NATIONAL SCIENCE FOUNDATION

National Science Board

SCIENCE AND 

ENGINEERING

INDICATORS
National Science Board

RAY M. BOWEN

Chairman, President Emeritus, Texas A&M University 

and Visiting Distinguished Professor, Rice University

ESIN GULARI

Vice Chairman, Dean of Engineering and Science, Clemson 

University

MARK R. ABBOTT

Dean and Professor, College of Oceanic and Atmospheric 

Sciences, Oregon State University

DAN E. ARVIZU

Director and Chief Executive, National Renewable Energy 

Laboratory

BONNIE BASSLER*

Howard Hughes Medical Institute Investigator and Squibb 

Professor of Molecular Biology, Princeton University

CAMILLA P. BENBOW

Patricia and Rodes Hart Dean of Education and Human 

Development, Peabody College of Education and Human 

Development, Vanderbilt University

JOHN T. BRUER

President, The James S. McDonnell Foundation

FRANCE A. CÓRDOVA

President, Purdue University

KELVIN K. DROEGEMEIER

Vice President for Research, Regents’ Professor of Meteorology 

and Weathernews Chair Emeritus, University of Oklahoma

PATRICIA D. GALLOWAY

Chief Executive Officer, Pegasus Global Holdings, Inc.

JOSÉ-MARIE GRIFFITHS

Vice President for Academic Affairs and University Professor, 

Bryant University

LOUIS J. LANZEROTTI*

Distinguished Research Professor of Physics, Center for SolarTerrestrial Research, Department of Physics, New Jersey Institute 

of Technology

ALAN I. LESHNER

Chief Executive Officer, American Association for the 

Advancement of Science, and Executive Publisher, Science

W. CARL LINEBERGER

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

Chemistry, University of Colorado

G. P. (BUD) PETERSON

President, Georgia Institute of Technology

DOUGLAS D. RANDALL

Professor and Thomas Jefferson Fellow and Director, 

Interdisciplinary Plant Group, University of Missouri

ARTHUR K. REILLY

Retired Senior Director, Strategic Technology Policy, Cisco 

Systems, Inc. 

ANNEILA I. SARGENT

Benjamin M. Rosen Professor of Astronomy and Vice President 

for Student Affairs, California Institute of Technology

DIANE L. SOUVAINE

Professor of Computer Science, Tufts University

ARNOLD F. STANCELL

Emeritus Professor and Turner Leadership Chair, Georgia 

Institute of Technology, School of Chemical and Biomolecular 

Engineering

CLAUDE M. STEELE 

Dean, School of Education, Stanford University

THOMAS N. TAYLOR

Roy A. Roberts Distinguished Professor, Department of Ecology 

and Evolutionary Biology, Curator of Paleobotany in the 

Natural History Museum and Biodiversity Research Center, The 

University of Kansas

RICHARD F. THOMPSON

Keck Professor of Psychology and Biological Sciences, 

University of Southern California

ROBERT J. ZIMMER

President, University of Chicago

SUBRA SURESH

Member ex officio, Director, National Science Foundation

MICHAEL L. VAN WOERT

Executive Officer, National Science Board, and Director, 

National Science Board Office

*Board Consultant

National Science Board Committee on Science and Engineering Indicators

José-Marie Griffiths, Chair

Camilla P. Benbow

John T. Bruer

Louis J. Lanzerotti

W. Carl Lineberger

G. P. (Bud) Peterson

Arthur K. Reilly

Richard F. Thompson

Executive Secretaries

Rolf Lehming

Robert Bell

NSB Staff Liaisons

Jean M. Pomeroy

Matthew B. Wilson
 SCIENCE AND 

ENGINEERING

 INDICATORS 

 2012
Cover Image

The cover for Science and Engineering Indicators 2012 was designed and produced by 

OmniStudio, Inc., Washington, DC.

Recommended Citation

National Science Board. 2012. Science and Engineering Indicators 2012. Arlington VA: 

National Science Foundation (NSB 12-01).
Errata

The following errors were discovered after publication of the print and PDF versions of Science

and Engineering Indicators 2012 and Science and Engineering Indicators Digest 2012. These

errors have been corrected in the online version of the volume and in the interactive Digest.

Updated 12 February 2013

Science and Engineering Indicators 2012

Chapter 3 

Page 3-15. The field of study “biological/agricultural/environmental life sciences” was 

incorrectly reported as “biological/agricultural sciences” and the percentage of degree holders in 

this field working in S&E occupations was incorrectly calculated. The correct percentage is 30%. 

Page 3-16. The percentages of degree holders in computer sciences and mathematics and those in 

engineering who work in the broad occupation group in which they were trained were incorrectly 

calculated. The correct value for computer sciences/mathematics is 53%, and for engineering, 

50%. 

Figure 3-7: The percentages of S&E degree holders working in S&E occupations were 

incorrectly calculated. The correct percentages for highest degree being in the field (f), doctorate 

in field (d), master’s in field (m), and bachelor’s in field (b), respectively, are as follows. All 

S&E degree holders: 38% (f), 74% (d), 51% (m), 31% (b). Biological/agricultural/environmental 

life sciences: 30% (f), 68% (d), 46% (m), no change (b). Computer/mathematical sciences: 56% 

(f), no change (d), 65% (m), 52% (b). Physical sciences: 54% (f), 78% (d), 60% (m), 44% (b). 

Social sciences: no change (f), 70% (d), 23% (m), 8% (b). Engineering: 64% (f), 80% (d), 73% 

(m), 60% (b).

Updated 14 September 2012 

Science and Engineering Indicators 2012

Chapter 2

Figure 2-4. Data in the rows “Mathematics/statistics” and “Computer sciences” are reversed. The

correct values for mathematics/statistics are 11.6% for research assistantship, 10.1% for 

fellowship, 0.8% for traineeship, 51.2% for teaching assistantship, 3.8% for other, and 22.4% for 

self-support. The correct values for computer sciences in these categories are 23.5%, 5.9%, 

0.5%, 14.7%, 7.3%, and 48.1%. 

Appendix table 2-5. Data in the rows “Mathematics/statistics” and “Computer sciences” are

reversed. The correct values for mathematics/statistics are 16,885 and 100.0% for all
mechanisms, 1,967 and 11.6% for research assistantship, 1,711 and 10.1% for fellowship, 139 

and 0.8% for traineeship, 8,651 and 51.2% for teaching assistantship, 639 and 3.8% for other, 

and 3,778 and 22.4% for self-support. The correct values for computer sciences in these 

categories are 32,198 and 100.0%, 7,556 and 23.5%, 1,887 and 5.9%, 174 and 0.5%, 4,735 and 

14.7%, 2,366 and 7.3%, and 15,480 and 48.1%. 

Page 2-13. Information for “Mathematics/statistics” and “Computer sciences” was reversed. The

corrected sentences should read as follows: In mathematics/statistics, more than half (51%) of 

full-time students were supported primarily through TAs and another 22% were self-supported. 

Full-time students in computer sciences and the social and behavioral sciences were mainly 

selfsupporting (48% for both) or received TAs (15% and 19%, respectively). 

Updated 6 July 2012

Science and Engineering Indicators 2012

Chapter 6

Figure 6-14. The legend for the third bar of each category is mislabeled 2009. It represents 2010 

data. The year range in the figure title should be corrected from 2008–09 to 2008–10. 

Updated 16 February 2012

Science and Engineering Indicators 2012

Chapter 2

Table 2-12. Data in the rows “Engineering” and “Science” are reversed for China, India, South 

Korea, and Taiwan. 

Chapter 3

Page 3-45. Salary differentials were incorrectly calculated for minorities. The correct 

percentages are as follows: American Indians/Alaska Natives earned 22% less than whites, 

blacks earned 22% less, and Hispanics 15% less. 

Figure 3-12. The top four segments of the stacked bars are mislabeled. The correct labels, in 

order from top to bottom, are 4-year institutions, 2-year and precollege institutions, Federal 

government, and State/local government.

Appendix table 3-16. The table title should be corrected from “workers with highest degree in 

S&E field” to “scientists and engineers employed full time.” 
Chapter 5

Page 5-11. The appendix table reference in the “Industry funds” bullet is incorrect. It should be 

appendix table 4-3. 

Figure 5-33. The units label was omitted. Units shown are percentages. 

Chapter 8

Table 8-24. Table notes should be added as follows. 

• NOTES: S&E degrees conferred include bachelor’s, master’s, and doctorate. S&E 

degrees include agricultural; biological; computer; earth, atmospheric, and ocean; 

physical; and social sciences; mathematics, psychology, and engineering. 

Table 8-44. The 2006 U.S. total for individuals in S&E occupations should be 5,407,710. The 

corrected total includes suppressed data to be consistent with totals presented in other years.

Table 8-48. Puerto Rico was incorrectly included in U.S. totals for academic S&E article output.

• U.S. totals should be 137,430 for 1997, 148,511 for 2003, and 167,587 for 2008. 

• U.S. values for academic articles/1,000 academic doctorate holders should be 559 for 

1997, 534 for 2003, and 579 for 2008. 

Table 8-49. Puerto Rico was incorrectly included in U.S. totals for academic S&E article output. 

• U.S. totals should be 136,897 for 2000, 142,320 for 2004, and 162,687 for 2009. 

Table 8-51. The source line should be corrected from 31 December 2009 to 31 December 2010. 

Science and Engineering Indicators Digest 2012

Page 4. The Asia 10 economies are incorrectly identified in the section “B. Where?” The correct 

list is China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, 

and Thailand. 

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 Rolf Lehming, Director of the Science and Engineering Indicators Program of the National Center for Science and Engineering Statistics (NCSES); Lynda T. Carlson, 

Director, NCSES; John R. Gawalt, Deputy Director, NCSES; 

and Myron P. Gutmann, Assistant Director, Directorate for 

Social, Behavioral and Economic Sciences. The authors of 

the manuscript were:

Overview. Rolf Lehming, NCSES

Chapter 1. Xianglei Chen, Leslie Hall, Susan Rotermund, 

Vera Bersudskaya, MPR Associates; Lawrence 

Burton, NCSES

Chapter 2. Jaquelina C. Falkenheim, NCSES

Chapter 3. Robert Bell, Nirmala Kannankutty, Emory P. 

Morrison, NCSES

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

M. Wolfe, NCSES

Chapter 5. Mark Boroush, Lawrence Burton, Mark K. 

Fiegener, NCSES

Chapter 6. Derek Hill, NCSES

Chapter 7. Carolyn L. Funk, SRI International

Chapter 8. Paula C. Dunnigan, Taratec Corporation; Jeri 

Mulrow, NCSES

Acknowledgments

The volume benefited from extensive contributions from 

NCSES staff. Joan S. Burrelli, Katherine Hale, and Mark C. 

Regets compiled and analyzed data for the volume. NCSES 

senior staff and survey managers assured availability of data 

under often stringent deadlines: Ronda K. Britt, Leslie Christovich, John Finamore, John E. Jankowski, Kelly H. Kang, 

Lynn Milan, Steven Proudfoot, Emilda B. Rivers, and Michael 

Yamaner. Jacqueline Durham assisted in acquiring data from 

outside sources. Stephen Cohen and Jock Black provided 

advice with statistical or data presentation issues. 

In managing production of the volume, Rolf Lehming was 

assisted by Robert Bell, Nirmala Kannankutty, and Cheryl 

Roesel. Along with Rolf Lehming, Robert Bell engaged with 

authors in the development, review, and preparation of chapter 

content. Nirmala Kannankutty coordinated the schedule for 

producing the volume. Cheryl Roesel managed the performance of editorial, printing, and publication services. Lynda T. 

Carlson worked closely with the management team to ensure 

that the volume was successfully produced.

Robin Pentola was responsible for overall direction and 

management of the website. Tanya Gore assisted with the 

final review of the page composition and appendix tables, 

and Rajinder Raut prepared files for review and provided a 

technical review of the website. Web design and programming 

were performed by De Vo, Bridget Tuthill, Jason Shaffer, Moe 

Than, Makiko Fisher, Micah Horvat, and Richard Ashley of 

Compuware Corporation. Deborah Collins helped check and 

review data for Chapter 8 and compiled the list of Acronyms 

and Abbreviations. Overall editing of the report was performed 

by David B. Fialkoff and the staff of Lockheed Martin Business Process Solutions. OmniStudio, Inc., Washington D.C., 

was responsible for design and production of the volume. 

Many others provided much appreciated advice and assistance 

and are listed under Contributors and Reviewers. 

The National Science Board Office, under the direction of 

Michael L. Van Woert, provided vital coordination throughout 

the project. The National Science Board is especially grateful 

to the S&E Indicators Committee, and Jean M. Pomeroy and 

Matthew B. Wilson for serving as Liaisons to the Committee. 

Robert Bell and Rolf Lehming were the Executive Secretaries.

iv 

Dr. Ray M. Bowen 

Chairman, National Science Board

Dr. José-Marie Griffiths

Chairman, Committee on Science and Engineering 

Indicators
v

The following persons contributed to the report by 

reviewing chapters or otherwise assisting in its 

preparation. Their help is greatly appreciated. 

Alice Abreu, International Council for Science

Robert Allen, U.S. Patent and Trademark Office

Nick Allum, University of Essex

Nicole Aspinall, Bloomberg New Energy Finance

Cheikhou Athie, Taratec Corporation

Robert D. Atkinson, Information Technology and Innovation 

Foundation

Susan L. Aud, National Center for Education Statistics

Samuel F. Barbett, National Center for Education Statistics

Roland Bardon, SRI International

Denise Barnes, National Science Foundation

Kristina Bartsch, Bureau of Labor Statistics

Steven C. Beering, Purdue University

Nathan Bell, Council of Graduate Schools

John Benskin, SRI International

John C. Besley, University of South Carolina 

Peter Billington, Higher Education Statistics Agency, Ltd., 

England

Richard E. Bissell, National Academy of Sciences

Henry Blount, National Science Foundation

Maria Borga, Bureau of Economic Analysis

Nava Brenner, Central Bureau of Statistics, Israel

Prudy Brown, SRI International

David B. Campbell, National Science Foundation

Patricia B. Campbell, Campbell-Kibler Associates, Inc.

Jill Cape, Taratec Corporation

Anthony Carnevale, Georgetown University

Nancy Chan, SRI International

Chiao-Ling Chien, UNESCO Institute for Statistics

Susan Choy, MPR Associates

Patrick Clemins, American Association for the Advancement 

of Science

Wesley Cohen, Duke University

Daniel Cohen-Vogel, State University System of Florida

Tammy D’Amato, The Patent Board

James J. Duderstadt, University of Michigan

John H. Falk, Oregon State University

Ghislaine Filliatreau, Observatoire des Sciences et des 

Techniques, Paris

Michael Finn, Oak Ridge Institute for Science and Education

Michael Fix, Migration Policy Institute

Patricia Flynn, Bentley University

John Fraser, The Florida State University

Fred Gault, UNU-MERIT

Donna K. Ginther, University of Kansas

Leonid Gokhberg, Higher School of Economics, Russia

Mary Good, University of Arkansas at Little Rock

Daniel L. Goroff, Alfred P. Sloan Foundation

Kerry Gruber, National Center for Education Statistics

Martin P. Grueber, Battelle

Gary Guenther, Congressional Research Service

Kimberly Hamilton, The Patent Board

Martha Harris, Internal Revenue Service

Robin Henke, MPR Associates

Paul Hitlin, Project for Excellence in Journalism

Ned Howenstine, Bureau of Economic Analysis

Tracy Hunt-White, National Center for Education Statistics

Harold Javitz, SRI International

Mark Jurkowitz, Project for Excellence in Journalism

Scott Keeter, Pew Research Center

Grace Kena, National Center for Education Statistics

Cheryl Kerr, Bureau of Labor Statistics

Beethika Khan, National Science Foundation

Eurry Kim, Internal Revenue Service

Tomoe Kiyosada, SRI International

Kei Koizumi, Office of Science and Technology Policy

Natalia Kovaleva, Higher School of Economics, Russia

Norris Krueger, Entrepreneurship Northwest

J. Steven Landefeld, Bureau of Economic Analysis

Julia Lane, National Science Foundation

Charles F. Larson, Innovation Research International

Bruce V. Lewenstein, Cornell University

Joanna Lewis, Georgetown University

Wendy Li, Bureau of Economic Analysis

James Lightbourne, National Science Foundation

Susan Carol Losh, Florida State University

B. Lindsay Lowell, Georgetown University

Jacques Mairesse, Observatoire des Sciences et des 

Techniques, Paris

Sunil Mani, Centre for Development Studies, India

Marsha K.McNutt, U.S. Geological Survey

Ram Mudambi, Temple University

Tom Nardone, Bureau of Labor Statistics

Jin Noh, SRI International

Norine E. Noonan, University of South Florida St. Petersburg

Sue Okubo, Bureau of Economic Analysis

Barbara M. Olds, National Science Foundation

Dominic Olivastro, The Patent Board

James C. Palmer, Illinois State University

Jongwon Park, SRI International

Valena Plisko, National Center for Education Statistics

Joshua Powers, Indiana State University

Prem Premakumar, IHS Global Insight

Sean F. Reardon, Stanford University

E.J. Reedy, Kauffman Foundation

Carol Robbins, Bureau of Economic Analysis

Terris Ross, National Center for Education Statistics

Erin Roth, National Center for Education Statistics

Allen Ruby, Institute of Education Sciences, U.S. Department 

of Education

Contributors and Reviewers
Mikyung Ryu, American Council on Education

Henry Sauermann, Georgia Institute of Technology

Scott Shane, Case Western Reserve University

Kazuko Shinohara, National Science Foundation, Tokyo 

Office

Sean Simone, National Center for Education Statistics

Thomas M. Smith, Vanderbilt University

Laurel Smith-Doerr, Boston University

Thomas Snyder, National Center for Education Statistics

Matthew Soldner, National Center for Education Statistics

Dixie Sommers, Bureau of Labor Statistics

Thomas L. Spreen, Bureau of Labor Statistics

George Stamas, Bureau of Labor Statistics

Madeleine Sumption, Migration Policy Institute

Anton Suslov, Higher School of Economics, Russia

Gregory Tassey, National Institute of Standards and 

Technology

Lori Thurgood, SRI International

Christopher P. Toumey, University of South Carolina

Serge Tran, University of California at Los Angeles

Andrew Tyndall, Tyndall Report

Jo Anne Vasquez, Helios Education Foundation

Thomas Weko, National Center for Education Statistics

Vincent Welch, National Opinion Research Center

John A. White, University of Arkansas

Laura Williams, ICF Macro

Sacha Wunsch-Vincent, World Intellectual Property 

Organization

Lisa Younger, Small Business Administration

Michael Zastrocky, The Leadership Board for CIO’s

Klarka Zeman, Statistics Canada

Ping Zhou, Institute of Scientific and Technical Information of 

China, Beijing

vi  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

Major Global Science and Technology Trends ..........................................................................O-3

Global Expansion of Research and Development Expenditures ................................................O-4

Overseas R&D by Multinational Corporations ...........................................................................O-5

Global Higher Education and Workforce Trends .......................................................................O-7

Expanding Global Researcher Pool ............................................................................................O-8

Research Outputs: Journal Articles and Patents .........................................................................O-9

Changing International Research Collaborations .....................................................................O-11

New Research Capacity Reflected in World’s Citations Base .................................................O-12

Inventive Activity Shown by Patents ........................................................................................O-12

Global Output of Knowledge- and Technology-Intensive Firms ............................................O-15

Employment in U.S. High-Technology Manufacturing ...........................................................O-16

Global Exports of Commercial Knowledge-Intensive Services ...............................................O-17

Changing Global High-Technology Trade Patterns .................................................................O-17

Deficit in Goods Trade, Surplus in Services and Intangibles ...................................................O-18

Conclusion ................................................................................................................................O-19

Notes .........................................................................................................................................O-20

Glossary ....................................................................................................................................O-21

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

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

Introduction ................................................................................................................................. 1-7

Student Learning in Mathematics and Science ........................................................................... 1-7

Student Coursetaking in High School Mathematics and Science ............................................. 1-16

Teachers of Mathematics and Science ...................................................................................... 1-21

Transition to Higher Education ................................................................................................. 1-30

Conclusion ................................................................................................................................ 1-34

Notes ......................................................................................................................................... 1-35

Glossary .................................................................................................................................... 1-38

References ................................................................................................................................. 1-39

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 ............................... 2-16

Graduate Education, Enrollment, and Degrees in the United States ........................................ 2-24

International S&E Higher Education ........................................................................................ 2-32

Conclusion ................................................................................................................................ 2-37

Notes ......................................................................................................................................... 2-38

Glossary .................................................................................................................................... 2-39

References ................................................................................................................................. 2-39

Contents

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

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

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

Scope of the S&E Workforce ..................................................................................................... 3-7

S&E Workers in the Economy .................................................................................................. 3-17

S&E Labor Market Conditions ................................................................................................. 3-29

Demographics of the S&E Workforce ...................................................................................... 3-40

Global S&E Labor Force .......................................................................................................... 3-56

Conclusion ................................................................................................................................ 3-62

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

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

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

Chapter 4. Research and Development: National Trends 

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

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

Introduction ................................................................................................................................. 4-7

Trends in National R&D Performance ....................................................................................... 4-7

U.S. Business R&D................................................................................................................... 4-17

R&D Performed Abroad by U.S.-Owned Companies ............................................................. 4-25

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

Exports and Imports of R&D-Related Services ........................................................................ 4-27

Federal R&D ............................................................................................................................. 4-28

Federal Technology Transfer and Other Innovation-Related Programs .................................. 4-37

International R&D Comparisons .............................................................................................. 4-40

Conclusion ................................................................................................................................ 4-53

Notes ......................................................................................................................................... 4-53

Glossary .................................................................................................................................... 4-55

References ................................................................................................................................. 4-56

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

Highlights .................................................................................................................................... 5-4

Chapter Overview ...................................................................................................................... 5-7

Expenditures and Funding for Academic R&D .......................................................................... 5-7

Infrastructure for Academic R&D ........................................................................................... 5-15

The Academic Doctoral S&E Workforce ................................................................................. 5-19

Outputs of S&E Research: Articles and Patents ....................................................................... 5-32

Conclusion ............................................................................................................................... 5-50

Notes ......................................................................................................................................... 5-51

Glossary .................................................................................................................................... 5-54

References ................................................................................................................................. 5-54

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

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

Introduction ................................................................................................................................. 6-8

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

Worldwide Distribution of Knowledge- and Technology-Intensive Industries ....................... 6-17

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

Innovation-Related Indicators of the U.S. and Other Major Economies .................................. 6-46

Investment and Innovation in Clean Energy and Technologies ............................................... 6-60

Conclusion ............................................................................................................................... 6-69

Notes ......................................................................................................................................... 6-70

Glossary .................................................................................................................................... 6-72

References ................................................................................................................................. 6-72

viii  Contents
Science and Engineering Indicators 2012 ix

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

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

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

Information Sources, Interest, and Involvement ......................................................................... 7-6

Public Knowledge About S&T ................................................................................................. 7-18

Public Attitudes About S&T in General ................................................................................... 7-27

Public Attitudes About Specific S&T-Related Issues .............................................................. 7-34

Conclusion ................................................................................................................................ 7-44

Notes ......................................................................................................................................... 7-45

Glossary .................................................................................................................................... 7-48

References ................................................................................................................................. 7-48

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

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

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

Elementary/Secondary Education ............................................................................................. 8-12

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

Workforce ................................................................................................................................. 8-74

Financial Research and Development Inputs ............................................................................ 8-88

Research and Development Outputs ....................................................................................... 8-104

Science and Technology in the Economy ............................................................................... 8-114

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-4

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

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

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

AAAS American Association for the Advancement 

of Science

ACA Angel Capital Association

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

BEA Bureau of Economic Analysis

BLS Bureau of Labor Statistics

BRDIS Business R&D and Innovation Survey

CCSSI Common Core State Standards Initiative

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

DNA deoxyribonucleic acid

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

EICC Interagency Coordinating Committee

EPA Environmental Protection Agency

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

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

HHS Department of Health and Human Services

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

KEI Knowledge Economy Index

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

NCES National Center for Education Statistics

NCLB The No Child Left Behind Act of 2001

NCRPA National Cooperative Research and 

Production Act

x 
Science and Engineering Indicators 2012 xi

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

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

RDD random direct dialing

RD&D R&D 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

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

VCU Virginia Commonwealth University

WebCASPAR Integrated Science and Engineering 

Resources Data System
xii 

About Science and Engineering Indicators

Science and Engineering Indicators (SEI) is first and foremost a volume of record comprising the major high-quality 

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, Web-based 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

 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 http://www.nsf.

gov/statistics/indicators/, contains detailed data tables keyed 

to each of the eight chapters. SEI includes a list of abbreviations/acronyms and an index.

The National Science Board authors one or more companion pieces, which draw upon 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 2012 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.

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.
Science and Engineering Indicators 2012 xiii

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.

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. 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. Data for the indicators are graphically 

displayed in United States maps that color code states into 

quartiles and in state-by-state tables. A small number of appendix tables for this chapter can be found online.

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.

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 http://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

Major Global Science and Technology Trends ..........................................................................O-3

Global Expansion of Research and Development Expenditures ................................................O-4

Overseas R&D by Multinational Corporations ...........................................................................O-5

Global Higher Education and Workforce Trends .......................................................................O-7

Expanding Global Researcher Pool ............................................................................................O-8

Research Outputs: Journal Articles and Patents .........................................................................O-9

Changing International Research Collaborations .....................................................................O-11

New Research Capacity Reflected in World’s Citations Base .................................................O-12

Inventive Activity Shown by Patents ........................................................................................O-12

Global Output of Knowledge- and Technology-Intensive Firms ............................................O-15

Employment in U.S. High-Technology Manufacturing ...........................................................O-16

Global Exports of Commercial Knowledge-Intensive Services ...............................................O-17

Changing Global High-Technology Trade Patterns .................................................................O-17

Deficit in Goods Trade, Surplus in Services and Intangibles ...................................................O-18

Conclusion ................................................................................................................................O-19

Notes .........................................................................................................................................O-20

Glossary ....................................................................................................................................O-21

List of Figures

Figure O-1. Estimated R&D expenditures worldwide: 1996–2009 ............................................O-4

Figure O-2. R&D expenditures for United States, EU, and 10 Asian economies: 

1996–2009 ..............................................................................................................................O-4

Figure O-3. R&D expenditures as a share of economic output of selected regions/ 

countries: 1996–2009 .............................................................................................................O-5

Figure O-4. Average annual growth of R&D expenditures for United States, EU, 

and selected Asian economies: 1996–2007, 2007–08, and 2008–09 .....................................O-5

Figure O-5. Location of estimated worldwide R&D expenditures: 1996 and 2009 ...................O-6

Figure O-6. R&D performed in the United States by U.S. affiliates of foreign companies, 

by investing region, and R&D performed abroad by foreign affiliates of U.S. 

multinational corporations, by host region: 1998 and 2008 ...................................................O-6

Figure O-7. U.S. multinational companies’ R&D performed abroad: 1999–2008 .....................O-6

Figure O-8. First university degrees, by selected region/country: 2008 or latest data ...............O-7

Figure O-9. First university degrees in natural sciences and engineering, by selected 

country/economy: 1998–2008 ................................................................................................O-8

Figure O-10. Doctoral degrees in natural sciences and engineering, by selected region/ 

country: 2000 to most recent year ..........................................................................................O-8

Figure O-11. Average annual growth in number of researchers, by region/country/ 

economy: 1995–2002 and 2002–09 .......................................................................................O-9

Figure O-12. R&D employment of U.S.-based multinational corporations: 1994, 1999, 

2004, and 2009 .......................................................................................................................O-9

Figure O-13. S&E journal articles produced, by selected region/country: 1995–2009 ............O-10
O-2 Overview

Figure O-14. Engineering journal articles produced, by selected region/country: 

1995–2009 ............................................................................................................................O-10

Figure O-15. Engineering journal articles as a share of total S&E journal articles, 

by selected region/country: 1995–2009 ...............................................................................O-11

Figure O-16. Research articles with international coauthors, by selected region/country/ 

economy: 1989–2009 ...........................................................................................................O-11

Figure O-17. Internationally coauthored articles with authors in Asia, by Asian author 

location: 1989–2009 .............................................................................................................O-12

Figure O-18. U.S. research collaborations with EU and selected Asian countries/ 

economies: 2000–10.............................................................................................................O-12

Figure O-19. EU research collaborations with the United States and selected Asian 

countries/economies: 2000–10 .............................................................................................O-13

Figure O-20. Chinese research collaborations with the United States, EU, and selected 

Asian countries/economies: 2000–10 ..................................................................................O-13

Figure O-21. Share of selected region’s/country’s citations to international literature: 

2000–10 ................................................................................................................................O-13

Figure O-22. Intra-Asia citation patterns for China, Asia-8, and Japan: 1999–2010 ...............O-14

Figure O-23. Citations of U.S. research articles in non-U.S. literature, by region/country: 

1998–2010 ............................................................................................................................O-14

Figure O-24. Share of U.S. patents granted to non-U.S. inventors, by inventor region/ 

country: 1992–2010 .............................................................................................................O-14

Figure O-25. Global high-value patents, by selected region/country: 2000–08 .......................O-14

Figure O-26. Global value added of knowledge- and technology-intensive industries: 

1998–2010 ............................................................................................................................O-15

Figure O-27. Value added of commercial knowledge-intensive services, by selected 

region/country: 1998–2010 ..................................................................................................O-15

Figure O-28. Value added of high-technology manufacturing industries, by selected 

region/country: 1998–2010 ..................................................................................................O-16

Figure O-29. Value added of computer and office machinery manufacturing, by selected 

region/country: 1998–2010 ..................................................................................................O-16

Figure O-30. U.S. high-technology manufacturing employment: 2000–10 .............................O-16

Figure O-31. Value of U.S. high-technology manufacturing output per 1,000 employees: 

2000–10 ................................................................................................................................O-17

Figure O-32. Exports of commercial knowledge-intensive services, by selected region/ 

country: 1998–2010 .............................................................................................................O-17

Figure O-33. High-technology exports, by selected region/country: 1998–2010 ....................O-18

Figure O-34. Share of global high-technology exports, by selected region/country: 

1998–2010 ............................................................................................................................O-18

Figure O-35. Asia-8 global high-technology exports to United States/EU and China: 

1998–2010 ............................................................................................................................O-18

Figure O-36. China’s high-technology exports to selected regions/countries: 1998–2010 ......O-19

Figure O-37. Trade balance of high-technology goods, by selected region/country: 

1998–2010 ............................................................................................................................O-19

Figure O-38. Trade balance in knowledge-intensive services and intangible assets, 

by selected region/country: 1997–2009 ...............................................................................O-19
Science and Engineering Indicators 2012 O-3

Introduction

This overview of the National Science Board’s Science 

and Engineering Indicators 2012 highlights some major developments in international and U.S. science and technology 

(S&T). It is not intended to be comprehensive; the reader 

will find more extensive data in the body of each chapter. 

Major findings on particular topics appear in the Highlights 

sections that precede chapters 1–7. 

The indicators included in Science and Engineering 

Indicators 2012 derive from a variety of national, international, public, and private sources and may not be strictly 

comparable in a statistical sense. As noted in the text, some 

data are weak, 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. 

The overview focuses on the trend in the United States 

and many other parts of the world toward the development 

of more knowledge-intensive economies in which research, 

its commercial exploitation, and other intellectual work are 

of growing importance. Industry and government play key 

roles in these changes. 

The overview examines how these S&T patterns and 

trends affect the position of the United States, using broadly comparable data wherever possible for the United States, 

the European Union (EU1), Japan, China, and other selected 

Asian economies (the Asia-8: India, Indonesia, Malaysia, the 

Philippines, Singapore, South Korea, Taiwan, and Thailand). 

The overview sketches an analytical framework for, and 

a broad outline of, the main S&T themes, which it then examines through the lens of various indicators. R&D and human resources indicators feature prominently, along with 

indicators of research outputs and their use in the form of 

article citations and patents. The overview then describes the 

growth and structural shifts in international high-technology 

markets, trade, and relative trade positions. 

Some of the data available as of this writing cover all or 

part of the period of the 2007–09 financial and economic 

crisis that continues to unsettle the world. The crisis has 

affected the range of S&T endeavors, from basic research 

to production and trade of high-technology goods and 

knowledge-intensive services. The full effects of these events 

will take years to become apparent, but, to the extent permitted by available data, the overview will comment on recession-induced changes in the major well-established trends.

Major Global Science and 

Technology Trends

Since the 1990s, a global wave of market liberalization 

has produced an interconnected world economy, accompanied by unprecedented levels of activity and growth and by 

ongoing structural changes. Governments in many parts of 

the developing world, viewing science and technology as 

integral to economic growth and development, have set out 

to build more knowledge-intensive economies. They have 

taken steps to open their markets to trade and foreign investment, develop their S&T infrastructures, stimulate industrial 

R&D, expand their higher education systems, and build indigenous R&D capabilities. Over time, global S&T capabilities have grown, nowhere more so than in Asia.

As more effective communication and management tools 

have been developed, multinational corporations (MNCs) 

seeking to access these new markets have evolved global 

corporate structures that draw on far-flung, specialized, 

global supplier networks. In turn, host governments have 

often attached conditions to market access that, along with 

technology spillovers, have aided in the development of 

indigenous S&T capabilities. Western- and Japan-based 

MNCs are increasingly joined in world S&T markets by 

newcomers headquartered in developing nations. 

In most broad aspects of S&T activities, the United States 

continues to maintain a position of leadership. But it has experienced a gradual erosion of its position in many specific 

areas. Two contributing developments to this erosion are the 

rapid increase in a broad range of Asian S&T capabilities outside of Japan and the effects of EU efforts to boost its relative 

competitiveness in R&D, innovation, and high technology.

Asia’s rapid ascent as a major world S&T center is 

chiefly driven by developments in China, which on most 

indicators continues to show long-term growth that would 

normally be regarded as unsustainable. But several other 

Asian economies (the Asia-8) have also played a role. All 

are intent on boosting quality of, and access to, higher education and developing world-class research and S&T infrastructures. The Asia-8 functions like a loosely structured 

supplier zone for China’s high-technology manufacturing 

export industries. This supplier zone increasingly appears 

to include Japan. Japan, a preeminent S&T nation, is continuing to lose ground relative to China and the Asia-8 in 

high-technology manufacturing and trade. India’s high gross 

domestic product (GDP) growth continues to contrast with a 

fledgling overall S&T performance.

The EU is seeking to hold its own in the face of these 

worldwide S&T shifts. Its innovation-focused policy initiatives have been supported by the creation of a shared currency and the elimination of internal trade and migration 

barriers. Much of the EU’s high-technology trade is with 

other EU members. EU research performance is strong and 

marked by pronounced EU-supported, intra-EU collaboration. The EU is also focused on boosting the quality and international standing of its universities.

Other countries share this heightened focus on S&T as a 

means of economic growth. Brazil and South Africa show 

high S&T growth rates, but from low bases. Among the 

more developed nations, Russia’s S&T establishment continues to struggle in both relative and absolute terms, whereas Israel, Canada, and Switzerland are examples of mature, 

high-performing S&T establishments. 
O-4 Overview

Global Expansion of Research 

and Development Expenditures

Global R&D expenditures over the past decade have 

grown faster than global GDP,2 an indication of widespread 

efforts to make economies more knowledge and technology intensive. The global total rose from an estimated $522 

billion in 1996 to approximately $1.3 trillion in 2009, with 

the rate of growth slowing in the 2008–09 recession years 

(figure O-1). Although the specific data points in figure O-1 

are imprecise estimates, the steady and strong upward trend 

illustrates the rapidly growing global focus on innovation 

through R&D.3

R&D investments of Western countries slowed markedly in the face of adverse economic conditions. After 2008, 

R&D growth stopped and decreased for both the United 

States and the EU, after accounting for inflation. Growth for 

the Asian region (China, Japan, and the Asia-8) and the rest 

of the world slowed somewhat in 2008 and 2009, but from 

very high rates in earlier years. 

The United States remained by far the single largest R&Dperforming country, with an R&D expenditure of $400 billion 

in 2009. For the first time, the Asian region’s total of $399 

billion4

 matched the U.S. total in 2009 (figure O-2). 

China’s 2008–09 R&D growth increased by a record 

28%—well above its 1997–2007 trendline growth of 22%—

and propelled it past Japan into second place. 2010 data 

released by China’s National Bureau of Statistics show a 

further 22% increase.

R&D expenditures can be viewed as long-term investments in innovation. The R&D/GDP ratio is a convenient 

indicator of how much of a nation’s economic activity is 

devoted to innovation through R&D. A U.S. goal in the 

1950s was to achieve an R&D investment of 1% of GDP 

by 1957. More recently, many governments have set their 

sights at 3% of GDP in pursuit of developing knowledgebased economies, a figure the EU has formally made its 

long-term planning target.5

However, decisions affecting the bulk of R&D expenditures are generally made by industry, thus removing achievement of such a target from direct government control. In the 

United States, industry funds about 62% of all R&D. The 

EU average is 54%, but with considerable range (e.g., nearly 

70% for Germany, but 45% for the United Kingdom). In 

China, Singapore, and Taiwan, industry funding ranges from 

60% upward. Nevertheless, government planners monitor 

the R&D/GDP ratio as an indicator of innovative capacity, 

even as few countries reach the 3% mark. 

Over the past decade, many developing economies in 

Asia have exhibited increased R&D/GDP ratios; conversely, the United States and the EU ratios have broadly held 

steady. Japan’s comparatively high R&D/GDP ratio reflects 

the confluence of contracting GDP and flat R&D. 

China’s R&D/GDP ratio almost tripled, from 0.6% in 

1996 to 1.7% in 2009, a period during which China’s GDP 

grew at 12% annually—an enormous, sustained increase. 

The gap in China’s R&D/GDP ratio relative to those of developed economies suggests that there is room for China’s 

R&D volume to continue to grow rapidly (figure O-3).

The decade-long (1996–2007) R&D growth rates of mature S&T economies were lower than those of developing 

ones. Growth of R&D expenditures in the United States, the 

Figure O-1

Estimated R&D expenditures worldwide: 1996–2009

Dollars (billions)

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of Organisation 

for Economic Co-operation and Development, Main Science and 

Technology Indicators (2011-1 and previous years) and United Nations 

Educational, Scientific and Cultural Organization Institute for Statistics, 

http://stats.uis.unesco.org.

Science and Engineering Indicators 2012

1996 1999 2001 2003 2005 2007 2009

0

200

400

600

800

1,000

1,200

1,400

Figure O-2

R&D expenditures for United States, EU, and 10 

Asian economies: 1996–2009

Dollars (billions)

Asia-10 = China, India, Indonesia, Japan, Malaysia, Philippines, 

Singapore, South Korea, Taiwan, Thailand; EU = European Union

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, special tabulations (2011) of 

Organisation for Economic Co-operation and Development, Main 

Science and Technology Indicators (2011-1 and previous years) and 

United Nations Educational, Scientific and Cultural Organization 

Institute for Statistics, http://stats.uis.unesco.org.

Science and Engineering Indicators 2012

1996 1999 2001 2003 2005 2007 2009

0

50

100

150

200

250

300

350

400

450

United States

EU

Asia-10

Rest of world
Science and Engineering Indicators 2012 O-5

EU, and Japan were in the 5.4%–5.8% range while growth 

ranged from about 9.5%–10.5% for Singapore and Taiwan, 

to 12% for South Korea. 

The effect of the global economic slowdown on R&D 

expenditures is dramatic—a sharp drop in growth in most 

locations in 2008–09 that is in stark contrast to a 28% rise in 

China’s R&D spending, its highest growth rate since 2000 

(figure O-4). 

The relatively greater R&D growth rates of Asian economies (excluding Japan) resulted in changes in the global distribution of estimated R&D expenditures. Compared to 1996, 

the North America region’s (United States, Canada, and 

Mexico) share of estimated world R&D activity decreased 

from 40% to 36% by 2009; the EU’s share declined from 31% 

to 24%. The Asia/Pacific region’s share increased from 24% 

to 35%, Japan’s low growth notwithstanding (figure O-5).

Overseas R&D by 

Multinational Corporations

The shift toward greater R&D expenditures in Asia is also 

reflected in R&D flows between MNCs and their overseas 

affiliates in which they hold majority ownership (figure O-6).

Overseas R&D expenditures of U.S.-based MNCs ($37 

billion in 2008) shifted toward Asian markets whose combined share increased from 11% in 1998 to 20% a decade 

later, with increases in China, South Korea, Taiwan, and 

Singapore. In 1998, about 83% of all overseas R&D by U.S.-

headquartered MNCs took place in Europe and Canada; by 

2008, their combined percentage had decreased to 74%. 

A crude indicator of the pace of utilization of overseas 

R&D talent and facilities by U.S. MNCs is the percentage 

of the MNCs’ total R&D that is conducted by their majorityowned overseas affiliates. Over the past decade, this share 

has gradually risen from about 13% to 16% (figure O-7).

Figure O-3

R&D expenditures as a share of economic output 

of selected regions/countries: 1996–2009

Percent of GDP

EU = European Union; GDP = gross domestic product

NOTE: 2009 data unavailable for South Korea.

SOURCE: Organisation for Economic Co-operation and Development, 

Main Science and Technology Indicators (2011-1 and previous 

years).

Science and Engineering Indicators 2012 

1996 1999 2001 2003 2005 2007 2009

0

1

2

3

4

United States

EU

Japan

China

South Korea

Figure O-4

Average annual growth of R&D expenditures for United States, EU, and selected Asian economies: 1996–2007, 

2007–08, and 2008–09

Percent

EU = European Union

NOTE: 2009 data unavailable for South Korea.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2011) of Organisation for Economic 

Co-operation and Development, Main Science and Technology Indicators (2011-1 and previous years).

Science and Engineering Indicators 2012

Singapore Japan United States EU Taiwan China South Korea

–15

–10

–5

0

5

10

15

20

25

30

1996–2007 2007–08 2008–09
O-6 Overview

Affiliates of foreign-headquartered MNCs in 2008 spent 

about $40.5 billion on R&D in the United States, virtually 

unchanged from the preceding year. The companies’ share 

of total U.S. business R&D has fluctuated between 13% and 

15% since 2000.

Figure O-5

Location of estimated worldwide R&D 

expenditures: 1996 and 2009

Percent

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of Organisation 

for Economic Co-operation and Development, Main Science and 

Technology Indicators (2011-1 and previous years) and United Nations 

Educational, Scientific and Cultural Organization Institute for Statistics, 

http://stats.uis.unesco.org.

Science and Engineering Indicators 2012

North America Europe Asia/Pacific Rest of world

0

10

20

30

40

50

1996: $522 billion 2009: $1.28 trillion

Science and Engineering Indicators 2012

NOTES: Preliminary estimates for 2008 (1998 data in parentheses). 

SOURCES: Bureau of Economic Analysis, Survey of Foreign Direct Investment in the United States (annual series); Survey of U.S. Direct Investment 

Abroad (annual series).

Figure O-6

R&D performed in the United States by U.S. affiliates of foreign companies, by investing region, and R&D

performed abroad by foreign affiliates of U.S. multinational corporations, by host region: 1998 and 2008

$1.21

($0.11) $1.80 ($0.40)

$1.47

($0.75)

$3.04 ($1.75)

$1.44 ($2.35) 

$5.44 ($2.96)

$7.21 ($1.60)

$30.28 ($16.43)

$0.36 ($0.13)

$24.16 ($10.39)

Current U.S. dollars (billions)

$1.06 ($0.14)

 $0.06 ($0.04) 

Figure O-7

U.S. multinational companies’ R&D performed 

abroad: 1999–2008

Percent

NOTES: Data for majority-owned affiliates. Preliminary estimate for 

2008.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of Bureau of 

Economic Analysis, Survey of U.S. Direct Investment Abroad.

Science and Engineering Indicators 2012

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0

5

10

15

20
Science and Engineering Indicators 2012 O-7

Global Higher Education 

and Workforce Trends

No comprehensive measures of the global S&E labor 

force exist, but fragmentary data indicate rapid growth, concentrated in developing countries, in the number of individuals who pursue education beyond the secondary level. Their 

number of degrees, especially degrees in the natural sciences 

and engineering (NS&E), has diminished the advantage that 

mature countries had in advanced education.6 The low U.S. 

share of global engineering degrees in recent years is striking; well above half of all such degrees are awarded in Asia7

(figure O-8). 

Governments in many Western countries and in Japan are 

concerned about lagging student interest in studying NS&E, 

fields they believe convey technical skills and knowledge 

that are essential for knowledge-intensive economies. In the 

developing world, the number of students earning first university degrees—that are considered broadly comparable to 

a U.S. baccalaureate—in NS&E is rising. 

China, especially, has driven the rise of first university 

NS&E degrees—from about 280,000 in 2000 to 1 million 

in 2008 (figure O-9). Its degree structure has a pronounced 

concentration on engineering degrees, which represent about 

30% of all first university degrees, 60% of S&E degrees, and 

70% of NS&E degrees (the U.S. equivalents are 4%, 14%, 

and 28%). 

South Korea, Taiwan, and Japan show similar field patterns. The combined NS&E degrees earned by their students, 

about 330,000 in 2008, exceeded the 248,000 earned by U.S. 

students, even though the U.S. population was considerably 

larger (300 million versus 200 million).

The expansion of NS&E degrees extends beyond first 

university degrees to degrees certifying completed advanced 

study. Since 2000, the number of NS&E doctorates awarded 

in Japan and India has increased to approximately 7,100 and 

8,000, respectively. NS&E doctorate awards from universities in China have more than tripled since 2000, to about 

26,000 in 2008, exceeding the comparable number of NS&E 

doctorates awarded in the United States (figure O-10). 

Moreover, unlike in China, in the United States a large 

proportion of these degrees go to non-U.S. citizens. Most 

of the post-2000 increase in U.S. NS&E doctorate production reflects degrees awarded to temporary visa holders, 

Figure O-8

First university degrees, by selected region/country: 2008 or latest data

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand; EU = European Union

SOURCES: Organisation for Economic Co-operation and Development, Education Online database, http://www.oecd.org/education; and national 

statistical offices.

Science and Engineering Indicators 2012

Engineering (2.0 million)

Natural sciences (1.7 million) Social sciences (1.3 million)

United States 10%

EU 18%

Japan 2%

China 17% Asia-8 26%

Russia 6%

Brazil 3%

All others 18% United States 19%

EU 23%

Japan 16%

China 11%

Asia-8 3%

Russia 6%

Brazil 2%

All others 20%

United States 4%

EU 17%

Japan 5%

China 34%

Asia-8 17%

Russia 7%

Brazil 2%

All others 14%

Non-S&E fields (9.9 million)

United States 11%

EU 18%

Japan 2%

China 11%

Asia-8 23%

Russia 11%

Brazil 7%

All others 18%
O-8 Overview

who in 2009 earned about 10,900 of the 24,700 U.S. NS&E 

doctorates.8 Temporary visa holders, not counting foreign 

students with permanent visas, have earned 39% to 48% of 

U.S. NS&E doctorates since 2000. More than half of these 

students are from China, India, and South Korea.

For engineering alone, the numbers are considerably more 

concentrated. Since 2000, the share of U.S. engineering doctorates earned by temporary visa holders has risen from 51% 

to as high as 63% in 2005–07, before dropping to 57% in 

2009. Nearly three-quarters of foreign national recipients of 

engineering doctorates were from East Asia or India.

Many of these individuals, especially those on temporary 

visas, will leave the United States after earning their doctorates, but if past trends continue, a large proportion—about 

60%—will stay. It appears, though, that graduates from toprated programs are somewhat less likely than others to stay.9

Expanding Global Researcher Pool

Estimates of the number of the world’s researchers provide broad support for the trends and shifts suggested by 

R&D and degree data. 

The estimated number of researchers grew from nearly 

4 million in 1995 to about 6 million in 2008.10 The United 

Figure O-9

First university degrees in natural sciences and 

engineering, by selected country/economy: 

1998–2008

NOTE: Natural sciences include physical, biological, environmental, 

agricultural, and computer sciences, and mathematics.

SOURCES: Organisation for Economic Co-operation and 

Development, Education Online database, http://www.oecd.org/ 

education; and national statistical offices. 

Science and Engineering Indicators 2012

Engineering

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0

50

100

150

200

250

300

350

China

United States

Japan

South Korea

France

Germany

Taiwan

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0

100

200

300

400

500

600

700

800

China

United States

Japan

South Korea

France

Germany

Taiwan

Thousands

Natural sciences

Figure O-10

Doctoral degrees in natural sciences and 

engineering, by selected region/country: 

2000 to most recent year

EU = European Union

NOTE: Natural sciences include physical, biological, environmental, 

agricultural, and computer sciences, and mathematics.

SOURCES: Organisation for Economic Co-operation and Development, 

Education Online database, http://www.oecd.org/education; and 

national statistical offices. 

Science and Engineering Indicators 2012

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

0

5

10

15

20

25

30

35

40

45

U.S. total

U.S. permanent resident

U.S. temporary resident

Selected EU

Russia

Japan

China

India

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

0

2

4

6

8

10

12

14

16

18

U.S. total

U.S. permanent resident

U.S. temporary resident

Selected EU

Russia

Japan

China

India

Thousands

Natural sciences

Engineering
Science and Engineering Indicators 2012 O-9

Research Outputs: 

Journal Articles and Patents

Research produces new knowledge, products, or processes. Research publications reflect contributions to knowledge, 

patents indicate useful inventions, and citations on patents to 

the scientific and technical literature indicate linkages between research and practical application. 

States and the 27 EU-member countries accounted for about 

1.4 and 1.5 million researchers each—a combined 49% of 

the total but below the 51% share they had held a decade 

earlier. China’s researchers tripled over the period.

Trends in researcher growth rates vary greatly by country 

and region (figure O-11). The United States and the EU had 

moderate annual growth in the 3%–4% range between 1995 

and 2002, after which U.S. growth moderated. Comparable 

rates for Japan fluctuated between ±1%; Russia’s researcher 

numbers kept contracting. Growth in the Asian region outside Japan was generally higher in the 2002–09 period than 

earlier and averaged 8%–9% for Taiwan, Singapore, and 

South Korea, capped by China’s 12% annual average.

The contribution of multinational corporations to researcher growth in the overseas markets in which they operate is unknown. But preliminary data, available every 5 years, suggest 

a pronounced expansion of R&D employment by overseas affiliates (majority-owned only) of U.S.-based MNCs in recent 

years.11 After gradually increasing from 102,000 in 1994 to 

138,000 in 2004, their R&D employment almost doubled to 

267,000 in 2009. Over the same 5 years, the MNCs’s R&D 

employment in the United States rose from about 716,000 to 

about 739,000. This boosted the overseas share of their total 

R&D employment from 16% to 27% (figure O-12). Not included are researchers in overseas firms in which MNCs hold 

less than majority ownership or in unaffiliated firms that perform contract research for MNCs. 

Data on employment of researchers by foreign-based 

MNCs in other countries are unavailable, except for those 

working in the United States. Growth in U.S. employment of 

researchers working for U.S. affiliates of foreign-based MNCs 

has been broadly in line with overall U.S. researcher trends. 

Figure O-11

Average annual growth in number of researchers, by region/country/economy: 1995–2002 and 2002–09

Percent

EU = European Union

NOTE: Growth rates through last available year in range indicated.

SOURCE: Organisation for Economic Co-operation and Development, Main Science and Technology Indicators (2011-1 and previous years).

Science and Engineering Indicators 2012

United States EU Russia Japan Singapore Taiwan South Korea China

–4

–2

0

2

4

6

8

10

12

14

1995–2002 2002–09

Figure O-12

R&D employment of U.S.-based multinational 

corporations: 1994, 1999, 2004, and 2009

Thousands (bars) Percent (line)

NOTES: Employment abroad limited to majority-owned affiliates. 2009 

data are preliminary.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of Bureau of 

Economic Analysis, International Economic Accounts, U.S. Direct 

Investment Abroad (2009 and previous years).

Science and Engineering Indicators 2012

1994 1999 2004 2009

0

100

200

300

400

500

600

700

800

900

Employment in United States Employment abroad

Percent abroad

0

5

10

15

20

25

30
O-10 Overview

The number of research articles published in a set of international, peer-reviewed journals has grown from about 

460,200 in 1988 to an estimated 788,300 in 2009.12 The geographical distribution of the authors13 provides an indication 

of the size of a country’s or region’s research enterprise and 

its production of research results (figure O-13). 

Researchers in the EU and the United States have long 

dominated world article production, but their combined 

share of published articles decreased steadily from 69% in 

1995 to 58% in 2009. In little more than a decade, Asia’s 

world article share expanded from 14% to 24%, driven by 

China’s 16% average annual growth. By 2007, China surpassed Japan’s article output and moved into second place 

behind the United States—up from 14th place in 1995. By 

2009, China accounted for about 9% of world article output. 

India’s output of scientific and technical articles, stagnating through the late 1990s, began to rise after 2000, but 

India’s ranking hardly changed from 12th to 11th place in 

2009. Japan’s output declined in volume and global share. 

Russia’s article output flattened after 2005, following a 

decade-long decline that resulted in a drop from 7th to 13th 

place in global output ranking.

The distribution of a country’s research publications 

across different fields is a broad reflection of its research 

priorities. A large portion of U.S. articles focused on the 

biomedical and other life sciences; scientists in Asia and 

some major European countries published a preponderance 

of articles in the physical sciences14 and engineering. Recent 

shifts in emphasis include China’s growing focus on chemistry R&D and South Korea’s growing output in biological 

and medical sciences. These changes reflect government 

policy choices as China is building up its chemicals industry, 

and South Korea is trying to develop a world-class reputation in health sciences.

Worldwide, the number of engineering research articles 

have increased substantially faster than total S&E article 

production, particularly in Asia outside Japan (figure O-14). 

Growth in the United States and Japan averaged less than 

2%; in the EU, about 4.4%. China’s engineering article output grew by close to 16% annually, and the Asia-8 economies expanded their combined output by 10% a year. 

Consequently, the production of engineering research 

articles has shifted away from established S&T nations. In 

1995, the U.S. share of engineering articles was 25%, by 

2009, 13%. Japan’s share declined from 10% to 5% during 

the same period. The EU’s share dipped from 25% to 19%. 

Asia’s share, excluding Japan, increased from 9% to 23%, 

with China producing nearly half of these articles by 2009. 

The relative preponderance of engineering articles in developing Asian economies reflects the region’s emphasis on 

Figure O-13

S&E journal articles produced, by selected region/

country: 1995–2009

Thousands

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South 

Korea, Taiwan, Thailand; Asia-10 = Asia-8 plus China and Japan; 

EU = European Union

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, special tabulations (2011) of 

Thomson-Reuters, Science and Social Sciences Citation Indexes, 

http://thomsonreuters.com/products_services/science/, and The 

Patent BoardTM. 

Science and Engineering Indicators 2012

1995 1997 1999 2001 2003 2005 2007 2009

0

50

100

150

200

250

300

United States

EU

Asia-10

Japan

China

Asia-8

Rest of world

Figure O-14

Engineering journal articles produced, by selected 

region/country: 1995–2009

Thousands

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South 

Korea, Taiwan, Thailand; Asia-10 = Asia-8 plus China and Japan; 

EU = European Union

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, special tabulations (2011) of 

Thomson-Reuters, Science and Social Sciences Citation Indexes, 

http://thomsonreuters.com/products_services/science/, and The 

Patent BoardTM. 

Science and Engineering Indicators 2012

1995 1997 1999 2001 2003 2005 2007 2009

0

5

10

15

20

25

30

35

United States

EU

Asia-10

Japan

China

Asia-8

Rest of world
Science and Engineering Indicators 2012 O-11

building high-technology manufacturing capabilities. In the 

United States and the EU, 7%–8% of all articles are in engineering, in Asia, 11%–20% (figure O-15).

Patents list the prior scientific and technological knowledge on which they are built. Increasingly, U.S. patents have 

cited scientific articles as one such source. The foreign share 

of such patent-to-article citations is rising, indicating growing utilization of published research in foreign inventions.

Changing International 

Research Collaborations

Collaborative research is becoming the norm, and collaboration across national boundaries is generally increasing, as 

reflected in international coauthorship on research articles. 

In 1988, only 8% of the world’s S&E articles had international coauthors; by 2009, this share had grown to 23%. For 

the world’s major S&T regions, the 2009 rate ranged from 

about 27% to 42%.

International coauthorship trends for China, South Korea, 

and Taiwan differ from this pattern. Each location had 

reached an international coauthorship level of 20%–30% 

of its total articles by the early 1990s. They broadly maintained the same relative level of international collaboration, 

indicating that the bulk of their rapid article growth was due 

to articles with only domestic authors (figure O-16).

As a result of the large volume of both U.S. and EU article outputs, along with EU policies that encourage intraUnion collaboration, U.S.-based authors appeared on 43%, 

and EU-based authors on 67%, of the world’s internationally coauthored articles in 2009. Increasingly, Asia-based 

authors are participating in international collaborations, signaling maturing of their scientific and engineering capabilities (figure O-17).

Size matters: China, with its rapidly growing research 

capacity, can support more international collaborations than 

Singapore. An index of international collaboration corrects 

for different-sized science establishments and allows comparisons of regional and country coauthorship patterns. On 

this index, values above “1” indicate higher-than-expected, 

and values below “1” lower-than-expected, degrees of collaboration with researchers in a particular country.15

U.S. international collaborations measured by this index 

were widespread. Links were strongest with South Korea, 

Taiwan, Canada, and Israel; collaboration with China, 

Japan, and India was also above the U.S. average. The pattern of U.S. international collaborations remained mostly 

steady over the past decade (2000–10), though ties increased 

with China and weakened somewhat with a number of other 

Asian economies (figure O-18). 

Figure O-15

Engineering journal articles as a share of total S&E 

journal articles, by selected region/country: 

1995–2009

Percent

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South 

Korea, Taiwan, Thailand; EU = European Union

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, special tabulations (2011) of 

Thomson-Reuters, Science and Social Sciences Citation Indexes, 

http://thomsonreuters.com/products_services/science/, and The 

Patent BoardTM. 

Science and Engineering Indicators 2012

1995 1997 1999 2001 2003 2005 2007 2009

0

5

10

15

20

25

United States

EU

Japan

China

Asia-8

Figure O-16

Research articles with international coauthors, 

by selected region/country/economy: 1989–2009

Percent

EU = European Union

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, special tabulations (2011) of 

Thomson-Reuters, Science and Social Sciences Citation Indexes, 

http://thomsonreuters.com/products_services/science/, and The 

Patent BoardTM. 

Science and Engineering Indicators 2012

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

0

5

10

15

20

25

30

35

40

45

United States

EU

Japan

China

South Korea

Taiwan
O-12 Overview

EU collaborations were equally widespread and increased measurably over the period, quite likely in response 

to explicit EU policies. Levels of collaboration with Asia 

were generally well below the expected level, and the EU 

collaboration index was lower (and declining) with China 

than with India (figure O-19).

Collaboration among Asia’s growing number of researchers was generally substantially higher than expected, with 

high levels of collaboration between China and Japan, South 

Korea, Singapore, and Taiwan. Collaboration between China 

and India, as measured by this index, diminished noticeably 

over the decade amid rising Indian collaboration with South 

Korea and Japan (figure O-20). The underlying index values 

suggest the genesis of an intra-Asian zone of scientific collaboration that has a counterpart in the region’s knowledgeand technology-intensive (KTI) economic activities.

New Research Capacity Reflected 

in World’s Citations Base

Citations to the work of others in the literature are a 

broad indicator of the usefulness of this work in ongoing 

research.16 In most major countries/regions, citations to the 

international literature have grown at the expense of citing 

purely domestic work. International citations make up 70% 

of all references in Japanese articles and 65% of all references in the combined output of the Asia-8. In the United 

States, the EU, and China, about half of all citations are to 

articles that include at least one author from another country 

(figure O-21).

Intra-Asian citation patterns show a distinct reliance by 

Chinese researchers on the growing domestic literature, and 

on intra-Asia-8 articles by Asia-8 scientists, accompanied 

by rising Asia-8-China citations and vice versa. Citations to 

Japanese science and engineering articles are gradually declining (figure O-22).

Increasingly, high-quality research is being done not only 

in the United States, the EU, and Japan, but in a broader set 

of economies. This is illustrated by the declining proportion 

of citations to U.S. publications in articles originating elsewhere (figure O-23). The same trend appears in the references found in the top 1% of all cited articles. 

Inventive Activity Shown by Patents

Government-issued patents protect inventions that 

are new, not obvious, and useful. The U.S. Patent and 

Trademark Office (USPTO) grants patents to inventors from 

all over the world, and the sheer volume of U.S. patents and 

Figure O-17

Internationally coauthored articles with authors in 

Asia, by Asian author location: 1989–2009

Percent

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South 

Korea, Taiwan, Thailand

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, special tabulations (2011) of 

Thomson-Reuters, Science and Social Sciences Citation Indexes, 

http://thomsonreuters.com/products_services/science/, and The 

Patent BoardTM. 

Science and Engineering Indicators 2012

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

0

2

4

6

8

10

12

14

Japan

China

Asia-8

India

Singapore

South Korea

Taiwan

Figure O-18

U.S. research collaborations with EU and selected 

Asian countries/economies: 2000–10

Index

EU = European Union

NOTE: Index value >1 indicates higher-than-expected degree of 

collaboration.

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, special tabulations (2011) of 

Thomson-Reuters, Science and Social Sciences Citation Indexes, 

http://thomsonreuters.com/products_services/science/, and The 

Patent BoardTM. 

Science and Engineering Indicators 2012

2000 2001 2003 2002 2004 2005 2006 2007 2008 2009 2010

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

EU

Japan

China

South Korea Taiwan

India

Singapore
Science and Engineering Indicators 2012 O-13

the importance of the U.S. market makes them a useful indicator of trends in the geography of inventive activity.

In 1992, about 54% of USPTO patents were granted 

to U.S.-based inventors; by 2010, this percentage had 

fallen to 49%, a possible indication of growing inventive 

activity elsewhere.17

Among patents granted to non-U.S. inventors, the shares 

of EU- and Japan-based individuals have eroded by 9–11 

percentage points since 1992. Asia-8’s share rose by 15 

points over the period, mostly because of activity by South 

Korea and Taiwan (figure O-24). 

The picture of inventive activity drawn by Chinese patents is mixed. Among USPTO awards to non-U.S. inventors, 

China’s share rose from below 0.5% to 3%. By this indicator, broad-based indigenous inventive activity, a focus of 

Chinese government policy, appears to remain elusive. But 

patents granted in China to China-based inventors rose from 

5,000 in 2001 to 65,000 in 2009, and the Chinese-inventor 

share of Chinese patent grants increased from 33% to more 

than 50%.

Not all patents are equal in presumed value. Seeking protection for the same invention in the United States, the EU, 

and Japan requires substantial resources, suggesting that such 

patents are considered especially valuable by their owners. 

Figure O-19

EU research collaborations with the United States 

and selected Asian countries/economies: 2000–10

Index

EU = European Union

NOTE: Index value >1 indicates higher-than-expected level of 

collaboration.

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, special tabulations (2011) of 

Thomson-Reuters, Science and Social Sciences Citation Indexes, 

http://thomsonreuters.com/products_services/science/, and The 

Patent BoardTM. 

Science and Engineering Indicators 2012

2000 2001 2003 2005 2007 2009 2002 2004 2006 2008 2010

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Singapore

Japan

China

United States

South Korea

Taiwan

India

Figure O-20

Chinese research collaborations with the United 

States, EU, and selected Asian countries/

economies: 2000–10

Index

EU = European Union

NOTE: Index value >1 indicates higher-than-expected levels of 

collaboration.

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, special tabulations (2011) of 

Thomson-Reuters, Science and Social Sciences Citation Indexes, 

http://thomsonreuters.com/products_services/science/, and The 

Patent BoardTM. 

Science and Engineering Indicators 2012

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

0

1

2

3

4

5

6

EU

Japan

United States

South Korea

Taiwan

India

Singapore

Figure O-21

Share of selected region’s/country’s citations to 

international literature: 2000–10

Percent

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South 

Korea, Taiwan, Thailand; EU = European Union

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, special tabulations (2011) of 

Thomson-Reuters, Science and Social Sciences Citation Indexes, 

http://thomsonreuters.com/products_services/science/, and The 

Patent BoardTM. 

Science and Engineering Indicators 2012

2000 2001 2003 2005 2007 2009 2002 2004 2006 2008 2010

0

10

20

30

40

50

60

70

80

Japan

Asia-8

United States

China

EU
O-14 Overview

Figure O-22

Intra-Asia citation patterns for China, Asia-8, and 

Japan: 1999–2010

Percent

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South 

Korea, Taiwan, Thailand

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, special tabulations (2011) of 

Thomson-Reuters, Science and Social Sciences Citation Indexes, 

http://thomsonreuters.com/products_services/science/, and The 

Patent BoardTM. 

Science and Engineering Indicators 2012

1999 20012000 20032002 20052004 20072006 20092008 2010

0

5

10

15

20

25

30

35

Asia-8 citing Japan

Asia-8 citing China

Asia-8 citing Asia-8

China citing Japan

China citing China

China citing Asia-8

Figure O-23

Citations of U.S. research articles in non-U.S. 

literature, by region/country: 1998–2010

Percent

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South 

Korea, Taiwan, Thailand; EU = European Union

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, special tabulations (2011) of 

Thomson-Reuters, Science and Social Sciences Citation Indexes, 

http://thomsonreuters.com/products_services/science/, and The 

Patent BoardTM. 

Science and Engineering Indicators 2012

1998 2000 2002 2004 2006 2008 2010

0

20

40

60

80

EU

Japan

China

Asia-8

EU

Japan

China

Asia-8

In top 1% cited articles

In all

Figure O-24

Share of U.S. patents granted to non-U.S. 

inventors, by inventor region/country: 1992–2010

Percent

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South 

Korea, Taiwan, Thailand; EU = European Union 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of U.S. Patent 

and Trademark Office, Number of Utility Patent Applications Filed 

in the United States, by Country of Origin: Calendar Years 1965 to 

Present, http://www.uspto.gov/web/offices/ac/ido/oeip/taf/appl_yr.pdf.

Science and Engineering Indicators 2012

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

0

10

20

30

40

50

60

China

EU

Japan

Asia-8

Rest of world

In 2008, U.S. and EU inventors each accounted for 30% 

of such high-value patents. Japan’s share declined since 2000, 

while that of the Asia-8 rose, largely on the strength of Korean 

patenting activity (figure O-25). In contrast, China-based inventors appeared on only 1% of these high-value patents.

Figure O-25

Global high-value patents, by selected region/

country: 2000–08

Percent

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South 

Korea, Taiwan, Thailand; EU = European Union

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of Organisation 

for Economic Co-operation and Development, OECD.StatExtracts, 

patent statistics, http://stats.oecd.org/index.aspx. 

Science and Engineering Indicators 2012

2000 2001 2002 2003 2004 2005 2006 2007 2008

0

5

10

15

20

25

30

35

United States

EU

Japan

Asia-8

Rest of world
Science and Engineering Indicators 2012 O-15

Global Output of Knowledgeand Technology-Intensive Firms 

Governments in developed countries believe that KTI 

economies create well-paying jobs, contribute high-value 

output, and ensure economic competitiveness. Governments 

in many developing countries believe the same and promote the growth of knowledge-intensive services and hightechnology manufacturing industries.18

In 2010, these KTI industries contributed a combined 

$18.2 trillion to global economic output—about 30% of 

world GDP, and a growing share of many countries’ economic output. Services are by far the largest aggregate, totaling $16.8 trillion: $10.9 trillion in tradable services, and 

$5.9 trillion in more location-bound health and education 

services. High-technology manufacturing added $1.4 trillion (figure O-26).

The effects of the 2007–09 recession on KTI industry 

output are visibly more severe than were those of the 2001 

recession. A slowdown in growth in 2008 was followed by 

contraction or, in the case of knowledge-intensive services, 

lack of growth in 2009 and a sharp upswing in 2010. Hightechnology manufacturing went from 4.9% growth to a 5.7% 

contraction to 13.5% growth. 

The largest aggregate in the KTI category is commercial knowledge-intensive services, which includes business 

and financial services and communications. The value of its 

global output increased from $4.9 trillion in 1998 to $9.4 trillion in 2007. As with all KTI industries, it showed recession 

effects that were more severe for the EU than for the United 

States (figure O-27). The United States, with $3.6 trillion in 

2010, generated the largest value of these industries, as its 

output expanded after flat growth in 2008 and 2009. 

The EU was particularly hard hit by the recession, resulting in declining output followed by shallow growth. The 

rest of the world suffered a 1-year slowdown or shallow declines, followed by sometimes vigorous growth. Increased 

production by China expanded its value-added output of 

commercial knowledge-intensive services and increased its 

global share from 3% in 2005 to 7% in 2010.

High-technology manufacturing value-added output suffered a global contraction in 2001 but offered a more varied 

picture in the 2007–09 recession: Brief but sharp contraction in Asia (excepting China) followed by an equally sizeable rebound in 2010; a sharper drop in the EU, followed by 

shallow growth; slowing growth followed by strong expansion in the United States; and unimpeded, rapid growth in 

China. By 2010, China’s global share was 19%, up from 3% 

in 1998 (figure O-28).

The five high-technology manufacturing industries are, 

in decreasing order of the $1.4 trillion 2010 global valueadded total: communications equipment and semiconductors ($512 billion), pharmaceuticals ($346 billion), scientific 

instruments ($275 billion), aerospace ($137 billion), and 

computers and office machinery ($127 billion). The United 

States ranked first overall in aerospace and tied with the 

EU in pharmaceuticals, but in communications equipment 

manufacturing it ranked behind Japan and the Asia-8, and in 

scientific instruments it ranked behind the EU. 

Figure O-26

Global value added of knowledge- and technologyintensive industries: 1998–2010

Dollars (trillions)

NOTE: Industries defined by Organisation for Economic 

Co-operation and Development. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of IHS Global 

Insight, World Industry Service database. 

Science and Engineering Indicators 2012

1998 2000 2002 2004 2006 2008 2010

0

2

4

6

8

10

12

14

16

18

20

All knowledge- and technologyintensive industries

Commercial technologyintensive services

Public knowledgeintensive services

High-technology manufacturing

Figure O-27

Value added of commercial knowledge-intensive 

services, by selected region/country: 1998–2010

Dollars (trillions)

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South 

Korea, Taiwan, Thailand; EU = European Union

NOTE: Industries defined by Organisation for Economic 

Co-operation and Development.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of IHS Global 

Insight, World Industry Service database. 

Science and Engineering Indicators 2012

1998 2000 2002 2004 2006 2008 2010

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

United States

EU

Japan

China

Asia-8

Rest of world
O-16 Overview

China accounts for almost half of the global value of 

computer and office machinery production. This category 

saw a particularly rapid shift in relative world positions 

(figure O-29).

Employment in U.S. 

High-Technology Manufacturing

The effects of recessions go well beyond their impact on 

the value of the outputs of production. More far-reaching effects may be felt in the labor markets. Although internationally comparable data on KTI employment are fragmentary, 

employment data on U.S. high-technology manufacturing 

are illuminating (figure O-30).

Employment in the five U.S. high-technology manufacturing sectors reached a peak in 2000, just before the 

8-month-long recession of 2001. This recession led to job 

losses in these industries that were substantial and permanent. The 18-month 2007–09 recession further squeezed 

employment in these industries. The total job loss in hightechnology manufacturing over the period was 687,000—a 

decline of 28% since 2000.

The value of output generated by these industries contracted in 2001 and again slowed in 2007–08. However, over 

the decade, output per 1,000 employees doubled (unadjusted 

for inflation) (figure O-31). 

Figure O-28

Value added of high-technology manufacturing 

industries, by selected region/country: 1998–2010

Dollars (billions)

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South 

Korea, Taiwan, Thailand; EU = European Union

NOTE: Industries defined by Organisation for Economic 

Co-operation and Development.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of IHS Global 

Insight, World Industry Service database. 

Science and Engineering Indicators 2012

1998 2000 2002 2004 2006 2008 2010

0

50

100

150

200

250

300

350

400

450

United States

EU

Japan

China

Asia-8

Rest of world

Figure O-29

Value added of computer and office machinery 

manufacturing, by selected region/country: 

1998–2010

Dollars (billions)

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South 

Korea, Taiwan, Thailand; EU = European Union

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of IHS Global 

Insight, World Industry Service database. 

Science and Engineering Indicators 2012

1998 2000 2002 2004 2006 2008 2010

0

10

20

30

40

50

60

70

United States

EU

Japan

China

Asia-8

Rest of world

Figure O-30

U.S. high-technology manufacturing employment: 

2000–10

Thousands

NOTES: Industries defined by Organisation for Economic Co-operation 

and Development. Bars define recession periods.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of Bureau of 

Labor Statistics, Current Employment Survey.

Science and Engineering Indicators 2012

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

0

500

1,000

1,500

2,000

2,500

3,000

2001

recession

2007–09

recession
Science and Engineering Indicators 2012 O-17

Global Exports of Commercial 

Knowledge-Intensive Services

The global value of trade in commercial knowledge-intensive services is gradually increasing but it represents less than 

10% of global production of such services. The value of commercial knowledge-intensive services grew from $453 billion 

in 1998 to $1.46 trillion in 2008 and then contracted to $1.36 

trillion in 2009. 

The EU is the largest exporter of commercial knowledgeintensive services (excluding intra-EU exports), accounting 

for about 30% of the world total, followed by the United 

States with 22% and the Asia-8 with 15% (mostly from India 

and Singapore). The EU suffered a 10% drop in export volume in 2009, followed by less than 1% growth in 2010; the 

United States loss was 2%, followed by 6% growth. 

Export declines in 2009 by China and the Asia-8 were in 

the 4%–6% range, after which the Asia-8 rebounded with 16% 

growth. 2010 data for China are unavailable (figure O-32).

Changing Global High-Technology 

Trade Patterns

Two global recessions notwithstanding, total export volume of high-technology products increased at an 8% annual 

rate from 1998 to 2010, not accounting for inflation. This 

ranged from a low of 2% for Japan to a high of 19% for 

China, with the United States and EU in the 5%–7% range. 

The increase reflects a number of developments: growing 

international capacity for high-technology manufacturing, 

expansion of multinational companies’ overseas production, 

and growing reliance on specialized and geographically dispersed supplier networks.

China became the largest single high-technology exporter in 2006 and, together with the Asia-8, has since accounted for about half of total world exports in high-technology 

goods. After relatively prolonged slowdowns following the 

2001 global recession, China and Asia-8 high-technology 

exports accelerated until sharp declines in 2009, which were 

followed immediately by expansion beyond the 2008 levels. The general patterns were similar for the United States 

and the EU, but with a less complete recovery for the EU. 

Japan’s high-technology exports have been flat, not accounting for inflation, for a decade or more (figure O-33).

These changes have affected the relative positions of the 

developed and developing countries. China’s share of world 

high-technology exports increased from 7% in 1998 to 22% 

in 2010, while the Asia-8 share dropped to 26% in 2009 before easing upward again. Shares of the United States, EU, 

Japan, and the rest of the world—mostly developing countries—were flat or declined (figure O-34). 

An Asian high-technology supplier zone has developed 

that is largely arrayed around China. The shift in output of 

high-technology goods toward developing Asian economies 

has been accompanied by the growth of intraregional supplier relationships that provide intermediate goods, many for 

further assembly and eventual export. The share of Asia-8 

high-technology exports going directly to the United States 

Figure O-31

Value of U.S. high-technology manufacturing 

output per 1,000 employees: 2000–10

Dollars (millions)

NOTE: Industries defined by Organisation for Economic 

Co-operation and Development. 

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, special tabulations (2011) of IHS 

Global Insight, World Industry Service database, and Bureau of 

Labor Statistics, Current Employment Survey. 

Science and Engineering Indicators 2012

2000 2001 2003 2005 2007 2009 2002 2004 2006 2008 2010

0

50

100

150

200

250

Figure O-32

Exports of commercial knowledge-intensive 

services, by selected region/country: 1998–2010

Dollars (billions)

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South 

Korea, Taiwan, Thailand; EU = European Union 

NOTES: EU excludes internal trade. China includes Hong Kong. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of World Trade 

Organization, International Trade and Tariff database. 

Science and Engineering Indicators 2012

1998 2000 2002 2004 2006 2008 2010

0

50

100

150

200

250

300

350

400

450

500

United States

EU

Japan

China

Asia-8

Rest of world
O-18 Overview

and the EU declined from more than 50% to under 30%, 

while exports to China rose from 10% to 37% of their total 

(figure O-35). 

China’s high-technology exports withstood the global recession very well. Slowing sharply from double-digit to high 

single-digit growth in 2007–08, they declined by 9% in 2009 

but rose by 22% in 2010. After 2009, China’s high-technology 

exports to the United States jumped from $107 billion to 

$137 billion. Similarly large increases were evident for other 

export destinations (figure O-36).

Deficit in Goods Trade, Surplus in 

Services and Intangibles

In high-technology goods trade, U.S. surpluses through 

the mid-1990s turned into substantial deficits after 1997 

which reached almost $100 billion in 2010 (figure O-37). 

Major deficit drivers were communications and computer 

goods, whose production shift to Asia coincided with growing U.S. demand. Pharmaceuticals contributed to the deficit, 

while aerospace and scientific instruments counteracted it.

The EU’s overall high-technology trade deficit was relatively stable, though lower than that of the United States. 

Its communications and computers deficit, however, was 

almost identical to that of the U.S., reflecting the same dynamic of rising domestic demand and relocated production. 

Figure O-34

Share of global high-technology exports, by 

selected region/country: 1998–2010

Percent

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South 

Korea, Taiwan, Thailand; EU external = European Union trade 

excluding intra-EU exports

NOTE: Industries defined by Organisation for Economic 

Co-operation and Development. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of IHS Global 

Insight, World Trade Service database. 

Science and Engineering Indicators 2012

1998 2000 2002 2004 2006 2008 2010

0

5

10

15

20

25

30

35

United States

EU external

Japan

China

Asia-8

Rest of world

Figure O-35

Asia-8 global high-technology exports to United 

States/EU and China: 1998–2010

Percent

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South 

Korea, Taiwan, Thailand; EU = European Union 

NOTE: Industries defined by Organisation for Economic 

Co-operation and Development. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of IHS Global 

Insight, World Trade Service database. 

Science and Engineering Indicators 2012

1998 2000 2002 2004 2006 2008 2010

0

10

20

30

40

50

60

Asia-8 to China

Asia-8 to United States and EU

Figure O-33

High-technology exports, by selected region/

country: 1998–2010

Dollars (billions)

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South 

Korea, Taiwan, Thailand; EU external = European Union trade 

excluding intra-EU exports

NOTE: Industries defined by Organisation for Economic 

Co-operation and Development. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of IHS Global 

Insight, World Trade Service database. 

Science and Engineering Indicators 2012

1998 2000 2002 2004 2006 2008 2010

0

100

200

300

400

500

600

700

United States

EU external

Japan

China

Asia-8

Rest of world
Science and Engineering Indicators 2012 O-19

China and the Asia-8 had substantial 2010 high-technology 

trade surpluses of $157 billion and $226 billion, respectively, indicating that recession-induced reductions in their previous surpluses were likely to prove only temporary. 

U.S. trade in commercial knowledge-intensive services 

and intangible assets—business, financial, and communications services, and payments of royalties and fees—has 

produced a consistent and growing surplus (figure O-38). It 

reached a record $108 billion in 2008, sufficient to counterbalance the high-technology goods deficit, and has been 

flat since then, reflecting the recession’s effect. The EU’s 

surplus was sharply off, and that of the Asia-8 fell as well—

reflections of the continuing effects of the global recession. 

Conclusion

Science and technology are becoming ubiquitous features 

of many developing countries, as they integrate into the 

global economy. As a group, developing countries appear 

to either have been less severely affected by the worldwide 

financial crisis and recession than the United States, EU, and 

Japan, or to have recovered more rapidly. Governments in 

these countries have held firm in building S&T into their development policies, as they vie to make their economies more 

knowledge- and technology-intensive to ensure their competitiveness. These policies include long-term investments 

in higher education to develop human talent, infrastructure development, support for research and development, 

Figure O-36

China’s high-technology exports to selected 

regions/countries: 1998–2010

Dollars (billions)

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South 

Korea, Taiwan, Thailand; EU = European Union

NOTE: Industries defined by Organisation for Economic 

Co-operation and Development. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of IHS Global 

Insight, World Trade Service database. 

Science and Engineering Indicators 2012

1998 2000 2002 2004 2006 2008 2010

0

20

40

60

80

100

120

140

Asia-8

EU

United States

Japan

Figure O-37

Trade balance of high-technology goods, 

by selected region/country: 1998–2010

Dollars (billions)

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South 

Korea, Taiwan, Thailand; EU = European Union 

NOTE: Industries defined by Organisation for Economic 

Co-operation and Development. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of IHS Global 

Insight, World Trade Service database. 

Science and Engineering Indicators 2012

1998 2000 2002 2004 2006 2008 2010

–150

–100

–50

0

50

100

150

200

250

United States

EU

Japan

Asia-8 China

Figure O-38

Trade balance in knowledge-intensive services 

and intangible assets, by selected region/country: 

1997–2009

Dollars (billions)

Asia-8 = India, Indonesia, Malaysia, Philippines, Singapore, South 

Korea, Taiwan, Thailand; EU = European Union

NOTE: Industries defined by Organisation for Economic 

Co-operation and Development. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of World Trade 

Organization, International Trade and Tariff database. 

Science and Engineering Indicators 2012

1997 1999 2001 2003 2005 2007 2009

–40

–20

0

20

40

60

80

100

120

140

United States

EU

Japan China

Asia-8
O-20 Overview

8. Both figures exclude those with unknown citizenship 

(1,600 in 2007) and those with degrees in medical/other life 

sciences. Engineering figures exclude about 630 with unknown citizenship. The U.S. figures include individuals with 

permanent visas.

9. Michael G. Finn, Stay Rates of Foreign Doctorate 

Recipients From U.S. Universities, 2007 (Oak Ridge, TN: 

Oak Ridge Institute for Science and Education, January 

2010).

10. Both estimates are based on data from a limited number of countries reporting their data, on a full-time equivalent basis, to OECD.

11. Preliminary 2009 data from Bureau of Economic 

Analysis, International Economic Accounts, U.S. Direct 

Investment Abroad (2009 and previous years); National 

Science Foundation tabulation.

12. The database used is the expanding set of journals 

included in the Thomson Scientific, Science and Social 

Sciences Citation Indexes; IpIQ, Inc.; and National Science 

Foundation tabulations.

13. Author location is determined by location of institutional affiliation. For example, an American scientist listed 

at a Japanese university is considered located in Japan; a 

Japanese scientist listed at a U.S. university is considered 

located in the United States.

14. The physical sciences are physics; chemistry; earth, 

atmospheric, and ocean sciences; and astronomy.

15. Expectation is based on a location’s total international collaborations. The index numerator is the percentage of 

country A’s international collaborations with country B; the 

denominator is country B’s percentage of the world’s international collaborations. See appendix table 5-41.

16. Citation indicators are subject to a number of distortions: self-citation, citation of failed theories, hypotheses, 

and approaches; citation of domestic versus foreign articles; 

language and cultural barriers; etc. However, when aggregated over many articles, citation indicators carry information about the relative use of an accumulated knowledge 

base in subsequent work.

17. In these data, USPTO patents are assigned to the location of the first-named inventor.

18. These industry groups are defined by OECD and form 

the basis for databases of economic activity that cover a large 

number of the world’s economies. Knowledge-intensive services industries include the commercially tradable business, 

financial, and communications services; and education and 

health services, which are considered more nearly locationbound and closer to government functions. High-technology 

manufacturing industries include aircraft and spacecraft; 

pharmaceuticals; office, accounting, and computing machinery; radio, television, and communication equipment; and 

medical, precision, and optical instruments.

19. See National Science Board, International Science 

and Engineering Partnerships: A Priority for U.S. Foreign 

Policy and Our Nation’s Innovation Enterprise, NSB-08-4 

(Arlington, VA: National Science Foundation, 2008).

attraction of foreign direct investment and technologically 

advanced multinational companies, and the eventual development of indigenous high-technology capabilities. 

The resulting developments open the way for widespread 

international collaboration in science and engineering research.19 The broad trend in this direction is reflected in increasing numbers of research articles in the world’s leading 

journals with authors in multiple countries. These researchers are increasingly able to draw on high-quality work done 

outside the traditional S&T locales, and international connections are deepened by globally mobile experts. 

Competitive elements, such as the quest for international 

talent, enter as well. Once largely limited to major Western 

nations, the quest for international talent is now pursued by 

many and “brain drain” has evolved into cross-national flows 

of highly trained specialists. Governments are eager to develop more modern economies that will increase the wealth of 

their populations. They seek to establish specialty niches and 

indigenous world-class capacity and to become competitive in 

international investment, development, and trade.

The globalization of the world economy has brought unprecedented levels of growth to many countries, demonstrating that benefits can accrue to all. These trends continue, but 

the structural changes that are part and parcel of rapid growth 

bring with them painful dislocations that are amplified by 

the continuing changes forced by the recent recession. 

Notes

1. Unless otherwise noted, EU refers to the 27 member 

countries of the European Union.

2. World Bank estimates of global gross national income.

3. These estimates rely on data from the Organisation 

for Economic Co-operation and Development (OECD) and 

the United Nations Educational, Scientific and Cultural 

Organization Institute for Statistics. They are not precise 

measures. Reported data are converted to dollar totals using purchasing power parities, the local costs of a standard 

market basket of goods and services. The accuracy of this 

standard economic conversion may degrade in the case 

of developing economies. In addition, estimation of some 

missing data and variable reporting mean that there is uncertainty about any specific point estimate. The reader’s 

focus is directed to the overall trend, which reflects an internally consistent estimate over time. 

4. The latest updated 2009 U.S. R&D estimate is $400.5 

billion. The overview uses the most recent OECD numbers 

to allow comparison with other countries’ values.

5. European Commission, Barcelona European Council, 

Presidency Conclusions (Barcelona, Spain, March 2002).

6. See Joan Burrelli and Alan Rapoport, Reasons for 

International Changes in the Ratio of Natural Science 

and Engineering Degrees to the College-Age Population,

SRS 09-308 (Arlington, VA: National Science Foundation, 

January 2009).

7. No data are available for India, making this share estimate an upper bound.
Science and Engineering Indicators 2012 O-21

Glossary

Asia-8: Includes India, Indonesia, Malaysia, Philippines, 

Singapore, South Korea, Taiwan, and Thailand.

Asia-10: Includes China, Japan, India, Indonesia, 

Malaysia, Philippines, Singapore, South Korea, Taiwan, and 

Thailand.

European Union: The 27 member states of the European 

Union since 2007 include 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.

High-technology manufacturing: Includes air- and 

spacecraft; pharmaceuticals; office, accounting, and computing machinery; radio, television, and communication 

equipment; and medical, precision, and optical instruments.

Knowledge-intensive services: Includes commercial business, financial, and communication services and 

largely publicly supported education and health services. 

Commercial knowledge-intensive services exclude education and health.

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 High School Mathematics and Science .............................................1-4

Teachers of Mathematics and Science ......................................................................................1-5

Transition to Higher Education ................................................................................................1-6

Introduction ..................................................................................................................................1-7

Student Learning in Mathematics and Science ............................................................................1-7

Mathematics and Science Performance in Grades 4, 8, and 12 ................................................1-8

Algebra Performance of Ninth Graders in 2009 .....................................................................1-13

International Comparisons of Mathematics and Science Performance ..................................1-14

Student Coursetaking in High School Mathematics and Science ..............................................1-16

High School Graduation Requirements and Curriculum Standards .......................................1-17

Mathematics and Science Coursetaking in High School ........................................................1-18

Teachers of Mathematics and Science .......................................................................................1-21

Characteristics of High-Quality Teachers ..............................................................................1-22

School Factors Contributing to Teachers’ Effectiveness ........................................................1-25

Transition to Higher Education ..................................................................................................1-30

Completion of High School ....................................................................................................1-30

Enrollment in Postsecondary Education .................................................................................1-32

Conclusion .................................................................................................................................1-34

Notes ..........................................................................................................................................1-35

Glossary .....................................................................................................................................1-38

References ..................................................................................................................................1-39

List of Sidebars

Development and Content of NAEP Technology and Engineering Literacy Assessment ..........1-9

Mathematics and Science Achievement in Charter Schools ......................................................1-11

Sample Items from PISA ...........................................................................................................1-16

Common Core State Standards ..................................................................................................1-18

Taking Algebra I Before High School .......................................................................................1-19

Measuring Teaching Quality ......................................................................................................1-23

Teacher Attrition ........................................................................................................................1-29

State Student Tracking Systems .................................................................................................1-33
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 ..............................................................................................................1-7

Table 1-2. Changes in NAEP mathematics scores of students in grades 4, 8, and 12, 

by student and school characteristics: Selected years, 1990–2009 .......................................1-10

Table 1-3. Distribution of all students in grades 4 and 8 and students in the top 1% 

taking the NAEP mathematics assessments, by student characteristic: 2009 .......................1-12

Table 1-4. Average NAEP mathematics scores of all students in grades 4 and 8 

and students in the top 1%: Selected years, 2000–09 ...........................................................1-12

Table 1-5. Changes in NAEP mathematics score gaps between selected groups 

of students, by grade level: Selected years, 1990–2009........................................................1-13

Table 1-6. State graduation requirements for mathematics and science, 

by number of years required: Selected years, 1987–2008 ....................................................1-18

Table 1-7. Public middle and high school teachers with regular or advanced certification, 

by teaching field: Academic years 2003–04 and 2007–08 ...................................................1-23

Table 1-8. Preparation of public school mathematics and science teachers for teaching 

in their field, by school level and teaching field: Academic years 2003–04 

and 2007–08 ..........................................................................................................................1-26

Table 1-9. On-time graduation rates of U.S. public high school students, 

by race/ethnicity: 2006 and 2009 ..........................................................................................1-32

Table 1-A. High school graduates completing first-year algebra before high school, 

by student characteristic: 2005 and 2009 ..............................................................................1-19
Science and Engineering Indicators 2012 1-3

List of Figures

Figure 1-1. Average NAEP mathematics scores of students in grades 4 and 8: 

Selected years, 1990–2009 ....................................................................................................1-10

Figure 1-2. Average NAEP science scores of students in grade 4, by student 

and school characteristics: 2009............................................................................................1-14

Figure 1-3. Ninth-graders proficient in various algebra skill areas, 

by race/ethnicity: 2009 ..........................................................................................................1-15

Figure 1-4. Average PISA mathematics and science literacy scores of 15-year-old 

students in the United States and OECD countries: 2003, 2006, and 2009 ..........................1-17

Figure 1-5. Average total and advanced mathematics and science credits 

earned by high school graduates: Selected years, 1990–2009 ..............................................1-20

Figure 1-6. High school graduates completing various advanced mathematics 

courses, by subject: Selected years, 1990–2009 ...................................................................1-20

Figure 1-7. High school graduates completing various advanced science 

and engineering courses, by subject: Selected years, 1990–2009 .........................................1-21

Figure 1-8. Participation of new public middle and high school teachers in practice 

teaching, by teaching field and minority enrollment: Academic year 2007–08 ...................1-25

Figure 1-9. Participation of public middle and high school teachers in 

professional development activities during past 12 months, by topic: 

Academic year 2007–08 ........................................................................................................1-27

Figure 1-10. Duration of professional development received by public middle 

and high school teachers in the content of subject(s) taught, by teaching field: 

Academic year 2007–08 ........................................................................................................1-28

Figure 1-11. Salaries of public middle and high school mathematics teachers 

and teacher satisfaction with salaries, by minority enrollment and school poverty level: 

Academic year 2007–08 ........................................................................................................1-30

Figure 1-12. Perceptions of working conditions of public middle and high school 

mathematics teachers, by minority enrollment and school poverty level: 

Academic year 2007–08 ........................................................................................................1-31

Figure 1-13. Serious student problems reported by public middle and high school 

mathematics teachers, by minority enrollment and school poverty level: 

Academic years 2003–04 and 2007–08 ................................................................................1-32

Figure 1-14. High school graduation rates, by OECD country: 2008 .......................................1-33

Figure 1-15. Immediate college enrollment rates among high school graduates, 

by sex: 1979–2009 ................................................................................................................1-34

Figure 1-A. Average NAEP mathematics scores of public school students 

in grades 4, 8, and 12, by charter school status: Selected years, 2003–09 ............................1-11

Figure 1-B. One-year attrition rate of public school teachers, by teaching field: 

Selected academic years, 1988–89 to 2008–09.....................................................................1-29
1-4 Chapter 1. Elementary and Secondary Mathematics and Science Education

Student Learning in Mathematics 

and Science

Gains in average mathematics scores on the National 

Assessment of Educational Progress (NAEP) between 

2007 and 2009 leveled off for grade 4 and continued for 

grade 8. For 12th graders, average mathematics scores 

improved from 2005 to 2009.

From 1990 to 2007, average mathematics scores increased 

by 27 points for fourth graders. Scores then leveled off in 

2009 across almost all demographic groups and performance levels and among students at public and private 

schools.

At grade 8, average mathematics scores steadily gained 20 

points from 1990 to 2009, with improvement for most demographic groups, performance levels, and school types.

At grade 12, average mathematics scores improved by 3 

points from 2005 to 2009, with improvement patterns similar to those of eighth graders.

Score gaps among demographic groups narrowed over 

time but remained substantial.

At grades 4, 8, and 12, white and Asian/Pacific Islander 

students had significantly higher scores than their black, 

Hispanic, and American Indian/Alaska Native counterparts. Students from higher income families also performed significantly better than their peers from lower 

income families. Although boys scored higher than girls, 

the differences were relatively small.

At grade 4, some gaps narrowed over time. Between 1990 

and 2009, the score gap between white and black students 

fell from 32 to 26 points, the score gap between public and 

private school students dropped from 12 to 7 points, and 

the score gap between low- and high-performing students 

narrowed by 9 points.

Few students in ninth grade mastered high level algebra 

skills in 2009, according to the High School Longitudinal 

Study assessment.

A majority of ninth graders demonstrated proficiency in 

lower level algebra skills such as algebraic expressions 

(86%) and multiplicative and proportional thinking (59%).

Few students reached proficiency in systems of equations 

(18%) and linear functions (9%), the two highest algebra 

skills assessed.

Relatively few students at grades 4, 8, and 12 reached 

their grade-specific proficiency levels in science on the 

2009 NAEP assessment. Science scores varied significantly across student subgroups.

At all three grade levels, whites, Asians/Pacific Islanders, and 

students from higher income families scored significantly 

higher than their counterparts. Boys also scored higher than 

girls at all three grade levels, but the difference was substantially smaller.

In both 2006 and 2009, U.S. 15-year-olds scored below those 

of many other developed countries in the Programme for 

International Student Assessment, a literacy assessment 

designed to test mathematics and science. Nonetheless, 

U.S. scores improved from 2006 to 2009.

The average mathematics literacy score of U.S. 15-yearolds declined about 9 points from 2003 to 2006, and then 

rose about 13 points in 2009, placing the United States 

below 17 of 33 other members of the Organisation for 

Economic Co-operation and Development (OECD).

The average science literacy score of U.S. 15-year-olds was 

not measurably different from the 2009 OECD average, 

though it improved by 3 points from 2006 to 2009. The 

U.S. score was lower than the score of 12 out of 33 other 

OECD nations participating in the assessment.

Student Coursetaking in High School 

Mathematics and Science

High school graduates in 2009 continued an upward trend 

of earning more credits in mathematics and science, including advanced mathematics and science courses.

The average number of credits earned in all mathematics 

courses was 3.9 in 2009, up from 3.2 in 1990. The average 

number of credits earned in all science courses was 3.5 in 

2009, up from 2.8 in 1990.

Graduates in 2009 earned an average of 1.7 credits in advanced mathematics and 1.9 credits in advanced science 

and engineering courses, compared with 0.9 and 1.1 credits, respectively, in 1990.

The percentages of students completing advanced mathematics and science courses increased in all subject areas.

In 2009, 76% of all graduates earned a credit for algebra II, 

compared with 53% of all graduates in 1990.

The percentage of students earning a credit in precalculus/

analysis more than doubled since 1990, with 35% of graduates completing precalculus/analysis in 2009, compared 

with 14% in 1990.

From 1990 to 2009, the percentage of students earning a 

credit in advanced chemistry increased from 45% to 70%. 

Increased rates were also seen in advanced biology (28% to 

45%) and physics (24% to 39%).

The percentage of students taking algebra I before high 

school increased. Twenty-six percent of high school graduates took algebra I before high school in 2009, up from 

20% in 2005.

Highlights
Although students in all racial/ethnic groups are earning 

more advanced mathematics and science credits, differences among these groups have persisted.

Asian/Pacific Islander students earned the most credits in 

advanced mathematics, an average of 2.4 credits in 2009. 

Hispanics and blacks earned the fewest credits in advanced 

mathematics, approximately 1.4 credits. White students 

earned more credits (1.8) than black or Hispanic students, 

but fewer than Asian/Pacific Islander students. Similar patterns were seen in science coursetaking.

Teachers of Mathematics and Science

The percentage of public middle and high school mathematics and science teachers with advanced degrees and 

full certification has increased since 2003, but school differences persist.

Fifty-four percent of mathematics teachers and 58% of 

science teachers had earned a master’s or higher degree in 

2007, compared with 48% and 52%, respectively, in 2003.

Eighty-seven percent of mathematics and science teachers 

held regular or advanced teaching certification in 2007—a 

significant increase for science teachers from 83% in 2003.

Degree and certification differences persist among schools 

with different student populations. For example, 69% of 

science teachers in low-poverty schools had advanced degrees versus 49% in schools with high poverty rates.

In 2007, about one in five new mathematics and science 

teachers was hired through an alternative certification 

program. Relatively more of these teachers were found in 

high-poverty or high-minority schools. For example, 26% 

of mathematics teachers in schools with the highest poverty levels became teachers through alternative certification, 

compared with 12% of those in schools with the lowest 

poverty levels. (Some alternative certification programs 

aim to place teachers in high-poverty schools.)

Novice teachers—those with 3 or fewer years of experience—are more prevalent at high-poverty and highminority schools.

In 2007, about 20% of all public middle and high school 

mathematics and science teachers were novice teachers. 

Proportionally, more of those in high-minority schools 

were novices: 22% of mathematics teachers and 25% of 

science teachers were novices, compared with 13% and 

15% in low-minority schools.

Most high school teachers of mathematics and science 

taught in field (i.e., they had a degree or full credential in 

the subject matter they taught) in 2007. In-field teaching 

is less prevalent among middle school teachers but has 

increased among middle school mathematics teachers 

since 2003.

In-field mathematics teachers in public middle schools increased from 53% in 2003 to 64% in 2007. Approximately 

70% of middle school science teachers taught in field in 

both 2003 and 2007.

Eighty-eight percent of high school mathematics teachers 

in 2007 taught in field, as did 93% of biology/life science 

teachers and 82% of physical science teachers.

Participation has increased in new teacher induction 

programs, which provide professional development and 

support during early teaching years, and the gap in participation rates between teachers at schools with different demographics has narrowed.

In 2007, 79% of new mathematics teachers and 73% of 

new science teachers in public middle and high schools 

had participated in an induction program. The corresponding rates in 2003 were 71% among mathematics teachers 

and 68% among science teachers.

In 2003, 63% of new mathematics teachers in high-minority 

schools had been in an induction program, 25 percentage 

points fewer than their counterparts at low-minority schools. 

In 2007, this gap narrowed to 8 percentage points because of 

higher participation in high-minority schools.

More than three-quarters of mathematics and science 

teachers in 2007 said that they had received some professional development in their subject matter. However, 

few participated for as many hours as research suggests 

is desirable.

In 2007, 83% of mathematics teachers and 77% of science 

teachers in public middle and high schools said they had 

received professional development in their subject matter 

during the previous 12 months.

Among those with professional development in their subject matter, 28% of mathematics teachers and 29% of science teachers received 33 hours or more. Research has 

suggested that 80 hours or more may be required to affect 

teacher knowledge and practice.

Teachers’ views of their working conditions varied with 

the characteristics of the student population at their 

schools, but some differences have narrowed since 2003.

Half of mathematics and science teachers at high-poverty 

or high-minority schools viewed student tardiness and class 

cutting as interfering with teaching. In contrast, a third of 

their counterparts at low-poverty and low-minority schools 

expressed this view.

Some differences have narrowed since 2003. Then, about 

half of mathematics teachers at high-poverty schools saw 

student apathy as a serious problem, compared with 12% 

at low-poverty schools. In 2007, that gap had narrowed by 

about 20 percentage points, reflecting more positive views 

of teachers at high-poverty schools. The gap in reported 

lack of student preparedness for learning also shrank.

Science and Engineering Indicators 2012 1-5
Transition to Higher Education

Rates of students graduating within 4 years of entering 

ninth grade (“on-time” graduation) increased slightly in 

recent years, but gaps among racial/ethnic groups persist.

In 2009, 76% of students completed high school on time, 

up from 73% in 2001.

The on-time graduation rates of black and Hispanic students increased between 2006 and 2009: from 59% to 64% 

for black students and from 61% to 66% for Hispanic students. Wide gaps remained between the on-time graduation rates of black and Hispanic students and those of white 

students, who graduated at a rate of 82% in 2009.

The U.S. high school graduation rate lags behind those of 

most other developed (OECD) nations.

The United States ranked 18th out of 25 OECD countries 

for which graduation rate data were available in 2008.

According to OECD estimates, the United States had an 

average graduation rate of 77% compared with the OECD 

average of 80%.

The majority of U.S. high school graduates enroll in a 

postsecondary institution immediately after high school 

completion.

Seventy percent of 2009 high school graduates had enrolled in a postsecondary institution by the October following high school completion, an increase of 19 percentage 

points since 1975.

Relatively more female graduates than male graduates 

enrolled immediately in postsecondary education in 2009 

(74% versus 66%).

Students from high-income families enrolled at a higher 

rate (84%) than did students from middle-income (67%) or 

low-income families (55%).

The rate for white students was 71%, compared with 63% 

for black and 62% for Hispanic students.

1-6 Chapter 1. Elementary and Secondary Mathematics and Science Education
Science and Engineering Indicators 2012 1-7

Introduction

National and state education policies continue to focus on 

improving learning by U.S. students. Policy goals include 

increasing student achievement overall, reducing disparities 

in performance among key subgroups of students, and moving the international ranking of U.S. students from the middle to the top over the next decade (The White House n.d.). 

STEM fields (science, technology, engineering, and mathematics) have been a strong focus of recent reform efforts, 

including developing common core standards across states, 

strengthening curricula, promoting advanced coursetaking, 

enhancing teacher quality, raising graduation requirements, 

and expanding technology use in education.

This chapter presents indicators of elementary and secondary mathematics and science education in the United 

States, drawing mainly on data from the National Center 

for Education Statistics (NCES) of the U.S. Department 

of Education. Table 1-1 presents an overview of the topics 

covered in this chapter and the indicators used to illuminate 

the topics.

The chapter begins by summarizing the most recent data 

on student achievement in mathematics and science, focusing on recent trends in student performance, changes in performance gaps, and the relative international standing of U.S. 

students.1 It also includes new indicators of mathematics and 

science performance by students in charter schools, trends in 

mathematics achievement among very high-scoring students, 

and the results of an algebra assessment of ninth graders.

The chapter then focuses on mathematics and science 

coursetaking in high school. This edition includes new data 

on trends in total and advanced mathematics and science 

credits earned by high school graduates and enrollment in 

algebra before high school. It also discusses the “common 

core standards” effort and state participation in that effort, 

subjects new to this volume.

The chapter turns next to public school mathematics and 

science teachers, examining their educational attainment, 

licensure, experience, professional development, attrition, 

salaries, and working conditions. All teacher indicators in 

this chapter use the latest available data, which derive from 

the 2007–08 Schools and Staffing Survey (SASS).

The chapter closes with indicators of students’ transitions 

from secondary to postsecondary education—the subject of 

chapter 2 in this volume. Updated indicators include on-time 

high school graduation rates, immediate college enrollment 

rates, and international comparisons of high school graduation rates and postsecondary enrollment.

The chapter focuses primarily on overall patterns but 

also reports variation in access to educational resources by 

schools’ minority concentration and poverty level and in 

student performance by sex, race/ethnicity, and family and 

school characteristics. Whenever a difference or change 

over time is cited in this chapter, it is statistically significant 

at the 0.05 probability level.2

Student Learning in 

Mathematics and Science

Increasing overall student achievement, especially lifting 

the performance of low achievers, is a central goal of education reform in the United States. This goal is reflected in the 

federal No Child Left Behind Act of 2001 (NCLB), which 

mandates that all students in each state reach the proficient 

level of achievement by 2014. This goal is also highlighted 

in the more recent federal Race to the Top program, which 

calls for states to design systemic and innovative educational reform strategies to improve student achievement and 

Table 1-1

Indicators of elementary and secondary school mathematics and science education

Topic Indicator

Student learning in 

mathematics and science

UÊ/Ài`ÃÊÊ{Ì]ÊnÌ]Ê>`Ê£ÓÌÊ}À>`iÀÃ1⁄2Ê>Ìi>ÌVÃÊ«iÀvÀ>ViÊÌÀÕ}ÊÓää

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Student coursetaking in 

mathematics and science

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Mathematics and science 

teachers

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UÊ/i>ViÀÊÃ>>ÀiÃÊ>`ÊÜÀ}ÊV`ÌÃÊÊÓään

Student transitions to higher 

education

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UÊi`>ÌiÊVi}iÊiÀiÌÊvÀÊ£ÇxÊÌÊÓää

UÊÌiÀ>Ì>ÊV«>ÀÃÃÊvÊVi}iÊiÀiÌÊÀ>ÌiÃÊÊÓään

Science and Engineering Indicators 2012
£nÊChapter 1. Elementary and Secondary Mathematics and Science Education

close performance gaps.3 The federal government also targets funds directly to low-performing schools through the 

School Improvement Grants program,4 for example, to support changes needed in the lowest achieving schools across 

the nation. These and other efforts to improve achievement 

are ongoing.

How has the performance of U.S. students changed over 

time? Are achievement gaps narrowing? How do U.S. students compare with their peers in other nations? This section 

addresses these questions by examining over time a series of 

indicators of student performance in mathematics and science in the United States. It begins with a review of recent 

results of mathematics and science assessments of U.S. students in grades 4, 8, and 12, followed by a review of the 

performance of ninth graders in algebra in 2009. The section 

ends by placing U.S. student performance in an international 

context, comparing the mathematics and science literacy of 

U.S. 15-year-olds with that of their peers in other countries.

Mathematics and Science Performance in 

Grades 4, 8, and 12

The National Assessment of Educational Progress 

(NAEP), a congressionally mandated program, has monitored changes in U.S. students’ academic performance in 

mathematics and science since 1969. NAEP has two assessment programs: main NAEP and NAEP Long-Term Trend 

(LTT).5 The main NAEP assesses national samples of 4th 

and 8th grade students at regular intervals and 12th grade 

students occasionally. These assessments are updated periodically to reflect contemporary curriculum standards in various subjects, including mathematics and science. (In 2014, 

NAEP will conduct its first nationwide assessment in technology and engineering literacy; see sidebar “Development 

and Content of NAEP Technology and Engineering Literacy 

Assessment.”)

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 3 decades. This section examines recent performance results using main NAEP data only. Findings based 

on NAEP LTT data have been reported in previous editions 

of Science and Engineering Indicators, and no new data 

were available from the NAEP LTT for this volume.6

Reporting NAEP Results

The main NAEP reports student performance in two 

ways: scale scores and achievement levels. Scale scores 

place students along a continuous scale based on their overall performance on the assessment. 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.

NAEP also reports student results in terms of achievement levels. Developed by the National Assessment 

Governing Board (NAGB), achievement levels are intended 

to measure how well students’ actual achievement matches 

the achievement expected of them in different subjects assessed by NAEP. Based on recommendations from educators, policymakers, and the general public, NAGB sets three 

achievement levels for all subjects assessed by NAEP 

(NCES 2010, 2011):

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.

7 

Because achievement levels were developed independently 

at each grade level, they cannot be compared across grade 

levels.8 Although the NAEP achievement levels are useful in understanding student results and have been widely 

used by national and state officials, there is disagreement 

about whether these achievement levels are appropriately 

defined. A study commissioned by the National Academy of 

Sciences asserted that NAEP achievement levels were “fundamentally flawed” (Pellegrino, Jones, and Mitchell 1999). 

The National Mathematics Advisory Panel concluded in 

2008 that NAEP scores for the two highest achievement categories (proficient and advanced) were set too high (NMAP 

2008). Both NCES and NAGB acknowledged this controversy, and NCES, upon review of congressionally mandated 

evaluations of NAEP, has recommended that achievement 

levels be used on a trial basis and interpreted with caution 

(NCES 2011).

The following review of NAEP results reports both average scale scores and achievement levels, focusing on the 

percentage of students performing at or above the proficient 

level both overall and among various subgroups of students.

Trends in Mathematics Performance 

Through 2009

Average Score. For grade 4, the average mathematics 

score increased by 27 points from 1990 to 2007 and leveled 

off from 2007 to 2009 (figure 1-1). This overall trend was repeated in almost all demographic subgroups, across students 

at all performance levels (i.e., 10th to 90th percentiles9), and 

among students at both public and private schools (table 1-2).

For grade 8, the average mathematics score increased steadily from 1990 to 2009 with a total gain of 20 points over the period, including a statistically significant 2-point gain from 2007 

to 2009 (figure 1-1). Rising scores were widespread, occurring 

among both male and female students; almost all racial/ethnic 

groups; students from families that were financially disadvantaged and advantaged; students in the low-middle, middle, and 

high ranges of performance (i.e., 25th to 90th percentiles); 

and students attending public schools (table 1-2) (see sidebar 

“Mathematics and Science Achievement in Charter Schools”). 

The score at the 10th percentile, however, was unchanged from 

2007 to 2009, indicating that mathematics performance did not 

improve significantly among very low-performing students 

during this period. 
Science and Engineering Indicators 2012 £

For grade 12, only 2005 and 2009 results are examined 

here; substantial revisions of the mathematics framework for 

the 2005 assessment made comparison with earlier assessments impossible.10 Between 2005 and 2009, the average 

mathematics score for students in grade 12 increased by 3 

points (appendix table 1-1). Improvement occurred across 

the board: for both sexes, across all racial/ethnic subgroups, 

for all performance levels, and among public school students 

(table 1-2).11 The gains in average scores were about 3−5 

points for many subgroups, with the exception of Asian/

Pacific Islander and American Indian/Alaska Native students, who posted gains of 12 and 10 points, respectively, 

from 2005 to 2009.

Achievement Level. Trends in the percentages of students in grades 4, 8, and 12 reaching the proficient level 

parallel the scale score trends. The percentage of fourth 

grade students performing at or above the proficient level 

increased steadily through 2007 but remained unchanged in 

2009. Eighth grade students, on the other hand, showed continuous improvement from 1990 to 2009. Among 12th grade 

students, the percentage of proficient students increased 

from 2005 to 2009 (appendix table 1-2).

Despite these gains, the percentage of students reaching 

the proficient level remains low. In 2009, the percentage of 

students performing at or above proficient was 39% for 4th 

graders, 34% for 8th graders, and 26% for 12th graders.

Beginning in 2014, the National Assessment of 

Educational Progress (NAEP) will administer the first nationwide student assessment in technology and engineering 

literacy. The framework defines key terms such as technology and engineering literacy, determines the content to be 

assessed, specifies the types of assessment questions to be 

asked, and guides the development of the assessment instrument (WestEd 2010).

Although the federal No Child Left Behind Act of 

2001 requires that every student be “technologically literate by the time the student finishes the eighth grade,” 

the law itself is vague in defining what technological literacy is, leaving states to determine what it means and 

how it should be assessed. Some states require engineering/technology education for students in at least some 

grades, but few have adopted formal assessments in this 

area (Metiri Group 2009). Technology- and engineeringrelated courses are typically offered in middle and high 

schools as electives or are embedded in other subject 

areas, such as science or social studies (WestEd 2010). 

Overall, coursetaking in these subjects is not widespread: 

in 2009, about 3% of high school graduates had taken an 

engineering course and 6% an engineering/science technology course (Nord et al. 2011). Currently, there are no 

national standards for K−12 engineering or technology 

education. Implementing such standards is difficult given 

limited experience with engineering/technology education at the K−12 level and insufficient numbers of teachers qualified to deliver instruction in this area (National 

Academy of Engineering 2010).

Definitions of Technology and Engineering Literacy. 

For the purpose of developing national assessments in 

this area, the NAEP framework defines technology, engineering, and technology and engineering literacy as follows (WestEd 2010, pp. 1–4):

Technology is any modification of the natural or designed world done to fulfill human needs or desires.

Engineering is a systematic and often iterative approach 

to designing objects, processes, and systems to meet human needs and wants.

Technology and engineering literacy is the capacity to 

use, understand, and evaluate technology as well as to 

understand technological principles and strategies needed to develop solutions and achieve goals.

Areas To Be Assessed. The 2014 NAEP assessment 

of technology and engineering literacy will test students 

in the following three areas:

Technology and Society covers the interaction of technology and people; effects of technology on society and 

the natural world; and questions of ethics, equity, and 

responsibility that arise from those effects.

Design and Systems includes the nature of technology; 

the engineering design process by which technologies 

are developed; and basic principles of dealing with 

everyday technologies, including maintenance and 

troubleshooting.

Information and Communication Technology involves 

computers and software learning tools; networking 

systems and protocols; and the selection and use of 

hand-held digital devices and other technologies for accessing, creating, and communicating information and 

for facilitating creative expression.

For examples of questions, see http://www.nagb.org/

publications/frameworks/prepub_naep_tel_framework_ 

2014.pdf (in chapters 3 and 4). Note that the grade level 

for these sample questions has not yet been determined.

Development and Content of NAEP Technology 

and Engineering Literacy Assessment
££äÊChapter 1. Elementary and Secondary Mathematics and Science Education

Average score

Figure 1-1

Average NAEP mathematics scores of students in 

grades 4 and 8: Selected years, 1990–2009

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 shown are for students allowed to 

use testing accommodations. 

SOURCE: National Science Foundation, National Center for Science and 

Engineering Statistics, special tabulations (2010) of NAEP 1990, 1992, 

1996, 2000, 2003, 2005, 2007, and 2009 mathematics assessments, 

National Center for Education Statistics. See appendix table 1-1.

Science and Engineering Indicators 2012

1990 1992 1996 2000 2003 2007 2009 2005

0

200

220

240

260

280

300

Grade 4

Grade 8

Trends in Mathematics Performance of 

Top Students

Although increasing student achievement is the central 

goal of educational reform in the United States, policies and 

reform efforts are aimed mainly at improving the achievement of low-achieving students (Hanushek, Peterson, and 

Woessmann 2010; Loveless 2008; NSB 2010a). Little nationally representative research has been conducted on highachieving students.

Advances in STEM, however, often depend on originality and leadership from exceptionally capable individuals. 

Although such individuals are not easily identified, data 

on students who score unusually well on standardized assessments provide some indication of performance trends 

among highly capable students. The following analysis uses 

NAEP assessment data to focus on students who score in the 

top 1% of mathematics performance in grades 4 and 8.

In 2009, the 37,000−38,000 fourth and eighth grade 

students who performed at or above the 99th percentile on 

the NAEP mathematics assessment resembled higher performing students in the general population.12 However, 

compared with fourth and eighth graders nationwide, these 

top performers were more likely to be male, to be white or 

Table 1-2

Changes in NAEP mathematics scores of students in grades 4, 8, and 12, by student and school characteristics: 

Selected years, 1990–2009

Grade 4 À>`iÊn Grade 12a

Student and school characteristic £äqÓää ÓääÇqä £äqÓää ÓääÇqä ÊÊÊÊÊÊÊÊÊÓääxqä

All students ......................................................................... 

-iÝ

Male ............................................................................. 

Female ......................................................................... 

Race/ethnicity

White ........................................................................... 

Black ........................................................................... 

Ã«>V ...................................................................... 

Ã>É*>VvVÊÃ>`iÀ .................................................. 

iÀV>Ê`>É>Ã>Ê >ÌÛi ................................... S S 

Free/reduced-price lunchb

Eligible ......................................................................... 

 ÌÊi}Li .................................................................. 

Score in percentile

£äÌ ............................................................................. 

25th ............................................................................. 

xäÌ ............................................................................. 

75th ............................................................................. 

äÌ ............................................................................. 

School type

*ÕLV .......................................................................... 

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Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 1-11

Charter schools are public schools that provide elementary or secondary education to students under a 

specific charter granted by the state legislature or other 

appropriate authority (Hoffman 2008). These schools 

are independent of direct control by local school districts 

and operate free of many regulations applicable to traditional public schools. Data from the National Alliance 

for Public Charter Schools (http://www.publiccharters.

org/dashboard/home) show that between 2000 and 2010, 

the number of charter schools more than tripled and the 

number of students attending these schools increased almost fivefold. In 2009–10, there were about 5,000 charter 

schools in 40 states and the District of Columbia with 

a total of 1.6 million students (3.4% of all U.S. public 

school students).

Comparison of student performance in charter versus 

traditional public schools is difficult because students in 

charter schools are self-selected (Garcia 2008; Grady and 

Bielick 2010). Some parents may enroll their children 

in charter schools because their children are struggling 

academically. Other parents may desire greater parent 

involvement or control. Still others may choose charter 

schools because they are dissatisfied with some aspect of 

local public schools. These selection factors may result in 

student populations in charter schools that are different 

from those in traditional public schools.

The data from the National Assessment of Educational 

Progress show that although average mathematics 

performance of fourth and eighth graders in charter 

schools improved from 2000 to 2009, charter school 

students overall had consistently lower scores than their 

counterparts in traditional public schools, and the gaps 

persisted over time (figure 1-A). In 2009, the average 

mathematics score of 12th graders in charter schools was 

also lower than that of their counterparts in traditional 

public schools. No measurable difference in average 

science scores, however, was found between students 

in charter and noncharter public schools (special NSF 

tabulations).

To mitigate the effects of selection factors, researchers have employed various research designs to control for 

different student characteristics in charter and noncharter 

schools (Abdulkadiroglu et al. 2009; Berends et al. 2010; 

Braun, Jenkins, and Grigg 2006; CREDO 2009; Hoxby, 

Murarka, and Kang 2009; Lubienski and Lubienski 2006; 

Zimmer et al. 2009). These studies produced mixed results on the effectiveness of charter schools, with impacts 

ranging from small (either positive or negative) to statistically insignificant (Betts and Tang 2008). There is wider variation in performance among charter schools than 

among public noncharter schools (Braun, Jenkins, and 

Grigg 2006). This may be due in part to wide variation 

in charter schools’ operation and organizational structure 

(Buddin and Zimmer 2005; Zimmer et al. 2003).

Mathematics and Science Achievement in Charter Schools

Figure 1-A

Average NAEP mathematics scores of public school students in grades 4, 8, and 12, by charter school status: 

Selected years, 2003–09

Average score

NAEP = National Assessment of Educational Progress 

NOTES: NAEP mathematics assessment scores range from 0 to 500 for grades 4 and 8 and from 0 to 300 for grade 12. Charter schools not identified 

prior to 2003 for grade 4, 2006 for grade 8, and 2009 for grade 12.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2010) of NAEP 2003, 2005, 2007, and 

2009 mathematics assessments, National Center for Education Statistics. See appendix table 1-1.

Science and Engineering Indicators 2012

Grade 4 Grade 8 Grade 12

2003 2005 2007 2009 2005 2007 2009 2009

0

100

200

300

400

Charter school Public noncharter school
1-12 Chapter 1. Elementary and Secondary Mathematics and Science Education

Asian/Pacific Islander, and to come from higher income 

families (table 1-3).13 Top performers in grade 8 were more 

likely than eighth graders overall to have parents with a 

college degree.14

Average mathematics scores for fourth grade students in 

this top 1% were not only much higher than those for the 

average fourth grader (304 versus 240 in 2009), they also exceeded the eighth grade average (304 versus 283 in 2009)15 

(table 1-4). Average mathematics scores for this top group 

rose steadily from 2000 to 2005 and then remained flat after 

2005. Between 2000 and 2009, the scores for the top 1% 

of fourth graders increased by 9 points, compared with a 

14-point increase in scores for all fourth graders.

Like fourth graders, the top 1% of eighth graders had 

much higher mathematics scores than average (e.g., 366 versus 283 in 2009). However, their trend pattern differed from 

that of their fourth grade counterparts: average mathematics scores for top eighth graders remained essentially unchanged between 2000 and 2003 and then increased steadily 

after 2003. The average scores for all eighth graders also increased (appendix table 1-1) so that the improvements overall and among the top 1% were not measurably different.

Table 1-4

Average NAEP mathematics scores of all students 

in grades 4 and 8 and students in the top 1%: 

Selected years, 2000–09

Grade Óäää ÓääÎ Óääx ÓääÇ Óää

Grade 4

All students ......... 226 235 ÓÎn Ó{ä Ó{ä

Top 1% ............... Óx Ón ÎäÎ ÎäÎ Îä{

GradeÊn

All students ......... 273 ÓÇn ÓÇ Ón£ ÓnÎ

Top 1% ............... Îxn Îx 362 364 366

 
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mathematics scores *ÊÌiÊÌÊ«iÀViÌiÊvÀÊÌiÀÊ}À>`iÊiÛi°

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iÌiÀÊvÀÊ
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Science and Engineering Indicators 2012

Table 1-3

Distribution of all students in grades 4 and 8 and students in the top 1% taking the NAEP mathematics 

assessments, by student characteristic: 2009

­*iÀViÌÊ`ÃÌÀLÕÌ®

Grade 4 À>`iÊn

Student characteristic All students Students in top 1% All students Students in top 1%

All students .................................................................... £ää°ä £ää°ä £ää°ä £ää°ä

-iÝ

Male ........................................................................ xä°n 62.4 xä°Î xÇ°

Female .................................................................... {°Ó 37.7 {°Ç 42.1

Race/ethnicity

7Ìi]ÊÃ«>V ............................................... 56.5 È°Ç xn°x 75.4

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Ã«>V ................................................................. 21.2 1.5 £° 1.4

Ã>É*>VvVÊÃ>`iÀ ............................................. x°ä 27.5 5.2 22.2

iÀV>Ê`>É>Ã>Ê >ÌÛi .............................. 1.2 ä°Î 1.1 ä°Î

Free/reduced-price lunch

 ÌÊi}Li ............................................................. 52.4 {°Ç 57.7 x°ä

Eligible .................................................................... 47.7 5.3 42.3 x°ä

}iÃÌÊiÛiÊvÊ«>ÀiÌ>Êi`ÕV>Ì

}ÊÃVÊÀÊiÃÃ .................................................   27.1 £°

Some college .........................................................   £n°Ó 4.4


i}iÊ`i}ÀiiÊÀÊ>LÛi ........................................   x{°n Î°Ç

 ÊrÊÌÊ>Û>>Li

 
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Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 1-13

Changes in Performance Gaps in Mathematics

Despite improvement in recent decades, gaps in mathematics performance persisted among many student subgroups (appendix table 1-1). In general, boys performed 

slightly better than girls.16 Gaps between students of different 

racial/ethnic backgrounds or family income remained large, 

with white and Asian/Pacific Islander students and those 

from higher income families posting significantly higher 

scores than their counterparts who were black, Hispanic, or 

American Indian/Alaska Native students or who were from 

lower income families. Large gaps were also observed by 

school type, with private school students scoring significantly higher than their peers in public schools.17

Some reductions in these gaps were observed among fourth 

grade students (table 1-5). For example, the white-black gap 

in mathematics performance among fourth grade students narrowed from 32 to 26 scale points between 1990 and 2009 because of larger gains by black students18 (appendix table 1-1). 

The gap between public and private school fourth grade students also narrowed during the same period because of greater 

gains by public school students. Finally, fourth graders’ score 

at the 10th percentile rose faster than that at the 90th percentile, reducing the gap between low- and high-performing students in grade 4. No similar gap reductions between 1990 and 

2009 were observed at grades 8 or 12.

Science Performance in 2009

The framework for the NAEP science assessment was 

updated in 2009 to reflect advances in science, curriculum 

standards, assessments, and research on science learning 

(NCES 2011). The new assessment placed a greater emphasis on what students can do with science knowledge. 

Because the framework changed significantly, the results 

from the 2009 assessment cannot be compared with earlier 

ones (NAGB 2008). This section, therefore, discusses only 

the 2009 assessment results, which will serve as a baseline 

for measuring students’ progress on future science assessments. For earlier results on NAEP science assessments, see 

Science and Engineering Indicators 2008, pp. 1-13 and 1-14 

(NSB 2008).

As in mathematics, science performance varies significantly by student demographics and by school type. At grade 

4, the average score for boys was slightly higher than that 

for girls (151 versus 149) (figure 1-2). Differences by racial/

ethnic background and family income were larger: scores for 

white and Asian/Pacific Islander students were at least 28 

points higher than those for black, Hispanic, and American 

Indian/Alaska Native students, and the score for students 

from higher income families was 29 points higher than that 

for students from lower income families. Students from private schools outperformed their peers in public schools by 

14 points. Similar performance gaps based on sex, race/ethnicity, and family income were observed among students in 

grades 8 and 12 (appendix table 1-3).

Most students failed to reach the proficient level on the 

science assessment. In 2009, 34% of 4th graders, 30% of 8th 

graders, and 21% of 12th graders performed at or above the 

proficient level in science (appendix table 1-4). At grade 12, 

only 4% of black students, 8% of Hispanic students, and 8% 

of low-income students reached the proficient level.

Algebra Performance of Ninth Graders 

in 2009

The first year of algebra is a prerequisite for higher level 

mathematics courses in high school (NMAP 2008), opening doors to more advanced mathematics and a college 

Table 1-5

Changes in NAEP mathematics score gaps between selected groups of students, by grade level: Selected 

years, 1990–2009

Change in score gap

 Grade 4 ÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÊÀ>`iÊn Grade 12

-VÀiÊ}>«ÊLiÌÜiiÊÃiiVÌi`Ê}ÀÕ«ÃÊvÊÃÌÕ`iÌÃ £äqÓää £äqÓää Óääxqäa

>iÃÊ>`Êvi>iÃ ........................................................................................ 

Whites and blacks ......................................................................................... 

7ÌiÃÊ>`ÊÃ«>VÃ .................................................................................... 

-ÌÕ`iÌÃÊvÀÊÜViÊv>iÃÊ>`ÊÌÃiÊvÀÊÌiÀÊv>iÃb .............. 

Ü«iÀvÀ}ÊÃÌÕ`iÌÃÊ>`Ê}«iÀvÀ}ÊÃÌÕ`iÌÃc

 ............................ 

*ÕLVÊÃVÊÃÌÕ`iÌÃÊ>`Ê«ÀÛ>ÌiÊÃVÊÃÌÕ`iÌÃ .................................... 

= no change; ÊrÊ`iVÀi>ÃiÆÊ ÊrÊÌÊ>Û>>LiÊ

 
*ÊrÊ >Ì>ÊÃÃiÃÃiÌÊvÊ
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Science and Engineering Indicators 2012
1-14 Chapter 1. Elementary and Secondary Mathematics and Science Education

below it. In order of increasing difficulty, these five skill 

areas are as follows:

Level 1, Algebraic expressions: Understands algebraic 

basics including evaluating simple algebraic expressions 

and translating between verbal and symbolic representations of expressions.

Level 2, Multiplicative and proportional thinking: Understands proportions and multiplicative situations and can 

solve proportional situation word problems, find the percent of a number, and identify equivalent algebraic expressions for multiplicative situations.

Level 3, Algebraic equivalents: Understands algebraic 

equivalents and can link equivalent tabular and symbolic 

representations of linear equations, identify equivalent 

lines, and find the sum of variable expressions.

Level 4, Systems of equations: Understands systems of linear equations and can solve such systems algebraically and 

graphically and characterize the lines (parallel, intersecting, collinear) represented by a system of linear equations.

Level 5, Linear functions: Understands linear functions 

and can find and use slopes and intercepts of lines and 

functional notation.

In 2009, a majority of ninth graders were proficient in 

lower level algebra skills such as algebraic expressions 

(86%) and multiplicative and proportional thinking (59%) 

(figure 1-3). Proportions demonstrating proficiency in more 

advanced algebra skills were lower and decreased as the difficulty level increased. Only 9% of ninth graders reached 

proficiency in linear functions, the highest algebra skill level 

assessed by HSLS.

Though there were no gender differences in algebra performance (appendix table 1-5), considerable differences 

were found among racial/ethnic subgroups (figure 1-3). In 

each skill area, Asian and white students demonstrated proficiency at higher rates than did black and Hispanic students. 

For example, 20% of Asians and 10% of whites were proficient in linear functions, compared with 6−7% of blacks 

and Hispanics.

Differences by parents’ education were also considerable 

(appendix table 1-5). In every skill area assessed, proportionally more students whose parents had a bachelor’s or 

advanced degree achieved proficiency than those whose parents had a high school education or less. For example, 35% 

of students whose parents had an advanced degree mastered 

systems of equations and 16% mastered linear functions; the 

corresponding percentages for students whose parents had 

not completed high school were 10% and 6%, respectively.

International Comparisons of Mathematics 

and Science Performance

This section examines the relative international standing 

of U.S. students in mathematics and science using assessment data from the Programme for International Student 

preparatory curriculum. These, in turn, are associated with 

higher college attendance rates, higher college graduation rates, greater job readiness, and higher earnings once 

students have entered the workforce (Achieve, Inc. 2008; 

Adelman 2006; Allensworth and Nomi 2009; Bozick and 

Lauff 2007; Gamoran and Hannigan 2000; Ma and Wilkins 

2007; Nord et al. 2011). The following section draws on the 

High School Longitudinal Study of 2009 (HSLS:09) to examine mathematics performance in algebra among a cohort 

of ninth graders in 2009.

HSLS:09, a nationally representative longitudinal study 

of more than 21,000 ninth graders in 944 schools, is following a sample of students who were ninth graders in 2009 

through secondary and postsecondary education, providing 

insight into students’ learning experiences from the beginning of high school into postsecondary education and work. 

The base year data collection of HSLS included an algebra 

assessment that provides indicators of ninth graders’ proficiency in five specific algebraic skill areas (Ingels et al. 

2011). These skill areas are arranged in a hierarchy such that 

proficiency at a higher level implies proficiency at all levels 

Figure 1-2

Average NAEP science scores of students in grade 

4, by student and school characteristics: 2009 

NAEP = National Assessment of Educational Progress

NOTE: NAEP science assessment scores range from 0 to 300 for 

grade 4. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2010) of NAEP 2009 

science assessment, National Center for Education Statistics. See 

appendix table 1-3. 

Science and Engineering Indicators 2012

Average score

0 40 80 120 160 200

Private

Public

Not eligible

Eligible for free/

reduced-price lunch

American Indian/

Alaska Native

Asian/Pacific Islander

Hispanic

Black, non-Hispanic

White, non-Hispanic

Female

Male

All students
Science and Engineering Indicators 2012 1-15

Shanghai-China (600), Singapore (562), and Hong Kong 

(555). In 2009, U.S. students demonstrated higher mathematical literacy than students in only 5 out of 34 OECD 

countries (Greece, Israel, Turkey, Chile, and Mexico).

The top mathematics performers in the United States 

trailed behind their peers in many other nations as well. In 

2009, the U.S. score at the 90th percentile in mathematics 

was 607, lower than the corresponding score in 12 of 33 

other OECD nations (620−659) (OECD 2010b).

Science Literacy Among U.S. 15-Year-Olds

U.S. students performed relatively better in the PISA 

science assessment. The average science literacy score of 

U.S. 15-year-olds improved by 3 points from 2006 to 2009 

(figure 1-4). Whereas U.S. students scored lower than the 

OECD average in 2006 (489 versus 498), this gap was not 

evident in 2009 (502 versus 501). The U.S. gains in science 

since 2006 were mainly driven by improvements at the bottom of the performance distribution; performance at the top 

remained unchanged (OECD 2010b).

Despite improvement, the 2009 U.S. score (502) was 

below that of 12 OECD nations (512−554) (appendix table 

1-6). For example, U.S. students scored lower than students in 5 top-performing OECD nations (Finland, Japan, 

Republic of Korea, New Zealand, and Canada) by 27−52 

points. U.S. students also lagged behind their peers in (nonOECD) Shanghai-China, Hong Kong, and Singapore (by 

40−73 points), The U.S. 90th percentile score in scientific 

literacy was 629, below the corresponding scores in 7 of 33 

other OECD nations (642−667) (OECD 2010b). Thus, U.S. 

top performers in science did better relative to other countries than did U.S. students on average.

Assessment (PISA).19 Sponsored by the Organisation for 

Economic Co-operation and Development (OECD) and initially implemented in 2000,20 PISA assesses the performance 

of 15-year-olds in mathematics and science literacy every 3 

years. Most countries participating in PISA are OECD members, although the number of participating non-OECD nations or regions has been increasing. Most OECD countries 

are economically advanced nations.

PISA is a literacy assessment, not a curriculum-based assessment; it measures how well students apply their knowledge and understanding to real-world situations.21 The term 

“literacy” indicates its focus on the application of knowledge learned in and out of school. In the PISA mathematics assessment, for example, students are asked to estimate 

an area, compare the best price for buying a product, or interpret the statistics in a news report or government document. In the PISA science assessment, students are asked 

to discuss acid rain, interpret erosion at the Grand Canyon, 

or predict the results of a controlled experiment (see sidebar 

“Sample Items from PISA”).

Mathematics Literacy Among U.S. 15-Year-Olds

Despite recent improvement, U.S. PISA scores in mathematics remain consistently below the OECD average and 

also below those of many non-OECD countries (figure 1-4). 

On the most recent PISA test in 2009, the U.S. average score 

of 487 fell below the OECD average of 496 and was lower 

than the scores of 17 of 33 other OECD nations, including 

Republic of Korea (546), Finland (541), Switzerland (534), 

Japan (529), Canada (527), and the Netherlands (526) (appendix table 1-6). The U.S. score was also lower than scores 

in several non-OECD regions/countries/economies, such as 

Figure 1-3

Ninth-graders proficient in various algebra skill areas, by race/ethnicity: 2009

Percent 

NOTES: Skill areas are arranged in a hierarchy such that proficiency in a given area assumes proficiency in all lower areas. “All ninth-graders” bars also 

include students in other racial/ethnic categories that are not shown separately.

SOURCE: Ingels SJ, Dalton B, Holder TE, Lauff E, Burns, LJ, High School Longitudinal Study of 2009 (HSLS:09): A First Look at Fall 2009 Ninth-Graders, 

NCES 2011-327 (2011). See appendix table 1-5.

Science and Engineering Indicators 2012

Algebraic expressions Multiplicative and

proportional thinking

Algebraic equivalent Systems of equations Linear functions

0

20

40

60

80

100

Increasing difficulty

All ninth-graders White Black Hispanic Asian
1-16 Chapter 1. Elementary and Secondary Mathematics and Science Education

Sample Items for Mathematics

1) A result of global warming is that the ice of some glaciers is melting. Twelve years after the ice disappears, 

tiny plants, called lichen, start to grow on the rocks. 

Each lichen grows approximately in the shape of a 

circle. The relationship between the diameter of this 

circle and the age of the lichen can be approximated 

with the formula:

 where d represents the diameter of the lichen in millimeters, and t represents the number of years after 

the ice has disappeared. Using the formula, calculate the diameter of the lichen, 16 years after the ice 

disappeared.

Correct answer: 14 mm.

Difficulty level: Correct answer corresponding to 484 

score points on the PISA mathematics scale ranging 

from 1 to 1,000.

2) In Mei Lin’s school, her science teacher gives tests 

that are marked out of 100. Mei Lin has an average 

of 60 marks on her first four Science tests. On the fifth 

test she got 80 marks.

 What is the average of Mei Lin’s marks in Science after all five tests?

Correct answer: 64.

Difficulty level: Correct answer corresponding to 556 

score points on the PISA mathematics scale ranging 

from 1 to 1,000

Sample Items for Science

1) Mary Montagu was a beautiful woman. She survived 

an attack of smallpox in 1715 but she was left covered with scars. While living in Turkey in 1717, she 

observed a method called inoculation that was commonly used there. This treatment involved scratching 

a weak type of smallpox virus into the skin of healthy 

young people who then became sick, but in most cases 

only with a mild form of the disease. Mary Montagu 

was so convinced of the safety of these inoculations 

that she allowed her son and daughter to be inoculated. In 1796, Edward Jenner used inoculations 

of a related disease, cowpox, to produce antibodies 

against smallpox. Compared with the inoculation of 

smallpox, this treatment had less side effects and the 

treated person could not infect others. The treatment 

became known as vaccination.

 What kinds of diseases can people be vaccinated 

against?

A. Inherited diseases like haemophilia.

B. Diseases that are caused by viruses, like polio.

C. Diseases from the malfunctioning of the body, 

like diabetes.

D. Any sort of disease that has no cure.

Correct answer: B. Diseases that are caused by viruses, like polio.

Difficulty level: Correct answer corresponding to 436 

score points on the PISA science scale ranging from 1 

to 1,000.

2) Regular but moderate physical exercise is good for 

our health.

 Is this an advantage of regular physical exercise: 

 Physical exercise helps prevent heart and circulation 

illnesses. Yes / No

 Physical exercise leads to a healthy diet. Yes / No

 Physical exercise helps to avoid becoming overweight. 

Yes / No

Correct answer: Yes, No, Yes in that order.

Difficulty level: Correct answer corresponding to 545 

score points on the PISA science scale ranging from 1 

to 1,000.

For additional sample questions, see http://www.pisa.

oecd.org/dataoecd/47/23/41943106.pdf.

Sample Items from PISA

Student Coursetaking in High School 

Mathematics and Science

Increasing mathematics and science coursetaking is one 

goal of current education reform efforts.22 Policymakers 

are calling for high school students to take more courses in 

mathematics and science, particularly at the advanced level, 

to ensure they are adequately prepared for college and careers and to keep the United States competitive in the global 

marketplace (NSB 2010a; President’s Council of Advisors 

on Science and Technology 2010). Strategies to increase 

mathematics and science coursetaking have focused on raising high school graduation requirements to include more 

mathematics and science courses, creating core academic 

standards to ensure that these courses are sufficiently rigorous, and encouraging students to take more rigorous mathematics and science courses.

This section provides indicators of mathematics and science coursetaking in the United States. The section begins with 

contextual information about programmatic efforts to increase 
Science and Engineering Indicators 2012 1-17

mathematics and science coursetaking and to standardize the 

quality of these courses. The section next examines various 

indicators of mathematics and science coursetaking by recent 

high school graduates in the United States, including trends 

in overall mathematics and science credits completed by high 

school graduates, the extent to which students take advanced

mathematics and science courses, enrollment in algebra I before high school, and differences in these indicators among 

various demographic groups.

The primary data source for this section is the NAEP 

High School Transcript Study (HSTS). Conducted every 

4 to 6 years since 1990, HSTS analyzes transcripts from a 

nationally representative sample of U.S. high school graduates. Results from the 2009 NAEP HSTS are compared to 

the results from the 2005, 2000, and 1990 studies. Because 

the HSTS has been conducted periodically for more than 

two decades, the data illuminate trends in coursetaking. In 

addition to course credits earned, HSTS collects student 

information such as gender and race/ethnicity, allowing 

comparisons of coursetaking, credits earned, and achievement across demographic groups.

High School Graduation Requirements and 

Curriculum Standards

The American Diploma Project (ADP) Network includes 

government and education leaders from 35 states. It seeks 

to improve student achievement by aligning high school 

academic content standards with the demands of college 

and careers and by requiring all graduating students to have 

completed a college-and-career-ready curriculum (Achieve, 

Inc. 2011). ADP also encourages states and school districts 

to adopt graduation benchmarks that align high school 

coursework with the expectations of colleges and employers. These benchmarks specify that students should take at 

least 3 years of science and 4 years of mathematics to earn a 

high school diploma and that some of these courses should 

be at the advanced level. For example, the benchmarks specify that students must complete mathematics courses at least 

through the level of precalculus and that science courses 

must include biology, chemistry, and physics. Currently, 

20 states and the District of Columbia have adopted these 

graduation requirements (Achieve, Inc. 2011).

The Council of Chief State School Officers has documented the nationwide trend of rising mathematics and science coursework requirements to earn a high school diploma 

(table 1-6). In the mid-1980s, the predominant graduation 

requirement for mathematics and science coursetaking was 

2 years in each subject. No state in 1987, for example, required 4 years of mathematics to graduate; by 2006, 6 states 

required 4 years of mathematics, and that number doubled to 

12 states in 2008. The number of states requiring 4 years of 

science to graduate jumped from 0 in 1987 to 1 in 2006 and 

4 in 2008. More than half of states (27) required 3 years of 

science to graduate in 2008, a substantial increase from the 3 

states with that requirement in 1987.

While graduation requirements for mathematics and 

science coursetaking show an upward trend, a recent ACT 

report (2010) found that nearly half of high school seniors 

planning to attend college had not completed the advanced 

courses necessary to enroll in credit-bearing college courses. Thus, ADP continues its efforts not only to increase the 

number of mathematics and science courses required to 

graduate, but also to have states specify that some of these 

courses be at an advanced level.

A complementary reform effort, the Common Core State 

Standards Initiative, focuses on the content of the courses 

that students take rather than the number or level of courses. 

Its goal is to ensure that academic standards across states are 

similar and that they include the rigorous content and higher 

order skills necessary to prepare all students for college and 

careers (see sidebar “Common Core State Standards”).

Figure 1-4

Average PISA mathematics and science literacy 

scores of 15-year-old students in the United States 

and OECD countries: 2003, 2006, and 2009

Average score

OECD = Organisation for Economic Co-operation and Development; 

PISA = Programme for International Student Assessment 

NOTE: The average scores for OECD countries cannot be compared 

across years because the number of OECD countries participating in 

PISA assessments changed over time.

SOURCES: Lemke M, Sen A, Pahlke E, Partelow L, Miller D, Williams 

T, Kastberg D, Jocelyn L, International Outcomes of Learning in 

Mathematics Literacy and Problem Solving: PISA 2003 Results From 

the U.S. Perspective, National Center for Education Statistics 

(NCES), 2005-003 (2004); Baldi S, Jin Y, Skemer M, Green PJ, 

Herget D, Highlights From PISA 2006: Performance of U.S. 

15-Year-Old Students in Science and Mathematics Literacy in an 

International Context, National Center for Education Statistics, NCES 

2008-016 (2007); Fleischman HL, Hopstock PJ, Pelczar MP, Shelley 

BE, Highlights From PISA 2009: Performance of U.S. 15-Year-Old 

Students in Reading, Mathematics, and Science Literacy in an 

International Context, National Center for Education Statistics, NCES 

2011-004 (2010). 

Science and Engineering Indicators 2012

2003 2006

Mathematics Science

2009 2006 2009

0

100

200

300

400

500

600

United States OECD
££nÊChapter 1. Elementary and Secondary Mathematics and Science Education

Mathematics and Science Coursetaking in 

High School

HSTS distinguishes between two levels of mathematics 

and science courses: general and advanced.23 General-level 

courses include introductory content needed for more advanced courses. General mathematics includes courses such 

as basic mathematics, prealgebra, algebra I, and geometry. 

General science courses include science survey, introduction 

to physics, and biology 1.

Advanced courses include higher level content and are 

sometimes the second-year courses in a subject.24 For example, advanced mathematics courses include algebra II, precalculus/analysis, trigonometry, statistics and probability, 

and calculus. Advanced science courses include advanced 

biology, chemistry, and physics. (Engineering is considered 

an advanced course and often is grouped with advanced science courses for analysis, as it is in this section.)

Researchers and policymakers suggest that it is not enough 

simply to require students to earn more credits in mathematics 

and science; students also need to earn credits in advanced

courses if goals for improved mathematics and science education and outcomes are to be met. Advanced mathematics and 

science coursetaking is a strong predictor of students’ educational success. For example, students who take advanced 

mathematics and science courses in high school are more 

likely to earn higher scores on academic assessments, enroll 

in college, pursue mathematics and science majors in college, 

Table 1-6

State graduation requirements for mathematics and science, by number of years required: Selected years, 

1987–2008

­ ÕLiÀÊvÊÃÌ>ÌiÃ®

State/local 

standard

 Mathematics Science

£nÇ £È ÓääÈ Óään £nÇ £È ÓääÈ Óään

Local decisiona .......................... 67666766

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3 years ....................................... £ä 15 26 25 3 n 27 27

4 years ....................................... ä 2 612 ä 214

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Science and Engineering Indicators 2012

To ensure that students graduate from high school adequately prepared for college and employment, a group 

of 48 states, led by the National Governors Association’s 

Center for Best Practices and the Council of Chief State 

School Officers, has developed the Common Core State 

Standards Initiative (CCSSI) (NGA 2009). The standards 

outline a body of knowledge and skills students must 

master at each grade level to graduate from high school 

ready for college and career in the 21st century. The 

standards clarify what students are expected to learn in 

each grade, permit cross-state comparisons, and seek to 

improve student achievement by increasing the rigor of 

courses required to meet the standards (Fine 2010).

To date, CCSSI has sponsored development of standards for English language arts (ELA) and mathematics 

for grades K−12. (Detailed information on the ELA and 

mathematics standards is available on the CCSSI website at http://www.corestandards.org/the-standards.) The 

National Research Council is currently working on a 

framework for new national science standards for grades 

K−12 that states will have the opportunity to include in 

their common core standards when the standards become 

available in 2012 (Achieve, Inc. 2011).

Of the 48 states participating in CCSSI (Texas and 

Alaska do not participate), 44 states and the District of 

Columbia had adopted the standards by the end of 2010 

(Gewertz 2010). States adopted the standards for a variety of reasons, including their rigor, the opportunity 

for cross-state comparisons, and increased chances of 

securing Race to the Top funds (EdSource 2010; Kober 

and Rentner 2011; The Opportunity Equation 2011). 

According to a recent survey, a majority of the states 

adopting the standards plan to develop new assessments, 

curriculum materials, instructional practices, teacher induction and professional development programs, and 

teacher evaluation systems based on the standards (Kober 

and Rentner 2011).

Common Core State Standards
Science and Engineering Indicators 2012 ££

and complete a bachelor’s degree (Bozick and Lauff 2007; 

Chen 2009; NCES 2010, 2011; Nord et al. 2011).

Trends in Total Science and Mathematics 

Credits Earned

Data from HSTS show that the graduating class of 2009 

continued the upward trend of having earned more total 

credits in mathematics and science.25 The average number of 

credits earned for all mathematics courses was 3.9 in 2009, 

up from 3.2 in 1990 (figure 1-5) The average number of 

credits earned for all science courses was 3.5 in 2009, up 

from 2.8 in 1990.

Trends in Advanced Science and Mathematics 

Credits Earned

HSTS data also show that U.S. high school students are 

taking increasing numbers of advanced mathematics and science courses. The average number of credits earned by high 

school graduates in advanced mathematics courses increased 

from 0.9 in 1990 to 1.7 in 2009 (figure 1-5). Graduates in 

1990 earned an average of 1.1 credits in advanced science 

and engineering courses, compared with 1.9 credits in 2009.

Credits earned for advanced mathematics courses.

From 1990 to 2009, the percentages of students taking advanced mathematics courses increased substantially (figure 

1-6). For example, 76% of all graduates earned a credit for 

algebra II in 2009 compared to 53% of all graduates in 1990. 

The percentage of students taking and completing precalculus/analysis has more than doubled since 1990: 35% in 2009 

compared to 14% in 1990.26 The overall percentage of students earning credits in calculus (17%) and AP/IB mathematics courses (15%) in 2009 has increased since 1990, when 7% 

of students took calculus and 4% took an AP/IB course.

One reason students have been able to increase the number of advanced mathematics courses taken in high school is 

that in recent years more of them have been taking algebra 

I before high school (Nord et al. 2011) (see sidebar “Taking 

Algebra I Before High School”).

Credits earned for advanced science courses. Many 

more students took advanced science courses in 2009 as 

well (figure 1-7).27 The percentage who earned an advanced 

chemistry credit increased from 45% in 1990 to 70% in 2009, 

and comparable increases for advanced biology (from 28% 

to 45%) and physics (from 24% to 39%) were also large. The 

percentage of students taking advanced environmental/earth 

science and AP/IB science courses showed similar upward 

trends, though fewer students took these courses. Fourteen 

percent of students took an AP/IB science course in 2009, 

compared to 11% in 2005.28

Compared with advanced mathematics and science, fewer 

students earned credits in engineering: 3% of 2009 graduates 

had taken engineering in high school, up from 1.5% in 2005.

Taking Algebra I 

Before High School

Algebra I is considered a “gateway” course leading 

to more advanced coursetaking in mathematics and 

science and to higher levels of achievement (Loveless 

2008). An increasing number of educators and researchers are calling for more students to take algebra 

I before high school (Ma and Wilkins 2007; Matthews 

and Farmer 2008; National Mathematics Advisory 

Panel 2008).

High school transcripts indicate credits earned 

for high school courses taken before ninth grade. 

According to HSTS data, 26% of high school graduates took algebra I before high school in 2009, up from 

20% in 2005 (table 1-A). Percentages of both male and 

female graduates taking algebra before high school 

increased, though females (27%) slightly outpaced 

males (25%) in 2009. Upward trends occurred in all 

racial/ethnic groups as well, with black, Hispanic, and 

white graduates posting increases of 4, 7, and 6 percentage points, respectively. Asian/Pacific Islander 

students outpaced their peers by increasing their rates 

of completing algebra 1 before high school from 30% 

in 2005 to 48% in 2009.

HSTS identifies three curriculum levels based on 

the types of courses students take: standard, midlevel, 

and rigorous. A rigorous curriculum includes 4 years 

of mathematics including up to at least precalculus and 

3 years of science, which must include biology, chemistry, and physics. HSTS data show that nearly twothirds of graduates who completed a rigorous high 

school curriculum took algebra I before high school 

(Nord et al. 2011).

Table 1-A

High school graduates completing first-year 

algebra before high school, by student 

characteristic: 2005 and 2009

­*iÀViÌ®

Student characteristic Óääx Óää

All students ................................................ Óä 26

-iÝ

Male .................................................... Óä 25

Female ................................................ Óä 27

Race/ethnicity

7Ìi]ÊÃ«>V ........................... 23 Ó

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Science and Engineering Indicators 2012
£ÓäÊChapter 1. Elementary and Secondary Mathematics and Science Education

Figure 1-5

Average total and advanced mathematics and science credits earned by high school graduates: Selected years, 

1990–2009

Number

AP = Advanced Placement; IB = International Baccalaureate

NOTES: “Advanced mathematics” courses include algebra II, trigonometry, statistics/probability, precalculus/analysis, calculus, and any AP/IB 

mathematics courses. “Advanced science” courses include advanced biology, chemistry, physics, advanced environmental/earth science, engineering, 

and any AP/IB science courses.

SOURCES: Nord C, Roey S, Perkins R, Lyons M, Lemanski N, Brown J, Schuknecht J, America’s High School Graduates: Results of the 2009 NAEP High 

School Transcript Study, NCES 2011-462 (2011); National Science Foundation, National Center for Science and Engineering Statistics, special tabulations

(2011) of National Assessment of Educational Progress 1990, 2000, 2005, and 2009 High School Transcript Studies, National Center for Education 

Statistics. See appendix table 1-7. 

Science and Engineering Indicators 2012

Mathematics (total) Science (total) Advanced mathematics Advanced science

0

1

2

3

4

1990 2000 2005 2009

Figure 1-6

High school graduates completing various advanced mathematics courses, by subject: Selected years, 

1990–2009

Percent

AP = Advanced Placement; IB = International Baccalaureate

NOTE: AP/IB courses are shown separately here but also could be included in other bars. For example, calculus includes any calculus course, including 

AP calculus.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2011) of National Assessment of 

Educational Progress 1990, 2000, 2005, and 2009 High School Transcript Studies, National Center for Education Statistics. See appendix table 1-8.

Science and Engineering Indicators 2012

Algebra II Trigonometry/statistics/

probability

Precalculus/analysis Calculus AP/IB mathematics

0

20

40

60

80

100

1990 2000 2005 2009
Science and Engineering Indicators 2012 1-21

Percentage taking advanced courses. The percentage 

of females taking precalculus/analysis (37%) was higher 

than that of males (34%), as was the percentage of females 

taking algebra II (78% compared to 74%) (appendix table 

1-8). An equal percentage of males and females (17%) took 

calculus. Asian/Pacific Islander students outpaced all other 

groups in taking advanced mathematics in 2009. The most 

striking disparities occurred in AP/IB mathematics coursetaking, with Asian/Pacific Islander students (42%) taking 

these courses at rates approximately 6 times that of black 

students (7%), 4 times that of Hispanic students (9%), and 

2.5 times that of white students (16%).

Gender differences in advanced science coursetaking varied by subject (appendix table 1-9). Whereas more females 

than males took advanced biology (50% versus 39%), males 

took physics at higher rates than females (42% versus 36%). 

Males were 6 times more likely to have taken engineering 

(6% versus 1%). Asian/Pacific Islander students took advanced science and engineering courses at rates higher than 

those of other ethnic groups.

Teachers of Mathematics and Science

Among the many factors that influence student learning, 

teacher quality is crucial. To ensure that all classrooms are 

led by high-quality teachers, NCLB mandated that schools 

and districts hire only highly qualified teachers, defining 

“highly qualified” as having state certification, a minimum of a bachelor’s degree, and demonstrated subject area 

Demographic Differences in Advanced 

Mathematics and Science Credits Earned

Although mathematics and science coursetaking has increased for all demographic groups, differences among these 

groups have persisted. White students are more likely to earn 

advanced credits than black or Hispanic students. Asian/Pacific 

Islander students outpace other groups of students in terms of 

credits earned and percentages taking advanced courses.

Credits earned in advanced courses. In 2009, females 

and males earned approximately equal credits in advanced 

mathematics—an average of 1.7 credits (appendix table 1-7). 

Among racial/ethnic groups, Asian/Pacific Islander students 

earned the most credits in advanced mathematics, an average of 2.4 credits in 2009. Hispanics (1.3) and blacks (1.4) 

earned the fewest credits in advanced mathematics. White 

students earned substantially more credits (1.8) than black or 

Hispanic students, but significantly fewer than Asian/Pacific 

Islander students.

In 2009, females earned an average of 1.9 advanced science and engineering credits, compared to 1.8 credits for 

males. Among major racial/ethnic groups, Asian/Pacific 

Islander students earned the highest number of credits in advanced science and engineering (2.8). Hispanic and black 

students earned 1.5 and 1.6 credits, respectively, in these 

subjects. White students earned more credits (2.0 credits in 

advanced science and engineering) than black or Hispanic 

students, but fewer than Asian/Pacific Islanders.

Figure 1-7

High school graduates completing various advanced science and engineering courses, by subject: Selected 

years, 1990–2009

Percent

AP = Advanced Placement; IB = International Baccalaureate

NOTES: “Advanced biology” includes AP/IB biology, physiology, anatomy, and genetics. “Advanced environmental and earth sciences” includes AP/IB 

environmental sciences, college preparatory earth science, and various geology courses. AP/IB courses are shown separately here but also included in 

other bars. For example, “Physics” includes any advanced physics course, including AP physics, and “Chemistry” includes any advanced chemistry 

course, including AP chemistry.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2011) of National Assessment of

Educational Progress 1990, 2000, 2005, and 2009 High School Transcript Studies, National Center for Education Statistics. See appendix table 1-9.

Science and Engineering Indicators 2012

Advanced biology Advanced chemistry Physics Advanced environmental/

earth science

Engineering AP/IB science

0

20

40

60

80

100

1990 2000 2005 2009
1-22 Chapter 1. Elementary and Secondary Mathematics and Science Education

competence. Teaching quality has remained in the national 

spotlight. The Race to the Top program, a component of the 

American Recovery and Reinvestment Act of 2009, called 

for applications from states to compete for more than $4 

billion for education innovation and reform, including recruitment, professional development, compensation, and retention of effective teachers.29 Salaries, working conditions, 

and opportunities for professional development contribute 

to keeping teachers in the profession and the best teachers 

in the classroom (Berry, Smylie, and Fuller 2008; Brill and 

McCartney 2008; Hanushek and Rivkin 2007; Ingersoll and 

May 2010).

This section presents indicators of public school mathematics and science teachers’ preparation, experience, professional development, salaries, and working conditions. It 

focuses on middle and high school teachers, as mathematics 

and science teachers are more common and more easily identified at these levels than at the elementary level.30 The primaU\GDWDVRXUFHLVWKHí6$66FRPSDUDEOHGDWDIURP

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over time. The section refers to 2007 and 2003 to indicate 

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measures are analyzed separately for schools with differing 

concentrations of minority and low-income students.31

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Characteristics of High-Quality Teachers

The effects of good teachers on student achievement have 

been well documented (Boyd et al. 2008; Clotfelter, Ladd, 

and Vigdor 2007; Goe 2008; Guarino, Santibanez, and 

Daley 2006; Harris and Sass 2007), but the specific teacher 

characteristics that contribute to student success are less 

clear (see sidebar “Measuring Teaching Quality”). 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 teaching effectiveness (Boyd et al. 2006; Buddin and Zamarro 2009a, 

2009b; Hanushek and Rivkin 2006). This section reports on 

indicators such as public school mathematics and science 

teachers’ educational attainment, professional certification, 

participation in practice teaching, self-assessment of preparation, and years of experience. Although these are not the 

only characteristics that contribute to teacher effectiveness, 

they are more easily measured than such other characteristics as teachers’ abilities to motivate students, manage the 

classroom, maximize instruction time, and diagnose and 

overcome students’ learning difficulties.

Highest Degree Attained

Virtually all mathematics and science teachers at public 

middle and high schools held at least a bachelor’s degree 

in 2007, and more than half had earned an advanced deJUHH HJPDVWHU¶VGHJUHHHGXFDWLRQ VSHFLDOLVWFHUWLILFDWH

of advanced graduate studies, doctorate, professional deJUHHDSSHQGL[WDEOH7KHSURSRUWLRQRIPDWKHPDWLFV

and science teachers with a master’s or higher degree has 

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Teachers with advanced degrees are not evenly distributed across schools, however. Proportionately more 

mathematics and science teachers in low-poverty and lowminority schools held master’s degrees than did their peers 

in high-poverty and high-minority schools.32 For example, 

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in high-poverty schools.

Certification and Entry into the Profession

The traditional path to becoming a teacher begins in 

an undergraduate education program, where future teachers earn a bachelor’s or master’s degree and full teaching 

certification prior to beginning to teach. In recent years, a 

growing proportion of new teachers have entered the profession through an alternative pathway, usually a program that 

recruits college graduates from other fields or mid-career 

professionals in non-teaching careers. These teachers often 

begin to teach with probationary or temporary certification 

while they work toward regular certification during the first 

few years of their teaching careers.33 Regardless of their 

pathway into the profession, all public school teachers must 

have some type of state certification to teach.

State Certification. Teacher certification refers to a 

OLFHQVH UHTXLUHG E\WKH VWDWH RI DOO SUDFWLFLQJWHDFKHUV UHquirements vary by state but typically include completing a 

bachelor’s degree, completing a period of practice teaching, 

and passing a formal test34 (GLWRULDO3URMHFWVLQ(GXFDWLRQ

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teachers of mathematics and science to have a degree or 

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some states allow general education preparation and others 

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standards and requirements for certification complicate measurement of the impact of teachers’ credentials on student 

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regular or advanced certification is associated with student 

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In 2007, 87% of public middle and high school mathematics and science teachers were fully certified (i.e., held regular 

or advanced state certification) (table 1-7). The percentage 

of science teachers with full certification has increased by 4 
Science and Engineering Indicators 2012 1-23

No research has conclusively identified the most effective teachers or the factors that contribute to their success, 

but efforts to improve measures of teaching quality have 

proliferated in recent years. For example, 21 states and 

over 100 teacher preparation programs have joined the 

Teacher Performance Assessment Consortium (TPAC) to 

develop a teacher evaluation instrument. The evaluation 

will be based on assessments embedded in teachers’ preparatory coursework and on documentation of teaching 

and learning during multi-day lessons.

Another effort has focused on establishing a composite indicator for effective teaching by measuring student 

gains on test scores, quality of teaching practice, teachers’ pedagogical content knowledge, student perceptions 

of the classroom environment, and teachers’ perceptions 

of working conditions and instructional support at their 

schools (Measures of Effective Teaching 2010). Through 

the Measures of Effective Teaching project, researchers 

have analyzed data in large school districts nationwide 

to identify effective teachers and teaching practices. Data 

collection began in the 2009−10 academic year and continued in 2010−11.

A similar effort focused on mathematics teaching 

quality is underway at the National Center for Teacher 

Effectiveness, which seeks to identify practices and characteristics that distinguish effective mathematics teachers 

and to develop practical instruments and training tools 

for school districts. The center’s core project, Developing 

Measures of Effective Mathematics Teaching, will combine measures of teacher characteristics, practice, and 

content knowledge and measures of student engagement 

and learning to build a composite measure of teaching 

effectiveness in mathematics. Data collection in approximately 50 schools and 200 classrooms began in 2010 and 

will continue through 2013. 

These projects are among the largest efforts to incorporate gains in student test scores into the measurement 

of quality, but they are not the first. Several researchers 

have sought to develop so called “value-added” models 

that link teacher effectiveness to student gains in achievement test scores (Hanushek and Rivkin 2010; Hanushek 

et al. 2005). These models do not directly measure variation in teaching practices; rather, they compare test score 

gains of students with similar background characteristics 

and initial scores within the same school and attribute 

students’ differences in progress to their teachers (Baker 

et al. 2010). Although some studies have validated the 

value-added approach (Jacob and Lefgren 2008; Kane et 

al. 2010; Kane and Staiger 2008), researchers have raised 

concerns about nonrandom assignment of students to 

teachers within a school; the use of standardized tests that 

do not adequately measure students’ knowledge, skills, 

and progress; and family support or other factors outside 

of school that contribute to students’ achievement (Baker 

et al. 2010; Hanushek and Rivkin 2010; Rothstein 2008).

Despite these concerns, there seems to be consensus 

that these models can contribute to current efforts to 

evaluate teaching when used along with other observable 

measures. However, researchers have not yet arrived at 

a comprehensive model for measuring teaching quality.

More information on the Teacher Performance 

Assessment Consortium is available at http://aacte.org/ 

index.php?/Programs/Teacher-Performance-AssessmentConsortium-TPAC/teacher-performance-assessmentconsortium.html. More information about the Measures 

of Effective Teaching project is available at http://www.

metproject.org/. More information about the Developing 

Measures of Effective Mathematics Teaching project is 

available at http://www.gse.harvard.edu/ncte/projects/ 

project1/default.php. 

Measuring Teaching Quality

percentage points since 2003 (from 83% to 87%), and has 

increased at a faster pace at low-minority schools (from 86% 

to 93%) (appendix table 1-12).

Fully certified mathematics and science teachers were 

more prevalent in low-minority schools (92% of mathematics and 93% of science teachers) than in high-minority 

schools (84% of mathematics and 83% of science teachers) 

(appendix table 1-12). Fully certified science teachers were 

also more prevalent in low-poverty schools (89%) than in 

high-poverty schools (81%). The percentage of fully certified mathematics and science teachers at high-minority and 

both high- and low-poverty schools has not changed significantly since 2003.

Alternative Entry into the Teaching Profession.

Rather than completing traditional undergraduate programs in education, some teachers enter teaching through 

Table 1-7

Public middle and high school teachers with 

regular or advanced certification, by teaching 

field: Academic years 2003–04 and 2007–08

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Academic year 

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Academic year 

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Mathematics ................ nx°£ nÇ°ä

Science ........................ nÓ°n nÇ°Î

"ÌiÀ ............................ nÈ°È nn°£

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Science and Engineering Indicators 2012
1-24 Chapter 1. Elementary and Secondary Mathematics and Science Education

alternative programs such as Teach for America, The New 

Teacher Project, and other programs administered by states, 

districts, universities, and other organizations to expedite 

the transition of nonteachers into teaching. Although these 

programs have expanded in recent years,35 researchers have 

observed few systematic differences in the training received 

by aspiring teachers in traditional versus alternative pathways (Humphrey, Weschler, and Hough 2008; NRC 2010; 

Zeichner and Conklin 2005).36 Much of the formal training 

for teachers in both traditional and alternative programs 

takes place in university schools of education (Walsh and 

Jacobs 2007); according to SASS, however, a significantly 

smaller proportion of alternative-pathway teachers participated in practice teaching prior to beginning teaching (see 

“Practice Teaching” section). Some characteristics of teachers who enter through traditional and alternative programs, 

such as the selectivity of their undergraduate institutions or 

the likelihood of holding advanced degrees, are also similar 

(Cohen-Vogel and Smith 2007). Research has found mixed 

or no effects of teachers’ pathway into the profession on 

students’ achievement (Constantine et al. 2009; Boyd et al. 

2006; Zeichner and Conklin 2005).

Some alternative entry programs place recruits in “highneed” schools, generally those with high levels of student 

poverty and low levels of student achievement. According 

to its website, the New Teacher Project has placed 43,000 

teachers of all subjects in high-need locations since 1997, 

and Teach for America’s annual placement of teachers in 

high-need schools has grown from about 2,000 to 5,000 between 2005 and 2010 (TFA 2006, 2008, 2009). Although 

statistics on the number of mathematics and science teachers placed are not available, the New Teacher Project and 

Teach for America include increasing the supply of teachers 

in those subject areas among their goals.37

In 2007, 19% of all public middle and high school mathematics teachers and 22% of science teachers had entered 

the profession through an alternative certification program, 

compared with 16% of teachers in other fields (appendix table 1-13). Teachers who had entered through alternative programs were more concentrated in schools with high rates of 

minority enrollment and school poverty. For example, 26% 

of mathematics teachers in schools with the highest poverty 

levels had entered teaching through an alternative program, 

compared with 12% of those in schools with the lowest poverty levels. Nationwide, the supply of new mathematics and 

science teachers may not be sufficient to replace those who 

retire or leave the profession for other reasons, and teacher 

shortages in these subjects are not distributed evenly across 

schools (Ingersoll and Perda 2009). High-poverty schools in 

urban areas tend to have the highest rates of teacher turnover; resulting shortages may contribute to schools’ decision 

to hire teachers from alternative entry programs.

National Board Certification

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completed 3 years of teaching and must hold state certification to be eligible. They must then complete 10 assessments 

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knowledge in specific certificate areas, and four portfolio 

submissions, including video recordings of classroom practice and examples of student work.

Research on the effects of National Board Certification 

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several states has shown that teachers holding this certification are less likely to teach in schools with high proportions 

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for Title I, a federal program to provide funds to schools and 

districts with high percentages of low-income students.39

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Practice Teaching

Practice teaching (also called student teaching) offers 

prospective teachers hands-on classroom experience to help 

them transfer what they learn from coursework into classroom teaching. Practical experience in the classroom affects 

teaching quality (Boyd et al. 2008),40 and SASS data support 

this finding: among teachers with fewer than 5 years of experience (referred to here as “new teachers”), those who had 

participated in practice teaching were more likely to report 

feeling well prepared or very well prepared for various aspects of teaching during their first year than did those who 

had not had practice teaching (appendix table 1-14).

Among new public middle and high school mathematics and science teachers in 2007, about three-quarters had 

participated in practice teaching (appendix table 1-15). 

The proportion differed by school composition: 91% of 

new mathematics and 90% of new science teachers at lowminority schools participated in practice teaching, compared with 73% and 68%, respectively, at high-minority 

schools (figure 1-8).

The proportion of new mathematics and science teachers 

who have participated in practice teaching has declined during 

recent years. Seventy-five percent of new mathematics and 

72% of new science teachers reported participation in practice 
Science and Engineering Indicators 2012 1-25

teaching in 2007, compared with 79% and 75%, respectively, 

in 2003 (appendix table 1-15). The decline may be due to 

the increasing number of teachers who enter the profession 

through alternative programs and who are significantly less 

likely to have participated in practice teaching. In 2007, 43% 

of mathematics teachers and 51% of science teachers who 

entered the profession through an alternative program had 

participated in practice teaching, lower than the 94% of mathematics and 92% of science teachers who entered teaching the 

traditional way (appendix table 1-16). Thirty-nine states require prospective teachers in traditional preparation programs 

to participate in practice teaching, while six require teachers 

in alternative programs to practice teach (Editorial Projects in 

Education Research Center 2010).

Self-Assessment of Preparedness

New middle and high school teachers (i.e., those with 

fewer than 5 years of experience) generally felt well prepared 

to perform various tasks during their first year of teaching, 

and science teachers in particular have seen improvements 

in feeling prepared (appendix table 1-17). In 2007, 88% of 

new mathematics teachers and 89% of new science teachers 

felt prepared to teach their subject matter. Among new science teachers, this represents an increase since 2003, when 

79% felt prepared to teach the subject matter. More new science teachers also felt prepared to use computers in instruction: 75% reported feeling prepared in 2007, compared with 

62% in 2003.

New teachers’ assessments of their preparation varied 

with the characteristics of their schools. For example, 99% 

of new mathematics teachers and 95% of new science teachers in low-minority schools felt prepared to teach their subject matter, compared with 84% and 85% of their peers in 

high-minority schools (appendix table 1-17).

Experience

Teachers generally are more effective in helping students 

learn as they gain years of experience, particularly during 

their first few years (Boyd et al. 2006; Clotfelter, Ladd, and 

Vigdor 2007; Harris and Sass 2008; Rice 2010). In 2007, 

about one-fifth of public middle and high school mathematics and science teachers were novices with 3 or fewer years 

of experience (appendix table 1-18). Proportionally more 

mathematics teachers at high-minority schools were novice 

teachers than at low-minority schools (22% versus 13%). 

Similarly, novice science teachers were more prevalent in 

high-poverty schools than in low-poverty schools (25% versus 15%).

School Factors Contributing to Teachers’ 

Effectiveness

Teachers bring a variety of knowledge, skills, and experience into their classrooms, but conditions in their schools 

and districts also influence their effectiveness in promoting 

student outcomes and their decisions about remaining in the 

profession. This section presents indicators of district and 

school attributes that affect teachers’ success, including the 

assignment of teachers to subjects, initial and ongoing professional development, salaries, and working conditions.

In-Field Teaching

Over the past decade, few issues related to teaching quality have received more attention than in-field teaching assignment in middle and high schools (Almy and Theokas 

2010; Dee and Cohodes 2008; Peske and Haycock 2006). 

NCLB mandates that all students have teachers who demonstrate competence in subject knowledge and teaching. 

NCLB does not provide specific guidance or criteria for adequate preparation to teach mathematics and science, however, leaving that task to states.

To determine whether teachers have subject-specific 

preparation for the fields they teach, recent research focused 

on matching teachers’ formal preparation (as indicated by 

degree major and certification field) with their teaching field 

(Hill and Gruber 2011; McGrath, Holt, and Seastrom 2005; 

Morton et al. 2008). Following this line of research, the 

National Science Board (2010b) distinguished four levels of 

formal preparation for teaching mathematics and science at 

the middle and high school levels.41 In order of decreasing 

rigor of preparation, they are as follows:

In field: Mathematics teachers with a degree and/or full 

certification in mathematics or mathematics education. 

Science teachers with a degree and/or full certification in 

science or science education.

Figure 1-8

Participation of new public middle and high school 

teachers in practice teaching, by teaching field and 

minority enrollment: Academic year 2007–08

Percent

NOTES: “New teachers” refers to those with fewer than 5 years of 

teaching experience. Minority students constitute 0%–5% of the 

student population at low-minority schools and more than 45% of 

the population at high-minority schools. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2010) of 2007–08 

Schools and Staffing Survey, National Center for Education Statistics. 

See appendix table 1-15.

Science and Engineering Indicators 2012

0

20

40

60

80

100

Low-minority schools High-minority schools

Mathematics Science
1-26 Chapter 1. Elementary and Secondary Mathematics and Science Education

Related field: Mathematics teachers with a degree and/or 

full certification in a field related to mathematics (e.g., 

science, science education, computer sciences, engineering). Science teachers with a degree and/or full certification in a field related to their teaching field (e.g., high 

school biology teachers with a degree and/or full certification in chemistry). This category is omitted for middle 

school science teachers because science teachers at this 

level are usually not distinguished by specific science 

fields such as physics, chemistry, or biology.

General preparation: Mathematics and science teachers 

with a degree and/or full certification in general elementary, middle, or secondary education.

Other: Mathematics and science teachers without a degree or certification in their teaching field, a related field, 

or general elementary, middle, or secondary education.

In-field mathematics teachers in public middle schools 

increased from 53% in 2003 to 64% in 2007 (table 1-8). 

Seventy percent of science teachers in public middle schools 

were teaching in field in 2007, not a significant increase over 

67% in 2003. In both years, between 27% and 38% of middle school mathematics and science teachers were teaching 

their subject with general education preparation.

The level of in-field mathematics and science teachers 

in high schools did not change between 2003 and 2007. 

In both years, large majorities of high school mathematics 

teachers (87% in 2003 and 88% in 2007), biology/life science teachers (92% in 2003 and 93% in 2007), and physical 

science teachers (78% in 2003 and 82% in 2007) taught in 

field. Relatively few (3% or lower) mathematics and science 

teachers in high schools had general education preparation.

In-field teachers were more likely in low-minority and 

low-poverty schools than in their high-minority and highpoverty counterparts (appendix table 1-19). In 2007, for 

example, 95% of high school mathematics teachers in 

low-minority schools were teaching in field, compared 

with 83% in high-minority schools, and 94% of high school 

mathematics teachers in low-poverty schools were teaching 

in field, compared with 81% in high-poverty schools.

In-field mathematics teaching became somewhat more 

common at high-poverty and high-minority middle schools 

between 2003 and 2007; for example, the rate of in-field 

mathematics teachers increased from 47% to 65% at highpoverty middle schools and from 51% to 61% at high-minority middle schools. 

Professional Development for Mathematics and 

Science Teachers

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 

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of professional development for mathematics and science 

teachers found that professional development had statistically significant effects on student performance in mathematics 

&&66242

New Teacher Induction and Support. Professional development often begins during a teachers’ first year in the 

classroom. Without sufficient support and guidance, teachers in their first and second years may struggle, become 

less committed to teaching, and leave the profession altogether (Smith and Ingersoll 2004; Smith and Rowley 2005). 

Teacher induction programs at the school, local, or state 

level are designed to help teachers in their first 2 years improve their professional practice, deepen their understanding 

of teaching, and prevent early attrition (Britton et al. 2003; 

Fulton, Yoon, and Lee 2005; Smith and Ingersoll 2004).

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teaching field: Academic years 2003–04 and 2007–08

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Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 1-27

Participation in new teacher induction programs is becoming more common. Among new public middle and high 

school teachers with fewer than 5 years of experience in 

2007, 79% of mathematics and 73% of science teachers had 

participated in an induction program during their first year, 

compared with 71% of mathematics teachers and 68% of 

science teachers in 2003 (appendix table 1-20). Teacher participation in induction programs was lower in schools with 

high concentrations of minority and low-income students, 

but gaps in participation narrowed over time. In 2003, 63% 

of mathematics teachers in high-minority schools had participated in an induction program, compared with 88% in lowminority schools—a gap of 25 percentage points. In 2007, 

that gap was 8 percentage points. Gaps narrowed mainly due 

to increasing percentages of teachers in high-minority and 

high-poverty schools participating in induction programs. 

Appendix table 1-21 shows data on other types of support 

provided to new teachers when they start their careers.

The extent to which these programs help new teachers be 

more effective is unclear: a recent nationwide study of induction programs at the elementary level found no effects on 

student achievement for teachers who received a single year 

of induction, and effects on student achievement for teachers 

in 2-year induction programs were evident only in teachers’ 

third year of teaching (Glazerman et al. 2010). The study 

found no relationship between participation in new teacher 

induction and retention of teachers during their first 4 years. 

Some research suggests that a subject-matter match between 

teachers and induction programs improves outcomes for 

teachers (Luft 2009; Luft et al. 2010), but this question was 

not examined in the national study.

Ongoing Professional Development. Teachers’ professional development does not end after their first few years 

of teaching. Ongoing training is often mandated by state 

regulations and delivered by school districts to teachers 

throughout their careers. In 2007, more than three-quarters 

of mathematics and science teachers in public middle and 

high schools received professional development in the content of their teaching subject during the previous 12 months 

(figure 1-9). Another common focus of teacher professional 

development programs was the use of computers for instruction: 66% of mathematics and 69% of science teachers received professional development on that topic (appendix 

table 1-22). Fewer than half received training in classroom 

discipline or management, teaching students with disabilities, or teaching Limited English Proficient (LEP) students.

The duration of professional development programs is 

often shorter than what research suggests may be desirable. 

Although more research is needed to establish a threshold, 

some studies have suggested 80 hours or more of professional development is necessary to affect teacher practice 

(Banilower et al. 2006; CCSSO 2009; NSB 2008). Among 

teachers who received professional development in their 

subject area, 28% of mathematics and 29% of science teachers received 33 hours or more (figure 1-10).43

The three top priority areas for professional development 

programs identified by mathematics and science teachers at 

public middle and high schools were student discipline and 

classroom management, the content of their main subject 

field, and use of technology in instruction (appendix table 

1-23). Teachers in different types of schools had different 

priorities. For example, 29% of science teachers in highpoverty schools identified student discipline and classroom 

Figure 1-9

Participation of public middle and high school teachers in professional development activities during past 

12 months, by topic: Academic year 2007–08

Percent

LEP = limited English proficiency

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2010) of 2007–08 Schools and 

Staffing Survey, National Center for Education Statistics. See appendix table 1-22.

Science and Engineering Indicators 2012

Content of

subject(s) taught

Use of computers

for instruction

Student discipline and

classroom management

Students

with disabilities

LEP students

0

20

40

60

80

100

Mathematics Science
£ÓnÊChapter 1. Elementary and Secondary Mathematics and Science Education

management as their top priority, compared with 10% of 

their peers at low-poverty schools.

Teacher Salaries

Financial incentives have been associated with increased 

teacher recruitment (Berry 2004; Steele, Murnane, and 

Willet 2009) and retention (Clotfelter et al. 2008; Hanushek, 

Kain, and Rivkin 2004) (see sidebar “Teacher Attrition”). In 

2007, 15% of school districts offered pay incentives in fields 

of shortage—usually mathematics, science, and special education—and 10% offered rewards for excellence in teaching 

(Aritomi and Coopersmith 2009). Whether these policies 

improve overall teaching quality has not been established 

(Fryer 2011; Hanushek et al. 2005; Hanushek and Rivkin 

2007; Rand Corporation 2006; Springer et al. 2010).

Research has indicated that teachers earn less than other 

professionals with similar levels of education (AFT 2008; 

Allegretto, Corcoran, and Mishel 2008; Hanushek and 

Rivkin 2007). The circumstances of employment and the 

nature of the work differ between teachers and non-teachers, 

however, and may account for salary differences to some 

extent. Teachers are more likely than other professionals to 

work in rural areas, for example, where costs of living and 

salaries are lower (Taylor 2008). Selecting the appropriate 

comparison group for teachers also complicates salary comparisons: some research uses figures for most fields requiring a bachelor’s degree (AFT 2008), and at least one study 

suggests that a smaller set of occupations requiring more 

similar skills may be more appropriate (Milanowski 2008).

In 2007, the average base salary of middle and high 

school mathematics and science teachers was approximately 

$50,000, based on teachers’ reports in SASS (appendix table 

1-24). Salaries varied among schools with different student 

populations. For example, the average salary of mathematics 

teachers in public middle and high schools with the lowest 

rates of minority enrollment was approximately $4,000 less 

than that of their colleagues in schools with the highest minority enrollment (figure 1-11). High-minority schools tend 

to be located in urban areas (Keigher 2009), where living expenses are usually higher than in other areas. The pattern is 

reversed when examining school poverty rates: the average 

salary for mathematics teachers at schools with the lowest 

poverty rates was about $7,000 higher than those at schools 

with the highest rates.

When asked to rate their satisfaction with their salaries, slightly more than half of mathematics teachers reported being satisfied (figure 1-11). Those in low-poverty 

and low-minority schools were more likely to be satisfied 

with their salaries than their colleagues in high-poverty 

and high-minority schools, even though teachers in highminority schools earned higher base salaries than those 

in low-minority schools. Patterns were similar among science teachers (appendix table 1-24).

Teacher Perceptions of Working Conditions

Like salaries, working conditions play a role in determining the supply of qualified teachers and influencing 

their decisions about remaining in the profession. Safe environments, strong administrative leadership, cooperation 

among teachers, high levels of parent involvement, and 

sufficient learning resources can improve teacher effectiveness, enhance commitment to their schools, and promote 

job satisfaction (Berry, Smylie, and Fuller 2008; Brill and 

McCartney 2008; Guarino, Santibanez, and Daley 2006; 

Ingersoll and May 2010).

SASS asked teachers whether they agreed with several 

statements about their school environments and working 

conditions. Although agreement was not unanimous, large 

majorities of mathematics and science teachers at public 

middle and high schools agreed with the following statements regarding their working conditions in 2007: 88% of 

mathematics and 86% of science teachers reported that the 

principal knows what kind of school he or she wants and has 

communicated it to the staff; 85% of mathematics and 82% 

of science teachers agreed that the necessary materials for 

teaching were available; and 76% of mathematics and 73% 

of science teachers agreed that staff were recognized for a 

job well done (appendix table 1-25).44

Responses to some questions differed, however, with 

the composition of the school’s student body. For example, 

about half of mathematics teachers at high-poverty and highminority schools reported that students’ tardiness and class 

cutting interfered with teaching, compared with 34−35% 

of teachers at low-poverty and low-minority schools (figure 1-12). Patterns were similar when mathematics teachers were asked whether student misbehavior interferes with 

teaching (53% agreed at high-minority schools and 56% 

Figure 1-10

Duration of professional development received by 

public middle and high school teachers in the 

content of subject(s) taught, by teaching field: 

Academic year 2007–08

NOTE: Figure includes mathematics and science teachers who 

received professional development in their subject area during past 

12 months.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2010) of 2007–08 

Schools and Staffing Survey, National Center for Education 

Statistics. See appendix table 1-22.

Science and Engineering Indicators 2012

8 hours 9–32 hours 33 hours

Percent

Science

Mathematics

0 20 40 60 80 100
Science and Engineering Indicators 2012 £Ó

agreed at high-poverty schools whereas 34% agreed at lowminority schools and 35% agreed at low-poverty schools).

Teacher perceptions about certain problems in their 

schools improved slightly between 2003 and 2007. The percentage of mathematics and science teachers at middle and 

high schools reporting student apathy and students coming 

to school unprepared to learn as serious problems declined 

from 2003 to 2007. For example, 28% of mathematics teachers in 2007, compared with 31% in 2003, identified student 

apathy as a serious problem at their schools (appendix 

table 1-27). About 33% of mathematics teachers in 2007, 

compared with 37% in 2003, identified unpreparedness for 

learning as a serious problem at their schools. Similar reductions were observed among science teachers.

Although these improvements were small overall, most of 

the improvement in teachers’ responses occurred at schools 

with high concentrations of low-income and minority students. For example, in 2003, 48% of mathematics teachers at 

high-poverty schools reported that student apathy was a serious problem, compared with 12% at low-poverty schools—a 

Concerns about K–12 teacher shortages, teaching 

quality, and the need to retain high-quality instructors 

in the nation’s elementary and secondary schools have 

led to considerable research on rates of attrition among 

teachers (Borman and Dowling 2008; Boyd et al. 2009; 

Ingersoll and Perda 2009; Jalongo and Heider 2006). A 

recent national study revealed that from 1988 to 2008, 

5−9% of public school mathematics and science teachers left the teaching profession each year (figure 1-B) 

(Keigher and Cross 2010). The annual attrition rates of 

mathematics and science teachers are not higher than the 

average for all teachers (8−9% versus 8% in 2008, for example). Mathematics and science teachers who left teaching were also no more likely than other teachers who left 

to take noneducation jobs (appendix table 1-26).

Another study found large school-to-school differences in mathematics and science turnover (defined as 

teachers leaving their schools by either moving to another 

school or leaving teaching altogether) (Ingersoll and May 

2010). High-poverty, high-minority, and urban public 

schools had among the highest mathematics and science 

teacher turnover rates. Reasons prompting mathematics 

teachers to leave their schools included lack of individual 

classroom autonomy, student discipline problems, and 

the extent to which teachers received useful contentfocused professional development. For science teachers, 

the strongest factors included the maximum potential salary, student discipline problems, and the extent to which 

teachers received useful content-focused professional development (Ingersoll and May 2010).

More research is needed to establish conclusively 

links between how teachers enter the profession and attrition, but some has suggested that teachers who enter 

through alternative programs may be more likely to leave 

their schools or the profession than traditional-pathway 

teachers (Boyd et al. 2006; Kane, Rockoff, and Staiger 

2006; Smith 2007).

Figure 1-B

One-year attrition rate of public school teachers, by teaching field: Selected academic years, 1988–89 to 2008–09

Percent

SOURCES: Whitener SD, Gruber KJ, Lynch H, Tingos K, Perona M, Fondelier S, Characteristics of Stayers, Movers, and Leavers: Results From the 

Teacher Follow-up Survey: 1994–95, National Center for Education Statistics (NCES), NCES 97-450 (1997); Luekens MT, Lyter DM, Fox EE, Teacher 

Attrition and Mobility: Results from the Teacher Follow-up Survey, 2000–01, NCES 2004-301 (2004); Marvel J, Lyter DM, Peltola P, Strizek GA, Morton BA, 

Teacher Attrition and Mobility: Results from the 2004–05 Teacher Follow-up Survey, NCES 2007-307 (2006); Keigher A, Teacher Attrition and Mobility: 

Results From the 2008–09 Teacher Follow-up Survey, NCES 2010-353 (2010).

Science and Engineering Indicators 2012

All teachers Mathematics teachers Science teachers

0

2

4

6

8

10

1988–89 1991–92 1994–95 2000–01 2004–05 2008–09

Teacher Attrition
£ÎäÊChapter 1. Elementary and Secondary Mathematics and Science Education

gap of 36 percentage points (figure 1-13). In 2007, that 

gap had closed by about 20 percentage points, with fewer 

teachers at high-poverty schools reporting this as a serious 

problem. A similar change occurred in mathematics teachers reporting students’ lack of preparedness for learning as a 

serious problem: the gap between teachers at high- and lowpoverty schools shrank from 52 percentage points in 2003 to 

about 36 in 2007.

Transition to Higher Education

Preparing students for postsecondary education is an important goal of high schools in the United States. This section presents indicators related to students’ transitions from 

high school to college. It begins with data on high school 

completion rates in the United States, followed by international comparisons of high school graduation rates. It then 

examines students’ expectations for enrolling in college, 

the proportion of students enrolling in college immediately 

after completing high school, and the relative international 

Figure 1-11

Salaries of public middle and high school 

mathematics teachers and teacher satisfaction 

with salaries, by minority enrollment and school 

poverty level: Academic year 2007–08

NOTES: School poverty level is percentage of students in school 

qualifying for free/reduced-price lunch. Average salaries have been 

rounded to the nearest 100.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2010) of 2007–08 

Schools and Staffing Survey, National Center for Education 

Statistics. See appendix table 1-24.

Science and Engineering Indicators 2012

Dollars (thousands)

Average annual salary

Percent

Teachers satisfied with salaries

Total 5% >45% 10% >50%

0

15

30

45

60

75

Minority enrollment School poverty level

Minority enrollment School poverty level

Total 5% >45% 10% >50%

0

20

40

60

80

100

standing of postsecondary enrollment rates in the United 

States. Together, these data present an overview of the nation’s effectiveness in preparing students for postsecondary 

education, the topic of the next chapter.

Completion of High School

On-Time Graduation Rates

The on-time graduation rate in the United States is the 

percentage of students who graduate with a regular high 

school diploma 4 years after entering ninth grade. In 2009, 

76% of students completed high school on time (table 1-9), 

an improvement from 73% in 2006 (Chapman, Laird, and 

KewalRamani 2010; Stillwell and Hoffman 2008). Asian/

Pacific Islander students graduated on time at a higher rate 

than white students did (92% versus 82%).

Students of other races and ethnicities graduated at lower 

rates. Rates of black, Hispanic, and American Indian/Alaska 

Native students were lowest, at 64%, 66%, and 65%, nearly 

20–30 percentage points below the rate of white and Asian/

Pacific Islander students. These rates have increased slightly 

since 2006, however, when they stood at 59%, 61%, and 

62% respectively (Stillwell and Hoffman 2008). The gaps in 

on-time graduation rates between white and black students 

and between white and Hispanic students have declined 

slightly since 2006, by 3 percentage points.

Many students who did not complete high school within 

4 years eventually went on to earn a high school diploma 

or equivalency credential. In 2008, an estimated 90% of 

18- to 24-year-olds who were not enrolled in high school 

had received a high school diploma (84%) or earned an 

equivalency credential (6%), such as a General Educational 

Development (GED) certificate (Chapman, Laird, and 

KewalRamani 2010). Although most colleges and employers accept the GED as an alternative to a regular high school 

diploma, GED recipients do not fare as well as diploma 

holders across a variety of measures, including college completion rates and lifetime earnings (Chapman, Laird, and 

KewalRamani 2010).

Historically, not all states have used the same method for 

calculating graduation rates, leading to wide variation in the 

rates reported by each state. To facilitate state-by-state comparisons, the National Governors Association endorsed the 

NCES method as the standard method for calculating graduation rates in 2005, and all 50 governors agreed to work toward implementing that method (NGA 2005). This method 

calculates the high school graduation rate by dividing the 

number of graduates in a given year by the number of students who entered ninth grade 4 years earlier, adjusting the 

denominator for transfers into and out of the state over those 

4 years.

Currently, 18 states use graduation rates calculated with 

this method to indicate whether they have met the graduation rate requirements for adequate yearly progress under 

NCLB (NGA 2010). Beginning with the 2011–12 school 

year, all states are required to use the NCES method. In addition, all states will be required to set and meet their own high 
Science and Engineering Indicators 2012 1-31

grants program designed to support states in their efforts to 

create statewide longitudinal data systems. These systems 

will track individual students from pre-kindergarten through 

high school, college, and beyond (see sidebar “State Student 

Tracking Systems”).

High School Graduation Rates in the United 

States and Other OECD Nations

U.S. high school graduation rates calculated by OECD 

to articulate with reporting of other OECD members show 

that U.S. graduation rates are lagging behind those of other 

member countries. OECD calculates graduation rates by 

dividing the number of high school graduates in a country 

by the number of students of typical graduation age (OECD 

2010a). Of the 25 OECD nations for which graduation rate 

data were available in 2008, the United States ranked 18th, 

with an average graduation rate of 77% compared with the 

OECD average of 80% (figure 1-14). The U.S. graduation 

rate remained at 77% from 2006 to 2008 according to OECD 

figures (OECD 2009, 2010b).

Figure 1-12

Perceptions of working conditions of public middle and high school mathematics teachers, by minority 

enrollment and school poverty level: Academic year 2007–08 

NOTES: Teachers asked to indicate their agreement with various statements about their school conditions. Response categories included “strongly 

agree,” “somewhat agree,” “somewhat disagree,” and “strongly disagree.” Percentages based on teachers responding “strongly agree” or “somewhat 

agree” to various statements. School poverty level is percentage of students in school qualifying for free/reduced-price lunch.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2010) of 2007–08 Schools and 

Staffing Survey, National Center for Education Statistics. See appendix table 1-25. 

Science and Engineering Indicators 2012

Percent Percent

Student

misbehavior

interferes

Supportive

parents

Necessary

materials

available

Principal

communicates

vision

Staff recognized

for job well done

Worry about job

security due to

test scores

Tardiness and

class cutting

interfere

0 20 40 60 80 100 0 20 40 60 80 100

Minority enrollment: >45%

Minority enrollment: 5%

School poverty level: >50%

School poverty level: 10%

school graduation rate goals by 2014. As of summer 2010, 

22 states had set the graduation rate goal at 90% or higher, 

and 27 states had set the goal between 80 and 89%, an improvement over previous years, when more than half the 

states set the goal at 75% or lower (NGA 2010; NSB 2010a). 

In 2008, the federal government issued revised graduation 

rate requirements, including the provision that, beginning 

in 2011–12, states and districts must meet not only overall 

graduation rate goals but also graduation rate goals for all 

student subgroups to achieve adequate yearly progress (U.S. 

Department of Education 2008).

Traditionally, rates of high school completion have been 

difficult to calculate accurately because of varying requirements for earning a regular diploma across states and districts and inadequate state data systems that track outcomes 

for individual students (Barton 2009). The increased demand for accurate data for federal accountability purposes, 

both for graduation rates and other school outcomes, has 

led states to develop data systems to track student progress 

more accurately. In 2005, the federal government created a 
1-32 Chapter 1. Elementary and Secondary Mathematics and Science Education

Enrollment in Postsecondary Education

A majority of high school seniors expect to continue their 

education after high school. Among the 2009 high school 

senior class, 86% of graduating students planned to attend a 

postsecondary institution in the first year after high school, 

with 62% planning to attend a 4-year institution, 19% planning to attend a 2-year college, and 5% planning to attend a 

vocational, technical, or business school (NCES 2010).

Not all students fulfilled these expectations for immediate college enrollment. Seventy percent of 2009 high school 

graduates had enrolled in a postsecondary institution by the 

October following high school completion (figure 1-15). Of 

these students, 28% enrolled in a 2-year college and 42% 

enrolled in a 4-year institution (appendix table 1-28).

From 1975 through 2009, the immediate college enrollment rate rose by 19 percentage points (from 51% to 70%). 

Female enrollment increased at a much higher rate (49% to 

74%) than did male enrollment during the same period (53% 

to 66%). (For more detail on the gender gap in U.S. higher 

Figure 1-13

Serious student problems reported by public middle and high school mathematics teachers, by minority 

enrollment and school poverty level: Academic years 2003–04 and 2007–08

Percent

NOTES: Teachers asked to indicate the seriousness of various student problems in their schools. Response categories include “serious problem,” 

“moderate problem,” “minor problem,” and “not a problem.” Percentages based on teachers viewing various student problems as “serious.” School 

poverty level is percentage of students in school qualifying for free/reduced-price lunch. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2010) of 2003-04 and 2007-08 

Schools and Staffing Survey, National Center for Education Statistics. See appendix table 1-27.

Science and Engineering Indicators 2012

2003–04

Student apathy Students come unprepared to learn

Student apathy Students come unprepared to learn

2007–08 2003–04 2007–08

0

20

40

60

80

100

Minority students: 5% Minority students: >5%–45% Minority students: >45%

2003–04 2007–08 2003–04 2007–08

0

20

40

60

80

100

Percent

School poverty level: 10% School poverty level: >10%–50% School poverty level: >50%

/>LiÊ£

On-time graduation rates of U.S. public high 

school students, by race/ethnicity: 2006 and 2009

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Race/ethnicity ÓääÈ Óää

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Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 1-33

State Student Tracking Systems

For the most part, existing state data systems are 

cross-sectional and do not track students over time. 

Statewide longitudinal data systems (SLDS) are designed to follow individual students from early childhood through high school and into postsecondary 

education and employment. The impetus for these new 

data systems comes from the need for more comprehensive and reliable data for accountability and evidencebased decisionmaking in education (DQC 2011a).

In 2005, the Institute of Education Sciences of the 

U.S. Department of Education introduced the SLDS 

Grant Program to encourage the development of these 

systems (IES 2011a). At the same time, a group of 

prominent education stakeholders launched the Data 

Quality Campaign to provide a national forum for discussions about SLDS implementation and to avoid duplication of effort and encourage collaboration across 

states (DQC 2011b). Although several states had been 

developing SLDS before 2005, most began designing 

their systems with the first round of federal funding in 

2005, and many have made significant progress over 

the past 6 years (DQC 2011c). As of early 2011, for 

example, all states and the District of Columbia had 

collected student-level data on graduation and dropout 

rates (DQC 2011a).

Since 2005, 41 states and the District of Columbia 

have received at least one SLDS grant through one 

of four federal funding opportunities, including the 

American Recovery and Reinvestment Act (ARRA) 

(IES 2011b). To obtain ARRA funds, all governors and 

most legislatures agreed to implement SLDS that link 

preschool, K–12, postsecondary education, and workforce data and that conform to the requirements outlined 

in the America Competes Act by 2013 (U.S. Department 

of Education 2009). In addition, some states are linking 

their education data with data on corrections and social 

welfare assistance (Carson et al. 2010).

SLDS not only improve the quality of secondary 

and postsecondary education data, but also expose 

problems, such as the misalignment of state programs 

and inconsistencies in articulation of the data, that 

can then be addressed to improve education. SLDS 

are limited, however, by their inability to track students across state borders and into private colleges. 

A pilot project in Florida, Georgia, and Texas aims 

to develop a possible remedy for this problem by 

linking state data with college enrollment data from 

the National Student Clearinghouse (Bill & Melinda 

Gates Foundation 2010).

Figure 1-14

High school graduation rates, by OECD country: 

2008 

OECD = Organisation for Economic Co-operation and Development

NOTES: High school graduation rate is percentage of population at 

typical upper secondary graduation age (e.g., 18 years old in United 

States) completing upper secondary education programs. OECD 

average based on all OECD countries with available data. To 

generate estimates that are comparable across countries, rates are 

calculated by dividing the number of graduates in the country by the 

population of the typical graduation age.

SOURCE: OECD, Education at a Glance: OECD Indicators 2010

(2010). 

Science and Engineering Indicators 2012

Percent

0 20 40 60 80 100

Turkey

Mexico

Portugal

Chile

Luxembourg

Spain

Sweden

United States

New Zealand

Hungary

OECD average

Slovak Republic

Poland

Denmark

Italy

Czech Republic

Iceland

Switzerland

United Kingdom

Norway

Greece

Korea

Finland

Japan

Ireland

Germany
1-34 Chapter 1. Elementary and Secondary Mathematics and Science Education

education enrollment and degree attainment, see chapter 2 

sidebar “Gender Gap in Undergraduate Education.”)

Immediate college enrollment in the United States is associated with parental education levels and family income. 

In 2009, 40% of students whose parents had less than a high 

school education enrolled in college immediately after high 

school completion, compared with 82% of students whose 

parents had a bachelor’s or advanced degree (appendix table 

1-28). Students from high-income families enrolled in college at higher rates than did students from middle- or lowincome families (84% versus 67% and 55%, respectively) 

in 2009.

The rate of immediate enrollment in college for white 

students was 71%, compared with 63% for black and 62% 

for Hispanic students. Immediate college enrollment rates 

for black and Hispanic students have increased over time, 

showing gains of about 6 percentage points for blacks and 

7 percentage points for Hispanics since 2002. However, the 

white-black and white-Hispanic gaps persisted over time.

Postsecondary Enrollment in an International 

Context

According to OECD data, the percentage of U.S. young 

adults enrolling in college for the first time was 64% in 2008. 

The overall average was 56% for the 25 countries participating in the study. The United States ranked 11th out of 25 

in 2008 (appendix table 1-29). The data show that females 

enroll in college at higher rates than males in most OECD 

countries, including the United States. In the United States, 

females enrolled at a rate of 72% (compared with the OECD 

average of 63%), and males enrolled at a rate of 57% (compared with 50% internationally) (OECD 2010a).

Conclusion

Indicators in this chapter produce a mixed picture of the 

progress of elementary and secondary mathematics and science education in the United States. Although improvements 

are evident in many areas, overall they are slow and uneven. 

