Research and Development

An index of international collaboration is useful for highlighting rates of international scientific collaboration that 

differ substantially from chance (see sidebar, “Normalizing 

Coauthorship and Citation Data”). When collaborative authorship between two countries is exactly proportional to 

their overall rates of international collaborative authorship, 

the index value is 1; a higher index value means that a country pair has a stronger-than-expected tendency to collaborate, and a lower index value means the opposite.

U.S. collaboration with countries as measured by the index of international collaboration shows variable trends (table 5-24; appendix tables 5-55 and 5-56). In North America, 

the Canada-U.S. index shows a rate of collaboration that is 

slightly greater than would be expected, and the index has 

not changed much over the past 15 years. The U.S.-Mexico 

index is just about as would be expected and has been stable. 

In scientific collaboration with EU member countries, 

the United States has a weaker-than-expected tendency to 

collaborate with the United Kingdom, Germany, and France 

despite a comparatively high volume of internationally coauthored articles. U.S. collaboration with these countries became slightly stronger between 1997 and 2012.

In contrast to EU member countries, U.S. collaboration 

with Asia has generally been stronger than expected. U.S. 

collaboration is relatively strong with China, South Korea, 

and Taiwan. However, U.S. collaboration with Japan is 

slightly weaker than expected despite a high volume of coauthored papers. Between 1997 and 2012, U.S.-Japan collaboration has shifted from as expected to weaker than expected.

Collaborations between Latin American countries are 

notably stronger than expected. The collaboration index of 

Mexico-Argentina is 3.88, far above expected levels. The 

collaboration index of Argentina-Brazil is even higher, at 

5.81, one of the highest in the world, and was high, at 4.94, 

even 15 years ago.

Among European countries, collaboration patterns are 

mixed, but most have increased between 1997 and 2012. 

Among the large publishing countries (Germany, the United 

Kingdom, and France), collaboration was less than expected 

in 1997 but grew to just about what would be expected in 

2012. A particularly strong collaboration network has developed between scientists in Poland and the Czech Republic, 

with the index for their countries standing at 5.97 in 2012.

The Scandinavian countries increased their collaboration 

indexes with many countries elsewhere in Europe over the 

last 15 years (appendix table 5-55).53 Within Scandinavia, 

the indexes are among the highest in the world (table 5-24).

Collaboration indexes within Asia and across the South 

Pacific between the large article producers are generally higher 

than expected, but some have declined between 1997 and 2012. 

The collaboration index of China-Japan declined from 1.61 to 

1.23; the South Korea–Japan index fell from 2.20 to 1.93. The 

Australia–New Zealand collaboration index, although much 

higher than expected, fell from 4.33 to 3.65. Other partnerships 

strengthened during this period. The Australia-China collaboration shifted from slightly weaker to slightly stronger than expected. India’s collaborations with both South Korea and Japan 

grew stronger between 1997 and 2012.

Figure 5-24

Selected country share of U.S. internationally coauthored articles: 1997 and 2012

Percent

NOTES: Article counts are from the set of journals covered by the Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). Articles are classied 

by the year they entered the database rather than their year of publication, and are assigned to a country/economy on the basis of the institutional address(es) 

listed in the article. Articles are credited on a whole-count basis (i.e., each collaborating institution or country is credited one count). Internationally coauthored 

articles may also have multiple domestic coauthors.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent Board,

TM special tabulations (2013) from 

Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/. 

Science and Engineering Indicators 2014

China United Kingdom Germany Canada France Italy Japan

0

5

10

15

20

1997 2012
Science and Engineering Indicators 2014 ♦ 5-45

Table 5-24

Index of international collaboration on S&E 

articles, by selected country/economy pair: 1997 

and 2012

(International collaboration index)

Country/economy pair 1997 2012

North/South America

Canada–United States................... 1.19 1.14

Mexico–United States.................... 1.01 1.08

United States–Brazil ...................... 0.83 0.96

Argentina–Brazil............................. 4.94 5.81

Mexico–Argentina .......................... 2.50 3.88

North Atlantic

UK–United States .......................... 0.68 0.77

Germany–United States................. 0.67 0.72

France–United States .................... 0.57 0.66

Canada–France.............................. 0.58 0.87

Europe

France–Germany ........................... 0.75 1.06

France–UK..................................... 0.78 0.97

Germany–UK ................................. 0.70 0.98

Belgium–Netherlands..................... 2.53 2.86

Italy–Switzerland............................ 1.46 1.65

Poland–Czech Republic................. 1.76 5.97

Hungary–Germany......................... 1.23 1.77

Germany–Czech Republic.............. 1.30 1.63

Scandinavia

Finland–Sweden ............................ 3.34 4.12

Norway–Sweden............................ 4.38 4.61

Sweden–Denmark.......................... 2.74 3.88

Finland–Denmark........................... 1.98 2.98

Pacific Rim

Japan–United States ..................... 1.00 0.86

China–United States...................... 0.79 1.10

South Korea–United 

States ......................................... 1.38 1.25

Taiwan–United States .................... 1.53 1.22

China–Canada ............................... 0.80 0.74

Japan–Canada............................... 0.61 0.67

Asia/South Pacific

China–Japan.................................. 1.61 1.23

South Korea–Japan ....................... 2.20 1.93

Australia–Singapore....................... 2.22 1.48

Australia–China.............................. 0.92 1.11

Australia–New Zealand.................. 4.33 3.65

India–Japan ................................... 0.78 1.06

India–South Korea ......................... 1.55 2.42

UK = United Kingdom.

NOTES: The international collaboration index shows the first 

country’s rate of collaboration with the second country, divided 

by the second country’s rate of international coauthorship. Article 

counts are from the set of journals covered by the Science Citation 

Index (SCI) and Social Sciences Citation Index (SSCI). Articles are 

classified by the year they entered the database, rather than their 

year of publication, and are assigned to a country/economy on the 

basis of the institutional address(es) listed in the article. Articles are 

credited on a whole-count basis (i.e., each collaborating country/

economy credited one count).

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, and The Patent Board,TM special 

tabulations (2013) from Thomson Reuters, SCI and SSCI, http://

thomsonreuters.com/products_services/science/. See appendix 

table 5-55. 

Science and Engineering Indicators 2014

Normalizing Coauthorship 

and Citation Data

Data for coauthorships and citations can be misleading if they do not take into account the size of a 

country’s scientific publication base. To aid interpretation, data should be normalized. The normalized 

measures used in this report have an expected value of 

1.00. If the measure is higher than expected, it will be 

greater than 1.00; if less than expected, it will be less 

than 1.00.

Index of International Collaboration. Eliminating 

other factors (language, geography, etc.), one might expect a large share of a country’s internationally coauthored articles to have coauthors from the United States 

simply due to the sheer size of the U.S. scientific base. 

Thus, if the United States is a coauthor on 43% of the 

world’s internationally coauthored articles, one would 

expect 43% of China’s internationally coauthored articles to have a U.S. partner. In fact, 47.5% of China’s 

internationally coauthored articles in 2012 have a U.S. 

coauthor. Dividing the actual share by the expected 

share yields an index value of 1.10. Thus, China coauthors with the United States 10% more than expected. 

Index values for any country pair are always symmetrical, so the United States also coauthors with China 10% 

more than expected. The data for calculating the 2012 

indexes in appendix table 5-55 are contained in appendix table 5-56.

Relative Citation Index. Similarly, normalizing 

citation counts by a country’s publication output is 

essential for correct interpretation of the data. The 

expected share of citations that one country receives 

from another depends on the number of articles that 

the cited country produces. Using the U.S.-China example above, the United States authored 26.6% of 

all 2008–10 articles (appendix table 5-57). All other 

things being equal, if Chinese authors showed no 

preference for U.S. science, 26.6% of their references 

in 2012 articles would be to U.S. articles. In actuality, 22.9% of Chinese references are to U.S. articles. 

Dividing the number of Chinese references to U.S. 

articles by the expected number of references yields 

an index value of 0.86. The relative citation index is 

not symmetrical; that is, the index for China citing the 

United States is not equal to the index for the United 

States citing China (0.32).
5-46 ♦ Chapter 5. Academic Research and Development

Collaboration among U.S. Sectors

U.S. coauthorship data at the sector level—academic, 

nonprofit, industry, FFRDCs, federal and state government—are indicators of collaboration among U.S. sectors 

and between U.S. sectors and foreign institutions. The academic sector, the largest article producer among U.S. sectors, is the center of U.S. sector and foreign collaboration. In 

2012, the academic sector published 119,371 articles coauthored with other U.S. sectors and foreign institutions, three 

and a half times more than the 33,973 such articles published 

by the nonprofit sector, the second largest (table 5-25). 

Although the largest producer of articles coauthored with 

other U.S. sectors and foreign institutions, academia has the 

lowest coauthored share of total articles, compared to other 

U.S. sectors. 

Figure 5-25 shows the share of U.S. articles coauthored 

with foreign institutions, U.S. academic institutions, and 

other U.S. sectors (outside of self and academia). FFRDCs 

are notable for their very high level of foreign collaboration (46%) compared to a 31%–34% range for most other 

U.S. sectors. With a high concentration of FFRDCs being 

focused on physics research (36% of FFRDC articles, table 

5-22), which often requires the use of globally shared instruments, a high degree of international collaboration can 

be expected. State and local governments have the lowest 

foreign collaboration shares but the highest share of collaboration with other U.S. sectors. Industry has the lowest collaboration share (57%) with academia, compared to 63% or 

higher for other U.S. sectors.

Over the last decade, collaboration with other U.S. sectors 

and with foreign institutions increased strongly in almost all 

sectors (table 5-25). In the academic sector, the number of 

articles coauthored with other U.S. sectors and foreign institutions increased by more than half, from 76,622 to 119,371. 

The largest increase was for articles coauthored with foreign institutions, which increased by 83% (from 41,978 

to 76,907). As a result, articles with foreign coauthors increased their share of all U.S. academic articles, from 24% 

to 34%. U.S. academic articles coauthored with other U.S. 

sectors increased by 41% (from 43,587 to 61,329 articles). 

The nonprofit sector had the largest increase in the number of coauthored articles with other U.S. sectors and foreign institutions (from 20,703 to 33,973, a 64% increase). 

Nonprofit articles coauthored with foreign institutions led 

the increase, more than doubling (from 6,337 to 13,740). 

The percentage of articles coauthored with foreign institutions increased their share from 22% to 34%.

Table 5-25

U.S. sector articles coauthored with other U.S. sectors and foreign institutions: 2002 and 2012

U.S. sector

Year Academic

Federal 

government Industry FFRDCs

Private 

nonprofit

State/local 

government

2002

All articles .............................................................. 176,756 24,824 23,485 9,502 28,372 3,868

Total coauthored................................................ 117,863 20,009 17,815 7,605 23,161 3,322

Total coauthored with another U.S. sector 

and/or foreign institution............................. 76,622 18,592 16,456 7,275 20,703 3,209

Coauthored with another U.S. sector......... 43,587 16,051 13,372 5,671 18,124 3,073

Coauthored with academic sector.......... na 14,014 11,187 4,925 16,457 2,614

Coauthored with non-academic sector.... 43,587 5,543 5,305 1,762 5,544 1,455

Coauthored with foreign ............................. 41,978 5,749 5,557 3,609 6,337 494

2012

All articles .............................................................. 226,753 29,099 25,268 13,316 40,672 4,550

Total coauthored................................................ 173,744 25,527 21,925 11,739 36,612 4,206

Total coauthored with another U.S. sector 

and/or foreign institution............................. 119,371 24,057 20,555 11,269 33,973 4,103

Coauthored with another U.S. sector......... 61,329 21,244 16,651 9,128 29,883 3,941

Coauthored with academic sector.......... na 19,095 14,382 8,404 27,870 3,485

Coauthored with non-academic sector.... 61,329 8,367 7,535 2,768 9,595 2,037

Coauthored with foreign ............................. 76,907 9,006 8,712 6,172 13,740 917

na = not applicable.

FFRDCs = federally funded R&D centers.

NOTES: Article counts are from the set of journals covered by the Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). Articles are 

classified by the year they entered the database, rather than their year of publication, and are assigned to a U.S. sector on the basis of the institutional 

address(es) listed in the article. Articles are credited on a whole-count basis (i.e., each collaborating institution type is credited one count in each 

qualifying group). The sum of articles coauthored with various sectors could exceed the total number of articles coauthored with another sector and/or 

foreign sector due to articles coauthored by multiple sectors. Articles from joint or unknown U.S. sectors are not shown.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent Board,TM special tabulations (2013) from 

Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/. 

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 5-47

Articles with at least one author from industry grew the 

least over the time period, less than 8%, and in turn had the 

smallest increase in articles coauthored with other U.S. sectors and foreign institutions (25%).

Much of the growth of industry-coauthored articles was 

with foreign institutions; foreign coauthorships increased by 

57%. Articles coauthored with the academic sector rose by 

only 29%, the smallest increase among sectors coauthoring 

with academia.

Trends in Citation of S&E Articles

Citations indicate influence, and they are increasingly 

international in scope.54 When scientists and engineers cite 

the published papers resulting from prior S&E research, they 

are formally crediting the influence of that research on their 

own work.

Citations are generally increasing with the volume of 

S&E articles. (For the analysis of citations from articles to 

articles, citation counts are limited to a fixed 3-year citation window that begins 4 years and ends 2 years prior to 

the year of the citing article.55) As cited by 1992 articles, 

an earlier S&E article received, on average, 1.85 citations. 

In contrast, an S&E article cited by 2012 articles received, 

on average, 2.47 citations (figure 5-26). Articles with U.S. 

authors tended to receive more citations than others, but that 

gap has narrowed slightly in the most recent 4 years. 

The next sections examine two aspects of article citations 

in a global context: the overall rate of citation of a country’s 

scientific publications, and the share of the world’s most 

highly cited literature authored by different countries. The 

discussion of article citations will conclude with an examination of citations to articles authored by researchers at U.S. 

academic institutions and in other U.S. sectors.

International Citation Patterns

Like the indicators of international coauthorship discussed earlier, cross-national citations are evidence that S&E 

research is increasingly international in scope. Citations to 

a country’s articles that come from articles authored outside that country are referred to as international citations. 

Between 1992 and 2012, the international share of citations 

increased in all but one of the world’s major S&E article–

producing countries.

China is the exception. In 1992, 69% of citations to 

Chinese S&E articles came from outside China; by 2012, the 

proportion had dropped to 49% (figure 5-27). This suggests 

that China’s expanding S&E article output is being used 

mostly within China. However, changes in the composition 

of the Thomson Reuters database probably also play a role in 

accounting for this trend.56 The trend toward domestic citations is also related to the unusually large role of domestic 

articles in Chinese output growth; the lack of international 

coauthors may explain, in part, the relatively low rate of international citations.

The relative citation index normalizes cross-national 

citation data for variations in publication output, much 

like the collaboration index (see sidebar, “Normalizing 

Coauthorship and Citation Data”). The expected value is 

1.0, but unlike the collaboration index, citation indexes are 

Figure 5-25

Share of U.S. sector articles coauthored with foreign institutions, academia, and other U.S. sectors: 2010

Percent

FFRDC = federally funded R&D center.

NOTES: Article counts are from the set of journals covered by the Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). Articles are 

classied by the year they entered the database, rather than their year of publication, and are assigned to a sector on the basis of the institutional 

address(es) listed in the article. Articles are credited on a whole-count basis (i.e., each collaborating institution type is credited one count in each 

qualifying group). The sum of shares may exceed 100 due to articles coauthored by multiple sectors. Articles from joint or unknown sectors are not 

shown. Articles with authors from a single sector are omitted.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent Board,TM special tabulations (2013) from 

Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/. 

Science and Engineering Indicators 2014

Foreign coauthor Academic coauthor Other sector coauthor, excluding academia

0

20

40

60

80

100

Academic Private nonprot Federal government Industry FFRDC State/local government
5-48 ♦ Chapter 5. Academic Research and Development

not symmetric. When country A cites an article by country 

B, this does not mean that country B is also citing an article 

by country A. Table 5-26 shows the relative citation index 

for the year 2012 for major publishing locations in four regions: North America, the EU, Asia, and South America. 

These data show the following:

♦ U.S. articles are most highly cited by articles from Canada 

(1.29) and the United Kingdom (1.15). 

♦ U.S. authors cite Chinese articles much less than expected 

(0.32).

♦ Mexico is heavily cited by South American countries, 

ranging from 22% to 44% more than expected (index 

values from 1.22 to 1.44); likewise, Mexican authors cite 

South American articles more than they cite articles from 

other areas of the world.

♦ Inter-European influence is strong, with most country 

pairs exhibiting index values greater than 1.0. Asian authors show similar interconnectedness, with the exception 

of Japan.

These data indicate the strong influence that geographic, 

cultural, and language ties have on citation patterns.

U.S. articles are more influential than those produced 

by the world’s other major publishing regions or countries. 

They receive 31% more citations than expected. U.S. index 

values for physics and chemistry are especially high, at 1.49 

and 1.43, respectively, but in every field, U.S. articles are 

disproportionately cited (see figure 5-28).57

Trends in Highly Cited S&E Literature by Country

Another indicator of the performance of a national or regional S&E system is the share of its articles that are highly 

cited. High citation rates generally indicate that an article 

has a relatively great impact on subsequent research. 

World citations to U.S. research articles show that, in 

all broad fields of S&E, U.S. articles continue to have the 

highest citation rates. In both 2002 and 2012, as displayed 

in appendix table 5-58, the U.S. share of articles in the 99th 

citation percentile was higher than its share in the 95th 

percentile, and these were higher than its share in the 90th 

Figure 5-26

Average citations per S&E article, by country of 

author: 1992–2012

Number

NOTES: Article/citation counts are from the set of journals covered 

by the Science Citation Index (SCI) and Social Sciences Citation 

Index (SSCI). Articles are classied by the year they entered the 

database, rather than year their of publication, and are assigned to a 

country/economy on the basis of the institutional address(es) listed 

in the article. Articles are credited on a fractional-count basis (i.e., for 

articles with collaborating institutions from multiple countries/ 

economies, each country/economy receives fractional credit on the 

basis of the proportion of its participating institutions). Citation 

counts are based on a 3-year period with a 2-year lag (e.g., citations 

for 2012 are references made in articles in the 2012 data tape to 

articles in the 2008–10 data tapes). 

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, and The Patent Board,TM special 

tabulations (2011) from Thomson Reuters, SCI and SSCI, http:// 

thomsonreuters.com/products_services/science/.

Science and Engineering Indicators 2014

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

All countries

United States

Not United States

Figure 5-27

Share of selected region/country citations that are 

international: 1992–2012

Percent

EU = European Union.

NOTES: Article/citation counts are from the set of journals covered 

by the Science Citation Index (SCI) and Social Sciences Citation 

Index (SSCI). Articles are classied by the year they entered the 

database rather than their year of publication, and are assigned to a 

country/region on the basis of the institutional address(es) listed in 

the article. Articles are credited on a fractional-count basis (i.e., for 

articles with collaborating institutions from multiple countries/ 

regions, each country/region receives fractional credit on the basis of 

the proportion of its participating institutions). See appendix table 

5-24 for countries included in the EU, which in this gure is treated 

as a single country. Citation counts are based on a 3-year period 

with a 2-year lag (e.g., citations for 2012 are references made in 

articles in the 2012 data tape to articles in the 2008–10 data tapes). 

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, and The Patent Board,TM special 

tabulations (2013) from Thomson Reuters, SCI and SSCI, http:// 

thomsonreuters.com/products_services/science/. 

Science and Engineering Indicators 2014

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

40

0

45

50

55

60

65

70

75

United States

EU

China

Japan
Science and Engineering Indicators 2014 ♦ 5-49

Table 5-26

Relative citation index, by selected country/economy pair: 2012

Cited country/economy

North America European Union Asia South America

Citing country/

economy

United 

States Canada Mexico France Germany UK China Japan

South 

Korea Taiwan Argentina Brazil Chile

North America

United States ... 2.15 0.96 0.35 0.72 0.84 0.97 0.32 0.53 0.40 0.34 0.36 0.31 0.46

Canada ............ 1.29 5.57 0.46 0.86 0.87 1.16 0.39 0.50 0.41 0.39 0.46 0.38 0.59

Mexico ............. 0.99 0.90 27.04 0.96 0.84 0.95 0.62 0.52 0.54 0.59 1.37 1.17 1.52

European Union

France.............. 1.02 0.87 0.45 5.35 1.15 1.13 0.39 0.63 0.41 0.35 0.58 0.40 0.70

Germany .......... 1.08 0.82 0.34 1.05 4.24 1.14 0.36 0.64 0.39 0.30 0.43 0.31 0.55

UK.................... 1.15 1.00 0.36 0.94 1.06 4.17 0.30 0.51 0.33 0.29 0.37 0.32 0.50

Asia

China................ 0.86 0.65 0.46 0.71 0.78 0.64 3.43 0.83 1.11 1.06 0.43 0.44 0.37

Japan ............... 0.99 0.65 0.31 0.81 0.95 0.79 0.56 5.16 0.74 0.56 0.31 0.27 0.31

South Korea..... 1.04 0.64 0.34 0.64 0.74 0.68 1.03 1.02 7.45 1.29 0.36 0.37 0.45

Taiwan.............. 0.95 0.71 0.43 0.65 0.73 0.68 1.12 0.90 1.51 11.37 0.41 0.40 0.41

South America

Argentina.......... 0.91 0.87 1.44 1.03 0.93 0.87 0.44 0.48 0.40 0.40 39.73 1.68 2.98

Brazil................ 0.84 0.79 1.22 0.87 0.75 0.83 0.53 0.50 0.49 0.53 1.92 13.93 1.07

Chile................. 1.02 0.90 1.31 1.13 1.05 1.06 0.42 0.46 0.41 0.37 2.74 1.19 55.46

World ................... 1.31 1.01 0.56 1.03 1.13 1.15 0.84 0.82 0.75 0.68 0.65 0.60 0.67

UK = United Kingdom.

NOTES: Article/citation counts are from the set of journals covered by the Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). 

Articles are classified by the year they entered database, rather than their year of publication, and are assigned to a country/economy on the basis of the 

institutional address(es) listed in the article. Articles are credited on a fractional-count basis (i.e., for articles with collaborating institutions from multiple 

countries/economies, each country/economy receives fractional credit on the basis of the proportion of its participating institutions). Citation counts 

are based on a 3-year period with a 2-year lag (e.g., citations for 2012 are references made in articles in the 2012 data tape to articles in the 2008–10 

data tapes). 

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent Board,TM special tabulations (2013) from 

Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/. 

Science and Engineering Indicators 2014

Figure 5-28

Relative citation index to the United States, by scientific field: 2012

Index

NOTES: Article/citation counts are from the set of journals covered by the Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). Articles 

are classied by the year they entered the database rather than their year of publication. Citation counts are based on a 3-year period with a 2-year lag 

(e.g., citations for 2012 are references made in articles in the 2012 data tape to articles in the 2008–10 data tapes). 

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent Board,TM special tabulations (2013) from 

Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/. 

Science and Engineering Indicators 2014

Physics Chemistry MathematicsBiological

sciences

EngineeringAstronomyMedical

sciences

GeosciencesComputer

sciences

Social

sciences

Psychology Other life

sciences

Agricultural

sciences

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6
5-50 ♦ Chapter 5. Academic Research and Development

percentile.58 In 2012, although the United States authored 

27% of the world’s total of 2.4 million articles in the cited 

period shown, the United States authored 46% of the articles 

in the 99th citation percentile. 

U.S. publications uniquely display the preferred citation 

pattern: the higher the citation percentile, the higher the 

share of U.S. articles in the citation percentile. In contrast, 

EU articles are found disproportionately in the middle citation percentiles, while Chinese and Japanese articles are 

found disproportionately in the lower citation percentiles 

(see appendix table 5-58). Nevertheless, as the U.S. share 

of all articles produced declined between 2002 and 2012, 

its share of articles in the 99th percentile (i.e., the top 1%) 

of cited articles also declined, particularly in some fields. 

Shares in the top percentile increased for the EU and China 

but dropped slightly for Japan. 

Between 2002 and 2012, 1.6%–1.8% of U.S.-authored 

S&E articles have appeared in the world’s top 1% of cited 

articles, compared with 0.7%–0.9% of articles from the EU 

(figure 5-29). The share of China’s articles in the top 1% 

remained behind the United States and the EU but increased 

from 0.1% to 0.6% over the period. 

The high citation of U.S. articles has changed little over 

the past 10 years, remaining much higher than expected 

when compared to the overall U.S. share of world articles 

(figure 5-30; appendix table 5-57). Between 2002 and 2012, 

the EU index of highly cited articles for all fields combined 

rose slightly, to almost 1.0. The Japanese index remained the 

same and well below the expected value. China’s index rose 

substantially from 0.1 in 2002 to 0.6 in 2012, the same as 

Japan’s index. 

U.S. articles are highly cited across all broad scientific 

fields, with indexes ranging from 1.3 to 2.2. The U.S. indexes across all these fields showed little change between 

2002 and 2012. The greatest gain in the index of highly cited 

articles was in engineering, which grew from 1.7 to 2.0. The 

indexes for two fields—chemistry and social sciences—declined slightly (appendix table 5-57). 

The EU’s articles are more highly cited than expected 

in two fields, agriculture (1.2) and physics (1.2) for 2012. 

The EU’s index values are what would be expected in two 

fields—astronomy and chemistry. 

China is less highly cited than expected in all science 

fields except computer sciences, chemistry, and geosciences. Impressively, China’s index in computer sciences leaped 

from 0.2 in 2002 to 1.3 in 2012. Chinese geosciences articles 

experienced a similar rise from 0.2 to 1.1, while the index for 

chemistry has now just reached the expected value of 1.0. 

Japan’s production of highly cited articles is lower than 

expected across all fields, although its index increased substantially in astronomy.

U.S. Cross-Sector Citation Trends

The relative citation index (described in the section on 

“International Citation Patterns”) can also be used to examine the influence that each U.S. sector has on U.S. S&E literature. Figure 5-31 shows the relative citation index values 

for each of the six sectors of U.S. institutions and how they 

have changed over the past 20 years. U.S. academic articles 

are at the citation level that would be expected and have 

maintained this level over the entire time period. State and 

local governments, industry, and FFRDCs historically have 

produced the U.S. articles with the lowest citation rates. 

Index values for industry articles have gradually declined 

over time. In contrast, articles authored at FFRDCs have 

shown a marked improvement since 2008, rising above the 

expected value of 1.0 by 2011 and finally ending the period 

as the second most highly cited U.S. sector.

Articles authored at federal government institutions always have been cited within the United States more than 

expected. Although the index value declined almost to 1.0 in 

the 1990s, it has since risen to 1.09. The U.S. articles with 

the relative greatest impact are those by nonprofit organizations. Counter to the federal government trend, index values 

rose over the 1990s to 1.29 but have been in decline in the 

past 10 years, dropping to 1.14 by 2012.

Figure 5-29

Share of U.S., EU, and China S&E articles that are 

in the world’s top 1% of cited articles: 2002–12

Percent

EU = European Union.

NOTES: Article/citation counts are from the set of journals covered 

by the Science Citation Index (SCI) and Social Sciences Citation 

Index (SSCI). Articles are classied by the year they entered the 

database, rather than their year of publication, and are assigned to a 

country/economy on the basis of the institutional address(es) listed 

in the article. Articles are credited on a fractional-count basis (i.e., for 

articles with collaborating institutions from multiple countries/ 

regions, each country/region receives fractional credit on the basis of 

the proportion of its participating institutions). See appendix table 

5-24 for countries included in the EU, which in this gure is treated 

as a single country. Citation counts are based on a 3-year period 

with a 2-year lag (e.g., citations for 2012 are references made in 

articles in the 2012 data tape to articles in the 2008–10 data tapes). 

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, and The Patent Board,TM special 

tabulations (2013) from Thomson Reuters, SCI and SSCI, http:// 

thomsonreuters.com/products_services/science/. See appendix 

table 5-57.

Science and Engineering Indicators 2014

2002 2004 2006 2008 2010 2012

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

United States

EU

China
Science and Engineering Indicators 2014 ♦ 5-51

Citation of S&E Articles by USPTO Patents

Citations to the S&E literature on the cover pages of issued patents are one indicator of the contribution of research 

to the development of inventions.59 To measure trends consistently, the analysis limits the cited article years to a specific moving window, just as is done for references from 

articles to articles. Unlike article-to-article citations, however, patents reference much older research, largely due to 

the length of time that passes from patent application to patent grant (i.e., pendency). Therefore, indicators in this section are based on an 11-year citation window after a 5-year 

lag. For example, citations from 2012 are references from 

patents issued in 2012 to articles published from 1997–2007.

According to this indicator, research links to invention 

increased sharply in the late 1980s and early 1990s (Narin, 

Hamilton, and Olivastro 1997). At the same time, patenting 

activity by academic institutions was increasing rapidly, as 

were patent citations to S&E literature produced across all 

sectors (NSB 2008:5-49–5-54).

After a slowdown in the late 1990s and early 2000s, referencing from patents to scientific literature is once again 

increasing. Of utility patents awarded to both U.S. and foreign assignees, 12% cited S&E articles in 2003, and this 

figure grew to 15% in 2012 (appendix table 5-59). In addition, the share of patent citations to foreign S&E articles 

has increased, coinciding with a growth in the percentage 

of U.S. utility patents awarded to foreign assignees and the 

share of world articles authored outside the United States. 

Starting in 2009, U.S. patents cited more foreign articles 

than U.S. articles.

Citations to U.S. articles in 2012 USPTO patents were 

dominated by articles in biological sciences (48%) and 

Figure 5-30

Index of highly cited articles, by selected S&E field and region/country: 2002 and 2012

EU = European Union.

NOTES: Article/citation counts are from the set of journals covered by the Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). 

Articles/citations are classied by the year they entered the database, rather than their year of publication, and are assigned to a country/region on the 

basis of the institutional address(es) listed in the article. See appendix table 5-24 for countries included in the EU. Citation counts are based on a 3-year 

period with a 2-year lag (e.g., citations for 2012 are references made in articles in the 2012 data tape to articles in the 2008–10 data tapes). The index of 

highly cited articles is a country’s share of the world’s top 1% cited articles divided by its share of world articles for the cited-year window. 

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent Board,TM special tabulations (2013) 

from Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/. See appendix table 5-57. 

Science and Engineering Indicators 2014

All S&E Index Engineering

Biological sciences Physics

United States EU China Japan

0

0.5

1.0

1.5

2.0

2.5

2002 2012

Expected value = 1

United States EU China Japan

0

0.5

1.0

1.5

2.0

2.5

2002 2012

Expected value = 1

United States EU China Japan

0

0.5

1.0

1.5

2.0

2.5

2002 2012

Expected value = 1

United States EU China Japan

0

0.5

1.0

1.5

2.0

2.5

2002 2012

Expected value = 1
5-52 ♦ Chapter 5. Academic Research and Development

medical sciences (23%), along with chemistry (11%), engineering (7%), and physics (7%). These five fields account 

for 96% of the total (figure 5-32; appendix table 5-60). The 

patents citing U.S. articles are concentrated in three technology areas—pharmaceuticals, chemicals, and biotechnology—that together make up 63% of the total (figure 5-32).

The proportion of U.S. articles cited in U.S. patents that 

were authored by industry and federal government dropped 

between 2003 and 2012, largely because citations to academic articles increased (appendix table 5-59). Citations to 

academia grew from 59% to 65% of total citations to U.S. 

articles in that time period. This trend was stronger in some 

fields than in others. It was especially pronounced in engineering (from 50% to 68%), mathematics (from 71% to 

89%), physics (from 51% to 68%), and psychology (from 

67% to 83%). Despite the increasing proportion of citations 

to academic articles overall, citations to academic agricultural science articles actually decreased (from 67% to 63%) 

(appendix table 5-60). 

Articles from other sectors receive far fewer citations in 

patents, but this varied by field (figure 5-33). After academia, 

industry articles capture the next-largest share of citations 

in every major field except medical sciences, ranging from 

12% (medical sciences) to 22% (engineering). In medical 

sciences, nonprofit articles garner 16% of patent citations. 

Energy and Environment–Related Patent Citations

Clean energy and energy conservation and related technologies—including biofuels, solar, wind, nuclear, energy 

efficiency, pollution prevention, smart grid, and carbon 

sequestration—are closely linked to scientific R&D and 

have become a policy focus in the United States and other 

countries. NSF developed a method for identifying patents 

Figure 5-31

Within-U.S. article citations: Relative citation index, 

1992–2012

Index

FFRDC = federally funded R&D center.

NOTES: Article/citation counts are from the set of journals covered 

by the Science Citation Index (SCI) and Social Sciences Citation 

Index (SSCI). Articles are classied by the year they entered the 

database, rather than their year of publication, and are assigned to a 

sector on the basis of the institutional address(es) listed in the article. 

Articles/citations are credited on a fractional-count basis (i.e., for 

articles with collaborating institutions from multiple countries/ 

sectors, each country/sector receives fractional credit on the basis of 

the proportion of its participating institutions). Citation counts are 

based on a 3-year period with a 2-year lag (e.g., citations for 2012 

are references made in articles in the 2012 data tape to articles in the 

2008–10 data tapes).

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, and The Patent Board,TM special 

tabulations (2013) from Thomson Reuters SCI and SSCI, http:// 

thomsonreuters.com/products_services/science/.

Science and Engineering Indicators 2014

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

0

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

Academic

FFRDC

Federal government

Industry

Private nonprot

State/local government

Figure 5-32

Citations of U.S. S&E articles in U.S. patents, by 

selected S&E article field and technology area: 2012 

NOTES: Citations are references to S&E articles in journals covered 

by the Science Citation Index (SCI) and Social Sciences Citation 

Index (SSCI). Citation counts are based on an 11-year window with 

a 5-year lag (e.g., citations for 2012 are references in U.S. patents 

issued in 2012 to articles published in 1997–2007).

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, and The Patent Board,TM special 

tabulations (2013) from U.S. Patent and Trademark Ofce (USPTO), 

Patent Grant Bibliographic Data, and Thomson Reuters, SCI and 

SSCI, http://www.thomsonreuters.com/products_services/science/. 

See appendix table 5-60.

Science and Engineering Indicators 2014

Biological

sciences

48.2%

Medical

sciences 22.9%

Chemistry

11.4%

Physics

7.0%

Engineering 6.7%

All others 3.9%

Cited article field

Technology area of citing patent

Pharmaceuticals

25.7%

Chemicals

19.9%

Biotechnology

17.4%

Semiconductors

and electronics

5.2%

Computers

and peripherals

5.1%

Agriculture

4.7%

All others

18.0%

Medical

electronics

4.0%
Science and Engineering Indicators 2014 ♦ 5-53

with potential application in these technologies. (See sidebar 

“Identifying Clean Energy and Pollution Control Patents” 

for details on the filters.) 

Chapter 6 of this volume presents extensive data on the 

patents in four technology areas related to clean energy—

alternative energy, pollution mitigation, smart grid, and 

energy storage—including the nationality of their inventors. (See chapter 6, “Industry, Technology, and the Global 

Marketplace,” section “Patenting of Clean Energy and 

Pollution Control Technologies.”) This section reports on the 

citations in those patents to the S&E literature, using those 

citations to indicate the linkages between S&E R&D and the 

potential for practical use of the results of those R&D projects in new inventions and technologies.60 The citation data 

are based on patents issued between 2003 and 2012.

U.S. patents in these four areas of clean energy technology cite more foreign literature than U.S. literature (appendix 

table 5-61). In contrast, patents in all technology areas have 

consistently cited more U.S. literature than foreign literature 

(appendix table 5-59). 

Within citations to U.S. literature, articles authored by 

the academic sector accounted for the most citations (70%) 

among U.S. sectors in 2012. Industry and FRRDCs were 

the next largest, accounting for 12% and 10% of citations, 

respectively. Between 2003 and 2012, academia’s share 

of citations to U.S. literature increased from 59% to 70%. 

Industry’s share fell from 22% to 12%. 

Four broad S&E fields dominate the citations to S&E 

literature in these four patent areas: chemistry, physics, 

engineering, and biological sciences. The range of S&E 

fields cited indicates that these developing technologies rely 

on a wide base of S&E knowledge. 

The S&E fields cited by these patents are shown in table 

5-27. These four categories of energy and environment–related patents show somewhat different patterns of reliance 

on S&E literature. In both energy storage and smart grid, referencing is concentrated in a single field. For energy storage 

patents, over half of all citations are to chemistry articles; 

for smart grid patents, engineering is similarly dominant. 

Alternative energy and pollution mitigation citations are 

more evenly distributed across the four fields; for both of 

these technologies, however, chemistry is the most heavily 

cited field, receiving roughly one-third of all citations. 

Using patent citations as an indicator, the data show that 

chemistry research contributes heavily to invention in all areas of green technology with the exception of smart grid, 

where engineering dominates. Geoscience articles, which in 

this taxonomy include environmental sciences, are prominent as well, but only in pollution mitigation. 

Academic Patenting

Academic institutions whose research leads to intellectual property attempt to protect and benefit from the fruits 

of their labor through patents and associated activities. The 

majority of U.S. universities did not become actively involved in managing their own intellectual property until late 

in the 20th century, when the Bayh-Dole Act of 1980 gave 

Figure 5-33

Citation of U.S. S&E articles in U.S. patents, by selected S&E field and article author sector: 2012

Percent

All elds Engineering Chemistry Physics Biological sciences Medical sciences

0

10

20

30

40

50

60

70

80

FFRDC = federally funded R&D center.

NOTES: Citations are references to U.S. S&E articles in journals covered by the Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). Citations 

are classied on a fractional-count basis (i.e., for cited articles with collaborating institutions from more than one sector, each sector receives fractional credit on 

the basis of the proportion of its participating institutions). Citation counts are based on an 11-year window with a 5-year lag (e.g., citations for 2012 are 

references in U.S. patents issued in 2012 to articles published in 1997–2007). Fields with less than 5% of 2012 citations to U.S. articles are omitted. Joint and 

unknown sectors are not shown.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent Board,

TM special tabulations (2013) from U.S. 

Patent and Trademark Ofce (USPTO), Patent Grant Bibliographic Data, and Thomson Reuters, SCI and SSCI, http://www.thomsonreuters.com/products_ 

services/science/. See appendix table 5-60.

Science and Engineering Indicators 2014

Federal government Industry Academic FFRDC Nonprot
5-54 ♦ Chapter 5. Academic Research and Development

colleges and universities a common legal framework for 

claiming ownership of income streams from patented discoveries that resulted from their federally funded research. 

Other countries implemented policies similar to the BayhDole Act by the early 2000s, giving their academic institutions (rather than inventors or the government) ownership of 

patents resulting from government-funded research (Geuna 

and Rossi 2011). To facilitate the conversion of new knowledge produced in their laboratories to patent-protected public knowledge that potentially can be licensed by others or 

form the basis for a startup firm, many U.S. research institutions established technology management/transfer offices 

(AUTM 2009). 

The following sections discuss overall trends in university patenting and related indicators through 2011 and 2012.

Trends and Patterns in Academic Patenting

USPTO granted 8,700 patents to U.S. and foreign universities and colleges in 2012, 3.4% of USPTO patents granted 

to all U.S. and foreign inventors (figure 5-34). U.S. universities and colleges were granted 5,100 USPTO patents, with 

foreign universities receiving 3,600. 

Patenting by academic institutions has increased markedly over the last two decades—from 1,800 in 1992 to 8,700 

in 2012—resulting in their share of all USPTO patents doubling from 1.8% to 3.4%. Patenting by U.S. institutions 

outpaced overall growth of USPTO patents in the 1990s, 

resulting in their share of all patents increasing from 1.6% in 

1992 to 2.4% in 1999. Although the number of U.S. academic patents continued to grow from 2000 to 2012, the U.S. 

university and college share of all USPTO patents declined 

slightly (appendix table 5-62). In contrast, USPTO patents 

granted to foreign universities and colleges grew much more 

rapidly than those granted to U.S. universities and colleges 

in the 2000–12 period. U.S. patents to foreign universities 

and colleges grew sixfold to reach 3,600 patents; their share 

of all USPTO patents rose from 0.4% in 2000 to 1.4% in 

2012 (figure 5-34).61

Patenting by U.S. and foreign universities and colleges 

in another major patent office, the European Patent Office 

(EPO), shows a similar trend of increasing activity (figure 

5-35). The academic share of all patents granted by EPO 

increased from 0.9% in 1992 to 2.4% in 2012. After steadily 

increasing in the 1990s and early 2000s, the number of EPO 

patents granted to U.S. universities and colleges has remained flat at approximately 500–600 patents since 2003. In 

contrast, patenting by foreign universities and colleges grew 

more rapidly in the 2000s, and they surpassed U.S. universities in 2007.

The top 200 R&D-performing institutions dominate 

among U.S. universities and university systems receiving 

patent protection, with 98% of the total patents granted to 

U.S. universities between 1997 and 2012 (appendix table 

5-62).62 Among these institutions, 19 accounted for more 

than 50% of all patents granted to the top 200 (some of these 

were multicampus systems, like the University of California 

and the University of Texas). The University of California 

system received 11.3% of all U.S. patents granted to U.S. 

universities over the period, followed by the Massachusetts 

Institute of Technology, with 4.2%.

Using a combination of U.S. Patent Classification and International Patent Classification codes and text strings, the 

National Science Foundation developed algorithms to identify U.S. Patent and Trademark Office–issued patents with 

potential application in four broad, green technology areas. The four technology areas and their main subcategories are 

listed below. The search codes used to locate relevant patents are available at http://www.patentboard.com/OurResearch/

PatentFilters/tabid/115/Default.aspx, which documents the process used in identifying relevant patents.

Alternative energy 

production Energy storage

Energy management 

(smart grid) Pollution mitigation

Bioenergy Batteries Advanced components Recycling

Geothermal Flywheels Sensing and measurement Air

Hydropower Superconducting magnetic 

energy systems

Advanced control methods Solid waste

Nuclear Ultracapacitors Improved interfaces and 

decision support

Water

Solar Hydrogen production 

and storage

Integrated communication Environmental remediation

Wave/tidal/ocean Thermal energy storage Cleaner coal

Wind Compressed air Carbon and greenhouse gas 

capture and storage

Electric/hybrid vehicles

Fuel cells

Identifying Clean Energy and Pollution Control Patents
Science and Engineering Indicators 2014 ♦ 5-55

Biotechnology patents accounted for the largest share 

(25%) of U.S. university patents in 2012 (appendix table 

5-63). Biotechnology has been the largest technology area 

for U.S. academic patenting since 1991. Pharmaceuticals, 

the next-largest technology area, has had a declining number 

of patents over the past decade, dropping from an average of 

491 a year in 1998–2002 to 369 a year in 2008–12 (figure 

5-36). Medical equipment shows a similar, but much smaller, decline. The other major technology areas have been 

increasing. Patents for semiconductors have made the greatest increase, from around 90 patents per year in 1993–97 to 

around 210 in 2008–12.

Commercialization of U.S. Academic Patents

Universities commercialize their intellectual property by 

granting licenses to commercial firms and supporting startup firms formed by their faculty. Data from the Association 

of University Technology Managers (AUTM) indicate continuing growth in a number of such patent-related activities. 

Invention disclosures filed with university technology management/transfer offices describe prospective inventions 

and are submitted before a patent application is filed. These 

grew from 12,600 in 2002 to 19,700 in 2011 (notwithstanding small shifts in the number of institutions responding 

Table 5-27

Patent citations to S&E articles, by selected 

patent technology area and article field: 2003–12

Technology/field Citations (n) Percent

Alternative energy.................. 24,800 100.0

Chemistry........................... 7,611 30.7

Physics .............................. 6,004 24.2

Engineering........................ 5,285 21.3

Biological sciences ............ 5,017 20.2

Geosciences ...................... 400 1.6

Agricultural sciences.......... 365 1.5

All others............................ 118 0.5

Energy storage ...................... 7,278 100.0

Chemistry........................... 3,771 51.8

Engineering........................ 1,555 21.4

Physics .............................. 1,164 16.0

Biological sciences ............ 685 9.4

All others............................ 103 1.4

Smart grid.............................. 1,695 100.0

Engineering........................ 900 53.1

Physics .............................. 595 35.1

Computer sciences............ 85 5.0

Biological sciences ............ 37 2.2

Geosciences ...................... 31 1.8

All others............................ 47 2.8

Pollution mitigation................ 8,578 100.0

Chemistry........................... 2,943 34.3

Engineering........................ 1,817 21.2

Geosciences ...................... 1,605 18.7

Biological sciences ............ 1,500 17.5

Physics .............................. 326 3.8

Agricultural sciences.......... 243 2.8

All others............................ 144 1.7

NOTES: Citations are references to S&E articles in journals covered 

by the Science Citation Index (SCI) and Social Sciences Citation 

Index (SSCI). Citation counts are based on an 11-year window with 

a 5-year lag (e.g., citations for 2012 are references in U.S. patents 

issued in 2012 to articles published in 1997–2007). Patents may 

appear in more than one technology area; thus, citation counts may 

overlap slightly. See sidebar “Identifying Clean Energy and Pollution 

Control Patents” for details on these technology areas.

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, and The Patent Board,TM special 

tabulations (2013) from U.S. Patent and Trademark Office (USPTO), 

Patent Grant Bibliographic Data, and Thomson Reuters, SCI and 

SSCI, http://www.thomsonreuters.com/products_services/science/.

Science and Engineering Indicators 2014

Figure 5-34 

USPTO patents granted to U.S. and non-U.S. 

academic institutions: 1992–2012

USPTO = U.S. Patent and Trademark Ofce.

SOURCE: The Patent Board,TM special tabulations (2013) of 

Proprietary Patent database. See appendix table 5-62.

Science and Engineering Indicators 2014

1992 1994 1996 1998 2000

Share of all USPTO patent grants

Number of USPTO patent grants

2002 2004 2006 2008 2010 2012

0

1,000

2,000

3,000

4,000

5,000

6,000

USPTO patents granted

to non-U.S. universities

USPTO patents granted

to U.S. universities

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

USPTO patents granted to U.S. universities

USPTO patents granted to non-U.S. universities
5-56 ♦ Chapter 5. Academic Research and Development

Figure 5-36

U.S. academic patents, by technology area: Selected 5-year averages, 1993–2012

Number

NOTES: Data include institutions afliated with academic institutions (e.g., university and alumni organizations, foundations, and university associations). 

Universities vary in how patents are assigned (e.g., to boards of regents, individual campuses, or entities with or without afliation with the university). The Patent 

BoardTM technology areas constitute an application-oriented classication system that maps the thousands of International Patent Classes (IPCs) at the main 

group level into 1 of 35 technology areas. If a patent has more than one IPC, only the primary IPC is considered in mapping. 

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent Board,

TM special tabulations (2013) from U.S. 

Patent and Trademark Ofce (USPTO), Patent Grant Bibliographic Data. See appendix table 5-63.

Science and Engineering Indicators 2014

Biotechnology Pharmaceuticals Measurement

techniques and

instrumentation

Materials Medical

equipment

Semiconductors Optics

0

200

400

600

800

1,000

1,200

1,400

1993–97 1998–2002 2003–07 2008–12

0

0.5

1.0

1.5

2.0

2.5

Figure 5-35 

EPO patents granted to U.S. and non-U.S. academic institutions: 1992–2012

EPO = European Patent Ofce.

SOURCE: The Patent Board,TM special tabulations (2013) of Proprietary Patent database.

Science and Engineering Indicators 2014

Number of EPO patent grants Share of all EPO patent grants

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

EPO patents granted to non-U.S. universities

EPO patents granted to U.S. universities

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

0

200

400

600

800

1,000

1,200

EPO patents granted

to non-U.S. universities

EPO patents granted

to U.S. universities
Science and Engineering Indicators 2014 ♦ 5-57

to the AUTM survey over the same period) (figure 5-37). 

Likewise, new U.S. patent applications filed by AUTM university respondents also increased, nearly doubling from 

6,500 in 2002 to 12,100 in 2011. However, U.S. patents 

awarded to AUTM respondents stayed flat over the period, 

rising only in the last 2 years and reflecting a similar rise in 

the number of patents granted to all assignees (see appendix 

table 5-62).63

Despite the economic slowdown of the past 5 years, the 

number of new startup companies formed continued to rise, 

as did the number of past startups still operating; AUTM 

survey respondents reported a low of 348 startup companies 

formed in 2003 and a maximum of 617 in 2011, with a total 

of extant startup companies in 2011 of 3,573 (appendix table 

5-64). Licenses and options that generated revenues also increased over the period. Active licenses increased steadily 

from 18,800 in 2001 to 33,300 in 2011. 

Most royalties from licensing agreements accrue for relatively few patents and the universities that own them, and 

many of the AUTM respondent offices report no income. 

(Thursby and colleagues [2001] report that maximizing 

royalty income is not the dominant objective of university 

technology management offices.) At the same time, large 

one-time payments to a university can affect the overall trend 

in university licensing income. In 2011, the 157 institutions 

that responded to the AUTM survey reported a total of $1.5 

billion in net royalties from their patent holdings. This is 

essentially the same amount reported for the last 3 years. 

Perhaps as a result of the nation’s economic downturn, this 

number is down sharply from the high value of $2.1 billion 

reported in 2008 (appendix table 5-64). 

Conclusion

The nation’s universities and colleges play a key role in 

U.S. R&D by providing the following services: 

♦ Educating and training S&E students in research practices 

and other advanced skills

♦ Performing a large share of the nation’s basic research

♦ Building and operating world-class research facilities and 

supporting the national research cyberinfrastructure

♦ Producing intellectual output through published research 

articles and patents

Over the past several decades, academic spending on R&D 

has continued to increase, with funding from ARRA being a 

major source of support since 2009. The federal government 

has long provided the majority of funding for academic S&E 

R&D. Other important sources of academic R&D funding 

are universities and colleges themselves, state and local governments, businesses, and nonprofit organizations. 

Academic R&D expenditures have long been concentrated within a relatively small number of universities and 

colleges. For over 20 years, less than 12 schools each year 

have received about one-fifth of total academic R&D funding, about 20 schools have received close to one-third of this 

funding, and about 100 have received four-fifths of the total. 

(The identities of the universities in each group have varied 

over time.) 

For decades, more than half of all academic R&D spending has been in the broad field of life sciences. Since the 

mid-1990s, about one-third of all U.S.-trained, academically 

employed S&E doctorate holders received their degree in 

life sciences (in 2010, over 50% of their foreign-trained 

counterparts had doctorates in life sciences). The dominance of life sciences is also seen in physical infrastructure, 

where two subfields of life sciences—biological sciences 

and biomedical sciences—account for the bulk of growth in 

research space and where the largest share of new university research construction has been undertaken to advance 

health and clinical sciences. Life sciences are also heavily 

featured in academic R&D output: biological sciences and 

medical sciences accounted for over 50% of U.S. S&E articles in 2011. 

Academic R&D is increasingly collaborative. More articles are authored by researchers from different university 

departments, from multiple universities, or from universities in different countries. Similarly, academic collaboration 

with researchers in other sectors of the U.S. economy—

such as federal, state, or local government; business; or 

FFRDCs—has been increasing. Three-quarters of all U.S. 

articles, many of them authored by U.S. universities and 

colleges, now have coauthors from multiple institutions and 

countries. Collaboration rates between the United States and 

Canada are higher than would be expected, based on publishing output, thus suggesting the importance of geographic proximity and a common language. Collaboration rates 

are also relatively high between the United States and Asia 

Figure 5-37

U.S. university patenting activities: 2002–11

Thousands

SOURCE: Association of University Technology Managers (AUTM), 

AUTM Licensing Surveys: 2002–11. See appendix table 5-64. 

Science and Engineering Indicators 2014

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

0

5

10

15

20

25

Invention disclosures received

New U.S. patent applications led

U.S. patents granted
5-58 ♦ Chapter 5. Academic Research and Development

(in particular, China, South Korea, and Taiwan), reflecting, 

in part, ties formed through large numbers of students from 

Asian locations having studied for advanced S&E degrees in 

the United States. In another indicator of growing research 

collaboration, R&D funds passed through universities to other 

universities or to non-academic institutions grew more rapidly 

over the last decade than total academic R&D funding. 

Working conditions for S&E doctorate holders within the 

nation’s universities and colleges as well as access to federal funds for research have undergone changes over the past 

20–30 years. Although full-time faculty positions in the professoriate continue to be the norm in academic employment, 

S&E doctorate holders are increasingly employed in parttime and nontenured positions. Since 1995, despite an aging 

academic doctoral workforce, there has been a decrease in 

the percentage of doctorate holders with tenured positions. 

The share of academic researchers receiving federal support, 

including early career S&E faculty, has declined since 1991. 

Higher education has also experienced notable changes 

in demographic diversity. In particular, the share of academic doctoral positions held by white, male, native-born 

citizens has declined. Women represent a growing share of 

academic doctoral employment in S&E, as do the foreign 

born and foreign trained. The share of Asians employed in 

the S&E academic doctoral workforce has grown dramatically over the past three decades, while the shares held by 

blacks, Hispanics, and American Indians or Alaska Natives 

have grown much more slowly; these latter groups remain 

underrepresented in the academic doctoral workforce. 

There have been further shifts in the degree to which the 

academic doctoral workforce is focused on research activities versus teaching. Compared with the early 1990s, there 

has been an increase in the proportion of the academic doctoral workforce, including full-time faculty, that reports 

research as its primary work activity. By contrast, there 

has been a decline since the early 1990s in the share of the 

workforce that reports teaching as its primary work activity. 

Of those in the academic doctoral workforce reporting research as their primary activity, two-thirds are employed at 

the nation’s most research-intensive academic institutions. 

Those who primarily teach are more evenly dispersed across 

academia. 

The United States has a strong position in the global 

academic R&D enterprise. With major input from the academic sector, the United States is the largest single-country producer of S&E articles, not far behind the entire EU. 

The global shares of the United States, the EU, and other 

developed countries have declined as China has become 

the world’s third-largest producer of S&E articles over the 

last decade. However, the United States continues to have a 

disproportionately high global share of the most-cited S&E 

articles, indicating that U.S. academic R&D continues to be 

highly influential for subsequent research around the globe. 

Academic R&D increasingly advances marketplace 

technologies. U.S. universities continue to commercialize 

their research, as evidenced in the growth in the number 

of U.S. patent applications and invention disclosures and 

in the formation of startup companies. This growing commercialization of U.S. science is particularly important in 

biological sciences, which have spawned new discoveries in 

pharmaceuticals, chemicals, and biotechnology. In addition, 

U.S. patents most frequently cite academic-authored articles 

within all U.S. articles, underscoring the important linkage 

of academic R&D to invention. 

Notes

1. The academic R&D totals presented here exclude expenditures at the federally funded research and development 

centers (FFRDCs) associated with universities. Those expenditures are tallied separately and discussed in chapter 4. 

Nevertheless, the FFRDCs and other national laboratories 

(including federal intramural laboratories) play an important role in academic research and education, providing 

research opportunities for students and faculty at academic 

institutions, often by providing highly specialized, shared 

research facilities.

2. For this discussion, the terms universities and colleges, higher education, and academic institutions are used 

interchangeably. 

3. Gross domestic product implicit price deflators were 

used to convert current dollars to constant 2005 dollars. 

4. From 2005 to 2008, prior to the enactment of the 

American Recovery and Reinvestment Act of 2009, academic R&D expenditures increased by an annual average 

rate of 1.5% after adjusting for inflation. 

5. For a more complete discussion of these concepts, see 

the chapter 4 “Glossary.” 

6. Starting in 2010, the Higher Education Research and 

Development Survey asked institutions to categorize their 

R&D expenditures as either basic research, applied research, 

or development; prior surveys had asked how much total 

S&E R&D the institution performed and requested an estimate of the percentage of their R&D expenditures devoted 

to basic research. By only mentioning basic research, the 

survey question may have caused some respondents to classify a greater proportion of their activities in this category. 

The 2010 question provided definitions and examples of the 

three R&D categories to aid institutions in making more accurate assignments. In debriefing interviews, institutional 

representatives cited the changes in the survey question as 

the most important factor affecting their somewhat lower 

estimates of the amount of basic research institutions performed. The explicit inclusion of clinical trials and research 

training grants and the addition of non-S&E R&D may also 

have contributed. 

7. Data on non-S&E R&D expenditures have been collected by the National Science Foundation since FY 2003. 

However, the response rates on these items for the years 

prior to 2006 make trend analysis unreliable.

8. The academic R&D reported here includes separately 

budgeted R&D and related recovered indirect costs and also 
Science and Engineering Indicators 2014 ♦ 5-59

institutional estimates of unrecovered indirect costs associated with externally funded R&D projects, including committed cost sharing. Indirect costs are general expenses that 

cannot be associated with specific research projects but pay 

for things that are used collectively by many research projects at an academic institution. Two major components of 

indirect costs exist: (1) facilities-related costs, such as the 

construction, maintenance, and operation of facilities used 

for research; and (2) administrative costs, including expenses 

associated with financial management, institutional review 

boards, and environment, health, and safety management. 

Some indirect costs are recovered as a result of indirect-cost 

proposals that universities submit based on their actual costs 

from the previous year. Unrecovered indirect costs are calculated as the difference between an institution’s negotiated 

indirect cost rate on a sponsored project and the amount it recovers from the sponsor. Committed cost sharing is the sum 

of the institutional contributions required by the sponsor for 

specific projects (mandatory cost sharing) and the institutional resources made available to a specific project at the 

discretion of the grantee institution (voluntary cost sharing). 

9. The Higher Education Research and Development 

Survey collects aggregate data not separated by field on 

universities’ estimates of basic research, applied research, 

and development. 

10. Statistics on R&D performance can differ depending on whether the reporting is by R&D performers—in 

this case, academic institutions—or R&D funders. Reasons 

for this difference are discussed in the chapter 4 sidebar, 

“Tracking R&D: The Gap between Performer- and SourceReported Expenditures.”

11. Institutionally financed research includes both organized research projects fully supported with internal funding 

and all other separately accounted-for funds for research. 

This category does not include funds spent on research that 

are not separately accounted for, such as estimates of faculty 

time budgeted for instruction that is spent on research. Funds 

for institutionally financed R&D may also derive from 

general-purpose state or local government appropriations; 

general-purpose awards from industry, foundations, or other 

outside sources; endowment income; and gifts. Universities 

may also use income from patents and licenses or revenue 

from patient care to support R&D. (See this chapter’s section “Commercialization of U.S. Academic Patents” for a 

discussion of patent and licensing income.) 

12. Federal grants, contracts, and awards from other 

sources that are passed through state and local governments 

to academic institutions are credited to the original provider 

of the funds.

13. The federally financed share of academic S&E R&D 

expenditures dipped slightly in 2012; in part, this is because 

universities and colleges spent more American Recovery 

and Reinvestment Act of 2009 funds in 2011 (about $4.2 

billion) than in 2012 (about $2.4 billion). 

14. In 1991, the Office of Management and Budget capped 

reimbursement of administrative costs at 26% of total direct 

costs. As a result, actual unrecovered indirect costs at both 

public and private universities may be somewhat higher than 

the amounts reported on the Higher Education Research and 

Development Survey. 

15. During the early years of the 2000 decade, survey 

questions on pass-through funding were voluntary, with relatively high nonresponse (11% in 2000 versus 4% in 2009). 

16. Research space here is defined as the space used for 

sponsored R&D activities at academic institutions and for 

which there is separate budgeting and accounteing. Research 

space is measured in net assignable square feet (NASF). This 

is the sum of all areas on all floors of a building assigned 

to, or available to be assigned to, an occupant for a specific 

use, such as research or instruction. NASF is measured from 

the inside faces of walls. Multipurpose space that is partially 

used for research is prorated to reflect the proportion of time 

and use devoted to research.

17. The S&E fields used in the National Science 

Foundation Survey of Science and Engineering Research 

Facilities are based on the National Center for Education 

Statistics Classification of Instructional Programs (CIP)—

which is updated every 10 years (the current version is dated 2010). The S&E fields used in the FY 2011 Survey of 

Science and Engineering Research Facilities reflect the 2010 

CIP update. Both the FY 2007 and FY 2009 surveys reflect 

the 2000 CIP standard. For a comparison of the subfields in 

the FY 2005 and FY 2007 surveys, see the detailed statistical tables for S&E Research Facilities: FY 2007. No major 

impacts on these data resulted from the CIP 2010 update.

18. The science and technology field and subfield definitions were updated to the 2000 Classification of Instructional 

Programs starting with the FY 2007 Survey of Science and 

Engineering Research Facilities. Some of the observed declines in research space for health and clinical sciences and 

for physical sciences between FY 2005 and FY 2007 could 

reflect definition changes.

19. Institutional sources includes an institution’s operating funds, endowments, private donations, tax-exempt 

bonds and other debt financing, and indirect costs recovered 

from federal and nonfederal sources.

20. Only projects whose prorated cost was estimated 

to be $250,000 or more for at least one field of S&E were 

included.

21. Because of rising capitalization thresholds, the dollar threshold for inclusion in the equipment category has 

changed over time. Generally, university equipment that 

costs less than $5,000 would be classified under the cost category of “supplies.”

22. The “bricks and mortar” section of the Survey of 

Science and Engineering Research Facilities asks institutions to report their research space only. Therefore, the reported figures do not include space used for other purposes, 

such as instruction or administration. In the “Computing and 

Networking Capacity” section of the survey, respondents are 

asked to identify all of their cyberinfrastructure resources, 
5-60 ♦ Chapter 5. Academic Research and Development

regardless of whether these resources were used for research 

or other functions.

23. Research-performing academic institutions are defined as colleges and universities that grant degrees in 

S&E and expend at least $1 million in R&D funds. Each 

institution’s R&D expenditures are determined through the 

National Science Foundation Higher Education Research 

and Development Survey.

24. Academic institutions provided data on all computing systems with peak theoretical performance of 1 teraflop 

or faster. This defined the threshold for high-performance 

computing in the “Computing and Networking Capacity” 

section of the Survey of Science and Engineering Research 

Facilities. A teraflop is a measure of computing speed equal 

to 1 trillion floating point operations per second (FLOPS). 

FLOPS reflect the number of multiplications that a computer 

processor can perform within 1 second. 

25. These points have been cited as rationales for centralizing cyberinfrastructure and high-performance computing 

at several institutions (University of Arizona 2013; UCSD 

2009; Bose et al. 2010).

26. Clusters use multiple commodity systems, each running its own operating system with a high-performance interconnect network to perform as a single system. Massively 

parallel processors use multiple processors within a single 

system with a specialized high-performance interconnect 

network. Each processor uses its own memory and operating 

system. Symmetric multiprocessors use multiple processors 

sharing the same memory and operating system to work simultaneously on individual pieces of a program. 

27. Usable storage is the amount of space for data storage 

that is available for use after the space overhead required by 

file systems and applicable redundant array of independent 

disks configurations is removed. Online storage includes all 

storage providing immediate access for files and data from 

high-performance computing systems of at least 1 teraflop. 

Storage can be either locally available or made available via 

a network.

28. In the discussion covering the age composition of 

the academic doctoral workforce, comparisons are made 

between 1995 and 2010 because the Age Discrimination in 

Employment Act of 1967 applied to the professoriate starting in 1994. In the section on federal support of doctoral 

researchers, comparisons are made between 1973, the very 

early 1990s, and 2010 because of the availability of relatively comparable data for these years. In all discussions of 

full-time faculty, comparisons are made between 1997 and 

2010 because comparable data on senior and junior faculty 

groupings are available for these years. 

29. These other positions included positions at universities and colleges where no tenure system exists and there are 

various non-tenure-track positions. 

30. In addition, individuals ages 70–75 years grew as a 

share of the total doctoral academic workforce from 1995 

to 2010. In 1995, less than 1% of the doctoral academic 

workforce was between 70 and 75 years of age; this increased 

to 2.4% in 2010. 

31. Despite these gains, the number of academically employed, U.S.-trained female S&E doctorate holders in 2010 

(105,000) was nearly identical to the number of their male 

counterparts four decades earlier (107,000). 

32. Because a larger share of foreign-trained doctorate 

holders working in U.S. universities and colleges are men 

(70% in 2010 versus 64% of the U.S.-trained doctorate holders), using the Survey of Doctorate Recipients as a measure 

of female participation in the doctoral academic workforce 

results in a slight overcount of women’s presence. 

33. For some fields—in particular, life sciences and 

psychology—the National Survey of College Graduates 

(NSCG) estimates are somewhat higher than the Survey of 

Doctorate Recipients (SDR) estimates because SDR employs a more restrictive definition of research doctorate. As 

a result, some complications exist in comparing NSCG estimates of foreign-trained S&E doctorate holders with SDR 

estimates of the U.S.-trained S&E doctorate holders.

34. Analysis of trends in minority and underrepresented 

minority representation in the U.S.-trained academic doctoral workforce is complicated by changes in the Survey 

of Doctorate Recipients question about race and ethnicity 

starting in 2001. Specifically, since 2001, respondents have 

been allowed to report more than one race. Because of this 

change, data from 2001 to 2010 are not directly comparable 

to earlier years’ data (Milan 2012). 

35. Estimates of the percentage of underrepresented minorities by gender in the U.S.-trained academic doctoral 

workforce are based on small samples and are particularly 

sensitive to sampling error. 

36. Asians or Pacific Islanders include Native Hawaiians 

and Other Pacific Islanders. 

37. Some academically employed S&E doctorate holders 

were older than 75 years of age in 1995 and in 2010, but 

the Survey of Doctorate Recipients does not report on this 

because it drops respondents from the survey sample after 

they have reached 75 years of age. It is generally believed 

that individuals over age 75 years hold a small but growing 

share of doctoral academic employment. 

38. The Survey of Doctorate Recipients presents respondents with a list of work activities and asks them to identify the activities that occupied the most hours and second 

most hours during their typical work week. This measure 

was constructed slightly differently prior to 1993, and the 

data are not strictly comparable across the two periods. Prior 

to 1993, the survey question asked the respondent to select 

their primary and secondary work activity from a list of activities. Beginning in 1993, respondents were given the same 

list and asked on which activity they spent the most hours 

and on which they spent the second most hours. 

39. University-reported data from the Higher Education 

Research and Development Survey indicate that approximately 154,000 personnel paid from R&D salaries and 
Science and Engineering Indicators 2014 ♦ 5-61

wages were designated as principal investigators in academic FY 2012. 

40. A higher share (just under 90%) of the nation’s foreign-trained academic doctoral personnel classified research 

as their primary or secondary work activity in 2010. 

41. Data on federal support of academic researchers for 

1985 and 1993–97 cannot be compared with results for the 

earlier years or with those from 1999 to 2010 because of 

changes in the survey question. In 1985, the question focused on 1 month and, from 1993 to 1997, on 1 week. In 

most other survey years, the reference was to the entire preceding year. Since the volume of academic research activity 

is not uniform over the entire academic year, a 1-week (or 

1-month) reference period seriously understates the number 

of researchers supported at some time during an entire year.

42. A somewhat larger share of the nation’s foreigntrained academic doctoral personnel working full-time 

(66%) received federal support in 2010. 

43. For more information on the World Bank economic 

classification of countries, see http://data.worldbank.org/

about/country-classifications/country-and-lending-groups.

44. Countries with indexed S&E articles can change their 

borders over time. Data on Hong Kong, for example, were 

formerly reported separately but are now included in totals 

for China. See appendix table 5-24 for a list of the locations 

represented in the data.

45. Statements that a country “authors” a certain number of articles are somewhat imprecise, especially given 

the growing rates of international collaboration discussed 

later in this chapter. See the sidebar “Bibliometric Data and 

Terminology” for more information on how S&E article 

production and collaboration are measured.

46. See Eades et al. (2005) for a discussion of recent 

reforms in Japan’s higher education system. Japan’s R&D 

expenditures increased by 14% to reach 17.4 trillion yen 

between 2000 and 2008, according to the Organisation for 

Economic Co-operation and Development (http://www.

oecd.org/sti/inno/researchanddevelopmentstatisticsrds.htm).

47. Publication traditions in broad S&E fields differ 

somewhat. For example, although all fields publish journal 

articles, computer scientists often publish their findings in 

conference proceedings, and social scientists often write 

books and also publish in journals. Proceedings and books 

are poorly covered in the data currently used in this chapter.

48. Social science journals tend to focus on local issues, 

have less international author diversity, and publish in a language other than English more often than natural sciences 

journals—all criteria for exclusion from the Thomson Reuters 

databases. The lower concentration of articles in social sciences, other life sciences, and psychology in foreign countries may be partially attributed, then, to journal coverage. 

For further details on Thomson’s journal selection process, 

see http://www.thomsonreuters.com/products_services/

science/free/essays/journal_selection_process/.

49. The U.S. sector identification in this chapter is quite 

precise; to date, sector identification has not been possible 

for other countries. 

50. The 16 federally funded research and development centers (FFRDCs) sponsored by the Department of 

Energy (DOE) dominated S&E publishing by this sector. 

Across all fields of S&E, DOE-sponsored labs accounted 

for 83% of the total for the sector in 2005 (NSB 2008). 

Scientists and engineers at DOE-sponsored FFRDCs published 96% of the sector’s articles in chemistry, 95% in 

physics, and 90% in engineering (see “S&E Articles From 

Federally Funded Research and Development Centers” 

[NSB 2008:5-47]). Nine other federal agencies (including 

the Departments of Defense, Energy, Health and Human 

Services, Homeland Security, Transportation, and Treasury; 

the National Aeronautics and Space Administration; the 

Nuclear Regulatory Commission; and the National Science 

Foundation) also sponsor another 23 FFRDCs (NSF/

SRS 2009).

51. Coauthorship is a broad, though limited, indicator of collaboration among scientists. Previous editions of 

Indicators discussed possible underlying drivers for increased collaboration, including scientific advantages of 

knowledge sharing and instrument sharing, decreased costs 

of travel and communication, and national policies (NSB 

2006). Katz and Martin (1997), Bordons and Gómez (2000), 

and Laudel (2002) analyze limitations of coauthorship as an 

indicator of research collaboration. Despite these limitations, 

other authors have continued to use coauthorship as a collaboration indicator (Adams et al. 2005; Gómez, Fernández, 

and Sebastián 1999; Lundberg et al. 2006; Wuchty, Jones, 

and Uzzi 2007; Zitt, Bassecoulard, and Okubo 2000).

52. Readers are reminded that the number of coauthored 

articles between any pair of countries is the same; each 

country is counted once per article in these data. However, 

countries other than the pairs discussed here may also appear 

on the article.

53. Finland is included here as one of the Scandinavian 

countries; Iceland is not.

54. “Influence” is used here broadly; even citations that 

criticize or correct previous research indicate the influence 

of that previous research on the citing article.

55. For example, 2012 citation rates are from references in articles in the 2012 data file to articles contained 

in the 2008–10 data files of the Thomson Reuters Science 

Citation Index and Social Sciences Citation Index databases. Analysis of the citation data shows that, in general, 

the 2-year citing lag captures the 3 peak citation years for 

most fields, with the following exceptions: in astronomy and 

physics, the peak citation years are generally captured with a 

1-year lag; in computer sciences, psychology, and the social 

sciences, the peak citation years are generally captured with 

a 3-year lag.
5-62 ♦ Chapter 5. Academic Research and Development

56. Some part of this percentage decrease may reflect the 

increase in Chinese journals in the Science Citation Index 

and Social Sciences Citation Index databases used in this 

chapter. Since more Chinese authors in these journals are 

available to cite their Chinese coauthors, international citations to Chinese-authored articles are declining as a share 

of total citations. However, accounting for the “nationality” 

of a journal is not straightforward, and the data file used 

by the National Science Foundation (NSF) excludes journals that are primarily of regional interest. NSF’s estimate 

of “Chinese” journals shows an increase of 75% over the 

past decade, compared to an increase of 334% for Chineseauthored articles.

57. Because different S&E fields have different citation 

behaviors, these indicators should be used with caution. 

For example, articles in life sciences tend to list more references than, for example, articles in engineering or mathematics. Thus, a country’s research portfolio that is heavily 

weighted toward life sciences (e.g., the United States) may 

receive proportionately more citations than a country whose 

portfolio is more heavily weighted toward engineering or 

mathematics.

58. Percentiles are specified percentages below which 

the remainder of the articles fall. Thus, the 99th percentile 

identifies the number of citations 99% of the articles failed 

to receive. Across all fields of science, 99% of articles from 

2008 to 2010 failed to receive at least 21 citations in 2012. 

Matching numbers of citations with a citation percentile is 

not precise because all articles with a specified number of 

citations must be counted the same. Therefore, the citation 

percentiles discussed in this section and used in appendix tables 5-57 and 5-58 have all been counted conservatively, and 

the identified percentile is in every case higher than specified 

(i.e., the 99th percentile is always greater than 99%, the 95th 

percentile is always greater than 95%, and so forth). Actual 

citations/percentiles per field vary widely because counts 

were cut off to remain within the identified percentile. For 

example, using this method of counting, the 75th percentile 

for engineering contained 2008 to 2010 articles with 3–4 citations from 2012 articles, whereas the 75th percentile for 

astronomy contained articles with 6–10 citations. A country 

whose research influence was high would have greater proportions of articles in the higher-citation percentiles, whereas a country whose influence was low would have greater 

proportions of articles in lower-citation percentiles.

59. Patent citations to S&E research discussed in this section are limited to the citations found on the cover pages of 

successful patent applications. These citations are entered 

by the patent examiner and may or may not reflect citations 

given by the applicant in the body of the application. Patent 

cover pages also contain references to scientific and technical materials not contained in the article data used in this 

chapter (e.g., other patents, conference proceedings, industry standards). Analyses of the data referred to in this section found that nonjournal references on patent cover pages 

accounted for 19% of total references in 2008. The journals/

articles in the Science Citation Index and Social Sciences 

Citation Index databases used in this chapter—a set of relatively high-impact journals—accounted for 83% of the journal references, or 67% of the total science references, on the 

patent covers.

60. In this discussion, patent data are patents granted by 

the U.S. Patent and Trademark Office to all assignees, not 

just U.S. assignees. S&E publication data are for all publications in all U.S. sectors and for all country authors.

61. Patent-based data must be interpreted with caution. 

Year-to-year changes in the data may reflect changes in U.S. 

Patent and Trademark Office processing times (so-called 

“patent pendency” rates) and attempts to reduce the backlog of patent applications that build up from time to time. 

Likewise, industries and companies have different tactics 

and strategies for pursuing patents and otherwise protecting 

intellectual property, and these also may change over time.

62. The institutions listed in appendix table 5-62 are 

slightly different from those listed in past volumes, and data 

for individual institutions may be different. In appendix table 5-62, an institution is credited with a patent even if it is 

not the first assignee, and therefore some patents may be 

double counted. Several university systems are counted as 

one institution, and medical schools may be counted with 

their home institution. Universities also vary in how they assign patents (e.g., to boards of regents, individual campuses, 

or entities with or without affiliation with the university).

63. Other than for general trends, the patent counts reported by Association of University Technology Managers 

respondents in figure 5-37 and appendix table 5-64 cannot 

be compared with the patent counts developed from U.S. 

Patent and Trademark Office data as in appendix tables 5-62 

and 5-63.
Science and Engineering Indicators 2014 ♦ 5-63

Glossary

Doctoral academic S&E workforce: Includes those 

with a research doctorate in science, engineering, or health 

who are employed in 2- or 4-year colleges or universities, 

including medical schools and university research institutes, 

in the following positions: full and associate professors (referred to as senior faculty); assistant professors (referred to 

as junior faculty); postdoctorates (postdocs); other full-time 

positions, such as instructors, lecturers, adjunct faculty, research associates, and administrators; and part-time positions of all kinds.

European Union (EU): As of June 2013, the EU comprised 27 member nations: Austria, Belgium, Bulgaria, 

Cyprus, Czech Republic, Denmark, Estonia, Finland, 

France, Germany, Greece, Hungary, Ireland, Italy, Latvia, 

Lithuania, Luxembourg, Malta, the Netherlands, Poland, 

Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, and 

the United Kingdom. Croatia joined the EU in July 2013. 

Unless otherwise noted, Organisation for Economic Cooperation and Development data on the EU include all 28 

members; data on the EU from other sources are limited to 

the 27 nations that were members as of June 2013.

Federally funded research and development center 

(FFRDC): R&D organization exclusively or substantially 

financed by the federal government, either to meet particular R&D objectives or, in some instances, to provide major 

facilities at universities for research and associated training 

purposes. Each FFRDC is administered either by an industrial firm, a university, or a nonprofit institution. 

File year: Year in which an S&E article entered Thomson 

Reuters’ S&E publication database, which may be later than 

the year in which the S&E article was published. 

Fractional counting: Method of counting S&E publications in which credit for coauthored articles is divided 

among the collaborating institutions or countries based on 

the proportion of their participating departments or institutions. For example, the United States and China would each 

be credited half of a count for an article with a U.S. coauthor 

and a Chinese coauthor.

Index of highly cited articles: A country’s share of the 

top 1% most-cited S&E articles divided by the country’s 

share of all cited S&E articles. An index greater than 1 

means that a country has a disproportionately higher share in 

highly cited articles; an index less than 1 means the opposite.

Index of international collaboration: A country’s share 

of another country’s internationally coauthored articles 

divided by the other country’s share of all internationally 

coauthored articles. An index greater than 1 means that a 

country pair has a stronger-than-expected tendency to collaborate; an index less than 1 means the opposite. 

Net assignable square feet (NASF): Unit for measuring 

research space. NASF is the sum of all areas on all floors 

of a building assigned to, or available to be assigned to, an 

occupant for a specific use, such as research or instruction. 

NASF is measured from the inside face of walls. 

Relative citation index: A country’s share of another 

country’s cited S&E articles divided by the other country’s 

share of all cited S&E articles. An index of greater than 1 

means that the country has a higher-than-expected tendency 

to cite the other country’s S&E literature; an index less than 

1 means the opposite.

Research space: The budgeted and accounted for space 

used for sponsored R&D activities at academic institutions. 

Research space is the net assignable square feet of space 

in buildings within which research activities take place. 

Research facilities are located within buildings. A building 

is a roofed structure for permanent or temporary shelter of 

persons, animals, plants, materials, or equipment. Structures 

are included as research space if they are (1) attached to a 

foundation; (2) roofed; (3) serviced by a utility, exclusive 

of lighting; and (4) a source of significant maintenance and 

repair activities. 

Underrepresented minority: Demographic category including blacks, Hispanics, and American Indians or Alaska 

Natives, groups considered to be underrepresented in academic institutions.

Whole counting: Method of counting S&E publications 

in which each institution or country receives one credit for 

its participation in the article. Whole counting is used for 

coauthorship data. For example, the United States and China 

would each be credited one count for an article with a U.S. 

and Chinese coauthor.

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47(3):627–57.
Chapter 6

Industry, Technology, 

and the Global Marketplace

♦ 6-1

Highlights.....................................................................................................................................6-5

Knowledge- and Technology-Intensive Industries in the World Economy .............................6-5

Worldwide Distribution of Knowledge- and Technology-Intensive Industries.......................6-5

Trade and Other Globalization Indicators ................................................................................6-5

Innovation-Related Indicators of the United States and Other Major Economies ...................6-6

Investment and Innovation in Clean Energy Technologies......................................................6-6

Introduction..................................................................................................................................6-7

Chapter Overview.....................................................................................................................6-7

Chapter Organization................................................................................................................6-9

Data Sources, Definitions, and Methodology.........................................................................6-10

Knowledge- and Technology-Intensive Industries in the World Economy...............................6-10

Growth of Knowledge- and Technology-Intensive Industries in the World and Major 

Economies..........................................................................................................................6-10

Productivity.............................................................................................................................6-17

Worldwide Distribution of Knowledge- and Technology-Intensive Industries ........................6-20

Health and Education Services...............................................................................................6-20

Commercial Knowledge-Intensive Service Industries ..........................................................6-21

High-Technology Manufacturing Industries ..........................................................................6-25

Trade and Other Globalization Indicators.................................................................................................. 6-29

Global Trade in Commercial Knowledge- and Technology-Intensive Goods 

and Services.......................................................................................................................6-30

U.S. Trade in Advanced Technology Products ......................................................................6-34

U.S. Multinational Companies in Knowledge- and Technology-Intensive Industries...........6-36

U.S. and Foreign Direct Investment in Knowledge- and Technology-Intensive 

Industries............................................................................................................................6-38

Innovation-Related Indicators of the United States and Other Major Economies.....................6-39

Innovation Activities by U.S. Businesses...............................................................................6-39

Global Trends in Patenting .....................................................................................................6-40

Patenting Valuable Inventions: Triadic Patents......................................................................6-44

Trade in Royalties and Fees....................................................................................................6-45

U.S. High-Technology Small Businesses...............................................................................6-45

Investment and Innovation in Clean Energy Technologies.......................................................6-49

Commercial Investment..........................................................................................................6-49

Venture Capital Investment ....................................................................................................6-51

Public Research, Development, and Demonstration Expenditures in Clean Energy 

Technologies......................................................................................................................6-52

Patenting of Clean Energy and Pollution Control Technologies............................................6-53

Conclusion .................................................................................................................................6-55

Notes..........................................................................................................................................6-56

Glossary .....................................................................................................................................6-58

References..................................................................................................................................6-59
6-2 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

List of Tables

Table 6-1. ICT infrastructure and per capita income of selected developing economies: 

2011 and 2012.......................................................................................................................6-16

Table 6-2. Employment and R&D for selected U.S. industries: 2012 or most recent year.......6-22

Table 6-3. India’s and China’s trade in commercial KI services: 2011.....................................6-31

Table 6-4. U.S. and EU commercial KI services trade, by category: 2004, 2008, and 2011 ....6-31

Table 6-5. HT product exports, by selected region/country/economy: 2012 ............................6-33

Table 6-6. U.S. ATP trade in selected technology areas, by selected region/country/ 

economy: 2012......................................................................................................................6-35

Table 6-7. USPTO patents granted for selected countries: 2003, 2008, and 2012....................6-41

Table 6-8. USPTO patents granted in selected technology areas: 2003, 2008, and 2012 .........6-43

Table 6-9. Number of firms and employment of U.S. HT microbusinesses, by selected 

industries: 2010 .....................................................................................................................6-46

Table 6-10. U.S. venture capital investment, by selected financing stage and technology/ 

industry: 2009–12..................................................................................................................6-48

Table 6-11. Government RD&D of selected developed countries in clean energy and 

nuclear technologies, by technology area: Selected years, 2004–11 ....................................6-52

Table 6-12. U.S. government RD&D expenditures on clean energy and nuclear 

technologies: 2007–11 ..........................................................................................................6-53

Table 6-13. USPTO patents granted in alternative-energy and pollution-control 

technologies, by technology area: Selected years, 1997–2012.............................................6-54

Table 6-14. Patenting activity in alternative-energy and pollution-control technologies, 

by selected country/economy: 2009–12................................................................................6-55

Table 6-A. Global value added for selected industries, by selected region/country/ 

economy: 1997, 2006, and 2012 .............................................................................................6-8

Table 6-B. Global value added for manufacturing industries, by selected technology 

level and selected region/country/economy: Selected years, 1997–2012...............................6-9

Table 6-C. U.S. trade balance in iPhones, by selected country/economy .................................6-34

List of Sidebars

Industries That Are Not Knowledge or Technology Intensive....................................................6-8

Comparison of Data Classification Systems Used.....................................................................6-11

Industry Data and Terminology.................................................................................................6-12

Indonesia’s Rapid Growth in Commercial Knowledge-Intensive Services ..............................6-22

Currency Exchange Rates of Major Economies........................................................................6-24

Australia’s Commercial Knowledge-Intensive Services Grow Strongly ..................................6-25

Brazil’s and India’s High-Technology Manufacturing Industries.............................................6-27

U.S. Manufacturing and Employment .......................................................................................6-28

Measurement and Limitations of Trade Data ............................................................................6-29

U.S. Trade in R&D Services......................................................................................................6-32

International Initiative to Measure Trade in Value-Added Terms.............................................6-34

List of Figures

Figure 6-1. KTI industries’ share of GDP of developed and developing economies: 

Selected years, 1997–2012....................................................................................................6-13

Figure 6-2. Output of KTI industries as a share of GDP of selected developed 

economies: 2012....................................................................................................................6-13

Figure 6-3. Output of KTI industries as a share of GDP for selected developing 

economies: 2012....................................................................................................................6-14

Figure 6-4. Selected industry category share of developing economies’ GDP: 1997, 

2005, and 2012......................................................................................................................6-15

Figure 6-5. ICT infrastructure indexes of selected developed economies: 2011.......................6-16

Figure 6-6. ICT infrastructure indexes of selected developing economies: 2011 .....................6-17

Figure 6-7. ICT business and consumer spending as a share of GDP for selected 

developed countries: 2010.....................................................................................................6-17
Science and Engineering Indicators 2014 ♦ 6-3

Figure 6-8. ICT business and consumer spending as share of GDP for selected 

developing economies: 2010.................................................................................................6-18

Figure 6-9. Labor productivity growth of developed and developing economies: 

1997–2012.............................................................................................................................6-18

Figure 6-10. Labor productivity growth of selected developing economies: 1997–2012 .........6-19

Figure 6-11. Labor productivity growth of selected developed economies: 1997–2012 ..........6-19

Figure 6-12. GDP per capita for selected developing economies: Selected years, 

2000–12.................................................................................................................................6-20

Figure 6-13. Output of commercial KI services for selected regions/countries/economies: 

1997–2012.............................................................................................................................6-21

Figure 6-14. U.S. employment in commercial KI services: Selected years, 2000–12 ..............6-23

Figure 6-15. Global value-added shares of selected regions/countries/economies 

for selected service industries: 2012 .....................................................................................6-23

Figure 6-16. Output of HT manufacturing industries for selected regions/countries/ 

economies: 1997–2012..........................................................................................................6-26

Figure 6-17. HT manufacturing industries of selected regions/countries/economies: 2012 .....6-26

Figure 6-18. Output of China’s ICT manufacturing industries: 2002–11 .................................6-27

Figure 6-19. U.S. employment in HT manufacturing industries: 2000–12 ...............................6-28

Figure 6-20. Commercial KI service exports, by selected region/country/economy: 

2004–11.................................................................................................................................6-30

Figure 6-21. Exports of HT products, by selected region/country/economy: 2003–12.............6-32

Figure 6-22. U.S. advanced technology product trade in ICT, by selected region/country/

economy: 2012......................................................................................................................6-35

Figure 6-23. U.S. trade in advanced technology products: 2000–12.........................................6-36

Figure 6-24. Globalization indicators of U.S. multinationals in commercial KI 

services: 2010........................................................................................................................6-37

Figure 6-25. Globalization indicators of U.S. multinationals in selected manufacturing 

industries: 2010 .....................................................................................................................6-37

Figure 6-26. U.S. outward foreign direct investment in selected industries: 2012....................6-38

Figure 6-27. Foreign direct investment in selected U.S. industries, by selected region/ 

country/economy: 2012.........................................................................................................6-38

Figure 6-28. Share of U.S. manufacturing companies reporting innovation activities, 

by selected industry: 2008–10...............................................................................................6-39

Figure 6-29. Share of U.S. nonmanufacturing companies reporting innovation activities, 

by selected industry: 2008–10...............................................................................................6-40

Figure 6-30. USPTO patents granted, by location of inventor: 2003–12 ..................................6-41

Figure 6-31. USPTO patents granted, by selected U.S. industry: 2011....................................6-42

Figure 6-32. USPTO patents granted, by selected technology areas for selected 

country/economy of inventor: 2010–12................................................................................6-43

Figure 6-33. USPTO patents granted, by selected technology areas for inventors 

located in South Korea and Taiwan: 2010–12 ......................................................................6-44

Figure 6-34. Global triadic patent families, by selected region/country/economy: 

1998–2010.............................................................................................................................6-44

Figure 6-35. Global exports of royalties and fees, by selected region/country/economy: 

2004–11.................................................................................................................................6-45

Figure 6-36. Exports of royalties and fees of selected developing countries: 2004–11 ............6-46

Figure 6-37. U.S. HT industries, by share of industry sector: 2010 ..........................................6-46

Figure 6-38. Venture capital investment, by selected region/country/economy: 2005–12 .......6-47

Figure 6-39. U.S. venture capital investment, by financing stage: Selected years, 2005–12....6-48

Figure 6-40. SBIR investment, by financing phase: 2000–10...................................................6-49

Figure 6-41. Financial new investment in clean energy technologies, by selected region/ 

country/economy: 2004–12...................................................................................................6-50

Figure 6-42. Financial new investment in clean energy technologies, by selected energy 

and technology: 2006–12 ......................................................................................................6-50
6-4 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

Figure 6-43. Financial new investment in clean energy technologies in China, the 

United States, and the EU, by technology: 2012 ..................................................................6-50

Figure 6-44. Global venture capital investment in clean energy technologies: 

2004–12.................................................................................................................................6-51

Figure 6-45. Global venture capital investment in clean energy technologies, 

by selected technology: 2006–12..........................................................................................6-51

Figure 6-46. Government RD&D expenditures of selected developed countries/ 

economies in clean energy and nuclear technologies: 2004–11 ..........................................6-52

Figure 6-47. USPTO patents in alternative energy and pollution control technologies, 

by selected region/country/economy of inventor: Selected years, 1997–2012.....................6-53

Figure 6-A. Indonesia’s commercial KI services and HT manufacturing industries: 

2003–12.................................................................................................................................6-22

Figure 6-B. U.S. dollar exchange rate with selected currencies: 2007–12................................6-24

Figure 6-C. Output of Japan’s HT manufacturing industries: 2007–12 ....................................6-24

Figure 6-D. Australia’s commercial KI services industries: 2003–12.......................................6-25

Figure 6-E. Selected manufacturing industries of Brazil and India: 2003 and 2012.................6-27
Knowledge- and Technology-Intensive 

Industries in the World Economy

Knowledge- and technology-intensive (KTI) industries 

have been a major and growing part of the global economy. The United States has the highest KTI share of gross 

domestic product (GDP) of any large economy.

♦ Ten KTI industries, consisting of five service industries 

and five high-technology (HT) manufacturing industries, 

represented 27% of world GDP in 2012. Among the KTI 

industries, the commercial knowledge-intensive (KI) services—business, financial, and communications—have 

the highest share (16% of GDP). The public KI services, 

education and health, have a 9% share. The five HT manufacturing industries—aircraft and spacecraft; communications and semiconductors; computers; testing, measuring, 

and control instruments; and pharmaceuticals—have a 

2% share.

♦ The U.S. economy had the highest concentration of KTI 

industries among major economies (40% of U.S. GDP). 

The KTI concentrations for the European Union (EU) and 

Japan were considerably lower at 29%–30%.

♦ Major developing countries have lower KTI shares than 

developed countries. The KTI shares in Brazil, China, and 

India were 19%–21%. Turkey had the highest KTI share 

(23%) among larger developing countries.

Productivity growth in the world’s developing countries 

since 2000 has been much faster than in developedcountries. 

♦ Labor productivity growth in developing countries accelerated from 2% in the early 2000s to 6% in the mid-2000s 

before falling to 4% in the latter half of the 2000s. China 

and India led productivity growth of developing countries, 

growing 10% and 6%, respectively, between 2003 and 2012.

♦ Labor productivity growth in the United States and other developed countries slowed from 2% in the early 2000s to negative growth during the global recession before rising to 1%. 

Worldwide Distribution of Knowledge- and 

Technology-Intensive Industries

The United States is the largest global provider of commercial KI services and HT manufactured goods. 

♦ The United States has the largest global share (32%) in commercial KI services industries (business, financial, and communications). The EU is the second-largest global provider (23%).

♦ China’s commercial KI services industries have been 

growing rapidly, but from a low base. China’s global share 

reached 8% in 2012 to tie with Japan as the third-largest 

global provider.

♦ In HT manufacturing, the United States has a global share 

of 27%, closely followed by China. China’s HT industries 

have grown exponentially from a global share of 4% in 

2000 to 24% in 2012. 

U.S. KTI industries generally fared better than those of 

the developed economies in the EU and Japan in the aftermath of the recession. 

♦ The U.S. commercial KI services industries did better than 

their EU competitors following the 2008–09 global recession. U.S. value-added output in these industries grew 9% 

in 2010–12, whereas value added in the EU was stagnant. 

♦ U.S. HT manufacturing industries fared better than those in 

the EU or Japan following the 2008–09 global recession. U.S. 

value-added output grew 2% in 2010–12, while value-added 

output of the EU and of Japan remained flat or declined. 

U.S. KTI industries are a major part of the U.S. economy, and they have mostly recovered from the recession.

♦ U.S. commercial KI services industries employ one of 

every seven U.S workers (18 million) and pay higherthan-average wages. These industries have a higher-thanaverage share of skilled workers and fund about one-fourth 

of U.S. business R&D.

♦ Although U.S. HT manufacturing industries are much 

smaller than commercial KI services, they fund nearly onehalf of U.S. business R&D. These industries employ 1.8 

million workers and have an even higher share of highly 

skilled workers than commercial KI services.

♦ The value-added outputs of U.S. commercial KI services 

and HT manufacturing in 2012 are higher than their levels prior to the recession. However, employment in U.S. 

commercial KI services and HT manufacturing industries 

remains below its pre-recession levels.

Trade and Other Globalization Indicators

The EU is the world’s largest exporter of commercial KI 

services, followed by the United States. Both the EU and 

the United States have substantial surpluses.

♦ The EU’s commercial KI services exports more than doubled to reach $432 billion between 2004 and 2011, with its 

surplus widening to $127 billion. 

♦ U.S. exports of commercial KI services grew as fast as the 

EU’s to reach $235 billion between 2004 and 2011; the U.S 

trade surplus climbed from $25 billion to $52 billion. 

♦ Commercial KI services exports of developing countries 

grew much faster than developed countries, but from a 

much lower base. In these services, China and India have 

the largest export shares (4%–5% each) among developing 

countries. India’s trade surplus widened from $11 billion in 

2004 to $51 billion in 2011.

In HT manufactured goods, China is the world’s largest 

exporter, followed by the EU and the United States. 

♦ China, the world’s second-largest manufacturer of electronic products, is the world’s largest exporter of HT products, with a surplus of over $200 billion. China imports 

Highlights

Science and Engineering Indicators 2014 ♦ 6-5
components and inputs from the United States, the EU, and 

Asia for final assembly in China.

♦ The U.S. share of global HT exports remained stable for much 

of the 2000s. However, the U.S. trade deficit in HT products 

widened from $50 billion to $130 billion during this period.

♦ The U.S. trade deficit in HT goods is almost entirely due 

to information and communications technologies (ICT) 

products—communications, computers, and semiconductors. In other HT manufactured goods, notably aircraft and 

spacecraft, the United States has a substantial trade surplus.

A separate measure of U.S. trade in advanced technology 

products (ATP) shows patterns similar to those found in 

internationally comparable HT product trade data. 

♦ In 2012, the United States exported $305 billion of ATP 

and imported $396 billion of ATP products. The $92 billion 

deficit of ATP trade is largely due to trade in ICT products, 

primarily with China. The United States has a substantial 

surplus in trade of aerospace products. 

U.S. overseas investment in foreign KTI industries exceeds foreign investment in U.S. KTI industries. 

♦ In the commercial KI services industries, the stock of U.S. 

overseas investment was $1 trillion in 2012. The EU is the 

largest recipient, followed by Asia, which in these data includes Australia and New Zealand. The stock of foreign 

direct investment in the United States in these industries 

was $600 billion, with the EU as the largest investor.

♦ In computer and electronics manufacturing, which includes 

three HT manufacturing industries, the stock of U.S. overseas investment was $102 billion. Asia, which in these data 

includes Australia and New Zealand, and the EU are the two 

largest destinations. The stock of foreign direct investment 

in these industries in the United States was $61 billion, with 

the EU and Asia and the Pacific regions being the two largest investors. 

Innovation-Related Indicators of the United 

States and Other Major Economies

U.S. firms in commercial KTI industries reported much 

higher incidences of innovation than firms in other industries.

♦ Five HT manufacturing industries—aircraft; computers; communications; testing, measuring, and control instruments; and 

pharmaceuticals—reported rates of product innovation that 

were at least double the U.S. manufacturing sector average.

♦ In the U.S. nonmanufacturing sector, software firms were 

the leading innovators, with 69% of companies reporting the 

introduction of a new product or service compared to the 9% 

average for all nonmanufacturing companies. Innovation is 

two to three times higher than the nonmanufacturing average in computer systems design; data processing, hosting, 

and related services; and scientific R&D services.

The U.S. Patent and Trademark Office (USPTO) granted U.S. inventors 127,000 patents in 2012, not quite half 

of all USPTO patents granted worldwide. 

♦ The share of patents granted by USPTO to U.S. inventors 

declined from 53% in 2003 to 48% in 2012. 

♦ The United States has a higher concentration relative to 

other major economies in USPTO patenting activity in 

several advanced and science-based technologies, including ICT, automation, biotechnology, and pharmaceuticals.

The United States has a similar share to the EU and 

Japan in triadic patents, which are considered an indicator of higher-value inventions. 

♦ Triadic patents are patents sought for protection in the world’s 

largest markets—the United States, the EU, and Japan. 

♦ The U.S. share of triadic patents has remained constant 

during the 2000s at 27%–30%. 

Investment and Innovation in 

Clean Energy Technologies

More of the world’s investment in clean energy technologies 

occurred in developing countries than in developed countries in 2012. More commercial investment in clean energy 

technologies occurred in China than in any other country. 

♦ Clean energy investment in China, largely in solar and 

wind technologies, rose exponentially over the last decade 

to reach $61 billion in 2012. 

♦ Commercial investment in clean energy was between $27 

billion and $29 billion in the United States and the EU in 

2012. Commercial investment in the EU is down sharply 

due to the EU’s economic difficulties and cutbacks in government support for clean energy production and investment.

♦ Worldwide venture capital investment in clean energy 

technologies was estimated at $4 billion in 2012. The 

United States is the largest recipient, accounting for more 

than 80% of all investment. Three technologies—energy 

smart and efficiency, solar, and biofuels—dominate venture capital investment.

♦ Worldwide venture capital investment rose rapidly, more than 

quadrupling from $1 billion to $4 billion from 2004 to 2012.

The United States and Japan were the largest investors 

in 2012 public research, development, and demonstration (RD&D) for clean energy technologies.

♦ Expenditures of most OECD countries on RD&D investment for clean energy and nuclear technologies were an 

estimated $13 billion in 2010. 

♦ U.S. public RD&D investment in clean energy technologies jumped from $1.5 billion in 2004 to spike at $7.0 billion in 2009 due to one-time stimulus funding under the 

American Recovery and Reinvestment Act of 2009. In 

2011, U.S. public RD&D dropped to $4.0 billion, still $2.5 

billion higher than its level in 2004.

6-6 ♦ Chapter 6. Industry, Technology, and the Global Marketplace
Science and Engineering Indicators 2014 ♦ 6-7

Introduction

Chapter Overview

Policymakers in many countries increasingly emphasize the central role of knowledge, particularly research 

and development and other activities that advance science 

and technology (S&T), in a country’s economic growth and 

competitiveness. This chapter examines the downstream effects of these activities on the performance of the United 

States and other major economies in the global marketplace. 

This chapter covers two main areas. The first is knowledge- and technology-intensive (KTI) industries in both the 

service and manufacturing sectors. KTI industries are 10 

categories of industries classified by the Organisation for 

Economic Co-operation and Development (OECD 2001, 

2007) that have a particularly strong link to S&T:1

♦ Five knowledge-intensive (KI) services industries incorporate high technology (HT) either in their services or in the 

delivery of their services. Three of these—financial, business, and communications services (including computer 

software and R&D)—are generally commercially traded. 

The others—education and health services—are publicly 

regulated or provided and remain relatively more location 

bound.

♦ Five HT manufacturing industries spend a large proportion 

of their revenues on R&D and make products that contain 

or embody technologies developed from R&D. These are 

aircraft and spacecraft, pharmaceuticals, computers and 

office machinery, semiconductors and communications 

equipment (treated separately in the text), and scientific 

(medical, precision, and optical) instruments.2 Trends in 

aircraft and spacecraft and pharmaceuticals are particularly sensitive to government policies. Aircraft and spacecraft 

trends are affected by funding for military aircraft, missiles, and spacecraft and by different national flight regulations. National regulations covering drug approval, prices, 

patent protection, and importation of foreign pharmaceuticals can affect pharmaceuticals.

This report gives special attention to KTI industries in 

information and communications technology (ICT). ICT 

combines the HT manufacturing industries of computers and office machinery, communications equipment, and 

semiconductors with the KI services of communications and 

computer programming (a subset of business services). ICT 

industries are important because they provide the infrastructure for many social and economic activities, facilitating innovation and economic growth.3

Industries that are less KTI, however, remain very important in the world economy and therefore receive some attention in the chapter (see sidebar, “Industries That Are Not 

Knowledge or Technology Intensive”).

The globalization of the world economy involves 

the rise of new centers of KTI industries.4

 Although the 

United States continues to be a leader in these industries, 

China, India, Brazil, and other developing economies have 

vigorously pursued national innovation policies in an effort to become major producers and exporters of KTI goods 

and services. Advances in S&T have enabled companies to 

spread KTI activity to more locations around the globe and 

to develop strong interconnections among geographically 

distant entities.

The second major focus of the chapter is innovation. 

Because innovation is closely associated with technologically led economic growth, the analysis of innovation in the 

chapter emphasizes the role of KTI industries. The measurement of innovation is an emerging field, and current data 

and indicators are limited. However, activities related to the 

commercialization of inventions and new technologies are 

regarded as important components of innovation indicators. 

Such activities include patenting, the creation and financing of new HT firms, and investment in intangible goods 

and services. 

In recent years, innovations aimed at developing improved technologies for generating clean and affordable energy have become increasingly important in both developed 

and developing countries or economies. Clean energy has 

a strong link to S&T. Like ICT, energy is a key element of 

infrastructure, the availability of which can strongly affect 

prospects for growth and development. For these reasons, 

the chapter pays special attention to energy technologies.

Several themes cross-cut the various indicators examined 

in the chapter: 

♦ The HT manufacturing industries are the most globalized 

among the KTI industries. Two HT manufacturing industries—communications; semiconductors and computers—have the most complex global value chains, where 

China is the dominant locale for final production. Three 

industries—aircraft and spacecraft; testing, measuring, and 

control instruments; pharmaceuticals—are less globally 

integrated, with final production largely located in developed countries.

♦ Globalization is increasing rapidly in the commercial KI 

services industries but remains substantially less than in 

the HT manufacturing industries. Data on trade and U.S. 

foreign investment suggest that these industries have substantial linkages among developed economies. Industries 

in developed economies also contract out some of their activities to developing economies. 

♦ Although KTI activity has increased in Brazil, India, 

Indonesia, Turkey, and other developing countries, China 

plays a unique role in this arena. Despite a per capita income comparable to that in other developing countries, 

China’s economic activity in several KTI industries has 

grown unusually quickly and is now comparable to or exceeds that of the United States, the European Union (EU; 

see “Glossary” for member countries), and Japan.

♦ KTI industries remain concentrated in developed countries despite much more rapid growth by China and other 

developing countries. Developed countries account for 
6-8 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

Science and technology (S&T) are used in many industries besides high-technology (HT) manufacturing and 

knowledge-intensive (KI) services. Service industries not 

classified as KI services—which include the wholesale 

and retail, restaurant and hotel, transportation and storage, and real estate industries—may incorporate advanced 

technology in their services or in the delivery of their services. Manufacturing industries not classified as HT by the 

Organisation for Economic Co-operation and Development 

(OECD) may use advanced manufacturing techniques, incorporate technologically advanced inputs in manufacture, 

and/or perform or rely on R&D. Industries not classified as 

either manufacturing or services—agriculture, construction, 

mining, and utility—also may incorporate recent S&T in 

their products and processes. For example, agriculture relies on breakthroughs in biotechnology, construction uses 

knowledge from materials science, mining depends on earth 

sciences, and utilities rely on advances in energy science. 

In the non-KI services industries—real estate; restaurants and hotels; transport and storage; and wholesale 

and retail—patterns and trends of the four largest producers—the United States, the EU, Japan, and China—were 

similar to those in HT manufacturing and commercial KI 

services (table 6-A). The United States and the EU, the 

Industries That Are Not Knowledge or Technology Intensive

Table 6-A

Global value added for selected industries, by selected region/country/economy: 1997, 2006, and 2012

(Percent distribution)

Service industry and region/country/economy 1997 2006 2012

Agriculture

Global value added (current $billions) .......................................... 1,140 1,461 2,879

China......................................................................................... 15.3 20.6 28.8

EU.............................................................................................. 19.5 15.6 10.3

Japan ........................................................................................ 6.6 4.4 3.8

United States ............................................................................ 9.5 8.4 5.9

Construction

Global value added (current $billions) .......................................... 1,610 2,585 3,657

China......................................................................................... 4.0 6.2 16.3

EU.............................................................................................. 27.2 31.1 22.4

Japan ........................................................................................ 21.3 10.6 10.6

United States ............................................................................ 21.5 25.2 15.3

Mining

Global value added (current $billions) .......................................... 573 1,713 3,038

China......................................................................................... 4.8 8.2 17.4

EU.............................................................................................. 11.7 7.4 3.9

Japan ........................................................................................ 1.2 0.2 0.1

United States ............................................................................ 16.6 13.4 9.4

Real estate

Global value added (current $billions) .......................................... 2,686 4,283 5,667

China......................................................................................... 1.7 3.2 8.3

EU.............................................................................................. 29.3 32.7 27.8

Japan ........................................................................................ 17.3 12.1 13.1

United States ............................................................................ 34.3 34.8 31.9

Restaurants and hotels ....................................................................

Global value added (current $billions) .......................................... 732 1,202 1,708

China......................................................................................... 3.3 5.5 10.2

EU.............................................................................................. 28.2 31.6 25.9

Japan ........................................................................................ 17.5 10.9 11.3

United States ............................................................................ 30.1 32.0 27.6

Transport and storage

Global value added (current $billions) .......................................... 524 855 1,255

China......................................................................................... 3.8 6.0 10.4

EU.............................................................................................. 30.5 34.6 26.8

Japan ........................................................................................ 14.2 8.6 9.3

United States ............................................................................ 23.9 20.2 16.7

Utilities

Global value added (current $billions) .......................................... 708.2 1,032.8 1,487.8

China......................................................................................... 4.0 9.5 20.5

EU.............................................................................................. 25.0 26.5 20.9

Japan ........................................................................................ 21.4 14.0 11.7

United States ............................................................................ 26.7 24.8 19.7

Wholesale and retail

Global value added (current $billions) .......................................... 3,713 5,607 8,042

China......................................................................................... 3.0 4.5 10.7

EU.............................................................................................. 24.9 26.1 20.5

Japan ........................................................................................ 18.5 11.3 9.9

United States ............................................................................ 30.0 30.4 23.7

EU = European Union.

NOTES: Value added is the amount contributed by a country, firm, or other entity to the value of a good or service and excludes purchases of domestic 

and imported materials and inputs. China includes Hong Kong. EU excludes Cyprus, Estonia, Latvia, Lithuania, Luxembourg, Malta, and Slovenia. 

SOURCE: IHS Global Insight, World Industry Service database (2013).

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 6-9

two largest providers, had modest declines in their global 

shares of value added between 1997 and 2012. Japan’s 

share declined more sharply. China’s global share grew 

rapidly to reach near or at Japan’s share in restaurants 

and hotels, transport and storage, and wholesale and retail 

during this period. 

Non-HT manufacturing industries are divided into three 

categories, as classified by the OECD: medium-high technology, medium-low technology, and low technology.*

 In 

these industries, patterns and trends were somewhat divergent from those in HT manufacturing (table 6-B). China’s 

global share of value added grew rapidly between 1997 and 

2012, and it became the world’s largest manufacturer in the 

three non-HT manufacturing segments. The global shares 

of the United States and EU declined sharply in contrast to 

their relatively more stable positions in HT manufacturing. 

Japan’s share also declined sharply in all three segments. 

The positions of the United States, the EU, China, and 

Japan in nonmanufacturing and nonservices industries—

agriculture, construction, and mining—are fairly similar to 

their positions in KTI industries (table 6-A). China’s global 

share grew rapidly between 1997 and 2012, and it became 

the world’s largest producer in agriculture and mining. The 

global shares of the United States and EU fell moderately. 

Japan had a steeper decline in these industries.

* Medium-high technology includes motor vehicle manufacturing and 

chemicals production, excluding pharmaceuticals; medium-low technology includes rubber and plastic production and basic metals; and low technology includes paper and food product production.

Industries That Are Not Knowledge or Technology Intensive—continued

Table 6-B

Global value added for manufacturing industries, by selected technology level and selected region/country/

economy: Selected years, 1997–2012

(Percent distribution)

Manufacturing technology level and region/country/economy 1997 2003 2006 2009 2012

Medium high

Global value added (current $billions) ..................................... 1,467 1,643 2,139 2,357 3,480

China.................................................................................... 3.4 7.0 11.6 23.1 28.2

EU......................................................................................... 33.2 33.9 32.4 28.0 23.0

Japan ................................................................................... 20.2 16.8 14.3 11.8 11.6

United States ....................................................................... 23.4 23.7 20.3 15.3 14.4

Medium low

Global value added (current $billions) ..................................... 1,346 1,482 2,212 2,418 3,512

China.................................................................................... 3.8 7.8 12.9 24.0 31.1

EU......................................................................................... 28.9 29.6 25.8 22.1 16.2

Japan ................................................................................... 19.5 15.1 11.3 9.8 9.6

United States ....................................................................... 23.5 22.4 20.2 14.9 13.4

Low

Global value added (current $billions) ..................................... 1,454 1,594 1,955 2,371 2,969

China.................................................................................... 4.6 8.1 13.4 20.4 29.1

EU......................................................................................... 30.2 30.0 27.9 25.1 19.3

Japan ................................................................................... 15.7 13.4 10.0 9.6 9.1

United States ....................................................................... 23.4 24.1 20.6 17.9 13.9

EU = European Union.

NOTES: Value added is the amount contributed by a country, firm, or other entity to the value of a good or service and excludes purchases of domestic and 

imported materials and inputs. The technology level of manufacturing is classified by the Organisation for Economic Co-operation and Development on the 

basis of R&D intensity of output. China includes Hong Kong. EU excludes Cyprus, Estonia, Latvia, Lithuania, Luxembourg, Malta, and Slovenia. 

SOURCE: IHS Global Insight, World Industry Service database (2013).

Science and Engineering Indicators 2014

three-quarters of global production of commercial KI services industries, the largest category of KTI industries. 

♦ KTI industries in developing countries have fared better 

than those in developed countries in the aftermath of the 

2008–09 global recession. Among the KTI industries in the 

developed countries, those in the United States rebounded 

more robustly from the economic downturn than those in 

other developed economies.

Chapter Organization

The chapter focuses on the United States, the EU, Japan, 

and the large and rapidly developing economy of China. 

Other major developing countries, including Brazil, India, 

and Indonesia, also receive significant attention. The timespan is from the late 1990s to the present.

This chapter is organized into five sections. The first section discusses the prominent role of KTI industries in regional and national economies around the world.
6-10 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

The second section describes the global spread of KTI industries and analyzes regional and national shares of worldwide production. It discusses shares for the KTI industry 

group as a whole, for KI services and HT manufacturing 

overall, and for particular services and manufacturing industries within these groups. Because advanced technology is 

increasingly essential for non-HT industries, some data on 

these industries are also presented.

The third section examines indicators of increased interconnection of KTI industries in the global economy. Data on 

patterns and trends in global trade in KTI industries make 

up the bulk of this section. The section also presents data on 

U.S. trade in advanced technology products (ATP), examining trends in U.S. trade with major economies and in key 

technologies. Data on domestic and foreign production and 

on employment in U.S. multinational companies (MNCs) in 

KTI industries are presented as indicators of the increasing 

involvement of these economically important firms in crossborder activities. To further illustrate the effects of globalization on the United States, the section presents data on 

U.S. and foreign direct investment abroad, showing trends 

by region and for individual KTI industries.

The fourth section presents innovation-related indicators. It examines countries’ shares in all patents granted 

by the United States in various technology areas. It next 

examines countries’ shares of high-value patents. It presents innovation-related data on U.S. industries from the 

National Science Foundation’s (NSF’s) Business R&D and 

Innovation Survey (BRDIS). A discussion of U.S. HT small 

businesses includes data on the number of HT small business startups and existing firms, employment, and venture 

capital and Small Business Innovation Research (SBIR) investment by industry. 

The last section presents data on clean energy and energy 

conservation and related technologies, which have become 

a policy focus in developed and developing nations. These 

energy technologies, like KTI industries, are closely linked 

to scientific R&D. Production, investment, and innovation 

in these energies and technologies are rapidly growing in the 

United States and other major economies.

Data Sources, Definitions, and Methodology

This chapter uses a variety of data sources. Although several are thematically related, they have different classification systems. The sidebar “Comparison of Data Classification 

Systems Used” describes these systems and aims to clarify 

the differences among them. The discussion of regional and 

country patterns and trends includes examination of developed and developing countries using the World Bank’s per 

capita income classification. Countries classified by the 

World Bank as high income are developed countries, while 

those classified in the other income levels—upper middle income, lower middle income, and low income—are classified 

as developing. In this chapter, “country” and “economy” are 

used interchangeably in these discussions.

Knowledge- and Technology-Intensive 

Industries in the World Economy

The first section of this chapter examines the role of KTI 

industries in the global economy. (For an explanation of KTI 

industries, please see “Chapter Overview.”) Data on value 

added in these industries can be used to examine their growing importance in the global economy, the United States, and 

other major economies. (For a discussion of value added and 

other measures of economic activity, see sidebar, “Industry 

Data and Terminology”). For context, selected data are presented on wealth, productivity growth, and ICT infrastructure of selected economies, with a focus on the United States 

and other economies in which KTI industries play a particularly large or rapidly growing role.

Growth of Knowledge- and TechnologyIntensive Industries in the World and Major 

Economies

KTI industries—commercial KI services, public KI services, and HT manufacturing—are a major part of the global 

economy, making up 27% of world gross domestic product (GDP) (appendix tables 6-1–6-3). Among the KTI industries, the commercial KI services—business, financial, 

and communications—have the highest share (16% of GDP) 

(appendix table 6-4). 

The public KI services—education and health—are the 

second largest (9%) (appendix tables 6-3, 6-5, and 6-6).5 The 

HT manufacturing industries—aircraft and spacecraft, communications, computers, pharmaceuticals, semiconductors, 

and testing, measuring and control instruments—are much 

smaller, with a 2% share (appendix table 6-7). 

The KTI share of the world economy remained roughly 

constant between 1997 and 2012 (appendix tables 6-2 and 

6-3). Among the KTI categories, the commercial KI services share gained 1 percentage point to reach 16% (appendix 

table 6-4). The expansion of commercial KI services reflects 

the continued shift in developed economies to services and 

the tendency for businesses and other organizations to purchase various services rather than maintain organizational 

units to provide them. This has spurred the growth of the 

business services industry. In developing economies, rapid 

economic growth and higher per capita income have stimulated demand for various services, including the commercial 

KI services of communications and financial services. 

The share of public KI services stayed stable at 9% between 1997 and 2012 (appendix tables 6-3, 6-5, and 6-6). 

The growth of education and health care in line with world 

GDP growth has occurred due to increased demand for and 

access to education and health care services, the aging of 

populations in many countries, and other demographic factors and technological advances, such as online education 

and electronic medical records. The share of HT manufacturing declined 1 percentage point to reach 2% (appendix 

table 6-7).
Science and Engineering Indicators 2014 ♦ 6-11

Comparison of Data Classification Systems Used

 Topic Data provider Variables Basis of 

classification

Coverage Methodology

Knowledgeintensive (KI) 

service and hightechnology (HT) 

manufacturing 

industries

IHS Global Insight, 

World Industry 

Service database 

(proprietary)

Production, value 

added

Industry basis 

using International 

Standard Industrial 

Classification

KI services—

business, financial, 

communications, 

health, and 

education services

HT manufacturing—

aircraft and 

spacecraft, 

pharmaceuticals, 

office and computer 

equipment, 

communications, 

and scientific 

and measuring 

equipment

Uses data from 

national statistical 

offices in developed 

countries and 

some developing 

countries and 

estimates by IHS 

Global Insight for 

some developing 

countries

Trade in commercial 

KI services 

World Trade 

Organization

Exports and imports Product basis 

using Extended 

Balance of 

Payments Services 

Classification

KI services—

business, financial, 

communications, 

and royalties and 

fees

Uses data from 

national statistical 

offices, International 

Monetary Fund, and 

other sources

Trade in HT goods IHS Global Insight, 

World Trade 

Service database 

(proprietary)

Exports and imports Product basis 

using Standard 

International Trade 

Classification

Aerospace, 

pharmaceuticals, 

office and 

computing 

equipment, 

communications 

equipment, 

and scientific 

and measuring 

instruments

Uses data from 

national statistical 

offices and 

estimates by IHS 

Global Insight

U.S. trade in 

advancedtechnology 

products

U.S. Census Bureau Exports and imports Product basis 

using Harmonized 

Commodity 

Description and 

Coding System, 10 

technology areas 

classified by U.S. 

Census

Advanced materials, 

aerospace, 

biotechnology, 

electronics, flexible 

manufacturing, 

information and 

communications, 

life sciences, 

nuclear technology, 

optoelectronics, 

and weapons

Data collected 

from automated 

reporting by U.S. 

Customs

Globalization of 

U.S. multinationals

U.S. Bureau of 

Economic Analysis 

(BEA)

Value added, 

employment, and 

inward and outward 

direct investment

Industry basis 

using North 

American Industrial 

Classification 

System (NAICS)

Commercial 

KI services—

business, financial, 

communications 

HT manufacturing—

aerospace, 

pharmaceuticals, 

office and computer 

equipment, 

communications, 

and scientific 

and measuring 

equipment

BEA annual 

surveys of U.S. 

multinationals and 

U.S. subsidiaries 

of non-U.S. 

multinationals

U.S. industry 

innovation activities

National Science 

Foundation, 

Business R&D and 

Innovation Survey

Innovation activities U.S. businesses 

with more than five 

employees

Industries classified 

on industry basis 

using NAICS

Survey of U.S.-

located businesses 

with more than 

five employees 

using nationally 

representative 

sample

Continued on following page
6-12 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

U.S. Patent and 

Trademark Office 

(USPTO) patents

The Patent Board Patent grants Inventor country of 

origin, technology 

area as classified by 

The Patent Board

More than 400 U.S. 

patent classes, 

inventors classified 

according to 

country of origin 

and technology 

codes assigned to 

grant

Source of data is 

USPTO 

Triadic patent 

families

Organisation for 

Economic Cooperation and 

Development 

(OECD)

Patent applications Inventor country of 

origin and selected 

technology area as 

classified by OECD

Broad technology 

areas as defined by 

OECD, inventors 

classified according 

to country of origin

Sources of data are 

USPTO, European 

Patent Office, and 

Japanese Patent 

Office

Venture capital Dow Jones 

VentureSource

Investment, 

technology area, 

country of investor 

origin

Technology areas as 

classified by Dow 

Jones classification 

system

Twenty-seven 

technology 

areas, investment 

classified by 

venture firms’ 

country location

Data collected by 

analysts from public 

and private sources, 

such as public 

announcements 

of venture capital 

investment deals

Comparison of Data Classification Systems Used—continued

The data and indicators reported here permit the tracing and analysis of broad patterns and trends that shed 

light on the spread and shifting distribution of global 

knowledge- and technology-intensive (KTI) capabilities. 

The industry data used in this chapter derive from a proprietary IHS Global Insight database that assembles data 

from the United Nations (UN) and the Organisation for 

Economic Co-operation and Development to cover 70 

countries in a consistent way. IHS estimates some missing data for some of the developing countries, including 

China. Data for developing countries may not be available on a timely basis or for specific industries.

The industry data follow the International Standard 

Industrial Classification, a UN system for classifying 

economic activities. Firms are classified according to 

their primary activity; a company that primarily manufactures pharmaceuticals, for example, but also operates 

a retail business would have all of its economic activity 

counted under pharmaceuticals. 

Production is measured as value added. Value added is 

the amount contributed by an economic entity—country, 

industry, or firm—to the value of a good or service. It 

excludes purchases of domestic and imported supplies as 

well as inputs from other countries, industries, or firms.

Value added is measured in current dollars. For countries outside the United States, value added is recorded in 

the local currency and converted at the prevailing nominal 

exchange rate. Industry data are reported in current dollar 

terms because most KTI industries are globally traded and 

because the majority of international trade and foreign 

direct investment is dollar denominated. However, current dollars are an imperfect measure. Economic research 

has found a weak link between nominal exchange rates 

of countries’ currencies that are globally traded and differences in their economic performance (Balke, Ma, and 

Wohar 2013). In addition, the exchange rates of some 

countries’ currencies are not market determined. 

Value added is also an imperfect measure. It is credited to countries or regions based on the reported location 

of the activity, but globalization and the fragmentation of 

supply chains mean that the precise location of an activity is often uncertain. Companies use different reporting 

and accounting conventions for crediting and allocating 

production performed by their subsidiaries in foreign 

countries. Moreover, the value added of a diversified 

company’s activity is assigned to a single industry based 

on the industry that accounts for the largest share of the 

company’s business. However, a company classified as 

manufacturing may include services, and a company 

classified in a service industry may include manufacturing or may directly serve a manufacturing company. 

For China and other developing countries, industry data 

may be estimated by IHS Global Insight or may be revised frequently because of rapid economic change or 

improvements in data collection by national statistical 

offices. Thus, value-added trends should be interpreted 

as broad and relatively internally consistent indicators 

of the changing distribution of where economic value is 

generated, and small differences and changes should not 

be overemphasized.

Industry Data and Terminology
Science and Engineering Indicators 2014 ♦ 6-13

Patterns and Trends of Knowledge- and 

Technology-Intensive Shares of Developed 

Economies 

The KTI share of developed economies is much higher 

than that of developing economies due to their much larger 

share of KI services (figure 6-1; appendix tables 6-2 and 

6-3). KTI shares vary widely among developed economies: 

♦ The United States has the largest KTI share of any large developed economy (40%), followed by Australia (39%) and 

the United Kingdom (36%) (figure 6-2). These countries 

have larger shares in KI services, particularly in commercial KI services (22%–28%). The commercial KI services’ 

shares of Australia and the United States are due, in part, 

to their higher shares in financial services (14% and 8%, 

respectively) relative to other developed economies (appendix tables 6-3 and 6-8). Some research suggests that 

the large size of financial sectors in the United States and 

other developed economies has fostered slowed economic 

growth and greater economic instability (Palley 2007:2–3). 

♦ The EU, Japan, Canada, and South Korea have KTI shares 

of 29%–30%, with considerably smaller shares than the 

United States in commercial KI services (14%–18% versus 24%) (figure 6-2). The EU and South Korea have 

smaller shares of financial services (5%–7%) compared to 

Australia and the United States.

Between 1997 and 2012, the KTI share of developed economies grew from 29% to 32% due to increases in the commercial 

and public KI services (figure 6-1; appendix tables 6-2–6-6). 

The HT manufacturing share fell from 3% to 2% (appendix 

table 6-7). The context for this development is the continued 

shift from manufacturing to services in developed economies. 

Trends in the KTI share varied somewhat among the developed economies: 

♦ The KTI shares of the United States, the United Kingdom, 

and Australia rose 6–9 percentage points from 1997 to 

2012 to reach 39%–40% in Australia and the United States 

and 36% in the United Kingdom (figure 6-2; appendix 

 

GDP = gross domestic product; KTI = knowledge and technology 

intensive.

NOTES: Output of KTI industries on a value-added basis. Value 

added is the amount contributed by a country, rm, or other entity to 

the value of a good or service and excludes purchases of domestic 

and imported materials and inputs. KTI industries include knowledgeintensive (KI) services and high-technology (HT) manufacturing 

industries classied by the Organisation for Economic Co-operation 

and Development. KI services include business, nancial, 

communications, education, and health. Commercial KI services 

include business, nancial, and communications services. HT 

industries include aerospace, communications and semiconductors, 

computers and ofce machinery, pharmaceuticals, and scientic 

instruments and measuring equipment. Developed countries are 

classied by the World Bank as high income. Developing economies 

are classied by the World Bank as higher- and lower-middle income 

and low income.

SOURCE: IHS Global Insight, World Industry Service database 

(2012). See tables 6-2 and 6-3. 

Science and Engineering Indicators 2014

Figure 6-1

KTI industries’ share of GDP of developed and 

developing economies: Selected years, 1997–2012

Percent

1997 2002 2007 2012

0

5

10

15

20

25

30

35

Developing economies Developed economies

EU = European Union; GDP = gross domestic product; HT = high 

technology; KI = knowledge intensive; KTI = knowledge and 

technology intensive.

NOTES: Output of KTI industries on a value-added basis. Value added 

is the amount contributed by a country, rm, or other entity to the 

value of a good or service and excludes purchases of domestic and 

imported materials and inputs. KTI industries include KI services and 

HT manufacturing industries classied by the Organisation for 

Economic Co-operation and Development. KI services include 

business, nancial, communications, education, and health. 

Commercial KI services include business, nancial, and communications services. Public KI services include education and health. HT 

manufacturing industries include aerospace, communications and 

semiconductors, computers and ofce machinery, pharmaceuticals, 

and measuring, testing, and control instruments. Developed 

economies are classied by the World Bank as high income.

SOURCE: IHS Global Insight, World Industry Service database (2013). 

See appendix tables 6-3–6-7.

Science and Engineering Indicators 2014

Percent

Figure 6-2

Output of KTI industries as a share of GDP of 

selected developed economies: 2012

Developed

economies

United

States

Australia United

Kingdom

Canada EU Japan South

Korea

0

10

20

30

40

50

Commercial KI industries

Public KI industries

HT manufacturing industries
6-14 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

tables 6-2 and 6-3). In the United States, the increase in 

the KTI share occurred largely from a rise in the share of 

financial services (from 7% to 8%) and public KI services 

(from 11% to 13%) (appendix tables 6-5, 6-6, and 6-8).

♦ The EU’s and Japan’s KTI shares rose 3 percentage points 

to reach 30% and 29%, respectively. 

♦ South Korea’s share rose 6 percentage points to reach 29%.

Patterns and Trends of Knowledge- and 

Technology-Intensive Shares of Developing 

Economies

The KTI share of developing economies is much lower 

than that of developed economies due to smaller shares of 

KI services (figure 6-1). The KTI shares of individual developing countries vary widely, reflecting considerable differences in their stage of development and level of per capita 

income (figure 6-3; appendix tables 6-2 and 6-3). Among the 

larger developing countries, Turkey, which has a relatively 

high per capita income, has the highest KTI share (23%). 

Five countries—Brazil, China, India, Mexico, and South 

Africa—have KTI shares of 19%–21%. Indonesia has the 

lowest KTI share of any large developing economy (14%). 

The KTI share of developing countries as a group edged up 

from 18% to 20% between 1997 and 2012 (figure 6-1). The commercial KI share grew slightly from 11% to 12%. The shares of 

public KI services and KI services were flat, as were shares of 

HT and non-HT manufacturing (figure 6-4). The shares of agriculture, construction mining, and utilities grew substantially in 

many of these countries, reflecting the continuing importance of 

resource extraction to their economies and growing domestic and 

global demand for food, energy, and minerals. 

Trends of individual developing countries varied widely 

(figure 6-3): 

♦ Turkey’s KTI share had the largest increase among larger developing countries, rising 7 percentage points to reach 23%; 

most of the increase occurred in commercial KI services.

♦ Mexico’s KTI share gained 5 percentage points to reach 

21% due to increases in commercial KI services. Its HT 

manufacturing share fell from 2% to 1% (appendix tables 

6-2–6-4 and 6-7).

♦ China’s KTI share grew by 3 percentage points to reach 

20% due entirely to a rise in its HT manufacturing share 

as it became the primary location for global production of 

electronic products.

♦ India’s KTI share rose from 16% to 19% due an increase in 

commercial KI services.

Information and Communications 

Technology Infrastructure 

Many economists regard ICT as a general-purpose platform technology that fundamentally changes how and where 

economic activity is carried out in today’s knowledge-based 

countries, much as earlier general-purpose technologies 

(e.g., the steam engine, automatic machinery) propelled 

growth during the Industrial Revolution.6 Thus, ICT facilitates broad development of new markets (e.g., for mobile 

computing, data exchange, and communications) and of new 

methods, products, organization, and processes. It also raises worker productivity in non-ICT industries. 

Because of the shift to knowledge-based production, ICT 

infrastructure can be as important as or more important than 

physical infrastructure to raising living standards and remaining economically competitive. A World Bank study of developed and developing countries estimated that a 10 percentage 

point increase in broadband penetration raises economic 

growth by 1.2–1.4 percentage points (World Bank 2009:45). 

This section examines two broad ICT indicators: an index 

of ICT infrastructure available to business, consumers, and 

the public sector; and data on ICT spending by consumers and 

businesses as a share of GDP. The indexes of ICT infrastructure are composite indicators developed by the Connectivity 

Scorecard that are composed of the following elements:

♦ The ICT consumer infrastructure measures include data 

on fixed broadband coverage and penetration, 3G coverage and penetration, wireless telephone penetration, and 

Internet download speeds.

GDP = gross domestic product; HT = high technology; KI = 

knowledge intensive; KTI = knowledge and technology intensive.

NOTES: Output of KTI industries is on a value-added basis. Value 

added is the amount contributed by a country, rm, or other entity to 

the value of a good or service and excludes purchases of domestic 

and imported materials and inputs. KTI industries include KI services 

and HT manufacturing industries classied by the Organisation for 

Economic Co-operation and Development. KI services include 

business, nancial, communications, education, and health. 

Commercial KI services include business, nancial, and communications services. Public KI services included education and health. HT 

manufacturing industries include aerospace, communications and 

semiconductors, computers and ofce machinery, pharmaceuticals, 

and measuring, testing, and control instruments. Developing 

economies are classied by the World Bank as higher- and 

lower-middle income and low income. 

SOURCE: IHS Global Insight, World Industry Service database 

(2013). See appendix tables 6-3–6-7.

Science and Engineering Indicators 2014

Percent

Figure 6-3

Output of KTI industries as a share of GDP for 

selected developing economies: 2012

Developing

economies

Turkey Brazil China India South

Africa

Indonesia

0

5

10

15

20

25

Commercial KI industries

Public KI industries

HT manufacturing industries
Science and Engineering Indicators 2014 ♦ 6-15

Figure 6-4

Selected industry category share of developing economies’ GDP: 1997, 2005, and 2012

Percent

GDP = gross domestic product; HT = high technology; KI = knowledge intensive; KTI = knowledge and technology intensive.

NOTES: Output of KTI industries on value-added basis. Value added is the amount contributed by a country, rm, or other entity to the value of a good or 

service and excludes purchases of domestic and imported materials and inputs. KTI industries include KI services and HT manufacturing industries 

classied by the Organisation for Economic Co-operation and Development. KI services include business, nancial, communications, education, and 

health. Commercial KI services include business, nancial, and communications services. Public KI services include education and health. HT 

manufacturing industries include aerospace, communications and semiconductors, computers and ofce machinery, pharmaceuticals, and measuring, 

testing, and control instruments. Developing economies are classied by the World Bank as higher- and lower-middle income and low income.

SOURCE: IHS Global Insight, World Industry Service database (2013). See appendix tables 6-3–6-7.

Science and Engineering Indicators 2014

Commercial

KI services

Public KI services Other services HT manufacturing Non-HT 

manufacturing

Agriculture, construction,

mining, and utilities

0

10

20

30

40

1997 2005 2012

♦ The ICT business infrastructure measures include Internet 

servers and personal computers per capita, ICT investment per capita, and business usage of broadband and 

mobile data.

♦ The ICT public sector infrastructure measures include government, health care, and education spending on ICT and a 

United Nations indicator of online e-government services.7

For developing countries, indexes have fewer components due to lack of data availability.

Developed countries. The U.S. ICT infrastructure compares favorably to other large developed countries as measured by these ICT indicators (figure 6-5): 

♦ U.S. businesses invest heavily in and intensively utilize 

ICT business infrastructure.

♦ The United States also scores high in public sector infrastructure because of high investment by government, education, and health care sectors in ICT and an extensive 

number of e-government services.

♦ The United States scores moderately high in consumer 

infrastructure. The United States is ahead of Western 

European countries (except Sweden) in deployment of 

high-speed broadband but trails Japan and South Korea on 

this measure. 

Other countries that have similar scores to the United 

States are the United Kingdom, Sweden, and Canada (figure 

6-5). These countries were early adopters of ICT, and their 

business sectors are ICT intensive, particularly in the United 

States and the United Kingdom, which have large sophisticated service industries.

European countries—including France, Germany, and 

Italy, which were later adopters of ICT—have substantially 

lower scores in ICT business and public sector infrastructure 

compared to the United States (figure 6-5). Their business 

and public sectors are less-intensive users of ICT and invest 

less in ICT, and their public sectors provide fewer e-government services. Italy and Greece have the weakest index 

scores among developed countries and, in this respect, are 

more comparable to developing countries. 

South Korea and Japan have the highest scores in consumer infrastructure, which reflects extensive government programs to provide near-universal broadband coverage and 3G 

networks (figure 6-5). However, these two countries score 

far weaker in business and public sector ICT infrastructure.

Developing countries. Separate ICT infrastructure indexes for major developing countries show wide variations 

among them, reflecting in part their level of per capita income (table 6-1; figure 6-6). The three Asian countries—

China, India, and Indonesia—have the lowest index scores 

among the larger developing countries. Indonesia and India 

have very low scores in the consumer, business, and public sectors because their domestic ICT usage and access 

for consumers and businesses are limited and uneven, even 

though India has a high level of ICT service exports and a 

large pool of skilled ICT workers. China scores somewhat 

higher on consumer infrastructure, with comparatively higher broadband and fixed-line usage by its populace. China’s 
6-16 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

Figure 6-5

ICT infrastructure indexes of selected developed economies: 2011

Index

ICT = information and communications technology.

NOTES: Scores are based on a variety of data and metrics. For more information on methodology and data sources, see http://www.connectivityscorecard.org/ 

methodology/.

SOURCE: ICT Connectivity Scorecard 2011, http://www.connectivityscorecard.org/, accessed 15 January 2013.

Science and Engineering Indicators 2014

United States United Kingdom Sweden Canada Germany France Italy South Korea Japan

0

0.2

0.4

0.6

0.8

1.0

Consumer Business Public sector

relatively weak score in ICT business infrastructure reflects 

very low penetration of secure Internet servers and limited 

international Internet bandwidth.

Developing countries outside of Asia have generally 

higher index scores, with wide variations (figure 6-6). South 

Africa has the highest score in public sector infrastructure 

among developing countries but far weaker scores in business and consumer indexes, which are close to those in the 

Asian countries. Brazil’s and Mexico’s scores are comparatively higher in the consumer and public sectors, with 

somewhat lower scores in business infrastructure, particularly for Mexico. Turkey is strong on consumer infrastructure, 

moderate on business, and poor in the public sector. 

Information and Communications Technology 

Share of Business and Consumer Spending

Among developed countries, the United States and 

Canada have the highest ICT spending of businesses and 

consumers as a share of their GDP (figure 6-7). The next 

Table 6-1

ICT infrastructure and per capita income of selected developing economies: 2011 and 2012

Economy

Per capita 

income (2012) Consumer Business Public sector a

Russia..................................................................................... 0.88 0.47 0.73 18,323

Brazil....................................................................................... 0.56 0.46 0.58 14,943

Turkey ..................................................................................... 0.67 0.55 0.38 13,380

China ...................................................................................... 0.51 0.22 0.31 10,568

Mexico.................................................................................... 0.60 0.36 0.52 10,292

South Africa............................................................................ 0.30 0.44 0.83 9,655

Indonesia................................................................................ 0.41 0.08 0.14 5,408

India........................................................................................ 0.22 0.04 0.18 4,431

ICT = information and communications technology.

a Per capita income is gross domestic product in 2012 dollars purchasing power parity, divided by population.

NOTES: ICT infrastructure scores are based on a variety of data and metrics. For more information on methodology and data sources, see http://www.

connectivityscorecard.org/methodology/.

SOURCES: ICT Connectivity Scorecard 2011, http://www.connectivityscorecard.org/, accessed 15 February 2013; The Conference Board, Total 

Economy Database on Output and Labor Productivity (January 2013), https://www.conference-board.org/data/productivity.cfm, accessed 15 January 2013. 

See appendix table 6-10.

Science and Engineering Indicators 2014

ICT infrastructure index score (2011)
Science and Engineering Indicators 2014 ♦ 6-17

highest are South Korea and the United Kingdom, with 5%, 

followed by Australia, the EU, and Japan, with 4%. 

The ICT business spending share is arguably a more important indicator than ICT consumer spending because of 

the large impact that businesses have on overall economic 

growth, employment, and productivity. The United States 

has the highest share of ICT business spending (4.4%), 

closely followed by Canada (4.0%). The high ICT business 

spending shares of these two countries coincide with their 

high scores on ICT business infrastructure (discussed in the 

previous section). Although scoring as high as the United 

States and Canada on ICT business infrastructure, the 

United Kingdom has a lower ICT business spending share 

of GDP that is nearly the same as the EU average. Japan and 

Australia have some of the lowest shares in ICT business 

spending. 

Many developing countries have ICT spending shares 

that are comparable to developed countries (figure 6-8). 

South Africa, which has the highest share among larger 

developed countries, matches the levels of Canada and the 

United States, although South Africa’s ICT business spending share is less than that of Canada and the United States. 

Three countries—Brazil, China, and Turkey—have ICT 

shares roughly the same as the EU, with similar levels of 

ICT business spending. India and Indonesia have the lowest 

ICT spending shares, with their ICT business spending GDP 

share at 2% or less, coinciding with their low index scores in 

ICT business infrastructure. 

Productivity

Productivity, which is the ratio of production outputs 

to resource inputs, is considered a key source of economic 

growth and an indicator of development. The rise in the KTI 

concentration of economic activity and in business investment in ICT and other knowledge-based assets in many 

countries has been associated with elevated or rapid productivity growth. This association is evidence that knowledge 

has become a crucial factor in productivity growth. Business 

investment in knowledge-based assets—computerized 

EU = European Union; GDP = gross domestic product; ICT = 

information and communications technology.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2013) from IHS Global 

Insight ICT Global Navigator.

Science and Engineering Indicators 2014

Percent

Figure 6-7

ICT business and consumer spending as a share of 

GDP for selected developed economies: 2010

All

developed

economies

Canada United

States

South

Korea

United

Kingdom

EU Japan Australia

0

1

2

3

4

5

6

7

Business

Consumer

Figure 6-6

ICT infrastructure indexes of selected developing economies: 2011

Index

ICT = information and communications technology.

NOTES: Scores are based on a variety of data and metrics. For more information on methodology and data sources, see http://www.connectivityscorecard.org/ 

methodology/.

SOURCE: ICT Connectivity Scorecard 2011, http://www.connectivityscorecard.org/, accessed 15 January 2013. 

Science and Engineering Indicators 2014

Brazil China India Indonesia Mexico South Africa Turkey

0

0.2

0.4

0.6

0.8

1.0

Consumer Business Public sector
6-18 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

GDP = gross domestic product; ICT = information and communications 

technology.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2013) from IHS Global 

Insight ICT Global Navigator.

Science and Engineering Indicators 2014

Percent

Figure 6-8

ICT business and consumer spending as share of 

GDP for selected developing economies: 2010

All

developing

economies

South

Africa

Brazil Turkey China Indonesia India

0

1

2

3

4

5

6

7

Business

Consumer

10% between 2003 and 2012, up from 8% between 1997 

and 2003.

♦ India grew the second fastest, increasing at an average annual rate of nearly 6% between 2003 and 2012, up from 

4% between 1997 and 2003.

♦ Three countries—Brazil, Indonesia, and South Africa—

had negative growth between 1997 and 2003, followed by 

modest positive growth between 2003 and 2012. Indonesia 

had the strongest performance among these countries, with 

an annual growth rate of 4% between 2003 and 2012. South 

Africa grew by 3%, with Brazil growing the slowest (1%).

In the developed countries, productivity growth declined 

from 2% in the early 2000s to negative growth during the 

2008–09 recession before rising to about 1% in 2011–12 

(figure 6-9; appendix table 6-9). Although the 2008–09 recession was a major factor in the slowdown, productivity 

growth of developing countries had been slowing prior to 

the recession. The recovery in productivity growth following the recession has been weak.

Productivity in the United States grew faster than almost 

all developed countries between 1997 and 2012, with annual 

average growth of 2.2% between 1997 and 2003 slowing to 

1.2% between 2003 and 2012 (figure 6-11; appendix table 

6-9). Only South Korea, whose transformation to become 

a fully developed country is relatively recent, grew faster. 

Observers and researchers have attributed the United States’ 

Figure 6-9

Labor productivity growth of developed and 

developing economies: 1997–2012

Percent

GDP = gross domestic product; PPP = purchasing power parity.

NOTES: Labor productivity growth is based on gross domestic 

product (GDP) per employed person. GDP is in 2012 purchasing 

power parity (PPP) dollars. Developed countries are those classied 

by the World Bank as high-income. Developing countries are 

classied by the World Bank as higher- and lower-middle-income 

economies and low-income economies.

SOURCE: The Conference Board, Total Economy Database on 

Output and Labor Productivity (January 2013), https://

www.conference-board.org/data/productivity.cfm, accessed 15 

January 2013. See appendix table 6-9.

Science and Engineering Indicators 2014

1997 2000 2002 2004 2006 2008 2010 2012

–4

–2

0

2

4

6

8

Developed economies

Developing economies

information and software, intellectual property, and economic competencies, including brand equity and training—are estimated to account for 20%–25% of productivity 

growth in Europe and 27% in the United States between 

1995 and 2007 (OECD 2012:2). Because the most accurate 

measure of productivity, output per hour, is unavailable for 

many developing countries, GDP per employed person is the 

proxy measure used here.8

After growing at the same pace as developed countries 

in the late 1990s, labor productivity of developing countries accelerated to reach 6% per annum in the mid-2000s 

(figure 6-9; appendix table 6-9). The rapid advancement in 

productivity of developing countries has been attributed to 

economic liberalization; investment in education, R&D, and 

physical infrastructure; foreign direct investment and technology transfer by subsidiaries of MNCs; and the migration 

of workers from agriculture to manufacturing and services. 

The pace of productivity growth declined in the late 2000s 

due to cyclical effects of the 2008–09 global recession. 

Some observers also believe that productivity growth will 

continue to moderate because China and other fast-growing 

countries have begun transitioning to a more consumer- and 

services sector–driven economy, which typically results in 

lower productivity growth (Conference Board 2013:10). 

Productivity growth trends among the large developing 

countries varied widely (figure 6-10; appendix table 6-9): 

♦ China registered the fastest growth of any large developing economy, growing at an average annual rate of nearly 
Science and Engineering Indicators 2014 ♦ 6-19

Figure 6-10

Labor productivity growth of selected developing economies: 1997–2012

Percent

NOTES: Labor productivity growth is based on gross domestic product (GDP) per employed person. GDP is in 2012 purchasing power parity (PPP) 

dollars. China includes Hong Kong. Developing countries are classied by the World Bank as higher- and lower-middle-income economies and 

low-income economies.

SOURCE: The Conference Board, Total Economy Database on Output and Labor Productivity (January 2013), https://www.conference-board.org/data/

productivity.cfm,accessed 15 January 2013. See appendix table 6-9. 

Science and Engineering Indicators 2014

All developing China India Indonesia Turkey South Africa Brazil

–2

0

2

4

6

8

10

12

1997–2003 2003–12

Figure 6-11

Labor productivity growth of selected developed economies: 1997–2012

Percent

EU = European Union. 

NOTES: Labor productivity growth is based on gross domestic product (GDP) per employed person. GDP is in 2012 purchasing power parity (PPP) 

dollars. Developing economies are classied by the World Bank as high-income economies.

SOURCE: The Conference Board, Total Economy Database on Output and Labor Productivity (January 2013), https://www.conference-board.org/data/

productivity.cfm, accessed 15 January 2013. See appendix table 6-9.

Science and Engineering Indicators 2014

All developed South Korea United States EU Japan Australia United Kingdom Canada

0

1

2

3

4

1997–2003 2003–12
6-20 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

better performance relative to the EU and Japan to several 

factors, including faster adoption of ICT technology, moreflexible labor markets, high-quality research universities, 

and an influx of highly skilled immigrants.

Rapidly rising living standards, expressed as per capita 

GDP, accompanied the acceleration of productivity growth 

in developing countries and narrowed their gap with developed countries (figure 6-12; appendix table 6-10). Despite 

sustained rapid productivity growth by China and several 

other developing countries, however, their gap with the 

United States and other developed countries is substantial 

and is likely to remain for some time, even if China sustains 

current growth rates. This is because the gap between the 

levels of per capita GDP in the United States and the developing world is very large. For example, U.S. per capita GDP 

in 2012 was $49,000 on a purchasing power parity (PPP) basis compared to $10,500 in China, about one-fifth the level 

of the United States. 

Worldwide Distribution of 

Knowledge- and TechnologyIntensive Industries

The second section of the chapter examines the changing shares of global activity in KTI industries attributed 

to the United States and other major economies (appendix 

table 6-1). (For an explanation of KTI industries, please see 

“Chapter Overview.”) As national and regional economies 

change, the worldwide centers of KTI industries shift in 

importance. Shifts take place for this entire group of industries and for individual service and manufacturing industries 

within the group. This section examines the positions of the 

United States and other major economies in KTI industries. 

Health and Education Services

Although health and education services are not as fully 

competitive or globally integrated as other KTI industries, 

these sectors are major sources of knowledge and innovation 

that benefit the entire economy. Education trains students 

for future work in science, technology, and other knowledge 

fields, and research universities are an important source of 

knowledge and innovation for other economic sectors. 

International comparison of the health and education sectors is complicated by variations in the size and distribution 

of each country’s population, market structure, and the degree 

of government involvement and regulation. As a result, differences in market-generated value added may not accurately 

reflect differences in the relative value of these services.

The United States and the EU are the world’s largest providers of education services, with world shares of 27%–30% 

(appendix tables 6-3 and 6-5). China is the third-largest 

provider, followed by Japan. Country and regional shares 

are similar in health care, except that Japan places ahead of 

China (appendix table 6-6).

The U.S. and EU global shares of education and health 

care fell modestly between 2003 and 2012 (appendix tables 

6-3, 6-5, and 6-6). Japan’s share fell more sharply. China’s 

global share of education and health care services at least 

doubled during this period, in line with its rapid economic 

growth. Brazil, India, and Indonesia showed a similar expansion in their global shares. The growth of education in China 

Figure 6-12

GDP per capita for selected developing economies: Selected years, 2000–12

United States = 100

GDP = gross domestic product.