Gaps among students of different demographic backgrounds 

and among schools with different student populations have 

been a persistent challenge in K−12 education in the United 

States. These gaps are reflected in many indicators in this 

chapter, including teacher qualifications, school environment, and, ultimately, learning outcomes.

NAEP mathematics and science assessment results show 

that, although average mathematics scores for 8th graders 

have improved steadily since 1990 and average mathematics 

scores for 12th graders have increased between 2005 and 

2009, improvement among 4th graders leveled off in 2009. 

Achievement gaps are found among many student subgroups. Whereas boys performed slightly better than girls 

in both subjects, relatively larger gaps existed among students of different racial/ethnic backgrounds or with different 

family incomes. Over time, some gaps narrowed at grade 4: 

gaps in mathematics achievement between white and black 

students, between high- and low-performing students, and 

between private and public school students were smaller in 

2009 than in 1990.

Overall, large majorities of 4th, 8th, and 12th graders 

did not demonstrate proficiency in the knowledge and skills 

taught at their grade level. While a majority of ninth graders 

reached proficiency in low-level algebra skills, few mastered 

higher level skills. Results of international mathematics and 

science literacy tests show that U.S. 15-year-olds continue to 

lag behind their peers in many other countries, even though 

their scores have improved somewhat in recent years.

Figure 1-15

Immediate college enrollment rates among high school graduates, by sex: 1979–2009

Percent

NOTES: Includes students ages 16–24 completing high school in survey year. Immediate college enrollment rates defined as rates of high school 

graduates enrolled in college in October after completing high school. 

SOURCE: Aud S, Hussar W, Kena G, Bianco K, Frohlich L, Kemp J, Tahan K. The Condition of Education 2011, National Center for Education Statistics 

(NCES), NCES 2011-033 (2011). See appendix table 1-28. 

Science and Engineering Indicators 2012 

1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

0

50

60

70

80

All graduates

Male

Female
Science and Engineering Indicators 2012 1-35

Efforts to improve student achievement include raising 

high school graduation requirements, strengthening the rigor 

of curriculum standards, increasing advanced coursetaking, 

promoting early participation in gatekeeper courses such 

as algebra I, and improving teaching quality. From 1987 

to 2008, the number of states requiring at least 3 years of 

mathematics and science courses for high school graduation increased from just a few states to more than 30. By 

the end of 2010, 44 states had adopted a common set of rigorous academic standards designed to ensure that students 

graduate from high school prepared for college and careers. 

Trend data from 1990 to 2009 show an upward trend of 

students earning more mathematics and science credits and 

participating in advanced mathematics and science courses. 

Nevertheless, completion rates in some advanced courses 

remained relatively low, and wide gaps in advanced mathematics and science coursetaking persisted among racial/

ethnic subgroups.

Indicators related to teaching quality show that virtually 

all mathematics and science teachers in public middle and 

high schools have such basic credentials as a bachelor’s 

degree and teaching certificate, and proportionally more 

mathematics and science teachers had advanced degrees in 

2007 than in 2003. Likewise, more science teachers held 

full certification in 2007 than in 2003. Large majorities of 

mathematics and science teachers in high school also had a 

degree and/or certificate in their teaching field. Although infield mathematics and science teachers are not as prevalent 

in middle schools as in high schools, the percentage of such 

teachers in middle schools has increased in recent years. 

Mathematics and science teachers with these qualifications 

are not evenly distributed across schools, however: schools 

with lower concentrations of minority and low-income students tend to have higher proportions of teachers with advanced degrees, full certification, in-field preparation, and 

more experience.

An increasing proportion of new mathematics and science teachers entered the profession through alternative 

programs. These teachers often begin teaching before completing their training, engaging in practice teaching, or earning full state certification, and they are more often found in 

schools with high concentrations of minority and poor students. Nevertheless, a majority of new mathematics and science teachers in public middle and high schools participate 

in practice teaching before entering the teaching force, and 

many of them also participate in induction programs during 

their first year in the classroom. In addition, a majority of 

mathematics and science teachers participate in professional 

development activities during the school year, although the 

duration of many such activities is relatively short.

Annual attrition rates for public school mathematics and 

science teachers fluctuated in the range of 5−9% between 

1988 and 2008. Although teachers’ salaries have not kept 

pace with those in occupations requiring comparable education, most teachers had favorable perceptions of their working conditions. Teachers in high-minority and high-poverty 

schools were less likely than others to have such positive 

perceptions, but some gaps have narrowed in recent years.

Most high school students graduate with a regular diploma 

4 years after entering ninth grade. On-time graduation rates 

have improved, though slowly. Significant racial/ethnic gaps 

exist, with white and Asian/Pacific Islander students having 

graduation rates higher than those of students of other races and ethnicities. The U.S. ranked 18th in graduation rates 

among 25 OECD countries with available data in 2008.

A majority of high school seniors expect to continue their 

education after high school, and many enroll in college directly after high school graduation. Immediate college enrollment rates have increased for all students as well as for 

many demographic subgroups. Gaps persisted, however. 

Black students, Hispanic students, low-income students, and 

students whose parents have less education enroll in college 

at rates lower than their counterparts.

Notes

1. The terms achievement and performance are used 

interchangeably in this section when discussing scores on 

mathematics and science assessments.

2. Differences between two estimates were tested using 

Student’s t-test statistic to minimize the chances of concluding that a 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 0.05, which means that a reported difference would occur 

by chance no more than once in 20 samples when there was 

no actual difference between the population means.

3. Race to the Top is a $4.35 billion competitive grant 

program funded by the U.S. Department of Education as part 

of the American Rec overy and Reinvestment Act of 2009. 

This program is designed to encourage and reward states 

creating the conditions for education innovation and reform, 

achieving significant improvement in student outcomes, and 

implementing reform plans in four core areas: 1) adopting 

standards and assessments that prepare students to succeed 

in college and the workplace; 2) building data systems that 

measure student growth and success and inform teachers and 

principals how to improve instruction; 3) recruiting, developing, rewarding, and retaining effective teachers and principals; and 4) turning around the lowest performing schools. 

In March 2010, Delaware and Tennessee won grants in the 

first phase of the competition, receiving approximately $100 

million and $500 million, respectively, to implement their 

comprehensive school reform plans. In August 2010, nine 

states (Florida, Georgia, Hawaii, Maryland, Massachusetts, 

New York, North Carolina, Ohio, and Rhode Island) and the 

District of Columbia won grants in the second phase of the 

competition. Grant levels depend on a state’s student population: large states like New York and Florida receive up 

to $700 million and smaller states like Hawaii and Rhode 

Island receive up to $75 million. See the Race to the Top 
1-36 Chapter 1. Elementary and Secondary Mathematics and Science Education

Fund website for more information: http://www2.ed.gov/

programs/racetothetop/index.html.

4. The U.S. Department of Education awarded School 

Improvement Grants to states under the Elementary and 

Secondary Education Act of 1965 (reauthorized in 2002 as 

the No Child Left Behind Act) to support focused school 

improvement efforts. In 2009, the department dramatically 

increased the funds that would be provided to states (from 

$491,265 in 2008 to $3.546 billion in 2009) and charged 

states with using the funds for leveraging changes needed to 

turn around persistently low-achieving schools.

5.7KHVHWZR1$(3DVVHVVPHQWSURJUDPVGLIIHULQPDQ\

aspects, including samples of students and assessment times, 

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UHSRUWFDUGDERXWOWWBPDLQBGLIIDVS

6. The 2010 volume reviewed long-term trends in mathHPDWLFVIURPWRDQGWKHYROXPHH[DPLQHG

WUHQGVLQVFLHQFHIURPWR7KHORQJWHUPWUHQGDVVHVVPHQWLQPDWKHPDWLFVZLOOEHDGPLQLVWHUHGDJDLQLQ

the long-term trend assessment in science has not been conducted since 1999.

7. 6WXGHQWVLQWKHEHORZEDVLFFDWHJRU\KDYHVFRUHVORZHU

WKDQWKHPLQLPXPVFRUHIRUWKHEDVLFOHYHO6WXGHQWVLQWKH

basic category have scores at or above the minimum score 

for the basic level, but lower than the minimum for the proILFLHQWOHYHO6WXGHQWVLQWKHSURILFLHQWFDWHJRU\KDYHVFRUHV

at or above the minimum score for the proficient level, 

but lower than the minimum score for the advanced level. 

6WXGHQWVLQWKHDGYDQFHGFDWHJRU\KDYHVFRUHVDWRUDERYH

the minimum score for the advanced level.

8. See NAEP’s mathematics and science achievement levels defined by grade at http://nces.ed.gov/nations 

reportcard/mathematics/achieveall.asp and http://nces.

ed.gov/nationsreportcard/science/achieveall.asp.

9. Percentiles are scores below which a specified percentage of the population falls. For example, among fourth graders in 2009, the 10th percentile score for mathematics was 

202. This means that 10% of fourth graders had mathematics scores at or below 202 and 90% scored above 202. The 

scores at various percentiles indicate students’ performance 

levels.

10. In 2005, NAGB adopted a new mathematics framework for the grade 12 assessment to reflect contemporary 

standards of high school curriculum and coursework. Based 

on this new framework, the 2005 assessment changed its 

content areas (e.g., increasing coverage on algebra, data 

analysis, and probability) and adopted a new reporting scale 

(i.e., 0−300 as opposed to 0−500 in earlier years). These 

changes made the 2005 assessment results not comparable 

to those in earlier years. Some changes were also made to the 

2009 framework; the purpose was to enable NAEP to better measure how well prepared 12th grade students are for 

postsecondary education and training (e.g., adding content 

that is beyond what is typically taught in a standard 3-year 

course of study in high school mathematics). However, special analyses of 2005 and 2009 data determined that the 2009 

grade 12 mathematics results could still be compared with 

results from the 2005 assessment despite the changes to the 

2009 framework. More information about the mathematics 

frameworks for the 2005 and 2009 grade 12 assessments 

and how they differ from the previous framework is available at http://nces.ed.gov/nationsreportcard/mathematics/ 

frameworkcomparison.asp.

11. Results for private school students in 2009 could not 

be reported separately due to the low participation rate for 

private schools.

12. Special NSF tabulations.

13. Students’ eligibility for free/reduced-price lunch is 

often used as a proxy measure of family poverty. Students 

who are eligible for free/reduced-price lunch are considered 

to come from low-income families, and those who are not 

eligible for free/reduced-price lunch are considered to come 

from relatively high-income families.

14. Data on parental education for grade 4 were unreliable and therefore excluded from the analysis.

15. Cross-grade comparisons are acceptable for mathematics scores of fourth and eighth graders because these scores 

were put on a common scale. However, mathematics scores 

for 4th and 8th graders cannot be compared to those of 12th 

graders because they used different score scales (0 to 500 

for grades 4 and 8 and 0 to 300 for grade 12). Cross-grade 

comparisons are also not appropriate for other subjects because the scales were derived independently at each grade 

level. See http://nces.ed.gov/nationsreportcard/mathematics/ 

interpret-results.asp.

16. Gender gaps are not consistent across racial/ethnic 

subgroups. For example, the results from the 2009 NAEP 

mathematics assessment show that, whereas white and 

Hispanic boys had higher scores than their girl counterparts 

at grade 4, the pattern was opposite among blacks—girls 

outperformed boys. Similar differences were also found 

among students in grade 8 (special NSF tabulations).

17. Differences in performance between public and private school students reflect in part different types of students 

enrolled in public and private schools. Proportionally, private schools enroll more white students and students from 

advantaged socioeconomic backgrounds than public schools 

(Snyder and Dillow 2011).

18. The reduction in the white-black gap at grade 4 is 

likely attributable to larger improvements made by black female students (Vanneman et al. 2009). From 1990 to 2007, 

the average mathematics score gains of black females at grade 

4 were greater than those of their white peers, reducing the 

white-black gap. However, among male students at grade 4, 

no similar gap reductions were observed during this period.

19. Previous volumes of Science and Engineering 

Indicators (e.g., NSB 2010b) also used data from the Trends 

in International Mathematics and Science Study (TIMSS) to 

examine the relative standing of U.S. students in mathematics and science achievement. No new data from TIMSS, 

however, were available when this chapter was prepared. 

The latest administration of TIMSS was in spring 2011, 

and international comparisons based on TIMSS data will be 
Science and Engineering Indicators 2012 1-37

available in the 2014 volume of Science and Engineering 

Indicators.

20. Information on OECD and its assessment programs 

is available at http://www.pisa.oecd.org/pages/0,2987,

en_32252351_32235731_1_1_1_1_1,00.html.

21. PISA differs from NAEP in several key aspects. 

NAEP assesses the knowledge and skills students need for 

an in-depth understanding of mathematics and science at 

various grade levels. PISA measures the “yield” of education systems, that is, the skills and competencies students 

have acquired and can apply in real-world contexts by age 

15. NAEP emphasizes curriculum-based knowledge, whereas PISA focuses on literacy and applications, drawing on 

learning both in and outside of school. Although NAEP and 

PISA both are sample-based assessments, NAEP uses gradebased samples of students in grades 4, 8, and 12, and PISA 

uses an age-based sample of 15-year-old students nearing 

completion of compulsory schooling in many countries. 

Both assessments are developed from a framework specifying the content and skills to be measured, but the PISA 

framework is organized around overarching ideas (e.g., 

space and shape) with emphasis on the contexts in which 

concepts are applied (e.g., in school, in society), as opposed 

to curriculum-based topics, such as geometry and algebra.

22. In this section, “coursetaking” refers only to completed courses for which students earned at least one credit. The 

High School Transcript Study contains no data on students 

who did not graduate or who may have enrolled in a course 

but did not complete it.

23. Not all high schools have the same standards for course 

titles and content. To allow comparisons, HSTS standardizes 

the transcript information. To control for variation in course 

titles, a coding system called the Classification of Secondary 

School Courses is used for classifying courses on the basis of 

information in school catalogs and other information sources. 

(For more information, see http://nces.ed.gov/surveys/hst/

courses.asp.)

24. Advanced mathematics course categories used in this 

edition are based on the categories reported by HSTS for 

2009. HSTS has changed these categories since 2005, so the 

percentages shown in figures 1-5 and 1-6 are not comparable 

to those reported in previous editions.

25. HSTS converts high schools’ transcript credits to 

standardized Carnegie units of credit (or Carnegie credits), 

in which a single unit is equal to 120 hours of classroom 

time over the course of a year. A credit is equivalent to a 

1-year course in a subject.

26. Precalculus/analysis includes courses referred to as 

mathematics analysis courses, but they include the same 

content as precalculus courses.

27. Advanced science course categories used in this edition are based on the categories reported by HSTS for 2009. 

HSTS has changed these categories since 2005, so the percentages for each subject area shown in figure 1-7 are not 

comparable to those reported in previous editions.

28. AP/IB science courses were not coded separately in 

1990 and therefore are not reported for that year.

29. Of 500 possible points awarded to grant applications, 

138 points, or 28% of the total, were given to plans for “Great 

Teachers and Leaders.” Specifically, plans were solicited for 

providing high-quality pathways for aspiring teachers and 

principals (21 points), improving teacher and principal effectiveness based on performance (58 points), ensuring equitable distribution of effective teachers and principals (25 

points), improving the effectiveness of teacher and principal preparation programs (14 points), and providing effective support to teachers and principals (20 points). Detailed 

information is available at http://www2.ed.gov/programs/

racetothetop/executive-summary.pdf.

30. Middle and high school teachers, included in these 

indicators, are identified using a SASS variable that indicates the level of the school at which teachers are employed. 

Middle schools are defined as those with no grade lower 

than 5 and no grade higher than 8; high schools are defined 

as those with no grade lower than 7 and at least one grade 

higher than 8. Elementary school teachers, not included in 

these indicators, typically teach multiple subjects, and most 

of them hold a certification in general education.

31. Based on the percentage of students in school qualifying for free/reduced-price lunch.

32. To simplify the discussion, schools in which 10% 

or fewer of the students are eligible for the federal free and 

reduced-price lunch program are called low-poverty schools, 

and schools in which more than 50% of the students are eligible are called high-poverty schools. Similarly, low-minority 

schools are those in which 5% or fewer of the students are 

members of a minority, and high-minority schools are those 

in which more than 45% of the students are members of 

a minority.

33. Probationary certification generally is awarded to 

those who have completed all requirements except for a 

probationary teaching period. Provisional or temporary certification is awarded to those who still have requirements 

to meet. States also issue emergency certification to those 

with insufficient teacher preparation who must complete a 

regular certification program to continue teaching (Henke et 

al. 1997). Teachers’ type of certification differs from their 

pathway into the profession: teachers from both traditional 

and alternative programs may have any type of state certification in order to teach. Alternative-pathway teachers, however, are more likely to begin teaching with a provisional or 

temporary certification.

34. As of 2009, 48 states required teachers to pass a test 

covering topics such as basic academic skills and pedagogical knowledge to obtain certification (Editorial Projects in 

Education Research Center 2010).

35. In 2010, the National Academy of Sciences counted 

130 alternative programs, differing in goals, requirements, 

structure, and candidate pools (NRC 2010). Some programs, 

such as Teach for America, receive direct federal support, 

and others are themselves federal programs, such as the U.S. 

Department of Defense’s “Troops to Teachers” program, 

which facilitates the entry of military personnel into teaching careers. Race to the Top, a federal competitive grant 
£ÎnÊChapter 1. Elementary and Secondary Mathematics and Science Education

program encouraging certain education reforms, awarded 

points to applicant states for providing high-quality alternative pathways for aspiring teachers.

36. Large variation has been observed between programs 

within each pathway (Boyd et al. 2008).

37. More information about these programs is available at 

http://www.teachforamerica.org and http://tntp.org/about-us/. 

Information about the Troops to Teachers program is available at http://www2.ed.gov/programs/troops/index.html.

38. More information about National Board Certification 

is available at http://www.nbpts.org.

39. Information on the number of teachers is available at 

http://www.nbpts.org/about_us/national_board_certifica1/

national_board_certifica; information on their diversity initiatives and teacher placement is at http://www.nbpts.org/

resources/diversity_initiatives. 

40. Research suggests that characteristics of the practice 

teaching placement and program affect subsequent teacher 

effectiveness. In New York City, teachers who were placed 

in easy-to-staff schools during their practice teaching were 

more likely to remain teaching in the district and see gains in 

student achievement, regardless of the characteristics of the 

school at which they were ultimately employed (Ronfeldt 

2010); teachers whose preparation programs provided oversight of their practice teaching and required a capstone project saw larger student achievement gains during their first 

year (Boyd et al. 2008). 

41. For a slightly different measurement of in-field 

teaching, see Education and Certification Qualifications 

of Departmentalized Public High School-Level Teachers 

of Core Subjects: Evidence From the 2007–08 Schools 

and Staffing Survey (NCES 2011-317), http://nces.ed.gov/

pubs2011/2011317.pdf.

42. A recent experimental study of professional development for middle school mathematics teachers found that a 

2-year training program for 7th-grade mathematics teachers 

had no effect on either teacher knowledge or student performance (Garet et al. 2011). A report from the study’s first 

year found that the training did significantly increase the 

frequency of one “good practice” for teaching mathematics: 

engaging in activities that elicit student thinking (Garet et 

al. 2010).

43. The maximum duration SASS provides as an option 

in its teacher questionnaire is “33 hours or more,” which is 

reported in this chapter. Research suggests that teachers who 

receive content-focused professional development already 

have relatively strong content knowledge (Desimone, Smith, 

and Ueno 2006).

44. The statements about working conditions included 

in this section represent a selection of those measured in 

SASS. For a complete list of questions and results for public 

elementary and secondary teachers, see http://nces.ed.gov/

programs/digest/d10/tables/dt10_076.asp. 

Glossary

Student Learning in Mathematics and Science

Eligibility for National School Lunch Program: Student 

eligibility for this program, which provides free or reducedprice 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.

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 2009. 

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.

Student Coursetaking in High School Mathematics 

and Science

Advanced Placement: Courses that teach college-level 

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.

International Baccalaureate: An internationally recognized pre-university academic subject course designed for 

high school students.

Teachers of Mathematics and Science

High schools: Schools that have at least one grade higher 

than 8 and no grade in K–6.

Main teaching assignment field: The field in which 

teachers teach the most classes in school.

Major: A field of study in which an individual has taken 

substantial academic coursework at the postsecondary level, 

implying that the individual has substantial knowledge of 

the academic discipline or subject area.

Middle schools: Schools that have any of grades 5–8 and 

no grade lower than 5 and no grade higher than 8. 

Practice teaching: Programs designed to offer prospective teachers hands-on classroom practice. Practice teaching 

is often a requirement for completing an educational degree 

or state certification, or both.

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. 

Secondary schools: Schools that have any of grades 

7–12 and no grade in K–6.

Teaching certification: A license or certificate awarded to teachers by the state to teach in a public school. 

Certification typically includes the following five types: 
Science and Engineering Indicators 2012 £Î

(1) regular or standard state certification or advanced professional certificate; (2) probationary certificate issued to 

persons who satisfy all requirements except the completion 

of a probationary period; (3) provisional certificate issued 

to persons who are still participating in what the state calls 

an “alternative certification program”; (4) temporary certificate issued to persons who need some additional college 

coursework, student teaching, and/or passage of a test before 

regular certification can be obtained; and (5) emergency certificate issued to persons with insufficient teacher preparation who must complete a regular certification program to 

continue teaching.

Teacher induction: Programs designed at the school, local, or state level for beginning teachers in their first few 

years of teaching. The purpose of the programs is to help 

new teachers improve professional practice, deepen their 

understanding of teaching, and prevent early attrition. One 

key component of such programs is that new teachers are 

paired with mentors or other experienced teachers to receive 

advice, instruction, and support.

Transition to Higher Education

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.

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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 ........................................................................................2-5

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

Financing Higher Education ...................................................................................................2-11

Undergraduate Education, Enrollment, and Degrees in the United States ................................2-16

Undergraduate Enrollment in the United States .....................................................................2-16

Undergraduate Degree Awards ...............................................................................................2-19

Persistence and Retention in Undergraduate Education (S&E Versus Non-S&E Fields) ......2-22

Graduate Education, Enrollment, and Degrees in the United States .........................................2-24

Graduate Enrollment in S&E ..................................................................................................2-24

S&E Master’s Degrees ...........................................................................................................2-25

S&E Doctoral Degrees ...........................................................................................................2-26

International S&E Higher Education .........................................................................................2-32

Higher Education Expenditures ..............................................................................................2-32

Educational Attainment ..........................................................................................................2-32

First University Degrees in S&E Fields .................................................................................2-32

S&E First University Degrees by Sex ....................................................................................2-33

Global Comparison of S&E Doctoral Degrees .......................................................................2-34

Global Student Mobility ........................................................................................................2-34

Conclusion .................................................................................................................................2-37

Notes ..........................................................................................................................................2-38

Glossary .....................................................................................................................................2-39

References ..................................................................................................................................2-39

List of Sidebars

Carnegie Classification of Academic Institutions ........................................................................2-8

State Appropriations to Public Research Universities: A Volatile Decade ...............................2-12

Gender Gap in Undergraduate Education ..................................................................................2-21

The Path Forward: The Future of Graduate Education in the United States ............................2-24

The National Research Council Ratings: Measuring Scholarly Quality of 

Doctoral Programs ................................................................................................................2-26

An Update on the Bologna Process ..........................................................................................2-34

Transnational Higher Education ...............................................................................................2-36

Please see chapter errata
2-2 Chapter 2. Higher Education in Science and Engineering

List of Figures

Figure 2-1. Community college attendance among recent recipients of S&E degrees, 

by field of highest degree: 1999 and 2008 ............................................................................2-10

Figure 2-2. Reasons for attending community college among recent S&E graduates: 

1999 and 2008 .......................................................................................................................2-11

Figure 2-3. Average annual tuition, fees, room, and board for public and private 4-year 

institutions, total student aid dollars, and median income: 2000–10 ....................................2-11

Figure 2-4. Full-time S&E graduate students, by field and mechanism of primary 

support: 2009 .........................................................................................................................2-14

Figure 2-5. Full-time S&E graduate students with primary support from federal 

government, by field: 2009 ...................................................................................................2-14

Figure 2-6. Freshmen intending S&E major, by race/ethnicity: 1995–2010 .............................2-17

Figure 2-7. Engineering: Freshmen intentions and degrees, by sex ..........................................2-17

Figure 2-8. Engineering: Freshmen intentions and degrees, by race/ethnicity ..........................2-17

Figure 2-9. Natural sciences: Freshmen intentions and degrees, by sex ...................................2-18

Figure 2-10. Natural sciences: Freshmen intentions and degrees, by race/ethnicity .................2-18

Figure 2-11. Foreign undergraduate student enrollment in U.S. universities, 

by top 10 places of origin and field: November 2010 ...........................................................2-19

Figure 2-12. U.S. engineering enrollment, by level: 1989–2009 ...............................................2-20

Figure 2-13. S&E bachelor’s degrees, by field: 2000–09 ..........................................................2-20

Figure 2-14. Women’s share of S&E bachelor’s degrees, by field: 2000–09 ...........................2-21

Figure 2-15. Share of S&E bachelor’s degrees, by race/ethnicity: 2000–09 .............................2-22

Figure 2-16. S&E master’s degrees, by field: 2000–09 .............................................................2-25

Figure 2-17. S&E master’s degrees, by sex: 2000–09 ...............................................................2-25

Figure 2-18. S&E master’s degrees, by race/ethnicity and citizenship: 2000–09 .....................2-26

List of Tables

Table 2-1. U.S. citizen/permanent resident S&E doctorate recipients who reported 

earning college credit from a community or 2-year college, by race/ethnicity: 2005–09 ......2-9

Table 2-2. Community college attendance among recent recipients of S&E bachelor’s 

and master’s degrees, by degree level and degree year: 1999–2008 ......................................2-9

Table 2-3. Community college attendance among recent recipients of S&E degrees, 

by sex, race/ethnicity, and citizenship status: 2008 ..............................................................2-10

Table 2-4. Full-time S&E graduate students, by source and mechanism of primary 

support: 2009 .........................................................................................................................2-13

Table 2-5. Primary support mechanisms for S&E doctorate recipients, by 2010 Carnegie 

classification of doctorate-granting institution: 2009 ...........................................................2-15

Table 2-6. Master’s degree recipients with debt from graduate student loans upon 

graduation and average amount owed, by broad field: 1999–2000 and 2007–08 ................2-16

Table 2-7. Foreign students enrolled in U.S. higher education institutions, 

by broad field and academic level: 2006–10.........................................................................2-19

Table 2-8. Persistence and outcome of postsecondary students beginning 4-year 

colleges or universities in 2004: 2009 ...................................................................................2-23

Table 2-9. Field switching among postsecondary students beginning 4-year colleges 

and universities in 2004: 2009 ..............................................................................................2-23

Table 2-10. Median number of years from entering graduate school to receipt of 

S&E doctorate, by 2010 Carnegie classification of doctorate-granting institution: 

1995–2009 .............................................................................................................................2-28

Table 2-11. Foreign recipients of U.S. S&E doctorates, by country/economy of origin: 

1989–2009 .............................................................................................................................2-29

Table 2-12. Asian recipients of U.S. S&E doctorates, by field and country/economy 

of origin: 1989–2009 .............................................................................................................2-30

Table 2-13. European and North American recipients of U.S. S&E doctorates, by field and 

region/country of origin: 1989–2009 ....................................................................................2-31
Science and Engineering Indicators 2012 2-3

Figure 2-19. S&E doctoral degrees earned in U.S. universities, by field: 2000–09 ..................2-27

Figure 2-20. S&E doctoral degrees earned by U.S. citizen and permanent resident 

underrepresented minorities, by race/ethnicity: 2000–09 .....................................................2-28

Figure 2-21. S&E doctoral degrees, by sex, race/ethnicity, and citizenship: 2000–09 .............2-29

Figure 2-22. U.S. S&E doctoral degree recipients, by selected Asian country/economy 

of origin: 1989–2009 .............................................................................................................2-29

Figure 2-23. U.S. S&E doctoral degree recipients, by selected Western European 

country: 1989–2009 ..............................................................................................................2-30

Figure 2-24. U.S. S&E doctoral degree recipients from Europe, by region: 1989–2009 ..........2-31

Figure 2-25. U.S. S&E doctoral degree recipients from Canada and Mexico: 1989–2009 .......2-31

Figure 2-26. Attainment of tertiary-type A and advanced research programs, 

by country and age group: 2008 ............................................................................................2-33

Figure 2-27. First university natural sciences and engineering degrees, 

by selected countries: 1999–2008 .........................................................................................2-33

Figure 2-28. Natural sciences and engineering doctoral degrees, by selected 

country: 2000–08 ..................................................................................................................2-35

Figure 2-29. S&E doctoral degrees earned by Chinese students at home universities 

and U.S. universities: 1994–2008 .........................................................................................2-35

Figure 2-30. Internationally mobile students enrolled in tertiary education, 

by country: 2009 ...................................................................................................................2-36

Figure 2-A. State appropriations to major public research universities: 2002–10 ....................2-12

Figure 2-B. State appropriations to major public research universities per enrolled 

student: 2002–10 ...................................................................................................................2-12
2-4 Chapter 2. Higher Education in Science and Engineering

Characteristics of the U.S. Higher 

Education System

Research institutions 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.

Baccalaureate colleges are the source of relatively few 

S&E bachelor’s degrees, but are a more prominent source 

of future S&E doctorate recipients. 

Master’s colleges and universities awarded more than onethird of S&E bachelor’s and master’s degrees in 2009.

Nearly one in five U.S. citizen/permanent residents who 

received a doctoral degree from 2005 to 2009 had earned 

some college credit from a community or 2-year college.

Over the past decade in the United States, tuition and 

fees for colleges and universities have grown faster than 

median income.

In the 2007–08 academic year, two-thirds of all undergraduates received some kind of financial aid and 39% took out 

loans to finance their education. 

At the time of doctoral degree conferral, 45% of 2009 S&E 

doctorate recipients had debt related to their undergraduate 

or graduate education.

In 2009, the federal government was the primary source 

of financial support for 18% of full-time S&E graduate 

students.

In 2009, the federal government funded 63% of S&E graduate students on traineeships, 49% of those with research 

assistantships, and 23% of those with fellowships.

Graduate students in the biological sciences, the physical 

sciences, and engineering received relatively more federal 

financial support compared with those in computer sciences, mathematics, other life sciences, psychology, and 

social sciences.

Undergraduate Education, Enrollment, 

and Degrees

Enrollment in U.S. higher education rose from 14.5 million in fall 1994 to 20.7 million in fall 2009. 

Between 2007 and 2009, enrollment increased faster than 

in most previous years.

Enrollment in higher education is projected to grow through 

2019 because of increases in the college-age population.

Postsecondary enrollment is projected to increase for all 

racial/ethnic groups, except for whites. The percentage for 

white students is projected to decrease from 63% in 2008 

to 58% in 2019, reflecting demographic changes.

The number of S&E bachelor’s degrees has risen steadily over the past 15 years, reaching a new peak of about 

half a million in 2009.

With the exception of computer sciences, most S&E fields 

experienced increases in the number of degrees awarded in 

2009. In computer sciences, the number of bachelor’s and 

master’s degrees awarded decreased sharply from 2004 to 

2007, but then remained stable 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. 

In general, men earn a majority of bachelor’s degrees in 

engineering, computer sciences, and physics. More women 

than men earn degrees in chemistry; biological, agricultural, and social sciences; and psychology.

In the last 10 years, the proportion of S&E bachelor’s degrees 

awarded to women has not grown measurably and has declined in computer sciences, mathematics, and engineering. 

The racial/ethnic composition of those earning S&E 

bachelor’s degrees is changing, reflecting both population change and an increase in college attendance by 

members of minority groups.

For all racial/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 generally increased since 2000.

Undergraduate students majoring in S&E fields persist 

and complete their degrees at a higher rate than nonS&E students.

Six years after enrolling in a 4-year college or university in 

academic year 2003–04, 63% of undergraduates with an 

S&E major had completed a bachelor’s degree, compared 

to 55% of students with other majors.

Among students who began 4-year colleges in 2003–04, 

the proportion majoring in S&E in 2009 was higher than 

the proportion majoring in S&E in 2004. Thus, the number 

of students switching majors out of S&E fields was lower 

than the number entering S&E fields as a whole. 

Within S&E, undergraduate attrition out of engineering 

was greater than transfers into this field, and transfers into 

social/behavioral sciences exceeded attrition. About 10% 

of engineering majors switched to mathematics or physical 

or computer sciences majors.

Highlights
Graduate Education, Enrollment, and Degrees

The proportion of women and minorities in S&E graduate education has been growing steadily but slowly.

Nearly half of the 611,600 S&E graduate students enrolled 

in the United States in fall 2009 were women, with considerable field variation. 

Women continued to enroll at disproportionately lower 

rates in engineering, computer sciences, physical sciences, 

and economics.

In 2009, underrepresented minority students (blacks, 

Hispanics, and American Indians/Alaska Natives) made up 

12% of students enrolled in graduate S&E programs, with 

Asian/Pacific Islanders representing 6% and whites 48%. 

Temporary residents accounted for remainder of graduate 

S&E enrollment.

The number of total foreign graduate students continued 

to increase through fall 2010, with all of the increase occurring in S&E fields. 

About 60% of all foreign graduate students in the United 

States in 2010 were enrolled in S&E fields, compared with 

32% at the undergraduate level.

Most of the growth in the number of foreign graduate students in S&E between 2009 and 2010 occurred in engineering and computer sciences.

India and China were the countries of origin for nearly twothirds of the foreign S&E graduates in the United States in 

November 2010.

Master’s degrees awarded in S&E fields increased from 

120,200 in 2007 to 134,000 in 2009, after holding steady 

for the previous 3 years.

Increases occurred in most major science fields. 

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/ethnic groups from 2000 to 2009. 

During this period, the proportion earned by blacks and 

Hispanics increased, that of Asians/Pacific Islanders and 

American Indians/Alaska Natives remained flat, and that 

of whites decreased. 

In 2009, U.S. academic institutions awarded 41,100 

S&E doctorates. 

The number of S&E doctorates conferred annually by U.S. 

universities increased steeply (43%) from 2003 to 2007, 

then flattened and declined slightly in 2009. 

Among fields that award large numbers of doctorates, the 

biggest increases between 2000 and 2009 were in engineering (47%) and biological sciences (49%).

Students on temporary visas earned high proportions 

of U.S. S&E doctorates and dominated degrees in some 

fields. They also earned large shares of the master’s degrees in S&E fields.

Foreign students earned 57% of all engineering doctorates, 

54% of all computer science degrees, and 51% of physics 

doctoral degrees. Their overall share of S&E degrees was 

one-third.

After a 64% growth from 2002 to 2008, the number of temporary residents earning S&E doctoral degrees declined by 

about 4% in 2009 to 13,400.

In 2009, temporary visa students earned 27% of S&E master’s degrees, receiving 46% of those in computer sciences, 

43% of those in engineering, and 36% of those in physics.

International S&E Higher Education

In 2008, about 5 million first university degrees were 

awarded in S&E worldwide. Students in China earned 

about 23%, those in the European Union earned about 

19%, and those in the United States earned about 10% 

of these degrees.

The number of S&E first university degrees awarded in 

China and Taiwan more than doubled between 2000 and 

2008. Those awarded in the United States and many other 

countries generally increased. Those awarded in France, 

Spain, and Japan decreased in recent years.

S&E degrees continue to account for about one-third of all 

bachelor’s degrees awarded in the United States. In Japan 

and China, more than half of first degrees were awarded in 

S&E fields in 2008.

In the United States, about 4% of all bachelor’s degrees 

awarded in 2008 were in engineering. This compares with 

about 19% throughout Asia and 31% in China specifically. 

In 2008, 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 number of S&E doctoral degrees awarded in China, 

the United States, and Italy has risen substantially in recent years; S&E doctorates awarded in India, Japan, South 

Korea, and many European countries have risen more 

modestly. The number in Russia increased from 2002 to 

2007, but fell sharply in 2008. 

In 2007, China overtook the United States as the world 

leader in the number of doctoral degrees awarded in the 

natural sciences and engineering. 

Women earned 41% of S&E doctoral degrees awarded 

in the United States in 2008, about the same as women’s 

percentages in Australia, Canada, the European Union, 

and Mexico.

Science and Engineering Indicators 2012 2-5
International student mobility expanded over the past 

two decades and countries are increasingly competing 

for foreign students. 

The United States remains the destination for the largest 

number of foreign students worldwide (undergraduate and 

graduate), although its share of foreign students worldwide 

decreased from 24% in 2000 to 19% in 2008.

Some countries expanded recruitment of foreign students 

as their own populations of college-age students decreased, 

both to attract highly skilled workers and to increase revenue for colleges and universities.

In addition to the United States, other countries that are 

among the top destinations for foreign students include the 

United Kingdom, Germany, and France.

2-6 Chapter 2. Higher Education in Science and Engineering
Science and Engineering Indicators 2012 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 

(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 by higher education.

Indicators presented in this chapter are discussed in the 

context of national and global events, including changing 

demographics, increasing foreign student mobility, and 

global competition in higher education. The U.S. collegeage population is currently increasing and projected to continue to grow for the next decade. Its composition is also 

changing, with Asians and Hispanics becoming an increasing share of the population. Recent enrollment and degree 

trends, to some extent, reflect these changes. 

As the world becomes more interconnected, more students travel to study in a different country, and more countries invest in their higher education systems. Increases in 

foreign students contributed to most of the growth in overall S&E graduate enrollment in the United States in recent 

years. Despite a decline in the number of foreign students 

coming to the United States after 11 September 2001, foreign graduate student enrollment in S&E has recovered. 

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 population and 

attracting growing numbers of foreign students. The effects 

of these trends, as well as the effects of the recent global 

financial crisis on domestic and foreign student enrollment 

in U.S. institutions, remain to be seen. 

Chapter Organization

This chapter describes characteristics of the U.S. higher 

education system and trends in higher education worldwide. 

It 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. Trends are discussed by field 

and demographic group, with a focus on the flow of foreign students into the United States by country. The chapter 

then presents various international higher education indicators, including comparative S&E degree production in several world regions and indicators that measure the growing 

dependence of all industrialized countries on foreign S&E 

students.

The data in this chapter come from a variety of federal 

and nonfederal sources, primarily from surveys conducted by the National Science Foundation’s (NSF) 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 Organisation for Economic 

Co-operation and Development (OECD) and the United 

Nations Educational, Scientific and Cultural Organization 

(UNESCO), and 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 is important because it 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. 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, religious affiliation, types of students served, types of degrees offered, 

and sector (public, private nonprofit, or private for-profit) 

(NCES 2010a). Among the approximately 4,500 postsecondary degree-granting institutions in the United States 

in the 2009–10 academic year, 62% offered bachelor’s or 

higher degrees, 31% offered associate’s degrees, and 8% offered degrees that were at least 2-year but less than 4-year 

as the highest degree awarded (NCES 2010b). In 2009, U.S. 

academic institutions awarded more than 3.1 million associate’s, bachelor’s, master’s, and doctoral degrees; 23% of the 

degrees were in S&E (appendix table 2-1).

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. In 2009, these research institutions awarded 75% of doctoral degrees, 42% 

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

fields. (See sidebar “Carnegie Classification of Academic 

Institutions.”) Master’s colleges and universities awarded 

another 29% of S&E bachelor’s degrees and 26% of S&E 

master’s degrees in 2009. Baccalaureate colleges were the 

source of relatively few S&E bachelor’s degrees (12%) (appendix table 2-1), but they produce a large proportion of future S&E doctorate recipients. 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 

hundred bachelor’s degrees awarded than other types of institutions, except research universities (NSF/NCSES 2008).
2-8 Chapter 2. Higher Education in Science and Engineering

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. They are 

likely to serve groups with lower college attendance rates 

in past generations. Community colleges are important in 

preparing students to enter the workforce with certificates 

or associate’s degrees and in preparing students to transition 

to 4-year colleges or universities (Karp 2008). They provide 

the education needed for S&E or S&E-related occupations 

that require less than a bachelor’s degree, and they provide 

the first 2 years of many students’ education before they 

transfer to an S&E program at a 4-year college or university. 

In the 2008–09 academic year, there were more than 

1,000 community colleges in the United States. These colleges enrolled about 7.2 million students, or about a third 

of all postsecondary students. Nearly six out of ten of these 

students were enrolled part-time (NCES 2011a). With the 

economic recession, enrollment in community colleges increased by about 800,000 students between 2007 and 2009 

(NCES 2009a and 2011a).

Community colleges play a significant role in the education of individuals with advanced S&E credentials. Among 

U.S. citizen and permanent resident S&E doctorate holders 

who received their doctorates between 2005 and 2009, nearly one in five indicated they had earned college credit from a 

community or 2-year college (table 2-1). According to data 

from the National Survey of Recent College Graduates, in 

the last decade, the proportion of recent bachelor’s S&E 

graduates who reported ever attending a community college 

increased (table 2-2). Forty-six percent of 2006 and 2007 

S&E graduates indicated they had attended a community 

college (49% of the bachelor’s recipients and 35% of the 

master’s recipients). Graduates in engineering and physical 

sciences1

 were the least likely to have attended a community college. Between 1999 and 2008, the proportion of S&E 

graduates who attended community colleges increased in the 

life sciences, social sciences, mathematics, and computer 

sciences (figure 2-1).

In 2008, female S&E bachelor’s and master’s degree 

recipients were more likely to have attended a community 

college than their male counterparts (table 2-3). Attendance 

was also higher among U.S. citizens and permanent visa 

holders than among temporary visa holders. Attendance was 

higher for Hispanic and black S&E graduates than for whites 

or Asians. The likelihood of attending a community college 

before receiving an S&E bachelor’s or master’s degree was 

related to parental education level. More than half of the 

S&E graduates who reported that their fathers or mothers 

had less than a high school diploma attended a community 

college, compared to about one-third of those whose fathers 

or mothers had a professional or a doctoral degree.

Over the last 10 years, the top reason for attending a community college among science and engineering graduates 

Carnegie Classification of 

Academic Institutions

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 same 

structure initially adopted in 2005 and illustrates the 

most current landscape of U.S. colleges and universities. Compared with the 2005 update, there are 483 

newly classified institutions in the 2010 classifications 

(from a universe of 4,633). More than three-quarters 

of the new institutions (77%) are from the private forprofit sector, 19% from the private nonprofit sector, 

and 4% from the public institution sector. 

Academic institutions are categorized primarily 

on the basis of highest degree conferred, level of degree production, and research activity.* In this report, 

several 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).

Master’s colleges and universities include the 727 

institutions that award at least 50 master’s degrees 

and fewer than 20 doctoral degrees per year.

Baccalaureate colleges include the 808 institutions 

for 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 in 

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 indices (aggregate level of 

research and per capita research activity) derived from a principal 

components analysis of data on R&D 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.
Science and Engineering Indicators 2012 2-9

remained the same—earning credits for a bachelor’s degree 

(figure 2-2). However, the prevalence of other reasons for 

attending a community college changed over time. The importance of community colleges as bridges between high 

school and college in the form of dual enrollment programs 

increased from 13% in 1999 to 28% in 2008. Attending a 

community college to facilitate a change in fields or for financial reasons also became more important, while gaining 

skills and knowledge in their fields, having opportunities to 

increase advancement, or attending for leisure or personal 

interest became less important.

For-Profit Institutions

Two-year, for-profit institutions enroll considerably fewer students than community colleges. Over the past 10 years, 

however, the number of for-profit institutions has grown rapidly and the number of degrees they awarded has more than 

doubled (NCES 2010c; appendix table 2-2). A large part of 

that increase is accounted for by the growth of University of 

Phoenix Online Campus. In 2009, about 2,900 academic institutions in the United States operated on a for-profit basis. 

About half of these institutions offer less-than-2-year programs and fewer than half are degree-granting institutions. 

Of the degree-granting institutions, close to half award associate’s degrees as their highest degree (NCES 2010b).

In 2009, for-profit academic institutions awarded between 1% and 5% of S&E degrees at the bachelor’s, master’s, and doctoral levels, and 31% of S&E degrees at the 

associate’s level. Computer sciences accounted for 91% of 

the associate’s degrees and 67% of the bachelor’s degrees 

awarded by for-profit institutions in science and engineering 

fields in 2009 (appendix table 2-3). For-profit institutions 

award relatively few S&E master’s and doctoral degrees; 

those degrees are mainly in psychology. In 2009, degrees 

in psychology represented 51% of the master’s and 81% of 

the doctoral degrees awarded by for-profit institutions in science and engineering fields.

Table 2-1

U.S. citizen/permanent resident S&E doctorate recipients who reported earning college credit from a 

community or 2-year college, by race/ethnicity: 2005–09

Earned college credit from a 

community or 2-year college

Race/ethnicity All Yes No Percent yes

All races/ethnicities ...................................................... 87,790 17,033 70,757 20

American Indian/Alaska Native................................. 313 122 191 39

Asian ......................................................................... 8,783 1,158 7,625 13

Black ......................................................................... 3,982 706 3,276 18

Hispanic .................................................................... 4,529 1,024 3,505 23

White ........................................................................ 67,250 13,369 53,881 20

Native Hawaiian/Other Pacific Islander .................... 200 50 150 25

Unknown/unreported ............................................... 2,733 604 2,129 22

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2010) of Survey of Earned 

Doctorates, 2005–09.

Science and Engineering Indicators 2012

Table 2-2

Community college attendance among recent recipients of S&E bachelor’s and master’s degrees, by degree 

level and degree year: 1999–2008

1999 2001 2003 2006 2008

Degree level

Recent 

degree 

recipients

Attended 

community 

college (%)

Recent 

degree 

recipients

Attended 

community 

college (%)

Recent 

degree 

recipients

Attended 

community 

college (%)

Recent 

degree 

recipients

Attended 

community 

college (%)

Recent 

degree 

recipients

Attended 

community 

college (%)

All graduates ...... 900,400 41 918,400 44 958,400 45 1,634,200 45 1,138,400 46

Bachelor’s ...... 743,400 43 758,300 46 794,400 47 1,343,000 47 934,300 49

Master’s .......... 157,000 35 160,100 34 164,000 34 291,200 34 204,100 35

NOTES: Recent graduates are those who earned degrees in the 2 academic years preceding survey year, or, for 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 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 (2010) of National Survey of Recent 

College Graduates, 1999, 2001, 2003, 2006, and 2008.

Science and Engineering Indicators 2012
2-10 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 to students in remote geographic locations 

and providing greater flexibility for students who have time 

constraints, physical impairments, responsibility for caring 

for dependents, etc. Online education is a relatively new 

phenomenon and online enrollment has grown substantially 

in recent years. In 2007–08, about 4.3 million undergraduate students (20% of all undergraduates) took at least one 

distance education course, up from 2.9 million (16% of all 

undergraduates) in 2003–04. In addition, nearly 800,000 

(22%) of all postbaccalaureate students took distance education courses in 2007–08 (NCES 2011b).2

At the undergraduate level, students at private for-profit 

4-year institutions were more likely to participate in distance 

education courses than students at public or private not-forprofit institutions (appendix table 2-4). Similarly, a higher 

proportion of students at private for-profit 4-year institutions 

took their entire program through distance education than 

students at any other type of institution. Most institutions, 

for-profit institutions in particular, believe that online education will be a critical part of their long-term strategy (Allen 

and Seaman 2010).

In recent years, academic institutions have begun developing online courses for public access—examples include 

the Open Learning Initiative at Carnegie Mellon and the 

MIT OpenCourseWare.3 Other kinds of initiatives involve 

working with faculty and organizations such as the National 

Center for Academic Transformation to redesign courses to 

incorporate the use of information technology.

Figure 2-1

Community college attendance among recent 

recipients of S&E degrees, by field of highest 

degree: 1999 and 2008 

NOTE: Recent graduates are those who earned degrees in the 2 

academic years preceding survey year.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, National Survey of Recent College 

Graduates, 1999 and 2008. 

Science and Engineering Indicators 2012

Percent

0 10 20 30 40 50 60

Engineering

Social sciences

and related sciences

Physical sciences

and related sciences

Life sciences and

related sciences

Computer sciences

and mathematical

sciences

2008

1999

Table 2-3

Community college attendance among recent 

recipients of S&E degrees, by sex, race/ethnicity, 

and citizenship status: 2008

Characteristic Number Percent

All graduates .................................... 1,138,400 46

Sex

Female .......................................... 570,500 49

Male .............................................. 567,900 43

Race/ethnicity

American Indian/Alaska Native ..... 2,000 61

Hispanic ........................................ 98,000 53

Black ............................................. 73,400 51

White ............................................. 713,900 45

Asian ............................................. 192,800 43

Native Hawaiian/other Pacific 

Islander ...................................... 6,800 66

Multiple race ................................. 51,500 45

Citizenship status

U.S. citizen .................................... 1,029,500 48

Permanent visa ............................. 30,900 46

Temporary visa ............................. 78,000 17

Father’s education

Less than high school ................... 68,800 55

High school diploma or 

equivalent .................................. 203,500 50

Some college, vocational, or 

trade school .............................. 219,900 54

Bachelor’s ..................................... 294,400 45

Master’s ........................................ 181,500 41

Professional degree ...................... 81,800 31

Doctorate ...................................... 70,100 36

Not applicable ............................... 18,400 46

Mother’s education

Less than high school ................... 77,500 58

High school diploma or 

equivalent .................................. 232,000 50

Some college, vocational, or 

trade school .............................. 268,000 51

Bachelor’s ..................................... 312,700 42

Master’s ........................................ 180,400 41

Professional degree ...................... 32,700 31

Doctorate ...................................... 27,400 34

Not applicable ............................... 7,700 56

NOTES: Recent graduates are those who earned degrees between 

1 July 2006 and 30 June 2007. Data rounded to nearest 100.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2010) of National 

Survey of Recent College Graduates, 2008.

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 2-11

Financing Higher Education

Cost of Higher Education

Affordability and access to U.S. higher education institutions are perennial concerns (NCPPHE 2008; NSB 2003). 

For at least the past 10 years, tuition and fees for colleges and 

universities in the United States have grown rapidly, faster 

than median income (figure 2-3). In the 2010–11 academic 

year, average tuition and fees for 4-year colleges rose faster 

than inflation. While the Consumer Price Index increased 

by 1.2% between July 2009 and July 2010 (College Board 

2010a), average tuition and fees rose 7.9% from the previous 

academic year for in-state students at public 4-year colleges, 

4.5% for students in private nonprofit 4-year colleges, and 

6% for students at public 2-year colleges. Another inflation 

index, the Higher Education Price Index, which measures 

the average relative level in the price of a fixed-market basket of goods and services purchased by colleges and universities each year, rose 0.9% in fiscal year 2010 (Commonfund 

Institute 2010). 

In the 5-year interval between 2005–06 and 2010–11, average published tuition and fees rose much faster than other 

prices in the economy. However, compared to 5 years ago, 

estimated average net tuition and fees (i.e., the published 

prices minus grant aid and tax benefits) are lower for all sectors. Large increases in federal Pell grants and veterans’ benefits in 2009–10 and the passage of the American Recovery 

and Reinvestment Act of 2009 largely drove the decline in 

average net prices (College Board, 2010a). According to the 

College Board (2010b), in the coming years, rising tuition 

prices are likely to continue in response to state reductions in 

higher education funding (see sidebar “State Appropriations 

to Public Research Universities: A Volatile Decade”), but 

the rate of increase in grant funds is not likely to keep pace.

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 

grants, student loans, and work study. A financial aid package may contain one or more of these kinds of support. In 

the 2007–08 academic year, two-thirds of all undergraduate 

students received some kind of financial aid: 52% received 

grants and 39% took out loans (NCES 2009b). A higher proportion of undergraduates in private for-profit institutions 

(96%) and in nonprofit 4-year institutions (85%) than those 

Figure 2-2

Reasons for attending community college among 

recent S&E graduates: 1999 and 2008 

NOTE: Recent graduates are those who earned degrees in the 2 

academic years preceding survey year.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, National Survey of Recent College 

Graduates, 1999 and 2008. 

Science and Engineering Indicators 2012

Thousands

0 7 605040302010 0 80

For opportunity to

increase advancement

To facilitate change

in academic/

occupational field

For leisure or

personal interest

To prepare for college

To earn credits for

a bachelor’s degree

2008

1999

To complete

associate’s degree

To earn college credits

while in high school

To gain further skills/

knowledge in academic/

occupational field

For financial reasons

Figure 2-3

Average annual tuition, fees, room, and board for 

public and private 4-year institutions, total student 

aid dollars, and median income: 2000–10 

Constant 2010 dollars (thousands)

NOTE: Data on median income and total student aid per full-time 

equivalent student not available for 2009–10.

SOURCES: College Board, Trends in College Pricing 2010; and 

Census Bureau, Historical Income Tables, Table P-7, http://www. 

census.gov/hhes/www/income/data/historical/people/P07AR_2009.

xls, accessed 15 March 2011.

Science and Engineering Indicators 2012

2000 2001 2003 2002 2004 2006 2008 2010 2005 2007 2009

0

5

10

15

20

25

30

35

Median income

Average annual tuition, fees, room,

and board at public 4-year institutions

Average annual tuition, fees, room,

and board at private 4-year institutions

Total student aid per full-time

equivalent student
2-12 Chapter 2. Higher Education in Science and Engineering

Increases in the number of students seeking an affordable college education and competing demands on state 

government budgets have affected the resources available for state-funded higher education. Because funding 

for major state research universities has been a particular 

focus of concern, this sidebar examines trends in state 

support for these institutions between 2002 and 2010.4

Data cover 101 public research universities with broad 

educational missions (i.e., excluding free-standing 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. 

According to data collected by the U.S. Department 

of Education’s National Center for Education Statistics 

and Illinois State University’s Center for the Study of 

Education Policy (CSEP), total state funding in current 

dollars for these 101 universities, including state appropriations and state operating grants and contracts, grew 

during the period of 2002 through 2010 from $23.8 billion in 2002 to $25.8 billion in 2010.5

 Funding fluctuated 

over this period, dipping in the early years and then rising 

until 2008 when it began to fall sharply. In constant dollars, this represented a decline of 10% (figure 2-A). As a 

percentage of the universities’ total revenues, state funding declined from 28% in 2001 to 19% in 2009. 

In constant dollars, 72 of the 101 universities experienced an overall reduction in state appropriations. More 

than half of the universities, 54, had reductions of more 

than 10%. For 29 institutions, state appropriations in 

2010 were between 90% and 110% of the 2002 level. 

The remaining 18 universities received increases of more 

than 10%. 

Funding changes varied widely by institution and by 

state. For example, all of the nine research universities in 

California experienced reductions ranging from 17% to 

35%. By contrast, the four State University of New York 

(SUNY) campuses received substantial increases ranging from 71% to 171%. In Texas, three universities had 

very different funding trends: the University of Texas at 

Dallas experienced a 19% increase, Texas A&M a 12% 

decrease, and the University of Texas at Austin had a 3% 

decrease. In Michigan, the University of Michigan–Ann 

Arbor experienced a 28% decrease and Michigan State 

University had a 21% decrease. 

While the value of overall state funding declined 

nationally, enrollment was growing consistently. As a 

result, state funding per enrolled student dropped in constant dollars by 20%, going from $10,195 per student in 

2002 to $8,157 per student in 2010 (figure 2-B). 

Preliminary data prepared by CSEP—available by 

state but not by university—suggest a continuing state 

funding decline. In particular, between 2009 and 2011, 

35 of the 50 states reported reductions in state appropriations and other state support, ranging from less than 1% 

to more than 28%. 

Additional indicators of state-level trends in the affordability of higher education, including state appropriations for operating expenses as a percentage of GDP and 

average undergraduate charges at public 4-year institutions, can be found in chapter 8. 

State Appropriations to Public Research Universities: A Volatile Decade

Figure 2-A

State appropriations to major public research 

universities: 2002–10

2005 constant dollars (billions)

NOTE: Data for 2010 are preliminary.

SOURCES: National Center for Education Statistics, Integrated 

Postsecondary Education Data System; Illinois State University, 

Center for the Study of Education Policy.

Science and Engineering Indicators 2012

0

5

10

15

20

25

30

2002 2003 2004 2005 2006 2007 2008 2009 2010

Figure 2-B

State appropriations to major public research 

universities per enrolled student: 2002–10

2005 constant dollars (thousands)

NOTE: Data for 2010 are preliminary.

SOURCES: National Center for Education Statistics, Integrated 

Postsecondary Education Data System; Illinois State University, 

Center for the Study of Education Policy.

Science and Engineering Indicators 2012

0

2

4

6

8

10

12

2002 2003 2004 2005 2006 2007 2008 2009 2010
Science and Engineering Indicators 2012 2-13

in public 4-year (71%) or public 2-year institutions (48%) 

received some type of financial aid. 

Undergraduate Debt. Undergraduate debt does not vary 

by undergraduate major (NSF/NCSES 2010a); 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. The median level of debt 

for 2007–08 bachelor’s degree recipients who took out loans 

was $20,000 for those who graduated from public colleges 

and universities and $24,600 for those who graduated from 

private nonprofit institutions. Students who attend private 

for-profit institutions are more likely to borrow than those 

who attend public and private nonprofit institutions (College 

Board 2010b).

Levels of debt varied widely by state. Average debt 

for 2009 graduates of public 4-year colleges and universities ranged from $14,739 in California to $29,675 in New 

Hampshire. Average debt for graduates of private nonprofit 

colleges and universities ranged from $11,312 in Utah to 

$32,434 in Rhode Island (Project on Student Debt 2009).

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; i.e, 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 (37%), RAs are the most prevalent primary mechanism of financial support for all fulltime S&E graduate students. In 2009, 27% of full-time S&E 

graduate students were supported primarily by RAs, 18% 

were supported primarily through TAs, and 12% relied primarily on fellowships or traineeships (table 2-4).

Primary mechanisms of support differ widely by S&E 

field of study (appendix table 2-5). For example, in fall 

2009, full-time students in physical sciences were financially supported mainly through RAs (42%) and TAs (38%) 

(figure 2-4, appendix table 2-5). RAs also were important in 

agricultural sciences (51%); earth, atmospheric, and ocean 

sciences (40%); biological sciences (39%); and engineering (40%). In computer science, more than half (51%) of 

full-time students were supported primarily through TAs 

and another 22% were self-supported. Full-time students 

in mathematics and the social and behavioral sciences were 

mainly self-supporting (48% respectively) or received TAs 

(15% and 19% respectively). Students in medical/other life 

sciences were mainly self-supporting (62%).

The federal government plays a substantial role in supporting S&E graduate students through some mechanisms 

in some fields, and a smaller role in others. Federal financial support for graduate education reaches relatively more 

students in the biological sciences; the physical sciences; 

Table 2-4

Full-time S&E graduate students, by source and mechanism of primary support: 2009

Source All

Research 

assistantship Fellowship Traineeship

Teaching 

assistantship Other Self-supporta

Number

All sources .............. 441,743 120,008 38,115 12,799 78,317 29,791 162,713

Federal ................ 81,205 58,341 8,592 8,068 1,248 4,956 NA

Nonfederal .......... 197,825 61,667 29,523 4,731 77,069 24,835 NA

Percent

All sources .............. 100.0 27.2 8.6 2.9 17.7 6.7 36.8

Federal ................ 100.0 71.8 10.6 9.9 1.5 6.1 NA

Nonfederal .......... 100.0 31.2 14.9 2.4 39.0 12.6 NA

NA = not available

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 life sciences). These fields reported separately in National Science Foundation, National 

Center for Science and Engineering Statistics, Graduate Students and Postdoctorates in Science and Engineering (annual series). S&E excludes fields that 

were collected in this survey (architecture, communication, and family and consumer sciences/human sciences) that are not included in other tables in this 

report from other data sources. Self-support not included in federal or nonfederal counts. Percentages may not add to 100% because of rounding.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Survey of Graduate Students and Postdoctorates in 

Science and Engineering, Integrated Science and Engineering Resources Data System (WebCASPAR), http://webcaspar.nsf.gov.

Science and Engineering Indicators 2012
2-14 Chapter 2. Higher Education in Science and Engineering

support comes from the federal government. In 2009, these 

institutions supported about 26,400 and 21,600 students respectively. NIH funded about 75% of such students in the biological sciences, 64% of those in the medical sciences, and 

40% of those in psychology. NSF supported nearly 60% of 

students in computer sciences or mathematics; nearly 50% 

of those in earth, atmospheric, and ocean sciences; and 34% 

of those in engineering (appendix table 2-7).

For doctoral degree students, notable differences exist in 

primary support mechanisms by type of doctorate-granting 

institution. In 2009, 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). 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 (table 2-5). These differences 

by type of institution hold for all S&E fields (NSF/NCSES 

2000). As noted earlier in this chapter, the majority of S&E 

doctorate recipients (about 75%) received their doctorate 

from research universities with very high research activity.

the earth, atmospheric, and ocean sciences; and engineering. Relatively fewer students in computer sciences, mathematics, other life sciences, psychology, and social sciences 

receive federal support (figure 2-5). Appendix table 2-6 provides detailed information by field and mechanism. 

The federal government was the primary source of financial support for 18% of full-time S&E graduate students in 

2009 (appendix table 2-6). In 2009, the federal government 

funded 63% of S&E graduate students on traineeships, 49% 

of those with RAs, and 23% of those with fellowships. 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 72% 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 2009, TAs were the most prominent mechanism (39%) 

followed by RAs (31%) (table 2-4).

The National Institutes of Health (NIH) and NSF support 

most of the full-time S&E graduate students whose primary 

Figure 2-5

Full-time S&E graduate students with primary 

support from federal government, by field: 2009 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, 2009 Survey of Graduate Students and 

Postdoctorates in Science and Engineering, WebCASPAR database, 

http://webcaspar.nsf.gov. See appendix table 2-6.

Science and Engineering Indicators 2012

Percent

0 5 10 15 20 25 30 35 40

Social sciences

Psychology

Other life sciences

Mathematics/

statistics

Computer sciences

Medical sciences

Agricultural

sciences

Engineering

Earth, atmospheric,

and ocean sciences

Physical sciences

Biological sciences

All S&E

Figure 2-4

Full-time S&E graduate students, by field and 

mechanism of primary support: 2009

NOTE: Self-support includes any loans (including federal) and 

support from personal or family financial contributions. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, 2009 Survey of Graduate Students and 

Postdoctorates in Science and Engineering, WebCASPAR database, 

http://webcaspar.nsf.gov. See appendix table 2-5. 

Science and Engineering Indicators 2012

Percent

Research

assistantship

Fellowship Traineeship Teaching

assistantship

Other Self-support

Social sciences

Physical sciences

Mathematics/statistics

Earth, atmospheric,

and ocean sciences

Agricultural sciences

S&E

Psychology

Computer sciences

Biological sciences

Engineering

Medical/other

life sciences

0 20 40 60 80 100
Science and Engineering Indicators 2012 2-15

debt. For some, debt levels were high, especially for graduate debt: 4% reported more than $40,000 of undergraduate 

debt and 6% reported more than $70,000 of graduate debt 

(appendix table 2-9).

Levels of debt vary widely by doctorate field. In 2009, 

high levels of graduate debt were most common among doctorate recipients in psychology, social sciences, and medical/other health sciences. Psychology doctorate recipients 

were most likely to report having graduate debt and also 

high levels of debt.6 In 2009, 20% of psychology doctoral degree recipients reported graduate debt of more than 

$70,000. Doctorate recipients in mathematics; computer sciences; physical sciences; engineering; biological sciences; 

and earth, atmospheric, and ocean sciences were least likely 

to report graduate debt. A higher percentage of doctorate recipients in non-S&E fields (49%) than those in S&E fields 

(32%) reported graduate debt.

Although men and women differed little in level of debt, 

U.S citizens and permanent residents accumulated more debt 

than temporary visa holders, and blacks and Hispanics had 

higher levels of graduate debt than whites, even accounting 

for differences in field of doctorate (NSF/NCSES 2010b). 

The proportion of S&E master’s recipients with debt increased between 2000 and 2008 (table 2-6). In 2000, about 

40% of all master’s students had incurred debt while studying for their master’s degree, with no meaningful differences 

between those in S&E and non-S&E. By 2008, this proportion had increased to 51% among S&E master’s recipients 

and 58% among those in non-S&E fields. Among graduates 

who had incurred debt, there was a statistically significant 

increase in the amount of the debt for those in non-S&E 

fields, but not for S&E students.7

Notable differences also exist in primary support mechanisms for doctoral degree students by sex, race/ethnicity, 

and citizenship. In 2009, among U.S. citizens and permanent 

residents, men were more likely than women to be supported 

by RAs (29% compared with 22%) and women were more 

likely than men to support themselves from personal sources 

(19% compared with 12%). Also, among U.S. citizens and 

permanent residents, whites and Asians were more likely 

than other racial/ethnic groups to receive primary support 

from RAs (27% and 33%, respectively), whereas underrepresented minorities depended more on fellowships or traineeships (38%). The primary source of support for doctoral 

degree students with temporary visas was an RA (50%) (appendix table 2-8).

To some extent, the sex, citizenship, and racial/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/

ethnicity, or citizenship remain, even after accounting for 

doctorate field (NSF/NCSES 2000, NSB 2010). 

Debt Levels of S&E Graduate Students. At the time of 

doctoral degree conferral, 45% of S&E doctorate recipients 

have debt related to their undergraduate or graduate education. In 2009, 27% of S&E doctorate recipients reported having undergraduate debt and 32 % reported having graduate 

Table 2-5

Primary support mechanisms for S&E doctorate recipients, by 2010 Carnegie classification of doctorate-granting 

institution: 2009

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) ......... 35,564 27,166 5,275 1,123 1,184 816

All mechanisms (%) ............... 100.0 100.0 100.0 100.0 100.0 100.0

Fellowship or traineeship ... 21.9 23.6 13.6 12.9 32.9 14.5

Grant .................................. 5.9 6.1 3.3 1.6 16.3 4.3

Teaching assistantship ...... 15.2 15.2 21.3 8.8 1.6 4.5

Research assistantship ...... 32.6 35.7 26.3 10.1 25.8 13.0

Other assistantship ............ 0.6 0.5 1.1 0.4 0.7 0.5

Personal ............................. 10.2 6.8 18.0 39.3 10.6 31.5

Other .................................. 3.1 2.6 4.8 6.8 3.3 4.4

Unknown ............................ 10.6 9.6 11.7 20.2 8.8 27.3

NOTES: Personal support mechanisms include personal savings, other personal earnings, other family earnings or savings, and loans. Traineeships 

include internships and residency. 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 (2010) of Survey of Earned Doctorates.

Science and Engineering Indicators 2012
2-16 Chapter 2. Higher Education in Science and Engineering

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 and intentions to major in S&E fields, recent trends in the number of 

earned degrees in S&E fields, and persistence and retention 

in undergraduate education and in S&E.

Undergraduate Enrollment in the 

United States

Recent trends in higher education enrollment reflect the 

expanding U.S. college-age population. This section examines trends in undergraduate enrollment by type of institution, field, and demographic characteristics. For information 

on enrollment rates of high school seniors, see chapter 1, 

“Transition to Higher Education.”

Overall Enrollment

Over the last 15 years studied, enrollment in U.S. institutions of higher education at all levels rose from 14.5 million 

students in fall 1994 to 20.7 million in fall 2009, with most 

of the growth occurring in the last 10 years (appendix table 

2-10). In 2009, the types of institutions enrolling the most 

students were associate colleges (8.2 million, 40% of all students enrolled), master’s colleges/universities (4.7 million, 

23%), and doctorate-granting universities with very high research activity (2.8 million, 14%). Between 1994 and 2009, 

enrollment nearly doubled at doctoral/research universities 

and increased by about 50% or more at associate’s colleges, 

master’s colleges, and medical schools/medical centers (appendix table 2-10). (See sidebar “Carnegie Classification of 

Academic Institutions” for definitions of the types of academic institutions.) These trends are expected to continue 

for the near future.

On the basis of demographics, household income, and 

age-specific unemployment rates,8

 NCES projects that undergraduate enrollment in higher education will increase 

16% between 2008 and 2019 (NCES 2011c).9 According to 

Census Bureau projections, the number of college-age individuals (ages 20–24) is expected to grow from 21.8 million in 2010 to 28.2 million by 2050 (appendix table 2-11). 

Enrollment of first-time freshmen is projected to increase 

by 13% between 2008 and 2019, although the number of 

high school graduates is projected to change little because 

of relatively flat numbers of 18-year-olds during this period 

(NCES 2011c). 

Increased enrollment in higher education at all levels is 

projected to come mainly from minority groups, particularly 

Hispanics. Enrollment of all racial/ethnic groups is projected 

to increase, but the percentage for whites is projected to decrease from 63% in 2008 to 58% in 2019, whereas the percentages for blacks and Hispanics are projected to increase 

from 14% and 12% respectively, to 15% for both groups. 

(For further information on assumptions underlying these 

projections, see “Projection Methodology” in Projections 

of Education Statistics to 2019 [NCES 2011c], http://nces.

ed.gov/pubs2011/2011017.pdf, accessed 14 March 2011.)

Undergraduate Enrollment in S&E

Freshmen Intentions to Major in S&E. Since 1972, the 

annual Survey of the American Freshman, National Norms, 

administered by the Higher Education Research Institute at 

the University of California–Los Angeles, has asked freshmen at a large number of universities and colleges about 

their intended majors.10 The data have proven to be a broadly 

accurate picture of trends in degree fields several years later.11 Between 1972 and 2007, about one-third of all freshmen 

planned to study S&E; this proportion gradually rose to 38% 

by 2010. Increases in the proportion of freshmen planning to 

major in biological/agricultural sciences in recent years account for most of this growth. In 2010, about 11% of freshmen intended to major in each of the following disciplines: 

Table 2-6

Master’s degree recipients with debt from graduate student loans upon graduation and average amount owed, 

by broad field: 1999–2000 and 2007–08

1999–2000a 2007–08b

Field

With debt from master’s 

degree program (%)

Average amount owed 

(constant 2000 dollars)c

With debt from master’s 

degree program (%)

Average amount owed 

(constant 2000 dollars)c

All fields ............................... 40.1 23,366 53.8 28,375

S&E .................................. 41.1 22,954 51.3 27,282

Non-S&E .......................... 40.2 22,452 57.9 30,000

a

Data as of late 2000. 

bData as of late 2008.

c

Average excludes respondents who did not owe any money from their master’s degree program upon graduation.

NOTE: Debt is total amount owed on all loans for graduate education.

SOURCE: U.S. Department of Education, National Center for Education Statistics, special tabulations (2011) of 1999–2000 and 2007–08 National 

Postsecondary Student Aid Study (NPSAS: 2000 and NPSAS: 2008), http://nces.ed.gov/datalab/index.aspx.

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 2-17

biological/agricultural sciences, social/behavioral sciences, 

and engineering. Between 1% and 3% intended to major in 

physical sciences, computer sciences, and mathematics/statistics (appendix table 2-12).

In 2010, about one in three white, black, and Hispanic 

freshmen; 28% of American Indian/Alaska Native freshmen; and 49% of Asian American/Asian freshmen reported 

that they intended to major in S&E (figure 2-6). The proportions planning to major in S&E were higher for men than for 

women in every racial/ethnic group (appendix table 2-12). 

For most racial/ethnic groups, about 10%–16% planned to 

major in social/behavioral sciences, about 6%–15% in engineering, about 9%–18% in biological/agricultural sciences, 

2%–3% in computer sciences, 2%–3% in physical sciences, 

and 1% in mathematics or statistics. Higher proportions of 

Asian American/Asian freshmen than of those from other 

racial/ethnic groups p lanned to major in biological/agricultural sciences (18%) and engineering (15%). The percentage 

of all freshmen intending to major in computer sciences has 

dropped in recent years, whereas the percentage intending to 

major in biological/agricultural sciences has increased. (See 

appendix table 2-19 and the section on “S&E Bachelor’s 

Degrees” for trends in bachelor’s degrees.) 

Generally, the percentages of students earning bachelor’s 

degrees in particular S&E fields are similar to the percentages planning to major in those fields, with the exception of 

engineering and social/behavioral sciences. (See section on 

“Persistence and Retention in Undergraduate Education and 

S&E.”) The percentage of students earning bachelor’s degrees in engineering is smaller than the percentage planning 

Figure 2-8

Engineering: Freshmen intentions and degrees, 

by race/ethnicity

Percent

NOTES: Data for freshmen intentions are for 2003; data for degrees 

are for 2009. Degrees do not reflect the same student cohort.

Asian American/Asian includes Native Hawaiian/Pacific Islander. 

SOURCES: Higher Education Research Institute, University of California 

at Los Angeles, Survey of the American Freshman: National Norms, 

special tabulations (2011); 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. See appendix tables 2-12 and 2-19.

Science and Engineering Indicators 2012

White/

Caucasian

Asian

American/

Asian

African

American/

Black

Hispanic/

Latino

0

5

10

15

20

25

Intentions Degrees

to major in it for men and women as well as for all ethnic/

racial groups, but the difference is larger for blacks (figures 

2-7 and 2-8). The percentage earning bachelor’s degrees in 

Figure 2-6

Freshmen intending S&E major, by race/ethnicity: 

1995–2010

Percent

NOTES: In 2001 Native Hawaiian/Pacific Islander was added as a 

category under Asian American/Asian. 

SOURCE: Higher Education Research Institute, University of 

California at Los Angeles, Survey of the American Freshman: 

National Norms, special tabulations (2011). See appendix table 2-12.

Science and Engineering Indicators 2012

1995 1998 2000 2002 2004 2006 2008 2010

0

10

20

30

40

50

60

Asian American/Asian

Hispanic/Latino

African American/Black

White/Caucasian

American Indian/Alaska Native

Figure 2-7

Engineering: Freshmen intentions and degrees, 

by sex

Percent

NOTES: Data for freshmen intentions are for 2003; data for degrees 

are for 2009. Degrees do not reflect the same student cohort.

SOURCES: Higher Education Research Institute, University of 

California at Los Angeles, Survey of the American Freshman: National 

Norms, special tabulations (2011); 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. See appendix tables 2-12 and 

2-18.

Science and Engineering Indicators 2012

Total Male Female

0

5

10

15

20

25

Intentions Degrees
2-18 Chapter 2. Higher Education in Science and Engineering

social/behavioral sciences is larger than previous years’ percentages planning to major in those fields. The proportion 

earning bachelor’s degrees in the natural sciences is smaller 

than the proportion planning to major in these fields for 

women, blacks, and Hispanics (figures 2-9 and 2-10). 

The demographic composition of students planning to 

major in S&E has become more diverse over time. The proportion of white students planning to major in S&E declined 

from 77% in 1995 to 71% in 2010. On the other hand, the 

proportion of Asian American/Asian students increased 

from 7% to 12% and the proportion of Hispanic students 

increased from 5% to 13%. American Indian/Alaska Native 

and black students accounted for roughly 2% and 11%, respectively, of freshmen intending to major in S&E in both 

1995 and 2010 (appendix table 2-13).

Foreign Undergraduate Enrollment.12 In the 2009–10 

academic year, the number of foreign students enrolled in 

bachelor’s degree programs in U.S. academic institutions 

rose 5% from the previous year, to approximately 206,000 

(IIE 2010). This continues a 3-year trend in which foreign 

student enrollment has risen after a 4-year decline (between 

the 2001–02 and 2005–06 academic years). The number of 

foreign undergraduates enrolled in 2009–10 was 5% above 

the peak in 2001–02. Among new foreign undergraduates, 

enrollment decreased 3% in 2009–10, the first decline in 

5 years following a 20% increase in 2008–09. The countries that accounted for the largest numbers of foreign undergraduates enrolled in a U.S. institution in 2009–10 

were China (almost 40,000), South Korea (36,200), India (15,200), Canada (13,600), and Japan (13,100). The number 

of Chinese undergraduates increased 52% over the previous 

year, and the numbers of South Korean and Indian undergraduates decreased 2% and 3% respectively. Among all 

foreign students (undergraduate and graduate) in 2009–10, 

the number of those studying agricultural sciences increased 

15%; engineering, 7%; and mathematics and computer sciences, 8%. The physical and life sciences decreased 1% 

compared with the preceding year (IIE 2010). 

More recent data from the Bureau of Citizenship and 

Immigration Services show a 7% increase in undergraduate enrollment of S&E foreign students in the U.S. from 

November 2009 to November 2010, mostly in engineering, social sciences, and mathematics. China, South Korea, 

Canada, Japan, and India were among the top countries 

sending foreign undergraduates in fall 2010, and were also 

among the top countries sending foreign S&E undergraduates (figure 2-11; appendix table 2-14). Although Nepal and 

Saudi Arabia sent comparatively fewer total undergraduates, 

they were also among the top countries sending foreign undergraduates in S&E fields—more than Canada and Japan. 

About one-third of all foreign students in undergraduate 

programs at U.S. institutions are enrolled in S&E fields.13 

Undergraduate foreign enrollment in S&E has increased 

each year between 2006 and 2010, while growth in non-S&E 

fields has slowed down (table 2-7). 

Figure 2-10

Natural sciences: Freshmen intentions and 

degrees, by race/ethnicity

Percent

NOTES: Data for freshmen intentions are for 2003; data for degrees 

are for 2009. Degrees do not reflect the same student cohort. Asian 

American/Asian includes Native Hawaiian/ Pacific Islander. 

SOURCES: Higher Education Research Institute, University of 

California at Los Angeles, Survey of the American Freshman: National 

Norms, special tabulations (2011); 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. See appendix tables 2-12 and 

2-19.

Science and Engineering Indicators 2012

White/

Caucasian

Asian

American/

Asian

African

American/

Black

Hispanic/

Latino

0

5

10

15

20

25

Intentions Degrees

Figure 2-9

Natural sciences: Freshmen intentions and 

degrees, by sex

Percent

NOTES: Data for freshmen intentions are for 2003; data for degrees 

are for 2009. Degrees do not reflect the same student cohort. 

SOURCES: Higher Education Research Institute, University of California 

at Los Angeles, Survey of the American Freshman: National Norms, 

special tabulations (2011); 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. See appendix tables 2-12 and 2-18.

Science and Engineering Indicators 2012

Total Male Female

0

5

10

15

20

25

Intentions Degrees
Science and Engineering Indicators 2012 2-19

Engineering Enrollment. For the most part, students 

do not declare majors until their sophomore year, therefore, undergraduate enrollment data are not available by 

field. However, engineering is an exception. Engineering 

programs generally require students to declare a 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 2010).

Undergraduate engineering enrollment was flat in the 

late 1990s, increased from 2000 to 2003, declined slightly 

through 2006, and rose for the next 3 years to a peak of 

468,100 in 2009 (figure 2-12; appendix table 2-15). The 

number of undergraduate engineering students increased 

15% between 2006 and 2009, with particularly steep increases in 2007 (7%) and 2009 (6%). Full-time freshman 

enrollment followed a similar pattern, reaching 114,700 

in 2009—the highest since 1982. These trends correspond 

with declines in the college-age population through the mid1990s, particularly the drop in white 20–24-year-olds, who 

account for the majority of engineering enrollment (NSF/

NCSES 2011). Similar trends in undergraduate engineering enrollment are reported by the American Society for 

Engineering Education (Gibbons 2009).

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 2019 (NCES 2011c). 

Table 2-7

Foreign students enrolled in U.S. higher education institutions, by broad field and academic level: 2006–10

Level and field 2006 2007 2008 2009 2010

All fields

All levels ................................................. 525,470 548,090 568,400 585,510 592,790

Undergraduate ................................... 256,090 266,870 281,550 291,440 295,550

Graduate ............................................ 269,380 281,210 286,840 294,070 297,240

S&E fields

All levels ............................................. 232,780 240,130 248,260 259,200 269,350

Undergraduate ................................ 74,740 77,150 81,700 86,950 93,230

Graduate ......................................... 158,040 162,980 166,560 172,250 176,120

Non-S&E fields

All levels ............................................. 292,680 307,960 320,130 326,300 323,450

Undergraduate ................................ 181,340 189,730 199,850 204,480 202,320

Graduate ......................................... 111,340 118,230 120,280 121,820 121,120

NOTES: Foreign doctorate recipients are those holding temporary visas. Undergraduate level includes associate’s and bachelor’s degrees; graduate level 

includes master’s and doctoral degrees. Numbers rounded to 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 (2010).

Science and Engineering Indicators 2012

Figure 2-11

Foreign undergraduate student enrollment in U.S. 

universities, by top 10 places of origin and field: 

November 2010 

SOURCE: Bureau of Citizenship and Immigration Services, Student 

and Exchange Visitor Information System database, special 

tabulations (2011). See appendix table 2-14. 

Science and Engineering Indicators 2012

Thousands

100 20 30 40 50 60

Nigeria

Malaysia

Vietnam

Japan

Canada

Nepal

Saudi Arabia

India

South Korea

China

All fields

S&E
2-20 Chapter 2. Higher Education in Science and Engineering

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.14 Many who 

transfer to baccalaureate-granting institutions do not earn associate’s degrees before transferring. Associate’s degrees in 

S&E and engineering technology accounted for about 11% 

of all associate’s degrees in 2009 (appendix table 2-16).

S&E associate’s degrees from all types of academic institutions rose from 38,400 in 2000 to 62,800 in 2003, declined 

to 47,500 through 2007, and increased to 54,300 in 2009. 

The overall trend mirrors the pattern of computer sciences, 

which also peaked in 2003, declined through 2007, and 

increased through 2009. Associate’s degrees earned in engineering technology (not included in S&E degree totals because of their applied focus) declined from about 40,500 in 

2000 to 29,700 in 2006, but have since increased to 33,200 

(appendix table 2-16). 

In 2009, women earned 62% of all associate’s degrees, 

up from 60% in 2000, and 40% 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 science degrees, 

from 42% in 2000 to 25% in 2009. In 2009, women’s share 

of S&E associate’s degrees rose slightly due largely to an 

increase in psychology degrees (appendix table 2-16). 

Students from underrepresented groups (blacks, Hispanics, 

and American Indians/Alaska Natives) earn a higher proportion of associate’s degrees than of bachelor’s or more advanced degrees.15 (See “S&E Bachelor’s Degrees by Race/

Ethnicity” and “Doctoral Degrees by Race/Ethnicity.”) In 

2009, underrepresented minorities earned 28% of S&E associate’s degrees—more than one-third of all associate’s 

degrees in social and behavioral sciences, and more than onequarter of all associate’s degrees in biological sciences, computer sciences, and mathematics (appendix table 2-17). In the 

last 10 years, the number of S&E associate’s degrees earned 

by these students increased by 52%, compared with the overall national increase of 41%.

S&E Bachelor’s Degrees

The baccalaureate is the most prevalent S&E degree, accounting for about 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 399,000 in 2000 to 505,000 in 2009 (appendix 

table 2-18).

In the last decade, the number of bachelor’s degrees 

awarded increased fairly consistently, though to different 

extents, in all S&E fields. The exception was computer sciences, where the number increased sharply from 1998 to 

2004, dropped as sharply through 2008, and remained flat in 

2009 (figure 2-13, appendix table 2-18).

S&E Bachelor’s Degrees by Sex. Since 1982, women 

have outnumbered men in undergraduate education. They 

Figure 2-13

S&E bachelor’s degrees, by field: 2000–09

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. See appendix table 2-18.

Science and Engineering Indicators 2012

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

0

50

100

150

200

250

300

Social/behavioral sciences

Biological/agricultural sciences

Engineering

Computer sciences

Physical sciences

Mathematics

Figure 2-12

U.S. engineering enrollment, by level: 1989–2009

Thousands

NOTE: Enrollment data include full- and part-time students.

SOURCE: American Association of Engineering Societies, 

Engineering Workforce Commission, Engineering & Technology 

Enrollments (various years). See appendix tables 2-15 and 2-22.

Science and Engineering Indicators 2012

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

0

100

200

300

400

500

Undergraduate

Graduate
Science and Engineering Indicators 2012 2-21

(by 5%), and engineering (by 2%) (figure 2-14). Fields where 

the proportion of bachelor’s degrees awarded to women grew 

during this period include astronomy (from 34% to 44%), atmospheric sciences (from 23% to 33%), agricultural sciences 

(from 46% to 51%), chemistry (from 47% to 50%), and anthropology (from 67% to 70%) (appendix table 2-18).

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 2009.16

S&E Bachelor’s Degrees by Race/Ethnicity. The racial/ethnic composition of S&E bachelor’s degree recipients has changed over time, reflecting population changes 

and increasing college attendance by members of minority 

groups.17 Between 2000 and 2009, the proportion of S&E 

degrees awarded to white students among U.S. citizens and 

permanent residents declined from 71% to 66%, although 

the number of S&E bachelor’s degrees earned by white 

students increased during that time (figure 2-15, appendix 

table 2-19). The proportion awarded to Hispanic students increased from 7% to 9% and to Asians/Pacific Islanders from 

9% to 10%. The shares to black and American Indian/Alaska 

Native students have remained flat since 2000. The number 

of S&E bachelor’s degrees earned by students of unknown 

race/ethnicity also increased. 

Despite considerable progress over the past couple of 

decades for underrepresented minority groups earning 

have earned relatively constant fractions of all bachelor’s 

and S&E bachelor’s degrees for several years (see sidebar 

“Gender Gap in Undergraduate Enrollment”). Since the 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 2011). 

Within S&E, men and women tend to study different 

fields. In 2009, men earned a majority of bachelor’s degrees 

awarded in engineering, computer sciences, and physics 

(82%, 82%, and 81%, respectively). Women earned half or 

more of the bachelor’s degrees in psychology (77%), agricultural sciences (51%), biological sciences (60%), chemistry (50%), and social sciences (54%) (appendix table 2-18).

In the last 10 years studied, changes have not followed a 

consistent pattern. The share of bachelor’s degrees awarded to 

women declined in computer sciences (by 10%), mathematics 

Gender Gap in 

Undergraduate Education

A sizeable gender gap in college enrollment 

emerged in the 1980s and has widened since. By 

1980, women achieved parity with men, receiving 

half of all college degrees. By 1990, women received 

54% of college degrees and by the end of the millennium, 58%. The latest update of the American Council 

on Education (ACE) publication Gender Equity in 

Higher Education (King 2010) reports that the gender 

gap in the United States has largely stabilized.

According to disaggregated data from ACE, the 

size of the gender gap varies with race, ethnicity, age, 

income, and the financial independence of students 

pursuing higher education. It is close to zero among 

affluent families with parents who pay for their children’s higher education. It is much larger for blacks 

and Hispanics, for low income families, and for independent students who pay for their own education.

Several indicators point to the stabilization of the 

gender gap. First, the distribution of enrollment and 

undergraduate degrees by gender has remained consistent since around 2000. Second, the number of bachelor’s degrees awarded to both men and women is on 

the rise. Third, for most racial/ethnic groups, the percentage of traditional-age, male undergraduates has 

been stable. 

Hispanics are the exception. Despite a large increase in the number of degrees awarded to Hispanics 

of both genders in recent years, the bachelor’s degree 

attainment rate for Hispanic males is the lowest of any 

major racial/ethnic group (10%) and has not changed 

much since the mid-1990s. This is due to immigration. 

Foreign-born Hispanics complete high school and college at much lower rates than their native-born peers, 

in particular male immigrants, who represent one out 

of every three Hispanic young adults. 

Figure 2-14

Women’s share of S&E bachelor’s degrees, by field: 

2000–09

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. See appendix table 2-18.

Science and Engineering Indicators 2012

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

0

10

20

30

40

50

60

70

80

90

Physical sciences

Biological/agricultural sciences

Mathematics

Computer sciences

Psychology

Social sciences

Engineering
2-22 Chapter 2. Higher Education in Science and Engineering

bachelor’s degrees in any field, the gap in educational attainment between young minorities and whites continues to 

be wide. The percentage of the population ages 25–29 with 

bachelor’s or higher degrees was 19% for blacks, 12% 

for Hispanics, and 37% for whites in 2009. These figures 

changed from 13%, 10%, and 26%, respectively, in 1989 

(NCES 2010a). Differences in completion of bachelor’s degrees in S&E by race/ethnicity reflect differences in high 

school completion rates, college enrollment rates, and college persistence and attainment rates. In general, blacks, 

Hispanics, and American Indian/Alaska Natives are less 

likely than whites and Asians/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 chapter 1, “Transition to 

Higher Education.”) Among those who do enroll in or graduate from college, blacks, Hispanics, and American Indians/

Alaska Natives are about as likely as whites to choose S&E 

fields; and Asians/Pacific Islanders are more likely than 

members of other racial/ethnic groups to choose these fields. 

For Asians/Pacific Islanders, almost half of all bachelor’s 

degrees received are in S&E, compared with about one-third 

of all bachelor’s degrees earned by each of the other racial/

ethnic groups. However, the proportion of Asians/Pacific 

Islanders earning degrees in the social sciences is similar to 

other racial/ethnic groups (appendix table 2-19).

The contrast in field distribution among whites, blacks, 

Hispanics, and American Indians/Alaska Natives on the 

one hand and Asians/Pacific Islanders on the other is apparent within S&E fields as well. White, black, Hispanic, and 

American Indian/Alaska Native S&E baccalaureate recipients 

share a similar distribution across broad S&E fields. In 2009, 

between 9% and 11% of all baccalaureate recipients in each 

of these racial/ethnic groups earned their degrees in the natural sciences,18 3%–4% in engineering, and 15%–18% in the 

social and behavioral sciences. Asian/Pacific Islander baccalaureate recipients earned 20% of their bachelor’s degrees in 

natural sciences and 8% in engineering (appendix table 2-19).

For all racial/ethnic groups, the total number of bachelor’s 

degrees, the number of S&E bachelor’s degrees, and the number of bachelor’s degrees in most S&E fields (with the exception of computer sciences) has generally increased since 

2000 (appendix table 2-19). Across all racial/ethnic groups, 

the number of degrees in computer sciences increased considerably through 2003–04 and then sharply declined through 

2008. Except for Asians/Pacific Islanders, whose numbers in 

computer sciences continued to fall in 2009, the decline in 

other racial/ethnic groups stabilized. In the case of Hispanics, 

the number of computer science degrees awarded increased.

Bachelor’s Degrees by Citizenship. Since 2000, 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. These students earned a larger share of 

bachelor’s degrees awarded in economics and in electrical 

and industrial engineering in 2009 (about 9%). The number of S&E bachelor’s degrees awarded to students on temporary visas increased from about 15,200 in 2000 to about 

18,800 in 2004, and then declined to 17,100 in 2009 (appendix table 2-19).

Persistence and Retention in Undergraduate 

Education (S&E Versus Non-S&E Fields)

Many students who start out in undergraduate programs 

drop out before completing a degree. This section examines 

differences between S&E and non-S&E students in persistence and completion of higher education. 

S&E students persist and complete undergraduate programs at a higher rate than non-S&E students. Six years after 

enrollment in a 4-year college or university in the 2003–04 

academic year, 63% of S&E students had completed a bachelor’s degree by spring 2009, compared to 55% of non-S&E 

students. About 12% of both S&E and non-S&E students 

were still enrolled and about 24% had not completed any 

degree and were no longer enrolled. Within S&E fields, persistence and completion is higher in agricultural, biological, 

and social sciences than in mathematics, and physical and 

computer sciences (table 2-8).

The number of undergraduates who switch out of S&E 

fields is lower than entry into S&E fields as a whole. 

Because many students begin college in the large pool of 

non-S&E and undeclared majors, even the relatively small 

proportion who later switch to S&E constitutes a large number. Among postsecondary students who began at 4-year 

Figure 2-15

Share of S&E bachelor’s degrees, by race/ethnicity: 

2000–09

URM = underrepresented minorities (black, Hispanic, and American 

Indian/Alaska Native)

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. See appendix table 2-19.

Science and Engineering Indicators 2012

URM and Asian/Pacific 

Islander (Percent) White (Percent)

White

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

0

2

4

6

8

10

12

Asian/Pacific Islander

Black

Hispanic

American Indian/Alaska Native

0

57

59

61

63

65

67

69

71

73

75

White
Science and Engineering Indicators 2012 2-23

colleges or universities in 2003–04, 25% reported an S&E 

major, 47% reported a non-S&E major, and 28% were missing data on major or had not declared a major. In cases where 

data on major were available, 35% reported an S&E major. 

Six years later, among those who had attained a bachelor’s 

degree, 34% were S&E majors. Although about 28% of agricultural/biological sciences majors, 31% of mathematics/

physical/computer sciences majors, 22% of engineering majors, and 32% of social sciences majors eventually switched 

to non-S&E majors before earning a bachelor’s degree, 35% 

of those with initially missing or undeclared majors and 15% 

of those with initial non-S&E majors switched into S&E 

fields before earning their bachelor’s degrees (table 2-9). 

Within S&E fields, undergraduate attrition out of agricultural/biological sciences, mathematics/physical/computer 

sciences, and engineering is greater than transfers into those 

fields, but transfers into social/behavioral sciences are greater than attrition. One in ten engineering majors switched into 

a mathematics/physical/computer sciences major. 

Among postsecondary students who began at 4-year colleges or universities in 2003–04 for whom data are available 

and who reported a major, 7% reported an agricultural/biological sciences major or a mathematics/physical/computer 

sciences major respectively, 10% reported an engineering 

major, 11% reported a social/behavioral sciences major, 

and 65% reported a non-S&E major. Six years later, among 

those who had attained a bachelor’s degree, 7% were agricultural/biological sciences majors, 6% were mathematics/

physical/computer sciences majors, 6% were engineering 

majors, 16% were social/behavioral sciences majors, and 

64% were non-S&E majors. 

Table 2-8

Persistence and outcome of postsecondary students beginning 4-year colleges or universities in 2004: 2009

Cumulative persistence outcome, 2009 (%)

Major in 2004 Number Bachelor’s

Associate’s 

or certificate Still enrolled

No longer 

enrolled

All majors ...................................................................... 1,657,800 57.8 6.2 12.2 23.7

S&E ........................................................................... 397,500 63.3 4.5 11.7 20.5

Agricultural/biological sciences ............................. 80,600 71.4 3.1 10.2 15.3

Physical/math/computer sciences ........................ 85,300 51.7 7.4 11.3 29.5

Engineering ............................................................ 107,300 60.8 4.5 14.2 20.5

Social/behavioral sciences .................................... 124,300 62.4 3.4 14.7 19.1

Non-S&E .................................................................... 790,900 55.2 7.3 13.0 24.5

Missing/undeclared ................................................... 469,400 57.5 5.9 11.3 25.3

NOTE: Physical sciences include earth, atmospheric, and ocean sciences. Social sciences include history.

SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 Beginning Postsecondary Students Longitudinal Study, 

Second Follow-Up (BPS:04/09), http://nces.ed.gov/datalab/index.aspx.

Science and Engineering Indicators 2012

Table 2-9

Field switching among postsecondary students beginning 4-year colleges and universities in 2004: 2009

Major in 2004 Number

Major when last enrolled in 2009 (%)

Agricultural/ 

biological 

sciences

Physical/math/ 

computer 

sciences Engineering

Social and 

behavioral 

sciences Non-S&E

Undeclared/

not in degree 

program

All majors ................................................... 1,387,700 6.8 5.7 5.8 15.5 60.7 5.6

S&E

Agricultural/biological sciences ......... 71,300 53.7 3.6 1.3 10.9 28.3 2.2

Physical/math/computer sciences ..... 68,900 5.0 43.0 5.6 8.2 31.1 7.1

Engineering ........................................ 95,500 2.7 10.1 55.9 3.5 22.3 5.4

Social/behavioral sciences ................. 108,600 2.2 1.1 1.0 60.7 31.8 3.3

Non-S&E ................................................ 651,500 3.5 2.5 1.5 7.7 79.4 5.4

Missing/undeclared ............................... 391,900 6.2 5.0 2.9 20.8 58.0 7.1

NOTES: Data excludes students who were not enrolled after July 2006, including those who had obtained their degree by that date. Physical sciences 

include earth, atmospheric, and ocean sciences. Social sciences include history.

SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 Beginning Postsecondary Students Longitudinal Study, 

Second Follow-Up (BPS:04/09), http://nces.ed.gov/datalab/index.aspx.

Science and Engineering Indicators 2012
2-24 Chapter 2. Higher Education in Science and Engineering

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. In 

2009, the Council of Graduate Schools and the Educational 

Testing Service formed a joint commission to investigate 

how graduate education can meet the challenges of the 

21st century (see sidebar “The Path Forward: The Future of 

Graduate Education in the United States”).

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 in S&E

There were 611,600 S&E graduate students enrolled in 

the United States in fall 2009; 48% of them were women 

(appendix table 2-20). The proportions of women graduate students enrolled in S&E differed considerably by field, 

with the lowest proportions in engineering (22%), computer 

sciences (26%), and physical sciences (33%). Women constituted the majority of graduate students in psychology 

(76%), medical/other life sciences (76%), biological sciences (57%), and social sciences (54%), and were close to 

half of graduate students in agricultural sciences (49%) and 

earth, atmospheric, and ocean sciences (46%). Among the 

social sciences, economics has an unusually low proportion 

of women (37%).

In 2009, underrepresented minority students (blacks, 

Hispanics, and American Indians/Alaska Natives) accounted 

for 12% of students enrolled in graduate S&E programs 

(appendix table 2-21). As a group, blacks, Hispanics, and 

American Indians/Alaska Natives made up 6%–7% of graduate enrollment in many S&E fields (engineering; mathematics; physical sciences; earth, atmospheric, and ocean 

sciences; and computer sciences), 9%–10% of graduate 

enrollment in agricultural and biological sciences, 15% in 

medical/other life sciences, 17% in social sciences, and 19% 

in psychology. Whites accounted for about 48% of S&E 

graduate enrollment in 2009 and Asians/Pacific Islanders 

for 6%.

Enrollment in engineering has been rising steadily in 

the last 20 years;19 the number of full-time engineering students reached a new peak of 114,600 in 2009 (figure 2-12; 

appendix table 2-22). According to more recent data from 

the Engineering Workforce Commission and the American 

Society for Engineering Education (Gibbons 2009), graduate engineering enrollment continued to rise in 2009. 

In 2009, approximately 130,000 full-time students were 

enrolled for the first time in S&E graduate programs—23% 

in engineering, 49% in the natural sciences, and 27% in the 

social and behavioral sciences (appendix table 2-23).

Foreign Student Enrollment

In 2009, 168,900 foreign students were enrolled in S&E 

graduate programs (appendix table 2-21). The concentration 

of foreign enrollment was highest in computer sciences, engineering, physical sciences, mathematics, and economics.20

Those were also the fields with the highest share of enrollment of first-time, full-time S&E foreign graduate students 

(appendix table 2-23).

According to data collected by the Institute of 

International Education (IIE 2010), the overall number of 

According to a 2010 report from the Commission on 

the Future of Graduate Education in the United States 

(Wendler et al. 2010), the main challenges facing graduate education and the U.S. educational system as a 

whole are as follows:

In the future, larger numbers of children entering 

schools will come from families with less education. 

Consequently, fewer domestic students may have 

the levels of math and reading skills that will enable 

them to pursue higher education. 

Population growth by the year 2015 will result for the 

most part from international migration, according to 

estimates by the Census Bureau. This will result in a 

growing number of first generation college students, 

many of whom are likely to require additional educational preparation.

The number of nontraditional students (students who 

are older, working adults) is growing. This population 

may see graduate education as a way to improve their 

employability rather than as a way to prepare for a 

first career.

The level of degree attrition is high and time to degree is long, particularly for doctoral students.

At the doctorate level, the decline in the availability 

of tenure track positions, which used to be an incentive for students who decided to pursue a doctorate, 

may result in many doctoral recipients looking for 

careers outside academia.

All of these changes indicate the need to reconsider 

how graduate students are financially supported and 

what kinds of additional resources they may need for 

success in graduate school. The changing demographics 

also may require a reconsideration of traditional time to 

degree expectations and career pathway opportunities.

The Path Forward: The Future of Graduate Education in the United States 
Science and Engineering Indicators 2012 2-25

foreign graduate students in all fields increased 4% from 

academic year 2008–09 to 2009–10. The number of new 

foreign graduate students declined slightly. India, China, 

South Korea, Taiwan, and Canada were the top countries/

economies of origin for foreign graduate students. 

More recent data from the Bureau of Citizenship and 

Immigration Services show a continuing increase in foreign 

graduate students from November 2009 to November 2010, 

with all of the increase occurring in S&E fields (table 2-7). 

About 60% of all foreign students in graduate programs at 

U.S. institutions were enrolled in S&E fields. In fall 2010, 

the number of foreign graduate students enrolled in S&E 

fields increased 2% over the previous year (appendix table 

2-24). In absolute numbers, most of the growth was in computer sciences and engineering, but the increase in computer 

sciences was proportionately higher than in engineering. 

India and China accounted for nearly two-thirds of the foreign S&E graduates in the United States in November 2010. 

South Korea, Taiwan, and Turkey also sent large numbers of 

S&E graduate students, although South Korea and Taiwan 

sent far larger numbers of graduate students in non-S&E 

fields (primarily business and humanities).

S&E Master’s Degrees

In some fields, such as engineering and geology, a master’s degree is often the terminal degree for students. In other 

fields, master’s degrees are a step toward doctoral degrees. 

Professional master’s degree programs, which stress interdisciplinary training, are a relatively new direction in graduate education (for details on professional science master’s 

degrees, see NSB 2010, page 2–22). 

Master’s degrees awarded in S&E fields increased from 

96,200 in 2000 to about 120,900 in 2005, remained fairly 

consistent through 2007, but increased 12% in the years 

2008–09 (appendix table 2-25). Since 2000, increases occurred in all major science fields. Master’s degrees awarded 

in engineering and computer sciences declined between 

2004 and 2007, but have since increased (figure 2-16).

Master’s Degrees by Sex

The number of S&E master’s degrees earned by both men 

and women rose between 2000 and 2009, but the number for 

women grew slightly faster (figure 2-17). In 2000, women 

earned 43% of all S&E master’s degrees; by 2009, they 

earned 45% (appendix table 2-25). Among U.S. citizens and 

permanent residents, women earned about half of all S&E 

bachelor’s degrees (NSF/NCSES 2011). 

Women’s share of S&E master’s degrees varies by field. 

As with bachelor’s degrees, in 2009, women earned a majority of master’s degrees in psychology, biological sciences, 

social sciences, and agricultural sciences and a smaller share 

of master’s degrees in engineering. Women’s share of master’s degrees in engineering in 2009, however, was slightly 

higher than their share in 2000 (appendix table 2-25). The 

number of master’s degrees awarded to women in most major S&E fields increased fairly consistently throughout the 

last decade. In earth, atmospheric, and ocean sciences, and 

in the physical sciences, the numbers increased through 2006 

but have since declined. In computer sciences, the numbers 

increased through 2004, declined sharply through 2007, but 

increased 14% in the years 2008–09.

Figure 2-16

S&E master’s degrees, by field: 2000–09

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. See appendix table 2-25.

Science and Engineering Indicators 2012

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

0

5

10

15

20

25

30

35

40

Engineering

Psychology

Social sciences

 Computer sciences

Biological/agricultural sciences

Physical sciences

Mathematics

Figure 2-17

S&E master’s degrees, by sex: 2000–09

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. See appendix table 2-25.

Science and Engineering Indicators 2012

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

0

10

20

30

40

50

60

70

80

Male

Female
2-26 Chapter 2. Higher Education in Science and Engineering

Master’s Degrees by Race/Ethnicity

The number of S&E master’s degrees awarded to U.S. 

citizens and permanent residents increased for all racial/ethnic groups between 2000 and 2009 (figure 2-18; appendix 

table 2-26).21

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 over 

the 10 years studied. Blacks accounted for 10% of master’s 

degree recipients in 2009, up from 8% in 2000, Hispanics 

from 5% in 2000 to 7% in 2009, and American Indians/

Alaska Natives from 0.5% to 0.6%. The proportion of Asian/

Pacific Islander recipients remained flat in this period.

The percentage of S&E master’s degrees earned by white 

students fell from 52% in 2000 to 45% in 2009, as the percentage of degrees earned by blacks, Hispanics, and temporary residents increased. The proportion of S&E master’s 

degrees with other/unknown race increased from 5% to 9% 

between 2000 and 2009 (appendix table 2-26). 

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 2009, foreign students earned 

27% of S&E master’s degrees. Their degrees were heavily 

concentrated in computer sciences, economics, and engineering, where they earned 46%, 45%, and 43%, respectively, of all master’s degrees awarded in 2009 (appendix 

table 2-26). 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 the decade in 

2009 (36,000), after a sharp decline between 2004 and 2007. 

Most of the drop during this time period was accounted for 

by decreases of temporary residents in the computer sciences and engineering fields, both of which rebounded by about 

one-third in the following 2 years.

S&E Doctoral Degrees

Doctoral education in the United States prepares a new 

generation of faculty and researchers in academia, as well as 

a highly skilled workforce for other sectors of the economy. 

It also generates new knowledge important for the society as 

a whole and for U.S. competitiveness in a global knowledgebased economy. Over the years, numerous attempts have 

been made to measure the quality of doctoral education in 

the United States (Berelson 1960; Cartter 1966; NRC 1982; 

NRC 1995; Roose and Andersen 1970). For information on 

the latest assessment, see sidebar “The National Research 

Council Ratings: Measuring Scholarly Quality of Doctoral 

Programs.”

The National Research Council 

Ratings: Measuring Scholarly 

Quality of Doctoral Programs

The National Research Council’s A Data-Based 

Assessment of Research Doctorate Programs in the 

United States (NRC 2010), released in September 

2010, is the latest attempt to measure the quality of 

U.S. doctoral education. The assessment sought to 

rely more heavily than past ratings on objective performance measures and to give less weight to faculty 

reputation. The study collected a wealth of data during 

the 2005–06 academic year, covering more than 5,000 

programs in 62 fields at 212 universities. 

Despite differences in the methodologies and the 

individual disciplines over time, the same universities—Harvard, Princeton, Stanford, University of 

California–Berkeley, MIT, and the California Institute 

of Technology—tend to have the top ranked departments (Jaschik 2010).

Not all observers agree that the latest ratings methodology is a clear improvement over past ratings. 

Major objections include (1) age of the data at the 

time of the release, (2) exclusion of books from the 

measure of faculty publication in some fields but not 

in others, and (3) disregard for the quality of the journals in which articles were published (Glenn 2010; 

Jaschik 2011).

Figure 2-18

S&E master’s degrees, by race/ethnicity and 

citizenship: 2000–09

Thousands

NOTE: Data on race/ethnicity include U.S. citizens and permanent 

residents. 

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. See appendix table 2-26.

Science and Engineering Indicators 2012

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

0

10

20

30

40

50

60

70

White

Asian/Pacific Islander

Black

Hispanic

American Indian/

Alaska Native

Temporary resident
Science and Engineering Indicators 2012 2-27

The number of S&E doctorates conferred annually by 

U.S. universities increased rapidly between 2003 and 2007, 

but growth slowed in 2008, and the number declined slightly to 41,100 in 2009 (appendix table 2-27).22 The growth 

through 2008 occurred among both U.S. citizens/permanent 

residents and temporary residents, although, in 2009, the 

number of temporary residents earning an S&E doctoral degree declined by about 4% (appendix table 2-28). The largest increases during the 2000–09 period were in engineering, 

biological/agricultural sciences, and medical/other life sciences (figure 2-19). 

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 2-29). The 

physical sciences, mathematics, biological sciences, and engineering had the shortest time to degree, while the social 

sciences and medical/other life sciences had the longest. 

Between 1995 and 2009, 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 2009, down from 7.7 years 

in 1995). Doctorate recipients at medical schools also finished quickly (6.8 years in 2009). Time to degree was longer at universities less-strongly oriented toward research 

(table 2-10).

Doctoral Degrees by Sex

Among U.S. citizens and permanent residents, the proportion of S&E doctoral degrees earned by women grew 

consistently between 2000 and 2007 (from 45% to 55%), but 

decreased slightly in 2008 and 2009 (appendix table 2-27). 

During this decade, women made gains in most major fields, 

but considerable differences continued in certain fields. In 

2009, women earned half or more of doctorates in non-S&E 

fields, in social/behavioral sciences, and in medical/other 

life sciences. However, they earned considerably fewer than 

half of the doctorates awarded in physical sciences (33%), 

mathematics/computer sciences (26%), and engineering 

(25%) (appendix table 2-27). Although the percentages of 

degrees earned by women in physical sciences and engineering are low, they are higher than those earned in 2000 (26% 

and 19% respectively).

The number of S&E doctoral degrees earned by women 

grew faster than that of men. The number of U.S. citizen 

and permanent resident women earning doctorates in S&E 

increased from 8,700 in 2000 to 15,000 in 2009, while the 

number earned by men increased from 10,700 to 12,800 in 

the same time interval (appendix table 2-27). 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 2009, biological sciences doctorates from 1,700 to 2,800, 

physical sciences doctorates from 600 to 800, and medical 

and other life sciences doctorates from 1,300 to 5,300. A 

decrease in the number of doctorates earned by men in the 

early years of the decade occurred in non-S&E fields and 

in most S&E fields (except for medical/other life sciences). 

Since 2005, the number of doctorates earned by U.S. citizen 

and permanent resident men has increased in all major S&E 

fields except for agricultural sciences and psychology.

Doctoral Degrees by Race/Ethnicity

The number and proportion of doctoral degrees in S&E 

fields earned by underrepresented minorities increased 

between 2000 and 2009. In 2009, blacks earned 1,451, 

Hispanics earned 1,335, and American Indians/Alaska 

Natives earned 154—accounting for 7% of all S&E doctoral 

degrees earned that year, up from 6% in 2000 (appendix 

table 2-28).23 Their share of the S&E doctorates earned by 

Figure 2-19

S&E doctoral degrees earned in U.S. universities, 

by field: 2000–09

Thousands

NOTES: Physical sciences include earth, atmospheric, and ocean 

sciences. Data differ from doctoral degree data in other tables and 

figures in this report that are based on NSF Survey of Earned 

Doctorates and that refer to research doctorates only. Greatest 

differences are in psychology and medical/other life 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 table 2-27.

Science and Engineering Indicators 2012

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

0

2

4

6

8

10

Engineering

Physical sciences

Mathematics

Computer sciences

Biological/agricultural

sciences

Social/behavioral sciences

Medical/other life sciences
2-28 Chapter 2. Higher Education in Science and Engineering

U.S. citizens and permanent residents rose from 9% to 11% 

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/Alaska Natives (figure 2-20). 

Asian/Pacific Islander U.S. citizens and permanent residents 

earned 6% of all S&E doctorates in 2009, similar to 2000. 

The number of S&E doctorates earned by white U.S. citizens and permanent residents increased between 2000 and 

2009. The number of S&E doctoral degrees earned by white 

U.S. citizen and permanent resident men declined through 

2003, then gradually increased (figure 2-21). The number of 

degrees earned by white U.S. citizen and permanent resident 

women increased through 2007, but declined somewhat in 

2008 and 2009. As the number of S&E doctorates awarded 

to minorities and temporary residents increased, the proportion of S&E doctoral degrees earned by white U.S. citizens 

and permanent residents decreased from 54% in 2000 to 

49% in 2009 (appendix table 2-28).

Foreign S&E Doctorate Recipients

Temporary residents earned approximately 13,400 S&E 

doctorates in 2009, up from 8,500 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 2-17, 2-19, 2-26, and 2-28). The temporary 

residents’ share of S&E doctorates rose from 30% in 2000 

to 33% in 2009. In some fields, foreign students earned sizeable shares of doctoral degrees. In 2009, foreign students 

on temporary visas earned half or more of doctoral degrees 

awarded in engineering, physics, computer sciences, and 

economics. They earned considerably lower proportions of 

doctoral degrees in other S&E fields, for example, 29% in 

biological sciences, 8% in medical/other life sciences, and 

7% in psychology (appendix table 2-28).

Figure 2-20

S&E doctoral degrees earned by U.S. citizen and 

permanent resident underrepresented minorities, 

by race/ethnicity: 2000–09

Number 

NOTES: Data differ from doctoral degree data in other tables and 

figures in this report that are based on NSF Survey of Earned 

Doctorates and that refer to research doctorates only. Greatest 

differences are in psychology and medical/other life 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 table 2-28.

Science and Engineering Indicators 2012

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

0

200

400

600

800

1,000

1,200

1,400

1,600

Black

Hispanic

American Indian/Alaska Native

Table 2-10

Median number of years from entering graduate school to receipt of S&E doctorate, by 2010 Carnegie 

classification of doctorate-granting institution: 1995–2009

Year of doctorate All institutions

Research universities 

(very high 

research activity)

Research 

universities (high 

research activity)

Doctoral/research 

universities

Medical schools 

and medical centers

Other/ 

not classified

1995.......................... 7.7 7.7 8.3 9.9 7.7 8.7

1996.......................... 7.7 7.7 8.6 9.2 7.7 8.7

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 8.9 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 7.7

2003.......................... 7.6 7.2 8.2 9.9 6.9 8.7

2004.......................... 7.2 7.0 8.0 9.2 6.9 7.6

2005.......................... 7.3 7.2 7.9 9.3 7.0 7.7

2006.......................... 7.2 7.0 7.9 9.0 6.9 7.5

2007.......................... 7.0 6.9 7.7 8.9 6.9 7.4

2008.......................... 7.0 6.9 7.7 8.9 6.7 7.4

2009.......................... 7.0 6.9 7.7 9.2 6.8 7.3

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2010) of Survey of Earned Doctorates.

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 2-29

Countries/Economies of Origin

The top 10 foreign countries/economies of origin of foreign S&E doctorate recipients together accounted for 67% 

of all foreign recipients of U.S. S&E doctoral degrees from 

1989 to 2009 (table 2-11). Six out of those top 10 locations 

are in Asia. The Asian countries/economies sending the most 

doctoral degree students to the United States have been, in 

descending order, China, India, South Korea, and Taiwan.

Asia. From 1989 to 2009, students from four Asian countries/economies (China, India, South Korea, and Taiwan) 

earned more than half of U.S. S&E doctoral degrees awarded to foreign students (122,200 of 223,200)—almost 4 times 

more than students from Europe (30,000). Most of these degrees were awarded in engineering, biological sciences, and 

physical sciences (table 2-12).

Students from China earned the largest number of U.S. 

S&E doctorates awarded to foreign students during the 

1989–2009 period (57,700), followed by those from India 

(24,800), South Korea (21,800), and Taiwan (17,800) (table 

2-11). The number of S&E doctorates earned by students 

from China dropped in the late 1990s, increased through 

2007, but declined nearly 13% in the following 2 years 

(figure 2-22). Over the 20-year period, however, the number 

of S&E doctorates earned by Chinese nationals increased 

nearly 6 times.24 The number of S&E doctorates earned by 

students from India also declined in the late 1990s, but has 

Table 2-11

Foreign recipients of U.S. S&E doctorates, by 

country/economy of origin: 1989–2009

Country/economy Number Percent

All foreign recipients ........ 223,245 100.0

Top 10 total .................. 149,774 67.1

China ........................ 57,705 25.8

India .......................... 24,809 11.1

South Korea ............. 21,846 9.8

Taiwan ...................... 17,848 8.0

Canada ..................... 7,193 3.2

Turkey ...................... 5,391 2.4

Thailand .................... 4,003 1.8

Japan ....................... 3,806 1.7

Mexico ...................... 3,589 1.6

Germany ................... 3,584 1.6

All others ...................... 73,471 32.9

NOTE: Foreign doctorate recipients include permanent and 

temporary residents. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2010) of Survey of 

Earned Doctorates.

Science and Engineering Indicators 2012

Figure 2-21

S&E doctoral degrees, by sex, race/ethnicity, 

and citizenship: 2000–09

Thousands

NOTES: Minority includes Asian/Pacific Islander, black, Hispanic, and 

American Indian/Alaska Native. Data differ from doctoral degree data 

in other tables and figures in this report that are based on NSF Survey 

of Earned Doctorates and that refer to research doctorates only. 

Greatest differences are in psychology and medical/other life 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 2-27 and 2-28.

Science and Engineering Indicators 2012

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

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

Figure 2-22

U.S. S&E doctoral degree recipients, by selected 

Asian country/economy of origin: 1989–2009

Number

NOTE: Degree recipients include permanent and temporary residents. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2010), Survey of 

Earned Doctorates.

Science and Engineering Indicators 2012

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

China

India

South

Korea

Taiwan
2-30 Chapter 2. Higher Education in Science and Engineering

increased almost every year since 2002; over the last two 

decades it more than tripled. The number of S&E doctoral 

degrees earned by South Korean students also dipped in 

the late 1990s and then rose, but the number did not rise 

as dramatically as those for China and India. In 1989, students from Taiwan earned more U.S. S&E doctoral degrees than students from China, India, or South Korea. 

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.

Europe. European students earned far fewer U.S. S&E 

doctorates than Asian students between 1989 and 2009, and 

they tended to focus less on engineering than did their Asian 

counterparts (tables 2-12 and 2-13). Western European 

countries whose students earned the largest number of U.S. 

S&E doctorates from 1989 to 2009 were Germany, the 

United Kingdom, Greece, Italy, and France, in that order. 

Individual country trends and patterns vary (figure 2-23).

The number of Central and Eastern European students 

earning S&E doctorates at U.S. universities increased from 

74 in 1989 to more than 800 in 2009, approaching the number of those from Western Europe (figure 2-24). A higher 

proportion (87%) of Central and Eastern European doctorate 

recipients than of Western European or Scandinavian doctorate recipients (73% and 76% respectively) earned their 

doctorates in S&E fields, particularly in mathematics and 

physical sciences (table 2-13).

North America. 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. S&E degrees earned by 

students from Canada doubled between 1989 and 2009, 

from about 240 to nearly 500. The number of doctoral degree recipients from Mexico increased through 2003, but 

has generally remained stable since then. In 2009, 193 S&E 

Figure 2-23

U.S. S&E doctoral degree recipients, by selected 

Western European country: 1989–2009

Number 

NOTE: Degree recipients include permanent and temporary residents. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2010), Survey of 

Earned Doctorates.

Science and Engineering Indicators 2012

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

0

50

100

150

200

250

Germany

Greece United Kingdom

Italy

France

Table 2-12

Asian recipients of U.S. S&E doctorates, by field and country/economy of origin: 1989–2009

Field Asia China India South Korea Taiwan

All fields ........................................................................... 183,457 61,888 27,981 28,079 22,095

S&E .............................................................................. 157,306 57,705 24,809 21,846 17,848

Engineering .............................................................. 58,557 38,903 13,847 13,356 9,992

Science .................................................................... 98,749 18,802 10,962 8,490 7,856

Agricultural sciences ............................................ 5,905 1,726 632 838 678

Biological sciences ............................................... 26,526 13,107 3,998 2,613 2,730

Computer sciences............................................... 8,462 2,831 2,147 937 916

Earth/atmospheric/ocean sciences ...................... 3,132 1,627 273 371 301

Mathematics ......................................................... 7,534 3,677 709 977 677

Medical/other life sciences ................................... 5,267 1,174 1,071 591 893

Physical sciences ................................................. 22,581 11,220 2,851 2,627 1,867

Psychology ........................................................... 2,423 422 300 413 320

Social sciences ..................................................... 16,919 3,119 1,866 3,989 1,610

Non-S&E ...................................................................... 26,151 4,183 3,172 6,233 4,247

NOTE: Includes permanent and temporary residents. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2010) of Survey of Earned Doctorates.

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 2-31

doctorate recipients from Mexico earned their degree in the 

United States (figure 2-25). A higher proportion of Mexican 

students (84%) than Canadian students (66%) earned U.S. 

doctorates in S&E fields (table 2-13). In particular, higher 

percentages of Mexican students than of Canadian students 

received U.S. doctoral degrees in engineering and agricultural sciences.

Table 2-13

European and North American recipients of U.S. S&E doctorates, by field and region/country of origin: 

1989–2009

Europea North America

Field All countries Western Scandinavia Central/Eastern All countries Canada Mexico

All fields ................................................ 38,644 24,433 2,151 12,060 15,275 10,943 4,283

S&E ................................................... 29,988 17,852 1,632 10,504 10,802 7,193 3,589

Engineering ................................... 5,876 3,741 291 1,844 1,859 1,035 824

Science ......................................... 24,112 14,111 1,341 8,660 8,943 6,158 2,765

Agricultural sciences ................. 828 606 59 163 854 266 588

Biological sciences .................... 4,534 2,753 258 1,523 2,109 1,513 589

Computer sciences.................... 1,621 868 75 678 337 235 101

Earth/atmospheric/ocean 

 sciences .................................. 1,075 720 80 275 375 236 138

Mathematics .............................. 2,957 1,305 105 1,547 526 330 195

Medical/other life sciences ........ 700 531 76 93 638 540 96

Physical sciences ...................... 6,068 2,956 220 2,892 1,164 873 289

Psychology ................................ 1,124 823 114 187 962 873 84

Social sciences .......................... 5,205 3,549 354 1,302 1,978 1,292 685

Non-S&E ........................................... 8,656 6,581 519 1,556 4,473 3,750 694

a

See figure 2-20 notes for countries included in Western Europe, Scandinavia, and Central/Eastern Europe.

NOTE: Includes permanent and temporary residents.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2010) of Survey of Earned Doctorates.

Science and Engineering Indicators 2012

Figure 2-24

U.S. S&E doctoral degree recipients from Europe, 

by region: 1989–2009

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, Armenia, Azerbaijan, Belarus, Bosnia-Herzegovina, Bulgaria, 

Croatia, Czech Republic, Estonia, Georgia, Hungary, Kazakhstan, 

Kosovo, Kyrgyzstan, Latvia, Lithuania, Macedonia, Moldova, Poland, 

Romania, Russia, Serbia-Montenegro, Slovakia, Slovenia, Tadjikistan, 

Turkmenistan, Ukraine, Uzbekistan, and Yugoslavia. Scandinavia 

includes Denmark, Finland, Iceland, Norway, and Sweden.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2010), Survey of 

Earned Doctorates.

Science and Engineering Indicators 2012

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

0

100

200

300

400

500

600

700

800

900

1,000

Western Europe

Central and Eastern Europe

Scandinavia

Figure 2-25

U.S. S&E doctoral degree recipients from Canada 

and Mexico: 1989–2009

Number 

NOTE: Degree recipients include permanent and temporary residents.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2010), Survey of Earned 

Doctorates.

Science and Engineering Indicators 2012

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

0

100

200

300

400

500

600

Canada

Mexico
2-32 Chapter 2. Higher Education in Science and Engineering

International S&E Higher Education

In the 1990s, many countries expanded their higher education systems and 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. 

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 most instances, government spending underwrites these initiatives. One indicator of the importance 

of higher education is the percentage of a nation’s resources 

devoted to higher education, as measured by expenditures 

on tertiary education (education beyond high school) as a 

percentage of gross domestic product (GDP). In 2007, U.S. 

expenditures on tertiary education as a percentage of GDP 

were double the OECD average. The United States, Canada, 

and Korea spent the highest percentage of GDP on higher 

education (appendix table 2-30).

Another indicator of the growing importance of higher 

education is the change in expenditures for higher education 

over time. Expenditures for tertiary education rose more in 

the United States than in many other OECD countries between 1995 and 2000, but less in the United States than in 

other OECD countries between 2000 and 2007. From 1995 

to 2000, educational expenditures in the United States increased faster than the OECD average and faster than most 

OECD countries. From 2000 to 2007, educational expenditures in the United States increased at a rate similar to the 

OECD average. During this period, several countries, including the United Kingdom and Poland, exceeded the OECD 

average increase in expenditures (appendix table 2-30). 

Higher education funding data can vary between countries for reasons unrelated to actual expenditures, such as 

changes in measurement, prevalence of public versus private 

institutions (private institutions are much more prevalent in 

the United States than in other countries), types and levels of 

government funding included, and types and levels of education included. In several European countries, governments 

plan to cut their investments in higher education as a result 

of the global recession and fiscal crisis; the results of these 

policies remain to be seen. 

Educational Attainment

Higher education in the United States expanded greatly 

after World War II and, for several decades, the United 

States’ population led the world in educational attainment. 

In the 1990s, many countries in Europe and Asia also began to expand their higher education systems. 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 2-26; appendix table 2-31).25

First University Degrees in S&E Fields

More than 14 million students worldwide earned first 

university degrees26 in 2008, with about 5 million of these 

in S&E fields (appendix table 2-32). These worldwide totals include only countries for which relatively recent data 

are available (primarily countries in Asia, Europe, and the 

Americas) and are, therefore, an underestimation. Asian 

universities accounted for 2.4 million of the world’s S&E 

first university degrees in 2008, more than 1 million of these 

in engineering. Students across Europe (including Eastern 

Europe and Russia) earned more than 1.2 million S&E degrees, and students in North and Central America earned 

nearly 700,000 in 2008.

In several countries/economies around the world, the proportion of first university degrees in S&E fields was higher 

than in the United States. More than half of first university 

degrees in Japan and China were in S&E fields, compared 

with about one-third in the United States. The disparity was 

especially large in engineering.27 China has traditionally 

awarded a large proportion of its first university degrees in 

engineering, although the percentage has declined in recent 

years (appendix table 2-33). In the United States, about 4% 

of all bachelor’s degrees are in engineering, compared with 

19% in Asia, and approximately one-third in China (appendix table 2-32). About 11% of all bachelor’s degrees awarded in the United States and worldwide are in natural sciences 

(physical, biological, computer, and agricultural sciences, 

and mathematics). 

The number of S&E first university degrees awarded in 

China and Taiwan more than doubled between 2000 and 

2008, and those in the United States and many other countries generally increased. Those awarded in Japan, France, 

and Spain decreased in recent years (appendix table 2-33). 

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 rose sharply from 2002 to 2008, and more 

than tripled between 2000 and 2008 (figure 2-27). In comparison, the number awarded in Germany, Japan, South Korea, 

the United Kingdom, and the United States remained relatively flat. In China, degrees awarded increased faster than 

the population, which is also growing. In Japan and Europe 

degree trends may be influenced by declining populations.

 In 1999, 29 European countries, through the Bologna 

Declaration, initiated a system of reforms in higher education in Europe. The goal of the Bologna Process is to 

harmonize certain aspects of higher education within participating countries so that degrees are comparable, credits 
Science and Engineering Indicators 2012 2-33

are transferable, and students, teachers, and researchers can 

move freely from institution to institution across national 

borders (for information on reforms affecting degree awards 

in Europe, see sidebar “An Update on the Bologna Process”). 

The Bologna Process is also stimulating discussions about 

higher education in the United States (Adelman 2009).

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 2008, including 

the United States and a number of smaller countries. Several 

large countries in Europe are not far behind, with more than 

40% of first university S&E degrees earned by women. In 

many Asian and African countries, women generally earn 

about one-third or less of the first university degrees awarded in S&E fields (appendix table 2-34). 

In Canada, Japan, the United States, and many smaller 

countries, more than half of the S&E first university degrees 

Figure 2-26

Attainment of tertiary-type A and advanced 

research programs, by country and age group: 2008

NOTES: Tertiary-type A programs (International Standard Classification of Education [ISCED] 5A) are largely theory-based and designed 

to provide sufficient qualifications for entry to advanced research 

programs and professions with high skill requirements such as 

medicine, dentistry, or architecture and have minimum duration of 3 

years’ full-time equivalent, although typically last 4 years. 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 qualification, e.g., doctorate. See 

appendix table 2-33.

SOURCE: Organisation for Economic Co-operation and Development (OECD), Education at a Glance 2010: OECD Indicators (2010).

Science and Engineering Indicators 2012

0 10 20 30 40 50

Percent

Austria

Chile (2004)

Turkey

Slovenia

Italy

Slovak Republic

Portugal

Czech Republic

Belgium

Mexico

Germany

Greece

France

Luxembourg

Hungary

Poland

Finland

Spain

Ireland

Switzerland

Russian

Federation (2002)

Sweden

United Kingdom

Estonia

Japan

New Zealand

Korea

Australia

Canada

Iceland

Denmark

Netherlands

Israel

United States

Norway

25–64 years old

25–34 years old

Figure 2-27

First university natural sciences and engineering 

degrees, by selected countries: 1999–2008

Thousands

NOTES: Natural sciences include physical, biological, earth, atmospheric, ocean, and agricultural sciences; computer science; and 

mathematics. Data for U.S. not available for 1999.

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, Higher Education Bureau, Monbusho 

Survey of Education; South Korea and Germany—Organisation for 

Economic Co-operation and Development, Education Online 

Database, http://www.oecd.org/education/database; 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. See appendix table 2-33. 

Science and Engineering Indicators 2012

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0

200

400

600

800

1,000

1,200

China

United States

Japan

South Korea

United Kingdom

Germany
2-34 Chapter 2. Higher Education in Science and Engineering

earned by women were in the social and behavioral sciences. 

In South Korea, nearly half of the S&E first university degrees earned by women were in engineering, a much higher 

proportion than in Europe and the United States.

Global Comparison of S&E Doctoral Degrees

About 194,000 S&E doctoral degrees were earned worldwide in 2008. The United States awarded the largest number 

of S&E doctoral degrees of any country (about 33,000),28 

followed by China (about 28,000), Russia (almost 15,000), 

Germany (about 11,000), and the United Kingdom (about 

9,500) (appendix table 2-35). About 55,000 S&E doctoral 

degrees were earned in the European Union.

Women earned 41% of S&E doctoral degrees awarded in 

the United States in 2008, about the same percentage earned 

by women in Australia, Canada, the European Union, and 

Mexico. In the United States, women earned nearly half of 

the S&E doctoral degrees awarded to U.S. citizens and permanent residents.29 Women earned more than half of S&E 

doctoral degrees in Portugal and less than one-quarter of 

S&E doctoral degrees in the Netherlands, South Korea, and 

Taiwan (appendix table 2-36). 

The number of S&E doctoral degrees awarded in China, 

Italy, and the United States has risen steeply in recent years; 

the number awarded in Russia increased considerably between 2002 and 2007, but decreased sharply in 2008 (appendix tables 2-37 and 2-38). Until 2006, the United States 

awarded the largest number of natural sciences and engineering doctoral degrees but, in 2007, China surpassed the 

United States (figure 2-28). 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. 

The number of doctoral degrees awarded in S&E stagnated 

or declined in many of these countries between 2000 and 

2004, although that number increased in later years in Italy, 

Switzerland, and the United States (appendix table 2-37).

In Asia, China was the largest producer of S&E doctoral 

degrees. As China’s capacity for advanced S&E education 

increased, the number of S&E doctorates awarded rose 

from about 2,700 in 1994 to almost 28,500 in 2008 (appendix table 2-38), a substantially faster rate of growth when 

compared to the number of doctorates earned by Chinese 

citizens in the United States during the same period (figure 2-29). In 2007, the Chinese State Council Academic 

Degrees Committee 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, Japan, South Korea, and 

Taiwan also rose from 1994 to 2008, but at a lower rate. In 

China, Japan, South Korea, and Taiwan, more than half of 

S&E doctorates were awarded in engineering. In India, almost three-quarters of the S&E doctorates were awarded in 

the physical and biological sciences (appendix table 2-38).

Global Student Mobility 

International migration of students has expanded in the 

past two decades, and countries are increasingly competing 

for foreign students. According to UNESCO, the number of 

internationally mobile students more than tripled between 

An Update on the 

Bologna Process 

Ten years after the Bologna Declaration, the 

European Higher Education Area (EHEA) was 

launched and higher education reform in Europe had 

been extended to more than 45 participating countries. 

The Trends 2010 report, published by the European 

University Association (Sursock and Smidt 2010), 

analyzes the implementation of the Bologna Process 

and its impact on higher education based on questionnaire responses from 821 universities, 27 university 

associations, and site visits to 16 countries.

Some of the key findings indicate that—

The vast majority of the institutions had implemented the three degree cycles (bachelor’s, master’s, and doctorate). However, implementation of 

degree structures had been more difficult in certain regulated professions such as medicine, law, 

and engineering.

At the bachelor’s level there has been greater emphasis on increasing and widening access, on studentcentered learning, and on flexible learning paths. 

The master’s degree was introduced as a new, separate qualification across Europe, and so far it seems 

to be a very flexible degree, but one that is defined 

differently by different countries and institutions. 

At the doctorate level, schools have been expanding 

rapidly and more attention has been focused on the 

supervision and training of doctoral students.

A growing majority of the universities used the 

credit-transfer system for all bachelor’s and master’s degrees.

Bologna was a catalyst to improve the quality of 

teaching and move toward student-centered learning. 

The majority of the universities had reviewed curricula in all departments under the Bologna Process. 

Institutions identified internationalization as an 

important driver of change. More institutions are 

developing integrated internationalization approaches to teaching and research through strategic partnerships. 

Despite efforts to promote mobility across institutions and national borders, not much data were 

available on how mobility flows had changed during the Bologna Process.
Science and Engineering Indicators 2012 2-35

1980 and 2009, to 3.4 million (UNESCO 2011).30 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, e.g., Australia, China, and 

South Korea for East Asia and South Africa for sub-Saharan 

Africa (UNESCO 2009). 

Some students migrate temporarily for education, whereas others remain permanently. 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 countries of destination; 

the financial support the countries of destination offer to 

international students; and the cost of living and exchange 

rates that impact the cost of international education. 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). In recent 

years, many countries, particularly English-speaking countries such as Australia, Canada, the United Kingdom, and 

the United States, have expanded their provision of transnational education, i.e., programs for foreign students in 

their home countries (see sidebar “Transnational Higher 

Education”). The influence of the worldwide economic and 

monetary crises that began in 2008 on future international 

flows of students is uncertain.

Some countries expanded recruitment of international 

students as their own populations of college-age students 

decreased, both to attract highly skilled workers and increase revenue for colleges and universities (OECD 2010). 

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 (appendix table 

2-39). The U.S. population of 20–24-year-olds is projected 

to increase.

The United States remains the destination of the largest 

number of internationally mobile students (both undergraduate and graduate) of all countries (figure 2-30), although its 

share declined in recent years. In 2009, the United States 

received 20% of international students, down from 25% 

in 2000 (UNESCO 2011). Other top destinations for international students include the United Kingdom (12%), 

Germany (9%), and France (9%). Together with the U.S., 

Figure 2-28

Natural sciences and engineering doctoral degrees, 

by selected country: 2000–08 

Thousands

UK = United Kingdom

NOTES: Natural sciences and engineering include physical, biological, earth, atmospheric, ocean, and agricultural sciences; computer 

science; mathematics; and engineering. Data for India not available 

for 2007 and 2008; data for Japan not available for 2008.

SOURCES: China—National Bureau of Statistics of China; Japan— 

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

Science and Technology, Higher Education Bureau, Monbusho 

Survey of Education; 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 Germany—Federal Statistical 

Office, Prüfungen an Hochschulen, and Organisation for Economic 

Co-operation and Development, Education Online database, http:// 

www.oecd.org/education/ database/; 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, Integrated Science and Engineering Resources Data System 

(WebCASPAR), http://webcaspar.nsf.gov. See appendix tables 2-37 

and 2-38. 

Science and Engineering Indicators 2012

2000 2001 2002 2003 2004 2005 2006 2007 2008

0

5

10

15

20

25

30

United States

Germany

Russia

China

UK

Japan

South Korea

India

Figure 2-29

S&E doctoral degrees earned by Chinese students 

at home universities and U.S. universities: 

1994–2008

Thousands

NOTE: Degree recipients in the United States include permanent and 

temporary residents. 

SOURCES: National Science Foundation, National Center for Science

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

Earned Doctorates; China—National Research Center for Science and

Technology for Development and Education Statistics Yearbook of

China (various years). 

Science and Engineering Indicators 2012

1994 1996 1998 2000 2002 2004 2006 2008

0

5

10

15

20

25

30

S&E doctorates

earned in China S&E doctorates 

earned by Chinese 

recipients in the 

United States
2-36 Chapter 2. Higher Education in Science and Engineering

Figure 2-30

Internationally mobile students enrolled in tertiary 

education, by country: 2009

NOTES: Data 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 for 2007 exclude 

private institutions. Data for Netherlands and Germany exclude 

advanced research programs, e.g., doctorate. Data for Belgium 

exclude social advancement education. Data for Russia exclude 

tertiary-type B programs (e.g., associate’s) in private institutions and 

advanced research programs (e.g., doctorate). Data for United 

Kingdom, United States, and Australia based on country of 

residence; data for Germany and Switzerland based on country of 

prior education; data from other countries based on country of 

citizenship.

SOURCE: UNESCO Institute for Statistics, Global Education Digest

(2011).

Science and Engineering Indicators 2012

Thousands

Hungary

Thailand

Poland

Norway

Saudi Arabia

Turkey

Kyrgyzstan

Lebanon

Netherlands

Jordan

Czech

Republic

Cuba

United Arab

Emirates

Switzerland

Egypt

Ukraine

New Zealand

Singapore

Malaysia

Spain

South Korea

Austria

South Africa

China

Italy

Canada

Japan

Russian

Federation

Germany

France

Australia

United

Kingdom

United States

0 100 200 300 400 500 600 700 800

Transnational Higher Education 

Although transnational higher education is not entirely new, the nature and scale of its global expansion 

are changing substantially (Naidoo 2009). Two growing trends are the establishment of branch campuses 

and collaborative programs such as joint/dual degrees.

According to research by the Observatory on 

Borderless Higher Education, between 2006 and 2009 

the number of branch campuses increased by 43%, to 

162 (Becker 2010). English-speaking countries dominate, led by U.S. institutions with 78 international 

branch campuses, followed by Australia (14), the 

United Kingdom (13), and France and India (11 each). 

The United States also led in campus growth between 

2006 and 2009; American institutions sponsored 15 

of the 49 branch campuses created during that time. 

The United Arab Emirates was the top host country, 

with 40 international branch campuses, two-thirds of 

which are located in Dubai International Academic 

City. China hosts 15 branch campuses, followed by 

Singapore and Qatar. 

Branch campuses give foreign students the opportunity to earn a Western degree without leaving their 

home country. For the institution venturing into a 

new country, meeting enrollment and financial goals 

without diluting quality standards is often a challenge. 

Following the closures of several branch campuses, 

higher education institutions have become more aware 

of the long-term risks involved and more frequently 

look for sponsors or partners to share and reduce such 

risks.

Recent data on joint and dual degree programs are 

scarce. In these programs, students study at two or 

more institutions. After successfully completing the 

requirements, in dual degree programs they receive 

a separate diploma from each institution and in joint 

degree programs they receive a single diploma representing both institutions (CGS 2010). Two member 

surveys conducted by the Council of Graduate Schools 

in 2007 and 2008 show that, at the graduate level in the 

United States, dual degrees are more prevalent than 

joint degrees and that these collaborative programs are 

more common in universities with high international 

student enrollment. U.S. graduate schools are more 

likely to have established dual/joint degree programs 

with higher education institutions in Europe, with 

China and India in second place. The most common 

fields for dual degrees at the master’s level are business, engineering, and the social sciences; at the doctoral level, they are engineering and physical sciences. 
Science and Engineering Indicators 2012 2-37

these countries receive more than half of all internationally 

mobile students worldwide.

Although Australia has a higher percentage (21%) of 

foreign higher education students (undergraduate and graduate) than the United States (3%), it has a lower share (7%) 

of foreign students worldwide.31 Other countries with relatively high percentages of foreign higher education students include Austria (16%), the United Kingdom (15%), 

Switzerland (14%), and New Zealand (13%). In Switzerland 

and the United Kingdom, more than 40% of doctoral students are foreign. A number of other countries, including 

New Zealand, Austria, Australia, Belgium, Canada, and the 

United States, have relatively high percentages (more than 

20%) of doctoral students who are foreign32 (OECD 2010).

The United Kingdom has been actively expanding its position in international education, both by recruiting foreign 

students to study in the country and expanding its provision 

of transnational education (British Council 2011). Foreign 

student enrollment in the United Kingdom has been increasing, especially at the graduate level, with increasing flows 

of students from China and India (appendix table 2-40). In 

2008, foreign students made up 47% of all graduate students 

studying S&E in the United Kingdom (an increase from 

32% in 1998). Foreign students now account for nearly 60% 

of graduate students in mathematics, computer sciences, and 

engineering. Students from China and India accounted for 

most of the increase, but the number of graduate students 

from Nigeria, Pakistan, Germany, and the United States also 

increased. The percentage of foreign undergraduate students 

increased little. 

Japan has increased its enrollment of foreign students 

in recent years and in 2008 announced plans to triple foreign enrollment in 12 years (McNeil 2008). In 2010, almost 

70,000 foreign students were enrolled in S&E programs in 

Japanese universities, up from 57,000 in 2004. Foreign S&E 

student enrollment in Japan is concentrated at the undergraduate level, accounting for 67% of all foreign S&E students. 

Foreign nationals accounted for 3% of undergraduate and 

16% of graduate S&E students in Japan. The vast majority 

of the foreign students were from Asian countries. In 2010, 

Chinese students accounted for 69% of the foreign S&E 

undergraduate students and 57% of graduate S&E students 

in Japan. South Koreans comprised 19% of the foreign undergraduates and 10% of the graduates. Indonesia, Vietnam, 

Malaysia, Thailand, Mongolia, and Nepal were among the 

top 10 countries of origin for both undergraduates and graduate students (appendix table 2-41).

Foreign students constitute an increasing share of enrollment in Canadian universities. Foreign S&E students accounted for about 7% of undergraduate and 22% of graduate 

S&E enrollment in Canada in 2008, up from 4% and 14% 

in 1999. In 2008, at both the undergraduate and graduate 

levels, the highest percentages of foreign S&E students were 

in mathematics/computer sciences and engineering. China 

was the top country of origin of foreign S&E students in 

Canada, accounting for 15% of foreign S&E graduate and 

15% of undergraduate students. The United States was also 

among the top countries of origin of foreign students, accounting for 7% of foreign S&E graduate students and 10% 

of foreign S&E undergraduate students in Canada. About 

10% of foreign S&E graduate students in Canada were from 

France and 9% from Iran. At the undergraduate level, 8% of 

Canada’s foreign S&E undergraduate population was from 

France (appendix table 2-42). 

Although foreign students make up a large share of U.S. 

higher education, U.S. students constitute a relatively small 

share of foreign students worldwide. About 52,328 U.S. 

students (in all fields) were reported as foreign students 

by OECD and OECD-partner countries in 2008, far fewer 

than the number of foreign students from China, France, 

Germany, India, Japan, or South Korea. The main destinations of U.S. students were the United Kingdom (13,900), 

Canada (9,900), Germany (3,300), France (3,200), Australia 

(3,100), New Zealand (2,900), Ireland (2,800)—mainly 

English-speaking countries (OECD 2010).

About 260,000 U.S. students from U.S. universities enrolled in study-abroad programs in the 2008–09 academic 

year, down slightly from 2007–08 (1%), but up 81% in 

the last 10 years (IIE 2010). Just over one-third enrolled in 

programs lasting one semester, a similar proportion in the 

summer term, and 12% in short-term programs (2–8 weeks). 

About 12% were graduate students; the rest were undergraduates, primarily juniors or seniors. About one-third were 

studying in S&E fields: 21% in social sciences, 7% in physical or life sciences, 3% in engineering, 2% in mathematics or 

computer sciences, and 1% in agricultural sciences.

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 2009. Most of the 

growth in undergraduate S&E education occurred in science 

fields, in particular in the social and behavioral sciences. In 

engineering, bachelor’s degrees increased since 2002 but 

have not yet reached the record high levels attained in the 

1980s. Computer sciences degree awards dropped precipitously between 2004 and 2007, but have began to rebound 

since then. A growth in the number of master’s degrees 

awarded occurred in all major S&E fields. The number of 

doctoral degrees awarded in all fields and in S&E increased 

between 2000 and 2008 and remained stable in 2009. In the 

last decade, growth in doctoral degrees awarded occurred 

mostly in the natural sciences and engineering fields. 

Foreign graduate student enrollment in S&E recovered 

since early in the decade when the number of entering foreign students dropped after 11 September 2001.

Globalization of higher education continues to expand. 

Although the United States continues to attract the largest number and fraction of foreign students worldwide, 

its share of foreign students has decreased in recent years. 
2-38 Chapter 2. Higher Education in Science and Engineering

Universities in several other countries have expanded their 

enrollment of foreign S&E students.

Notes

1. The physical sciences include earth, atmospheric, and 

ocean sciences.

2. In this NCES report, distance education courses include live, interactive audio- or videoconferencing; prerecorded instructional videos; webcasts; CD-ROMs or DVDs; 

or computer-based systems accessed over the Internet. 

Distance education does not include correspondence courses.

3. For information on site traffic statistics at the 

MIT OpenCourseWare, see http://ocw.mit.edu/about/

site-statistics.

4. In May 2010, an ad hoc committee of the National 

Academy of Sciences began a 2-year project to report on 

the top 10 actions that Congress, the federal government, 

state governments, research universities, and others could 

take to assure the ability of the American research university 

to maintain excellence in research and doctoral education. 

Among other areas, the committee is focusing on the financial capacity of public research universities in the United 

States.

5. 2010 data are preliminary.

6. Clinical psychology programs and programs that emphasize professional practice (professional schools and 

Psy.D. programs) are associated with higher debt, but even 

in the more research-focused subfields of psychology, lower percentages of doctorate recipients were debt free and 

higher percentages had high 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 20 June 2011).

7. In table 2-6, the difference in the average amount owed 

in constant 2000 dollars by S&E master’s recipients between 

2000 and 2008 was not statistically significant.

8. Household income is a measure of ability to pay and 

age-specific unemployment rates is a measure of opportunity costs.

9. Based on previous projections, NCES estimated that 

the mean absolute percentage error for enrollment in degreegranting institutions projected 9 years out was 10.1 (NCES 

2011c).

10. These data are from sample surveys and are subject 

to sampling error. Information on estimated standard errors can be found in appendix E of the annual report The 

American Freshman: National Norms Fall 2010, published by The Cooperative Institutional Research Program 

of the Higher Education Research Institute, University 

of California–Los Angeles (http://gseis.ucla.edu/heri/ 

pr-display.php?prQry=55, accessed 15 February 2011). 

Data reported here are significant at the .05 level.

11. The number of S&E degrees awarded to a particular 

freshmen cohort is lower than the number of students reporting such intentions and reflects losses of students from S&E, 

gains of students from non-S&E fields after their freshman 

year, and general attrition from bachelor’s degree programs.

12. The data in this section come from the Institute for 

International Education (IIE) and the Student and Exchange 

Visitor Information System (SEVIS). IIE conducts an annual survey of institutions during the fall of a specific year 

and the spring and summer of the following year. An international student in this survey is 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 all 

foreign national students enrolled in colleges and universities in the United States. SEVIS collects data for the fall and 

the spring of each year. Data on exchange visitors are not 

included in this chapter.

13. These data include foreign students pursuing both 

bachelor’s and associate’s degrees. Comparable data for 

U.S. citizen/permanent resident students do not exist. 

However, the proportion of S&E associate’s and bachelor’s 

degree awards for U.S. citizens and permanent residents is 

considerably lower.

14. About 14% of S&E bachelor’s degree recipients 

who earned their degree between 1 July 1 2002 and 30 June 

2005 had previously earned an associate’s degree (National 

Science Foundation, National Center for Science and 

Engineering Statistics, Scientists and Engineers Statistical 

Data System [SESTAT] 2006, special tabulation). 

15. Data for racial/ethnic groups are for U.S. citizens and 

permanent residents only.

16. For longer trends in degrees, see NSB 2010. For more 

detail on enrollment and degrees by sex and by race/ethnicity, see Women, Minorities, and Persons with Disabilities in 

Science and Engineering: 2011 (NSF/NCSES 2011). 

17. Data for racial/ethnic groups are for U.S. citizens and 

permanent residents only.

18. The natural sciences include agricultural; biological; 

computer; earth, atmospheric, and ocean; and physical sciences and mathematics.

19. The reason for the differences in the number of engineering students in appendix table 2-21 and appendix table 

2-22 is because the Engineering Workforce Commission includes in its engineering counts computer science students 

enrolled in engineering schools. Data on graduate enrollment 

from the Survey of Graduate Students and Postdoctorates in 

Science and Engineering counts such students as computer 

science students. 

20. See Women, Minorities, and Persons with Disabilities 

in Science and Engineering: 2011 (NSF/NCSES 2011) for 

more detail on enrollment of foreign students by sex.

21. Data for racial/ethnic groups are for U.S. citizens and 

permanent residents only.

22. At the doctorate level, the data on degrees awarded 

in the United States includes health fields because they 
Science and Engineering Indicators 2012 2-39

are research-oriented and not professional fields (as health 

fields are at the bachelor’s and master’s level). However, 

health fields at the doctorate level are not included in international comparisons because international sources cannot 

separate the MD degrees from the health fields, and the MDs 

are professional and not research degrees.

23. For the corresponding proportions in the 1990s see 

NSB 2008.

24. The number of S&E doctoral degrees earned by students in Chinese universities continued to increase throughout this period, from 1,894 in 1993 to 28,439 in 2008.

25. 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- to 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- to 34-year-olds with a 

bachelor’s or higher degree is roughly 0.4.

26. A first university degree refers to the completion of 

a terminal undergraduate degree program. These degrees 

are classified as level 5A in the International Standard 

Classification of Education, although individual countries 

use different names for the first terminal degree (e.g., laureata in Italy, diplome in Germany, maîtrise in France, and 

bachelor’s degree in the United States and Asian countries). 

27. Differences in the taxonomies of engineering programs and level of reporting detail across countries make 

exact comparisons difficult.

28. In international comparisons, S&E fields do not include medical or health fields.

29. This proportion excludes medical/other life sciences doctorate awards in the United States because international sources cannot separate the MD degrees from the 

health fields, and the MDs are professional and not research 

degrees.

30. 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.

31. Foreign students are those who do not hold the citizenship of the country for which the data were collected.

32. In many OECD countries, students in S&E fields 

make up a considerable proportion of international students. 

No data are available by degree level and field of study. 

Glossary

Distance education: Formal education process in which 

the student and instructor are not in the same place.

First university degree: A terminal undergraduate degree program; these degrees are classified as level 5A in the 

International Standard Classification of Education, which 

is developed by the United Nations Educational, Scientific 

and Cultural Organization, although individual countries use 

different names for the first terminal degree (e.g., laureata

in Italy, diplome in Germany, maîtrise in France, and bachelor’s degree in the United States and in Asian countries).

Internationally mobile students: Students who have 

crossed a national or territorial border for the purposes of education and are now enrolled outside their country of origin.

Net price: The published price of an undergraduate college education minus the average grant aid and tax benefits 

that students receive.

Online education: A type of distance education where 

the medium of instruction is computer technology via the 

Internet.

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 

degrees in the United States.

Underrepresented minorities: Blacks, Hispanics, and 

American Indians/Alaska Natives are considered to be underrepresented minorities in S&E. 

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Chapter 3

Science and Engineering 

Labor Force

3-1

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

Scope of the S&E Workforce ...................................................................................................3-5

S&E Workers in the Economy .................................................................................................3-5

S&E Labor Market Conditions .................................................................................................3-5

Demographics of the S&E Workforce ......................................................................................3-5

Global S&E Labor Force ..........................................................................................................3-6

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

Chapter Overview .....................................................................................................................3-7

Chapter Organization ................................................................................................................3-7

Scope of the S&E Workforce ......................................................................................................3-7

Measures of the S&E Workforce ..............................................................................................3-8

Size of the S&E Workforce ....................................................................................................3-10

Growth of the S&E Workforce ...............................................................................................3-10

Educational Distribution of Those in S&E Occupations ........................................................3-14

S&E Degree Holders in Non-S&E Occupations ....................................................................3-15

Relationships Between Jobs and Degrees ...............................................................................3-16

S&E Workers in the Economy ...................................................................................................3-17

Characteristics of Employers of Scientists and Engineers .....................................................3-18

S&E Workers by Employment Sector ...................................................................................3-22

Scientists and Engineers and Innovation-Related Activities ..................................................3-25

S&E Labor Market Conditions ..................................................................................................3-29

Unemployment in the S&E Labor Force ................................................................................3-29

Earnings ..................................................................................................................................3-32

Earnings at Different Degree Levels ......................................................................................3-32

Recent S&E Graduates ...........................................................................................................3-33

General Labor Market Indicators for Recent Graduates .........................................................3-33

Recent Doctorate Recipients ...................................................................................................3-34

Postdoc Positions ....................................................................................................................3-36

Demographics of the S&E Workforce .......................................................................................3-40

Sex Differences in the S&E Workforce ..................................................................................3-40

Racial/Ethnic Differences in the S&E Workforce ..................................................................3-43

Salary Differentials for Women and Minorities .....................................................................3-45

S&E Immigrants .....................................................................................................................3-47

New Foreign-Born Workers ..................................................................................................3-48

Age and Retirement ................................................................................................................3-52

Global S&E Labor Force ...........................................................................................................3-56

Size and Growth of Global S&E Labor Force ........................................................................3-56

High-Skill Migration .............................................................................................................3-57

R&D Employment Abroad by U.S. Companies .....................................................................3-58

International Engagement by the Domestic S&E Workforce .................................................3-60

Conclusion .................................................................................................................................3-62

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

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

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

Please see chapter errata
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 employed S&E workforce: 2003, 2008, and 2009 .................3-10

Table 3-4. Educational background of workers in S&E occupations: 2008 ..............................3-15

Table 3-5. Relationship of highest degree to job among S&E highest degree holders 

not in S&E occupations, by degree level: 2008 ....................................................................3-16

Table 3-6. Employment sector of employed scientists and engineers, by broad occupation 

and degree field: 2008 ..........................................................................................................3-19

Table 3-7. Average annual salaries of workers, by industries’ proportion of employment 

in S&E occupations: May 2010 ............................................................................................3-21

Table 3-8. Workers in S&E and STEM occupations in largest metropolitan statistical 

areas: May 2010 ....................................................................................................................3-21

Table 3-9. Metropolitan areas with largest proportion of workers in S&E occupations, 

by occupation category: May 2010 .......................................................................................3-22

Table 3-10. Metropolitan areas with largest number of workers in S&E occupations, 

by occupation category: May 2010 .......................................................................................3-23

Table 3-11. Self-employed scientists and engineers, by education, occupation, and type 

of business: 2008 ...................................................................................................................3-24

Table 3-12. Employed S&E degree holders with R&D work activities, by 

occupation: 2008 ...................................................................................................................3-26

Table 3-13. Domestic industrial and R&D employment, by company size: 2009 ....................3-27

Table 3-14. Patenting indicators for employed U.S.-trained SEH doctorate holders, 

by field of doctorate: 2003–08 ..............................................................................................3-28

Table 3-15. Scientists and engineers participating in work-related training, by employment 

status and occupation: 2008 ..................................................................................................3-29

Table 3-16. Alternative measures of labor underutilization ......................................................3-31

Table 3-17. Annual earnings and earnings growth in science and technology and related 

occupations: May 2007–May 2010 .......................................................................................3-33

Table 3-18. Labor market indicators for recent S&E degree recipients up to 5 years 

after receiving degree, by field: 2008 ...................................................................................3-35

Table 3-19. Employment characteristics of recent SEH doctorate recipients up to 3 years 

after receiving doctorate, by field: 2001–08 .........................................................................3-36

Table 3-20. Employed SEH doctorate recipients holding tenure and tenure-track 

appointments at academic institutions, by years since degree and field: 1993–2008 ...........3-37

Table 3-21. Salary of recent SEH doctorate recipients up to 5 years after receiving degree, 

by field and percentile: 2008 .................................................................................................3-37

Table 3-22. Median annual salary of recent SEH doctorate recipients up to 5 years after 

receiving degree, by field and employment sector: 2008 .....................................................3-38

Table 3-23. Median salary of U.S. SEH doctorate holders in postdoc positions: 2008 .............3-39

Table 3-24. Age distribution of workers in S&E occupations, by sex and 

race/ethnicity: 2008 ...............................................................................................................3-41

Table 3-25. Racial/ethnic distribution of individuals in S&E occupations, S&E degree 

holders, college graduates, and U.S. residents: 2008 ............................................................3-43

Table 3-26. Distribution of workers in S&E occupations, by race/ethnicity and year: 

1993–2008 .............................................................................................................................3-44

Table 3-27. Field of highest degree among workers with highest degree in S&E, 

by race/ethnicity: 2008 .........................................................................................................3-45

Table 3-28. Foreign-born workers in S&E occupations, by education level: Selected years, 

2000–09 ................................................................................................................................3-49

List of Sidebars

Technical Expertise on the Job ....................................................................................................3-9

Projected Growth of Employment in S&E Occupations ...........................................................3-12
Science and Engineering Indicators 2012 3-3

List of Figures

Figure 3-1. Science and technology employment: 1950–2009 ..................................................3-11

Figure 3-2. Average annual growth rates of S&E occupations and total workforce: 

1960–2009 .............................................................................................................................3-11

Figure 3-3. U.S. workforce in S&E occupations: 1983–2010 ...................................................3-11

Figure 3-4. Annual average growth rate of degree production and occupational 

employment, by S&E field: 1980–2000................................................................................3-14

Figure 3-5. Educational attainment, by type of occupation: 2009 .............................................3-14

Figure 3-6. S&E degree background of workers in S&E occupations: 2008 ............................3-15

Figure 3-7. S&E degree holders working in S&E occupations, by degree field: 2008 .............3-16

Figure 3-8. S&E degree holders employed in jobs related to highest degree, by years 

since highest degree: 2008 ....................................................................................................3-17

Figure 3-9. S&E bachelor’s highest degree holders employed in jobs closely related 

to degree, by degree field and years since degree: 2008 .......................................................3-17

Figure 3-10. Intersection of individuals with highest degree in S&E and S&E 

occupation: 2008 ...................................................................................................................3-18

Figure 3-11. Measures of the S&E workforce: 2008 .................................................................3-18

Figure 3-12. Employed scientists and engineers, by employment sector: 1993–2008 ..............3-19

Figure 3-13. S&E highest degree holders and S&E workers employed in business/ 

industry sector, by employer size: 2008 ................................................................................3-20

Figure 3-14. Industries that employ workers in S&E occupations: May 2010 ..........................3-20

Figure 3-15. Self-employment rates of workers with highest degrees in S&E, by degree 

level and age: 2008................................................................................................................3-25

Figure 3-16. R&D activity rate of employed S&E degree holders, by field and level 

of highest degree: 2008 .........................................................................................................3-25

Figure 3-17. SEH doctorate holders with R&D as major work activity, by field and years 

since degree: 2008 .................................................................................................................3-26

Figure 3-18. Domestic R&D employment in selected industries: 2009 ....................................3-27

Figure 3-19. Unemployment rate, by occupation: 1983–2010 ..................................................3-30

Figure 3-20. Estimated unemployment rates over previous 3 months for workers in 

S&E occupations and selected other categories: March 2008–September 2011 ..................3-30

Figure 3-21. Measures of labor underutilization for S&E occupations and all occupations: 

March 2008–September 2011 ...............................................................................................3-31

Figure 3-22. Unemployment rates for individuals with S&E as highest degree, by degree level 

and years since degree: 1999 and 2003 .................................................................................3-32

Figure 3-23. Individuals with highest degree in S&E who are involuntarily working 

out of field, by degree level and years since highest degree: 1999 and 2003 .......................3-32

Figure 3-24. Median salaries for bachelor’s degree holders, by broad field and years 

since degree: 2003 .................................................................................................................3-33

Figure 3-25. Salary distribution of S&E degree holders employed full time, by degree 

level: 2003 .............................................................................................................................3-34

Figure 3-26. Median salaries of individuals with highest degree in S&E, by degree 

level and years since degree: 2003 ........................................................................................3-34

Table 3-29. Average annual salary of new H-1B visa recipients, by occupation and 

education level: FY 2009 ......................................................................................................3-52

Table 3-30. Temporary U.S. residents who received S&E doctorates in 2002, by program 

rating and year: 2003–07 ......................................................................................................3-54

Table 3-31. Employed S&E doctorate holders who left full-time employment after 

April 2006, by employment sector and age: October 2008 ..................................................3-56

Table 3-32. Domestic and foreign business-sector employment, by company 

characteristics: 2009 ..............................................................................................................3-59

Table 3-33. Scientists and engineers reporting international engagement, by demographic 

characteristics, education, employment sector, occupation, and salary: 2006 ......................3-61

Table 3-A. Bureau of Labor Statistics projections of employment and job openings 

in S&E and other selected occupations: 2008–18 ................................................................3-13
3-4 Chapter 3. Science and Engineering Labor Force

Figure 3-27. U.S.-educated SEH doctorate holders in postdoctorate positions, 

by doctorate field: 2008 .......................................................................................................3-38

Figure 3-28. Women in S&E occupations: 1993–2008 .............................................................3-40

Figure 3-29. Highest degree holders in S&E not in the labor force, by sex and age: 2008 .......3-42

Figure 3-30. Employed women with highest degree in S&E, by degree level: 1993–2008 ......3-42

Figure 3-31. Level of S&E degree among workers with highest degree in S&E field, 

by race/ethnicity: 2008 ..........................................................................................................3-45

Figure 3-32. Estimated differences in full-time salary between women and men with 

highest degree in S&E, controlling for selected employment and other characteristics, 

by degree level: 2008 ............................................................................................................3-46

Figure 3-33. Estimated differences in full-time salary between underrepresented minorities 

and whites with highest degree in S&E, controlling for selected employment and other 

characteristics, by degree level: 2008 ...................................................................................3-47

Figure 3-34. Estimated differences in full-time salary between men and women with 

highest degree in S&E, controlling for selected employment and other characteristics, 

by marital and parental status and degree level: 2008 ..........................................................3-48

Figure 3-35. Foreign-born individuals with highest degree in S&E living in the United 

States, by place of birth: 2003 ...............................................................................................3-49

Figure 3-36. Temporary work visas issued in categories with many high-skilled 

workers: FY 1989–2009 ........................................................................................................3-50

Figure 3-37. Citizenship of new recipients of U.S. H-1B temporary work visas: FY 2009 ......3-51

Figure 3-38. Plans of U.S. S&E doctorate recipients with temporary visas at graduation 

to stay in United States, by year of doctorate: 1989–2009 ...................................................3-52

Figure 3-39. Plans of U.S. S&E doctorate recipients with temporary visas at graduation 

to stay in the United States, by place of origin and year of doctorate: 1998–2001 

and 2006–09 ..........................................................................................................................3-53

Figure 3-40. Stay rates for U.S. S&E doctorate recipients with temporary visas at 

graduation, by selected year of doctorate: 1995–2009..........................................................3-53

Figure 3-41. Workers older than age 50 in S&E occupations, by highest degree level 

and year: 1993–2008 .............................................................................................................3-54

Figure 3-42. Age distribution of employed individuals with highest degree in S&E, 

by degree level and broad occupational area: 2008 ..............................................................3-54

Figure 3-43. Age distribution among employed individuals with highest degree in 

S&E, by degree field: 2008 ...................................................................................................3-55

Figure 3-44. Older individuals with highest degree in S&E who work full time, 

by age and degree level: 2008 ..............................................................................................3-55

Figure 3-45. Estimated number of researchers in selected countries/regions: 1995–2009 ........3-57

Figure 3-46. Researchers as a share of total employment in selected countries/regions: 

1995–2009 .............................................................................................................................3-58

Figure 3-47. Top countries of origin of foreign-born persons having at least a tertiary 

education and residing in an OECD country: 2000 ..............................................................3-58

Figure 3-48. R&D employment of U.S. multinational corporations at their foreign 

affiliates, and foreign MNCs at their U.S. affiliates: 1994, 1999, 2004, and 2009...............3-59

Figure 3-49. R&D employment of U.S. multinational corporations’ parent companies 

in the United States and their foreign affiliates: 1994, 1999, 2004, and 2009 ......................3-60

Figure 3-A. Bureau of Labor Statistics projected increases in employment for S&E 

and selected other occupations: 2008–18 ..............................................................................3-12

Figure 3-B. Bureau of Labor Statistics projected job openings in S&E and selected 

other occupations: 2008–18 ..................................................................................................3-13
Scope of the S&E Workforce

The S&E workforce has shown sustained growth for 

more than half a century.

The number of workers in S&E occupations grew from 

about 182,000 in 1950 to 5.4 million in 2009. This represents an average annual growth rate of 5.9%, much greater 

than the 1.2% growth rate for the total workforce older than 

age 18 during this period.

Workforce growth in S&E occupations from 2000 to 2009 

was slower than in the two preceding decades. Nonetheless, 

at 1.4% annually, it exceeded the rate (0.2%) for the general workforce, which barely grew at all.

Many workers outside S&E occupations have S&E 

training or use related knowledge and skills in their jobs.

Individuals with an S&E bachelor’s degree or higher (17.2 

million in 2008) or whose highest degree was in S&E (12.6 

million in 2008) substantially outnumbered those working 

in S&E occupations.

In 2008, about two-thirds of those with an S&E highest degree but not working in an S&E occupation reported that their 

job was either closely or somewhat related to their degree.

S&E Workers in the Economy

Scientists and engineers work for all types of employers.

For-profit firms employed 59% of all individuals whose 

highest degree was in S&E but only 35% of those holding 

S&E doctorates.

Academic institutions employed about 41% of individuals 

with S&E doctorates, including those in postdoc or other 

temporary positions.

About 19% of workers whose highest degree was in S&E 

reported they were self-employed in 2008, with two-thirds 

in incorporated businesses.

Small firms are important employers of those with S&E 

highest degrees. Firms with fewer than 100 persons employ 36% of them.

S&E Labor Market Conditions

Workers with S&E degrees or occupations tend to earn 

more than other comparable workers.

Half of the workers in S&E occupations earned $73,290 

or more in 2010, more than double the median earnings 

($33,840) of the total U.S. workforce.

Workers with S&E degrees, regardless of their occupations, earn more than workers with comparable-level degrees in other fields.

Industries with above-average proportions of S&E jobs 

tend to pay higher average salaries to both their S&E and 

non-S&E workers.

People 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-degreed individuals and 

much lower than those of persons with less than a bachelor’s degree.

Unemployment rates for S&E doctorate holders are generally much lower than for those at other degree levels.

Demographics of the S&E Workforce

Women remain underrepresented in the S&E workforce, 

although to a lesser degree than in the past.

Women constituted 38% of employed individuals with a 

highest degree in an S&E field in 2008, but their proportion is smaller in most S&E occupations. 

From 1993 through 2008, growth occurred in both the share 

of workers with a highest degree in an S&E field who are 

women (increasing from 31% to 38%) and the share of 

women in S&E occupations (increasing from 21% to 26%).

Female scientists and engineers are concentrated in different occupations than are men, with relatively high shares of 

women in the social sciences (53%) and biological and medical sciences (51%) and relatively low shares in engineering 

(13%) and computer and mathematical sciences (26%).

Race and ethnicity are salient factors in rates of participation in the S&E workforce.

Hispanics, blacks, and American Indians/Alaska Natives 

make up a smaller share of the S&E workforce, with 9% of 

workers in S&E occupations and 11% of S&E degree holders in 2008, than their proportion in the general population, 

with 26% of U.S. residents from ages 20 to 70.

Asians work in S&E occupations at higher rates (17%) than 

their representation in the U.S. working-age population 

(5%). Asians are particularly highly concentrated in computer and information science occupations (22% Asian). 

Within every S&E occupation, more than half of all workers are non-Hispanic whites.

A variety of indicators point to a decline during the recent economic downturn in the immigration of foreign 

scientists and engineers.

After an upward trend in the number of temporary work 

visas issued to scientists and engineers for most of the decade, the number fell sharply in 2009. H-1B visas fell to 

2003 levels, dropping to 72% of the number issued in 2007.

Highlights

Science and Engineering Indicators 2012 3-5
Both the number and percentage of S&E doctoral degree 

recipients with temporary visas reporting plans to stay in 

the United States peaked in 2007 and declined in 2009 after rising since 2002. 

The proportion of S&E doctoral degree recipients with 

temporary visas who remained in the United States 5 years 

after receiving their degrees rose from 45% to 67% between 1989 and 2005 but fell to 62% in 2009. 

The baby boom portion of the S&E workforce continues 

to age, nearing retirement. 

From 1993 to 2008, the median age of scientists and engineers in the U.S. workforce rose from 37 to 41. The proportion over age 50 increased from 18% to 27%.

Between 1993 and 2008, increasing percentages of scientists and engineers in their 60s reported that they were still 

in the labor force. Whereas 59% of S&E degree holders 

between the ages of 60 and 64 were employed in 1993, the 

comparable percentage rose to 66% in 2006 before declining slightly in 2008.

Global S&E Labor Force

Worldwide, the number of workers engaged in research 

has been growing since at least 1995.

Among countries with large numbers of researchers, 

growth has been most rapid in China, where the number 

of researchers tripled, and South Korea, where it doubled.

The United States and the European Union experienced 

steady growth but at a lower rate than in China or South 

Korea; both increased from about 1 million in 1995 to 

nearly 1.5 million in 2007.

Japan and Russia were exceptions to the worldwide trend: 

in Japan, the number of researchers remained essentially 

unchanged, and in Russia the number declined.

Among businesses located in the United States, R&D employment is disproportionately domestic.

Although about one-third of total employment in these 

firms is located abroad, only one-quarter of R&D employment is in foreign locations.

In manufacturing, the disparity between overall employment in foreign locations (41%) and R&D employment in 

these locations (25%) is substantial; for nonmanufacturing 

employment, the comparable proportions—24% for overall 

employment and 23% for R&D employment—are similar.

Preliminary 2009 data indicate a substantial shift in the balance between R&D employment by U.S. firms abroad and 

R&D employment by foreign firms in the United States.

Whereas R&D employment abroad by U.S. multinational companies (MNCs) nearly doubled between 2004 and 

2009, domestic R&D employment by these firms increased 

by less than 5% in the same period.

U.S. MNCs employed many more R&D workers in foreign locations in 2009 than foreign firms employed in the 

United States. In contrast, these two numbers had been 

similar in 2004.

3-6 Chapter 3. Science and Engineering Labor Force
Science and Engineering Indicators 2012 3-7

Introduction

Chapter Overview

Policymakers and researchers have increasingly emphasized the importance of skilled people—what social scientists refer to as human capital—to both innovation and 

economic growth. As technical content spreads throughout 

our knowledge-based economy, the knowledge and skills associated with science and engineering (S&E) are increasingly necessary for workers with formal training in S&E who 

work in non-S&E jobs as well as for those in occupations 

traditionally classified as part of the S&E labor force.

Chapter Organization

The chapter is divided into five sections. The first section defines the S&E labor force and reports on its size and 

growth. It analyzes the interplay among occupational roles, 

educational credentials, and use of S&E expertise on the 

job. This section also includes a chart describing the main 

sources of data on the U.S. S&E labor force.

Section two explores the distribution of S&E workers 

in the economy. It describes employment patterns by sector and industry, with some special emphasis on the role 

private-sector firms play as employers of scientists and engineers. This section also reports data on federal workers in 

S&E occupations, thereby showing the roles of scientists 

and engineers in both scientific and other federal agencies.

Section three looks at recent and long-term trends in the 

economic rewards of participating in the S&E labor force. 

It includes data on recent labor market conditions, earnings, 

unemployment, and workers unable to find jobs in their field. 

Where possible, it contrasts S&E and non-S&E degree holders at comparable degree and experience levels. The section 

also includes broader measures of labor underutilization that 

go beyond long- and short-term unemployment rates. 

Labor force demographics are covered in section four, 

including the growing role of women, minorities, and immigrants in the S&E labor force. This section also examines 

the distribution of S&E workers across occupations, sectors, 

and industries by degree levels and fields. Data on the aging 

of the S&E labor force and on its retirement patterns also 

appear in this section. 

In addition, section four features a detailed analysis of salary differences among different demographic groups. This 

analysis explores the role of factors that are relevant to a 

worker’s productivity (e.g., years of experience) and factors 

that are not directly related to job skill (e.g., demographic or 

personal background characteristics, such as race/ethnicity 

and sex). Trends in salary differences are also considered. 

The final section of the chapter deals with the global S&E 

labor force. Although there are indications that the global 

S&E labor force has grown, there is little solid worldwide 

data on this broader labor force or its characteristics. Several 

U.S. and international data sources are used in this section to present indicators of worldwide R&D employment, 

international employment by multinational companies, and 

international engagement by U.S. S&E workers. 

Scope of the S&E Workforce

Measures of the S&E Workforce

The terms scientist and engineer can include very different 

sets of workers. This section presents three types of measures 

that can be used to estimate the size and describe the characteristics of the U.S. S&E labor force.1

 Different categories 

of measures are better adapted for addressing some questions 

than others, and not all general population and workforce surveys include questions in each category (table 3-1).

Occupation

U.S. federal occupation data classify workers by the activities or tasks they primarily perform in their jobs. The 

Occupational Employment Statistics (OES) survey administered by the Bureau of Labor Statistics (BLS) relies on 

employers to classify their workers using standard occupational definitions. National Science Foundation (NSF) and 

Census Bureau occupational data in this chapter come from 

surveys in which individuals (NSF) or members of their 

household (Census Bureau) supplied information about job 

titles and work activities. With this information, jobs can 

be coded into standard occupational categories. Differences 

between employer- and employee-provided information can 

affect the content of occupational data.

NSF has developed a widely used set of occupational 

categories that it calls S&E occupations. These occupations 

are generally associated with a bachelor’s degree level of 

knowledge and education in S&E fields. A second category of occupations, S&E-related occupations, also requires 

some S&E knowledge or training, but not necessarily as a 

required credential for being hired or at the bachelor’s degree level. Examples of such occupations are S&E technicians or managers of the S&E enterprise who may supervise 

people working in S&E occupations. Other occupations, although classified as non-S&E, may include individuals who 

use their 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.

Other general terms, including science, technology, engineering, or 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.

In this chapter, the narrow classification of S&E occupations is sometimes expanded to include S&E technicians, 

computer programmers, and S&E managers. This broader 

grouping is referred to here as STEM occupations.
3-8 Chapter 3. Science and Engineering Labor Force

Education

The pool of S&E workers can also be identified by educational credentials. Individuals who possess an S&E degree, 

whose highest degree is in S&E, or whose most recent degree 

is in S&E may be qualified to hold jobs that require S&E 

knowledge and skills and may seek such jobs if they do not 

currently hold them. However, a focus on people with relevant educational credentials also includes individuals who 

hold jobs that are not generally identified with S&E and who 

are not likely to seek S&E jobs in the future. Furthermore, 

workers with degrees in S&E may not have kept up to date 

with the fields in which they were trained, may lack interest 

in working in jobs that require skills associated with S&E 

education, or may have advanced in their careers to a point 

where other skills have become more important.

S&E Technical Expertise

The S&E workforce may also be defined by the expertise 

required to perform a job or the extent to which job requirements are related to formal training in S&E. Many people, 

including some outside S&E occupations or without S&E 

degrees, report that their jobs require at least a bachelor’s 

degree level of technical expertise in engineering, computer 

sciences, mathematics, the natural sciences, or social sciences, which we refer to in this report as S&E technical 

expertise. Unlike defining the S&E workforce by occupational groupings or educational credentials, defining it by 

the use of technical knowledge, skills, or expertise involves 

assessing the content and characteristics of individual jobs. 

However, it also involves asking survey respondents to 

make a complex judgment about their jobs and apply a criterion that they are likely to interpret differently.2

 A recent 

survey provides clues to how college-educated Americans 

understand job-related technical expertise. (See sidebar, 

“Technical Expertise on the Job.”)

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 2010 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 

(SESTAT)—comprises

Survey of Doctorate 

Recipients, 

National Survey of 

College Graduates,

National Survey 

of Recent College 

Graduates

National Science 

Foundation, National 

Center for Science and 

Engineering Statistics

Through 2008 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 2009 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 2010 Employment status

Occupation

Educational 

attainment

Demographic 

characteristics

Households U.S. population

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 3-9

The Joint Program on Survey Methodology (JPSM) provides advanced education for survey research professionals 

through a collaboration among the University of Maryland, 

the University of Michigan, and Westat, a survey research 

firm. As part of their training, JPSM students take a course 

in which they design and analyze a survey on a topic of interest to a federal statistical agency.

In 2009, JPSM’s survey probed the meaning of SESTAT 

data indicating that many college-educated Americans who 

are not employed in S&E occupations say their jobs require 

the technical expertise of an S&E bachelor’s degree. The 

survey asked a nationally representative group of college 

graduates who are members of the Knowledge Networks 

Internet survey panel about the knowledge and skills they 

use on the job and the education and experience through 

which they acquired them. The survey also collected various additional information about the survey respondents—

the colleges they attended; their major fields of study; and 

the characteristics of their current and previous jobs, including respondents’ occupations, salaries, job satisfaction, and 

employer characteristics.

Preliminary analyses suggest that asking about either 

“knowledge and skills” or “technical expertise” produces 

roughly equivalent response patterns; if anything, a higher 

percentage of respondents claim that “knowledge and skills” 

associated with a degree are required on the job than make 

the equivalent claim about “technical expertise.” In addition, 

the data suggest that graduates in different major fields vary 

in how often they claim that their jobs require bachelor’s 

level competency in a field. Along with education majors, 

people who major in natural sciences and engineering appear to more frequently view their jobs as requiring bachelor’s degree level competency in some field of study. Those 

who major in health-related fields and social sciences rank 

somewhat below them. College graduates with degrees in 

arts, humanities, business administration, communications, 

and other fields outside the sciences less often report that 

their jobs need this kind of competency. However, these 

data offer numerous opportunities for further analysis of the 

relationships among knowledge, skills, and job activities, 

and such analyses might cast these preliminary findings in 

a different light. 

Technical Expertise on the Job

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: 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/docs/occ03maj.html and http://sestat.nsf.gov/docs/ed03maj.html.

Science and Engineering Indicators 2012
3-10 Chapter 3. Science and Engineering Labor Force

Growth of the S&E Workforce

However defined, the S&E workforce has for decades 

grown faster than the total workforce. Defined by occupation, growth in the S&E workforce can be examined over 

nearly seven decades using Census Bureau data. The number of workers in S&E occupations grew from about 182,000 

in 1950 to 5.4 million in 2009. This represents an average 

annual growth rate of 5.9%, much greater than the 1.2% 

growth rate for the total workforce older than age 18 during 

this period. The somewhat broader category of S&T occupations grew from 205,000 to 6.6 million (a 6.1% growth rate) 

(figure 3-1). 

In each decade, the growth rate of S&E occupations exceeded that of the total workforce (figure 3-2). During the 

1960s, 1980s, and 1990s, the difference in growth rates was 

very large (about 3 times the rate for the total labor force). It 

was smallest during the slower growth period of the 1970s. 

Between 2000 and 2007, the ratio of the S&E growth rate 

to the overall workforce was 1.6, which was comparable to 

the 1970s. The economic downturn at the end of this decade resulted in almost no overall workforce growth for the 

decade as a whole, well below the 1.4% growth rate for the 

S&E workforce for the same period. While both the total 

and S&E employment experienced smaller growth rates in 

the 2000s compared to the 1990s, the trend of higher growth 

rates in S&E occupations relative to other jobs continues, 

even through the recent economic downturn. S&E occupational employment has grown from 2.6% of the workforce 

in 1983 to 4.8% of all employment in 2010 (figure 3-3).

Size of the S&E Workforce

In the most recent estimates, the U.S. S&E workforce 

(defined by occupation) totaled between 4.8 million and 

6.4. million people (table 3-3). Those in S&E occupations 

who also had bachelor’s degrees were estimated at between 

4.8 million (Census Bureau 2009) and 4.9 million (NSF, 

National Center for Science and Engineering Statistics 

[NCSES], Scientists and Engineers Statistical Data System 

[SESTAT]).3

 SESTAT’s 2008 estimates for individuals with 

an S&E degree at the bachelor’s level or higher (17.2 million) or whose highest degree was in S&E (12.6 million) 

were substantially higher than the number of current workers 

in S&E occupations. Many of those whose highest degree is 

in S&E reported that their job, although not in an occupation 

classified as S&E, was closely (2.2 million) or somewhat 

(2.1 million) related to their highest degree. Counting these 

people, along with those in S&E occupations, as part of the 

S&E workforce increases the SESTAT S&E workforce estimate from 4.9 million to 9.1 million, an 84% increase. 

The 2003 SESTAT surveys provide a recent estimate for 

a different assessment of S&E work—whether workers believe their jobs require technical expertise at the bachelor’s 

degree level or higher in S&E fields. According to these surveys, 12.9 million bachelor’s degree holders reported that 

their jobs required at least this level of expertise in one or 

more S&E fields. This contrasts with 2003 SESTAT estimates of 4.8 million workers in S&E occupations and 11.9 

million whose highest degree was in an S&E field.

Table 3-3

Measures and size of employed S&E workforce: 2003, 2008, and 2009

Measure Education coverage Data source Workers 

Occupation

Employment in S&E occupations ................................... All degree levels 2009 BLS OES 5,786,000

Employment in S&E occupations ................................... Bachelor’s and above 2008 NSF/NCSES SESTAT 4,874,000

Employment in S&E occupations ................................... All degree levels 2009 Census Bureau ACS 6,416,000

Employment in S&E occupations ................................... Bachelor’s and above 2009 Census Bureau ACS 4,750,000

Education

At least one degree in S&E field ..................................... Bachelor’s and above 2008 NSF/NCSES SESTAT 17,214,000

Highest degree in S&E field ............................................ Bachelor’s and above 2008 NSF/NCSES SESTAT 12,588,000

Job closely related to highest degree ......................... Bachelor’s and above 2008 NSF/NCSES SESTAT 4,802,000

S&E occupation ....................................................... Bachelor’s and above 2008 NSF/NCSES SESTAT 2,635,000

Other occupation ..................................................... Bachelor’s and above 2008 NSF/NCSES SESTAT 2,168,000

Job somewhat related to highest degree ................... Bachelor’s and above 2008 NSF/NCSES SESTAT 3,101,000

S&E occupation ....................................................... Bachelor’s and above 2008 NSF/NCSES SESTAT 996,000

Other occupation ..................................................... Bachelor’s and above 2008 NSF/NCSES SESTAT 2,105,000

Job requires S&E technical expertise at bachelor’s level

In one or more S&E fields ............................................... Bachelor’s and above 2003 NSF/NCSES SESTAT and NSCG 12,855,000

Engineering, computer science, mathematics, 

 or natural sciences ................................................... Bachelor’s and above 2003 NSF/NCSES SESTAT and NSCG 9,215,000

Social sciences ........................................................... Bachelor’s and above 2003 NSF/NCSES SESTAT and NSCG 5,335,000

ACS = American Community Survey; BLS = Bureau of Labor Statistics; OES = Occupational Employment Statistics Survey; NSF/NCSES = National 

Science Foundation, National Center for Science and Engineering Statistics; SESTAT = Scientists and Engineers Statistical Data System; NSCG = 

National Survey of College Graduates

SOURCES: BLS, 2009 OES; Census Bureau, 2009 ACS; NSF/NCSES, 2008 SESTAT integrated file and special analytic file comprising 2003 SESTAT 

integrated file and 2003 NSCG. 

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 3-11

Recent OES employment estimates for workers in S&E 

occupations indicate that the S&E workforce has remained 

steady while the total workforce has declined. The OES estimate was 5.5 million in May 2010, compared to 5.6 million 

in May 2007. The total workforce declined from 134 million to 127 million in this time frame. The broader STEM 

aggregate (including technicians, S&E managers, etc.) also 

remained relatively steady at 7.4 million in May 2010, compared to 7.6 million in May 2007. OES projections for 2008 

to 2018 are that S&E occupations will grow at a faster rate 

than the total workforce. (See sidebar, “Projected Growth of 

Employment in S&E Occupations.”)

Between 1980 and 2000, although the number of S&E 

degree holders in the workforce grew more than the number 

of people working in S&E occupations, degree production in 

all broad categories of S&E fields rose at a slower rate than 

employment in S&E jobs (figure 3-4). (See chapter 2 for a 

fuller discussion of S&E degrees.) During this period, S&E 

employment grew from 2.1 million to 4.8 million (4.2% average annual growth), while annual S&E degree production 

increased from 526,000 to 676,000 (1.5% average annual 

growth). Except for mathematics, computer sciences, and 

the social sciences, the growth rate for advanced degrees 

was higher than for bachelor’s degrees.

This growth in the S&E labor force was possible largely 

because of three factors: (1) increases in U.S. S&E degrees 

earned by both native and foreign-born students who entered 

the labor force, (2) temporary and permanent migration to 

the United States of those with foreign S&E educations, and 

(3) the relatively small proportion of scientists and engineers 

retiring from the S&E labor force. Many have expressed 

concerns about the effects of changes in any or all of these 

factors on the future of the U.S. S&E labor force (see NRC 

2010 and NSB 2003).

Figure 3-1

Science and technology employment: 1950–2009

Employees (millions)

S&T = science and technology

NOTE: Data include bachelor’s degrees or higher in science 

occupations, some college and above in engineering occupations, 

and any education level for technicians and computer programmers. 

No estimates were calculated below level of S&E and S&T from 2009 

American Community Survey.

SOURCES: Adapted from Lowell BL, Regets MC, A Half-Century Snapshot of the STEM Workforce, 1950 to 2000, Commission on professionals in Science and Technology (2006); with additional estimates from the 

Census Bureau, American Community Survey (2009).

Science and Engineering Indicators 2012

1950 1960 1970 1980 1990 2000 2009

0

1

2

3

4

5

6

7

All S&T employees

S&E

Life scientists

Physical scientists

Engineers

Mathematicians/computer scientists

Social scientists

Technicians

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) from 1960– 

2000 Decennial Census, Public-Use Microdata Sample (PUMS), and 

American Community Survey (2009). 

Science and Engineering Indicators 2012

Figure 3-2

Average annual growth rates of S&E occupations 

and total workforce: 1960–2009

Percent

1960–70 1970–80 1980–90 1990–2000 2000–09

0

1

2

3

4

5

6

7

8

9

S&E total All workers 18 years old

Growth rate

ratios

S&E employees (millions) (bars) Workforce (percent) (line)

Figure 3-3

U.S. workforce in S&E occupations: 1983–2010

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) from Bureau of 

Labor Statistics, Current Population Survey Monthly Outgoing 

Rotation files (1983–2010). 

Science and Engineering Indicators 2012

0

1

2

3

4

5

1983 1986 1989 1992 1995 1998 2001 2004 2007 2010

0

1

2

3

4

5

6

7

8
3-12 Chapter 3. Science and Engineering Labor Force

Projections of employment growth are plagued by 

uncertain assumptions and notoriously difficult to make. 

Many corporate and government spending decisions on 

R&D are impossible to anticipate. In addition, R&D 

funds increasingly cross borders in search of the best 

place to have particular research performed. Finally, it 

may be difficult to anticipate new products and industries 

that may be created via the innovation processes that are 

most closely associated with scientists and engineers.

The worldwide economic crisis and the dynamics of 

recovery from it compound the already difficult problem 

of making employment projections, because recent economic upheavals may produce long-term changes in employment patterns and trends. The reader is cautioned that 

the assumptions underlying projections such as those that 

follow, which rely on past empirical relationships, may 

no longer be valid.

The most recent Bureau of Labor Statistics (BLS) 

occupational projections, for the period 2008–18, suggest 

that total employment in occupations that NSF classifies as S&E will increase at more than double the overall 

growth rate for all occupations (figure 3-A). S&E occupations are projected to grow by 20.6% between 2008 and 

2018, while employment in all occupations is projected 

to grow 10.1% over the same period (table 3-A, appendix 

table 3-1).*

 These projections involve only the demand 

for strictly defined S&E occupations and do not include 

the wider range of jobs in which S&E degree holders 

often use their training.

Approximately 58% of BLS’s projected increase in 

S&E jobs is in computer and mathematical scientist occupations (table 3-A). Although life scientists account 

for a smaller number of job openings, they have a higher 

projected growth rate (26.7%) than computer and mathematical scientists (25.6%). The growth rates projected 

for physical scientists and social scientists are also above 

those for all occupations. Engineering occupations, with 

projected growth of 11.3%, are expected to grow at only 

slightly more than the rate for all jobs.

Table 3-A also shows occupations that either contain significant numbers of S&E-trained people or represent other career paths that are often chosen by S&E 

bachelor’s degree holders who pursue graduate training. 

Among these, the occupation healthcare practitioners and 

technicians is projected to grow faster than all S&E occupations, from 7.5 million to 9.1 million workers over the 

decade between 2008 and 2018—an increase of 21.4%. 

Postsecondary teacher, which includes all fields of instruction, is projected to grow 15.1%. In contrast, BLS 

projects computer programmers to decrease by 2.9%.

BLS also projects that job openings in NSF-identified 

S&E occupations over the 2008–18 period will represent a greater proportion of current employment than 

openings in all other occupations—41.7% versus 33.7% 

(figure 3-B). Job openings include both growth in total 

employment and openings caused by attrition.

*Although BLS labor force projections do a reasonable job of forecasting employment in many occupations (see Alpert and Auyer 2003), 

the mean absolute percentage error in the 1988 forecast of employment 

in detailed occupations in 2000 was 23.2%.

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: 2008–18

Percent

SOURCE: Bureau of Labor Statistics, Office of Occupational Statistics and Employment Projections, National Industry-Occupation Employment 

Projections 2008–18. See appendix table 3-1.

Science and Engineering Indicators 2012

All S&E Mathematical/

computer 

scientists

Physical

scientists

Social

scientists

Life

scientists

Engineers All

occupations

Postsecondary

teachers

Healthcare

practitioners

and technicians

Lawyers Computer

programmers

–5

0

5

10

15

20

25

30
Science and Engineering Indicators 2012 3-13

Table 3-A

Bureau of Labor Statistics projections of employment and job openings in S&E and other selected occupations: 

2008–18 

(Thousands)

Occupation 

BLS National 

Employment 

Matrix 

2008 estimate

BLS 

projected 

2018 

employment

Job openings 

from growth and 

net replacements, 

2008–18

10-year 

growth in total 

employment (%)

10-year job 

openings % 

of 2008 

employment

All occupations ................................................... 150,932 166,206 50,929 10.1 33.7

All S&E ............................................................ 5,571 6,717 2,321 20.6 41.7

Computer/mathematical scientists ............. 3,101 3,895 1,353 25.6 43.6

Life scientists .............................................. 279 354 144 26.7 51.4

Physical scientists ....................................... 276 317 123 15.1 44.6

Social scientists/related occupations ......... 343 400 170 16.5 49.4

Engineers .................................................... 1,572 1,750 531 11.3 33.8

S&E-related occupations

S&E managers ............................................. 522 589 166 13.0 31.8

S&E technicians .......................................... 855 925 298 8.2 34.9

Computer programmers .............................. 427 414 80 -2.9 18.8

Healthcare practitioners and technicians .... 7,491 9,091 3,139 21.4 41.9

Selected other occupations

Postsecondary teachers ............................. 1,699 1,956 553 15.1 32.5

Lawyers ....................................................... 759 858 240 13.0 31.7

BLS = Bureau of Labor Statistics

NOTES: Estimates of current and projected employment for 2008–18 from BLS’s National Employment Matrix. Data in matrix from Occupational 

Employment Statistics (OES) survey and 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 table based on sum of BLS projections in occupations that National Science Foundation considers as S&E.

SOURCE: BLS, Office of Occupational Statistics and Employment Projections, special tabulations (2011) of 2008–18 National Industry-Occupation 

Employment Projections.

Science and Engineering Indicators 2012

Figure 3-B

Bureau of Labor Statistics projected job openings in S&E and selected other occupations: 2008–18

Percentage of 2008 employment

SOURCE: Bureau of Labor Statistics, Office of Occupational Statistics and Employment Projections, National Industry-Occupation Employment 

Projections 2008–18. See appendix table 3-1.

Science and Engineering Indicators 2012

All S&E

occupations

Mathematical/

computer

scientists

Physical

scientists

Social

scientists

Life

scientists

Engineers All

occupations

Postsecondary

teachers

Healthcare

practitioners

and technicians

Lawyers Computer

programmers

0

10

20

30

40

50

60

Projected Growth of Employment in S&E Occupations—continued
3-14 Chapter 3. Science and Engineering Labor Force

GED = General Equivalency Diploma

SOURCE: Census Bureau, American Community Survey (2009). 

Science and Engineering Indicators 2012

Percent

Figure 3-5

Educational attainment, by type of occupation: 2009

S&E occupations

except postsecondary

teachers

All other

occupations

0

20

40

60

80

100

High school

High school 

graduate/GED

Some college, 

no degree

Associate’s–academic

Bachelor’s

Master’s

Professional

Doctorate

Figure 3-4

Annual average growth rate of degree production and occupational employment, by S&E field: 1980–2000

Percent

SOURCES: University of Michigan, Integrated Public Use Microdata Series, 1980–2000 Decennial Census files, http://usa.ipums.org/usa; and National 

Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2011) of WebCASPAR database, https://webcaspar. 

nsf.gov. 

Science and Engineering Indicators 2012

All S&E Engineering Life sciences Mathematics/

computer sciences

Physical sciences Social sciences

–2

0

2

4

6

8

10

Bachelor’s Master’s Doctorate All degrees Employment in occupation

Educational Distribution of Those in 

S&E Occupations

Workers in S&E occupations have undergone more 

formal education than the general workforce (figure 3-5). 

Nonetheless, these occupations include workers with a range 

of educational qualifications. For all workers in S&E occupations except postsecondary teachers,4

 data from the 2009 

U.S. Census Bureau’s American Community Survey (ACS) 

indicate that slightly more than one-quarter had not earned 

a bachelor’s degree. For an additional 44%, a bachelor’s 

was their highest degree. The proportion of workers with 

advanced degrees was about equal to that of those without a 

bachelor’s degree. Only about 6% of all S&E workers (except postsecondary teachers) had doctorates.

Technical issues related to occupational classification 

may inflate the estimated size of the nonbaccalaureate S&E 

workforce. Even so, these data indicate that many individuals enter the S&E workforce with marketable technical skills 

from technical or vocational schools (with or without earned 

associate’s degrees) 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 “Who Performs 

R&D?” and the discussion in chapter 2.)

Among individuals with at least a bachelor’s degree who 

work in S&E occupations, a large proportion (88%) have 

at least one S&E degree, and 75% have S&E degrees only 
Science and Engineering Indicators 2012 3-15

(table 3-4). S&E workers who have both S&E and non-S&E 

degrees very likely earned their first bachelor’s degree in 

S&E, even if their highest degree was not in an S&E field. 

Among workers in S&E occupations, the most common degrees are in engineering (38%) and computer sciences and 

mathematics (22%) (figure 3-6).

S&E Degree Holders in Non-S&E Occupations

S&E degree holders work in all manner of jobs. For example, they work in S&E-related jobs such as health occupations (1.4 million workers) or in S&E managerial positions 

(367,000 workers), but they also hold non-S&E jobs such as 

college and precollege teachers in non-S&E areas (655,000 

workers) or work in social services occupations (634,000 

workers) (appendix table 3-2). 

In 2008, 6.3 million workers whose highest degree was 

in an S&E field did not work in an S&E occupation. Some 

1.3 million worked in S&E-related occupations, while 5.1 

million worked in non-S&E jobs. The largest category of 

non-S&E jobs was management and management-related 

occupations, with 1.5 million workers, followed by sales 

and marketing occupations, with 882,000 workers (appendix table 3-2).

Only about 38% of college graduates whose highest degree is in an S&E field work in S&E occupations (figure 

3-7). The proportion is higher for those with more advanced 

degrees. The overall proportion varies substantially by field, 

ranging from engineering (64%) at the top, followed closely 

by computer sciences and mathematics (56%) and physical 

sciences (54%). Although a smaller percentage (31%) of 

biological/agricultural sciences degree holders work in S&E 

occupations, an additional 26% of persons with degrees in 

these fields work in S&E-related occupations (appendix table 3-2). Individuals with social science degrees (14%) are 

least likely to work in S&E occupations. This pattern of field 

differences generally characterizes individuals whose highest degree is either a bachelor’s or a master’s. At the doctoral 

level, the size of these field differences shrinks substantially.

Table 3-4

Educational background of workers in S&E 

occupations: 2008

Educational background Workers Percent

S&E occupations .................................. 4,874,000 100.0

At least one S&E degree ................... 4,275,000 87.7

First bachelor’s degree in 

 S&E field ..................................... 4,022,000 82.5

Highest degree in S&E field........... 3,881,000 79.6

All degrees in S&E fields ............... 3,644,000 74.8

At least one degree in field

Computer and mathematical 

 sciences .................................. 1,056,000 21.7

Biological, agricultural, and 

 other life sciences ................... 591,000 12.1

Physical sciences ...................... 479,000 9.8

Social sciences .......................... 675,000 13.9

Engineering ................................ 1,839,000 37.7

No S&E degrees but at least one 

 S&E-related degree........................ 217,000 4.4

No S&E or S&E-related degrees ....... 382,000 7.8

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) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

Figure 3-6

S&E degree background of workers in S&E occupations: 2008

Percent

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

At least one

S&E degree

Computer/

mathematical 

sciences degree(s)

Biological/agricultural/

environmental life

sciences degree(s)

Physical sciences

degree(s)

Social sciences

degree(s)

Engineering

degree(s)

0

20

40

60

80

100
3-16 Chapter 3. Science and Engineering Labor Force

By field, holders of degrees in computer sciences and 

mathematics and engineering most often work in the broad 

occupation group in which they were trained (49% and 

42%, respectively). S&E doctorate holders more often work 

in an S&E occupation similar to their doctoral field (55%) 

compared with individuals whose highest degree is an S&E 

bachelor’s (23%) (appendix table 3-3). 

Relationships Between Jobs and Degrees

Most individuals with S&E highest degrees who work 

in S&E-related or non-S&E occupations do not see themselves as working entirely outside their field of degree. 

Rather, most indicate that their jobs are either closely (34%) 

or somewhat (33%) related to their degree field (table 3-5). 

Among those in managerial and management-related occupations, for example, 33% characterize their jobs as closely 

related and 42% as somewhat related. More than half (52%) 

of workers in sales and marketing say their S&E degrees are 

closely or somewhat related to their jobs. Among S&E precollege teachers whose highest degree is in S&E, 72% say 

their jobs are closely related to their degrees. 

Workers with more advanced S&E education more often do work that is at least somewhat related to their field 

of degree. Up to 5 years after receiving their degrees, 96% 

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 75% of bachelor’s degree 

holders (figure 3-8). Even when the fit between an individual’s job and degree is assessed using the stricter criterion of 

closely related, the data indicate that many S&E bachelor’s 

degree holders who received their degree up to 5 years earlier are working in jobs that use skills developed during their 

college training (figure 3-9). In the natural sciences and engineering fields (i.e., S&E degree fields excluding the social 

sciences), half or more characterized their jobs as closely 

related to their field of degree: 58% in engineering, 57% in 

physical sciences, 60% in computer/mathematical sciences, 

and 46% in biological, agricultural, and environmental life 

sciences. The comparable figure for social science graduates 

(30%) was substantially lower.

Figure 3-7

S&E degree holders working in S&E occupations, by degree field: 2008

Percent

NOTE: Individuals may have degrees in more than one S&E degree field.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (2008), http://sestat.nsf.gov.

Science and Engineering Indicators 2012

S&E degree holders Biological/agricultural/

environmental life

sciences

Computer/

mathematical sciences

Physical sciences Social sciences Engineering

0

20

40

60

80

100

Highest degree = in field Highest degree = doctorate in field Highest degree = master’s in field Highest degree = bachelor’s 

in field

Table 3-5

Relationship of highest degree to job among S&E 

highest degree holders not in S&E occupations, by 

degree level: 2008

(Percent)

Degree related to job

Highest degree Workers Closely

Somewhat Not

All degree levelsa .... 6,335,000 34.2 33.2 32.6

Bachelor’s ........... 5,108,000 30.8 33.6 35.6

Master’s .............. 1,027,000 49.3 30.6 20.1

Doctorate ............ 193,000 45.1 36.9 18.0

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) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 3-17

The stronger relationship between S&E jobs and S&E 

degrees at higher degree levels holds at all 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. However, for each group, 

the relationship between job and field of degree becomes 

weaker over time. There are many reasons for this decline: 

individuals may change their career interests, gain skills in 

different areas, take on general management responsibilities, forget some of their original college training, or even 

find that some of their original training has become obsolete. 

Against this background, the career-cycle decline in the relevance of an S&E degree appears modest.

The loose relationship among jobs, degrees, and individuals’ perceptions of the expertise they need to do their work 

can be seen in figures 3-10 and 3-11. In figure 3-10, the intersecting area shows individuals whose highest degree is 

in S&E who are also working in S&E occupations. Less 

than one-third of SESTAT respondents fall in this area—the 

rest have one or the other attribute but not both. Figure 3-11 

compares three groups of individuals who hold at least a 

bachelor’s degree: those whose highest degree is in S&E and 

who say their job is at least somewhat related to their degree, 

those who say they need at least a bachelor’s degree level of 

S&E expertise to perform their job, and those in S&E occupations. In 2008, about 13 million Americans had one or 

more of these characteristics.5 Yet these three characteristics 

are not strongly associated with each other:

Only 27% had all three characteristics, and 43% had only one. 

Even among those in S&E occupations, only about 71% 

also had S&E degrees, had jobs at least somewhat related 

to S&E, and believed they needed at least a bachelor’s 

degree level of S&E expertise. 

Among the people who claimed they needed the technical 

expertise associated with an S&E bachelor’s degree for 

their job, more than 40% said either that their job was unrelated to their actual degree or that their highest degree 

was not in S&E.

S&E Workers in the Economy

This section profiles how the S&E labor force is distributed across employment sectors in the U.S. economy. 

It shows that members of the S&E labor force work in all 

sectors, including for-profit businesses, nonprofit organizations, educational institutions, and government. The section 

begins with a brief description of patterns and trends in the 

proportions of the S&E labor force in these different employment sectors, and in the characteristics of organizations 

that employ S&E workers. The section looks at employment 

patterns in sectors and industries that have unusually high 

concentrations of S&E workers and variations among employers of different sizes. It then closes with a brief presentation of data on geographical areas with major concentrations 

of S&E workers. This includes data both on areas where 

workers in S&E occupations constitute a large percentage of 

Figure 3-8

S&E degree holders employed in jobs related 

to highest degree, by years since highest 

degree: 2008

Percent

NOTE: Includes those who say their job is either closely related or 

somewhat related to field of their highest degree.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

Years since degree

<5 5–9 10–14 15–19 20–24 25–29 30–34 35

0

20

40

60

80

100

Bachelor’s

Master’s

Doctorate

Figure 3-9

S&E bachelor’s highest degree holders employed 

in jobs closely related to degree, by degree field 

and years since degree: 2008

Percent

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

Years since degree

<5 5–9 10–14 15–19 20–24 25–29 30–34 35

0

10

20

30

40

50

60

70

Computer/mathematical

sciences

Biological/

agricultural/

environmental 

life sciences

Physical

sciences

Social sciences

Engineering
3-18 Chapter 3. Science and Engineering Labor Force

the labor force and areas where large numbers of workers in 

these occupations are geographically concentrated.

The section then analyzes S&E employment in the different economic sectors. In the business/industry sector, it 

describes differences between for-profit and nonprofit organizations and in the proportion of S&E workers by industrial 

sector. The section also examines self-employed workers 

with S&E degrees and in S&E occupations. Throughout the 

section, the analysis distinguishes between employment sectors for individuals with S&E degrees and for those working 

in S&E occupations. 

A brief analysis of the education sector, including all 

levels of education at both public and private institutions, 

and the government sector follows. In light of specialized 

scientific missions and the scope of scientific activities supported by the U.S. government, this section focuses on federal employment.

The S&E labor force is often seen as a major contributor to innovation. The section concludes, therefore, with 

data on various activities associated with innovation, such 

as performing R&D, patenting, and enhancing knowledge 

and skills through work-related training. This includes a description of data on job changes among S&E workers, which 

enable them to apply work-related learning in new contexts 

and may thereby spur innovation. 

Characteristics of Employers of Scientists 

and Engineers

Employment Sector

In general, the labor market is divided into workers in 

the public sector and those in the private sector. This classification works awkwardly for analysis of the S&E labor 

force. Because educational institutions are significant employers of scientists and engineers in the United States, 

these institutions are better treated as a distinct sector, which 

spans public and private institutions and includes 4- and 

2-year colleges and universities and precollege institutions. 

Employees in the business/industry sector work in for-profit 

businesses and nonprofit organizations, as well as being 

self-employed. The government sector includes local, state, 

and federal employees. 

The S&E workforce includes both those working in S&E 

occupations and those trained in S&E fields. In 2008, approximately 70% of individuals trained or working in S&E 

worked in the business/industry sector, 12% in the government sector, and 18% in the education sector. This distribution has stayed relatively stable since the early 1990s (see 

figure 3-12), with some minor shifts. Although the overall 

percentage of scientists and engineers working in educational institutions has stayed at approximately 18% of overall 

employment, the relative proportion working in 4-year institutions versus other educational institutions has changed 

from about 50/50 in 1993 to 40/60 in 2008. Compared with 

1993, a smaller proportion of scientists and engineers are 

working in the federal government in 2008 (6.4% versus 

4.5%). The largest change has been within the nonprofit sector. In 1993, the proportion working in this sector was 5.8%; 

by 2008, it was 10.4%, an 80% increase. 

The different sectors in which scientists and engineers are 

employed are shown in table 3-6. The sector distributions 

of scientists and engineers by highest degree in S&E versus 

any degree in S&E are very similar, and mirror the distributions found among all employed S&Es. Workers in different 

Figure 3-10

Intersection of individuals with highest degree in 

S&E and S&E occupation: 2008

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

Individuals with highest

degree in S&E

12.6 million

Individuals with

highest degree in

S&E working

in S&E occupations

3.9 million

Individuals

working

in S&E

occupations

4.9 million

Figure 3-11

Measures of the S&E workforce: 2008

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

22.1%

30.0%

10.7%

26.6%

7.0%

2.5%

Individuals with highest

degree in S&E in jobs

related to field of degree

7.9 million

Individuals with job

requiring at least

bachelor’s-level technical

expertise in one

or more S&E fields

11.2 million

Individuals

working in

S&E occupations

4.9 million

1.2%
Science and Engineering Indicators 2012 3-19

broad occupational categories are concentrated in different 

employment sectors. Four-year educational institutions, for 

example, employ a higher percentage of workers in S&E occupations than other institutions in the education sector. A 

larger proportion of S&E-related workers are employed in 

nonprofit organizations, compared to those in S&E or nonS&E occupations.

Employer Size

Employer size can affect the breadth and depth of S&E 

employment concentration. Educational institutions and government entities that employ scientists and engineers are, 

primarily, larger employers. A large majority of these organizations have 100 or more employees (88% in the education sector, 91% in the government sector). Scientists and 

engineers working in the business/industry sector are more 

broadly distributed across firms of many sizes.

S&E degree holders who work in for-profit businesses 

are distributed particularly broadly. Moreover, within the 

business/industry sector, workers at different degree levels 

are distributed similarly across firms of different sizes (figure 3-13). Companies with fewer than 100 employees, for 

example, employ 36% of S&E highest degree holders who 

work in the business/industry sector, ranging from 32% of 

master’s degree holders to 38% of doctorate holders. S&E 

doctorate holders in this sector, however, are concentrated 

at very small and very large firms. Some 23% work at the 

Percent

Figure 3-12

Employed scientists and engineers, by employment 

sector: 1993–2008 

NOTE: Scientists and engineers refers to all persons who work in an 

S&E occupation or who received a bachelor’s degree or higher in an 

S&E degree field in 1993–99 or an S&E or S&E-related field in 

2003–08. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (1993–2008), http://sestat.nsf.gov.

Science and Engineering Indicators 2012

1993 1995 1997 1999 2003 2006 2008

100

80

60

40

20

0

4-year

institutions

2-year and

precollege

institutions

Federal

government

State/local 

government

Self-employed,

unincorporated

businesses

Nonprofit

organizations

For-profit

businesses

Table 3-6

Employment sector of employed scientists and engineers, by broad occupation and degree field: 2008 

(Number and percent)

Education

Employment sector

All employed 

scientists and 

engineers S&E

S&E

related

NonS&E

Highest 

degree

 in S&E

Any 

degree 

in S&E

Total (n) ......................................................................... 19,244,000 4,874,000 5,542,000 8,828,000 10,216,000 14,145,000 

Business/industry (%) ............................................... 69.8 70.9 69.6 69.3 71.3 69.8

For-profit businesses ............................................ 53.0 63.2 45.0 52.4 58.9 55.5

Nonprofit organizations ......................................... 10.4 4.4 18.4 8.6 6.9 7.8

Self-employed, unincorporated businesses ......... 6.4 3.3 6.1 8.3 5.5 6.5

Education (%) .......................................................... 18.0 16.4 21.0 17.0 15.5 17.2

4-year institutions .................................................. 7.5 13.3 7.1 4.5 8.3 8.0

2-year institutions .................................................. 1.0 1.6 0.6 0.9 0.9 0.9

Precollege and other institutions........................... 9.5 1.5 13.5 11.5 6.3 8.2

Government (%) ........................................................ 12.2 12.7 9.4 13.7 13.2 13.0

Federal .................................................................. 4.5 6.1 3.4 4.4 5.3 5.0

State ...................................................................... 3.7 3.7 2.8 4.2 4.0 3.9

Local ...................................................................... 4.0 3.0 3.2 5.1 3.9 4.2

NOTE: Scientists and engineers refers to all persons who have received a bachelor’s degree or higher in a science or engineering (S&E) field or S&Erelated field or occupation. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

Occupation
3-20 Chapter 3. Science and Engineering Labor Force

Industries with higher proportions of individuals in S&E 

occupations tend to pay higher average salaries to both their 

S&E and non-S&E workers (table 3-7). The average salary 

of workers in non-S&E occupations employed in industries 

where more than 40% of workers are in S&E occupations 

is nearly double the average salary of workers in non-S&E 

occupations in industries with below-average proportions of 

workers in S&E occupations ($79,540 versus $29,970). 

smallest firms (under 10 employees), but the proportion of 

them at firms with fewer than 500 employees is similar to 

that among S&E highest degree holders generally. At the 

other end of the spectrum, close to 20% of doctorate holders 

work at firms of 25,000 or more employees.

The distribution of employees in the business/industry sector in S&E occupations, however, shows a different 

pattern. Among this group, there is a greater concentration 

of employment in firms with more than 5,000 employees 

(44%), compared to those in smaller firms of 100 employees 

or fewer (25%).

S&E Occupation Density by Type of Industry

Industries vary in their proportions of S&E workers 

(table 3-6). The OES survey provides detailed estimates for 

employment by type of industry, although it excludes the 

self-employed and those employed in recent startups. OES 

classifies the government sector within the broad category 

“government,” and educational institutions within the broad 

category of “educational services.” In the for-profit sector, 

the industry with the highest percentage of S&E workers 

was “professional, scientific, and technical services” with 

29%, followed by information with 16% (figure 3-14). The 

government (federal, state, and local) had 6% and the educational services sector had 5% of total employment in S&E 

occupations in 2010. 

In 2010, slightly more than 1 million workers in S&E 

jobs were employed in industries whose S&E employment 

component was less than the national average of 4.4% (table 

3-7). These industries employ 75% of all workers and 21% 

of all workers in S&E occupations. Examples include local 

government (at 3.0%, with 165,960 S&E jobs), hospitals (at 

1.5%, with 77,890 S&E jobs), and plastic parts manufacturers (at 2.6%, with 13,000 S&E jobs). 

Percent

Figure 3-13

S&E highest degree holders and S&E workers employed in business/industry sector, by employer size: 2008

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

Employer size (employees)

10 11–24 25–99 100–499 500–999 1,000–4,999 5,000–24,999 25,000

0

5

10

15

20

25

30

All degree levels Bachelor’s Master’s Doctorate Workers in S&E occupations

Figure 3-14

Industries that employ workers in S&E occupations: 

May 2010 

NOTE: Industries defined by North American Industry Classification 

System.

SOURCE: Bureau of Labor Statistics, Occupational Employment 

Statistics Survey (May 2010).

Science and Engineering Indicators 2012

Information

16%

Management of

companies and

enterprises 12%

Utilities 9%

Manufacturing 7%

Mining 7%

Government 6%

Educational services

5%

Finance and

insurance 5%

Wholesale trade 4%

Administrative and support/waste

management and remediation 2%

Health care/social assistance 1%

Other 3%

Professional, 

scientific, 

and technical

services 23%
Science and Engineering Indicators 2012 3-21

S&E Workers by Metropolitan Area

The availability of highly skilled workers can affect an 

area’s economic competitiveness and its ability to attract 

business investment. 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).

Two measures indicate availability of workers in S&E 

occupations: (1) the number of these workers in a metropolitan area or division and (2) the proportion of the entire metropolitan workforce in S&E occupations. For both 

measures, estimates are affected by the geographic scope 

of a metropolitan area, which can vary significantly. Thus, 

comparisons between areas can be strongly affected by how 

much territory outside the urban core is included in the metropolitan area. 

Table 3-8 presents the total number and proportion of 

workers in STEM and S&E occupations in the very large 

metropolitan areas with multiple metropolitan subdivisions. 

Metropolitan divisions with the largest estimated proportion of the workforce employed in S&E occupations are 

shown in table 3-9; those with the largest estimated number 

of workers employed in S&E occupations are listed in table 

3-10. The metropolitan areas with the highest estimated 

Table 3-7

Average annual salaries of workers, by industries’ proportion of employment in S&E occupations: May 2010

Average annual salary ($)

Workers in S&E occupations (%) All occupations

S&E 

occupations

Non-S&E 

occupations

All 

occupations

S&E 

occupations

Non-S&E 

occupations

All industries ................................... 127,097,160 5,549,980 121,547,180 44,410 80,170 42,770

>40.0 ........................................... 2,464,060 1,183,480 1,280,580 82,770 86,250 79,540

20.1–40.0 .................................... 3,459,430 2,492,720 966,710 67,570 87,720 57,810

10.1–20.0 .................................... 11,084,360 1,585,440 9,498,920 64,680 80,590 47,750

4.4–10.0 ...................................... 9,533,170 8,861,610 671,560 53,680 74,290 35,490

<4.4 (below national average) ..... 95,119,520 1,139,620 93,979,900 40,480 70,320 29,970

SOURCE: Bureau of Labor Statistics, Occupational Employment Statistics Survey (May 2010). 

Science and Engineering Indicators 2012

Table 3-8

Workers in S&E and STEM occupations in largest metropolitan statistical areas: May 2010

Workers employed (n) Percentage of workforce

Metropolitan statistical area All occupations

S&E 

occupations

STEM 

occupations

S&E 

occupations

STEM 

occupations

U.S. total ..................................................................... 127,097,160 5,549,980 7,427,350 4.4 5.8

New York-Northern New Jersey-Long Island, 

 NY-NJ-PA ............................................................ 8,101,890 S 443,200 S 5.5

Washington-Arlington-Alexandria, 

 DC-VA-MD-WV .................................................... 2,840,740 298,180 360,580 10.5 12.7

Los Angeles-Long Beach-Santa Ana, CA............... 5,191,880 237,430 308,090 4.6 5.9

Boston-Cambridge-Quincy, MA-NH ...................... 2,413,780 190,260 244,740 7.9 10.1

Chicago-Naperville-Joliet, IL-IN-WI ........................ 4,169,840 155,760 214,310 3.7 5.1

Dallas-Fort Worth-Arlington, TX.............................. 2,832,560 151,090 198,860 5.3 7.0

Seattle-Tacoma-Bellevue, WA ................................ 1,601,010 138,350 174,920 8.6 10.9

San Francisco-Oakland-Fremont, CA .................... 1,900,110 138,280 177,380 7.3 9.3

Philadelphia-Camden-Wilmington, 

 PA-NJ-DE-MD ..................................................... 2,619,360 129,910 168,720 5.0 6.4

Detroit-Warren-Livonia, MI ..................................... 1,686,920 102,210 135,190 6.1 8.0

Miami-Fort Lauderdale-Pompano Beach, FL ......... 2,143,470 63,060 83,940 2.9 3.9

S = suppressed for reasons of confidentiality and/or reliability

STEM = science, technology, engineering, and mathematics

NOTES: Includes only metropolitan statistical areas with multiple metropolitan divisions. Differences among employment estimates may not be statistically 

significant. For additional information see appendix table 3-4.

SOURCE: Bureau of Labor Statistics, Occupational Employment Statistics Survey (May 2010). 

Science and Engineering Indicators 2012
3-22 Chapter 3. Science and Engineering Labor Force

proportion of S&E employment are mainly smaller and 

perhaps less economically diverse. However, some large 

areas, such as Washington, D.C.; Seattle; Boston; and San 

Jose, also appear on the list of metropolitan areas with the 

greatest intensity of S&E employment. Differences between 

estimates for different areas are not necessarily statistically 

significant. More detailed information on all metropolitan 

areas can be found in appendix table 3-4. 

S&E Workers by Employment Sector 

Education Sector

Overall, the education sector employs 18% of scientists 

and engineers and 16% of those in S&E occupations (table 

3-6). Depending on the population, however, the proportion 

working within different parts of the education sector varies. 

For example, for workers with an S&E doctorate, 4-year colleges and universities are the most important employer (appendix table 3-5). However, only a minority (41%) of S&E 

doctorate holders work in this sector, and not all of these 

are tenured or tenure-track faculty. This figure also includes 

individuals holding postdoc and other temporary positions, 

working in various other S&E teaching and research jobs, 

performing administrative functions, and 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.) 

Within the education sector, the portion of the workforce 

in S&E occupations is concentrated in 4-year institutions 

(81%). In contrast, most education sector workers in S&Erelated or non-S&E occupations are found in precollege or 

other institutions (63% and 68%, respectively). These workers are primarily teachers in these types of institutions. 

Business/Industry Sector

For-profit businesses. For-profit businesses employ the 

greatest number of individuals with S&E degrees (figure 

3-12). In 2008, they employed 59% of all individuals whose 

highest degree is in S&E and 35% of S&E doctorate holders 

(appendix table 3-5). By occupation, they employ 53% of 

those working in S&E occupations. 

Table 3-9

Metropolitan areas with largest proportion of workers in S&E occupations, by occupation category: May 2010

Percentage of workforce Workers employed (n)

Metropolitan area

S&E 

occupations

STEM 

occupations

All 

occupations

S&E 

occupations

STEM 

occupations

U.S. total ..................................................................... 4.4 5.8 127,097,160 5,549,980 7,427,350

San Jose-Sunnyvale-Santa Clara, CA .................... 15.4 19.2 857,160 131,890 164,640

Huntsville, AL .......................................................... 13.7 17.5 202,410 27,780 35,500

Boulder, CO ............................................................ 13.6 15.9 152,100 20,640 24,220

Corvallis, OR ........................................................... 12.4 17.4 32,770 4,050 5,700

Durham, NC ............................................................ 11.8 15.1 266,990 31,590 40,260

Framingham, MA NECTA Division .......................... 11.4 14.8 154,760 17,710 22,960

Lowell-Billerica-Chelmsford, MA-NH NECTA 

 Division ................................................................ 11.1 14.7 113,630 12,630 16,660

Washington-Arlington-Alexandria, DC-VA-MD-WV

 Metropolitan Division .......................................... 10.6 12.7 2,289,200 243,350 291,730

Bethesda-Frederick-Gaithersburg, MD 

 Metropolitan Division ........................................... 9.9 12.5 551,550 54,820 68,860

Seattle-Bellevue-Everett, WA Metropolitan 

 Division ................................................................ 9.7 12.3 1,346,300 131,130 164,980

Kennewick-Pasco-Richland, WA ............................ 9.2 12.6 96,390 8,830 12,100

Bloomington-Normal, IL ......................................... 8.8 11.4 85,760 7,570 9,750

College Station-Bryan, TX ...................................... 8.8 11.1 92,510 8,110 10,230

Palm Bay-Melbourne-Titusville, FL ......................... 8.6 11.3 189,730 16400 21,480

Boston-Cambridge-Quincy, MA NECTA Division ... 8.4 10.7 1,658,000 139,620 177,930

Olympia, WA ........................................................... 8.4 10.3 93,910 7,870 9,640

Kokomo, IN ............................................................. 8.4 10.9 37,790 3,160 4,120

Fort Collins-Loveland, CO ...................................... 8.0 10.0 125,100 10,070 12,500

Austin-Round Rock, TX .......................................... 8.0 10.4 759,910 60,600 79,210

Colorado Springs, CO ............................................ 7.9 9.5 240,000 19,050 22,700

NECTA = New England City and Town Area; STEM = science, technology, engineering, and mathematics

NOTES: Excludes metropolitan statistical areas where S&E proportions were suppressed. Larger metropolitan areas broken into component metropolitan 

divisions. Differences among employment estimates may not be statistically significant. For additional details, see appendix table 3-4.

SOURCE: Bureau of Labor Statistics, Occupational Employment Statistics Survey (May 2010). 

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 3-23

Nonprofit organizations. Nonprofit organizations have 

shown substantial growth in the percentage of scientists and 

engineers that they employ (see figure 3-12). However, this 

is primarily driven by those working in S&E-related occupations (which include health-related jobs); 18.4% of the 

workers in S&E-related occupations work in nonprofit organizations (table 3-6). Among those in S&E occupations, the 

proportion is much smaller—4.4%. 

Self-employment. More than 3.6 million individuals 

with S&E degrees or working in S&E occupations were 

self-employed in 2008—18.8% of all scientists and engineers in the United States (table 3-11; NSF/NCSES 2008). 

This SESTAT estimate of self-employment is much higher 

than others that have been published elsewhere because it 

includes those self-employed individuals who work in incorporated businesses. In contrast, most reports of federal 

data on self-employment are limited to individuals whose 

businesses are unincorporated. 

Although only about one-third of all self-employed workers in the United States work in incorporated businesses 

(Census Bureau 2009), about two-thirds of self-employed 

scientists and engineers in the broad SESTAT population 

work in such businesses (table 3-11). The rate of incorporated self-employment is much higher for individuals with 

S&E degrees (12%), with S&E highest degrees (11%), or 

working in S&E occupations (8%) than for the U.S. workforce as a whole, where the comparable rate is 3% (Census 

Bureau 2009). 

Scientists and engineers working in S&E-related or nonS&E occupations reported higher levels of self-employment 

(20% and 22%, respectively) than those working in S&E occupations. Some 16% of social scientists indicated that they 

are self-employed, but unlike the general pattern of higher 

Table 3-10

Metropolitan areas with largest number of workers in S&E occupations, by occupation category: May 2010

Workers employed (n) Percentage of workforce

Metropolitan area All occupations

S&E 

occupations

STEM 

occupations

S&E 

occupations

STEM 

occupations

U.S. total ..................................................................... 127,097,160 5,549,980 7,427,350 4.4 5.8

Washington-Arlington-Alexandria, 

 DC-VA-MD-WV Metropolitan Division ................. 2,289,200 243,350 291,730 10.6 12.7

New York-White Plains-Wayne, NY-NJ 

 Metropolitan Division ........................................... 4,982,650 182,350 250,050 3.7 5.0

Los Angeles-Long Beach-Glendale, CA 

 Metropolitan Division ........................................... 3,817,570 169,040 217,670 4.4 5.7

Houston-Sugar Land-Baytown, TX......................... 2,497,880 135,170 184,640 5.4 7.4

Chicago-Naperville-Joliet, IL Metropolitan 

 Division ................................................................ 3,542,180 131,980 182,380 3.7 5.1

Boston-Cambridge-Quincy, MA NECTA Division ... 1,658,000 139,620 177,930 8.4 10.7

Seattle-Bellevue-Everett, WA Metropolitan 

 Division ................................................................ 1,346,300 131,130 164,980 9.7 12.3

San Jose-Sunnyvale-Santa Clara, CA .................... 857,160 131,890 164,640 15.4 19.2

Dallas-Plano-Irving, TX Metropolitan Division ........ 2,001,860 115,340 150,490 5.8 7.5

Atlanta-Sandy Springs-Marietta, GA ...................... 2,200,660 108,840 139,950 4.9 6.4

Minneapolis-St. Paul-Bloomington, MN-WI ........... 1,678,090 99,380 132,040 5.9 7.9

Philadelphia, PA Metropolitan Division ................... 1,804,600 93,760 120,330 5.2 6.7

San Diego-Carlsbad-San Marcos, CA .................... 1,238,720 83,330 111,550 6.7 9.0

Denver-Aurora, CO ................................................. 1,183,990 82,610 101,300 7.0 8.6

Phoenix-Mesa-Scottsdale, AZ ................................ 1,683,500 73,680 100,060 4.4 5.9

Baltimore-Towson, MD ........................................... 1,238,860 72,670 93,740 5.9 7.6

San Francisco-San Mateo-Redwood City, CA 

 Metropolitan Division ........................................... 948,970 73,800 92,600 7.8 9.8

Santa Ana-Anaheim-Irvine, CA Metropolitan 

 Division ................................................................ 1,374,310 68,390 90,420 5.0 6.6

Warren-Troy-Farmington Hills, MI Metropolitan 

 Division ................................................................ 1,017,660 65,640 86,600 6.5 8.5

Oakland-Fremont-Hayward, CA Metropolitan 

 Division ................................................................ 951,150 64,470 84,770 6.8 8.9

NECTA = New England City and Town Area; STEM = science, technology, engineering, and mathematics

NOTES: Larger metropolitan areas broken into component metropolitan divisions. Differences among employment estimates may not be statistically 

significant. For additional details see appendix table 3-4.

SOURCE: Bureau of Labor Statistics, Occupational Employment Statistics Survey (May 2010). 

Science and Engineering Indicators 2012
3-24 Chapter 3. Science and Engineering Labor Force

incorporated self-employment exhibited among scientists 

and engineers in general, this group reported higher rates of 

unincorporated self-employment. This is largely driven by 

psychologists, 30% of whom are self-employed, mostly in 

unincorporated businesses (NSF/NCSES 2008). Many scientists and engineers who are self-employed are working in 

small businesses. Some 81% of self-employed individuals in 

unincorporated businesses and 46% of self-employed people 

in incorporated businesses are working in businesses with 10 

or fewer employees. Some proportion of these scientists and 

engineers are likely to be working as independent professionals, rather than in small businesses. 

The proportion of self-employed workers generally 

decreases by level of degree and increases with age (figure 3-15). Across all ages, 18% of S&E bachelor’s degree 

holders are self-employed, but the proportion falls to 12% 

for S&E doctorate holders. However, self-employment increases with age at all degree levels. By ages 60–64, selfemployment reaches about 35% for bachelor’s degree, 27% 

for master’s degree, and 21% for doctorate holders.

Government Sector 

Federal government. The United States’ federal government is a major employer of scientists and engineers. 

However, its employees are largely limited to those with 

U.S. citizenship.6

 According to data from the U.S. Office of 

Personnel Management, the federal government employed 

approximately 235,000 persons in S&E occupations in 2009. 

Many of these workers were 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. Among federal employees 

in S&E occupations, 60% were in science occupations and 

40% were in engineering occupations. 

The five federal agencies with the largest proportions of 

scientists and engineers among their workforce are those 

with strong scientific missions: the National Aeronautics 

and Space Administration (NASA), Nuclear Regulatory 

Commission (NRC), Environmental Protection Agency 

(EPA), National Science Foundation (NSF), and Department 

of Energy. The Department of Defense employed the largest 

number of scientists and engineers, with 43% of the federal 

S&E workforce (NSF/NCSES 2012b, forthcoming).7

Overall, scientists and engineers represent approximately 

11.5% of the entire federal workforce. Among federal executives in the Senior Executive Service (SES),8

 22% are 

scientists and engineers. 

State and local government. Data from the 2010 OES 

survey show that there are approximately 7.89 million 

employees of state and local governments in the United 

States. In 2008, SESTAT estimated 1.48 million scientists 

and engineers working in this sector. Approximately 8% of 

Table 3-11

Self-employed scientists and engineers, by education, occupation, and type of business: 2008

(Percent)

Characteristic Total

Unincorporated 

business

Incorporated 

business

All self-employed scientists and engineers ..................................... 18.8 6.4 12.4

S&E degree holders

At least one degree in S&E field .................................................. 18.7 6.5 12.2

Highest degree in S&E field...................................................... 16.7 5.6 11.1

Computer and mathematical sciences ................................. 13.9 3.3 10.6

Biological, agricultural, and environmental life sciences ...... 17.1 6.5 10.6

Physical sciences ................................................................. 15.1 5.7 9.4

Social sciences ..................................................................... 18.0 7.4 10.6

Engineering ........................................................................... 16.8 3.5 13.3

Occupation

S&E occupation ........................................................................... 11.1 3.3 7.8

Computer and mathematical scientists ................................... 10.3 2.4 7.9

Biological, agricultural, and environmental life scientists ........ 5.7 1.6 4.1

Physical scientists .................................................................... 9.3 3.2 6.1

Social scientists ....................................................................... 16.1 11.1 5.0

Engineers ................................................................................. 12.3 2.4 9.9

S&E-related occupations ............................................................. 20.1 6.1 14.0

Non-S&E occupations ................................................................. 22.3 8.3 14.0

NOTE: Scientists and engineers include those with one or more S&E or S&E-related degrees at bachelor’s level or higher or who have a non-S&E degree 

at bachelor’s level or higher and were employed in an S&E or S&E-related occupation in 2008. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 3-25

individuals with highest degrees in S&E work in this sector; 

7% of those with S&E occupations also work there (appendix table 3-5). Within S&E occupations, a larger proportion 

of biological and physical scientists work in state and local 

governments (11.3% and 10.5%, respectively), relative to 

other S&E occupations. 

Scientists and Engineers and InnovationRelated Activities

Who Performs R&D?

Because R&D creates new knowledge and new types of 

goods and services that can fuel economic growth, individuals 

with S&E expertise who use their knowledge in R&D attract 

special interest. Using SESTAT data, this section reports two 

broad indicators of R&D work. One involves whether performing R&D is a major work activity constituting at least 

10% of the worker’s job. The other is whether workers report 

R&D as a primary or secondary work activity—an activity 

ranking first or second in work hours from a list of 14 choices. 

In 2008, just over 14.1 million employed individuals had 

one or more S&E degrees (NSF/NCSES 2008). Overall, 31% 

of S&E degree holders report R&D as a major work activity 

in their principal jobs. The majority of them have bachelor’s 

(52%) or master’s (32%) degrees, while individuals with 

doctorates, who constitute only 6% of all individuals with 

S&E degrees, represent 12% of individuals who report R&D 

as a major work activity. 

R&D as a work activity varies among S&E degree holders depending on the field of their highest degree. Figure 

3-16 shows the proportion of S&E degree holders who report R&D as their primary or secondary work activity, by 

their highest degree level and field (which may not be in 

S&E). Among S&E fields, the highest degree holders in engineering reported the highest aggregate R&D activity rate 

(51%), while those in the social sciences reported the lowest 

rate (22%). 

In all fields, doctorate holders report higher R&D activity 

rates than those at lower levels of educational attainment. 

Engineering doctorate holders report the highest R&D rates, 

with other doctorate holders in natural and mathematical sciences fields having slightly lower rates. Social sciences and 

health doctorates report the lowest R&D rates (figure 3-16). 

This pattern of differences among fields is similar to that 

found among all degree holders.

Figure 3-15

Self-employment rates of workers with highest 

degrees in S&E, by degree level and age: 2008

Percent

NOTE: Self-employment includes unincorporated self-employed and 

incorporated self-employed. All degree levels includes professional 

degrees not broken out separately.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

Age

24 25–

29

30–

34

35–

39

40–

44

45–

49

50–

54

55–

59

60–

64

65–

69

70–

75

0

5

10

15

20

25

30

35

40

45

50

All degree levels

Bachelor’s

Master’s

Doctorate

Figure 3-16

R&D activity rate of employed S&E degree holders, 

by field and level of highest degree: 2008

NOTES: “All degree levels” includes professional degrees not 

broken out separately. R&D activity rate is proportion of individuals 

who report that basic research, applied research, design, or 

development is primary or secondary work activity. For classification 

of degrees by S&E, S&E-related, and non-S&E, see table 3-1.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

Percent

0 20 30 40 50 60 70 80 10

All degree

levels

Bachelor’s

Master’s

Doctorate

Non-S&E fields

S&E-related

fields

Engineering

Social

sciences

Physical 

sciences

Computer/

math sciences

Biological/

agricultural/

environmental 

life sciences

S&E fields
3-26 Chapter 3. Science and Engineering Labor Force

Doctorate holders in all fields engaged in declining 

amounts of R&D activity over the course of their careers 

(figure 3-17). The decline may reflect movement into 

management or other career interests. It may also reflect 

increased opportunity for more experienced scientists to 

perform functions involving the interpretation and use of, as 

opposed to the creation of, scientific knowledge.

Many S&E degree holders subsequently earn degrees in 

other fields, such as medicine, law, or business. Figure 3-16 

includes individuals who have at least one S&E degree, but 

then may have earned other degrees in S&E-related and nonS&E fields. These individuals report substantial R&D activity rates less often than workers whose highest degrees are in 

S&E fields. Nonetheless, the proportions who report R&D 

as their primary or secondary activity—18% for those whose 

highest degree is in an S&E-related field and 21% for those 

whose degree is in a non-S&E field—are still substantial and 

are similar to those for people with their highest degree in 

the social sciences. 

R&D activity spans a broad range of occupations. Table 

3-12 shows the occupational distribution of S&E degree 

holders who spend at least 10% of their time on R&D or report R&D as a major work activity. Among the former, 39% 

are in non-S&E occupations (lawyers or non-S&E managers, for example). Twenty-seven percent of those for whom 

R&D is a major work activity are in non-S&E occupations. 

R&D Employment in the Business/Industry Sector

A large proportion (78%) of scientists and engineers who 

work in the business/industry sector report spending at least 

10% of their work hours on R&D activities; this proportion 

is 80% for those employed in the for-profit sector (NSF/

NCSES 2008). The 2009 Business R&D and Innovation 

Survey, which includes only U.S.-located companies that 

fund or perform R&D, allows for further examination of 

R&D employment in this sector. 

Figure 3-17

SEH doctorate holders with R&D as major work 

activity, by field and years since degree: 2008

Percent

SEH = science, engineering, and health

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

Years since degree

<5 5–9 10–14 15–19 20–24 25–29 30–34 35

0

10

20

30

40

50

60

70

80

90

100

Computer/mathematical sciences

Biological/agricultural/environmental life sciences

Physical sciences

Social sciences

Engineering

Health

Table 3-12

Employed S&E degree holders with R&D work activities, by occupation: 2008

R&D at least 10% of work time R&D as major work activity

Occupation Number Percent Number Percent

R&D 

activity 

rate (%) Number Percent

R&D 

activity 

rate (%)

All occupations ............................................... 14,145,000 100.0 7,670,000 100.0 54.2 4,403,000 100.0 31.1

S&E occupations ........................................ 4,275,000 30.2 3,397,000 44.3 79.5 2,590,000 58.8 60.6

Biological, agricultural, environmental 

 life scientists .......................................... 455,000 3.2 399,000 5.2 87.8 338,000 7.7 74.3

Computer and mathematical 

 scientists ............................................... 1,577,000 11.1 1,163,000 15.2 73.7 811,000 18.4 51.4

Physical scientists ................................... 308,000 2.2 263,000 3.4 85.5 219,000 5.0 71.1

Social scientists ...................................... 449,000 3.2 303,000 4.0 67.6 228,000 5.2 50.7

Engineers ................................................ 1,487,000 10.5 1,268,000 16.5 85.3 994,000 22.6 66.8

S&E-related occupations ............................ 2,507,000 17.7 1,258,000 16.4 50.2 631,000 14.3 25.2

Non-S&E occupations ................................ 7,363,000 52.1 3,015,000 39.3 40.9 1,182,000 26.8 16.1

NOTE: Detail may not add to total because of rounding. R&D as major work activity includes those reporting basic research, applied research, design, or 

development as activities they spent the most or second-most hours engaged in during a typical work week.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

 Employed S&E 

 degree holders
Science and Engineering Indicators 2012 3-27

The proportion of R&D employment relative to total employment, or R&D employment intensity, is one indicator 

of a company’s involvement in R&D activity. Companies 

located in the United States that performed or funded research and development domestically or overseas employed 

an estimated 27.1 million workers worldwide in 2009 (NSF/

NCSES 2012a, forthcoming). The domestic employment of 

these companies totaled 17.8 million workers, including 1.4 

million domestic R&D employees. Thus, domestic R&D 

employment accounted for 8% of companies’ total domestic 

employment (table 3-13). 

Smaller companies reported higher proportions of domestic R&D employment than did larger companies, with companies of 250 or more reporting 10% or fewer of their domestic 

employees as R&D employees, and small companies reporting rates higher than 10% (table 3-13). The greatest proportion of R&D employment (27.0%) is among companies of 

5–24 employees, whereas the smallest proportion (5.1%) is 

among very large companies of 25,000 or more. 

R&D employment is found in both manufacturing and 

nonmanufacturing industries, but at different rates. R&D 

employment intensity is 8.6% in manufacturing industries 

and 7.3% in nonmanufacturing industries (figure 3-18). 

Examination of this indicator across industries shows the 

highest R&D employment intensity rates in scientific R&D 

services (36%), communications equipment (30%), software 

publishers (28%), semiconductor and other electronics equipment (27%), and pharmaceuticals and medicines (20%). 

Table 3-13

Domestic industrial and R&D employment, by 

company size: 2009

(Thousands of employees)

 Domestic employees

Company size All R&D

% R&D 

employees

All companies ......... 17,788 1,424 8.0

Small companies

5–499 .................. 3,045 459 15.1

5–99 .................... 1,471 295 20.1

5–49 .................... 869 197 22.7

5–24 .................... 429 116 27.0

25–49 .................. 440 81 18.4

50–99 .................. 602 99 16.4

100–249 .............. 853 91 10.7

250–499 .............. 721 72 10.0

Medium and large 

 companies

500–999 .............. 795 64 8.1

1,000–4,999 ........ 2,349 204 8.7

5,000–9,999 ........ 1,603 112 7.0

10,000–24,999 .... 2,679 212 7.9

25,000 ............... 7,316 374 5.1

NOTES: Data representative of companies where worldwide R&D 

expense plus worldwide R&D costs funded by others are greater 

than zero. Size based on number of domestic employees. Includes 

2002 North American Industry Classification System (NAICS) 

codes 21–23, 31–33, and 42–81. Upper bound of “small company” 

classification based on U.S. Small Business Administration’s 

definition of small business; Business R&D and Innovation Survey 

(BRDIS) does not include companies with fewer than 5 domestic 

employees. Detail may not add to total because of rounding. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, BRDIS (2009 preliminary). 

Science and Engineering Indicators 2012

Figure 3-18

Domestic R&D employment in selected industries: 

2009

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Business R&D and Innovation Survey 

(2009 preliminary). 

Science and Engineering Indicators 2012

Percent

Newspaper, periodical,

book, and directory

publishers

Computer systems design

and related services

Architectural, engineering,

and related services

Software publishers

Scientific research and

development services

Nonmanufacturing

industries

Resin, synthetic rubber,

fibers, and filament

Basic chemicals

Engine, turbine, and power

transmission equipment

Soap, cleaning compound,

and toilet preparations

Automobiles, bodies,

trailers, and parts

Paint, coating, adhesive,

and other chemicals

Pesticide, fertilizer, and

other agricultural chemicals

Aerospace products

and parts

Medical equipment

and supplies

Navigational, measuring,

electromedical, and

control instruments

Pharmaceuticals

and medicines

Semiconductor and other

electronic components

Communications

equipment

Manufacturing

industries

All industries

0 15 20 25 30 35 40 105
3-28 Chapter 3. Science and Engineering Labor Force

Patenting Activity of Scientists and Engineers

The U.S. Patent and Trademark Office 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.

In its 2003 SESTAT surveys of the S&E workforce, NSF 

asked scientists and engineers to report on their recent patenting activities. Among those who had ever worked, 2.6% 

reported that from fall 1998 to fall 2003 they had been named 

as an inventor on a U.S. patent application (NSB 2010). 

Patenting activity rates were highest among those employed 

in the business/industry sector. 

The patent office does not grant all patent applications, 

and not all granted patents produce useful commercial products or processes. NSF estimates that in the 5-year period 

for which data were collected, U.S. scientists and engineers 

filed 1.8 million patent applications. The patent office granted some 1 million patents (although applicants may have applied for some of these at an earlier period).

Of those patents granted between 1998 and 2003, about 

54% resulted in a commercialized product, process, or license during the same period. Scientists and engineers employed in the business/industry sector reported the highest 

commercialization success rate (58%), much higher than the 

education (43%) and government (13%) sectors.9

 The overall 

commercialization rate varies by degree level, at 60%–65% 

for bachelor’s and master’s degree holders but 38% for doctorate holders (many of whom work in education, which has 

a low commercialization rate relative to other sectors).

In 2003, the patent activity rate of doctorate holders was 

15.7%, compared with 0.7% among those whose highest 

degree was at the bachelor’s level.10 However, there are far 

fewer doctoral-level scientists and engineers, so they accounted for only about a quarter of all survey respondents 

named on a U.S. patent application. Bachelor’s and master’s 

degree holders accounted for 41% and 31%, respectively, of 

all patenting activity reported in the survey.

More recent data from 2008 on a subset of scientists and 

engineers—U.S.-trained science, engineering, and health 

(SEH) doctorates—show that the patent activity rate of this 

set of employed doctorate holders from 2003 to 2008 was 

16.2% (table 3-14). The highest patenting activity rates were 

among doctorate holders in engineering (38.6%) and physical sciences (25.0%). Doctorate holders in these two fields 

also report the highest average number of applications per 

person (5.9 in both fields) and the highest average number 

granted (3.6 and 3.4, respectively). Doctorate holders in 

engineering and computer/information sciences report the 

highest average number commercialized (1.5 in both fields).

Table 3-14

Patenting indicators for employed U.S.-trained SEH doctorate holders, by field of doctorate: 2003–08

Indicator

All

fields

Biological

sciences

Computer/

information 

sciences

Mathematics/

statistics

Physical 

sciences

PsychologySocial 

sciences

Engineering Health

Employed U.S. SEH doctorates .... 651,168 163,981 16,152 30,035 115,376 99,157 81,596 115,994 28,878

Patent applicants ....................... 105,196 26,159 4,780 2,034 30,621 1,010 591 38,368 1,632

Patent activity rate (%)............... 16.2 17.2 15.1 5.4 25.0 1.0 8.0 38.6 6.7

Patent grantees ......................... 73,169 16,905 3,064 1,367 22,664 677 343 27,047 1,099

Patent commercializers ............. 40,365 7,779 1,787 692 12,256 349 255 16,593 653

Grantee’s commercialization 

 success rate (%) ..................... 55.2 46.0 58.3 50.6 54.1 51.6 74.3 61.3 59.4

Average number

Applications ............................... 5.18 3.63 5.08 4.89 5.87 2.35 3.56 5.91 2.82

Patents granted ......................... 2.92 1.77 2.45 2.66 3.57 1.38 1.24 3.40 1.32

Commercialized products,

 processes, or licenses ............. 1.16 0.69 1.49 1.06 1.14 0.66 0.79 1.50 0.69

Number of patents

Applied for ................................. 545,058 95,080 24,312 9,940 179,737 2,378 2,113 227,002 4,609

Granted ...................................... 307,583 46,199 11,682 5,405 109,389 1,395 729 130,686 2,160

Commercialized ......................... 122,182 18,055 7,114 2,147 34,914 673 464 57,700 1,131

Patent commercialization 

 success rate (%) ......................... 39.7 39.1 60.9 39.7 31.9 48.2 63.6 43.9 52.4

SEH = science, engineering, and health

NOTES: Patenting indicators include activities between October 2003 and October 2008. Patenting indicators defined in Morgan R, Kruytbosch C, 

Kannankutty N, Patenting and invention activity of U.S. scientists and engineers in the academic sector: Comparisons to industry, Journal of Technology 

Transfer 26:173–83 (2001). Biological sciences includes agricultural and environmental life sciences.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Survey of Doctorate Recipients (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 3-29

Work-Related Training

In addition to formal education, scientists and engineers 

very often engage in work-related training. Such training 

can contribute to innovation in the economy by enhancing 

skills and knowledge within the S&E labor force. According 

to SESTAT, about three-fifths of scientists and engineers 

participated in work-related training in 2008. Among those 

who were employed, the rate was approximately 67%; for 

the unemployed, it was 32% (table 3-15). Among employed 

scientists and engineers, those in S&E-related occupations 

(health-related occupations, S&E managers, S&E precollege 

teachers, and S&E technicians and technologists) had the 

highest participation rate (79%).

Most who took training did so to improve skills or knowledge in their current occupational field (53%) (appendix 

table 3-6). Others did so for licensure/certification in their 

current occupational field (24%) or because it was required 

or expected by their employer (14%). Relative to those who 

were employed or not in the labor force, those who were 

unemployed more often reported that they engaged in workrelated 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. Women participated in workrelated training at a higher rate than men: 61% compared 

with 55% of men (appendix table 3-7). This difference exists 

regardless of labor force status or highest degree level.

S&E Labor Market Conditions

Labor market conditions for scientists and engineers affect the attractiveness of S&E fields to both students and 

those already in the labor force. Assessing the state of the 

labor market generally includes examining a variety of indicators that can include employment and unemployment 

conditions and earnings, and the interplay of these indicators with other economic measures. The most recent recession officially began in December 2007 and ended in June 

2009.11 These two endpoints represent the peak of a business 

cycle through the trough. Although there are no fixed definitions that identify peaks and troughs of business activity, 

factors such as the gross domestic product, aggregate employment, and national income are considered relevant. As 

various measures are presented in this section, it is important to note that many of these measures are lagging indicators. That is, they are economic factors that sometimes do 

not change until the economy has already begun to follow a 

particular trend. For example, unemployment rates can continue to rise or can remain the same although a recession has 

ended. Unemployment rates, involuntarily out-of-field rates, 

and earnings should all be considered in this context. This 

section looks at both long-term and recent trends in these 

indicators using NSF, Census Bureau, and BLS data ranging 

from before and continuing after the recession.

Unemployment in the S&E Labor Force

In general, those who hold S&E degrees or those working in S&E occupations have had lower rates of unemployment than other college graduates and much lower rates than 

those without a college education. However, this does not 

exempt them from unemployment due to overall business 

cycles or specific events affecting individuals with training 

in their fields. 

Unemployment rates in S&E occupations are also generally less volatile than unemployment rates for these other 

groups (figure 3-19). The Bureau of Labor Statistics’ Current 

Population Survey data for 1983–2010 indicate that the unemployment rate for all individuals in S&E occupations ranged 

from 1.3% to 4.3%, which contrasted favorably with rates 

for all U.S. workers (from 4.0% to 9.6%) and all workers 

with a bachelor’s degree or higher (from 1.8% to 7.8%). The 

rate for S&E technicians and computer programmers ranged 

from 2.1% to 7.4%. During most of the period, computer 

Table 3-15

Scientists and engineers participating in workrelated training, by employment status and 

occupation: 2008

Employment status 

and occupation Number Percent

All scientists and engineers .................. 23,232,000 57.8

Employed .......................................... 19,244,000 66.5

S&E occupations ........................... 4,874,000 58.1

Computer and mathematical 

 scientists ................................. 1,970,000 54.1

Biological, agricultural, 

 other life scientists .................. 498,000 56.2

Physical scientists ..................... 322,000 53.1

Social scientists ......................... 502,000 62.9

Engineers ................................... 1,582,000 63.1

S&E-related occupations .............. 5,542,000 78.5

Non-S&E occupations ................... 8,828,000 63.6

Unemployed ..................................... 604,000 31.8

S&E occupations ........................... 140,000 25.9

Computer and mathematical 

 scientists ................................. 61,000 28.8

Biological, agricultural, 

 other life scientists .................. 12,000 24.7

Physical and related scientists ... 10,000 22.5

Social and related scientists ...... 11,000 36.8

Engineers ................................... 45,000 20.2

S&E-related occupations .............. 106,000 44.0

Non-S&E occupations ................... 359,000 30.5

Not in labor force .............................. 3,383,000 13.1

NOTES: Scientists and engineers include those with one or more 

S&E or S&E-related degrees at bachelor’s level or higher, or who 

have non-S&E degree at bachelor’s level or higher and employed 

in S&E or S&E-related occupation in 2006. Unemployed individuals 

are those not working but who looked for job in preceding 4 weeks. 

For unemployed, the last job held was used for classification. Total 

excludes scientists and engineers who never worked. 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) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012
3-30 Chapter 3. Science and Engineering Labor Force

programmers had an unemployment rate similar to that of 

workers in S&E occupations, but with greater volatility (from 

1.2% to 6.7%). By 2010 unemployment rates for all U.S. 

workers were still increasing, while the unemployment rate 

for workers in S&E occupations had begun to go down.

The recent economic downturn that began in late 2007 

generally follows the historic pattern. In 2008, workers in 

S&E occupations or S&E technician and computer programmer occupations had lower unemployment rates (2.1% or 

3.9%, respectively) than all workers (5.8%). By 2009, when 

unemployment had reached much higher levels, workers in 

S&E occupations and S&E technicians and technologists 

still had lower rates (4.3% and 7.0%, respectively) than all 

workers in general (9.3%); a similar pattern existed for 2010. 

Three-month unemployment rates tell a somewhat more 

nuanced story. College-educated S&E workers generally 

have lower unemployment rates than all college graduates; 

this pattern was still valid in the period from 2007 to 2010. 

However, in the 3-month period ending in September 2009, 

the unemployment rate of college educated S&E workers 

rose to 5.5%, approximately the same rate as for all college 

graduates (5.4%). S&E technicians and computer programmers continued to experience a considerably lower unemployment rate (8.2%) than that of the general labor force 

(9.7%) (figure 3-20). These rates immediately followed the 

end of the official recession (June 2009). Moving forward 

to the 3-month period ending in September 2011, the more 

classic pattern emerges of college-educated S&E workers having a significantly lower unemployment rate (3.8%) 

than all college graduates (4.8%). It should be noted, however, that unemployment rates for college graduates have 

remained relatively stable since approximately April 2011, 

while they have risen for college-educated S&E workers. 

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 standard (U3) unemployment rate. In addition to U3, the Bureau 

of Labor Statistics reports five other rates of labor underutilization (U1, U2, U4, U5, and U6; see table 3-16). These 

provide additional detail about differences in employment 

patterns between the S&E labor force and the general U.S. 

labor force (appendix table 3-8). 

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. Moreover, the advantages for 

workers in S&E occupations increased over the course of 

the economic downturn. Figure 3-21 shows the growing gap 

between these workers and the general labor force in both 

standard (U3) and long-term (U1) unemployment rates. The 

difference between their monthly standard rates ranged between 3.2 and 4.1 percentage points in 2008, between 4.0 

and 4.9 percentage points in 2009, and between 5.0 and 6.1 

Figure 3-19

Unemployment rate, by occupation: 1983–2010

Percent

SOURCES: National Bureau of Economic Research, Merged 

Outgoing Rotation Group files (various years); Bureau of Labor 

Statistics, Current Population Survey (various years). 

Science and Engineering Indicators 2012

1983 1986 1989 1992 1995 1998 2001 2004 2007 2010

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)

Figure 3-20

Estimated unemployment rates over previous 

3 months for workers in S&E occupations 

and selected other categories: March 2008–

September 2011

Percent

NOTES: Estimates not seasonally adjusted. Estimates from pooled 

microrecords of Current Population Survey and, although similar, are 

not same as 3-month moving average.

SOURCE: Bureau of Labor Statistics, Current Population Survey, 

Public-Use Microdata Sample (PUMS), March 2008–September 

2011. 

Science and Engineering Indicators 2012

3/08 9/08 1/09 5/09 9/09 1/10 5/10 9/10 1/11 5/11 9/11

0

2

4

6

8

10

12

All U.S. workers

S&E technicians and 

computer programmers 

(any education level)

Workers with bachelor’s

degree or higher

Workers in S&E occupations 

(bachelor’s degree or higher)
Science and Engineering Indicators 2012 3-31

percentage points in 2010. It remained near 6 percentage 

points for most of 2011. Whereas general unemployment 

peaked at 10.5% (March 2010), S&E unemployment rose 

only as high as 5.6% in October 2009. 

Similarly, the difference in long-term unemployment, defined as more than 15 weeks, grew as the downturn went on. It 

rose from about 1 to 1.3 percentage points in 2008 to between 

1.5 and 2.6 percentage points in 2009, and over 3 percentage 

points in the first half of 2010 before dropping later in the 

year. Beginning near 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 U3 

unemployment rate, this indicator captures the difference 

between workers’ labor market aspirations and outcomes. 

During the downturn, the gap between this measure and the 

standard unemployment rate among workers in S&E occupations was substantially smaller than the comparable gap 

in the general labor force (appendix table 3-8). Thus, the 

proportion of underutilized workers who were unemployed 

in the standard sense of the term was consistently higher 

among S&E workers than it was in the general labor force.

Unemployment Rates by Degree and Field

In most economic downturns, workers with advanced 

S&E degrees have been less vulnerable to changes in economic conditions than individuals who hold only S&E bachelor’s degrees. Figure 3-22 compares unemployment rates 

over career cycles for persons with S&E bachelor’s degrees 

and doctorates, regardless of their occupation, for 1999 and 

2003—periods of relatively good and relatively difficult labor market conditions, respectively. The relatively difficult 

2003 labor market had a greater effect on bachelor’s degree 

holders: for individuals at various points in their careers, the 

unemployment rate increased by between 1.6 and 3.5 percentage points between 1999 and 2003. Labor market conditions had a smaller effect on doctorate holders, but some 

increases in unemployment rates affected individuals in 

most years-since-degree cohorts.

Similarly, among those who said they were working involuntarily outside the field of their highest degree, labor 

market conditions from 1999 to 2003 had a greater effect 

on the proportion of bachelor’s degree holders than on doctorate holders (figure 3-23). These rates ranged from 7% to 

12% for bachelor’s degree holders in 2003 versus 2% to 5% 

for those with doctorates. Rates of working involuntarily 

out-of-field (IOF) for doctorate holders changed little between 1999 and 2003.

Although S&E qualifications may help workers weather 

recessions, they do not make them immune to the adverse 

labor market conditions that recessions bring. The estimated 4.3% unemployment rate for S&E occupations in April 

2009, although low relative to other occupations, was the 

highest in 25 years.

Figure 3-21

Measures of labor underutilization for S&E 

occupations and all occupations: March 2008–

September 2011

Percent

U1 = % of labor force unemployed for 15 weeks or more; U3 = % of 

labor force without jobs who have looked for work in past 4 weeks 

(official unemployment rate)

NOTES: Estimates not seasonally adjusted. Estimates made from 

pooled microrecords of Current Population Survey and, although 

similar, are not same as 3-month moving average.

SOURCE: Bureau of Labor Statistics, Current Population Survey, 

Public-Use Microdata Sample (PUMS), March 2008–September 

2011. 

Science and Engineering Indicators 2012

3/08 9/08 1/09 5/09 9/09 1/10 5/10 9/10 1/11 5/11 9/11

0

2

4

6

8

10

12

U3, all occupations

U1, all occupations

U3, S&E occupations

U1, S&E occupations

Table 3-16

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 four 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 2012
3-32 Chapter 3. Science and Engineering Labor Force

Earnings

The estimated annual wages of individuals in S&E occupations, based on the OES survey, are considerably higher 

than the average of the total workforce. Median annual wages in 2010 (regardless of education level or field) in S&E 

occupations were $75,820, more than double the median 

($33,840) for all U.S. workers (table 3-17). The spread in 

average (mean) wage was less dramatic but still quite wide, 

with individuals in S&E occupations again earning considerably more on average ($80,170) than workers in all occupations ($44,410). Mean S&E wages ranged from $71,860 

for social science occupations to $87,980 for engineering 

occupations. 

The 2007–10 annual growth in mean and median wages 

for both the S&E and STEM occupation groups were similar 

to those for employed U.S. workers in the OES data. 

Workers with S&E degrees also have higher earnings 

than those with degrees in other fields. Figure 3-24 shows 

estimates of median salary at different points in life for individuals with a bachelor’s degree as their highest degree 

in a variety of fields. Except in the first 4 years after earning their degrees, holders of S&E bachelor’s degrees earn 

more than those with non-S&E degrees at every year since 

degree. Median salaries for S&E bachelor’s degree holders 

in 2003 peaked at $65,000 at 15–19 years after receiving 

their degree, compared with $49,000 for those with nonS&E bachelor’s degrees. Median salaries of individuals 

with bachelor’s degrees in S&E-related fields (such as technology, architecture, or health) peaked at $52,000 at 25–29 

years after degree, but were higher than those for non-S&E 

bachelor’s degree holders regardless of years of experience. 

Earnings at Different Degree Levels

Data on educational histories of all college graduates have 

been periodically collected by the National Survey of College 

Graduates, allowing for detailed comparisons of S&E and 

other college degree holders. Figure 3-25 illustrates the distribution of median salaries earned by individuals with S&E degrees at various levels. (Because the distributions are heavily 

skewed, the median is the preferred summary statistic.) Not 

surprisingly, salaries are higher for those with more advanced 

degrees. In 2003 (the most recent data available), 11% of S&E 

bachelor’s degree holders had salaries higher than $100,000, 

compared with 28% of doctorate holders. Similarly, 22% of 

bachelor’s degree holders earned less than $30,000, compared 

with 8% of doctorate holders.12

Figure 3-26 shows a cross-sectional profile of median 

2003 salaries for S&E degree holders over the course of 

their career. Median earnings generally increase with time 

since degree, as workers add on-the-job knowledge to their 

formal training. For holders of bachelor’s and master’s degrees in S&E, average earnings adjusted for inflation begin 

to decline in mid to late career, a common pattern that is 

often attributed to “skill depreciation.” In contrast, earnings 

for S&E doctorate holders continue to rise even late in their 

Figure 3-22

Unemployment rates for individuals with S&E as 

highest degree, by degree level and years since 

degree: 1999 and 2003

Percent

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (1999 and 2003), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

Years since degree

1–4 5–9 10–14 15–19 20–24 25–29 30–34 35

0

2

4

6

8

Bachelor’s 1999

Bachelor’s 2003

Doctorate 1999

Doctorate 2003

Figure 3-23

Individuals with highest degree in S&E who are 

involuntarily working out of field, by degree level 

and years since highest degree: 1999 and 2003

Percent

NOTE: Individuals involuntarily employed out of their field include 

those in jobs not related to field of highest degree because job in 

that field not available, and those employed part time because 

full-time work not available. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (1999 and 2003), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

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

Bachelor’s 1999

Bachelor’s 2003

Doctorate 1999

Doctorate 2003
Science and Engineering Indicators 2012 3-33

careers. Median salaries in 2003 peaked at $65,000 for bachelor’s degree holders, $73,000 for master’s degree holders, 

and $96,000 for doctorate holders.

Recent S&E Graduates

Compared with experienced S&E workers, recent S&E 

graduates more often bring newly acquired skills to the labor 

market and have relatively few work or family commitments 

that limit their job mobility. As a result, measures of the success of recent graduates in securing good jobs can be sensitive indicators of changes in the S&E labor market. 

This section looks at a number of standard labor market 

indicators for recent S&E degree recipients at all degree levels and examines a number of other indicators that may apply only to recent S&E doctorate recipients. 

General Labor Market Indicators for 

Recent Graduates

Table 3-18 summarizes some basic labor market statistics 

in 2008 for recent recipients of S&E degrees, with recent

meaning up to 5 years from receiving the degree. Across all 

fields of S&E degrees, there was a 5.3% unemployment rate 

for bachelor’s degree holders who received their degrees in 

Table 3-17

Annual earnings and earnings growth in science and technology and related occupations: May 2007–May 2010

Mean Median

Occupation

2007

annual 

earnings ($)

2010

annual 

earnings ($)

Annual

growth rate 

since 2007 (%)

2007

annual 

earnings ($)

2010

annual 

earnings ($)

Annual

growth rate 

since 2007 (%)

All U.S. employment ....................................... 40,690 44,410 2.2 31,410 33,840 1.9

STEM occupations ..................................... 72,000 79,000 2.3 66,950 73,290 2.3

S&E occupations ..................................... 74,070 80,170 2.0 70,600 75,820 1.8

Computer/mathematical scientists ...... 71,940 77,320 1.8 68,910 73,790 1.7

Life scientists ....................................... 71,700 77,850 2.1 63,170 68,740 2.1

Physical scientists ............................... 73,720 80,490 2.2 67,190 72,850 2.0

Social scientists ................................... 66,370 71,860 2.0 60,380 65,540 2.1

Engineers ............................................. 81,050 87,980 2.1 77,750 83,610 1.8

Technology occupations ......................... 67,870 74,510 2.4 NA 62,180 NA

S&E managers ..................................... 114,470 NA NA NA NA NA

S&E technicians/computer 

 programmers .................................... 53,165 NA NA NA NA NA

S&E-related occupations (not included 

 above) ...................................................... 66,150 72,580 2.3 50,540 71,320 9.0

Health-related occupations ..................... 66,000 72,480 2.4 55,310 59,350 1.8

Other S&E-related occupations .............. 73,110 78,350 1.7 50,250 71,320 9.1

NA = not available

STEM = science, technology, engineering, and mathematics

NOTE: 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 2007 and May 2010). 

Science and Engineering Indicators 2012

Figure 3-24

Median salaries for bachelor’s degree holders, 

by broad field and years since degree: 2003

Dollars (thousands)

NOTE: See table 3-2 for definitions of S&E, S&E-related, and 

non-S&E degrees.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, National Survey of College Graduates 

(2003). 

Science and Engineering Indicators 2012

Years since degree

 1–4 5–9 10–14 15–19 20–24 25–29 30–34

0

30

40

50

60

70

S&E related

Non-S&E

S&E
3-34 Chapter 3. Science and Engineering Labor Force

the previous 5 years. This ranged from 2.1% for those with 

engineering degrees to 6.7% for social science degree recipients. Early in their careers, individuals tend to change 

jobs more often and have a higher incidence of unemployment. However, with the exception of those who earned a 

bachelor’s degree in the social sciences, the unemployment 

rate for those with recent S&E degrees was less than the 

unemployment rate of 6.5% for the full U.S. labor force in 

October 2008. 

A useful but more subjective indicator of labor market 

conditions for recent graduates is the proportion reporting 

that a job in their degree field was not available. This involuntarily out-of-field (IOF) rate is a measure unique to NSF’s 

labor force surveys. At the bachelor’s degree level, across 

all S&E fields, the IOF rate in 2008 was 7.9%, but it ranged 

from 2.4% for recent engineering graduates to 12.0% for recent graduates in the social sciences. In all fields of degrees, 

the IOF rate decreases as the level of education increases, 

reaching a low of 1.5% for recent doctorate recipients.

The median salary for recent S&E bachelor’s degree recipients in 2008 was $39,800, ranging from $30,000 in the 

life sciences to $59,000 in engineering. Recent master’s degree recipients had average salaries of $57,000, with recent 

doctorate recipients earning $65,000. 

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 doctoral S&E labor market; a range of relevant labor 

market indicators are discussed below, including unemployment rates, IOF employment, employment in academia 

versus other sectors, employment in postdoc positions, and 

salaries. Although a doctorate opens both career and salary 

Density (percent)

Figure 3-25

Salary distribution of S&E degree holders employed full time, by degree level: 2003

NOTE: Salary distribution smoothed using kernel density techniques.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (2003), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

Salary in U.S. dollars (thousands)

2 14 26 38 50 62 74 86 98 110 122 134 146 158 170 182 194 206 218 230 242 254 266 278 290 300

0

0.000002

0.000004

0.000006

0.000008

0.000010

0.000012

0.000014

Bachelor’s

Master’s

Doctorate

Figure 3-26

Median salaries of individuals with highest degree 

in S&E, by degree level and years since degree: 

2003

Dollars (thousands)

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2003), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

Years since degree

 1–4 5–9 10–14 15–19 20–24 25–29 30–34

0

20

40

60

80

100

Bachelor’s

Master’s

Doctorate
Science and Engineering Indicators 2012 3-35

opportunities, these opportunities come at the price of many 

years of lost labor market earnings. For some doctorate holders, a postdoc position further extends this period of low 

earnings. In addition, some doctorate holders do not obtain 

the jobs they desire after they complete their education.

Although the official recession began in the United States 

in December 2007 and overall unemployment rose precipitously after April 2008, as of October 2008, the labor market 

indicators for individuals who recently earned an SEH doctoral degree in the United States remained relatively positive. Their unemployment rate was only modestly higher 

than in April 2006; the rate of working involuntarily outside 

of one’s field was slightly lower than in 2006; the decline 

in the proportion of recent doctorate holders who had secured either tenure or tenure-track faculty appointments was 

modest; and inflation-adjusted salaries rose considerably between 2006 and 2008. 

Unemployment

As of October 2008, the 1.5% unemployment rate for 

SEH doctorate recipients up to 3 years after receiving their 

doctorates was considerably lower than the unemployment 

rate of the civilian labor force in general (6.5%) and the unemployment rate for recent recipients of S&E bachelor’s degrees (5.3%). Among recent SEH doctoral degree recipients, 

the unemployment rate in each of the broad SEH degree areas was lower in 2008 than it was in 2003 with the exception 

of the physical sciences (table 3-19). With a 3% unemployment rate, the physical sciences had considerably higher unemployment among recent doctoral degree recipients than 

other SEH areas. Indeed, in all other broad SEH fields except the social sciences the unemployment rate among recent SEH doctoral degree recipients was below 2% in 2008. 

Working Involuntarily Outside the Field

In addition to the 1.5% who were unemployed in 2008, 

another 1.3% of recent SEH doctorate recipients in the labor 

force reported that they took a job that was not related to 

the field of their doctorate because a job in their field was 

not available. The share of recent SEH doctoral degree recipients who have reported involuntarily working outside of 

their field has declined steadily from 2001, when the IOF 

rate was 2.8% (table 3-19). 

The highest IOF rates were found for recent doctorate 

recipients in the physical sciences and the social sciences. 

However, within the physical sciences the IOF rate declined 

from 5.4% to 2.3% between 2001 and 2008.

Tenure-Track Positions

Many SEH doctorate recipients may aspire to tenure-track 

academic appointments, but most will end up working in other 

positions and sectors. In 2008, 16% of all those who had earned 

their SEH doctoral degree within the previous 3 years had a 

tenure or tenure-track faculty appointment, a share that has held 

Table 3-18

Labor market indicators for recent S&E degree recipients up to 5 years after receiving degree, by field: 2008

Highest degree field

Indicator and degree All S&E fields

Computer/

mathematical 

sciences

Biological/ 

agricultural/ 

environmental 

life sciences

Physical 

sciences

Social 

sciences Engineering

Unemployment rate (%)

All degree levels .......................................... 4.6 3.2 5.1 3.4 6.1 2.0

Bachelor’s ............................................... 5.3 3.2 6.0 3.9 6.7 2.1

Master’s .................................................. 2.9 3.5 2.4 2.5 3.5 2.0

Doctorate ................................................ 1.5 0.3 2.1 2.5 1.2 1.0

Involuntarily out-of-field rate (%)

All degree levels .......................................... 7.9 4.0 7.6 5.6 12.0 2.4

Bachelor’s ............................................... 9.7 5.4 9.1 7.6 13.6 2.5

Master’s .................................................. 3.5 0.7 4.1 1.8 6.1 2.6

Doctorate ................................................ 1.5 0.9 1.2 3.1 1.9 0.8

Median annual salary ($)

All degree levels .......................................... 42,000 55,000 34,000 40,000 36,000 63,000

Bachelor’s ............................................... 39,800 51,000 30,000 32,000 34,000 59,000

Master’s .................................................. 57,000 72,000 44,000 47,000 43,000 70,000

Doctorate ................................................ 65,000 80,000 50,000 67,000 60,000 86,000

NOTES: Median annual salaries are rounded to nearest $1,000. All degree levels includes professional degrees not broken out separately. Includes 

degrees earned from October 2003 to October 2008. Involuntarily out-of-field rate is proportion of individuals employed in job not related to field of 

highest degree because job in that field was not available.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System (SESTAT) 

(2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012
3-36 Chapter 3. Science and Engineering Labor Force

broadly steady since 1993, with a 2003 peak approaching 19% 

and subsequent modest declines (table 3-20). 

The share of SEH degree recipients who hold a tenure or 

tenure-track faculty appointment increases with increasing 

time since earning the doctorate. In 2008, the proportion of 

SEH doctorates with tenure or tenure-track appointments who 

were less than 3 years from completing their doctorate was 

16.2%; for those who had been in the labor market for 3 to 

5 years, the comparable rate was 22.9%. In computer and information sciences, 22.0% of individuals who had less than 3 

years in the labor force since earning their doctoral degree had 

a tenure or tenure-track faculty appointment; the proportion 

increases by 15.8 percentage points to 37.8% for those 3 to 5 

years from the doctoral degree. Psychology and the social sciences are the only areas that do not show a dramatic rise in the 

share of the labor force with a tenure or tenure-track appointment among those with 3 to 5 years of labor market exposure 

compared to those with less than 3 years of labor market exposure. (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. One of the quickest declines among 

recent doctoral degree recipients in tenure-track employment occurred in computer sciences, from 31.5% in 1993 

to 18.2% in 1999 despite the high demand for computer sciences faculty (table 3-20).

Salaries for Recent SEH Doctorate Recipients

For all SEH degree fields in 2008, the median annual salary for recent doctorate recipients up to 5 years after they 

received their degrees was $67,000. Across various SEH 

fields of degree, median annual salaries ranged from a low 

of $50,000 in the biological sciences to a high of $88,000 in 

computer and information sciences (table 3-21). From 2006 

to 2008, salaries for recent recipients of doctoral degrees 

rose considerably. After adjusting for inflation, the median 

salary for recent doctoral degree recipients rose by 17%. 

By type of employment, salaries for recent doctorate recipients ranged from $42,000 for postdoc positions to $85,000 for 

those employed by private for-profit businesses (table 3-22).

Postdoc Positions

The growing number of recent doctorate recipients in 

postdoctoral appointments, generally known as postdocs,

13

has become a major concern in science policy. Neither the 

reasons for this growth nor its effect on the health of science are well understood. Increases in competition for tenure-track academic research jobs, collaborative research in 

large teams, and needs for specialized training are possible 

factors explaining this growth. Although individuals in postdoc positions often perform cutting-edge research, there is 

concern that time spent in a postdoc position is time added 

onto the already long time spent earning a doctorate, thereby 

delaying the start and advancement of independent careers. 

Because postdoc positions usually offer low pay, forgone 

earnings add significantly to the costs of a doctoral education and may discourage doctoral-level careers in S&E. 

How Many Postdocs Are There? 

In 2010, Science and Engineering Indicators (NSB 2010) 

included an analysis of a one-time postdoc module from the 

2006 Survey of Doctorate Recipients (SDR), and compared it 

Table 3-19

Employment characteristics of recent SEH doctorate recipients up to 3 years after receiving doctorate, 

by field: 2001–08

(Number and percent)

 Recent doctorates (n) Unemployment rate (%)

Involuntarily 

out-of-field rate (%)

Field 2001 2003 2006 2008 2001 2003 2006 2008 2001 2003 2006 2008

All recent SEH doctorates ........................ 48,700 43,700 49,500 52,600 1.3 2.5 1.2 1.5 2.8 2.1 1.4 1.3

Biological, agricultural, and 

 environmental life sciences ................ 12,300 11,200 12,600 13,400 1.4 2.4 0.9 1.7 2.6 1.0 0.3 1.0

Computer/information sciences .......... 1,600 1,400 1,500 2,400 0.3 4.1 1.9 S S S 2.6 1.4

Mathematics and statistics ................... 2,200 1,600 2,000 2,400 0.2 3.4 S S 1.4 3.4 2.2 1.1

Physical sciences ................................ 7,700 6,500 7,400 7,500 1.5 1.3 1.1 3.0 5.4 4.2 2.6 2.3

Psychology .......................................... 7,200 6,300 7,000 5,800 1.5 2.7 1.2 0.8 3.0 1.5 1.4 0.8

Social sciences .................................... 5,800 6,000 6,200 5,900 1.6 3.1 1.4 2.1 3.3 3.0 2.3 3.4

Engineering .......................................... 9,400 8,000 9,500 12,000 1.5 3.0 1.8 1.2 2.0 3.0 1.6 0.7

Health ................................................... 2,400 2,700 3,200 3,300 0.4 0.7 0.9 1.2 S 1.1 S S

S = suppressed for reasons of confidentiality and/or reliability

SEH = science, engineering, and health

NOTES: Involuntarily out-of-field rate is proportion of individuals employed in job not related to field of doctorate because job in that field was not 

available. 2001 and 2006 data include graduates from 12 to 36 months prior to survey reference date; 2003 and 2008 data include graduates from 15 to 

36 months prior to survey reference date. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Survey of Doctorate Recipients (SDR), (2001–08), http://

sestat.nsf.gov. 

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 3-37

to data collected on NSF’s Survey of Graduate Students and 

Postdocs in Science and Engineering, in order to estimate the 

total number of postdocs in the United States. Similar more 

recent data from the SDR are not available. However, there 

are several point estimates from more recent years. 

In October 2008, the SDR measured 27,100 individuals 

with SEH doctorates who were employed in postdoc positions. The SDR covers U.S. residents with research doctorates in SEH fields from U.S. universities, but not those with 

non-U.S. doctorates. The NSF Graduate Student Survey 

(GSS) gathers information on postdocs from U.S. academic 

graduate departments, regardless of where these individuals 

earned their doctorates. It does not cover people in nonacademic employment, at some university research centers, or 

at academic departments that lack graduate programs. The 

fall 2008 estimate from the GSS was 54,100 postdocs. The 

SDR and GSS estimates overlap in some populations (U.S.-

trained doctorates and those working in academia), but differ in others (GSS covers foreign-trained doctorates, but not 

those in the industry or government sectors).

Postdocs by Academic Discipline

More than half of all U.S.-educated SEH doctorates in 

postdoctoral positions in 2008 (57%) had doctorates in 

biological or health sciences (figure 3-27). In these fields, 

Table 3-20

Employed SEH doctorate recipients holding tenure and tenure-track appointments at academic institutions, 

by years since degree and field: 1993–2008

(Percent)

Years since doctorate and field 1993 1995 1997 1999 2001 2003 2006 2008

<3 years

All SEH fields ......................................................................... 18.1 16.3 15.8 13.5 16.5 18.6 17.7 16.2

Biological, agricultural, and environmental life sciences ... 9.0 8.5 9.3 7.7 8.6 7.8 7.2 6.5

Computer/information sciences ......................................... 31.5 36.5 23.4 18.2 20.7 32.5 31.2 22.0

Mathematics and statistics ................................................ 40.9 39.8 26.9 18.9 25.2 38.4 31.6 31.3

Physical sciences ............................................................... 8.8 6.9 8.5 7.8 10.0 13.3 9.8 8.8

Psychology ......................................................................... 12.8 13.6 14.7 16.0 15.6 14.6 17.0 18.1

Social sciences .................................................................. 43.5 35.9 37.4 35.4 38.5 44.8 39.3 45.4

Engineering ........................................................................ 15.0 11.5 9.4 6.4 11.3 10.8 12.4 9.3

Health ................................................................................. 33.9 34.2 30.1 28.1 32.1 30.3 36.2 27.7

3–5 years

All SEH fields ......................................................................... 27.0 24.6 24.2 21.0 18.5 23.8 25.9 22.9

Biological, agricultural, and environmental life sciences ... 17.3 17.0 18.1 16.4 14.3 15.5 13.7 14.3

Computer/information sciences ......................................... 55.7 37.4 40.7 25.9 17.3 32.2 45.7 37.8

Mathematics and statistics ................................................ 54.9 45.5 48.1 41.0 28.9 45.5 50.6 40.7

Physical sciences ............................................................... 18.8 15.5 14.5 11.9 15.8 18.3 19.7 16.5

Psychology ......................................................................... 17.0 20.7 16.8 17.6 17.5 19.9 23.8 18.3

Social sciences .................................................................. 54.3 52.4 50.4 46.5 38.8 46.0 50.4 48.9

Engineering ........................................................................ 22.7 19.3 19.4 12.6 10.8 15.9 16.3 15.5

Health ................................................................................. 47.4 40.2 41.1 39.5 25.1 40.8 43.1 34.4

SEH = science, engineering, and health

NOTES: Proportions calculated on basis of all doctorates working in all sectors of economy. Data for 1993–1999, 2001, and 2006 includes graduates 

from 12 to 60 months prior to survey reference date; 2003 and 2008 data include graduates from 15 to 60 months prior to survey reference date.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Survey of Doctorate Recipients (1993–2008), http://

sestat.nsf.gov. 

Science and Engineering Indicators 2012

Table 3-21

Salary of recent SEH doctorate recipients up 

to 5 years after receiving degree, by field and 

percentile: 2008

(Dollars)

Field of doctorate

25th 

percentile

50th 

percentile

75th 

percentile

All SEH fields ............................ 48,000 67,000 88,000

Biological, agricultural, and 

 environmental life 

 sciences ............................. 41,000 50,000 68,000

Computer and information 

 sciences ............................. 72,000 88,000 107,000

Mathematics and statistics ... 52,000 65,000 90,000

Physical sciences ................. 50,000 68,000 85,000

Psychology ........................... 48,000 58,000 75,000

Social sciences ..................... 50,000 62,000 82,000

Engineering ........................... 70,000 86,000 100,000

Health .................................... 60,000 76,000 95,000

SEH = science, engineering, and health

NOTES: Salaries are rounded to nearest $1,000. Includes graduates 

from 15 to 60 months prior to survey reference date.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Doctorate Recipients (2008), 

http://sestat.nsf.gov. 

Science and Engineering Indicators 2012
3-38 Chapter 3. Science and Engineering Labor Force

Table 3-22

Median annual salary of recent SEH doctorate recipients up to 5 years after receiving degree, by field and 

employment sector: 2008

(Dollars)

Education

4-year institution

Field of doctorate All sectors

Tenured or 

tenure-track 

position Postdoc

Other 

academic 

positions

2-year or 

precollege 

institution

GovernmentBusiness/ 

industry

All SEH fields ...................................................... 67,000 65,000 42,000 55,000 60,000 71,000 85,000

Biological, agricultural, and environmental 

 life sciences ................................................. 50,000 62,000 41,000 50,000 55,000 60,000 65,000

Computer and information sciences............... 88,000 80,000 46,000 80,000 80,000 90,000 100,000

Mathematics and statistics ............................. 65,000 59,000 52,000 50,000 60,000 86,000 97,000

Physical sciences ........................................... 68,000 60,000 43,000 53,000 52,000 69,000 85,000

Psychology ..................................................... 58,000 57,000 42,000 55,000 62,000 75,000 65,000

Social sciences ............................................... 62,000 60,000 50,000 52,000 56,000 87,000 85,000

Engineering ..................................................... 86,000 80,000 43,000 68,000 45,000 78,000 95,000

Health .............................................................. 76,000 75,000 43,000 68,000 69,000 82,000 85,000

SEH = science, engineering, and health

NOTES: Salaries are rounded to nearest $1,000. Includes graduates from 15 to 60 months prior to survey reference date.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Survey of Doctorate Recipients (2008), http://sestat.nsf.

gov. 

Science and Engineering Indicators 2012

Figure 3-27

U.S.-educated SEH doctorate holders in postdoctorate positions, by doctorate field: 2008 

SEH = science, engineering, and health

NOTE: Percentages do not add to 100% because of rounding. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Survey of Doctorate Recipients (2008), http://sestat.nsf.gov.

Science and Engineering Indicators 2012

Cell and molecular biology 10%

Biochemistry and biophysics 8%

Microbiology 4%

Genetics, animal and plant 3%

Physiology and pathology 3%

Agricultural and food sciences 2%

Pharmacology 2%

Botany 2%

Health 3% Other biological sciences 20%

Mathematics 2%

Computer sciences 1%

Physics/astronomy/astrophysics 9%

Chemistry 8%

Geosciences 3%

Psychology 8%

Social sciences 2%

Electrical engineering 2%

Mechanical engineering 2%

Chemical engineering 2%

Materials engineering 1% Other engineering 4%
Science and Engineering Indicators 2012 3-39

postdoc training has been common for a long time and individuals remain in postdoc positions longer than in other 

fields. Psychology, chemistry, and physics also have high 

rates of graduates entering postdoc positions and together 

make up another one-quarter of postdoc positions. The remaining quarter come from all other SEH fields, most of 

which do not have a strong postdoc tradition as part of their 

career paths. 

Increase in the Likelihood and Length of 

Postdoc Positions

 Among holders of U.S. SEH doctorates received before 

1972, 31% reported having had a postdoc position earlier 

in their careers (NSB 2010). This proportion has risen over 

time to 46% among 2002–05 graduates and has increasingly 

involved fields in which, formerly, only a small number of 

doctorate recipients went on to postdoc positions. In traditionally high-postdoc fields such as the life sciences (from 

46% to 60%) and the physical sciences (from 41% to 61%), 

most doctorate recipients now have a postdoc position as 

part of their career path. Similar increases were found in 

mathematical and computer sciences (19% to 31%), social 

sciences (18% to 30%), and engineering (14% to 38%). 

Recent engineering doctorate recipients are now almost as 

likely to take a postdoc position as physical sciences doctorate holders were 35 years ago. 

Postdoc Pay and Benefits

Low pay and fewer benefits for postdocs are frequently 

raised as concerns by those worried about the effect of the 

increasing number of postdoc positions on the attractiveness 

of science careers. The median academic postdoc salary is 

44% less than the median salary for nonpostdocs up to 5 

years after receiving their doctorates (table 3-23). Among 

engineering doctorates, academic postdocs are paid half the 

salary of those who are not in postdoc positions up to 5 years 

after receiving their doctorate. Among social sciences doctorates, this gap is closer to one-quarter (24%). Nonacademic 

postdocs are better paid than academic postdocs, but their 

median salary is still 33% less than that of those who are not 

in postdoc positions.

The 2006 Survey of Earned Doctorates asked about employment benefits among postdocs. Across all S&E fields, 

90% of postdocs reported having medical benefits and 49% 

reported having retirement benefits. It is not possible to 

know from the survey how extensive medical benefits may 

be or how transferable retirement benefits are. In the social 

sciences, medical benefits are less available, with only 75% 

of postdocs reporting that they had medical benefits.

Postdoc Positions as a Sign of Labor 

Market Distress

In 2006, former postdoc position holders reported reasons for accepting their appointment that are consistent with 

the traditional intent of a postdoc position as a type of apprenticeship, such as seeking “additional training in doctorate field” or “training in an area outside of doctorate field.” 

However, 10% of SDR respondents in a postdoc position 

in October 2008 reported that they took their current postdoc position because “other employment not available.” 

This reason was given by 9% of postdocs in the biological 

and agricultural sciences, 5% in the health sciences, 12% 

in computer sciences and mathematics, 12% in the physical 

sciences, 6% in the social sciences, and 16% in engineering. 

Postdoc Outcomes

In 2006, most former postdocs reported that their most recent postdoc appointment had enhanced their career opportunities, and the proportions who said this were similar for 

different cohorts (NSB 2010). Across all S&E fields and cohorts, 53%–56% of former postdocs said that their postdoc 

appointment enhanced their career opportunities to a “great 

Table 3-23

Median salary of U.S. SEH doctorate holders in postdoc positions: 2008

Median salary ($)

Field of doctorate Academic postdocs Nonacademic postdocs Nonpostdocs

All SEH .......................................................................................... 42,000 50,000 75,000

Biological/agricultural/environmental life sciences .................... 41,000 47,000 65,000

Computer/information sciences ................................................ 46,000 S 90,000

Mathematical sciences .............................................................. 52,000 S 71,000

Physical sciences ...................................................................... 43,000 57,000 75,000

Psychology ................................................................................ 42,000 48,000 60,000

Social sciences .......................................................................... 47,000 S 62,000

Engineering ................................................................................ 43,000 57,000 90,000

Health ......................................................................................... 43,000 63,000 80,000

S = suppressed for reasons of confidentiality and/or reliability

SEH = science, engineering, and health

NOTE: Salaries are rounded to nearest $1,000.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Survey of Doctorate Recipients (2008), http://sestat.nsf.gov.

Science and Engineering Indicators 2012
3-40 Chapter 3. Science and Engineering Labor Force

extent”; an additional 33%–38% said that their postdoc appointment “somewhat” enhanced their career opportunities. 

The proportion of those completing postdoc positions who 

said that it was no help to their career opportunities ranged 

from only 8% for the 2002–05 graduation cohort to 12% for 

the 1987–91 cohort. For a more detailed look at perceived 

and actual outcomes from a postdoc experience, see chapter 3 in the 2008 edition of Science and Engineering (NSB 

2008) and NSF/SRS (2008b).

Demographics of the S&E Workforce

This section describes the demographic composition 

of the U.S. S&E workforce by sex, race/ethnicity, foreign 

origin, and age. It also addresses the relationship between 

workforce demographics and selected indicators of labor 

force rewards and participation. 

The section begins with a focus on differences by sex 

among workers in S&E occupations and among S&E degree holders. Similar comparisons will be made across race/

ethnicity categories. Historically, in the United States, very 

high proportions of workers in S&E occupations have been 

male and white (non-Hispanic). Engineering and physical 

science occupations have had particularly low concentrations of women and of members of most underrepresented 

minority groups (i.e., blacks, Hispanics, American Indians, 

and Alaska Natives), both relative to the concentrations 

of these groups in other occupational areas and relative to 

their representation in the population in general. However, 

both women and minorities increasingly have been entering 

a wide range of S&E occupations. Asians have also been 

increasing their participation in S&E occupations, although 

with concentrations in areas different from women and underrepresented minorities. This section documents, across 

S&E occupations, the extent to which the numbers and the 

share of workers who are women, underrepresented minorities, and Asians have risen, and provides indicators of their 

contemporary levels of participation. 

The presentation of indicators of levels of participation 

will be followed by an analysis of the relationship between 

wage differences and demographic factors. Historically, 

women and minorities in S&E occupations have received 

lower salaries than white men. This section will provide data 

on contemporary salary differences as well as findings regarding how various factors contribute to these differences. 

This discussion of wage differences will be followed by 

a presentation of indicators pertaining to S&E immigration 

trends. Increasing global competition for S&E workers and 

changes in economic conditions influence levels of immigration. This section describes recent trends in immigration of 

S&E workers that can be compared with other factors (like 

economic growth). Indicators are collected from population 

data from the U.S. Census Bureau and visa data from the 

U.S. Citizenship and Immigration Service, as well as S&E 

workforce data from the NSF SESTAT data system. Data 

from the Survey of Earned Doctorates will be presented to 

capture stay rates: rates at which noncitizen recipients of 

U.S. S&E doctoral degrees remain in the United States. 

The demographics section ends with a presentation of 

indicators of the aging of the S&E workforce as the babyboom generation moves toward retirement age. The high 

concentration of workers over age 50 suggests that the S&E 

workforce will soon experience high levels of turnover. 

Thus, indicators will also be presented pertaining to levels 

of workforce participation and engagement of individuals at 

the ages near the end of their career cycle. 

Sex Differences in the S&E Workforce

Sex Differences in S&E Occupations

Historically, men in S&E occupations have outnumbered 

women by wide margins. Yet the number of women in these 

occupations has been on the rise, increasing over the past two 

decades by more than half-a-million workers. These recent 

increases in the number of women have narrowed overall disparities by sex, but only modestly. In 2008, overall disparities remained pronounced, with women constituting 27% of 

workers, only a slightly higher share than in the previous decade when women made up 23% of workers (figure 3-28).

Sex disparities vary across occupations (appendix table 

3-9). The most extreme disparities are within engineering, 

where women constituted 13% of the workforce in 2008. 

Among large engineering occupations, the disparity between men and women is greatest among mechanical engineers, with men outnumbering women by more than 12 

Figure 3-28

Women in S&E occupations: 1993–2008

Percent

NOTE: National estimates not available from Scientists and 

Engineers Statistical Data System (SESTAT) in 2001.

SOURCE: National Science Foundation, National Center for Science and 

Engineering Statistics, SESTAT (1993–2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

1993 1995 1997 1999 2003 2006 2008

0

10

20

30

40

50

60

70

All S&E occupations

Computer/mathematical scientists

Biological/agricultural/

environmental life scientists

Physical scientists

Social scientists

Engineers
Science and Engineering Indicators 2012 3-41

to 1. Other large engineering occupations in which women 

account for as few as 9% of workers include electrical and 

electronics engineers and aerospace, aeronautical, and astronautical engineers. 

Both computer and mathematical scientists (26% women) and physical scientists (30% women) are disproportionately male. Within physical science occupations, physicists 

have the largest imbalance by sex. Within computer and 

mathematical scientist occupations, the largest component, 

computer and information scientists (25% women), is the 

most imbalanced. Mathematical scientists (45% women) are 

much closer to parity. 

Sex parity in participation was nearly achieved by 2008 

among biological and medical scientists (51% women). 

With 53% of women in the field in 2008, parity in the social 

sciences has been long established. Occupations within social sciences, however, vary with respect to the proportion 

of female workers. For example, women account for slightly 

less than one-third of economists, but more than two-thirds 

of psychologists. Psychology, with about 185,000 total 

workers, is the only large S&E occupation with substantially 

more women than men.

The number of women working in each occupational area 

has risen since the early 1990s. Growth has been strongest 

in the biological and related sciences, where the number of 

female workers doubled between 1993 and 2008. This rate of 

growth has far outstripped that of men in these occupations, 

thus women’s share of workers has also increased (from 34% 

in 1993 to 45% in 2008, see figure 3-28). During the same 

period, women have also increased their share among workers in engineering (from 9% to 13%) and in the physical sciences (from 21% to 30%). In these two occupational areas, 

women’s increased share emerged as women’s numbers in 

the workforce expanded (roughly by 60%) but men’s numbers 

did not, remaining roughly similar between 1993 and 2008. 

In social science occupations, the growth in women’s participation has occurred at levels similar to those in engineering and the physical sciences. However, men’s participation 

in these occupations has grown at similar levels and, therefore, the balance between men and women has changed little.

With 230,000 more female computer and mathematical 

scientists in 2008 than in 1993, women have added more 

workers in this area than in any of the other S&E occupations. The rate of growth of women in this area is also higher 

than in any other area, except for life scientists. However, 

unlike in the other four areas, men’s rate of growth in this 

occupational area is higher than women’s. Thus, women’s 

share of this occupation has been declining. From 1993 to 

2008 women’s share of computer and mathematical scientists dropped from 31% to 26%, making the sex disparity 

here even greater than in physical science occupations. The 

declining share of women in the computer and mathematical 

science occupations reflects increasing disparities in participation among those whose highest degree is at the bachelor’s 

degree level. Among those with a doctoral degree, women’s 

share of workers in computer science occupations increased 

from 13% to 18% over this period.

Sex Differences in Age and Racial/Ethnic Groups

With the recent, greater growth among women than 

among men in S&E occupations, women in the field tend 

to be somewhat younger than the men (table 3-24). Age disparities are greatest among life scientists, physical scientists, 

and engineers, where women’s participation levels have 

been increasing relative to men’s. Age disparities are small 

among computer and mathematical scientists, where women 

have lost ground relative to men in levels of participation. 

Overall, in 2008 28% of men working in S&E occupations 

were over age 50 compared with 22% of women. Only 13% 

of men were younger than 30, but 17% of women were. The 

median age of women in S&E occupations was 41 years 

compared with 43 years among men.

Women in S&E occupations were more likely than men 

to be classified as an American Indian/Alaska Native, black, 

Hispanic, Native Hawaiian/Pacific Islander or of two or 

more races. In 2008, 14% of women in S&E occupations 

identified themselves within one of these groups compared 

with 10% of men (appendix table 3-10). Neither occupational area nor age explains the increased likelihood for women 

to be from a minority group, and less likely to be white. 

Women are more likely to be minorities within all five broad 

occupational areas whether or not age is controlled.

Sex Differences Among S&E Degree Holders

Sex disparities among the general U.S. workforce with 

S&E degrees are somewhat smaller than disparities within 

S&E occupations. In 2008, among individuals with their 

highest degree in an S&E field, women constituted 38% of 

those who were employed, up from 31% in 1993. Over the 

Table 3-24

Age distribution of workers in S&E occupations, by 

sex and race/ethnicity: 2008

(Percent)

 Age (years)

Sex and race/ethnicity <30 30–50 >50

All S&E occupations ........................... 14.1 59.3 26.7

Sex

Male ................................................ 12.9 58.6 28.4

Female ............................................ 17.2 61.0 21.9

Race/ethnicity

Asian ............................................... 17.8 67.7 14.5

American Indian/Alaska Native ....... 8.7 70.5 20.8

Black ............................................... 12.5 65.8 21.8

Hispanic .......................................... 18.2 65.1 16.7

White ............................................... 12.6 56.6 30.8

Native Hawaiian/Other Pacific 

 Islander ........................................ 24.8 65.3 9.9

Two or more races .......................... 29.8 53.6 16.6

NOTE: All single-race categories include non-Hispanics only.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012
3-42 Chapter 3. Science and Engineering Labor Force

same period, the share of women among unemployed workers with an S&E degree rose more dramatically, from 34% 

to 45%. Among those out of the labor market, the share of 

women rose from 46% to 50%. 

At every age, women with their highest degree in an S&E 

field are more likely than men to be out of the labor market 

(figure 3-29). However, at typical ages for career entry and 

exit (before age 30 and after age 60) these differences are 

modest. The sex disparity in the likelihood of being out of the 

labor market is particularly pronounced in the middle years of 

the career cycle. Between ages 30 and 55, 16.1% of women 

were out of the labor market compared with 2.2% of men.

Many women between ages 30 and 55 with S&E degrees 

who were not in the labor market identified family reasons 

as an important factor: 69% of women reported that family 

was a factor compared with 25% 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 

(46% of women and 26% of men). 

Sex Differences in Degree Fields and 

Degree Levels

With respect to the proportion of men and women among 

S&E highest degree holders, the pattern of variation among 

degree fields echoes the pattern of variation among occupations associated with those fields (see appendix table 3-11). 

In 2008, more than half (54%) of degree holders in the social 

science fields were women, as were nearly half (46%) of 

those with a degree in the biological and related sciences. 

Men outnumbered women among computer sciences and 

mathematics degree holders (31% women) and among 

physical science degree holders (28% women). Disparities, 

however, were greatest among those with a degree in engineering, where only 13% of degree holders were women. In 

all fields except computer and mathematical sciences, the 

share of women with degrees in the workforce has been increasing over the past two decades. In computer science and 

mathematics this share has remained flat.

Sex differences are not limited to the field of degree, but 

also to the level of the S&E degree. Men in the workforce 

are more likely to have a more advanced S&E degree. For 

example, women accounted for 38% of those whose highest 

degree in S&E is at the bachelor’s level but 29% of workers 

whose highest degree in S&E is at the doctoral level (figure 

3-30). At the doctoral degree level, however, women’s share 

has been steadily increasing. Women’s share of S&E bachelor’s degree holders in the workforce has also been rising 

since the early 1990s, but in 2008 this share was not larger 

than it had been in 2006.

Working men and women with S&E degrees also differ 

in the extent to which they are employed in the same field 

as their S&E degree. However, this disparity is 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 outside of S&E occupations. 

Figure 3-29

Highest degree holders in S&E not in the labor 

force, by sex and age: 2008

Percent

NOTE: Not in labor force includes those not working nor looking for 

work in the 4 weeks prior to October 2008.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System, SESTAT (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

24 25–

29

30–

34

35–

39

40–

44

45–

49

Age

50–

54

55–

59

60–

64

65–

69

70–

75

0

10

20

30

40

50

60

70

80

90

Female Male

Figure 3-30

Employed women with highest degree in S&E, by 

degree level: 1993–2008

Percent

NOTE: National estimates not available from Scientists and 

Engineers Statistical Data System (SESTAT) in 2001. 

SOURCE: National Science Foundation, National Center for Science and 

Engineering Statistics, SESTAT (1993–2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

1993 1995 1997 1999 2003 2006 2008

0

5

10

15

20

25

30

35

40

45

Bachelor’s

Master’s

Doctorate
Science and Engineering Indicators 2012 3-43

In 2008, across all degree areas, 21% of women with a highest degree in S&E compared with 35% of men were employed 

in the field in which they earned their degree (appendix table 

3-12). About 26% of women were working in an S&E occupation compared with 45% of men. However, within most degree 

areas, a similar proportion of men and women work in an occupation that matches their degree field, and similar proportions 

work in non-S&E jobs. Computer and mathematical science 

fields are exceptions, where men are more likely to work in an 

occupation that matches their degree field.

Racial/Ethnic Differences in the S&E 

Workforce

This section addresses the level of diversity in science and 

engineering by describing the cross-cutting social categories of 

race and Hispanic status. Like the preceding section, this section draws on data from the NSF science and engineering labor 

force surveys to report on levels of participation in science and 

engineering: first, across occupations, and next, across the overall workforce with science and engineering 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 white. The 

next largest group of scientists and engineers are Asians, 

who have been increasing their share in the S&E field since 

the early 1980s. On the other hand, several minority groups, 

including blacks, Hispanics, and Native Americans, have 

low levels of participation in science and engineering occupations both compared with other groups and compared with 

their proportion of the general working-age population (table 

3-25). Both blacks and Hispanics also have low levels of 

participation in S&E relative to their proportion in the general population with a college degree. The composition of 

the S&E workforce across these groups has been a concern 

of policymakers who are interested in the development and 

utilization of human capital to maintain the United States’ 

global competitiveness in science and engineering. 

In 2008, with 3.5 million workers in S&E occupations, 

whites made up over 70% of the country’s scientists and 

engineers. Whites accounted for more than 50% of workers within each of the S&E occupations (see appendix table 

3-13). Whites are particularly highly concentrated in areas 

that focus on macrophysical systems. For example, whites 

were a strong majority of forestry and conservation scientists (91%); earth, atmospheric, and ocean scientists (86%); 

and agricultural and food scientists (82%). 

Asians, with 824,000 workers in S&E occupations, accounted for 17% of scientists and engineers. They are strongly 

concentrated in computer engineering fields, constituting 40% 

of computer hardware engineers, 30% of computer software 

engineers, and 23% of the related occupations of electrical 

and electronics engineering. On the other hand, Asians participate in social science occupations at much lower rates than 

whites. For example, Asians account for 4% of psychologists 

and just 3% of sociologists and anthropologists. 

The social sciences are the one occupation within S&E 

in which the underrepresented minorities (American Indian/

Alaska Natives, blacks, Hispanics, and Native Hawaiians/

Pacific Islanders) outnumber Asians. Collectively, these 

groups account for 17% of sociologists and anthropologists, 

and 12% of psychologists. These minorities also account for 

a comparatively high share of computer support specialists 

(16%) and statisticians (14%). On the other hand, underrepresented minorities account for relatively few physicists 

and astronomers (6%). Moreover, among these minority 

physicists and astronomers, only one-third were born in the 

United States compared with the more than two-thirds of underrepresented minorities who are in other S&E occupations 

and were born in the United States. U.S.-born underrepresented minorities accounted for less than 2% of physicists 

and astronomers.

Table 3-25

Racial/ethnic distribution of individuals in S&E occupations, S&E degree holders, college graduates, and U.S. 

residents: 2008

(Percent)

Race/ethnicity

S&E

occupations

S&E 

degree

holders

College 

degree 

holders

Total U.S. 

residential 

population

S&E occupations

Asian ............................................................. 16.9 11.2 8.5 4.7

American Indian/Alaska Native ..................... 0.3 0.4 0.3 0.7

Black ............................................................. 3.9 5.5 7.2 11.7

Hispanic ........................................................ 4.9 5.6 6.2 13.9

White ............................................................ 71.8 75.2 76.5 67.6

Native Hawaiian/Other Pacific Islander ........ 0.4 0.4 0.1 0.1

Two or more races ........................................ 1.7 1.7 1.1 1.2

SOURCES: Census Bureau, American Community Survey (2008); National Science Foundation, National Center for Science and Engineering Statistics, 

Scientists and Engineers Statistical Data System (SESTAT) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012
3-44 Chapter 3. Science and Engineering Labor Force

Race/Ethnicity Trends in S&E Occupations

Over the past two decades, the U.S. workforce in S&E 

occupations has been becoming more diverse with increasing numbers of minorities and Asians and a decreasing share 

of whites. In 1993, 84% of workers in S&E occupations 

reported their race as white. By 2008, this proportion declined to 72%. Some of this decline reflects changes to the 

NCSES workforce surveys that collect information on race 

in the S&E workforce. 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 if they had the option, which would have decreased the estimated numbers 

of whites. However, because less than 2% of S&E workers self-reported two or more races in years when the option 

was available, it is unlikely that this change contributed to 

much of the decline in the share of whites between 1993 and 

2008. Most of the decline in whites was offset by growth 

among Asians during this period and some by growth in 

other groups, particularly Hispanics (table 3-26).

Age Differences Among Racial/Ethnic Groups

The age structure of different demographic groups (see 

table 3-24) reflects the fact that members of the different 

groups entered the S&E workforce in different numbers at 

different times. The largest demographic group, whites, is 

also the oldest, with a median age of 44. Almost one-third 

of whites were older than 50 and only 13% were age 30 or 

younger. Blacks (median age of 42) and Hispanics (median 

age of 39) are somewhat younger. Asians are even younger, with a median age of 38. The comparative youthfulness 

of Asians reflects the age distribution of Asians working 

in S&E who were born in the United States. Native-born 

Asians were dramatically younger than other demographic 

groups, including foreign-born Asians. The median age 

among U.S. native-born Asians working in S&E occupations was 30, and only 9% were older than 50. 

Racial/Ethnic Differences Among S&E 

Degree Holders 

Most patterns across demographic groups among workers in S&E occupations also hold for members of the workforce with a highest degree in an S&E field. Additionally, 

outcomes that vary by race among S&E degree holders deal 

with unemployment rates and level of degree attainment. 

In 2008, among those whose highest degree was in an 

S&E field, Hispanics and blacks had the highest unemployment rate (5.2% and 5.1%, respectively), which was roughly 

two percentage points higher than the unemployment rate 

for whites (3.2%). Although whites had the lowest unemployment rate, they also had the highest labor force nonparticipation rate (17%). Because of the large numbers of 

whites who are out of the labor force, whites have the lowest 

rates of employment among S&E highest degree holders.

Among those who are employed and whose highest degree is in an S&E field, race/ethnicity groups have concentrations in different degree fields. Differences in degree fields 

resemble those among S&E occupations. Both blacks and 

Hispanics are more concentrated in the social sciences, and 

Asians are more concentrated in engineering and in computer and mathematical sciences. In 2008, among blacks, 

more than half had their highest S&E degree in the social 

sciences, while 46% of Hispanics did (table 3-27). For both 

of these groups, close to one-third had their highest S&E 

degrees in engineering or in the computer and mathematical 

sciences. Asians, on the other hand, are heavily concentrated 

in the computer and mathematical sciences and in engineering, with 59% having their highest degree in one of these 

two fields and 20% having their highest degree in the social 

sciences. The distribution of degree fields for whites more 

closely resembles that for non-Asian groups. (See appendix 

table 3-14 for more detailed data on S&E degrees by race and 

Hispanic status.) On the whole, the field differences among 

S&E degree holders are more pronounced than are the corresponding differences among workers in S&E occupations.

Table 3-26

Distribution of workers in S&E occupations, by race/ethnicity and year: 1993–2008

(Percent)

Race/ethnicity 1993 1995 1997 1999 2003 2006 2008

Asian .................................................................. 9.1 9.6 10.4 11.0 14.2 16.1 16.9

American Indian/Alaska Native.......................... 0.2 0.3 0.3 0.3 0.3 0.4 0.3

Black .................................................................. 3.6 3.4 3.4 3.4 4.3 3.9 3.9

Hispanic ............................................................. 2.9 2.8 3.1 3.4 4.4 4.6 4.9

White ................................................................. 84.1 83.9 82.9 81.8 75.2 73.2 71.8

Native Hawaiian/Other Pacific Islander ............. NA NA NA NA 0.3 0.5 0.4

Two or more races ............................................ NA NA NA NA 1.4 1.4 1.7

NA = not available

NOTES: Before 2003, respondents could not classify themselves in more than one racial/ethnic category. Before 2003, Asian included Native Hawaiians 

and other Pacific Islanders. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (1993–2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 3-45

In addition to having concentrations in different fields, the 

demographic groups differ in the level of their highest degree. 

For example, among Asians with a highest degree in an S&E 

field, 56% have their highest degree at the bachelor’s level 

and 12% have a doctoral degree (figure 3-31). In comparison, 

among both blacks and Hispanics 79% have their highest degree at the bachelor’s level and 4% have a doctoral degree. 

Asians whose highest degree is in an S&E field are more 

likely than are others to work in an S&E occupation and 

are more likely than are others to work in the area in which 

they earned their degree (see appendix table 3-12). Among 

blacks, only one-quarter work in an S&E occupation; among 

Hispanics and American Indians/Alaska Natives nearly onethird work in an S&E occupation. By comparison, more than 

half of Asians work in these occupations. 

Race/ethnicity matters even for those with similar credentials. Some, but not all, of the high concentration of 

black S&E degree holders working outside of science and 

engineering, and the high concentration of Asian S&E degree holders working within S&E, can be explained by their 

different degree levels or fields. But Asians with an S&E 

degree have a higher propensity to work in S&E occupations 

than others even among individuals with similar degree levels and fields. Thus differences between Asians and blacks 

in the propensity among degree holders to work in S&E occupations remain even among those with the same degrees. 

Salary Differentials for Women and Minorities

Women and minority groups generally receive less pay 

than their male and white counterparts. The median salary in 

2008 among women with a highest degree in an S&E field 

and working full time was one-third lower than the median 

salary among similar men (appendix table 3-15). Salary differences between men and women are much greater among 

those who are not working in S&E occupations. Among 

those working full time in S&E occupations, women’s salaries were 18% lower than men’s. 

Racial/ethnic salary differences were somewhat smaller 

than salary differences between men and women (appendix 

table 3-16). American Indians/Alaska Natives with a highest degree in an S&E field and working full time earned 

19% less than whites; blacks earned 16% less than whites; 

and Hispanics earned 14% less than whites. These salary 

Table 3-27

Field of highest degree among workers with highest degree in S&E, by race/ethnicity: 2008 

(Percent)

Race/ethnicity All S&E fields

Computer/

mathematical 

sciences

Biological/ 

agricultural/ 

environmental 

life sciences

Physical 

sciences

Social 

sciences Engineering

Employed with highest degree in S&E ........ 12,588,000 15.3 15.6 6.7 39.3 23.2

Asian ........................................................ 1,545,000 24.7 13.8 7.4 20.2 33.9

American Indian/Alaska Native ................ 46,000 19.9 17.3 6.3 41.3 15.1

Black ........................................................ 638,000 17.0 12.3 3.5 54.0 13.1

Hispanic ................................................... 722,000 11.7 14.7 5.1 45.8 22.7

White ........................................................ 9,348,000 14.0 16.1 7.0 40.6 22.3

Native Hawaiian/Other Pacific Islander ... 53,000 14.1 14.9 3.6 40.2 27.2

Two or more races ................................... 236,000 11.1 14.7 5.5 50.3 18.5

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

Percent

Figure 3-31

Level of S&E degree among workers with highest 

degree in S&E field, by race/ethnicity: 2008 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2008), http://sestat.nsf.gov.

Science and Engineering Indicators 2012

Bachelor’s

Master’s

Doctorate

Asian American

Indian/

Alaska

Native

Black Hispanic White Native

Hawaiian/

Other

Pacific

Islander

Two or

more

races

0

20

40

60

80

100
3-46 Chapter 3. Science and Engineering Labor Force

differences were generally more modest among those who 

worked in S&E occupations. 

Overall, both salary differences between men and women 

and race/ethnicity salary differences remained largely unchanged in the 15-year period between 1993 and 2008.

Differences in average age, work experience, field of 

degree, sector of employment, and other characteristics can 

make direct comparison of salary and earnings statistics 

misleading. Statistical models can estimate the size of the 

wage difference between men and women, as well as the 

wage difference between minorities and whites 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 estimates of the expected size of the 

wage difference between men and women among individuals 

who are similar in age, work experience, field of degree, and 

other relevant characteristics; data bearing on wage differences between non-Asian minorities and whites are also included. 

These estimates are substantively consistent with many of the 

other published analyses on these topics (see, for example, 

Xie and Shauman 2003). Without accounting for any factors 

except level of degree, women working full time whose highest degree is a bachelor’s in an S&E field were paid salaries 

that were 38% lower than those of men (figure 3-32).14 This 

salary difference is substantial, but it is smaller at both the 

master’s level (28%) and at the doctoral level (24%). The salary differences for minorities relative to whites are narrower 

(figure 3-33). Minority salary levels are 10% lower than those 

of whites at the bachelor’s level, 16% lower at the master’s 

level, and 4% lower at the doctoral level. All estimated baseline differences are statistically significant. 

Effects of Occupation and Experience on Salary 

Differences

Salaries differ across occupations. For example, in the 

three S&E occupations with the lowest concentrations of 

women—aerospace, aeronautical, or astronautical engineers; 

mechanical engineers; and electrical and electronics engineers—the combined median salary among men is $90,000, 

and among women it is $81,000. These figures are substantially higher than the combined sex-specific median salary 

($65,750 for men and $54,000 for women) in the three large 

S&E occupations with the highest concentrations of women—psychologists; medical scientists, except practitioners; 

and biological scientists (see appendix table 3-15). Salary 

also varies by indicators of experience, including both age 

and years since completing education. Estimates of salary 

differences are made by applying controls for occupation, 

age, and years since completing the highest degree.15 After 

controlling for these factors, the estimated wage difference 

between men and women narrows. However, among men 

and women in similar jobs and with similar levels of experience, women are still paid 16% less than men (among individuals whose highest degree is at the bachelor’s level) and 

9% less than men (among individuals whose highest degree 

is at the master’s and doctoral level). Minorities with their 

highest degree at the bachelor’s level also earn somewhat 

less (6%) than whites, after controlling for occupation and 

experience. Among those with a doctoral degree, the wage 

difference between minorities and whites is mostly attenuated (3%) and at the master’s degree level, the difference is 

fully attenuated after controlling for occupation and experience. This illustrates that at higher degree levels, minorities 

and white degree holders in similar S&E occupations and 

with similar experiences receive about the same salaries.

Effects of Other Factors: Sector, Field of Degree, 

and Region 

Salaries vary by other work-related factors beyond occupation and experience. For example, salaries differ across 

sector. Academic and nonprofit employers typically pay 

less for the same skills than employers pay in the private 

sector, and government compensation falls somewhere 

between the two groups. These differences are salient for 

understanding salary variations by sex and race/ethnicity because whites and males are more highly concentrated in the 

Figure 3-32

Estimated differences in full-time salary between 

women and men with highest degree in S&E, 

controlling for selected employment and other 

characteristics, by degree level: 2008

NOTES: Salary differentials represent estimated percentage 

differential of women’s full-time salary relative to men’s full-time 

salary. Coefficients are estimated in a mixed-effects regression 

model using natural log of full-time annual salary as dependent 

variable. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2008), http://sestat.nsf.gov.

Science and Engineering Indicators 2012

Negative salary differential (percent)

Women’s salaries compared with men’s

Total difference

in full-time

salary

Plus field of

degree, sector,

region, and

parents’

education

Controlling for

occupation,

age, and years

since degree

Bachelor’s

Master’s

Doctorate

0 5 10 15 20 25 30 35 40
Science and Engineering Indicators 2012 3-47

private for-profit sector. Salaries also differ across regions. 

For example, at $86,000, the Pacific census division had the 

highest median salaries for scientists and engineers among 

the nine U.S. census divisions and the west-north-central, 

at $75,000, had the lowest. Almost one-quarter (23%) of 

U.S.-born underrepresented minorities worked in the Pacific 

division compared with 15% of whites, whereas whites had 

a higher concentration in the west-north-central (9%) than 

underrepresented minorities (4%). 

Salaries also vary by degree field. Salaries among those 

with degrees in engineering, the physical sciences, and in 

computer and mathematical sciences are higher than salaries 

among those with degrees in the environmental and life sciences, and among those with degrees in the social sciences. 

Degree areas with lower salaries also have higher concentrations of women and minorities. 

However, taking these factors into account16 in addition to 

occupation and experience results in only marginal changes 

in the estimated salary differences between men and women 

compared with estimates generated accounting for occupation and experience alone. Women who are similar to men 

along all seven of these factors receive salaries that are 13% 

(among bachelor’s degree holders) to 8% (among master’s 

degree and doctoral degree holders) lower than their male 

counterparts. The salary difference between minorities and 

whites fully attenuates when all seven factors are simultaneously controlled. 

Effects of Family on Salary Differences 

The family roles of wife and mother are associated with 

lower salaries for women. In contrast, the roles of husband 

and father are associated with higher salaries among men. 

To evaluate the effects of family status on wage differences 

between men and women, these differences are estimated 

separately for the set of workers in science and engineering 

occupations who are unmarried and without young children, 

who are married and without young children, and who are 

married and with young children. Each estimate is made 

accounting for occupation, age, time since degree, employment sector, field of degree, region, and parents’ educational 

attainment, as described above. The analysis presented in 

figure 3-34 considers a household to include young children 

if a child age 12 or younger was present.17

Among full-time workers with a highest degree in an 

S&E field who are both unmarried and childless, men and 

women tend to be paid similar salaries. At the bachelor’s 

level, the estimated salary difference is 3% among men and 

women who are similar in occupation, age, experience, work 

sector, degree field, region, and parents’ education (figure 

3-34). At the master’s and doctoral levels, estimated salary 

differences between men and women among the unmarried 

and childless are statistically insignificant. The presence or 

absence of children under age 12 does not consistently affect the size of salary differences between men and women 

beyond what would be expected considering other factors.18

S&E Immigrants

The foreign born constitute a considerable proportion of 

workers in science and engineering occupations, and both 

the number and share of foreign-born workers have been increasing. However, immigration of scientists and engineers 

to the United States has declined during the recent economic 

downturn. Most indicators presented in this section apply 

to all foreign born, despite the fact that the foreign born is 

a broad category comprising long-term U.S. residents with 

strong roots in the United States as well as recent immigrants 

who compete in global job markets or whose main social ties 

are in their countries of origin. 

Several sources yield broadly consistent estimates of U.S. 

reliance on foreign-born scientists and engineers. Table 3-28 

Figure 3-33

Estimated differences in full-time salary between 

underrepresented minorities and whites with 

highest degree in S&E, controlling for selected 

employment and other characteristics, by degree 

level: 2008

* Differences not significant at p < .05. 

NOTES: Salary differentials represent estimated percentage 

differential of underrepresented minorities’ full-time salary relative to 

whites’ full-time salary. Coefficients estimated in a mixed-effects 

regression model using natural log of full-time annual salary as 

dependent variable. Asians and multiracial individuals, representing 

15% of employed highest S&E degree holders, are not included in 

regression. Underrepresented minorities include American Indian/ 

Alaska Natives, blacks, Hispanics, and Native Hawaiian/Other 

Pacific Islanders. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2008), http://sestat.nsf.gov.

Science and Engineering Indicators 2012

Negative salary differential (percent)

Total difference

in full-time

salary

Plus field of

degree, sector,

region, and

parents’

education

Controlling for

occupation,

age, and years

since degree

Bachelor’s

Master’s

Doctorate

0 5 10 15 20 25 30 35 40

*

*

*

Underrepresented minorities’

salaries compared with whites’
3-48 Chapter 3. Science and Engineering Labor Force

shows upward trends in the percentage of foreign-born individuals in U.S. S&E occupations over the first decade of 

the century. The share of nonacademic scientists and engineers who are foreign born rose from 22% in 2000 to 25% 

in 2009, although some evidence suggests that the rate of 

growth slowed in the last years of the decade. 

The similarity in the estimates from SESTAT and the 

U.S. Census Bureau American Community Survey (ACS) 

is noteworthy because the two surveys differ methodologically. SESTAT surveys include only individuals who were 

counted in the most recent Decennial Censuses or who 

received a U.S. S&E degree, thereby excluding recently 

arrived foreign-born and foreign-educated scientists and engineers. The potential for an undercount of the foreign born 

is smallest in the earliest portion of the decade—the closer 

in time to the Decennial Census—and increases over the 

course of the decade.19 The ACS, on the other hand, draws 

a new sample of the U.S. residential population every year. 

However, ACS occupation coding is less precise, and the 

ACS does not distinguish postsecondary teachers in science 

and engineering fields from other postsecondary teachers. 

The similarity in the estimates from these surveys, despite 

their contrasting limitations, suggests that the overall picture 

the surveys provide is broadly accurate. 

Characteristics of the Foreign Born

The foreign born in S&E occupations tend to have higher 

levels of education than the U.S. native born. In most S&E 

occupations, the higher the degree level, the greater the 

proportion of the workforce who are foreign born (appendix table 3-17). This relationship is weakest among social 

scientists and strongest among computer and mathematical 

scientists and engineers. In 2003, at the bachelor’s degree 

level, the proportion of foreign-born individuals within occupational areas ranged between 10% (social scientists) and 

19% (computer and mathematical scientists). However, at 

the doctoral degree level, about half of the workers in computer and mathematical sciences and in engineering were 

foreign born. 

In 2003, more than half (55%) of foreign born in the 

United States with a highest degree in an S&E field came 

from Asian countries. Just over one-fifth were born in 

Europe. North America (Canada), Central America, the 

Caribbean, South America, and Africa each supply roughly 

equal numbers (each accounting for from 4% to 5% of the 

foreign born). The leading country of origin among immigrant S&E workers in the United States is India, which accounted for 16% of the foreign born. China (with 11%) is 

the second leading country. Source countries for the 276,000 

foreign-born holders of S&E doctorates are somewhat more 

concentrated, with China providing 22% and India 14% (figure 3-35 and appendix table 3-18).

Source of Education 

The majority of foreign-born scientists and engineers in 

the United States came to the United States before completing their higher education, but a substantial number came 

to the United States after receiving their university training 

abroad. Although almost half of the foreign-born, university-educated individuals working in the United States have 

a degree from a foreign university, two-thirds earned their 

highest degree from a U.S. educational institution. Among 

the foreign born with a doctoral degree, just over two-thirds 

received this degree from a U.S. institution, although nearly 

80% have at least one degree from a foreign institution. 

New Foreign-Born Workers 

The number and share of foreign-born S&E workers have 

been rising, but the volume of new foreign workers entering 

U.S. S&E occupations has shown signs of decline during the 

recent economic downturn. One indicator of new foreignborn S&E workers joining the U.S. workforce is the number 

of temporary work visas issued by the U.S. government in 

visa classes for high-skilled workers. A second indicator is 

Figure 3-34

Estimated differences in full-time salary between 

men and women with highest degree in S&E, 

controlling for selected employment and other

characteristics, by marital and parental status and 

degree level: 2008

* Not significantly different than zero at p = .05.

NOTES: Salary differentials represent estimated percentage 

differential of women's full-time salary relative to men’s full-time 

salary when controlling for occupation, age, years of experience, 

field of degree, employment sector, region, and parents’ education. 

Coefficients estimated in a mixed-effects regression model using 

natural log of full-time annual salary as dependent variable. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2008), http://sestat.nsf.gov.

Science and Engineering Indicators 2012

Negative salary differential (percent)

Population

0 5 10 15 20 25 30 35 40

Married,

with children

younger

than 12

Married,

without children

younger

than 12

Unmarried

and childless

Bachelor’s

Master’s

Doctorate

*

*
Science and Engineering Indicators 2012 3-49

the rate at which foreign-born recipients of U.S. doctoral degrees remain in the United States after earning their degree 

(‘stay-rates’).

Temporary Visas

The number of temporary work visas issued for high-skill 

workers provides an indication of the volume of immigration of these workers. However, 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. 

The largest classes of these temporary visas declined during the recent economic downturn, after several years of 

growth (figure 3-36). Data for 2010, however, suggest that 

this period of decline may be short-lived. The previous period 

of decline in the use of these visas occurred during the more 

mild recession in the earlier part of the decade, and these declines were unevenly experienced across visa categories.

H-1B temporary work visas account for a larger number 

of high-skill workers than other visa classes. This visa is issued to individuals who seek temporary entry into the United 

States in a specialty occupation that requires the skills of a 

professional. It is issued for up to 3 years with the possibility 

of an extension to 6 years. In 2010, the United States issued 

Figure 3-35

Foreign-born individuals with highest degree in S&E living in the United States, by place of birth: 2003

UK = United Kingdom

NOTE: Percents 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) 

(2003), http://sestat.nsf.gov. See appendix table 3-18.

Science and Engineering Indicators 2012

 India 16%

 China 11%

 Philippines

 6%

Former Soviet 

Union 5%

 Canada 4%

UK 4%

 Taiwan 4% South Korea 4% Germany 3%

 Vietnam 3%

 Mexico 3%

 Iran 3%

 Japan 2%

 Cuba 1%

Other foreign

born 31%

China 22%

 India 14%

UK 7%

Former Soviet Union 6%

 Taiwan 4% Canada 4%

 Germany 4%

 South Korea 3%

 Iran 2%

 Japan 2%

All S&E degrees S&E doctorate

Other foreign

born 32%

Table 3-28

Foreign-born workers in S&E occupations, by education level: Selected years, 2000–09 

(Percent)

Education

Decennial 

census SESTAT ACS SESTAT ACS SESTAT ACS ACS

All college educateda

 .......... 22.4 22.6 24.2 24.0 25.3 24.8 24.9 25.2

Bachelor’s ....................... 16.5 16.4 17.7 17.5 18.1 17.2 18.4 18.3

Master’s .......................... 29.0 30.3 32.0 32.8 33.5 33.9 32.7 33.4

Doctorate ........................ 37.6 40.5 37.8 40.9 41.8 41.4 40.9 41.6

ACS = American Community Survey; SESTAT = Scientists and Engineers Statistical Data System

a

Includes professional degrees not broken out separately.

NOTES: Includes all S&E occupations except postsecondary teachers because these occupations not separately reported in 2000 Census or ACS data 

files. SESTAT 2006 and 2008 data do not include foreign workers who arrived in the United States after 2000 Decennial Census and also did not earn S&E 

degree in United States.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, SESTAT (2003–08), http://sestat.nsf.gov; Census 

Bureau, 2000 Decennial Census Public Use Microdata Sample (PUMS) and ACS (2003, 2006, 2008, 2009). 

Science and Engineering Indicators 2012

 2003 2006 2008 2009

2000
3-50 Chapter 3. Science and Engineering Labor Force

more than 118,000 H-1B visas, down almost 25% from the 

nearly 155,000 issued in 2007.

Similarly, many fewer J-1 exchange visas—visas issued 

for limited periods of study, research, or teaching—were 

issued in 2010 than in 2007. For L-1 visas, which support 

intercompany transfers, the number was 12% lower in 2010 

than in 2007. The smaller, more specialized visa programs 

for high-skilled workers also fell slightly in 2010. These visa 

classes include O-1 (a person of outstanding ability), O-2 

(an assistant to an O-1, sometimes a postdoc), TN (collegedegreed citizens of Canada and Mexico), and E-3 (collegedegreed citizens of Australia). 

Characteristics of H-1B Visa Recipients

The H-1B visa, which is the most common visa for new 

foreign entrants into the U.S. S&E workforce, is not issued 

exclusively for scientists and engineers. Other professional 

workers who use an H-1B visa include those in administrative occupations, legal occupations, and cultural occupations 

(such as artists and entertainers). However, because the U.S. 

Citizenship and Immigration Services do not classify occupations with the same taxonomy used by the National Science 

Foundation, precise counts of H-1B visas issued to scientists 

and engineers cannot be obtained. Nevertheless, it is safe to 

say that the bulk of H-1B visa recipients work in S&E or S&Erelated occupations (appendix table 3-19). In 2009, workers in 

computer-related occupations were the most common recipients of H-1B visas, accounting for 35% of H-1B visas issued. 

The total number of newly initiated H-1B visas for workers in 

computer-related fields declined by nearly half from 2008 to 

2009 while the share of total recipients who worked in these 

fields declined from 50% to 35%. Despite this drop, the proportion of H-1B recipients who worked in computer sciences 

was considerably higher than it was early in the decade. For 

example, in 2002, only 25% of these visa recipients worked in 

computer-related fields.

H-1B visa recipients tend to possess advanced degrees. In 

FY 2009, 58% of new H-1B visa recipients had an advanced 

degree, including 40% with master’s degrees, 6% with professional degrees, and 13% with doctorates. This degree distribution differs by occupation, with 83% of mathematical 

and physical scientists holding advanced degrees (44% with 

doctorates). Among life scientists, 87% hold advanced degrees (61% with doctorates).

Almost half of recent H-1B visa recipients were from 

India (39%) or China (10%). Among doctorate holders, 29% 

were from China and another 16% from India (figure 3-37). 

Altogether, Asian citizens made up nearly two-thirds of all 

H-1B visa recipients with a doctoral degree.20 

Table 3-29 shows salaries paid to new recipients of 

H-1B temporary work visas by occupation group and level 

of degree. These starting salaries, taken from final visa application forms sent to U.S. Citizenship and Immigration 

Services, are different from—and generally higher than—

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 average salaries for doctorate holders in the life sciences may reflect the common 

use of H-1B visas to hire individuals for relatively low-paying postdoc fellowships.

Short-Term Stay Rates for U.S. Doctorate 

Recipients

Among doctoral recipients, the period immediately after 

earning the doctoral degree is a pivotal point at which longterm career trajectories may be set. Foreign doctoral recipients who remain in the United States may set themselves on 

a pathway toward long-term residency. 

At time of award, foreign students who receive doctoral 

degrees from U.S. universities report whether they intend to 

stay in the United States and whether they have a firm offer 

(either a postdoc or employment opportunity) to stay in the 

United States.21 These responses provide estimates of shortterm stay rates. 

Most foreign U.S. doctorate recipients plan to stay in the 

United States after graduation. At the time of doctorate receipt, three-quarters of foreign recipients of U.S. S&E doctorates, including those on both temporary and permanent 

visas, plan to stay in the United States, and about half have 

either accepted an offer of postdoc study or employment 

or are continuing employment in the United States (figure 

3-38).22 Through the 1980s, about half of foreign students 

who earned S&E degrees at U.S. universities reported that 

they planned to stay in the United States after graduation, and 

about one-third said they had firm offers for postdoc study 

or employment (NSB 1998). In the 1990s, however, these 

percentages increased substantially. Thus, the proportion 

Thousands

Figure 3-36

Temporary work visas issued in categories 

with many high-skilled workers: FY 1989–2009

NOTE: J-1 exchange visitor visa used for many different skill levels. 

SOURCE: U.S. Department of State, Nonimmigrant Visa Issuances 

by Visa Class and by Nationality, http://www.travel.state.gov/visa/ 

statistics/nivstats/nivstats_4582.html (accessed May 4, 2011). 

Science and Engineering Indicators 2012

1989 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

0

50

100

150

200

250

300

350

400

J-1 (Exchange visitors)

H1-B and earlier

high-skilled H’s

L-1 (Intercompany transfers)
Science and Engineering Indicators 2012 3-51

of foreign S&E doctoral degree recipients reporting plans 

to stay in the United States rose to 72% in the 1998–2001 

period and to 77% in the 2006–09 period (appendix table 

3-20). In 2009, both the percentage who reported plans to 

stay in the United States and those with firm offers to stay 

declined modestly from 2008. The number of foreign doctoral degree recipients also declined in 2009, making the 

drop from 2008 to 2009 in numbers of foreign-born doctoral 

recipients with plans to stay in the United States somewhat 

more pronounced, with 6% fewer foreign-born doctoral recipients reporting plans to stay in the United States. 

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. Between 2006 and 2009, the short-term stay 

rate in each of these four fields was about 80%, as measured 

by reports of intentions to stay in the United States. However, 

the short-term stay rate for foreign doctoral recipients in the 

social sciences and in health fields was considerably lower. 

Stay rates vary by place of origin. In the period 2006–09, 

89% of U.S. S&E doctoral recipients from China and from 

India reported plans to stay in the United States, and close 

to 60% reported accepting firm offers for employment or 

postdoc research in the United States. 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). Close to half of U.S. S&E doctoral degree recipients from Europe had firm plans to stay after graduation. In 

North America, the percentage of 2006–09 doctoral degree 

students who had definite plans to stay in the United States 

was higher for those from Canada than those from Mexico 

(see appendix table 3-20).

Between 2002–05 and 2006–09, the percentage of U.S. 

S&E doctoral degree recipients from the two top countries 

of origin (China and India) who were reporting definite plans 

to stay in the United States declined. Other countries, however, experienced sharp increases in short-term stay rates 

among S&E doctoral degree recipients in the United States, 

including Indonesia, New Zealand, Mexico, and Colombia. 

Long-Term Stay Rates

The rate at which foreign recipients of U.S. doctoral degrees who stayed in the United States immediately after they 

received their degree continue to remain in the United States 

over longer durations can also be observed.23 Recent trends 

in long-term stay rates show that within cohorts, long-term 

stay rates are similar to short-term rates. This similarity is 

particularly evident for the cohort of foreign S&E doctoral 

recipients who earned their degrees in 1993 (figure 3-40). 

Two years after receiving the doctoral degree, 53% of these 

foreign doctorates who were temporary residents when they 

earned their degree remained in the United States. By 2009, 

52% remained, with little variation along the way. More recent cohorts have had higher short-term stay rates, but these 

stay rates have declined over time. The cohort of degree recipients who earned their doctorates in 1999 had a stay rate 

after 2 years of 68%. After 10 years, this rate declined by 7 

percentage points, but the rate of decline gradually attenuated. The cohort of foreign S&E doctoral degree recipients 

of 2004 had a 2-year stay rate of 66%, which declined to 

62% by 2009 (figure 3-40; Finn 2012, forthcoming). 

The stability of stay rates over time applies whether or not 

these rates are calculated for foreign doctoral recipients from 

U.S. institutions who received their doctoral degree while on 

a temporary visa status or for those who held either a temporary or permanent visa. Temporary visa holders make up 

the largest share of foreign S&E doctoral degree recipients. 

They also have lower stay rates than do permanent residents. 

For example, among foreign S&E doctoral degree recipients 

from the 1993 cohort, those who were permanent residents 

at the time they earned their degree had stay rates that were 

24 percentage points higher than those with temporary visas. 

This difference persisted through 2009. Among more recent 

cohorts, the difference in stay rates between permanent and 

Figure 3-37

Citizenship of new recipients of U.S. H-1B 

temporary work visas: FY 2009 

EU = European Union

NOTE: Other Asia includes Hong Kong, Japan, Malaysia, Singapore, 

and Thailand. 

SOURCE: Department of Homeland Security (DHS), U.S. Citizenship 

and Immigration Services; National Science Foundation, National 

Center for Science and Engineering Statistics, special tabulations 

(2011) of DHS administrative records.

Science and Engineering Indicators 2012

China 10%

Canada 5%

South Korea 3%

Philippines 4%

United Kingdom 2%

Taiwan 2%

Pakistan 1%

Germany 1%

Mexico 2%

All new H-1B recipients

France 1%

Turkey 1%

India 39%

Other countries 29%

China

29%

Canada 6%

South Korea 8%

Taiwan 3%

Doctorate holders who are new H-1B recipients 

India 16%

Other countries 19%

Other Asia 4%

EU 15%
3-52 Chapter 3. Science and Engineering Labor Force

temporary residents was initially much smaller, but increased 

rapidly over the 5 years after receipt of the doctorate.

Because of the persistence of stay rates over time, factors 

that are associated with the level of short-term stay rates are 

similarly associated with the level of longer-term stay rates. 

For example, countries with the highest levels of short-term 

stay rates (e.g., China and India) are among the countries 

with the highest long-term stay rates. Similarly, academic 

fields that have the highest short-term stay rates (e.g., the 

physical sciences) also have the highest long-term stay rates, 

and the field with the lowest short-term stay rates, the social 

sciences, has the lowest long-term stay rates.

Some evidence suggests that stay rates may vary for doctorate recipients from graduate programs of different quality based on ratings of faculty by the publication U.S. News 

and World Report and on separate ratings by the National 

Research Council (Finn 2009). Doctorate recipients from the 

graduate programs designated among the top 25 were somewhat less likely to remain in the United States than were 

graduates of other programs (see table 3-30). The difference 

in 1-year stay rates was 2 percentage points: 69% of those 

from the top-rated programs and 71% of other doctorate recipients remained in the United States 1 year after receiving 

their degrees. By 5 years after receiving their degree, the two 

groups showed differences that rose to 5 percentage points, 

with stay rates of 59% and 64%, respectively.

Age and Retirement

The baby boom generation—the unusually large cohort 

born between 1946 and 1964 (with birth rates in the United 

States peaking in 1957)—affected the age structure of the 

S&E labor force in much the same way it affected the general labor force. Thus, in the early 1990s, this bulge produced a relatively large concentration of S&E workers in 

their late 20s to mid-40s contributing to a comparatively 

Table 3-29

Average annual salary of new H-1B visa recipients, by occupation and education level: FY 2009

(Dollars)

Occupation All degree levels Bachelor’s Master’s Professional Doctorate

Administrative specializations ................................... 57,700 56,000 59,000 77,000 89,000

Architecture/engineering/surveying........................... 71,300 71,000 68,000 77,000 82,000

Art .............................................................................. 47,800 47,000 50,000 na na

Computer-related occupations ................................. 66,300 65,000 66,000 73,000 94,000

Education .................................................................. 53,200 40,000 48,000 78,000 57,000

Entertainment/recreation ........................................... 36,600 37,000 36,000 na na

Law/jurisprudence ..................................................... 108,200 83,000 74,000 137,000 na

Life sciences .............................................................. 53,300 47,000 52,000 54,000 55,000

Managers/officials nec .............................................. 87,200 83,000 89,000 132,000 138,000

Mathematics/physical sciences ................................ 69,800 70,000 68,000 84,000 71,000

Medicine/health ......................................................... 76,500 55,000 58,000 100,000 64,000

Miscellaneous professional/technical/managerial..... 75,300 72,000 76,000 91,000 101,000

Museum/library/archival sciences ............................. 49,900 na 46,000 na na

Religion/theology ....................................................... 37,800 41,000 36,000 na na

Social sciences .......................................................... 67,400 60,000 70,000 na 95,000

Writing ....................................................................... 44,600 44,000 43,000 na na

na = not applicable; nec = not elsewhere classified

SOURCE: Department of Homeland Security (DHS), U.S. Citizenship and Immigration Services; National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of DHS administrative records.

Science and Engineering Indicators 2012

Thousands

Figure 3-38

Plans of U.S. S&E doctorate recipients with 

temporary visas at graduation to stay in United 

States, by year of doctorate: 1989–2009

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of Survey of 

Earned Doctorates.

Science and Engineering Indicators 2012

1989 1991 1993 1995 1997 1999

Year of doctorate

2001 2003 2005 2007 2009

0

2

4

6

8

10

12

14

Plans to stay

Firm plans to stay

Recipients
Science and Engineering Indicators 2012 3-53

youthful S&E workforce. By 2008, these cohorts had aged 

into their early 40s to early 60s, with the oldest nearing traditional retirement ages. One indicator of the aging of the 

S&E workforce is the increasing percentage of individuals 

in this workforce above age 50 (as seen in figure 3-41). In 

2008, 27% of individuals with S&E degrees and in S&E occupations were in that age group, whereas 15 years earlier 

just 18% were in that age group.

Another indication of the aging of the S&E labor force 

is the increase over time of the median age of individuals 

working in S&E occupations. From 1993 to 2008, the median age rose by 4 years, from 37 to 41 years of age. The median age of workers with a highest degree at the bachelor’s 

level rose by 5 years (from 35 to 40), at the master’s level by 

3 years (from 39 to 42), and at the doctoral level by 3 years 

(from 44 to 47). 

The increasing average age of S&E workers may mean 

increased experience and greater productivity among them. 

However, it could also reduce opportunities for younger researchers to make productive contributions by working independently. In many scientific fields, folklore and empirical 

evidence indicate that the most creative research comes from 

younger people (Stephan and Levin 1992).

Age Differences Among Occupations

Individuals with S&E degrees who work in S&E occupations are younger than individuals with S&E degrees who 

work in S&E-related occupations. They also are younger 

than those whose jobs are not in, nor related to, S&E. Figure 

3-42 shows, for 2008, age distributions for S&E-degree 

holders by highest degree level and broad occupational area. 

Age differences across broad occupational areas are more 

pronounced at higher degree levels. Among those whose 

highest S&E degree is at the master’s level, the median age 

of workers in S&E occupations was 42; for workers in S&Erelated occupations it was 47; for workers in jobs not in nor 

related to S&E occupations it was 49. Among those whose 

highest S&E degree is at the doctoral level, the median age 

of workers in S&E occupations was 47 compared with 50 

for workers in S&E-related occupations and 53 for workers 

in jobs not in nor related to S&E. The flow of workers out 

of S&E occupations into other occupations compared with 

the reverse flow from other occupations to S&E occupations 

contributes to much of the differences in age distributions 

across broad occupational areas. For example, among workers in S&E occupations who were observed in 2003, 16% 

were no longer in such occupations in 2006. On the other 

hand, only 5% of those workers in other occupations in 2003 

were in S&E occupations in 2006. Among the S&E workers 

who moved into other occupations, one-third (approximately 200,000 workers) went into management positions, many 

of which involve supervising S&E workers.

Age Differences Among S&E Degree Fields

In 2008, the median age among those in the labor force 

with any degree in S&E was 43. Degree holders in different areas varied in their ages. Degree holders in the physical 

Figure 3-39

Plans of U.S. S&E doctorate recipients with 

temporary visas at graduation to stay in the United 

States, by place of origin and year of doctorate: 

1998–2001 and 2006–09

NOTE: Rates are proportions of each group reporting firm 

commitment to postgraduation employment in United States.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of Survey of 

Earned Doctorates.

Science and Engineering Indicators 2012

Percent

0 20 40 60 80

Brazil

Mexico

Japan

Taiwan

Canada

South Korea

France

Italy

Germany

Turkey

China

India

Iran

1998–2001

2006–09

Percent

Figure 3-40

Stay rates for U.S. S&E doctorate recipients with 

temporary visas at graduation, by selected year 

of doctorate: 1995–2009

SOURCE: Finn M, Stay rates of foreign doctorate recipients from 

U.S. universities: 2012, Oak Ridge Institute for Science and 

Education (forthcoming). 

Science and Engineering Indicators 2012

1995 1997 1999 2001 2003 2005 2007 2009

0

50

60

70

80

1993 graduates

1999 graduates

2004 graduates
3-54 Chapter 3. Science and Engineering Labor Force

sciences were comparatively old with a median age of 47 

and 38% of the field’s workers over age 50 (figure 3-43). 

Degree holders in computer and mathematical sciences were 

relatively young, with a median age of 42 and only 22% over 

age 50. Within degree areas, specific fields differed considerably in the ages of their workers. For example, within 

engineering the youngest degree holders were in bioengineering and biomedical engineering, with a median age of 

34 and with 39% younger than age 30 (see appendix table 

3-21). On the other hand, more than 40% of the workers in 

metallurgical engineering and mining and mineral engineering were older than 50.

Leaving the Labor Force and Retirement

The increasing share of the S&E labor force over age 50 

makes retirement patterns among S&E workers more important in terms of how they will affect the supply of these workers. Recent patterns of labor force exit and work reduction 

among the older members of the workforce suggest that by 

age 55 rates of participation in the S&E workforce begin to 

decline and are markedly reduced by the time workers reach 

Figure 3-41

Workers older than age 50 in S&E occupations, by 

highest degree level and year: 1993–2008

Percent

NOTES: Total includes professional degrees not broken out separately. 

National estimates not available from Scientists and Engineers 

Statistical Data System (SESTAT) in 2001.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, SESTAT (1993–2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

1993 1995 1997 1999 2003 2006 2008

0

10

20

30

40

50

All degree levels

Bachelor’s

Master’s

Doctorate

Percent

Figure 3-42

Age distribution of employed individuals with 

highest degree in S&E, by degree level and 

broad occupational area: 2008

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

30–50

<30

>50

 S&E S&Erelated

Bachelor’s Master’s Doctorate

NonS&E S&E S&ErelatedNonS&E S&E S&ErelatedNonS&E

0

20

40

60

80

100

Table 3-30

Temporary U.S. residents who received S&E doctorates in 2002, by program rating and year: 2003–07 

(Percent)

Program rating

 Foreign doctorate 

recipients (n) 2003 2004 2005 2006 2007

All programs .................................... 7,850 70 67 65 63 63

Top-rated programs..................... 2,611 69 65 62 60 59

All other programs ....................... 5,239 71 69 67 65 64

NOTE: Characterization of programs as “top-rated” by Finn, using ratings of faculty reputation in research from U.S. News and World Report and National 

Research Council.

SOURCE: Finn M, Stay rates of foreign doctorate recipients from U.S. universities: 2012, Oak Ridge Institute for Science and Education (forthcoming).

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 3-55

their mid-60s. One indication of the relationship between 

age and the level of labor force participation is illustrated 

by figure 3-44, which shows full-time work rates among 

older S&E degree holders by highest level of education. In 

2008, at age 55, 78% of those whose highest degree was 

at the bachelor’s level, 75% of those whose highest degree 

was at the master’s level, and 89% of those whose highest 

degree was at the doctoral level worked full time. However, 

at all degree levels, full-time labor force participation rates 

decline quickly as S&E workers age into their late 50s. By 

age 61, more than half of S&E bachelor’s degree holders are 

not working full time. Among those whose highest degree 

is at the master’s level, this milestone is reached at age 62. 

For S&E doctoral degree holders, half are not working full 

time by age 64. After age 65, no more than one-quarter of 

the workforce with a highest degree at the master’s or bachelor’s level worked full time. Among those with a doctoral 

degree, this proportion is reached at age 71.

Another indicator of the relationship between age and 

labor force participation is the proportion of S&E degree 

holders who reported that they were out of the labor market. 

In 2008, at age 55, 12% of those whose highest degree is at 

the bachelor’s level, 7% of those whose highest degree is at 

the master’s level, and 5% of those whose highest degree 

was at the doctoral level were out of the labor force. By the 

early 60s, the proportion of people who are out of the labor 

force takes a sharp turn upwards, and by age 65 about half 

of those whose highest degree is at the master’s level and 

half of those whose highest degree is at the bachelor’s level 

report that they are neither working nor looking for work. 

Among those with a doctoral degree, more than half report 

neither working nor looking for work at age 68. 

Table 3-31 shows the rates at which holders of U.S. S&E 

doctorates left full-time employment, by sector of employment, between April 2006 and October 2008. Rates of leaving full-time employment for S&E doctorate holders were 

higher for those working in the private sector than those 

Percent

Figure 3-43

Age distribution among employed individuals with highest degree in S&E, by degree field: 2008

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Scientists and Engineers Statistical Data System 

(SESTAT) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

All fields Computer/

mathematical

sciences

Biological/agricultural/

environmental

life sciences

Physical sciences Social sciences Engineering

0

20

40

60

80

100

<30

30–50

>50

Figure 3-44

Older individuals with highest degree in S&E who 

work full time, by age and degree level: 2008 

Percent

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012

Years of age

55 57 59 61 63 65 67 69 71 73 75

0

20

40

60

80

100

Bachelor’s

Master’s

Doctorate
3-56 Chapter 3. Science and Engineering Labor Force

employed in education or government, although in the oldest group this sector difference largely disappears. 

Between 1993 and 2008, increasing percentages of 

SESTAT respondents in their 60s reported that they were 

still in the labor force. Whereas 59% of S&E degree holders between the ages of 60 and 64 were employed in 1993, 

the comparable percentage rose to 66% in 2006. For S&E 

degree holders between ages 65 and 69, the increase was 

larger, rising from 32% in 1993 to 44% in 2006. After peaking in 2006, rates of employment among workers in their 

60s declined slightly in 2008, but remained above rates prior 

to 2006. Other indicators, including full-time employment 

rates and retirement rates, show similar patterns, as do comparisons restricted to workers with similar highest degree 

levels and degrees in similar fields. In recent years, labor 

force participation has also risen slightly among S&E degree 

holders in their early 70s, but has not changed among those 

in their late 50s. 

Global S&E Labor Force

Work that involves science and engineering occurs 

throughout the world. Such work is concentrated in developed nations, where most 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 in S&E 

occupations, are increasingly mobile, and the number who 

leave their native countries to pursue education and career 

goals is growing. In recent years many nations, recognizing 

the value of high-skill 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).

Ideally, data on the global S&E labor force would include statistics on its overall size and growth, enable detailed 

comparisons of S&E labor force characteristics in different 

countries, and track flows of S&E workers across national 

boundaries. 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. The U.S. SESTAT 

system, for example, 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 member 

countries of the Organisation for Economic Co-operation 

and Development (OECD), they do not cover several countries—including Brazil, India, and Israel—that have been 

making concerted efforts to build knowledge economies in 

which S&E play a central role, and they do not provide fully 

comparable data for China.

This section begins with information about the size and 

growth of workforce segments whose jobs involve S&E in 

nations for which relevant data exist. It then reports limited 

data on high-skill migration trends. Data on the role of immigrants in the U.S. S&E labor force are reported earlier in 

this chapter (see “Demographics in the S&E Labor Force”). 

The section closes with data on international employment by 

U.S. multinational companies and international engagement 

by members of the U.S. S&E workforce. 

Size and Growth of 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 mostly 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, p. 

93). Because this definition can be applied differently when 

different nations conduct surveys, international comparisons 

should be made with caution. The OECD also reports data 

on personnel employed directly in R&D. These data include 

clerical and administrative staff employed in R&D organizations as well as professionals whose skills and career paths 

are more closely connected to R&D. 

OECD reports an estimated increase in researchers in its 

member countries from 2.8 million in 1995 to 4.2 million in 

2007. OECD also publishes estimates for eight non-member 

economies, including China and Russia; adding these to the 

OECD member total for 2007 yields a worldwide estimate of 6.3 

million. Numerous uncertainties affect this estimate, however: 

Table 3-31

Employed S&E doctorate holders who left full-time 

employment after April 2006, by employment 

sector and age: October 2008

(Percent)

April 2006 employment sector

Age 

(years)

All

sectors Education Government

 Business/

 industry

50–55 ...... 4.7 3.3 2.5 6.9

56–62 ...... 9.7 7.9 10.2 11.7

63–70 ...... 27.6 26.3 28.0 29.3

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2006, 2008), http://sestat.nsf.gov. 

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 3-57

Some non-member countries that engage in large and 

growing amounts of research (e.g., India, Brazil) are 

omitted entirely from these totals. 

China’s data for 2009, collected in accordance with 

OECD definitions and standards, yield an estimate of 

about 440,000 fewer researchers than China’s data for the 

preceding year.

For some countries and regions, including the United 

States and the European Union, OECD estimates are derived from multiple national data sources and not from a 

uniform or standardized data collection procedure.

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 

3-45). 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 2008. The 

United States and the European Union experienced steady 

growth but at a lower rate between 1995 and 2007, both starting the period at about 1 million researchers and increasing 

to almost 1.5 million. Japan (little change) and Russia (decline, especially early in the period; see also Gokhberg and 

Nekipelova 2002) were exceptions to the overall worldwide 

trend. 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 3-22).

OECD also estimates the proportion of researchers in 

the workforce in different economies. In OECD’s most recent estimates, small economies in Scandinavia (Denmark, 

Finland, Iceland, Norway, Sweden) and East Asia (Singapore, 

Taiwan) report that at least 1% of their workforce are researchers (appendix table 3-23).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 (1.04%), South Korea (1.00%), and the 

United States (0.95%). Although China reports a large number of researchers, they are a much smaller percentage of its 

workforce (0.15%) than in OECD member countries.

Several Asian economies have shown marked and continuous increases since 1995 in the percentage of their workforce employed as researchers. These include China, South 

Korea, Singapore, and Taiwan. In the United States and 

Japan, where growth occurred at all, it took place mostly 

between 1995 and 2003 (figure 3-46). Patterns and trends in 

the proportion of the workforce classified as R&D personnel 

are generally similar to those for researchers.

High-Skill Migration 

Worldwide or internationally comparable data on migration 

of workers in S&E occupations or with college-level S&E degrees do not exist. Docquier, Lowell, and Marfouk (2009; see 

also Docquier and Marfouk 2006) compiled and analyzed data 

on migrants to OECD countries in 1990 and 2000. Their data 

come from almost 200 source locations, all but a handful of them 

independent nations. They report several characteristic patterns 

in high-skill migrations, defined as emigration of people with 

some postsecondary education from the country of their birth:

Between 1990 and 2000, the overall number of immigrants to OECD countries increased from about 42 million to about 58 million.

Rates of legal emigration were much greater among highskill persons than among persons with less education.

In countries the World Bank classifies as low income, 

the gap in emigration rates between high- and low-skill 

groups (6.1% compared with a total emigration rate of 

0.5%) was especially large. 

The proportion of women among high-skill migrants 

rose, partly but not entirely because of the worldwide increase in the proportion of people with some postsecondary education who are women.

Countries estimated to have the largest number of highskill emigrants living in OECD countries in 2000 were 

the United Kingdom (1.5 million), the Philippines (1.1 

Figure 3-45

Estimated number of researchers in selected 

countries/regions: 1995–2009

Thousands

EU = European Union

NOTES: 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 (2010/1 

and earlier years).

Science and Engineering Indicators 2012

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

1995 1997 1999 2001 2003 2005 2007 2009

United States

EU

Japan

South Korea

China

Russia
3-58 Chapter 3. Science and Engineering Labor Force

million), India (1.0 million), Mexico (0.9 million), and 

Germany (0.9 million) (figure 3-47).

In both 1990 and 2000, about half of the immigrants with 

tertiary education living in OECD countries were in the 

United States.

In a more limited study covering six major destination 

countries, Defoort (2008) concluded that worldwide emigration rates for high-skill persons were stable between 1975 

and 2000; Docquier and Marfouk (2006) calculate an increase in the migration rate for these persons from 5.0% to 

5.4% between 1990 and 2000. Nonetheless, because worldwide education levels are rising, the proportion of high-skill 

persons among those who immigrated to OECD countries 

rose between 1990 and 2000 (Docquier and Marfouk 2006).

Insofar as S&E workers, especially those in natural science 

and engineering fields, are less dependent on language- and 

culture-specific skills than highly educated workers trained 

in other fields, they may be more internationally mobile than 

other high-skill workers. Thus, in the United States high-skill 

immigrants are disproportionately found in S&E occupations 

and disproportionately have degrees in the natural sciences 

and engineering. However, current international data do not 

enable researchers to assess whether and how migration rates 

vary among different categories of high-skill workers.

R&D Employment Abroad by U.S. Companies

R&D jobs located abroad in U.S.-owned companies are 

an indicator of global engagement in the world’s S&E workforce. Data from the 2009 Business R&D and Innovation 

Survey 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 (table 3-32). 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. 

Among firms with five or more employees, R&D employment is disproportionately domestic. About one-third of 

all employees are located abroad, compared with about onequarter of R&D employees. There is a large disparity between the overall proportion of manufacturing employment 

that is foreign (41%) and the proportion of manufacturing 

R&D employment that is foreign (25%). In contrast, the proportions in nonmanufacturing industries are similar: 24% for 

overall employment and 23% for R&D employment. 

Larger companies locate more of their R&D employment 

outside the country than small ones. In firms with 1,000 

or more employees, 30% of R&D employment is foreignbased, whereas only 11% is foreign-based in firms with 

Figure 3-46

Researchers as a share of total employment in 

selected countries/regions: 1995–2009

Per thousand

EU = European Union

NOTES: 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 (2010/1 

and earlier years).

 Science and Engineering Indicators 2012

1995 1997 1999 2001 2003 2005 2007 2009

0

2

4

6

8

10

12

United States

EU

Japan

South Korea

China

Russia

Figure 3-47

Top countries of origin of foreign-born persons 

having at least a tertiary education and residing 

in an OECD country: 2000

OECD = Organisation for Economic Co-operation and Development 

SOURCE: Docquier F, Lowell BL, Marfouk A. A Gendered 

Assessment of Highly Skilled Emigration (2009), http://perso. 

uclouvain.be/frederic.docquier/filePDF/DLM_PDR09.pdf. 

Science and Engineering Indicators 2012

Emigrants (thousands)

0 200 400 600 800 1,000 1,200 1,400 1,600

United Kingdom

Philippines

India

Mexico

Germany

China

South Korea

Canada

Vietnam

Poland

United States
Science and Engineering Indicators 2012 3-59

Table 3-32

Domestic and foreign business-sector employment, by company characteristics: 2009

Company size Industry type

5–999 1,000 Manufacturing Nonmanufacturing

Education Number Percent Number Percent Number Percent Number Percent

Total employment

Worldwide ....................... 4,915 100 22,177 100 16,679 100 10,415 100

Domestic ..................... 3,840 78 13,947 63 9,882 59 7,906 76

Foreign ........................ 1,075 22 8,321 37 6,798 41 2,509 24

R&D employment

Worldwide ....................... 587 100 1,290 100 1,137 100 742 100

Domestic ..................... 523 89 902 70 850 75 574 77

Foreign ........................ 65 11 391 30 287 25 167 23

NOTES: Data are representative of companies where worldwide R&D expense plus worldwide R&D costs funded by others are greater than zero. 

Includes 2002 North American Industry Classification System (NAICS) codes 21–23, 31–33, and 42–81. Detail may not add to total because of rounding. 

Industry classification based on dominant business code for domestic R&D performance, where available. For companies not reporting business codes, 

classification used for sampling was assigned. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Business R&D and Innovation Survey (2009 preliminary). 

Science and Engineering Indicators 2012

fewer than 1,000 employees. In both cases, comparable percentages are higher for overall employment (37% and 22%, 

respectively). 

The domestic and foreign R&D workforces of U.S.-

located businesses have similar occupational and demographic profiles. Data on broad occupational categories, 

levels of educational attainment, and sex distributions for 

businesses in different sectors and of different sizes are in 

appendix table 3-24.

Multinational companies (MNCs) perform a substantial 

proportion of R&D through foreign direct investment (FDI) 

(see chapter 4). Data on MNC R&D employment count 

managers, scientists, engineers, and other professional and 

technical employees engaged in R&D. The Survey of U.S. 

Direct Investment Abroad, conducted by the Bureau of 

Economic Analysis (BEA), provides data on R&D employment of parent companies of U.S. MNCs and their overseas 

affiliates every 5 years. Preliminary data for this indicator 

are available for 2009. Separately, BEA’s Survey of Foreign 

Direct Investment in the United States includes data on U.S. 

R&D employment by foreign-based MNCs.25

Between 1994 and 2004, R&D employment in the United 

States by foreign firms grew slightly faster than R&D employment abroad by U.S. firms. During this period, R&D 

employment in the United States by majority-owned affiliates26 of foreign firms rose from 89,800 to 128,500, a 43% 

increase (figure 3-48). Over the same 10 years, R&D employment by U.S. firms at their majority-owned foreign affiliates grew 35%, from 102,000 in 1994 to 137,800 in 2004. 

Adding U.S. parent company R&D employment of 716,400 

workers, U.S. MNCs employed 854,200 R&D workers globally (figure 3-49) in 2004.

The average annual growth in R&D employment abroad 

by U.S. firms from 1994 to 2004 was 3%. This shifted their 

proportion of overseas employment slightly, increasing it 

from 14% to 16% of total employment.

The 2009 data on MNC R&D employment abroad show 

a markedly different trend after 2004 from the trend in the 

preceding decade. About 85% of MNC R&D employment 

growth occurred abroad. Whereas employment abroad nearly doubled, domestic employment during the same period 

Figure 3-48

R&D employment of U.S. multinational corporations 

at their foreign affiliates, and foreign MNCs at their 

U.S. affiliates: 1994, 1999, 2004, and 2009

Thousands

MNC = multinational corporation

NOTE: Includes only employment at majority-owned affiliates.

SOURCE: Bureau of Economic Analysis, Survey of Foreign Direct 

Investment in the United States and Survey of U.S. Direct Investment 

Abroad (various years). 

Science and Engineering Indicators 2012

1994 1999 2004 2009

(preliminary)

0

50

100

150

200

250

300

U.S. MNCs abroad Foreign MNCs in United States
3-60 Chapter 3. Science and Engineering Labor Force

grew by less than 5%. As a result, the proportion of MNC 

R&D employment located outside the United States went 

from 16% to 27%. 

The unprecedented increase in U.S. MNC R&D employment abroad contrasts with the continuation of modest 

growth in R&D employment by foreign firms in the United 

States. Because of this, unlike in 2004 and prior years, the 

amount of R&D employment attributed to U.S. MNCs 

abroad is much larger than the comparable figure for foreign 

firms in the United States (figure 3-48).

The data in figures 3-48 and 3-49 are consistent with two 

trends discussed in this chapter: growth in S&T employment 

in the United States coinciding with a general expansion 

throughout the world of the capacity to do S&T work. 

International Engagement by the Domestic 

S&E Workforce

Working with people in foreign countries is an indicator of how globally engaged the S&E workforce is. In 

2006, SESTAT asked survey respondents whether they had 

worked “with individuals located in other countries” during 

a particular week. Seventeen percent of respondents reported that they had.

Thousands

Figure 3-49

R&D employment of U.S. multinational corporations’ 

parent companies in the United States and their 

foreign affiliates: 1994, 1999, 2004, and 2009

MNC = multinational corporation

NOTE: Includes only employment at majority-owned affiliates.

SOURCE: Bureau of Economic Analysis, Survey of U.S. Direct 

Investment Abroad (various years). 

Science and Engineering Indicators 2012

1994 1999 2004 2009

(preliminary)

0

200

400

600

800

1,000

1,200

Parent companies of U.S. MNCs

Foreign affiliates of U.S. MNCs

The proportion of the workforce that reported this kind of 

international engagement varied depending on differences 

in their work roles and demographic characteristics (table 

3-33; appendix table 3-25) (NSF/NCSES 2012c, forthcoming). The following patterns were found among SESTAT 

respondents:

Workers in for-profit organizations (24%) had the highest 

rates of international work, more often reporting such work 

than those in government, education, self-employment, 

or nonprofit organizations. Federal government workers 

had higher rates than state or local employees, and those 

in 4-year higher educational institutions had higher rates 

than persons teaching at institutions serving less advanced 

students. 

Workers in S&E occupations had much higher rates of 

international engagement (28%) than those in non-S&E 

(16%) or S&E-related (8%) occupations.

Among those in S&E occupations, computer and mathematical scientists and engineers had the highest rates 

of international engagement and social scientists had the 

lowest rates. However, within employment sectors field 

differences did not consistently follow this pattern.

Doctorate holders had substantially higher rates of international engagement than individuals whose highest degrees were at the master’s or bachelor’s level. Professional 

degree holders had the lowest rates of all.

Men (21%) reported international engagement more often than women (11%). 

Foreign-born survey respondents (24%) reported international engagement more often than U.S.-born individuals 

(15%).

SESTAT respondents who earned degrees both in the 

United States and abroad had the highest rates of international engagement (31%). The comparable figure for 

those who earned their degrees abroad was 23%, and for 

those with only U.S. degrees it was 16%.

SESTAT respondents showed substantial variation in 

international engagement depending on their work activities. For persons reporting either computer applications, 

programming, and systems or R&D as a primary or secondary work activity, the rate of international engagement was 

high—about one-quarter reported an international interaction. Rates for teaching (6%) and for professional services 

(7%) were substantially lower than for other activities.

Data on another indicator of international engagement, 

international coauthorship of S&E journal articles, are reported in chapter 5.
Science and Engineering Indicators 2012 3-61

Table 3-33

Scientists and engineers reporting international engagement, by demographic characteristics, education, 

employment sector, occupation, and salary: 2006

Reporting international engagement

Characteristic Total employment Number Percent

All employed scientists and engineers ...................... 18,927,000 3,157,000 16.7

Sex

Male ....................................................................... 10,683,000 2,293,000 21.5

Female ................................................................... 8,244,000 865,000 10.5

Place of birth

U.S. born ................................................................ 15,714,000 2,397,000 15.3

Not U.S. born ......................................................... 3,213,000 761,000 23.7

Age group (years)

24 ......................................................................... 619,000 86,000 13.9

25–34 ..................................................................... 3,951,000 679,000 17.2

35–44 ..................................................................... 5,169,000 1,006,000 19.5

45–54 ..................................................................... 5,381,000 886,000 16.5

55–64 ..................................................................... 3,165,000 425,000 13.4

65 ......................................................................... 641,000 75,000 11.8

Place of postsecondary education

All degrees earned in United States ...................... 17,031,000 2,675,000 15.7

Degrees earned abroad and in United States ....... 730,000 229,000 31.4

All degrees earned abroad..................................... 114,000 254,000 22.8

Highest degree

Bachelor’s .............................................................. 10,886,000 1,761,000 16.2

Master’s ................................................................. 5,384,000 970,000 18.0

Professional ........................................................... 1,774,000 171,000 9.7

Doctorate ............................................................... 883,000 254,000 28.8

Employment sector

Business/industry .................................................. 13,137,000 2,653,000 20.2

For-profit ............................................................ 7,682,000 2,048,000 26.7

Self-employeda

 ................................................... 3,624,000 478,000 13.2

Non-profit ........................................................... 1,830,000 127,000 6.9

Government ........................................................... 2,228,000 216,000 9.7

Federal ............................................................... 824,000 146,000 17.8

State/local .......................................................... 1,405,000 69,000 4.9

Education ............................................................... 3,562,000 289,000 8.1

4-year educational institutionsb .......................... 1,549,000 229,000 14.8

Other educational institutionsc

 ........................... 2,014,000 60,000 3.0

Occupation

S&E occupations ................................................... 5,024,000 1,416,000 28.2

Computer/mathematical scientists ................... 2,112,000 667,000 31.6

Biological/agricultural/other life scientists ......... 487,000 116,000 23.9

Physical scientists .............................................. 334,000 80,000 23.9

Social scientists ................................................. 47,000 70,000 14.8

Engineers ........................................................... 1,621,000 483,000 29.8

S&E-related occupations ....................................... 5,246,000 394,000 7.5

Non-S&E occupations ........................................... 8,657,000 1,348,000 15.6

Salary

<$30,000 ................................................................ 2,923,000 190,000 6.5

$30,000–49,999 ..................................................... 4,127,000 362,000 8.8

$50,000–69,999 ..................................................... 3,872,000 522,000 13.5

$70,000–89,999 ..................................................... 2,986,000 636,000 21.3

$90,000–109,999 ................................................... 2,068,000 551,000 26.6

$110,000 .............................................................. 2,950,000 897,000 30.4

a

Includes self-employed or business owners in incorporated or unincorporated businesses, professional practices, or farms. 

b4-year educational institutions include 4-year colleges or universities, medical schools (including university-affiliated hospitals or medical centers), and 

university-affiliated research institutions.

c

Other educational institutions include 2-year colleges, community colleges, or technical institutes and other precollege institutions. 

NOTES: International engagement defined as working with individuals located in other countries during survey reference week. Scientists and engineers

refers to all persons who have received a bachelor’s degree or higher in an S&E or S&E-related field, plus persons holding a non-S&E bachelor’s or 

higher degree who were employed in an S&E or S&E-related occupation in 2003. Numbers rounded to nearest 1,000. Detail may not add to total because 

of rounding.

SOURCE: National Science Foundation, National Center for Science and Engineering, Scientists and Engineers Statistical Data System (SESTAT) (2006), 

http://sestat.nsf.gov. 

Science and Engineering Indicators 2012
3-62 Chapter 3. Science and Engineering Labor Force

Conclusion

The S&E labor force may be defined in a variety of 

ways. At its core are individuals in S&E occupations, with 

S&E degrees, using knowledge and skills closely related to 

their S&E training, and working in jobs that make use of 

this expertise. But in a modern knowledge-based economy 

many workers have one or two of these attributes rather 

than all of them. Nonetheless, by any plausible definition, the S&E labor force experienced strong growth in the 

United States and the world throughout the second half of 

the twentieth century.

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 amounts of education and experience. Workers with S&E training or in S&E 

occupations are less likely to be unemployed. Industries 

with higher proportions of workers in S&E occupations tend 

to offer higher pay even to their employees who are in other 

lines of work. 

Worldwide, growing numbers of workers are engaged in 

research. Growth has been especially marked in rapidly developing economies, such as South Korea and China, 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, while the number of researchers in the struggling Japanese economy has been stagnant. 

The United States has shown some recent signs of slower 

growth: little change in the number of trained workers in 

S&E occupations, an aging S&E workforce that is drawing 

nearer to retirement (though showing signs of delaying retirement to somewhat later ages), and a modest drop during the most recent recession in the proportion of foreign 

recipients of U.S. advanced S&E degrees who join the U.S. 

labor force. At the same time, members of historically underrepresented groups (e.g., women, blacks) have played an 

increasing role in the U.S. S&E labor force, although more 

so in some fields (e.g., biological and social sciences) than 

in others (e.g., mathematical and physical sciences and engineering). In addition, the United States has remained an 

attractive destination for foreign workers with advanced 

S&E training. 

Numerous factors beyond the availability of workers 

equipped to use S&E knowledge and skills on the job will 

affect the kinds of jobs that the U.S. economy generates in 

the future. As a result, data on current labor force trends do 

not necessarily portend future patterns that will emerge in a 

dynamic world economy recovering from the shocks produced by a prolonged economic downturn. 

Notes

1. The standard definition of the term labor force includes the population that is employed or not working 

but seeking work (unemployed); other individuals are not 

considered 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. Despite the limitations of this subjective measure, 

variations among occupations in the proportions of workers who say they need this level of S&E technical expertise 

accord with common sense. For example, among doctoral 

level postsecondary teachers of physics, 99.7% said they 

needed at least a bachelor’s degree level of knowledge in 

engineering, computer sciences, mathematics, or the natural 

sciences, compared with 5% among doctoral level postsecondary teachers of English. Likewise, among the small numbers of 

S&E bachelor’s degree holders whose occupation is secretary/

receptionist/typist, fewer than one in six reported that their job 

needed bachelor’s level S&E expertise of any kind.

3. 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 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 coding. 

All of these differences can affect the estimates.

4. Many comparisons using Census Bureau data on occupations are limited to looking at all S&E occupations except 

postsecondary teachers because the Census Bureau aggregates all postsecondary teachers into one occupation code. 

Only NSF surveys of scientists and engineers and some BLS 

surveys collect data on postsecondary teachers by field.

5. SESTAT/National Survey of College Graduates (NSCG) 

2003 and 2008 estimates for the data displayed in figure 3-11 

are not comparable. The 2003 estimates include a full complement of respondents to the 2003 NSCG, many of whom 

report that their jobs require S&E expertise, even though 

they lack degrees in S&E fields. SESTAT 2008 continues to 

gather data from S&E degree holders identified in the NSCG, 

but does not include individuals who are not either in S&E 

occupations or holders of S&E degrees. Thus, SESTAT 2003 

data, although less current, are in some ways better suited for 

analyzing the relationships among occupations, degrees, and 

subjective assessments of job requirements. Relevant 2003 

data were reported in Science and Engineering Indicators 

2010. Because of the limitations of the 2008 SESTAT data, 

table 3-3 uses 2003 estimates.

6. Only U.S. citizens and nationals may be appointed 

in the competitive civil service; however, federal agencies may employ certain noncitizens who meet specific 

employability requirements in the excepted service or the 

Senior Executive Service.
Science and Engineering Indicators 2012 3-63

7. This list does not include the National Institutes of 

Health, which is a part of the Department of Health and 

Human Services (DHHS). The proportion of all federal scientists and engineers working at DHHS is 5%.

8. SES includes occupations of senior managerial, supervisory, and policy positions in the executive branch of the 

federal government who generally serve as the link between 

political appointees and the rest of the federal workforce.

9. The commercialization success rate is the ratio of patents commercialized to patents granted.

10. The patent activity rate is the proportion who report 

having been named as an inventor on a patent application in 

the previous 5 years.

11. 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. 

12. Many doctorate holders with salaries at this level are 

postdocs in temporary training positions.

13. 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 traditionally defined as a temporary position that individuals take 

primarily for additional training—a period of advanced professional apprenticeship—after completion of a doctorate.

14. This estimate differs slightly from the observed median difference in salary by sex because the former addresses 

mean differences and the latter addresses median differences. The former is influenced by extreme cases and outliers, 

and the latter is not.

15. Occupation, age, and years since completion of education are each controlled for as a random effect. SESTAT 

respondents working in science and engineering have been 

classified into 62 distinct occupations. Age is observed in 

one of eleven 5-year brackets. Years of experience are observed in one of twelve 5-year brackets.

16. Occupational sector, region, field of degree, and parents’ education are each controlled for as a random effect. 

Employers are classified into one of seven sectors: 4-year 

colleges and universities, 2-year colleges, for-profit private 

sector, nonprofit private sector, self-employment, federal 

government, and state and local government. Regions are 

classified into the nine U.S. census divisions. Field of degree

is observed in 1 of 142 distinct degree fields among individuals whose highest degree is at the bachelor’s level, and 

within 123 distinct degree fields among individuals whose 

highest degree is at the doctoral level. Parents’ education

measures the highest level of education completed by either 

parent and is observed in one of eight categories. 

17. The analysis was repeated with different age cut-points 

defining young children. Results did not change substantially 

when this age limit was adjusted (from ages 0–18 to ages 

0–6), indicating that the finding in the text is not substantively sensitive to where this cut-point is set.

18. Among married workers with children younger than 

age 12, the estimated salary differences between men and 

women are generally similar in magnitude to the estimates 

for all scientists and engineers. For example, among workers whose highest degree is a bachelor’s in an S&E field, the 

estimated salary difference by sex is 13% among all workers 

and is also 13% among workers who are married and with 

children younger than age 12. At the doctoral level, the estimated 8% salary difference by sex applies to all workers and 

to workers who are married with children. Only at the master’s degree level is the estimated salary difference between 

men and women among the married with children larger (at 

15%) than the difference among all workers (7%).

19. In the future, however, the largest component of 

SESTAT, the National Survey of College Graduates, will 

be refreshed on a biennial basis using respondents from the 

ACS, and so the undercount of recent foreign arrivals will 

be minimized.

20. This includes East Asians, South Asians, and Southeast 

Asians, but excludes individuals from countries in the Middle 

East and from the former Soviet Republics. 

21. This question is part of the Survey of Earned Doctorates, 

which is administered to all recipients of U.S. doctoral degrees. 

22. The growth in the number of doctoral students from 

China accounts for much of the rapid increase in foreign recipients of doctoral degrees from the early 1980s through 

1996. During this period, the annual count of Chinese recipients of doctoral degrees rose from fewer than 10 to more 

than 3,000 (from 0.1% to 27.4% of all foreign doctoral 

degree recipients). The decline in foreign doctoral degree 

awards following 1996 also is partially, but not fully, accounted for by changes in the numbers of Chinese doctoral 

degree recipients. One contributing factor to the decline in 

1996 was the Chinese Student Protection Act of 1992.

23. Long-term stay rates are observed by annually calculating the ratio of the number of noncitizen Survey of 

Earned Doctorate respondents who made Social Security 

contributions to the number of noncitizen Survey of Earned 

Doctorate respondents. 

24. OECD’s 2009 estimates for Norway and Singapore 

exceeded 1%, although the 2008 estimates reported in appendix table 3-23 did not. Iceland, which is not included 

in appendix table 3-23, was also above 1% in both years. 

OECD’s estimate for Japan reported in the text is also more 

recent than that in the appendix table.

25. Although R&D employment by subsidiaries is an important indicator of international R&D activity, it has a significant limitation in that it does not include various external 

arrangements for performing R&D, ranging from R&D contracting to consulting work and strategic collaborations. 

26. An affiliate is a company or business enterprise located in one country but owned or controlled by a parent 

company in another country. Majority-owned affiliates are 

those in which the ownership stake of parent companies is 

more than 50%.
3-64 Chapter 3. Science and Engineering Labor Force

Glossary

Career path job: A job that helps graduates fulfill their 

future career plans.

European Union (EU): A union of 27 member states 

on the continent of Europe, including 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.

Federally funded research and development center 

(FFRDC): An organization that performs research and development and is exclusively or substantially financed by 

the federal government either to meet a particular research 

and development objective or, in some instances, to provide 

major facilities at universities for research and associated 

training purposes.

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.

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 

30 countries headquartered in Paris, France. The member 

countries are Australia, Austria, Belgium, Canada, Czech 

Republic, Denmark, Finland, France, Germany, Greece, 

Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, 

Mexico, Netherlands, New Zealand, Norway, Poland, 

Portugal, Slovak Republic, 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 nonprofit organization, primarily for gaining additional education and training in research 

after completion of a doctorate.

SESTAT: Scientists and Engineers Statistical Data 

System, a system of three surveys conducted by the National 

Science Foundation that measure the educational, occupational, and demographic characteristics of the science and 

engineering 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 students on temporary visas 

who stay in the United States 1–10 years after receiving a 

doctorate.

Tertiary education: 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.

Workforce: A subset of the labor force that includes only 

employed individuals.

References

Alpert A, Auyer J. 2003. Evaluating the BLS 1988–2000 

employment projections. Monthly Labor Review

(October):13–37.

Census Bureau. 2009. American Community Survey PUMS 

file, special tabulations. http://www.census.gov/acs.

Davis T, Hart DM. 2010. International cooperation to manage high-skill migration: The case of India-U.S. relations. 

Review of Policy Research 27(4):509–26.

Defoort C. 2008. Long-term trends in international migration: An analysis of the six main receiving countries. 

Population-E 63(2):285–318.

Docquier F, Lowell BF, Marfouk A. 2009. A gendered assessment of highly skilled emigration. Population and 

Development Review 35(2):297–321.

Docquier F, Marfouk A. 2006. International migration 

by educational attainment, 1990–2000. In Ozden C, 

Schiff M, editors, International Migration, Remittances 

and Development. New York: Palgrave Macmillan. p. 

151–200.

Finn M. 2012 (forthcoming). Stay rates of foreign doctoral 

recipients from U.S. universities. Oak Ridge, TN: Oak 

Ridge Institute for Science and Education.

Gokhberg L, Nekipelova E. 2002. International migration of 

scientists and engineers in Russia. In OECD, International 

Mobility of the Highly Skilled. Paris: OECD. p. 177–188.

National Research Council (NRC). 2010. Rising Above 

the Gathering Storm, Revisited: Rapidly Approaching 

Category 5. Washington, DC: National Academies Press.

National Science Board (NSB). 1998. Science and 

Engineering Indicators 1998. Arlington, VA: National 

Science Foundation (NSB 98-1).

National Science Board (NSB). 2003. Report of the National 

Science Board Committee on Education and Human 

Resources Task Force on National Workforce Policies 

for Science and Engineering. Arlington, VA: National 

Science Foundation.

National Science Board (NSB). 2008. Science and 

Engineering Indicators 2008. Two volumes. Arlington, 

VA: National Science Foundation (volume 1, NSB 08-

01; volume 2, NSB 08-01A). 

National Science Board (NSB). 2010. Science and 

Engineering Indicators 2010. Arlington, VA: National 

Science Foundation (NSB 10-01).

National Science Foundation, National Center for Science 

and Engineering Statistics (NSF/NCSES). 2003. Scientists 

and Engineers Statistical Data System (SESTAT), special 

tabulations. http://sestat.nsf.gov. 

National Science Foundation, National Center for Science 

and Engineering Statistics (NSF/NCSES). 2008. Scientists 

and Engineers Statistical Data System (SESTAT), special 

tabulations. http://sestat.nsf.gov. 

National Science Foundation, Division of Science Resources 

Statistics (NSF/SRS). 2008b. Postdoc participation of 

science, engineering, and health doctorate recipients. 
Science and Engineering Indicators 2012 3-65

NSF 08-307, Hoffer T, Grigorian K, and Hedberg E. 

Arlington, VA: NSF/SRS.

National Science Foundation, National Center for Science 

and Engineering Statistics (NSF/NCSES). 2012a (forthcoming). BRDIS 2008-9 DST. Arlington, VA.

National Science Foundation, National Center for Science 

and Engineering Statistics (NSF/NCSES). 2012b (forthcoming). Federal S&E 2009 DST. Arlington, VA.

National Science Foundation, National Center for Science 

and Engineering Statistics (NSF/NCSES). 2012c (forthcoming). International collaboration of scientists and 

engineers.

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Statistical Area Definitions and Guidance on Their 

Uses. OMB Bulletin No. 10.02. http://www.whitehouse.

gov/sites/default/files/omb/assets/bulletins/b10-02.pdf. 

Accessed 26 June 2011.

Organisation for Economic Co-operation and Development 

(OECD). 2002. Frascati Manual. Paris: OECD.

Shachar A. 2006. The race for talent: Highly skilled migrants and competitive immigration regimes. New York 

University Law Review 81(1):148–206.

Stephan P, Levin S. 1992. Striking the Mother Lode in 

Science: The Importance of Age, Place, and Time. New 

York: Oxford University Press.

Xie Y, Shauman K. 2003. Women in Science: Career 

Processes and Outcomes. Cambridge Massachusetts: 

Harvard University Press.

Chapter 4

Research and Development: 

National Trends and 

International Comparisons

4-1

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

Trends in National R&D Performance .....................................................................................4-4

R&D and GDP Growth .............................................................................................................4-4

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

R&D by Multinational Companies ...........................................................................................4-5

Exports and Imports of R&D-Related Services ........................................................................4-5

Federal R&D .............................................................................................................................4-5

Federal R&E Tax Credit ...........................................................................................................4-5

International R&D Comparisons ..............................................................................................4-5

Introduction ..................................................................................................................................4-7

Chapter Organization ................................................................................................................4-7

Trends in National R&D Performance ........................................................................................4-7

Trends in U.S. R&D Performance and R&D Intensity ............................................................4-7

Performers of R&D ...................................................................................................................4-8

Sources of R&D Funding .......................................................................................................4-12

R&D by Character of Work ...................................................................................................4-15

R&D, GDP Growth, and Innovation-Related Metrics ............................................................4-17

U.S. Business R&D....................................................................................................................4-17

Domestic R&D Performance and Funding Sources ...............................................................4-18

Domestic R&D Performance Funded by Others ....................................................................4-19

Domestic R&D Performance by Size of Company ................................................................4-19

Largest Domestic R&D-Performing Industries ......................................................................4-21

Business Activities for Domestic R&D ..................................................................................4-21

R&D Performed Abroad by U.S.-Owned Companies ..............................................................4-25

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

U.S. Affiliates of Foreign Companies ....................................................................................4-25

U.S. MNCs Parent Companies and Their Foreign Affiliates .................................................4-26

Exports and Imports of R&D-Related Services .........................................................................4-27

Federal R&D ..............................................................................................................................4-28

Federal R&D Budget by National Objectives ........................................................................4-28

Federal Spending on R&D by Agency ...................................................................................4-31

Federal Spending on Research by Field .................................................................................4-33

Federal R&E Tax Credits .......................................................................................................4-35

Federal Technology Transfer and Other Innovation-Related Programs ...................................4-37

Federal Technology Transfer ..................................................................................................4-38

Small Business Innovation-Related Programs ........................................................................4-38

International R&D Comparisons ...............................................................................................4-40

Global Patterns of R&D Expenditures ...................................................................................4-40

Comparison of Country R&D Intensities ...............................................................................4-42

R&D by Performing Sector and Source of Funds ..................................................................4-44

Government R&D Priorities ...................................................................................................4-46

Business R&D Focus .............................................................................................................4-48

Business Support for Academic R&D ....................................................................................4-52
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: 2004–09 ......4-9

Table 4-2. Annual rates of growth in U.S. R&D expenditures, total and by performing 

sectors: 1989–2009................................................................................................................4-10

Table 4-3. U.S. R&D expenditures, by performing sector, source of funds, and character 

of work: 2009 ........................................................................................................................4-14

Table 4-4. U.S. R&D expenditures, by character of work and performing sectors: 

1979–2009 .............................................................................................................................4-16

Table 4-5. U.S. average annual real GDP growth rates, unadjusted and R&D adjusted: 

1959–2007 .............................................................................................................................4-17

Table 4-6. Domestic R&D performed by the company, by industry and company 

size: 2008 ..............................................................................................................................4-19

Table 4-7. Sources of funds for domestic R&D performed by the company, by 

selected industry and company size: 2008 ............................................................................4-20

Table 4-8. Business R&D performed in the United States by the company, paid for 

by the company and by others, by industry group: 2008 ......................................................4-22

Table 4-9. Domestic R&D performance paid for by the company for top 10 business 

activities: 2008 ......................................................................................................................4-24

Table 4-10. Domestic R&D performance paid for by others for top 10 business 

activities: 2008 ......................................................................................................................4-24

Table 4-11. R&D performed abroad by U.S.-owned companies: 2008 .....................................4-25

Table 4-12. R&D performed by majority-owned affiliates of foreign companies 

in the United States, by selected NAICS industry of affiliate and country: 2008 ................4-26

Table 4-13. R&D performed by U.S. multinational companies: 1999–2008 ............................4-27

Table 4-14. R&D performed abroad by majority-owned foreign affiliates of U.S. 

parent companies, by selected NAICS industry of affiliate and region/country/ 

economy: 2008 ......................................................................................................................4-29

Table 4-15. U.S. trade balance in research, development, and testing services, by 

affiliation: 2006–09 ...............................................................................................................4-30

List of Sidebars

Measured and Unmeasured R&D ................................................................................................4-8

Location of R&D Performance by State ....................................................................................4-11

Recent Developments in Innovation-Related Metrics ...............................................................4-18

U.S. Business R&D and Innovation Survey ..............................................................................4-23

Foreign Direct Investment in R&D ............................................................................................4-25

Linking MNC Data from International Investment and Business R&D Surveys ......................4-26

Federal Budgetary Concepts and Related Terms .......................................................................4-30

Tracking R&D: The Gap between Performer- and Source-Reported Expenditures ..................4-34

Major Federal Legislation Related to Technology Transfer and Commercializing R&D .........4-39

Federal Technology Transfer: Activities and Metrics ...............................................................4-40

Comparing International R&D Expenditures ............................................................................4-42

R&D Intensity and the Composition of Gross Domestic Product ..............................................4-44

Global R&D Expenses of Public Corporations ........................................................................4-51

Government Funding Mechanisms for Academic Research .....................................................4-52

Conclusion .................................................................................................................................4-53

Notes ..........................................................................................................................................4-53

Glossary .....................................................................................................................................4-55

References ..................................................................................................................................4-56
Science and Engineering Indicators 2012 4-3

List of Figures

Figure 4-1. U.S. total R&D expenditures: 1953–2009 ..............................................................4-10

Figure 4-2. Ratio of U.S. R&D to gross domestic product, roles of federal and 

nonfederal funding for R&D: 1953–2009 .............................................................................4-10

Figure 4-3. Shares of U.S. total R&D expenditures, by performing sector and funding 

source: 2009 ..........................................................................................................................4-13

Figure 4-4. U.S. R&D, by performing and funding sectors: 1953–2009 ...................................4-13

Figure 4-5. U.S. total R&D expenditures, by source of funding: 1953–2009 ...........................4-15

Figure 4-6. U.S. R&D by character of work, basic research by performing sector, 

and basic research by source of funds: 2009 .........................................................................4-17

Figure 4-7. Domestic R&D performed and paid for by the company as a percentage 

of domestic net sales: 2008 ...................................................................................................4-19

Figure 4-8. Shares of domestic R&D performed in the United States, by industry 

group: 2008 ...........................................................................................................................4-23

Figure 4-9. Regional shares of R&D performed abroad by foreign affiliates of U.S. 

MNCs: 1997–2008 ................................................................................................................4-28

Figure 4-10. Federal budget authority for R&D, by budget function: FY 1980–2010 ..............4-31

Figure 4-11. Federal obligations for R&D and R&D plant: FY 1980–2009 .............................4-33

Figure 4-12. Federal obligations for R&D, by agency and character of work: FY 2009 .........4-36

Figure 4-13. Federal obligations for research, by agency and major S&E field: FY 2009 .......4-37

Figure 4-14. Global R&D expenditures by region: 2009 ..........................................................4-43

Figure 4-15. Gross domestic expenditures on R&D by the United States, EU, 

and selected other countries: 1981–2009 ..............................................................................4-44

Figure 4-16. Gross expenditures on R&D as share of gross domestic product, for 

selected countries: 1981–2009 ..............................................................................................4-46

Figure 4-17. Share of industrial R&D, by industry sector and selected country: 2007–10 .......4-50

Figure 4-18. Academic R&D financed by business, for selected countries: 1981–2009 ..........4-52

Figure 4-A. Differences in U.S. performer-reported and agency-reported federal R&D: 

1985–2009 .............................................................................................................................4-34

Figure 4-B. Composition of gross domestic product, for selected countries/economies, 

by sector: 2010 ......................................................................................................................4-44

Table 4-16. Federal obligations for R&D and R&D plant, by agency and performer: 

FY 2009 .................................................................................................................................4-32

Table 4-17. Federal obligations for R&D, by agency and character of work: FY 2009 ...........4-35

Table 4-18. Federal laboratory technology transfer activity indicators, total and 

selected U.S. agencies: FY 2004 and FY 2009 .....................................................................4-41

Table 4-19. International comparisons of gross domestic expenditures on R&D and 

R&D share of gross domestic product, by region and selected country/economy: 

2009 or most recent year .......................................................................................................4-45

Table 4-20. Expenditures on R&D as share of gross domestic product for all R&D, 

nondefense R&D, and basic research, by selected country/economy: 2009 or most 

recent year .............................................................................................................................4-47

Table 4-21. Gross expenditures on R&D by performing sector, by selected country/ 

economy: 2009 or most recent year ......................................................................................4-47

Table 4-22. Gross expenditures on R&D by funding source, by selected country/ 

economy: 2009 or most recent year ......................................................................................4-48

Table 4-23. Government R&D support by major socioeconomic objectives, 

by selected region/country: 1981–2009 ................................................................................4-49

Table 4-A. Top 10 U.S. states in R&D performance, by sector and intensity: 2008 .................4-12

Table 4-B. Global R&D spending by top 20 corporations: 2009 ..............................................4-51
4-4 Chapter 4. Research and Development: National Trends and International Comparisons

Trends in National R&D Performance

Growth in total U.S. R&D performance slowed noticeably 

in 2009, compared to the last several years, but the broader 

trend remains that R&D spending growth continues to significantly outpace growth of the U.S. economy as a whole.

Overall R&D performed in the United States in 2009 totaled an estimated $400 billion (current dollars)—somewhat below the $403 billion level in 2008, but well above 

the $377 billion in 2007. Adjusted for inflation, the 2009 

estimate represents a $6 billion or 1.7% decline from 2008. 

The 2009 slowdown primarily reflects a drop in business 

R&D in the face of the 2008–09 financial crisis and the 

economic recession. At the same time, R&D spending 

in other performing sectors continued to rise, notably for 

federal and academic R&D, in part because of the onetime federal R&D funding increase appropriated in the 

American Recovery and Reinvestment Act of 2009. 

U.S. R&D performance has increased largely uninterrupted since 1953. Over the last 5 years (2004–09), annual 

growth in U.S. R&D spending averaged 5.8%, compared 

to annual average growth of 3.3% for U.S. gross domestic product (GDP). Indeed, over the last several decades, 

average annual growth in R&D spending has substantially 

outpaced that of GDP. 

The business sector continues to account for most of both 

U.S. R&D performance and R&D funding.

The business sector performed an estimated $282 billion of 

R&D in 2009, or 71% of the U.S. total, drawing on business, 

federal sources, and other sources of R&D support. The 

business sector itself provided an estimated $247 billion of 

funding for R&D in 2009, or 62% of the U.S. total; almost 

all of which supported R&D performed by business. 

The levels of business R&D performance and funding 

were both higher in 2008 than in 2009 ($291 billion and 

$259 billion, respectively). Even with the decline in 2009, 

expanded business spending 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 $54 billion in 2009, 

or about 14% of the national total. 

The federal government is the second-largest funder of 

U.S. R&D, providing an estimated $124 billion, or 31% of 

the U.S. total in 2009. 

U.S. R&D is dominated by development activities, largely performed by the business sector. The business sector 

also performs the majority of applied research, but most 

basic research is conducted at universities and colleges 

and funded by the federal government.

In 2009, basic research was about 19% ($76 billion) of total U.S. R&D performance, applied research was about 18% 

($71 billion), and development was about 63% ($253 billion). 

Universities and colleges historically have been the main 

performers of U.S. basic research—and accounted for about 

53% of all U.S. basic research in 2009. The federal government remains the primary source of basic research funding, 

accounting for about 53% of all such funding in 2009. 

The business sector is the predominant performer of applied research, accounting for 58% of all U.S. applied 

research in 2009. Business is also the largest source of 

funding for applied research, providing 48% in 2009.

Development is by far the largest component of U.S. R&D. 

Funding for development comes primarily from the business sector, 78% in 2009; nearly all of the rest comes from 

the federal government. 

R&D and GDP Growth

Treating R&D as an investment, rather than as an expense, affects estimates of GDP growth.

When R&D is treated as an investment, estimates of average annual GDP growth between 1959 and 2007 are 0.07 

points higher than when R&D is treated as an expense.

The difference in estimated average annual growth is higher in recent periods: 0.17 percentage points for 1995 to 

2001 and 0.12 percentage points from 2002 to 2007. 

U.S. Business R&D

Domestic R&D performed by the business sector reached 

$291 billion in 2008. 

More than three-quarters of U.S. business R&D is performed in six industry groups—four in manufacturing 

(chemicals, computer and electronic products, aerospace 

and defense, and automotive) and two in services (software 

and computer-related products, and R&D services). 

Highlights
R&D by Multinational Companies

The majority of R&D by U.S. multinational companies 

(MNCs) continues to be performed in the United States. 

Outside the United States, R&D by U.S.-owned foreign affiliates is performed mostly in Western Europe, 

Canada, and Japan, followed more recently by other locations in the Asia-Pacific region.

In 2008, U.S. MNC parent companies and their majority-owned foreign affiliates performed $236.1 billion in 

R&D worldwide, according to the Bureau of Economic 

Analysis. This included $199.1 billion performed by the 

parent companies in the United States and $37.0 billion by 

their majority-owned foreign affiliates. 

The share of R&D performed by Asia-located affiliates 

(other than in Japan) increased from 5.3% to 14.4% from 

1997 to 2008. In particular, the share of U.S.-owned affiliates R&D performed in China, South Korea, Singapore, 

and India rose from a half percentage point or less in 1997 

to 4% for China, just under 3% for South Korea, and just 

under 2% each for Singapore and India in 2008. 

Majority-owned affiliates of foreign MNCs located in the 

United States (U.S. affiliates) performed $40.5 billion of 

R&D in 2008 virtually unchanged from the $41.0 billion 

they performed in 2007. Since 1999, the share of these 

companies in total business R&D has fluctuated narrowly 

between 13% and 15%.

Exports and Imports of R&D-Related Services

Trends in cross-border transactions in research, development, and testing (RDT) services are another indicator of global linkages.

In 2009, U.S. RDT exports and imports stood at $18.2 billion and $15.8 billion, respectively, for a balance of $2.5 

billion.

In 2008, the proportion of RDT exports ($17.4 billion) to 

domestic U.S. business R&D performance ($290.7 billion) 

was 5.6%. This proportion was about 3.8% in 2001.

Most transactions in RDT services—around 85% of 

total annual RDT exports—occur within multinational 

companies. 

Federal R&D

Federal spending on R&D has continued to grow, although at a slower pace, when adjusted for inflation, in 

the last several years. Defense continues to account for 

more than half of annual federal R&D spending. Healthrelated R&D accounts for the majority of federal nondefense R&D.

Eight federal agencies accounted for 97% of federal R&D 

spending in FY 2009: the departments of Commerce, 

Defense, Energy, Health and Human Services, and 

Homeland Security, and the National Science Foundation 

and National Air and Space Administration. Federal obligations for R&D have increased annually since the late 

1990s. When adjusted for inflation, growth has been flatter 

after FY 2005. 

In FY 2009, federal obligations for R&D reached $133.3 

billion and an additional $3.6 billion for R&D plant. The 

American Recovery and Reinvestment Act of 2009 obligated an additional $8.7 billion for R&D and $1.4 billion 

for R&D plant for the same fiscal year. 

In the last 10 years, federal funding for basic and applied 

research has grown faster in the life sciences, mathematics/computer sciences, and psychology than in other fields. 

In the environmental sciences, growth has not kept pace 

with inflation.

Over the last two decades, the greatest change in federal 

R&D priorities has been the rise in health-related R&D, 

which currently accounts for just over half of nondefense 

R&D spending.

Federal R&E Tax Credit

To counteract potential business underinvestment in 

R&D, the federal government makes available tax credits for companies that expand their R&D activities.

Business research and experimentation (R&E) tax credit 

claims were about $8.3 billion both in 2007 and in 2008. 

Five industries accounted for 75% of R&E credit claims in 

2008: computer and electronic products; chemicals, including pharmaceuticals and medicines; transportation equipment, including motor vehicles and aerospace; information, 

including software; and professional, scientific, and technical services, including computer and R&D services.

International R&D Comparisons

The top three R&D-performing countries: United States, 

China—now the second largest R&D performer—and 

Japan represented just over half of the estimated $1.28 

trillion in global R&D in 2009. 

The United States, the largest single R&D-performing 

country, accounted for about 31% of the 2009 global total, 

down from 38% in 1999. 

Asian countries—including China, India, Japan, Malaysia, 

Singapore, South Korea, Taiwan, and Thailand—represented 24% of the global R&D total in 1999 but accounted 

for 32% in 2009, including China (12%) and Japan (11%).

Science and Engineering Indicators 2012 4-5
4-6 Chapter 4. Research and Development: National Trends and International Comparisons

The pace of real growth over the past 10 years in China’s overall R&D remains exceptionally high at about 20% annually. 

The European Union accounted for 23% total global R&D 

in 2009, down from 27% in 1999. 

Wealthy economies generally devote larger shares of 

their GDP to R&D than do less developed economies.

The U.S. R&D/GDP ratio (or R&D intensity) was about 

2.9% in 2009 and has fluctuated between 2.6% and 2.8% 

during the past 10 years, largely reflecting changes in business R&D spending. 

In 2009, the United States ranked eighth in R&D intensity—surpassed by Israel, Sweden, Finland, Japan, South 

Korea, Switzerland, and Taiwan—all of which perform far 

less R&D annually than the United States. 

Among the top European R&D-performing countries, 

Germany reported a 2.8% R&D/GDP ratio in 2008; 

France, 2.2%; and the United Kingdom, 1.9%. 

The Japanese and South Korean R&D/GDP ratios were 

among the highest in the world in 2008, each at about 

3.3%. China’s ratio remains relatively low, at 1.7%, but has 

more than doubled from 0.8% in 1999. 
Science and Engineering Indicators 2012 4-7

Introduction

Research and development activities are an important input to commercial innovation and the objectives of government agencies. R&D is part of a class of intangible inputs that 

also include software, higher education, and worker training. 

Intangibles are at least as important sources of long-term 

economic growth as are physical investments in machinery, 

equipment, and other infrastructure (Corrado et al. 2006; 

Jorgenson 2007; Van Ark and Hulten 2007). Indeed, the 

America COMPETES Act1 specifically recognizes the role of 

innovation, STEM education, entrepreneurship, and technology transfer based on federally performed or funded R&D in 

strengthening U.S. competitiveness. 

This chapter focuses on R&D, presenting data on public 

and private funding and performance in the United States. It 

also examines related international investments or transactions involving R&D financing or performance.

Chapter Organization

This chapter is organized into eight main sections. A section on trends in national R&D performance is followed by four 

sections on the business sector. Business R&D, the second section, covers domestic R&D in detail. The third section covers 

foreign operations of U.S.-owned companies. The fourth section examines R&D by U.S. multinational companies (MNCs) 

and foreign-owned MNCs with U.S. activities, and the fifth describes international transactions in R&D services. 

The sixth section presents patterns of federal government 

R&D, including mission areas such as defense, energy, and 

health, and concludes with federal tax incentives for business R&D. This is followed by a section on selected federal 

programs to aid small businesses and activities in technology 

transfer and commercialization.

The eighth and last section discusses international comparisons of R&D, including national R&D expenditures by 

performer and source (including universities), national R&D 

intensities, and government R&D priorities across member 

countries of the Organisation for Economic Cooperation and 

Development (OECD). The chapter also includes two appendix tables (appendix tables 4-1 and 4-2) that contain information on how R&D comparisons across time and among 

different countries can be made.

Trends in National R&D Performance

The U.S. R&D system consists of a variety of performers and sources of funding, including businesses, the federal 

government, universities and colleges, other (nonfederal) government, 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. (See 

sidebar, “Measured and Unmeasured R&D.”) The discussion 

throughout this section examines current levels and key trends 

in U.S. R&D performance and funding (see Glossary for definitions).2

 Supporting this section is a series of appendix tables 

(appendix tables 4-3 through 4-10) that report core data on U.S. 

national patterns of R&D funding and performance.

Trends in U.S. R&D Performance and 

R&D Intensity

Overall spending on R&D conducted in the United States 

in calendar year 2009 is estimated to have totaled $400.5 billion, somewhat below the 2008 level of $403.0 billion, but 

well above the $377.0 billion in 2007 (current dollars) (table 

4-1). Adjusted for inflation, the 2009 level is a $6 billion or 

1.7% decline from 2008.3

The 2009 spending slowdown primarily reflects a drop 

in business R&D in both current and constant dollars in the 

face of the 2008–09 financial crisis and economic recession. However, R&D spending in other sectors continued to 

rise, in both current and constant dollar terms. Some of this 

was the effect—notably for federal and academic R&D and 

R&D infrastructure—of the one-time $18.3 billion funding increase appropriated in the American Recovery and 

Reinvestment Act of 2009 (ARRA, Public Law 111-5, enacted in February 2009).

The 2009 slowdown in spending growth notwithstanding, 

increases in national R&D spending have occurred largely 

uninterrupted since 1953 in both current and real dollars 

(figure 4-1). U.S. R&D spending crossed the $100 billion 

(current dollars) threshold in 1984, passed $200 billion in 

1997, exceeded $300 billion in 2004, and was at or above 

$400 billion in both 2008 and 2009. 

The year-over-year rate of R&D funding growth outpaced 

that of gross domestic product in each of the last 3 years—

even during the economic downturn (table 4-2). Over the 

last 5 years (2004–09), annual growth in the total of R&D 

spending averaged 5.8%, compared to GDP at 3.3%. And, 

similarly, growth in total R&D spending outpaces that of 

GDP when the averaging period is either 10 or 20 years. The 

same relative findings prevail when the dollars are adjusted 

for inflation (table 4-2). 

R&D intensity—a country’s national R&D expenditures 

expressed as a percentage of its GDP—provides another 

gauge of overall national R&D performance and is a widely 

used target-setting tool internationally. 

In 2009, the U.S. R&D/GDP ratio was nearly 2.9%, rising from around 2.8% in 2008 and 2.7% in 2007 (figure 

4-2). The ratio has ranged from 1.4% in 1953 to a high of 

nearly 2.9% in 1964 and has fluctuated in the range of 2.1% 

to 2.8% in the subsequent years. 