NOTES: GDP per capita income is expressed as an index where 100 equals the per capita income of the United States. GDP per capita income is in 2012 

purchasing power parity (PPP) dollars. China includes Hong Kong.

SOURCE: The Conference Board, Total Economy Database on Output and Labor Productivity (January 2013), https://www.conference-board.org/data/

productivity.cfm, accessed 15 January 2013. See appendix table 6-10.

Science and Engineering Indicators 2014

China Brazil India Indonesia South Africa Turkey

0

5

10

15

20

25

30

2000 2005 2009 2012
Science and Engineering Indicators 2014 ♦ 6-21

and India coincided with increases in both of these countries 

in earned doctorates in the natural S&E fields (see chapter 2). 

Commercial Knowledge-Intensive 

Service Industries 

The global value added of commercial KI services—business, financial, and telecommunications—was $11.5 trillion 

in 2012 (figure 6-13; appendix table 6-4). Business services, 

which includes the technologically advanced industries of 

computer programming and R&D services, is the largest service industry ($5.6 trillion), closely followed by financial 

services ($4.3 trillion), with telecommunications far smaller 

($1.6 trillion) (appendix tables 6-8, 6-11, and 6-12). 

Patterns and Trends in Developing Countries

Developing countries comprise about one-fifth of global 

value added of commercial KI services industries (figure 6-13; 

appendix table 6-4). China (8% global share) is the largest 

provider among developing countries and essentially ties with 

Japan as the third-largest global provider. Other large developing countries have global shares of 2% or less.

From 1997 to 2003, the value added of commercial KI services grew at roughly the same rate in developed and developing countries (figure 6-13; appendix table 6-4). Starting in 

2003, growth accelerated in developing countries, resulting in 

their share of global output doubling from 10% to 21% in 2012. 

Figure 6-13

Output of commercial KI services for selected 

regions/countries/economies: 1997–2012

Trillions of dollars

EU = European Union; KI = knowledge intensive.

NOTES: Output of knowledge- and technology-intensive industries is 

on a value-added basis. Value added is the amount contributed by a 

country, rm, or other entity to the value of a good or service and 

excludes purchases of domestic and imported materials and inputs. 

The EU excludes Cyprus, Estonia, Latvia, Lithuania, Luxembourg, 

Malta, and Slovenia. China includes Hong Kong. Developed 

economies are classied by the World Bank as high income. 

Developing economies are classied by the World Bank as upperand lower-middle income and low income. 

SOURCE: IHS Global Insight, World Industry Service database 

(2013). See appendix table 6-4. 

Science and Engineering Indicators 2014

1997 2000 2002 2004 2006 2008 2010 2012

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

United States

EU

Japan

Other developed

Other developing China

China grew the fastest among developing countries and 

accounted for 45% of the expansion of all developing countries between 2003 and 2012 (appendix table 6-4). China’s 

world share more than doubled to reach 8% to tie with Japan 

as the third-largest provider (figure 6-13). Among the commercial KI services, China had the largest gain in financial 

services, which may reflect the substantial role of publicowned or public-supported financial institutions and development banks in that country. 

Brazil and India also had sizeable gains in commercial KI 

services, with each reaching global shares of 2% (appendix 

table 6-4). Brazil’s expansion was led by financial services 

and telecommunications (appendix tables 6-8 and 6-12). 

India gained the most in business services, particularly in 

computer programming, reflecting, in part, the success of 

firms providing information technology (IT), accounting, 

legal, and other services to developed countries (appendix tables 6-11 and 6-13). Indonesia, which has a smaller 

global share than these two countries, grew the second 

fastest among the larger developing countries (see sidebar, 

“Indonesia’s Rapid Growth in Commercial KnowledgeIntensive Services”). 

Patterns and Trends in Developed Countries

Commercial KI services industries in developed economies comprise four-fifths of global value added (figure 6-13; 

appendix table 6-4). The United States has the largest commercial KI services industries, with a 32% share of global 

value added. U.S. commercial KI services industries employ 

18 million workers, 14% of the U.S. labor force, and pay 

higher-than-average wages (table 6-2; figure 6-14). In addition, these industries have a much higher concentration of 

skilled workers as measured by the proportion of those in 

S&E occupations. These industries fund roughly one-fourth 

of U.S. industry R&D. 

The EU is the second-largest global provider of commercial KI services, with a 23% global share, followed by Japan 

(9%), which is essentially tied with China (figure 6-13). 

After growing rapidly between 2003 and 2008, the valued 

added of commercial KI services of developed economies 

contracted in 2009 before rebounding in 2010–12 (figure 

6-13; appendix table 6-4). However, growth in developed 

economies lagged developing economies, primarily due to 

China’s rapid expansion. As a result, the global share of developed countries fell from 90% in 2003 to 79% in 2012. 

After expanding rapidly prior to the global recession, 

value added of U.S. commercial KI services dipped in 2009 

before rebounding to reach $3.7 trillion in 2012, 12% higher 

than its level prior to the global recession (figure 6-13; appendix table 6-4). Between 2003 and 2012, the U.S. global 

share slid from 40% to plateau at 32% beginning in 2011. 

Employment in U.S. commercial KI services has had a 

weaker recovery (figure 6-14). Commercial KI services lost 

1.0 million jobs during the recession. Although jobs grew 

modestly in 2011–12, employment in 2012 remains 300,000 

jobs below its pre-recession level. 

The United States is the leading global provider of business 

services, which led the growth of U.S. commercial KI industries 
6-22 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

Indonesia’s commercial knowledge-intensive services more than doubled between 2007 and 2012, 

expanding 40% faster than the average for all developing countries (figure 6-A). Among the three individual industries, telecommunications grew the fastest, 

closely followed by business services. Indonesia’s 

high-technology manufacturing industries also grew 

rapidly, with their value-added output more than doubling between 2003 and 2012. Indonesia’s economy 

has benefitted from a sharp reduction in its budget 

deficit and from government programs to improve education, health care, and technological development. 

Unlike many of its more export-dependent neighbors, 

Indonesia has managed to skirt the recession, helped 

by strong domestic demand (which makes up about 

two-thirds of the economy) and a government fiscal 

stimulus package of about 1.4% of GDP. In addition, 

the government has implemented various programs to 

expand and improve education and health care and to 

increase technological development. 

Figure 6-A

Indonesia’s commercial KI services and HT 

manufacturing industries: 2003–12

Billions of current dollars

HT = high technology; KI = knowledge intensive.

NOTES: Output is on a value-added basis. Value added is the 

amount contributed by a country, rm, or other entity to the value of 

a good or service and excludes purchases of domestic and imported 

materials and inputs. Commercial KI services consist of business, 

nancial, and communication services. Business services include 

computer programming, R&D, and other business services. Financial 

service includes leasing. HT manufacturing industries are classied 

by the Organisation for Economic Co-operation and Development 

and consist of aircraft and spacecraft, communications, computers, 

pharmaceuticals, semiconductors, and testing, measuring, and 

control instruments. 

SOURCE: IHS Global Insight, World Industry Service database 

(2013). See appendix tables 6-8 and 6-11–6-13. 

Science and Engineering Indicators 2014

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

0

5

10

15

20

25

30

Business services

Financial services

Telecommunications

HT manufacturing

Indonesia’s Rapid Growth in Commercial Knowledge-Intensive Services

Table 6-2

Employment and R&D for selected U.S. industries: 2012 or most recent year

Industry

Employment 

(millions of persons) S&E share

Average salary 

(actual $)

Business R&D (2009) 

($ billions)

All industries ........................................... 133.7 4.4 45,000 282.4

Commercial KI services ...................... 18.4 15.8 68,000 78.8

HT manufacturing ............................... 1.8 26.4 70,000 135.9

HT = high technology; KI = knowledge intensive.

NOTES: Business R&D consists of domestic funding by companies’ own internal funds and funds from other sources. Employment consists of the 

nonagricultural workforce. HT manufacturing industries and KI services are classified by the Organisation for Economic Co-operation and Development. 

HT manufacturing includes computers, communications, semiconductors, electronic and measuring instruments, aircraft and space vehicles, and 

pharmaceuticals. KI services include health, education, business, information, and financial services. Commercial KI services include business, 

information, and financial services. Business R&D of commercial KI services consists of professional and technical services and information. Coverage of 

some industries may vary among data sources due to differences in classification of industries. Salaries are rounded to the nearest thousand.

SOURCES: Bureau of Economic Analysis, Annual Industry Accounts, http://www.bea.gov/industry/index.htm#annual; Bureau of Labor Statistics, Current 

Employment Survey, http://www.bls.gov/ces/; Bureau of Labor Statistics, Occupational Employment Survey, special tabulations; National Science 

Foundation, National Center for Science and Engineering Statistics, Business Research and Development and Innovation Survey, http://www.nsf.gov/

statistics/srvyindustry/.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 6-23

between 2003 and 2012 (figure 6-15; appendix table 6-11). 

Value added of business services grew slightly faster than all 

commercial KI industries (55% versus 45%), with value added 

of computer programming expanding 66% (appendix table 

6-13). One source of growth of U.S. business services has been 

the infrastructure boom in developing countries that have employed U.S. firms in areas including architecture, engineering, 

and consulting.9 U.S. employment in business services grew 

from 8.3 million in 2003 to reach 9.9 million in 2012, 400,000 

jobs greater than the pre-recession level (figure 6-14). 

The EU, which is the second-largest global provider, has 

fared worse than the United States since the recession. In 

the midst of the EU’s financial and economic difficulties, 

the value added of its commercial KI services has remained 

stagnant in 2009–12 and below its pre-recession peak (figure 

6-13; appendix table 6-4). As a result, the EU’s global share 

dropped from 30% in 2008 to 23% 2012. 

In the aftermath of the recession, Japan has performed better 

than the United States or the EU in this industry group. Valueadded output continued to expand during and following the recession to reach a level nearly 25% higher than the pre-recession 

peak (figure 6-13; appendix table 6-4). Japan’s share fell slightly, 

from 11% in 2003 to 9% in 2006, where it has remained steady. 

However, the substantial appreciation of the Japanese yen relative to the dollar during this period may have overstated the 

strength of Japan’s commercial KI services industries (see sidebar, “Currency Exchange Rates of Major Economies”).

 

EU = European Union.

NOTES: Output on a value-added basis is shown above bars in trillions of dollars. Value added is the amount contributed by a country, rm, or other 

entity to the value of a good or service and excludes purchases of domestic and imported materials and inputs. Business services include computer 

programming, R&D, and other business services. Data on computer programming, a componenet of business services, is provided separately. Financial 

services include leasing. China includes Hong Kong. The EU excludes Cyprus, Estonia, Latvia, Lithuania, Luxembourg, Malta, and Slovenia. Developed 

countries are classifed as high-income countries by the World Bank. Developing countries are classied by the World Bank as upper- and 

lower-middle-income countries and low-income countries.

SOURCE: IHS Global Insight, World Industry Service database (2013). See appendix tables 6-11–6-13. 

Science and Engineering Indicators 2014

Figure 6-15

Global value-added shares of selected regions/countries/economies for selected service industries: 2012

Percent

Business services Computer programming Financial services Telecommunications services

0

10

20

30

40

50

United States EU Japan Other developed China Other developing

Figure 6-14

U.S. employment in commercial KI services: 

Selected years, 2000–12

Millions

KI = knowledge intensive.

NOTES: KI services are classied by the Organisation for Economic 

Co-operation and Development. Commercial KI services include 

business, information, and nancial services. 

SOURCE: Bureau of Labor Statistics, Current Employment Statistics 

(August 2013), http://www.bls.gov/ces/, accessed 8 August 2013.

Science and Engineering Indicators 2014

2000 2003 2005 2008 2010 2012

0

2

4

6

8

10

12

Business services

Financial services

Information services
6-24 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

International comparisons of industry, trade, investment, and other global economic activities often use 

current dollars at market exchange rates. Most global 

economic activities are dollar denominated, which facilitates comparison. In addition, many economists believe 

that market exchange rates reflect, at least to some degree, differences in economic performance among various countries (Balke, Ma, and Wohar 2013:2). 

However, fluctuations in exchange rates may reflect 

factors other than economic performance. Governments 

can and do take action to influence the level of their exchange rates, ranging from intervening in currency exchange markets so as to exercise almost complete control 

of rates to using macroeconomic policies and other mechanisms so as to exercise more limited and indirect influence 

on markets. In addition, factors such as political instability or the short-term effects of global financial events on 

a country’s economy can cause currency fluctuations that 

are unrelated to enduring differences in national economic 

performance. Factors such as these mean that comparing 

economic data from different countries in current dollar 

terms can sometimes provide an inaccurate and misleading measure of a country’s relative economic performance. 

Between 2007 and 2012, during the global financial 

crisis, the worldwide recession, and the subsequent economic recovery, the exchange rates of the four largest 

economies—China, the EU member countries that use 

the euro (the Eurozone), Japan, and the United States—

exhibited considerable fluctuations (figure 6-B). The 

Japanese yen showed the largest change among these 

currencies, with an appreciation of 30% against the 

U.S. dollar to a nearly post–World War II high. Some 

experts attributed the strong appreciation of the yen to 

its attractiveness as a safe haven in response to Europe’s 

debt problems and doubts about U.S. economic growth 

(Tabuchi 2011). The yuan’s exchange rate, which is controlled by China’s government, also appreciated against 

the dollar, although at a more modest pace. 

The substantial appreciation of the yen and yuan 

against the dollar from 2007 to 2012 made Japan’s and 

China’s positions in economic activities denominated in 

current U.S. dollars appear progressively stronger during 

this period. Denominated in local currency terms, however, their economic performance looked weaker. The 

disparity was particularly large for Japan. For example, 

the value added of Japan’s high-technology manufacturing industries in current dollars exhibited a slight decline 

(4%) from 2007 to 2012 (figure 6-C). The value added in 

yen shows a much deeper decline (35%). 

Currency Exchange Rates of Major Economies

Figure 6-B

U.S. dollar exchange rate with selected currencies: 

2007–12

2007 = 100

SOURCE: Federal Reserve, Economic and Research and Data, Foreign 

Exchange Rates, http://www.federalreserve.gov/releases/h10/current/, 

accessed 15 May 2013.

Science and Engineering Indicators 2014

2007 2008 2009 2010 2011 2012

0

20

40

60

80

100

120

140

Yuan

Euro

Yen

Figure 6-C

Output of Japan’s HT manufacturing 

industries: 2007–12

2007 = 100

HT = high technology.

NOTES: Output of HT manufacturing industries is on a value-added 

basis. Value added is the amount contributed by a country, rm, or 

other entity to the value of a good or service and excludes purchases 

of domestic and imported materials and inputs. HT manufacturing 

industries are classied by the Organisation for Economic 

Co-operation and Development and include aircraft and spacecraft, 

communications, computers, pharmaceuticals, semiconductors, and 

testing, measuring, and control instruments. See appendix table 6-7. 

SOURCES: Federal Reserve, Economic Research and Data, Foreign 

Exchange Rates http://www.federalreserve.gov/releases/h10/current/, 

accessed 15 May 2013; IHS Global Insight, World Industry Service 

database (2013).

Science and Engineering Indicators 2014

2007 2008 2009 2010 2011 2012

0

20

40

60

80

100

120

Dollar terms

Yen terms
Science and Engineering Indicators 2014 ♦ 6-25

Australia’s Commercial 

Knowledge-Intensive 

Services Grow Strongly

Australia’s commercial KI services grew four 

times faster than the average of all developed countries between 2003 and 2012 (figure 6-D; appendix 

table 6-4). The financial sector grew the fastest among 

the commercial KI services, with telecommunications 

and business services growing considerably slower. 

Australia’s high-technology manufacturing industries also grew significantly faster than the developed 

country average, largely because of rapid growth in 

its pharmaceuticals industry. Australia’s economy has 

had two decades of uninterrupted growth and was one 

of the few developed economies to escape the global 

recession. A primary factor in its growth has been 

booming demand by China and other Asian countries 

for its iron ore and other mining commodities. Its dependence on commodity exports has prompted the 

government to develop policies to make its economic 

growth more broad based. 

 

KI = knowledge intensive.

NOTES: Output is on a value-added basis. Value added is the 

amount contributed by a country, rm, or entity to the value of a 

good or service and excludes purchases of domestic and imported 

materials and inputs. Commercial KI services consist of business, 

nancial, and communications services.See appendix tables 6-8, 

6-11, and 6-12. 

SOURCE: IHS Global Insight, World Industry Service database (2013).

Science and Engineering Indicators 2014

Figure 6-D

Australia’s commercial KI services industries: 

2003–12

Billions of current dollars

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

0

50

100

150

200

250

300

350

400

450

Business services

Financial services

Information

Australia had the fastest growth in commercial KI services among large developing economies during this period 

(appendix table 6-4). Its global share doubled from 1.7% in 

2003 to 3.7% in 2012. Australia’s rapid expansion is due 

in part to its growing economic integration with China (see 

sidebar, “Australia’s Commercial Knowledge-Intensive 

Services Grow Strongly”). 

High-Technology Manufacturing Industries

Global value added of HT manufacturing was $1.5 trillion in 2012, making up 14% of the manufacturing sector 

(figure 6-16; appendix tables 6-7 and 6-14). The three ICT 

manufacturing industries—communications, computers, 

and semiconductors—make up a collective $0.6 trillion in 

global value added (appendix tables 6-15–6-17). The three 

remaining industries are scientific instruments and pharmaceuticals, each with about $350 billion in value added, and 

aircraft and spacecraft, with $180 billion (appendix tables 

6-18–6-20). 

Patterns and Trends in Developing Countries

China is the second-largest global producer of HT products (24% global share) (figure 6-16; appendix table 6-7). 

These HT products are largely exported to the rest of the 

world. Most of China’s production is performed in plants 

controlled by MNCs using imported inputs and components. 

Other large developing countries have global shares of 2% 

or less. 

Growth of HT manufacturing in developing countries 

sharply accelerated starting in 2003 almost entirely due to 

China’s rapid expansion (figure 6-16; appendix table 6-7). 

Between 2003 and 2012, China’s value added rose more 

than fivefold, resulting in its global share climbing from 

8% in 2003 to 24% in 2012. China’s output fell slightly in 

2009 during the 2008–09 recession, at a time when output 

declined more substantially in most other developing and 

developed countries. Among the HT industries, China made 

the most rapid gain in ICT manufacturing industries, with 

its global share reaching 36% in 2012 (figure 6-17; appendix tables 6-15–6-17). China also made huge gains in pharmaceuticals, reaching a global share of 25% in 2012 to tie 

with the EU as the world’s largest producer (appendix table 

6-18). Production of generic drugs by Chinese-based firms 

and the establishment of production facilities controlled by 

U.S. and EU multinationals were major factors in this industry’s rapid expansion.

Despite some progress in producing globally competitive HT goods, notably in telecommunications equipment, 

Chinese HT manufacturing companies largely continue to 

be limited to lower-value activities, such as final assembly.10

For example, within the semiconductor industry, Chinese 

firms have a limited share (20%) of China’s rapidly growing 

market for integrated circuits, which foreign firms continue 

to dominate (PwC 2012). In addition, Chinese HT companies have not met many of the ambitious targets and goals of 

the Chinese government’s indigenous innovation program.
6-26 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

Anecdotal reports suggest that some multinationals are relocating their facilities from China to other developing countries 

with lower labor costs or reshoring production in developed 

countries in response to increases in transportation costs and in 

China’s manufacturing wages.11 China’s growth in ICT manufacturing industries appears to have slowed during the 2000s 

even prior to the global recession, although the slowdown may 

reflect the limitations of further expanding China’s huge capacity (figure 6-18; appendix tables 6-15–6-17). However, China 

remains an attractive location for foreign MNCs because of its 

well-developed and globally capable manufacturing infrastructure. In addition, China’s growing and potentially huge domestic market is prompting some foreign HT firms to expand their 

production facilities and establish R&D laboratories to develop 

products for China’s rapidly growing consumer market. 

Other large developing countries that grew rapidly included Brazil and India (see sidebar, “Brazil’s and India’s HighTechnology Manufacturing Industries”). 

Patterns and Trends in Developed Countries

Developed countries make up 66% of global value added 

of HT manufacturing industries (appendix table 6-7). The 

United States, which has a 27% global share, is the largest global producer (figure 6-16). U.S. HT manufacturing 

industries employ 1.8 million workers, 16% of the manufacturing labor force, and pay higher-than-average wages due, 

in part, to their high concentration of highly skilled S&E 

workers (table 6-2). Although a small part of the U.S economy, U.S. HT manufacturing industries fund about one-half 

of U.S. business R&D. 

The EU and Japan are the third- and fourth-largest global 

producers with shares of 18% and 8%, respectively (figure 

6-16; appendix table 6-7). Several Asian economies are both 

EU = European Union; HT = high technology; ICT = information and communications technology.

NOTES: HT manufacturing industries are classied by the Organisation for Economic Co-operation and Development and include aircraft and spacecraft, 

communications, computers, pharmaceuticals, semiconductors, and testing, measuring, and control instruments. ICT manufacturing industries consist of 

computers, communications, and semiconductors. Value added is the amount contributed by a country, rm, or other entity to the value of a good or 

service and excludes purchases of domestic and imported materials and inputs. China includes Hong Kong. The EU excludes Cyprus, Estonia, Latvia, 

Lithuania, Luxembourg, Malta, and Slovenia. Developed countries classied as high-income countries by the World Bank. Developing countries classied 

as upper- and lower-middle-income countries and low-income countries by the World Bank.

SOURCE: IHS Global Insight, World Industry Service database (2013). See appendix tables 6-21 and 6-25–6-31. 

Science and Engineering Indicators 2014

Figure 6-17

HT manufacturing industries of selected regions/countries/economies: 2012

Percent

ICT manufacturing industries Pharmaceuticals Testing, measuring,

and control instruments

Aircraft and spacecraft

0

10

20

30

40

50

United States EU Japan Other developed China Other developing

Figure 6-16

Output of HT manufacturing industries for 

selected regions/countries/economies: 1997–2012

Billions of dollars

EU = European Union; HT = high technology.

NOTES: Output of HT manufacturing industries is on a value-added 

basis. Value added is the amount contributed by a country, rm, or 

other entity to the value of a good or service and excludes purchases 

of domestic and imported materials and inputs. HT manufacturing 

industries are classied by the Organisation for Economic 

Co-operation and Development and include aircraft and spacecraft, 

communications, computers, pharmaceuticals, semiconductors, and 

testing, measuring, and control instruments. The EU excludes 

Cyprus, Estonia, Latvia, Lithuania, Luxembourg, Malta, and Slovenia. 

China includes Hong Kong. Developed countries classied as 

high-income countries by the World Bank. Developing countries 

classied as upper- and lower-middle-income countries and 

low-income countries by the World Bank.

SOURCE: IHS Global Insight, World Industry Service database 

(2013). See appendix table 6-7. 

Science and Engineering Indicators 2014

1997 2000 2002 2004 2006 2008 2010 2012

0

100

200

300

400

500

United States

EU

Japan

Other developed

China

Other developing
Science and Engineering Indicators 2014 ♦ 6-27

Figure 6-18

Output of China’s ICT manufacturing 

industries: 2002–11

Year-over-year (percent)

ICT = information and communications technology.

NOTES: Growth is on a 3-year moving-average basis of the value 

added of ICT manufacturing industries. Value added is the amount 

contributed by a country, rm, or other entity to the value of a good 

or service and excludes purchases of domestic and imported 

materials and inputs. ICT manufacturing industries consist of 

communications, computers, and semiconductors. China includes 

Hong Kong. 

SOURCE: IHS Global Insight, World Industry Service database 

(2013). See appendix tables 6-15–6-17. 

Science and Engineering Indicators 2014

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

–20

0

20

40

60

80

100

Communications

Computers

Semiconductors

Brazil’s high-technology (HT) manufacturing industries grew more than twice as fast as the average for all 

developing countries, excluding China, between 2003 

and 2012. Pharmaceuticals and aircraft and spacecraft 

led the growth of Brazil’s HT industries (figure 6-E). The 

expansion of Brazil’s pharmaceuticals industry has been 

boosted by the establishment of manufacturing plants 

by foreign multinationals to capitalize on Brazil’s growing consumer market. Brazil is a major global producer 

of aircraft and has invested heavily in R&D for spacecraft and satellites. Growth was also rapid in scientific 

instruments. 

India’s pharmaceuticals industry, a globally competitive industry, has led the growth of its HT manufacturing industries, which quadrupled in value added between 

2003 and 2012 (figure 6-E). India’s pharmaceuticals industry is a major global manufacturer of generic drugs 

and, more recently, has been conducting clinical trials 

and manufacturing drugs for Western pharmaceutical 

companies. India, which has been weak in manufacture of electronics, has also had significant growth in its 

three information and communications technology (ICT) 

manufacturing industries. Most production of ICT manufacturing has been low value-added assembly in plants 

controlled by foreign multinational companies; however, the government has recently released its strategy for 

strengthening its electronic manufacturing industry. 

Brazil’s and India’s High-Technology Manufacturing Industries

ICT = information and communications technologies.

NOTES: Output is on a value-added basis. Value added is the 

amount contributed by a country, rm, or entity to the value of a 

good or service and excludes purchases of domestic and imported 

materials and inputs. ICT manufacturing industries consist of 

communications, computers, and semiconductors. See appendix 

tables 6-15–6-20. 

SOURCE: IHS Global Insight, World Industry Service database (2013).

Science and Engineering Indicators 2014

Billions of dollars

Figure 6-E

Selected manufacturing industries of Brazil and 

India: 2003 and 2012

Brazil India

2003 2012 2003 2012

0

5

10

15

20

25

30

35

40

ICT

Pharmaceuticals

Testing, measuring, 

and controlling

Aircraft and spacecraft

major domestic producers and suppliers of inputs and components to China. The largest—Singapore, South Korea, and 

Taiwan—have a collective share of 8%.

After expanding briskly prior to the recession, the value 

added by HT manufacturing industries of developed countries contracted by 5% in 2008, a far larger decline than in 

developed countries’ commercial KI services (figure 6-16; 

appendix table 6-7). The recovery of HT manufacturing industries following the global recession was modest. Between 

2003 and 2012, the global share of developed countries fell 

steadily from 86% in 2003 to 69% in 2012, due entirely to a 

collective 18 percentage point decline in the global shares of 

the United States, the EU, and Japan. 

In the United States, value added dipped slightly in 2008 

during the recession before rebounding strongly to reach 

14% higher than its pre-recession level (figure 6-16; appendix table 6-7). After falling from 33% in the early 2000s to 

27% in 2008, the U.S. global share has remained roughly 

steady in 2009–12. 

U.S. employment has fared worse prior to and following 

the recession. HT manufacturing jobs fell from 2.5 million 

in 2000 to 2.0 million in 2008 before shedding 200,000 more 

jobs during the global recession (figure 6-19). Furthermore, 

HT manufacturing employment has remained stagnant following the recession. The steady loss of employment reflects 

the relocation of production to China and other countries and 
6-28 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

U.S. Manufacturing 

and Employment

Several signs point to an increase in U.S. manufacturing activity after years of decline. After falling 

continuously in the previous decade, employment in 

the U.S. manufacturing sector increased somewhat 

in 2011–12, coinciding with a rebound in this sector’s output following the 2008–09 global recession.* 

According to press reports, several firms, including 

Apple, GE, and Lenovo, are building new manufacturing facilities in the United States (Booth 2013:1). 

Furthermore, some analysts and researchers predict a 

resurgence in U.S. manufacturing production, pointing to low transportation and energy costs, modest 

U.S. labor costs, and favorable currency exchange 

rates as factors conducive to manufacturing growth 

(PwC 2012:3). 

However, other observers doubt that large-scale increases in employment will accompany increased U.S. 

manufacturing production. Many U.S. manufacturing 

industries are highly productive, which allows them 

to increase output substantially without increasing employment much. Although manufacturers in the United 

States and other high-income economies will continue 

to hire more high-skilled workers, manufacturing employment is likely to continue to decline over the next 

several decades due to further advances in productivity and global competitive pressures (McKinsey Global 

Institute 2012:4). 

In interpreting recent trends in manufacturing production and employment, it is helpful to take into account several broader trends and patterns:

♦ The share of manufacturing production and employment has steadily declined in the United States and 

other advanced countries over the past several decades (Shipp et al. 2012:61). 

♦ In wealthy countries, manufacturing continues to 

play a key role in innovation, productivity, and exports, even as its share of output and employment 

declines. 

♦ As a share of a country’s economy, manufacturing 

production and employment peak when a country’s 

per capita income reaches a middle level (McKinsey 

Global Institute 2012:3). At higher per capita income 

levels, output and employment grow more rapidly in 

the service sector than in manufacturing. 

* Employment in the U.S. manufacturing sector increased by 

about 200,000 jobs in both 2011 and 2012, according to the U.S. Bureau of Labor Statistics’ Current Employment Survey, http://www.

bls.gov/ces/data.htm, accessed 10 June 2013.

also the rapid productivity growth of U.S. HT manufacturing industries, which have eliminated some jobs, particularly those in routine tasks (see sidebar, “U.S. Manufacturing 

and Employment”). Researchers and policymakers have 

concluded that the location of HT manufacturing and R&D 

activities may lead to the migration of higher-value activities 

abroad (Fuchs and Kirchain 2010:2344).

Trends among individual U.S. industries were variable: 

♦ Testing, measuring, and control instruments led growth 

of U.S. HT manufacturing industries due to increased 

demand for these products for a variety of purposes, including meeting environmental standards (appendix table 

6-19). However, employment declined from 490,000 jobs 

in 2000 to 400,000 jobs in 2012 (figure 6-19).

♦ The United States is also the largest producer in aircraft 

and spacecraft, reflecting its historical dominance and the 

U.S. government’s procurement of military aircraft and 

spacecraft (figure 6-17; appendix table 6-20). Employment 

remained flat in this industry at about 500,000 jobs 

(figure 6-19).

♦ Value-added output in ICT industries contracted, reflecting 

the relocation of production abroad and labor saving from 

rapid productivity growth (appendix tables 6-15–6-17). 

Employment dropped from 1.2 million in 2000 to 650,000 

in 2012 (figure 6-19). 

Figure 6-19

U.S. employment in HT manufacturing industries: 

2000–12

Thousands

HT = high technology.

NOTES: HT manufacturing industries are classied by the 

Organisation for Economic Co-operation and Development. HT 

manufacturing industries include aircraft and spacecraft, 

communications, computers, pharmaceuticals, semiconductors, and 

testing, measuring, and control instruments. 

SOURCE: Bureau of Labor Statistics, Current Employment Statistics 

(August 2013), http://www.bls.gov/ces/, accessed 8 August 2013.

Science and Engineering Indicators 2014

2000 2002 2004 2006 2008 2010 2012

0

200

400

600

800

Aircraft and spacecraft

Communications

Computers

Pharmaceuticals

Semiconductors

Testing, measuring, and control
Science and Engineering Indicators 2014 ♦ 6-29

♦ Pharmaceuticals showed little growth during this period 

(appendix table 6-18). The expiration of patents on highly 

profitable blockbuster drugs, the lack of new breakthrough 

drugs, increasing competition from generic drugs, and the 

relocation of production to other countries were among the 

factors accounting for tepid growth. 

Other major Asian producers—Singapore, South Korea, 

and Taiwan—showed little change in their global shares during this period. After rapid expansion in HT manufacturing in 

the prior two decades, companies based in these economies 

have relocated some of their production facilities to China and 

other low-cost locations. For example, many Taiwanese ICT 

firms have shifted their production to mainland China.

Trade and Other 

Globalization Indicators

The third section of this chapter examines several trade 

and globalization measures associated with KTI industries in 

the United States and other economies. (For an explanation 

of KTI industries, please see “Chapter Overview.”) In the 

modern world economy, production is more often globalized

(i.e., value is added to a product or service in more than one 

nation) and less often vertically integrated (i.e., conducted 

under the auspices of a single company and its subsidiaries) 

than in the past. These trends have affected all industries, 

but their impact has been pronounced in many commercial 

KTI industries. The broader context is the rapid expansion 

of these industrial and service capabilities in many developing countries, both for export and internal consumption, 

accompanied by an increasing supply of skilled, internationally mobile workers. (See chapter 3 for a discussion on the 

migration of highly skilled labor.) 

This section focuses on cross-border trade of international KI services and HT trade and on U.S. trade of ATP. (See 

“U.S. Trade in Advanced Technology Products” later in this 

chapter for a discussion of how the U.S. Census Bureau’s 

classification of ATP differs from the classification of HT 

products based on the OECD industry classification.) It will 

also examine trade and other globalization measures of U.S. 

multinationals in KTI industries. Trade data are a useful although imperfect indicator of globalization (for a discussion, 

see sidebar, “Measurement and Limitations of Trade Data”).

This discussion of trade trends in KI services and HT 

manufactured products focuses on (1) the trading zones of 

the North American Free Trade Agreement (NAFTA), with a 

particular focus on the United States, and the EU; (2) China, 

which is rapidly taking on an increasingly important role in 

KTI trade; (3) Japan and other Asian countries; and (4) large 

developing countries, including Brazil, India, and Indonesia. 

The EU, East Asia, and NAFTA have substantial volumes of intraregional trade. This section treats trade within 

these three regions in different ways. Intra-EU and NAFTA 

exports are not counted because they are integrated trading 

Measurement and 

Limitations of Trade Data

Trade data are based on a classification of goods 

or services themselves. In the case of product trade, 

trade is assigned one product code according to the 

Harmonized Commodity Description and Coding 

System, or Harmonized System (HS).*

 The product 

classification of trade is fundamentally different from 

the industry classification used in the last section, 

which is based on the primary activity of the industry 

that produced a product and not on the characteristics 

of the product itself. Thus, the two classifications cannot be mapped onto each other. For example, an export 

classified as a computer service in the product-based 

system may be classified in the industrial classification as computer manufacturing because it originated 

from a firm in that industry.

Data on exports and imports represent the market 

value of products and services in international trade. 

Exports of products are assigned by the importing 

country’s port of entry to a single country of origin. 

For goods manufactured in multiple countries, the 

country of origin is determined by where the product 

was “substantially transformed” into its final form. 

The value of product trade entering or exiting a 

country’s ports may include the value of components, 

inputs, or services classified in different product categories or originating from countries other than the 

country of origin. For example, China is credited with 

the full value (i.e., factory price plus shipping cost) 

of a smart phone when it is assembled in China, although made with components imported from other 

countries. In these data, countries whose firms provide 

high-value services such as design, marketing, and 

software development are typically not credited for 

these contributions.

* HS is a system for classifying goods traded internationally 

that was developed under the auspices of the Customs Cooperation 

Council. Beginning on 1 January 1989, HS numbers replaced schedules previously adhered to in more than 50 countries, including the 

United States. For more information, see http://www.census.gov/

foreign-trade/guide/sec2.html#htsusa.

zones with common external trade tariffs and few restrictions on intraregional trade. This kind of trade is treated 

as essentially equivalent to trade between China and Hong 

Kong, which is excluded because it is essentially intraeconomy trade. (Data on trade in commercial KI services between 

China and Hong Kong are not available.) Intra-Asian trade 

is counted for other Asian countries because they have a far 

smaller degree of trade integration. 
6-30 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

Global Trade in Commercial Knowledge- and 

Technology-Intensive Goods and Services

Exporting goods and services to other countries is one measure of a country’s economic success in the global market—the 

goods and services it produces compete in a world market. In 

addition, exports have an important advantage over domestic 

purchases in that they bring in income from external sources 

and do not consume the income of a nation’s own residents. 

Global trade in commercial KTI goods and services consists of four services—business, communications, computer 

and information, and finance—and six HT products—aerospace, communications, computers, pharmaceuticals, semiconductors, and scientific instruments.12 Global cross-border 

exports of commercial KTI goods and services were an estimated $3.7 trillion, consisting of $2.3 trillion of exports 

of HT products and $1.4 trillion of commercial KI services 

(figure 6-20; appendix table 6-21). 

Commercial Knowledge-Intensive Services

Global exports of commercial KI made up one-third of all 

commercial services. Among the commercial KI services, 

business services was the largest ($800 billion), followed by 

finance (which includes insurance) ($300 billion), computer 

and information services ($170 billion), and communications ($80 million).13

The United States, the EU, Japan, and other developed 

countries export $1.0 trillion in commercial KI services, 

comprising 77% of global exports (figure 6-20). China and 

other developing countries export far less than developed 

countries ($0.3 trillion). 

Patterns and Trends in Developing Countries

Exports of developing countries make up a small share 

(22%) of global exports of commercial KI services. China 

and India have the largest global export shares of any developing economy (6%–7% each), and they are tied as the third 

largest in the world, behind the United States and the EU 

(table 6-3; figure 6-20).14

India is notable for being the largest exporter of computer 

and information services, attesting to the strong market position of Indian firms providing IT and related services to 

the rest of the world (table 6-3). China and India both have 

substantial surpluses in trade of commercial KI services. 

Other developed countries have global export shares of less 

than 2%.

Between 2004 and 2011, cross-border commercial KI exports of developing countries nearly tripled to reach $296 

billion, expanding much faster than in developed countries but from a much lower base (figure 6-20). The global 

share of developing countries rose from 16% to 22% during 

this period. 

China’s exports tripled during this period, resulting in its 

global export share climbing from 4% to 7% (table 6-3; figure 6-20). China’s trade balance in commercial KI services 

widened from a surplus of $3 billion to $11 billion in 2010.15

India’s exports also expanded rapidly, with its global 

share rising from 4% to 7%. India’s surplus expanded from 

$11 billion to $50 billion during this period.16

Patterns and Trends in Developed Countries

The EU is the largest exporter of commercial KI services, 

with a global share of 32% (figure 6-20). The United States 

is the second-largest exporter, with a global share of 17%. 

The EU and United States both have surpluses in trade of 

commercial KI services in contrast to their deficits in HT 

product trade (table 6-4). Japan, which has a small deficit in 

commercial KI services trade, is the fifth-largest exporter, 

behind India and China.17

Between 2004 and 2011, growth of commercial KI exports of developed economies trailed developing economies, 

resulting in their global share falling from 83% to 77% (figure 6-20). 

U.S. exports of commercial KI services more than doubled to reach $235 billion; the U.S trade surplus climbed 

Figure 6-20

Commercial KI service exports, by selected 

region/country/economy: 2004–11

Billions of dollars

EU = European Union; KI = knowledge intensive.

NOTES: Commercial KI service exports consist of communications, 

business services, nancial services, and computer and information 

services. Financial services includes nance and insurance services. 

EU exports do not include intra-EU exports. Developed countries are 

classied as high-income economies by the World Bank. Developing 

countries are classied as higher- and lower-middle-income 

economies and low-income economies by the World Bank. The sum 

of the regions/countries/economies does not add to the world total 

due to rounding and discrepancies.

SOURCE: World Trade Organization, International trade and tariff 

data, http://www.wto.org/english/res_e/statis_e/statis_e.htm, 

accessed 8 August 2013.

Science and Engineering Indicators 2014

2004 2005 2006 2007 2008 2009 2010 2011

0

200

400

600

800

1,000

1,200

1,400

1,600

World

EU

United States

Japan

Other developed

China

Other developing
Science and Engineering Indicators 2014 ♦ 6-31

from $33 billion to $52 billion (table 6-4; figure 6-20). 

Exports of business services, the largest component, slighted lagged overall export growth. The trade surplus in other 

business services increased from $29 billion to $39 billion. 

U.S. exports of R&D services, a component of business services, rose from $13 billion in 2006 to $22 billion in 2010. 

The trade surplus edged down from $4 billion to $2 billion 

(see sidebar, “U.S. Trade in R&D Services”).

In the EU, commercial KI services grew at a similar pace, 

reaching more than $400 billion in 2011, with the EU’s surplus more than doubling to reach $127 billion (table 6-4; 

figure 6-20). Among the commercial KI services, computer information services grew the fastest, nearly tripling to 

reach $57 billion. Exports of business services, the largest 

component, slightly lagged overall growth. The EU’s trade 

surpluses of these two commercial KI exports both grew 

substantially. EU’s exports of financial services (which 

include insurance) also grew rapidly with the surplus widening from $25 billion to $51 billion.

High-Technology Goods

Global HT product exports—aircraft and spacecraft; 

computers; communications; semiconductors; pharmaceuticals; and testing, measuring, and control instruments—were 

$2.3 trillion in 2012, making up 16% of the $14.7 trillion 

in exports of all manufactured goods (figure 6-21; appendix tables 6-21 and 6-24). Among the HT products, ICT 

products—communications, computers, and semiconductors—are the largest, with a collective value of $1.4 trillion 

(appendix tables 6-25–6-28). The remaining three industries—testing, measuring, and control instruments; pharmaceuticals; and aircraft and spacecraft—range from $200 

billion to $400 billion each (appendix tables 6-29–31). 

Table 6-3

India’s and China’s trade in commercial KI services: 2011

(Billions of dollars)

India China

Category Exports Imports Balance Exports Imports Balance

All commercial KI services ..................................... 94 43 50.7 76 65 10.9

Computer information services........................... 44 2 41.8 12 4 8.3

Financial services................................................ 9 14 -5.5 4 20 -16.6

Other business services...................................... 39.5 25.5 14.1 58.3 39.6 18.7

Communications services................................... 1.7 1.4 0.3 1.7 1.2 0.5

KI = knowledge intensive.

NOTES: Commercial KI services trade consists of communications, business services, financial services, computer and information services, and other 

business services. Financial services includes finance and insurance.

SOURCE: World Trade Organization, International trade and tariff data, http://www.wto.org/english/res_e/statis_e/statis_e.htm, accessed 8 August 2013.

Science and Engineering Indicators 2014

Table 6-4

U.S. and EU commercial KI services trade, by category: 2004, 2008, and 2011

(Billions of dollars)

2004 2008 2011

Category Exports Imports Balance Exports Imports Balance Exports Imports Balance

United States

All commercial KI services..................... 118.9 86.4 32.5 201.7 165.7 36.0 235.1 183.6 51.5

Computer and information services... 8.7 8.6 0.1 13.1 16.9 -3.8 15.5 24.5 -9.0

Financial services............................... 43.7 40.2 3.5 72.2 76.1 -3.9 81.0 72.8 8.2

Other business services..................... 61.6 32.3 29.3 101.8 64.3 37.5 117.2 78.2 39.0

Communications services.................. 4.9 5.2 -0.3 10.3 8.4 1.9 12.9 8.1 4.8

EU

All commercial KI services..................... 207.5 141.1 52.7 376.2 252.5 123.9 431.6 274.7 126.6

Computer and information services... 20.2 10.0 7.3 44.7 18.8 20.3 57.1 20.3 30.4

Financial services............................... 49.8 24.9 41.6 93.1 40.0 54.7 96.3 45.5 41.6

Other business services..................... 129.3 97.2 4.8 220.2 175.4 50.0 254.2 187.6 53.0

Communications services.................. 8.2 9.0 -1.0 18.2 18.3 -1.1 24.0 21.2 1.7

EU = European Union; KI = knowledge intensive.

NOTES: Commercial KI services trade consists of communications, other business services, financial services, and computer and information services. 

Financial services includes finance and insurance. EU trade does not include intra-EU trade.

SOURCE: World Trade Organization, International trade and tariff data, http://www.wto.org/english/res_e/statis_e/statis_e.htm, accessed 8 August 2013.

Science and Engineering Indicators 2014
6-32 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

Between 2003 and 2012, global HT exports doubled to 

reach $2.3 trillion (appendix table 6-21). The HT share of 

manufactured exports declined from 22% to 16% during this 

period (appendix table 6-24).

Patterns and Trends in Developing Countries

China is the largest exporter of HT products among developing countries and is also the world’s largest exporter, 

with a 28% share of global HT exports (table 6-5; figure 

6-21; appendix table 6-21). Other developing countries have 

global shares of 3% or less.

Between 2003 and 2012, HT exports of developing countries grew twice as fast as those of developed countries. 

As a result, the developing countries increased their share 

of global HT exports from 29% to 40% (figure 6-21; appendix table 6-21). China grew the fastest among the developing countries, with its exports reaching $632 billion, 

becoming the world’s largest exporter. China’s trade surplus 

climbed from $30 billion to $280 billion during this period. 

U.S. Trade in R&D Services

Trade in research and development services is part 

of U.S. trade in business services, a component of 

commercial KI services. In 2011, companies located 

in the U.S. exported $24 billion in these services and 

imported $22 billion, based on Bureau of Economic 

Analysis (BEA) statistics.*

 Most of this trade occurs 

between affiliated parties, that is, within multinational 

companies (MNCs) (appendix table 6-22). 

Details by regions and countries (available for total trade, not by affiliation) show that Europe is the 

top destination for U.S. R&D services exports, with 

a 64.9% share in 2011. For R&D services imports, 

Europe is also the largest trading partner but with a 

lower share, at 46.6% in 2011. The Asia-Pacific region was the second-largest destination for R&D services exports, receiving 15.9% of U.S. exports in these 

services. The region’s share as a source of imports was 

higher, at 29.4% in 2011.

Data for earlier years were collected under the category “research, development, and testing (RDT) services” (appendix table 6-23). These data show that U.S. 

exports of RDT services rose from $13 billion to $24 billion between 2006 and 2010. The trade surplus fell from 

$4 billion to $2 billion during this period. The European 

imports share of RDT services declined steadily from 

62.3% in 2006 to 49.4% in 2010. At the same time, the 

share of RDT services imports from the Asia-Pacific region increased from 17.4% in 2006 to 22.7% in 2007 to 

just below 30% annually from 2008 to 2010. 

R&D and testing services imports from the AsiaPacific region increased most notably from India (from 

$427 million in 2006 to $1.6 billion in 2010), China 

(from $92 million to $955 million) and Japan (from 

$550 million to $1.3 billion). This trend is consistent 

with increased R&D activities in these countries both 

overall (gross expenditures in R&D) and by affiliates 

of U.S. MNCs (see the “International Comparisons 

of R&D Performance” and “R&D by Multinational 

Companies” sections in chapter 4).

* Statistics for 2011 are from the Benchmark Survey of Transactions in Selected Services and Intellectual Property with Foreign 

Persons. See appendix table 6-22 for details.

The bulk of global exports ($1.4 trillion) originate from 

developed countries—primarily from the EU, the United 

States, Japan, and several Asian economies, including 

Singapore, South Korea, and Taiwan (figure 6-21; appendix 

tables 6-21 and 6-32). A large share of HT exports of developed countries is made up of components and inputs that are 

imported by China, Mexico, and other developing countries 

for final assembly. Exports of developing countries, which 

make up $0.9 trillion, are largely finished goods imported by 

developed countries (figure 6-21).

Figure 6-21

Exports of HT products, by selected region/

country/economy: 2003–12

Billions of dollars

EU = European Union; HT = high technology.

NOTES: HT products include aerospace, communications and 

semiconductors, computers and ofce machinery, pharmaceuticals, 

and scientic instruments and measuring equipment. China includes 

Hong Kong. The EU excludes Cyprus, Estonia, Latvia, Lithuania, 

Luxembourg, Malta, and Slovenia. Exports of the United States 

exclude exports to Canada and Mexico. Exports of the EU exclude 

intra-EU exports. Exports of China exclude exports between China 

and Hong Kong. Other selected Asia consists of Malaysia, 

Phillippines, Singapore, South Korea, Taiwan, and Thailand.

SOURCE: IHS Global Insight, World Trade Service database (2013). 

See appendix table 6-21. 

Science and Engineering Indicators 2014

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

0

100

200

300

400

500

600

700

United States

EU

Japan

China

Other selected Asia
Science and Engineering Indicators 2014 ♦ 6-33

Table 6-5

HT product exports, by selected region/country/economy: 2012

(Billions of dollars)

Region/country/economy All HT products ICT

Aircraft and 

spacecraft Pharmaceuticals

Testing, measuring, 

and control 

instruments

China ............................................ 631.7 557.1 3.4 13.5 57.7

EU................................................. 377.9 105.3 51.4 141.9 79.3

United States................................ 286.7 94.3 96.3 38.7 57.4

Japan............................................ 128.1 74.2 4.5 4.8 44.6

Other selected Asia ...................... 560.8 457.0 6.1 15.9 81.8

EU = European Union; HT = high technology; ICT = information and communications technologies.

NOTES: HT products include aircraft and space vehicles, communications and semiconductors, computers and office machinery, pharmaceuticals, and 

scientific instruments and measuring equipment. ICT products include communications, semiconductors and computers, and office machinery. China 

includes Hong Kong. Exports of China exclude exports between China and Hong Kong. Exports of the United States exclude exports to Canada and 

Mexico. The EU excludes Cyprus, Luxembourg, Malta, and Slovenia. Exports of the EU exclude exports to EU member countries. Other selected Asia 

includes Malaysia, Singapore, South Korea, Taiwan, and Thailand.

SOURCE: IHS Global Insight, World Trade Service database (2013). See appendix tables 6-21 and 6-25–6-30.

Science and Engineering Indicators 2014

However, because many of China’s exports consist of inputs and components imported from other countries, China’s 

trade surplus is likely much less in value-added terms 

(see sidebar, “International Initiative to Measure Trade in 

Value-Added Terms”).

China’s ICT exports, which dominate China’s HT product exports, more than tripled to reach almost $560 billion 

during this period (table 6-5; appendix tables 6-25–6-28). 

China’s ICT trade surplus expanded from almost $40 billion 

to over $280 billion. Its exports of testing, measuring, and 

control instruments grew at the same pace to reach almost 

$60 billion (appendix table 6-31). 

Trends varied widely among other developing countries 

(appendix table 6-21): 

♦ Vietnam grew the fastest of any developing country, with 

its HT exports growing from less than $1 billion to $17 

billion. Vietnam has become a low-cost location for assembly of cell phones and other ICT products, with some 

firms shifting production out of China and other developing countries, where labor costs are higher.

♦ India’s exports rose sevenfold to reach $26 billion due to 

expansion in pharmaceuticals and ICT products.

Patterns and Trends in Developed Countries

The bulk of global exports of HT goods ($1.4 trillion) 

originate from developed countries—primarily the EU, the 

United States, Japan, and several Asian economies (figure 6-21; appendix tables 6-21 and 6-32). The EU and the 

United States are the largest and second-largest global exporters among developed economies. Japan, South Korea, 

and Taiwan are the next-largest exporters, each with a global 

share of between 6% and 8%. 

Between 2003 and 2012, exports of developed economies nearly doubled to reach $1.4 trillion in 2012 (figure 

6-21; appendix table 6-21). Because exports of developing 

economies grew much faster than developed economies, the 

global share of developed economies fell from 71% to 60%. 

In the United States, HT product exports grew slightly 

faster than the average for all developed economies’ exports 

(appendix table 6-21). The U.S. global share slipped from 

14% to 13%. The U.S. HT product trade position, which 

had been in balance in the late 1990s, experienced a widening deficit during this period, going from $88 billion to 

$130 billion.18

U.S. growth of HT product exports was led by pharmaceuticals and by aircraft and spacecraft (appendix tables 

6-29 and 6-30). Pharmaceutical exports more than doubled 

in value to reach $39 billion, with the trade deficit widening from $13 billion to $24 billion. Exports of aircraft and 

spacecraft climbed to $96 billion, with the U.S. trade surplus 

at nearly $80 billion in 2012, up from $21 billion in 2003. 

Exports of ICT products, the largest component, grew 

slower than the average for all HT products to reach $94 

billion (appendix tables 6-25–6-28). The U.S. trade deficit 

in ICT products widened from $95 billion to $192 billion. 

The EU exhibited a similar trend, with growth in its HT 

product exports led by aircraft and spacecraft, pharmaceuticals, and testing, measuring, and control instruments (appendix tables 6-29–6-31). The trade surpluses in these three 

products widened substantially. The EU’s trade deficit in 

ICT products deepened from $65 billion to $112 billion (appendix tables 6-25–6-28).

Other major Asian exporters—Japan, South Korea, and 

Taiwan—showed divergent trends (appendix table 6-21). 

Japan’s exports trailed the average for all developed countries, with its global share falling from 12% to 6%. Japan’s 

decline from an export powerhouse in electronics reflects 

its lengthy economic stagnation, the financial difficulties 

of Japanese electronics firms, and Japanese companies offshoring their production to Taiwan, China, and other lowercost locations.
6-34 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

The Organisation for Economic Co-operation and 

Development (OECD)/World Trade Organization (WTO) 

Trade in Value Added (TiVA) initiative is developing estimates of trade measured in value-added terms to complement conventional measures of trade. In a world where 

goods and services are often produced through global supply chains, value-added measures of international trade 

have two substantial advantages over conventional trade 

measures. First, they record the amount of global trade 

more accurately; they record value only once, in the country in which it is added. In contrast, conventional trade 

measures overstate the value of internationally traded 

goods and services, recording the entire (gross) value of 

an item every time it crosses a national border. Second, 

value-added measures produce better estimates of national 

contributions to the value of goods and services in international trade. In contrast, conventional trade measures attribute the entire (gross) value of the goods and services 

a country trades to that country, even if a portion of the 

value was produced by other countries in the supply chain. 

The OECD’s estimate of the U.S. trade balance in iPhones 

shows that the United States has a much smaller estimated 

trade deficit with China, the location of final assembly and 

export of iPhones, and larger trade deficits with countries 

that supply inputs to the iPhone (table 6-C).

OECD/WTO estimates of trade in value-added terms 

are derived from OECD country-level input-output tables. 

Input-output tables track the interrelationships among 

domestic industries and also between domestic industries and consumers—households, government, industry, 

and export customers. OECD/WTO built international 

input-output tables that link exports in one country to the 

purchasing industries or final-demand consumers in the 

importing country. The international input-output tables 

estimate trade among countries on an industry basis using 

coefficients derived from bilateral product and services 

trade data, which are not collected on an industry basis.

OECD/WTO estimates of trade in value-added terms 

assume that the share of imports in any product consumed 

directly as intermediate consumption or final demand 

(except imports) is the same for all users. This assumption is reasonable for developed countries, where there 

is little product differentiation between what is produced 

for export and what is produced for the domestic market. 

This assumption is probably less realistic for developing 

countries because the import content of exports is usually 

higher than the import content of products destined for 

domestic consumption. 

The most recent version of the OECD/WTO database, 

released in May 2013, covers 58 economies (including all 

OECD countries, Brazil, China, India, Indonesia, Russia, 

and South Africa) and the years 1995, 2000, 2005, 2008, 

and 2009. Trade in value-added indicators and additional 

information are available at http://www.oecd.org/industry/ind/measuringtradeinvalue-addedanoecd-wtojointinitiative.htm.

International Initiative to Measure Trade in Value-Added Terms

Table 6-C

U.S. trade balance in iPhones, by selected country/economy

(Millions of dollars)

Type of trade China Germany South Korea Taiwan ROW

Balance (gross).................................................. -1,646 0 0 0 0

Balance (value added)....................................... -65 -161 -800 -207 -413

ROW = rest of world.

SOURCE: Organisation for Economic Co-operation and Development, Trade in Value-Added: Concepts, Methodologies and Challenges, http://www.

oecd.org/sti/ind/49894138.pdf, accessed 15 March 2013.

Science and Engineering Indicators 2014

Taiwan’s HT exports doubled during this period, and it 

surpassed Japan in 2010 to become the largest developed 

Asian exporter of HT products. South Korea’s HT exports 

also doubled, and it reached Japan’s level in 2012. Both 

of these economies’ rapid gains in HT exports were due to 

growth of ICT product exports, which make up most of their 

HT exports (appendix tables 6-25–6-28).

U.S. Trade in Advanced Technology Products

The Census Bureau has developed a classification system 

for internationally traded products based on the degree to 

which they embody new or leading-edge technologies. This 

classification system has significant advantages for determining whether products are HT and may be a more precise 

and comprehensive measure than the product classification 

based on the OECD classification for HT industry production. It categorizes ATP trade into 10 major technology 
Science and Engineering Indicators 2014 ♦ 6-35

areas, including aerospace, biotechnology, electronics, ICT, 

life sciences, and optoelectronics.19

U.S. trade in ATP products is an important component 

of overall U.S. trade, accounting for about one-fifth of combined nonpetroleum exports and imports. Five technology 

areas—ICT, aerospace, electronics, life sciences, and optoelectronics—account for more than 90% of the total value 

of U.S. ATP exports and imports (table 6-6; appendix tables 

6-33–6-38). ICT is the largest, with a share of 44%, followed 

by aerospace, with a 21% share. Life sciences and electronics each have a share of 11%. Optoelectronics has a share 

of 5%. The largest U.S. ATP trading partners are China; 

other Asian countries, including Japan, South Korea, and 

Malaysia; the EU; and NAFTA partners Canada and Mexico.

In 2012, the United States exported $305 billion in ATP 

goods and imported $396 billion, resulting in a deficit of $92 

billion (figures 6-22 and 6-23; appendix table 6-33). Trends 

varied widely by technology area (table 6-6): 

♦ Trade in ICT products produced a deficit of $128 billion, 

the largest of any technology area. The largest trading partner is China, which dominates this area.

♦ In the life sciences area, the United States ran a small deficit of $12 billion, largely with the EU.

♦ The United States has a surplus of $66 billion in aerospace, 

the largest of any technology area. The largest trading partner in this area is the EU.

Table 6-6

U.S. ATP trade in selected technology areas, by selected region/country/economy: 2012

(Billions of dollars)

Technology area NAFTA EU China Japan Other Asia ROW

Aerospace

Exports...................................... 8.2 29.6 11.0 8.1 9.6 38.5

Imports...................................... 9.0 20.2 0.7 4.7 1.2 3.2

Balance..................................... -0.9 9.3 10.3 3.4 8.4 35.2

Electronics

Exports...................................... 8.5 2.4 6.6 1.3 13.2 9.2

Imports...................................... 2.6 2.5 3.1 2.6 12.3 10.8

Balance..................................... 5.9 -0.1 3.4 -1.3 0.9 -1.5

ICT

Exports...................................... 36.6 13.5 8.0 3.5 5.1 24.7

Imports...................................... 33.3 7.0 127.4 8.4 30.5 12.9

Balance..................................... 3.3 6.5 -119.4 -4.9 -25.4 11.8

Life sciences

Exports...................................... 3.7 11.8 3.3 3.7 2.0 7.1

Imports...................................... 4.4 27.2 2.1 1.8 2.0 7.0

Balance..................................... -0.7 -15.4 1.2 2.0 0.0 0.1

ATP = advanced technology products; EU = European Union; ICT = information and communications technology; NAFTA = North American Free Trade 

Agreement; ROW = rest of world.

NOTES: China includes Hong Kong. EU includes current member countries. Other Asia includes Malaysia, South Korea, Singapore, and Taiwan. 

ATP trade is classified by the Census Bureau and consists of advanced materials, aircraft and space vehicles, biotechnology, electronics, flexible 

manufacturing, information and communications technology, life sciences, optoelectronics, nuclear, and weapons.

SOURCE: U.S. Census Bureau, Foreign Trade Statistics, Advanced Technology Trade database, http://www.census.gov/foreign-trade/statistics/country/

index.html, accessed 15 January 2013. See appendix tables 6-34–6-37.

Science and Engineering Indicators 2014

 

EU = European Union; ICT = information and communications 

technology; NAFTA = North America Free Trade Agreement; ROW = 

rest of world. 

NOTES: China includes Hong Kong. Other Asia includes Malaysia, 

Singapore, South Korea, and Taiwan. NAFTA includes Canada and 

Mexico. Advanced technology product trade is classied by the 

Census Bureau and consists of advanced materials, aerospace, 

biotechnology, electronics, exible manufacturing, ICT, life sciences, 

optoelectronics, nuclear, and weapons.

SOURCE: U.S. Census Bureau, Foreign Trade Statistics, Advanced 

Technology Trade database, http://www.census.gov/foreign-trade/ 

statistics/country/index.html, accessed 15 January 2013. See 

appendix table 6-34.

Science and Engineering Indicators 2014

Figure 6-22

U.S. advanced technology product trade in ICT, 

by selected region/country/economy: 2012

Billions of current dollars

NAFTA EU China Japan Other Asia ROW

0

20

40

60

80

100

120

140

Exports Imports
6-36 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

♦ The United States had a small surplus ($7 billion) in 

electronics. Leading trading partners are Malaysia and 

South Korea.

Trends in U.S. Advanced Technology 

Products Trade

Between 2003 and 2012, U.S. ATP imports grew faster 

than exports, resulting in the trade deficit widening from 

$27 billion to $92 billion (figure 6-23; appendix table 6-33). 

Among the four largest technology areas, exports of life sciences grew the fastest (143%), with imports increasing at the 

same rate, resulting in the trade deficit remaining roughly 

stable (appendix table 6-37). 

Aerospace exports grew the next fastest, and outpaced 

growth of imports, resulting in the trade surplus widening 

from $27 billion to $66 billion (appendix table 6-35). Trends 

in exports and imports in these two technology areas have 

largely been driven by trade with the EU, the largest partner 

in these two areas.

Exports of ICT products grew the slowest among these 

four technology areas, with much faster growth of imports 

(appendix table 6-34). The trade deficit in ICT products 

more than doubled to reach nearly $130 billion, with the 

trade deficit with China reaching nearly $100 billion. As in 

U.S. HT international trade, the rising deficit in U.S. ATP 

trade has largely occurred in ICT products and with China. 

In electronics, the United States had a surplus of between 

$16 billion and $25 billion for much of the 2000s. Between 

2011 and 2012, the trade surplus fell to $7 billion because 

of a decline in exports combined with an increase in imports 

(appendix table 6-36). 

U.S. Multinational Companies in Knowledgeand Technology-Intensive Industries

The Bureau of Economic Analysis (BEA) conducts an 

annual survey of U.S. multinationals that includes firms in 

KTI industries. The BEA data are not directly comparable 

with the world industry data used in the previous sections. 

However, the BEA data provide additional information on 

the globalization of activity and employment in U.S. multinationals in these industries. 

Commercial Knowledge-Intensive 

Service Industries

U.S. multinationals in commercial KI services industries 

generated $1.1 trillion in value added in 2010 (preliminary), 

of which $873 billion (76%) occurred in the United States 

(appendix table 6-39). Financial services ranks first by value 

added ($471 billion), followed by information services ($384 

billion) and business services ($297 billion). Production in 

business services was the most globalized, as measured by 

the distribution between U.S. and foreign value added, with 

31% of value added originating from foreign economies in 

2010 (figure 6-24). Financial services were the next highest 

(28%), followed by information services (15%). 

U.S. multinationals in commercial KI services industries 

employed 7.4 million workers worldwide, of whom 5.4 million (72%) were employed in the United States (appendix 

table 6-39). Employment was highest in financial services, 

at 2.5 million, followed by 1.6 million employed in information services and 1.2 million employed in business services. 

Employment was most globalized in business services (foreign share of 44%), followed by financial services (24%) 

and information services (19%) (figure 6-24). 

High-Technology Manufacturing Industries

U.S. multinationals in the HT manufacturing industries 

(excluding aircraft and spacecraft) generated more than $400 

billion worldwide in value added in 2010 (preliminary), of 

Figure 6-23

U.S. trade in advanced technology products: 

2000–12

Billions of dollars

NOTE: Advanced technology product trade is classied by the 

Census Bureau and consists of advanced materials, aerospace, 

biotechnology, electronics, exible manufacturing, information and 

communications technologies, life sciences, optoelectronics, 

nuclear, and weapons.

SOURCE: U.S. Census Bureau, Foreign Trade Statistics, Advanced 

Technology Trade database, http://www.census.gov/foreign-trade/ 

statistics/country/index.html, accessed 15 January 2013. See 

appendix table 6-33.

Science and Engineering Indicators 2014

2000 2002 2004 2006

Balance

2008 2010 2012

0

50

100

150

200

250

300

350

400

450

Imports

Exports

2000 2002 2004 2006 2008 2010 2012

–120

–100

–80

–60

–40

–20

0

20
Science and Engineering Indicators 2014 ♦ 6-37

which about two-thirds originated in the United States (figure 6-25; appendix table 6-39). Production in the computer 

industry was the most globalized, as measured by the distribution between U.S. and foreign value added, with 45% of 

value added originating from foreign locations in 2010 (figure 6-25). Pharmaceuticals was the second highest (40%), 

followed by semiconductors (35%) and then by testing, 

measuring, and control instruments (28%). Communications 

is the least-globalized industry, with 17% of value added 

produced outside of the United States.

U.S. multinationals in HT manufacturing employed 2.4 

million workers worldwide, with 1.2 million workers (about 

Figure 6-24

Globalization indicators of U.S. multinationals in commercial KI services: 2010

Percent

KI = knowledge intensive.

NOTES: Value added is the amount contributed by a country, rm, or other entity to the value of a good or service and excludes purchases of domestic 

and imported materials and inputs. Commercial KI services are classied by the Organisation for Economic Co-operation and Development and include 

business, nancial, and communications. Internet and data processing are part of communications. Management, scientic, and technicals and computer 

system design are part of business services.

SOURCE: Bureau of Economic Analysis, International Economic Accounts, U.S. Direct Investment Abroad: Financial and Operating Data for U.S. 

Multinational Companies (2009–10), http://www.bea.gov/international/di1usdop.htm, accessed 15 February 2013. See appendix table 6-39.

Science and Engineering Indicators 2014

Commercial

 KI services

Financial

services

Information Internet and

data processing

Business

services

Management, scientic,

and technical services

Computer

system design

0

10

20

30

40

50

60

Foreign share of value added Foreign share of employment

Figure 6-25

Globalization indicators of U.S. multinationals in selected manufacturing industries: 2010

Percent

NOTE: Value added is the amount contributed by a country, rm, or other entity to the value of a good or service and excludes purchases of domestic 

and imported materials and inputs. 

SOURCE: Bureau of Economic Analysis, International Economic Accounts, U.S. Direct Investment Abroad: Financial and Operating Data for U.S. 

Multinational Companies, 2009–10, http://www.bea.gov/international/di1usdop.htm, accessed 15 February 2013. See appendix table 6-39.

Science and Engineering Indicators 2014

Pharmaceuticals

and medicines

Computers and

peripheral equipment

Communications

equipment

Semiconductors and other

electronic components

Navigational, measuring,

and other instruments

0

10

20

30

40

50

60

70

Foreign share of value added Foreign share of employment
6-38 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

50%) employed in the United States in 2010 (preliminary) 

(appendix table 6-39). More than 60% of the semiconductor 

workforce of 600,000 workers is employed abroad, the highest share among these industries (figure 6-25). Multinational 

companies in two industries—computers and pharmaceuticals—employ around 50% of their workforce abroad. The 

communications and testing, measuring, and control instruments industries have less than 40% of their workforces employed abroad. 

U.S. and Foreign Direct Investment in 

Knowledge- and Technology-Intensive 

Industries

Foreign direct investment (FDI) has the potential to generate employment, raise productivity, transfer skills and 

technology, enhance exports, and contribute to long-term 

economic development (Kumar 2007). Receipt of FDI may 

indicate a developing country’s emerging capability and integration with countries that have more established industries. 

FDI in specific industries may suggest the potential for these 

industries’ evolution and the creation of new technologies. 

This section uses data from BEA on U.S. direct investment abroad and foreign investment in the United States in 

KTI industries. The rising volume of trade by U.S.-based 

KTI firms has been accompanied by increases in U.S. direct 

investment abroad and FDI in the United States. Estimates 

of U.S. direct investment abroad and FDI in the United 

States are lower-bound estimates because a substantial share 

of outward and inward investment is allocated to holding 

companies that own companies in other industries.

U.S. Direct Investment Abroad

The stock of U.S. direct investment abroad in computer 

and electronic products, which includes the HT industries 

of communications, semiconductors, and testing, measuring, 

and control instruments, was $102 billion in 2012 (figure 

6-26). The Asia and Pacific region receives 43% of U.S. direct investment abroad.20 The EU is the next-largest recipient, with a share of 39%. 

The stock of U.S. direct investment abroad in commercial KI services industries was $1.0 trillion in 2012 (figure 

6-26). Financial services accounted for most U.S. direct investment abroad, with far smaller shares for information and 

professional, scientific, and technical services. The EU is the 

largest recipient in these three industries, with shares ranging from 44% to 54%. The Asia and Pacific region, including Japan, is the next largest, with shares of 18%–28% in 

these industries.

Foreign Direct Investment in the United States

The stock of inward FDI in U.S. computer electronics 

manufacturing industries was $61 billion in 2012, less than 

the amount the United States invested abroad in these industries (figure 6-27). Limited data on the geographical region 

show that the Asia and Pacific region is the largest investor, 

EU = European Union.

NOTES: Finance excludes depository institutions. Other Asia and 

Pacic includes Australia, China, Hong Kong, India, Indonesia, 

Malaysia, New Zealand, Philippines, Singapore, South Korea, 

Taiwan, Thailand, and others.

SOURCE: Bureau of Economic Analysis, International Economic 

Accounts, U.S. Direct Investment Abroad: Financial and Operating 

Data for U.S. Multinational Companies 2012, http://www.bea. 

gov/international/di1usdop.htm, accessed 10 August 2013.

Science and Engineering Indicators 2014

Share (percent)

Figure 6-26

U.S. outward foreign direct investment in selected 

industries: 2012

Computers

and electronic

products

Information Finance Professional,

scientic,

and technical

services

0

20

40

60

80

100

EU

Japan

Other Asia

and Pacic

All others

All countries total (billions of dollars)

102.1 146.6 775.6 94.1

EU = European Union.

NOTES: Investment in billions of dollars is shown above each 

industry. Finance excludes depository institutions. Other Asia and 

Pacic includes Australia, China, Hong Kong, India, Indonesia, 

Malaysia, New Zealand, Philippines, Singapore, South Korea, 

Taiwan, Thailand, and others.

SOURCE: Bureau of Economic Analysis, International Economic 

Accounts, Foreign Direct Investment in the U.S.: Balance of 

Payments and Direct Investment Position Data, http://www.bea. 

gov/international/di1fdibal.htm, accessed 15 January 2013.

Science and Engineering Indicators 2014

Share (percent)

Figure 6-27

Foreign direct investment in selected U.S. industries, 

by selected region/country/economy: 2012

Computers

and electronic

products

Information Finance Professional,

scientic,

and technical

services

0

20

40

60

80

100

EU

Japan

Other Asia

and Pacic

All others

All countries total (billions of dollars)

61.2 123.8 365.8 106.7
Science and Engineering Indicators 2014 ♦ 6-39

with a share of 39%. The EU is the second largest, with a 

share of 33%.

Similarly, the stock of inward FDI in U.S. commercial KI 

services, at $596 billion in 2011, was less than the amount 

the United States invested abroad in these industries (figure 

6-27). The EU is the largest investor in these industries, with 

shares of 65%–83% in these industries.

Innovation-Related Indicators 

of the United States and 

Other Major Economies

The fourth section of this chapter examines several innovation-related measures in industries, with a focus on 

KTI industries. OECD defines innovation as the “implementation of a new or significantly improved product (good 

or service), or process, a new marketing method, or a new 

organizational method” (OECD/Eurostat 2005:46–47). 

Innovation is widely recognized as instrumental to the realization of commercial value in the marketplace and as a driver of economic growth. New ICT technologies, for example, 

have stimulated the creation of new products, services, and 

industries that have transformed the world economy over the 

past several decades.

This section presents data on how innovation activity 

varies among U.S. industries, using information from NSF’s 

BRDIS. The section also includes three indicators of activities that can facilitate innovation but do not themselves 

constitute innovation. Two of these, patents and trade in 

royalties and fees, are indicators of invention—they protect 

intellectual property in inventions that can have value for 

commercial innovations. The third indicator concerns early 

stage financing for U.S. HT small businesses, which can be 

an important milestone in the process of bringing new products and services to market. 

Innovation Activities by U.S. Businesses

BRDIS provides innovation indicators that are representative of all U.S.-located businesses with five or more employees. Survey results indicate which kinds of companies 

introduced new goods, services, or processes between 2008 

and 2010.21 Data from the 2010 survey suggest that U.S. KTI 

industries have a much higher incidence of innovation than 

other industries. 

In the U.S. manufacturing sector, five of the six HT manufacturing industries—aircraft; communications; computers; 

pharmaceuticals; and testing, measuring, and control instruments—reported rates of product and process innovation that 

were at least double the manufacturing sector average (figure 

6-28). Most of these industries reported significantly higher 

rates of innovation in both goods and services, suggesting 

that high rates of innovation by manufacturing companies go 

hand-in-hand with innovations in services. 

Several of these industries—notably, aerospace; computers; pharmaceuticals; and testing, measuring, and control 

instruments—reported higher-than-average rates of process 

innovations, particularly in production methods, logistics, 

and delivery methods. Innovation is also higher in several 

commercial KI services industries in comparison to other 

nonmanufacturing industries (figure 6-29).22 Software firms 

lead in incidence of innovation, with 69% of companies reporting the introduction of a new product or service, compared to the 9% average for all nonmanufacturing industries. 

Figure 6-28

Share of U.S. manufacturing companies reporting innovation activities, by selected industry: 2008–10

Percent

NOTES: The survey asked companies to identify innovations introduced from 2008 to 2010. Figures are preliminary and may later be revised. Data may 

not be internationally comparable. The sum of yes plus no percentages may not add to 100% due to item response to some innovation question items. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Business R&D and Innovation Survey (2010).

Science and Engineering Indicators 2014

All manufacturing

industries

Communications Aircraft Testing, measuring,

and control instruments

Pharmaceuticals Computers Soap, cleaning

compound, and

toilet preparation

0

10

20

30

40

50

60

Any good or service Any process
6-40 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

Innovation is also three to four times higher than the nonmanufacturing average in three other industries—computer 

systems design, data processing and hosting, and scientific 

R&D services. 

Global Trends in Patenting

To foster innovation, nations assign property rights to 

inventors in the form of patents. These rights allow the 

inventor to exclude others from making, using, or selling 

the invention for a limited period in exchange for publicly 

disclosing details and licensing the use of the invention.23 

Inventors obtain patents from government-authorized agencies for inventions judged to be “new . . . useful . . . and . . . 

nonobvious.”24

Patenting is an intermediate step toward innovation, and 

patent data provide indirect and partial indicators of innovation. Not all inventions are patented, and the propensity 

to patent differs by industry and technology area. Not all 

patents are of equal value, and not all foster innovation—

patents may be obtained to block rivals, negotiate with competitors, or help in infringement lawsuits (Cohen, Nelson, 

and Walsh 2000). In HT industries, where innovation is 

cumulative, firms may build “thickets” of patents that impede or raise the cost of R&D and innovation (Noel and 

Schankerman 2009:2).

Indeed, the vast majority of patents are never commercialized. However, the smaller number of patents that are 

commercialized result in new or improved products or processes or even entirely new industries. In addition, their licensing may provide an important source of revenue, and 

patents may provide important information for subsequent 

inventions and technological advances. 

This discussion focuses largely on patent activity at the 

U.S. Patent and Trademark Office (USPTO). It is one of the 

largest patent offices in the world and has a significant share 

of applications and grants from foreign inventors because of 

the size and openness of the U.S. market.25 Although U.S. 

patents are naturally skewed toward U.S. inventions, these 

market attributes make U.S. patent data useful for identifying trends in global inventiveness. 

This section also deals with patents filed in all three of 

the world’s largest patenting centers: the United States, the 

EU, and Japan. Because of the high costs associated with 

patent filing and maintenance in these three patent offices, 

inventions covered by these patents are likely to be valuable. 

U.S. Patent and Trademark Office Grants

The USPTO granted inventors more than 250,000 patents 

in 2012 (appendix tables 6-40 and 6-41). U.S. inventors were 

granted 120,000 patents, making them the largest recipient, 

with a share of nearly one-half of patents granted worldwide. 

Japan, the next largest, was granted 51,000 patents. The 

EU, ranked third, received 36,000 patents. Other developed 

economies, largely South Korea and Taiwan, were together 

granted the same number as the EU. Developing countries 

received 9,000 patents (less than 4% of total patents). China 

and India received by far the largest number of patents granted to developing countries. 

The number of USPTO patents remained essentially flat 

at 170,000 patents between 2003 and 2009 before rising rapidly to reach 250,000 in 2012 (appendix table 6-40). The 

rapid growth in 2010–12 may reflect recovery from the recession, along with USPTO efforts to decrease its backlog of 

patent applications. The United States enacted a new patent 

law in 2011 that was aimed in part at reducing the backlog 

of USPTO patent applications. 

Between 2003 and 2012, the number of USPTO patents granted to U.S.-based inventors grew from 87,000 to 

120,000 patents, trailing the pace of growth of all patents 

(appendix table 6-40). As a result of U.S. growth lagging behind overall growth, the U.S. share fell 5 percentage points 

to reach 48% (figure 6-30). The decline in the U.S. share 

likely indicates increased technological capabilities abroad, 

globalization that makes patent protection in foreign countries more important, and patenting by U.S.-based inventors 

located abroad, such as patents granted to inventors located 

in subsidiaries of U.S. MNCs. 

 

NOTES: The survey asked companies to identify innovations 

introduced in 2008–10. The sum of yes plus no percentages may not 

add to 100% due to item nonresponse to some innovation question 

items. Figures are preliminary and may later be revised. Data may not 

be internationally comparable. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Business R&D and Innovation Survey (2010).

Science and Engineering Indicators 2014

Figure 6-29

Share of U.S. nonmanufacturing companies 

reporting innovation activities, by selected industry: 

2008–10

Percent

Nonmanufacturing

industries

Software

publishers

Computer

systems

design and 

related

services

Data

processing,

hosting,

and

related

services

Scientic

R&D

services

0

10

20

30

40

50

60

70

80

Any good or service Any process
Science and Engineering Indicators 2014 ♦ 6-41

Patents granted to Japan and the EU grew slightly slower 

than the growth of overall patents, resulting in their shares 

slightly declining to 20% and 14%, respectively (figure 

6-30; appendix table 6-40). Slow growth of USPTO patenting by Japan and the EU may indicate sluggish economic 

activity or an increased preference to patent in their home 

patent offices.

Patents granted to other developed economies rose three 

times faster than growth of all patents to reach 37,000 patents 

(appendix table 6-40). South Korea and Taiwan led growth 

of these developed economies, with their patent grants rising 

to 13,000 and 11,000, respectively. 

Patents granted to developing countries rose exponentially (but from a very low base) to reach 9,000 patents (table 

6-7; figure 6-30; appendix table 6-40). China and India led 

growth of developing countries, with their patents reaching 

5,000 and 2,000 patents, respectively. 

U.S. Patent and Trademark Office Patenting 

Activity by U.S. Companies

Patenting by U.S. industry provides an indication of inventive activity, mediated by the relative importance in different industries of patenting as a business strategy. 

According to the NSF BRDIS survey, U.S. KTI industries account for a large share of USPTO patent grants (figure 6-31; appendix table 6-42). The BRDIS data on USPTO 

patents are not comparable with the USPTO patent data 

presented in the previous and following section.26 U.S. HT 

industries were granted 29,000 of the 58,000 patents granted 

EU = European Union; USPTO = U.S. Patent and Trademark Ofce.

NOTES: Technologies are classied by The Patent Board.TM Patent grants are fractionally allocated among countries on the basis of the proportion of the 

residences of all named inventors. 

SOURCE: The Patent Board,TM special tabulations (2013) from Proprietary Patent database. See appendix table 6-40.

Science and Engineering Indicators 2014

Share (percent)

Figure 6-30

USPTO patents granted, by location of inventor: 2003–12

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

0

20

40

60

80

100

United

States

Japan

EU

Other

developed

Developing

to all U.S. manufacturing industries in 2011. The HT industry share of patents granted to all manufacturing industries 

(50%) is far higher than its share of value added of all manufacturing industries (19%). The U.S. semiconductor industry 

was issued the largest number of patents (10,000) among 

these HT industries, followed by 2,000 to 5,000 each for 

aerospace, computers, communications equipment, pharmaceuticals, and testing, measuring, and control instruments. 

U.S. commercial KI services received 46% of the 43,000 

patents issued to nonmanufacturing industries in 2011 (figure 6-31; appendix table 6-42). These industries’ share of 

patents is much higher than their value-added share of all 

Table 6-7

USPTO patents granted for selected countries: 

2003, 2008, and 2012

Country 2003 2008 2012

Brazil............................... 132 101 201

China .............................. 613 1,607 5,351

India................................ 354 651 1,756

Malaysia ......................... 48 159 213

South Africa.................... 111 90 140

USPTO = U.S. Patent and Trademark Office.

NOTE: Patent grants are fractionally allocated between the United 

States and all other countries on the basis of the proportion of the 

residences of all named inventors. 

SOURCE: The Patent Board,TM special tabulations (2013) of the 

Proprietary Patent database. See appendix table 6-40.

Science and Engineering Indicators 2014
6-42 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

nonmanufacturing industries (32%), similar to the position 

of HT manufacturing industries. The software industry accounted for 10,000 patents, more than half of the patents issued to commercial KI services; professional and technical 

services were ranked second, with 6,000 patents. Two industries in professional and technical services—scientific R&D 

services and computer systems design—reported significant 

patenting activity.

U.S. Patent and Trademark Office Patents 

Granted, by Technology Area

This section discusses trends in four broad, NSF-classified 

technology areas that are closely linked to science or KTI industries—ICT; biotechnology and pharmaceuticals; medical 

electronics and medical equipment; and automation, control, 

and measuring technologies. This NSF classification assigns 

patents to technology areas on the basis of information contained in the patents; it is not comparable to patent data from 

BRDIS presented in the previous section, which classify 

patents based on the industry of the company to which the 

patent was issued. 

Patents granted in the four broad, NSF-classified technology areas make up more than half of all U.S. patents: 

♦ The largest area is ICT, which consists of networking, information processing, telecommunications, semiconductors, and computer systems (table 6-8; appendix tables 

6-43–6-47). It accounts for nearly 40% of all USPTO 

patents.

♦ Health-related technologies consist of two broad areas, 

biotechnology and pharmaceuticals and medical electronics and medical equipment. These two technology areas 

each have shares of 6% (appendix tables 6-48–6-51).

♦ A fourth broad area includes automation and control and 

measuring and instrumentation technologies, with a share 

of 6% (appendix tables 6-52 and 6-53). 

♦ Between 2003 and 2012, USPTO patents granted in ICT 

technologies more than doubled, compared to a 50% increase in patents in all technologies (appendix tables 

6-43–6-47). Trends varied widely among the five ICT 

technology areas: 

• Patents granted in information processing and networking at least tripled to reach 14,000 and 24,000, 

respectively.

• Patents in telecommunication nearly doubled to reach 

17,000.

• Patents in computer systems lagged overall growth 

(55%) to reach 15,000.

• Patents in semiconductors grew the slowest (18%) to 

reach 16,000.

♦ Biotechnology and pharmaceuticals trailed growth of patents in all technologies (36% versus 50%) (appendix tables 

6-48 and 6-49). Growth was particularly weak in pharmaceuticals, which grew 16%. This weak growth coincides 

with consolidation of the pharmaceutical industry in the 

last several years, stronger price and safety regulation of 

drugs in many developed countries, increased competition 

from generics, and little growth in U.S. Food and Drug 

Administration approval of new drugs. 

Positions of Major Patenting Regions and 

Countries in Selected Technology Areas

This section presents shares of the United States, the 

EU, and several Asian countries in these four broad technology areas averaged over 2010–12. A technology area 

USPTO = U.S. Patent and Trademark Ofce.

NOTES: Detail may not add to total because of rounding. Industry 

classication is based on dominant business code for domestic R&D 

performance where available. For companies that did not report 

business codes, classication used for sampling was assigned. 

Statistics are based on companies in the United States that reported 

to the survey, regardless of whether they did or did not perform or 

fund R&D. These statistics do not include an adjustment to the 

weight to account for unit nonresponse. For a small number of 

companies that were issued more than 100 patents by USPTO, 

counts from USPTO.gov were used to supplement survey data. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Business R&D and Innovation Survey, 

2011. See appendix table 6-42.

Science and Engineering Indicators 2014

Thousands

Figure 6-31

USPTO patents granted, by selected U.S. 

industry: 2011

Manufacturing industries

Nonmanufacturing industries

0

10

20

30

40

50

60

0

10

20

30

40

50

60

Semiconductor and other 

electronic components

Other computer and electronic 

components

Navigational, measuring, 

electromedical, and control 

instruments

Pharmaceuticals and medicines

Communications equipment

Aerospace products 

and parts

All other industries

Software

Telecommunications

Professional, scientic, and 

technical services

Finance and insurance

Data processing, hosting, and 

related services

All other industries
Science and Engineering Indicators 2014 ♦ 6-43

share greater (less) than the share of all patents signifies that 

patents by a region, country, or economy are concentrated 

(weaker) in a particular technology. 

ICT. U.S. patenting activity is concentrated in the broad 

ICT technology area, with a share 4 percentage points 

higher than its share of all patents (figure 6-32). However, 

the U.S. position varies widely among the individual 

technology areas: 

♦ The United States is highly concentrated in two areas—information processing and networking—with shares more 

than 10 percentage points higher (appendix tables 6-43 

and 6-44).

♦ The United States has average activity in two areas—computer systems and telecommunications (appendix tables 

6-45 and 6-47). The United States is weak in semiconductors, with its share more than 10 percentage points below 

its share of all patents (appendix table 6-46).

EU patenting activity in ICT is comparatively low (figure 6-32). Several studies suggest that the EU has lagged 

behind the United States in ICT technology, but the pattern 

may also reflect a preference of EU inventors to patent in the 

European Patent Office. 

In Asia, Japan and Taiwan have similar ICT patterns, with 

an overall weakness in ICT (figures 6-32 and 6-33). They 

have weaker activity in three technologies—networking, 

information processing, and telecommunications (appendix 

Table 6-8

USPTO patents granted in selected technology areas: 2003, 2008, and 2012

Technology area 2003 2008 2012

Automation, control, and measurement........................................... 11,062 12,583 15,773 

Biotechnology and pharmaceuticals................................................ 10,969 9,499 14,969 

ICT.................................................................................................... 40,441 51,842 90,140 

Computer systems ....................................................................... 9,789 11,148 15,260 

Information processing................................................................. 7,533 13,268 27,880 

Networking.................................................................................... 2,626 5,806 10,986 

Semiconductors............................................................................ 13,108 11,080 15,272 

Telecommunications ..................................................................... 7,385 10,540 20,743 

Medical electronics and equipment ................................................. 9,987 6,262 14,555 

ICT = information and communications technology; USPTO = U.S. Patent and Trademark Office.

NOTE: Technologies are classified by The Patent Board.TM

SOURCE: The Patent Board,TM special tabulations (2013) of the Proprietary Patent database. See appendix tables 6-43–6-53.

Science and Engineering Indicators 2014

Figure 6-32

USPTO patents granted, by selected technology areas for selected country/economy of inventor: 2010–12

Share (percent)

EU = European Union; ICT = information and communications technologies; USPTO = U.S. Patent and Trademark Ofce.

NOTES: Technologies are classied by The Patent Board.TM Patents are fractionally allocated among countries on the basis of the proportion of the 

residences of all named inventors. 

SOURCE: The Patent Board,TM special tabulations (2013) from Proprietary Patent database. See appendix tables 6-40 and 6-43–6-53. 

Science and Engineering Indicators 2014

United States Japan EU

0

20

40

60

80

All technology areas ICT Biotechnology and

pharmaceuticals

Medical equipment

and electronics

Automation and control and 

measuring and instrumentation
6-44 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

tables 6-43–45). They have concentrated patenting activity 

in computer systems and semiconductors (appendix tables 

6-46 and 6-47). 

Biotechnology and Pharmaceuticals. The United States 

is concentrated in this area, with a high concentration in biotechnology and a somewhat high concentration in pharmaceuticals (figure 6-32; appendix tables 6-48 and 6-49). The EU is 

highly concentrated in this area, with very strong activity in 

pharmaceuticals and above-average activity in biotechnology. 

South Korea and Taiwan are weak in this area (figure 6-33).

Medical Electronics and Equipment. The United States 

has a very high concentration in medical electronics and 

equipment with a share that is 20 percentage points higher 

than its share of all patents (figure 6-32; appendix tables 

6-50 and 6-51). The United States is equally strong in the 

two individual technology areas. The EU’s patenting activity is average in this area, and South Korea and Taiwan have 

much weaker activity (figure 6-33).

Automation and Control; Measuring and Instrumentation. The United States has a somewhat higher concentration in automation and control and average activity in 

measuring and instrumentation (figure 6-32; appendix tables 

6-52 and 6-53). The EU has higher-than-average concentration in these two technology areas. South Korea and 

Taiwan have weaker activity in these two technology areas 

(figure 6-33). 

Patenting Valuable Inventions: Triadic Patents

Using patent counts as an indicator of national inventive 

activity does not differentiate between inventions of minor 

and substantial economic potential. Inventions for which 

patent protection is sought in three of the world’s largest 

markets—the United States, the EU, and Japan—are likely 

to be viewed by their owners as justifying the high costs of 

filing and maintaining these patents in three markets. These 

triadic patents serve here as an indicator of higher-value inventions, although growing patent activity in China, India, 

South Korea, and other locations may limit the utility of this 

measure. The number of triadic patents is strongly correlated 

with expenditures on industry R&D, suggesting that countries with higher patenting activity make greater investments 

to foster innovation (OECD 2009:36).

Between 2000 and 2010, the number of triadic patents 

grew slightly from 45,000 to 49,000 (figure 6-34; appendix 

 

ICT = information and communications technologies; USPTO = U.S. 

Patent and Trademark Ofce.

NOTES: Technologies are classied by The Patent Board.TM Patents 

are fractionally allocated among countries/economies on the basis of 

the proportion of the residences of all named inventors. 

SOURCE: The Patent Board,TM special tabulations (2013) from 

Proprietary Patent database. See appendix tables 6-40 and 6-43–53.

Science and Engineering Indicators 2014

Figure 6-33

USPTO patents granted, by selected technology 

areas for inventors located in South Korea and 

Taiwan: 2010–12

Share (percent)

South Korea Taiwan

0

1

2

3

4

5

6

7

8

9

10

All technology areas ICT Biotechnology and

pharmaceuticals

Medical equipment

and electronics

Automation and control and 

measuring and instrumentation

Figure 6-34

Global triadic patent families, by selected region/

country/economy: 1998–2010

Number

EU = European Union; ROW = rest of world. 

NOTES: Triadic patent families include patents applied in the U.S. 

Patent and Trademark Ofce, European Patent Ofce, and Japan 

Patent Ofce. Patent families are fractionally allocated among 

regions/countries/economies based on the proportion of the 

residences of all named inventors.

SOURCE: Organisation for Economic Co-operation and Development, 

Patents Statistics, http://stats.oecd.org/WBOS/index.aspx, Patents by 

Region database, accessed 15 January 2011. See appendix table 6-54.

Science and Engineering Indicators 2014

1998 2000 2002 2004 2006 2008 2010

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

United States

EU

Japan

South Korea

ROW
Science and Engineering Indicators 2014 ♦ 6-45

table 6-54). During this period, the United States, the EU, 

and Japan had roughly equal numbers of triadic patents.27

South Korea’s filings rose much faster than overall growth, 

resulting in its share of triadic patents doubling from 2% to 

4%. Filings by all other countries remained at less than 1% 

of all triadic patents during this period.

Trade in Royalties and Fees

Firms trade intellectual property when they license or 

franchise proprietary technologies, trademarks, and entertainment products to entities in other countries. Trade in 

intellectual property can involve patented and unpatented 

techniques, processes, formulas, and other intangible assets and proprietary rights; broadcast rights and other intangible rights; and the rights to distribute, use, and reproduce 

general-use computer software. These transactions generate 

revenues in the form of royalties and licensing fees. Trade in 

royalties and fees is a rough indicator of technology transfer across the global economy and the international value of 

an economy’s intellectual property. However, differences in 

tax policies and protection of intellectual property also likely 

influence the volume and geographic patterns of global trade 

in royalties and fees (Gravelle 2010:8; Mutti and Grubert 

2007:112).

Global exports of royalties and fees were estimated at 

$241 billion in 2011 (figure 6-35). The United States, the 

EU, and Japan are collectively the largest global exporters, 

with a global share of 85%. 

The United States is by far the world’s largest exporter of royalties and fees, with exports of $121 billion and a 

large and growing surplus (figure 6-35). The volume and 

geographic patterns of U.S. trade in royalties and fees have 

been influenced by U.S.-based multinationals transferring 

their intellectual property to low-tax jurisdictions or their 

foreign subsidiaries to reduce their U.S. and foreign taxes 

(Gravelle 2010:8; Mutti and Grubert 2007:112). The EU is 

the second largest, with exports of $54 billion. The EU has 

a small deficit in trade of royalties and fees. Japan is the 

third largest, with exports of $29 billion, and has a substantial trade surplus. 

Exports of major developing countries are much lower 

than those of developed countries (figure 6-36). Developing 

countries are typically net importers of royalties and fees as 

they seek to acquire technology from abroad to foster development of their economies. China is the largest developing country exporter of royalties and fees, with $743 million 

(figure 6-36). Brazil is the second largest, with $590 million, 

followed by India ($300 million). These three countries have 

had growing deficits in their trade of royalties and fees.

U.S. High-Technology Small Businesses

Many of the new technologies and industries seen as 

critical to U.S. innovation and economic growth are identified with small businesses. Many large HT businesses 

invest in and acquire small businesses as part of their 

efforts to develop and commercialize new technologies. 

Biotechnology, the Internet, and computer software are examples of industries built around new technologies in whose 

initial commercialization microbusinesses—those with fewer than five employees—played an important role. Trends in 

the number of microbusinesses in emerging or established 

HT sectors may point to innovative industries with future 

areas of growth. This section covers patterns and trends that 

characterize microbusinesses operating in HT industries as 

classified by the Bureau of Labor Statistics (BLS), which 

is different than OECD’s HT classification. Two sources 

of financing for HT small businesses—venture capital and 

the U.S. government’s SBIR—are also examined using data 

from Dow Jones and other sources. 

Characteristics of Microbusinesses in U.S. 

High-Technology Industries

The number of microbusinesses in industries classified as 

HT by BLS is about 320,000, two-thirds of all firms operating in these industries (table 6-9; figure 6-37; appendix table 

6-55).28 Services account for 95% (300,000) of U.S. HT 

Figure 6-35

Global exports of royalties and fees, by selected 

region/country/economy: 2004–11

Billions of dollars

EU = European Union.

NOTES: EU exports do not include intra-EU exports. Developed 

countries are classied as high-income economies by the World 

Bank. Developing countries are classied as upper- and lowermiddle income and low income by the World Bank. Sum of 

regions/countries/economies does not add up to total due to 

rounding and discrepancies.

SOURCE: World Trade Organization, International trade and tariff 

data, http://www.wto.org/english/res_e/statis_e/statis_e.htm, 

accessed 8 August 2013.

Science and Engineering Indicators 2014

2004 2005 2006 2007 2008 2009 2010 2011

0

40

80

120

160

200

240

280

World 

United States 

EU 

Japan 

Other developed 

China 

Other developing
6-46 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

microbusinesses; manufacturing accounts for 4% (12,000), 

with the remainder in other industries (e.g., agriculture, 

mining, and construction). Similarly, services dominate employment in HT microbusinesses, with a very small share 

employed in manufacturing. 

Figure 6-36

Exports of royalties and fees of selected 

developing countries: 2004–11

Millions of dollars

NOTE: Developing countries are classied as upper- and lowermiddle income and low income by the World Bank. 

SOURCE: World Trade Organization, International trade and tariff 

data, http://www.wto.org/english/res_e/statis_e/statis_e.htm, 

accessed 8 August 2013.

Science and Engineering Indicators 2014

2004 2005 2006 2007 2008 2009 2010 2011

0

200

400

600

800

1,000

China

Brazil

Thailand

India

Table 6-9

Number of firms and employment of U.S. HT microbusinesses, by selected industries: 2010

Industry Number of firms Employment

All industries ....................................................................................................................... 316,636 437,604

All manufacturing industries ........................................................................................... 11,512 20,683

Navigational, measuring, electromedical, and control instruments ............................ 1,645 3,025

Other general-purpose machinery .............................................................................. 1,589 3,036

Industrial machinery.................................................................................................... 1,128 2,129

Semiconductors and other electronic components.................................................... 1,121 1,954

All others ..................................................................................................................... 6,029 10,539

All services industries ..................................................................................................... 300,259 408,968

Management, scientific, and technical consulting...................................................... 117,678 140,953

Computer systems design and related ....................................................................... 80,767 107,719

Architectural, engineering, and related ....................................................................... 61,046 95,055

All others ..................................................................................................................... 40,768 65,241

All other industries .......................................................................................................... 4,865 7,953

HT = high technology.

NOTES: Firms with less than 5 employees include those reporting no employees on their payroll. A firm is an entity that is either a single location with no 

subsidiary or branches or the topmost parent of a group of subsidiaries or branches. HT industries are defined by the Bureau of Labor Statistics (BLS) 

by the basis of employment intensity of the technology-oriented occupations based on BLS’s 2011 Occupation Employment Survey. HT small business 

employment is a lower-bound estimate because employment data are not available for a few industries due to data suppression. 

SOURCES: U.S. Census Bureau, Statistics of U.S. Businesses, http://www.census.gov/econ/susb/, accessed 15 May 2013; Hecker DE. 2006. Hightechnology employment: A NAICS-based update, Monthly Labor Review 128(7):57–72, http://www.bls.gov/opub/mlr/2005/07/art6full.pdf, accessed 

15 March 2013. See appendix table 6-55.

Science and Engineering Indicators 2014

HT = high technology.

NOTES: Firms with fewer than ve employees include those reporting 

no employees on their payroll. A rm is an entity that is either a single 

location with no subsidiary or branches or the topmost parent of a 

group of subsidiaries or branches. HT industries are dened by the 

Bureau of Labor Statistics (BLS) by the basis of employment intensity 

of the technology-oriented occupations, based on the BLS 

Occupational Employment Survey of 2011. HT small business 

employment is a lower-bound estimate because employment data are 

not available for a few industries due to data suppression. 

SOURCES: U.S. Census Bureau, Statistics of U.S. Businesses, 

http://www.census.gov/econ/susb/, accessed 15 May 2013; Hecker 

DE. 2006. High-technology employment: A NAICS-based update, 

Monthly Labor Review 128(7):57–72, 

http://www.bls.gov/opub/mlr/2005/07/art6full.pdf, accessed 15 May 

2013. See appendix table 6-55.

Science and Engineering Indicators 2014

Share (percent)

Figure 6-37

U.S. HT industries, by share of industry sector: 2010

< 5

employees

Number of firms Employment

> 5

employees

< 5

employees

> 5

employees

0

20

40

60

80

100

Services

Manufacturing

Other
Science and Engineering Indicators 2014 ♦ 6-47

Three HT services—management, scientific, and technical consulting; computer systems design; and architectural 

and engineering—dominate HT services with a collective 

share of more than 80% of all firms and employment (table 

6-9). In HT manufacturing, four industries—navigational, 

measuring, electromedical, and control instruments; other 

general purpose machinery; industrial machinery; and semiconductors—are large employers with a collective share of 

nearly 50%. 

Entrepreneurial Investment in 

HT Small Businesses

Entrepreneurs seeking to start or expand a small firm with 

new or unproven technology may not have access to public or credit-oriented institutional funding. (In this section, 

business denotes anything from an entrepreneur with an idea 

to a legally established operating company.) Often, entrepreneurs rely on friends and family for financing. However, 

when they need or can get access to larger amounts of financing, venture capital investment and SBIR financing are 

often critical to financing nascent and entrepreneurial HT 

businesses. This section examines patterns and trends of 

these two types of financing in the United States and internationally (venture capital only). 

Venture capital investment. The United States accounted for $29 billion in venture capital, nearly 70% of global 

venture capital in 2012 (figure 6-38; appendix table 6-56). 

Europe and China are the next largest, accounting for $6 billion and $4 billion, respectively. Venture capital financing 

in India was $1 billion. Much of the financing occurring 

outside of the United States probably originates from U.S.-

based venture capital firms.

Between 2005 and 2012, global venture capital financing 

rose by 30% to reach $42 billion (figure 6-38). After falling 

sharply during the recession, venture capital bounced back 

to its pre-recession level in 2011 before falling $8 billion 

in 2012. Venture capital invested in the United States grew 

more slowly than outside the United States, with the result 

that the U.S. share of global venture capital fell from 75% to 

70% (figure 6-38). The expansion of venture capital outside 

of the United States coincides with the globalization of finance, greater commercial opportunities in rapidly growing 

developing countries, and the decline of yields on existing 

venture capital investments in U.S. companies. In China, 

venture capital grew from $1 billion in 2005 to $4 billion 

in 2012, resulting in its global share more than doubling to 

reach 10% (figure 6-38). Venture capital investment in India 

grew from $300 million to $1.4 billion, with India’s global 

share rising from 1% to 3%.

Venture capital investment is generally categorized into 

four broad stages of financing:

♦ Seed supports proof-of-concept development and initial 

product development and marketing.

♦ First round supports product development and marketing 

and the initiation of commercial manufacturing and sales.

♦ Expansion provides working capital for company expansion; funds for major growth (including plant expansion, 

marketing, or development of an improved product); and 

financing to prepare for an initial public offering (IPO).

♦ Later stage includes acquisition financing and management and leveraged buyouts. Acquisition financing provides resources for the purchase of another company, and 

management and leveraged buyouts provide funds to enable operating management to acquire a product line or 

business from either a public or a private company. 

In 2012, later stage venture capital investment comprised 

60% ($17 billion) of total U.S. venture capital investment, 

up from 50% in 2005 (figure 6-39; appendix table 6-56). 

Knowledgeable observers have attributed the shift to laterstage investment because of a desire for lower investment 

risk, a decline in yields on existing investments of venture 

capitalists, and a sharp decline in IPOs and acquisitions of 

venture capital–backed firms, which has required venture 

capital investors to provide additional rounds of financing.29 

In contrast to the predominance of later-stage investment, 

investment in the seed stage, the earliest stage, amounted to 

1% ($300 million) of total U.S. venture capital investment 

(figure 6-39; appendix table 6-56). Despite the amount tripling in value between 2005 and 2012, seed’s share of venture capital investment remained at 1% or less. Investment 

in the first-round stage, which follows seed, represented 

21% ($6.0 billion) of venture capital investment in 2012. 

Investment in this stage remained constant, resulting in its 

share falling 6 percentage points to 21% in 2012. Financing 

Figure 6-38

Venture capital investment, by selected region/

country/economy: 2005–12

Billions of dollars

ROW = rest of world.

NOTE: ROW consists of Canada and Israel.

SOURCE: Dow Jones, special tabulations (2013) from VentureSource 

database, http://www.dowjones.com/info/venture-capital-data.asp.

Science and Engineering Indicators 2014

2005 2006 2007 2008 2009 2010 2011 2012

0

5

10

15

20

25

30

35

40

United States

Europe

China

India

ROW
6-48 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

of the expansion stage, which follows first round, represented 18% ($5.0 billion) of venture capital investment in 2012. 

Investment in this stage stayed constant between 2002 and 

2012, resulting in its share falling from 22% to 18%. 

Five technologies—biopharmaceuticals, business support services, consumer information services, medical devices and equipment, and software—dominate U.S. venture 

capital financing (table 6-10). During 2009–12, these five 

technologies accounted for more than 60% of total and seed 

stage investment. 

Software led these technologies in venture capital investment, receiving $19.2 billion in 2009–12 (table 6-10; appendix table 6-56). Total and early stage investment in software 

rose between 2005 and 2012, resulting in software’s share 

of total investment remaining steady (23%) and its share 

of early stage investment increasing from 16% to 34%. 

Biopharmaceuticals was second, receiving $14.7 billion. 

Total investment in biopharmaceuticals fell from $4.0 billion in 2005 to $3.4 billion in 2012, resulting in its share falling from 17% to 12%. Seed stage financing dropped from $7 

million to $6 million during this period. Consumer information services received $13.5 billion in 2009–12. Total venture capital investment in this technology area rose from less 

than $700 million in 2005 to $2.8 billion in 2012. Growth 

in early stage financing was also rapid, rising from less than 

$10 million to $79 million, resulting in its share more than 

doubling from 11% to 26%. 

Small Business Innovation Research Financing. The 

U.S. federal government’s SBIR program provides early stage public financing to help U.S. small or start-up 

 

NOTES: Seed consists of proof-of-concept development and initial 

product development and marketing. First round consists of product 

development and marketing and the initiation of commercial 

manufacturing and sales. Expansion consists of second-round 

nancing that provides working capital for company expansion and 

nancing to prepare for an initial public offering. Later stage includes 

acquisition nancing and management and leverage buyouts.

SOURCE: Dow Jones, special tabulations (2013) from VentureSource 

database, http://www.dowjones.com/info/venture-capital-data.asp. 

See appendix table 6-56. 

Science and Engineering Indicators 2014

Figure 6-39

U.S. venture capital investment, by financing stage: 

Selected years, 2005–12

Billions of dollars

2005 2007 2009 2011 2012

0

5

10

15

20

25

Seed First round Expansion Later stage

Table 6-10

U.S. venture capital investment, by selected financing stage and technology/industry: 2009–12

(Millions of U.S. dollars)

Technology/industry 2009 2010 2011 2012 2009–12 total

All financing stages

All technologies/industries................................................. 23,291 28,131 34,006 29,208 114,636

Software ......................................................................... 3,350 4,183 4,973 6,663 19,169

Biopharmaceuticals........................................................ 3,820 3,466 4,043 3,380 14,709

Consumer information services ..................................... 2,264 4,107 4,328 2,823 13,522

Business support services ............................................. 2,248 2,748 4,261 3,698 12,955

Medical devices and equipment .................................... 3,060 2,551 3,403 2,765 11,779

Seed stage

All technologies/industries................................................. 120 230 376 302 1,028

Software ......................................................................... 22 44 128 102 296

Consumer information services ..................................... 36 60 95 79 270

Business support services ............................................. 18 39 46 23 126

Media and content ......................................................... 6 3 10 21 40

Medical software and information services.................... 3 6 4 13 26

NOTES: Technologies are classified by Dow Jones. Seed stage consists of proof of concept and initial product development.

SOURCE: Dow Jones, special tabulations (2013) of VentureSource database, http://www.dowjones.com/info/venture-capital-data.asp. See appendix 

table 6-56.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 6-49

companies to commercialize technology derived from federal R&D. (For more information on SBIR, see chapter 4, 

“Small Business Innovation-Related Programs.”) The SBIR 

program provides financing in two phases: 

♦ Phase I funds the evaluation of the scientific and technical 

merit and feasibility of a company’s new ideas. 

♦ Phase II funds further scientific and technical review and 

requires a commercialization plan. 

SBIR provided $2.3 million in financing for nearly 6,000 

awards in 2010 (figure 6-40).30 The majority of SBIR financing occurs in Phase II, which provided $1.4 million to fund 

more than 4,000 awards in 2010. The next largest financing 

stage, Phase I, provided $0.5 million for nearly 2,000 awards 

in 2010. The remainder ($0.3 million) provided funding for 

technical assistance, commercial outreach, and other activities. After nearly doubling from $1.1 million in 2000 to $2.0 

million in 2004, SBIR financing grew far more slowly in 

the latter half of the decade to reach $2.2 million in 2010. 

Between 2000 and 2010, Phase II financing lagged the overall growth of SBIR financing, resulting in the share of Phase 

II declining from 77% to 64%. In contrast, Phase I’s share of 

SBIR financing remained roughly steady at 20%–24% during this period. 

Investment and Innovation 

in Clean Energy Technologies

The fifth section of this chapter examines clean energy 

and energy-conservation and related technologies. Clean energy, like KTI industries, has a strong link to S&T. Clean 

energy and energy-conservation and related technologies—

including biofuels, solar, wind, nuclear, energy efficiency, 

pollution prevention, smart grid, and carbon sequestration—

have become a policy focus in developed and developing nations. These technologies are KTI and thus are closely linked 

to scientific R&D. Production, investment, and innovation 

in these energies and technologies are rapidly growing in 

many countries. Prompted by concerns over the high cost 

of fossil fuels and their impact on the climate, governments 

have developed various inducements, such as subsidies and 

tax incentives, and increased funding for clean energy R&D. 

This section examines venture capital and total private financing data from Bloomberg New Energy Finance and public 

research, development, and demonstration (RD&D) data from 

the International Energy Agency (IEA). The IEA data discussed here cover RD&D. They are not comparable to the energy R&D data described in chapter 4, which focus on R&D.31

Commercial Investment

Global commercial investment in clean energy technologies, including early stage angel and venture capital investment and later-stage financing, was $160 billion in 2012 

(figure 6-41).32 Two technologies—wind and solar—dominate clean energy investment, with a combined share of 85% 

(figure 6-42). 

Between 2005 and 2012, global clean energy investment 

rose from less than $30 billion to $159 billion (figure 6-41). 

The rapid rise of investment was interrupted by a dip during 

the global recession before climbing back to its level prior 

to the recession. This rise has been spurred by government 

policies to encourage clean energy financing and production 

and by falling costs in wind, solar, and other energy technologies. Global investment appears to have plateaued since 

the global recession due to several factors, including the 

sluggish global economy, cutbacks by many governments 

on subsidies, tax and other incentives for clean energy, and 

a substantial decline in natural gas prices due to hydraulic 

fracturing technologies.

Patterns and Trends in Developing Countries

In 2012, almost $100 billion in commercial investment 

in clean energy occurred in China and other developing 

countries, making up over 61% of global investment (figure 6-41). Clean energy financing in China was an estimated 

$61 billion, more than in any economy in the world (35% 

share of global investment). The comparable amount for 

other developing countries was $36 billion. 

Between 2005 and 2012, clean energy investment in developing countries rose from $8 billion to nearly $100 billion (figure 6-41). The global share of developing countries 

Figure 6-40

SBIR investment, by financing phase: 2000–10

Billions of dollars

SBIR = Small Business Innovation Program.

NOTES: SBIR investment is by scal year. Investment is the amount 

budgeted by U.S. federal agencies for SBIR nancing. Phase I 

evaluates the scientic and technical merit and feasibility of ideas. 

Phase II is subject to further scientic and technical review and 

requires a commercialization plan. Other includes technical 

assistance and commercial outreach.

SOURCE: SBIR Report Data, http://www.sbir.gov/awards/annual-reports,

accessed 15 June 2013. 

Science and Engineering Indicators 2014

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

0

0.5

1.0

1.5

2.0

2.5

Total

Phase I 

Phase II

Other
6-50 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

climbed from about one-third of clean energy investment to 

nearly two-thirds during this period. 

China was the primary driver of investment in developing 

countries; China’s commercial investment rose exponentially from less than $2 billion in 2004 to $61 billion in 2012 

(figure 6-41). The uninterrupted growth of clean energy investments in China reflects the government’s policies targeted at wind and solar energy to make China a major world 

producer in these technologies and to reduce China’s reliance on fossil fuels. Investment in wind energy, which was 

$28 billion in 2012, made up the largest share of China’s investment between 2004 and 2012 (figure 6-43). Investment 

in solar also rose rapidly. It reached $27 billion in 2012, reflecting China’s emergence as a major manufacturer of lowcost photovoltaic modules.

Clean energy investment in other developing countries 

has also risen rapidly, from $6 billion to $36 billion (figure 6-41). The rapid rise of investment in countries such as 

Brazil, India, Indonesia, and Mexico reflects the adoption of 

policies by these countries to encourage clean energy, lower 

costs relative to developed countries, and rapid economic 

growth and growing energy demand. 

Figure 6-41

Financial new investment in clean energy 

technologies, by selected region/country/economy: 

2004–12

Billions of dollars

EU = European Union. 

NOTES: Clean energy technologies include biomass, geothermal, 

wind, solar, biofuels, and energy smart and efciency technologies. 

Financial new investment includes private and public R&D, venture 

capital, private equity, and public markets. Mergers and acquisitions 

are excluded. 

SOURCE: Bloomberg New Energy Finance, http://bnef.com/, special 

tabulations (2013). 

Science and Engineering Indicators 2014

2004 2005 2006 2007 2008 2009 2010 2011 2012

0

10

20

30

40

50

60

70

United States

EU

Other developed

China

Other developing

 

NOTES: Clean energy technologies include biomass, geothermal, 

wind, solar, biofuels, and energy smart and efciency technologies. 

Financial new investment includes private and public R&D, venture 

capital, private equity, and public markets. Mergers and acquisitions 

are excluded. 

SOURCE: Bloomberg New Energy Finance, http://bnef.com/, special 

tabulations (2013). 

Science and Engineering Indicators 2014

Figure 6-42

Financial new investment in clean energy 

technologies, by selected energy and technology: 

2006–12

Billions of dollars

2006 2008 2010 2012

0

20

40

60

80

100

120

Wind Solar Biofuels, biomass, 

and waste

All others

 

EU = European Union.

NOTES: Clean energy technologies include biomass, geothermal, 

wind, solar, biofuels, and energy smart and efciency technologies. 

Financial new investment includes private and public R&D, venture 

capital, private equity, and public markets. Mergers and acquisitions 

are excluded. 

SOURCE: Bloomberg New Energy Finance, http://bnef.com/, special 

tabulations (2013).

Science and Engineering Indicators 2014

Figure 6-43

Financial new investment in clean energy 

technologies in China, the United States, and 

the EU, by technology: 2012

Billions of dollars

China United States EU

0

5

10

15

20

25

30

Wind Solar All others
Science and Engineering Indicators 2014 ♦ 6-51

Patterns and Trends in Developed Economies

Investment in the United States, the EU, and other developed economies was $63 billion, 39% of global investment 

(figure 6-41). The United States and the EU, with from $27 

billion to $29 billion each, tied as the second-largest locations of clean energy investment, behind China. Investment 

in other developed economies is much smaller, amounting to 

a collective $7 billion. 

Between 2004 and 2012, clean energy investment in 

developed economies rose from $19 billion to $63 billion 

(figure 6-41). Investment has been volatile in the aftermath 

of the global recession. Investment rebounded in 2010 and 

reached a new high of $110 billion in 2011 before plunging 

to $63 billion in 2012, its lowest level since 2006. 

After rising steadily prior to the global recession, U.S. 

investment fell sharply in 2008 before recovering to $32 

billion in 2010, near its pre-recession level (figure 6-41). 

Investment spiked in 2011 to $45 billion before falling to $29 

billion in 2012 due to the expiration of temporary financing 

provisions and subsidies. Wind and solar energy have led the 

growth of U.S. investment between 2004 and 2012 (figure 

6-43). Wind investment reached $14 billion in 2012, closely 

followed by solar energy, which was $10 billion.

In the EU, the global recession had less impact on commercial investment compared to the United States (figure 

6-41). However, investment fell by half in 2012 to $27 

billion due to the EU’s economic and financial crisis and 

sharp cutbacks in government support for solar and other 

clean energies in Germany, Spain, and the United Kingdom. 

Investment in solar energy in 2012 was $7 billion, less than 

half its level in 2008 (figure 6-43). Investment in wind energy was also down sharply.

Venture Capital Investment

Venture capital investment is a useful indicator of market 

assessment of nascent and future trends in clean energy technologies. Global venture capital investment in clean energy 

was $4.4 billion in 2012, making up 3% of commercial financial investment (figure 6-44). The United States is the main 

location of venture capital financing for clean energy technologies, with more than 80% of global investment in 2012. 

Among the technology areas, energy smart and efficiency 

technologies make up nearly half of venture capital financing (figure 6-45). The energy smart and efficiency category 

covers a wide range of technologies, from digital energy 

applications to efficient lighting, electric vehicles, and the 

smart grid that maximizes the energy efficiency of existing 

energy sources and networks. Two other technology areas—

solar and biofuels—accounted for about 20% each of all 

venture capital financing. 

After rising rapidly to reach $5 billion prior to the global 

recession, venture capital investment plunged in 2009. It 

then rebounded from $4 billion to $5 billion in 2010–12 (figure 6-44). Between 2004 and 2012, three technology areas—

energy smart and efficiency, solar, and biofuels—led growth 

(figure 6-45). Biofuels grew the fastest among these technologies, but from a low base, to reach $0.9 billion. Solar 

rose from less than $0.2 billion to reach $1.0 billion. Energy 

smart and efficiency, the largest technology area, grew from 

$0.8 billion to $2.0 billion. 

NOTE: Clean energy technologies include biomass, geothermal, 

wind, solar, biofuels, and energy smart and efciency technologies.

SOURCE: Bloomberg New Energy Finance, http://bnef.com/, special 

tabulations (2013).

Science and Engineering Indicators 2014

Billions of dollars

Figure 6-45

Global venture capital investment in clean energy 

technologies, by selected technology: 2006–12

2006 2008 2010 2012

0

1

2

3

4

5

Wind

Energy smart 

and efciency 

technologies

Solar

Biofuels

All others

Figure 6-44

Global venture capital investment in clean energy 

technologies: 2004–12

Billions of dollars

ROW = rest of world.

NOTE: Clean energy technologies include biomass, geothermal, 

wind, solar, biofuels, and energy smart and efciency technologies.

SOURCE: Bloomberg New Energy Finance, http://bnef.com/, special 

tabulations (2013).

Science and Engineering Indicators 2014

2004 2005 2006 2007 2008 2009 2010 2011 2012

–2

0

2

4

6

United States

ROW
6-52 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

U.S. venture capital investment in the energy smart and 

efficiency and the solar areas is likely a result of several factors, including American Recovery and Reinvestment Act 

of 2009 (ARRA) funding of R&D in these technologies and 

U.S. loan guarantees for companies operating in these areas. 

In addition, energy efficiency technologies are less capital 

intensive than other clean energy technologies, have a shorter time horizon than most other energy technologies, can be 

applied to a wider range of energy products and services, 

and are less reliant on government incentives or subsidies 

that may be withdrawn. 

Public Research, Development, and 

Demonstration Expenditures in Clean 

Energy Technologies

Major developed economies invested an estimated $13.0 

billion on public RD&D in clean energy and nuclear technologies in 2011 (table 6-11; figure 6-46). Clean energy 

technologies include renewables (solar, wind, ocean), bioenergy, hydrogen, fuel cells, carbon capture and storage, energy efficiency, and other power and storage.33

Nuclear energy was the largest area, receiving $5.6 billion in 2011, nearly one-third of total RD&D (table 6-11). 

The next two largest areas are energy efficiency and renewable energy (solar, wind, ocean, bioenergy), which received 

$3.6 and $2.4 billion, respectively. The fourth largest, other 

power and storage, received $1.1 billion. 

The United States and Japan are the largest investors in clean 

energy and nuclear RD&D, with each spending $4.0 billion in 

2012 (figure 6-46). The EU is the next largest, with expenditures of $2.6 billion. Three other countries—Canada, South 

Korea, and Australia—had significant expenditures. Canada’s 

RD&D was $1 billion, and Australia and South Korea each 

spent between $500 million and $600 million.

Between 2004 and 2008, clean energy and nuclear RD&D 

rose steadily to reach $12 billion in 2008 before spiking up to 

$17.6 billion in 2009 due to stimulus spending in the United 

States and the EU (table 6-11; figure 6-46). Clean energy and 

nuclear RD&D fell in 2010 and 2011 with the fading of stimulus spending to reach $13.1 billion in 2011. Trends among the 

individual technology areas varied between 2004 and 2011: 

♦ CO2 capture and storage had the fastest growth, rising from 

$100 million to $1.1 billion.

Table 6-11

Government RD&D of selected developed countries in clean energy and nuclear technologies, by technology 

area: Selected years, 2004–11

(Billions of dollars)

Year

All clean energy 

and nuclear 

technologies Nuclear

Energy 

efficiency

Renewable 

energy

Hydrogen 

and fuel cells

Other power 

and storage

CO2 capture 

and storage

2004............................ 9.3 5.2 1.5 1.3 0.6 0.5 0.1

2008............................ 12.0 5.7 2.4 1.9 1.0 0.6 0.4

2009............................ 17.6 5.7 4.3 4.1 0.9 1.6 1.0

2010............................ 15.9 5.7 3.9 3.5 0.8 0.9 1.0

2011............................ 13.0 4.6 2.4 3.6 0.6 0.8 1.1

RD&D = research, development, and demonstration.

NOTES: Clean energy and nuclear technologies include solar, wind, bioenergy, nuclear, fuel cells, hydrogen, CO2

 capture and storage, other power and 

storage, and energy efficiency. Detail may not add to total because of rounding. Countries included are Australia, Austria, Belgium, Canada, Czech 

Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, Netherlands, Norway, Poland, Portugal, Slovakia, South Korea, 

Spain, Sweden, Switzerland, United Kingdom, and United States.

SOURCE: International Energy Agency, Statistics and Balances, http://www.iea.org/stats/index.asp, accessed 15 March 2013.

Science and Engineering Indicators 2014

Figure 6-46

Government RD&D expenditures of selected 

developed countries/economies in clean energy 

and nuclear technologies: 2004–11

Billions of dollars

EU = European Union; RD&D = research, development, and 

demonstration.

NOTES: Clean energy and nuclear technologies include solar, wind, 

bioenergy, nuclear, fuel cells, hydrogen, CO2

 capture and storage, 

other power and storage, and energy efciency. The EU includes 

Austria, Belgium, Czech Republic, Denmark, Finland, France, 

Germany, Greece, Hungary, Ireland, Luxembourg, Netherlands, 

Poland, Portugal, Slovakia, Spain, Sweden, and the United Kingdom. 

All others include Australia, Canada, and South Korea. 

SOURCE: International Energy Agency, Statistics and Balances, 

http://www.iea.org/stats/index.asp, accessed 15 January 2013.

Science and Engineering Indicators 2014

2004 2005 2006 2007 2008 2009 2010 2011

0

1

2

3

4

5

6

7

8

United States

EU

Japan

All others
Science and Engineering Indicators 2014 ♦ 6-53

♦ Spending on renewable energy nearly tripled to reach 

$3.6 billion.

♦ Energy efficiency expenditures rose by 50% to reach 

$2.4 billion.

♦ Nuclear energy declined from $5.2 billion to $4.6 billion.

The United States outpaced the EU and Japan in growth 

of clean energy and nuclear RD&D during this period (table 

6-12; figure 6-46). U.S. RD&D rose from $1.5 billion in 2004 

to $2.8 billion in 2008 before surging to $7.1 billion in 2009 

due to ARRA spending. Renewable and energy efficiency 

received the bulk of ARRA spending, which temporarily increased spending in each technology area by about $1.5 billion. U.S. RD&D dropped in 2010 and 2011 to reach $4.0 

billion, $2.5 billion higher than its RD&D in 2004. The EU’s 

RD&D increased from $2.2 billion in 2004 to reach a stimulus-induced high of $5.0 billion in 2010 before dropping to 

$2.6 billion in 2011, still 18% higher than its level in 2004. 

Japan’s RD&D declined from $4.5 billion to $3.9 billion.

Patenting of Clean Energy and Pollution 

Control Technologies

USPTO patents granted in clean energy and pollution 

control technologies can be classified using a taxonomy developed for this purpose. The taxonomy classifies patents 

involving bioenergy, nuclear, wind, solar, energy storage, 

smart grid, and pollution mitigation. The number of patents in these technologies jumped to a record high in 2012, 

which could reflect USPTO efforts to speed up processing 

of applications (figure 6-47; appendix table 6-57).34 (For a 

more detailed description of how this taxonomy identifies 

clean energy and pollution control patents, see the sidebar in 

chapter 5, “Identifying Clean Energy and Pollution Control 

Patents.”) U.S. resident inventors were granted slightly less 

than half of the 8,800 clean energy and pollution control 

technology patents in 2012, continuing the advantage of 

non-U.S. inventors in these fields since 2003.

Among non-U.S. inventors, Japan, the EU, and South 

Korea, in that order, are the main recipients of U.S. patents 

for clean energy and pollution control technologies, with a 

collective share of 44% of total patents granted (figure 6-47; 

Table 6-12

U.S. government RD&D expenditures on clean energy and nuclear technologies: 2007–11

(Millions of dollars)

Year

All clean energy 

and nuclear 

technologies

Energy 

efficiency

Renewable 

energy

Nuclear 

energy

Hydrogen and 

fuel cells

Other power 

and storage 

technologies

2007............................. 2,690 585 594 898 343 140

2008............................. 2,831 692 468 1,008 335 127

2009............................. 7,131 2,196 2,280 871 368 951

2010............................. 4,519 1,422 1,338 907 340 281

2011............................. 3,996 882 1,161 1,225 260 178

RD&D = research, development, and demonstration.

NOTE: Clean energy and nuclear technologies include solar, wind, bioenergy, nuclear, fuel cells, hydrogen, CO2

 capture and storage, other power and 

storage, and energy efficiency.

SOURCE: International Energy Agency, Statistics and Balances, http://www.iea.org/stats/index.asp, accessed 15 March 2013.

Science and Engineering Indicators 2014

Figure 6-47

USPTO patents in alternative energy and pollution 

control technologies, by selected region/country/

economy of inventor: Selected years, 1997–2012

Number

.

EU = European Union; USPTO = U.S. Patent and Trademark Ofce.

NOTES: Clean energy and pollution control technologies include 

alternative energy, energy storage, smart grid, and pollution 

mitigation. Alternative energy includes solar, wind, nuclear, 

hydropower, wave/tidal/ocean, geothermal, and electric/hybrid. 

Energy storage includes batteries, compressed air, ywheels, 

superconductivity, magnet energy systems, ultracapacitors, 

hydrogen production and storage, and thermal energy. Pollution 

mitigation includes recycling; control of air, water, and solid waste 

pollution; environmental remediation; cleaner coal; and capture and 

storage of carbon and other greenhouse gases. Technologies are 

classied by The Patent Board.TM Patent grants are fractionally 

allocated among regions/countries on the basis of the proportion of 

the residences of all named inventors. 

SOURCE: The Patent Board,TM Proprietary Patent database, special 

tabulations (2013). See appendix table 6-57.

Science and Engineering Indicators 2014

1997 1999 2001 2004 2006 2008 2010 2012

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

United States

Japan

South Korea

EU
6-54 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

Table 6-13

USPTO patents granted in alternative-energy and pollution-control technologies, by technology area: Selected 

years, 1997–2012

Technology 1997 2002 2007 2010 2012

All alternative-energy and pollution-control technologies........................ 3,087 4,094 3,701 6,260 8,834

Alternative energy ................................................................................. 846 1,522 1,605 3,094 5,214

Bioenergy .......................................................................................... 52 74 101 226 564

Electric and hybrid vehicles .............................................................. 189 405 396 543 896

Fuel cells ........................................................................................... 95 374 549 1,093 1,143

Solar.................................................................................................. 212 397 261 671 1,472

Wind .................................................................................................. 29 65 173 362 856

All others ........................................................................................... 269 207 125 199 283

Energy storage...................................................................................... 349 576 508 989 1,098

Batteries ............................................................................................ 220 329 227 523 632

Hydrogen production and storage .................................................... 77 141 186 307 284

All others ........................................................................................... 52 106 95 159 182

Pollution mitigation ............................................................................... 1,719 1,856 1,382 1,916 2,064

Air...................................................................................................... 696 877 731 1,084 1,183

Capture and storage of carbon and other greenhouse gases .......... 57 89 64 157 215

Cleaner coal ...................................................................................... 96 61 41 171 240

Water................................................................................................. 271 371 306 321 311

All others ........................................................................................... 599 458 240 183 115

Smart grid ............................................................................................. 291 304 366 543 811

USPTO = U.S. Patent and Trademark Office.

NOTES: Alternative-energy and pollution-control technologies include alternative energy, energy storage, smart grid, and pollution mitigation. Alternative 

energy includes solar, wind, nuclear, bioenergy, hydropower, wave, tidal, ocean, geothermal, and electric and hybrid automobiles. Pollution mitigation 

includes recycling; control of air, water, and solid waste pollution; environmental remediation; cleaner coal; and capture and storage of carbon and other 

greenhouse gasses. Energy storage includes batteries, compressed air, flywheels, superconductivity, magnet energy systems, ultracapacitors, hydrogen 

production and storage, and thermal energy. Technologies are classified by The Patent Board.TM The sum of individual technologies may exceed broad 

areas, and the sum of the broad categories may exceed the total because some of the patents are assigned to multiple individual technologies or 

broad areas.

SOURCE: The Patent Board,TM special tabulations (2013) of the Proprietary Patent database. See appendix tables 6-57–6-75.

Science and Engineering Indicators 2014

appendix table 6-57). Japan received 22%, and EU inventors 

received 16%. South Korean inventors received 6% of total 

patents, up from 2% in 2003. Patents granted to inventors in 

China and Taiwan have been increasing rapidly, although 

from a low base. In 2012, China’s and Taiwan’s shares of 

total patents were 2% each, up from 1% or less in 2003.

Clean energy and pollution control technology patents 

comprise four broad areas: alternative energy, with 5,000 

patents granted; energy storage, with 1,000 patents; smart 

grid, with 800 patents; and pollution mitigation, with 2,000 

patents (table 6-13; appendix tables 6-58–6-61). The proportion of alternative energy patents rose from 27% in 1997 to 

59% in 2012, with major share gains by fuel cells and solar patents. Pollution mitigation technologies declined from 

56% to 23%, driven by share losses of air and water quality.

Patent technology activity indexes measure the world 

share of a region, country, or economy in clean energy and 

clean technologies relative to its world share in patents in all 

technologies. A ratio greater than 1 signifies that patents by 

a region, country, or economy are concentrated in a particular technology (table 6-14).

In alternative energy patents, the U.S. has a high concentration in bioenergy and solar technologies and relatively 

low patent activity in fuel cells, hybrid vehicles, and wind 

energy (table 6-14; appendix tables 6-62–6-66). The EU 

has relatively high concentrations in bioenergy, wind, and 

nuclear and a relatively low concentration in electric hybrid 

technologies (appendix table 6-67). Japan has a high concentration of patents in electric hybrid technologies and 

fuel cells but relatively low activity in bioenergy, solar, and 

wind. South Korea has a high concentration in fuel cells but 

low concentrations in bioenergy, solar, and wind.

The United States and the EU have relatively low concentrations of patents in energy storage because of their low activity 

in battery technology, but this is an area of high concentration 

for Japan and South Korea (table 6-14; appendix tables 6-59 

and 6-68). Despite its overall low concentration of patents in 

energy storage, the United States has a high concentration of 

patents in hydrogen power and storage (appendix table 6-69). 

In smart grid, the United States has a high concentration 

of patents, the EU has a slightly above-average concentration, and Japan and South Korea have relatively low concentrations (table 6-14; appendix table 6-60).

In pollution mitigation technologies, the United States 

has a slightly above-average concentration of patents, with 

high concentrations in carbon capture and storage and in 

cleaner coal (table 6-14; appendix tables 6-61, 6-70, and 

6-71). The EU has a particularly high concentration of patents in air pollution and a high concentration in carbon capture and storage (appendix table 6-72). Japan has average 

patenting activity in this area, with high concentrations in 

air pollution and in carbon capture and storage. South Korea 

has relatively low concentrations in all pollution mitigation 

technologies (appendix tables 6-73–6-75). 
Science and Engineering Indicators 2014 ♦ 6-55

Conclusion

The U.S. economy continues to be the leading global 

economy in technology-based industries, as measured by 

its overall performance, market position in these industries, 

and position in patenting and other measures of innovationrelated activities. 

The strong competitive position of the U.S. economy 

overall is tied to continued U.S. global leadership in many 

KTI industries. The United States continues to hold the 

dominant market position in commercial KI services, which 

account for nearly one-fifth of global economic activity, 

and in HT manufacturing industries. The U.S. trading position in commercial KI services and licensing of patents and 

trade secrets remains strong, as evidenced by the continued 

U.S. surpluses in these areas. The United States is the leading source of RD&D and venture capital financing of clean 

energy technologies. 

The overall U.S. ranking notwithstanding, its market position in almost all of these industries has not been improving; in many cases, it has slipped. China, the second-largest 

producer in HT manufacturing industries, has narrowed its 

gap with the United States. U.S. production and employment 

have fallen sharply in the HT manufacturing industries of 

communications, computers, and semiconductors, coinciding with U.S. companies moving assembly and other activities to China and other countries. The U.S. trade position 

in these products has shifted to deficit because, although 

Table 6-14

Patenting activity in alternative-energy and pollution-control technologies, by selected country/economy: 2009–12

(Activity index)

Technology United States EU Japan South Korea

All alternative-energy and pollution-control technologies........................ 0.97 1.12 1.10 1.11

Alternative energy ................................................................................. 0.95 1.21 1.10 1.06

Bioenergy .......................................................................................... 1.45 1.04 0.22 0.21

Fuel cells ........................................................................................... 0.71 0.77 1.83 2.18

Hybrid electric ................................................................................... 0.79 0.83 2.00 0.97

Solar.................................................................................................. 1.14 0.97 0.69 0.86

Wind .................................................................................................. 0.86 2.81 0.37 0.08

Energy storage...................................................................................... 0.71 0.53 1.68 3.06

Batteries ............................................................................................ 0.40 0.39 2.11 4.67

Hydrogen power and storage ........................................................... 1.15 0.75 0.95 1.22

Smart grid ............................................................................................. 1.26 1.08 0.43 0.50

Pollution mitigation ............................................................................... 1.07 1.25 0.97 0.44

Air...................................................................................................... 0.94 1.43 1.36 0.42

Capture and storage of carbon and other greenhouse gases .......... 1.33 1.11 0.37 0.45

Cleaner coal ...................................................................................... 1.50 0.70 0.31 0.18

EU = European Union.

NOTES: Alternative-energy and pollution-control technologies include alternative energy, energy storage, smart grid, and pollution mitigation. Alternative 

energy includes solar, wind, nuclear, bioenergy, hydropower, wave, tidal, ocean, geothermal, and electric and hybrid automobilies. Pollution mitigation 

includes recycling; control of air, water, and solid waste pollution; environmental remediation; cleaner coal; and capture and storage of carbon and other 

greenhouse gases. Energy storage includes batteries, compressed air, flywheels, superconductivity, magnet energy systems, ultracapacitors, hydrogen 

production and storage, and thermal energy. Technologies are classified by The Patent Board.TM Patent grants are fractionally allocated among countries/

economies on the basis of the proportion of the residences of all named inventors. The EU includes current member countries. The activity index consists 

of the ratio of the countries’/economies’ share of the indicated technology to the countries’/economies’ share of the total grants. A ratio of greater than 

1.00 signifies more active patenting in the selected technology; a ratio of less than 1.00 signifies less active patenting.

SOURCE: The Patent Board,TM special tabulations (2011) of the Proprietary Patent database. See appendix tables 6-57–6-75.

Science and Engineering Indicators 2014

exports have increased, imports have increased even more. 

In addition, productivity growth of the U.S. economy has 

slowed in the 2000s relative to the 1990s. 

For much of the 2000s, the EU’s position was similar to 

that of the United States—relatively strong overall economic performance, with a slowdown in productivity and flatlining or slight declines in its market position in KTI industries. 

During this period, Japan’s economy showed less dynamism 

compared with the economies of the United States and the 

EU, and its market position declined steeply in many KTI 

industries. Japan’s loss of market position in HT manufacturing industries was due, in part, to Japanese companies 

shifting production to China and other Asian economies.

Among large developing countries, China’s progress 

clearly stands out. China has become a leading provider of 

commercial KI services and the second-largest global producer in HT manufacturing industries. China has become the 

largest global exporter in HT manufacture products and has 

developed surpluses in trade of HT manufacturing products 

and commercial KI services. China has become the world’s 

largest source of commercial financing for clean energy and 

a leading producer in the solar industry. China has led the 

acceleration of productivity growth in developing countries 

over the last decade. However, China’s indigenous capability in KTI industries and other indicators is uneven. Much 

of China’s HT manufacturing output is controlled by MNCs 

that import higher-value components from other countries. 

Chinese companies have made limited progress in more 
6-56 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

technologically advanced and higher-end manufacturing activities. In addition, China’s share of USPTO and economically valuable patents remains very small. 

Other developing economies—including Brazil, India, 

and Indonesia—are showing rapid progress in their overall economic growth and technological capabilities. Their 

market positions in many KTI industries have strengthened, 

coinciding with their rapid economic growth and development. Productivity growth has accelerated in most developing countries. 

Led by China, KTI industries in developing countries have 

grown much faster than developed economies in the aftermath of the recession. The United States has generally fared 

better than other developed countries in most KTI industries 

in the aftermath of the 2008–09 global recession. Although 

productivity growth has been weak, the United States continues to grow faster than most other developed countries. 

The EU’s market position in KTI industries has eroded because of the EU’s economic and financial problems. Japan 

continues to lose market share in many KTI industries.

Notes

1. See the Organisation for Economic Co-operation and 

Development (OECD) (2001) for a discussion of classifying economic activities according to degree of “knowledge 

intensity.” Like all classification schemes, the OECD classification has shortcomings. For example, knowledge- and 

technology-intensive (KTI) industries produce some goods 

or services that are neither knowledge intensive nor technologically advanced. In addition, multiproduct companies 

that produce a mix of goods and services, only some of 

which are KTI, are assigned to their largest business segment. Nevertheless, data based on the OECD classification 

allows researchers and analysts to trace, in broad outline, the 

worldwide trends toward greater interdependence in science 

and technology and the development of KTI sectors in many 

of the world’s economies. 

2. In designating these high-technology (HT) manufacturing industries, the Organisation for Economic Co-operation 

and Development (OECD) estimated the degree to which 

different industries utilized R&D expenditures made directly 

by firms in these industries and the R&D embedded in purchased inputs (indirect R&D) for 13 countries: the United 

States, Japan, Germany, France, the United Kingdom, 

Canada, Italy, Spain, Sweden, Denmark, Finland, Norway, 

and Ireland. Direct R&D intensities were calculated as the 

ratio of total R&D expenditure to output (production) in 22 

industrial sectors. Each sector was weighted according to its 

share of the total output among the 13 countries, using purchasing power parities as exchange rates. Indirect intensities 

were calculated using the technical coefficients of industries 

on the basis of input-output matrices. OECD then assumed 

that, for a given type of input and for all groups of products, the proportions of R&D expenditure embodied in value 

added remained constant. The input-output coefficients were 

then multiplied by the direct R&D intensities. For further 

details concerning the methodology used, see OECD (2001). 

It should be noted that several nonmanufacturing industries have R&D intensities equal to or greater than those 

of industries designated by OECD as HT manufacturing. 

For additional perspectives on OECD’s methodology, see 

Godin (2004).

3. See Atkinson and McKay (2007:16–17) for a discussion of and references to the impact of information technology on economic growth and productivity.

4. See Mudambi (2008) and Reynolds (2010) for a discussion on the shift to knowledge-based production and geographical dispersion of economic activity.

5. Data on the health care sector include social services.

6. See Bresnahan and Trajtenberg (1995) and DeLong and 

Summers (2001) for discussions of information and communications technologies and general-purpose technologies.

7. These information and communications technologies 

(ICT) infrastructure indexes originate from the Connectivity 

Scorecard, which has developed a variety of ICT indexes for 

developed and developing countries. The ICT infrastructure 

indexes are benchmarked against the best-in-class country 

among developed and developing countries. The business 

ICT infrastructure index is composed of metrics on business 

hardware and software and penetration of business lines. 

The consumer infrastructure index is composed of indicators on penetration of telephone lines and broadband. The 

government infrastructure index is composed of metrics 

related to e-government capacity and the share of schools 

connected to the Internet. More information on the methodology can be found at http://www.connectivityscorecard.

org/methodology/.

8. Gross domestic product (GDP) per person employed 

is an imprecise measure of labor productivity. For example, labor productivity using this measure is skewed in 

countries that are major petroleum exporters because their 

GDP is boosted by their petroleum exports, with little input 

from labor. 

9. See Jensen (2012) for a discussion of U.S business services firms helping to build infrastructure in 

developing countries.

10. See Williamson and Raman (2011) for a discussion of 

China’s acquisition of foreign companies.

11. See Economist (Coming home 2013) for a discussion 

of multinational firms choosing to have more of their manufacturing take place in developed countries.

12. Commercial knowledge-intensive services and goods 

trade does not correspond to commercial knowledge- and 

technology-intensive industries because industry and trade 

data are collected on different bases. Industry production 

data are classified by primary industry, and trade data are 

classified by product or service.

13. Data on services exports are available from the World 

Trade Organization (2013).

14. India’s export share is for 2009; 2010 data are 

not available. 
Science and Engineering Indicators 2014 ♦ 6-57

15. Data for China’s trade balance in commercial KI 

services are available from the World Trade Organization 

(2013).

16. Data for India’s trade balance in commercial KI services are available from the World Trade Organization (2013).

17. Data on commercial KI exports by country are available from the World Trade Organization (2013).

18. The U.S. trade balance is affected by many other factors, including currency fluctuations, differing fiscal and 

monetary policies, and export subsidies and trade restrictions between the United States and its trading partners.

19. The 10 technology areas are advanced materials, 

aerospace, biotechnology, electronics, flexible manufacturing, information and communications technology, life 

sciences, optoelectronics, nuclear, and weapons. More information on collection, definition, and measurement of advanced technology products trade data can be found at http://

www.census.gov/foreign-trade/guide/sec2.html.

20. The Asia and Pacific region includes Australia, 

China, Hong Kong, India, Indonesia, Japan, Malaysia, New 

Zealand, Philippines, Singapore, South Korea, Taiwan, 

and Thailand.

21. The National Science Foundation (NSF) Business 

R&D and Innovation Survey’s (BRDIS’s) definition of innovation is very similar to the Organisation for Economic 

Co-operation and Development definition. For more information, see NSF, BRDIS, http://www.nsf.gov/statistics/

srvyindustry/about/brdis/.

22. Business R&D and Innovation Survey data are not 

available for the entire U.S. service sector.

23. Two legal concepts define who has the right to the 

grant of a patent—first to file and first to invent. In a firstto-file system, the patent is granted to the first person to file 

for protection. In the first-to-invent system, the patent is 

granted to the person who is determined to be the first inventor. The first-to-file system is used in all countries, including 

the United States, which switched to a first-to-file system in 

March 2013 after the enactment of the America Invents Act 

of 2011. 

24. U.S. patent law states that any person who “invents 

or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent.” The law defines 

nonobvious as “sufficiently different from what has been 

used or described before that it may be said to be nonobvious 

to a person having ordinary skill in the area of technology 

related to the invention.” These terms are part of the criteria in U.S. patent law. For more information, see the U.S. 

Patent and Trademark Office, “What Is a Patent?” (http://

www.uspto.gov/patents/index.jsp#). 

25. The Japan Patent Office is also a major patent office but has a much smaller share of foreign patents than 

the U.S. Patent and Trademark Office and the European 

Patent Office.

26. The Business R&D and Innovation Survey data are 

collected from a sample of U.S. firms, whereas the U.S. 

Patent and Trademark Office data are from administrative records of all U.S inventors, including individuals 

and nonprofits.

27. Triadic patent families with coinventors residing in 

different countries are assigned to their respective regions, 

countries, or economies on a fractional-count basis (i.e., each 

region, country, or economy receives fractional credit on the 

basis of the proportion of its inventors listed on the patent). 

Patents are listed by priority year, which is the year of the 

first patent filing. Data for 1998–2003 are estimated by the 

Organisation for Economic Co-operation and Development.

28. The high-technology (HT) definition used here is 

from the Bureau of Labor Statistics and differs from that 

used in earlier sections. See Hecker (2005) for a definition 

and the methodology for determining HT industries.

29. Another possibility is that the behavior of venture 

capital investors changed because fewer opportunities for 

attractive risky investments were available in the 2000s than 

in the 1990s.

30. Data on number of awards are available at http://

www.sbir.gov/awards/annual-reports.

31. The International Energy Agency (IEA) manual 

states: “The IEA concept of Energy RD&D differs from the 

Frascati concept of R&D, in that (i) it focuses on energy 

related programmes only; (ii) it includes ‘demonstration 

projects’; and (iii) it includes state owned companies. . . . 

The energy RD&D data collected by the IEA should not be 

confused with the data on government budget appropriations 

or outlays on R&D (GBAORD) collected by the OECD 

Directorate for Science, Technology, and Industry for the 

socio-economic objective ‘Production, distribution and rational utilisation of energy’” (IEA 2011:16–17).

32. Bloomberg’s data include investment in renewable 

energy, biofuels, energy efficiency, smart grid and other 

energy technologies, carbon capture and storage, and infrastructure investments targeted purely at integrating clean 

energy. Investment in solar hot water, combined heat and 

power, renewable heat, and nuclear are excluded, as are the 

proceeds of mergers and acquisitions (which do not contribute to new investment).

33. The International Energy Agency has no official 

definition of clean energy. This discussion includes public research, development, and demonstration in energy 

efficiency, renewable energy, nuclear, hydrogen and fuel 

cells, CO2 capture and storage, and other power and storage 

technologies.

34. The U.S. Patent and Trademark Office initiated a 

green technology pilot program on 7 December 2009 that 

expedites processing of some applications related to green 

technologies. For more information, see http://www.uspto.

gov/patents/init_events/green_tech.jsp.
6-58 ♦ Chapter 6. Industry, Technology, and the Global Marketplace

Glossary

Affiliate: A company or business enterprise located in 

one country but owned or controlled (10% or more of voting securities or equivalent) by a parent company in another 

country; may be either incorporated or unincorporated.

Commercial knowledge-intensive (KI) services: KI 

that are generally privately owned and compete in the marketplace without public support. These services are business, communications, and financial services.

Company or firm: A business entity that is either in a 

single location with no subsidiaries or branches or the topmost parent of a group of subsidiaries or branches.

European Union (EU): As of June 2013, the EU comprised 27 member nations: Austria, Belgium, Bulgaria, 

Cyprus, Czech Republic, Denmark, Estonia, Finland, 

France, Germany, Greece, Hungary, Ireland, Italy, Latvia, 

Lithuania, Luxembourg, Malta, the Netherlands, Poland, 

Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, and 

the United Kingdom. Croatia joined the EU in July 2013. 

Unless otherwise noted, Organisation for Economic Cooperation and Development data on the EU include all 28 

members; data on the EU from other sources are limited to 

the 27 nations that were members as of June 2013.

Foreign direct investment: Financial investment by 

which a person or an entity acquires a lasting interest in and 

a degree of influence over the management of a business 

enterprise in a foreign country.

Gross domestic product (GDP): The market value of all 

final goods and services produced within a country within a 

given period of time.

High-technology (HT) manufacturing industries:

Those that spend a relatively high proportion of their revenue 

on R&D, consisting of aerospace, pharmaceuticals, computers and office machinery, communications equipment, and 

scientific (medical, precision, and optical) instruments.

Hydraulic fracturing: The procedure of fracturing rock 

by a pressurized liquid to extract oil, gas, and other hydrocarbons that formerly had been inaccessible with conventional technologies. The slang term for hydraulic fracturing 

is “fracking.”