Most of this continuity in the U.S. R&D/GDP ratio reflects the growth in nonfederal R&D spending, which rose 

from about 0.6% of GDP in 1953 to just below 2.0% in the 

last several years. The increase reflects the growing role of 

business R&D in the national R&D system and, more broadly, the growing prominence of R&D-derived goods and services in the national and global economies. 

The peaks and valleys in the U.S. R&D/GDP ratio also 

reflect changing federal R&D priorities. The ratio’s drop 

from its peak in 1964 resulted largely from federal cutbacks 
4-8 Chapter 4. Research and Development: National Trends and International Comparisons

in defense and space R&D programs. From 1975 to 1979, 

gains in energy R&D activities worked to keep the ratio stable. Beginning in the late 1980s, cuts in defense-related R&D 

lowered the federal R&D/GDP ratio, which was counterbalanced by a steady or rising nonfederal ratio. Since 2000, increased federal spending for, notably, defense and biomedical 

research have helped to push upward the federal ratio.

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, intramural R&D activities of 

federal agencies, federally funded R&D centers (FFRDCs), 

universities and colleges, and other nonprofit organizations. For state-level detail see sidebar, “Location of R&D 

Performance by State” and chapter 8. 

The statistics on U.S. R&D discussed in this section reflect the National Science Foundation’s periodic National 

Patterns of R&D Resources reports and data series with a 

comprehensive account of total U.S. R&D performance. 

The National Patterns data, in turn, derive from five major NSF surveys of organizations that perform the bulk of 

U.S. R&D. These are: 

Survey of Federal Funds for R&D

Survey of R&D Expenditures at Federally Funded 

R&D Centers

Business R&D and Innovation Survey

Survey of R&D Expenditures at Universities and Colleges

Survey of R&D Funding and Performance by Nonprofit 

Organizations

National Patterns integrates the R&D spending and funding data from these separate surveys into U.S. R&D totals, 

which are calculated on a calendar-year basis, disaggregated 

for the main performing sectors and funding sources. Due to 

practical constraints, 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.

To reduce cost and respondent burden, the U.S. business R&D estimates are derived from a survey of R&Dperforming companies with five or more employees. 

Accordingly, no estimates of R&D performance currently 

are available for companies with fewer than five employees. (NSF is currently working on the design and implementation of a Microbusiness Innovation and Science 

and Technology (MIST) Survey, which will collect data 

from companies with fewer than five employees.)

Social science R&D had, until 2008, been excluded 

from the U.S. business R&D statistics. R&D in the humanities and other non-S&E fields (such as law) has been 

excluded from the U.S. academic R&D statistics. (Other 

countries include both in their national statistics, making 

their national R&D expenditures relatively larger when 

compared with those of the United States.) Changes are 

now underway in both these respects in the U.S. surveys. 

NSF’s new U.S. Business R&D and Innovation Survey 

(see BRDIS sidebar later in this chapter), fielded for the 

first time in 2009 (to collect 2008 data), now includes 

social science R&D ($1.2 billion in 2008) and will also 

better capture the full range of business R&D funded by 

others. NSF is also now fielding a redesigned Higher 

Education R&D Survey (starting with the 2010 academic 

fiscal year), which will include non-S&E R&D expenditures in the reported totals. 

The statistics for academic R&D track research expenditures that are separately budgeted and accounted 

(notably, sponsored research). But U.S. universities 

generally do not maintain records for the “departmental 

research” performed by faculty, which then cannot be 

included in the academic R&D totals. This can be a significant limitation in international R&D comparisons, as 

department research estimates are often included in the 

national statistics of other countries. (For a further discussion, see sidebar “Government Funding Mechanisms for 

Academic Research” later in this chapter.) 

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 performance by state governments 

had only been sporadically collected until 2006 and 2007, 

when NSF and the U.S. Census Bureau first fielded a survey on this topic (now being conducted every 2 years; 

state government R&D performance totals only several 

hundred million dollars annually). Finally, 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 has commissioned the National Research 

Council’s Committee on National Statistics (CNSTAT) 

to form a panel to review the methodologies used in developing the National Patterns dataset. The panel began 

work in mid-2011.

Measured and Unmeasured R&D
Science and Engineering Indicators 2012 4-9

Business Sector 

The business sector is by far the largest performer of U.S. 

R&D. R&D performed by businesses in the United States 

totaled an estimated $282.4 billion in 2009 (table 4-1), about 

71% of total U.S. R&D (figure 4-3). This predominance of 

the business sector has long been the case (figure 4-4), with 

shares of national R&D performance ranging from 69% to 

75% over the course of the last 20 years. The business sector 

is also the nation’s largest R&D funder, accounting for about 

62% of the U.S. total.

Table 4-1

U.S. R&D expenditures, by performing sector and source of funding: 2004–09

Sector 2004 2005 2006 2007 2008 2009

Current $millions

All performing sectors ............................................ 302,503 324,993 350,162 376,960 403,040 400,458

Business ............................................................. 208,301 226,159 247,669 269,267 290,681 282,393

Federal government ............................................ 37,685 39,568 41,611 43,906 44,674 46,151

Federal intramurala

 .......................................... 24,898 26,322 28,240 29,859 29,839 30,901

FFRDCs ........................................................... 12,788 13,246 13,371 14,047 14,835 15,250

Industry administeredb ................................. 2,485 2,601 3,122 5,165 6,346 6,446

U&C administeredb ...................................... 7,659 7,817 7,306 5,567 4,766 4,968

Nonprofit administered ................................ 2,644 2,828 2,943 3,316 3,724 3,835

Universities and colleges .................................... 43,122 45,190 46,955 49,010 51,650 54,382

Other nonprofit organizations ............................. 13,394 14,077 13,928 14,777 16,035 17,531

All funding sectors .................................................. 302,503 324,993 350,162 376,960 403,040 400,458

Business ............................................................. 191,266 207,680 227,057 246,679 258,626 247,357

Federal government ............................................ 91,656 96,276 100,768 105,822 117,611 124,432

Universities and colleges .................................... 7,936 8,578 9,285 9,959 10,707 11,436

Nonfederal government ...................................... 2,883 2,922 3,021 3,265 3,518 3,675

Other nonprofit organizations ............................. 8,761 9,538 10,031 11,235 12,578 13,559

Constant 2005 $millions

All performing sectors ............................................ 312,548 324,993 339,202 354,864 371,184 364,951

Business ............................................................. 215,218 226,159 239,917 253,484 267,706 257,355

Federal government ............................................ 38,937 39,568 40,308 41,332 41,143 42,059

Federal intramurala

 .......................................... 25,724 26,322 27,356 28,109 27,480 28,161

FFRDCs ........................................................... 13,212 13,246 12,953 13,224 13,663 13,897

Industry administeredb ................................. 2,568 2,601 3,024 4,862 5,844 5,875

U&C administeredb ...................................... 7,913 7,817 7,078 5,241 4,389 4,528

Nonprofit administered ................................ 2,732 2,828 2,851 3,121 3,429 3,495

Universities and colleges .................................... 44,554 45,190 45,485 46,137 47,568 49,561

Other nonprofit organizations ............................. 13,839 14,077 13,492 13,911 14,767 15,977

All funding sectors .................................................. 312,548 324,993 339,202 354,864 371,184 364,951

Business ............................................................. 197,617 207,680 219,950 232,220 238,184 225,425

Federal government ............................................ 94,700 96,276 97,614 99,619 108,315 113,399

Universities and colleges .................................... 8,200 8,578 8,995 9,375 9,861 10,422

Nonfederal government ...................................... 2,979 2,922 2,926 3,074 3,240 3,349

Other nonprofit organizations ............................. 9,052 9,538 9,717 10,576 11,584 12,356

FFRDC = federally funded research and development center; U&C = universities and colleges

a

 Includes expenditures of federal intramural R&D and costs associated with administering extramural R&D.

b Los Alamos National Laboratory (approximately $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 the 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 July 1 instead of October 1. Some of the figures for other nonprofit 

organizations are estimated and may later be revised. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, National Patterns of R&D Resources (annual series). See 

appendix tables 4-3 and 4-7.

Science and Engineering Indicators 2012
4-10 Chapter 4. Research and Development: National Trends and International Comparisons

Table 4-2

Annual rates of growth in U.S. R&D expenditures, total and by performing sectors: 1989–2009

(Percent)

Longer term trend Most recent years

Expenditures and gross domestic product 1989–2009 1999–2009 2004–09 2006–07 2007–08 2008–09

Current dollars

Total R&D, all performers ........................... 5.3 5.0 5.8 7.7 6.9 -0.6

Business ................................................. 5.3 4.5 6.3 8.7 8.0 -2.9

Federal government ................................ 3.6 5.7 4.1 5.5 1.8 3.3

Federal intramurala

 .............................. 3.6 5.6 4.4 5.7 -0.1 3.6

FFRDCs ............................................... 3.6 5.8 3.6 5.1 5.6 2.8

Universities and colleges ........................ 6.4 6.8 4.7 4.4 5.4 5.3

Other nonprofit organizations ................. 8.1 7.9 5.5 6.1 8.5 9.3

Gross domestic product............................. 4.8 4.1 3.3 4.9 1.9 -2.5

Constant 2005 dollars

Total R&D, all performers ........................... 2.9 2.6 3.1 4.6 4.6 –1.7

Business ................................................. 3.0 2.1 3.6 5.7 5.6 –3.9

Federal government ................................ 1.3 3.2 1.6 2.5 –0.5 2.2

Federal intramurala .............................. 1.3 3.2 1.8 2.8 –2.2 2.5

FFRDCs ............................................... 1.3 3.3 1.0 2.1 3.3 1.7

Universities and colleges ........................ 4.0 4.3 2.2 1.4 3.1 4.2

Other nonprofit organizations ................. 5.7 5.4 2.9 3.1 6.2 8.2

Gross domestic product............................. 2.4 1.7 0.7 1.9 –0.3 –3.5

a

Includes expenditures of federal intramural R&D as well as 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 2012

Figure 4-1

U.S. total R&D expenditures: 1953–2009

Dollars (billions)

NOTE: Some figures involve estimates and may later be revised.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, National Patterns of R&D Resources 

(annual series). See appendix table 4-3.

Science and Engineering Indicators 2012

1953 1961 1969 1977 1985 1993 2001 2009

0

50

100

150

200

250

300

350

400

450

Current dollars

Constant 2005 dollars

Figure 4-2

Ratio of U.S. R&D to gross domestic product, roles 

of federal and nonfederal funding for R&D: 

1953–2009

Percent

GDP = gross domestic product

NOTES: Some figures involve estimates and may later be revised. 

Federal R&D/GDP ratios represent the federal government as a 

funder of R&D by all performers; the nonfederal ratios reflect 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 2012

1953 1961 1969 1977 1985 1993 2001 2009

0

0.5

1.0

1.5

2.0

2.5

3.0

Total R&D/GDP

Federal R&D/GDP

Nonfederal R&D/GDP
Science and Engineering Indicators 2012 4-11

A decline of business R&D performance from $290.7 billion in 2008 to $282.4 billion in 2009 was the first such yearto-year decline since 2002. Nevertheless, business R&D 

performance rose on average (table 4-2) at 6.3% annually 

from 2004 to 2009, outpacing the growth rates of both total 

U.S. R&D (5.8%) and gross domestic product (3.3%). After 

adjusting for inflation, business R&D grew at a 3.6% annual 

rate, total R&D at 3.1%, and U.S. GDP at 0.7%).

Universities and Colleges

Universities and colleges performed $54.4 billion of 

R&D in 2009 (table 4-1). This was almost 14% of total 

U.S. R&D spending that year, making academia the secondlargest performer of U.S. R&D (figure 4-3). 

Academic R&D spending increased in each of the last 

5 years (in both current dollars and constant dollars). The 

academic share in total U.S. R&D has ranged between 11% 

and 14% over the past 20 years. 

Universities and colleges have a special niche in the nation’s R&D system: they performed more than half (53%) of 

the nation’s basic research in 2009. Academic institutions also 

rely much more extensively than the business sector on external sources of funding, particularly the federal government, at 

about 60%, to support the R&D they perform. (See chapter 5 

for an extensive analysis of academic R&D.)

Distribution of R&D expenditures among the U.S. states

In 2008, 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, New 

Jersey, Texas, Massachusetts, Washington, New York, 

Maryland, Michigan, Pennsylvania, and Illinois (table 

4-A).* California alone accounted for 22% of the U.S. 

total, exceeding the next-highest state, Massachusetts, by 

almost 4 times. The top 20 states accounted for 84% of 

the R&D total; the 20 lowest-ranking states, around 5% 

(see appendix tables 4-11 and 4-12).

The states with the biggest R&D expenditures are not 

necessarily those with the greatest relative concentration 

of R&D. Among those with the highest R&D/GDP ratios in 2008 were New Mexico, the District of Columbia, 

Maryland, and Massachusetts (table 4-A). New Mexico is 

the location of a number of major government research facilities. The District of Columbia is home to major federal 

science and technology agencies with intramural research 

labs and R&D management activities. Maryland is also 

the site of many government research facilities and growing research universities. Massachusetts benefits from both 

leading research universities and thriving high-technology 

industries. California has relatively high R&D intensity, but 

nonetheless is ninth from the top. (Chapter 8 provides additional information on R&D related activities in the states.) 

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 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 2008, R&D performed by the business sector accounted for about 73% of the U.S. R&D total 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. Connecticut, 8th in business-sector R&D and 

home to substantial pharmaceutical R&D activity, surpasses Maryland in the business R&D ranking.

University-performed R&D accounts for 14% 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 

and FFRDCs)—about 12% of the U.S. total—is more concentrated geographically than that in other sectors. Only 5 

states—Maryland, California, New Mexico, the District of 

Columbia, and Virginia—account for 65% of all federal 

R&D performance.** This figure rises to 80% when the 

other 5 of the top 10 states—Massachusetts, Tennessee, 

Washington, Illinois, and Alabama—are included.

Federal R&D accounts for the bulk of total R&D in 

several states, including New Mexico, which is home 

to the nation’s two largest FFRDCs (Los Alamos and 

Sandia National Laboratories) and Tennessee (36%) 

home to Oak Ridge National Laboratory. The high figures for Maryland (55%), the District of Columbia (80%), 

and Virginia (37%) 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 2008. Total U.S. R&D expenditures that year are 

estimated at $403.0 billion. Of this total, $372.7 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 $8.4 billion in 2008) could not be allocated by state; statelevel 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
4-12 Chapter 4. Research and Development: National Trends and International Comparisons

Location of R&D Performance by State—continued

Table 4-A

Top 10 U.S. states in R&D performance, by sector and intensity: 2008

All R&Da R&D intensity (R&D/GDP ratio)

Rank State

Amount 

(current 

$millions) Business U&C

Federal intramural 

and FFRDCb State

R&D/GDP 

(%)

GDP 

(current 

$billions)

1 California 81,323 California California Maryland New Mexico 7.58 78.0

2 New Jersey 20,713 New Jersey New York California District of Columbia 6.15 96.8

3 Texas 20,316 Texas Texas New Mexico Maryland 5.92 280.5

4 Massachusetts 20,090 Massachusetts Maryland District of Columbia Massachusetts 5.53 363.1

5 Washington 16,696 Washington Pennsylvania Virginia Connecticut 5.10 222.2

6 Maryland 16,605 Michigan Massachusetts Massachusetts Washington 4.96 336.3

7 New York 16,486 New York North Carolina Tennessee New Jersey 4.28 484.3

8 Michigan 15,507 Connecticut Illinois Washington New Hampshire 4.24 58.8

9 Pennsylvania 13,068 Pennsylvania Ohio Illinois California 4.22 1,925.5

10 Illinois 11,961 Illinois Michigan Alabama Michigan 4.12 376.2

FFRDC = federally funded research and development center; GDP = gross domestic product; U&C = universities and colleges

a

Includes in-state total R&D performance of business sector, universities and colleges, federal agencies, FFRDCs, and federally financed nonprofit R&D. 

bIncludes costs associated with 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 2012

Sector ranking

Federal Agencies and FFRDCs

R&D performed by the federal government includes the 

activities of agency intramural research laboratories and federally funded research and development centers (FFRDCs). 

The figures for intramural R&D also include expenditures for 

agency planning and administration of both intramural and 

extramural R&D projects. Federal agencies’ intramural R&D 

performance is funded entirely by the federal government. 

FFRDCs are R&D-performing organizations 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. Each FFRDC is administered by an industrial firm, 

a university, a nonprofit institution, or a consortium. 

R&D spending by federal intramural labs and FFRDCs 

was $46.2 billion in 2009, about 12% of all U.S. R&D (table 

4-1). Of this amount, $30.9 billion (8% of all U.S. R&D) was 

intramural and $15.3 billion (4%) was R&D by FFRDCs. 

Spending on this federal R&D performance grew rapidly 

from 2001 to 2003, primarily reflecting increased defense 

spending following the terrorist attacks in the United States 

on September 11, 2001. A slower pace of growth has prevailed, however, since then.

The volume of the federal government’s R&D performance is small compared with that of the U.S. business 

sector. Nonetheless, the $46.2 billion performance total in 

2009 exceeds domestic R&D expenditures of every country 

except Japan, China, and Germany. And this figure does not 

include 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 

university to undertake.

Other Nonprofit Organizations

R&D performed in the United States by nonprofit organizations other than universities and certain FFRDCs is estimated at $17.5 billion in 2009. This amount represents just 

over 4% of all U.S. R&D in that year, a share that has been 

fairly stable since 2000.

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 other governments, academic institutions, and 

other nonprofit organizations. The mix of funding sources 

varies by performer. 

R&D Funding by Business

The business sector, the largest performer of U.S. R&D, 

is also its largest funder, at about $247.4 billion in 2009 or 

about 62% of the U.S. total (table 4-1, figure 4-3), virtually all in support of business R&D.4

 The business sector’s 

predominant role in funding R&D began in the early 1980s, 

when its support began to exceed 50% of all U.S. R&D 

funding (figure 4-5)—a share that has continually increased 

over the last 30 years. Just about all business funding for 
Science and Engineering Indicators 2012 4-13

R&D (98%) is directed toward business R&D performance 

(table 4-3). The small remainder has gone to academic and 

other nonprofit performers. (For a fuller discussion, see the 

“U.S. Business R&D” section later in this chapter.) 

R&D Funding by the Federal Government

The federal government was once the predominant sponsor of the nation’s R&D, funding some 67% of all U.S. R&D 

in 1964 (figure 4-5). But the federal share decreased in subsequent years to less than half in 1979 and to a low of 25% 

in 2000. Changing business conditions and expanded federal funding of health, defense, and counterterrorism R&D 

pushed it back up above 30% in 2009.

The federal government remains a major source of funds 

for all U.S. performer sectors except private business, where 

its role (while not negligible) is substantially overshadowed 

by business’s own funds. 

In 2009, according to the reports of R&D performers,5 

the federal government provided an estimated $124.4 billion (current dollars) of R&D funds, about 31% of all U.S. 

spending on R&D that year (table 4-1). 

In 2009, the largest recipient of federal R&D funding, 

$46.2 billion, was federal agencies and their FFRDCs (table 

4-3). FFRDCs also received about $400 million from nonfederal sources, less than 1% of their total support. 

Figure 4-3

Shares of U.S. total R&D expenditures, by 

performing sector and funding source: 2009 

NOTES: Some figures involve estimates and may later be revised. 

National R&D expenditures are estimated to be $400.5 billion in 

2009. Federal performing sector includes federal agencies and 

federally funded research and development 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 4-3 and 4-7.

Science and Engineering Indicators 2012

Funding source

Performing sector

Business 61.7%

Federal

government

31.1%

Other nonprofit organizations 3.4% Universities and colleges 

2.9%

Nonfederal government 0.9%

Business 70.5%

Federal

government

11.5%

Universities and

colleges 13.6%

Other nonprofit organizations 4.4%

Figure 4-4

U.S. R&D, by performing and funding sectors: 

1953–2009

NOTES: Some figures involve estimates and may later be revised. 

Current dollar figures are converted to constant dollars based on the 

implicit gross domestic product price deflator. Federal performers of 

R&D include federal agencies and federally funded research and 

development centers. Other funding includes support from 

universities and colleges, nonfederal government, and nonprofit 

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 4-3 and 4-7. 

Science and Engineering Indicators 2012

Performing sector

Funding sector

Constant 2005 dollars (billions) 

1953 1961 1969 1977 1985 1993 2001 2009

0

50

100

150

200

250

300

350

400

Business

U.S. total

Federal government

Universities and colleges

Nonprofit 

0

50

100

150

200

250

300

350

400

Business

U.S. total

Federal government

Other

1953 1961 1969 1977 1985 1993 2001 2009
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: 2009

Source of funds ($millions)

Performing sector and character of work Total Business

Federal 

government

Universities 

and colleges

Other 

nonprofit 

organizations

Total 

expenditures 

(% distribution)

R&D ............................................................. 400,458 247,357 124,431 15,111 13,559 100.0

Business .................................................. 282,393 242,820 39,573   70.5

Federal government ................................. 46,150 **  46,150 ** ** 11.5

Federal intramural ................................ 30,901 ** 30,901   7.7

FFRDCs ................................................ 15,249 **  15,249 ** ** 3.8

Industry administered ........................ 6,446 ** 6,446   1.6

U&C administered ............................. 4,968 **  4,968 ** ** 1.2

Nonprofit administered ...................... 3,835 ** 3,835   1.0

Universities and colleges ......................... 54,383 3,279 31,575 15,111 4,418 13.6

Other nonprofit organizations .................. 17,532 1,258 7,133 **  9,141 4.4

Percent distribution by source ................. 100.0 61.8 31.1 3.8 3.4 –

Basic research ......................................... 75,970 16,486 40,451 10,800 8,233 100.1

Business ............................................... 14,784 13,444 1,340 ** ** 19.5

Federal government ............................. 11,373 ** 11,373   15.0

Federal intramural ........................... 5,507 **  5,507 ** ** 7.2

FFRDCs .......................................... 5,866 ** 5,866   7.7

Industry administered .................. 2,550 **  2,550 ** ** 3.4

U&C administered ........................ 1,808 ** 1,808   2.4

Nonprofit administered ................ 1,508 **  1,508 ** ** 2.0

Universities and colleges ..................... 40,544 2,344 24,242 10,800 3,158 53.4

Other nonprofit organizations .............. 9,269 698 3,496 ** 5,075 12.2

Percent distribution by source ............. 100.0 21.7 53.2 14.2 10.8 –

Applied research ...................................... 71,330 34,344 30,101 3,535 3,350 100.1

Business ............................................... 41,055 33,258 7,797   57.6

Federal government ............................. 12,665 **  12,665 ** ** 17.8

Federal intramural ........................... 8,006 ** 8,006   11.2

FFRDCs .......................................... 4,659 **  4,659 ** ** 6.5

Industry administered .................. 1,930 ** 1,930   2.7

U&C administered ........................ 1,289 **  1,289 ** ** 1.8

Nonprofit administered ................ 1,440 ** 1,440   2.0

Universities and colleges ..................... 11,912 767 6,577 3,535 1,033 16.7

Other nonprofit organizations .............. 5,698 319 3,062 **  2,317 8.0

Percent distribution by source ............. 100.0 48.1 42.2 5.0 4.7 –

Development ............................................ 253,161 196,527 53,882 776 1,976 100.0

Business ............................................... 226,554 196,118 30,436 ** ** 89.5

Federal government ............................. 22,115 ** 22,115   8.7

Federal intramural ........................... 17,389 **  17,389 ** ** 6.9

FFRDCs .......................................... 4,726 ** 4,726   1.9

Industry administered .................. 1,967 **  1,967 ** ** 0.8

U&C administered ........................ 1,872 ** 1,872   0.7

Nonprofit administered ................ 887 **  887 ** ** 0.4

Universities and colleges ..................... 1,927 168 756 776 227 0.8

Other nonprofit organizations .............. 2,565 241 575 ** 1,749 1.0

Percent distribution by source ............. 100.0 77.6 21.3 0.3 0.8 –

** = small to negligible amount, included in other funding sectors

FFRDC = federally funded research and development center; U&C = universities and colleges 

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. State and local government support to U&C ($3,675 million) is included in 

U&C support for U&C performance. Some figures for other nonprofit organizations are estimates and may later be revised. 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, National Patterns of R&D Resources (annual series). 

See appendix tables 4-3–4-10.

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 4-15

R&D by Character of Work 

R&D encompasses a range of activities: from fundamental research in the physical, life, and social sciences; to 

research addressing such critical societal issues as global 

climate change, energy efficiency, and health care; to the 

development of platform or general-purpose technologies 

and new goods and services. Because the activities are so 

diverse, it helps to classify them in separate categories when 

analyzing R&D expenditures. The most widely used classifications distinguish among basic research, applied research, and (experimental) development (see definitions in 

Glossary).6 Nevertheless, these categories are not always 

mutually exclusive and any particular R&D activity may 

have aspects of more than one category. 

Basic Research

In 2009, spending on basic research activities amounted 

to about $76.0 billion (19%) of the $400.5 billion of total 

U.S. R&D (table 4-4, figure 4-6). The basic research share 

has gradually moved upward, from about 14% in 1979 to 

19% in 2009 (table 4-4). 

Universities and colleges continue to occupy a unique position in U.S. basic research. They are the primary performer 

of U.S. basic research (53% in 2009), while also training 

the next generation of researchers (table 4-4). The business 

sector performs nearly 20%; the federal government (agency 

intramural labs and FFRDC s), 15%; and other nonprofit organizations, 12%.

The federal government remains by far the prime source 

of funding for basic research, accounting for about 53% of 

all such funding in 2009 (table 4-3). Universities and colleges themselves provide about 14% of the funding. Other 

nonprofit organizations provide 11%. 

Business’s $16.5 billion devoted to basic research is 

small by comparison to its $247.4 billion of funding for total 

R&D in 2009, but it still accounted for about 22% of the 

overall funding for basic research. 

Business views about performing basic research involve 

considerations about the appropriability of results, commercialization risks, and uncertain investment returns. However, 

involvement in basic research can help boost human capital, 

attract and retain talent, absorb external knowledge, and 

strengthen innovation capacity. Businesses that invest most 

heavily in basic research are those whose new products are 

most directly tied to ongoing science and technological advances, such as the pharmaceuticals and scientific R&D service sectors.

Applied Research

Applied research activities accounted for about 18% 

($71.3 billion) of total U.S. R&D in 2009, modestly under 

the amount spent on basic research that year (table 4-4). 

Looking back over two decades, the share of applied research is somewhat lower at present than in the past: 23% 5 

years ago, 21% 10 years ago, and 23% 20 years ago. 

The second largest recipient was the business sector, 

for which, in 2009, the federal government provided $39.6 

billion of the $282.4 billion that funded business R&D. 

Through the early 1960s, more than half of the nation’s 

business R&D had been funded by the federal government. 

This share fell below 10% by 2000 and had rebounded to 

143% by 2009 (appendix table 4-3). 

Federal funds to academia provided $31.6 billion (58%) 

of the $54.4 billion spent on academic R&D in 2009. Of the 

$17.5 billion spent on R&D by other nonprofit organizations, 

$7.1 billion (about 41%) was supported by federal funds. 

R&D Funding from Other Sources

The balance of R&D funding from other sources is 

small: $28.6 billion in 2009, or about 7% of all funding. 

This includes academia’s own institutional funds (which 

support academic institution’s own R&D), other nonprofits 

(the majority of which fund their own R&D, but also contribute to academic research), and state and local governments (primarily for academic research).

Nonetheless, this segment of funding has been growing 

fairly rapidly for some time. From 1999 to 2009, growth 

in funding from these sectors averaged 5.4% per year in 

real-dollar terms—ahead of the pace of funding growth in 

both the federal and business sectors. Most R&D funded 

by these nonfederal sources is performed by the academic 

sector, which also provided about one-fifth of its own total 

spending on R&D.

Figure 4-5

U.S. total R&D expenditures, by source of funding: 

1953–2009

Percent

NOTES: Some figures involve estimates and may later be revised. 

Other includes universities and colleges, state and local government, 

and other nonprofit organizations. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, National Patterns of R&D Resources 

(annual series). See appendix table 4-7.

Science and Engineering Indicators 2012

1953 1961 1969 1977 1985 1993 2001 2009

0

10

20

30

40

50

60

70

80

Business

Federal government

Other
4-16 Chapter 4. Research and Development: National Trends and International Comparisons

The business sector performed 90% ($226.6 billion) of 

this development total, and the federal government (agency 

intramural labs, FFRDCs), another 9%—much of it defenserelated, with the federal government the main consumer. By 

contrast, academic and other nonprofit organizations perform very little of U.S. development, each performing less 

than 1% of the total in 2009. 

The business sector also provided about three-quarters 

(78%) of development funding ($196.5 billion) in 2009, nearly all of it in support of business development activities (table 

4-3). The federal government provided 21% ($53.9 billion) of 

the funding, with more than half going to business development—especially in defense-related industries—and most of 

the remainder going to federal intramural labs and FFRDCs. 

Universities and colleges and other nonprofit organizations provide small amounts of funding to support development performance in their own sectors. 

The business sector performed 58% of all applied research in 2009; the federal government (federal agency intramural labs and FFRDCs), 18%; universities and colleges, 

another 17%; nonprofit organizations, 8% (table 4-4). 

Business provided the bulk of funding for applied research in 2009, 48%. The federal government provided 

42%, and academia and other nonprofit organizations each 

contributed around 5%. 

Business sectors that perform relatively large amounts 

of applied research include chemicals and aerospace. The 

federal funding is spread broadly across all the performers, 

with the largest amounts (in 2009) going to federal intramural labs, the business sector, and universities and colleges. 

Development

Development, the most sizable component of U.S. R&D, 

accounted for 63% ($253.2 billion) of total national R&D 

in 2009 (table 4-4).7

 Development’s share of total national 

R&D has been near or above 60% for several decades. 

Table 4-4

U.S. R&D expenditures, by character of work and performing sectors: 1979–2009

Character of work and sector 1979 1989 1999 2004 2009

$billions

All R&D ....................................................... 55.4 141.9 245.0 302.5 400.5 

Basic ....................................................... 7.8 21.9 38.9 56.1 76.0 

Applied .................................................... 12.1 32.3 52.0 69.2 71.3 

Development ........................................... 35.4 87.7 154.4 177.2 253.2 

Percent distribution

All R&D ....................................................... 100.0 100.0 100.0 100.0 100.0

Basic ....................................................... 14.1 15.4 15.9 18.5 19.0

Applied .................................................... 21.8 22.8 21.2 22.9 17.8

Development ........................................... 63.9 61.8 63.0 58.6 63.2

Basic research ........................................ 100.0 100.0 100.0 100.0 100.0

Business .............................................. 13.5 22.0 17.1 14.0 19.5

Federal intramural ............................... 14.2 10.5 8.6 8.4 7.2

FFRDCs ............................................... 14.7 12.9 9.6 8.9 7.7

Universities and colleges .................... 48.9 46.7 54.0 57.0 53.4

Other nonprofit organizations ............. 8.8 7.9 10.8 11.8 12.2

Applied research ..................................... 100.0 100.0 100.0 100.0 100.0

Business .............................................. 57.7 69.1 70.4 65.7 57.6

Federal intramural ............................... 20.0 11.0 10.6 10.8 11.2

FFRDCs ............................................... 5.0 3.2 3.2 4.5 6.5

Universities and colleges .................... 11.7 13.0 11.1 13.0 16.7

Other nonprofit organizations ............. 5.6 3.6 4.7 6.1 8.0

Development ........................................... 100.0 100.0 100.0 100.0 100.0

Business .............................................. 81.9 82.9 89.9 87.5 89.5

Federal intramural ............................... 11.1 10.7 6.0 7.2 6.9

FFRDCs ............................................... 4.0 4.1 2.1 2.6 1.9

Universities and colleges .................... 1.3 1.4 0.9 1.2 0.8

Other nonprofit organizations ............. 1.7 0.9 1.0 1.5 1.0

FFRDC = federally funded research and development center

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, National Patterns of R&D Resources (annual series).

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 4-17

R&D, GDP Growth, and Innovation-Related 

Metrics

Intangible inputs such as R&D are important sources of 

long-term economic growth (Corrado et al. 2006; Jorgenson 

2007; Van Ark and Hulten 2007). The role of R&D in U.S. 

GDP has been estimated based on a methodology published 

in the Bureau of Economic Analysis (BEA)/NSF R&D 

Satellite Account (Lee and Schmidt 2010). This methodology treats R&D as an investment rather than as an expense. 

Using this methodology, a preliminary estimate of R&D on 

inflation-adjusted GDP from 1959 to 2007 suggests faster 

average annual GDP growth of 0.07 percentage point over 

treatment of R&D as an expense.8

Over this period, the difference in average growth estimates using these two methodologies was higher in the immediate post-War boom, dropped to almost zero from 1974 

to 1994 (a period that includes the productivity slowdown of 

the 1970s), and then increased relative to the overall average since 1995—years associated with IT-led productivity 

growth (Jorgenson et al. 2005b) (table 4-5). For other data 

developments activities, see sidebar, “Recent Developments 

in Innovation-Related Metrics.” 

U.S. Business R&D

Businesses engage in R&D with a variety of objectives 

and partners on a global basis. Most business R&D is aimed 

at developing new and improved goods, services, and processes; maintaining or increasing market share; and improving operating efficiency. Such activities reflect firms’ 

perceptions of the market’s demand and expectations about 

the profitability of new or newly applied technology. 

Businesses located in the United States, both domesticand foreign-owned, performed $290.7 billion in R&D in the 

United States in 2008 (table 4-6).9 Among these, companies 

that owned firms outside the United States performed an 

Figure 4-6

U.S. R&D by character of work, basic research 

by performing sector, and basic research by source 

of funds: 2009 

NOTES: Some figures involve estimates and may later be revised. 

National R&D expenditures estimated at $400.5 billion in 2009. 

National basic research expenditures estimated at $76.0 billion in 

2009. Federal performers include federal agencies and federally 

funded research and development centers. State and local 

government support to industry included in industry support for 

industry performance. State and local government support to 

universities and colleges included in universities and colleges 

support of universities and college performance.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, National Patterns of R&D Resources 

(annual series). See appendix tables 4-3, 4-6, and 4-8.

Science and Engineering Indicators 2012

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

17.8%

Development

63.2%

Business 19.5%

Federal

government

15.0%

Other nonprofit

organizations 12.2%

Business 21.7%

Other nonprofit

organizations 10.8%

Universities and

colleges 53.3%

Universities

and colleges

14.2%

Federal

government 53.3%

Table 4-5

U.S. average annual real GDP growth rates, 

unadjusted and R&D adjusted: 1959–2007 

(Percent)

Period

Unadjusted 

real GDPa

R&D-adjusted 

real GDPb Difference

1959–2007 ..... 3.32 3.39 0.07

1959–73 ..... 4.20 4.33 0.13

1974–94 ...... 3.02 3.03 0.01

1995–2001 ... 3.76 3.93 0.17

2002–07 ...... 2.75 2.87 0.12

GDP = gross domestic product

a

As published in the national income and product accounts.

bReal GDP with R&D treated as investment, deflated by aggregate 

output price index. Double-counting of R&D software removed.

SOURCE: Bureau of Economic Analysis estimates in Lee and 

Schmidt (2010).

Science and Engineering Indicators 2012
4-18 Chapter 4. Research and Development: National Trends and International Comparisons

additional $61.5 billion abroad (appendix table 4-14). This 

section will also cover details on funding sources (appendix 

table 4-15).

Domestic R&D Performance and 

Funding Sources

U.S. business R&D performance can be paid with funds 

from company-owned units, other businesses not owned by 

the company, and other external sources. Internal and external 

funders may be located in the United States or abroad. 

U.S. business R&D performance totaled $290.7 billion in 

2008, including $232.5 billion (80%) from businesses’ own 

funds and $58.2 billion (20%) paid for by others not owned by 

the company, regardless of the location of funders (table 4-6). 

Companies in manufacturing industries performed 

$203.8 billion of R&D domestically representing 70% of 

all business R&D performed in the United States in 2008; 

nonmanufacturing industries performed $86.9 billion. The 

split between own company funds and funding by others for 

manufacturing and nonmanufacturing industries was similar 

to the split for overall business R&D (about 80% and 20%).

Businesses vary in R&D intensity—how much R&D they 

do relative to production, value added, or sales—across industry and size. In this section, business R&D intensity is the 

ratio of domestic R&D performed and paid for by the company to domestic net sales. In 2008, the ratio across all businesses within scope of the Business R&D and Innovation 

Survey (BRDIS) was 3.0%; 3.5% for manufacturers and 

2.2% for companies in nonmanufacturing industries (appendix table 4-16). Some industries have considerably higher 

R&D intensities, such as the computer and electronic products, chemicals, and information industries (figure 4-7).

Innovation is defined as 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). R&D and other intangible investments such as investments in software, higher education, 

and worker training are key inputs driving innovation. 

Improved and internationally comparable measurements of 

these inputs and associated outcomes have been identified 

as important components in evidence-based policymaking. New analytical and policy questions suggest the need 

for continuous enhancements (NRC 2005, 2007; OECD 

2010c, 2010d). Questions include how innovation addresses ultimate social and economic goals, how it may affect 

(or be affected by) business cycles (economic downturns 

and recovery), business dynamics (new or small firms), 

and globalization (Filippetti and Archibugi 2011; Hasan 

and Tucci 2010; OECD 2010b, 2010c, 2010d; Stiglitz et 

al. 2009). Ongoing research and data development initiatives in innovation-related metrics include:

As part of its Innovation Strategy in support of economic growth and recovery, the OECD* has been working 

on a measurement agenda for innovation, including 

links between innovation and macroeconomic performance (OECD 2010c). International statistical manuals 

have been updated or developed. The latter include an 

updated United Nations System of National Accounts 

(SNA) manual (EU et al. 2009), which recognizes R&D 

and other intangibles as investments or capital assets, 

and a new OECD Handbook on the treatment of intangibles in national economic accounts (OECD 2010a). 

In the United States, the Commerce Department’s Bureau 

of Economic Analysis and NSF’s National Center for 

Science and Engineering Statistics (NCSES) have jointly 

developed an R&D Satellite Account** which considers R&D as a capital investment with long-term benefits rather than an expense (that is, it capitalizes R&D). 

This work will guide the incorporation of R&D in U.S. 

GDP and other national income and product accounts 

(NIPAs) in 2013, consistent with the revised SNA manual (Aizcorbe et al. 2009; Jorgenson et al. 2006).

NCSES’s new Business R&D and Innovation Survey 

covers global activities of U.S.-located companies on a 

broad range of R&D, employment, intellectual property 

(IP), technology transfer, and innovation variables. See 

sidebar, “U.S. Business R&D and Innovation Survey.” 

NSF Science of Science and Innovation Policy 

(SciSIP) program supports theoretical and empirical 

research designed to advance the scientific basis of 

science and innovation policy. SciSIP-funded research 

aims to develop, improve, and expand theories, models, analytical tools, data, and metrics bearing on science policy and innovation. 

STAR METRICS (Science and Technology for America’s 

Reinvestment: Measuring the Effect of Research on 

Innovation, Competitiveness, and Science) is an interagency project conducted under the auspices of the 

White House Office of Science Technology and Policy 

(OSTP).*** It seeks to build an infrastructure to integrate 

data on R&D inputs, outputs, and outcomes in order to 

analyze the impacts of science investments (Lane and 

Bertuzzi 2011). 

* http://www.oecd.org/innovation/strategy 

** http://www.bea.gov/national/newinnovation.htm 

*** https://www.starmetrics.nih.gov; http://scienceofscience 

policy.net

Recent Developments in Innovation-Related Metrics
Science and Engineering Indicators 2012 4-19

Domestic R&D Performance Funded by 

Others

Of the $58.2 billion (20% of $290.7 billion) in funding by 

others outside of individual companies, the U.S. federal government accounted for $36.4 billion, independent domestic 

firms $12.2 billion, and $8.9 billion was funded by companies located outside of the United States (both independent 

companies and foreign parents). Other nonfederal sources 

accounted for less than $1 billion. 

Federal R&D funding figures prominently in two defense-related industries. The aerospace products and parts 

industry performed $25.8 billion in federally funded R&D, 

almost 70% of their $36.9 billion in domestically performed 

R&D in 2008. The navigational, measuring, electromedical, 

and control instruments industry performed $3.6 billion in 

federally funded R&D, almost a quarter of its $15.5 billion 

of domestic R&D performance (table 4-7 and appendix table 

4-15). 

Domestic R&D Performance by 

Size of Company

Small companies, those with 5–499 domestic employees, 

performed $58.1 billion (20%) of $290.7 billion in U.S. business R&D performance; 80% ($46.4 billion) of the domestic 

Table 4-6

Domestic R&D performed by the company, by industry and company size: 2008

(Millions of U.S. dollars)

Industry and company size

 Domestic R&D performance

NAICS code Total

Paid for by

company

Paid for by 

others

All industries ......................................................................... 21–23, 31–33, 42–81 290,681 232,505 58,176

Manufacturing industries .................................................. 31–33 203,754 164,386 39,368

Nonmanufacturing industries ............................................ 21–23, 42–81 86,925 68,118 18,807

All companies ....................................................................... 21–23, 31–33, 42–81 290,681 232,505 58,176

Small companies (number of domestic employees)a

 ........ — 58,136 46,395 11,741

5–99 ............................................................................... — 33,256 24,890 8,366

100–249 ......................................................................... — 14,662 12,933 1,729

250–499 ......................................................................... — 10,218 8,572 1,646

Medium and large companies (number of domestic 

 employees) .................................................................. — 232,544 186,110 46,434

500–999 ......................................................................... — 11,886 9,673 2,213

1,000–4,999 ................................................................... — 46,337 39,010 7,327

5,000–9,999 ................................................................... — 24,764 20,358 4,406

10,000–24,999 ............................................................... — 48,737 43,049 5,688

25,000 or more .............................................................. — 100,820 74,020 26,800

NAICS = North American Industry Classification System

a

Upper bound based on U.S. Small Business Administration’s definition of a small business; Business R&D and Innovation Survey does not include 

companies with fewer than five domestic employees.

NOTES: Detail may not add to total because of rounding. Industry classification based on dominant business code for domestic R&D performance where 

available. For companies that did not report business codes, classification used for sampling was assigned.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics and Census Bureau, Business R&D and Innovation 

Survey (2008). 

Science and Engineering Indicators 2012

Figure 4-7

Domestic R&D performed and paid for by the 

company as a percentage of domestic net sales: 

2008 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics and U.S. Census Bureau, Business R&D 

and Innovation Survey (2008). 

Science and Engineering Indicators 2012

Percent

0 2 6 8 10 12 4

Transportation

equipment

Electrical equipment,

appliances, and components

Machinery

Professional, scientific,

and technical services

Information

Chemicals

Computer and

electronic products
4-20 Chapter 4. Research and Development: National Trends and International Comparisons

R&D performance by small companies was paid for with their 

own funds, the remainder by other business or organizations, 

regardless of location (table 4-6). The largest companies, 

those with 25,000 or more domestic employees, performed 

$100.8 billion (35%) of U.S. business R&D, with 73% of that 

amount ($74.0 billion) paid for with their own funds.

The domestic operations of small companies were more 

R&D intensive (6.3%) than the domestic operations of the 

largest companies (2.3%) in 2008 (appendix table 4-16).10

Table 4-7

Sources of funds for domestic R&D performed by the company, by selected industry and company size: 2008

(Millions of U.S. dollars)

Industry and company size

R&D paid 

for by the 

company

 R&D paid for by others

NAICS code Total Total

U.S. federal 

government

Nonfederal 

sourcesa

All industries ................................................................ 21–23, 31–33, 42–81 290,681 232,505 58,176 36,360 21,816

Manufacturing .......................................................... 31–33 203,754 164,386 39,368 31,102 8,266

Chemicals ............................................................ 325 58,249 55,042 3,207 197 3,010

Pharmaceuticals and medicines ....................... 3254 48,131 45,169 2,962 137 2,825

Other chemicals ................................................ other 325 10,118 9,873 245 60 185

Computer and electronic products ...................... 334 60,463 52,912 7,551 4,646 2,905

Navigational, measuring, electromedical, 

 and control instruments ................................ 3345 15,460 10,463 4,997 3,635 1,362

Other computer and electronic products ......... other 334 45,003 42,449 2,554 1,011 1,543

Transportation equipment .................................... 336 50,552 23,039 27,513 25,941 i 1,572

Aerospace products and parts ......................... 3364 36,941 10,371 26,570 25,805 i 765

Other transportation equipment ....................... other 336 13,611 12,668 943 136 807

Other manufacturing ............................................ other 31–33 34,490 33,393 1,097 318 779

Nonmanufacturing ................................................... 21–23, 42–81 86,925 68,118 18,807 5,258 13,549

Professional, scientific, and technical services .... 54 37,954 20,539 17,415 4,844 12,571

Scientific research and development services ... 5417 17,913 8,708 9,205 2,115 7,090

Other professional, scientific, and technical

 services ......................................................... other 54 20,041 11,831 8,210 2,729 5,481

Other nonmanufacturing ...................................... other 21–23, 42–81 48,971 47,579 1,392 414 978

All companies .............................................................. 21–23, 31–33, 42–81 290,681 232,505 58,176 36,360 21,816

Small companies (number of domestic employees)b .... — 58,136 46,395 11,741 4,117 7,624

5–99 ......................................................................... — 33,256 24,890 8,366 2,667 5,699

100–249 ................................................................... — 14,662 12,933 1,729 718 1,011

250–499 ................................................................... — 10,218 8,572 1,646 732 914

Medium and large companies 

 (number of domestic employees) ............................. — 232,544 186,110 46,434 32,243 14,191

500–999 ................................................................... — 11,886 9,673 2,213 747 1,466

1,000–4,999 ............................................................. — 46,337 39,010 7,327 2,162 i 5,165

5,000–9,999 ............................................................. — 24,764 20,358 4,406 1,168 3,238

10,000–24,999 ......................................................... — 48,737 43,049 5,688 3,024 2,664

25,000 or more ........................................................ — 100,820 74,020 26,800 25,142 i 1,658

i = more than 50% of the estimate is a combination of imputation and reweighting to account for nonresponse

NAICS = North American Industry Classification System

a

Companies located in the United States funded $12.2 billion; $8.9 billion was funded by companies located outside of the United States, including 

R&D paid for by the foreign parents of U.S. affiliates. For manufacturing industries, the amounts were $3.4 billion and $4.7 billion, respectively, and for 

nonmanufacturing industries, $8.7 billion and $4.2 billion, respectively.

bUpper bound based on U.S. Small Business Administration's definition of a small business; the Business R&D and Innovation Survey does not include 

companies with fewer than five domestic employees.

NOTES: Detail may not add to total because of rounding. Industry classification based on dominant business code for domestic R&D performance where 

available. For companies that did not report business codes, classification used for sampling was assigned.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics and Census Bureau, Business R&D and Innovation 

Survey (2008).

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 4-21

Largest Domestic R&D-Performing Industries

Business R&D intensity is generally greater in the manufacturing sector (3.5% overall) than in nonmanufacturing 

(2.2%). Nonetheless, R&D plays a large role in some service 

industries and R&D intensity in some manufacturing sectors 

is relatively low (appendix table 4-16).

Six industry groups—four in manufacturing (chemicals, 

computer and electronic products, aerospace and defense 

manufacturing, and automotive manufacturing) and two in 

services (software and computer-related products, and R&D 

services)—accounted for three-quarters of both 2008 total business R&D performed in the United States ($225.8 

billion) and company-funded/company-performed R&D 

($173.3 billion) in the United States. They also accounted 

for 96% ($34.8 billion) of federally funded U.S. business 

R&D in 2008 (table 4-8).11

Chemicals (Including Pharmaceuticals)

The chemical industries accounted for the largest share 

of business R&D performed in the United States—20% or 

$58.2 billion of $290.7 billion in 2008 (figure 4-8). Within 

the chemicals industry, the largest subsector is pharmaceuticals and medicines. In 2008, pharmaceutical companies performed $45.2 billion of company-funded R&D in the United 

States and $10.9 billion abroad (appendix table 4-14).

Software and Computer-Related Services

Software and computer-related services industries—

software publishing, computer systems design and Internet 

service providers, web search portals and data processing services industries—performed $46.9 billion of domestic R&D 

in 2008, making it the second largest industry group for domestic R&D performance, and $8.5 billion abroad. The R&D 

of these industries, 16% of business R&D performance in the 

United States, combined with that of the computer and electronic product manufacturers (below), accounted for 32% of 

all business R&D performed in the United States in 2008 (table 4-8). As computing, information technology, and Internetlinked activity has become more integrated with every sector 

of the economy, the demand for services associated with these 

technologies has increased.

Computer and Electronic Products

Companies in the computer and electronic product manufacturing industries include producers of communications 

equipment, semiconductors, computers and computer peripherals, and components for such products.12 The design 

and use of integrated circuits and the application of highly 

specialized miniaturization technologies are common elements in the production processes of the computer and electronic products sector. In 2008, companies in this industry 

group performed $45.0 billion of R&D, or 15% of all domestic business R&D, and $13.0 billion abroad. Funds for 

domestic R&D came mostly from the companies themselves 

($42.4 billion) and relatively little ($2.6 billion) came from 

other sources. Two relatively high R&D-intensive 4-digit 

NAICS (North American Industry Classification System) 

industries are included in this group, semiconductor and 

communications equipment manufacturing. Their domestic 

R&D/domestic sales ratios were 20% and 13%, respectively 

(appendix table 4-16).

Aerospace and Defense Manufacturing

Although it is common to refer to the “defense industry,” 

the NAICS system does not include such a classification. 

Thus, to approximate the cost of defense-related R&D, included here are data on aerospace products and parts plus 

federally funded R&D in the following industries: navigational, measuring, electromedical, and control instruments, 

as well as other transportation manufacturing industries. 

Companies in this “defense sector” perform the majority 

of the Department of Defense’s (DOD’s) extramural R&D 

(table 4-8). In 2008, these industries reported performing 

$40.7 billion of R&D in the United States. Federally funded 

R&D accounted for 73% ($29.6 billion) of the sector total 

and 81% of all federally funded business R&D performed in 

the United States. This total accounts for more than half of 

the $56.0 billion in DOD outlays for FY 2008 (NSF 2010d). 

R&D and Related Services

The R&D and related services group includes companies 

that provide scientific R&D, engineering, and architectural 

services to other firms or for their own use.13 Companies in 

this group performed $21.3 billion of R&D in the United 

States during 2008; $10.1 billion paid for from company 

funds and $11.2 billion paid for by others.14 Of the $11.2 billion paid for by others, $3.0 billion was funded by the U.S. 

federal government, the highest amount outside the aerospace and defense manufacturing group (table 4-8). 

Automotive Manufacturing

Companies in automotive manufacturing industries reported performing $14 billion of company-funded R&D 

in 2008, accounting for 5% of all such R&D performed by 

businesses in the United States (table 4-8). In 2008, out of 

the about 4,000 companies in the automobiles, bodies, trailers, and parts industries (NAICS 3361, 3362, and 3363), 13 

reported domestic R&D performed by the company of $100 

million or more, collectively representing 84% of R&D performed by that group of industries. 

Business Activities for Domestic R&D

Industry-based data above are the result of classifying 

each company’s R&D in only one industry. However, companies in different industries and even in the same industry perform R&D relating to a variety of different business 

lines of activities. For example, a company classified as a 

pharmaceutical company may also perform R&D in medical 

equipment. A feature of BRDIS is the collection of information on R&D performed by business activity—both R&D 

paid for by the company and paid for by others. See sidebar, 
4-22 Chapter 4. Research and Development: National Trends and International Comparisons

Table 4-8

Business R&D performed in the United States by the company, paid for by the company and by others, by 

industry group: 2008

(Millions of U.S. dollars)

Industry

R&D paid for 

by the 

company

 R&D paid for by others

Total Total

U.S. federal 

government

 Nonfederal 

 sources

All ................................................................................. 290,681 232,505 58,176 36,360 21,816

Highlighted industries .............................................. 225,813 L 173,318 52,495 L 34,788 L 17,707

Chemicals ............................................................ 58,249 55,042 3,207 197 3,010

Basic chemicals ................................................ 4,074 4,012 62 33 29

Pharmaceuticals and medicines ....................... 48,131 45,169 2,962 137 2,825

Soap, cleaning compound, and toilet 

 preparation .................................................... 2,108 2,099 9 6 3

Other chemicals ................................................ 3,936 3,762 174 21 153

Software and computer-related servicesa

 ............ 46,935 42,727 4,208 961 3,247

Software publishers .......................................... 28,221 27,665 556 176 380

Computer systems design and related 

 services .......................................................... 12,146 8,569 3,577 784 2,793

Internet service providers, web search portals, 

 and data processing services ........................ 6,568 6,493 75 1 74

Computer and electronic productsb ..................... 45,003 42,449 2,554 1,011 1,543

Communications equipment ............................ 12,903 11,484 1,419 D D

Semiconductor and other electronic 

 components ................................................... 22,324 21,588 736 D D

Computer and peripheral equipment and other 

 computer and electronic products ................ 9,776 9,377 399 D D

Aerospace and defense manufacturingc .............. 40,712 10,371 30,341 i 29,576 i 765

Aerospace products and parts ......................... 36,941 10,371 26,570 i 25,805 i 765

Navigational, measuring, electromedical, and 

 control instruments (U.S. federal government 

 funded only) ................................................... 3,635 — 3,635 3,635 —

Transportation equipment (U.S. federal 

 government funded only) ............................... 136 — 136 i 136 i —

R&D and related servicesd .................................... 21,335 10,086 11,249 3,043 8,206

Architectural, engineering, and related services .... 3,422 1,378 2,044 928 1,116

Scientific research and development services ... 17,913 8,708 9,205 2,115 7,090

Automotive manufacturinge

 .................................. 13,579 L 12,643 936 L D 936

Automobiles, bodies, trailers, and parts ........... 13,140 L 12,234 906 L D 906

Other transportation equipment (not aerospace 

 or defense related) ......................................... 439 L 409 30 L D 30

All other .................................................................... 64,868 L 59,187 5,681 L 1,572 L 4,109

D = suppressed to avoid disclosure of confidential information; i = more than 50% of the estimate is a combination of imputation and reweighting to 

account for nonresponse; L = lower-bound estimate, potential disclosure of individual company operations only allows lower-bound estimates for some 

industry groups

NAICS = North American Industry Classification System

a

Includes domestic R&D performance for software publishers (NAICS 5112), computer systems design and related service industries (NAICS 5415), and 

Internet service providers, web search portals, and data processing services (NAICS 518).

bIncludes domestic R&D performance for the computer and electronics industry (NAICS 334), except for federal R&D for the navigational, measuring, 

electromedical, and control instruments industry (NAICS 3345), which is included in the aerospace and defense manufacturing sector.

c

Includes domestic R&D performance for the aerospace products and parts industry (NAICS 3364), plus all federal R&D for the navigational, measuring, 

electromedical, and control instruments (NAICS 3345) and transportation equipment manufacturing industries.

dIncludes domestic R&D performance for the architectural, engineering, and related services (NAICS 5413) and scientific R&D services industries 

(NAICS 5417).

e

Includes domestic R&D performance for automobiles, bodies, trailers, and parts (NAICS 3361-3363) and transportation equipment (NAICS 336) 

industries, except federally funded components that are included in the aerospace and defense manufacturing group.

NOTES: Detail may not add to total because of rounding. Industry classification based on dominant business code for domestic R&D performance where 

available. For companies that did not report business codes, classification used for sampling was assigned.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics and Census Bureau, Business R&D and Innovation 

Survey (2008).

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 4-23

Figure 4-8

Shares of domestic R&D performed in the United 

States, by industry group: 2008 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics and U.S. Census Bureau, Business R&D 

and Innovation Survey (2008).

Science and Engineering Indicators 2012

Chemicals 20.0%

Software and

computerrelated services

16.1%

Computer and electronic

products 15.5%

Aerospace and defense

manufacturing 14.0%

R&D and related

services 7.3%

Automotive

manufacturing

4.7%

All other 22.3%

“U.S. Business R&D and Innovation Survey.”15 The number of these activities is large, as indicated in appendix table 

4-17, but most companies performed R&D in only one business activity area. However, some companies, especially 

large diversified firms, performed R&D in multiple business 

activity areas. In BRDIS, 92% of companies reported domestic R&D paid for by the company related to only one 

business activity; 4% related to two business activities, and 

4% related to three or more business activities. For domestic 

R&D paid for by others, the percentages were 91%, 6%, and 

3%, respectively. 

The top 10 business activities for which companies used 

their own funds to perform R&D in the United States accounted for 60% of these companies’ R&D funds ($140.6 

billion of $232.5 billion) (table 4-9). The top 10 activities 

for which companies used others’ funds to perform R&D accounted for 68% of the total amount of company-performed 

R&D paid for by others (table 4-10). 

To better understand and measure how R&D is conducted in today’s innovation- and global-based economy 

(NRC 2005), NSF and the U.S. Census Bureau launched 

a new Business R&D and Innovation Survey (BRDIS). 

BRDIS expands on R&D data collected by its predecessor, the Survey of Industrial Research and Development, 

to cover (among other areas) global R&D funding or expenses by U.S.-located businesses, and introduces preliminary innovation and intellectual property questions 

that will be further developed. 

Chapters 3, 4, 6, and 8 in this edition of Science and 

Engineering Indicators include selected preliminary data 

from the 2008 pilot survey. Detailed 2008 estimates and 

data for subsequent survey cycles are available at http://

www.nsf.gov/statistics/industry/. BRDIS questionnaires 

are available at http://www.nsf.gov/statistics/question.

cfm#13. Listed below are the main data collection areas. 

Company information:

s/WNERSHIP

s"USINESSACTIVITIES

Measures of R&D activity paid for by the company: 

s$OMESTICANDWORLDWIDESALESREVENUEAND2$

activity

s#OMPANY
FUNDED2$BYBUSINESSACTIVITYTYPEOF

costs, and location

s0ROJECTED2$COSTS

s#APITALEXPENDITURESFOR2$BUILDINGSSOFTWARE

equipment) 

Measures related to R&D management and strategy: 

s#HARACTEROFWORKBASICRESEARCHAPPLIEDRESEARCH

and development) 

s2$APPLICATIONSAND2$INNEWBUSINESSAREAS

s2$RELATIONSHIPSWITHOTHERSOUTSIDETHECOMPANY

Measures of company R&D activity funded by others: 

s&UNDSFORWORLDWIDEANDDOMESTIC2$ACTIVITY

s2$FUNDEDBYOTHERSBYBUSINESSACTIVITYTYPEOF

organization, type of cost, state, and location (domestic vs. foreign) 

Measures of R&D employment: 

s2$ HEADCOUNT DOMESTIC AND WORLDWIDE	 BY OCcupation and sex 

s.UMBEROF532$EMPLOYEESWORKINGUNDERA

visa (H-1B, L-1, etc.) 

s2$FULL
TIMEEQUIVALENTCOUNTS

Measures of intellectual property (IP), technology 

transfer, and innovation: 

s0ARTICIPATIONINACTIVITIESTOINTRODUCENEWORSIGNIFIcantly improve existing goods, services, methods of 

production and distribution, or support systems 

s3ELECTEDPATENTINGANDLICENSINGINFORMATION

s0ARTICIPATION IN SPECIFIC TECHNOLOGY TRANSFER ACTIVIties and importance of types of IP protection

For more information see NSF 2008, 2010a, 2010b, and 

2010c.

U.S. Business R&D and Innovation Survey
4-24 Chapter 4. Research and Development: National Trends and International Comparisons

Table 4-9

Domestic R&D performance paid for by the company for top 10 business activities: 2008

(Millions of U.S. dollars)

Business activity Business codea R&D paid for by the company

All business activities ................................................................................... 21100–81390 232,505

Top 10 business activities ......................................................................... — 140,632

Pharmaceutical, medicinal, botanical, and biological products 

 (except diagnostic) manufacturing ..................................................... 32541 41,593

Software publishers (except Internet) ................................................... 51120 23,860

Semiconductor and other electronic components 

 manufacturing .................................................................................... 33440 22,674

Computers and peripheral equipment manufacturing .......................... 33410 9,223

Medical equipment and supplies manufacturing .................................. 33910 8,521

Scientific research and development services ...................................... 54170 8,464

Computer systems design and related services ................................... 54150 8,384

Motor vehicles manufacturing ............................................................... 33610 8,305

Telephone apparatus manufacturing ..................................................... 33421 5,424

Radio and television broadcasting and wireless 

 communications equipment manufacturing....................................... 33422 4,184

All other business activities including undistributed amounts .................. — 91,873

a

Business codes and descriptions based on North American Industry Classification System industry definitions.

NOTES: Data tabulated independent of the industry classification of the company. Detail may not add to total because of rounding

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics and Census Bureau, Business R&D and Innovation 

Survey (2008).

Science and Engineering Indicators 2012

Table 4-10

Domestic R&D performance paid for by others for top 10 business activities: 2008

(Millions of U.S. dollars)

Business activity Business codea R&D paid for by others

All business activities ................................................................................... 21100–81390 58,176

Top 10 business activities ......................................................................... — 39,776

Scientific research and development services ...................................... 54170 8,093

Aircraft manufacturing ........................................................................... 33641 5,881

Architectural, engineering, and related services ................................... 54130 5,558

Search, detection, navigation, guidance, aeronautical, 

 and nautical system and instruments manufacturing ........................ 33452 4,962

Guided missiles, space vehicles, and parts manufacturing .................. 33644 4,725

Computer systems design and related services ................................... 54150 3,085

Pharmaceutical, medicinal, botanical, and biological products 

 (except diagnostic) manufacturing ..................................................... 32541 2,797

Aircraft engine and engine parts manufacturing ................................... 33642 1,918

Electromedical, electrotherapeutic, and irradiation apparatus 

 manufacturing .................................................................................... 33451 1,899

Radio and television broadcasting and wireless 

 communications equipment manufacturing....................................... 33422 858

All other business activities including undistributed amounts .................. — 18,400

a

Business codes and descriptions based on North American Industry Classification System industry definitions.

NOTES: Data tabulated independent of the industry classification of the company. Detail may not add to total because of rounding.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics and Census Bureau, Business R&D and Innovation 

Survey (2008).

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 4-25

R&D Performed Abroad 

by U.S.-Owned Companies 

Foreign operations of U.S. businesses performed $61.5 

billion in R&D outside the United States, based on 2008 

BRDIS pilot survey. Of this performance abroad, $56.9 billion was paid by the own company and $4.6 billion was paid 

by others outside of the company (table 4-11).16

By far, the industry that performed the most R&D outside 

of the United States was the pharmaceuticals and medicines 

industry ($10.9 billion), based on BRDIS data (appendix 

table 4-14). Other industries with high levels of R&D performed abroad were automobiles, automobile bodies, trailers, and parts manufacturers ($8.4 billion), semiconductor 

and other electronic components manufacturers ($7.1 billion), and software publishers ($6.3 billion).

R&D by Multinational Companies 

This section covers statistics on R&D performed by majority-owned affiliates of foreign multinational corporations 

(MNCs) located in the United States, and R&D performed 

by U.S. MNCs and their majority-owned foreign affiliates, 

collected by the Bureau of Economic Analysis (BEA). See 

sidebar, “Foreign Direct Investment in R&D.” 

R&D arising from foreign direct investment (FDI) activities is quantitatively important since MNCs are the largest 

performers of business R&D in the United States (discussed 

below) and in other economies (Dunning and Lundan 2009). 

Both home country and international opportunities and policies affect R&D and other innovation-related activities by 

MNCs (Breznitz 2009; Athukorala and Kohpaiboon 2010). 

In turn, MNC activities influence the ultimate impacts of national and international R&D on national economic growth 

and productivity. 

The majority of R&D by U.S. MNCs continues to be performed in the United States. Outside the United States, R&D 

by U.S.-owned foreign affiliates is performed mostly in 

Western Europe, Canada, and Japan, followed more recently by other locations in the Asia-Pacific region. Information 

on character of work for MNCs’ R&D is presented in the 

sidebar, “Linking MNC Data from International Investment 

and Business R&D Surveys.” 

U.S. Affiliates of Foreign Companies

Majority-owned affiliates of foreign MNCs located in 

the United States (U.S. affiliates) performed $40.5 billion 

of R&D or 13.9% of the $290.7 in U.S. business R&D performed in 2008 (preliminary BEA estimate).17 Since 1999, 

the share of these companies in U.S.-located business R&D, 

Table 4-11

R&D performed abroad by U.S.-owned companies: 2008

(Millions of U.S. dollars)

Foreign performance

Industry NAICS code Total Paid for by company Paid for by others

All industries ............................................ 21–23, 31-33, 42–81 61,475 56,899 4,576

Manufacturing industries ..................... 31–33 46,572 45,274 1,298

Nonmanufacturing industries ............... 21–23, 42–81 14,903 11,625 3,278

NAICS = North American Industry Classification System

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics and Census Bureau, Business R&D and Innovation 

Survey (2008).

Science and Engineering Indicators 2012

Foreign direct investment (FDI) is one of several 

channels for the creation, exploitation, and diffusion 

of new knowledge along with international trade, licensing, and technology partnerships (Saggi 2002). 

Direct investment is defined as ownership or control 

of 10% or more of the voting securities of a business 

(affiliate) in another country. The cross-country location of R&D activities via FDI is driven by factors 

ranging from costs and long-term market and technological opportunities to infrastructure and policy considerations, such as human resources and intellectual 

property protection. 

Statistics on R&D by affiliates of foreign companies located in the United States, and by foreign affiliates of U.S. MNCs and their parent companies, are 

from BEA’s Survey of Foreign Direct Investment in 

the United States (FDIUS) and BEA’s Survey of U.S. 

Direct Investment Abroad (USDIA). Affiliate data 

presented in this section cover majority-owned affiliates, that is, those in which the ownership stake of parent companies totals more than 50%. Annual changes 

in FDI R&D reflect a combination of mergers and 

acquisitions; newly built factories, service centers, or 

laboratories; and activities in existing facilities. Data 

exclude commercial banks, savings institutions, credit 

unions, bank holding companies, and financial holding companies

Foreign Direct Investment in R&D
4-26 Chapter 4. Research and Development: National Trends and International Comparisons

as collected in NSF R&D surveys, has fluctuated narrowly 

between 13% and 15%. About 90% of R&D by U.S. affiliates of foreign MNCs is performed by firms owned by 

European, Japanese, and Canadian parent companies (appendix table 4-18). 

The share of U.S. affiliates’ R&D performed by manufacturing companies has decreased from over 80% in the late 

1990s to 74.7% in 2007 and 69.6% in 2008. Country ownership patterns and industry focus have remained relatively 

unchanged, with Swiss- and British-owned companies, for 

example, performing close to two-thirds of R&D by U.S. 

affiliates classified in chemicals (which includes pharmaceuticals) and German-owned companies performing close 

to one-quarter of R&D by U.S. affiliates classified in transportation equipment in 2008 (table 4-12). Among the largest nonmanufacturing R&D-performing industries for U.S. 

affiliates in 2008 were wholesale trade in electrical goods 

($2.4 billion); professional, scientific, and technical services 

($2.3 billion); and information services ($2.1 billion) (see 

appendix tables 4-19, 4-20, and 4-21).

U.S. MNCs Parent Companies and Their 

Foreign Affiliates

In 2008, parent companies of U.S. MNCs performed 

$199.1 billion of the $290.7 billion of R&D performed by 

businesses in the United States. Their majority-owned foreign affiliates performed $37.0 billion according to preliminary BEA data (see table 4-13 and appendix tables 4-22 

through 4-26).).18 Since 1999, U.S. MNCs have performed, 

on average, about 86% of their annual global R&D in the 

United States. In turn, U.S. MNC parents accounted, on 

average, for about 72% of annual U.S. business R&D performed over the same period. 

Table 4-12

R&D performed by majority-owned affiliates of foreign companies in the United States, by selected NAICS 

industry of affiliate and country: 2008

(Millions of current U.S. dollars)

Manufacturing Nonmanufacturing

Country

All 

industries Total Chemicals Machinery

Computer 

and 

electronic 

products

Electrical 

equipment

Transportation 

equipment Information

Professional, 

technical, 

scientific 

services

Wholesale 

trade

All countries ..... 40,519 28,190 14,121 2,535 4,259 499 4,015 2,108 2,347 7,404

Canada ............ 1,435 429 124 D D D 194 D D D 

France ............. 5,978 4,672 1,408 D D D 102 D 91 D 

Germany .......... 5,520 4,763 2,017 D 101 21 930 D D 227

Netherlands ..... 1,789 D D D 236 4 16 0 3 D 

Switzerland ...... 6,926 5,743 5,435 43 11 D 9 3 934 245

United 

Kingdom ....... 7,369 6,683 3,665 47 292 6 D D 106 D 

Japan ............... 4,637 1,643 516 64 515 42 282 11 678 2,242

Other ................ 6,865 D D 595 D D D D D 2,911

D = suppressed to avoid disclosure of confidential information

NAICS = North American Industry Classification System

NOTES: Preliminary 2008 estimates for majority-owned (>50%) nonbank affiliates of nonbank U.S. parents by country of ultimate beneficial owner and industry of affiliate. Expenditures included for R&D conducted by foreign affiliates, whether for themselves or others under contract. Expenditures excluded 

for R&D conducted by others for affiliates under contract. 

SOURCE: Bureau of Economic Analysis, Survey of Foreign Direct Investment in the United States (annual series), http://www.bea.gov/international/index.

htm#omc, accessed 4 February 2011.

Science and Engineering Indicators 2012

In 2007, the nature of R&D carried out by U.S. affiliates of foreign-owned MNCs was very similar to 

U.S.-based R&D of U.S. MNC parents: 4.4%–4.5% 

of R&D expenditures was devoted to basic research, 

19.4%–19.9% to applied research, and 75.8%–76.1% 

to development.

This new insight into the distribution of character 

of work of MNCs R&D is made possible by linking 

and comparing reports for 2004–07 from the same 

set of companies responding to NSF/Census Survey 

of Industrial Research and Development (SIRD),* the 

predecessor of BRDIS, with two different BEA surveys: Foreign Direct Investment in the United States 

and U.S. Direct Investment Abroad.

* http://www.nsf.gov/statistics/industry/

Linking MNC Data from 

International Investment and 

Business R&D Surveys
Science and Engineering Indicators 2012 4-27

R&D by foreign affiliates of U.S. MNCs has gradually 

shifted from traditional host countries, including Japan, towards other Asian venues. The combined share of Europe, 

Canada, and Japan as hosts of R&D by U.S.-owned foreign 

affiliates declined from about 90% in the mid- and late 

1990s to around 80% since 2006. European-located affiliates have performed about two-thirds of R&D by affiliates 

of U.S. MNCs since 2003, after declining in the late 1990s 

and early 2000s (figure 4-9 and appendix table 4-22). 

On the other hand, the share of R&D performed by Asialocated affiliates (other than in Japan) increased from about 

5% to 14% from 1997 to 2008. In particular, the share of 

U.S.-owned affiliates’ R&D performed in China, South 

Korea, Singapore, and India rose from a half percentage 

point or less in 1997 to 4% for China, just under 3% for 

South Korea, and just under 2% each for Singapore and 

India in 2008. 

Manufacturing affiliates accounted for 80% of foreign 

affiliates’ R&D in 2008, including two-thirds performed in 

three industries: transportation equipment (25%), chemicals 

(including pharmaceuticals) (24%), and computer and electronic products (17%) (table 4-14). Professional, technical, 

and scientific services accounted for another 11% and information services for 5% (see appendix tables 4-23 and 4-24).

The country and industry distribution of U.S. MNCs’ 

foreign R&D is related, among other factors, to historical 

S&T strengths in the host countries. United Kingdom and 

Germany, for example, hosted 20.4% and 12.0% of U.S.-

owned overseas R&D in chemicals (which includes pharmaceuticals), whereas affiliates located in Germany performed 

almost two-fifths of R&D by transportation equipment affiliates (table 4-14). 

Within the professional, technical, and scientific services 

industry, affiliates located in the UK performed 22.3% of 

foreign affiliates’ R&D total, followed by Canada (12.5%) 

and India (6.3%), according to available country detail. 

Lastly, about four-fifths of affiliates’ R&D in information 

services (which includes software and Internet publishing 

and telecommunications) in 2008 was performed in three 

areas: Ireland (30.7%), Canada (22.3%), and Asia outside 

Japan (25.5%). 

Exports and Imports 

of R&D-Related Services

Cross-border transactions of business services, published 

by BEA as part of international transactions accounts, include research, development, and testing (RDT) services 

under the category of business, professional, and technical 

services. RDT services include commercial and noncommercial research as well as product development and testing 

services. In 2009, U.S. RDT exports and imports stood at 

$18.2 billion and $15.8 billion, respectively, for a balance of 

$2.5 billion (appendix table 4-27).19

Transactions in RDT services provide insights into business R&D-related transactions, including exchanges among 

unaffiliated or independent companies (unaffiliated trade) 

and trade within MNCs (affiliated trade). As described below, 

most transactions in these R&D-related services occur within 

multinational corporations. Further, the patterns of U.S. RDT 

exports and imports differ for U.S. and foreign MNCs.

Most RDT trade occurs within companies. Since 2001, 

when affiliated RDT trade data were first available, transactions among MNCs members (parent companies and 

subsidiaries) have represented around 85% of total RDT 

exports annually. This share is consistent with the large 

role of MNCs (including U.S. parents and foreign-owned 

Table 4-13

R&D performed by U.S. multinational companies: 1999–2008

Location of R&D performed 

(current US$millions)

Shares of U.S. MNCs R&D 

performance (%)

Year

United States 

(by parents of U.S. MNCs)

Outside United States 

(by MOFAs)

Total by 

U.S. MNCs United States

Outside United 

States

1999.................. 126,291 18,144 144,435 87.4 12.6

2000.................. 135,467 20,457 155,924 86.9 13.1

2001.................. 143,017 19,702 162,719 87.9 12.1

2002.................. 136,977 21,063 158,040 86.7 13.3

2003.................. 139,884 22,793 162,677 86.0 14.0

2004.................. 164,189 25,840 190,029 86.4 13.6

2005.................. 177,598 27,653 205,251 86.5 13.5

2006.................. 184,428 29,583 214,011 86.2 13.8

2007.................. 203,678 34,446 238,124 85.5 14.5

2008.................. 199,105 36,991 236,096 84.3 15.7

MNC = multinational company; MOFA = majority-owned foreign affiliate

NOTE: MOFAs are affiliates in which combined ownership of all U.S. parents is >50%.

SOURCE: Bureau of Economic Analysis. Survey of Foreign Direct Investment in the United States (annual series). 

Science and Engineering Indicators 2012
4-28 Chapter 4. Research and Development: National Trends and International Comparisons

companies) in U.S. business R&D performance. (See section “R&D by Multinational Companies.”) Likewise, within-company imports accounted for 66% to 78% of total RDT 

imports annually over the same period. The large share of 

this affiliated or within-company RDT trade reflects the 

need for management control and proprietary protection in 

cross-border transactions involving intangible assets. 

Foreign MNCs with operations in the U.S. are exporting more RDT services to their foreign parents (and other 

members of the foreign MNCs) compared with their level of 

imports. Foreign MNCs with activities in the U.S. reported 

average annual net exports of $3.9 billion between 2006 and 

2009, with net exports fluctuating between $3.4 billion to 

$4.5 billion over this period. On the other hand, U.S. MNC 

parents imported annually about the same or slightly more of 

those services relative to their exports over the same period 

(table 4-15).20 In 2009, U.S. parents imported $602 million 

more RDT services from their foreign affiliates than they 

exported to their affiliates.

Europe accounted for about half of U.S. total RDT exports and imports in 2009 (appendix table 4-27). Latin 

America was the second largest destination of RDT exports 

(23%) whereas Asia was the second largest origin of RDT 

imports (about 30%). The latter included 9.1% of RDT imports from India in 2009 (compared with 4.6% in 2006), 

8.6% from Japan (compared with 5.9% in 2006), and 5.4% 

from China (compared with 1.0% in 2006).

Federal R&D

The U.S. government supports the nation’s R&D system through various policy tools. The most direct is federal 

performance and funding of R&D. This section provides 

statistics on these federally performed or funded activities, including budget authority by national objectives, obligations by agency, and obligations by research field (for 

definition of these terms see sidebar, “Federal Budgetary 

Concepts and Related Terms”). This section also covers federal tax credits for business R&D.

Federal R&D Budget by National Objectives

Federal support for the nation’s R&D spans a range of 

broad objectives, including defense, health, space, energy, 

natural resources/environment, general science, and various other categories. To assist the president and Congress in 

planning and setting the federal budget and its components, 

the Office of Management and Budget classifies agency budget requests into specific categories called budget functions. 

These functions include a number of categories that distinguish the various R&D objectives. Descriptions of the budget authority provided annually to federal agencies in terms 

of these R&D budget functions afford a useful picture of the 

present priorities and trends in federal support for U.S. R&D. 

In FY 2009, budget authority for federal agency spending on R&D totaled an estimated $156.0 billion, including a one-time $15.1 billion increase provided under the 

American Recovery and Investment Act of 2009 (appendix 

tables 4-28 and 4-29). 

Defense-Related R&D

As in previous years, defense was the largest of the R&D 

budget functions, accounting for 55% ($85.2 billion) of the 

total. Defense R&D is supported primarily by the Department 

Figure 4-9

Regional shares of R&D performed abroad by 

foreign affiliates of U.S. MNCs: 1997–2008 

MNC = multinational company; OWH = other Western Hemisphere

NOTES: Data for majority-owned affiliates. Preliminary estimates for 

2008. 

SOURCE: Bureau of Economic Analysis, Survey of U.S. Direct 

Investment Abroad (annual series).

Science and Engineering Indicators 2012

Percent

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0

65

70

75

Europe

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0

2

4

6

8

10

12

14

Canada

Japan

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0

2

4

6

8

10

12

14

16

Asia/Pacific excluding Japan

Latin America/OWH

Middle East Africa
Science and Engineering Indicators 2012 4-29

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).

Defense has accounted for the majority of R&D budget 

authority throughout the last two decades (figure 4-10, appendix table 4-28); the share has fluctuated year to year in 

the 50%–70% range. In FY 1980, it roughly equaled nondefense R&D, but by FY 1985 it was more than double. 

From 1986 to 2001, nondefense R&D surged, and the share 

of defense R&D shrank to 53%. After September 11, 2001, 

defense R&D became more prominent, accounting for 59% 

of the federal R&D budget in FY 2008. The drop to 55% in 

FY 2009 reflects chiefly the effect of the one-time ARRA 

Table 4-14

R&D performed abroad by majority-owned foreign affiliates of U.S. parent companies, by selected NAICS 

industry of affiliate and region/country/economy: 2008

(Millions of current U.S. dollars)

Manufacturing

Region/country/ 

economy

All 

industries Total Chemicals Machinery

Computer 

and 

electronic 

products

Electrical 

equipment, 

appliances, 

and 

components

Transportation 

equipment Information

Professional, 

technical, 

scientific 

services

Wholesale 

trade

All countries ........... 36,991 29,385 8,754 1,457 6,354 586 9,163 1,954 3,963 1,461

Canada .............. 3,040 1,981 685 28 482 19 653 436 494 78

Europe ............... 24,155 19,416 6,255 1,076 2,762 313 6,601 870 2,680 1,089

Belgium .......... 1,259 891 783 24 D 15 14 * 350 12

France ............ 2,171 1,878 448 133 501 35 456 45 188 53

Germany......... 7,039 6,485 1,051 388 858 132 3,527 D 194 D 

Ireland ............ 1,503 848 438 0 313 0 2 599 D D 

Italy ................. 582 475 246 52 22 4 73 * D D 

Netherlands .... 1,484 1,267 778 41 73 D D D D D 

Sweden .......... 1,576 1,478 33 12 59 D D D D 7

Switzerland ..... 1,123 728 262 88 173 24 D D 229 118

United 

Kingdom...... 5,157 3,844 1,790 185 464 49 912 76 884 341

Latin America/ 

OWH ................ 1,465 1,354 371 40 191 D 642 1 37 29

Brazil .............. 791 770 175 35 D 0 444 1 4 16

Mexico ........... 329 D 80 4 7 D 169 * D 3

Africa .................. 57 44 14 1 * 0 23 2 * 2

South 

Africa ........... 43 34 13 * * 0 D 2 * 2

Middle East ........ 1,063 869 D D 650 0 0 5 174 15

Israel ............... 1,060 867 D D 650 0 0 D 174 D 

Asia and 

Pacific .............. 7,210 5,722 D D 2,268 D 1,244 640 578 247

Australia ......... 923 851 234 10 6 20 D 7 20 41

China .............. 1,517 1,180 D 24 965 66 40 D D 43

Hong Kong ..... 102 52 9 0 10 5 0 5 37 7

India ............... 582 222 58 D D D 32 D 250 D 

Japan ............. 1,872 1,529 930 64 244 D 81 142 D D 

Malaysia ......... 360 358 3 * 345 1 0 0 * 2

Singapore ....... 621 390 30 1 343 1 2 D D 9

South Korea ... 966 931 34 17 207 D D 7 7 D 

Taiwan ............ 102 D D 1 48 5 2 D 8 8

Thailand .......... 69 67 7 3 27 0 17 0 0 2

* = ) $500,000; D = suppressed to avoid disclosure of confidential information

NAICS = North American Industry Classification System; OWH = other Western Hemisphere

NOTES: Preliminary 2008 estimates for majority-owned (>50%) nonbank affiliates of nonbank U.S. parents by country of ultimate beneficial owner 

and industry of affiliate. Expenditures included for R&D conducted by foreign affiliates, whether for themselves or others under contract. Expenditures 

excluded for R&D conducted 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 4 February 2011.

Science and Engineering Indicators 2012

Nonmanufacturing
4-30 Chapter 4. Research and Development: National Trends and International Comparisons

spending authority that expanded health, energy, and general science research. 

Nondefense R&D

Budget authority for nondefense R&D totaled $70.8 billion in FY 2009, or about 45% of the total that year (appendix table 4-28). Nondefense R&D includes health, space 

research/technology, energy, general science, natural resources/environment, transportation, agriculture, education, international affairs, veterans’ benefits, and a number 

of other small categories related to economic and governance matters. 

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 well over 

half of all nondefense R&D (figure 4-10). Health R&D has 

risen from about 12% of total federal R&D budget authority 

in FY 1980 to 21% in FY 2008 and 26% in FY 2009 because 

of the ARRA increment. The increase in share accelerated 

after 1998, when policymakers set the National Institutes of 

Health (NIH) budget on course to double by FY 2003.

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 authority throughout the 1990s. The loss of 

the space shuttle Columbia and its entire crew in February 

2003 prompted curtailment of manned space missions. In 

FY 2005, the space R&D share was down to about 6%; by 

FY 2009, it had declined to around 4%. 

Federal nondefense R&D classified as general science 

had about a 4% share of total federal R&D in the mid 1990s, 

growing to 8% in FY 2009. However, this change reflected 

chiefly a reclassification, starting in FY 1998, of several 

DOE programs from energy to general science.

Federal Budget for Basic Research

In FY 2009, federal budget authority for all basic research totaled $36.4 billion (appendix table 4-29). This 

represented some 23% of the $156.0 billion of total federal 

budget authority for R&D that year. The vast majority of 

basic research reflects the budgets of agencies with nondefense objectives, such as general science (notably NSF), 

health (NIH), and space research and technology (NASA). 

Over the past several years, budget authority levels for 

basic research have been mostly flat, after adjusting for 

inflation, excepting the 2009 ARRA boost. In FY 2002, 

basic research budget authority was $25.8 billion (constant 

2005 dollars); in FY 2008, $26.4 billion; and $33.0 billion 

in FY 2009.

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.

Federal Budgetary Concepts 

and Related Terms

Table 4-15

U.S. trade balance in research, development, and testing services, by affiliation: 2006–09

(Millions of current U.S. dollars)

Affiliation 2006 2007 2008 2009

Total ........................................................................................................ 3,534 2,593 1,142 2,481

Unaffiliated .......................................................................................... –660 –1,008 –1,473 –1,020

Affiliated .............................................................................................. 4,193 3,601 2,615 3,500

By U.S. parents from/to their foreign affiliates ................................ –334 185 –1,100 –602

By U.S. affiliates from/to their foreign parent groupsa

 .................... 4,528 3,416 3,715 4,103

a

In addition to transactions with its foreign parent, U.S. affiliates’ exports and imports include transactions with other members of their foreign 

parent group. 

NOTES: Trade balance is exports minus imports. Positive amounts represent a trade surplus; negative amounts represent a trade deficit.

SOURCE: Bureau of Economic Analysis, U.S. International Services, http://www.bea.gov/international/international_services.htm, accessed 

4 February 2011. 

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 4-31

Federal Spending on R&D by Agency

Budget authority, discussed above, lays out the themes of 

the broad federal spending plan. Federal obligations reflect 

federal dollars as they are spent, that is, the implementation 

of the plan by federal agencies (see appendix tables 4-30 

and 4-31).

 In FY 2009, federal obligations for R&D and R&D plant 

together totaled an estimated $137.0 billion: $133.3 billion for R&D and an additional $3.6 billion for R&D plant 

(table 4-16). Federal obligations for R&D have, in general, increased annually on a current-dollar basis since the 

mid-1990s (figure 4-11). Earlier figures are $68.2 billion for 

R&D in FY 1995 and an additional $2.3 billion for R&D 

plant, $75.9 billion and $4.5 billion in FY 2000, $118.9 

billion and $3.8 billion in FY 2005 (appendix table 4-30). 

When adjusted for inflation, however, the growth has been 

slower after FY 2005. NSF’s latest statistics indicate that the 

boost to R&D from the ARRA appropriations translated to 

an additional $10.1 billion of federal R&D obligations in FY 

2009—$8.7 billion for R&D, another $1.4 billion for R&D 

plant, with the main recipients the Department of Health and 

Human Services (HHS), NSF, and DOE (table 4-16). 

(The figures for federal funding of U.S. R&D cited in 

table 4-1 earlier in this chapter are somewhat lower. These 

earlier 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. See sidebar, 

“Tracking R&D: The Gap between Performer- and SourceReported Expenditures.”) 

Fifteen federal departments and a dozen other agencies 

engage in and/or fund R&D in the U.S.21 Seven departments/

agencies that reported spending on R&D in excess of $1 billion annually accounted for 97% of the total (table 4-16). 

Another eight of the departments/agencies reported spending above $100 million annually. 

Department of Defense

In FY 2009, DOD obligated a total of $68.2 billion for 

R&D and R&D plant (table 4-16)—which represented half 

(50%) of all federal spending on R&D and R&D plant that 

year. Nearly the entire DOD total was R&D spending ($68.1 

billion) with the remainder spent on R&D plant. 

Twenty-seven percent ($18.7 billion) of the total was 

spending by the department’s intramural labs, related 

agency R&D program activities, and FFRDCs (table 4-16). 

Extramural performers—private businesses, universities/

colleges, state/local governments, other nonprofit organizations, and foreign performers—accounted for 73% ($49.5 

billion) of the obligations, with the bulk going to business 

firms ($46.3 billion). 

Considering just the R&D component, relatively small 

amounts were spent on basic research ($1.7 billion, 3%) and 

applied research ($5.1 billion, 7%) in FY 2009 (table 4-17). 

The vast majority of obligations, $61.3 billion (90%), went 

to development. Furthermore, the bulk of this DOD development ($54.9 billion) was allocated for “major systems development,” which includes the main activities in developing, 

testing, and evaluating combat systems (figure 4-12). The 

remaining DOD development ($6.4 billion) was allocated 

for “advanced technology development,” which is more 

similar to other agencies’ development obligations.

Department of Health and Human Services

HHS is the main federal source of spending for healthrelated R&D. In FY 2009, the department obligated an estimated $35.7 billion for R&D and R&D plant, or 26% of the 

Figure 4-10

Federal budget authority for R&D, by budget 

function: FY 1980–2010

NOTES: Data for FY 2010 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, such as agriculture 

and transportation.1998 increase in general science and decrease in 

energy, and 2000 decrease in space were results of reclassification. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Federal R&D Funding by Budget Function 

(FY 2009–11). See appendix table 4-28. 

Science and Engineering Indicators 2012

Budget function

Nondefense budget function

Curent dollars (billions) 

1980 1983 1986 19921989 1995 1998 20042001 2007 2010

0

20

40

60

80

100

120

140

160

Total

 Defense

Nondefense

1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010

0

5

10

15

20

25

30

35

40

45

Health

Space

Energy

General science

Other
4-32 Chapter 4. Research and Development: National Trends and International Comparisons

total of federal obligations that year. Nearly all of this was 

for R&D ($35.6 billion). Furthermore, much of the total, 

$34.6 billion, represented the R&D activities of the NIH. 

Obligations from the ARRA-appropriated funds totaled 

$4.9 billion for HHS in FY 2009, the largest by far of all the 

federal agencies (table 4-16). Again, nearly all of this was 

NIH R&D. 

For the department as a whole, R&D and R&D plant 

obligations for agency intramural activities and FFRDCs 

accounted for 21% ($7.5 billion) of the total. Extramural 

performers accounted for 79% ($28.2 billion). Universities 

and colleges ($20.5 billion) and other nonprofit organizations ($5.3 billion) conducted the most sizable of these extramural activities (appendix table 4-31). 

Nearly all of HHS R&D funding is allocated to research—almost 53% for basic research and 47% for applied 

research (table 4-17). 

Department of Energy

DOE obligated an estimated $11.6 billion for R&D and 

R&D plant in FY 2009, about 8% of the federal obligations 

total that year. Of this amount, $9.9 billion was for R&D and 

$1.7 billion for R&D plant. Obligations this year stemming 

from the ARRA appropriation totaled $2.2 billion, the third 

largest among the agencies (behind HHS and NSF). 

The department’s intramural laboratories and FFRDCs 

accounted for 77% of the total obligations. Many of DOE’s 

research activities require specialized equipment and facilities available only at its intramural laboratories and FFRDCs. 

Accordingly, DOE invests more resources in its intramural 

laboratories and FFRDCs than other federal agencies. The 

23% of obligations to extramural performers were chiefly to 

businesses and universities/colleges. 

For the $9.9 billion obligated to R&D, basic research 

accounted for 41%, applied research 32%, and development 27%. DOE R&D activities are rather evenly distributed among defense (much of it funded by the department’s 

Table 4-16

Federal obligations for R&D and R&D plant, by agency and performer: FY 2009

(Millions of dollars)

Total by performers

Agency Total R&D

R&D 

plant R&D

R&D 

plant

Intramural 

and 

FFRDCs 

Percent 

of total

Extramural 

performers

Percent 

of total

All agencies .......................................... 136,996.5 133,349.0 3,647.5 8,714.1 1,367.8 42,954.7 31.4 94,041.8 68.6

Department of Defense ..................... 68,230.2 68,113.0 117.2 184.2 0.0 18,695.1 27.4 49,535.1 72.6

Department of Health and 

Human Services ............................ 35,735.9 35,584.0 151.9 4,889.0 49.7 7,546.7 21.1 28,189.2 78.9

Department of Energy ....................... 11,562.2 9,889.9 1,672.3 1,393.4 813.2 8,853.3 76.6 2,709.0 23.4

National Science Foundation ............ 6,924.8 6,095.2 829.6 1,807.6 388.5 303.8 4.4 6,618.2 95.6

National Aeronautics and Space 

Administration .............................. 5,937.1 5,937.0 0.1 314.7 0.0 1,958.1 33.0 3,979.0 67.0

Department of Agriculture ................ 2,347.2 2,269.7 77.5 0.4 11.0 1,576.9 67.2 770.3 32.8

Department of Commerce ................ 1,533.3 1,146.9 386.4 46.0 98.7 1,181.4 77.0 351.9 23.0

Department of Homeland Security ... 983.6 672.5 311.1 0.0 0.0 596.5 60.6 387.0 39.3

Department of Transportation .......... 846.3 826.0 20.3 0.0 0.0 280.5 33.2 565.7 66.8

Department of the Interior................. 738.8 732.4 6.4 59.6 0.0 602.5 81.6 136.3 18.4

Environmental Protection Agency .... 552.8 552.8 0.0 0.0 0.0 414.1 74.9 138.8 25.1

Department of Veterans Affairs......... 510.0 510.0 0.0 0.0 0.0 510.0 100.0 0.0 0.0

Department of Education .................. 322.4 322.4 0.0 0.0 0.0 15.6 4.8 306.7 95.2

Smithsonian Institution ..................... 226.7 152.0 74.7 0.0 6.7 226.7 100.0 0.0 0.0

Agency for International 

Development ................................. 160.1 160.1 0.0 0.0 0.0 10.6 6.6 149.5 93.4

All other agencies ............................. 385.1 385.1 0.0 19.2 0.0 183.0 47.5 204.9 53.2

FFRDC = federally funded research and development center

NOTES: Table lists all agencies with R&D obligations greater than $100 million in FY 2009. Data include obligations from the additional federal R&D 

funding appropriated by the American Recovery and Reinvestment Act of 2009. R&D is basic research, applied research, and development; 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 industry, 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 Justice, Department of Labor, Department of State, Department of the Treasury, 

Appalachian Regional Commission, Federal Communications Commission, Federal Trade Commission, Library of Congress, National Archives and 

Records Commission, Nuclear Regulatory Commission, and Social Security Administration.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Federal Funds for Research and Development (FY 

2009–11). See appendix table 4-31.

Science and Engineering Indicators 2012

ARRA funds
Science and Engineering Indicators 2012 4-33

National Nuclear Security Administration), energy, and general science (much of which is funded by the department’s 

Office of Science). 

National Science Foundation

NSF obligated $6.9 billion for R&D and R&D plant in 

FY 2009, or 5% of the federal total. Extramural performers, 

chiefly universities and colleges ($6.6 billion), represented 

96% of this total. ARRA-related obligations were $2.2 billion (R&D and R&D plant), the second largest among the 

agencies. Basic research accounted for about 92% 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. 

National Aeronautics and Space Administration

NASA obligated an estimated $5.9 billion to R&D in 

FY 2009, 4% of the federal total. Sixty-seven percent of 

these obligations were for extramural R&D, given chiefly 

to industry performers. Agency intramural R&D and that by 

FFRDCs represented 33% of the NASA obligations total. 

By character of work, 71% of the NASA R&D obligations 

funded development activities; 17%, basic research; and 

12%, applied research.

Department of Agriculture

USDA obligated an estimated $2.3 billion for R&D in 

FY 2009, 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 2009, about 67% ($1.6 billion) 

funded R&D by agency intramural performers, chiefly the 

Agricultural Research Service. Basic research accounts for 

about 41%; applied research, 51%; and development, 8%. 

Department of Commerce

DOC obligated an estimated $1.5 billion for R&D in 

FY 2009, most of which represented the R&D and R&D 

plant spending of the National Oceanic and Atmospheric 

Administration (NOAA) and the National Institute of 

Standards and Technology (NIST). Seventy-seven percent 

of this total was for agency intramural R&D; 23% went to 

extramural performers, primarily businesses and universities/colleges. For the R&D component, 12% was basic research; 72%, applied research; and 16%, development. 

Department of Homeland Security

DHS obligated an estimated $1.0 billion for R&D and 

R&D plant in FY 2009, nearly all of which was for activities 

by the department’s Science and Technology Directorate. 

Sixty-one percent of this obligations total was for agency 

intramural and FFRDC activities. Just over 39% was conducted by extramural performers—mainly businesses, but 

also universities/colleges and other nonprofit organizations. 

Of the obligations for R&D, 15% was basic research; 37%, 

applied research; and 48%, development.

Other Agencies

The eight other departments/agencies obligating more 

than $100 million annually for R&D in FY 2009 were the 

Departments of Education, Interior, Justice, Transportation, 

and Veterans Affairs; and the Environmental Protection 

Agency, Agency for International Development, and Smithsonian Institution (tables 4-16 and 4-17). These agencies 

varied with respect to the character of the research and the 

roles of intramural, FFRDC, and extramural performers. 

Federal Spending on Research by Field

Federal agencies’ research covers the whole range of science and engineering fields. These fields vary in their funding levels and have different growth paths (see appendix 

tables 4-34 and 4-35).

Funding for basic and applied research combined accounted for $63.7 billion (about 48%) of the $133.3 billion total of 

federal obligations for R&D in FY 2009 (table 4-17). Of this 

amount, $33.3 billion (52% of $63.7 billion) supported research in the life sciences (figure 4-13; appendix table 4-34). 

The fields with the next-largest amounts were engineering 

($10.3 billion, 16%) and the physical sciences ($5.8 billion, 

9%), followed by environmental sciences ($3.8 billion, 6%), 

and mathematics and computer sciences ($3.6 billion, 6%). 

The balance of federal obligations for research in FY 2009 

supported psychology, the social sciences, and all other sciences ($7.0 billion overall, or 11% of the total for research). 

HHS accounted for the largest share (56%) of federal obligations for research in FY 2009 (appendix table 4-34). Most 

Figure 4-11

Federal obligations for R&D and R&D plant: 

FY 1980–2009

Current dollars (billions)

NOTES: Data for FY 2009 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 (FY 2009–11). See appendix table 4-30.

Science and Engineering Indicators 2012

1980 1985 1988 1991 1994 1997 2000 2003 2006 2009

0

20

40

60

80

100

120

140

160

R&D and R&D plant

R&D

R&D plant
4-34 Chapter 4. Research and Development: National Trends and International Comparisons

In the United States—and in some other 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 in the data provided by funders and 

performers can arise for numerous reasons, such as the 

different calendars for reporting government obligations 

(fiscal years) and performance expenditures (calendar 

years). In the U.S., there has been a sizable gap between 

performer and funder data for federal R&D over the past 

decade or more.

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 through 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 $106 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% smaller 

than the federally reported R&D support to industry in 

FY 2007 (see figure 4-A and appendix table 4-32). These 

differences in federal R&D totals were seen primarily in 

DOD funding of development activities by industry. The 

figures for 2008 and 2009 suggest a narrowing of the federal agency reporting excess, but 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. According to a General Accounting Office investigation (GAO 2001): 

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. 

Echoing this assessment, the National Research 

Council (2005) noted that comparing federal outlays for 

R&D (as opposed to obligations) to performer expenditures results in a smaller discrepancy. (In FY 2009, federal agencies reported total R&D outlays of $127 billion, 

compared to a total R&D figure of $124 billion reported 

by all performers that year. In FY 2007, federal agencies 

reported R&D outlays of $109 billion, compared to the 

performer-reported total of $106 billion.) 

Tracking R&D: The Gap between Performer- and Source-Reported Expenditures

Percent

Figure 4-A

Differences in U.S. performer-reported and 

agency-reported federal R&D: 1985–2009 

NOTE: Difference is defined as percent 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 (FY 2009–11). See appendix table 4-32.

Science and Engineering Indicators 2012

All performers

Industrial performers

1985 1989 1993 1997 2001 2005 2009

–60

–40

–20

0

20

1985 1989 1993 1997 2001 2005 2009

–60

–40

–20

0

20
Science and Engineering Indicators 2012 4-35

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 (11%), 

DOD (11%), NSF (10%), USDA (3%), and NASA (3%). 

DOE’s $7.2 billion in research obligations provided 

funding for research in the physical sciences ($2.6 billion) 

and engineering ($2.5 billion), along with mathematics and 

computer sciences ($1.0 billion). DOD’s $6.8 billion of research funding emphasized engineering ($3.5 billion), but 

also included mathematics and computer sciences ($0.9 

billion), physical sciences ($0.8 billion) and life sciences 

($0.9 billion). NSF—not a mission agency in the traditional sense—is charged with “promoting the health of science.” Consequently, it had a relatively diverse $6.1 billion 

research portfolio that allocated about $1.0 billion to $1.3 

billion in each of the following fields: environmental, life, 

mathematics/computer, and physical sciences; and engineering. Lesser amounts were allocated to psychology and the 

social and other sciences. USDA’s $2.1 billion was directed 

primarily at the life (agricultural) sciences ($1.7 billion). 

NASA’s $1.7 billion for research emphasized engineering 

($0.6 billion), followed by the physical sciences ($0.5 billion) and environmental sciences ($0.4 billion). 

Growth in federal research obligations has slowed since 

2004. Federal obligations for research in all S&E fields expanded on average at 3.6% annually (in current dollars) over 

the last 5 years (FY 2004–09), a much higher 6.6% over the 

last 10 years, and 5.8% over the last 20 years (appendix table 

4-35). Adjusted for inflation, the 2004–09 average growth 

turns into an average annual increase of only 0.9%, which 

contrasts with a 10-year real growth of 4.1% and 3.3% over 

the last 20 years. 

Since the late 1990s, growth in federal research obligations in the life sciences and psychology has exceeded the 

S&E average, leading to growing shares for these fields. 

Growth for the mathematics and computer sciences was just 

below the S&E average. The shares of research funding going to physical sciences, behavioral and other social sciences, and engineering, declined. Environmental sciences grew 

slower than both total research and inflation. 

Federal R&E Tax Credits

The federal government makes available tax credits for 

companies that expand their R&D activities, as a way of 

counteracting potential business underinvestment in R&D. 

Table 4-17

Federal obligations for R&D, by agency and character of work: FY 2009

(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 .................................................... 133,349.0 32,877.9 30,830.9 69,640.2 24.7 23.1 52.2

Department of Defense ............................... 68,113.0 1,735.0 5,071.4 61,306.5 2.5 7.4 90.0

Department of Health and 

Human Services ...................................... 35,584.0 18,772.2 16,717.7 94.1 52.8 47.0 0.3

Department of Energy ................................. 9,889.9 4,061.0 3,127.2 2,701.8 41.1 31.6 27.3

National Science Foundation ...................... 6,095.2 5,623.9 471.3 0.0 92.3 7.7 0.0

National Aeronautics and Space 

 Administration ......................................... 5,937.0 1,021.6 681.8 4,233.5 17.2 11.5 71.3

Department of Agriculture .......................... 2,269.7 924.0 1,154.0 191.7 40.7 50.8 8.4

Department of Commerce .......................... 1,146.9 138.3 820.8 187.8 12.1 71.6 16.4

Department of Transportation .................... 826.0 0.0 586.7 239.2 0.0 71.0 29.0

Department of the Interior........................... 732.4 47.1 610.5 74.8 6.4 83.4 10.2

Department of Homeland Security ............. 672.5 101.3 245.9 325.3 15.1 36.6 48.4

Environmental Protection Agency .............. 552.8 83.7 384.4 84.7 15.1 69.5 15.3

Department of Veterans Affairs................... 510.0 203.3 274.0 32.7 39.9 53.7 6.4

Department of Education ............................ 322.4 4.2 198.4 119.7 1.3 61.5 37.1

Agency for International Development ....... 160.1 0.6 159.6 0.0 0.4 99.7 0.0

Smithsonian Institution ............................... 152.0 152.0 0.0 0.0 100.0 0.0 0.0

All other agencies ....................................... 385.1 9.7 327.2 48.4 2.5 85.0 12.6

NOTES: Table lists all agencies with R&D obligations greater than $100 million in FY 2009. Data include obligations from the additional federal R&D 

funding appropriated by the American Recovery and Reinvestment Act of 2009 (ARRA). ARRA funds obligated for R&D totaled $8,714.1 million in FY 

2009: $5,115.9 million for basic research, $2,611.3 million for applied research, and $987 million for development. All other agencies includes Department 

of Housing and Urban Development, Department of Justice, Department of Labor, Department of State, Department of the Treasury, Appalachian 

Regional Commission, Federal Communications Commission, Federal Trade Commission, Library of Congress, National Archives and Records 

Commission, Nuclear Regulatory Commission, and Social Security Administration.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Federal Funds for Research and Development (FY 

2009–11). See appendix table 4-31.

Science and Engineering Indicators 2012
4-36 Chapter 4. Research and Development: National Trends and International Comparisons

Governments stimulate the conduct of R&D through tax incentives—allowances, exemptions, deductions, or tax credits—each of which can be designed with differing criteria for 

eligibility, allowable expenses, and baselines (OECD 2003). 

In the United States, federal tax incentives for qualified business R&D expenditures include a deduction under Internal 

Revenue Code (IRC) Section 174 (C.F.R. Title 26) and a research and experimentation (R&E) tax credit under Section 

41.22 The latter was established in 1981 by the Economic 

Recovery Tax Act (Public Law 97-34). It was last renewed 

by the Tax Relief, Unemployment Insurance Reauthorization, 

and Job Creation Act of 2010, through 31 December 2011.23

The Obama administration has proposed making this credit 

permanent (U.S Department of Treasury 2011). 

Along with the United States, over 20 OECD countries 

offer fiscal incentives for business R&D (OECD 2011b). 

Fiscal incentives for R&D are typically predicated on 

R&D’s role in economic growth along with the recognition 

that R&D can generate social benefits well beyond those 

captured by companies investing in such activities (see 

Hemphill 2009 and references therein). 

In the United States there were about $8.3 billion in 

business R&E tax credit claims both in 2007 and in 2008 

(see appendix table 4-36).24 Five industries accounted for 

75% of these claims in 2008: computer and electronic products; chemicals, including pharmaceuticals, and medicines; 

transportation equipment, including motor vehicles and 

aerospace; information, including software; and professional, scientific, and technical services, including computer and R&D services.

Since 1998, R&E credit claims have grown at about the 

same average annual rate as has company-funded domestic 

R&D, keeping the ratio of R&E credit claims to companyfunded domestic R&D in a narrow range (3.3% in 2008).25

In 2008, more than 12,700 corporate returns claimed at least 

one component of the R&E tax credit (appendix table 4-37). 

Corporations with more than $250 million in business receipts accounted for 14% of returns claiming the credit in 

2008 and 82% of the dollar value of all claims. In 2001, they 

had accounted for 9% of returns and 73% of dollar claims.26

The federal R&E tax credit encompasses a regular credit and as many as two forms of alternative credit formulas 

since 1996.27 Under the regular credit, companies can take 

a 20% credit for qualified research above a base amount 

for activities undertaken in the United States (IRC section 

41(a)(1)). Thus, the regular credit is characterized as a fixedbase incremental credit. An incremental design is intended 

to encourage firms to spend more on R&D than they otherwise would by lowering after-tax costs (Guenther forthcoming). Expenses paid or incurred for qualified research 

include company-funded expenses for wages paid, supplies used in the conduct of qualified research, and certain 

Figure 4-12

Federal obligations for R&D, by agency and character of work: FY 2009 

DOC = Department of Commerce; DOD = Department of Defense; DOE = Department of Energy; DHS = Department of Homeland Security; 

HHS = Department of Health and Human Services; NASA = National Aeronautics and Space Administration; NSF = National Science Foundation; 

USDA = Department of Agriculture

NOTES: Detail may not add to total because of rounding. Includes 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 

(FY 2009–11). See appendix table 4-31.

Science and Engineering Indicators 2012

Other than DOD DOD

Basic research 3%

Applied research 7%

Advanced

technology

development 9%

Major systems

development 81%

Basic research 48%

Applied research 39%

Development 13%

DOD 51%

HHS 27%

NASA 4%

USDA 2%

DOE 7%

NSF 5%

DOC 1%

DHS 1% Other 3%
Science and Engineering Indicators 2012 4-37

Figure 4-13

Federal obligations for research, by agency and major S&E field: FY 2009

Current dollars (billions)

0 5 10 15

Total, all agencies HHS DOD

20 25 30 35

DOE NASA NSF

USDA DOC Other agencies

Other sciences nec

Social sciences

Psychology

Math/computer sciences

Environmental sciences

Physical sciences

Engineering

Life sciences

Other sciences nec

Social sciences

Psychology

Math/computer sciences

Environmental sciences

Physical sciences

Engineering

Life sciences

Other sciences nec

Social sciences

Psychology

Math/computer sciences

Environmental sciences

Physical sciences

Engineering

Life sciences

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 classified; NSF = National Science Foundation; USDA = Department of 

Agriculture

NOTES: Scale differs for Total, all agencies and HHS compared to other agencies listed. Includes obligations from the additional federal R&D funding 

appropriated by the American Recovery and Reinvestment Act of 2009. Research includes basic and applied research.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Federal Funds for Research and Development 

(FY 2009–11). See appendix table 4-34.

Science and Engineering Indicators 2012

0 5 10 15 20 25 30 35

contract expenses. Further, research “must be undertaken 

for discovering information that is technological in nature, 

and its application must be intended for use in developing a 

new or improved business component.”28 The credit covers 

U.S.-performed R&D by both domestic and foreign-owned 

firms and excludes R&D conducted abroad by U.S. companies. Activities generally disallowed for the purposes of the 

credit include those conducted after the beginning of commercial production and adapting an existing product or process. Research in the social sciences, arts, or humanities and 

research funded by another entity is also excluded.

Federal Technology Transfer and 

Other Innovation-Related Programs 

This section reviews data on two types of federal programs that support public-private collaboration for technology transfer and innovation.29 (For academic patents and 

related knowledge diffusion indicators, see chapter 5; for international business licensing fees and royalties, see chapter 

6.) The first type includes federal programs for technology 

transfer from R&D funded and performed by agencies and 

laboratories. The second type supports new or small U.S. 

companies in R&D or technology deployment with R&D 

funds or technical assistance. 

In the late 1970s, concerns about the strength of U.S. 

industries and their ability to be competitive in the global 
4-38 Chapter 4. Research and Development: National Trends and International Comparisons

economy intensified. Issues included the question of whether inventions from federally funded academic research were 

adequately exploited for the benefit of the national economy 

and the need to create or strengthen public-private R&D 

partnerships. Since the 1980s, several U.S. policies have 

facilitated cross-sector R&D collaboration and technology transfer. One major policy thrust was to enhance formal mechanisms for transferring knowledge arising from 

federally funded and performed R&D (Crow and Bozeman 

1998; NRC 2003). Other policies addressed federally funded 

academic R&D, the transition of early-stage technologies 

into the marketplace, and R&D and innovation by small or 

minority-owned businesses. For an overview of these initiatives, see sidebar, “Major Federal Legislation Related to 

Technology Transfer and Commercializing R&D.” 

Federal Technology Transfer

Federal 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. Technology transfer may also involve linking 

R&D capabilities and the resources of federal laboratories 

with outside public or private organizations for mutual benefit (FLC 2006).

In response to the Stevenson-Wydler Act of 1980 (as 

amended) federal agencies with laboratory operations have 

active efforts to engage in technology transfer as defined 

above, identify and manage intellectual assets created by 

their R&D, and participate in collaborative R&D relationships with nonfederal parties (including private businesses, 

universities, nonprofit organizations) consistent with agency 

mission goals. Federal labs have also been required to have 

technology transfer offices (termed an Office of Research 

and Technology Applications or ORTA) to assist in identifying transfer opportunities and establishing appropriate arrangements for relationships with nonfederal parties 

(see 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 2004 and 2009, spanning the main activity areas of invention disclosures and patenting, intellectual 

property licensing, and collaborative relationships for R&D, 

appear in table 4-18.30 (Similar statistics for a larger set of 

agencies, going back over time to FY 2001, appear in appendix table 4-38.) 

As is apparent in the distribution of the statistics across 

the activity types in table 4-18, 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 USDA 

and DOC). Some agencies have unique transfer authorities 

which can confer practical advantages. NASA, for example, 

can establish collaborative R&D relationships through special authorities it has under the Space Act of 1958; USDA 

has a number of special options for establishing R&D collaborations other than through CRADAs; DOE’s contractoroperated national labs, with their nonfederal staffs, are not 

constrained by the normal federal limitation on copyright by 

federal employees and are able to 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, technologies principally targeted for development, 

intellectual property protection tools and policies, and the 

types of external parties through which transfer and collaboration are chiefly pursued. 

Small Business Innovation-Related Programs

This section focuses on several small business programs. 

The Small Business Innovation Research (SBIR) program was 

established by the Small Business Innovation Development 

Act of 198231 to stimulate technological innovation by increasing the participation of small companies in Federal R&D 

projects, increase private sector commercialization of innovation derived from federal R&D, and foster participation by 

minority and disadvantaged persons in technological innovation. The Small Business Technology Transfer (STTR) program was created in 1992 to stimulate cooperative R&D and 

federal technology transfer.32 SBIR and STTR are both administered by the Small Business Administration (SBA). The 

last portion of this section covers the Technology Innovation 

Program (TIP), created by the America COMPETES Act of 

2007 and administered by NIST. 

The focus on smaller or startup R&D-based companies 

in these programs is an example of the promotion of innovation-based entrepreneurship via public-private partnerships 

that enable not only financing but also R&D collaboration 

and commercialization opportunities (Gilbert et al. 2004; 

Link and Scott 2010). 

According to the SBIR statute, federal agencies with extramural R&D obligations exceeding $100 million must set 

aside a fixed percentage of such obligations (2.5% since FY 

1997) for projects involving small business (those with fewer than 500 employees). In FY 2009, SBIR awards totaled 

$1.9 billion (SBA 2010). In FY 2008, 11 federal agencies 

awarded a total of $1.8 billion to about 5,400 SBIR projects 

(appendix tables 4-39 and 4-40). DOD provides about 50% 

of total SBIR funds annually, followed by HHS (around 

30% since 1999), consistent with their large extramural 

R&D budgets.

The SBIR program is structured in three phases. Phase 

I evaluates the scientific and technical merit and feasibility 

of ideas. Phase II builds on phase I findings, is subject to 

further scientific and technical review, and requires a commercialization plan. During phase III, the results from phase 

II R&D are further developed and introduced into private 

markets or federal procurement using private or non-SBIR 
Science and Engineering Indicators 2012 4-39

Technology Innovation Act of 1980 (Stevenson-Wydler 

Act) (Public Law 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) (Public Law 96-517)—permitted 

small businesses, universities, and nonprofits to obtain 

titles to inventions developed with federal funds. Also 

permitted government-owned and government-operated 

laboratories to grant exclusive patent rights to commercial organizations.

Small Business Innovation Development Act of 1982 

(Public Law 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 (Public Law 

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 (Public Law 

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 (Public Law 

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 (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 (Public 

Law 100-418)—in addition to measures on trade and intellectual property protection, the act directed attention 

to public-private cooperation on R&D, technology transfer, and commercialization. It also established NIST’s 

Manufacturing Extension Partnership (MEP) program.

National Competitiveness Technology Transfer Act 

of 1989 (Public Law 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 

(Public Law 102-564)—extended the existing SBIR program, increased the percentage of an agency’s budget to 

be devoted to SBIR, and increased the amounts of the 

awards. Also established the Small Business Technology 

Transfer (STTR) program to enhance the opportunities 

for collaborative R&D efforts between governmentowned/contractor-operated federal laboratories and small 

businesses, universities, and nonprofit partners.

National Cooperative Research and Production Act of 

1993 (Public Law 103-42)—relaxed restrictions on cooperative production activities, which enable research joint 

venture participants to work together in the application of 

technologies that they jointly acquire.

National Technology Transfer and Advancement Act of 

1995 (Public Law 104-113)—amended the StevensonWydler Act to make CRADAs more attractive to federal 

laboratories, scientists, and private industry.

Technology Transfer Commercialization Act of 2000 

(Public Law 106-404)—broadened CRADA licensing 

authority to make such agreements more attractive to private industry and increase the transfer of federal technology. Established procedures for performance reporting 

and monitoring by federal agencies on technology transfer activities.

America COMPETES Act of 2007 (America Creating 

Opportunities to Meaningfully Promote Excellence in 

Technology, Education, and Sciences [COMPETES] Act) 

(Public Law 110-69)—authorized increased investment 

in R&D; strengthened educational opportunities in science, technology, engineering, and mathematics from elementary through graduate school; and further developed 

the nation’s innovation infrastructure. Among other measures, the act established NIST’s Technology Innovation 

Program (TIP) and called for a President’s Council on 

Innovation and Competitiveness.

America COMPETES Reauthorization Act of 2010 (Public 

Law 111–358)—updates the America COMPETES Act of 

2007 and authorizes additional funding to science, technology, and education programs over the succeeding 3 years. 

The Act’s numerous provisions broadly directed strengthening the foundation of the U.S. economy, creating new 

jobs, and increasing U.S. competitiveness abroad.

Major Federal Legislation Related to 

Technology Transfer and Commercializing R&D
4-40 Chapter 4. Research and Development: National Trends and International Comparisons

federal funding.33 Several participating R&D agencies also 

offer bridge funding to phase III and other commercialization support for startups (NRC 2008:208–216).34

Federal agencies with extramural R&D budgets exceeding $1 billion are required to set aside 0.3% of their extramural R&D budget for STTR awards. The program is also 

structured in three phases and involves R&D performed 

jointly by small businesses, universities, and nonprofit research organizations. In FY 2008, federal agencies awarded 

734 STTR grants valued at $240 million (appendix tables 

4-39 and 4-41).

The Technology Innovation Program was set up for “the 

purpose of assisting U.S. businesses and institutions of higher 

education or other organizations, such as national laboratories 

and nonprofit research institutions, to support, promote, and 

accelerate innovation in the United States through high-risk, 

high-reward research in areas of critical national need.”35 Two 

areas of focus in recent funding competitions were advanced 

manufacturing materials and advanced sensors to support 

monitoring and assessment of civil infrastructure, such as 

water pipelines, roads, bridges, and tunnels. From FY 2008 

to FY 2010, TIP made 38 competitive awards involving 78 

participants including small businesses and universities. Over 

this period, awards reached $281 million, including $136 million from TIP and $145 million in industry-cost sharing funds 

(appendix table 4-42).

International R&D Comparisons

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 OECD cover the organization’s 34 member countries and 

7 nonmembers.36 The United Nations Educational, Scientific, 

and Cultural Organization’s (UNESCO’s) Institute for 

Statistics provides data on additional countries. The discussion in this section draws on both of these datasets. 

International comparisons necessarily involve currency 

conversions. The analysis in this section follows the international convention of converting foreign currencies into U.S. 

dollars via purchasing power parity (PPP) exchange rates. 

(See sidebar, “Comparing International R&D Expenditures.”) 

Global Patterns of R&D Expenditures

Worldwide R&D expenditures totaled an estimated 

$1,276 billion (purchasing power parities) in 2009. The corresponding estimate, 5 years earlier in 2004 was $873 billion. Ten years earlier, in 1999, it was $641 billion. By these 

figures, growth in these global totals has been rapid, averaging nearly 8% annually over the last 5 years and 7% over the 

last 10 years. 

Overall, global R&D performance remains highly concentrated in three geographic regions, North America, Asia, 

and Europe (figure 4-14). North America (United States, 

Canada, Mexico) accounted for 34% ($433 billion) of 

worldwide R&D performance in 2009; the combination of 

East/Southeast and South Asia (including China, Taiwan, 

Japan, India, South Korea), 32% ($402 billion); and Europe, 

including (but not limited to) European Union (EU) countries, 25% ($319 billion). The remainder, approximately 

10%, reflects the R&D of countries in the regions of Central 

and South America, Central Asia, Middle East, Australia/

Oceania, and Africa.

Federal technology transfer can take a variety of 

forms (FLC 2006), including the following:

Commercial transfer. Movement of knowledge or 

technology developed by a federal lab to private organizations in 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 lab 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 lab to enhance the existing internal capabilities. 

Dual use. Development of technologies, products, or 

families of products with both commercial and federal 

applications. 

Federal tech transfer metrics cover activities among 

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 lab’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)

In addition, the statutory annual tech transfer performance reporting by agencies with federal labs, established by the Technology Transfer Commercialization 

Act of 2000, provides data on downstream outcomes 

and impacts.

Federal Technology Transfer: 

Activities and Metrics
Science and Engineering Indicators 2012 4-41

The geographic concentration is more apparent when 

looking at specific countries (table 4-19). Three countries 

account for more than half of global R&D. The United 

States is by far the largest R&D performer ($402 billion 

in 2009), accounting for about 31% of the global total, but 

down from 38% in 1999. China became the second largest performer ($154 billion) in 2009, accounting for about 

12% of the global total. Japan moved down to third, at 11% 

($138 billion). The largest EU performers spend comparatively less: Germany ($83 billion, 6%), France ($48 billion, 

4%), and the United Kingdom ($40 billion, 3%). The most 

recent figure available for South Korea is 2008, with $44 

billion of R&D—in recent years South Korea has typically 

been among the top seven R&D performing countries, representing 3%–4% of the global total. Taken together, these 

top seven countries account for about 71% of the global total. Russia, Italy, Canada, India, Brazil, Taiwan, and Spain 

comprise a next lower rung, with national R&D expenditures ranging from $20 billion to $33 billion. 

Besides the generally vigorous pace at which the global 

total of R&D is now growing, the other major trend has been 

the rapid expansion of R&D performance in the regions of 

East/Southeast Asia and South Asia, including countries 

such as China, India, Japan, Malaysia, Singapore, South 

Korea, Taiwan, and Thailand. The R&D performed in these 

two Asian regions represented only 24% of the global R&D 

total in 1999, but accounted for 32% in 2009, including 

China (12%) and Japan (11%). 

China continues to exhibit the most dramatic R&D growth 

pattern (figure 4-15). 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 real 

growth over the past 10 years (1999–2009) in China’s overall R&D remains exceptionally high at about 20% annually. 

Table 4-18

Federal laboratory technology transfer activity indicators, total and selected U.S. agencies: FY 2004 and FY 2009

Technology transfer activity All federal labs DOD HHS DOE NASA USDA DOC

FY 2009

Invention disclosures and patenting

Inventions disclosed .................................. 4,422 831 389 1,439 1,373 153 49

Patent applications .................................... 2,080 690 156 919 126 117 19

Patents issued ........................................... 1,494 404 397 520 114 21 7

Licensing

All licenses, total active in fiscal year ........ 10,913 432 1,584 5,752 2,497 316 40

Invention licenses ................................... 4,226 386 1,304 1,452 504 316 40

Other intellectual property licenses ........ 6,730 46 327 4,300 1,993 0 0

Collaborative relationships for R&D ..............

CRADAs, total active in fiscal year ............ 7,733 2,870 457 744 1 233 2,386

Traditional CRADAs ............................... 4,219 2,247 284 744 1 191 77

Other collaborative R&D relationships ....... 16,319 1 0 0 2,743 9,960 3,608

FY 2004

Invention disclosures and patenting

Inventions disclosed .................................. 5,454 1,369 461 1,617 1,612 142 25

Patent applications .................................... 1,768 517 216 661 207 81 12

Patents issued ........................................... 1,391 426 167 520 189 50 12

Licensing

All licenses, total active in fiscal year ........ 7,567 369 1,424 4,345 861 296 125

Invention licenses ................................... 3,804 364 1,173 1,362 338 296 125

Other intellectual property licenses ........ 3,775 5 251 2,983 523 0 0

Collaborative relationships for R&D

CRADAs, total active in fiscal year ............ 6,015 2,833 220 610 0 205 1,969

Traditional CRADAs ............................... 3,546 2,425 119 610 0 185 67

Other collaborative R&D relationships ....... 7,454 0 0 0 3,987 1,166 2,301

CRADA = Cooperative Research and Development Agreement; 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; USDA = U.S. Department of 

Agriculture

NOTES: Other federal agencies not listed but included in the All federal labs 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 (IP) refers to IP 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 lab and one or more nonfederal organizations. Federal agencies have varying authorities for other kinds of collaborative R&D relationships. 

SOURCE: National Institute of Standards and Technology, Federal Laboratory Technology Transfer, Fiscal Year 2009 Summary Report to the President and 

the Congress, March 2011, http://www.nist.gov/tpo/publications/upload/Federall-Lab-TT-Report-FY2009.pdf See also appendix table 4-38.

Science and Engineering Indicators 2012
4-42 Chapter 4. Research and Development: National Trends and International Comparisons

The rate of growth in South Korea’s R&D has also been 

relatively high, averaging nearly 10% annually over the 10-

year period. Growth in Japan has been slower, at an annual 

average rate of 4.0%.

By comparison, while the U.S. remains atop the list of the 

world’s R&D performing nations, its pace of growth in R&D 

performance has averaged 5.0% over the same 1999–2009 

period, and its share of global R&D has declined from 38% 

to 31% over this time. Total R&D by EU nations has been 

growing (current dollars) over the same 10 years at an average annual rate of 5.8%. The pace of growth during the same 

period for Germany, France, and the United Kingdom has 

been somewhat slower, averaging 5.3%, 4.5%, and 4.5%, respectively. The EU countries accounted for 23% total global 

R&D in 2009, down from 27% in 1999.37

Comparison of Country R&D Intensities

R&D intensity provides another basis for international 

comparisons of R&D performance. This approach does not 

require conversion of a country’s currency to a standard 

international benchmark yet still provides a way to adjust 

for differences in the sizes of national economies. (For additional background on R&D intensity and how it is affected 

by the economic make-up of a country, see sidebar, “R&D 

Intensity and the Composition of Gross Domestic Product.”) 

Total R&D/GDP Ratios

The U.S. R&D/GDP ratio was just under 2.9% in 2009 

(table 4-19). At this level, the United States is eighth among 

the economies tracked by the OECD and UNESCO. Israel 

continues to have the highest ratio, at 4.3%—although 

Finland is not far back at 4%. Sweden, Japan, and South 

Korea all have ratios well above 3%; Switzerland and 

Taiwan are slightly above the U.S. figure.

The R&D/GDP ratio in the United States has ranged from 

1.4% in 1953 to a high of 2.9% in 1964, and has fluctuated 

in the range of 2.6% to 2.7% in recent years (figure 4-16). 

Most of the growth over time in the U.S. R&D/GDP ratio 

can be attributed to increases in nonfederal R&D spending, 

financed primarily by business. Nonfederally financed R&D 

increased from about 0.6% of GDP in 1953 to about 2.0% of 

GDP in 2009. 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. 

Comparisons of international R&D statistics are 

hampered by the lack of R&D-specific exchange rates. 

Two approaches are commonly used to facilitate international R&D comparisons: (1) express national R&D 

expenditures as a percentage of 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 choosing 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 non-traded 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 cross-country 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 2006 

R&D expenditures (as reported to the OECD) are $38 billion using MERs but $87 billion using PPPs. (Appendix 

table 4-2 lists the relative difference between MERs and 

PPPs for a number of countries.)

However, PPPs for large, developing countries such 

as India and China 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 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) conclude 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.” The issue 

remains unresolved.

Comparing International R&D Expenditures
Science and Engineering Indicators 2012  4-43

Among other top seven R&D-performing countries, total 

R&D/GDP ratios over the 1999–2009 period show mixed 

trends (figure 4-16). Compared with 1999 R&D/GDP ratios, 

the 2009 ratios were substantially higher in Japan, Germany, 

and South Korea. (However, Japan’s rising ratio reflects the 

confluence of declining GDP and largely flat R&D spending.) 

Most notably, China’s ratio more than doubled over this 10-

year period. For the United Kingdom, the 2009 ratio remained 

about the same, and for France, it slightly increased.

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.

Nondefense R&D and Basic Research

Further perspective is provided by the ratio of nondefense 

R&D expenditures to GDP. This ratio more directly measures civilian R&D intensity and is useful when comparing 

nations with substantially different financial commitments 

to national defense. Table 4-20 provides such figures for the 

top seven R&D performing nations, for 2009 or most recent 

data year. The U.S. ratio (2.3% in 2009) ranks ahead of that 

for the United Kingdom and France but lags behind Japan, 

South Korea, and Germany. (Data on this metric for China 

are not currently available.) 

Another perspective comes from the extent to which 

spending on basic research accounts for a country’s total 

R&D/GDP ratio. Estimates of the relative volume of basic research spending can provide a glimpse of the extent 

to which R&D resources are directed toward advancing the 

scientific knowledge base. 

In 2009, the U.S. basic research/R&D ratio is about 0.6% 

and accounts for about a fifth of the total R&D/GDP ratio 

(table 4-20). France’s basic research ratio is slightly below the 

U.S. figure and accounts for just over a quarter of its total ratio. South Korea’s basic research ratio is close to the U.S. and 

French figures. The basic research ratios for Japan, the United 

Kingdom, and, especially, China are below the U.S. figure.

World total = $1,276

PPP = purchasing power parity

NOTES: Foreign currencies converted to U.S. dollars through purchasing power parities. Some country figures are estimated. Countries are grouped 

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

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, estimates, July 2011. Based on data from Organisation 

for Economic Co-operation and Development, Main Science and Technology Indicators (2011/1); and United Nations Educational, Scientific, and 

Cultural Organization (UNESCO) Institute for Statistics, http://stats.uis.unesco.org/unesco/ReportFolders/ReportFolders.aspx, table 25, accessed 13 

July 2011.

Science and Engineering Indicators 2012

South America 

$31 (2.4%)

Africa

$9 (0.7%)

North America 

$433 (33.9%)

Europe

$319 (25.0%)

South Asia

$34 (2.6%)

Australia

& Oceania

$22 (1.8%)

Middle East

$26 (2.0%)

Central Asia

$33 (2.6%)

East, Southeast

Asia $369 (28.9%)

Central America 

& Caribbean 

$0.6 (<0.1%)

Figure 4-14

Global R&D expenditures by region: 2009

U.S. PPP dollars (billions)
4-44 Chapter 4. Research and Development: National Trends and International Comparisons

R&D by Performing Sector and Source 

of Funds

The business sector is the predominant R&D performer for all seven of the top R&D performing nations (table 

4-21).38 For the U.S., the business sector accounted for 70% 

of gross expenditures on R&D in 2009. Japan’s business 

sector was the highest, accounting for almost 76% of the 

country’s total R&D. China and South Korea were also well 

above the U.S. level. France and the United Kingdom were 

somewhat lower, at, respectively, 62% and 60%.

R&D performance by the government ranges over 9%–

19% of total national R&D for the seven countries. Japan 

(9%) and the United Kingdom (9%) are on the lower end of 

this range. China (19%) and France (16%) are at the high end. 

The U.S., South Korea, and Germany lie in between. 

Academic R&D ranges from 8% to 28% of total national 

R&D performance for these countries. China is the low point, 

at 8%. The United Kingdom is the highest, at 28%. The U.S. 

(14%), Japan (13%), and South Korea (11%) have lower 

shares; Germany (18%) and France (21%), higher shares. 

The structure of a nation’s economy can be a consideration in interpreting and comparing national R&D intensity statistics. That is, the relative prominence of major 

sectors such as agriculture, manufacturing, and services 

can directly influence the ratio of overall R&D expenditures to gross domestic product. Businesses and organizations differ widely in their relative need for investment 

in the latest science and technology. So, countries whose 

overall GDP depends more heavily on advanced technology industries will typically exhibit higher R&D/GDP ratios than other countries. 

Agriculture is a comparatively small component (6% 

or less) for all but 2 of the top 14 R&D performing countries (figure 4-B). The exceptions are India, where agriculture currently accounts for about 16% of its GDP, and 

China, where it is 10%. Industrial production (manufacturing) is 20%–30% of GDP for all but three of the countries. China is a much higher 47%; South Korea is 39% 

and Russia 34%. Services are 60%–70% of national GDP 

for all but 2 of the 14 countries. China is substantially less 

services-dependent, at 44% of GDP, and India is somewhat less so, at 55%. 

R&D Intensity and the Composition 

of Gross Domestic Product 

Figure 4-B

Composition of gross domestic product, for selected 

countries/economies, by sector: 2010

NOTES: Data are estimates. Latest data for South Korea are 2008. 

Fourteen largest R&D performing countries (see table 4-20).

SOURCE: Central Intelligence Agency, The World Factbook, 

https://www.cia.gov/library/publications/the-world-factbook/index.

html, accessed 16 March 2011. 

Science and Engineering Indicators 2012

Agriculture Industry Services

40 60 80 100

Percent

0 20

Spain

Brazil

Taiwan

Italy

Russia

Canada

India

United

Kingdom

France

South

Korea

Germany

China

Japan

United

States

Figure 4-15

Gross domestic expenditures on R&D by the 

United States, EU, and selected other countries: 

1981–2009

Constant PPP dollars (billions) 

EU = European Union; PPP = purchasing power parity

NOTES: Data not available for all countries in all years. Data for 

United States in this figure reflect international standards for 

calculating gross expenditures on R&D, which vary slightly from 

NSF approach to tallying U.S. total R&D. Data for Japan for 1996 

onward may not be consistent with earlier data due to changes in 

methodology. EU data for all years based on current 27 EU 

member countries. 

SOURCE: Organisation for Economic Co-operation and Development, Main Science and Technology Indicators (2011/1). See 

appendix table 4-43. 

Science and Engineering Indicators 2012

1981 1985 1989 1993 1997 2001 2005 2009

0

50

100

150

200

250

300

350

400

450

United States

EU

Germany

France

United Kingdom

Japan

China

South Korea
Science and Engineering Indicators 2012 4-45

With regard to the funding of R&D, the business sector is 

again the predominant source for all seven of the top R&D 

performing nations (table 4-22). In 2009, funding for about 

75% 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 67%–73% range. R&D 

funding from business is lower, but still predominant, in the 

U.S., at 60%. The corresponding figures for France (51%) 

and the United Kingdom (45%) are notably lower. 

Government is the second major source of R&D funding for these seven countries. France is the highest, at 39%. 

The lowest is Japan at 18%. The United Kingdom (33%), 

Table 4-19

International comparisons of gross domestic expenditures on R&D and R&D share of gross domestic product, 

by region and selected country/economy: 2009 or most recent year

GERD GERD/GDP GERD GERD/GDP

Region/country/economy (PPP $millions) (%) Region/country/economy (PPP $millions) (%)

North America Middle East

United States (2009)a

 ................ 401,576.5 2.88 Israel (2009) .............................. 8,810.1 4.28

Canada (2009) ........................... 24,551.3 1.92 Turkey (2009) ............................ 8,681.2 0.85

Mexico (2007) ........................... 5,719.6 0.37 Iran (2008) ................................. 6,465.2 0.79

South America Africa

Brazil (2008) .............................. 21,649.4 1.08 South Africa (2008) ................... 4,689.3 0.93

Argentina (2007) ........................ 2,678.8 0.51 Egypt (2009) .............................. 997.3 0.21

Chile (2004) ............................... 1,227.7 0.68 Morocco (2006) ......................... 765.1 0.64

Tunisia (2009) ............................ 1,048.5 1.21

Europe

Germany (2009) ........................ 82,730.7 2.78 Central Asia

France (2009) ............................ 47,953.5 2.21 Russian Federation (2009) ........ 33,368.1 1.24

United Kingdom (2009) ............. 40,279.5 1.85

Italy (2009) ................................ 24,752.6 1.27 South Asia

Spain (2009) .............................. 20,496.4 1.38 India (2007) ............................... 24,439.4 0.76

Sweden (2009) .......................... 12,494.9 3.62 Pakistan (2009) ......................... 2,055.2 0.46

Netherlands (2009) .................... 12,273.8 1.82

Switzerland (2008) .................... 10,512.7 3.00 East, Southeast Asia

Austria (2009) ............................ 8,931.3 2.75 Japan (2009) ............................. 137,908.6 3.33

Belgium (2009) .......................... 7,684.9 1.96 China (2009) .............................. 154,147.4 1.70

Finland (2009) ........................... 7,457.8 3.96 South Korea (2008) ................... 43,906.4 3.36

Denmark (2009) ......................... 6,283.8 3.02 Taiwan (2009) ............................ 21,571.8 2.93

Norway (2009) ........................... 4,734.1 1.76 Singapore (2009) ....................... 5,626.5 2.35

Poland (2009) ............................ 4,874.9 0.68 Malaysia (2006) ......................... 2,090.9 0.64

Portugal (2009) .......................... 4,411.0 1.66 Thailand (2007) ......................... 1,120.8 0.21

Czech Republic (2009) .............. 4,094.8 1.53

Ireland (2009) ............................ 3,164.6 1.79 Australia, Oceania

Ukraine (2009) ........................... 2,485.7 0.86 Australia (2008) ......................... 18,755.0 2.21

Hungary (2009) ......................... 2,333.8 1.15 New Zealand (2007) .................. 1,422.5 1.17

Romania (2009) ......................... 1,471.5 0.47

Greece (2007) ........................... 1,867.9 0.59 Selected country groups

Belarus (2009) ........................... 813.3 0.65 EU (2009) .................................. 297,889.6 1.90

Slovenia (2009) ......................... 1,043.6 1.86 OECD (2008) ............................. 965,629.1 2.33

Croatia (2009) ........................... 743.1 0.84 G-20 countries (2009) ............... 1,181,263.7 2.01

Luxembourg (2009) ................... 708.5 1.68

Slovak Republic (2009) ............. 595.5 0.48

EU = European Union; GDP = gross domestic product; GERD = gross expenditures (domestic) on R&D; OECD = Organisation for Economic Co-operation 

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 NSF protocol for tallying U.S. total R&D. 

NOTES: Year of data listed in parentheses. Foreign currencies converted to dollars through purchasing power parities. Countries with annual GERD of 

$500 million or more. Countries are grouped according to the regions described by the The CIA World Factbook, www.cia.gov/library/publications/theworld-factbook/index.html. No countries in the Central America/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: OECD, Main Science and Technology Indicators (2011/1); United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics, http://stats.uis.unesco.org/unesco/ReportFolders/ReportFolders.aspx, table 25, accessed 13 July 2011.

Science and Engineering Indicators 2012
4-46 Chapter 4. Research and Development: National Trends and International Comparisons

Germany (28%), and United States (31%) are on the higher 

side. South Korea (25%) and China (23%) are in between. 

Funding from abroad refers to funding from businesses, 

universities, governments, and other organizations located 

outside of the country. Table 4-22 shows this funding category for selected OECD countries. For the U.S., data on 

funding from abroad is available only for the business sector. 

Government R&D Priorities

The mix of government funding for R&D across differing objectives (e.g., defense, health, space, general research) provides insights into government R&D priorities. 

The OECD compiles such statistics annually on its member 

countries and selected others: government budget appropriations or outlays for R&D (GBAORD). GBAORD indicators 

for the United States and other top R&D performing countries appear in table 4-23, broken down by a number of major socioeconomic objectives. 

Defense is an objective for government funding of R&D 

for all the top R&D-performing countries, but the share 

varies widely (table 4-23). Defense accounted for 52% of 

U.S. federal R&D support in 2009, but was markedly lower 

elsewhere: a smaller but still sizable 28% in France and 18% 

in the United Kingdom, 17% in South Korea, and below 6% 

in both Germany and Japan.

Defense has remained the focus of more than 50% of the 

federal R&D budget in the United States for much of the 

past 25 years. It was 63% in 1990 as the long Cold War 

period drew to a close, but dropped in subsequent years. The 

defense share of government R&D funding for the other 

countries over the past 25 years has generally declined or 

remained at a stable, low level.

The health and environment objective now accounts for 

some 56% of nondefense federal R&D budget support in the 

United States and 29% in the United Kingdom. For both countries, the share has expanded dramatically over the share prevailing several decades ago. The health and environment share 

is currently 19% in South Korea, 15% in France, and 10% or 

less in Germany and Japan. The funding under this objective 

goes primarily into the health arena in the United States and the 

United Kingdom (appendix table 4-45). 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. The share of nondefense government R&D support 

allocated to economic development has generally declined 

over the past 25 years across the OECD countries. In the 

United States, it was 36% of all nondefense federal support for R&D in 1981, dropping to 13% in 2009.39 In the 

United Kingdom, it was 39% in 1981, declining to 9% in 

2009. Despite a decline, support for this objective remains 

substantial in some countries: 23% in Germany and 24% in 

France (both with particular attention to industrial production and technology) and 31% in Japan (notably in energy 

and industrial production and technology). South Korea currently has by far the largest share for this objective, 52%, 

with a particularly strong emphasis in recent years on industrial production and technology. 

The civil space objective now accounts for 11% of nondefense federal R&D funding in the United States. The share has 

been above or around 20% in the United States for much of the 

past 25 years. The share in France is currently about 13%, 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 non-oriented research and general university 

funds (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: Germany (58%), Japan (54%), the United 

Kingdom (54%), and France (45%). The corresponding 2009 

shares for the United States (18%) and South Korea (23%) are 

substantially smaller. Nevertheless, cross-national comparisons of these particular indicators can be difficult, since some 

countries (notably the U.S.) do not use the GUF mechanism to 

Figure 4-16

Gross expenditures on R&D as share of gross 

domestic product, for selected countries: 

1981–2009

GDP = gross domestic product

NOTES: Top seven R&D performing countries. Data not available for 

all countries for all years. Figures for the United States reflect 

international standards for calculating gross expenditures on R&D, 

which differ slightly from the NSF protocol for tallying U.S. total R&D. 

Data for Japan, for 1996 onward, may not be consistent with earlier 

data due to changes in methodology. 

SOURCE: Organisation for Economic Co-operation and Development, Main Science and Technology Indicators (2011/1). See 

appendix table 4-43.

Science and Engineering Indicators 2012

Total R&D/GDP

Percent

1981 1985 1989 1993 1997 2001 2005 2009

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

United States

Germany

France

United Kingdom

Japan

China

South Korea
Science and Engineering Indicators 2012 4-47

Table 4-20

Expenditures on R&D as share of gross domestic product for all R&D, nondefense R&D, and basic research, 

by selected country/economy: 2009 or most recent year

Country/economy All R&D/GDP

Nondefense 

R&D/GDP

Fraction of 

all (%)

Basic research 

R&D/GDP

Fraction of 

all (%)

United States (2009)a

 ......................... 2.88 2.3 81 0.55 19

China (2009) ...................................... 1.70 NA NA 0.08 5

Japan (2009) ...................................... 3.33 3.3 99 0.42 13

Germany (2008) ................................. 2.68 2.6 97 NA NA

France (2008) ..................................... 2.11 1.9 90 0.54 26

South Korea (2008) ............................ 3.36 3.2 95 0.54 16

United Kingdom (2009) ...................... 1.85 1.7 92 0.21 11

Russian Federation (2009) ................. 1.24 NA NA 0.25 20

India (2007) ........................................ 0.76 NA NA NA NA

Italy (2009) ......................................... 1.27 1.3 102 0.33 26

Canada (2009) ................................... 1.92 NA NA NA NA

Brazil (2008) ....................................... 1.08 NA NA NA NA

Taiwan (2009) .................................... 2.93 2.9 99 0.30 10

Spain (2008) ...................................... 1.35 1.3 96 0.23 17

NA = not available 

GDP = gross domestic product

a

Figures for United States in this table reflect international standards for calculating gross expenditures on R&D, which vary slightly from NSF protocol for 

tallying U.S. total R&D. 

NOTES: Top 14 countries globally in annual gross expenditures on R&D. Year of data listed in parentheses.

SOURCE: Organisation for Economic Co-operation and Development, Main Science and Technology Indicators (2011/1). Data for Brazil and India 

from United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics, http://stats.uis.unesco.org/unesco/ 

ReportFolders/ReportFolders.aspx, table 26, accessed 13 July 2011.

Science and Engineering Indicators 2012

Table 4-21

Gross expenditures on R&D by performing sector, by selected country/economy: 2009 or most recent year

(Percent)

Country/economy Business Government Higher education Private nonprofit

United States (2009)a

 ....................... 70.3 11.7 13.5 4.4

China (2009) .................................... 73.2 18.7 8.1 0.0

Japan (2009) .................................... 75.8 9.2 13.4 1.6

Germany (2009) ............................... 67.5 14.9 17.6 **

France (2009) ................................... 61.9 16.3 20.6 1.2

South Korea (2008) .......................... 75.4 12.1 11.1 1.4

United Kingdom (2009) .................... 60.4 9.2 27.9 2.5

Russian Federation (2009) ............... 62.4 30.3 7.1 0.2

India (2007) ...................................... 33.9 61.7 4.4 **

Italy (2009) ....................................... 51.5 13.9 31.4 3.2

Canada (2009) ................................. 51.7 10.1 37.6 0.6

Brazil (2004) ..................................... 40.2 21.3 38.4 0.1

Taiwan (2008) .................................. 70.1 16.8 12.8 0.4

Spain (2009) .................................... 51.9 20.1 27.8 0.2

** = included in other performing sectors

a

Figures for the United States in this table reflect international standards for calculating gross expenditures on R&D, which vary slightly from NSF protocol 

for tallying U.S. total R&D. 

NOTES: Top 14 R&D performing countries. Year of data listed in parentheses. Percentages may not add to 100 due to rounding. 

SOURCES: Organisation for Economic Co-operation and Development, Main Science and Technology Indicators (2011/1). Data for Brazil and India from 

United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics, http://stats.uis.unesco.org/unesco/ReportFolders/

ReportFolders.aspx, table 27, accessed 18 July 2011.

Science and Engineering Indicators 2012
4-48 Chapter 4. Research and Development: National Trends and International Comparisons

fund general advancement of knowledge R&D, 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). For a further discussion of this 

topic, see the sidebar “Government Funding Mechanisms for 

Academic Research” later in this chapter. 

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 (4%), and the 

United Kingdom (6%), than in the United States (2%) and 

Japan (1%). South Korea is in between at 3%.

Business R&D Focus 

Business R&D varies substantially among countries in 

terms of both industry concentration and sources of funding. Because businesses account for the largest share of total R&D performance in the United States and most OECD 

countries, differences in business structure can help explain 

international differences in more aggregated statistics such 

as R&D/GDP. For example, countries with higher concentrations of R&D-intensive industries (such as communication, television, and radio equipment manufacturing) are 

likely to also have higher R&D/GDP ratios than countries 

whose business structures are weighted more heavily toward 

less R&D-intensive industries.

Using internationally comparable data, no one industry accounted for more than 19% of total business R&D in 

the United States in 200840 (figure 4-17 and appendix table 

4-46), based on OECD’s Analytical Business Enterprise 

R&D-Statistical Analysis Database (ANBERD-STAN) 

(OECD 2011a). This is largely a result of the size of business R&D expenditures in the United States, which makes 

it difficult for any one sector to dominate. However, the diversity of R&D investment by industry in the United States 

is also an indicator of how the nation’s accumulated stock 

of knowledge and well-developed S&T infrastructure have 

made it an attractive location for R&D performance in a 

broad range of industries.

Compared with the United States, smaller economies 

shown in figure 4-17 display much higher concentration in 

particular industries. For example, in South Korea, one of 

the world’s top producers of communication, TV, and radio 

equipment industry, which includes semiconductors, this industry accounted for 46% of the country’s business R&D.41 

The spread of global production networks and value 

chains is also reflected in these indicators. Automotive manufacturers rank among the largest R&D-performing companies in the world (see sidebar, “Global R&D Expenses 

of Public Corporations”). The automotive industry has also 

highly distributed production and technical sites globally. 

Thus, countries that are home to major automotive MNCs 

and/or serve as host countries for MNCs affiliates, their 

Table 4-22

Gross expenditures on R&D by funding source, by selected country/economy: 2009 or most recent year

(Percent)

Country/economy Business Government Other domestic From abroad

United States (2009)a

 ...................................... 59.7 31.3 7.2 1.9

China (2009) ................................................... 71.7 23.4 NA 1.3

Japan (2009) ................................................... 75.3 17.7 6.6 0.4

Germany (2008) .............................................. 67.3 28.4 0.3 4.0

France (2008) .................................................. 50.7 38.9 2.3 8.0

South Korea (2008) ......................................... 72.9 25.4 1.4 0.3

United Kingdom (2009) ................................... 44.5 32.6 6.3 16.6

Russian Federation (2009) .............................. 26.6 66.5 0.5 6.5

India (2007) ..................................................... 33.9 66.1 ** NA

Italy (2008) ...................................................... 45.2 42.9 4.1 7.8

Canada (2009) ................................................ 47.6 33.4 12.1 6.9

Brazil (2008) .................................................... 43.9 54.0 2.2 NA

Taiwan (2009) ................................................. 69.7 28.9 1.3 *

Spain (2008) ................................................... 45.0 45.6 3.8 5.7

NA = not available; * = <0.05%.; ** = included in other funding sectors

a

Figures for the United States in this table reflect international standards for calculating gross expenditures on R&D, which vary slightly from NSF protocol 

for tallying U.S. total R&D. Figures for funding from abroad based primarily on funding for business R&D.

NOTES: Top 14 R&D performing countries. Year of data listed in parentheses. Percentages may not add to 100 due to rounding. For the United States, 

data on R&D funding from abroad are not separately identified and instead are included in sector totals. In most other countries, funding from abroad is a 

distinct and separate category. 

SOURCES: Organisation for Economic Co-operation and Development, Main Science and Technology Indicators (2011/1). Data for Brazil and India from 

United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics, http://stats.uis.unesco.org/unesco/ReportFolders/

ReportFolders.aspx, table 28, accessed 18 July 2011.

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 4-49

Table 4-23

Government R&D support by major socioeconomic objectives, by selected region/country: 1981–2009

Percent of nondefense

Region/country 

and year

GBAORD 

(current US$ 

millions, PPP) Defense Nondefense

Economic 

development 

programs

Health 

and 

environment

Education 

and 

society

Civil 

space 

Non-oriented 

research

General 

university 

funds (GUF)

United States

1981 ...................... 33,735.0 54.6 45.4 36.1 31.2 3.6 20.3 8.7 na

1990 ...................... 63,781.0 62.6 37.4 22.2 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

2009 ...................... 164,292.0 51.6 48.4 13.3 55.9 1.5 11.4 17.8 na

EU

1981 ...................... na na na na na na na na na

1990 ...................... na na na na na na na na na

2000 ...................... 73,559.9 13.1 86.9 22.7 11.6 3.4 6.1 15.7 34.9

2008 ...................... 110,238.5 9.6 90.4 23.5 14.8 5.9 4.9 17.3 33.8

Germany

1981 ...................... 8,572.5 8.9 91.1 34.9 9.6 4.5 4.5 46.5 0.0

1990 ...................... 13,269.1 13.5 86.5 25.9 10.8 2.9 6.8 15.2 37.6

2000 ...................... 16,806.2 7.8 92.2 21.6 9.4 3.9 5.1 17.5 42.4

2009 ...................... 25,857.8 5.7 94.3 23.0 10.3 4.1 5.4 18.2 39.6

France

1981 ...................... 8,531.3 38.4 61.6 37.9 13.3 2.0 6.7 39.1 0.0

1990 ...................... 13,228.6 40.0 60.0 32.8 9.3 0.8 13.0 24.6 18.9

2000 ...................... 14,738.0 21.4 78.6 17.7 9.7 1.1 13.2 27.4 28.5

2008 ...................... 16,171.9 28.3 71.7 24.3 15.0 3.6 12.5 6.4 39.0

United Kingdom

1981 ...................... 6,731.2 46.3 53.7 38.5 13.1 1.5 3.8 10.6 29.6

1990 ...................... 8,113.8 43,5 56.5 31.9 18.1 4.0 5.5 10.3 29.8

2000 ...................... 10,357.6 36.2 63.8 12.1 28.3 6.4 3.5 18.8 30.4

2009 ...................... 15,146.3 18.3 81.7 9.3 28.8 5.7 2.3 23.8 30.1

Japan

1981 ...................... NA NA NA NA NA NA NA NA NA

1990 ...................... 10,142.0 5.4 94.6 33.9 4.5 1.1 6.9 8.4 45.1

2000 ...................... 21,223.0 4.1 95.9 33.4 6.6 1.0 5.8 14.6 37.0

2009 ...................... 31,072.5 3.7 96.3 30.5 7.2 1.0 7.5 18.3 35.5

China

1981 ...................... NA NA NA NA NA NA NA NA NA

1990 ...................... NA NA NA NA NA NA NA NA NA

2000 ...................... NA NA NA NA NA NA NA NA NA

2009 ...................... NA NA NA NA NA NA NA NA NA

South Korea

1981 ...................... NA NA NA NA NA NA NA NA NA

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 **

2009 ...................... 13,209.6 16.7 83.3 52.0 18.6 2.8 3.7 22.8 **

** = 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 converted to dollars through purchasing power parities. Most recent data available for France and the EU are 2008. EU 

data for all years based on current 27 member countries. 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 (NABS).

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (August 2010) of federal R&D budget 

authority by spending category; Organisation for Economic Co-operation and Development, Main Science and Technology Indicators (2011/1). See 

appendix table 4-45.

Science and Engineering Indicators 2012

Percent of GBAORD
4-50 Chapter 4. Research and Development: National Trends and International Comparisons

Figure 4-17

Share of industrial R&D, by industry sector and selected country: 2007–10

Science and Engineering Indicators 2012

Pharmaceuticals Motor vehicles

Communication, TV,

and radio equipment

All services R&D services Computer and related services

Percent

NOTES: Source data for U.S. business R&D in this figure are preliminary (NSF 2010a); final U.S. statistics were used elsewhere in chapter 4. Countries 

listed in descending order by amount of total business R&D. Data years are in parentheses.

SOURCES: Organisation for Economic Co-operation and Development, Analytical Business Enterprise R&D (ANBERD)-Statistical Analysis Database 

(STAN)-R&D Expenditure in Industry, http://www.oecd.org/document/17/0,3746,en_2649_34451_1822033_1_1_1_1,00.html, accessed 27 July 2011; 

National Science Foundation, National Center for Science and Engineering Statistics and U.S. Census Bureau, Business R&D and Innovation Survey (2008).

Slovak Republic (2009)

Slovenia (2008)

Greece (2007)

Hungary (2008)

Portugal (2008)

Czech Republic (2009)

Norway (2008)

Mexico (2007)

Turkey (2008)

Belgium (2007)

Austria (2007)

Australia (2010)

Italy (2010)

United Kingdom (2008)

France (2007)

South Korea (2008)

Germany (2008)

Japan (2009)

United States (2008)

0 10 20 30 40 50 60 0 10 20 30 40 50 60 0 10 20 30 40 50 60

Slovak Republic (2009)

Slovenia (2008)

Greece (2007)

Hungary (2008)

Portugal (2008)

Czech Republic (2009)

Norway (2008)

Mexico (2007)

Turkey (2008)

Belgium (2007)

Austria (2007)

Australia (2010)

Italy (2010)

United Kingdom (2008)

France (2007)

South Korea (2008)

Germany (2008)

Japan (2009)

United States (2008)

0 10 20 30 40 50 60 0 10 20 30 40 50 60 0 10 20 30 40 50 60
Science and Engineering Indicators 2012 4-51

Most firms that make significant investments in 

R&D track their R&D expenses separately in their accounting records and financial statements. The annual 

reports of public corporations often include data on these 

R&D expenses. Research organizations and consulting 

companies interested in tracking and ranking businesses 

compile R&D expenditures and related operations and 

performance data. According to one such ranking, the 

20 public corporations with the largest reported worldwide R&D expenditures spent $129 billion on R&D in 

2009 (Booz & Company 2010). The six companies with 

the largest reported R&D expenses—Roche Holding, 

Microsoft, Nokia, Toyota, Pfizer, and Novartis—each 

spent between $7.4 billion and $9.1 billion (table 4-B). 

Eight companies in the computing and electronic sector 

spent a total of $50.4 billion (39% of the total for the top 

20). Seven companies in the health sector spent a total of 

$49.5 billion (38% of the total). The remaining five companies on the list are automobile manufacturers and they 

reported combined spending of $29.1 billion on R&D 

(23% of the total). The top 20 companies are headquartered in 8 countries, with 9 headquartered in the United 

States. In addition, most companies in this list have production, distribution, and/or research and technical facilities in multiple countries. (For related industry-level 

information, see “R&D by Multinational Companies” in 

this chapter and chapter 6.)

Global R&D Expenses of Public Corporations 

Table 4-B

Global R&D spending by top 20 corporations: 2009

R&D rank Company Country

R&D expense 

($millions)

Sales

($millions)

R&D intensity

(%)

 1................ Roche Holding AG Switzerland 9,120 45,606 20.1

 2................ Microsoft Corp United States 9,010 58,437 15.4

 3................ Nokia OYJ Finland 8,240 57,150 14.4

 4................ Toyota Motor Corp Japan 7,822 204,363 3.8

 5................ Pfizer Inc United States 7,739 50,009 15.5

 6................ Novartis AG Switzerland 7,469 44,267 16.9

 7................ Johnson & Johnson United States 6,986 61,897 11.3

 8................ Sanofi-Aventis SA France 6,391 40,866 15.6

 9................ GlaxoSmithKline PLC United Kingdom 6,187 44,422 13.9

10................ Samsung Electronics Co Ltd South Korea 6,002 109,541 5.5

11................ General Motors Co United States 6,000 104,589 5.7

12................ International Business Machines United States 5,820 95,759 6.1

13................ Intel Corp United States 5,653 35,127 16.1

14................ Merck & Co Inc United States 5,613 27,428 20.5

15................ Volkswagen AG Germany 5,359 146,677 3.7

16................ Siemens AG Germany 5,285 103,866 5.1

17................ Cisco Systems Inc United States 5,208 36,117 14.4

18................ Panasonic Corp Japan 5,143 79,994 6.4

19................ Honda Motor Co Ltd Japan 4,996 92,516 5.4

20................ Ford Motor Co United States 4,900 118,308 4.1

SOURCE: Booz & Company, The global innovation1000-how the top innovators keep winning (2010). http://www.booz.com/media/file/sb61_10408-R.pdf 

and http://www.booz.com/media/file/keep_winning_11_2010.pdf. Both accessed 10 August 2011.

Science and Engineering Indicators 2012

part suppliers, or technical contractors, may have relatively 

larger share of motor vehicles R&D, as shown for Germany, 

the Czech Republic, and Turkey. 

A significant trend in both U.S. and international business R&D activity has been the growth of R&D in the service sector. According to national statistics for recent years, 

the service sector accounted for 30% or more of all business 

R&D in 8 of the 19 OECD countries shown in figure 4-17 

and less than 10% in only one of the countries. In the United 

States, service industries accounted for 32% of all business 

R&D in 2008.42

Internationally comparable data for selected nonOECD members are also available from the same database 

(ANBERD-STAN) (OECD 2011a). Percentage shares 

by industry of total business R&D for China, the Russian 

Federation, Singapore, South Africa, and Taiwan are given in appendix table 4-46. Among these economies, the 

communication, television, and radio equipment industry, 

which includes semiconductors, accounted for over 50% 

of all business R&D in Singapore (2008). Motor vehicle 

R&D accounted for 5% of business R&D in South Africa 

(2007); pharmaceutical R&D accounted for 3% in China 

(2009) and R&D in the computer, office and accounting 

machines industry accounted for 3% of the business R&D 

performed in Taiwan (2009). Among OECD countries, the 

service sector accounted for as little as 8% of business R&D 
4-52 Chapter 4. Research and Development: National Trends and International Comparisons

U.S. universities generally do not maintain data on departmental research (i.e., research that is not separately 

budgeted and accounted). 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, 

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.

Government Funding Mechanisms for Academic Research

Figure 4-18

Academic R&D financed by business, for selected 

countries: 1981–2009

Percent 

NOTES: Top seven R&D performing countries. Data 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 National Science 

Foundation.

SOURCE: Organisation for Economic Co-operation and Development, 

Main Science and Technology Indicators (2011/1).

Science and Engineering Indicators 2012

1981 1985 1989 1993 1997 2001 2005 2009

0

5

10

15

20

25

30

35

40

United States

Germany

France

United Kingdom

Japan

China

South Korea

in South Korea (2008) to as much as 65% in Israel (2010). 

For the non-OECD economies examined here, the percentage of business R&D accounted for by the service sectors 

ranged from 7% in Taiwan (2009) to 86% in the Russian 

Federation (2009).

Business Support for Academic R&D

For most countries, the government is (and has long 

been) the largest source of academic research funding. (See 

sidebar, “Government Funding Mechanisms for Academic 

Research.”) Nevertheless, business support for academic 

R&D has increased over the past 25 years among the OECD 

countries as a whole. It was around 3% in the early 1980s, 

nearly 6% in 1990, almost 7% in 2000, and still around 7% 

in 2007.

In the United States, business support for academic 

R&D was about 4% in the early 1980s and rose to about 7% 

later in that decade and through the 1990s, but has dropped 

to below 6% since 2000. Some commentators note concern 

about this recent trend of decline, given the significant role 

that academic basic research plays in providing a foundation for technological innovation that is important to the 

national economy. 

The proportion of academic R&D financed by business is 

more varied among the other top R&D-performing countries 

(figure 4-18). Among the other top seven R&D-performing 

countries, the highest figure for business support of academic R&D is currently in China (37%). The figures are also 

high in Germany (15%) and South Korea (12%), whereas 

Japan, France, and the United Kingdom occupy the low end, 

with figures under 5%.
Science and Engineering Indicators 2012 4-53

Conclusion

Growth in global R&D has been rapid, averaging 7% annually over the last 10 years, reaching an estimated $1,276 

billion (in purchasing power parities) in 2009. The United 

States is by far the largest R&D performer, accounting for 

about 31% of the global total, but down from 38% in 1999. 

Average annual growth in U.S. R&D spending has outpaced 

U.S. GDP growth over the last several decades. However, 

in 2009 U.S. R&D spending was somewhat below the 2008 

level. The 2009 slowdown primarily reflects a drop in business R&D in the face of the 2008–09 financial crisis and the 

economic recession. On the other hand, U.S. R&D spending in other performing sectors continued to rise, notably for 

federal and academic R&D, in part because of the one-time 

federal R&D funding increase appropriated in the American 

Recovery and Reinvestment Act of 2009. 

The other major trend has been the rapid expansion of 

R&D performance in Asia. The region represented 24% 

of the global R&D total in 1999 but accounted for 32% in 

2009, including China (12%) and Japan (11%). The pace of 

real growth over the past 10 years in China’s overall R&D 

remains exceptionally high at about 20% annually. The rate 

of growth in South Korea’s R&D has also been relatively 

high, averaging nearly 10% annually over the 10-year period. Growth in Japan has been slower, at an annual average 

rate of 4.0%.

The R&D/GDP ratio, or R&D intensity, constitutes another basis for international comparisons. The U.S. ratio was 

about 2.9% in 2009 and has fluctuated between 2.6% and 

2.8% during the prior 10 years, largely reflecting changes in 

business R&D spending. In 2009, the United States ranked 

eighth in R&D intensity—surpassed by Israel, Sweden, 

Finland, Japan, South Korea, Switzerland, and Taiwan (but 

all perform far less R&D annually than the U.S.). China’s 

ratio remains relatively low, at 1.7%, but has more than doubled from 0.8% in 1999.

The majority of R&D by U.S. MNCs continues to be performed in the United States. Indeed, parent companies of 

U.S. MNCs performed just over two-thirds of U.S. business 

R&D. U.S. MNCs performed most of their foreign R&D in 

Europe, Canada, and Japan. However, from 1997 to 2008 

the share of R&D performed by U.S. majority-owned affiliates in Asia (other than Japan) more than doubled, including increases in the share performed in China, South Korea, 

Singapore, and India.

Notes

1. America Creating Opportunities to Meaningfully 

Promote Excellence in Technology, Education, and Science 

[COMPETES] Act (Public Law 110-69, January 4, 2007) 

and America COMPETES Reauthorization Act of 2010 

(Public Law 111-358, January 4, 2011).

2. For an annotated compilation of definitions of R&D by 

U.S. statistical agencies, tax statutes, accounting bodies, and 

other official sources, see NSF (2006).

3. Adjustments for inflation reported in this chapter are 

based on the GDP implicit price deflator. GDP deflators are 

calculated on an economy-wide rather than an R&D-specific 

basis. As such, they should be interpreted as measures of real 

resources engaged in R&D rather than in other activities, 

such as consumption or physical investment. They are not a 

measure of cost changes in performing R&D. See appendix 

table 4-1 for GDP deflators used in this chapter.

4. 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.

5. Federal support for R&D reported by federal agencies in the form of obligations differs from expenditures of 

federal R&D funds reported by R&D performers. For a discussion of the reasons for, and the magnitude of these discrepancies, see sidebar “Tracking R&D: The Gap Between 

Performer- and Source-Reported Expenditures” later in this 

chapter.

6. Contemporary discussions often note the extensive 

feedback loops among basic research, applied research, and 

development that prevail in the conduct of R&D. On this 

basis, there is often-heard criticism that this standard trio 

is simplistic and erroneously implies a linear progression. 

Even so, an alternative framework has yet to be identified 

to wide acceptance. Accordingly, the chapter relies for its 

analysis on the standard trio of categories, which have been 

longstanding, widely used, and internationally comparable 

(OECD 2002). 

7. The OECD notes that in measuring R&D, the greatest 

source of error often is the difficulty of locating the cutoff 

point between experimental development and the related activities required 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 manufacturers 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. These estimates measure solely the direct impact of 

R&D investment. Although indirect productivity impacts of 

R&D are included in BEA’s industry output measures, estimates of the impact of R&D based on the R&D Satellite 

Account do not separately identify spillovers, the indirect 

benefits to firms that did not pay for the R&D. For R&D 

spillovers in the context of national accounts measures, see 

Sveikauskas (2007).

9. The sample for the Business R&D and Innovation 

Survey (BRDIS) was selected to represent all for-profit nonfarm companies with five or more domestic employees, publicly or privately held, that perform or fund R&D or engage 

in innovative activities in the United States. For worldwide 

expense data from this survey, see appendix table 4-13.

10. Recall that BRDIS excludes companies with fewer 

than five domestic employees.
4-54 Chapter 4. Research and Development: National Trends and International Comparisons

11. Because federal R&D funding is concentrated among 

a few companies in a small number of industries than is R&D 

in general, estimates for federally funded business R&D are 

often suppressed. Consequently, the percentage of federally 

funded business R&D for these six industry groups is based 

on a lower bound estimate.

12. Estimates for computer and electronic product manufacturing in this section refer to NAICS 334 except the 

federally funded R&D component of navigational, measuring, electromedical, and control instruments industry 

(NAICS 3345), which is included in aerospace and defense 

manufacturing.

13. Specifically, this industry group includes domestic 

R&D performance for architectural, engineering, and related 

services (NAICS 5413) and scientific R&D services industries (NAICS 5417).

14. Although companies in the R&D and related-services 

sector and their R&D activities are classified as nonmanufacturing, they serve many manufacturing industries. For 

example, many biotechnology companies in this sector license their technology to companies in the pharmaceutical 

manufacturing industry. The R&D of a research firm that is 

a subsidiary of a manufacturing company rather than an independent contractor would be classified as R&D in a manufacturing industry. Consequently, growth in R&D services 

may, in part, reflect a more general pattern of industry’s increasing reliance on outsourcing and contract R&D.

15. Data are tabulated independent of the industry classification of the company.

16. Funded by others outside the company includes funded by foreign parents.

17. See appendix tables 4-18 through 4-21.

18. The BEA estimate for R&D performance by majority-owned foreign affiliates of U.S. MNCs is much lower 

than the $61.5 billion based on BRDIS 2008. BEA, NSF, 

and the Census Bureau are researching measures of R&D by 

foreign affiliates as part of the R&D linking project, which is 

discussed in sidebar “Linking MNC Data from International 

Investment and Business R&D Surveys.” This research 

should lead to improvements in both data sets. 

19. Data in this section cover international transactions 

in RDT services by U.S.-located companies from BEA’s 

Survey of Transactions in Selected Services and Intangible 

Assets with Foreign Persons. Separate data for R&D versus 

“testing” services are not available (further, testing services 

may have both R&D and non-R&D components). Other feebased measures on intangibles trade include international 

licensing and royalty payments and receipts (see chapter 6). 

RDT services cover activities by companies in any industrial classification, not just companies classified in services 

or in NAICS 5417 (Scientific research and development services). For further methodological information, see http://

www.bea.gov/surveys/iussurv.htm. 

20. U.S. RDT exports by foreign MNCs in 2008 were 

about 16% of their U.S. R&D performance as reported in 

the section “R&D by Multinational Companies,” whereas 

the corresponding ratio for U.S. parents was 4%. Thus a substantial share of foreign-owned R&D in the U.S. is apparently devoted to service foreign parents and other members 

of the foreign MNC. See Moris (2009) for caveats on these 

cross-survey comparisons.

21. Federal agencies also sponsor FFRDCs; see appendix 

table 4-33.

22. For information on R&D credits at the state level, see 

NSB (2008, chapter 4) and Wilson (2009).

23. See Section 731 of H. R. 4853, Public Law 111-

312. The statute also renewed the credit retroactively for 

activities after December 31 2009, given that the credit 

had expired on the latter date according to the Emergency 

Economic Stabilization Act of 2008 (H.R.1424, Public Law 

110-343, Division C, Title III, Section 301). This credit has 

now been extended 14 times despite its temporary status 

since its inception.

24. Based on data from the Internal Revenue Service/

Statistics of Income (IRS/SOI). Data are sample-based estimates and are subject to sampling and nonsampling errors. 

For statistical methodology, see section 3 in IRS (2010).

25. This percentage is based on company and other nonfederal funds for business R&D.

26. Based on IRS/SOI figures B and C in http://www.

irs.gov/taxstats/article/0,,id=164402,00.html (accessed 25 

February 2011). See also IRS (2008).

27. The alternative incremental tax credit was in place 

from 1996 to 2008; a simplified alternative credit has 

been in place since 2006. See IRS (2008) and Guenther 

(forthcoming). 

28. See IRS tax form 6765 at http://www.irs.gov/pub/irspdf/f6765.pdf.

29. Science or research parks, another example of publicprivate collaboration, may facilitate knowledge diffusion, 

technology development and deployment, and entrepreneurship by involving universities, government laboratories, and 

business startups. Two recent U.S. workshops focused on 

science parks. A December 2007 NSF workshop was aimed 

at fostering a better understanding and measurement of science parks’ activities, including the role of science parks 

in the national innovation system. Participants identified a 

need for systematic studies on topics such as the social benefits of public investment in science parks, ways in which 

the university-science park interaction engenders entrepreneurial activity, and lessons that U.S. science parks can learn 

from comparative studies with European and Asian parks. 

For material from this workshop, see http://www.nsf.gov/

statistics/workshop/sciencepark07. A subsequent workshop 

sponsored by the National Academies explored international 

models and best practices in science parks (NRC 2009). 

30. Notably missing among these indicators are technical articles published in professional journals, conference 

papers, and other kinds of scientific communications. Most 

federal lab scientists, engineers, and managers view this traditional form of new knowledge dissemination as an essential tech transfer component. Nevertheless, few agencies and 
Science and Engineering Indicators 2012 4-55

their associated federal labs regularly tabulate and report this 

information.

31. P.L. 97–219. At the time of writing, SBIR was authorized until November 18, 2011 (Public Law 112–36).

32. Small Business Technology Transfer Act of 1992 

(Public Law 102-564, Title II). 

33. To obtain federal funding under this program, a small 

company applies for a phase I SBIR grant of up to $100,000 

for up to 6 months to assess the scientific and technical feasibility of ideas with commercial potential. If the concept 

shows further potential, the company may receive a phase 

II grant of up to $750,000 over a period of up to 2 years for 

further development.

34. SBA’s Federal and State Technology (FAST) partnership program also provides support associated with 

SBIR/STTR. The Consolidated Appropriations Act of 2010 

(Public Law 111-117) authorized $2 million for FAST. In 

October 2010, SBA granted $100,000 awards to 20 state and 

local economic development agencies, business development centers, and colleges and universities. The program is 

designed to help socially and economically disadvantaged 

firms compete in SBIR and STTR. The project and budget 

periods are for 12 months, starting September 30, 2010. See 

SBA Press Release No. 10-62, http://www.sba.gov/aboutsba-services/7367/11391, accessed 4 March 2011.

35. Public Law 110-69, Section 3012. See NSB (2010) 

pages 4–57 and appendix table 4-47 of that publication 

for information and data on the predecessor program, the 

Advanced Technology Program.

36. See appendix tables 4-43 through 4-46.

37. EU real growth over 1999–2009 and the 1999 share 

are based on all current 27 EU member countries.

38. For related 2008 data, see appendix table 4-44.

39. 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.

40. Data for the United States included in the Organisation 

for Economic Co-operation and Development’s Analytical 

Business Enterprise R&D (ANBERD)-Statistical Analysis 

Database (STAN) are preliminary (NSF 2010a); final statistics were used for the business R&D analyses earlier in 

chapter 4.

41. For information on global valued added, trade, and related statistics for high technology industries, see chapter 6.

42. Share in OECD/ANBERD based on preliminary U.S. 

business R&D data (NSF 2010a); final U.S. statistics were 

used elsewhere in chapter 4.

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.

Company-funded R&D: R&D paid for with a company’s own funds, no matter the location of R&D activity 

or who performs or conducts the R&D (the company itself 

or others outside the funding company). Company-funded 

R&D is also known as R&D expense for certain tax, accounting, and data collection purposes.

EU: Prior to 2004, the European Union (EU) consisted 

of 15 member nations: Austria, Belgium, Denmark, Finland, 

France, Germany, Greece, Ireland, Italy, Luxembourg, 

Netherlands, Portugal, Spain, Sweden, and the United 

Kingdom. In 2004, the membership expanded to include an 

additional 10 countries: Cyprus, Czech Republic, Estonia, 

Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and 

Slovenia. Bulgaria and Romania were added in January 2007, 

bringing the total of current EU member countries to 27.

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.

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.

General university fund (GUF): Block grants provided 

by all levels of government in Europe, Canada, and Japan 

to the academic sector that can be used to support departmental R&D programs that are not separately budgeted; the 
4-56 Chapter 4. Research and Development: National Trends and International Comparisons

U.S. federal government does not provide research support 

through a GUF equivalent.

Gross domestic product (GDP): The market value of 

goods and services produced within a country. It is one of 

the main measures in the NIPAs.

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 (NIPAs): 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, 

Korea, Luxembourg, Mexico, 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 nonmember countries.

Public-private partnership: Collaboration between 

private or commercial organizations and at least one public or nonprofit organization such as a university, research 

institute, or government laboratory. Examples include cooperative research and development agreements (CRADAs), 

industry-university alliances, and science parks.

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, companyfunded R&D to net sales ratio, and R&D expenditures per 

employee.

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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Chapter 5

Academic Research 

and Development

5-1

Highlights .....................................................................................................................................5-4

Spending and Funding for Academic R&D ..............................................................................5-4

Infrastructure for Academic R&D ............................................................................................5-4

Cyberinfrastructure ...................................................................................................................5-4

Doctoral Scientists and Engineers in Academia .......................................................................5-4

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

Chapter Overview .......................................................................................................................5-7

Expenditures and Funding for Academic R&D ...........................................................................5-7

Academic R&D in the National R&D Effort ...........................................................................5-7

Sources of Support for Academic R&D ...................................................................................5-7

Academic R&D Expenditures by Field .................................................................................5-11

Academic R&D by Institution ................................................................................................5-13

Infrastructure for Academic R&D ............................................................................................5-15

Research Facilities ..................................................................................................................5-16

Research Equipment ..............................................................................................................5-17

Cyberinfrastructure ................................................................................................................5-18

The Academic Doctoral S&E Workforce ..................................................................................5-19

Trends in Academic Employment of Doctoral Scientists and Engineers ...............................5-21

Academic Researchers ............................................................................................................5-25

Academic Employment in Postdoc Positions .........................................................................5-27

Government Support of Academic Doctoral Researchers ......................................................5-28

Outputs of S&E Research: Articles and Patents ........................................................................5-32

S&E Article Output ................................................................................................................5-32

Coauthorship and Collaboration .............................................................................................5-35

Trends in Output and Collaboration Among U.S. Sectors .....................................................5-40

Trends in Citation of S&E Articles .......................................................................................5-43

Academic Patents, Licenses, Royalties, and Startups ............................................................5-45

Patent-to-Literature Citations ................................................................................................5-48

Conclusion ................................................................................................................................5-50

Notes ..........................................................................................................................................5-51

Glossary .....................................................................................................................................5-54

References ..................................................................................................................................5-54

List of Sidebars

Data on the Financial and Infrastructure Resources for Academic R&D ...................................5-8

Congressional Earmarks ............................................................................................................5-10

EPSCoR: The Experimental Program to Stimulate Competitive Research ...............................5-12

Postdoctoral Researchers ...........................................................................................................5-29

Bibliometric Data and Terminology ..........................................................................................5-33

Calculating the Index of International Collaboration ................................................................5-40

Identifying Clean Energy and Pollution Control Patents ...........................................................5-49

Please see chapter errata
5-2 Chapter 5. Academic Research and Development

List of Tables

Table 5-1. R&D expenditures at academic institutions in non-S&E fields: FY 2007–09 .........5-14

Table 5-2. New construction of S&E research space in academic institutions, by field 

and time of construction: FY 2002–09..................................................................................5-17

Table 5-3. Bandwidth of commodity internet (Internet1) and Internet2 at academic 

institutions: FY 2005–10 ......................................................................................................5-19

Table 5-4. Institutions with high-performance network connections, by type of institution: 

FY 2005–09 ...........................................................................................................................5-20

Table 5-5. Highest internal network speeds, by highest degree granted: FY 2003–09 .............5-21

Table 5-6. Average annual growth rate for employment of SEH doctorate holders, 

by sector: 1973–2008 ............................................................................................................5-21

Table 5-7. SEH doctorate holders employed in academia, by years since doctorate: 

Selected years, 1973–2008 ....................................................................................................5-22

Table 5-8. Women as percentage of SEH doctorate holders employed in academia, 

by position: Selected years, 1973–2008 ................................................................................5-23

Table 5-9. Underrepresented minorities as percentage of SEH doctorate holders employed 

in academia, by position: Selected years, 1973–2008...........................................................5-24

Table 5-10. SEH doctorate holders employed in academia, by Carnegie institution type 

and race/ethnicity: 2008 ........................................................................................................5-24

Table 5-11. SEH faculty reporting research as primary work activity, by years since 

doctorate and degree field: 2008 ...........................................................................................5-26

Table 5-12. Full-time SEH graduate students and graduate research assistants at 

universities and colleges, by degree field: Selected years, 1973–2008 ................................5-28

Table 5-13. SEH doctorate holders and graduate research assistants employed in 

academia, by Carnegie institution type: 1973–2008 .............................................................5-29

Table 5-14. SEH doctorate holders with academic employment in postdoc position, 

by demographic group: Selected years, 1973–2008 .............................................................5-30

Table 5-15. SEH doctorate holders with academic employment in postdoc position, 

by Carnegie institution type and years since doctorate: 2008 ...............................................5-31

Table 5-16. SEH doctorate holders employed in academia, by years since doctorate, 

number of postdoc positions held during career, and degree field: 1995 and 2006 ..............5-32

Table 5-17. S&E articles in all fields, by country/economy: 1999 and 2009 ............................5-34

Table 5-18. Share of internationally coauthored S&E articles worldwide, 

by region/country: 2000 and 2010 ........................................................................................5-38

Table 5-19. International collaboration on S&E articles, by selected region/country: 

2000 and 2010 .......................................................................................................................5-38

Table 5-20. International coauthorship of S&E articles with the United States, by selected 

country/economy: 2000 and 2010 .........................................................................................5-39

Table 5-21. Index of international collaboration on S&E articles, by selected country/ 

economy pair: 1995 and 2010 ...............................................................................................5-41

Table 5-22. U.S. S&E articles, by sector: Selected years, 1995–2010 ......................................5-41

Table 5-23. U.S. S&E article coauthorship, by sector, foreign coauthorship, and U.S. 

coauthor sector: 2000 and 2010 ............................................................................................5-42

Table 5-24. S&E articles, citations, and international citations, by selected region/ 

country: 2000 and 2010 .........................................................................................................5-44

Table 5-25. Patent citations to S&E articles, by selected patent technology area and 

article field: 1998–2010 ........................................................................................................5-50

Table 5-A. EPSCoR and EPSCoR-like program budgets, by agency: FY 2001–10 .................5-12
Science and Engineering Indicators 2012 5-3

List of Figures

Figure 5-1. Academic share of U.S. R&D performance: 1970–2009 ..........................................5-9

Figure 5-2. Academic R&D expenditures, by source of funding: 1972–2009 ............................5-9

Figure 5-3. Federal and nonfederal funding of academic R&D expenditures: 1996–2009 .........5-9

Figure 5-4. Academic R&D expenditures, by S&E field: 1999–2009 ......................................5-11

Figure 5-5. Changes in share of academic R&D, by selected S&E field: 1990–2000 

and 2000–09 ..........................................................................................................................5-13

Figure 5-6. Federally financed academic R&D expenditures, by agency and S&E field: 

FY 2009 .................................................................................................................................5-13

Figure 5-7. Sources of R&D funding for public and private academic institutions: 

FY 2009 .................................................................................................................................5-14

Figure 5-8. Components of institutional R&D expenditures for public and private 

academic institutions: 1999–2009 .........................................................................................5-14

Figure 5-9. Share of academic R&D, by institution rank in R&D expenditures: 

FY 1989–2009 .......................................................................................................................5-15

Figure 5-10. Total and federally funded academic R&D pass-throughs: FY 2000–09 .............5-15

Figure 5-11. S&E research space at academic institutions, by field: FY 1999 and 2009 ..........5-16

Figure 5-12. Current fund expenditures for S&E research equipment at academic 

institutions, by field: 1999–2009...........................................................................................5-18

Figure 5-13. SEH doctorate holders employed in academia, by type of position: 

1973–2008 .............................................................................................................................5-22

Figure 5-14. SEH doctorate holders employed in academia, by degree field: 1973–2008 .......5-23

Figure 5-15. Women as percentage of SEH doctorate holders with full-time employment 

in academia, by academic rank: Selected years, 1973–2008 ................................................5-23

Figure 5-16. SEH doctorate holders employed in academia, by birthplace: 1973–2008 ..........5-25

Figure 5-17. Academic researchers as percentage of SEH doctoral employment, 

by position and involvement in research: 1973–2008...........................................................5-25

Figure 5-18. Primary work activity of full-time doctoral SEH faculty: 1973–2008 .................5-26

Figure 5-19. SEH doctorate holders with academic employment in postdoc position, 

by degree field: Selected years, 1973–2008 ..........................................................................5-30

Figure 5-20. Recent SEH doctorate holders employed in academia, by position 

and years since doctorate: 1979–2008 ..................................................................................5-31

Figure 5-21. SEH doctorate holders employed in academia with federal support, 

by position and years since doctorate: 1973–2008 ...............................................................5-32

Figure 5-22. World S&E articles, by author characteristic: 1988–2009 ....................................5-35

Figure 5-23. U.S. academic S&E articles, institutional authors, and author names: 

1990–2010 .............................................................................................................................5-36

Figure 5-24. Number of authors per U.S. academic S&E article, by S&E field: 

Selected years, 1990–2010 ....................................................................................................5-36

Figure 5-25. World and U.S. academic S&E articles coauthored domestically and 

internationally: 1990–2010 ...................................................................................................5-37

Figure 5-26. Average citations per S&E article, by country of author: 1992–2010 ..................5-43

Figure 5-27. Share of selected country/region citations that are international: 2000–10 ..........5-44

Figure 5-28. Share of U.S., EU, and China S&E articles that are in the world’s top 1% 

of cited articles: 2000–10 ......................................................................................................5-45

Figure 5-29. Index of highly cited articles, by selected S&E field and region/country: 

2000 and 2010 .......................................................................................................................5-46

Figure 5-30. Chinese index of highly cited articles, by selected S&E field: 2000 and 2010 ....5-47

Figure 5-31. U.S. academic patents, by technology area: Selected 5-year averages, 

1991–2010 .............................................................................................................................5-47

Figure 5-32. U.S. university patenting activities: 2002–2009 ...................................................5-48

Figure 5-33. Citations of U.S. S&E articles in U.S. patents, by selected S&E article 

field: 2010 .............................................................................................................................5-48

Figure 5-34. Citations of U.S. S&E articles in U.S. patents, by selected S&E field and article 

author sector: 2010 ...............................................................................................................5-49
5-4 Chapter 5. Academic Research and Development

Spending and Funding for Academic R&D

In 2009, U.S. academic institutions spent $54.9 billion on 

science and engineering R&D and an additional $2.4 billion in non-S&E fields. 

In 2009, academic institutions performed nearly half (53%) 

of the nation’s total basic research, a percent that has risen 

steadily from 47% in the later 1980s. 

Academia performed 36% of all U.S. research (basic plus 

applied) and 14% of total U.S. R&D.

Higher education’s share of total U.S. research expenditures (basic plus applied) has gradually increased, rising 

from 24% in 1982 to 36% in 2009. 

The federal government provides the bulk of funds for 

academic R&D; during the past two decades, its share 

has fluctuated around 60%.

The federal government provided 59% ($32.6 billion) of 

the $54.9 billion of academic spending on S&E R&D in 

FY 2009. The federal share was somewhat higher in the 

1970s and early 1980s. 

Six agencies provide almost all (97% in 2009) federal academic R&D support—the National Institutes of 

Health, National Science Foundation, Department of 

Defense, National Aeronautics and Space Administration, 

Department of Energy, and Department of Agriculture. 

The bulk of academic R&D funding from nonfederal 

sources is provided by the universities themselves.

The share of support provided by institutional funds increased steadily between 1972 (12%) and 1991 (19%) but 

since then has remained fairly stable at roughly one-fifth of 

total academic R&D funding. 

Industry’s percentage of funding for academic R&D declined steeply after the 1990s, from above 7% in 1999 

down to about 5% by 2004, but has seen a 5-year increase 

to about 6% in 2009.

Support from other governmental agencies, chiefly state 

funds, declined from 10% in the late 1970s to about 8% 

through the 1990s and stood at less than 7% in 2009.

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 2009, the life sciences represented the largest share 

(60%) of expenditures in academic S&E R&D.

Over the last 20 years, the life sciences were the only broad 

field to experience a sizable increase in share—6 percentage points—of total academic R&D. Over the same period, the physical sciences share of total academic R&D 

dropped 3 percentage points.

Infrastructure for Academic R&D

Research space at academic institutions has continued to 

grow annually over the last 20 years. Nonetheless, the pace 

of growth has noticeably slowed in the last few years.

Total research space at research-performing universities 

and colleges was 2.2% greater at the end of 2009 than 

it was in 2007, continuing a two decade long period of 

expansion. 

The rate of annual increase for all S&E fields combined in 

the 2001–03 period was 11%, but it has gradually slowed 

since then. Unlike in other fields, in recent years research 

space for the biological/biomedical sciences and agricultural sciences has continued to expand at substantial rates.

In 2009, about $2.0 billion in current funds was spent for 

academic research equipment (i.e., movable items such 

as computers or microscopes), a 2% increase over 2008, 

after adjusting for inflation. 

Equipment spending as a share of total R&D expenditures 

fell from 4.8% in FY 1999 to a three decade low of 3.6% 

in FY 2009. 

Three S&E fields accounted for 82% of equipment expenditures in 2009: the life sciences (41%), engineering 

(24%), and the physical sciences (17%).

In FY 2009, the federal share of support for all academic 

research equipment funding was 55%. This share has fluctuated between 55% and 63% over the last 20 years.

Cyberinfrastructure

Academic networking infrastructure is rapidly expanding in capability and coverage. 

Research performing institutions had more connections, 

bandwidth, and campus coverage compared with earlier in 

the decade.

Colleges and universities reported external network connections with greater bandwidth, faster internal network 

distribution speeds, more connections to high-speed networks, and greater on-campus wireless coverage. 

In FY 2003, 66% of institutions had bandwidth of less than 

1 gigabit per second and no institutions had speeds faster 

than 2.5 gigabits per second. By FY 2009, 82% of institutions had bandwidth speeds of 1 gigabit per second or faster and 24% had speeds faster than 2.5 gigabits per second.

Doctoral Scientists and Engineers 

in Academia

The size of the doctoral academic S&E workforce was 

an estimated 272,800 in 2008, almost unchanged from 

2006. Total academic doctoral employment grew less in 

this period than in any comparable period since 1973. 

Highlights
Full-time faculty positions, although still the predominant type of employment, increased more slowly than 

postdoc and other full- and part-time positions.

The share of all S&E doctorate holders employed in academia dropped from 55% in 1973 to 44% in 2008.

The percentage of S&E doctorate holders employed in 

academia who held full-time faculty positions declined 

from 88% in the early 1970s to 73% in 2008. Over that 

same period, other full-time positions rose from 6% to 

15% of total academic employment, and postdoc and parttime appointments increased from 4% and 2% to 7% and 

6%, respectively.

The demographic profile of academic researchers shifted substantially between 1973 and 2008. The increasing 

proportion of women was a particularly striking change.

The number of women in academia increased more than 

eightfold between 1973 and 2008, from 10,700 to about 

93,400, raising their share of all academic S&E doctoral 

employment from 9% to 34%. Women employed as fulltime doctoral S&E faculty increased from 7% to 31%.

In 2008, underrepresented minorities (blacks, Hispanics, 

and American Indians/Alaska Natives) constituted about 

9% of both total academic S&E doctoral employment and 

full-time faculty positions, up from 2% in 1973. 

The foreign-born share of U.S. S&E doctorate holders in 

academia increased from 12% in 1973 to nearly 25% in 

2008, and nearly half (46%) of postdoc positions in 2008 

were held by foreign-born U.S. S&E doctorate holders. No 

comparable data exist for foreign-born, foreign-degreed 

doctorate holders.

Between 1973 and 2008, the number of academic researchers with S&E doctorates more than doubled. 

Among full-time faculty, the balance of emphasis in work 

activity shifted toward research and away from teaching. Young faculty—those within 3 years of a doctorate 

award—were less likely than other faculty to report research as a primary work activity.

About two-thirds of doctoral scientists and engineers employed in academic institutions in 2008 were engaged in research as either a primary or secondary work activity. The 

proportions of researchers were highest in the life sciences, 

engineering, and computer sciences.

The share of full-time S&E faculty identifying research as 

their primary work activity climbed from 19% in 1973 to 

36% in 2008, while the share identifying teaching as their 

primary activity fell from 68% to 47%. 

In 2008, 33% of recently degreed S&E doctoral faculty 

identified research as their primary work activity, a smaller 

share than reported by faculty cohorts who had earned 

S&E doctorate degrees 4 to 7 years earlier (48%), 8 to 11 

years earlier (41%), and 12 or more years earlier (35%). 

A substantial pool of academic researchers—including 

graduate research assistants and doctorate holders employed in postdoc positions—has developed outside the 

ranks of full-time faculty. 

The number of S&E doctorate holders employed in academic postdoc positions climbed from 4,000 in 1973 to 

18,000 in 2008.

In 2008, 36% of recently degreed S&E doctorate holders in 

academia were employed in postdoc positions, a figure that 

approached the share (42%) employed in full-time faculty 

positions. Among S&E doctorate holders 4 to 7 years beyond their doctorate degrees, 11% held postdoc positions.

For S&E as a whole and for many fields, the share of academic S&E doctorate holders receiving federal support 

declined since the early 1990s.

Throughout the 1973–2008 period, fewer than half of fulltime S&E faculty received federal support, whereas the 

share of postdocs who received federal support was more 

than 70%.

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

S&E article output worldwide grew at an average annual 

rate of 2.6% between 1999 and 2009. The U.S. growth 

rate was much lower, at 1.0%.

The United States accounted for 26% of the world’s total 

S&E articles in 2009, down from 31% in 1999. The share 

for the European Union also declined, from 36% in 1999 

to 32% in 2009.

In Asia, average annual growth rates were high—for example, 16.8% in China and 10.1% in South Korea. In 

2009, China, the world’s second-largest national producer 

of S&E articles, accounted for 9% of the world total.

Very rapid annual growth rates of over 10% between 

1999 and 2009 were also experienced by Iran, Thailand, 

Malaysia, Pakistan, and Tunisia. However, some of these 

countries had low S&E article production in 1999.

Two-thirds of all S&E articles were coauthored in 2010. 

Articles with authors from different institutions and different countries have continued to increase, indicating 

increasing knowledge creation, transfer, and sharing 

among institutions and across national boundaries. 

Coauthored articles grew from 40% of the world’s total 

S&E articles in 1988 to 67% in 2010. Articles with only 

domestic coauthors increased from 32% of all articles in 

1988 to 43% in 2010. Internationally coauthored articles 

grew from 8% to 24% over the same period.

Science and Engineering Indicators 2012 5-5
U.S.-based researchers were coauthors of 43% of the 

world’s total internationally coauthored articles in 2010. 

Three other nations—Germany, the United Kingdom, and 

France—had high, though declining, shares of international coauthorships. Chinese authors increased their share of 

the world’s internationally coauthored S&E articles from 

5% to 13% between 2000 and 2010.

In the United States, because of the predominance of the 

academic sector in S&E article publishing, academic scientists and engineers have been on the forefront of the integration of S&E research across sectors. In non-academic 

sectors, cross-sector coauthorship with academic authors 

ranged from 55% to 76% in 2010. 

Like indicators of international coauthorship, crossnational citations provide mixed evidence of changes in 

the worldwide scope, influence, and quality of U.S. S&E 

research. 

Between 2000 and 2010, the U.S. share of the world’s total citations in S&E articles declined from 45% to 36%, 

reflecting the broad expansion of the global literature. 

China’s share of these citations increased from 1% to 6%. 

The EU share remained steady at 33%, and Japan’s share 

fell from 7% to 6%.

The percentage of U.S.-authored S&E articles receiving 

the highest number of citations—an indicator of quality 

and impact on subsequent research—has changed little. In 

2010, U.S. articles represented 28% of all articles in the 

cited period, but 49% of the articles in the top 1% of all 

cited articles. 

Data on citations per publication suggest that the quality 

of U.S.-authored articles has changed little over the past 

10 years. 

In 2010, articles with U.S. authors were highly cited about 

76% more often than expected based on the U.S. share of 

world articles, compared to 85% in 2000. Between 2000 

and 2010, EU-authored articles improved on this indicator, 

from 27% less often than expected to 6% less often. 

In 2010, China’s rate of high citation was nearly equal to 

its rate of publication in engineering and computer science, 

but its citation rate did not exceed its publication rate in 

any field. In most broad fields, China’s rate of high citations compared to its publication rate was higher in 2010 

than in 2000. 

U.S. Patent and Trademark Office (USPTO) data show 

that annual patent grants to universities and colleges 

ranged from 2,900 to 4,500 between 1998 and 2010. 

College and university patents have been about 4.2% 

to 4.7% of U.S. nongovernmental patents for a decade. 

Biotechnology patents accounted for most U.S. university 

patents in 2010, at 30%, a percentage that has grown over 

the past 15 years. 

Data from the Association of University Technology 

Managers (AUTM) indicate continuing growth in a number of patent-related activities. Invention disclosures grew 

from 12,600 in 2002 to 18,200 in 2009. New U.S. patent 

applications filed by AUTM university respondents also 

increased, from 6,500 in 2001 to 11,300 in 2009. In contrast, the number of issued patents reported by AUTM respondents has remained flat.

5-6 Chapter 5. Academic Research and Development
Science and Engineering Indicators 2012 5-7

Chapter Overview 

U.S. universities and colleges occupy a unique position in 

the nation’s overall R&D system. They perform more than 

half of U.S. basic research and, because they link graduate 

education and research, prepare the next generation of researchers (see chapter 2). 

This chapter discusses the role of the academic sector 

within the national R&D enterprise. The first section 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 importance of federal agency support for overall 

R&D and for individual fields is explored in some detail. 

Other significant sources of funding include the institutions 

themselves, businesses, and state and local government. The 

first section also traces recent changes in the distribution of 

funds among academic institutions and the types of academic institutions that receive federal R&D support. 

The chapter’s second section reviews the status of infrastructure for academic R&D. This discussion provides data 

on the current trends in academic research facilities, research 

equipment, and cyberinfrastructure.

The next section discusses trends in the employment of 

academic doctoral scientists and engineers. Major trends examined include the numbers of academic doctoral scientists 

and engineers, their changing demographic composition, 

and the types of positions they hold. This section also examines employment patterns in the segment of the academic 

workforce that is engaged in research, with particular attention to full-time faculty, postdocs, graduate research assistants, and the academic scientists and engineers receiving 

research support from the federal government. 

The chapter concludes with an analysis of trends in two 

types of research outputs: S&E articles and patents issued to 

U.S. universities. (A third major output of academic R&D, 

educated and trained personnel, is discussed in chapter 2.) 

This section looks at the volume of research articles for selected countries/regions and focuses (when appropriate) on 

S&E articles by U.S. academic researchers. Coauthored articles, both across U.S. sectors and internationally, are indicators of increasing collaboration in S&E research. The 

number of influential articles from U.S. institutions, as measured by the frequency with which they are cited, is examined and compared with citations to S&E articles produced 

around the globe. 

The final section explores academic patenting activities 

and 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 some attention—new in 

this edition—to S&E literature citations in patents for clean 

energy and related technologies. 

Expenditures and Funding 

for Academic R&D

Academic R&D is a key part 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, the 

research facilities, and the 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 the 

sidebar, “Data on the Financial and Infrastructure Resources 

for Academic R&D.”) 

Academic R&D in the National R&D Effort

Expenditures by U.S. colleges and universities on R&D 

in S&E fields totaled $54.9 billion in 2009.2 Academic 

spending in non-S&E fields that year was another $2.4 billion. The corresponding figures for 2008 were $51.9 billion 

and $2.2 billion. In 2004, these figures were $43.3 billion 

and $1.6 billion, respectively. 

Academic R&D spending is primarily for research (basic 

and applied)—in 2009, about 96% was spent on research 

(75% basic, 22% applied) and almost 4% was spent on development.3

 These shares are not appreciably different from 

the proportions that prevailed 5 and 10 years ago (appendix 

table 5-1).

Universities and colleges performed about 14% of all 

U.S. R&D in 2009. Higher education’s prominence as a national R&D performer has generally increased over the last 

30 years, rising from about 10% of all R&D performed in 

the United States in the early 1970s to an estimated 14% in 

2009 (figure 5-1).

Universities and colleges accounted for just under 36% 

of all U.S. research in 2009. This was slightly higher than 

the 35% reported in 2002—and the previously highest share 

of the U.S. research total over the last 30 years (figure 5-1).

In regard to basic research, the academic sector is by far the 

country’s largest performer. In 2009, it accounted for 53% of 

all the basic research performed in the United States. Indeed, 

institutions of higher education have accounted for more than 

half of all U.S. basic research since 1998 (figure 5-1). 

(For a comparison of the academic R&D profiles of 

other countries, see the section on “International R&D 

Comparisons” in chapter 4.)

Sources of Support for Academic 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, industry, nonprofits, and other organizations. 

Nevertheless, the federal government has consistently provided the majority of funding.
5-8 Chapter 5. Academic Research and Development

Data on the financial and infrastructure resources supporting U.S. academic R&D are drawn from three ongoing National Science Foundation (NSF) surveys: 

Survey of Research and Development Expenditures at 

Universities and Colleges 

Survey of Science and Engineering Research Facilities

Survey of Federal Funds for Research and Development.

The data definitions and classifications in these three 

surveys are similar, but not identical. Furthermore, the 

respondents differ across the surveys: universities and 

colleges for the first two, federal agencies for the third.

Some of the data presented in the first part of this 

section (see “Academic R&D in the National R&D 

Effort”) come from the NSF’s National Patterns of R&D 

Resources series, which integrates data from NSF’s R&D 

expenditure surveys to yield a comprehensive account of 

national R&D spending and funding. These separate data 

sets are adjusted for internal consistency and to reflect a 

calendar year. Some of the National Patterns figures for 

2009 are considered “preliminary.” 

The data subsequently covered are derived from the 

Survey of Research and Development Expenditures at 

Universities and Colleges. These data are not adjusted 

and represent reporting on an academic year basis (e.g., 

FY 2009 covers July 2008 through June 2009). 

Data on “Top Agency Supporters” and “Agency 

Support by Character of Work” come from NSF’s Survey 

of Federal Funds for Research and Development, which 

collects data on the R&D obligations of 30 federal agencies for each federal fiscal year (e.g., FY 2009 covers 

October 2008 through September 2009). The 2009 federal funds figures remain preliminary. 

The federal obligations data for academic R&D (e.g., 

$26.0 billion in FY 2008) do not match the federally 

funded expenditures data reported by academic institutions (e.g., $31.3 billion in 2008). Several factors account 

for this discrepancy: the spans of the academic and federal fiscal years differ slightly, there is a time lag between 

obligating and spending funds, awards may span multiple 

years, and federal funds passed to other recipient organizations are sometimes double-counted. 

The data on research equipment come from the 

Survey of Research and Development Expenditures at 

Universities and Colleges. The data on research facilities 

and cyberinfrastructure come from the Survey of Science 

and Engineering Research Facilities. In these surveys, 

academic R&D expenditures are reported by academic 

fiscal year.

Research equipment is purchased from operating 

funds and included in R&D expenditures. Although some 

large instrument systems may be classified as either facilities or equipment, facilities are generally treated as 

capital projects for accounting purposes.

The survey population for the facilities survey includes all universities and colleges in the Academic R&D 

Expenditures survey with $1 million or more in R&D expenditures. Starting in 2003, the facilities survey included data on computing and networking capacities. Fixed 

items such as buildings, which often cost millions of dollars, are not included in the reported R&D expenditures.

Redesign of the Survey of R&D 

Expenditures at Universities and Colleges

NSF’s Survey of Research and Development Expenditures 

at Universities and Colleges has been conducted annually 

since 1972. In 2007, an effort was started to evaluate and 

redesign the survey. The goals of the redesign were (1) to 

update the survey instrument to reflect current accounting 

principles in order to obtain more valid and reliable measurements of the amount of U.S. academic R&D expenditures and (2) to expand the current survey items to collect 

some additional detail on topics most often requested by 

data users. Data from the revised and expanded survey, renamed the “Higher Education R&D (HERD) Survey,” is 

expected to be publicly available in late 2011.

The HERD survey will continue to capture comparable 

information on R&D expenditures by sources of funding 

and field, which will allow for continued trend analysis. 

It will also include a more comprehensive treatment of 

S&E and non-S&E fields, an expanded population of surveyed institutions, explicit treatment of research training 

grants and clinical trials, greater detail about the sources 

of funding for R&D expenditures by field, and headcounts 

on principal investigators, other research personnel, and 

postdocs. Britt (2010) provides a more complete list of improvements in the redesigned survey.

Data on the Financial and Infrastructure Resources for Academic R&D 
Science and Engineering Indicators 2012 5-9

Federal Funding

The federal government provided $32.6 billion (59%) of 

the $54.9 billion of academic spending on S&E R&D in FY 

(academic) 2009.4 The federal share was somewhat higher in 

the 1970s and early 1980s, although the federal government 

has long contributed the majority of funds for academic 

R&D (figure 5-2 and figure 5-3). 

This $32.6 billion of federal funding in FY 2009 was 4.2% 

above the level of the previous year. The rates of growth in 

2008 and 2007 were 2.8% and 1.0%, respectively. Over the 

previous 10 years, the level of federal funding for academic 

R&D has been consistently up, averaging 3.3% annually for 

the 5-year period of 2004–09 and 7.3% annually for the 10-

year period of FY 1999–2009. But when adjusted for inflation, the 5-year annual average increase was 0.8% and the 10 

year average was 4.8%—FY 2006 and 2007 were years with 

constant dollar declines in federal funding. 

An additional perspective on funding trends is provided 

by inflation-adjusted obligations for academic S&E R&D 

reported by the federal agencies (i.e., the funds in constant 

dollars going to academic institutions in a given federal fiscal year that will be spent on R&D activities in the current 

and subsequent years). In constant 2005 dollars, federal academic R&D obligations peaked in FY 2004 at $25.0 billion, 

fell in the three subsequent years, reaching $24.0 billion 

in FY 2008, and then spiked upward in FY 2009, reaching 

$28.8 billion (appendix table 5-3). 

Federal obligations for S&E R&D grew more than 10% 

each year on a constant dollar basis between FY 1998 and 

2001. This reflected, for the most part, the federal commitment to double the R&D budget of the National Institutes 

of Health (NIH) over a 5-year period. Between FY (federal) 1998 and 2004, NIH’s share of federal academic R&D 

funding increased from 57% to 63%. Then in FY 2009, the 

American Recovery and Reinvestment Act (ARRA), which 

was signed into law on February 17, 2009, provided an additional $18.3 billion in appropriations for federal R&D and 

R&D facilities and equipment in FY 2009.5

 The significant uptick in obligations observed in FY 2009 reflects the 

Figure 5-1

Academic share of U.S. R&D performance: 

1970–2009

Percent

NOTES: Data based on annual reports by performers. Because of 

changes in survey procedures, character of work data before FY 

1998 are not directly comparable with later years.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, National Patterns of R&D Resources 

(annual series). See appendix table 5-1. 

Science and Engineering Indicators 2012

1970 1977 1981 1985 1989 1993 1997 20052001 2009

0

10

20

30

40

50

60

70

U.S. total R&D performance

Total research (basic and applied)

Basic research

Figure 5-2

Academic R&D expenditures, by source of funding: 

1972–2009

Percent

NOTE: Science and engineering R&D; non-S&E R&D not included.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Research and Development 

Expenditures at Universities and Colleges. See appendix table 5-2.

Science and Engineering Indicators 2012

1972 1977 1981 1985 1989 1993 1997 2001 2005 2009

0

10

20

30

40

50

60

70

Federal government

State/local government

Industry

Academic institutions

All other sources

Figure 5-3

Federal and nonfederal funding of academic R&D 

expenditures: 1996–2009

Dollars (billions)

NOTES: Science and engineering R&D; non-S&E R&D not included. 

See appendix table 4-1 for gross domestic product implicit price 

deflators used to convert current dollars to constant 2005 dollars.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Research and Development 

Expenditures at Universities and Colleges. See appendix table 5-2.

Science and Engineering Indicators 2012

0

5

10

15

20

25

30

35

1996 1999 2001 2003 2005 2007 2009

Federal (current dollars)

Nonfederal (current dollars)

Federal (constant 2005 dollars)

Nonfederal 

(constant 2005 

dollars)
5-10 Chapter 5. Academic Research and Development

presence of nearly $5 billion of these ARRA funds (appendix table 5-3). 

Top Federal Agency Supporters

Six agencies are responsible for the vast majority of 

annual federal obligations for higher education R&D: 

the National Institutes of Health (NIH), National Science 

Foundation (NSF), Department of Defense (DOD), National 

Aeronautics and Space Administration (NASA), Department 

of Energy (DOE), and Department of Agriculture (USDA). 

In federal FY 2009, these six agencies represented about 

96% of the estimated $31.6 billion obligated for S&E R&D 

that year (appendix table 5-3).6

NIH is by far the largest funder, providing about 65% of 

total federal academic R&D obligations in FY 2009. NSF provided 13%; DOD, 9%; NASA, 3%; DOE, 4%; and USDA, 3%. 

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, all 

of which have differing missions and objectives, which in 

turn affect the priorities for research funding in the academic 

sector. For the most part, federal R&D funding to the higher 

education sector is allocated through competitive peer review. Nevertheless, congressional priorities and concerns in 

the course of the annual federal budget process can influence funding outcomes—see the sidebar, “Congressional 

Earmarks.” 

Federal Agency Support by Character of Work

Basic research activities represented about 58% of federal obligations for academic R&D in FY 2009 and about 

56% in both FY 2007 and 2008 (appendix table 5-4). The 

two agencies funding the majority of basic research in the 

academic sector were NIH and NSF. 

Applied research represented about 38% of federal obligations for academic R&D in FY 2009, 37% in FY 2008, and 

38% in FY 2007. NIH provided the vast majority of funding 

in this category. Federal obligations for development activities in academia were 4–7% throughout FY 2007–09, with 

DOD and NASA the principal funders. 

Other Sources of Funding

Notwithstanding the continuing dominant federal role in 

academic R&D funding, funding from nonfederal sources 

has grown steadily in recent years (figure 5-3). Adjusted for 

inflation, annual growth in nonfederal funding for academic 

R&D has averaged 4.8% over the last 5 years, and 4.4% for 

the last 10 years. The corresponding growth rates for federal 

funding have been 0.8% and 4.8%. 

University and college institutional funds. In FY 2009, 

institutional funds from universities and colleges comprised the second largest source of funding for academic 

R&D, accounting for about 20% ($11.2 billion) of the total 

(appendix table 5-2). Institutional funds encompass institutionally financed research expenditures and unrecovered 

indirect costs and cost sharing. They exclude departmental 

research, which is a more informal type of research that is 

usually coupled with instructional activities in departmental budget accounts and thus does not meet the Office of 

Management and Budget definition of organized research. 

The share of support represented by institutional funds increased steadily from 12% in 1972 to 19% in 1991, and 

it has remained near 20% in the subsequent years. Funds 

for institutionally financed R&D may derive from generalpurpose state or local government appropriations; generalpurpose 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 section “Patent-Related 

Activities and Income” later in this chapter for a discussion 

of patent and licensing income.) 

State and local government funds. State and local governments provided 7% ($3.6 billion) of higher education 

R&D funding in FY 2009. Although their absolute funding total has continued to rise annually, their funding 

share has declined from a peak of 10% in the early 1970s 

to below 7% 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 only reflect funds that these governments directly target to academic R&D activities.7

 They 

exclude any general-purpose state or local government appropriations that academic institutions designate and use to 

Congressional Earmarks

Broadly defined, academic earmarking is the congressional practice of directing federal funds to educational institutions for facilities or projects that are not 

required to undergo merit-based peer review. However, 

this characterization contains enough ambiguity about 

how to classify individual projects that estimates of the 

number of earmarked projects or the amount of earmarked funds may reasonably differ. 

Detailed assessments of academic earmarks have 

been prepared by staff of The Chronicle of Higher 

Education. The most recent of these analyses estimated a total of $2.3 billion in academic earmarks in FY 

2008 (Brainard and Hermes, 2008). A similar analysis 

for FY 2003 puts the academic earmark total at $2.0 

billion (Brainard and Borrego, 2003). Approximately 

two-thirds ($1.6 billion) of the FY 2008 funds and $1.4 

billion of the FY 2003 funds were for R&D projects, 

R&D equipment, or construction or renovation of R&D 

laboratories. A more recent estimate, published in the 

Chronicle but prepared by an outside watchdog group, 

put the academic earmark total for FY 2010 at $1.5 billion (Kiley, 2010).

Recently, both the Senate and House of 

Representatives agreed to federal budget rules that aim 

to eliminate earmarks. There are no earmarks in the final budget appropriations for FY 2011. 
Science and Engineering Indicators 2012 5-11

fund separately budgeted research or 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.) 

Industry funds. Industrial support accounts for the smallest share of academic R&D funding (just under 6%), and 

support for academia has never been a major component 

of industry-funded R&D. After a 3-year decline between 

2001 and 2004, industry funding of academic R&D has 

been steadily increasing, reaching $3.2 billion in FY 

2009. (See appendix table 4-5.) 

Other sources of funds. In FY 2009, all other sources 

of support accounted for 8% ($4.3 billion) of academic 

R&D funding, a level that has stayed about the same since 

1972. This category of funds includes, but is not limited 

to, grants and contracts for R&D from nonprofit organizations and voluntary health agencies. 

EPSCoR

The Experimental Program to Stimulate Competitive 

Research (EPSCoR) is a long standing multi-agency federal 

program that has the objective of improving the geographical distribution of federal support for academic R&D. An 

overview of the program and recent statistics on its activities are discussed in the sidebar EPSCoR: The Experimental 

Program to Stimulate Competitive Research.

Academic R&D Expenditures by Field 

Investment in academic R&D has long been concentrated 

in the life sciences, which have received more than half of all 

academic R&D expenditures for more than three decades. 

Science and Engineering R&D

In FY 2009, academic R&D in the life sciences accounted 

for $32.8 billion (60%) of the $54.9 billion academic S&E 

R&D total (appendix table 5-5). Within the life sciences, the 

medical sciences accounted for 33% of the academic total 

and the biological sciences accounted for another 18%.8

Adjusted for inflation, academic R&D expenditures 

in the medical sciences increased by more than 75% from 

FY 1990 to FY 2000, and by almost 65% from FY 2000 to 

FY 2009 (figure 5-4 and appendix table 5-6). This sizable 

increase shifted the distribution of academic R&D expenditures (figure 5-5). The life sciences gained more than 4 

percentage points in financial share over the decade of the 

1990s (from 54% to 58%) and nearly another 2 percentage 

points since 2000 (up to 60%). By contrast, the physical sciences lost 2 percentage points in share over FY 1990–2000 

(from 11% to 9%) and an additional percentage point since 

FY 2000 (down to 8%).

Federal R&D Funds by Field

R&D projects in the life sciences also constitute a majority of federally supported academic S&E R&D. They accounted for $19.3 billion (59%) of the $32.6 billion of federal 

support in FY 2009 (appendix table 5-7). The Department 

of Health and Human Services (HHS)—of which NIH is a 

part—supports the vast majority of this life science funding 

(83%). 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 (figure 5-6). In 

FY 2009, NSF was the lead federal funding agency for academic research in the physical sciences (30% of federally 

funded R&D expenditures), mathematics (56%), computer 

sciences (40%), and environmental sciences (34%). DOD 

was the lead funding agency in engineering (32%). 

Federal funding has played a larger role in overall support 

for some fields than others (appendix table 5-8). The federal 

government is the dominant funder in fields such as the atmospheric sciences (78% in FY 2009), physics (73%), and 

aeronautical/astronautical engineering (70%). But it plays a 

much smaller role in other fields, such as economics (32% 

in FY 2009), political science (37%), and agricultural sciences (28%). 

The federally financed proportion of R&D spending in 

all of the broad S&E fields has been stable or increased since 

1990 (appendix table 5-8). This reverses the trend between 

1975 and 1990, when the federal share had declined in all 

the broad fields. 

Figure 5-4

Academic R&D expenditures, by S&E field: 

1999–2009

Constant 2005 dollars (billions)

NOTE: See appendix table 4-1 for gross domestic product implicit 

price deflators used to convert current dollars to constant 2005 dollars.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Research and Development 

Expenditures at Universities and Colleges. See appendix table 5-6.

Science and Engineering Indicators 2012

1999 2000 2001 2002 2003 2004 2005 2006 2008 2007 2009

0

2

4

6

8

10

12

14

16

18

Medical sciences

Biological sciences

Engineering

Physical sciences

Environmental sciences

Social sciences

Computer sciences

Psychology

Mathematical 

sciences
5-12 Chapter 5. Academic Research and Development

EPSCoR, the Experimental Program to Stimulate 

Competitive Research, is based on the premise that universities and their S&E faculty and students are valuable resources that can potentially influence a state’s development in the 

21st century in much the same way that agricultural, industrial, and natural resources did in the 20thcentury. 

EPSCoR originated as a response to a number of stated 

federal objectives. Section 3(e) of the National Science 

Foundation Act of 1950, as amended, states that “it shall be an 

objective of the Foundation to strengthen research and education in the sciences and engineering, including independent 

research by individuals, throughout the United States, and to 

avoid undue concentration of such research and education.” 

Prior to this, in 1947, a Steelman report, titled “Science and 

Public Policy,” in discussing the formation of NSF, stated “it 

is clear that a portion of the funds expended by the National 

Science Foundation should be used to strengthen the weaker, 

but promising, colleges and universities, and thus to increase 

our total scientific potential.”

In 1978, Congress authorized the NSF to conduct EPSCoR 

in response to broad public concerns about the extent of 

geographical concentration of federal funding for Research 

and Development (R&D). Eligibility for EPSCoR participation was limited to those jurisdictions that have historically 

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.

The success of the NSF EPSCoR program during 

the 1980s subsequently prompted Congress to authorize 

the creation of EPSCoR and EPSCoR-like programs in 

six other federal agencies: the Departments of Energy, 

Defense, and Agriculture; the National Aeronautics and 

Space Administration; the National Institutes of Health; 

and the Environmental Protection Agency. In FY 1992, 

the EPSCoR Interagency Coordinating Committee (EICC) 

was established between the federal agencies with EPSCoR 

or EPSCoR-like programs. The major objectives of the 

EICC focused on improving coordination among and between the federal agencies in implementing EPSCoR or 

EPSCoR-like programs consistent with the policies of participating agencies. The participating agencies agreed to the 

following objectives:

Coordinate federal EPSCoR and EPSCoR-like programs 

to maximize the impact of federal support while eliminating duplication in states receiving EPSCoR support from 

more than one agency.

Coordinate agency objectives with state and institutional 

goals, where appropriate, to obtain continued nonfederal support of science and technology (S&T) research 

and training.

Coordinate the development of criteria to assess gains 

in academic research quality and competitiveness and in 

S&T human resource development.

Exchange information on pending legislation, agency 

policies, and relevant programs related to S&T research 

and training and, when appropriate, to provide responses 

on issues of common concern.

EPSCoR seeks to increase the R&D competitiveness of 

an eligible state through the development and utilization of 

the S&T resources residing in its major research universities. It strives to achieve this objective by (1) stimulating 

sustainable S&T infrastructure improvements at the state 

and institutional levels that 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.

In FY 2010, five EICC agencies spent a total of $460.1 

million on EPSCoR and EPSCoR-like programs, up from 

$225.3 million in 2001 (table 5-A). The Environmental 

Protection Agency and the Department of Defense discontinued issuing separate EPSCoR program solicitations in FY 

2006 and 2010, respectively.

Table 5-A

EPSCoR and EPSCoR-like program budgets, by agency: FY 2001–10

(Millions of dollars)

Agency 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

All agencies .......................... 225.3 288.9 358.0 353.3 367.4 367.1 363.1 418.9 437.2 460.1

DOD .................................. 18.7 15.7 15.7 8.4 11.4 11.5 9.5 17.0 14.1 0.0

DOE .................................. 7.7 7.7 11.7 7.7 7.6 7.3 7.3 14.7 16.8 21.6

EPA ................................... 2.5 2.5 2.5 2.5 2.4 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

NIH .................................... 100.0 160.0 210.0 214.0 222.0 220.0 218.0 223.6 224.3 228.8

NSF ................................... 74.8 79.3 88.8 93.7 93.4 97.8 101.5 120.0 133.0 147.1

USDA ................................ 11.6 13.7 19.3 17.0 18.6 18.0 14.0 28.1 29.0 37.6

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

NOTE: EPA discontinued issuing separate EPSCoR program solicitations in FY 2006. 

SOURCE: Data provided by agency EPSCoR representatives; collected by NSF Office of Integrative Activities, Office of EPSCoR, April 2011.

Science and Engineering Indicators 2012

EPSCoR: The Experimental Program to Stimulate Competitive Research
Science and Engineering Indicators 2012 5-13

Non-S&E R&D

Academic institutions spent a total of $2.4 billion on 

R&D in non-S&E fields in FY 2009 (table 5-1), an increase 

of 8% over the $2.2 billion spent in 2008.9,10 This $2.4 billion 

is in addition to the $54.9 billion expended on S&E R&D. 

The federal government funds smaller proportions of R&D 

in non-S&E than in S&E fields: 36% of the $2.4 billion in 

non-S&E R&D in FY 2009.

The largest amounts reported for R&D in non-S&E fields 

were for education ($921 million), business and management ($341 million), and humanities ($253 million). Other 

areas of non-S&E R&D include law, social work, communication, journalism, library science, and the visual and performing arts. 

Academic R&D by Institution

The prior discussion examined R&D for the academic 

sector as a whole. This section discusses some of the differences that prevail across the types of academic institutions. 

R&D Funding for Public and Private Universities 

and Colleges 

In FY 2009, public institutions received $37.5 billion in 

academic S&E R&D and private institutions received $17.4 

billion (appendix table 5-9). 

Although public and private universities rely on the same 

major sources of R&D funding, the importance of the different sources varies substantially (figure 5-7). The federal 

government provided 71% of the R&D funding for private 

institutions but only 54% for public institutions. Conversely, 

public institutions received around 9% of their R&D funding 

from state and local governments, while private institutions 

received a little over 2%.

Public academic institutions also supported a larger portion of their R&D from their own sources—24%, compared 

to 12% 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 have directed toward R&D. Private institutions in turn report a larger proportion of unrecovered indirect costs (54% of their institutional total in FY 2009, versus 

42% for public institutions). For both types of institutions, 

these unrecovered indirect costs have declined over the past 

decade, from 63% to 54% for private institutions and from 

44% to 42% for public institutions (figure 5-8).

Figure 5-5

Changes in share of academic R&D, by selected 

S&E field: 1990–2000 and 2000–09

NOTE: Fields ranked by change in share during 2000–09, in ascending 

order. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Research and Development 

Expenditures at Universities and Colleges. See appendix table 5-6. 

Science and Engineering Indicators 2012

Percentage points

–4 –3 –2 –1 0 1 2 3 4 5 6 7

Life sciences

Engineering

Psychology

Computer sciences

Mathematics

Environmental sciences

Social sciences

Physical sciences

1990–2000

2000–09

Figure 5-6

Federally financed academic R&D expenditures, 

by agency and S&E field: FY 2009

Current dollars (billions)

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, Survey of Research and Development 

Expenditures at Universities and Colleges (FY 2009). See appendix 

table 5-7. 

Science and Engineering Indicators 2012

0

2

4

6

8

10

12

14

16

18

20

DOD DOE HHS NASA NSF USDA

Engineering

Other sciences

Social sciences

Psychology

Physical sciences

Mathematical sciences

Life sciences

Environmental sciences

Computer sciences
5-14 Chapter 5. Academic Research and Development

Distribution of R&D Funds across Academic 

Institutions 

Academic R&D expenditures are concentrated in a relatively small number of institutions. In FY 2009, 711 institutions reported spending at least $150,000 on S&E R&D. Of 

these, the top-spending 20 institutions accounted for 30% of 

total academic R&D spending and the top 100 for 80% of 

this spending (figure 5-9). 

Both public and private institutions received approximately 6% of their R&D support from industry in FY 2009. 

The share of total R&D expenditures funded by all other 

sources was also comparable, at 7% in public and 9% in private institutions. 

Figure 5-7

Sources of R&D funding for public and private 

academic institutions: FY 2009

Percent

NOTE: Science and engineering R&D; non-S&E R&D not included.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Research and Development 

Expenditures at Universities and Colleges (FY 2009). See appendix 

table 5-9.

Science and Engineering Indicators 2012

Federal

government

State/local

government

Industry Academic

institutions

Other

0

10

20

30

40

50

60

70

80

Public Private

Figure 5-8

Components of institutional R&D expenditures for 

public and private academic institutions: 1999–2009

Percent

NOTE: Science and engineering R&D; non-S&E R&D not included.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2010) of Survey of 

Research and Development Expenditures at Universities and 

Colleges.

Science and Engineering Indicators 2012

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

0

10

20

30

40

50

60

70

Unrecovered indirect

costs: private Institutionally financed

organized research: public

Unrecovered indirect

costs: public

Institutionally financed

organized research: private

Table 5-1

R&D expenditures at academic institutions in non-S&E fields: FY 2007–09

(Millions of current dollars)

Field

 2007 2008 2009

Total

expenditures

Federal

expenditures

Total

expenditures

Federal

expenditures

Total

expenditures

Federal

expenditures

All non-S&E fields .................................... 2,058 808 2,206 831 2,386 867

Business and management ................. 275 54 326 66 341 68

Communication, journalism, 

 and library science ............................ 90 31 90 29 108 30

Education ............................................. 902 473 869 450 921 480

Humanities ........................................... 242 60 246 56 253 60

Law ...................................................... 74 29 89 28 107 23

Social work .......................................... 93 40 124 59 139 62

Visual and performing arts ................... 46 4 59 4 73 4

Other non-S&E fields ........................... 335 116 404 139 445 140

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, Survey of Research and Development Expenditures at 

Universities and Colleges. 

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 5-15

Overall, $3.8 billion was passed through institutions to all 

types of subrecipients in FY 2009 (including both academic 

and nonacademic institutions), and $4.1 billion was received 

as subrecipient funding from all types of pass-through entities (appendix table 5-12). Again, the majority of these funds 

(85% of pass-through funds and 90% of subrecipient expenditures) were from federal sources.

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 (such as 

laboratories and computer rooms) and instrumentation. 

Accordingly, the square footage of a designated research 

space and counts of instruments are principal indicators of 

the status of research infrastructure. 

Over the last 20 years, however, advances in information technology have brought significant changes to both the 

methods of scientific research and the infrastructure needed 

to conduct R&D. The technologies, human interfaces, and 

associated processing capabilities resulting from these innovations are often included in the term cyberinfrastructure. Cyberinfrastructure may involve mainly one resource, 

The top 100 institutions are listed in appendix table 5-10. 

The concentration of academic R&D funds among the top 

100 institutions has remained largely constant over the past 

two decades (figure 5-9). Similarly, the shares held by both 

the top 10 and the top 20 institutions have not changed much 

over the same period. (Even so, the identities of the universities in each of these groups have varied over time, as 

universities increase or decrease their R&D activities. For 

example, 5 of the top 20 institutions in FY 1988 were no 

longer in the top 20 in FY 2008.) 

A similar concentration is found among universities that 

perform non-S&E R&D. The top 20 performers accounted 

for 36% of the total non-S&E R&D expenditures in FY 2009 

(appendix table 5-11). 

R&D Collaboration between Academic Institutions

A persistent trend in academic R&D has been the growth 

of research collaboration—notably evident in the growth of 

jointly authored research articles (see later in this chapter for 

details). This trend is also evident in flows of funds among 

institutions to support collaborative research activities. One 

indicator of this collaboration is the amount of total R&D 

expenditures that is passed through to other academic institutions or received by institutions as subrecipient funding.11

On this basis, the R&D funds for joint projects passed 

through universities to other university subrecipients more 

than doubled from FY 2000 to 2009, from $699 million to 

$1.9 billion (figure 5-10 and appendix table 5-12). The FY 

2009 value is about 3% of total academic R&D expenditures that year, compared with 2% in 2000. In FY 2009, 

$1.7 billion (89%) of these pass-through funds came from 

federal sources.

Figure 5-9

Share of academic R&D, by institution rank in R&D 

expenditures: FY 1989–2009

Percent

NOTE: Science and engineering R&D; non-S&E R&D not included.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2010) of Survey of 

Research and Development Expenditures at Universities and 

Colleges. 

Science and Engineering Indicators 2012

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

0

20

40

60

80

100

Top 100 institutions

Top 20 institutions

Top 10 institutions

Figure 5-10

Total and federally funded academic R&D 

pass-throughs: FY 2000–09

Dollars (billions)

NOTE: Science and Engineering R&D; non S&E R&D not included.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Research and Development 

Expenditures at Universities and Colleges. See appendix table 5-12. 

Science and Engineering Indicators 2012

0

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

All academic R&D passed 

to other universities

Federal academic R&D 

passed to other universities

All academic R&D received 

from other universities

Federal academic R&D received 

from other universities
5-16 Chapter 5. Academic Research and Development

such as a network used to transfer data, or it may involve a 

complex interaction of many resources resulting in sophisticated capabilities, such as high-performance computation 

or remote use of scientific instrumentation. No matter how 

simple or complex these technologies and their human interfaces may be, cyberinfrastructure has become an essential 

resource for science. 

Indicators for research facilities, research equipment, and 

cyberinfrastructure capacity are discussed in this section. 

(For an overview of the sources of data used see the sidebar, “Data on the Financial and Infrastructure Resources for 

Academic R&D,” earlier in this chapter.) 

Research Facilities

Research Space

At the close of academic FY 2009, research-performing 

colleges and universities had 196.1 million net assignable 

square feet (NASF) of research space available (appendix 

table 5-13).12 This was 2.2% above the assignable square 

footage at the end of FY 2007. 

This increase represented continuity in a now two-decade 

long trend of academic institutions investing to expand their 

research space. Even so, the pace of growth has slowed in 

the last few years. The 2.2% expansion over the FY 2007–09 

period was the slowest since the 1988–90 period. (The rate 

of increase peaked in 2001–03 at 11%, and has gradually 

declined since then.)

The S&E field of biological/biomedical sciences currently accounts for the largest portion of research space, or 50.3 

million NASF in FY 2009 and 26% of the academic total 

(figure 5-11 and appendix table 5-13).13 The related field of 

health/clinical sciences was the second largest, accounting 

for 36.3 million NASF and 19% of the total. Still sizable are 

engineering (30.2 million NASF, 15%), agricultural/natural 

resources (29.5 million NASF, 15%), and physical sciences 

(28.5 million NASF, 15%). The other fields are substantially 

smaller: social sciences (5.5 million NASF, 3%), computer/

information sciences (5.2 million NASF, 3%), psychology 

(5.2 million NASF, 3%), mathematics/statistics (1.5 million 

NASF, 1%), and all other sciences (3.9 million NASF, 2%). 

The aforementioned slowing pace of growth in overall 

academic research space since FY 2005 has played out in a 

variety of ways across the S&E fields (appendix table 5-13). 

The large amount of space for biological/biomedical sciences continued to expand at a substantial rate in both the 

FY 2005–07 and 2007–09 periods. The agricultural/natural 

resources field also increased its research NASF in both 

periods. Engineering expanded in FY 2005–07, but had no 

growth in the 2007–09 period. 

Even so, the amount of research space available to a sizable number of S&E fields has experienced no growth or a 

decline since FY 2005. Health/clinical sciences and physical 

sciences, both fields with large amounts of research space, 

experienced declines in each of the FY 2005–07 and 2007–

09 periods.14 The decline in the health/clinical sciences is 

particularly notable, because this field exhibited some of the 

largest increases in research space in any S&E field in the 

first half of the 2000 decade. While much smaller in NASF 

size, the social sciences exhibited a research space decline in 

both FY 2005–07 and 2007–09. And, also small, the mathematics/statistics field exhibited a decline in 2007–09. 

Compared with other fields, the computer sciences exhibited among the largest rates of increase in research space 

from FY 2001 to 2007 (appendix table 5-13). Nonetheless, 

its total research space, currently at 5.2 million NASF, is less 

than most fields. 

New Construction

Concomitant with the slowing expansion of overall academic research space, new construction also slowed in the 

second half of the 2000 decade (table 5-2). The 16.2 million 

NASF of new construction in FY 2002–03 dropped to about 

8.8 million in FY 2006–07, even if up somewhat, to 9.9 million in FY 2008–09. Similarly, within the broad decline of 

total research space, the amount and direction of change in 

new construction varied significantly across the S&E fields.

The construction starts for new research space in the biological/biomedical sciences was the largest among all the 

fields in FY 2006–07 and 2008–09, or 2.9 million NASF 

Figure 5-11

S&E research space at academic institutions, 

by field: FY 1999 and 2009

NOTE: S&E fields are those used in the National Center for Educational 

Statistcs (NCES) Classification of Instructional Programs (CIP). NCES 

updates the CIP every 10 years. S&E fields here reflect the NCES 2000 

CIP update. 

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Science and Engineering 

Research Facilities. See appendix table 5-13. 

Science and Engineering Indicators 2012

Net assignable square feet (millions)

Research

animal space

All other sciences

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 1999

FY 2009

0 10 20 30 40 50 60
Science and Engineering Indicators 2012 5-17

and 3.5 million NASF, respectively. Further back were the 

health/clinical sciences (1.7 million NASF in FY 2006–07, 

1.9 million in 2008–09) and engineering (respectively, 1.3 

million and 2.1 million). All the other fields reported some 

new construction starts in both FY 2006–07 and 2008–09, 

but at levels well below the top three fields. 

Academic institutions draw on various sources to fund 

their capital projects, including the institutions’ own funds, 

state or local governments, and the federal government. 

For the construction of new research space initiated in FY 

2008–09, about 62% of the funding came from institutions’ 

internal sources, 36% from state/local government, and the 

remaining 3% from the federal government. This was similar 

to the new construction initiated in FY 2006–07, where the 

funding shares were, 62%, 32%, and 6%, respectively. In 

recent years, the federal portion of funding has been under 

10% and declining, with the FY 2009 level the lowest for 

several decades.

Research Equipment 

In FY 2009, about $2.0 billion in current funds was spent 

for academic research equipment (i.e., moveable items, such 

as computers or microscopes) necessary for the conduct of 

organized research projects (appendix table 5-14).15 The corresponding totals in earlier years were $1.9 billion in FY 

Table 5-2

New construction of S&E research space in academic institutions, by field and time of construction: FY 2002–09

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

Net assignable square feet (millions)

All fields .............................................................. 16.2 10.2 8.8 9.9

Agricultural and natural resources .................. 0.8 0.4 0.5 0.4

Biological and biomedical sciences ............... 4.0 3.2 2.9 3.5

Computer and information sciences............... 1.0 0.3 0.6 0.3

Engineering ..................................................... 2.2 1.5 1.3 2.1

Health and clinical sciences ........................... 5.0 3.3 1.7 1.9

Mathematics and statistics ............................. * * * *

Physical sciences ........................................... 2.1 0.8 1.0 1.0

Earth, atmospheric, and ocean sciences .... 0.6 0.3 0.3 0.1

Astronomy, chemistry, and physics............. 1.5 0.5 0.7 0.9

Psychology ..................................................... 0.2 0.2 0.1 0.3

Social sciences ............................................... 0.2 0.1 0.1 0.2

Other sciences ................................................ 0.7 0.3 0.7 0.3

 

Research animal space ................................... 1.4 1.2 1.0 0.8

Share of total new construction square feet (%)

All fields .............................................................. 100.0 100.0 100.0 100.0

Agricultural and natural resources .................. 4.9 3.9 5.7 4.0

Biological and biomedical sciences ............... 24.7 31.4 33.0 35.4

Computer and information sciences............... 6.2 2.9 6.8 3.0

Engineering ..................................................... 13.6 14.7 14.8 21.2

Health and clinical sciences ........................... 30.9 32.4 19.3 19.2

Mathematics and statistics ............................. * * * * 

Physical sciences ........................................... 13.0 7.8 11.4 10.1

Earth, atmospheric, and ocean sciences .... 3.7 2.9 3.4 1.0

Astronomy, chemistry, and physics............. 9.3 4.9 8.0 9.1

Psychology ..................................................... 1.2 2.0 1.1 3.0

Social sciences ............................................... 1.2 1.0 1.1 2.0

Other sciences ................................................ 4.3 2.9 8.0 3.0

Research animal space ................................... 8.6 11.8 11.4 8.1

* = >0 but <50,000 net assignable square feet

NOTES: Detail may not add to total because of rounding. Figures for research animal space listed separately and also included in individual field totals. 

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 2000 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 2012
5-18 Chapter 5. Academic Research and Development

2008, $1.9 billion in FY 2004, and $1.3 billion in FY 1999. 

Adjusted for inflation, the change in this spending from 

2008 to 2009 was a 2% increase, which was an increase of 

16% over the 1999 spending level, but a 9% decline from 

the 2004 level. 

The $2.0 billion of equipment spending in FY 2009 was 

just under 4% of the $54.9 billion of total academic R&D 

expenditures that year. In FY 2004, the share was somewhat 

above 4% of the academic R&D total. In FY 1999, the fraction was closer to 5%.

This equipment spending continues to be concentrated in 

just a few S&E fields. In FY 2009, three fields accounted 

for 82% of the annual total: life sciences (41%), engineering 

(24%), and the physical sciences (17%). The shares for these 

three fields have remained similarly predominant for many 

years (appendix table 5-14). Even so, when adjusted for inflation, the annual level of equipment spending in all three 

fields has declined since 2005—reversing a trend of steady 

growth from FY 2001 to 2004 (figure 5-12). 

Some of the funding for academic research equipment 

comes from the federal government. These federal funds are 

generally received as part of research grants or as separate 

equipment grants. In FY 2009, the federal government supported 55% of total academic research equipment funding—

a figure that has fluctuated between 55% and 63% over the 

last 20 years (appendix table 5-15). Nevertheless, the federal 

share of funding varies significantly by S&E field, ranging 

from 26% to 77% in 2009. In FY 2009, computer sciences 

had the largest proportion of federally funded R&D equipment (77%), with atmospheric sciences a close second (76%).

Cyberinfrastructure 

Networking is an essential component of cyberinfrastructure. It facilitates research-related activities such as communication, data transfer, high-performance computation, and 

remote use of instrumentation.16 In FY 2009, networking infrastructure on many academic campuses was pervasive and 

still rapidly expanding in capability and coverage. Researchperforming institutions had more connections, bandwidth, 

and campus coverage than they did earlier in the decade.17

(Network “bandwidth” is the amount of data that can be 

transmitted in a given amount of time, typically measured in 

bits per second.) Colleges and universities reported external 

network connections with greater bandwidth, faster internal 

network distribution speeds, more connections to high-speed 

networks, and greater wireless coverage on campus.

Some academic cyberinfrastructure is dedicated primarily to research activities. For example, universities may 

have high-performance networks (such as the National 

LambdaRail or networks to government agencies) available 

almost exclusively for research activities, and this bandwidth 

capacity is only for these activities. Nonetheless, universities may have other networks that are available to the entire campus community for both research and non-research 

activities, and this bandwidth capacity is not an indicator 

solely of research capacity. 

Bandwidth to External Connections

Academic institutions can have multiple networking resources, at varying connection speeds. Internet1—the public 

multiuse, commodity network often called the “Internet”—

is one such component. Many institutions also have direct or 

indirect connections to high-performance networks that support the development and use of advanced applications and 

technologies. In the academic community, these high-performance networks are chiefly Internet2 (a high performance 

backbone network providing leading-edge network services 

to member colleges, universities, and research laboratories 

across the country), the National LambdaRail (an advanced 

optical network for research and education, organized by a 

consortium of universities, private companies, and federal 

labs), and connections to federal research networks. 

Early in the 2000 decade, some academic institutions reported no Internet1 connections of any kind. By mid-decade, 

all institutions had Internet1 connections and bandwidth 

speeds were increasing. Between FY 2005 and FY 2009, 

the fraction of institutions with total Internet1 and Internet2 

bandwidth of more than 100 megabits per second increased 

Figure 5-12

Current fund expenditures for S&E research 

equipment at academic institutions, by field: 

1999–2009

Constant 2005 dollars (millions)

NOTE: See appendix table 4-1 for gross domestic product implicit 

price deflators used to convert current dollars to constant 2005 

dollars.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Research and Development 

Expenditures at Universities and Colleges. See appendix table 5-14. 

Science and Engineering Indicators 2012

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

0

100

200

300

400

500

Engineering

Medical 

sciences

Physical sciences Biological sciences

Environmental sciences

Computer sciences
Science and Engineering Indicators 2012 5-19

from 52% to 80% (table 5-3). Furthermore, the share of institutions with total Internet1 and Internet2 bandwidths of 

1 gigabit per second or faster rose from 22% in FY 2005 to 

45% in FY 2009. (If current institutional estimates are realized, the percent of institutions with total bandwidths of 1 

gigabit or faster will reach 52% in FY 2010.) 

Bandwidth has increased broadly across all types of academic institutions. Nevertheless, a greater fraction of doctorate-granting institutions have the faster bandwidths. In 

FY 2009, 87% of the institutions that granted doctorates had 

total Internet1 and Internet2 bandwidth of at least 1 gigabit 

per second, and 32% had bandwidth greater than 2.4 gigabits. In contrast, 71% of nondoctorate granting institutions 

had total bandwidth at 1 gigabit per second or above and 8% 

above 2.4 gigabits. 

Part of the increase in institutions’ bandwidth can be 

attributed to an increase in the number of connections to 

high-performance networks (table 5-4). The number of connections to Internet2 has grown gradually over the current 

decade; by the end of FY 2009, a large majority (75%) of 

institutions had Internet2 connections. Between FY 2007 

and 2009, the percentage of institutions with connections to 

the National LambdaRail increased, from 25% to 34% of all 

institutions. The number of institutions anticipating connections to federal government high-performance networks such 

as the Department of Energy’s ESnet or NASA’s NREN further increased in FY 2009. Institutions have also begun connecting to more than one high-performance network—for 

example, in FY 2009, 34% had connections to both Internet2 

and the National LambdaRail.

Internal Institutional Networks

The bandwidth speeds of academic institutions’ internal 

networks have also increased considerably. Since early in 

the present decade, the percentage of institutions with slower bandwidth has rapidly decreased while the percentage 

with faster bandwidths has rapidly increased. In FY 2003, 

66% of institutions had bandwidth less than 1 gigabit per 

second, but by the end of FY 2009, only 19% did (table 5-5). 

In FY 2003, no institutions had bandwidth speeds faster than 

2.5 gigabits per second, but by FY 2009, 24% of academic 

institutions did. By FY 2009, 82% of institutions had speeds 

of 1 gigabit per second or faster. 

In FY 2009, all academic institutions had at least some 

wireless coverage in their campus buildings. In FY 2003, 

only 14% of these institutions had more than half of their 

building infrastructure covered by wireless; by FY 2009, the 

comparable figure was 74%.

The Academic Doctoral 

S&E Workforce

S&E doctorate holders in academia influence the nation’s 

academic R&D enterprise in two key ways. They work in institutions that conduct academic R&D and produce the bulk of 

academic articles and patents. Moreover, they teach individuals who then go on to earn S&E doctorates, many of whom 

Table 5-3

Bandwidth of commodity internet (Internet1) and Internet2 at academic institutions: FY 2005–10 

(Percent distribution)

Bandwidth FY 2005 FY 2007 FY 2009 FY 2010a

All bandwidth ......................................... 100 100 100 100

No bandwidth .................................... 0000

10 mb ............................................... 6311

11–100 mb ......................................... 42 33 19 13

101–999 mb ....................................... 30 31 35 34

1–2.4 gb ............................................. 15 23 25 25

2.5–9 gb ............................................. 4456

10 gb .................................................. *247

>10 gb ................................................ 2 4 11 14

Other .................................................. *000

Number of institutions ........................... 449 448 495 494

* = >0 but <0.5%

mb = megabits per second; gb = gigabits per second

a

Figures for 2010 are estimated.

NOTES: Details may not add to 100% due to rounding. Internet1, also termed commodity internet, is the general public, multiuse network often called 

the “Internet.” Internet2 is a high-performance backbone network that enables the development of advanced Internet applications and the deployment 

of leading-edge network services to member colleges, universities, and research laboratories across the country. Total bandwidth for FY 2009 and 2010 

includes National LamdaRail bandwidth. The response categories in the FY 2005 survey varied slightly from those in the FY 2007 and 2009 surveys; in 

the FY 2005 survey, the categories were “1 to 2.5 gb” and “2.6 to 9 gb.”

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Survey of Science and Engineering Research Facilities.

Science and Engineering Indicators 2012
5-20 Chapter 5. Academic Research and Development

will work in academia and contribute to academic R&D. The 

focus of this section is on the research aspects of the employment of doctoral scientists and engineers in academia.

This section examines trends in the doctoral S&E academic workforce in terms of its demographic composition 

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. 

The discussion in this section is limited to individuals, including foreign-born individuals, who received their 

S&E doctorate at a U.S. institution. (More than two-thirds 

of foreign-born doctorate holders employed in the United 

States earned their doctorate degree from a U.S. institution; 

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 

S&E doctorate holders, including those in nonfaculty positions. At various points the characteristics of full-time faculty are discussed.

Table 5-4

Institutions with high-performance network connections, by type of institution: FY 2005–09

(Percent)

Type of institution Internet2

National

LambdaRail

A federal

 government 

research network

State or regional

high-performance

 network Other

FY 2005

All academic .......................... 68 10 11 na 12

Doctorate granting ............. 82 11 13 na 15

Nondoctorate granting ....... 38 7 6 na 6

Public ................................. 73 11 12 na 14

Private ................................ 58 8 9 na 9

All biomedical ........................ 24 2 1 na 3

Research institutions .......... 19 1 1 na 3

Hospitals ............................ 35 4 2 na 2

FY 2007

All academic .......................... 70 25 11 55 3

Doctorate granting ............. 81 32 13 59 4

Nondoctorate granting ....... 46 10 4 43 1

 

Public ................................. 75 29 12 61 3

Private ................................ 61 17 8 41 3

 

All biomedical ........................ 26 4 0 13 2

Research institutions .......... 20 3 0 10 3

Hospitals ............................ 37 6 0 19 2

FY 2009

All academic .......................... 75 34 13 60 8

Doctorate granting ............. 87 43 17 70 9

Nondoctorate granting ....... 51 18 6 39 5

 

Public ................................. 83 41 16 73 7

Private ................................ 59 22 8 36 10

 

All biomedical ........................ 29 10 2 15 1

Research institutions .......... 22 11 1 16 1

Hospitals ............................ 47 8 4 14 2

na = not applicable; data were not collected in FY 2005.

NOTES: Internet2 is a high-performance hybrid optical packet network. The network was designed to provide next-generation production services as 

well as a platform for the development of new networking ideas and protocols. National LambdaRail (NLR) is an advanced optical network infrastructure 

for research and education. NLR enables cutting-edge exploration in the sciences and network research. An institution may have a connection to more 

than one high-performance network.

SOURCE: National Science Foundation/National Center for Science and Engineering Statistics, Survey of Science and Engineering Research Facilities.

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 5-21

Trends in Academic Employment of Doctoral 

Scientists and Engineers

Academic employment of doctoral scientists and engineers grew over the past three decades and reached a record 

high of 272,800 in 2008, about the same as the employment 

numbers in 2006 (appendix table 5-16).18 However, the 

change from 2006 was the smallest single-period increase 

in estimated total academic employment since at least 1973. 

The long-term growth rate in the number of doctoral scientists and engineers employed in the academic sector was 

slower than the rate of growth in the business and government sectors (table 5-6). As a result, the share of all S&E doctorate holders employed in academia dropped from 55% to 

44% during the 1973–2008 period (table 5-7). In 2008, nearly 

half of those with recently awarded S&E doctorate degrees 

(that is, a degree awarded within 3 years of the survey year) 

were employed in academia, with 18% of recent doctorate 

holders employed in academic postdoc positions.19

Academic Employment of S&E Doctorate Holders

The academic doctoral S&E workforce includes those 

with a doctorate in an S&E field and employed in the following positions: full and associate professors (referred to 

as “senior faculty”); assistant professors and instructors (referred to as “junior faculty”); postdoctoral researchers (referred to as “postdocs”); other full-time positions such as 

lecturers, adjunct faculty, research associates, and administrators; and part-time positions of all kinds. Academic employment is limited to those employed in 2-year or 4-year 

colleges or universities. 

Full-time faculty positions continue to be the norm in 

academic employment, but S&E doctorate holders are increasingly employed in other full-time positions, postdocs, 

and part-time positions (figure 5-13). The share of full-time 

faculty among all academic S&E doctorate holders fell from 

88% in the early 1970s to 73% in 2008 (appendix table 5-16). 

Over the same period, the share of other full-time positions 

rose from 6% to 15%, the share of postdocs increased from 

4% to 7%, and the share of part-time positions increased 

from 2% to 6% of all academic S&E doctorate holders. 

Table 5-5

Highest internal network speeds, by highest 

degree granted: FY 2003–09

(Percent distribution)

Fiscal year and 

connection 

speed

All academic 

institutions Doctorate Nondoctorate

FY 2003 .............. 100 100 100

10 mb .......... 2 3 2

11–999 mb .... 64 55 88

1–2.5 gb ........ 33 43 10

2.6–9 gb ........ 0 0 0

10 gb ............. 0 0 0

>10 gb ........... 0 0 0

Other ............. 0 0 0

FY 2005 ............ 100 100 100

10 mb .......... 0 0 1

11–999 mb .... 46 38 64

1–2.5 gb ........ 50 56 35

2.6–9 gb ........ 1 1 0

10 gb ............. 3 4 0

>10 gb ........... * * 0

Other ............. 0 0 0

FY 2007 ............ 100 100 100

10 mb .......... 1 1 1

11–999 mb .... 24 18 39

1–2.4 gb ........ 61 63 55

2.5–9 gb ........ 2 2 1

10 gb ............. 10 13 3

>10 gb ........... 1 2 0

Other ............. 1 1 1

FY 2009 ............ 100 100 100

10 mb .......... 1 * 1

11–999 mb .... 18 13 28

1–2.4 gb ........ 58 55 63

2.5–9 gb ........ 2 3 1

10 gb ............. 18 24 5

>10 gb ........... 3 4 1

Other ............. 1 1 1

* = >0 but <0.5%

mb = megabits per second; gb = gigabits per second

NOTE: Details may not add to 100% due to rounding.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Survey of Science and Engineering 

Research Facilities.

Science and Engineering Indicators 2012

Highest degree granted

Table 5-6

Average annual growth rate for employment of SEH doctorate holders, by sector: 1973–2008

(Percent)

Sector 1973–2008 1973–83 1983–93 1993–2003 2003–08

All sectors ...................................................................... 3.3 5.4 2.5 2.0 1.8

Academia ................................................................... 2.4 4.1 2.0 2.0 1.0

Industry ...................................................................... 4.6 7.9 4.1 2.7 2.9

Government ............................................................... 3.2 5.5 2.5 3.1 0.6

Other .......................................................................... 2.7 5.3 0.5 –1.6 11.9

SEH = science, engineering, and health 

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2011) of 1973–2008 Surveys of 

Doctorate Recipients.

Science and Engineering Indicators 2012
5-22 Chapter 5. Academic Research and Development

The proportion of full-time faculty among S&E doctorate 

holders in higher education fell in all fields during 1973–

2008, with the life sciences and psychology experiencing the 

largest relative declines. Growth in postdoc positions and 

other full-time positions accounted for the declining share of 

full-time faculty positions in the life sciences, whereas the 

growth in part-time and other full-time positions explained 

the drop in share of faculty positions in psychology (appendix table 5-16).

Over the past three decades, growth in the number of 

life scientists with academic employment was consistently 

stronger than for doctorate holders in other S&E fields (figure 5-14). Growth in academic employment slowed in the 

early 1990s for engineering, social sciences, physical sciences, and mathematics, but has increased since then in social sciences and mathematics (appendix table 5-16). 

Women in Academic S&E Workforce 

The number of women with S&E doctorates employed 

in academia grew from 10,700 in 1973 to 93,400 in 2008, 

more than an eightfold increase. In comparison, the number of male S&E doctorate holders increased 67% over the 

period, from 107,200 in 1973 to 179,400 in 2008 (appendix 

table 5-17). 

These differential rates of increase are reflected in the 

steadily rising share of women in the academic S&E workforce. Women constituted 34% of all academic S&E doctoral employment and 31% of full-time faculty in 2008, 

up from 9% and 7%, respectively, in 1973 (table 5-8 and 

appendix table 5-17). 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. 

Women have held a larger share of junior faculty positions 

(includes assistant professors and instructors) than positions 

at either the associate or full professor rank. However, as a 

result of the 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 three faculty ranks rose significantly between 1973 and 2008. In 2008, women constituted 21% of 

full professors, 37% of associate professors, and 42% of junior faculty (figure 5-15). 

Compared with their male counterparts in the academic 

doctoral S&E workforce, women were more heavily concentrated in the fields of life sciences, social sciences, and 

Figure 5-13

SEH doctorate holders employed in academia, 

by type of position: 1973–2008

Thousands

SEH = science, engineering, and health

NOTES: Academic employment limited to U.S. doctorate holders 

employed at 2- or 4-year colleges or universities. Senior faculty 

includes full and associate professors; junior faculty includes 

assistant professors and instructors. Other full-time positions include 

positions such as research associates, adjunct appointments, 

lecturers, and administrative positions. Part-time positions exclude 

those held by students or retired persons.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of 1973–2008 

Surveys of Doctorate Recipients. 

Science and Engineering Indicators 2012 

0

50

100

150

200

250

300

1973 1979 1983 1987 1991 1995 1999 2003 2008

Full-time senior faculty

Full-time junior faculty

Other full-time positions

Postdocs

Part-time positions

Table 5-7

SEH doctorate holders employed in academia, by years since doctorate: Selected years, 1973–2008

(Percent)

Years since doctorate 1973 1983 1993 2003 2008

All employed doctorate holders .............. 54.8 48.4 45.9 45.6 43.8

3 ......................................................... 55.2 48.0 50.5 53.7 49.6

4–7 ....................................................... 55.8 44.9 47.0 47.7 48.3

8 ......................................................... 54.2 49.4 45.0 44.2 42.1

SEH = science, engineering, and health

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2011) of 1973, 1983, 1993, 2003, and 

2008 Surveys of Doctorate Recipients. 

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 5-23

psychology, with correspondingly lower shares in engineering, the physical sciences, mathematics, and computer sciences. Women’s share of doctorate holders in each of these 

fields grew during the 1973–2008 period (appendix table 

5-17), with the most pronounced growth in share occurring 

in engineering, the field in which women were the least well 

represented. 

Minorities in Academic S&E Workforce

Although the number of academic S&E doctorate holders who are members of underrepresented minority groups 

(i.e., blacks, Hispanics, and American Indians/Alaska 

Natives) has increased over time, they remain a small percentage of the total (appendix table 5-18). These groups 

constituted about 9% of both total academic employment 

Figure 5-14

SEH doctorate holders employed in academia, 

by degree field: 1973–2008 

Thousands

SEH = science, engineering, and health

NOTES: Academic employment 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.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of 1973–2008 

Surveys of Doctorate Recipients.

Science and Engineering Indicators 2012

1973 1979 2008 1983 1987 1991 1995 1999 2003

0

50

100

150

200

250

300

Life sciences

Social sciences

Physical sciences

Psychology

Engineering

Mathematics

Computer sciences

Table 5-8

Women as percentage of SEH doctorate holders employed in academia, by position: Selected years, 1973–2008

(Percent)

Position 1973 1983 1993 2003 2008

All positions .................................................................... 9.1 15.0 21.9 30.3 34.2

Full-time senior faculty................................................ 5.8 9.3 14.2 22.8 26.8

Full-time junior faculty ................................................ 11.3 23.5 32.2 40.5 41.9

Other full-time positions ............................................. 14.5 23.1 30.2 33.1 40.9

Postdocs ..................................................................... 14.3 30.1 30.8 38.0 39.4

Part-time positions ..................................................... 48.3 41.7 61.0 54.5 55.2

SEH = science, engineering, and health

NOTES: Academic employment limited to U.S. doctorate holders employed at 2- or 4-year colleges or universities. Senior faculty includes full and 

associate professors; junior faculty includes assistant professors and instructors. Other full-time positions include positions such as research associates, 

adjunct appointments, 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 (2011) of 1973, 1983, 1993, 2003, and 

2008 Surveys of Doctorate Recipients. 

Science and Engineering Indicators 2012

Figure 5-15

Women as percentage of SEH doctorate holders 

with full-time employment in academia, by academic 

rank: Selected years, 1973–2008

Percent

SEH = science, engineering, and health

NOTES: Academic employment 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. Junior faculty 

includes assistant professors and instructors.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of 1973, 1983, 

1993, 2003, and 2008 Surveys of Doctorate Recipients. 

Science and Engineering Indicators 2012

Full professor Associate professor Junior faculty

0

5

10

15

20

25

30

35

40

45

1973 1983 1993 2003 2008
5-24 Chapter 5. Academic Research and Development

and full-time faculty positions in 2008, up from 2% in 1973. 

Underrepresented minority groups have a relatively higher 

share of employment in other positions, which includes parttime positions, than in the full-time faculty and postdoc employment categories (table 5-9). 

Underrepresented minorities were concentrated in different degree fields and different types of institutions than 

whites. Compared with white S&E doctorate holders employed in academia, underrepresented minorities were relatively concentrated in the social sciences and relatively less 

represented in the physical sciences and the life sciences 

(appendix table 5-18). Relatively fewer underrepresented 

minorities were employed at research universities than 

whites in 2008, and relatively more were employed at master’s colleges and universities (table 5-10). (See chapter 2 

sidebar, “Carnegie Classification of Academic Institutions,” 

for a brief description of the Carnegie categories.) 

The share of Asians/Pacific Islanders employed in the 

S&E academic doctoral workforce grew dramatically over 

the past three decades, rising from 4% in 1973 to 14% in 

2008. Asians/Pacific Islanders were heavily represented in 

engineering and computer sciences, where they constituted 

27% and 35%, respectively, of the S&E academic doctoral 

workforce in 2008. Far smaller proportions of Asians/Pacific 

Islanders were present in social sciences (8%) and psychology (5%) (appendix table 5-18). A larger share of Asians/

Pacific Islanders than whites was employed at research universities and medical schools in 2008 (table 5-10). 

Table 5-9

Underrepresented minorities as percentage of SEH doctorate holders employed in academia, by position: 

Selected years, 1973–2008

(Percent)

Position 1973 1983 1993 2003 2008

All positions ................................................................... 2.0 3.7 5.0 7.9 8.9

Full-time faculty ......................................................... 1.9 3.6 5.0 7.8 8.7

Postdocs .................................................................... 2.4 4.8 4.5 7.0 8.3

Other positions .......................................................... 2.9 4.1 5.3 8.4 9.9

SEH = science, engineering, and health

NOTES: Underrepresented minorities include blacks, Hispanics, and American Indians/Alaska Natives. Academic employment limited to U.S. doctorate 

holders employed at 2- or 4-year colleges or universities. Faculty includes full, associate, and assistant professors plus instructors. Other positions 

include part-time positions and full-time positions such as research associates, adjunct appointments, lecturers, and administrative positions. Other 

positions excludes those employed part time who are students or retired.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2011) of 1973, 1983, 1993, 2003, and 

2008 Surveys of Doctorate Recipients. 

Science and Engineering Indicators 2012

Table 5-10

SEH doctorate holders employed in academia, by Carnegie institution type and race/ethnicity: 2008

(Percent distribution)

Institution type

 All S&E doctorate

 holders

 Asian/Pacific 

 Islander White

 Underrepresented

 minority

All institutions ................................................ 100.0 100.0 100.0 100.0

Doctorate-granting, very high research ..... 41.7 50.0 40.9 33.6

Other doctorate-granting institutions ........ 17.8 17.1 18.0 17.8

Master’s colleges and universities ............. 18.1 13.8 18.4 22.3

Medical schools/medical centers .............. 5.0 6.1 4.7 5.6

Baccalaureate colleges .............................. 8.1 3.3 8.9 8.4

Two-year institutions .................................. 3.6 1.8 3.8 4.6

Other .......................................................... 5.8 7.8 5.3 7.7

SEH = science, engineering, and health

NOTES: Underrepresented minorities include blacks, Hispanics, and American Indians/Alaska Natives. Academic employment 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. Institutions designated by 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 (2011) of 2008 Survey of Doctorate 

Recipients.

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 5-25

Foreign-Born U.S. S&E Doctorate Holders

Academia has long relied on foreign-born doctorate holders, many of them with doctorate degrees from U.S. universities, to staff faculty and other academic positions. No 

current information is available about the number of foreignborn individuals with foreign doctorates who are employed 

at U.S. universities and colleges. The following discussion 

is limited to foreign-born individuals with U.S. doctorates. 

Academic employment of foreign-born U.S. 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 (figure 5-16). 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 nearly 25% in 2008 (figure 5-16), and reached 

particularly high proportions in engineering (46%) and computer sciences (51%) (appendix table 5-19). In all fields, foreign-born doctorate holders were a larger share of postdoc 

employment than of full-time faculty employment. Overall, 

46% of postdoc positions were held by foreign-born U.S. 

S&E doctorate holders, compared with 23% of full-time faculty positions and 23% of other full-time positions. 

Of the 39,000 Asian/Pacific Islander doctorate holders 

employed in academia in 2008, 9% were native-born U.S. 

citizens, 44% were naturalized U.S. citizens, and 47% were 

noncitizens. In 2008, Asians/Pacific Islanders represented 

50% of the foreign-born faculty employed full-time in the 

United States and 62% of the foreign-born doctorate holders 

with postdoc appointments. In contrast, only 1% of nativeborn full-time faculty and 5% of native-born postdocs were 

Asians/Pacific Islanders.

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, because individuals employed in the same position 

may be involved in research activities to differing degrees or 

not involved in research. Therefore, self-reported research 

involvement is a better measure than position title for gauging research activity.20 This section limits the analysis to 

“academic researchers”—academic S&E doctorate holders 

who reported that research is either their primary work activity (that is, the activity that occupies the most hours of 

their work time during a typical work week) or their secondary work activity (the activity that occupies the second most 

work hours per week). 

Doctoral S&E Researchers

From 1973 to 2008, the number of academic researchers 

grew from 82,300 in 1973 to 184,700 in 2008 (appendix table 

5-20). The 2008 total included 137,800 individuals employed 

in full-time faculty positions. The proportion of academically 

employed S&E doctorate holders that are researchers declined 

slightly from 1993 (70%) to 2008 (68%) (figure 5-17). A 

Figure 5-16

SEH doctorate holders employed in academia, 

by birthplace: 1973–2008 

Thousands

SEH = science, engineering, and health

NOTES: Academic employment 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.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of 1973–2008 

Surveys of Doctorate Recipients. 

Science and Engineering Indicators 2012 

0

50

100

150

200

250

300

U.S. born

Foreign born

1973 1979 1983 1987 1991 1995 1999 2003 2008

Figure 5-17

Academic researchers as percentage of SEH 

doctoral employment, by position and involvement 

in research: 1973–2008 

Percent

SEH = science, engineering, and health

NOTES: Academic employment 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. Full-time faculty 

includes full, associate, and assistant professors plus instructors. 

Research includes basic or applied research, development, and 

design.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of 1973–2008 

Surveys of Doctorate Recipients.

Science and Engineering Indicators 2012

0

10

20

30

40

50

60

70

80

All researchers, research

is primary or secondary activity

All researchers,

research is primary

activity

Full-time faculty, research

is primary or secondary activity

Full-time faculty,

research is primary activity

1973 1979 1983 1987 1991 1995 1999 2003 2008
5-26 Chapter 5. Academic Research and Development

nearly identical pattern of decline was observed for the share 

of full-time faculty that reported research as a primary or secondary work activity. The proportions of researchers among 

all academic S&E doctorate holders and all full-time faculty 

were higher in the life sciences, engineering, and computer 

sciences than in the social sciences and psychology (appendix 

table 5-20). In most fields, the share of academic researchers 

declined between 1993 and 2008.

A different picture emerges when only considering researchers 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 steadily increased from 1973 to 2008 (figure 

5-17). Taken together, these trends suggest that while research as an important work activity is not becoming more 

widespread among S&E doctorate holders employed in academia, a growing share of academic S&E positions are becoming research intensive. 

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 for many faculty. From 1973 to 2008, the 

proportion of full-time faculty identifying research as their 

primary work activity climbed from 19% to 36%, while the 

share with teaching as their primary activity fell from 68% to 

47% (figure 5-18). The balance of emphasis between teaching and research varied across the disciplines, with a higher 

share of faculty in the life sciences identifying research as 

their primary work activity, and a higher share of faculty in 

mathematics and social sciences reporting teaching as their 

primary activity. Since 1991, the proportion of doctorate 

holders who list research as a primary work activity declined 

in physical sciences, computer sciences, and life sciences 

fields, but grew in mathematics, psychology, engineering, 

and the social sciences (appendix table 5-20).

S&E Full-Time Faculty Researchers

Table 5-11 examines the relationship between research 

and the career stage of S&E full-time faculty. The smallest share of primary researchers occurred among the most 

recently degreed faculty (33%). The share of faculty who 

indicated research as their primary work activity increased 

Figure 5-18

Primary work activity of full-time doctoral SEH 

faculty: 1973–2008 

Percent

SEH = science, engineering, and health

NOTES: Academic employment 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. Full-time faculty 

includes full, associate, and assistant professors plus instructors. 

Research includes basic or applied research, development, or 

design.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of 1973–2008 

Surveys of Doctorate Recipients. 

Science and Engineering Indicators 2012 

0

20

40

60

80

100

Research

Teaching

Other

1973 1979 1983 1987 1991 1995 1999 2003 2008

Table 5-11

SEH faculty reporting research as primary work activity, by years since doctorate and degree field: 2008

(Percent)

Years since doctorate All fields

Physical 

sciences

Mathematics 

and

statistics

Computer 

and information 

sciences

Life

sciences Psychology

Social 

sciences Engineering

All years since doctorate ...... 35.8 33.4 30.8 37.2 42.7 33.7 27.7 38.8

1–3 .................................... 33.2 22.8 32.6 44.2 27.5 30.6 29.5 58.7

4–7 .................................... 48.3 32.3 59.6 56.0 53.1 37.8 41.0 71.0

8–11 .................................. 40.8 35.1 33.5 38.5 49.3 36.8 31.0 46.8

12 .................................... 34.5 34.3 27.6 35.3 42.5 34.4 25.7 31.6

SEH = science, engineering, and health

NOTES: Academic employment 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. Faculty includes full, associate, and assistant professors plus instructors. 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 (2011) of 2008 Survey of Doctorate 

Recipients.

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 5-27

with time since doctorate in the succeeding two cohorts, and 

then fell in the last reported cohort (12 years or more since 

doctorate). The higher share (48%) of primary researchers 

within the second cohort, 4 to 7 years since doctorate, coincides with the period during which many early career faculty 

would be preparing to apply for tenure at their university, 

and would have heightened motivation to complete research 

projects and publish results. In the last cohort reported in 

the table, 12 years or more beyond the doctorate, the share 

of full-time faculty reporting research as a primary activity fell to 35%. Other responsibilities—such as mentoring 

younger faculty, advising doctoral students, and accepting 

major committee assignments or faculty leadership roles—

may become primary work activities for many faculty at this 

career stage. 

A similar pattern prevailed in most degree fields—the 

share of faculty who indicated that research was their primary work activity increased through the early career cohorts 

and then fell as faculty approached mid-career. Research 

was more frequently a primary work activity for early career 

faculty in engineering and computer sciences than for faculty in other fields (table 5-11). 

Collaborative Research

Research in many fields has increasingly involved collaboration. This section describes S&E doctorate holders’ selfreports of their collaboration with others using data from 

the 2006 Survey of Doctorate Recipients.21 Information on 

trends in coauthorship can be found later in this chapter under “Coauthorship and Collaboration.”

In 2006, roughly 70% of S&E full-time research faculty 

employed in academic institutions reported working with an 

immediate work team or with others working elsewhere in 

the same organization, nearly 60% worked with individuals in other organizations in the United States, and nearly 

one-third worked with individuals located in other countries. 

Team work is most common among life scientists, physical 

scientists, and engineers, and least common among mathematicians and social scientists. 

International collaboration was more common among foreign-born S&E full-time research faculty. Communication 

by e-mail or telephone was, by far, the most commonly used 

mode of international collaboration, followed by travel to 

the United States by the foreign collaborator(s), foreign 

travel by the U.S.-based collaborator, and communication 

through web-based or virtual technology. 

For a more extensive discussion of these topics, see the 

“Collaborative Research” section in chapter 5 of the 2010 

edition of Indicators (NSB 2010). For data on international 

collaborative activity in the S&E workforce more generally, 

see the “International Engagement by the Domestic S&E 

Workforce” in chapter 3.

Graduate Research Assistants

The close coupling of advanced training with hands-on 

research experience is a key strength of U.S. graduate education. Many of the 434,100 full-time S&E graduate students 

in 2008 (table 5-12) contributed significantly to the conduct 

of academic research. 

The number of research assistants (RAs)—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 2008, up from 22% in 1973. In 

the field distribution of RAs, there was a shift away from 

the physical sciences and social sciences and into the life 

sciences, computer sciences, and engineering. In engineering and the physical sciences in 2008, the proportion of RAs 

was high relative to graduate enrollment; 42% of graduate 

students in the physical sciences and 40% of engineering 

graduate students were supported in their graduate study primarily by research assistantships. In the life sciences, the 

proportion of RAs relative to graduate enrollment was similar to the overall proportion across all fields (27%), possibly 

reflecting the heavier reliance on postdoctoral researchers 

rather than RAs in the life sciences fields (table 5-12). 

The majority of the academic research workforce remains 

employed in the intensive and very intensive research universities, although the research universities’ shares of both 

academic researchers and of RAs have declined since 1973. 

(See chapter 2 sidebar, “Carnegie Classification of Academic 

Institutions,” for a brief description of the Carnegie categories.) During the 2003–2008 period, the research universities employed 48% of all S&E doctorate holders in academic 

positions, 57% of those reporting research as their primary 

or secondary activity, and 79% of S&E graduate students 

for whom an RA was their primary means of support (table 

5-13). Trends indicate a growing research presence by fulltime academic researchers at institutions not classified as 

research universities, although RAs remain highly concentrated in the research universities. 

Academic Employment in Postdoc Positions

The number of S&E doctorate holders employed in 

academic postdoc positions climbed from 4,000 in 1973 

to 18,000 in 2008 (appendix table 5-16).22 (See sidebar, 

“Postdoctoral Researchers.”) During that time period, the 

share of postdocs increased from 4% to 7% of all academically employed S&E doctorate holders. Postdocs were 

much more prevalent in the life sciences, engineering, and 

the physical sciences than in social sciences, although the 

proportion of postdoc positions in physical sciences has declined since the mid-1990s (figure 5-19 and appendix table 

5-16). 

The demographic profile of individuals employed in 

academic postdoc positions has changed dramatically over 

time. The proportions of postdocs held by women, racial/

ethnic minorities, and foreign-born individuals has climbed 

since 1973 (table 5-14).
5-28 Chapter 5. Academic Research and Development

Early Career Postdocs

A temporary postdoc appointment is a common stop 

along the career path of S&E doctorate holders, particularly 

during their early career stages. In 2008, 36% of recently degreed S&E doctorate holders in academia were employed in 

postdoc positions, a figure that approached the share (42%) 

employed in full-time faculty positions (appendix table 

5-21). With the exception of 2003, the share of recently degreed S&E doctorate holders in academic postdoc positions 

has exceeded the share holding full-time tenured or tenuretrack faculty positions since 1995 (figure 5-20). S&E doctorate holders 4 to 7 years beyond the doctorate degree were 

far less likely than their recently degreed counterparts to be 

employed in academic postdoc positions; in 2008, only 11% 

of these doctorate holders held postdoc positions. 

The vast majority of academic postdocs are employed at 

very high research activity universities. In 2008, the share of 

all academic postdocs employed at these institutions reached 

75% (table 5-15). At the research universities, 70% of S&E 

postdoc appointments in 2008 were held by recently degreed individuals, and 5% by doctorate holders who were 

8 or more years past their degree. The postdoc populations 

employed at medical schools and other universities and colleges included a larger pool of doctorate holders who had not 

recently earned the doctorate degree. 

In comparison to 1995, a larger share of S&E doctorate 

holders employed in academia in 2006, 45% versus 41%, had 

held a postdoc appointment at some point in their career, and a 

slightly larger share than in 1995 had been employed in postdoc positions two or more times (table 5-16). Postdocs and 

multiple postdocs are relatively more prevalent among early 

career S&E doctorate holders than among the total pool of 

S&E doctorate holders. Early career postdoc employment and 

multiple instances of postdoc employment are typical for academic careers in the life sciences and the physical sciences 

(table 5-16), the two fields of study that have had the highest 

incidence of postdocs over the years (figure 5-19). 

Government Support of Academic 

Doctoral Researchers

The federal government provides academic researchers with a substantial portion of overall research support. 

This section presents data from S&E doctorate holders in 

academia who reported on the presence or absence (but not 

magnitude) of federal support for their work.23

Table 5-12

Full-time SEH graduate students and graduate research assistants at universities and colleges, by degree field: 

Selected years, 1973–2008

1973 1983 1993 2003 2008

Group and degree field Thousands Percent Thousands Percent Thousands Percent Thousands Percent Thousands Percent

Graduate students ................ 161.6 100 252.0 100 329.6 100 398.0 100 434.1 100

Physical sciences ............. 28.9 18 37.2 15 41.9 13 41.9 11 44.5 10

Mathematics ..................... 10.3 6 11.0 4 14.5 4 14.6 4 16.2 4

Computer sciences ........... 2.9 2 10.6 4 17.4 5 30.9 8 31.3 7

Life sciences ..................... 40.6 25 69.2 28 91.6 28 123.2 31 138.8 32

Psychology ....................... 15.2 9 26.6 11 34.8 11 35.8 9 42.1 10

Social sciences ................. 32.4 20 43.5 17 55.6 17 61.3 15 68.2 16

Engineering ....................... 31.3 19 53.9 21 73.8 22 90.4 23 93.0 21

Graduate research 

 assistants ....................... 35.9 100 54.9 100 90.2 100 114.3 100 118.3 100

Physical sciences ............. 8.9 25 12.6 23 17 19 18.1 16 18.7 16

Mathematics ..................... 0.7 2 0.8 2 1.4 2 1.8 2 1.9 2

Computer sciences ........... 0.7 2 1.4 3 3.8 4 7.5 7 7.3 6

Life sciences ..................... 9.4 26 16.5 30 28.0 31 35.5 31 37.4 32

Psychology ....................... 1.9 5 3.0 5 4.6 5 5.6 5 6.1 5

Social sciences ................. 4.0 11 5.0 9 7.4 8 8.4 7 8.1 7

Engineering ....................... 10.4 29 15.6 28 28.0 31 37.4 33 37.0 31

SEH = science, engineering, and health

NOTES: Detail may not add to total because of rounding. Graduate research assistants are full-time graduate students with research assistantships 

as 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 (2011) of 1973, 1983, 1993, 2003, and 

2008 Surveys of Graduate Students and Postdoctorates in Science and Engineering.

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 5-29

Academic Scientists and Engineers Who Receive 

Federal Support

In 2008, 45% of all 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 (appendix table 5-22). For S&E as a whole and 

for many fields, the share of S&E doctorate holders and 

researchers receiving federal support has declined since the 

early 1990s. 

Faculty and other full-time S&E doctoral employees 

were less likely than postdocs to receive federal support. 

Throughout the 1973–2008 period, fewer than half of fulltime S&E faculty received federal support, whereas the 

share of postdocs receiving federal support was above 70%. 

Table 5-13

SEH doctorate holders and graduate research assistants employed in academia, by Carnegie institution type: 

1973–2008

(Percent distribution)

Group and institution type 1973–83 1983–93 1993–2003 2003–08

All employed S&E doctorate holders............... 100.0 100.0 100.0 100.0

Research universities ................................... 53.7 53.4 50.0 48.4

Doctorate-granting institutions .................... 11.5 11.4 11.0 10.5

Comprehensive institutions ......................... 18.0 18.5 18.3 18.6

Other institutions ......................................... 16.8 16.8 20.7 22.6

Researchers ................................................. 100.0 100.0 100.0 100.0

Research universities ............................... 64.8 62.2 57.8 56.8

Doctorate-granting institutions ................ 10.9 11.2 11.3 10.8

Comprehensive institutions ...................... 12.4 13.9 14.5 15.1

Other institutions ...................................... 11.9 12.8 16.4 17.3

Graduate research assistants .......................... 100.0 100.0 100.0 100.0

Research universities ................................... 87.5 84.0 80.4 79.3

Doctorate-granting institutions .................... 9.3 10.1 11.8 11.8

Comprehensive institutions ......................... 2.2 3.5 4.9 5.4

Other institutions ......................................... 1.0 2.4 2.9 4.5

SEH = science, engineering, and health

NOTES: Detail may not add to total because of rounding. Academic employment of S&E doctorate holders limited to those employed at 2- or 4-year 

colleges or universities, excluding those employed part time who are students or retired. Researchers are S&E doctorate holders employed in academia 

reporting research as a primary or secondary work activity; research includes basic or applied research, development, and design. Graduate research 

assistants are full-time graduate students with research assistantships as primary mechanism of support. Institutions designated by 1994 Carnegie 

classification code. Freestanding schools of engineering and technology included under comprehensive institutions. Other instititions includes 

freestanding medical schools, 4-year colleges, specialized institutions, and institutions without Carnegie code. For information on these institutional 

categories, see The Carnegie Classification of Institutions of Higher Education, http://classifications.carnegiefoundation.org/index.php. 

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2011) of 1973–2008 Surveys of 

Doctorate Recipients and Survey of Graduate Students and Postdoctorates in Science and Engineering. 

Science and Engineering Indicators 2012

A postdoc is a temporary position in academia, industry, a nonprofit organization, or government, 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 advisor, and with the administrative 

and infrastructural support of a host institution and 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 science and engineering enterprise and perform a 

substantial portion of the nation’s research. Most have 

recently earned the doctorate 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 junior members, help write 

grant proposals and papers, and present research results 

at professional society meetings (COSEPUP 2000).

Postdoctoral Researchers
5-30 Chapter 5. Academic Research and Development

Since 1991, the share of academic S&E doctorate holders 

receiving federal support has declined in all position categories (appendix table 5-22).

Federal support is more prevalent in very high research 

activity universities and medical schools. More than 60% 

of S&E doctorate holders and full-time faculty employed in 

research universities and medical schools received federal 

support in 2008 (appendix table 5-23). The percentage with 

federal support was less than 30% among those employed 

in doctoral/research universities, master’s-granting universities, and baccalaureate colleges. 

Table 5-14

SEH doctorate holders with academic employment in postdoc position, by demographic group: Selected years, 

1973–2008

(Percent distribution)

Demographic group 1973 1983 1993 2003 2008

Sex

Female ....................................................................... 16.7 30.1 30.8 37.6 39.4

Male ........................................................................... 83.3 69.9 69.2 62.4 60.6

Race/ethnicity

White .......................................................................... 85.7 81.9 68.4 63.1 57.8

Asian/Pacific Islander ................................................ 11.9 13.3 27.1 30.6 33.9

Underrepresented minority ........................................ 2.4 4.8 4.5 7.0 8.3

Place of birth

United States ............................................................. 82.5 81.7 60.9 57.0 53.9

Foreign ....................................................................... 17.5 18.3 39.1 43.0 46.1

SEH = science, engineering, and health

NOTES: Academic employment 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. Underrepresented minorities include blacks, Hispanics, and American Indians/Alaska Natives. Asian/Pacific Islander includes 

Pacific Islanders through 1999 but excludes them in 2001–08.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2011) of 1973, 1983, 1993, 2003, and 

2008 Surveys of Doctorate Recipients.

Science and Engineering Indicators 2012

Figure 5-19

SEH doctorate holders with academic employment in postdoc position, by degree field: Selected years, 1973–2008

Percent

SEH = science, engineering, and health

NOTES: Data on computer sciences not available for 1973. Academic employment 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.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2011) of 1973, 1983, 1993, 2003, and 

2008 Surveys of Doctorate Recipients. 

Science and Engineering Indicators 2012

Physical sciences Mathematics Computer sciences Life sciences Psychology Social sciences Engineering

0

2

4

6

8

10

12

14

1973 1983 1993 2003 2008
Science and Engineering Indicators 2012 5-31

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. From 1973–2008, S&E doctorate holders with recently earned doctorates (i.e., doctorates earned 

within 3 years of the survey) employed in full-time faculty 

positions were far less likely to receive federal support than 

those in postdoc positions (figure 5-21). In 2008, 27% of 

recent doctorate recipients in full-time faculty positions received federal support, down from 38% in 1991. Of recent 

S&E doctorate recipients employed in postdoc positions in 

2008, 71% received federal support, which was a substantial 

decline from 1991 (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 (figure 5-21). As with recent doctorate recipients, the share of full-time faculty and 

postdocs 4–7 years beyond the doctorate who received federal support also declined from 1991. The shares of early 

career full-time faculty and postdocs with federal support 

were higher in some fields (life sciences, physical sciences, 

and engineering) than in others (mathematics and social sciences) (appendix table 5-24).

Table 5-15

SEH doctorate holders with academic employment in postdoc position, by Carnegie institution type and years 

since doctorate: 2008

(Percent distribution)

Institution type

Number of 

postdocs 

(thousands) Total 1–3 4–7 8

All institutions ................................................................ 18.0 100.0 68.6 25.6 5.8

Doctorate-granting, very high research ..................... 13.5 100.0 69.7 25.2 5.1

Other doctorate-granting institutions ........................ 1.4 100.0 75.1 24.7 S

Medical schools/medical centers .............................. 1.6 100.0 60.4 29.0 10.7

Other universities and colleges .................................. 1.5 100.0 61.5 26.3 12.2

S = data suppressed for reasons of confidentiality and/or reliability

SEH = science, engineering, and health

NOTES: Academic employment 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. Institutions designated by 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 (2011) of 2008 Survey of Doctorate 

Recipients.

Science and Engineering Indicators 2012

Years since doctorate

Figure 5-20

Recent SEH doctorate holders employed in 

academia, by position and years since doctorate: 

1979–2008 

Percent

SEH = science, engineering, and health

NOTES: Academic employment 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. Full-time tenured or 

tenure-track faculty includes full, associate, and assistant professors 

plus instructors. 

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of 1973–2008 

Surveys of Doctorate Recipients.

Science and Engineering Indicators 2012

1979 1983 1987 1991 1995 1999 2003 2008

0

10

20

30

40

50

60

70

80

Full-time tenured or tenuretrack faculty,1–3 years since doctorate

Postdoc, 1–3 years since doctorate

Full-time tenured or tenure-track faculty, 

4–7 years since doctorate

Postdoc, 4–7 years since doctorate
5-32 Chapter 5. Academic Research and Development

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 continues 

that theme by examining the intellectual output of academic 

S&E research using indicators derived from published research articles and U.S. patent and related data. 

Researchers have traditionally published the results of 

their work in the world’s peer-reviewed S&E journals.24

Article-level data from these journals are indicators of 

S&E research output by countries and—within the United 

States—by academia and other sectors of the economy.25

(See sidebar “Bibliometric Data and Terminology.”) These 

bibliometric data can also be used to track trends in S&E 

research collaboration, using measures of coauthorship between and among departments, institutions, sectors, and 

countries. Finally, citations in more current research articles 

to previous research, and in patents to published research 

articles, offer insight into the importance and impact of previous research and its connection to inventions.

S&E Article Output

Between 1999 and 2009, the total world S&E article 

output in the SCI/SSCI database grew at an average annual 

rate of 2.6% (table 5-17). Leading this growth was China at 

16.8% per year, which propelled it from ninth largest S&E 

article producer26 in 1999 to second largest in 2009 behind 

the United States. Very rapid growth of over 10% per year 

Figure 5-21

SEH doctorate holders employed in academia 

with federal support, by position and years since 

doctorate: 1973–2008 

Percent

SEH = science, engineering, and health

NOTES: 1985 and 1993–97 data not comparable with other years and 

understate degree of federal support. In 1985 and 1993–97, federal 

support question asks whether work performed during week of April 15 

was supported by government; in other years, question pertains to work 

conducted over course of entire year. Academic employment 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. Faculty 

includes full, associate, and assistant professors plus instructors.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, special tabulations (2011) of 1973–2008 

Surveys of Doctorate Recipients. 

Science and Engineering Indicators 2012

1973 1979 1983 1987 1991 1999 2003 2008

0

20

40

60

80

100

Postdoc, 1–3 years

since doctorate

Full-time faculty, 1–3 

years since doctorate

Full-time faculty, 4–7

years since doctorate

Postdoc, 4–7 years

since doctorate

Table 5-16

SEH doctorate holders employed in academia, by years since doctorate, number of postdoc positions held 

during career, and degree field: 1995 and 2006

(Percent distribution)

 All fields Life sciences Physical sciences

Years since doctorate and postdocs (n) 1995 2006 1995 2006 1995 2006

All years since doctorate ............................ 100.0 100.0 100.0 100.0 100.0 100.0

0 .............................................................. 59.3 54.7 36.1 36.6 37.5 35.7

1 .............................................................. 29.2 33.2 45.9 43.6 43.7 47.2

2 ............................................................ 11.4 12.1 15.5 19.9 18.9 17.1

1–3 years since doctorate....................... 100.0 100.0 100.0 100.0 100.0 100.0

0 .......................................................... 47.1 42.7 33.7 31.9 21.0 24.2

1 .......................................................... 44.0 49.8 54.3 57.2 63.9 69.5

2 ........................................................ 8.9 7.5 12.0 10.8 15.1 6.2

4–7 years since doctorate....................... 100.0 100.0 100.0 100.0 100.0 100.0

0 .......................................................... 54.5 52.6 20.5 26.1 22.6 32.5

1 .......................................................... 30.9 35.6 46.0 52.5 50.0 47.3

2 ........................................................ 14.6 11.8 17.5 31.3 27.5 20.1

SEH = science, engineering, and health

NOTES: Academic employment 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. The number of postdoc positions held during career includes postdoc appointments outside academia.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations (2011) of 1995 and 2006 Surveys of 

Doctorate Recipients.

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 5-33

was also experienced by South Korea and, from low bases, 

by Iran, Tunisia, Thailand, Pakistan, and Malaysia.

Viewed regionally, growth in S&E article output over 

the decade has been uneven. Mature economies had modest growth or decline: the United States averaged 1.0%, EU 

member countries 1.4%, while Japan declined by –1.1% per 

year and Russia by –2.0%. Developing economies, mainly in 

Asia, far outpaced this growth in S&E articles, where China 

(16.8%) and South Korea (10.1%) were joined by Taiwan at 

7.7%, Singapore at 8.2%, and India at 6.9% (table 5-17 and 

appendix table 5-27). 

The research portfolios of the U.S., EU, and Asian economies differ in important ways (NSB 2010; and appendix 

tables 5-27 through 5-40):

China and Japan emphasize the physical sciences more 

than the United States and European Union;

The United States, European Union, and Japan produce 

relatively more articles in the life sciences than China or 

other Asian nations; and

S&E research publications with authors in Asian countries 

are more heavily concentrated in engineering than those 

with authors in the United States or European Union.

Countries in Central and South America together increased their S&E article output between 1999 and 2009 at 

an annual rate of 5.6%. Brazil had the highest growth rate 

in the region, at 7.7% (table 5-17 and appendix table 5-27).

The countries or other entities with indexed S&E articles 

are always evolving.27 In the current volume, 199 receive 

credit for publishing S&E articles (appendix table 5-25). 

Of these, a small number account for most of the publications.28 Table 5-17 shows that five countries (the United 

States, China, Japan, the United Kingdom, and Germany) 

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 Thomson Scientific in the 

Science Citation Index and Social Sciences Citation 

Index (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 the SCI 

and SSCI. The journals selected are notable for their relatively high citation rank within their corresponding S&E 

subfields; journals of only regional interest are excluded.

The number of journals analyzed by NSF from SCI/

SSCI was 4,093 in 1988 and 5,085 in 2010, an annual 

growth rate of about 1.0%. These journals give good 

coverage of a core set of internationally recognized peerreviewed scientific journals. The coverage extends to 

electronic-only journals and print journals with electronic 

versions. In the period 1988–2010, the database contained 

14.6 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 115 to 

154, an annual growth rate of about 1.3%.

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 

co-authored 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. (If authors list more than one departmental or institutional affiliation, these are fractionalized 

for article and citation counts; whole counts are used for 

each affiliation in coauthorship data.)

Data in the first section only (“S&E Article Output”) 

are reported by publication year through 2009 as reported 

in the data files through late January, 2011. These data 

are noted as “by year of publication.” Publication data 

in the remaining bibliometrics sections (“Coauthorship 

and Collaboration,” “Trends in Output and Collaboration 

Among U.S. Sectors,” and “Trends in Citation of S&E 

Articles”) are reported through 2010. These data are noted as “by data file year.”

The country/economy breakouts are reported in appendix table 5-25. Data reported in this section are grouped 

into 13 broad S&E fields and 125 subfields (appendix 

table 5-26).

Bibliometric Data and Terminology
5-34 Chapter 5. Academic Research and Development

Table 5-17

S&E articles in all fields, by country/economy: 1999 and 2009

Rank Country 1999 2009

Average annual 

change (%)

2009 world 

total (%)

2009 cumulative 

world total (%)

– World........................................................... 610,203 788,347 2.6 na na

1 United States ............................................. 188,004 208,601 1.0 26.5 26.5

2 China .......................................................... 15,715 74,019 16.8 9.4 35.8

3 Japan .......................................................... 55,274 49,627 -1.1 6.3 42.1

4 United Kingdom ........................................ 46,788 45,649 -0.2 5.8 47.9

5 Germany ..................................................... 42,963 45,003 0.5 5.7 53.6

6 France ......................................................... 31,345 31,748 0.1 4.0 57.7

7 Canada ....................................................... 22,125 29,017 2.7 3.7 61.4

8 Italy ............................................................. 20,327 26,755 2.8 3.4 64.7

9 South Korea ............................................... 8,478 22,271 10.1 2.8 67.6

10 Spain ........................................................... 14,514 21,543 4.0 2.7 70.3

11 India ............................................................ 10,190 19,917 6.9 2.5 72.8

12 Australia ...................................................... 14,341 18,923 2.8 2.4 75.2

13 Netherlands ................................................ 12,168 14,866 2.0 1.9 77.1

14 Russia ......................................................... 17,145 14,016 -2.0 1.8 78.9

15 Taiwan ......................................................... 6,643 14,000 7.7 1.8 80.7

16 Brazil ........................................................... 5,859 12,306 7.7 1.6 82.2

17 Sweden ...................................................... 9,890 9,478 -0.4 1.2 83.4

18 Switzerland ................................................ 8,195 9,469 1.5 1.2 84.6

19 Turkey ......................................................... 3,223 8,301 9.9 1.1 85.7

20 Poland ........................................................ 5,100 7,355 3.7 0.9 86.6

21 Belgium ...................................................... 5,713 7,218 2.4 0.9 87.5

22 Iran .............................................................. 665 6,313 25.2 0.8 88.3

23 Israel ........................................................... 5,929 6,304 0.6 0.8 89.1

24 Denmark ..................................................... 4,783 5,306 1.0 0.7 89.8

25 Finland ........................................................ 4,719 4,949 0.5 0.6 90.4

26 Greece ........................................................ 2,626 4,881 6.4 0.6 91.1

27 Austria ........................................................ 4,158 4,832 1.5 0.6 91.7

28 Norway ....................................................... 3,043 4,440 3.9 0.6 92.2

29 Singapore ................................................... 1,897 4,187 8.2 0.5 92.8

30 Portugal ...................................................... 1,711 4,157 9.3 0.5 93.3

31 Mexico ........................................................ 2,884 4,128 3.7 0.5 93.8

32 Czech Republic ......................................... 2,360 3,946 5.3 0.5 94.3

33 Argentina .................................................... 2,636 3,655 3.3 0.5 94.8

34 New Zealand .............................................. 2,915 3,188 0.9 0.4 95.2

35 South Africa ............................................... 2,303 2,864 2.2 0.4 95.5

36 Ireland ......................................................... 1,459 2,798 6.7 0.4 95.9

37 Hungary ...................................................... 2,200 2,397 0.9 0.3 96.2

38 Egypt .......................................................... 1,293 2,247 5.7 0.3 96.5

39 Thailand ...................................................... 549 2,033 14.0 0.3 96.7

40 Chile ............................................................ 1,059 1,868 5.8 0.2 97.0

41 Ukraine ....................................................... 2,355 1,639 -3.6 0.2 97.2

42 Romania ..................................................... 917 1,367 4.1 0.2 97.4

43 Malaysia ..................................................... 471 1,351 11.1 0.2 97.5

44 Slovenia ...................................................... 708 1,234 5.7 0.2 97.7

45 Serbia ......................................................... NA 1,173 NA 0.1 97.8

46 Croatia ........................................................ 647 1,164 6.0 0.1 98.0

47 Pakistan ...................................................... 296 1,043 13.4 0.1 98.1

48 Tunisia ......................................................... 257 1,022 14.8 0.1 98.3

49 Slovakia ...................................................... 979 1,000 0.2 0.1 98.4

na = not applicable; NA = not available

NOTES: Countries/economies shown produced 1,000 articles or more in 2009. Countries/economies ranked on 2009 total. Article counts from set of 

journals covered by Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). Articles classified by year of publication and assigned to 

country/economy on basis of institutional address(es) listed on article. Articles on fractional-count basis, i.e., for articles with collaborating institutions 

from multiple countries/economies, each country/economy receives fractional credit on basis of 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 BoardTM, special tabulations (2011) 

from Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/. See appendix table 5-27.

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 5-35

accounted for more than 50% of the total world S&E article 

output in 2009. The 49 countries in table 5-17—one quarter 

of the countries in the data—produced 98% of the world total of S&E articles. 

The number of journals covered by SCI/SSCI has expanded to accommodate the rising number of research articles. 

Most of the increase reflects activity in new S&T centers. 

Figure 5-22 shows how the number of published articles has 

grown over the past 20 years, from 485,000 articles in 1989 

to 788,000 in 2009. Non-U.S. articles have increasingly 

dominated world S&E article output, growing from 63% to 

74% of the total. The expansion of non-U.S. S&E articles 

signals the return on decades of increased investments in 

higher education and the more recent conviction that R&D 

is essential to economic growth and competitiveness. It also 

reflects a slowdown in the growth of U.S. S&E article output 

to around 1% or less in recent years. 

In Figure 5-22, co-authored articles are pro-rated to U.S. 

sectors and foreign countries, depending on their fraction 

of the institutional addresses. These fractions were then resummed to produce the shares shown in the figure. But that 

method of allocating credit for S&E article authorship does 

not show the relationships among the authors, author sectors, 

and country authors that together illuminate the extent to 

which S&E research is an increasingly global, collaborative 

undertaking. The following sections explore these growing 

collaborative and international dimensions of world S&E research as indicated by data on S&E publications. Together 

these indicators will describe a growing globalization of the 

social system of scientific knowledge production and the 

global use of its outputs. 

Coauthorship and Collaboration

Article output trends since about the mid-1990s have two 

defining features: the rapid growth of articles with authors 

from the developing world, and a rise in the percentage of 

global article output that is the result of collaboration among 

researchers internationally. Articles with authors from different institutions in the United States and from different countries have continued to increase, indicating rising 

knowledge creation, transfer, and sharing among institutions and across national boundaries.29, 30 This section covers broad trends in coauthorship for the world as a whole 

and continues with an examination of country-level trends, 

including selected country-to-country coauthorship patterns 

and indexes of international collaboration.31 Indicators of 

cross-sector coauthorship, which are available only for the 

United States, are examined below in the section “Trends in 

Output and Collaboration Among U.S. Sectors.” 

Article Author Names and Institutions 

Earlier volumes of this report have noted the imbalance 

between the growth in number of S&E articles and the 

growth in the number of authorship credits to institutions 

and individuals that produced those articles (NSB 2008, 08-

01, figure 5-29; NSB 2010, 10-01, table 5-16). The much 

faster growth in authorship credits to institutions and individuals—in all broad fields—has been used as an indicator 

of a steady rise in the collaborative nature of S&E research, 

both domestically and internationally.

Figure 5-23 shows the same trend, but here data are restricted to articles with at least one U.S. academic author. 

Over the period 1990–2010, the number of such articles in 

the data analyzed in this section increased by an average of 

1.6% annually. In contrast, the number of institutions listed on these articles grew over twice as fast at 4.1% annually, and the number of author names grew even faster, at 

4.4% annually. 

Figure 5-24 focuses on the authors per paper for S&E articles by field with an author from the U.S. academic sector 

over the same 20-year period. In two decades, the average 

number of author names per paper in all S&E fields grew from 

3.2 to 5.6. The average number of authors per paper more than 

quadrupled in astronomy (3.1 to 13.8) and doubled in physics (4.5 to 10.1). Growth in the average number of coauthors 

was slowest in the social sciences (from 1.6 authors per paper 

in 1990 to 2.1 in 2010) and in mathematics (from 1.7 to 2.2). 

In short, papers authored by a single U.S. academic scientist 

or engineering are becoming an increasingly small minority 

of the published literature. NSF analysis shows that in 2010, 

Figure 5-22

World S&E articles, by author characteristic: 

1988–2009 

Thousands

NOTES: Article counts from set of journals covered by Science 

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

Articles classified by year of publication and assigned to country and 

sector on basis of institutional address(es) listed on article. Articles 

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

institutions from multiple countries/sectors, each country/sector 

receives fractional credit on basis of proportion of its participating 

institutions. Sector not available for non-U.S. articles.

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, and The Patent BoardTM, 

special tabulations (2011) from Thomson Reuters, SCI and SSCI, 

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

Science and Engineering Indicators 2012 

1988 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

0

100

200

300

400

500

600

700

800

Non-U.S. institution

U.S. non-academic institution

U.S. academic institution
5-36 Chapter 5. Academic Research and Development

92.4% of all S&E articles with at least one U.S. academic author had two or more author names.

A closely related indicator, coauthored articles (i.e., articles with authors in different institutions or departments or 

in more than one country) has also increased steadily. Figure 

5-25 contrasts these trends for the world as a whole with 

those for articles with at least one U.S. academic author. 

Coauthored articles grew from 42% of the world’s total S&E 

articles in 1990 to 67% in 2010. This growth has two parts. 

Coauthored articles that list only domestic institutions grew 

from 33% of all articles in 1990 to 43% in 2010. Articles that 

list institutions from more than one country, that is, internationally coauthored articles (which also may have multiple 

domestic institutional authors), grew more dramatically—

from 10% to 24% over the same period. 

The percent of S&E articles with a U.S. academic author 

that is internationally coauthored is higher than the percent 

of total world international coauthorships (figure 5-25). 

Purely domestic coauthorship in this sector has been relatively flat in the United States, at about 43% of total U.S. 

academic articles from 1990 to 2010. Over the same period 

U.S. academic articles with a non-U.S. coauthor have grown 

strongly, from 12% to 32%. (These coauthorships may also 

include multiple domestic U.S. coauthors.) The remainder of 

this section takes a closer look at patterns within this broad 

increase in international coauthorship around the world.

Figure 5-24

Number of authors per U.S. academic S&E article, by S&E field: Selected years, 1990–2010

Number

NOTES: Data from set of journals covered by Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). Articles classified by year they 

entered database. All articles have at least one U.S. academic author and may have authors from other sectors and from outside United States.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent BoardTM, special tabulations (2011) from 

Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/. 

Science and Engineering Indicators 2012

All fields Physics Astronomy Medical

sciences

Biological

sciences

GeosciencesAgricultural

sciences

Chemistry Engineering Other life

sciences

Psychology Computer

sciences

MathematicsSocial

sciences

0

2

4

6

8

10

12

14

1990 2000 2010

Figure 5-23

U.S. academic S&E articles, institutional authors, 

and author names: 1990–2010

Thousands

NOTES: Article counts from set of journals covered by Science 

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

Articles classified by year they entered database. Articles counted on 

a whole-count basis. All articles have at least one U.S. academic 

author and may have authors from other sectors and from outside 

the U.S. Author name counted each time it appears in data set. 

Authors assigned to institution on basis of institutional address listed 

on article; authors from separate departments each counted as 

individual institutional author; multiple authors from same 

department of institution considered as one institutional author.

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, and The Patent BoardTM, special 

tabulations (2011) from Thomson Reuters, SCI and SSCI, 

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

Science and Engineering Indicators 2012

1990 19941992 1996 20001998 20042002 2006 2008 2010

0

200

400

600

800

1,000

1,200

1,400

Author names

Articles

Institutional authors
Science and Engineering Indicators 2012 5-37

International Coauthorship Patterns From a 

Country Perspective

International coauthorship can be considered from two perspectives: (1) a country’s level of participation in the world’s 

total S&E coauthorships, and (2) a country’s international coauthorship vis-à-vis the country’s total S&E authorship.

World total S&E coauthorship. Table 5-18 shows the 

world’s countries/economies that account for 1% or more of 

internationally coauthored S&E articles, and how their relative standing, or rank, has changed over the past 10 years. 

U.S.-based researchers were coauthors of 43% of the world’s 

total internationally coauthored articles in 2010, well above 

the global percentage of U.S. article output. Germany, the 

United Kingdom, and France were also leading contributors 

to the world’s internationally coauthored articles. The most 

notable trend in this indicator, however, was the rise of authors from China, who increased their share of world internationally coauthored S&E articles from 5% to 13% over the 

last 10 years. 

Individual region/country coauthorship. Table 5-19 compares a region/country’s share of total world international coauthorship with the region/country’s internal or domestic rate 

of international coauthorship. The table is restricted to countries that had institutional authors on at least 5% or more of the 

world’s internationally coauthored S&E articles in 2010 (see 

also appendix table 5-41). 

The sheer volume of U.S. internationally coauthored 

articles dominates these measures: 32% of U.S. articles in 

2010 were internationally coauthored, up from 23% in 2000. 

Figure 5-25

World and U.S. academic S&E articles coauthored domestically and internationally: 1990–2010

NOTES: Article counts from set of journals covered by Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). Articles classified by year they 

entered database, rather than year of publication, and assigned to country/economy on basis of institutional address(es) listed on article. Articles on 

whole-count basis, i.e., each collaborating institution or country 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 BoardTM, special tabulations (2011) from 

Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/ science/. 

Science and Engineering Indicators 2012

Percent Percent

World articles U.S. academic articles

1990 1994 1998 2002 2006 2010

0

20

40

60

80

Domestic coauthorship only

International coauthorship

All coauthorship

1990 1994 1998 2002 2006 2010

0

20

40

60

80

All coauthorship

Domestic coauthorship only

International coauthorship

Even higher rates of international coauthorship are evident 

among the countries of the European Union, where large 

Framework Research Programs have strongly encouraged it, 

and in Switzerland. Both Japan’s and Asia-8’s international 

coauthorship rates have increased over the past 10 years, and 

more countries passed the 50% mark over the decade.

Table 5-19 also shows China’s idiosyncratic position on 

this indicator. Table 5-17 shows that China’s S&E article 

output grew sufficiently over the decade to place it as the 

world’s second largest S&E article-producing nation. At the 

same time, China’s internationally coauthored articles as 

a share of its total article output remained almost flat and, 

at 27%, was the lowest percentage of all countries/regions 

shown on Table 5-19. This atypical measure shows that 

China’s very rapid S&E article growth has been driven by 

articles with solely domestic authors (see discussion below 

of China’s rates of internal and international citations). 

What accounts for specific coauthorship relationships? 

Linguistic and historical factors (Narin et al. 1991), geography, and cultural relations (Glänzel and Schubert 2005) 

play a role. In recent years, coauthorships in Europe have 

risen in response to EU policies and incentives that actively encouraged intra-European cross-border collaboration. 

However, strong ties among science establishments in the 

Asian region, without the formal framework that characterizes Europe, indicate that regional dynamics can play a 

strong role in the development of collaborative ties. The discussion below in the section “International Collaboration in 

S&E” identifies strong coauthorship relationships in specific 

country pairs across the world, based on the strength of their 

coauthorship rates. 
5-38 Chapter 5. Academic Research and Development

International Coauthorship With the United States

Table 5-20 lists the 31 countries whose institutions appeared on at least 1% of U.S. internationally coauthored articles in 2010. U.S. authors are most likely to coauthor articles 

with colleagues from the United Kingdom (14.1%), China 

(13.7%), Germany (13.3%), and Canada (11.8%). 

Table 5-19 shows that the rate at which U.S. researchers participate in international collaboration is below that 

of many countries with smaller science establishments. The 

large size of the U.S. S&E establishment results in a share 

Table 5-19

International collaboration on S&E articles, by 

selected region/country: 2000 and 2010

(Percent distribution)

Region/country 2000 2010 2000 2010

United States ................. 23 32 44 43

EU

France ........................ 42 56 15 14

Germany .................... 39 54 20 19

Italy ............................ 39 48 9 9

Netherlands ............... 45 56 7 7

Spain .......................... 36 50 6 8

United Kingdom ......... 35 53 19 19

Other Western Europe

Switzerland ................ 52 68 6 6

Asia

China .......................... 26 27 5 13

Japan ......................... 19 28 10 8

Asia-8 ......................... 24 30 8 13

Other

Australia ..................... 33 49 5 7

Canada ...................... 36 48 9 10

EU = European Union

NOTES: Internationally coauthored articles have at least one 

collaborating institution from indicated country/economy and an 

institution from outside that country/economy. Article counts from 

set of journals covered by Science Citation Index (SCI) and Social 

Sciences Citation Index (SSCI). Articles classified by year they 

entered database, rather than year of publication, and assigned 

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

on article. Articles on whole-count basis, i.e., each collaborating 

country/economy credited one count. Countries/economies with 

less than 5% of 2010 international total omitted. See appendix 

table 5-25 for countries/economies included in Asia-8, which in this 

table is treated as a single country. Detail adds to more than 100% 

because articles may have authors from more than two countries/

economies.

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, and The Patent BoardTM, special 

tabulations (2011) from Thomson Reuters, SCI and SSCI, http://

thomsonreuters.com/products_services/science/. 

Science and Engineering Indicators 2012

Share of 

world’s 

internationally 

coauthored 

articles

Share of 

region/country 

total article 

output

Table 5-18

Share of internationally coauthored S&E articles 

worldwide, by region/country: 2000 and 2010

(Percent)

Country/economy 2000 2010

United States ...................... 43.8 42.9

Germany ............................. 20.0 18.8

United Kingdom ................ 19.0 18.7

France ................................. 15.3 13.8

China .................................. 5.0 13.0

Canada ............................... 9.3 10.1

Italy ..................................... 9.3 9.4

Japan .................................. 10.4 8.2

Spain ................................... 6.1 8.1

Australia .............................. 5.3 7.1

Netherlands ........................ 6.7 6.9

Switzerland ........................ 5.8 6.1

Sweden .............................. 5.4 4.8

South Korea ....................... 2.3 4.4

Belgium .............................. 4.0 4.3

Russia ................................. 6.9 3.7

India .................................... 2.1 3.3

Brazil ................................... 2.8 3.0

Austria ................................ 2.6 2.9

Denmark ............................. 3.1 2.9

Poland ................................ 3.2 2.6

Israel ................................... 3.0 2.3

Finland ................................ 2.6 2.2

Taiwan ................................. 1.4 2.2

Norway ............................... 1.7 2.1

Portugal .............................. 1.2 1.9

Singapore ........................... 0.8 1.8

Czech Republic ................. 1.5 1.8

Greece ................................ 1.4 1.6

Mexico ................................ 1.7 1.6

New Zealand ...................... 1.3 1.5

Ireland ................................. 0.9 1.4

South Africa ....................... 1.0 1.4

Argentina ............................ 1.3 1.3

Turkey ................................. 0.8 1.2

Hungary .............................. 1.7 1.2

Chile .................................... 0.8 1.1

Iran ...................................... 0.2 1.0

NOTES: Internationally coauthored articles have at least one 

collaborating institution from indicated country/economy and an 

institution from outside that country/economy. Article counts from 

set of journals covered by Science Citation Index (SCI) and Social 

Sciences Citation Index (SSCI). Articles classified by year they 

entered database, rather than year of publication, and assigned 

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

on article. Articles on whole-count basis, i.e., each collaborating 

country/economy credited one count. Countries/economies with less 

than 1% of world’s 2010 international articles omitted.

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, and The Patent BoardTM, special 

tabulations (2011) from Thomson Reuters, SCI and SSCI, http://

thomsonreuters.com/products_services/science/. See appendix 

table 5-41.

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 5-39

of U.S. internationally coauthored articles that is lower than 

those of most other countries. Scientists and engineers in 

countries with smaller S&E establishments, in order to find 

an appropriate coauthor, must more frequently turn to coauthors abroad, resulting in relatively larger shares of those 

countries’ S&E articles that are coauthored with U.S. scientists and engineers. These relationships are summarized in 

table 5-20. 

For example, 2.8% of U.S. internationally coauthored 

articles in 2010 had an Israeli coauthor. The corresponding figure for Israel, with its much smaller scientific infrastructure, is 53.9%. Also, 49.9% of Canada’s internationally 

coauthored articles had a U.S. coauthor, but only 11.8% of 

U.S. international coauthorship was with a colleague at a 

Canadian institution.32 Linguistic, geographic, and other ties 

underlie these collaborations. 

Notable changes in these patterns of U.S. international 

coauthorship parallel changes in other indicators discussed 

Table 5-20

International coauthorship of S&E articles with the United States, by selected country/economy: 2000 and 2010

(Percent)

2000 2010

Country/economy

U.S. share of 

country/economy 

international articles

Country/economy 

share of U.S. 

international articles

U.S. share of 

country/economy 

international articles

Country/economy 

share of U.S. 

international articles

World ............................................... 43.8 na 42.9 na

United Kingdom ........................... 30.9 13.4 32.3 14.1

China ............................................ 35.2 4.0 45.2 13.7

Germany ...................................... 29.7 13.6 30.4 13.3

Canada ........................................ 52.1 11.0 49.9 11.8

France .......................................... 25.6 8.9 27.5 8.8

Italy .............................................. 32.0 6.8 33.4 7.3

Japan ........................................... 42.3 10.0 36.9 7.0

South Korea ................................. 59.8 3.2 53.8 5.5

Australia ....................................... 35.4 4.3 32.0 5.3

Spain ............................................ 27.0 3.8 27.9 5.3

Netherlands ................................. 29.7 4.5 31.1 5.0

Switzerland .................................. 31.2 4.1 31.1 4.4

Sweden ........................................ 27.6 3.4 29.2 3.3

Israel ............................................ 51.8 3.5 53.9 2.8

Brazil ............................................ 38.9 2.5 39.7 2.8

Taiwan .......................................... 61.2 1.9 51.2 2.6

India ............................................. 38.1 1.8 33.5 2.5

Belgium ........................................ 23.3 2.1 24.7 2.5

Russia .......................................... 24.8 3.9 27.1 2.3

Denmark ...................................... 29.6 2.1 30.8 2.0

Austria .......................................... 26.7 1.6 25.9 1.8

Poland .......................................... 26.3 1.9 27.4 1.7

Mexico ......................................... 42.3 1.6 44.4 1.6

Finland ......................................... 30.8 1.8 28.8 1.5

Norway ......................................... 28.0 1.1 28.9 1.4

Greece ......................................... 27.2 0.9 35.0 1.3

Singapore ..................................... 27.0 0.5 31.0 1.3

New Zealand ................................ 33.1 1.0 34.4 1.2

South Africa ................................. 33.2 0.8 36.5 1.2

Turkey .......................................... 39.7 0.7 40.6 1.1

Argentina ...................................... 34.6 1.0 35.0 1.1

na = not applicable

NOTES: Internationally coauthored articles have at least one collaborating institution from indicated country/economy and an institution from outside that 

country/economy. Article counts from set of journals covered by Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). Articles classified 

by year they entered database, rather than year of publication, and assigned to country/economy on basis of institutional address(es) listed on article. 

Articles on whole-count basis, i.e., each collaborating country/economy credited one count. Countries/economies ranked on percentage of their share of 

U.S.’s international articles in 2010; countries/economies with less than 1% of U.S.’s 2010 international articles omitted.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent BoardTM, special tabulations (2011) from 

Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/. See appendix table 5-41.

Science and Engineering Indicators 2012
5-40 Chapter 5. Academic Research and Development

in this section. As China’s total S&E article output grew rapidly, so did its coauthorship with U.S. authors: the U.S. share 

of China’s internationally coauthored articles increased 

about 10 percentage points to 45.2% over the past decade, 

and China’s share of U.S. internationally coauthored articles 

increased 9.7 percentage points to 13.7% (table 5-20). In 

contrast, U.S. scientists and engineers lost relative share of 

international coauthorship elsewhere (e.g. Japan, Australia, 

Taiwan, and South Korea) as their counterparts broadened 

the geographic scope of their collaborations with foreign scientists and engineers. 

An Index of International Collaboration in S&E

The size of countries’ S&E systems conditions the scope 

and reach of their international collaborations (Glänzel and 

Schubert 2004). An index of international collaboration 

addresses this issue. This index is a ratio of country A’s 

percentage of country B’s international coauthorships to 

country A’s percentage of total international coauthorship 

(Narin et al. 1991) (see sidebar, “Calculating the Index of 

International Collaboration”). An index value substantially 

greater than 1 indicates strong collaborative ties, and a value 

substantially below 1 signals relatively infrequent collaboration. The 1995 and 2010 indexes for country pairs that 

produced more than 1% of all internationally coauthored articles in 2010 are shown in appendix table 5-42. 

Table 5-21 lists the international collaboration index for 

selected pairs of countries. In North America, the CanadaUnited States index shows a rate of collaboration that is 

slightly greater than would be expected based solely on the 

number of internationally coauthored articles produced by 

these two countries, and the index has changed little over the 

past 15 years. The United States-Mexico index is just about 

as would be expected and is also stable. 

Mexico-Argentina scientific collaboration networks are 

strong at 3.5, well above expected levels. In South America, 

the collaboration index of Argentina-Brazil, at 5.1, is one of 

the highest in the world.

Collaboration indexes between pairs of countries on 

opposite sides of the North Atlantic are all low and have 

changed little over the past 15 years. In Europe, collaboration patterns are mixed but most have increased, indicating 

growing integration across the European Union for S&E 

article publishing. Among the large publishing countries 

(Germany, the United Kingdom, and France) collaboration was less than expected, but grew in all three countries 

over 15 years. A particularly strong collaboration network 

has developed between scientists in Poland and the Czech 

Republic.

The Scandinavian countries33 increased their collaboration indexes with many countries elsewhere in Europe (appendix table 5-42). Within Scandinavia, the indexes are 

among the highest in the world (table 5-21).

Cross-Pacific collaboration patterns are mixed. JapanUnited States collaboration fell below the expected value 

over the 15 years, while the United States-China index rose 

to 1. U.S. collaboration with South Korea and Taiwan weakened but remained higher than expected in both cases. The 

international collaboration indexes between Canada and 

countries in Asia are lower than the U.S.-Asia indexes. 

Collaboration indexes within Asia and across the South 

Pacific between the large article producers are generally 

higher than expected, but have experienced some weakening. 

Australia’s coauthorships are strongly linked to New Zealand. 

Two strongly collaborating pairs are South Korea-Japan and 

Australia-Singapore, but each of these networks has declined 

in strength. India’s collaborations with both South Korea and 

Japan grew stronger between 1995 and 2010. 

Trends in Output and Collaboration Among 

U.S. Sectors

In the U.S. innovation system, ties between and among 

universities, industry, and government can be beneficial for 

all sides. These ties include the flows of knowledge among 

these sectors, for which research article outputs and collaboratively produced articles are proxy indicators. S&E 

articles authored at academic institutions have for decades 

Calculating the Index of 

International Collaboration

Appendix table 5-41 contains the raw data for calculating the 2010 indexes of international collaboration contained in appendix table 5-42. Using the data 

for the world, China, and the United States, the 2010 

U.S.-China index is computed as follows: 

China-U.S coauthorships as a proportion of U.S. 

international coauthorship = 10,917 / 79,581 = 

0.1372

China’s percentage of total international coauthorship = 24,164 / 185,303 = 0.1304

U.S.-China coauthorships as a percentage of 

China’s international coauthorship = 10,917 / 

24,164 = 0.4518

U.S. percentage of total international coauthorship 

= 79,581 / 185,303 = 0.4295

The indexes for any country pair are always symmetrical. The China-U.S. and U.S.-China index are the 

same, as follows:

China-U.S. index: 0.1372 / 0.1304 = 1.05 and

U.S.-China index: 0.4518 / 0.4295 = 1.05

The 2010 China-U.S. index value is essentially 1, 

the “expected” index value when two countries coauthor with each other at the same rate as they coauthor with all countries. This is an increase since 1995, 

when the index was 0.83.
Science and Engineering Indicators 2012 5-41

accounted for more than 70% of all U.S. articles, and this 

percentage has been slowly rising—to 76% in 2010 (table 

5-22), primarily as a result of declines in articles with authors from industry (for a discussion of this shift, see NSB 

2008). This section contrasts U.S. academic authorship with 

nonacademic authorship, including output trends by sector 

and trends in coauthorship, both between U.S. sectors and 

between U.S. sectors and authors abroad. 

Article Output by Sector

Total annual S&E articles by authors in U.S. nonacademic sectors changed little over the past decade, ranging from 

48,000 to 55,000 articles34 per year between 1995 and 2010 

(appendix table 5-43). The number of articles produced by 

scientists and engineers in the federal government and in 

industry was more than 15,000 in each sector in 1995 but 

slowly declined through 2010, and each sector lost share 

over that period (table 5-22). State and local government authorship, dominated by articles in the medical and biological 

sciences, has remained constant. Scientists and engineers in 

the private nonprofit sector increased their output to about 

18,000 in 2008 and then declined to near 17,000 in 2010 

(appendix table 5-43).

Federally funded research and development centers 

(FFRDCs) are research institutions that are sponsored by 

federal agencies and administered by universities, industry, 

or other nonprofit institutions. FFRDCs have specialized 

research agendas closely related to the mission of the sponsoring agency and may house large and unique research instruments not otherwise available in other research venues. 

Table 5-21

Index of international collaboration on S&E 

articles, by selected country/economy pair: 

1995 and 2010

International 

collaboration 

index

Country/economy pair 1995 2010

North/South America

Canada–United States ............................ 1.16 1.16

Mexico–United States ............................. 0.97 1.03

United States–Brazil ............................... 0.89 0.92

Argentina–Brazil ...................................... 3.93 5.12

Mexico–Argentina ................................... 2.48 3.46

North Atlantic

UK–United States ................................... 0.68 0.75

Germany–United States .......................... 0.66 0.71

France–United States ............................. 0.59 0.64

Canada–France ....................................... 0.60 0.78

Europe

France–Germany .................................... 0.74 0.98

France–UK .............................................. 0.71 0.93

Germany–UK .......................................... 0.64 0.93

Belgium–Netherlands .............................. 2.41 2.85

Italy–Switzerland ..................................... 1.48 1.53

Poland–Czech Republic.......................... 1.96 3.93

Hungary–Germany .................................. 1.22 1.42

Germany–Czech Republic ...................... 1.23 1.40

Scandinavia

Finland–Sweden ..................................... 3.45 3.97

Norway–Sweden ..................................... 4.30 4.16

Sweden–Denmark ................................... 3.29 3.54

Finland–Denmark .................................... 2.73 3.02

Pacific Rim

Japan–United States .............................. 1.04 0.86

China–United States ............................... 0.83 1.05

South Korea–United States .................... 1.39 1.25

Taiwan–United States ............................. 1.59 1.19

China–Canada ........................................ 0.75 0.74

Japan–Canada ........................................ 0.64 0.56

Asia/South Pacific

China–Japan ........................................... 1.49 1.26

South Korea–Japan ................................ 2.49 1.94

Australia–Singapore ................................ 2.01 1.66

Australia–China ....................................... 1.11 1.06

Australia–New Zealand ........................... 4.49 3.92

India–Japan ............................................ 0.72 1.13

India–South Korea .................................. 1.25 2.12

UK = United Kingdom

NOTES: International collaboration index shows first country’s rate 

of collaboration with second country divided by second country’s 

rate of international coauthorship. Article counts from set of journals 

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

Index (SSCI). Articles classified by year they entered database, rather 

than year of publication, and assigned to country/economy on basis 

of institutional address(es) listed on article. Articles on 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 BoardTM, special 

tabulations (2011) from Thomson Reuters, SCI and SSCI, http://

thomsonreuters.com/products_services/science/. See appendix 

table 5-42.

Science and Engineering Indicators 2012

Table 5-22

U.S. S&E articles, by sector: Selected years, 

1995–2010

(Percent)

Sector 1995 2000 2005 2010

Federal government ....... 7.9 7.2 6.6 6.1

Industry ........................... 8.1 7.3 6.4 5.8

Academic ....................... 71.6 72.8 74.6 76.1

FFRDCs .......................... 2.8 2.7 2.8 2.4

Private nonprofit ............. 8.0 8.5 8.2 8.6

State/local government ... 1.0 0.9 0.9 0.9

FFRDCs = federally funded research and development centers

NOTES: Detail does not add to 100% because joint and unknown 

sectors omitted. Article counts from set of journals covered by 

Science Citation Index (SCI) and Social Sciences Citation Index 

(SSCI). Articles classified by year they entered database, rather than 

year of publication, and assigned to sector on basis of institutional 

address(es) listed on article. Articles on fractional-count basis, i.e., 

for articles with collaborating institutions from multiple sectors, 

each sector receives fractional credit on basis of proportion of its 

participating institutions. 

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, and The Patent BoardTM, special 

tabulations (2011) from Thomson Reuters, SCI and SSCI, http://

thomsonreuters.com/products_services/science/. See appendix 

table 5-43.

Science and Engineering Indicators 2012
5-42 Chapter 5. Academic Research and Development

Although authors at FFRDCs published articles in all of the 

broad S&E fields considered in this chapter, articles in physics, chemistry, and engineering together represented 71% 

of publication by this sector in 2010, reflecting the more 

specialized research programs in FFRDCs (appendix table 

5-43).35

In contrast, articles published by authors in the private 

nonprofit sector are primarily in the medical sciences (54% 

of the sector’s articles in 2010) and biological sciences 

(25%) (appendix table 5-43). Federal government authors 

show a similar pattern, with 30% in the biological sciences 

and 28% in the medical sciences.

Trends in Sector Coauthorship

Coauthorship data are indicators of collaboration at the 

sectoral level between U.S. institutional authors and between 

U.S. sectors and foreign institutions.36 These data show that 

the growing integration of R&D activities, as measured by 

coauthorship, is occurring across R&D-performing U.S. institutions in all sectors. 

Overall, the largest increases in this integration have been 

driven by increased coauthorship between U.S. academic 

authors and non-U.S. authors (in all sectors; NSF data do 

not identify the sectors of non-U.S. authors) (table 5-23). 

Co-authorship between non-U.S. authors and U.S. academic 

authors increased over the decade by 9.9 percentage points.

Between 2000 and 2010, coauthorship within sectors increased for all U.S. sectors.37 Coauthorship within academia 

rose from 39% in 2000 to 47% in 2010. FFRDC to FFRDC 

coauthorship increased 6 percentage points (table 5-23). 

Because most publishing scientists and engineers are in the 

academic sector, non-academic scientists and engineers turn 

to academia for collaborators, so the resulting rates of crosssector coauthorship with academic authors are quite high 

and continue to increase. Because of the predominance of 

the academic sector in S&E article publishing in the United 

States, academic scientists and engineers have been on the 

forefront of integrating S&E research across institutions, 

both nationally and internationally. 

Table 5-23

U.S. S&E article coauthorship, by sector, foreign coauthorship, and U.S. coauthor sector: 2000 and 2010

(Percent)

U.S. coauthor sector

Sector

Foreign 

coauthor

Federal 

government Industry Academic FFRDCs

Private 

nonprofit

State/local 

government

2000

Federal government .................. 21.2 18.1 9.2 55.0 3.1 9.6 2.5

Industry ..................................... 22.5 9.6 14.5 45.6 3.1 10.3 1.5

Academic .................................. 21.7 7.8 6.2 38.6 2.8 9.2 1.5

FFRDCs .................................... 35.0 8.0 7.6 51.2 13.4 4.2 0.2

Private nonprofit ....................... 20.4 8.4 8.6 57.0 1.4 25.1 2.4

State/local government ............. 11.7 16.2 9.4 68.2 0.6 18.2 13.8

2010

Federal government .................. 29.6 21.8 10.6 64.8 4.6 14.9 3.1

Industry ..................................... 31.5 11.8 19.3 55.4 3.8 16.0 2.1

Academic .................................. 31.6 8.3 6.4 46.9 3.4 11.7 1.6

FFRDCs .................................... 46.4 10.9 8.0 62.2 19.4 8.0 0.3

Private nonprofit ....................... 31.6 10.8 10.5 66.5 2.5 30.3 2.8

State/local government ............. 18.8 18.8 11.5 75.6 0.7 23.1 15.6

2000–10 change (percentage points)

Federal government .................. 8.4 3.7 1.4 9.8 1.5 5.2 0.6

Industry ..................................... 9.1 2.2 4.8 9.8 0.7 5.7 0.6

Academic .................................. 9.9 0.5 0.2 8.3 0.6 2.4 0.1

FFRDCs .................................... 11.3 2.9 0.4 11.0 6.0 3.7 *

Private nonprofit ....................... 11.2 2.5 1.9 9.5 1.0 5.2 0.3

State/local government ............. 7.1 2.6 2.1 7.4 0.1 4.9 1.8

* = rounds to zero

FFRDCs = federally funded research and development centers

NOTES: Article counts from set of journals covered by Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). Articles classified by year 

they entered the database, rather than year of publication, and assigned to sector on basis of institutional address(es) listed on article. Articles on wholecount basis, i.e., each collaborating country or sector credited one count. Articles from joint or unknown sectors omitted. Articles may have authors from 

more than two sectors. Articles with authors from a single sector omitted from table.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent BoardTM, special tabulations (2011) from 

Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/. 

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 5-43

Figure 5-26

Average citations per S&E article, by country of 

author: 1992–2010

NOTES: Article/citation counts from set of journals covered by Science 

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

classified by year they entered database, rather than year of publication, 

and assigned to country/economy on basis of institutional address(es) 

listed on article. Articles on fractional-count basis, i.e., for articles with 

collaborating institutions from multiple countries/economies, each 

country/economy receives fractional credit on basis of proportion of its 

participating institutions. Citation counts based on 3-year period with 

2-year lag, e.g., citations for 2008 are references made in articles in 

2008 data tape to articles in 2004–06 data tapes. 

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, and The Patent BoardTM, special tabulations 

(2011) from Thomson Reuters, SCI and SSCI, http://thomsonreuters. 

com/products_services/ science/. 

Science and Engineering Indicators 2012

Number

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

All countries

United States

Non-U.S.

As discussed earlier in this chapter, international collaboration has increased rapidly in the United States. 

International coauthorship across the U.S. sectors rose by 

7–11 percentage points between 2000 and 2010 (table 5-23). 

Articles from FFRDCs reached the highest rate of collaboration with foreign authors, at 46%, followed by those from 

academia, private nonprofit institutions, industry, and the 

federal government, at roughly 30% each. 

Trends in Citation of S&E Articles38

Citations indicate influence, and they are increasingly international in scope. 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.39 Citations are generally increasing in volume relative 

to S&E articles. In 1992, an S&E article received, on average, 1.85 citations. In contrast, an S&E article in 2010 received on average 2.32 citations (Figure 5-26). Articles with 

U.S. authors tended to receive more citations than others, but 

the gap narrowed over the period as the total share of U.S.-

authored articles declined.40

Like the indicators of international coauthorship discussed above, cross-national citations are evidence that 

S&E research is increasingly international in scope. Two 

other trends accompanied the steady growth of international citations in the world’s S&E literature: changing shares 

of total citations across countries and changing shares of 

highly cited S&E literature. These are discussed in the following sections.

Citation Trends in a Global Context

Shares of the world total of citations to S&E research articles have changed concurrently with shares of the world 

total of these articles. Table 5-24 shows, for example, that 

between the periods 1996–98 and 2006–08, the U.S. share 

of world S&E articles declined from 32% to 28% across all 

fields.41 The U.S. share declined in every broad field, although the decline varied in size. Table 5-24 shows parallel 

trends for the U.S. share of citations and indicates an even 

larger decline, from 45% to 36%. 

China’s share of total world S&E articles and citations 

increased over the same period. However, in contrast to the 

global trend of increasing international citations, China’s 

pattern has been different. Unlike the United States and 

other large article-producing countries/regions, the share of 

China’s citations that are international citations decreased

between 2000 and 2010, from 60% to 51% (figure 5-27), 

suggesting that much of the use of China’s expanding S&E 

article output—as indicated by citations to those articles—is 

occurring within China.42

Trends in Highly Cited S&E Literature

Another indicator of performance of a national or regional S&E system is the share of its articles that are highly cited. 

High citation rates can indicate that an article has a greater 

impact on subsequent research than articles with lower citation rates. 

Appendix table 5-44 shows citation percentiles for 2000 

and 2010 by field for the top five S&E article-producing 

countries/regions.43 In that table, a country whose global 

research influence was high would have higher proportions 

of articles in higher citation percentiles, whereas a country 

whose influence was low would have greater proportions of 

articles in lower citation percentiles. In other words, a country whose research is highly influential would have higher 

shares of its articles in higher citation percentiles.

World citations to U.S. research articles show that U.S. 

articles continue to have the highest citation rates across all 

broad fields of S&E. In both 2000 and 2010, as displayed 

in appendix table 5-44, the U.S. share of articles in the 99th 

percentile was higher than its share in the 95th percentile, 

and these were higher than its share in the 90th percentile, 

and so forth, even while U.S. shares of all articles and all 

citations were decreasing. In 2010, U.S. articles represented 

28% of the world’s total of 2.3 million articles in the cited 

period shown; the U.S. authored 49% of the rare 21,900 articles in the 99th percentile and 24% of the 1.3 million articles 

in the 50th percentile. 

Only U.S. publications display the preferred relationship 

of strongly higher proportions of articles in the higher percentiles of article citations. When cited, articles with authors 
5-44 Chapter 5. Academic Research and Development

from the European Union, China, Japan, and the Asia-8 are 

more often found in the lower citation percentiles. (These 

data are summarized in appendix table 5-45.) Nevertheless, 

as the U.S. share of all articles produced declined between 

2000 and 2010, 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 

European Union, China, Japan, and the Asia-8. 

To control for changing shares of the world’s S&E articles, Figure 5-28 shows the percentage of total articles for 

each of the United States, European Union, and China that 

appears in the world’s top 1% of cited articles. Across the 

decade, 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. China’s articles in the 

top 1% of cited articles remained behind the United States 

and European Union but increased from 0.1% to 0.5% over 

the period. 

When citation rates are normalized by the share of world 

articles during the citation period to produce an index of highly cited articles, the influence of U.S. articles has changed 

little over the past 10 years. Between 2000 and 2010, the U.S. 

index of highly cited articles barely changed (from 1.85 to 

1.76) (figure 5-29 and appendix table 5-45) and remained 

well above the expected index value of 1. During the same 

period, the EU increased its index from 0.73 to 0.93, and 

China, Japan, and the Asia-8 increased their index values but 

remained below their expected values. In other words, the 

United States had 76% more articles than expected in the 99th 

percentile of cited articles in 2010, and the EU had 7% fewer 

than expected. China had 51% fewer articles in the 99th percentile than expected in 2010, and Japan 39% fewer. 

Figure 5-27

Share of selected country/region citations that are 

international: 2000–10

EU = European Union

NOTES: Article/citation counts from set of journals covered by Science 

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

classified by year they entered database, rather than year of publication, 

and assigned to country/region on basis of institutional address(es) 

listed on article. Articles on fractional-count basis, i.e., for articles with 

collaborating institutions from multiple countries/regions, each 

country/region receives fractional credit on basis of proportion of its 

participating institutions. See appendix table 5-25 for countries included 

in EU and Asia-8, which in this table are treated as single countries. 

Citation counts based on 3-year period with 2-year lag, e.g., citations for 

2009 are references made in articles in 2009 data tape to articles in 

2005–07 data tapes. 

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, and The Patent BoardTM, special tabulations 

(2011) from Thomson Reuters, SCI and SSCI, http://thomsonreuters. 

com/products_services/science/.

Science and Engineering Indicators 2012

Percent

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

0

40

50

60

70

80

Japan

Asia-8

United States

China

EU

Table 5-24

S&E articles, citations, and international citations, by selected region/country: 2000 and 2010

(Percent)

Share of world articles 

(cited years)

 Share of world 

citations (citing year)

 Share of region/ 

 country citations 

 that are international 

 (citing year)

Region/country 1996–98 2006–08 2000 2010 2000 2010

United States ................................ 32.4 27.8 44.8 36.4 48.1 53.7

EU ................................................. 35.4 32.4 33.3 32.8 44.8 49.7

China ............................................ 2.0 7.5 0.9 6.0 60.3 50.8

Japan ............................................ 8.8 7.0 7.1 5.7 62.3 70.2

Asia-8 ........................................... 4.1 7.4 1.8 5.3 62.9 65.0

EU = European Union

NOTES: Article/citation counts from set of journals covered by Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). Articles classified 

by year they entered database, rather than year of publication, and assigned to country/economy on basis of institutional address(es) listed on article. 

Articles on fractional-count basis, i.e., for articles with collaborating institutions from multiple countries/economies, each country/economy receives 

fractional credit on basis of proportion of its participating institutions. See appendix table 5-25 for countries/economies included in EU and Asia-8, which 

in this table are treated as single countries. Citation counts based on 3-year period with 2-year lag (e.g., citations for 2000 are references made in articles 

in 2000 data tape to articles in 1996–98 data tapes); data shown are for the 3 years in cited year window. 

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent BoardTM, special tabulations (2011) from 

Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/. 

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 5-45

The United States experienced gains on the index of 

highly cited articles in engineering, astronomy, other life 

sciences, and psychology and declines in chemistry, geosciences, and mathematics, although all remained well above 

expectation (appendix table 5-45). The EU reached its expected value in engineering, chemistry, physics, and the agricultural sciences. Japan and the Asia-8 countries did not 

achieve the expected value of 1 in any broad field.

Notably, China achieved an index value of near 1 in engineering and computer sciences (figure 5-30). In most broad 

fields, China’s indexes of highly cited articles were higher in 

2010 than in 2000. In a few fields—the biological, medical, 

and social sciences—the Chinese index remained low, and 

these fields kept the index for all fields below 0.5 in 2010 

(appendix table 5-45). 

Academic Patents, Licenses, Royalties, 

and Startups 

Other indicators of academic R&D outputs reflect universities’ efforts to develop their intellectual property for possible commercial use in the form of 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, although some were granted patents much earlier.44 The Bayh-Dole Act of 1980 gave 

colleges and universities a common legal framework for 

claiming ownership of income streams from patented discoveries that resulted from their federally funded research. 

To facilitate the conversion of new knowledge produced in 

their laboratories to patent-protected public knowledge that 

can be potentially licensed by others or form the basis for 

a startup firm, more and more research institutions established technology management/transfer offices (Association 

of University Technology Managers 2009). 

The following sections discuss overall trends in university patenting and related indicators through 2009–10.

University Patenting Trends

U.S. Patent and Trademark Office (USPTO) data show 

that annual patent grants to universities and colleges ranged 

from 2,900 to 4,500 between 1998 and 2010 (appendix table 

5-46).45

The top 200 R&D-performing institutions, with 97% of 

the total patents granted to U.S. universities during the 1998–

2010 period, dominate among universities and university 

systems receiving patent protection.46 College and university 

patents have been about 4.2–4.7% of U.S. nongovernmental 

patents for a decade. Among the top R&D-performing institutions that received patents between 1998 and 2010, 19 

accounted for more than 50% of all patents granted to these 

institutions (although these included a few multicampus systems, including the Universities of California and Texas). 

The University of California system received 11.9% of all 

U.S. patents granted to U.S. universities over the period, followed by the Massachusetts Institute of Technology with 

4.2% of all U.S. patents granted to U.S. universities.

Biotechnology patents account for the largest percent 

(30%) of U.S. university patents in 2010 (appendix table 

5-47), and have grown over the past 15 years (figure 5-31). 

Pharmaceutical patents, the next largest technology area, 

have more recently begun to decline, from nearly 450 a year 

in the late 1990s to about 300 in more recent years. Patents 

for measuring devices, semiconductors, and optics have all 

increased gradually over the past two decades. 

Patent-Related Activities and Income 

Data from the Association of University Technology 

Managers (AUTM) indicate continuing growth in a number 

of patent-related activities. Invention disclosures filed with 

university technology management offices describe prospective inventions and are submitted before a patent application is filed. These grew from 12,600 in 2002, to 18,200 in 

Figure 5-28

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

the world’s top 1% of cited articles: 2000–10

EU = European Union

NOTES: Article/citation counts from set of journals covered by Science 

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

classified by year they entered database, rather than year of publication, 

and assigned to country/economy on basis of institutional address(es) 

listed on article. Articles on fractional-count basis, i.e., for articles with 

collaborating institutions from multiple countries/regions, each 

country/region receives fractional credit on basis of proportion of its 

participating institutions. See appendix table 5-25 for countries included 

in EU, which in this figure is treated as a single country. Citation counts 

based on 3-year period with 2-year lag, e.g., citations for 2009 are 

references made in articles in 2009 data tape to articles in 2005–07 data 

tapes. 

SOURCES: National Science Foundation, National Center for Science 

and Engineering Statistics, and The Patent BoardTM, special tabulations 

(2011) from Thomson Reuters, SCI and SSCI, http://thomsonreuters. 

com/products_services/science/.

Science and Engineering Indicators 2012

Percent

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

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
5-46 Chapter 5. Academic Research and Development

2009 (notwithstanding small shifts in the number of institutions responding to the AUTM survey over the same period) 

(figure 5-32). Likewise, new U.S. patent applications filed 

by AUTM university respondents also increased, from 6,500 

in 2001, to 11,300 in 2009. U.S. patents awarded to AUTM 

respondents stayed flat over the period, at about 3,000 per 

year with some fluctuation.47

The AUTM survey respondents reported 348 startup 

companies formed in 2003 and 555 in 2009, with a total of 

extant startup companies in 2009 of 3,175 (appendix table 

5-48). Licenses and options that generated revenues also 

increased over the period. However, active licenses, while 

increasing steadily from 1999 to 2008, declined slightly in 

2009; this decline may reflect the downturn in the U.S. economy in that period. 

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 the objectives 

of university technology management offices include more 

than royalty income.) At the same time, large one-time payments to a university can affect the overall trend in university licensing income. In 2009, the 153 institutions that 

responded to the AUTM survey reported a total of $1.5 billion in net royalties from their patent holdings, down sharply 

from the previous 2 years, perhaps as a result of the nation’s 

economic downturn in 2008–09 (appendix table 5-48). 

Figure 5-29

Index of highly cited articles, by selected S&E field and region/country: 2000 and 2010

EU = European Union

NOTES: Article/citation counts from set of journals covered by Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). Articles/citations 

classified by year they entered database, rather than year of publication, and assigned to country/region on basis of institutional address(es) listed on 

article. See appendix table 5-25 for countries included in EU and Asia-8. Citation counts based on 3-year period with 2-year lag, e.g., citations for 2010 

are references made in articles in 2010 data tape to articles in 2006–08 data tapes. Index of highly cited articles is country’s share of 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 BoardTM, special tabulations (2011) 

from Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/. See appendix table 5-45.

Science and Engineering Indicators 2012

All S&E

Index Engineering

Biological sciences Physics

United States EU China Japan Asia-8

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

Expected value = 1

2000 2010

United States EU China Japan Asia-8

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

Expected value = 1

2000 2010

United States EU China Japan Asia-8

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

Expected value = 1

2000 2010

United States EU China Japan Asia-8

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

Expected value = 1

2000 2010
Science and Engineering Indicators 2012 5-47

Figure 5-30

Chinese index of highly cited articles, by selected S&E field: 2000 and 2010

Index

NOTES: Article/citation counts from set of journals covered by Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). Articles/citations 

classified by year they entered database, rather than year of publication, and assigned to country on basis of institutional address(es) listed on article. 

Citation counts based on 3-year period with 2-year lag, e.g., citations for 2010 are references made in articles in 2010 data tape to articles in 2006–08 

data tapes. Index of highly cited articles is country’s share of 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 BoardTM, special tabulations (2011) from 

Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/. See appendix table 5-45. 

Science and Engineering Indicators 2012

All fields Engineering Chemistry Physics Geosciences Mathematics Computer

 sciences

Agricultural

sciences

Biological

sciences

Medical

sciences

0

0.2

0.4

0.6

0.8

1.0

1.2

Expected value = 1

2000 2010

Figure 5-31

U.S. academic patents, by technology area: Selected 5-year averages, 1991–2010

Number

NOTES: Data include institutions affiliated with academic institutions, such as university and alumni organizations, foundations, and university associations. Universities vary in how patents assigned, e.g., to boards of regents, individual campuses, or entities with or without affiliation with university. The 

Patent BoardTM technology areas constitute an application-oriented classification system that maps the thousands of International Patent Classes (IPCs) 

at main group level into 1 of 35 technology areas. If patent has more than one IPC, only primary IPC is considered in mapping. Data in figure not comparable to previous versions of the figure due to changes in classification system.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent BoardTM, special tabulations (2011) from 

U.S. Patent and Trademark Office (USPTO), Patent Grant Bibliographic Data. 

Science and Engineering Indicators 2012

Biotechnology Pharmaceuticals Measurement

techniques and

instrumentation

Materials Medical equipment Semiconductors Optics

0

200

400

600

800

1,000

1,200

1991–95 1996–2000 2001–05 2006–10
5-48 Chapter 5. Academic Research and Development

Patent-to-Literature Citations 

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 practical innovations.48 This indicator 

of how science links to invention increased sharply in the 

late 1980’s and early 1990’s (Narin, Hamilton, and Olivastro 

1997), due in part to developments in U.S. policy, industry growth and maturation, and court interpretation. 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, pp. 5-49 to 5-54).

Between 1998 and 2010, growth for this indicator was 

much slower. Of utility patents awarded to both U.S. and 

foreign assignees, 11% cited the S&E articles analyzed in 

this chapter in 2010 (appendix table 5-49). Concomitant with 

a growth in the percentage of U.S. utility patents awarded to 

foreign assignees, nearly 50% of the citations to the S&E 

literature in 2010 cited non-U.S. S&E articles. 

In 2010, five broad S&E fields (biological sciences, medical sciences, chemistry, physics, and engineering) accounted for 96% of the citations to U.S. articles in USPTO patents 

(figure 5-33 and appendix table 5-50). These citations are 

dominated by articles in the biological sciences, at 46% of 

the total (compare with patents awarded by technology area, 

figure 5-31).

Considering only citations to U.S. articles, growth in 

citations has been uneven across the sectors and thus sector shares have changed somewhat (appendix table 5-49). 

Citations to articles authored in the industry, nonprofit, and 

government sectors have lost share, largely to articles from 

academia, which grew from 58% to 64% of the total citations to U.S. articles between 1998 and 2010. Appendix table 5-50 summarizes the increasing role of citations to U.S. 

academic articles in the science linkage to U.S. patents. Of 

the five broad fields of S&E that accounted for virtually all 

patent citations to U.S. academic articles, increased shares 

of academic citations were notable in engineering (from 

46% to 63%) and physics (from 43% to 66%). 

Figure 5-34 shows, within the most cited S&E fields, the 

distribution by U.S. sector of citations to articles in U.S. patents in 2010. As noted above, academic articles dominate 

across all of the fields shown, from 62% in the biological sciences to 68% in chemistry. U.S. government-authored articles 

received 7% of the 2010 patent citations in both the biological 

and medical sciences. S&E articles from industry accounted 

for 27% of the engineering citations and about one-fifth of 

the articles cited in chemistry and physics. FFRDC-authored 

articles accounted for 6% of the physics citations. 

Energy and Environment-Related Patent Citations 

NSF developed a set of four filters for identifying patents 

with potential application in pollution mitigation and in alternative means of energy production, storage, and management. (See sidebar “Identifying Clean Energy and Pollution 

Control Patents” for details on the filters.) These include patents slated by the federal government for fast-track review 

at USPTO.49

Chapter 6 of this volume presents extensive data on the 

patents in these four technology areas, including the nationality of their assignees. (See chapter 6, “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 

Figure 5-33

Citations of U.S. S&E articles in U.S. patents, 

by selected S&E article field: 2010 

NOTES: Citations are references to S&E articles in journals covered 

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

(SSCI). Citation counts based on a 6-year window with 5-year lag, 

e.g., citations for 2010 are references in U.S. patents issued in 2010 

to articles published in 2000–05.

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, and The Patent BoardTM, special 

tabulations (2011) 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/. 

See appendix table 5-50. 

Science and Engineering Indicators 2012

Biological

sciences

46.4

Medical

sciences 22.1

Chemistry

12.2

Physics

8.5

Engineering 7.0

All others 3.9

Figure 5-32

U.S. university patenting activities: 2002–2009

SOURCE: Association of University Technology Managers (AUTM), 

AUTM Licensing Surveys: 2002–2009. See appendix table 5-48. 

Science and Engineering Indicators 2012

Thousands

2002 2003 2004 2005 2006 2007 2008 2009

0

5

10

15

20

Invention disclosures received

New U.S. patent applications filed

U.S. patents granted
Science and Engineering Indicators 2012 5-49

Using a combination of U.S. Patent Classification and 

International Patent Classification codes and text strings, 

NSF developed algorithms to identify USPTO-issued patents with potential application in four broad “green” technology areas. The four technology areas and their main 

sub-categories are listed below. The search codes used to 

locate relevant patents will be available in D’Amato et al. 

(2012 forthcoming), which documents the process used 

in developing these patent filters.

Identifying Clean Energy and Pollution Control Patents

Alternative energy 

production Energy storage

Energy management 

(smart grid) Pollution mitigation

Figure 5-34

Citations of U.S. S&E articles in U.S. patents, by selected S&E field and article author sector: 2010 

Percent

FFRDC = federally funded research and development center

NOTES: Citations are references to U.S. S&E articles in journals covered by Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). 

Citations on fractional-count basis, i.e., for cited articles with collaborating institutions from more than one sector, each sector receives fractional credit 

on basis of proportion of its participating institutions. Citation counts based on a 6-year window with 5-year lag, e.g., citations for 2010 are references in 

U.S. patents issued in 2010 to articles published in 2000–05. Detail may not add to total because of rounding.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, and The Patent BoardTM, special tabulations (2011) 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 2012

All fields Engineering Chemistry Physics Biological sciences Medical sciences

0

20

40

60

80

Federal government Industry Academia FFRDCs Nonprofit

Bioenergy

Geothermal

Hydropower

Nuclear

Solar

Wave/tidal/ocean

Wind

Electric/hybrid vehicles

Fuel cells

Batteries

Flywheels

Superconducting magnetic 

energy systems

Ultracapacitors

Hydrogen production and 

storage

Thermal energy storage

Compressed air

Advanced components

Sensing and measurement

Advanced control methods

Improved interfaces and 

decision support

Integrated communication

Recycling

Air

Solid waste

Water

Environmental remediation

Cleaner coal

Carbon and greenhouse gas 

capture and storage
5-50 Chapter 5. Academic Research and Development

R&D50 and the potential for practical use of the results of 

those R&D projects in new inventions and technologies.

Five broad S&E fields dominate the citations to S&E 

literature in these four patent areas: chemistry, physics, engineering, the biological sciences, and geosciences (which 

in this taxonomy includes the environmental sciences). The 

range of S&E fields cited indicates that these developing 

technologies rely on a wide base of S&E knowledge.51

The S&E fields cited by these patents are shown in table 

5-25. Thirty-five percent of the citations in alternative energy patents that cite S&E articles were to chemistry articles, 

followed by articles from physics (28%), engineering (20%), 

and the biological sciences (15%).

Chemistry also dominates the citations in patents for energy storage systems, at 54%., followed by citations to articles in engineering (20%), physics (16%), and the biological 

sciences (9%). 

Patents with potential for application in pollution mitigation processes cite S&E articles most often in chemistry, at 

31%. The biological sciences, geosciences, and engineering 

each receive about one-fifth of the citations in these patents.

Smart grid is a set of patents related to efficient use and distribution of energy. Two fields dominate the S&E article citations in these patents: physics (52%) and engineering (40%). 

Conclusion

U.S. universities and colleges continue to be key performers of U.S. R&D, particularly for basic research. Academic 

spending on R&D has continued to increase yearly over the 

last 10 years, both in current dollar and inflation-adjusted 

terms. Academic R&D spending primarily supports basic 

research—it accounted for 75% in 2009, with another 21% 

supporting applied research and 4% for development—proportions that have been stable over the decade. The federal 

government has long provided the majority of funding for 

academic R&D, at 59% in FY 2009. This federal support 

has grown yearly over the last 10 years—although when adjusted for inflation, FYs 2006 and 2007 were years of real 

dollar declines. Academic R&D has also long been concentrated in just a few S&E fields. For decades, more than half 

of all academic R&D spending has been in the life sciences. 

The structure and organization of academic R&D have 

also changed. Research-performing colleges and universities continued to expand their research space, particularly 

in the biological and medical sciences, which are the fields 

with the bulk of R&D expenditures. 

Both the overall academic S&E doctoral workforce and 

the academic research workforce have continued to increase, 

although the change since 2006 was the smallest single-period increase on record. The life sciences accounted for much 

of the growth in the academic S&E doctoral workforce, and 

life scientists represented more than a third of academic 

S&E doctoral researchers in 2008. The growth in the number of new PhDs has outpaced the growth in the number of 

full-time faculty positions since the late 1980s, particularly 

among life scientists. The following long-term academic 

workforce trends continue: a relative shift of S&E doctorate holders away from full-time faculty positions toward 

other full-time positions, part-time positions, and (in some 

years) postdocs; a relative shift toward greater employment 

of women and minorities; a steadily increasing proportion 

of foreign-born faculty and postdocs; and a decline in share 

of academic researchers receiving federal support. Federal 

support has been less available to early career S&E doctoral 

Table 5-25

Patent citations to S&E articles, by selected 

patent technology area and article field: 

1998–2010

Technology/field Citations (n) Percent

Alternative energy .......................... 7,852 100.0

Chemistry ................................... 2,770 35.3

Physics ...................................... 2,171 27.6

Engineering ................................ 1,532 19.5

Biological sciences .................... 1,179 15.0

Geosciences .............................. 116 1.5

All others .................................... 84 1.1

Energy storage .............................. 3,909 100.0

Chemistry ................................... 2,106 53.9

Engineering ................................ 783 20.0

Physics ...................................... 637 16.3

Biological sciences .................... 338 8.6

All others .................................... 45 1.2

Smart grid ...................................... 1,433 100.0

Physics ...................................... 750 52.3

Engineering ................................ 572 39.9

Computer sciences .................... 33 2.3

Biological sciences .................... 31 2.2

Geosciences .............................. 20 1.4

Chemistry ................................... 19 1.3

All others .................................... 8 0.6

Pollution mitigation ........................ 5,390 100.0

Chemistry ................................... 1,643 30.5

Biological sciences .................... 1,162 21.6

Geosciences .............................. 1,088 20.2

Engineering ................................ 1,068 19.8

Physics ...................................... 211 3.9

Agricultural sciences .................. 136 2.5

All others .................................... 82 1.5

NOTES: Citations are references to S&E articles in journals covered 

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

(SSCI). Citation counts based on a 6-year window with 5-year lag, 

e.g., citations for 2002 are references in U.S. patents issued in 2002 to 

articles published in 1992–97. Patents may appear in more than one 

technology area and 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 BoardTM, special 

tabulations (2011) 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 2012
Science and Engineering Indicators 2012 5-51

faculty than to more established faculty, and the percentage 

of early career S&E faculty with federal support has declined since 1991. 

The intimate links between research and U.S. graduate 

education, regarded as a model by other countries, helps 

to bring large numbers of foreign students to the United 

States, many of whom stay in the country after graduation. Academia has also been able to attract many talented 

foreign-born scientists and engineers into its workforce. In 

research institutions, foreign-born faculty who received their 

degrees in the United States approach half the total of all 

U.S. degrees granted in engineering and computer science.

Data on S&E research articles suggest that research is 

increasingly done in team settings: the number of authors 

per article has steadily increased over the past 20 years. 

Academic R&D is also becoming more international, and 

this trend is reflected in the data on S&E articles. U.S. academic scientists and engineers are collaborating extensively 

with colleagues in other countries—in 2010, nearly onethird of S&E articles with a U.S. author also had at least one 

coauthor from abroad, and U.S. authors appeared on more 

than 40% of all internationally coauthored articles. 

Citation data indicate that U.S. scientific publications 

remain highly influential relative to publications from other countries. However, the relative volume of U.S. article 

output has not kept up with the increasing outputs of the 

European Union and several countries in Asia. In recent 

years, China has become the second-largest national producer of S&E articles.

Notes

1. For this discussion, the terms universities and colleges,

higher education, and academic institutions are used interchangeably and include only those schools that grant a bachelor’s or higher degree in science or engineering and spend 

at least $150,000 for separately budgeted R&D in S&E.

2. 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 and highly specialized, shared research facilities.

3. For the definitions used in National Science Foundation 

(NSF) surveys and a more complete discussion of these concepts, see the chapter 4 sidebar, “Definitions of R&D.”

4. The academic R&D reported here includes separately 

budgeted R&D and related recovered indirect costs, as well 

as institutional estimates of unrecovered indirect costs associated with externally funded R&D projects, including 

mandatory and voluntary cost sharing.

5. Under the act the funding was to be obligated by the 

end of FY 2009. However, the expenditures for these projects could span several years.

6. Statistics on R&D performance can differ depending 

on whether the reporting is by R&D performers or R&D 

funders. There are a number of reasons for this difference; for 

a discussion see the chapter 4 sidebar, “Tracking R&D: Gap 

Between Performer- and Source-Reported Expenditures.”

7. 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.

8. The medical sciences include subfields such as pharmacy, neuroscience, oncology, and pediatrics. The biological sciences include subfields such as microbiology, 

genetics, epidemiology, and pathology. These distinctions 

may blur at times because the boundaries between fields often are not well defined.

9. Data reported on non-S&E R&D expenditures are lower-bound estimates (slightly) for the national totals because 

NSF did not attempt to adjust for the 2.7% nonresponse rate 

on this survey item. Also, only institutions that conducted 

at least $150,000 of S&E R&D were surveyed. The activities of institutions that do not perform S&E R&D (but may 

conduct substantial amounts of non-S&E R&D) are not reflected here.

10. Data on non-S&E R&D expenditures have been collected by NSF since FY 2003. However, the response rates 

on these items for the years prior to 2006 make trend analysis unreliable. 

11. This financial pass through is far from a complete indicator, as it provides little indication of the nature of the 

collaborative relationships involved.

12. Research space here is defined as the space used for 

sponsored R&D activities at academic institutions that is 

separately budgeted and accounted for. 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.

13. The S&E fields used in the NSF Survey of Science and 

Engineering Research Facilities are based on the National 

Center for Education Statistics (NCES) Classification of 

Instructional Programs (CIP)—which is updated every 10 

years (the current version is dated 2000). The S&E fields 

used in both the FY 2007 and FY 2009 Survey of Science 

and Engineering Research Facilities reflect the 2000 CIP update. 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.

14. The S&T field and subfield definitions were updated 

to the 2000 CIP starting with the FY 2007 facilities survey. 

Therefore, some of the observed declines in research space 

for health/clinical sciences and physical sciences between 

FY 2005 and FY 2007 could reflect definition changes.
5-52 Chapter 5. Academic Research and Development

15. 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.”

16. 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 cyberinfrastructure section of the survey, however, respondents are asked to 

identify all of their cyberinfrastructure resources, regardless 

of whether these resources were used for research or other 

functions.

17. 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 

NSF Survey of Research and Development Expenditures at 

Universities and Colleges.

18. Unless specifically noted, data on S&E doctorate 

holders in this section come from the Survey of Doctorate 

Recipients, a biennial NSF survey. All numbers are rounded 

to the nearest 100. Small estimates may be unreliable.

19. The United States is unlike many other countries in 

the fraction of doctorate holders who are 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 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 (Organisation for 

Economic Co-operation and Development 2009). 

20. Respondents were presented with a list of work activities and asked to identify the activities which occupied the 

most and second most hours during the 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 the primary and secondary work activity from a list of activities. Beginning in 1993, respondents 

were asked on which activity they spent the most hours and 

on which they spent the second most hours. Therefore, the 

crossing over of the two trends between 1991 and 1993 

could partly reflect a difference in methodology. However, 

the faster growth rate for researchers in both the 1973–91 

and 1993–2008 periods means that changes in question 

wording cannot fully explain the observed trend. Because 

individuals may select both a primary and a secondary work 

activity, they can be counted in both groups.

21. On the 2006 Survey of Doctorate Recipients, respondents were asked to indicate whether they “Work with an 

immediate work group or team?”; “Work with others in the 

same organization (company, university, agency, etc.), but 

not the same group or team?”; “Work with individuals in 

other organizations in the U.S.?”; and “Work with individuals located in other countries?” For respondents who indicated that they had collaborated with individuals located 

in other countries, subsequent questionnaire items inquired 

about the nature of the collaboration (for example, sharing 

information, sharing facilities, preparing a joint publication) 

and the mode of collaboration (for example, collaboration 

via telephone or e-mail, travel to foreign country).

22. These data include only U.S.-trained postdocs 

employed in U.S. academic institutions. A 2003 survey 

conducted by the Sigma Xi honor society, which was nonrepresentative and likely to undercount foreign-degreed 

postdocs, found that 46% of responding postdocs had received their doctorate from a non-U.S. institution.

23. Interpretation of the data on federal support of academic researchers is complicated by a technical difficulty. Between 1993 and 1997, respondents to the Survey of 

Doctorate Recipients were asked whether work performed 

during the week of April 15 was supported by the federal 

government. In most other survey years, the reference was 

to the entire preceding year, and in 1985, it was to the month 

of April. However, 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. 

Thus, the numbers for 1985 and 1993–97 cannot be compared with results for the earlier years or with those from 

the 1999 through 2008 surveys, which also used an entire 

reference year.

The discussion in this edition of Indicators generally compares data for 2008 with data for 1991. All calculations express 

the proportion of researchers with federal support relative to the 

number responding to this question. The reader is cautioned 

that, given the nature of these data, the trends discussed are 

broadly suggestive rather than definitive. The reader also is reminded that trends in the proportion of all academic researchers 

supported by federal funds occurred against a background of 

rising overall numbers of academic researchers.

24. Publication traditions in broad S&E fields differ 

somewhat. For example, computer scientists often publish their findings in conference proceedings, and social 

scientists often write books as well as publish in journals. 

Proceedings and books are poorly covered in the data currently used in this chapter.

25. The U.S. sector identification in this chapter is quite 

precise; to date, sector identification has not been possible 

for other countries. 

26. 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. This chapter follows the convention of counting a country’s articles in fractions (i.e., articles with more 

than one country’s participation are fractionalized according 

to the number of different institutional authors listed on the 

article). These fractions are then allocated to the respective 
Science and Engineering Indicators 2012 5-53

country and totaled to produce a national article count. This 

chapter uses the more straightforward if less precise terminology “country X produces some number of the world’s 

S&E articles.” It also refers to the percentage of the world’s 

total S&E articles accounted for by certain countries. 

27. For example, Vatican City is not strictly a country; 

the Union of Soviet Socialist Republics (USSR) and Hong 

Kong are contained in the data in earlier years, but the USSR 

no longer exists and Hong Kong data are now reported as 

part of China. See appendix table 5-25 for a list of the locations represented in the data.

28. Distributions of data in which a small percentage 

of cases account for a significant amount of the total value 

across all cases belong to a group of statistical distributions 

collectively referred to as power law distributions (Adamic, 

2000). Examples of other phenomena with such distributions include earthquakes (only a few among a large number 

of earthquakes have great power) and Internet traffic (visits 

to a relatively small number of sites account for a very large 

proportion of visits to all sites).

29. 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- 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). 

30. The reader is reminded that the data on which these 

indicators are based give the nationality of the institutional 

addresses listed on the article. Authors themselves are not 

associated with a particular institution and may be of any 

nationality. Therefore the discussion in this section is based 

on the nationality of institutions, not authors, and makes no 

distinction between nationality of institutions and nationality of authors.

31. For a consideration of current limitations in identifying interdisciplinary S&E research using bibliometrics 

techniques, see Wagner et al. (2011) and the sidebar “Can 

Bibliometric Data Provide Indicators of Interdisciplinary 

Research?” in NSB 2010.

32. 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. 

33. Finland is included here as one of the Scandinavian 

countries. Iceland is not.

34. Article counts in this section are based on the year in 

which the article appeared in the database, not on the year of 

publication, and therefore are not the same counts as in the 

earlier discussion of total world article output.

35. The 16 FFRDCs sponsored by the Department of 

Energy 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, p. 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 National Science Foundation 

also sponsor another 23 FFRDCs (NSF/SRS 2009). 

36. Identification of the sector of the non-U.S. institution 

is not possible with the current data set.

37. Readers are reminded that coauthors from different departments in an institution are coded as different 

institutions.

38. This chapter uses the convention of a 3-year citation 

window with a 2-year lag. For example, 2008 citation rates 

are from references in articles in the 2008 data file to articles contained in the 2004, 2005, and 2006 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, and in computer sciences, psychology, and the social sciences with a 3-year lag.

39. “Influence” is used here broadly; even citations that 

criticize or correct previous research indicate the influence 

of that previous research on the citing article. 

40. Because different S&E fields have different citation 

behaviors, these indicators should be used with caution. For 

example, articles in the 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 the life sciences (e.g., the U.S.) may receive proportionately more citations than a country whose 

portfolio is more heavily weighted toward engineering or 

mathematics. 

41. The reader is reminded that articles in this section are 

counted by the year they entered the database, not by year of 

publication. Therefore article counts, and percentages based 

on them, are different from the data presented earlier in this 

section.

42. Some part of this percentage decrease may reflect 

the increase in Chinese journals in the SCI and SSCI 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 is declining as a share of total citations. However, accounting for 

the “nationality” of a journal is not straightforward, and the 

data file used by NSF excludes journals that are primarily of 

regional interest. NSF’s count of “Chinese” journals shows 
5-54 Chapter 5. Academic Research and Development

an increase of 75% over the past decade, compared to an 

increase of 334% for Chinese-authored articles. 

43. Percentiles are specified percentages below which the 

remainder of the articles falls. For example, the 99th percentile identifies the number of citations 99% of the articles 

failed to receive. For example, across all fields of science, 

99% of articles from 2005 to 2007 failed to receive at least 

21 citations in 2009. 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 5-44 and 5-45 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 articles with three to four citations in 2005 through 

2007, whereas the 75th percentile for astronomy contained 

articles with 6 to 10 citations.

44. For an overview of these developments in the 20th 

century, see Mowery (2002).

45. Sharp changes in the number of patents granted are 

related to the speed of processing at United States Patent and 

Trademark Office. 

46. The institutions listed in appendix table 5-46 are 

slightly different from those listed in past volumes, and data 

for individual institutions may be different. In appendix table 5-46, 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).

47. The patent counts reported by Association of 

University Technology Managers respondents in figure 5-32 

and appendix table 5-48 cannot be compared with the patent 

counts developed from USPTO data as in appendix tables 

5-46 and 5-47.

48. Patent-based data must be interpreted with caution. Year-to-year changes in the data may reflect changes 

in USPTO processing times (so-called “patent pendency” 

rates). Likewise, industries and companies have different 

tactics and strategies for pursuing patents, and these may 

also change over time. 

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, etc.). 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 SCI/SSCI database 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.

49. Pilot Program for Green Technologies Including 

Greenhouse Gas Reduction, 74 Fed. Reg. 64,666 (USPTO, 

December 8, 2009).

50. Due to data limitations, this discussion is limited to 

the following: patent data are patent awards made by the 

USPTO to all assignees, not just U.S. assignees. S&E publication data are for all publications in all U.S. sectors and all 

country authors.

51. Compare with Organisation for Economic Cooperation and Development, 2010, p.36.

Glossary

Academic doctoral S&E workforce: Includes those 

with a U.S. doctorate in an S&E field employed in 2- or 

4-year colleges or universities in the following positions: 

full and associate professors (referred to as senior faculty); 

assistant professors and instructors (referred to as junior faculty); postdocs; other full-time positions such as lecturers, 

adjunct faculty, research associates, and administrators; and 

part-time positions of all kinds.

Academic institution: In the Expenditures and Funding 

for Academic R&D section of this chapter, an academic institution is generally defined as an institution that grants a 

bachelors’ or higher degree in science or engineering and 

that has spent at least $150,000 for separately budgeted R&D 

in S&E within the fiscal year being measured. Elsewhere in 

the chapter, this term encompasses any accredited institution 

of higher education. 

Underrepresented minority: Demographic category 

including blacks, Hispanics, and American Indians/Alaska 

Natives; groups considered to be underrepresented in academic institutions.

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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 and Technologies ...............................................6-7

Introduction ..................................................................................................................................6-8

Chapter Overview .....................................................................................................................6-8

Chapter Organization ................................................................................................................6-8

Data Sources, Definitions, and Methodology ...........................................................................6-8

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

Growth of Knowledge- and Technology-Intensive Industries in the World and 

Major Economies ...............................................................................................................6-11

Productivity .............................................................................................................................6-15

Information and Communications Technology Infrastructure ...............................................6-16

Worldwide Distribution of Knowledge- and Technology-Intensive Industries ........................6-17

Health and Education Services ...............................................................................................6-18

Commercial Knowledge-Intensive Service Industries ...........................................................6-18

High-Technology Manufacturing Industries ..........................................................................6-21

Information and Communications Technology Industries .....................................................6-25

Industries That Are Not Knowledge or Technology Intensive ..............................................6-26

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

Global Trade in Commercial KTI Goods and Services .........................................................6-31

Trade in Medium- and Low-Technology Manufactured Products .........................................6-39

U.S. Trade in Advanced Technology Products .....................................................................6-39

U.S. Multinational Companies in Knowledge- and Technology-Intensive Industries ...........6-42

U.S. and Foreign Direct Investment in Knowledge- and Technology-Intensive Industries ...6-45

Innovation-Related Indicators of the U.S. and Other Major Economies ...................................6-46

Innovation Activities by U.S. Businesses ...............................................................................6-47

Global Trends in Patenting and Trademarks ..........................................................................6-47

USPTO Patents Granted, by Technology Area ......................................................................6-51

Patenting Valuable Inventions: “Triadic” Patents ..................................................................6-53

Trademark Applications .........................................................................................................6-54

U.S. High-Technology Small Businesses ...............................................................................6-55

Investment and Innovation in Clean Energy and Technologies ................................................6-60

Commercial Investment ..........................................................................................................6-61

Venture Capital Investment ....................................................................................................6-63

Public Research, Development, and Demonstration Expenditures in Clean Energy 

and Technologies ..............................................................................................................6-64

Patenting of Clean Energy and Pollution Control Technologies ............................................6-65

Conclusion ................................................................................................................................6-69

Notes ..........................................................................................................................................6-70

Glossary .....................................................................................................................................6-72

References ..................................................................................................................................6-72

Please see chapter errata
6-2 Chapter 6. Industry, Technology, and the Global Marketplace

List of Tables

Table 6-1. Global value added of health and education services, by selected region/country/

economy: 1995, 2000, 2005, and 2010 .................................................................................6-13

Table 6-2. Indexes for ICT infrastructure for selected countries, by economic 

sector: 2010 ...........................................................................................................................6-18

Table 6-3. Global value added for selected service industries, by region/country/ 

economy: Selected years, 1995–2010 ...................................................................................6-28

Table 6-4. Global value added for manufacturing industries, by selected technology 

level and region/country/economy: Selected years, 1995–2010 ...........................................6-29

Table 6-5. Exports of high-technology products, by selected product and region/country/ 

economy: Selected years, 1995–2010 ...................................................................................6-36

Table 6-6. Exports of manufactured products, by selected technology level and region/ 

country/economy: Selected years, 1995–2010 ......................................................................6-41

Table 6-7. Stock of U.S. direct investment abroad and foreign direct investment in 

the United States, by selected industry: 2000, 2005, and 2009.............................................6-46

Table 6-8. USPTO patents granted, by selected technology area: Selected years, 

2000–10 .................................................................................................................................6-52

Table 6-9. Activity in USPTO patent grants in selected technology areas, 

by selected region/country/economy: 2007–10 average .......................................................6-52

Table 6-10. U.S. high-technology microbusinesses, by number of firms and employment 

for selected industries: 2008..................................................................................................6-56

Table 6-11. U.S. venture capital investment, by selected financing stage and 

industry/technology: Selected years, 2002–10 ......................................................................6-61

Table 6-12. Financial new investment, by selected region/country and energy/ 

technology: 2004–10 .............................................................................................................6-64

Table 6-13. USPTO patents granted in clean energy and pollution control technologies, 

by selected area: Selected years, 1995–2010 ........................................................................6-68

Table 6-14. Patenting activity in selected clean energy and pollution control technologies, 

by selected region/country: 2007–10 ....................................................................................6-69

Table 6-A. Share of global value added for selected industries, by region/country/ 

economy: Selected years, 1995–2010 ...................................................................................6-27

Table 6-B. Value chain of Apple iPad, by location and activity: 2010 .....................................6-30

Table 6-C. Patents granted by Brazil, China, India, and Russia, by share of resident 

and nonresident inventors: Selected years, 1995–2009 ........................................................6-50

List of Sidebars

Comparison of Data Classification Systems Used .......................................................................6-9

Industry and Trade Data and Terminology ................................................................................6-11

Apparent Consumption of High-Technology Manufactured Goods .........................................6-23

China’s Progress in Supercomputers .........................................................................................6-25

Trends in Industries Not Classified as Services or Manufacturing ............................................6-27

Tracing the Geography of the Value Chain of Products ............................................................6-30

Product Classification and Determination of Country of Origin of Trade Goods .....................6-38

New U.S. Patent Law .................................................................................................................6-49

Trends in Patents Granted in China, India, and Russia ..............................................................6-50

Government Stimulus Funding for Clean Energy .....................................................................6-62
Science and Engineering Indicators 2012 6-3

List of Figures

Figure 6-1. Global value added of knowledge- and technology-intensive industries 

for developed and developing countries: 1995 and 2010......................................................6-12

Figure 6-2. Output of knowledge- and technology-intensive industries as share 

of GDP: 1995–2010 ..............................................................................................................6-12

Figure 6-3. Output of knowledge- and technology-intensive industries as a share 

of GDP, by selected region/country: 1995 and 2010 ............................................................6-13

Figure 6-4. Commercial KI service share of nongovernment services, by selected 

region/country: Selected years, 1995–2009 ..........................................................................6-14

Figure 6-5. High-technology share of manufacturing sector for selected regions/countries: 

1995–2010 .............................................................................................................................6-14

Figure 6-6. Growth in GDP per employed person for selected regions/countries: 

1990–2008 .............................................................................................................................6-15

Figure 6-7. GDP per capita for selected developing economies: 1990–2008 ............................6-16

Figure 6-8. Household broadband penetration, by selected region/country: 2009 ....................6-17

Figure 6-9. ICT share of fixed capital investment for selected countries: 2000–08 ..................6-17

Figure 6-10. Share of U.S. workers with ICT skills, by selected industry: 2009 ......................6-19

Figure 6-11. Value added of commercial knowledge-intensive services, by selected 

region/country: 1995–2010 ...................................................................................................6-20

Figure 6-12. Growth of HT manufacturing and commercial KI industries for 

developed and developing countries: 1995–2010 .................................................................6-20

Figure 6-13. Growth of HT manufacturing and commercial KI services for 

developed and developing countries: 2008–10 .....................................................................6-20

Figure 6-14. Growth of selected U.S. industries: 2008–09 ........................................................6-21

Figure 6-15. Value added for communications services, by selected region/country: 

1995–2010 .............................................................................................................................6-21

Figure 6-16. Value added of high-technology manufacturing industries, by selected 

region/country: 1995–2010 ...................................................................................................6-22

Figure 6-17. Value added for selected manufacturing industries, by global share 

of selected region/country/economy:1995, 2000, 2005, and 2010 .......................................6-24

Figure 6-18. Value added for selected high-technology manufacturing industries, 

by global share of selected region/country/economy: 1995, 2000, 2005, and 2010 .............6-24

Figure 6-19. Value added for ICT industries, by selected region/country/economy: 

1995–2010 .............................................................................................................................6-26

Figure 6-20. Global commercial KTI exports and production: 1995–2010 ..............................6-32

Figure 6-21. Global commercial KTI exports, by selected region/country/ 

economy: 1995–2009 ............................................................................................................6-32

Figure 6-22. Exports of commercial knowledge-intensive services, by selected 

region/country/economy: 1995–2009 ...................................................................................6-33

Figure 6-23. Global exports of selected services, by selected region/country/ 

economy: 2008 ......................................................................................................................6-33

Figure 6-24. Trade balance in commercial KI services for selected region/country/ 

economy: 1995–2009 ............................................................................................................6-34

Figure 6-25. Export share of high-technology manufacturing production, by selected 

region/country/economy: 1995–2010 ...................................................................................6-35

Figure 6-26. Exports of high-technology goods, by selected region/country/economy: 

1995–2010 .............................................................................................................................6-35

Figure 6-27. Trade balance of high-technology products, by selected product and 

region/country/economy: 1995–2010 ...................................................................................6-37

Figure 6-28. U.S., EU, and Japan imports of communications and computer products, 

by selected origin: 2000 and 2010 ........................................................................................6-37

Figure 6-29. U.S. exports of semiconductors, by selected destination: 2000 and 2010 ............6-38

Figure 6-30. Asia-8 exports of selected goods, by type and destination: 2000 and 2010 .........6-39

Figure 6-31. China’s imports of semiconductors, by selected origin: 2000 and 2010 ..............6-39

Figure 6-32. India and Singapore’s exports of pharmaceuticals, by selected 

destination: 2010 ...................................................................................................................6-40

Figure 6-33. U.S. trade in advanced technology products and U.S. exchange rate: 

1995–2010 .............................................................................................................................6-42
6-4 Chapter 6. Industry, Technology, and the Global Marketplace

Figure 6-34. U.S. trade in advanced technology products, by selected region/ 

country/economy and technology: 2010 ...............................................................................6-43

Figure 6-35. Globalization indicators of U.S. multinationals in commercial 

knowledge-intensive services: 2000, 2004, and 2008 ...........................................................6-44

Figure 6-36. Globalization indicators of U.S. multinationals in high-technology 

manufacturing: 2000, 2004, and 2008...................................................................................6-45

Figure 6-37. Share of U.S. manufacturing companies reporting innovation 

activities: 2006–08 ................................................................................................................6-47

Figure 6-38. Share of nonmanufacturing U.S. companies reporting innovation, 

by selected industry: 2006–08 ...............................................................................................6-48

Figure 6-39. USPTO patents granted, by nationality of inventor: 1995–2010 ..........................6-48

Figure 6-40. USPTO patents granted to non-U.S. inventors, by selected region/country/ 

economy: 1992–2010 ............................................................................................................6-49

Figure 6-41. USPTO patents granted, by selected U.S. industry: 2009 .....................................6-51

Figure 6-42. New drugs approved by the FDA: 2000–10 .........................................................6-53

Figure 6-43. Global triadic patent families, by share of selected region/country/ 

economy: 1999–2008 ............................................................................................................6-53

Figure 6-44. U.S. trademark applications by U.S. and non-U.S.applicants: 1998–2008 ..........6-54

Figure 6-45. U.S. trademark applications from non-U.S. applicants, by share of 

selected region/country: 2000, 2004, and 2008 ....................................................................6-54

Figure 6-46. Share of scientific and advanced-technology related classes of U.S. 

 trademark applications: 2008 ...............................................................................................6-55

Figure 6-47. Number of U.S. high-technology firms, by size: Selected years, 2000–08 ..........6-55

Figure 6-48. Growth in number of U.S. microbusinesses, by selected industry: 2000–08 .......6-57

Figure 6-49. Estimated U.S. angel investment: 2001–10 ..........................................................6-58

Figure 6-50. Investment in financing stages preferred by Angel Capital Association 

member groups: 2009 ............................................................................................................6-58

Figure 6-51. Investment in technology areas preferred by member groups of the 

Angel Capital Association: 2009 ..........................................................................................6-59

Figure 6-52. Global and U.S. venture capital investment: 2002–10 ..........................................6-59

Figure 6-53. Non-U.S. venture capital investment, by region/country: 2005–10 ......................6-60

Figure 6-54. U.S. venture capital investment, by financing stage: Selected years, 2002–10 ....6-60

Figure 6-55. Financial new investment in clean energy and technologies, 

by selected region/country/economy: 2004–10 ....................................................................6-62

Figure 6-56. Financial new investment in clean energy and technologies, 

by select energy and technology: 2004–10 ...........................................................................6-63

Figure 6-57. Global venture capital investment in clean energy and technologies: 2004–10 ...6-64

Figure 6-58. Global venture capital investment in clean energy and technologies: 2004–10 ...6-65

Figure 6-59. Global government RD&D in clean energy and technologies, 

by technology area: 2003, 2005, 2007, and 2009 .................................................................6-65

Figure 6-60. Government RD&D expenditures for clean energy and technologies, 

by selected region/country: 2000–09 ....................................................................................6-66

Figure 6-61. U.S. government RD&D expenditures on clean energy and 

technologies: 2007–10 ..........................................................................................................6-66

Figure 6-62. U.S. government RD&D in clean energy and technologies, 

by share of technology area: 2005, 2007, 2009, and 2010 ....................................................6-66

Figure 6-63. USPTO patents in clean energy and pollution control technologies, 

by U.S. and non-U.S. inventors: 1995–2010 ........................................................................6-67

Figure 6-64. USPTO patents granted to non-U.S. inventors in clean energy and 

pollution control technologies, by selected region/country: 1995–2010 ..............................6-67

Figure 6-A. Apparent domestic consumption of high-technology manufacturing 

industries, by selected region/country/economy: 1995–2010 ...............................................6-23

Figure 6-B. Top 500 supercomputers by selected region/country: Selected years, 2004–10 ....6-25

Figure 6-C. Components of value added and value capture ......................................................6-31

Figure 6-D. Public stimulus funding for clean energy and technologies, 

by selected region/country: 2008–09 ....................................................................................6-62
Knowledge- and Technology-Intensive 

Industries in the World Economy

KTI industries have been a major and growing part of the 

global economy, with the United States having the highest 

KTI share of GDP of any large economy.

Global value added of knowledge- and technology-intensive (KTI) industries, consisting of five knowledgeintensive (KI) service and five high-technology (HT) 

manufacturing industries, totaled $18.2 trillion in 2010. 

This represents 30% of estimated world gross domestic 

product (GDP) in 2010, compared with a 27% share in 

1995.

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 32% and 30%, respectively. 

Major developing economies have lower KTI shares than 

developed economies. China’s KTI industries created 20% 

of GDP in 2010 compared to 17% in 1995. The KTI shares 

in Brazil, India, and Russia were similar to China’s. 

Rising KTI shares in most countries have coincided with 

growth in productivity. But productivity growth in the 

world’s developed economies since 2000 has been slower 

than in developing economies. 

Labor productivity growth in the United States and other developed countries slowed from 1.9% in the 1990s 

to 1.3% from 2000 to 2008, coinciding with slackening 

growth in their per capita GDP. 

Labor productivity growth in developing countries accelerated from 1.4% in the 1990s to 4.9% from 2000 to 2008, 

led by China, India, and Russia. China’s labor productivity 

grew at a 10% annual average with its per capita GDP increasing from 8% to 20% of U.S. per capita GDP. 

Worldwide Distribution of Knowledgeand Technology-Intensive Industries

The commercial KI service and HT manufacturing industries in the United States are collectively larger than 

in any other country. China’s KI and HT industries have 

been growing rapidly, making China a major center of 

global activity. 

The United States has larger output ($3.6 trillion) than any 

other country in commercial KI service industries (business, financial, and communications). However, the U.S. 

share of world output fell substantially in the last decade 

from 42% in 2000 to 33% in 2010. 

China’s world share of commercial KI service industries 

rose from 2% in 1995 to 7% in 2010, led by 20% average 

annual growth of its communications industry.

U.S. HT manufacturing industries have a larger share of 

global output than any other economy. The U.S. global 

share fell from 34% in 1998 to 28% in 2010. 

China’s share of the world’s HT manufacturing rose sixfold from 3% in 1995 to 19% in 2010, surpassing Japan 

in 2007. Its share grew rapidly across all HT manufacturing industries, reaching nearly 50% in computers, 26% in 

communications, and 17%–18% in pharmaceuticals and 

semiconductors. 

Global output of commercial KI services was flat and HT 

manufacturing declined in 2009 in the midst of the recession. Global output of commercial KI services and HT 

manufacturing recovered in 2010 with China and other 

developing economies leading the recovery. 

Global output of commercial KI services was flat in 2009 

as part of the worldwide recession. Output in developed 

countries declined by 1%. But output grew by 4% in developing economies, led by double-digit growth in China. 

Commercial KI services resumed growing in 2010, led by 

a 20% increase by developing countries. 

Global output of HT manufacturing industries declined by 

6% in 2009. It dropped by 7% for developed economies, 

but was flat in developing countries, with China growing 

by 9%. Global output bounced back in 2010, rising 14%, 

propelled by China and other developing countries. 

Trade and Other Globalization Indicators

Worldwide, commercial KTI exports have grown faster 

than their KTI production, indicating increased globalization in these industries. 

The export share of commercial KI production rose from 

5% in 1995 to 8% in 2010 suggesting a modest rate of globalization. Advances in information and communications 

technology (ICT) and emerging capabilities in both developed and developing countries, such as India, are driving 

globalization of commercial KI services.

The export share of HT manufacturing production rose 

from 36% to 53% in 2006 before drifting downward to 

50% in 2010. 

The United States is the second-largest exporter behind 

the EU of commercial KI services and runs a large surplus. In HT goods, the United States has lost export share 

and faces a widening trade deficit. 

The United States exported $290 billion of commercial KI 

services (business, computer and information services, finance, and royalties and fees), with a 22% share of global 

exports behind the EU’s 30%. The Asia-8 and China are the 

next two largest exporters with global shares of 15% and 

8%, respectively.

Highlights

Science and Engineering Indicators 2012 6-5
The U.S. trade surplus in commercial KI services rose 

from $55 billion in 2000 to reach more than $100 billion 

in 2009; during this same period, however, the U.S. trade 

deficit in HT manufacturing goods grew. 

China’s and the Asia-8’s surpluses in commercial KI services have grown over the last decade to reach about $30 

billion in 2009. The increase in the Asia-8’s surplus reflects 

rising surpluses in computer and information services.

While the U.S. share of global HT exports declined, 

China became the world’s largest exporter of HT goods.

The U.S. share of global HT exports rose from 19% to 22% 

from 1995 to 1998 before declining to 14%–15% during the 

period from 2003 to 2010 because of losses in communications and computers. The U.S. deficit in HT trade widened 

from $67 billion to $94 billion during the 2000s, driven by 

rising deficits in communications and computer goods. 

China’s share of global HT goods exports more than tripled, 

from 6% in 1995 to 22% in 2010, making it the single largest exporting country for HT products. China’s trade surplus in these products increased from less than $20 billion 

in 2002 to nearly $160 billion in 2010, largely because of 

rising surpluses in computer and communications goods. 

China’s rise as the world’s major assembler and exporter 

of many electronic goods is reflected in a sharp increase in 

China’s share of imports of intermediate communications 

and computer goods originating from other Asian economies. Most of China’s exports of electronics goods are destined for the United States, the EU, and Japan. 

A separate measure of U.S. HT trade shows patterns in 

U.S. HT trade similar to those found in internationally 

comparable trade data. 

According to U.S. Census data on U.S. trade in advanced 

technology products (ATP), the United States first generated a trade deficit in ATP in 2002 that widened to $82 

billion by 2010. The deficit in ICT products alone reached 

more than $120 billion in 2010. Aerospace and electronics 

generated a combined surplus of $70 billion in 2010. 

The largest U.S. trade deficit in ATP was $87 billion with 

China, its largest trading partner country in total goods and 

ATP trade, followed by $17 billion with the Asia-8, and $8 

billion with Japan. ICT deficits with these Asian economies were higher, offset by lower deficits or positive trade 

balances in other ATP categories. 

U.S. foreign overseas investment in KTI industries exceeds foreign investment in U.S. KTI industries. 

The stock of U.S. overseas investment in KTI industries was 

$1.1 trillion, and the stock of foreign direct investment in the 

United States in these industries was almost $700 billion.

The bulk of U.S. overseas KTI investment was in service 

industries ($1 trillion), with less than 15% in HT manufacturing industries ($125 billion) in 2009. 

Financial services had by far the largest share in the stock of 

U.S. overseas investment in commercial KI service industries (74%), followed by business services (19%). Among 

HT manufacturing industries, pharmaceuticals (41%) and 

semiconductors (25%) had the largest shares. 

The stock of foreign direct investment (FDI) in the United 

States in commercial KI service industries stood at $433 

billion in 2009; FDI in U.S. HT manufacturing industries 

stood at $222 billion.

Financial services had the largest share (68%) in the stock 

of FDI in commercial KI service industries, followed by 

business services (19%) and communications (13%). 

Pharmaceuticals accounted for 68% of the share for HT 

manufacturing industries. 

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 other industries.

Four HT manufacturing industries—computers, communications, scientific and measuring instruments, and 

pharmaceuticals—reported rates of product and process 

innovation that were at least double the U.S. manufacturing 

sector average.

In the U.S. nonmanufacturing sector, software firms lead, 

with 77% of companies reporting the introduction of a new 

product or service compared to the 7% average for all nonmanufacturing companies. Innovation is also two to three 

times higher than the nonmanufacturing average in telecommunications/Internet industries.

The U.S. share of patents granted by the U.S. Patent 

and Trademark Office has declined over the last decade, 

which may indicate increased technological capacity 

abroad.

The U.S. resident share of U.S. Patent and Trademark 

Office (USPTO) patents granted has gradually fallen since 

the late 1990s, from 54% in 1998 to 52% in 2002 and down 

to 49% in 2010. The EU, Japan, and the Asia-8 were the 

main recipients of USPTO patents granted to non-U.S. 

countries, with a collective share of nearly 90%.

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 patents sought in three of the world’s largest markets—

the United States, the EU, and Japan. The United States, 

the EU, and Japan have similar shares of these high-value 

patents, accounting for nearly 90% of the total. 

U.S. microbusinesses (those with fewer than five employees) in industries classified as HT by the Bureau of Labor 

Statistics (BLS) grew much faster than in other industries 

6-6 Chapter 6. Industry, Technology, and the Global Marketplace
Science and Engineering Indicators 2012 6-7

during the period 2000–08. Growth of microfirms in services classified as HT was three times that of other service 

industries. 

The three HT services with the largest number of microbusinesses are management, scientific, and technical consulting; computer systems design; and architectural and 

engineering. HT manufacturing industries with large number of microfirms include navigational, measuring, and 

electromedical equipment and semiconductors.

Investment and Innovation in Clean Energy 

and Technologies

According to commercial investment data from Bloomberg, 

China in 2010 provided more investment in clean energy 

and technologies than any other country. 

Chinese commercial investment in clean energy and technologies, which Bloomberg defines to include wind, solar, 

biofuels, and energy efficiency, rose exponentially from 

less than $1 billion in 2004 to $53 billion in 2010. The bulk 

of China’s investment was in wind energy ($45 billion).

The United States and the EU each provided about $30 billion in clean energy finance in 2010. Wind energy accounts 

for the largest share (60%) of U.S. investment, with solar 

the second largest.

The United States is the leading investor of venture capital in clean energy and technologies. 

Worldwide venture capital investment in clean energy and 

technologies rose rapidly, more than quadrupling from $1 

billion to $4 billion from 2004 to 2010. The United States 

is the largest source of this type of investment, providing 

more than 80% of global energy-related venture capital. 

Two technologies, energy smart/efficiency and solar, dominate venture capital investment. Each has a 40% share.

According to data from the International Energy 

Administration (IEA), the United States in 2009 invested more in public research, development, and demonstration for clean energy and technologies than other 

countries/regions.

Global public research, development, and demonstration 

(RD&D) investment for clean energy and related technologies was an estimated $17 billion in 2009. IEA data cover 

renewable energy, nuclear, fuel cells, carbon capture and 

storage, and energy efficiency.

U.S. public RD&D investment in clean energy and technologies jumped from $2.8 billion in 2008 to $7.0 billion 

in 2009. However, this increase reflected one-time stimulus 

funding under the American Recovery and Reinvestment 

Act (ARRA). In 2010, U.S. public RD&D fell to $4.4 billion, when ARRA funding declined.

The EU and Japan each funded about $4 billion in 2009, 

equivalent to a 24% global share. 
6-8 Chapter 6. Industry, Technology, and the Global Marketplace

Introduction

Chapter Overview

Policymakers in many countries increasingly emphasize 

the central role of knowledge, particularly R&D and other 

activities that advance science and technology, in a country’s economic growth and competitiveness. This chapter 

examines the downstream effects of these activities on the 

economies of the United States and its major competitors in 

the global marketplace. 

Knowledge- and technology-intensive (KTI) industries in 

both the service and manufacturing sectors are a major focus 

of the chapter. These industries are considered to have a particularly strong link to science and technology. In many cases, 

these industries develop technological infrastructure that diffuses across the entire economy. Information and communications technology (ICT), for example, is widely regarded 

as a transformative “platform” technology that has altered 

lifestyles and the conduct of business across a wide range of 

sectors. Industries that are less knowledge and technology intensive, however, remain very important in the world economy and therefore receive some attention in the chapter.

The globalization of the world economy involves the rise 

of new centers of KTI industries. Although the United States 

continues to be a leader in these industries, developing economies, especially in Asia, have vigorously pursued national 

innovation policies in an effort to become major producers and exporters of KTI goods and services. Advances in 

science and technology have enabled companies to spread 

KTI activity to more locations around the globe while also 

maintaining strong interconnections among geographically 

distant entities.

Innovation is closely associated with technologically led 

economic growth, and observers regard it as important for 

advancing living standards. 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 high-technology 

(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. Clean energy has a strong link to 

science and technology. 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.

Chapter Organization

This chapter is organized into five sections. The first 

section discusses the increasingly prominent role of KTI 

industries in regional/national economies around the world. 

The focus is on the United States, the European Union 

(EU), Japan, China, and the Asia-8—India, Indonesia, 

Malaysia, Philippines, Singapore, South Korea, Taiwan, and 

Thailand—which are included because of their substantial 

activity in KTI production and trade and growing trade ties 

with China. The timespan is from the early 1990s, roughly 

the end of the Cold War, to the present.

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 groups as a whole and for particular services and 

manufacturing industries within them. Because technology 

is increasingly essential for non-HT industries, some data on 

the latter are presented as well.

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. It presents bilateral trade data 

to provide a rough indication of the internationalization of 

the supply chains of HT manufacturing industries, with a 

special focus on Asia. The section also presents data on U.S. 

trade in advanced technology products, examining trends in 

U.S. trade with major economies and in key technologies. 

Domestic and foreign production and employment of U.S. 

multinationals in KTI industries are presented as indicators 

of the increasing involvement of these economically important firms in cross-border 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 by KTI industries.

The fourth section presents innovation-related indicators. Using a new classification system, it examines country shares in patents granted by the United States in various 

technologies. It next examines patterns in country shares 

of high-value patents. It presents innovation-related data 

on U.S. industries from the National Science Foundation’s 

new Business R&D and Innovation Survey. A discussion of 

U.S. HT small businesses includes data on the number of HT 

small business startups and existing firms, employment, and 

venture and angel capital 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. They 

are knowledge and technology intensive and thus are closely 

linked to scientific research and development. 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,” shows the classification systems used in this chapter. 
Science and Engineering Indicators 2012 6-9

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 (ISIC)

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 for 

some developing 

countries

Trade in commercial 

KI services (new for 

2012)

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 (SITC) 

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 science, 

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

NSF, 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
6-10 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

Organization 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

U.S. trademarks 

(new for 2012)

USPTO Trademark 

applications

Applicant country 

of origin, trademark 

class as determined 

by USPTO

45 trademark 

goods/services 

classes; 

trademarks, 

applicants classified 

by country of origin

Source of data is 

USPTO

Venture capital Dow Jones Venture 

source (new for 

2012)

Investment, 

technology area, 

country of investor 

origin

Technology areas as 

classified by Dow 

Jones classification 

system

27 technology 

areas, investment 

classified by 

venture firms’ 

country of 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

Knowledge- and Technology-Intensive 

Industries in the World Economy

Science and technology are widely regarded as important 

for the growth and competitiveness of individual industries 

and for overall national economic growth. Indeed, global 

economic growth increasingly depends on science, technology, and other knowledge-based assets. Policymakers in 

developed and developing countries are striving to attract, 

cultivate, and retain knowledge-based companies and workers to foster national prosperity and to increase national access to the global economy.1

The Organisation for Economic Co-operation and 

Development (OECD 2001, 2007) has identified 10 categories of industries that have a particularly strong link to science and technology.2

 Data on worldwide production in these 

industries can be used to examine their growing importance in 

the United States and other major economies.3 These industries include both knowledge-intensive (KI) service industries 

and industries that produce high-technology (HT) manufactured goods. Collectively referred to as knowledge- and technology-intensive (KTI) industries, they include:

Five KI service industries that incorporate 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 that 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.4

 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.5

This section examines the role of KTI industries in the 

global economy. (For a discussion of value added and other 

measures of economic activity, see sidebar, “Industry and 

Trade 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. 
Science and Engineering Indicators 2012 6-11

(figure 6-3). In India and Russia, the KTI shares each rose 

2–4 percentage points to reach 19% and 20% of GDP, respectively, driven by the increases in commercial and public 

KI service shares. 

 Commercial Knowledge-Intensive Services 

Value added of commercial KI services more than doubled from $4.4 trillion in 1995 to $10.9 trillion in 2010, 

representing 60% of the value added of all KTI industries 

($18.2 trillion) (figure 6-1 and appendix table 6-3). In the 15 

years leading up to 2010, commercial KI services increased 

their share of world economic activity from 15% to 18% (appendix table 6-2). Public KI services, especially education, 

also increased their share of the growing global GDP (figure 

6-2 and appendix tables 6-4 and 6-5). 

In the United States, value added of commercial KI services increased from 20% to 25% of GDP, the highest share 

of any large economy (figure 6-3 and appendix table 6-3). For 

the EU, the comparable figure rose by 4 percentage points to 

reach 18%, with France and Germany near the EU average 

and the UK above it. Japan’s share rose from 15% to 17%. 

The trend in large developing economies varied, with the 

shares of China and Brazil remaining roughly steady at 12%–

14% (figure 6-3 and appendix tables 6-2 and 6-3). India’s and 

Russia’s shares each climbed by 3 percentage points to reach 

13% and 14%, respectively. The differences among these 

economies reflect their stage of development and government 

policies, and may also reflect differences in the difficulty in 

measuring economic activity of service industries.

Commercial KI services as a percentage of non-government 

services (i.e., including health, education, and all commercial services) also increased (figure 6-4), and national 

Growth of Knowledge- and TechnologyIntensive Industries in the World and Major 

Economies

KTI industries have become a major part of the global 

economy and represent a growing share of many countries’ 

total economic activity. Global value added of these industries totaled $18.2 trillion in 2010 (figure 6-1 and appendix 

table 6-1). This represents 30% of estimated world gross 

domestic product (GDP), compared with a 27% share of a 

much smaller global economy 15 years earlier (figure 6-2 

and appendix table 6-2). Almost all of the share increase 

occurred between 1995 and 2001. Most of the increase in 

the KTI share of the world economy stemmed from growth 

in KTI industries in the United States, the European Union 

(EU), Japan, and several developing economies. 

The KTI shares of the total economic output of the United 

States, EU, and Japan rose by 4–7 percentage points from 

1995 to 2010, reaching 40% in the United States, 32% in the 

EU, and 30% in Japan (figure 6-3). The higher U.S. share 

relative to the EU and Japan reflects a greater intensity of 

commercial KI services, notably finance and business services. The KTI share increases in the economies of South 

Korea and Taiwan were larger, rising by 7–10 percentage 

points to 29% and 32%, respectively, with increases occurring in both manufacturing and service industries. South 

Korea and Taiwan both became wealthy, developed economies during this period. 

KTI shares also grew in most of the developing economies. China’s KTI share grew by 3 percentage points to 

reach 20%, driven by a doubling of HT manufacturing share 

and increases in commercial KI services and education 

The data and indicators reported here permit the tracing 

and analysis of broad patterns and trends that shed light on 

the broadening and shifting distribution of global knowledge- and technology-intensive capabilities. The industrylevel production and trade data used in this chapter derive 

from a proprietary IHS Global Insight database that assembles data from the United Nations 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. 

Two measures of industry activity—value added and 

trade volume—are expressed in current dollars. 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 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 or companies in foreign 

countries. Moreover, the value added of a company’s activity is assigned to a single industry based on 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. Thus, valueadded trends should be interpreted as broad and relatively 

internally consistent indicators of the changing distribution 

of where economic value is generated.

Data on exports and imports represent the market 

value of products in international trade. This measure is 

not comparable with the value-added measure of industry 

production. Exports and imports are credited to the country where the product was “substantially transformed” 

into final form, but for exports produced in multiple 

economies, the assigned country may not be the location 

with the highest value added.

Industry and Trade Data and Terminology
6-12 Chapter 6. Industry, Technology, and the Global Marketplace

differences in rates of increase were generally, but not always, similar to those for commercial KI services alone.

The three commercial KI service industries contributed 

uneven value-added amounts. The largest, business services, 

provided $5.7 trillion (52% of global total value added in 

2010) (appendix table 6-6). Business services include the 

S&T intensive R&D services and computer programming 

industries (appendix tables 6-7 and 6-8). The second-largest, 

finance, provided $3.9 trillion (36% of global value added) 

(appendix table 6-9). Communications, crucial for information and data transactions in today’s knowledge-based economies, provided $1.3 trillion (12% of global value added) 

(appendix table 6-10).6

Education and Health Services

The education and health sectors generated an estimated 

global value added of $2.6 and $3.3 trillion, respectively, in 2010 (table 6-1 and appendix tables 6-4 and 6-5).7

International comparison of these two sectors is complicated 

by variations in market structure, the size and distribution of 

each country’s population, and the degree of government involvement and regulation. As a result, differences in marketgenerated value added may not accurately reflect differences 

in the relative value of these services. 

Between 2000 and 2010, the value added generated by 

education services in developed countries nearly doubled, 

rising from $1.1 trillion to $2.0 trillion (appendix table 6-4). 

Output in the developing world tripled, increasing from $190 

billion to $600 billion. China’s output more than quadrupled, 

and Brazil’s output nearly tripled. Russia’s and India’s outputs, starting from a low base, expanded more than fivefold 

KI = knowledge-intensive; KTI = knowledge- and technology-intensive

NOTES: Output of knowledge- and technology-intensive industries on value-added basis. Value added is amount contributed by country, firm, or other entity to 

value of good or service and excludes purchases of domestic and imported materials and inputs. Knowledge- and technology-intensive industries include 

knowledge-intensive services and high-technology manufacturing industries classified by the Organisation for Economic Co-operation and Development. 

Knowledge-intensive services include business, financial, communications, education, and health. Commercial knowledge-intensive services include business, 

financial, and communications services. Public knowledge-intensive services include education and health. High-technology manufacturing industries include 

aerospace, communications and semiconductors, computers and office machinery, pharmaceuticals, and scientific instruments and measuring equipment. 

SOURCE: IHS Global Insight, World Industry Service database (2011). See appendix tables 6-1, 6-3, 6-4, 6-5, and 6-11. 

Science and Engineering Indicators 2012

Dollars (trillions)

Figure 6-1

Global value added of knowledge- and technology-intensive industries for developed and developing countries: 

1995 and 2010

All KTI

industries

Commercial

KI services

Public

KI services

1995 2010

High-technology

manufacturing

All KTI

industries

Commercial

KI services

Public

KI services

High-technology

manufacturing

0

4

8

12

16

20

Developed

Developing

GDP = gross domestic product

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

value-added basis. Value added is amount contributed by country, firm, 

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

domestic and imported materials and inputs. Knowledge- and 

technology-intensive industries include knowledge-intensive services 

and high-technology manufacturing industries classified by the 

Organisation for Economic Co-operation and Development. 

Knowledge-intensive services include business, financial, 

communications, education, and health. Commercial knowledgeintensive services include business, financial, and communications 

services. Public knowledge-intensive services include education and 

health. High-technology manufacturing industries include aerospace, 

communications and semiconductors, computers and office machinery, 

pharmaceuticals, and scientific instruments and measuring equipment.

SOURCE: IHS Global Insight, World Industry Service database (2011). 

See appendix tables 6-1, 6-2, 6-4, 6-5, and 6-11. 

Science and Engineering Indicators 2012

Percent

Figure 6-2

Output of knowledge- and technology-intensive 

industries as share of GDP: 1995–2010

1995 1999 2003 2007 2010

0

5

10

15

20

25

30

35

40

Commercial knowledge-intensive services

Public knowledge-intensive services

High-technology manufacturing industries
Science and Engineering Indicators 2012 6-13

and threefold, respectively (table 6-1). Increases by these 

large developing economies coincided with the rapid expansion of university enrollments and graduation of new degree 

holders. (See “Global Trends in Higher Education in S&E” 

in chapter 2 for a discussion of international trends in S&E 

higher education.)

As with education services, production of health care 

services in developed countries also doubled from 2000 

to 2010, rising from $1.4 trillion to $2.9 trillion (appendix 

table 6-5). The United States and the EU have the largest 

health care sectors, as measured by share of global value 

added (34% each) (table 6-1). The growth trend in health 

care for these two developed economies was similar to that 

in education.