Information and communications technologies (ICT) 

industries: A subset of knowledge- and technology-intensive industries, consisting of two high-technology manufacturing industries, computers and office machinery and 

communications equipment and semiconductors, and two 

knowledge-intensive service industries, communications 

and computer services, which is a subset of business services.

Intellectual property: Intangible property resulting from 

creativity that is protected in the form of patents, copyrights, 

trademarks, and trade secrets.

Intra-EU exports: Exports from European Union (EU) 

countries to other EU countries.

Knowledge- and technology-intensive (KTI) industries: Those that have a particularly strong link to science 

and technology. These industries are five service industries, financial, business, communications, education, and 

health, and five manufacturing industries, aerospace, pharmaceuticals, computers and office machinery, communications equipment, and scientific (medical, precision, and 

optical) instruments.

Knowledge-intensive (KI) industries: Those that incorporate science, engineering, and technology into their services or the delivery of their services, consisting of business, 

communications, education, financial, and health services.

Normalizing: To adjust to a norm or standard.

Not obvious: One criterion (along with “new” and “useful”) that an invention must meet to be patentable.

Productivity: The efficiency with which resources are 

employed within an economy or industry, measured as labor 

or multifactor productivity. Labor productivity is measured 

by gross domestic product (GDP) or output per unit of labor. 

Multifactor productivity is measured by GDP or output per 

combined unit of labor and capital.

Purchasing power parity (PPP): Procedure that normalizes currency exchange rates based on the funds required 

to purchase an equivalent market basket of goods in different countries.

R&D intensity: The proportion of R&D expenditures to 

the number of technical people employed (e.g., scientists, 

engineers, and technicians) or the value of revenues.

Triadic patent: A patent for which patent protection 

has been applied within the three major world markets: the 

United States, Europe, and Japan.

Utility patent: A type of patent issued by the U.S. Patent 

and Trademark Office for inventions, including new and 

useful processes, machines, manufactured goods, or composition of matter.

Value added: A measure of industry production that is 

the amount contributed by a country, firm, or other entity to 

the value of the good or service. It excludes the country, industry, firm, or other entity’s purchases of domestic and imported supplies and inputs from other countries, industries, 

firms, and other entities.

Value chain: A chain of activities to produce goods and 

services that may extend across firms or countries. These 

activities include design, production, marketing and sales, 

logistics, and maintenance.

Venture capitalist: Venture capitalists manage the 

pooled investments of others (typically wealthy investors, 

investment banks, and other financial institutions) in a 

professionally managed fund. In return, venture capitalists 

receive ownership equity and almost always participate in 

managerial decisions.
Science and Engineering Indicators 2014 ♦ 6-59

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Chapter 7

Science and Technology: Public 

Attitudes and Understanding

♦ 7-1

Highlights.....................................................................................................................................7-4

Interest, Information Sources, and Involvement.......................................................................7-4

Public Knowledge about S&T..................................................................................................7-4

Public Attitudes about S&T in General....................................................................................7-4

Public Attitudes about Specific S&T-Related Issues ...............................................................7-5

Introduction..................................................................................................................................7-6

Chapter Overview.....................................................................................................................7-6

Chapter Organization................................................................................................................7-6

A Note about Data and Terminology........................................................................................7-6

Interest, Information Sources, and Involvement........................................................................7-10

Public Interest in S&T ............................................................................................................7-10

Availability of S&T News in the Media.................................................................................7-12

S&T Information Sources.......................................................................................................7-15

Involvement ............................................................................................................................7-18

Public Knowledge about S&T ...................................................................................................7-20

Understanding Scientific Terms and Concepts.......................................................................7-20

Reasoning and Understanding the Scientific Process.............................................................7-23

Perceived Knowledge about Causes and Solutions to Environmental Problems...................7-26

Public Attitudes about S&T in General .....................................................................................7-26

Promises and Reservations about S&T...................................................................................7-28

Federal Funding of Scientific Research..................................................................................7-30

Confidence in the Science Community’s Leadership.............................................................7-32

Views of S&E Occupations....................................................................................................7-32

Which Fields and Activities Are Seen as Scientific ..............................................................7-35

Influence of Scientific Experts on Public Issues ....................................................................7-37

Public Attitudes about Specific S&T-Related Issues.................................................................7-37

Environment ...........................................................................................................................7-38

Climate Change ......................................................................................................................7-40

Nuclear Power and Other Energy Sources .............................................................................7-42

Genetically Modified Food.....................................................................................................7-43

Nanotechnology .....................................................................................................................7-44

Stem Cell Research and Human Cloning ...............................................................................7-44

Teaching Evolution in the Schools.........................................................................................7-45

Animal Research.....................................................................................................................7-45

Science, Engineering, and Mathematics Education................................................................7-46

Conclusion .................................................................................................................................7-46

Notes..........................................................................................................................................7-47

Glossary .....................................................................................................................................7-49

References..................................................................................................................................7-50
7-2 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

List of Tables

Table 7-1. News followed “very closely” by American public: 1996–2012.............................7-11

Table 7-2. Traditional media coverage of science and technology, by topic area: 2007–12.....7-13

Table 7-3. Leading traditional media story lines on science and technology, by topic 

area: 2011 and 2012 ..............................................................................................................7-14

Table 7-4. Leading nightly news story lines on science and technology, by topic area: 

2011 and 2012.......................................................................................................................7-15

Table 7-5. Most-discussed subjects in the new media: 2011 and 2012.....................................7-16

Table 7-6. Visits to informal science and other cultural institutions, by country/region: 

Most recent year....................................................................................................................7-19

Table 7-7. Correct answers to factual knowledge and scientific process questions 

in physical and biological sciences, by sex: 1999–2012.......................................................7-22

Table 7-8. Correct answers to factual knowledge questions in physical and biological 

sciences, by country/region: Most recent year......................................................................7-23

Table 7-9. Correct answers to scientific process questions: Selected years, 1999–2012 ..........7-24

Table 7-10. Public perceptions of science and engineering occupations: 2012 ........................7-35

Table 7-11. Public perceptions of degree to which certain fields and work activities 

are scientific: 2012 ................................................................................................................7-36

List of Sidebars

U.S. Survey Data Sources............................................................................................................7-7

International Survey Data Sources...............................................................................................7-8

Nuclear Energy and the Fukushima Accident ...........................................................................7-42
Science and Engineering Indicators 2014 ♦ 7-3

List of Figures

Figure 7-1. Public interest in selected issues: 2012 ...................................................................7-10

Figure 7-2. Public interest in selected science-related issues: 1981–2012 ................................7-11

Figure 7-3. Network nightly news coverage of science and technology: 1988–2012...............7-13

Figure 7-4. Primary source of information about current news events, science and 

technology, and specific scientific issues: 2012 ...................................................................7-16

Figure 7-5. Primary source of information about current news events, science and 

technology, and specific scientific issues: 2001–12 .............................................................7-17

Figure 7-6. Mean number of correct answers to trend factual knowledge of science scale: 

1992–2012.............................................................................................................................7-21

Figure 7-7. Correct answers to trend factual knowledge of science scale, by respondent 

characteristic: 2012 ...............................................................................................................7-21

Figure 7-8. Understanding scientific inquiry, by respondent characteristic: 2012....................7-25

Figure 7-9. Public self-assessment of knowledge about causes of and solutions to 

environmental problems, by country/economy: 2010...........................................................7-27

Figure 7-10. Public assessment of scientific research: 2012–1979 ...........................................7-28

Figure 7-11. Public assessment of belief in science versus faith, and whether science 

does more harm than good, by country/economy: 2010.......................................................7-29

Figure 7-12. Public opinion on whether the federal government should fund basic 

scientific research: 1985–2012..............................................................................................7-30

Figure 7-13. Public assessment of amount of government spending for scientific research: 

1981–2012.............................................................................................................................7-31

Figure 7-14. Public assessment of government spending in various policy areas: 2012...........7-31

Figure 7-15. Public confidence in institutional leaders, by type of institution: 2012................7-32

Figure 7-16. Public self-assessment of knowledge of what scientists and engineers do 

day-to-day on their jobs: 2012 ..............................................................................................7-33

Figure 7-17. Public opinion on science and engineering careers for one’s children: 

1983, 2001, and 2012............................................................................................................7-33

Figure 7-18. Worry about quality of environment: 2001–12.....................................................7-38

Figure 7-19. Public concern about environmental issues, by country/economy: 2010 .............7-39

Figure 7-20. Public assessment of science’s ability to solve environmental problems, 

by country/economy: 2010....................................................................................................7-39

Figure 7-21. Public assessment of danger to environment of climate change and nuclear 

power stations, by country/economy: 2010 ..........................................................................7-41

Figure 7-22. Public assessment of danger to environment of modifying genes of crops, 

by country/economy: 2010....................................................................................................7-44
7-4 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

Interest, Information Sources, 

and Involvement

Four out of five Americans say they are interested in 

“new scientific discoveries.” 

♦ Other science and technology (S&T) related issues also 

interest many Americans; these include new medical discoveries, environmental pollution, and new inventions 

and technologies. 

♦ A survey of the United States and 10 European countries, 

including the 5 largest, suggests that interest in S&T in 

the United States is somewhat higher than in Europe.

The Internet has surpassed television as Americans’ primary source for information about S&T. 

♦ About 4 in 10 Americans cited the Internet as their primary source of S&T information in 2012 compared with 

about one-third in 2010. The percentage of Americans 

saying they relied on television as their primary source of 

S&T information dropped between 2010 and 2012.

♦ Most of those who used the Internet for S&T information 

said they used online editions of newspapers.

A majority of Americans said they had visited a zoo or 

aquarium, natural history museum, or S&T museum 

in 2012. 

♦ Reported attendance at informal science and cultural institutions in 2012 was down slightly from 2008. The primary drop was for zoos and aquariums.

♦ Attendance at informal science institutions was associated with higher education and income.

Public Knowledge about S&T

Americans correctly answered 5.8 out of 9 factual 

knowledge questions in 2012, a score similar to those in 

recent years. 

♦ A survey experiment showed that 48% of respondents 

said they thought it was true that “human beings, as 

we know them today, developed from earlier species of 

animals,” but 72% gave this response when the same 

statement was prefaced by “according to the theory of 

evolution.” Similarly, 39% of respondents said that “the 

universe began with a huge explosion,” but 60% gave this 

response when the statement was prefaced by “according 

to astronomers.”

♦ Levels of factual knowledge in the United States are comparable to those in Europe and are generally higher than 

levels in countries in other parts of the world.

♦ Americans with more formal education do better on science knowledge questions.

♦ Men do better on questions focused on the physical sciences, but there are few differences between men and 

women in terms of responses to questions focused on the 

biological sciences.

Most Americans could correctly answer two multiplechoice questions dealing with probability in the context of medical treatment and the best way to conduct 

a drug trial but had difficulty providing a rationale for 

the use of a control group or describing what makes 

something scientific. 

♦ Americans performed better than the average for residents 

of 10 European countries on a similar multiple-choice 

measure of probability, although the residents of several 

individual countries had better scores than U.S. residents.

Fewer Americans rejected astrology in 2012 than in 

recent years.

♦ In 2012, slightly more than half of Americans said that 

astrology was “not at all scientific,” whereas nearly twothirds gave this response in 2010. The comparable percentage has not been this low since 1983.

Public Attitudes about S&T in General

Most Americans continue to say that the benefits of science outweigh the potential harms and that the federal 

government should fund research that “advances the 

frontiers of knowledge.” 

♦ As in past years, about 4 in 10 Americans said the government was spending “too little on research.” In 2012, 

about half of respondents said government spending on 

scientific research was “about right,” and about 1 in 10 

said there was too much research spending. 

♦ Americans are most likely to say that education has remained the area in which the government spends too little 

money. Majorities have also consistently said that they 

believe health, “alternative energy,” and environmental 

improvement and protection receive too little funding. 

The only area in which majorities say government spends 

“too much” is on “assistance to other countries.”

Americans are more likely to have a “great deal of confidence” in leaders of both the scientific community and 

the medical community than in leaders of any group except the military.

♦ The scientific and medical communities are also 

among the most highly regarded groups in most other 

countries surveyed.

Highlights
Americans hold positive views about both scientists and 

engineers. Attitudes are similar to those expressed about 

scientists in 1983 and 2001.

♦ Less than half of Americans say they have an “excellent” 

or “good” understanding of what scientists and engineers 

do at work. Americans say they have a better understanding of engineers’ work than scientists’ work. 

♦ Many Americans say they think that “scientific work” and 

“engineering work” are “dangerous,” although scientific 

work is seen as more dangerous than engineering work.

♦ Most Americans see scientists and engineers as “dedicated people who work for the good of humanity.”

Americans see many traditional research fields, as well 

as a range of applied fields, as “scientific.”

♦ Only about half of Americans see the social science 

fields of economics and sociology as scientific. More 

Americans see applied activities such as computer programming, farming, and firefighting as scientific.

Public Attitudes about Specific 

S&T-Related Issues

Americans are about as concerned about the overall environment as respondents in many other 

developed countries.

♦ In 2010, about one-third of Americans said they worried 

about “the quality of the environment.” Responses to this 

question have been similar in recent years.

Americans remain divided on views about climate change 

and hold views that are different from those of citizens of 

other countries.

♦ A majority of Americans worried “a great deal” or a “fair 

amount” about climate change in 2013.

♦ About 3 in 10 Americans say that “dealing with global warming” should be a priority for the president and 

Congress. In recent years, dealing with climate issues has been near the bottom of Americans’ list of 

potential priorities. 

♦ Many of the other countries surveyed show more concern 

than the United States about climate change.

♦ Americans are more likely than residents of other countries 

to say they believe that any apparent change in temperatures is the result of natural rather than man-made causes. 

Americans’ support for oil and nuclear energy has rebounded or stabilized following declines associated with 

major accidents. 

♦ About two-thirds of Americans supported “allowing 

more offshore oil and gas drilling” in 2012. Less than half 

of Americans supported drilling in a survey conducted in 

2010, shortly after the Deepwater Horizon oil spill in the 

Gulf of Mexico.

♦ Most Americans continue to express support for nuclear 

energy as “one of the ways to provide electricity,” although support remains lower than before the 2011 nuclear accident in Fukushima, Japan.

♦ Americans are more supportive of nuclear energy than 

residents of most other countries.

Americans are less concerned about “modifying the genes 

of certain crops” than residents of most other countries 

surveyed, although most still see potential danger.

♦ In 2010, about one-quarter of U.S. respondents said that 

modification could be “very” or “extremely dangerous.” Belgium was the only country where residents saw 

less danger.

Most Americans see using stem cells from human embryos in medical research as “morally acceptable.” 

♦ In 2013, 6 in 10 of Americans saw using stem cells from 

human embryos as acceptable. This percentage has stayed 

relatively stable since 2005.

Science and Engineering Indicators 2014 ♦ 7-5
7-6 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

Introduction

Chapter Overview

Science and technology (S&T) is central to American 

life. Whether at home, work, school, or out in our communities, S&T affects our daily activities and how we interact in 

a host of different ways. Many Americans work in jobs in 

which they innovate using S&T, whereas others use these 

innovations to produce the goods and services that improve 

and reshape our lives. S&T gives us new opportunities to 

get healthy and stay healthy. It affects what and how we eat 

while providing technologies that keep us entertained and 

connected. S&T also gives us things to talk about, whether 

as part of political discussions or simply because so much 

about S&T can be interesting and important to how the 

world works. Such conversations are common because S&T 

is integral to American society. This centrality means that 

Americans’ attitudes and understanding about S&T matter 

a great deal.

Sometimes S&T debates involve potential risks to health 

or the environment or changes to what it takes for individuals or companies to succeed. Societies can do a better job 

addressing potential concerns when these concerns are well 

understood, even if some concerns turn out to be unfounded. 

Americans’ ability to deal with potential risks may affect 

what kinds of S&T development occurs within the country 

as well as whether we can take advantage of the S&T that 

already exists. Individuals may also choose where to focus 

their careers based on both their personal interests as well as 

where they feel they can make a meaningful impact. 

Given the centrality of S&T to life in the United States, 

this chapter presents indicators about interest in S&T news, 

where people encounter S&T in the media, trend data regarding knowledge of S&T, and indicators of people’s attitudes about S&T-related issues. To put U.S. data in context, 

the chapter examines trend indicators for past years and 

comparative indicators for other countries.

Chapter Organization

This chapter is divided into four main sections. The first 

includes indicators of the public’s interest in S&T news, 

sources of information, and involvement in informal S&T 

activities. The second section reports indicators of public 

knowledge, including trend measures of factual knowledge 

of S&T and people’s understanding of the scientific process. This second section also includes the way individuals 

respond to knowledge questions. The third and fourth sections of the chapter describe public attitudes toward S&T. 

The third section presents data on attitudes about S&T in 

general, including support for government funding of basic research, confidence in the leadership of the scientific 

community, and perceptions of scientists and engineers. 

Also included is a focus on the degree to which the public 

views various fields and activities as “scientific.” The fourth 

section addresses attitudes on public issues in which S&T 

plays an important role, such as the environment, climate 

change, energy, nuclear power, and the use of animals in scientific research. It also includes indicators of public opinion 

about several emerging lines of research and new technologies, including nanotechnology, genetically modified (GM) 

food, stem cell research, and cloning.

A Note about Data and Terminology

This chapter emphasizes trends over time, patterns of 

variation within the U.S. population, and international patterns. It reviews recent survey data from national samples 

with sound, representative sampling designs. The emphasis 

in the text is on the trends and patterns in the data. 

Like all survey data, the data in this chapter are subject to 

numerous sources of error and random variation that should 

be kept in mind when interpreting the findings. Caution is 

especially warranted for data from surveys that omit significant portions of the target population, have low response 

rates, or have topics that are particularly sensitive to subtle 

differences in question wording (see sidebars “U.S. Survey 

Data Sources” and “International Survey Data Sources”). 

Also, although many of the international comparisons involve identical questions asked in different countries, these 

comparisons can be affected by language and cultural differences that cause survey respondents to interpret questions 

differently. International comparisons therefore require 

careful consideration.

S&T questions asked in the biennial General Social 

Survey (GSS) are a major source of data for this chapter. The 

GSS is a high-quality, nationally representative data source 

on attitudes and behavior of the U.S. population. Questions 

about S&T information, knowledge, and attitudes have been 

included in the GSS since 2006 and have formed the basis 

of this chapter in Science and Engineering Indicators since 

2008. The GSS collects data primarily through in-person interviews. Comparable survey data collected between 1982 

and 2004 used telephone interviewing; prior to 1982, these 

data were collected via in-person interviews. Changes in 

data collection methods over these years, particularly prior 

to 2006, may affect comparisons over time.

Another important limitation is that recent, high-quality, 

relevant data are not always available. In some cases, there 

are large gaps between data collections or only a small number of questions on any given topic. This challenge is particularly acute when it comes to international data. There 

is a substantial amount of survey work on S&T in Europe, 

but these data are not collected as regularly as data from the 

GSS. Asian data are collected even less frequently. Data 

from Africa and South America are also limited. In general, 

the current chapter focuses on surveys that have become 

public after the preparation of the 2012 Indicators report. 

Earlier data can be found in past editions of Indicators. In 

addition, Bauer, Shukla, and Allum (2012) summarize survey data up to 2006 from a range of countries and regions. 
Science and Engineering Indicators 2014 ♦ 7-7

U.S. Survey Data Sources

Sponsoring 

organization Title Years used Information used

Data collection 

method

Respondents (n); margin of 

error of general population 

estimates

National Science 

Foundation (NSF)

Survey of Public Attitudes 

Toward and Understanding 

of Science and Technology 

(1979–2001); University 

of Michigan Survey of 

Consumer Attitudes (2004) 

1979–2001, 

2004

Information sources; interest; visits 

to informal science institutions; 

general attitudes; attitudes toward 

government spending, science/

mathematics education, and animal 

research

Telephone 

interviews

n = 1,574–2,041; 

± 2.5%–3.0%

National Opinion 

Research Center 

(NORC) at the 

University of Chicago

General Social Survey 

(GSS) 

1973–2012 Attitudes toward government 

spending, confidence in institutional 

leaders

Face-to-face 

interviews, 

supplemented 

by telephone 

interviews

Government spending 

(2000–12): 

n = 1,372–4,510; 

± 2.8%–3.9%

Confidence in institutional 

leaders (1973–2012): 

n = 876–2,223; ± 2.5%–4.4%

NORC at the 

University of Chicago

GSS environment module 1993–94, 

2000, 2010

Attitudes toward environment Face-to-face 

interviews, 

supplemented 

by telephone 

interviews

n = 1,276–1,557; 

± 2.5%–3.3%

NORC at the 

University of Chicago

GSS Science and 

Technology (S&T) module 

2006, 2008, 

2010, 2012

Information sources; interest; visits 

to informal science institutions; 

science knowledge; general attitudes; 

attitudes toward government 

spending, science/mathematics 

education, animal research, and 

nanotechnology

Face-to-face 

interviews, 

supplemented 

by telephone 

interviews

n = 1,864–2,256; 

± 2.5%–3.3%

National Survey of 

American Public 

Opinion on Climate 

Change

American Belief in Climate 

Change

2012 Attitudes toward climate change Telephone 

interviews

n = 726; ± 4.0%

Gallup Various ongoing surveys 1982–2013 Federal priorities; attitudes toward 

environmental protection, climate 

change, nuclear energy, alternative 

energy, animal research, stem cell 

research, and quality of science/

mathematics education in U.S. 

public schools

Telephone 

interviews

n = ~ 1,000; ± 3.0%–4.0% 

GfK Roper/ 

Bisconti Research

U.S. Public Opinion Survey 1983–2013 Attitudes toward nuclear energy Telephone 

interviews

n = ~ 1,000; ± 3.0%

Harris Interactive The Harris Poll 1977–2009 Views on occupational prestige Telephone 

interviews

n = ~ 1,000 (~ 500 asked 

about each occupation) 

Pew Initiative on Food 

and Biotechnology, 

The Pew Charitable 

Trusts

Poll on consumer attitudes 

toward genetically 

modified foods and genetic 

engineering

2001–06 Attitudes toward genetically 

modified foods

Telephone 

interviews

n = 1,000; ± 3.1%

Pew Internet & 

American Life Project, 

Pew Research Center

Pew Internet & American 

Life Survey

2006, 2012 Information sources, interest, 

involvement, Internet use, library use

Telephone 

interviews

2006: n = 2,000; ± 3.0%

2012: n = 2,252; ± 2.3%

Pew Research Center Biennial News 

Consumption Survey 

1994–2012 Information sources, interest, 

credibility of information sources, 

top stories, time spent following the 

news

Telephone 

interviews

1994, 1998–2012: n = 

3,000–3,667; ± 2.0%–2.5%

1996: n = 1,751; ± 3.0%

Pew Research Center General Public Science 

Survey

2009 Public’s beliefs about S&T-related 

issues and benefits of science to 

well-being of society 

Telephone 

interviews

n = 2,001; ± 2.5%

Pew Research Center Media surveys (various) 1985–2012 Attitudes toward news media, media 

believability

Telephone 

interviews

n = ~ 1,000–1,505; 

± 3.4%–4.0%

Pew Research Center Political surveys (various) 2008–13 Information sources; Internet use; 

attitudes toward national policy on 

environment, climate change, and 

energy; attitudes toward government 

spending for scientific research

Telephone 

interviews

n = ~ 1,000–2,250; 

± 2.5%–3.5%

Thomson Reuters National Survey of 

Healthcare Consumers: 

Genetically Engineered 

Food

2010 Attitudes toward genetically 

modified foods

Telephone 

interviews

n = 3,025; ± 1.8%

Virginia 

Commonwealth 

University (VCU)

VCU Life Sciences Survey 2001–08, 

2010

Attitudes toward animal research, 

stem cell research, and cloning 

technology

Telephone 

interviews

n = ~ 1,000; ± 3.0%–3.8%
7-8 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

U.S. Survey Data Sources—continued

Sponsoring 

organization Title Years used Information used

Data collection 

method

Respondents (n); margin of 

error of general population 

estimates

Yale Project on 

Climate Change 

Communication and 

the George Mason 

University Center 

for Climate Change 

Communication

Climate Change in the 

American Mind

2008–12 Attitudes toward climate change Online (probabilitybased sample)

n = ~ 1,000; ± 4.0%

NOTES: All surveys are national in scope and based on probability sampling methods. Statistics on the number of respondents and the margin of error are as 

reported by the sponsoring organization. When a margin of error is not cited, none was given by the sponsor.

International Survey Data Sources

Sponsoring 

organization Title Years used Information used

Data collection 

method

Respondents (n); margin of 

error of general population 

estimates

BBVA Foundation 

(Fundación BBVA)

BBVA Foundation 

International Study on 

Scientific Culture

2011 Media use, various knowledge and 

attitudes items

Face-to-face 

interviews

n = 1,500 for each of 15 

countries; ± 2.6%

British Council, Russia Survey of Public Attitudes 

Toward Science and 

Technology in Russia

2003 Various knowledge items Paper questionnaires n = 2,107

Chinese Association 

for Science and 

Technology, China 

Research Institute for 

Science Popularization

Chinese National Survey of 

Public Scientific Literacy

2007, 2010 Interest, various knowledge and 

attitude items, information sources, 

visits to informal science institutions, 

views on occupational prestige 

Face-to-face 

interviews

2007: n = 10,059

2010: n = 68,416

European Commission Special Eurobarometer 

52.2: The Europeans and 

Biotechnology (1999)

1999 Attitudes toward nuclear energy Face-to-face 

interviews

(EU total) n = 16,082; 

(Germany) 2,000; 

(UK) 1,300; 

(Luxembourg) 600; 

(12 other countries) ~ 1,000 

Special Eurobarometer 224/

Wave 63.1: Europeans, 

Science and Technology

(2005)

2005 Views on academic fields, visits to 

informal science institutions

(EU total) n = 26,403; 

(Germany) 1,507; 

(UK) 1,307; 

(Slovakia) 1,241; 

(19 other countries) ~ 1,000; 

(3 other countries) ~ 500 

Special Eurobarometer 224/

Wave 64.3: Europeans and 

Biotechnology in 2005: 

Patterns and Trends (2006)

2005 Various knowledge items (EU total) n = ~ 25,000; 

(each member country/state) 

~ 1,000

Special Eurobarometer 300/

Wave 69.2: Europeans’ 

Attitudes Towards Climate 

Change (2008)

2008 Attitudes toward climate change (EU total) n = ~ 26,661; 

(Germany) 1,534; 

(UK) 1,306; 

(22 other countries) ~ 1,000; 

(3 other countries) ~ 500

Special Eurobarometer 340/

Wave 73.1: Science and 

Technology Report (2010)

2010 Attitudes toward science and 

technology, animal research

(EU total) n = ~ 26,671; 

(Germany) 1,531; 

(UK) 1,311; 

(22 other countries) ~ 1,000; 

(3 other countries) ~ 500

Special Eurobarometer 341/

Wave 73.1: Biotechnology

(2010)

2010 Attitudes toward cloning and nuclear 

energy

(EU total) n = ~ 26,676; 

(Germany) 1,531; 

(UK) 1,316; 

(22 other countries) ~ 1,000; 

(3 other countries) ~ 500

Special Eurobarometer 365/

Wave 75.2: Attitudes of 

European Citizens Toward 

the Environment

2011 Attitudes toward the environment (EU total) n = ~ 26,825; 

(Germany) 1,588; 

(UK) 1,317; 

(22 other countries) ~ 1,000; 

(3 other countries) ~ 500
Science and Engineering Indicators 2014 ♦ 7-9

International Survey Data Sources—continued

Sponsoring 

organization Title Years used Information used

Data collection 

method

Respondents (n); margin of 

error of general population 

estimates

Gallup Global Gallup Reports 2007–08, 

2010

Attitudes toward climate change Face-to-face 

interviews

Telephone 

interviews

2007–08: (Total) 

n = 206,193; ± 1.0%–6.0% 

(United States and 

127 other countries) 

~ 2,000 in most countries

2010: (Total) n = ~ 111,000; 

± 1.7%–5.7% 

(United States and 110 other 

countries) ~ 1,000 each

India National Council 

of Applied Economic 

Research

National Science Survey 2004 Various knowledge items, visits 

to informal science institutions, 

information sources

Face-to-face 

interviews

n = 30,255

International Social 

Survey Programme

Environment Module 1993, 2000, 

2010

Various environment and science 

items

Face-to-face 

interviews

Paper questionnaires

1993: (Total) n = 28,301; 

(United States) 1,430; 

(22 other countries) 

767–1,931

2000: (Total) n = 31,042; 

(United States) 1,276; 

(37 other countries) 

527–1,609

2010: (Total) n = 45,199; 

(United States) 2,044; 

(31 other countries) 

527–1,609

Japanese Cabinet 

Office 

A Public Opinion Poll 

on Science, Technology, 

and Society (except 1998, 

when it is “...Science and 

Technology in the Future”)

1990, 1995, 

1998, 2004, 

2007, 2010

Interest Face-to-face 

interviews

1990, 1995, 1998, 2004, 

2010: n = ~ 1,900–2,200

2007: n = 1,667

Japan National 

Institute of Science 

and Technology 

Policy, Ministry of 

Education, Culture, 

Sports, Science and 

Technology

Survey of Public Attitudes 

Toward and Understanding 

of Science and Technology 

in Japan

2001, 2011 Interest, various knowledge and 

attitude items, information sources, 

visits to informal science institutions

Face-to-face 

interviews

2001: n = 2,146

2011 (July): n =1,010

2011 (Dec.): n =1,208

Korea Foundation for 

the Advancement of 

Science and Creativity 

(formerly Korea 

Science Foundation)

Survey of Public Attitudes 

Toward and Understanding 

of Science and Technology 

2004, 2008, 

2010

Interest, various knowledge and 

attitude items, information sources, 

funding, visits to informal science 

institutions

Face-to-face 

interviews

n = 1,000; ± 3.0%–3.1%

Malaysian Science 

and Technology 

Information Center, 

Ministry of Science, 

Technology and 

Innovation

Survey of the Public’s 

Awareness of Science and 

Technology: Malaysia

2008 Interest, awareness, various 

knowledge and attitude items, 

information sources, visits to 

informal science institutions

Face-to-face 

interviews

n = 18,447; ± 1.0%

Ministry of Science 

and Technology of 

Brazil

Public Perceptions of 

Science and Technology 

2010 Attitudes toward government 

spending

Face-to-face 

interviews

n = ~ 2,000; ± 2.2%

Pew Global Attitudes 

Project, Pew Research 

Center

Global Attitudes Survey 2010 Climate change concerns (Varies by country)

Face-to-face 

interviews

Telephone 

interviews

(United States) n = 1,002; 

± 4.0% (21 other countries)

n = 700–3,262; 

± 2.5%–5.0%

EU = European Union; UK = United Kingdom.

NOTES: All surveys are national in scope and based on probability sampling methods. Statistics on the number of respondents and margin of error are as 

reported by the sponsoring organization. When a margin of error is not cited, none was given by the sponsor.
7-10 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

Throughout this chapter, the terminology used in the text 

reflects the wording in corresponding survey questions. In 

general, survey questions asking respondents about their primary sources of information, interest in issues in the news, 

and general attitudes use the phrase “science and technology.” Thus, S&T is used when discussing these data. Survey 

questions asking respondents about their confidence in institutional leaders, the prestige of occupations, and their views 

on different disciplines use terms such as “scientific community,” “scientists,” “researchers,” and “engineers,” so S&E is 

used when examining issues related to occupations, careers, 

and fields of research. Although science and engineering are 

distinct fields, national survey data that make this distinction are scarce. The term Americans, as well as equivalent 

terms for other countries, is meant to refer to U.S. residents 

included in a national survey. However, not all respondents 

were citizens of the countries in which they were surveyed.

Interest, Information 

Sources, and Involvement

Americans’ understanding and attitudes about topics such 

as S&T depend, in part, on how much exposure they get to 

such content throughout their life, as well as how much attention they pay to such content (Slater, Hayes, and Ford 

2007). Exposure and attention to S&T can make residents 

more informed, shape attitudes, and help them make decisions that are better for themselves, their families, and their 

communities. Media use can also spur interest in S&T issues 

and foster a desire to seek out and consider new information.

This section reviews overall expressed interest in media reports about S&T, the sources of material about S&T 

that are available to the public, and the type of S&T-related 

content the public uses. It concludes with indicators of behavioral involvement in S&T through visits to museums and 

other cultural institutions. 

Public Interest in S&T

U.S. Patterns and Trends

Most Americans say they are interested in science news, 

although several other subjects draw more interest. Less 

than half of Americans (40%) in 2012 said that they were 

“very interested” in news about “new scientific discoveries,” 

which is about the same as the percentage who expressed 

high levels of interest in news about “military and defense 

policy” (37%) and the “use of new inventions and technologies” (42%). Interest in other issues that touch on S&T 

ranged from a high of 58% for “new medical discoveries” to 

a low of 23% for “space exploration.” “Environmental pollution” issues (45%) were also popular (figure 7-1; appendix 

tables 7-1 and 7-2).1

Current findings for science news are within their historical range. For 2012, the percentage of Americans who said 

they find news about scientific discovery “very” interesting 

stayed stable from 2010, but the percentage saying they are 

“not at all interested” in scientific discovery climbed from 

8% in 2010 to 14%. Between 1981 and 2012, the percentage 

of uninterested respondents has ranged between 17% (1981) 

and 8% (2001), whereas the percentage of “very interested” respondents has ranged between 37% (1981) and 49% 

(1997). The topic of medical discoveries has consistently 

stayed at the top of the list alongside nonscience issues such 

as local school issues and economic issues. Space exploration has remained near the bottom alongside nonscience 

subjects such as international affairs (figure 7-2; appendix 

tables 7-1 and 7-2).

Also, although most Americans may say they have an interest in S&T, Pew Research data show that the percentage of 

Americans who actually followed news about “Science and 

Technology” “very closely” was just 16% in 2012 and has 

stayed between 13% and 18% since 2000. The 2012 percentage is down from highs of 20% and 22% in 1996 and 1998, 

respectively. Weather is the most common subject respondents 

say they follow “very closely” (52%). About the same percentage of people paid close attention to S&T as paid close attention 

Figure 7-1

Public interest in selected issues: 2012

NOTE: Responses to There are a lot of issues in the news, and it is 

hard to keep up with every area. I’m going to read you a short list of 

issues, and for each one I would like you to tell me if you are very 

interested, moderately interested, or not at all interested.

SOURCE: University of Chicago, National Opinion Research Center, 

General Social Survey (2012). See appendix table 7-1. 

Science and Engineering Indicators 2014

Very interested

Don’t know

Moderately interested Not at all interested

Cumulative percent

International and

foreign policy issues

Agricultural and

farm issues

Space

exploration

Military and 

defense policy

New scientic

discoveries

Use of new inventions

and technologies

Environmental

pollution

Economic issues and

business conditions

Local school issues

New medical

discoveries

0 20 40 60 80 100
Science and Engineering Indicators 2014 ♦ 7-11

to politics, business and finance, and international affairs. 

Although some issues have stayed relatively stable, most issues 

have seen at least small declines in the percentage of Americans 

who say they follow that topic closely. One of the largest declines has been in the percentage of Americans interested in 

health news (Pew Research Center 2012a) (table 7-1). 

International Comparisons 

Americans generally report higher levels of interest in 

S&T issues than do residents of many European countries. 

A survey conducted by the BBVA Foundation in the United 

States and 10 European countries—including the 5 largest 

(France, Germany, Italy, Spain, and the United Kingdom) 

and 5 others (Austria, the Czech Republic, Denmark, the 

Netherlands, and Poland)—asked respondents to use a 

0-to-10–point scale to rate their interest in six issues. These 

included three S&T-related issues (“scientific issues,” “environmental issues,” and “health issues”) and three nonS&T issues (“economic issues,” “international issues,” and 

“political issues”). For scientific issues, the United States 

had an average interest level of 6.0, which was greater than 

the 10-country European average of 5.6. The Netherlands 

had the highest score (6.4), and several countries were in 

the same general range as the United States. The U.S. average for interest in environmental issues (6.9) tied the 

Netherlands, the highest of the included European countries, 

but was only a little higher than the overall average of 6.6. 

For health issues, the U.S. average of 7.8 was just below 

Figure 7-2

Public interest in selected science-related issues: 

1981–2012

Percent “very interested”

NOTE: Responses to There are a lot of issues in the news, and it is 

hard to keep up with every area. I’m going to read you a short list of 

issues, and for each one I would like you to tell me if you are very 

interested, moderately interested, or not at all interested.

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Public Attitudes Toward and 

Understanding of Science and Technology (1981–2001); University of 

Chicago, National Opinion Research Center, General Social Survey 

(2008–12). See appendix table 7-1.

Science and Engineering Indicators 2014

1981 1985 1988 1992 1997 2001 2008 2012

0

20

40

60

80

New medical discoveries

Use of new inventions

and technologies

New scientic discoveries

Space exploration

Table 7-1

News followed “very closely” by American public: 1996–2012

(Percent)

Type of news 1996 1998 2000 2002 2004 2006 2008 2012

Weather ................................................................................... NA NA NA NA 53 50 48 52

Crime....................................................................................... 41 36 30 30 32 29 28 28

Community .............................................................................. 35 34 26 31 28 26 22 26

Sports...................................................................................... 26 27 27 25 25 23 20 26

Health news............................................................................. 34 34 29 26 26 24 20 23

Local government.................................................................... 24 23 20 22 22 20 20 21

Politics/Washington news ....................................................... 16 19 17 21 24 17 21 17

Science and technology.......................................................... 20 22 18 17 16 15 13 16

Business and finance .............................................................. 13 17 14 15 14 14 16 15

International affairs.................................................................. 16 16 14 21 24 17 16 14

Entertainment.......................................................................... 15 16 15 14 15 12 10 11

Education ................................................................................ NA NA NA NA NA NA 23 NA

Environment ............................................................................ NA NA NA NA NA NA 21 NA

Religion.................................................................................... 17 18 21 19 20 16 17 NA

Consumer news ...................................................................... 14 15 12 12 13 12 13 NA

Culture and arts....................................................................... 9 12 10 9 10 9 11 NA

Celebrity news......................................................................... NA NA NA NA NA NA 7 NA

Travel ....................................................................................... NA NA NA NA NA NA 6 NA

NA = not available, question not asked.

NOTE: Data reflect respondents who said they followed a type of news “very closely.” 

SOURCES: Pew Research Center for the People and the Press, Audience Segments in a Changing News Environment: Key News Audiences Now 

Blend Online and Traditional Sources (17 August 2008), p. 39; Biennial News Consumption Survey (30 April–1 June 2008), http://www.people-press.org/

reports/pdf/444.pdf, accessed 21 September 2009; Biennial News Consumption Survey (9 May–3 June 2012), http://www.people-press.org/files/legacyquestionnaires/News%20Consumption%20topline%20for%20release.pdf, accessed 25 January 2013.

Science and Engineering Indicators 2014
7-12 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

that of Spain (7.9%), which had the highest average of the 

European countries. The overall European average for health 

issues was quite high at 7.4. The U.S. averages for non-S&T 

issues were also relatively high (BBVA Foundation 2012b).2

A separate 2010 all-European survey found that 30% of 

respondents across 27 European nations reported being “very 

interested” in new scientific discoveries and technological developments, 49% were “moderately interested,” and 

20% were “not interested.” Thus, again, expressed interest 

in S&T appears lower in the European Union (EU) than in 

the United States, where 40% of Americans in 2010 reported 

being “very interested” in S&T. However, several European 

countries—the Netherlands (48%), the United Kingdom 

(43%), Sweden (43%), Luxembourg (42%), France (41%), 

and Hungary (41%)—had percentages similar to the U.S. 

percentage (European Commission 2010a).3 

A majority of residents of China, Japan, and Korea report 

interest in science and technology, although the varied questions and survey structures used make direct comparisons 

with the United States unwise. In 2010, 72% of Chinese 

respondents said they were “interested” in “new scientific 

discoveries,” and 68% said they were interested in “new inventions and technologies” (CRISP 2010). Interest in both 

topics appears to be up from a 2007 survey (NSB 2010). In 

Japan, the percentage saying they were interested in “science and technology” climbed from 63% in January of 2010 

to 76% in July of 2011, before and after the major earthquake that damaged the nuclear energy plant in Fukushima. 

It dropped back to 65% in December of 2011. Japanese interest in S&T was in the mid-50% range from 1990 to 2004 

(NISTEP 2012). In Korea, a 2010 survey found that 51% of 

respondents said they had an interest in “new inventions and 

technologies,” and 49% had an interest in “new scientific 

discoveries” (KOFAC 2011). Korean interest in scientific 

discovery was up from 24% in a 2008 survey (NSB 2012). 

Respondents in China and Korea were asked about both 

S&T and non-S&T topics, whereas the Japanese surveys addressed only S&T topics.

The 2011 BBVA Foundation survey, as well as the 2010 

Chinese survey, reported two novel indicators of science 

interest and involvement: how much people discussed science and whether they knew someone who was a scientist. 

Interpersonal discussion and contact with opinion leaders 

within one’s social network influence views about S&T 

issues (Hwang and Southwell 2007; Nisbet and Kotcher 

2009). About 36% of Americans said that S&T issues were 

“part of [their] conversations with family members, friends, 

or work colleagues” “very often” or “quite often.” The 

10-country European average was 27%, although countries 

such as Denmark (50%), the United Kingdom (38%), and 

the Netherlands (37%) had scores at or above the U.S. level. 

The percentage of Americans who said they are “personally 

acquainted with someone who is a scientist” (44%) was close 

to the 10-country European average of 40% but lower than 

those of a number of countries, including the Netherlands 

(74%), Denmark (67%), the United Kingdom (55%), and 

Germany (53%). In total, 1 in 5 Americans (20%) reported 

having a friend who was a scientist. This was about the same 

as the 10-country European average (22%) but once again 

was less than the scores for the Netherlands (34%), Denmark 

(30%), and the United Kingdom (28%) (BBVA Foundation 

2012a). In China, 43% of respondents said that “conversations with people” were a main source of S&T information. 

Further, 61% said they had “often” or “sometimes” engaged 

in talk about S&T with “relatives, friends, and colleagues,” 

and 14% said they had been involved in “discussions or 

hearings” related to S&T.

Availability of S&T News in the Media

Americans’ knowledge and attitudes about S&T, particularly in areas of emerging knowledge, partially depend on 

the availability of S&T news. Media coverage often sets the 

public agenda (Soroka 2002) and frames the debate related 

to scientific issues (Nisbet and Scheufele 2009). A range of 

social processes associated with journalism, science, and 

public decision making determine which issues get attention 

from journalists at particular periods of time (Nisbet and 

Huge 2006). For example, natural or human disasters may 

increase the likelihood that relevant S&T issues are covered 

by the news while decreasing the likelihood that unrelated 

issues are covered. Quantity and prominence of coverage 

may also affect topical knowledge within society (Barabas 

and Jerit 2009). Other research suggests that different types 

of media have different effects on attitudes, with newspaper 

and Internet use being associated with more favorable attitudes than television (e.g., Dudo et al. 2011). Given the 

potential impact of media use, indicators that address how 

much and what kinds of S&T news coverage are available in 

the media can be important for understanding the development of views about S&T. 

The Project for Excellence in Journalism (PEJ 2012) 

conducted an extensive content analysis of media coverage 

between January 2007 and May 2012 using 52 outlets in the 

following media sectors: print, Internet, network television, 

cable television, and radio. Each week, stories were classified into 1 of 26 broad topic areas, including S&T, the environment, and “health and medicine.”4

Special tabulations of PEJ data show that S&T coverage made up a small percentage of the total amount of news 

in the traditional media—less than 2% annually—between 

2007 and 2012. News coverage of the environment made up 

a similarly small percentage of the news, dropping to 1.0% 

of all coverage in 2011 and 1.2% in the first part of 2012. 

Coverage of health and medicine consistently made up a 

greater percentage of the news, ranging from 3.1% in 2011 

to 8.9% in 2009 (table 7-2).5

Many issues that dominated coverage in previous years 

remained prominent in 2011 and early 2012. For S&T, “cyberspace” issues have been near the top of the media agenda 

since 2009 (NSB 2010, 2012). The National Aeronautics and 

Space Administration (NASA) led coverage in 2011 with 
Science and Engineering Indicators 2014 ♦ 7-13

the final launch of the Space Shuttle Atlantis and the end 

of the shuttle program. (table 7-3) (NSB 2012). The most 

prominent environmental issue in the news has varied over 

recent years. The energy debate and global warming/climate 

change, as well as the oil spill in the Gulf of Mexico, have 

all received prominent coverage in recent years (NSB 2012). 

News programming on the three major broadcast networks (ABC, CBS, and NBC) shows a similar pattern. The 

Tyndall Report has tracked the content of the three major 

broadcast networks for more than 20 years. Tyndall tabulates the amount of airtime devoted to different topics using 18 different categories (Tyndall Report 2013). Two 

categories with large science, engineering, and technology 

components are “science, space, and technology” and “biotechnology and basic medical research.”6

 Neither category 

has ever occupied a large percentage of the approximately 

15,000 minutes of annual nightly weekday newscast coverage on the networks. The airtime devoted to “science, space, 

and technology” averaged about 2% of broadcast news between 2000 and 2012. Time devoted to “biotechnology and 

basic medical research” was even lower, almost always 1% 

or less of broadcast news (figure 7-3).

The leading stories in these two science-related categories on nightly news broadcasts in 2011 were the death of 

Apple chief executive officer and technology innovator 

Steve Jobs and the end of NASA’s Space Shuttle program. 

In 2012, the social networking site Facebook’s initial public 

offering of stock led technology coverage. NASA stayed in 

the news with its Curiosity rover mission to Mars as well as 

additional coverage of the end of the space shuttle program. 

In the category of “biotechnology and basic medical research,” cancer research garnered the most coverage in both 

2011 and 2012 (table 7-4). Since 2006, cancer research has 

received more attention than other medical research topics 

(NSB 2008, 2010, 2012).

The PEJ also tracked new media and social media—a 

segment of the Internet that continues to grow at high rates 

around the world (Pew Research Global Attitudes Project 

2012)—between January 2009 and June 2012. The New 

Media Index focused specifically on the five main topics 

linked to by blog and Twitter posts from Monday to Friday 

of each week.7

 Discussion of specific technology companies (e.g., Apple, Google, Samsung, Facebook, and Twitter) 

dominated both blogs and Twitter. In 2012, technology companies remained among the most common topics of discussion on blogs, but other subjects dominated Twitter (table 

7-5). The one environmental issue that made the top five list 

multiple times was “global warming.”

Table 7-2

Traditional media coverage of science and technology, by topic area: 2007–12

(Percent)

Year Number of stories Science and technology Environment Health and medicine

2007.............................................. 70,737 1.3 1.6 3.6

2008.............................................. 69,942 1.1 1.3 2.7

2009.............................................. 68,717 1.8 1.5 8.9

2010.............................................. 52,613 1.5 1.6 5.0

2011.............................................. 48,555 1.4 1.0 3.1

2012 (January–May) ..................... 20,452 1.2 1.2 4.1

NOTES: Data reflect the percentage of news stories in each topic area that are based on content analysis of coverage by media outlets in five sectors: print, 

Internet, network television, cable television, and radio. Data for 2012 reflect only the first 5 months of the year; data were not collected after May 2012.

SOURCE: Project for Excellence in Journalism, News Coverage Index, special tabulations (21 March 2011, 10 December 2012), received via e-mail. For 

methodology, see http://www.journalism.org/commentary_backgrounder/new_media_index_methodology, accessed 18 January 2013.

Science and Engineering Indicators 2014

Figure 7-3

Network nightly news coverage of science and 

technology: 1988–2012

Percent of news

NOTES: Data reect the percentage of approximately 15,000 total 

annual minutes of weekday nightly newscasts on ABC, CBS, and 

NBC that were spent on science, space, and technology and on 

biotechnology and basic medical research. Excluded from science, 

space, and technology are stories on forensic science and media 

content. Excluded from biotechnology and basic medical research 

are stories on clinical research and medical technology. 

SOURCE: Tyndall Report, special tabulations (21 March 2011, 12 

January 2013, 22 January 2013). 

Science and Engineering Indicators 2014

1988 1992 1996 2000 2004 2008 2012

0

1

2

3

4

5

6

Science, space, and technology

Biotechnology and basic medical research
7-14 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

Table 7-3

Leading traditional media story lines on science and technology, by topic area: 2011 and 2012

(Percent of news in each topic area)

Topic area/leading story line 2011 Topic area/leading story line January–May 2012

Science, space, and technology (n = 693 stories) Science, space, and technology (n = 255 stories)

NASA/shuttle missions ................................................ 26.2 Cyberspace issues ............................................................. 12.7

Cyberspace issues ...................................................... 13.2 Online piracy legislation...................................................... 12.6

Apple news .................................................................. 7.2 Facebook/Zuckerberg news............................................... 7.8

Supreme Court actions................................................ 3.4 NASA/shuttle missions ....................................................... 7.3

Facebook/Zuckerberg news........................................ 2.4 SpaceX rocket launch......................................................... 4.5

Texting and driving/multitasking.................................. 1.9 Google news....................................................................... 4.2

Gabrielle Giffords shooting.......................................... 1.7 Kony 2012 viral video.......................................................... 3.9

Google news................................................................ 1.2 Apple news ......................................................................... 3.1

Iran............................................................................... 1.1 Texting and driving/multitasking......................................... 2.2

Economy...................................................................... 0.9 Education system/debate................................................... 1.8

Japan earthquake/tsunami (March 2011) .................... 0.9 Japan earthquake/tsunami (March 2011) ........................... 0.8

Nobel prizes................................................................. 0.8 New Year celebrations ........................................................ 0.8

Pollution/emissions/going green ................................. 0.8 Economy............................................................................. 0.6

Education system/debate............................................ 0.8

Environment (n = 244 stories)

Environment (n = 467 stories) Energy debate..................................................................... 30.4

Energy debate.............................................................. 28.0 Keystone oil pipeline........................................................... 13.1

Japan earthquake/tsunami (March 2011) .................... 14.1 Gas/oil prices...................................................................... 11.0

Pollution/emissions/going green ................................. 13.3 Global warming................................................................... 10.3

Global warming............................................................ 7.1 Pollution/emissions/going green ........................................ 6.9

Solyndra scandal ......................................................... 6.8 Nuclear policy ..................................................................... 3.7

Gas/oil prices............................................................... 5.5 BP oil spill in the Gulf of Mexico......................................... 2.3

BP oil spill in the Gulf of Mexico.................................. 5.2 Japan earthquake/tsunami (March 2011) ........................... 1.9

Economy...................................................................... 2.1 Solyndra scandal ................................................................ 1.4

2012 presidential election............................................ 1.4 2012 presidential election................................................... 1.2

District of Columbia–area earthquake ......................... 0.6 Economy............................................................................. 1.2

Supreme Court actions....................................................... 0.8

Health and medicine (n = 1,499 stories)

Health care reform debate........................................... 42.8 Health and medicine (n = 839 stories)

2012 presidential election............................................ 2.9 Health care reform debate.................................................. 60.3

Economy...................................................................... 2.7 2012 presidential election................................................... 3.7

Gabrielle Giffords shooting.......................................... 1.8 Autism research.................................................................. 1.5

Cigarette warning labels .............................................. 1.4 Heart disease research....................................................... 1.2

World AIDS Day 2011 .................................................. 1.2 TruvadaTM—promising HIV/AIDS medication....................... 1.0

Japan earthquake/tsunami (March 2011) .................... 1.1 Flesh-eating bacteria .......................................................... 0.9

Education system/debate............................................ 1.0 Bloomberg big soda ban .................................................... 0.9

Stem cell controversy .................................................. 1.0 Education system/debate................................................... 0.9

Avastin® loses FDA approval ....................................... 0.9 U.S. airline industry............................................................. 0.8

Listeria-tainted melons ................................................ 0.8 Stem cell controversy ......................................................... 0.6

WHO cell phone study (June 2011) ............................. 0.8 Trayvon Martin shooting ..................................................... 0.6

Heart disease research................................................ 0.8

Dr. Oz and apple juice.................................................. 0.8

HPV cervical cancer vaccine ....................................... 0.6

German E. coli outbreak .............................................. 0.5

FDA = Food and Drug Administration; HPV = human papillomavirus; NASA = National Aeronautics and Space Administration; WHO = World Health 

Organization.

NOTES: Data reflect story lines with the greatest percentage of news in each topic area based on content analysis of coverage by media outlets in five 

sectors: print, Internet, network television, cable television, and radio. Data for 2012 reflect only the first 5 months of the year; data were not collected 

after May 2012.

SOURCE: Project for Excellence in Journalism, News Coverage Index, special tabulations (10 December 2012). For methodology, see http://www.

journalism.org/commentary_backgrounder/new_media_index_methodology, accessed 18 January 2013.

Science and Engineering Indicators 2014

Entertainment television can also shape views. 

However, one recent study showed that, between 2000 

and 2008, scientists represented just 1% of characters 

on prime-time network shows. Of these scientists, 7 out 

of 10 were men and almost 9 of 10 were white. Medical 

professionals were 8% of the characters. Generic “professionals” were the most common type of character (21%). 

In general, about 8 of 10 scientists were coded as being 

“good” (Dudo et al. 2011).8 
Science and Engineering Indicators 2014 ♦ 7-15

getting information about current events from the Internet 

has increased steadily since about 2001, and the percentage 

using newspapers for current events has declined. Television 

use declined for several years but has held steady at current 

levels since about 2008 (figure 7-5; appendix table 7-3). 

For news specifically about S&T, Americans are now more 

likely to rely on the Internet than on television. In 2012, 42% 

of Americans cited the Internet as their primary source of S&T 

information, up from 35% in 2010. The percentage citing the 

Internet as their primary source of S&T information has also 

grown steadily since 2001. Conversely, reliance on television 

has dropped; about 32% of Americans reported that television 

was their primary source of S&T news in 2012, down from 

39% in 2008. Some 7% said they get their S&T information 

from newspapers, and another 8% said they get their S&T information from magazines (figure 7-5; appendix table 7-4). 

S&T Information Sources

U.S. Patterns and Trends

The media environment has changed repeatedly over the last 

century. The available data show clear trends in what sources 

Americans say they use to get news about current events and 

S&T, as well as where they would look for new S&T information. Overall, Pew Research reports that Americans said they 

spent 67 minutes with the news per day in 2012, similar to previous years. The main difference was a clear shift toward online 

sources (Pew Research Center 2012a).

For news about current events, television remains the 

primary source of information for 43% of Americans. 

Substantial percentages also reported in 2012 that most of 

their current event news comes from the Internet (33%) or 

newspapers (13%) (figure 7-4). The percentage of Americans 

Table 7-4

Leading nightly news story lines on science and technology, by topic area: 2011 and 2012

(Annual minutes of coverage)

Topic area/leading story line 2011 Topic area/leading story line 2012

Science, space, and technology Science, space, and technology

Computer CEO Steve Jobs of Apple dies at age 56 ........ 68 Internet social network Facebook launches IPO............... 69

NASA Space Shuttle program discontinued ....................... 62 Mars astronomy: NASA Curiosity rover mission................ 34

Cellular telephone/computer combination: smartphones .... 27 NASA Space Shuttle program ends as a museum piece ... 31

Cellular telephone radiation safety worries....................... 20 Solar astronomy: storms, flares, Northern Lights.............. 22

Computer networks targeted by coordinated hackers..... 15 Computer networks targeted by coordinated hackers...... 18

Cellular telephone billing abuses, surcharges .................. 14 Space transportation uses privatized rockets ................... 16

NASA research satellite falls out of orbit .......................... 14 Cellular telephone/computer combination: smartphones .... 14

Internet online commerce volume increases .................... 13 Computer flat-screen tablet technology innovation .......... 10

Immigrant quotas on work visas for high-technology jobs ... 11 Computer manufacturer Apple posts record profits.......... 10

Computer flat-screen tablet technology innovation ........... 10 Science and mathematics education in schools ............... 9

NASA Apollo manned moon missions remembered ........ 9 International Space Station program................................. 9

International Space Station program................................ 9 NASA manned space flights from the 1960s..................... 9

Inventions/innovations in technology surveyed................ 9 Teenage girl is a science achiever despite homelessness ... 9

Asteroids/astronomy: rock to pass close to Earth............ 8 Internet search engine Google monitors browsing............ 9

Internet used for social networking: Facebook grows...... 8 Highway safety: drivers’ cell phone use dangers .............. 8

Mars astronomy: search for signs of life........................... 8 Internet online commerce volume increases ..................... 8

NASA Space Shuttle Challenger disaster 25th anniversary ... 8 Physicists build supercollider, search for particle.............. 7

Air safety: in-cabin cellular telephone use risks ............... 8 High school science fair competitions held for students..... 7

Internet BlackBerry e-mail service is addictive................. 7 Solar eclipses visible in western states, Australia ............. 7

Science and mathematics education in schools .............. 5 Internet copyright piracy crackdown proposed................. 6

Space transportation to use privatized rockets................ 5 Internet social network photographs from Instagram ....... 6

Flash mobs assemble via instant message networks ...... 5 NASA Apollo manned moon missions remembered ......... 6

Telecommunications billing consumer fraud: 

crammed surcharges .................................................... 5

Telemarketing abuses: automated robocalls increase....... 6

Venus astronomy: transit visible across the face of the sun... 6

NASA Space Shuttle astronaut Sally Ride dies at age 61... 6

Biotechnology and basic medical research Computer systems are vulnerable to viruses, worms........ 5

War on cancer research efforts......................................... 59 NASA Apollo astronaut Neil Armstrong dies at age 82 ..... 5

Spinal cord injuries and paralysis research ...................... 16 Air safety: in-cabin use of electronic devices .................... 5

Asteroids/astronomy: rock passes close to Earth ............. 5

Digital surveillance spycams are miniaturized................... 5

Biotechnology and basic medical research

War on cancer research efforts.......................................... 28

Bone marrow stem cell transplants save lives................... 9

Spinal cord injuries and paralysis research ....................... 5

CEO = chief executive officer; IPO = initial public offering; NASA = National Aeronautics and Space Administration.

NOTES: Data reflect annual minutes of story coverage on these topics by major networks ABC, CBS, and NBC, out of approximately 15,000 total annual 

minutes on weekday nightly newscasts. Story lines receiving at least 5 minutes of coverage in 2011 or 2012 are shown. Excluded from science, space, 

and technology are stories on forensic science and media content. Excluded from biotechnology and basic medical research are stories on clinical 

research and medical technology. 

SOURCE: Tyndall Report, special tabulations (12 January 2013, 22 January 2013).

Science and Engineering Indicators 2014
7-16 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

In 2012, the GSS also included questions aimed at unpacking what people mean when they say they go online for 

S&T information and whether people are using traditional 

media sources’ online content. These analyses point to the 

importance of newspapers’ online presence. Of the 42% who 

said they go online for S&T news, 63% indicated they used 

online newspapers. Of the 7% who said newspapers were the 

primary source of S&T information, about one-sixth (16%) 

said they used an online edition. Combined, this means that 

33% got S&T news from newspapers, with 27% getting their 

newspaper online and 6% getting it in traditional form. It 

also means that newspaper content is described as a primary 

S&T source by about the same percentage of people who 

said television was their primary source of S&T information 

(32%). Another 11% said their online source was magazines. 

This represents about 5% of all respondents and means that 

about 13% of all S&T media use was from magazines. All 

other potential online sources—which might include blogs 

and other forms of social media—were chosen by less than 

10% of respondents who indicated they went online for S&T 

news. The data do not address attention to individual issues. 

Since at least 2001, the Internet has also been the most 

common resource that respondents say they would use to 

seek out information about specific scientific issues. In 

2012, the highest ever percentage of Americans (63%) said 

they would go online to find information about a specific 

S&T issue. Another 17% said they would turn to television 

and just 3% said they would use newspapers (figure 7-5; appendix table 7-5). 

Generally, newspaper reliance is more common for 

relatively older respondents, and Internet reliance is more 

common for relatively younger and higher earning respondents. Television use is also somewhat less common for 

younger respondents, although the pattern is not nearly as 

pronounced. Those with lower incomes and lower levels of 

Table 7-5

Most-discussed subjects in the new media: 2011 and 2012

Subject

2011a

Subject

2012b

Weeks in 

top 5 (n)

Weeks in 

top 5 (%)

Weeks in 

top 5 (n)

Weeks in 

top 5 (%)

Blogs Blogs

Apple................................................. 20 40 Apple........................................................... 16 70

2012 presidential election................... 13 26 Google ........................................................ 11 48

Google............................................... 12 24 Search engine optimization ........................ 10 43

California budget .............................. 6 12 2012 presidential election........................... 6 26

Samsung........................................... 5 10 Application programming interfaces (tie).... 4 17

Samsung Galaxy (tie).................................. 4 17

Twitter

Facebook........................................... 19 38 Twitter

Google............................................... 19 38 One Direction (music) ................................. 16 70

Twitter ............................................... 18 36 Justin Bieber (music) .................................. 10 43

Apple................................................. 16 32 Super Junior (music)................................... 10 43

Justin Bieber (music) ........................ 11 22 @The90sLife ............................................... 4 17

Lady Gaga (music) (tie) ............................... 3 13

Trayvon Martin shooting (tie)....................... 3 13

a

 Blogs and Twitter content analysis for 2011 is based on 50 weeks in the year.

b Blogs and Twitter content analysis for 2012 is based on the first 23 weeks in the year.

NOTES: Data reflect the number and percentage of weeks a subject appeared in the Project for Excellence in Journalism’s (PEJ’s) New Media Index. PEJ 

stopped regularly producing the New Media Index in June 2012.

SOURCE: PEJ New Media Index, special tabulations (January–February 2013), http://www.journalism.org/news_index/100, accessed 8 February 2013.

Science and Engineering Indicators 2014

Figure 7-4

Primary source of information about current news 

events, science and technology, and specific 

scientific issues: 2012

NOTE: “All other” includes radio, magazines, books, government 

agencies, family, and friends/colleagues.

SOURCE: University of Chicago, National Opinion Research Center, 

General Social Survey (2012). See appendix tables 7-3–7-5.

Science and Engineering Indicators 2014

Television Internet Newspapers All other Don’t know

Cumulative percent

0 20 40 60 80 100

Specic scientic

issues

Science and

technology

Current

news events
Science and Engineering Indicators 2014 ♦ 7-17

education are more likely to say they get their news, including S&T-related news, from television, whereas those with 

more education and income get their news from newspapers, 

television, and the Internet (appendix tables 7-3–7-5).

Blending traditional and online news sources was also 

addressed in the context of S&T for the 2012 Indicators report based on 2010 GSS data. That survey asked half of the 

sample a question with response options that distinguished 

between online and print-format sources for newspapers and 

magazines. Overall, there was a clear pattern of increasing 

reliance on online sources for increasingly specific content 

(NSB 2012). More recent information on what other online 

sources people may use for S&T information and the degree 

to which people encounter S&T information as a byproduct 

of attention to other issues is not available.9

Another important aspect to understanding media use is to 

recognize that people make choices about what media to use 

based partially on the degree to which they trust that source. 

Both Pew Research and Gallup data suggest that Americans 

trust the media less than they did in previous years (Morales 

2012; Pew Research Center 2011a, 2012b). Evidence about 

how Americans judge the credibility of S&T-specific media 

is, however, scant. A 2006 Pew Internet & American Life 

Project study of how Americans acquire science information indicates that Internet users who seek science information online do not always assume that the information they 

find there is accurate. The vast majority reported that they 

checked information by comparing it to other information 

they found online, comparing it to offline sources (e.g., science journals, encyclopedia) or by looking up the original 

source of the information (Horrigan 2006; NSB 2008).

International Comparisons 

The 2011 BBVA Foundation survey found that residents 

of all countries made similar uses of television, newspapers, 

the Internet, and radio to acquire S&T content. The survey 

found that 47% of Americans watched television programs 

addressing S&T topics “very” or “quite” often. The average of the 10 European countries surveyed was 41% but 

residents of two countries—the United Kingdom (54%) 

and Denmark (54%)—watched more S&T television than 

Americans. About one-third (34%) of Americans said they 

read news items about S&T “very” or “quite” often in newspapers. This was similar to the 10-country European average 

of 32%. Residents of the Netherlands were the most likely to say they often read S&T news in newspapers (52%), 

although Denmark (48%) and the United Kingdom (43%) 

also had relatively high S&T readership. About 32% of 

Americans said they often read S&T news online, which was 

a percentage comparable to those of the largest European 

countries and substantially above the 10-country European 

average of 24% (BBVA Foundation 2012a). Although these 

data, compared with the GSS information on media use, may 

suggest a less prominent role for the Internet, this may reflect 

a difference in the questions on the two surveys. Whereas 

the GSS asks people for their primary source of information, 

the BBVA Foundation survey asked about overall use for 

each channel. 

Outside of Europe and North America, research has 

also suggested that television is the leading source of S&T 

Figure 7-5

Primary source of information about current news 

events, science and technology, and specific 

scientific issues: 2001–12

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, Survey of Public Attitudes 

Toward and Understanding of Science and Technology (2001); 

University of Michigan, Survey of Consumer Attitudes (2004); 

University of Chicago, National Opinion Research Center, General 

Social Survey (2006–12). See appendix tables 7-3–7-5.

Science and Engineering Indicators 2014

Current news events

Science and technology

Specific scientific issues

Percent

2001 2004 2006 2008 2010 2012

2001 2004 2006 2008 2010 2012

0

10

20

30

40

50

60

70

Television

Newspapers

Internet

0

10

20

30

40

50

60

70

Television

Internet

Newspapers

2001 2004 2006 2008 2010 2012

0

10

20

30

40

50

60

70

Television

Internet

Newspapers
7-18 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

information; newspapers are generally second, and relatively fewer survey respondents cite the Internet as an important 

source of S&T information. This was true in countries such 

as Malaysia (MASTIC 2010) and India (Shukla 2005). A 

2010 Chinese survey allowed respondents to choose up to 

three sources of information. About 88% of Chinese indicated that television was a primary source of their S&T information, 59% said newspapers, and 27% said the Internet 

(CRISP 2010). However, in more widely connected South 

Korea, a 2010 survey found that more respondents named 

the Internet (23%) as their primary source of S&T information than newspapers (12%). About 57% said television was 

their primary source of S&T information. A separate set of 

measures show that 30% said they “almost never” get S&T 

information from television. About 53% said they rarely get 

S&T information from newspapers, and 56% said they rarely get S&T information from the Internet (KOFAC 2011).

Americans and Europeans also appear to differentiate the 

degree to which they trust scientific information provided by 

various sources. The 2011 BBVA Foundation survey of 10 

European countries and the United States asked respondents 

to score a range of different groups on an 11-point scale, 

where “0” meant they did “not trust it at all” and “10” meant 

they trusted it a “great deal.” The results suggest substantial agreement over who should be trusted as an information 

source. In the United States, professional medical associations were the most trusted, with a mean score of 7.6, but 

universities (7.4), science museums (7.2), and government 

(7.2) were also highly trusted. In Europe, universities were 

the most trusted information sources, with a mean score of 

7.2, but medical associations (7.0) and science museums 

(6.9) were also highly regarded. The score for government 

was about a point lower in Europe (6.1) than in the United 

States (7.2) but varied widely across countries. The news 

media was the least trusted source in both the United States 

(4.8) and Europe (5.1), but again scores varied widely in 

Europe. Consumer organizations and environmental organizations had midrange scores in both the United States (6.1 

and 6.2, respectively) and in the European countries surveyed (both 6.3) (BBVA Foundation 2012b).

Although the media received relatively low trust scores 

on the BBVA Foundation S&T survey, a 2011 U.S. survey by Pew Research suggested the media was among the 

most trusted sources of general information (Pew Research 

Center 2011a). This difference may reflect the comparison 

groups involved in the two studies. The Pew Research study 

asked about the trustworthiness of information from the 

media versus various actors typically involved in political 

decision making, and the BBVA study asked about actors 

from a broader range of sources. The Pew Research study 

also focused on general media trust, whereas the BBVA 

Foundation study focused specifically on science.

Involvement

U.S. Patterns and Trends

U.S. residents may also come in contact with S&T 

through America’s rich and diverse informal science and 

cultural institutions. Many of these institutions actively try 

to broaden and deepen Americans’ intellectual and emotional engagement with science (Bell, Lewenstein, Shouse, 

and Feder 2009).10 By offering visitors the flexibility to pursue individual curiosity, such institutions provide exposure 

to S&T that is well-suited to helping people develop their 

interests and improve their knowledge, and such institutions 

can sometimes even change patrons’ attitudes.

The 2012 GSS shows that reported attendance at informal science and cultural institutions was down slightly from 

2008, although the changes were all quite small.11 Zoos and 

aquariums were the most popular type of informal science 

institutions with 47% of Americans saying they had visited 

such an organization in the previous year. This represents a 

drop from 52% in 2008 and 58% in 2001. The Association 

for Zoos and Aquariums’ member surveys have also consistently shown that about half of Americans visit a zoo or 

aquarium in any given year, but their numbers suggest that 

attendance stayed relatively stable between 2008 and 2011 

at about 175 million visitors and then climbed to 181 million 

in 2012.12 According to the GSS, natural history museums 

(28%) and science and technology museums (25%) continued to attract about the same percentage of people in 2012 as 

they did in 2008, although these percentages are also down 

from 2001. In total, 58% of Americans said they had visited 

at least one of these three types of cultural institutions in the 

12 months prior to the 2012 survey, down from 61% in 2008 

and 66% in 2001.13

The public library remains a widely used resource in 

communities across America, with 60% of respondents saying that they had visited a library in the previous 12 months. 

This number was down from 2008 (64%) and 2001 (75%). 

The percentage visiting art museums (33%)—the other cultural institution in the survey—stayed essentially unchanged 

from 2008 (34%) and the earlier 2001 survey (32%) (table 

7-6; appendix table 7-6).

Americans with more years of formal education are 

more likely than others to engage in these informal science 

activities. Those in higher income brackets are more likely 

to have visited a zoo or aquarium, a natural history or S&T 

museum, or an art museum but are just as likely as those in 

the lowest income bracket to have visited a public library. 

In general, visits to informal science institutions are less 

common among Americans who are 45 or older (appendix 

table 7-7). 

A 2012 Pew Research study focused on libraries found 

similar results. It found that 53% of Americans aged 16 or 

older said they had visited a library in the “past year” and 

that women (59%) and residents aged 16–17 (62%) were 
Science and Engineering Indicators 2014 ♦ 7-19

most likely to have done so. Almost everyone (91%) agreed 

that libraries are “very” or “somewhat” important to their 

“community as a whole.” Many also said they used the library for activities such as researching a “topic of interest” 

(54%), using a “research database” (46%), and attending a 

“class, program or lecture for adults” (21%) (Pew Internet & 

American Life Project 2013).

International Comparisons

The available data—some of which are relatively dated—

suggest that Americans are particularly active in the degree 

to which they make use of a range of informal science and 

cultural institutions.

China and Japan are the only countries where zoo and 

aquarium attendance is similar to that in the United States, 

and China also has similar levels of S&T and natural history museum attendance. Chinese attendance at these 

types of institutions also appears to be growing, with average attendance up about 8% from 2007 across the 

five types of cultural institutions measured (NSB 2012) 

(table 7-6).

The 2011 BBVA Foundation survey of 10 European 

countries and the United States asked slightly different 

questions and found that attendance varies greatly between 

countries. About 32% of Americans said they had visited an 

S&T museum or exhibition in the previous 12 months. This 

was higher than the 10-country European average of 25% 

but similar to the rate of attendance by residents of several 

specific countries such as Germany (35%), the Netherlands 

(32%), Denmark (29%), Austria (29%), and France (29%). 

Also, about 12% of Americans said they had attended a “conference or talk on science or technology topics.” This was 

about the same as the European average (12%) but substantially lower than for countries such as the Netherlands (25%) 

and Denmark (27%). Americans were, however, nearly twice 

as likely as those in the 10 European countries surveyed to 

have made a “virtual visit to a science and technology museum via the Internet.” About 20% of Americans said they had 

made such a “visit” in the previous 12 months, whereas the 

10-country European average was 8%, and the highest percentage for an individual country was for Denmark (12%) 

(BBVA Foundation 2012a). As noted previously, the BBVA 

Foundation also found that both Americans and Europeans 

in the 10 countries surveyed see science information from 

museums as more trustworthy than information from many 

other groups (BBVA Foundation 2012b).

Table 7-6

Visits to informal science and other cultural institutions, by country/region: Most recent year

(Percent)

Institution

United 

States

(2012)

Brazil

(2010)

China

(2010)

EU

(2005)

India

(2004)

Japan

(2001)

Malaysia

(2008)

South Korea

(2010)

Zoo/aquariuma ............................. 47 22 58 27 35 43 30 28

Natural history museum .............. 28 NA 22 NA NA 19 NA NA

Science/technology museumb..... 25 8 27 16 12 12 11 9

Public libraryc

............................... 60 29 50 34 27 46 NA 27

Art museumd................................ 33 14 27 23 22 34 30 27

NA = not available, question not asked.

EU = European Union; data are not available for Bulgaria and Romania.

a

 “Zoo” for Brazil, India, and Malaysia; “Zoo, aquarium, botanical garden” for China. 

b “Science museums or technology museums or science centers” for EU; “Science parks” for India; “National Science Centre” for Malaysia; “Science 

museum or exhibition” for South Korea. 

c

 “Library” for Brazil and India.

d “Art gallery or exhibition hall” for China; “Museum” for India and Malaysia; “Museum/art gallery” for South Korea.

NOTES: Responses to (United States, Japan) I am going to read you a short list of places and ask you to tell me how many times you visited each type of 

place during the last year, that is, the last 12 months (percentage includes those who visited each institution one or more times); (Brazil, China, EU) Which 

of the following have you visited in the last 12 months? (multiple answers possible); (India) How frequently did you visit the following during the last 12 

months? (percentage includes those who visited each institution one or more times); (Malaysia, South Korea) In the past year, how many times did you 

visit the following places? (percentage includes those who visited each institution one or more times).

SOURCES: United States—University of Chicago, National Opinion Research Center, General Social Survey (2012); Brazil—Ministry of Science and 

Technology of Brazil, Public Perceptions of Science and Technology (2010); China—Chinese Association for Science and Technology/China Research 

Institute for Science Popularization, Chinese National Survey of Public Scientific Literacy (2010); EU—European Commission, Eurobarometer 224/Wave 

63.1: Europeans, Science and Technology (2005); India—National Council of Applied Economic Research, National Science Survey (2004); Japan—

National Institute of Science and Technology Policy/Ministry of Education, Culture, Sports, Science and Technology, Survey of Public Attitudes Toward 

and Understanding of Science and Technology in Japan (2001); Malaysia—Malaysian Science and Technology Information Center/Ministry of Science, 

Technology and Innovation, Survey of the Public’s Awareness of Science and Technology: Malaysia (2008); South Korea—Korea Foundation for the 

Advancement of Science and Creativity, Survey of Public Understanding of Science and Technology (2010). See appendix table 7-6 for U.S. trends.

Science and Engineering Indicators 2014
7-20 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

help make decisions about the natural world and the 

changes made to it through human activity. (OECD 

2003:132–33)

The degree to which respondents demonstrate an understanding of basic scientific terms, concepts, and facts; an ability 

to comprehend how S&T generates and assesses evidence; 

and a capacity to distinguish science from pseudoscience are 

widely used indicators of basic scientific literacy. 

The 2012 GSS continues to show that many Americans 

provide multiple incorrect answers to basic questions 

about scientific facts and do not apply appropriate reasoning strategies to questions about selected scientific issues. 

Residents of other countries, including highly developed 

ones, appear to perform no better, on balance, when asked 

similar questions.

Understanding Scientific Terms and Concepts

U.S. Patterns and Trends

A primary indicator of public understanding of science 

in the United States comes from a nine-question index of 

factual knowledge questions included in the GSS. In 2012, 

Americans were able to correctly answer an average of 5.8 

of the 9 items (65%), which is slightly up from 2010 (5.6 of 

9 items, or 63%) (appendix table 7-8). 

The public’s level of factual knowledge about science has 

not changed much over the past two decades (figure 7-6). 

Since 2001, the average number of correct answers to a series of nine questions for which fully comparable data have 

been collected has ranged from 5.6 to 5.8 correct responses, 

although scores for individual questions have varied somewhat over time (appendix tables 7-8 and 7-9). Pew Research 

used several of the same questions in a 2013 survey and received nearly identical results (Pew Research Center 2013a).

Factual knowledge of science is strongly related to people’s level of formal schooling and the number of science 

and mathematics courses completed. For example, those 

who had not completed high school answered 45% of the 

nine questions correctly, and those who had completed a 

bachelor’s degree answered 78% of the questions correctly. 

The average percentage correct rose to 83% among those 

who had taken three or more science and mathematics courses in college (figure 7-7). Respondents aged 65 or older are 

less likely than younger Americans to answer the factual 

science questions correctly (appendix table 7-8). Younger 

generations have had more formal education, on average, 

than Americans coming into adulthood some 50 years ago; 

these long-term societal changes make it difficult to know 

whether the association between age and factual knowledge 

is due primarily to aging processes, cohort differences in 

education, or other factors. Analyses of surveys conducted 

between 1979 and 2006 concluded that public understanding of science has increased over time and by generation, 

even after controlling for formal education levels (Losh 

2010, 2012).

Public Knowledge about S&T

Science and Engineering Indicators has been assessing 

Americans’ knowledge about science and technology since 

1979. Initial questions focused on the proper design of a 

scientific study and views about whether pseudoscientific 

belief systems, such as astrology, could be considered scientific. Questions focused on an understanding of probability 

and an understanding of basic constructs were added in the 

late 1980s and early 1990s (Miller 2004). These later questions remain the core of the available data on trends in adult 

Americans’ knowledge of science.

Researchers have questioned both the degree to which 

scientific literacy has a substantial impact on how people 

make decisions in their public and private lives (see, for example, NSB 2012:7-27; Bauer, Allum, and Miller 2007) and 

whether a short battery of questions can assess scientific literacy. Despite the limitations of these indicators, evidence 

suggests that knowledge about science, as measured by the 

GSS, has a small but meaningful impact on attitudes and 

behaviors (Allum et al. 2008). In addition, adult responses 

to an expanded list of knowledge questions drawn from 

tests given to students nationwide indicate that people who 

“answered the additional factual questions accurately also 

tended to provide correct answers to the trend factual knowledge questions” included in the GSS (NSB 2010:7-20). This 

finding suggests that the trend questions used in this report 

represent a reasonable indicator of basic science knowledge. 

At the same time, in light of the limitations of using a small 

number of questions largely keyed to knowledge taught in 

school, generalizations about Americans’ knowledge of 

science should be made cautiously. Toumey et al. (2010) 

recommended additional research aimed at developing a 

measure of S&T literacy focused on how people actually 

use S&T knowledge. Similar challenges confront attempts 

to study health literacy (Berkman, Davis, and McCormack 

2010) and political literacy (Delli Carpini and Keeter 1996). 

More generally, in developing measures for what is often 

termed scientific literacy across nations, the Organisation 

for Economic Co-operation and Development (OECD 2003) 

emphasizes that scientific literacy is a matter of degree and 

that people cannot be classified as either literate or not literate. The OECD noted that literacy had several components:

Current thinking about the desired outcomes of 

science education for all citizens emphasizes the development of a general understanding of important 

concepts and explanatory frameworks of science, of 

the methods by which science derives evidence to support claims for its knowledge, and of the strengths and 

limitations of science in the real world. It values the 

ability to apply this understanding to real situations 

involving science in which claims need to be assessed 

and decisions made...

Scientific literacy is the capacity to use scientific 

knowledge, to identify questions and to draw evidence-based conclusions in order to understand and 
Science and Engineering Indicators 2014 ♦ 7-21

focused on conclusions that the scientific community has 

drawn about the natural world (“according to the theory of 

evolution, human beings, as we know them today, developed from earlier species of animals” and “according to astronomers, the universe began with a big explosion”). 

In 2012, respondents were much more likely to answer 

both questions correctly if the questions were framed as being about scientific theories or ideas rather than about natural world facts. For evolution, 48% of Americans answered 

“true” when presented with the statement that human beings 

evolved from earlier species with no preface, whereas 72% 

of those who received the preface said “true,” a 24 percentage 

point difference.14 These results replicate the pattern from 

2004, when the percentage answering “true” went from 42% 

to 74%, a 32 percentage point difference (NSB 2008). For 

the big bang question, the pattern was very similar: in 2012, 

39% of Americans answered “true” when presented with the 

statement about the origin of the universe without the preface, whereas 60% of those who heard the statement with 

the preface answered “true.” This represents a 21 percentage 

point difference. The 2004 experiment found that including 

the preface increased the percentage who answered correctly 

Factual knowledge about science is also associated with 

sex of the respondent. On average, men tend to answer more 

factual science knowledge questions correctly (70% correct) 

than do women (60% correct) (figure 7-7). However, this pattern depends on the science domain referenced in the question. Men typically score higher than women on questions 

in the physical sciences but not on questions in the biological sciences. Women tend to score at least equally as high as 

men on the biological science questions and often a bit higher 

(table 7-7; appendix table 7-10).

Evolution and the Big Bang

The GSS survey includes two additional true-or-false 

science questions that are not included in the index calculation because Americans’ responses appear to reflect factors beyond unfamiliarity with basic elements of science. 

One of these questions addresses evolution, and the other 

addresses the origins of the universe. To better understand 

Americans’ responses, the 2012 GSS replicated an experiment first conducted in 2004 (NSB 2006). Half of the survey 

respondents were randomly assigned to receive questions 

focused on information about the natural world (“human beings, as we know them today, developed from earlier species 

of animals” and “the universe began with a big explosion”). 

The other half were asked the questions with a preface that 

Figure 7-6

Mean number of correct answers to trend factual 

knowledge of science scale: 1992–2012

Mean 

NOTES: Mean number of correct answers to the nine questions that 

are included in the trend factual knowledge of science scale; see 

appendix table 7-8 for explanation, list of questions, and percentage 

of questions answered correctly. See appendix tables 7-9 and 7-10 

for responses to individual questions. 

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Public Attitudes Toward and 

Understanding of Science and Technology (1992–2001); University of 

Chicago, National Opinion Research Center, General Social Survey 

(2006–12).

Science and Engineering Indicators 2014

1992 1995 1997 1999 2001 2006 2008 2010 2012

0

1

2

3

4

5

6

7

Figure 7-7

Correct answers to trend factual knowledge of 

science scale, by respondent characteristic: 2012

NOTES: Data reect the average percentage of nine questions 

answered correctly. “Don’t know” responses and refusals to respond 

are counted as incorrect. See appendix table 7-8 for explanation, list 

of questions, and additional respondent characteristics. See 

appendix tables 7-9 and 7-10 for responses to individual questions. 

SOURCE: University of Chicago, National Opinion Research Center, 

General Social Survey (2012). 

Science and Engineering Indicators 2014

≥ 3 college science/

mathematics courses

1–2 college science/

mathematics courses

No college science/

mathematics courses

No college

Graduate/professional

degree

Bachelor’s degree

Some college

High school diploma

< High school

Female

Male

All respondents

Average percent of questions answered correctly

1009080706050403020100
7-22 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

from 33% to 62%, a 29 percentage point difference (NSB 

2008). Residents of other countries have been more likely 

than Americans to answer “true” to the evolution question.15

International Comparisons 

Researchers in a range of countries have asked adults 

in their countries identical or substantially similar questions to test their factual knowledge of science in past years. 

Knowledge scores for individual items vary from country to 

country, and no country consistently outperforms the others. 

For the physical science and biological science questions, 

knowledge scores are relatively low in China, Russia, and 

Malaysia. Compared with scores in the United States and 

the EU overall, scores in Japan are also relatively low for 

several questions (table 7-8).16

Science knowledge scores have also varied across Europe, 

with northern European countries, led by Sweden, scoring 

the highest on a set of 13 questions. For a smaller set of four 

questions, administered in 12 European countries in 1992 

and 2005, each country performed better in 2005. In contrast, 

U.S. data on science knowledge did not show upward trends 

over the same period. In Europe, as in the United States, men, 

younger adults, and more highly educated people tend to score 

higher on these questions (NSB 2008).

The 2011 BBVA Foundation survey of 10 European countries and the United States included a set of 22 knowledge 

questions that were mostly different from those that have traditionally been included in Indicators. On average, the United 

States—with a mean score of 14.3 correct answers—performed similarly to many of the European countries surveyed, 

with a score close to the European average (13.4). The highest 

scoring countries were Denmark (15.6) and the Netherlands 

(15.3). Germany (14.8), the Czech Republic (14.6), Austria 

(14.2), the United Kingdom (14.1), and France (13.8) all had 

scores similar to those of the United States.

There were some questions on which Europeans, however, did much better than Americans. For example, for the 

statement, “the earliest humans lived at the same time as the 

dinosaurs,” about 43% of Americans correctly answered 

“false,” whereas 61% of Europeans in the 10 countries surveyed gave the correct response. Another question on which 

Americans did substantially worse focused on nuclear energy. About 47% of Americans correctly indicated that the 

“greenhouse effect” is not caused by the use of nuclear energy, in comparison to 58% of Europeans. Conversely, there 

were several questions on which Americans did substantially better (BBVA Foundation 2012a).17 

Table 7-7

Correct answers to factual knowledge and scientific process questions in physical and biological sciences, 

by sex: 1999–2012

(Average percent correct)

Science topic/sex 1999 2001 2004 2006 2008 2010 2012

Physical science indexa

Male ...................................................................... 72 73 73 74 74 73 75

Female .................................................................. 57 59 55 59 61 60 61

Biological science indexb

Male ...................................................................... 59 61 62 63 60 62 59

Female .................................................................. 61 65 65 66 64 64 62

a

 Physical science index includes five questions:

• The center of the Earth is very hot. (True)

• All radioactivity is man-made. (False)

• Lasers work by focusing sound waves. (False)

• Electrons are smaller than atoms. (True)

• The continents have been moving their location for millions of years and will continue to move. (True) b Biological science index includes six questions (questions 3 and 4 have two parts):

• It is the father’s gene that decides whether the baby is a boy or a girl. (True)

• Antibiotics kill viruses as well as bacteria. (False)

• A doctor tells a couple that their genetic makeup means that they’ve got one in four chances of having a child with an inherited illness. (1) Does this 

mean that if their first child has the illness, the next three will not? (No); (2) Does this mean that each of the couple’s children will have the same risk of 

suffering from the illness? (Yes) Data represent a composite of correct responses to both questions.

• Two scientists want to know if a certain drug is effective against high blood pressure. The first scientist wants to give the drug to 1,000 people with 

high blood pressure and see how many of them experience lower blood pressure levels. The second scientist wants to give the drug to 500 people with 

high blood pressure and not give the drug to another 500 people with high blood pressure, and see how many in both groups experience lower blood 

pressure levels. Which is the better way to test this drug? Why is it better to test the drug this way? (The second way because a control group is used 

for comparison.) Data represent a composite of correct responses to both questions. 

NOTES: Data reflect the average percentage of questions in the index answered correctly. “Don’t know” responses and refusals to respond are counted 

as incorrect.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, Survey of Public Attitudes Toward and Understanding 

of Science and Technology (1999, 2001); University of Michigan, Survey of Consumer Attitudes (2004); University of Chicago, National Opinion Research 

Center, General Social Survey (2006–12). See appendix tables 7-9 and 7-10 for factual knowledge questions. See appendix tables 7-11 and 7-12 for 

scientific process questions (probability and experiment).

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 7-23

Little international polling is done on the question of evolution or the big bang. However, residents of other countries have typically been more likely than Americans to say 

they believe that “human beings, as we know them today, 

developed from an earlier species of animals.” For example, 70% of European respondents in 2005 (NSB 2006) and 

76% of Japanese respondents in 2011 (NISTEP 2012) gave 

this response. 

Reasoning and Understanding 

the Scientific Process

U.S. Patterns and Trends

Another indicator of public understanding of science focuses on understanding of how science generates and assesses evidence, rather than knowledge of particular facts. Such 

measures reflect recognition that knowledge of specific 

Table 7-8

Correct answers to factual knowledge questions in physical and biological sciences, by country/region: 

Most recent year

(Percent giving correct answer)

Question

United 

Statesa

(2012)

China

(2010)

EU

(2005)

India

(2004)

Japan

(2011)

Malaysia

(2008)

Russia

(2003)

South Korea

(2004)

Physical science

The center of the Earth is 

very hot. (True)...................... 84 56 86 57 84 66 NA 87

The continents have been 

moving their location for 

millions of years and will 

continue to move. (True)....... 83 50 87 32 88 44 40 87

Does the Earth go around the 

Sun, or does the Sun go 

around the Earth? (Earth 

around Sun).......................... 74 NA 66 70 NA 72 NA 86

All radioactivity is manmade. (False)........................ 72 48 59 NA 69 14 35 48

Electrons are smaller than 

atoms. (True)......................... 53 27 46 30 32 33 44 46

Lasers work by focusing 

sound waves. (False) ............ 47 23 47 NA 32 16 24 31

The universe began with a 

huge explosion. (True) .......... 39 NA NA 34 NA NA 35 67

Biological science

It is the father’s gene that 

decides whether the baby is 

a boy or a girl.

b (True)............ 63 58 64 38 29 40 22 59

Antibiotics kill viruses as well 

as bacteria.

c

 (False) .............. 51 28 46 39 33 8 18 30

Human beings, as we know 

them today, developed from 

earlier species of animals. 

(True)..................................... 48 66 70 56 76 NA 44 64

NA = not available, question not asked.

EU = European Union; data are not available for Bulgaria and Romania.

a

 See appendix table 7-9 for U.S. trends.

b China and Europe surveys asked about “mother’s gene” instead of “father’s gene.”

c

 Japan survey asked about “antibodies” instead of “antibiotics.”

SOURCES: United States—University of Chicago, National Opinion Research Center, General Social Survey (2012); China—Chinese Association 

for Science and Technology/China Research Institute for Science Popularization, Chinese National Survey of Public Scientific Literacy (2010); EU—

European Commission, Eurobarometer 224/Wave 63.1: Europeans, Science and Technology (2005), and Eurobarometer 224/Wave 64.3: Europeans and 

Biotechnology in 2005: Patterns and Trends (2006); India—National Council of Applied Economic Research, National Science Survey (2004); Japan—

National Institute of Science and Technology Policy/Ministry of Education, Culture, Sports, Science and Technology, Survey of Public Attitudes Toward 

and Understanding of Science and Technology in Japan (2011); Malaysia—Malaysian Science and Technology Information Centre/Ministry of Science, 

Technology and Innovation, Survey of the Public’s Awareness of Science and Technology: Malaysia (2008); Russia—Gokhberg L, Shuvalova O, Russian 

Public Opinion of the Knowledge Economy: Science, Innovation, Information Technology and Education as Drivers of Economic Growth and Quality of 

Life, British Council, Russia (2004); South Korea—Korea Science Foundation (now Korea Foundation for the Advancement of Science and Creativity), 

Survey of Public Attitudes Toward and Understanding of Science and Technology (2004).

Science and Engineering Indicators 2014
7-24 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

S&T facts is conceptually different from knowledge about 

the overall scientific processes (Miller 1998). 

Data on three general topics—probability, experimental 

design, and the scientific method—show trends in Americans’ 

understanding of the process of scientific inquiry. One set of 

questions tests how well respondents apply the principles of 

probabilistic reasoning to a series of questions about a couple 

whose children have a 1 in 4 chance of suffering from an inherited disease. A second set of questions deals with the logic 

of experimental design, asking respondents about the best 

way to design a test of a new drug for high blood pressure. A 

third, open-ended question probes what respondents think it 

means to “study something scientifically.” Because probability, experimental design, and the scientific method are all central to scientific research, these questions are relevant to how 

respondents evaluate scientific evidence. These measures are 

reviewed separately and then as a combined indicator of public understanding about scientific inquiry.

With regard to probability, 82% of Americans in 2012 

correctly indicated that the fact that a couple’s first child 

has the illness has no relationship to whether three future 

children will have the illness. About 72% of Americans correctly responded that the odds of a genetic illness are equal 

for all of a couple’s children. Overall, 65% got both probability questions correct. Understanding of probability has 

been fairly stable over time, with the percentage giving both 

correct responses ranging from 64% to 69% since 1999 and 

going no lower than 61% dating back to 1990 (table 7-9; appendix tables 7-11 and 7-12).18

With regard to understanding experiments, one-third (34%) 

of Americans were able to answer a question about how to test 

a drug and then provide a correct response to an open-ended 

question that required them to explain the rationale for an experimental design (i.e., giving 500 people a drug while not 

giving the drug to 500 additional people as a control group). 

A smaller percentage of people were able to answer this set 

of questions in 2012 than were in 2010, when 51% answered 

correctly (table 7-9). However, this change should be treated 

with particular caution because of the way these types of survey responses rely on human coders to categorize responses 

and because the 2010 figure represents an historical high.19

The percentage of people the 2012 GSS judged as understanding what it means to study something scientifically 

was more consistent with previous surveys. About 20% of 

Americans were scored as correctly answering the GSS 

question on this topic. When describing the scientific method, these respondents mentioned that it involves at least one 

of the following: testing a theory using hypotheses, conducting an experiment with a control group, or making rigorous 

and systematic comparisons. The percentage of Americans 

providing at least one of these acceptable answers has declined somewhat from a high of 26% in 2001, although the 

2012 result is similar to percentages in recent years.

Table 7-9

Correct answers to scientific process questions: Selected years, 1999–2012

(Percent)

Question 1999 2001 2004 2006 2008 2010 2012

Understanding of scientific inquiry scalea ....................... 32 40 39 41 36 42 33

Components of understanding scientific inquiry scale

Understanding of probabilityb ...................................... 64 67 64 69 64 66 65

Understanding of experimentc ..................................... 34 40 46 42 38 51 34

Understanding of scientific studyd............................... 21 26 23 25 23 18 20

a

 To be classified as understanding scientific inquiry, the survey respondent had to (1) answer correctly the two probability questions stated in footnote b 

and (2) either provide a theory-testing response to the open-ended question about what it means to study something scientifically (see footnote d) or a 

correct response to the open-ended question about experiment (i.e., explain why it is better to test a drug using a control group [see footnote c]). 

b To be classified as understanding probability, the survey respondent had to answer correctly A doctor tells a couple that their genetic makeup means 

that they’ve got one in four chances of having a child with an inherited illness. (1) Does this mean that if their first child has the illness, the next three will 

not have the illness? (No); and (2) Does this mean that each of the couple’s children will have the same risk of suffering from the illness? (Yes). c

 To be classified as understanding experiment, the survey respondent had to answer correctly (1) Two scientists want to know if a certain drug is effective 

against high blood pressure. The first scientist wants to give the drug to 1,000 people with high blood pressure and see how many of them experience 

lower blood pressure levels. The second scientist wants to give the drug to 500 people with high blood pressure and not give the drug to another 500 

people with high blood pressure, and see how many in both groups experience lower blood pressure levels. Which is the better way to test this drug? and 

(2) Why is it better to test the drug this way? (The second way because a control group is used for comparison). d To be classified as understanding scientific study, the survey respondent had to answer correctly (1) When you read news stories, you see certain 

sets of words and terms. We are interested in how many people recognize certain kinds of terms. First, some articles refer to the results of a scientific 

study. When you read or hear the term scientific study, do you have a clear understanding of what it means, a general sense of what it means, or little 

understanding of what it means? and (2) (If “clear understanding” or “general sense” response) In your own words, could you tell me what it means to 

study something scientifically? (Formulation of theories/test hypothesis, experiments/control group, or rigorous/systematic comparison).

NOTES: Data reflect the percentage of survey respondents who gave a correct response to each concept. “Don’t know” responses and refusals to 

respond are counted as incorrect and are not shown. See appendix table 7-11 for more detail on the probability questions and for years before 1999.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, Survey of Public Attitudes Toward and Understanding 

of Science and Technology (1999, 2001); University of Michigan, Survey of Consumer Attitudes (2004); University of Chicago, National Opinion Research 

Center, General Social Survey (2006–12).

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 7-25

Overall, when these questions are combined into an overall measure of “understanding of scientific inquiry,” the 

2012 results are relatively low compared with those from 

other years. About 33% of Americans could both correctly 

respond to the two questions about probability and provide 

a correct response to at least one of the open-ended questions about experimental design or what it means to study 

something scientifically. The 2010 survey represents a high 

point (42%), and the current result is closest to scores seen 

in the late 1990s but lower than scores in the other surveys 

conducted since 2001 (table 7-9; appendix table 7-11). In 

general, respondents with more education did better on the 

scientific inquiry questions (figure 7-8; appendix table 7-12). 

International Comparisons

The 2011 BBVA Foundation survey of 10 European 

countries and the United States included the standard question about probability in the context of genetic disease. In this 

instance, 61% of Americans could correctly indicate that a 

child’s susceptibility to a genetic disease was unaffected by 

whether the child’s siblings suffered from the disease. This 

percentage is substantially lower than the 82% found in the 

2012 GSS (see previous section). The 10-country European 

average was 49%, but residents of both Denmark (81%) 

and the Netherlands (79%) did better on this question than 

Americans. UK residents (60%) had a score nearly identical 

to that of U.S. residents (BBVA Foundation 2012a).

Recent surveys from Asia also touch on reasoning and 

understanding. A 2010 Chinese survey reported that 49% 

understood the idea of probability, 20% understood the need 

for comparisons in research, and 31% understood the idea of 

“scientific research” (CRISP 2010). The exact wording of 

the questions used was not available, but given that much of 

the survey replicated past U.S. questions reported in Science 

and Engineering Indicators, it seems likely that these questions were similar to those asked in the United States. In 

a July 2011 Japanese survey, 62% correctly answered a 

multiple choice question about the use of control groups 

in research experiments, whereas 57% answered correctly 

in a follow-up December 2011 survey (NISTEP 2012). A 

Korean survey used self-report measures of knowledge. 

Koreans were most likely to say they knew “well” or “very 

well” about diseases (54%) and least likely to say they knew 

about nanotechnology (14%). Koreans were also unlikely to 

say they knew about stem cell research (15%) and genetic 

modification (20%) (KOFAC 2011).

Comparisons of Adult and K–12 Student 

Understanding

The 2008 GSS included several additional questions 

on the scientific process that also indicated that many 

Americans lack an understanding of experimental design.20

Between 29% and 57% of Americans responded correctly to 

various questions measuring the concepts of scientific experiment and controlling variables. Only 12% of Americans 

responded correctly to all the questions on this topic, and 

nearly 20% did not respond correctly to any of them (NSB 

2010). These data raise further questions about how well 

Americans can reliably apply a generalized understanding of 

experimental design across different situations. Responses to 

these questions also allowed a comparison between adults’ 

understanding of experimentation and that of middle school 

students tested on the same questions. On the three experimental knowledge questions in which direct comparison is 

possible, adults’ scores were similar to a national sample of 

middle school students on one question but were lower on 

two others (NSB 2010). 

Pseudoscience

Another indicator of public understanding about S&T 

comes from a measure focused on the public’s capacity to 

distinguish science from pseudoscience. Since 1979, surveys have asked Americans whether they view astrology 

as being scientific. In 2012, about half of Americans (55%) 

said astrology is “not at all scientific.” One-third (32%) said 

they thought astrology was “sort of scientific,” and 10% said 

it was “very scientific.” About 4% said they did not know. In 

comparison, in 2010, 62% of Americans said that astrology 

was not scientific, and this percentage has hovered between 

55% (2012) and 66% (2004) since 1985. The only years 

Figure 7-8

Understanding scientific inquiry, by respondent 

characteristic: 2012

NOTES: See appendix table 7-11 for an explanation of understanding 

scientic inquiry and questions included in the index. See appendix 

table 7-12 for additional respondent characteristics.

SOURCE: University of Chicago, National Opinion Research Center, 

General Social Survey (2012). 

Science and Engineering Indicators 2014

Percent understanding scientic inquiry 

≥ 3 college science/

mathematics courses

1–2 college science/

mathematics courses

No college science/

mathematics courses

No college

Graduate/professional

degree

Bachelor’s degree

Some college

High school diploma

< High school

Female

Male

All respondents

1009080706050403020100
7-26 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

when a smaller percentage of respondents said that astrology was not at all scientific were in 1979, when 50% gave 

this response, and in 1983, when 51% gave this response.

Respondents with more years of formal education and 

higher income were less likely to see astrology as scientific. 

For example, in 2012, 72% of those with graduate degrees 

indicated that astrology is “not at all scientific,” compared 

with 34% of those who did not graduate from high school. 

Between 2010 and 2012, responses to the astrology question changed more among Americans with less education 

and factual knowledge than among other Americans. For 

example, in 2010, 79% of those high in factual knowledge 

said astrology was “not at all scientific,” which was only 

5% more than the 74% who gave this response in 2012. In 

contrast, 52% of those with the lowest factual knowledge 

said astrology was unscientific in 2010 compared with 35% 

in 2012, which is a 17% change.

Age was also related to perceptions of astrology. Younger 

respondents, in particular, were the least likely to regard astrology as unscientific, with 42% of the youngest age group 

(18–24) saying that astrology is “not at all scientific.” The 

largest change, however, occurred in the 35–44 age group. 

In 2010, 64% of respondents in this group said that astrology 

was not scientific, whereas 51% gave this response in 2012, 

which is a 13% change (appendix table 7-13).21

International Comparisons

A 2010 Chinese survey had multiple questions about superstition. It found that 80% of respondents did not believe 

in “fortune telling sticks,” 82% did not believe in face reading, 87% did not believe in dream interpretation, 92% did 

not believe in horoscopes, and 95% did not believe in “computer fortune telling” (CRISP 2010). 

Perceived Knowledge about Causes and 

Solutions to Environmental Problems

U.S. Patterns and Trends

Along with actual knowledge, perceived knowledge may 

also affect individuals’ attitudes and behaviors (Ladwig et 

al. 2012; Griffin, Dunwoody, and Yang 2013). The 2010 

GSS included two questions about how much Americans 

believed they personally knew about the causes of and solutions to environmental problems. These questions used a 

5-point scale that went from “1” for “know nothing at all” 

to “5” for “know a great deal.” About 27% of Americans 

chose a “4” or “5” when asked to assess their knowledge of 

the causes of environmental problems, and 14% chose “4” or 

“5” to describe their knowledge of environmental solutions 

(figure 7-9; appendix tables 7-14 and 7-15). 

International Comparisons

The 2010 International Social Survey Programme (ISSP) 

allows for international comparisons of perceived science 

knowledge. The 2010 ISSP asked questions in 31 countries, 

including the United States, about perceived knowledge 

bearing on environmental issues. The results show that residents of most other countries surveyed expressed more confidence than Americans about their knowledge of the causes 

of and solutions to environmental problems. The country 

with the highest percentage of survey takers choosing “4” 

or “5” on the 5-point scale for perceived knowledge of the 

causes of environmental problems was Norway (50%). 

The United States (27%) had a much lower percentage, 

although its percentage was similar to that of many other 

countries. Only Slovak Republic respondents reported less 

knowledge, on average, than U.S. respondents about causes 

of environmental problems. Residents of more than half of 

the countries surveyed gave responses that suggested they 

knew more. On the subject of environmental solutions, the 

top countries saw about one-third of residents saying they 

understood the solutions to environmental problems. The 

United States (14%) was among the countries with the lowest percentages of residents who said they understood the solutions to environmental problems. Only the Russians (13%) 

reported less knowledge, on average, than the Americans 

about environmental solutions. It is also noteworthy that no 

country’s citizens thought they knew more about solutions 

than causes but that the difference in mean scores for the 

two questions was almost always less than half a point on the 

5-point scale used by the ISSP (figure 7-9; appendix tables 

7-14 and 7-15). 

Public Attitudes about S&T in General

How people perceive science can matter in a range of different ways. It can affect the public’s willingness to fund 

S&T through public investment, young people’s willingness to enter into S&T training and choose jobs in S&T, 

and parents’ willingness to encourage such career paths. 

Committing resources—whether time or money—to S&T 

means trusting that our commitment will pay off over the 

long term for ourselves, our families, and our communities. 

General views about S&T may also affect our views about 

specific technologies and research programs that could enhance our lives or pose new risks. 

This section presents general indicators of public attitudes 

and orientations toward S&T in the United States and other 

countries. It covers views on the promises of S&T and reservations about science, overall support for government funding of research, confidence in scientific community leaders, 

views of science and engineering as occupations, and views 

about the degree to which specific fields and work activities 

are scientific. Overall, the data make it clear that Americans 

support both S&T and the people involved in S&T.
Science and Engineering Indicators 2014 ♦ 7-27

Figure 7-9

Public self-assessment of knowledge about causes of and solutions to environmental problems, by country/

economy: 2010

NA = not available.

NOTES: Responses to How much do you feel you know about the causes of/solutions to these sorts of environmental problems, where 1 indicates you feel 

you know nothing at all and 5 indicates you feel you know a great deal? Percentages may not add to 100% because of rounding. 

SOURCE: International Social Survey Program, Environment Module (2010). See appendix tables 7-14 and 7-15.

Science and Engineering Indicators 2014

Cumulative percent

0 20 40 60 80 100

Cumulative percent

5-point scale, where 5 = knows a great deal and 1 = knows nothing at all. 5 and 4 3 2 and 1 Can’t choose/no answer

Knowledge about causes Knowledge about solutions

Slovak Republic

Japan

Lithuania

South Korea

Czech Republic

Spain

Chile

Russia

Mexico

Bulgaria

Latvia

Argentina

Turkey

Belgium

South Africa

United Kingdom

Austria

Denmark

Croatia

Sweden

Taiwan

Germany

New Zealand

Philippines

France

Slovenia

Switzerland

Canada

Finland

Israel

Norway

United States

0 20 40 60 80 100

Turkey NA

Japan

Slovak Republic

Russia

Lithuania

Spain

Chile

Czech Republic

South Korea

Latvia

Belgium

Bulgaria

Germany

Taiwan

Denmark

Argentina

Austria

Sweden

United Kingdom

Switzerland

South Africa

Croatia

Israel

Mexico

Canada

New Zealand

France

Slovenia

Norway

Finland

Philippines

United States
7-28 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

Promises and Reservations about S&T

U.S. Patterns and Trends

Overall, Americans remain strong believers in the benefits of S&T even while seeing potential risks. Surveys since 

at least 1979 show that roughly 7 in 10 Americans see the effects of scientific research as more positive than negative for 

society. In 2012, this included 50% who said they believed 

the benefits “strongly” outweigh the negatives and 22% 

who said the benefits slightly outweigh the potential harms 

(appendix table 7-16). About 7% said science creates more 

harms than benefits. These numbers are generally consistent 

with earlier surveys; Americans saying the benefits strongly 

or slightly outweigh the harmful results have ranged from 

68% to 80% since this question was initially asked in the 

1970s (figure 7-10). 

Americans with more education, income, and scientific 

knowledge hold a stronger belief in the benefits of science 

than others. For example, 55% of those who had not completed high school said they believe science does more good 

than harm, but 89% of those with bachelor’s degrees and 

92% of those with graduate degrees expressed this view. 

Similarly, 86% of those in the top income quartile saw 

more benefits than harms from science, whereas 60% of 

those in the lowest bracket expressed this view. Almost all 

(87%) of those in the top knowledge quartile said they saw 

more benefits than harms, but just half (50%) of those in 

the lowest knowledge quartile gave this response (appendix 

table 7-16).22

Americans also overwhelmingly agree that S&T will foster “more opportunities for the next generation” but continue 

to express worry that it may make life change too quickly. 

In 2012, about 87% of Americans “agreed” or “strongly 

agreed” that S&T will create more opportunities (appendix table 7-17). This was down very slightly from the 2006 

through 2010 surveys, during which time 89%–91% agreed 

about the relative value of S&T (NSB 2008, 2010, 2012). 

Fewer Americans, however, said they were worried about 

the pace of change. In 2012, 42% of Americans agreed that 

“science makes our way of life change too fast” (appendix 

table 7-18). This represents a substantial drop from 2010, 

when 51% expressed worry about the pace of change (NSB 

2012). It also represents a shift in the trend line as worry had 

previously increased steadily from 33% in 2004 (NSB 2006, 

2008, 2010).

International Comparisons

The 2010 ISSP also included two questions about the 

promises of science. It asked respondents in 31 countries 

whether they thought that societies were putting too much 

faith in science and whether science may do more harm than 

good. Comparable data were also collected by the ISSP program in multiple countries in 1993 and 2000.

In 2010, about 41% of U.S. residents “agreed” or 

“strongly agreed” that “we believe too often in science, and 

not enough in feelings and faith.” The average response of 

U.S. residents put the United States in the middle range of 

countries. Over time, Americans have become more likely to 

disagree with the statement, along with several other countries (figure 7-11; appendix table 7-19). A small proportion 

of Americans (14%) also said they “strongly agreed” or 

“agreed” that “modern science does more harm than good” 

in 2010 (figure 7-11). The average response has remained 

relatively stable across the three survey years in most countries, and most other countries surveyed also expressed more 

negative views toward science (appendix table 7-20).

The 2011 BBVA Foundation survey also asked a range 

of questions about general attitudes toward science. It found 

Figure 7-10

Public assessment of scientific research: 2012–1979

NOTES: Responses to People have frequently noted that scientific 

research has produced benefits and harmful results. Would you say 

that, on balance, the benefits of scientific research have outweighed 

the harmful results, or have the harmful results of scientific research 

been greater than its benefits? In this gure, “Benets...outweigh 

harmful results” and “Harmful results...outweigh benets” each 

combine responses of “strongly outweigh” and “slightly outweigh.” 

Figure includes all years for which data were collected. Percentages 

may not add to 100% because of rounding.

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Public Attitudes Toward and 

Understanding of Science and Technology (1979–2001); University of 

Michigan, Survey of Consumer Attitudes (2004); University of 

Chicago, National Opinion Research Center, General Social Survey 

(2006–12). See appendix table 7-16.

Science and Engineering Indicators 2014

Cumulative percent

0 20 40 60 80 100

Benets of scientic research strongly/slightly outweigh 

harmful results

Benets of scientic research are about equal to harmful results

Harmful results of scientic research strongly/slightly outweigh 

benets

Don’t know

1979

1981

1985

1988

1990

1992

1995

1997

1999

2001

2004

2006

2008

2010

2012
Science and Engineering Indicators 2014 ♦ 7-29

that Europeans and Americans were similar in endorsing the 

benefits of science but that Europeans in the 10 countries 

surveyed expressed more reservations. The survey used an 

11-point scale that went from “totally disagree” at “0” to 

“totally agree” at “10” for all questions. Seven questions 

assessed perceptions about the “positive facets of science,” and 11 questions addressed reservations (appendix 

table 7-21).

As noted, it appears that Americans hold similar views 

to the 10-country European average and, in some cases, 

Figure 7-11

Public assessment of belief in science versus faith, and whether science does more harm than good, by country/

economy: 2010

NOTES: Responses to How much do you agree or disagree with the statements: We believe too often in science, and not enough in feelings and faith and

Overall, modern science does more harm than good? Percentages may not add to 100% because of rounding. 

SOURCE: International Social Survey Program, Environment Module (2010). See appendix tables 7-19 and 7-20.

Science and Engineering Indicators 2014

Cumulative percent

0 20 40 60 80 100

Cumulative percent

Strongly disagree/disagree Neither agree nor disagree Agree/strongly agree Can’t choose/no answer

Level of agreement with “We believe too often 

in science, and not enough in feelings and faith”

Level of agreement with “Overall,

modern science does more harm than good”

0 20 40 60 80 100

Chile

Philippines

Russia

Bulgaria

Japan

South Africa

South Korea

Mexico

Spain

Switzerland

Latvia

Slovenia

Argentina

Lithuania

Czech Republic

Austria

Germany

Slovak Republic

Belgium

Israel

Croatia

United Kingdom

France

New Zealand

Sweden

Finland

Denmark

Taiwan

Norway

Canada

Turkey

United States

Philippines

Chile

South Africa

Mexico

Slovenia

Argentina

Bulgaria

Japan

Croatia

Russia

Slovak Republic

Turkey

Latvia

Lithuania

Austria

France

Switzerland

Czech Republic

Belgium

South Korea

United Kingdom

New Zealand

Israel

Taiwan

Spain

Sweden

Germany

Canada

Denmark

Finland

Norway

United States
7-30 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

see less promise for science than the residents of the other 

countries surveyed. For example, survey recipients were 

asked whether they disagreed or agreed with the statement 

that “science is the motor of progress.” The U.S. average 

agreement was 6.9, lower than the European average of 7.4 

and tied with the United Kingdom for the lowest average. 

The Czech Republic (7.9) and Poland (7.9) had the highest 

average agreement. Another statement addressed whether 

“science is central to a society’s culture.” The U.S. average was 6.3, lower than the overall European average of 6.8, 

although a few European countries had lower scores. The 

lowest was Denmark, with an average score of 5.3, and the 

highest was Germany, with an average score of 7.3.

On several questions, however, Americans expressed 

fewer reservations than Europeans. For example, fewer 

Americans agreed that “people would be better off if they 

lived a simpler life, without so much science and technology.” Americans had an average score of 4.4 on this question, whereas the 10-country European average was 5.1. 

Germany (4.0) and Denmark (3.4) were the only countries 

that provided a more pro-science response than the United 

States. Indeed, Denmark and Germany were the only two 

countries that were consistently as positive, or more positive, than the United States. The United Kingdom was also 

often similar to the United States. Americans were the most 

likely to disagree that “science drives out religion” and that 

“science makes our way of life change too fast.” The U.S. 

score on the religion question was 3.9, whereas the 10-country European average was 4.9. The U.S. score on the “way 

of life question” was 4.7, whereas the 10-country European 

average was 6.0 (BBVA Foundation 2012b).

Within Asia, different question wording makes comparisons difficult, but most respondents appeared to support 

S&T. In 2010, 75% of Chinese respondents “fully” or “basically” agreed that S&T brings more advantages than disadvantages, whereas only one-fifth (20%) said they thought 

that “we are too dependent on science such that we overlook 

belief” (CRISP 2010). In 2011, 54% of Japanese respondents said “there are more pluses” or “on the whole, there are 

more pluses” to S&T development (NISTEP 2012). Koreans 

were asked separate questions about the risks and benefits 

of S&T. About 78% “agreed” or “somewhat agreed” that 

S&T promotes a “healthy and convenient life,” and 76% 

agreed that S&T “helps in everyday life.” However, 65% 

also agreed that S&T “creates problems” (KOFAC 2011). 

Federal Funding of Scientific Research

U.S. Patterns and Trends

U.S. public opinion consistently and strongly supports 

federal spending on basic scientific research. In 2012, 83% 

of Americans “agreed” or “strongly agreed” that “even if 

it brings no immediate benefits, scientific research that advances the frontiers of knowledge is necessary and should 

be supported by the federal government.” This is similar to 

both 2010 (82%) and 2008 (84%). Since 1985, agreement 

Figure 7-12

Public opinion on whether the federal government 

should fund basic scientific research: 1985–2012

Percent

NOTES: Responses to Even if it brings no immediate benefits, 

scientific research that advances the frontiers of knowledge is 

necessary and should be supported by the federal government. Do 

you strongly agree, agree, disagree, or strongly disagree? Responses 

of “don’t know” are not shown. 

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Public Attitudes Toward and 

Understanding of Science and Technology (1985–2001); University of 

Michigan, Survey of Consumer Attitudes (2004); University of 

Chicago, National Opinion Research Center, General Social Survey 

(2006–12). See appendix tables 7-22 and 7-23.

Science and Engineering Indicators 2014

1985 1988 1992 1997 2001 2004 2008 2012

0

20

40

60

80

100

Strongly agree/agree

Disagree/strongly disagree

with this statement has ranged from a low of 76% in 1992 

to a high of 87% in 2006 (figure 7-12; appendix table 7-22). 

Americans with relatively higher levels of education 

and more science knowledge are particularly likely to support funding scientific research. For example, 75% of those 

who had not completed high school agreed that funding was 

needed, but 94% of those with graduate degrees expressed 

this view. Also, 73% of those in the lowest quartile of S&T 

knowledge agreed that support was needed, whereas 88% of 

those in the highest knowledge quartile expressed this view 

(appendix table 7-23). 

Another indicator of views about S&T is the percentage 

of Americans who say they think the government is spending 

too little on scientific research. In 2012, 38% of respondents 

said government was spending “too little,” 45% said the 

amount was “about right,” and 12% said it was “too much.” 

The percentage who said they thought the government spent 

too little on science gradually increased from 1981 to 2006, 

fluctuating between 29% and 34% in the 1980s, between 

30% and 37% in the 1990s, and between 34% and 41% in 

the 2000s and 2010s (figure 7-13; appendix table 7-24). Pew 

Research also found that about one-third of Americans support more spending on scientific research (Pew Research 

Center 2011b). Other research showed that more than half 

of Americans reject cuts to science (Pew Research Center 
Science and Engineering Indicators 2014 ♦ 7-31

2012c) and nearly three-quarters of Americans expect that 

spending on scientific research will pay off in the long term 

(Pew Research Center 2009).

Compared with support for government spending in other 

areas, however, support for spending on scientific research 

is not especially strong, according to the GSS. Americans 

are more likely to say several other areas need government 

spending more than S&T. Education (75%) consistently 

receives the most support from Americans, compared with 

about 6 in 10 who say that government should spend more 

on assistance to the poor (61%), health (61%), development 

of alternative energy sources (60%), and environmental protection (58%). Support for increased spending on scientific 

research (38%) is roughly comparable to that for spending 

on improving mass transportation (38%) but garners more 

support than parks and recreation (31%), national defense 

(24%), space exploration (22%), and assistance to foreign 

countries (7%) (figure 7-14; appendix table 7-24).23

International Comparisons

In other countries where similar, although not identical, 

questions have been asked, respondents also express strong 

support for government spending on scientific research. In 

2010, 72% of EU residents agreed that “even if it brings 

no immediate benefits, scientific research which adds to 

knowledge should be supported by government,” and only 

9% disagreed (European Commission 2010a). In 2010, 

77% of Chinese agreed to a similar statement regarding the 

need for support (CRISP 2010). Although the comparable 

U.S. percentages for agreement with the need for support 

are nominally higher (83%), the absence of a middle option 

(e.g., “neither agree nor disagree”) rather than a difference in 

underlying opinions may account for this difference. Levels 

of agreement in South Korea, Malaysia, Japan, and Brazil 

Figure 7-13

Public assessment of amount of government 

spending for scientific research: 1981–2012

Percent

NOTES: Responses to We are faced with many problems in this 

country, none of which can be solved easily or inexpensively. I’m going 

to name some of these problems, and for each one, I’d like you to tell 

me if you think that the government is spending too little money on it, 

about the right amount, or too much: [supporting scientific research].

Responses of “right amount” and “don’t know” are not shown.

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Public Attitudes Toward and 

Understanding of Science and Technology (1981–2001); University of 

Chicago, National Opinion Research Center, General Social Survey 

(2002–12). See appendix table 7-24.

Science and Engineering Indicators 2014

1981 1985 1988 1992 1999 2004 2008 2012

0

10

20

30

40

50

Too little

Too much

Figure 7-14

Public assessment of government spending in 

various policy areas: 2012

NOTE: Responses to We are faced with many problems in this 

country, none of which can be solved easily or inexpensively. I’m 

going to name some of these problems, and for each one, I’d like you 

to tell me if you think that the government is spending too little money 

on it, about the right amount, or too much.

SOURCE: University of Chicago, National Opinion Research Center, 

General Social Survey (2012). See appendix table 7-24. 

Science and Engineering Indicators 2014

Cumulative percent

40200 60 80 100

Too little About right Too much Don’t know

Assistance to

other countries

Welfare

Space exploration

National defense

Assistance

to big cities

Assistance to blacks

Parks and recreation

Supporting

scientic research

Mass transportation

Highways

and bridges

Assistance

for child care

Drug rehabilitation

Law enforcement

Social Security

Environment

Developing alternative

energy sources

Health

Assistance

for the poor

Education
7-32 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

have also been similar to the United States and Europe (NSB 

2012). In 2010, 64% of Koreans said S&T “requires public 

support,” and 35% said they wanted to see more investment 

in S&T research (KOFAC 2011).

Confidence in the Science Community’s 

Leadership

U.S. Patterns and Trends

Few members of the public have the background knowledge or resources to fully evaluate scientific questions in the 

public sphere. People, therefore, often rely on how they perceive decision makers as a decision aid (Earle, Siegrist, and 

Gutscher 2007; Kahan, Jenkins-Smith, and Braman 2011). 

Public confidence in leaders of the scientific community 

can therefore affect public acceptance of findings and conclusions based on scientific research. Since 1973, the GSS 

has tracked public confidence in the leadership of various 

institutions, including the scientific community. The GSS 

asks respondents whether they have “a great deal of confidence,” “only some confidence,” or “hardly any confidence 

at all” in the leaders of different institutions. In 2012, 41% 

of Americans expressed “a great deal of confidence” in leaders of the scientific community, nearly half (49%) expressed 

“some confidence,” and fewer than 1 in 10 (7%) expressed 

“hardly any confidence at all” (figure 7-15).

These results suggest that leaders of the scientific community compare well to leaders of other institutions in 

America. Only military leaders generated greater public 

confidence in 2012, with 53% of Americans saying they had 

a “great deal of confidence” in them. The scientific community (41%) and the medical community (40%) shared about 

equal levels of confidence. Since at least the 1970s, a similar 

percentage of Americans have said they place a “great deal 

of confidence” in the scientific community, whereas the percentage saying this about the medical community has fallen 

from highs of 61% in the mid-1970s (appendix table 7-25).

International Comparisons

The 2011 BBVA Foundation survey also found that scientists were among the most positively viewed groups in 

both the United States and the 10 European countries surveyed. Teachers and engineers were also viewed positively. 

The survey used an 11-point scale in which “0” means the 

respondent believed “that [the] group does not contribute at 

all to the welfare and progress of society” and “10” means 

“it contributes a great deal.” Doctors scored 8.4 in the United 

States and 8.2 in Europe. Scientists scored 8.1 in the United 

States and 7.9 in Europe. Teachers were more positively 

viewed in the United States (8.5) than in the 10 countries 

surveyed in Europe (7.6), but they were still near the top 

for both locations. Engineers received scores of 7.9 in the 

United States and 7.6 in Europe (BBVA Foundation 2012b).

Levels of reported trust varied in two Asian surveys 

that used different questions. A 2010 Korean survey found 

that 32% “strongly agreed” or “agreed” that “scientists can 

always be trusted” (KOFAC 2011). In contrast, a 2011 survey 

in Japan found that 69% of respondents said scientists could 

be “trusted” or “somewhat trusted.” Even more respondents 

(77%) said engineers could be trusted (NISTEP 2012).

Views of S&E Occupations

U.S. Patterns and Trends

Data on public esteem for S&E occupations are an indicator of the attractiveness of these occupations and their ability to recruit talented people into their ranks. Such data may 

therefore have a bearing on the degree to which S&E affects 

the nation’s well-being in the future. Perceptions of specific 

occupations may also provide a picture of the degree to which 

people have confidence in those involved in S&E. Past research shows that when people—especially children—are 

asked to “draw a scientist,” they often rely on relatively unflattering stereotypes (Losh, Wilke, and Pop 2008). 

The 2012 GSS included questions aimed at assessing 

how people view scientists and engineers. Half of the respondents were asked questions about scientists, and half 

were asked identical questions about engineers. Many of 

Figure 7-15

Public confidence in institutional leaders, by type of 

institution: 2012

NOTE: Responses to As far as the people running these institutions are 

concerned, would you say that you have a great deal of confidence, 

only some confidence, or hardly any confidence at all in them?

SOURCE: University of Chicago, National Opinion Research Center, 

General Social Survey (2012). See appendix table 7-25. 

Science and Engineering Indicators 2014

Cumulative percent

200 40 8060 100

A great deal Some Hardly any Don’t know

Congress

Press

Television

Banks and

nancial institutions

Organized labor

Executive branch of

the federal government

Major companies

Organized religion

Education

U.S. Supreme Court

Medicine

Scientic community

Military
Science and Engineering Indicators 2014 ♦ 7-33

the scientist-focused questions were also asked in 1983 and 

2001. An analysis of these earlier surveys concluded that 

views about scientists were shaped by a range of factors; 

older respondents, women, and those who believe society 

relies too much on science had more negative views about 

scientists. In contrast, those with more education and more 

college courses in science were more positive about scientists (Losh 2010).

More Americans said they had an “excellent” or “good” 

understanding of what engineers (42%) than of what scientists (35%) do in their jobs. In contrast, more respondents 

said they had “considered working” in a science-related 

(33%) than in an engineering-related (26%) career. The percentage interested in a science career was down from 41% 

in 2001 and similar to the 34% who gave this response in 

1983. There were few clear demographic patterns, although 

younger and older respondents were both less likely to say 

they understood S&E careers, and more education and 

knowledge were generally associated with more self-reported understanding (figure 7-16; appendix table 7-26). 

Almost all Americans said they would be “happy” if their 

son or daughter were to become a scientist or engineer. In 

2012, four out of five Americans (80%) said they would be 

happy if their son or daughter became a scientist, and even 

more would be happy to see their child become an engineer 

(84% for daughters and 85% for sons). The 2001 survey 

similarly found that 80% of Americans would be happy 

about a scientific career for their child, up from 67% for both 

sexes in 1983 (figure 7-17).24

In general, these patterns were consistent across demographic groups, although those who scored well on the test 

of science knowledge were somewhat more likely to be 

happy if their son or daughter were to become an engineer 

than those who scored relatively less well. For example, in 

2012, 79% of respondents in the bottom quartile for science 

knowledge said they would be happy if their son became an 

engineer, whereas 88% of those in the top quartile gave this 

response. This pattern was not apparent in those asked about 

scientists (appendix table 7-27).

Americans’ views about specific facets of S&E occupations are also quite positive. Americans generally believe 

that both scientists and engineers have a positive impact 

on society, and these beliefs appear to have remained stable over the past decade. Americans almost universally 

“strongly agree” or “agree” that scientists (95%) and engineers (91%) “are helping to solve challenging problems.” 

Figure 7-16

Public self-assessment of knowledge of what 

scientists and engineers do day-to-day on their 

jobs: 2012

Percent 

NOTES: Responses to Would you say your knowledge of what 

scientists/engineers do day-to-day on their jobs is excellent, good, 

fair, poor, or very poor? Percentages may not add to 100% because 

of rounding.

SOURCE: University of Chicago, National Opinion Research Center, 

General Social Survey (2012). See appendix table 7-26.

Science and Engineering Indicators 2014

What scientists do What engineers do

0

10

20

30

40

Excellent Good Fair

Poor Very poor Don’t know

Figure 7-17

Public opinion on science and engineering careers 

for one’s children: 1983, 2001, and 2012

NOTES: Responses to If you had a daughter/son, how would you feel if 

she/he wanted to be a scientist/engineer—would you feel happy, 

unhappy, or would you not care one way or the other? Percentages 

may not add to 100% because of rounding.

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Public Attitudes Toward and 

Understanding of Science and Technology (1983, 2001); University of 

Chicago, National Opinion Research Center, General Social Survey 

(2012). See appendix table 7-27.

Science and Engineering Indicators 2014

Cumulative percent

0 20 40 60 80 100

Happy Wouldn’t care

either way

Unhappy Don’t know

2012: engineer

2012: scientist

2001: scientist

1983: scientist

How would you feel if your

son wanted to be a...

How would you feel if your

daughter wanted to be a...

2012: engineer

2012: scientist

2001: scientist

1983: scientist
7-34 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

This is similar to the 96% who gave such responses in 2001 

when asked only about scientists (NSB 2002). Americans 

also believe these groups are made up of “dedicated people 

who work for the good of humanity.” Although both groups 

are seen positively, more respondents agreed that this description fits scientists (88%) than agreed that this description fits engineers (79%). The finding for scientists is also 

similar to that in 2001, when 86% of respondents gave this 

answer (NSB 2002). There is no meaningful difference in 

Americans’ belief that scientists (86%) and engineers (86%) 

“work on things that will make life better for the average 

person” (table 7-10). About 89% also said this about scientists in 2001.

Americans’ views about S&E careers include several elements that could be perceived by some as negative. Respondents were more likely to provide such 

comments when asked about scientists rather than engineers. 

Specifically, 50% of respondents said they “strongly agree” 

or “agree” with the statement that “scientific work is dangerous,” but just 38% said they thought engineering work 

is dangerous. The percentage seeing scientific work as dangerous is essentially unchanged from 2001, when 53% of 

respondents gave this response. In 2012, more Americans 

saw scientists than saw engineers as not likely “to be very religious people” (33%, compared with 15% for engineers); as 

having “few other interests but their work” (28%, compared 

with 16% for engineers); and as likely to “earn less than 

other people with equally demanding jobs” (17%, compared 

with 9% for engineers). These numbers are also similar to 

those from 2001, when 30% said they thought scientists 

were unlikely to be religious and 29% said they believed 

scientists were too interested in work. About one-third of 

Americans saw scientists and engineers as “apt to be odd and 

peculiar people” (36% for scientists, compared with 28% for 

engineers). This percentage rose for scientists from 25% in 

2001 (NSB 2002), but it is not far from the 31% response in 

1983 (table 7-10; appendix table 7-28). 

Americans saw few differences between scientists and 

engineers in 2012 for some of the less common negative 

ideas about which they were asked. Few Americans said they 

believe that scientists and engineers “don’t get as much fun 

out of life as other people do” (19% for scientists, compared 

with 16% for engineers); that scientists or engineers “usually 

work alone” (20% for scientists, compared with 23% for engineers); or that being a scientist or engineer “would be boring” (17% for scientists, compared with 14% for engineers) 

(table 7-10). The percentage of people who believed that scientists have less fun was 20% in 2001 and 24% in 1983. The 

percentage of people who believed that scientists work alone 

was lower in 2001 (16%) and similar in 1983 (21%). In previous surveys, respondents were not asked about whether 

science was boring (appendix table 7-28).

It is also noteworthy that the Harris Poll (Harris 

Interactive 2009) asked about the prestige of a large number of occupations, including scientists and engineers, over 

a period of about 30 years. In 2009, the last year for which 

data are available, 57% of Americans said that scientists had 

“very great prestige,” and 39% expressed this view about 

engineers. Most occupations in the surveys were rated well 

below engineers.25 In recent years, scientists’ ratings were 

comparable to those of nurses, doctors, firefighters, and 

teachers and ahead of those of military and police officers. 

Engineers’ standing was comparable to those of occupations 

clustered just below the top group of occupations rated, including clergy, military officers, farmers, and police officers 

(NSB 2012). 

International Comparisons

Elsewhere, S&E occupations are also highly regarded. 

The BBVA Foundation research in Europe and the United 

States found that both groups reject negative portrayals 

of scientists and embrace positive ones. The 2011 BBVA 

Foundation survey presented respondents with the idea that 

“films often use particular images to portray scientists” and 

then asked if the respondents believed these portrayals “reflect what scientists are like.” About 42% of Americans and 

46% of residents of the 10 European countries surveyed said 

they thought that a depiction of scientists as “people doing 

research beyond the bounds of what is morally acceptable” 

would reflect scientists “fairly well” or “very well.” Fewer 

respondents—27% of Americans and 29% of Europeans—

said that depictions of scientists as “people who lie about 

their research for personal gain” would be accurate. Even 

fewer—23% of Americans and 25% of Europeans—said 

they believed that depictions of scientists as “dangerous 

people” would be accurate. Americans and Europeans diverged on the degree to which residents said they believed 

that scientists were “people with a lot of power” or “absentminded people.” About 53% of Americans and 45% of 

Europeans said that they thought depictions of scientists as 

powerful would accurately reflect scientists. Also, 22% of 

Americans said they thought an absent-minded depiction 

would be accurate, but 35% of Europeans held this view 

(BBVA Foundation 2012b). 

The BBVA Foundation survey also found that more 

Americans had “considered the possibility of taking up a career related to science” than most other countries in the survey. One-third of Americans (33%) said they had considered 

such a career, but only 17% of those surveyed in the other 

10 European countries said they had considered this option 

(BBVA Foundation 2012b).

Earlier data from other countries indicate that scientists 

are well regarded. Chinese respondents were asked in 2010 

to choose up to three occupations that they thought were the 

most prestigious and three that they would like their child 

to choose. Scientist (44%) rated close to doctor (44%) as an 

occupation that was among the most “prestigious,” although 

both were behind teacher (55%). Engineering was seen as 

a prestigious career by 22% of Chinese respondents. When 

it came to careers, 36% said they would like their child to 

become a scientist. Teacher (51%) and doctor (49%) were 

the only occupations more preferred. About 17% said they 
Science and Engineering Indicators 2014 ♦ 7-35

Which Fields and Activities Are Seen 

as Scientific 

U.S. Patterns and Trends

The 2012 GSS included a series of questions about the 

degree to which Americans see various fields of research 

and practical activities as scientific. Such questions are important because they can provide an indicator of the degree 

that Americans see a role for science in everyday life. Some 

of these questions were also asked in the 2006 GSS as well 

as in a 2005 EU survey. The new data include both the earlier list of fields as well as an additional list of activities, many 

would like their child to become an engineer (CRISP 2010). 

A 2010 Korean survey also included questions about scientists and found that 56% of respondents “strongly” or 

“somewhat” agreed that scientists “serve the interests of humankind,” 38% agreed scientists are “neutral and objective,” 

and 32% agreed scientists are “unique and different people.” 

Overall, 24% said they would “strongly support” their children in pursuing an S&E career, although most (66%) indicated they would let their children choose their own path 

(KOFAC 2011). In 2006, the majority of Israelis said they 

would be pleased if their children became scientists (77%), 

engineers (78%), or physicians (78%) (Yaar 2006). 

Table 7-10

Public perceptions of science and engineering occupations: 2012

(Percent)

Field/work activity

Level of agreement

Don’t know Mean score

Strongly 

agree Agree Disagree

Strongly 

disagree

Scientists are helping to solve challenging problems.......... 21 74 1 1 3 3.2

Engineers are helping to solve challenging problems.......... 19 72 3 * 6 3.2

Scientific researchers are dedicated people who work 

for the good of humanity.................................................. 19 69 6 1 5 3.1

Engineering researchers are dedicated people who 

work for the good of humanity......................................... 11 68 11 1 9 3.0

Most scientists want to work on things that will make 

life better for the average person..................................... 14 72 8 1 5 3.0

Most engineers want to work on things that will make 

life better for the average person..................................... 11 75 7 * 7 3.0

Scientific work is dangerous................................................ 6 44 39 4 6 2.6

Engineering work is dangerous............................................ 6 32 48 5 9 2.4

Scientists are apt to be odd and peculiar people................ 4 32 51 6 8 2.4

Engineers are apt to be odd and peculiar people................ 4 24 55 7 10 2.3

Scientists are not likely to be very religious people............. 4 29 47 6 13 2.4

Engineers are not likely to be very religious people............. 1 14 57 6 22 2.1

Scientists have few other interests but their work............... 2 26 55 5 11 2.3

Engineers have few other interests but their work............... 2 14 63 6 14 2.2

A scientist usually works alone............................................ 3 17 64 10 7 2.1

An engineer usually works alone.......................................... 3 20 57 11 9 2.2

Scientists don’t get as much fun out of life as other 

people do......................................................................... 2 17 59 11 11 2.1

Engineers don’t get as much fun out of life as other 

people do......................................................................... 2 14 63 10 12 2.1

Scientists earn less than other people with equally 

demanding jobs................................................................ 2 15 60 4 19 2.2

Engineers earn less than other people with equally 

demanding jobs................................................................ 1 8 69 7 14 2.0

A job as a scientist would be boring.................................... 2 15 66 11 6 2.1

A job as an engineer would be boring................................. 2 12 68 8 11 2.1

* = < 0.5% responded.

NOTES: Responses to Now I’d like to read you some statements about scientists/engineers. Please tell me if you agree or disagree with each one. If you 

feel especially strongly about a statement, please say that you strongly agree or strongly disagree. Mean agreement score is based on a 4-point scale, 

where 4 = strongly agree, 3 = agree, 2 = disagree, and 1 = strongly disagree. Percentages may not add to 100% because of rounding.

SOURCE: University of Chicago, National Opinion Research Center, General Social Survey (2012). See appendix table 7-28.

Science and Engineering Indicators 2014
7-36 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

of which require practical applications of S&T knowledge, 

such as farming, computer programming, and counseling. 

Engineering was included as both a field and an activity. The 

results clearly show that Americans differentiate between 

different fields and activities. 

Many of the fields and activities that Americans saw 

as scientific are those where the S&T element is clear. 

Medicine (94%) and medical treatment (96%) were the 

most likely to be seen as “very” or “pretty scientific.” The 

percentage for medicine was down slightly from 97% in 

2006. Many also saw the fields of physics (88%), biology 

(90%), and engineering (80%)—as well as the activities of 

engineering (90%) and architecture (75%)—as scientific. 

Biology was down slightly from 94% in 2006, and physics 

was down from 90%. Engineering (as a field) was about the 

same (77%) in 2006, whereas architecture was not included 

in the earlier survey. Respondents saw engineering as more 

scientific when grouped with other “activities” than when 

grouped with “fields.” The fact that “engineering” followed 

“medicine” in the list of fields on the underlying GSS survey but followed “law enforcement” in the list of activities, 

may have contributed to this difference in perceptions (table 

7-11; appendix table 7-29).

Three fields were seen as marginally scientific. About 

half of Americans saw the social science fields of economics 

(45%) and sociology (45%) as “very scientific” or “pretty 

scientific.” These are down slightly from 2006 when economics had been at 51% and sociology at 49%. About onethird of respondents (31%) said they saw history as scientific 

in 2012, which is about the same as in 2006 (30%).

Americans also saw many activities as scientific and distinguished these from other activities that they saw as unscientific. Most respondents saw computer programming 

(85%) and farming (72%) as scientific, whereas about half 

of respondents saw firefighting (57%) and law enforcement 

(44%) as scientific.

In general, respondents with more education and more 

scientific knowledge were more likely to see almost all fields 

and activities as at least somewhat scientific. Patterns are 

also apparent in the percentage describing certain fields or 

activities as “pretty scientific.” For example, the percentage 

of respondents saying that economics is “pretty scientific” 

climbs from 20% for the lowest knowledge quartile to 44% 

for the highest knowledge quartile. No such pattern is apparent when looking at the “very scientific” percentage for 

economics. Similarly, 21% of those who had not completed 

Table 7-11

Public perceptions of degree to which certain fields and work activities are scientific: 2012

(Percent)

Field/work activity

Degree to which scientific

Haven’t 

heard of it Mean score

Very 

scientific

Pretty 

scientific

Not too 

scientific

Not 

scientific 

at all

Field

Medicine .......................................................................... 80 14 2 1 3 3.8

Physics ............................................................................ 69 19 4 2 6 3.7

Biology............................................................................. 67 23 4 1 5 3.6

Engineering...................................................................... 49 31 10 6 5 3.3

Economics ....................................................................... 15 30 31 18 6 2.5

Sociology ......................................................................... 9 36 33 8 13 2.5

History.............................................................................. 9 22 41 24 4 2.2

Accounting....................................................................... 8 19 35 33 5 2.0

Work activity

Medical treatment............................................................ 77 19 2 1 1 3.7

Engineering...................................................................... 59 31 5 3 2 3.5

Computer programming .................................................. 52 33 10 3 2 3.4

Architecture...................................................................... 35 40 15 7 3 3.1

Farming............................................................................ 18 54 20 5 2 2.9

Firefighting ....................................................................... 17 40 28 13 2 2.6

Law enforcement............................................................. 12 32 33 21 2 2.3

Financial counseling ........................................................ 8 25 36 28 3 2.1

Journalism ....................................................................... 4 16 46 29 4 2.0

Marriage counseling ........................................................ 7 18 33 39 3 1.9

Salesmanship .................................................................. 4 12 39 42 3 1.8

NOTES: Responses to How scientific are each of the following fields/work activities? If you have not heard of a particular field/work activity, just say you 

haven’t heard of it. Is [field/work activity] very scientific, pretty scientific, not too scientific, or not scientific at all? Mean scientific score is based on a 

4-point scale, where 4 = very scientific, 3 = pretty scientific, 2 = not too scientific, and 1 = not scientific at all. Percentages may not add to 100% because 

of rounding.

SOURCE: University of Chicago, National Opinion Research Center, General Social Survey (2012). See appendix table 7-29.

Science and Engineering Indicators 2014
Science and Engineering Indicators 2014 ♦ 7-37

high school said they thought law enforcement was “very 

scientific,” but only 4% of those with graduate degrees gave 

this opinion. In contrast, 18% of those with less than a high 

school diploma viewed law enforcement as “pretty scientific,” but 47% of those with bachelor’s degrees gave this 

response. Similar patterns are apparent for education and/

or literacy measures applied to occupations such as farming, firefighting, marriage counseling, law enforcement, and 

financial counseling. These results suggest that Americans 

with more understanding of science may be more likely to 

recognize a partial natural- or social-scientific element to 

fields or activities in which S&T plays a supporting role.26

International Comparisons

The pattern of results in the 2012 GSS remains similar to 

those found in a 2005 survey of EU countries. This survey 

used a five-point scale anchored by “not at all scientific” 

and “very scientific.” Some 89% of Europeans chose one of 

the two highest categories for medicine (i.e., above the midpoint). About 83% gave such a score for physics, and 75% 

gave such a score for biology. About 40% indicated they 

believed economics was scientific, and 34% said they saw 

history as scientific (European Commission 2005).

Influence of Scientific Experts on 

Public Issues

U.S. Patterns and Trends

The 2010 GSS included a battery of questions that focused on what role the public wants scientists and others to 

play in policy decision making. These questions were also 

asked in 2006. In 2010, the survey focused on four issues: 

global climate change,27 research using human embryonic 

stem cells, federal income taxes, and nuclear power.28 In 

2006, the issues included GM foods but not nuclear power. 

Respondents were asked how much influence a group of 

scientists or engineers with relevant expertise (e.g., medical 

researchers, economists, nuclear engineers) should have in 

deciding about each issue, how well the experts understood 

the issue, and to what extent each would “support what is 

best for the country as a whole versus what serves their own 

narrow interests.” The same questions were asked about 

elected officials and either religious leaders (for stem cell 

research) or business leaders (for the other issues). Thus, the 

questions allow a comparison among leadership groups at a 

single point in time as well as a comparison of perceptions 

about these groups over time.

The 2010 GSS data indicate that most Americans believe 

that scientists and engineers should have either a “great deal” 

or “a fair amount” of influence on these public decisions. 

More said that scientists and engineers should have a “great 

deal” of influence about these issues than said the same 

about other groups when it comes to global warming, stem 

cell research, nuclear power, and GM foods. Americans also 

gave scientists relatively high marks for understanding each 

issue and for being relatively impartial. For all issues, compared with other leadership groups, S&E groups were more 

likely to be seen as supporting what is best for the country 

rather than their own narrow interests. Nonetheless, the 2010 

GSS also assessed perceived consensus among scientists and 

found that the public thought that scientists disagreed among 

themselves on most issues. The public perceived the greatest consensus on stem cells and nuclear energy and the least 

consensus on taxes. Past research suggests that a lack of perceived consensus may limit the influence of the scientific 

community (Krosnick et al. 2006; NSB 2010). Americans 

with more education and more science knowledge tended to 

have more favorable perceptions of the knowledge, impartiality, and level of agreement among scientists.

Public Attitudes about 

Specific S&T-Related Issues

In addition to general views about S&T, most people 

also develop views about specific issues, and these views 

can shape personal and political decisions. Such specific 

attitudes are usually associated with general attitudes and 

knowledge and may come from a range of experiences. 

Both general and specific views about S&T may affect what 

people decide to study, what they decide to consume, and 

whom they trust. Likewise, attitudes about emerging areas 

of research and new technologies may influence innovation 

activity in important ways. The climate of opinion concerning new research areas can shape public and private investment in related technological innovations and, eventually, 

the adoption of new technologies and the growth of industries based on these technologies. 

Even in democratic societies, public opinion about new 

S&T developments rarely translates directly into actions or 

policy. Instead, institutions selectively assess what the public believes and may magnify or minimize the effects of divisions in public opinion on public discourse and government 

policy (Jasanoff 2005). It is noteworthy that the public’s attitudes about specific S&T issues such as climate change and 

biotechnology can differ markedly from the views of scientists (Pew Research Center 2009). This is partly because 

attitudes toward S&T involve a multitude of factors, not just 

knowledge or understanding of relevant science. Values, 

morals, judgments of prudence, and numerous other factors 

come into play; judgments about scientific fact are often secondary (Kahan, Jenkins-Smith, and Braman 2011).

This section describes data about views on environmental 

issues, including global climate change, nuclear power, and 

energy development; nanotechnology; agricultural biotechnology (i.e., GM food); cloning and stem cell research; and 

teaching evolution in schools. It concludes with recent data 

on attitudes toward scientific research on animals and toward science and mathematics education. 
7-38 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

Environment

U.S. Patterns and Trends

Environmental issues—especially climate change and 

energy technologies—are often the subject of both public 

policy debate and news interest. The massive 2010 oil spill 

in the United States was followed by a 2011 nuclear accident 

in Japan and attendant calls for the development of new energy alternatives. Recent years also saw the reemergence of 

a domestic natural gas industry as new technologies made 

hydraulic fracturing (“fracking”) technologically and economically feasible. A review of general public views about 

the environment and specific environmental issues follows, 

along with reviews of views about climate change and 

energy technologies.

Concern about Environmental Quality

The environment is important to many Americans, but 

other issues rate higher on their list of priorities. A 2012 

Gallup survey on Americans’ concerns for the nation shows 

“worry” about the environment rebounded slightly after tying record lows in 2010 and 2011. The 2012 poll found that 

37% said they worry “a great deal” about “the quality of the 

environment,” compared with 34% in 2010 and 2011. The 

percentage that worries “a great deal” has, however, fluctuated within a 9% range (34% to 43%) since Gallup began 

asking the question in 2001. These most recent figures are 

well within that range, suggesting long-term stability (figure 7-18). Overall, environmental concerns are relatively 

low on the list of issues about which Gallup respondents 

worry (Saad 2012), and in 2013, just 47% of respondents 

said the government is doing “too little” in terms of protecting the environment. This was down from 51% in 2012 

and relatively low compared with data going back to 1983 

(Newport 2013). Similar results from Pew Research said 

86% of Americans think “strengthening the nation’s economy” should be a top priority for the President and Congress 

for the year, whereas 56% said “protecting the environment” 

should be a top priority. About 45% said “dealing with the 

nation’s energy problem” should be a top priority. Both 

environmental protection and energy issues have also fluctuated within a relatively narrow range in past polls (Pew 

Research Center 2013b). Another way survey researchers 

assess what issues are most salient in the public mind is to 

ask an open-ended question about what respondents believe 

to be “the most important problem facing the country” at the 

beginning of a survey. Neither Gallup (Jones and Saad 2013) 

nor the Pew Research Center (2012e) have found that more 

than about 1% of respondents offer environmental or energy 

issues as the country’s biggest problem in recent years.