High Technology Manufacturing 

The global value-added output of HT manufacturing industries increased from about $700 billion in 1995 to $1.4 

trillion in 2010 (appendix table 6-11). However, the share 

of HT manufacturing industries in the global economy remained broadly steady during this period (figure 6-2 and 

appendix table 6-2) because of stronger overall growth in 

service industries than in manufacturing. In most nations, 

the HT manufacturing share of the economy remained flat 

or declined somewhat (figure 6-3). China was an exception. The HT manufacturing share of its economy doubled 

from 2% to 4%. This likely reflects a shift of final assembly 

of these goods from other Asian economies and developed 

economies to China. 

EU = European Union; GDP = gross domestic product; KI = knowledge-intensive

NOTES: Output of knowledge- and technology-intensive industries on value-added basis. Value added is amount contributed by country, firm, or other entity to 

value of good or service and excludes purchases of domestic and imported materials and inputs. Knowledge- and technology-intensive industries include 

knowledge-intensive services and high-technology manufacturing industries classified by the Organisation for Economic Co-operation and Development. 

Knowledge-intensive services include business, financial, communications, education, and health. Commercial knowledge-intensive services include business, 

financial, and communications services. Public knowledge-intensive services include education and health. High-technology manufacturing industries include 

aerospace, communications and semiconductors, computers and office machinery, pharmaceuticals, and scientific instruments and measuring equipment. EU 

excludes Cyprus, Estonia, Latvia, Lithuania, Luxembourg, Malta, and Slovenia. China includes Hong Kong.

SOURCE: IHS Global Insight, World Industry Service database (2011). See appendix tables 6-2, 6-3, 6-4, 6-5, and 6-11. 

Science and Engineering Indicators 2012

Percent

Figure 6-3

Output of knowledge- and technology-intensive industries as a share of GDP, by selected region/country: 1995 

and 2010

United States EU Japan China India Brazil Russia

1995 2010 1995 2010 1995 2010 1995 2010 1995 2010 1995 2010 1995 2010

0

10

20

30

40

50

Commercial KI services

Public KI services

High-technology manufacturing

Table 6-1

Global value added of health and education 

services, by selected region/country/economy: 

1995, 2000, 2005, and 2010

Characteristic 1995 2000 2005 2010

Education

World ($ billions) ............ 1,209.9 1,329.9 1,882.8 2,552.8 

United States ........ 30.5 36.3 33.9 31.6

EU.......................... 34.1 28.5 33.3 29.9

Japan .................... 15.9 12.8 8.4 6.9

China ..................... 1.7 3.1 4.0 6.7

Asia-8 .................... 4.3 4.7 5.4 6.2

ROW ...................... 13.5 14.6 15.0 18.7

Health and social services

World ($ billions) ............ 1,394.7 1,553.6 2,370.3 3,334.9 

United States ........ 33.3 38.1 35.2 33.3

EU.......................... 37.3 31.0 36.0 33.9

Japan .................... 13.8 14.1 11.0 10.3

China ..................... 1.1 1.7 1.9 2.8

Asia-8 .................... 2.5 3.1 3.4 4.4

ROW ...................... 12.0 11.9 12.5 15.3

EU = European Union; ROW = rest of world

NOTES: Value added is amount contributed by country, firm, or 

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

domestic and imported materials and inputs. Asia-8 includes India, 

Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan, 

and Thailand. China includes Hong Kong. EU excludes Cyprus, 

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

SOURCE: IHS Global Insight, World Industry Service database (2011). 

See appendix tables 6-4 and 6-5.

Science and Engineering Indicators 2012
6-14 Chapter 6. Industry, Technology, and the Global Marketplace

Within the manufacturing sector, many economies experienced a modest shift toward HT industries. In both 

developed and developing economies, the HT share of the 

manufacturing sector has increased by 2 percentage points 

since 1995, reaching 16% and 10%, respectively (figure 6-5 

and appendix tables 6-11 and 6-12). The HT share of the 

U.S. manufacturing sector, at 21% in 2010, is larger than in 

either the EU or in Japan. In China, the HT share increased 

from 7% to 13% of its total manufacturing base, similar to 

the proportion in the EU. However, other large developing 

countries underwent almost no change on this indicator.

Information and Communications 

Technology Industries

Many economists regard information and communications 

technology (ICT) as a general-purpose platform technology 

that fundamentally changes how and where economic activity 

is carried out in today’s knowledge based economies, much as 

earlier general-purpose technologies (e.g., the steam engine, 

automatic machinery) propelled growth during the Industrial 

Revolution.8

 Thus ICT facilitates broad development of new 

markets (e.g., for mobile computing, data exchange, and communications). 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. 

Figure 6-4

Commercial KI service share of nongovernment 

services, by selected region/country: Selected 

years, 1995–2009

EU = European Union; KI = knowledge-intensive

NOTES: Output of commercial knowledge-intensive and 

nongovernment service industries on value-added basis. Value 

added is amount contributed by country, firm, or other entity to value 

of good or service and excludes purchases of domestic and imported 

materials and inputs. Commercial knowledge-intensive services are 

classified by the Organisation for Economic Co-operation and 

Development and include business, financial, and communications 

services. EU excludes Cyprus, Estonia, Latvia, Lithuania, 

Luxembourg, Malta, and Slovenia. China includes Hong Kong.

SOURCE: IHS Global Insight, World Industry Service database 

(2011).

Science and Engineering Indicators 2012

0

10

20

30

40

Percent

1995 2002 2007 2010

All developed United States Japan EU

All developing China India Russia Brazil

0

10

20

30

40

Developed countries

Developing countries

Figure 6-5

High-technology share of manufacturing sector for 

selected regions/countries: 1995–2010

EU = European Union

NOTES: Output of manufacturing industries on a value-added basis. 

Value added is amount contributed by country, firm, or other entity to 

value of good or service and excludes purchases of domestic and 

imported materials and inputs. High-technology manufacturing 

industries are classified by the Organisation for Economic Co-operation 

and Development and include aerospace, communications and 

semiconductors, computers and office machinery, pharmaceuticals, 

and scientific instruments and measuring equipment. EU excludes 

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

China includes Hong Kong. 

SOURCE: IHS Global Insight, World Industry Service database 

(2011). See appendix tables 6-11 and 6-12.

Science and Engineering Indicators 2012

Percent

Developed

Developing

All developed United States EU Japan

0

5

10

15

20

25

1995 2002 2007 2010

All developing China Brazil India Russia

0

5

10

15

20

25
Science and Engineering Indicators 2012 6-15

The OECD has identified four ICT industries: two are 

manufacturing industries—semiconductors and communications equipment and computers—and two are service industries—communications and computer programming and 

data processing.

Value added of ICT industries more than doubled from 

$1.2 trillion in 1995 to $2.8 trillion in 2010 (appendix table 

6-13). In 2010, developed countries generated a collective 

$1.9 trillion in value added, with $1.7 trillion generated by 

the United States, the EU, and Japan. The ICT share of the 

global economy, and of most major economies, showed little 

change between 1995 and 2010 (increasing from a 4% to a 

6% share of GDP) (appendix table 6-2). In contrast, the ICT 

share of the Chinese economy doubled from 3% to 6%, driven by its huge expansion in ICT goods produced for export 

and rapid growth of its communications services.

Productivity

Productivity growth is considered essential for maintaining or advancing living standards. The growth and rise in 

the concentration of KTI industries in the United States, the 

EU, Japan, and many developing economies coincided with 

elevated or rapidly rising productivity. The most accurate 

measure of productivity—output per hour—is unavailable 

for many emerging economies. GDP per employed person is 

the proxy measure used here, spanning 1990 to 2008.

Labor productivity growth of developed economies 

slowed from 1.9% in the 1990s to 1.6% from 2000 to 2005 

and dropped to 0.9% from 2005 to 2008 (figure 6-6 and appendix table 6-14). Growth trends in the United States and 

the EU were very similar to the developed world average. 

After lagging behind the United States and the EU in the 

1990s, Japan’s growth accelerated to reach the rate of the 

United States and the EU in the 2000s. South Korea’s productivity slowed but continued to grow twice as fast (3%) as 

most of the large developed economies. 

The growth in labor productivity in developing economies accelerated from 1.4% in the 1990s to 4.4% from 2000 

to 2005 and to 5.6% for 2005–08 (figure 6-6 and appendix 

table 6-14). China drove this increase; its labor productivity 

registered the fastest growth of any large economy, from 6% 

in the 1990s to more than 10% for both periods in the 2000s. 

Figure 6-6

Growth in GDP per employed person for selected regions/countries: 1990–2008

EU = European Union; GDP = gross domestic product; PPP = purchasing power parity

NOTES: GDP is in 2010 PPP dollars. EU includes current member countries. China includes Hong Kong. Brazil's growth in 2000–05 was –0.1%.

SOURCE: The Conference Board, Total Economy Database on Output and Labor Productivity (November 2010), http://www.conference-board.org/ 

data/productivity.cfm, accessed 15 November 2010. See appendix table 6-14. 

Science and Engineering Indicators 2012

Developing

Developed

1990–2000 2000–05 2005–08

Percent

All developed United States EU Japan South Korea Australia Canada

0

1

2

3

4

5

6

All developing China India Indonesia Brazil Turkey South Africa Russia

–4

–2

0

2

4

6

8

10

12
6-16 Chapter 6. Industry, Technology, and the Global Marketplace

Russia’s labor productivity moved from negative growth 

in the 1990s to a 5.4% growth rate from 2000 to 2005 and 

further increased to a 6.2% growth rate from 2005 to 2008. 

India’s growth in labor productivity advanced from 3.7% to 

4.4% to 5.9% over these three periods. Brazil’s labor productivity grew much more slowly for much of the 2000s 

than the other three large developing economies, but its 

growth accelerated from –0.1% from 2000 to 2005 to nearly 

3% from 2005 to 2008. 

Rapidly rising living standards, expressed as per capita 

GDP, accompanied the acceleration of productivity growth 

in developing economies and narrowed their gap with developed countries (figure 6-7 and appendix table 6-15). Despite 

sustained rapid productivity growth by China and several 

other emerging economies, however, their gap with the 

United States and other developed economies is substantial 

and is likely to remain so for some time even if their high 

growth is sustained. Per capita GDP in China and Brazil remains at less than a fifth of that in the United States and in 

Russia at less than half. India’s and Indonesia’s per capita 

GDP remains at less than 10% of that in the United States.

Information and Communications 

Technology Infrastructure

This section examines three broad ICT indicators: the 

percentage of households with broadband access; the ICT 

share of total fixed capital investment; and indexes of business, consumer, and government ICT infrastructure.9

 For 

developing economies, only the ICT infrastructure indexes 

are available. 

The U.S. ICT infrastructure compares favorably in these 

three indicators to other large developed economies. South 

Korea is the leading country in fixed broadband penetration, 

with nearly 100% of its households having broadband access (figure 6-8). The United States is in the next group with 

household penetration of about 60% along with Australia, 

Canada, and Germany. The United States exceeds the EU 

average, France, and Japan in broadband penetration. 

The United States has the highest ICT share of fixed capital investment (26%) of large OECD economies, with the 

United Kingdom a close second (figure 6-9). Five countries, 

Australia, Canada, Japan, France, and Germany, have shares 

of 13%–15%. In all of these countries, the ICT investment 

share has declined by large percentages since 2000; this 

most likely reflects rapidly falling prices of semiconductors, 

computers, and other ICT goods.

The United States is the leader in ICT business infrastructure among the larger developed economies (table 6-2), with 

an index score substantially higher than those of France, 

Germany, the United Kingdom, Japan, and South Korea. 

The United States scores near the top in ICT government 

infrastructure and about the same as France, Germany, the 

United Kingdom, Australia, and Canada in consumer infrastructure. South Korea and Japan have significantly higher 

scores in consumer infrastructure than the other developed 

economies, reflecting their lead in deployment of 3G connectivity and advanced mass-market broadband over other 

developed economies. 

Employment data reinforce the close connection between 

ICT infrastructure and KTI industrial activity generally. 

In the United States, for example, commercial KI service 

industries employed about 16 million workers in 2009, or 

1 of every 7 workers in the private sector, and they had a 

higher share of highly skilled workers than other service industries. Four commercial KI services—finance; scientific, 

technical, and professional services; telecommunications; 

and data processing hosting—have twice as high a share of 

workers with ICT skills compared to all service industries 

(figure 6-10).

Separate ICT infrastructure indexes for developing 

economies show wide variation among Brazil, China, India, 

and Russia (table 6-2). China scores third among these four 

economies in business infrastructure and second in consumer and government infrastructure. China’s relatively 

weak score in ICT business infrastructure reflects very low 

penetration of secure Internet servers and limited international Internet bandwidth. India scores the lowest among the 

four in the three indexes, reflecting factors such as limited 

Figure 6-7

GDP per capita for selected developing economies: 

1990–2008

United States = 100

GDP = gross domestic product; PPP = purchasing power parity

NOTES: GDP is in 2010 PPP dollars. China includes Hong Kong.

SOURCE: The Conference Board, Total Economy Database on 

Output and Labor Productivity (November 2010), http://www. 

conference-board.org/data/productivity.cfm, accessed 15 November 

2010.

Science and Engineering Indicators 2012

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

0

5

10

15

20

25

30

35

40

45

50

China

India

Indonesia

Brazil

Russia

South Africa

Turkey
Science and Engineering Indicators 2012 6-17

availability of public telephone lines, modest Internet usage 

and subscriber levels, and very low penetration of secure 

Internet servers. 

Of the four large developing economies, Brazil ties with 

Russia as having the highest score in business infrastructure and 

with China for second in consumer infrastructure (table 6-2). 

Brazil’s score in business infrastructure reflects higher penetration rates of secure Internet servers and personal computers. 

Brazil has the highest score in ICT government infrastructure. 

Among the four large developing economies, Russia 

leads in consumer infrastructure, ties with Brazil in business 

infrastructure, and scores roughly the same as China in government infrastructure. Russia’s relatively high score in consumer infrastructure reflects its levels of fixed and mobile 

telephone penetration and strong Internet and broadband 

subscription levels. Russia’s business infrastructure score 

reflects a relatively high penetration of personal computers 

and telephones offset by low penetration of secure Internet 

servers and limited international Internet bandwidth.

Worldwide Distribution of 

Knowledge- and TechnologyIntensive Industries

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 will examine the positions of the United States 

and other major economies in KTI industries. 

EU = European Union

NOTE: EU includes current member countries.

SOURCE: Organisation for Economic Co-operation and Development (OECD), Directorate for Science Technology and Industry, OECD Broadband Portal, 

http://www.oecd.org/document/54/0,3746,en_2649_33703_38690102_1_1_1_1,00.html, accessed 15 February 2011. 

Science and Engineering Indicators 2012

Percent share of households

Figure 6-8

Household broadband penetration, by selected region/country: 2009

South

Korea

Netherlands United

Kingdom

Canada Germany United

States

Australia Japan France EU Poland

0

20

40

60

80

100

Figure 6-9

ICT share of fixed capital investment for selected 

countries: 2000–08

Percent

ICT = information and communications technology

SOURCE: Organisation for Economic Co-operation and Development, 

Statistics Portal, Productivity, http://www.oecd.org/topicstatsportal/ 

0,3398,en_2825_30453906_1_1_1_1_1,00.html, accessed 15 

February 2011.

Science and Engineering Indicators 2012

2000 2001 2003 2002 2004 2005 2006 2007 2008

0

5

10

15

20

25

30

35

United States

France

Germany

United Kingdom

Japan

Australia

Canada
6-18 Chapter 6. Industry, Technology, and the Global Marketplace

Health and Education Services

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 32% 

and 30%, respectively (table 6-1 and appendix table 6-4). 

Other large economies have comparatively small shares—

Japan (7%); China (7%); and the Asia-8, a group of economies consisting of India, Indonesia, Malaysia, Philippines, 

Singapore, South Korea, Taiwan, and Thailand (6%). 

The U.S. global share of education services fell 4 percentage points from 36% to 32% during the first decade of the 

century, whereas the EU’s share stayed roughly flat (30%) 

(table 6-1 and appendix table 6-4). Third-ranked Japan’s 

share fell from 13% to 7% because of stagnant growth. 

China’s global share of education services more than doubled from 3% to 7% to nearly equal Japan’s share.

Patterns and trends in the health care sector are similar 

to those for education—domination of both sectors by the 

EU and the United States, declining global shares of production in the United States and Japan, and a growing share by 

China (table 6-1 and appendix table 6-5). 

Commercial Knowledge-Intensive 

Service Industries

The United States has the largest commercial KI service 

industries—business, financial, and communications—with 

$3.6 trillion of value added in 2010 (figure 6-11 and appendix table 6-3). The EU was second at $2.9 trillion, trailed by 

Japan with $900 billion. China had the largest output among 

developing countries, nearly equal to Japan, with $700 billion. The Asia-8 region was in fifth place with $600 billion. 

From 1995 to 2010, the value added of developing countries grew far faster than in the developed world (figure 6-12 

and appendix table 6-3). The value added of developing 

countries more than quadrupled from $500 billion to $2.3 

trillion, whereas value added of developed countries more 

than doubled from $3.9 trillion to $8.6 trillion. Two factors driving the growth of KI service industries in developing countries are the rapid advancement of living standards 

in these economies and the growth of international trade 

in these services. Although these industries remain largely 

based in developed economies, these factors are helping to 

build local capacity in the developing world.

Faster growth of KI services industries in developing 

countries during the last 15 years resulted in their share 

of global output rising from 12% to 21% (appendix table 

6-3). China’s output rose sevenfold, tripling its world share 

from 2% to 6% (figure 6-11). Brazil, India, and Russia each 

reached shares of 2%–3%. 

Rapidly rising output by China across all commercial KI 

service industries, combined with the declining Japanese 

share of worldwide production in these industries through 

2007, has substantially altered the national distribution of 

these services within the Asian region. 

Because of the worldwide recession, total global output 

of commercial KI service industries was stagnant in 2009, 

compared to 8% growth in 2008 (appendix table 6-3). But 

developed and developing countries were affected very differently. Output was flat in the developed countries (–0.1%), 

but it grew by 4% in developing countries (figure 6-13). As 

a result, a growing share of world output shifted to the developing world. Double-digit growth in China was largely 

responsible for the difference, but India also increased its 

output rapidly. The recovery in global output in 2010 (8%) 

was led by double-digit increases by most major developing 

economies, continuing the shift in global share from developed to the developing countries. Output of developed countries grew by 5%, with the United States and Japan growing 

at the same rate. The EU had stagnant growth. 

The U.S. share of worldwide commercial KI services, 

which rose from 1995 to 2001 to reach a peak of 44%, 

dropped steadily thereafter to 33% in 2010 (figure 6-11 and 

Table 6-2

Indexes for ICT infrastructure for selected 

countries, by economic sector: 2010

 ICT infrastructure index

Country Business Consumer Government

Developed countries

United States ...... 85 57 79

Australia .............. 80 53 77

Canada ............... 79 48 79

France ................. 60 46 73

Germany ............. 59 49 72

Japan .................. 63 77 75

South Korea ........ 57 96 88

Sweden ............... 85 72 87

United Kingdom ... 74 50 71

Developing countries

Brazil ................... 50 64 85

China ................... 23 69 48

India .................... 6 24 39

Iran ...................... 17 53 32

Malaysia .............. 60 77 78

Russia ................. 52 93 51

Turkey ................. 59 77 80

South Africa ........ 56 52 74

ICT = information and communications technology

NOTES: Developed and developing countries have separate index 

scores. Country scores are benchmarked against the highest scoring 

developed and developing country. 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 2010, http://www.connectivity 

scorecard.org/, accessed 15 February 2011.

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 6-19

appendix table 6-3). The United States had a slight loss in 

its share of the commercial KI services market during the 

recent global recession. In 2009, however, U.S. commercial KI services outperformed other U.S. service industries, 

maintaining their production level while other private services experienced a 1% decline. U.S. commercial KI services grew by 5% in 2010, faster than other services (3%) in 

that year (figure 6-14). 

The EU’s share of worldwide commercial KI services 

rose from 24% in 2000 to 30% in 2007–08 before dropping 

to 26% in 2010 (figure 6-11 and appendix table 6-3). Japan’s 

world share dropped from 17% in 1995 to 8%–9% for the 

2006–10 period. (Fluctuations in the shares of the United 

States, the EU, and Japan may in part reflect changes in the 

dollar/euro/yen exchange rates.)

Trends in national and regional shares of production in 

individual commercial KI service industries sometimes varied substantially from the corresponding trends for the group 

as a whole: 

The U.S. share of the world’s communications services 

declined continuously from 39% in the early 2000s to 

26% in 2010 (figure 6-15 and appendix table 6-10). 

The EU’s share remained roughly steady in business services and finance for the latter half of the 2000s before 

falling 2–3 percentage points in 2009–10 to reach 31% 

for business services and 22% for finance during the recession (appendix tables 6-6 and 6-9). The EU share in 

communications showed a more pronounced drop from 

26% in 2004 to 19% in 2010 (figure 6-15 and appendix 

table 6-10). 

Some large developing economies showed gains in some 

of these industries but from a low base. Brazil’s share in 

finance rose from 2% to 3% between 2001 and 2010 (appendix table 6-9). Its share in communications more than 

doubled from 2% to 5% (appendix table 6-10). Russia’s 

share in finance rose from less than 0.5% in 1995 to 2% in 

2010. India’s share in communications doubled from 1% in 

1995 to 2% in 2010. 

ICT = information and communications technology

NOTE: U.S. workers with ICT skills based on those with occupations that use narrow or broadly related ICT skills based on Organisation for Economic 

Co-operation and Development (OECD) methodology. 

SOURCES: Bureau of Labor Statistics, Occupational Employment Statistics, http://www.bls.gov/oes/#data, accessed 15 October 2010; OECD, New 

Perspectives on ICT Skills and Employment (2005), http://www.oecd.org/dataoecd/ 26/35/34769393.pdf, accessed 15 October 2010. 

Science and Engineering Indicators 2012

Percent

Figure 6-10

Share of U.S. workers with ICT skills, by selected industry: 2009

Service industries

Manufacturing industries

All private services Data processing

and hosting

Finance Management

of companies

Professional, scientific,

and technical services

Telecommunications

0

20

40

60

80

100

Manufacturing Aerospace Communications

equipment

Computers Scientific

instruments

Pharmaceuticals Semiconductors

0

20

40

60

80

100

ICT broad

ICT narrow
6-20 Chapter 6. Industry, Technology, and the Global Marketplace

Figure 6-11

Value added of commercial KI services, by 

selected region/country: 1995–2010

EU = European Union; KI = knowledge-intensive

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

value-added basis. Value added is amount contributed by country, 

firm, or other entity to value of good or service and excludes purchases 

of domestic and imported materials and inputs. Commercial KI 

services are classified by the Organisation for Economic Co-operation 

and Development and include business, financial, and communications 

services. Asia-8 includes India, Indonesia, Malaysia, Philippines, 

Singapore, South Korea, Taiwan, and Thailand. EU excludes Cyprus, 

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

includes Hong Kong. 

SOURCE: IHS Global Insight, World Industry Service database 

(2011). See appendix table 6-3.

Science and Engineering Indicators 2012

Dollars (trillions)

Percent

Value added

Global value-added share

1995 1998 2000 2002 2004 2006 2008 2010

1995 1998 2000 2002 2004 2006 2008 2010

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

United States

EU

Japan

China

Asia-8

Other developed

Other developing

0

10

20

30

40

50

United States

EU

Japan

China

Asia-8

Other developed

Other developing

Figure 6-12

Growth of HT manufacturing and commercial KI 

industries for developed and developing countries: 

1995–2010

Average annual percent change

HT = high-technology; KI = knowledge-intensive; OECD = 

Organisation for Economic Co-operation and Development

NOTES: Output of commercial KI and HT manufacturing industries on 

value-added basis. Value added is amount contributed by country, 

firm, or other entity to value of good or service and excludes 

purchases of domestic and imported materials and inputs. 

Commercial KI services are classified by the OECD and include 

business, financial, and communications services. Public KI services 

include education and health. HT manufacturing industries are 

classified by the OECD and include aerospace, communications and 

semiconductors, computers and office machinery, pharmaceuticals, 

and scientific instruments and measuring equipment.

SOURCE: IHS Global Insight, World Industry Service database (2011). 

See appendix tables 6-3 and 6-11.

Science and Engineering Indicators 2012

0

4

8

12

16

20

HT manufacturing Commercial KI

Developed Developing

Figure 6-13

Growth of HT manufacturing and commercial KI 

services for developed and developing countries: 

2008–10

Average annual percent change

HT = high-technology; KI = knowledge-intensive; OECD = Organisation 

for Economic Co-operation and Development

NOTES: Output of commercial KI and HT manufacturing industries on 

value-added basis. Value added is amount contributed by country, firm, 

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

domestic and imported materials and inputs. Commercial KI services 

are classified by the OECD and include business, financial, and 

communications services. Public KI services include education and 

health. HT manufacturing industries are classified by the OECD and 

include aerospace, communications and semiconductors, computers 

and office machinery, pharmaceuticals, and scientific instruments and 

measuring equipment.

SOURCE: IHS Global Insight, World Industry Service database (2011). 

See appendix tables 6-3 and 6-11.

Science and Engineering Indicators 2012

2008 2009 2010

Developed Developing Developed Developing Developed Developing

–15

–10

–5

0

5

10

15

20

25

Commercial 

KI

HT

manufacturing
Science and Engineering Indicators 2012 6-21

High-Technology Manufacturing Industries

The United States has the world’s largest set of HT 

manufacturing industries, with $390 billion of global value 

added in 2010 (figure 6-16 and appendix table 6-11). The 

EU and China are the second and third largest with about 

$270 billion and $260 billion, respectively, of global value 

added in 2010. The EU and China lead the world in apparent 

domestic consumption of HT goods with the United States 

close behind (see sidebar, “Apparent Consumption of HighTechnology Manufactured Goods”). The Asia-8 and Japan 

each have HT manufacturing output of about $175 billion. 

The dampening effects of the recessions in the early and 

late 2000s on these industries’ output are clearly visible and 

remarkably similar. Overall worldwide output declined by 

about 13% from 2000 to 2001, from $850 to $740 billion (appendix table 6-11). Output slipped by 14% in the developed 

economies but maintained its volume in the developing world. 

From 2008 to 2009, total world HT manufacturing output declined by 6%. It dropped by 7% for developed economies, 

but stayed constant for the rest of the world (figure 6-13). 

Only China’s output grew throughout the entire period (figure 

6-16). World HT manufacturing output rebounded in 2010, 

growing at 13%, with developing countries averaging more 

than 20% growth in their output. Output of developed countries rose by 10%, led by a 30% increase in Japan’s output. 

Figure 6-14

Growth of selected U.S. industries: 2008–09

Percent

KI = knowledge-intensive; OECD = Organisation for Economic 

Co-operation and Development

NOTES: Output of commercial knowledge-intensive and hightechnology manufacturing industries on value-added basis. Value added 

is amount contributed by country, firm, or other entity to value of good 

or service and excludes purchases of domestic and imported materials 

and inputs. Commercial knowledge-intensive services are classified by 

the OECD and include business, financial, and communications 

services. Public knowledge-intensive services include education and 

health. High-technology manufacturing industries are classified by the 

OECD and include aerospace, communications and semiconductors, 

computers and office machinery, pharmaceuticals, and scientific 

instruments and measuring equipment. Growth rate for commercial KI 

services in 2009 was –0.1 percent.

SOURCE: IHS Global Insight, World Industry Service database (2011). 

See appendix tables 6-3 and 6-11.

Science and Engineering Indicators 2012

Hightechnology

manufacturing

All other

manufacturing

Commercial

KI services

Other private

services

–6

–2

2

6

10

14

2008 2009 2009

Figure 6-15

Value added for communications services, 

by selected region/country: 1995–2010

EU = European Union

NOTES: Output on value-added basis. Value added is amount 

contributed by country, firm, or other entity to value of good or 

service and excludes purchases of domestic and imported materials 

and inputs. Asia-8 includes India, Indonesia, Malaysia, Philippines, 

Singapore, South Korea, Taiwan, and Thailand. EU excludes Cyprus, 

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

includes Hong Kong. 

SOURCE: IHS Global Insight, World Industry Service database 

(2011). See appendix table 6-10.

Science and Engineering Indicators 2012

Dollars (billions)

Percent

Value added

Global value added share

1995 1998 2000 2002 2004 2006 2008 2010

0

50

100

150

200

250

300

350

United States

China

Japan

EU

Asia-8

Other developed

Other developing

1995 1998 2000 2002 2004 2006 2008 2010

0

5

10

15

20

25

30

35

40

45 United States

China

Japan

Asia-8

EU

Other developed

Other developing
6-22 Chapter 6. Industry, Technology, and the Global Marketplace

Output of the United States and EU grew far more slowly, 

expanding by 5% and 3%, respectively. The relatively less 

severe effects of the two recessions on developing nations 

combined with China’s rapid, uninterrupted growth to produce global share shifts: from 3% in 1995 to 19% in 2010 for 

China and from 9% in 1995 to 29% in 2010 for the developing world as a whole (figure 6-16 and appendix table 6-11). 

The U.S. share declined from 34% in 1998 to 28% in 2010, 

while the EU’s share, long at 25%, dropped to 20% by 2010. 

Japan’s share plummeted from 27% in 1995 to 11% in 2009 

before rising to 13% in 2010. 

The six HT manufacturing industries contribute uneven 

value-added amounts. The largest, pharmaceuticals, provided $346 billion, 25% of the global total in 2010. The others, 

in order, were semiconductors ($312 billion, 22%); scientific and measuring equipment, which includes medical and 

measuring equipment ($275 billion, 20%); communications 

equipment ($200 billion, 14%); aircraft and spacecraft ($137 

billion; 10%); and computers ($127 billion, 9%) (appendix 

tables 6-16, 6-17, 6-18, 6-19, 6-20, 6-21, and 6-22). Size 

variations have not been stable over the 1995–2010 period, 

in part reflecting steep price declines for computers, semiconductors, and communications equipment. 

The U.S. share of global value added was relatively stable 

in the aircraft and spacecraft, computer, and pharmaceutical 

industries between 1995 and 2010 (figures 6-17 and 6-18 and 

appendix tables 6-16, 6-21, and 6-22). The United States is 

the world’s leading producer in aircraft and spacecraft (51% 

of global value added in 2010) and ties with the EU as the 

leading producer of pharmaceuticals. The U.S. share in scientific and measuring instruments rose modestly (from 31% 

to 35%), surpassing the EU in 2010 to become the world’s 

largest producer (appendix table 6-18). The U.S. share fell 

in communications (from 26% to 20%), and semiconductors (from 25% to 19%) (appendix tables 6-17 and 6-20). 

Researchers and policymakers have concluded that the location of HT manufacturing and R&D activities overseas may 

also lead to the migration of higher value activities abroad. 

China’s communications and semiconductor industries 

grew more than fivefold over the decade, their world shares 

climbing from 5%–6% to 17% in semiconductors and 26% 

in communications equipment (figure 6-18 and appendix 

tables 6-17 and 6-20). China surpassed the United States and 

Japan to become the largest producer in communications and 

overtook the EU to become the third largest in semiconductors, narrowing its gap with the United States. China’s rapid 

growth in these two industries owes much to the establishment in China of manufacturing operations of U.S., EU, and 

developed Asian-based companies, but Chinese-based companies in these industries are also emerging and successfully 

competing both domestically and globally. China’s computer industry grew even faster than its communications and 

semiconductor industries, expanding from 4% to 47% of the 

world total (figure 6-18 and appendix table 6-22). China’s 

dominant position in computer manufacturing has been 

largely due to its success as the low-cost assembly center of 

computer components primarily manufactured and designed 

Figure 6-16

Value added of high-technology manufacturing 

industries, by selected region/country: 1995–2010

EU = European Union

NOTES: Output of high-technology manufacturing industries on 

value-added basis. Value added is amount contributed by country, 

firm, or other entity to value of good or service and excludes 

purchases of domestic and imported materials and inputs. 

High-technology manufacturing industries are classified by the 

Organisation for Economic Co-operation and Development and 

include aerospace, communications and semiconductors, 

computers and office machinery, pharmaceuticals, and scientific 

instruments and measuring equipment. Asia-8 includes India, 

Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan, 

and Thailand. EU excludes Cyprus, Estonia, Latvia, Lithuania, 

Luxembourg, Malta, and Slovenia. China includes Hong Kong. 

SOURCE: IHS Global Insight, World Industry Service database 

(2011). See appendix table 6-11.

Science and Engineering Indicators 2012

Dollars (billions)

1995 1998 2000 2002 2004 2006 2008 2010

1995 1998 2000 2002 2004 2006 2008 2010

0

Percent

5

10

15

20

25

30

35

40 United States

China

Japan

EU

Asia-8

Other developed

Other developing

0

50

100

150

200

250

300

350

400

United States

China

Japan

EU

Asia-8

Other developed

Other developing
Science and Engineering Indicators 2012 6-23

Apparent Consumption of High-Technology Manufactured Goods

Figure 6-A

Apparent domestic consumption of high-technology 

manufacturing industries, by selected region/

country/economy: 1995–2010

EU = European Union

NOTES: Apparent consumption is sum of domestic production and 

inputs less exports. High-technology manufacturing industries are 

classified by the Organisation for Economic Co-operation and 

Development and include aerospace, communications and 

semiconductors, computers and office machinery, pharmaceuticals, 

and scientific instruments and measuring equipment. Asia-8 includes 

India, Indonesia, Malaysia, Philippines, Singapore, South Korea, 

Taiwan, and Thailand. EU excludes Cyprus, Estonia, Latvia, 

Lithuania, Luxembourg, Malta, and Slovenia. 

SOURCE: IHS Global Insight, World Industry Service database 

(2011).

Science and Engineering Indicators 2012

Dollars (billions)

Percent

1995 1998 2000 2002 2004 2006 2008 2010

0

200

400

600

800

1,000

1,200

1,400

United States

China

Japan

Asia-8

EU

Other developed

Other developing

1995 1998 2000 2002 2004 2006 2008 2010

0

10

20

30

40

50

United States

China

Japan

Asia-8

EU

Other developed

Other developing

Production of HT goods feeds both domestic and foreign markets. A broad measure of domestic use is provided by adding domestic sales to imports and subtracting 

exports. However, use so defined encompasses two types 

of economic activity, consumption of final goods and 

capital investment for further production (intermediate 

goods). Available data series do not permit the examination of these two types of activity separately.

Patterns of the world’s use of HT manufactures have 

changed considerably over the past decade. The U.S. 

share of domestic use, as defined above, fell from 30% 

in 2000 to 19% in 2010 (figure 6-A). The EU’s share 

stayed broadly the same at 26%–27% over much of the 

decade before falling to 21% in 2010. The EU overtook 

the United States in 2003 to become the leading consumer of HT goods between 2003 and 2009. China’s share 

surged from 5% in 2000 to 21% in 2010, overtaking the 

United States and reaching the EU’s level. Japan’s share 

declined from 17% in 2000 to 11% in 2010.

The Chinese trend underscores the difficulty of teasing out final consumption from use as intermediate 

goods. The strong rise in the Chinese trend is considered 

by many observers to reflect the rising flow of intermediate goods—often previously produced in China—from 

other Asian manufacturing centers into China, where 

they undergo further assembly before being exported to 

final consumers.

in other countries; acquisition of Western computer companies also played a role.10 China’s achievement of designing 

and building the world’s fastest supercomputer—albeit as 

yet with largely foreign-designed input— indicates its drive 

to become a global competitor in a range of technologically sophisticated, high-value-added activities (see sidebar, 

“China’s Progress in Supercomputers”).

China’s growth in other HT industries was also rapid—

China more than tripled its world share in pharmaceuticals, 

scientific instruments, and aircraft and spacecraft (figure 

6-17 and appendix tables 6-16, 6-18, and 6-21).

The EU’s share stayed roughly stable over the decade in 

two industries: aircraft and spacecraft (25%) and pharmaceuticals (26%) (figure 6-17 and appendix tables 6-16 and 

6-21). Its share fell in computers (from 16% to 8%), communications (from 13% to 9%), semiconductors (from 15% to 

12%), and scientific instruments (from 38% to 30%) (figure 

6-18 and appendix tables 6-17, 6-18, 6-20, and 6-22). 

Japan’s share loss, driven primarily by the communications, semiconductor, and computer and office machinery 

industries, also extended to pharmaceuticals and scientific 

instruments (figures 6-17 and 6-18 and appendix tables 6-16, 

6-17, 6-18, 6-20, and 6-22). However, the decline of Japan’s 

semiconductor industry was interrupted by very strong 

growth in 2010 that raised its world share from 18% in 2009 
6-24 Chapter 6. Industry, Technology, and the Global Marketplace

to 22% in 2010, resulting in a 7-percentage-point fall in its 

world share over the decade. This broad downward trend may 

reflect the Japanese economy’s lengthy stagnation and the 

shift of production to China and other Asian economies. 

The Asia-8 rapidly increased its global share in semiconductors from 20% to 26% over the decade, surpassing Japan 

and the United States to become the largest world producer 

in this industry (figure 6-18 and appendix table 6-17). The 

Asia-8’s rapid rise was driven by Taiwan and South Korea, 

which together had a 20% global share. The success of South 

Korea and Taiwan in this industry reflects both the output of 

companies based in these locations and investments in manufacturing facilities by Intel and other multinational firms. 

Many Taiwanese firms have shifted production to mainland 

China, which may overstate China’s global market share and 

understate Taiwan’s. 

The Asia-8 slightly increased its share in pharmaceuticals from 5% to 7%, with growth driven by activity in India 

Figure 6-17

Value added for selected manufacturing industries, 

by global share of selected region/country/economy:

1995, 2000, 2005, and 2010

EU = European Union

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

amount contributed by country, firm, or other entity to value of good 

or service and excludes purchases of domestic and imported 

materials and inputs. Asia-8 includes India, Indonesia, Malaysia, 

Philippines, Singapore, South Korea, Taiwan, and Thailand. EU 

excludes Cyprus, Estonia, Latvia, Lithuania, Luxembourg, Malta, and 

Slovenia. China includes Hong Kong. 

SOURCE: IHS Global Insight, World Industry Service database 

(2011). See appendix tables 6-16, 6-18, and 6-21.

Science and Engineering Indicators 2012

Percent

Pharmaceuticals

Aircraft and spacecraft

Scientific and measuring instruments

United States EU Japan China Asia-8

0

10

20

30

40

50

60

1995 2000 2005 2010

United States EU Japan China Asia-8

0

10

20

30

40

50

60

United States EU Japan China Asia-8

0

10

20

30

40

50

60

Figure 6-18

Value added for selected high-technology 

manufacturing industries, by global share of 

selected region/country/economy: 1995, 2000, 

2005, and 2010

EU = European Union

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

amount contributed by country, firm, or other entity to value of good 

or service and excludes purchases of domestic and imported 

materials and inputs. Asia-8 includes India, Indonesia, Malaysia, 

Philippines, Singapore, South Korea, Taiwan, and Thailand. EU 

excludes Cyprus, Estonia, Latvia, Lithuania, Luxembourg, Malta, and 

Slovenia. China includes Hong Kong. 

SOURCE: IHS Global Insight, World Industry Service database 

(2011). See appendix tables 6-17, 6-20, and 6-22.

Science and Engineering Indicators 2012

Percent

Semiconductors

Communications equipment

Computers

United States EU Japan China Asia-8

0

10

20

30

40

50

60

United States EU Japan China Asia-8

0

10

20

30

40

50

60

United States EU Japan China Asia-8

0

10

20

30

40

50

60

1995 2000 2005 2010
Science and Engineering Indicators 2012 6-25

and Singapore (figure 6-17 and appendix table 6-16). Indian 

firms have become significant world producers, particularly 

in generic drugs. In addition, U.S. firms and other multinationals have established a presence in India to access the 

growing consumer market and collaborate with India-based 

firms. Firms based in India and Singapore have also become 

contractors for manufacturing and clinical trials conducted 

by U.S. and EU-based firms. 

Information and Communications 

Technology Industries

In 2010, the United States had the largest ICT industry 

with $729 billion (26% global share), closely followed by 

the EU with $625 billion (22%) (figure 6-19 and appendix 

table 6-13). China and Japan, each with about $340 billion 

in value added, tied for third place, with 12% global shares. 

The TOP500, an organization composed of computer 

scientists and industry specialists, has been tracking the 

world’s fastest performing supercomputers since 1993. 

It provides an annual update with information, including 

the origin, performance, type of application, and technology of high-performance supercomputers. According to 

the November 2010 report, China was ranked for the first 

time as having the world’s fastest supercomputer at the 

National Supercomputing Center in Tianjin. The Tianjin 

supercomputer uses existing component technology from 

the United States and other countries with energy-saving technology developed in China.* A second Chinese 

supercomputer was ranked third, giving China 2 slots in 

the top 10 supercomputers. The United States was ranked 

second, and had 4 other supercomputers in the top 10. 

In 2005, TOP500 had ranked the United States first, 

and 6 other U.S. supercomputers were ranked in the top 

10. China’s highest ranking in that year was 26th. The 

United States continues to dominate in the number of supercomputers ranked in the top 500 and in the number 

of high-performance supercomputers. China’s share of 

high-performance supercomputers has increased rapidly, 

from 1% in 2008 to 9% in 2010 (figure 6-B).

*See Ernst (2011) for information on China’s Taijin supercomputer.

China’s Progress in Supercomputers

Figure 6-B

Top 500 supercomputers by selected region/country: Selected years, 2004–10 

EU = European Union; ROW = rest of world

NOTES: Asia-8 includes India, Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan, and Thailand. Data on Philippines and Thailand are not 

available. EU includes Austria, Belgium, Cyprus, Czech Republic, Denmark, Finland, France, Germany, Ireland, Italy, Luxembourg, Netherlands, Poland, 

Portugal, Slovenia, Spain, Sweden, and the United Kingdom. China includes Hong Kong.

SOURCE: Top 500 Supercomputer Sites, Statistics, http://www.top500.org/drilldown, accessed 15 March 2011. 

Science and Engineering Indicators 2012

Percent

Share of sites

Share of performance

United States Asia-8 China EU Japan ROW

0

10

20

30

40

50

60

70

United States Asia-8 China EU Japan ROW

0

10

20

30

40

50

60

70

2004 2006 2008 2010
6-26 Chapter 6. Industry, Technology, and the Global Marketplace

The U.S. global share rose from 31% in 1995 to 34% in 

the early 2000s before falling steadily to reach 26% in 2010 

(figure 6-19 and appendix table 6-13). The EU’s share remained roughly stable at 26%–27% for much of the 2000s 

before falling to 22% in 2010. Japan’s share fell steeply 

from 22% in 1995 to reach 11%–12% in the latter half of 

the 2000s, mirroring its downward trends in share in both 

HT manufacturing and commercial KI service industries. 

China’s share rose by sixfold from 2% to 12% because of 

strong gains in its shares of both HT and KI industries. The 

Asia-8’s share was roughly steady at 8% during this period. 

India’s share rose from 0.5% to 1.5%; Brazil’s and Russia’s 

shares had similar trends.

Industries That Are Not Knowledge or 

Technology Intensive 

Science and technology are used in many industries besides HT manufacturing and KI services. Services not classified as knowledge intensive may incorporate advanced 

technology in their services or in the delivery of their services, albeit at a lower intensity than the KI services discussed 

above. Manufacturing industries not classified as HT by the 

OECD may use advanced manufacturing techniques, incorporate technologically advanced inputs in manufacture, and/

or perform or rely on R&D. Some industries not classified 

as either manufacturing or services also incorporate recent 

science and technology in their products and processes (see 

sidebar, “Trends in Industries Not Classified as Services or 

Manufacturing”).

Non-Knowledge-Intensive Commercial Services

Commercial services not classified as KI include the 

wholesale and retail, restaurant and hotel, transportation and 

storage, and real estate industries. The United States and the 

EU are the two largest providers in the wholesale and retail 

industry—the largest of these industries ($7.0 trillion)—and 

in the real estate and restaurant and hotel industries (table 

6-3). The EU is the largest provider in transportation and storage 

(27% share of global value added), leading the next two economies, the United States and China (14% share each of global 

value added), by a wide margin. Allowing for fluctuations, the 

U.S. and EU shares declined and the Asia-8’s share remained 

stable or showed a slightly upward trend between 1995 and 2010. 

China showed rapid growth, with its shares of global value added 

at least tripling across all these industries. Japan’s global shares 

fell significantly across all of these industries. 

Non-High-Technology Manufacturing Industries

Non-HT manufacturing industries are divided into three 

categories, as classified by the OECD: medium-high technology, medium-low technology, and low technology. 

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. 

Figure 6-19

Value added for ICT industries, by selected region/

country/economy: 1995–2010

EU = European Union; ICT = information and communications 

technology

NOTES: Output of ICT industries on value-added basis. Value added 

is amount contributed by country, firm, or other entity to value of 

good or service and excludes purchases of domestic and imported 

materials and inputs. ICT industries are classified by the Organisation for Economic Co-operation and Development and include 

communications and computer and data processing services and 

semiconductors and communications and computer manufacturing 

industries. Asia-8 includes India, Indonesia, Malaysia, Philippines, 

Singapore, South Korea, Taiwan, and Thailand. EU excludes Cyprus, 

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

includes Hong Kong.

SOURCE: IHS Global Insight, World Industry Service database 

(2011). See appendix table 6-13.

Science and Engineering Indicators 2012

Dollars (billions)

1995 1998 2000 2002 2004 2006 2008 2010

1995 1998 2000 2002 2004 2006 2008 2010

Percent

Global value-added share

0

200

400

600

800

United States

China

Japan

Asia-8

EU

Other developed

Other developing

0

5

10

15

20

25

30

35

40

45 United States

China

Japan

EU

Asia-8

Other developed

Other developing
Science and Engineering Indicators 2012 6-27

The share trends in all of these industry segments are generally the same as for HT—share losses for the United States 

and the EU, larger share losses for Japan, stable or slight 

increases for the Asia-8, and strong share gains across all 

segments for China.

Agriculture, construction, mining, and utilities are not 

classified as either manufacturing or service industries 

and are not categorized by their level of technology or 

knowledge intensity. However, these industries depend 

on or use science and technology. 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.

The United States ranks second in construction, mining, and utilities, and third in agriculture as measured 

by share of global value added among the five major 

economies—United States, EU, Japan, China, and the 

Asia-8 (table 6-A). The U.S. share in construction fell 

from 29% in 2002 to 20% in 2008 and 16% in 2010, in 

part because of the recession and crisis in the housing 

sector. The U.S. share remained stable in agriculture and 

fell slightly in mining and utilities. The EU’s share was 

steady in construction and utilities but fell substantially in 

mining and agriculture. Japan’s share fell sharply in all of 

these industries. China had gains across all industries, and 

became the largest producer among the five economies in 

agriculture and mining. The Asia-8’s shares were stable 

or grew slightly during the 2000s. 

Trends in Industries Not Classified as Services or Manufacturing

Table 6-A

Share of global value added for selected industries, by region/country/economy: Selected years, 1995–2010

(Percent distribution)

Industry and 

region/country/economy 1995 1999 2002 2005 2008 2010

Agriculture

Global value added (current $billions) .... 1,108.1 1,034.4 1,043.6 1,385.3 2,052.6 2,359.0

All countries .......................................... 100.0 100.0 100.0 100.0 100.0 100.0

United States .................................... 8.2 9.0 9.0 9.2 7.8 6.5

EU ...................................................... 22.0 19.5 17.7 16.4 14.1 10.7

Japan ................................................ 9.3 7.9 6.5 5.0 3.4 3.3

China ................................................ 13.1 17.3 19.2 19.8 23.6 26.3

Asia-8 ................................................ 18.8 18.7 17.9 18.7 19.0 21.2

ROW .................................................. 28.6 27.6 29.7 30.9 32.1 32.0

Construction

Global value added (current $billions) .... 1,641.6 1,627.8 1,680.3 2,352.2 3,174.0 3,100.1

All countries .......................................... 100.0 100.0 100.0 100.0 100.0 100.0

United States .................................... 17.9 26.3 29.4 26.0 19.6 16.3

EU ...................................................... 29.7 27.7 28.1 31.2 32.3 27.2

Japan ................................................ 26.5 20.6 16.1 12.3 9.1 10.1

China ................................................ 3.1 4.4 5.0 5.6 8.7 13.8

Asia-8 ................................................ 7.4 5.7 5.9 7.1 7.8 9.6

ROW .................................................. 15.4 15.3 15.5 17.8 22.5 23.0

Mining

Global value added (current $billions) .... 494.1 481.7 657.0 1,388.9 2,497.9 2,358.4

All countries .......................................... 100.0 100.0 100.0 100.0 100.0 100.0

United States .................................... 15.5 17.0 16.7 13.8 12.7 11.9

EU ...................................................... 14.6 12.4 10.6 7.7 6.5 5.0

Japan ................................................ 1.8 1.2 0.7 0.3 0.1 0.1

China ................................................ 4.4 6.0 6.1 7.8 11.2 14.5

Asia-8 ................................................ 7.4 7.3 7.1 6.1 5.7 7.4

ROW .................................................. 56.3 56.1 58.8 64.3 63.8 61.1

Utilities

Global value added (current $billions) .... 713.7 687.1 694.2 922.3 1,268.6 1,298.6

All countries .......................................... 100.0 100.0 100.0 100.0 100.0 100.0

United States .................................... 24.6 25.1 26.1 22.3 20.7 21.2

EU ...................................................... 26.7 24.9 23.7 26.9 28.9 24.6

Japan ................................................ 25.6 23.8 21.1 17.0 10.9 13.4

China ................................................ 2.8 4.8 6.6 8.6 12.9 15.8

Asia-8 ................................................ 5.2 5.6 6.3 6.0 4.7 5.3

ROW .................................................. 15.1 15.8 16.2 19.2 21.9 19.7

EU = European Union; ROW = rest of world

NOTES: Value added is amount contributed by country, firm, or other entity to value of good or service and excludes purchases of domestic and imported 

materials and inputs. Asia-8 includes India, Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan, and Thailand. China includes Hong Kong. 

EU excludes Cyprus, Estonia, Latvia, Lithuania, Luxembourg, Malta, and Slovenia. Percents may not add to 100% because of rounding.

SOURCE: IHS Global Insight, World Industry Service database (2011).

Science and Engineering Indicators 2012
6-28 Chapter 6. Industry, Technology, and the Global Marketplace

Medium-High-Technology Industries: These industries 

produced $2.9 trillion in global value added in 2010. The 

U.S. share fell from 22% to 14% between 1995 and 2010 

(table 6-4), and the EU’s share fell from 34% to 24%. 

Japan’s share fell from 24% to 13%. China’s share grew 

more than eightfold from 3% to 26%, as it joined the EU 

as one of the two largest producers among these economies. The Asia-8’s share rose slightly from 6% to 8%. 

Medium-Low-Technology Industries: The U.S. share of 

these industries ($3.0 trillion global value added) fell 1 

percentage point between 1995 and 2010, to 18% in 2010 

(table 6-4). The EU’s share fell more steeply, from 31% 

to 23%. China’s share rose nearly sevenfold, from 3% to 

20%, making it the second-largest producer among these 

economies. Japan’s share fell from 24% to 10%, its steepest loss among these three segments.

Low-Technology Industries: These industries produced 

$1.2 trillion in global value added in 2010. The U.S. share 

fell from 25% in 1994 to 19% in 2010, and the EU’s share 

was down more sharply, from 33% to 22% (table 6-4). 

China’s share grew by ninefold, from 3% to 28%. 

Table 6-3

Global value added for selected service industries, by region/country/economy: Selected years, 1995–2010

(Percent distribution)

Service industry and 

region/country/economy 1995 1997 1999 2001 2004 2006 2008 2010

Wholesale and retail

Global value added (current $billions) .... 3,692.4 3,732.6 3,791.4 3,836.2 4,855.6 5,570.3 6,775.7 6,956.5

All countries/regions/economies .......... 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

United States .................................... 26.7 29.9 32.5 34.3 30.5 29.5 24.5 24.7

EU ...................................................... 26.1 24.8 25.2 23.6 27.8 26.3 26.9 23.2

Japan ................................................ 23.0 18.4 18.2 16.1 13.9 11.4 10.7 10.6

China ................................................. 2.3 3.0 3.2 3.8 3.9 4.5 6.3 8.7

Asia-8 ................................................ 5.9 6.2 5.7 5.9 6.3 7.3 7.5 8.8

ROW .................................................. 16.0 17.7 15.2 16.2 17.5 20.9 24.1 24.2

Real estate

Global value added (current $billions) .... 2,592.6 2,625.7 2,770.6 2,899.1 3,745.9 4,217.9 5,165.1 5,094.3

All countries/regions/economies .......... 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

United States .................................... 31.9 35.1 36.9 40.1 35.7 35.3 32.7 31.5

EU ...................................................... 31.5 30.0 29.3 26.9 33.1 33.1 34.4 31.0

Japan ................................................ 21.9 17.7 18.0 16.7 14.8 12.3 11.6 13.4

China ................................................. 1.4 1.7 1.8 2.2 2.5 3.2 4.2 6.7

Asia-8 ................................................ 3.3 3.7 3.1 3.2 3.2 3.6 3.5 3.8

ROW .................................................. 10.0 11.8 10.9 10.9 10.8 12.4 13.5 13.6

Transport and storage

Global value added (current $billions) .... 1,181.3 1,179.6 1,199.6 1,218.0 1,616.9 1,876.3 2,342.3 2,426.5

All countries/regions/economies .......... 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

United States .................................... 16.5 18.7 20.3 20.7 17.7 17.4 14.8 14.1

EU ...................................................... 30.6 29.8 30.7 28.8 33.3 31.7 32.4 27.2

Japan ................................................ 23.7 17.8 17.6 15.9 13.4 10.6 9.8 10.5

China ................................................. 4.1 5.1 6.0 7.6 7.7 8.8 10.4 13.9

Asia-8 ................................................ 6.8 7.2 6.6 6.7 7.3 8.0 7.8 8.8

ROW .................................................. 18.3 21.4 18.7 20.3 20.6 23.5 24.8 25.6

Restaurants and hotels

Global value added (current $billions) .... 704.2 733.6 799.8 817.1 1,053.7 1,202.2 1,441.0 1,483.3

All countries/regions/economies .......... 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

United States .................................... 26.9 30.1 33.1 36.3 32.6 32.0 27.8 27.6

EU ...................................................... 29.0 28.1 28.4 26.9 32.2 31.6 32.6 29.4

Japan ................................................ 21.2 17.5 16.8 14.9 13.1 10.9 10.7 11.8

China ................................................. 2.7 3.3 3.5 4.1 4.6 5.4 7.0 7.9

Asia-8 ................................................ 6.0 6.0 4.9 5.0 5.2 6.0 6.0 6.8

ROW .................................................. 14.2 15.1 13.3 13.0 12.2 14.1 15.8 16.5

EU = European Union; ROW = rest of world

NOTES: Value added is amount contributed by country, firm, or other entity to value of good or service and excludes purchases of domestic and imported 

materials and inputs. Asia-8 includes India, Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan, and Thailand. China includes Hong Kong. 

EU excludes Cyprus, Estonia, Latvia, Lithuania, Luxembourg, Malta, and Slovenia. Percents may not add to 100% because of rounding.

SOURCE: IHS Global Insight, World Industry Service database (2011).

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 6-29

Table 6-4

Global value added for manufacturing industries, by selected technology level and region/country/economy: 

Selected years, 1995–2010

(Percent distribution)

Manufacturing technology level and 

region/country/economy 1995 1998 2000 2002 2004 2006 2008 2010

Medium high

Global value added (current $billions) .... 1,526.9 1,431.8 1,459.6 1,452.9 1,820.9 2,114.5 2,653.2 2,897.1

All countries/regions/economies .......... 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

United States .................................... 21.7 25.5 25.5 24.9 20.4 18.8 14.3 14.4

EU ...................................................... 33.7 35.0 30.1 32.2 34.9 32.7 31.6 24.2

Japan ................................................ 23.6 18.4 20.8 17.4 16.7 14.4 12.5 12.5

China ................................................. 2.8 3.6 4.6 6.2 8.2 12.2 18.9 26.0

Asia-8 ................................................ 5.8 4.4 6.0 6.3 6.7 7.3 7.0 8.1

ROW .................................................. 12.3 13.0 13.1 13.0 13.0 14.5 15.6 14.7

Medium low

Global value added (current $billions) .... 1,365.9 1,280.8 1,328.0 1,280.2 1,784.1 2,198.8 2,878.3 2,983.2

All countries/regions/economies .......... 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

United States .................................... 19.4 22.8 24.5 25.3 22.4 21.7 18.4 17.6

EU ...................................................... 30.8 29.3 30.1 28.0 29.7 26.8 26.4 23.3

Japan ................................................ 23.5 19.8 18.9 17.4 15.4 13.4 9.6 9.8

China ................................................. 3.4 3.9 4.2 5.7 7.4 10.0 14.4 19.7

Asia-8 ................................................ 7.4 7.5 6.7 6.7 7.6 7.8 8.0 7.5

ROW .................................................. 15.4 16.8 15.7 17.0 17.4 20.2 23.1 22.1

Low 

Global value added (current $billions) .... 815.6 741.3 759.7 725.0 864.8 968.2 1,160.3 1,221.1

All countries/regions/economies .......... 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

United States .................................... 24.8 29.0 30.5 29.8 26.4 24.5 19.3 18.6

EU ...................................................... 32.5 33.0 28.3 30.1 31.9 29.1 28.6 22.4

Japan ................................................ 16.7 12.0 12.7 9.8 8.8 6.5 6.1 5.9

China ................................................. 3.3 4.3 5.1 6.8 9.1 14.2 20.6 27.7

Asia-8 ................................................ 7.7 5.6 7.0 7.2 6.6 6.9 6.2 6.7

ROW .................................................. 15.0 16.1 16.4 16.3 17.2 18.8 19.2 18.8

EU = European Union; ROW = rest of world

NOTES: Value added is amount contributed by country, firm, or other entity to value of good or service and excludes purchases of domestic and imported 

materials and inputs. Technology level of manufacturing classified by Organisation for Economic Co-operation and Development on basis of R&D 

intensity of output. Asia-8 includes India, Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan, and Thailand. China includes Hong Kong. 

EU excludes Cyprus, Estonia, Latvia, Lithuania, Luxembourg, Malta, and Slovenia. Percents may not add to 100% because of rounding.

SOURCE: IHS Global Insight, World Industry Service database (2011).

Science and Engineering Indicators 2012

Trade and Other 

Globalization Indicators

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 particularly strong 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 will focus on international KI services and HT 

trade and U.S. trade of advanced technology products (ATP). 

(See “U.S. Trade in Advanced Technology Products” later in 

this chapter for a discussion of how the U.S. Census Bureau’s 

product-based classification of advanced technology products differs from the OECD’s industry-based classification 

of HT products.) It will also examine several globalization 

measures of U.S. multinationals in KTI industries. 

Trade data are a useful though imperfect indicator 

of globalization. Trade data are classified by product or 

type of service, while corresponding production data are 

classified by industry (see sidebars “Industry and Trade 

Data and Terminology” and “Product Classification and 

Determination of Country of Origin of Trade Goods”). An 

export classified as a computer service may originate from a 

firm classified as a computer manufacturer. Trade data also 

cannot provide a precise measure of where value is added 

to a product or service. For example, China is credited with 

the full value (i.e., factory price plus shipping cost) even 

when exporting a smart phone that was assembled in China 

with inputs and components imported from other countries. 
6-30 Chapter 6. Industry, Technology, and the Global Marketplace

Countries whose firms provide these high-value components 

and services (design, marketing, software development, etc.) 

are not credited for their contributions (see sidebar, “Tracing 

the Geography of the Value Chain of Products”). 

This discussion of trade trends in KI services and HT 

manufactured products focuses on (1) the world’s large, 

highly developed countries and regions—the United States, 

the EU, and Japan; (2) China, which is rapidly taking on an 

increasingly important role in KTI trade; and (3) the Asia-8, 

which generates a substantial and increasing trade volume 

within the group and maintains strong trade ties with China. 

Both Europe and East Asia have substantial volumes 

of intraregional trade. This section treats trade within 

these two regions in different ways. Intra-EU exports are 

not counted because the EU is an integrated trading bloc 

with common external trade tariffs and few restrictions 

Several studies have attempted to estimate more precisely the geographic contribution of the global value chain involved in the production of several electronic goods. These 

studies essentially show that the largest returns accrue to the 

firms and countries that harbor special design, engineering, 

and marketing expertise. Because value-added data are not 

readily available at the product or firm level, these studies 

estimate the cost of direct labor, inputs, design, marketing, 

and distribution and retail (table 6-B). 

A study of Apple’s iPad estimates that the United States receives 33% of the retail price of the iPad, almost all of it (30%) 

consisting of Apple’s gross profit (figure 6-C). The estimated 

share for manufacture and assembly of components for the 

iPad is 23%, largely apportioned to South Korea with smaller 

distributions to Japan, Taiwan, the EU, and the United States. 

China, the location of final assembly, receives an estimated 2% share of the iPad’s price (figure 6-C). The study 

estimates that China’s value added is very small because 

final assembly of these products requires only a few minutes 

and China’s wages for assembly workers are very low compared to those in more developed countries. 

Because final assembly of the iPad and other electronic 

goods manufactured by foreign multinationals yields little 

value for China, observers claim that bilateral trade statistics 

are misleading. The large U.S. trade deficit with China in electronic goods is due in part to crediting China for the entire 

shipping cost of these goods, even though much of the value 

of these goods derives from imported parts and components 

from other Asian countries, the EU, and the United States. 

A study by Xing (2010) estimates that crediting exports 

to countries on the basis of their value-added contribution 

would lower the value of China’s exports of Apple iPhones 

to the United States in 2009 from an estimated $2 billion to 

less than $100 million. The remaining $1.9 billion would 

be credited to countries that supply components to China—

South Korea, Japan, Germany, and others.

Tracing the Geography of the Value Chain of Products

Table 6-B

Value chain of Apple iPad, by location and activity: 2010

(Percent)

Characteristic Activity Location

Amount/cost 

(dollars)

Share of 

retail price (%)

Distribution and retail .... Manufacturer’s suggested retail price Worldwide 499 100.0

 Distribution Worldwide 75 15.0

 Wholesale price (received by Apple) United States 424 85.0

Value capture ................. Total value capture 238 47.7

 U.S. total United States 162 32.5

 Design/marketing Apple 150 30.1

 Manufacturing of components U.S. suppliers 12 2.4

 Manufacturing of components Japan 7 1.4

 Manufacturing of components South Korea 34 6.8

 Manufacturing of components Taiwan 7 1.4

 Manufacturing of components EU 1 0.2

 Manufacturing of components Unidentified 27 5.4

Direct labor .................... Total direct labor 33 6.6

 Labor to manufacture components Unidentified 25 5.0

 Labor for final assembly China 8 1.6

Inputs ............................. Nonlabor costs Worldwide 154 30.9

EU = European Union

NOTES: iPad is configured with 16GB of memory and no cellular access. Value added is amount contributed by country, firm, or other entity to value 

of good or service and excludes purchases of domestic and imported materials and inputs. Value capture is value added excluding the cost of direct 

labor, which is the same as gross profit. Detail may not add to total due to rounding.

SOURCE: Linden G, Kraemer KL, Dedrick J. Who profits from innovation in global value chains? Estimates for the iPhone and iPad, Personal 

Computing Center, University of California–Irvine (2011), unpublished manuscript dated June 15.

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 6-31

on intra-EU trade. By the same token, HT trade between 

China and Hong Kong is excluded because it is essentially 

intra-country trade. Intra-Asian trade is counted because it 

allows the delineation of a developing Asia-8/China trade 

zone in the absence of the kind of formal structures that 

bind the EU together. 

Global Trade in Commercial KTI 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. 

Global trade in commercial KTI goods and services consists of three services—business, communications, and finance—and six HT products—aerospace, communications, 

computers, pharmaceuticals, semiconductors, and scientific 

instruments.11 The data on commercial KI service trade also 

include trade in royalties and fees, which do not correspond 

to a specific industry. 

The value of commercial KTI exports has risen faster 

than their global production, resulting in an increase in the 

export share of production from 12% in 1995 to plateau at 

15%–16% in the latter half of the 2000s (figure 6-20). The 

rise in export intensity indicates the growing importance of 

international suppliers involved in production of goods and 

provision of services. Data on multinational companies and 

cross-border investment likewise indicate growing interconnection among the world’s economies.

The global value of commercial KTI exports increased 

from $1 trillion in 1995 to $3.5 trillion in 2008, then declined 

to a recession-induced $3.2 trillion in 2009 but rebounded to 

$3.6 trillion in 2010 (figure 6-20). This mirrored the trend 

in global output of commercial KTI industries during this 

period (figure 6-11 and appendix table 6-3). The decline of 

commercial KTI exports in 2009 was far sharper than in the 

recession in the early 2000s (figure 6-20). 

The EU is the largest exporter of commercial KI goods 

and services, with $719 billion in 2009 (23% of global 

value) (figure 6-21). The Asia-8 closely follows with $683 

billion. The United States is next largest with $564 billion 

(18% of global value), followed by China with $500 billion 

(16% of global value). Japan trails with $199 billion. 

The U.S. global share fluctuated between 20% and 23% 

from 1995 to 2001 before declining to 17%–18% from 2002 to 

2009 (figure 6-21). The EU and Asia-8’s shares fluctuated between 20% and 23% for much of the period. China’s share rose 

rapidly from 6% to 15% from 1995 to 2006, surpassing Japan 

in 2003, then rose more slowly to 16% from 2006 to 2009.

Commercial Knowledge-Intensive Services

Global exports of commercial KI services grew faster than 

global production of these services over the 15-year period 

from 1995 to 2010 (figure 6-20). The gradual rise in the export 

share of commercial KI production (from 5% to 8%) suggests 

a modest rate of globalization in these service industries, in 

contrast to the earlier and more rapid pace in HT manufacturing. Advances in ICT technologies and emerging capabilities 

in other developed and developing countries, such as India, 

are driving globalization of commercial KI services. 

The EU is the largest exporter of commercial KI services with $409 billion in 2009 (30% of global value) (figure 

6-22). The United States is the second-largest economy and 

single largest country exporter with $293 billion in 2009 

(22% of global value). The Asia-8 is the third-largest exporter group with $204 billion (15% of global value), with 

India and Singapore being the major exporters in this region. 

China is the fourth-largest exporter with $110 billion, although its exports include trade between China and Hong 

Kong, which is likely substantial.12 Japan is the fifth largest 

with $84 billion. 

The dollar value of total global exports (excluding intraEU) of commercial KI services rose almost fourfold over a 

decade and a half, from $360 billion in 1995 to $1.5 trillion 

Figure 6-C

Components of value added and value capture

Sales price

Cost of goods sold Purchased inputs

Direct labor

Value added

Selling, general, and administrative

Value capture Research and development

Depreciation

Net profit

NOTES: Value added is amount contributed by country, firm, or other entity to value of good or service and excludes purchases of domestic and 

imported materials and inputs. Value capture is value added excluding the cost of direct labor.

SOURCE: Dedrick J, Kraemer KL, Linden G, Who Profits from Innovation in Global Value Chains? A Study of the iPod and notebook PCs, Personal 

Computing Industry Center, University of California–Irvine (2008), http://pcic.merage.uci.edu/index.htm, accessed 7 November 2009.

Science and Engineering Indicators 2012 

Tracing the Geography of the Value Chain of Products—continued
6-32 Chapter 6. Industry, Technology, and the Global Marketplace

Figure 6-20

Global commercial KTI exports and production: 

1995–2010

HT = high-technology; KI = knowledge-intensive; KTI = knowledge- and 

technology-intensive

NOTES: Production is gross revenue, which includes purchases of 

domestic and imported materials and inputs. KTI industries include 

knowledge-intensive services and high-technology manufacturing 

industries classified by Organisation for Economic Co-operation and 

Development. Knowledge-intensive services include business, financial, 

communications, education, and health. Commercial knowledgeintensive services include business, financial, and communications 

services. Public knowledge-intensive services include education and 

health. High-technology manufacturing industries include aerospace, 

communications and semiconductors, computers and office machinery, 

pharmaceuticals, and scientific instruments and measuring equipment. 

KTI trade consists of trade of four services (business, financial, 

computer and communications services, and royalties and fees) and five 

products (aerospace, communications and semiconductors, scientific 

instruments, computers, and pharmaceutical products). EU exports in KI 

services for 2010 is estimated.

SOURCES: IHS Global Insight, World Trade Service database (2010); 

World Trade Organisation, International trade and tariff data, 

http://www.wto.org/english/res_e/statis_e/statis_e.htm, accessed 15 

November 2010.

Science and Engineering Indicators 2012

Percent

Current dollars (trillions)

Global commercial KTI production

Export share of commercial KTI production

0

10

20

30

40

50

60

KI services

HT manufacturing industries

KTI industries

1995 1998 2000 2002 2004 2006 2008 2010

1995 1998 2000 2002 2004 2006 2008 2010

0

5

10

15

20

25

KI industries

HT manufacturing industries

KTI industries

Dollars (trillions)

Global commercial KTI exports

1995 1998 2000 2002 2004 2006 2008 2010

0

1

2

3

4

KI services

HT manufacturing

industries

KTI industries

Figure 6-21

Global commercial KTI exports, by selected 

region/country/economy: 1995–2009

EU = European Union; KTI = knowledge- and technology-intensive; 

ROW = rest of world

NOTES: KTI trade consists of trade of four services (business, 

financial, computer and communications services, and royalties and 

fees) and five products (aerospace, communications and 

semiconductors, scientific instruments, computers, and 

pharmaceutical products). Asia-8 includes India, Indonesia, 

Malaysia, Philippines, Singapore, South Korea, Taiwan, and 

Thailand. China includes Hong Kong. EU includes current member 

countries. Data for China and ROW not available for 2010.

SOURCES: IHS Global Insight, World Trade Service database (2010); 

World Trade Organisation, International trade and tariff data, 

http://www.wto.org/english/res_e/statis_e/statis_e.htm, accessed 15 

November 2010.

Science and Engineering Indicators 2012

Dollars (billions)

Percent

Global share

Amount

0

200

400

600

800

1,000

1995 1997 1999 2001 2003 2005 2007 2009

United States

EU

Japan

China

Asia-8

ROW

1995 1997 1999 2001 2003 2005 2007 2009

0

5

10

15

20

25

30

35

United States

EU

Japan

China

Asia-8

ROW
Science and Engineering Indicators 2012 6-33

in 2008, before declining to $1.4 trillion in 2009, in contrast 

to the flattening of output during the earlier recession (figure 

6-20). Global exports resumed growth in 2010, returning to 

their 2008 level ($1.5 trillion). 

The U.S. and EU global shares fluctuated at 22%–26% 

and 29%–31%, respectively, over the period (figure 6-22). 

The Asia-8’s share rose from 11% to 15%, led by India and 

Singapore. China’s share nearly doubled from 5% to 8%, 

surpassing Japan in 2007. Japan’s share declined from 11% 

to 6% during this period. 

Commercial KI service exports comprise four categories: 

business services (including legal, management, advertising, R&D, and engineering services), valued at $675 billion; 

financial services (banking and insurance), valued at $267 

billion; royalties and licensing fees, valued at $183 billion; 

and computer and information services, valued at $129 billion (figure 6-23).13

Figure 6-22

Exports of commercial knowledge-intensive services, 

by selected region/country/economy: 1995–2009

EU = European Union; ROW = rest of world

NOTES: Commercial knowledge-intensive trade consists of trade in 

business, financial, computer and communications services, and 

royalties and licensing fees. Asia-8 includes India, Indonesia, 

Malaysia, Philippines, Singapore, South Korea, Taiwan, and 

Thailand. China includes Hong Kong and trade between China and 

Hong Kong. EU includes current member countries. China and ROW 

not available for 2010.

SOURCE: World Trade Organisation, International trade and tariff 

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

accessed 15 November 2010.

Science and Engineering Indicators 2012

Dollars (billions)

Percent

Global share

1995 1997 1999 2001 2003 2005 2007 2009

0

100

200

300

400

500

United States

EU

Japan

China

Asia-8

ROW

1995 1997 1999 2001 2003 2005 2007 2009

0

10

20

30

40

50

United States

EU

Japan

China

Asia-8

ROW

Amount

EU = European Union

NOTES: EU includes current member countries. Royalty and 

licensing fees data not available for China and India. Financial 

services data not available for China.

SOURCE: World Trade Organisation, International trade and tariff 

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

accessed 15 November 2010.

Science and Engineering Indicators 2012

Figure 6-23

Global exports of selected services, by selected 

region/country/economy: 2008

Dollars (billions)

Other

business

services

Financial

services

Royalty

and

licensing

fees

Computer

and

information

services

0

100

200

300

400

500

600

700

United States

EU

India

China

Japan

Hong Kong

All others
6-34 Chapter 6. Industry, Technology, and the Global Marketplace

The EU is the largest world exporter of business services 

with value added of $222 billion in 2008 (33% of global 

value) (figure 6-23). The United States is the second largest 

with $102 billion (15% of global value added), far below 

the EU’s level. China (including Hong Kong) is slightly below the United States with $79 billion (12% of global value 

added). 

The EU is the largest exporter of financial services with 

$95 billion in 2008 (36% of global value added), closely followed by the United States with $63 billion (28% of global 

value added) (figure 6-23). Data on China, Japan and the 

Asia-8 economies show much lower levels of financial services exports. 

The United States is the world’s largest exporter in royalties and licensing fees with $102 billion (51% of global 

value) (figure 6-23). The EU and Japan are the second and 

third largest with $36 billion and $26 billion, respectively. 

These three economies collectively account for 85% of global value of these exports. 

The EU is the largest exporter of communications and 

information services with $43 billion (33% of global value) 

(figure 6-23). India is the second-largest exporter with $36 

billion (28% of global value), reflecting its strong position in 

providing these services for companies based in the United 

States, EU, and other developed countries. The United States 

is the third largest with $13 billion (10% of global value). 

Trade Balance Trends in Commercial KnowledgeIntensive Services

The EU and the United States have enjoyed substantial and 

rising positive balances in their trade of commercial KI services, particularly over the last decade (figure 6-24). Both exceeded $80 billion in 2009, even as the EU’s surplus dropped 

steeply and the U.S. surplus flattened as a result of the 2008–

09 recession. The U.S. surplus rose from $55 billion in 2000 

to more than $100 billion in 2007–09, even as the U.S. trade 

deficit in HT goods deepened during the same period. 

The United States has substantial surpluses in royalties 

and fees ($68 billion) and other business services ($36 billion). It has small deficits in financial services and computer 

and information services ($2–$3 billion). The composition 

of the EU’s surplus is similar to that of the United States. 

China had a surplus of $28 billion in 2009, up from the 

$13–$16 billion surplus it had run in the early 2000s (figure 

6-24). The Asia-8 as a group had a surplus of $34 billion 

in 2009 with India having a $32 billion surplus, the largest among these economies (figure 6-24). The rise in India’s 

surplus was driven by its substantial rise in computer and 

information services. Brazil and Russia have deficits in their 

KI services trade, ranging up to $29 billion for Russia. 

High-Technology Goods

The global production of HT manufacturing industries 

more than doubled from $2.0 trillion to $4.3 trillion over the 

last 15 years. The value of HT export goods grew faster than 

global production, suggesting that globalization has continued in these already highly competitive and geographically 

dispersed industries. The export share rose from 36% to 53% 

in 2006 before drifting downward to 50% in 2010 (figure 

6-20). 

The HT export shares of the major economies—i.e., the 

percentage of total production that is exported—vary widely, with the shares of the United States and EU and Japan 

considerably lower than those of China and the Asia-8, the 

largest global exporters (figure 6-25). The export shares of 

the United States and the EU each rose about 15 percentage 

points between 1995 and 2010 to reach 43% in the United 

States and 38% in the EU. Japan’s share stayed roughly stable at 29%. The Asia-8’s export share fluctuated between 

80% and 90% of their total production. China’s export share 

rose from 63% to 71% from 1995 to 2004 before falling 

sharply to 43% in 2010, helping to account for the slight decline in the proportion of global HT production that was exported. The decline in China’s export share could be a result 

of growing domestic consumption of these goods, higher 

labor costs in China that have prompted some relocation of 

manufacturing facilities to other countries, and higher shipping costs. Conversely, it may reflect the impact of the global recession that caused a sharper decline in China’s exports 

than in production in 2009. 

Figure 6-24

Trade balance in commercial KI services for 

selected region/country/economy: 1995–2009

EU = European Union; KI = knowledge-intensive

NOTES: Commercial knowledge-intensive trade consists of trade in 

business, financial, computer and communications services, and 

royalties and licensing fees. Asia-8 includes India, Indonesia, 

Malaysia, Philippines, Singapore, South Korea, Taiwan, and 

Thailand. China includes Hong Kong. EU includes current member 

countries. Data for China not available for 2010.

SOURCE: World Trade Organisation, International trade and tariff 

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

accessed 15 November 2010.

Science and Engineering Indicators 2012

Dollars (billions)

–40

–20

0

20

40

60

80

100

120

140

1995 1997 1999 2001 2003 2005 2007 2009

United States

EU

Japan

China

Asia-8
Science and Engineering Indicators 2012 6-35

Global exports of HT goods in 2010 were $2.1 trillion, 

including a combined $1.1 trillion exported by China and the 

Asia-8 and a collective $800 billion exported by the United 

States, the EU, and Japan (figure 6-26 and appendix table 

6-24). Global HT exports comprised nearly one-fifth of the 

$11 trillion in exports of all manufactured goods (appendix 

table 6-25). The largest single exporter in HT manufacturing is the Asia-8 group with $570 billion (27% of global 

value) (figure 6-26). The second-largest exporter is China 

with $476 billion (22% of global value). The United States 

and EU follow China with exports of around $330 billion 

each (16% of global value). Japan was fifth with exports of 

$140 billion. 

The value of global exports rose from $700 billion in 

1995 to $2.0 trillion in 2008 before falling sharply in 2009 

to $1.8 trillion, coinciding with the contraction of global HT 

manufacturing output during the recession (figures 6-16 and 

Figure 6-25

Export share of high-technology manufacturing 

production, by selected region/country/economy: 

1995–2010

EU = European Union

NOTES: Production is gross revenue, which includes purchases of 

domestic and imported materials and inputs. High-technology 

manufacturing industries are classified by the Organisation for 

Economic Co-operation and Development and include aerospace, 

communications and semiconductors, computers and office 

machinery, pharmaceuticals, and scientific instruments and 

measuring equipment. High-technology exports are on a product 

basis, and include exports of aerospace, communications and 

semiconductors, computers and office machinery, pharmaceuticals, 

and scientific instruments and measuring equipment. Asia-8 includes 

India, Indonesia, Malaysia, Philippines, Singapore, South Korea, 

Taiwan, and Thailand. China includes Hong Kong. EU includes 

current member countries.

SOURCE: IHS Global Insight, World Trade Service database (2011). 

Science and Engineering Indicators 2012

Percent

1995 1998 2000 2002 2004 2006 2008 2010

0

20

40

60

80

100

World total

United States

EU

Japan

China

Asia-8

Figure 6-26

Exports of high-technology goods, by selected 

region/country/economy: 1995–2010

EU = European Union; ROW = rest of world

NOTES: High-technology products include aerospace, communications 

and semiconductors, computers and office machinery, pharmaceuticals, and scientific instruments and measuring equipment. 

Asia-8 includes India, Indonesia, Malaysia, Philippines, Singapore, 

South Korea, Taiwan, and Thailand. China includes Hong Kong. EU 

excludes Cyprus, Estonia, Latvia, Lithuania, Luxembourg, Malta, and 

Slovenia. 

SOURCE: IHS Global Insight, World Trade Service database (2011). 

See appendix tables 6-24 and 6-32.

Science and Engineering Indicators 2012

Dollars (billions)

Percent

Global share

1995 1998 2000 2002 2004 2006 2008 2010

0

100

200

300

400

500

600

700

United States

EU

Japan

China

Asia-8

ROW

1995 1998 2000 2002 2004 2006 2008 2010

0

10

20

30

40

50

United States

EU

Japan

China

Asia-8

ROW

Amount
6-36 Chapter 6. Industry, Technology, and the Global Marketplace

6-26 and appendix table 6-24). Global exports sharply rebounded in 2010 to reach $2.1 trillion, slightly greater than 

their 2008 levels.14

The U.S. share of global HT manufacturing exports rose 

from 19% to 22% from 1995 to 1998 before declining to a 

range of 13%–15% from 2003 to 2010 (figure 6-26 and appendix table 6-24). China’s share nearly quadrupled from 

6% to 22%. The Asia 8’s global share fluctuated between 

27% and 30% from 1995 to 2010. Japan’s share fell sharply 

from 19% to 7% over the 15-year period.

Among the six HT products, ICT products account for 

$1.3 trillion (61%) of the $2.1 trillion in global exports. 

These include communications ($505 billion), semiconductors ($422 billion), and computers ($385 billion) (appendix 

tables 6-26, 6-27, and 6-28). The others are, in decreasing 

order: scientific and measuring instruments ($361 billion), 

pharmaceuticals ($286 billion), and aerospace ($176 billion) 

(table 6-5 and appendix tables 6-29, 6-30, and 6-31).

The U.S. global export share in computers declined substantially, driving the loss in the U.S. overall ICT export 

share (table 6-5 and appendix tables 6-26, 6-27, and 6-28). 

The U.S. share was down by about half, reaching a level of 

11%. The United States had a more modest decline in communications (from 13% to 11%) and semiconductors (from 

15% to 11%). The EU had comparatively greater declines 

in communications and semiconductors and a smaller decline in computers. Japan had steep losses across all three 

goods categories. 

China’s share rose sharply in communications and 

computers, becoming the world’s largest exporter in these 

two goods (table 6-5 and appendix tables 6-26 and 6-28). 

China’s share increased from 10% to 39% in communications and from 6% to 45% in computers. China’s rise in 

semiconductors was more modest, increasing from 4% to 

10% (appendix table 6-27). The Asia-8’s share in communications fluctuated between 24% and 29% and was down in 

computers (from 39% to 27%). The Asia-8’s share in semiconductors rose from 40% to 59%, driven by rapid gains in 

South Korea and Taiwan. The Asia-8’s sizeable export share 

in ICT goods reflects its role as a manufacturing supplier 

zone for ICT goods assembled in China. 

The U.S. share in scientific and measuring instruments 

fell slightly from 22% to 19% (table 6-5 and appendix table 

6-29). The EU’s share also fell slightly, declining from 20% 

to 18%. Japan’s share was down by half from 23% to 12%. 

The Asia-8 region’s share more than doubled from 10% to 

22%. China’s share rose sharply from 8% to 14%. 

The U.S. share in pharmaceutical exports was stable at 

16% between 1995 and 2010 (table 6-5 and appendix table 

6-30). The EU’s share declined from 48% to 44%. China’s 

share was stable at 4%. The Asia-8’s share rose from 4% to 

6%, driven by gains in India and Singapore. 

The United States maintained a dominant position in aerospace exports, with its share rising from 40% in 1995 to 48% 

in 2005 before dropping to 44% in 2010 (table 6-5 and appendix table 6-31). The EU’s share dropped from 40% to 31%. 

Trade Balance Trends in High-Technology Goods 

The United States had a trade surplus in HT manufactured products throughout the 1980s and early 1990s, in 

contrast to deficits for other U.S. manufacturing products.15

Growing U.S. imports in the late 1990s shifted the U.S. 

Table 6-5

Exports of high-technology products, by selected 

product and region/country/economy: Selected 

years, 1995–2010

Export 1995 2000 2005 2010

Communications

World ($billions) ............ 150.5 225.2 394.9 506.9

United States ............ 13.4 13.1 7.1 9.1

EU .............................. 16.1 17.2 15.4 9.2

Japan ........................ 19.7 13.7 9.5 6.6

China ......................... 10.2 12.9 28.1 38.5

Asia-8 ........................ 29.1 24.5 26.6 23.8

Semiconductors

World ($billions) ............ 163.5 274.0 327.9 421.8

United States ............ 15.2 17.4 13.2 11.2

EU .............................. 9.2 9.1 7.0 4.4

Japan ........................ 26.5 16.5 12.6 10.7

China ......................... 3.8 4.0 7.2 10.2

Asia-8 ........................ 39.9 46.0 55.6 58.7

Computers

World ($billions) ............ 185.7 300.6 380.8 385.1

United States ............ 19.2 15.3 9.7 10.0

EU .............................. 9.1 8.7 8.8 6.6

Japan ........................ 20.1 12.8 7.0 2.4

China ......................... 5.8 10.9 34.5 45.4

Asia-8 ........................ 39.1 44.1 34.1 27.2

Scientific instruments

World ($billions) ............ 106.8 150.9 236.0 361.4

United States ............ 22.4 26.0 19.7 19.1

EU .............................. 20.0 19.3 21.4 17.6

Japan ........................ 22.7 19.8 15.6 11.6

China ......................... 8.1 9.0 10.6 13.9

Asia-8 ........................ 9.5 8.7 16.0 22.0

Pharmaceuticals

World ($billions) ............ 44.1 69.3 156.8 285.6

United States ............ 14.6 18.6 16.4 15.6

EU .............................. 47.6 47.5 50.3 44.0

Japan ........................ 4.3 4.4 2.9 2.3

China ......................... 3.5 2.7 2.5 3.9

Asia-8 ........................ 3.5 3.7 4.5 6.4

Aerospace

World ($billions) ............ 62.4 81.9 101.9 176.5

United States ............ 39.5 48.7 48.2 44.9

EU .............................. 39.9 27.7 29.2 31.2

Japan ........................ 1.0 1.9 1.6 1.7

China ......................... 0.4 0.4 0.5 0.8

Asia-8 ........................ 2.3 1.5 2.1 3.3

EU = European Union

NOTES: Asia-8 includes India, Indonesia, Malaysia, Philippines, 

Singapore, South Korea, Taiwan, and Thailand. China includes Hong 

Kong. EU excludes Cyprus, Estonia, Latvia, Lithuania, Luxembourg, 

Malta, and Slovenia. 

SOURCE: IHS Global Insight, World Trade Service database (2011). 

See appendix tables 6-26–6-31.

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 6-37

balance into a $67 billion deficit by 2000. After reaching a 

level of $100–120 billion in 2004–07 prior to the recession, 

the deficit dropped to $90 billion by 2010 (figure 6-27 and 

appendix table 6-24).

The EU had a small deficit from 1995 to 2005, which 

widened to $50–$60 billion in 2006-10 (figure 6-27 and appendix table 6-24). Japan’s surplus declined from $90 billion to $30 billion over the 15-year period. The other Asian 

economies also ran surpluses: China’s trade position in HT 

products increased from a small surplus in 2000 to almost 

$160 billion in surplus in 2010. The Asia-8’s trade surplus 

doubled over the last decade to reach $230 billion in 2010.

Two categories of ICT goods, communications and computers, are largely responsible for producing the substantial 

shifts in the trade positions of the United States, the EU, 

Japan, and China (figure 6-27 and appendix tables 6-26 and 

6-28). The U.S. deficit in these goods rose from $39 billion 

in 1995 to nearly $100 billion in 2002 and further widened 

to $150 billion in 2010; the EU’s trend was similar. Japan’s 

trade surplus in these ICT goods fell from $40 billion to a 

small deficit. 

The widening EU and U.S. deficits in these goods and 

the shrinking Japanese surplus were driven by a sharp rise 

in their imports from China. This in turn reflected the structural shifts towards Asia in production of these ICT goods 

(Athukorala and Yamashita 2006, Ng and Yeats 2003, Rosen 

and Wing 2005). China’s share of U.S., EU, and Japanese 

global imports of these ICT goods rose from 13%–15% in 

2000 to 49% or more by 2010 (figure 6-28 and appendix 

tables 6-33 and 6-34). China’s surplus in these ICT goods 

Figure 6-27

Trade balance of high-technology products, by 

selected product and region/country/economy: 

1995–2010

EU = European Union

NOTES: High-technology products include aerospace, communications 

and semiconductors, computers and office machinery, pharmaceuticals, and scientific instruments and measuring equipment. 

Asia-8 includes India, Indonesia, Malaysia, Philippines, Singapore, 

South Korea, Taiwan, and Thailand. China includes Hong Kong. EU 

excludes Cyprus, Estonia, Latvia, Lithuania, Luxembourg, Malta, and 

Slovenia. 

SOURCE: IHS Global Insight, World Trade Service database (2011). 

See appendix tables 6-24, 6-26–6-28, and 6-32.

Science and Engineering Indicators 2012

Dollars (billions)

Communications and computer products

High-technology products

Semiconductors

1995 1998 2000 2002 2004 2006 2008 2010

–200

–100

0

100

200

300

United States

EU

Japan

China

Asia-8

1995 1998 2000 2002 2004 2006 2008 2010

–200

–100

0

100

200

300

United States

EU

Japan

China

Asia-8

1995 1998 2000 2002 2004 2006 2008 2010

–200

–100

0

100

200

300

United States

EU

Japan

China

Asia-8

EU = European Union; ROW = rest of world

NOTES: Asia-8 includes India, Indonesia, Malaysia, Philippines, 

Singapore, South Korea, Taiwan, and Thailand. China includes Hong 

Kong. EU excludes Cyprus, Estonia, Latvia, Lithuania, Luxembourg, 

Malta, and Slovenia. 

SOURCE: IHS Global Insight, World Trade Service database (2011). 

See appendix tables 6-32 and 6-33.

Science and Engineering Indicators 2012

Figure 6-28

U.S., EU, and Japan imports of communications 

and computer products, by selected origin: 2000 

and 2010

Percent

0

20

40

60

80

100

United

States

EU

China

Japan

Asia-8

ROW

EU

2000

EU

2010

U.S.

2000

U.S.

2010

Japan

2000

Japan

2010
6-38 Chapter 6. Industry, Technology, and the Global Marketplace

rose from $3 billion in 1995 to $28 billion in 2000, and then 

leaped to more than $200 billion in 2006 and almost $300 

billion in 2010 (figure 6-27 and appendix tables 6-26, 6-27, 

and 6-28). 

In semiconductors, the United States and Japan ran modest surpluses over the last decade (figure 6-27 and appendix 

table 6-27). The largest market for U.S. exports of semiconductors was the Asia-8, largely South Korea and Taiwan 

(41% of U.S. exports), with China the second largest at 20%, 

up sharply from only 6% in 2000 (figure 6-29 and appendix 

table 6-34). The Asia-8 ran surpluses in semiconductors, reflecting their growing role as suppliers to each other’s and 

China’s factories and assembly lines. The surpluses widened 

over the decade from less than $20 billion in 2000 to $100 

billion in 2010, coinciding with rapid growth in Asia-8 exports destined for China for final assembly or manufactured 

under contract by U.S.- and Japanese-based semiconductor 

firms (figure 6-30 and appendix table 6-27). 

China must import semiconductors for use in its production. Its deficit in semiconductor trade widened from $20 

billion in 2000 to $110 billion in 2010, driven by increased 

imports from the Asia-8 (figure 6-27 and appendix tables 

6-27 and 6-34). The Asia-8’s share of Chinese semiconductor 

imports rose from 61% to 77%, with Taiwan accounting for 

about half of China’s imports from this region (figure 6-31). 

In aerospace, the United States has run a consistent surplus over the decade and a half from 1995 to 2010 (appendix 

tables 6-31 and 6-35). The U.S. surplus increased from about 

$20 billion in the early 2000s to $60 billion in 2010, partially 

offsetting its growing deficit in communications and computers. The EU ran a small surplus. 

In scientific instruments and pharmaceuticals, the United 

States had small deficits in most years since 1995, while the 

EU had a surplus in pharmaceuticals that grew from $11 billion in 1995 to $55 billion in 2010 (appendix tables 6-29 and 

6-30). The Asia-8’s trade position in scientific instruments 

shifted from a small deficit to surplus in 2005, and steadily 

grew to $31 billion in 2010. The trend was similar in pharmaceuticals, driven by exports from India and Singapore. 

Since 1995, the United States and EU have become more 

important destinations for pharmaceutical exports from 

India and Singapore. The U.S. share of India’s pharmaceutical exports rose from 5% in 2000 to 29% in 2010, and its 

share of Singapore’s pharmaceutical exports jumped from 

5% to 30% during the same period (figure 6-32 and appendix table 6-36). The trend was similar in the EU.

EU = European Union; ROW = rest of world

NOTES: Asia-8 includes India, Indonesia, Malaysia, Philippines, 

Singapore, South Korea, Taiwan, and Thailand. China includes Hong 

Kong. EU excludes Cyprus, Estonia, Latvia, Lithuania, Luxembourg, 

Malta, and Slovenia. 

SOURCE: IHS Global Insight, World Trade Service database (2011). 

See appendix tables 6-32 and 6-34.

Science and Engineering Indicators 2012

Figure 6-29

U.S. exports of semiconductors, by selected 

destination: 2000 and 2010

Percent

0

20

40

60

80

100

Mexico

EU

China

Japan

Asia-8

ROW

2000 2010

Product Classification and 

Determination of Country 

of Origin of Trade Goods

Trade data are based on a classification of goods 

or services themselves, not the industry that produces 

them. Data on product trade are recorded at the exporting country’s ports of exit and the importing country’s 

ports of entry. Because many imported products are 

assessed an import duty and these duties vary by product category, a customs agent for the receiving country 

inspects or reviews the shipment to make the final determination of the proper product code and country of 

origin. The customs agent assigns a product trade code 

according to the Harmonized System.*

The value of products 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.

Data on international product trade assign products 

to a single country of origin. For goods manufactured 

with international components, the country of origin 

is determined by where the product was “substantially 

transformed” into its final form. 

*The Harmonized Commodity Description and Coding System, 

or Harmonized System (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.
Science and Engineering Indicators 2012 6-39

Trade in Medium- and Low-Technology 

Manufactured Products

The U.S. export performance in manufactured products associated with less knowledge intensity and less use 

of R&D provides a context for interpreting its HT trade. In 

these industries, the United States has world export shares 

below those of the EU and Asia-8 (across all three categories: medium-high, medium-low, and low technology) and 

China (medium-low and low technology).

The U.S. share of world exports in medium-high-technology products (i.e., motor vehicles, chemicals, railroad equipment) was 14% in 2010, roughly the same as its share in HT 

industries (table 6-6), placing it at about the same level as 

China, Japan, and the Asia-8. The world export shares of 

these economies are significantly below the leading global 

exporter, the EU (23% share of global value). The U.S. and 

EU shares have fallen 3 percentage points over the past decade and a half, while Japan’s share has fallen more steeply 

from 22% to 13%. China has rapidly expanded its share of 

global exports from 4% to 14% (excluding trade between 

China and Hong Kong), reaching rough parity with the 

United States, the Asia-8, and Japan. 

The United States has roughly the same share (8%) as 

Japan in world exports in medium-low-technology products, behind the EU (15%), China (11%), and the Asia-8 

(21%) (table 6-6). The U.S. share of global exports of lowtechnology products in 2010 (11%) placed it well behind all 

the other major economies except for Japan (2% share). In 

both of these industry groups, China’s world export share 

expanded greatly since the mid-1990s but not to the same 

degree as for HT exports.

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 

EU = European Union; ROW = rest of world

NOTES: Asia-8 includes India, Indonesia, Malaysia, Philippines, 

Singapore, South Korea, Taiwan, and Thailand. China includes Hong 

Kong. EU excludes Cyprus, Estonia, Latvia, Lithuania, Luxembourg, 

Malta, and Slovenia. 

SOURCE: IHS Global Insight, World Trade Service database (2011). 

See appendix table 6-33 and 6-34.

Science and Engineering Indicators 2012

Figure 6-30

Asia-8 exports of selected goods, by type and 

destination: 2000 and 2010

0

20

40

60

80

100

Percent

United States

EU

China

Japan

Asia-8

ROW

2000 2010

Semiconductors

Communications and computers

0

20

40

60

80

100

2000 2010

United States

EU

China

Japan

Asia-8

ROW

EU = European Union; ROW = rest of world

NOTES: Other Asia-8 includes India, Indonesia, Malaysia, 

Philippines, and Thailand. China includes Hong Kong. EU excludes 

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

SOURCE: IHS Global Insight, World Trade Service database (2011). 

See appendix table 6-34.

Science and Engineering Indicators 2012

Figure 6-31

China’s imports of semiconductors, by selected 

origin: 2000 and 2010

Percent

0

20

40

60

80

100

United States

EU

Taiwan

South Korea

Singapore

Japan

Other Asia-8

ROW

2000 2010
6-40 Chapter 6. Industry, Technology, and the Global Marketplace

determining whether products are HT and may be a more 

precise and comprehensive measure than the industry-based 

OECD classification. 

This system allows a highly disaggregated, focused examination of technologies embodied in U.S. imports and 

exports. It categorizes advanced technology product (ATP) 

trade into 10 major technology areas: 

Advanced materials—the development of materials, 

including semiconductor materials, optical fiber cable, 

and videodisks, that enhance the application of other advanced technologies. 

Aerospace—the development of aircraft technologies, such 

as most new military and civilian airplanes, helicopters, 

spacecraft (excluding communications satellites), turbojet 

aircraft engines, flight simulators, and automatic pilots. 

Biotechnology—the medical and industrial application 

of advanced genetic research to the creation of drugs, 

hormones, and other therapeutic items for both agricultural and human uses. 

Electronics—the development of electronic components 

(other than optoelectronic components), including integrated circuits, multilayer printed circuit boards, and 

surface-mounted components (such as capacitors and 

resistors) that improve performance and capacity and, in 

many cases, reduce product size. 

Flexible manufacturing—the development of products 

for industrial automation, including robots, numerically 

controlled machine tools, and automated guided vehicles, 

that permit greater flexibility in the manufacturing process and reduce human intervention. 

Information and communications—the development of 

products that process increasing amounts of information 

in shorter periods of time, including computers, videoconferencing, routers, radar apparatus, communications satellites, central processing units, and peripheral units such as 

disk drives, control units, modems, and computer software.

Life sciences—the application of nonbiological scientific advances to medicine. For example, advances such as 

nuclear magnetic resonance imaging, echocardiography, 

and novel chemistry, coupled with new drug manufacturing techniques, have led to new products that help control 

or eradicate disease. 

Optoelectronics—the development of electronics and 

electronic components that emit or detect light, including 

optical scanners, optical disk players, solar cells, photosensitive semiconductors, and laser printers. 

Nuclear—the development of nuclear production apparatus (other than nuclear medical equipment), including 

nuclear reactors and parts, isotopic separation equipment, 

and fuel cartridges. (Nuclear medical apparatus is included in the life sciences rather than this category.) 

Weapons—the development of technologies with military 

applications, including guided missiles, bombs, torpedoes, 

mines, missile and rocket launchers, and some firearms. 

U.S. trade in ATP products is an important component of 

overall U.S. trade, accounting for about one-fifth of its combined nonpetroleum exports and imports for the past two 

decades. In 2010, U.S. exports of ATP products were $273 

billion (24% of total U.S. goods exports) and imports were 

$355 billion (23% of total U.S. goods imports) (figure 6-33 

and appendix table 6-37). As with world HT product trade 

accounts, U.S. imports of ATP products have grown faster 

than exports since the early 1990s. This sent the U.S. trade 

balance in ATP products into deficit in 2002. The deficit leveled off at $55–60 billion for 2007–09 before reaching a new 

record high of $82 billion in 2010. 

After growing for much of the last decade, exports and 

imports both fell 10% in 2009 during the global recession 

(figure 6-33 and appendix table 6-37). Both bounced back 

in 2010, exports growing 11% and imports growing 18%. 

Exports returned to their 2008 value, and imports reached a 

new high of $354 billion. 

The growing U.S. trade deficit in these goods reflects not 

only changing world production and trade patterns but also 

factors that are hard to measure and cannot be adequately 

accounted for, including exchange rate movements and new 

business and production processes.

EU = European Union; ROW = rest of world

NOTES: Asia-8 includes India, Indonesia, Malaysia, Philippines, 

Singapore, South Korea, Taiwan, and Thailand. China includes Hong 

Kong. EU excludes Cyprus, Estonia, Latvia, Lithuania, Luxembourg, 

Malta, and Slovenia. 

SOURCE: IHS Global Insight, World Trade Service database (2011). 

See appendix tables 6-32 and 6-36.

Science and Engineering Indicators 2012

Figure 6-32

India and Singapore’s exports of pharmaceuticals, 

by selected destination: 2010

0

20

40

60

80

100

Percent

United States

EU

China

Russia

Brazil

Asia-8

ROW

Singapore India
Science and Engineering Indicators 2012 6-41

U.S. Advanced Technology Product Trade, 

by Technology 

Four technology areas—ICT, aerospace, electronics, and 

the life sciences—accounted for a combined share of 85% of 

U.S. ATP product exports in 2010 (figure 6-34 and appendix 

tables 6-37, 6-38, 6-39, 6-40, 6-41, 6-42, and 6-43). Aerospace 

had the largest single share (30%), followed by ICT (28%), 

electronics (17%), and the life sciences (10%). ICT technologies have generated the largest trade deficits of any technology area—$127 billion in 2010. This deficit in ICT, widening 

from $35 billion to more than $120 billion over the decade, 

drove the increase in the U.S. ATP trade deficit. 

Two technologies, aerospace and electronics, have generated a combined trade surplus of $70 billion in 2010 (figure 6-34 and appendix tables 6-39 and 6-40). The United 

States is the leading producer of aerospace products; it had a 

trade surplus of $51 billion in 2010 ($24 billion more than in 

2000), as exports jumped from $53 billion to $81 billion and 

imports increased more moderately from $26 billion to $29 

billion. The surplus in electronics was $18 billion in 2010.

U.S. Advanced Technology Products Trade, by 

Region and Country

About 80% of U.S. ATP exports go to three regions: the 

EU (24%), Asia (Asia-8, China, and Japan) (36%), and the 

North American Free Trade Agreement (NAFTA) trade 

zone (20%) (figure 6-34 and appendix table 6-37). 

China, Japan, and the Asia-8. China is the single largest U.S. trading partner in both total goods trade and ATP 

products, exporting $117 billion worth of ATP products to 

the United States (about one-third of U.S. imports of these 

products) and importing $30 billion from the United States 

in 2010 (figure 6-34 and appendix table 6-37).16 The U.S. 

deficit in ATP and all products with China is larger than its 

deficits with any other country. Nearly 90% of U.S. ATP 

Table 6-6

Exports of manufactured products, by selected technology level and region/country/economy: 

Selected years, 1995–2010

(Percent distribution)

Manufacturing technology level and region/country/

economy 1995 1998 2001 2004 2006 2008 2010

Medium high

Global exports (current $billions) ........................... 646.0 715.7 816.7 1,189.6 1,523.7 1,987.5 1,877.4

All countries ........................................................ 100.0 100.0 100.0 100.0 100.0 100.0 100.0

United States .................................................. 16.5 17.0 15.7 13.3 13.9 13.1 13.7

EU .................................................................... 25.9 25.7 24.7 25.2 23.9 24.6 23.1

Japan .............................................................. 22.0 19.0 17.4 16.7 15.4 14.1 12.9

China ............................................................... 3.8 4.9 6.6 9.3 11.5 13.4 14.3

Asia-8 .............................................................. 1.5 1.3 1.8 2.7 2.9 2.7 3.4

All other countries ........................................... 30.3 32.1 33.8 32.8 32.4 32.1 32.6

Medium low

Global exports (current $billions) ........................... 417.7 433.8 520.0 855.2 1,304.6 1,976.0 1,871.4

All countries ........................................................ 100.0 100.0 100.0 100.0 100.0 100.0 100.0

United States .................................................. 9.3 10.5 9.6 7.0 7.3 7.0 7.6

EU .................................................................... 20.8 19.7 17.0 16.8 16.8 16.1 14.8

Japan .............................................................. 12.6 10.4 8.3 7.7 6.6 6.4 7.4

China ............................................................... 5.0 5.4 6.5 8.9 10.2 11.6 10.6

Asia-8 .............................................................. 14.8 16.1 15.9 16.5 17.8 18.7 20.8

All other countries ........................................... 37.5 38.0 42.2 42.5 41.3 39.5 37.1

Low

Global exports (current $billions) ........................... 608.5 621.8 649.5 909.7 1,075.6 1,291.0 1,266.0

All countries ........................................................ 100.0 100.0 100.0 100.0 100.0 100.0 100.0

United States .................................................. 9.7 12.5 12.2 9.5 10.8 10.8 11.0

EU .................................................................... 16.4 20.1 18.7 19.5 18.4 18.3 16.5

Japan .............................................................. 3.5 4.0 3.7 2.7 2.5 2.1 2.1

China .............................................................. 9.9 11.3 13.6 15.4 17.9 20.2 21.0

Asia-8 .............................................................. 14.7 13.5 13.4 12.0 11.4 10.3 12.4

All other countries ........................................... 45.8 38.6 38.4 40.9 39.0 38.3 37.0

EU = European Union

NOTES: Global exports exclude intra-EU exports and exports between China and Hong Kong. EU exports exclude intra-EU exports, and China 

exports exclude exports between China and Hong Kong. Manufacturing technology level classified by Organisation for Economic Co-operation and 

Development. Asia-8 includes India, Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan, and Thailand. China includes Hong Kong. EU 

excludes Cyprus, Luxembourg, Malta, and Slovenia. Percents may not add to 100% because of rounding.

SOURCE: IHS Global Insight, World Trade Service database (2011).

Science and Engineering Indicators 2012
6-42 Chapter 6. Industry, Technology, and the Global Marketplace

imports from China are ICT goods (appendix table 6-38). 

U.S. ATP exports to China include aerospace, electronics, 

and ICT (appendix tables 6-39 and 6-40). 

U.S. ATP data show that ICT imports from China have 

increased much faster than its exports to China (appendix 

table 6-38). The steep rise in imports and flat export growth 

widened the U.S. deficit with China in ICT from $6 billion 

in 2000 to $87 billion in 2010 (figure 6-34).

ICT products also constituted 40% of all U.S. imports 

from Japan in 2010 (figure 6-34 and appendix table 6-38). 

Among U.S. ATP exports to Japan, aerospace accounted for 

the largest share (34%); life sciences ranked second (22%) 

(appendix tables 6-39 and 6-40). 

The United States exported $36 billion of ICT goods to 

the Asia-8 and imported $60 billion from this region (figure 

6-34 and appendix table 6-38). The $17 billion U.S. deficit with the Asia-8 in ICT consists of $5–$7 billion deficits 

with Malaysia, South Korea, Taiwan, and Thailand and a 

small surplus with Singapore. As with China, ICT products 

constituted the largest share of total U.S. ATP trade with 

the Asia-8. Important suppliers are Malaysia ($10 billion), 

South Korea ($13 billion), and Taiwan ($11 billion). U.S. 

imports of $48 billion and exports of $7 billion produced 

a deficit with these Asian economies of $41 billion in ICT 

products in 2010. 

The European Union. The EU exported $60 billion 

to the United States and imported $66 billion from it, for 

a $6-billion U.S. surplus in 2010 (figure 6-34 and appendix table 6-37). Four EU members—France, Germany, the 

Netherlands, and the United Kingdom (UK)—accounted 

for nearly 75% of U.S. ATP exports. Three technology areas—aerospace, ICT, and the life sciences—had a combined 

75% share of U.S. exports to the EU, with aerospace having 

the single largest export share (40%) (appendix tables 6-38, 

6-39, and 6-41). 

The United States had substantial surpluses with the EU 

in aerospace ($11 billion) and ICT goods ($6 billion) (figure 6-34 and appendix tables 6-38 and 6-39). Important EU 

customers of aerospace and ICT are France, Germany, and 

the UK; the Netherlands purchases the most U.S. ICT goods 

of any EU country. The life sciences produced a $16-billion 

deficit (appendix table 6-41). Ireland was by far the largest 

EU supplier of life sciences products to the United States, 

accounting for more than half of the EU’s $27 billion in exports to the United States in 2010. Other substantial suppliers were Belgium, France, Germany, and the UK. 

The U.S. trade surplus in ATP goods with the EU narrowed from $16 billion in 2000 to $400 million in 2010, 

reflecting the deficit in the life sciences, which rose from 

$6 billion to $16 billion because of accelerating growth of 

imports (figure 6-34 and appendix tables 6-37 and 6-41). 

NAFTA Trade Zone. The United States exported $55 

billion to Canada and Mexico in 2010 and imported $62 

billion from those countries (figure 6-34 and appendix table 6-37). The United States has a $22 billion deficit with 

Mexico, largely in ICT and optoelectronics, reflecting in 

part Mexico’s duty-free imports of U.S. components and 

their assembly and free re-export to the United States (appendix tables 6-38 and 6-42). The United States imported 

$13 billion from Canada and exported $24 billion, resulting 

in a surplus of $12 billion, largely in ICT goods.17

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 the employment of U.S. 

multinationals in these industries. 

Since 2000, an increasing proportion of the goods and 

services produced by U.S. multinational companies in KTI 

industries has been produced outside the United States. The 

Figure 6-33

U.S. trade in advanced technology products and U.S. 

exchange rate: 1995–2010

SOURCES: U.S. Census Bureau, Foreign Trade Statistics, Advanced 

Technology Trade database, http://www.census.gov/foreign-trade/ 

statistics/country/index.html, accessed 15 March 2011; U.S. Federal 

Reserve, Statistical Releases, Exchange rates and international data, 

http://www.federalreserve.gov/releases/g17/, accessed 15 March 2011. 

See appendix table 6-37. 

Science and Engineering Indicators 2012

Dollars (billions)

U.S. broad dollar exchange rate index

–100

0

100

200

300

400

Exports

Imports

1995 1998 2000 2002 2004 2006 2008 2010

Balance

1995 1998 2000 2002 2004 2006 2008 2010

0

90

100

110

120

130

1997 = 100
Science and Engineering Indicators 2012 6-43

Communications ranks first by value added ($264 billion), 

followed by business services ($261 billion) and finance 

($197 billion).18 The proportion of U.S. value added was 

highest in communications (90%), followed by Internet and 

data processing and financial services (76%–77%) and business services (70%). The U.S. share of value added declined 

across all these industries between 2000 and 2008, suggesting globalization of their production. 

Figure 6-34

U.S. trade in advanced technology products, by selected region/country/economy and technology: 2010

EU = European Union; ICT = information and communications technology; NAFTA = North American Free Trade Agreement

NOTES: China includes Hong Kong. Asia-8 includes India, Indonesia, Malaysia, Singapore, South Korea,Taiwan,and Thailand. EU includes current 

member countries. Advanced technology product trade classified by the Census Bureau and consists of advanced materials, aerospace, 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 March 2011. See appendix tables 6-37–6-41.

Science and Engineering Indicators 2012

Dollars (billions)

Exports

Imports

Balance

All technologies Aerospace Electronics ICT Life science

0

20

40

60

80

100

120

China Japan Asia-8 EU NAFTA

All technologies Aerospace Electronics ICT Life science

0

20

40

60

80

100

120

All technologies Aerospace Electronics ICT Life science

–100

–80

–60

–40

–20

0

20

proportion of jobs in these companies that are outside the 

United States has likewise increased.

Commercial Knowledge-Intensive 

Service Industries

U.S. multinationals in commercial KI service industries 

generated $722 billion in value added in 2008, of which 79% 

($573 billion) occurred in the United States (figure 6-35). 
6-44 Chapter 6. Industry, Technology, and the Global Marketplace

U.S. multinationals in commercial KI service industries 

employed 5.2 million workers worldwide, of which 3.8 million (73%) were employed in the United States (figure 6-35). 

U.S. employment was highest in communications services, 

at 1.4 million, closely followed by 1.3 million employed in 

business services and 1.0 million employed in financial services. The financial and communications industry employed 

81% of their workers in the United States, with business 

services employing a smaller share of their workers in the 

United States (63%). Between 2000 and 2008, the U.S. share 

of employment fell nearly 10 percentage points in business 

and communications services, but by larger amounts (17%–

21%) for computer systems design and management and for 

scientific, and technical services. The U.S. share in financial 

services stayed stable. 

High-Technology Manufacturing Industries

U.S. multinationals in four of five HT manufacturing 

industries generated more than $300 billion worldwide in 

value added in 2008, of which about two-thirds originated in 

the United States, down from three-quarters in 2000 (figure 

6-36). Production in the semiconductor industry was the 

most globalized, as measured by the distribution between 

U.S. and foreign value added, with 57% of value added 

originating from the United States in 2008, down from 77% 

in 2000. Pharmaceuticals and communication equipment 

showed a more modest shift, with the U.S. shares of value 

added falling 5 percentage points to 65% and 81%, respectively. The distribution of value added of the other two industries remained stable between 2000 and 2008.

U.S. multinationals in HT manufacturing employed 2.2 

million workers worldwide with 1.3 million workers (about 

60%) employed in the United States in 2008 (figure 6-36). 

More than half (58%) of the semiconductor workforce of 

half a million workers is employed abroad, the highest share 

among these industries. Three industries—computers, communications and pharmaceuticals—employ around 40% of 

their workforce abroad, equal to the average for all manufacturing industries. The navigational and measuring equipment industry has 25% of its workforce abroad, much lower 

than other industries. The U.S. share of worldwide employment showed little change or increase in computers and 

Figure 6-35

Globalization indicators of U.S. multinationals in commercial knowledge-intensive services: 2000, 2004, and 2008

NOTES: Value added is amount contributed by country, firm, or other entity to value of good or service and excludes purchases of domestic and 

imported materials and inputs. Commercial knowledge-intensive services are classified by Organisation for Economic Co-operation and Development 

and include business, financial, and communications. Internet and data processing is part of communications. Management, scientific, 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 1999–2008, http://www.bea.gov/international/di1usdop.htm, accessed 15 December 2010. 

Science and Engineering Indicators 2012

Percent

U.S. share of value added

U.S. share of employment

All services Commercial

knowledgeintensive services

Financial

services

Communications Internet and

data processing

Business

services

Management,

scientific, and

technicals

Computer

system design

All services Commercial

knowledgeintensive services

Financial

services

Communications Internet and

data processing

Business

services

Management,

scientific, and

technicals

Computer

system design

0

20

40

60

80

100

2000 2004 2008

0

20

40

60

80

100
Science and Engineering Indicators 2012 6-45

navigational and measuring equipment from 2000 to 2008. 

The U.S. employment shares in communications equipment 

fell from 76% in 2000 to 56% in 2008 and in semiconductors 

fell from to 48% in 2000 to 41% in 2008. 

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 

their evolution and the creation of new technologies. 

This section uses data from the 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. 

U.S. Direct Investment Abroad

U.S. firms have long invested abroad and have substantial 

overseas investment positions in both KTI services and manufacturing. The U.S. KI services stock abroad exceeds foreign 

counterpart investments in the United States; the opposite is 

the case with HT manufacturing investments (table 6-7). The 

stock of U.S. direct investment abroad had reached $125 billion in HT manufactures and $1 trillion in commercial KI service industries by 2009.19 This represented one-quarter of the 

stock of all U.S. direct overseas investment in all manufacturing industries ($500 billion) and about one-third of U.S. direct 

overseas investment in all services ($2.8 trillion). 

The stock of U.S. direct investment abroad in HT manufacturing industries increased from $87 billion in 2000 to 

$125 billion in 2009 (table 6-7). Semiconductors and pharmaceuticals have a combined share of 66% of investments 

in HT industries. The value of pharmaceuticals investments 

doubled between 2000 and 2009 to reach $51 billion. The 

investment value in semiconductors rose 25% to reach $31 

billion. The stock of investment in the other three HT industries is $10–$13 billion. 

Figure 6-36

Globalization indicators of U.S. multinationals in high-technology manufacturing: 2000, 2004, and 2008

NOTES: Value added is amount contributed by country, firm, or other entity to value of good or service and excludes purchases of domestic and 

imported materials and inputs. High-technology manufacturing industries are classified by Organisation for Economic Co-operation and Development 

and include communications and semiconductors, computers and office machinery, scientific and measuring instruments, and pharmaceuticals. 

SOURCE: Bureau of Economic Analysis, International Economic Accounts, U.S. Direct Investment Abroad: Financial and Operating Data for U.S. 

Multinational Companies 1999–2006, http://www.bea.gov/international/di1usdop.htm, accessed 15 December 2010. 

Science and Engineering Indicators 2012

Percent

U.S. share of value added

U.S. share of employment

All manufacturing All high-technology

industries

Communications

equipment

Computers and

office machinery

Navigational and

measuring equipment

Pharmaceuticals Semiconductors

All manufacturing All high-technology

industries

Communications

equipment

Computers and

office machinery

Navigational and

measuring equipment

Pharmaceuticals Semiconductors

0

20

40

60

80

100

2000 2004 2008

0

20

40

60

80

100
6-46 Chapter 6. Industry, Technology, and the Global Marketplace

The stock of U.S. direct investment abroad in commercial 

KI service industries was $1 trillion in 2009, exceeding onethird of the stock of total U.S. direct investment abroad in all 

services (table 6-7). Financial services accounted for 86% 

($861 billion) of these investments, up from $257 billion 

in 2000. Business services grew from $61 billion in 2000 

to $197 billion in 2009. Within business services, software 

investments grew from $10 billion to $51 billion, and investment in the professional, scientific, and technical industries 

more than doubled from $33 billion to $78 billion. 

Foreign Direct Investment in the United States

The value of FDI stock in U.S. HT manufacturing industries stood at $222 billion in 2009, up from $133 billion in 

2000, larger than the $125 billion FDI stock in U.S. investment abroad (table 6-7). The FDI stock in the U.S. pharmaceuticals industry was $152 billion in 2009, almost 70% of 

the total. The stock of FDI in pharmaceuticals more than 

tripled between 2000 and 2009 from $45 billion to $152 billion, coinciding with the acquisition of U.S. drug companies 

by EU- and India-based firms. The stock of FDI grew rapidly in computers from $3 billion in 2000 to $20 billion in 

2009. However, FDI in semiconductors fell from $29 billion 

to $11 billion during this period, reflecting a relative decline 

in the U.S. world position in this industry. 

FDI stock in U.S. commercial KI service industries was 

$433 billion in 2009, compared with the $1 trillion in the 

stock of U.S. investment abroad in these industries (table 

6-7). The largest industry was financial services ($292 billion), followed by $84 billion in business services and $56 

billion in communications. The stock of FDI in software increased $13 billion to $22 billion over this 9-year period. 

Innovation-Related Indicators of the 

U.S. and Other Major Economies 

The 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.”20 Innovation is widely recognized as instrumental 

Table 6-7

Stock of U.S. direct investment abroad and foreign direct investment in the United States, by selected industry: 

2000, 2005, and 2009

(Billions of dollars)

 U.S. direct 

 investment abroad

 Foreign direct 

 investment in the United States

Industry/service 2000 2005 2009 2000 2005 2009

All knowledge- and technology-intensive industries ...... 420.9 723.7 1,137.2 424.0 455.5 655.2

Commercial KI services .............................................. 333.6 631.3 1,012.0 291.5 353.9 433.1

Business services .................................................... 61.0 129.1 196.7 47.0 71.3 83.5

Software .............................................................. 10.4 17.5 50.8 7.4 10.9 21.6

Professional, technical, and scientific services ... 32.9 57.2 77.5 30.5 51.5 46.1

Architectural and engineering services ............ 3.1 1.9 3.5 2.6 4.2 10.2

Computer system design ................................. 15.0 28.5 33.9 13.7 9.1 9.1

Management, scientific, and technical 

 consulting ....................................................... 4.3 11.0 16.5 1.0 9.9 7.9

Communications ..................................................... 55.5 38.1 68.3 77.5 41.0 56.4

Finance .................................................................... 217.1 464.0 747.0 167.0 241.6 293.2

All services .................................................................. 874.6 1,683.7 2,779.6 735.9 1,026.7 1,333.6

High-technology manufacturing ..................................... 87.3 92.4 125.2 132.5 101.6 222.1

Aerospace products and parts ................................... 2.9 4.5 10.9 4.5 8.6 16.6

Communications equipment ....................................... 16.7 10.6 12.6 33.0 2.0 10.9

Computers and peripheral equipment ........................ 14.1 6.3 9.3 2.5 2.2 19.5

Navigational, measuring, and other instruments ........ 3.1 6.4 9.5 19.0 10.9 12.3

Pharmaceuticals ......................................................... 25.3 38.7 51.4 44.7 65.6 151.6

Semiconductors and other electronic components ... 25.2 26.1 31.4 28.7 12.4 11.2

All manufacturing ........................................................... 343.9 430.7 541.1 480.6 499.9 790.6

KI = knowledge-intensive

NOTES: Knowledge- and technology-intensive industries are commercial knowledge-intensive services and high-technology manufacturing industries. 

High-technology manufacturing industries and commercial knowledge-intensive services classified by Organisation for Economic Co-operation and 

Development. High technology manufacturing industries include aerospace, communications and semiconductors, computers and office machinery, 

navigational, measuring and other instruments, and pharmaceuticals. Knowledge-intensive services include business, financial, communications, 

education, and health. Commercial knowledge intensive services include business, financial, and communications services. Communications includes 

broadcasting, telecommunications, and Internet publishing and broadcasting. Finance does not include depository institutions. Detail may not add to 

total because of rounding. 

SOURCE: Bureau of Economic Analysis, International Economic Accounts, U.S. Direct Investment Abroad, Balance of Payments and Direct Investment 

Position Data, http://www.bea.gov/international/di1usdbal.htm, and 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 2011.

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 6-47

to the realization of commercial value in the marketplace 

and as a driver of economic growth.21 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 will present data on how innovation activity varies among U.S. industries, using information from the 

National Science Foundation’s (NSF’s) Business R&D and 

Innovation Survey (BRDIS). The section also includes three 

indicators of activities that are related to innovation, but do 

not actually constitute innovation. Two of these, patents 

and trademarks, are indicators of invention—they protect 

intellectual property in inventions that can have value for 

commercial innovations. The third indicator concerns earlystage financing for U.S. HT small businesses, which can be 

an important milestone in the process of bring new products 

and services to market. 

Innovation Activities by U.S. Businesses

The NSF BRDIS survey 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 2006 and 2008.22 Preliminary data from a 2008 

pilot survey suggest that U.S. KTI industries have a much 

higher incidence of innovation than other industries. 

In the U.S. manufacturing sector, four of the six HT 

manufacturing industries—computers, communications, 

scientific and measuring instruments, and pharmaceuticals—reported rates of product and process innovation that 

were at least double the manufacturing sector average (figure 

6-37 and appendix table 6-44). 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 computers, communications, and scientific and measuring instruments, 

reported significantly higher than average rates of process 

innovations, particularly in production methods and logistics and delivery methods. Innovation is also higher in several commercial KI service industries in comparison to other 

service industries (figure 6-38 and appendix table 6-44).23

Software firms lead in incidence of innovation, with 77% 

of companies reporting the introduction of a new product or 

service compared to the 7% average for all nonmanufacturing industries. Innovation is also 2 to 3 times higher than 

the nonmanufacturing average in the telecommunications/

Internet industries. The average rate of innovation in the 

professional, scientific, and technical industries is close to 

the nonmanufacturing average, but computer systems design 

and scientific R&D services reported much higher rates of 

innovation, comparable to those in the telecommunications/

Internet industries. 

Global Trends in Patenting and Trademarks

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.24 Inventors obtain patents from government-authorized agencies for inventions judged to be “new...useful...and...nonobvious.”25

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 

Figure 6-37

Share of U.S. manufacturing companies reporting innovation activities: 2006–08

Percent

NOTES: Survey asked companies to identify innovations introduced in 2006 to 2008. 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 (2008). See 

appendix table 6-44.

Science and Engineering Indicators 2012

Manufacturing

industries

Pharmaceuticals/

medicines

Computers/

peripheral

equipment

Communications

equipment

Semiconductor/

other electronic

components

Navigational/

measuring/

electromedical/

 control instruments

Aerospace

products/parts

0

10

20

30

40

50

60

70

Any good or service Any process
6-48 Chapter 6. Industry, Technology, and the Global Marketplace

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). 

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.26 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.27 Because of the high costs associated with patent 

filing and maintenance in these three patent offices, inventions covered by these patents are presumed to be valuable. 

Applications for USPTO Grants

The USPTO granted inventors 220,000 patents in 2010, 

50,000 more than in 2009 (figure 6-39 and appendix table 

6-45). The sharp increase in 2010 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 aimed in part to reduce the backlog of USPTO patent applications (see sidebar, “New U.S. 

Patent Law”). The number of U.S. patent grants jumped in 

the late 1990s, coinciding with a strengthening of the patent 

system, extension of patent protection into new technology 

areas through policy changes and judicial decisions during the 

1980s and 1990s, and administrative changes (NRC 2004). 

Figure 6-38

Share of nonmanufacturing U.S. companies reporting innovation, by selected industry: 2006–08

Percent

NOTES: Survey asked companies to identify innovations introduced in 2006 to 2008. 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 (2008). See 

appendix table 6-44.

Science and Engineering Indicators 2012

Nonmanufacturing

industries

Software

publishers

Telecommunications/

Internet services

providers/Web search 

portals/data

processing services

Computer

systems

design/related

services

Scientific

R&D services

Professional/scientific/

technical services

Finance/

insurance

0

20

40

60

80

100

Any good or service Any process

Figure 6-39

USPTO patents granted, by nationality of inventor: 

1995–2010

USPTO = U.S. Patent and Trademark Office

NOTE: Technologies classified by The Patent BoardTM. Patent 

grants fractionally allocated between United States and all other 

countries on basis of proportion of residences of all named 

inventors. 

SOURCE: The Patent BoardTM, Proprietary Patent database, special 

tabulations (2011). See appendix tables 6-45 and 6-62.

Science and Engineering Indicators 2012

Thousands

1995 1998 2000 2002 2004 2006 2008 2010

0

50

100

150

200

250

U.S. inventors

Non-U.S. inventors

Total
Science and Engineering Indicators 2012 6-49

Inventors residing in the United States were granted 

107,000 patents in 2010, a 30% increase over 2009 (figure 

6-39 and appendix table 6-45).28 The U.S. resident share 

has gradually fallen since the late 1990s, from 54% to 52% 

in 2002 and to 49% in 2010. The decline in the U.S. share 

may indicate increased technological capabilities abroad, 

globalization, and the increasing recognition by developing 

countries of the potential value of intellectual property protection in the United States. 

The overall growth of patent grants, accompanied by a 

decline in the U.S. share in these grants over the past two 

decades, reflects a marked increase in patents granted to 

non-U.S. countries. The USPTO granted 112,000 patents 

to non-U.S. inventors in 2010 compared to 46,000 in 1995 

(figure 6-39 and appendix table 6-45). The EU, Japan, and 

the Asia-8 are the main recipients, with a collective share 

of nearly 90% of patents granted to all non-U.S. inventors 

(figure 6-40). 

New U.S. Patent Law

The America Invents Act, Public Law 112-29, 125 

Stat. 283, signed into law on September 16, 2011, is 

the most significant reform of U.S. patent law since 

1952. The act aims to foster innovation and improve 

productivity by making the U.S. patent system more 

compatible with the systems in other countries. 

Supporters of the act believe it will reduce a growing 

backlog of U.S. patent applications, reverse a decline 

in U.S. patent quality, decrease the number of patents 

for frivolous inventions, and diminish the amount 

of expensive and time-consuming patent litigation. 

Economists and legal scholars who have studied the 

U.S. patent system have advocated reforms such as 

those in the new law (see Jaffe and Lerner [2006] and 

Burk and Lemley [2011])

The America Invents Act has three major provisions: 

First-to-file system. The law changes the primary 

standard for granting U.S. patent applications from 

the longstanding “first to invent” doctrine to “first 

to file.” Under the new law, patents would go to the 

inventor that files an application first, and disputes 

about who was first to invent would be avoided. 

The goals of this provision are to harmonize the 

U.S. patenting system with most other national patent offices and to reduce litigation over disputes 

about when a product or idea was invented. 

Postgrant review. The law establishes an administrative postgrant review process similar to opposition proceedings in European patent offices. 

The process allows a third party to challenge the 

validity of a patent within 9 months of the patent’s 

issuance. The aims of this provision are to provide 

an alternative to litigation, improve the quality of 

patents, and generate better decisions about alleged 

patent infringements. 

Budget and operations of the U.S. Patent and 

Trademark Office. The law gives the U.S. Patent 

and Trademark Office (USPTO) authority to set 

its own fees, which had previously been set by 

Congress. The purpose of this provision is to give 

USPTO greater budget autonomy and allow it to 

reduce its application backlog by hiring more examiners and modernizing its IT systems when the 

need arises.

Figure 6-40

USPTO patents granted to non-U.S. inventors, 

by selected region/country/economy: 1992–2010

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

NOTES: Technologies classified by The Patent BoardTM. Patent 

grants fractionally allocated among regions/countries on basis of 

proportion of residences of all named inventors. Asia-8 includes 

India, Indonesia, Malaysia, Philippines, Singapore, South Korea, 

Taiwan, and Thailand. China includes Hong Kong. EU includes 

current member countries. 

SOURCE: The Patent BoardTM, Proprietary Patent database, special 

tabulations (2011). See appendix tables 6-45 and 6-62.

Science and Engineering Indicators 2012

Thousands

Percent

Share of non-U.S. inventors

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

0

10

20

30

40

50

China

EU

Japan

Asia-8

All others

0

10

20

30

40

50

60

China

EU

Japan

Asia-8 All others
6-50 Chapter 6. Industry, Technology, and the Global Marketplace

Japan has the largest share of foreign patent grants by 

the USPTO, 40%, down slightly from the early 2000s (figure 6-40 and appendix table 6-45). The EU is second, with 

a 27% share, a decline of 6 percentage points from 2000. 

The Asia-8 group was in third place with 20%; its share 

nearly doubled from 2000 to 2010, largely because of rapid 

growth by South Korea and Taiwan. Chinese patenting activities in the U.S. remained insubstantial, as did those of 

Brazil, Russia, and India, in contrast to much higher activity of Chinese and other national patent offices (see sidebar, 

“Trends in Patents Granted in China, India, and Russia”).

USPTO 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. 

Table 6-C

Patents granted by Brazil, China, India, and Russia, by share of resident and nonresident inventors: Selected 

years, 1995–2009

Share (percent)

Country 1995 1999 2001 2003 2005 2007 2008 2009

Brazil

Resident ....................................... 19.7 13.2 19.1 NA 10.2 NA 9.5 NA

Nonresident ................................. 80.3 86.8 80.9 NA 89.8 NA 90.5 NA

Patents (number) ......................... 2,659 3,219 3,589 NA 2,439 NA 2,451 NA

China

Resident ....................................... 45.1 40.6 33.1 30.7 38.8 47.0 49.7 50.9

Nonresident ................................. 54.9 59.4 66.9 69.3 61.2 53.0 50.3 49.1

Patents (number) ......................... 3,393 7,637 16,296 37,154 53,305 67,948 93,706 128,489

India

Resident ....................................... 25.7 29.3 34.2 40.3 32.3 NA NA NA

Nonresident ................................. 74.3 70.7 65.8 59.7 67.7 NA NA NA

Patents (number) ......................... 1,613 2,160 1,549 1,526 4,320 15,318 18,230 NA 

Russia

Resident ....................................... 81.4 78.7 84.6 83.3 83.1 80.0 77.3 75.5

Nonresident ................................. 18.6 21.3 15.4 16.7 16.9 20.0 22.7 24.5

Patents (number) ......................... 25,633 19,508 16,292 24,758 23,390 23,028 28,808 34,824 

NA = not available

NOTES: Country of origin is based on first named applicant. Year of patent is based on date of patent grant.

SOURCE: World Intelllectual Property Organization, Intellectual Property Statistics, Patent grants by patent office by resident and nonresident, http://

www.wipo.int/ipstats/en/statistics/patents/, accessed 15 June 2011.

Science and Engineering Indicators 2012

The number of Chinese patent grants grew exponentially during the 2000s. Chinese patents granted to domestic residents rose more than 10-fold from 6,000 in 

2002 to 65,000 in 2009 (table 6-C). During this period, 

the Chinese inventor share of Chinese patent grants 

increased from 28% to slightly more than 50%, suggesting that patent protection is becoming increasingly 

important for Chinese companies that sell to the large 

and growing Chinese consumer market. The bulk of 

applications by Chinese inventors have been in utility 

model and industrial designs, which are quicker, cheaper, and have a lower standard than invention patents. 

Observers have criticized Chinese utility and industrial 

design patents as low quality but innovation economists 

note that these types of patents have played an important 

role in fostering indigenous innovation in Japan, South 

Korea, and Taiwan.* Chinese patents granted to nonresident inventors have also risen rapidly, coinciding 

with the growing sales and interest of U.S. and other 

companies in the Chinese domestic market. The growth 

of patent grants by residents and nonresidents may also 

reflect the strengthening of China’s patent protection 

during the 2000s (Zhao 2010). 

India and Russia show divergent trends in patenting 

activity by domestic and foreign inventors. A minority 

of India’s patents are granted to domestic investors, with 

their share falling from 40% in 2002 to 25% in 2006 

(table 6-C). The rising share of patents granted to nonIndian inventors may reflect the strengthening of patent 

protection for pharmaceuticals and other goods by the 

Indian patent system during this period. Russian-based 

inventors dominate patents granted by their country with 

a share of 76%. This may reflect the orientation of many 

Russian companies to the domestic market.

*See Ernst (2011) for a discussion on Chinese patenting.

Trends in Patents Granted in China, India, and Russia
Science and Engineering Indicators 2012 6-51

According to the NSF BRDIS survey, U.S. KTI industries 

account for a large share of USPTO patent grants (figure 6-41 

and appendix table 6-46). U.S. HT industries were granted 

23,000 patents, 57% of the 40,000 patents granted to all U.S. 

manufacturing industries in 2009. The U.S. semiconductor 

industry was issued the largest number of patents (7,000) 

among these HT industries, followed by 3,000 to 4,000 each 

for aerospace, computers, communications equipment, pharmaceuticals, and scientific and measuring equipment. 

U.S. commercial KI services received 86% of the 17,000 

patents issued to nonmanufacturing industries (figure 6-41 

and appendix table 6-46). The software industry accounted 

for 9,000 patents, more than half of the patents issued to commercial KI services; professional and technical services were 

ranked second with 5,000 patents. Two industries in professional and technical services—scientific research and development services and computer systems design—reported 

significant patenting activity.

USPTO Patents Granted, by Technology Area

This section discusses trends in several technology areas 

in a new technology classification system that includes broad 

science and technologically advanced areas that are emerging and technologies closely aligned with HT industries. The 

largest area is ICT, which consists of networking, information 

processes, telecommunications, semiconductors, and computer systems (table 6-8 and appendix tables 6-45 and 6-47). It 

accounts for nearly 40% of all USPTO patents. Health-related 

technologies consist of biotechnology, pharmaceuticals, medical electronics, and medical equipment. A third broad area 

includes automation, control, and measuring technologies. 

Several of these advanced and emerging technologies 

were among the fastest growing patent areas during the 

2000s (table 6-8). Patents in networking grew at a nearly 

20% average annual pace over the decade, information processes grew by 13%, and telecommunications and automation and control grew by 9%, compared to a 3% growth in 

total patents granted (appendix tables 6-45, 6-48, 6-49, 6-50, 

and 6-51). Other fast-growing technologies were medical 

electronics, semiconductors, optics, and measurement techniques and instrumentation (appendix tables 6-52, 6-53, 

6-54, and 6-55). 

Technologies that lagged behind overall growth in patents included pharmaceuticals, materials, and aerospace and 

defense (table 6-9 and appendix tables 6-56, 6-57, and 6-58). 

Weak activity in pharmaceuticals 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 Food and Drug Administration approval of 

new drugs (figure 6-42). 

The next section will present patent technology activity 

indexes for selected regions/countries/economies, which 

measure the world share of a region, country, or economy in 

patents in a particular technology relative to its world share 

in all patents. A ratio greater than 1 signifies that patents by 

a region/country/economy are concentrated in a particular 

technology. 

ICT: Computer Systems, Information Processes, 

Networking, Semiconductors, and Telecommunications. 

U.S. patents are concentrated in three ICT-related technologies: information processes, networking, and telecommunications, with special strength in information processes and 

networking (table 6-9 and appendix tables 6-45, 6-48, 6-49, 

and 6-50). U.S. patenting activity, however, is comparatively weak in semiconductors (appendix table 6-53). 

NOTES: Detail may not add to total because of rounding. Industry 

classification based on dominant business code for domestic R&D 

performance where available. For companies that did not report 

business codes, classification used for sampling was assigned. 

Companies with fewer than five domestic employees not included.

SOURCE: National Science Foundation, National Center for Science 

and Engineering Statistics, Business R&D and Innovation Survey, 

2008. See appendix table 6-46.

Science and Engineering Indicators 2012

Thousands

Figure 6-41

USPTO patents granted, by selected U.S. 

industry: 2009

0

5

10

15

20

25

30

35

40

45

Semiconductor and other 

electronic components

Computer and peripheral 

equipment and other computer 

and electronic products

Navigational, measuring, 

electromedical, and control 

instruments

Pharmaceuticals and medicines

Communications equipment

Aerospace products 

and parts

All other industries

Manufacturing industries

Nonmanufacturing industries

0

5

10

15

20

25

30

35

40

45

Software

Professional, scientific, 

and technical services

Telecommunications

Internet service providers, 

web search portals, and data 

processing services

Finance and insurance

All other industries
6-52 Chapter 6. Industry, Technology, and the Global Marketplace

EU patenting activity in ICT is comparatively low (table 6-9 and appendix tables 6-45, 6-48, 6-49, 6-50, 6-53, 

and 6-59). 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. The United Kingdom is an 

Table 6-8

USPTO patents granted, by selected technology area: Selected years, 2000–10

Technology area 2000 2003 2005 2007 2009 2010

Average annual 

change: 2000–10 

(%)

All technologies ...................................................... 157,489 169,020 143,805 157,282 167,350 219,642 3.4

Networking .......................................................... 1,785 2,626 3,321 4,859 6,921 9,861 18.6

Information processing ....................................... 6,539 7,533 8,141 11,672 15,075 22,038 12.9

Automation and control ...................................... 1,591 1,843 1,856 2,773 3,225 3,951 9.5

Telecommunications ........................................... 6,526 7,385 7,125 10,264 11,138 14,727 8.5

Medical electronics ............................................. 2,066 2,575 2,026 2,439 2,565 3,489 5.4

Semiconductors .................................................. 10,856 13,108 11,036 11,440 11,974 16,665 4.4

Optics ................................................................. 4,550 6,417 6,696 6,597 6,683 6,875 4.2

Measurement techniques and instrumentation .... 7,391 9,219 8,406 9,478 9,790 10,918 4.0

Biotechnology ..................................................... 5,606 5,379 4,117 5,940 5,826 8,206 3.9

Computer systems ............................................. 8,848 9,789 9,711 10,506 11,680 12,654 3.6

Aerospace and defense ...................................... 1,702 2,110 1,781 1,434 1,679 2,098 2.1

Medical equipment ............................................. 6,929 7,412 4,913 4,582 4,691 7,424 0.7

Pharmaceuticals ................................................. 5,388 5,590 3,911 3,835 4,275 5,471 0.2

Materials ............................................................. 5,580 5,793 4,006 3,658 3,582 5,193 –0.7

USPTO = U.S. Patent and Trademark Office

NOTE: Technologies classified by The Patent BoardTM. 

SOURCE: The Patent BoardTM, Proprietary Patent database, special tabulations (2011). See appendix tables 6-48–6-61.

Science and Engineering Indicators 2012

Table 6-9

Activity in USPTO patent grants in selected technology areas, by selected region/country/economy: 2007–10 

average

Activity index

Technology area

United 

States EU Japan

 South 

 Korea Taiwan China India

All ICT ..................................................................... 1.09 0.73 0.90 1.38 1.02 0.95 1.60

Computer systems ............................................. 0.99 0.53 1.22 1.66 1.15 1.01 1.48

Information processes ........................................ 1.31 0.75 0.66 0.43 0.37 1.01 2.35

Networking .......................................................... 1.31 0.80 0.50 0.68 0.38 1.03 2.30

Semiconductors .................................................. 0.83 0.59 1.28 2.47 2.08 0.60 0.69

Telecommunications ........................................... 1.03 1.03 0.73 1.59 0.94 1.12 1.31

Automation and control .......................................... 1.10 0.94 0.85 0.72 1.17 1.39 0.92

Measuring and instrumentation .............................. 1.01 1.25 0.94 0.56 0.63 1.05 0.78

Optics ..................................................................... 0.65 0.72 1.74 2.33 1.52 0.64 0.12

Biotechnology ........................................................ 1.23 1.28 0.42 0.35 0.29 0.86 1.57

Pharmaceuticals ..................................................... 1.03 1.95 0.45 0.26 0.20 0.75 4.64

Medical electronics ................................................ 1.26 1.18 0.62 0.18 0.11 0.57 0.55

Medical equipment ................................................. 1.40 1.03 0.33 0.14 0.39 0.38 0.18

Aerospace and defense.......................................... 1.30 1.60 0.19 0.10 0.26 0.15 0.50

Materials ................................................................. 0.81 1.45 1.32 0.82 0.39 0.80 1.63

EU = European Union; ICT = information and communications technology; USPTO = U.S. Patent and Trademark Office

NOTES: Activity index consists of ratio of region/country/economy’s share of indicated technology to region/country/economy’s share of total grants. A 

ratio of greater than one signifies more active patenting in the selected technology; a ratio of less than one signifies less active patenting. Technologies 

classified by The Patent BoardTM. Patent grants fractionally allocated among regions/countries/economies on basis of proportion of residences of all 

named inventors. China includes Hong Kong. 

SOURCE: The Patent BoardTM, Proprietary Patent database, special tabulations (2011). See appendix tables 6-47–6-56 and 6-58–6-61.

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 6-53

exception in the EU with stronger activity in networking, 

telecommunications, and information processes, similar to 

the United States. 

In Asia, Japan, South Korea, and Taiwan have similar 

ICT patterns, with strength in computer systems and semiconductors balanced by weaker activity in networking and 

information processes (table 6-9 and appendix tables 6-45, 

6-48, 6-49, 6-50, 6-53, and 6-59). China has an uneven pattern in ICT technologies, with relative strength in telecommunications but average or low activity in other ICT areas. 

In a pattern that is consistent with an emphasis on developing ICT service industries, India scores high in all ICT areas 

but semiconductors. 

Biotechnology, Medical Electronics, Medical Equipment, 

and Pharmaceuticals. The United States and the EU have relatively strong patenting activity in these health-related technologies (table 6-9 and appendix tables 6-45, 6-52, 6-56, 6-60, 

and 6-61). The United States is much weaker in pharmaceuticals, where the EU excels, and stronger in medical equipment. 

Four of the Asian economies are very weak in these biomedical technologies (table 6-9 and appendix tables 6-45, 

6-52, 6-56, 6-60, and 6-61). The exception is India, which 

has very strong activity in pharmaceuticals and biotechnology that coincides with its market presence in these industries.

Automation and Control, Measuring and Instrumentation, 

and Optics. These are areas of generally low patent activity. Relative strengths are automation and control for the 

United States, measuring techniques and instrumentation 

for the EU, and optics for Japan, South Korea, and Taiwan 

(table 6-9, and appendix tables 6-45, 6-51, 6-54, and 6-55). 

China’s relative strength is in automation and control. 

Aerospace and Defense and Materials. The United 

States and EU have a strong concentration in aerospace and 

defense, to which the EU adds strength in materials (table 

6-9 and appendix tables 6-45, 6-57, and 6-58). This is also 

a strength for Japan, but the other Asian economies have 

comparatively low activity levels in these areas. 

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.

The number of such “triadic” patents was estimated at 

about 48,000 in 2008 (the last year for which these data 

are available), up from 45,000 in 1999, and showing little 

growth after 2004 (figure 6-43 and appendix table 6-63). 

The United States, the EU, and Japan held basically equal 

shares and their nearly identical positions in triadic patents 

contrast with the far greater gap between them in USPTO 

Figure 6-42

New drugs approved by the FDA: 2000–10

FDA = U.S. Food and Drug Administration

NOTE: New drugs consist of FDA-approved new molecular entities, 

ester, salt, or other noncovalent derivatives. 

SOURCE: U.S. Food and Drug Administration, Drugs@FDA, 

http://www.accessdata.fda.gov/scripts/cder/drugsatfda/index.cfm?

fuseaction=Reports.ReportsMenu, accessed 15 May 2011.

Science and Engineering Indicators 2012

Number

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

0

5

10

15

20

25

30

35

Figure 6-43

Global triadic patent families, by share of selected 

region/country/economy: 1999–2008

EU = European Union; ROW = rest of world 

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

Patent and Trademark Office, European Patent Office, and Japan 

Patent Office. Patent families fractionally allocated among 

regions/countries/economies based on proportion of residences of 

all named inventors. Asia-8 includes India, Indonesia, Malaysia, 

Philippines, Singapore, South Korea, Taiwan, and Thailand. EU 

includes current member countries.

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 6-63.

Science and Engineering Indicators 2012

Percent

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0

10

20

30

40

United States

EU

Japan

Asia-8

ROW
6-54 Chapter 6. Industry, Technology, and the Global Marketplace

patent grants.29 The United States, the EU, and Japan together accounted for more than 93% of triadic patents in 1997, 

but that share dropped to 88% by 2008, largely reflecting a 

rapid rise in South Korean filings to 5% of the total. 

Trademark Applications

Firms use trademarks to launch new products and services, promote their brand, signal novelty, and appropriate 

the benefits of their innovation. Trademarks enable companies to establish exclusive identities for their new goods and 

services and to distinguish their products from those of competitors. Trademarks are considered a downstream indicator 

of innovation, showing the efforts of firms to build brand 

equity in new products and services. Because the U.S. market is large and open, this section will use applications for 

U.S trademarks as a measure of innovation activity for both 

the United States and other countries. 

The total number of U.S. trademark applications was 

about 300,000 in 2008, with 250,000 applications originating from within the United States (figure 6-44 and appendix table 6-64). The EU, Canada, Switzerland, Japan, and 

China are the main sources of U.S. trademark applications 

from outside the United States (figure 6-45). The EU had 

the largest number of applications from abroad with 22,000, 

followed by Canada (6,500). Japan and China had the most 

activity among Asian economies with 3,200 and 2,500, 

respectively. 

The number of U.S. trademark applications rose 20% 

from 1998 to 2008, although it dropped sharply during the 

recession of the early 2000s, and again showed signs of 

slowing during the late-decade recession (figure 6-44 and 

appendix table 6-64). The U.S. share has fluctuated between 

83% and 88%. Among foreign applications, the EU share 

was consistently just below 50%, and Japan’s share was approximately 7%–8% for the period (figure 6-45). China’s 

share grew from 1% in 1998 to 5% in 2008. South Korea 

and India, although they have growing numbers of patent 

grants, have little trademark activity. 

Patterns in trademark applications by class may indicate 

innovation activity in related technology or industry areas. 

Classes related to KTI industries are among those with the 

most applications in 2008. After advertising, the scientific 

and measuring category had the second-largest share of 

applications (10%) (figure 6-46). Several other classes—

insurance and finance, science and technology, R&D and 

computer design, pharmaceuticals, and medical services—

had shares of 2%–5% each. 

Figure 6-44

U.S. trademark applications by U.S. and non-U.S.

applicants: 1998–2008

SOURCE: World Intellectual Property Organisation, Statistics on 

Trademarks, http://www.wipo.int/ipstats/en/statistics/marks/, 

accessed 15 February 2011. See appendix table 6-64.

Science and Engineering Indicators 2012

Thousands

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0

50

100

150

200

250

300

U.S. applicants

Non-U.S. applicants

Figure 6-45

U.S. trademark applications from non-U.S. applicants, by share of selected region/country: 2000, 2004, and 2008

Percent

EU = European Union; ROW = rest of world

NOTES: EU includes current member countries. China includes Hong Kong.

SOURCE: World Intellectual Property Organisation, Statistics on Trademarks, http://www.wipo.int/ipstats/en/statistics/marks/, accessed 15 February 2011. 

See appendix table 6-64. 

Science and Engineering Indicators 2012

EU Japan China Canada Australia Switzerland ROW

0

10

20

30

40

50

2000 2004 2008
Science and Engineering Indicators 2012 6-55

U.S. High-Technology Small Businesses

Many of the new technologies and industries seen as critical to U.S. innovation and economic growth are also 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, based on 

data from the Census Bureau. Two sources of financing for 

HT small businesses—angel investment and venture capital 

investment—are also examined using data from the National 

Venture Capital Association and other sources. 

Characteristics of Microbusinesses in U.S. HighTechnology Industries

According to U.S. Census data, the number of microbusinesses in industries classified as HT by the Bureau of Labor 

Statistics (BLS) is about 325,000, more than 60% of all 

firms operating in these industries (figure 6-47).30 Services 

account for more than 90% (300,000) of U.S. HT microbusinesses, 20,000 operate in HT manufacturing, and 5,000 

are in other industries. The proportion of services in non-HT 

microbusinesses is lower at 81%.31

The three HT services with the largest number of microbusinesses are management, scientific, and technical 

consulting; computer systems design; and architectural and 

engineering. HT manufacturing industries with large numbers of microfirms include navigational, measuring, and 

electromedical equipment and semiconductors (table 6-10). 

Figure 6-46

Share of scientific and advanced-technology related classes of U.S. trademark applications: 2008

Percent

S&T = science and technology

SOURCE: World Intellectual Property Organisation, Statistics on Trademarks, http://www.wipo.int/ipstats/en/statistics/marks/, accessed 15 February 2011.

Science and Engineering Indicators 2012

Advertising Scientific,

measuring

S&T, R&D,

and computer

design

Pharmaceuticals

and herbicides

Medical

services

Legal

services

TelecommunicationsMedical

Instruments

Metal

and alloys

0

2

4

6

8

10

12

14

NOTES: Firms with less than five employees include those reporting 

no employees on their payroll. Firm is an entity that is either a single 

location with no subsidiary or branches or topmost parent of a group 

of subsidiaries or branches. High-technology industries are defined 

by Bureau of Labor Statistics on basis of employment intensity of 

technology-oriented occupations. High-technology small business 

employment is lower bound estimate because employment 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 March 2011; 

Hecker DE. High-technology employment: A NAICS-based update, 

Monthly Labor Review 128(7):57–72 (2006), http://www. bls.gov/ 

opub/mlr/2005/07/art6full.pdf, accessed 15 March 2011. 

Science and Engineering Indicators 2012

Thousands

Figure 6-47

Number of U.S. high-technology firms, by size: 

Selected years, 2000–08

2000 2002 2004 2006 2008

0

100

200

300

400

500

600

<5

>5
6-56 Chapter 6. Industry, Technology, and the Global Marketplace

The number of microfirms in BLS-classified HT industries grew much faster than in other industries from 2000 to 

2008 (figure 6-48). Growth of microfirms in services classified as HT was three times that in other service industries. 

However, the number of microfirms in manufacturing classified as HT had a deeper decline than in other manufacturing industries. 

Financing of High-Technology 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. Often, they rely 

on friends and family for financing. However, when they 

need or can get access to larger amounts of financing, angel capital and venture capital investment are often critical 

to financing nascent and entrepreneurial HT businesses. (In 

this section, business denotes anything from an entrepreneur 

with an idea to a legally established operating company.)

Table 6-10

U.S. high-technology microbusinesses, by number of firms and employment for selected industries: 2008

 Firms Employment

High-technology Industry Number

Industry share 

of firms (%) Employees

Industry share of 

employees (%)

All high-technology microbusinesses ............................................ 325,447 100.0 465,162 100.0

All high-technology services ...................................................... 300,487 92.3 420,099 90.3

Management, scientific, and technical consulting services ... 111,965 34.4 138,337 29.7

Computer systems design and related services .................... 80,670 24.8 107,524 23.1

Architectural, engineering, and related services .................... 62,234 19.1 99,932 21.5

Professional and commercial equipment and supplies 

 merchant wholesalers ......................................................... 13,301 4.1 23,786 5.1

Scientific research and development services ....................... 7,088 2.2 11,001 2.4

Data processing, hosting, and related services ..................... 4,598 1.4 6,883 1.5

Management of companies and enterprises.......................... 3,392 1.0 4,365 0.9

Software publishers ............................................................... 2,310 0.7 3,977 0.9

Wired telecommunications carriers ........................................ 1,521 0.5 2,477 0.5

Wireless telecommunications carriers (except satellite) ........ 717 0.2 1,245 0.3

Facilities support services ...................................................... 642 0.2 1,030 0.2

Satellite telecommunications ................................................. 388 0.1 583 0.1

All high-technology manufacturing ............................................ 19,682 6.0 36,515 7.8

Metalworking machinery manufacturing ................................ 2,600 0.8 5,306 1.1

Other fabricated metal product manufacturing ...................... 2,032 0.6 3,759 0.8

Navigational, measuring, electromedical, and control 

 instruments manufacturing ................................................. 1,640 0.5 3,036 0.7

Other general purpose machinery manufacturing ................. 1,602 0.5 3,044 0.7

Motor vehicle parts manufacturing ........................................ 1,555 0.5 2,934 0.6

Industrial machinery manufacturing ....................................... 1,118 0.3 2,189 0.5

Semiconductor and other electronic component 

 manufacturing ..................................................................... 1,098 0.3 1,982 0.4

Electrical equipment manufacturing ...................................... 648 0.2 1,289 0.3

Computer and peripheral equipment manufacturing ............. 495 0.2 804 0.2

Pharmaceutical and medicine manufacturing ........................ 466 0.1 848 0.2

Communications equipment manufacturing .......................... 382 0.1 659 0.1

Aerospace product and parts manufacturing ........................ 366 0.1 592 0.1

Audio and video equipment manufacturing ........................... 207 0.1 387 0.1

Resin, synthetic rubber, and artificial synthetic fibers and 

 filaments manufacturing ...................................................... 154 0.0 263 0.1

All other industries ..................................................................... 5,278 1.6 8,548 1.8

Oil and gas extraction ............................................................ 4,545 1.4 7,433 1.6

Electric power generation, transmission, and distribution ..... 342 0.1 547 0.1

NOTES: Microbusinesses are firms with fewer than five employees and include those reporting no employees on their payroll. Firm is an entity that is 

either a single location with no subsidiary or branches or topmost parent of a group of subsidiaries or branches. High-technology industries defined by 

Bureau of Labor Statistics on basis of employment intensity of technology-oriented occupations. 

SOURCE: U.S. Census Bureau, Statistics of U.S. Businesses, http://www.census.gov/econ/susb/, accessed 15 March 2011; and Hecker DE. Hightechnology employment: A NAICS-based update, Monthly Labor Review 128(7):57–72 (2006), http://www.bls.gov/opub/mlr/2005/07/art6full.pdf, accessed 

15 March 2011. 

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 6-57

An angel investor is a person who provides capital, in the 

form of debt or equity, from his or her own funds to a private business owned and operated by someone else who is 

neither friend nor family (Shane 2008). Angel investors may 

invest on their own as individuals or through an informal 

network of affiliated investors. Angel funds are more formal 

organizations where groups of investors pool their resources 

and jointly invest in businesses. 

Venture capitalists pool the investments of others (typically wealthy investors, investment banks, retirement funds, 

and other financial institutions) in a professionally managed fund. They receive ownership equity in the companies 

in which they invest, and they almost always participate in 

managerial decisions.

Angel and venture capital investment are generally categorized into four broad stages of financing:

Seed and startup supports proof-of-concept development (seed) and initial product development and marketing (startup or first round).

Early stage supports 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 

a management and leveraged buyout provides funds to 

enable operating management to acquire a product line or 

business from either a public or a private company. 

This section examines angel capital and venture capital 

investment patterns in the United States and internationally, 

focusing on the period from 2001 to 2008. The section examines (1) changes in the overall level of angel and venture 

capital investment, (2) venture capital investment outside 

the United States, (3) angel and venture capital investment 

by stage of financing, and (4) the technology areas that U.S. 

angel and venture capitalists find attractive.

U.S. angel investment. There are no sources of current, nationally representative data that directly measure 

U.S. angel investment. Data on U.S. angel investment have 

largely been restricted to samples that are not nationally representative or that rely disproportionately on angel groups 

and thereby exclude individual investors. This section will 

examine two data sources that provide some data on the 

level and activities of U.S. angel investment, the Global 

Entrepreneurship Monitor’s (GEM’s) survey of U.S. informal investment and the Angel Capital Association.

The GEM’s U.S. survey is a nationally representative survey that provides a variety of data on patterns of U.S. entrepreneurship, including informal investment. The survey asks 

respondents who identify themselves as informal investors 

about their relationship with the person that received their 

investment, ranging from close family members to strangers. The proportion of strangers provides a crude estimate 

of the level of U.S. angel investment. By that measure, U.S. 

angel investment was estimated at $9 billion in 2010 (figure 6-49). Estimated U.S. angel investment has fluctuated 

widely between 2001 and 2010, from a low of $1 billion in 

2007 to a high of $9 billion in 2010. 

Figure 6-48

Growth in number of U.S. microbusinesses, by selected industry: 2000–08

Percent

NOTES: Microbusinesses are firms with less than five employess and those reporting no employees on their payroll. Firm is an entity that is either a single 

location with no subsidiary or branches or topmost parent of a group of subsidiaries or branches. High-technology industries are defined by Bureau of 

Labor Statistics on basis of employment intensity of technology-oriented occupations. High-technology small business employment is lower bound 

estimate because employment 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 March 2011; Hecker DE, Hightechnology employment: A NAICS-based update, Monthly Labor Review 128(7):57–72 (2006), http://www.bls.gov/opub/mlr/2005/07/art6full.pdf, 

accessed 15 March 2011. 

Science and Engineering Indicators 2012

-10

0

10

20

30

40

All high-technology

industries

High-technology

services

High-technology

manufacturing

All non-hightechnology

industries

All non-hightechnology

services

All non-hightechnology

manufacturing
6-58 Chapter 6. Industry, Technology, and the Global Marketplace

Figure 6-49

Estimated U.S. angel investment: 2001–10

NOTES: U.S. angel investment estimated from the Global Entrepreneurship Monitor’s annual survey of the United States. Angel 

investment is estimated from the proportion of informal investors that 

lend to strangers multiplied by the average amount of investment per 

investor and the share of informal investors of the U.S. population of 

ages 18–99. 

SOURCES: Global Entrepreneurship Monitor, http://www.gem 

consortium.org/default.aspx; U.S. Census Bureau, Population 

Estimates, http://www.census.gov/popest/estbygeo.html, accessed 

15 May 2011.

Science and Engineering Indicators 2012

Dollars (billions)

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

0

2

4

6

8

10

The estimated level of angel investment is significantly 

lower than that of venture capital investment during this period, and anecdotal evidence suggests that HT areas receive 

a minority of U.S. angel investment. The returns to angel investors in lower technology industries can be very high, and 

many individual angel investors make limited or one-time investments, often in lower technology industries (Shane 2008).

In contrast with individual angel investors, angel networks and groups are more likely to invest a larger share in 

HT industries. Angel groups allow angels to exchange and 

analyze information about industries and talk with experts 

on technologies. Angel groups that pool their investments 

can invest larger amounts that may be required for HT industries, such as biotechnology or medical devices. 

The Angel Capital Association (ACA) is a trade association of 150 leading angel groups in North America. According 

to ACA’s survey of its members, the average investment for 

an ACA group fell from $1.8–$1.9 million in 2007–08 to $1.4 

million in 2009 during the recession. The majority prefer to 

invest in the earlier stages of financing of companies, with 

70%–80% reporting preferences for seed/startup or earlystage financing (figure 6-50). Financing for the later stages 

of business operations—expansion and later stage—is far 

less preferred, with 33% preferring the expansion stage and 

only 10% preferring later stage financing. ACA members expressed strong interest in investing in HT industries (figure 

6-51). Software and medical devices have the highest level of 

interest, with more than 70% of members showing interest, 

followed by biotechnology with 60%. Half or more of members expressed interest in investing in IT services, industrial/

energy, telecommunications, and networking equipment.

Venture capital investment. Data from Dow Jones 

Venture Capital show that global venture capital investment 

rose more than 40% from $28 billion in 2005 to $41 billion 

in 2008 (figure 6-52). It fell sharply to $28 billion in 2009 

in the midst of the recession. Investment rebounded in 2010 

to reach $34 billion. The United States is the main source of 

venture capital financing, providing nearly 80% of global 

investment in 2010. U.S. venture capital investment grew 

29%, from $24 billion to $31 billion, during this period. U.S. 

investment fell sharply in 2009 before growing modestly in 

2010 to reach $26 billion. U.S. venture capital investment 

lagged behind the growth in non-U.S. investment between 

2005 and 2010. As a result, the U.S. share of global venture 

capital investment fell from 85% to 78% during that period. 

Venture capital investment originating outside the United 

States grew rapidly but from a low level, nearly doubling 

from $4 billion in 2005 to $7 billion in 2010 (figure 6-53). 

China led the growth in non-U.S. venture capital investment, 

with its investment tripling from $1.3 billion to $4 billion 

during the period from 2005 to 2010. China surpassed Europe 

in 2006 to become the largest source of non-U.S. investment, with its share reaching more than 50% in 2010. The 

remaining countries and regions—Canada, Europe, Israel, 

and India—provide small and relatively stable amounts of 

venture capital, with their shares of non-U.S venture capital 

investment ranging from 8% to 14%. 

Figure 6-50

Investment in financing stages preferred by Angel 

Capital Association member groups: 2009

Percent

NOTES: Percent is share of Angel Capital Association member groups 

that express preferrence in investing in indicated investment stage. 

Seed and startup round supports proof-of-concept development 

(seed) and initial product development and marketing (startup). Early 

stage supports initiation of commercial manufacturing and sales. 

Expansion stage 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. Later-stage funds include acquisition financing 

and management and leveraged buyouts. 

SOURCE: Angel Capital Association Group, Investing through Angel 

Groups, http://www.angelcapitalassociation.org/, accessed 15 May 

2011.

Science and Engineering Indicators 2012

Seed/startup Early stage Expansion Later stage

0

20

40

60

80

100
Science and Engineering Indicators 2012 6-59

U.S. venture capital investment by financing stage. 

Knowledgeable observers believe that venture capital investment has become generally more conservative during 

the 2000s.32 Later stage venture capital investment has both 

grown in absolute terms and as a share of total investment, 

from $10.8 billion (50% share of total investment) in 2002 

to $17.4 billion (65% share) in 2010 (figure 6-54 and appendix table 6-65). The shift to later stage, more conservative investing has been attributed to a desire for lowered 

investment risk, higher minimum investment levels, which 

typically exceed earlier stages, a shorter time horizon for 

realizing gains, a decline in yields of venture capital investment, and the sharp decline in IPOs and acquisitions of venture capital-backed firms, which has required venture capital 

investors to provide additional rounds of financing. 

In 2010, U.S. venture capital investment in the early stage, 

consisting of seed, startup, and initiation of commercial activities, was $4.6 billion, slightly higher than its level in 2002 

but well below its prerecession peak of $7.9 billion in 2007 

(figure 6-54 and appendix table 6-65). The early-stage share 

of total venture capital investment has declined steadily, from 

about 33% in the late 1990s to 20%–25% for much of the 

NOTE: Percent is share of Angel Capital Association member groups that express preferrence in indicated technology area. 

SOURCE: Angel Capital Association Group, Investing through Angel Groups, http://www.angelcapitalassociation.org/, accessed 15 May 2011. 

Science and Engineering Indicators 2012

Percent

Figure 6-51

Investment in technology areas preferred by member groups of the Angel Capital Association: 2009

Medical

devices

Software Biotechnology IT services Business

related

Industrial/

energy

Electronics Networking

equipment

0

20

40

60

80

100

TelecommunicationsConsumer

related

Health

services

Computer Financial

services

Internet/media Semiconductors Retail/

distribution

0

20

40

60

80

100

Figure 6-52

Global and U.S. venture capital investment: 

2002–10

ROW = rest of world

NOTE: Data on non-U.S. venture capital investment not available for 

2002–04. 

SOURCE: Dow Jones, special tabulations (2011) from VentureSource 

database, http://www.dowjones.com/info/venture-capital-data.asp. 

See appendix table 6-65. 

Science and Engineering Indicators 2012

Dollars (billions)

2002 2003 2004 2005 2006 2007 2008 2009 2010

0

10

20

30

40

50

United States

Total

ROW
6-60 Chapter 6. Industry, Technology, and the Global Marketplace

2000s, and down to 17%–19% in 2009–10. The decline in 

early-stage investment both in absolute terms and as a share of 

total investment has amplified concerns that there is a growing 

lack of adequate financing for very young HT firms seeking to 

grow and successfully commercialize their technologies. 

U.S. venture capital financing by technology. Five 

technologies—software, biopharmaceuticals, medical devices and equipment, consumer information services, and 

business support services—dominate venture capital financing (table 6-11 and appendix table 6-65). During 2007–10, 

these five technologies accounted for more than 60% of total 

and early-stage investment. Software and biopharmaceuticals received the most financing, with each receiving nearly 

$18 billion in total financing. Total and early-stage investment in software dropped sharply (33%–44%) between 

2002 and 2010, reducing software’s share of venture capital 

investment by half. Total investment in biopharmaceuticals 

remained roughly flat but early-stage financing dropped 

from $900 million to $600 million during this period. 

Medical devices and equipment were second, receiving $13 

billion in total financing (table 6-11 and appendix table 6-65). 

Total investment in this technology increased 27% from $1.8 

billion in 2002 to $2.2 billion in 2010. Consumer information 

services and business support services were third, receiving 

$10–$11 billion. Consumer information services had the fastest growth among these five technologies, with total investment rising exponentially from less than $200 million in 2002 

to $4.5 billion in 2010. Growth in early-stage financing was 

also rapid, rising from less than $50 million to $600 million. 

Total investment in business support services rose by 70% 

from $1.5 billion to $2.7 billion, and early-stage investment 

more than doubled from $200 million to $500 million. 

Investment and Innovation in Clean 

Energy and Technologies

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 knowledge and technology intensive 

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 directed both stimulus funding and long-term 

investments into these technologies. Private investors have also 

shown increased interest.

This section will examine public research, development, 

and demonstration (RD&D) data from the International 

Energy Agency (IEA) and venture capital and total private financing data from Bloomberg New Energy Finance, 

by technology and key region. A sidebar, “Government 

Stimulus Funding for Clean Energy,” will summarize various countries’ initiatives related to clean energy as part of 

their stimulus measures or long-range policies. The IEA 

data discussed here cover research, development, and 

NOTES: Early stage consists of seed, startup, and initiation of 

commercial sales. Expansion consists of second round financing 

that provides working capital for company expansion, and financing 

to prepare for initial public offering. Later stage includes acquistion 

financing and management and leverage buyouts. 

SOURCE: Dow Jones, special tabulations (2011) from VentureSource 

database, http://www.dowjones.com/info/venture-capital-data.asp. 

See appendix table 6-65. 

Science and Engineering Indicators 2012

Dollars (billions)

Figure 6-54

U.S. venture capital investment, by financing 

stage: Selected years, 2002–10

2002 2005 2007 2008 2009 2010

0

5

10

15

20

25

30

35

Later stage

Expansion

Early stage

Figure 6-53

Non-U.S. venture capital investment, by region/

country: 2005–10

Dollars (billions)

SOURCE: Dow Jones, special tabulations (2011) from VentureSource 

database, http://www.dowjones.com/info/venture-capital-data.asp. 

Science and Engineering Indicators 2012

2005 2006 2007 2008 2009 2010

0

1

2

3

4

5

China

Europe

Israel

India

Canada
Science and Engineering Indicators 2012 6-61

demonstration. They are not comparable to energy RD&D 

data described in Chapter 4, which focus on research and 

development.33

Commercial Investment

According to Bloomberg New Energy Finance, global 

commercial investment in clean energy and technology from 

all sources, including early-stage angel and venture capital 

investment and later stage financing raised from private equity and public capital markets, has risen rapidly from less 

than an estimated $20 billion in 2004 to nearly $154 billion 

in 2010 (figure 6-55).34 This rise has been spurred by government policies, financial incentives, and funding to foster the 

development of clean energy production and technologies; 

falling costs in wind and solar energy; and investor perception that this area is ready for large-scale commercialization. 

The United States, EU, China, and other countries provided 

additional support of nearly $200 billion to this sector from 

stimulus funding to help spur recovery from the global recession (see sidebar, “Government Stimulus Funding for 

Clean Energy”). 

The United States generated an estimated $30 billion 

(19% global share) in clean energy commercial investment 

in 2010, placing it behind China and roughly equal to the 

EU (figure 6-55). After peaking at $34 billion in 2008, U.S. 

commercial investment declined sharply to $20 billion in 

2009 during the global financial crisis before recovering in 

2010 to reach nearly its pre-crisis peak. 

China provided an estimated $54 billion in clean energy 

financing in 2010, more than any economy in the world (35% 

share of global investment) (figure 6-55). China’s commercial investment rose exponentially from less than $2 billion 

in 2004 to $54 billion in 2010, surpassing the United States 

in 2009 and the EU in 2010. The uninterrupted growth of 

clean energy investments in China reflects the government’s 

commitment to reduce China’s reliance on fossil fuels, considerable financing from state development banks (less affected by the financial crisis than other countries/regions), 

low labor costs, and subsidies to encourage large renewable 

energy projects, particularly in wind and solar energy. 

The EU ties with the United States in clean energy investment, providing an estimated $34 billion in 2010 (22% share 

of global investment) (figure 6-55). Clean energy investment 

in the EU has been spurred by government policies such as 

feed-in tariffs for solar power in Germany and Spain and 

large-scale investment in offshore wind by the UK. However, 

EU clean energy investment dropped from $50 billion in 

2008 to $23 billion in 2010, reflecting the global recession 

and sharp cutbacks by Spain, the UK, and other EU countries 

in their support of solar and other clean energies. 

Brazil and the Asia-8 have comparatively low activity in 

clean energy financing (an estimated $8 and $6 billion, respectively) (figure 6-55). India provides the largest amount 

of financing ($3.8 billion) from the Asia-8. Investment from 

both Brazil and the Asia-8 grew rapidly from 2004 to 2010, 

though from a low base. Japan provided less than $1 billion 

in clean energy investment in 2010, down sharply from $7 

billion in 2004. 

Wind technology is the largest recipient of global clean 

energy financing, with an estimated $99 billion (65% share 

of total investment) in 2010 (figure 6-56). Wind energy accounted for nearly 60% of total clean energy investment by 

the EU and the United States and more than 80% by China 

in 2010 (table 6-12). 

Table 6-11

U.S. venture capital investment, by selected financing stage and industry/technology: Selected years, 2002–10

(Millions of U.S. dollars)

Technology/industry 2002 2005 2007 2008 2009 2010 2007–10 total

All financing stages

All technologies/industries ..................................... 21,509 24,207 32,200 31,243 22,348 26,415 112,206

Software .............................................................. 5,612 5,591 5,669 5,153 3,231 3,762 17,815

Biopharmaceuticals ............................................ 3,243 4,074 5,822 4,626 4,030 3,246 17,725

Medical devices and equipment ......................... 1,776 2,361 3,791 3,615 2,959 2,249 12,614

Consumer information services .......................... 152 661 1,740 2,525 1,792 4,552 10,610

Business support services .................................. 1,471 1,719 2,989 2,808 2,120 2,516 10,433

Early-stage financing

All technologies/industries ...................................... 4,351 5,958 8,104 6,999 4,242 4,570 23,916

Biopharmaceuticals ............................................ 932 1,139 1,601 1,345 854 578 4,378

Software .............................................................. 1,299 1,139 1,190 839 788 726 3,543

Consumer information services .......................... 43 340 878 870 382 623 2,753

Business support services .................................. 217 514 905 841 503 462 2,711

Medical devices and equipment ......................... 337 437 722 784 356 333 2,194

NOTES: Technologies classified by Dow Jones. Early-stage financing consists of seed, startup, and initiation of commercial sales. 

SOURCE: Dow Jones, special tabulations (2011) of VentureSource database, http://www.dowjones.com/info/venture-capital-data.asp. See appendix 

table 6-65.

Science and Engineering Indicators 2012
6-62 Chapter 6. Industry, Technology, and the Global Marketplace

Figure 6-55

Financial new investment in clean energy and 

technologies, by selected region/country/economy: 

2004–10

EU = European Union; ROW = rest of world

NOTES: Clean energy and technologies include biomass, 

geothermal, wind, solar, biofuels, and energy smart and efficiency. 

Financial new investment includes private and public R&D, venture 

capital, private equity, and public markets. Mergers and acquisitions 

are excluded. Asia-8 includes India, Indonesia, Malaysia, Philippines, 

Singapore, South Korea, Taiwan, and Thailand.

SOURCE: Bloomberg New Energy Finance, http://bnef.com/, special 

tabulations (2011).

Science and Engineering Indicators 2012

Dollars (billions)

Percent share

2004 2005 2006 2007 2008 2009 2010

0

10

20

30

40

50

60

United

States

EU

Japan

China

Asia-8

Brazil ROW

2004 2005 2006 2007 2008 2009 2010

0

10

20

30

40

50

United States

EU

Japan

China

Asia-8

Brazil

ROW

Government Stimulus Funding 

for Clean Energy

A number of economies pledged an estimated $194 

billion in late 2008 and early 2009 for clean energy 

and low carbon energy projects as part of their stimulus programs undertaken in response to the global 

economic recession (figure 6-D). Four of these economies, the United States, China, South Korea, and the 

EU, led stimulus funding of clean energy with a collective $168 billion in spending commitments. The 

United States had the largest amount with $67 billion 

in commitments for energy efficiency, renewable energy deployment, transportation, and smart grid technology. China announced $47 billion in funding for 

energy efficiency, clean vehicles, grid infrastructure, 

and other energy technologies. The EU and South 

Korea each committed $27–$28 billion in funding.

Progress was slow in 2009 with governments 

spending an estimated $20.3 billion (10%) of the total $194 billion in stimulus commitments. The pace 

accelerated in 2010 with 38% of the stimulus funding commitments (an estimated $74.5 billion) being 

spent, largely by the United States, China, Germany, 

and South Korea. Disbursement of stimulus spending 

commitments in 2009–10 is estimated at $94.8 billion. 

The majority of this funding has gone to energy efficiency, renewables, smart grid, and R&D. Energy efficiency has received an estimated $35.5 billion (37% 

share) followed by $20.2 billion (21%) allocated to renewables. R&D and the smart grid have each received 

$17–$18 billion.

Figure 6-D

Public stimulus funding for clean energy and 

technologies, by selected region/country: 2008–09

Dollars (billions)

EU = European Union; ROW = rest of world

NOTE: Funding amounts are commitments announced by 

governments in 2008 and 2009.

SOURCE: Pew Charitable Trust, “Who’s winning the clean energy 

race,” http://www.pewenvironment.org/uploadedFiles/PEG/ 

Publications/Report/G-20Report-LOWRes-FINAL.pdf, accessed 15 

May 2011.

Science and Engineering Indicators 2012

United

States

China South

Korea

EU Japan ROW

0

20

40

60

80
Science and Engineering Indicators 2012 6-63

China is the world’s largest source of investment in wind 

technology with an estimated $45 billion in 2010, more than 

twice as much as the EU ($19 billion) and the United States 

($17 billion) (table 6-12). China’s rapid growth in this field, 

from less than $1 billion in 2004 to $45 billion in 2010, was 

spurred by aggressive government policies and comparatively low labor and financing costs. Solar is the secondlargest clean energy technology area with an estimated $26 

billion of investment in 2010 (17% share of global investment) (figure 6-56). Commercial investment in solar grew 

rapidly from less than $1 billion in 2004 to a peak of $34 

billion in 2008 before falling to $26 billion in 2009–10. The 

fall in investment may reflect volatility in the price of photovoltaic modules and, in the case of the EU, reductions in 

government support and incentives in Germany, Spain, and 

other EU countries. The EU is the world’s largest source of 

financing for solar with an estimated $11 billion in 2010, 

down sharply from $22 billion in 2008. The marked decline in EU financing reflects the recession and cutbacks by 

Germany, Spain, and the United Kingdom in government 

support and incentives for solar power. The United States is 

the second-largest source of financing in solar with $6 billion in 2010, up from $4 billion in 2009 but below the peak 

of $8 billion in 2008. China is the third-largest source of 

solar investment with $4 billion. Chinese investment in this 

area was negligible in 2004–05 before rising to $2 billion in 

2006 and doubling to $4 billion in 2010. 

Biomass/waste was the third-largest area of investment, 

with an estimated $11 billion in 2010 (figure 6-56). After 

rising rapidly from $4 billion to $10 billion from 2004 to 

2006, investment leveled off at $10–11 billion from 2006 to 

2010. Biofuels is the fourth-largest area of investment, with 

$6 billion in 2010. Investment in this sector is down sharply from its $23 billion peak in 2006 due to excess capacity 

and overinvestment, particularly in the U.S. ethanol sector; 

volatility in the price of oil; and falls in the prices of corn 

and other commodities used in biofuels production.35 U.S. 

investment slid from $9 billion in 2006 to $1 billion in 2010, 

and EU investment also fell sharply (table 6-12). 

Venture Capital Investment

Venture capital investment is a useful indicator of market assessment of future technology trends. As an important 

source of financing for new firms, it may indicate nascent 

areas of clean energy technologies. 

Data from Bloomberg New Energy Finance show that 

global venture capital investment in clean energy rose rapidly, more than quadrupling from an estimated $1 billion in 

2004 to $4 billion in 2010, after a sharp recession-induced 

dip in 2009 (figure 6-57). The United States is the main provider of venture capital financing for clean energy technologies, with more than 90% of global investment in 2010. The 

EU, China, and other Asian economies have been negligible 

sources of venture capital. 

Two major technology areas, energy smart/efficiency and 

solar, dominate global venture capital investments in clean 

energy, receiving an estimated $2 billion and $1.5 billion, 

respectively (figure 6-58). The energy smart/efficiency category covers a wide range of technologies from digital energy 

applications to efficient lighting, electric vehicles, and the 

Figure 6-56

Financial new investment in clean energy and 

technologies, by select energy and technology: 

2004–10

NOTES: Clean energy and technologies include biomass, 

geothermal, wind, solar, biofuels, and energy smart and efficiency. 

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 (2011).

Science and Engineering Indicators 2012

Dollars (billions)

Percent

2004 2005 2006 2007 2008 2009 2010

0

20

40

60

80

100

Wind

Solar

Biofuels

Biomass and waste

Energy smart technologies

All others

2004 2005 2006 2007 2008 2009 2010

0

20

40

60

80

Wind

Solar

Biofuels

Biomass and waste

Energy smart technologies

All others

Share by selected energy and technology

Amount
6-64 Chapter 6. Industry, Technology, and the Global Marketplace

smart grid that maximizes the energy efficiency of existing 

energy sources and networks. 

The attractiveness of these technologies may be enhanced by sizable public R&D funding. 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. 

This sector has also benefited from increased U.S. public 

research spending. Investor interest has been in electric cars 

and the smart grid, both of which have received U.S. stimulus funding. 

Biofuels is the third-largest technology in terms of venture capital investment, with a share of 10% in 2010 (figure 

6-58). Wind energy has received less than 5% of venture 

capital investment, far less than its dominant share in total 

commercial investment.

Public Research, Development, and 

Demonstration Expenditures in Clean Energy 

and Technologies 

According to IEA data, the estimated amount of global 

public R&D and demonstration (RD&D) investment for 

clean energy and related technologies was $16.7 billion in 

2009 (figure 6-59). Clean energy RD&D includes solar, 

wind, ocean, nuclear, bioenergy, hydrogen, fuel cells, carbon capture and storage, and energy efficiency.36

Nuclear energy was the largest area, receiving $5.3 billion in 2009, one-third of total RD&D (figure 6-59). RD&D 

funding for nuclear energy has remained relatively flat during the 2000s. The next two largest areas are energy efficiency and renewable energy (solar, wind, ocean, bioenergy), 

which each received about $4 billion in public RD&D. Other 

power and storage was third, receiving $1.6 billion (figure 

6-59). Renewable energy had the fastest growth between 

2000 and 2009, more than quadrupling from $900 million 

to $3.9 billion. Growth was also rapid in hydrogen and fuel 

cells, which increased from $32 million in 2002 to $900 million in 2009. 

The United States in 2009 had the largest investment 

in clean energy RD&D; its $7.0 billion accounted for 42% 

of global RD&D (figure 6-60). However, this figure included one-time funding from the American Recovery and 

Table 6-12

Financial new investment, by selected region/country and energy/technology: 2004–10

(Millions of dollars)

Region/country and technology 2004 2005 2006 2007 2008 2009 2010

EU

Wind .................................................................... 6,728 12,034 12,413 25,316 22,546 20,660 19,243

Solar .................................................................... 319 1,809 5,359 13,718 22,200 15,222 11,491

Biofuels ............................................................... 477 1,510 4,450 4,694 1,994 1,726 167

United States

Wind .................................................................... 1,574 3,962 9,210 11,167 17,593 10,479 17,142

Solar .................................................................... 153 1,124 2,389 5,514 7,834 3,666 5,580

Biofuels ............................................................... 989 2,651 10,448 9,136 4,078 935 1,155

China

Wind .................................................................... 220 1,473 3,678 7,472 17,368 30,764 44,875

Solar .................................................................... 3 90 562 197 1,981 3,967 3,856

Biofuels ............................................................... 17 64 1,117 1,397 187 43 NA

EU = European Union; NA = not available

NOTES: Clean energy technologies include biomass, geothermal, wind, solar, biofuels, and energy smart and efficiency. Financial new investment 

includes venture capital financing raised from private equity, and public capital markets. Mergers and acquisitions are excluded. 

SOURCE: Bloomberg New Energy Finance, http://bnef.com/, special tabulations (2011).

Science and Engineering Indicators 2012

Figure 6-57

Global venture capital investment in clean energy 

and technologies: 2004–10

ROW = rest of world

NOTE: Clean energy and technologies include biomass, geothermal, 

wind, solar, biofuels, and energy smart and efficiency.

SOURCE: Bloomberg New Energy Finance, http://bnef.com/, special 

tabulations (2011).

Science and Engineering Indicators 2012

Dollars (billions)

2004 2005 2006 2007 2008 2009 2010

0

1

2

3

4

5

United States

ROW
Science and Engineering Indicators 2012 6-65

Reinvestment Act (ARRA). For much of the 2000s, U.S. 

public investments had been the third largest behind the EU 

and Japan, which in 2009 invested about $4.0 billion each, 

nearly a quarter each of global RD&D. 

Global public RD&D investment more than doubled 

between 2000 and 2009 from $8.2 billion to $16.7 billion 

(figure 6-60). Increases in funding in the United States and 

the EU propelled growth after 2003; Japan’s public RD&D 

expenditures stayed flat during this period. More recent U.S. 

data show a sharp decline in U.S. clean energy RD&D investment from $7.0 billion in 2009 to $4.4 billion in 2010, 

when ARRA funding declined (figure 6-61). 

U.S. energy-related RD&D funding across technologies 

has been volatile (figure 6-62). Energy efficiency, including 

smart grid, and renewable energy were the two largest areas, 

each receiving about 30% of funding in 2010. In the renewable energy area, biofuels received the largest share of funding, followed by solar and smaller amounts for wind and 

ocean energy. The shares of energy efficiency and renewable energy jumped starting in 2009 because most ARRA 

funding was allocated to these two areas. Nuclear is the 

third-largest area, receiving 20% of expenditures. Nuclear 

had been the largest area for much of the 2000s but received 

scant funding from ARRA, resulting in its share falling from 

36% in 2008 to 20% in 2010. 

Patenting of Clean Energy and Pollution 

Control Technologies

USPTO patents granted in clean energy and pollution control 

technologies can be classified using a new taxonomy developed 

for this purpose. The taxonomy classifies patents involving bioenergy, nuclear, wind, solar, energy storage, smart grid, and 

NOTE: Clean energy and technologies include biomass, geothermal, wind, solar, biofuels, and energy smart and efficiency.

SOURCE: Bloomberg New Energy Finance, http://bnef.com/, special tabulations (2011). 

Science and Engineering Indicators 2012

Dollars (billions)

Figure 6-58

Global venture capital investment in clean energy and technologies: 2004–10

2004 2005 2006 2007 2008 2009 2010

0

1

2

3

4

5

Energy smart technologies

Solar

Biofuels

Wind

All others

EU = European Union; RD&D = Research, development, and 

demonstration

NOTES: RD&D includes research, development, and demonstration 

projects. Clean energy and technologies include solar, wind, 

bioenergy, nuclear, fuel cells, hydrogen, CO2

 capture and storage, 

other power and storage, and energy efficiency. EU consists of 

Austria, Belgium, Czech Republic, Denmark, Finland, France, 

Germany, Greece, Hungary, Ireland, Luxembourg, Netherlands, 

Poland, Portugal, Slovak Republic, Spain, Sweden, and United 

Kingdom. Data not available for China. 

SOURCE: International Energy Agency, Statistics and Balances, 

http://www.iea.org/stats/index.asp, accessed 15 March 2011.

Science and Engineering Indicators 2012

Figure 6-59

Global government RD&D in clean energy and 

technologies, by technology area: 2003, 2005, 2007, 

and 2009

Dollars (billions)

2003 2005 2007 2009

0

2

4

6

8

10

12

14

16

18

Nuclear

Energy 

efficiency

Renewable 

energy

Hydrogen and 

fuel cells

Other power 

and storage

CO2 capture 

and storage
6-66 Chapter 6. Industry, Technology, and the Global Marketplace

pollution mitigation. The number of patents in these technologies jumped to a record high in 2010, which may mostly reflect 

USPTO efforts to speed up processing of applications (figure 

6-63 and appendix table 6-66).37 (For a more detailed description of how this taxonomy identifies clean energy and pollution 

control patents, see the section in Chapter 5, “Identifying clean 

energy and pollution control patents.”) U.S. resident inventors 

Figure 6-60

Government RD&D expenditures for clean energy 

and technologies, by selected region/country: 

2000–09

EU = European Union; RD&D = Research, development, and 

demonstration; ROW = rest of world

NOTES: RD&D includes research, development, and demonstration 

projects.Clean energy and technologies includes solar, wind, 

bioenergy, nuclear, fuel cells, hydrogen, CO2

 capture and storage, 

other power and storage, and energy efficiency. EU includes Austria, 

Belgium, Czech Republic, Denmark, Finland, France, Germany, 

Greece, Hungary, Ireland, Luxembourg, Netherlands, Poland, 

Portugal, Slovakia, Spain, Sweden, and United Kingdom. ROW 

includes Australia, Canada, and South Korea. Data not available for 

China.

SOURCE: International Energy Agency, Statistics and Balances, 

http://www.iea.org/stats/index.asp, accessed 15 April 2011.

Science and Engineering Indicators 2012

Dollars (billions)

Percent

Global share

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

0

1

2

3

4

5

6

7

8

United States

EU

Japan

ROW

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

0

10

20

30

40

50

60

United States

EU

Japan

ROW

ARRA = American Recovery and Reinvestment Act of 2009;

RD&D = Research, development, and demonstration 

NOTES: RD&D includes research, development, and demonstration 

projects. Amount of R&DD shown in billions of current dollars above 

each year. Clean energy and 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 2011.

Science and Engineering Indicators 2012

Figure 6-62

U.S. government RD&D in clean energy and 

technologies, by share of technology area: 2005, 

2007, 2009, and 2010

0

20

40

60

80

100

Percent

Nuclear

Energy 

efficiency

Renewable 

energy

Hydrogen and 

fuel cells

Other power 

and storage

CO2 capture 

and storage

2005

2.2

2007

2.6

2009

7.0

2010

4.4

ARRA = American Recovery and Reinvestment Act of 2009; 

RD&D = Research, development, and demonstration 

NOTES: RD&D includes research, development, and demonstration 

projects. Clean energy and technologies includes 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 2011.

Science and Engineering Indicators 2012

Figure 6-61

U.S. government RD&D expenditures on clean 

energy and technologies: 2007–10

0

2

4

6

8

10

Dollars (billions)

2007 2008 2009 2010
Science and Engineering Indicators 2012 6-67

were granted about 45% of the 6,100 clean energy and pollution 

control technology patents in 2010, continuing the advantage of 

non-U.S. inventors in these fields since 2000.38 The decline in 

the U.S. share of U.S. patent awards since 2000 suggests increased foreign technological capabilities in this area.

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 84% of patents granted to all non U.S. 

inventors (figure 6-64 and appendix table 6-66). Japan received 43% (down from more than 50% in the early 2000s); 

EU inventors received 30% (down from 36% in 2000). South 

Korean inventors received 12% of these non-U.S. inventor 

patents, up steeply from 3% in 2000. No other country has a 

substantial share of U.S. patents in this area. 

Clean energy and pollution control technology patents 

comprise four broad areas: alternative energy with 3,000 

patents granted, energy storage with 1,000 patents, smart 

grid with 500 patents, and pollution mitigation with 1,900 

patents (table 6-13 and appendix tables 6-67, 6-68, 6-69, 

and 6-70). The proportion of clean energy patents rose from 

26% in 1995 to 49% in 2010, with major share gains by fuel 

cell and losses by nuclear patents (appendix tables 6-71 and 

6-72). Energy storage patents advanced from 8% to 16%, 

and pollution mitigation technologies declined from 58% to 

31%, driven by share losses of air quality, water quality, and 

recycling (appendix tables 6-73, 6-74, and 6-75).

Figure 6-63

USPTO patents in clean energy and pollution 

control technologies, by U.S. and non-U.S. 

inventors: 1995–2010

USPTO = U.S. Patent and Trademark Office

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, flywheels, 

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 

classified by The Patent BoardTM. Patent grants fractionally allocated 

among regions/countries on basis of proportion of residences of all 

named inventors. 

SOURCE: The Patent BoardTM, Proprietary Patent database, special 

tabulations (2011). See appendix table 6-66.

Science and Engineering Indicators 2012

Number

1995 1998 2000 2002 2004 2006 2008 2010

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

U.S. inventors

Non-U.S. inventors

Total

Figure 6-64

USPTO patents granted to non-U.S. inventors in 

clean energy and pollution control technologies, 

by selected region/country: 1995–2010

EU = European Union; ROW = rest of world; USPTO = U.S. Patent 

and Trademark Office

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, flywheels, 

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 

classified by The Patent BoardTM. Patent grants fractionally allocated 

among regions/countries on basis of proportion of residences of all 

named inventors. EU includes current member countries.

SOURCE: The Patent BoardTM, Proprietary Patent database, special 

tabulations (2011). See appendix table 6-66.

Science and Engineering Indicators 2012

Number

Percent 

Share of selected region/country

1995 1998 2000 2002 2004 2006 2008 2010

0

200

400

600

800

1,000

1,200

1,400

1,600

Japan

South Korea

EU

ROW

1995 1998 2000 2002 2004 2006 2008 2010

0

10

20

30

40

50

60

Japan

South Korea

EU

ROW
6-68 Chapter 6. Industry, Technology, and the Global Marketplace

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/economy are concentrated in a particular 

technology (table 6-14).

 In clean 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 and appendix tables 6-45, 6-71, 6-76, 6-77, 6-78, and 

6-79). The EU has relatively high concentrations in bioenergy, wind, and nuclear and relatively low concentration in 

electric hybrid technologies (appendix table 6-72). Japan has 

a high concentration of patents in electric hybrid technologies and fuel calls, but relatively low activity in bioenergy, 

nuclear, and solar. South Korea’s concentration of patent activity is low across the range of clean energy. 

The United States and 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 and 

appendix tables 6-45, 6-68, and 6-80). 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 6-81). 

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 (appendix tables 6-45 and 6-69).

In pollution mitigation technologies, the United States 

has a slightly above average concentration of patents, with 

very high concentration in clean coal and slightly higher 

concentration in carbon capture and storage (table 6-14 and 

appendix tables 6-45, 6-82, and 6-83). The EU has a particularly high concentration of patents in air pollution, carbon capture and storage, and solid waste (appendix tables 

6-73 and 6-84). Japan has a relatively low concentration in 

this area, with the exception of air pollution. South Korea 

has relatively low concentrations in all pollution mitigation 

technologies. 

Table 6-13

USPTO patents granted in clean energy and pollution control technologies, by selected area: Selected years, 

1995–2010

Technology area 1995 2000 2005 2008 2010

All clean energy and pollution control technologies ...................................... 2,991 3,641 3,533 3,688 6,145 

Alternative energy ...................................................................................... 825 1,154 1,482 1,606 2,993 

Bioenergy ............................................................................................... 43 71 60 100 222 

Electric and hybrid vehicles ................................................................... 129 247 365 377 532 

Fuel cells ................................................................................................ 104 219 518 534 1,031 

Geothermal ............................................................................................. 23 21 19 29 41 

Hydropower ............................................................................................ 24 32 30 43 72 

Nuclear ................................................................................................... 263 144 127 83 120 

Solar ....................................................................................................... 210 377 244 238 651 

Wind ....................................................................................................... 28 55 137 197 355 

Energy storage ........................................................................................... 227 476 461 526 980 

Batteries ................................................................................................. 124 285 224 235 547 

Hydrogen production and storage ......................................................... 54 114 161 193 278 

Ultracapacitors ....................................................................................... 24 52 61 83 131 

All others ................................................................................................ 28 37 34 27 41 

Smart grid .................................................................................................. 295 277 288 385 528 

Pollution mitigation .................................................................................... 1,717 1,864 1,456 1,321 1,887 

Air ........................................................................................................... 701 835 819 719 1,076 

Capture and storage of carbon and other greenhouse gases ............... 41 71 72 74 152 

Cleaner coal ........................................................................................... 64 72 60 54 158 

Environmental remediation ..................................................................... 160 140 76 78 89 

Recycling ................................................................................................ 327 322 170 120 186 

Solid waste ............................................................................................. 304 235 137 116 129 

Water ...................................................................................................... 301 385 274 281 319 

USPTO = U.S. Patent and Trademark Office

NOTES: Clean 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/hybrid. 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 classified by The Patent BoardTM. Sum of individual technologies may exceed broad areas and sum of broad 

categories may exceed total because some patents are assigned to multiple individual technologies or broad areas. 

SOURCE: The Patent BoardTM, special tabulations (2011) of Proprietary Patent database. See appendix tables 6-66–6-84.

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 6-69

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 KTI industries, 

and position in patenting and other measures of innovationrelated activities. 

The strong competitive position of the U.S. economy 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 service industries, which 

account for nearly one-fifth of global economic activity. The 

U.S. trading position in technology-oriented services remains 

strong, as evidenced by the continued U.S. surplus in commercial KI services and licensing of patents and trade secrets. 

The United States is the leading source of RD&D and venture 

capital financing of clean energy and technologies. 

The overall U.S. ranking notwithstanding, its market position in most of these industries has either flattened or slipped. 

Productivity growth of the U.S. economy has slowed in the 

2000s relative to the 1990s. The historically strong U.S. trade 

position in advanced technology products has shifted to deficit because of the faster growth of imports than exports. This 

shift is due in part to U.S. companies moving assembly and 

other activities to China, other East Asian countries, and elsewhere. However, the U.S. deficit also reflects the development of indigenous capability by China and other East Asian 

countries in HT manufacturing industries.

China and other emerging Asian economies are showing 

rapid progress in their overall economic growth and technological capabilities. Productivity growth has accelerated, 

coinciding with an increase in the concentration of KTI industries in many of their economies. Their market positions in 

KTI industries—particularly HT manufacturing industries—

have strengthened, and their shares of U.S. and economically 

valuable patents have risen, led by South Korea and Taiwan. 

The number of Chinese patents has soared, with Chinese and 

non-Chinese inventors each having a 50% share, suggesting 

the expansion of technological activity by domestic and foreign companies in China’s rapidly growing economy.

Among individual large countries, China’s progress clearly stands out. China has become a leading global producer 

and exporter of HT manufacturing goods. It has become a 

major global assembly center, supplied by components and 

inputs from East Asian economies. However, China’s rapid 

progress in other indicators of technological capability and 

Table 6-14

Patenting activity in selected clean energy and pollution control technologies, by selected region/country: 

2007–10

(Activity index)

Technology United States EU Japan South Korea

All clean energy and pollution control technologies ...................................... 0.95 1.14 1.16 0.94

Alternative energy ...................................................................................... 0.92 1.24 1.22 0.87

Bioenergy ............................................................................................... 1.32 1.26 0.23 0.11

Fuel cells ................................................................................................ 0.79 0.92 1.65 1.46

Hybrid electric ........................................................................................ 0.76 0.89 2.02 0.78

Nuclear ................................................................................................... 0.96 1.94 0.79 1.26

Solar ....................................................................................................... 1.13 1.01 0.80 0.50

Wind ....................................................................................................... 0.86 2.92 0.31 0.19

Energy storage ........................................................................................... 0.73 0.54 1.71 2.58

Batteries ................................................................................................. 0.42 0.35 2.09 4.74

Hydrogen power and storage ................................................................ 1.16 0.86 0.95 0.62

Smart grid .................................................................................................. 1.23 1.02 0.50 0.48

Pollution mitigation .................................................................................... 1.03 1.33 0.96 0.40

Air ........................................................................................................... 0.92 1.50 1.30 0.38

Capture and storage of carbon and other greenhouse gases ............... 1.18 1.55 0.48 0.36

Cleaner coal ........................................................................................... 1.43 0.89 0.34 0.33

Solid waste ............................................................................................. 1.10 1.39 0.41 0.56

Water ...................................................................................................... 1.20 0.98 0.53 0.56

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

NOTES: Clean 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/hybrid. Energy storage includes batteries, 

compressed air, flywheels, 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 classified by The Patent BoardTM. Patent grants fractionally allocated among regions/countries on basis 

of proportion of residences of all named inventors. EU includes current member countries. Activity index consists of ratio of region/country’s share of 

indicated technology to region/country’s share of total grants. A ratio of greater than one signifies more active patenting in the selected technology; a 

ratio of less than one signifies less active patenting.

SOURCE: The Patent BoardTM, special tabulations (2011) of Proprietary Patent database. See appendix tables 6-66–6-84.

Science and Engineering Indicators 2012
6-70 Chapter 6. Industry, Technology, and the Global Marketplace

the nascent rise of globally competitive Chinese companies 

suggest that China is moving to more technologically challenging and higher end manufacturing activities. China has 

become the world’s largest source of commercial financing 

for clean energy and is home to rapidly growing wind and 

solar industries. 

The EU’s position has been similar to that of the United 

States for much of the 2000s—relatively strong overall economic performance with a slowdown in productivity and flat 

or slight declines in its market position in KTI industries. 

However, the EU has suffered more severe losses in its market position in KTI industries than the United States during 

the worldwide recession in part because of the EU’s debt 

and fiscal problems. Japan’s economy has shown less dynamism compared with the United States and the EU, and its 

market position has declined steeply in many KTI industries. 

Japan’s loss of market position in HT manufacturing industries is due, in part, to Japanese companies shifting production to China and other Asian economies.

The global recession had a disproportionately severe impact on the United States, the EU, and other developed economies, with production of their technology-intensive industries 

declining in 2009. In contrast, technology-intensive industries 

of developing economies, led by China, continued to grow 

during the global recession and increased their market positions relative to developed economies. Worldwide output of 

technology-intensive industries recovered in 2010, with much 

faster growth by China and other developing economies. 

Recovery of technology-intensive industries in the developed 

economies in 2010 was more evident in the United States and 

Japan than in the EU. Whether the global downturn will lead 

to fundamental changes in the market positions of the United 

States and other developed economies in the production and 

trade of KTI industries remains uncertain.

Notes

1. See Mudambi (2008) and Reynolds (2010) for a discussion on the shift to knowledge-based production and geographical dispersion of economic activity. 

2. See OECD (2001) for a discussion of classifying 

economic activities according to degree of “knowledge 

intensity.” Part of the discussion on trade uses a different, 

product-based classification of the U.S. Census Bureau under the terminology advanced technology products.

3. Like all classification schemes, the OECD classification has shortcomings. For example, 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 allow researchers and analysts to trace, in broad outline, 

the worldwide trends towards greater interdependence in 

science and technology and the development of KTI sectors 

in many of the world’s economies.

4. In designating these HT manufacturing industries, 

OECD took into account both the R&D expenditures made 

directly by firms and 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 

the OECD as HT manufacturing. For additional perspectives 

on OECD’s methodology, see Godin (2004).

5. See Atkinson and McKay (2007: 16–17) for a discussion of and references to the impact of IT on economic 

growth and productivity.

6. The sum of the value added attributable to individual 

commercial KI services does not add to the total because of 

rounding. 

7. Data on the health sector includes social services.

8 See Bresnahan and Trajtenberg (1995) and DeLong 

and Summers (2001) for a discussion of ICT and generalpurpose technologies.

9. These 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 bestin-class country in 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 line 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/

10. See Williamson and Raman (2011) for a discussion of 

China’s acquisition of foreign companies.

11. Commercial KTI services and goods trade does not 

correspond to commercial KTI 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. 

12. Data on commercial KI trade between China and 

Hong Kong are not available. 
Science and Engineering Indicators 2012 6-71

13. The sums of the categories do not add to the total, 

which also includes a small amount of trade in noncommercial KI services, including construction services. 

14. IHS Global Insight data as of July 2009.

15. The U.S. trade balance is affected by many other factors, including currency fluctuations, differing fiscal and 

monetary policies, and export subsidies between the United 

States and its trading partners.

16. China is the single largest trading partner for the United 

States in goods trade according to recent data from the U.S. 

Census. For more information, see http://www.census.gov/

foreign-trade/index.html. 

17. The discrepancy in the trade figures is because of 

rounding.

18. U.S. multinational financial services data for 1999 

and 2006 do not include banks and depository institutions, 

which are included in the global industry data on financial 

services. 

19. U.S. direct investment abroad by industry and country is a lower bound estimate because an increasing share 

of U.S. direct investment (36% in 2008) is through holding 

companies that invest in other industries that may be in a different country. For more information, see Ibarra and Koncz 

(2008). 

20. OECD (2005).

21. Definitions of innovation differ widely, but a common element is the commercialization of something that did 

not previously exist.

22. The NSF BRDIS survey’s definition of innovation 

is very similar to the OECD definition. For more information, see NSF, Business R&D and Innovation Survey, http://

www.nsf.gov/statistics/srvyindustry/about/brdis/.

23. BRDIS data are not available for the entire U.S. service sector.

24. Rather than granting property rights to the inventor, 

as is the practice in the United States and many other countries, some countries grant property rights to the applicant, 

which may be a corporation or other organization. 

25. 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 USPTO, 

“What Is a Patent?” Available at http://www.uspto.gov/web/

offices/pac/doc/general/index.html#patent. Accessed 19 

June 2009. 

26. The Japan Patent Office is also a major patent office but has a much smaller share of foreign patents than the 

USPTO and the European Patent Office. 

27. Although the USPTO grants several types of patents, 

this discussion is limited to utility patents, commonly known 

as patents for inventions. They include any new, useful, or 

improved-on method, process, machine, device, manufactured item, or chemical compound.

28. Unless otherwise noted, USPTO assigns patents to 

countries on the basis of the residence of the first-named 

inventor. 

29. Triadic patent families with co-inventors residing in 

different countries are assigned to their respective countries/

economies on a fractional-count basis (i.e., each country/

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 OECD.

30. The high-technology 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.

31. According to U.S. Census data, the number of U.S. 

microbusinesses in non-HT industries in 2008 was 3.3 million, with 2.7 million operating in service industries. 

32. Another possibility is that venture capital investor behavior changed because fewer opportunities for attractive risky 

investments were available in the 2000s than in the 1990s.

33. The 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’...” See IEA 

(2011), http://www.iea.org/stats/RDD%20Manual.pdf, pp. 

16–17.

34. 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).

35. See UNEP 2009, p. 18, for a discussion of the biofuels sector.

36. The IEA has no official definition of clean energy. 

This discussion includes public RD&D in energy efficiency, 

renewable energy, nuclear, hydrogen and fuel cells, CO2

 capture and storage, and other power and storage technologies.

37. The USPTO initiated a green technology pilot program on December 7, 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. 

38. See note 28. 
6-72 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.

Angel investment: Financing from affluent individuals 

for business startups, usually in exchange for ownership equity. Angel investors typically invest their own funds or organize themselves into networks or groups to share research 

and pool investment capital.

Asia-8: India, Indonesia, Malaysia, the Philippines, 

Singapore, South Korea, Taiwan, and Thailand.

Commercial knowledge-intensive services: Knowledgeintensive services 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 a single location with no subsidiary or branches or the topmost 

parent of a group of subsidiaries or branches.

EU (European Union): The 27 member states of the 

European Union since 2007 include 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.

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 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.

Information and communications technology 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 knowledgeintensive 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 EU countries to other 

EU countries.

Knowledge-intensive 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.

Knowledge- and technology-intensive 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.

Normalizing: To adjust to a norm or standard.

Not obvious: One criterion (along with “new” and 

“useful”) by which an invention is judged to determine its 

patentability.

Productivity: The efficiency with which resources are 

employed within an economy or industry, measured as labor 

or multifactor productivity. Labor productivity is measured 

by 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): The exchange rate required to purchase an equivalent market basket of goods.

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 the 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.

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http://dx.doi.org/10.1787/5km7fmtw4qmv-en
Chapter 7

Science and Technology: Public 

Attitudes and Understanding

7-1

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

Information Sources, Interest, 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 Issues ............................................................................7-5

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

Chapter Overview .....................................................................................................................7-6

Chapter Organization ................................................................................................................7-6

A Note About Data and Terminology ......................................................................................7-6

Information Sources, Interest, and Involvement ..........................................................................7-6

S&T Information Sources .......................................................................................................7-10

Public Interest in S&T ............................................................................................................7-12

Availability of S&T News in the Media .................................................................................7-14

Involvement ............................................................................................................................7-16

Public Knowledge About S&T ..................................................................................................7-18

Understanding Scientific Terms and Concepts .......................................................................7-19

Reasoning and Understanding the Scientific Process .............................................................7-23

Other Indicators of Public Knowledge and Understanding About S&T ................................7-26

Public Attitudes About S&T in General ....................................................................................7-27

Promise and Reservations About S&T ...................................................................................7-28

Federal Funding of Scientific Research ..................................................................................7-29

Confidence in the Science Community’s Leadership .............................................................7-31

Influence of Scientific Experts on Public Issues ....................................................................7-32

Views of S&E Occupations ....................................................................................................7-33

Public Attitudes About Specific S&T-Related Issues ...............................................................7-34

Environment, Climate Change, and Energy Development .....................................................7-36

Stem Cell Research and Human Cloning ...............................................................................7-40

Teaching Evolution in the Schools .........................................................................................7-41

Genetically Modified Food .....................................................................................................7-42

Nanotechnology .....................................................................................................................7-42

Other Emerging Technologies ................................................................................................7-43

Animal Research .....................................................................................................................7-43

Science, Engineering, and Mathematics Education ................................................................7-44

Conclusion .................................................................................................................................7-44

Notes ..........................................................................................................................................7-45

Glossary .....................................................................................................................................7-48

References ..................................................................................................................................7-48
7-2 Chapter 7. Science and Technology: Public Attitudes and Understanding

List of Tables

Table 7-1. News followed “very closely” by American public: 1996–2008 .............................7-14

Table 7-2. Traditional media coverage on science and technology, 

by topic area: 2007–10 ..........................................................................................................7-14

Table 7-3. Leading traditional media story lines on science and technology, 

by topic area: 2009 and 2010 ................................................................................................7-15

Table 7-4. Leading nightly news story lines on science and technology, by topic area: 

2009 and 2010 .......................................................................................................................7-16

Table 7-5. “Most-linked-to” subjects in the new media, by topic area: 2009 and 2010 ............7-17

Table 7-6. Visits to informal science and other cultural institutions, by country/region: 

Most recent year ....................................................................................................................7-18

Table 7-7. Correct answers to factual knowledge and process questions in physical 

and biological sciences, by sex: 1999–2010 .........................................................................7-21

Table 7-8. Comparison of correct answers given by adults and students to factual 

knowledge questions: Most recent year ................................................................................7-22

Table 7-9. Correct answers to factual knowledge questions in physical and biological 

sciences, by country/region: Most recent year ......................................................................7-24

Table 7-10. Correct answers to scientific process questions: Selected years, 1999–2010 ........7-25

Table 7-11. Public perceptions of various groups’ contributions to the well-being 

of society: 2009 .....................................................................................................................7-29

Table 7-12. Public preferences about various groups’ influence on decisions about 

public issues: 2010 or most recent year ................................................................................7-33

Table 7-13. Public perceptions of various groups’ understanding of public issues: 

2010 or most recent year .......................................................................................................7-34

Table 7-14. Public perceptions of various groups’ impartiality in making policy 

recommendations about public issues: 2010 or most recent year .........................................7-35

Table 7-15. Public perceptions of scientific consensus on public issues: 2010 or most 

recent year .............................................................................................................................7-35

Table 7-16. Public perceptions of prestige of various occupations: Selected years, 

 1977–2009...............................................................................................................................7-36

Table 7-17. Public assessment of potential environmental problems: 1993–2010 ....................7-39

Table 7-18. Public opinion on medical technologies derived from stem cell research: 

2010 or most recent year ......................................................................................................7-41

Table 7-A. Online and print information sources: 2010 ............................................................7-11

Table 7-B. Correct responses on trend factual knowledge of science scale by longer 

factual knowledge scale: 2010 ..............................................................................................7-20

Table 7-C. Comparison of general public’s and scientists’ attitudes toward specific 

S&T-related issues: 2009 ......................................................................................................7-37

List of Sidebars

U.S. Survey Data Sources ............................................................................................................7-7

International Survey Data Sources ...............................................................................................7-8

The Blending of Print and Online Sources of Science News ....................................................7-11

Measuring Factual Science Knowledge Over Time ..................................................................7-20

Public Understanding of Science and Its Role in Everyday Life ...............................................7-27

Differences Between Scientists and the Public on S&T-related Issues .....................................7-37
Science and Engineering Indicators 2012 7-3

List of Figures

Figure 7-1. Primary source of information about current news events, science and 

technology, and specific scientific issues: 2010 ...................................................................7-10

Figure 7-2. Primary source of information about current news events, science and 

technology, and specific scientific issues: 2001–10 .............................................................7-10

Figure 7-3. Public interest in selected issues: 2010 ...................................................................7-12

Figure 7-4. Public interest in selected science-related issues: 1979–2010 ................................7-13

Figure 7-5. Network nightly news coverage of science and technology: 1988–2010 ...............7-16

Figure 7-6. Attendance at informal science and other cultural institutions, by institution 

type and education level: 2008 ..............................................................................................7-17

Figure 7-7. Mean number of correct answers to trend factual knowledge of science scale: 

1992–2010 .............................................................................................................................7-19

Figure 7-8. Correct answers to trend factual knowledge of science scale, by respondent 

characteristic: 2010 ...............................................................................................................7-19

Figure 7-9. Mean number of correct answers to nanotechnology questions, by correct 

answers to trend factual knowledge of science scale: 2010 ..................................................7-23

Figure 7-10. Understanding scientific inquiry, by respondent characteristic: 2010 ..................7-26

Figure 7-11. Public assessment of scientific research: 1979–2010 ...........................................7-28

Figure 7-12. Public opinion on whether government should fund basic scientific research: 

1985–2010 .............................................................................................................................7-30

Figure 7-13. Public assessment of amount of government spending for scientific research: 

1981–2010 .............................................................................................................................7-30

Figure 7-14. Public attitudes toward government spending in various policy areas: 2010 .......7-31

Figure 7-15. Public confidence in institutional leaders, by type of institution: 2010 ................7-31

Figure 7-16. Worry about quality of environment: 2001–11 .....................................................7-38

Figure 7-17. Public attitudes toward stem cell research: 2001–10 ............................................7-41

Figure 7-18. Public attitudes toward whether scientific research that causes pain to 

animals should be allowed: 1988–2008 ................................................................................7-43

Figure 7-19. Public assessment of whether the quality of science and mathematics 

education in America is inadequate: 1985–2008 ..................................................................7-44
Information Sources, Interest, and 

Involvement

The Internet is the main source of information for learning about specific scientific issues such as global climate 

change and biotechnology. 

Americans are now about equally likely to rely on the 

Internet as on television as their primary source of general 

science and technology (S&T) information. 

Americans have consistently expressed interest in S&T, 

with 41% reporting they were “very interested” and 

50% reporting they were “moderately interested” in new 

scientific discoveries. 

However, Americans also express similar or higher levels 

of interest in a range of other news topics. 

On average, Europeans appear to express lower levels of 

public interest in “new scientific discoveries and technological developments” relative to Americans, although there is 

considerable variation among different European countries.

In 2008, a majority of Americans said they had visited 

an informal science institution such as a zoo or natural 

history museum within the past year. This proportion is 

generally consistent with results from surveys conducted 

since the 1980s.

Americans with more formal education are more likely to 

visit informal science institutions.

Visits to informal science institutions tend to be less common in Europe, Japan, and Brazil. Visits to a zoo are about 

equally common in China and the United States.

Public Knowledge About S&T

Many Americans continue to give multiple incorrect 

answers to questions about basic factual knowledge of 

science or the scientific inquiry process. In the United 

States, levels of factual knowledge of science have been 

stable for more than a decade.

Americans’ factual knowledge of science is positively 

related to their formal education level and the number of 

science and math courses they have taken. Younger generations also exhibit higher levels of factual knowledge about 

science than older generations. 

Men tend to score higher than women on factual knowledge questions in the physical sciences; women score 

equally well as men on questions in the biological sciences. 

People who score well on factual knowledge measures also 

tend to know more about emerging science topics such as 

nanotechnology.

Levels of factual knowledge of science in the United 

States are comparable to those in Europe and appear to 

be higher than those in Japan, China, or Russia.

In Europe, China, and South Korea, demographic variations in factual knowledge are similar to those in the 

United States.

Americans’ understanding of the process of scientific 

inquiry is stable, after modest improvements since the 

mid-1990s. Understanding of what constitutes an experiment is greater in 2010 than in previous years. 

Americans’ understanding of scientific inquiry is strongly 

associated with their factual knowledge of science, their 

level of formal education, and the number of science and 

mathematics courses they have completed.

Men and women obtain similar scores on understanding of 

scientific inquiry. 

Public Attitudes About S&T in General

Americans in all demographic groups consistently endorse the past achievements and future promise of S&T.

In 2010, 69% of Americans said that the benefits of scientific research have strongly or slightly outweighed the 

harmful results; 9% said the harmful results outweighed 

the benefits. 

Americans tend to have more favorable attitudes about the 

promise of S&T than Europeans, the Japanese, Malaysians, 

Indians, and the Chinese. Attitudes in South Korea tend to 

be more favorable than those in the United States. 

Reservations about science accompany these favorable 

attitudes. Nearly half of Americans agree that “science 

makes our way of life change too fast,” and large proportions of Chinese and South Korean residents voice the 

same sentiment.

Support for government funding of scientific research 

remains strong.

In 2010, 82% of Americans expressed support for government funding of basic research.

In 2009, 73% of Americans said spending on basic scientific research “usually pays off in the long run”; fewer than 

two in ten said such spending was “not worth it.” About the 

same percentage (74%) said spending on engineering and 

technology “usually pays off in the long run.”

The public continues to expresses confidence in science 

leaders.

In 2010, roughly equal percentages of Americans expressed 

“a great deal” of confidence in medical leaders and scientific leaders; military leaders were the only group in whom 

more Americans expressed a great deal of confidence. 

Highlights

7-4 Chapter 7. Science and Technology: Public Attitudes and Understanding
On science-related public policy issues (global climate 

change, stem cell research, nuclear power, and genetically 

modified foods), Americans regard science and engineering leaders as both knowledgeable and impartial—relative 

to other leaders—and believe they should be influential in 

decisions about these topics. 

However, Americans also perceive a considerable lack of 

consensus among scientists on these issues.

A majority of Americans accord scientists “very great 

prestige.” Ratings for engineers are lower but nonetheless better than those of most other occupations.

In 2009, more Americans rated scientists as having “very 

great prestige” than did so for almost any other occupation 

surveyed, second only to firefighters.

Nearly four in ten (39%) Americans rated engineers as 

having “very high prestige”—well above most other occupations considered on the survey. 

Public Attitudes About Specific S&T Issues

Americans’ support for the development of alternative 

sources of energy increased in the 2000s. Assessments of 

environmental hazards from pollution, nuclear power, and 

climate change were largely stable between 1993 and 2010.

A majority of Americans said the government spends too 

little on developing alternative energy sources, and most 

favor providing incentives for using solar and other alternative energy sources. 

In 2010 and 2011, about one-third of Americans (34%) 

said they worry about environmental quality “a great deal,” 

following an increase from 2006 to 2008. More Americans 

considered water pollution as “very” or “extremely dangerous” to the environment than they did several other potential problems. 

Climate change continues to divide opinion. In a 22-nation 

survey, respondents from the United States, China, and the 

UK were less likely to consider climate change a “very serious problem” than those in a number of other countries. 

Respondents from only two nations (Poland and Pakistan) 

were less likely than Americans to consider climate change 

a “very serious problem.” 

Support for the use of nuclear power to generate electricity 

increased from 53% in 2007 to 62% in 2010. However, a 

substantial minority says that nuclear power plants are not 

safe—a proportion that may increase after the 2011 earthquake and tsunami in Japan.

A majority of Americans favor medical research that 

uses human embryonic stem cells. However, Americans 

are overwhelmingly opposed to reproductive cloning and 

wary of innovations using “cloning technology.”

Support for embryonic stem cell research has increased 

since 2004, with 62% in favor of embryonic stem cell research in 2010. A higher proportion (71%) favors stem cell 

research when it does not involve human embryos.

More than three-quarters of Americans oppose human 

cloning.

Americans remain largely unfamiliar with nanotechnology, despite increased funding and a growing numbers of 

products on the market that use nanotechnology.

Public awareness of nanotechnology remains limited. Even 

among respondents who had heard of nanotechnology, 

knowledge levels are not high.

Those who have heard “a lot” or “some” about nanotechnology are more likely to say the benefits of such technology will outweigh any harms than to say the harmful 

results will outweigh the benefits. 

Europeans are split, on average, over whether nanotechnology use in consumer products should be encouraged or not 

(44% to 35%, respectively, with 22% holding no opinion). 

Science and Engineering Indicators 2012 7-5
7-6 Chapter 7. Science and Technology: Public Attitudes and Understanding

Introduction

Chapter Overview

Science and technology (S&T) affect all aspects of 

American life, including work, leisure, family, and civic 

activities. In the workforce, Americans use technology 

to improve productivity in ways that could not have been 

imagined a generation ago, applying recently invented 

tools and applications. In their leisure time, they entertain 

themselves with high technology electronic products and 

make friends, communicate, and stay informed about the 

world through the Internet and social media. As citizens, 

they may engage in discussions on climate change, stem 

cell research, and nuclear power—issues about which atmospheric scientists, microbiologists, and nuclear engineers have formal training and expertise—or benefit from 

advances in new technologies.

It is increasingly difficult for Americans to be competent workers, consumers, and citizens without some degree 

of competency in S&T. How the American public collectively deal with S&T-related issues may, in turn, affect what 

kinds of S&T development the United States will support. 

Therefore, this chapter presents not only indicators about 

media sources, information, and knowledge of S&T, but 

indicators of people’s attitudes about S&T-related issues 

as well. To put U.S. data in context, this 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 

section includes indicators of the public’s sources of information about, level of interest in, and active involvement with 

S&T. The second section reports indicators of public knowledge, including measures of factual knowledge of science 

and engineering and people’s understanding of the scientific 

process. When possible, American adults’ understanding of 

science is compared to that of American students. 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, perceptions of the prestige of S&E occupations, and opinions about how much influence science 

and scientists should have on public affairs. The fourth section addresses public attitudes on issues in which S&T plays 

an important role, such as the environment, climate change, 

nuclear power, the quality of science and math education, 

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 stem cell research, 

cloning, genetically modified (GM) food, nanotechnology, 

and synthetic biology.

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 survey data from national samples with 

sound representative sampling designs. The emphasis in 

the text is on the trends and patterns presented in the data. 

All survey data are subject to numerous sources of error; 

interpretation of the data should be mindful of the limits of 

survey data. 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.”) Most of the international comparisons involve identical questions asked in different countries. 

However, language and cultural differences can affect how 

respondents interpret questions and can introduce numerous 

complexities, so international comparisons require careful 

consideration.

Throughout this chapter, the terminology used in the 

text reflects the wording in the corresponding survey question. 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.

Information Sources, Interest, 

and Involvement

Americans’ awareness and understanding of S&T are 

dependent, in part, on how much they monitor new S&T 

developments throughout their adult life. Because S&T 

are relevant to so many aspects of daily life and are often 

changing and evolving, information about S&T can help 

Americans make informed decisions and more easily navigate the world around them. Interest in and involvement 

with S&T can lead Americans to acquire more information 

and achieve greater understanding.

This section reviews the sources of information about 

S&T that are available to and used by the public, interest in 

and attention to media reports about S&T, and the amount 

of S&T news available from traditional and new media 

sources. It concludes with indicators of behavioral involvement in S&T through visits to museums and other cultural 

institutions. 
Science and Engineering Indicators 2012 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)

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, 

informal science institution visits, 

general attitudes, government 

spending attitudes, science/math 

education attitudes, animal research 

attitudes 

Telephone 

interviews

n = 1,574–2,041; ± 2.47%– 

3.03%

NORC at the 

University of Chicago

General Social Survey 

(GSS) 

1973–2010 Government spending attitudes, 

confidence in institutional leaders

Face-to-face 

interviews

Government spending 

(2000–10): 

n = 1,358– 4,901; ± 2.7%– 3.9%

Confidence in institutional 

leaders, (1973–2010): 

n = 876–3,278; ± 1.3%–3.3%

NORC at the 

University of Chicago

GSS environment module 1993–94, 

2000, 2010

Environmental dangers attitudes Face-to-face 

interviews

n = 1,276–1,557; ± 2.5%– 

3.3%

NORC at the 

University of Chicago

GSS S&T module 2006, 2008, 

2010

Information sources, interest, 

informal science institution visits, 

general attitudes, government 

spending attitudes, science/math 

education attitudes, animal research 

attitudes, nanotechnology awareness 

and attitudes, science knowledge 

Face-to-face 

interviews

n = 1,864–2,021; ± 2.5%– 

3.3%

ABC News/Planet 

Green/Stanford 

University 

ABC News/Planet Green/

Stanford University Poll

2008 Environmental problem attitudes Telephone 

interviews

n = 1,000; ± 3.0%

CBS News/New York 

Times

CBS News/New York Times

Poll

2008 Genetically modified food awareness 

and attitudes

Telephone 

interviews

n = 1,065; ± 3.0%

American Association 

for the Advancement 

of Science (AAAS)

AAAS Project 2061 

(unpublished results, 2008)

2007 (middle 

school 

students)

Science knowledge Paper questionnaires n = 2,047 middle school 

students; n = 1,597 

(follow-up question)

Department of 

Education, National 

Center for Education 

Statistics (NCES)

National Assessment of 

Education Progress (NAEP)

2000 

(grade 8), 

2005 (grades 

4 and 8)

Science knowledge Paper questionnaires 2000 (independent national 

sample): n = 15,955 8th 

graders; ± 2.2% (one 

question used)

2005 (combined national/

state sample): n = 147,700 

4th graders; ± 1.0% (one 

question used)

n = 143,400 8th graders; 

± 0.8%–1.2% (three 

questions used)

The Gallup 

Organization

Various ongoing surveys 2001–11 Federal priorities, environmental 

protection, climate change, global 

warming, nuclear power, alternative 

energy, animal research, stem cell 

research, quality of science/math 

education in U.S. public schools 

attitudes

Telephone 

interviews

n = ~1,000; ± 3.0–4.0% 

Harris Interactive The Harris Poll 1977–2009 Occupational prestige attitudes 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 Genetically modified foods attitudes Telephone 

interviews

n = 1,000; ± 3.1%

Pew Internet & 

American Life Project, 

Pew Research Center

Pew Internet & American 

Life Survey

2006, 2010 Information sources, interest, 

involvement, Internet use

Telephone 

interviews

2006: n = 2,000; ± 3.0%

2010: n = 2,252; ± 2.4%

Pew Research Center 

for the People and the 

Press

Biennial News 

Consumption Survey 

2008, 2010 Information sources, interest, 

credibility of information sources, 

top stories, time spent following the 

news

Telephone 

interviews

2008: n = 3,615; ± 2.0%

2010: n = 3,006; ± 2.5%

Pew Research Center 

for the People and the 

Press

General Public Science 

Survey, separate survey of 

AAAS scientists

2009 Public’s and scientists’ beliefs 

about S&T-related issues, benefits 

of science to well-being of society, 

animal research attitudes

Telephone 

interviews (survey 

of general public)

Internet (survey of 

scientists)

Public: n = 2,001; ± 2.5%

Scientists: n = 2,533; 

± 2.5%

Pew Research Center 

for the People and the 

Press

News Interest Index Survey 2010–11 Top stories, nuclear power and 

offshore oil drilling attitudes

Telephone 

interviews

n = ~1,000; ± 4.0%
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

Pew Research Center 

for the People and the 

Press

Political Survey (various) 2008–11 Information sources, Internet 

use, national policy attitudes 

(environment, global warming, 

energy, stem cell research), 

government spending for scientific 

research attitudes

Telephone 

interviews

n = ~1,300–2,250; ± 2.5%– 

3.5%

Virginia 

Commonwealth 

University (VCU)

VCU Life Sciences Survey 2001–08, 

2010

Interest, science and government 

spending for scientific research 

attitudes, energy sources, animal 

research, stem cell research, cloning 

technology attitudes

Telephone 

interviews

n = ~1,000; ± 3.0%–3.8% 

The Woodrow 

Wilson International 

Center for Scholars, 

conducted by Peter 

D. Hart Research 

Associates

Synthetic Biology Project 2010 Synthetic biology awareness and 

attitudes

Telephone 

interviews

n = 1,000; ± 3.1%

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 

(Fundacion BBVA)

BBVA Foundation 

International Study on 

Attitudes To Stem Cell 

Research and Hybrid 

Embryos

2007/2008 

combined

Stem cell research knowledge, 

awareness, and attitudes 

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 and attitude items Paper questionnaires n = 2,107

Canadian 

Biotechnology 

Secretariat

Canada–U.S. Survey on 

Biotechnology

2005 Biotechnology, nanotechnology, 

genetically modified foods, and other 

technology attitudes (includes U.S. 

data on specific issues)

Telephone 

interviews

(Canada): n = 2,000; ± 2.19%

(United States): n = 1,200; 

± 2.81%

Chinese Association 

for Science and 

Technology (CAST), 

China Research 

Institute for Science 

Popularization 

(CRISP)

Chinese National Survey of 

Public Scientific Literacy

2001, 2007 Various knowledge and attitude 

items, interest, occupational prestige, 

informal science institution visits

Face-to-face 

interviews

2001: n = 8,350 

2007: n = 10,059; ± 3.0%

European Commission Special Eurobarometer 224/

Wave 63.1: Europeans, 

Science and Technology 

(2005)

Special Eurobarometer 224/

Wave 64.3: Europeans and 

Biotechnology in 2005: 

Patterns and Trends (2006)

Special Eurobarometer 300/

Wave 69.2: Europeans’ 

Attitudes Towards Climate 

Change (2008) 

Special Eurobarometer 340/

Wave 73.1: Science and 

Technology Report (2010)

Special Eurobarometer 341/

Wave 73.1: Europeans and 

Biotechnology in 2010: 

Winds of change? (2010) 

2005

2005

2008

2010

2010

Knowledge, trust in scientists, public 

support for basic research, other 

attitudes, informal science institution 

visits 

Biotechnology attitudes

Climate change attitudes

S&T attitudes and interest, support 

for basic research, animal research 

attitudes

Nuclear energy, nanotechnology, 

emerging biotechnologies, synthetic 

biology, and genetically modified 

foods attitudes

Face-to-face 

interviews

(EU total) n = 26,403; 

(Germany) 1,507; 

(UK) 1,307; (Slovakia) 1,241; 

(19 other countries) ~1,000; 

(3 other countries) ~500 

(EU total) n = ~25,000; 

(each member country/state) 

~1,000

(EU total) n = ~26,661; 

(Germany) 1,534; 

(UK) 1,306; 

(22 other countries) ~1,000; 

(3 other countries) ~500

(EU total) n = ~26,671; 

(Germany) 1,531; 

(UK) 1,311; 

(22 other countries) ~1,000; 

(3 other countries) ~500

(EU total) n = ~26,676; 

(Germany) 1,531; 

(UK) 1,316; 

(22 other countries) ~1,000; 

(3 other countries) ~500
Science and Engineering Indicators 2012 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

India National Council 

of Applied Economic 

Research

National Science Survey 2004 Various knowledge and attitude 

items, informal science institution 

visits

Face-to-face 

interviews

n = 30,255

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 & Technology 

in Japan

2001 Various knowledge and attitude 

items, informal science institution 

visits

Face-to-face 

interviews

n = 2,146 

Korea Foundation for 

the Advancement of 

Science and Creativity 

(KOFAC, formerly 

Korea Science 

Foundation)

Survey of Public Attitudes 

Toward and Understanding 

of Science and Technology 

2004, 2006, 

2008

Interest, various knowledge and 

attitude items, informal science 

institution visits 

Face-to-face 

interviews

n = 1,000; ± 3.1%

Malaysian Science 

and Technology 

Information Center 

(MASTIC), 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, 

informal science institution visits

Face-to-face 

interviews

n = 18,447; ± 1.0%

Ministry of Science 

and Technology (MCT) 

of Brazil

Public Perceptions of 

Science and Technology 

2006, 2010 Interest, informal science institution 

visits

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%

Samuel Neaman 

Institute for Advanced 

Studies in Science and 

Technology (Israel)

Survey of attitudes of Israeli 

public toward science and 

technology

2006 Prestige of science careers Telephone 

interviews

n = 490

U.S. Department of 

Education, NCES

Trends in International 

Mathematics and Science 

Study (TIMSS)

2003 

(grade 8)

Science knowledge Paper questionnaires (United States) n = 8,912; 

± 1.4% (for all TIMSS 

questions); (44 other 

countries) n = 2,943–8,952; 

± 1.0%–2.4% (for all 

TIMSS questions) 

WorldPublicOpinion.

org/ The World Bank, 

managed by Program 

on International Policy 

Attitudes at University 

of Maryland

WorldPublicOpinion.org 

Poll

2009 Attitudes toward climate change as 

government priority

(Varies by country)

Face-to-face 

interviews

Telephone 

interviews

n = 18,578 in 19 nations 

comprising 60% of world’s 

population; ± 3.0%–4.0%

EU = European Union; UK = United Kingdom

NOTES: All surveys are national in scope and based on probability sampling methods. Statistics on number of respondents and margin of error are as reported 

by the sponsoring organization. When a margin of error was not cited, none was given by the sponsor.
7-10 Chapter 7. Science and Technology: Public Attitudes and Understanding

S&T Information Sources

U.S. Patterns and Trends

The media environment has been changing over the past 

decade. Although a plurality of Americans say that television is their primary source of news about current events, 

fewer said that they relied on television news for S&T information in 2010 than in previous years. Also, a majority 

turn to the Internet as their primary source of information on 

specific scientific issues such as global climate change, stem 

cell research, GM foods, and nuclear power. 

For news about current events, television is the primary 

source of information for 45% of Americans. Substantial percentages report that most of their current event news comes 

from the Internet (24%) or newspapers (16%) (figure 7-1; appendix table 7-1). The proportion of Americans getting information about current events from the Internet has increased 

considerably since the 1990s, and the proportion using newspapers for current events has declined (figure 7-2). Newspaper 

readership has strongly declined over the past decade (Project 

for Excellence in Journalism, PEJ 2010e). Patterns of reported 

media use over time are complicated by the fact that some 

of the readership for newspapers has shifted to online news 

sources by the same organizations that produce print newspapers.1

 Thus, the separation between print and online news 

sources is often blurred. (Also see sidebar, “The Blending of 

Print and Online Sources of Science News.”) 

For news about S&T, Americans are about equally likely to rely on the Internet as on television. According to the 

2010 General Social Survey (GSS), 35% of Americans cite 

Figure 7-1

Primary source of information about current news 

events, science and technology, and specific 

scientific issues: 2010

NOTE: “All other” includes radio, magazines, books, government 

agencies, family, and friends/colleagues. 

SOURCE: University of Chicago, National Opinion Research Center, 

General Social Survey (2010). See appendix tables 7-1, 7-2, and 7-3. 

Science and Engineering Indicators 2012

Television Internet Newspapers All other Don’t know

Cumulative percent

0 20 40 60 80 100

Specific scientific

issues

Current

news events

Science and

technology

Figure 7-2

Primary source of information about current news 

events, science and technology, and specific

scientific issues: 2001–10

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, 2008, 2010). See appendix tables 7-1, 7-2, 

and 7-3.

Science and Engineering Indicators 2012

Current news events

Science and technology

Specific scientific issues

Percent

2001 2004 2006 2008 2010

0

10

20

30

40

50

60

Internet

Newspapers

2001 2004 2006 2008 2010

0

10

20

30

40

50

60

2001 2004 2006 2008 2010

0

10

20

30

40

50

60

Television

Television

Television

Newspapers

Newspapers

Internet

Internet

the Internet as their primary source of S&T information, up 

from 29% in 2008. The proportion citing the Internet as their 

primary source of S&T information has grown steadily since 
Science and Engineering Indicators 2012 7-11

no reason to expect younger generations who grew up relying more heavily on the Internet to shift to traditional media 

as they age.

National data that address the processes through which 

Americans acquire and sort through S&T information are 

scarce. A Pew Internet and American Life Project survey 

examined how Americans use the Internet to acquire information about science (Horrigan 2006). It found that a clear 

majority of Internet users had engaged in some information 

search activities, including “look[ing] up the meaning of a 

particular scientific term or concept” (70%), “look[ing] for 

an answer to a question you have about a scientific concept 

or theory” (68%), and “learn[ing] more about a science story 

or scientific discovery you first heard or read about offline” 

(65%). In addition, just over half had used the Internet to 

“complete a science assignment for school, either for yourself or for a child” (55%) or to “check the accuracy of a scientific fact or statistic” (52%). Fewer had used the Internet 

to “download scientific data, graphs, or charts” (43%) or to 

“compare different or opposing scientific theories” (37%). 

How skillfully or how often Americans engage in the search 

Internet news sites sometimes represent new providers of news and other times represent an alternative 

outlet for reporting done by print or broadcast media 

organizations. The 2010 General Social Survey asked 

half the sample a question with response options that 

distinguish between online and print-format sources for 

newspapers and magazines.

Print media organizations are more likely to serve 

as a primary source of Americans’ information about 

current news events than they are about either S&T or 

specific scientific issues. When it comes to news about 

current events, a roughly equal proportion of Americans 

who primarily rely on the Internet do so via online 

venues of print media organizations and other online 

sources (12% and 11% of adults, respectively). Print 

media organizations are less dominant as sources of 

news about general S&T. Eleven percent of Americans 

rely on Internet sources for S&T news provided by print 

media organizations; nearly twice as many use other 

online sources (20%). A majority of Americans seeking information about specific scientific issues say the 

Internet would be their primary source, 12% would rely 

on online information from print media organizations, 

and 48% would rely on other online sources. 

Table 7-A

Online and print information sources: 2010

(Percent)

Where do you get most of your information about...?

Current news

events

Science

and technology

Specific scientific 

issues

Online Sources

Online newspapers ............................................................ 12 8 8

Online magazines .............................................................. * 3 4

Other online sources .......................................................... 11 20 48

Print sources

Print newspapers ............................................................... 16 7 3

Print magazines ................................................................. 1 8 3

Other sources ........................................................................ 59 53 33 

Don’t know ............................................................................ * 1 1

* = <0.5% responded

NOTES: “Other sources” includes television, radio, books, family, friends/colleagues. Percentages may not add to 100% because of rounding.

SOURCE: University of Chicago, National Opinion Research Center, General Social Survey (2010).

Science and Engineering Indicators 2012

The Blending of Print and Online Sources of Science News

2001. Conversely, reliance on television has dropped; only 

34% of Americans report that television is their primary 

source of S&T news, down from 39% in 2008 (figures 7-1 

and 7-2; appendix table 7-2).2

When Americans are seeking specific information related 

to S&T, they turn to the Internet as the dominant resource.3 

Asked “If you wanted to learn about scientific issues such as 

global warming or biotechnology, where would you get information?” 59% of Americans cited the Internet, up slightly 

from 55% in 2008. Television ranked as a distant second at 

15%, down from 21% in 2008 (figures 7-1 and 7-2; appendix table 7-3). 

In general, use of the Internet for news and information, 

including S&T information, is greater among younger audiences and increases with education and income. Conversely, 

the use of television decreases with education and income 

and increases with age (appendix tables 7-1, 7-2, and 7-3). 

According to a recent Pew Research Center survey, the 

Internet now outranks television as the primary source of 

news about national and international issues among younger 

adults (ages 18–29) (Pew Research Center 2011b).4

 There is 
7-12 Chapter 7. Science and Technology: Public Attitudes and Understanding

for scientific information—whether on the Internet or elsewhere—remains unknown. 

Using information effectively involves more than just 

finding it. In an information-saturated society, people often 

need to assess the quality of the information they encounter 

and determine its credibility. Survey data provide some indication of how Americans assess the credibility of public 

information. For the past 10 years, Americans have become 

more skeptical of the information they encounter in major 

broadcast and print media, but recently this trend has leveled 

off. Americans’ judgments of media credibility are shaped 

by factors other than critical thinking skills and the quality of the information provided. For example, judgments of 

the credibility of particular mass media information sources 

are associated with political party affiliations (Pew Research 

Center 2010a). 

Evidence about how Americans judge the credibility 

of S&T information in the media is scant. The 2006 Pew 

Internet and 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 (80%) reported they have checked information at least 

once, either by comparing it to other information they found 

online, comparing it to offline sources (e.g., science journals, encyclopedia), or looking up the original source of the 

information (Horrigan 2006). (For additional details, see 

NSB 2008.)

International Comparisons 

Information sources in other countries depend, in part, 

on access to the Internet and the prevalence of Internet 

news sources (Internet World Statistics 2010). Internet access is currently greater in North America than in any other 

region of the world. In many other countries, television is 

the leading source of S&T information, newspapers generally rank second, and relatively fewer survey respondents 

cite the Internet as an important source of S&T information. 

In Malaysia, for example, 82% cite television as their leading source of S&T news and information, whereas 62% cite 

newspapers, and 25% cite the Internet (respondents could 

choose multiple sources of S&T information). Television is 

also the dominant source of S&T information in India, where 

about two-thirds of survey respondents in 2004 said it was 

their main information source (Shukla 2005). Radio (13%) 

and friends/relatives (12%) ranked ahead of print sources 

such as newspapers, books, and magazines, which together 

accounted for 9% of responses. India’s relatively low literacy rate (144th of 176 countries in a 2005 ranking) may contribute to this reliance on non-printed sources. On the other 

hand, in more widely connected South Korea, a 2008 survey 

found that more respondents named the Internet (28%) as 

their primary source of S&T information than newspapers 

(16%) (KOFAC 2009).

Public Interest in S&T

U.S. Patterns and Trends

Americans regularly express relatively high levels of interest in S&T news. More than four in ten Americans (41%) 

report being “very interested” in new scientific discoveries, 

half say they are “moderately interested,” and just 8% are 

“not at all interested,” according to the 2010 GSS survey 

(figure 7-3). The proportion of respondents “very interested” 

in new scientific discoveries in 2010 is about the same as 

in 2008 and down from 47% in 2001 (figure 7-4; appendix 

table 7-4).5 Comparable data from Virginia Commonwealth 

University (VCU) show a stable trend in public interest in 

new scientific discoveries between 2001 and 2006; during 

this period, the proportion of Americans who said they had 

a lot of interest in new scientific discoveries fluctuated between 43% and 47% (VCU 2006). Interest in new scientific 

discoveries was greater among those with more formal education and more coursework in science and mathematics 

(appendix table 7-5). 

Relative to other topics, however, the level of interest 

in S&T is not particularly high. Interest in new scientific 

discoveries and use of new inventions and technologies 

Figure 7-3

Public interest in selected issues: 2010

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 (2010). See appendix table 7-4. 

Science and Engineering Indicators 2012

Very interested

Don’t know

Moderately interested Not at all interested

Cumulative percent

International and

foreign policy issues

Space exploration

Agricultural

and farm issues

Military and

defense policy

Use of new inventions

and technologies

New scientific

discoveries

Economic issues and

business conditions

Environmental

pollution

Local school issues

New medical

discoveries

0 20 40 60 80 100
Science and Engineering Indicators 2012 7-13

ranked in the middle among 10 areas considered on the 

2010 GSS survey. Interest in S&T is roughly comparable 

to interest in economic issues and business conditions, and 

military and defense policy. It ranks well ahead of interest 

in agriculture and farming, space exploration, and foreign 

policy; and lags behind interest in new medical discoveries, environmental pollution, and local school issues (figure 7-3). Of course, a more inclusive concept of S&T might 

treat several of the topics on this list, such as space exploration and new medical discoveries, as part of the S&T 

category; furthermore, other topic areas often include substantial S&T content.6

Survey reports about attention to news show a smaller 

percentage of Americans paying close attention to news 

reports about S&T in 2008 relative to earlier years. In the 

2008 Pew Research Center survey on media consumption, 

13% of the respondents reported following S&T news “very 

closely.” S&T news ranked 13th among 18 topics, tied with 

consumer news and ahead of entertainment, culture and the 

arts, celebrity news, and travel (table 7-1). As is the case 

for many other news topics, the percentage of Americans 

who said they follow S&T closely declined between 1996 

and 2008. S&T’s relative standing on the list of topics also 

slipped; it ranked ahead of seven topics in 1996, but ahead 

of only two of the same topics in 2008 (Pew Research 

Center 2008).

International Comparisons 

International surveys often find similar or lower expressed 

interest in S&T, but few ask about interest levels using the 

exact same question wording, making direct comparisons 

difficult. In the 2010 European survey (“Eurobarometer”), 

30% of respondents across all 27 European nations surveyed 

report being “very interested” in new scientific discoveries 

and technological developments, 49% are “moderately interested,” and 20% are “not interested.” Thus, expressed interest in S&T tends to be lower in the European Union (EU) 

than in the United States. The EU’s average self-reported 

interest in S&T-related issues is about the same in 2010 as it 

was 2005,7

 but there is considerable variation among different countries. In both the United States and in Europe, men 

show more interest in S&T than women (EC 2010).8

About half of Chinese respondents (52%) report being 

interested in new scientific discovery; somewhat lower percentages are interested in new discovery and new technology (CRISP 2008).9

 Interest is lower in South Korea, where 

24% of respondents were very interested in new scientific 

discovery (KOFAC 2009).

In other countries, the questions asked are not directly 

comparable to those asked in the United States. Brazilians 

showed a marked increase in interest about S&T in 2010 

compared with 2006, along with a marked increase in interest 

about the environment (MCT of Brazil 2010). In Malaysia, 

interest toward S&T has been fairly stable between 1998 and 

2008, whereas interest in environmental pollution has shown 

a gradual decline (MASTIC 2010). 

Interest in medicine tends to be on a par with interest in 

S&T in Europe and China. Europeans are about equally likely 

to report being very interested in “new medical discoveries” 

as they are in “new scientific discoveries and technological 

development” (EC 2010). The Chinese are equally likely to 

report being interested in “new medical progress” and “new 

scientific discovery” (CRISP 2008). More Brazilians report 

being interested or very interested in medicine and health 

than in S&T (MCT of Brazil 2010); this pattern is consistent 

with U.S. survey data. The same pattern holds in Malaysia 

(MASTIC 2010).

Interest in space exploration has consistently ranked low 

in the United States and around the world, relative to other 

S&T topic areas. Surveys in Russia, China, and Japan have 

documented this general pattern in the past, though no recent 

data are available on this subject. In India, 19% of the public reported being “interested” in space exploration—lower 

than any other topic asked (Shukla 2005). Malaysia recently 

developed a space exploration program and put its own astronauts into space for the first time in 2007. In 2008, half of 

Percent “very interested”

Figure 7-4

Public interest in selected science-related issues: 

1979–2010

NOTES: 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. Figure 

shows only “very interested” responses. Figure includes all years for 

which data collected; other years extrapolated.

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 Chicago, National Opinion Research Center, General 

Social Survey (2008, 2010). See appendix table 7-4.

Science and Engineering Indicators 2012

1979 1985 1988 1992 1997 2001 2010

0

10

20

30

40

50

60

70

80

90

100

New scientific

discoveries

Use of new inventions 

and technologies

New medical discoveries

Space

exploration
7-14 Chapter 7. Science and Technology: Public Attitudes and Understanding

Table 7-1

News followed “very closely” by American public: 1996–2008

(Percent)

Type of news 1996 1998 2000 2002 2004 2006 2008

Weather ................................. NA NA NA NA 53 50 48

Crime ..................................... 41 36 30 30 32 29 28

Education .............................. NA NA NA NA NA NA 23

Community ............................ 35 34 26 31 28 26 22

Environment .......................... NA NA NA NA NA NA 21

Politics/Washington news ..... 16 19 17 21 24 17 21

Local government .................. 24 23 20 22 22 20 20

Health news ........................... 34 34 29 26 26 24 20

Sports .................................... 26 27 27 25 25 23 20

Religion .................................. 17 18 21 19 20 16 17

International affairs ................ 16 16 14 21 24 17 16

Business and finance ............ 13 17 14 15 14 14 16

Consumer news .................... 14 15 12 12 13 12 13

Science and technology ........ 20 22 18 17 16 15 13

Culture and arts ..................... 9 12 10 9 10 9 11

Entertainment ........................ 15 16 15 14 15 12 10

Celebrity news ....................... NA NA NA NA NA NA 7

Travel ..................................... NA NA NA NA NA NA 6

NA = not available, question not asked

NOTES: Data reflect respondents who said they followed type of news “very closely.” Table includes all years for which data collected.

SOURCE: 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–01 June 2008), http://people-press.org/

reports/pdf/444.pdf, accessed 21 September 2009.

Science and Engineering Indicators 2012

Malaysians indicated they were “interested” or “very interested” in space exploration (MASTIC 2010). 

Availability of S&T News in the Media

The sources of information Americans rely on for news 

about S&T are at least partly a function of the availability 

of S&T information from different venues and news media. Recent research on media coverage across a range of 

public policy domains found that the amount and prominence of media coverage is positively associated with public 

awareness of specific policy-related facts (Barabas and Jerit 

2009). Thus, the amount and depth of media coverage of 

S&T could both reflect public interest in the topic and also 

influence the amount of public attention to and awareness of 

developments in S&T. 

How much and what kinds of S&T news coverage 

are available in the media? The Project for Excellence in 

Journalism (PEJ 2010a) has conducted an extensive content analysis of media coverage since 2007 using a broad 

sample of about 50 outlets in the following media sectors: 

print, Internet, network television, cable television, and radio. Each week, stories are classified into 1 of 26 broad topic 

areas, including a category for S&T.10

These data show that S&T make up a small percentage of 

the total amount of news in the traditional media—less than 

2% annually from 2007 to 2010 (table 7-2).11 News coverage 

on the environment makes up a similarly small proportion of 

the news. By comparison, coverage of health and medicine 

makes up a greater proportion of the news but is also more 

variable, ranging from approximately 3% to 9% during the 

4-year period. 

Which stories about S&T are covered by the media? 

Within the S&T news coverage, stories on cyberspace issues are most common—about 27% in 2010 and 18% in 

2009. Other stories compose a much smaller portion of the 

S&T news coverage in the media. In 2010, stories about the 

Table 7-2

Traditional media coverage on science and 

technology, by topic area: 2007–10

(Percent)

Year

Number of 

stories

Science and 

technology

EnvironmentHealth 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

NOTE: Data reflect percentage of news stories in each topic area 

based on content analysis of coverage by media outlets in five 

sectors: print, Internet, network TV, cable TV, and radio. 

SOURCE: Project for Excellence in Journalism, News Coverage 

Index, special tabulations (21 March 2011), http://www.journalism.

org/about_news_index/methodology, accessed 11 February 2011. 

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 7-15

NASA Space Shuttle mission accounted for 8% of the S&T 

news, roughly equal to the proportion of stem cell-related 

news in 2009 (table 7-3). 

Analyses of the content on the three major broadcast networks (ABC, CBS, NBC) tell a similar story. The Tyndall 

Report has tracked the content of the three major broadcast 

networks for more than 20 years; the amount of air time 

on each nightly newscast is classified into 18 categories 

(Tyndall Report 2011a). Two categories with large science, engineering, and technology components are “science, 

space, and technology,” and “biotechnology and basic medical research.”12 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 339 minutes—about 2% of broadcast news—between 

2000 and 2010, but fluctuated from 1% to 5% during this 

period (figure 7-5).13 Time devoted to “biotechnology and 

basic medical research” was considerably lower, accounting for 1% or less of broadcast news (with some variation 

depending on the year).

The leading story on nightly news broadcasts in 2010 was 

the oil spill in the Gulf of Mexico (9% of the year’s news). 

Table 7-3

Leading traditional media story lines on science and technology, by topic area: 2009 and 2010

(Percent of news in each topic area)

Topic area/leading story line 2009 Topic area/leading story line 2010

Science, space, and technology (n = 1,212 stories) Science, space, and technology (n = 796 stories)

Cyberspace issues ...................................................... 17.6 Cyberspace issues .............................................................. 26.7

Stem cell research ....................................................... 8.6 NASA/shuttle missions ........................................................ 8.1

40th anniversary of Apollo space mission ................... 6.4 Apple product and business news ...................................... 8.0

Hubble Telescope ........................................................ 5.2 China .................................................................................... 2.2

Space Shuttle Endeavour ............................................ 4.4 Education ............................................................................. 2.0

Moon bombing by NASA ............................................. 3.2 Google ................................................................................. 1.7

TV switch to digital ...................................................... 2.5 WikiLeaks ............................................................................. 1.5

NASA/shuttle missions ................................................ 2.5 Stem cell research ............................................................... 1.3

Global warming/climate change .................................. 1.9 Terror threats/homeland security ......................................... 1.2

Texting and driving ....................................................... 1.7 Texting and driving ............................................................... 1.0

 

Environment (n = 1,007 stories) Environment (n = 830 stories)

Global warming/climate change .................................. 37.1 BP oil spill in Gulf of Mexico ................................................ 42.2

Pollution/emissions/going green ................................. 18.9 Energy debate ...................................................................... 15.4

Energy debate .............................................................. 13.8 Global warming/climate change .......................................... 11.5

Economy ...................................................................... 3.4 Pollution/emissions/going green ......................................... 7.0

G8 Summit ................................................................... 2.7 China .................................................................................... 1.0

Health and medicine (n = 6,101 stories) Health and medicine (n = 3,271 stories)

Health care reform debate in Congress ....................... 65.2 Health care reform debate in Congress ............................... 62.8

Swine flu outbreak ....................................................... 15.7 Egg recall ............................................................................. 2.3

Government mammogram recommendations ............. 1.5 2010 elections ...................................................................... 1.4

Economy ...................................................................... 0.8 Stem cell research ............................................................... 1.4

Chemotherapy refused by teen cancer patient ............. 0.6 Avandia ................................................................................ 1.3

NOTE: Data reflect story lines with greatest percentage of news in each topic area based on content analysis of coverage by media outlets in five 

sectors: print, Internet, network TV, cable TV, and radio. 

SOURCE: Project for Excellence in Journalism, News Coverage Index, special tabulations (21 March 2011), http://www.journalism.org/about_news_

index/methodology, accessed 11 February 2011. For methodology, see http://www.journalism.org/commentary_backgrounder/new_media_index_

methodology, accessed 11 February 2011.

Science and Engineering Indicators 2012

Although not classified as such, stories on the oil spill often 

included substantial attention to science and engineering issues (Tyndall Report 2011b). The most-covered stories on 

science, space, and technology in 2009 and 2010 focused 

on developments in the nation’s space program and new 

developments in high technology products and tools for 

consumers, such as flat screen tablet computers and social 

networking websites (table 7-4). In the category of “biotechnology and basic medical research,” cancer research garnered the most coverage, as it has done since 2006. 

The media environment is rapidly changing, with new media and social media outlets continuing to proliferate and attract users. The Project for Excellence in Journalism conducts 

a new media content analysis focusing primarily on newsfocused blogs and Twitter posts (PEJ 2010c). The analysis 

tracks the most-linked-to news subjects on a sample of blogs 

in order to capture the priorities of bloggers. The same procedure is used for Twitter posts.14 This provides another indicator of interest in and availability of S&T news. In 2010, S&T 

stories composed 12% of the most-linked-to blog subjects in 

a given week; in 2009, that figure was 17%. On Twitter, S&T 

made up 38% of the most-linked-to subjects in a given week 

in 2010, down from 48% in 2009 (table 7-5). 
7-16 Chapter 7. Science and Technology: Public Attitudes and Understanding

Figure 7-5

Network nightly news coverage of science and 

technology: 1988–2010

Percent of news

NOTES: Data reflect 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 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), 

http://www.tyndallreport.com, accessed 3 February 2011.

Science and Engineering Indicators 2012

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

0

1

2

3

4

5

6

Biotechnology and 

basic biomedical

research

Science, space,

and technology

What kinds of stories go viral on blogs and Twitter posts? 

There is no available quantitative measure of the mostlinked-to science stories. Recent examples of most-linkedto blog stories on science include the discovery of a new 

kind of large rat in Papua New Guinea, news that a chemical 

found in blue M&Ms might have therapeutic qualities, and 

the discovery of a meat-eating plant (PEJ 2010d). Climate 

change and the controversy surrounding e-mails from a 

British researcher on the subject was one of the top five subjects covered by bloggers in December 2009 and made up a 

third of the weekly blog links 3 months later, shortly after a 

BBC interview on the subject (PEJ 2010b).

Involvement

Involvement with S&T outside the classroom in informal, 

voluntary, and self-directed settings—such as museums, science centers, zoos, and aquariums—is another indicator of 

the public’s interest in S&T.15 By offering visitors the flexibility to pursue individual curiosity, such institutions provide a kind of exposure to S&T that is well-suited to helping 

people develop further interest.16

In the 2008 GSS, 61% of Americans indicated that they 

had visited an informal science venue during the previous 

year (appendix table 7-6).17 About half (52%) said they had 

visited a zoo or aquarium, and more than one-quarter had 

visited a natural history museum (28%) or an S&T museum 

(27%). One in three Americans had visited an art museum 

and 64% had visited a public library. These data are generally consistent with data collected by the Pew Internet and 

Table 7-4

Leading nightly news story lines on science and technology, by topic area: 2009 and 2010

(Annual minutes of coverage)

Topic area/leading story line 2009 Topic area/leading story line 2010

Science, space, and technology Science, space, and technology

NASA Hubble Space Telescope repairs ........................... 40 Internet used for social networking: Facebook ................ 34

Moon astronomy: NASA searches for evidence of water .... 20 Computer flatscreen table technology: iPad .................... 20

NASA anniversary of Apollo manned moon missions ........ 19 Cellular telephone/computer combination: iPhone .......... 18

NASA Space Shuttle program .......................................... 17 NASA manned space flights to be discontinued .............. 18

Computer networks targeted by coordinated hackers ..... 15 UFO speculation fascinates skywatchers ......................... 13

Internet used for social networking: Facebook ................ 12 Internet classified ads posted online: Craigslist ............... 11

Apple Computer CEO Steve Jobs returns to work ........... 12 High-technology multitasking is distracting ..................... 9

Internet online commerce volume increases .................... 12 Office copier machines have hard drive memories .......... 9

NASA plans renewed manned missions to moon ............ 11 Videostreams shared online in viral networks: YouTube ..... 8

Videostreams shared online in viral networks: YouTube ..... 6 China censors Internet access, e-mail traffic ................... 8

Biotechnology/basic medical research Biotechnology/basic medical research

War on cancer/research efforts ........................................ 37 War on cancer/research efforts ........................................ 48

Human embryo stem cell biotechnology research ........... 23 Human embryo stem cell biotechnology research ........... 14

Genetic DNA biotech analysis predicts disease ............... 11

Salmon genetically modified to accelerate growth ........... 7

Spinal cord injuries and paralysis research ...................... 5

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. Shown are the story lines receiving at least 5 minutes of coverage in 2009 and 2010. Excluded from science, 

space, and technology are stories on forensic science and media content. Excluded from biotechnology/basic medical research are stories on clinical 

research and medical technology. 

SOURCE: Tyndall Report, special tabulations (2 March 2011), http://www.tyndallreport.com, accessed 3 February 2011.

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 7-17

Table 7-5

“Most-linked-to” subjects in the new media, by topic area: 2009 and 2010

(Percent)

Topic area

Blogs 2009 

(n = 235)

Blogs 2010 

(n = 256)

Twittera 2009 

(n = 132)

Twittera 2010

(n = 255)

Science, space, and technology ......... 17 12 48 38

Environment ..................................... 4 4 4 2

Health and medicine ........................ 8 6 6 1

n = number of subjects coded

a Twitter content analysis for 2009 based on 6 months starting June 15; analysis for 2010 based on 12 months.

NOTE: Data reflect percentage of “most-linked-to” subjects in a given week, based on content analysis of news-focused blogs and social media sites. 

SOURCE: Project for Excellence in Journalism, New Media Index, special tabulations (17 February 2011), http://www.journalism.org/commentary_

backgrounder/new_media_index_methodology, accessed 11 February 2011. 

Science and Engineering Indicators 2012

American Life Project and the Institute for Museum and 

Library Services. (For more detail on these surveys, see NSB 

2008.) Among those who visited each of these institutions, 

the number of annual visits was highest for public libraries, 

which averaged about 15 visits per year. 

The proportion of respondents who reported visiting either a zoo or aquarium, an S&T museum, and a public library is down slightly from the last time these questions were 

asked in 2001.18 Respondents in households with children 18 

or younger were more likely to visit a zoo or aquarium, a 

public library, and also a natural history museum. Minors in 

the household did not make a difference in the proportion of 

adults who visited an art museum or an S&T museum (appendix table 7-7). 

Americans with more years of formal education are more 

likely than others to engage in these informal science activities (figure 7-6; appendix table 7-7). 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 65 

or older. 

In addition, respondents who get most of their information about S&T from the Internet or use this medium to learn 

about scientific issues are more likely to have visited any 

informal science institution, even after controlling for expressed interest in scientific issues. This suggests that users 

experience these different sources of science information as 

complementing, rather than replacing, one another. 

International Comparisons

Compared with the United States, visits to S&T museums 

are less common in China, Japan, South Korea, Malaysia, 

India, Europe, and Brazil (table 7-6). The proportion of 

respondents who indicate they have visited a zoo is similar in the United States, China, and Japan. Visiting a zoo 

is more common in the United States19 than it is in South 

Korea, India, Malaysia, Europe, and Brazil. Unmeasured 

Figure 7-6

Attendance at informal science and other cultural 

institutions, by institution type and education 

level: 2008 

NOTES: Responses to 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 indicates 

respondents who had attended the noted institution at least once.

SOURCE: University of Chicago, National Opinion Research Center, 

General Social Survey (2008). See appendix tables 7-6 and 7-7. 

Science and Engineering Indicators 2012

Percent

80706050403020100 90 100

Science or

technology

 museum

Natural

history

museum

Art museum

Zoo or

 aquarium

Public

library

High school

graduate

<High school

Some college

Bachelor’s

degree

Graduate/

professional

degree
7-18 Chapter 7. Science and Technology: Public Attitudes and Understanding

Table 7-6

Visits to informal science and other cultural institutions, by country/region: Most recent year

(Percent)

Institution

United 

States 

(2008)

China 

(2007)

Japan 

(2001)

South 

Korea 

(2008)

India

(2004)

Malaysia 

(2008)

EU 

(2005)

Brazil

(2010)

Zoo/aquariuma

 ................................ 52 52 43 36 35 30 27 22

Natural history museum ................. 28 14 19 NA NA NA NA NA

Science/technology museumb ........ 27 17 12 11 12 11 16 8

Public libraryc .................................. 64 41 46 34 27 NA 34 29

Art museumd ................................... 34 18 34 34 22 30 23 14

NA = not available, question not asked

EU = European Union; data not available for Bulgaria and Romania

a “Zoo, aquarium, botanic garden” for China; “Zoo” for India, Malaysia, Brazil. 

b “Science museum” for South Korea; “Science parks” for India; “National Science Centre” for Malaysia; “Science museums or technology museums or 

science centers” for EU.

c “Library” for India, Brazil.

d “Art museum or exhibition hall” for China; “Museum/art gallery” for South Korea; “Museum” for India, Malaysia.

NOTES: Responses to (United States, Japan, South Korea) 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); (China, EU, 

Brazil) Which of the following have you visited in the last 12 months? (multiple answers possible); (Malaysia) In the past year, how many times did you visit 

the following places? (percentage includes those who visited each institution one or more times); (India) How frequently did you visit the following during 

the last 12 months? (percentage includes those who visited each institution one or more times).

SOURCES: United States–University of Chicago, National Opinion Research Center, General Social Survey (2008); China–Chinese Association for 

Science and Technology/China Research Institute for Science Popularization, Chinese National Survey of Public Scientific Literacy (2007); 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); South Korea–Korea Foundation for the Advancement of Science and Creativity (formerly 

Korea Science Foundation), Survey of Public Attitudes Toward and Understanding of Science and Technology (2008); India–National Council of Applied 

Economic Research, National Science Survey (2004); 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); EU–European Commission, Eurobarometer 224/Wave 

63.1: Europeans, Science and Technology (2005); Brazil–Ministry of Science and Technology of Brazil, Public Perceptions of Science and Technology 

(2010). See appendix table 7-6 for U.S. trends.

Science and Engineering Indicators 2012

differences in the prevalence and accessibility of informal 

science learning opportunities across countries prohibit attributing different visit patterns to differences in interest. 

Public Knowledge About S&T

Knowledge and understanding of S&T can be relevant 

to public policy and the personal choices that people make. 

In developing measures for what is often termed scientific 

literacy across nations, the Organisation for Economic Cooperation 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 help make decisions about the natural 

world and the changes made to it through human 

activity. (pp. 132–33)

A good 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 scientific 

literacy. U.S. survey data indicate 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. However, 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.
Science and Engineering Indicators 2012 7-19

Figure 7-7

Mean number of correct answers to trend factual 

knowledge of science scale: 1992–2010

Mean

NOTES: Mean number of correct answers to nine questions included 

in 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. Table includes all years for which data were 

collected.

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, 2008, 2010). 

Science and Engineering Indicators 2012

1992 1995 1997 1999 2001 2006 2008 2010

0

1

2

3

4

5

6

7

8

9

Understanding Scientific Terms and Concepts

U.S. Patterns and Trends

One common indicator of public understanding about 

science comes from an index of factual science knowledge 

questions covering a range of science disciplines. Responses 

to nine questions are used in a combined scale as an indicator of general knowledge about S&T. In 2010, Americans, 

on average, were able to correctly answer 5.6 out of the 

9 items, for an average percent correct of 63%.

The public’s level of factual knowledge about science 

has not changed much over the past two decades (figure 

7-7). Since 2001, the average number of correct answers 

to a series of mostly true-false science questions in years 

for which fully comparable data were collected has ranged 

from 5.6 correct responses to 5.8 correct responses, although 

knowledge on individual questions has varied somewhat 

over time (appendix tables 7-8 and 7-9).20 (Also see sidebar, 

“Measuring Factual Science Knowledge Over Time.”)

Some individuals know more about science than others, 

of course. Factual knowledge of science is strongly related 

to people’s level of formal schooling and the number of 

science and mathematics courses completed. Among those 

who have no more than a high school education, 49% of the 

questions were answered correctly, on average. Individuals 

who had attended college answered more items correctly; 

the average percent correct rose to 81% among those who 

had taken three or more science and mathematics courses in 

college (figure 7-8; appendix table 7-8).

Respondents age 65 and 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. An analysis 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 2009, 2011). (Also 

see Bauer 2009.) 

Factual knowledge about science is also associated with 

sex. Men tend to answer more factual science knowledge 

questions correctly than do women. However, this pattern depends on the science domain referenced in the question. In the 

factual questions included in NSF surveys since 1979, men 

Figure 7-8

Correct answers to trend factual knowledge of 

science scale, by respondent characteristic: 2010 

NOTES: Data reflect average percentage of nine questions answered 

correctly. “Don’t know” responses and refusals to respond 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 (2010). 

Science and Engineering Indicators 2012

Average percent of questions answered correctly

3 or more college

science/math courses

1–2 college science/

math courses

No college science/

math courses

No college

Graduate/professional

degree

Bachelor’s degree

Some college

High school

<High school

Female

Male

All respondents

1009080706050403020100
7-20 Chapter 7. Science and Technology: Public Attitudes and Understanding

How to measure factual knowledge about science 

over time is a difficult puzzle, in part because the generally accepted principles and facts of scientific fields are 

constantly in flux. The items in the factual knowledge 

index were first developed in the 1970s and aimed to tap 

a selection of science facts that would likely withstand 

the “test of time” (Miller 1998, 2011). The index aims 

to measure the extent to which the public has a clear 

understanding of the factual aspects of major scientific 

fields in the biological and physical sciences. The proportion of the public that provides the correct answer 

on any one question is less important than the pattern of 

responses across the set of questions used in the factual 

knowledge index. 

As science changes and public knowledge about science changes, the exact questions that best distinguish 

individuals who tend to know more about science from 

those who tend to know less are likely to vary over time. 

As a result, periodic review of indicators such as these is 

warranted. A number of studies and analyses have been 

commissioned by NSF for this purpose over the years. 

NSF is in the process of undertaking further review and 

experimentation with the factual knowledge questions.

Two items used in past versions of the index have 

received considerable scrutiny; one concerned the “big 

bang” and the other concerned evolution. In the 2010 

GSS, 45% of Americans answered “true” that “the universe began with a huge explosion.” There was some 

concern that the wording of this question erred too heavily on the side of using easily comprehensible language 

at the cost of scientific precision. This may prompt some 

highly knowledgeable respondents to think that the item 

blurs or neglects important distinctions, and in a few 

cases may lead respondents to answer the question incorrectly. The other item of some concern was “human 

beings, as we know them today, developed from earlier 

species of animals.” In the 2010 GSS, half of Americans 

answered “true” to the question about evolution. As discussed elsewhere in the chapter, evidence from a 2004 

survey-based experiment suggests that responses to 

these items reflect more than familiarity with the concepts. (Also see NSB 2008.)

As measures of science knowledge, these questions 

correlate with the overall index, but the correlations for 

other items are generally stronger. A statistical review 

conducted by the Research Triangle Institute on behalf 

of NSF in 2004 found that all the knowledge questions, 

including the evolution and “big bang” questions, reflect a single underlying dimension of factual knowledge (Bann and Schwerin 2004). Later analyses have 

replicated this finding over time. Thus, the social science foundation for using either 11 items or 9 items together in one scale is well-supported. 

This chapter relies on the 9-item factual knowledge 

scale for analysis of trends in knowledge over time. 

Responses to the 9-item factual knowledge scale and an 

11-item factual knowledge scale that includes responses 

to the questions on evolution and the “big bang” are 

highly correlated with each other. Whether or not these 

two questions are included in a scale of factual science 

knowledge has little bearing on the summary portrait of 

Americans’ knowledge that the scale conveys. In addition, knowledge differences between population groups 

(e.g., men and women) are similar (appendix table 7-10). 

Table 7-B shows that, on average, respondents in the top 

quartile on the trend factual knowledge scale answered 

87% of the questions on the 11-item version of the scale 

correctly and 59% of the two additional items correctly. 

Those in the lower quartiles on the trend factual knowledge scale answered fewer items correctly. 

Table 7-B

Correct responses on trend factual knowledge of science scale by longer factual knowledge scale: 2010

(Average percent correct)

Respondent scorea

Trend factual knowledge 

of science scale, 9 items

11-item

scaleb

2-item

scalec

Top quartile ............................................ 94 87 59

2nd quartile ............................................ 73 68 47

3rd quartile ............................................. 51 48 37

Bottom quartile ...................................... 25 24 22

a

Quartile based on correct answers to trend factual knowledge of science scale, 9 items.

b11-item scale that includes the same 9 items plus responses to 2 additional items.

c

2-item scale consisting of responses to the evolution and “big bang” questions.

NOTES: Data reflect average percentage of questions in index answered correctly. “Don’t know” responses and refusals to respond counted as incorrect.

SOURCE: University of Chicago, National Opinion Research Center, General Social Survey (2010).

Science and Engineering Indicators 2012

Measuring Factual Science Knowledge Over Time
Science and Engineering Indicators 2012 7-21

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).

Comparisons of Adult and K–12 Student 

Knowledge

The factual knowledge questions that have been repeatedly asked in U.S. surveys involve information that was 

being taught in grades K–12 when most respondents were 

young. Because science continually generates new knowledge that reshapes how people understand the world, scientific literacy requires lifelong learning so that citizens 

become familiar with terms, concepts, and facts that emerge 

after they complete their schooling. 

The 2008 GSS included several different kinds of factual 

science knowledge questions; seven of those questions can 

be directly compared with national student assessments of 

science knowledge. Adult Americans received a higher or 

similar score to fourth and eighth grade students in five of 

the seven factual science knowledge questions where comparisons scores were possible (table 7-8). 

Comparisons should be made cautiously because of the 

differences in circumstances in which students and adults 

responded to these science knowledge questions. Students’ 

tests were self-administered on paper, whereas the majority of respondents in the GSS answered orally to questions 

asked by an interviewer. Also, elementary and middle 

school students had an advantage over adults in that classroom preparation preceded their tests. (For more details, see 

NSB 2010.) 

Knowledge About Nanotechnology and the 

Polar Regions

New developments in S&T are always on the horizon. 

Indicators of factual science knowledge need to probe 

knowledge and understanding about newly emerging science topics, as well as more established topics. Recent GSS 

surveys included indicators of public understanding for one 

such emerging area––nanotechnology. 

A small minority report having heard “a lot” about nanotechnology; 31% of Americans correctly indicate that “nanotechnology involves manipulating extremely small units of 

matter, such as individual atoms, in order to produce better materials” is true.21 About two in ten (18%) Americans 

correctly indicate that “the properties of nanoscale materials often differ fundamentally and unexpectedly from the 

properties of the same materials at larger scales.” (Also see 

“Public Attitudes About Specific S&T-Related Issues.”) 

Table 7-7

Correct answers to factual knowledge and process questions in physical and biological sciences, by sex: 

1999–2010

(Average percent correct)

Science topic/sex 1999 2001 2004 2006 2008 2010

Physical science indexa

Male ............................................................................. 72 73 73 74 74 73

Female ......................................................................... 57 59 55 59 61 60

Biological science indexb

Male ............................................................................. 59 61 62 63 60 62

Female ......................................................................... 62 65 65 66 64 65

a

Physical science index includes five questions:

UÊThe center of the Earth is very hot. (True)

UÊAll radioactivity is man-made. (False)

UÊLasers work by focusing sound waves. (False)

UÊElectrons are smaller than atoms. (True)

UÊÊThe continents have been moving their location for millions of years and will continue to move. (True) bBiological science index includes six questions (questions 3 and 4 have two parts):

UÊÊIt is the father’s gene that decides whether the baby is a boy or a girl. (True)

UÊAntibiotics kill viruses as well as bacteria. (False)

UÊÊA 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.

UÊÊTwo 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 average percentage of questions in index answered correctly. “Don’t know” responses and refusals to respond 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); and University of Chicago, National Opinion 

Research Center, General Social Survey (2006, 2008, 2010). See appendix tables 7-9 and 7-10 for factual knowledge questions. See appendix tables 

7-13 and 7-14 for scientific process questions (probability and experiment). 

Science and Engineering Indicators 2012
7-22 Chapter 7. Science and Technology: Public Attitudes and Understanding

Table 7-8

Comparison of correct answers given by adults and students to factual knowledge questions: Most recent year

(Percent)

Adult Student

Question Field of study Concepts measured

United 

States

United 

States International

Question 

source

1. A farmer thinks that the vegetables 

on her farm are not getting enough 

water. Her son suggests that they use 

water from the nearby ocean to water 

the vegetables. Is this a good idea?

Earth and space 

sciences

Water cycle; nature of the 

oceans and their effects on 

water and climate; location 

of water, its distribution, 

characteristics, and its 

effect and influence on 

human activity

84 61 NA NAEP 2005, 

grade 4

2. Traits are transferred from generation 

to generation through the...

Life sciences Reproduction and heredity 79 86 74 TIMSS 2003, 

grade 8

3. How do most fish get the oxygen 

they need to survive? 

Life sciences Change and evolution; 

adaptation and natural 

selection

75 78 NA NAEP 2005, 

grade 8

4. What property of water is most 

important for living organisms?

Physical 

sciences

Matter and its 

transformations

68 76 NA NAEP 2000, 

grade 8

5. Which one of the following is NOT an 

example of erosion?

Earth and space 

sciences

Composition of the Earth; 

forces that alter the Earth’s 

surface; rocks: their 

formation, characteristics, 

and uses; soil: its changes 

and uses; natural resources 

used by humankind; and 

forces within the Earth

54 37 NA NAEP 2005, 

grade 8

6. Lightning and thunder happen at the 

same time, but you see the lightning 

before you hear the thunder. Explain 

why this is so.

Physical 

sciences

Frames of reference; force 

and changes in position and 

motion; action and reaction; 

vibrations and waves as 

motion; electromagnetic 

radiation and interactions 

of electomagnetic radiation 

with matter

44 36 NA NAEP 2005, 

grade 8

7. A solution of hydrochloric acid (HCl) 

in water will turn blue litmus paper 

red. A solution of the base sodium 

hydroxide (NaOH) in water will turn 

red litmus paper blue. If the acid and 

base solutions are mixed in the right 

proportion, the resulting solution will 

cause neither red nor blue litmus 

paper to change color. Explain why 

the litmus paper does not change 

color in the mixed solution.

Chemistry Acids and bases 20 17 21 TIMSS 2003, 

grade 8

NA = not available, question not asked

NAEP = National Assessment of Educational Progress; TIMSS = Trends in International Mathematics and Science Study

NOTES: Questions appeared in 2008 General Social Survey; see appendix table 7-17 for complete questions. Original sources of questions are NAEP and 

TIMSS. 

SOURCES: University of Chicago, National Opinion Research Center, General Social Survey (2008), see appendix table 7-18; NAEP, http://nces.ed.gov/

nationsreportcard/itmrls/startsearch.asp, accessed 22 September 2009; TIMSS, http://nces.ed.gov/timss/results03.asp, accessed 22 September 2009. 

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 7-23

Those who scored higher on the general factual knowledge scale were also more likely to answer the two questions 

about nanotechnology correctly (figure 7-9).22 Likewise, the 

educational and demographic characteristics associated with 

higher scores on the trend factual knowledge questions are 

also associated with higher knowledge of nanotechnology 

(appendix table 7-11). These data suggest that the trend factual knowledge scale, although focused on the kind of scientific facts and principles learned in school, is a reasonable 

indicator of factual science knowledge in general, including 

knowledge on newly emerging topics acquired later in life. 

The 2006 and 2010 GSSs included a series of knowledge 

questions about the polar regions. Knowledge about the polar 

regions was measured using a 4-item scale of true-false questions. In 2010, Americans answered 60% of the four items 

correctly, on average, up from 55% in 2006. Increased knowledge about the polar region was indicated especially by two 

of the four questions: “The North Pole is on a sheet of ice 

that floats on the Arctic Ocean” (from 41% in 2006 to 48% 

in 2010), and “Hunting is more likely than climate change to 

make polar bears become extinct” (from 36% in 2006 to 44% 

in 2010) (appendix table 7-12). It is possible that this increase 

in knowledge stems, in part, from increased attention to the 

polar regions during the 2007–2008 International Polar Year. 

However, there may be other reasons for the change including increased public attention to global climate change and its 

implications for the polar regions. 

International Comparisons on Factual 

Knowledge Questions

Adults in different countries and regions have been 

asked identical or substantially similar questions to test their 

factual knowledge of science. 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 reported in table 7-9, 

knowledge scores are relatively low in China, Russia, and 

Malaysia. Compared to the United States and the EU, scores 

in Japan are also relatively low.23

Science knowledge scores vary considerably across 

Europe, with northern European countries, led by Sweden, 

scoring the highest on a set of 13 questions. For a smaller 

set of 4 questions that were administered in 12 European 

countries in 1992 and 2005, each country performed better 

in 2005. In contrast, U.S. data on science knowledge do 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. 

Reasoning and Understanding the 

Scientific Process

Another indicator of public understanding of science focuses on understanding of how S&T generates and assesses scientific evidence, rather than knowledge of particular 

facts. Past NSF surveys have used questions on three general 

topics—probability, experimental design, and the scientific 

method—to assess 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 one in four chance of suffering from an inherited 

disease.24 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 (table 

7-10; appendix table 7-13).

In 2010, two-thirds of Americans correctly responded 

to two questions about probability of a child’s genetic inheritance of illness. Understanding of probability has been 

fairly stable over time, with the percentage giving a correct 

response ranging from 64% to 69% since 1999. About half 

(51%) of Americans correctly identified the concept of using 

an experimental design or control group in the context of a 

medical study in 2010. This represents a marked increase in 

Figure 7-9

Mean number of correct answers to nanotechnology 

questions, by correct answers to trend factual 

knowledge of science scale: 2010

Mean

NOTES: Mean number of correct responses to two factual questions 

on nanotechnology. Respondents saying they had heard “nothing at 

all” about nanotechnology were not asked questions; these respondents count as zero (0) correct. See appendix table 7-11 for responses to nanotechnology questions. Trend factual knowledge of 

science scale includes nine questions; see appendix table 7-8 for 

explanation and list of questions.

SOURCE: University of Chicago, National Opinion Research Center, 

General Social Survey (2010). 

Science and Engineering Indicators 2012

Quartile based on correct answers to

trend factual knowledge of science scale

Top quartile 2nd quartile 3rd quartile Bottom quartile

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0
7-24 Chapter 7. Science and Technology: Public Attitudes and Understanding

Table 7-9

Correct answers to factual knowledge questions in physical and biological sciences, by country/region: 

Most recent year

(Percent giving correct answer)

Question

United 

Statesa

(2010)

(n = 1,932)

South

Korea

(2004)

(n = 1,000)

EU

(2005)

(n =16,029)

Japan

(2001)

(n = 2,146)

Malaysia

(2008)

(n = 18,447)

India

(2004)

(n = 30,255)

China

(2007)

(n = 10,059)

Russia

(2003)

(n = 2,107)

Physical science

The center of the Earth is very hot. 

(True) ................................................. 84 87 86 77 66 57 49 NA

The continents have been moving their 

location for millions of years and will 

continue to move. (True) ................... 80 87 87 83 44 32 44 40

Does the Earth go around the Sun, or 

does the Sun go around the Earth? 

(Earth around Sun) ........................... 73 86 66 NA 72 70 78 NA

All radioactivity is man-made. (False)... 67 48 59 56 14 NA 40 35

Electrons are smaller than atoms. 

(True) ................................................. 51 46 46 30 33 30 22 44

Lasers work by focusing sound waves. 

(False) ............................................... 47 31 47 28 16 NA 20 24

The universe began with a huge 

explosion. (True) ............................... 38 67 NA 63 NA 34 22 35

Biological science

The cloning of living things produces 

genetically identical copies. (True) .... 80 NA 68 NA 53 NA NA NA

It is the father’s gene that decides 

whether the baby is a boy or a girl.b

(True) ................................................. 61 59 64 25 40 38 55 22

Ordinary tomatoes do not contain 

genes, while genetically modified 

tomatoes do.

c

 (False) ........................ 47 NA 41 NA NA NA NA 22

Antibiotics kill viruses as well as 

bacteria. (False) ................................ 50 30 46 23 8 39 21 18

Human beings, as we know them 

today, developed from earlier 

species of animals. (True) ................. 47 64 70 78 NA 56 69 44

NA = not available, question not asked

EU = European Union; data not available for Bulgaria and Romania

a

See appendix table 7-9 for U.S. trends.

bChina and Europe surveys asked about “mother’s gene” instead of “father’s gene.”

c

Russia survey asked about “ordinary plants” instead of “ordinary tomatoes.”

SOURCES: United States–University of Chicago, National Opinion Research Center, General Social Survey (2010); 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); 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); 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 Centre/Ministry of Science, Technology and Innovation, Survey of the Public’s Awareness 

of Science and Technology: Malaysia (2008); India–National Council of Applied Economic Research, National Science Survey (2004); China–Chinese 

Association for Science and Technology/China Research Institute for Science Popularization, Chinese National Survey of Public Scientific Literacy (2007); 

Russia–Gokhberg L and 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).

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 7-25

understanding from 38% in 2008 (table 7-10; appendix table 

7-13).25 Understanding of what it means to study something 

scientifically is considerably lower, at 18% in 2010. Correct 

responses on this question are lower, in part, because the 

task of expressing a concept in one’s own words is more 

difficult than recognizing a correct response to a multiplechoice style closed-ended survey question. Correct responses on these questions have ranged from a low of 18% in 

2010 to a high of 26% in 2001. 

Taken together, 42% of Americans exhibit an understanding of scientific inquiry in 2010, up from 36% in 2008.26 As 

was found for factual science knowledge, public understanding of scientific inquiry is strongly associated with people’s 

level of formal schooling and the number of science and 

mathematics courses completed. Among those who have no 

more than a high school education, 23% are able to provide a 

correct response on the measure of understanding scientific 

inquiry. Understanding of scientific inquiry is somewhat 

higher among college attendees who did not take collegelevel science or mathematics courses. However, it is notably higher (71% correct) among individuals who completed 

at least three science and mathematics courses in college 

(figure 7-10; appendix table 7-14).

Americans age 65 and older score lower than younger 

adults on the scientific process measures. The differences 

are greatest on understanding of an experimental or control 

group design and on the open-ended questions about the 

meaning of scientific study. These differences may be related to the lower levels of formal education among older generations in the United States. The same pattern was found for 

factual science knowledge. 

Unlike the patterns found on factual knowledge, particularly on facts related to the physical sciences, men and 

women obtain similar scores on understanding of scientific 

inquiry (figure 7-10; appendix table 7-14).

Comparisons of Adult and K-12 Student 

Understanding

The 2008 GSS included several additional questions on 

the scientific process that provide an opportunity to examine 

Americans’ understanding of experimental design in more 

detail. From 29% to 57% of Americans responded correctly 

to questions measuring the concepts of scientific experiment 

and controlling variables, only 12% responded correctly to 

all the questions on this topic, and nearly 20% of Americans 

did not respond correctly to any of them (appendix table 

7-15). These data raise questions about how well Americans 

can reliably apply a generalized understanding of experimental design across different situations.

Table 7-10

Correct answers to scientific process questions: Selected years, 1999–2010

(Percent)

Question

1999

(n = 1,882)

2001

(n = 1,574)

2004

(n = 2,025)

2006

(n = 1,864)

2008

(n = 2,021)

2010

(n = 1,454)

Understanding of scientific inquiry scalea

 ........................... 32 40 39 41 36 42

Components of understanding scientific inquiry scale

Understanding of probabilityb .......................................... 64 67 64 69 64 66

Understanding of experimentc

 ......................................... 34 40 46 42 38 51

Understanding of scientific studyd ................................... 21 26 23 25 23 18

a To be classified as understanding scientific inquiry, survey respondent had to (1) answer correctly the two probability questions stated in footnote b and 

(2) either provide “theory-testing” response to open-ended question about what it means to study something scientifically (see footnote d) or correct 

response to 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, 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, 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, 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 percentage giving a correct response to each concept. “Don’t know” responses and refusals to respond counted as incorrect and 

not shown. See appendix table 7-13 for more detail on probability questions and for years prior to 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); and University of Chicago, National Opinion 

Research Center, General Social Survey (2006, 2008, 2010). 

Science and Engineering Indicators 2012
7-26 Chapter 7. Science and Technology: Public Attitudes and Understanding

These questions allow a comparison between adults’ understanding of experimentation and that of middle school 

students tested on the exact same questions. Out of the three 

experimental knowledge questions where direct comparison 

is possible, adults’ scores are similar to a national sample of 

middle school students on one question, but lower on two 

others (appendix table 7-16). 

Other Indicators of Public Knowledge and 

Understanding About S&T

The trend factual knowledge and process understanding 

questions are both indicators used to gauge public knowledge and understanding about S&T over time. These are but 

two of the potential indicators that might be useful, however (Miller 1998). A handful of other approaches have been 

used in recent years. These are reviewed briefly below. One 

provides an alternative measure of factual public knowledge 

about science that is rooted in national standards for what students are expected to know about science. Other approaches 

include indicators of understanding about statistics and the 

interpretation of charts, as well as indicators of the ability to 

distinguish between science and pseudoscience. Taken together, these approaches provide a more complete portrait 

of public understanding about S&T. Other approaches are 

currently being developed that seek to add indicators of the 

understanding of science as it applies to everyday life and 

measure public understanding of institutions and how they 

influence the development of S&T. (See sidebar, “Public 

Understanding of Science and Its Role in Everyday Life.”)

National Standards and Applying Science 

Knowledge to Specific Problems

Recently devised measures developed in light of national 

standards for what students should know about scientific topics provide additional information about public knowledge 

and understanding. These standards go beyond the factual 

knowledge questions that have been used to measure trends 

in public knowledge of science on NSF surveys since 1979 

and often include the ability to apply science knowledge to 

specific problems. Questions of this kind were administered 

as part of the 2008 GSS and were reported in NSB 2010. The 

2008 GSS questions were selected from Project 2061, an initiative by the American Association for the Advancement of 

Science (AAAS) that develops assessment materials aligned 

with current curricular standards, and from three national 

exams administered to students.27 The series of questions included nine factual questions, two questions that measured 

chart reading and the statistical concept of a “mean,” and 

five questions that tested reasoning and understanding of the 

scientific process. Two of the 16 questions were open-ended 

and the rest were multiple-choice. (For details on the measures, see appendix table 7-17.28)

Respondents who answered these additional factual 

knowledge questions correctly (on the “scale 2” index reflecting national standards) also tended to answer the trend 

factual knowledge questions correctly. This suggests that the 

trend factual knowledge questions are a reasonable indicator 

of the type of knowledge students are tested on in national 

assessments (appendix table 7-18).

Understanding of Statistics and Charts

Americans encounter basic statistics and charts in everyday life. Many media reports cite studies in health, social, 

economic, and political trends. Understanding statistical 

concepts is important to understanding the meaning of these 

studies and, consequently, to scientific literacy (Crettaz von 

Roten 2006). One test of these concepts included on the 

2008 GSS found that 74% of Americans could read a simple 

chart correctly and 66% understood the concept of “mean” 

in statistics. Understanding these two concepts was associated with both formal education and the number of math and 

science courses taken. Older respondents were less likely 

than younger adults to respond correctly to these two questions. Men and women were about equally likely to answer 

these questions correctly (appendix table 7-15). 

Figure 7-10

Understanding scientific inquiry, by respondent 

characteristic: 2010 

NOTES: See appendix table 7-13 for explanation of understanding 

scientific inquiry and questions included in the index. See appendix 

table 7-14 for additional respondent characteristics.

SOURCE: University of Chicago, National Opinion Research Center, 

General Social Survey (2010). 

Science and Engineering Indicators 2012

Percent understanding scientific inquiry 

3 or more college

science/math courses

1–2 college science/

math courses

No college science/

math courses

No college

Graduate/professional

degree

Bachelor’s degree

Some college

High school

<High school

Female

Male

All respondents

1009080706050403020100
Science and Engineering Indicators 2012 7-27

Pseudoscience

Another indicator of public understanding about S&T 

comes from measuring the public’s capacity to distinguish 

science from pseudoscience. One such indicator, on astrology, is available over time on the NSF surveys conducted 

since 1979. Recent surveys show a downward trend toward 

fewer Americans considering astrology as scientific. In the 

2010 GSS, 62% of Americans indicated that they believe 

that astrology is “not at all scientific,” 28% said that it is 

“sort of scientific,” and just 6% considered it “very scientific.” Respondents with more years of formal education 

were less likely to perceive astrology to be at all scientific. 

In 2010, 78% of college graduates indicated that astrology 

is “not at all scientific,” compared with 58% of high school 

graduates. Those who scored highest on the factual knowledge measures were less likely to perceive astrology to be 

at all scientific (79%) than those who scored lowest (52%). 

Respondents who correctly understood the concept of scientific inquiry were more likely to say that astrology is “not 

at all scientific” (73%) than those who did not understand 

the concept (54%). However, the youngest age group (18–

24) was less likely to say astrology is “not at all scientific” 

(46%) and more likely to say it is “very” or “sort of scientific” (54%) (appendix table 7-19).29 

Public Attitudes About S&T in General

Public support for S&T can make a difference in many 

ways. Public openness to technological change can give 

U.S. businesses opportunities to build a domestic customer 

base, create a foundation for worldwide technological competitiveness, and foster the national advantages that flow 

from pioneering innovations. Broad public and political 

support for long-term commitments to S&T research, especially in the face of pressing immediate needs, facilitates 

ambitious proposals for sustained federal S&T investments 

to reach fruition. Public confidence that S&E community 

leaders are trustworthy, S&T research findings are reliable, 

and S&E experts bring valuable judgment and knowledge 

to bear on public issues encourages reliance on scientific 

knowledge in practical affairs. In addition, positive public 

perceptions of S&E occupations encourage young people to 

pursue S&E careers. 

This section presents general indicators of public attitudes and orientations toward S&T in the United States and 

other countries. It covers views of the promise of S&T and 

reservations about science, overall support for government 

funding of research, confidence in scientific community 

leaders, perceptions of the proper influence of scientists on 

Indicators of public understanding about S&T can 

serve many purposes. NSF held two workshops in fall 

2010 with social science experts from multiple disciplines and backgrounds to review how best to conceptualize and measure public understanding of science and 

engineering (Guterbock et al. 2010; Toumey et al. 2010). 

The workshop participants endorsed the past measures reported by NSF as useful indicators of public understanding and suggested approaches for developing additional 

or improved indicators. The workshop participants also 

endorsed the need to monitor and evaluate all indicators 

on an ongoing basis so that adjustments to the indicators 

can be implemented when needed. 

The NSF-sponsored workshops identified three key 

functions of public knowledge about S&T. First, knowledge facilitates civic engagement with science, particularly when technologies raise emerging issues that intersect 

science and society. Examples of these kinds of situations 

include public debates at the local, state, or national levels about nuclear power and nuclear waste disposal, and 

debates about the role and funding of embryonic stem cell 

research. Second, knowledge facilitates decisionmaking 

in everyday life, particularly when S&T intersects with 

citizens’ work, home, and leisure activities. Some examples include knowledge about antibiotic medications 

and their appropriate usage, and the principles of heat and 

electricity as they relate to home use. A third function of 

science knowledge is broadly framed as knowledge for 

the sake of knowing more about the world and how it 

works, addressing human curiosity in ways that go beyond instrumental needs for practical knowledge. This 

three-part framework for the role and function of public 

knowledge about S&T helps inform the standards against 

which one can judge the kinds of knowledge that are important for citizens to hold and whether the public knows 

“enough” about science for these three purposes. 

Three different types of knowledge were identified: 

factual science knowledge, knowledge of scientific processes and standards for evaluating scientific evidence, 

and knowledge about the institutions that play a role in 

scientific development and how those institutions operate (also see Shen 1975). NSF surveys have included 

measures of both factual science knowledge and understanding of scientific processes for a number of years. 

Indicators of how well the public understands the workings of institutions engaged in S&T development have 

not been included in past NSF surveys. Research by 

Bauer, Petkova, and Boyadjieva (2000) developed one 

set of measures along these lines in surveys of the British 

and Bulgarian publics.

Apart from evaluating the purposes and function of the 

NSF indicators of public knowledge, the workshops also 

raised additional questions for social scientists to explore, 

such as research on the kinds of things that motivate 

greater learning about S&T and a better understanding of 

how such adult learning occurs. 

Public Understanding of Science and Its Role in Everyday Life
7-28 Chapter 7. Science and Technology: Public Attitudes and Understanding

controversial public issues about which the research community claims expertise, and views of S&E as occupations. 

Promise and Reservations About S&T

A majority of Americans see science as having, on balance, a positive effect on society and regard scientists and 

engineers as contributing to the well-being of society. At the 

same time, a majority of Americans also express reservations about the role of S&T in society.

NSF surveys dating back to 1979 show that roughly seven in ten Americans see the effects of scientific research, in 

general, as more positive than negative for society. In 2010, 

46% of GSS respondents said the benefits of scientific research strongly outweigh the harmful results, and 23% said 

that benefits slightly outweigh harms. Only 9% of respondents said the harms either slightly or strongly outweigh the 

benefits. Of the remaining respondents, 14% volunteered 

that the two are about equal and 8% gave no response. These 

numbers are generally consistent with earlier surveys; those 

saying the benefits strongly or slightly outweigh the harmful results ranged from 68% to 79% over the 30-year survey 

period (figure 7-11; appendix table 7-20). In practically any 

major American social grouping, few individuals express 

strong doubt about the benefits of science. 

Americans overwhelmingly agree that S&T will foster 

“more opportunities for the next generation” (appendix table 

7-21). Agreement with this statement has been increasing 

moderately for more than a decade; nine in ten Americans 

agreed in 2010.30

The annual VCU Life Sciences Surveys show similar 

results. The percentage of Americans who agreed that “developments in science helped make society better” ranged 

from 83% to 87% over the past decade, with about half of 

the public (48%) saying that science helped make society “a 

lot” better in 2010 and 34% saying it made society “somewhat better.” Similarly, between 2002 and 2010, the surveys 

asked respondents whether they believed that “scientific 

research is essential for improving the quality of human 

lives” and found that agreement ranged between 87% and 

92% (VCU 2010). During the same period, between 88% 

and 92% of respondents agreed that “new technology used 

in medicine allows people to live longer and better.”

A survey conducted by the Pew Research Center for the 

People and the Press (2009a) also demonstrates a strong public regard for the benefits to society from S&E. Respondents 

considered a series of occupational groups and rated each 

in terms of their contribution to the well-being of society. 

Seven in ten Americans said that scientists contribute “a lot” 

to the well-being of our society; 64% said the same about 

engineers. Medical doctors were evaluated similarly, with 

69% of respondents saying they contribute a lot to society. 

Only the military and teachers were considered by more 

Americans to contribute a lot to society (table 7-11).

What kinds of contributions do Americans have in mind? 

The Pew Research Center survey asked respondents to express, in their own words, some of the ways science has had 

a positive effect on society. More than half of all responses 

referred to medical contributions: 32% of responses referred to general improvements in healthcare and medicine 

and 24% referred to specific vaccines and disease research. 

Other responses were less common. These included space 

exploration (8% of responses), the environment (7% of responses), and communication and computer technologies 

(7% of responses).

Americans who have more years of formal education 

and score higher on measures of science knowledge express 

Figure 7-11

Public assessment of scientific research: 1979–2010

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 figure, “benefits outweigh 

harmful results” and “harmful results outweigh benefits” each 

combine responses of “strongly outweigh” and “slightly outweigh.” 

Figure includes all years for which data collected. Detail may not 

add to total 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, 2008, 2010). See appendix tables 7-20 and 

7-23. 

Science and Engineering Indicators 2012

Cumulative percent

0 20 40 60 80 100

Benefits of scientific research strongly/slightly outweigh 

harmful results

Benefits of scientific research are about equal to harmful results

Harmful results of scientific research strongly/slightly outweigh 

benefits

Don’t know

1979

1981

1985

1988

1990

1992

1995

1997

1999

2001

2004

2006

2008

2010
Science and Engineering Indicators 2012 7-29

more favorable attitudes about S&T. A review of numerous 

surveys from around the world found—other things being 

equal—a weak but consistent relationship between greater 

knowledge of science and more favorable attitudes toward 

science. This relationship was stronger in the United States 

than in any of the other countries in the study (Allum et al. 

2008). (For more details, see NSB 2008.)

Americans also express reservations about S&T. The VCU 

Life Sciences Surveys found that a majority of Americans 

agree that “scientific research these days doesn’t pay enough 

attention to the moral values of society.” In 2010, 58% of 

respondents agreed with this statement and 35% disagreed; 

however, the percentage that agreed has dropped substantially, from a high of 73% in 2001. Majorities or near majorities agree with statements expressing reservations about 

science in other surveys, as well. For example, in the 2010 

GSS, about half (51%) agreed that “science makes our way 

of life change too fast”; 47% disagreed. Men and women are 

about equally likely to express reservations about science. 

Those expressing fewer reservations about science on this 

statement tend to have more formal education, more science 

and math education, and more factual knowledge of science 

(appendix table 7-22). 

International Comparisons

International surveys also indicate strong public support 

for S&T. Although data from other countries are not entirely 

comparable, they appear to indicate that Americans hold at 

least equally or somewhat more positive attitudes about the 

benefits of S&T than Europeans, Russians, and Japanese. 

Attitudes in China and South Korea are comparable with the 

United States; on some questions, attitudes are even more favorable, but reservations about science are somewhat higher 

in China and South Korea as well (appendix table 7-23). 

Attitudes about S&T have grown increasingly positive in 

Malaysia over recent years; in 2008, 74% of Malaysians 

agreed that scientific research has more positive than negative effects, up from 45% in 1998. In all of the countries 

and regions where survey data exist, statements about the 

achievements and promise of science elicit substantially 

more agreement than disagreement. 

As in the United States, respondents abroad also express 

reservations about S&T. Numerous international surveys 

have asked for agreement or disagreement with a statement 

that “science makes our way of life change too fast” (appendix table 7-23). Levels of agreement with this statement 

appear to be lower in the United States than in several other 

countries, although there are large differences of viewpoint 

across European nations.

Federal Funding of Scientific Research

U.S. public opinion consistently and strongly supports 

federal spending on basic research. Since 1985, NSF surveys have asked Americans whether, “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.” In 2010, 82% agreed or strongly 

agreed with this statement; 14% disagreed. Agreement with 

this statement has ranged from a low of 76% in 1992 to a 

high of 87% in 2006 (figure 7-12; appendix tables 7-24 

and 7-25). 

The 2009 Pew Research Center Survey found that nearly three-quarters of Americans express support for federal 

spending on S&E. Asked whether government investments 

“usually pay off in the long run,” or are “not worth it,” 73% 

said spending on basic scientific research “usually pays off 

in the long run”; 74% said the same about engineering and 

technology. Furthermore, six in ten Americans said “government investment in research is essential for scientific 

Table 7-11

Public perceptions of various groups’ contributions to the well-being of society: 2009

(Percent)

Occupational group A lot Some Not very much Nothing at all Don’t know

Members of the military .............................................. 84 11 3 1 1

Teachers ..................................................................... 77 17 3 1 2

Scientists .................................................................... 70 23 3 2 3

Medical doctors .......................................................... 69 24 4 1 2

Engineers .................................................................... 64 25 4 2 5

Clergy ......................................................................... 40 37 10 5 9

Journalists .................................................................. 38 41 13 4 4

Artists ......................................................................... 31 43 15 7 4

Lawyers ...................................................................... 23 46 18 9 5

Business executives ................................................... 21 43 22 9 5

NOTES: Responses to Thinking about some different professions, how much do you think the following contribute to the well-being of our society? 

Do [people in occupational group] contribute a lot, some, not very much, or nothing at all to the well-being of our society? Detail may not add to total 

because of rounding. 

SOURCE: Pew Research Center for the People and the Press, Public Praises Science: Scientists Fault Public, Media (9 July 2009), http://people-press.

org/report/528/, accessed 6 January 2011. 

Science and Engineering Indicators 2012
7-30 Chapter 7. Science and Technology: Public Attitudes and Understanding

progress,” 29% said “private investment will ensure that 

enough scientific progress is made, even without government investment,” and the remainder gave no response.

Another indicator, the proportion of Americans who 

thought the government was spending too little on scientific 

research, 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. In 2010, 

36% of respondents said government spending on scientific 

research was “too little,” 47% said it was “about right,” and 

12% said it was “too much” (figures 7-13 and 7-14; appendix 

table 7-26). Support for increased government spending is 

greater for a number of other program areas, with the highest 

support for spending on education (74%). About six in ten 

Americans say government should spend more on developing alternative energy sources (61%), assistance to the poor 

(61%), health (58%), and environmental protection (57%). 

Support for increased spending in other areas is lower. 

Support for increased spending on scientific research (36%) 

is roughly comparable to that for spending on improving 

mass transportation (40%) and parks and recreation (32%). 

Still, based on the proportion of the U.S. population favoring 

increased spending, scientific research garners more support 

than spending in national defense (25%), space exploration 

(16%), and assistance to foreign countries (8%).31

A more recent survey by the Pew Research Center 

(2011a) suggests that the economic downturn of recent years 

and other factors have dampened Americans’ appetite for 

increased government spending in a number of areas. In the 

Pew Research Center survey, public support for increasing 

spending on scientific research was 36%, down from 39% 

in 2009; support for decreasing scientific research spending 

was 23% in 2011, up from 14% in 2009. 

International Comparisons

In other countries where similar though not precisely comparable questions have been asked, respondents also express 

strong support for government spending on basic scientific 

research. In 2010, 72% of Europeans agreed that “even if it 

brings no immediate benefits, scientific research which adds 

to knowledge should be supported by government,” and only 

9% disagreed. In 2007, 74% of Chinese agreed to a similar 

statement. These percentages may be lower because of a difference in question wording, however. Both the European 

survey and the Chinese survey offered a middle option (“neither agree nor disagree”), whereas no middle category was 

offered in the United States (appendix table 7-23). Agreement 

in South Korea, Malaysia, Japan, and Brazil is comparable to 

that in the United States and Europe. 

Figure 7-12

Public opinion on whether government should 

fund basic scientific research: 1985–2010

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” not shown. Survey results in 1985, 1988, 1990, 

1992, 1995, 1997, 1999, 2001, 2004, 2006, 2008, and 2010; other 

years extrapolated.

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, Survey of Public Attitudes 

Toward and Understanding of Science and Technology (years 

through 2001); University of Michigan, Survey of Consumer Attitudes 

(2004); University of Chicago, National Opinion Research Center, 

General Social Survey (2006, 2008, 2010). See appendix tables 7-24 

and 7-25.

Science and Engineering Indicators 2012

Percent

1985 1988 1992 1997 2001 2006 2010

0

20

40

60

80

100

Strongly agree/agree

Disagree/strongly disagree

Figure 7-13

Public assessment of amount of government 

spending for scientific research: 1981–2010

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: [scientific 

research]. Responses of “right amount” and “don’t know” not shown. 

Survey results in 1981, 1985, 1988, 1990, 1992, 1997, 1999, 2001, 

2002, 2004, 2006, 2008, and 2010; other years extrapolated. 

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 (2002–10). See appendix table 7-26.

Science and Engineering Indicators 2012

Percent

0

10

20

30

40

50

1981 1985 1988 1992 1997 2001 2006 2010

Too little

 Too much
Science and Engineering Indicators 2012 7-31

research would require mastery and evaluation of a great 

deal of evidence. In addition to relying on direct evidence 

from scientific studies, citizens who want to draw on scientific evidence may consult the judgments of leaders and 

other experts who they believe can speak authoritatively 

about the scientific knowledge that is relevant to an issue.

Public confidence in leaders of the scientific community is 

one indicator of public willingness to rely on science. 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 2010, 

four in ten Americans expressed “a great deal of confidence” 

in leaders of the scientific community, nearly half (49%) expressed “some confidence,” and fewer than one in ten (7%) 

expressed “hardly any confidence at all” in the scientific community (figure 7-15; appendix table 7-27). 

About the same proportion expressed “a great deal of 

confidence” in leaders of the medical community (41%) as 

in leaders of the scientific community. The military was the 

Figure 7-15

Public confidence in institutional leaders, by type of 

institution: 2010

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 (2010). See appendix table 7-27.

Science and Engineering Indicators 2012

Cumulative percent

200 40 60 80 100

A great deal Some Hardly any Don’t know

Press

Banks and

financial institutions

Congress

Television

Organized labor

Major companies

Executive branch of

federal government

Organized religion

Education

U.S. Supreme Court

Scientific community

Medicine

Military

Confidence in the Science Community’s 

Leadership

For the science-related decisions that citizens face, a 

comprehensive understanding of the relevant scientific 

Figure 7-14

Public attitudes toward government spending in 

various policy areas: 2010

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 (2010). See appendix table 7-26. 

Science and Engineering Indicators 2012

Too little About right Too much Don’t know

0 20 40 60 80 100

Cumulative percent

Assistance to

other countries

Space exploration

Welfare

National defense

Assistance

to blacks

Assistance to

big cities

Parks and

recreation

Supporting

scientific research

Mass

transportation

Highways

and bridges

Assistance

for childcare

Drug rehabilitation

Social Security

Law enforcement

Environment

Health

Assistance

for the poor

Developing

alternative

energy sources

Education
7-32 Chapter 7. Science and Technology: Public Attitudes and Understanding

only institution with higher levels of expressed confidence 

(52%). This pattern is consistent with past surveys where 

science usually ranked second or third in public confidence, 

with medicine or the military ranking first. The consistently 

high confidence in the leadership of the scientific community contrasts with views of other institutional leaders over 

the years. For example, confidence in the military has fluctuated more widely over the past three decades. The medical 

community has seen a long-term decline in confidence during the 1970s and 1980s. More than half of Americans expressed a great deal of confidence in medical leaders in the 

mid-1970s, compared to about 40% in recent years. Thirty 

years ago, confidence in the medical community was higher 

than confidence in scientific leaders. However, during the 

past decade, the public was about equally likely to express 

confidence in medical and scientific leaders. 

Influence of Scientific Experts on 

Public Issues

Government support for scientific research derives partly 

from the notion that science can support policymakers in 

shaping many public decisions. Science can play this role 

more effectively if the general public supports the use of scientific knowledge in such decisions and shares the view that 

science is relevant.

In 2006 and 2010, the GSS asked about the appropriate 

influence of science on four public policy issues to which 

scientific research might be considered relevant. In 2010, 

those issues were global climate change,32 research using human embryonic stem cells, federal income taxes, and nuclear 

power.33 In 2006, those issues included GM foods but not 

nuclear power. Survey 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 occupational groups over time.

The 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. 

Relative to other groups, more say that scientists and engineers should have a great deal of influence about these issues than say the same about other groups when it comes to 

global warming, stem cell research, nuclear power, and GM 

foods (table 7-12).

The only exception to that pattern was found on tax issues. When it comes to decisions about reducing federal income taxes, 18% said that economists should have “a great 

deal” of influence and 23% said the same about elected officials. Both the 2006 GSS and the 2010 GSS found the same 

patterns in Americans’ preferences about each group’s influence on these public issues (see appendix table 7-28). 

Americans also gave scientists relatively high marks for 

understanding each issue, a pattern that underscores the perception of scientists and engineers as experts in these areas 

(table 7-13). The GSS asked respondents to rate each leadership group’s understanding of a largely factual aspect of each 

issue on a 5-point scale ranging from “very well” to “not 

at all.” For the issues dealing with biological or geophysical phenomena, the differences in perceived understanding 

were significant: between 27% and 58% of the public placed 

the relevant S&E group in the top category of understanding, whereas only 3% to 16% placed any of the other groups 

in that category. The contrast among groups was smallest 

for the tax issue, with economists (27%) ranking ahead of 

business leaders (16%) and elected officials (10%) as understanding the likely effects of reducing federal income taxes 

“very well.” The same patterns were found in 2006 and 2010 

(see appendix table 7-29). 

Perceptions of impartiality in judgments about these issues may also influence preferences about the role of leadership groups in public issue debates and decisions. When 

asked which groups would “support what is best for the 

country as a whole versus what serves their own narrow 

interests,” the patterns were similar, with more Americans 

saying the relevant S&E group would support what is best 

for the country than saying the same about other leadership 

groups. For all issues, S&E groups were more likely to be 

seen as supporting what is best for the country than other 

leadership groups (table 7-14; appendix table 7-30).

One factor that may limit the influence of scientific 

knowledge and the scientific community on public issues 

is the perception that significant scientific disagreement exists, making scientific knowledge uncertain (Krosnick et al. 

2006). GSS respondents were asked to rate the degree of 

scientific consensus on a largely factual aspect of each of 

the issues using a 5-point scale ranging from “near complete agreement” to “no agreement at all.” The degree of 

perceived consensus was highest for medical researchers 

on “the importance of stem cells for research” (58% rated 

this group at one of the two points nearest the “complete 

agreement” scale point.) A 53% majority also saw nuclear 

engineers as at or near complete agreement about “the risks 

and benefits of using nuclear power to generate electricity.” 

About four in ten (38%) gave the same level of rating for 

perceived consensus to environmental scientists on “the existence and causes of global warming.” Lower proportions 

of respondents chose one of these two points when asked 

about the extent to which medical researchers agree on “the 

risks and benefits of genetically modified foods” in 2006 

(28%), or economists on “the effects of reducing federal 

income taxes” in 2010 (21%) (table 7-15; appendix table 

7-31). (See sidebar, “Differences Between Scientists and the 

Public on S&T-related Issues,” for more perceptions of scientific consensus.)
Science and Engineering Indicators 2012 7-33

With a few exceptions, responses to these questions do 

not differ markedly among demographic groups. Americans 

with more education and more science knowledge tend to 

have more favorable perceptions of the knowledge, impartiality, and level of agreement among scientists. 

Views of S&E Occupations

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 also 

have a bearing on the public’s sense that S&E affects the nation’s well-being in the future. 

For more than 30 years, the Harris Poll (Harris Interactive 

2009) has asked about the prestige of a large number of occupations, including scientists and engineers (table 7-16). In 

2009, 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.34

The percentage of survey respondents attributing “very 

great prestige” to scientists has fluctuated narrowly between 

52% and 57% since 2003. More Americans rated scientists 

as having “very great prestige” than did so for almost any 

other occupation considered in the Harris surveys. In recent 

years, their rating was comparable to that of nurses, doctors, 

firefighters, and teachers, and ahead of military and police 

officers. In 2009, it was second only to firefighters. 

Engineers’ standing is comparable to occupations clustered just below the top group of occupations rated (including clergy, military officers, farmers, and police officers). 

A plurality of Americans said engineers have “very great 

prestige”; this figure has fluctuated between a low of 28% 

in 2003 and a high of 40% in 2008, and was about the same 

in 2009, at 39%. 

The relative ratings of each occupation are, of course, dependent on the set of occupations considered on the surveys. 

Prestige appears to reflect perceived service orientation 

and public benefit more than high income or celebrity; for 

Table 7-12

Public preferences about various groups’ influence on decisions about public issues: 2010 or most recent year

(Percent and mean score)

 Preferred degree of influence

Public issue/group

A great

deal

A fair 

amount A little

None

at all

Don’t

know Mean score

Global warming: deciding what to do about global 

 warming policy

Environmental scientists .................................................. 48 37 9 3 3 3.3

Elected officials ................................................................ 12 35 34 17 3 2.4

Business leaders .............................................................. 8 22 40 27 3 2.1

Genetically modified (GM) foods: deciding whether to 

 restrict the sale of GM foods

Medical researchers ......................................................... 41 40 10 3 5 3.3

Elected officials ................................................................ 7 30 37 21 5 2.2

Business leaders .............................................................. 3 16 41 35 5 1.9

Stem cell research: deciding about government funding 

 for stem cell research

Medical researchers ......................................................... 41 39 11 5 4 3.2

Elected officials ................................................................ 9 32 34 21 4 2.3

Religious leaders.............................................................. 7 18 36 35 4 2.0

Nuclear power: deciding whether to expand the use of 

 nuclear power

Nuclear engineers ............................................................ 38 41 11 4 6 3.2

Elected officials ................................................................ 10 38 35 11 6 2.5

Business leaders .............................................................. 5 27 42 21 5 2.2

Federal income taxes: deciding whether to reduce federal 

 income taxes

Economists ...................................................................... 18 55 18 4 6 2.9

Elected officials ................................................................ 23 39 24 9 5 2.8

Business leaders .............................................................. 11 41 33 10 5 2.6

NOTES: Responses to How much influence should each of the following groups have in deciding: what to do about global warming policy; what to do 

about government funding for stem cell research; whether to reduce federal income taxes; whether to expand the use of nuclear power; whether to 

restrict sale of genetically modified foods? Responses on global warming, stem cell research, federal income taxes, and nuclear power are for 2010. 

Responses on genetically modified foods are for 2006. Mean preferred influence score based on 4-point scale, where 4 = a great deal of influence, 3 = a 

fair amount, 2 = a little influence, and 1 = none at all. Detail may not add to total because of rounding. 

SOURCE: University of Chicago, National Opinion Research Center, General Social Survey (2006, 2010). See appendix table 7-28. 

Science and Engineering Indicators 2012
7-34 Chapter 7. Science and Technology: Public Attitudes and Understanding

Table 7-13

Public perceptions of various groups’ understanding of public issues: 2010 or most recent year

(Percent and mean score)

Degree of understanding (on scale of 1 to 5)

Public issue/group

Very well

5432

Not at all

1

Don’t

know

Mean

score

Nuclear power: understand the likely effects of using nuclear 

 power to generate electricity

Nuclear engineers ....................................................................... 58 18 11 4 3 5 4.3

Elected officials ........................................................................... 5 11 32 33 15 5 2.6

Business leaders ......................................................................... 5 11 32 30 17 5 2.5

Stem cell research: understand stem cell research

Medical researchers .................................................................... 49 26 14 3 2 5 4.2

Elected officials ........................................................................... 4 7 34 28 24 5 2.4

Religious leaders......................................................................... 5 6 27 28 29 5 2.3

Global warming: understand the causes of global warming

Environmental scientists ............................................................. 40 20 21 10 6 4 3.8

Elected officials ........................................................................... 5 10 28 26 26 4 2.4

Business leaders ......................................................................... 4 9 29 28 26 4 2.3

Genetically modified foods: understand the risks posed by 

 genetically modified foods 

Medical researchers .................................................................... 32 32 18 8 5 6 3.8

Elected officials ........................................................................... 3 6 24 33 29 5 2.2

Business leaders ......................................................................... 4 7 24 31 28 6 2.2

Federal income taxes: understand the likely effects of reducing 

 federal income taxes

Economists ................................................................................. 27 21 31 8 6 7 3.6

Elected officials ........................................................................... 10 18 32 18 17 6 2.8

Business leaders ......................................................................... 16 24 31 14 9 7 3.3

NOTES: Responses to How well do the following groups understand: causes of global warming; stem cell research; likely effects of reducing federal 

income taxes; likely effects of using nuclear power to generate electricity; risks posed by genetically modified foods? Responses on global warming, stem 

cell research, federal income taxes, and nuclear power are for 2010. Responses on genetically modified foods are for 2006. Mean understanding score 

based on 5-point scale, where 5 = understands very well and 1 = understands not at all. Detail may not add to total because of rounding.

SOURCE: University of Chicago, National Opinion Research Center, General Social Survey (2006, 2010). See appendix table 7-29. 

Science and Engineering Indicators 2012

instance, fewer than two in ten Americans attributed “very 

great prestige” to entertainers or actors (table 7-16). 

International Comparisons

Elsewhere, S&E occupations are also highly regarded. 

Among the Chinese in 2008, science (40%) rated close to 

medicine (41%) and teaching (43%) as an occupation that 

survey respondents hoped their children would pursue 

(CRISP 2008). In 2006, the majority of Israelis said they 

would be pleased if their children became scientists (77%), 

engineers (78%), or physicians (78%) (Yaar 2006). On at 

least one measure, Americans rated scientific careers more 

positively than was the case in at least some other countries. 

In 2004, a little more than 50% of South Koreans said they 

would feel happy if their son or daughter wanted to become 

a scientist. In the United States, 80% of those surveyed in 

2001 expressed positive views regarding their children becoming scientists. 

Public Attitudes About 

Specific S&T-Related Issues

Public attitudes can affect the speed and direction of S&T 

development. When science plays a substantial role in a national policy controversy, more than the specific policies under debate may be at stake. The policy debate may also shape 

public opinion and government decisions about investments 

in general categories of research. Less directly, a highly visible debate involving S&T issues may shape overall public 

impressions of either the credibility of science or the proper 

role of science in other, less visible public decisions. 

Likewise, public attitudes about emerging areas of research and new technologies may have an influence on innovation. The climate of opinion concerning new research 

areas can influence levels of public and private investment 

in related technological innovations and, eventually, the 

adoption of new technologies and the growth of industries 

based on these technologies.
Science and Engineering Indicators 2012 7-35

Table 7-14

Public perceptions of various groups’ impartiality in making policy recommendations about public issues: 

2010 or most recent year

(Percent and mean score)

Extent to which group would support (on scale of 1 to 5)

Public issue/group

What is best 

for country 

5432

Own narrow

interests

1 Don’t know Mean score

Global warming

Environmental scientists .................. 42 22 18 7 7 4 3.9

Elected officials ................................ 11 10 25 22 28 4 2.5

Business leaders .............................. 6 4 24 27 34 4 2.2

Genetically modified foods

Medical researchers ......................... 34 29 19 7 6 5 3.8

Elected officials ................................ 6 10 32 25 21 5 2.5

Business leaders .............................. 2 4 25 32 32 5 2.1

Stem cell research

Medical researchers ......................... 30 28 21 9 7 5 3.7

Elected officials ................................ 6 10 25 26 29 4 2.4

Religious leaders.............................. 9 10 24 24 27 6 2.5

Nuclear power

Nuclear engineers ............................ 27 28 22 9 8 6 3.6

Elected officials ................................ 8 16 32 22 17 6 2.7

Business leaders .............................. 6 9 28 28 23 6 2.4

Federal income taxes

Economists ...................................... 19 27 28 11 9 6 3.4

Elected officials ................................ 10 11 27 22 24 6 2.6

Business leaders .............................. 5 11 23 29 27 6 2.4

NOTES: Responses to When making policy decisions about [public issue], to what extent do you think [group] would support doing what is best for the 

country as a whole or what serves their own narrow interests? Responses on global warming, stem cell research, federal income taxes, and nuclear 

power are for 2010. Responses on genetically modified foods are for 2006. Mean impartiality score based on 5-point scale, where 5 = best for the country 

and 1 = own narrow interests. Detail may not add to total because of rounding. 

SOURCE: University of Chicago, National Opinion Research Center, General Social Survey (2006, 2010). See appendix table 7-30. 

Science and Engineering Indicators 2012

Table 7-15

Public perceptions of scientific consensus on public issues: 2010 or most recent year

(Percent and mean score)

Degree of consensus (on scale of 1 to 5)

Group/public issue

Near

complete

agreement

5432

No

agreement

at all

1

Don’t

know Mean score

Medical researchers on importance of stem 

cells for research .................................................. 28 30 26 6 4 7 3.8

Nuclear engineers on risks and benefits of 

nuclear power to generate electricity .................. 19 34 28 6 3 11 3.7

Environmental scientists on existence and 

causes of global warming .................................... 15 23 35 11 10 6 3.2

Medical researchers on risks and benefits of 

genetically modified foods .................................. 9 19 41 11 7 13 3.1

Economists on effects of reducing federal 

income taxes ....................................................... 5 16 38 14 15 12 2.8

NOTES: Responses to To what extent do [people in group] agree on [public issue]? Responses on global warming, stem cell research, federal income 

taxes, and nuclear power are for 2010. Responses on genetically modified foods are for 2006. Mean consensus score based on 5-point scale, where 

5 = near complete agreement and 1 = no agreement at all. Detail may not add to total because of rounding. 

SOURCE: University of Chicago, National Opinion Research Center, General Social Survey (2006, 2010). See appendix table 7-31. 

Science and Engineering Indicators 2012
7-36 Chapter 7. Science and Technology: Public Attitudes and Understanding

For these reasons, survey responses regarding controversies over policies involving science, specific research 

areas, and emerging technologies are relevant. In addition, 

responses about relatively specific matters provide insight 

into the practical decisions through which citizens translate 

more general attitudes into actions, although, like all survey responses, how these responses relate to actual behavior 

remains uncertain. More generally, even in democratic societies, public opinion about new S&T developments does 

not translate directly into actions or policy. Instead, it filters 

through institutions that selectively measure what the public 

believes and either magnify or minimize the effects of divisions in public opinion on public discourse and government 

policy (Jasanoff 2005). Public attitudes about specific S&T 

issues can differ markedly from the views of scientists. (See 

sidebar, “Differences Between Scientists and the Public on 

S&T-related Issues.”)

Public attitudes toward policy issues involve a multitude 

of factors, not just knowledge or understanding of relevant 

science. Values, morals, judgments of prudence, and numerous other factors can come strongly into play; judgments 

about scientific fact are often secondary. In assessing the 

same issue, different people may find different considerations relevant.

This section discusses data on environmental issues, including global climate change, nuclear power, and energy 

development; cloning and stem cell research; teaching evolution in schools; agricultural biotechnology (i.e., GM food); 

and attitudes toward recent and novel technologies, including nanotechnology and medical biotechnology. It concludes 

with recent data on attitudes toward scientific research on 

animals and toward science and mathematics education. 

Environment, Climate Change, and Energy 

Development

Environmental issues, such as climate change, and the 

closely related issue of sustainable energy sources, have become of increased salience in national policy debates and 

international meetings such as those at the United Nations 

Climate Change Conference, held in Copenhagen, Denmark 

in December 2009. For Americans, the April 2010 oil spill 

in the Gulf of Mexico further increased the salience of environmental issues—particularly the environmental hazards 

of offshore oil drilling—with long-running media coverage 

and sustained public attention (Pew Research Center 2010c). 

Table 7-16

Public perceptions of prestige of various occupations: Selected years, 1977–2009

(Percent saying “very great prestige”)

Occupation 1977 1982 1992 1997 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Firefighter ........................... NA NA NA NA NA NA NA NA 55 48 56 63 61 57 62

Scientist .............................. 66 59 57 51 55 56 53 51 57 52 56 54 54 56 57

Doctor ................................. 61 55 50 52 61 61 61 50 52 52 54 58 52 53 56

Nurse .................................. NA NA NA NA NA NA NA NA 47 44 50 55 50 52 54

Teacher ............................... 29 28 41 49 53 53 54 47 49 48 47 52 54 52 51

Military officer ..................... NA 22 32 29 34 42 40 47 46 47 49 51 52 46 51

Police officer ....................... NA NA 34 36 41 38 37 40 42 40 40 43 46 46 44

Priest/minister/clergy .......... 41 42 38 45 46 45 43 36 38 32 36 40 42 40 41

Engineer ............................. 34 30 37 32 34 32 36 34 28 29 34 34 30 40 39

Farmer ................................ NA NA NA NA NA NA NA NA NA NA NA 36 41 41 36

Architect ............................. NA NA NA NA 26 26 28 27 24 20 27 27 23 28 29

Member of Congress .......... NA NA 24 23 25 33 24 27 30 31 26 28 26 28 28

Lawyer ................................ 36 30 25 19 23 21 18 15 17 17 18 21 22 24 26

Business executive ............. 18 16 19 16 18 15 12 18 18 19 15 11 14 17 23

Athlete ................................ 26 20 18 21 20 21 22 21 17 21 23 23 16 20 21

Journalist ............................ 17 16 15 15 15 16 18 19 15 14 14 16 13 18 17

Union leader ....................... NA NA 12 14 16 16 17 14 15 16 15 12 13 18 17

Entertainer .......................... 18 16 17 18 19 21 20 19 17 16 18 18 12 15 17

Banker ................................ 17 17 17 15 18 15 16 15 14 15 15 17 10 15 16

Actor ................................... NA NA NA NA NA NA NA NA 13 16 16 12 9 16 15

Stockbroker ........................ NA NA NA NA NA NA NA NA 8 10 8 11 12 10 13

Accountant ......................... NA 13 14 18 17 14 15 13 15 10 13 17 11 15 11

Real estate agent/broker .... NA NA NA NA NA NA NA NA 6 5 9 6 5 6 5

NA = not available, question not asked

NOTES: Responses to I am going to read off a number of different occupations. For each, would you tell me if you feel it is an occupation of very great 

prestige, considerable prestige, some prestige, or hardly any prestige at all? Data reflect responses of “very great prestige.”

SOURCE: Harris Interactive, Firefighters, Scientists, and Doctors Seen as Most Prestigious Occupations: Real Estate Brokers, Accountants, and 

Stockbrokers Are at the Bottom of the List. The Harris Poll (#86, 4 August 2009), http://www.harrisinteractive.com/vault/Harris-Interactive-Poll-ResearchPres-Occupations-2009-08.pdf, accessed 7 February 2011. 

Science and Engineering Indicators 2012
Science and Engineering Indicators 2012 7-37

Directly comparable data on the degree to which 

public attitudes align with those of scientists is scarce. 

A study conducted by the Pew Research Center in 2009 

asked the same questions of a sample of scientists belonging to the AAAS and a representative sample of the 

general public. The study found a striking difference between the groups across a number of specific issues including climate change, nuclear power, embryonic stem 

cell research, evolution, and animal research. 

The public tends to underestimate the degree of consensus among scientists about evolution. Six in ten said 

that scientists generally agree that humans have evolved 

over time, and 28% said they do not generally agree 

about this. The survey of scientists found that 97% of 

scientists say that humans and other living things have 

evolved over time. 

The public also tends to underestimate the degree of 

consensus among scientists about climate change; 56% 

said that scientists generally agree that the earth is getting warmer because of human activity, and 35% said 

scientists do not generally agree about this. The survey 

of scientists found 84% of scientists say that “the earth 

is getting warmer mostly because of human activity 

such as burning fossil fuels.” A survey of earth scientists by Doran and Zimmerman (2009) also found strong 

consensus among scientists that the earth’s temperature 

is rising and that human activity is “a significant contributing factor in changing mean global temperatures.”

Table 7-C

Comparison of general public’s and scientists’ attitudes toward specific S&T-related issues: 2009

(Percent)

S&T-related issue Scientistsa General publicb

Climate change

 The earth is getting warmer due to human activity .................................................. 84 49

 Climate change is due to natural changes in the atmosphere ................................. 10 36

 No solid evidence earth is warming .......................................................................... 4 11

 Don’t know/no answer .............................................................................................. 2 4

Nuclear power

 Favor building more nuclear power plants to generate electricity ............................ 70 51

 Oppose building more nuclear power plants to generate electricity ........................ 27 42

 Don’t know/no answer .............................................................................................. 3 7

Embryonic stem cell research

 Favor federal funding for embryonic stem cell research .......................................... 93 58

 Oppose federal funding for embryonic stem cell research ....................................... 6 35

 Don’t know/no answer .............................................................................................. 1 7

Evolution

 Humans and other living things evolved over time ................................................... 97 61

 Humans and other living things always existed in present form .............................. 2 31

 Don’t know/no answer .............................................................................................. 1 8

Animal research

 Favor use of animals in scientific research ............................................................... 93 52

 Oppose use of animals in scientific research ........................................................... 5 43

 Don’t know/no answer .............................................................................................. 2 6

a

Survey of scientists based on sample survey of AAAS members conducted by Internet, 1 May–14 June 2009 (n = 2,533). bSurvey of general public conducted by landline and cellular telephone, 28 April–12 May 2009 (n = 2,001).

SOURCE: Pew Research Center for the People and the Press, 9 July 2009.

Science and Engineering Indicators 2012

Differences Between Scientists and the Public on S&T-related Issues

Surveys taken shortly after the oil spill in the Gulf found 

increased willingness to trade off energy production for environmental protection when compared with surveys conducted before the oil spill (Jones 2010). In addition, there 

was decreased public support for offshore oil drilling shortly 

after the spill; since that time, public support has returned to 

previous levels (Pew Research Center 2010b).

Concern About Environmental Quality

The Gallup Organization’s annual survey on environmental issues indicates that Americans were somewhat less 

concerned about environmental quality in 2010 and early 

2011, after an increase in expressed concern between 2006 

and 2008. The 2011 Gallup Poll found 34% of Americans 

worry “a great deal” about the environment, 34% worry 

“a fair amount,” and 31% worry “only a little” or “not at 
7-38 Chapter 7. Science and Technology: Public Attitudes and Understanding

all” (Saad 2011a).35 The percentage saying they worry “a 

great deal” was the same in 2010 (figure 7-16). Relative to 

other concerns, environmental quality ranked lower on the 

list than a number of other concerns including the economy 

(71%) and federal spending and the budget deficit (64%). 

Environmental concerns are infrequently mentioned in 

response to open-ended questions about the most important 

problems facing the nation. Only about 2% of Americans 

mentioned the environment or pollution in an open-ended 

question asking “What do you think is the most important 

problem facing this country today?” (Jones 2011c).

Climate Change

Climate change (often colloquially referred to as global 

warming), has become a prominent environmental issue for 

the American public. In a 2008 survey asking Americans to 

report, in their own words, the “single biggest environmental problem the world faces at this time,” the most common 

response was climate change (25%), followed by pollution 

(24%), energy problems (11%), and toxic substances in the 

environment (6%) (ABC News 2008).

Other surveys, using structured questions, also show 

evidence of widespread awareness of the issue of climate 

change. The Gallup Polls registered gradual increases in the 

percentage of Americans who say they understand the “global warming” issue “very well” or “fairly well,” from 68% in 

2004 to 82% in 2010 (The Gallup Organization 2010). 

Public debate about climate change has centered on both 

the existence of climate change and the likely causes of any 

change occurring. Gallup surveys found a decline in the 

percentage of Americans who consider climate change to be 

primarily due to human activities. When asked whether “the 

increases in the earth’s temperature over the last century” 

are largely the result of human activities rather than natural 

changes, half of Americans said human activities in 2010, 

down 8 points from 2008, and 46% said natural changes 

(Newport 2010).36

A large number of surveys have been conducted about climate change, both in the United States and abroad. The Pew 

Global Attitudes survey conducted in 2010 among 22 nations 

found 37% of Americans consider global climate change a 

“very serious problem,” one third said it was “somewhat serious,” and a minority said it was “not too serious” (15%) 

or “not a problem” (13%). The percentage of Americans 

saying climate change is “very serious” decreased 10 points 

from 47% in 2007. Americans express less concern about 

climate change than individuals in a number of other countries where majorities consider climate change a very serious problem: Germany, Japan, South Korea, India, Kenya, 

Argentina, Brazil, Mexico, Lebanon, and Turkey. Half or 

near half of the citizens in Spain, Jordan, and Indonesia say 

the same. The Chinese and British are about equally likely as 

Americans to say climate change is a very serious problem 

(41% and 40%, respectively). The only publics with lower 

concern than Americans about climate change were those in 

Poland (31%) and Pakistan (22%). A World Public Opinion 

survey conducted in 2009 in 15 nations found a similar pattern, with Americans, Russians, and Chinese least likely to 

consider climate change a “very serious” problem. 

Assessment of Potential Problems 

Public assessments of the degree to which potential hazards pose a threat to the environment have been surprisingly 

stable over the past two decades. A series of questions on 

the GSS surveys conducted in 1993, 1994, 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 potential problems; in 2010, 69% 

considered water pollution to be very or extremely dangerous. Air pollution caused by industry was considered very or 

extremely dangerous to the environment by 63%, whereas 

air pollution caused by cars was less likely to be considered 

very or extremely dangerous to the environment (43%) (table 7-17).

Furthermore, 48% of Americans considered the “rise 

in temperature caused by climate change” to be very or 

extremely dangerous to the environment, according to the 

2010 GSS. A decade earlier, that figure was 40%. The percentage saying that climate change was not very or not at all 

dangerous to the environment rose during the same period, 

from 11% in 2000 to 18% in 2010. The percentage holding 

no opinion decreased during the same period. 

Nuclear power stations were considered very or extremely dangerous to the environment by 45% of Americans in 

2010. Perceptions of environmental danger from nuclear 

power stations were about the same as when this question 

Percent

Figure 7-16

Worry about quality of environment: 2001–11

NOTES: Responses to the following: How much do you personally 

worry about the quality of the environment: a great deal, a fair 

amount, only a little, or none at all? Figure shows only responses for 

“a great deal.” Poll conducted annually in March. 

SOURCE: Saad L, Americans’ worries about economy, budget top 

other issues, The Gallup Poll (21 March 2011), http://www.gallup.

com/poll/146708/Americans-Worries-Economy-Budget-Top-Issues.

aspx, accessed 22 March 2011. 

Science and Engineering Indicators 2012

2001 2002 2003 2004 2005 2006 2007 2008 2010 2011

0

5

10

15

20

25

30

35

40

45

50

Respondents who worry “a great deal” 

about the quality of the environment
Science and Engineering Indicators 2012 7-39

was first asked in 1993. However, it is important to note that 

these data were collected prior to concerns about the risk to 

human health and the environment from damage to nuclear 

energy plants in the aftermath of the earthquake and tsunami 

in Japan in March 2011.

Assessments of environmental dangers changed substantially on only one issue—pesticides and chemicals used in 

farming. About half of Americans (52%) called these very 

or extremely dangerous to the environment in 2010, up from 

45% in 2000 and 37% in 1993. 

Concern about environmental dangers from GM crops appears to be modest. In the 2010 GSS, a quarter of Americans 

said modifying the genes of crops is very or extremely 

dangerous to the environment, and a roughly equal portion 

Table 7-17

Public assessment of potential environmental problems: 1993–2010

(Percent)

Potential problem/opinion 1993 1994 2000 2010

Pollution of America’s lakes, rivers, and streams

Extremely/very dangerous .................................................................. 66 61 66 69

Somewhat dangerous ......................................................................... 27 29 23 24

Not very/not dangerous ...................................................................... 4554

Don’t know .......................................................................................... 3572

Air pollution caused by industry

Extremely/very dangerous .................................................................. 61 53 62 63

Somewhat dangerous ......................................................................... 30 37 29 31

Not very/not dangerous ...................................................................... 4524

Don’t know .......................................................................................... 4562

Pesticides and chemicals used in farming

Extremely/very dangerous .................................................................. 37 33 45 52

Somewhat dangerous ......................................................................... 48 49 39 37

Not very/not dangerous ...................................................................... 11 13 8 8

Don’t know .......................................................................................... 4573

Rise in temperature caused by climate changea

Extremely/very dangerous .................................................................. 41 35 40 48

Somewhat dangerous ......................................................................... 34 35 33 27

Not very/not dangerous ...................................................................... 14 16 11 18

Don’t know .......................................................................................... 12 14 15 6

Nuclear power stations

Extremely/very dangerous .................................................................. 40 41 NA 45

Somewhat dangerous ......................................................................... 34 35 NA 30

Not very/not dangerous ...................................................................... 16 15 NA 19

Don’t know .......................................................................................... 9 9 NA 7

Air pollution caused by cars

Extremely/very dangerous .................................................................. 48 43 45 43

Somewhat dangerous ......................................................................... 38 42 41 46

Not very/not dangerous ...................................................................... 7968

Don’t know .......................................................................................... 7783

Modifying the genes of certain crops

Extremely/very dangerous .................................................................. NA NA 21 25

Somewhat dangerous ......................................................................... NA NA 32 33

Not very/not dangerous ...................................................................... NA NA 25 26

Don’t know .......................................................................................... NA NA 22 16

NA = not available, question not asked

a Wording was changed from “the greenhouse effect” to “climate change” in 2010.

NOTES: Responses to In general, do you think that [potential problem] is extremely dangerous for the environment, very dangerous, somewhat 

dangerous, not very dangerous, or not dangerous at all for the environment? Table includes all years for which data collected. Detail may not add to total 

because of rounding. 

SOURCE: University of Chicago, National Opinion Research Center, General Social Survey (1993–2010).

Science and Engineering Indicators 2012
7-40 Chapter 7. Science and Technology: Public Attitudes and Understanding

(26%) said this is not very or not at all dangerous to the 

environment. Another 16% of Americans held no opinion 

about the dangers of GM crops, suggesting that the public 

has a more limited awareness or understanding of this issue. 

Nuclear Power and Other Energy Sources

Public debate about energy sources in recent years has 

emphasized the need for lessened U.S. reliance on imported 

oil and more focus on alternative, renewable energy sources. 

A Gallup/USA Today poll conducted in early 2011 found 

more than eight in ten (83%) Americans favor legislation 

that “provides incentives for using solar and other alternative energy sources,” and 15% are opposed. Two-thirds favor legislation that “expands drilling and exploration for oil 

and gas” (Jones 2011b). These findings are in keeping with 

public preferences on government spending in the 2010 GSS 

survey; 61% of Americans said the government is spending 

“too little” on developing alternative sources of energy. 

Support for nuclear energy has varied over the past 15 years. 

American public opinion was fairly evenly divided in the late 

1990s and support increased in the late 2000s. According to the 

2010 GSS, about six in ten (61%) Americans favor or strongly 

favor increasing the use of nuclear energy to generate electricity 

in the United States, about three in ten (28%) oppose or strongly 

oppose, and the remainder gave no opinion. Similarly, the proportion of Americans who favor the use of nuclear power as 

“one of the ways to provide electricity” ranged from 57% to 

62% between 2009 and early 2011 on Gallup surveys (Jones 

2011a). The 2011 survey was conducted prior to damage to 

nuclear energy plants in Japan stemming from the earthquake 

and tsunami in March 2011. A Pew Research Center survey 

conducted shortly after the disaster in Japan suggests that 

Americans’ support for nuclear power declined, but the longterm effect on Americans’ attitudes toward nuclear power is unknown at this time (Pew Research Center 2011c). A substantial 

minority of Americans (42%) said nuclear power plants are not 

safe in a 2009 Gallup Poll, and prior surveys indicate that three 

out of five Americans oppose the construction of a nuclear energy plant in their local communities (Jones 2009).37

International Comparisons

In 2010, Europeans were divided about whether or not 

nuclear energy will “improve our way of life” (39%) or 

“make things worse” (39%). The remainder said nuclear energy will have no effect (10%) or held no opinion (13%). 

Assessments of nuclear energy were more negative when 

this question was first asked in 1999, and have been increasingly divided since that time (EC 2010). Support for nuclear 

energy varies a great deal among European countries. In 

general, citizens of countries that have operational nuclear 

power plants are considerably more likely to support nuclear 

energy than citizens of other countries (see NSB 2010).38

Stem Cell Research and Human Cloning

Unlike many issues involving scientific research, studies using embryonic stem cells have generated considerable 

public controversy. In the case of stem cell research, many 

people’s attitudes are strongly related to their views about 

moral fundamentals. There is less reason to believe that this 

is the case for other S&T issues, such as nuclear power. 

Public support for “medical research that uses stem cells 

from human embryos” grew over the past decade, from a 

low of 35% in favor in 2002. Since 2004, a majority of the 

public has favored stem cell research, with 62% favoring in 

2010 and 31% opposed (VCU 2010) (figure 7-17). Annual 

Gallup Poll data draw a similar picture: the percentage of 

Americans who find stem cell research “morally acceptable” 

has fluctuated from a low of 52% in 2002 to a high of 64% 

in 2007; in 2011, 62% said it was “morally acceptable” and 

30% said it was “morally wrong” (Saad 2011b). 

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 seven 

out of ten respondents (71%) favored this type of research 

in 2010, down slightly from 75% in 2007 (VCU 2010). 

Support is also greater when the question is framed as an 

emotionally compelling personal issue (“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?”) In this case, 70% of Americans support treatments 

that use stem cells and 21% do not (VCU 2006). 

Americans are overwhelmingly opposed to human cloning when there is no mention of a medical purpose. In a 

2010 survey, the idea of cloning or genetically altering humans was rejected by eight in ten Americans (VCU 2010). 

Opinions are more mixed when questions mention “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, with 55% in favor and 40% opposed in 2010 (table 

7-18). The specter of reproductive cloning can generate apprehension about therapeutic cloning. Asked how concerned 

they were that “the use of human cloning technology to create stem cells for human therapeutic purposes will lead to a 

greater chance of human reproductive cloning,” more than 

two-thirds of Americans said they were either very (31%) or 

somewhat (38%) concerned (VCU 2006). 

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 the 

2008 VCU survey, most Americans (64%) said they were 

“not very clear” or “not clear at all” about this distinction, 

with 26% saying they were “somewhat clear” and only 8% 

characterizing themselves as “very clear” about it. The number of Americans who professed greater comprehension in 

2008 was lower than it was when VCU began asking this 

question in 2002. Additionally, self-assessed understanding 

of stem cell research declined between 2008 and 2010. 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, down 

from 64% in 2008 (VCU 2010).
Science and Engineering Indicators 2012 7-41

An international survey on attitudes toward stem cell 

research in a dozen European countries, the United States, 

Japan, and Israel found that awareness, knowledge, and attitudes about this type of research vary widely (Fundacion 

BBVA 2008). Overall, Americans were more aware of stem 

cell research than residents of most other countries and more 

often responded correctly to knowledge questions on this 

subject. All the same, Americans were somewhat more likely than residents of several countries in Europe to believe 

that stem cell research is immoral (appendix table 7-32). 

Teaching Evolution in the Schools

In the United States, the topic of whether and how evolution should be taught in the school system has been a frequent 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. In surveys sponsored by NSF between 1979 and 2010, 

many Americans appear skeptical of established scientific 

ideas about evolution. For example, when asked about the 

statement “human beings, as we know them today, developed from earlier species of animals” on the 2010 GSS survey, 38% considered this statement false and 47% said it 

was true (appendix table 7-10). 

An experimental study included in the 2004 Michigan 

Survey of Consumer Attitudes suggests that survey responses to such questions reflect more than unfamiliarity with 

basic elements of science. Some of the survey respondents 

were asked a question that tested knowledge about evolution 

(“human beings, as we know them today, developed from 

earlier species of animals”). Other respondents were asked a 

question about what the theory of evolution asserts (“according to the theory of evolution, human beings, as we know 

Table 7-18

Public opinion on medical technologies derived from stem cell research: 2010 or most recent year 

(Percent)

Question Favor Oppose

1. 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? 

(Yes or no) .......................................................................................................................................................... 70 21

2. Do you favor or oppose medical research that uses stem cells from sources that do NOT involve human 

embryos? (Strongly favor, somewhat favor, somewhat oppose, or strongly oppose) ...................................... 71 21

3. How much do you favor or oppose using human cloning technology IF it is used ONLY to help medical 

research develop new treatments for disease? (Strongly favor, somewhat favor, somewhat oppose, or 

strongly oppose) ................................................................................................................................................ 55 40

4. The technology now exists to clone or genetically alter animals. How much do you favor or oppose 

allowing the same thing to be done in humans? (Strongly favor, somewhat favor, somewhat oppose, or 

strongly oppose) ................................................................................................................................................ 15 80

NOTES: Question 1 asked during 7–21 November 2006. Questions 2, 3, and 4 asked during 12–18 May 2010. Detail does not add to total because “don’t 

know” responses not shown. 

SOURCE: Virginia Commonwealth University (VCU), VCU Life Sciences Survey (2006 for question 1; 2010 for questions 2, 3, and 4), http://www.vcu.edu/

lifesci/images2/survey2010.pdf, accessed 4 March 2011. 

Science and Engineering Indicators 2012

Figure 7-17

Public attitudes toward stem cell research: 2001–10 

NOTES: Responses to On the whole, how much do you favor or 

oppose medical research that uses stem cells from human embryos?

Question most recently asked 12–18 May 2010. Survey not conducted in 2009. Detail may not add to total because of rounding.

SOURCE: Virginia Commonwealth University (VCU), VCU Life 

Sciences Survey (2010), http://www.vcu.edu/lifesci/images2/ 

survey2010.pdf, accessed 4 March 2011. 

Science and Engineering Indicators 2012

Cumulative percent

Somewhat oppose Strongly oppose

Strongly favor Somewhat favor Don’t know

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007 2008 2010
7-42 Chapter 7. Science and Technology: Public Attitudes and Understanding

them today, developed from earlier species of animals”). 

Respondents were much more likely to answer correctly 

if the question was framed as being about scientific theories rather than as being about the natural world. When the 

question about evolution was prefaced by “according to the 

theory of evolution,” 74% responded that the statement was 

true; conversely, only 42% considered the statement true 

when it was not prefaced as such. (For more details, see NSB 

2008.) These differences may indicate that many Americans 

hold religious or other beliefs that cause them to be skeptical 

of certain established scientific ideas, even when they have 

some basic familiarity with those ideas. 

When surveys ask for opinions about whether and how 

evolution should be taught in U.S. public schools, two key 

patterns emerge. First, when asked whether creation should 

be taught alongside of or in addition to evolution, a majority of Americans favor this pluralistic 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, while a sizeable 

minority favor it. In the most recent survey, 49% opposed 

teaching creation instead of evolution in the public schools 

and 38% favored it (Plutzer and Berkman 2008; Berkman 

and Plutzer 2010).

Genetically Modified Food

Although the introduction of GM crops has provoked 

much less public controversy in the United States than in 

Europe, U.S. public support for this application of biotechnology is limited. According to a 2008 CBS/New York Times

poll, 44% of Americans indicate they have heard nothing 

or “not much” about GM ingredients added to foods to 

make them taste better and last longer (CBS-NYT 2008). 

However, 87% believe that these foods should be labeled 

and 53% expect that it is “not very likely” or “not at all likely” that they would buy food that is labeled as such.

Overall, these results are consistent with a series of 

five surveys conducted by the Pew Initiative on Food and 

Biotechnology between 2001 and 2006. These studies consistently found that only about one-fourth of U.S. consumers 

favor “the introduction of genetically modified foods into 

the U.S. food supply” (Mellman Group, Inc. 2006). The percentage of U.S. survey respondents reporting a negative reaction to the phrase “genetically modified food” (44%) was 

more than twice the 20% that reported a positive reaction 

(Canadian Biotechnology Secretariat 2005). Nonetheless, 

consumers in the United States express more favorable 

views than Europeans, with Canadians falling somewhere in 

between (Gaskell et al. 2006).

Although the FDA proposed guidelines for the approval 

process for genetically engineered animals in September 

2008 (Maugh and Kaplan 2008), past surveys have generally found that U.S. residents are even more wary of genetic 

modification of animals than they are of genetic modification of plants (Mellman Group, Inc. 2005). Many express 

support for regulatory responses, but this support appears to 

be quite sensitive to the way issues are framed. Thus, whereas 29% expressed a great deal of confidence in “the Food 

and Drug Administration or FDA,” only about half as many 

expressed the same confidence when the question was posed 

about “government regulators” (Mellman Group, Inc. 2006). 

(Additional findings from earlier U.S. surveys can be found 

in NSB 2006 and NSB 2008.)

Nanotechnology 

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 almost a decade, and innovations based on 

nanotechnology are increasingly common. However, relative to other new technologies, nanotechnology is still in an 

early stage of development and the degree of risk remains 

uncertain (Chatterjee 2008, Barlow et al. 2009). 

As noted earlier, public awareness and understanding of 

nanotechnology remains limited despite increased federal 

funding and more than 600 nanotechnology products already 

on the market (The National Academies 2008a).39 According 

to the 2010 GSS, 24% of Americans report having heard “a 

lot” or “some” about nanotechnology, up four percentage 

points from 2008 and 2006. A plurality (44%) of Americans 

report having heard “nothing at all” about nanotechnology 

(appendix table 7-33). In 2010, among the minority of respondents who had heard “a lot” or “some” about this technology, 

65% correctly indicated that “nanotechnology involves manipulating extremely small units of matter, such as individual 

atoms, in order to produce better materials,” and 39% correctly indicated that “the properties of nanoscale materials often 

differ fundamentally and unexpectedly from the properties of 

the same materials at larger scales.” 

After receiving a brief explanation of nanotechnology, 

GSS respondents were asked about the likely balance between the benefits and harms of nanotechnology. Among all 

respondents to the 2010 GSS, regardless of their awareness 

of nanotechnology, 37% said the benefits will outweigh the 

harmful results, 11% expected the harms to predominate, 

and 43% held no opinion (appendix table 7-34). The balance 

of opinion was similar in 2006 and 2008. 

In the GSS data, favorable attitudes toward and familiarity with nanotechnology are strongly associated. That 

is, Americans who say they are more familiar with nanotechnology are more likely to believe that its benefits will 

outweigh the risks. Among those who have heard “a lot” or 

“some” about nanotechnology, 65% said the benefits will 

outweigh the harms, 8% said harmful results will outweigh 

any benefits, 5% said benefits and harms would be about 

equal, and 22% had no opinion.40 However, this association does not mean that when people become more familiar with nanotechnology their attitudes necessarily become 

positive (Cobb 2005; Lee, Scheufele, and Lewenstein 2005). 

Furthermore, recent research suggests that attitudes toward 

nanotechnology are likely to vary depending on the context 
Science and Engineering Indicators 2012 7-43

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

In Europe, 45% of survey respondents said they had 

heard of nanotechnology on the 2010 Eurobarometer, which 

described nanotechnology in terms of consumer product applications. Overall, 44% of Europeans agreed that nanotechnology should be encouraged, 35% disagreed, and 22% had 

no opinion about this issue (Gaskell et al. 2010).

Other Emerging Technologies

Opinions on other new and emerging technologies show 

an often receptive public, but one where opinion is likely to 

be fluid due to low levels of familiarity with the issue and 

any relevant concerns for public debate. 

Synthetic biology, an emerging field that applies biologic 

science to design and construct new biological parts, organisms, or artificially engineered biological systems, provides 

one example. About one-quarter (26%) of Americans have 

heard “some” or “a lot” about synthetic biology, up from 

9% in 2008 (Peter D. Hart Research Associates, Inc. 2010). 

When first asked to weigh the benefits and harms from synthetic biology, one-third thought the benefits and risks would 

be about equal, a similar percentage had no opinion, and the 

remainder was split about equally between those who felt 

the benefits would outweigh the risks and those who felt the 

risks would outweigh the benefits. After hearing a balanced 

description of the benefits and risks of synthetic biology, a 

greater proportion said the risks will outweigh the benefits 

than said the benefits will outweigh the risks. 

International Comparisons

The 2010 Eurobarometer survey included an extensive 

series of questions about new and emerging biotechnologies. 

As in the United States, familiarity with synthetic biology 

tends to be limited. These data show that public familiarity with new technologies is often associated with opinions about the technology. In the case of nanotechnology, 

Europeans who are more familiar with the technology are 

more likely to see nanotechnology as safe and beneficial. In 

the cases of GM foods and animal cloning, greater familiarity with the technology is not associated with positive assessments of it (Gaskell, et al. 2010).

Animal Research

The medical research community conducts experimental 

tests on animals for many purposes, including to advance 

scientific understanding of biological processes and test the 

effectiveness of drugs and procedures that may eventually 

be used to improve human health. 

Most Americans support at least some kind of animal 

research. A 52% majority favors the use of animals in scientific research, whereas 43% are opposed, according to a 

2009 Pew Research Center survey. Nearly two-thirds said 

they favor “using animals in medical research” (VCU 2007). 

Further, 55% of Americans consider “medical testing on 

animals” to be “morally acceptable,” whereas 38% say it is 

“morally wrong,” according to a 2011 Gallup survey (Saad 

2011b). A 2008 Gallup survey also found a majority of respondents supported this kind of research; 64% opposed 

“banning all medical research on laboratory animals” and 

59% opposed “banning all product testing on laboratory animals” (Newport 2008).

There is a sizeable gender gap in opinions about animal 

research. Women are less likely than men to support animal 

research; 42% of women favor the use of animals in research, 

compared with 62% of men (Pew Research Center 2009a). 

Similarly, women are less likely than men to say that medical testing on animals is “morally acceptable” (Saad 2010). 

Opposition to using animals in research has grown in the 

past two decades. When asked whether scientists should be 

allowed to do “research that causes pain and injury to animals like dogs and chimpanzees if it produces new information about human health problems,” between 42% and 45% 

of Americans disagreed in the early 1990s. This proportion 

increased to 51% in 2001 and 56% in 2008 (figure 7-18; appendix tables 7-35 and 7-36).41

Past NSF surveys suggest that the public is more comfortable with the use of mice in scientific experiments than the 

use of dogs and chimpanzees (NSB 2002). In 2001, 68% of 

Figure 7-18

Public attitudes toward whether scientific research 

that causes pain to animals should be allowed: 

1988–2008 

NOTES: Responses to Scientists should be allowed to do research 

that causes pain and injury to animals like dogs and chimpanzees if it 

produces new information about human health problems. Do you 

strongly agree, agree, disagree, or strongly disagree? Responses of 

“don’t know” not shown. Survey results in 1988, 1990, 1992, 1995, 

1997, 1999, 2001, and 2008; other years extrapolated. 

SOURCES: National Science Foundation, National Center for 

Science and Engineering Statistics, Survey of Public Attitudes 

Toward and Understanding of Science and Technology (1988–2001); 

University of Chicago, National Opinion Research Center, General 

Social Survey (2008). See appendix tables 7-35 and 7-36.

Science and Engineering Indicators 2012

Percent

1988 1990 1992 1995 1997 1999 2001 2008

0

10

20

30

40

50

60

Strongly agree/agree

Disagree/strongly disagree
7-44 Chapter 7. Science and Technology: Public Attitudes and Understanding

Americans agreed that “scientists should be allowed to do 

research that causes pain and injury to animals like mice if 

it produces new information about human health problems,” 

compared to 44% who expressed agreement when the question focused on dogs and chimpanzees (NSB 2002).

International comparisons on animal research are scarce. 

Half of Malaysians agree that “although research on animals 

may cause suffering, it has to be done for the sake of mankind.” In Europe, two-thirds agree that “scientists should be 

allowed to do research on animals like mice if it produces 

new information about human health problems.” A survey 

conducted by the Gallup Organization in 2003 showed that 

Americans and Canadians were more likely to tolerate scientific research on animals than the British. When asked, 

“Regardless of whether or not you think it should be legal, 

please tell me whether you personally believe that in general 

medical testing on animals is morally acceptable or morally wrong,” the majority of adults in the United States and 

Canada believed it was morally acceptable (63% and 59%, 

respectively). In contrast, the majority of British respondents 

thought it was morally wrong (54%) (Mason Kiefer 2003). 

Science, Engineering, and Mathematics 

Education

In much of the public discourse about how Americans 

will fare in an increasingly S&T-driven world, quality education in science and mathematics is seen as crucial for 

both individuals and the nation as a whole. 

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 

(appendix tables 7-37 and 7-38). Their level of agreement 

increased with education, science knowledge, income, and 

age. Dissatisfaction with the quality of math and science 

education increased from 63% in 1985 to 70% in 2008, 

peaking at 75% in 1992 (figure 7-19). 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 proportion of Americans who said they 

believe the federal government is spending too little money on improving education in the biennial GSS surveys 

has remained greater than 70% since the early 1980s. This 

is consistently one of the top areas where the public feels 

government spending is too low (figure 7-14; appendix 

table 7-26).

Conclusion

The portrait of public knowledge and attitudes concerning S&T depends, in part, on the standard used for judgment. 

One standard involves comparing a country’s knowledge 

and attitudes with those recorded in other countries. When 

the data are examined using other countries as a benchmark, 

the United States compares relatively favorably. Compared 

with adult residents of other developed countries, American 

adults appear to know as much or more about science, and 

they express as much or more optimism about technology. 

A second standard involves comparing Americans’ 

knowledge and attitudes today with those of the past. By 

this standard, the survey data, while not showing marked 

improvements in public understanding, provide little or no 

evidence of declining knowledge. Relative to Americans in 

the recent past, today’s American public scores as well on 

knowledge measures and tends to be more skeptical about 

scientific claims for pseudoscience, such as astrology. 

Additionally, younger Americans are more knowledgeable 

about S&T than older cohorts; this pattern suggests that the 

long-term outlook for public knowledge is promising. 

Similarly, general U.S. attitudes about the promise and 

contribution of science to society remain strongly positive. 

Three decades of data consistently show that Americans 

endorse the past achievements and future promise of S&T 

and are favorably predisposed to continued government investment in science. When Americans compare science with 

other institutions, science’s relative ranking is equally or 

more favorable than in the past. In addition, the prestige of 

the engineering profession has increased in recent years.

A third standard involves assessing what a technologically advanced society requires (either today or in the future) to 

compete in the world economy and enable its citizens to better take advantage of scientific progress in their own lives. 

Figure 7-19

Public assessment of whether the quality of 

science and mathematics education in America is 

inadequate: 1985–2008 

NOTES: Responses to The quality of science and mathematics 

education in American schools is inadequate. Do you strongly agree, 

agree, disagree, or strongly disagree? Responses of “don’t know” 

not shown. Survey results in 1985, 1988, 1990, 1992, 1995, 1997, 

1999, 2001, and 2008; other years extrapolated. 

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 Chicago, National Opinion Research Center, General 

Social Survey (2008). See appendix tables 7-37 and 7-38.

Science and Engineering Indicators 2012

Percent

1985 1988 1990 1992 1995 1997 1999 2001 2008

0

10

20

30

40

50

60

70

80

90

Strongly agree/agree

Disagree/strongly disagree
Science and Engineering Indicators 2012 7-45

By this standard, there is more reason for concern. Trend 

data show that significant minorities of Americans cannot 

answer relatively simple knowledge questions about S&T, 

often express basic misconceptions about emerging technologies such as nanotechnology, and believe that relatively great scientific uncertainty surrounds the existence and 

causes of global climate change. Sizable proportions of the 

population express reservations about how the speed of technological change affects our way of life, and about the use of 

animals in medical research. 

Regardless of the standard used in assessing public attitudes and understanding of S&T, one pattern in the data stands 

out: Americans who are more highly educated—particularly 

those who are college-educated and have completed college 

courses in science and mathematics—tend to know and understand more about S&T. Although it is not clear whether 

this association is causal, the pattern underscores the need for 

continued attention to the education system and the possible 

role of science, technology, engineering, and mathematics 

education in fostering public understanding of S&T. 

Notes 

1. Data from Pew show that the proportion of Americans 

who read the newspaper declined from 40% to 34% between 

2006 and 2008, and that newspapers would have lost more 

readers if they did not have online versions. Most of the loss 

in newspaper readership since 2006 came from those who 

read the print version of the newspaper—in 2008, 27% said 

they had read only the print version of a daily newspaper the 

day before, compared to 34% in 2006. However, audiences 

are getting news from both traditional sources (television, 

print) and the Internet and blending these sources together, 

rather than choosing between one or the other (Pew Research 

Center 2008). 

2. The 2010 GSS included two alternatives for distinguishing between print and online sources of information. 

The data in figure 7-1 are based on an approach that used 

followup questions asking whether references to newspapers, magazines, or the Internet included primarily print or 

online sources. An alternative approach offered response options that distinguish between online and print-format sources for newspapers and magazines, without branching into 

followup questions (see sidebar, “The Blending of Print and 

Online Sources of Science News”). Estimates of information 

sources for television, books, and other sources where there 

is no need to distinguish between print and online venues are 

comparable for both approaches to measurement. For most 

respondents, a response of newspapers appears to reflect reliance on print newspapers. Using the branched approach, 

the percentage indicating reliance on printed newspapers is 

similar to the percentage saying the same on the question 

with direct response options; less than 1% initially indicated 

a reliance on newspapers and then responded that they primarily relied on online newspapers in a followup question. 

When respondents are initially given options which distinguish between online newspapers and other online sources, 

however, somewhat fewer respondents indicate a reliance on 

any type of Internet source (31% vs. 35%). 

3. The Internet is also a primary source of information for 

most Americans when they are seeking information on other 

topics, such as health. See Pew Internet and American Life 

Project surveys (Fox 2010; Jansen 2010).

4. Analyses that examine age differences in patterns of 

media use through repeated cross-sectional surveys hide 

considerable generational effects, because they only show a 

snapshot of a single point in time (Losh 2009).

5. In 2001, this question was part of a single-purpose 

telephone survey focused on S&T. In 2008, these data were 

collected as part of 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. 

6. In interpreting survey data that use the phrase “science 

and technology,” it is important to take into account the uncertainties surrounding its meaning and the different associations Americans make when they hear it. 

7. Note that the Eurobarometer surveys include a different set of countries because the composition of the European 

Union changes over time. In 2010, the survey included 27 

countries; in 2005, it included 25 countries.

8. 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.

9. The China survey asked about interest levels on a 

3-point scale with response options translated as “interested,” “ordinary” interest level, and “not interested.”

10. 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 is designed to capture 

the main news stories covered each week. Coding of programs and articles is 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 is coded on 17 variables according to 

an established coding protocol. The team of individuals performing the content analysis is directed by a coding manager, a training coordinator, a methodologist, and a senior 

researcher. Intensive tests of intercoder reliability are conducted annually. For variables that require little or no inference, intercoder agreement was 97% in 2010. 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.

11. 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 TV sources.

12. “Science, space, and technology” includes stories on 

manned and unmanned space flight, astronomy, scientific 

research, computers, the Internet, and telecommunications 
7-46 Chapter 7. Science and Technology: Public Attitudes and Understanding

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.

13. The peak in the coverage of the category “Science, 

space, and technology” in 1999 includes major network 

coverage of stories about the so-called Millennium Bug and 

business issues from the dot.com boom, such as the rise of 

Internet commerce and the browser antitrust wars.

14. The sample of news links on blogs and Twitter posts 

comes from two prominent Web-tracking sites, Icerocket 

and Tweetmeme, using the links to articles embedded on the 

sites as a proxy for the subject of the blog post or Tweet. The 

Web-tracking sites 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 capture 

the list of most-linked-to stories each weekday, and the coding staff categorize 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/commentary_backgrounder/

new_media_index_methodology. 

15. People can become involved with S&T through many 

kinds of non-classroom activities. Examples of such activities include participating in government policy processes, 

going to movies that feature S&T, 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.

16. Involvement in informal S&T activities is also 

thought to foster learning and knowledge about S&T (see 

Falk and Dierking 2010).

17. In the 2008 GSS, respondents received two different introductions to this question. Response patterns did not 

vary depending on which introduction was given. 

18. In 2001, this question was part of a single-purpose 

telephone survey focused on science and technology. In 

2008, these data were collected as part of 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 visit behavior account for the decline 

observed between 2001 and 2008.

19. In the United States, this measure included visits to a 

zoo or aquarium.

20. 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 feel 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.

21. Respondents who say they know “nothing at all” 

about nanotechnology were not asked the two knowledge 

questions about this topic; they are classified as holding incorrect responses to both questions.

22. The two nanotechnology questions were asked only 

of respondents who said they had some familiarity with nanotechnology, and a sizable majority of the respondents who 

ventured a response different from “don’t know” answered 

the questions correctly. To measure nanotechnology knowledge more reliably, researchers would prefer a scale with 

more than two questions.

23. In its own international comparison of scientific literacy, Japan ranked itself 10th among the 14 countries it 

evaluated (National Institute of Science and Technology 

Policy 2002).

24. Early NSF surveys 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. 

25. A change of this magnitude in a 2-year period is unusual. Because classification of knowledge on these items 

includes open-ended questions, it is possible that some of 

the change could stem from unknown differences in coding 

practices by the GSS staff over time. 

26. Classification as understanding scientific inquiry is 

based on providing a correct response to the measure of understanding probability and providing a correct response to 

either the measure of understanding an experiment or the 

open-ended measure of understanding a scientific study. 

27. The questions were selected from the Trends in 

Mathematics and Science Studies (TIMSS), National 

Assessment of Educational Progress (NAEP), practice 

General Educational Development (GED) exams, and 

AAAS Project 2061. 

28. The scoring of the open-ended questions closely followed the scoring of the corresponding test administered to 

middle-school students. 

For the NAEP question, “Lightning and thunder happen 

at the same time, but you see the lightning before you hear 

the thunder. Explain why this is so,” the question was scored 

as follows: 

1) Complete: The response provided a correct explanation 

including the relative speeds at which light and sound travel. 

For example, “Sound travels much slower than light so you 

see the light sooner at a distance.” 

2) Partial: The response addressed speed and used terminology such as thunder for sound and lightning for light, or 

made a general statement about speed but did not indicate 
Science and Engineering Indicators 2012 7-47

which is faster. For example, “One goes at the speed of light 

and the other at the speed of sound.” 

3) Unsatisfactory/Incorrect: Any response that did not relate or mention the faster speed of light or its equivalent, 

the slower speed of sound. For example, “Because the storm 

was further out,” or “Because of static electricity.” 

For the TIMSS question, “A solution of hydrochloric acid 

(HCl) in water will turn blue litmus paper red. A solution of 

the base sodium hydroxide (NaOH) in water will turn red 

litmus paper blue. If the acid and base solutions are mixed in 

the right proportion, the resulting solution will cause neither 

red nor blue litmus paper to change color. Explain why the 

litmus paper does not change color in the mixed solution,” 

the question was scored as follows: 

1) Correct: The response had to refer to a neutralization or 

a chemical reaction that results in products that do not react 

with litmus paper. Three kinds of answers were classified 

as correct: 

a. The response referred explicitly to the formation of water (and salt) from the neutralization reaction. For example, 

“Hydrochloric acid and sodium hydroxide will mix together 

to form water and salt, which is neutral.”

b. The response referred to neutralization (or the equivalent) 

even if the specific reaction is not mentioned. For example, 

“The mixed solution is neutral, so litmus paper does not 

react.”)

c. The response referred to a chemical reaction taking place 

(implicitly or explicitly) to form products that do not react 

with litmus paper (or a similar substance), even if neutralization was not explicitly mentioned. For example, “The 

acid and base react, and the new chemicals do not react with 

litmus paper.”

2) Partially correct: The response mentioned only that acids 

and bases are “balanced,” “opposites,” “cancel each other 

out,” or that it changes to a salt without mentioning the neutralization reaction. These answers suggest that the respondent remembered the concept but the terminology they used 

was less precise, or that the answer was partial. For example, 

“They balance each other out.” 

3) Incorrect: The response did not mention any of the above 

in a–c or is too partial or incomplete, and/or uses terminology 

that is too imprecise. For example, “Because they are base 

solutions—the two bases mixed together there is no reaction,” 

or “There is no change. Both colors change to the other.”

29. 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. 

30. Methodological issues make fine-grained comparisons of data from different survey years suspect. For 

instance, 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 (though 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. 

31. 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. 

32. The GSS questions on global climate change used the 

term “global warming.”

33. The 2010 GSS survey included ratings of nuclear engineers in addition to medical researchers, environmental 

scientists, and economists. As discussed below, the patterns 

of results were similar whether the group with relevant expertise was engineers or scientists. 

34. There are many different types of specializations 

within occupations, and prestige may well vary within the 

same occupation or industry.

35. This survey was conducted prior to the earthquake 

and tsunami in Japan on March 11, 2011.

36. 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 suggested that such 

wording differences had little effect (EC 2008; Villar and 

Krosnick 2010).

37. The two questions from the 2009 Gallup survey were 

each asked to half of the sample (n = 500).

38. Countries with nuclear plants include Belgium, 

Bulgaria, the Czech Republic, Finland, France, Germany, 

Hungary, Lithuania, the Netherlands, Romania, Slovakia, 

Slovenia, Spain, Sweden, and the United Kingdom. Two 

exceptions to this pattern are Romania and Spain, both of 

which have operational nuclear power plants but where the 

level of support for nuclear energy is below the EU average. 

An earlier Eurobarometer study showed that respondents in 

Spain and Romania were less aware of the fact that their 

countries have nuclear power plants than respondents in 

other countries with nuclear plants in operation (EC 2008a). 

This low level of awareness regarding the operation of a 

nuclear plant in their country may lead to a less positive attitude about nuclear energy.

39. According to a report from The National Academies, 

more than 600 products involving nanotechnology are already on the market; most of them are health and fitness 

products such as skin care products and cosmetics (The 

National Academies, 2008b).
7-48 Chapter 7. Science and Technology: Public Attitudes and Understanding

40. This pattern of data is consistent with findings from 

a meta-analysis of 22 studies conducted in the United States 

and elsewhere by Satterfield et al. (2009).

41. The increase in the proportion of respondents who 

disagree with this statement may be related to methodological issues, because of the changes in data collection. See 

note 5.

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.

EU: Austria, Belgium, Bulgaria, Cyprus, Czech Republic, 

Denmark, Estonia, Finland, France, Germany, Greece, 

Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, 

Malta, Netherlands, Poland, Portugal, Romania, Slovakia, 

Slovenia, Spain, Sweden, United Kingdom.

Genetically modified (GM) food: A food product containing some quantity of any genetically modified 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.

Synthetic biology: An emerging field that applies biologic science to design and construct new biological parts, 

organisms, or artificially engineered biological systems.

Reproductive cloning: Technology used to generate genetically identical individuals with the same nuclear DNA as 

another individuals. 

Therapeutic cloning: Use of cloning technology in medical research to develop new treatments for diseases; differentiated from human reproductive cloning.

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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

High-Technology Industries ....................................................................................................8-9

Appendix Tables ....................................................................................................................8-10

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

Elementary/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 Current Expenditures as a Percentage 

of Gross Domestic Product ...................................................................................................8-30

Current Expenditures per Pupil for Elementary and Secondary Public Schools ......................8-32

Percentage of Public High School Students Taking Advanced Placement Exams ..................8-34

Percentage of Public High School Students Scoring 3 or Higher on at Least One 

Advanced Placement Exam ..................................................................................................8-36

Percentage of Public High School Students Scoring 3 or Higher on Advanced Placement 

Calculus AB Exam ................................................................................................................8-38

High School Graduates Among Individuals 25–44 Years Old .................................................8-40

Higher Education

Bachelor’s Degrees Conferred per 1,000 Individuals 18–24 Years Old ..................................8-42

Bachelor’s Degrees in Science and Engineering Conferred per 1,000 

Individuals 18–24 Years Old ................................................................................................8-44

Bachelor’s Degrees in Natural Sciences and Engineering Conferred 

per 1,000 Individuals 18–24 Years Old ................................................................................8-46

Science and Engineering Degrees as a Percentage of Higher Education Degrees Conferred ...8-48

Natural Sciences and Engineering Degrees as a Percentage of Higher Education 

Degrees Conferred ................................................................................................................8-50

Science and Engineering Graduate Students per 1,000 Individuals 25–34 Years Old .............8-52

Advanced Science and Engineering Degrees as a Percentage of S&E Degrees Conferred .....8-54

Advanced Natural Sciences and Engineering Degrees as a Percentage of NS&E Degrees 

Conferred ..............................................................................................................................8-56

Science and Engineering Doctoral Degrees as a Percentage of S&E Degrees Conferred ........8-58

Please see chapter errata
8-2  Chapter 8. State Indicators

Average Undergraduate Charge at Public 4-Year Institutions .................................................8-60

Average Undergraduate Charge at Public 4-Year Institutions as a Percentage of 

Disposable Personal Income .................................................................................................8-62

Appropriations of State Tax Funds for Operating Expenses of Higher Education as a 

Percentage of Gross Domestic Product .................................................................................8-64

State Expenditures on Student Aid per Full-Time Undergraduate Student ..............................8-66

State Funding for Major Public Research Universities per Enrolled Student ..........................8-68

Postsecondary Degree Holders Among Individuals 25–44 Years Old .....................................8-70

Bachelor’s Degree Holders Among Individuals 25–44 Years Old ............................................8-72

Workforce

Bachelor’s Degree Holders Potentially in the Workforce ........................................................8-74

Individuals in Science and Engineering Occupations as a Percentage of the Workforce ........8-76

Employed Science and Engineering Doctorate Holders as a Percentage of the Workforce .....8-78

Engineers as a Percentage of the Workforce ............................................................................8-80

Life and Physical Scientists as a Percentage of the Workforce ...............................................8-82

Computer Specialists as a Percentage of the Workforce ..........................................................8-84

Technical Workers as a Percentage of the Workforce ..............................................................8-86

Financial Research and Development Inputs

R&D as a Percentage of Gross Domestic Product .....................................................................8-88

Federal R&D Obligations per Employed Worker .....................................................................8-90

Federal R&D Obligations per Individual in Science and Engineering Occupation ..................8-92

State Agency R&D Expenditures per $1 Million of Gross Domestic Product .........................8-94

State Agency R&D Expenditures per Employed Worker .........................................................8-96

State Agency R&D Expenditures per Individual in Science and Engineering Occupation ......8-98

Business-Performed R&D as a Percentage of Private-Industry Output ..................................8-100

Academic Science and Engineering R&D per $1,000 of Gross Domestic Product ..............8-102

Research and Development Outputs

Science and Engineering Doctorates Conferred per 1,000 S&E Doctorate Holders ..............8-104

Academic Science and Engineering Article Output per 1,000 S&E Doctorate Holders 

in Academia ........................................................................................................................8-106

Academic Science and Engineering Article Output per $1 Million of Academic 

S&E R&D ............................................................................................................................8-108

Academic Patents Awarded per 1,000 Science and Engineering Doctorate Holders 

in Academia .........................................................................................................................8-110

Patents Awarded per 1,000 Individuals in Science and Engineering Occupations ................8-112

Science and Technology in the Economy

High-Technology Establishments as a Percentage of All Business Establishments ...............8-114

Net High-Technology Business Formations as a Percentage of All Business 

Establishments .....................................................................................................................8-116

Employment in High-Technology Establishments as a Percentage of Total Employment .....8-118

Average Annual Federal Small Business Innovation Research Funding per $1 Million 

of Gross Domestic Product .................................................................................................8-120

Venture Capital Disbursed per $1,000 of Gross Domestic Product ........................................8-122

Venture Capital Deals as a Percentage of High-Technology Business Establishments ..........8-124

Venture Capital Disbursed per Venture Capital Deal ..............................................................8-126
Science and Engineering Indicators 2012 8-3

List of Tables

Table 8-1. Average fourth grade mathematics performance, by state: 2000, 2005, and 2009 ...... 8-13

Table 8-2. Students reaching proficiency in fourth grade mathematics, by state: 2000, 2005, 

and 2009 ..................................................................................................................................... 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: 2000, 2005, and 2009 ..... 8-21

Table 8-6. Students reaching proficiency in eighth grade mathematics, by state: 2000, 2005, 

and 2009 ..................................................................................................................................... 8-23

Table 8-7. Average eighth grade science performance, by state: 2009 ........................................ 8-25

Table 8-8. Students reaching proficiency in eighth grade science, by state: 2009 ....................... 8-27

Table 8-9. Public school teacher salaries, by state: 2000, 2005, and 2010 ................................... 8-29

Table 8-10. Elementary and secondary public school current expenditures as a percentage 

of gross domestic product, by state: 2000, 2005, and 2009 ....................................................... 8-31

Table 8-11. Current expenditures per pupil for elementary and secondary public schools, 

by state: 2000, 2005, and 2009 .................................................................................................. 8-33

Table 8-12. Public high school students taking Advanced Placement Exams, by state: 

2000, 2005, and 2010 ................................................................................................................. 8-35

Table 8-13. Public high school students scoring 3 or higher on at least one Advanced 

Placement Exam, by state: 2000, 2005, and 2010 ..................................................................... 8-37

Table 8-14. Public high school students scoring 3 or higher on Advanced Placement 

Calculus AB Exam, by state: 2000, 2005, and 2010 ................................................................. 8-39

Table 8-15. High school graduates among individuals 25–44 years old, by state: 

2000, 2005, and 2009 ................................................................................................................. 8-41

Table 8-16. Bachelor’s degrees conferred per 1,000 individuals 18–24 years old, by state: 

2000, 2005, and 2009 ................................................................................................................. 8-43

Table 8-17. Bachelor’s degrees in science and engineering conferred per 1,000 individuals 

18–24 years old, by state: 2000, 2005, and 2009 ...................................................................... 8-45

Table 8-18. Bachelor’s degrees in natural sciences and engineering conferred 

per 1,000 individuals 18–24 years old, by state: 2000, 2005, and 2009 ................................... 8-47

Table 8-19. Science and engineering degrees as a percentage of higher education 

degrees conferred, by state: 2000, 2005, and 2009 ................................................................... 8-49

Table 8-20. Natural sciences and engineering degrees as a percentage of higher 

education degrees conferred, by state: 2000, 2005, and 2009 ................................................... 8-51

Table 8-21. Science and engineering graduate students per 1,000 individuals 25–34 

years old, by state: 2000, 2005, and 2009 ................................................................................... 8-53

Table 8-22. Advanced science and engineering degrees as a percentage of S&E degrees 

conferred, by state: 2000, 2005, and 2009 ................................................................................. 8-55

Table 8-23. Advanced natural sciences and engineering degrees as a percentage of NS&E 

degrees conferred, by state: 2000, 2005, and 2009 ................................................................... 8-57

Table 8-24. Science and engineering doctoral degrees as a percentage of S&E degrees 

conferred, by state: 2000, 2005, and 2009 ................................................................................. 8-59

Table 8-25. Average undergraduate charge at public 4-year institutions, by state: 

2000, 2005, and 2010 ................................................................................................................. 8-61

Table 8-26. Average undergraduate charge at public 4-year institutions as a percentage 

of disposable personal income, by state: 2000, 2005, and 2009 ............................................... 8-63

Table 8-27. Appropriations of state tax funds for operating expenses of higher education 

as a percentage of gross domestic product, by state: 2000, 2005, and 2010 ............................ 8-65

Table 8-28. State expenditures on student aid per full-time undergraduate student, by state: 

2000, 2004, and 2008 .................................................................................................................. 8-67
8-4  Chapter 8. State Indicators

Table 8-29. State funding for major public research universities per enrolled student, by state: 

2002, 2006, and 2010 ................................................................................................................. 8-69

Table 8-30. Postsecondary degree holders among individuals 25–44 years old, by state: 

2000, 2005, and 2009 ................................................................................................................. 8-71

Table 8-31. Bachelor’s degree holders among individuals 25–44 years old, by state: 

2000, 2005, and 2009 ................................................................................................................. 8-73

Table 8-32. Bachelor’s degree holders potentially in the workforce, by state: 2000, 2005, 

and 2009 ..................................................................................................................................... 8-75

Table 8-33. Individuals in science and engineering occupations as a percentage of the 

workforce, by state: 2003 2006, and 2010 ................................................................................. 8-77

Table 8-34. Employed science and engineering doctorate holders as a percentage of the 

workforce, by state: 1997, 2003, and 2008 ................................................................................. 8-79

Table 8-35. Engineers as a percentage of the workforce, by state: 2004, 2007, and 2010 ........... 8-81

Table 8-36. Life and physical scientists as a percentage of the workforce, by state: 

2004, 2007, and 2010 ................................................................................................................. 8-83

Table 8-37. Computer specialists as a percentage of the workforce, by state: 

2004, 2007, and 2010 ................................................................................................................. 8-85

Table 8-38. Technical workers as a percentage of the workforce, by state: 

2004, 2007, and 2010 .................................................................................................................. 8-87

Table 8-39. R&D as a percentage of gross domestic product, by state: 

2000, 2004, and 2008 .................................................................................................................. 8-89

Table 8-40. Federal R&D obligations per employed worker, by state: 2000, 2004, and 2008 ..... 8-91

Table 8-41. Federal R&D obligations per individual in science and engineering occupation, 

by state: 2003, 2005, and 2008 .................................................................................................. 8-93

Table 8-42. State agency R&D expenditures per $1 million of gross domestic product, 

by state: 2006 and 2007 .............................................................................................................. 8-95

Table 8-43. State agency R&D expenditures per employed worker, by state: 2006 and 2007 .... 8-97

Table 8-44. State agency R&D expenditures per individual in science and engineering 

occupation, by state: 2006 and 2007 .......................................................................................... 8-99

Table 8-45. Business-performed R&D as a percentage of private-industry output, by state: 

2000, 2004, and 2008 ...............................................................................................................8-101

Table 8-46. Academic science and engineering R&D per $1,000 of gross domestic 

product, by state: 2000, 2005, and 2009 ..................................................................................8-103

Table 8-47. Science and engineering doctorates conferred per 1,000 employed S&E doctorate 

holders, by state: 1997, 2003, and 2008 ..................................................................................8-105

Table 8-48. Academic science and engineering article output per 1,000 S&E doctorate 

holders in academia, by state: 1997, 2003, and 2008 ..............................................................8-107

Table 8-49. Academic science and engineering article output per $1 million of academic 

S&E R&D, by state: 2000, 2004, and 2009 ............................................................................8-109

Table 8-50. Academic patents awarded per 1,000 science and engineering doctorate 

holders in academia, by state: 1997, 2003, and 2008 ..............................................................8-111

Table 8-51. Patents awarded per 1,000 individuals in science and engineering 

occupations, by state: 2003, 2006, and 2010 ...........................................................................8-113

Table 8-52. High-technology establishments as a percentage of all business establishments, 

by state: 2003, 2006, and 2008 ................................................................................................8-115

Table 8-53. Net high-technology business formations as a percentage of all business 

establishments, by state: 2004, 2006, and 2008 ......................................................................8-117

Table 8-54. Employment in high-technology establishments as a percentage of total 

employment, by state: 2003, 2006, and 2008 ..........................................................................8-119

Table 8-55. Average annual federal Small Business Innovation Research funding 

per $1 million of gross domestic product, by state: 2000–02, 2004–06, and 2008-10 ...........8-121
Science and Engineering Indicators 2012 8-5

Table 8-56. Venture capital disbursed per $1,000 of gross domestic product, by state: 

2000, 2005, and 2010 ................................................................................................................8-123

Table 8-57. Venture capital deals as a percentage of high-technology business 

establishments, by state: 2003, 2006, and 2008 .......................................................................8-125

Table 8-58. Venture capital disbursed per venture capital deal, by state: 

2000, 2005, and 2010 ................................................................................................................8-127

Table 8-A. 2002 NAICS codes that constitute high-technology industries ................................... 8-11

List of Figures

Figure 8-1. Average fourth grade mathematics performance: 2009 ..........................................8-12

Figure 8-2. Students reaching proficiency in fourth grade mathematics: 2009 .........................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: 2009 ..........................................8-20

Figure 8-6. Students reaching proficiency in eighth grade mathematics: 2009 .........................8-22

Figure 8-7. Average eighth grade science performance: 2009 ..................................................8-24

Figure 8-8. Students reaching proficiency in eighth grade science: 2009 .................................8-26

Figure 8-9. Public school teacher salaries: 2010 .......................................................................8-28

Figure 8-10. Elementary and secondary public school current expenditures as a percentage 

of gross domestic product: 2009 ............................................................................................8-30

Figure 8-11. Current expenditures per pupil for elementary and secondary public 

schools: 2009 .........................................................................................................................8-32

Figure 8-12. Percentage of public high school students taking Advanced Placement 

Exams: 2010 ...........................................................................................................................8-34

Figure 8-13. Percentage of public high school students scoring 3 or higher on at least one 

Advanced Placement Exam: 2010 .........................................................................................8-36

Figure 8-14. Percentage of public high school students scoring 3 or higher on Advanced 

Placement Calculus AB Exam: 2010 .....................................................................................8-38

Figure 8-15. High school graduates among individuals 25–44 years old: 2009 ........................8-40

Figure 8-16. Bachelor’s degrees conferred per 1,000 individuals 18–24 years old: 2009 ........8-42

Figure 8-17. Bachelor’s degrees in science and engineering conferred per 1,000 individuals 

18–24 years old: 2009 ............................................................................................................8-44

Figure 8-18. Bachelor’s degrees in natural sciences and engineering conferred per 1,000 

individuals 18–24 years old: 2009 .........................................................................................8-46

Figure 8-19. Science and engineering degrees as a percentage of higher education 

degrees conferred: 2009 .........................................................................................................8-48

Figure 8-20. Natural sciences and engineering degrees as a percentage of higher 

education degrees conferred: 2009 ........................................................................................8-50

Figure 8-21. Science and engineering graduate students per 1,000 individuals 25–34 years 

old: 2009 ................................................................................................................................8-52

Figure 8-22. Advanced science and engineering degrees as a percentage of S&E degrees 

conferred: 2009 ......................................................................................................................8-54

Figure 8-23. Advanced natural sciences and engineering degrees as a percentage of 

NS&E degrees conferred: 2009 .............................................................................................8-56

Figure 8-24. Science and engineering doctoral degrees as a percentage of S&E degrees 

conferred: 2009 ......................................................................................................................8-58

Figure 8-25. Average undergraduate charge at public 4-year institutions: 2010 .......................8-60

Figure 8-26. Average undergraduate charge at public 4-year institutions as a percentage 

of disposable personal income: 2009 .....................................................................................8-62
8-6  Chapter 8. State Indicators

Figure 8-27. Appropriations of state tax funds for operating expenses of higher education 

 as a percentage of gross domestic product: 2010 ..................................................................8-64

Figure 8-28. State expenditures on student aid per full-time undergraduate student: 2008 ......8-66

Figure 8-29. State funding for major public research universities per enrolled student: 

2010 ........................................................................................................................................8-68

Figure 8-30. Postsecondary degree holders among individuals 25–44 years old: 2009 ...............8-70

Figure 8-31. Bachelor’s degree holders among individuals 25–44 years old: 2009 ...................8-72

Figure 8-32. Bachelor’s degree holders potentially in the workforce: 2009 ...............................8-74

Figure 8-33. Individuals in science and engineering occupations as a percentage of the 

workforce: 2010 .....................................................................................................................8-76

Figure 8-34. Employed science and engineering doctorate holders as a percentage of the 

workforce: 2008 ......................................................................................................................8-78

Figure 8-35. Engineers as a percentage of the workforce: 2010 .................................................8-80

Figure 8-36. Life and physical scientists as a percentage of the workforce: 2010 .......................8-82

Figure 8-37. Computer specialists as a percentage of the workforce: 2010 ................................8-84

Figure 8-38. Technical workers as a percentage of the workforce: 2010 ....................................8-86

Figure 8-39. R&D as a percentage of gross domestic product: 2008 ..........................................8-88

Figure 8-40. Federal R&D obligations per employed worker: 2008 ...........................................8-90

Figure 8-41. Federal R&D obligations per individual in science and engineering 

occupation: 2008.....................................................................................................................8-92

Figure 8-42. State agency R&D expenditures per $1 million of gross domestic product: 2007 ... 8-94

Figure 8-43. State agency R&D expenditures per employed worker: 2007 ...............................8-96

Figure 8-44. State agency R&D expenditures per individual in science and engineering 

occupation: 2007.....................................................................................................................8-98

Figure 8-45. Business-performed R&D as a percentage of private-industry output: 2008 .......8-100

Figure 8-46. Academic science and engineering R&D per $1,000 of gross domestic 

product: 2009 ........................................................................................................................8-102

Figure 8-47. Science and engineering doctorates conferred per 1,000 employed S&E doctorate 

holders: 2008 .......................................................................................................................8-104

Figure 8-48. Academic science and engineering article output per 1,000 S&E doctorate 

holders in academia: 2008 ....................................................................................................8-106

Figure 8-49. Academic science and engineering article output per $1 million of academic 

S&E R&D: 2009 ..................................................................................................................8-108

Figure 8-50. Academic patents awarded per 1,000 science and engineering doctorate 

holders in academia: 2008 ...................................................................................................8-110

Figure 8-51. Patents awarded per 1,000 individuals in science and engineering 

occupations: 2010 .................................................................................................................8-112

Figure 8-52. High-technology establishments as a percentage of all business 

establishments: 2008 .............................................................................................................8-114

Figure 8-53. Net high-technology business formations as a percentage of all business 

establishments: 2008 ............................................................................................................8-116

Figure 8-54. Employment in high-technology establishments as a percentage of total 

employment: 2008 ...............................................................................................................8-118

Figure 8-55. Average annual federal Small Business Innovation Research funding 

per $1 million of gross domestic product: 2008-10 ...............................................................8-120

Figure 8-56. Venture capital disbursed per $1,000 of gross domestic product: 2010 ................8-122

Figure 8-57. Venture capital deals as a percentage of high-technology business 

establishments: 2008 .............................................................................................................8-124

Figure 8-58. Venture capital disbursed per venture capital deal: 2010 ......................................8-126

Figure 8-A. U.S. map and list of abbreviations ...........................................................................8-8

Figure 8-B. Example state distribution chart ...............................................................................8-9
Science and Engineering Indicators 2012 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 58 indicators for individual states, the 

District of Columbia, and Puerto Rico.

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 ranked 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.

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 which case, data are given only for the years in which 

comparisons are appropriate. Most indicators contain data 

for 2008–09; some contain data for 2010.

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

Fifty-eight indicators are included in this chapter and 

grouped into the following areas:

Elementary and secondary education

Higher education

Workforce

Financial R&D inputs

Research and development outputs

S&T in the economy

The first two areas address state educational attainment. 

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 the expected level of 

competence on the standardized test.

Comparable state-level performance data are not available 

for high school students. Although performance and proficiency data in science are available for students in grade 12 at the 

national level but not at the state level, data on performance 

and proficiency in mathematics is not available at either the 

federal or state 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 the top-performing high school students. 

Other indicators in education focus on state spending, teacher 

salaries, student costs, and undergraduate and graduate degrees in S&E. Three indicators measure the level of education 

in the populations of individual states.

Workforce indicators focus on the level of S&E training in the employed labor force. These indicators reflect the 

higher education level of the labor force and the degree of 

specialization in S&E disciplines and occupations.

Financial indicators address 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. This section enables readers to compare the extent to which R&D is conducted by industrial, academic, or state agency performers.

The final two sections provide measures of outputs. The 

first focuses on the work products of the academic community. It includes 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.

The last section of output indicators examines the robustness of a region’s S&T-related economic activity. These 

indicators include venture capital activity, Small Business 

Innovation Research awards, and high-technology business activity. Although data that adequately address both 

the quantity and quality of R&D results are difficult to find, 

these indicators offer a reasonable information base.

This edition includes six new indicators. Consistent with 

other indicators in the chapter, they are normalized. The first 

covers AP Calculus AB exams and is presented as a percentage of high school students scoring 3 or higher on the 

exam. The second covers the number of bachelor’s degrees 

in science and engineering that were conferred relative to the 

size of the population in the appropriate age range. The third 

provides an indication of the degree to which a state’s educational infrastructure provides the highest level of training 

in science and engineering and is presented as the number of 

doctorate degrees conferred in science and engineering as a 

percentage of all science and engineering degrees conferred. 

The fourth indicator covers state funds for higher education 

and is presented as the percentage of state gross domestic 

product. The fifth addresses the amount of state funding for 

public research universities per enrolled student. Finally, 

the last new indicator focuses on the percentage of technical 

workers in a state’s workforce.
8-8  Chapter 8. State Indicators

Data Sources and Considerations

Raw data for each indicator are presented in the tables. 

Each table provides an average value for all states, labeled 

“United States.” For most indicators, the state average was 

calculated by summing the values for the 50 states and the 

District of Columbia for both the numerator and the denominator and then dividing the two. Any alternate approach is 

indicated in the notes at the bottom of the table.

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 sparsely populated 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.

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.

The sample map below shows the outline of each state. 

The state is identified by its postal code. 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 

(IdeA) program at the National Institutes of Health. States 

shown with a gray background in figure 8-A are states in the 

EPSCoR group. The EPSCoR group of states are those eligible 

NOTE: Gray states indicate EPSCoR states

AK

WA

OR

CA

NV

ID

MT ND

MN

SD

WY

UT

CO

AZ NM

TX

NE

KS

OK

HI

IA

WI

MI

MO

AR

LA

MS AL

TN

IL IN OH

KY

WV VA

NC

GA

SC

FL

PA

NY

ME

NH

VT

MA

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
Science and Engineering Indicators 2012 8-9

for EPSCoR-like programs in at least five federal agencies or 

departments. The 24 EPSCoR states are Alabama, Alaska, 

Arkansas, Delaware, Hawaii, Idaho, Kansas, Kentucky, 

Louisiana, Maine, Mississippi, Montana, Nebraska, Nevada, 

New Hampshire, New Mexico, North Dakota, Oklahoma, 

Rhode Island, South Carolina, South Dakota, Vermont, West 

Virginia, and Wyoming. The EPSCoR Program is discussed 

further in chapter 5, “Academic Research and Development,” 

in the sidebar “EPSCoR: The Experimental Program to 

Stimulate Competitive Research.” The remaining 26 states 

are considered states in the non-EPSCoR group.

The second element is a state distribution chart illustrating 

state values for the latest data year for that indicator (figure 

8-B). States are listed alphabetically by postal code and are 

centered over the mid-point of the range for their indicator 

values. Indicator values are presented along the x-axis of the 

chart. States stacked together have indicator values in the 

same range but not necessarily identical values. 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. Although most of 

the findings are directly related to the data, some represent 

interpretations that are meant to stimulate further investigation and discussion.

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.

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 8-39, 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 first and second 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 24 EPSCoR 

states and dividing by 24. All state ratios count equally in 

this computation. Examples of this calculation are shown in 

tables 8-5 and 8-18.

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.

High-technology occupations include scientific, engineering, and technician occupations. These occupations employ 

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
8-10  Chapter 8. State Indicators

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 hightechnology 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 2002 North American Industry 

Classification System (NAICS). The 2002 NAICS codes 

contain a number of additions and changes from the previous 

1997 NAICS codes that were used to classify business establishments in data sets covering the period 1998–2002, and 

therefore cannot be applied to data sets from earlier years.

The list of high-technology industries used in this chapter 

includes the 46 four-digit codes from the 2002 NAICS listing 

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. Additional data on the 

coefficient of variation for data sources in the chapter also are 

presented in the appendix tables when they are available.

Reference

Hecker D. 2005. High-technology employment: A NAICSbased update. Monthly Labor Review 128(7):57–72.
Science and Engineering Indicators 2012 8-11

Table 8-A

2002 NAICS codes that constitute high-technology industries

NAICS code Industry

1131, 1132.............................. Forestry

2111........................................ Oil and gas extraction

2211........................................ Electric power generation, transmission, and distribution

3241........................................ Petroleum and coal products manufacturing

3251........................................ Basic chemical manufacturing

3252........................................ Resin, synthetic rubber, and artificial synthetic fibers and filaments manufacturing

3253........................................ Pesticide, fertilizer, and other agricultural chemical manufacturing

3254........................................ Pharmaceutical and medicine manufacturing

3255........................................ Paint, coating, and adhesive manufacturing

3259........................................ Other chemical product and preparation manufacturing

3332........................................ Industrial machinery manufacturing

3333........................................ Commercial and service industry machinery manufacturing

3336........................................ Engine, turbine, and power transmission equipment manufacturing

3339........................................ Other general purpose machinery manufacturing

3341........................................ Computer and peripheral equipment manufacturing

3342........................................ Communications equipment manufacturing

3343........................................ Audio and video equipment manufacturing

3344........................................ Semiconductor and other electronic component manufacturing

3345........................................ Navigational, measuring, electromedical, and control instruments manufacturing

3346........................................ Manufacturing and reproducing magnetic and optical media

3353........................................ Electrical equipment manufacturing

3364........................................ Aerospace product and parts manufacturing

3369........................................ Other transportation equipment manufacturing

4234........................................ Professional and commercial equipment and supplies, merchant wholesalers

4861........................................ Pipeline transportation of crude oil

4862........................................ Pipeline transportation of natural gas

4869........................................ Other pipeline transportation

5112........................................ Software publishers

5161........................................ Internet publishing and broadcasting

5171........................................ Wired telecommunications carriers

5172........................................ Wireless telecommunications carriers (except satellite)

5173........................................ Telecommunications resellers

5174........................................ Satellite telecommunications

5179........................................ Other telecommunications

5181........................................ Internet service providers and Web search portals

5182........................................ Data processing, hosting, and related services

5211........................................ Monetary authorities, central bank

5232........................................ Securities and commodity exchanges

5413........................................ Architectural, engineering, and related services

5415........................................ Computer systems design and related services

5416........................................ Management, scientific, and technical consulting services

5417........................................ Scientific research and development services

5511........................................ Management of companies and enterprises

5612........................................ Facilities support services

8112........................................ Electronic and precision equipment repair and maintenance

NAICS = North American Industry Classification System

Science and Engineering Indicators 2012
8-12  List of Appendix Tables

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 is a federally authorized 

measure of student performance in which all 50 states and 

the District of Columbia participated in 2009.

Student performance is presented in terms of average 

scores on a scale from 0 to 500. 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., extended time, individual testing, or 

small group testing). All data presented here represent scores 

from tests taken with accommodations offered.

Findings

U In 2009, the nationwide average mathematics score of fourth 

grade public school students was 239, a significant increase 

from 224 in 2000. This improvement occurred almost entirely 

during the initial portion of the decade, with no change in the 

nationwide average math score between 2007 and 2009.

U The states with the highest average fourth grade 

performance scores are concentrated in the northern United 

States.

U The gap in mathematics scores between white and black 

fourth graders decreased from 30 points to 26 points 

between 2000 and 2009. The gap in mathematics scores 

between white and Hispanic fourth graders decreased from 

26 points to 21 points between 2000 and 2009. There were 

no significant changes in either of these gaps between 2007 

and 2009.

U The average mathematics scores for both male and female 

fourth grade students increased over the decade, but the 

size of the gender gap in fourth grade mathematics scores 

remained unchanged at 2 points.

Fourth Grade Mathematics Performance

Figure 8-1

Average fourth grade mathematics performance: 2009

1st quartile (244–252)

2nd quartile (241–243)

3rd quartile (236–240)

4th quartile (219–235)

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. 216 220 224 228 232 236 240 244 248 252 256

DC MS

AK

AR

DE

GA

HI

IL

KY

MI

NE

OK

OR

RI

SC

AL

AZ

LA

NM

CO

FL

IA

ID

IN

MO

NY

SD

TX

UT

VA

WA

WY

CT

KS

MD

ME

MT

NC

ND

NJ

OH

PA

WI

MN

NH

VT MA

CA

NV

TN

WV

Average score
Science and Engineering Indicators 2012 8-13

Table 8-1

Average fourth grade mathematics performance, by state: 2000, 2005, 

and 2009

(Score out of 500)

State 2000 2005 2009

United States ................................................................... 224* 237* 239

Alabama ....................................................................... 217* 225* 228

Alaska .......................................................................... NA 236 237

Arizona ......................................................................... 219* 230 230

Arkansas ...................................................................... 216* 236 238

California ...................................................................... 213* 230 232

Colorado ...................................................................... NA 239 243

Connecticut ................................................................. 234* 242 245

Delaware ...................................................................... NA 240* 239

District of Columbia ..................................................... 192* 211* 219

Florida .......................................................................... NA 239* 242

Georgia ........................................................................ 219* 234 236

Hawaii .......................................................................... 216* 230* 236

Idaho ............................................................................ 224* 242 241

Illinois ........................................................................... 223* 233* 238

Indiana ......................................................................... 233* 240* 243

Iowa ............................................................................. 231* 240* 243

Kansas ......................................................................... 232* 246 245

Kentucky ...................................................................... 219* 231* 239

Louisiana ...................................................................... 218* 230 229

Maine ........................................................................... 230* 241 244

Maryland ...................................................................... 222* 238 244

Massachusetts ............................................................. 233* 247* 252

Michigan ...................................................................... 229* 238 236

Minnesota .................................................................... 234* 246 249

Mississippi ................................................................... 211* 227 227

Missouri ....................................................................... 228* 235* 241

Montana ....................................................................... 228* 241* 244

Nebraska ...................................................................... 225* 238 239

Nevada ......................................................................... 220* 230 235

New Hampshire ........................................................... NA 246* 251

New Jersey .................................................................. NA 244* 247

New Mexico ................................................................. 213* 224* 230

New York ...................................................................... 225* 238* 241

North Carolina .............................................................. 230* 241 244

North Dakota ............................................................... 230* 243* 245

Ohio ............................................................................. 230* 242 244

Oklahoma ..................................................................... 224* 234* 237

Oregon ......................................................................... 224* 238 238

Pennsylvania ................................................................ NA 241* 244

Rhode Island ................................................................ 224* 233 239

South Carolina ............................................................. 220* 238 236

South Dakota ............................................................... NA 242 242

Tennessee .................................................................... 220* 232 232

Texas ............................................................................ 231* 242 240

Utah ............................................................................. 227* 239 240

Vermont ........................................................................ 232* 244* 248

Virginia ......................................................................... 230* 240* 243

Washington .................................................................. NA 242 242

West Virginia ................................................................ 223* 231* 233

Wisconsin .................................................................... NA 241* 244

Wyoming ...................................................................... 229* 243 242

Puerto Rico .................................................................. NA NA NA

*significantly different (p < .05) from the 2009 score for the jurisdiction; NA = not available

NOTES: National average for United States is reported value in National Assessment of Educational 

Progress (NAEP) reports. NAEP grade 4 mathematics scores for public schools only.