International Comparisons

The availability of the 2010 ISSP also makes it possible 

to provide a number of international comparisons related to 

environmental issues. Particularly relevant to general environmental concerns is one general question that asked respondents “how concerned” they were “about environmental 

issues.” It asked them to respond on a five-point scale where 

1 meant “not at all concerned” and 5 meant “very concerned.” About 63% of American respondents chose 4 or 

5. The U.S. average score in 2010 was relatively low—residents of more than a dozen countries were more concerned 

about such issues—but also was statistically similar to the 

scores of many large, developed countries (figure 7-19; appendix table 7-30).29 Also, in 2010, about one-quarter of 

Americans (23%) said they “strongly agreed” or “agreed” 

that “modern science will solve our environmental problems 

with little change to our way of life.” Americans were again 

in the middle range of countries. In many of the countries 

where multiple years of data (i.e., 1993 and 2000) were 

available, confidence increased over time (figure 7-20; appendix table 7-31).

Within Europe alone, a 2011 Eurobarometer found that 

95% of EU residents said that “protecting the environment” 

was personally “very important” or “important” (European 

Commission 2011). This figure was essentially unchanged 

from 2007, when it was at 96%. Further, 76% of EU residents agreed that “environmental problems” have a “direct 

effect” on their lives; this, too, was similar to 2007 (78%) 

(European Commission 2011).

Assessment of Specific Environmental Problems 

The U.S. public’s perceptions of hazards to the environment have been mostly stable over the past two decades. 

Responses to a series of questions on GSS surveys conducted in 1993, 2000, and 2010 show that Americans consider pollution of America’s rivers, lakes, and streams to be 

more dangerous to the environment than any of several other 

Figure 7-18

Worry about quality of environment: 2001–12

Percent

NOTES: Responses to How much do you personally worry about the 

quality of the environment: a great deal, a fair amount, only a little, or 

not at all? Poll is conducted annually in March. 

SOURCE: Saad L, Economic Issues Still Dominate Americans’ 

National Worries, The Gallup Poll (28 March 2012), http://www.gallup. 

com/poll/153485/Economic-Issues-Dominate-Americans-NationalWorries.aspx, accessed 25 January 2013. 

Science and Engineering Indicators 2014

2001 2002 2003 2004 2005 2006 2007 2008 2010 2011 2012

0

10

20

30

40

50

Respondents who worry “a great deal”

about the quality of the environment
Science and Engineering Indicators 2014 ♦ 7-39

potential problems; in 2010, 68% considered water pollution 

to be very or extremely dangerous. Air pollution caused by 

industry was considered very or extremely dangerous to the 

environment by 62%, whereas air pollution caused by cars 

was less likely to be considered very or extremely dangerous 

to the environment (43%). Assessments of environmental 

Figure 7-19

Public concern about environmental issues, by 

country/economy: 2010

NOTES: Responses to Generally speaking, how concerned are you 

about environmental issues, where 1 means you are not at all 

concerned and 5 means you are very concerned? Percentages may 

not add to 100% because of rounding. 

SOURCE: International Social Survey Program, Environment Module 

(2010). See appendix table 7-30.

Science and Engineering Indicators 2014

Cumulative percent

0 20 40 60 80 100

5 and 4 3 2 and 1 Can’t choose/no answer

5-point scale, where 5 = very concerned and 1 = not concerned at all

Czech Republic

Slovak Republic

South Africa

Lithuania

Croatia

Denmark

Sweden

Norway

Russia

Mexico

Turkey

Austria

United Kingdom

Bulgaria

Latvia

Chile

Israel

Germany

Belgium

Philippines

France

Finland

South Korea

Taiwan

Spain

Japan

Argentina

New Zealand

Switzerland

Canada

Slovenia

United States

Figure 7-20

Public assessment of science’s ability to solve 

environmental problems, by country/economy: 2010

NOTES: Responses to How much do you agree or disagree with the 

statement: Modern science will solve our environmental problems 

with little change to our way of life? Percentages may not add to 

100% because of rounding. 

SOURCE: International Social Survey Program, Environment Module 

(2010). See appendix table 7-31.

Science and Engineering Indicators 2014

Cumulative percent

0 20 40 60 80 100

Strongly agree/agree Neither agree nor disagree

Disagree/strongly disagree Can’t choose/no answer

Japan

Sweden

Finland

Slovak Republic

Switzerland

Canada

Slovenia

Belgium

France

New Zealand

Czech Republic

Latvia

South Korea

Chile

Croatia

United Kingdom

Norway

Bulgaria

Germany

Lithuania

Israel

Spain

Austria

Denmark

Argentina

Russia

Mexico

Turkey

Taiwan

Philippines

South Africa

United States
7-40 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

dangers changed substantially on only one issue—pesticides 

and chemicals used in farming. About half of Americans 

(51%) called these very or extremely dangerous to the environment in 2010, up from 37% in 1993. 

The 2010 ISSP data also allow U.S. concerns about specific issues to be compared with concerns in other countries. 

In 2010, the United States sat in the middle range of concern 

on most issues. As in the United States, the only clear trend 

for most other countries surveyed in multiple years was that, 

over time, people viewed agricultural pesticides and chemicals as more dangerous (appendix tables 7-32–7-35).

Climate Change

U.S. Patterns and Trends

Climate change (sometimes referred to as global warming) has become a central environmental issue for the 

American public. It has also been the subject of widespread 

polling in recent years, with evidence showing clear shifts 

in views.30

Gallup has polled on “global warming” since 1989, when 

it found that 63% of Americans “worry a great deal” or “a 

fair amount” about the issue. In March 2013, the comparable 

statistic was 58%, but this percentage has risen and fallen 

multiple times. A much smaller percentage (34%), however, 

told Gallup that they believed “global warming would pose 

a serious threat” to their “way of life” during their lifetime. 

As with the question about “worry,” responses to this question have fluctuated over time (Saad 2013). Data from other 

sources show similar fluctuations (Pew Research Center 

2012f; Leiserowitz et al. 2012), and these shifts come alongside shifts in the percentage of Americans who say “there is 

solid evidence that the average temperature on earth has been 

getting warmer over the past few decades” (Pew Research 

Center 2012f). The Brookings Institution found that people 

were increasingly pointing to changes in weather patterns as 

“the primary factor” that has led them to conclude “that temperatures on earth are increasing” (Borick and Rabe 2012).

Within the subset of Americans who believe the earth is 

getting warmer (i.e., 67% of Americans), about two-thirds 

(42% of all respondents) said it was likely because of “human activity such as burning fossil fuels,” whereas the remaining third (19% of all respondents) attributed the change 

to “natural patterns in the earth’s environment.”31 The percentage attributing perceived change to human activity 

reached a high of 50% in July 2006 but declined to as low as 

36% in October 2009 (Pew Research Center 2012f). 

Despite widespread concern, Pew Research Center also 

reports that “dealing with global warming” has been at, or 

near, the bottom of the public’s priorities for the president 

and Congress since at least 2007. About 28% of Americans 

said it should be a priority in 2013, which is down from 

38% in 2007 (Pew Research Center 2013b). Pew Research’s 

September 2012 survey also found, however, that most 

Americans said they believe that the threat of climate change 

is relatively distant from their lives (Pew Research Center 

2012f). Risk researchers have long known that people often see risks as more likely to harm others than themselves 

(Spence, Poortinga, and Pidgeon 2012).

Both Pew Research and Gallup have also asked questions 

about the degree to which Americans believe there is a scientific consensus around climate change. Gallup reported 

that, in 2013, 62% of Americans said that “most scientists 

believe that global warming is occurring.” Gallup’s research 

also shows that the percentage saying a consensus exists rose 

from 48% in 1998 to a high of 65% in 2008 before falling 

again (Saad 2013). Several other surveys report similar findings (Pew Research Center 2012f; Leiserowitz et al. 2012). 

Survey organizations that collect public opinion data on climate change consistently find views on this topic to be related 

to party affiliation (Pew Research Center 2012f; Saad 2013).

International Comparisons

The most recent internationally comparable, representative data on public views about climate change are from 

2010, a year in which Americans were at (or near) relative 

lows in their concerns about climate change.

The 2010 ISSP indicated that the United States is among 

the countries with the least concern about climate change 

(figure 7-21). There was no clear pattern, however, between 

countries over time, with some countries becoming more 

concerned (e.g., Japan and Spain) and others becoming less 

concerned (e.g., the Czech Republic and New Zealand) between 1993 and 2010 (appendix table 7-36). Almost half 

(45%) of Americans said climate change was “very” or “extremely dangerous” in 2010 (NSB 2012). 

Gallup similarly reported that, in 2010, 53% of Americans 

saw global warming as a “very” or “somewhat” serious 

threat to themselves and their families, putting it in the 

middle range of the 111 countries/economies Gallup polled. 

The average for Western Europe was 56%. Higher percentages of respondents were concerned in Southern and Eastern 

Europe (60%), Canada (71%), Latin America (73%), and 

the developed parts of Asia (74%) than in the United States. 

Conversely, residents of less developed areas were less concerned than those in the United States, including those in the 

Commonwealth of Independent States (44%), the Middle 

East and North Africa (37%), Sub-Saharan Africa (34%), 

and developing countries in Asia (31%). Gallup also reported that the perceived threat of climate change declined between 2007–08 and 2010 in many developed countries (Ray 

and Pugliese 2011a).

Americans were also more likely than residents of any 

other country surveyed to say they believe rising temperatures are “a result of natural causes.” About 47% of U.S. 

respondents gave this response, whereas 35% said that 

temperature rises are “a result of human activity.” Another 

14% volunteered that they believed both human and natural 

causes are at play (i.e., they were not explicitly given that 

choice but offered the opinion anyway). The next closest 
Science and Engineering Indicators 2014 ♦ 7-41

country to the United States was the United Kingdom, where 

39% said climate change is due to natural causes, 37% said 

human causes, and 18% said both. Gallup reported that 

the average in “developed Asia” was 76%. About 49% of 

Western Europeans and 46% of Eastern Europeans said 

they think climate change is a result of human factors (Ray 

and Pugliese 2011b). Pew Research has also reported that 

Americans express less concern about climate change than 

people in many other countries (Pew Research Global 

Attitudes Project 2010).

Figure 7-21

Public assessment of danger to environment of climate change and nuclear power stations, by country/

economy: 2010

NOTES: Responses to Do you think that a rise in the world's temperature caused by climate change is extremely dangerous for the environment, very 

dangerous, somewhat dangerous, not very dangerous, or not dangerous at all for the environment? and Do you think that nuclear power stations are...? 

Percentages may not add to 100% because of rounding. 

SOURCE: International Social Survey Program, Environment Module (2010). See appendix tables 7-36 and 7-37.

Science and Engineering Indicators 2014

Cumulative percent

0 20 40 60 80 100

Cumulative percent

Extremely/very dangerous Somewhat dangerous Not very/not at all dangerous Can’t choose/no answer

Climate change Nuclear power stations

0 20 40 60 80 100

Belgium

Norway

Latvia

Denmark

United Kingdom

New Zealand

Sweden

France

Finland

Czech Republic

Lithuania

Israel

Canada

Switzerland

Russia

South Africa

Slovak Republic

Slovenia

Austria

Germany

South Korea

Bulgaria

Croatia

Philippines

Spain

Japan

Mexico

Turkey

Argentina

Taiwan

Chile

United States

Czech Republic

Sweden

United Kingdom

Japan

Denmark

Belgium

South Korea

Finland

France

Canada

Slovak Republic

New Zealand

Taiwan

Bulgaria

Norway

Lithuania

South Africa

Germany

Spain

Latvia

Russia

Slovenia

Israel

Philippines

Argentina

Mexico

Switzerland

Turkey

Croatia

Chile

Austria

United States
7-42 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

Nuclear Power and Other Energy Sources

U.S. Patterns and Trends

Accidents such as the 2010 Deepwater Horizon oil spill 

in the Gulf of Mexico and the 2011 nuclear accident in 

Fukushima, Japan, have put energy decisions at the center 

of policy debates. Questions about the health, environmental, and social impacts of hydraulic fracturing (“fracking”) 

have also emerged in many parts of the country. Overall, 

public opinion about energy appears to change temporarily 

in response to new events, while showing no consistent trend 

over time (see sidebar, “Nuclear Energy and the Fukushima 

Accident”).

About half of Americans support the use of nuclear energy. Gallup reports that 57% of Americans said they “strongly” or “somewhat” favored nuclear energy in 2012 (Newport 

2012b), while the Pew Research Center (2012d) put the level 

of support at 44%. 

For other energy issues, Gallup reports that Americans 

are about equally divided over whether “protection of the 

environment should be given a priority, even at the risk 

of limiting the amount of energy supplies—such as oil, 

gas, and coal—which the U.S. produces” or whether the 

“development of U.S. energy supplies...should be given 

priority, even if the environment suffers to some extent.” 

Environmental protection was clearly more favored by respondents in 2001, when 52% chose environmental protection, and this percentage rose to 58% in 2007. However, 

41% and 44% of respondents chose environmental protection in 2011 and 2012, respectively. Respondents were also 

asked how they thought the country should deal with “the 

nation’s energy problems.” The percentage of people choosing “more conservation by consumers of existing energy 

supplies” over producing “more oil, gas and coal supplies” 

has remained about evenly divided since 2010. Preference 

for conservation climbed from 56% in 2001 up to 64% in 

2007 before falling back to 48% in 2011 and 51% in 2012 

(Jones 2012).

The majority of Americans support both offshore energy 

development and alternative energy spending, but opinion on these topics has shifted in recent years. About twothirds (67%) of Americans said they supported “allowing 

more offshore oil and gas drilling” in September 2008. This 

dropped to a low of 44% in June 2010, after the Deepwater 

Horizon spill, but climbed back to 65% by March 2012. In 

contrast, the percentage that favored “increasing federal 

spending for research on wind, solar and hydrogen technology” has steadily declined from highs of 82% in polls 

The combination of the 2011 Fukushima accident 

and a 2012 decision by the U.S. Nuclear Regulatory 

Commission to grant its first new license to build a new 

nuclear plant in decades (Wald 2012) has made nuclear 

energy a vibrant area of public opinion research. The 

Fukushima accident had a small impact on public opinion, but Americans’ views appear to be relatively resilient, with more than half of Americans continuing to 

support nuclear energy. 

Survey research by Gallup from March 2001 had 

about equal numbers of respondents favoring (46%) and 

opposing (48%) “nuclear energy as one of the ways to 

provide electricity for the U.S.” Support climbed to 62% 

favoring by March 2010, a year before the Fukushima 

accident. Gallup conducted a poll about a month after the 

accident and saw favorability drop to 57%. It was still 

at 57% a year later, in March 2012 (Newport 2012b). A 

similar pattern—but with even higher levels of support 

for nuclear energy—was found by a GfK Roper survey 

that used a similar question between 1983 and 2013 

(Bisconti Research 2013).

Pew Research’s polling indicated a similar pattern. 

Support for “promoting the increased use of nuclear power” started at 39% in September 2005 and then moved 

upward to 52% in February 2010 before falling back to 

about 45% in October 2010. A poll in March 2011, about 

a month after Fukushima, and then another in November 

2011 saw support down to 39%. A more recent March 

2012 poll had support for nuclear energy back to 44%.

Question wording might explain the differences in 

expressed support for nuclear energy. Gallup and GfK 

Roper asked about nuclear energy “as one of the ways,” 

while Pew Research asked about “promoting” nuclear 

energy. A comprehensive review of nuclear energy polling showed that opposition to nuclear energy declined 

from the 1970s, stabilized through the 1980s, and then 

began to rise in the 2000s (Bolsen and Cook 2008).

A Swiss study that surveyed the same people both before 

and after the Fukushima accident found that acceptance of 

nuclear energy, perceived benefits of nuclear energy, and 

trust in nuclear energy operators declined as a result of the 

accident, while risk perceptions increased. This research 

argued that the key drivers of acceptance stayed the same 

over time, and it was the decline in trust and benefits perceptions, as well as the increase in risk perceptions, that 

changed the level of nuclear acceptance (Visschers and 

Siegrist 2012). Some studies have also shown high levels of support in areas that already have nuclear facilities 

(Besley 2010; Greenberg and Truelove 2011). 

Nuclear Energy and the Fukushima Accident
Science and Engineering Indicators 2014 ♦ 7-43

from February 2006 and April 2009. Support reached lows 

of 68% in November 2011 and 69% in March 2012 (Pew 

Research Center 2012d).

Beyond government support, however, Americans say 

they would like the United States “as a country” to put “more 

emphasis” on “producing domestic energy” from renewable 

sources. About 76% of respondents told Gallup they would 

like more emphasis on solar power, and 71% said they would 

like more emphasis on wind power. In contrast, 65% would 

like more emphasis on natural gas, 46% would like more 

emphasis on oil, 37% would like more emphasis on nuclear, 

and 31% would like more emphasis on coal (Jacobe 2013). 

International Comparisons

In the United Kingdom—which has also been debating 

whether to update its nuclear energy infrastructure—support 

for nuclear energy has declined in recent years, although the 

decline may have leveled off. Ipsos MORI found that the 

percentage of respondents who said they had a “very favourable” or “mainly favourable” “impression...of the nuclear 

energy industry” was 33% in 2009, 40% in 2010 (just before Fukushima), and 28% in 2011 (just after Fukushima). 

Similarly, the percentage who said they would “strongly 

support” or “tend to support” “the building of new nuclear 

power stations in Britain” went from 42% in 2009 up to 47% 

in 2010 and then down to 36% in 2011 (Ipsos MORI 2011).

Questions about nuclear energy were also included in the 

environment module of the ISSP that was fielded in multiple 

countries in 1993, 2000, and 2010. In 2010, pre-Fukushima, 

44% of Americans said that nuclear power stations were very 

or extremely dangerous; this percentage was relatively low, 

although it was still similar to a range of countries. There 

were also many countries where concern was quite high 

(figure 7-21). In some countries, concern increased between 

surveys, while in others, concern decreased (appendix table 

7-37). As noted in the 2012 NSB report, a Eurobarometer 

survey from 2010 showed that EU residents were split on 

whether or not nuclear energy will “improve our way of 

life” (39%) or “make things worse” (39%). Many also said 

that nuclear energy would have no effect (10%) or that they 

held no opinion (13%). Assessments of nuclear energy were 

more negative when this question was first asked in 1999 

(Gaskell et al. 2010). 

Genetically Modified Food

U.S. Patterns and Trends

Genetic modification of food has engendered less opposition in the United States than in much of Europe (Jasanoff 

2005), but it remains an active issue of public debate around 

the world as new products continue to enter the market. 

Scholars often point to the emergence of an anti-GM movement as something that might have been limited if the scientific community had better communicated with the public 

during the early research and commercialization phases 

(Einsiedel and Goldenberg 2006). There has also been active 

discussion on the question of whether the public wants mandatory labeling of food that contains genetically modified 

ingredients despite arguments by scientists that such labeling would inappropriately suggest risks to buyers (Roe and 

Teisl 2007). The U.S. Food and Drug Administration was 

also reviewing an application concerning the first potential 

use of genetic engineering in an animal species—Atlantic 

salmon—in 2013.

The 2010 ISSP included a question asking about the perceived danger of “modifying the genes of certain crops.” 

The survey found that 25% of U.S. respondents said that 

modification would be very or extremely dangerous to the 

environment. The 2000 ISSP yielded similar results (figure 

7-22; appendix table 7-38). 

Most U.S. surveys are focused on safety rather than the 

environment. A 2010 survey by Thomson Reuters found that 

about 21% of respondents were willing to say that “genetically engineered foods are safe” (Thomson Reuters 2010). 

This is consistent with a series of five surveys conducted by 

the Pew Initiative on Food and Biotechnology between 2001 

and 2006, which found that only about one-fourth of U.S. 

consumers favored “the introduction of genetically modified 

foods into the U.S. food supply” (Mellman Group 2006).

How genetic modification is used matters to Americans. 

The Thomson Reuters survey found that 35% of respondents 

said they would eat GM fish, 38% said they would eat GM 

meat, and 60% said they would eat GM vegetables, fruit, or 

grain (Thomson Reuters 2010). Past surveys also generally 

found that Americans are more wary of genetic modification 

of animals than they are of genetic modification of plants 

(Mellman Group 2006).

In total, 69% of respondents said they knew that GM 

foods are already in U.S. stores, and 93% of respondents 

said “foods should be labeled to indicate that they have been 

genetically engineered or contain ingredients that have been 

genetically engineered” (Thomson Reuters 2010). 

International Comparisons

The 2010 GSS/ISSP results show that the United States 

(25%) is less concerned about genetic modification than 

most other countries. There were several countries that were 

similar to the United States but none were more positive, on 

average. Also, residents of some countries became more concerned between 2000 and 2010 (e.g., Bulgaria and Mexico), 

while others became less concerned (e.g., Denmark and 

Japan) (appendix table 7-38). 
7-44 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

Nanotechnology 

U.S. Patterns and Trends

Nanotechnology involves manipulating matter at unprecedentedly small scales to create new or improved products 

that can be used in a wide variety of ways. Nanotechnology 

has been the focus of relatively large public and private investments for more than a decade, and innovations based on 

nanotechnology are increasingly common. More than 1,000 

nanotechnology products—more than 5 times the number available in 2006—were on the market by 2011 (Pew 

Project on Emerging Technologies 2011). However, relative 

to other new technologies, the public generally reports relatively low levels of understanding (Ladwig et al. 2012).

The 2010 GSS found that 24% of U.S. respondents said 

they had heard “a lot” or “some” about nanotechnology, up 

4 percentage points from both 2006 and 2008. A plurality 

(44%) of Americans in the 2010 GSS reported having heard 

“nothing at all” about nanotechnology (NSB 2010). About 

37% of 2010 GSS respondents also said the benefits would 

outweigh the harms, 9% said the benefits and harms would 

be about equal, and 11% expected the harms to predominate. 

The remaining 43% held no opinion (NSB 2010). The balance of opinion was similar in 2006 and 2008. As with GM 

food, attitudes toward nanotechnology vary depending on 

the context in which it is applied, with energy applications 

viewed much more positively than those in health and human enhancements (Pidgeon et al. 2009). 

International Comparisons

More Europeans than Americans appear to have heard 

about nanotechnology. About 45% of EU residents said that 

they had heard of nanotechnology in 2010. Overall, 44% of 

EU residents agreed that nanotechnology should be encouraged, 35% disagreed, and 22% had no opinion about this 

issue (Gaskell et al. 2010). One recent study of UK residents 

found that providing balanced information resulted in more 

positive views about nanotechnology for those who started 

out positive about nanotechnology, while those who started 

out negative became more negative. Such individuals were 

also less likely to be “ambivalent” after receiving balanced 

information. Those who started out with a neutral attitude, 

however, became more ambivalent about nanotechnology 

after receiving balanced information (Fischer et al. 2012). 

Stem Cell Research and Human Cloning

U.S. Patterns and Trends

Stem cell and cloning research focuses on understanding how to use genetic material to produce living cells, 

tissues, and organisms. Such research creates opportunities for enhanced understanding of life as well as opportunities to develop new health care treatments. The focus on 

health, human life, and the destruction of human embryos, 

however, creates a range of ethical issues that have spurred 

public debate.

Most Americans appear to support the use of stem cells 

for medical research, and support has stayed within a 5% 

range in recent years. Annual Gallup Poll data showed that, 

in 2013, 60% of Americans saw using stem cells from human embryos in medical research as “morally acceptable.” 

Figure 7-22

Public assessment of danger to environment of 

modifying genes of crops, by country/economy: 2010

NOTES: Responses to Do you think that modifying the genes of certain 

crops is extremely dangerous for the environment, very dangerous, 

somewhat dangerous, not very dangerous, or not dangerous at all for the 

environment? Percentages may not add to 100% because of rounding. 

SOURCE: International Social Survey Program, Environment Module 

(2010). See appendix table 7-38.

Science and Engineering Indicators 2014

Cumulative percent

0 20 40 60 80 100

Extremely/very dangerous Somewhat dangerous

Not very/not at all dangerous Can’t choose/no answer

Belgium

Norway

Denmark

United Kingdom

Taiwan

Sweden

New Zealand

Finland

Canada

Japan

Israel

Czech Republic

South Africa

Slovak Republic

Switzerland

Spain

Philippines

Latvia

France

South Korea

Germany

Slovenia

Argentina

Croatia

Mexico

Bulgaria

Austria

Lithuania

Russia

Chile

Turkey

United States
Science and Engineering Indicators 2014 ♦ 7-45

Teaching Evolution in the Schools

In the United States, the topic of whether and how evolution should be taught in the public schools has been a source 

of controversy for almost a century. Public views about evolution and the role of teaching evolution in the schools have 

been relatively stable over the course of 30 years. 

Public opinion about how evolution should be taught 

in U.S. public schools consistently shows two key patterns. First, when asked whether intelligent design should 

be taught alongside or in addition to evolution, a majority of Americans favor this approach to education. Second, 

when asked whether creation should be taught instead of 

evolution—thereby replacing it in the science curriculum—

a majority oppose this idea, but a sizeable minority favor 

it. Opposition to replacing evolution ranged between 44% 

and 54% from 1999 to 2005, whereas support ranged from 

37% to 44% over the same period (Plutzer and Berkman 

2008; Berkman and Plutzer 2010). A 2007 survey of 926 

high school biology teachers also found that 28% might 

be classified as advocates for evolutionary biology in their 

classrooms, whereas about 13% of teachers said they tell 

their students that “creationism or intelligent design” are 

“valid, scientific” theories about the “origin of the species.” 

Teachers who had taken a college-level course addressing 

evolution were significantly more likely to advocate for 

evolutionary biology (Berkman and Plutzer 2011). The difficulty of sampling in such surveys of special populations, 

however, means that this type of data should be interpreted 

with caution.

Animal Research

U.S. Patterns and Trends

The medical research community conducts experimental 

tests on animals for many purposes, including testing the effectiveness of drugs and procedures that may eventually be 

used to improve human health and advancing scientific understanding of biological processes. 

Most Americans support at least some kind of animal research, but support has fallen in recent years. About 56% of 

Americans said they saw “medical testing on animals” as 

“morally acceptable” in 2013, similar to the 55% who gave 

this response in 2011 and 2012 (Newport 2012a). These figures put support at the lowest level registered since Gallup 

began asking the question in 2001, when 65% said they saw 

such testing as acceptable (Newport and Himelfarb 2013). A 

comparison of surveys from 1988 and 2008 found a similar 

pattern of declining support (NSB 2012). 

The 2011 and 2012 Gallup numbers also suggest less support than research by VCU (2007) that showed nearly twothirds of respondents favoring “using animals in medical 

research.” A comprehensive 2008 Gallup survey also found 

that a majority of respondents wanted to maintain access 

to animal testing animal research; 64% opposed “banning 

all medical research on laboratory animals,” and 59% opposed “banning all product testing on laboratory animals” 

About 32% said it was “morally wrong.” The percentage 

of Americans seeing the use of human embryos as moral 

climbed from 52% in 2002, when Gallup started polling on 

the issue, to a high of 64% in 2007. Since then, the percentage of Americans viewing stem cell research as morally acceptable has ranged between 57% and 62% (Newport and 

Himelfarb 2013). 

Support for stem cell research is greater when the question posed asks about research that uses stem cells from 

sources that do not involve human embryos. About 7 out of 

10 respondents (71%) favored this type of research in 2010, 

down slightly from 75% in 2007 (VCU 2010). Support was 

also greater when the question was framed as an emotionally compelling personal issue (i.e., “If you or a member of 

your family had a condition such as Parkinson’s Disease, or 

a spinal cord injury, would you support the use of embryonic 

stem cells in order to pursue a treatment for that condition?”) 

(VCU 2006). 

Gallup has also asked Americans about human cloning. 

In 2013, Gallup found that only 13% of Americans said human cloning is “morally acceptable” and that 83% said it 

was “morally wrong.” The percentage indicating that cloning is morally acceptable was 7% in 2001 and 2002 and 

has stayed between 8% and 13% since then (Newport and 

Himelfarb 2013).

It appears that Americans are particularly opposed to human cloning when there is no mention of a medical purpose. 

As reported in the 2012 Indicators, a 2010 survey showed that 

8 in 10 Americans rejected the idea of cloning or genetically 

altering humans (VCU 2010). Opinions were more mixed 

when questions mentioned “cloning technology” that is used 

only to help medical research develop new treatments for 

disease; opinion about therapeutic cloning has been slowly 

growing more positive in recent years. Public attitudes toward cloning technology are not grounded in a strong grasp 

of the difference between reproductive and therapeutic cloning (see “Glossary” for definitions). In 2010, a 54% majority 

of Americans were “very clear” or “somewhat clear” about 

the difference between stem cells that come from human 

embryos, stem cells that come from adults, and stem cells 

that come from other sources (VCU 2010).

International Comparisons

A 2010 Eurobarometer found that 63% of those surveyed 

across the EU supported the use of stem cells from human 

embryos either with no special laws (12%) or “as long as 

this is regulated by strict laws” (51%). The use of adult stem 

cells, in contrast, was supported by 69% of Europeans, including 15% who saw no need for special laws and 54% 

who would approve of “strict laws.” The survey did not address human cloning, but it included several questions about 

animal cloning, and the results also show widespread disapproval. About 17% said that they saw it as “safe for future generations,” and 70% of EU residents disagreed that 

“animal cloning in food production should be encouraged” 

(European Commission 2010b).
7-46 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

(Newport 2008). There also appears to be a sizeable gender gap in opinions about animal research, with women less 

likely than men to support animal testing (Saad 2010), as 

well as an age gap with younger respondents being less supportive of animal testing (Wilke and Saad 2013). 

International Comparisons

A 2010 European-wide survey showed that EU residents 

have a range of views about animal research but are, on balance, supportive of such practices. Respondents were asked 

whether “scientists should be allowed to experiment on 

animals like dogs and monkeys if this can help sort out human health problems.” About 44% of EU residents said they 

“totally” or “tend to” agree that such experiments should 

be allowed, whereas 37% said they “totally” or “tend to” 

disagree. The report also indicated that, across the countries 

surveyed, men (49%) were much more likely to agree that 

animal testing should be allowed than women (39%). Those 

who said they were well informed about science (47%) or 

interested in science (48%) were also more favorable to animal testing than the average. When asked about animal research using mice—instead of dogs and monkeys—66% of 

EU residents indicated that would be acceptable (European 

Commission 2010a).

Science, Engineering, and 

Mathematics Education

Although the news media are important to how adults 

think about S&T, the formal education system remains 

most people’s primary introduction to S&T. A 2013 Pew 

Research study found that 11% of Americans named science 

as the subject that K–12 schools should emphasize more 

than other subjects. This made science the third most named 

subject. The most commonly named subject was math 

(30%), followed by “English/Grammar/Writing/Reading.” 

“Computers/Computer Science” came sixth (4%). When 

asked, 46% of Americans said the reason “many young people don’t pursue degrees in math and science” is because 

these subjects “are too hard.” About equal numbers said 

these subjects might be “too boring” (20%) or “not useful 

for their careers” (22%) (Pew Research Center 2013a).

In the 2008 GSS, the majority of Americans in all demographic groups agreed that the quality of science and mathematics education in American schools was inadequate. The 

level of dissatisfaction increased with education, science 

knowledge, income, and age. Dissatisfaction has also varied 

over time: it was 63% in 1985, peaked at 75% in 1992, and 

declined to 70% in 2008 (NSB 2010). Further, about half of 

Americans said that their local public schools did not put 

enough emphasis on teaching science and math, an equal 

portion (48%) said the emphasis was about right, and just 

2% said there was too much emphasis on teaching science 

and math in the local schools (Rose and Gallup 2007). In addition, the percentage of Americans in the biennial GSS surveys who said they believe the government is spending too 

little money on improving education has remained greater 

than 70% since the early 1980s. This is consistently one of 

the top areas in which the public says government spending 

is too low.

Conclusion

Assessing public attitudes and understanding about S&T 

can involve looking at what a technologically advanced society requires to succeed, either currently or in the future. 

Comparisons over time and between countries can also help 

identify achievements and areas for concern.

Those who believe that advanced societies require strong 

S&T performance will likely find many of the available indicators about S&T heartening. Americans remain interested 

in S&T, and a majority of Americans continue to say that 

they visit at least one informal science institution, such as 

a zoo or aquarium, annually. Most Americans are also able 

to answer basic S&T knowledge questions. In terms of attitudes, a large majority of Americans say that they want 

funding for scientific research and hold scientists and engineers in high regard. Most Americans also express positive 

views about various emerging technologies, including nuclear energy, biotechnology, and stem cells. In most cases, 

indicators for these attitudes have changed little in recent 

years, and Americans are more positive and have more factual knowledge about S&T than residents of other countries.

However, proponents of S&T may also find some indicators less reassuring. In particular, they may note that indicators of media content show that S&T has represented just 

a small percentage of the available news content in recent 

years. Likewise, data showing that many Americans have 

difficulty answering relatively simple knowledge questions 

about S&T are not encouraging. Also, while Americans say 

they are interested in S&T and want to fund S&T, other issues generate greater interest and elicit more support for 

government funding. Although most of the available indicators have remained stable, stability may represent cause for 

concern to those who hope to see Americans become more 

knowledgeable or more supportive of science. Comparisons 

with other countries are not unambiguously reassuring either. Although Americans generally score better on factual knowledge questions and are more positive about S&T 

than residents of other countries, multinational surveys 

have identified several countries where residents have more 

knowledge or are more supportive of S&T in specific areas. 

Although most of the indicators are stable, changes appearing in the most recent data might also cause concern. 

In 2012, fewer Americans could provide an adequate description of what makes something scientific or were willing to reject astrology as unscientific. Americans were also 

less supportive of stem cell research than in previous years. 

People focused on environmental issues might also worry 

that some indicators show that Americans are becoming 

less concerned about the environment than in previous years 

and are less concerned about such issues than residents of 
Science and Engineering Indicators 2014 ♦ 7-47

many other countries. Climate change is one topic for which 

substantial evidence suggests that Americans have become 

less concerned than in the past and where residents of most 

other countries are closer to sharing the assessment of the 

evidence that prevails in the scientific community.

One limitation of the available indicators is that much of 

the data come from Europe, with only limited recent data 

from the Asia-Pacific region, where there is a high level of 

S&T activity. 

Regardless of the standard used in assessing public attitudes and understanding of S&T, one pattern in the data 

continues to stands out. Year after year, Americans who are 

more highly educated—particularly those who are college 

educated and have completed college courses in science and 

mathematics—tend to understand more about S&T, tend to 

see S&T in a more positive light, and tend to engage with 

S&T more often. Although it is not clear whether this association is causal, the pattern underscores the role of science, technology, engineering, and mathematics education 

in fostering public understanding of S&T and possibly in 

developing orientations toward S&T that are similar to those 

that prevail in the scientific community.

Notes

1. This is an example in which, in 2001, the question was 

part of a single-purpose telephone survey focused on S&T. 

In 2008, these data were collected as part of the General 

Social Survey, a face-to-face, multipurpose survey covering 

a broad range of behavior and attitudes. It is unclear whether 

these differences in data collection or a change in public 

opinion account for the decline in interest observed between 

2001 and 2008.

2. The report for the survey did not provide confidence 

intervals or formal tests to assess the differences in means.

3. The question asked on the Eurobarometer surveys 

has changed over time, making the data not always strictly 

comparable with previous Eurobarometer surveys or with 

U.S. data.

4. The analysis is based on a purposive selection of five 

media sectors, outlets within each sector, and specific programs or articles for study. The index was designed to capture the main news stories covered each week. Coding of 

programs and articles was limited to the first 30 minutes of 

most radio, cable, and network news programs; the front 

page of newspapers; and the top five stories on websites. 

Each selected unit of study was coded on 17 variables, according to an established coding protocol. The team of individuals performing the content analysis was directed by 

a coding manager, a training coordinator, a methodologist, 

and a senior researcher. For variables that require little or 

no inference, intercoder agreement was 97% for 2010, the 

last year in which statistics were reported. For variables requiring more inference, intercoder agreement ranged from 

78% to 85% in 2010. Intercoder agreement was similar in 

earlier years. For more details, see http://www.journalism.

org/about_news_index/methodology.

5. The total amount of news consists of the space devoted 

to news in print and online news sources and the time devoted to news on radio and television sources.

6. “Science, space, and technology” includes stories on 

manned and unmanned space flight, astronomy, scientific 

research, computers, the Internet, and telecommunications 

media technology. It excludes forensic science and telecommunications media content. “Biotechnology and basic medical research” includes stem cell research, genetic research, 

cloning, and agribusiness bioengineering and excludes clinical research and medical technology. Stories often do not fall 

neatly into a single category or theme. The Tyndall and PEJ 

data should not be directly compared because they involve 

different definitions of content. The coverage of health research in the Tyndall television data represents only a small 

percentage of the overall health coverage on television.

7. After 11 August 2011, the PEJ used the tracking services 

Technorati and Icerocket to monitor blogs and Tweetmeme 

and Twitturly to monitor social media. Prior to August 2011, 

the data collection was done using Icerocket and Tweetmeme. 

In all cases, the services used the links embedded on the sites 

as a proxy for the subject of the blog post or tweet. The sites 

thus provide a list of the most-linked-to news stories based on 

the number of blogs, tweets, or other sites that link to each. 

Typically, the linked-to stories originate from traditional media 

sources. PEJ staff manually captured the list of most-linkedto stories each weekday, and the coding staff categorized the 

top five linked-to articles from this list of approximately 50 

linked-to articles each week. The coding procedures are similar 

to those used for the News Coverage Index of traditional media 

sources. For more, see http://www.journalism.org/node/14356.

8. In general, it is difficult to obtain information about 

S&T content within entertainment programming, although 

substantial evidence suggests that the entertainment people 

view shapes their attitudes about a range of issues, including 

S&T (Brossard and Dudo 2012).

9. A 2013 report by the PEJ reported that the most popular 

news sites were those associated with the news divisions of 

the main television broadcasters and cable networks, with the 

Yahoo!–ABC News Network leading the way. No clear science source was listed in the summaries of various measures 

of news site popularity, although several weather-focused 

sites (e.g., http://www.weather.com) appeared (PEJ 2013).

10. People become involved with S&T through many 

kinds of nonclassroom activities beyond attendance at informal science institutions. Examples of such activities 

include participating in government policy processes, going to movies that feature S&T, attending talks or lectures, 

bird watching, and building computers. Citizen science is a 

term used for activities by citizens with no specific science 

training who participate in the research process through activities such as observation, measurement, or computation. 

Nationally representative data on this sort of involvement 

with S&T are unavailable.
7-48 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

11. In the 2008 GSS, respondents received two different 

introductions to this set of questions. Response patterns did 

not vary depending on which introduction was given.

12. S. Feldman, Senior Vice President of External Affairs, 

Association of Zoos and Aquariums, personal communication to author, 1 May 2013.

13. This question was part of a single-purpose telephone 

survey focused on science and technology in 2001. In 2008, 

these data were collected as part of a face-to-face multipurpose survey. It is unclear whether these differences in data 

collection or a change in visit behavior account for changes 

seen between 2001, 2008, and 2012.

14. Survey items that test factual knowledge sometimes 

use easily comprehensible language at the cost of scientific 

precision. This may prompt some highly knowledgeable respondents to believe that the items blur or neglect important 

distinctions, and in a few cases may lead respondents to answer questions incorrectly. In addition, the items do not reflect the ways that established scientific knowledge evolves 

as scientists accumulate new evidence. Although the text of 

the factual knowledge questions may suggest a fixed body 

of knowledge, it is more accurate to see scientists as making 

continual, often subtle modifications in how they understand 

existing data in light of new evidence. When the answer to 

a factual knowledge question is categorized as “correct,” it 

means that the answer accords with the current consensus 

among knowledgeable scientists and that the weight of scientific evidence clearly supports the answer.

15. Although the data clearly show a difference in how 

respondents answer to different question types, these data 

do not provide guidance as to what caused the difference. A 

range of explanations are possible.

16. In its own international comparison of scientific literacy, Japan ranked itself 10th among the 14 countries it 

evaluated (NISTEP 2002).

17. Twenty questions used a true-or-false format. These 

included: (1) “Hot air rises” (true; Europe correct: 91%, 

United States correct: 95%); (2) “The continents have been 

moving for millions of years and will continue to move in 

the future” (true; Europe correct: 86%, United States correct: 80%); (3) “The oxygen we breathe comes from plants” 

(true; Europe correct: 83%, United States correct: 94%); 

(4) “The gene is the basic unit of heredity of living beings” 

(true; Europe correct: 82%, United States correct: 82%); 

(5) “Earth’s gravity pulls objects towards it without being 

touched” (true; Europe correct: 79%, United States correct: 

80%); (6) “Energy cannot be created or destroyed, but only 

changed from one form to another” (true; Europe correct: 

66%, United States correct: 80%); (7) “Almost all microorganisms are harmful to human beings” (false; Europe 

correct: 63%, United States correct: 56%); (8) “Generally 

speaking, human cells do not divide” (false; Europe correct: 

63%, United States correct: 58%); (9) “The earliest humans 

lived at the same time as the dinosaurs” (false; Europe correct: 61%, United States correct: 43%); (10); “Plants have 

no DNA” (false; Europe correct: 60%, United States correct: 

64%); (11); “The greenhouse effect is caused by the use of 

nuclear power” (false; Europe correct: 58%, United States 

correct: 47%); (12) “All radioactivity is a product of human 

activity” (false; Europe correct: 56%, United States correct: 

62%); (13) “Ordinary tomatoes, the ones we normally eat, 

do not have genes, whereas genetically engineered tomatoes do” (false; Europe correct: 54%, United States correct: 

48%); (14) “It is the father’s gene that determines a newborn 

baby’s sex, whether it is a boy or a girl” (true; Europe correct: 52%, United States correct: 75%); (15) “Lasers work 

by sound waves” (false; Europe correct: 48%, United States 

correct: 54%); (16) “The light that reaches the Earth from the 

sun is made up of a single color: white” (false; Europe correct: 44%, United States correct: 55%); (17) “Today it is not 

possible to transfer genes from humans to animals” (false; 

Europe correct: 41%, United States correct: 43%); (18) 

“Atoms are smaller than electrons” (false; Europe correct: 

38%, United States correct: 50%); (19) “Antibiotics destroy 

viruses” (false; Europe correct: 36%, United States correct: 

47%); (20) “Human stem cells are extracted from human 

embryos without destroying the embryos” (false; Europe 

correct: 29%, United States correct: 54%). Two additional 

questions used a multiple choice format. These asked about 

(21) whether the sun moves around the Earth, whether the 

Earth moves around the sun (correct), or neither the sun nor 

the Earth moves (Europe correct: 80%, United States correct: 82%); and (22) whether light travels faster than sound 

(correct), sound travels faster than light, or whether they 

travel at equal speed (Europe correct: 74%, United States 

correct: 78%).

18. Earlier NSF surveys used for the Indicators report 

used additional questions to measure understanding of 

probability. Bann and Schwerin (2004) identified a smaller 

number of questions that could be administered to develop 

a comparable indicator. Starting in 2004, the NSF surveys 

used these questions for the trend factual knowledge scale.

19. The evidence for the 2012 decline in understanding of 

experimental design needs to be regarded with caution. It is 

important to note that the percentage of Americans who correctly answered the initial, multiple choice question about 

how to conduct a pharmaceutical trial stayed stable between 

2010 and 2012. It was only the follow-up question that 

asked respondents to use their own words to justify the to 

use of a control group that saw a decline. For this question, 

interviewers recorded the response and then trained coders 

to use a standard set of rules to judge whether the response 

is correct. Although the instructions and training have remained the same in different years, small changes in survey 

administration practices can sometimes substantially affect 

such estimates.

20. The questions were selected from the Trends in 

Mathematics and Science Studies, National Assessment 

of Educational Progress, practice General Educational 

Development exams, and the American Association for the 

Advancement of Science Project 2061.
Science and Engineering Indicators 2014 ♦ 7-49

21. The pseudoscience section focuses on astrology 

because of the availability of long-term national trend indicators on this subject. Other examples of pseudoscience 

include the belief in lucky numbers, the existence of unidentified flying objects (UFOs), extrasensory perception (ESP), 

or magnetic therapy. One difficulty with this question is that 

astrology is based on observation of planets and stars and 

respondents might believe that this makes it “sort of scientific.” However, the fact that those with more formal education and higher factual knowledge scores are consistently 

more likely to reject astrology as a science suggests that this 

nuance has a limited impact on results.

22. Methodological issues make fine-grained comparisons of data from different survey years particularly difficult for this question. For example, although the question 

content and interviewer instructions were identical in 2004 

and 2006, the percentage of respondents who volunteered 

“about equal” (an answer not among the choices given) was 

substantially different. This difference may have been produced by the change from telephone interviews in 2004 to 

in-person interviews in 2006 (although telephone interviews 

in 2001 produced results that are similar to those in 2006). 

More likely, customary interviewing practices in the three 

different organizations that administered the surveys affected their interviewers’ willingness to accept responses other 

than those that were specifically offered on the interview 

form, including “don’t know” responses.

23. This type of survey question asks respondents about 

their assessment of government spending in several areas 

without mentioning the possible negative consequences of 

spending (e.g., higher taxes, less money available for higher 

priority expenditures). A question that focused respondents’ 

attention on such consequences might yield response patterns less sympathetic to greater government funding.

24. As noted previously, the 1983 and 2001 surveys were 

telephone surveys, whereas the 2012 GSS survey was primarily a face-to-face survey. Similarly, there are only three 

data points for comparison, and these are separated by about 

a decade each. It is difficult to know the degree to which 

the change in survey mode may have affected the results, 

and the widely dispersed data points make determining the 

presence of a trend difficult. The between-year comparisons 

are therefore made with caution. Not all of the questions discussed were included each year.

25. There are many different types of specializations 

within occupations, and prestige may well vary within the 

same occupation or industry.

26. Given the relationship between education, knowledge, and views about professions, it may be that the ability 

to assess the degree to which a field or occupation involves 

the use of S&T concepts or ideas represents a form of science literacy relevant to the question of the role of science in 

everyday life (NSB 2012; Toumey et al. 2010).

27. The GSS questions on global climate change used the 

term global warming.

28. The 2010 GSS included ratings of nuclear engineers 

in addition to medical researchers, environmental scientists, 

and economists. As discussed, the patterns of results were 

similar whether the group with relevant expertise was engineers or scientists.

29. Similarity comments for ISSP data are based on post 

hoc statistical tests using mean scores. Also, countries described as being the most or least concerned are those that 

are statistically similar but in group with the highest or lowest mean score based on mean testing.

30. There is some evidence from a large-scale experimental study that the wording used in such questions (“global 

warming” or “climate change”) can have an effect on reported beliefs about global climate change (Schuldt, Konrath, 

and Schwarz 2011). Earlier studies, however, suggested 

that such wording differences had little effect (European 

Commission 2008; Villar and Krosnick 2010).

31. This question was only asked to those who said 

they believed there was “solid evidence of increasing 

global temperatures.”

Glossary

Biotechnology: The use of living things to make products.

Climate change: Any distinct change in measures of 

climate lasting for a long period of time. Climate change 

means major changes in temperature, rainfall, snow, or wind 

patterns lasting for decades or longer. Climate change may 

result from natural factors or human activities.

European Union (EU): Eurobarometer survey data for 

2008, 2010, and 2011 include data for 27 EU member nations: Austria, Belgium, Bulgaria, Cyprus, Czech Republic, 

Denmark, Estonia, Finland, France, Germany, Greece, 

Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, 

Malta, the Netherlands, Poland, Portugal, Romania, 

Slovakia, Slovenia, Spain, Sweden, and the United Kingdom. 

Eurobarometer survey data for years prior to 2008 include 

data for EU member nations as of the survey year (25 countries in 2005 and 15 in 1999).

Genetically modified (GM) food: A food product containing some quantity of any GM organism as an ingredient.

Global warming: An average increase in temperatures 

near the Earth’s surface and in the lowest layer of the atmosphere. Increases in temperatures in the Earth’s atmosphere 

can contribute to changes in global climate patterns. Global 

warming can be considered part of climate change along 

with changes in precipitation, sea level, etc.

Nanotechnology: Manipulating matter at unprecedentedly small scales to create new or improved products that 

can be used in a wide variety of ways.

Reproductive cloning: Technology used to generate genetically identical individuals with the same nuclear DNA as 

another individual. 

Therapeutic cloning: Use of cloning technology in medical research to develop new treatments for diseases; differentiated from human reproductive cloning.
7-50 ♦ Chapter 7. Science and Technology: Public Attitudes and Understanding

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Pew Research Global Attitudes Project. 2010. Obama more 

popular abroad than at home, global image of U.S. continues to benefit. http://www.pewglobal.org/2010/06/17/chapter-8-environmental-issues-2. Accessed 24 January 2013.

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poll/147242/Worldwide-Blame-Climate-Change-FallsHumans.aspx#1. Accessed 14 January 2013. 

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♦ 8-1

Chapter 8

State Indicators

Introduction..................................................................................................................................8-7

Chapter Overview....................................................................................................................8-7

Types of Indicators ..................................................................................................................8-7

Data Sources and Considerations.............................................................................................8-8

Key Elements for Indicators ....................................................................................................8-8

Technical Note: High-Technology Industries........................................................................8-10

Appendix Tables....................................................................................................................8-10

Reference ...................................................................................................................................8-10

Elementary and Secondary Education

Fourth Grade Mathematics Performance ...................................................................................8-12

Fourth Grade Mathematics Proficiency.....................................................................................8-14

Fourth Grade Science Performance ...........................................................................................8-16

Fourth Grade Science Proficiency .............................................................................................8-18

Eighth Grade Mathematics Performance ...................................................................................8-20

Eighth Grade Mathematics Proficiency.....................................................................................8-22

Eighth Grade Science Performance ...........................................................................................8-24

Eighth Grade Science Proficiency .............................................................................................8-26

Public School Teacher Salaries..................................................................................................8-28

Elementary and Secondary Public School Expenditures as a Percentage of Gross 

Domestic Product...................................................................................................................8-30

Expenditures per Pupil for Elementary and Secondary Public Schools....................................8-32

Public High School Students Taking Advanced Placement Exams ..........................................8-34

Public High School Students Scoring 3 or Higher on at Least One 

Advanced Placement Exam ...................................................................................................8-36

Public High School Students Scoring 3 or Higher on Advanced Placement 

Calculus AB Exam.................................................................................................................8-38

High School Graduates or Higher Among Individuals 25–44 Years Old .................................8-40

Higher Education

Associate’s Degrees in Science, Engineering, and Technology Conferred per 1,000 

Individuals 18–24 Years Old .................................................................................................8-42

Bachelor’s Degrees Conferred per 1,000 Individuals 18–24 Years Old ...................................8-44

Bachelor’s Degrees in Science and Engineering Conferred per 1,000 

Individuals 18–24 Years Old .................................................................................................8-46

Bachelor’s Degrees in Natural Sciences and Engineering Conferred 

per 1,000 Individuals 18–24 Years Old .................................................................................8-48

Science and Engineering Degrees as a Percentage of Higher Education Degrees Conferred ......8-50

Natural Sciences and Engineering Degrees as a Percentage of Higher Education 

Degrees Conferred .................................................................................................................8-52

Science and Engineering Graduate Students per 1,000 Individuals 25–34 Years Old..............8-54

Advanced Science and Engineering Degrees as a Percentage of S&E Degrees Conferred ......8-56

Advanced Natural Sciences and Engineering Degrees as a Percentage of NS&E Degrees 

Conferred ...............................................................................................................................8-58
8-2 ♦ Chapter 8. State Indicators

Science and Engineering Doctoral Degrees as a Percentage of S&E Degrees Conferred ........8-60

Average Undergraduate Charge at Public 4-Year Institutions .................................................8-62

Average Undergraduate Charge at Public 4-Year Institutions as a Percentage of 

Disposable Personal Income .................................................................................................8-64

Appropriations of State Tax Funds for Operating Expenses of Higher Education as a 

Percentage of Gross Domestic Product..................................................................................8-66

State Expenditures on Student Aid per Full-Time Undergraduate Student ..............................8-68

State Funding for Public Research Universities per Full-Time Equivalent Student ................8-70

Postsecondary Degree Holders among Individuals 25–44 Years Old ......................................8-72

Bachelor’s Degree Holders among Individuals 25–44 Years Old.............................................8-74

Workforce

Bachelor’s Degree Holders Potentially in the Workforce ........................................................8-76

Individuals in Science and Engineering Occupations as a Percentage of All Occupations ......8-78

Employed Science and Engineering Doctorate Holders as a Percentage of the Workforce .....8-80

Engineers as a Percentage of All Occupations .........................................................................8-82

Life and Physical Scientists as a Percentage of All Occupations ............................................8-84

Computer Specialists as a Percentage of All Occupations .......................................................8-86

Technical Workers as a Percentage of All Occupations ...........................................................8-88

Financial Research and Development Inputs

R&D as a Percentage of Gross Domestic Product.....................................................................8-90

Federal R&D Obligations per Employed Worker .....................................................................8-92

Federal R&D Obligations per Individual in Science and Engineering Occupation ..................8-94

State Agency R&D Expenditures per $1 Million of Gross Domestic Product .........................8-96

State Agency R&D Expenditures per Employed Worker .........................................................8-98

State Agency R&D Expenditures per Individual in Science and Engineering Occupation ....8-100

Business-Performed R&D as a Percentage of Private-Industry Output ..................................8-102

Academic Science and Engineering R&D per $1,000 of Gross Domestic Product ..............8-104

Research and Development Outputs

Science and Engineering Doctorates Conferred per 1,000 Employed S&E Doctorate 

Holders ................................................................................................................................8-106

Academic Science and Engineering Article Output per 1,000 S&E Doctorate Holders 

in Academia ........................................................................................................................8-108

Academic Science and Engineering Article Output per $1 Million of Academic 

S&E R&D............................................................................................................................8-110

Academic Patents Awarded per 1,000 Science and Engineering Doctorate Holders 

in Academia .........................................................................................................................8-112

Patents Awarded per 1,000 Individuals in Science and Engineering Occupations ................8-114

Science and Technology in the Economy

High-Technology Establishments as a Percentage of All Business Establishments ...............8-116

Net High-Technology Business Formations as a Percentage of All Business 

Establishments.....................................................................................................................8-118

Employment in High-Technology Establishments as a Percentage of Total Employment.....8-120

Average Annual Federal Small Business Innovation Research Funding per $1 Million 

of Gross Domestic Product .................................................................................................8-122

Venture Capital Disbursed per $1,000 of Gross Domestic Product ........................................8-124

Venture Capital Deals as a Percentage of High-Technology Business Establishments..........8-126

Venture Capital Disbursed per Venture Capital Deal..............................................................8-128
Science and Engineering Indicators 2014 ♦ 8-3

List of Tables

Table 8-1. Average fourth grade mathematics performance, by state: 2003, 2007, 

and 2011.................................................................................................................................8-13

Table 8-2. Students reaching proficiency in fourth grade mathematics, by state: 2003, 

2007, and 2011 ........................................................................................................................... 8-15

Table 8-3. Average fourth grade science performance, by state: 2009 .........................................8-17

Table 8-4. Students reaching proficiency in fourth grade science, by state: 2009 .......................8-19

Table 8-5. Average eighth grade mathematics performance, by state: 2003, 2007, and 2011 ..... 8-21

Table 8-6. Students reaching proficiency in eighth grade mathematics, by state:

2003, 2007, and 2011 ................................................................................................................. 8-23

Table 8-7. Average eighth grade science performance, by state: 2009 and 2011 ........................8-25

Table 8-8. Students reaching proficiency in eighth grade science, by state: 2009 and 2011 ........8-27

Table 8-9. Public school teacher salaries, by state: 2002, 2007, and 2012 ...................................8-29

Table 8-10. Elementary and secondary public school expenditures as a percentage 

of gross domestic product, by state: 2000, 2005, and 2010 ....................................................... 8-31

Table 8-11. Expenditures per pupil for elementary and secondary public schools, 

by state: 2000, 2005, and 2010 ................................................................................................... 8-33

Table 8-12. Public high school students taking Advanced Placement Exams, 

by state: 2002, 2007, and 2012 ................................................................................................... 8-35

Table 8-13. Public high school students scoring 3 or higher on at least one Advanced 

Placement Exam, by state: 2002, 2007, and 2012...................................................................... 8-37

Table 8-14. Public high school students scoring 3 or higher on Advanced Placement 

Calculus AB Exam, by state: 2002, 2007, and 2012.................................................................. 8-39

Table 8-15. High school graduates or higher among individuals 25–44 years old, 

by state: 2001, 2006, and 2011 ................................................................................................... 8-41

Table 8-16. Associate’s degrees in science, engineering, and technology conferred 

per 1,000 individuals 18–24 years old, by state: 2001, 2006, and 2011.................................... 8-43

Table 8-17. Bachelor’s degrees conferred per 1,000 individuals 18–24 years old, by state: 

2001, 2006, and 2011.................................................................................................................. 8-45

Table 8-18. Bachelor’s degrees in science and engineering conferred per 1,000 individuals 

18–24 years old, by state: 2001, 2006, and 2011 ....................................................................... 8-47

Table 8-19. Bachelor’s degrees in natural sciences and engineering conferred per 1,000 

individuals 18–24 years old, by state: 2001, 2006, and 2011 .................................................... 8-49

Table 8-20. Science and engineering degrees as a percentage of higher education 

degrees conferred, by state: 2001, 2006, and 2011 .................................................................... 8-51

Table 8-21. Natural sciences and engineering degrees as a percentage of higher 

education degrees conferred, by state: 2001, 2006, and 2011.................................................... 8-53

Table 8-22. Science and engineering graduate students per 1,000 individuals 25–34 years 

old, by state: 2001, 2006, and 2011 ............................................................................................ 8-55

Table 8-23. Advanced science and engineering degrees as a percentage of S&E degrees 

conferred, by state: 2001, 2006, and 2011 ................................................................................. 8-57

Table 8-24. Advanced natural sciences and engineering degrees as a percentage of NS&E 

degrees conferred, by state: 2001, 2006, and 2011 ................................................................... 8-59

Table 8-25. Science and engineering doctoral degrees as a percentage of S&E degrees 

conferred, by state: 2001, 2006, and 2011.................................................................................. 8-61

Table 8-26. Average undergraduate charge at public 4-year institutions, by state: 

2001, 2006, and 2011.................................................................................................................. 8-63

Table 8-27. Average undergraduate charge at public 4-year institutions as a percentage 

of disposable personal income, by state: 2001, 2006, and 2011................................................ 8-65

Table 8-28. Appropriations of state tax funds for operating expenses of higher education 

as a percentage of gross domestic product, by state: 2004, 2008, and 2012 ............................. 8-67

Table 8-29. State expenditures on student aid per full-time undergraduate student, by state: 

2001, 2006, and 2011.................................................................................................................. 8-69
8-4 ♦ Chapter 8. State Indicators

Table 8-30. State funding for major public research universities per full-time equivalent 

student: 2010 .............................................................................................................................. 8-71

Table 8-31. Postsecondary degree holders among individuals 25–44 years old, by state: 

2001, 2006, and 2011.................................................................................................................. 8-73

Table 8-32. Bachelor’s degree holders among individuals 25–44 years old, by state: 

2001, 2006, and 2011.................................................................................................................. 8-75

Table 8-33. Bachelor’s degree holders potentially in the workforce, by state: 

2001, 2006, and 2011.................................................................................................................. 8-77

Table 8-34. Individuals in science and engineering occupations as a percentage of all 

occupations, by state: 2003, 2008, and 2012.............................................................................. 8-79

Table 8-35. Employed science and engineering doctorate holders as a percentage of the 

workforce, by state: 2001, 2006, and 2010................................................................................. 8-81

Table 8-36. Engineers as a percentage of all occupations, by state: 2003, 2008, 

and 2012 ...................................................................................................................................... 8-83

Table 8-37. Life and physical scientists as a percentage of all occupations, by state: 

2003, 2008, and 2012.................................................................................................................. 8-85

Table 8-38. Computer specialists as a percentage of all occupations, by state: 

2003, 2008, and 2012.................................................................................................................. 8-87

Table 8-39. Technical workers as a percentage of all occupations, by state: 2003, 2008, 

and 2012 ...................................................................................................................................... 8-89

Table 8-40. R&D as a percentage of gross domestic product, by state: 2000, 2005, and 2010....8-91

Table 8-41. Federal R&D obligations per employed worker, by state: 2001, 2006, and 2011.....8-93

Table 8-42. Federal R&D obligations per individual in science and engineering occupation, 

by state: 2003, 2007, and 2011 ................................................................................................... 8-95

Table 8-43. State agency R&D expenditures per $1 million of gross domestic product, 

by state: 2006, 2009, and 2011 ................................................................................................... 8-97

Table 8-44. State agency R&D expenditures per employed worker, by state: 2006, 2009, 

and 2011 ...................................................................................................................................... 8-99

Table 8-45. State agency R&D expenditures per individual in science and engineering 

occupation, by state: 2006, 2009, and 2011 .............................................................................8-101

Table 8-46. Business-performed domestic R&D as a percentage of private-industry output, 

by state: 2001, 2006, and 2011 .................................................................................................8-103

Table 8-47. Academic science and engineering R&D per $1,000 of gross domestic product, 

by state: 2002, 2007, and 2012 .................................................................................................8-105

Table 8-48. Science and engineering doctorates conferred per 1,000 employed S&E 

doctorate holders, by state: 2001, 2006, and 2010 ...................................................................8-107

Table 8-49. Academic science and engineering article output per 1,000 S&E doctorate 

holders in academia, by state: 2001, 2006, and 2010...............................................................8-109

Table 8-50. Academic science and engineering article output per $1 million of academic 

S&E R&D, by state: 2003, 2008, and 2012 .............................................................................8-111

Table 8-51. Academic patents awarded per 1,000 science and engineering doctorate 

holders in academia, by state: 2001, 2006, and 2010...............................................................8-113

Table 8-52. Patents awarded per 1,000 individuals in science and engineering occupations, 

by state: 2003, 2008, and 2012 .................................................................................................8-115

Table 8-53. High-technology establishments as a percentage of all business establishments, 

by state: 2003, 2007, and 2010 .................................................................................................8-117

Table 8-54. Net high-technology business formations as a percentage of all business 

establishments, by state: 2004, 2007, and 2010 .......................................................................8-119

Table 8-55. Employment in high-technology establishments as a percentage of total 

employment, by state: 2003, 2007, and 2010...........................................................................8-121

Table 8-56. Average annual federal Small Business Innovation Research funding per 

$1 million of gross domestic product, by state: 2002–04, 2006–08, and 2010–12.................8-123
Science and Engineering Indicators 2014 ♦ 8-5

Table 8-57. Venture capital disbursed per $1,000 of gross domestic product, by state: 

2002, 2007, and 2012................................................................................................................8-125

Table 8-58. Venture capital deals as a percentage of high-technology business 

establishments, by state: 2003, 2007, and 2010 .......................................................................8-127

Table 8-59. Venture capital disbursed per venture capital deal, by state: 2002, 2007, and 2012 ...8-129

Table 8-A. NAICS codes that constitute high-technology industries............................................ 8-11

List of Figures

Figure 8-1. Average fourth grade mathematics performance: 2011..........................................8-12

Figure 8-2. Students reaching proficiency in fourth grade mathematics: 2011 ........................8-14

Figure 8-3. Average fourth grade science performance: 2009 .................................................8-16

Figure 8-4. Students reaching proficiency in fourth grade science: 2009 ................................8-18

Figure 8-5. Average eighth grade mathematics performance: 2011 .........................................8-20

Figure 8-6. Students reaching proficiency in eighth grade mathematics: 2011 ........................8-22

Figure 8-7. Average eighth grade science performance: 2011 .................................................8-24

Figure 8-8. Students reaching proficiency in eighth grade science: 2011 ................................8-26

Figure 8-9. Public school teacher salaries: 2012 ......................................................................8-28

Figure 8-10. Elementary and secondary public school expenditures as a percentage 

of gross domestic product: 2010................................................................................................8-30

Figure 8-11. Expenditures per pupil for elementary and secondary public 

schools: 2010 .........................................................................................................................8-32

Figure 8-12. Public high school students taking Advanced Placement Exams: 2012...............8-34

Figure 8-13. Public high school students scoring 3 or higher on at least one Advanced 

Placement Exam: 2012 ..........................................................................................................8-36

Figure 8-14. Public high school students scoring 3 or higher on Advanced Placement 

Calculus AB Exam: 2012.......................................................................................................8-38

Figure 8-15. High school graduates or higher among individuals 25–44 years old: 2011 ........8-40

Figure 8-16. Associate’s degrees in science, engineering, and technology conferred 

per 1,000 individuals 18–24 years old: 2011.........................................................................8-42

Figure 8-17. Bachelor’s degrees conferred per 1,000 individuals 18–24 years old: 2011 ........8-44

Figure 8-18. Bachelor’s degrees in science and engineering conferred per 1,000 

individuals 18–24 years old: 2011 .........................................................................................8-46

Figure 8-19. Bachelor’s degrees in natural sciences and engineering conferred per 

1,000 individuals 18–24 years old: 2011 ...............................................................................8-48

Figure 8-20. Science and engineering degrees as a percentage of higher education 

degrees conferred: 2011.........................................................................................................8-50

Figure 8-21. Natural sciences and engineering degrees as a percentage of higher 

education degrees conferred: 2011 ........................................................................................8-52

Figure 8-22. Science and engineering graduate students per 1,000 individuals 25–34 

years old: 2011.......................................................................................................................8-54

Figure 8-23. Advanced science and engineering degrees as a percentage of S&E degrees 

conferred: 2011 .....................................................................................................................8-56

Figure 8-24. Advanced natural sciences and engineering degrees as a percentage of 

NS&E degrees conferred: 2011 ............................................................................................8-58

Figure 8-25. Science and engineering doctoral degrees as a percentage of S&E degrees 

conferred: 2011 ......................................................................................................................8-60

Figure 8-26. Average undergraduate charge at public 4-year institutions: 2011.......................8-62

Figure 8-27. Average undergraduate charge at public 4-year institutions as a percentage 

of disposable personal income: 2011.....................................................................................8-64

Figure 8-28. Appropriations of state tax funds for operating expenses of higher education 

as a percentage of gross domestic product: 2012...................................................................8-66

Figure 8-29. State expenditures on student aid per full-time undergraduate student: 2011 ......8-68
8-6 ♦ Chapter 8. State Indicators

Figure 8-30. State funding for major public research universities per full-time equivalent 

student: 2010 .........................................................................................................................8-70

Figure 8-31. Postsecondary degree holders among individuals 25–44 years old: 2011............8-72

Figure 8-32. Bachelor’s degree holders among individuals 25–44 years old: 2011..................8-74

Figure 8-33. Bachelor’s degree holders potentially in the workforce: 2011 .............................8-76

Figure 8-34. Individuals in science and engineering occupations as a percentage of all 

occupations: 2012 ..................................................................................................................8-78

Figure 8-35. Employed science and engineering doctorate holders as a percentage 

of the workforce: 2010...........................................................................................................8-80

Figure 8-36. Engineers as a percentage of all occupations: 2012..............................................8-82

Figure 8-37. Life and physical scientists as a percentage of all occupations: 2012 ..................8-84

Figure 8-38. Computer specialists as a percentage of all occupations: 2012 ............................8-86

Figure 8-39. Technical workers as a percentage of all occupations: 2012 ................................8-88

Figure 8-40. R&D as a percentage of gross domestic product: 2010 ........................................8-90

Figure 8-41. Federal R&D obligations per employed worker: 2011.........................................8-92

Figure 8-42. Federal R&D obligations per individual in science and engineering 

occupation: 2011....................................................................................................................8-94

Figure 8-43. State agency R&D expenditures per $1 million of gross domestic 

product: 2011 .........................................................................................................................8-96

Figure 8-44. State agency R&D expenditures per employed worker: 2011..............................8-98

Figure 8-45. State agency R&D expenditures per individual in science and engineering 

occupation: 2011..................................................................................................................8-100

Figure 8-46. Business-performed domestic R&D as a percentage of private-industry 

output: 2011 .........................................................................................................................8-102

Figure 8-47. Academic science and engineering R&D per $1,000 of gross domestic 

product: 2012 .......................................................................................................................8-104

Figure 8-48. Science and engineering doctorates conferred per 1,000 employed 

S&E doctorate holders: 2010...............................................................................................8-106

Figure 8-49. Academic science and engineering article output per 1,000 S&E doctorate 

holders in academia: 2010 ...................................................................................................8-108

Figure 8-50. Academic science and engineering article output per $1 million of 

academic S&E R&D: 2012..................................................................................................8-110

Figure 8-51. Academic patents awarded per 1,000 science and engineering doctorate 

holders in academia: 2010 ...................................................................................................8-112

Figure 8-52. Patents awarded per 1,000 individuals in science and engineering 

occupations: 2012 ................................................................................................................8-114

Figure 8-53. High-technology establishments as a percentage of all business 

establishments: 2010............................................................................................................8-116

Figure 8-54. Net high-technology business formations as a percentage of all business 

establishments: 2010............................................................................................................8-118

Figure 8-55. Employment in high-technology establishments as a percentage of total 

employment: 2010 ...............................................................................................................8-120

Figure 8-56. Average annual federal Small Business Innovation Research funding 

per $1 million of gross domestic product: 2010–12 ............................................................8-122

Figure 8-57. Venture capital disbursed per $1,000 of gross domestic product: 2012 .............8-124

Figure 8-58. Venture capital deals as a percentage of high-technology business 

establishments: 2010............................................................................................................8-126

Figure 8-59. Venture capital disbursed per venture capital deal: 2012 ...................................8-128

Figure 8-A. U.S. map and list of abbreviations ...........................................................................8-9

Figure 8-B. Example state distribution chart .............................................................................8-10
Science and Engineering Indicators 2014 ♦ 8-7

Introduction

Chapter Overview

To address the interest of the policy and research communities in the role of science and technology (S&T) in state and 

regional economic development, this chapter presents findings on state trends in S&T education, the employed workforce, finance, and research and development. This chapter 

includes 59 indicators for individual states, the District of 

Columbia, and Puerto Rico.

The indicators are designed to present information about 

various aspects of state S&T infrastructure. The data used to 

calculate the indicators were gathered from public and private sources. When possible, data covering a 10-year span 

are presented to assist in identifying trends. However, consistent data were not always available for the 10-year period; in 

these cases, data are given only for the years in which comparisons are appropriate. Most indicators contain data for 

2010–11; some contain data for 2012.

Ready access to accurate and timely information is an 

important tool for formulating effective S&T policies at the 

state level. By studying the programs and performance of 

their peers, state policymakers may be able to better assess 

and enhance their own programs and performance. Corporations and other organizations considering investments at the 

state level may also benefit from this information. The tables 

are intended to provide quantitative data that may be relevant 

to technology-based economic development. More generally, 

the chapter aims to foster further consideration of the appropriate uses of state-level indicators.

Types of Indicators

The 59 indicators are divided into six categories. 

1. Elementary and secondary education

Indicators in this area cover three topics:

♦ Student achievement at elementary and 

secondary levels

♦ Public school expenditures

♦ Persons with high school credentials

Student achievement is expressed in terms of performance, 

which refers to the average state score on a standardized test, 

and proficiency, which is expressed as the percentage of students who have achieved at least an expected level of competence on the test.

State-level performance data are not available for high 

school students. Performance and proficiency data in mathematics are available for students in grade 12 at the national 

level but for students in fewer than one-quarter of the states 

at the state level. Performance and proficiency data in science 

are only available at the national level for students in grade 

12. Instead, mastery of college-level material through performance on Advanced Placement Exams has been included as 

a measure of the skills being developed by top-performing 

high school students.

2. Higher education

Indicators in this area cover three topics:

♦ Credentials awarded and sought in S&E 

♦ Persons with higher education credentials

♦ State and student resources supporting higher 

education 

These indicators measure the higher education different 

states provide, the level of education in their populations, 

the cost of college attendance at the undergraduate level, and 

state expenditures to public universities. 

3. Workforce

Indicators in this area cover two topics:

♦ Higher education credentials of the workforce

♦ S&E workers in the labor force

Workforce indicators focus on the level of S&E training 

and occupations of the employed labor force. These indicators reflect the higher education level of the labor force and 

the extent of S&E employment.

4. Financial R&D inputs

Indicators in this area cover two topics:

♦ Level of R&D activity

♦ Public-sector support for R&D activities

Financial indicators present the sources and level of funding for R&D. They show how much R&D is being performed 

relative to the size of a state’s business base. The indicators 

also present the extent to which R&D is conducted by industrial and academic performers.

5. Research and development outputs

Indicators in this area cover two topics:

♦ Human capital outputs

♦ Research-based outputs

These indicators show the number of new doctorates conferred, the publication of academic articles, and patent activity from the academic community and from all sources in 

the state.

6. S&T in the economy

Indicators in this area cover two topics:

♦ High-technology business activity

♦ Early-stage, high-risk capital investments

These indicators include venture capital activity, Small 

Business Innovation Research (SBIR) awards, and hightechnology business activity. 

Unlike other chapters in this volume, this chapter presents 

indicators individually. Indicators are normalized to enable 

comparisons among states of different sizes, but indicators 

are presented discretely rather than in a continuous text that 

describes the relationships among them. Because these indicators span a broad range of topics across the entire S&E landscape—inputs and outputs, people and dollars, businesses and 

universities, R&D and education—a validated model synthesizing interrelationships among these specific indicators does not 

exist. Moreover, states are both heterogeneous, with hubs of 

intense S&E activity alongside areas without substantial S&E 

infrastructure, and porous, with limited control over movements 
8-8 ♦ Chapter 8. State Indicators

of people and funds across their borders. As a result, smaller 

regions, which form more tightly coupled economic systems, 

and nations, which create stronger barriers to movement, are 

often considered to be better units of analysis for studying geographic variation in S&E activity.

Nonetheless, state governments and other state-based actors have significant leverage and can affect S&T-related 

economic development in their states and regions. The 

data in this chapter offer ample opportunities for exploratory analysis of variations among states and the interplay 

of education, R&D, and economic activity. The online state 

data tool (http://www.nsf.gov/statistics/seind14/c8/interactive/)—which includes the state data in this chapter plus, 

when available, additional data on state S&T over the past 20 

years—enables readers to examine the relationships among 

the different indicators in the chapter.

Some examples of possible issues that could be explored 

with the current set of indicators include the following:

♦ What is the relationship between K–12 student achievement and the S&E workforce within a state?

♦ How do state commitments of resources for education at 

different levels relate to R&D performance?

♦ Do states whose universities provide advanced S&E training to large numbers of students have correspondingly 

large segments of their workforces in S&E occupations?

♦ How do indicators of educational attainment within a 

state’s population relate to R&D performance and hightechnology business activity?

♦ What state characteristics are associated with relatively 

high investments of tax dollars in S&E?

♦ Are states whose universities produce more articles and patents also involved in more high-technology business activity?

The data in this chapter cannot be expected to provide definitive answers to any of these questions. Additional data, 

well-defined theoretical models, and more refined geographical comparisons will be required as social scientists grapple 

with these complex relationships. But exploring relationships 

in the existing data via the online state data tool can stimulate 

policymakers and other stakeholders to think more broadly 

and deeply about the possible implications of strategies used 

to address state-level S&E policy topics. 

The tool offers users the following capabilities:

♦ Long-term trend data on each indicator are available for 

download. This provides users with the option to combine 

data from existing indicators to produce new indicators. 

Visualizations of the trend data—such as quartile maps, 

histograms, and charts—are also available.

♦ Standard error tables for each indicator with sample-based 

data are available for download.

♦ Financial information can be translated from current into 

constant dollars.

Data Sources and Considerations

The tables present estimates for the components that make 

up each indicator. Each table provides an average value for 

all states, labeled “United States.” For census-based data, the 

national average is the sum of numerator values for the 50 

states and the District of Columbia divided by the sum of 

the denominator values. For sample-based data, the national 

totals were estimated directly, and the national average is the 

ratio of the estimated totals. 

The values for most indicators are expressed as ratios or 

percentages to facilitate comparison between states that differ substantially in size. For example, an indicator of higher 

education achievement is not defined as the absolute number 

of degrees conferred in a state because less populous states 

are unlikely to have or need as extensive a higher education system as states with larger populations. Instead, the 

indicator is defined as the number of degrees per number 

of residents in the college-age cohort, which measures the 

intensity of educational services relative to the size of the 

resident population.

Although data for Puerto Rico are reported whenever 

available, they frequently were collected by a different 

source, making it unclear whether the methodology used for 

data collection and analysis is comparable with that used for 

the states. For this reason, Puerto Rico was not listed with the 

states, not assigned a quartile value, and not displayed on the 

maps. Data for United States territories and protectorates—

such as American Samoa, Guam, Northern Mariana Islands, 

and Virgin Islands—were available only on a sporadic basis 

and thus are not included.

Readers must exercise caution when evaluating the indicator values for the District of Columbia. Frequently, the 

indicator value for the District of Columbia is appreciably 

different from the indicator values for any of the states. The 

District of Columbia is unique because it is an urban region 

with a large federal presence and many universities. In addition, it has a large student population and provides employment for many individuals who live in neighboring states. 

Indicator values can be quite different depending on whether 

data attributed to the District of Columbia are based on where 

people live or where they work.

Key Elements for Indicators 

Six key elements are provided for each indicator. The first 

element is a map color coded to show in which quartile each 

state placed on that indicator for the latest year that data were 

available. This helps the reader quickly grasp geographic patterns. On the indicator maps, the darkest color indicates states 

that rank in the first or highest quartile, and white indicates 

states that rank in the fourth or lowest quartile. Cross-hatching 

indicates states for which no data are available.
Science and Engineering Indicators 2014 ♦ 8-9

The sample map (figure 8-A) shows the outline of each 

state. Each state is identified by its U.S. Postal Service abbreviation. In 1978, Congress initiated the Experimental Program 

to Stimulate Competitive Research (EPSCoR) at the National 

Science Foundation to build R&D capacity in states that have 

historically been less competitive in receiving federal R&D 

funding. Subsequently, several federal agencies established 

similar programs, the largest of which is the Institutional 

Development Award program at the National Institutes of 

Health. Four other agencies with programs used for the 

EPSCoR-like programs are the National Science Foundation, 

the Department of Energy, the National Aeronautics and Space 

Administration, and the U.S. Department of Agriculture. States 

shown with a gray background in figure 8-A were eligible for 

EPSCoR-like programs at four of the five federal agencies 

or departments in 2012. The 22 states in the EPSCoR group 

are Alabama, Alaska, Arkansas, Delaware, Idaho, Kansas, 

Kentucky, Louisiana, Maine, Mississippi, Montana, Nebraska, 

Nevada, New Hampshire, New Mexico, Oklahoma, Rhode 

Island, South Carolina, South Dakota, Vermont, West Virginia, 

and Wyoming. The remaining 28 states are considered states in 

the non-EPSCoR group. Puerto Rico (an EPSCoR jurisdiction), 

and the District of Columbia (a non-EPSCoR jurisdiction) are 

not included in EPSCoR tabulations in the tables. The EPSCoR 

Program is discussed in greater depth in chapter 5, “Academic 

Research and Development,” in the sidebar “Experimental 

Program to Stimulate Competitive Research.” 

The second element is a state distribution chart below the 

map, illustrating state values for the latest data year for that indicator (figure 8-B). States are listed alphabetically by U.S. Postal 

Service abbreviation and are centered over the midpoint of the 

range for their indicator values. Indicator values are presented 

along the x-axis of the chart. States stacked together have indicator values that are in the same range but are not necessarily 

identical. The reader is referred to the table for values of the 

indicators. All of the indicators are broad measures, and several 

rely on sample estimates that have a margin of error. Small differences in state values generally carry little useful information.

The third element, at the bottom of the map box, is a short 

citation for the data source. The full citation appears under the 

table on the facing page.

The fourth element, in a shaded box on the lower left side 

of the page, is a summary of findings that includes the national 

average and comments on national and state trends and patterns for the particular indicator. Only statistically significant 

findings are presented; adjustments in the testing to account 

for multiple comparisons have been made, when appropriate. 

Although most of the findings are directly related to the data, 

some represent interpretations that are meant to stimulate further investigation and discussion.

Figure 8-A. U.S. map and list of abbreviations

NOTE: Gray shading indicates Experimental Program to Stimulate Competitive Research (EPSCoR) states.

AK

AL

AR

AZ

CA

CO

CT

DC

DE

FL

Alaska

Alabama

Arkansas

Arizona

California

Colorado

Connecticut

District of Columbia

Delaware

Florida

......

......

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......

......

......

......

......

GA

HI

IA

ID

IL

IN

KS

KY

LA

MA

Georgia

Hawaii

Iowa

Idaho

Illinois

Indiana

Kansas

Kentucky

Louisiana

Massachusetts

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MD

ME

MI

MN

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Maryland

Maine

Michigan

Minnesota

Missouri

Mississippi

Montana

North Carolina

North Dakota

Nebraska

......

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......

......

......

......

......

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......

NH

NJ

NM

NV

NY

OH

OK

OR

PA

RI

New Hampshire

New Jersey

New Mexico

Nevada

New York

Ohio

Oklahoma

Oregon

Pennsylvania

Rhode Island

......

......

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......

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......

......

......

SC

SD

TN

TX

UT

VA

VT

WA

WI

WV

WY

South Carolina

South Dakota

Tennessee

Texas

Utah

Virginia

Vermont

Washington

Wisconsin

West Virginia

Wyoming

......

......

......

......

......

......

......

......

......

......

......

AK

WA

OR

CA

NV

ID

MT ND

MN

SD

WY

UT

CO

AZ

NM

TX

NE

KS

OK

HI

IA

WI

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AR

LA

MS AL

TN

IL IN

OH

KY

WV VA

NC

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PA

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ME

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VT

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CT

RI

NJ

DE

DC

MD

AK........ Alaska HI .........Hawaii ME........Maine NJ.........New Jersey SD ........South Dakota

AL ........ Alabama IA .........Iowa MI .........Michigan NM .......New Mexico TN ........Tennessee

AR........ Arkansas ID .........Idaho MN........Minnesota NV ........Nevada TX.........Texas

AZ........ Arizona IL..........Illinois MO .......Missouri NY........New York UT.........Utah

CA........ California IN .........Indiana MS........Mississippi OH........Ohio VA.........Virginia

CO ....... Colorado KS........Kansas MT ........Montana OK........Oklahoma VT.........Vermont

CT........ Connecticut KY........Kentucky NC ........North Carolina OR........Oregon WA........Washington

DC ....... District of Columbia LA ........Louisiana ND ........North Dakota PA.........Pennsylvania WI.........Wisconsin

DE........ Delaware MA .......Massachusetts NE.........Nebraska RI..........Rhode Island WV........West Virginia

FL ........ Florida MD.......Maryland NH ........New Hampshire SC........South Carolina WY........Wyoming

GA ....... Georgia

Figure 8-A

U.S. map and list of abbreviations
8-10 ♦ Chapter 8. State Indicators

The fifth element, on the lower right side of the page, is a 

description of the indicator and includes information pertaining to the underlying data.

The final element is the data table, which appears on the 

facing page. Up to 3 years of data and the calculated values 

of the indicator are presented for each state, the District of 

Columbia, and Puerto Rico. Puerto Rico is included in the data 

table only when data are available.

For selected indicators, the data table has been expanded to 

include the average data and indicator value for the 50 states and 

the District of Columbia, and the averages for the EPSCoR and 

non-EPSCoR states. These averages have been calculated in two 

ways. The first two lines, “EPSCoR states” and “Non-EPSCoR 

states,” treat each group as a single geographical unit, ignoring 

the division of that unit into separate states. The ratio for the 

group is calculated by totaling the numerator value of each of the 

states in the group and the denominator value of each of the states 

in the group and dividing to compute an average. For example, 

the EPSCoR states’ average of R&D by gross domestic product 

by state, shown in table 8-40, is calculated by summing the R&D 

of all the EPSCoR states, summing the gross domestic product 

of these states, and dividing to compute an average. States with 

more R&D and a larger gross domestic product affect this average more than smaller ones do, just as data on California affect 

U.S. totals more than data on Wyoming do.

The third and fourth lines, “Average EPSCoR state value” 

and “Average non-EPSCoR state value,” represent the average of the individual state ratios for an indicator. The average 

EPSCoR state value for R&D by gross domestic product by 

state is calculated by summing the ratios for the 22 EPSCoR 

states and dividing by 22. All state ratios count equally in this 

computation. Examples of this calculation are shown in tables 

8-5 and 8-18.

Technical Note: High-Technology Industries

To define high-technology industries, this chapter uses a 

modification of the approach employed by the Bureau of Labor 

Statistics (BLS) (Hecker 2005). BLS’s approach is based on the 

intensity of high-technology employment within an industry.

1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0

AK

AL

AR

AZ

CA

CO

CT

DC

DE

FL

GA

HI

IA

ID

OR

PA

RI

SC

SD

TN

TX

UT

IL

IN

KS

KY

LA

MA

MD

ME

MN

MO

MS

VA

VT

WA

WI

WV WY

MS

MT

NC

ND

NE

NH

NJ

NM

NV

NY

OH

OK

Figure 8-B

Example state distribution chart

High-technology occupations include scientific, engineering, 

and technician occupations. These occupations employ workers 

who possess an in-depth knowledge of the theories and principles of science, engineering, and mathematics, which is generally 

acquired through postsecondary education in some field of technology. An industry is considered a high-technology industry if 

employment in technology-oriented occupations accounts for a 

proportion of that industry’s total employment that is at least twice 

the 4.9% average for all industries (i.e., 9.8% or higher).

In this chapter, the category “high-technology industries” 

refers only to private-sector businesses. In contrast, BLS includes the “Federal Government, excluding Postal Service” 

in its listing of high-technology industries.

Each industry is defined by a four-digit code that is based 

on the listings in the North American Industry Classification 

System (NAICS). The NAICS codes change over time, thereby affecting the trend data presented in the tables. For data 

years up through 2008, the 2002 NAICS codes were used to 

define business establishments. Subsequent data years reflect 

the use of the 2007 NAICS codes. The list of high-technology 

industries used in this chapter includes the four-digit codes 

from the 2002 and 2007 NAICS listings shown in table 8-A.

Appendix Tables

Additional data tables pertaining to the indicators in this 

chapter have been included in the appendix. These tables provide supplemental information to assist the reader in evaluating the data used in an indicator. The appendix tables contain 

state-level data on the performance of students in different racial/ethnic and gender groups on the National Assessment of 

Educational Progress evaluations. 

Reference

Hecker D. 2005. High-technology employment: A NAICSbased update. Monthly Labor Review 128(7):57–72.
Science and Engineering Indicators 2014 ♦ 8-11

Table 8-A

NAICS codes that constitute high-technology industries

2002 NAICS code 2007 NAICS code Industry

1131 1131 Timber track operations

1132 1132 Forest nurseries and gathering of forest products

2111 2111 Oil and gas extraction

2211 2211 Electric power generation, transmission, and distribution

3241 3241 Petroleum and coal products manufacturing

3251 3251 Basic chemical manufacturing

3252 3252 Resin, synthetic rubber, and artificial synthetic fibers and filaments manufacturing

3253 3253 Pesticide, fertilizer, and other agricultural chemical manufacturing

3254 3254 Pharmaceutical and medicine manufacturing

3255 3255 Paint, coating, and adhesive manufacturing

3259 3259 Other chemical product and preparation manufacturing

3332 3332 Industrial machinery manufacturing

3333 3333 Commercial and service industry machinery manufacturing

3336 3336 Engine, turbine, and power transmission equipment manufacturing

3339 3339 Other general purpose machinery manufacturing

3341 3341 Computer and peripheral equipment manufacturing

3342 3342 Communications equipment manufacturing

3343 3343 Audio and video equipment manufacturing

3344 3344 Semiconductor and other electronic component manufacturing

3345 3345 Navigational, measuring, electromedical, and control instruments manufacturing

3346 3346 Manufacturing and reproducing magnetic and optical media

3353 3353 Electrical equipment manufacturing

3364 3364 Aerospace product and parts manufacturing

3369 3369 Other transportation equipment manufacturing

4234 4234 Professional and commercial equipment and supplies, merchant wholesalers

4861 4861 Pipeline transportation of crude oil

4862 4862 Pipeline transportation of natural gas

4869 4869 Other pipeline transportation

5112 5112 Software publishers

5161 na Internet publishing and broadcasting

na 519130 Internet publishing and broadcasting and Web search portals

5171 5171 Wired telecommunications carriers

5172 5172 Wireless telecommunications carriers (except satellite)

5173 na Telecommunications resellers

5174 5174 Satellite telecommunications

5179 5179 Other telecommunications

5181 na Internet service providers and Web search portals

5182 5182 Data processing, hosting, and related services

5211 5211 Monetary authorities, central bank

5232 5232 Securities and commodity exchanges

5413 5413 Architectural, engineering, and related services

5415 5415 Computer systems design and related services

5416 5416 Management, scientific, and technical consulting services

5417 5417 Scientific research and development services

5511 5511 Management of companies and enterprises

5612 5612 Facilities support services

na 561312 Executive search services

8112 8112 Electronic and precision equipment repair and maintenance

na = not applicable. 

NAICS = North American Industry Classification System.

NOTES: Data on high-tech industries for 2008 and earlier years were compiled using the 2002 NAICS codes. Data for 2009 and 2010 were compiled 

using the 2007 NAICS codes.

Science and Engineering Indicators 2014
8-12 ♦ Chapter 8. State Indicators

This indicator represents each state’s average score on the National 

Assessment of Educational Progress (NAEP) in mathematics for its 

fourth grade students in public schools. The NAEP mathematics assessment, conducted by the National Center for Education Statistics, is part 

of a legally mandated federal effort to measure student performance. 

It measures students’ knowledge and skills in mathematics and their 

ability to apply that knowledge in the content areas of number properties and operations; measurement; geometry; data analysis, statistics, 

and probability; and algebra. Student performance is presented in 

terms of average scores on a scale from 0 to 500.

All 50 states and the District of Columbia participated in the 2011 

NAEP mathematics assessment. Students with disabilities or limited 

English-language proficiency are allowed to use certain accommodations (e.g., extra testing time or individual rather than group administration). All data presented here represent scores from tests taken with 

accommodations offered. For additional details on NAEP scores by 

gender and race/ethnicity, see appendix tables 8-1 to 8-12.

Findings

• In 2011, the nationwide average mathematics score 

of fourth grade public school students was 240, an 

increase from 234 in 2003. Fourth graders scored 

higher in mathematics in 2011 than in any previous 

assessment year.

• The states with the highest average fourth grade 

performance scores are concentrated in the northern United States.

• Nationally, the 2011 average mathematics score for 

white public school fourth grade students was 249 

compared to 224 for black students, a gap of 25 

points, and 229 for Hispanic students, a gap of 20 

points, based upon racial classifications provided 

by the schools. In 2003, these score gaps were 27 

and 22 points, respectively, indicating that these 

demographic gaps are not shrinking.

Fourth Grade Mathematics Performance

Figure 8-1

Average fourth grade mathematics performance: 2011

1st quartile (244–253)

2nd quartile (241–243)

3rd quartile (237–240)

4th quartile (222–236)

Achievement levels

(Scores range from 0–500)

Advanced ......................... 282–500

Proficient .......................... 249–281

Basic................................. 214–248

SOURCE: National Center for Education Statistics, National Assessment of 

Educational Progress. 220 224 228 232 236 240 244 248 252 256

DC

AK

AR

GA

HI

IL

MI

NV

NY

OK

OR

SC

AL

LA

MS

CT

DE

FL

IA

ID

KY

MO

NE

RI

SD

TX

UT

WA

CO

IN

KS

MD

ME

MT

NC

ND

OH

PA

VA

VT

WI

WY

MN

NJ

MA

NH

AZ

CA

NM

TN

WV

Average score
Science and Engineering Indicators 2014 ♦ 8-13

Table 8-1

Average fourth grade mathematics performance, by state: 2003, 2007, 

and 2011

(Score out of 500)

State 2003 2007 2011

United States................................................................... 234 239 240

Alabama....................................................................... 223 229 231

Alaska .......................................................................... 233 237 236

Arizona......................................................................... 229 232 235

Arkansas ...................................................................... 229 238 238

California...................................................................... 227 230 234

Colorado ...................................................................... 235 240 244

Connecticut ................................................................. 241 243 242

Delaware ...................................................................... 236 242 240

District of Columbia ..................................................... 205 214 222

Florida.......................................................................... 234 242 240

Georgia ........................................................................ 230 235 238

Hawaii .......................................................................... 227 234 239

Idaho............................................................................ 235 241 240

Illinois........................................................................... 233 237 239

Indiana ......................................................................... 238 245 244

Iowa ............................................................................. 238 243 243

Kansas ......................................................................... 242 248 246

Kentucky...................................................................... 229 235 241

Louisiana...................................................................... 226 230 231

Maine ........................................................................... 238 242 244

Maryland...................................................................... 233 240 247

Massachusetts............................................................. 242 252 253

Michigan ...................................................................... 236 238 236

Minnesota .................................................................... 242 247 249

Mississippi ................................................................... 223 228 230

Missouri ....................................................................... 235 239 240

Montana....................................................................... 236 244 244

Nebraska...................................................................... 236 238 240

Nevada......................................................................... 228 232 237

New Hampshire ........................................................... 243 249 252

New Jersey .................................................................. 239 249 248

New Mexico................................................................. 223 228 233

New York...................................................................... 236 243 238

North Carolina.............................................................. 242 242 245

North Dakota ............................................................... 238 245 245

Ohio ............................................................................. 238 245 244

Oklahoma..................................................................... 229 237 237

Oregon......................................................................... 236 236 237

Pennsylvania................................................................ 236 244 246

Rhode Island................................................................ 230 236 242

South Carolina ............................................................. 236 237 237

South Dakota............................................................... 237 241 241

Tennessee.................................................................... 228 233 233

Texas............................................................................ 237 242 241

Utah ............................................................................. 235 239 243

Vermont........................................................................ 242 246 247

Virginia ......................................................................... 239 244 245

Washington.................................................................. 238 243 243

West Virginia ................................................................ 231 236 235

Wisconsin .................................................................... 237 244 245

Wyoming...................................................................... 241 244 244

Puerto Rico.................................................................. NA NA NA

NA = not available.

NOTES: National Assessment of Educational Progress (NAEP) grade 4 mathematics scores are for 

public schools only. For additional details on NAEP scores by sex and race or ethnicity, see appendix 

tables 8-1–8-12.

SOURCE: National Center for Education Statistics, NAEP (various years). 

Science and Engineering Indicators 2014
8-14 ♦ Chapter 8. State Indicators

This indicator represents the proportion of a state’s fourth grade students in 

public schools that has met or exceeded the proficiency standard in mathematics. The National Assessment Governing Board sets performance standards 

that provide a context for interpreting National Assessment of Educational 

Progress (NAEP) results. The standards define “proficiency” as well as “advanced” and “basic” accomplishment. For the fourth grade, the proficient level 

(scores 249–281) represents solid academic performance and demonstrates 

competency over challenging subject-matter knowledge. The advanced level 

(282–500) signifies superior performance. The basic level (214–248) denotes 

partial mastery of knowledge and skills that are prerequisite for proficient work. 

The National Center for Education Statistics has determined that achievement 

levels should be used on a trial basis and interpreted with caution.

Approximately 210,000 fourth grade students in 8,500 schools participated 

in the 2011 NAEP mathematics assessment. Students with disabilities or limited English-language proficiency are allowed to use certain accommodations 

(e.g., extra testing time or individual rather than group administration). All 

data presented here represent scores from tests taken with accommodations 

offered. For additional details on NAEP scores by gender and race/ethnicity, 

see appendix tables 8-1 to 8-12.

Findings

• In 2011, 40% of fourth grade public school 

students nationwide performed at or above 

the proficient level in mathematics, an 

increase from 31% in 2003.

• Of the 51 jurisdictions that participated in 

both the 2003 and 2011 fourth grade mathematics assessments, 50 showed increases 

in mathematics proficiency among public 

school fourth graders over the period.

• Nationally, the percentage of fourth grade 

white public school students demonstrating 

proficient performance in mathematics was 

52% in 2011 compared to 17% for black 

students, a gap of 35 percentage points, 

and 24% for Hispanic students, a gap of 28 

percentage points, based upon racial classifications provided by the schools. In 2003, 

these gaps were 32 and 27 points, respectively, indicating that these demographic 

gaps are not shrinking.

Fourth Grade Mathematics Proficiency

Figure 8-2

Students reaching proficiency in fourth grade mathematics: 2011

(Percentage of students scoring 249 or above)

1st quartile (45%–58%)

2nd quartile (40%–44%)

3rd quartile (36%–39%)

4th quartile (22%–35%)

Achievement levels

(Scores range from 0–500)

Advanced ......................... 282–500

Proficient .......................... 249–281

Basic................................. 214–248

SOURCE: National Center for Education 

Statistics, National Assessment of Educational Progress. 20 24 28 32 36 40 44 48 52 56 60

DC

AZ

CA

MI

OK

AL

LA

MS

AK

AR

DE

FL

GA

ID

IL

KY

NE

NV

NY

OR

SC

TX

HI

IA

MO

RI

SD

UT

CO

CT

IN

ME

MT

NC

ND

OH

VA

WA

WI

WY MN

KS

MD

NJ

PA

VT

MA

NH

NM

TN

WV

Percent
Science and Engineering Indicators 2014 ♦ 8-15

Table 8-2

Students reaching proficiency in fourth grade mathematics, by state: 2003, 

2007, and 2011

(Percent)

State 2003 2007 2011

United States................................................................... 31 39 40

Alabama....................................................................... 19 26 27

Alaska .......................................................................... 30 38 37

Arizona......................................................................... 25 31 34

Arkansas ...................................................................... 26 37 37

California...................................................................... 25 30 34

Colorado ...................................................................... 34 41 47

Connecticut ................................................................. 41 45 45

Delaware ...................................................................... 31 40 39

District of Columbia ..................................................... 7 14 22

Florida.......................................................................... 31 40 37

Georgia ........................................................................ 27 32 37

Hawaii .......................................................................... 23 33 40

Idaho............................................................................ 31 40 39

Illinois........................................................................... 32 36 38

Indiana ......................................................................... 35 46 44

Iowa ............................................................................. 36 43 43

Kansas ......................................................................... 41 51 48

Kentucky...................................................................... 22 31 39

Louisiana...................................................................... 21 24 26

Maine ........................................................................... 34 42 45

Maryland...................................................................... 31 40 48

Massachusetts............................................................. 41 58 58

Michigan ...................................................................... 34 37 35

Minnesota .................................................................... 42 51 53

Mississippi ................................................................... 17 21 25

Missouri ....................................................................... 30 38 41

Montana....................................................................... 31 44 45

Nebraska...................................................................... 34 38 39

Nevada......................................................................... 23 30 36

New Hampshire ........................................................... 43 52 57

New Jersey .................................................................. 39 52 51

New Mexico................................................................. 17 24 30

New York...................................................................... 33 43 36

North Carolina.............................................................. 41 41 44

North Dakota ............................................................... 34 46 46

Ohio ............................................................................. 36 46 45

Oklahoma..................................................................... 23 33 33

Oregon......................................................................... 33 35 37

Pennsylvania................................................................ 36 47 48

Rhode Island................................................................ 28 34 43

South Carolina ............................................................. 32 36 36

South Dakota............................................................... 34 41 40

Tennessee.................................................................... 24 29 30

Texas............................................................................ 33 40 39

Utah ............................................................................. 31 39 43

Vermont........................................................................ 42 49 49

Virginia ......................................................................... 36 42 46

Washington.................................................................. 36 44 45

West Virginia ................................................................ 24 33 31

Wisconsin .................................................................... 35 47 47

Wyoming...................................................................... 39 44 44

Puerto Rico.................................................................. NA NA NA

NA = not available.

NOTES: National Assessment of Educational Progress (NAEP) grade 4 mathematics scores are for 

public schools only. For additional details on NAEP scores by sex and race or ethnicity, see appendix 

tables 8-1–8-12.

SOURCE: National Center for Education Statistics, NAEP (various years). 

Science and Engineering Indicators 2014
8-16 ♦ Chapter 8. State Indicators

This indicator represents each state’s average score on the National 

Assessment of Educational Progress (NAEP) in science for its fourth 

grade students in public schools. The NAEP science assessment, conducted 

by the National Center for Education Statistics, is part of a legally mandated federal effort to measure student performance. It measures students’ 

knowledge and skill in science and their ability to apply that knowledge 

in the content areas of physical, life, and earth and space science. The 

NAEP assessment in science was updated in 2009 to keep pace with recent 

developments in science and science education. Because it is based on a 

new framework, 2009 results cannot be compared to those from previous 

science assessments. Student performance is presented in terms of average 

scores on a scale from 0 to 300.

An average score designated as “NA” (not available) indicates that 

the state either did not participate in the assessment or did not meet the 

minimum guidelines for reporting. Students with disabilities or limited 

English-language proficiency are allowed to use certain accommodations 

(e.g., extra testing time or individual rather than group administration). All 

data presented here represent scores from tests taken with accommodations offered. For additional details on NAEP scores by gender and race/

ethnicity, see appendix tables 8-1 to 8-12.

Findings

• In 2009, the nationwide average science score 

of fourth grade public school students was 149. 

Average scores for individual states ranged 

between 133 and 163.

• Of the 46 jurisdictions that participated in the 

2009 fourth grade science assessment, 24 had 

scores that were higher than the national average, 12 were not significantly different, and 10 

were lower.

• Nationally, the 2009 average science score for 

white public school fourth grade students was 

162 compared to 127 for black students, a gap 

of 35 points, and 130 for Hispanic students, a 

gap of 32 points, based upon racial classifications provided by the schools.

• Male fourth grade public school students scored 

1 point higher in science than female fourth 

grade public school students although females 

scored higher in the life science subsection than 

did males.

Fourth Grade Science Performance

Figure 8-3

Average fourth grade science performance: 2009 

132 135 138 141 144 147 150 153 156 159 162 165

MS CA

AR

GA

AZ

HI

IL

NC

NY

OK

SC

TN

TX

WV

FL

MD

MI

OR

RI

WA

CO

DE

ID

IN

NJ

PA

UT

KY

MA

ME

MT

CT

IA

MN

MO

OH

SD

WI

WY

ND

NH

VA

AL

LA

NM

NV

Average score

1st quartile (156–163)

2nd quartile (151–155)

3rd quartile (148–150)

4th quartile (133–146)

No data

Achievement levels

(Scores range from 0–300)

Advanced ......................... 224–300

Proficient .......................... 167–223

Basic................................. 131–166

SOURCE: National Center for Education 

Statistics, National Assessment of Educational Progress.
Science and Engineering Indicators 2014 ♦ 8-17

Table 8-3

Average fourth grade science performance, by state: 2009

(Score out of 300)

State 2009

United States........................................................................................ 149

Alabama............................................................................................ 143

Alaska ............................................................................................... NA

Arizona.............................................................................................. 138

Arkansas ........................................................................................... 146

California........................................................................................... 136

Colorado ........................................................................................... 155

Connecticut ...................................................................................... 156

Delaware ........................................................................................... 153

District of Columbia .......................................................................... NA

Florida............................................................................................... 151

Georgia ............................................................................................. 144

Hawaii ............................................................................................... 140

Idaho................................................................................................. 154

Illinois................................................................................................ 148

Indiana .............................................................................................. 153

Iowa .................................................................................................. 157

Kansas .............................................................................................. NA

Kentucky........................................................................................... 161

Louisiana........................................................................................... 141

Maine ................................................................................................ 160

Maryland........................................................................................... 150

Massachusetts.................................................................................. 160

Michigan ........................................................................................... 150

Minnesota ......................................................................................... 158

Mississippi ........................................................................................ 133

Missouri ............................................................................................ 156

Montana............................................................................................ 160

Nebraska........................................................................................... NA

Nevada.............................................................................................. 141

New Hampshire ................................................................................ 163

New Jersey ....................................................................................... 155

New Mexico...................................................................................... 142

New York........................................................................................... 148

North Carolina................................................................................... 148

North Dakota .................................................................................... 162

Ohio .................................................................................................. 157

Oklahoma.......................................................................................... 148

Oregon.............................................................................................. 151

Pennsylvania..................................................................................... 154

Rhode Island..................................................................................... 150

South Carolina .................................................................................. 149

South Dakota.................................................................................... 157

Tennessee......................................................................................... 148

Texas................................................................................................. 148

Utah .................................................................................................. 154

Vermont............................................................................................. NA

Virginia .............................................................................................. 162

Washington....................................................................................... 151

West Virginia ..................................................................................... 148

Wisconsin ......................................................................................... 157

Wyoming........................................................................................... 156

Puerto Rico....................................................................................... NA

NA = not available.

NOTES: National Assessment of Educational Progress (NAEP) grade 4 science scores are for 

public schools only. For additional details on NAEP scores by sex and race or ethnicity, see 

appendix tables 8-1–8-12.

SOURCE: National Center for Education Statistics, NAEP (various years). 

Science and Engineering Indicators 2014
8-18 ♦ Chapter 8. State Indicators

This indicator represents the proportion of a state’s fourth grade students in public 

schools that has met or exceeded the proficiency standard in science. The National 

Assessment Governing Board sets performance standards that provide a context 

for interpreting National Assessment of Educational Progress (NAEP) results. The 

standards define “proficiency” as well as “advanced” and “basic” accomplishment. 

For the fourth grade, the proficient level (scores 167–223) represents solid academic 

performance and demonstrates competency over challenging subject-matter knowledge. The advanced level (224–300) signifies superior performance. The basic level 

(131–166) denotes partial mastery of knowledge and skills that are prerequisite for 

proficient work. The National Center for Education Statistics has determined that 

achievement levels should be used on a trial basis and interpreted with caution.

Approximately 156,500 fourth grade students in 9,330 schools participated in 

the 2009 NAEP science assessment. A designation of “NA” (not available) indicates 

that the state either did not participate in the assessment or did not meet minimum 

guidelines for reporting. Students with disabilities or limited English-language 

proficiency are allowed to use certain accommodations (e.g., extra testing time or 

individual rather than group administration). All data presented here represent scores 

from tests taken with accommodations offered. For additional details on NAEP scores 

by gender and race/ethnicity, see appendix tables 8-1 to 8-12.

Findings

• In 2009, 32% of fourth grade public 

school students nationwide performed at or above the proficient 

level in science. Among the states, 

there were substantial differences in 

the percentage of fourth grade public 

school students who demonstrated 

proficiency in science. State values 

for this indicator ranged from 17% 

to 47%.

• Nationally, the percentage of fourth 

grade white public school students 

demonstrating proficient performance in science was 46% in 2009 

compared to 10% for black students, 

a gap of 36 percentage points, and 

13% for Hispanic students, a gap of 

33 percentage points, based upon 

racial classifications provided by 

the schools.

Fourth Grade Science Proficiency

Figure 8-4

Students reaching proficiency in fourth grade science: 2009

(Percentage of students scoring 167 or above)

16 18 21 24 27 30 33 36 39 42 45 48

MS

AL

AR

GA

OK

TX

WV

AZ

CA

NV

FL

IL

NC

NY

DE

ID

IN

MD

MI

OR

RI

SC

TN

WA

PA

UT

WY

ME

MN

MT

CO

CT

IA

MO

NJ

OH

SD

WI

KY

MA

ND

NH

VA

HI

LA

NM

Percent

1st quartile (40%–47%)

2nd quartile (34%–39%)

3rd quartile (28%–33%)

4th quartile (17%–27%)

No data

Achievement levels

(Scores range from 0–300)

Advanced ......................... 224–300

Proficient .......................... 167–223

Basic................................. 131–166

SOURCE: National Center for Education 

Statistics, National Assessment of Educational Progress.
Science and Engineering Indicators 2014 ♦ 8-19

Table 8-4

Students reaching proficiency in fourth grade science, by state: 2009

(Percent)

State 2009

United States........................................................................................ 32

Alabama............................................................................................ 27

Alaska ............................................................................................... NA

Arizona.............................................................................................. 22

Arkansas ........................................................................................... 29

California........................................................................................... 22

Colorado ........................................................................................... 39

Connecticut ...................................................................................... 40

Delaware ........................................................................................... 34

District of Columbia .......................................................................... NA

Florida............................................................................................... 32

Georgia ............................................................................................. 27

Hawaii ............................................................................................... 25

Idaho................................................................................................. 35

Illinois................................................................................................ 32

Indiana .............................................................................................. 35

Iowa .................................................................................................. 41

Kansas .............................................................................................. NA

Kentucky........................................................................................... 45

Louisiana........................................................................................... 25

Maine ................................................................................................ 42

Maryland........................................................................................... 33

Massachusetts.................................................................................. 45

Michigan ........................................................................................... 34

Minnesota ......................................................................................... 43

Mississippi ........................................................................................ 17

Missouri ............................................................................................ 40

Montana............................................................................................ 43

Nebraska........................................................................................... NA

Nevada.............................................................................................. 23

New Hampshire ................................................................................ 47

New Jersey ....................................................................................... 39

New Mexico...................................................................................... 24

New York........................................................................................... 30

North Carolina................................................................................... 30

North Dakota .................................................................................... 45

Ohio .................................................................................................. 41

Oklahoma.......................................................................................... 28

Oregon.............................................................................................. 34

Pennsylvania..................................................................................... 38

Rhode Island..................................................................................... 34

South Carolina .................................................................................. 33

South Dakota.................................................................................... 40

Tennessee......................................................................................... 33

Texas................................................................................................. 29

Utah .................................................................................................. 38

Vermont............................................................................................. NA

Virginia .............................................................................................. 46

Washington....................................................................................... 35

West Virginia ..................................................................................... 28

Wisconsin ......................................................................................... 41

Wyoming........................................................................................... 37

Puerto Rico....................................................................................... NA

NA = not available.

NOTES: National Assessment of Educational Progress (NAEP) grade 4 science scores are for 

public schools only. For additional details on NAEP scores by sex and race or ethnicity, see 

appendix tables 8-1–8-12.

SOURCE: National Center for Education Statistics, NAEP (various years). 

Science and Engineering Indicators 2014
8-20 ♦ Chapter 8. State Indicators

This indicator represents each state’s average score on the National 

Assessment of Educational Progress (NAEP) in mathematics for its 

eighth grade students in public schools. The NAEP mathematics assessment, conducted by the National Center for Education Statistics, is part 

of a legally mandated federal effort to measure student performance. 

It measures students’ knowledge and skills in mathematics and their 

ability to apply that knowledge in the content areas of number properties 

and operations; measurement; geometry; data analysis, statistics, and 

probability; and algebra. Student performance is presented in terms of 

average scores on a scale from 0 to 500.

All 50 states and the District of Columbia participated in the 2011 

NAEP mathematics assessment. Students with disabilities or limited 

English-language proficiency are allowed to use certain accommodations (e.g., extra testing time or individual rather than group administration). All data presented here represent scores from tests taken with 

accommodations offered. For additional details on NAEP scores by 

gender and race/ethnicity, see appendix tables 8-1 to 8-12.

Findings

• In 2011, the nationwide average mathematics score 

of eighth grade public school students was 283, an 

increase from 276 in 2003. Eighth graders scored 

higher in mathematics in 2011 than in any previous 

assessment year.

• Of the 51 jurisdictions that participated in both 

the 2003 and 2011 mathematics assessments, 46 

showed increases in mathematics scores among 

public school eighth graders over the period. Since 

2007, eighth grade mathematics scores showed an 

increase for public school students in 28 states.

• Nationally, the 2011 average mathematics score for 

white public school eighth grade students was 293 

compared to 262 for black students, a gap of 31 

points, and 269 for Hispanic students, a gap of 24 

points, based upon racial classifications provided by 

the schools. In 2003, these score gaps were 35 and 

29 points, respectively.

Eighth Grade Mathematics Performance

Figure 8-5

Average eighth grade mathematics performance: 2011

1st quartile (288–299)

2nd quartile (283–287)

3rd quartile (278–282)

4th quartile (260–274)

Achievement levels

(Scores range from 0–500)

Advanced ......................... 333–500

Proficient .......................... 299–332

Basic................................. 262–298

SOURCE: National Center for Education 

Statistics, National Assessment of Educational Progress. 260 265 270 275 280 285 290 295 300

DC

AR

AZ

FL

GA

HI

NV

OK

AL

MS

AK

DE

IL

KY

MI

MO

NE

NY

OR

RI

SC

UT

CT

IA

ID

IN

MD

ME

NC

OH

PA

VA

WA

WI

WY

CO

KS

MN

MT

ND

NH

NJ

SD

TX

VT MA

CA

LA

NM

TN

WV

Average score
Science and Engineering Indicators 2014 ♦ 8-21

Table 8-5

Average eighth grade mathematics performance, by state: 2003, 2007, 

and 2011

(Score out of 500)

State 2003 2007 2011

Average EPSCoR state value ......................................... 276 279 282

Average non-EPSCoR state value.................................. 279 283 285

United States.................................................................. 276 280 283

Alabama...................................................................... 262 266 269

Alaska ......................................................................... 279 283 283

Arizona........................................................................ 271 276 279

Arkansas ..................................................................... 266 274 279

California..................................................................... 267 270 273

Colorado ..................................................................... 283 286 292

Connecticut ................................................................ 284 282 287

Delaware ..................................................................... 277 283 283

District of Columbia .................................................... 243 248 260

Florida......................................................................... 271 277 278

Georgia ....................................................................... 270 275 278

Hawaii ......................................................................... 266 269 278

Idaho........................................................................... 280 284 287

Illinois.......................................................................... 277 280 283

Indiana ........................................................................ 281 285 285

Iowa ............................................................................ 284 285 285

Kansas ........................................................................ 284 290 290

Kentucky..................................................................... 274 279 282

Louisiana..................................................................... 266 272 273

Maine .......................................................................... 282 286 289

Maryland..................................................................... 278 286 288

Massachusetts............................................................ 287 298 299

Michigan ..................................................................... 276 277 280

Minnesota ................................................................... 291 292 295

Mississippi .................................................................. 261 265 269

Missouri ...................................................................... 279 281 282

Montana...................................................................... 286 287 293

Nebraska..................................................................... 282 284 283

Nevada........................................................................ 268 271 278

New Hampshire .......................................................... 286 288 292

New Jersey ................................................................. 281 289 294

New Mexico................................................................ 263 268 274

New York..................................................................... 280 280 280

North Carolina............................................................. 281 284 286

North Dakota .............................................................. 287 292 292

Ohio ............................................................................ 282 285 289

Oklahoma.................................................................... 272 275 279

Oregon........................................................................ 281 284 283

Pennsylvania............................................................... 279 286 286

Rhode Island............................................................... 272 275 283

South Carolina ............................................................ 277 282 281

South Dakota.............................................................. 285 288 291

Tennessee................................................................... 268 274 274

Texas........................................................................... 277 286 290

Utah ............................................................................ 281 281 283

Vermont....................................................................... 286 291 294

Virginia ........................................................................ 282 288 289

Washington................................................................. 281 285 288

West Virginia ............................................................... 271 270 273

Wisconsin ................................................................... 284 286 289

Wyoming..................................................................... 284 287 288

Puerto Rico................................................................. NA NA NA

NA = not available.

EPSCoR = Experimental Program to Stimulate Competitive Research.

NOTES: National Assessment of Educational Progress (NAEP) grade 8 mathematics scores are for public 

schools only. For additional details on NAEP scores by sex and race or ethnicity, see appendix tables 

8-1–8-12. For an explanation of EPSCoR and non-EPSCoR averages, see the chapter introduction.

SOURCE: National Center for Education Statistics, NAEP (various years).

Science and Engineering Indicators 2014
8-22 ♦ Chapter 8. State Indicators

This indicator represents the proportion of a state’s eighth grade students in public schools that has met or exceeded the proficiency standard 

in mathematics. The National Assessment Governing Board sets performance standards that provide a context for interpreting National Assessment of Educational Progress (NAEP) results. The standards define 

“proficiency” as well as “advanced” and “basic” accomplishment. For 

the eighth grade, the proficient level (scores 299–332) represents solid 

academic performance and demonstrates competency over challenging 

subject-matter knowledge. The advanced level (333–500) signifies superior performance. The basic level (262–298) denotes partial mastery 

of knowledge and skills that are prerequisite for proficient work. The 

National Center for Education Statistics has determined that achievement levels should be used on a trial basis and interpreted with caution.

Approximately 175,200 eighth grade students in 7,610 schools participated in the 2011 NAEP mathematics assessment. Students with disabilities or limited English-language proficiency are allowed to use certain 

accommodations (e.g., extra testing time or individual rather than group 

administration). All data presented here represent scores from tests taken 

with accommodations offered. For additional details on NAEP scores by 

gender and race/ethnicity, see appendix tables 8-1 to 8-12.

Findings

• In 2011, 34% of eighth grade public school students 

nationwide performed at or above the proficient level 

in mathematics, a sizable increase from 27% in 2003.

• Of the 51 jurisdictions that participated in both the 

2003 and 2011 eighth grade mathematics assessments, 43 showed increases in mathematics proficiency among public school eighth graders during the 

period. Only 25 showed a significant increase from 

2007 to 2011.

• Nationally, the percentage of eighth grade white public school students demonstrating proficient performance in mathematics was 43% in 2011 compared 

to 13% for black students, a gap of 30 percentage 

points, and 20% for Hispanic students, a gap of 23 

percentage points, based upon racial classifications 

provided by the schools. In 2003, these gaps were 29 

and 25 percentage points, respectively.

• The percentage of eighth grade students proficient in 

mathematics increased for both sexes between 2003 

and 2011, and the size of the gender gap decreased 

from 3% to 1% during this period. 

Eighth Grade Mathematics Proficiency

Figure 8-6

Students reaching proficiency in eighth grade mathematics: 2011

(Percentage of students scoring 299 or above)

1st quartile (40%–51%)

2nd quartile (34%–39%)

3rd quartile (29%–33%)

4th quartile (17%–28%)

Achievement levels

(Scores range from 0–500)

Advanced ......................... 333–500

Proficient .......................... 299–332

Basic................................. 262–298

SOURCE: National Center for Education 

Statistics, National Assessment of Educational Progress. 15 20 25 30 35 40 45 50 55

DC

MS

AL

LA

NM

TN

WV

AK

CT

ID

ME

NC

OH

PA

UT

WY

AR

CA

FL

GA

NV

OK

CO

KS

MD

ND

NH

SD

TX

VA

WA

WI

MN

MT

NJ

VT MA

AZ

DE

HI

IA

IL

IN

KY

MI

MO

NE

NY

OR

RI

SC

Percent
Science and Engineering Indicators 2014 ♦ 8-23

Table 8-6

Students reaching proficiency in eighth grade mathematics, by state: 

2003, 2007, and 2011

(Percent)

State 2003 2007 2011

United States.................................................................. 27 31 34

Alabama...................................................................... 16 18 20

Alaska ......................................................................... 30 32 35

Arizona........................................................................ 21 26 31

Arkansas ..................................................................... 19 24 29

California..................................................................... 22 24 25

Colorado ..................................................................... 34 37 43

Connecticut ................................................................ 35 35 38

Delaware ..................................................................... 26 31 32

District of Columbia .................................................... 6 8 17

Florida......................................................................... 23 27 28

Georgia ....................................................................... 22 25 28

Hawaii ......................................................................... 17 21 30

Idaho........................................................................... 28 34 37

Illinois.......................................................................... 29 31 33

Indiana ........................................................................ 31 35 34

Iowa ............................................................................ 33 35 34

Kansas ........................................................................ 34 40 41

Kentucky..................................................................... 24 27 31

Louisiana..................................................................... 17 19 22

Maine .......................................................................... 29 34 39

Maryland..................................................................... 30 37 40

Massachusetts............................................................ 38 51 51

Michigan ..................................................................... 28 29 31

Minnesota ................................................................... 44 43 48

Mississippi .................................................................. 12 14 19

Missouri ...................................................................... 28 30 32

Montana...................................................................... 35 38 46

Nebraska..................................................................... 32 35 33

Nevada........................................................................ 20 23 29

New Hampshire .......................................................... 35 38 44

New Jersey ................................................................. 33 40 47

New Mexico................................................................ 15 17 24

New York..................................................................... 32 30 30

North Carolina............................................................. 32 34 37

North Dakota .............................................................. 36 41 43

Ohio ............................................................................ 30 35 39

Oklahoma.................................................................... 20 21 27

Oregon........................................................................ 32 35 33

Pennsylvania............................................................... 30 38 39

Rhode Island............................................................... 24 28 34

South Carolina ............................................................ 26 32 32

South Dakota.............................................................. 35 39 42

Tennessee................................................................... 21 23 24

Texas........................................................................... 25 35 40

Utah ............................................................................ 31 32 35

Vermont....................................................................... 35 41 46

Virginia ........................................................................ 31 37 40

Washington................................................................. 32 36 40

West Virginia ............................................................... 20 19 21

Wisconsin ................................................................... 35 37 41

Wyoming..................................................................... 32 36 37

Puerto Rico................................................................. NA NA NA

NA = not available.

NOTES: National Assessment of Educational Progress (NAEP) grade 8 mathematics scores are for public 

schools only. For additional details on NAEP scores by sex and race or ethnicity, see appendix tables 

8-1–8-12.

SOURCE: National Center for Education Statistics, NAEP (various years).

Science and Engineering Indicators 2014
8-24 ♦ Chapter 8. State Indicators

This indicator represents each state’s average score on the National Assessment of Educational Progress (NAEP) in science for its eighth grade 

students in public schools. The NAEP science assessment, conducted by the 

National Center for Education Statistics, is part of a legally mandated federal 

effort to measure student performance. It measures students’ knowledge and 

skill in science and their ability to apply that knowledge in the content areas 

of physical, life, and earth and space science. The NAEP assessment in science was updated in 2009 to keep pace with recent developments in science 

and science education. Because it is based on a new framework, 2009 results 

cannot be compared to those from previous science assessments. Student 

performance is presented in terms of average scores on a scale from 0 to 300.

An average score designated as “NA” (not available) indicates that the 

state either did not participate in the assessment or did not meet the minimum guidelines for reporting. NAEP allows students with disabilities or 

limited English-language proficiency to use certain accommodations (e.g., 

extra testing time or individual rather than group administration). All data 

presented here represent scores from tests taken with accommodations offered. For additional details on NAEP scores by gender and race/ethnicity, 

see appendix tables 8-1 to 8-12.

Findings

• In 2011, the nationwide average science 

score of eighth grade public school students 

was 151, an increase from 149 in 2009. 

Average scores in 2011 for individual states 

ranged from a high of 164 to a low of 137.

• Of the 46 jurisdictions that participated in 

both the 2009 and 2011 eighth grade science assessments, 22 showed increases in 

science scores among public school eighth 

graders over the period.

• Nationally, the 2011 average science score 

for white public school eighth grade students was 163 compared to 128 for black 

students, a gap of 35 points, and 136 for 

Hispanic students, a gap of 27 points, based 

upon racial classifications provided by the 

schools. In 2009, these gaps were 36 and 30 

points, respectively.

Eighth Grade Science Performance

Figure 8-7

Average eighth grade science performance: 2011

110 115 135 140 145 150 155 160 165

DC MS

AL

AZ

CA

HI

LA

NV

AR

FL

IL

NC

NM

NY

OK

RI

SC

WV

CT

IA

ID

KS

KY

MI

MO

NE

NJ

OH

OR

WA

WI

AK

DE

GA

IN

MD

PA

TN

TX

CO

MA

ME

MN

MT

ND

NH

SD

UT

VA

VT

WY

Average score

1st quartile (159–164)

2nd quartile (153–158)

3rd quartile (148–152)

4th quartile (112–147)

Achievement levels

(Scores range from 0–300)

Advanced ......................... 215–300

Proficient .......................... 170–214

Basic................................. 141–169

SOURCE: National Center for Education 

Statistics, National Assessment of Educational Progress.
Science and Engineering Indicators 2014 ♦ 8-25

Table 8-7

Average eighth grade science performance, by state: 2009 and 2011

(Score out of 300)

State 2009 2011

United States.................................................................. 149 151

Alabama...................................................................... 139 140

Alaska ......................................................................... NA 153

Arizona........................................................................ 141 144

Arkansas ..................................................................... 144 148

California..................................................................... 137 140

Colorado ..................................................................... 156 161

Connecticut ................................................................ 155 155

Delaware ..................................................................... 148 150

District of Columbia .................................................... NA 112

Florida......................................................................... 146 148

Georgia ....................................................................... 147 151

Hawaii ......................................................................... 139 142

Idaho........................................................................... 158 159

Illinois.......................................................................... 148 147

Indiana ........................................................................ 152 153

Iowa ............................................................................ 156 157

Kansas ........................................................................ NA 156

Kentucky..................................................................... 156 157

Louisiana..................................................................... 139 143

Maine .......................................................................... 158 160

Maryland..................................................................... 148 152

Massachusetts............................................................ 160 161

Michigan ..................................................................... 153 157

Minnesota ................................................................... 159 161

Mississippi .................................................................. 132 137

Missouri ...................................................................... 156 156

Montana...................................................................... 162 163

Nebraska..................................................................... NA 157

Nevada........................................................................ 141 144

New Hampshire .......................................................... 160 162

New Jersey ................................................................. 155 155

New Mexico................................................................ 143 145

New York..................................................................... 149 149

North Carolina............................................................. 144 148

North Dakota .............................................................. 162 164

Ohio ............................................................................ 158 158

Oklahoma.................................................................... 146 148

Oregon........................................................................ 154 155

Pennsylvania............................................................... 154 151

Rhode Island............................................................... 146 149

South Carolina ............................................................ 143 149

South Dakota.............................................................. 161 162

Tennessee................................................................... 148 150

Texas........................................................................... 150 153

Utah ............................................................................ 158 161

Vermont....................................................................... NA 163

Virginia ........................................................................ 156 160

Washington................................................................. 155 156

West Virginia ............................................................... 145 149

Wisconsin ................................................................... 157 159

Wyoming..................................................................... 158 160

Puerto Rico................................................................. NA NA

NA = not available.

NOTES: National Assessment of Educational Progress (NAEP) grade 8 science scores are for public 

schools only. For additional details on NAEP scores by sex and race or ethnicity, see appendix tables 

8-1–8-12.

SOURCE: National Center for Education Statistics, NAEP (various years). 

Science and Engineering Indicators 2014
8-26 ♦ Chapter 8. State Indicators

This indicator represents the proportion of a state’s eighth grade students in public 

schools that has met or exceeded the proficiency standard in science. The National 

Assessment Governing Board sets performance standards that provide a context for 

interpreting National Assessment of Educational Progress (NAEP) results. The standards 

define “proficiency” as well as “advanced” and “basic” accomplishment. For the eighth 

grade, the proficient level (scores 170–214) represents solid academic performance and 

demonstrates competency over challenging subject-matter knowledge. The advanced 

level (215–300) signifies superior performance. The basic level (141–169) denotes partial 

mastery of knowledge and skills that are prerequisite for proficient work. The National 

Center for Education Statistics has determined that achievement levels should be used 

on a trial basis and interpreted with caution.

Approximately 122,000 eighth grade students in 7,290 schools participated in the 

2011 NAEP science assessment. A designation of “NA” (not available) indicates that 

the state either did not participate in the assessment or did not meet minimum guidelines for reporting. NAEP allows students with disabilities or limited English-language 

proficiency to use certain accommodations (e.g., extra testing time or individual rather 

than group administration). All data presented here represent scores from tests taken 

with accommodations offered. For additional details on NAEP scores by gender and 

race/ethnicity, see appendix tables 8-1 to 8-12.

Findings

• In 2011, 31% of eighth grade 

public school students nationwide performed at or above the 

proficient level in science. State 

values for this indicator ranged 

from 19% to 45%.

• Nationally, the percentage of 

eighth grade white students 

demonstrating proficient performance in science was 43% in 

2011 compared to 9% for black 

students, a gap of 34 percentage 

points, and 16% for Hispanic 

students, a gap of 27 percentage points, based upon the 

racial classifications provided 

by the schools. In 2009, these 

gaps were 33 and 29 percentage 

points, respectively.

Eighth Grade Science Proficiency

Figure 8-8

Students reaching proficiency in eighth grade science: 2011

(Percentage of students scoring 170 or above)

16 20 24 28 32 36 40 44 48

AZ

CA

HI

LA

NM

NV

AR

IL

NC

OK

WV

DE

FL

GA

NY

RI

SC

TN

AK

CT

IA

IN

KS

KY

MD

NJ

OR

PA

TX

WA

CO

MN

NH

SD

UT

VA

VT

WI

ID

ME

MI

MO

NE

OH

WY

MA

MT

ND

AL

MS

Percent

8 12

DC

1st quartile (38%–45%)

2nd quartile (34%–37%)

3rd quartile (26%–33%)

4th quartile (8%–24%)

Achievement levels

(Scores range from 0–300)

Advanced ......................... 215–300

Proficient .......................... 170–214

Basic................................. 141–169

SOURCE: National Center for Education 

Statistics, National Assessment of Educational Progress.
Science and Engineering Indicators 2014 ♦ 8-27

Table 8-8

Students reaching proficiency in eighth grade science, by state: 

2009 and 2011

(Percent)

State 2009 2011

United States.................................................................. 29 31

Alabama...................................................................... 19 19

Alaska ......................................................................... NA 34

Arizona........................................................................ 22 23

Arkansas ..................................................................... 24 26

California..................................................................... 20 22

Colorado ..................................................................... 36 42

Connecticut ................................................................ 35 35

Delaware ..................................................................... 25 28

District of Columbia .................................................... NA 8

Florida......................................................................... 25 28

Georgia ....................................................................... 27 30

Hawaii ......................................................................... 17 22

Idaho........................................................................... 37 38

Illinois.......................................................................... 28 26

Indiana ........................................................................ 32 33

Iowa ............................................................................ 35 35

Kansas ........................................................................ NA 35

Kentucky..................................................................... 34 34

Louisiana..................................................................... 20 22

Maine .......................................................................... 35 37

Maryland..................................................................... 28 32

Massachusetts............................................................ 41 44

Michigan ..................................................................... 35 38

Minnesota ................................................................... 40 42

Mississippi .................................................................. 15 19

Missouri ...................................................................... 36 36

Montana...................................................................... 43 44

Nebraska..................................................................... NA 38

Nevada........................................................................ 20 23

New Hampshire .......................................................... 39 42

New Jersey ................................................................. 34 34

New Mexico................................................................ 21 22

New York..................................................................... 31 29

North Carolina............................................................. 24 26

North Dakota .............................................................. 42 45

Ohio ............................................................................ 37 38

Oklahoma.................................................................... 25 26

Oregon........................................................................ 35 35

Pennsylvania............................................................... 35 33

Rhode Island............................................................... 26 31

South Carolina ............................................................ 23 28

South Dakota.............................................................. 40 42

Tennessee................................................................... 28 31

Texas........................................................................... 29 32

Utah ............................................................................ 39 43

Vermont....................................................................... NA 43

Virginia ........................................................................ 36 40

Washington................................................................. 34 35

West Virginia ............................................................... 22 24

Wisconsin ................................................................... 38 40

Wyoming..................................................................... 36 38

Puerto Rico................................................................. NA NA

NA = not available.

NOTES: National Assessment of Educational Progress (NAEP) grade 8 science scores are for public 

schools only. For additional details on NAEP scores by sex and race or ethnicity, see appendix tables 

8-1–8-12.

SOURCE: National Center for Education Statistics, NAEP (various years). 

Science and Engineering Indicators 2014
8-28 ♦ Chapter 8. State Indicators

This indicator represents the average salary of all full-time 

public school teachers. The year is the end date of the academic 

year. For example, 2012 data represent salaries for the 2011–12 

academic year. The figures include salaries for teachers with 

varying amounts of teaching experience and various types and 

levels of formal education.

Salary estimates for public elementary and secondary 

teachers are provided by the National Education Association's 

Estimates of School Statistics, 1969–70 through 2011–12.

Public school teacher salaries may reflect a range of factors, 

including the value that the state places on primary and secondary education, the state’s cost of living, the teachers’ experience 

and education level, and the local supply and demand in the job 

market. Relatively low teacher salaries may hinder recruitment 

into the teaching profession.

Findings

• In 2012, salaries for public school teachers nationwide averaged $55,418, ranging from a state low of $38,804 to a high 

of $73,398.

• Fifteen states and the District of Columbia had average public 

school teacher salaries higher than the national average in 

2012, an increase from 13 states and the District of Columbia 

that were higher than the national average in 2002.

• Between 2002 and 2012, average teacher salaries across 

the nation rose by 24% in unadjusted dollars. Average 

teacher salaries declined by 1% after adjusting for inflation. 

• Average state salaries for public school teachers and state 

achievement scores on the NAEP mathematics and science 

tests are not correlated: some states rank high on one measure and low on the other.

Public School Teacher Salaries

Figure 8-9

Public school teacher salaries: 2012

36,400 40,800 45,200 49,600 54,000 58,400 62,800 67,200 71,600 76,000

SD

MS

OK

AL

AR

AZ

CO

FL

ID

KS

ME

MO

MT

NC

ND

NE

NM

SC

TN

TX

UT

VA

WV

AK

DE

MI

PA

RI

GA

IA

IN

KY

LA

VT

WA

WI

MD

NJ

CA

CT

DC

MA

NY

HI

IL

MN

NH

NV

OH

OR

WY

Dollars

1st quartile ($57,636–$73,398)

2nd quartile ($50,240–$57,348)

3rd quartile ($47,338–$50,179)

4th quartile ($38,804–$47,082)

SOURCE: National Education Association 

Research, Estimates Database.
Science and Engineering Indicators 2014 ♦ 8-29

Table 8-9

Public school teacher salaries, by state: 2002, 2007, and 2012

(Dollars)

State 2002 2007 2012

United States............................................. 44,683 50,816 55,418

Alabama................................................. 37,194 43,389 48,003

Alaska .................................................... 49,418 54,658 62,425

Arizona................................................... 39,973 45,941 48,691

Arkansas ................................................ 36,962 44,245 46,314

California................................................ 54,348 63,640 68,531

Colorado ................................................ 40,659 45,833 49,049

Connecticut ........................................... 53,551 60,822 69,465

Delaware ................................................ 48,363 54,680 58,800

District of Columbia ............................... 47,049 59,000 68,720

Florida.................................................... 39,275 45,308 46,479

Georgia .................................................. 44,073 49,905 52,938

Hawaii .................................................... 42,615 51,922 54,070

Idaho...................................................... 39,591 42,798 48,551

Illinois..................................................... 49,435 58,246 57,636

Indiana ................................................... 44,195 47,831 50,516

Iowa ....................................................... 38,230 43,130 50,240

Kansas ................................................... 37,093 43,334 46,718

Kentucky................................................ 37,951 43,646 49,730

Louisiana................................................ 36,328 42,816 50,179

Maine ..................................................... 37,300 41,596 47,338

Maryland................................................ 48,251 56,927 63,634

Massachusetts....................................... 50,293 58,624 71,721

Michigan ................................................ 52,676 54,895 61,560

Minnesota .............................................. 42,194 49,634 54,959

Mississippi ............................................. 33,295 40,182 41,646

Missouri ................................................. 37,996 41,839 46,406

Montana................................................. 34,379 41,225 48,546

Nebraska................................................ 36,236 42,044 48,154

Nevada................................................... 40,764 45,342 54,559

New Hampshire ..................................... 39,915 46,527 54,177

New Jersey ............................................ 53,192 59,920 67,078

New Mexico........................................... 36,440 42,780 45,622

New York................................................ 52,000 58,537 73,398

North Carolina........................................ 42,680 46,410 45,947

North Dakota ......................................... 32,253 38,822 46,058

Ohio ....................................................... 44,029 51,937 56,715

Oklahoma............................................... 34,744 42,379 44,391

Oregon................................................... 46,081 50,911 57,348

Pennsylvania.......................................... 50,599 54,970 61,934

Rhode Island.......................................... 49,758 55,956 62,186

South Carolina ....................................... 39,923 44,133 47,428

South Dakota......................................... 31,295 35,378 38,804

Tennessee.............................................. 38,515 43,816 47,082

Texas...................................................... 39,232 44,897 48,373

Utah ....................................................... 37,414 40,566 48,159

Vermont.................................................. 39,240 48,370 51,306

Virginia ................................................... 41,731 44,727 48,703

Washington............................................ 43,464 47,882 52,232

West Virginia .......................................... 36,751 40,531 45,320

Wisconsin .............................................. 42,232 47,901 53,792

Wyoming................................................ 37,837 50,692 57,222

Puerto Rico............................................ NA NA NA

NA = not available.

NOTES: The 2002 and 2007 national averages for the United States are the reported values 

from the Digest of Education Statistics; the 2012 national average for the United States is the 

reported value from the National Education Association. 

SOURCE: National Center for Education Statistics, Digest of Education Statistics (various 

years). National Education Association Research, Estimates Database (2012).

Science and Engineering Indicators 2014
8-30 ♦ Chapter 8. State Indicators

This indicator represents the relative amount of resources that state governments expend to support public education in prekindergarten through grade 12. 

It is calculated by dividing a state’s expenditures for elementary and secondary public schools by the state’s GDP. Expenditures include instruction and 

instruction-related costs, student support services, administration, and operations and exclude funds for school construction and other capital outlays, debt 

service, and programs outside of public elementary and secondary education. 

State and local support represent the largest sources of funding for elementary 

and secondary education.

Expenditure data on public elementary and secondary education are reported 

by the National Center for Education Statistics, Department of Education. They 

are part of the National Public Education Financial Survey and are included in 

the Common Core of Data, a comprehensive annual national statistical database 

that covers approximately 100,000 public elementary and secondary schools 

and 18,000 regular school districts in the 50 states, the District of Columbia, and 

outlying areas, as well as Department of Defense Schools. Most of the data are 

obtained from administrative records maintained by state education agencies.

Expenditures are expressed in actual dollars and their data year is the end date 

of the academic year. For example, expenditure data for 2010 represent expenditures for the 2009–10 academic year. GDP data refer to the 2010 calendar year.

Findings

• The 2010 national average for spending on 

elementary and secondary education was 

3.65% of the gross domestic product (GDP), an 

increase from 3.28% in 2000. Among individual 

states, the value for this indicator ranged from 

2.47% to 5.67% of the state’s GDP in 2010, 

indicating that some states were directing a 

much higher percentage of their resources 

toward elementary and secondary education.

• Spending for elementary and secondary public 

education as a percentage of the state’s GDP 

decreased in 10 states during the 2000–10 

period.

• Several states spending the highest percentage of their GDP on elementary and secondary 

education had relatively small student populations (100,000–300,000 students), suggesting 

that some level of expenditure for educational 

infrastructure (e.g., assessments of curriculum 

development) may be largely independent of 

the size of the student population.

Elementary and Secondary Public School Expenditures as a Percentage of 

Gross Domestic Product

Figure 8-10

Elementary and secondary public school expenditures as a percentage of gross domestic

product: 2010

1.20 1.60 2.00 2.40 2.80 3.20 3.60 4.00 4.40 4.80 5.20 5.60 6.00

CA

CO

HI

NC

ND

NV

OR

SD

TN

UT

VA

WA

AL

CT

GA

IL

IN

KS

KY

MA

MO

WY

AK

AR

MD

MS

MT

NH

NM

OH

PA

SC

DC DE WI

AZ

FL

IA

ID

LA

MN

NE

OK

TX NJ

ME

MI

NY

RI WV VT

Percent

1st quartile (4.10%–5.67%)

2nd quartile (3.66%–4.07%)

3rd quartile (3.12%–3.57%)

4th quartile (1.40%–3.11%)

SOURCES: National Center for Education 

Statistics (NCES), NCES Common Core of 

Data, National Public Education Financial 

Survey; Bureau of Economic Analysis, 

Gross Domestic Product data.
Science and Engineering Indicators 2014 ♦ 8-31

Table 8-10

Elementary and secondary public school expenditures as a percentage of gross domestic product, by state: 

2000, 2005, and 2010

Public school expenditures 

($thousands) State GDP ($millions)

School expenditures/

GDP (%)

State 2000 2005 2010 2000 2005 2010 2000 2005 2010

United States................... 323,808,910 424,562,096 525,497,899 9,884,170 12,539,116 14,388,814 3.28 3.39 3.65

Alabama....................... 4,176,082 5,164,406 6,670,517 116,009 150,968 172,842 3.60 3.42 3.86

Alaska .......................... 1,183,499 1,442,269 2,084,019 25,911 37,774 47,910 4.57 3.82 4.35

Arizona......................... 4,262,182 6,451,870 8,587,889* 161,792 222,569 247,329 2.63 2.90 3.47

Arkansas ...................... 2,380,331 3,546,999 4,459,910* 68,335 88,501 103,170 3.48 4.01 4.32

California...................... 38,129,479 50,918,654 58,248,662* 1,319,472 1,688,949 1,845,249 2.89 3.01 3.16

Colorado ...................... 4,400,888 5,994,440 7,429,302 172,037 217,329 254,551 2.56 2.76 2.92

Connecticut ................. 5,402,868 7,080,396 8,853,337* 163,455 196,307 221,767 3.31 3.61 3.99

Delaware ...................... 937,630 1,299,349 1,549,812 40,614 54,422 62,832 2.31 2.39 2.47

District of Columbia ..... 780,192 1,023,952 1,451,870 58,267 82,488 103,745 1.34 1.24 1.40

Florida.......................... 13,885,988 19,042,877 23,349,314* 481,239 681,225 727,972 2.89 2.80 3.21

Georgia ........................ 9,158,624 12,528,856 15,730,409* 293,966 363,177 402,006 3.12 3.45 3.91

Hawaii .......................... 1,213,695 1,648,086 2,110,864 41,450 56,901 67,274 2.93 2.90 3.14

Idaho............................ 1,302,817 1,618,215 1,961,857* 36,147 48,683 55,639 3.60 3.32 3.53

Illinois........................... 14,462,773 18,658,428 24,695,773* 474,520 568,114 642,769 3.05 3.28 3.84

Indiana ......................... 7,110,930 9,108,931 9,921,243* 198,238 239,321 270,739 3.59 3.81 3.66

Iowa ............................. 3,264,336 3,808,200 4,794,308 93,312 119,998 138,378 3.50 3.17 3.46

Kansas ......................... 2,971,814 3,718,153 4,731,676 85,722 104,869 126,640 3.47 3.55 3.74

Kentucky...................... 3,837,794 4,812,591 6,091,814 113,233 138,772 161,064 3.39 3.47 3.78

Louisiana...................... 4,391,214 5,554,766 7,393,452* 131,289 196,917 227,373 3.34 2.82 3.25

Maine ........................... 1,604,438 2,056,266 2,356,312* 36,438 45,520 51,343 4.40 4.52 4.59

Maryland...................... 6,545,135 8,682,586 11,883,677* 182,923 247,241 295,981 3.58 3.51 4.02

Massachusetts............. 8,511,065 11,357,857 14,067,276* 273,006 323,314 376,908 3.12 3.51 3.73

Michigan ...................... 13,994,294 16,353,921 17,227,515 337,459 375,753 367,107 4.15 4.35 4.69

Minnesota .................... 6,140,442 7,310,284 8,927,288* 188,818 237,813 268,578 3.25 3.07 3.32

Mississippi ................... 2,510,376 3,243,888 3,990,876* 65,625 81,360 95,763 3.83 3.99 4.17

Missouri ....................... 5,655,531 7,115,207 8,923,448* 180,967 216,336 243,876 3.13 3.29 3.66

Montana....................... 994,770 1,193,182 1,498,252 21,633 30,054 36,521 4.60 3.97 4.10

Nebraska...................... 1,926,500 2,512,914 3,247,970 57,333 72,505 90,910 3.36 3.47 3.57

Nevada......................... 1,875,467 2,722,264 3,592,994 75,895 114,478 124,838 2.47 2.38 2.88

New Hampshire ........... 1,418,503 2,021,144 2,576,956 44,161 53,693 61,147 3.21 3.76 4.21

New Jersey .................. 13,327,645 19,669,576 24,261,392 350,110 430,246 483,007 3.81 4.57 5.02

New Mexico................. 1,890,274 2,554,638 3,217,328 50,294 67,763 77,686 3.76 3.77 4.14

New York...................... 28,433,240 38,866,853 50,251,461* 769,291 959,867 1,136,417 3.70 4.05 4.42

North Carolina.............. 7,713,293 9,567,000 12,200,362 281,542 354,664 426,875 2.74 2.70 2.86

North Dakota ............... 638,946 786,870 1,000,095 18,266 24,670 35,357 3.50 3.19 2.83

Ohio ............................. 12,974,575 17,167,866 19,801,670 380,895 444,083 465,679 3.41 3.87 4.25

Oklahoma..................... 3,382,581 4,161,024 5,192,124 91,273 120,529 147,649 3.71 3.45 3.52

Oregon......................... 3,896,287 4,458,028 5,401,667 113,180 143,429 181,523 3.44 3.11 2.98

Pennsylvania................ 14,120,112 18,711,100 22,733,518 395,602 482,200 558,818 3.57 3.88 4.07

Rhode Island................ 1,393,143 1,825,900 2,136,582* 33,584 44,189 48,572 4.15 4.13 4.40

South Carolina ............. 4,087,355 5,312,739 6,566,165 115,443 141,877 162,292 3.54 3.74 4.05

South Dakota............... 737,998 916,563 1,115,861 24,038 31,549 38,297 3.07 2.91 2.91

Tennessee.................... 4,931,734 6,446,691 7,894,661 177,540 224,288 253,602 2.78 2.87 3.11

Texas............................ 25,098,703 31,919,107 42,621,886 731,064 968,553 1,226,714 3.43 3.30 3.47

Utah ............................. 2,102,655 2,627,022 3,635,085 69,489 90,616 118,225 3.03 2.90 3.07

Vermont........................ 870,198 1,177,478 1,463,792 18,039 22,743 25,809 4.82 5.18 5.67

Virginia ......................... 7,757,598 10,705,162 13,193,633 261,759 356,370 422,763 2.96 3.00 3.12

Washington.................. 6,399,883 7,870,979 9,832,913 227,704 279,333 342,702 2.81 2.82 2.87

West Virginia ................ 2,086,937 2,527,767 3,315,648 41,386 51,857 62,732 5.04 4.87 5.29

Wisconsin .................... 6,852,178 8,435,359 9,918,809 177,355 218,689 245,415 3.86 3.86 4.04

Wyoming...................... 683,918 863,423 1,334,655 17,050 26,250 36,459 4.01 3.29 3.66

Puerto Rico.................. 2,086,414 2,865,945 3,464,044* 69,208 86,158 NA 3.00 3.30 NA

* = value is affected by the redistribution of reported values to correct for missing data items and/or to distribute state direct-support expenditures; 

NA = not available.

GDP = gross domestic product.

SOURCES: National Center for Education Statistics (NCES), NCES Common Core of Data, National Public Education Financial Survey (various years); 

Bureau of Economic Analysis, Gross Domestic Product data (June 2013); Government of Puerto Rico, Office of the Governor (various years); United Nations Statistics Division. 

Science and Engineering Indicators 2014
8-32 ♦ Chapter 8. State Indicators

Expenditures per Pupil for Elementary and Secondary Public Schools

This indicator represents the amount that local, state, and federal 

governments spend on elementary and secondary education in a state, 

adjusted for the size of the student body. It is calculated by dividing the 

expenditures over the entire academic year for prekindergarten through 

grade 12 by the number of students in those grades in public schools. 

Expenditures include expenditures for instruction and instruction-related 

costs, student support services, administration, and operations and exclude 

funds for school construction and other capital outlays, debt service, 

and programs outside of public elementary and secondary education. 

The number of pupils enrolled in prekindergarten through grade 12 is 

determined during the fall of the academic year. Expenditures represent 

actual spending in current dollars and have not been adjusted for inflation 

or for the cost of living in a state, which affects the amount of goods and 

services that can be purchased.

During the 2009–10 school year, 66.1% of expenses were used for 

instructional costs, 17.7% for operational costs, 10.7% for administrative 

costs, and 5.6% for student support services.

The year is the end date of the academic year. For example, data for 

2010 represent costs for the 2009–10 academic year.

Findings

• Per-pupil spending on day-to-day operations 

grew nationwide from $6,911 in 2000 to $10,652 

in 2010, an increase of 54% in unadjusted 

dollars. This was equivalent to an increase of 

approximately 23% after adjusting for inflation.

• In 2010, all states showed substantial increases 

in per-pupil spending relative to 2000, and 

only one state did not exceed the 2000 

national average.

• Per-pupil spending in individual states varied 

widely, ranging from a low of $6,452 to a high of 

$18,167 in 2010. The District of Columbia had 

the highest per-pupil spending of any jurisdiction 

at $20,910.

• Several states that ranked in the lower two quartiles of this indicator ranked in the upper quartiles of the National Assessment of Educational 

Progress indicators.

Figure 8-11

Expenditures per pupil for elementary and secondary public schools: 2010

5,600 7,000 8,400 9,800 11,200 12,600 14,000 15,400 16,800 18,200 19,600 21,000

UT

AZ

ID

MS

NC

NV

OK

TN

DE

HI

IL

ME

NE

OH

WI

WV

AL

AR

CA

CO

FL

GA

IA

IN

KY

MO

NM

OR

SC

SD

TX

WA

NH

PA

MA

MD

RI

WY

NJ

NY

AK

CT

VT DC

KS

LA

MI

MN

MT

ND

VA

Dollars

1st quartile ($12,452–$20,910)

2nd quartile ($10,447–$12,222)

3rd quartile ($8,957–$9,972)

4th quartile ($6,452–$8,926)

SOURCE: National Center for Education 

Statistics (NCES), NCES Common Core 

of Data, State Nonfiscal Survey of Public 

Elementary/Secondary Education, National 

Public Education Financial Survey.
Science and Engineering Indicators 2014 ♦ 8-33

Table 8-11

Expenditures per pupil for elementary and secondary public schools, by state: 2000, 2005, and 2010

Public school expenditures 

($thousands) Student enrollment

Per-pupil 

expenditures ($)

State 2000 2005 2010 2000 2005 2010 2000 2005 2010

United States................... 323,808,910 424,562,096 525,497,899 46,857,149 48,794,911 49,333,543 6,911 8,701 10,652

Alabama....................... 4,176,082 5,164,406 6,670,517 740,732 730,140 748,889 5,638 7,073 8,907

Alaska .......................... 1,183,499 1,442,269 2,084,019 134,391 132,970 131,661 8,806 10,847 15,829

Arizona......................... 4,262,182 6,451,870 8,587,889* 852,612 1,043,298 1,077,831 4,999 6,184 7,968

Arkansas ...................... 2,380,331 3,546,999 4,459,910* 451,034 463,115 480,559 5,277 7,659 9,281

California...................... 38,129,479 50,918,654 58,248,662* 6,038,590 6,441,557 6,263,438 6,314 7,905 9,300

Colorado ...................... 4,400,888 5,994,440 7,429,302 708,109 765,976 832,368 6,215 7,826 8,926

Connecticut ................. 5,402,868 7,080,396 8,853,337* 553,993 577,390 563,968 9,753 12,263 15,698

Delaware ...................... 937,630 1,299,349 1,549,812 112,836 119,091 126,801 8,310 10,911 12,222

District of Columbia ..... 780,192 1,023,952 1,451,870 77,194 76,714 69,433 10,107 13,348 20,910

Florida.......................... 13,885,988 19,042,877 23,349,314* 2,381,396 2,639,336 2,634,522 5,831 7,215 8,863

Georgia ........................ 9,158,624 12,528,856 15,730,409* 1,422,762 1,553,437 1,667,685 6,437 8,065 9,432

Hawaii .......................... 1,213,695 1,648,086 2,110,864 185,860 183,185 180,196 6,530 8,997 11,714

Idaho............................ 1,302,817 1,618,215 1,961,857* 245,136 256,084 276,299 5,315 6,319 7,100

Illinois........................... 14,462,773 18,658,428 24,695,773* 2,027,600 2,097,503 2,103,813 7,133 8,896 11,739

Indiana ......................... 7,110,930 9,108,931 9,921,243* 988,702 1,021,348 1,046,661 7,192 8,919 9,479

Iowa ............................. 3,264,336 3,808,200 4,794,308 497,301 478,319 491,842 6,564 7,962 9,748

Kansas ......................... 2,971,814 3,718,153 4,731,676 472,188 469,136 474,489 6,294 7,926 9,972

Kentucky...................... 3,837,794 4,812,591 6,091,814 648,180 674,796 680,089 5,921 7,132 8,957

Louisiana...................... 4,391,214 5,554,766 7,393,452* 756,579 724,281 690,915 5,804 7,669 10,701

Maine ........................... 1,604,438 2,056,266 2,356,312* 209,253 198,820 189,225 7,667 10,342 12,452

Maryland...................... 6,545,135 8,682,586 11,883,677* 846,582 865,561 848,412 7,731 10,031 14,007

Massachusetts............. 8,511,065 11,357,857 14,067,276* 971,425 975,574 957,053 8,761 11,642 14,699

Michigan ...................... 13,994,294 16,353,921 17,227,515 1,725,639 1,750,919 1,649,082 8,110 9,340 10,447

Minnesota .................... 6,140,442 7,310,284 8,927,288* 854,034 838,503 837,053 7,190 8,718 10,665

Mississippi ................... 2,510,376 3,243,888 3,990,876* 500,716 495,376 492,481 5,014 6,548 8,104

Missouri ....................... 5,655,531 7,115,207 8,923,448* 914,110 905,449 917,982 6,187 7,858 9,721

Montana....................... 994,770 1,193,182 1,498,252 157,556 146,705 141,807 6,314 8,133 10,565

Nebraska...................... 1,926,500 2,512,914 3,247,970 288,261 285,761 283,414 6,683 8,794 11,460

Nevada......................... 1,875,467 2,722,264 3,592,994 325,610 400,083 428,947 5,760 6,804 8,376

New Hampshire ........... 1,418,503 2,021,144 2,576,956 206,783 206,852 197,140 6,860 9,771 13,072

New Jersey .................. 13,327,645 19,669,576 24,261,392 1,289,256 1,393,347 1,396,029 10,337 14,117 17,379

New Mexico................. 1,890,274 2,554,638 3,217,328 324,495 326,102 334,419 5,825 7,834 9,621

New York...................... 28,433,240 38,866,853 50,251,461* 2,887,776 2,836,337 2,766,052 9,846 13,703 18,167

North Carolina.............. 7,713,293 9,567,000 12,200,362 1,275,925 1,385,754 1,483,397 6,045 6,904 8,225

North Dakota ............... 638,946 786,870 1,000,095 112,751 100,513 95,073 5,667 7,829 10,519

Ohio ............................. 12,974,575 17,167,866 19,801,670 1,836,554 1,840,032 1,764,297 7,065 9,330 11,224

Oklahoma..................... 3,382,581 4,161,024 5,192,124 627,032 629,476 654,802 5,395 6,610 7,929

Oregon......................... 3,896,287 4,458,028 5,401,667 545,033 552,322 582,839 7,149 8,071 9,268

Pennsylvania................ 14,120,112 18,711,100 22,733,518 1,816,716 1,828,089 1,785,993 7,772 10,235 12,729

Rhode Island................ 1,393,143 1,825,900 2,136,582* 156,454 156,498 145,118 8,904 11,667 14,723

South Carolina ............. 4,087,355 5,312,739 6,566,165 666,780 703,736 723,143 6,130 7,549 9,080

South Dakota............... 737,998 916,563 1,115,861 131,037 122,798 123,713 5,632 7,464 9,020

Tennessee.................... 4,931,734 6,446,691 7,894,661 916,202 941,091 972,549 5,383 6,850 8,117

Texas............................ 25,098,703 31,919,107 42,621,886 3,991,783 4,405,215 4,850,210 6,288 7,246 8,788

Utah ............................. 2,102,655 2,627,022 3,635,085 480,255 503,607 563,361 4,378 5,216 6,452

Vermont........................ 870,198 1,177,478 1,463,792 104,559 98,352 91,451 8,323 11,972 16,006

Virginia ......................... 7,757,598 10,705,162 13,193,633 1,133,994 1,204,739 1,245,340 6,841 8,886 10,594

Washington.................. 6,399,883 7,870,979 9,832,913 1,003,714 1,020,005 1,035,347 6,376 7,717 9,497

West Virginia ................ 2,086,937 2,527,767 3,315,648 291,811 280,129 282,662 7,152 9,024 11,730

Wisconsin .................... 6,852,178 8,435,359 9,918,809 877,753 864,757 866,072 7,806 9,755 11,453

Wyoming...................... 683,918 863,423 1,334,655 92,105 84,733 87,621 7,425 10,190 15,232

Puerto Rico.................. 2,086,414 2,865,945 3,464,044* 613,019 575,648 493,393 3,404 4,979 7,021

* = value is affected by the redistribution of reported values to correct for missing data items and/or to distribute state direct-support expenditures.

SOURCES: National Center for Education Statistics (NCES), NCES Common Core of Data, State Nonfiscal Survey of Public Elementary/Secondary Education (various years); National Public Education Financial Survey (various years). 

Science and Engineering Indicators 2014
8-34 ♦ Chapter 8. State Indicators

Participation in the Advanced Placement (AP) program provides 

a measure of the extent to which a rigorous curriculum is available 

to and used by high school students. This indicator represents the 

percentage of students in the graduating class who have taken one 

or more AP Exams.

Throughout the United States, more than 954,000 public school 

students from the class of 2012 took nearly 2.9 million AP Exams 

during their high school careers. Generally, students who take AP 

Exams have completed a rigorous course of study in a specific subject 

area in high school with the expectation of obtaining college credit 

or advanced placement. AP Exams were taken most frequently in 

U.S. history, English literature and composition, English language 

and composition, calculus AB, and U.S. government and politics.

Students from the class of 2012 attended 13,383 U.S. public high 

schools that participated in the AP program. These schools make 

many different AP courses available to their students.

Findings

• Nationwide, the percentage of public school students 

who took an AP Exam rose from 18.0% of the class 

of 2002 to 31.2% of the class of 2012.

• The percentage of public school students taking an 

AP Exam varied greatly among states and ranged 

from 11.1% to 51.0% of the class of 2012. Forty-one 

states and the District of Columbia exceeded the 

2002 national average in 2012, compared with 16 

states and the District of Columbia that exceeded the 

national average in 2002.

• AP participation levels were higher for all jurisdictions 

in 2012 than in 2002. Arkansas showed the largest 

increase, with the class of 2012 exceeding the participation of the class of 2002 by 34 percentage points.

Public High School Students Taking Advanced Placement Exams

Figure 8-12

Public high school students taking Advanced Placement Exams: 2012

10.0 14.8 19.6 24.4 29.2 34.0 38.8 43.6 48.4 53.2

LA

MS

ND

NE

IL

IN

KY

MN

NC

NJ

VT

WA

AK

AL

AZ

MT

NH

OH

OK

OR

PA

RI

WV

CA

CO

CT

MA

ME

NY

TX

UT

AR

GA

VA

DC

MD FL

DE

HI

MI

NM

NV

SC

WI

IA

ID

KS

MO

SD

TN

WY

Percent

1st quartile (34.4%–51.0%)

2nd quartile (26.7%–34.0%)

3rd quartile (20.7%–26.5%)

4th quartile (11.1%–20.2%)

SOURCES: Western Interstate Commission for Higher Education, Knocking at the 

College Door: Projections of High School 

Graduates, 2012; College Board, Advanced 

Placement Report to the Nation.
Science and Engineering Indicators 2014 ♦ 8-35

Table 8-12

Public high school students taking Advanced Placement Exams, by state: 2002, 2007, and 2012

Public high school graduates 

who took an Advanced 

Placement Exam High school graduates

High school graduates 

who took an Advanced 

Placement Exam (%)

State 2002 2007 2012 2002 2007 2012 2002 2007 2012

United States................... 471,404 694,705 954,070 2,621,534 2,893,045 3,053,230 18.0 24.0 31.2

Alabama....................... 3,103 4,181 9,852 35,887 38,912 44,317 8.6 10.7 22.2

Alaska .......................... 1,085 1,497 1,621 6,945 7,666 7,813 15.6 19.5 20.7

Arizona......................... 5,100 9,087 14,407 47,175 55,954 61,958 10.8 16.2 23.3

Arkansas ...................... 2,630 8,781 12,175 26,984 27,166 27,990 9.7 32.3 43.5

California...................... 78,638 110,253 144,801 325,895 356,641 384,080 24.1 30.9 37.7

Colorado ...................... 8,585 13,753 18,358 40,760 45,628 50,176 21.1 30.1 36.6

Connecticut ................. 6,790 9,819 13,332 32,327 37,541 36,836 21.0 26.2 36.2

Delaware ...................... 1,017 1,843 2,417 6,482 7,205 8,395 15.7 25.6 28.8

District of Columbia ..... 584 1,017 1,512 3,090 2,944 3,194 18.9 34.5 47.3

Florida.......................... 28,170 49,234 76,128 119,537 142,284 149,219 23.6 34.6 51.0

Georgia ........................ 13,518 21,730 33,647 65,983 77,829 84,813 20.5 27.9 39.7

Hawaii .......................... 1,239 1,702 2,905 10,452 11,063 10,990 11.9 15.4 26.4

Idaho............................ 1,795 2,507 3,150 15,874 16,242 17,043 11.3 15.4 18.5

Illinois........................... 18,833 27,798 40,653 116,657 130,220 135,636 16.1 21.3 30.0

Indiana ......................... 7,575 11,306 21,260 56,722 59,887 63,354 13.4 18.9 33.6

Iowa ............................. 2,667 3,989 5,542 33,789 34,127 32,833 7.9 11.7 16.9

Kansas ......................... 2,458 3,519 5,167 29,541 30,139 30,428 8.3 11.7 17.0

Kentucky...................... 4,537 7,036 12,218 36,337 39,099 41,038 12.5 18.0 29.8

Louisiana...................... 1,399 1,957 3,931 37,905 34,274 35,501 3.7 5.7 11.1

Maine ........................... 2,572 3,680 4,576 12,593 13,151 13,468 20.4 28.0 34.0

Maryland...................... 12,019 20,232 26,640 50,881 57,564 58,009 23.6 35.1 45.9

Massachusetts............. 12,084 17,036 22,808 55,272 63,903 63,701 21.9 26.7 35.8

Michigan ...................... 14,706 20,129 26,822 95,001 111,838 107,956 15.5 18.0 24.8

Minnesota .................... 8,926 12,527 16,780 57,440 59,497 57,486 15.5 21.1 29.2

Mississippi ................... 1,659 2,605 3,615 23,740 24,186 25,756 7.0 10.8 14.0

Missouri ....................... 3,895 5,846 9,235 54,487 60,275 61,471 7.1 9.7 15.0

Montana....................... 1,367 1,543 1,913 10,554 10,122 9,466 13.0 15.2 20.2

Nebraska...................... 1,199 1,882 2,886 19,910 19,873 19,656 6.0 9.5 14.7

Nevada......................... 2,239 4,371 6,890 16,270 17,149 25,710 13.8 25.5 26.8

New Hampshire ........... 1,919 2,850 3,238 12,452 14,452 13,917 15.4 19.7 23.3

New Jersey .................. 15,350 21,944 27,433 77,664 93,013 93,211 19.8 23.6 29.4

New Mexico................. 2,496 3,434 4,815 18,094 16,131 18,141 13.8 21.3 26.5

New York...................... 42,000 54,201 64,946 140,139 168,333 181,454 30.0 32.2 35.8

North Carolina.............. 15,008 22,315 26,633 65,955 76,031 88,421 22.8 29.3 30.1

North Dakota ............... 562 768 882 8,114 7,159 6,785 6.9 10.7 13.0

Ohio ............................. 14,057 19,929 25,170 110,608 117,658 119,318 12.7 16.9 21.1

Oklahoma..................... 5,032 7,018 8,140 36,852 37,100 37,792 13.7 18.9 21.5

Oregon......................... 3,643 6,107 8,059 31,153 33,446 34,662 11.7 18.3 23.3

Pennsylvania................ 15,890 21,887 28,750 114,943 128,603 127,773 13.8 17.0 22.5

Rhode Island................ 1,118 1,438 2,176 9,006 10,384 9,809 12.4 13.8 22.2

South Carolina ............. 6,444 8,142 10,564 31,302 35,108 39,496 20.6 23.2 26.7

South Dakota............... 1,003 1,268 1,545 8,796 8,346 8,345 11.4 15.2 18.5

Tennessee.................... 5,193 7,954 10,743 40,894 54,502 60,444 12.7 14.6 17.8

Texas............................ 43,308 65,788 96,166 225,167 241,193 279,291 19.2 27.3 34.4

Utah ............................. 7,744 8,737 10,439 30,183 28,276 30,229 25.7 30.9 34.5

Vermont........................ 1,280 1,913 2,151 7,083 7,317 6,827 18.1 26.1 31.5

Virginia ......................... 17,825 25,627 33,626 66,519 73,997 80,354 26.8 34.6 41.8

Washington.................. 8,513 14,741 20,581 58,311 62,801 64,002 14.6 23.5 32.2

West Virginia ................ 1,806 2,505 3,722 17,128 17,407 17,017 10.5 14.4 21.9

Wisconsin .................... 10,205 14,454 18,076 60,575 63,968 62,111 16.8 22.6 29.1

Wyoming...................... 619 825 974 6,106 5,441 5,538 10.1 15.2 17.6

Puerto Rico.................. NA NA NA NA NA NA NA NA NA

NA = not available.

NOTE: The national average for the United States is the reported value in the Advanced Placement Report to the Nation.

SOURCES: Western Interstate Commission for Higher Education, Knocking at the College Door: Projections of High School Graduates, 2012. College 

Board, Advanced Placement Report to the Nation (various years). 

Science and Engineering Indicators 2014
8-36 ♦ Chapter 8. State Indicators

This indicator represents the extent to which high school students 

are successfully demonstrating mastery of college-level material in 

specific disciplines. State scores on this indicator reflect students’ access to rigorous coursework as well as their success in comprehending 

and using it. The indicator value is defined as the percentage of U.S. 

public high school graduates who have scored 3 or higher on at least 

one Advanced Placement (AP) Exam. Many colleges and universities 

grant college credit or advanced placement for AP Exam scores of 3 or 

higher. Students who score a 3 or higher typically experience greater 

academic success in college and higher graduation rates.

More than 30 different AP Exams are offered each spring by the 

College Board. The exams include a multiple choice section and a 

free response section. To prepare for the AP Exam in a subject area, 

most students enroll in an AP class that employs a curriculum of high 

academic intensity. Performance on AP Exams has been shown in 

research to be one of the best predictors of success in college.

Findings

• Nationally, 18.8% of public high school students in 

the class of 2012 demonstrated the ability to do college-level work by obtaining a score of 3 or higher on 

at least one AP Exam, a substantial increase from the 

11.6% of the class of 2002 who obtained that score.

• Students from all states and the District of Columbia 

demonstrated greater success on AP Exams in 2012 

than in 2002, but this success was not evenly distributed. In 2012, 13 states had percentages below the 

2002 national average of 11.6% compared with 34 

jurisdictions in 2002.

• The percentage of students who scored 3 or higher 

on an AP Exam varied widely among states. For the 

class of 2012, this percentage ranged from a low of 

4.3% to a high of 28.1%.

Public High School Students Scoring 3 or Higher on at Least One Advanced 

Placement Exam

Figure 8-13

Public high school students scoring 3 or higher on at least one Advanced Placement Exam: 2012

4.4 6.8 9.2 11.6 14.0 16.4 18.8 21.2 23.6 26.0 28.4

LA

MS

MO

ND

NE

AR

DE

IN

KY

MI

NV

OR

PA

SC

AL

HI

IA

KS

OK

TN

WV

WY

NC

NH

TX

GA

IL

MN

VT

WA

WI

CO

ME

NJ

NY

CT

FL

MD

VA

CA

MA

UT

AK

AZ

DC

ID

MT

NM

OH

RI

SD

Percent

1st quartile (20.9%–28.1%)

2nd quartile (14.8%–20.3%)

3rd quartile (10.9%–14.6%)

4th quartile (4.3%–10.6%)

SOURCES: Western Interstate Commission for Higher Education, Knocking at the 

College Door: Projections of High School 

Graduates, 2012; College Board, Advanced 

Placement Report to the Nation.
Science and Engineering Indicators 2014 ♦ 8-37

Table 8-13

Public high school students scoring 3 or higher on at least one Advanced Placement Exam, by state: 

2002, 2007, and 2012

Public high school graduates 

who scored 3+ on an Advanced 

Placement Exam High school graduates

High school graduates 

who scored 3+ on an 

Advanced Placement 

Exam (%)

State 2002 2007 2012 2002 2007 2012 2002 2007 2012

United States................... 305,098 424,004 573,472 2,621,534 2,893,045 3,053,230 11.6 14.7 18.8

Alabama....................... 1,710 2,398 4,258 35,887 38,912 44,317 4.8 6.2 9.6

Alaska .......................... 762 957 1,062 6,945 7,666 7,813 11.0 12.5 13.6

Arizona......................... 3,285 5,428 8,307 47,175 55,954 61,958 7.0 9.7 13.4

Arkansas ...................... 1,333 2,620 4,227 26,984 27,166 27,990 4.9 9.6 15.1

California...................... 53,816 72,097 95,695 325,895 356,641 384,080 16.5 20.2 24.9

Colorado ...................... 5,582 8,569 11,442 40,760 45,628 50,176 13.7 18.8 22.8

Connecticut ................. 5,006 7,089 9,685 32,327 37,541 36,836 15.5 18.9 26.3

Delaware ...................... 617 979 1,257 6,482 7,205 8,395 9.5 13.6 15.0

District of Columbia ..... 234 211 389 3,090 2,944 3,194 7.6 7.2 12.2

Florida.......................... 17,256 26,360 39,306 119,537 142,284 149,219 14.4 18.5 26.3

Georgia ........................ 7,686 11,592 17,767 65,983 77,829 84,813 11.6 14.9 20.9

Hawaii .......................... 682 867 1,200 10,452 11,063 10,990 6.5 7.8 10.9

Idaho............................ 1,156 1,605 2,115 15,874 16,242 17,043 7.3 9.9 12.4

Illinois........................... 13,666 18,857 26,461 116,657 130,220 135,636 11.7 14.5 19.5

Indiana ......................... 4,134 5,786 9,634 56,722 59,887 63,354 7.3 9.7 15.2

Iowa ............................. 1,828 2,640 3,481 33,789 34,127 32,833 5.4 7.7 10.6

Kansas ......................... 1,631 2,208 3,117 29,541 30,139 30,428 5.5 7.3 10.2

Kentucky...................... 2,396 3,518 6,067 36,337 39,099 41,038 6.6 9.0 14.8

Louisiana...................... 775 920 1,531 37,905 34,274 35,501 2.0 2.7 4.3

Maine ........................... 1,701 2,275 2,933 12,593 13,151 13,468 13.5 17.3 21.8

Maryland...................... 8,414 12,882 16,327 50,881 57,564 58,009 16.5 22.4 28.1

Massachusetts............. 8,773 12,307 16,251 55,272 63,903 63,701 15.9 19.3 25.5

Michigan ...................... 9,594 13,062 17,262 95,001 111,838 107,956 10.1 11.7 16.0

Minnesota .................... 5,631 7,815 11,067 57,440 59,497 57,486 9.8 13.1 19.3

Mississippi ................... 696 845 1,145 23,740 24,186 25,756 2.9 3.5 4.4

Missouri ....................... 2,566 3,686 5,554 54,487 60,275 61,471 4.7 6.1 9.0

Montana....................... 929 1,033 1,205 10,554 10,122 9,466 8.8 10.2 12.7

Nebraska...................... 733 1,105 1,724 19,910 19,873 19,656 3.7 5.6 8.8

Nevada......................... 1,375 2,430 3,607 16,270 17,149 25,710 8.5 14.2 14.0

New Hampshire ........... 1,341 2,052 2,430 12,452 14,452 13,917 10.8 14.2 17.5

New Jersey .................. 11,230 15,772 20,283 77,664 93,013 93,211 14.5 17.0 21.8

New Mexico................. 1,215 1,642 2,108 18,094 16,131 18,141 6.7 10.2 11.6

New York...................... 28,196 35,707 42,627 140,139 168,333 181,454 20.1 21.2 23.5

North Carolina.............. 9,016 12,858 16,558 65,955 76,031 88,421 13.7 16.9 18.7

North Dakota ............... 402 542 553 8,114 7,159 6,785 5.0 7.6 8.2

Ohio ............................. 8,896 12,301 16,201 110,608 117,658 119,318 8.0 10.5 13.6

Oklahoma..................... 2,620 3,268 4,023 36,852 37,100 37,792 7.1 8.8 10.6

Oregon......................... 2,477 3,812 5,025 31,153 33,446 34,662 8.0 11.4 14.5

Pennsylvania................ 10,918 14,442 18,665 114,943 128,603 127,773 9.5 11.2 14.6

Rhode Island................ 666 900 1,302 9,006 10,384 9,809 7.4 8.7 13.3

South Carolina ............. 3,944 4,765 6,231 31,302 35,108 39,496 12.6 13.6 15.8

South Dakota............... 610 793 1,005 8,796 8,346 8,345 6.9 9.5 12.0

Tennessee.................... 3,153 4,344 5,790 40,894 54,502 60,444 7.7 8.0 9.6

Texas............................ 24,801 34,869 49,062 225,167 241,193 279,291 11.0 14.5 17.6

Utah ............................. 5,586 5,896 7,298 30,183 28,276 30,229 18.5 20.9 24.1

Vermont........................ 910 1,311 1,425 7,083 7,317 6,827 12.8 17.9 20.9

Virginia ......................... 11,198 16,007 21,524 66,519 73,997 80,354 16.8 21.6 26.8

Washington.................. 5,619 8,938 12,542 58,311 62,801 64,002 9.6 14.2 19.6

West Virginia ................ 886 1,148 1,631 17,128 17,407 17,017 5.2 6.6 9.6

Wisconsin .................... 7,100 10,053 12,590 60,575 63,968 62,111 11.7 15.7 20.3

Wyoming...................... 347 443 523 6,106 5,441 5,538 5.7 8.1 9.4

Puerto Rico.................. NA NA NA NA NA NA NA NA NA

NA = not available.

NOTE: The national average for the United States is the reported value in the Advanced Placement Report to the Nation.

SOURCE: Western Interstate Commission for Higher Education, Knocking at the College Door: Projections of High School Graduates, 2012. College 

Board, Advanced Placement Report to the Nation (various years). 

Science and Engineering Indicators 2014
8-38 ♦ Chapter 8. State Indicators

The Advanced Placement (AP) Calculus AB exam seeks to assess how well 

a student has mastered the concepts and techniques of differential and integral 

calculus. The indicator value is defined as the percentage of U.S. public high 

school graduates who have scored 3 or higher on the AP Calculus AB exam 

during their high school careers. Many colleges and universities grant college 

credit or advanced placement for AP exam scores of 3 or higher.

AP courses in calculus consist of a full high school academic year of work 

and are comparable to calculus courses taught at colleges and universities. Prior 

to taking an AP Calculus course, students are expected to have completed 4 years 

of secondary mathematics intended for college-bound students consisting of 

courses in algebra, geometry, trigonometry, analytic geometry, and elementary 

functions. Even though a Calculus AB course may cover elementary functions, 

most of its topics will address differential and integral calculus. The use of a 

graphing calculator in AP Calculus is considered an integral part of the course, 

and graphing calculators are required on portions of the AP Exam.

Successful performance on the Calculus AB exam indicates that the student 

has a solid mathematical background and is prepared to undertake advanced 

training in mathematics, science, or engineering at the college or university level.

Findings

• Nationally, the share of the graduating class 

that demonstrated a mastery of Calculus AB by 

scoring a 3 or higher on the AP Exam increased 

from 4.7% in 2002 to 6.9% in 2012. 

• Values for individual states ranged from a low of 

1.9% to a high of 10.0% for the class of 2012.

• Between 2002 and 2012, all but 2 states 

increased the percentage of high school graduates that successfully completed the Calculus 

AB exam. For the class of 2012, improvements 

of 4 percentage points or higher as compared with the class of 2002 were reported in 

Maryland, Indiana, and Vermont.

• Because the percentages are small, yearto-year comparisons should be made with 

caution. Variability in students’ course selection and level of performance can affect 

the numbers.

Public High School Students Scoring 3 or Higher on Advanced Placement 

Calculus AB Exam

Figure 8-14

Public high school students scoring 3 or higher on Advanced Placement Calculus AB Exam: 2012

1.5 2.3 3.1 3.9 4.7 5.5 6.3 7.1 7.9 8.7 9.5 10.3

LA

MS

MO

ND

NE

TN

AZ

HI

NV

OR

WY

IA

NM

OK

WV

AL

MI

NH

OH

PA

RI

SC

SD

TX

DC

KY

NC

WI

DE

FL

GA

IL

MN

NJ

NY

CA

MA

ME

VA

AK

AR

CO

CT

UT

WA

IN

MD

VT

ID

KS

MT

Percent

1st quartile (7.8%–10.0%)

2nd quartile (6.2%–7.7%)

3rd quartile (4.3%–6.0%)

4th quartile (1.9%–4.0%)

SOURCES: The College Board, special 

tabulation (2013), copyright 2012 The 

College Board, www.collegeboard.org; 

Western Interstate Commission for Higher 

Education, Knocking at the College Door: 

Projections of High School Graduates, 2012.
Science and Engineering Indicators 2014 ♦ 8-39

Table 8-14

Public high school students scoring 3 or higher on Advanced Placement Calculus AB Exam, by state: 2002, 

2007, and 2012

Public high school graduates 

who scored 3+ on Advanced 

Placement Calculus AB Exam High school graduates

High school graduates 

who scored 3+ on 

Advanced Placement 

Calculus AB Exam (%)

State 2002 2007 2012 2002 2007 2012 2002 2007 2012

United States................... 123,388 166,239 211,570 2,621,534 2,893,045 3,053,230 4.7 5.7 6.9

Alabama....................... 696 1,022 2,441 35,887 38,912 44,317 1.9 2.6 5.5

Alaska .......................... 338 431 616 6,945 7,666 7,813 4.9 5.6 7.9

Arizona......................... 1,344 2,118 2,971 47,175 55,954 61,958 2.8 3.8 4.8

Arkansas ...................... 622 1,709 2,419 26,984 27,166 27,990 2.3 6.3 8.6

California...................... 19,653 27,410 36,107 325,895 356,641 384,080 6.0 7.7 9.4

Colorado ...................... 1,705 3,020 4,108 40,760 45,628 50,176 4.2 6.6 8.2

Connecticut ................. 1,691 2,235 3,032 32,327 37,541 36,836 5.2 6.0 8.2

Delaware ...................... 385 449 608 6,482 7,205 8,395 5.9 6.2 7.2

District of Columbia ..... 141 158 205 3,090 2,944 3,194 4.6 5.4 6.4

Florida.......................... 5,987 9,113 11,670 119,537 142,284 149,219 5.0 6.4 7.8

Georgia ........................ 3,798 4,715 6,619 65,983 77,829 84,813 5.8 6.1 7.8

Hawaii .......................... 375 443 566 10,452 11,063 10,990 3.6 4.0 5.2

Idaho............................ 433 628 727 15,874 16,242 17,043 2.7 3.9 4.3

Illinois........................... 5,069 6,950 9,807 116,657 130,220 135,636 4.3 5.3 7.2

Indiana ......................... 3,415 4,696 6,348 56,722 59,887 63,354 6.0 7.8 10.0

Iowa ............................. 764 914 1,178 33,789 34,127 32,833 2.3 2.7 3.6

Kansas ......................... 565 968 1,224 29,541 30,139 30,428 1.9 3.2 4.0

Kentucky...................... 1,334 1,934 2,800 36,337 39,099 41,038 3.7 4.9 6.8

Louisiana...................... 288 423 695 37,905 34,274 35,501 0.8 1.2 2.0

Maine ........................... 737 907 1,208 12,593 13,151 13,468 5.9 6.9 9.0

Maryland...................... 2,614 3,935 5,801 50,881 57,564 58,009 5.1 6.8 10.0

Massachusetts............. 3,285 4,588 6,006 55,272 63,903 63,701 5.9 7.2 9.4

Michigan ...................... 4,062 5,765 6,736 95,001 111,838 107,956 4.3 5.2 6.2

Minnesota .................... 3,030 3,639 4,309 57,440 59,497 57,486 5.3 6.1 7.5

Mississippi ................... 304 428 495 23,740 24,186 25,756 1.3 1.8 1.9

Missouri ....................... 969 1,168 1,583 54,487 60,275 61,471 1.8 1.9 2.6

Montana....................... 288 348 411 10,554 10,122 9,466 2.7 3.4 4.3

Nebraska...................... 258 322 585 19,910 19,873 19,656 1.3 1.6 3.0

Nevada......................... 519 968 1,227 16,270 17,149 25,710 3.2 5.6 4.8

New Hampshire ........... 625 810 830 12,452 14,452 13,917 5.0 5.6 6.0

New Jersey .................. 4,363 5,323 6,783 77,664 93,013 93,211 5.6 5.7 7.3

New Mexico................. 596 665 695 18,094 16,131 18,141 3.3 4.1 3.8

New York...................... 11,776 12,620 13,992 140,139 168,333 181,454 8.4 7.5 7.7

North Carolina.............. 4,120 5,398 5,850 65,955 76,031 88,421 6.2 7.1 6.6

North Dakota ............... 157 224 185 8,114 7,159 6,785 1.9 3.1 2.7

Ohio ............................. 4,567 5,986 6,864 110,608 117,658 119,318 4.1 5.1 5.8

Oklahoma..................... 999 1,100 1,374 36,852 37,100 37,792 2.7 3.0 3.6

Oregon......................... 868 1,325 1,744 31,153 33,446 34,662 2.8 4.0 5.0

Pennsylvania................ 4,203 5,708 6,985 114,943 128,603 127,773 3.7 4.4 5.5

Rhode Island................ 301 425 554 9,006 10,384 9,809 3.3 4.1 5.6

South Carolina ............. 2,119 2,384 2,460 31,302 35,108 39,496 6.8 6.8 6.2

South Dakota............... 372 445 491 8,796 8,346 8,345 4.2 5.3 5.9

Tennessee.................... 1,086 1,554 1,781 40,894 54,502 60,444 2.7 2.9 2.9

Texas............................ 9,724 14,278 17,397 225,167 241,193 279,291 4.3 5.9 6.2

Utah ............................. 1,905 2,049 2,518 30,183 28,276 30,229 6.3 7.2 8.3

Vermont........................ 396 576 654 7,083 7,317 6,827 5.6 7.9 9.6

Virginia ......................... 4,228 5,450 7,297 66,519 73,997 80,354 6.4 7.4 9.1

Washington.................. 2,797 4,212 5,369 58,311 62,801 64,002 4.8 6.7 8.4

West Virginia ................ 402 519 655 17,128 17,407 17,017 2.3 3.0 3.8

Wisconsin .................... 2,894 3,526 4,315 60,575 63,968 62,111 4.8 5.5 6.9

Wyoming...................... 221 258 275 6,106 5,441 5,538 3.6 4.7 5.0

Puerto Rico.................. NA NA NA NA NA NA NA NA NA

NA = not available.

SOURCES: Derived from data provided by the College Board, special tabulations (2013), copyright 2001–12 The College Board, www.collegeboard.org; 

Western Interstate Commission for Higher Education, Knocking at the College Door: Projections of High School Graduates, 2012. 

Science and Engineering Indicators 2014
8-40 ♦ Chapter 8. State Indicators

This indicator represents the percentage of a state’s early- to midcareer population that has earned at least a high school credential. The 

indicator displays results based on where high school graduates live 

rather than where they were educated. High values indicate a resident 

population and potential workforce with widespread basic education credentials.

Estimates of educational attainment have been developed by the 

U.S. Census Bureau. Data from 2005 and later are derived from the 

American Community Survey (ACS), the largest household survey in 

the United States, with a sample size of about 3 million addresses. The 

ACS collects information on an annual basis. Data prior to 2005 were 

derived from the Decennial Census.

Estimates of the population aged 25–44 are provided by the Census 

Bureau based on the 2000 and 2010 Decennial Censuses. Estimates for 

states with smaller populations are generally less precise than estimates 

for states with larger populations.

Findings

• Nationwide, 87.5% of the early- to midcareer 

population had at least a high school credential 

in 2011, an increase from the 84.0% who held 

such a credential in 2001.

• Between 2001 and 2011, 23 states and the 

District of Columbia showed a significant 

increase in the percentage of their early- to 

midcareer population with at least a high school 

credential. Two states had 2011 values below 

the 2001 national average of 84.0% compared 

with 8 in 2001.

• In 2011, the early- to midcareer population 

with at least a high school credential varied 

greatly among states, ranging from 82.2% to 

94.9%. States at or near the southern border 

of the United States tended to rank lowest on 

this indicator.

High School Graduates or Higher among Individuals 25–44 Years Old

Figure 8-15

High school graduates or higher among individuals 25–44 years old: 2011

82.0 83.7 85.4 87.1 88.8 90.5 92.2 93.9 95.6

CA

NV

TX

AR

DE

FL

IN

KY

NY

OK

OR

SC

TN

AL

AZ

MS

NM

CO

ID

IL

KS

MD

MO

NE

RI

UT

WA

WV

CT

DC

MA

MI

NJ

OH

PA

SD

VA

WI

AK

IA

ME

MN

NH

WY

HI

MT

ND

VT

GA

LA

NC

Percent

1st quartile (91.6%–94.9%)

2nd quartile (89.6%–91.4%)

3rd quartile (87.4%–89.4%)

4th quartile (82.2%–87.3%)

SOURCES: Census Bureau, 2010 Decennial 

Census, Population Estimates Program, and 

American Community Survey.
Science and Engineering Indicators 2014 ♦ 8-41

Table 8-15

High school graduates or higher among individuals 25–44 years old, by state: 2001, 2006, and 2011

Graduates 25–44 years old Population 25–44 years old

Graduates/ 

population 

25–44 

years old (%)

State 2001 2006 2011 2001 2006 2011 2001 2006 2011

United States................... 71,031,901 72,494,658 72,168,571 84,523,274 82,638,980 82,432,298 84.0 87.7 87.5

Alabama....................... 1,060,262 1,041,196 1,038,840 1,266,952 1,233,767 1,223,076 83.7 84.4 84.9

Alaska .......................... 176,545 177,011 186,627 198,158 188,470 198,914 89.1 93.9 93.8

Arizona......................... 1,167,520 1,461,333 1,436,551 1,526,458 1,664,223 1,688,279 76.5 87.8 85.1

Arkansas ...................... 608,424 644,782 651,633 743,315 747,504 745,421 81.9 86.3 87.4

California...................... 8,038,587 8,641,477 8,684,128 10,750,718 10,578,738 10,565,342 74.8 81.7 82.2

Colorado ...................... 1,211,816 1,253,856 1,304,869 1,412,620 1,380,451 1,448,033 85.8 90.8 90.1

Connecticut ................. 908,023 876,847 815,391 1,017,477 944,217 898,232 89.2 92.9 90.8

Delaware ...................... 207,147 208,667 200,262 233,890 232,516 227,578 88.6 89.7 88.0

District of Columbia ..... 156,642 168,221 198,756 190,251 187,870 216,233 82.3 89.5 91.9

Florida.......................... 3,808,005 4,201,616 4,153,736 4,591,807 4,804,621 4,758,046 82.9 87.4 87.3

Georgia ........................ 2,205,133 2,389,315 2,356,862 2,668,017 2,727,666 2,741,412 82.7 87.6 86.0

Hawaii .......................... 318,552 336,004 348,052 357,271 358,311 366,855 89.2 93.8 94.9

Idaho............................ 307,283 344,342 359,687 362,154 383,267 402,781 84.8 89.8 89.3

Illinois........................... 3,239,703 3,204,663 3,119,362 3,756,180 3,572,420 3,491,104 86.2 89.7 89.4

Indiana ......................... 1,532,633 1,519,940 1,471,266 1,769,492 1,705,535 1,665,758 86.6 89.1 88.3

Iowa ............................. 723,260 691,725 693,855 793,288 747,836 749,530 91.2 92.5 92.6

Kansas ......................... 700,664 650,567 653,237 755,887 713,707 727,160 92.7 91.2 89.8

Kentucky...................... 983,313 1,022,178 997,294 1,194,291 1,162,541 1,138,883 82.3 87.9 87.6

Louisiana...................... 1,007,477 956,118 1,027,440 1,268,704 1,152,042 1,203,069 79.4 83.0 85.4

Maine ........................... 332,327 321,680 292,585 364,111 337,692 312,002 91.3 95.3 93.8

Maryland...................... 1,456,139 1,442,853 1,410,513 1,652,198 1,598,650 1,565,884 88.1 90.3 90.1

Massachusetts............. 1,801,547 1,660,827 1,588,629 1,967,815 1,795,786 1,738,118 91.6 92.5 91.4

Michigan ...................... 2,623,986 2,467,686 2,192,907 2,905,689 2,658,755 2,414,603 90.3 92.8 90.8

Minnesota .................... 1,403,384 1,332,478 1,298,651 1,486,814 1,412,852 1,400,438 94.4 94.3 92.7

Mississippi ................... 620,634 641,562 639,787 794,888 767,066 760,122 78.1 83.6 84.2

Missouri ....................... 1,387,967 1,392,466 1,371,220 1,606,777 1,547,126 1,523,458 86.4 90.0 90.0

Montana....................... 207,722 213,879 223,374 238,899 228,548 237,269 86.9 93.6 94.1

Nebraska...................... 434,381 421,144 423,823 478,968 458,133 469,737 90.7 91.9 90.2

Nevada......................... 517,456 625,412 634,590 648,880 747,896 766,544 79.7 83.6 82.8

New Hampshire ........... 348,880 337,033 298,047 378,536 348,846 319,411 92.2 96.6 93.3

New Jersey .................. 2,372,904 2,220,609 2,118,729 2,603,347 2,446,589 2,338,637 91.1 90.8 90.6

New Mexico................. 421,618 431,947 436,332 506,151 507,378 519,946 83.3 85.1 83.9

New York...................... 4,931,556 4,751,228 4,630,437 5,775,563 5,417,603 5,280,570 85.4 87.7 87.7

North Carolina.............. 2,014,720 2,155,551 2,219,453 2,504,293 2,518,651 2,577,307 80.5 85.6 86.1

North Dakota ............... 162,511 147,532 159,980 168,631 156,114 170,010 96.4 94.5 94.1

Ohio ............................. 2,947,087 2,765,830 2,612,564 3,259,384 3,037,836 2,873,075 90.4 91.0 90.9

Oklahoma..................... 774,640 820,764 856,716 960,435 938,630 975,445 80.7 87.4 87.8

Oregon......................... 853,696 893,508 913,086 992,783 994,743 1,031,267 86.0 89.8 88.5

Pennsylvania................ 3,046,733 2,921,526 2,860,909 3,435,158 3,224,924 3,123,097 88.7 90.6 91.6

Rhode Island................ 259,555 252,937 233,864 306,912 284,670 261,020 84.6 88.9 89.6

South Carolina ............. 1,003,899 1,006,117 1,040,906 1,175,787 1,179,555 1,193,581 85.4 85.3 87.2

South Dakota............... 187,100 180,183 185,252 202,454 193,284 201,235 92.4 93.2 92.1

Tennessee.................... 1,414,180 1,473,533 1,484,697 1,699,828 1,690,961 1,678,144 83.2 87.1 88.5

Texas............................ 4,978,683 5,507,182 5,937,001 6,529,822 6,742,164 7,180,834 76.2 81.7 82.7

Utah ............................. 565,686 643,346 708,544 633,099 701,224 793,074 89.4 91.7 89.3

Vermont........................ 159,184 150,033 138,202 172,405 155,997 146,497 92.3 96.2 94.3

Virginia ......................... 1,907,914 1,977,743 2,014,374 2,227,441 2,190,642 2,215,775 85.7 90.3 90.9

Washington.................. 1,602,454 1,634,206 1,683,779 1,805,606 1,791,998 1,868,055 88.7 91.2 90.1

West Virginia ................ 401,838 414,635 404,537 487,860 468,119 455,269 82.4 88.6 88.9

Wisconsin .................... 1,410,448 1,378,346 1,322,584 1,561,327 1,479,447 1,440,314 90.3 93.2 91.8

Wyoming...................... 120,083 121,024 134,651 134,483 131,399 145,854 89.3 92.1 92.3

Puerto Rico.................. NA 878,376 798,311 1,055,380 1,080,801 955,369 NA 81.3 83.6

SOURCES: Census Bureau, 2000 and 2010 Decennial Censuses, Population Estimates Program (various years), and American Community Survey (various years). 

Science and Engineering Indicators 2014
8-42 ♦ Chapter 8. State Indicators

Educational attainment in a science, engineering, or technology (SET) field gives people 

greater opportunities to work in higher-paying technical jobs than are generally available 

to those in other fields of study. Earning an associate’s degree in a SET field also prepares 

an individual for more advanced technical education. 

This indicator represents the extent to which a state provides associate’s level training in SET fields, controlling for the size of its college-age population. The cohort 18–24 

years old was chosen to approximate the age range of most students who are pursuing an 

associate’s degree. 

The National Center for Education Statistics counts the number of associate’s degrees 

awarded in SET fields; these data include degrees in science and engineering technology 

fields, which are not included in measures of S&E degrees. Associate’s degrees are awarded 

at both 2-year and 4-year institutions in the United States; states and regions vary in the 

kinds of institutions that are accredited to award degrees in different fields. Estimates of 

the population aged 18–24 years old are provided by the U.S. Census Bureau. Small differences in the indicator value between states or across time generally are not meaningful. 

Because students may move across state lines after receiving their associate’s degrees, 

this indicator does not necessarily predict the qualifications of a state’s future technical workforce. 

Findings

• In 2011, nearly 116,000 associate’s degrees in SET were conferred 

nationally, which is up from 85,000 

in 2001 and represents an increase 

of 37%. Between 2001 and 2011, 

the number of associate’s degrees in 

SET fields conferred per 1,000 individuals 18–24 years old in the population increased by 23% nationwide.

• In 2011, state values on this indicator 

varied greatly. They ranged from 1.3 

to 20.0 associate’s degrees in SET 

fields conferred per 1,000 individuals 

18–24 years old.

• California has consistently awarded 

the largest number of SET associate’s degrees, at between 12% and 

15% of the national total.

Associate’s Degrees in Science, Engineering, and Technology Conferred per 

1,000 Individuals 18–24 Years Old

Figure 8-16

Associate’s degrees in science, engineering, and technology conferred per 1,000 individuals 18–24 years old: 2011

1.1 2.2 3.3 4.4 5.5 6.6 7.7 8.8 19.8 20.9

AL

AR

CO

CT

DC

FL

GA

IL

ME

SC

AK

CA

HI

IN

MO

MS

MT

NE

NH

NV

NY

PA

WA

WV

MI

MN

OH

RI

WI AZ

Degrees

DE

ID

KS

KY

LA

MA

MD

NC

NJ

NM

OR

TN

TX

UT

VT

IA

ND

OK

SD

VA WY

1st quartile (4.3–20.0)

2nd quartile (3.3–4.1)

3rd quartile (2.4–3.1)

4th quartile (1.3–2.1)

SOURCES: National Center for Education Statistics, Integrated Postsecondary 

Education Data System; Census Bureau, 

2010 Decennial Census and Population 

Estimates Program.
Science and Engineering Indicators 2014 ♦ 8-43

Table 8-16

Associate’s degrees in science, engineering, and technology conferred per 1,000 individuals 18–24 years old, by 

state: 2001, 2006, and 2011

 SET associate’s degrees Population 18–24 years old

Degrees/1,000 

individuals 

18–24 years old

State 2001 2006 2011 2001 2006 2011 2001 2006 2011

EPSCoR states................ 14,248 12,088 15,610 4,617,353 4,785,938 4,930,966 3.1 2.5 3.2

Non-EPSCoR states........ 69,513 67,171 99,175 23,105,302 24,522,581 25,834,531 3.0 2.7 3.8

Average EPSCoR 

 state value .................... na na na na na na 3.5 2.9 3.5

Average non-EPSCoR 

 state value .................... na na na na na na 2.8 2.7 4.0

United States................... 84,856 80,346 115,838 27,992,652 29,602,839 31,067,478 3.0 2.7 3.7

Alabama....................... 1,011 679 1,026 447,963 459,114 482,405 2.3 1.5 2.1

Alaska .......................... 125 145 262 61,774 74,369 77,271 2.0 1.9 3.4

Arizona......................... 2,009 2,291 12,854 536,018 591,986 643,726 3.7 3.9 20.0

Arkansas ...................... 378 400 545 268,131 273,882 287,131 1.4 1.5 1.9

California...................... 11,291 9,961 17,166 3,488,933 3,700,882 3,975,377 3.2 2.7 4.3

Colorado ...................... 1,148 761 922 453,712 477,387 498,956 2.5 1.6 1.8

Connecticut ................. 432 409 427 278,499 310,891 333,524 1.6 1.3 1.3

Delaware ...................... 230 258 280 78,530 84,759 92,520 2.9 3.0 3.0

District of Columbia ..... 226 217 106 72,568 76,839 84,786 3.1 2.8 1.3

Florida.......................... 3,850 3,759 3,682 1,401,785 1,652,892 1,773,048 2.7 2.3 2.1

Georgia ........................ 500 1,210 1,631 866,190 915,634 996,088 0.6 1.3 1.6

Hawaii .......................... 524 490 440 120,970 131,489 133,388 4.3 3.7 3.3

Idaho............................ 776 337 466 144,212 153,844 155,895 5.4 2.2 3.0

Illinois........................... 1,936 2,317 2,295 1,230,513 1,238,706 1,246,926 1.6 1.9 1.8

Indiana ......................... 2,121 2,428 2,424 628,372 638,724 656,136 3.4 3.8 3.7

Iowa ............................. 796 1,132 1,759 303,271 312,319 310,985 2.6 3.6 5.7

Kansas ......................... 1,101 652 800 282,851 296,431 291,056 3.9 2.2 2.7

Kentucky...................... 1,031 1,024 1,272 411,270 405,029 418,168 2.5 2.5 3.0

Louisiana...................... 1,359 819 1,225 485,975 459,662 474,817 2.8 1.8 2.6

Maine ........................... 217 295 236 107,177 117,346 116,333 2.0 2.5 2.0

Maryland...................... 510 735 1,380 470,318 525,903 567,560 1.1 1.4 2.4

Massachusetts............. 1,636 1,473 1,655 594,747 637,145 685,891 2.8 2.3 2.4

Michigan ...................... 2,967 3,152 4,546 955,459 975,541 986,710 3.1 3.2 4.6

Minnesota .................... 1,576 1,578 2,433 486,444 517,679 505,955 3.2 3.0 4.8

Mississippi ................... 688 670 1,037 316,243 306,076 308,468 2.2 2.2 3.4

Missouri ....................... 1,813 1,450 2,095 552,622 583,691 591,301 3.3 2.5 3.5

Montana....................... 265 243 343 89,343 99,357 96,660 3.0 2.4 3.5

Nebraska...................... 925 762 651 178,947 188,966 183,949 5.2 4.0 3.5

Nevada......................... 337 399 868 190,232 229,614 250,650 1.8 1.7 3.5

New Hampshire ........... 463 379 450 107,717 121,400 125,008 4.3 3.1 3.6

New Jersey .................. 1,545 1,628 1,932 690,374 729,181 779,067 2.2 2.2 2.5

New Mexico................. 813 573 644 184,493 202,027 206,918 4.4 2.8 3.1

New York...................... 8,257 5,673 6,724 1,802,422 1,869,014 1,996,795 4.6 3.0 3.4

North Carolina.............. 1,610 2,056 2,810 824,717 876,910 953,966 2.0 2.3 2.9

North Dakota ............... 345 380 507 76,459 85,992 83,807 4.5 4.4 6.0

Ohio ............................. 3,766 4,266 5,231 1,079,689 1,083,220 1,106,053 3.5 3.9 4.7

Oklahoma..................... 1,349 1,748 2,189 369,614 381,715 385,762 3.6 4.6 5.7

Oregon......................... 840 699 1,007 337,357 350,178 362,400 2.5 2.0 2.8

Pennsylvania................ 4,810 4,518 5,078 1,121,223 1,200,427 1,269,203 4.3 3.8 4.0

Rhode Island................ 704 616 593 109,990 116,788 120,607 6.4 5.3 4.9

South Carolina ............. 909 788 972 418,111 440,769 481,483 2.2 1.8 2.0

South Dakota............... 324 314 479 79,716 84,428 82,667 4.1 3.7 5.8

Tennessee.................... 1,274 1,319 1,685 563,268 572,909 613,516 2.3 2.3 2.7

Texas............................ 7,256 6,165 7,950 2,283,119 2,463,849 2,628,169 3.2 2.5 3.0

Utah ............................. 1,010 859 937 328,513 322,408 321,208 3.1 2.7 2.9

Vermont........................ 252 215 184 58,845 65,566 65,966 4.3 3.3 2.8

Virginia ......................... 2,644 3,490 5,044 706,828 781,520 815,225 3.7 4.5 6.2

Washington.................. 2,037 1,840 2,727 586,456 628,998 665,328 3.5 2.9 4.1

West Virginia ................ 651 402 642 174,409 168,204 170,502 3.7 2.4 3.8

Wisconsin .................... 1,879 2,002 2,781 534,453 564,587 551,418 3.5 3.5 5.0

Wyoming...................... 340 370 446 51,810 56,592 56,730 6.6 6.5 7.9

Puerto Rico.................. 1,348 952 1,130 429,366 400,529 376,652 3.1 2.4 3.0

na = not applicable.

EPSCoR = Experimental Program to Stimulate Competitive Research; SET = science, engineering, and technology.

NOTES: SET associate’s degrees include engineering, physical sciences, computer and mathematical sciences, agricultural and biological sciences, 

social sciences, science technologies, and engineering technologies.

SOURCES: National Center for Education Statistics, Integrated Postsecondary Education Data System (various years); Census Bureau, 2000 and 2010 

Decennial Censuses and Population Estimates Program (various years). 

Science and Engineering Indicators 2014
8-44 ♦ Chapter 8. State Indicators

Educational attainment gives people greater opportunities to work in higherpaying jobs than are generally available to those with less education. Earning a 

bachelor’s degree also prepares them for advanced education.

Educational attainment varies by several demographic characteristics including age. The cohort 18–24 years old was chosen to approximate the age 

range of most students who are pursuing an undergraduate degree. This indicator represents the extent to which the 18–24-year-old population has earned a 

bachelor’s degree.

The number of bachelor’s degrees awarded is based on an actual count provided by the National Center for Education Statistics. Estimates of the population 

aged 18–24 years are provided by the U.S. Census Bureau. Small differences in 

the indicator value between states or across time generally are not meaningful.

A high value for this indicator may suggest the successful provision of 

educational opportunity at this level. Student mobility after graduation is not 

accounted for, which may make this indicator less meaningful in predicting 

the qualifications of a state’s future workforce. A state’s value for this indicator 

may also be high when its higher education system draws a large percentage of 

out-of-state students—a situation characteristic of the District of Columbia and 

of some states with small resident populations.

Findings

• In 2011, more than 1.7 million bachelor’s 

degrees were conferred nationally in all 

fields, which is up from 1.2 million in 2001 

and represents an increase of 38%. 

• Between 2001 and 2011, the number of 

bachelor’s degrees conferred per 1,000 individuals 18–24 years old in the population has 

increased by more than 24% nationwide.

• In 2011, state values on this indicator varied greatly. They ranged from 22.9 to 116.6 

bachelor’s degrees conferred per 1,000 individuals 18–24 years old.

• Except in the District of Columbia and 

Montana, the number of bachelor’s degrees 

conferred per 1,000 individuals 18–24 years 

old increased in all jurisdictions between 

2001 and 2011.

Bachelor’s Degrees Conferred per 1,000 Individuals 18–24 Years Old

Figure 8-17

Bachelor’s degrees conferred per 1,000 individuals 18–24 years old: 2011

22.5 32.0 41.5 51.0 60.5 70.0 79.5 89.0 98.5 108.0 117.5

AK

NV

AR

CA

FL

GA

HI

KY

LA

MS

NJ

OK

SC

TN

WA

AZ

MA

MO

NE

NH

UT

WV

AL

CT

ID

IL

MD

MI

MT

NC

OH

OR

VA IA

CO

DE

IN

KS

ME

MN

ND

NY

PA

SD

WI

Degrees

NM

TX

WY

RI

VT DC

1st quartile (67.7–116.6)

2nd quartile (57.8–66.3)

3rd quartile (47.6–57.4)

4th quartile (22.9–47.2)

SOURCES: National Center for Education 

Statistics, Integrated Postsecondary Education Data System; Census Bureau, 2010 

Decennial Census and Population Estimates 

Program.
Science and Engineering Indicators 2014 ♦ 8-45

Table 8-17

Bachelor’s degrees conferred per 1,000 individuals 18–24 years old, by state: 2001, 2006, and 2011

Bachelor’s degrees Population 18–24 years old

Degrees/ 

1,000 individuals 

18–24 years old

State 2001 2006 2011 2001 2006 2011 2001 2006 2011

United States................... 1,244,171 1,485,242 1,715,913 27,992,652 29,602,839 31,067,478 44.4 50.2 55.2

Alabama....................... 20,823 21,995 27,248 447,963 459,114 482,405 46.5 47.9 56.5

Alaska .......................... 1,338 1,573 1,770 61,774 74,369 77,271 21.7 21.2 22.9

Arizona......................... 20,856 32,708 50,928 536,018 591,986 643,726 38.9 55.3 79.1

Arkansas ...................... 9,628 11,340 13,259 268,131 273,882 287,131 35.9 41.4 46.2

California...................... 123,382 151,021 169,623 3,488,933 3,700,882 3,975,377 35.4 40.8 42.7

Colorado ...................... 22,272 28,554 30,570 453,712 477,387 498,956 49.1 59.8 61.3

Connecticut ................. 14,245 17,997 19,970 278,499 310,891 333,524 51.1 57.9 59.9

Delaware ...................... 4,504 5,410 5,877 78,530 84,759 92,520 57.4 63.8 63.5

District of Columbia ..... 8,166 10,556 8,402 72,568 76,839 84,786 112.5 137.4 99.1

Florida.......................... 52,557 69,899 86,281 1,401,785 1,652,892 1,773,048 37.5 42.3 48.7

Georgia ........................ 28,790 36,332 45,075 866,190 915,634 996,088 33.2 39.7 45.3

Hawaii .......................... 4,896 5,813 5,751 120,970 131,489 133,388 40.5 44.2 43.1

Idaho............................ 4,646 7,781 9,171 144,212 153,844 155,895 32.2 50.6 58.8

Illinois........................... 55,633 68,016 71,580 1,230,513 1,238,706 1,246,926 45.2 54.9 57.4

Indiana ......................... 31,881 38,093 43,519 628,372 638,724 656,136 50.7 59.6 66.3

Iowa ............................. 18,652 21,435 36,266 303,271 312,319 310,985 61.5 68.6 116.6

Kansas ......................... 14,734 16,731 18,191 282,851 296,431 291,056 52.1 56.4 62.5

Kentucky...................... 15,434 18,646 21,078 411,270 405,029 418,168 37.5 46.0 50.4

Louisiana...................... 19,990 19,936 21,509 485,975 459,662 474,817 41.1 43.4 45.3

Maine ........................... 5,429 6,544 7,347 107,177 117,346 116,333 50.7 55.8 63.2

Maryland...................... 23,001 26,685 30,264 470,318 525,903 567,560 48.9 50.7 53.3

Massachusetts............. 42,792 47,074 53,749 594,747 637,145 685,891 71.9 73.9 78.4

Michigan ...................... 46,115 51,756 56,217 955,459 975,541 986,710 48.3 53.1 57.0

Minnesota .................... 23,355 28,927 33,386 486,444 517,679 505,955 48.0 55.9 66.0

Mississippi ................... 11,232 11,803 13,230 316,243 306,076 308,468 35.5 38.6 42.9

Missouri ....................... 30,102 35,161 41,648 552,622 583,691 591,301 54.5 60.2 70.4

Montana....................... 5,183 5,118 5,512 89,343 99,357 96,660 58.0 51.5 57.0

Nebraska...................... 10,782 12,150 13,510 178,947 188,966 183,949 60.3 64.3 73.4

Nevada......................... 4,358 6,595 7,556 190,232 229,614 250,650 22.9 28.7 30.1

New Hampshire ........... 7,254 8,030 9,479 107,717 121,400 125,008 67.3 66.1 75.8

New Jersey .................. 26,948 32,251 37,087 690,374 729,181 779,067 39.0 44.2 47.6

New Mexico................. 6,551 7,491 8,179 184,493 202,027 206,918 35.5 37.1 39.5

New York...................... 97,415 113,094 128,472 1,802,422 1,869,014 1,996,795 54.0 60.5 64.3

North Carolina.............. 34,767 39,969 48,670 824,717 876,910 953,966 42.2 45.6 51.0

North Dakota ............... 4,688 5,487 5,674 76,459 85,992 83,807 61.3 63.8 67.7

Ohio ............................. 50,856 58,522 63,882 1,079,689 1,083,220 1,106,053 47.1 54.0 57.8

Oklahoma..................... 15,932 18,909 19,511 369,614 381,715 385,762 43.1 49.5 50.6

Oregon......................... 13,887 17,631 19,542 337,357 350,178 362,400 41.2 50.3 53.9

Pennsylvania................ 66,514 79,791 88,205 1,121,223 1,200,427 1,269,203 59.3 66.5 69.5

Rhode Island................ 8,222 9,636 10,863 109,990 116,788 120,607 74.8 82.5 90.1

South Carolina ............. 16,316 19,313 23,034 418,111 440,769 481,483 39.0 43.8 47.8

South Dakota............... 4,223 4,850 5,211 79,716 84,428 82,667 53.0 57.4 63.0

Tennessee.................... 22,823 26,330 31,026 563,268 572,909 613,516 40.5 46.0 50.6

Texas............................ 76,074 92,027 107,438 2,283,119 2,463,849 2,628,169 33.3 37.4 40.9

Utah ............................. 17,091 20,677 24,461 328,513 322,408 321,208 52.0 64.1 76.2

Vermont........................ 4,697 4,981 6,100 58,845 65,566 65,966 79.8 76.0 92.5

Virginia ......................... 32,822 38,775 49,077 706,828 781,520 815,225 46.4 49.6 60.2

Washington.................. 23,441 28,570 31,398 586,456 628,998 665,328 40.0 45.4 47.2

West Virginia ................ 8,704 10,033 12,978 174,409 168,204 170,502 49.9 59.6 76.1

Wisconsin .................... 28,493 31,434 35,279 534,453 564,587 551,418 53.3 55.7 64.0

Wyoming...................... 1,677 1,792 1,860 51,810 56,592 56,730 32.4 31.7 32.8

Puerto Rico.................. 15,758 17,129 17,698 429,366 400,529 376,652 36.7 42.8 47.0

SOURCES: National Center for Education Statistics, Integrated Postsecondary Education Data System (various years); Census Bureau, 2000 and 2010 

Decennial Censuses and Population Estimates Program (various years). 

Science and Engineering Indicators 2014
8-46 ♦ Chapter 8. State Indicators

Educational attainment in an S&E field gives people greater opportunities to work 

in higher-paying technical jobs than are generally available to those in other fields 

of study. Earning a bachelor’s degree in an S&E field also prepares an individual for 

advanced technical education. S&E fields include the physical, life, earth, ocean, atmospheric, computer, and social sciences; mathematics; engineering; and psychology.

Educational attainment varies by several demographic characteristics including 

age. The cohort 18–24 years old was chosen to approximate the age range of most 

students who are pursuing an undergraduate degree. This indicator represents the 

extent to which a state provides bachelor’s level training in S&E fields, controlling 

for the size of its college-age population.

The number of bachelor’s degrees awarded in S&E fields is based on an actual 

count provided by the National Center for Education Statistics. Estimates of the population aged 18–24 years old are provided by the U.S. Census Bureau. Small differences 

in the indicator value between states or across time generally are not meaningful.

A high value for this indicator may suggest the successful provision of undergraduate training in S&E fields. Student mobility after graduation is not accounted 

for, which may make this indicator less meaningful in predicting the qualifications of 

a state’s future technical workforce. A state’s value for this indicator may also be high 

when its higher education system draws a large percentage of out-of-state students, 

a situation characteristic of the District of Columbia and of some states with small 

resident populations.

Findings

• In 2011, nearly 550,000 bachelor’s degrees 

in S&E fields were conferred nationally, which 

is up from 396,000 in 2001 and represents 

an increase of 39%. 

• Between 2001 and 2011, the number of 

bachelor’s degrees in S&E fields conferred 

per 1,000 individuals 18–24 years old in 

the population increased by nearly 25% 

nationwide.

• In 2011, state values on this indicator varied 

greatly. They ranged from 9.0 to 39.4 bachelor’s degrees in S&E fields conferred per 

1,000 individuals 18–24 years old.

• The number of bachelor’s degrees in S&E 

fields conferred per 1,000 individuals 18–24 

years old decreased in Kansas and the 

District of Columbia between 2001 and 2011.

• The states producing the largest numbers 

of S&E bachelor’s degrees were the same 

as those producing the largest numbers of 

bachelor’s degrees in natural sciences and 

engineering (see indicator 8-19). 

Bachelor’s Degrees in Science and Engineering Conferred per 1,000 

Individuals 18–24 Years Old

Figure 8-18

Bachelor’s degrees in science and engineering conferred per 1,000 individuals 18–24 years old: 2011

8.5 13.0 17.5 22.0 26.5 31.0 35.5 40.0 44.5 49.0

AK

AR

KY

LA

MS

NM

NV

OK

TX

WY

IA

VT

AZ

CT

DE

IN

MD

MI

MN

MO

MT

NE

NY

OR

SD

VA

WA

WI

WV

CO

ME

NH

PA

UT DC

MA

RI

Degrees

AL

CA

FL

GA

HI

ID

IL

KS

NC

ND

NJ

OH

SC

TN

1st quartile (21.6–46.9)

2nd quartile (17.7–21.5)

3rd quartile (14.2–17.5)

4th quartile (9.0–13.6)

SOURCES: National Center for Education Statistics, Integrated Postsecondary Education Data System; Census 

Bureau, 2010 Decennial Census and 

Population Estimates Program.
Science and Engineering Indicators 2014 ♦ 8-47

Table 8-18

Bachelor’s degrees in science and engineering conferred per 1,000 individuals 18–24 years old, by state: 

2001, 2006, and 2011

S&E bachelor’s degrees Population 18–24 years old

Degrees/1,000 

individuals 

18–24 years old

State 2001 2006 2011 2001 2006 2011 2001 2006 2011

EPSCoR states................ 56,960 65,336 74,420 4,617,353 4,785,938 4,930,966 12.3 13.7 15.1

Non-EPSCoR states........ 332,493 401,258 468,059 23,105,302 24,522,581 25,834,531 14.4 16.4 18.1

Average EPSCoR 

 state value .................... na na na na na na 14.1 15.3 17.3

Average non-EPSCoR 

 state value .................... na na na na na na 15.2 17.4 19.8

United States................... 396,149 474,650 549,871 27,992,652 29,602,839 31,067,478 14.2 16.0 17.7

Alabama....................... 5,520 6,019 7,406 447,963 459,114 482,405 12.3 13.1 15.4

Alaska .......................... 420 515 693 61,774 74,369 77,271 6.8 6.9 9.0

Arizona......................... 5,159 8,174 13,812 536,018 591,986 643,726 9.6 13.8 21.5

Arkansas ...................... 2,404 2,659 3,196 268,131 273,882 287,131 9.0 9.7 11.1

California...................... 47,715 60,588 68,228 3,488,933 3,700,882 3,975,377 13.7 16.4 17.2

Colorado ...................... 8,727 11,024 11,173 453,712 477,387 498,956 19.2 23.1 22.4

Connecticut ................. 5,161 6,272 7,213 278,499 310,891 333,524 18.5 20.2 21.6

Delaware ...................... 1,449 1,729 1,916 78,530 84,759 92,520 18.5 20.4 20.7

District of Columbia ..... 3,880 4,814 3,979 72,568 76,839 84,786 53.5 62.7 46.9

Florida.......................... 14,374 20,500 25,263 1,401,785 1,652,892 1,773,048 10.3 12.4 14.2

Georgia ........................ 9,119 11,219 13,327 866,190 915,634 996,088 10.5 12.3 13.4

Hawaii .......................... 1,602 1,956 1,995 120,970 131,489 133,388 13.2 14.9 15.0

Idaho............................ 1,415 2,159 2,444 144,212 153,844 155,895 9.8 14.0 15.7

Illinois........................... 16,150 19,132 20,589 1,230,513 1,238,706 1,246,926 13.1 15.4 16.5

Indiana ......................... 8,748 10,397 12,224 628,372 638,724 656,136 13.9 16.3 18.6

Iowa ............................. 5,375 6,122 11,742 303,271 312,319 310,985 17.7 19.6 37.8

Kansas ......................... 4,405 4,598 4,515 282,851 296,431 291,056 15.6 15.5 15.5

Kentucky...................... 4,041 4,830 5,271 411,270 405,029 418,168 9.8 11.9 12.6

Louisiana...................... 5,490 5,574 5,925 485,975 459,662 474,817 11.3 12.1 12.5

Maine ........................... 2,062 2,390 2,854 107,177 117,346 116,333 19.2 20.4 24.5

Maryland...................... 8,878 11,170 12,388 470,318 525,903 567,560 18.9 21.2 21.8

Massachusetts............. 16,189 17,794 20,023 594,747 637,145 685,891 27.2 27.9 29.2

Michigan ...................... 13,682 15,675 17,573 955,459 975,541 986,710 14.3 16.1 17.8

Minnesota .................... 7,497 9,544 11,012 486,444 517,679 505,955 15.4 18.4 21.8

Mississippi ................... 2,836 2,821 3,191 316,243 306,076 308,468 9.0 9.2 10.3

Missouri ....................... 8,360 9,605 10,473 552,622 583,691 591,301 15.1 16.5 17.7

Montana....................... 1,718 1,763 1,888 89,343 99,357 96,660 19.2 17.7 19.5

Nebraska...................... 2,564 3,064 3,415 178,947 188,966 183,949 14.3 16.2 18.6

Nevada......................... 975 1,836 2,270 190,232 229,614 250,650 5.1 8.0 9.1

New Hampshire ........... 2,477 2,811 3,284 107,717 121,400 125,008 23.0 23.2 26.3

New Jersey .................. 10,617 11,668 12,819 690,374 729,181 779,067 15.4 16.0 16.5

New Mexico................. 1,819 2,163 2,466 184,493 202,027 206,918 9.9 10.7 11.9

New York...................... 33,187 37,365 42,904 1,802,422 1,869,014 1,996,795 18.4 20.0 21.5

North Carolina.............. 11,826 13,300 16,543 824,717 876,910 953,966 14.3 15.2 17.3

North Dakota ............... 1,214 1,286 1,418 76,459 85,992 83,807 15.9 15.0 16.9

Ohio ............................. 14,001 15,723 17,872 1,079,689 1,083,220 1,106,053 13.0 14.5 16.2

Oklahoma..................... 4,067 4,839 4,815 369,614 381,715 385,762 11.0 12.7 12.5

Oregon......................... 5,177 6,456 7,242 337,357 350,178 362,400 15.3 18.4 20.0

Pennsylvania................ 21,007 25,095 28,273 1,121,223 1,200,427 1,269,203 18.7 20.9 22.3

Rhode Island................ 2,340 2,975 3,326 109,990 116,788 120,607 21.3 25.5 27.6

South Carolina ............. 4,951 5,910 7,017 418,111 440,769 481,483 11.8 13.4 14.6

South Dakota............... 1,396 1,582 1,669 79,716 84,428 82,667 17.5 18.7 20.2

Tennessee.................... 6,281 7,080 8,370 563,268 572,909 613,516 11.2 12.4 13.6

Texas............................ 20,778 25,896 30,453 2,283,119 2,463,849 2,628,169 9.1 10.5 11.6

Utah ............................. 5,090 6,774 7,751 328,513 322,408 321,208 15.5 21.0 24.1

Vermont........................ 1,834 2,020 2,596 58,845 65,566 65,966 31.2 30.8 39.4

Virginia ......................... 12,522 14,209 17,594 706,828 781,520 815,225 17.7 18.2 21.6

Washington.................. 8,055 10,158 11,652 586,456 628,998 665,328 13.7 16.1 17.5

West Virginia ................ 2,176 2,422 3,539 174,409 168,204 170,502 12.5 14.4 20.8

Wisconsin .................... 8,818 10,318 11,546 534,453 564,587 551,418 16.5 18.3 20.9

Wyoming...................... 601 657 724 51,810 56,592 56,730 11.6 11.6 12.8

Puerto Rico.................. 4,208 4,076 4,370 429,366 400,529 376,652 9.8 10.2 11.6

na = not applicable.

EPSCoR = Experimental Program to Stimulate Competitive Research. 

NOTE: For an explanation of EPSCoR and non-EPSCoR averages, see the chapter introduction.

SOURCES: National Center for Education Statistics, Integrated Postsecondary Education Data System (various years); Census Bureau, 2000 and 2010 

Decennial Censuses and Population Estimates Program (various years). 

Science and Engineering Indicators 2014
8-48 ♦ Chapter 8. State Indicators

Natural sciences and engineering (NS&E) fields include the physical, earth, ocean, 

atmospheric, biological, agricultural, and computer sciences; mathematics; and engineering. NS&E fields do not include social sciences and psychology. This indicator is 

the ratio of new NS&E bachelor’s degrees to the population aged 18–24 years old and 

represents the extent to which a state prepares young people to enter technology-intensive 

occupations that are fundamental to a knowledge-based, technology-driven economy. 

In addition, the presence of higher education institutions that produce such degrees may 

generate resources for the state. The cohort 18–24 years old was chosen to approximate 

the age range of most students who are pursuing an undergraduate degree.

The number of NS&E bachelor’s degrees awarded is based on an actual count provided by the National Center for Education Statistics. Estimates of the population aged 

18–24 years old are provided by the U.S. Census Bureau. Small differences in the value 

of the indicator between states or across time generally are not meaningful.

Because students often relocate after graduation, this measure does not necessarily 

indicate the qualifications of a state’s future workforce. A state’s value for this indicator 

may also be high when its higher education system draws a large number of out-of-state 

students who study NS&E fields—a situation characteristic of the District of Columbia 

and some states with small resident populations.

Findings

• Between 2001 and 2011, the value of 

this indicator nationwide increased from 

7.5 to 8.9 bachelor’s degrees conferred 

in NS&E fields per 1,000 individuals 

aged 18–24 years old.

• In 2011, the value of this indicator 

for individual states ranged from 4.5 

to 19.9.

• The states conferring the largest number of bachelor’s degrees in NS&E 

fields were California, New York, Texas, 

and Pennsylvania.

• States that ranked in the top two quartiles on this indicator were generally the 

same as those in the top two quartiles 

for the number of bachelor’s degrees 

conferred per 1,000 individuals 18–24 

years old (see indicator 8-17).

Bachelor’s Degrees in Natural Sciences and Engineering Conferred per 1,000 

Individuals 18–24 Years Old

Figure 8-19

Bachelor’s degrees in natural sciences and engineering conferred per 1,000 individuals 18–24 years old: 2011

4.2 5.8 7.4 9.0 10.6 12.2 13.8 15.4 17.0 18.6 20.2

AK

HI

NV

CA

CT

IL

KS

NC

NJ

OH

SC

WA

WY

MA

ME

MT

PA

SD

UT

AL

AZ

DC

DE

ID

MI

MO

NE

NY

OR VT

IA

RI

CO

IN

MD

MN

ND

NH

VA

WI

WV

Degrees

AR

FL

GA

KY

LA

MS

NM

OK

TN

TX

1st quartile (11.8–19.9)

2nd quartile (9.5–11.2)

3rd quartile (7.3–9.3)

4th quartile (4.5–7.2)

SOURCES: National Center for Education Statistics, Integrated Postsecondary 

Education Data System; Census Bureau, 

2010 Decennial Census and Population 

Estimates Program.
Science and Engineering Indicators 2014 ♦ 8-49

Table 8-19

Bachelor’s degrees in natural sciences and engineering conferred per 1,000 individuals 18–24 years old, by 

state: 2001, 2006, and 2011

 NS&E bachelor’s degrees Population 18–24 years old

Degrees/1,000 

individuals 

18–24 years old

State 2001 2006 2011 2001 2006 2011 2001 2006 2011

EPSCoR states................ 33,480 35,875 41,515 4,617,353 4,785,938 4,930,966 7.3 7.5 8.4

Non-EPSCoR states........ 172,100 199,802 233,396 23,105,302 24,522,581 25,834,531 7.4 8.1 9.0

Average EPSCoR 

 state value .................... na na na na na na 8.0 8.2 9.5

Average non-EPSCoR 

 state value .................... na na na na na na 7.9 8.7 9.8

United States................... 208,747 239,201 277,549 27,992,652 29,602,839 31,067,478 7.5 8.1 8.9

Alabama....................... 3,659 3,662 4,499 447,963 459,114 482,405 8.2 8.0 9.3

Alaska .......................... 230 312 399 61,774 74,369 77,271 3.7 4.2 5.2

Arizona......................... 3,110 5,423 6,285 536,018 591,986 643,726 5.8 9.2 9.8

Arkansas ...................... 1,492 1,531 1,897 268,131 273,882 287,131 5.6 5.6 6.6

California...................... 22,337 27,014 30,766 3,488,933 3,700,882 3,975,377 6.4 7.3 7.7

Colorado ...................... 4,614 5,892 5,906 453,712 477,387 498,956 10.2 12.3 11.8

Connecticut ................. 1,902 2,171 2,734 278,499 310,891 333,524 6.8 7.0 8.2

Delaware ...................... 689 729 895 78,530 84,759 92,520 8.8 8.6 9.7

District of Columbia ..... 1,699 1,821 893 72,568 76,839 84,786 23.4 23.7 10.5

Florida.......................... 7,348 9,524 12,184 1,401,785 1,652,892 1,773,048 5.2 5.8 6.9

Georgia ........................ 5,206 6,009 7,208 866,190 915,634 996,088 6.0 6.6 7.2

Hawaii .......................... 670 790 746 120,970 131,489 133,388 5.5 6.0 5.6

Idaho............................ 900 1,384 1,530 144,212 153,844 155,895 6.2 9.0 9.8

Illinois........................... 9,184 10,920 11,153 1,230,513 1,238,706 1,246,926 7.5 8.8 8.9

Indiana ......................... 4,953 5,744 6,961 628,372 638,724 656,136 7.9 9.0 10.6

Iowa ............................. 3,055 3,327 4,533 303,271 312,319 310,985 10.1 10.7 14.6

Kansas ......................... 2,606 2,469 2,555 282,851 296,431 291,056 9.2 8.3 8.8

Kentucky...................... 2,132 2,367 2,828 411,270 405,029 418,168 5.2 5.8 6.8

Louisiana...................... 3,481 3,327 3,354 485,975 459,662 474,817 7.2 7.2 7.1

Maine ........................... 1,060 1,138 1,430 107,177 117,346 116,333 9.9 9.7 12.3

Maryland...................... 4,737 5,793 6,287 470,318 525,903 567,560 10.1 11.0 11.1

Massachusetts............. 7,209 7,707 9,182 594,747 637,145 685,891 12.1 12.1 13.4

Michigan ...................... 8,348 9,265 10,044 955,459 975,541 986,710 8.7 9.5 10.2

Minnesota .................... 4,026 5,016 6,027 486,444 517,679 505,955 8.3 9.7 11.9

Mississippi ................... 1,755 1,659 1,856 316,243 306,076 308,468 5.5 5.4 6.0

Missouri ....................... 4,837 5,152 5,628 552,622 583,691 591,301 8.8 8.8 9.5

Montana....................... 1,171 1,133 1,233 89,343 99,357 96,660 13.1 11.4 12.8

Nebraska...................... 1,495 1,676 1,940 178,947 188,966 183,949 8.4 8.9 10.5

Nevada......................... 527 883 1,137 190,232 229,614 250,650 2.8 3.8 4.5

New Hampshire ........... 1,198 1,116 1,386 107,717 121,400 125,008 11.1 9.2 11.1

New Jersey .................. 5,199 5,217 5,920 690,374 729,181 779,067 7.5 7.2 7.6

New Mexico................. 1,166 1,296 1,383 184,493 202,027 206,918 6.3 6.4 6.7

New York...................... 15,134 16,418 19,196 1,802,422 1,869,014 1,996,795 8.4 8.8 9.6

North Carolina.............. 6,183 6,396 8,342 824,717 876,910 953,966 7.5 7.3 8.7

North Dakota ............... 798 913 999 76,459 85,992 83,807 10.4 10.6 11.9

Ohio ............................. 7,748 8,254 9,746 1,079,689 1,083,220 1,106,053 7.2 7.6 8.8

Oklahoma..................... 2,491 2,672 2,808 369,614 381,715 385,762 6.7 7.0 7.3

Oregon......................... 2,372 2,886 3,349 337,357 350,178 362,400 7.0 8.2 9.2

Pennsylvania................ 11,901 13,781 15,723 1,121,223 1,200,427 1,269,203 10.6 11.5 12.4

Rhode Island................ 1,202 1,531 1,744 109,990 116,788 120,607 10.9 13.1 14.5

South Carolina ............. 2,760 3,201 3,878 418,111 440,769 481,483 6.6 7.3 8.1

South Dakota............... 913 1,052 1,070 79,716 84,428 82,667 11.5 12.5 12.9

Tennessee.................... 3,281 3,491 4,330 563,268 572,909 613,516 5.8 6.1 7.1

Texas............................ 11,798 14,150 17,196 2,283,119 2,463,849 2,628,169 5.2 5.7 6.5

Utah ............................. 2,797 3,432 4,028 328,513 322,408 321,208 8.5 10.6 12.5

Vermont........................ 846 898 1,312 58,845 65,566 65,966 14.4 13.7 19.9

Virginia ......................... 5,956 6,519 8,632 706,828 781,520 815,225 8.4 8.3 10.6

Washington.................. 3,861 4,631 5,416 586,456 628,998 665,328 6.6 7.4 8.1

West Virginia ................ 1,296 1,407 1,918 174,409 168,204 170,502 7.4 8.4 11.2

Wisconsin .................... 5,004 5,670 6,620 534,453 564,587 551,418 9.4 10.0 12.0

Wyoming...................... 411 432 463 51,810 56,592 56,730 7.9 7.6 8.2

Puerto Rico.................. 3,054 2,925 3,000 429,366 400,529 376,652 7.1 7.3 8.0

na = not applicable.

EPSCoR = Experimental Program to Stimulate Competitive Research; NS&E = natural sciences and engineering. 

NOTE: For an explanation of EPSCoR and non-EPSCoR averages, see the chapter introduction.

SOURCES: National Center for Education Statistics, Integrated Postsecondary Education Data System (various years); Census Bureau, 2000 and 2010 

Decennial Censuses and Population Estimates Program (various years). 

Science and Engineering Indicators 2014
8-50 ♦ Chapter 8. State Indicators

This indicator represents the extent to which a state’s higher education programs are concentrated in S&E fields. S&E 

fields include the physical, life, earth, ocean, atmospheric, 

computer, and social sciences; mathematics; engineering; 

and psychology. Counts of both S&E degrees and higher 

education degrees conferred include bachelor’s, master’s, 

and doctoral degrees; associate’s degrees are not included.

Degree data reflect the location of the degree-granting 

institution, not the state where degree-earning students 

permanently reside. The year indicates the end date of the 

academic year. For example, data for 2011 represent degrees 

conferred during the 2010–11 academic year. All degree data 

are actual counts.

Findings

• In 2011, nearly 734,000 S&E bachelor’s, master’s, and doctoral degrees were conferred nationwide, an increase of 41% 

since 2001.

• Nationally, the proportion of S&E degrees as a share of total 

degrees conferred remained almost unchanged at 29% 

between 2001 and 2011.

• There are noteworthy differences in the proportions of S&E 

higher education degrees conferred in different states. In 

some states, only about 20% of higher education degrees 

were awarded in S&E fields. In others, nearly 40% of higher 

education degrees were awarded in S&E fields.

• The District of Columbia has a high value because of the 

large number of programs in political science and public 

administration at several of its academic institutions.

Science and Engineering Degrees as a Percentage of Higher Education 

Degrees Conferred

Figure 8-20

Science and engineering degrees as a percentage of higher education degrees conferred: 2011

19.5 21.5 23.5 25.5 27.5 29.5 31.5 33.5 35.5 37.5 39.5 41.5

AZ

DE

IA

MN

NM

NY

PA

SC

TX

UT

AL

ND

NE

OK

TN

AR

KS

KY

MO

MS

MI

NC

NH

RI

WI

CO

CT

HI

MA

NJ

OR

SD

VA

ME

MT

WA

CA

VT

AK

MD

DC

WY

FL

GA

ID

IL

IN

LA

NV

OH

WV

Percent

1st quartile (32.8%–40.8%)

2nd quartile (28.5%–32.1%)

3rd quartile (26.1%–28.4%)

4th quartile (20.4%–26.0%)

SOURCE: National Center for Education 

Statistics, Integrated Postsecondary Education Data System.
Science and Engineering Indicators 2014 ♦ 8-51

Table 8-20

Science and engineering degrees as a percentage of higher education degrees conferred, by state: 

2001, 2006, and 2011

All S&E degrees All higher education degrees

All S&E degrees/

all higher education 

degrees (%)

State 2001 2006 2011 2001 2006 2011 2001 2006 2011

United States................... 520,476 625,163 733,609 1,757,551 2,135,374 2,506,134 29.6 29.3 29.3

Alabama....................... 7,489 8,313 9,933 29,471 32,889 39,751 25.4 25.3 25.0

Alaska .......................... 604 719 921 1,771 2,176 2,509 34.1 33.0 36.7

Arizona......................... 6,800 10,072 18,154 32,089 58,452 88,964 21.2 17.2 20.4

Arkansas ...................... 2,844 3,235 4,121 12,039 14,662 18,344 23.6 22.1 22.5

California...................... 63,360 80,172 91,643 175,179 213,725 244,200 36.2 37.5 37.5

Colorado ...................... 11,606 14,320 15,209 31,418 41,242 45,772 36.9 34.7 33.2

Connecticut ................. 6,929 8,341 9,790 22,459 27,331 29,759 30.9 30.5 32.9

Delaware ...................... 1,868 2,216 2,512 6,174 7,795 8,840 30.3 28.4 28.4

District of Columbia ..... 6,870 8,294 7,743 16,005 20,458 19,033 42.9 40.5 40.7

Florida.......................... 18,843 25,887 32,260 74,159 96,870 121,004 25.4 26.7 26.7

Georgia ........................ 12,083 14,677 17,367 40,652 50,258 64,301 29.7 29.2 27.0

Hawaii .......................... 2,227 2,531 2,594 6,793 7,982 8,074 32.8 31.7 32.1

Idaho............................ 1,756 2,661 2,942 5,809 9,613 11,076 30.2 27.7 26.6

Illinois........................... 22,867 27,863 30,855 85,474 108,371 117,325 26.8 25.7 26.3

Indiana ......................... 11,188 13,241 15,768 41,533 50,859 59,294 26.9 26.0 26.6

Iowa ............................. 6,389 7,413 13,296 22,762 26,669 47,054 28.1 27.8 28.3

Kansas ......................... 5,576 5,872 6,012 20,315 23,069 25,962 27.4 25.5 23.2

Kentucky...................... 5,015 6,317 6,883 20,615 26,057 29,932 24.3 24.2 23.0

Louisiana...................... 6,908 7,282 7,562 26,403 26,933 29,072 26.2 27.0 26.0

Maine ........................... 2,236 2,625 3,103 6,659 8,238 9,173 33.6 31.9 33.8

Maryland...................... 12,710 15,870 18,039 34,837 41,487 48,527 36.5 38.3 37.2

Massachusetts............. 22,843 25,439 28,989 70,397 77,949 90,422 32.4 32.6 32.1

Michigan ...................... 18,618 21,124 23,488 68,860 75,553 79,340 27.0 28.0 29.6

Minnesota .................... 9,319 12,388 15,853 32,426 45,705 57,518 28.7 27.1 27.6

Mississippi ................... 3,472 3,618 4,278 14,904 16,011 18,366 23.3 22.6 23.3

Missouri ....................... 11,306 12,690 14,323 44,466 53,706 63,525 25.4 23.6 22.5

Montana....................... 2,076 2,189 2,313 6,216 6,333 6,814 33.4 34.6 33.9

Nebraska...................... 3,261 3,892 4,478 14,309 16,517 18,564 22.8 23.6 24.1

Nevada......................... 1,279 2,378 2,814 5,966 8,904 10,532 21.4 26.7 26.7

New Hampshire ........... 3,082 3,520 4,047 9,767 11,273 13,293 31.6 31.2 30.4

New Jersey .................. 13,842 15,603 17,439 37,760 46,061 52,919 36.7 33.9 33.0

New Mexico................. 2,558 3,164 3,339 9,412 11,117 11,725 27.2 28.5 28.5

New York...................... 44,628 51,277 59,701 150,970 181,303 203,148 29.6 28.3 29.4

North Carolina.............. 14,543 16,541 20,896 45,316 53,738 66,530 32.1 30.8 31.4

North Dakota ............... 1,397 1,522 1,743 5,597 6,805 7,385 25.0 22.4 23.6

Ohio ............................. 18,238 20,505 23,094 70,489 81,292 88,516 25.9 25.2 26.1

Oklahoma..................... 5,914 6,243 6,503 21,668 24,726 26,299 27.3 25.2 24.7

Oregon......................... 6,473 8,004 8,767 19,198 24,144 27,306 33.7 33.2 32.1

Pennsylvania................ 26,514 32,358 37,253 90,967 110,726 127,084 29.1 29.2 29.3

Rhode Island................ 2,872 3,649 4,077 10,400 12,083 13,718 27.6 30.2 29.7

South Carolina ............. 6,052 6,997 8,223 21,323 24,830 29,523 28.4 28.2 27.9

South Dakota............... 1,775 2,030 2,153 5,305 6,043 6,729 33.5 33.6 32.0

Tennessee.................... 7,787 8,752 10,243 31,683 36,299 43,362 24.6 24.1 23.6

Texas............................ 28,242 35,293 42,413 103,513 127,836 153,270 27.3 27.6 27.7

Utah ............................. 6,151 8,075 9,399 21,069 25,753 31,967 29.2 31.4 29.4

Vermont........................ 2,153 2,576 3,343 6,116 6,827 8,535 35.2 37.7 39.2

Virginia ......................... 15,823 18,582 23,672 44,783 53,760 72,067 35.3 34.6 32.8

Washington.................. 10,011 12,592 14,269 31,856 38,392 42,255 31.4 32.8 33.8

West Virginia ................ 2,699 3,056 5,050 11,225 13,265 19,040 24.0 23.0 26.5

Wisconsin .................... 10,549 12,328 13,761 36,813 40,999 46,009 28.7 30.1 29.9

Wyoming...................... 831 857 981 2,161 2,288 2,407 38.5 37.5 40.8

Puerto Rico.................. 4,847 4,992 5,437 18,678 22,551 23,611 26.0 22.1 23.0

NOTES: All S&E degrees include bachelor’s, master’s, and doctorate. All S&E degrees include physical, computer, agricultural, biological, earth, 

atmospheric, ocean, and social sciences; psychology; mathematics; and engineering. All higher education degrees include bachelor’s, master’s, and 

doctorate.

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education Data System (various years). 

Science and Engineering Indicators 2014
8-52 ♦ Chapter 8. State Indicators

This indicator represents the extent to which a state’s higher education programs are concentrated in natural sciences and engineering 

(NS&E) fields. The indicator is expressed as the percentage of higher 

education degrees that were conferred in NS&E fields.

NS&E fields include the physical, life, earth, ocean, atmospheric, 

and computer sciences; mathematics; and engineering. Social sciences such as anthropology, economics, political science and public 

administration, psychology, and sociology are not included. Counts 

of both NS&E degrees and higher education degrees conferred include bachelor’s, master’s, and doctoral degrees; associate’s degrees 

are not included.

Degree data reflect the location of the degree-granting institution, 

not the state in which degree-earning students permanently reside. 

The year reflects the end date of the academic year. For example, data 

for 2010 represent degrees conferred during the 2009–10 academic 

year. All degree data are actual counts.

Findings

• In 2011, more than 392,000 NS&E bachelor’s, master’s, and doctoral degrees were conferred nationwide, 

an increase of 37% since 2001.

• The proportion of NS&E degrees as a share of total 

degrees conferred remained unchanged at 15.6% 

between 2006 and 2011.

• There are noteworthy differences in the proportions of 

NS&E higher education degrees conferred in different 

states. In 2011, the proportions ranged between 9.1% 

and 26.4%.

• Nationally, more than half (53%) of all S&E degrees 

were in NS&E fields in 2011, down from 55% of all 

S&E degrees in 2001.

• States with the highest percentage of higher education degrees in NS&E fields tended to be located in 

the western United States, and four of the top five 

are Experimental Program to Stimulate Competitive 

Research states.

Natural Sciences and Engineering Degrees as a Percentage of 

Higher Education Degrees Conferred

Figure 8-21

Natural sciences and engineering degrees as a percentage of higher education degrees conferred: 2011

9.0 10.6 12.2 13.8 15.4 17.0 18.6 20.2 21.8 23.4 25.0 26.6

AZ

CT

GA

IN

LA

MA

ND

OR

RI

SC

TX

UT

VA

WA

HI

KS

KY

MN

MO

TN

WV

DC

IA

CA

ID

ME

MI

NC

NJ

NM

PA

VT

WI

AK

MD

CO SD MT WY

AL

AR

DE

FL

IL

MS

NE

NH

NV

NY

OH

OK

Percent

1st quartile (17.4%–26.4%)

2nd quartile (15.7%–17.2%)

3rd quartile (14.0%–15.4%)

4th quartile (9.1%–13.9%)

SOURCE: National Center for Education 

Statistics, Integrated Postsecondary Education Data System.
Science and Engineering Indicators 2014 ♦ 8-53

Table 8-21

Natural sciences and engineering degrees as a percentage of higher education degrees conferred, by state: 

2001, 2006, and 2011

NS&E degrees All higher education degrees

NS&E degrees/higher 

education degrees (%)

State 2001 2006 2011 2001 2006 2011 2001 2006 2011

United States................... 287,290 333,677 392,170 1,757,551 2,135,374 2,506,134 16.3 15.6 15.6

Alabama....................... 4,578 4,854 5,927 29,471 32,889 39,751 15.5 14.8 14.9

Alaska .......................... 346 451 543 1,771 2,176 2,509 19.5 20.7 21.6

Arizona......................... 4,251 6,666 8,107 32,089 58,452 88,964 13.2 11.4 9.1

Arkansas ...................... 1,783 1,932 2,608 12,039 14,662 18,344 14.8 13.2 14.2

California...................... 31,205 38,756 44,876 175,179 213,725 244,200 17.8 18.1 18.4

Colorado ...................... 6,514 8,017 8,532 31,418 41,242 45,772 20.7 19.4 18.6

Connecticut ................. 3,078 3,637 4,592 22,459 27,331 29,759 13.7 13.3 15.4

Delaware ...................... 906 1,021 1,271 6,174 7,795 8,840 14.7 13.1 14.4

District of Columbia ..... 3,230 3,100 2,243 16,005 20,458 19,033 20.2 15.2 11.8

Florida.......................... 10,142 12,964 16,847 74,159 96,870 121,004 13.7 13.4 13.9

Georgia ........................ 7,296 8,233 10,120 40,652 50,258 64,301 17.9 16.4 15.7

Hawaii .......................... 983 1,042 1,041 6,793 7,982 8,074 14.5 13.1 12.9

Idaho............................ 1,174 1,797 1,939 5,809 9,613 11,076 20.2 18.7 17.5

Illinois........................... 13,417 16,244 17,297 85,474 108,371 117,325 15.7 15.0 14.7

Indiana ......................... 6,455 7,596 9,295 41,533 50,859 59,294 15.5 14.9 15.7

Iowa ............................. 3,830 4,300 5,672 22,762 26,669 47,054 16.8 16.1 12.1

Kansas ......................... 3,368 3,309 3,533 20,315 23,069 25,962 16.6 14.3 13.6

Kentucky...................... 2,705 3,247 3,656 20,615 26,057 29,932 13.1 12.5 12.2

Louisiana...................... 4,480 4,522 4,550 26,403 26,933 29,072 17.0 16.8 15.7

Maine ........................... 1,198 1,301 1,604 6,659 8,238 9,173 18.0 15.8 17.5

Maryland...................... 7,300 9,075 10,167 34,837 41,487 48,527 21.0 21.9 21.0

Massachusetts............. 11,261 12,275 14,560 70,397 77,949 90,422 16.0 15.7 16.1

Michigan ...................... 12,070 13,262 14,238 68,860 75,553 79,340 17.5 17.6 17.9

Minnesota .................... 5,083 6,492 7,767 32,426 45,705 57,518 15.7 14.2 13.5

Mississippi ................... 2,242 2,265 2,660 14,904 16,011 18,366 15.0 14.1 14.5

Missouri ....................... 6,125 6,760 7,733 44,466 53,706 63,525 13.8 12.6 12.2

Montana....................... 1,448 1,433 1,530 6,216 6,333 6,814 23.3 22.6 22.5

Nebraska...................... 1,943 2,209 2,602 14,309 16,517 18,564 13.6 13.4 14.0

Nevada......................... 728 1,232 1,498 5,966 8,904 10,532 12.2 13.8 14.2

New Hampshire ........... 1,648 1,545 1,953 9,767 11,273 13,293 16.9 13.7 14.7

New Jersey .................. 7,530 7,769 9,266 37,760 46,061 52,919 19.9 16.9 17.5

New Mexico................. 1,666 1,988 2,021 9,412 11,117 11,725 17.7 17.9 17.2

New York...................... 21,845 24,238 29,236 150,970 181,303 203,148 14.5 13.4 14.4

North Carolina.............. 8,204 8,745 11,550 45,316 53,738 66,530 18.1 16.3 17.4

North Dakota ............... 928 1,087 1,226 5,597 6,805 7,385 16.6 16.0 16.6

Ohio ............................. 10,649 11,501 13,299 70,489 81,292 88,516 15.1 14.1 15.0

Oklahoma..................... 3,361 3,658 3,875 21,668 24,726 26,299 15.5 14.8 14.7

Oregon......................... 3,162 3,799 4,359 19,198 24,144 27,306 16.5 15.7 16.0

Pennsylvania................ 15,425 18,459 21,701 90,967 110,726 127,084 17.0 16.7 17.1

Rhode Island................ 1,537 1,935 2,207 10,400 12,083 13,718 14.8 16.0 16.1

South Carolina ............. 3,635 4,004 4,789 21,323 24,830 29,523 17.0 16.1 16.2

South Dakota............... 1,159 1,325 1,369 5,305 6,043 6,729 21.8 21.9 20.3

Tennessee.................... 4,235 4,522 5,509 31,683 36,299 43,362 13.4 12.5 12.7

Texas............................ 17,085 20,867 25,952 103,513 127,836 153,270 16.5 16.3 16.9

Utah ............................. 3,477 4,377 5,136 21,069 25,753 31,967 16.5 17.0 16.1

Vermont........................ 1,001 1,158 1,553 6,116 6,827 8,535 16.4 17.0 18.2

Virginia ......................... 8,032 9,182 11,887 44,783 53,760 72,067 17.9 17.1 16.5

Washington.................. 5,102 6,009 6,907 31,856 38,392 42,255 16.0 15.7 16.3

West Virginia ................ 1,629 1,861 2,428 11,225 13,265 19,040 14.5 14.0 12.8

Wisconsin .................... 6,269 7,099 8,303 36,813 40,999 46,009 17.0 17.3 18.0

Wyoming...................... 572 557 636 2,161 2,288 2,407 26.5 24.3 26.4

Puerto Rico.................. 3,417 3,392 3,520 18,678 22,551 23,611 18.3 15.0 14.9

NS&E = natural sciences and engineering.

NOTES: NS&E degrees include bachelor’s, master’s, and doctorate. NS&E degrees include physical, computer, agricultural, biological, earth, atmospheric, and ocean sciences; mathematics; and engineering. All higher education degrees include bachelor’s, master’s, and doctorate. 

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education Data System (various years).

Science and Engineering Indicators 2014
8-54 ♦ Chapter 8. State Indicators

Graduate students in S&E fields may become the technical leaders of the future. 

This indicator is a relative measure of a state’s population with graduate training in S&E 

and is defined as the ratio of S&E graduate students to a state’s population aged 25–34.

Graduate students are counted on the basis of their university enrollment and include 

state residents, residents of other states, and noncitizens. The cohort includes all state 

residents aged 25–34 and was chosen to approximate the age of most graduate students.

Data on S&E graduate students are counts obtained from all academic institutions 

in the United States that offer doctoral or master’s degree programs in any S&E field, 

including the physical, life, earth, ocean, atmospheric, computer, and social sciences; 

mathematics; engineering; and psychology. Graduate students enrolled in schools of 

nursing, public health, dentistry, veterinary medicine, and other health-related disciplines 

are not included.

Estimates of the population aged 25–34 years old are provided by the U.S. Census 

Bureau. Small differences in the value of the indicator between states or across years 

generally are not meaningful.

Findings

• The number of S&E graduate 

students in the United States 

grew from approximately 426,000 

in 2001 to 558,000 in 2011, a 

31% increase.

• Among the 50 states, the value 

of this indicator ranged from 5.2 

to 30.5.

• Growth in the number of S&E graduate students was most significant 

in Texas and California during this 

period. Other states with sizeable 

increases included New York, 

Florida, Minnesota, and Maryland.

Science and Engineering Graduate Students per 1,000 Individuals 

25–34 Years Old

Figure 8-22

Science and engineering graduate students per 1,000 individuals 25–34 years old: 2011

5.0 8.0 11.0 14.0 17.0 20.0 23.0 26.0 29.0 32.0 77.0 80.0

CT

DE

MD

MN

ND

NY

RI

AK

AZ

FL

GA

ID

KY

LA

MS

NH

OR

TN

VT

WA

WV

CA

HI

MO

MT

NC

NJ

OK

SD

TX

UT

WI

WY

AR

ME

NV

SC

AL

CO

IA

IL

IN

KS

MI

NE

NM

OH

PA

VA ND MA DC

Degrees

1st quartile (14.9–79.6)

2nd quartile (12.6–14.8)

3rd quartile (9.7–12.0)

4th quartile (5.2–9.5)

SOURCES: National Science Foundation, 

National Center for Science and Engineering 

Statistics, Survey of Graduate Students and 

Postdoctorates in Science and Engineering; 

Census Bureau, 2010 Decennial Census 

and Population Estimates Program.
Science and Engineering Indicators 2014 ♦ 8-55

Table 8-22

Science and engineering graduate students per 1,000 individuals 25–34 years old, by state: 2001, 2006, and 2011

S&E graduate students Population 25–34 years old

S&E graduate 

students/1,000 

individuals 

25–34 years old

State 2001 2006 2011 2001 2006 2011 2001 2006 2011

United States................... 426,094 482,626 557,993 39,471,522 39,395,179 41,797,950 10.8 12.3 13.3

Alabama....................... 5,257 6,097 8,639 588,949 591,061 614,667 8.9 10.3 14.1

Alaska .......................... 619 825 1,139 87,154 89,788 106,872 7.1 9.2 10.7

Arizona......................... 6,789 7,395 9,398 748,567 838,414 867,462 9.1 8.8 10.8

Arkansas ...................... 2,069 2,677 2,533 348,283 366,132 381,599 5.9 7.3 6.6

California...................... 54,730 64,120 68,576 5,234,401 5,169,993 5,405,582 10.5 12.4 12.7

Colorado ...................... 9,048 9,316 12,326 674,645 673,414 746,654 13.4 13.8 16.5

Connecticut ................. 6,937 7,308 8,309 437,292 403,277 428,825 15.9 18.1 19.4

Delaware ...................... 1,461 1,817 2,055 106,005 107,873 114,146 13.8 16.8 18.0

District of Columbia ..... 7,448 9,798 10,626 102,420 105,154 133,464 72.7 93.2 79.6

Florida.......................... 16,414 19,550 23,822 2,071,777 2,221,655 2,348,449 7.9 8.8 10.1

Georgia ........................ 9,345 10,848 12,473 1,295,862 1,312,377 1,357,886 7.2 8.3 9.2

Hawaii .......................... 1,455 1,896 2,142 168,560 176,055 192,265 8.6 10.8 11.1

Idaho............................ 1,547 1,841 2,039 169,900 191,279 210,906 9.1 9.6 9.7

Illinois........................... 24,266 24,483 27,237 1,788,465 1,737,829 1,787,899 13.6 14.1 15.2

Indiana ......................... 8,510 9,761 12,125 817,892 812,260 835,258 10.4 12.0 14.5

Iowa ............................. 4,705 5,124 5,601 356,466 352,021 389,147 13.2 14.6 14.4

Kansas ......................... 5,846 5,722 5,958 343,487 343,137 383,886 17.0 16.7 15.5

Kentucky...................... 4,017 4,693 4,543 556,948 555,069 569,299 7.2 8.5 8.0

Louisiana...................... 5,739 5,515 5,980 587,213 560,262 643,209 9.8 9.8 9.3

Maine ........................... 605 675 761 152,867 143,767 146,381 4.0 4.7 5.2

Maryland...................... 9,209 11,219 13,688 733,474 725,791 784,346 12.6 15.5 17.5

Massachusetts............. 20,191 23,011 26,539 907,376 818,399 870,207 22.3 28.1 30.5

Michigan ...................... 15,695 15,206 17,439 1,327,644 1,219,056 1,172,148 11.8 12.5 14.9

Minnesota .................... 6,663 11,940 13,443 666,671 661,468 729,934 10.0 18.1 18.4

Mississippi ................... 2,629 3,010 3,419 374,462 373,805 389,945 7.0 8.1 8.8

Missouri ....................... 6,320 7,687 8,715 727,911 736,036 787,984 8.7 10.4 11.1

Montana....................... 1,268 1,456 1,496 101,315 107,547 125,164 12.5 13.5 12.0

Nebraska...................... 2,428 2,905 3,598 220,445 223,330 250,051 11.0 13.0 14.4

Nevada......................... 1,584 2,053 2,133 314,776 367,992 388,454 5.0 5.6 5.5

New Hampshire ........... 1,337 1,426 1,608 156,956 144,611 146,960 8.5 9.9 10.9

New Jersey .................. 11,322 12,513 12,676 1,164,876 1,091,700 1,124,825 9.7 11.5 11.3

New Mexico................. 3,269 3,656 4,045 229,301 247,445 273,317 14.3 14.8 14.8

New York...................... 38,946 44,139 49,332 2,705,918 2,560,803 2,716,521 14.4 17.2 18.2

North Carolina.............. 10,640 12,419 16,027 1,207,640 1,192,318 1,261,660 8.8 10.4 12.7

North Dakota ............... 1,078 1,362 1,890 74,377 75,538 94,738 14.5 18.0 19.9

Ohio ............................. 16,388 18,161 20,589 1,481,232 1,420,465 1,425,637 11.1 12.8 14.4

Oklahoma..................... 4,166 4,095 6,113 445,894 463,304 515,926 9.3 8.8 11.8

Oregon......................... 3,990 4,409 5,064 472,656 488,957 530,602 8.4 9.0 9.5

Pennsylvania................ 18,585 20,218 22,851 1,514,109 1,460,972 1,547,226 12.3 13.8 14.8

Rhode Island................ 1,646 1,885 2,376 137,032 128,875 128,843 12.0 14.6 18.4

South Carolina ............. 3,240 3,397 4,202 551,336 564,942 600,606 5.9 6.0 7.0

South Dakota............... 982 924 1,272 89,533 92,913 108,359 11.0 9.9 11.7

Tennessee.................... 5,797 6,302 7,081 800,705 809,202 834,804 7.2 7.8 8.5

Texas............................ 28,440 33,083 42,542 3,186,416 3,361,877 3,690,821 8.9 9.8 11.5

Utah ............................. 4,034 5,049 5,755 333,952 395,758 449,404 12.1 12.8 12.8

Vermont........................ 597 610 685 72,130 66,702 70,651 8.3 9.1 9.7

Virginia ......................... 12,286 13,044 15,984 1,025,675 1,023,967 1,121,245 12.0 12.7 14.3

Washington.................. 5,891 6,542 8,741 837,266 855,936 961,616 7.0 7.6 9.1

West Virginia ................ 2,031 2,166 2,101 222,472 221,677 221,255 9.1 9.8 9.5

Wisconsin .................... 7,871 8,460 9,227 692,200 678,122 731,644 11.4 12.5 12.6

Wyoming...................... 764 818 1,080 58,619 64,854 79,201 13.0 12.6 13.6

Puerto Rico.................. 3,062 3,585 2,842 533,518 551,751 480,337 5.7 6.5 5.9

NOTE: S&E graduate students include students pursuing degrees in physical, computer, agricultural, biological, earth, atmospheric, ocean, and social 

sciences; psychology; mathematics; and engineering.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, Survey of Graduate Students and Postdoctorates in 

Science and Engineering; Census Bureau, 2000 and 2010 Decennial Censuses and Population Estimates Program (various years).

Science and Engineering Indicators 2014
8-56 ♦ Chapter 8. State Indicators

This indicator represents the extent to which a state’s 

higher education programs in S&E are concentrated at 

the graduate level. S&E fields include the physical, life, 

earth, ocean, atmospheric, computer, and social sciences; 

mathematics; engineering; and psychology. Advanced S&E 

degrees include master’s and doctoral degrees. Total S&E 

degrees include bachelor’s, master’s, and doctoral degrees 

but exclude associate’s degrees.

The indicator value is computed by dividing the number 

of advanced S&E degrees by the total number of S&E degrees 

awarded by the higher education institutions within the state. 

The number of degrees are actual counts provided by the 

National Center for Education Statistics.

Findings

• In 2011, nearly 184,000 advanced S&E degrees were 

awarded nationwide, 48% more degrees than were awarded 

in 2001. The share of advanced degrees as a percentage of 

all S&E degrees conferred increased by 5% between 2001 

and 2011.

• In 2011, the value of this indicator for individual states 

ranged from 8.0% to 33.3% of S&E graduates completing training at the master’s or doctoral level. Between 2001 

and 2011, 29 states and the District of Columbia showed 

increases in the share of their S&E graduates completing training at the master’s or doctoral level and 21 states 

showed decreases.

• In states with few S&E graduate programs, the number of 

advanced S&E degrees conferred varies considerably from 

year to year. Readers should use caution when making 

annual comparisons for those states with small numbers of 

S&E graduate students.

Advanced Science and Engineering Degrees as a Percentage of S&E 

Degrees Conferred

Figure 8-23

Advanced science and engineering degrees as a percentage of S&E degrees conferred: 2011

8.0 12.1 16.2 20.3 24.4 28.5 32.6 36.7 40.8 44.9 49.0

IA

ME

SC

WI

ID

MT

ND

NH

NV

OR

RI

TN

UT

WA

MA

MD

MN

WV

AR

AZ

DE

FL

GA

HI

IN

KY

LA

NC

NE

OH

PA

SD

VT IL DC

AK

AL

CA

CO

CT

KS

MI

MO

MS

NJ

NM

NY

OK

TX

VA

WY

Percent

1st quartile (26.2%–48.6%)

2nd quartile (23.7%–26.1%)

3rd quartile (18.9%–23.4%)

4th quartile (8.0%–18.6%)

SOURCE: National Center for Education 

Statistics, Integrated Postsecondary 

Education Data System.
Science and Engineering Indicators 2014 ♦ 8-57

Table 8-23

Advanced science and engineering degrees as a percentage of S&E degrees conferred, by state: 2001, 2006, 

and 2011

Advanced S&E degrees All S&E degrees

Advanced S&E 

degrees/all 

S&E degrees (%)

State 2001 2006 2011 2001 2006 2011 2001 2006 2011

United States................... 124,327 150,513 183,738 520,476 625,163 733,609 23.9 24.1 25.0

Alabama....................... 1,969 2,294 2,527 7,489 8,313 9,933 26.3 27.6 25.4

Alaska .......................... 184 204 228 604 719 921 30.5 28.4 24.8

Arizona......................... 1,641 1,898 4,342 6,800 10,072 18,154 24.1 18.8 23.9

Arkansas ...................... 440 576 925 2,844 3,235 4,121 15.5 17.8 22.4

California...................... 15,645 19,584 23,415 63,360 80,172 91,643 24.7 24.4 25.6

Colorado ...................... 2,879 3,296 4,036 11,606 14,320 15,209 24.8 23.0 26.5

Connecticut ................. 1,768 2,069 2,577 6,929 8,341 9,790 25.5 24.8 26.3

Delaware ...................... 419 487 596 1,868 2,216 2,512 22.4 22.0 23.7

District of Columbia ..... 2,990 3,480 3,764 6,870 8,294 7,743 43.5 42.0 48.6

Florida.......................... 4,469 5,387 6,997 18,843 25,887 32,260 23.7 20.8 21.7

Georgia ........................ 2,964 3,458 4,040 12,083 14,677 17,367 24.5 23.6 23.3

Hawaii .......................... 625 575 599 2,227 2,531 2,594 28.1 22.7 23.1

Idaho............................ 341 502 498 1,756 2,661 2,942 19.4 18.9 16.9

Illinois........................... 6,717 8,731 10,266 22,867 27,863 30,855 29.4 31.3 33.3

Indiana ......................... 2,440 2,844 3,544 11,188 13,241 15,768 21.8 21.5 22.5

Iowa ............................. 1,014 1,291 1,554 6,389 7,413 13,296 15.9 17.4 11.7

Kansas ......................... 1,171 1,274 1,497 5,576 5,872 6,012 21.0 21.7 24.9

Kentucky...................... 974 1,487 1,612 5,015 6,317 6,883 19.4 23.5 23.4

Louisiana...................... 1,418 1,708 1,637 6,908 7,282 7,562 20.5 23.5 21.6

Maine ........................... 174 235 249 2,236 2,625 3,103 7.8 9.0 8.0

Maryland...................... 3,832 4,700 5,651 12,710 15,870 18,039 30.1 29.6 31.3

Massachusetts............. 6,654 7,645 8,966 22,843 25,439 28,989 29.1 30.1 30.9

Michigan ...................... 4,936 5,449 5,915 18,618 21,124 23,488 26.5 25.8 25.2

Minnesota .................... 1,822 2,844 4,841 9,319 12,388 15,853 19.6 23.0 30.5

Mississippi ................... 636 797 1,087 3,472 3,618 4,278 18.3 22.0 25.4

Missouri ....................... 2,946 3,085 3,850 11,306 12,690 14,323 26.1 24.3 26.9

Montana....................... 358 426 425 2,076 2,189 2,313 17.2 19.5 18.4

Nebraska...................... 697 828 1,063 3,261 3,892 4,478 21.4 21.3 23.7

Nevada......................... 304 542 544 1,279 2,378 2,814 23.8 22.8 19.3

New Hampshire ........... 605 709 763 3,082 3,520 4,047 19.6 20.1 18.9

New Jersey .................. 3,225 3,935 4,620 13,842 15,603 17,439 23.3 25.2 26.5

New Mexico................. 739 1,001 873 2,558 3,164 3,339 28.9 31.6 26.1

New York...................... 11,441 13,912 16,797 44,628 51,277 59,701 25.6 27.1 28.1

North Carolina.............. 2,717 3,241 4,353 14,543 16,541 20,896 18.7 19.6 20.8

North Dakota ............... 183 236 325 1,397 1,522 1,743 13.1 15.5 18.6

Ohio ............................. 4,237 4,782 5,222 18,238 20,505 23,094 23.2 23.3 22.6

Oklahoma..................... 1,847 1,404 1,688 5,914 6,243 6,503 31.2 22.5 26.0

Oregon......................... 1,296 1,548 1,525 6,473 8,004 8,767 20.0 19.3 17.4

Pennsylvania................ 5,507 7,263 8,980 26,514 32,358 37,253 20.8 22.4 24.1

Rhode Island................ 532 674 751 2,872 3,649 4,077 18.5 18.5 18.4

South Carolina ............. 1,101 1,087 1,206 6,052 6,997 8,223 18.2 15.5 14.7

South Dakota............... 379 448 484 1,775 2,030 2,153 21.4 22.1 22.5

Tennessee.................... 1,506 1,672 1,873 7,787 8,752 10,243 19.3 19.1 18.3

Texas............................ 7,464 9,397 11,960 28,242 35,293 42,413 26.4 26.6 28.2

Utah ............................. 1,061 1,301 1,648 6,151 8,075 9,399 17.2 16.1 17.5

Vermont........................ 319 556 747 2,153 2,576 3,343 14.8 21.6 22.3

Virginia ......................... 3,301 4,373 6,078 15,823 18,582 23,672 20.9 23.5 25.7

Washington.................. 1,956 2,434 2,617 10,011 12,592 14,269 19.5 19.3 18.3

West Virginia ................ 523 634 1,511 2,699 3,056 5,050 19.4 20.7 29.9

Wisconsin .................... 1,731 2,010 2,215 10,549 12,328 13,761 16.4 16.3 16.1

Wyoming...................... 230 200 257 831 857 981 27.7 23.3 26.2

Puerto Rico.................. 639 916 1,067 4,847 4,992 5,437 13.2 18.3 19.6

NOTES: Advanced S&E degrees include only master’s and doctorate. All S&E degrees include bachelor’s, master’s, and doctorate. S&E degrees include 

physical, computer, agricultural, biological, earth, atmospheric, ocean, and social sciences; psychology; mathematics; and engineering.

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education Data System (various years).

Science and Engineering Indicators 2014
8-58 ♦ Chapter 8. State Indicators

This indicator represents the extent to which a state’s 

higher education programs in NS&E are concentrated at the 

graduate level. NS&E fields include the physical, life, earth, 

ocean, atmospheric, and computer sciences; mathematics; 

and engineering. The social sciences, including anthropology, economics, political science and public administration, 

psychology, and sociology, are not included. Advanced NS&E 

degrees include master’s and doctoral degrees. Total NS&E 

degrees include bachelor’s, master’s, and doctoral degrees but 

exclude associate’s degrees.

The indicator value is computed by dividing the number of 

advanced NS&E degrees by the total number of NS&E degrees 

awarded by the higher education institutions within the state.

The number of degrees are actual counts provided by the 

National Center for Education Statistics.

Findings

• In 2011, nearly 115,000 advanced natural sciences and engineering (NS&E) degrees were awarded nationwide. This total 

represented approximately 46% more than were awarded 

in 2001. The share of advanced degrees as a percentage of 

all NS&E degrees conferred rose by 1.9 percentage points 

between 2001 and 2011.

• In 2011, the value of this indicator for states ranged from a 

low of 10.8% to a high of 40.5%.

• Nationally, about 62% of all advanced S&E degrees were in 

NS&E fields in 2011, a slight decline from 63% in 2001.

• In states with few NS&E graduate programs, the number 

of advanced NS&E degrees conferred varies considerably 

from year to year. Readers should use caution when making 

annual comparisons for those states with small numbers of 

NS&E graduate students.

Advanced Natural Sciences and Engineering Degrees as a Percentage of 

NS&E Degrees Conferred

Figure 8-24

Advanced natural sciences and engineering degrees as a percentage of NS&E degrees conferred: 2011

10.0 15.6 21.2 26.8 32.4 38.0 43.6 49.2 54.8 60.4

ME

VT

IL

MA

NJ

NY

TX

AK

AL

AZ

IN

KY

LA

MN

NE

NV

OH

OR

SD

TN

UT

WA

CT

MD DC

AR

CA

CO

DE

FL

GA

HI

KS

MI

MO

MS

NC

NH

NM

OK

PA

VA

WY

IA

ID

MT

ND

RI

SC

WI

WV

Percent

1st quartile (29.6%–60.2%)

2nd quartile (25.8%–30.8%)

3rd quartile (21.6%–26.7%)

4th quartile (10.8%–21.4%)

SOURCE: National Center for Education 

Statistics, Integrated Postsecondary Education Data System.
Science and Engineering Indicators 2014 ♦ 8-59

Table 8-24

Advanced natural sciences and engineering degrees as a percentage of NS&E degrees conferred, by state: 

2001, 2006, and 2011

Advanced NS&E degrees NS&E degrees conferred

Advanced NS&E 

degrees/NS&E degrees 

conferred (%)

State 2001 2006 2011 2001 2006 2011 2001 2006 2011

United States................... 78,543 94,476 114,621 287,290 333,677 392,170 27.3 28.3 29.2

Alabama....................... 919 1,192 1,428 4,578 4,854 5,927 20.1 24.6 24.1

Alaska .......................... 116 139 144 346 451 543 33.5 30.8 26.5

Arizona......................... 1,141 1,243 1,822 4,251 6,666 8,107 26.8 18.6 22.5

Arkansas ...................... 291 401 711 1,783 1,932 2,608 16.3 20.8 27.3

California...................... 8,868 11,742 14,110 31,205 38,756 44,876 28.4 30.3 31.4

Colorado ...................... 1,900 2,125 2,626 6,514 8,017 8,532 29.2 26.5 30.8

Connecticut ................. 1,176 1,466 1,858 3,078 3,637 4,592 38.2 40.3 40.5

Delaware ...................... 217 292 376 906 1,021 1,271 24.0 28.6 29.6

District of Columbia ..... 1,531 1,279 1,350 3,230 3,100 2,243 47.4 41.3 60.2

Florida.......................... 2,794 3,440 4,663 10,142 12,964 16,847 27.5 26.5 27.7

Georgia ........................ 2,090 2,224 2,912 7,296 8,233 10,120 28.6 27.0 28.8

Hawaii .......................... 313 252 295 983 1,042 1,041 31.8 24.2 28.3

Idaho............................ 274 413 409 1,174 1,797 1,939 23.3 23.0 21.1

Illinois........................... 4,233 5,324 6,144 13,417 16,244 17,297 31.5 32.8 35.5

Indiana ......................... 1,502 1,852 2,334 6,455 7,596 9,295 23.3 24.4 25.1

Iowa ............................. 775 973 1,139 3,830 4,300 5,672 20.2 22.6 20.1

Kansas ......................... 762 840 978 3,368 3,309 3,533 22.6 25.4 27.7

Kentucky...................... 573 880 828 2,705 3,247 3,656 21.2 27.1 22.6

Louisiana...................... 999 1,195 1,196 4,480 4,522 4,550 22.3 26.4 26.3

Maine ........................... 138 163 174 1,198 1,301 1,604 11.5 12.5 10.8

Maryland...................... 2,563 3,282 3,880 7,300 9,075 10,167 35.1 36.2 38.2

Massachusetts............. 4,052 4,568 5,378 11,261 12,275 14,560 36.0 37.2 36.9

Michigan ...................... 3,722 3,997 4,194 12,070 13,262 14,238 30.8 30.1 29.5

Minnesota .................... 1,057 1,476 1,740 5,083 6,492 7,767 20.8 22.7 22.4

Mississippi ................... 487 606 804 2,242 2,265 2,660 21.7 26.8 30.2

Missouri ....................... 1,288 1,608 2,105 6,125 6,760 7,733 21.0 23.8 27.2

Montana....................... 277 300 297 1,448 1,433 1,530 19.1 20.9 19.4

Nebraska...................... 448 533 662 1,943 2,209 2,602 23.1 24.1 25.4

Nevada......................... 201 349 361 728 1,232 1,498 27.6 28.3 24.1

New Hampshire ........... 450 429 567 1,648 1,545 1,953 27.3 27.8 29.0

New Jersey .................. 2,331 2,552 3,346 7,530 7,769 9,266 31.0 32.8 36.1

New Mexico................. 500 692 638 1,666 1,988 2,021 30.0 34.8 31.6

New York...................... 6,711 7,820 10,040 21,845 24,238 29,236 30.7 32.3 34.3

North Carolina.............. 2,021 2,349 3,208 8,204 8,745 11,550 24.6 26.9 27.8

North Dakota ............... 130 174 227 928 1,087 1,226 14.0 16.0 18.5

Ohio ............................. 2,901 3,247 3,553 10,649 11,501 13,299 27.2 28.2 26.7

Oklahoma..................... 870 986 1,067 3,361 3,658 3,875 25.9 27.0 27.5

Oregon......................... 790 913 1,010 3,162 3,799 4,359 25.0 24.0 23.2

Pennsylvania................ 3,524 4,678 5,978 15,425 18,459 21,701 22.8 25.3 27.5

Rhode Island................ 335 404 463 1,537 1,935 2,207 21.8 20.9 21.0

South Carolina ............. 875 803 911 3,635 4,004 4,789 24.1 20.1 19.0

South Dakota............... 246 273 299 1,159 1,325 1,369 21.2 20.6 21.8

Tennessee.................... 954 1,031 1,179 4,235 4,522 5,509 22.5 22.8 21.4

Texas............................ 5,287 6,717 8,756 17,085 20,867 25,952 30.9 32.2 33.7

Utah ............................. 680 945 1,108 3,477 4,377 5,136 19.6 21.6 21.6

Vermont........................ 155 260 241 1,001 1,158 1,553 15.5 22.5 15.5

Virginia ......................... 2,076 2,663 3,255 8,032 9,182 11,887 25.8 29.0 27.4

Washington.................. 1,241 1,378 1,491 5,102 6,009 6,907 24.3 22.9 21.6

West Virginia ................ 333 454 510 1,629 1,861 2,428 20.4 24.4 21.0

Wisconsin .................... 1,265 1,429 1,683 6,269 7,099 8,303 20.2 20.1 20.3

Wyoming...................... 161 125 173 572 557 636 28.1 22.4 27.2

Puerto Rico.................. 363 467 520 3,417 3,392 3,520 10.6 13.8 14.8

NS&E = natural sciences and engineering.

NOTES: Advanced NS&E degrees include only master’s and doctorate. NS&E degrees conferred includes bachelor’s, master’s, and doctorate. NS&E 

degrees include physical, computer, agricultural, biological, earth, atmospheric, and ocean sciences; mathematics; and engineering.

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education Data System (various years).

Science and Engineering Indicators 2014
8-60 ♦ Chapter 8. State Indicators

This indicator represents the extent to which a state’s higher education programs in S&E are focused on producing individuals with 

the highest level of technical expertise. The academic and technical 

leaders of the future are often drawn from individuals receiving S&E 

doctoral degrees. S&E fields include the physical, life, earth, ocean, 

atmospheric, computer, and social sciences; mathematics; engineering; and psychology. Total S&E degrees conferred include bachelor’s, 

master’s, and doctoral degrees but exclude associate’s degrees.

The indicator value is computed by dividing the number of 

doctoral degrees awarded in S&E fields by the total number of S&E 

degrees awarded by the higher education institutions within the state. 

The number of degrees are counts provided by the National Center 

for Education Statistics.

Findings

• The number of S&E doctoral degrees awarded nationwide rose from 25,000 in 2001 to 34,000 in 2011, 

an increase of 34%. California showed the largest 

increase in the number of S&E doctorates awarded 

during this period.

• Nationally, the percentage of S&E degrees awarded 

that were doctoral degrees has declined from 4.9% in 

2001 to 4.6% in 2011. 

• In 2011, the value of this indicator for individual states 

ranged from a low of 1.2% to a high of 6.8%.

• In states with a small number of S&E graduate programs, the number of S&E doctoral degrees awarded 

varies considerably from year to year. Readers should 

use caution when making annual comparisons for 

those states with small numbers of S&E doctorates.

Science and Engineering Doctoral Degrees as a Percentage of S&E Degrees 

Conferred

Figure 8-25

Science and engineering doctoral degrees as a percentage of S&E degrees conferred: 2011

1.2 1.8 2.4 3.0 3.6 4.2 4.8 5.4 6.0 6.6 7.2

ME

VT ID

SD

WV

AK

AR

AZ

IA

MO

MT

NH

CO

CT

FL

MD

MS

NC

NE

NY

PA

TN

VA

WI

WY

CA

GA

HI

IL

KS

MI

MN

NJ

NM

NV

OH

RI

TX IN

DE

MA

AL

DC

KY

LA

ND

OK

OR

SC

UT

WA

Percent

1st quartile (4.9%–6.8%)

2nd quartile (4.4%–4.8%)

3rd quartile (3.6%–4.3%)

4th quartile (1.2%–3.5%)

SOURCE: National Center for Education 

Statistics, Integrated Postsecondary Education Data System.
Science and Engineering Indicators 2014 ♦ 8-61

Table 8-25

Science and engineering doctoral degrees as a percentage of S&E degrees conferred, by state: 2001, 2006, 

and 2011

S&E doctoral degrees S&E degrees conferred

S&E doctoral/S&E 

degrees conferred (%)

State 2001 2006 2011 2001 2006 2011 2001 2006 2011

United States................... 25,352 30,291 34,045 520,476 625,163 733,609 4.9 4.8 4.6

Alabama....................... 268 309 367 7,489 8,313 9,933 3.6 3.7 3.7

Alaska .......................... 22 18 29 604 719 921 3.6 2.5 3.1

Arizona......................... 392 514 626 6,800 10,072 18,154 5.8 5.1 3.4

Arkansas ...................... 61 106 127 2,844 3,235 4,121 2.1 3.3 3.1

California...................... 3,664 4,365 4,805 63,360 80,172 91,643 5.8 5.4 5.2

Colorado ...................... 511 546 647 11,606 14,320 15,209 4.4 3.8 4.3

Connecticut ................. 370 453 457 6,929 8,341 9,790 5.3 5.4 4.7

Delaware ...................... 108 135 170 1,868 2,216 2,512 5.8 6.1 6.8

District of Columbia ..... 306 363 321 6,870 8,294 7,743 4.5 4.4 4.1

Florida.......................... 838 1,211 1,466 18,843 25,887 32,260 4.4 4.7 4.5

Georgia ........................ 607 819 901 12,083 14,677 17,367 5.0 5.6 5.2

Hawaii .......................... 141 110 136 2,227 2,531 2,594 6.3 4.3 5.2

Idaho............................ 50 72 68 1,756 2,661 2,942 2.8 2.7 2.3

Illinois........................... 1,528 1,603 1,541 22,867 27,863 30,855 6.7 5.8 5.0

Indiana ......................... 621 700 848 11,188 13,241 15,768 5.6 5.3 5.4

Iowa ............................. 322 373 442 6,389 7,413 13,296 5.0 5.0 3.3

Kansas ......................... 236 247 302 5,576 5,872 6,012 4.2 4.2 5.0

Kentucky...................... 174 254 264 5,015 6,317 6,883 3.5 4.0 3.8

Louisiana...................... 317 289 309 6,908 7,282 7,562 4.6 4.0 4.1

Maine ........................... 30 27 44 2,236 2,625 3,103 1.3 1.0 1.4

Maryland...................... 604 791 822 12,710 15,870 18,039 4.8 5.0 4.6

Massachusetts............. 1,436 1,689 1,909 22,843 25,439 28,989 6.3 6.6 6.6

Michigan ...................... 868 1,060 1,132 18,618 21,124 23,488 4.7 5.0 4.8

Minnesota .................... 531 705 847 9,319 12,388 15,853 5.7 5.7 5.3

Mississippi ................... 115 142 201 3,472 3,618 4,278 3.3 3.9 4.7

Missouri ....................... 412 512 496 11,306 12,690 14,323 3.6 4.0 3.5

Montana....................... 39 66 80 2,076 2,189 2,313 1.9 3.0 3.5

Nebraska...................... 131 136 195 3,261 3,892 4,478 4.0 3.5 4.4

Nevada......................... 50 93 138 1,279 2,378 2,814 3.9 3.9 4.9

New Hampshire ........... 111 129 126 3,082 3,520 4,047 3.6 3.7 3.1

New Jersey .................. 652 708 835 13,842 15,603 17,439 4.7 4.5 4.8

New Mexico................. 134 181 165 2,558 3,164 3,339 5.2 5.7 4.9

New York...................... 2,157 2,495 2,730 44,628 51,277 59,701 4.8 4.9 4.6

North Carolina.............. 665 805 974 14,543 16,541 20,896 4.6 4.9 4.7

North Dakota ............... 43 45 68 1,397 1,522 1,743 3.1 3.0 3.9

Ohio ............................. 1,023 1,138 1,167 18,238 20,505 23,094 5.6 5.5 5.1

Oklahoma..................... 198 195 239 5,914 6,243 6,503 3.3 3.1 3.7

Oregon......................... 298 320 324 6,473 8,004 8,767 4.6 4.0 3.7

Pennsylvania................ 1,143 1,551 1,724 26,514 32,358 37,253 4.3 4.8 4.6

Rhode Island................ 168 223 207 2,872 3,649 4,077 5.8 6.1 5.1

South Carolina ............. 205 231 294 6,052 6,997 8,223 3.4 3.3 3.6

South Dakota............... 31 38 55 1,775 2,030 2,153 1.7 1.9 2.6

Tennessee.................... 351 395 486 7,787 8,752 10,243 4.5 4.5 4.7

Texas............................ 1,500 1,845 2,228 28,242 35,293 42,413 5.3 5.2 5.3

Utah ............................. 197 225 348 6,151 8,075 9,399 3.2 2.8 3.7

Vermont........................ 52 49 40 2,153 2,576 3,343 2.4 1.9 1.2

Virginia ......................... 667 787 984 15,823 18,582 23,672 4.2 4.2 4.2

Washington.................. 422 525 578 10,011 12,592 14,269 4.2 4.2 4.1

West Virginia ................ 56 102 122 2,699 3,056 5,050 2.1 3.3 2.4

Wisconsin .................... 494 559 618 10,549 12,328 13,761 4.7 4.5 4.5

Wyoming...................... 33 37 43 831 857 981 4.0 4.3 4.4

Puerto Rico.................. 101 161 160 4,847 4,992 5,437 2.1 3.2 2.9

NOTES: S&E degrees conferred include bachelor’s, master’s, and doctorate. S&E degrees include physical, computer, agricultural, biological, earth, 

atmospheric, ocean, and social sciences; psychology; mathematics; and engineering.

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education Data System (various years).

Science and Engineering Indicators 2014
8-62 ♦ Chapter 8. State Indicators

The average annual charge for an undergraduate student to attend a public 4-year academic institution is one indicator of how 

accessible higher education is to a state’s students. The annual 

charge includes standard in-state charges for tuition, required fees, 

room, and board for a full-time undergraduate student who is a 

resident of that state. These charges were weighted by the number 

of full-time undergraduates attending each public institution within 

the state. The total charge for all public 4-year institutions in the 

state was divided by the total number of full-time undergraduates 

attending all public 4-year institutions in the state. The year is the 

end date of the academic year. For example, data for 2011 represent 

costs for the 2010–11 academic year.

To improve educational attainment, the federal government, 

state governments, and academic institutions provide various kinds 

of financial aid that reduce the charge to students. The data in this 

indicator do not include any adjustments for such financial aid.

Findings

• During 2011, the total annual nominal charge for a fulltime undergraduate student to attend a public 4-year 

institution averaged $15,918 nationally, an increase of 

84% since 2001. This was equivalent to an increase of 

approximately 47% after adjusting for inflation.

• All states showed major increases in undergraduate 

charges at public institutions from 2001 to 2011. In several states, undergraduate charges more than doubled 

during this period.

• In 2011, the state average for a year of undergraduate 

education at a public 4-year institution ranged from a low 

of $10,768 to a high of $22,592.

• Tuition and required fees averaged 47% of the total 

charges at public 4-year institutions in 2011, but individual states had different cost structures.

Average Undergraduate Charge at Public 4-Year Institutions

Figure 8-26

Average undergraduate charge at public 4-year institutions: 2011

10,500 11,750 13,000 14,250 15,500 16,750 18,000 19,250 20,500 21,750 23,000

UT

WY

AK

GA

KS

NE

NV

TN

WI

AZ

IN

MD

ME

OH

SC

AL

HI

IA

KY

MO

TX

CA

MA

MI

VA

CT

DE

IL

PA

RI

NH

VT NJ

CO

MN

NY

OR

WA

Dollars

AR

FL

ID

LA

MS

MT

NC

ND

NM

OK

SD

WV

1st quartile ($17,964–$22,592)

2nd quartile ($15,110–$17,767)

3rd quartile ($12,874–$15,002)

4th quartile ($10,768–$12,799)

No data

SOURCE: National Center for Education 

Statistics, Integrated Postsecondary 

Education Data System.
Science and Engineering Indicators 2014 ♦ 8-63

Table 8-26

Average undergraduate charge at public 4-year institutions, by state: 

2001, 2006, and 2011

(Dollars)

State 2001 2006 2011

United States.................................................................. 8,653 12,108 15,918

Alabama...................................................................... 7,349 9,625 14,416

Alaska ......................................................................... 8,390 10,620 14,053

Arizona........................................................................ 7,874 11,480 17,083

Arkansas ..................................................................... 6,797 9,192 12,580

California..................................................................... 9,590 13,685 18,933

Colorado ..................................................................... 8,362 11,569 16,208

Connecticut ................................................................ 10,521 14,658 19,400

Delaware ..................................................................... 10,283 14,326 19,541

District of Columbia .................................................... NA NA NA

Florida......................................................................... 7,947 10,141 12,774

Georgia ....................................................................... 7,463 10,062 14,019

Hawaii ......................................................................... 8,272 9,042 15,133

Idaho........................................................................... 6,765 8,982 11,773

Illinois.......................................................................... 9,532 13,976 20,054

Indiana ........................................................................ 9,239 12,388 16,912

Iowa ............................................................................ 7,587 12,329 14,855

Kansas ........................................................................ 6,654 9,980 13,229

Kentucky..................................................................... 6,923 10,663 15,002

Louisiana..................................................................... 6,329 8,506 11,856

Maine .......................................................................... 9,371 12,568 17,767

Maryland..................................................................... 10,834 14,793 16,963

Massachusetts............................................................ 9,207 14,651 19,164

Michigan ..................................................................... 9,825 13,693 18,333

Minnesota ................................................................... 8,127 12,777 16,385

Mississippi .................................................................. 7,195 9,461 12,051

Missouri ...................................................................... 8,203 11,861 15,110

Montana...................................................................... 7,615 10,613 12,891

Nebraska..................................................................... 7,355 11,286 14,081

Nevada........................................................................ 8,247 10,865 14,172

New Hampshire .......................................................... 11,720 15,479 21,481

New Jersey ................................................................. 12,007 17,708 22,592

New Mexico................................................................ 7,086 9,579 12,520

New York..................................................................... 10,260 13,275 16,606

North Carolina............................................................. 7,076 9,675 12,874

North Dakota .............................................................. 6,418 9,829 12,503

Ohio ............................................................................ 10,451 16,032 17,964

Oklahoma.................................................................... 6,022 9,404 11,938

Oregon........................................................................ 9,394 12,720 16,402

Pennsylvania............................................................... 11,091 15,464 19,916

Rhode Island............................................................... 11,095 14,315 19,815

South Carolina ............................................................ 9,096 13,145 17,641

South Dakota.............................................................. 6,975 9,493 12,603

Tennessee................................................................... 7,658 9,956 13,759

Texas........................................................................... 7,614 10,973 14,585

Utah ............................................................................ 6,598 8,745 10,768

Vermont....................................................................... 12,847 16,571 21,530

Virginia ........................................................................ 8,751 12,279 18,110

Washington................................................................. 8,909 12,384 16,253

West Virginia ............................................................... 7,290 9,992 12,799

Wisconsin ................................................................... 7,396 10,560 13,819

Wyoming..................................................................... 7,017 8,946 11,467

Puerto Rico................................................................. NA NA NA

NA = not available.

NOTES: The national average for the United States is from the Digest of Education Statistics. Average 

charges are for full-time equivalent students but are not adjusted for student residency. Average charges 

include tuition, fees, room, and board. 

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education Data System 

(various years). 

Science and Engineering Indicators 2014
8-64 ♦ Chapter 8. State Indicators

This indicator represents a broad measure of how affordable higher 

education at a public institution is for the average resident. It is calculated by dividing the average undergraduate charge at all public 4-year 

institutions in the state by the per capita disposable personal income 

of state residents. The average undergraduate charge includes standard 

in-state tuition, room, board, and required fees for a student who is a 

resident of the state. The year is the end date of the academic year. For 

example, data for 2011 represent costs for the 2010–11 academic year.

Disposable personal income is the income available to state residents for spending or saving. It is calculated as personal income minus 

personal current taxes paid to federal, state, and local governments. 

High values indicate that a year of undergraduate education consumes 

a high percentage of the disposable personal income of state residents. 

However, the data in this indicator do not include any adjustment for 

financial aid that a student might receive.

Findings

• In 2011, a year of undergraduate education at a state 

institution would have consumed, on average, 42.9% 

of a resident’s disposable income, an increase from the 

32.3% it would have consumed in 2001.

• The cost of a year of undergraduate education at a 

public institution exceeded 50% of the per capita disposable income for residents of 12 states in 2011.

• All states showed an increase in this indicator between 

2001 and 2011.

• Residents in 26 states experienced major increases in 

the cost of a year of undergraduate education relative 

to their purchasing power (in excess of 10 percentage 

points of their per capita disposable income) between 

2001 and 2011.

Average Undergraduate Charge at Public 4-Year Institutions as a Percentage 

of Disposable Personal Income

Figure 8-27

Average undergraduate charge at public 4-year institutions as a percentage of disposable personal income: 2011

25.6 28.9 32.2 35.5 38.8 42.1 45.4 48.7 52.0 55.3 58.6

WY

HI

KS

MD

NE

NY

ND

SD

AR

CO

CT

IA

ID

MA

MN

MS

MT

NC

NM

TN

TX

WA

WI

AL

GA

MO

NV

VA

WV

AZ

DE

IN

OH

PA

CA

IL

KY

ME

NH

NJ

OR

RI

MI

SC

VT

AK

FL

LA

OK

UT

Percent

1st quartile (49.4%–57.8%)

2nd quartile (41.3%–49.3%)

3rd quartile (38.7%–40.8%)

4th quartile (26.5%–38.2%)

No data

SOURCES: National Center for Education 

Statistics, Integrated Postsecondary Education Data System; Bureau of Economic 

Analysis, State and Local Personal Income 

data.
Science and Engineering Indicators 2014 ♦ 8-65

Table 8-27

Average undergraduate charge at public 4-year institutions as a percentage of disposable personal income, by 

state: 2001, 2006, and 2011

Average undergraduate 

charge ($)

Per capita disposable 

personal income ($)

Undergraduate charge/

disposable personal 

income (%)

State 2001 2006 2011 2001 2006 2011 2001 2006 2011

United States................... 8,653 12,108 15,918 26,828 33,197 37,078 32.3 36.5 42.9

Alabama....................... 7,349 9,625 14,416 22,353 28,054 31,854 32.9 34.3 45.3

Alaska .......................... 8,390 10,620 14,053 28,711 35,380 41,420 29.2 30.0 33.9

Arizona......................... 7,874 11,480 17,083 23,815 30,557 32,015 33.1 37.6 53.4

Arkansas ...................... 6,797 9,192 12,580 21,291 26,627 30,819 31.9 34.5 40.8

California...................... 9,590 13,685 18,933 28,526 36,042 38,308 33.6 38.0 49.4

Colorado ...................... 8,362 11,569 16,208 30,455 36,194 39,221 27.5 32.0 41.3

Connecticut ................. 10,521 14,658 19,400 35,266 43,728 48,873 29.8 33.5 39.7

Delaware ...................... 10,283 14,326 19,541 27,520 33,875 36,721 37.4 42.3 53.2

District of Columbia ..... NA NA NA 38,342 52,769 65,233 NA NA NA

Florida.......................... 7,947 10,141 12,774 26,158 33,847 36,173 30.4 30.0 35.3

Georgia ........................ 7,463 10,062 14,019 25,438 30,144 32,430 29.3 33.4 43.2

Hawaii .......................... 8,272 9,042 15,133 25,706 33,249 39,073 32.2 27.2 38.7

Idaho............................ 6,765 8,982 11,773 22,558 28,045 30,111 30.0 32.0 39.1

Illinois........................... 9,532 13,976 20,054 28,564 35,081 38,797 33.4 39.8 51.7

Indiana ......................... 9,239 12,388 16,912 24,560 29,146 32,199 37.6 42.5 52.5

Iowa ............................. 7,587 12,329 14,855 24,736 30,320 37,406 30.7 40.7 39.7

Kansas ......................... 6,654 9,980 13,229 26,012 31,761 36,807 25.6 31.4 35.9

Kentucky...................... 6,923 10,663 15,002 22,221 26,894 30,758 31.2 39.6 48.8

Louisiana...................... 6,329 8,506 11,856 22,635 30,118 35,308 28.0 28.2 33.6

Maine ........................... 9,371 12,568 17,767 24,610 29,915 34,713 38.1 42.0 51.2

Maryland...................... 10,834 14,793 16,963 30,640 38,686 44,404 35.4 38.2 38.2

Massachusetts............. 9,207 14,651 19,164 32,414 40,663 45,960 28.4 36.0 41.7

Michigan ...................... 9,825 13,693 18,333 26,080 29,830 32,651 37.7 45.9 56.1

Minnesota .................... 8,127 12,777 16,385 28,570 34,831 39,257 28.4 36.7 41.7

Mississippi ................... 7,195 9,461 12,051 20,699 25,681 29,514 34.8 36.8 40.8

Missouri ....................... 8,203 11,861 15,110 25,010 30,394 34,383 32.8 39.0 43.9

Montana....................... 7,615 10,613 12,891 22,509 28,655 32,618 33.8 37.0 39.5

Nebraska...................... 7,355 11,286 14,081 26,293 31,715 38,457 28.0 35.6 36.6

Nevada......................... 8,247 10,865 14,172 27,199 34,314 33,536 30.3 31.7 42.3

New Hampshire ........... 11,720 15,479 21,481 30,157 36,822 41,472 38.9 42.0 51.8

New Jersey .................. 12,007 17,708 22,592 33,318 41,046 45,850 36.0 43.1 49.3

New Mexico................. 7,086 9,579 12,520 22,162 27,241 31,392 32.0 35.2 39.9

New York...................... 10,260 13,275 16,606 29,031 37,417 43,524 35.3 35.5 38.2

North Carolina.............. 7,076 9,675 12,874 24,715 29,553 32,505 28.6 32.7 39.6

North Dakota ............... 6,418 9,829 12,503 23,915 29,891 42,492 26.8 32.9 29.4

Ohio ............................. 10,451 16,032 17,964 25,361 30,027 33,943 41.2 53.4 52.9

Oklahoma..................... 6,022 9,404 11,938 23,198 29,555 34,327 26.0 31.8 34.8

Oregon......................... 9,394 12,720 16,402 25,252 30,299 33,361 37.2 42.0 49.2

Pennsylvania................ 11,091 15,464 19,916 26,546 32,603 37,647 41.8 47.4 52.9

Rhode Island................ 11,095 14,315 19,815 26,918 33,819 39,383 41.2 42.3 50.3

South Carolina ............. 9,096 13,145 17,641 22,722 27,646 30,528 40.0 47.5 57.8

South Dakota............... 6,975 9,493 12,603 25,253 31,024 41,133 27.6 30.6 30.6

Tennessee.................... 7,658 9,956 13,759 24,845 30,026 33,954 30.8 33.2 40.5

Texas............................ 7,614 10,973 14,585 25,867 31,844 36,631 29.4 34.5 39.8

Utah ............................. 6,598 8,745 10,768 22,576 27,468 30,405 29.2 31.8 35.4

Vermont........................ 12,847 16,571 21,530 25,754 31,946 37,714 49.9 51.9 57.1

Virginia ......................... 8,751 12,279 18,110 28,302 35,857 40,608 30.9 34.2 44.6

Washington.................. 8,909 12,384 16,253 28,868 35,545 39,960 30.9 34.8 40.7

West Virginia ................ 7,290 9,992 12,799 21,093 25,747 30,369 34.6 38.8 42.1

Wisconsin .................... 7,396 10,560 13,819 26,038 31,404 35,359 28.4 33.6 39.1

Wyoming...................... 7,017 8,946 11,467 27,278 38,553 43,194 25.7 23.2 26.5

Puerto Rico.................. NA NA NA NA NA NA NA NA NA

NA = not available.

NOTES: The national average for the United States is from the Digest of Education Statistics. Average charges are for full-time equivalent students but are 

not adjusted for student residency. Average charges include tuition, fees, room, and board. 

SOURCES: National Center for Education Statistics, Integrated Postsecondary Education Data System (various years); Bureau of Economic Analysis, 

State and Local Personal Income data.

Science and Engineering Indicators 2014
8-66 ♦ Chapter 8. State Indicators

This indicator represents the extent of state spending for 

higher education operating expenses as a proportion of its 

GDP. A higher value on this indicator indicates that a state 

has made financial support of its higher education system 

more of a priority.

Because of decreases in state tax collections in FY 2009–

11, state monies allocated to higher education decreased in 

many states. This decrease was offset to a degree by federal 

stimulus funds that were used to restore the level of state 

support for public higher education. The state monies used to 

calculate this indicator do not include federal stimulus funds 

for education stabilization or government funds for the modernization, renovation, or repair of higher education facilities.

Findings

• Nationally, state appropriations for operating expenses of 

higher education as a share of the state’s gross domestic 

product (GDP) decreased from 0.53% in 2004 to 0.46% 

in 2012.

• In 2012, the value of this indicator ranged from 0.13% to 

0.99% across the states.

• Between 2004 and 2012, 38 states increased their appropriations for higher education. The states that showed the 

largest increase in appropriations of state tax funds for the 

operating expenses of higher education were North Carolina, 

New York, Texas, and Illinois.

• While many states reduced the percentage of their GDP 

that was allocated to higher education, the states of North 

Carolina and Alaska made significant increases between 

2004 and 2012.

Appropriations of State Tax Funds for Operating Expenses of Higher 

Education as a Percentage of Gross Domestic Product

Figure 8-28

Appropriations of state tax funds for operating expenses of higher education as a percentage of gross domestic 

product: 2012

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.90 0.90 1.00

NH

AZ

DE

MI

MO

NJ

NV

NY

PA

RI

VA

VT

WA

AK

GA

NE

OK

CA

CT

FL

IA

MN

OH

SC

SD

TX

WI

HI

KY

ND

WV

AL

AR

NC

WY

MS

NM

ID

IL

IN

KS

LA

MD

ME

MT

TN

UT

Percent

CO

MA

OR

1st quartile (0.62%–0.99%)

2nd quartile (0.49%–0.61%)

3rd quartile (0.36%–0.47%)

4th quartile (0.13%–0.35%)

No data

SOURCES: State Higher Education Executive Officers/College Board, State Higher 

Education Finance; Bureau of Economic 

Analysis, Gross Domestic Product data; 

United Nations Statistics Division.
Science and Engineering Indicators 2014 ♦ 8-67

Table 8-28

Appropriations of state tax funds for operating expenses of higher education as a percentage of gross domestic 

product, by state: 2004, 2008, and 2012

Appropriations of state tax 

funds for operating 

expenses of higher 

education ($millions) State GDP ($millions)

Appropriations of state 

tax funds for operating 

expenses of higher 

education/state GDP (%)

State 2004 2008 2012 2004 2008 2012 2004 2008 2012

United States................... 62,375 80,698 72,220 11,774,408 14,193,121 15,566,076 0.53 0.57 0.46

Alabama....................... 1,168 1,962 1,495 141,974 170,203 183,547 0.82 1.15 0.81

Alaska .......................... 216 299 357 34,367 49,809 51,859 0.63 0.60 0.69

Arizona......................... 922 1,326 824 201,006 261,128 266,891 0.46 0.51 0.31

Arkansas ...................... 661 880 904 83,806 100,369 109,557 0.79 0.88 0.83

California...................... 8,715 11,634 9,379 1,569,816 1,900,463 2,003,479 0.56 0.61 0.47

Colorado ...................... 578 747 647 201,564 252,487 274,048 0.29 0.30 0.24

Connecticut ................. 748 1,034 950 187,545 219,449 229,317 0.40 0.47 0.41

Delaware ...................... 191 243 213 50,575 57,974 65,984 0.38 0.42 0.32

District of Columbia ..... NA NA NA 77,737 96,792 109,793 NA NA NA

Florida.......................... 3,285 4,449 3,631 621,417 748,117 777,164 0.53 0.59 0.47

Georgia ........................ 2,356 2,960 2,635 342,863 404,335 433,569 0.69 0.73 0.61

Hawaii .......................... 399 554 512 52,290 65,978 72,424 0.76 0.84 0.71

Idaho............................ 340 411 334 44,069 55,143 58,243 0.77 0.75 0.57

Illinois........................... 2,682 2,949 3,594 545,591 631,962 695,238 0.49 0.47 0.52

Indiana ......................... 1,360 1,525 1,549 231,762 260,971 298,625 0.59 0.58 0.52

Iowa ............................. 738 874 740 115,581 133,910 152,436 0.64 0.65 0.49

Kansas ......................... 686 826 740 99,733 124,330 138,953 0.69 0.66 0.53

Kentucky...................... 1,109 1,321 1,238 131,701 153,570 173,466 0.84 0.86 0.71

Louisiana...................... 1,245 1,708 1,237 171,461 213,970 243,264 0.73 0.80 0.51

Maine ........................... 232 271 269 44,352 49,500 53,656 0.52 0.55 0.50

Maryland...................... 1,149 1,555 1,613 231,963 281,112 317,678 0.50 0.55 0.51

Massachusetts............. 915 1,347 1,158 310,341 361,716 403,823 0.29 0.37 0.29

Michigan ...................... 1,984 2,034 1,548 365,609 368,963 400,504 0.54 0.55 0.39

Minnesota .................... 1,286 1,561 1,284 227,091 262,105 294,729 0.57 0.60 0.44

Mississippi ................... 767 1,046 954 77,539 95,461 101,490 0.99 1.10 0.94

Missouri ....................... 905 1,022 933 208,375 241,406 258,832 0.43 0.42 0.36

Montana....................... 151 197 202 27,831 35,802 40,422 0.54 0.55 0.50

Nebraska...................... 509 657 650 69,572 85,181 99,557 0.73 0.77 0.65

Nevada......................... 480 620 473 100,663 131,976 133,584 0.48 0.47 0.35

New Hampshire ........... 112 133 83 51,335 58,473 64,697 0.22 0.23 0.13

New Jersey .................. 1,741 2,045 1,998 410,790 482,099 508,003 0.42 0.42 0.39

New Mexico................. 710 1,016 799 64,196 77,117 80,600 1.11 1.32 0.99

New York...................... 3,313 4,853 4,719 891,462 1,079,719 1,205,930 0.37 0.45 0.39

North Carolina.............. 2,475 3,837 3,915 327,343 407,360 455,973 0.76 0.94 0.86

North Dakota ............... 200 254 344 23,333 31,769 46,016 0.86 0.80 0.75

Ohio ............................. 2,072 2,288 2,014 428,172 465,527 509,393 0.48 0.49 0.40

Oklahoma..................... 766 1,099 998 112,298 153,223 160,953 0.68 0.72 0.62

Oregon......................... 589 726 566 137,290 174,990 198,702 0.43 0.41 0.28

Pennsylvania................ 1,947 2,193 1,801 461,721 544,712 600,897 0.42 0.40 0.30

Rhode Island................ 184 191 161 42,925 47,231 50,956 0.43 0.40 0.32

South Carolina ............. 926 1,211 859 134,793 159,203 176,217 0.69 0.76 0.49

South Dakota............... 154 199 181 30,569 37,266 42,464 0.50 0.53 0.43

Tennessee.................... 1,090 1,640 1,415 213,537 247,961 277,036 0.51 0.66 0.51

Texas............................ 5,190 6,348 6,464 903,679 1,209,267 1,397,369 0.57 0.52 0.46

Utah ............................. 614 812 729 82,463 113,789 130,486 0.74 0.71 0.56

Vermont........................ 77 91 90 21,876 24,445 27,296 0.35 0.37 0.33

Virginia ......................... 1,346 1,886 1,624 329,557 397,894 445,876 0.41 0.47 0.36

Washington.................. 1,361 1,768 1,362 257,979 333,720 375,730 0.53 0.53 0.36

West Virginia ................ 405 562 543 48,691 58,227 69,380 0.83 0.97 0.78

Wisconsin .................... 1,125 1,243 1,154 208,904 236,094 261,548 0.54 0.53 0.44

Wyoming...................... 201 291 338 23,301 38,853 38,422 0.86 0.75 0.88

Puerto Rico.................. NA NA NA 82,809 95,708 NA NA NA NA

NA = not available.

GDP = gross domestic product.

SOURCES: State Higher Education Executive Officers College Board, State Higher Education Finance (various years); Bureau of Economic Analysis, 

Gross Domestic Product data (June 2013).

Science and Engineering Indicators 2014
8-68 ♦ Chapter 8. State Indicators

The cost of an undergraduate education can be reduced with financial assistance from the state or federal government or from an academic institution. 

This indicator is calculated by dividing the amount of financial support from state 

grants by the number of full-time undergraduate students who attend both public 

and private institutions in the state. A high value is one indicator of state efforts 

to provide access to higher education at a time of escalating undergraduate costs. 

The actual distribution of state grants to individual students may be affected by 

the percentage of undergraduates who are state residents.

This indicator should be viewed relative to the tuition charged to undergraduates in a state, as some states have chosen to subsidize tuition for all students at 

public institutions rather than provide grants. Other differences between states, 

such as the amount of scholarship aid available from other sources, the percentage of students attending out-of-state institutions, and their eligibility for state 

funding, mean that readers should exercise caution when making comparisons 

between states and examining changes over time.

Total state grant expenditures for financial aid include need-based and non–

need-based grants. State assistance through subsidized or unsubsidized loans 

and awards to students at the graduate and first professional degree levels is not 

included. The year is the end date of the academic year. For example, data for 

2011 represent costs for the 2010–11 academic year.

Findings

• The total amount of state financial aid from 

grants provided to undergraduates doubled 

nationwide, increasing from $4.6 billion in 

2001 to $9.1 billion in 2011.

• On a per-student basis, state funding for 

student grants across the United States 

increased from $613 per undergraduate in 

2001 to $858 per undergraduate in 2011.

• There are major differences in the amount 

of state aid provided to undergraduate students in different states. State values for this 

indicator ranged from $16 to $2,553 in 2011. 

Seven jurisdictions averaged less than $100 

per undergraduate student, while 14 provided more than $1,000 per student.

• Eleven states reported spending less per 

student for student financial aid in 2011 than 

in 2001, even though the cost of undergraduate education rose rapidly during this period.

State Expenditures on Student Aid per Full-Time Undergraduate Student

Figure 8-29

State expenditures on student aid per full-time undergraduate student: 2011

0 260 520 780 1,040 1,300 1,560 1,820 2,080 2,340 2,600

AK

AL

AZ

HI

ID

KS

MI

MT

NE

NH

OR

RI

SD

UT

WY

CT

DC

DE

FL

MD

MN

OK

VA

VT

WI

AR

KY

NC

NM

IN

PA

WV

CA

NJ TN

GA

SC

IL

LA

NY

TX

WA

Dollars

CO

IA

MA

ME

MO

MS

ND

NV

OH

1st quartile ($1,159–$2,553)

2nd quartile ($557–$1,098)

3rd quartile ($223–$545)

4th quartile ($16–$175)

SOURCES: National Association of 

State Student Grant and Aid Programs, 

Annual Survey Report; National Center 

for Education Statistics, Integrated 

Postsecondary Education Data System.
Science and Engineering Indicators 2014 ♦ 8-69

Table 8-29

State expenditures on student aid per full-time undergraduate student, by state: 2001, 2006, and 2011

State expenditures on student 

aid ($thousands)

Undergraduate enrollment 

at 4-year institutions

State expenditures on student aid/

undergraduate enrollment 

at 4-year institutions ($)

State 2001 2006 2011 2001 2006 2011 2001 2006 2011

United States................... 4,565,162 6,789,273 9,052,204 7,450,520 8,653,820 10,547,386 613 785 858

Alabama....................... 7,413 7,626 19,193 127,475 143,386 176,759 58 53 109

Alaska .......................... NA 502 1,514 24,939 26,382 30,749 NA 19 49

Arizona......................... 2,990 2,798 19,932 124,389 262,642 433,785 24 11 46

Arkansas ...................... 39,151 28,364 146,496 73,369 82,437 97,013 534 344 1,510

California...................... 461,914 757,809 1,269,917 636,105 714,347 809,090 726 1,061 1,570

Colorado ...................... 54,151 60,737 70,970 138,846 168,860 196,643 390 360 361

Connecticut ................. 44,763 39,366 63,611 87,335 93,832 106,932 513 420 595

Delaware ...................... 1,432 10,240 19,435 28,125 28,268 31,640 51 362 614

District of Columbia ..... 781 33,856 34,713 52,262 65,318 48,459 15 518 716

Florida.......................... 302,633 410,758 577,736 310,145 504,557 884,228 976 814 653

Georgia ........................ 310,995 461,615 768,449 198,254 236,664 322,458 1,569 1,951 2,383

Hawaii .......................... 535 410 3,339 27,637 33,963 38,071 19 12 88

Idaho............................ 1,138 5,424 5,064 50,969 57,505 65,074 22 94 78

Illinois........................... 382,566 380,349 407,825 281,619 330,331 351,725 1,358 1,151 1,159

Indiana ......................... 111,618 182,281 251,254 222,510 243,669 288,387 502 748 871

Iowa ............................. 53,100 53,815 57,848 99,468 127,886 221,384 534 421 261

Kansas ......................... 12,819 15,168 17,590 86,126 92,962 100,750 149 163 175

Kentucky...................... 66,931 172,866 193,206 112,935 127,630 146,072 593 1,354 1,323

Louisiana...................... 91,166 116,432 178,753 146,230 141,348 144,475 623 824 1,237

Maine ........................... 11,961 13,387 15,230 43,082 44,270 43,972 278 302 346

Maryland...................... 50,416 76,362 90,535 122,430 135,317 155,690 412 564 582

Massachusetts............. 116,892 80,093 87,823 235,697 246,799 268,140 496 325 328

Michigan ...................... 102,164 197,674 85,612 295,912 320,345 341,985 345 617 250

Minnesota .................... 120,465 131,010 130,073 152,381 170,616 203,079 791 768 641

Mississippi ................... 20,163 22,285 22,198 62,595 65,791 75,152 322 339 295

Missouri ....................... 43,882 42,068 90,774 182,463 212,159 254,357 240 198 357

Montana....................... 3,195 3,760 5,877 33,462 33,677 39,110 95 112 150

Nebraska...................... 5,975 9,918 15,671 59,388 63,983 70,231 101 155 223

Nevada......................... 13,449 39,671 47,838 34,274 81,180 93,013 392 489 514

New Hampshire ........... 1,497 3,753 2,967 42,534 44,860 48,794 35 84 61

New Jersey .................. 197,619 256,047 333,404 161,329 171,282 201,059 1,225 1,495 1,658

New Mexico................. 38,736 61,780 89,254 43,285 50,546 59,125 895 1,222 1,510

New York...................... 659,394 895,129 896,266 581,671 624,730 712,725 1,134 1,433 1,258

North Carolina.............. 121,153 192,018 378,366 196,748 230,576 259,914 616 833 1,456

North Dakota ............... 1,152 1,864 12,198 29,951 34,017 41,423 38 55 294

Ohio ............................. 173,868 221,411 109,731 309,285 337,332 412,936 562 656 266

Oklahoma..................... 29,035 58,216 92,122 102,808 114,011 128,649 282 511 716

Oregon......................... 19,711 29,429 19,287 80,385 91,031 112,271 245 323 172

Pennsylvania................ 325,234 403,957 368,459 386,220 423,915 457,606 842 953 805

Rhode Island................ 6,164 12,883 13,170 50,452 54,189 56,056 122 238 235

South Carolina ............. 98,095 255,744 325,348 95,652 105,408 127,415 1,026 2,426 2,553

South Dakota............... NA 3,367 4,418 33,125 37,090 42,527 NA 91 104

Tennessee.................... 30,156 173,907 353,309 142,697 162,843 192,406 211 1,068 1,836

Texas............................ 108,628 366,873 684,905 449,177 538,069 623,983 242 682 1,098

Utah ............................. 2,511 7,409 9,674 128,285 140,967 190,067 20 53 51

Vermont........................ 14,414 17,560 17,328 26,395 28,648 31,116 546 613 557

Virginia ......................... 115,242 132,720 188,585 180,228 218,857 285,448 639 606 661

Washington.................. 98,533 173,835 234,238 110,310 128,585 183,650 893 1,352 1,275

West Virginia ................ 18,217 70,981 100,916 69,795 66,302 111,857 261 1,071 902

Wisconsin .................... 71,145 93,583 119,616 170,859 184,922 219,649 416 506 545

Wyoming...................... NA 163 167 8,907 9,516 10,287 NA 17 16

Puerto Rico.................. 40,231 33,840 53,318 156,795 165,366 181,489 257 205 294

NA = not available.

SOURCES: National Association of State Student Grant and Aid Programs, Annual Survey Report (various years); National Center for Education Statistics, Integrated Postsecondary Education Data System (various years).

Science and Engineering Indicators 2014
8-70 ♦ Chapter 8. State Indicators

Public research universities rely on state support for a substantial share of their operating 

revenues, most of which support their education function. The amount of funding provided 

per full-time equivalent (FTE) student is an indicator of states’ investment in the education 

of their students. Eventually, changes in these funds affect the institutions’ financial health 

and the quality of education they provide.

Data for this indicator cover 101 public research universities with broad educational 

missions (excluding freestanding medical and engineering schools when possible). These 

institutions are either the leading recipient of academic R&D funding in their state or among 

the nation’s top 100 recipients of academic R&D funding to public universities in 2008. 

State funds include state and local operating grants and contracts as well as state appropriations. Enrollment includes total FTE enrollment measured in the fall of each academic year. 

Data were drawn from the National Center for Education Statistics Integrated Postsecondary Education Data System (IPEDS) Analytics: Delta Cost Project Database: 2000–2010. 

To maintain comparability over time, the database groups institutions that included data 

for branch campuses in their reporting to IPEDS in 1 or more years. Specifically, for 34 

institutions in the database, data cover branch campuses with little or no research activity as 

well as main campuses for which research is central to the university’s mission. Comparison between states and analysis of funding trends at the nation’s most research-intensive 

institutions should take this into account. 

State funds are one of many sources of public university revenue. This indicator does 

not include changes in these other revenue sources.

Findings

• Across the nation, state funding 

for public research universities and 

their branch campuses increased 

from nearly $25 billion in 2000 to 

nearly $37 billion in 2010. Only two 

states did not increase their funding for public research universities 

and their branch campuses during 

this period.

• When adjusted for inflation, total 

state expenditures for public 

research universities increased 

by 18% between 2000 and 2010, 

while FTE enrollment increased by 

over 22%. 

• Between 2000 and 2010, perstudent state support to public 

research universities dropped 

by an average of 3% in inflationadjusted dollars. 

State Funding for Public Research Universities per Full-Time Equivalent Student 

Figure 8-30

State funding for major public research universities per full-time equivalent student: 2010

2,500 4,800 7,100 9,400 11,700 14,000 16,300 18,600 20,900 23,200 25,500

CO

DE

IN

ND

SC

SD

VA

AL

AR

KY

NV

TN

TX

AZ

ID

MA

ME

MI

MO

MS

NE

OH

WV

AK

NY

CA

WI

CT

HI

MN

WY NC

FL

GA

IA

IL

KS

LA

MD

NJ

NM

OK

UT

WA

Dollars

MT

NH

OR

PA

RI

VT

1st quartile ($14,741–$24,402)

2nd quartile ($11,792–$14,669)

3rd quartile ($8,835–$11,742)

4th quartile ($2,945–$8,646)

No data

SOURCES: National Center for Education 

Statistics, Integrated Postsecondary Education Data System (IPEDS) Analytics; Delta 

Cost Project Database.
Science and Engineering Indicators 2014 ♦ 8-71

Table 8-30

State funding for public research universities per full-time equivalent student, by state: 2000, 2005, and 2010

State funding for major public 

research universities ($thousands) FTE enrolled students

State funding for public 

research universities/

FTE enrolled student ($)

State 2000 2005 2010 2000 2005 2010 2000 2005 2010

United States................. 24,865,422 31,273,013 36,859,219 2,353,173 2,622,287 2,877,469 10,567 11,926 12,810

Alabama..................... 404,754 531,561 620,604 37,325 39,842 42,101 10,844 13,342 14,741

Alaska ........................ 185,564 286,687 400,048 16,096 18,722 19,614 11,529 15,313 20,396

Arizona....................... 621,501 758,956 918,021 66,712 75,172 95,139 9,316 10,096 9,649

Arkansas .................... 176,714 200,887 251,151 13,196 14,605 17,121 13,391 13,755 14,669

California.................... 2,618,451 3,337,918 4,082,603 193,944 221,004 244,519 13,501 15,103 16,696

Colorado .................... 218,806 223,086 161,033 47,525 52,820 54,687 4,604 4,224 2,945

Connecticut ............... 380,456 445,039 608,193 19,219 24,158 26,294 19,796 18,422 23,130

Delaware .................... 109,653 131,880 148,308 18,722 19,350 19,506 5,857 6,816 7,603

District of Columbia ... NA NA NA NA NA NA NA NA NA

Florida........................ 1,455,743 2,298,758 2,616,966 140,621 172,048 188,691 10,352 13,361 13,869

Georgia ...................... 920,671 986,136 1,055,577 58,504 67,614 76,588 15,737 14,585 13,783

Hawaii ........................ 183,164 296,004 359,025 14,398 17,103 16,899 12,721 17,307 21,245

Idaho.......................... 108,404 144,959 122,931 9,585 11,014 10,513 11,310 13,161 11,693

Illinois......................... 927,641 1,001,944 1,122,660 79,863 82,597 86,474 11,615 12,131 12,983

Indiana ....................... 518,069 630,767 669,915 69,150 72,063 77,482 7,492 8,753 8,646

Iowa ........................... 563,423 591,598 611,004 48,712 49,286 52,037 11,566 12,003 11,742

Kansas ....................... 319,712 367,085 563,987 40,721 43,759 46,904 7,851 8,389 12,024

Kentucky.................... 499,773 615,607 653,892 35,765 39,764 42,144 13,974 15,482 15,516

Louisiana.................... 278,804 342,635 348,428 28,644 30,035 26,914 9,733 11,408 12,946

Maine ......................... 192,289 243,768 261,284 24,276 27,369 27,088 7,921 8,907 9,646

Maryland.................... 433,340 588,915 593,753 37,442 41,522 44,629 11,574 14,183 13,304

Massachusetts........... 519,962 614,223 632,434 46,182 46,238 54,469 11,259 13,284 11,611

Michigan .................... 1,002,738 1,253,549 1,241,363 94,551 102,013 107,487 10,605 12,288 11,549

Minnesota .................. 592,159 783,777 1,001,761 35,330 41,521 43,254 16,761 18,877 23,160

Mississippi ................. 260,204 267,310 294,249 24,684 27,367 31,205 10,541 9,768 9,430

Missouri ..................... 470,857 530,539 632,596 43,157 49,720 56,507 10,910 10,671 11,195

Montana..................... 55,167 64,978 73,074 10,467 10,533 10,617 5,271 6,169 6,883

Nebraska.................... 256,913 278,534 350,481 29,715 30,523 33,970 8,646 9,125 10,317

Nevada....................... 135,194 198,948 197,496 9,677 13,039 13,946 13,971 15,258 14,161

New Hampshire ......... 82,863 115,288 149,608 20,933 23,263 26,275 3,958 4,956 5,694

New Jersey ................ 570,620 689,066 731,360 47,407 49,603 55,272 12,037 13,892 13,232

New Mexico............... 403,774 519,765 651,942 40,718 45,155 51,884 9,916 11,511 12,565

New York.................... 725,971 1,063,088 1,547,586 63,096 69,203 77,091 11,506 15,362 20,075

North Carolina............ 794,917 990,950 1,350,270 45,492 49,458 55,334 17,474 20,036 24,402

North Dakota ............. 136,245 178,899 258,148 21,900 26,713 27,976 6,221 6,697 9,227

Ohio ........................... 779,803 972,021 1,143,700 91,254 98,065 110,237 8,545 9,912 10,375

Oklahoma................... 346,788 390,760 506,624 37,533 43,948 41,226 9,240 8,891 12,289

Oregon....................... 220,361 235,773 243,593 30,644 36,497 40,774 7,191 6,460 5,974

Pennsylvania.............. 692,819 857,409 952,440 117,563 126,593 139,967 5,893 6,773 6,805

Rhode Island.............. 81,968 95,139 71,880 11,806 12,618 14,566 6,943 7,540 4,935

South Carolina ........... 376,372 389,544 383,147 35,248 38,235 43,365 10,678 10,188 8,835

South Dakota............. 48,496 64,110 78,464 7,252 9,163 10,367 6,687 6,997 7,569

Tennessee.................. 444,873 794,529 706,906 37,970 38,289 43,905 11,716 20,751 16,101

Texas.......................... 2,293,537 2,781,144 3,914,463 190,114 225,357 246,847 12,064 12,341 15,858

Utah ........................... 321,948 476,882 541,400 35,839 41,437 44,037 8,983 11,509 12,294

Vermont...................... 35,626 77,270 76,552 8,922 9,745 12,172 3,993 7,929 6,289

Virginia ....................... 815,759 866,207 933,792 94,170 104,625 120,454 8,663 8,279 7,752

Washington................ 565,588 726,238 822,927 52,296 58,997 69,789 10,815 12,310 11,792

West Virginia .............. 219,310 239,083 281,888 22,640 26,121 29,613 9,687 9,153 9,519

Wisconsin .................. 396,056 573,924 678,788 36,825 37,769 38,822 10,755 15,196 17,485

Wyoming.................... 101,602 159,876 240,903 9,368 10,632 10,697 10,846 15,037 22,521

Puerto Rico................ NA NA NA NA NA NA NA NA NA

NA = not available.

FTE = full-time equivalent. 

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education Data System (IPEDS) Analytics; Delta Cost Project Database: 

2000–2010. 

Science and Engineering Indicators 2014
8-72 ♦ Chapter 8. State Indicators

This indicator represents the percentage of the early- to midcareer 

population that has earned a postsecondary degree. That degree may be 

an associate’s, bachelor’s, master’s, or doctoral degree. The indicator 

represents where postsecondary degree holders live rather than where 

they were educated. The age cohort of 25–44 years represents the group 

most likely to have completed a postsecondary program.

Estimates of educational attainment and of the population of individuals aged 25–44 years old are provided by the U.S. Census Bureau. 

Small differences in the value of this indicator between states and across 

time generally are not meaningful.

Findings

• The early- to midcareer population with a postsecondary degree was 40.4% nationwide in 

2011, an increase from 35.3% in 2001.

• In 2011, the percentage of this cohort with a 

postsecondary degree varied greatly among 

states, ranging from 28.3% to 53.5%.

• Between 2001 and 2011, all states, except 

Rhode Island and Kansas, showed an increase 

in the percentage of their early- to midcareer 

population with a postsecondary degree, ranging from approximately 2 to 9 percentage points 

over the time period.

• States with the lowest cost of living tended to 

rank lowest on this indicator.

Postsecondary Degree Holders among Individuals 25–44 Years Old

Figure 8-31

Postsecondary degree holders among individuals 25–44 years old: 2011

28.0 32.5 37.0 41.5 46.0 50.5 55.0 59.5 64.0 68.5

AR

LA

MS

NM

NV

WV

CO

CT

MD

ND

NH

NJ

NY

VA

VT

CA

DE

FL

GA

HI

ME

MI

MO

NC

OH

OR

UT

WY

MA

MN DC

IA

IL

KS

MT

NE

PA

RI

SD

WA

WI

AK

AL

AZ

ID

IN

KY

OK

SC

TN

TX

Percent

1st quartile (45.7%–66.5%)

2nd quartile (39.8%–44.9%)

3rd quartile (35.3%–39.6%)

4th quartile (28.3%–34.6%)

SOURCES: Census Bureau, Population 

Estimates Program and American 

Community Survey.
Science and Engineering Indicators 2014 ♦ 8-73

Table 8-31

Postsecondary degree holders among individuals 25–44 years old, by state: 2001, 2006, and 2011

Postsecondary degree holders 

25–44 years old Individuals 25–44 years old

Postsecondary 

degree holders/ 

individuals 25–44 

years old (%)

State 2001 2006 2011 2001 2006 2011 2001 2006 2011

United States.................. 29,834,331 31,653,483 33,261,456 84,523,274 82,638,980 82,432,298 35.3 38.3 40.4

Alabama...................... 368,495 382,124 410,753 1,266,952 1,233,767 1,223,076 29.1 31.0 33.6

Alaska ......................... 59,787 63,331 68,881 198,158 188,470 198,914 30.2 33.6 34.6

Arizona........................ 460,938 586,052 595,767 1,526,458 1,664,223 1,688,279 30.2 35.2 35.3

Arkansas .................... 185,372 202,594 225,881 743,315 747,504 745,421 24.9 27.1 30.3

California..................... 3,618,304 3,942,407 4,095,176 10,750,718 10,578,738 10,565,342 33.7 37.3 38.8

Colorado ..................... 591,620 616,562 686,256 1,412,620 1,380,451 1,448,033 41.9 44.7 47.4

Connecticut ................ 445,850 425,024 426,066 1,017,477 944,217 898,232 43.8 45.0 47.4

Delaware ..................... 81,009 92,375 89,768 233,890 232,516 227,578 34.6 39.7 39.4

District of Columbia .... 94,690 110,819 143,688 190,251 187,870 216,233 49.8 59.0 66.5

Florida......................... 1,564,716 1,766,287 1,807,574 4,591,807 4,804,621 4,758,046 34.1 36.8 38.0

Georgia ....................... 874,366 993,730 1,032,857 2,668,017 2,727,666 2,741,412 32.8 36.4 37.7

Hawaii ......................... 129,438 149,841 151,199 357,271 358,311 366,855 36.2 41.8 41.2

Idaho........................... 102,492 133,521 145,300 362,154 383,267 402,781 28.3 34.8 36.1

Illinois.......................... 1,488,602 1,515,472 1,567,172 3,756,180 3,572,420 3,491,104 39.6 42.4 44.9

Indiana ........................ 561,655 582,550 614,145 1,769,492 1,705,535 1,665,758 31.7 34.2 36.9

Iowa ............................ 299,357 314,362 342,653 793,288 747,836 749,530 37.7 42.0 45.7

Kansas ........................ 304,258 288,709 308,235 755,887 713,707 727,160 40.3 40.5 42.4

Kentucky..................... 321,357 367,007 377,645 1,194,291 1,162,541 1,138,883 26.9 31.6 33.2

Louisiana..................... 318,160 316,725 365,789 1,268,704 1,152,042 1,203,069 25.1 27.5 30.4

Maine .......................... 121,914 126,998 127,825 364,111 337,692 312,002 33.5 37.6 41.0

Maryland..................... 690,575 727,195 732,628 1,652,198 1,598,650 1,565,884 41.8 45.5 46.8

Massachusetts............ 974,665 925,921 929,155 1,967,815 1,795,786 1,738,118 49.5 51.6 53.5

Michigan ..................... 1,030,376 1,001,040 940,460 2,905,689 2,658,755 2,414,603 35.5 37.7 38.9

Minnesota ................... 665,624 674,897 710,688 1,486,814 1,412,852 1,400,438 44.8 47.8 50.7

Mississippi .................. 218,184 230,625 241,695 794,888 767,066 760,122 27.4 30.1 31.8

Missouri ...................... 518,314 560,544 602,937 1,606,777 1,547,126 1,523,458 32.3 36.2 39.6

Montana...................... 79,849 90,992 99,689 238,899 228,548 237,269 33.4 39.8 42.0

Nebraska..................... 194,601 198,392 207,888 478,968 458,133 469,737 40.6 43.3 44.3

Nevada........................ 152,489 208,988 216,971 648,880 747,896 766,544 23.5 27.9 28.3

New Hampshire .......... 164,905 161,622 151,036 378,536 348,846 319,411 43.6 46.3 47.3

New Jersey ................. 1,137,167 1,115,872 1,105,760 2,603,347 2,446,589 2,338,637 43.7 45.6 47.3

New Mexico................ 141,690 160,603 166,658 506,151 507,378 519,946 28.0 31.7 32.1

New York..................... 2,335,677 2,450,307 2,534,197 5,775,563 5,417,603 5,280,570 40.4 45.2 48.0

North Carolina............. 826,717 921,080 1,024,597 2,504,293 2,518,651 2,577,307 33.0 36.6 39.8

North Dakota .............. 73,317 74,021 82,796 168,631 156,114 170,010 43.5 47.4 48.7

Ohio ............................ 1,104,034 1,085,140 1,110,323 3,259,384 3,037,836 2,873,075 33.9 35.7 38.6

Oklahoma.................... 264,301 290,732 329,554 960,435 938,630 975,445 27.5 31.0 33.8

Oregon........................ 337,122 377,058 404,801 992,783 994,743 1,031,267 34.0 37.9 39.3

Pennsylvania............... 1,262,106 1,282,537 1,335,850 3,435,158 3,224,924 3,123,097 36.7 39.8 42.8

Rhode Island............... 126,318 124,009 113,495 306,912 284,670 261,020 41.2 43.6 43.5

South Carolina ............ 380,304 383,505 424,313 1,175,787 1,179,555 1,193,581 32.3 32.5 35.5

South Dakota.............. 78,120 81,430 85,994 202,454 193,284 201,235 38.6 42.1 42.7

Tennessee................... 495,231 524,652 574,932 1,699,828 1,690,961 1,678,144 29.1 31.0 34.3

Texas........................... 1,979,704 2,183,005 2,486,642 6,529,822 6,742,164 7,180,834 30.3 32.4 34.6

Utah ............................ 222,744 278,685 323,281 633,099 701,224 793,074 35.2 39.7 40.8

Vermont....................... 70,565 68,160 70,427 172,405 155,997 146,497 40.9 43.7 48.1

Virginia ........................ 892,862 949,488 1,049,674 2,227,441 2,190,642 2,215,775 40.1 43.3 47.4

Washington................. 704,284 771,750 813,490 1,805,606 1,791,998 1,868,055 39.0 43.1 43.5

West Virginia ............... 110,362 130,214 143,431 487,860 468,119 455,269 22.6 27.8 31.5

Wisconsin ................... 568,689 599,292 610,184 1,561,327 1,479,447 1,440,314 36.4 40.5 42.4

Wyoming..................... 41,055 45,237 55,280 134,483 131,399 145,854 30.5 34.4 37.9

Puerto Rico................. NA 411,191 390,538 1,055,380 1,080,801 955,369 NA 38.0 40.9

NA = not available.

SOURCES: Census Bureau, 2000 and 2010 Decennial Censuses, Population Estimates Program (various years), and American Community Survey (various years).

Science and Engineering Indicators 2014
8-74 ♦ Chapter 8. State Indicators

This indicator represents the percentage of the earlyto midcareer population that has earned at least a 4-year 

undergraduate degree. The indicator represents where 

college degree holders live rather than where they were 

educated. The age cohort of 25–44 years represents a group 

of individuals who are potential long-term participants in 

a state’s workforce.

Estimates of educational attainment are developed by 

the U.S. Census Bureau. Small differences in the value of 

this indicator between states and across time generally are 

not meaningful.

Findings

• The early- to midcareer population with at least a bachelor’s 

degree was 31.7% nationwide in 2011, an increase from 27.4% 

in 2001.

• All states, except South Dakota and New Mexico, showed an 

increase in the percentage of their early-career population with 

at least a bachelor’s degree between 2001 and 2011.

• In 2011, the percentage of the early-career population with at 

least a bachelor’s degree varied among states, ranging from 

21.1 % to 45.8%. The highest percentages tended to be found 

in the New England and Middle Atlantic states.

• States with the lowest cost of living tended to rank lowest on 

this indicator.

• The difference between Experimental Program to Stimulate 

Competitive Research (EPSCoR) and non-EPSCoR states, as a 

group, remained relatively unchanged and may have increased 

slightly between 2001 and 2011.

Bachelor’s Degree Holders among Individuals 25–44 Years Old

Figure 8-32

Bachelor’s degree holders among individuals 25–44 years old: 2011

20.0 25.0 30.0 35.0 40.0 45.0 50.0 55.0 60.0 65.0

AL

AR

KY

LA

MS

NM

NV

WV

CA

DE

GA

IA

KS

ME

MO

MT

NC

ND

NE

OR

PA

RI

UT

WA

WI

CO

CT

IL

MN

NH

NY

VA

VT MA DC

MD

NJ

Percent

AK

AZ

FL

HI

ID

IN

MI

OH

OK

SC

SD

TN

TX

WY

1st quartile (33.5%–64.2%)

2nd quartile (30.1%–32.9%)

3rd quartile (26.5%–29.8%)

4th quartile (21.1%–26.4%)

SOURCES: Census Bureau, Population 

Estimates Program and American Community Survey.
Science and Engineering Indicators 2014 ♦ 8-75

Table 8-32

Bachelor’s degree holders among individuals 25–44 years old, by state: 2001, 2006, and 2011

Bachelor’s degree holders 

25–44 years old Individuals 25–44 years old

Bachelor’s degree 

holders/individuals 

25–44 years old (%)

State 2001 2006 2011 2001 2006 2011 2001 2006 2011

EPSCoR states................ 2,881,370 3,071,085 3,318,921 12,874,120 12,561,027 12,630,814 22.0 24.0 26.0

Non-EPSCoR states........ 20,034,473 21,178,682 22,506,261 70,933,001 69,375,658 69,048,386 28.0 31.0 33.0

Average EPSCoR state 

 value ............................ na na na na na na 23.6 26.2 28.1

Average non-EPSCoR 

 state value ................... na na na na na na 28.7 31.1 33.2

United States................... 23,146,638 24,511,980 26,126,620 84,523,274 82,638,980 82,432,298 27.4 29.7 31.7

Alabama ...................... 260,871 279,106 302,531 1,266,952 1,233,767 1,223,076 20.6 22.6 24.7

Alaska .......................... 41,179 49,715 54,019 198,158 188,470 198,914 20.8 26.4 27.2

Arizona......................... 345,585 439,549 447,271 1,526,458 1,664,223 1,688,279 22.6 26.4 26.5

Arkansas ...................... 140,169 154,707 171,718 743,315 747,504 745,421 18.9 20.7 23.0

California...................... 2,902,253 3,139,598 3,325,565 10,750,718 10,578,738 10,565,342 27.0 29.7 31.5

Colorado ...................... 479,814 502,928 559,124 1,412,620 1,380,451 1,448,033 34.0 36.4 38.6

Connecticut ................. 364,317 353,075 357,969 1,017,477 944,217 898,232 35.8 37.4 39.9

Delaware ...................... 62,714 73,850 73,414 233,890 232,516 227,578 26.8 31.8 32.3

District of Columbia ..... 90,167 104,724 138,826 190,251 187,870 216,233 47.4 55.7 64.2

Florida.......................... 1,116,118 1,261,961 1,291,828 4,591,807 4,804,621 4,758,046 24.3 26.3 27.2

Georgia ........................ 711,728 788,910 829,700 2,668,017 2,727,666 2,741,412 26.7 28.9 30.3

Hawaii .......................... 91,816 108,311 106,724 357,271 358,311 366,855 25.7 30.2 29.1

Idaho............................ 74,079 94,112 106,168 362,154 383,267 402,781 20.5 24.6 26.4

Illinois........................... 1,188,735 1,207,920 1,280,116 3,756,180 3,572,420 3,491,104 31.6 33.8 36.7

Indiana ......................... 414,582 421,398 448,594 1,769,492 1,705,535 1,665,758 23.4 24.7 26.9

Iowa ............................. 210,690 219,911 238,650 793,288 747,836 749,530 26.6 29.4 31.8

Kansas ......................... 234,772 228,389 243,650 755,887 713,707 727,160 31.1 32.0 33.5

Kentucky...................... 242,002 265,168 279,374 1,194,291 1,162,541 1,138,883 20.3 22.8 24.5

Louisiana...................... 258,592 250,100 283,642 1,268,704 1,152,042 1,203,069 20.4 21.7 23.6

Maine ........................... 85,043 90,374 96,292 364,111 337,692 312,002 23.4 26.8 30.9

Maryland...................... 580,246 605,876 627,067 1,652,198 1,598,650 1,565,884 35.1 37.9 40.0

Massachusetts............. 797,799 775,630 795,926 1,967,815 1,795,786 1,738,118 40.5 43.2 45.8

Michigan ...................... 765,478 759,116 711,953 2,905,689 2,658,755 2,414,603 26.3 28.6 29.5

Minnesota .................... 500,619 500,908 535,146 1,486,814 1,412,852 1,400,438 33.7 35.5 38.2

Mississippi ................... 154,507 159,018 162,731 794,888 767,066 760,122 19.4 20.7 21.4

Missouri ....................... 421,368 436,851 466,569 1,606,777 1,547,126 1,523,458 26.2 28.2 30.6

Montana....................... 60,140 68,280 76,273 238,899 228,548 237,269 25.2 29.9 32.1

Nebraska...................... 138,097 145,722 153,063 478,968 458,133 469,737 28.8 31.8 32.6

Nevada......................... 112,005 151,981 161,563 648,880 747,896 766,544 17.3 20.3 21.1

New Hampshire ........... 119,801 121,670 118,254 378,536 348,846 319,411 31.6 34.9 37.0

New Jersey .................. 965,627 940,691 945,454 2,603,347 2,446,589 2,338,637 37.1 38.4 40.4

New Mexico................. 107,707 116,419 119,738 506,151 507,378 519,946 21.3 22.9 23.0

New York...................... 1,807,717 1,934,138 2,066,561 5,775,563 5,417,603 5,280,570 31.3 35.7 39.1

North Carolina.............. 620,220 689,731 776,393 2,504,293 2,518,651 2,577,307 24.8 27.4 30.1

North Dakota ............... 48,812 49,178 55,888 168,631 156,114 170,010 28.9 31.5 32.9

Ohio ............................. 829,739 820,656 840,370 3,259,384 3,037,836 2,873,075 25.5 27.0 29.2

Oklahoma..................... 203,189 217,214 249,805 960,435 938,630 975,445 21.2 23.1 25.6

Oregon......................... 256,959 291,514 317,301 992,783 994,743 1,031,267 25.9 29.3 30.8

Pennsylvania................ 966,550 981,799 1,045,715 3,435,158 3,224,924 3,123,097 28.1 30.4 33.5

Rhode Island................ 92,810 98,999 91,168 306,912 284,670 261,020 30.2 34.8 34.9

South Carolina ............. 274,797 276,216 315,050 1,175,787 1,179,555 1,193,581 23.4 23.4 26.4

South Dakota............... 56,014 56,968 59,989 202,454 193,284 201,235 27.7 29.5 29.8

Tennessee.................... 390,824 406,110 455,453 1,699,828 1,690,961 1,678,144 23.0 24.0 27.1

Texas............................ 1,562,573 1,710,381 1,972,064 6,529,822 6,742,164 7,180,834 23.9 25.4 27.5

Utah ............................. 156,274 201,175 238,953 633,099 701,224 793,074 24.7 28.7 30.1

Vermont........................ 53,430 51,825 56,930 172,405 155,997 146,497 31.0 33.2 38.9

Virginia ......................... 739,779 777,630 878,899 2,227,441 2,190,642 2,215,775 33.2 35.5 39.7

Washington.................. 534,153 578,583 610,787 1,805,606 1,791,998 1,868,055 29.6 32.3 32.7

West Virginia ................ 78,782 91,847 104,925 487,860 468,119 455,269 16.1 19.6 23.0

Wisconsin .................... 404,726 432,643 442,833 1,561,327 1,479,447 1,440,314 25.9 29.2 30.7

Wyoming...................... 30,670 29,405 38,624 134,483 131,399 145,854 22.8 22.4 26.5

Puerto Rico.................. NA 279,953 278,782 1,055,380 1,080,801 955,369 NA 25.9 29.2

na = not applicable; NA = not available.

EPSCoR = Experimental Program to Stimulate Competitive Research.

NOTE: For an explanation of EPSCoR and non-EPSCoR averages, see the chapter introduction.

SOURCES: Census Bureau, 2000 and 2010 Decennial Censuses, Population Estimates Program (various years), and American Community Survey 

(various years).

Science and Engineering Indicators 2014
8-76 ♦ Chapter 8. State Indicators

The ratio of degree holders (bachelor’s, graduate, or professional) to 

the population potentially available for work is an indicator of the concentration of individuals with higher education qualifications in a jurisdiction. This indicator does not imply that all degree holders are currently 

employed; rather, it indicates the educational level of the workforce if all 

degree holders were employed. Knowledge-intensive businesses seeking 

to relocate may be attracted to states with high values on this indicator. 

Workers with at least a bachelor’s degree have a clear advantage over 

less-educated workers in expected lifetime earnings.

Estimates of degree data are provided by the U.S. Census Bureau 

and are limited to individuals 25–64 years old, the age range most representative of a jurisdiction’s workforce. Individuals younger than age 

25 are considered to be in the process of completing their education. 

Individuals older than 64 are considered to be largely retired, so their 

educational attainment would have limited applicability to the quality of 

the workforce. Employed workforce data are Bureau of Labor Statistics 

estimates of employed civilians based on Local Area Unemployment 

Statistics. Estimates for states with smaller populations are generally less 

precise than estimates for states with larger populations.

Findings

• In 2011, nearly 50 million individuals between ages 

25 and 64 held bachelor’s degrees in the United 

States, up from nearly 41 million in 2001. 

• Nationwide, the ratio of bachelor’s degree holders to 

the size of the workforce rose from 29.6% in 2001 to 

35.4% in 2011. This ratio varied considerably among 

the states, ranging from 26.1% to 47.2% in 2011.

• The value of this indicator increased in all jurisdictions, except Alaska, between 2001 and 2011. This 

increase may reflect a replacement of older cohorts 

of workers with younger, more educated ones. It may 

also indicate the restructuring of state economies to 

emphasize work that requires a higher level of education or credentials.

• In 2011, the jurisdictions in which the highest 

concentrations of bachelor’s degree holders lived 

included the District of Columbia, Massachusetts, 

New Jersey, Colorado, and Maryland.

Bachelor’s Degree Holders Potentially in the Workforce

Figure 8-33

Bachelor’s degree holders potentially in the workforce: 2011

25.0 30.0 35.0 40.0 45.0 50.0 55.0 60.0 65.0

AK

AZ

FL

ID

KS

ME

MI

MO

MT

NC

NM

OH

PA

SC

TN

TX

UT

WI MA

CA

DE

GA

HI

IL

MN

NH

OR

RI

VT

WA DC

CO

CT

MD

NJ

NY

VA

Percent

AL

AR

IA

IN

KY

LA

MS

ND

NE

NV

OK

SD

WV

WY

1st quartile (37.4%–63.3%)

2nd quartile (32.7%–37.0%)

3rd quartile (29.8%–32.2%)

4th quartile (26.1%–29.3%)

SOURCES: Census Bureau, American 

Community Survey; Bureau of Labor 

Statistics, Local Area Unemployment 

Statistics.
Science and Engineering Indicators 2014 ♦ 8-77

Table 8-33

Bachelor’s degree holders potentially in the workforce, by state: 2001, 2006, and 2011

Bachelor’s degree holders 

25–64 years old Employed workforce

Bachelor’s degree 

holders/employed 

workforce (%)

State 2001 2006 2011 2001 2006 2011 2001 2006 2011

United States.................. 40,527,497 45,935,309 49,761,556 137,107,779 143,729,350 140,695,662 29.6 32.0 35.4

Alabama...................... 476,157 550,302 593,859 2,034,909 2,098,462 1,992,522 23.4 26.2 29.8

Alaska ......................... 89,504 102,153 105,780 301,694 326,109 337,796 29.7 31.3 31.3

Arizona........................ 621,567 834,211 881,399 2,453,453 2,836,638 2,761,984 25.3 29.4 31.9

Arkansas ..................... 260,535 289,510 326,754 1,194,024 1,286,887 1,251,877 21.8 22.5 26.1

California..................... 5,140,460 5,788,525 6,209,917 16,220,033 16,821,266 16,237,286 31.7 34.4 38.2

Colorado ..................... 851,265 949,265 1,067,811 2,303,494 2,541,828 2,490,004 37.0 37.3 42.9

Connecticut ................ 656,272 693,564 740,397 1,700,046 1,745,993 1,732,807 38.6 39.7 42.7

Delaware ..................... 111,152 131,601 143,550 404,135 424,618 407,772 27.5 31.0 35.2

District of Columbia .... 141,159 160,332 197,942 286,649 303,791 312,859 49.2 52.8 63.3

Florida......................... 2,086,928 2,516,214 2,657,913 7,624,718 8,584,095 8,322,237 27.4 29.3 31.9

Georgia ....................... 1,144,463 1,426,071 1,524,738 4,112,868 4,500,150 4,295,113 27.8 31.7 35.5

Hawaii ......................... 180,610 218,941 221,769 589,216 617,807 614,824 30.7 35.4 36.1

Idaho........................... 152,726 184,486 210,656 644,816 718,077 702,920 23.7 25.7 30.0

Illinois.......................... 1,987,145 2,143,825 2,307,808 6,113,536 6,225,095 5,942,809 32.5 34.4 38.8

Indiana ........................ 707,529 782,232 843,402 3,020,985 3,080,047 2,874,722 23.4 25.4 29.3

Iowa ............................ 368,722 408,648 449,951 1,568,638 1,595,136 1,562,156 23.5 25.6 28.8

Kansas ........................ 407,954 440,261 474,072 1,347,715 1,403,938 1,401,055 30.3 31.4 33.8

Kentucky..................... 410,170 495,800 529,836 1,852,056 1,904,467 1,875,447 22.1 26.0 28.3

Louisiana..................... 453,105 477,352 528,592 1,922,110 1,900,240 1,919,021 23.6 25.1 27.5

Maine .......................... 171,041 199,868 217,867 650,699 665,856 649,312 26.3 30.0 33.6

Maryland..................... 1,015,855 1,144,963 1,228,462 2,712,268 2,892,733 2,868,191 37.5 39.6 42.8

Massachusetts............ 1,350,105 1,423,262 1,519,049 3,275,343 3,255,504 3,216,160 41.2 43.7 47.2

Michigan ..................... 1,330,224 1,427,656 1,422,628 4,876,338 4,722,716 4,189,792 27.3 30.2 34.0

Minnesota ................... 822,940 914,823 994,234 2,755,808 2,774,524 2,777,285 29.9 33.0 35.8

Mississippi .................. 284,057 295,278 317,872 1,229,884 1,199,871 1,197,641 23.1 24.6 26.5

Missouri ...................... 731,969 818,224 889,754 2,867,853 2,889,461 2,767,043 25.5 28.3 32.2

Montana...................... 127,026 146,640 155,461 447,827 476,412 466,372 28.4 30.8 33.3

Nebraska..................... 242,112 271,596 287,925 925,783 943,176 961,786 26.2 28.8 29.9

Nevada........................ 214,614 292,151 329,238 1,042,182 1,222,277 1,207,799 20.6 23.9 27.3

New Hampshire .......... 215,907 248,086 258,118 680,706 708,748 697,383 31.7 35.0 37.0

New Jersey ................. 1,644,820 1,745,454 1,835,382 4,117,543 4,257,899 4,120,017 39.9 41.0 44.5

New Mexico................ 227,129 261,942 274,058 821,003 886,708 862,043 27.7 29.5 31.8

New York..................... 3,054,065 3,493,031 3,725,582 8,743,924 9,062,464 8,740,642 34.9 38.5 42.6

North Carolina............. 1,043,271 1,265,162 1,461,123 3,929,977 4,261,325 4,183,052 26.5 29.7 34.9

North Dakota .............. 85,926 92,568 103,117 336,228 349,368 368,677 25.6 26.5 28.0

Ohio ............................ 1,423,694 1,528,942 1,623,724 5,566,735 5,602,764 5,303,655 25.6 27.3 30.6

Oklahoma.................... 379,436 433,967 490,304 1,614,627 1,650,070 1,678,953 23.5 26.3 29.2

Oregon........................ 497,208 587,174 629,810 1,711,041 1,792,039 1,785,400 29.1 32.8 35.3

Pennsylvania............... 1,676,416 1,863,711 2,006,801 5,874,153 6,021,084 5,892,519 28.5 31.0 34.1

Rhode Island............... 167,178 182,749 188,849 520,677 543,973 499,481 32.1 33.6 37.8

South Carolina ............ 495,647 546,986 607,286 1,834,871 1,970,912 1,941,654 27.0 27.8 31.3

South Dakota.............. 98,686 108,994 116,560 400,352 421,799 422,696 24.6 25.8 27.6

Tennessee................... 676,912 765,687 863,852 2,728,523 2,852,509 2,828,617 24.8 26.8 30.5

Texas........................... 2,714,923 3,162,391 3,660,238 9,991,920 10,757,510 11,493,519 27.2 29.4 31.8

Utah ............................ 266,153 354,651 410,367 1,108,547 1,285,389 1,254,151 24.0 27.6 32.7

Vermont....................... 107,928 118,680 126,695 330,099 343,149 338,632 32.7 34.6 37.4

Virginia ........................ 1,292,274 1,467,254 1,640,903 3,537,719 3,862,508 3,928,267 36.5 38.0 41.8

Washington................. 988,658 1,123,956 1,215,053 2,863,705 3,155,384 3,161,818 34.5 35.6 38.4

West Virginia ............... 156,241 179,015 202,206 758,904 777,210 740,175 20.6 23.0 27.3

Wisconsin ................... 714,317 812,662 865,610 2,897,937 2,932,482 2,832,826 24.6 27.7 30.6

Wyoming..................... 63,342 64,493 77,282 259,508 276,882 284,893 24.4 23.3 27.1

Puerto Rico................. NA 465,722 498,515 1,128,704 1,270,693 1,032,765 NA 36.7 48.3

NA = not available.

NOTES: Bachelor’s degree holders include those who completed a bachelor’s or higher degree. Workforce represents the employed component of the 

civilian labor force and is reported as annual data not seasonally adjusted.

SOURCES: Census Bureau, 2000 and 2010 Decennial Censuses, and American Community Survey (various years); Bureau of Labor Statistics, Local Area 

Unemployment Statistics (various years).

Science and Engineering Indicators 2014
8-78 ♦ Chapter 8. State Indicators

This indicator represents the extent to which a state’s workforce 

is employed in S&E occupations. A high value indicates that a state’s 

economy has a high percentage of technical jobs relative to other states.

S&E occupations are defined by standard occupational codes. They 

include engineers and computer, mathematical, life, physical, and social 

scientists. Managers, technicians, elementary and secondary schoolteachers, and medical personnel are not included.

Data on individuals in S&E occupations and total occupations come 

from a survey of workplaces that assigns workers to a state based on 

where they work. Estimates do not include self-employed persons and 

are developed by the Bureau of Labor Statistics from data provided by 

state workforce agencies. Due to the way the data are collected, faculty 

teaching in S&E fields are not included as workers in S&E occupations. 

Estimates for states with smaller populations are generally less 

precise than estimates for states with larger populations.

Findings

• In 2012, about 5.97 million people worked in occupations classified as S&E. This is an increase from 

the 4.96 million S&E workers in 2003.

• In 2012, the percentage of the workforce engaged 

in S&E occupations ranged from 2.19% to 7.63% 

in individual states.

• The highest percentages of employment in S&E 

occupations were found in the District of Columbia 

and the adjacent states of Maryland and Virginia as 

well as in Massachusetts and Colorado in 2012.

Individuals in Science and Engineering Occupations as a Percentage of 

All Occupations

Figure 8-34

Individuals in science and engineering occupations as a percentage of all occupations: 2012

1.75 2.75 3.75 4.75 5.75 6.75 7.75 8.75 9.75

CA

CO

DE

FL

HI

IA

IN

KY

ME

MT

ND

OK

SC

SD

TN

WV

WY

AL

GA

ID

IL

KS

MO

NC

NE

NH

NM

NY

OR

RI

TX

UT

VT

WI

AR

LA

MS

NV

AK

AZ

CT

MI

MN

NJ

MA

MD

VA DC

Percent

1st quartile (4.84%–9.73%)

2nd quartile (3.99%–4.78%)

3rd quartile (3.36%–3.90%)

4th quartile (2.19%–3.30%)

No data

SOURCE: Bureau of Labor Statistics, 

Occupational Employment Statistics Survey.
Science and Engineering Indicators 2014 ♦ 8-79

Table 8-34

Individuals in science and engineering occupations as a percentage of all occupations, by state: 2003, 2008, and 

2012

Individuals in S&E occupations All occupations

S&E occupations/ 

all occupations (%)

State 2003 2008 2012 2003 2008 2012 2003 2008 2012

United States.................. 4,961,550 5,781,460 5,968,240 127,420,170 135,185,230 130,287,700 3.89 4.28 4.58

Alabama...................... 56,380 68,580 72,880 1,817,240 1,945,300 1,824,400 3.10 3.53 3.99

Alaska ......................... 10,600 13,260 16,260 290,740 307,790 318,700 3.65 4.31 5.10

Arizona........................ 92,120 102,100 116,930 2,275,410 2,637,830 2,414,340 4.05 3.87 4.84

Arkansas ..................... 21,340 29,310 29,530 1,118,690 1,176,050 1,155,020 1.91 2.49 2.56

California..................... 676,180 791,750 821,780 14,460,860 15,212,610 14,303,630 4.68 5.20 5.75

Colorado ..................... 124,140 147,000 149,020 2,097,650 2,302,340 2,226,160 5.92 6.38 6.69

Connecticut ................ 81,380 80,290 78,450 1,631,610 1,697,810 1,620,620 4.99 4.73 4.84

Delaware ..................... 17,370 22,330 23,440 403,650 425,210 405,750 4.30 5.25 5.78

District of Columbia .... 54,890 63,360 63,600 595,220 635,500 653,760 9.22 9.97 9.73

Florida......................... 221,070 248,200 248,300 7,217,400 7,771,740 7,273,850 3.06 3.19 3.41

Georgia ....................... 144,170 147,380 148,830 3,770,430 4,068,270 3,815,530 3.82 3.62 3.90

Hawaii ......................... 16,090 18,830 20,930 557,400 612,420 588,210 2.89 3.07 3.56

Idaho........................... 22,150 23,310 25,260 563,200 650,240 598,540 3.93 3.58 4.22

Illinois.......................... 211,230 224,370 220,170 5,719,150 5,910,630 5,640,740 3.69 3.80 3.90

Indiana ........................ 78,410 90,840 94,620 2,851,210 2,927,620 2,811,920 2.75 3.10 3.36

Iowa ............................ 37,320 46,180 50,950 1,413,220 1,502,600 1,470,740 2.64 3.07 3.46

Kansas ........................ 51,970 54,260 50,930 1,292,170 1,374,560 1,320,920 4.02 3.95 3.86

Kentucky..................... 45,230 NA 51,830 1,719,620 1,817,860 1,764,750 2.63 NA 2.94

Louisiana..................... 41,900 41,790 45,920 1,851,870 1,887,370 1,868,210 2.26 2.21 2.46

Maine .......................... 15,020 17,000 17,910 591,750 604,150 581,110 2.54 2.81 3.08

Maryland..................... 149,250 167,070 179,550 2,448,580 2,561,530 2,510,680 6.10 6.52 7.15

Massachusetts............ 184,690 217,310 229,160 3,130,720 3,234,860 3,202,080 5.90 6.72 7.16

Michigan ..................... 182,940 204,290 198,610 4,310,420 4,142,750 3,918,120 4.24 4.93 5.07

Minnesota ................... 117,120 134,440 131,690 2,591,720 2,704,860 2,641,110 4.52 4.97 4.99

Mississippi .................. 22,190 27,270 23,640 1,089,350 1,138,210 1,080,420 2.04 2.40 2.19

Missouri ...................... 84,150 105,390 109,650 2,623,020 2,740,170 2,605,910 3.21 3.85 4.21

Montana...................... 11,450 NA 15,360 394,820 444,090 432,380 2.90 NA 3.55

Nebraska..................... 30,710 31,820 34,720 879,550 928,120 914,830 3.49 3.43 3.80

Nevada........................ 22,330 27,300 27,000 1,086,110 1,278,230 1,127,160 2.06 2.14 2.40

New Hampshire .......... 23,430 29,150 28,950 607,570 634,570 612,710 3.86 4.59 4.72

New Jersey ................. 161,420 198,060 181,480 3,878,020 3,986,310 3,793,720 4.16 4.97 4.78

New Mexico................ 33,600 34,560 35,310 747,050 819,480 773,860 4.50 4.22 4.56

New York..................... 272,440 326,510 321,480 8,236,200 8,633,580 8,542,280 3.31 3.78 3.76

North Carolina............. 132,440 153,680 167,900 3,702,170 4,063,420 3,878,800 3.58 3.78 4.33

North Dakota .............. 8,430 9,450 13,120 314,620 350,360 403,290 2.68 2.70 3.25

Ohio ............................ 177,100 206,320 NA 5,308,270 5,323,130 5,054,250 3.34 3.88 NA

Oklahoma.................... 44,360 48,900 50,420 1,416,640 1,557,750 1,529,900 3.13 3.14 3.30

Oregon........................ 61,230 70,070 75,780 1,537,000 1,706,740 1,609,900 3.98 4.11 4.71

Pennsylvania............... 185,560 227,170 NA 5,494,430 5,705,170 5,596,480 3.38 3.98 NA

Rhode Island............... 18,740 18,090 20,180 477,320 478,420 453,020 3.93 3.78 4.45

South Carolina ............ 48,740 57,770 63,170 1,764,170 1,892,690 1,796,550 2.76 3.05 3.52

South Dakota.............. 9,150 11,870 12,000 364,970 395,960 398,680 2.51 3.00 3.01

Tennessee................... 63,680 72,760 79,830 2,614,830 2,755,800 2,657,280 2.44 2.64 3.00

Texas........................... 365,270 463,850 493,980 9,248,660 10,391,420 10,579,400 3.95 4.46 4.67

Utah ............................ 45,570 52,570 54,720 1,043,500 1,230,320 1,200,850 4.37 4.27 4.56

Vermont....................... 11,420 12,360 12,870 291,400 301,130 294,090 3.92 4.10 4.38

Virginia ........................ 209,280 259,280 274,280 3,412,070 3,670,980 3,597,100 6.13 7.06 7.63

Washington................. 150,230 NA NA 2,560,190 2,868,910 2,764,080 5.87 NA NA

West Virginia ............... 16,220 17,000 19,900 680,200 717,740 710,540 2.38 2.37 2.80

Wisconsin ................... 93,320 101,680 103,030 2,687,400 2,776,690 2,673,280 3.47 3.66 3.85

Wyoming..................... 6,130 8,850 8,710 240,730 283,980 278,040 2.55 3.12 3.13

Puerto Rico................. 19,940 22,970 21,750 962,000 999,010 942,080 2.07 2.30 2.31

NA = not available.

NOTES: United States total includes states with suppressed data. Occupational Employment Statistics survey estimates for 2003 are based on November data; estimates for the remaining years are based on May data.

SOURCE: Bureau of Labor Statistics, Occupational Employment Statistics Survey (various years).

Science and Engineering Indicators 2014
8-80 ♦ Chapter 8. State Indicators

This indicator represents a state’s ability to attract and retain highly trained 

scientists and engineers. These individuals often conduct R&D, manage R&D 

activities, or are otherwise engaged in knowledge-intensive activities. A high 

value for this indicator in a state suggests employment opportunities for individuals with highly advanced training in S&E fields.

Data on employed S&E doctorate holders include those with doctoral 

degrees in computer and mathematical sciences; the biological, agricultural, 

or environmental life sciences; physical sciences; social sciences; psychology; 

engineering; and health fields. S&E doctorate data exclude individuals with 

doctorates from foreign institutions and those older than the age of 75. S&E 

doctorate holders are assigned to a state based on where they work.

Employed workforce data are developed by the Bureau of Labor Statistics, 

which assigns workers to a state based on where they live. Workforce data 

represent annual estimates of the employed civilian labor force; estimates are 

not seasonally adjusted.

Small differences in the values of the indicator between states or across 

time are generally not meaningful.

Findings

• The number of employed S&E doctorate 

holders in the United States rose from 

almost 573,000 in 2001 to about 688,000 

in 2010, an increase of 20%.

• Overall, the value of this indicator rose 

from 0.42% in 2001 to 0.49% in 2010 

because the number of employed S&E 

doctorate holders nationwide increased 

more rapidly than the size of the 

workforce.

• In 2010, the values for this indicator in 

individual states ranged from 0.23% to 

1.16% of a state’s workforce.

• States in the top quartile tended to be 

homes to major research laboratories, 

research universities, or research-intensive industries.

Employed Science and Engineering Doctorate Holders as a Percentage of 

the Workforce

Figure 8-35

Employed science and engineering doctorate holders as a percentage of the workforce: 2010

0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 1.10 1.20 4.70 4.80

CA

CO

CT

WA

AL

AZ

GA

IA

IN

ME

MO

NE

OK

SC

SD

TX

WI

WV

AK

ID

IL

MI

ND

NH

OH

TN

UT NM

AR

FL

KS

KY

LA

MS

NV

WY

HI

MN

MT

NC

NJ

NY

OR

PA

RI

VA

VT DE MD MA DC

Percent

1st quartile (0.56%–4.79%)

2nd quartile (0.42%–0.53%)

3rd quartile (0.33%–0.41%)

4th quartile (0.23%–0.32%)

SOURCES: National Science Foundation, 

National Center for Science and Engineering 

Statistics, Survey of Doctorate Recipients; 

Bureau of Labor Statistics, Local Area 

Unemployment Statistics.
Science and Engineering Indicators 2014 ♦ 8-81

Table 8-35

Employed science and engineering doctorate holders as a percentage of the workforce, by state: 2001, 2006, and 2010

Employed S&E doctorate holders Employed workforce

S&E doctorate 

holders/all workers (%)

State 2001 2006 2010 2001 2006 2010 2001 2006 2010

United States.................. 572,800 618,400 688,300 137,107,779 143,729,350 139,395,958 0.42 0.43 0.49

Alabama...................... 5,300 5,900 6,600 2,034,909 2,098,462 1,969,557 0.26 0.28 0.34

Alaska ......................... 1,200 1,100 1,400 301,694 326,109 333,538 0.40 0.34 0.42

Arizona........................ 7,100 8,400 9,000 2,453,453 2,836,638 2,780,328 0.29 0.30 0.32

Arkansas ..................... 2,600 2,800 2,900 1,194,024 1,286,887 1,242,088 0.22 0.22 0.23

California..................... 80,900 87,400 102,300 16,220,033 16,821,266 16,063,550 0.50 0.52 0.64

Colorado ..................... 11,800 13,100 14,800 2,303,494 2,541,828 2,475,831 0.51 0.52 0.60

Connecticut ................ 9,500 10,300 11,300 1,700,046 1,745,993 1,735,059 0.56 0.59 0.65

Delaware ..................... 3,500 3,100 3,000 404,135 424,618 402,612 0.87 0.73 0.75

District of Columbia .... 14,200 13,300 14,900 286,649 303,791 310,842 4.95 4.38 4.79

Florida......................... 15,700 17,600 20,600 7,624,718 8,584,095 8,141,447 0.21 0.21 0.25

Georgia ....................... 12,000 13,000 15,200 4,112,868 4,500,150 4,241,718 0.29 0.29 0.36

Hawaii ......................... 2,600 2,800 3,000 589,216 617,807 603,894 0.44 0.45 0.50

Idaho........................... 2,200 2,800 2,800 644,816 718,077 694,976 0.34 0.39 0.40

Illinois.......................... 22,100 24,100 25,300 6,113,536 6,225,095 5,925,554 0.36 0.39 0.43

Indiana ........................ 9,600 9,900 10,900 3,020,985 3,080,047 2,843,268 0.32 0.32 0.38

Iowa ............................ 4,400 4,900 5,600 1,568,638 1,595,136 1,566,307 0.28 0.31 0.36

Kansas ........................ 4,000 4,300 4,000 1,347,715 1,403,938 1,398,046 0.30 0.31 0.29

Kentucky..................... 4,600 5,000 5,100 1,852,056 1,904,467 1,854,279 0.25 0.26 0.28

Louisiana..................... 5,300 5,500 5,300 1,922,110 1,900,240 1,920,732 0.28 0.29 0.28

Maine .......................... 2,000 2,400 2,400 650,699 665,856 643,499 0.31 0.36 0.37

Maryland..................... 22,700 26,200 29,800 2,712,268 2,892,733 2,831,069 0.84 0.91 1.05

Massachusetts............ 29,100 32,400 36,900 3,275,343 3,255,504 3,187,622 0.89 1.00 1.16

Michigan ..................... 17,400 17,900 18,000 4,876,338 4,722,716 4,147,952 0.36 0.38 0.43

Minnesota ................... 11,400 11,800 13,700 2,755,808 2,774,524 2,744,470 0.41 0.43 0.50

Mississippi .................. 3,200 3,300 3,300 1,229,884 1,199,871 1,177,276 0.26 0.28 0.28

Missouri ...................... 9,300 9,300 10,700 2,867,853 2,889,461 2,755,946 0.32 0.32 0.39

Montana...................... 1,400 2,000 2,400 447,827 476,412 462,278 0.31 0.42 0.52

Nebraska..................... 2,900 3,000 3,100 925,783 943,176 944,562 0.31 0.32 0.33

Nevada........................ 2,000 2,600 3,000 1,042,182 1,222,277 1,199,517 0.19 0.21 0.25

New Hampshire .......... 2,500 2,500 3,000 680,706 708,748 693,679 0.37 0.35 0.43

New Jersey ................. 22,700 20,800 23,000 4,117,543 4,257,899 4,111,155 0.55 0.49 0.56

New Mexico................ 7,700 8,300 8,000 821,003 886,708 861,503 0.94 0.94 0.93

New York..................... 44,000 45,900 50,900 8,743,924 9,062,464 8,760,743 0.50 0.51 0.58

North Carolina............. 16,800 18,900 20,600 3,929,977 4,261,325 4,136,257 0.43 0.44 0.50

North Dakota .............. 1,100 1,400 1,500 336,228 349,368 360,921 0.33 0.40 0.42

Ohio ............................ 20,100 20,500 21,700 5,566,735 5,602,764 5,271,394 0.36 0.37 0.41

Oklahoma.................... 4,400 4,400 4,900 1,614,627 1,650,070 1,657,099 0.27 0.27 0.30

Oregon........................ 7,000 8,300 9,100 1,711,041 1,792,039 1,761,867 0.41 0.46 0.52

Pennsylvania............... 26,100 29,100 31,300 5,874,153 6,021,084 5,854,537 0.44 0.48 0.53

Rhode Island............... 2,600 3,000 3,000 520,677 543,973 505,131 0.50 0.55 0.59

South Carolina ............ 5,100 5,900 6,400 1,834,871 1,970,912 1,917,747 0.28 0.30 0.33

South Dakota.............. 1,000 1,000 1,300 400,352 421,799 420,171 0.25 0.24 0.31

Tennessee................... 9,000 10,000 11,500 2,728,523 2,852,509 2,777,213 0.33 0.35 0.41

Texas........................... 32,500 36,000 42,400 9,991,920 10,757,510 11,273,239 0.33 0.33 0.38

Utah ............................ 4,800 5,500 5,900 1,108,547 1,285,389 1,252,466 0.43 0.43 0.47

Vermont....................... 1,800 1,700 1,800 330,099 343,149 337,049 0.55 0.50 0.53

Virginia ........................ 17,500 19,800 22,000 3,537,719 3,862,508 3,840,619 0.49 0.51 0.57

Washington................. 14,800 16,900 18,900 2,863,705 3,155,384 3,166,880 0.52 0.54 0.60

West Virginia ............... 1,900 2,000 2,200 758,904 777,210 737,115 0.25 0.26 0.30

Wisconsin ................... 8,700 9,500 10,600 2,897,937 2,932,482 2,820,453 0.30 0.32 0.38

Wyoming..................... 800 700 800 259,508 276,882 280,903 0.31 0.25 0.28

Puerto Rico................. 1,400 1,700 2,300 1,128,704 1,270,693 1,061,519 0.12 0.13 0.22

NOTE: Employed S&E doctorate holders are classified by employment lcoation; employed workers are classified by residence.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, Survey of Doctorate Recipients, (various years); Bureau 

of Labor Statistics, Local Area Unemployment Statistics (various years).

Science and Engineering Indicators 2014
8-82 ♦ Chapter 8. State Indicators

Engineers design and operate production processes and create new 

products and services. This indicator represents the percentage of trained 

engineers in a state’s workforce. It includes the standard occupational codes 

for engineering fields: aerospace, agricultural, biomedical, chemical, civil, 

computer hardware, electrical and electronics, environmental, industrial, 

marine and naval architectural, materials, mechanical, mining and geological, 

nuclear, and petroleum.

Data on individuals in engineering occupations and total occupations 

come from a survey of workplaces that assigns workers to a state based on 

where they work. Estimates do not include self-employed persons and are 

developed by the Bureau of Labor Statistics from data provided by state 

workforce agencies. 

Estimates for states with smaller populations are generally less precise 

than estimates for states with larger populations.

Findings

• In the United States, 1.63 million individuals 

were employed in engineering occupations 

in 2012, an increase from the 1.47 million 

engineers employed in 2003. Between 2003 

and 2012, the percentage of the workforce 

employed in engineering occupations 

increased from 1.15% to 1.25%.

• The concentration of engineers in individual 

states ranged from 0.60% to 2.25% in 2012.

• States ranking highest on this indicator also 

ranked high on employment in high-technology 

establishments as a share of total employment.

Engineers as a Percentage of All Occupations

Figure 8-36

Engineers as a percentage of all occupations: 2012

0.60 0.82 1.04 1.26 1.48 1.70 1.92 2.14 2.36

FL

GA

HI

IA

KY

ME

MO

MT

NC

ND

NJ

TN

AL

CA

CO

DC

MA

MD

NM

VA

AR

NE

NV

NY

SD

WV

DE

IL

IN

KS

LA

OH

OK

PA

RI

VT

WI

WY AK MI

AZ

CT

ID

MN

NH

OR

SC

TX

UT

Percent

1st quartile (1.45%–2.25%)

2nd quartile (1.15%–1.44%)

3rd quartile (0.93%–1.14%)

4th quartile (0.60%–0.89%)

No data

SOURCE: Bureau of Labor Statistics, 

Occupational Employment Statistics Survey.
Science and Engineering Indicators 2014 ♦ 8-83

Table 8-36

Engineers as a percentage of all occupations, by state: 2003, 2008, and 2012

Engineers All occupations

Engineers in all 

occupations (%)

State 2003 2008 2012 2003 2008 2012 2003 2008 2012

United States.................. 1,465,670 1,626,330 1,629,470 127,420,170 135,185,230 130,287,700 1.15 1.20 1.25

Alabama...................... 21,660 26,430 29,240 1,817,240 1,945,300 1,824,400 1.19 1.36 1.60

Alaska ......................... 3,570 4,450 5,540 290,740 307,790 318,700 1.23 1.45 1.74

Arizona........................ 34,500 35,850 32,880 2,275,410 2,637,830 2,414,340 1.52 1.36 1.36

Arkansas ..................... 5,610 7,340 6,940 1,118,690 1,176,050 1,155,020 0.50 0.62 0.60

California..................... 212,620 240,860 234,600 14,460,860 15,212,610 14,303,630 1.47 1.58 1.64

Colorado ..................... 35,180 41,130 37,050 2,097,650 2,302,340 2,226,160 1.68 1.79 1.66

Connecticut ................ 26,020 23,920 23,480 1,631,610 1,697,810 1,620,620 1.59 1.41 1.45

Delaware ..................... 3,440 5,120 4,910 403,650 425,210 405,750 0.85 1.20 1.21

District of Columbia .... 10,070 8,220 9,840 595,220 635,500 653,760 1.69 1.29 1.51

Florida......................... 58,300 69,040 62,860 7,217,400 7,771,740 7,273,850 0.81 0.89 0.86

Georgia ....................... 30,060 36,020 36,360 3,770,430 4,068,270 3,815,530 0.80 0.89 0.95

Hawaii ......................... 4,670 5,020 5,800 557,400 612,420 588,210 0.84 0.82 0.99

Idaho........................... 8,530 7,870 8,760 563,200 650,240 598,540 1.51 1.21 1.46

Illinois.......................... 58,150 55,840 61,420 5,719,150 5,910,630 5,640,740 1.02 0.94 1.09

Indiana ........................ 30,110 30,780 32,230 2,851,210 2,927,620 2,811,920 1.06 1.05 1.15

Iowa ............................ NA 10,270 12,100 1,413,220 1,502,600 1,470,740 NA 0.68 0.82

Kansas ........................ 19,870 16,930 15,320 1,292,170 1,374,560 1,320,920 1.54 1.23 1.16

Kentucky..................... 13,090 13,880 14,690 1,719,620 1,817,860 1,764,750 0.76 0.76 0.83

Louisiana..................... 15,940 18,270 19,940 1,851,870 1,887,370 1,868,210 0.86 0.97 1.07

Maine .......................... 4,880 4,480 5,070 591,750 604,150 581,110 0.82 0.74 0.87

Maryland..................... 33,610 39,390 41,130 2,448,580 2,561,530 2,510,680 1.37 1.54 1.64

Massachusetts............ 49,430 54,330 52,610 3,130,720 3,234,860 3,202,080 1.58 1.68 1.64

Michigan ..................... 92,190 92,190 87,980 4,310,420 4,142,750 3,918,120 2.14 2.23 2.25

Minnesota ................... 30,650 29,490 33,210 2,591,720 2,704,860 2,641,110 1.18 1.09 1.26

Mississippi .................. 7,770 10,160 NA 1,089,350 1,138,210 1,080,420 0.71 0.89 NA

Missouri ...................... 20,090 25,950 23,100 2,623,020 2,740,170 2,605,910 0.77 0.95 0.89

Montana...................... 2,680 3,570 4,020 394,820 444,090 432,380 0.68 0.80 0.93

Nebraska..................... 5,890 6,350 6,330 879,550 928,120 914,830 0.67 0.68 0.69

Nevada........................ 6,660 7,870 7,180 1,086,110 1,278,230 1,127,160 0.61 0.62 0.64

New Hampshire .......... 7,490 7,870 8,290 607,570 634,570 612,710 1.23 1.24 1.35

New Jersey ................. 37,190 40,720 39,140 3,878,020 3,986,310 3,793,720 0.96 1.02 1.03

New Mexico................ 12,710 11,500 12,350 747,050 819,480 773,860 1.70 1.40 1.60

New York..................... 65,600 74,570 61,980 8,236,200 8,633,580 8,542,280 0.80 0.86 0.73

North Carolina............. 31,020 33,400 36,710 3,702,170 4,063,420 3,878,800 0.84 0.82 0.95

North Dakota .............. 2,130 2,530 3,520 314,620 350,360 403,290 0.68 0.72 0.87

Ohio ............................ 61,960 60,120 60,790 5,308,270 5,323,130 5,054,250 1.17 1.13 1.20

Oklahoma.................... 12,830 14,040 17,740 1,416,640 1,557,750 1,529,900 0.91 0.90 1.16

Oregon........................ 17,970 18,740 21,440 1,537,000 1,706,740 1,609,900 1.17 1.10 1.33

Pennsylvania............... NA 63,340 66,970 5,494,430 5,705,170 5,596,480 NA 1.11 1.20

Rhode Island............... 5,080 5,150 5,110 477,320 478,420 453,020 1.06 1.08 1.13

South Carolina ............ 19,960 22,750 26,470 1,764,170 1,892,690 1,796,550 1.13 1.20 1.47

South Dakota.............. 1,990 2,440 2,770 364,970 395,960 398,680 0.55 0.62 0.69

Tennessee................... 20,880 23,130 26,420 2,614,830 2,755,800 2,657,280 0.80 0.84 0.99

Texas........................... 116,160 146,520 152,120 9,248,660 10,391,420 10,579,400 1.26 1.41 1.44

Utah ............................ 12,120 14,350 15,940 1,043,500 1,230,320 1,200,850 1.16 1.17 1.33

Vermont....................... 3,600 3,790 3,120 291,400 301,130 294,090 1.24 1.26 1.06

Virginia ........................ 46,400 54,280 54,050 3,412,070 3,670,980 3,597,100 1.36 1.48 1.50

Washington................. 45,460 55,490 NA 2,560,190 2,868,910 2,764,080 1.78 1.93 NA

West Virginia ............... 4,890 5,320 4,990 680,200 717,740 710,540 0.72 0.74 0.70

Wisconsin ................... 29,850 32,010 30,570 2,687,400 2,776,690 2,673,280 1.11 1.15 1.14

Wyoming..................... 2,110 3,260 3,050 240,730 283,980 278,040 0.88 1.15 1.10

Puerto Rico................. 7,200 7,990 7,640 962,000 999,010 942,080 0.75 0.80 0.81

NA = not available.

NOTE: United States total includes states with suppressed data. 

SOURCE: Bureau of Labor Statistics, Occupational Employment Statistics Survey (various years).

Science and Engineering Indicators 2014
8-84 ♦ Chapter 8. State Indicators

This indicator represents the percentage of life and physical scientists in a state’s 

workforce. Life scientists are identified from standard occupational codes and include agricultural and food scientists, biological scientists, conservation scientists 

and foresters, and medical scientists. Physical scientists are identified from standard 

occupational codes and include astronomers, physicists, atmospheric and space 

scientists, chemists, materials scientists, environmental scientists, and geoscientists. 

A high share of life and physical scientists in a state’s workforce could be due to a 

variety of factors, ranging from a cluster of life sciences companies in the state to 

the presence of forests or national parks, which require foresters, wildlife specialists, 

and conservationists to manage the natural assets in these areas.

Data on individuals in life and physical sciences occupations and total occupations come from a survey of workplaces that assigns workers to a state based on 

where they work. Estimates do not include self-employed persons and are developed 

by the Bureau of Labor Statistics from data provided by state workforce agencies. 

Estimates for states with smaller populations are generally less precise than 

estimates for states with larger populations.

Findings

• About 648,000 individuals (0.50% of the 

workforce) were employed as life and 

physical scientists in the United States 

in 2012, an increase from the 544,000 

life and physical scientists employed 

in 2003, which represented 0.43% of 

the workforce.

• In 2012, individual states had indicator 

values ranging from 0.27% to 1.37%, 

which showed major differences in the 

concentration of jobs in the life and physical sciences.

• States with the highest concentrations of 

life and physical scientists in their workforces were widely distributed throughout 

the United States.

Life and Physical Scientists as a Percentage of All Occupations

Figure 8-37

Life and physical scientists as a percentage of all occupations: 2012

0.25 0.40 0.55 0.70 0.85 1.00 1.15 1.30 1.45

CT

IA

IN

KS

ME

MO

MS

ND

NE

NH

RI

TX

UT

VA

WI

WV MT

AL

AR

AZ

FL

GA

IL

KY

MI

NV

NY

OH

OK

SC

TN

CA

HI

ID

MN

NC

NJ

OR

VT MD DE AK

CO

DC

WA

WY

Percent

1st quartile (0.61%–1.37%)

2nd quartile (0.46%–0.60%)

3rd quartile (0.36%–0.45%)

4th quartile (0.29%–0.35%)

No data

SOURCE: Bureau of Labor Statistics, 

Occupational Employment Statistics Survey.
Science and Engineering Indicators 2014 ♦ 8-85

Table 8-37

Life and physical scientists as a percentage of all occupations, by state: 2003, 2008, and 2012

Life and physical scientists All occupations

Life and physical 

scientists in all 

occupations (%)

State 2003 2008 2012 2003 2008 2012 2003 2008 2012

United States................. 543,540 621,020 647,850 127,420,170 135,185,230 130,287,700 0.43 0.46 0.50

Alabama..................... 5,720 7,570 6,370 1,817,240 1,945,300 1,824,400 0.31 0.39 0.35

Alaska ........................ 3,020 3,720 4,380 290,740 307,790 318,700 1.04 1.21 1.37

Arizona....................... 6,760 7,660 9,210 2,275,410 2,637,830 2,414,340 0.30 0.29 0.38

Arkansas .................... 2,950 3,180 4,020 1,118,690 1,176,050 1,155,020 0.26 0.27 0.35

California.................... 65,400 92,000 99,360 14,460,860 15,212,610 14,303,630 0.45 0.60 0.69

Colorado .................... NA 15,040 17,220 2,097,650 2,302,340 2,226,160 NA 0.65 0.77

Connecticut ............... 8,210 7,550 8,640 1,631,610 1,697,810 1,620,620 0.50 0.44 0.53

Delaware .................... 3,250 3,420 5,210 403,650 425,210 405,750 0.81 0.80 1.28

District of Columbia ... 5,650 5,650 5,240 595,220 635,500 653,760 0.95 0.89 0.80

Florida........................ 19,820 22,280 23,140 7,217,400 7,771,740 7,273,850 0.27 0.29 0.32

Georgia ...................... 12,290 9,610 11,200 3,770,430 4,068,270 3,815,530 0.33 0.24 0.29

Hawaii ........................ 2,450 3,570 3,570 557,400 612,420 588,210 0.44 0.58 0.61

Idaho.......................... 3,630 3,100 3,980 563,200 650,240 598,540 0.64 0.48 0.66

Illinois......................... 18,990 20,370 19,240 5,719,150 5,910,630 5,640,740 0.33 0.34 0.34

Indiana ....................... 9,100 11,530 11,350 2,851,210 2,927,620 2,811,920 0.32 0.39 0.40

Iowa ........................... 4,370 5,900 7,190 1,413,220 1,502,600 1,470,740 0.31 0.39 0.49

Kansas ....................... 4,470 6,010 5,520 1,292,170 1,374,560 1,320,920 0.35 0.44 0.42

Kentucky.................... 5,410 NA 5,680 1,719,620 1,817,860 1,764,750 0.31 NA 0.32

Louisiana.................... 6,170 NA NA 1,851,870 1,887,370 1,868,210 0.33 NA NA

Maine ......................... 2,290 2,750 2,870 591,750 604,150 581,110 0.39 0.46 0.49

Maryland.................... 18,630 22,630 25,880 2,448,580 2,561,530 2,510,680 0.76 0.88 1.03

Massachusetts........... 20,480 26,930 NA 3,130,720 3,234,860 3,202,080 0.65 0.83 NA

Michigan .................... 10,450 NA 13,300 4,310,420 4,142,750 3,918,120 0.24 NA 0.34

Minnesota .................. 11,530 13,990 15,400 2,591,720 2,704,860 2,641,110 0.44 0.52 0.58

Mississippi ................. 4,430 4,890 4,760 1,089,350 1,138,210 1,080,420 0.41 0.43 0.44

Missouri ..................... 9,370 10,620 10,400 2,623,020 2,740,170 2,605,910 0.36 0.39 0.40

Montana..................... 2,870 NA 4,010 394,820 444,090 432,380 0.73 NA 0.93

Nebraska.................... 4,070 3,580 4,280 879,550 928,120 914,830 0.46 0.39 0.47

Nevada....................... 3,130 3,400 3,920 1,086,110 1,278,230 1,127,160 0.29 0.27 0.35

New Hampshire ......... 1,720 2,690 2,720 607,570 634,570 612,710 0.28 0.42 0.44

New Jersey ................ 20,970 25,170 21,380 3,878,020 3,986,310 3,793,720 0.54 0.63 0.56

New Mexico............... 7,580 6,870 NA 747,050 819,480 773,860 1.01 0.84 NA

New York.................... 30,430 28,460 30,780 8,236,200 8,633,580 8,542,280 0.37 0.33 0.36

North Carolina............ 18,330 21,860 23,190 3,702,170 4,063,420 3,878,800 0.50 0.54 0.60

North Dakota ............. 1,610 1,650 2,130 314,620 350,360 403,290 0.51 0.47 0.53

Ohio ........................... 15,550 19,040 18,140 5,308,270 5,323,130 5,054,250 0.29 0.36 0.36

Oklahoma................... 6,500 5,720 5,820 1,416,640 1,557,750 1,529,900 0.46 0.37 0.38

Oregon....................... 8,130 9,170 9,610 1,537,000 1,706,740 1,609,900 0.53 0.54 0.60

Pennsylvania.............. 25,470 28,610 NA 5,494,430 5,705,170 5,596,480 0.46 0.50 NA

Rhode Island.............. 2,670 2,080 2,200 477,320 478,420 453,020 0.56 0.43 0.49

South Carolina ........... 4,920 5,220 5,200 1,764,170 1,892,690 1,796,550 0.28 0.28 0.29

South Dakota............. 1,800 2,350 NA 364,970 395,960 398,680 0.49 0.59 NA

Tennessee.................. 7,240 7,920 8,470 2,614,830 2,755,800 2,657,280 0.28 0.29 0.32

Texas.......................... 47,660 46,710 47,770 9,248,660 10,391,420 10,579,400 0.52 0.45 0.45

Utah ........................... 5,730 6,520 5,200 1,043,500 1,230,320 1,200,850 0.55 0.53 0.43

Vermont...................... 1,230 1,460 1,860 291,400 301,130 294,090 0.42 0.48 0.63

Virginia ....................... 14,750 14,810 15,420 3,412,070 3,670,980 3,597,100 0.43 0.40 0.43

Washington................ 17,970 NA 21,730 2,560,190 2,868,910 2,764,080 0.70 NA 0.79

West Virginia .............. 2,830 2,890 2,930 680,200 717,740 710,540 0.42 0.40 0.41

Wisconsin .................. 11,600 14,580 12,200 2,687,400 2,776,690 2,673,280 0.43 0.53 0.46

Wyoming.................... 1,670 2,320 2,290 240,730 283,980 278,040 0.69 0.82 0.82

Puerto Rico................ 4,870 5,380 4,070 962,000 999,010 942,080 0.51 0.54 0.43

NA = not available.

NOTE: United States total includes states with suppressed data. 

SOURCE: Bureau of Labor Statistics, Occupational Employment Statistics Survey (various years).

Science and Engineering Indicators 2014
8-86 ♦ Chapter 8. State Indicators

This indicator represents the percentage of specialists with 

advanced computer training in a state’s workforce. Computer 

specialists are identified from standard occupational codes that 

include computer and information scientists, programmers, software engineers, support specialists, systems analysts, database 

administrators, and network and computer system administrators. Higher values may indicate a state workforce that is better 

able to thrive in an information economy or to embrace and use 

computer technology.

Data on individuals in computer occupations and total occupations come from a survey of workplaces that assigns workers 

to a state based on where they work. Estimates do not include 

self-employed persons and are developed by the Bureau of Labor 

Statistics from data provided by state workforce agencies. 

Estimates for states with smaller populations are generally less 

precise than estimates for states with larger populations.

Findings

• In the United States, 3.46 million individuals (2.65% of 

the workforce) were employed as computer specialists in 

2012, an increase from the 2.73 million computer specialists employed in 2003, which accounted for 2.14% of 

the workforce.

• States showed large differences in the intensity of computerrelated operations in their economies, with 0.88% to 5.35% 

of their workforces employed in computer-related occupations in 2012.

• Computer-intensive occupations were especially concentrated in the District of Columbia and the adjacent states 

of Maryland and Virginia. This may be due to the presence 

of many government offices, colleges and universities, and 

government contractors in the area that employ individuals 

in computer occupations.

• Experimental Program to Stimulate Competitive Research 

states tended to have smaller percentages of computer specialists in their workforces and accounted in total for 10% of 

computer specialists nationally.

Computer Specialists as a Percentage of All Occupations

Figure 8-38

Computer specialists as a percentage of all occupations: 2012

0.85 1.30 1.75 2.20 2.65 3.10 3.55 4.00 4.45 4.90 5.35

AZ

GA

MN

MO

NE

NH

TX

UT

AK

AR

HI

ID

IN

KY

ME

MT

ND

NV

OK

SC

SD

TN

WV

AL

FL

IA

KS

NM

RI

WI

LA

MS

WY

CT

IL

MI

NC

NY

OH

OR

PA

VT

MA

MD

DC

WA

CA

DE

NJ CO VA

Percent

1st quartile (2.82%–5.35%)

2nd quartile (2.23%–2.78%)

3rd quartile (1.56%–2.17%)

4th quartile (0.88%–1.55%)

SOURCE: Bureau of Labor Statistics, 

Occupational Employment Statistics Survey.
Science and Engineering Indicators 2014 ♦ 8-87

Table 8-38

Computer specialists as a percentage of all occupations, by state: 2003, 2008, and 2012

Computer specialists All occupations

Computer specialists 

in all occupations (%)

State 2003 2008 2012 2003 2008 2012 2003 2008 2012

EPSCoR states................ 261,450 321,940 337,260 19,688,810 21,058,900 20,239,580 1.33 1.53 1.67

Non-EPSCoR states........ 2,389,820 2,826,110 3,067,180 106,264,140 112,528,090 108,402,850 2.25 2.51 2.83

Average EPSCoR 

 state value .................... na na na na na na 1.37 1.57 1.74

Average non-EPSCoR 

 state value .................... na na na na na na 2.30 2.55 2.87

United States................... 2,732,640 3,198,050 3,456,500 127,420,190 135,185,230 130,287,700 2.14 2.37 2.65

Alabama....................... 28,010 33,570 37,140 1,817,240 1,945,300 1,824,400 1.54 1.73 2.04

Alaska .......................... 3,170 4,120 5,000 290,740 307,790 318,700 1.09 1.34 1.57

Arizona......................... 45,020 55,840 68,770 2,275,410 2,637,830 2,414,340 1.98 2.12 2.85

Arkansas ...................... 11,770* 18,230 18,020* 1,118,690 1,176,050 1,155,020 1.05 1.55 1.56

California...................... 361,640 401,690 458,630 14,460,860 15,212,610 14,303,630 2.50 2.64 3.21

Colorado ...................... 73,490 84,680 88,840 2,097,650 2,302,340 2,226,160 3.50 3.68 3.99

Connecticut ................. 42,600 44,470 41,610 1,631,610 1,697,810 1,620,620 2.61 2.62 2.57

Delaware ...................... 8,930* 11,800* 13,130 403,650 425,210 405,750 2.21 2.78 3.24

District of Columbia ..... 26,590 32,170 31,340 595,220 635,500 653,760 4.47 5.06 4.79

Florida.......................... 132,520 147,920 153,030 7,217,400 7,771,740 7,273,850 1.84 1.90 2.10

Georgia ........................ 86,970 91,760 101,120 3,770,430 4,068,270 3,815,530 2.31 2.26 2.65

Hawaii .......................... 7,170 8,040 8,950 557,400 612,420 588,210 1.29 1.31 1.52

Idaho............................ 7,720 10,990* 9,010* 563,200 650,240 598,540 1.37 1.69 1.51

Illinois........................... 120,840* 138,900 142,000 5,719,150 5,910,630 5,640,740 2.11 2.35 2.52

Indiana ......................... 36,440 43,090 45,450* 2,851,210 2,927,620 2,811,920 1.28 1.47 1.62

Iowa ............................. 20,640 26,920 29,300 1,413,220 1,502,600 1,470,740 1.46 1.79 1.99

Kansas ......................... 19,980 28,170 27,790 1,292,170 1,374,560 1,320,920 1.55 2.05 2.10

Kentucky...................... 24,370 27,770 27,410 1,719,620 1,817,860 1,764,750 1.42 1.53 1.55

Louisiana...................... 18,190 16,770 19,050 1,851,870 1,887,370 1,868,210 0.98 0.89 1.02

Maine ........................... 6,730 7,960 8,550 591,750 604,150 581,110 1.14 1.32 1.47

Maryland...................... 87,350 91,600 101,660 2,448,580 2,561,530 2,510,680 3.57 3.58 4.05

Massachusetts............. 102,180 117,580 133,370 3,130,720 3,234,860 3,202,080 3.26 3.63 4.17

Michigan ...................... 71,830* 88,570 91,460 4,310,420 4,142,750 3,918,120 1.67 2.14 2.33

Minnesota .................... 67,110 79,500 79,410 2,591,720 2,704,860 2,641,110 2.59 2.94 3.01

Mississippi ................... 8,200 9,800 9,490 1,089,350 1,138,210 1,080,420 0.75 0.86 0.88

Missouri ....................... 55,730 66,140* 74,530 2,623,020 2,740,170 2,605,910 2.12 2.41 2.86

Montana....................... 4,790* 5,270* 6,250 394,820 444,090 432,380 1.21 1.19 1.45

Nebraska...................... 15,960* 20,110 24,330 879,550 928,120 914,830 1.81 2.17 2.66

Nevada......................... 10,490 13,890 14,780 1,086,110 1,278,230 1,127,160 0.97 1.09 1.31

New Hampshire ........... 12,780 17,560 17,030 607,570 634,570 612,710 2.10 2.77 2.78

New Jersey .................. 109,960 131,090 121,030 3,878,020 3,986,310 3,793,720 2.84 3.29 3.19

New Mexico................. 11,380* 12,050 13,980 747,050 819,480 773,860 1.52 1.47 1.81

New York...................... 167,790 201,100* 206,960 8,236,200 8,633,580 8,542,280 2.04 2.33 2.42

North Carolina.............. 68,320 87,410 99,940 3,702,170 4,063,420 3,878,800 1.85 2.15 2.58

North Dakota ............... 3,050 4,660* 6,740 314,620 350,360 403,290 0.97 1.33 1.67

Ohio ............................. 92,040 116,010 124,070 5,308,270 5,323,130 5,054,250 1.73 2.18 2.45

Oklahoma..................... 21,600* 25,790 22,860 1,416,640 1,557,750 1,529,900 1.52 1.66 1.49

Oregon......................... 31,430 37,010 40,350 1,537,000 1,706,740 1,609,900 2.04 2.17 2.51

Pennsylvania................ 98,860 118,710 127,390 5,494,430 5,705,170 5,596,480 1.80 2.08 2.28

Rhode Island................ 9,190* 9,180* 9,770 477,320 478,420 453,020 1.93 1.92 2.16

South Carolina ............. 19,560 28,010 28,940 1,764,170 1,892,690 1,796,550 1.11 1.48 1.61

South Dakota............... 4,910 5,950 6,120 364,970 395,960 398,680 1.35 1.50 1.54

Tennessee.................... 35,700 38,250 41,480 2,614,830 2,755,800 2,657,280 1.37 1.39 1.56

Texas............................ 197,310 257,960 285,120 9,248,660 10,391,420 10,579,400 2.13 2.48 2.70

Utah ............................. 25,930 32,220 33,850 1,043,500 1,230,320 1,200,850 2.48 2.62 2.82

Vermont........................ 5,080 5,460 6,560 291,400 301,130 294,090 1.74 1.81 2.23

Virginia ......................... 142,270 172,550 192,490 3,412,070 3,670,980 3,597,100 4.17 4.70 5.35

Washington.................. 79,320 104,850 127,310 2,560,190 2,868,910 2,764,080 3.10 3.65 4.61

West Virginia ................ 6,960 7,360 9,520 680,200 717,740 710,540 1.02 1.03 1.34

Wisconsin .................... 36,530 50,290 58,010 2,687,400 2,776,690 2,673,280 1.36 1.81 2.17

Wyoming...................... 1,680 2,130 2,530 240,730 283,980 278,040 0.70 0.75 0.91

Puerto Rico.................. 7,070 8,750 9,200 962,000 999,010 942,080 0.73 0.88 0.98

* = value may be underreported because one or more codes for computer occupations were suppressed by the state or the Bureau of Labor Statistics 

and were not reported at the state level; na = not applicable.

EPSCoR = Experimental Program to Stimulate Competitive Research.

NOTES: United States total includes states with suppressed data. For an explanation of EPSCoR and non-EPSCoR averages, see the chapter introduction.

SOURCE: Bureau of Labor Statistics, Occupational Employment Statistics Survey (various years).

Science and Engineering Indicators 2014
8-88 ♦ Chapter 8. State Indicators

Technical workers include managers in the areas of 

computer and information science, engineering, or the 

natural sciences; computer programmers; drafters working 

in architecture, civil engineering, electronics, or mechanical 

engineering; and technicians in a wide variety of technical 

fields. Individuals who work as scientists and engineers are 

not included in this indicator.

Data on technical occupations and total occupations 

come from a survey of workplaces that assigns workers to a 

state based on where they work. Estimates are developed by 

the Bureau of Labor Statistics from data provided by state 

workforce agencies and do not include self-employed persons.

Estimates for states with smaller populations are generally 

less precise than estimates for states with larger populations.

Findings

• Only 2 states increased in the use of technical workers between 

2003 and 2012. The number of technical workers exceeded the 

number of doctorate holders, engineers, or life and physical scientists in the workforce during this period.

• Nearly 1.89 million individuals (1.45% of the U.S. workforce) 

were employed as technical workers in 2012, a decrease from 

the more than 2 million technical workers employed in 2003, 

which accounted for 1.61% of the workforce.

• Individual states showed large differences in the percentage of 

technical workers in their workforce, with 0.98% to 2.11% of 

their workforce employed as technical workers in 2012.

• Experimental Program to Stimulate Competitive Research states 

tended to have smaller percentages of technical workers in 

their workforces and accounted in total for 14% of technical 

workers nationally.

Technical Workers as a Percentage of All Occupations

Figure 8-39

Technical workers as a percentage of all occupations: 2012

0.97 1.13 1.29 1.45 1.61 1.77 1.93 2.09 2.25

AR

FL

HI

KY

MS

NE

NV

SD

WV

GA

IA

IL

IN

LA

MO

ND

NY

RI

SC

TN

AL

KS

ME

NC

OK

WI

AZ

CO

NH

NJ

TX

VT

WY

CA

CT

MI

MN

VA

DC

DE

MT

OR

UT

ID

MA

MD

NM AK

Percent

1st quartile (1.72%–2.11%)

2nd quartile (1.39%–1.70%)

3rd quartile (1.20%–1.32%)

4th quartile (0.98%–1.18%)

No data

SOURCE: Bureau of Labor Statistics, 

Occupational Employment Statistics Survey.
Science and Engineering Indicators 2014 ♦ 8-89

Table 8-39

Technical workers as a percentage of all occupations, by state: 2003, 2008, and 2012

Technical workers All occupations

Technical workers 

in all occupations (%)

State 2003 2008 2012 2003 2008 2012 2003 2008 2012

EPSCoR states................ 220,020 290,540 264,220 19,688,810 21,058,900 20,239,580 1.12 1.38 1.31

Non-EPSCoR states........ 1,323,500 1,754,580 1,409,960 106,264,140 112,528,090 108,402,850 1.25 1.56 1.30

Average EPSCoR 

 state value .................... na na na na na na 1.15 1.48 1.40

Average non-EPSCoR 

 state value .................... na na na na na na 1.27 1.58 1.50

United States................... 1,561,840 2,071,260 1,888,050 127,420,190 135,185,230 130,287,700 1.23 1.53 1.45

Alabama....................... 22,040 25,000 23,770 1,817,240 1,945,300 1,824,400 1.21 1.29 1.30

Alaska .......................... 5,140 6,950 6,730 290,740 307,790 318,700 1.77 2.26 2.11

Arizona......................... 30,770 46,960 37,860 2,275,410 2,637,830 2,414,340 1.35 1.78 1.57

Arkansas ...................... 10,040 13,470 12,160 1,118,690 1,176,050 1,155,020 0.90 1.15 1.05

California...................... 173,750 248,640 246,680 14,460,860 15,212,610 14,303,630 1.20 1.63 1.72

Colorado ...................... 29,940 37,480 33,200 2,097,650 2,302,340 2,226,160 1.43 1.63 1.49

Connecticut ................. 21,800 31,020 26,880 1,631,610 1,697,810 1,620,620 1.34 1.83 1.66

Delaware ...................... 5,100 9,170 7,190 403,650 425,210 405,750 1.26 2.16 1.77

District of Columbia ..... 6,840 11,310 11,880 595,220 635,500 653,760 1.15 1.78 1.82

Florida.......................... 83,390 94,870 78,110 7,217,400 7,771,740 7,273,850 1.16 1.22 1.07

Georgia ........................ 39,760 49,960 48,930 3,770,430 4,068,270 3,815,530 1.05 1.23 1.28

Hawaii .......................... 4,580 7,090 6,570 557,400 612,420 588,210 0.82 1.16 1.12

Idaho............................ 7,760 15,520 12,350 563,200 650,240 598,540 1.38 2.39 2.06

Illinois........................... 59,280 78,950 71,670 5,719,150 5,910,630 5,640,740 1.04 1.34 1.27

Indiana ......................... 30,740 36,430 35,600 2,851,210 2,927,620 2,811,920 1.08 1.24 1.27

Iowa ............................. 13,360 19,560 17,410 1,413,220 1,502,600 1,470,740 0.95 1.30 1.18

Kansas ......................... 14,650 20,380 17,460 1,292,170 1,374,560 1,320,920 1.13 1.48 1.32

Kentucky...................... 15,420 18,670 17,330 1,719,620 1,817,860 1,764,750 0.90 1.03 0.98

Louisiana...................... 20,860 25,160 23,430 1,851,870 1,887,370 1,868,210 1.13 1.33 1.25

Maine ........................... 6,610 8,450 7,540 591,750 604,150 581,110 1.12 1.40 1.30

Maryland...................... 35,560 47,210 48,880 2,448,580 2,561,530 2,510,680 1.45 1.84 1.95

Massachusetts............. 43,010 64,150 63,310 3,130,720 3,234,860 3,202,080 1.37 1.98 1.98

Michigan ...................... 75,280 72,440 67,450 4,310,420 4,142,750 3,918,120 1.75 1.75 1.72

Minnesota .................... 35,540 47,300 45,380 2,591,720 2,704,860 2,641,110 1.37 1.75 1.72

Mississippi ................... 8,920 14,790 10,630 1,089,350 1,138,210 1,080,420 0.82 1.30 0.98

Missouri ....................... 29,980 37,420 30,340 2,623,020 2,740,170 2,605,910 1.14 1.37 1.16

Montana....................... 5,090 6,730 8,050 394,820 444,090 432,380 1.29 1.52 1.86

Nebraska...................... 9,820 10,780 10,290 879,550 928,120 914,830 1.12 1.16 1.12

Nevada......................... 9,420 13,310 11,270 1,086,110 1,278,230 1,127,160 0.87 1.04 1.00

New Hampshire ........... 7,020 10,410 9,280 607,570 634,570 612,710 1.16 1.64 1.51

New Jersey .................. 54,950 68,530 57,440 3,878,020 3,986,310 3,793,720 1.42 1.72 1.51

New Mexico................. 13,740 16,560 16,120 747,050 819,480 773,860 1.84 2.02 2.08

New York...................... 84,250 126,730 106,730 8,236,200 8,633,580 8,542,280 1.02 1.47 1.25

North Carolina.............. 43,910 62,530 50,890 3,702,170 4,063,420 3,878,800 1.19 1.54 1.31

North Dakota ............... 3,330 4,710 4,870 314,620 350,360 403,290 1.06 1.34 1.21

Ohio ............................. 59,130 68,590 NA 5,308,270 5,323,130 5,054,250 1.11 1.29 NA

Oklahoma..................... 15,280 20,040 21,810 1,416,640 1,557,750 1,529,900 1.08 1.29 1.43

Oregon......................... 21,270 31,440 29,410 1,537,000 1,706,740 1,609,900 1.38 1.84 1.83

Pennsylvania................ 65,160 82,850 NA 5,494,430 5,705,170 5,596,480 1.19 1.45 NA

Rhode Island................ 5,090 6,600 5,430 477,320 478,420 453,020 1.07 1.38 1.20

South Carolina ............. 21,170 27,370 22,870 1,764,170 1,892,690 1,796,550 1.20 1.45 1.27

South Dakota............... 3,470 3,990 4,040 364,970 395,960 398,680 0.95 1.01 1.01

Tennessee.................... 27,640 31,230 30,050 2,614,830 2,755,800 2,657,280 1.06 1.13 1.13

Texas............................ 130,250 186,370 163,880 9,248,660 10,391,420 10,579,400 1.41 1.79 1.55

Utah ............................. 14,890 23,910 21,620 1,043,500 1,230,320 1,200,850 1.43 1.94 1.80

Vermont........................ 2,570 4,450 4,390 291,400 301,130 294,090 0.88 1.48 1.49

Virginia ......................... 50,660 62,770 61,190 3,412,070 3,670,980 3,597,100 1.48 1.71 1.70

Washington.................. 39,510 56,080 NA 2,560,190 2,868,910 2,764,080 1.54 1.95 NA

West Virginia ................ 8,090 8,530 7,910 680,200 717,740 710,540 1.19 1.19 1.11

Wisconsin .................... 29,720 41,160 37,050 2,687,400 2,776,690 2,673,280 1.11 1.48 1.39

Wyoming...................... 2,720 4,210 4,170 240,730 283,980 278,040 1.13 1.48 1.50

Puerto Rico.................. 9,560 10,750 10,640 962,000 999,010 942,080 0.99 1.08 1.13

na = not applicable; NA = not available.

EPSCoR = Experimental Program to Stimulate Competitive Research.

NOTES: United States total includes states with suppressed data. For an explanation of EPSCoR and non-EPSCoR averages, see the chapter introduction. 

SOURCE: Bureau of Labor Statistics, Occupational Employment Statistics Survey (various years).

Science and Engineering Indicators 2014
8-90 ♦ Chapter 8. State Indicators

This indicator represents the extent to which R&D plays a role in a 

state’s economy. A high value indicates that a state has a high intensity 

of R&D activity, which may support future growth in knowledge-based 

industries. Industries that have a high percentage of R&D activity include 

pharmaceuticals, chemicals, computer equipment and services, electronic 

components, aerospace, and motor vehicles.

“R&D performed” refers to R&D activities conducted or funded by 

federal and state agencies, businesses, universities, and nonprofit organizations. In 2010, business performed nearly 69% of the total R&D at 

the national level. The remaining R&D was performed by colleges and 

universities, government facilities (including federally funded R&D 

centers), and nonprofit institutions.

The methodology for assigning industry R&D activity at the state 

level was modified in 2001, and 1998–2000 data were recalculated using 

the new methodology.

Findings

• The national value of this indicator rose slightly 

between 2000 and 2010, from 2.48% to 2.61%.

• In 2010, state values for this indicator ranged from 

0.29% to 8.01%, indicating large differences in the 

geographic concentration of R&D activity.

• New Mexico has a large amount of federal R&D 

activities and a relatively small GDP, giving it the 

highest value for this indicator.

• States with high rankings on this indicator also 

tended to rank high on S&E doctorate holders as a 

share of the workforce.

• The total R&D performed in states in the 

Experimental Program to Stimulate Competitive 

Research (EPSCoR) group was approximately 9% of 

that performed in states in the non-EPSCoR group 

in 2010.

R&D as a Percentage of Gross Domestic Product

Figure 8-40

R&D as a percentage of gross domestic product: 2010

0.00 0.80 1.60 2.40 3.20 4.00 4.80 5.60 6.40 7.20 8.00 8.80

AK

AR

LA

NV

OK

SD

WY

FL

GA

HI

KS

KY

ME

MS

MT

ND

NE

NY

SC

TN

TX

WV

CT

DC

DE

ID

MO

NH

NJ

AL

AZ

IA

IN

NC

OH

PA

VA

VT

WI

CA

MI

MA

WA MD NM

CO

IL

MN

OR

RI

UT

Percent

1st quartile (3.20%–8.01%)

2nd quartile (2.16%–2.96%)

3rd quartile (1.03%–2.05%)

4th quartile (0.29%–1.02%)

SOURCES: National Science Foundation, 

National Center for Science and Engineering Statistics, National Patterns of R&D 

Resources; Bureau of Economic Analysis, 

Gross Domestic Product data.
Science and Engineering Indicators 2014 ♦ 8-91

Table 8-40

R&D as a percentage of gross domestic product, by state: 2000, 2005, and 2010

R&D performed ($millions) State GDP ($millions)

R&D performed/

state GDP (%)

State 2000 2005 2010 2000 2005 2010 2000 2005 2010

EPSCoR states................ 18,292 27,056 31,731 1,309,452 1,725,273 2,017,488 1.40 1.57 1.57

Non-EPSCoR states........ 223,827 279,001 339,269 8,456,735 10,649,784 12,164,950 2.65 2.62 2.79

Average EPSCoR 

 state value .................... na na na na na na 1.62 1.76 1.75

Average non-EPSCoR 

 state value .................... na na na na na na 2.47 2.65 2.83

United States................... 244,852 310,197 375,724 9,884,170 12,539,116 14,388,814 2.48 2.47 2.61

Alabama....................... 1,730 2,804 3,738 116,009 150,968 172,842 1.49 1.86 2.16

Alaska .......................... 196 266 347 25,911 37,774 47,910 0.76 0.70 0.72

Arizona......................... 3,107 4,139 5,481 161,792 222,569 247,329 1.92 1.86 2.22

Arkansas ...................... 454 528 590 68,335 88,501 103,170 0.66 0.60 0.57

California...................... 55,093 63,874 81,005 1,319,472 1,688,949 1,845,249 4.18 3.78 4.39

Colorado ...................... 4,230 5,807 6,164 172,037 217,329 254,551 2.46 2.67 2.42

Connecticut ................. 4,888 8,987 7,435 163,455 196,307 221,767 2.99 4.58 3.35

Delaware ...................... 1,532 1,635 2,327 40,614 54,422 62,832 3.77 3.00 3.70

District of Columbia ..... 2,296 3,342 3,568 58,267 82,488 103,745 3.94 4.05 3.44

Florida.......................... 4,663 6,224 7,952 481,239 681,225 727,972 0.97 0.91 1.09

Georgia ........................ 2,796 3,867 5,451 293,966 363,177 402,006 0.95 1.06 1.36

Hawaii .......................... 291 513 688 41,450 56,901 67,274 0.70 0.90 1.02

Idaho............................ 1,434 1,030 1,779 36,147 48,683 55,639 3.97 2.12 3.20

Illinois........................... 12,767 12,519 15,820 474,520 568,114 642,769 2.69 2.20 2.46

Indiana ......................... 3,252 5,455 6,339 198,238 239,321 270,739 1.64 2.28 2.34

Iowa ............................. 1,017 1,669 2,765 93,312 119,998 138,378 1.09 1.39 2.00

Kansas ......................... 1,420 2,366 2,002 85,722 104,869 126,640 1.66 2.26 1.58

Kentucky...................... 866 1,136 1,498 113,233 138,772 161,064 0.76 0.82 0.93

Louisiana...................... 627 966 1,200 131,289 196,917 227,373 0.48 0.49 0.53

Maine ........................... 319 524 488 36,438 45,520 51,343 0.88 1.15 0.95

Maryland...................... 8,634 14,136 18,429 182,923 247,241 295,981 4.72 5.72 6.23

Massachusetts............. 13,004 17,757 20,195 273,006 323,314 376,908 4.76 5.49 5.36

Michigan ...................... 18,892 18,372 14,702 337,459 375,753 367,107 5.60 4.89 4.00

Minnesota .................... 4,299 7,137 7,393 188,818 237,813 268,578 2.28 3.00 2.75

Mississippi ................... 513 777 852 65,625 81,360 95,763 0.78 0.96 0.89

Missouri ....................... 2,583 3,627 9,253 180,967 216,336 243,876 1.43 1.68 3.79

Montana....................... 170 318 390 21,633 30,054 36,521 0.79 1.06 1.07

Nebraska...................... 439 800 935 57,333 72,505 90,910 0.77 1.10 1.03

Nevada......................... 377 614 939 75,895 114,478 124,838 0.50 0.54 0.75

New Hampshire ........... 775 1,776 2,159 44,161 53,693 61,147 1.75 3.31 3.53

New Jersey .................. 13,133 14,900 17,876 350,110 430,246 483,007 3.75 3.46 3.70

New Mexico................. 3,085 5,265 6,225 50,294 67,763 77,686 6.13 7.77 8.01

New York...................... 13,556 14,103 17,141 769,291 959,867 1,136,417 1.76 1.47 1.51

North Carolina.............. 5,045 7,329 8,746 281,542 354,664 426,875 1.79 2.07 2.05

North Dakota ............... 146 285 468 18,266 24,670 35,357 0.80 1.16 1.32

Ohio ............................. 7,662 8,267 10,048 380,895 444,083 465,679 2.01 1.86 2.16

Oklahoma..................... 660 814 1,029 91,273 120,529 147,649 0.72 0.68 0.70

Oregon......................... 2,116 3,920 5,250 113,180 143,429 181,523 1.87 2.73 2.89

Pennsylvania................ 9,842 11,916 13,074 395,602 482,200 558,818 2.49 2.47 2.34

Rhode Island................ 1,501 1,990 1,439 33,584 44,189 48,572 4.47 4.50 2.96

South Carolina ............. 1,126 2,108 2,384 115,443 141,877 162,292 0.98 1.49 1.47

South Dakota............... 85 157 270 24,038 31,549 38,297 0.35 0.50 0.71

Tennessee.................... 2,057 3,009 3,955 177,540 224,288 253,602 1.16 1.34 1.56

Texas............................ 11,552 15,867 19,504 731,064 968,553 1,226,714 1.58 1.64 1.59

Utah ............................. 1,361 1,886 3,197 69,489 90,616 118,225 1.96 2.08 2.70

Vermont........................ 465 493 452 18,039 22,743 25,809 2.58 2.17 1.75

Virginia ......................... 5,069 8,568 10,063 261,759 356,370 422,763 1.94 2.40 2.38

Washington.................. 10,516 11,864 16,685 227,704 279,333 342,702 4.62 4.25 4.87

West Virginia ................ 457 567 584 41,386 51,857 62,732 1.10 1.09 0.93

Wisconsin .................... 2,693 3,802 5,346 177,355 218,689 245,415 1.52 1.74 2.18

Wyoming...................... 61 122 104 17,050 26,250 36,459 0.36 0.46 0.29

Puerto Rico.................. NA NA NA 69,208 86,158 NA NA NA NA

na = not applicable; NA = not available.

EPSCoR = Experimental Program to Stimulate Competitive Research; GDP = gross domestic product.

NOTES: R&D includes R&D performed by federal agencies, businesses, universities, other nonprofit organizations, and state agencies. For an explanation 

of EPSCoR and non-EPSCoR averages, see the chapter introduction. 

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, National Patterns of R&D Resources (various years); 

Bureau of Economic Analysis, Gross Domestic Product data (June 2013).

Science and Engineering Indicators 2014
8-92 ♦ Chapter 8. State Indicators

This indicator represents how federal R&D obligations are disbursed geographically relative to the size of a state’s employed civilian workforce. Federal R&D 

dollars are attributed to the states in which the recipients are located.

Data on federal obligations for R&D come from the National Center for Science and Engineering Statistics, which aggregates reports from 11 federal agencies. The Department of Defense (DoD) disburses the most federal R&D funding, 

approximately 50% of the total. The geographic distribution of DoD R&D funding 

for development to industry reflects the location of prime contractors only, not 

the subcontractors who perform much of the R&D. A high value may indicate the 

existence of a number of large prime contractors or major federally funded R&D 

facilities in a state.

The estimate of a state’s workforce is provided by the Bureau of Labor Statistics 

(BLS). It represents the employed component of the civilian labor force and is not 

seasonally adjusted. BLS assigns workers to a location based on residence. Estimates for states with smaller populations are generally less precise than estimates 

for states with larger populations.

Findings

• Federal R&D obligations have 

increased from about $82 billion in 

2001 to about $132 billion in 2011, an 

increase of 60%.

• In 2011, federal R&D obligations per 

civilian worker were concentrated in 

a few states; only 12 states and the 

District of Columbia exceeded the 

national average of $940 per worker.

• Federal R&D obligations in 2011 

varied greatly among the states, ranging from $131 to $5,633 per civilian 

worker. Higher values were found in 

the states surrounding the District of 

Columbia and in sparsely populated 

states with national laboratories or 

federal facilities.

Federal R&D Obligations per Employed Worker

Figure 8-41

Federal R&D obligations per employed worker: 2011

100 300 500 700 900 1,100 1,300 1,500 1,700 2,000 3,000 4,000 5,000 6,000 10,000 11,000

RI NM

FL

GA

IA

IN

LA

MN

MO

MT

NC

ND

NV

OR

VT

WV

AK

HI

ID

IL

MI

MS

NH

NJ

NY

OH

TX

PA

TN

CO

WA

AL

MA

AZ VA

AR

DE

KS

KY

ME

NE

OK

SC

SD

WI

WY

CA

CT

UT MD DC

Dollars

1st quartile ($1,067–$10,372)

2nd quartile ($504–$783)

3rd quartile ($316–$495)

4th quartile ($131–$309)

SOURCES: National Science Foundation, 

National Center for Science and Engineering 

Statistics, Federal Funds for Research and 

Development; Bureau of Labor Statistics, 

Local Area Unemployment Statistics.
Science and Engineering Indicators 2014 ♦ 8-93

Table 8-41

Federal R&D obligations per employed worker, by state: 2001, 2006, and 2011

Federal R&D 

obligations ($thousands) Employed workers

Federal R&D 

obligations/

employed worker ($)

State 2001 2006 2011 2001 2006 2011 2001 2006 2011

United States................. 82,445,122 107,446,145 132,214,369 137,107,779 143,729,350 140,695,662 601 748 940

Alabama..................... 2,456,769 2,161,708 5,295,260 2,034,909 2,098,462 1,992,522 1,207 1,030 2,658

Alaska ........................ 254,444 209,038 202,715 301,694 326,109 337,796 843 641 600

Arizona....................... 1,881,000 2,056,284 2,947,365 2,453,453 2,836,638 2,761,984 767 725 1,067

Arkansas .................... 183,867 156,164 185,011 1,194,024 1,286,887 1,251,877 154 121 148

California.................... 13,001,687 21,156,522 23,766,358 16,220,033 16,821,266 16,237,286 802 1,258 1,464

Colorado .................... 1,450,603 2,029,641 3,956,160 2,303,494 2,541,828 2,490,004 630 798 1,589

Connecticut ............... 1,377,388 1,591,960 2,519,649 1,700,046 1,745,993 1,732,807 810 912 1,454

Delaware .................... 83,546 108,657 115,097 404,135 424,618 407,772 207 256 282

District of Columbia ... 2,775,973 4,091,852 3,244,864 286,649 303,791 312,859 9,684 13,469 10,372

Florida........................ 2,774,006 2,319,079 2,572,256 7,624,718 8,584,095 8,322,237 364 270 309

Georgia ...................... 3,438,270 1,251,445 1,467,224 4,112,868 4,500,150 4,295,113 836 278 342

Hawaii ........................ 435,685 340,094 426,895 589,216 617,807 614,824 739 550 694

Idaho.......................... 209,344 297,094 461,143 644,816 718,077 702,920 325 414 656

Illinois......................... 1,825,057 1,975,552 2,993,498 6,113,536 6,225,095 5,942,809 299 317 504

Indiana ....................... 568,761 559,860 909,639 3,020,985 3,080,047 2,874,722 188 182 316

Iowa ........................... 361,455 497,173 702,407 1,568,638 1,595,136 1,562,156 230 312 450

Kansas ....................... 306,656 212,152 372,210 1,347,715 1,403,938 1,401,055 228 151 266

Kentucky.................... 370,384 239,141 246,577 1,852,056 1,904,467 1,875,447 200 126 131

Louisiana.................... 275,788 321,096 612,183 1,922,110 1,900,240 1,919,021 143 169 319

Maine ......................... 450,735 226,468 181,441 650,699 665,856 649,312 693 340 279

Maryland.................... 9,473,728 12,499,496 16,155,528 2,712,268 2,892,733 2,868,191 3,493 4,321 5,633

Massachusetts........... 4,499,835 6,104,611 7,789,148 3,275,343 3,255,504 3,216,160 1,374 1,875 2,422

Michigan .................... 1,196,424 1,680,908 2,156,949 4,876,338 4,722,716 4,189,792 245 356 515

Minnesota .................. 900,936 1,237,266 973,293 2,755,808 2,774,524 2,777,285 327 446 350

Mississippi ................. 415,671 544,029 610,041 1,229,884 1,199,871 1,197,641 338 453 509

Missouri ..................... 927,045 1,225,269 1,197,459 2,867,853 2,889,461 2,767,043 323 424 433

Montana..................... 136,825 149,876 182,103 447,827 476,412 466,372 306 315 390

Nebraska.................... 145,189 159,986 262,929 925,783 943,176 961,786 157 170 273

Nevada....................... 360,215 422,449 482,238 1,042,182 1,222,277 1,207,799 346 346 399

New Hampshire ......... 474,423 371,808 407,597 680,706 708,748 697,383 697 525 584

New Jersey ................ 1,673,959 2,110,673 2,622,723 4,117,543 4,257,899 4,120,017 407 496 637

New Mexico............... 2,890,565 3,100,110 3,553,015 821,003 886,708 862,043 3,521 3,496 4,122

New York.................... 3,528,344 5,225,241 5,252,630 8,743,924 9,062,464 8,740,642 404 577 601

North Carolina............ 1,400,937 1,765,859 2,069,117 3,929,977 4,261,325 4,183,052 356 414 495

North Dakota ............. 77,903 112,067 118,061 336,228 349,368 368,677 232 321 320

Ohio ........................... 3,051,142 2,420,136 3,609,891 5,566,735 5,602,764 5,303,655 548 432 681

Oklahoma................... 324,002 262,336 475,596 1,614,627 1,650,070 1,678,953 201 159 283

Oregon....................... 522,640 505,321 729,170 1,711,041 1,792,039 1,785,400 305 282 408

Pennsylvania.............. 2,772,116 3,227,533 4,528,148 5,874,153 6,021,084 5,892,519 472 536 768

Rhode Island.............. 437,455 615,902 612,766 520,677 543,973 499,481 840 1,132 1,227

South Carolina ........... 314,287 370,562 515,713 1,834,871 1,970,912 1,941,654 171 188 266

South Dakota............. 54,941 75,926 61,931 400,352 421,799 422,696 137 180 147

Tennessee.................. 1,039,488 1,455,622 2,213,857 2,728,523 2,852,509 2,828,617 381 510 783

Texas.......................... 3,189,399 5,263,822 5,904,027 9,991,920 10,757,510 11,493,519 319 489 514

Utah ........................... 421,569 737,850 1,757,902 1,108,547 1,285,389 1,254,151 380 574 1,402

Vermont...................... 112,704 105,544 125,857 330,099 343,149 338,632 341 308 372

Virginia ....................... 4,995,665 8,882,441 8,637,382 3,537,719 3,862,508 3,928,267 1,412 2,300 2,199

Washington................ 1,725,258 4,039,292 4,980,150 2,863,705 3,155,384 3,161,818 602 1,280 1,575

West Virginia .............. 376,105 301,416 261,253 758,904 777,210 740,175 496 388 353

Wisconsin .................. 487,948 635,645 752,236 2,897,937 2,932,482 2,832,826 168 217 266

Wyoming.................... 36,986 36,169 37,709 259,508 276,882 284,893 143 131 132

Puerto Rico................ 90,790 98,790 98,891 1,133,988 1,260,703 1,032,765 80 78 96

NOTES: Only 11 agencies are required to report federal R&D obligations: Departments of Agriculture, Commerce, Defense, Energy, Health and Human Services, Homeland Security (established in 2002), Interior, and Transportation; Environmental Protection Agency; National Aeronautics and Space Administration; 

and National Science Foundation. These obligations represent approximately 98% of total federal R&D obligations. Civilian workers represent the employed 

component of the civilian labor force and are reported as annual data not seasonally adjusted. Federal R&D obligations are reported in current dollars.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, Federal Funds for Research and Development (various 

years); Bureau of Labor Statistics, Local Area Unemployment Statistics (various years).

Science and Engineering Indicators 2014
8-94 ♦ Chapter 8. State Indicators

This indicator represents the relationship between federal R&D spending in a state 

and the number of employees in the state who work in S&E occupations. Federal R&D 

dollars are attributed to the states in which the recipients of federal obligations are located.

Data on federal obligations for R&D come from the National Center for Science and 

Engineering Statistics, which aggregates reports from 11 federal agencies. The Department of Defense (DoD) disburses the most funding, approximately 50% of the total. 

The geographic distribution of DoD R&D funding to industry, mostly for development, 

reflects the location of prime contractors only, not the numerous subcontractors who 

perform much of the R&D.

S&E occupations are defined by standard occupational codes. They include engineers 

and computer, mathematical, life, physical, and social scientists. Managers, technicians, 

elementary and secondary schoolteachers, and medical personnel are not included.

Data on individuals in S&E occupations come from a survey of workplaces that 

assigns workers to a state based on where they work. Estimates do not include selfemployed persons and are developed by the Bureau of Labor Statistics from data provided 

by state workforce agencies. Data on people in S&E occupations are sample based.

Estimates for states with smaller populations are generally less precise than estimates 

for states with larger populations.

Findings

• The federal government obligated 

approximately $132 billion for R&D in 

2011—nearly $23,000 for each person 

employed in an S&E occupation.

• Federal R&D obligations per person 

employed in an S&E occupation 

ranged across the states from $4,502 

to $100,028 in 2011.

• The distribution for this indicator was 

highly skewed in 2011, with only 14 

states and the District of Columbia 

above the national average. High 

values were reported in the District of 

Columbia and adjoining states and 

also in states where federal facilities or 

major defense contractors are located.

• The majority of states in the lowest 

quartile for this indicator in 2011 were 

Experimental Program to Stimulate 

Competitive Research states.

Federal R&D Obligations per Individual in Science and Engineering Occupation

Figure 8-42

Federal R&D obligations per individual in science and engineering occupation: 2011

0 11,122 22,244 33,366 44,488 55,610 66,732 77,854 88,976 100,098

AR

DE

FL

GA

IN

KS

KY

ME

MN

ND

NE

OK

SC

SD

VT

WI

WY DC

AZ

CA

CO

CT

MS

RI

TN

VA

WA AL

MD

NM

MA

UT

AK

HI

IA

ID

IL

LA

MI

MO

MT

NC

NH

NJ

NV

NY

OH

TX

WV

Dollars

1st quartile ($26,024–$100,028)

2nd quartile ($14,262–$25,553)

3rd quartile ($9,776–$13,944)

4th quartile ($4,502–$9,718)

No data

SOURCES: National Science Foundation, National Center for Science and 

Engineering Statistics, Federal Funds for 

Research and Development; Bureau of 

Labor Statistics, Occupational Employment Statistical Survey.
Science and Engineering Indicators 2014 ♦ 8-95

Table 8-42

Federal R&D obligations per individual in science and engineering occupation, by state: 2003, 2007, and 2011

Federal R&D 

 obligations ($millions) Individuals in S&E occupations

Federal R&D 

obligations/individual in 

S&E occupation ($)

State 2003 2007 2011 2003 2007 2011 2003 2007 2011

United States................. 100,982 124,684 132,213 4,961,550 5,591,990 5,794,980 20,353 22,297 22,815

Alabama..................... 3,212 2,248 5,295 56,380 69,650 70,290 56,971 32,276 75,331

Alaska ........................ 399 264 203 10,600 11,990 15,680 37,642 22,018 12,946

Arizona....................... 2,385 2,422 2,947 92,120 102,380 115,330 25,890 23,657 25,553

Arkansas .................... 145 178 185 21,340 28,460 29,350 6,795 6,254 6,303

California.................... 20,170 26,987 23,766 676,180 753,570 800,430 29,829 35,812 29,692

Colorado .................... 1,735 2,798 3,956 124,140 138,990 144,750 13,976 20,131 27,330

Connecticut ............... 2,068 2,117 2,520 81,380 80,280 76,550 25,412 26,370 32,920

Delaware .................... 95 131 115 17,370 22,140 22,430 5,469 5,917 5,127

District of Columbia ... 2,986 4,278 3,245 54,890 63,150 62,660 54,400 67,743 51,787

Florida........................ 2,854 4,078 2,572 221,070 244,140 244,970 12,910 16,704 10,499

Georgia ...................... 2,133 1,686 1,467 144,170 136,880 147,820 14,795 12,317 9,924

Hawaii ........................ 414 379 427 16,090 18,740 20,100 25,730 20,224 21,244

Idaho.......................... 218 288 461 22,150 24,330 25,950 9,842 11,837 17,765

Illinois......................... 1,935 2,145 2,993 211,230 225,180 204,420 9,161 9,526 14,641

Indiana ....................... 574 597 910 78,410 83,080 93,640 7,320 7,186 9,718

Iowa ........................... 500 662 702 37,320 45,430 48,930 13,398 14,572 14,347

Kansas ....................... 269 318 372 51,970 50,040 51,530 5,176 6,355 7,219

Kentucky.................... 247 222 247 45,230 49,030 51,990 5,461 4,528 4,751

Louisiana.................... 453 419 612 41,900 38,450 42,760 10,811 10,897 14,312

Maine ......................... 167 379 181 15,020 15,960 17,490 11,119 23,747 10,349

Maryland.................... 8,027 11,906 16,156 149,250 162,540 173,020 53,782 73,250 93,376

Massachusetts........... 5,492 7,529 7,789 184,690 205,610 220,670 29,736 36,618 35,297

Michigan .................... 1,693 1,726 2,157 182,940 212,040 188,380 9,254 8,140 11,450

Minnesota .................. 866 1,387 973 117,120 129,840 130,340 7,394 10,682 7,465

Mississippi ................. 1,181 434 610 22,190 25,520 23,440 53,222 17,006 26,024

Missouri ..................... 1,350 1,221 1,197 84,150 102,170 106,930 16,043 11,951 11,194

Montana..................... 131 654 182 11,450 13,240 14,960 11,441 49,396 12,166

Nebraska.................... 168 230 263 30,710 31,420 33,800 5,471 7,320 7,781

Nevada....................... 419 321 482 22,330 26,920 28,370 18,764 11,924 16,990

New Hampshire ......... 512 340 408 23,430 28,450 29,260 21,852 11,951 13,944

New Jersey ................ 2,088 2,192 2,623 161,420 186,120 182,210 12,935 11,777 14,395

New Mexico............... 3,090 3,478 3,553 33,600 33,440 35,520 91,964 104,007 100,028

New York.................... 4,383 5,368 5,253 272,440 322,520 310,510 16,088 16,644 16,917

North Carolina............ 1,617 1,828 2,069 132,440 142,970 161,880 12,209 12,786 12,781

North Dakota ............. 107 116 118 8,430 9,660 12,070 12,693 12,008 9,776

Ohio ........................... 2,967 3,661 3,610 177,100 196,390 208,140 16,753 18,641 17,344

Oklahoma................... 570 253 476 44,360 51,430 47,090 12,849 4,919 10,108

Oregon....................... 514 506 729 61,230 67,890 NA 8,395 7,453 NA

Pennsylvania.............. 3,989 3,360 4,528 185,560 218,890 NA 21,497 15,350 NA

Rhode Island.............. 566 628 613 18,740 18,400 19,450 30,203 34,130 31,517

South Carolina ........... 454 422 516 48,740 54,120 61,020 9,315 7,797 8,456

South Dakota............. 55 62 62 9,150 11,550 11,790 6,011 5,368 5,259

Tennessee.................. 1,131 1,908 2,214 63,680 70,820 77,630 17,761 26,942 28,520

Texas.......................... 5,414 6,693 5,904 365,270 441,410 469,080 14,822 15,163 12,586

Utah ........................... 803 991 1,758 45,570 51,340 51,350 17,621 19,303 34,236

Vermont...................... 201 108 126 11,420 12,760 13,100 17,601 8,464 9,618

Virginia ....................... 6,709 9,088 8,637 209,280 254,710 267,620 32,058 35,680 32,273

Washington................ 2,442 4,751 4,980 150,230 183,900 196,760 16,255 25,835 25,310

West Virginia .............. 383 219 261 16,220 16,560 18,300 23,613 13,225 14,262

Wisconsin .................. 658 671 752 93,320 99,380 102,320 7,051 6,752 7,349

Wyoming.................... 43 37 38 6,130 8,110 8,440 7,015 4,562 4,502

Puerto Rico................ 112 86 99 19,940 23,630 20,990 5,617 3,639 4,717

NA = not available.

NOTES: Only 11 agencies are required to report federal R&D obligations: Departments of Agriculture, Commerce, Defense, Energy, Health and Human 

Services, Homeland Security (established in 2002), Interior, and Transportation; Environmental Protection Agency; National Aeronautics and Space Administration; and National Science Foundation. These obligations represent approximately 98% of total federal R&D obligations. Federal R&D obligations 

are reported in current dollars. Occupational Employment Statistics estimates for 2003 are based on November data; estimates for the remaining years 

are based on May data.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, Federal Funds for Research and Development (various 

years); Bureau of Labor Statistics, Occupational Employment Statistics Survey (various years).

Science and Engineering Indicators 2014
8-96 ♦ Chapter 8. State Indicators

This indicator represents the ratio of state agency R&D funding to 

the size of a state’s economy. State R&D expenditures include stateadministered funds from all sources that support R&D performed by 

either a state agency or an external performer.

Data on state R&D funding cover funding administered by state 

government departments, agencies, independent commissions, and other 

state-run entities. They exclude state-run colleges and universities as 

well as laboratories or experiment stations controlled by state universities; funding administered by these institutions is classified as academic 

R&D. The data also exclude state legislatures’ direct appropriations to 

nonstate agencies. Some data may include expenditures for non-R&D 

activities such as commercialization, environmental testing, and routine 

survey work.

Because of differences in the survey populations, definition of 

covered R&D activities, and collection methods, the results of National 

Science Foundation surveys on state government R&D prior to 2006 

are not comparable. 

Findings

• Nationally, state government agencies spent a total 

of $1.4 billion on R&D in 2011. This represented $94 

for each $1 million of a state’s gross domestic product (GDP).

• National, state agency R&D expenditures accounted 

for less than one-half of 1% of total R&D expenditures in 2001, 2006, and 2011; most R&D was 

funded by nonstate sources.

• In 2011, the state values for this indicator ranged 

from $13 to $537 per $1 million of state GDP.

• Five Experimental Program to Stimulate Competitive 

Research (EPSCoR) states are among those with 

the highest values for this indicator, suggesting that 

there is a state-level effort to improve R&D infrastructure in these states, not just a federal effort. 

The average value of this indicator for EPSCoR 

states exceeded that of non-EPSCoR states from 

2006 to 2011.

State Agency R&D Expenditures per $1 Million of Gross Domestic Product

Figure 8-43

State agency R&D expenditures per $1 million of gross domestic product: 2011

0 60 120 180 240 300 360 420 480 540

DC

DE

GA

IL

IN

KS

LA

MA

MI

MN

MO

NE

NH

NJ

NM

NV

RI

TN

TX

VA

AR

CT

ID

KY

MT

NY

OK

PA

WY

AK

FL

HI

ME

ND OH WV

IA

SC

UT

Dollars

AL

AZ

CA

CO

MD

MS

NC

OR

SD

VT

WA

WI

1st quartile ($164–$537)

2nd quartile ($73–$156)

3rd quartile ($39–$69)

4th quartile ($11–$36)

SOURCES: National Science Foundation, 

National Center for Science and Engineering Statistics, Survey of State Research 

and Development Expenditures; Bureau 

of Economic Analysis, Gross Domestic 

Product data.
Science and Engineering Indicators 2014 ♦ 8-97

Table 8-43

State agency R&D expenditures per $1 million of gross domestic product, by state: 2006, 2009, and 2011

State agency R&D 

expenditures ($) State GDP ($millions)

State agency R&D ($)/

$1 million GDP 

State 2006 2009 2011 2006 2009 2011 2006 2009 2011

EPSCoR states................ 162,703,455 170,308,360 243,823,609 1,827,039 1,928,030 2,102,177 89 88 116

Non-EPSCoR states........ 824,010,424 1,012,336,215 1,137,595,278 11,288,411 11,746,303 12,640,405 73 86 90

Average EPSCoR 

 state value .................... na na na na na na 107 94 123

Average non-EPSCoR 

 state value .................... na na na na na na 73 93 95

United States................... 1,021,016,894 1,213,524,157 1,403,816,235 13,289,242 13,869,679 14,959,781 77 87 94

Alabama....................... 7,269,319 12,929,167 19,684,063 159,059 166,315 178,533 46 78 110

Alaska .......................... 10,019,060 7,741,467 11,349,400 41,782 45,149 51,237 240 171 222

Arizona......................... 37,151,471 9,363,943 18,626,577 246,099 245,216 255,989 151 38 73

Arkansas ...................... 4,869,648 11,465,214 14,705,327 93,792 99,530 106,557 52 115 138

California...................... 107,793,045 146,793,247 149,810,643 1,798,197 1,818,627 1,908,985 60 81 78

Colorado ...................... 8,997,236 15,563,581 18,141,931 230,236 245,362 264,733 39 63 69

Connecticut ................. 19,209,064 28,559,052 39,192,091 209,487 217,103 225,409 92 132 174

Delaware ...................... 2,812,102 1,683,562 2,609,902 56,262 60,201 64,377 50 28 41

District of Columbia ..... 1,173,076 487,411 1,221,108 86,736 98,355 107,201 14 5 11

Florida.......................... 42,329,624 66,513,756 150,764,438 731,467 721,175 746,439 58 92 202

Georgia ........................ 10,620,188 6,662,887 11,690,663 380,530 393,964 417,438 28 17 28

Hawaii .......................... 12,067,849 13,976,364 13,103,983 60,993 64,787 70,006 198 216 187

Idaho............................ 2,280,873 8,552,058 9,366,052 50,509 54,285 57,096 45 158 164

Illinois........................... 37,184,281 9,570,893 17,207,125 600,668 625,423 670,247 62 15 26

Indiana ......................... 6,220,575 47,549,928 6,983,364 248,630 252,488 284,344 25 188 25

Iowa ............................. 13,564,062 37,976,643 36,992,222 124,057 134,659 146,057 109 282 253

Kansas ......................... 14,348,384 12,305,385 6,635,626 111,658 121,967 134,767 129 101 49

Kentucky...................... 17,558,997 13,938,134 20,498,849 146,409 152,040 168,019 120 92 122

Louisiana...................... 11,216,568 8,285,478 9,203,635 204,437 204,370 237,389 55 41 39

Maine ........................... 17,509,051 6,400,019 9,918,765 47,594 50,048 52,489 368 128 189

Maryland...................... 24,945,119 21,093,331 20,084,540 259,792 284,724 305,175 96 74 66

Massachusetts............. 10,729,419 3,290,198 4,878,927 337,483 360,675 388,575 32 9 13

Michigan ...................... 75,016,589 8,630,209 9,802,873 376,208 349,195 385,123 199 25 25

Minnesota .................... 6,219,201 16,655,913 11,653,327 245,026 257,596 279,987 25 65 42

Mississippi ................... 2,744,882 3,623,953 7,420,851 85,854 92,614 97,533 32 39 76

Missouri ....................... 18,465,303 15,797,247 13,658,961 223,721 237,774 249,546 83 66 55

Montana....................... 8,606,319 7,200,442 6,474,190 32,232 35,027 38,933 267 206 166

Nebraska...................... 5,602,163 4,415,644 4,061,651 76,549 86,323 96,230 73 51 42

Nevada......................... 1,397,463 1,510,607 1,868,869 123,754 123,115 129,421 11 12 14

New Hampshire ........... 2,040,544 1,860,269 1,921,421 56,103 58,951 63,333 36 32 30

New Jersey .................. 25,900,482 15,146,838 17,068,781 454,701 471,957 493,175 57 32 35

New Mexico................. 3,105,000 1,655,529 1,821,583 71,426 75,308 79,555 43 22 23

New York...................... 103,597,135 151,467,015 182,736,305 1,030,373 1,080,441 1,169,436 101 140 156

North Carolina.............. 14,344,310 40,404,202 29,611,785 378,241 412,912 436,144 38 98 68

North Dakota ............... 21,062,090 16,415,807 8,072,257 26,063 32,204 39,992 808 510 202

Ohio ............................. 55,068,629 121,394,963 159,322,228 452,884 451,574 490,265 122 269 325

Oklahoma..................... 8,922,036 15,930,878 20,304,740 132,176 142,078 156,058 68 112 130

Oregon......................... 7,382,722 11,120,140 20,001,272 159,899 171,535 188,981 46 65 106

Pennsylvania................ 117,320,158 102,958,404 71,098,139 506,362 540,231 581,256 232 191 122

Rhode Island................ 150,000 1,877,724 1,947,727 46,450 47,443 49,423 3 40 39

South Carolina ............. 22,427,746 28,599,885 47,795,394 149,104 157,825 168,716 150 181 283

South Dakota............... 5,791,586 4,430,602 3,629,155 32,304 37,040 41,667 179 120 87

Tennessee.................... 5,355,000 3,752,587 3,606,726 236,313 246,617 263,626 23 15 14

Texas............................ 28,019,645 49,381,346 47,372,367 1,054,414 1,140,218 1,321,005 27 43 36

Utah ............................. 3,214,170 26,442,711 34,418,764 100,221 112,995 124,454 32 234 277

Vermont........................ 1,680,533 738,707 1,711,673 23,613 24,394 26,545 71 30 64

Virginia ......................... 11,579,623 17,412,519 17,241,804 374,566 404,005 433,611 31 43 40

Washington.................. 22,834,218 13,892,247 24,500,489 300,145 332,600 357,056 76 42 69

West Virginia ................ 6,024,577 10,357,006 35,475,338 55,205 59,575 66,109 109 174 537

Wisconsin .................... 10,949,155 24,942,415 21,128,936 228,691 237,237 253,349 48 105 83

Wyoming...................... 6,326,604 4,806,630 5,419,398 30,767 34,432 38,190 206 140 142

Puerto Rico.................. 1,458,790 NA 537,869 88,902 NA NA 16 NA NA

na = not applicable; NA = not available.

EPSCoR = Experimental Program to Stimulate Competitive Research; GDP = gross domestic product.

NOTE: For an explanation of EPSCoR and non-EPSCoR averages, see the chapter introduction. 

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, Survey of State Research and Development Expenditures (various years); Bureau of Economic Analysis, Gross Domestic Product data (June 2013).

Science and Engineering Indicators 2014
8-98 ♦ Chapter 8. State Indicators

This indicator represents the extent of R&D activity funded by state government 

agencies relative to the size of the state’s employed civilian workforce. State R&D 

expenditures include state-administered funds from all sources that support R&D 

performed by either a state agency or an external performer.

Data on state R&D cover funding administered by state government departments, agencies, independent commissions, and other state-run entities. They 

exclude state-run colleges and universities as well as laboratories or experiment 

stations controlled by state universities; funding administered by these institutions 

is classified as academic R&D. The data also exclude state legislatures’ direct 

appropriations to nonstate agencies. Some data may include expenditures for nonR&D activities such as commercialization, environmental testing, and routine 

survey work.

Estimates of the size of a state’s workforce are provided by the Bureau of Labor 

Statistics and represent the employed component of the civilian labor force. The 

data are not seasonally adjusted and workers are assigned to a location based on 

residence. Estimates for states with smaller populations are generally less precise 

than estimates for states with larger populations.

Findings

• In 2011, state government agency R&D 

expenditures averaged $9.98 per employed 

civilian worker nationwide.

• State agency R&D funding per civilian 

worker across the United States was 

approximately 1% of the $940 in federal 

R&D obligations per worker in 2011.

• State agency R&D spending per civilian 

worker varied greatly among the states in 

2011, ranging from a low of $1.28 to a high 

of $47.93.

• Seven Experimental Program to Stimulate 

Competitive Research states are 

among those with the highest values for 

this indicator.

State Agency R&D Expenditures per Employed Worker

Figure 8-44

State agency R&D expenditures per employed worker: 2011

0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00

DC

GA

IL

IN

KS

LA

MA

MI

MN

MO

NE

NH

NJ

NM

NV

RI

TN

TX

VA

AR

ID

KY

MT

OK

OR

PA UT

AK

OH WV

CT

HI

IA

ND

NY

SC

FL

ME

WY

Dollars

AL

AZ

CA

CO

DE

MD

MS

NC

SD

VT

WA

WI

1st quartile ($15.28–$47.93)

2nd quartile ($7.29–$13.88)

3rd quartile ($4.14–$7.08)

4th quartile ($1.28–$4.12)

SOURCES: National Science Foundation, 

National Center for Science and Engineering Statistics, Survey of State Research 

and Development Expenditures; Bureau of 

Labor Statistics, Local Area Unemployment 

Statistics. 
Science and Engineering Indicators 2014 ♦ 8-99

Table 8-44

State agency R&D expenditures per employed worker, by state: 2006, 2009, and 2011

State agency R&D 

expenditures ($) Employed workers

State agency R&D 

expenditures/

employed worker ($)

State 2006 2009 2011 2006 2009 2011 2006 2009 2011

United States................. 1,021,016,894 1,213,524,157 1,403,816,235 143,729,350 139,594,700 140,695,662 7.10 8.69 9.98

Alabama..................... 7,269,319 12,929,167 19,684,063 2,098,462 1,930,230 1,992,522 3.46 6.70 9.88

Alaska ........................ 10,019,060 7,741,467 11,349,400 326,109 331,099 337,796 30.72 23.38 33.60

Arizona....................... 37,151,471 9,363,943 18,626,577 2,836,638 2,820,086 2,761,984 13.10 3.32 6.74

Arkansas .................... 4,869,648 11,465,214 14,705,327 1,286,887 1,250,526 1,251,877 3.78 9.17 11.75

California.................... 107,793,045 146,793,247 149,810,643 16,821,266 16,151,063 16,237,286 6.41 9.09 9.23

Colorado .................... 8,997,236 15,563,581 18,141,931 2,541,828 2,511,189 2,490,004 3.54 6.20 7.29

Connecticut ............... 19,209,064 28,559,052 39,192,091 1,745,993 1,740,974 1,732,807 11.00 16.40 22.62

Delaware .................... 2,812,102 1,683,562 2,609,902 424,618 405,553 407,772 6.62 4.15 6.40

District of Columbia ... 1,173,076 487,411 1,221,108 303,791 302,687 312,859 3.86 1.61 3.90

Florida........................ 42,329,624 66,513,756 150,764,438 8,584,095 8,152,332 8,322,237 4.93 8.16 18.12

Georgia ...................... 10,620,188 6,662,887 11,690,663 4,500,150 4,289,819 4,295,113 2.36 1.55 2.72

Hawaii ........................ 12,067,849 13,976,364 13,103,983 617,807 593,514 614,824 19.53 23.55 21.31

Idaho.......................... 2,280,873 8,552,058 9,366,052 718,077 695,476 702,920 3.18 12.30 13.32

Illinois......................... 37,184,281 9,570,893 17,207,125 6,225,095 5,937,296 5,942,809 5.97 1.61 2.90

Indiana ....................... 6,220,575 47,549,928 6,983,364 3,080,047 2,869,556 2,874,722 2.02 16.57 2.43

Iowa ........................... 13,564,062 37,976,643 36,992,222 1,595,136 1,573,085 1,562,156 8.50 24.14 23.68

Kansas ....................... 14,348,384 12,305,385 6,635,626 1,403,938 1,400,319 1,401,055 10.22 8.79 4.74

Kentucky.................... 17,558,997 13,938,134 20,498,849 1,904,467 1,848,505 1,875,447 9.22 7.54 10.93

Louisiana.................... 11,216,568 8,285,478 9,203,635 1,900,240 1,916,952 1,919,021 5.90 4.32 4.80

Maine ......................... 17,509,051 6,400,019 9,918,765 665,856 642,434 649,312 26.30 9.96 15.28

Maryland.................... 24,945,119 21,093,331 20,084,540 2,892,733 2,814,180 2,868,191 8.62 7.50 7.00

Massachusetts........... 10,729,419 3,290,198 4,878,927 3,255,504 3,187,538 3,216,160 3.30 1.03 1.52

Michigan .................... 75,016,589 8,630,209 9,802,873 4,722,716 4,201,763 4,189,792 15.88 2.05 2.34

Minnesota .................. 6,219,201 16,655,913 11,653,327 2,774,524 2,713,601 2,777,285 2.24 6.14 4.20

Mississippi ................. 2,744,882 3,623,953 7,420,851 1,199,871 1,168,581 1,197,641 2.29 3.10 6.20

Missouri ..................... 18,465,303 15,797,247 13,658,961 2,889,461 2,778,671 2,767,043 6.39 5.69 4.94

Montana..................... 8,606,319 7,200,442 6,474,190 476,412 465,005 466,372 18.06 15.48 13.88

Nebraska.................... 5,602,163 4,415,644 4,061,651 943,176 939,290 961,786 5.94 4.70 4.22

Nevada....................... 1,397,463 1,510,607 1,868,869 1,222,277 1,209,252 1,207,799 1.14 1.25 1.55

New Hampshire ......... 2,040,544 1,860,269 1,921,421 708,748 696,145 697,383 2.88 2.67 2.76

New Jersey ................ 25,900,482 15,146,838 17,068,781 4,257,899 4,135,921 4,120,017 6.08 3.66 4.14

New Mexico............... 3,105,000 1,655,529 1,821,583 886,708 873,960 862,043 3.50 1.89 2.11

New York.................... 103,597,135 151,467,015 182,736,305 9,062,464 8,832,592 8,740,642 11.43 17.15 20.91

North Carolina............ 14,344,310 40,404,202 29,611,785 4,261,325 4,104,049 4,183,052 3.37 9.84 7.08

North Dakota ............. 21,062,090 16,415,807 8,072,257 349,368 355,641 368,677 60.29 46.16 21.90

Ohio ........................... 55,068,629 121,394,963 159,322,228 5,602,764 5,320,715 5,303,655 9.83 22.82 30.04

Oklahoma................... 8,922,036 15,930,878 20,304,740 1,650,070 1,648,556 1,678,953 5.41 9.66 12.09

Oregon....................... 7,382,722 11,120,140 20,001,272 1,792,039 1,753,853 1,785,400 4.12 6.34 11.20

Pennsylvania.............. 117,320,158 102,958,404 71,098,139 6,021,084 5,898,301 5,892,519 19.48 17.46 12.07

Rhode Island.............. 150,000 1,877,724 1,947,727 543,973 504,616 499,481 0.28 3.72 3.90

South Carolina ........... 22,427,746 28,599,885 47,795,394 1,970,912 1,908,839 1,941,654 11.38 14.98 24.62

South Dakota............. 5,791,586 4,430,602 3,629,155 421,799 420,278 422,696 13.73 10.54 8.59

Tennessee.................. 5,355,000 3,752,587 3,606,726 2,852,509 2,713,058 2,828,617 1.88 1.38 1.28

Texas.......................... 28,019,645 49,381,346 47,372,367 10,757,510 11,070,143 11,493,519 2.60 4.46 4.12

Utah ........................... 3,214,170 26,442,711 34,418,764 1,285,389 1,275,514 1,254,151 2.50 20.73 27.44

Vermont...................... 1,680,533 738,707 1,711,673 343,149 335,132 338,632 4.90 2.20 5.05

Virginia ....................... 11,579,623 17,412,519 17,241,804 3,862,508 3,842,447 3,928,267 3.00 4.53 4.39

Washington................ 22,834,218 13,892,247 24,500,489 3,155,384 3,194,251 3,161,818 7.24 4.35 7.75

West Virginia .............. 6,024,577 10,357,006 35,475,338 777,210 745,150 740,175 7.75 13.90 47.93

Wisconsin .................. 10,949,155 24,942,415 21,128,936 2,932,482 2,843,857 2,832,826 3.73 8.77 7.46

Wyoming.................... 6,326,604 4,806,630 5,419,398 276,882 281,106 284,893 22.85 17.10 19.02

Puerto Rico................ 1,458,790 NA 537,869 1,260,703 1,101,862 1,032,765 1.16 NA 0.52

NA = not available.

NOTE: Civilian workers represent the employed component of the civilian labor force and are reported as annual data not seasonally adjusted. 

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, Survey of State Research and Development Expenditures (various 

years); Bureau of Labor Statistics, Local Area Unemployment Statistics (various years).

Science and Engineering Indicators 2014
8-100 ♦ Chapter 8. State Indicators

This indicator represents the ratio of state agency R&D funding to the number of 

individuals who work in S&E occupations in the state.

Data on state agency R&D cover funding administered by state government 

departments, agencies, independent commissions, and other state-run entities. They 

exclude state-run colleges and universities as well as laboratories or experiment 

stations controlled by state universities; funding administered by these institutions 

is classified as academic R&D. The data also exclude state legislatures’ direct appropriations to nonstate agencies. Some data may include expenditures for non-R&D 

activities such as commercialization, environmental testing, and routine survey work.

S&E occupations are defined by standard occupational codes. They include 

engineers and computer, mathematical, life, physical, and social scientists. Managers, technicians, elementary and secondary schoolteachers, and medical personnel 

are not included.

Data on individuals in S&E occupations come from a survey of workplaces that 

assigns workers to a state based on where they work. Estimates do not include selfemployed persons and are developed by the Bureau of Labor Statistics from data 

provided by state workforce agencies. Because of the way data are collected, faculty 

teaching in S&E fields are not included as workers in S&E occupations. Data on 

people in S&E occupations are sample based.

Findings

• Nationally, state government agencies 

spent about $1.4 billion for R&D in 2011. 

By comparison, the federal government 

obligated more than $132 billion for R&D 

in 2011.

• In 2011, the average state agency R&D 

expenditure per person employed in an 

S&E occupation was $242, compared to 

about $23,000 the federal government 

averaged for each person employed in an 

S&E occupation.

• State agency R&D funding per person 

employed in an S&E occupation ranged 

from $22 to $1,939 per state in 2011.

• Several Experimental Program to 

Stimulate Competitive Research states 

had the highest state agency R&D spending per S&E worker.

State Agency R&D Expenditures per Individual in Science and Engineering 

Occupation

Figure 8-45

State agency R&D expenditures per individual in science and engineering occupation: 2011

0 100 200 300 400 500 600 700 800 1,900 2,000

DC

GA

IL

IN

MA

MI

MN

NH

NJ

NM

NV

TN

VA

AZ

CA

CO

DE

KS

MD

MO

NC

NE

RI

TX

VT

WA

AL

LA

WI WV

ID

KY

MS

SD

MT

OK

AR

CT

ME

NY

FL

HI

ND

UT

WY

AK

IA

OH

SC

Dollars

1st quartile ($512–$1,939)

2nd quartile ($183–$501)

3rd quartile ($94–$162)

4th quartile ($19–$89)

No data

SOURCES: National Science Foundation, 

National Center for Science and Engineering Statistics, Survey of State Research 

and Development Expenditures; Bureau of 

Labor Statistics, Occupational Employment 

Statistics Survey.
Science and Engineering Indicators 2014 ♦ 8-101

Table 8-45

State agency R&D expenditures per individual in science and engineering occupation, by state: 2006, 2009, and 2011

State agency R&D 

expenditures ($)

Individuals in S&E 

occupations

State agency R&D 

expenditures/

individual in S&E 

occupation ($)

State 2006 2009 2011 2006 2009 2011 2006 2009 2011

United States................. 1,021,016,894 1,213,524,157 1,403,816,235 5,407,710 5,785,710 5,794,980 189 210 242

Alabama..................... 7,269,319 12,929,167 19,684,063 66,100 68,670 70,290 110 188 280

Alaska ........................ 10,019,060 7,741,467 11,349,400 10,720 14,780 15,680 935 524 724

Arizona....................... 37,151,471 9,363,943 18,626,577 98,110 102,820 115,330 379 91 162

Arkansas .................... 4,869,648 11,465,214 14,705,327 24,860 31,420 29,350 196 365 501

California.................... 107,793,045 146,793,247 149,810,643 730,010 795,240 800,430 148 185 187

Colorado .................... 8,997,236 15,563,581 18,141,931 133,730 148,090 144,750 67 105 125

Connecticut ............... 19,209,064 28,559,052 39,192,091 79,380 79,160 76,550 242 361 512

Delaware .................... 2,812,102 1,683,562 2,609,902 21,550 22,200 22,430 130 76 116

District of Columbia ... 1,173,076 487,411 1,221,108 64,120 61,430 62,660 18 8 19

Florida........................ 42,329,624 66,513,756 150,764,438 246,190 247,070 244,970 172 269 615

Georgia ...................... 10,620,188 6,662,887 11,690,663 136,470 NA 147,820 78 NA 79

Hawaii ........................ 12,067,849 13,976,364 13,103,983 18,940 19,020 20,100 637 735 652

Idaho.......................... 2,280,873 8,552,058 9,366,052 NA 23,520 25,950 NA 364 361

Illinois......................... 37,184,281 9,570,893 17,207,125 222,470 221,170 204,420 167 43 84

Indiana ....................... 6,220,575 47,549,928 6,983,364 80,110 90,750 93,640 78 524 75

Iowa ........................... 13,564,062 37,976,643 36,992,222 43,670 47,080 48,930 311 807 756

Kansas ....................... 14,348,384 12,305,385 6,635,626 48,620 56,200 51,530 295 219 129

Kentucky.................... 17,558,997 13,938,134 20,498,849 44,680 51,200 51,990 393 272 394

Louisiana.................... 11,216,568 8,285,478 9,203,635 40,180 43,630 42,760 279 190 215

Maine ......................... 17,509,051 6,400,019 9,918,765 15,950 17,910 17,490 1,098 357 567

Maryland.................... 24,945,119 21,093,331 20,084,540 159,470 169,540 173,020 156 124 116

Massachusetts........... 10,729,419 3,290,198 4,878,927 198,670 217,690 220,670 54 15 22

Michigan .................... 75,016,589 8,630,209 9,802,873 208,520 187,760 188,380 360 46 52

Minnesota .................. 6,219,201 16,655,913 11,653,327 125,930 134,060 130,340 49 124 89

Mississippi ................. 2,744,882 3,623,953 7,420,851 24,910 25,940 23,440 110 140 317

Missouri ..................... 18,465,303 15,797,247 13,658,961 96,420 104,310 106,930 192 151 128

Montana..................... 8,606,319 7,200,442 6,474,190 13,010 14,210 14,960 662 507 433

Nebraska.................... 5,602,163 4,415,644 4,061,651 32,500 31,790 33,800 172 139 120

Nevada....................... 1,397,463 1,510,607 1,868,869 26,930 27,560 28,370 52 55 66

New Hampshire ......... 2,040,544 1,860,269 1,921,421 27,680 30,550 29,260 74 61 66

New Jersey ................ 25,900,482 15,146,838 17,068,781 176,460 195,690 182,210 147 77 94

New Mexico............... 3,105,000 1,655,529 1,821,583 30,800 36,950 35,520 101 45 51

New York.................... 103,597,135 151,467,015 182,736,305 306,810 315,480 310,510 338 480 589

North Carolina............ 14,344,310 40,404,202 29,611,785 138,790 158,920 161,880 103 254 183

North Dakota ............. 21,062,090 16,415,807 8,072,257 9,360 9,930 12,070 2,250 1,653 669

Ohio ........................... 55,068,629 121,394,963 159,322,228 185,190 207,930 208,140 297 584 765

Oklahoma................... 8,922,036 15,930,878 20,304,740 50,770 45,730 47,090 176 348 431

Oregon....................... 7,382,722 11,120,140 20,001,272 64,520 69,630 NA 114 160 NA

Pennsylvania.............. 117,320,158 102,958,404 71,098,139 214,910 NA NA 546 NA NA

Rhode Island.............. 150,000 1,877,724 1,947,727 18,060 18,120 19,450 8 104 100

South Carolina ........... 22,427,746 28,599,885 47,795,394 53,230 57,370 61,020 421 499 783

South Dakota............. 5,791,586 4,430,602 3,629,155 10,120 11,570 11,790 572 383 308

Tennessee.................. 5,355,000 3,752,587 3,606,726 67,040 68,970 77,630 80 54 46

Texas.......................... 28,019,645 49,381,346 47,372,367 408,710 470,010 469,080 69 105 101

Utah ........................... 3,214,170 26,442,711 34,418,764 49,690 51,270 51,350 65 516 670

Vermont...................... 1,680,533 738,707 1,711,673 12,780 12,780 13,100 131 58 131

Virginia ....................... 11,579,623 17,412,519 17,241,804 251,720 264,090 267,620 46 66 64

Washington................ 22,834,218 13,892,247 24,500,489 171,780 196,850 196,760 133 71 125

West Virginia .............. 6,024,577 10,357,006 35,475,338 17,150 16,350 18,300 351 633 1,939

Wisconsin .................. 10,949,155 24,942,415 21,128,936 96,860 100,850 102,320 113 247 206

Wyoming.................... 6,326,604 4,806,630 5,419,398 7,640 8,920 8,440 828 539 642

Puerto Rico................ 1,458,790 NA 537,869 23,850 22,760 20,990 61 NA 26

NA = not available.

NOTES: The national total for S&E occupations includes states with suppressed data. Occupational Employment Statistics estimates for S&E occupations are based 

on May data.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, Survey of State Research and Development Expenditures (various 

years); Bureau of Labor Statistics, Occupational Employment Statistics Survey (various years).

Science and Engineering Indicators 2014
8-102 ♦ Chapter 8. State Indicators

This indicator represents the role of R&D in a state’s business activity. 

The business sector is the largest performer of U.S. R&D. It accounts for 

more than half of all U.S. applied research funding and a significant portion, over 80%, of all development funding. A high value for this indicator 

means that the businesses within a state are making a large investment 

in their R&D activities.

The methodology for data collection, assignment to individual states, 

and developing estimates of R&D spending has changed during the last 

decade as the transition was made from the Survey of Industrial R&D to 

the Business R&D and Innovation Survey. Estimates from the two surveys 

are consistent. Estimates for states with smaller economies are generally 

less precise than those for states with larger economies.

Private-industry output is the portion of state gross domestic product 

contributed by state businesses.

Findings

• The amount of R&D performed by business rose 

from more than $202 billion in 2001 to about 

$294 billion in 2011, an increase of 46%.

• The value of this indicator for the United States 

remained virtually unchanged between 2001 

and 2011.

• Business-performed R&D as a share of private 

industry output varied greatly among states in 

2011, ranging from 0.14 to 4.78.

• Business R&D was concentrated in a few 

states—only 12 states had indicator values that 

exceeded the national average in 2011.

Business-Performed R&D as a Percentage of Private-Industry Output

Figure 8-46

Business-performed domestic R&D as a percentage of private-industry output: 2011

0.00 0.60 1.20 1.80 2.40 3.00 3.60 4.20 4.80

AK

AR

DC

HI

LA

MS

MT

NV

OK

SD

WV

WY

IN

MN

OR

AL

IA

KS

NC

OH

RI

TX

VA

VT NJ

CT

DE

MI

NH

CA

MA

WA

AZ

CO

ID

IL

MD

PA

UT

WI

FL

GA

KY

ME

ND

NE

NM

NY

SC

TN

Percent

1st quartile (2.25%–4.78%)

2nd quartile (1.32%–2.22%)

3rd quartile (0.62%–1.30%)

4th quartile (0.14%–0.59%)

No data

SOURCES: National Science Foundation, 

National Center for Science and 

Engineering Statistics, Survey of Industrial 

Research and Development (various 

years) and Business R&D and Innovation 

Survey; Bureau of Economic Analysis, 

Gross Domestic Product data.
Science and Engineering Indicators 2014 ♦ 8-103

Table 8-46

Business-performed domestic R&D as a percentage of private-industry output, by state: 2001, 2006, and 2011

Business-performed domestic 

R&D ($millions) Private-industry output ($millions)

Business-performed 

domestic R&D/ 

private-industry 

output (%)

State 2001 2006 2011 2001 2006 2011 2001 2006 2011

United States................... 202,017 247,669 294,093 9,010,778 11,709,405 13,081,829 2.24 2.12 2.25

Alabama....................... 905 1,835 1,879 101,748 134,131 148,510 0.89 1.37 1.27

Alaska .......................... 68 49e 84e 22,208 34,059 41,756 0.31 0.14 0.20

Arizona......................... 2,707 3,590 4,931 148,971 215,927 222,442 1.82 1.66 2.22

Arkansas ...................... 254e 285 344 62,027 80,964 91,227 0.41 0.35 0.38

California...................... 44,628 58,424 75,035 1,190,581 1,603,109 1,687,543 3.75 3.64 4.45

Colorado ...................... 3,082 4,657 4,310 160,347 202,993 230,650 1.92 2.29 1.87

Connecticut ................. 4,686 8,273 7,504 152,898 190,578 204,211 3.06 4.34 3.67

Delaware ...................... 1,232 1,446 2,097 40,005 51,333 58,313 3.08 2.82 3.60

District of Columbia ..... 242 276 415 41,378 57,397 69,787 0.58 0.48 0.59

Florida.......................... 3,755 4,139 5,988 445,856 654,411 650,652 0.84 0.63 0.92

Georgia ........................ 1,912 2,786 3,839 267,990 330,692 358,180 0.71 0.84 1.07

Hawaii .......................... 93 155 252 33,397 47,688 52,975 0.28 0.33 0.48

Idaho............................ 884 625 1,171 31,084 43,450 49,304 2.84 1.44 2.38

Illinois........................... 8,232 10,765 12,038 440,581 544,055 601,979 1.87 1.98 2.00

Indiana ......................... 3,583 4,858 6,158 180,282 220,584 256,124 1.99 2.20 2.40

Iowa ............................. 817 1,055 2,314 83,002 109,971 129,407 0.98 0.96 1.79

Kansas ......................... 1,299 i 2,064 i 1,509 77,240 95,821 114,751 1.68 2.15 1.32

Kentucky...................... 636 839 1,278 100,203 124,402 140,036 0.63 0.67 0.91

Louisiana...................... 316e 367 459 120,435 182,648 211,357 0.26 0.20 0.22

Maine ........................... 249 253 295 32,916 41,042 45,163 0.76 0.62 0.65

Maryland...................... 3,682 3,421 5,101 162,500 215,095 248,615 2.27 1.59 2.05

Massachusetts............. 11,756 15,562 15,722 257,702 307,528 352,398 4.56 5.06 4.46

Michigan ...................... 14,283 16,477 13,660 301,664 333,606 339,886 4.73 4.94 4.02

Minnesota .................... 4,355 6,296 6,174 173,971 219,748 251,930 2.50 2.87 2.45

Mississippi ................... 219e 231 235 56,068 71,207 79,490 0.39 0.32 0.30

Missouri ....................... 1,792 2,675 NA 163,803 196,615 217,573 1.09 1.36 NA

Montana....................... 70e 103 i 136 19,184 27,064 32,709 0.36 0.38 0.42

Nebraska...................... 306 447 636 51,532 66,099 83,460 0.59 0.68 0.76

Nevada......................... 290 535 638 71,072 112,123 115,577 0.41 0.48 0.55

New Hampshire ........... 1,339 1,774 i 2,069 40,680 50,826 56,693 3.29 3.49 3.65

New Jersey .................. 10,164 14,606 13,930 329,143 408,330 439,353 3.09 3.58 3.17

New Mexico................. 231 676 472 41,609 57,760 64,097 0.56 1.17 0.74

New York...................... 10,884 9,518 12,072 726,643 923,574 1,045,302 1.50 1.03 1.15

North Carolina.............. 4,437 5,486 6,193 256,039 328,722 371,955 1.73 1.67 1.66

North Dakota ............... 347 120 261 16,005 21,956 34,991 2.17 0.55 0.75

Ohio ............................. 6,694 6,852 6,993 341,402 403,379 434,407 1.96 1.70 1.61

Oklahoma..................... 543e 474 604 80,579 109,859 129,709 0.67 0.43 0.47

Oregon......................... 2,677 3,419 4,631 97,526 141,073 166,817 2.74 2.42 2.78

Pennsylvania................ 8,967 9,819 9,718 367,480 457,197 524,349 2.44 2.15 1.85

Rhode Island................ 1,134 i 1,330 i 542 31,263 40,777 42,800 3.63 3.26 1.27

South Carolina ............. 921 1,396 1,399 101,790 124,815 139,836 0.90 1.12 1.00

South Dakota............... 87e 95 136 21,906 28,132 36,560 0.40 0.34 0.37

Tennessee.................... 1,503 1,428 1,434 163,834 208,571 232,226 0.92 0.68 0.62

Texas............................ 9,839 13,334 15,309 677,945 940,631 1,174,548 1.45 1.42 1.30

Utah ............................. 1,173 1,274 2,438 62,231 86,675 108,306 1.88 1.47 2.25

Vermont........................ 339 360 374 i 16,466 20,432 22,821 2.06 1.76 1.64

Virginia ......................... 2,957 4,816 5,562 232,954 308,886 353,751 1.27 1.56 1.57

Washington.................. 8,933 i 11,320 14,558 199,011 257,173 304,420 4.49 4.40 4.78

West Virginia ................ 211 221 247 35,755 44,991 54,212 0.59 0.49 0.46

Wisconsin .................... 2,469 i 3,020 4,053 163,999 204,541 225,709 1.51 1.48 1.80

Wyoming...................... 28e 27e 46e 15,873 26,765 32,962 0.18 0.10 0.14

Puerto Rico.................. NA NA NA NA NA NA NA NA NA

e = estimated; more than 50% of value is imputed due to raking of state data. i = more than 50% of value is imputed. NA = not available.

NOTE: The national totals for business-performed R&D in the United States, from the National Science Foundation/National Center for Science and 

Engineering Statistics, and Census Bureau Business R&D and Innovation Survey, include undistributed business performed R&D and states with suppressed data. 

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, Survey of Industrial Research and Development (various 

years) and Business R&D and Innovation Survey; Bureau of Economic Analysis, Gross Domestic Product data (June 2013).

Science and Engineering Indicators 2014
8-104 ♦ Chapter 8. State Indicators

This indicator represents the ratio of S&E R&D 

expenditures at a state’s colleges and universities to the 

size of the state’s economy. Academic R&D performers 

account for slightly more than half of U.S. basic research, 

about one-third of total research (basic plus applied), 

and roughly 10% of all R&D conducted in the United 

States. Academic R&D can be a valuable basis for future 

economic development.

Data on academic R&D are provided by the National 

Center for Science and Engineering Statistics and represent S&E R&D at U.S. colleges and universities with 

more than $150,000 in R&D expenditures.

Findings

• Expenditures for research performed in academic institutions have 

increased by 71% between 2002 and 2012, rising from $36.3 billion to 

$62.1 billion.

• In the United States, growth in academic research increased more rapidly than gross domestic product (GDP), causing the value of this indicator to increase by 16% between 2002 and 2012.

• In 2012, the value of this indicator ranged from $1.12 to $10.44 

across states.

• The amount of spending for academic S&E R&D declined as a share 

of GDP in 15 states between 2002 and 2012. Ten of these states were 

Experimental Program to Stimulate Competitive Research (EPSCoR) 

states.

• Although non-EPSCoR states had 7.6 times the amount of spending for 

academic S&E R&D as EPSCoR states in 2012, the average indicator 

value for both groups increased between 2002 and 2012.

Academic Science and Engineering R&D per $1,000 of Gross Domestic 

Product

Figure 8-47

Academic science and engineering R&D per $1,000 of gross domestic product: 2012

0.95 1.90 2.85 3.80 4.75 5.70 6.65 7.60 8.55 9.50 10.45

NV

WY

AK

AR

DE

FL

ID

LA

ME

NJ

OK

WV

MI

NM

PA

WI

AZ

IL

KS

KY

MN

OR

SC

SD

TN

TX

VA

WA

NC

NH

RI MA MD

AL

CA

CO

CT

DC

GA

HI

IA

IN

MO

MS

MT

ND

NE

NY

OH

UT

VT

Dollars

1st quartile ($4.56–$10.44)

2nd quartile ($3.94–$4.53)

3rd quartile ($2.85–$3.85)

4th quartile ($1.12–$2.69)

SOURCES: National Science Foundation, 

National Center for Science and Engineering 

Statistics, Academic Research and Development Expenditures; Bureau of Economic 

Analysis, Gross Domestic Product data.
Science and Engineering Indicators 2014 ♦ 8-105

Table 8-47

Academic science and engineering R&D per $1,000 of gross domestic product, by state: 2002, 2007, and 2012

Academic S&E R&D ($thousands) State GDP ($millions)

Academic S&E R&D ($)/ 

$1,000 GDP

State 2002 2007 2012 2002 2007 2012 2002 2007 2012

EPSCoR states................ 4,575,040 6,292,689 7,120,907 1,412,084 1,918,622 2,164,567 3.24 3.28 3.29

Non-EPSCoR states........ 31,201,781 42,290,351 53,976,573 9,027,189 11,833,059 13,173,276 3.46 3.57 4.10

Average EPSCoR 

 state value .................... na na na na na na 3.29 3.41 3.42

Average non-EPSCoR 

 state value .................... na na na na na na 3.65 3.76 4.31

United States................... 36,316,382 49,363,499 62,094,377 10,572,388 13,936,196 15,566,076 3.44 3.54 3.99

Alabama....................... 503,470 655,245 808,194 125,168 165,665 183,547 4.02 3.96 4.40

Alaska .......................... 128,875 130,637 135,751 28,894 44,540 51,859 4.46 2.93 2.62

Arizona......................... 531,106 782,671 987,123 177,068 259,157 266,891 3.00 3.02 3.70

Arkansas ...................... 140,813 240,321 275,503 74,167 97,470 109,557 1.90 2.47 2.51

California...................... 4,887,918 6,733,546 8,092,820 1,387,213 1,870,916 2,003,479 3.52 3.60 4.04

Colorado ...................... 645,291 872,576 1,249,413 186,529 242,633 274,048 3.46 3.60 4.56

Connecticut ................. 538,488 691,408 925,024 168,865 221,133 229,317 3.19 3.13 4.03

Delaware ...................... 88,319 125,663 176,743 43,672 59,592 65,984 2.02 2.11 2.68

District of Columbia ..... 260,819 336,618 455,063 67,924 91,896 109,793 3.84 3.66 4.14

Florida.......................... 1,085,764 1,557,504 1,934,951 536,061 760,936 777,164 2.03 2.05 2.49

Georgia ........................ 1,076,706 1,388,976 1,708,803 313,952 399,579 433,569 3.43 3.48 3.94

Hawaii .......................... 172,664 274,373 328,168 44,752 64,070 72,424 3.86 4.28 4.53

Idaho............................ 93,323 114,224 142,976 37,729 54,273 58,243 2.47 2.10 2.45

Illinois........................... 1,441,156 1,867,003 2,270,759 497,802 626,611 695,238 2.90 2.98 3.27

Indiana ......................... 650,718 893,808 1,150,327 208,674 261,755 298,625 3.12 3.41 3.85

Iowa ............................. 485,756 586,786 695,465 98,584 134,053 152,436 4.93 4.38 4.56

Kansas ......................... 299,806 375,960 479,721 91,671 120,599 138,953 3.27 3.12 3.45

Kentucky...................... 334,208 503,293 547,025 121,436 150,487 173,466 2.75 3.34 3.15

Louisiana...................... 476,785 604,007 653,885 139,202 207,312 243,264 3.43 2.91 2.69

Maine ........................... 75,063 137,425 118,589 39,989 49,065 53,656 1.88 2.80 2.21

Maryland...................... 1,895,382 2,542,336 3,316,156 206,624 271,985 317,678 9.17 9.35 10.44

Massachusetts............. 1,697,182 2,171,596 3,009,019 288,352 352,378 403,823 5.89 6.16 7.45

Michigan ...................... 1,233,887 1,509,953 2,091,921 351,832 386,591 400,504 3.51 3.91 5.22

Minnesota .................... 504,398 636,920 840,731 201,559 253,374 294,729 2.50 2.51 2.85

Mississippi ................... 289,412 410,637 437,775 69,527 92,107 101,490 4.16 4.46 4.31

Missouri ....................... 705,593 941,445 1,057,351 192,189 232,959 258,832 3.67 4.04 4.09

Montana....................... 122,375 179,137 184,754 23,781 35,085 40,422 5.15 5.11 4.57

Nebraska...................... 266,930 364,842 419,163 61,384 82,135 99,557 4.35 4.44 4.21

Nevada......................... 126,713 192,081 149,323 82,764 133,185 133,584 1.53 1.44 1.12

New Hampshire ........... 220,061 307,074 397,916 46,730 57,868 64,697 4.71 5.31 6.15

New Jersey .................. 690,642 864,737 1,061,201 376,922 471,372 508,003 1.83 1.83 2.09

New Mexico................. 292,691 410,375 390,548 53,662 74,356 80,600 5.45 5.52 4.85

New York...................... 2,765,484 3,971,652 5,166,092 822,408 1,076,255 1,205,930 3.36 3.69 4.28

North Carolina.............. 1,279,377 1,884,244 2,619,095 302,201 396,740 455,973 4.23 4.75 5.74

North Dakota ............... 106,078 169,468 213,666 20,439 28,549 46,016 5.19 5.94 4.64

Ohio ............................. 1,116,116 1,767,903 2,017,681 397,966 467,138 509,393 2.80 3.78 3.96

Oklahoma..................... 282,062 298,663 412,978 98,778 140,378 160,953 2.86 2.13 2.57

Oregon......................... 386,666 574,521 680,956 119,571 167,088 198,702 3.23 3.44 3.43

Pennsylvania................ 1,913,687 2,438,312 3,120,745 424,103 531,098 600,897 4.51 4.59 5.19

Rhode Island................ 163,052 230,281 335,957 38,135 47,293 50,956 4.28 4.87 6.59

South Carolina ............. 399,982 569,347 563,599 124,391 157,712 176,217 3.22 3.61 3.20

South Dakota............... 38,449 81,544 123,383 27,610 34,885 42,464 1.39 2.34 2.91

Tennessee.................... 491,274 761,388 968,317 193,069 242,220 277,036 2.54 3.14 3.50

Texas............................ 2,535,237 3,417,082 4,413,601 782,780 1,147,404 1,397,369 3.24 2.98 3.16

Utah ............................. 359,556 414,690 608,199 74,603 108,474 130,486 4.82 3.82 4.66

Vermont........................ 90,189 115,025 119,802 19,599 24,043 27,296 4.60 4.78 4.39

Virginia ......................... 693,668 971,377 1,283,852 290,904 389,570 445,876 2.38 2.49 2.88

Washington.................. 784,186 981,229 1,376,467 237,117 325,118 375,730 3.31 3.02 3.66

West Virginia ................ 100,830 167,208 183,510 44,533 56,864 69,380 2.26 2.94 2.64

Wisconsin .................... 806,543 1,066,688 1,330,504 190,241 236,522 261,548 4.24 4.51 5.09

Wyoming...................... 41,632 79,700 63,812 19,262 33,708 38,422 2.16 2.36 1.66

Puerto Rico.................. NA NA NA 74,827 93,263 NA NA NA NA

na = not applicable; NA = not available.

EPSCoR = Experimental Program to Stimulate Competitive Research; GDP = gross domestic product.

NOTES: Academic R&D is reported for institutions with R&D over $150,000. For an explanation of EPSCoR and non-EPSCoR averages, see the chapter 

introduction.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, Academic Research and Development Expenditures 

(various years); Bureau of Economic Analysis, Gross Domestic Product data (June 2013).

Science and Engineering Indicators 2014
8-106 ♦ Chapter 8. State Indicators

This indicator represents the rate at which the states are training new S&E 

doctorate recipients for entry into the workforce. High values indicate relatively 

large production of new doctorate holders compared with the existing stock of 

employed doctorate holders. States with relatively low values may need to attract 

S&E doctorate holders from elsewhere to meet the needs of local employers.

Data on doctorates conferred and on employed doctorate holders include 

those with doctoral degrees in computer and mathematical sciences; the biological, agricultural, or environmental life sciences; physical sciences; social 

sciences; psychology; engineering; and health fields. Both sets of data exclude 

individuals with doctorates from foreign institutions. The employed doctorate 

data also exclude those older than the age of 75. Data for doctorates conferred 

are presented by the location where the doctorate was earned; employment data 

for S&E doctorate holders are presented by employment location regardless of 

residence. Estimates for states with smaller populations of employed doctorate 

holders are generally less precise than estimates for states with larger populations.

The indicator does not take into account any postgraduation mobility of 

recent S&E doctorate recipients to their place of employment. Doctorate recipients with temporary visas may decide to return home after graduation to begin 

their careers. The indicator also does not cover individuals with non-U.S. S&E 

doctorates who are working in the United States.

Findings

• In 2010, nearly 33,000 S&E doctorates were 

awarded by U.S. academic institutions, 

approximately 28% more than in 2001. 

• The national value for this indicator rose from 

44.3 S&E doctorates conferred per 1,000 

employed S&E doctorate holders in 2001 to 

47.2 in 2010.

• State values for S&E doctorates conferred 

per 1,000 S&E doctorate holders employed 

ranged from 15.0 to 76.1 in 2010. 

• Low state values on this indicator may 

indicate either a small S&E graduate-level 

educational program or a concentration of 

S&E doctorate-level employment opportunities that attract significant numbers of S&E 

doctorate holders who were educated elsewhere. Low-ranking Experimental Program to 

Stimulate Competitive Research states tend 

to fall into the former category.

Science and Engineering Doctorates Conferred per 1,000 Employed S&E 

Doctorate Holders 

Figure 8-48

Science and engineering doctorates conferred per 1,000 employed S&E doctorate holders: 2010

14.0 21.0 28.0 35.0 42.0 49.0 56.0 63.0 70.0 77.0

DC

ME

MT

NJ

OR

VT

WA

AL

KY

MN

NY

OH

OK

PA

TX

WI

WV

CO

CT

HI

SC

SD

TN

VA

GA

IL

MS

NE

WY

AZ

FL

KS

MI

RI

IA

IN

LA

AR

CA

DE

MA

MO

NC

ND

NH

NV

UT

Degrees

AK

ID

MD

NM

1st quartile (57.0–76.1)

2nd quartile (47.9–55.9)

3rd quartile (39.0–47.5)

4th quartile (15.0–37.8)

SOURCES: National Science Foundation, 

National Center for Science and Engineering 

Statistics, Survey of Earned Doctorates and 

Survey of Doctorate Recipients.
Science and Engineering Indicators 2014 ♦ 8-107

Table 8-48

Science and engineering doctorates conferred per 1,000 employed S&E doctorate holders, by state: 2001, 2006, 

and 2010

 S&E doctorates conferred

Employed S&E 

doctorate holders

S&E doctorates 

conferred/1,000 employed 

S&E doctorate holders

State 2001 2006 2010 2001 2006 2010 2001 2006 2010

United States................. 25,352 30,291 32,509 572,800 618,400 688,300 44.3 49.0 47.2

Alabama..................... 268 309 357 5,300 5,900 6,600 50.6 52.4 54.1

Alaska ........................ 22 18 38 1,200 1,100 1,400 18.3 16.4 27.1

Arizona....................... 392 514 567 7,100 8,400 9,000 55.2 61.2 63.0

Arkansas .................... 61 106 126 2,600 2,800 2,900 23.5 37.9 43.4

California.................... 3,664 4,365 4,589 80,900 87,400 102,300 45.3 49.9 44.9

Colorado .................... 511 546 611 11,800 13,100 14,800 43.3 41.7 41.3

Connecticut ............... 370 453 470 9,500 10,300 11,300 38.9 44.0 41.6

Delaware .................... 108 135 145 3,500 3,100 3,000 30.9 43.5 48.3

District of Columbia ... 306 363 263 14,200 13,300 14,900 21.5 27.3 17.7

Florida........................ 838 1,211 1,389 15,700 17,600 20,600 53.4 68.8 67.4

Georgia ...................... 607 819 867 12,000 13,000 15,200 50.6 63.0 57.0

Hawaii ........................ 141 110 122 2,600 2,800 3,000 54.2 39.3 40.7

Idaho.......................... 50 72 63 2,200 2,800 2,800 22.7 25.7 22.5

Illinois......................... 1,528 1,603 1,518 22,100 24,100 25,300 69.1 66.5 60.0

Indiana ....................... 621 700 818 9,600 9,900 10,900 64.7 70.7 75.0

Iowa ........................... 322 373 426 4,400 4,900 5,600 73.2 76.1 76.1

Kansas ....................... 236 247 259 4,000 4,300 4,000 59.0 57.4 64.8

Kentucky.................... 174 254 263 4,600 5,000 5,100 37.8 50.8 51.6

Louisiana.................... 317 289 377 5,300 5,500 5,300 59.8 52.5 71.1

Maine ......................... 30 27 36 2,000 2,400 2,400 15.0 11.3 15.0

Maryland.................... 604 791 817 22,700 26,200 29,800 26.6 30.2 27.4

Massachusetts........... 1,436 1,689 1,788 29,100 32,400 36,900 49.3 52.1 48.5

Michigan .................... 868 1,060 1,153 17,400 17,900 18,000 49.9 59.2 64.1

Minnesota .................. 531 705 713 11,400 11,800 13,700 46.6 59.7 52.0

Mississippi ................. 115 142 200 3,200 3,300 3,300 35.9 43.0 60.6

Missouri ..................... 412 512 495 9,300 9,300 10,700 44.3 55.1 46.3

Montana..................... 39 66 70 1,400 2,000 2,400 27.9 33.0 29.2

Nebraska.................... 131 136 191 2,900 3,000 3,100 45.2 45.3 61.6

Nevada....................... 50 93 126 2,000 2,600 3,000 25.0 35.8 42.0

New Hampshire ......... 111 129 133 2,500 2,500 3,000 44.4 51.6 44.3

New Jersey ................ 652 708 775 22,700 20,800 23,000 28.7 34.0 33.7

New Mexico............... 134 181 168 7,700 8,300 8,000 17.4 21.8 21.0

New York.................... 2,157 2,495 2,576 44,000 45,900 50,900 49.0 54.4 50.6

North Carolina............ 665 805 987 16,800 18,900 20,600 39.6 42.6 47.9

North Dakota ............. 43 45 69 1,100 1,400 1,500 39.1 32.1 46.0

Ohio ........................... 1,023 1,138 1,175 20,100 20,500 21,700 50.9 55.5 54.1

Oklahoma................... 198 195 271 4,400 4,400 4,900 45.0 44.3 55.3

Oregon....................... 298 320 295 7,000 8,300 9,100 42.6 38.6 32.4

Pennsylvania.............. 1,143 1,551 1,571 26,100 29,100 31,300 43.8 53.3 50.2

Rhode Island.............. 168 223 209 2,600 3,000 3,000 64.6 74.3 69.7

South Carolina ........... 205 231 264 5,100 5,900 6,400 40.2 39.2 41.3

South Dakota............. 31 38 51 1,000 1,000 1,300 31.0 38.0 39.2

Tennessee.................. 351 395 448 9,000 10,000 11,500 39.0 39.5 39.0

Texas.......................... 1,500 1,845 2,192 32,500 36,000 42,400 46.2 51.3 51.7

Utah ........................... 197 225 280 4,800 5,500 5,900 41.0 40.9 47.5

Vermont...................... 52 49 58 1,800 1,700 1,800 28.9 28.8 32.2

Virginia ....................... 667 787 832 17,500 19,800 22,000 38.1 39.7 37.8

Washington................ 422 525 540 14,800 16,900 18,900 28.5 31.1 28.6

West Virginia .............. 56 102 115 1,900 2,000 2,200 29.5 51.0 52.3

Wisconsin .................. 494 559 593 8,700 9,500 10,600 56.8 58.8 55.9

Wyoming.................... 33 37 50 800 700 800 41.3 52.9 62.5

Puerto Rico................ 101 161 140 1,400 1,700 2,300 72.1 94.7 60.9

NOTE: Data on U.S. S&E doctorate holders are classified by employment location.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, Survey of Earned Doctorates, and Survey of Doctorate 

Recipients (various years).

Science and Engineering Indicators 2014
8-108 ♦ Chapter 8. State Indicators

The volume of peer-reviewed articles per 1,000 academic S&E doctorate holders is an 

approximate measure of their contribution to scientific knowledge. Publications are only one 

measure of academic productivity, which includes trained personnel, patents, trademarks, 

copyrights, and other outputs. A high value on this indicator shows that the S&E faculty 

in a state’s academic institutions are generating a high volume of publications relative to 

other states. Academic institutions include 2-year colleges, 4-year colleges and universities, 

medical schools, and university-affiliated research centers.

Publication counts are based on the number of articles that appear in a set of journals 

tracked by Thomson Reuters in the Science Citation Index and Social Sciences Citation 

Index. Academic article output is based on the most recent journal set; data for earlier years 

may differ slightly from previous publications due to changes in the journal set. Articles with 

authors from different institutions were counted fractionally. For instance, for a publication 

with authors at N institutions, each institution would be credited with 1/N of the article.

S&E doctorates include those in the biological, agricultural, or environmental life 

sciences; computer, physical, and social sciences; mathematics; psychology; engineering; 

and health fields. S&E doctorate data are estimates and exclude those with doctorates from 

foreign institutions and those older than the age of 75. Estimates for states with smaller 

populations of S&E doctorate holders are generally less precise than estimates for states 

with larger populations. Data for S&E doctorate holders in academia are presented by 

employment location.

Findings

• Between 2001 and 2010, the number 

of scientific and engineering articles 

published by academia increased 

from 141,000 to 154,000 and the 

number of S&E doctorate holders in 

academia increased from 251,000 

to 300,000.

• In 2010, the value of this indicator 

across the states ranged from 255 to 

705 S&E articles per 1,000 S&E doctorate holders in academia.

• The publication rate for academic 

S&E doctorate holders in states in 

the top quartile of this indicator was 

nearly twice as high as for states 

in the bottom quartile. The average 

indicator value for Experimental 

Program to Stimulate Competitive 

Research (EPSCoR) states was considerably lower than the average indicator value for non-EPSCoR states.

Academic Science and Engineering Article Output per 1,000 S&E Doctorate 

Holders in Academia

Figure 8-49

Academic science and engineering article output per 1,000 S&E doctorate holders in academia: 2010

250 301 352 403 454 505 556 607 658 709

ID

ME

MT

SD

AL

CO

DC

FL

GA

KS

NE

NJ

OH

OR

UT

VT

WI

WY

HI

NH

VA

IA

IN

MI

MO

NC

NY

PA

TX

AZ

CT

IL

LA

CA

DE

MD

RI MA

KY

MN

MS

SC

TN

WA

AK

AR

ND

NM

NV

OK

WV

Articles

1st quartile (510–705)

2nd quartile (469–509)

3rd quartile (397–465)

4th quartile (255–372)

SOURCES: The Patent Board,TM special 

tabulations from Thomson Reuters, Science 

Citation Index, and Social Sciences Citation Index; National Science Foundation, 

National Center for Science and Engineering 

Statistics, Survey of Doctorate Recipients.
Science and Engineering Indicators 2014 ♦ 8-109

Table 8-49

Academic science and engineering article output per 1,000 S&E doctorate holders in academia, by state: 2001, 

2006, and 2010

 Academic S&E article output

 S&E doctorate holders

in academia

Academic S&E articles/

1,000 S&E doctorate holders 

in academia

State 2001 2006 2010 2001 2006 2010 2001 2006 2010

EPSCoR states................ 15,686 17,489 16,921 37,300 40,500 41,000 421 432 413

Non-EPSCoR states........ 123,090 136,312 135,402 208,500 233,600 254,000 590 584 533

Average EPSCoR 

 state value .................... na na na na na na 402 423 403

Average non-EPSCoR 

 state value .................... na na na na na na 570 563 515

United States................... 140,601 155,818 154,331 250,600 278,900 300,400 561 559 514

Alabama....................... 1,851 1,868 1,688 3,000 3,300 3,600 617 566 469

Alaska .......................... 184 215 245 500 600 700 368 358 350

Arizona......................... 2,082 2,333 2,342 3,200 3,800 3,900 651 614 601

Arkansas ...................... 585 713 616 1,600 1,900 2,000 366 375 308

California...................... 17,440 19,566 19,781 25,100 27,600 31,800 695 709 622

Colorado ...................... 2,536 2,694 2,673 4,800 5,300 5,600 528 508 477

Connecticut ................. 2,632 2,949 2,910 4,200 4,500 5,200 627 655 560

Delaware ...................... 535 604 572 800 800 900 669 755 636

District of Columbia ..... 1,047 1,077 1,044 2,500 2,300 2,300 419 468 454

Florida.......................... 4,043 5,029 5,152 7,800 9,000 10,700 518 559 481

Georgia ........................ 3,372 3,862 4,084 6,300 7,400 8,400 535 522 486

Hawaii .......................... 515 569 595 1,500 1,600 1,600 343 356 372

Idaho............................ 300 356 321 900 1,400 1,200 333 254 268

Illinois........................... 6,598 7,153 7,089 10,600 11,400 12,200 622 627 581

Indiana ......................... 2,891 3,291 3,415 5,600 6,100 6,700 516 540 510

Iowa ............................. 2,068 2,099 1,980 3,200 3,500 3,900 646 600 508

Kansas ......................... 1,194 1,172 1,241 2,200 2,600 2,500 543 451 496

Kentucky...................... 1,291 1,509 1,415 3,200 3,600 3,500 403 419 404

Louisiana...................... 1,733 1,782 1,770 3,300 3,400 3,000 525 524 590

Maine ........................... 221 301 280 1,100 1,200 1,100 201 251 255

Maryland...................... 4,786 5,158 4,922 5,800 7,200 7,900 825 716 623

Massachusetts............. 9,167 10,073 9,945 12,900 14,300 14,100 711 704 705

Michigan ...................... 4,833 5,328 5,549 8,700 9,300 10,200 556 573 544

Minnesota .................... 2,289 2,410 2,483 5,300 5,600 6,000 432 430 414

Mississippi ................... 669 816 805 2,000 2,000 1,900 335 408 424

Missouri ....................... 3,111 3,165 3,202 5,600 5,600 6,300 556 565 508

Montana....................... 317 412 348 800 1,200 1,300 396 343 268

Nebraska...................... 972 1,076 977 1,900 1,800 2,100 512 598 465

Nevada......................... 414 521 491 1,300 1,600 1,400 318 326 351

New Hampshire ........... 582 715 638 1,200 1,200 1,600 485 596 399

New Jersey .................. 2,896 3,201 2,977 5,400 6,000 6,300 536 534 473

New Mexico................. 754 839 753 2,800 2,100 2,300 269 400 327

New York...................... 11,722 12,468 12,075 20,200 21,800 23,700 580 572 509

North Carolina.............. 4,932 5,590 5,740 8,600 9,900 10,400 573 565 552

North Dakota ............... 263 371 369 700 1,000 1,100 376 371 335

Ohio ............................. 4,857 5,339 4,969 9,800 10,100 10,100 496 529 492

Oklahoma..................... 862 973 1,020 2,800 2,800 3,300 308 348 309

Oregon......................... 1,497 1,835 1,648 3,100 3,500 3,500 483 524 471

Pennsylvania................ 7,941 8,756 8,723 13,300 15,800 16,000 597 554 545

Rhode Island................ 840 894 971 1,700 2,000 1,600 494 447 607

South Carolina ............. 1,299 1,453 1,509 2,900 3,500 3,600 448 415 419

South Dakota............... 124 154 196 600 700 700 207 220 280

Tennessee.................... 2,178 2,574 2,555 4,700 5,500 6,000 463 468 426

Texas............................ 8,709 9,884 9,842 13,600 16,600 19,300 640 595 510

Utah ............................. 1,509 1,734 1,692 3,000 3,500 3,500 503 495 483

Vermont........................ 400 451 410 1,000 1,000 900 400 451 456

Virginia ......................... 2,896 3,161 3,332 6,500 7,600 8,400 446 416 397

Washington.................. 3,217 3,381 3,216 6,200 7,000 7,200 519 483 447

West Virginia ................ 375 428 425 1,100 1,300 1,300 341 329 327

Wisconsin .................... 2,888 3,279 3,106 5,000 5,700 6,700 578 575 464

Wyoming...................... 184 237 230 600 500 500 307 474 460

Puerto Rico.................. 185 254 271 1,000 1,300 1,500 185 195 181

na = not applicable.

EPSCoR = Experimental Program to Stimulate Competitive Research.

NOTE: For an explanation of EPSCoR and non-EPSCoR averages, see the chapter introduction. 

SOURCES: The Patent Board,TM special tabulations (2013) from Thomson Reuters, Science Citation Index and Social Sciences Citation Index http://thomsonreuters.com/products_services/science/; National Science Foundation, National Center for Science and Engineering Statistics, Survey of Doctorate 

Recipients (various years).

Science and Engineering Indicators 2014
8-110 ♦ Chapter 8. State Indicators

This indicator represents the relationship between the number of academic 

S&E publications and the amount of money expended for academic R&D. 

Academic institutions include 2-year colleges, 4-year colleges or universities, 

medical schools, and university-affiliated research centers. This indicator is 

not an efficiency measure; it is affected by the highly variable costs of R&D 

and by publishing conventions in different fields and institutions. It may also 

reflect variations in field emphasis among states and institutions.

Publication counts are based on the number of articles that appear in a set of 

journals tracked by Thomson Reuters in the Science Citation Index and Social 

Sciences Citation Index. Academic article output is based on the most recent 

journal set; data for earlier years may differ slightly from previous publications 

due to changes in the journal set. Articles with authors from different institutions were counted fractionally. For instance, for a publication with authors 

at N institutions, each institution would be credited with 1/N of the article.

Findings

• From 2003 to 2012, the number of academic 

S&E publications rose from about 148,000 to 

about 163,000—an increase of 10% that may 

reflect both an increase in publications and 

an increase in the number of journals in the 

Thomson Reuters database.

• In 2012, academic researchers produced an 

average of 2.62 publications per $1 million of 

academic R&D, compared with 3.71 in 2003. 

This partly reflects the effect of general price 

inflation but may also indicate rising academic 

research costs.

• The value of this indicator ranged from 1.62 to 

4.08 across the states in 2012.

• Between 2003 and 2012, the value for this 

indicator decreased 29% nationwide and 

in all states but Wyoming, Alaska, Nevada, 

and Vermont.

Academic Science and Engineering Article Output per $1 Million of Academic 

S&E R&D

Figure 8-50

Academic science and engineering article output per $1 million of academic S&E R&D: 2012

1.60 1.90 2.20 2.50 2.80 3.10 3.40 3.70 4.00 4.30

AK

MD

MS

ND

NH

FL

IA

MN

MO

NJ

PA

SC

TN

UT

AL

AR

AZ

CO

DC

GA

ID

NC

NE

NY

TX

WA

WI

WV

CT

IL

IN

NV

RI

DE

MA

VT WY

CA

KS

KY

LA

ME

MI

OH

OK

OR

VA

HI

MT

NM

SD

Articles

1st quartile (2.99–4.08)

2nd quartile (2.53–2.90)

3rd quartile (2.40–2.52)

4th quartile (1.62–2.36)

SOURCES: The Patent Board,TM special 

tabulations from Thomson Reuters, Science 

Citation Index, and Social Sciences Citation Index; National Science Foundation, 

National Center for Science and Engineering 

Statistics, Higher Education R&D Survey.
Science and Engineering Indicators 2014 ♦ 8-111

Table 8-50

Academic science and engineering article output per $1 million of academic S&E R&D, by state: 2003, 2008, and 2012

 Academic S&E article output Academic S&E R&D ($millions)

Academic S&E articles/ 

$1 million academic S&E R&D

State 2003 2008 2012 2003 2008 2012 2003 2008 2012

United States................. 148,487 167,518 162,877 40,002 51,736 62,097 3.71 3.24 2.62

Alabama..................... 1,851 1,976 1,824 551 708 808 3.36 2.79 2.26

Alaska ........................ 195 285 238 142 128 136 1.37 2.23 1.75

Arizona....................... 2,152 2,455 2,388 618 831 987 3.48 2.95 2.42

Arkansas .................... 664 716 683 184 247 276 3.61 2.90 2.47

California.................... 18,756 21,003 20,485 5,358 7,026 8,093 3.50 2.99 2.53

Colorado .................... 2,615 2,854 3,044 695 924 1,249 3.76 3.09 2.44

Connecticut ............... 2,748 3,070 3,108 595 732 925 4.62 4.19 3.36

Delaware .................... 581 650 627 105 133 177 5.53 4.89 3.54

District of Columbia ... 1,060 1,106 1,096 281 375 455 3.77 2.95 2.41

Florida........................ 4,549 5,678 5,543 1,205 1,592 1,935 3.78 3.57 2.86

Georgia ...................... 3,641 4,300 4,187 1,177 1,521 1,709 3.09 2.83 2.45

Hawaii ........................ 572 696 622 185 279 328 3.09 2.49 1.90

Idaho.......................... 305 360 322 105 113 143 2.90 3.19 2.25

Illinois......................... 6,958 7,655 7,393 1,614 1,973 2,271 4.31 3.88 3.26

Indiana ....................... 3,022 3,645 3,681 726 955 1,150 4.16 3.82 3.20

Iowa ........................... 2,212 2,215 2,015 499 528 695 4.43 4.20 2.90

Kansas ....................... 1,235 1,292 1,327 310 404 480 3.98 3.20 2.76

Kentucky.................... 1,434 1,604 1,471 378 499 547 3.79 3.21 2.69

Louisiana.................... 1,759 1,753 1,645 514 660 654 3.42 2.66 2.52

Maine ......................... 267 285 300 84 128 119 3.18 2.23 2.52

Maryland.................... 4,947 5,453 5,381 2,041 2,747 3,316 2.42 1.99 1.62

Massachusetts........... 9,451 10,846 10,649 1,822 2,272 3,009 5.19 4.77 3.54

Michigan .................... 5,071 5,804 5,829 1,390 1,594 2,092 3.65 3.64 2.79

Minnesota .................. 2,287 2,633 2,515 518 699 841 4.42 3.77 2.99

Mississippi ................. 710 840 764 324 406 438 2.19 2.07 1.74

Missouri ..................... 3,122 3,443 3,215 807 960 1,057 3.87 3.59 3.04

Montana..................... 363 396 376 141 186 185 2.57 2.13 2.03

Nebraska.................... 991 1,115 1,023 301 376 419 3.29 2.97 2.44

Nevada....................... 458 571 471 155 191 149 2.95 2.99 3.16

New Hampshire ......... 627 681 657 252 302 398 2.49 2.25 1.65

New Jersey ................ 3,150 3,327 3,186 754 878 1,061 4.18 3.79 3.00

New Mexico............... 792 835 763 307 417 391 2.58 2.00 1.95

New York.................... 12,140 13,317 12,816 3,078 3,982 5,166 3.94 3.34 2.48

North Carolina............ 5,321 6,170 6,182 1,398 1,979 2,619 3.81 3.12 2.36

North Dakota ............. 315 411 363 134 181 214 2.35 2.27 1.70

Ohio ........................... 5,090 5,635 5,302 1,268 1,827 2,018 4.01 3.08 2.63

Oklahoma................... 933 1,081 1,131 295 333 413 3.16 3.25 2.74

Oregon....................... 1,650 1,974 1,853 437 595 681 3.78 3.32 2.72

Pennsylvania.............. 8,263 9,421 8,944 2,015 2,604 3,121 4.10 3.62 2.87

Rhode Island.............. 871 1,020 1,102 187 237 336 4.66 4.30 3.28

South Carolina ........... 1,428 1,587 1,584 435 576 564 3.28 2.76 2.81

South Dakota............. 165 202 259 50 92 123 3.30 2.20 2.11

Tennessee.................. 2,310 2,826 2,810 600 787 968 3.85 3.59 2.90

Texas.......................... 9,423 10,756 10,584 2,765 3,744 4,414 3.41 2.87 2.40

Utah ........................... 1,539 1,786 1,851 385 426 608 4.00 4.19 3.04

Vermont...................... 383 475 434 107 117 120 3.58 4.06 3.62

Virginia ....................... 2,991 3,593 3,551 776 1,053 1,284 3.85 3.41 2.77

Washington................ 3,412 3,605 3,334 871 1,058 1,376 3.92 3.41 2.42

West Virginia .............. 375 417 451 125 169 184 3.00 2.47 2.45

Wisconsin .................. 3,129 3,445 3,237 878 1,117 1,331 3.56 3.08 2.43

Wyoming.................... 204 255 261 60 75 64 3.40 3.40 4.08

Puerto Rico................ 212 265 243 78 NA NA 2.72 NA NA

NA = not available.

NOTE: Academic R&D expenditures are reported in current dollars.

SOURCES: The Patent Board,TM special tabulations (2013) from Thomson Reuters, Science Citation Index and Social Sciences Citation Index http://

thomsonreuters.com/products_services/science/; National Science Foundation, National Center for Science and Engineering Statistics, Academic R&D 

Expenditure Survey, Higher Education R&D Survey (various years).

Science and Engineering Indicators 2014
8-112 ♦ Chapter 8. State Indicators

Since the early 1980s, academic institutions have increasingly been viewed as engines of 

economic growth. Growing attention has been paid to the role of academic R&D in creating new 

products, processes, and services. One indicator of such R&D results is the volume of patents 

assigned to academic institutions. Academic patenting is highly concentrated and partly reflects 

the resources devoted to institutional patenting offices.

This indicator relates the number of academic-owned utility patents to the size of the doctoral 

S&E workforce in academia and is one approximate measure of the degree to which results 

with perceived economic value are generated by the doctoral academic workforce. Academia 

includes 2-year colleges, 4-year colleges and universities, medical schools, and university-affiliated 

research centers. Utility patents, commonly known as patents for inventions, include any new, useful, or improved method, process, machine, device, manufactured item, or chemical compound 

and represent a key measure of intellectual property. Patent assignments are made on the basis 

of the address of their original assignee(s). For patents with multiple U.S. university assignees 

from different U.S. states, the database credits each participating U.S. state as owning one patent.

S&E doctorates include those in computer sciences; mathematics; biological, agricultural, 

or environmental life sciences; physical sciences; social sciences; psychology; engineering; and 

health fields. S&E doctorate data exclude those with doctorates from foreign institutions and 

those older than the age of 75. For states with smaller populations, estimates of doctorate holders 

in academia are generally less precise than estimates for states with larger populations. Data for 

S&E doctorate holders are presented by employment location regardless of residence.

Findings

• Throughout the United States, the 

number of new patents assigned 

to academic institutions increased 

28% from 2001 to 2010; the number of academic S&E doctorate 

holders rose by 20% during the 

same period.

• In 2010, states varied widely on 

this indicator, with values ranging 

from 0 to 25.5 patents per 1,000 

S&E doctorate holders employed 

in academia.

• California showed the highest level 

of both academic patenting and 

venture capital investment.

• The value of this indicator fluctuates over time and across states.

Academic Patents Awarded per 1,000 Science and Engineering Doctorate 

Holders in Academia

Figure 8-51

Academic patents awarded per 1,000 science and engineering doctorate holders in academia: 2010

0.00 3.25 6.50 9.75 13.00 16.25 19.50 22.75 26.00

AK

HI

SD

AR

CT

IA

IL

MI

NC

NH

NJ

OH

PA

VT

WA

WY

AZ

CO

DC

ID

LA

ME

MN

MS

ND

NM

NV

OR

RI

TN

WV

GA

MD

NY

CA

FL

MA

WI

DE

UT

AL

IN

KS

KY

MO

NE

OK

SC

TX

MT VA

Patents

1st quartile (15.1–25.5)

2nd quartile (11.8–15.0)

3rd quartile (8.2–11.7)

4th quartile (0.0–7.9)

SOURCES: National Science Foundation, 

National Center for Science and Engineering 

Statistics, and The Patent Board,TM special 

tabulations (2013) from the U.S. Patent and 

Trademark Office (USPTO), Patent Grant 

Bilbiographic Data.
Science and Engineering Indicators 2014 ♦ 8-113

Table 8-51

Academic patents awarded per 1,000 science and engineering doctorate holders in academia, by state: 2001, 

2006, and 2010

 Patents awarded 

 to academic institutions

S&E doctorate holders

in academia

Academic patents/1,000

academic S&E doctorate holders

State 2001 2006 2010 2001 2006 2010 2001 2006 2010

United States................. 3,680 3,646 4,699 250,600 278,900 300,400 14.7 13.1 15.6

Alabama..................... 43 31 37 3,000 3,300 3,600 14.3 9.4 10.3

Alaska ........................ 0 0 2 500 600 700 0.0 0.0 2.9

Arizona....................... 24 38 31 3,200 3,800 3,900 7.5 10.0 7.9

Arkansas .................... 30 27 27 1,600 1,900 2,000 18.8 14.2 13.5

California.................... 703 728 787 25,100 27,600 31,800 28.0 26.4 24.7

Colorado .................... 35 32 46 4,800 5,300 5,600 7.3 6.0 8.2

Connecticut ............... 44 53 68 4,200 4,500 5,200 10.5 11.8 13.1

Delaware .................... 5 5 20 800 800 900 6.3 6.3 22.2

District of Columbia ... 14 13 15 2,500 2,300 2,300 5.6 5.7 6.5

Florida........................ 118 178 258 7,800 9,000 10,700 15.1 19.8 24.1

Georgia ...................... 87 87 139 6,300 7,400 8,400 13.8 11.8 16.5

Hawaii ........................ 8 10 5 1,500 1,600 1,600 5.3 6.3 3.1

Idaho.......................... 6 11 11 900 1,400 1,200 6.7 7.9 9.2

Illinois......................... 151 132 191 10,600 11,400 12,200 14.2 11.6 15.7

Indiana ....................... 41 42 79 5,600 6,100 6,700 7.3 6.9 11.8

Iowa ........................... 70 57 62 3,200 3,500 3,900 21.9 16.3 15.9

Kansas ....................... 19 6 25 2,200 2,600 2,500 8.6 2.3 10.0

Kentucky.................... 23 30 41 3,200 3,600 3,500 7.2 8.3 11.7

Louisiana.................... 49 29 29 3,300 3,400 3,000 14.8 8.5 9.7

Maine ......................... 2 5 10 1,100 1,200 1,100 1.8 4.2 9.1

Maryland.................... 133 148 148 5,800 7,200 7,900 22.9 20.6 18.7

Massachusetts........... 257 255 359 12,900 14,300 14,100 19.9 17.8 25.5

Michigan .................... 121 136 147 8,700 9,300 10,200 13.9 14.6 14.4

Minnesota .................. 42 39 42 5,300 5,600 6,000 7.9 7.0 7.0

Mississippi ................. 15 14 15 2,000 2,000 1,900 7.5 7.0 7.9

Missouri ..................... 65 48 62 5,600 5,600 6,300 11.6 8.6 9.8

Montana..................... 5 6 7 800 1,200 1,300 6.3 5.0 5.4

Nebraska.................... 21 21 22 1,900 1,800 2,100 11.1 11.7 10.5

Nevada....................... 4 5 10 1,300 1,600 1,400 3.1 3.1 7.1

New Hampshire ......... 11 20 21 1,200 1,200 1,600 9.2 16.7 13.1

New Jersey ................ 92 92 95 5,400 6,000 6,300 17.0 15.3 15.1

New Mexico............... 22 11 21 2,800 2,100 2,300 7.9 5.2 9.1

New York.................... 317 317 444 20,200 21,800 23,700 15.7 14.5 18.7

North Carolina............ 159 137 152 8,600 9,900 10,400 18.5 13.8 14.6

North Dakota ............. 4 4 8 700 1,000 1,100 5.7 4.0 7.3

Ohio ........................... 104 104 139 9,800 10,100 10,100 10.6 10.3 13.8

Oklahoma................... 23 27 40 2,800 2,800 3,300 8.2 9.6 12.1

Oregon....................... 26 24 24 3,100 3,500 3,500 8.4 6.9 6.9

Pennsylvania.............. 243 165 240 13,300 15,800 16,000 18.3 10.4 15.0

Rhode Island.............. 30 14 15 1,700 2,000 1,600 17.6 7.0 9.4

South Carolina ........... 15 23 42 2,900 3,500 3,600 5.2 6.6 11.7

South Dakota............. 1 0 0 600 700 700 1.7 0.0 0.0

Tennessee.................. 53 40 53 4,700 5,500 6,000 11.3 7.3 8.8

Texas.......................... 179 217 244 13,600 16,600 19,300 13.2 13.1 12.6

Utah ........................... 51 40 79 3,000 3,500 3,500 17.0 11.4 22.6

Vermont...................... 5 5 12 1,000 1,000 900 5.0 5.0 13.3

Virginia ....................... 51 56 107 6,500 7,600 8,400 7.8 7.4 12.7

Washington................ 67 50 94 6,200 7,000 7,200 10.8 7.1 13.1

West Virginia .............. 6 2 9 1,100 1,300 1,300 5.5 1.5 6.9

Wisconsin .................. 83 108 157 5,000 5,700 6,700 16.6 18.9 23.4

Wyoming.................... 3 4 8 600 500 500 5.0 8.0 16.0

Puerto Rico................ 6 3 2 1,000 1,300 1,500 6.0 2.3 1.3

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent Board,TM special tabulations (2013) from 

U.S. Patent and Trademark Office, Patent Grant Bibliographic Data.

Science and Engineering Indicators 2014
8-114 ♦ Chapter 8. State Indicators

This indicator represents state patent activity normalized to the size of its S&E 

workforce, specifically employees in S&E occupations. People in S&E occupations 

include engineers and computer, mathematical, life, physical, and social scientists. 

Managers, technicians, elementary and secondary schoolteachers, and medical 

personnel are not included.

Although the U.S. Patent and Trademark Office (USPTO) grants several types 

of patents, this indicator covers only utility patents, commonly known as patents for 

inventions. Utility patents can be granted for any new, useful, or improved method, 

process, machine, device, manufactured item, or chemical compound and represent a 

key measure of intellectual property. USPTO classifies patents geographically according to the residence of the first-named inventor. Only U.S.-origin patents are included.

Data on individuals in S&E occupations come from a survey of workplaces that 

assigns workers to a state based on where they work. Estimates do not include selfemployed persons and are developed by the Bureau of Labor Statistics.

Situations in which workers live in one state and work in another introduce some 

imprecision into the calculation of this indicator. The treatment of postsecondary 

teachers is another source of imprecision. Due to the way the data are collected, 

faculty teaching in S&E fields are not included as workers in S&E occupations. Estimates for states with smaller populations are generally less precise than estimates 

for states with larger populations.

Findings

• About 121,000 utility patents were 

awarded to inventors residing in the 

United States in 2012, an increase 

from the 88,000 utility patents awarded 

in 2003.

• In 2012, the national average for this 

indicator was 20.3 patents per 1,000 

individuals in an S&E occupation, 

higher than the average of 17.7 in 2003. 

• Values for individual states varied 

widely, ranging from 1.7 to 39.1 patents per 1,000 individuals in S&E 

occupations in 2012.

• Almost 27% of all 2012 U.S. utility 

patents were awarded to residents of 

California. Texas and New York were 

each awarded approximately 8,000 utility patents in 2012, together representing more than 13% of the national total.

Patents Awarded per 1,000 Individuals in Science and Engineering 

Occupations

Figure 8-52

Patents awarded per 1,000 individuals in science and engineering occupations: 2012

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0

AK

DC

MI

NJ

NY

UT

FL

GA

KY

ME

NM

SC

TN

WY

CT

MA

MN

NH

NV

OR

CA

ID

VT

AZ

CO

DE

IA

IL

IN

KS

NC

RI

TX

WI

AL

AR

HI

LA

MD

MO

MS

MT

ND

NE

OK

SD

VA

WV

Patents

1st quartile (23.2–39.1)

2nd quartile (16.3–21.3)

3rd quartile (9.0–14.8)

4th quartile (1.7–8.4)

No data

SOURCES: U.S. Patent and Trademark 

Office, Electronic Information Products 

Division/Patent Technology Monitoring 

Branch, Patent Counts by Country/State 

and Year, Utility Patents, 1 January 1963–31 

December 2011; Bureau of Labor Statistics, 

Occupational Employment Statistics Survey.
Science and Engineering Indicators 2014 ♦ 8-115

Table 8-52

Patents awarded per 1,000 individuals in science and engineering occupations, by state: 2003, 2008, and 2012

Patents awarded Individuals in S&E occupations

Patents/1,000 

individuals in 

 S&E occupations

State 2003 2008 2012 2003 2008 2012 2003 2008 2012

United States................. 87,864 77,480 120,989 4,961,550 5,781,460 5,968,240 17.7 13.4 20.3

Alabama..................... 397 279 413 56,380 68,580 72,880 7.0 4.1 5.7

Alaska ........................ 37 20 28 10,600 13,260 16,260 3.5 1.5 1.7

Arizona....................... 1,584 1,584 2,210 92,120 102,100 116,930 17.2 15.5 18.9

Arkansas .................... 152 108 178 21,340 29,310 29,530 7.1 3.7 6.0

California.................... 19,688 19,182 32,107 676,180 791,750 821,780 29.1 24.2 39.1

Colorado .................... 2,069 1,621 2,442 124,140 147,000 149,020 16.7 11.0 16.4

Connecticut ............... 1,667 1,357 2,108 81,380 80,290 78,450 20.5 16.9 26.9

Delaware .................... 346 325 445 17,370 22,330 23,440 19.9 14.6 19.0

District of Columbia ... 49 68 131 54,890 63,360 63,600 0.9 1.1 2.1

Florida........................ 2,563 2,046 3,686 221,070 248,200 248,300 11.6 8.2 14.8

Georgia ...................... 1,333 1,344 2,128 144,170 147,380 148,830 9.2 9.1 14.3

Hawaii ........................ 75 77 108 16,090 18,830 20,930 4.7 4.1 5.2

Idaho.......................... 1,803 1,162 930 22,150 23,310 25,260 81.4 49.8 36.8

Illinois......................... 3,296 2,741 4,345 211,230 224,370 220,170 15.6 12.2 19.7

Indiana ....................... 1,385 985 1,741 78,410 90,840 94,620 17.7 10.8 18.4

Iowa ........................... 665 561 854 37,320 46,180 50,950 17.8 12.1 16.8

Kansas ....................... 428 425 1,004 51,970 54,260 50,930 8.2 7.8 19.7

Kentucky.................... 439 413 543 45,230 NA 51,830 9.7 NA 10.5

Louisiana.................... 390 260 364 41,900 41,790 45,920 9.3 6.2 7.9

Maine ......................... 150 113 211 15,020 17,000 17,910 10.0 6.6 11.8

Maryland.................... 1,453 1,232 1,609 149,250 167,070 179,550 9.7 7.4 9.0

Massachusetts........... 3,908 3,516 5,734 184,690 217,310 229,160 21.2 16.2 25.0

Michigan .................... 3,857 2,996 4,598 182,940 204,290 198,610 21.1 14.7 23.2

Minnesota .................. 2,953 2,535 3,902 117,120 134,440 131,690 25.2 18.9 29.6

Mississippi ................. 162 102 140 22,190 27,270 23,640 7.3 3.7 5.9

Missouri ..................... 823 615 1,015 84,150 105,390 109,650 9.8 5.8 9.3

Montana..................... 121 91 119 11,450 NA 15,360 10.6 NA 7.7

Nebraska.................... 185 191 292 30,710 31,820 34,720 6.0 6.0 8.4

Nevada....................... 389 375 752 22,330 27,300 27,000 17.4 13.7 27.9

New Hampshire ......... 677 477 734 23,430 29,150 28,950 28.9 16.4 25.4

New Jersey ................ 3,522 2,722 4,224 161,420 198,060 181,480 21.8 13.7 23.3

New Mexico............... 390 280 417 33,600 34,560 35,310 11.6 8.1 11.8

New York.................... 6,234 4,885 7,640 272,440 326,510 321,480 22.9 15.0 23.8

North Carolina............ 1,871 1,841 2,977 132,440 153,680 167,900 14.1 12.0 17.7

North Dakota ............. 55 63 96 8,430 9,450 13,120 6.5 6.7 7.3

Ohio ........................... 3,183 2,227 3,387 177,100 206,320 NA 18.0 10.8 NA

Oklahoma................... 516 417 471 44,360 48,900 50,420 11.6 8.5 9.3

Oregon....................... 1,665 1,781 2,059 61,230 70,070 75,780 27.2 25.4 27.2

Pennsylvania.............. 3,182 2,414 3,483 185,560 227,170 NA 17.1 10.6 NA

Rhode Island.............. 266 218 329 18,740 18,090 20,180 14.2 12.1 16.3

South Carolina ........... 571 395 850 48,740 57,770 63,170 11.7 6.8 13.5

South Dakota............. 80 54 113 9,150 11,870 12,000 8.7 4.5 9.4

Tennessee.................. 797 586 930 63,680 72,760 79,830 12.5 8.1 11.6

Texas.......................... 6,029 5,712 8,367 365,270 463,850 493,980 16.5 12.3 16.9

Utah ........................... 638 642 1,167 45,570 52,570 54,720 14.0 12.2 21.3

Vermont...................... 429 437 487 11,420 12,360 12,870 37.6 35.4 37.8

Virginia ....................... 1,110 1,030 1,691 209,280 259,280 274,280 5.3 4.0 6.2

Washington................ 2,285 3,517 5,390 150,230 NA NA 15.2 NA NA

West Virginia .............. 139 74 135 16,220 17,000 19,900 8.6 4.4 6.8

Wisconsin .................. 1,787 1,349 1,785 93,320 101,680 103,030 19.1 13.3 17.3

Wyoming.................... 71 35 120 6,130 8,850 8,710 11.6 4.0 13.8

Puerto Rico................ 27 14 34 19,940 22,970 21,750 1.4 0.6 1.6

NA = not available.

NOTES: Origin of utility patent is determined by the residence of the first-named inventor. National totals for S&E occupations include states with suppressed 

data. Occupational Employment Statistics estimates for 2003 are based on November data; estimates for the remaining years are based on May data.

SOURCES: U.S. Patent and Trademark Office, Electronic Information Products Division/Patent Technology Monitoring Branch, Patent Counts by Country/

State and Year, Utility Patents, 1 January 1963–31 December 2011; Bureau of Labor Statistics, Occupational Employment Statistics Survey (various years).

Science and Engineering Indicators 2014
8-116 ♦ Chapter 8. State Indicators

This indicator represents the portion of a state’s business establishments that are classified as being part of high-technology industries. 

High-technology industries are defined as those in which the proportion 

of employees in technology-oriented occupations is at least twice the average proportion for all industries. High-technology occupations include 

scientific, engineering, and technician occupations that employ workers 

who generally possess in-depth knowledge of the theories and principles 

of science, engineering, and mathematics at a postsecondary level.

States often consider such industries desirable, in part because they 

tend to compensate workers better than other industries do. This indicator includes all establishments with an employer identification number 

regardless of the number of people they employ.

The data pertaining to establishments for the years 2003 to 2008 are 

based on their classification according to the 2002 edition of the North 

American Industry Classification System (NAICS). The data for the 

years 2009 and 2010 are based on their classification according to the 

2007 edition of the NAICS. See table 8-A in the chapter introduction 

for a list of the industries (by NAICS code) that are defined as high 

technology. Data for years prior to 2003 are not directly comparable.

Findings

• The number of establishments in high-technology industries rose from about 590,000 in 2003 to nearly 649,000 in 

2010, an increase of 10%.

• The percentage of U.S. establishments in high-technology industries went from 8.17% to 8.79% of the total 

business establishments during the 2003–10 period, and 

most states showed an upward trend in the percentage of 

their establishments in high-technology industries.

• Between 2003 and 2010, the largest growth in the number of establishments in high-technology industries 

occurred in California and Florida, which added approximately 8,200 and 6,500 establishments, respectively.

• The state distribution of this indicator is similar to that of 

three other indicators: bachelor’s degree holders, S&E 

doctoral degree holders, and workers in S&E occupations, all expressed as a share of the workforce.

• Experimental Program to Stimulate Competitive Research 

(EPSCoR) states have a lower average value on this indicator than non-EPSCoR states.

High-Technology Establishments as a Percentage of All Business 

Establishments

Figure 8-53

High-technology establishments as a percentage of all business establishments: 2010

5.00 6.15 7.30 8.45 9.60 10.75 11.90 13.05 14.20 15.35 16.50

IA

MS

ND

SD

AR

CT

KS

LA

MI

NC

NM

NY

OH

OR

PA

VT

WY MD

AK

AZ

FL

IL

MN

NH

OK

WA

CO

VA DE DC

CA

GA

MA

NJ

NV

TX

UT

Percent

AL

HI

ID

IN

KY

ME

MO

MT

NE

RI

SC

TN

WI

WV

1st quartile (9.47%–16.33%)

2nd quartile (8.08%–9.21%)

3rd quartile (6.97%–8.01%)

4th quartile (5.28%–6.88%)

SOURCE: Census Bureau, special tabulations (2013) of the Business Information 

Tracking Series.
Science and Engineering Indicators 2014 ♦ 8-117

Table 8-53

High-technology establishments as a percentage of all business establishments, by state: 2003, 2007, and 2010

 High-technology 

 establishments All business establishments

High-technology/all 

business establishments (%)

State 2003 2007 2010 2003 2007 2010 2003 2007 2010

EPSCoR states................ 80,403 87,616 88,727 1,150,925 1,221,996 1,173,884 6.99 7.17 7.56

Non-EPSCoR states........ 504,364 556,553 553,299 6,001,637 6,392,070 6,135,209 8.40 8.71 9.02

Average EPSCoR 

 state value .................... na na na na na na 7.20 7.34 7.73

Average non-EPSCoR 

 state value .................... na na na na na na 8.23 8.52 8.86

United States................... 590,417 650,707 648,993 7,223,240 7,689,821 7,384,267 8.17 8.46 8.79

Alabama....................... 6,347 6,783 6,786 99,453 105,388 99,097 6.38 6.44 6.85

Alaska .......................... 1,345 1,538 1,698 19,037 20,146 19,922 7.07 7.63 8.52

Arizona......................... 10,433 12,540 11,875 120,966 142,649 131,661 8.62 8.79 9.02

Arkansas ...................... 4,012 4,550 4,852 64,058 67,513 65,069 6.26 6.74 7.46

California...................... 77,614 87,815 85,787 822,751 889,726 848,238 9.43 9.87 10.11

Colorado ...................... 15,532 18,016 18,306 143,398 157,570 151,765 10.83 11.43 12.06

Connecticut ................. 7,827 7,868 7,472 91,207 93,444 89,078 8.58 8.42 8.39

Delaware ...................... 3,964 3,573 3,256 24,739 25,476 24,263 16.02 14.02 13.42

District of Columbia ..... 2,589 3,158 3,507 19,357 20,957 21,478 13.38 15.07 16.33

Florida.......................... 38,118 44,745 44,577 458,823 522,710 490,492 8.31 8.56 9.09

Georgia ........................ 18,820 21,586 21,413 208,350 231,418 216,787 9.03 9.33 9.88

Hawaii .......................... 2,097 2,305 2,309 30,950 33,321 31,904 6.78 6.92 7.24

Idaho............................ 2,515 3,107 3,071 39,582 47,284 43,365 6.35 6.57 7.08

Illinois........................... 27,606 29,222 28,886 310,589 324,628 313,654 8.89 9.00 9.21

Indiana ......................... 9,626 10,355 10,276 147,073 152,604 144,802 6.55 6.79 7.10

Iowa ............................. 4,316 4,679 4,745 80,745 83,008 80,637 5.35 5.64 5.88

Kansas ......................... 5,716 6,076 6,144 74,637 76,984 74,163 7.66 7.89 8.28

Kentucky...................... 5,453 5,850 5,913 90,358 93,428 90,665 6.03 6.26 6.52

Louisiana...................... 7,218 7,574 7,850 101,933 104,459 103,234 7.08 7.25 7.60

Maine ........................... 2,466 2,612 2,652 40,519 42,409 40,506 6.09 6.16 6.55

Maryland...................... 13,428 15,151 15,589 132,782 141,076 134,417 10.11 10.74 11.60

Massachusetts............. 17,183 17,470 17,148 177,910 176,304 169,475 9.66 9.91 10.12

Michigan ...................... 16,937 17,321 16,555 236,221 234,971 218,752 7.17 7.37 7.57

Minnesota .................... 12,834 13,590 13,014 145,364 151,304 145,247 8.83 8.98 8.96

Mississippi ................... 3,269 3,405 3,496 59,565 61,727 59,196 5.49 5.52 5.91

Missouri ....................... 9,562 10,238 9,956 149,753 154,201 149,628 6.39 6.64 6.65

Montana....................... 2,108 2,515 2,593 33,616 37,645 35,950 6.27 6.68 7.21

Nebraska...................... 2,797 3,257 3,361 50,213 52,452 51,803 5.57 6.21 6.49

Nevada......................... 5,387 6,087 6,031 53,080 62,706 59,113 10.15 9.71 10.20

New Hampshire ........... 3,511 3,575 3,539 38,119 39,363 37,385 9.21 9.08 9.47

New Jersey .................. 24,286 24,688 23,686 237,097 242,967 228,577 10.24 10.16 10.36

New Mexico................. 3,322 3,658 3,611 43,386 46,763 44,134 7.66 7.82 8.18

New York...................... 35,926 38,368 38,636 500,559 518,608 518,527 7.18 7.40 7.45

North Carolina.............. 14,869 17,671 17,967 207,500 227,444 217,768 7.17 7.77 8.25

North Dakota ............... 964 1,075 1,151 20,371 21,477 21,792 4.73 5.01 5.28

Ohio ............................. 19,875 20,486 20,180 269,202 269,855 253,136 7.38 7.59 7.97

Oklahoma..................... 6,859 7,512 7,610 85,633 91,054 89,868 8.01 8.25 8.47

Oregon......................... 7,500 8,453 8,587 102,462 113,054 107,181 7.32 7.48 8.01

Pennsylvania................ 22,266 23,778 23,956 297,040 304,721 296,514 7.50 7.80 8.08

Rhode Island................ 1,976 2,108 2,071 29,172 30,299 28,477 6.77 6.96 7.27

South Carolina ............. 5,869 6,942 7,010 98,735 107,685 101,902 5.94 6.45 6.88

South Dakota............... 1,206 1,347 1,426 24,314 25,797 25,562 4.96 5.22 5.58

Tennessee.................... 8,196 8,980 8,702 129,458 137,547 131,302 6.33 6.53 6.63

Texas............................ 45,062 49,237 50,180 481,804 520,405 521,248 9.35 9.46 9.63

Utah ............................. 5,474 6,960 7,139 60,011 71,722 68,725 9.12 9.70 10.39

Vermont........................ 1,453 1,570 1,581 21,747 22,298 21,422 6.68 7.04 7.38

Virginia ......................... 18,868 22,607 23,623 182,783 200,131 192,780 10.32 11.30 12.25

Washington.................. 13,171 15,138 15,335 166,229 183,984 175,486 7.92 8.23 8.74

West Virginia ................ 2,257 2,352 2,490 40,225 40,415 38,599 5.61 5.82 6.45

Wisconsin .................... 9,035 9,591 9,709 141,560 146,019 139,332 6.38 6.57 6.97

Wyoming...................... 1,353 1,625 1,686 18,804 20,705 20,189 7.20 7.85 8.35

Puerto Rico.................. NA NA NA NA NA NA NA NA NA

na = not applicable; NA = not available.

EPSCoR = Experimental Program to Stimulate Competitive Research.

NOTE: For an explanation of EPSCoR and non-EPSCoR averages, see the chapter introduction.

SOURCE: Census Bureau, special tabulations (2007, 2010, 2013) of the Business Information Tracking Series (various years).

Science and Engineering Indicators 2014
8-118 ♦ Chapter 8. State Indicators

The business base of a state is constantly changing as new businesses form and 

others cease to exist. The term “net business formations” refers to the difference between the number of businesses that are formed and the number that cease operations 

during any particular year.

The ratio of the number of net business formations that occur in high-technology 

industries to the number of business establishments in a state indicates the changing 

role of high-technology industries in a state’s economy. High positive values indicate 

an increasingly prominent role for these industries.

The data on business establishments in high-technology industries in 2003 through 

2008 are based on their classification according to the 2002 edition of the North American Industry Classification System (NAICS). The data for the years 2009 and 2010 

are based on their classification according to the 2007 edition of the NAICS. See table 

8-A in the chapter introduction for a list of the industries (by NAICS code) that are 

defined as high technology. Data for years prior to 2003 are not directly comparable.

Changes in company name, ownership, or address are not counted as business 

formations or business deaths. Net business formations cannot be used to directly link 

the number of high-technology business establishments in different years because 

the primary industry of some establishments may have changed during the period.

Findings

• In 2010, about 4,600 more businesses in 

high-technology industries were formed 

than ceased operations in the United States. 

From a base of approximately 7.4 million 

total business establishments, 78,862 new 

business establishments were formed in 

high-technology industries and 74,231 

ceased operations in those same industries.

• The lingering effects of the business downturn were still evident in 2010 as 12 states 

had more businesses in high-technology 

industries ceasing operations than were 

being formed.

• Many of the top-ranking states on this 

indicator were Experimental Program to 

Stimulate Competitive Research states. 

However, the largest numbers of net 

new businesses were formed in Florida 

and Texas.

Net High-Technology Business Formations as a Percentage of All Business 

Establishments

Figure 8-54

Net high-technology business formations as a percentage of all business establishments: 2010

-0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

AZ

CT

DE

ME

MO

NM

TN

AK

MD

UT

VA

WV

WY

AL

CA

CO

GA

IL

IN

KS

KY

MI

MS

MT

NE

NJ

OH

OK

PA

RI

SC

SD

VT DC

AR

FL

IA

LA

NC

ND

NV

NY

OR

TX

WA

WI

HI

ID

MA

MN

NH

Percent

1st quartile (0.12% to 0.62%)

2nd quartile (0.06% to 0.11%)

3rd quartile (0.00% to 0.05%)

4th quartile (–0.18% to –0.02%)

SOURCE: Census Bureau, special tabulations (2013) of the Business Information 

Tracking Series.
Science and Engineering Indicators 2014 ♦ 8-119

Table 8-54

Net high-technology business formations as a percentage of all business establishments, by state: 2004, 2007, 

and 2010

Net high-technology 

business formations All business establishments

High-technology 

business formations/

all business 

establishments (%)

State 2004 2007 2010 2004 2007 2010 2004 2007 2010

United States................. 11,598 15,113 4,631 7,355,122 7,689,821 7,384,267 0.16 0.20 0.06

Alabama..................... 63 141 -3 100,402 105,388 99,097 0.06 0.13 0.00

Alaska ........................ 22 55 49 19,266 20,146 19,922 0.12 0.27 0.25

Arizona....................... 357 464 -213 125,126 142,649 131,661 0.30 0.33 -0.16

Arkansas .................... 123 114 120 65,022 67,513 65,069 0.19 0.17 0.18

California.................... 1,099 1,929 -8 836,783 889,726 848,238 0.13 0.22 0.00

Colorado .................... 490 751 97 146,747 157,570 151,765 0.34 0.48 0.06

Connecticut ............... -47 62 -113 92,552 93,444 89,078 -0.05 0.07 -0.13

Delaware .................... -52 -131 -36 25,311 25,476 24,263 -0.21 -0.51 -0.15

District of Columbia ... 66 143 134 19,498 20,957 21,478 0.34 0.68 0.62

Florida........................ 1,743 873 616 482,910 522,710 490,492 0.38 0.17 0.13

Georgia ...................... 642 731 106 213,906 231,418 216,787 0.31 0.32 0.05

Hawaii ........................ 51 -30 -32 31,497 33,321 31,904 0.16 -0.09 -0.10

Idaho.......................... 54 185 -13 41,121 47,284 43,365 0.14 0.39 -0.03

Illinois......................... 452 545 92 314,707 324,628 313,654 0.15 0.17 0.03

Indiana ....................... 208 171 122 148,864 152,604 144,802 0.14 0.11 0.08

Iowa ........................... 12 97 91 81,216 83,008 80,637 0.01 0.12 0.11

Kansas ....................... 160 41 31 75,478 76,984 74,163 0.21 0.05 0.04

Kentucky.................... 116 48 14 91,437 93,428 90,665 0.13 0.05 0.02

Louisiana.................... -38 225 117 102,732 104,459 103,234 -0.04 0.22 0.11

Maine ......................... 81 0 -44 41,061 42,409 40,506 0.20 0.00 -0.11

Maryland.................... 475 478 342 135,515 141,076 134,417 0.36 0.34 0.25

Massachusetts........... 156 304 -36 175,154 176,304 169,475 0.09 0.17 -0.02

Michigan .................... 44 267 113 237,062 234,971 218,752 0.02 0.11 0.05

Minnesota .................. 185 276 -54 148,053 151,304 145,247 0.13 0.18 -0.04

Mississippi ................. 7 79 25 60,267 61,727 59,196 0.01 0.13 0.04

Missouri ..................... 195 62 -262 153,328 154,201 149,628 0.13 0.04 -0.18

Montana..................... 108 87 26 34,473 37,645 35,950 0.32 0.23 0.07

Nebraska.................... 64 144 32 50,735 52,452 51,803 0.13 0.27 0.06

Nevada....................... 169 181 112 55,590 62,706 59,113 0.32 0.29 0.19

New Hampshire ......... 30 -23 -28 38,650 39,363 37,385 0.08 -0.06 -0.07

New Jersey ................ -80 -77 31 239,692 242,967 228,577 -0.03 -0.03 0.01

New Mexico............... 37 93 -69 44,024 46,763 44,134 0.09 0.20 -0.16

New York.................... 702 977 530 509,079 518,608 518,527 0.14 0.19 0.10

North Carolina............ 514 559 383 212,170 227,444 217,768 0.25 0.25 0.18

North Dakota ............. -1 52 25 20,739 21,477 21,792 0.00 0.24 0.11

Ohio ........................... 204 205 173 270,693 269,855 253,136 0.08 0.08 0.07

Oklahoma................... 75 245 47 87,064 91,054 89,868 0.09 0.27 0.05

Oregon....................... 156 309 198 104,808 113,054 107,181 0.15 0.27 0.18

Pennsylvania.............. 474 233 198 300,408 304,721 296,514 0.16 0.08 0.07

Rhode Island.............. 67 69 22 29,845 30,299 28,477 0.23 0.23 0.08

South Carolina ........... 175 294 44 100,759 107,685 101,902 0.18 0.27 0.04

South Dakota............. 16 76 5 24,646 25,797 25,562 0.07 0.29 0.02

Tennessee.................. 39 99 -156 131,161 137,547 131,302 0.03 0.07 -0.12

Texas.......................... 401 1,588 600 488,935 520,405 521,248 0.08 0.31 0.12

Utah ........................... 283 397 156 62,539 71,722 68,725 0.47 0.55 0.23

Vermont...................... 42 37 17 22,041 22,298 21,422 0.19 0.17 0.08

Virginia ....................... 845 844 567 188,281 200,131 192,780 0.46 0.42 0.29

Washington................ 346 657 185 170,428 183,984 175,486 0.21 0.36 0.11

West Virginia .............. 16 40 99 40,665 40,415 38,599 0.04 0.10 0.26

Wisconsin .................. 215 78 138 143,466 146,019 139,332 0.15 0.05 0.10

Wyoming.................... 37 69 41 19,216 20,705 20,189 0.20 0.33 0.20

Puerto Rico................ NA NA NA NA NA NA NA NA NA

NA = not available.

SOURCE: Census Bureau, special tabulations (2007, 2010, 2013) of the Business Information Tracking Series (various years).

Science and Engineering Indicators 2014
8-120 ♦ Chapter 8. State Indicators

This indicator represents the extent to which a state’s workforce is employed 

in high-technology industries. High-technology industries are defined as those 

in which the proportion of employees in technology-oriented occupations is at 

least twice the average proportion for all industries. High-technology occupations 

include scientific, engineering, and technician occupations that employ workers who generally possess in-depth knowledge of the theories and principles of 

science, engineering, and mathematics at a postsecondary level.

The data pertaining to establishments in 2003 through 2008 are based on 

their classification according to the 2002 edition of the North American Industry 

Classification System (NAICS). The data for the years 2009 and 2010 are based 

on their classification according to the 2007 edition of the NAICS. See table 

8-A in the chapter introduction for a list of the industries (by NAICS code) that 

are defined as high technology. Data on total employment and NAICS industry 

establishment employment in high-technology establishments are provided by 

the U.S. Census Bureau and differ from workforce data provided by the Bureau 

of Labor Statistics. Total employment refers to all U.S. business establishments 

with paid employees, but does not include crop and animal production, rail 

transportation, the postal service, or most government employees.

Findings

• Employment in high-technology industries 

in the United States decreased slightly from 

13.6 million in 2003 to 13.4 million in 2010.

• Nationwide, the value of this indicator 

changed little from 2003 (11.96%) to 2010 

(11.99%).

• States varied greatly on this indicator in 

2010, ranging from 6.26% to 17.67% 

of their workforce employed in hightechnology industries.

• During the 2003–10 period, Michigan and 

New York recorded the largest net losses 

of jobs in high-technology industries, while 

Virginia, Georgia, and Colorado posted the 

largest net gains of jobs in high-technology 

industries.

• States were distributed similarly on the 

high-technology employment and hightechnology establishment indicators.

Employment in High-Technology Establishments as a Percentage of Total 

Employment

Figure 8-55

Employment in high-technology establishments as a percentage of total employment: 2010

6.00 7.20 8.40 9.60 10.80 12.00 13.20 14.40 15.60 16.80 18.00

HI

NV

SD

FL

IN

KY

LA

RI

WV

WY

DE

IL

MN

OR

AL

AR

NC

NY

SC

TN

VT

WI

CA

CT

GA

KS

NH

TX

AK

CO

DC

MA

NJ

MD

WA VA

AZ

ID

MI

MO

NM

OH

OK

PA

UT

Percent

IA

ME

MS

MT

ND

NE

1st quartile (13.40%–17.67%)

2nd quartile (11.07%–13.36%)

3rd quartile (8.84%–10.96%)

4th quartile (6.26%–8.44%)

SOURCE: Census Bureau, special tabulations (2013) of the Business Information 

Tracking Series.
Science and Engineering Indicators 2014 ♦ 8-121

Table 8-55

Employment in high-technology establishments as a percentage of total employment, by state: 2003, 2007, 

and 2010

Employment in high-technology 

establishments Total employment

High-technology/ 

total employment (%)

State 2003 2007 2010 2003 2007 2010 2003 2007 2010

United States................. 13,563,122 14,152,153 13,428,176 113,373,663 120,579,971 111,956,736 11.96 11.74 11.99

Alabama..................... 152,879 160,545 164,889 1,597,265 1,722,354 1,567,725 9.57 9.32 10.52

Alaska ........................ 21,851 30,766 36,813 216,707 244,560 254,694 10.08 12.58 14.45

Arizona....................... 234,603 263,246 238,931 1,997,990 2,403,472 2,065,596 11.74 10.95 11.57

Arkansas .................... 95,180 89,819 95,963 988,822 1,031,129 964,899 9.63 8.71 9.95

California.................... 1,781,830 1,838,795 1,715,056 12,986,496 13,767,970 12,534,832 13.72 13.36 13.68

Colorado .................... 274,979 302,681 303,701 1,883,883 2,075,404 1,955,624 14.60 14.58 15.53

Connecticut ............... 210,114 208,417 195,895 1,550,615 1,538,977 1,436,754 13.55 13.54 13.63

Delaware .................... 52,349 47,144 44,535 385,098 396,251 358,811 13.59 11.90 12.41

District of Columbia ... 54,314 59,284 67,828 422,912 454,512 463,070 12.84 13.04 14.65

Florida........................ 576,274 632,765 585,325 6,548,276 7,423,816 6,624,005 8.80 8.52 8.84

Georgia ...................... 413,384 435,409 447,211 3,386,590 3,647,746 3,315,180 12.21 11.94 13.49

Hawaii ........................ 25,777 28,932 29,955 458,952 518,928 478,798 5.62 5.58 6.26

Idaho.......................... 55,706 60,056 54,000 466,379 544,337 487,901 11.94 11.03 11.07

Illinois......................... 646,285 644,910 606,229 5,204,887 5,397,867 4,978,701 12.42 11.95 12.18

Indiana ....................... 219,598 240,529 220,867 2,540,554 2,647,861 2,400,566 8.64 9.08 9.20

Iowa ........................... 102,387 98,655 103,641 1,232,709 1,303,265 1,252,828 8.31 7.57 8.27

Kansas ....................... 155,023 160,739 156,591 1,109,699 1,168,907 1,127,221 13.97 13.75 13.89

Kentucky.................... 121,838 132,400 122,950 1,471,622 1,550,035 1,456,297 8.28 8.54 8.44

Louisiana.................... 137,029 145,219 141,384 1,603,492 1,645,547 1,600,202 8.55 8.82 8.84

Maine ......................... 35,184 39,004 37,840 488,788 503,725 480,302 7.20 7.74 7.88

Maryland.................... 315,887 353,025 331,437 2,088,552 2,238,894 2,075,391 15.12 15.77 15.97

Massachusetts........... 460,984 495,550 442,407 2,974,164 3,073,572 2,928,453 15.50 16.12 15.11

Michigan .................... 499,133 449,369 390,824 3,884,881 3,686,604 3,287,170 12.85 12.19 11.89

Minnesota .................. 315,994 351,940 308,919 2,381,860 2,525,488 2,358,867 13.27 13.94 13.10

Mississippi ................. 66,566 64,539 63,467 912,004 941,215 881,489 7.30 6.86 7.20

Missouri ..................... 254,299 265,680 254,464 2,387,245 2,457,551 2,292,521 10.65 10.81 11.10

Montana..................... 20,296 23,340 24,377 302,932 353,717 338,366 6.70 6.60 7.20

Nebraska.................... 68,975 65,653 63,881 774,858 795,489 769,404 8.90 8.25 8.30

Nevada....................... 61,847 74,288 64,882 970,678 1,195,473 1,002,731 6.37 6.21 6.47

New Hampshire ......... 63,264 66,235 75,371 540,132 573,026 562,382 11.71 11.56 13.40

New Jersey ................ 550,224 563,587 518,502 3,578,674 3,661,138 3,365,857 15.38 15.39 15.40

New Mexico............... 60,399 71,616 70,027 571,057 642,068 600,269 10.58 11.15 11.67

New York.................... 823,992 796,369 744,789 7,415,430 7,528,488 7,264,463 11.11 10.58 10.25

North Carolina............ 349,424 379,831 347,943 3,337,552 3,585,951 3,233,868 10.47 10.59 10.76

North Dakota ............. 20,584 29,850 24,590 258,878 292,851 294,794 7.95 10.19 8.34

Ohio ........................... 531,491 520,079 490,703 4,769,406 4,781,448 4,353,123 11.14 10.88 11.27

Oklahoma................... 132,887 142,168 136,082 1,184,312 1,307,578 1,241,178 11.22 10.87 10.96

Oregon....................... 152,140 162,690 164,454 1,338,380 1,476,970 1,350,947 11.37 11.02 12.17

Pennsylvania.............. 566,406 558,193 556,446 5,028,650 5,194,723 4,975,537 11.26 10.75 11.18

Rhode Island.............. 35,806 40,738 37,962 427,369 441,293 398,960 8.38 9.23 9.52

South Carolina ........... 163,373 166,710 154,343 1,550,227 1,647,759 1,502,540 10.54 10.12 10.27

South Dakota............. 18,890 21,680 23,558 299,723 330,071 328,930 6.30 6.57 7.16

Tennessee.................. 219,898 244,256 218,686 2,298,836 2,475,155 2,262,886 9.57 9.87 9.66

Texas.......................... 1,158,481 1,222,727 1,173,479 8,049,300 9,038,702 8,785,658 14.39 13.53 13.36

Utah ........................... 99,856 123,602 120,574 900,331 1,102,528 1,021,872 11.09 11.21 11.80

Vermont...................... 29,402 26,216 27,984 256,401 268,023 264,076 11.47 9.78 10.60

Virginia ....................... 459,017 545,693 529,869 2,932,471 3,196,510 2,999,409 15.65 17.07 17.67

Washington................ 401,413 380,962 387,568 2,292,462 2,500,835 2,325,825 17.51 15.23 16.66

West Virginia .............. 46,635 46,395 47,252 561,317 580,953 560,311 8.31 7.99 8.43

Wisconsin .................. 233,967 260,033 245,381 2,382,979 2,483,664 2,320,269 9.82 10.47 10.58

Wyoming.................... 15,008 19,824 18,351 180,866 215,571 205,184 8.30 9.20 8.94

Puerto Rico................ NA NA NA NA NA NA NA NA NA

NA = not available.

SOURCE: Census Bureau, special tabulations (2007, 2010, 2013) of the Business Information Tracking Series (various years).

Science and Engineering Indicators 2014
8-122 ♦ Chapter 8. State Indicators

Funds awarded through the federal Small Business Innovation Research (SBIR) program support technological innovation in companies 

with 500 or fewer employees. Awards are made to evaluate the feasibility 

and scientific merit of new technology (Phase 1—up to $150,000) and 

to develop the technology to a point where it can be commercialized 

(Phase 2—up to $750,000). The total award dollars include both Phase 

1 and Phase 2 SBIR awards.

Because of year-to-year fluctuations, this indicator is calculated using 

3-year averages. The 3-year average annual SBIR award dollars won 

by small businesses in a state are divided by the 3-year average annual 

gross domestic product for the same period. A high value indicates that 

small business firms in a state are doing innovative development work 

that attracts federal support.

Findings

• The SBIR program decreased in size from $1.7 

billion in 2002–04 to $700 million in 2010–12.

• Over the 3-year period of 2010–12, SBIR funds 

were concentrated in relatively few states; the 

average annual state awards during this period 

ranged from $23,000 to nearly $133 million.

• Many of the states with the highest rankings on 

this indicator are locations of federal laboratories 

or well-recognized academic research institutions from which innovative small businesses 

have emerged.

• States with a high ranking on this indicator also 

tended to rank high on the high-technology and 

venture capital indicators.

Average Annual Federal Small Business Innovation Research Funding per 

$1 Million of Gross Domestic Product

Figure 8-56

Average annual federal Small Business Innovation Research funding per $1 million of gross domestic product: 

2010–12

0 28 56 84 112 140 168 196 224 252

AK

AR

DC

FL

GA

IA

ID

IL

IN

KS

KY

LA

MO

MS

ND

NE

NV

OK

RI

SC

SD

TN

TX

UT

WV

WY

CO

MD

NH

NM

AL

CA

CT

MT

OH

OR MA

VA

VT

AZ

DE

HI

ME

MI

MN

NC

NJ

NY

PA

WA

WI

Dollars

1st quartile ($56–$225)

2nd quartile ($25–$51)

3rd quartile ($17–$24)

4th quartile ($0–$16)

SOURCES: Small Business Administration, 

Office of Technology, Small Business Innovation Research program statistics; Bureau of 

Economic Analysis, Gross Domestic Product 

data.
Science and Engineering Indicators 2014 ♦ 8-123

Table 8-56

Average annual federal Small Business Innovation Research funding per $1 million of gross domestic product, 

by state: 2002–04, 2006–08, and 2010–12

Average SBIR funding 

($thousands)

Average state GDP 

($millions)

SBIR funding ($)/ 

$1 million GDP

State 2002–04 2006–08 2010–12 2002–04 2006–08 2010–12

 2002– 

04

2006– 

08

2010– 

12

United States................. 1,729,004 1,801,190 699,675 11,138,210 13,806,188 14,971,555 155 130 47

Alabama..................... 33,192 39,376 11,461 132,668 164,976 178,307 250 239 64

Alaska ........................ 495 707 23 31,382 45,377 50,335 16 16 0

Arizona....................... 28,534 29,003 11,280 189,045 255,461 256,736 151 114 44

Arkansas .................... 3,240 6,965 2,463 78,672 97,210 106,428 41 72 23

California.................... 361,242 350,717 133,814 1,472,700 1,856,525 1,919,238 245 189 70

Colorado .................... 81,320 86,126 31,818 193,378 241,785 264,444 421 356 120

Connecticut ............... 29,454 24,046 12,721 176,775 216,690 225,498 167 111 56

Delaware .................... 4,195 7,215 3,292 47,198 57,943 64,398 89 125 51

District of Columbia ... 5,840 3,339 709 72,509 91,808 106,913 81 36 7

Florida........................ 37,543 44,269 16,919 577,287 746,840 750,525 65 59 23

Georgia ...................... 16,484 17,983 7,069 327,221 394,815 417,671 50 46 17

Hawaii ........................ 5,772 7,820 3,350 48,379 63,680 69,901 119 123 48

Idaho.......................... 3,664 3,496 1,138 40,426 53,308 56,993 91 66 20

Illinois......................... 22,500 28,052 14,109 520,654 619,747 669,418 43 45 21

Indiana ....................... 10,739 17,080 7,193 220,221 257,119 284,569 49 66 25

Iowa ........................... 4,875 4,290 1,949 106,253 130,673 145,624 46 33 13

Kansas ....................... 4,971 4,626 1,115 95,996 118,862 133,453 52 39 8

Kentucky.................... 4,237 5,602 4,085 126,177 150,155 167,516 34 37 24

Louisiana.................... 3,126 4,326 2,416 155,554 208,573 236,009 20 21 10

Maine ......................... 5,604 7,378 1,464 41,950 48,720 52,496 134 151 28

Maryland.................... 93,753 79,860 39,756 218,398 270,963 306,278 429 295 130

Massachusetts........... 242,347 228,472 87,740 298,795 350,526 389,769 811 652 225

Michigan .................... 33,544 44,746 19,609 360,031 377,254 384,245 93 119 51

Minnesota .................. 24,805 26,318 8,603 213,719 253,502 281,098 116 104 31

Mississippi ................. 3,232 1,040 1,070 73,636 91,141 98,262 44 11 11

Missouri ..................... 7,342 8,792 5,161 200,162 232,695 250,751 37 38 21

Montana..................... 7,045 8,278 2,561 25,765 34,373 38,625 273 241 66

Nebraska.................... 2,998 2,471 1,575 65,767 81,288 95,566 46 30 16

Nevada....................... 7,772 3,364 2,452 90,871 129,638 129,281 86 26 19

New Hampshire ......... 22,208 24,343 8,703 48,944 57,481 63,059 454 423 138

New Jersey ................ 47,712 42,988 22,392 393,407 469,391 494,728 121 92 45

New Mexico............... 21,965 24,776 8,918 58,588 74,300 79,280 375 333 112

New York.................... 75,741 83,045 33,447 852,183 1,062,116 1,170,594 89 78 29

North Carolina............ 22,739 36,235 14,732 313,544 394,114 439,664 73 92 34

North Dakota ............. 1,960 918 691 22,033 28,794 40,455 89 32 17

Ohio ........................... 67,568 73,611 30,032 411,939 461,850 488,446 164 159 61

Oklahoma................... 6,630 7,104 2,065 105,269 141,926 154,887 63 50 13

Oregon....................... 19,556 27,406 11,352 127,142 167,326 189,735 154 164 60

Pennsylvania.............. 65,170 78,761 25,430 442,445 527,391 580,324 147 149 44

Rhode Island.............. 7,783 7,056 1,004 40,575 46,991 49,650 192 150 20

South Carolina ........... 7,397 4,630 3,146 129,895 155,340 169,075 57 30 19

South Dakota............. 1,291 526 110 29,034 34,818 40,809 44 15 3

Tennessee.................. 9,411 13,239 4,072 202,354 242,165 264,755 47 55 15

Texas.......................... 71,023 79,874 25,681 836,983 1,137,028 1,315,029 85 70 20

Utah ........................... 14,299 15,540 2,820 78,275 107,495 124,388 183 145 23

Vermont...................... 4,808 5,286 2,685 20,671 24,034 26,550 233 220 101

Virginia ....................... 98,902 102,923 38,430 309,279 387,343 434,083 320 266 89

Washington................ 46,963 47,165 13,361 247,384 319,661 358,496 190 148 37

West Virginia .............. 6,002 2,186 1,060 46,373 56,765 66,074 129 39 16

Wisconsin .................. 17,592 25,174 12,125 199,081 233,769 253,437 88 108 48

Wyoming.................... 2,418 2,647 504 21,223 34,443 37,690 114 77 13

Puerto Rico................ 216 8 120 78,948 92,624 NA 3 0 NA

NA = not available.

GDP = gross domestic product; SBIR = Small Business Innovation Research.

SOURCES: Small Business Administration, Office of Technology, Small Business Innovation Research program statistics (various years); Bureau of 

Economic Analysis, Gross Domestic Product data (June 2013).

Science and Engineering Indicators 2014
8-124 ♦ Chapter 8. State Indicators

Venture capital represents an important source of funding 

for startup companies. It supports the growth and expansion 

of these companies early in their development, before they 

establish a predictable sales history that would qualify them 

for other types of financing.

This indicator represents the relative magnitude of venture 

capital investments in a state after adjusting for the size of the 

state’s economy. The indicator is expressed as dollars of venture 

capital disbursed per $1,000 of gross domestic product. High 

values indicate that companies in those states are successfully 

attracting venture capital to fuel their growth. Access to venture 

capital financing varies greatly among states.

Venture capital data measure cash-for-equity investments by 

the professional venture capital community in private emerging 

companies in the United States. Data exclude debt, buy-outs, 

recapitalizations, initial public offerings, and other forms of 

private equity that do not involve cash. Results are updated 

periodically. All data are subject to change at any time.

Findings

• The total amount of venture capital invested in the United 

States has increased from $22 billion in 2002 to nearly $27 

billion in 2012. The average value for venture capital disbursed per $1,000 of gross domestic product for the United 

States was $2.08 in 2002 and $1.73 in 2012.

• Venture capital investment is concentrated in relatively few 

states. Companies in California received more than 50% of 

the total venture capital disbursed in the United States in 

2012, followed by companies in Massachusetts with 12%. 

Three states reported no venture capital investment in 2012, 

and a total of 11 states reported less than $10 million.

• In 2012, the value of this indicator across states ranged from 

$0.00 to $7.74.

• The average indicator value for Experimental Program to 

Stimulate Competitive Research (EPSCoR) states was substantially lower than that for non-EPSCoR states. The state 

distribution of venture capital was similar to indicators of 

high-technology business activity.

Venture Capital Disbursed per $1,000 of Gross Domestic Product

Figure 8-57

Venture capital disbursed per $1,000 of gross domestic product: 2012

0.00 0.42 0.84 1.26 1.68 2.10 2.52 6.72 7.14 7.56 7.98

CO

UT

WA

AZ

CT

DC

GA

IL

MI

NC

NM

OH

OR

TX

VA

MD

MN

NH

NJ

PA

AK

AL

AR

DE

FL

HI

IA

ID

IN

KS

KY

LA

ME

MO

MS

MT

ND

NE

NV

OK

SC

SD

TN

VT

WI

WV

WY

NY

RI CA MA

Dollars

1st quartile ($0.83–$7.74)

2nd quartile ($0.34–$0.82)

3rd quartile ($0.13–$0.31)

4th quartile ($0.00–$0.11)

SOURCES: PricewaterhouseCoopers and 

National Venture Capital Association, special tabulations (2013) of the MoneyTreeTM 

Survey; Bureau of Economic Analysis, 

Gross Domestic Product data.
Science and Engineering Indicators 2014 ♦ 8-125

Table 8-57

Venture capital disbursed per $1,000 of gross domestic product, by state: 2002, 2007, and 2012

Venture capital disbursed 

($millions) State GDP ($millions)

Venture capital 

($)/$1,000 GDP

State 2002 2007 2012 2002 2007 2012 2002 2007 2012

United States................. 22,010 30,871 26,873 10,572,388 13,936,196 15,566,076 2.08 2.22 1.73

Alabama..................... 57 31 23 125,168 165,665 183,547 0.46 0.19 0.13

Alaska ........................ 0 0 0 28,894 44,540 51,859 0.00 0.00 0.00

Arizona....................... 197 203 222 177,068 259,157 266,891 1.11 0.78 0.83

Arkansas .................... 10 0 5 74,167 97,470 109,557 0.13 0.00 0.05

California.................... 9,528 14,735 14,194 1,387,213 1,870,916 2,003,479 6.87 7.88 7.08

Colorado .................... 537 610 585 186,529 242,633 274,048 2.88 2.51 2.13

Connecticut ............... 183 296 158 168,865 221,133 229,317 1.08 1.34 0.69

Delaware .................... 19 7 9 43,672 59,592 65,984 0.44 0.12 0.14

District of Columbia ... 20 91 64 67,924 91,896 109,793 0.29 0.99 0.58

Florida........................ 410 768 199 536,061 760,936 777,164 0.76 1.01 0.26

Georgia ...................... 565 475 262 313,952 399,579 433,569 1.80 1.19 0.60

Hawaii ........................ 4 5 1 44,752 64,070 72,424 0.09 0.08 0.01

Idaho.......................... 11 16 15 37,729 54,273 58,243 0.29 0.29 0.26

Illinois......................... 309 505 570 497,802 626,611 695,238 0.62 0.81 0.82

Indiana ....................... 40 83 84 208,674 261,755 298,625 0.19 0.32 0.28

Iowa ........................... 2 6 5 98,584 134,053 152,436 0.02 0.04 0.03

Kansas ....................... 7 82 47 91,671 120,599 138,953 0.08 0.68 0.34

Kentucky.................... 14 53 24 121,436 150,487 173,466 0.12 0.35 0.14

Louisiana.................... 19 16 11 139,202 207,312 243,264 0.14 0.08 0.05

Maine ......................... 15 5 13 39,989 49,065 53,656 0.38 0.10 0.24

Maryland.................... 637 613 284 206,624 271,985 317,678 3.08 2.25 0.89

Massachusetts........... 2,530 3,714 3,127 288,352 352,378 403,823 8.77 10.54 7.74

Michigan .................... 108 105 237 351,832 386,591 400,504 0.31 0.27 0.59

Minnesota .................. 403 488 253 201,559 253,374 294,729 2.00 1.93 0.86

Mississippi ................. 5 6 10 69,527 92,107 101,490 0.07 0.07 0.10

Missouri ..................... 76 92 21 192,189 232,959 258,832 0.40 0.39 0.08

Montana..................... 0 4 6 23,781 35,085 40,422 0.00 0.11 0.15

Nebraska.................... 13 0 11 61,384 82,135 99,557 0.21 0.00 0.11

Nevada....................... 32 29 7 82,764 133,185 133,584 0.39 0.22 0.05

New Hampshire ......... 208 135 61 46,730 57,868 64,697 4.45 2.33 0.94

New Jersey ................ 905 632 444 376,922 471,372 508,003 2.40 1.34 0.87

New Mexico............... 54 129 36 53,662 74,356 80,600 1.01 1.73 0.45

New York.................... 799 1,130 1,864 822,408 1,076,255 1,205,930 0.97 1.05 1.55

North Carolina............ 563 547 197 302,201 396,740 455,973 1.86 1.38 0.43

North Dakota ............. 0 0 2 20,439 28,549 46,016 0.00 0.00 0.05

Ohio ........................... 268 193 286 397,966 467,138 509,393 0.67 0.41 0.56

Oklahoma................... 33 8 34 98,778 140,378 160,953 0.33 0.06 0.21

Oregon....................... 151 312 124 119,571 167,088 198,702 1.26 1.87 0.62

Pennsylvania.............. 456 819 521 424,103 531,098 600,897 1.08 1.54 0.87

Rhode Island.............. 96 7 85 38,135 47,293 50,956 2.52 0.15 1.67

South Carolina ........... 80 87 39 124,391 157,712 176,217 0.64 0.55 0.22

South Dakota............. 18 4 0 27,610 34,885 42,464 0.65 0.11 0.00

Tennessee.................. 116 125 87 193,069 242,220 277,036 0.60 0.52 0.31

Texas.......................... 1,296 1,468 934 782,780 1,147,404 1,397,369 1.66 1.28 0.67

Utah ........................... 136 188 318 74,603 108,474 130,486 1.82 1.73 2.44

Vermont...................... 4 9 4 19,599 24,043 27,296 0.20 0.37 0.16

Virginia ....................... 429 557 372 290,904 389,570 445,876 1.47 1.43 0.83

Washington................ 580 1,383 908 237,117 325,118 375,730 2.45 4.25 2.42

West Virginia .............. 16 10 15 44,533 56,864 69,380 0.36 0.18 0.22

Wisconsin .................. 51 90 95 190,241 236,522 261,548 0.27 0.38 0.36

Wyoming.................... 0 0 0 19,262 33,708 38,422 0.00 0.00 0.00

Puerto Rico................ NA NA 0 74,827 93,263 NA NA NA NA

NA = not available.

GDP = gross domestic product.

SOURCES: PricewaterhouseCoopers and National Venture Capital Association, special tabulations (2011, 2011, 2013) of the MoneyTreeTM Survey (various years); Bureau of Economic Analysis, Gross Domestic Product data (June 2013).

Science and Engineering Indicators 2014
8-126 ♦ Chapter 8. State Indicators

This indicator represents the extent to which high-technology companies in a 

state receive venture capital investments. The value of the indicator is calculated by 

dividing the number of venture capital deals by the number of companies operating 

in high-technology industries in that state. High values indicate that high-technology 

companies in a state are frequently using venture capital to facilitate their growth and 

development. In most cases, a company will not receive more than one infusion of 

venture capital in a given year.

Venture capital data measure cash-for-equity investments by the professional venture 

capital community in private emerging companies in the United States. Data exclude 

debt, buy-outs, recapitalizations, initial public offerings, and other forms of private equity 

that do not involve cash. Results are updated periodically. All data are subject to change 

at any time. Venture capital investment can help to grow a high-technology company.

Data on business establishments operating in high-technology industries for the years 

2003 through 2008 are based on their classification according to the 2002 edition of the 

North American Industry Classification System (NAICS). The data for the years 2009 

and 2010 are based on their classification according to the 2007 edition of the NAICS. 

See table 8-A in the chapter introduction for a list of the industries (by NAICS code) that 

are defined as high technology. Data for years prior to 2003 are not directly comparable.

Findings

• The number of venture capital deals that involved 

U.S. companies increased from about 2,900 

deals in 2003 to nearly 3,300 deals in 2010.

• In 2010, venture capital deals were concentrated 

in only a few states. Indicator values ranged from 

a low of 0.00% to a high of 2.05% with a median 

value of 0.21%.

• Companies in high-technology industries located 

in Massachusetts were the most successful in 

accessing venture capital investments in 2010, 

with a 2.05% rate. California companies in hightechnology industries obtained venture capital 

investment at a rate of 1.50%. No other states 

reached a rate of 1.00%.

• In 2010, companies in Experimental Program 

to Stimulate Competitive Research (EPSCoR) 

states tended to receive little venture capital 

investment, and no venture capital deals were 

reported in three EPSCoR states.

Venture Capital Deals as a Percentage of High-Technology Business 

Establishments

Figure 8-58

Venture capital deals as a percentage of high-technology business establishments: 2010

0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25

AK

AL

AR

AZ

FL

HI

IA

ID

IL

IN

KY

LA

ME

MI

MN

MO

MS

MT

ND

NE

NV

OK

SC

SD

TN

VA

WI

WV

WY

CT

KS

NY

PA

RI WA CA MA

CO

DC

DE

GA

MD

NC

NH

NJ

NM

OH

OR

TX

UT

VT

Percent

1st quartile (0.36%–2.05%)

2nd quartile (0.21%–0.35%)

3rd quartile (0.09%–0.20%)

4th quartile (0.00%–0.08%)

SOURCES: PricewaterhouseCoopers, 

and National Venture Capital Association, special tabulations (2013) of the 

MoneyTreeTM Survey; Census Bureau, 

special tabulations (2013) of the Business 

Information Tracking Series.
Science and Engineering Indicators 2014 ♦ 8-127

Table 8-58

Venture capital deals as a percentage of high-technology business establishments, by state: 2003, 2007, and 2010

Venture capital deals High-technology establishments

Venture capital deals/

high-technology 

establishments (%)

State 2003 2007 2010 2003 2007 2010 2003 2007 2010

United States................. 2,903 3,948 3,266 590,417 650,707 648,993 0.49 0.61 0.50

Alabama..................... 9 4 2 6,347 6,783 6,786 0.14 0.06 0.03

Alaska ........................ 0 0 0 1,345 1,538 1,698 0.00 0.00 0.00

Arizona....................... 16 28 17 10,433 12,540 11,875 0.15 0.22 0.14

Arkansas .................... 3 1 1 4,012 4,550 4,852 0.07 0.02 0.02

California.................... 1,122 1,626 1,289 77,614 87,815 85,787 1.45 1.85 1.50

Colorado .................... 72 97 77 15,532 18,016 18,306 0.46 0.54 0.42

Connecticut ............... 34 37 52 7,827 7,868 7,472 0.43 0.47 0.70

Delaware .................... 1 4 9 3,964 3,573 3,256 0.03 0.11 0.28

District of Columbia ... 6 17 11 2,589 3,158 3,507 0.23 0.54 0.31

Florida........................ 61 65 39 38,118 44,745 44,577 0.16 0.15 0.09

Georgia ...................... 55 76 63 18,820 21,586 21,413 0.29 0.35 0.29

Hawaii ........................ 6 4 5 2,097 2,305 2,309 0.29 0.17 0.22

Idaho.......................... 5 4 4 2,515 3,107 3,071 0.20 0.13 0.13

Illinois......................... 58 69 60 27,606 29,222 28,886 0.21 0.24 0.21

Indiana ....................... 8 17 14 9,626 10,355 10,276 0.08 0.16 0.14

Iowa ........................... 1 3 2 4,316 4,679 4,745 0.02 0.06 0.04

Kansas ....................... 2 16 36 5,716 6,076 6,144 0.03 0.26 0.59

Kentucky.................... 3 8 14 5,453 5,850 5,913 0.06 0.14 0.24

Louisiana.................... 1 7 3 7,218 7,574 7,850 0.01 0.09 0.04

Maine ......................... 2 7 5 2,466 2,612 2,652 0.08 0.27 0.19

Maryland.................... 84 96 70 13,428 15,151 15,589 0.63 0.63 0.45

Massachusetts........... 378 446 351 17,183 17,470 17,148 2.20 2.55 2.05

Michigan .................... 17 22 33 16,937 17,321 16,555 0.10 0.13 0.20

Minnesota .................. 58 57 26 12,834 13,590 13,014 0.45 0.42 0.20

Mississippi ................. 4 2 0 3,269 3,405 3,496 0.12 0.06 0.00

Missouri ..................... 23 17 14 9,562 10,238 9,956 0.24 0.17 0.14

Montana..................... 1 1 2 2,108 2,515 2,593 0.05 0.04 0.08

Nebraska.................... 2 0 3 2,797 3,257 3,361 0.07 0.00 0.09

Nevada....................... 6 8 3 5,387 6,087 6,031 0.11 0.13 0.05

New Hampshire ......... 32 23 10 3,511 3,575 3,539 0.91 0.64 0.28

New Jersey ................ 88 92 71 24,286 24,688 23,686 0.36 0.37 0.30

New Mexico............... 5 25 13 3,322 3,658 3,611 0.15 0.68 0.36

New York.................... 119 193 266 35,926 38,368 38,636 0.33 0.50 0.69

North Carolina............ 76 69 57 14,869 17,671 17,967 0.51 0.39 0.32

North Dakota ............. 2 1 0 964 1,075 1,151 0.21 0.09 0.00

Ohio ........................... 25 56 52 19,875 20,486 20,180 0.13 0.27 0.26

Oklahoma................... 2 6 2 6,859 7,512 7,610 0.03 0.08 0.03

Oregon....................... 21 42 33 7,500 8,453 8,587 0.28 0.50 0.38

Pennsylvania.............. 90 158 153 22,266 23,778 23,956 0.40 0.66 0.64

Rhode Island.............. 10 4 13 1,976 2,108 2,071 0.51 0.19 0.63

South Carolina ........... 4 10 8 5,869 6,942 7,010 0.07 0.14 0.11

South Dakota............. 1 2 0 1,206 1,347 1,426 0.08 0.15 0.00

Tennessee.................. 22 20 18 8,196 8,980 8,702 0.27 0.22 0.21

Texas.......................... 165 172 143 45,062 49,237 50,180 0.37 0.35 0.28

Utah ........................... 22 33 25 5,474 6,960 7,139 0.40 0.47 0.35

Vermont...................... 6 8 6 1,453 1,570 1,581 0.41 0.51 0.38

Virginia ....................... 80 94 51 18,868 22,607 23,623 0.42 0.42 0.22

Washington................ 81 175 116 13,171 15,138 15,335 0.61 1.16 0.76

West Virginia .............. 5 4 4 2,257 2,352 2,490 0.22 0.17 0.16

Wisconsin .................. 8 21 19 9,035 9,591 9,709 0.09 0.22 0.20

Wyoming.................... 1 1 1 1,353 1,625 1,686 0.07 0.06 0.06

Puerto Rico................ 1 NA 1 NA NA NA NA NA NA

NA = not available.

SOURCES: PricewaterhouseCoopers and National Venture Capital Association, special tabulations (2011, 2011, 2013) of the MoneyTreeTM Survey (various years); Census Bureau, special tabulations (2007, 2010, 2013) of the Business Information Tracking Series (various years).

Science and Engineering Indicators 2014
8-128 ♦ Chapter 8. State Indicators

This indicator represents the average size of the venture capital investments being made in a state. The indicator is expressed as the total dollars 

of venture capital invested in millions divided by the number of companies 

receiving venture capital. The availability of venture capital may vary widely 

based on stage of investment, type of company, and numerous other factors.

Venture capital data measure cash-for-equity investments by the professional venture capital community in private emerging companies in the United 

States. Data exclude debt, buy-outs, recapitalizations, initial public offerings, 

and other forms of private equity that do not involve cash. Results are updated 

periodically. All data are subject to change at any time.

This indicator provides some measure of the magnitude of investment that 

developing companies in a state have attracted from venture capital sources. 

Some states have relatively few venture capital deals taking place in a given 

year; thus, the value of this indicator may show large fluctuations on a yearto-year basis. Twenty-two states reported fewer than 10 venture capital deals 

in 2012. In such states, a single large or small venture capital investment can 

substantially affect the value of this indicator.

Findings

• In 2012, the size of the average venture capital 

investment in the United States was about $7.1 

million per deal. This is essentially unchanged 

from the same investment per deal in 2002.

• The value of this indicator continued to show 

a high level of variability from year to year and 

among states.

• The total number of venture capital deals fluctuated between 2002 and 2012. There were 3,101 

such deals in 2002, which rose to 3,948 in 2007 

and then decreased slightly to 3,769 in 2012.

• Among those states that received venture capital 

investments in 2012, the state distribution on 

this indicator was skewed from a high value of 

nearly $14 million per deal to a low of $300,000 

per deal, with a median value of about $4.9 million per deal. 

Venture Capital Disbursed per Venture Capital Deal

Figure 8-59 

Venture capital disbursed per venture capital deal: 2012

0.0 1.5 3.0 4.5 6.0 7.5 9.0 10.5 12.0 13.5 15.0

AK

DE

HI

MT

SD

VT

WY

AL

CT

ID

KS

KY

OR

AR

CO

FL

GA

IA

IN

MD

MI

NC

NY

OH

OK

RI

TX

VA

WV AZ

Dollars ($millions)

DC

LA

ME

MO

MS

ND

NE

NM

NV

PA

TN

IL

UT

WI CA

MA

MN

NH

NJ

SC

WA

1st quartile ($5.8–$13.9)

2nd quartile ($4.7–$5.7)

3rd quartile ($2.4–$4.6)

4th quartile ($0.0–$2.2)

SOURCE: PricewaterhouseCoopers and 

National Venture Capital Association, special tabulations (2013) of the MoneyTreeTM 

Survey.
Science and Engineering Indicators 2014 ♦ 8-129

Table 8-59

Venture capital disbursed per venture capital deal, by state: 2002, 2007, and 2012

Venture capital disbursed 

($millions) Venture capital deals

Venture capital/deal 

($millions)

State 2002 2007 2012 2002 2007 2012 2002 2007 2012

United States................. 22,010 30,871 26,873 3,101 3,948 3,769 7.10 7.82 7.13

Alabama..................... 57 31 23 13 4 6 4.38 7.75 3.83

Alaska ........................ 0 0 0 0 0 0 0.00 0.00 0.00

Arizona....................... 197 203 222 24 28 16 8.21 7.25 13.88

Arkansas .................... 10 0 5 5 1 1 2.00 0.00 5.00

California.................... 9,528 14,735 14,194 1,074 1,626 1,551 8.87 9.06 9.15

Colorado .................... 537 610 585 88 97 103 6.10 6.29 5.68

Connecticut ............... 183 296 158 38 37 52 4.82 8.00 3.04

Delaware .................... 19 7 9 2 4 7 9.50 1.75 1.29

District of Columbia ... 20 91 64 6 17 27 3.33 5.35 2.37

Florida........................ 410 768 199 58 65 34 7.07 11.82 5.85

Georgia ...................... 565 475 262 79 76 53 7.15 6.25 4.94

Hawaii ........................ 4 5 1 2 4 3 2.00 1.25 0.33

Idaho.......................... 11 16 15 2 4 4 5.50 4.00 3.75

Illinois......................... 309 505 570 77 69 83 4.01 7.32 6.87

Indiana ....................... 40 83 84 11 17 17 3.64 4.88 4.94

Iowa ........................... 2 6 5 1 3 1 2.00 2.00 5.00

Kansas ....................... 7 82 47 7 16 11 1.00 5.13 4.27

Kentucky.................... 14 53 24 3 8 7 4.67 6.63 3.43

Louisiana.................... 19 16 11 8 7 4 2.38 2.29 2.75

Maine ......................... 15 5 13 4 7 6 3.75 0.71 2.17

Maryland.................... 637 613 284 91 96 51 7.00 6.39 5.57

Massachusetts........... 2,530 3,714 3,127 373 446 419 6.78 8.33 7.46

Michigan .................... 108 105 237 26 22 50 4.15 4.77 4.74

Minnesota .................. 403 488 253 56 57 30 7.20 8.56 8.43

Mississippi ................. 5 6 10 3 2 4 1.67 3.00 2.50

Missouri ..................... 76 92 21 28 17 10 2.71 5.41 2.10

Montana..................... 0 4 6 0 1 6 0.00 4.00 1.00

Nebraska.................... 13 0 11 3 0 4 4.33 0.00 2.75

Nevada....................... 32 29 7 6 8 4 5.33 3.63 1.75

New Hampshire ......... 208 135 61 38 23 8 5.47 5.87 7.63

New Jersey ................ 905 632 444 91 92 57 9.95 6.87 7.79

New Mexico............... 54 129 36 7 25 15 7.71 5.16 2.40

New York.................... 799 1,130 1,864 154 193 333 5.19 5.85 5.60

North Carolina............ 563 547 197 85 69 37 6.62 7.93 5.32

North Dakota ............. 0 0 2 0 1 1 0.00 0.00 2.00

Ohio ........................... 268 193 286 49 56 61 5.47 3.45 4.69

Oklahoma................... 33 8 34 4 6 7 8.25 1.33 4.86

Oregon....................... 151 312 124 26 42 29 5.81 7.43 4.28

Pennsylvania.............. 456 819 521 96 158 182 4.75 5.18 2.86

Rhode Island.............. 96 7 85 14 4 15 6.86 1.75 5.67

South Carolina ........... 80 87 39 7 10 5 11.43 8.70 7.80

South Dakota............. 18 4 0 2 2 1 9.00 2.00 0.00

Tennessee.................. 116 125 87 22 20 32 5.27 6.25 2.72

Texas.......................... 1,296 1,468 934 172 172 160 7.53 8.53 5.84

Utah ........................... 136 188 318 28 33 44 4.86 5.70 7.23

Vermont...................... 4 9 4 6 8 4 0.67 1.13 1.00

Virginia ....................... 429 557 372 85 94 81 5.05 5.93 4.59

Washington................ 580 1,383 908 108 175 116 5.37 7.90 7.83

West Virginia .............. 16 10 15 8 4 3 2.00 2.50 5.00

Wisconsin .................. 51 90 95 11 21 14 4.64 4.29 6.79

Wyoming.................... 0 0 0 0 1 0 0.00 0.00 0.00

Puerto Rico................ NA NA 0 NA NA 1 NA NA 0.00

NA = not available.

NOTE: Venture capital amounts are reported in current dollars.

SOURCE: PricewaterhouseCoopers and National Venture Capital Association, special tabulations (2011, 2011, 2013) of the MoneyTreeTM Survey (various years).

Science and Engineering Indicators 2014

Appendix

Methodology and Statistics

♦ A-1

Introduction

Science and Engineering Indicators (SEI) contains data 

compiled from a variety of sources. This appendix explains 

the methodological and statistical criteria used to assess 

possible data sources for inclusion in SEI and to develop 

statements about the data. It also provides basic information 

about how statistical procedures and reasoning are applied.

This appendix has four main sections. The first describes 

the statistical considerations that are part of the selection 

process for data sets to be included in SEI. The second 

discusses the different types of data (e.g., sample surveys, 

censuses, and administrative records) used in the report and 

provides information about each type. The third discusses 

factors that can affect accuracy at all stages of the survey 

process. The fourth discusses the statistical testing employed 

to determine whether differences between sample surveybased estimates are statistically significant, i.e., greater than 

could be expected by chance. The appendix concludes with 

a glossary of statistical terms commonly used or referred to 

in the text.

Selection of Data Sources

Four criteria guide the selection of data for SEI:

Representativeness. Data should represent national or 

international populations of interest.

Relevance. Data should include indicators central to the 

functioning of the science and technology enterprise.

Timeliness. Data that are not part of a time series should 

be timely, i.e., substantial and unmeasured changes in the 

population under study should not have occurred since the 

data were collected.

Statistical and methodological quality. Survey methods used to acquire data should provide sufficient assurance 

that statements based on statistical analysis of the data are 

valid and reliable.

Data that are collected by U.S. government agencies and 

are products of the federal statistical system meet rigorous 

statistical and methodological criteria as described below. 

Unless otherwise indicated, these data are representative of 

the nation as a whole and of the demographic, organizational, or geographic subgroups that constitute it.

For data collected by governments in other countries and 

by nongovernment sources, including private survey firms 

and academic researchers, methodological information is 

examined to assess conformity with the criteria U.S. federal 

agencies typically use. Government statistical agencies in 

the developed world cooperate extensively both in developing data-quality standards and in improving international 

comparability for key data, and methodological information 

about the data generated by this international statistical system is relatively complete.

Often, methodological information about data from nongovernmental sources and from governmental agencies 

outside the international statistical system is less well documented. These data must meet basic scientific standards for 

representative sampling of survey respondents and adequate 

and unbiased coverage of the population under study. The 

resulting measurements must be sufficiently relevant and 

meaningful to warrant publication despite methodological 

uncertainties that remain after the documentation has been 

scrutinized. The most important statistical criteria are described in general terms below and in greater detail in the 

following sections.

Many data sources that contain pertinent information 

about a segment of the S&E enterprise are not cited in SEI 

because their coverage of the United States is partial in terms 

of geography, incomplete in terms of segments of the population, or otherwise not representative. For example, data 

may be available for only a limited number of states, or studies may be based on populations not representative of the 

United States as a whole. Similarly, data for other countries 

should cover and be representative of the entire country. (In 

some cases, data that have limited coverage or are otherwise 

insufficiently representative are referenced in sidebars.)

Data included in SEI must be of high quality. Data quality can be measured in a variety of ways, some of which are 

described in the following sections. Some key dimensions of 

quality include the following.

Validity. Data have validity to the degree that they accurately measure the phenomenon they are supposed 

to represent.

Reliability. Data have reliability to the degree that 

the same results would be produced if the same measurement or procedure were performed multiple times on the 

same population.

Lack of bias. Data are unbiased to the degree that estimates from the data do not deviate from the population value 

of a phenomenon in a systematic fashion.
A-2 ♦ Appendix. Methodology and Statistics

Data Sources

Much of the data cited in SEI come from surveys. Surveys 

strive to measure characteristics of target populations. To 

generalize survey results correctly to the population of interest, a survey’s target population must be rigorously defined 

and the criteria determining membership in the population 

must be applied consistently in determining which units to 

include in the survey.

Some surveys are censuses (also known as universe surveys), in which the survey attempts to obtain data for all 

population units. The decennial census, in which the target 

population is all U.S. residents, is the most familiar census 

survey. SEI uses data from the Survey of Earned Doctorates, 

an annual census of individuals who earn research doctorates 

from accredited U.S. institutions, for information about the 

numbers and characteristics of new U.S. doctorate holders.

Other surveys are sample surveys, in which data are obtained for only a representative portion of the population 

units. The National Survey of Recent College Graduates, 

which gathers data on individuals who recently received 

bachelor’s or master’s degrees in science, engineering, or 

health fields from U.S. institutions, is an example of a sample survey.

A sample is a probability sample if each unit in the sampling frame has a known, nonzero probability of being selected for the sample. Probability samples are necessary for 

inferences about a population to be evaluated statistically. 

Except for some Asian surveys referenced in chapter 7, 

sample surveys included in SEI use probability sampling. In 

nonprobability sampling, a sample is selected haphazardly, 

purposively, or conveniently, and inferences about the population cannot be evaluated statistically. Internet surveys and 

phone-in polls that elicit responses from self-selected individuals are examples of nonprobability sample surveys.

In sample surveys, after a survey’s target population 

has been defined, the next step is to establish a list of all 

members of that target population (i.e., a sampling frame). 

Members of the population must be selected from this list 

in a scientific manner so that it will be possible to generalize from the sample to the population as a whole. Surveys 

frequently sample from lists that to varying extents omit 

members of the target population, because complete lists are 

typically unavailable.

Surveys may be conducted of individuals or of organizations, such as businesses, universities, or government 

agencies. Surveys of organizations are often referred to as 

establishment surveys. An example of an establishment 

survey used in SEI is the Higher Education Research and 

Development Survey.

Surveys may be longitudinal or cross-sectional. In a longitudinal survey, the same individuals (or organizations) are 

surveyed repeatedly. The primary purpose of longitudinal 

surveys is to investigate how individuals or organizations 

change over time. The Survey of Doctorate Recipients is 

a longitudinal sample survey of individuals who received 

research doctorates from U.S. institutions. SEI uses results 

from this survey to analyze the careers of doctorate holders.

Cross-sectional surveys provide a “snapshot” at a given 

point of time. When conducted periodically, cross-sectional 

surveys produce repeated snapshots of a population, enabling analysis of how the population changes over time. 

However, because the same individuals or organizations are 

not included in each survey cycle, cross-sectional surveys 

cannot, in general, track changes for specific individuals 

or organizations. National and international assessments of 

student achievement in K–12 education, such as those discussed in chapter 1, are examples of repeated cross-sectional 

surveys. Most of the surveys cited in SEI are conducted periodically, although the frequency with which they are conducted varies.

Surveys are conducted using a variety of modes (e.g., 

postal mail, telephone, the Internet, or in person). They 

can be self or interviewer administered. Many surveys are 

conducted in more than one mode. The National Survey of 

College Graduates, a sample survey that collects data on individuals with S&E-related degrees and/or occupations, is 

an example of a multimode survey. It is conducted primarily 

via the Web, with potential participants who did not respond 

to the questionnaire later contacted by telephone.

Some of the data in SEI come from administrative records

(data previously collected for the purpose of administering 

various programs). Examples of data drawn directly from 

administrative records in SEI include patent data from the 

records of government patent offices; bibliometric data on 

publications in S&E journals, compiled from information collected and published by the journals themselves; and data on 

foreign S&E workers temporarily in the United States, drawn 

from the administrative records of immigration agencies.

Many of the establishment surveys that SEI uses depend 

heavily, although indirectly, on administrative records. 

Universities and corporations that respond to surveys about 

their R&D activities often use administrative records developed for internal management or income tax reporting purposes to respond to these surveys. 

Data Accuracy

Accurate information is a primary goal of censuses and 

sample surveys. Accuracy can be defined as the extent to 

which results deviate from the true values of the characteristics in the target population. Statisticians use the term “error” to refer to this deviation. Good survey design seeks to 

minimize survey error.

Statisticians usually classify the factors affecting the accuracy of survey data into two categories: nonsampling and 

sampling errors. Nonsampling error applies to all surveys, 

including censuses, whereas sampling error applies only to 

sample surveys. The sources of nonsampling error in surveys have analogues for administrative records: the processes through which such records are created affect the degree 

to which the records accurately indicate the characteristics 
Science and Engineering Indicators 2014 ♦ A-3

of relevant populations (e.g., patents, journal articles, immigrant scientists and engineers).

Nonsampling Error

Nonsampling error refers to error related to survey design, 

data collection, and processing procedures. Nonsampling error may occur at each stage of the survey process; however, 

the extent of nonsampling error has no practical method of 

measurement. A brief description of five sources of nonsampling error follows. Although for convenience the descriptions occasionally refer to samples, they apply equally to 

censuses. 

Specification Error. Survey questions often do not perfectly measure the concept for which they are intended as indicators. For example, the number of patents is not the same 

as the amount of invention. 

Frame Error. The sampling frame, the list of the target 

population members used for selecting survey respondents, 

is often inaccurate. If the frame has omissions or other flaws, 

the survey is less representative because coverage of the target population is incomplete. Frame errors often require extensive effort to correct. 

Nonresponse Error. Nonresponse errors occur because not all members of the sample respond to the survey. 

Response rates indicate what proportion of sample members respond to the survey. Other things being equal, lower 

response rates create a greater possibility that, had nonrespondents supplied answers to the questionnaire, the survey 

estimates might have been different. 

Nonresponse can cause nonresponse bias, which occurs 

when the people or establishments that respond to a question, or to the survey as a whole, differ in systematic ways 

from those who do not respond. For example, in surveys 

of national populations, complete or partial nonresponse 

is often more likely among lower-income or less-educated 

respondents. Evidence of nonresponse bias is an important 

factor in decisions about whether survey data should be included in SEI. 

Managers of high-quality surveys, such as those in the 

U.S. federal statistical system, do research on nonresponse 

patterns to assess whether and how nonresponse might bias 

survey estimates. SEI notes instances where reported data 

may be subject to substantial nonresponse bias. 

The response rate does not indicate whether a survey has 

a problem of nonresponse bias. Surveys with high response 

rates sometimes have substantial nonresponse bias, and surveys with relatively low response rates, if nonrespondents 

do not differ from respondents on important variables, may 

have relatively little. 

Measurement Error. There are many sources of measurement error, but respondents, interviewers, and survey 

questionnaires are the most important. Knowingly or unintentionally, respondents may provide incorrect information. 

Interviewers may inappropriately influence respondents’ 

answers or record their answers incorrectly. The questionnaire can be a source of error if there are ambiguous, poorly 

worded, or confusing questions, instructions, or terms, or if 

the questionnaire layout is confusing.

In addition, the records or systems of information that a 

respondent may refer to, the mode of data collection, and 

the setting for the survey administration may contribute to 

measurement error. Perceptions about whether data will be 

treated as confidential may affect the accuracy of survey responses to sensitive questions about business profits or personal incomes. 

Processing Error. Processing errors include errors in 

recording, checking, coding, and preparing survey data to 

make them ready for analysis.

Sampling Error

Sampling error is probably the best-known source of 

survey error and the most commonly reported measure of 

a survey’s precision or accuracy. Unlike nonsampling error, 

sampling error can be quantitatively estimated in most scientific sample surveys. 

Chance is involved in selecting the members of a sample. If the same, random procedures were used repeatedly 

to select samples from the population, numerous samples 

would be selected, each containing different members of the 

population with different characteristics. Each sample would 

produce different population estimates. When there is great 

variation among the samples drawn from a given population, 

the sampling error is high and there is a large chance that 

the survey estimate is far from the true population value. In 

a census, because the entire population is surveyed, there is 

no sampling error.

Sampling error is reduced when samples are large, 

and most of the surveys used in SEI have large samples. 

Typically, sampling error is a function of the sample size, 

the variability of the measure of interest, and the methods 

used to produce estimates from the sample data.

Sampling error associated with an estimate is measured by 

the coefficient of variation or margin of error, both of which 

are measures of the amount of uncertainty in the estimate.

Statistical Testing for Data 

from Sample Surveys

Statistical tests determine whether differences observed in 

sample survey data could have happened by chance (i.e., as 

the result of random variation in which people or establishments in the population were sampled). Differences that are 

very unlikely to have been produced by chance variations in 
A-4 ♦ Appendix. Methodology and Statistics

sample selection are termed statistically significant. When 

SEI reports statements about differences on the basis of sample surveys, the differences are statistically significant at the 

.10 level. This means that, if there were no true difference 

in the population, the chance of drawing a sample with the 

observed difference would be no more than 10%.

A statistically significant difference is not necessarily large, important, or significant in the usual sense of the 

word. It is simply a difference that cannot be attributed to 

chance variation in sampling. With the large samples common in SEI data, extremely small differences can be found 

to be statistically significant. Conversely, quite large differences may not be statistically significant if the sample or 

population sizes of the groups being compared are small. 

Occasionally, apparently large differences are noted in the 

text as not being statistically significant to alert the reader 

that these differences may have occurred by chance.

Numerous differences are apparent in every table in SEI 

that reports sample data. The tables permit comparisons between different groups in the survey population and in the 

same population in different years. It would be impractical 

to test and indicate the statistical significance of all possible 

comparisons in tables involving sample data. 

As explained in “About Science and Engineering 

Indicators” at the beginning of this volume, SEI presents 

indicators. It does not model the dynamics of the S&E enterprise, although analysts could construct models using the 

data in SEI. Accordingly, SEI does not make use of statistical procedures suitable for causal modeling and does not 

compute effect sizes for models that might be constructed 

using these data.

Glossary

Most glossary definitions are drawn from U.S. Office of 

Management and Budget, Office of Statistical Policy (2006), 

“Standards and Guidelines for Statistical Surveys” and U.S. 

Bureau of the Census (2006), “Organization of Metadata, 

Census Bureau Standard Definitions for Surveys and Census 

Metadata.” In some cases, glossary definitions are somewhat 

more technical and precise than those in the text, where fine 

distinctions are omitted to improve readability.

Administrative records: Data collected for the purpose 

of carrying out various programs (e.g., tax collection). 

Bias: Systematic deviation of the survey estimated value 

from the true population value. Bias refers to systematic errors that can occur with any sample under a specific design.

Coverage: Extent to which all elements on a frame list are 

members of the population and to which every element in a 

population appears on the frame list once and only once.

Coverage error: Discrepancy between statistics calculated on the frame population and the same statistics calculated 

on the target population. Undercoverage errors occur when 

target population units are missed during frame construction, 

and overcoverage errors occur when units are duplicated or 

enumerated in error. 

Cross-sectional sample survey: Based on a representative sample of respondents drawn from a population at a 

particular point in time. 

Estimate: A numerical value for a population parameter 

derived from information collected from a survey or other 

sources. 

Estimation error: Difference between a survey estimate 

and the true value of the parameter in the target population. 

Frame: A mapping of the universe elements (i.e., sampling units) onto a finite list (e.g., the population of schools 

on the day of the survey).

Item nonresponse: Occurs when a respondent fails to respond to one or more relevant item(s) on a survey.

Longitudinal sample survey: Follows the experiences 

and outcomes over time of a representative sample of respondents (i.e., a cohort).

Measurement error: Difference between observed values of a variable recorded under similar conditions and some 

fixed true value (e.g., errors in reporting, reading, calculating, or recording a numerical value).

Nonresponse bias: Occurs when the observed value 

deviates from the population parameter due to differences 

between respondents and nonrespondents. Nonresponse bias 

may occur as a result of not obtaining 100% response from 

the selected units.

Nonresponse error: Overall error observed in estimates caused by differences between respondents and 

nonrespondents. It consists of a variance component and 

nonresponse bias.

Nonsampling error: Includes measurement errors due 

to interviewers, respondents, instruments, and mode; nonresponse error; coverage error; and processing error.

Population: See “target population.”

Precision of survey results: How closely results from 

a sample can reproduce the results that would be obtained 

from a complete count (i.e., census) conducted using the 

same techniques. The difference between a sample result and 

the result from a complete census taken under the same conditions is an indication of the precision of the sample result.

Probabilistic methods: Any of a variety of methods for 

survey sampling that gives a known, nonzero probability of 

selection to each member of a target population. The advantage of probabilistic sampling methods is that sampling error 

can be calculated. Such methods include random sampling, 

systematic sampling, and stratified sampling. They do not 

include convenience sampling, judgment sampling, quota 

sampling, and snowball sampling.

Reliability: Degree to which a measurement technique 

would yield the same result each time it is applied. A measurement can be both reliable and inaccurate.

Response bias: Deviation of the survey estimate from 

the true population value due to measurement error from the 

data collection. Potential sources of response bias include 

the respondent, the instrument, and the interviewer.

Response rates: Measure the proportion of the sample 

frame represented by the responding units in each study.
Science and Engineering Indicators 2014 ♦ A-5

Sample design: Sampling plan and estimation procedures.

Sampling error: Error that occurs because all members 

of the frame population are not measured. It is associated 

with the variation in samples drawn from the same frame 

population. The sampling error equals the square root of 

the variance.

Standard error: Standard deviation of the sampling distribution of a statistic. Although the standard error is used to 

estimate sampling error, it includes some nonsampling error.

Statistical significance: Attained when a statistical procedure applied to a set of observations yields a p value that 

exceeds the level of probability at which it is agreed that the 

null hypothesis will be rejected.

Target population: Any group of potential sample units 

or individuals, businesses, or other entities of interest.

Unit nonresponse: Occurs when a respondent fails to respond to all required response items (i.e., fails to fill out or 

return a data collection instrument).

Universe survey: Involves the collection of data covering all known units in a population (i.e., a census).

Validity: Degree to which an estimate is likely to be true 

and free of bias (systematic errors).
List of Appendix Tables

Detailed appendix tables are available online at https://www.nsf.gov/statistics/indicators/appendix/.

Chapter 1. Elementary and Secondary Mathematics and Science Education

1-1. First-time kindergarteners and their average mathematics and science assessment scores, by child, family, and 

school characteristics: Academic year 2010−11

1-2. Average NAEP mathematics scores of students in grades 4 and 8, by student and school characteristics: 

1990–2011

1-3. Students in grades 4 and 8 scoring at or above NAEP’s proficient level in mathematics for their grade, by 

student and school characteristics: 1990–2011

1-4. Average NAEP science scores of students in grade 8, by student and school characteristics: 2009 and 2011

1-5. Students in grade 8 scoring at or above NAEP’s proficient level in science for their grade level, by student and 

school characteristics: 2009 and 2011

1-6. Average TIMSS mathematics scores of students in grades 4 and 8, by country/jurisdiction: 2011

1-7. Average TIMSS science scores of students in grades 4 and 8, by country/jurisdiction: 2011

1-8. Highest-level mathematics course in which ninth graders enrolled, by student and family characteristics: 2009

1-9. Highest-level science course in which ninth graders enrolled, by student and family characteristics: 2009

1-10. Public school students in graduating class of 2012 who took AP exams in mathematics and science in high 

school, by sex and race or ethnicity

1-11. Mathematics and science classes taught by teachers experienced in teaching their subject, by years of experience 

and school and class characteristics: 2012

1-12. Middle and high school science teachers with various levels of preparation in their subject, by grade level and 

subject taught: 2012

1-13. Middle and high school mathematics teachers who completed mathematics courses in college: 2012

1-14. Middle and high school mathematics and science teachers with an undergraduate or graduate degree in their 

subject, by school and class characteristics: 2012

1-15. Elementary teachers’ self-assessment of their preparedness to teach selected mathematics and science 

topics: 2012

1-16. Middle and high school science teachers considering themselves very well prepared to teach various science 

topics: 2012

1-17. Middle and high school mathematics teachers considering themselves very well prepared to teach various 

mathematics topics: 2012

1-18. School program representatives rating the effect of various school conditions on mathematics and science 

instruction: 2012

1-19. Beginning public elementary and secondary school teachers (2007−08) who had left teaching by 2009−10, by 

teaching level, field, and selected teacher and school characteristics

1-20. On-time graduation rates of U.S. public high school students, by sex and race or ethnicity: 2006−10

1-21. High school graduation rates, by OECD country: 2010

1-22. High school graduates enrolled in college in October after completing high school, by demographic 

characteristics and institution type: 1975–2011

1-23. First-time entry rates into university-level education, by OECD country and sex of student: 2010

Chapter 2. Higher Education in Science and Engineering

2-1. S&E degrees awarded, by degree level, Carnegie institution type, and field: 2011

2-2. Degrees awarded by private for-profit academic institutions, by broad field and degree level: 2000–11

2-3. Degrees awarded by private for-profit academic institutions, by field and degree level: 2011

2-4. Average revenue per FTE by institution type: 1987–2010

2-5. Average expenditures per FTE by institution type: 1987–2010

2-6. Full-time S&E graduate students, by source and mechanism of primary support: 1997–2011

2-7. Full-time S&E graduate students, by field and mechanism of primary support: 2011

2-8. Full-time S&E graduate students primarily supported by federal government, by field and mechanism of 

primary support: 2011

t B-1
B-2 t List of Appendix Tables

2-9. Full-time S&E graduate students primarily supported by federal government, by agency: 1997–2011

2-10. Full-time S&E graduate students primarily supported by federal government, by field and agency: 2011

2-11. Primary support mechanisms for S&E doctorate recipients, by citizenship, sex, and race or ethnicity: 2011

2-12. Amount of undergraduate and graduate debt of S&E doctorate recipients, by field: 2011

2-13. Doctorate recipients with graduate education-related debt, by broad field of study: 2001–11

2-14. Undergraduate and total enrollment in higher education, by Carnegie institution type: 1996–2011

2-15. Projections of U.S. population age 20–24, by sex and race or ethnicity: 2015–60

2-16. Freshmen intending S&E major, by field, sex, and race or ethnicity: 1998–2012

2-17. Earned bachelor’s degrees, by sex and field: 2000–11

2-18. Freshmen intending to major in selected S&E fields, by sex and race or ethnicity: 1998–2012

2-19. Foreign undergraduate student enrollment in U.S. universities, by field and selected places of origin: November 

2011 and November 2012

2-10. Undergraduate enrollment in engineering and engineering technology programs: 1997–2011

2-21. Earned associate’s degrees, by sex and field: 2000–11

2-22. Earned associate’s degrees, by citizenship, field, and race or ethnicity: 2000–11

2-23. Earned bachelor’s degrees, by citizenship, field, and race or ethnicity: 2000–11

2-24. S&E graduate enrollment, by sex and field: 2000–11

2-25. Engineering enrollment, by enrollment level and attendance: 1991–2011

2-26. First-time full-time S&E graduate students, by citizenship and field: 2000–11

2-27. S&E graduate enrollment, by citizenship, field, and race or ethnicity: 2000–11

2-28. Foreign graduate student enrollment in U.S. universities, by field and selected places of origin: November 2011 

and November 2012

2-29. Earned master’s degrees, by sex and field: 2000–11

2-30. Earned master’s degrees, by citizenship, field, and race or ethnicity: 2000–11

2-31. Earned doctoral degrees, by citizenship, field, and sex: 2000–11

2-32. Median number of years from S&E doctorate recipients’ entry to graduate school to receipt of doctorate, by 

field: 1981–2011

2-33. Earned doctoral degrees, by citizenship, field, and race or ethnicity: 2000–11

2-34. Expenditures on tertiary education as a percentage of GDP: 1995, 2000, 2005, and 2009

2-35. Tertiary-type A, advanced research programs, and tertiary education, by age group and country: 2010

2-36. First university degrees, by selected region and country/economy: 2010 or most recent year

2-37. S&E first university degrees, by selected Western or Asian country/economy and field: 2000–10

2-38. First university degrees, by field, sex, and region/country/economy: 2010 or most recent year

2-39. Earned S&E doctoral degrees, by selected region/country/economy and field: 2010 or most recent year

2-40. Earned S&E doctoral degrees, by sex, selected region/country/economy, and field: 2010 or most recent year

2-41. S&E doctoral degrees in the United States and selected European countries, by field: 2000–10 

2-42. S&E doctoral degrees, by selected Asian country/economy and field: 1996–2010

2-43. Trends in population age 20–24, by selected country and region: 2010–60

2-44. Foreign S&E student enrollment in UK universities, by enrollment level, place of origin, and field: Academic 

years 1994–95, 2010–11, and 2011–12

2-45. Foreign S&E student enrollment in Japanese universities, by enrollment level, place of origin, and field: 2004 

and 2012

2-46. S&E student enrollment in Canadian universities, by enrollment level, top place of origin, and field: 2000 

and 2010

Chapter 3. Science and Engineering Labor Force

3-1. S&E occupations in the 1960 U.S. Census and 2011 American Community Survey

3-2. Bureau of Labor Statistics projections of occupational employment: 2010–20

3-3. Scientists and engineers, by occupation and degree field: 2010

3-4. Employment sector of S&E highest degree holders, by level and field of highest degree: 2010

3-5. Scientists and engineers employed in the business sector, by employer size: 2010

3-6. Scientists and engineers participating in work-related training, by labor force status, highest degree level, 

and sex: 2010

3-7. Most important reason for scientists and engineers to participate in work-related training, by labor force 

status: 2010
Science and Engineering Indicators 2014 t B-3

3-8. Unemployment rates of scientists and engineers, by level of highest degree and broad occupational category: 

2003–10

3-9. Alternate rates of labor underutilization for S&E and all occupations: March 2008–April 2013

3-10. Labor market indicators for recent S&E degree recipients up to 5 years after receiving degree, by degree 

level: 2010

3-11. Postgraduation plans of doctorate recipients with definite commitments, by broad field of study: Selected years, 

1991–2011

3-12. Age profile of employed scientists and engineers, by sex and field of highest degree: 2010

3-13. Employed scientists and engineers, by sex and occupation: 2010

3-14. Employed S&E highest degree holders, by sex and field of degree: 2010

3-15. Employed S&E highest degree holders, by sex, race, ethnicity, field of highest degree, and broad occupational 

category: 2010

3-16. Employed scientists and engineers, by race, ethnicity, and occupation: 2010

3-17. Employed S&E highest degree holders, by race, ethnicity, and field of degree: 2010

3-18. Estimate and median salary of full-time workers with highest degree in S&E field, by sex and occupation: 2010

3-19. Estimate and median salary of full-time workers with highest degree in S&E field, by race, ethnicity, and 

occupation: 2010

3-20. Race and ethnic distribution of workers in S&E occupations, by nativity: 2010

3-21. Occupations of new H-1B visa recipients: FY 2011

3-22. Plans of foreign recipients of U.S. doctorates to stay in the United States, by field of doctorate and place of 

origin: 2000–11

3-23. R&D personnel in selected regions/countries: 1995–2011

3-24. Researchers as a share of total employment in selected regions/countries/economies: 1995, 2003, 2011

Chapter 4. Research and Development: National Trends and International Comparisons

4-1. Gross domestic product, R&D, and ratio of R&D and gross domestic product: 1953–2011

4-2. U.S. R&D expenditures, by performing sector and source of funds: 1953–2011

4-3. U.S. basic research expenditures, by performing sector and source of funds: 1953–2011

4-4. U.S. applied research expenditures, by performing sector and source of funds: 1953–2011

4-5. U.S. development expenditures, by performing sector and source of funds: 1953–2011

4-6. U.S. R&D expenditures, by source of funds and performing sector: 1953–2011

4-7. U.S. basic research expenditures, by source of funds and performing sector: 1953–2011

4-8. U.S. applied research expenditures, by source of funds and performing sector: 1953–2011

4-9. U.S. development expenditures, by source of funds and performing sector: 1953–2011

4-10. R&D expenditures of federally funded R&D centers, by FFRDC and source of funds: FY 2010 and FY 2011

4-11. U.S. R&D expenditures, by state, performing sector, and source of funds: 2010

4-12. U.S. R&D and gross domestic product, by state: 2010

4-13. Gross expenditures for R&D and expenditures for R&D as share of gross domestic product, for selected 

countries: 1981–2011

4-14. Gross expenditures on R&D, by performing and funding sectors, for selected countries: 2010

4-15. Domestic business R&D by major funding source, by industry and company size: 2008–11

4-16. Domestic R&D performed by the company and paid for by others, by source of funds, industry, and company 

size: 2010

4-17. Domestic R&D paid for by sources located outside the United States and performed by the company, by source 

of funds, industry, and company size: 2010

4-18. Domestic R&D paid for by others and performed by the company, by source of funds, industry, and company 

size: 2011

4-19. Domestic R&D paid for by sources located outside the United States and performed by the company, by source 

of funds, industry, and company size: 2011 

4-20. Domestic R&D paid for by the company and others and performed by the company, by business activity: 2011

4-21. Federal research and experimentation tax credit claims, by NAICS industry: 2001–09

4-22. Corporate tax returns claiming the federal research and experimentation tax credit, by NAICS industry: 

2001–09
B-4 t List of Appendix Tables

4-23. R&D performed by majority-owned affiliates of foreign companies in the United States, by region/country/

economy of ultimate beneficial owner: 1997–2010

4-24. R&D performed by majority-owned affiliates of foreign companies in the United States, by NAICS industry of 

affiliate: 2002–06

4-25. R&D performed by majority-owned affiliates of foreign companies in the United States, by NAICS industry of 

affiliate: 2007–10

4-26. R&D performed abroad by majority-owned foreign affiliates of U.S. parent companies, by region/country/

economy: 1997–2010

4-27. R&D performed abroad by majority-owned foreign affiliates of U.S. parent companies, by selected NAICS 

industry of affiliate: 2004–08

4-28. R&D performed abroad by majority-owned foreign affiliates of U.S. parent companies, by selected NAICS 

industry of affiliate: 2009 and 2010

4-29. R&D performed in the United States by U.S. multinational company parent companies, by NAICS industry: 

2004–08

4-30. R&D performed in the United States by U.S. multinational company parent companies, by NAICS industry: 

2009–10

4-31. Business expenditures for R&D, by industry and selected country/economy: 2010 or most recent year

4-32. Federal budget authority for R&D and R&D plant, by budget function: FYs 2000–13

4-33. Federal budget authority for R&D, R&D plant, and basic research, by budget function: FYs 2000–13

4-34. Federal obligations for R&D and R&D plant, by character of work: FYs 1953–2012 

4-35. Federal obligations for R&D and R&D plant, by agency, performer, and character of work: FY 2011

4-36. Differences in federal R&D support, as reported by performers and federal agencies: 1985–2011

4-37. Federal obligations for research, by agency and S&E field: FY 2011

4-38. Federal obligations for research, by detailed S&E field: FYs 1991–2011

4-39. Government budget appropriations or outlays for R&D, by socioeconomic objectives and selected 

countries: 2011

4-40. Federal technology transfer activity indicators for U.S. agencies with federal laboratories: FYs 2001–10

4-41. SBIR and STTR awards, by type of award: FYs 1983–2011

4-42. SBIR award funding, by type of award and federal agency: FYs 1983–2011

4-43. STTR award funding, by type of award and federal agency: FYs 1995–2011

Chapter 5. Academic Research and Development

5-1. Higher education R&D expenditures, by R&D field: FYs 2005–12

5-2. Total and federally financed higher education R&D expenditures, by character of work: FYs 1953–2012

5-3. Sources of S&E R&D funds at private and public academic institutions: Selected years, 1990–2012

5-4. Federally financed higher education R&D expenditures, by federal agency and R&D field: FY 2012

5-5. Higher education R&D expenditures, by source of funds and R&D field: FY 2012

5-6. Top 100 academic institutions in S&E R&D expenditures in S&E fields, ranked by FY 2012 R&D 

expenditures: FYs 2005–12

5-7. Expenditures for academic R&D passed through to and received by subrecipients: FYs 2000–09 

5-8. S&E research space in academic institutions, by field: FYs 1988–2011

5-9. Source of funds for new construction of S&E research space in academic institutions, by year of project start 

and type of institution: FYs 2002–11

5-10. Costs for repair and renovation of S&E research space in academic institutions, by field and time of repair and 

renovation: FYs 2010–13

5-11. Current fund expenditures for research equipment at academic institutions, by S&E field: Selected years, 

1985–2012

5-12. Federal share of current funding for research equipment at academic institutions, by S&E field: Selected years, 

FYs 1985–2012

5-13. Centrally administered high-performance computing in academic institutions, by type of institution and 

computing architecture: FY 2011

5-14. SEH doctorate holders employed in academia, by type of position and degree field: 1973–2010

5-15. SEH doctorate holders employed in academia, by type of position, sex, and degree field: 1973–2010

5-16. SEH doctorate holders employed in academia, by type of position, degree field, race, and ethnicity: 1973–2010

5-17. SEH doctorate holders employed in academia, by type of position, degree field, and citizenship: 2010
Science and Engineering Indicators 2014 t B-5

5-18. Age distribution of SEH doctorate holders in full-time faculty positions at research universities and other 

academic institutions: 1973–2010

5-19. SEH doctorate holders employed in academia, by research priority, type of position, and degree field: 

1973–2010

5-20. Early career SEH doctorate holders employed in academia, by Carnegie institution type, years since doctorate, 

and type of position: 1997–2010

5-21. Academic SEH doctorate holders with federal support, by degree field, research activity, and type of position: 

1973–2010

5-22. SEH doctorate holders and full-time faculty with federal support, by degree field and Carnegie classification of 

employer: 2010

5-23. Early career SEH doctorate holders employed in academia with federal support, by degree field, years since 

doctorate, and type of position: 1973–2010

5-24. Regions and countries/economies in S&E publications data

5-25. Fields and subfields of S&E publications data

5-26. S&E articles in all fields combined, by region/country/economy: 1997–2011

5-27. S&E articles in agricultural sciences, by region/country/economy: 1997–2011

5-28. S&E articles in astronomy, by region/country/economy: 1997–2011

5-29. S&E articles in biological sciences, by region/country/economy: 1997–2011

5-30. S&E articles in chemistry, by region/country/economy: 1997–2011

5-31. S&E articles in computer sciences, by region/country/economy: 1997–2011

5-32. S&E articles in engineering, by region/country/economy: 1997–2011

5-33. S&E articles in geosciences, by region/country/economy: 1997–2011

5-34. S&E articles in mathematics, by region/country/economy: 1997–2011

5-35. S&E articles in medical sciences, by region/country/economy: 1997–2011

5-36. S&E articles in other life sciences, by region/country/economy: 1997–2011

5-37. S&E articles in physics, by region/country/economy: 1997–2011

5-38. S&E articles in psychology, by region/country/economy: 1997–2011

5-39. S&E articles in social sciences, by region/country/economy: 1997–2011

5-40. U.S. S&E articles, by field and sector: 1997–2012

5-41. S&E articles in all fields combined, by coauthorship attribute and selected country/economy: 1997–2012

5-42. S&E articles in engineering, by coauthorship attribute and selected country/economy: 1997–2012

5-43. S&E articles in astronomy, by coauthorship attribute and selected country/economy: 1997–2012

5-44. S&E articles in chemistry, by coauthorship attribute and selected country/economy: 1997–2012

5-45. S&E articles in physics, by coauthorship attribute and selected country/economy: 1997–2012

5-46. S&E articles in geosciences, by coauthorship attribute and selected country/economy: 1997–2012

5-47. S&E articles in mathematics, by coauthorship attribute and selected country/economy: 1997–2012

5-48. S&E articles in computer sciences, by coauthorship attribute and selected country/economy: 1997–2012

5-49. S&E articles in agricultural sciences, by coauthorship attribute and selected country/economy: 1997–2012

5-50. S&E articles in biological sciences, by coauthorship attribute and selected country/economy: 1997–2012

5-51. S&E articles in medical sciences, by coauthorship attribute and selected country/economy: 1997–2012

5-52. S&E articles in other life sciences, by coauthorship attribute and selected country/economy: 1997–2012

5-53. S&E articles in psychology, by coauthorship attribute and selected country/economy: 1997–2012

5-54. S&E articles in social sciences, by coauthorship attribute and selected country/economy: 1997–2012

5-55. Indexes of internationally coauthored S&E articles, by selected country/economy pairs: 1997 and 2012

5-56. Internationally coauthored S&E articles, by selected country/economy pairs: 1997 and 2012

5-57. Share of all S&E articles, top 1% of cited articles, and index of highly cited articles, by field and selected 

country/economy: 2002 and 2012

5-58. S&E articles, by field, citation percentile, and country/economy of institutional author: 2002 and 2012

5-59. U.S. utility patents citing S&E literature, by patent assignee sector, article author sector, and patent issue year: 

2003–12

5-60. Citation of S&E articles in USPTO patents, by cited field and cited country/sector: 2003–12

5-61. U.S. utility patents in clean energy and pollution control technologies citing S&E literature, by patent assignee 

sector, article author sector, and patent issue year: 2003–12

5-62. U.S. utility patent awards, by selected characteristics of patent owner: 1997–2012

5-63. U.S. university patent awards, by technology area: 1992–2012

5-64. Academic patenting and licensing activities: 2001–11
B-6 t List of Appendix Tables

Chapter 6. Industry, Technology, and the Global Marketplace

6-1 Regions/countries/economies in world industry data

6-2. Value added of KTI industries, by region/country/economy: 1997–2012

6-3. Nominal GDP, by region/country/economy: Selected years, 1997–2012

6-4. Value added of commercial KI services, by region/country/economy: 1997–2012

6-5. Value added of education services, by region/country/economy: 1997–2012

6-6. Value added of health and social services, by region/country/economy: 1997–2012

6-7. Value added of HT manufacturing industries, by region/country/economy: 1997–2012

6-8. Value added of financial services, by region/country/economy: 1997–2012

6-9. Real GDP per employed person, by region/country/economy: Selected years, 1995–2012

6-10. Real GDP per capita, by region/country/economy: Selected years, 1995–2012

6-11. Value added of business services, by region/country/economy: 1997–2012

6-12. Value added of telecommunications services, by region/country/economy: 1997–2012

6-13. Value added of computer programming and related services, by region/country/economy: 1997–2012

6-14. Value added of all manufacturing industries, by region/country/economy: 1997–2012

6-15. Value added of semiconductor industry, by region/country/economy: 1997–2012

6-16. Value added of computers and office machinery, by region/country/economy: 1997–2012

6-17. Value added of communications, by region/country/economy: 1997–2012

6-18. Value added of pharmaceuticals industry, by region/country/economy: 1997–2012

6-19. Value added of testing, measuring, and control instruments industries, by region/country/economy: 1997–2012

6-20. Value added of aircraft and spacecraft, by region/country/economy: 1997–2012

6-21. Exports and imports of HT products, by region/country/economy: Selected years, 1997–2012

6-22. U.S. trade in R&D services, by affiliation and by selected region/country/economy: 2011

6-23. U.S. trade in research, development, and testing services, by affiliation and by selected region/country/

economy: 2006–10 

6-24. Exports and imports of all manufactured products, by region/country/economy: Selected years, 1997–2012

6-25. Exports and imports of ICT products, by region/country/economy: Selected years, 1997–2012

6-26. Exports and imports of communications products, by region/country/economy: Selected years, 1997–2012

6-27. Exports and imports of computer products, by region/country/economy: Selected years, 1997–2012

6-28. Exports and imports of semiconductor products, by region/country/economy: Selected years, 1997–2012

6-29. Exports and imports of pharmaceutical products, by region/country/economy: Selected years, 1997–2012

6-30. Exports and imports of aircraft products, by region/country/economy: Selected years, 1997–2012

6-31. Exports and imports of measuring, testing, and control instrument products, by region/country/economy: 

Selected years, 1997–2012

6-32. Regions and countries/economies in world trade data

6-33. U.S. trade in AT products, by region/country/economy: 2000–12

6-34. U.S. trade in ICT products, by region/country/economy: 2000–12

6-35. U.S. trade in aerospace products, by region/country/economy: 2000–12

6-36. U.S. trade in electronics products, by region/country/economy: 2000–12

6-37. U.S. trade in life sciences products, by region/country/economy: 2000–12

6-38. U.S. trade in optoelectronics products, by region/country/economy: 2000–12

6-39. Value added and employment of U.S. multinationals, by selected industry and location: 2010

6-40. USPTO patent grants, by region/country/economy: Selected years, 1997–2012

6-41. Regions/countries/economies in USPTO patent data

6-42. USPTO patent applications and grants, by industry: 2011

6-43. USPTO patents granted in networking technology, by region/country/economy: Selected years, 1997–2012

6-44. USPTO patents granted in information processing technology, by region/country/economy: Selected years, 

1997–2012

6-45. USPTO patents granted in telecommunications technology, by region/country/economy: Selected years, 

1997–2012

6-46. USPTO patents granted in semiconductor technology, by region/country/economy: Selected years, 1997–2012

6-47. USPTO patents granted in computer systems technology, by region/country/economy: Selected years, 

1997–2012

6-48. USPTO patents granted in biotechnology, by region/country/economy: Selected years, 1997–2012

6-49. USPTO patents granted in pharmaceuticals technology, by region/country/economy: Selected years, 1997–2012
Science and Engineering Indicators 2014 t B-7

6-50. USPTO patents granted in medical electronics technology, by region/country/economy: Selected years, 

1997–2012

6-51. USPTO patents granted in medical equipment technology, by region/country/economy: Selected years, 

1997–2012

6-52. USPTO patents granted in automation and control technology, by region/country/economy: Selected years, 

1997–2012

6-53. USPTO patents granted in measuring techniques and instrumentation technology, by region/country/economy: 

Selected years, 1997–2012

6-54. Triadic patent families, by region/country/economy: 1999–2010

6-55. U.S. HT industries by number of firms and employees: 2010

6-56. U.S. venture capital investment, by financing stage and selected industry/technology: 1997–2012

6-57. USPTO patents granted in alternative energy and pollution-control technologies, by region/country/economy: 

Selected years, 1997–2012

6-58. USPTO patents granted in alternative energy technologies, by region/country/economy: Selected years, 

1997–2012

6-59. USPTO patents granted in energy storage technology, by region/country/economy: Selected years, 1997–2012

6-60. USPTO patents granted in smart grid technology, by region/country/economy: Selected years, 1997–2012

6-61. USPTO patents granted in pollution mitigation technologies, by region/country/economy: Selected years, 

1997–2012

6-62. USPTO patents granted in fuel cell technology, by region/country/economy: Selected years, 1997–2012

6-63. USPTO patents granted in bioenergy technology, by region/country/economy: Selected years, 1997–2012

6-64. USPTO patents granted in solar energy technology, by region/country/economy: Selected years, 1997–2012

6-65. USPTO patents granted in hybrid and electric vehicle technology, by region/country/economy: Selected years, 

1997–2012

6-66. USPTO patents granted in wind energy technology, by region/country/economy: Selected years, 1997–2012

6-67. USPTO patents granted in nuclear energy technology, by region/country/economy: Selected years, 1997–2012

6-68. USPTO patents granted in battery technology, by region/country/economy: Selected years, 1997–2012

6-69. USPTO patents granted in hydrogen production and storage technology, by region/country/economy: Selected 

years, 1997–2012

6-70. USPTO patents granted in cleaner coal technology, by region/country/economy: Selected years, 1997–2012

6-71. USPTO patents granted in capture and storage of carbon and other greenhouse gases technologies, by region/

country/economy: Selected years, 1997–2012

6-72. USPTO patents granted in air pollution control technology, by region/country/economy: Selected years, 

1997–2012

6-73. USPTO patents granted in solid waste control technology, by region/country/economy: Selected years, 

1997–2012

6-74. USPTO patents granted in water pollution control technology, by region/country/economy: Selected years, 

1997–2012

6-75. USPTO patents granted in recycling technology, by region/country/economy: Selected years, 1997–2012

Chapter 7. Science and Technology: Public Attitudes and Understanding

7-1. Public interest in selected issues: 1981–2012

7-2. Public interest in selected issues, by respondent characteristic: 2012

7-3. Primary source of information about current news events, by respondent characteristic: 2012

7-4. Primary source of information about science and technology, by respondent characteristic: 2012

7-5. Primary source of information about specific scientific issues, by respondent characteristic: 2012

7-6. Visitors to informal science and other cultural institutions: 1981–2012

7-7. Visitors to informal science and other cultural institutions, by respondent characteristic: 2012

7-8. Correct answers to trend factual knowledge of science questions, by respondent characteristic: 1992–2012 

7-9. Correct answers to factual knowledge questions in physical and biological sciences: 1988–2012

7-10. Correct answers to factual knowledge questions in physical and biological sciences, by respondent 

characteristic: 2012

7-11. Correct answers to scientific process questions: 1990–2012

7-12. Correct answers to scientific process questions, by respondent characteristic: 2012

7-13. Public assessment of astrology, by respondent characteristic: 1979–2012

7-14. Public self-assessment of knowledge about causes of environmental problems, by country/economy: 2010
B-8 t List of Appendix Tables

7-15. Public self-assessment of knowledge about solutions to environmental problems, by country/economy: 2010

7-16. Public assessment of benefits and harms of scientific research, by respondent characteristic: 2012

7-17. Public assessment of whether science and technology result in more opportunities for next generation, by 

respondent characteristic: 2012

7-18. Public assessment of whether science makes life change too fast, by respondent characteristic: 2012

7-19. Public assessment of whether we believe too often in science, and not enough in feelings and faith, by country/

economy: 1993, 2000, and 2010

7-20. Public assessment of whether modern science does more harm than good, by country/economy: 1993, 2000, 

and 2010

7-21. Public attitudes toward science and technology in the United States and Europe, by country/economy: 2011

7-22. Public opinion on whether federal government should fund basic scientific research: 1985–2012

7-23. Public opinion on whether federal government should fund basic scientific research, by respondent 

characteristic: 2012

7-24. Public assessment of government spending, by policy area: 1981–2012

7-25. Public confidence in institutional leaders, by type of institution: 2012–1973

7-26. Public self-assessment of knowledge of what scientists and engineers do day-to-day on their jobs, by respondent 

characteristic: 2012

7-27. Public opinion on science and engineering careers for one’s children, by respondent characteristic: 2012

7-28. Public perceptions of science and engineering occupations, by respondent characteristic: 1983, 2001, and 2012

7-29. Public assessment of degree to which certain fields and work activities are scientific: 2006 and 2012

7-30. Public concern about environmental issues, by country/economy: 2010

7-31. Public assessment of whether modern science will solve environmental problems with little change to our way 

of life, by country/economy: 1993, 2000, and 2010

7-32. Public assessment of danger to environment of river, lake, and stream pollution, by country/economy: 1993, 

2000, and 2010

7-33. Public assessment of danger to environment of air pollution from industry, by country/economy: 1993, 2000, 

and 2010

7-34. Public assessment of danger to environment of air pollution from cars, by country/economy: 1993, 2000, 

and 2010

7-35. Public assessment of danger to environment of pesticides and chemicals used in farming, by country/economy: 

1993, 2000, and 2010

7-36. Public assessment of danger to environment of climate change, by country/economy: 1993, 2000, and 2010

7-37. Public assessment of danger to environment of nuclear power stations, by country/economy: 1993, 2000, 

and 2010

7-38. Public assessment of danger to environment of modifying genes of crops, by country/economy: 2000 and 2010

Chapter 8. State Indicators

8-1. Average mathematics NAEP scores and achievement-level results for white students in grades 4 and 8 in public 

schools, by state: 2003 and 2011

8-2. Average science NAEP scores and achievement-level results for white students in grades 4 and 8 in public 

schools, by state: 2009 and 2011

8-3. Average mathematics NAEP scores and achievement-level results for black students in grades 4 and 8 in public 

schools, by state: 2003 and 2011

8-4. Average science NAEP scores and achievement-level results for black students in grades 4 and 8 in public 

schools, by state: 2009 and 2011

8-5. Average mathematics NAEP scores and achievement-level results for Hispanic students in grades 4 and 8 in 

public schools, by state: 2003 and 2011

8-6. Average science NAEP scores and achievement-level results for Hispanic students in grades 4 and 8 in public 

schools, by state: 2009 and 2011

8-7. Average mathematics NAEP scores and achievement-level results for Asian or Pacific Islander students in 

grades 4 and 8 in public schools, by state: 2003 and 2011

8-8. Average science NAEP scores and achievement-level results for Asian or Pacific Islander students in grades 4 

and 8 in public schools, by state: 2009 and 2011

8-9. Average mathematics NAEP scores and achievement-level results for female students in grades 4 and 8 in 

public schools, by state: 2003 and 2011
Science and Engineering Indicators 2014 t B-9

8-10. Average science NAEP scores and achievement-level results for female students in grades 4 and 8 in public 

schools, by state: 2009 and 2011

8-11. Average mathematics NAEP scores and achievement-level results for male students in grades 4 and 8 in public 

schools, by state: 2003 and 2011

8-12. Average science NAEP scores and achievement-level results for male students in grades 4 and 8 in public 

schools, by state: 2009 and 2011
A

Academia. See also College(s); University(ies)

applied research by, 4.15–4.16

basic research by, 4.4, 4.10, 4.15

development funding, 4.16

development performance, 4.16

doctoral researchers in, federal support of, 5.34–5.35

doctoral scientists and engineers in, 5.5–5.6, 5.23–5.35

R&D performance, international comparisons, 4.21

research article output/production, 5.37–5.40, 5.46–5.47, 5.53

underrepresented minorities in, 5.6, 5.27–5.28

women in, 5.6, 5.26–5.27

Academic employment. See Employment

Academic institution(s). See also College(s); University(ies)

high-performance computing systems, 5.23

Academic patenting. See Patenting

Academic research, O.15, 4.4. See also Academic Research and 

Development (Chapter 5)

funding for, O.19–O.20, 4.4, 4.41

international comparisons, 4.21

outputs, 5.6–5.7

pass-through funding for, O.14, 5.17–5.18

S&E fields and, O.19

Academic Research and Development (Chapter 5), 5.1–5.64

Advanced Placement (AP). See Elementary and Secondary 

Mathematics and Science Education (Chapter 1)

Advanced technology products (ATP)

U.S. trade in, 6.6, 6.34–6.36

Aeronautical/aerospace/astronautical engineering

race and ethnicity trends in, 3.46–3.47

women in, 3.43

Aerospace industry. See also Aeronautical/aerospace/astronautical 

engineering

advanced technology products in, U.S. trade in, 6.35–6.36

applied research in, 4.15–4.16

innovation activities, 6.39

patents in, 6.41

R&D, 4.23, 4.30

women in, 3.43

Africa. See also North Africa; specific country

R&D performance in, 4.17

sub-Saharan, public concern about climate change, 7.40

African American(s). See Black(s) or African American(s)

Age composition

of S&E labor force, 3.6, 3.40–3.42

of tenure status, among academically employed SEH doctorate 

holders, 5.25–5.26

of U.S.-trained academic S&E doctoral workforce, 5.6, 5.29

Agency(ies), federal. See also specific agency

research funding, by field, 4.38–4.39

support for academic R&D, 5.11

Agricultural science(s)

academic R&D in, 5.14–5.15

academic research equipment, federal funding, 5.21

doctoral degrees in, 2.36

occupational distribution of S&E highest degree holders in, by field 

of highest degree, 3.17

percentages of bachelor’s degrees in, O.17

research space at academic institutions, 5.19–5.20

Agriculture

in global marketplace, 6.8–6.9

R&D, federal funding for, 4.32–4.33

Agriculture, Department of (USDA)

EPSCoR and EPSCoR-like program budgets, 5.14

R&D expenditures, 4.5, 4.36

research funding, by field, 4.38–4.39

support for academic R&D, 5.11

technology transfer activities, 4.43–4.46

Aircraft and spacecraft. See also Aeronautical/aerospace/astronautical 

engineering

global trade in, 6.31, 6.33

innovation in, O.12, 6.39

U.S. production of, 6.28, 6.33

Air pollution. See also Pollution mitigation

public concern about, 7.39–7.40

Alabama. See also State Indicators (Chapter 8)

R&D performance in, 4.12–4.13

Alaska. See State Indicators (Chapter 8)

Algebra. See Elementary and Secondary Mathematics and Science 

Education (Chapter 1)

Alternative energy production

patents potentially applicable to, 5.52–5.54

public attitudes about, U.S. patterns and trends, 7.42–7.43

American Indian(s) or Alaska Native(s)

doctoral degrees awarded, 2.32–2.33

graduate enrollment, 2.28

K–12 students’ performance in mathematics and science, 1.4

master’s degrees earned, 2.30

on-time graduation from high school, 1.6, 1.38

and performance gaps in grades 4 and 8, 1.15–1.16

in S&E academic doctoral workforce, 5.27–5.28

in S&E labor force, O.17, 3.6, 3.45–3.46

American Recovery and Reinvestment Act of 2009 (ARRA)

and academic research equipment funding, 5.21

and investment in clean energy, 6.6, 6.52

and R&D, O.20, 4.9, 4.32–4.33, 5.5, 5.9, 5.21

and RD&D for clean energy technologies, 6.6, 6.52

Americas. See also specific country

recipients of U.S. S&E doctorates from, 2.35–2.36

Analytical Business Enterprise R&D, 4.29–4.30

AP. See Advanced Placement (AP)

Applied research

federal expenditures on, by field, 4.37–4.39

funding for, O.19, 4.4, 4.15–4.16

higher education expenditures on, 5.9

performers, 4.4, 4.15–4.16

public attitudes about, 7.5

Argentina, research article output/production, preferred collaboration 

partners, 5.44

Arizona. See State Indicators (Chapter 8)

Arkansas. See State Indicators (Chapter 8)

ARRA. See American Recovery and Reinvestment Act of 2009 

(ARRA)

Asia. See Asia-Pacific region

Asian(s) or Pacific Islander(s), 1.42

and Advanced Placement (AP), 1.26

doctoral degrees earned, 2.33

graduate enrollment, 2.28

master’s degrees earned, 2.30

math coursetaking by ninth graders, 1.22

on-time graduation from high school, 1.6, 1.38

and performance gaps in grades 4 and 8, 1.15–1.16

in S&E labor force, O.17, 3.6, 3.45–3.47, 5.27–5.28

science coursetaking by ninth graders, 1.23

Index

♦ I-1
I-2 ♦ Index

Asia-Pacific region. See also specific country; specific region

doctoral degrees awarded in, 2.41

first university degrees in S&E, 2.6, 2.38–2.39

foreign-born scientists and engineers from, 3.53

foreign direct investment in U.S. KTI industries, 6.38–6.39

high-technology manufacturing in, 6.26–6.27, 6.29

information and communication technology infrastructure in, 6.15–

6.16

KTI economic activity in, O.3–O.4, 6.29

patenting activity, by technology area, 6.42–6.44

public concern about climate change, 7.40

public confidence in science community’s leadership, 7.32

public views on cause of climate change, 7.41

public’s general attitudes about science in, 7.30

recipients of U.S. S&E doctorates from, 2.34

research article output/production, 5.37, 5.42, 5.44

researchers in workforce, 3.60

trade

 in high-technology goods, 6.32–6.34

 KTI, 6.29

 in R&D services, 6.32

as U.S. advanced technology product trading partner, 6.35

U.S. direct investment in, 6.38

U.S. MOFA R&D in, 4.5, 4.27–4.29

workers with S&E skills, O.8

Associate’s degree(s). See also Tertiary degree(s)

S&E, 2.25

Astronomy

academic R&D in, 5.15

academic research equipment, federal funding, 5.21

research article output/production, international collaboration in, 

5.41

Atmospheric science(s)

academic research equipment, federal funding, 5.21

Attitude(s), public. See Science and Technology: Public Attitudes and 

Understanding (Chapter 7)

Australia

commercial knowledge-intensive services, 6.25

commodity exports, 6.25

as destination for foreign students, 2.6

doctoral degrees awarded in, by sex, 2.41

economic integration with China, 6.25

first university degrees in S&E, 2.6, 2.39

foreign students in, O.9, 2.42–2.43

information and communication technology share of business and 

consumer spending, 6.17

KTI share of economy, 6.13–6.14

R&D performance in, 4.17

RD&D of clean energy and nuclear technologies in, 6.52–6.53

research article output/production, preferred collaboration partners, 

5.44

U.S. students in, as foreign students, 2.44

Austria

foreign students in, 2.43

public interest in S&T, 7.11–7.12

public visits to informal science and other cultural institutions, 7.19

B

Bachelor’s degree(s). See also Tertiary degree(s)

citizenship status and, 2.27

and employment involuntarily out of field, 3.31, 3.34

field distribution, 2.4, 2.6

institutions awarding, 2.4

international comparisons, O.8, 2.6

numbers, trends in, O.8, O.16–O.17, 2.4, 2.6, 3.10

racial and ethnic distributions, O.16–O.17, 2.4, 2.26–2.27

and salaries of recent graduates, 3.34

S&E, 2.25–2.27, 3.10

in S&E fields vs. non-S&E fields, O.16–O.17

by sex, 2.4, 2.25–2.26

Basic research

federal expenditures on, by field, 4.37–4.39

federal funding of, public attitudes about, 7.30

funding for, O.19, 4.4, 4.15

higher education expenditures on, 5.9

international comparisons, 4.21

performers, 4.4, 4.10, 4.15

Belgium

public attitudes about genetically modified crops, 7.5

R&D performed in, by affiliates of foreign MNCs, 4.30

Bibliometric data, 5.35–5.36. See also Literature, scientific and 

technical

Bioenergy

patenting activity, 6.53–6.55

patents potentially applicable to, identification of, 5.52–5.54

public RD&D expenditures in, 6.52–6.53

Bioengineering, race and ethnicity trends in, 3.45

Biofuels

investment in, venture capital, 6.51–6.52

patents potentially applicable to, identification of, 5.52–5.54

Biological science(s). See also Agricultural science(s); Biology; 

Biomedical science(s); Environmental science(s); Evolution

academic R&D in, 5.14–5.15

academic research equipment expenditures and funding, 5.21

doctoral degrees, 2.5, 2.34, 2.41

graduate enrollment in, 2.5

occupational distribution of S&E highest degree holders in, by field 

of highest degree, 3.17

percentages of bachelor’s degrees in, O.17

research article output/production, 5.39–5.41, 5.53

research space at academic institutions, 5.5, 5.19–5.21

S&E degree holders working in, by level and field of highest S&E 

degree, 3.17–3.19

women in, 3.43–3.44

Biology. See also Biological science(s)

Advanced Placement (AP), 1.5, 1.23–1.26

ninth graders’ coursetaking in, 1.4, 1.22–1.23

seen as scientific by public, 7.36–7.37

Biomedical engineering. See Engineering, biomedical

Biomedical science(s). See also Engineering, biomedical

doctorates in, employment, 3.39

research space at academic institutions, 5.5, 5.19–5.21

Biotechnology, patents granted in, 6.42

academic, 5.55

international comparisons, O.11, 6.44

Black(s) or African American(s)

and advanced placement (AP), 1.5, 1.26

doctoral degrees awarded, 2.32–2.33

graduate enrollment, 2.28

K–12 students’ performance in mathematics and science, 1.4

master’s degrees earned, 2.30

ninth graders, math coursetaking, 1.22

on-time graduation from high school, 1.6, 1.38

and performance gaps in grades 4 and 8, 1.15–1.16

in S&E labor force, O.17, 3.6, 3.45–3.47, 5.27–5.28

Blended learning. See also Online learning

definition of, 1.6

enrollment in, 1.6, 1.36–1.37

Brazil

commercial knowledge-intensive services, 6.21

expenditures on higher education, 2.37

high-technology manufacturing in, 6.26–6.27
Science and Engineering Indicators 2014 ♦ I-3

information and communication technology in, 6.16–6.17

intellectual property trade, O.13, 6.45

investment in clean energy technologies, 6.50

KTI economic activity in, O.3, 6.5, 6.7, 6.29

KTI share of economy, 6.14

labor productivity growth in, 6.18

R&D performance, O.6–O.7, 4.17

recipients of U.S. S&E doctorates from, 2.35–2.36

research article output/production, O.10, 5.6, 5.37, 5.43–5.44

researchers in, numbers of, 3.60

tertiary education attainment in, 2.38

trade

 KTI, 6.29

 in royalties and fees, O.13, 6.45

 in value-added indicators, 6.34

U.S. MOFA R&D in, 4.5, 4.27–4.29

Budget appropriations, federal government, for R&D

cross-national comparisons, 4.39

by national objectives, 4.31–4.33

total, 4.31–4.32

Budget authority/appropriations, 4.31, 4.33

Budget function(s), 4.31

Bulgaria, public attitudes about genetically modified food, 7.43

Bureau of Labor Statistics (BLS)

alternative measures of labor underutilization, 3.29–3.30

unemployment rate, 3.28

Business sector

applied research by, 4.4, 4.15–4.16

basic research by, 4.15

development funding, 4.16

development performance, 4.16

innovation activities, 6.39–6.40

R&D funding, O.19–O.20, 4.4, 4.21–4.25, 5.5, 5.13

R&D performance, 4.4–4.5, 4.7–4.9, 4.20–4.25, 4.29–4.30

S&E employment in, 3.5, 3.19–3.24. See also Industry(ies)

support for academic R&D, by institution type, 5.16

Business services

employment in, 6.23, 6.36

global trade in, 6.30

providers, 6.21–6.25

U.S. multinational companies in, 6.36

C

Calculus, Advanced Placement (AP). See Elementary and Secondary 

Mathematics and Science Education (Chapter 1)

California. See also State Indicators (Chapter 8)

R&D performance in, 4.12–4.13

Canada

doctoral degrees awarded in, by sex, 2.41

expenditures on higher education, 2.37–2.38

first university degrees in S&E, 2.6, 2.39

foreign students in, 2.43–2.44

information and communication technology in, 6.15–6.17

KTI share of economy, O.3, 6.13–6.14

public concern about climate change, 7.40

R&D performance in, 4.17

RD&D of clean energy and nuclear technologies in, 6.52–6.53

recipients of U.S. S&E doctorates from, 2.35–2.36

research article output/production, preferred collaboration partners, 

5.42, 5.44

as U.S. advanced technology product trading partner, 6.35

U.S. MOFA R&D in, 4.5, 4.27–4.29

U.S. students in, as foreign students, 2.44

Carbon dioxide (CO2

) capture and storage. See Carbon sequestration

Carbon sequestration

patents potentially applicable to, identification of, 5.52–5.54

public RD&D expenditures in, 6.52–6.53

Central America. See also specific country

R&D performance in, 4.17

Central Asia. See also specific country

R&D performance in, 4.17

Chemical industry

applied research in, 4.15–4.16

domestic R&D performance, 4.23

multinational companies in, R&D performed by affiliates of, 4.27, 

4.29

R&D funding, 4.23–4.25

Chemistry

Advanced Placement (AP), participation in, 1.23

ninth graders’ coursetaking in, 1.22

research article output/production, 5.40–5.41, 5.53

Chile, research article output/production, preferred collaboration 

partners, 5.43

China

and Australia, economic integration, 6.25

basic research in, 4.21–4.22

business R&D in, distribution by industry, 4.30

commercial knowledge-intensive services, 6.21

consumer market in, 6.26

currency exchange rate, 6.24

doctoral degrees awarded in, numbers, 2.6, 2.41

first university degrees in S&E, O.8–O.9, 2.6, 2.39

foreign-born scientists and engineers from, 3.53

foreign students from, 2.44

foreign students in, 2.42, 2.44

GDP per capita in, 6.20

as global provider of knowledge-intensive services, 6.5

high-technology manufacturing in, 6.5, 6.25–6.26, 6.29

information and communication technology in, 6.15–6.17

intellectual property trade, O.13, 6.45

investment in clean energy technologies, 6.6, 6.49–6.51

knowledge-intensive services exports, 6.5

KTI economic activity in, O.3–O.4, 6.5, 6.7, 6.14, 6.29

labor productivity growth in, 6.5, 6.18

multinational companies in, 6.25–6.26

in non-knowledge-intensive services industries, 6.8–6.9

in nonmanufacturing and nonservices industries, 6.9

patenting activity, O.11

pharmaceutical industry in, 6.25

public interest in S&T, 7.12

public perceptions of S&E occupations in, 7.34–7.35

public visits to informal science and other cultural institutions, 7.19

public’s general attitudes about science in, 7.30

R&D in, O.5–O.7, 4.4, 4.17, 4.20–4.22

R&D intensity, 4.5, 4.20

recipients of U.S. S&E doctorates from, 2.34, 2.41

research article output/production, O.10, 5.6, 5.35–5.37, 5.42–5.44

researchers in, O.7–O.8, 3.6, 3.59–3.60

semiconductor manufacturing in, 6.25

sources of S&T information used by public in, 7.18

students from, in United States, 2.5

tertiary education attainment in, 2.38

trade

 in commercial knowledge-intensive services, 6.30

 in high-technology goods, 6.5–6.6, 6.32–6.33

 KTI, 6.29

 in R&D services, 6.32

 in royalties and fees, O.13, 6.45

 in value-added indicators, 6.34

as U.S. advanced technology product trading partner, 6.35–6.36

U.S. MOFA R&D in, 4.5, 4.27–4.29

U.S. patents granted to, 6.40–6.41
I-4 ♦ Index

U.S. students in, as foreign students, 2.44

value added for manufacturing industries, 6.9

workers with S&E skills, O.7–O.8

Civil engineering

academic research equipment, federal funding, 5.22

race and ethnicity trends in, 3.46–3.47

Clean energy technology(ies). See also Energy; Industry, Technology, 

and the Global Marketplace (Chapter 6)

innovation in, 6.6

investment in, 6.6, 6.49–6.55

patenting activity, 6.53–6.55

patents potentially applicable to, 5.52–5.54

RD&D, public expenditures for, 6.52–6.53

Climate change

public attitudes about, 7.5, 7.40–7.41

public policy on, influence of scientific experts on, public 

assessment of, 7.37

Collaboration. See also Overview

in academia, 5.17–5.18, 5.46–5.47

cross-institution, O.13–O.14, 5.7, 5.40

cross-national, O.13–O.14, 5.7, 5.40–5.47

cross-sector, O.13–O.15, 5.46–5.47

interinstitutional, O.13–O.14

international, O.10, 5.7, 5.40–5.47

in research publications, O.10, O.13–O.15, 5.7, 5.40–5.47

in transnational higher education, 2.42

in United States, O.13–O.15, 5.46–5.47

College(s). See also Academia; Community college(s)

institutional funding for S&E academic R&D, 5.11

patents granted to, 5.54–5.55

support for academic R&D, 5.16

College enrollment. See Postsecondary education, enrollment

Colorado. See State Indicators (Chapter 8)

Commerce, Department of (DOC)

R&D expenditures, 4.5, 4.36

research funding, by field, 4.38–4.39

technology transfer activities, 4.43–4.46

Commercialization

of academic patents, 5.55–5.57

of federal R&D, 4.5, 4.39–4.46

of patents, 6.40

Commercial knowledge-intensive services. See Knowledge-intensive 

services

Common Core State Standards (CCSS), 1.20

Communications equipment/equipment manufacturing

business R&D for, cross-national comparisons, 4.29–4.30

employment in, by U.S. multinational companies, 6.38

global trade in, 6.30–6.31

innovation in, O.12, 6.39

patents in, 6.41

U.S. direct investment abroad in, 6.38

U.S. multinational companies in, 6.37–6.38

Community college(s). See also Postsecondary institution(s)

attendance, among S&E degree recipients, 2.4, 2.9–2.11, O.15

revenues and expenditures, 2.14–2.15

Computer(s)

global trade in, 6.31

innovation in, O.12, 6.39

patents in, 6.41

Computer and electronics manufacturing

advanced technology products in, U.S. trade in, 6.35–6.36

domestic R&D performance, 4.23

employment in, by U.S. multinational companies, 6.38

multinational companies in, R&D performed by affiliates of, 4.27, 

4.29

R&D funding, 4.23

U.S., foreign direct investment in, 6.6, 6.38–6.39

U.S. multinational companies in, 6.37–6.38

U.S. overseas investment in, 6.6

Computer industry. See Computer and electronics manufacturing

Computer programming

employment projections for, 3.12

growth, 6.23

seen as scientific by public, U.S. patterns and trends, 7.36

Computer science(s)

academic R&D in, 5.13–5.15

Advanced Placement (AP), 1.5, 1.23, 1.26

age distribution of degree holders in, 3.41–3.42

doctoral degrees in, 2.5

employment projections for, 3.11–3.12

graduate enrollment in, unemployment and, 2.27

graduate students in, 2.5

master’s degrees in, 2.5, 2.29

occupational distribution of S&E highest degree holders in, by field 

of highest degree, 3.17

percentages of bachelor’s degrees in, O.17

race and ethnicity trends in, 3.45–3.47

R&D activity in, 3.25

research, federal spending on, 4.37–4.39

research article output/production, international collaboration in, 

5.41

research space at academic institutions, 5.19–5.20

salaries in, 3.49

S&E degree holders working in, by level and field of highest S&E 

degree, 3.17, 3.19

women in, 3.43–3.44

Computer services

business R&D, industry share, cross-national comparisons, 4.30

global trade in, 6.30

Computer support specialists, race and ethnicity trends in, 3.46

Connecticut. See State Indicators (Chapter 8)

Conservation science(s), race and ethnicity trends in, 3.45

Cyberinfrastructure, for academic R&D. See Academic Research and 

Development (Chapter 5)

Cyprus, foreign students from, 2.40

Czech Republic

public concern about climate change, 7.40

public interest in S&T, 7.11–7.12

public’s general attitudes about science in, 7.30

research article output/production, preferred collaboration partners, 

5.44

and study-abroad programs, 2.40

D

Defense, Department of (DOD)

EPSCoR and EPSCoR-like program budgets, 5.14

R&D expenditures, O.19, 4.5, 4.32, 4.34

research funding, by field, 4.38–4.39

S&E employment in, 3.23

support for academic R&D, 5.11, 5.15

support for graduate students, 2.18–2.19

technology transfer activities, 4.43–4.46

Defense industry, R&D. See also Aerospace industry

federal funding for, 4.5, 4.32

government support for, cross-national comparisons, 4.39

industry share, cross-national comparisons, 4.30

Degree(s)

associate’s, S&E, 2.25

bachelor’s. See Bachelor’s degree(s)

doctoral. See Doctoral degree(s)

dual, in transnational higher education, 2.42

foreign-born workers with, O.17–O.19
Science and Engineering Indicators 2014 ♦ I-5

and freshmen’s intentions to major in S&E, 2.20–2.23

institutions awarding, 2.4, 2.7–2.12

international comparisons, O.8–O.9

joint, in transnational higher education, 2.42

master’s. See Master’s degree(s)

in S&E, O.8–O.9, O.15–O.19, 2.4, 2.7–2.12, 2.20–2.24

undergraduate, 2.24–2.27

U.S. trends in, O.15–O.17

Delaware. See also State Indicators (Chapter 8)

R&D performance in, 4.12–4.13

Demographics

of college-age populations, international comparisons, 2.42

and K–12 students’ performance disparities in mathematics and 

science, 1.4

of postdocs in academic employment, 5.31

and salary gap, 3.51

of U.S. S&E labor force, O.17–O.19, 3.6

of U.S.-trained S&E doctorate holders, 5.6

Denmark

public attitudes about genetically modified food, 7.43

public interest in S&T, 7.11–7.12

public visits to informal science and other cultural institutions, 7.19

public’s general attitudes about science in, 7.30

R&D intensity, 4.4, 4.18

R&D performance, O.5

researchers in workforce, 3.60

sources of S&T information used by public in, 7.17

Developing country(ies)/economy(ies), 5.35

commercial knowledge-intensive services, 6.21

GDP per capita in, 6.20

high-technology manufacturing in, 6.25–6.26

information and communication technology infrastructure, 6.15–

6.16

intellectual property trade, O.13, 6.45

investment in clean energy technologies, 6.49–6.50

knowledge-intensive services exports, 6.5

KTI economic activity in, O.3–O.4, 6.5, 6.7–6.9, 6.29

labor productivity growth in, 6.5, 6.18

patenting activity, O.11, 6.40–6.41

R&D performance and expenditures in, 4.17

research article output/production, O.10, 5.36–5.37

trade

 in commercial knowledge-intensive services, 6.30

 in high-technology goods, 6.32–6.33

 KTI, 6.29

 in royalties and fees, O.13, 6.45

U.S. patents granted to, 6.40–6.41

World Bank classification of, 6.9

Development

as component of U.S. R&D, 4.4, 4.16

higher education expenditures on, 5.9

DHS. See Homeland Security, Department of (DHS)

Digital education, 1.34

Digital learning, 1.34

Disability(ies)

and doctoral degrees awarded, 2.32

and graduate enrollment, 2.28

and undergraduate enrollment, 2.24

Discipline-based education research, 2.23

Discovery(ies), scientific, public interest in, 7.4, 7.10–7.11

Distance education. See also Online learning

access to, 1.6

in higher education, 2.12

for K–12 students, 1.36–1.37

District of Columbia. See also State Indicators (Chapter 8)

R&D performance in, 4.12–4.13

DOC. See Commerce, Department of (DOC)

Doctoral degree(s)

by citizenship status, 2.33

by disability status, 2.32

and employment involuntarily out of field, 3.31, 3.34

foreign students earning, 2.5, 2.33–2.37, 3.6

institutions awarding, 2.4

international comparisons, 2.6, 2.41

non-S&E, 2.34

numbers, trends in, O.16, 2.5, 2.31, 3.10

by race or ethnicity, 2.32–2.33

and salaries of recent graduates, 3.34

S&E, 2.31–2.37, 2.41, 3.10

by sex, 2.32

time to completion, 2.31–2.32

Doctoral scientists and engineers, in academia. See Academic 

Research and Development (Chapter 5)

DOD. See Defense, Department of (DOD)

DOE. See Energy, Department of (DOE)

DOI. See Interior, Department of (DOI)

DOT. See Transportation, Department of (DOT)

Drug(s). See Pharmaceutical(s)/pharmaceutical industry

E

Earth science

ninth graders’ coursetaking in, 1.4, 1.22–1.23

race and ethnicity trends in, 3.45

East Asia

KTI economic activity in, O.3–O.4

R&D performance, O.5–O.6, 4.4, 4.17

research article output/production, O.10

researchers in workforce, 3.60

Economic downturn. See Recession, global

Economics

doctoral degrees in, foreign students earning, 2.5

master’s degrees in, foreign students earning, 2.5

seen as scientific by public, 7.36–7.37

women in, 3.43–3.44

Economy(ies). See also Overview

developed

 investment in clean energy technologies, 6.51

 KTI share of, 6.13–6.14

developing, KTI share of, 6.14

global. See also Developed country(ies); Developing country(ies)

 knowledge-intensive services in, O.3–O.4, 6.5

 KTI industries in, O.3–O.4, 6.5, 6.10–6.20

knowledge-intensive, O.3–O.4

KTI share of, 6.13–6.14

science and technology in, state indicators, 8.116–8.129

S&E labor force in, 3.5, 3.19–3.24

U.S., KTI industries in, 6.5

ED. See Education, Department of (ED)

Education. See also Precollege education

in engineering, public attitudes about, 7.46

federal funding of, public attitudes about, 7.4

in mathematics, public attitudes about, 7.46

parental, and ninth graders’ coursetaking, 1.4, 1.22–1.23

public attitudes about, 7.46

R&D, federal funding for, 4.32–4.33

S&E employment in, 3.5, 3.19, 3.21, 3.23

in science, public attitudes about, 7.46

Education, Department of (ED)

Beginning Teacher Longitudinal Study (BTLS), 1.26, 1.33–1.34

National Education Technology Plan (NETP), 1.34

R&D expenditures, 4.36

Educational services
I-6 ♦ Index

S&E employment in, 3.24

worldwide distribution of, 6.20–6.21

Electrical engineering, women in, 3.43

Electrical equipment, appliances, and components manufacturing, 

foreign multinational companies in, R&D performed by U.S. 

affiliates of, 4.27

Electronic product manufacturing. See Computer and electronics 

manufacturing

Electronics. See also Medical electronics

global trade in, 6.33

Elementary and secondary education in S&E. See also Elementary 

and Secondary Mathematics and Science Education 

(Chapter 1)

state indicators, 8.12–8.41

Elementary and Secondary Mathematics and Science Education 

(Chapter 1), 1.1–1.53

Elementary school(s), 1.45

Employment. See also Labor force; Unemployment

academic, O.15, 3.21

of biomedical sciences doctorates, 3.39

in business sector, 3.5, 3.19, 3.21, 3.23

in business services, 6.23, 6.36

in commercial knowledge-intensive services, 6.21

in education, 3.5, 3.19–3.21

employer size and, 3.5, 3.23–3.24

in financial services, 6.36

geographic distribution, differences, 3.5, 3.24

global recession and, 6.27–6.28

globalized, in knowledge-intensive services, 6.36

in government, 3.5, 3.19, 3.21, 3.23–3.24

growth of, 3.5

by highest degree, 3.5

in high-technology manufacturing, 6.26–6.28, 6.37–6.38

in industry, 3.5

in information services, 6.36

involuntarily out of field, 3.31, 3.34

in knowledge-intensive services, 6.36

in KTI industries, 6.5

male-female gap in, credentials and, 3.44–3.46

in manufacturing sector, 6.28

metropolitan areas and, 3.5, 3.24

of postdocs, 3.37–3.40

projections for, 3.5

R&D, abroad, by U.S. companies, 3.61

salaries/earnings for, 3.5, 3.32–3.33

in S&E occupations, 3.5, 3.19–3.24, 3.32–3.33, 3.44–3.46

small firms and, 3.5, 3.23–3.24

trends in, 3.5

U.S., 6.21, 6.23, 6.26–6.28

by U.S. multinational companies, 6.36–6.38

Employment sector(s), of S&E workforce, 3.5, 3.19, 3.21, 3.23

Energy. See also Alternative energy; Clean energy technology(ies); 

Fossil fuel(s); Fuel cell(s); Nuclear energy; Solar energy; 

Wind energy

conservation, patents potentially applicable to, identification of, 

5.52–5.54

management, patents potentially applicable to, identification of, 

5.52–5.54

public attitudes about, U.S. patterns and trends, 7.42–7.43

R&D, federal funding for, 4.32–4.33

Energy, Department of (DOE)

EPSCoR and EPSCoR-like program budgets, 5.14

R&D expenditures, 4.5, 4.32–4.36

research funding, by field, 4.38–4.39

S&E employment in, 3.23

support for academic R&D, 5.11, 5.15

technology transfer activities, 4.43–4.46

Energy efficiency

patents potentially applicable to, identification of, 5.52–5.54

public RD&D expenditures in, 6.52–6.53

Energy smart and efficiency technologies, investment in, venture 

capital, 6.51–6.52

Energy storage

patenting activity, 6.53–6.55

patents potentially applicable to, 5.52–5.54

Engineer(s)

employment projections for, 3.12

influence on public issues, public assessment of, 7.37

public attitudes about, 7.5

public confidence in, international comparisons, 7.32

public perceptions of, 7.32–7.35

Engineering. See also Biotechnology; Higher Education in Science 

and Engineering (Chapter 2); Science and Engineering Labor 

Force (Chapter 3); specific field

academic R&D in, 5.13–5.15

academic research equipment expenditures and funding, 5.21

aeronautical/aerospace/astronautical. See Aeronautical/aerospace/

astronautical engineering

bachelor’s degrees in, O.17, 2.6

biomedical, race and ethnicity trends in, 3.45–3.47

civil. See Civil engineering

degrees in, by sex, 2.21

doctoral degrees, 2.5, 2.34–2.37, 2.41

doctorate holders, tenure status, 5.25

and employment involuntarily out of field, 3.31

first university degrees in, international comparisons, 2.39

foreign students in, 2.5

freshmen intending to major in, 2.21–2.22

graduate enrollment in, 2.5, 2.27

master’s degrees in, 2.5, 2.29

mechanical, women in, 3.43

occupational distribution of S&E highest degree holders in, by field 

of highest degree, 3.17

research, federal spending on, 4.37–4.39

research article output/production, 5.40

 citations in U.S. patents, 5.53

research space at academic institutions, 5.19–5.20

salaries in, 3.49–3.51

S&E degree holders working in, by level and field of highest S&E 

degree, 3.17–3.19

seen as scientific by public, U.S. patterns and trends, 7.36

undergraduate degrees in, international comparisons, 2.39

unemployment and, 2.27

women in, 3.43–3.44

Enrollment(s). See also Higher education in Science and Engineering 

(Chapter 2); Postsecondary education, enrollment

Environment. See also Air pollution; Climate change

public concern about, 7.5, 7.38–7.40

Environmental Protection Agency, U.S. (EPA)

EPSCoR and EPSCoR-like program budgets, 5.14

R&D expenditures, 4.36

S&E employment in, 3.23

Environmental quality, public concern about, 7.38

Environmental science(s)

academic R&D in, 5.13–5.15

Advanced Placement (AP), 1.5, 1.24, 1.26

ninth graders’ coursetaking in, 1.4

occupational distribution of S&E highest degree holders in, by field 

of highest degree, 3.17

research, federal spending on, 4.37–4.39

S&E degree holders working in, by level and field of highest S&E 

degree, 3.17, 3.19
Science and Engineering Indicators 2014 ♦ I-7

EPA. See Environmental Protection Agency, U.S. (EPA)

EPO. See European Patent Office (EPO)

EPSCoR. See Experimental Program to Stimulate Competitive 

Research (EPSCoR)

EU. See European Union (EU)

Europe. See also European Union (EU); specific country

first university degrees in S&E, O.8–O.9, 2.38–2.39

public attitudes about nuclear energy, 7.43

public concern about climate change, 7.40

public confidence in science community’s leadership, 7.32

public interest in S&T, 7.11–7.12

public views on cause of climate change, 7.41

public visits to informal science and other cultural institutions, 7.19

public’s general attitudes about science in, 7.28–7.30

R&D performance, 4.17, 4.30

recipients of U.S. S&E doctorates from, 2.34–2.35

sources of S&T information used by public in, 7.17–7.18

trade, in R&D services, 6.32

U.S. MOFA R&D in, 4.5, 4.27–4.29

European Patent Office (EPO), 6.43

academic share of patents granted by, 5.54

European Union (EU)

commercial knowledge-intensive services, 6.21–6.25

currency exchange rate, 6.24

doctoral degrees awarded in, 2.41

first university degrees in S&E, O.8–O.9

foreign direct investment in U.S. KTI industries, 6.39

as global provider of knowledge-intensive services, 6.5

high-technology manufacturing in, 6.26–6.27

information and communication technology in, 6.15, 6.17

intellectual property trade, O.13, 6.45

investment in clean energy technologies, 6.6, 6.51

knowledge-intensive services exports, 6.5

KTI share of economies, O.3–O.4, 6.5, 6.13–6.14, 6.29

labor productivity growth in, 6.20

in non-knowledge-intensive services industries, 6.8–6.9

in nonmanufacturing and nonservices industries, 6.9

number of researchers in, O.8

patenting activity, O.10–O.12, 6.40–6.41

 in clean energy and pollution control, 6.53–6.55

 by technology area, 6.42–6.44

pharmaceutical industry in, 6.25

public attitudes

 about animal research, 7.46

 about nanotechnology, 7.44

public interest in S&T, 7.12

R&D performance, O.5–O.6, 4.4, 4.17–4.18

RD&D of clean energy and nuclear technologies in, 6.52–6.53

research article output/production, O.10, 5.6, 5.36–5.37, 5.42

researchers in, numbers of, 3.6, 3.60

trade

 in commercial knowledge-intensive services, 6.30–6.31

 in high-technology goods, 6.31–6.33

 KTI, 6.29

 in royalties and fees, O.13, 6.45

as U.S. advanced technology product trading partner, 6.35–6.36

U.S. direct investment in, 6.38

U.S. patents granted to, 6.40–6.41

value added for manufacturing industries, 6.9

Evolution

public understanding of, 7.21–7.23

teaching about, in schools, public attitudes about, 7.45

Experimental Program to Stimulate Competitive Research (EPSCoR), 

5.11, 5.14

Exports. See Trade

F

Faculty. See also Academic Research and Development (Chapter 

5); Non-tenure-track faculty positions; Tenure-track faculty 

positions; specific faculty

Federal funding

for academic R&D, 5.5, 5.10–5.11, 5.15–5.16

for academic research equipment, 5.21–5.22

for applied research, 4.15–4.16, 4.37–4.39, 5.10–5.11

for basic research, 4.15, 4.37–4.39, 5.10–5.11

for development, 4.16

for doctoral researchers in academia, 5.34–5.35

for domestic business R&D performance, 4.22–4.23

and gap between performer- and source-reported expenditures, 

4.34–4.35

for graduate students, 2.5, 2.17–2.19

obligations, 4.33–4.34

priorities, public attitudes about, 7.4

for public universities, 2.4

for R&D, O.19–O.20, 4.4–4.5, 4.22–4.23, 4.31–4.39

for research, by field, 4.37–4.39

for S&E graduate students, 2.5

for scientific research, public attitudes about, 7.4, 7.30–7.32

in various policy areas, public assessment of, 7.31

Federal government, U.S.

applied research by, 4.15–4.16

basic research by, 4.15

and commercialization of federal R&D, 4.5, 4.39–4.46

development performance, 4.16

promotion of technology transfer, 4.5, 4.39–4.46

R&D expenditures, by agency, 4.33–4.37

R&D performance, 4.31–4.39

research article output/production, 5.37–5.40

 citations in U.S. patents, 5.53

 collaboration patterns, 5.46–5.47

R&E tax credit, 4.23–4.24

S&E workforce employed in, 3.5, 3.19, 3.21, 3.23–3.24

support for R&D. See also Federal funding

 direct and indirect, 4.23–4.24

Federally funded research and development center(s) (FFRDC[s])

R&D performance, 4.10–4.11, 4.16

research article output/production, 5.37–5.40

 citations in U.S. patents, 5.53

 collaboration patterns, 5.46–5.47

Fellowships. See Financial aid

FFRDC(s). See Federally funded research and development center(s) 

(FFRDC[s])

Fields of research and practical activities, seen as scientific by public

international comparisons, 7.37

U.S. patterns and trends, 7.35–7.37

Fields of science and engineering. See specific field

Finance, global trade in, 6.30

Financial aid

fellowships, 2.17–2.19

for graduate education, 2.5, 2.17–2.19

postsecondary, 2.5, 2.17–2.19

for undergraduate education, 2.15–2.16

Financial services

employment in, globalized, 6.36

global trade in, 6.31

U.S. direct investment abroad in, 6.38

U.S. multinational companies in, 6.36

Finland

foreign students in, 2.40

R&D intensity, 4.4, 4.18

R&D performance, O.5–O.6
I-8 ♦ Index

researchers in workforce, 3.60

Florida. See State Indicators (Chapter 8)

Foreign-born scientists and engineers

age distribution of, 3.53

characteristics of, 3.52–3.53

from China, O.19

countries of origin, 3.53

educational levels of, 3.52–3.53

highest degree levels of, 3.52–3.53

from India, O.19

occupational fields of, 3.52–3.53

reasons for migration, 3.53–3.54

in S&E labor force, O.17–O.19, 3.6

sex distribution of, 3.53

source of education, 3.53–3.54

in U.S. economy, 3.51–3.52

Foreign direct investment

in KTI industries, 6.38–6.39

in R&D, 4.25–4.26

in U.S. high-technology industries, 6.6

in U.S. KTI industries, 6.6

Foreign students. See also Higher Education in Science and 

Engineering (Chapter 2); Internationally mobile students

Forestry, race and ethnicity trends in, 3.45–3.47

France

basic research in, 4.21–4.22

business R&D in, distribution by industry, 4.30

defense R&D in, distribution by industry, 4.30

as destination for foreign students, 2.6

first university degrees in S&E, 2.6, 2.39

foreign students from, 2.44

foreign students in, O.9, 2.40, 2.42, 2.44

government R&D support, by socioeconomic objectives, 4.39

information and communication technology infrastructure in, 6.15

multinational companies based in, R&D performed by U.S. 

affiliates of, 4.26–4.27

public interest in S&T, 7.11–7.12

public visits to informal science and other cultural institutions, 7.19

R&D in, O.5, O.7, 4.17–4.18, 4.20–4.22

R&D intensity, 4.4, 4.20

recipients of U.S. S&E doctorates from, 2.34–2.35

research article output/production, 5.37, 5.42, 5.44

and study-abroad programs, 2.40

U.S. students in, as foreign students, 2.44

Free/reduced-price lunch, eligibility for, concentrations of, as 

economic indicator, 1.27

Fuel cell(s), public RD&D expenditures in, 6.52–6.53

Fukushima accident, effect on public opinion, 7.42

Funding. See also Federal funding; Overview

from abroad, for R&D, 4.21, 4.24–4.25

for higher education, international comparisons, 2.37–2.38

for new construction of research space at academic institutions, 

5.20

pass-through, O.14, 5.17–5.18

R&D, O.19–O.20, 4.21

G

GDP. See Gross domestic product (GDP)

Gender. See also Women

and factual knowledge of S&T, 7.4, 7.21–7.22

and underrepresented minorities in academic S&E doctoral 

employment, 5.27

General science, R&D, federal funding for, 4.32–4.33

Geology, race and ethnicity trends in, 3.45

Georgia. See State Indicators (Chapter 8)

Geoscience(s), research article output/production

citations in U.S. patents, 5.53

international collaboration in, 5.41

Geothermal energy, patents potentially applicable to, identification of, 

5.52–5.54

Germany

business R&D in, distribution by industry, 4.29–4.30

as destination for foreign students, 2.6

doctoral degrees awarded in, 2.6, 2.41

first university degrees in S&E, O.8–O.9, 2.6, 2.39

foreign students from, 2.44

foreign students in, O.9, 2.40, 2.42

government R&D support, by socioeconomic objectives, 4.39

high-skill emigrants in, 3.58

information and communication technology infrastructure in, 6.15

investment in clean energy technologies, 6.51

KTI economic activity in, O.4

multinational companies based in, R&D performed by U.S. 

affiliates of, 4.26–4.29

public interest in S&T, 7.11–7.12

public visits to informal science and other cultural institutions, 7.19

public’s general attitudes about science in, 7.30

R&D in, O.7, 4.17–4.18, 4.20–4.21

R&D intensity, 4.4, 4.18–4.20

recipients of U.S. S&E doctorates from, 2.34–2.35

research article output/production, 5.37, 5.42, 5.44

and study-abroad programs, 2.40

U.S. students in, as foreign students, 2.44

Global recession. See Recession, global

Global trade. See Trade

Global warming

causes of, public understanding of, 7.40–7.41

public concern about, 7.5, 7.40–7.41

Globalization. See also Trade

of commercial knowledge-intensive services, 6.7

data classification systems, 6.10–6.12

of high-technology manufacturing, 6.7

indicators, 6.5–6.6, 6.29

Government. See also Federal government, U.S.

R&D funding, international comparisons, 4.21

R&D performance, international comparisons, 4.21

R&D priorities, cross-national comparisons, 4.39

S&E employment in, 3.5, 3.19, 3.21, 3.23–3.24

state/local. See State/local government

Graduate education. See Higher Education in Science and 

Engineering (Chapter 2)

Graduate students. See Higher Education in Science and Engineering 

(Chapter 2)

Greece, recipients of U.S. S&E doctorates from, 2.34–2.35

Gross domestic product (GDP)

growth, U.S. R&D expenditures and, 4.7

per capita, in developing economies, 6.20

H

Hawaii. See State Indicators (Chapter 8)

HBCUs. See Historically black colleges and universities

Health and clinical science(s). See also Biomedical science(s); 

Medical and health sciences; Pharmaceutical(s)/

pharmaceutical industry

research space at academic institutions, 5.19–5.21

Health and environment objective, government R&D support for, 

cross-national comparisons, 4.39

Health and Human Services, Department of (HHS)

R&D expenditures, 4.5, 4.34

research funding, by field, 4.37–4.39

support for academic R&D, 5.11, 5.15

technology transfer activities, 4.43–4.46
Science and Engineering Indicators 2014 ♦ I-9

Health care practitioner(s), employment projections for, 3.12

Health-related occupations, women in, 3.44

Health-related R&D, federal funding for, 4.32–4.33

Health-related technology(ies), patents granted in, 6.42

Health services, worldwide distribution of, 6.20–6.21

HHEs. See High Hispanic enrollment institutions

HHS. See Health and Human Services, Department of (HHS)

Higher education. See also Higher Education in Science and 

Engineering (Chapter 2); Overview

international comparisons, O.8–O.9, 2.6, 2.37–2.44

organization, in United States, O.15, 2.7–2.20

revenues and expenditures, O.15–O.16, 2.4, 2.12–2.15

state indicators, 8.42–8.75

transition to, among high school graduates, 1.6–1.7

Higher Education in Science and Engineering (Chapter 2), 2.1–2.51

Higher Education Research and Development Survey (HERD), 5.9

High Hispanic enrollment institutions, 2.8–2.9

High-performance computing, for academic R&D, 5.23

High school(s). See Elementary and Secondary Mathematics and 

Science Education (Chapter 1)

High-skill migration, worldwide, 3.58

High technology. See also Knowledge- and technology-intensive 

(KTI) industry(ies); Knowledge-intensive services

exports, 6.5–6.6

foreign direct investment in, 6.6

product innovation, 6.6

High-technology manufacturing. See Industry, Technology, and the 

Global Marketplace (Chapter 6)

High-technology services, U.S. small businesses in, 6.47

Hispanics

and Advanced Placement (AP), 1.5, 1.26

doctoral degrees awarded, 2.32–2.33

graduate enrollment, 2.28

K–12 students’ performance in mathematics and science, 1.4

master’s degrees earned, 2.30

ninth graders, math coursetaking, 1.22

on-time graduation from high school, 1.6, 1.38

and performance gaps in grades 4 and 8, 1.15–1.16

in S&E labor force, O.17, 3.6, 3.45–3.47, 5.27–5.28

Historically black colleges and universities, 2.8–2.9

Homeland Security, Department of (DHS), R&D expenditures, 4.5, 

4.36

Hong Kong, grades 4 and 8 students’ TIMSS test scores in, 1.4

Hungary

public interest in S&T, 7.12

and study-abroad programs, 2.40

Hydrogen technology

public attitudes about, U.S. patterns and trends, 7.42–7.43

public RD&D expenditures in, 6.52–6.53

I

ICT. See Information and communication technology

Idaho. See State Indicators (Chapter 8)

Illinois. See also State Indicators (Chapter 8)

R&D performance in, 4.12–4.13

Immigration. See also Stay rate(s)

by high-skill workers, 3.58

and S&E workforce, 3.51–3.52

Imports. See Trade

India

commercial knowledge-intensive services, 6.21

doctoral degrees awarded in, 2.41

foreign-born scientists and engineers from, 3.53

foreign students from, 2.44

high-skill emigrants in, 3.58

high-technology manufacturing in, 6.26–6.27

information and communication technology in, 6.15–6.17

investment in clean energy technologies, 6.50

knowledge-intensive services exports, 6.5

KTI economic activity in, 6.5, 6.7, 6.14, 6.29

labor productivity growth in, 6.5, 6.18

patenting activity, O.11

R&D performance, O.5–O.7, 4.4, 4.17

recipients of U.S. S&E doctorates from, 2.34

research article output/production, O.10, 5.6, 5.37

researchers in, numbers of, 3.60

sources of science and technology information used by public in, 

7.18

trade

 in commercial knowledge-intensive services, 6.30

 in high-technology goods, 6.33

 KTI, 6.29

 in R&D services, 6.32

 in royalties and fees, 6.45

 in value-added indicators, 6.34

U.S. MOFA R&D in, 4.5, 4.27–4.29

U.S. patents granted to, 6.40–6.41

Indiana. See State Indicators (Chapter 8)

Indians, American. See American Indian(s) or Alaska Native(s)

Indonesia

commercial knowledge-intensive services, 6.21–6.22

foreign students from, 2.44

information and communication technology in, 6.15–6.17

investment in clean energy technologies, 6.50

KTI economic activity in, 6.7, 6.14, 6.29

labor productivity growth in, 6.18

trade

 KTI, 6.29

 in value-added indicators, 6.34

Industry(ies). See also Industry, Technology, and the Global 

Marketplace (Chapter 6); Knowledge- and technologyintensive (KTI) industry(ies); specific industry

applied research in, 4.15–4.16

domestic R&D performance, 4.23

high-technology, 8.10

nonmanufacturing

 domestic R&D performance, 4.23

 patents in, 6.41–6.42

research article output/production, 5.37–5.40

 citations in U.S. patents, 5.53

 collaboration patterns, 5.46–5.47

S&E employment in, 3.5, 3.24

that are not knowledge or technology intensive, 6.8–6.9

Industry, Technology, and the Global Marketplace (Chapter 6), 

6.1–6.60

Information and communication technology. See Industry, 

Technology, and the Global Marketplace (Chapter 6)

Information processing and networking, patents granted in, 6.42

international comparisons, 6.43

Information science

age distribution of degree holders in, 3.42

research space at academic institutions, 5.19–5.20

Information security analysts, race and ethnicity trends in, 3.46

Information services

domestic R&D performance, 4.23

employment in, globalized, 6.36

global trade in, 6.30

multinational companies in, R&D performed by affiliates of, 4.27, 

4.29

R&D funding, 4.23

S&E employment in, 3.24

U.S. direct investment abroad in, 6.38
I-10 ♦ Index

U.S. multinational companies in, 6.36

Innovation, 6.7, 6.39–6.49. See also Industry, Technology, and 

the Global Marketplace (Chapter 6); Overview; Patent(s); 

Technology transfer

Instructional technology, 1.6, 1.34–1.38. See also Distance education; 

Internet access; Online learning

Intellectual property. See also Patent(s)

trade, O.12–O.13, 6.45; see also specific country

Interior, Department of (DOI), R&D expenditures, 4.36

International comparisons. See also Research and Development: 

National Trends and International Comparisons (Chapter 4)

of educational achievement, 2.6

 in grades 4 and 8, 1.17–1.19

of enrollments, 1.40

of first-time enrollment in university-level education, 1.7, 1.40

of first university degrees in S&E, O.8–O.9, 2.6, 2.38–2.39

of high school graduation rates, 1.7, 1.39

of K–12 students’ TIMSS test scores, 1.4, 1.17–1.19

of KTI industries, O.3–O.4

of postsecondary degrees, 2.6

of public acceptance/rejection of pseudoscience, 7.26

of public assessment of government spending on scientific research, 

7.31–7.32

of public attitudes

 about animal research, 7.46

 about climate change, 7.40–7.41

 about cloning, 7.45

 about environmental problems, 7.38

 about genetically modified food, 7.43

 about nanotechnology, 7.44

 about nuclear energy, 7.43

 about promises of science, 7.28–7.30

 about stem cell research, 7.45

of public confidence in science community’s leadership, 7.32

of public esteem for S&E occupations, 7.34–7.35

of public factual knowledge of science, 7.22–7.23

of public interest in science and technology, 7.11–7.12

of public reservations about science, 7.28–7.30

of public understanding of scientific process, 7.25

of public’s perceived knowledge about causes and solutions to 

environmental problems, 7.26

of R&D, O.5–O.7, 4.4–4.5, 4.16–4.22

of R&D trends, O.5–O.7

of S&E doctoral degrees, 2.41

of workforces, O.7–O.9, 2.38, 3.59–3.61

Internationally mobile students, 2.6, 2.42–2.44. See also Foreign 

students

International trade. See Trade

Invention(s), valuable, patenting. See Patent(s), triadic

Iowa. See State Indicators (Chapter 8)

iPhones, global trade in, metrics for, 6.34

Iran

first university degrees in S&E, 2.39

foreign students from, 2.44

recipients of U.S. S&E doctorates from, 2.37

research article output/production, 5.37, 5.41

Ireland

foreign students in, 2.43

multinational companies based in, R&D performed by affiliates of, 

4.30

Israel

multinational companies based in, R&D performed by affiliates of, 

4.5, 4.27–4.30

public perceptions of S&E occupations in, 7.35

R&D intensity, 4.4, 4.18

R&D performance, O.5–O.6, 4.5, 4.27–4.30

research article output/production, preferred collaboration partners, 

5.43

Italy

doctoral degrees awarded in, by sex, 2.41

first university degrees in S&E, 2.6, 2.39

information and communication technology infrastructure in, 6.15

public interest in science and technology, 7.11–7.12

R&D performance in, 4.17

recipients of U.S. S&E doctorates from, 2.34–2.35

research article output/production, preferred collaboration partners, 

5.42

U.S. students in, as foreign students, 2.44

J

Japan

basic research in, 4.21–4.22

commercial knowledge-intensive services, 6.21–6.25

currency exchange rate, 6.24

doctoral degrees awarded in, 2.6, 2.41

first university degrees in S&E, O.9, 2.6, 2.39

foreign students in, 2.42–2.44

as global provider of knowledge-intensive services, 6.5

government R&D support, by socioeconomic objectives, 4.39

high-technology manufacturing in, 6.26–6.27

information and communication technology in, 6.15, 6.17

intellectual property trade, O.13

KTI share of economy, O.3–O.4, 6.5, 6.13–6.14, 6.29

labor productivity growth in, 6.20

multinational companies based in, R&D performed by affiliates of, 

4.5, 4.26–4.30

in non-knowledge-intensive services industries, 6.8–6.9

in nonmanufacturing and nonservices industries, 6.9

number of researchers in, O.8

patenting activity, O.10–O.11

 in clean energy and pollution control, 6.53–6.55

 by technology area, 6.43–6.44

public attitudes about genetically modified food, 7.43

public concern about climate change, 7.40

public confidence in science community’s leadership, 7.32

public interest in science and technology, 7.12

public visits to informal science and other cultural institutions, 7.19

public’s general attitudes about science in, 7.30

R&D funding, 4.21–4.22

R&D in, O.5–O.7, 4.4, 4.17–4.18, 4.20–4.21, 4.26–4.27, 4.30

R&D intensity, 4.4–4.5, 4.18–4.20

RD&D of clean energy and nuclear technologies in, 6.52–6.53

research article output/production, O.10, 5.6, 5.36–5.37, 5.42–5.44

researchers in, 3.6, 3.60–3.61

trade

 in commercial knowledge-intensive services, 6.30–6.31

 in high-technology goods, 6.32–6.34

 KTI, 6.29

 in R&D services, 6.32

as U.S. advanced technology product trading partner, 6.35

U.S. direct investment in, 6.38

U.S. patents granted to, 6.40–6.41

value added for manufacturing industries, 6.9

Jordan

foreign students in, 2.42

recipients of U.S. S&E doctorates from, 2.37

Journal articles. See Literature, scientific and technical

Justice, Department of, R&D expenditures, 4.32, 4.36

K

Kansas. See State Indicators (Chapter 8)

K–12 education. See Elementary and secondary education in S&E; 
Science and Engineering Indicators 2014 ♦ I-11

Elementary and Secondary Mathematics and Science 

Education (Chapter 1)

Kentucky. See State Indicators (Chapter 8)

Kindergarten. See also Elementary and secondary education in 

S&E; Elementary and Secondary Mathematics and Science 

Education (Chapter 1)

mathematics and science performance in, 1.10–1.12

student learning in, nonschool factors affecting, 1.12–1.13

Knowledge- and technology-intensive (KTI) industry(ies) and 

services, O.3–O.4, O.12. See also Industry, Technology, and 

the Global Marketplace (Chapter 6)

Korea. See South Korea

KTI. See Knowledge- and technology- intensive (KTI) industry(ies) 

and services

L

Labor force, S&E, O.7–O.9, O.17–O.19. See also Employment; 

Researcher(s); Science and Engineering Labor Force (Chapter 

3); Unemployment

academic, 5.5–5.6, 5.27

college-educated workers in, 2.38, 3.5, 3.10–3.11, 3.14–3.15

doctorates in, 5.5–5.6

foreign-trained, 5.5–5.6, 5.27

international comparisons, O.7–O.9, 2.38

state indicators, 8.76–8.89

U.S.-trained, 5.5–5.6

Labor market. See also Unemployment

and earnings/salaries, 3.32–3.33

and involuntarily working out of field, 3.31, 3.34

for recent S&E graduates, 3.33–3.40

Labor market indicators, 3.28

Labor productivity, growth

in developed countries, 6.5, 6.18–6.20

in developing countries, 6.5, 6.18

Labor underutilization. See also Unemployment

alternative measures of, 3.29–3.30

Latin America. See also specific country

public concern about climate change, 7.40

research article output/production, preferred collaboration partners, 

5.44

U.S. MOFA R&D in, 4.5, 4.27–4.29

Law enforcement, seen as scientific by public, U.S. patterns and 

trends, 7.36–7.37

Library(ies), public, public attitudes toward, 7.18–7.19

Life science(s)

academic R&D in, 5.5, 5.13–5.15

academic research equipment expenditures and funding, 5.21

advanced technology products in, U.S. trade in, 6.35–6.36

doctorate holders, 5.25

and employment involuntarily out of field, 3.31

employment projections for, 3.11–3.12

occupational distribution of S&E highest degree holders in, by field 

of highest degree, 3.17

race and ethnicity trends in, 3.46–3.47

R&D activity in, 3.25

research, federal spending on, 4.37–4.39

research article output/production, 5.39–5.41

salaries in, 3.49–3.51

women in, 3.43–3.44

Literature, scientific and technical

academic and non-academic, in United States, 5.39

article output/production, O.10, 5.6, 5.35–5.53

Chinese-authored, citation data, 5.7, 5.48–5.50

citation by USPTO patents, 5.51–5.53

citation data, O.10, 5.7, 5.45

coauthorship, O.14, 5.7, 5.40–5.41, 5.46–5.47

collaboration patterns, O.10, O.13–O.15, 5.7, 5.40–5.47

by country, 5.36–5.37

EU-authored, citation data, 5.7, 5.48–5.50

by field, international comparisons, 5.37, 5.39–5.40

highly cited, trends, by country, 5.7, 5.48–5.50

international comparisons, O.10, 5.6–5.7, 5.36, 5.46–5.48

Japan-authored, citation data, 5.50

relative citation index, 5.45, 5.48, 5.50

U.S.-authored, citation data, 5.7, 5.48–5.50

by U.S. sector, 5.37–5.40

Louisiana. See State Indicators (Chapter 8)

Luxembourg, public interest in science and technology, 7.12

M

Maine. See State Indicators (Chapter 8)

Malaysia

foreign students from, 2.44

R&D performance, O.5, 4.4, 4.17

research article output/production, 5.37

sources of science and technology information used by public in, 

7.18

as U.S. advanced technology product trading partner, 6.35–6.36

Malta, foreign students in, 2.40

Manager(s), S&E employment, 3.24

projections for, 3.12–3.13

Manufacturing sector, 6.8–6.9

foreign multinational companies in, R&D performed by U.S. 

affiliates of, 4.26–4.27

innovation activities, 6.39–6.40

non-high-technology, 6.8–6.9

R&D performance, 4.23, 4.29–4.30

S&E employment in, 3.24

U.S. employment in, 6.28

U.S. MOFA R&D in, 4.29

Maryland. See also State Indicators (Chapter 8)

R&D performance in, 4.12–4.13

Massachusetts. See also State Indicators (Chapter 8)

R&D performance in, 4.12–4.13

Master’s degree(s)

citizenship status and, 2.29–2.31

and employment involuntarily out of field, 3.31, 3.34

by field, 2.29

foreign students earning, 2.5, 2.31

institutions awarding, 2.4

numbers, trends in, O.16, O.18, 2.5, 3.10

professional science, 2.29–2.30

by race and ethnicity, 2.29–2.30

and salaries of recent graduates, 3.34

S&E, 2.29–2.31, 3.10

by sex, 2.29

success rates, 2.29–2.30

Mathematical science(s)

academic R&D in, 5.13–5.15

age distribution of degree holders in, 3.41–3.42

doctoral degrees in, 2.35

doctorate holders employed in academia, 5.25

employment projections for, 3.11–3.12

foreign-born students in, 2.5

graduate students in, 2.5

occupational distribution of S&E highest degree holders in, by field 

of highest degree, 3.17

percentages of bachelor’s degrees in, O.17

race and ethnicity trends in, 3.45–3.47

R&D activity in, 3.25

research, federal spending on, 4.37–4.39

salaries in, 3.49–3.51

S&E degree holders working in, by level and field of highest S&E 

degree, 3.17–3.19
I-12 ♦ Index

and tenure status, 5.25

women in, 3.43–3.44

Mathematics. See also Elementary and Secondary Mathematics and 

Science Education (Chapter 1)

K–12 students’ learning in, 1.4

K–12 students’ proficiency in, 1.4

ninth graders’ coursetaking in, 1.21–1.22

research article output/production, international collaboration in, 

5.41

students’ coursetaking in, 1.4, 1.19–1.26

Measuring and instrumentation. See Automation, control, and 

measuring technology(ies)

Mechanical engineering. See Engineering, mechanical

Medical and health sciences

academic R&D in, 5.14–5.15

academic research equipment expenditures and funding, 5.21

doctoral degrees in, 2.33–2.34

graduate students in, federal financial support, 2.5

research article output/production, 5.39–5.40

women in, 3.43–3.44

Medical electronics, patents granted in, 6.42

international comparisons, O.11, 6.44

Medical equipment, patents granted in, 6.42

academic, 5.55

international comparisons, O.11, 6.44

Medical science(s). See Medical and health sciences

Medicine. See also Biomedical science(s)

Mexico

doctoral degrees awarded in, by sex, 2.41

high-skill emigrants in, 3.58

information and communication technology infrastructure in, 6.16

investment in clean energy technologies, 6.50

KTI share of economy, 6.14

public attitudes about genetically modified food, 7.43

R&D performance in, 4.17

recipients of U.S. S&E doctorates from, 2.35–2.36

research article output/production, preferred collaboration partners, 

5.43–5.44

as U.S. advanced technology product trading partner, 6.35

Michigan. See also State Indicators (Chapter 8)

R&D performance in, 4.12–4.13

Microbusiness(es), U.S., high-technology, 6.45–6.49

Middle East

public concern about climate change, 7.40

R&D performance in, 4.17

recipients of U.S. S&E doctorates from, 2.37

U.S. MOFA R&D in, 4.5, 4.27–4.29

Minnesota. See State Indicators (Chapter 8)

Minority(ies). See also Underrepresented minority(ies); specific 

minority

as postdocs in academic employment, 5.31

salaries for, 3.49–3.51

in S&E labor force, 3.43, 3.45–3.51

Mississippi. See State Indicators (Chapter 8)

Missouri. See also State Indicators (Chapter 8)

R&D performance in, 4.12–4.13

MOFA(s). See Multinational company(ies), majority-owned foreign 

affiliate(s) (MOFA[s])

Mongolia, foreign students from, 2.44

Montana. See State Indicators (Chapter 8)

MOOC(s) (massive open online course[s]). See Online learning

Motor vehicle industry, R&D performance, cross-national 

comparisons, 4.29–4.30

Multinational company(ies)

in China, 6.25–6.26

foreign, U.S. affiliates, R&D performance, 4.26–4.27

foreign affiliates, business enterprise R&D and R&D, 4.29–4.30

in high-technology manufacturing, 6.36–6.38

in KTI industries, 6.36–6.38

majority-owned foreign affiliate(s) (MOFA[s]), R&D performance, 

4.5, 4.27–4.29

parent companies and foreign affiliates, R&D performance, 4.5, 

4.27–4.29

and pharmaceutical industry, in China, 6.25

R&D investments, O.6–O.7, 4.5, 4.25–4.29, 6.32

U.S., 4.27–4.29, 6.36–6.38

N

Nanotechnology, public attitudes about

international comparisons, 7.44

U.S. patterns and trends, 7.44

NASA. See National Aeronautics and Space Administration (NASA)

National Aeronautics and Space Administration (NASA)

EPSCoR and EPSCoR-like program budgets, 5.14

media coverage of, 7.12–7.13

R&D expenditures, 4.5, 4.36

research funding, by field, 4.38–4.39

S&E employment in, 3.23

support for academic R&D, 5.11, 5.15

technology transfer activities, 4.43–4.46

National Institutes of Health (NIH)

biomedical employment working group report, 3.39

EPSCoR and EPSCoR-like program budgets, 5.14

R&D funding for, 4.33

support for academic R&D, 5.11, 5.15

support for graduate students, 2.18–2.19

National Research Council, recommendations for strengthening 

research universities, 5.12

National Science Foundation (NSF). See also Industry/University 

Cooperative Research Centers (I/UCRC) Program; Scientists 

and Engineers Statistical Data System (SESTAT)

classification of degree fields and occupations, 3.8

EPSCoR and EPSCoR-like program budgets, 5.14

R&D expenditures, 4.5, 4.36

research funding, by field, 4.38–4.39

S&E employment in, 3.23

support for academic R&D, 5.11, 5.15

support for graduate students, 2.18

Native Hawaiian or Other Pacific Islander. See Asian(s) or Pacific 

Islander(s)

Natural resources, research space at academic institutions, 5.19–5.20

Natural resources and environment, R&D, federal funding for, 

4.32–4.33

Natural sciences

degrees in, 2.21

doctoral degrees, international comparisons, 2.41

first university degrees in, international comparisons, 2.39

freshmen intending to major in, 2.21–2.22

Nebraska. See State Indicators (Chapter 8)

Nepal, foreign students from, 2.44

Netherlands

doctoral degrees awarded in, by sex, 2.41

public interest in science and technology, 7.11–7.12

public visits to informal science and other cultural institutions, 7.19

sources of science and technology information used by public in, 

7.17

Networking. See also Information processing and networking, patents 

granted in

as component of cyberinfrastructure for academic R&D, 5.22–5.23

Nevada. See State Indicators (Chapter 8)

New Hampshire. See also State Indicators (Chapter 8)

R&D performance in, 4.12–4.13

New Jersey. See also State Indicators (Chapter 8)

R&D performance in, 4.12–4.13
Science and Engineering Indicators 2014 ♦ I-13

New Mexico. See also State Indicators (Chapter 8)

R&D performance in, 4.12–4.13

New York. See also State Indicators (Chapter 8)

R&D performance in, 4.12–4.13

New Zealand

foreign students in, 2.43

public concern about climate change, 7.40

research article output/production, preferred collaboration partners, 

5.44

U.S. students in, as foreign students, 2.44

NIH. See National Institutes of Health (NIH)

No Child Left Behind Act of 2001 (NCLB), 1.8

Nondefense R&D

federal funding for, 4.32–4.33

government support for, cross-national comparisons, 4.39

Nonprofit organization(s)

applied research by, 4.15–4.16

basic research by, 4.15

development funding, 4.16

development performance, 4.16

funding for S&E academic R&D, 5.5, 5.12–5.13, 5.16

R&D, 4.11–4.12, 4.15

research article output/production, 5.37–5.40

 citations in U.S. patents, 5.53

 collaboration patterns, 5.46–5.47

S&E employment in, 3.19, 3.21

support for academic R&D, by institution type, 5.16

Non-tenure-track faculty positions, O.15

North Africa, public concern about climate change, 7.40

North America. See also Canada; Mexico; United States

R&D performance in, 4.17

sources of science and technology information used by public in, 7.17

North American Free Trade Agreement (NAFTA)

and KTI trade, 6.29

U.S. advanced technology product trading partners in, 6.35

North Carolina. See also State Indicators (Chapter 8)

R&D performance in, 4.12–4.13

North Dakota. See State Indicators (Chapter 8)

Norway, researchers in workforce, 3.60

NSF. See National Science Foundation (NSF)

Nuclear energy

Fukushima accident and, 7.42

international comparisons, 7.43

patenting activity, 6.53–6.55

patents potentially applicable to, identification of, 5.52–5.54

public attitudes about, 7.5, 7.42–7.43

public policy on, influence of scientific experts on, public assessment 

of, 7.37

public RD&D expenditures in, 6.52–6.53

U.S. patterns and trends, 7.42–7.43

Nuclear Regulatory Commission, S&E employment in, 3.23

O

Occupation(s), 7.32–7.35; See also Science and Engineering Labor 

Force (Chapter 3)

Ocean science(s), doctoral degrees in

Asian recipients of, 2.34

European recipients of, 2.35

OECD. See Organisation for Economic Co-operation and Development 

(OECD)

Offshore energy development, public attitudes about, 7.5, 7.42–7.43

Ohio. See also State Indicators (Chapter 8)

R&D performance in, 4.12–4.13

Oil drilling, public attitudes about, 7.5

Oklahoma. See State Indicators (Chapter 8)

Online learning

access to, 1.6

benefits of, 1.37

effectiveness of, research on, 1.38

enrollment in, 1.6

in higher education, 2.12

for K–12 students, 1.36–1.37

massive open online courses, 2.12

reasons for offering, 1.6

Optoelectronics, advanced technology products in, U.S. trade in, 6.35

Oregon. See State Indicators (Chapter 8)

Organisation for Economic Co-operation and Development (OECD)

data

 on first-time enrollment in university-level education, 1.7

 on global S&E workforce, 3.59–3.61

 on higher-education expenditures, 2.38

 on high school graduation rate, 1.7, 1.39

 on high-skill migration, 3.58

 on internationally mobile students, 2.42–2.44

 on R&D, 4.16–4.22

definition of tertiary-type A program, 1.40

estimate of trade in value-added terms, 6.34

estimate of U.S. trade balance in iPhones, 6.34

KTI industry classification, 6.7

Overview, O.1–O.23

P

Pacific Islander(s). See Asian(s) or Pacific Islander(s)

Pakistan, research article output/production, 5.37

Pass-through funding, O.14

for academic R&D, 5.17–5.18

Patent(s)

academic, 5.7, 5.53–5.57

applications, O.10, 3.26

biotechnology, 5.7

clean energy technology, 6.53–6.55

commercialization rate for, 6.40

in computer systems design, 6.42–6.43

in developed countries, 6.40–6.41

in developing countries, 6.40–6.41

energy-related, identification of, 5.52–5.54

environment-related, identification of, 5.52–5.54

global trends in, 6.40–6.44

higher education and, O.14–O.15

in high-technology industries, 6.40

by industry, 6.40–6.42

international comparisons, O.10–O.13, 6.40–6.44

by location of inventor, O.10–O.13, 6.6, 6.40–6.41

numbers, 6.40

pendency, 5.51

pollution control, 6.53–6.55

scientists and engineers and, 3.26

S&E article citation by, 5.51–5.53

S&E degrees and disciplines and, 3.26

by technology area, 6.42

thickets of, 6.40

triadic, O.11, 6.6, 6.44–6.45

to U.S. inventors, 6.6, 6.40

Patent activity rate, 3.26

Patent and Trademark Office (USPTO), 3.26

patents granted, O.10–O.13, 5.54–5.55, 6.40–6.44

Pennsylvania. See also State Indicators (Chapter 8)

R&D performance in, 4.12–4.13

Pharmaceutical(s)/pharmaceutical industry

in China, 6.25

cross-national comparisons, 4.29–4.30

domestic R&D in, 4.23
I-14 ♦ Index

employment in, by U.S. multinational companies, 6.38

in EU, 6.25

foreign multinational companies in, R&D performed by U.S. 

affiliates of, 4.27

global trade in, 6.31, 6.33

innovation in, O.12, 6.39

international comparisons, O.11–O.12, 6.44

patents granted in, O.11–O.12, 5.55, 6.41–6.42, 6.44

R&D funding, from abroad, 4.24–4.25

R&D performance, 4.23, 4.29–4.30

U.S. multinational companies in, 6.37–6.38

U.S., trends in, 6.29, 6.33

Pharmaceutical science(s). See Pharmaceutical(s)/pharmaceutical 

industry

Philippines, high-skill emigrants in, 3.58

Physical science(s). See also Astronomy; Atmospheric science(s); 

Chemistry; Earth science; Geoscience(s); Ocean science(s); 

Physics

academic R&D in, 5.13–5.15

academic research equipment expenditures and funding, 5.21

doctoral degrees, 2.34–2.35, 2.41

doctorate holders employed in academia, 5.25

and employment involuntarily out of field, 3.31

employment projections for, 3.12–3.13

graduate students in, federal financial support, 2.5

ninth graders’ coursetaking in, 1.4

occupational distribution of S&E highest degree holders in, by field 

of highest degree, 3.17

percentages of bachelor’s degrees in, O.17

race and ethnicity trends in, 3.46–3.47

research, federal spending on, 4.37–4.39

research space at academic institutions, 5.19–5.20

salaries in, 3.49–3.51

S&E degree holders working in, by level and field of highest S&E 

degree, 3.17–3.19

and tenure status, 5.25

women in, 3.43–3.44

Physics

academic research equipment, federal funding, 5.21

Advanced Placement (AP), 1.5, 1.23–1.26

doctoral degrees in, foreign students earning, 2.5

master’s degrees in, foreign students earning, 2.5

ninth graders’ coursetaking in, 1.22

research article output/production, 5.40

 citations in U.S. patents, 5.53

 international collaboration in, 5.41

seen as scientific by public, 7.36–7.37

Poland

first university degrees in S&E, 2.6, 2.39

foreign students in, 2.40

public interest in science and technology, 7.11–7.12

public’s general attitudes about science in, 7.30

research article output/production, preferred collaboration partners, 

5.44

researchers in, by sex, 3.61

Political science

master’s degrees in, 2.5, 2.29

race and ethnicity trends in, 3.45–3.47

Pollution control technology(ies), patenting activity, 6.53–6.55

Pollution mitigation

patenting activity, 6.53–6.55

patents potentially applicable to

 citations to S&E literature, 5.53

 identification of, 5.52–5.54

Portugal

doctoral degrees awarded in, by sex, 2.41

foreign students in, 2.40

Postdoc fellowship. See Postdocs

Postdocs

in academic employment, 3.38, 5.6, 5.24–5.25, 5.29, 5.31–5.33

compensation for, 3.38

demographics, 5.31

by discipline, 3.38, 5.31

early-career, 5.31–5.32

employment characteristics of, 3.37–3.40

foreign-born share of, 5.6, 5.29

by institution type, 5.31–5.32

number of, 3.38, 5.6, 5.31

reasons for taking positions, 3.38–3.40, 5.32

recently degreed, 5.31–5.32

as researchers, 5.32

salaries and benefits for, 3.37–3.38

SEH doctorate holders in, 5.6, 5.24–5.25

as share of academic positions, 5.6

trends in, 3.38

U.S.-trained, 5.6, 5.31

by years since doctorate, 5.31–5.32

Postsecondary education

enrollment, 1.7, 1.39–1.40, 2.4, 2.20–2.24

and freshmen’s intentions to major in S&E, 2.20–2.23

by high school graduates, 1.7, 1.39–1.40

international comparisons, 1.40

remedial courses in, 1.40–1.41

sex differences in, 1.39–1.40

socioeconomic status and, 1.40

Postsecondary institution(s). See also College(s); University(ies)

four-year

 math remediation rate for, 1.7, 1.40

 revenues and expenditures, 2.14

 S&E workforce employed in, 3.19–3.21

graduate public, revenues and expenditures, 2.14

international comparisons, 2.38

private, revenues and expenditures, 2.38

public, revenues and expenditures, 2.15, 2.38

two-year

 math remediation rate for, 1.7, 1.40

 S&E workforce employed in, 3.19–3.21

Postsecondary teacher(s), employment projections for, 3.12–3.13

Precollege education, S&E workforce employed in, 3.19–3.21

Private university(ies). See University(ies), private

Professional, scientific, and technical services

domestic R&D performance, 4.23

patents in, 6.42

R&D funding, 4.23, 4.25

S&E employment in, 3.5, 3.24

U.S. direct investment abroad in, 6.38

U.S. MOFA R&D in, 4.29

Professional development, for teachers, 1.6, 1.31–1.32

PST services. See Professional, scientific, and technical services

Psychology

academic R&D in, 5.13–5.15

doctorate holders employed in academia, 5.25

doctorate recipients, 2.19, 2.34

foreign graduate students in, 2.5

graduate students in, federal financial support, 2.5

master’s degrees in, 2.5, 2.29

percentages of bachelor’s degrees in, O.17

research, federal spending on, 4.37–4.39

research article output/production, international collaboration in, 5.41

research space at academic institutions, 5.19–5.20

and student debt levels, 2.19

and tenure status, 5.25
Science and Engineering Indicators 2014 ♦ I-15

women in, 3.44

Publications. See Literature, scientific and technical

Public attitude(s). See Science and Technology: Public Attitudes and 

Understanding (Chapter 7)

Public knowledge-intensive services. See Knowledge-intensive services

Public university(ies). See University(ies), public

R

Race and ethnicity. See also specific ethnicity; specific race

and Advanced Placement (AP), 1.5, 1.25–1.26

and doctoral degrees awarded, 2.32–2.33

and freshmen intending S&E major, 2.21

and graduate enrollment, 2.5

and master’s degrees, 2.5

and ninth graders’ coursetaking, 1.22–1.23

and on-time graduation from high school, 1.6, 1.38

and performance gaps in grades 4 and 8, 1.15–1.16

and salaries among S&E workforce, 3.49–3.51

and S&E degrees, O.16–O.17, 2.4

and S&E labor force, O.17–O.18, 3.45

Recent graduates, S&E. See Science and Engineering Labor Force 

(Chapter 3)

Recession, global

and commercial knowledge-intensive services, 6.21, 6.23

and employment out of field, among recent graduates, 3.34

and high-technology industries, 6.5

and high-technology manufacturing, 6.27

and immigration of scientists and engineers to United States, 3.6

and investment in clean energy technologies, 6.49, 6.51

and KTI economic activity, 6.5, 6.9

and labor productivity growth, 6.5, 6.18

and R&D expenditures, O.19–O.21

and S&E activity, O.21

and unemployment, 3.28–3.30, 3.34

and U.S. employment, 6.27–6.28

Relative citation index, for S&E articles, 5.45, 5.48, 5.50

Renewable energy, public RD&D expenditures in, 6.52–6.53

Republic of Korea. See South Korea

Research. See also Animal research; Discipline-based education 

research

academic. See Academic research

applied. See Applied research

basic. See Basic research

business sector, O.19–O.20, 4.4, 4.15–4.16

federal expenditures on, by field, 4.37–4.39

federal funding of, public attitudes about, 7.4, 7.30–7.32

non-oriented, government funding of, cross-national comparisons, 

4.39

as primary activity of full-time faculty, 5.6

scientific, public assessment of, 7.28

Research, development, and demonstration (RD&D), for clean energy 

technologies, 6.6, 6.52–6.53

Research and development. See also Academic Research and 

Development (Chapter 5); Research; Research and 

Development: National Trends and International Comparisons 

(Chapter 4); Researcher(s)

academic. See Academic Research and Development (Chapter 5)

comparative composition, by country, 4.20–4.22

employment abroad, by U.S. companies, 3.61

expenditures, 4.16–4.18, 4.21–4.22

federal funding for, 4.32–4.33

financial inputs, state indicators, 8.7, 8.90–8.105

funding from abroad, 4.21

funding sources, 4.21

geographic concentration, 4.17

government priorities in, cross-national comparisons, 4.39

international comparisons, O.7, 4.4–4.5, 4.16–4.22

by multinational companies, 4.5, 4.25–4.29

performers, international comparisons, 4.20–4.22

U.S., 4.12–4.13

 from abroad, 4.24–4.25

 academic, O.16, 4.4, 4.10, 4.12–4.13, 4.15

 basic research as share of, 4.21–4.22

 business sector, 4.4–4.5, 4.7–4.10, 4.12, 4.20, 4.22–4.25

 by character of work, 4.15–4.16

 collaborative research and, O.13–O.15

 commercialization, 4.5, 4.39–4.46

 defense-related, 4.5, 4.30, 4.32, 4.39

 expenditures, 4.21–4.22, 4.34–4.35

 federal, 4.4–4.5, 4.9–4.13, 4.22–4.23, 4.39–4.46

 federally funded R&D center, 4.10–4.13

 foreign funding for, 4.24–4.25

 funding for, O.19–O.20, 4.4–4.5, 4.9–4.12, 4.15, 4.22, 4.24–4.25

 gap between performer- and source-reported, 4.34–4.35

 health-related, O.19, 4.5

 national trends, O.13–O.16, 4.4, 4.6–4.16

 nongovernmental sector, 4.4–4.5, 4.9, 4.12, 4.22

 by nonprofits, 4.11–4.12, 4.15

 performance, 4.13

 performers, 4.4–4.5, 4.9–4.13

 scientists and engineers in, 3.24–3.26

 by sector, 4.12–4.13

 sources, 4.12–4.15

 by state, 4.12–4.13

 total, 4.6–4.9

 in U.S. National Income and Product Accounts, 4.6

Research and development intensity, O.5–O.6

international comparisons, 4.4–4–5, 4.17–4.20

by state, 4.12–4.13

Research and Development: National Trends and International 

Comparisons (Chapter 4), 4.1–4.50

Research and development services

industry share, cross-national comparisons, 4.30

innovation in, 6.40

patents in, 6.42

scientific, 6.40, 6.42

U.S. trade in, 6.31–6.32

Research and experimentation (R&E), federal tax credit, 4.23–4.24

Research assistantships, 2.17–2.19

in academia, 5.6

graduate, 5.31

Research associate(s). See Postdocs

Researcher(s)

academic, 5.6, 5.29–5.31

definition of, 3.59

doctoral S&E, 5.29–5.30

female, international comparisons, 3.61

international comparisons, O.7–O.8, 3.6, 3.59–3.61

numbers of, O.7–O.8, 3.6, 3.59–3.61

outside tenure-track faculty, 5.6

postdoctoral, 5.32

S&E full-time faculty, 5.30–5.31

in workforce, international comparisons, 3.60

Research facility(ies), for academic R&D, 5.5, 5.19–5.21

Research output, 5.35–5.57

academic, 5.6–5.7, 5.35–5.57. See also Literature, scientific and 

technical; Patent(s)

state indicators, 8.7, 8.106–8.115

Research space, for academic R&D, 5.5, 5.19–5.21

by field, 5.5, 5.19–5.20

growth in, 5.5, 5.19–5.20

new construction, 5.20–5.21
I-16 ♦ Index

repair and renovation, 5.21

Restaurants and hotels, in global marketplace, 6.8–6.9

Retirement, of scientists and engineers, 3.42–3.43

Rhode Island. See State Indicators (Chapter 8)

Romania, recipients of U.S. S&E doctorates from, 2.34–2.35

Royalties and fees, global trade in, O.13, 6.45

Russia

doctoral degrees awarded in, 2.6, 2.41

intellectual property trade, O.13

number of researchers in, O.8

recipients of U.S. S&E doctorates from, 2.34–2.35

researchers in, numbers of, 3.6, 3.59–3.60

tertiary education attainment in, 2.38

trade, in value-added indicators, 6.34

Russian Federation, R&D performance in, 4.17

S

Salary(ies)

education and, 3.49–3.51

for employed college-educated individuals, 3.32

experience and, 3.49–3.51

of H1-B visa recipients, 3.55

highest degree and, 3.33, 3.49–3.51

for minorities in S&E workforce, 3.49–3.51

for non-S&E occupations, 3.32–3.33

for postdoctoral positions, 3.37–3.38

for recent S&E graduates, 3.34, 3.37

for S&E occupations, 3.5, 3.32–3.33

for S&E-related occupations, 3.32–3.33

for women in S&E workforce, 3.49–3.51

for workers without a bachelor’s degree, 3.33

Salary gap

among recent graduates, 3.51

demographics and, 3.51

education and, 3.49–3.51

employment/occupation and, 3.49–3.51

experience and, 3.49–3.51

for racial and ethnic minorities in S&E workforce, 3.49–3.51

for women in S&E workforce, 3.49–3.51

Saudi Arabia

expenditures on higher education, 2.37

research article output/production, international collaboration in, 5.41

students from, in United States, 2.5

SBIR. See Small Business Innovation Research (SBIR)

Scandinavia, research article output/production, preferred collaboration 

partners, 5.44

Science and Engineering Labor Force (Chapter 3), 3.1–3.61

Science and Technology: Public Attitudes and Understanding (Chapter 

7), 7.1–7.53

Science Citation Index (SCI), 5.36

Science education, public attitudes about, 7.46

Science instruction

barriers to, 1.6

school support for, 1.6

Scientific literacy, 7.20

Scientific R&D services, funding from abroad, 4.25

Self-employment, of scientists and engineers, 3.19–3.23

business size and, 3.23–3.24

Semiconductor(s)/semiconductor industry

business R&D for, cross-national comparisons, 4.29–4.30

in China, 6.25

employment in, by U.S. multinational companies, 6.38

global trade in, 6.31

international comparisons, 6.43

patents in, 5.55, 6.41–6.43

U.S. direct investment abroad in, 6.38

U.S. multinational companies in, 6.37–6.38

Service industry(ies), 6.8

knowledge-intensive, 6.7. See also Knowledge-intensive services

non-knowledge-intensive, 6.8

R&D, industry share, cross-national comparisons, 4.30

SEVIS. See Student Exchange Visitor Information System (SEVIS)

Sex differences. See also Women

in Advanced Placement (AP), 1.5, 1.25–1.26

in fields of S&E degrees, 3.44–3.45

in high school graduates enrolling in postsecondary education, 1.7

in K–12 students’ performance in mathematics and science, 1.4

in levels of S&E degrees, 3.44–3.45

in on-time high school graduation rates, 1.38–1.39

and performance gaps in grades 4 and 8, 1.15–1.16

in postsecondary enrollment, 1.39–1.40

in salaries among S&E workforce, 3.49–3.51

in S&E occupations, 3.43–3.46

Singapore

first university degrees in S&E, 2.39

foreign students in, 2.42

high-technology manufacturing in, 6.27, 6.29

K–12 students’ TIMSS test scores in, 1.4

R&D performance, O.5, 4.4, 4.17

research article output/production, 5.37

researchers in workforce, 3.60

trade, in high-technology goods, 6.32

workers with S&E skills, O.8

Slovakia, foreign students in, 2.40

Small business, U.S.

entrepreneurial investment in, 6.47–6.49

high-technology, 6.45–6.49

Small Business Innovation Research (SBIR), 4.5, 4.42–4.46

financing, 6.48–6.49

Small Business Technology Transfer, 4.5, 4.42–4.46

Smart grid

patenting activity, 6.53–6.55

patents potentially applicable to

 citations to S&E literature, 5.53

 identification of, 5.52–5.54

Social media, S&T topics in, 7.13

Social science(s). See also Economics; Political science; Psychology; 

Public administration; Sociology

academic R&D in, 5.13–5.15

doctoral degrees in, 2.34

doctorate holders employed in academia, 5.25

and employment involuntarily out of field, 3.31

employment projections for, 3.11–3.12

foreign graduate students in, 2.5

foreign undergraduate students in, 2.5

graduate enrollment in, 2.5

graduate students in, federal financial support, 2.5

occupational distribution of S&E highest degree holders in, by field 

of highest degree, 3.17

percentages of bachelor’s degrees in, O.17

public attitudes about, 7.5

race and ethnicity trends in, 3.45–3.47

R&D activity in, 3.25

research, federal spending on, 4.37–4.39

research article output/production, international collaboration in, 5.41

salaries in, 3.49–3.51

S&E degree holders working in, by level and field of highest S&E 

degree, 3.17–3.19

and tenure status, 5.25

women in, 3.43–3.44

Sociology

academic R&D in, 5.15
Science and Engineering Indicators 2014 ♦ I-17

race and ethnicity trends in, 3.46–3.47

seen as scientific by public, U.S. patterns and trends, 7.36

Software industry

innovation in, O.12, 6.6, 6.39

patents in, 6.42

race and ethnicity trends in, 3.45–3.47

Solar energy

investment in, 6.49–6.52

patenting activity, 6.53–6.55

patents potentially applicable to, identification of, 5.52–5.54

public attitudes about, U.S. patterns and trends, 7.42–7.43

public RD&D expenditures in, 6.52–6.53

South Africa

information and communication technology in, 6.16–6.17

KTI economic activity in, O.3

labor productivity growth in, 6.18

trade, in value-added indicators, 6.34

South America, R&D performance in, 4.17

South Asia, R&D performance, O.5, 4.4, 4.17

South Carolina. See State Indicators (Chapter 8)

South Dakota. See State Indicators (Chapter 8)

Southeast Asia

KTI economic activity in, O.3–O.4

R&D performance, O.5–O.6, 4.4, 4.17

research article output/production, O.10

South Korea

business R&D in, distribution by industry, 4.30

doctoral degrees awarded in, 2.6, 2.41

expenditures on higher education, 2.38

first university degrees in S&E, O.9, 2.6, 2.39

foreign students from, 2.44

foreign students in, 2.42

government R&D support, by socioeconomic objectives, 4.39

high-technology manufacturing in, 6.27, 6.29

information and communication technology in, 6.15, 6.17

K–12 students’ TIMSS test scores in, 1.4

KTI share of economy, O.3, 6.13–6.14

labor productivity growth in, 6.18–6.20

patenting activity, O.11

 in clean energy and pollution control, 6.53–6.55

 by technology area, 6.44

public confidence in science community’s leadership, 7.32

public interest in S&T, 7.12

public perceptions of S&E occupations in, 7.35

public’s general attitudes about science in, 7.30

R&D in, O.5–O.6, 4.4, 4.17–4.18, 4.20–4.21

R&D intensity, 4.4–4.5, 4.18, 4.20

RD&D of clean energy and nuclear technologies in, 6.52–6.53

recipients of U.S. S&E doctorates from, 2.34

research article output/production, 5.37, 5.43–5.44

researchers in, 3.6, 3.60–3.61

sources of S&T information used by public in, 7.18

students from, in United States, 2.5

trade, in high-technology goods, 6.32–6.34

U.S. patents granted to, 6.40–6.41

as U.S. advanced technology product trading partner, 6.35–6.36

workers with S&E skills, O.8

Space research and technology, R&D, federal funding for, 4.32–4.33

Spain

first university degrees in S&E, 2.6, 2.39

foreign students in, 2.40

investment in clean energy technologies, 6.51

public concern about climate change, 7.40

public interest in S&T, 7.11–7.12

R&D performance in, 4.17

researchers in, by sex, 3.61

U.S. students in, as foreign students, 2.44

State Indicators (Chapter 8), 8.1–8.129

State/local government

funding for S&E academic R&D, 5.5, 5.12

research article output/production, 5.37–5.40, 5.46–5.47

S&E workforce employed in, 3.19–3.21, 3.23

Stay rate(s), 3.51

for U.S. S&E doctorate recipients, 3.6, 3.55–3.58

Stem cell research

public attitudes about, 7.5, 7.44–7.45

public policy on, influence of scientific experts on, public assessment 

of, 7.37

STEM education

K–12, monitoring progress in, 1.8–1.9

retention of undergraduates in, 2.22–2.23

teacher training and retention for, 100Kin10 program, 1.34

STTR. See Small Business Technology Transfer

Student aid. See Financial aid

Student debt, 2.4

graduate, 2.4, 2.19–2.20

undergraduate, 2.4, 2.16–2.17

Student Exchange Visitor Information System (SEVIS), 2.23, 2.29

Sweden

foreign students in, 2.43

information and communication technology infrastructure in, 6.15

public interest in S&T, 7.12

R&D intensity, 4.4, 4.18

R&D performance, O.5

researchers in, 3.60–3.61

Switzerland

foreign students in, 2.43

multinational companies based in, R&D performed by U.S. affiliates 

of, 4.26–4.27

R&D intensity, 4.4, 4.18

R&D performance, O.5, O.7

T

Taipei, K–12 students’ TIMSS test scores in, 1.4

Taiwan

doctoral degrees awarded in, by sex, 2.41

first university degrees in S&E, 2.6, 2.39

high-technology manufacturing in, 6.27, 6.29

patenting activity, by technology area, 6.43–6.44

R&D intensity, 4.4, 4.18

R&D performance, O.5, 4.4, 4.17

recipients of U.S. S&E doctorates from, 2.34

research article output/production, 5.37, 5.43–5.44

researchers in workforce, 3.60

trade, in high-technology goods, 6.32–6.34

U.S. patents granted to, 6.40–6.41

workers with S&E skills, O.8

Teacher(s)

beginning, attrition among, 1.33–1.34

elementary mathematics

 degrees held by, 1.5, 1.29

 self-assessment of preparedness to teach, 1.5, 1.30–1.31

elementary school, professional development activity, 1.6, 1.31

elementary science

 degrees held by, 1.5, 1.29

 self-assessment of preparedness to teach, 1.5, 1.30–1.31

high school, professional development activity, 1.6, 1.31

high school mathematics

 degrees held by, 1.5, 1.29

 self-assessment of preparedness to teach, 1.30–1.31

high school science

 degrees held by, 1.5, 1.29
I-18 ♦ Index

 self-assessment of preparedness to teach, 1.30–1.31

K–12, 1.26–1.34

middle school, professional development activity, 1.6, 1.31

middle school mathematics, self-assessment of preparedness to teach, 

1.30–1.31

middle school science, self-assessment of preparedness to teach, 

1.30–1.31

novice, 1.5, 1.27

professional development for, 1.6, 1.31–1.32

public confidence in, international comparisons, 7.32

in schools with high-poverty students, 1.5, 1.27

in schools with minority students, 1.5, 1.27

secondary, attrition rates, 1.6, 1.33–1.34

training, 100Kin10 program, 1.34

Teaching assistantships, 2.17

Technician(s), S&E, employment projections for, 3.12–3.13

Technology. See also Industry, Technology, and the Global Marketplace 

(Chapter 6); Science and Technology: Public Attitudes and 

Understanding (Chapter 7); specific technology

Technology transfer, federal

activities, 4.43

metrics, 4.43

programs promoting, 4.5, 4.39–4.46

Telecommunications services, patents granted in, 6.42

international comparisons, 6.43

Tennessee. See also State Indicators (Chapter 8)

R&D performance in, 4.12–4.13

Tenure

SEH doctorates with, 3.35–3.37

U.S.-trained S&E doctorate holders with, 5.6

Tenure-track faculty positions, O.15

recent doctorate recipients and, 3.35, 3.37

Tertiary degree(s), 2.38

Tertiary education, internationally mobile students enrolled in, 

international comparisons, O.9, 2.6, 2.42–2.44

Tertiary-type A program, OECD definition of, 1.40

Testing, measuring and control instruments

employment in, by U.S. multinational companies, 6.38

global trade in, 6.31, 6.33

innovation in, O.12, 6.39

patents in, 6.41

U.S. direct investment abroad in, 6.38

U.S. multinational companies in, 6.37–6.38

U.S. production of, 6.28

Texas. See also State Indicators (Chapter 8)

R&D performance in, 4.12–4.13

Thailand

foreign students from, 2.44

R&D performance in, 4.17

research article output/production, 5.37

Trade, 6.5–6.6. See also Industry, Technology, and the Global 

Marketplace (Chapter 6)

international, 6.5–6.6. See also Globalization

measured in value-added terms, 6.34

Trade balance(s), U.S.

in advanced technology products, 6.35–6.36

in iPhones, 6.34

Trade deficit(s)

EU, in information and communication technology products, 6.33

U.S.

 in advanced technology products, 6.6, 6.35–6.36

 in high-technology products, 6.6

 in information and communication technology products, 6.33

Trade in Value Added (TiVA), OECD/WTO initiative, 6.34

Trade surplus(es)

Chinese, in high-technology goods, 6.32–6.33

EU

 in business services, 6.31

 in commercial knowledge-intensive services, 6.31

 in high-technology goods, 6.33

in knowledge-intensive services, 6.5

U.S.

 in advanced technology products, 6.35–6.36

 in business services, 6.30–6.31

 in commercial knowledge-intensive services, 6.30–6.31

 in high-technology goods, 6.33

Traineeships, 2.17–2.19

Training, work-related, of S&E labor force, 3.15, 3.26–3.28

Transport and storage, in global marketplace, 6.8–6.9

Transportation, Department of (DOT), R&D expenditures, 4.5, 4.36

Transportation equipment manufacturing

domestic R&D performance, 4.23

multinational companies in, R&D performed by affiliates of, 4.27, 

4.29

R&D for, cross-national comparisons, 4.30

R&D funding, 4.23

Trends in International Mathematics and Science Study (TIMSS)

K–12 students’ test scores, international comparisons, 1.4, 1.17–1.19

mathematics performance of students in grades 4 and 8, 1.18

performance trends, 1.19

science performance of students in grades 4 and 8, 1.18–1.19

Triadic patent(s), O.11, 6.44–6.45

EU share of, 6.6, 6.44–6.45

Japan’s share of, 6.6, 6.44–6.45

U.S. share of, 6.6, 6.44–6.45

Tuition and fees. See also Student debt

for colleges and universities, trends in, 2.15

at community colleges, 2.14

in private 4-year colleges, 2.15

at public institutions, comparison, 2.15

at public 2-year colleges, 2.15

at public 4-year colleges, 2.15

in U.S. higher education, 2.4

Tunisia, research article output/production, 5.37

Turkey

first university degrees in S&E, 2.6, 2.39

foreign students from, 2.44

information and communication technology in, 6.16–6.17

KTI economic activity in, O.3, 6.5, 6.7, 6.14

recipients of U.S. S&E doctorates from, 2.37

research article output/production, preferred collaboration partners, 

5.43

researchers in, by sex, 3.61

U

Undergraduate education. See also Higher Education in Science and 

Engineering (Chapter 2)

financial support for, 2.15–2.16

Underrepresented minority(ies). See also American Indian(s) or Alaska 

Native(s); Black(s) or African American(s); Hispanics

in academic S&E doctoral employment, 5.6, 5.27–5.28

doctoral degrees awarded, 2.32–2.33

female, in academic S&E doctoral employment, 5.27

graduate enrollment, 2.28

master’s degrees earned, 2.30

in S&E labor force, 3.6, 3.43, 3.45–3.47, 3.49

Unemployment

alternative measures of, 3.29–3.30

career stage and, 3.28–3.29

in entire labor force, compared to S&E labor force, 3.28–3.30

global economic downturn and, 3.28–3.30

and graduate enrollment by field, 2.27
Science and Engineering Indicators 2014 ♦ I-19

highest degree and, 3.28–3.29

non-S&E occupations and, 3.28–3.29

of recent doctorate recipients, 3.35

recent S&E graduates and, 3.34

S&E occupations and, 3.5, 3.28–3.29

UNESCO. See United Nations Educational, Scientific and Cultural 

Organization (UNESCO)

United Kingdom

business R&D in, distribution by industry, 4.29–4.30

defense R&D in, distribution by industry, 4.30

as destination for foreign students, 2.6

doctoral degrees awarded in, numbers, 2.6, 2.41

expenditures on higher education, 2.38

first university degrees in S&E, 2.6, 2.39

foreign students in, O.9, 2.40, 2.42–2.43

government R&D support, by socioeconomic objectives, 4.39

high-skill emigrants in, 3.58

information and communication technology in, 6.15, 6.17

investment in clean energy technologies, 6.51

KTI economic activity in, O.4, 6.13–6.14

multinational companies based in, R&D performed by affiliates of, 

4.26–4.29

public attitudes

 about nanotechnology, 7.44

 about nuclear energy, 7.43

public interest in S&T, 7.11–7.12

public views on cause of climate change, 7.41

public’s general attitudes about science in, 7.30

R&D in, O.5, O.7, 4.17–4.18, 4.20–4.21

R&D intensity, 4.4, 4.20

recipients of U.S. S&E doctorates from, 2.34–2.35

research article output/production, 5.37, 5.42, 5.44

sources of S&T information used by public in, 7.17

U.S. students in, as foreign students, 2.44

United Nations Educational, Scientific and Cultural Organization 

(UNESCO), R&D data, 4.16

United States

basic research in, 4.21–4.22

business R&D in, distribution by industry, 4.29–4.30

commercial knowledge-intensive services, 6.21–6.25

currency exchange rate, 6.24

defense R&D in, 4.5, 4.30, 4.32

as destination for foreign students, 2.6

doctoral degrees awarded in, 2.6, 2.41

expenditures on higher education, 2.37–2.38

first university degrees in S&E, O.8, 2.6, 2.39

foreign students in, O.9, 2.6, 2.42–2.44

GDP per capita in, 6.20

as global provider of knowledge-intensive services, 6.5

government R&D support, by socioeconomic objectives, 4.39

high-skill migration to, 3.58

high-technology manufacturing in, 6.5, 6.26–6.29

 employment in, 6.26–6.28

 R&D funding, 6.26

 skilled workers in, 6.26

information and communication technology in, 6.15–6.17

intellectual property trade, O.12–O.13, 6.45

investment in clean energy technologies, 6.6, 6.51

knowledge-intensive services exports, 6.5

KTI share of economy, O.3–O.4, 6.5, 6.13–6.14

labor productivity growth in, 6.5, 6.18–6.20

in non-knowledge-intensive services industries, 6.8–6.9

in nonmanufacturing and nonservices industries, 6.9

number of researchers in, O.7–O.8

patenting activity, O.11–O.12

 in clean energy and pollution control, 6.53–6.55

 by technology area, 6.42–6.44

R&D funding, O.19–O.20, 4.6–4.9, 4.21–4.22

R&D intensity, 4.4, 4.6–4.9, 4.18–4.20

R&D performance, O.5–O.6, 4.4, 4.17–4.18

R&D performers, 4.20–4.21

RD&D investment for clean energy technologies, 6.6, 6.52–6.53

research article output/production, O.10, 5.6, 5.35–5.40, 5.42–5.44

researchers in, 3.6, 3.60

students from

 as foreign students, 2.44

 in study-abroad programs, 2.44

tertiary education attainment in, 2.38

trade

 in advanced technology products, 6.6, 6.34–6.36

 in business services, 6.30–6.31

 in commercial knowledge-intensive services, 6.30–6.31

 in high-technology goods, 6.6, 6.32–6.33

 KTI, 6.29

 in R&D services, 6.31–6.32

 in royalties and fees, O.12–O.13, 6.45

 in value-added indicators, 6.34

value added for manufacturing industries, 6.9

University(ies). See also Academia; Higher education

Carnegie classification of, 2.8

institutional funding for S&E academic R&D, 5.11

international, branch campuses, 2.42

patents granted to, 5.54–5.55

private

 revenues and expenditures, 2.13

 support for academic R&D, 5.16

public

 revenues and expenditures, 2.4, 2.13

 support for academic R&D, 5.16

research-intensive

 doctorate granting by, O.15, 2.4

 NRC recommendations for strengthening, 5.12

 R&D performance, O.16

 revenues and expenditures, O.15–O.16, 2.4, 2.13, 5.5

USDA. See Agriculture, Department of (USDA)

USPTO. See Patent and Trademark Office (USPTO)

Utah. See State Indicators (Chapter 8)

Utility(ies), in global marketplace, 6.8–6.9

V

VA. See Veterans Affairs

Value added, 6.12

of high-technology manufacturing, 6.25

indicators, trade measured in, 6.34

in knowledge-intensive services, 6.5

for manufacturing industries, 6.9

metrics, 6.12

for selected industries, 6.8–6.9

for service industries, by region/country/economy, 6.21–6.23

Venture capital investment

in clean energy technologies, 6.6, 6.51–6.52

in U.S. small business, 6.47–6.48

Vermont. See State Indicators (Chapter 8)

Veterans Affairs, R&D expenditures, 4.36

Veterans benefits, R&D, federal funding for, 4.32–4.33

Vietnam

foreign students from, 2.44

trade, in high-technology goods, 6.33

Virginia. See also State Indicators (Chapter 8)

R&D performance in, 4.12–4.13

Visa(s)

H1-B, 3.54–3.55
I-20 ♦ Index

J-1, 3.54

L-1, 3.54

SEVIS, 2.29

student (temporary). See also Foreign students

 and doctorates earned, 2.5, 2.33–2.34

 and undergraduate degrees earned, 2.27

work (temporary), trends in, 3.6, 3.54–3.55

W

Washington. See also State Indicators (Chapter 8)

R&D performance in, 4.12–4.13

West Virginia. See State Indicators (Chapter 8)

White(s)

doctoral degrees awarded, 2.33

graduate enrollment, 2.28

K–12 students’ performance in mathematics and science, 1.4

master’s degrees earned, 2.30

ninth graders, math coursetaking, 1.21–1.22

on-time graduation from high school, 1.6, 1.38

and performance gaps in grades 4 and 8, 1.15–1.16

in S&E labor force, 3.6, 3.45–3.47

Wind energy

investment in, 6.49–6.51

patenting activity, 6.53–6.55

patents potentially applicable to, identification of, 5.52–5.54

public attitudes about, U.S. patterns and trends, 7.42–7.43

public RD&D expenditures in, 6.52–6.53

Wisconsin. See State Indicators (Chapter 8)

Women

academic rank of, 5.6, 5.26–5.27

in academic S&E workforce, 5.6, 5.26–5.27

age distribution in labor force, 3.41

doctoral degrees awarded to, 2.6, 2.32, 2.41

as doctoral S&E faculty, 5.6, 5.26–5.27

as employed S&E highest degree holders, 3.44–3.45

field of work, 5.27

foreign-trained, 5.27

graduate enrollment, 2.5

as high-skill migrants, 3.58

labor force nonparticipation rates, 3.45

master’s degrees earned, 2.5, 2.29

occupations of, 3.43–3.44

out of field employment, 3.45

as postdocs in academic employment, 5.31

as researchers, international comparisons, 3.61

salaries for, 3.49–3.51

and S&E degrees, O.16–O.17, 2.4

in S&E labor force, O.17, 3.6, 3.41, 3.43–3.45, 3.49–3.51

work-related training of, 3.27

Workforce. See also Labor force, S&E; Science and Engineering Labor 

Force (Chapter 3)

S&E, definition of, 3.7–3.10

World Bank, economic classification of countries, 6.10

World Trade Organization (WTO), estimate of trade in value-added 

terms, 6.34

Wyoming. See State Indicators (Chapter 8)