SOURCE: National Center for Education Statistics, NAEP (various years).

Science and Engineering Indicators 2012
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.

Approximately 168,800 fourth grade students in 9,510 schools 

participated in the 2009 NAEP mathematics assessment.

NAEP allows students with disabilities or limited Englishlanguage proficiency to use certain accommodations (e.g., extended time, individual testing, or small group testing). All data 

presented here represent scores from tests taken with accommodations offered.

Findings

U In 2009, 38% of fourth grade public school students 

nationwide performed at or above the proficient level in 

mathematics, which represents a statistically significant 

increase from 22% in 2000 and 35% in 2005.

U All 41 jurisdictions that participated in both the 2000 

and 2009 assessments showed significant increases in 

mathematics proficiency levels for public school fourth 

graders in 2009.

U Substantial differences in mathematics proficiency exist 

among racial/ethnic groups of fourth graders. The gap 

between white and black students increased from 26% 

to 35% between 2000 and 2009. The gap between 

white and Hispanic students increased from 23% to 

29% during this period. All racial/ethnic groups showed 

gains between 2000 and 2009, but at varying rates.

U The gender gap in mathematics proficiency among 

fourth graders decreased from 5% to 3% between 2000 

and 2009. The range by state in 2009 was 

17%–59% for males and 17%–55% for females.

Fourth Grade Mathematics Proficiency

Figure 8-2

Students reaching proficiency in fourth grade mathematics: 2009

(Percentage of students scoring 249 or above)

1st quartile (45%–57%)

2nd quartile (40%–44%)

3rd quartile (34%–39%)

4th quartile (17%–33%)

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. 16 20 24 28 32 36 40 44 48 52 56 60

DC

LA

MS

GA

MI

NV

OK

SC

AL

NM

AK

AR

DE

HI

IL

KY

NE

OR

RI

TX

FL

IA

ID

IN

MO

NC

NY

SD

UT

VA

WA

WY

CO

CT

KS

MD

ME

MT

ND

OH

PA

WI MN

NJ

VT

MA

NH

AZ

CA

TN

WV

Percent
Science and Engineering Indicators 2012 8-15

Table 8-2

Students reaching proficiency in fourth grade mathematics, by state: 2000, 

2005, and 2009

(Percent)

State 2000 2005 2009

United States .................................................................. 22* 35* 38

Alabama ...................................................................... 13* 21* 24

Alaska ......................................................................... NA 34 38

Arizona ........................................................................ 16* 28 28

Arkansas ..................................................................... 14* 34 36

California ..................................................................... 13* 28 30

Colorado ..................................................................... NA 39 45

Connecticut ................................................................ 31* 43 46

Delaware ..................................................................... NA 36* 36

District of Columbia .................................................... 5* 10* 17

Florida ......................................................................... NA 37* 40

Georgia ....................................................................... 17* 30 34

Hawaii ......................................................................... 14* 27* 37

Idaho ........................................................................... 20* 41 41

Illinois .......................................................................... 20* 32* 38

Indiana ........................................................................ 30* 38* 42

Iowa ............................................................................ 26* 37* 41

Kansas ........................................................................ 29* 47 46

Kentucky ..................................................................... 17* 26* 37

Louisiana ..................................................................... 14* 24 23

Maine .......................................................................... 23* 39 45

Maryland ..................................................................... 21* 38 44

Massachusetts ............................................................ 31* 49* 57

Michigan ..................................................................... 28* 37 35

Minnesota ................................................................... 33* 47 54

Mississippi .................................................................. 9* 19 22

Missouri ...................................................................... 23* 31* 41

Montana ...................................................................... 24* 38* 45

Nebraska ..................................................................... 24* 36 38

Nevada ........................................................................ 16* 26* 32

New Hampshire .......................................................... NA 47* 56

New Jersey ................................................................. NA 46* 49

New Mexico ................................................................ 12* 19* 26

New York ..................................................................... 21* 36* 40

North Carolina ............................................................. 25* 40 43

North Dakota .............................................................. 25* 40* 45

Ohio ............................................................................ 25* 43 45

Oklahoma .................................................................... 16* 29 33

Oregon ........................................................................ 23* 37 37

Pennsylvania ............................................................... NA 41* 46

Rhode Island ............................................................... 22* 31* 39

South Carolina ............................................................ 18* 36 34

South Dakota .............................................................. NA 40 42

Tennessee ................................................................... 18* 28 28

Texas ........................................................................... 25* 40 38

Utah ............................................................................ 23* 37 41

Vermont ....................................................................... 29* 44* 51

Virginia ........................................................................ 24* 40 43

Washington ................................................................. NA 42 43

West Virginia ............................................................... 17* 25* 28

Wisconsin ................................................................... NA 40* 45

Wyoming ..................................................................... 25* 42 40

Puerto Rico ................................................................. NA NA NA

*significantly different (p < .05) from the 2009 score for the jurisdiction; NA = not available

NOTES: National average for United States is reported value in National Assessment of Educational 

Progress (NAEP) reports. NAEP grade 4 mathematics scores for public schools only.

SOURCE: National Center for Education Statistics, NAEP (various years).

Science and Engineering Indicators 2012
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 national science assessment was updated in 

2009 to keep pace with key developments in science. It contains questions 

covering the content areas of physical, life, and earth and space science. 

The 2009 assessment is based on a new framework, and these results, 

therefore, cannot be compared to those from previous science assessments. 

They provide a current snapshot of what fourth graders can do in science 

and will provide a basis for comparisons for the future.

Student performance is presented in terms of average scores on a 

scale from 0 to 300 with a mean of 150 and a standard deviation of 35. 

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., extended time, individual 

testing, or small group testing). All data presented here represent scores 

from tests taken with accommodations offered.

Findings

U 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.

U Of the 47 states and jurisdictions that 

participated in the fourth grade science 

assessment, 24 states had scores that were 

higher than the national average, 13 were not 

significantly different, and 10 were lower.

U Nationally, the gap in science scores between 

white and black public school fourth grade 

students was 35 points, and the gap between 

white and Hispanic public fourth grade students 

was 32 points.

U Male fourth grade public school students scored 

1 point higher in science than female fourth 

grade public school students although females 

scored higher in life science 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 (157–163)

2nd quartile (151–156)

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 2012 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 average for United States is reported value in National Assessment of 

Educational Progress (NAEP) reports. NAEP grade 4 science scores for public schools only.

SOURCE: National Center for Education Statistics, NAEP (various years).

Science and Engineering Indicators 2012
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 advised that science 

achievement levels are to be used on a trial basis and should be 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. NAEP allows 

students with disabilities or limited English-language proficiency to use 

certain accommodations (e.g., extended time, individual testing, or small 

group testing). All data presented here represent scores from tests taken with 

accommodations offered.

Findings

U In 2009, 32% of fourth grade public school 

students nationwide performed at or above 

the proficient level in science. Among the 

states, there were significant 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%.

U Nationally, the percentage of fourth grade 

white students demonstrating proficient 

performance in science was 46% compared 

to 13% for Hispanic students and 10% for 

black students.

U A gender difference was reported with 34% 

of male fourth grade public school students 

scoring at or above the proficient level in 

science compared to 31% of their female 

counterparts. The range by state was 18%–

49% for males and 16%–49% for females.

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 (29%–33%)

4th quartile (17%–28%)

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 2012 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 average for United States is reported value in National Assessment of 

Educational Progress (NAEP) reports. NAEP grade 4 science scores for public schools only.

SOURCE: National Center for Education Statistics, NAEP (various years).

Science and Engineering Indicators 2012
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 is a federally authorized 

measure of student performance in which all 50 states and the 

District of Columbia participated in 2009.

Student performance is presented in terms of average scores 

on a scale from 0 to 500. An average score designated as NA 

(not applicable) 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., extended time, individual testing, or small group testing). 

All data presented here represent scores from tests taken with 

accommodations offered.

Findings

U In 2009, the nationwide average mathematics score of 

eighth grade public school students was 282, a statistically 

significant increase from 272 in 2000. Eighth graders 

scored higher in mathematics in 2009 than in any previous 

assessment year.

U Of the 40 jurisdictions that participated in both the 2000 and 

2009 mathematics assessments, 35 showed statistically 

significant increases over the decade.

U Since 2007, eighth grade mathematics scores increased 

for public school students in 15 states; nearly half of those 

states showing an increase were located in the West. No 

states showed a decline. 

U The gaps in mathematics scores between white eighth 

graders and black or Hispanic eighth graders narrowed 

between 2000 and 2009 although significant gaps still exist.

U The average mathematics scores for both male and female 

eighth grade students increased over the decade, but the 

size of the gender gap remained unchanged at 2 points.

Eighth Grade Mathematics Performance

Figure 8-5

Average eighth grade mathematics performance: 2009

1st quartile (288–299)

2nd quartile (284–287)

3rd quartile (277–283)

4th quartile (254–276)

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. 250 255 260 265 270 275 280 285 290 295 300

DC

AR

AZ

FL

GA

KY

MI

OK

RI

TN

AL

MS

AK

DE

IA

IL

NC

NE

NY

SC

UT

CO

CT

ID

IN

KS

MD

ME

MO

OH

OR

PA

TX

VA

WA

WI

WY

MN

MT

ND

NH

NJ

SD

VT MA

CA

HI

LA

NM

NV

WV

Average score
Science and Engineering Indicators 2012 8-21

Table 8-5

Average eighth grade mathematics performance, by state: 2000, 2005, 

and 2009

(Score out of 500)

State 2000 2005 2009

Average EPSCoR state value ......................................... 270 275 280

Average non-EPSCoR state value .................................. 274 280 284

United States .................................................................. 272 * 278* 282

Alabama ...................................................................... 264 262 269

Alaska ......................................................................... NA 279* 283

Arizona ........................................................................ 269 * 274 277

Arkansas ..................................................................... 257 * 272 276

California ..................................................................... 260 * 269 270

Colorado ..................................................................... NA 281* 287

Connecticut ................................................................ 281 281 289

Delaware ..................................................................... NA 281* 284

District of Columbia .................................................... 235 * 245* 254

Florida ......................................................................... NA 274 279

Georgia ....................................................................... 265 * 272 278

Hawaii ......................................................................... 262 * 266* 274

Idaho ........................................................................... 277 * 281* 287

Illinois .......................................................................... 275 * 278 282

Indiana ........................................................................ 281 * 282* 287

Iowa ............................................................................ NA 284 284

Kansas ........................................................................ 283 * 284* 289

Kentucky ..................................................................... 270 * 274* 279

Louisiana ..................................................................... 259 * 268* 272

Maine .......................................................................... 281 * 281* 286

Maryland ..................................................................... 272 * 278* 288

Massachusetts ............................................................ 279 * 292* 299

Michigan ..................................................................... 277 277 278

Minnesota ................................................................... 287 * 290 294

Mississippi .................................................................. 254 * 262 265

Missouri ...................................................................... 271 * 276* 286

Montana ...................................................................... 285 286 292

Nebraska ..................................................................... 280 * 284 284

Nevada ........................................................................ 265 * 270 274

New Hampshire .......................................................... NA 285* 292

New Jersey ................................................................. NA 284* 293

New Mexico ................................................................ 259 * 263* 270

New York ..................................................................... 271 * 280 283

North Carolina ............................................................. 276 * 282 284

North Dakota .............................................................. 282 * 287* 293

Ohio ............................................................................ 281 * 283 286

Oklahoma .................................................................... 270 * 271* 276

Oregon ........................................................................ 280 282 285

Pennsylvania ............................................................... NA 281* 288

Rhode Island ............................................................... 269 * 272* 278

South Carolina ............................................................ 265 * 281 280

South Dakota .............................................................. NA 287 291

Tennessee ................................................................... 262 * 271* 275

Texas ........................................................................... 273 * 281* 287

Utah ............................................................................ 274 * 279 284

Vermont ....................................................................... 281 * 287* 293

Virginia ........................................................................ 275 * 284* 286

Washington ................................................................. NA 285 289

West Virginia ............................................................... 266 * 269 270

Wisconsin ................................................................... NA 285 288

Wyoming ..................................................................... 276 * 282* 286

Puerto Rico ................................................................. NA NA NA

*significantly different (p < .05) from the 2009 score for the jurisdiction; NA = not available

EPSCoR = Experimental Program to Stimulate Competitive Research

NOTES: National average for United States is reported value in National Assessment of Educational 

Progress (NAEP) reports. NAEP grade 8 mathematics scores for public schools only. For explanation of 

EPSCoR and non-EPSCoR averages, see chapter introduction.

SOURCE: National Center for Education Statistics, NAEP (various years).

Science and Engineering Indicators 2012
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.

Approximately 161,700 eighth grade students in 7,030 

schools participated in the 2009 NAEP mathematics assessment. NAEP allows students with disabilities or limited 

English-language proficiency to use certain accommodations 

(e.g., extended time, individual testing, or small group testing). 

All data presented here represent scores from tests taken with 

accommodations offered.

Findings

U In 2009, 33% of eighth grade public school students 

nationwide performed at or above the proficient level in 

mathematics, which represents a significant increase 

from 25% in 2000 and 28% in 2005. Of the 39 states that 

participated in both the 2000 and 2009 assessments, 30 

showed statistically significant increases in mathematics 

proficiency among public school eighth graders over the 

decade. Only eight showed a significant increase from 2007 

to 2009.

U Substantial differences in mathematics proficiency exist 

among racial/ethnic groups of eighth graders even though 

the gap in performance scores narrowed. The gap between 

white and black students who are proficient in mathematics 

increased from 28% to 31% between 2000 and 2009. The 

gap between white and Hispanic students remained around 

25% during this period. All racial/ethnic groups showed 

gains between 2000 and 2009, but at varying rates.

U The gender gap in mathematics proficiency among eighth 

graders remained at 3% between 2000 and 2009 although 

the percentage of proficient students increased for both 

sexes during this period. The range by state for 2009 was 

12%–53% for males and 11%–50% for females.

Eighth Grade Mathematics Proficiency

Figure 8-6

Students reaching proficiency in eighth grade mathematics: 2009

(Percentage of students scoring 299 or above)

1st quartile (39%–52%)

2nd quartile (35%–38%)

3rd quartile (27%–34%)

4th quartile (11%–25%)

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. 10 15 20 25 30 35 40 45 50 55

DC

MS

WV

AL

CA

LA

NM

OK

ID

IN

KS

ME

MO

NC

NE

OH

OR

TX

UT

VA

WA

WI

WY

AR

AZ

FL

GA

HI

KY

NV

RI

TN

CO

CT

MD

MT

ND

NH

NJ

PA

SD

VT MN MA

AK

DE

IA

IL

MI

NY

SC

Percent
Science and Engineering Indicators 2012 8-23

Table 8-6

Students reaching proficiency in eighth grade mathematics, by state: 

2000, 2005, and 2009

(Percent)

State 2000 2005 2009

United States .................................................................. 25* 28* 33

Alabama ...................................................................... 16 15 20

Alaska ......................................................................... NA 29 33

Arizona ........................................................................ 20* 26 29

Arkansas ..................................................................... 13* 22 27

California ..................................................................... 17* 22* 23

Colorado ..................................................................... NA 32* 40

Connecticut ................................................................ 33 35 40

Delaware ..................................................................... NA 30 32

District of Columbia .................................................... 6 7 11

Florida ......................................................................... NA 26 29

Georgia ....................................................................... 19* 23 27

Hawaii ......................................................................... 16* 18* 25

Idaho ........................................................................... 26* 30* 38

Illinois .......................................................................... 26* 29 33

Indiana ........................................................................ 29* 30* 36

Iowa ............................................................................ NA 34 34

Kansas ........................................................................ 34* 34* 39

Kentucky ..................................................................... 20* 23* 27

Louisiana ..................................................................... 11* 16 20

Maine .......................................................................... 30 30* 35

Maryland ..................................................................... 27* 30* 40

Massachusetts ............................................................ 30* 43* 52

Michigan ..................................................................... 28 29 31

Minnesota ................................................................... 39 43 47

Mississippi .................................................................. 9* 14 15

Missouri ...................................................................... 21* 26* 35

Montana ...................................................................... 36 36 44

Nebraska ..................................................................... 30* 35 35

Nevada ........................................................................ 18* 21 25

New Hampshire .......................................................... NA 35 43

New Jersey ................................................................. NA 36* 44

New Mexico ................................................................ 12* 14* 20

New York ..................................................................... 24* 31 34

North Carolina ............................................................. 27* 32 36

North Dakota .............................................................. 30* 35* 43

Ohio ............................................................................ 30* 34 36

Oklahoma .................................................................... 18 20 24

Oregon ........................................................................ 31 33 37

Pennsylvania ............................................................... NA 31* 40

Rhode Island ............................................................... 22* 24* 28

South Carolina ............................................................ 17* 30 30

South Dakota .............................................................. NA 36 42

Tennessee ................................................................... 16* 21 25

Texas ........................................................................... 24* 31* 36

Utah ............................................................................ 25* 30 35

Vermont ....................................................................... 31* 38* 43

Virginia ........................................................................ 25* 33 36

Washington ................................................................. NA 36 39

West Virginia ............................................................... 17 17 19

Wisconsin ................................................................... NA 36 39

Wyoming ..................................................................... 23* 29* 35

Puerto Rico ................................................................. NA NA NA

*significantly different (p < .05) from the 2009 score for the jurisdiction; NA = not available

NOTES: National average for United States is reported value in National Assessment of Educational 

Progress (NAEP) reports. NAEP grade 8 mathematics scores for public schools only.

SOURCE: National Center for Education Statistics, NAEP (various years).

Science and Engineering Indicators 2012
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 national science assessment was updated in 

2009 to keep pace with key developments in science. It contains questions 

covering the content areas of physical, life, and earth and space science. The 

2009 assessment is based on a new framework, therefore, these results cannot 

be compared to those from previous science assessments. They provide a 

current snapshot of what eighth graders can do in science and will provide 

a basis for comparisons for the future.

Student performance is presented in terms of average scores on a scale 

from 0 to 300 with a mean of 150 and a standard deviation of 35. An average 

score designated as NA (not applicable) 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., extended time, individual 

testing, or small group testing). All data presented here represent scores from 

tests taken with accommodations offered.

Findings

U In 2009, the nationwide average science 

score of eighth grade public school students 

was 149. Average scores for individual 

states ranged from a high of 162 to low of 

132.

U Of the 47 states and jurisdictions that 

participated, 25 had scores that were higher 

than the national average and 15 had scores 

that were lower.

U Nationally, the science score gap between 

white and black public school eighth 

grade students was 36 points, and the gap 

between white and Hispanic public eighth 

grade students was 30 points.

U Male eighth grade public school students 

nationally scored 4 points higher in science 

than female eighth grade public school 

students.

Eighth Grade Science Performance

Figure 8-7

Average eighth grade science performance: 2009

132 135 138 141 144 147 150 153 156 159 162 165

MS CA

AR

FL

NC

OK

RI

WV

AL

HI

LA

DE

GA

IL

MD

NY

TN

IN

TX

CT

MI

NJ

OR

PA

WA

MA

MN

NH

SD

CO

IA

ID

KY

ME

MO

OH

UT

VA

WI

WY

MT

ND

AZ

NM

NV

SC

Average score

1st quartile (157–162)

2nd quartile (153–156)

3rd quartile (145–152)

4th quartile (132–144)

No data

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 2012 8-25

Table 8-7

Average eighth grade science performance, by state: 2009

(Score out of 300)

State 2009

United States ............................................................................................ 149

Alabama ................................................................................................ 139

Alaska ................................................................................................... NA

Arizona .................................................................................................. 141

Arkansas ............................................................................................... 144

California ............................................................................................... 137

Colorado ............................................................................................... 156

Connecticut .......................................................................................... 155

Delaware ............................................................................................... 148

District of Columbia .............................................................................. NA

Florida ................................................................................................... 146

Georgia ................................................................................................. 147

Hawaii ................................................................................................... 139

Idaho ..................................................................................................... 158

Illinois .................................................................................................... 148

Indiana .................................................................................................. 152

Iowa ...................................................................................................... 156

Kansas .................................................................................................. NA

Kentucky ............................................................................................... 156

Louisiana ............................................................................................... 139

Maine .................................................................................................... 158

Maryland ............................................................................................... 148

Massachusetts ...................................................................................... 160

Michigan ............................................................................................... 153

Minnesota ............................................................................................. 159

Mississippi ............................................................................................ 132

Missouri ................................................................................................ 156

Montana ................................................................................................ 162

Nebraska ............................................................................................... NA

Nevada .................................................................................................. 141

New Hampshire .................................................................................... 160

New Jersey ........................................................................................... 155

New Mexico .......................................................................................... 143

New York ............................................................................................... 149

North Carolina ....................................................................................... 144

North Dakota ........................................................................................ 162

Ohio ...................................................................................................... 158

Oklahoma .............................................................................................. 146

Oregon .................................................................................................. 154

Pennsylvania ......................................................................................... 154

Rhode Island ......................................................................................... 146

South Carolina ...................................................................................... 143

South Dakota ........................................................................................ 161

Tennessee ............................................................................................. 148

Texas ..................................................................................................... 150

Utah ...................................................................................................... 158

Vermont ................................................................................................. NA

Virginia .................................................................................................. 156

Washington ........................................................................................... 155

West Virginia ......................................................................................... 145

Wisconsin ............................................................................................. 157

Wyoming ............................................................................................... 158

Puerto Rico ........................................................................................... NA

NA = not available

NOTES: National average for United States is reported value in National Assessment of 

Educational Progress (NAEP) reports. NAEP grade 8 science scores for public schools only.

SOURCE: National Center for Education Statistics, NAEP (various years).

Science and Engineering Indicators 2012
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 are to be used on a trial basis and should be interpreted with caution.

Approximately 151,100 eighth grade students in 6,920 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.

NAEP allows students with disabilities or limited English-language proficiency to use certain accommodations (e.g., extended time, individual testing, 

or small group testing). All data presented here represent scores from tests 

taken with accommodations offered.

Findings

U In 2009, 29% of eighth grade public 

school students nationwide performed at 

or above the proficient level in science. 

Among the states, there were significant 

differences in the percentage of eighth 

grade public school students who 

demonstrated proficiency in science. State 

values for this indicator ranged from 15% 

to 43%.

U Nationally, the percentage of eighth grade 

white students demonstrating proficient 

performance in science was 41% 

compared to 12% for Hispanic students 

and 8% for black students.

U A gender difference was reported 

with 32% of male eighth grade public 

school students scoring at or above the 

proficient level in science compared to 

26% of female eighth grade public school 

students. The range by state was 17%–

47% for males and 13%–38% for females.

Eighth Grade Science Proficiency

Figure 8-8

Students reaching proficiency in eighth grade science: 2009

(Percentage of students scoring 170 or above)

14.0 16.8 19.6 22.4 25.2 28.0 30.8 33.6 36.4 39.2 42.0 44.8

MS

AL

HI

GA

RI

AZ

CA

LA

NM

NV

WV

IL

MD

TN

TX

IN

NY

CO

CT

IA

KY

ME

MI

MO

NJ

OR

PA

VA

WA

WY

MA

MN

SD

ID

NH

OH

UT

WI

MT

ND

AR

DE

FL

NC

OK

SC

Percent

1st quartile (36%–43%)

2nd quartile (34%–35%)

3rd quartile (24%–32%)

4th quartile (15%–23%)

No data

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 2012 8-27

Table 8-8

Students reaching proficiency in eighth grade science, by state: 2009

(Percent)

State 2009

United States ............................................................................................ 29

Alabama ................................................................................................ 19

Alaska ................................................................................................... NA

Arizona .................................................................................................. 22

Arkansas ............................................................................................... 24

California ............................................................................................... 20

Colorado ............................................................................................... 36

Connecticut .......................................................................................... 35

Delaware ............................................................................................... 25

District of Columbia .............................................................................. NA

Florida ................................................................................................... 25

Georgia ................................................................................................. 27

Hawaii ................................................................................................... 17

Idaho ..................................................................................................... 37

Illinois .................................................................................................... 28

Indiana .................................................................................................. 32

Iowa ...................................................................................................... 35

Kansas .................................................................................................. NA

Kentucky ............................................................................................... 34

Louisiana ............................................................................................... 20

Maine .................................................................................................... 35

Maryland ............................................................................................... 28

Massachusetts ...................................................................................... 41

Michigan ............................................................................................... 35

Minnesota ............................................................................................. 40

Mississippi ............................................................................................ 15

Missouri ................................................................................................ 36

Montana ................................................................................................ 43

Nebraska ............................................................................................... NA

Nevada .................................................................................................. 20

New Hampshire .................................................................................... 39

New Jersey ........................................................................................... 34

New Mexico .......................................................................................... 21

New York ............................................................................................... 31

North Carolina ....................................................................................... 24

North Dakota ........................................................................................ 42

Ohio ...................................................................................................... 37

Oklahoma .............................................................................................. 25

Oregon .................................................................................................. 35

Pennsylvania ......................................................................................... 35

Rhode Island ......................................................................................... 26

South Carolina ...................................................................................... 23

South Dakota ........................................................................................ 40

Tennessee ............................................................................................. 28

Texas ..................................................................................................... 29

Utah ...................................................................................................... 39

Vermont ................................................................................................. NA

Virginia .................................................................................................. 36

Washington ........................................................................................... 34

West Virginia ......................................................................................... 22

Wisconsin ............................................................................................. 38

Wyoming ............................................................................................... 36

Puerto Rico ........................................................................................... NA

NA = not available

NOTES: National average for United States is reported value in National Assessment of 

Educational Progress (NAEP) reports. NAEP grade 8 science scores for public schools only.

SOURCE: National Center for Education Statistics, NAEP (various years).

Science and Engineering Indicators 2012
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, 2010 data represent salaries for the 2009–2010 academic year. 

The figures (given in current dollars) 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 National Education Association, Estimates of School Statistics, 

1969–70 through 2009–10.

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

U In 2010, salaries for public school teachers 

nationwide averaged $52,418, ranging from a 

state low of $35,136 to a high of $71,470.

U Twenty states and the District of Columbia had 

average public school teacher salaries higher 

than the national average in 2010 compared to 

22 states plus DC in 2000.

U Between 2000 and 2010, average teacher 

salaries across the nation rose by 33% in terms 

of current dollars. Average teacher salaries 

increased by 5% when expressed in constant 

dollars.

U States with high salaries for public school 

teachers do not necessarily have high student 

achievement scores on the NAEP mathematics 

and science tests.

Public School Teacher Salaries

Figure 8-9

Public school teacher salaries: 2010

32,000 36,000 40,000 44,000 48,000 52,000 56,000 60,000 64,000 68,000 72,000

SD

ND

UT

GA

MN

OH

OR

WA

WI

WY

AL

AZ

FL

ID

KS

ME

MO

MS

MT

NE

NM

OK

TN

TX

WV

AK

DE

HI

MI

PA

RI IL

CT

DC

MD

NJ

CA

MA

NY

AR

CO

IA

IN

KY

LA

NC

NH

NV

SC

VA

VT

Dollars

1st quartile ($57,958–$71,470)

2nd quartile ($49,999–$57,080)

3rd quartile ($46,401–$49,986)

4th quartile ($35,136–$46,290)

SOURCE: National Center for Education 

Statistics, Digest of Education Statistics.
Science and Engineering Indicators 2012 8-29

Table 8-9

Public school teacher salaries, by state: 2000, 2005, and 2010

(Dollars)

State 2000 2005 2010

United States .......................................................................................... 39,354 45,089 52,418

Alabama .............................................................................................. 36,689 38,186 47,156

Alaska ................................................................................................. 46,462 52,424 59,729

Arizona ................................................................................................ 36,902 42,905 46,952

Arkansas ............................................................................................. 33,386 40,495 49,051

California ............................................................................................. 47,680 57,876 70,458

Colorado ............................................................................................. 38,163 43,949 49,505

Connecticut ........................................................................................ 51,780 57,737 64,350

Delaware ............................................................................................. 44,435 50,595 57,080

District of Columbia ............................................................................ 47,076 58,456 64,548

Florida ................................................................................................. 36,722 41,590 46,912

Georgia ............................................................................................... 41,023 46,526 54,274

Hawaii ................................................................................................. 40,578 46,149 58,168

Idaho ................................................................................................... 35,547 42,122 46,283

Illinois .................................................................................................. 46,486 55,421 62,077

Indiana ................................................................................................ 41,850 46,583 49,986

Iowa .................................................................................................... 35,678 39,284 50,547

Kansas ................................................................................................ 34,981 39,345 46,957

Kentucky ............................................................................................. 36,380 40,522 48,354

Louisiana ............................................................................................. 33,109 39,022 50,349

Maine .................................................................................................. 35,561 39,610 46,106

Maryland ............................................................................................. 44,048 52,331 65,333

Massachusetts .................................................................................... 46,580 54,679 68,000

Michigan ............................................................................................. 49,044 56,973 57,958

Minnesota ........................................................................................... 39,802 46,906 53,069

Mississippi .......................................................................................... 31,857 36,590 45,644

Missouri .............................................................................................. 35,656 39,067 45,317

Montana .............................................................................................. 32,121 38,485 45,759

Nebraska ............................................................................................. 33,237 39,456 46,080

Nevada ................................................................................................ 39,390 43,394 51,524

New Hampshire .................................................................................. 37,734 43,941 51,365

New Jersey ......................................................................................... 52,015 56,682 64,809

New Mexico ........................................................................................ 32,554 39,391 46,401

New York ............................................................................................. 51,020 56,200 71,470

North Carolina ..................................................................................... 39,404 43,348 48,648

North Dakota ...................................................................................... 29,863 36,695 42,964

Ohio .................................................................................................... 41,436 48,692 55,931

Oklahoma ............................................................................................ 31,298 37,879 44,143

Oregon ................................................................................................ 42,336 48,330 55,224

Pennsylvania ....................................................................................... 48,321 53,258 58,124

Rhode Island ....................................................................................... 47,041 53,473 59,636

South Carolina .................................................................................... 36,081 42,189 48,417

South Dakota ...................................................................................... 29,071 34,040 35,136

Tennessee ........................................................................................... 36,328 42,076 46,290

Texas ................................................................................................... 37,567 41,011 47,157

Utah .................................................................................................... 34,946 39,456 43,068

Vermont ............................................................................................... 37,758 44,535 49,053

Virginia ................................................................................................ 38,744 42,768 49,999

Washington ......................................................................................... 41,043 45,718 53,653

West Virginia ....................................................................................... 35,009 38,360 45,959

Wisconsin ........................................................................................... 41,153 44,299 52,644

Wyoming ............................................................................................. 34,127 40,497 55,694

Puerto Rico ......................................................................................... NA NA NA

NA = not available

NOTES: National average for United States is reported value from Digest of Education Statistics. Average salaries reported in current dollars.

SOURCE: National Center for Education Statistics, Digest of Education Statistics (various years).

Science and Engineering Indicators 2012
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 current expenditures for elementary and 

secondary public schools by the state’s gross domestic product (GDP). Current 

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 2009 Common Core of Data, a comprehensive annual national statistical 

database that covers approximately 104,000 public elementary and secondary 

schools and 13,800 regular school districts in the United States.

Current expenditures are expressed in actual dollars and their data year is the 

end date of the academic year. For example, current expenditure data for 2009 

represent expenditures for the 2008–09 academic year. GDP data refer to the 

2009 calendar year in current dollars.

Findings

U The 2009 national average for spending on 

elementary and secondary education was 

3.70% of the gross domestic product (GDP), 

an increase from 3.32% in 2000. Among 

individual states, the value for this indicator 

ranged from 2.50% to 5.74% of the state’s 

GDP in 2009, indicating that some states 

were directing a much higher percentage 

of their resources toward elementary and 

secondary education.

U Spending for elementary and secondary 

public education as a percentage of the 

state’s GDP decreased in 9 states during the 

2000-2009 period.

U Several states spending the highest percentage 

of their GDP on elementary and secondary 

education tended to have relatively small 

student populations (100,000-300,000 

students), indicating that some level of state 

spending may be required regardless of the 

size of the student population or the GDP.

Elementary and Secondary Public School Current Expenditures as a 

Percentage of Gross Domestic Product

Figure 8-10

Elementary and secondary public school current expenditures as a percentage of gross domestic

product: 2009

1.2 1.6 2.0 2.4 2.8 3.2 3.6 4.0 4.4 4.8 5.2 5.6 6.0

CO

FL

NC

ND

NV

SD

TN

WA

CT

ID

IL

IN

KS

KY

MA

MO

PA

AK

AL

AR

GA

MD

MS

MT

NH

NM

OH

SC

DC DE WI

AZ

CA

HI

IA

LA

MN

NE

OK

OR

TX

UT

VA

WY

NJ

WV

ME

MI

NY

RI VT

Percent

1st quartile (4.17%–5.74%)

2nd quartile (3.72%–4.14%)

3rd quartile (3.30%–3.71%)

4th quartile (1.37%–3.27%)

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 2012 8-31

Table 8-10

Elementary and secondary public school current expenditures as a percentage of gross domestic product, 

by state: 2000, 2005, and 2009

Public school expenditures 

($thousands) State GDP ($millions)

School expenditures/

GDP (%)

State 2000 2005 2009 2000 2005 2009 2000 2005 2009

United States ................... 323,808,910 425,047,568 518,997,426 9,884,171 12,554,535 14,014,842 3.28 3.39 3.70

Alabama ....................... 4,176,082 5,164,406 6,683,843 116,014 151,096 166,819 3.60 3.42 4.01

Alaska .......................... 1,183,499 1,442,269 2,006,114 25,913 37,824 45,861 4.57 3.81 4.37

Arizona ......................... 4,262,182 6,579,957 8,625,276 161,901 222,968 249,711 2.63 2.95 3.45

Arkansas ...................... 2,380,331 3,546,999 4,240,839 68,146 88,227 98,795 3.49 4.02 4.29

California ...................... 38,129,479 50,918,654 60,080,929 1,317,343 1,691,991 1,847,048 2.89 3.01 3.25

Colorado ...................... 4,400,888 5,994,440 7,187,267 171,930 217,412 250,664 2.56 2.76 2.87

Connecticut ................. 5,402,868 7,080,396 8,708,294 163,943 197,055 227,550 3.30 3.59 3.83

Delaware ...................... 937,630 1,299,349 1,518,786 40,957 54,749 60,660 2.29 2.37 2.50

District of Columbia ..... 780,192 1,067,500 1,352,905 58,269 82,837 98,892 1.34 1.29 1.37

Florida .......................... 13,885,988 19,042,877 23,328,028 481,115 680,277 732,782 2.89 2.80 3.18

Georgia ........................ 9,158,624 12,528,856 15,976,945 294,479 363,154 394,117 3.11 3.45 4.05

Hawaii .......................... 1,213,695 1,648,086 2,225,437 41,372 56,869 65,428 2.93 2.90 3.40

Idaho ............................ 1,302,817 1,618,215 1,957,740 36,091 48,675 53,661 3.61 3.32 3.65

Illinois ........................... 14,462,773 18,658,428 23,495,271 474,444 569,544 631,970 3.05 3.28 3.72

Indiana ......................... 7,110,930 9,108,931 9,680,895 198,020 239,575 259,894 3.59 3.80 3.72

Iowa ............................. 3,264,336 3,808,200 4,731,463 93,287 120,258 136,062 3.50 3.17 3.48

Kansas ......................... 2,971,814 3,718,153 4,805,310 85,742 105,164 122,544 3.47 3.54 3.92

Kentucky ...................... 3,837,794 4,812,591 5,886,890 113,108 139,336 155,789 3.39 3.45 3.78

Louisiana ...................... 4,391,214 5,554,766 7,276,651 131,430 197,163 205,117 3.34 2.82 3.55

Maine ........................... 1,604,438 2,056,266 2,350,447 36,395 45,587 50,039 4.41 4.51 4.70

Maryland ...................... 6,545,135 8,682,586 11,591,965 182,953 248,139 285,116 3.58 3.50 4.07

Massachusetts ............. 8,511,065 11,357,857 13,942,586 272,680 323,301 360,538 3.12 3.51 3.87

Michigan ...................... 13,994,294 16,353,921 17,217,584 336,786 375,260 369,671 4.16 4.36 4.66

Minnesota .................... 6,140,442 7,310,284 9,270,281 188,449 238,367 258,499 3.26 3.07 3.59

Mississippi ................... 2,510,376 3,243,888 3,967,232 65,615 81,500 94,406 3.83 3.98 4.20

Missouri ....................... 5,655,531 7,115,207 8,827,224 180,982 216,633 237,955 3.12 3.28 3.71

Montana ....................... 994,770 1,193,182 1,436,062 21,629 30,088 34,999 4.60 3.97 4.10

Nebraska ...................... 1,926,500 2,512,914 3,053,575 57,233 72,504 86,411 3.37 3.47 3.53

Nevada ......................... 1,875,467 2,722,264 3,606,035 75,907 114,771 125,037 2.47 2.37 2.88

New Hampshire ........... 1,418,503 2,021,144 2,490,623 44,067 53,653 59,086 3.22 3.77 4.22

New Jersey .................. 13,327,645 19,669,576 23,589,224 349,334 429,985 471,946 3.82 4.57 5.00

New Mexico ................. 1,890,274 2,554,638 3,186,252 50,262 67,776 76,871 3.76 3.77 4.14

New York ...................... 28,433,240 38,866,853 48,635,363 770,621 961,941 1,094,104 3.69 4.04 4.45

North Carolina .............. 7,713,293 9,835,550 12,470,470 281,418 354,973 407,032 2.74 2.77 3.06

North Dakota ............... 638,946 832,157 928,528 18,250 24,672 31,626 3.50 3.37 2.94

Ohio ............................. 12,974,575 17,167,866 19,397,511 381,175 444,715 462,015 3.40 3.86 4.20

Oklahoma ..................... 3,382,581 4,161,024 5,082,062 91,292 120,662 142,388 3.71 3.45 3.57

Oregon ......................... 3,896,287 4,458,028 5,529,831 112,974 143,349 167,481 3.45 3.11 3.30

Pennsylvania ................ 14,120,112 18,711,100 21,831,816 395,811 482,324 546,538 3.57 3.88 3.99

Rhode Island ................ 1,393,143 1,825,900 2,139,317 33,522 44,169 47,470 4.16 4.13 4.51

South Carolina ............. 4,087,355 5,312,739 6,626,763 115,392 141,929 158,786 3.54 3.74 4.17

South Dakota ............... 737,998 916,563 1,080,054 24,009 31,641 38,255 3.07 2.90 2.82

Tennessee .................... 4,931,734 6,446,691 7,768,052 177,582 224,522 243,849 2.78 2.87 3.19

Texas ............................ 25,098,703 31,919,107 40,688,181 732,987 970,997 1,146,647 3.42 3.29 3.55

Utah ............................. 2,102,655 2,627,022 3,638,775 69,483 90,748 111,301 3.03 2.89 3.27

Vermont ........................ 870,198 1,177,478 1,413,329 18,033 22,773 24,625 4.83 5.17 5.74

Virginia ......................... 7,757,598 10,705,162 13,505,290 261,894 356,852 409,732 2.96 3.00 3.30

Washington .................. 6,399,883 7,870,979 9,940,056 227,828 279,405 331,639 2.81 2.82 3.00

West Virginia ................ 2,086,937 2,527,767 3,059,420 41,419 51,964 61,043 5.04 4.86 5.01

Wisconsin .................... 6,852,178 8,435,359 9,696,228 177,638 218,923 239,613 3.86 3.85 4.05

Wyoming ...................... 683,918 863,423 1,268,407 17,047 26,238 36,760 4.01 3.29 3.45

Puerto Rico .................. 2,086,414 2,865,945 3,502,757 69,208 86,157 NA 3.01 3.33 NA

GDP = gross domestic product; NA = not available

NOTE: GDP reported in current dollars.

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 (various years); Government of Puerto Rico, Office of the Governor (various years); United 

Nations Statistics Division.

Science and Engineering Indicators 2012
8-32  Chapter 8. State Indicators

Current 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, adjusted for 

the size of the student body. It is calculated by dividing the current expenditures over the entire academic year for prekindergarten through grade 

12 by the number of students in those grades in public schools. Current 

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 could affect the amount of goods and 

services that can be purchased.

During the 2008–09 school year, 65.8% of current expenses were used 

for instructional costs, 18.0% for operational costs, 10.8% for administrative costs, and 5.4% for student support services.

The year is the end date of the academic year. For example, data for 

2009 represent costs for the 2008–09 academic year.

Findings

U Per-pupil spending on day-to-day operations 

grew nationwide in current dollars from $6,911 

in 2000 to $10,591 in 2009, an increase of 53% 

in unadjusted dollars. This was equivalent to an 

increase of approximately 23% after adjusting 

for inflation.

U In 2009, all states showed substantial increases 

in per-pupil spending relative to 2000, and only 1 

state did not exceed the 2000 national average, 

compared with 30 states in 2000.

U Per-pupil spending in individual states varied 

widely, ranging from a low of $6,612 to a high of 

$17,746 in 2009.

U 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

Current expenditures per pupil for elementary and secondary public schools: 2009

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

NV

OK

TN

DE

HI

IL

ME

NH

PA

AL

AR

CA

CO

FL

GA

IN

KY

NC

NM

OR

SC

SD

TX

WA MD

AK

CT

MA

RI

VT

WY

NJ

NY DC

IA

KS

LA

MI

MN

MO

MT

ND

NE

OH

VA

WI

WV

Dollars

1st quartile ($12,299–$19,698)

2nd quartile ($10,189–$12,183)

3rd quartile ($8,867–$10,055)

4th quartile ($6,612–$8,854)

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 2012 8-33

Table 8-11

Current expenditures per pupil for elementary and secondary public schools, by state: 2000, 2005, and 2009

Public school expenditures 

($thousands) Student enrollment

Per-pupil 

expenditures ($)

State 2000 2005 2009 2000 2005 2009 2000 2005 2009

United States ................. 323,808,910 425,047,568 518,997,426 46,857,149 48,794,911 49,003,001 6,911 8,711 10,591

Alabama ..................... 4,176,082 5,164,406 6,683,843 740,732 730,140 739,198 5,638 7,073 9,042

Alaska ........................ 1,183,499 1,442,269 2,006,114 134,391 132,970 130,662 8,806 10,847 15,353

Arizona ....................... 4,262,182 6,579,957 8,625,276 852,612 1,043,298 1,087,817 4,999 6,307 7,929

Arkansas .................... 2,380,331 3,546,999 4,240,839 451,034 463,115 478,965 5,277 7,659 8,854

California .................... 38,129,479 50,918,654 60,080,929 6,038,590 6,441,557 6,322,528 6,314 7,905 9,503

Colorado .................... 4,400,888 5,994,440 7,187,267 708,109 765,976 818,443 6,215 7,826 8,782

Connecticut ............... 5,402,868 7,080,396 8,708,294 553,993 577,390 567,198 9,753 12,263 15,353

Delaware .................... 937,630 1,299,349 1,518,786 112,836 119,091 125,430 8,310 10,911 12,109

District of Columbia ... 780,192 1,067,500 1,352,905 77,194 76,714 68,681 10,107 13,915 19,698

Florida ........................ 13,885,988 19,042,877 23,328,028 2,381,396 2,639,336 2,631,020 5,831 7,215 8,867

Georgia ...................... 9,158,624 12,528,856 15,976,945 1,422,762 1,553,437 1,655,792 6,437 8,065 9,649

Hawaii ........................ 1,213,695 1,648,086 2,225,437 185,860 183,185 179,478 6,530 8,997 12,399

Idaho .......................... 1,302,817 1,618,215 1,957,740 245,136 256,084 275,051 5,315 6,319 7,118

Illinois ......................... 14,462,773 18,658,428 23,495,271 2,027,600 2,097,503 2,026,925 7,133 8,896 11,592

Indiana ....................... 7,110,930 9,108,931 9,680,895 988,702 1,021,348 1,046,147 7,192 8,919 9,254

Iowa ........................... 3,264,336 3,808,200 4,731,463 497,301 478,319 470,537 6,564 7,962 10,055

Kansas ....................... 2,971,814 3,718,153 4,805,310 472,188 469,136 471,060 6,294 7,926 10,201

Kentucky .................... 3,837,794 4,812,591 5,886,890 648,180 674,796 651,370 5,921 7,132 9,038

Louisiana .................... 4,391,214 5,554,766 7,276,651 756,579 724,281 684,873 5,804 7,669 10,625

Maine ......................... 1,604,438 2,056,266 2,350,447 209,253 198,820 192,935 7,667 10,342 12,183

Maryland .................... 6,545,135 8,682,586 11,591,965 846,582 865,561 843,861 7,731 10,031 13,737

Massachusetts ........... 8,511,065 11,357,857 13,942,586 971,425 975,574 958,910 8,761 11,642 14,540

Michigan .................... 13,994,294 16,353,921 17,217,584 1,725,639 1,750,919 1,659,921 8,110 9,340 10,373

Minnesota .................. 6,140,442 7,310,284 9,270,281 854,034 838,503 836,048 7,190 8,718 11,088

Mississippi ................. 2,510,376 3,243,888 3,967,232 500,716 495,376 491,962 5,014 6,548 8,064

Missouri ..................... 5,655,531 7,115,207 8,827,224 914,110 905,449 892,436 6,187 7,858 9,891

Montana ..................... 994,770 1,193,182 1,436,062 157,556 146,705 140,936 6,314 8,133 10,189

Nebraska .................... 1,926,500 2,512,914 3,053,575 288,261 285,761 281,544 6,683 8,794 10,846

Nevada ....................... 1,875,467 2,722,264 3,606,035 325,610 400,083 433,371 5,760 6,804 8,321

New Hampshire ......... 1,418,503 2,021,144 2,490,623 206,783 206,852 197,934 6,860 9,771 12,583

New Jersey ................ 13,327,645 19,669,576 23,589,224 1,289,256 1,393,347 1,381,420 10,337 14,117 17,076

New Mexico ............... 1,890,274 2,554,638 3,186,252 324,495 326,102 330,245 5,825 7,834 9,648

New York .................... 28,433,240 38,866,853 48,635,363 2,887,776 2,836,337 2,740,592 9,846 13,703 17,746

North Carolina ............ 7,713,293 9,835,550 12,470,470 1,275,925 1,385,754 1,463,967 6,045 7,098 8,518

North Dakota ............. 638,946 832,157 928,528 112,751 100,513 94,728 5,667 8,279 9,802

Ohio ........................... 12,974,575 17,167,866 19,397,511 1,836,554 1,840,032 1,779,290 7,065 9,330 10,902

Oklahoma ................... 3,382,581 4,161,024 5,082,062 627,032 629,476 645,108 5,395 6,610 7,878

Oregon ....................... 3,896,287 4,458,028 5,529,831 545,033 552,322 575,393 7,149 8,071 9,611

Pennsylvania .............. 14,120,112 18,711,100 21,831,816 1,816,716 1,828,089 1,775,029 7,772 10,235 12,299

Rhode Island .............. 1,393,143 1,825,900 2,139,317 156,454 156,498 145,342 8,904 11,667 14,719

South Carolina ........... 4,087,355 5,312,739 6,626,763 666,780 703,736 718,113 6,130 7,549 9,228

South Dakota ............. 737,998 916,563 1,080,054 131,037 122,798 126,429 5,632 7,464 8,543

Tennessee .................. 4,931,734 6,446,691 7,768,052 916,202 941,091 971,950 5,383 6,850 7,992

Texas .......................... 25,098,703 31,919,107 40,688,181 3,991,783 4,405,215 4,752,148 6,288 7,246 8,562

Utah ........................... 2,102,655 2,627,022 3,638,775 480,255 503,607 550,298 4,378 5,216 6,612

Vermont ...................... 870,198 1,177,478 1,413,329 104,559 98,352 93,625 8,323 11,972 15,096

Virginia ....................... 7,757,598 10,705,162 13,505,290 1,133,994 1,204,739 1,235,795 6,841 8,886 10,928

Washington ................ 6,399,883 7,870,979 9,940,056 1,003,714 1,020,005 1,026,023 6,376 7,717 9,688

West Virginia .............. 2,086,937 2,527,767 3,059,420 291,811 280,129 282,729 7,152 9,024 10,821

Wisconsin .................. 6,852,178 8,435,359 9,696,228 877,753 864,757 867,035 7,806 9,755 11,183

Wyoming .................... 683,918 863,423 1,268,407 92,105 84,733 86,709 7,425 10,190 14,628

Puerto Rico ................ 2,086,414 2,865,945 3,502,757 613,019 575,648 503,635 3,404 4,979 6,955

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 2012
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, nearly 853,000 public school students 

from the class of 2010 took nearly 2.5 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 2010 attended 12,705 U.S. public schools 

that participated in the AP program. These schools make an average of 

10 different AP courses available to their students.

Findings

U Nationwide, the percentage of public school 

students who took an AP Exam rose from 15.9% 

of the class of 2000 to 28.3% of the class 

of 2010.

U The percentage of public school students 

taking an AP Exam varied greatly among states 

and ranged from 10.4% to 43.5% of the class 

of 2010. Forty-two states and the District of 

Columbia exceeded the 2000 national average in 

2010, compared with 15 states and the District 

of Columbia that exceeded the national average 

in 2000.

U AP participation levels were higher for all 

jurisdictions in 2010 than in 2000. Arkansas 

showed the largest increase, with the class of 

2010 exceeding the participation of the class of 

2000 by more than 28 percentage points.

Percentage of Public High School Students Taking Advanced Placement 

Exams

Figure 8-12

Percentage of public high school students taking Advanced Placement Exams: 2010

8 12 16 20 24 28 32 36 40 44

LA

ND

DC

IL

KY

MN

NJ

SC

WI

AL

HI

ID

KS

MT

OH

PA

RI

SD

TN

WV

DE

IN

ME

NC

NV

TX

UT

VT

WA

CA

CO

CT

MA

AR

GA

NY

VA

FL

MD

AK

MI

NH

NM

OK

OR

AZ

IA

MO

MS

NE

WY

Percent

1st quartile (30.2%–43.5%)

2nd quartile (24.4%–29.3%)

3rd quartile (18.4%–23.4%)

4th quartile (10.4%–18.0%)

SOURCE: College Board, Advanced Placement Report to the Nation.
Science and Engineering Indicators 2012 8-35

Table 8-12

Public high school students taking Advanced Placement Exams, by 

state: 2000, 2005, and 2010

(Percent)

State 2000 2005 2010

United States ............................................. 15.9 22.7 28.3

Alabama ................................................. 7.2 9.7 19.5

Alaska .................................................... 15.4 18.8 22.3

Arizona ................................................... 11.3 14.6 15.6

Arkansas ................................................ 8.1 24.0 36.6

California ................................................ 22.2 30.2 34.0

Colorado ................................................ 18.6 26.7 34.6

Connecticut ........................................... 19.1 26.0 32.2

Delaware ................................................ 13.3 24.8 28.1

District of Columbia ............................... 17.3 27.0 25.1

Florida .................................................... 22.7 32.9 43.5

Georgia .................................................. 17.2 24.4 37.3

Hawaii .................................................... 10.6 16.5 19.6

Idaho ...................................................... 9.6 14.3 16.3

Illinois ..................................................... 13.4 19.8 26.3

Indiana ................................................... 11.9 18.4 29.3

Iowa ....................................................... 6.9 10.2 14.4

Kansas ................................................... 7.0 9.8 16.0

Kentucky ................................................ 10.6 17.1 24.4

Louisiana ................................................ 3.2 4.9 11.4

Maine ..................................................... 14.8 22.3 31.6

Maryland ................................................ 20.2 31.5 43.4

Massachusetts ....................................... 19.6 26.2 33.2

Michigan ................................................ 13.9 18.0 23.2

Minnesota .............................................. 13.4 17.6 26.4

Mississippi ............................................. 5.6 8.7 14.1

Missouri ................................................. 5.5 9.2 13.4

Montana ................................................. 10.1 15.2 18.0

Nebraska ................................................ 5.0 7.3 12.4

Nevada ................................................... 15.1 20.3 28.3

New Hampshire ..................................... 13.3 17.6 22.7

New Jersey ............................................ 17.9 23.0 25.6

New Mexico ........................................... 11.1 18.0 22.3

New York ................................................ 27.3 34.8 38.0

North Carolina ........................................ 19.7 29.7 28.8

North Dakota ......................................... 5.9 8.8 10.4

Ohio ....................................................... 11.3 16.4 18.9

Oklahoma ............................................... 9.5 17.7 20.8

Oregon ................................................... 10.5 16.4 23.4

Pennsylvania .......................................... 12.4 15.7 19.7

Rhode Island .......................................... 10.7 12.4 17.9

South Carolina ....................................... 17.7 21.6 26.8

South Dakota ......................................... 9.6 14.1 18.4

Tennessee .............................................. 10.4 15.1 18.6

Texas ...................................................... 16.6 25.1 30.2

Utah ....................................................... 24.5 29.1 28.4

Vermont .................................................. 16.6 22.7 31.8

Virginia ................................................... 25.0 30.1 38.1

Washington ............................................ 11.5 21.1 28.0

West Virginia .......................................... 8.4 12.0 18.4

Wisconsin .............................................. 15.2 21.1 26.3

Wyoming ................................................ 6.1 11.4 15.7

Puerto Rico ............................................ NA NA NA

NA = not available

NOTE: National average for United States is reported value in Advanced Placement Report to 

the Nation.

SOURCE: College Board, Advanced Placement Report to the Nation (various years).

Science and Engineering Indicators 2012
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.

A total of 33 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

U Nationally, 16.9% of public school students in the 

class of 2010 demonstrated the ability to do collegelevel work by obtaining a score of 3 or higher on at 

least one AP Exam, a substantial increase from the 

10.2% of the class of 2000 who obtained that score.

U Students from all states and the District of Columbia 

demonstrated greater success on AP Exams in 

2010 than in 2000, but this success was not evenly 

distributed. In 2010, 13 states and the District of 

Columbia had percentages below the 2000 national 

average of 10.2% compared with 38 jurisdictions in 

2000.

U The percentage of students who scored 3 or higher 

on an AP Exam varied widely among states. For the 

class of 2010, this percentage ranged from a low of 

4.4% to a high of 26.4% across states.

Percentage of Public High School Students Scoring 3 or Higher on at Least 

One Advanced Placement Exam

Figure 8-13

Percentage of public high school students scoring 3 or higher on at least one Advanced Placement Exam: 2010

4.4 6.6 8.8 11.0 13.2 15.4 17.6 19.8 22.0 24.2 26.4 28.6

LA

MS

DC

MO

ND

NE

WV

WY

AK

MI

NV

OR

SC

AL

AZ

HI

IA

KS

NM

OK

RI

TN

DE

IL

MN

NC

NH

TX

WA

GA

ME

NJ

UT

WI

CO

VT NY

CA

CT

FL

MA

VA MD

AR

ID

IN

KY

MT

OH

PA

SD

Percent

1st quartile (18.6%–26.4%)

2nd quartile (14.1%–18.3%)

3rd quartile (9.5%–12.7%)

4th quartile (4.4%–9.4%)

SOURCE: College Board, Advanced Placement Report to the Nation.
Science and Engineering Indicators 2012 8-37

Table 8-13

Public high school students scoring 3 or higher on at least one 

Advanced Placement Exam, by state: 2000, 2005, and 2010

(Percent)

State 2000 2005 2010

United States ................................................... 10.2 14.1 16.9

Alabama ....................................................... 3.9 5.3 9.0

Alaska .......................................................... 10.1 12.4 14.3

Arizona ......................................................... 7.2 9.2 8.8

Arkansas ...................................................... 4.3 7.7 12.5

California ...................................................... 15.0 19.7 22.3

Colorado ...................................................... 12.2 16.9 21.4

Connecticut ................................................. 13.6 19.1 23.2

Delaware ...................................................... 7.6 12.9 15.4

District of Columbia ..................................... 6.6 8.7 6.9

Florida .......................................................... 13.5 18.5 22.3

Georgia ........................................................ 9.7 13.5 19.1

Hawaii .......................................................... 5.8 8.2 9.4

Idaho ............................................................ 6.5 9.6 11.0

Illinois ........................................................... 9.9 14.1 17.2

Indiana ......................................................... 6.0 8.9 12.4

Iowa ............................................................. 4.9 6.7 8.8

Kansas ......................................................... 4.4 6.5 9.5

Kentucky ...................................................... 5.5 8.3 12.2

Louisiana ...................................................... 1.9 2.5 4.6

Maine ........................................................... 10.1 14.4 19.0

Maryland ...................................................... 14.1 21.0 26.4

Massachusetts ............................................. 14.5 18.7 23.1

Michigan ...................................................... 8.8 11.6 15.0

Minnesota .................................................... 8.1 11.5 16.8

Mississippi ................................................... 2.3 3.3 4.4

Missouri ....................................................... 3.7 6.0 7.5

Montana ....................................................... 6.8 10.0 11.7

Nebraska ...................................................... 3.2 4.4 7.4

Nevada ......................................................... 9.1 12.0 15.0

New Hampshire ........................................... 9.2 11.5 16.6

New Jersey .................................................. 12.9 16.5 18.6

New Mexico ................................................. 6.1 8.5 10.2

New York ...................................................... 17.9 22.8 24.6

North Carolina .............................................. 11.3 17.1 17.5

North Dakota ............................................... 4.4 6.0 6.8

Ohio ............................................................. 7.1 10.1 11.8

Oklahoma ..................................................... 5.4 8.2 10.3

Oregon ......................................................... 7.1 10.7 14.1

Pennsylvania ................................................ 8.3 10.5 12.7

Rhode Island ................................................ 6.9 8.1 10.9

South Carolina ............................................. 10.0 12.6 15.1

South Dakota ............................................... 5.9 8.8 11.0

Tennessee .................................................... 6.2 8.9 9.7

Texas ............................................................ 9.9 13.7 15.5

Utah ............................................................. 17.4 20.5 19.2

Vermont ........................................................ 11.5 15.4 21.8

Virginia ......................................................... 15.9 19.3 23.7

Washington .................................................. 7.6 13.2 17.1

West Virginia ................................................ 4.6 5.8 7.6

Wisconsin .................................................... 10.5 14.5 18.3

Wyoming ...................................................... 3.8 5.8 8.5

Puerto Rico .................................................. NA NA NA

NA = not available

NOTE: National average for United States is reported value in Advanced Placement Report to the 

Nation.

SOURCE: College Board, Advanced Placement Report to the Nation (various years).

Science and Engineering Indicators 2012
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. 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 four 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 Examination.

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

U In 2010, a total of 237,000 Calculus AB exams were 

taken in the United States, and 137,000 were scored 3 

or higher. Public school students took 200,000 Calculus 

AB exams and 108,000 of those were scored at 3 or 

above. The remaining 37,000 Calculus AB exams were 

taken by students who did not attend public schools, 

i.e., those who attended independent or religious 

schools, home schools, or did not identify their school.

U 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 2.7% in 2000 

to 3.5% in 2010. Values for individual states ranged 

from a low of 0.5% to a high of 6.0% in 2010.

U Between 2000 and 2010, nearly all states increased 

the percentage of high school graduates that had 

demonstrated their ability in Calculus AB. However, four 

states showed lower percentages in 2010.

U Because the percentages are small, year-to-year 

comparisons should be made with caution. Variability 

in students’ course selection and level of performance 

can affect the numbers.

Percentage of Public High School Students Scoring 3 or Higher on Advanced 

Placement Calculus AB Exam

Figure 8-14

Percentage of public high school students scoring 3 or higher on Advanced Placement Calculus AB

Exam: 2010

0.0 0.6 1.2 1.8 2.4 3.0 3.6 4.2 4.8 5.4 6.0 6.6

MS

DC

LA

NE

KS

KY

NV

OR

TX

WY

AZ

IA

MO

NM

OK

TN

WV

FL

GA

ID

IN

NC

OH

PA

RI

SC

SD

DE

IL

MI

MN

WI

AK

CO

ME

NH

NJ

UT

VA

WA

MA

MD

CA

CT

NY VT

AL

AR

HI

MT

ND

Percent

1st quartile (4.2%–6.0%)

2nd quartile (3.2%–4.1%)

3rd quartile (1.8%–3.1%)

4th quartile (0.5%–1.7%)

SOURCE: College Board, Advanced Placement Report to the Nation.
Science and Engineering Indicators 2012 8-39

Table 8-14

Public high school students scoring 3 or higher on Advanced 

Placement Calculus AB Exam, by state: 2000, 2005, and 2010

(Percent)

State 2000 2005 2010

United States ................................................... 2.7 3.2 3.5

Alabama ....................................................... 0.8 1.0 1.8

Alaska .......................................................... 3.2 4.0 4.6

Arizona ......................................................... 1.8 2.1 1.6

Arkansas ...................................................... 2.2 1.7 2.1

California ...................................................... 3.4 4.3 4.8

Colorado ...................................................... 2.4 3.0 4.4

Connecticut ................................................. 3.4 4.0 5.0

Delaware ...................................................... 2.2 3.5 3.8

District of Columbia ..................................... 0.7 1.7 0.8

Florida .......................................................... 3.2 3.2 3.3

Georgia ........................................................ 2.5 3.0 3.3

Hawaii .......................................................... 1.5 1.9 2.0

Idaho ............................................................ 1.6 2.8 3.0

Illinois ........................................................... 3.2 3.6 3.9

Indiana ......................................................... 2.3 2.5 3.3

Iowa ............................................................. 1.4 1.6 1.7

Kansas ......................................................... 1.2 1.8 2.4

Kentucky ...................................................... 1.6 0.4 2.7

Louisiana ...................................................... 0.4 0.3 0.8

Maine ........................................................... 2.7 3.4 4.3

Maryland ...................................................... 3.4 4.3 5.4

Massachusetts ............................................. 4.0 4.6 5.6

Michigan ...................................................... 2.5 3.0 3.6

Minnesota .................................................... 3.0 3.3 4.1

Mississippi ................................................... 0.5 0.7 0.5

Missouri ....................................................... 0.9 1.3 1.5

Montana ....................................................... 1.3 1.7 2.2

Nebraska ...................................................... 0.6 0.9 1.1

Nevada ......................................................... 2.1 2.6 2.6

New Hampshire ........................................... 3.0 2.8 4.2

New Jersey .................................................. 3.8 3.8 4.2

New Mexico ................................................. 1.4 1.7 1.6

New York ...................................................... 5.1 5.0 5.2

North Carolina .............................................. 3.4 3.9 3.2

North Dakota ............................................... 1.2 1.6 1.8

Ohio ............................................................. 2.3 3.0 3.0

Oklahoma ..................................................... 1.3 1.1 1.3

Oregon ......................................................... 1.8 2.2 2.9

Pennsylvania ................................................ 2.4 2.8 3.2

Rhode Island ................................................ 1.6 1.9 3.1

South Carolina ............................................. 3.4 3.7 3.5

South Dakota ............................................... 2.4 3.2 3.4

Tennessee .................................................... 1.5 1.7 1.5

Texas ............................................................ 1.9 2.4 2.6

Utah ............................................................. 5.0 4.9 4.7

Vermont ........................................................ 3.5 3.9 6.0

Virginia ......................................................... 3.7 3.6 4.2

Washington .................................................. 2.7 4.0 4.6

West Virginia ................................................ 1.3 1.4 1.3

Wisconsin .................................................... 3.1 3.7 4.0

Wyoming ...................................................... 1.6 1.6 2.5

Puerto Rico .................................................. NA NA NA

NA = not available

NOTE: National average for United States is reported value in Advanced Placement Report to the 

Nation.

SOURCE: College Board, Advanced Placement Report to the Nation (various years).

Science and Engineering Indicators 2012
8-40  Chapter 8. State Indicators

This indicator represents the percentage of the early- to mid-career 

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 population ages 25–44 are provided by the Census 

Bureau based on the 2000 Decennial Census. Estimates for states with 

smaller populations are generally less precise than estimates for states 

with larger populations.

Findings

U Nationwide, 87.1% of the early- to mid-career 

population had at least a high school credential 

in 2009, a slight increase from the 85.0% who 

held such a credential in 2000.

U Forty-six states and the District of Columbia 

showed an increase in the percentage of their 

early- to mid-career population with at least a 

high school credential between 2000 and 2009. 

Six states had 2009 values below the 2000 

national average of 85.0%, compared with 17 

states and the District of Columbia in 2000.

U In 2009, the early- to mid-career population with 

at least a high school credential varied greatly 

among states, ranging from 80.8% to 98.3%. 

States in close proximity to the southern border 

of the United States tended to rank lowest on 

this indicator.

High School Graduates Among Individuals 25–44 Years Old

Figure 8-15

High school graduates among individuals 25–44 years old: 2009

79.2 81.0 82.8 84.6 86.4 88.2 90.0 91.8 93.6 95.4 97.2 99.0

TX

CA

NM

NV

FL

KY

NC

NY

OK

SC

AZ TN

CO

DE

ID

IL

IN

MD

MO

OR

VA

WA

WV

AK

CT

DC

KS

MA

MI

NE

NJ

OH

PA

RI

UT

WI

IA

ME

MN

MT

NH

SD

VT

WY HI ND

AL

AR

GA

LA

MS

Percent

1st quartile (91.5%–98.3%)

2nd quartile (89.4%–91.3%)

3rd quartile (86.8%–89.3%)

4th quartile (80.8%–86.6%)

SOURCE: Census Bureau, 2000 Decennial 

Census, Population Estimates Program, and 

American Community Survey.
Science and Engineering Indicators 2012 8-41

Table 8-15

High school graduates among individuals 25–44 years old, by state: 2000, 2005, and 2009

Graduates 25–44 years old Population 25–44 years old

Graduates/population 

25–44 years old (%)

State 2000 2005 2009 2000 2005 2009 2000 2005 2009

United States ................... 72,241,876 71,215,646 72,337,536 85,040,251 83,257,116 83,096,278 85.0 85.5 87.1

Alabama ....................... 1,064,945 1,035,193 1,055,386 1,288,527 1,231,043 1,235,509 82.6 84.1 85.4

Alaska .......................... 186,160 162,669 179,001 203,522 191,837 197,248 91.5 84.8 90.7

Arizona ......................... 1,232,818 1,367,583 1,528,930 1,511,469 1,695,189 1,826,751 81.6 80.7 83.7

Arkansas ...................... 622,698 633,557 651,055 750,972 747,630 755,915 82.9 84.7 86.1

California ...................... 8,286,071 8,316,850 8,590,128 10,714,403 10,668,824 10,604,180 77.3 78.0 81.0

Colorado ...................... 1,242,919 1,240,697 1,279,279 1,400,850 1,388,046 1,445,400 88.7 89.4 88.5

Connecticut ................. 926,614 852,932 820,616 1,032,689 951,020 899,649 89.7 89.7 91.2

Delaware ...................... 207,799 206,583 209,049 236,441 234,823 232,837 87.9 88.0 89.8

District of Columbia ..... 157,077 163,027 180,722 189,439 190,118 197,983 82.9 85.8 91.3

Florida .......................... 3,840,710 4,000,762 4,170,014 4,569,347 4,787,948 4,789,059 84.1 83.6 87.1

Georgia ........................ 2,238,995 2,368,999 2,418,543 2,652,764 2,746,294 2,830,740 84.4 86.3 85.4

Hawaii .......................... 333,762 308,637 344,834 362,336 354,560 360,037 92.1 87.0 95.8

Idaho ............................ 316,815 327,870 355,984 362,401 375,247 400,329 87.4 87.4 88.9

Illinois ........................... 3,265,416 3,200,557 3,146,716 3,795,544 3,611,958 3,544,995 86.0 88.6 88.8

Indiana ......................... 1,567,100 1,500,650 1,502,416 1,791,828 1,716,726 1,689,050 87.5 87.4 89.0

Iowa ............................. 740,397 713,525 685,787 808,259 746,659 734,622 91.6 95.6 93.4

Kansas ......................... 687,268 656,920 649,026 769,204 713,752 717,645 89.3 92.0 90.4

Kentucky ...................... 1,009,246 993,094 1,014,340 1,210,773 1,172,770 1,162,402 83.4 84.7 87.3

Louisiana ...................... 1,044,255 1,026,229 1,022,069 1,293,128 1,217,593 1,186,325 80.8 84.3 86.2

Maine ........................... 339,227 317,653 301,407 370,597 344,295 322,409 91.5 92.3 93.5

Maryland ...................... 1,487,216 1,399,879 1,398,327 1,664,677 1,611,882 1,557,085 89.3 86.8 89.8

Massachusetts ............. 1,795,438 1,690,234 1,635,794 1,989,783 1,857,726 1,787,350 90.2 91.0 91.5

Michigan ...................... 2,630,713 2,455,339 2,293,446 2,960,544 2,743,365 2,536,880 88.9 89.5 90.4

Minnesota .................... 1,395,170 1,345,742 1,299,949 1,497,320 1,420,387 1,394,305 93.2 94.7 93.2

Mississippi ................... 650,242 648,458 644,971 807,170 771,676 761,785 80.6 84.0 84.7

Missouri ....................... 1,426,806 1,378,001 1,388,876 1,626,302 1,566,374 1,554,391 87.7 88.0 89.4

Montana ....................... 225,105 216,509 214,586 245,220 226,076 231,769 91.8 95.8 92.6

Nebraska ...................... 441,527 421,008 412,126 487,107 453,659 451,666 90.6 92.8 91.2

Nevada ......................... 508,173 585,942 631,069 628,572 719,501 769,608 80.8 81.4 82.0

New Hampshire ........... 350,744 330,926 310,704 381,240 357,080 333,694 92.0 92.7 93.1

New Jersey .................. 2,313,820 2,165,296 2,135,875 2,624,146 2,485,721 2,363,679 88.2 87.1 90.4

New Mexico ................. 425,745 411,608 430,512 516,100 510,063 523,059 82.5 80.7 82.3

New York ...................... 4,926,064 4,786,794 4,697,650 5,831,622 5,548,409 5,351,598 84.5 86.3 87.8

North Carolina .............. 2,117,289 2,148,501 2,217,822 2,500,535 2,485,963 2,553,673 84.7 86.4 86.8

North Dakota ............... 164,893 155,297 150,983 174,891 151,681 153,582 94.3 102.4 98.3

Ohio ............................. 2,965,744 2,759,770 2,716,279 3,325,210 3,122,259 2,998,151 89.2 88.4 90.6

Oklahoma ..................... 836,030 807,209 828,944 975,169 929,451 957,235 85.7 86.8 86.6

Oregon ......................... 861,602 872,276 907,376 997,269 988,164 1,028,645 86.4 88.3 88.2

Pennsylvania ................ 3,136,195 2,908,593 2,922,659 3,508,562 3,280,173 3,187,617 89.4 88.7 91.7

Rhode Island ................ 265,033 264,154 247,679 310,636 296,463 274,622 85.3 89.1 90.2

South Carolina ............. 990,207 999,627 1,036,562 1,185,955 1,172,501 1,200,366 83.5 85.3 86.4

South Dakota ............... 188,052 180,013 180,578 206,399 194,122 196,143 91.1 92.7 92.1

Tennessee .................... 1,439,729 1,459,559 1,502,094 1,718,428 1,698,113 1,710,134 83.8 86.0 87.8

Texas ............................ 5,115,457 5,248,281 5,709,404 6,484,321 6,665,252 7,064,651 78.9 78.7 80.8

Utah ............................. 555,513 646,632 700,665 626,600 686,668 775,481 88.7 94.2 90.4

Vermont ........................ 162,109 150,073 138,869 176,456 158,184 148,584 91.9 94.9 93.5

Virginia ......................... 1,962,040 1,896,614 1,959,386 2,237,655 2,194,670 2,194,699 87.7 86.4 89.3

Washington .................. 1,617,766 1,592,550 1,654,322 1,816,217 1,783,093 1,855,094 89.1 89.3 89.2

West Virginia ................ 420,900 411,155 408,715 501,343 468,846 459,606 84.0 87.7 88.9

Wisconsin .................... 1,429,331 1,367,667 1,326,456 1,581,690 1,495,775 1,449,006 90.4 91.4 91.5

Wyoming ...................... 126,931 117,952 129,556 138,619 127,487 139,035 91.6 92.5 93.2

Puerto Rico .................. 794,579 868,650 879,051 1,049,995 1,076,844 1,084,239 75.7 80.7 81.1

SOURCES: Census Bureau, 2000 Decennial Census, Population Estimates Program (various years), and American Community Survey (various years).

Science and Engineering Indicators 2012
8-42  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 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 of Education Statistics. Estimates of the population 

ages 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 outof-state students, a situation that sometimes occurs in states with small resident 

populations and the District of Columbia.

Findings

U In 2009, over 1.6 million bachelor’s degrees 

were conferred nationally in all fields, which 

is up from 1.2 million in 2000 and represents 

an increase of 29%. Between 2000 and 

2009, the number of bachelor’s degrees 

conferred per 1,000 individuals 18–24 years 

old in the population has increased by nearly 

16% nationwide.

U In 2009, state values on this indicator varied 

greatly. They ranged from 20.4 to 91.8 

bachelor’s degrees conferred per 1,000 

individuals 18–24 years old.

U The number of bachelor’s degrees conferred 

per 1,000 individuals 18–24 years old 

increased in all but 4 states between 2000 

and 2009.

Bachelor’s Degrees Conferred per 1,000 Individuals 18–24 Years Old

Figure 8-16

Bachelor’s degrees conferred per 1,000 individuals 18–24 years old: 2009

14 28 42 56 70 84 98 112 126 140 154

AK

AL

AR

CA

FL

HI

IL

KY

LA

MD

MI

MT

NC

NJ

OH

OK

OR

SC

TN

VA

WA

RI

VT

AZ

CO

CT

DE

ID

IN

KS

ME

MN

MO

ND

NE

NH

NY

PA

SD

UT

WI

WV DC

IA

MA

Degrees

GA

MS

NM

NV

TX

WY

1st quartile (63.5–151.5)

2nd quartile (54.9–63.1)

3rd quartile (46.5–53.4)

4th quartile (20.4–46.4)

SOURCES: National Center for Education 

Statistics, Integrated Postsecondary Education Data System; Census Bureau, 2000 

Decennial Census and Population Estimates 

Program.
Science and Engineering Indicators 2012 8-43

Table 8-16

Bachelor’s degrees conferred per 1,000 individuals 18–24 years old, by state: 2000, 2005, and 2009

Bachelor’s degrees Population 18–24 years old

Degrees/1,000 

individuals 

18–24 years old

State 2000 2005 2009 2000 2005 2009 2000 2005 2009

United States ................... 1,237,875 1,439,264 1,601,368 27,316,537 29,404,797 30,412,035 45.3 48.9 52.7

Alabama ....................... 21,293 21,616 24,245 442,009 453,363 465,249 48.2 47.7 52.1

Alaska .......................... 1,364 1,427 1,626 57,724 78,831 79,696 23.6 18.1 20.4

Arizona ......................... 20,865 29,133 39,898 519,381 570,206 610,920 40.2 51.1 65.3

Arkansas ...................... 9,405 11,191 12,027 263,326 269,720 273,263 35.7 41.5 44.0

California ...................... 121,546 146,959 160,930 3,389,475 3,601,422 3,746,026 35.9 40.8 43.0

Colorado ...................... 22,485 26,142 29,879 434,165 479,582 500,695 51.8 54.5 59.7

Connecticut ................. 15,072 16,833 19,178 273,000 315,265 340,550 55.2 53.4 56.3

Delaware ...................... 4,665 5,247 5,472 75,857 79,909 83,522 61.5 65.7 65.5

District of Columbia ..... 6,806 9,199 10,957 72,867 67,720 72,339 93.4 135.8 151.5

Florida .......................... 51,333 65,839 80,275 1,340,658 1,583,393 1,667,090 38.3 41.6 48.2

Georgia ........................ 29,219 35,515 40,461 844,924 912,133 979,688 34.6 38.9 41.3

Hawaii .......................... 5,091 5,300 5,797 115,683 128,311 124,841 44.0 41.3 46.4

Idaho ............................ 4,711 7,295 9,066 140,017 157,526 161,387 33.6 46.3 56.2

Illinois ........................... 55,036 63,913 69,339 1,217,816 1,273,336 1,298,744 45.2 50.2 53.4

Indiana ......................... 31,970 36,655 39,583 618,463 637,074 643,920 51.7 57.5 61.5

Iowa ............................. 18,750 20,786 26,239 299,438 320,586 321,355 62.6 64.8 81.7

Kansas ......................... 14,291 16,267 17,521 277,164 306,834 307,284 51.6 53.0 57.0

Kentucky ...................... 15,643 17,862 19,996 404,146 417,504 416,470 38.7 42.8 48.0

Louisiana ...................... 19,844 21,494 21,425 475,946 496,032 477,506 41.7 43.3 44.9

Maine ........................... 5,672 6,500 6,909 104,527 117,289 118,353 54.3 55.4 58.4

Maryland ...................... 22,089 25,990 27,909 454,129 515,830 547,538 48.6 50.4 51.0

Massachusetts ............. 42,308 45,769 50,106 582,619 621,142 668,112 72.6 73.7 75.0

Michigan ...................... 45,754 51,207 54,641 937,626 997,376 995,230 48.8 51.3 54.9

Minnesota .................... 23,175 28,275 31,275 473,816 523,797 526,091 48.9 54.0 59.4

Mississippi ................... 10,988 11,681 12,430 312,663 317,707 313,729 35.1 36.8 39.6

Missouri ....................... 29,978 34,306 38,370 538,883 586,404 592,454 55.6 58.5 64.8

Montana ....................... 5,171 5,177 5,252 86,241 103,362 104,243 60.0 50.1 50.4

Nebraska ...................... 10,747 11,999 12,575 175,359 197,081 196,793 61.3 60.9 63.9

Nevada ......................... 4,245 5,608 7,119 181,984 210,962 228,809 23.3 26.6 31.1

New Hampshire ........... 7,776 8,107 8,879 104,064 123,750 130,242 74.7 65.5 68.2

New Jersey .................. 26,939 31,987 34,625 679,702 716,880 756,033 39.6 44.6 45.8

New Mexico ................. 6,727 7,342 7,875 177,978 202,397 202,276 37.8 36.3 38.9

New York ...................... 95,558 111,201 122,186 1,772,439 1,839,901 1,923,887 53.9 60.4 63.5

North Carolina .............. 35,257 39,303 44,834 814,485 855,830 942,328 43.3 45.9 47.6

North Dakota ............... 4,877 5,161 5,604 73,371 89,501 88,808 66.5 57.7 63.1

Ohio ............................. 49,849 56,969 60,048 1,062,062 1,084,194 1,084,493 46.9 52.5 55.4

Oklahoma ..................... 15,578 18,266 19,634 358,410 387,237 386,532 43.5 47.2 50.8

Oregon ......................... 14,428 16,867 17,918 330,074 351,657 364,365 43.7 48.0 49.2

Pennsylvania ................ 66,273 77,765 84,692 1,099,275 1,169,151 1,219,844 60.3 66.5 69.4

Rhode Island ................ 8,402 9,417 10,291 107,100 109,690 112,088 78.5 85.9 91.8

South Carolina ............. 16,033 18,795 21,058 410,784 427,535 452,903 39.0 44.0 46.5

South Dakota ............... 4,494 4,771 5,031 78,087 87,891 87,586 57.6 54.3 57.4

Tennessee .................... 22,958 26,032 29,388 552,177 570,664 585,173 41.6 45.6 50.2

Texas ............................ 75,834 88,757 102,157 2,213,346 2,429,659 2,523,258 34.3 36.5 40.5

Utah ............................. 17,058 20,799 21,504 320,147 342,610 341,926 53.3 60.7 62.9

Vermont ........................ 4,832 4,892 5,788 56,918 66,960 68,869 84.9 73.1 84.0

Virginia ......................... 33,599 36,970 42,483 686,011 776,170 814,917 49.0 47.6 52.1

Washington .................. 24,002 28,265 30,091 563,091 617,519 644,616 42.6 45.8 46.7

West Virginia ................ 8,545 9,574 11,366 173,092 172,766 169,767 49.4 55.4 67.0

Wisconsin .................... 27,543 31,144 33,651 523,861 585,387 590,593 52.6 53.2 57.0

Wyoming ...................... 1,797 1,695 1,765 50,157 57,751 59,634 35.8 29.4 29.6

Puerto Rico .................. 16,164 16,646 17,116 429,220 406,548 394,800 37.7 40.9 43.4

SOURCES: National Center for Education Statistics, Integrated Postsecondary Education Data System (various years); Census Bureau, 2000 Decennial 

Census and Population Estimates Program (various years).

Science and Engineering Indicators 2012
8-44  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.

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 of Education Statistics. 

Estimates of the population ages 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 

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 that sometimes occurs in states with small resident populations and the District of Columbia.

Findings

U In 2009, more than 501,000 bachelor’s degrees in 

S&E fields were conferred nationally, which is up 

from 394,000 in 2000 and represents an increase 

of 27%. Between 2000 and 2009, 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 15% nationwide.

U In 2009, state values on this indicator varied greatly. 

They ranged from 7.0 to 31.4 bachelor’s degrees 

in S&E fields conferred per 1,000 individuals 18–24 

years old.

U The number of bachelor’s degrees in S&E fields 

conferred per 1,000 individuals 18–24 years old 

decreased in 7 states between 2000 and 2009.

U 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 science and engineering 

(NS&E). However, in terms of educational output 

adjusted for population, the concentration of S&E 

bachelor’s degrees was highest in the northeastern 

states unlike NS&E bachelor’s degrees that were 

concentrated in the north central states.

Bachelor’s Degrees in Science and Engineering Conferred per 1,000 

Individuals 18–24 Years Old

Figure 8-17

Bachelor’s degrees in science and engineering conferred per 1,000 individuals 18–24 years old: 2009

6 12 18 24 30 36 42 48 54 60 66

AK

AR

KY

MS

NM

NV

TX

WY

CO

CT

DE

IA

MD

ME

MN

MT

NH

NY

OR

PA

UT

VA

WI

MA

RI DC VT

Degrees

AL

AZ

CA

FL

GA

HI

ID

IL

IN

KS

LA

MI

MO

NC

ND

NE

NJ

OH

OK

SC

SD

TN

WA

WV

1st quartile (20.2–62.2)

2nd quartile (16.6–20.0)

3rd quartile (13.6–15.9)

4th quartile (7.0–13.2)

SOURCES: National Center for Education 

Statistics, Integrated Postsecondary Education Data System; Census Bureau, 2000 

Decennial Census and Population Estimates 

Program.
Science and Engineering Indicators 2012 8-45

Table 8-17

Bachelor’s degrees in science and engineering conferred per 1,000 individuals 18–24 years old, by state: 2000, 

2005, and 2009

 S&E bachelor’s degrees Population 18–24 years old

Degrees/1,000 individuals 

18–24 years old

State 2000 2005 2009 2000 2005 2009 2000 2005 2009

EPSCoR states ................ 61,035 67,202 71,999 4,702,607 5,059,909 5,109,850 13.0 13.3 14.1

Non-EPSCoR states ........ 330,351 394,690 424,576 22,541,063 24,277,168 25,229,846 14.7 16.3 16.8

Average EPSCoR 

 state value .................... na na na na na na 15.1 14.7 15.7

Average non-EPSCoR 

 state value .................... na na na na na na 15.6 17.1 17.8

United States ................... 394,301 466,065 501,076 27,316,537 29,404,797 30,412,035 14.4 15.8 16.5

Alabama ....................... 5,575 5,767 6,490 442,009 453,363 465,249 12.6 12.7 13.9

Alaska .......................... 393 440 559 57,724 78,831 79,696 6.8 5.6 7.0

Arizona ......................... 5,154 7,741 10,173 519,381 570,206 610,920 9.9 13.6 16.7

Arkansas ...................... 2,392 2,748 2,751 263,326 269,720 273,263 9.1 10.2 10.1

California ...................... 46,406 58,536 63,381 3,389,475 3,601,422 3,746,026 13.7 16.3 16.9

Colorado ...................... 8,822 10,375 10,865 434,165 479,582 500,695 20.3 21.6 21.7

Connecticut ................. 5,139 5,945 6,803 273,000 315,265 340,550 18.8 18.9 20.0

Delaware ...................... 1,546 1,656 1,688 75,857 79,909 83,522 20.4 20.7 20.2

District of Columbia ..... 2,915 4,173 4,501 72,867 67,720 72,339 40.0 61.6 62.2

Florida .......................... 14,094 19,400 22,608 1,340,658 1,583,393 1,667,090 10.5 12.3 13.6

Georgia ........................ 8,990 11,354 11,782 844,924 912,133 979,688 10.6 12.4 12.0

Hawaii .......................... 1,660 1,854 2,074 115,683 128,311 124,841 14.3 14.4 16.6

Idaho ............................ 1,492 1,960 2,340 140,017 157,526 161,387 10.7 12.4 14.5

Illinois ........................... 15,960 18,943 19,471 1,217,816 1,273,336 1,298,744 13.1 14.9 15.0

Indiana ......................... 8,921 10,477 10,211 618,463 637,074 643,920 14.4 16.4 15.9

Iowa ............................. 5,556 6,117 7,656 299,438 320,586 321,355 18.6 19.1 23.8

Kansas ......................... 4,157 4,713 4,599 277,164 306,834 307,284 15.0 15.4 15.0

Kentucky ...................... 4,153 4,537 4,944 404,146 417,504 416,470 10.3 10.9 11.9

Louisiana ...................... 5,568 6,076 5,782 475,946 496,032 477,506 11.7 12.2 12.1

Maine ........................... 2,117 2,354 2,518 104,527 117,289 118,353 20.3 20.1 21.3

Maryland ...................... 8,598 11,057 11,393 454,129 515,830 547,538 18.9 21.4 20.8

Massachusetts ............. 16,062 17,589 18,463 582,619 621,142 668,112 27.6 28.3 27.6

Michigan ...................... 13,642 15,591 16,873 937,626 997,376 995,230 14.5 15.6 17.0

Minnesota .................... 7,434 9,271 10,060 473,816 523,797 526,091 15.7 17.7 19.1

Mississippi ................... 2,769 2,784 3,036 312,663 317,707 313,729 8.9 8.8 9.7

Missouri ....................... 8,169 9,532 9,908 538,883 586,404 592,454 15.2 16.3 16.7

Montana ....................... 1,734 1,807 1,874 86,241 103,362 104,243 20.1 17.5 18.0

Nebraska ...................... 2,657 3,039 3,133 175,359 197,081 196,793 15.2 15.4 15.9

Nevada ......................... 1,050 1,481 2,039 181,984 210,962 228,809 5.8 7.0 8.9

New Hampshire ........... 2,788 2,826 2,929 104,064 123,750 130,242 26.8 22.8 22.5

New Jersey .................. 10,822 11,856 12,028 679,702 716,880 756,033 15.9 16.5 15.9

New Mexico ................. 1,939 2,188 2,308 177,978 202,397 202,276 10.9 10.8 11.4

New York ...................... 32,141 37,642 39,595 1,772,439 1,839,901 1,923,887 18.1 20.5 20.6

North Carolina .............. 12,021 13,488 14,833 814,485 855,830 942,328 14.8 15.8 15.7

North Dakota ............... 1,329 1,305 1,326 73,371 89,501 88,808 18.1 14.6 14.9

Ohio ............................. 13,874 15,485 16,257 1,062,062 1,084,194 1,084,493 13.1 14.3 15.0

Oklahoma ..................... 4,001 4,718 4,878 358,410 387,237 386,532 11.2 12.2 12.6

Oregon ......................... 5,381 6,261 6,545 330,074 351,657 364,365 16.3 17.8 18.0

Pennsylvania ................ 21,115 24,723 26,514 1,099,275 1,169,151 1,219,844 19.2 21.1 21.7

Rhode Island ................ 2,503 2,837 3,082 107,100 109,690 112,088 23.4 25.9 27.5

South Carolina ............. 4,996 5,784 6,151 410,784 427,535 452,903 12.2 13.5 13.6

South Dakota ............... 1,506 1,494 1,571 78,087 87,891 87,586 19.3 17.0 17.9

Tennessee .................... 6,532 7,156 7,727 552,177 570,664 585,173 11.8 12.5 13.2

Texas ............................ 20,831 25,294 27,723 2,213,346 2,429,659 2,523,258 9.4 10.4 11.0

Utah ............................. 5,245 6,870 6,986 320,147 342,610 341,926 16.4 20.1 20.4

Vermont ........................ 1,821 1,992 2,349 56,918 66,960 68,869 32.0 29.7 34.1

Virginia ......................... 12,933 13,748 15,158 686,011 776,170 814,917 18.9 17.7 18.6

Washington .................. 7,905 10,121 10,692 563,091 617,519 644,616 14.0 16.4 16.6

West Virginia ................ 2,204 2,285 2,929 173,092 172,766 169,767 12.7 13.2 17.3

Wisconsin .................... 8,604 10,118 10,871 523,861 585,387 590,593 16.4 17.3 18.4

Wyoming ...................... 685 557 649 50,157 57,751 59,634 13.7 9.6 10.9

Puerto Rico .................. 4,187 4,042 4,237 429,220 406,548 394,800 9.8 9.9 10.7

na = not applicable

EPSCoR = Experimental Program to Stimulate Competitive Research 

NOTE: For explanation of EPSCoR and non-EPSCoR averages, see chapter introduction.

SOURCES: National Center for Education Statistics, Integrated Postsecondary Education Data System (various years); Census Bureau, 2000 Decennial 

Census and Population Estimates Program (various years).

Science and Engineering Indicators 2012
8-46  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 ages18–24 years and 

represents the extent to which a state prepares young people to enter technologyintensive 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 pursing 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 

ages 18–24 years 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 that occurs in the District 

of Columbia and some states with small resident populations.

Findings

U Between 2000 and 2009, the value of this 

indicator did not change appreciably.

U In 2009, the value of this indicator ranged 

from 3.9 to16.0 natural sciences and 

engineering (NS&E) bachelor’s degrees 

conferred per 1,000 individuals 18–24 

years old for individual states.

U The states conferring the largest number 

of bachelor’s degrees in NS&E fields 

were California, New York, Texas, and 

Pennsylvania.

U States that ranked in the top 2 quartiles on 

this indicator were generally the same as 

those in the top 2 quartiles for the number 

of bachelor’s degrees conferred per 1,000 

individuals 18–24 years old.

Bachelor’s Degrees in Natural Sciences and Engineering Conferred per 1,000 

Individuals 18–24 Years Old

Figure 8-18

Bachelor’s degrees in natural sciences and engineering conferred per 1,000 individuals 18–24 years old: 2009

3.2 4.8 6.4 8.0 9.6 11.2 12.8 14.4 16.0 17.6 19.2

AK

NV

CA

CT

FL

HI

IL

LA

NC

NJ

NM

OH

OK

SC

TN

WA

WY

CO

MA

MT

PA

SD

AL

AZ

DE

ID

IN

KS

MO

NE

NH

NY

OR

VA RI VT DC

IA

MD

ME

MI

MN

ND

UT

WI

WV

Degrees

AR

GA

KY

MS

1st quartile (10.2–19.1)

2nd quartile (8.6–9.9)

3rd quartile (7.1–8.4)

4th quartile (3.9–6.7)

SOURCES: National Center for Education 

Statistics, Integrated Postsecondary Education Data System; Census Bureau, 2000 

Decennial Census and Population Estimates 

Program.
Science and Engineering Indicators 2012 8-47

Table 8-18

Bachelor’s degrees in natural sciences and engineering conferred per 1,000 individuals 18–24 years old, by 

state: 2000, 2005, and 2009

 NS&E bachelor’s degrees Population 18–24 years old

Degrees/1,000 individuals 

18–24 years old

State 2000 2005 2009 2000 2005 2009 2000 2005 2009

EPSCoR states ................ 35,201 37,177 38,960 4,702,607 5,059,909 5,109,850 7.5 7.3 7.6

Non-EPSCoR states ........ 171,285 198,206 209,080 22,541,063 24,277,168 25,229,846 7.6 8.2 8.3

Average EPSCoR 

 state value .................... na na na na na na 8.5 8.0 8.4

Average non-EPSCoR 

 state value .................... na na na na na na 8.1 8.6 8.8

United States ................... 207,375 236,700 249,420 27,316,537 29,404,797 30,412,035 7.6 8.0 8.2

Alabama ....................... 3,556 3,511 3,891 442,009 453,363 465,249 8.0 7.7 8.4

Alaska .......................... 240 248 313 57,724 78,831 79,696 4.2 3.1 3.9

Arizona ......................... 2,928 5,069 5,284 519,381 570,206 610,920 5.6 8.9 8.6

Arkansas ...................... 1,440 1,630 1,563 263,326 269,720 273,263 5.5 6.0 5.7

California ...................... 22,036 26,786 28,386 3,389,475 3,601,422 3,746,026 6.5 7.4 7.6

Colorado ...................... 4,742 5,475 5,714 434,165 479,582 500,695 10.9 11.4 11.4

Connecticut ................. 1,852 2,116 2,540 273,000 315,265 340,550 6.8 6.7 7.5

Delaware ...................... 696 694 740 75,857 79,909 83,522 9.2 8.7 8.9

District of Columbia ..... 889 1,317 1,380 72,867 67,720 72,339 12.2 19.4 19.1

Florida .......................... 7,353 9,097 10,769 1,340,658 1,583,393 1,667,090 5.5 5.7 6.5

Georgia ........................ 5,190 6,063 6,190 844,924 912,133 979,688 6.1 6.6 6.3

Hawaii .......................... 719 754 842 115,683 128,311 124,841 6.2 5.9 6.7

Idaho ............................ 1,013 1,234 1,437 140,017 157,526 161,387 7.2 7.8 8.9

Illinois ........................... 8,971 11,002 10,080 1,217,816 1,273,336 1,298,744 7.4 8.6 7.8

Indiana ......................... 5,113 5,797 5,573 618,463 637,074 643,920 8.3 9.1 8.7

Iowa ............................. 3,135 3,249 3,563 299,438 320,586 321,355 10.5 10.1 11.1

Kansas ......................... 2,436 2,637 2,463 277,164 306,834 307,284 8.8 8.6 8.0

Kentucky ...................... 2,266 2,293 2,503 404,146 417,504 416,470 5.6 5.5 6.0

Louisiana ...................... 3,395 3,600 3,216 475,946 496,032 477,506 7.1 7.3 6.7

Maine ........................... 1,091 1,136 1,172 104,527 117,289 118,353 10.4 9.7 9.9

Maryland ...................... 4,422 5,911 5,669 454,129 515,830 547,538 9.7 11.5 10.4

Massachusetts ............. 7,328 7,623 8,215 582,619 621,142 668,112 12.6 12.3 12.3

Michigan ...................... 8,315 9,174 9,620 937,626 997,376 995,230 8.9 9.2 9.7

Minnesota .................... 4,067 4,861 5,346 473,816 523,797 526,091 8.6 9.3 10.2

Mississippi ................... 1,733 1,630 1,757 312,663 317,707 313,729 5.5 5.1 5.6

Missouri ....................... 4,767 5,350 5,329 538,883 586,404 592,454 8.8 9.1 9.0

Montana ....................... 1,173 1,127 1,223 86,241 103,362 104,243 13.6 10.9 11.7

Nebraska ...................... 1,581 1,642 1,782 175,359 197,081 196,793 9.0 8.3 9.1

Nevada ......................... 548 739 989 181,984 210,962 228,809 3.0 3.5 4.3

New Hampshire ........... 1,281 1,130 1,186 104,064 123,750 130,242 12.3 9.1 9.1

New Jersey .................. 5,249 5,354 5,376 679,702 716,880 756,033 7.7 7.5 7.1

New Mexico ................. 1,243 1,392 1,306 177,978 202,397 202,276 7.0 6.9 6.5

New York ...................... 14,451 16,705 17,096 1,772,439 1,839,901 1,923,887 8.2 9.1 8.9

North Carolina .............. 6,172 6,774 7,308 814,485 855,830 942,328 7.6 7.9 7.8

North Dakota ............... 893 913 929 73,371 89,501 88,808 12.2 10.2 10.5

Ohio ............................. 7,828 8,106 8,504 1,062,062 1,084,194 1,084,493 7.4 7.5 7.8

Oklahoma ..................... 2,491 2,636 2,727 358,410 387,237 386,532 7.0 6.8 7.1

Oregon ......................... 2,440 2,831 3,068 330,074 351,657 364,365 7.4 8.1 8.4

Pennsylvania ................ 11,671 13,719 14,453 1,099,275 1,169,151 1,219,844 10.6 11.7 11.8

Rhode Island ................ 1,236 1,531 1,566 107,100 109,690 112,088 11.5 14.0 14.0

South Carolina ............. 2,684 3,130 3,199 410,784 427,535 452,903 6.5 7.3 7.1

South Dakota ............... 981 1,039 996 78,087 87,891 87,586 12.6 11.8 11.4

Tennessee .................... 3,455 3,541 3,921 552,177 570,664 585,173 6.3 6.2 6.7

Texas ............................ 11,868 13,692 15,185 2,213,346 2,429,659 2,523,258 5.4 5.6 6.0

Utah ............................. 2,817 3,412 3,766 320,147 342,610 341,926 8.8 10.0 11.0

Vermont ........................ 840 865 1,102 56,918 66,960 68,869 14.8 12.9 16.0

Virginia ......................... 6,414 6,310 7,096 686,011 776,170 814,917 9.3 8.1 8.7

Washington .................. 3,850 4,615 4,985 563,091 617,519 644,616 6.8 7.5 7.7

West Virginia ................ 1,208 1,288 1,637 173,092 172,766 169,767 7.0 7.5 9.6

Wisconsin .................... 4,851 5,574 6,044 523,861 585,387 590,593 9.3 9.5 10.2

Wyoming ...................... 457 378 421 50,157 57,751 59,634 9.1 6.5 7.1

Puerto Rico .................. 3,013 2,848 3,039 429,220 406,548 394,800 7.0 7.0 7.7

na = not applicable

EPSCoR = Experimental Program to Stimulate Competitive Research; NS&E = natural sciences and engineering 

NOTE: For explanation of EPSCoR and non-EPSCoR averages, see chapter introduction.

SOURCES: National Center for Education Statistics, Integrated Postsecondary Education Data System (various years); Census Bureau, 2000 Decennial 

Census and Population Estimates Program (various years).

Science and Engineering Indicators 2012
8-48  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 2009 represent degrees conferred during the 

2008–09 academic year. All degree data are actual counts.

Findings

U In 2009, nearly 668,000 S&E bachelor’s, master’s, 

and doctoral degrees were conferred nationwide, an 

increase of 30% since 2000.

U Nationally, the proportion of S&E degrees as a share 

of total degrees conferred decreased by 3% between 

2005 and 2009.

U 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.

U 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 8-19

Science and engineering degrees as a percentage of higher education degrees conferred: 2009

16.8 18.9 21.0 23.1 25.2 27.3 29.4 31.5 33.6 35.7 37.8 39.9

AZ

FL

ID

IL

LA

MN

NV

OH

TX

AR

MO

ND

DE

GA

IA

MI

NM

NY

PA

RI

SC

NC

NH

SD

UT

WI

CO

CT

HI

MA

ME

NJ

OR

WA

CA

DC

MD

WY

AK

MT

VA VT

AL

IN

KS

KY

MS

NE

OK

TN

WV

Percent

1st quartile (32.6%–39.1%)

2nd quartile (28.1%–32.1%)

3rd quartile (24.9%–27.6%)

4th quartile (18.6%–24.6%)

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education 

Data System.
Science and Engineering Indicators 2012 8-49

Table 8-19

Science and engineering degrees as a percentage of higher education degrees conferred, by state: 2000, 2005, 

and 2009

All S&E degrees All higher education degrees

All S&E 

degrees/all higher 

education degrees (%)

State 2000 2005 2009 2000 2005 2009 2000 2005 2009

United States ................... 515,298 614,694 667,790 1,739,739 2,066,513 2,322,054 29.6 29.7 28.8

Alabama ....................... 7,512 8,038 8,899 29,848 32,180 35,725 25.2 25.0 24.9

Alaska .......................... 578 676 802 1,901 2,107 2,267 30.4 32.1 35.4

Arizona ......................... 6,890 9,613 13,432 31,863 49,921 72,140 21.6 19.3 18.6

Arkansas ...................... 2,828 3,306 3,390 11,916 14,291 16,089 23.7 23.1 21.1

California ...................... 61,903 77,557 84,579 171,337 207,416 230,605 36.1 37.4 36.7

Colorado ...................... 11,733 13,745 14,280 31,689 38,125 43,834 37.0 36.1 32.6

Connecticut ................. 6,887 8,154 9,615 23,703 26,359 29,078 29.1 30.9 33.1

Delaware ...................... 1,940 2,163 2,241 6,296 7,512 8,118 30.8 28.8 27.6

District of Columbia ..... 5,740 7,490 8,469 14,433 18,348 22,525 39.8 40.8 37.6

Florida .......................... 18,293 24,845 29,004 71,408 92,917 112,165 25.6 26.7 25.9

Georgia ........................ 11,754 14,807 15,775 40,661 49,851 57,165 28.9 29.7 27.6

Hawaii .......................... 2,235 2,412 2,656 6,986 7,497 8,001 32.0 32.2 33.2

Idaho ............................ 1,823 2,384 2,797 5,943 9,057 10,816 30.7 26.3 25.9

Illinois ........................... 22,464 27,237 28,725 84,112 102,657 111,875 26.7 26.5 25.7

Indiana ......................... 11,404 13,317 13,251 41,655 49,048 54,032 27.4 27.2 24.5

Iowa ............................. 6,611 7,378 9,079 23,165 25,774 33,315 28.5 28.6 27.3

Kansas ......................... 5,317 6,068 5,920 19,617 22,348 24,284 27.1 27.2 24.4

Kentucky ...................... 5,091 6,088 6,481 20,865 24,937 28,070 24.4 24.4 23.1

Louisiana ...................... 6,998 7,849 7,199 26,338 28,930 28,256 26.6 27.1 25.5

Maine ........................... 2,302 2,550 2,749 6,916 8,188 8,717 33.3 31.1 31.5

Maryland ...................... 12,235 15,675 16,514 33,753 40,381 44,369 36.2 38.8 37.2

Massachusetts ............. 22,662 25,251 26,712 69,410 76,108 83,467 32.6 33.2 32.0

Michigan ...................... 18,430 21,335 22,804 67,567 75,675 78,332 27.3 28.2 29.1

Minnesota .................... 9,043 11,915 13,887 31,839 42,566 52,323 28.4 28.0 26.5

Mississippi ................... 3,397 3,577 3,937 14,598 15,931 17,023 23.3 22.5 23.1

Missouri ....................... 10,965 12,984 13,464 43,783 52,849 59,466 25.0 24.6 22.6

Montana ....................... 2,102 2,254 2,298 6,187 6,416 6,559 34.0 35.1 35.0

Nebraska ...................... 3,304 3,847 4,193 14,008 16,427 17,411 23.6 23.4 24.1

Nevada ......................... 1,365 1,963 2,585 5,813 7,776 9,884 23.5 25.2 26.2

New Hampshire ........... 3,342 3,460 3,684 10,330 11,025 12,375 32.4 31.4 29.8

New Jersey .................. 13,940 15,667 16,535 37,278 45,515 49,544 37.4 34.4 33.4

New Mexico ................. 2,636 3,065 3,255 9,664 10,874 11,366 27.3 28.2 28.6

New York ...................... 42,901 51,470 54,412 146,896 176,054 193,777 29.2 29.2 28.1

North Carolina .............. 14,651 16,665 18,671 46,045 52,192 60,854 31.8 31.9 30.7

North Dakota ............... 1,519 1,539 1,610 5,798 6,454 7,128 26.2 23.8 22.6

Ohio ............................. 18,123 20,287 21,129 68,854 79,349 83,813 26.3 25.6 25.2

Oklahoma ..................... 5,982 6,342 6,384 21,374 24,398 25,995 28.0 26.0 24.6

Oregon ......................... 6,608 7,805 7,975 19,647 23,492 24,858 33.6 33.2 32.1

Pennsylvania ................ 26,563 31,478 34,537 90,495 107,174 119,761 29.4 29.4 28.8

Rhode Island ................ 3,012 3,447 3,783 10,524 11,883 13,002 28.6 29.0 29.1

South Carolina ............. 5,953 6,855 7,279 20,995 24,268 26,666 28.4 28.2 27.3

South Dakota ............... 1,813 1,966 1,990 5,456 6,077 6,377 33.2 32.4 31.2

Tennessee .................... 8,029 8,749 9,601 31,502 35,453 40,553 25.5 24.7 23.7

Texas ............................ 27,962 34,612 38,196 103,283 124,122 142,321 27.1 27.9 26.8

Utah ............................. 6,289 8,153 8,339 20,866 25,382 27,003 30.1 32.1 30.9

Vermont ........................ 2,230 2,508 3,144 6,350 6,638 8,031 35.1 37.8 39.1

Virginia ......................... 16,299 17,810 20,822 45,870 51,210 60,840 35.5 34.8 34.2

Washington .................. 9,720 12,449 13,215 32,085 37,831 40,136 30.3 32.9 32.9

West Virginia ................ 2,750 2,945 3,772 11,144 12,522 15,787 24.7 23.5 23.9

Wisconsin .................... 10,260 12,187 12,881 35,426 40,806 43,694 29.0 29.9 29.5

Wyoming ...................... 910 757 839 2,247 2,202 2,262 40.5 34.4 37.1

Puerto Rico .................. 4,807 4,817 5,293 19,261 21,111 23,501 25.0 22.8 22.5

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 2012
8-50  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 2009 represent degrees conferred during the 2008–09 academic 

year. All degree data are actual counts.

Findings

U In 2009, nearly 354,000 NS&E bachelor’s, master’s, 

and doctoral degrees were conferred nationwide, an 

increase of 25% since 2000.

U The proportion of NS&E degrees as a share of total 

degrees conferred showed a decline of 5% between 

2005 and 2009.

U There are noteworthy differences in the proportions 

of natural sciences and engineering higher education 

degrees conferred in different states. In 2009, the 

proportions ranged between 9.7% and 24.4%.

U Nationally, over half, 53%, of all S&E degrees were 

in NS&E fields in 2009, down from 55% of all S&E 

degrees in 2000.

U States with the highest percentage of higher education 

degrees in natural science or engineering fields tended 

to be located in the western United States, and four of 

the top five are EPSCoR states.

Natural Sciences and Engineering Degrees as a Percentage of 

Higher Education Degrees Conferred

Figure 8-20

Natural sciences and engineering degrees as a percentage of higher education degrees conferred: 2009

9.0 10.5 12.0 13.5 15.0 16.5 18.0 19.5 21.0 22.5 24.0 25.5

AZ

CT

GA

LA

MA

ME

ND

OR

RI

TX

WA

AR

DC

DE

FL

KY

MN

MO

NV

NY

TN

WV

CA

CO

ID

MI

NC

NJ

NM

PA

UT

VA

VT

WI

MD

SD MT AK WY

AL

HI

IA

IL

IN

KS

MS

NE

NH

OH

OK

SC

Percent

1st quartile (17.0%–24.4%)

2nd quartile (15.1%–16.7%)

3rd quartile (13.5%–15.0%)

4th quartile (9.7%–13.4%)

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education 

Data System.
Science and Engineering Indicators 2012 8-51

Table 8-20

Natural sciences and engineering degrees as a percentage of higher education degrees conferred, by state: 

2000, 2005, and 2009 

NS&E degrees All higher education degrees

NS&E/higher education 

degrees (%)

State 2000 2005 2009 2000 2005 2009 2000 2005 2009

United States ................... 283,371 331,247 353,509 1,739,739 2,066,513 2,322,054 16.3 16.0 15.2

Alabama ....................... 4,464 4,732 5,261 29,848 32,180 35,725 15.0 14.7 14.7

Alaska .......................... 360 419 475 1,901 2,107 2,267 18.9 19.9 21.0

Arizona ......................... 4,063 6,343 6,989 31,863 49,921 72,140 12.8 12.7 9.7

Arkansas ...................... 1,734 2,022 1,970 11,916 14,291 16,089 14.6 14.1 12.2

California ...................... 30,833 38,181 40,990 171,337 207,416 230,605 18.0 18.4 17.8

Colorado ...................... 6,823 7,663 7,827 31,689 38,125 43,834 21.5 20.1 17.9

Connecticut ................. 3,007 3,634 4,624 23,703 26,359 29,078 12.7 13.8 15.9

Delaware ...................... 917 980 1,069 6,296 7,512 8,118 14.6 13.0 13.2

District of Columbia ..... 2,091 2,561 2,949 14,433 18,348 22,525 14.5 14.0 13.1

Florida .......................... 9,942 12,636 15,036 71,408 92,917 112,165 13.9 13.6 13.4

Georgia ........................ 7,099 8,354 9,025 40,661 49,851 57,165 17.5 16.8 15.8

Hawaii .......................... 1,004 1,011 1,109 6,986 7,497 8,001 14.4 13.5 13.9

Idaho ............................ 1,272 1,568 1,797 5,943 9,057 10,816 21.4 17.3 16.6

Illinois ........................... 13,053 16,027 15,937 84,112 102,657 111,875 15.5 15.6 14.2

Indiana ......................... 6,723 7,637 7,493 41,655 49,048 54,032 16.1 15.6 13.9

Iowa ............................. 3,939 4,205 4,633 23,165 25,774 33,315 17.0 16.3 13.9

Kansas ......................... 3,196 3,557 3,290 19,617 22,348 24,284 16.3 15.9 13.5

Kentucky ...................... 2,819 3,215 3,392 20,865 24,937 28,070 13.5 12.9 12.1

Louisiana ...................... 4,357 4,895 4,260 26,338 28,930 28,256 16.5 16.9 15.1

Maine ........................... 1,228 1,272 1,305 6,916 8,188 8,717 17.8 15.5 15.0

Maryland ...................... 6,854 9,110 9,142 33,753 40,381 44,369 20.3 22.6 20.6

Massachusetts ............. 11,309 12,222 13,219 69,410 76,108 83,467 16.3 16.1 15.8

Michigan ...................... 11,873 13,474 13,861 67,567 75,675 78,332 17.6 17.8 17.7

Minnesota .................... 5,056 6,333 6,873 31,839 42,566 52,323 15.9 14.9 13.1

Mississippi ................... 2,195 2,225 2,427 14,598 15,931 17,023 15.0 14.0 14.3

Missouri ....................... 5,974 6,935 7,182 43,783 52,849 59,466 13.6 13.1 12.1

Montana ....................... 1,458 1,455 1,529 6,187 6,416 6,559 23.6 22.7 23.3

Nebraska ...................... 1,958 2,212 2,393 14,008 16,427 17,411 14.0 13.5 13.7

Nevada ......................... 756 1,035 1,326 5,813 7,776 9,884 13.0 13.3 13.4

New Hampshire ........... 1,656 1,563 1,730 10,330 11,025 12,375 16.0 14.2 14.0

New Jersey .................. 7,469 7,859 8,283 37,278 45,515 49,544 20.0 17.3 16.7

New Mexico ................. 1,704 2,015 1,982 9,664 10,874 11,366 17.6 18.5 17.4

New York ...................... 20,638 24,534 25,832 146,896 176,054 193,777 14.0 13.9 13.3

North Carolina .............. 8,070 9,084 10,110 46,045 52,192 60,854 17.5 17.4 16.6

North Dakota ............... 1,026 1,087 1,147 5,798 6,454 7,128 17.7 16.8 16.1

Ohio ............................. 10,671 11,425 11,767 68,854 79,349 83,813 15.5 14.4 14.0

Oklahoma ..................... 3,366 3,821 3,740 21,374 24,398 25,995 15.7 15.7 14.4

Oregon ......................... 3,224 3,852 3,947 19,647 23,492 24,858 16.4 16.4 15.9

Pennsylvania ................ 15,119 18,344 19,701 90,495 107,174 119,761 16.7 17.1 16.5

Rhode Island ................ 1,589 1,914 1,988 10,524 11,883 13,002 15.1 16.1 15.3

South Carolina ............. 3,417 3,971 3,967 20,995 24,268 26,666 16.3 16.4 14.9

South Dakota ............... 1,142 1,324 1,253 5,456 6,077 6,377 20.9 21.8 19.6

Tennessee .................... 4,430 4,560 5,083 31,502 35,453 40,553 14.1 12.9 12.5

Texas ............................ 17,004 20,489 22,601 103,283 124,122 142,321 16.5 16.5 15.9

Utah ............................. 3,541 4,327 4,699 20,866 25,382 27,003 17.0 17.0 17.4

Vermont ........................ 1,008 1,097 1,365 6,350 6,638 8,031 15.9 16.5 17.0

Virginia ......................... 8,627 8,689 10,456 45,870 51,210 60,840 18.8 17.0 17.2

Washington .................. 5,009 5,981 6,415 32,085 37,831 40,136 15.6 15.8 16.0

West Virginia ................ 1,571 1,764 2,042 11,144 12,522 15,787 14.1 14.1 12.9

Wisconsin .................... 6,105 7,124 7,466 35,426 40,806 43,694 17.2 17.5 17.1

Wyoming ...................... 628 510 552 2,247 2,202 2,262 27.9 23.2 24.4

Puerto Rico .................. 3,314 3,244 3,586 19,261 21,111 23,501 17.2 15.4 15.3

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 2012
8-52  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 ages 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 ages 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 ages 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

U The number of S&E graduate students in 

the United States grew from approximately 

411,000 in 2000 to 523,000 in 2009, a 27% 

increase.

U Among the 50 states, the value of this 

indicator ranged from 5.1 to 29.4.

U Growth in the number of S&E graduate 

students was most significant in California 

during this period. Other states with 

sizeable increases included Texas, New 

York, Minnesota, and Florida.

Science and Engineering Graduate Students per 1,000 Individuals 

25–34 Years Old

Figure 8-21

Science and engineering graduate students per 1,000 individuals 25–34 years old: 2009

5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 25.0 27.5 30.0 85.0 87.5

CO

DE

KS

MD

RI

CT

MN

ND

AZ

FL

GA

ID

LA

MS

OR

TN

VT

WA

AK

AL

CA

HI

MO

MT

NC

NH

NJ

OK

TX

UT

WV

WY

AR

KY

ME

NV

SC

IA

IL

IN

MI

NE

NM

OH

PA

SD

VA

WI NY MA DC

Graduate students

1st quartile (14.4–86.1)

2nd quartile (12.2–14.1)

3rd quartile (9.3–11.7)

4th quartile (5.1–9.2)

SOURCES: National Science Foundation, 

National Center for Science and Engineering 

Statistics, Survey of Graduate Students and 

Postdoctorates in Science and Engineering; 

Census Bureau, 2000 Decennial Census and 

Population Estimates Program.
Science and Engineering Indicators 2012 8-53

Table 8-21

Science and engineering graduate students per 1,000 individuals 25–34 years old, by state: 2000, 2005, and 2009

S&E graduate students Population 25–34 years old

S&E graduate 

students/1,000 

individuals 25–34 years old

State 2000 2005 2009 2000 2005 2009 2000 2005 2009

United States ................... 410,505 474,550 522,511 39,825,518 39,712,639 41,566,322 10.3 11.9 12.6

Alabama ....................... 5,161 6,232 6,562 600,351 593,380 619,371 8.6 10.5 10.6

Alaska .......................... 597 806 1,064 89,013 92,286 106,145 6.7 8.7 10.0

Arizona ......................... 6,477 6,976 8,161 744,892 864,337 956,944 8.7 8.1 8.5

Arkansas ...................... 1,958 2,447 2,799 351,690 368,207 389,042 5.6 6.6 7.2

California ...................... 53,437 63,837 66,695 5,233,641 5,223,518 5,385,409 10.2 12.2 12.4

Colorado ...................... 8,688 8,997 11,000 665,276 676,923 734,263 13.1 13.3 15.0

Connecticut ................. 6,266 6,973 8,660 450,434 405,974 411,196 13.9 17.2 21.1

Delaware ...................... 1,414 1,760 1,907 108,699 110,244 115,495 13.0 16.0 16.5

District of Columbia ..... 7,126 8,662 9,700 101,272 102,601 112,704 70.4 84.4 86.1

Florida .......................... 15,463 19,126 22,486 2,084,041 2,229,778 2,357,049 7.4 8.6 9.5

Georgia ........................ 8,809 10,675 11,151 1,300,057 1,329,899 1,392,988 6.8 8.0 8.0

Hawaii .......................... 1,412 1,892 2,013 170,502 176,998 191,254 8.3 10.7 10.5

Idaho ............................ 1,309 2,013 1,933 169,287 186,405 209,877 7.7 10.8 9.2

Illinois ........................... 22,964 23,387 25,617 1,806,793 1,745,946 1,783,102 12.7 13.4 14.4

Indiana ......................... 7,931 9,648 10,489 828,362 817,981 838,888 9.6 11.8 12.5

Iowa ............................. 4,646 5,020 5,463 361,434 345,064 367,097 12.9 14.5 14.9

Kansas ......................... 5,740 5,804 5,574 347,757 337,097 368,979 16.5 17.2 15.1

Kentucky ...................... 3,390 4,625 4,076 566,024 562,610 579,199 6.0 8.2 7.0

Louisiana ...................... 5,496 4,809 5,824 597,966 593,933 624,512 9.2 8.1 9.3

Maine ........................... 588 684 760 156,941 148,575 149,643 3.7 4.6 5.1

Maryland ...................... 8,995 11,228 12,453 746,862 729,811 756,962 12.0 15.4 16.5

Massachusetts ............. 19,536 22,638 25,120 924,695 843,944 855,592 21.1 26.8 29.4

Michigan ...................... 15,080 15,454 16,072 1,357,578 1,261,427 1,202,738 11.1 12.3 13.4

Minnesota .................... 6,749 10,685 14,484 671,894 653,869 699,621 10.0 16.3 20.7

Mississippi ................... 2,628 3,175 3,246 380,294 379,067 393,163 6.9 8.4 8.3

Missouri ....................... 5,947 7,278 8,369 736,623 739,026 785,730 8.1 9.8 10.7

Montana ....................... 1,199 1,454 1,468 102,764 104,944 120,074 11.7 13.9 12.2

Nebraska ...................... 2,452 2,811 3,272 222,460 216,423 232,221 11.0 13.0 14.1

Nevada ......................... 1,423 1,992 2,137 308,307 357,001 394,105 4.6 5.6 5.4

New Hampshire ........... 1,340 1,448 1,544 159,784 149,554 149,648 8.4 9.7 10.3

New Jersey .................. 11,135 12,267 13,580 1,186,931 1,112,730 1,110,420 9.4 11.0 12.2

New Mexico ................. 3,109 3,762 3,792 233,360 250,621 277,897 13.3 15.0 13.6

New York ...................... 37,782 42,399 46,786 2,749,299 2,602,535 2,649,054 13.7 16.3 17.7

North Carolina .............. 10,034 12,167 14,457 1,212,550 1,187,546 1,235,447 8.3 10.2 11.7

North Dakota ............... 1,053 1,512 1,811 76,253 71,313 81,735 13.8 21.2 22.2

Ohio ............................. 16,092 19,054 19,580 1,513,069 1,470,729 1,478,233 10.6 13.0 13.2

Oklahoma ..................... 3,478 4,274 5,095 449,552 456,812 504,985 7.7 9.4 10.1

Oregon ......................... 3,815 4,387 4,737 469,904 483,361 525,411 8.1 9.1 9.0

Pennsylvania ................ 18,300 20,209 21,542 1,552,979 1,489,438 1,538,441 11.8 13.6 14.0

Rhode Island ................ 1,709 1,971 2,127 140,153 134,692 131,818 12.2 14.6 16.1

South Carolina ............. 3,185 3,339 3,499 559,245 565,038 598,990 5.7 5.9 5.8

South Dakota ............... 866 930 1,452 90,667 91,888 102,810 9.6 10.1 14.1

Tennessee .................... 5,366 6,585 6,431 813,532 820,169 848,633 6.6 8.0 7.6

Texas ............................ 27,855 32,788 37,774 3,164,710 3,339,356 3,647,847 8.8 9.8 10.4

Utah ............................. 3,821 4,884 4,929 327,177 381,597 441,598 11.7 12.8 11.2

Vermont ........................ 627 644 655 74,260 67,274 69,085 8.4 9.6 9.5

Virginia ......................... 11,552 12,566 14,624 1,035,588 1,027,412 1,084,710 11.2 12.2 13.5

Washington .................. 5,905 6,570 7,534 839,575 851,102 948,773 7.0 7.7 7.9

West Virginia ................ 2,024 2,247 2,273 227,512 224,330 225,178 8.9 10.0 10.1

Wisconsin .................... 7,822 8,572 8,817 704,005 675,890 707,393 11.1 12.7 12.5

Wyoming ...................... 754 887 917 59,504 61,984 74,853 12.7 14.3 12.3

Puerto Rico .................. 2,944 3,661 3,068 534,332 550,170 556,543 5.5 6.7 5.5

NOTE: S&E graduate students include students pursuing graduate 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 Engineering; Census Bureau, 2000 Decennial Census and Population Estimates Program (various years).

Science and Engineering Indicators 2012
8-54  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 of Education Statistics.

Findings

U In 2009, nearly 167,000 advanced S&E degrees were awarded 

nationwide, 38% more degrees than were awarded in 2000. The 

share of advanced degrees as a percentage of all S&E degrees 

conferred increased by 6% between 2000 and 2009.

U In 2009, the value of this indicator for individual states ranged 

from 8.4 % to 32.2% of S&E graduates completing training at 

the master’s or doctoral level. California produced the largest 

number of advanced S&E degrees, consistent over the decade 

and approximately 110 times the number produced in Wyoming.

U Between 2000 and 2009, 33 states showed increases in the 

share of their S&E graduates completing training at the master’s 

or doctoral level and 16 states and the District of Columbia 

showed decreases.

U 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 8-22

Advanced science and engineering degrees as a percentage of S&E degrees conferred: 2009

8 12 16 20 24 28 32 36 40 44 48

ME

AR

ID

LA

MT

ND

OR

RI

TN

UT

WA

AK

CT

MA

MD

NM

CO

FL

HI

IN

KS

KY

MS

NC

NH

NV

OH

OK

PA

SD

WV

WY IL DC

AL

AZ

CA

DE

GA

MI

MN

MO

NE

NJ

NY

TX

VA

VT

Percent

IA

SC

WI

1st quartile (27.1%–46.9%)

2nd quartile (23.1%–26.4%)

3rd quartile (19.5%–22.9%)

4th quartile (8.4%–19.1%)

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education 

Data System.
Science and Engineering Indicators 2012 8-55

Table 8-22

Advanced science and engineering degrees as a percentage of S&E degrees conferred, by state: 2000, 2005, 

and 2009

 Advanced S&E degrees All S&E degrees

 Advanced S&E/ 

 all S&E degrees (%)

State 2000 2005 2009 2000 2005 2009 2000 2005 2009

United States ................... 120,997 148,629 166,714 515,298 614,694 667,790 23.5 24.2 25.0

Alabama ....................... 1,937 2,271 2,409 7,512 8,038 8,899 25.8 28.3 27.1

Alaska .......................... 185 236 243 578 676 802 32.0 34.9 30.3

Arizona ......................... 1,736 1,872 3,259 6,890 9,613 13,432 25.2 19.5 24.3

Arkansas ...................... 436 558 639 2,828 3,306 3,390 15.4 16.9 18.8

California ...................... 15,497 19,021 21,198 61,903 77,557 84,579 25.0 24.5 25.1

Colorado ...................... 2,911 3,370 3,415 11,733 13,745 14,280 24.8 24.5 23.9

Connecticut ................. 1,748 2,209 2,812 6,887 8,154 9,615 25.4 27.1 29.2

Delaware ...................... 394 507 553 1,940 2,163 2,241 20.3 23.4 24.7

District of Columbia ..... 2,825 3,317 3,968 5,740 7,490 8,469 49.2 44.3 46.9

Florida .......................... 4,199 5,445 6,396 18,293 24,845 29,004 23.0 21.9 22.1

Georgia ........................ 2,764 3,453 3,993 11,754 14,807 15,775 23.5 23.3 25.3

Hawaii .......................... 575 558 582 2,235 2,412 2,656 25.7 23.1 21.9

Idaho ............................ 331 424 457 1,823 2,384 2,797 18.2 17.8 16.3

Illinois ........................... 6,504 8,294 9,254 22,464 27,237 28,725 29.0 30.5 32.2

Indiana ......................... 2,483 2,840 3,040 11,404 13,317 13,251 21.8 21.3 22.9

Iowa ............................. 1,055 1,261 1,423 6,611 7,378 9,079 16.0 17.1 15.7

Kansas ......................... 1,160 1,355 1,321 5,317 6,068 5,920 21.8 22.3 22.3

Kentucky ...................... 938 1,551 1,537 5,091 6,088 6,481 18.4 25.5 23.7

Louisiana ...................... 1,430 1,773 1,417 6,998 7,849 7,199 20.4 22.6 19.7

Maine ........................... 185 196 231 2,302 2,550 2,749 8.0 7.7 8.4

Maryland ...................... 3,637 4,618 5,121 12,235 15,675 16,514 29.7 29.5 31.0

Massachusetts ............. 6,600 7,662 8,249 22,662 25,251 26,712 29.1 30.3 30.9

Michigan ...................... 4,788 5,744 5,931 18,430 21,335 22,804 26.0 26.9 26.0

Minnesota .................... 1,609 2,644 3,827 9,043 11,915 13,887 17.8 22.2 27.6

Mississippi ................... 628 793 901 3,397 3,577 3,937 18.5 22.2 22.9

Missouri ....................... 2,796 3,452 3,556 10,965 12,984 13,464 25.5 26.6 26.4

Montana ....................... 368 447 424 2,102 2,254 2,298 17.5 19.8 18.5

Nebraska ...................... 647 808 1,060 3,304 3,847 4,193 19.6 21.0 25.3

Nevada ......................... 315 482 546 1,365 1,963 2,585 23.1 24.6 21.1

New Hampshire ........... 554 634 755 3,342 3,460 3,684 16.6 18.3 20.5

New Jersey .................. 3,118 3,811 4,507 13,940 15,667 16,535 22.4 24.3 27.3

New Mexico ................. 697 877 947 2,636 3,065 3,255 26.4 28.6 29.1

New York ...................... 10,760 13,828 14,817 42,901 51,470 54,412 25.1 26.9 27.2

North Carolina .............. 2,630 3,177 3,838 14,651 16,665 18,671 18.0 19.1 20.6

North Dakota ............... 190 234 284 1,519 1,539 1,610 12.5 15.2 17.6

Ohio ............................. 4,249 4,802 4,872 18,123 20,287 21,129 23.4 23.7 23.1

Oklahoma ..................... 1,981 1,624 1,506 5,982 6,342 6,384 33.1 25.6 23.6

Oregon ......................... 1,227 1,544 1,430 6,608 7,805 7,975 18.6 19.8 17.9

Pennsylvania ................ 5,448 6,755 8,023 26,563 31,478 34,537 20.5 21.5 23.2

Rhode Island ................ 509 610 701 3,012 3,447 3,783 16.9 17.7 18.5

South Carolina ............. 957 1,071 1,128 5,953 6,855 7,279 16.1 15.6 15.5

South Dakota ............... 307 472 419 1,813 1,966 1,990 16.9 24.0 21.1

Tennessee .................... 1,497 1,593 1,874 8,029 8,749 9,601 18.6 18.2 19.5

Texas ............................ 7,131 9,318 10,473 27,962 34,612 38,196 25.5 26.9 27.4

Utah ............................. 1,044 1,283 1,353 6,289 8,153 8,339 16.6 15.7 16.2

Vermont ........................ 409 516 795 2,230 2,508 3,144 18.3 20.6 25.3

Virginia ......................... 3,366 4,062 5,664 16,299 17,810 20,822 20.7 22.8 27.2

Washington .................. 1,815 2,328 2,523 9,720 12,449 13,215 18.7 18.7 19.1

West Virginia ................ 546 660 843 2,750 2,945 3,772 19.9 22.4 22.3

Wisconsin .................... 1,656 2,069 2,010 10,260 12,187 12,881 16.1 17.0 15.6

Wyoming ...................... 225 200 190 910 757 839 24.7 26.4 22.6

Puerto Rico .................. 620 775 1,056 4,807 4,817 5,293 12.9 16.1 20.0

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 2012
8-56  Chapter 8. State Indicators

This indicator represents the extent to which a state’s 

higher education programs in natural sciences and engineering (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 of Education Statistics.

Findings

U In 2009, more than 104,000 advanced natural sciences 

and engineering (NS&E) degrees were awarded nationwide. 

This total represented approximately 37% more than were 

awarded in 2000. The share of advanced degrees as a 

percentage of all NS&E degrees conferred rose by 10% 

between 2000 and 2009.

U In 2009, the value of this indicator ranged from a low of 

10.2% to a high of 45.1% of NS&E graduates completing 

training at the master’s or doctoral level.

U Nationally, about 62% of all advanced S&E degrees were in 

NS&E fields in 2009, nearly unchanged from a decade ago in 

2000.

U 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 8-23

Advanced natural sciences and engineering degrees as a percentage of NS&E degrees conferred: 2009

10 15 20 25 30 35 40 45 50 55

ME

AK

CA

DE

GA

MI

NH

NM

NY

TX

VA

AR

AZ

HI

IA

ID

LA

MN

MT

OR

RI

SD

TN

WA

WY

IL

MA

MD

NJ CT DC

AL

CO

FL

IN

KS

KY

MO

MS

NC

NE

NV

OH

OK

PA

ND

SC

UT

VT

WI

WV

Percent

1st quartile (31.4%–53.2%)

2nd quartile (25.8%–30.8%)

3rd quartile (22.2%–25.6%)

4th quartile (10.2%–21.2%)

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education 

Data System.
Science and Engineering Indicators 2012 8-57

Table 8-23

Advanced natural sciences and engineering degrees as a percentage of NS&E degrees conferred, by state: 

2000, 2005, and 2009

Advanced NS&E degrees NS&E degrees conferred

Advanced NS&E degrees/ 

NS&E degrees conferred (%)

State 2000 2005 2009 2000 2005 2009 2000 2005 2009

United States ................... 75,996 94,547 104,089 283,371 331,247 353,509 26.8 28.5 29.4

Alabama ....................... 908 1,221 1,370 4,464 4,732 5,261 20.3 25.8 26.0

Alaska .......................... 120 171 162 360 419 475 33.3 40.8 34.1

Arizona ......................... 1,135 1,274 1,705 4,063 6,343 6,989 27.9 20.1 24.4

Arkansas ...................... 294 392 407 1,734 2,022 1,970 17.0 19.4 20.7

California ...................... 8,797 11,395 12,604 30,833 38,181 40,990 28.5 29.8 30.7

Colorado ...................... 2,081 2,188 2,113 6,823 7,663 7,827 30.5 28.6 27.0

Connecticut ................. 1,155 1,518 2,084 3,007 3,634 4,624 38.4 41.8 45.1

Delaware ...................... 221 286 329 917 980 1,069 24.1 29.2 30.8

District of Columbia ..... 1,202 1,244 1,569 2,091 2,561 2,949 57.5 48.6 53.2

Florida .......................... 2,589 3,539 4,267 9,942 12,636 15,036 26.0 28.0 28.4

Georgia ........................ 1,909 2,291 2,835 7,099 8,354 9,025 26.9 27.4 31.4

Hawaii .......................... 285 257 267 1,004 1,011 1,109 28.4 25.4 24.1

Idaho ............................ 259 334 360 1,272 1,568 1,797 20.4 21.3 20.0

Illinois ........................... 4,082 5,025 5,857 13,053 16,027 15,937 31.3 31.4 36.8

Indiana ......................... 1,610 1,840 1,920 6,723 7,637 7,493 23.9 24.1 25.6

Iowa ............................. 804 956 1,070 3,939 4,205 4,633 20.4 22.7 23.1

Kansas ......................... 760 920 827 3,196 3,557 3,290 23.8 25.9 25.1

Kentucky ...................... 553 922 889 2,819 3,215 3,392 19.6 28.7 26.2

Louisiana ...................... 962 1,295 1,044 4,357 4,895 4,260 22.1 26.5 24.5

Maine ........................... 137 136 133 1,228 1,272 1,305 11.2 10.7 10.2

Maryland ...................... 2,432 3,199 3,473 6,854 9,110 9,142 35.5 35.1 38.0

Massachusetts ............. 3,981 4,599 5,004 11,309 12,222 13,219 35.2 37.6 37.9

Michigan ...................... 3,558 4,300 4,241 11,873 13,474 13,861 30.0 31.9 30.6

Minnesota .................... 989 1,472 1,527 5,056 6,333 6,873 19.6 23.2 22.2

Mississippi ................... 462 595 670 2,195 2,225 2,427 21.0 26.7 27.6

Missouri ....................... 1,207 1,585 1,853 5,974 6,935 7,182 20.2 22.9 25.8

Montana ....................... 285 328 306 1,458 1,455 1,529 19.5 22.5 20.0

Nebraska ...................... 377 570 611 1,958 2,212 2,393 19.3 25.8 25.5

Nevada ......................... 208 296 337 756 1,035 1,326 27.5 28.6 25.4

New Hampshire ........... 375 433 544 1,656 1,563 1,730 22.6 27.7 31.4

New Jersey .................. 2,220 2,505 2,907 7,469 7,859 8,283 29.7 31.9 35.1

New Mexico ................. 461 623 676 1,704 2,015 1,982 27.1 30.9 34.1

New York ...................... 6,187 7,829 8,736 20,638 24,534 25,832 30.0 31.9 33.8

North Carolina .............. 1,898 2,310 2,802 8,070 9,084 10,110 23.5 25.4 27.7

North Dakota ............... 133 174 218 1,026 1,087 1,147 13.0 16.0 19.0

Ohio ............................. 2,843 3,319 3,263 10,671 11,425 11,767 26.6 29.1 27.7

Oklahoma ..................... 875 1,185 1,013 3,366 3,821 3,740 26.0 31.0 27.1

Oregon ......................... 784 1,021 879 3,224 3,852 3,947 24.3 26.5 22.3

Pennsylvania ................ 3,448 4,625 5,248 15,119 18,344 19,701 22.8 25.2 26.6

Rhode Island ................ 353 383 422 1,589 1,914 1,988 22.2 20.0 21.2

South Carolina ............. 733 841 768 3,417 3,971 3,967 21.5 21.2 19.4

South Dakota ............... 161 285 257 1,142 1,324 1,253 14.1 21.5 20.5

Tennessee .................... 975 1,019 1,162 4,430 4,560 5,083 22.0 22.3 22.9

Texas ............................ 5,136 6,797 7,416 17,004 20,489 22,601 30.2 33.2 32.8

Utah ............................. 724 915 933 3,541 4,327 4,699 20.4 21.1 19.9

Vermont ........................ 168 232 263 1,008 1,097 1,365 16.7 21.1 19.3

Virginia ......................... 2,213 2,379 3,360 8,627 8,689 10,456 25.7 27.4 32.1

Washington .................. 1,159 1,366 1,430 5,009 5,981 6,415 23.1 22.8 22.3

West Virginia ................ 363 476 405 1,571 1,764 2,042 23.1 27.0 19.8

Wisconsin .................... 1,254 1,550 1,422 6,105 7,124 7,466 20.5 21.8 19.0

Wyoming ...................... 171 132 131 628 510 552 27.2 25.9 23.7

Puerto Rico .................. 301 396 547 3,314 3,244 3,586 9.1 12.2 15.3

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 2012
8-58  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 of Education 

Statistics.

Findings

U The number of S&E doctoral degrees awarded 

nationwide rose from 25,000 in 2000 to 33,000 in 

2009, an increase of 31%. California showed the 

largest increase in the number of S&E doctorates 

awarded during this period.

U Nationally, about 5% of the S&E degrees awarded 

have been doctoral degrees. In 2009, the value of 

this indicator for individual states ranged from a 

low of 1.4% to a high of 7.0% of S&E graduates 

completing training at the doctorate level.

U 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 8-24

Science and engineering doctoral degrees as a percentage of S&E degrees conferred: 2009

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

AL

AZ

CO

CT

DC

FL

HI

IA

KS

LA

MD

MN

ND

NE

OR

PA

IL

MI

NJ

NY

RI

TN

WI

WY

CA

NC

NM

NV

OH

TX

GA

IN

DE

MA

AK

AR

KY

MO

MS

MT

NH

OK

SC

UT

VA

WA

Percent

1st quartile (5.2%–7.0%)

2nd quartile (4.6%–5.1%)

3rd quartile (4.0%–4.5%)

4th quartile (1.4%–3.9%)

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education 

Data System.
Science and Engineering Indicators 2012 8-59

Table 8-24

Science and engineering doctoral degrees as a percentage of S&E degrees conferred, by state: 2000, 2005, and 

2009

S&E doctoral degrees S&E degrees conferred

S&E doctoral degrees/ 

S&E degrees conferred (%)

State 2000 2005 2009 2000 2005 2009 2000 2005 2009

United States ................. 25,350 28,398 33,137 515,298 614,694 667,790 4.9 4.6 5.0

Alabama ..................... 281 322 384 7,512 8,038 8,899 3.7 4.0 4.3

Alaska ........................ 13 20 29 578 676 802 2.2 3.0 3.6

Arizona ....................... 431 466 598 6,890 9,613 13,432 6.3 4.8 4.5

Arkansas .................... 67 111 127 2,828 3,306 3,390 2.4 3.4 3.7

California .................... 3,553 3,939 4,724 61,903 77,557 84,579 5.7 5.1 5.6

Colorado .................... 527 708 659 11,733 13,745 14,280 4.5 5.2 4.6

Connecticut ............... 413 433 435 6,887 8,154 9,615 6.0 5.3 4.5

Delaware .................... 112 116 151 1,940 2,163 2,241 5.8 5.4 6.7

District of Columbia ... 355 364 385 5,740 7,490 8,469 6.2 4.9 4.5

Florida ........................ 878 1,112 1,342 18,293 24,845 29,004 4.8 4.5 4.6

Georgia ...................... 552 719 964 11,754 14,807 15,775 4.7 4.9 6.1

Hawaii ........................ 126 114 121 2,235 2,412 2,656 5.6 4.7 4.6

Idaho .......................... 56 49 73 1,823 2,384 2,797 3.1 2.1 2.6

Illinois ......................... 1,438 1,470 1,534 22,464 27,237 28,725 6.4 5.4 5.3

Indiana ....................... 646 659 850 11,404 13,317 13,251 5.7 4.9 6.4

Iowa ........................... 319 321 428 6,611 7,378 9,079 4.8 4.4 4.7

Kansas ....................... 223 230 260 5,317 6,068 5,920 4.2 3.8 4.4

Kentucky .................... 188 241 256 5,091 6,088 6,481 3.7 4.0 4.0

Louisiana .................... 345 312 330 6,998 7,849 7,199 4.9 4.0 4.6

Maine ......................... 39 24 42 2,302 2,550 2,749 1.7 0.9 1.5

Maryland .................... 567 688 783 12,235 15,675 16,514 4.6 4.4 4.7

Massachusetts ........... 1,433 1,562 1,877 22,662 25,251 26,712 6.3 6.2 7.0

Michigan .................... 941 1,019 1,200 18,430 21,335 22,804 5.1 4.8 5.3

Minnesota .................. 463 609 645 9,043 11,915 13,887 5.1 5.1 4.6

Mississippi ................. 144 140 161 3,397 3,577 3,937 4.2 3.9 4.1

Missouri ..................... 415 491 495 10,965 12,984 13,464 3.8 3.8 3.7

Montana ..................... 41 65 83 2,102 2,254 2,298 2.0 2.9 3.6

Nebraska .................... 142 154 182 3,304 3,847 4,193 4.3 4.0 4.3

Nevada ....................... 78 92 142 1,365 1,963 2,585 5.7 4.7 5.5

New Hampshire ......... 102 150 149 3,342 3,460 3,684 3.1 4.3 4.0

New Jersey ................ 650 641 808 13,940 15,667 16,535 4.7 4.1 4.9

New Mexico ............... 136 159 184 2,636 3,065 3,255 5.2 5.2 5.7

New York .................... 2,237 2,440 2,605 42,901 51,470 54,412 5.2 4.7 4.8

North Carolina ............ 652 790 1,010 14,651 16,665 18,671 4.5 4.7 5.4

North Dakota ............. 41 42 68 1,519 1,539 1,610 2.7 2.7 4.2

Ohio ........................... 963 1,022 1,146 18,123 20,287 21,129 5.3 5.0 5.4

Oklahoma ................... 205 205 251 5,982 6,342 6,384 3.4 3.2 3.9

Oregon ....................... 254 272 336 6,608 7,805 7,975 3.8 3.5 4.2

Pennsylvania .............. 1,135 1,432 1,633 26,563 31,478 34,537 4.3 4.5 4.7

Rhode Island .............. 153 167 193 3,012 3,447 3,783 5.1 4.8 5.1

South Carolina ........... 198 234 294 5,953 6,855 7,279 3.3 3.4 4.0

South Dakota ............. 26 38 49 1,813 1,966 1,990 1.4 1.9 2.5

Tennessee .................. 356 354 496 8,029 8,749 9,601 4.4 4.0 5.2

Texas .......................... 1,495 1,727 2,116 27,962 34,612 38,196 5.3 5.0 5.5

Utah ........................... 238 251 303 6,289 8,153 8,339 3.8 3.1 3.6

Vermont ...................... 40 38 43 2,230 2,508 3,144 1.8 1.5 1.4

Virginia ....................... 668 758 863 16,299 17,810 20,822 4.1 4.3 4.1

Washington ................ 365 490 544 9,720 12,449 13,215 3.8 3.9 4.1

West Virginia .............. 80 97 93 2,750 2,945 3,772 2.9 3.3 2.5

Wisconsin .................. 520 508 650 10,260 12,187 12,881 5.1 4.2 5.0

Wyoming .................... 50 33 43 910 757 839 5.5 4.4 5.1

Puerto Rico ................ 73 163 147 4,807 4,817 5,293 1.5 3.4 2.8

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education Data System (various years).

Science and Engineering Indicators 2012
8-60  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 2010 represent costs for the 2009–10 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

U During 2010, the total annual nominal charge for a 

full-time undergraduate student to attend a public 

4-year institution averaged $15,014 nationally, an 

increase of 82% since 2000 in current dollars. This 

was equivalent to an increase of approximately 

43% after adjusting for inflation.

U All states showed major increases in 

undergraduate charges at public institutions in 

2010, compared with 2000. In several states, 

undergraduate charges more than doubled during 

this period.

U In 2010, the state average for a year of 

undergraduate education at a public 4-year 

institution ranged from a low of $10,109 to a high 

of $21,591.

U Tuition and required fees averaged 45% of the 

total charges at public 4-year institutions in 2010, 

but individual states had different cost structures.

Average Undergraduate Charge at Public 4-Year Institutions

Figure 8-25

Average undergraduate charge at public 4-year institutions: 2010

9,600 10,800 12,000 13,200 14,400 15,600 16,800 18,000 19,200 20,400 21,600

UT

AL

GA

KS

MT

SD

TN

WI

WV

AZ

MD

MN

NY

OR

SC

VA

AK

HI

IA

KY

MO

NE

NV

TX

CA

MA

ME

MI

OH

CT

DE

PA

RI IL

NH

NJ

VT

CO

IN

WA

Dollars

AR

FL

ID

LA

MS

NC

ND

NM

OK

WY

1st quartile ($17,020–$21,591)

2nd quartile ($14,182–$16,788)

3rd quartile ($12,022–$14,174)

4th quartile ($10,109–$11,891)

No data

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education 

Data System.
Science and Engineering Indicators 2012 8-61

Table 8-25

Average undergraduate charge at public 4-year institutions, by state: 

2000, 2005, and 2010

(Dollars)

State 2000 2005 2010

United States ............................................. 8,265 11,441 15,014

Alabama ................................................. 6,742 9,819 13,052

Alaska .................................................... 8,333 9,936 13,281

Arizona ................................................... 7,362 10,863 15,710

Arkansas ................................................ 6,416 8,734 11,841

California ................................................ 9,183 13,356 17,652

Colorado ................................................ 7,994 10,243 15,056

Connecticut ........................................... 10,136 13,824 18,331

Delaware ................................................ 9,876 13,353 18,383

District of Columbia ............................... NA NA NA

Florida .................................................... 7,474 9,335 11,659

Georgia .................................................. 7,295 9,439 12,552

Hawaii .................................................... 8,056 9,131 14,182

Idaho ...................................................... 6,323 9,066 10,895

Illinois ..................................................... 9,002 12,803 19,355

Indiana ................................................... 8,845 12,240 15,590

Iowa ....................................................... 7,210 11,541 14,174

Kansas ................................................... 6,324 9,397 12,578

Kentucky ................................................ 6,481 9,400 14,228

Louisiana ................................................ 5,910 7,973 10,873

Maine ..................................................... 9,089 11,826 17,020

Maryland ................................................ 10,345 14,108 16,407

Massachusetts ....................................... 9,212 13,687 17,819

Michigan ................................................ 9,513 12,658 17,852

Minnesota .............................................. 7,665 11,958 15,730

Mississippi ............................................. 6,456 9,019 11,583

Missouri ................................................. 8,185 11,356 14,368

Montana ................................................. 7,463 9,867 12,399

Nebraska ................................................ 7,258 10,704 13,265

Nevada ................................................... 7,812 10,464 13,682

New Hampshire ..................................... 11,052 14,651 20,492

New Jersey ............................................ 11,450 16,349 21,591

New Mexico ........................................... 6,600 8,675 11,809

New York ................................................ 9,998 12,441 16,147

North Carolina ........................................ 6,483 9,450 11,874

North Dakota ......................................... 6,994 9,011 11,891

Ohio ....................................................... 9,900 15,256 17,133

Oklahoma ............................................... 5,735 8,451 11,444

Oregon ................................................... 9,065 12,177 15,629

Pennsylvania .......................................... 10,534 14,771 19,017

Rhode Island .......................................... 10,595 13,541 18,509

South Carolina ....................................... 7,703 12,165 16,788

South Dakota ......................................... 6,520 8,944 12,022

Tennessee .............................................. 6,555 9,445 12,748

Texas ...................................................... 7,497 10,233 13,764

Utah ....................................................... 6,299 8,348 10,109

Vermont .................................................. 12,478 15,658 20,735

Virginia ................................................... 8,619 11,616 15,616

Washington ............................................ 8,314 11,902 15,189

West Virginia .......................................... 7,105 9,450 12,426

Wisconsin .............................................. 7,268 9,872 13,190

Wyoming ................................................ 7,091 8,514 10,952

Puerto Rico ............................................ NA NA NA

NA = not available

NOTES: National average for United States from Digest of Education Statistics data tables. 

Average charges for entire academic year (reported in current dollars). Tuition and fees 

weighted by number of full-time-equivalent undergraduates but not adjusted to reflect student 

residency. Room and board based on full-time students.

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education Data 

System (various years).

Science and Engineering Indicators 2012
8-62  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 2009 represent costs for the 2008–09 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

U In 2009, a year of undergraduate education 

at a state institution would have consumed, 

on average, 39.7% of a resident’s disposable 

income, an increase from the 31.8% it would 

have consumed in 2000.

U The cost of a year of undergraduate education at 

a public institution was equivalent to one-quarter 

to one-half of the per capita disposable income 

for residents of most states in 2009.

U Wyoming is the only state to show a decrease in 

this indicator between 2000 and 2009.

U Residents in 14 states experienced major 

increases in the cost of a year of undergraduate 

education relative to their purchasing power (in 

excess of 10% of their per capita disposable 

income) between 2000 and 2009.

Average Undergraduate Charge at Public 4-Year Institutions as a Percentage 

of Disposable Personal Income

Figure 8-26

Average undergraduate charge at public 4-year institutions as a percentage of disposable personal

income: 2009

22.4 25.6 28.8 32.0 35.2 38.4 41.6 44.8 48.0 51.2 54.4 57.6

WY

CT

GA

ID

MD

MT

NC

NE

NM

NV

NY

OK

TN

TX

UT

VA

WA

WI

FL

LA

ND

SD

AL

AR

CA

IA

MA

MN

MS

WV MO

AZ

DE

KY

NJ

OR

RI

MI

SC

IL

IN

ME

NH

OH

PA VT

AK

HI

KS

Percent

1st quartile (45.9%–55.1%)

2nd quartile (37.6%–45.8%)

3rd quartile (35.7%–37.5%)

4th quartile (24.0%–35.2%)

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 2012 8-63

Table 8-26

Average undergraduate charge at public 4-year institutions as a percentage of disposable personal income, 

by state: 2000, 2005, and 2009

Average undergraduate 

charge ($)

Per capita disposable 

personal income ($)

Undergraduate 

charge/disposable 

personal income (%)

State 2000 2005 2009 2000 2005 2009 2000 2005 2009

United States ................. 8,265 11,441 14,256 25,955 31,342 35,916 31.8 36.5 39.7

Alabama ..................... 6,742 9,819 12,183 21,357 27,031 30,758 31.6 36.3 39.6

Alaska ........................ 8,333 9,936 12,970 27,101 33,573 39,620 30.7 29.6 32.7

Arizona ....................... 7,362 10,863 14,098 22,939 28,159 30,807 32.1 38.6 45.8

Arkansas .................... 6,416 8,734 11,708 20,034 25,344 29,861 32.0 34.5 39.2

California .................... 9,183 13,356 15,679 27,664 33,810 38,300 33.2 39.5 40.9

Colorado .................... 7,994 10,243 14,250 28,857 34,160 37,899 27.7 30.0 37.6

Connecticut ............... 10,136 13,824 17,358 33,837 40,689 47,797 30.0 34.0 36.3

Delaware .................... 9,876 13,353 17,199 26,427 32,252 36,130 37.4 41.4 47.6

District of Columbia ... NA NA NA 33,459 47,478 60,751 NA NA NA

Florida ........................ 7,474 9,335 11,487 25,392 31,726 36,031 29.4 29.4 31.9

Georgia ...................... 7,295 9,439 11,532 24,606 28,653 31,096 29.6 32.9 37.1

Hawaii ........................ 8,056 9,131 13,358 25,495 31,764 38,556 31.6 28.7 34.6

Idaho .......................... 6,323 9,066 10,403 21,575 26,572 29,171 29.3 34.1 35.7

Illinois ......................... 9,002 12,803 18,228 27,877 32,972 37,913 32.3 38.8 48.1

Indiana ....................... 8,845 12,240 14,976 23,983 28,016 30,983 36.9 43.7 48.3

Iowa ........................... 7,210 11,541 13,828 24,136 29,298 34,385 29.9 39.4 40.2

Kansas ....................... 6,324 9,397 11,999 24,841 29,714 35,714 25.5 31.6 33.6

Kentucky .................... 6,481 9,400 13,213 21,726 25,512 29,526 29.8 36.8 44.8

Louisiana .................... 5,910 7,973 10,380 21,073 27,557 34,249 28.0 28.9 30.3

Maine ......................... 9,089 11,826 16,162 23,227 28,675 33,359 39.1 41.2 48.4

Maryland .................... 10,345 14,108 16,112 29,231 36,787 42,902 35.4 38.4 37.6

Massachusetts ........... 9,212 13,687 17,103 30,786 37,546 43,884 29.9 36.5 39.0

Michigan .................... 9,513 12,658 17,034 25,285 28,924 31,475 37.6 43.8 54.1

Minnesota .................. 7,665 11,958 15,097 27,780 33,302 37,583 27.6 35.9 40.2

Mississippi ................. 6,456 9,019 11,093 19,491 24,795 28,387 33.1 36.4 39.1

Missouri ..................... 8,185 11,356 14,056 24,335 28,884 32,781 33.6 39.3 42.9

Montana ..................... 7,463 9,867 11,970 20,781 27,192 31,853 35.9 36.3 37.6

Nebraska .................... 7,258 10,704 12,652 25,070 30,967 35,939 29.0 34.6 35.2

Nevada ....................... 7,812 10,464 12,824 26,882 33,743 34,914 29.1 31.0 36.7

New Hampshire ......... 11,052 14,651 19,228 29,273 34,591 39,124 37.8 42.4 49.1

New Jersey ................ 11,450 16,349 20,727 32,333 38,127 44,416 35.4 42.9 46.7

New Mexico ............... 6,600 8,675 11,261 20,200 26,242 30,721 32.7 33.1 36.7

New York .................... 9,998 12,441 14,878 28,623 34,598 40,348 34.9 36.0 36.9

North Carolina ............ 6,483 9,450 11,354 24,253 28,546 31,635 26.7 33.1 35.9

North Dakota ............. 6,994 9,011 11,426 23,121 29,667 37,286 30.2 30.4 30.6

Ohio ........................... 9,900 15,256 16,567 24,757 28,721 32,445 40.0 53.1 51.1

Oklahoma ................... 5,735 8,451 12,355 21,723 27,435 32,831 26.4 30.8 37.6

Oregon ....................... 9,065 12,177 15,183 24,536 28,493 32,717 36.9 42.7 46.4

Pennsylvania .............. 10,534 14,771 18,147 25,999 30,808 36,255 40.5 47.9 50.1

Rhode Island .............. 10,595 13,541 17,289 25,340 32,140 37,636 41.8 42.1 45.9

South Carolina ........... 7,703 12,165 16,137 22,165 26,368 29,900 34.8 46.1 54.0

South Dakota ............. 6,520 8,944 11,357 23,881 30,611 35,662 27.3 29.2 31.8

Tennessee .................. 6,555 9,445 12,057 24,011 28,810 32,135 27.3 32.8 37.5

Texas .......................... 7,497 10,233 13,222 25,166 30,175 35,472 29.8 33.9 37.3

Utah ........................... 6,299 8,348 10,301 21,454 25,554 28,856 29.4 32.7 35.7

Vermont ...................... 12,478 15,658 19,688 24,523 29,914 35,703 50.9 52.3 55.1

Virginia ....................... 8,619 11,616 14,850 26,780 34,039 39,606 32.2 34.1 37.5

Washington ................ 8,314 11,902 14,153 27,951 33,216 39,699 29.7 35.8 35.7

West Virginia .............. 7,105 9,450 12,128 19,815 24,249 29,416 35.9 39.0 41.2

Wisconsin .................. 7,268 9,872 12,400 25,078 29,864 33,857 29.0 33.1 36.6

Wyoming .................... 7,091 8,514 10,556 25,330 35,371 43,929 28.0 24.1 24.0

Puerto Rico ................ NA NA NA NA NA NA NA NA NA

NA = not available

NOTES: National average undergraduate charge for United States from Digest of Education Statistics data tables. Average charges for entire academic 

year (reported in current dollars). Tuition and fees weighted by number of full-time-equivalent undergraduates but not adjusted to reflect student residency. 

Room and board based on full-time students. National value for disposable personal income is value reported by Bureau of Economic Analysis.

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 2012
8-64  Chapter 8. State Indicators

This indicator represents the extent of state spending for 

higher education operating expenses as a proportion of its gross 

domestic product. 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 and 

FY 2010, 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

U Nationally, state appropriations for operating expenses 

of higher education as a share of gross domestic 

product decreased from 0.57% in 2000 to 0.52% in 

2010 but has remained unchanged since 2005.

U In 2010, the value of this indicator ranges from 0.22% 

to 1.10% across the states.

U Between 2000 and 2010, most states increased 

their appropriations for higher education in terms of 

current dollars. Notable exceptions were Michigan, 

Massachusetts, Iowa, Ohio, and Colorado which 

decreased their appropriations.

U While many states reduced the percentage of their 

gross domestic product that was allocated to higher 

education, the states of Georgia and North Carolina 

made significant increases between 2000 and 2010.

Appropriations of State Tax Funds for Operating Expenses of Higher 

Education as a Percentage of Gross Domestic Product

Figure 8-27

Appropriations of state tax funds for operating expenses of higher education as a percentage of gross domestic 

product: 2010

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2

CO

MA

NH

AZ

CT

IL

MI

MO

NJ

NV

NY

OH

WA

WI

GA

ID

KY

WV

WY

FL

IA

IN

KS

MD

ME

MN

MT

SC

TN

TX

AL

AR

HI

ND NC MS NM

AK

LA

NE

OK

UT

Percent

DE

OR

PA

RI

SD

VA

VT

1st quartile (0.69%–1.10%)

2nd quartile (0.51%–0.68%)

3rd quartile (0.41%–0.50%)

4th quartile (0.22%–0.40%)

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 2012 8-65

Table 8-27

Appropriations of state tax funds for operating expenses of higher education as a percentage of gross domestic 

product, by state: 2000, 2005, and 2010

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 2000 2005 2010 2000 2005 2010 2000 2005 2010

United States ................... 56,682 65,140 75,182 9,884,171 12,554,535 14,551,782 0.57 0.52 0.52

Alabama ....................... 1,095 1,215 1,449 116,014 151,096 172,567 0.94 0.80 0.84

Alaska .......................... 176 235 333 25,913 37,824 49,120 0.68 0.62 0.68

Arizona ......................... 866 987 1,104 161,901 222,968 253,609 0.53 0.44 0.44

Arkansas ...................... 605 655 905 68,146 88,227 102,566 0.89 0.74 0.88

California ...................... 7,684 9,067 10,793 1,317,343 1,691,991 1,901,088 0.58 0.54 0.57

Colorado ...................... 719 598 680 171,930 217,412 257,641 0.42 0.28 0.26

Connecticut ................. 699 788 1,032 163,943 197,055 237,261 0.43 0.40 0.43

Delaware ...................... 176 203 227 40,957 54,749 62,280 0.43 0.37 0.36

District of Columbia ..... NA NA NA 58,269 82,837 103,288 NA NA NA

Florida .......................... 2,786 3,581 3,714 481,115 680,277 747,735 0.58 0.53 0.50

Georgia ........................ 1,560 2,467 2,977 294,479 363,154 403,070 0.53 0.68 0.74

Hawaii .......................... 342 410 575 41,372 56,869 66,760 0.83 0.72 0.86

Idaho ............................ 279 351 389 36,091 48,675 55,435 0.77 0.72 0.70

Illinois ........................... 2,554 2,686 3,040 474,444 569,544 651,518 0.54 0.47 0.47

Indiana ......................... 1,227 1,417 1,564 198,020 239,575 275,676 0.62 0.59 0.57

Iowa ............................. 827 743 722 93,287 120,258 142,698 0.89 0.62 0.51

Kansas ......................... 622 728 754 85,742 105,164 127,170 0.73 0.69 0.59

Kentucky ...................... 924 1,077 1,204 113,108 139,336 163,269 0.82 0.77 0.74

Louisiana ...................... 885 1,288 1,411 131,430 197,163 218,853 0.67 0.65 0.64

Maine ........................... 213 241 264 36,395 45,587 51,643 0.59 0.53 0.51

Maryland ...................... 1,043 1,185 1,669 182,953 248,139 295,304 0.57 0.48 0.57

Massachusetts ............. 1,047 1,131 842 272,680 323,301 378,729 0.38 0.35 0.22

Michigan ...................... 2,074 1,948 1,837 336,786 375,260 384,171 0.62 0.52 0.48

Minnesota .................... 1,281 1,273 1,427 188,449 238,367 270,039 0.68 0.53 0.53

Mississippi ................... 917 761 1,006 65,615 81,500 97,461 1.40 0.93 1.03

Missouri ....................... 978 925 1,036 180,982 216,633 244,016 0.54 0.43 0.42

Montana ....................... 138 153 179 21,629 30,088 36,067 0.64 0.51 0.50

Nebraska ...................... 474 520 623 57,233 72,504 89,786 0.83 0.72 0.69

Nevada ......................... 306 502 501 75,907 114,771 125,650 0.40 0.44 0.40

New Hampshire ........... 96 115 138 44,067 53,653 60,283 0.22 0.22 0.23

New Jersey .................. 1,520 1,890 2,010 349,334 429,985 487,335 0.43 0.44 0.41

New Mexico ................. 544 762 877 50,262 67,776 79,678 1.08 1.12 1.10

New York ...................... 3,127 3,642 4,879 770,621 961,941 1,159,540 0.41 0.38 0.42

North Carolina .............. 2,293 2,781 3,848 281,418 354,973 424,935 0.81 0.78 0.91

North Dakota ............... 187 202 301 18,250 24,672 34,685 1.03 0.82 0.87

Ohio ............................. 2,061 2,102 1,968 381,175 444,715 477,699 0.54 0.47 0.41

Oklahoma ..................... 739 787 1,018 91,292 120,662 147,543 0.81 0.65 0.69

Oregon ......................... 650 586 663 112,974 143,349 174,151 0.58 0.41 0.38

Pennsylvania ................ 1,880 2,016 2,039 395,811 482,324 569,679 0.47 0.42 0.36

Rhode Island ................ 151 188 163 33,522 44,169 49,234 0.45 0.43 0.33

South Carolina ............. 813 977 924 115,392 141,929 164,445 0.70 0.69 0.56

South Dakota ............... 130 163 152 24,009 31,641 39,893 0.54 0.51 0.38

Tennessee .................... 985 1,302 1,474 177,582 224,522 254,806 0.55 0.58 0.58

Texas ............................ 4,093 5,110 6,543 732,987 970,997 1,207,494 0.56 0.53 0.54

Utah ............................. 547 647 687 69,483 90,748 114,538 0.79 0.71 0.60

Vermont ........................ 63 78 91 18,033 22,773 25,620 0.35 0.34 0.36

Virginia ......................... 1,480 1,481 1,576 261,894 356,852 423,860 0.57 0.41 0.37

Washington .................. 1,238 1,412 1,576 227,828 279,405 340,460 0.54 0.51 0.46

West Virginia ................ 373 426 503 41,419 51,964 64,642 0.90 0.82 0.78

Wisconsin .................... 1,075 1,122 1,192 177,638 218,923 248,265 0.61 0.51 0.48

Wyoming ...................... 140 218 305 17,047 26,238 38,527 0.82 0.83 0.79

Puerto Rico .................. NA NA NA 69,208 86,157 NA NA NA NA

NA = not available

GDP = gross domestic product

NOTE: FY 2010 appropriations figures represent initial allocations or estimates as of 10 February 2010 and are subject to change; appropriations and 

GDP reported in current dollars.

SOURCES: State Higher Education Executive Officers College Board, State Higher Education Finance (various years); Bureau of Economic Analysis, 

Gross Domestic Product data; United Nations Statistics Division.

Science and Engineering Indicators 2012
8-66  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 nonneed-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 

2008 represent costs for the 2007–08 academic year.

Findings

U The total amount of state financial aid from 

grants provided to undergraduates nearly 

doubled nationwide increasing from $4.0 

billion in 2000 to $7.9 billion in 2008.

U On a per-student basis, state funding for 

student grants across the United States 

increased from $553 per undergraduate in 

2000 to $839 per undergraduate in 2008 (in 

current dollars).

U There are major differences in the amount 

of state aid provided to undergraduate 

students in different states. State values for 

this indicator ranged from $12 to $2,577 in 

2008. Eight jurisdictions averaged less than 

$100 per undergraduate student, while 12 

provided more than $1,000 per student.

U Eight states reported spending less, in 

current dollars, per student for student 

financial aid in 2008 than in 2000, even 

though the cost of undergraduate education 

rose rapidly during this period.

State Expenditures on Student Aid per Full-Time Undergraduate Student

Figure 8-28

State expenditures on student aid per full-time undergraduate student: 2008

0 260 520 780 1,040 1,300 1,560 1,820 2,080 2,340 2,600

AK

AL

AZ

HI

ID

KS

MT

ND

NE

NH

SD

UT

WY

CT

DE

MD

MI

OH

OK

VA

VT KY

FL

IN

LA

MN

TX

WV

NJ

TN GA SC

CA

IL

NC

NM

NY

PA

WA

Dollars

AR

CO

DC

IA

MA

ME

MO

MS

NV

OR

RI

WI

1st quartile ($988–$2,577)

2nd quartile ($585–$980)

3rd quartile ($200–$503)

4th quartile ($12–$191)

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 2012 8-67

Table 8-28

State expenditures on student aid per full-time undergraduate student, by state: 2000, 2004, and 2008

State expenditures on 

student aid ($thousands)

Undergraduate enrollment 

at 4-year institutions

State expenditures 

on student aid/ 

undergraduate ($)

State 2000 2004 2008 2000 2004 2008 2000 2004 2008

United States ................. 3,975,263 5,688,877 7,864,532 7,193,814 8,220,306 9,379,112 553 692 839

Alabama ..................... 7,487 5,084 19,841 130,189 139,033 183,162 58 37 108

Alaska ........................ NA NA 670 24,573 27,377 27,132 NA NA 25

Arizona ....................... 2,727 2,865 12,179 111,429 192,819 378,866 24 15 32

Arkansas .................... 30,936 29,705 33,774 70,538 79,007 86,667 439 376 390

California .................... 369,785 654,549 813,374 599,658 679,920 750,965 617 963 1,083

Colorado .................... 54,151 60,266 75,259 133,500 154,427 181,904 406 390 414

Connecticut ............... 37,401 36,771 64,475 85,143 90,774 96,753 439 405 666

Delaware .................... 1,293 12,703 16,980 25,761 27,996 28,438 50 454 597

District of Columbia ... 743 27,571 33,496 40,703 59,930 76,586 18 460 437

Florida ........................ 225,553 326,468 533,073 288,143 457,808 587,097 783 713 908

Georgia ...................... 240,458 431,951 497,667 188,383 234,213 266,808 1,276 1,844 1,865

Hawaii ........................ 490 408 408 26,290 30,956 34,471 19 13 12

Idaho .......................... 1,127 4,982 6,221 39,343 55,877 59,010 29 89 105

Illinois ......................... 360,177 347,565 415,298 276,559 298,037 343,104 1,302 1,166 1,210

Indiana ....................... 106,169 161,263 223,676 217,294 230,454 259,489 489 700 862

Iowa ........................... 51,823 49,285 59,573 97,241 110,724 166,514 533 445 358

Kansas ....................... 12,397 14,073 19,090 84,620 91,803 95,529 147 153 200

Kentucky .................... 48,444 143,338 186,988 109,981 121,518 130,274 440 1,180 1,435

Louisiana .................... 68,391 111,602 137,347 146,259 157,201 140,163 468 710 980

Maine ......................... 10,360 12,561 17,916 42,093 44,104 43,462 246 285 412

Maryland .................... 45,683 50,390 99,726 117,720 130,334 140,784 388 387 708

Massachusetts ........... 103,301 79,735 86,342 235,263 238,366 256,937 439 335 336

Michigan .................... 91,109 162,225 189,078 287,233 314,782 323,385 317 515 585

Minnesota .................. 113,750 119,641 156,433 142,734 164,791 183,329 797 726 853

Mississippi ................. 20,163 21,367 22,923 61,043 65,345 67,127 330 327 341

Missouri ..................... 39,504 41,233 109,998 180,799 203,040 218,700 218 203 503

Montana ..................... 2,953 2,542 4,367 32,393 33,615 33,391 91 76 131

Nebraska .................... 5,645 8,742 12,537 58,789 62,427 65,693 96 140 191

Nevada ....................... 6,083 34,535 39,109 32,012 76,669 92,307 190 450 424

New Hampshire ......... 1,506 3,651 3,732 40,367 43,821 45,831 37 83 81

New Jersey ................ 189,294 225,282 292,500 156,867 167,863 183,112 1,207 1,342 1,597

New Mexico ............... 33,872 29,821 67,781 43,089 50,348 52,659 786 592 1,287

New York .................... 611,167 875,299 824,174 569,260 609,027 678,593 1,074 1,437 1,215

North Carolina ............ 109,004 156,604 286,773 191,117 215,536 243,631 570 727 1,177

North Dakota ............. 2,431 1,756 3,778 28,462 34,010 38,778 85 52 97

Ohio ........................... 163,994 200,787 254,785 302,681 326,174 353,284 542 616 721

Oklahoma ................... 26,595 28,841 75,823 98,512 114,090 117,799 270 253 644

Oregon ....................... 17,891 21,782 35,036 76,071 89,212 96,641 235 244 363

Pennsylvania .............. 280,402 360,816 460,451 377,646 409,046 438,118 742 882 1,051

Rhode Island .............. 61,722 12,296 15,336 49,484 52,831 55,312 1,247 233 277

South Carolina ........... 33,198 225,297 295,627 92,074 103,533 114,705 361 2,176 2,577

South Dakota ............. 857 NA 2,416 32,310 37,315 38,420 27 NA 63

Tennessee .................. 21,383 42,395 279,300 139,743 154,060 171,186 153 275 1,632

Texas .......................... 93,814 156,529 484,807 432,747 495,259 554,233 217 316 875

Utah ........................... 2,735 4,081 10,705 120,151 138,646 151,316 23 29 71

Vermont ...................... 48,840 17,149 18,804 25,972 27,166 30,393 1,880 631 619

Virginia ....................... 70,717 110,621 173,813 180,573 202,472 239,213 392 546 727

Washington ................ 76,581 134,280 205,891 105,470 122,495 159,407 726 1,096 1,292

West Virginia .............. 18,982 52,087 82,487 68,435 67,215 83,509 277 775 988

Wisconsin .................. 52,020 75,920 102,533 168,547 177,251 205,226 309 428 500

Wyoming .................... 155 163 162 8,550 9,589 9,699 18 17 17

Puerto Rico ................ 34,004 30,999 31,891 149,699 165,293 173,409 227 188 184

NA = not available

NOTE: State expenditures on student aid reported in current dollars.

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 2012
8-68  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. An 

indicator of states’ investment in the education of their students is the amount 

of funding provided per enrolled student. Eventually, changes in these funds 

affect the institutions’ financial health and the quality of education they provide.

Data for this indicator cover 102 public research universities with broad 

educational missions (excluding free-standing 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. State funds include state 

operating grants and contracts as well as state appropriations. Enrollment 

includes total enrollment measured in the fall of each academic year. Total 

state funds are shown in current dollars; the Findings also address constant 

dollar results.

Data were drawn from annual surveys of the National Center for Education 

Statistics (NCES), and supplemented with data from Illinois State University’s 

Center for the Study of Education Policy when NCES data were unavailable 

or incomplete.

State funds are one of many sources of public university revenue. This 

indicator does not include changes in these other sources of revenue.

Findings

U From 2002 to 2010, state funds for operating 

expenses of all public institutions of higher 

education increased by 21%. For major public 

research universities, state funds increased by 

only 8% dropping the states’ share of their total 

operating funds from 28% to 19%.

U When adjusted for inflation, total state 

expenditures for public higher education 

were essentially flat over the decade, while 

the amount going to major public research 

universities decreased by 10%.

U Between 2002 and 2008, total enrollment at 

major public research universities increased by 

8% and undergraduate enrollment at all public 

4-year institutions increased by 22%.

U Over the decade, per-student state support 

to major research universities dropped by an 

average of 20% in inflation-adjusted dollars. In 

10 states, the decline ranged from 30% to 48%. 

New York and Wyoming increased their funds 

per enrollment by over half. Their funds per 

student in 2010 put them among the top 5 states 

on this measure.

State Funding for Major Public Research Universities per Enrolled Student

Figure 8-29

State funding for major public research universities per enrolled student: 2010

3,200 4,800 6,400 8,000 9,600 11,200 12,800 14,400 16,000 17,600 19,200

CO

NH

RI

VT

AZ

DE

FL

IN

MI

MS

ND

OH

OK

SC

CA

DC

KY

MN

NJ

NM

CT

GA

IA

IL

MA

ME

NV

PA

TX

UT

WA

WV

NY

TN NC

AK

WY

AL

AR

HI

ID

LA

MD

MO

NE

WI

Dollars

KS

MT

OR

SD

VA

1st quartile ($10,825–$18,901)

2nd quartile ($8,800–$10,773)

3rd quartile ($7,126–$8,657)

4th quartile ($3,803–$7,106)

SOURCES: National Center for Education 

Statistics, Integrated Postsecondary Education Data System; Illinois State University 

Center for the Study of Education Policy.
Science and Engineering Indicators 2012 8-69

Table 8-29

State funding for major public research universities per enrolled student, by state: 2002, 2006, and 2010

State funding for major public 

research universities ($ thousands) Enrolled students

State funding for major 

public research universities/

enrolled student ($)

State 2002 2006 2010 2002 2006 2010 2002 2006 2010

United States ................. 23,826,069 24,969,781 25,835,178 2,527,368 2,663,015 2,844,570 9,427 9,377 9,082

Alabama ..................... 440,152 537,351 529,456 43,918 46,989 49,157 10,022 11,436 10,771

Alaska ........................ 97,177 125,062 164,911 7,142 8,228 9,137 13,606 15,200 18,049

Arizona ....................... 618,184 705,582 761,297 81,440 88,648 106,831 7,591 7,959 7,126

Arkansas .................... 166,884 184,955 214,863 15,752 17,821 19,849 10,594 10,378 10,825

California .................... 3,317,385 2,887,676 3,161,359 222,500 232,159 253,013 14,910 12,438 12,495

Colorado .................... 355,986 262,707 235,480 58,166 59,369 61,912 6,120 4,425 3,803

Connecticut ............... 188,379 205,800 239,156 19,876 23,185 25,029 9,478 8,876 9,555

Delaware .................... 120,736 139,441 148,308 20,949 20,982 21,138 5,763 6,646 7,016

District of Columbia ... 46,068 61,266 62,070 5,456 5,363 4,960 8,444 11,424 12,514

Florida ........................ 1,457,282 1,808,109 1,704,160 186,295 213,259 223,509 7,822 8,478 7,625

Georgia ...................... 908,772 912,554 804,648 73,635 76,762 85,603 12,342 11,888 9,400

Hawaii ........................ 198,539 231,273 220,156 17,532 20,644 20,435 11,324 11,203 10,773

Idaho .......................... 130,026 138,166 118,466 12,067 12,476 11,957 10,775 11,075 9,908

Illinois ......................... 954,329 753,066 766,844 85,844 88,191 91,071 11,117 8,539 8,420

Indiana ....................... 532,994 532,791 563,341 77,845 78,109 83,399 6,847 6,821 6,755

Iowa ........................... 590,225 565,168 541,689 56,591 54,167 56,932 10,430 10,434 9,515

Kansas ....................... 313,888 320,226 317,732 48,178 50,116 52,823 6,515 6,390 6,015

Kentucky .................... 514,549 602,485 559,642 43,583 46,398 47,311 11,806 12,985 11,829

Louisiana .................... 288,601 330,639 320,314 32,059 34,128 28,643 9,002 9,688 11,183

Maine ......................... 96,188 101,515 102,969 10,698 11,435 11,894 8,991 8,878 8,657

Maryland .................... 529,049 466,392 507,435 45,397 47,019 50,065 11,654 9,919 10,136

Massachusetts ........... 227,241 263,877 218,861 24,678 25,093 27,016 9,208 10,516 8,101

Michigan .................... 1,040,859 937,704 923,999 113,515 117,319 120,531 9,169 7,993 7,666

Minnesota .................. 611,601 606,249 621,463 46,597 51,175 51,659 13,125 11,847 12,030

Mississippi ................. 224,924 233,863 257,505 29,504 31,002 34,533 7,624 7,543 7,457

Missouri ..................... 223,274 252,801 306,294 23,667 27,930 31,237 9,434 9,051 9,805

Montana ..................... 62,345 62,875 63,841 11,670 12,143 12,348 5,342 5,178 5,170

Nebraska .................... 217,607 222,523 250,498 22,764 21,675 24,100 9,559 10,266 10,394

Nevada ....................... 146,094 198,620 148,492 14,316 16,336 16,875 10,205 12,158 8,800

New Hampshire ......... 65,447 66,162 73,144 14,766 14,511 15,253 4,432 4,559 4,795

New Jersey ................ 646,252 682,817 572,428 44,512 42,507 46,206 14,519 16,064 12,389

New Mexico ............... 383,411 459,827 534,212 38,977 42,244 45,767 9,837 10,885 11,672

New York .................... 556,999 1,161,303 1,295,820 76,717 80,289 86,291 7,260 14,464 15,017

North Carolina ............ 760,128 915,647 1,050,584 54,780 57,424 62,735 13,876 15,945 16,746

North Dakota ............. 132,543 153,291 204,312 22,298 25,053 27,361 5,944 6,119 7,467

Ohio ........................... 702,689 731,819 776,038 96,079 97,637 109,212 7,314 7,495 7,106

Oklahoma ................... 368,093 362,186 386,065 47,112 50,198 48,914 7,813 7,215 7,893

Oregon ....................... 217,853 218,217 213,453 36,969 39,571 44,285 5,893 5,515 4,820

Pennsylvania .............. 774,683 827,682 946,362 97,410 100,963 110,020 7,953 8,198 8,602

Rhode Island .............. 91,296 91,073 67,353 14,264 15,095 16,389 6,400 6,033 4,110

South Carolina ........... 391,965 378,784 347,733 40,101 44,230 47,593 9,774 8,564 7,306

South Dakota ............. 52,069 60,701 67,324 9,260 10,938 12,376 5,623 5,550 5,440

Tennessee .................. 400,660 433,396 458,400 26,033 28,512 29,934 15,390 15,200 15,314

Texas .......................... 1,067,096 1,118,929 1,227,595 140,695 144,430 152,480 7,584 7,747 8,051

Utah ........................... 391,336 416,127 396,268 50,669 45,016 44,896 7,723 9,244 8,826

Vermont ...................... 43,369 50,131 51,893 10,078 11,597 13,391 4,303 4,323 3,875

Virginia ....................... 841,601 831,820 796,312 120,467 131,914 143,477 6,986 6,306 5,550

Washington ................ 591,118 602,357 635,081 58,485 62,795 72,044 10,107 9,592 8,815

West Virginia .............. 218,982 206,953 232,538 22,774 26,051 28,898 9,615 7,944 8,047

Wisconsin .................. 388,883 382,379 432,132 40,922 40,793 41,654 9,503 9,374 10,374

Wyoming .................... 120,257 167,445 234,883 12,366 13,126 12,427 9,725 12,757 18,901

Puerto Rico ................ NA NA NA NA NA NA NA NA NA

NA = not available

NOTES: National average state funds per enrolled student from Integrated Postsecondary Education Data System. For District of Columbia, funds are local 

education taxes. For Colorado, Connecticut, Kansas, New Jersey, and Tennessee, data for certain years include data from the Center for the Study of Education 

Policy’s Grapevine tables. For Colorado, Connecticut, and Kansas, 2010 state funds are projected based on 2009. All other 2010 funds are preliminary. 

SOURCES: National Center for Education Statistics, Integrated Postsecondary Education Data System; Illinois State University Center for the Study of Education 

Policy.

Science and Engineering Indicators 2012
8-70  Chapter 8. State Indicators

This indicator represents the percentage of the early- to mid-career 

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 post-secondary program.

Estimates of educational attainment and of the population of individuals 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

U The early- to mid-career population with a postsecondary degree was 39.2% nationwide in 

2009, an increase from 34.7% in 2000.

U In 2009, the percentage of this cohort with a 

postsecondary degree varied greatly among 

states, ranging from 28.3% to 52.6%.

U Between 2000 and 2009, all states showed an 

increase in the percentage of their early- to midcareer population with a postsecondary degree, 

ranging from nearly 1% to almost 10% over the 

time period.

U States with the lowest cost of living tended to 

rank lowest on this indicator.

Postsecondary Degree Holders Among Individuals 25–44 Years Old

Figure 8-30

Postsecondary degree holders among individuals 25–44 years old: 2009

28 32 36 40 44 48 52 56 60 64

AK

AR

LA

MS

NM

NV

OK

WV

CO

CT

IA

MD

NE

NH

NJ

VA

VT

CA

DE

FL

GA

ME

MI

MO

NC

OH

OR

UT

MN

ND

NY MA DC

HI

IL

KS

MT

PA

RI

SD

WA

WI

AL

AZ

ID

IN

KY

SC

TN

TX

WY

Percent

1st quartile (44.8%–61.4%)

2nd quartile (39.0%–44.2%)

3rd quartile (33.9%–38.1%)

4th quartile (28.3%–33.3%)

SOURCES: Census Bureau, 2000 Decennial 

Census, Population Estimates Program, and 

American Community Survey.
Science and Engineering Indicators 2012 8-71

Table 8-30

Postsecondary degree holders among individuals 25–44 years old, by state: 2000, 2005, and 2009

Postsecondary degree holders 

25–44 years old Individuals 25–44 years old

Postsecondary 

degree holders/ 

individuals 25–44 

years old (%)

State 2000 2005 2009 2000 2005 2009 2000 2005 2009

United States .................. 29,471,612 31,382,831 32,606,255 85,040,251 83,257,116 83,096,278 34.7 37.7 39.2

Alabama ...................... 370,196 389,490 405,826 1,288,527 1,231,043 1,235,509 28.7 31.6 32.8

Alaska ......................... 61,646 58,631 61,366 203,522 191,837 197,248 30.3 30.6 31.1

Arizona ........................ 472,901 552,805 608,775 1,511,469 1,695,189 1,826,751 31.3 32.6 33.3

Arkansas .................... 177,657 202,622 216,126 750,972 747,630 755,915 23.7 27.1 28.6

California ..................... 3,670,622 3,892,099 4,024,498 10,714,403 10,668,824 10,604,180 34.3 36.5 38.0

Colorado ..................... 596,036 636,437 654,274 1,400,850 1,388,046 1,445,400 42.5 45.9 45.3

Connecticut ................ 443,608 432,451 426,214 1,032,689 951,020 899,649 43.0 45.5 47.4

Delaware ..................... 84,170 87,994 92,531 236,441 234,823 232,837 35.6 37.5 39.7

District of Columbia .... 90,097 103,236 121,521 189,439 190,118 197,983 47.6 54.3 61.4

Florida ......................... 1,513,345 1,694,517 1,761,941 4,569,347 4,787,948 4,789,059 33.1 35.4 36.8

Georgia ....................... 884,108 1,013,471 1,039,426 2,652,764 2,746,294 2,830,740 33.3 36.9 36.7

Hawaii ......................... 136,758 129,858 157,298 362,336 354,560 360,037 37.7 36.6 43.7

Idaho ........................... 112,690 121,718 135,622 362,401 375,247 400,329 31.1 32.4 33.9

Illinois .......................... 1,444,942 1,530,725 1,553,736 3,795,544 3,611,958 3,544,995 38.1 42.4 43.8

Indiana ........................ 537,644 562,483 603,097 1,791,828 1,716,726 1,689,050 30.0 32.8 35.7

Iowa ............................ 289,740 317,772 331,841 808,259 746,659 734,622 35.8 42.6 45.2

Kansas ........................ 282,475 289,848 297,294 769,204 713,752 717,645 36.7 40.6 41.4

Kentucky ..................... 317,109 353,170 385,497 1,210,773 1,172,770 1,162,402 26.2 30.1 33.2

Louisiana ..................... 316,348 340,337 358,275 1,293,128 1,217,593 1,186,325 24.5 28.0 30.2

Maine .......................... 122,958 123,129 122,981 370,597 344,295 322,409 33.2 35.8 38.1

Maryland ..................... 672,460 693,317 713,321 1,664,677 1,611,882 1,557,085 40.4 43.0 45.8

Massachusetts ............ 942,748 932,197 939,443 1,989,783 1,857,726 1,787,350 47.4 50.2 52.6

Michigan ..................... 982,169 1,013,031 946,696 2,960,544 2,743,365 2,536,880 33.2 36.9 37.3

Minnesota ................... 631,677 684,727 688,731 1,497,320 1,420,387 1,394,305 42.2 48.2 49.4

Mississippi .................. 208,866 231,759 226,353 807,170 771,676 761,785 25.9 30.0 29.7

Missouri ...................... 517,750 543,130 582,137 1,626,302 1,566,374 1,554,391 31.8 34.7 37.5

Montana ...................... 81,428 85,590 94,468 245,220 226,076 231,769 33.2 37.9 40.8

Nebraska ..................... 185,090 202,182 202,445 487,107 453,659 451,666 38.0 44.6 44.8

Nevada ........................ 152,536 193,902 226,510 628,572 719,501 769,608 24.3 26.9 29.4

New Hampshire .......... 156,434 161,161 153,958 381,240 357,080 333,694 41.0 45.1 46.1

New Jersey ................. 1,076,450 1,114,215 1,101,798 2,624,146 2,485,721 2,363,679 41.0 44.8 46.6

New Mexico ................ 149,398 153,406 164,083 516,100 510,063 523,059 28.9 30.1 31.4

New York ..................... 2,359,507 2,499,314 2,566,265 5,831,622 5,548,409 5,351,598 40.5 45.0 48.0

North Carolina ............. 844,019 933,034 1,004,796 2,500,535 2,485,963 2,553,673 33.8 37.5 39.3

North Dakota .............. 71,509 73,974 77,223 174,891 151,681 153,582 40.9 48.8 50.3

Ohio ............................ 1,075,353 1,098,912 1,115,603 3,325,210 3,122,259 2,998,151 32.3 35.2 37.2

Oklahoma .................... 276,525 296,769 298,455 975,169 929,451 957,235 28.4 31.9 31.2

Oregon ........................ 333,963 361,760 401,129 997,269 988,164 1,028,645 33.5 36.6 39.0

Pennsylvania ............... 1,230,548 1,269,457 1,326,259 3,508,562 3,280,173 3,187,617 35.1 38.7 41.6

Rhode Island ............... 117,758 127,598 120,458 310,636 296,463 274,622 37.9 43.0 43.9

South Carolina ............ 357,570 389,378 425,929 1,185,955 1,172,501 1,200,366 30.2 33.2 35.5

South Dakota .............. 73,128 82,619 80,521 206,399 194,122 196,143 35.4 42.6 41.1

Tennessee ................... 489,940 521,417 579,010 1,718,428 1,698,113 1,710,134 28.5 30.7 33.9

Texas ........................... 1,973,279 2,112,582 2,312,816 6,484,321 6,665,252 7,064,651 30.4 31.7 32.7

Utah ............................ 222,534 276,707 302,339 626,600 686,668 775,481 35.5 40.3 39.0

Vermont ....................... 70,277 68,447 68,179 176,456 158,184 148,584 39.8 43.3 45.9

Virginia ........................ 874,239 925,208 970,871 2,237,655 2,194,670 2,194,699 39.1 42.2 44.2

Washington ................. 693,591 739,976 782,873 1,816,217 1,783,093 1,855,094 38.2 41.5 42.2

West Virginia ............... 115,337 125,231 130,226 501,343 468,846 459,606 23.0 26.7 28.3

Wisconsin ................... 566,244 596,923 595,646 1,581,690 1,495,775 1,449,006 35.8 39.9 41.1

Wyoming ..................... 44,235 42,115 49,575 138,619 127,487 139,035 31.9 33.0 35.7

Puerto Rico ................. 358,595 424,718 412,249 1,049,995 1,076,844 1,084,239 34.2 39.4 38.0

SOURCES: Census Bureau, 2000 Decennial Census, Population Estimates Program (various years), and American Community Survey (various years).

Science and Engineering Indicators 2012
8-72  Chapter 8. State Indicators

This indicator represents the percentage of the early- to mid-career 

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

U The early- to mid-career population with at least 

a bachelor’s degree was 30.9% nationwide in 

2009, an increase from 26.8% in 2000.

U All states showed an increase in the percentage 

of their early-career population with at least a 

bachelor’s degree between 2000 and 2009.

U In 2009, the percentage of the early-career 

population with at least a bachelor’s degree 

varied among states, ranging from 20.3 % 

to 45.1%. The highest percentages tended 

to be found in the New England and Middle 

Atlantic states.

U States with the lowest cost of living tended to 

rank lowest on this indicator.

U The difference between EPSCoR and nonEPSCoR states, as a group, remained relatively 

unchanged and may have increased slightly over 

the decade.

Bachelor’s Degree Holders Among Individuals 25–44 Years Old

Figure 8-31

Bachelor’s degree holders among individuals 25–44 years old: 2009

20 24 28 32 36 40 44 48 52 56 60

AK

AR

MS

NM

NV

OK

WV

CA

DE

GA

HI

IA

ME

MI

MO

MT

NC

OH

OR

SD

UT

WI

IL

KS

ND

NE

NH

PA

RI

VT

WA MA DC

CO

CT

MD

MN

NJ

NY

VA

Percent

AL

AZ

FL

ID

IN

KY

LA

SC

TN

TX

WY

1st quartile (34.3%–58.6%)

2nd quartile (30.1%–33.1%)

3rd quartile (25.1%–29.8%)

4th quartile (20.3%–24.7%)

SOURCES: Census Bureau, 2000 Decennial 

Census, Population Estimates Program, and 

American Community Survey.
Science and Engineering Indicators 2012 8-73

Table 8-31

Bachelor’s degree holders among individuals 25–44 years old, by state: 2000, 2005, and 2009

Bachelor’s degree holders 

25–44 years old Individuals 25–44 years old

Bachelor’s degree 

holders/individuals 

25–44 years old (%)

State 2000 2005 2009 2000 2005 2009 2000 2005 2009

EPSCoR states ................ 3,004,954 3,199,506 3,399,367 13,582,778 13,120,340 13,171,410 22.1 24.4 25.8

Non-EPSCoR states ........ 19,692,206 21,057,298 22,147,292 71,268,034 69,946,658 69,726,885 27.6 30.1 31.8

Average EPSCoR state 

 value ............................ na na na na na na 23.4 26.0 27.4

Average non-EPSCoR 

 state value ................... na na na na na na 28.0 30.7 32.4

United States ................... 22,781,996 24,353,620 25,662,617 85,040,251 83,257,116 83,096,278 26.8 29.3 30.9

Alabama ...................... 275,759 288,817 303,640 1,288,527 1,231,043 1,235,509 21.4 23.5 24.6

Alaska .......................... 45,560 45,315 45,858 203,522 191,837 197,248 22.4 23.6 23.2

Arizona ......................... 355,836 408,522 458,075 1,511,469 1,695,189 1,826,751 23.5 24.1 25.1

Arkansas ...................... 136,883 152,225 161,439 750,972 747,630 755,915 18.2 20.4 21.4

California ...................... 2,882,717 3,112,603 3,270,704 10,714,403 10,668,824 10,604,180 26.9 29.2 30.8

Colorado ...................... 480,984 512,178 537,204 1,400,850 1,388,046 1,445,400 34.3 36.9 37.2

Connecticut ................. 362,272 362,929 354,994 1,032,689 951,020 899,649 35.1 38.2 39.5

Delaware ...................... 65,811 71,090 73,657 236,441 234,823 232,837 27.8 30.3 31.6

District of Columbia ..... 84,836 96,816 115,958 189,439 190,118 197,983 44.8 50.9 58.6

Florida .......................... 1,081,551 1,212,200 1,270,000 4,569,347 4,787,948 4,789,059 23.7 25.3 26.5

Georgia ........................ 718,591 820,695 844,470 2,652,764 2,746,294 2,830,740 27.1 29.9 29.8

Hawaii .......................... 99,378 95,029 111,896 362,336 354,560 360,037 27.4 26.8 31.1

Idaho ............................ 80,235 89,959 98,196 362,401 375,247 400,329 22.1 24.0 24.5

Illinois ........................... 1,149,688 1,216,933 1,264,186 3,795,544 3,611,958 3,544,995 30.3 33.7 35.7

Indiana ......................... 397,050 408,107 440,159 1,791,828 1,716,726 1,689,050 22.2 23.8 26.1

Iowa ............................. 202,004 221,497 228,596 808,259 746,659 734,622 25.0 29.7 31.1

Kansas ......................... 223,467 224,946 234,605 769,204 713,752 717,645 29.1 31.5 32.7

Kentucky ...................... 234,921 256,209 287,622 1,210,773 1,172,770 1,162,402 19.4 21.8 24.7

Louisiana ...................... 256,363 267,429 285,090 1,293,128 1,217,593 1,186,325 19.8 22.0 24.0

Maine ........................... 86,989 85,987 91,692 370,597 344,295 322,409 23.5 25.0 28.4

Maryland ...................... 566,294 582,280 613,136 1,664,677 1,611,882 1,557,085 34.0 36.1 39.4

Massachusetts ............. 773,569 780,522 805,478 1,989,783 1,857,726 1,787,350 38.9 42.0 45.1

Michigan ...................... 719,607 757,970 714,045 2,960,544 2,743,365 2,536,880 24.3 27.6 28.1

Minnesota .................... 476,707 511,402 520,361 1,497,320 1,420,387 1,394,305 31.8 36.0 37.3

Mississippi ................... 144,488 152,606 161,959 807,170 771,676 761,785 17.9 19.8 21.3

Missouri ....................... 407,449 429,501 456,593 1,626,302 1,566,374 1,554,391 25.1 27.4 29.4

Montana ....................... 62,682 63,693 71,370 245,220 226,076 231,769 25.6 28.2 30.8

Nebraska ...................... 134,516 149,233 148,518 487,107 453,659 451,666 27.6 32.9 32.9

Nevada ......................... 111,517 143,301 167,403 628,572 719,501 769,608 17.7 19.9 21.8

New Hampshire ........... 114,745 122,682 118,843 381,240 357,080 333,694 30.1 34.4 35.6

New Jersey .................. 899,016 943,939 935,352 2,624,146 2,485,721 2,363,679 34.3 38.0 39.6

New Mexico ................. 110,360 110,562 117,143 516,100 510,063 523,059 21.4 21.7 22.4

New York ...................... 1,817,661 1,964,870 2,072,041 5,831,622 5,548,409 5,351,598 31.2 35.4 38.7

North Carolina .............. 636,799 697,740 768,610 2,500,535 2,485,963 2,553,673 25.5 28.1 30.1

North Dakota ............... 46,291 48,381 50,787 174,891 151,681 153,582 26.5 31.9 33.1

Ohio ............................. 806,803 833,138 852,179 3,325,210 3,122,259 2,998,151 24.3 26.7 28.4

Oklahoma ..................... 209,025 218,272 224,298 975,169 929,451 957,235 21.4 23.5 23.4

Oregon ......................... 257,875 284,778 315,664 997,269 988,164 1,028,645 25.9 28.8 30.7

Pennsylvania ................ 938,930 979,367 1,035,976 3,508,562 3,280,173 3,187,617 26.8 29.9 32.5

Rhode Island ................ 88,647 98,477 94,159 310,636 296,463 274,622 28.5 33.2 34.3

South Carolina ............. 259,773 283,280 315,848 1,185,955 1,172,501 1,200,366 21.9 24.2 26.3

South Dakota ............... 51,213 56,951 55,005 206,399 194,122 196,143 24.8 29.3 28.0

Tennessee .................... 380,929 401,027 458,483 1,718,428 1,698,113 1,710,134 22.2 23.6 26.8

Texas ............................ 1,571,951 1,668,865 1,848,862 6,484,321 6,665,252 7,064,651 24.2 25.0 26.2

Utah ............................. 162,495 197,780 225,717 626,600 686,668 775,481 25.9 28.8 29.1

Vermont ........................ 52,787 53,693 52,956 176,456 158,184 148,584 29.9 33.9 35.6

Virginia ......................... 722,081 763,865 815,770 2,237,655 2,194,670 2,194,699 32.3 34.8 37.2

Washington .................. 520,382 554,104 602,269 1,816,217 1,783,093 1,855,094 28.7 31.1 32.5

West Virginia ................ 83,441 91,539 93,104 501,343 468,846 459,606 16.6 19.5 20.3

Wisconsin .................... 402,965 430,486 438,368 1,581,690 1,495,775 1,449,006 25.5 28.8 30.3

Wyoming ...................... 30,103 29,830 34,279 138,619 127,487 139,035 21.7 23.4 24.7

Puerto Rico .................. 245,975 276,934 286,174 1,049,995 1,076,844 1,084,239 23.4 25.7 26.4

na = not applicable

EPSCoR = Experimental Program to Stimulate Competitive Research

NOTE: For explanation of EPSCoR and non-EPSCoR averages, see chapter introduction.

SOURCES: Census Bureau, American Community Survey (various years), 2000 Decennial Census, and Population Estimates Program (various years).

Science and Engineering Indicators 2012
8-74  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

U In 2009, 48 million individuals between ages 25 

and 64 held bachelor’s degrees in the United 

States, up from 39 million in 2000. Nationwide, 

the ratio of bachelor’s degree holders to the 

size of the workforce rose from 28.5% in 2000 

to 34.6% in 2009. This ratio varied considerably 

among the states, ranging from 23.9% to 47.1% 

in 2009.

U The value of this indicator increased in all 

jurisdictions between 2000 and 2009. 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.

U In 2009, the jurisdictions in which the highest 

concentrations of bachelor’s degree holders 

lived included the District of Columbia, 

Massachusetts, New Jersey, and Maryland.

Bachelor’s Degree Holders Potentially in the Workforce

Figure 8-32

Bachelor’s degree holders potentially in the workforce: 2009

20 24 28 32 36 40 44 48 52 56 60

AR

AK

AL

AZ

FL

ID

IN

KY

MO

MT

NE

NM

OH

SC

TN

TX

UT

WI

CO

CT

MD

NJ

NY

DE

GA

KS

ME

MI

MN

NC

NH

PA

VT MA DC

CA

HI

IL

OR

RI

VA

WA

Percent

IA

LA

MS

ND

NV

OK

SD

WV

WY

1st quartile (36.6%–58.4%)

2nd quartile (31.8%–36.2%)

3rd quartile (28.8%–31.1%)

4th quartile (23.9%–28.4%)

SOURCES: Census Bureau, American Community Survey, 2000 Decennial Census, and 

Population Estimates Program; Bureau of 

Labor Statistics, Local Area Unemployment 

Statistics.
Science and Engineering Indicators 2012 8-75

Table 8-32

Bachelor’s degree holders potentially in the workforce, by state: 2000, 2005, and 2009

Bachelor’s degree holders 

25–64 years old Employed workforce

Bachelor’s degree 

holders/workforce (%)

State 2000 2005 2009 2000 2005 2009 2000 2005 2009

United States .................. 39,078,598 44,972,214 48,338,792 136,955,714 141,077,688 139,594,700 28.5 31.9 34.6

Alabama ...................... 479,734 549,086 583,697 2,067,147 2,051,893 1,959,849 23.2 26.8 29.8

Alaska ......................... 87,739 96,854 102,828 299,324 320,590 330,597 29.3 30.2 31.1

Arizona ........................ 638,515 781,932 886,445 2,404,916 2,724,859 2,851,063 26.6 28.7 31.1

Arkansas ..................... 247,079 287,058 300,180 1,207,352 1,270,930 1,256,136 20.5 22.6 23.9

California ..................... 4,960,210 5,732,017 6,060,404 16,024,341 16,592,204 16,141,519 31.0 34.5 37.5

Colorado ..................... 819,906 936,007 1,030,750 2,300,192 2,455,773 2,501,834 35.6 38.1 41.2

Connecticut ................ 633,867 707,700 720,251 1,697,670 1,718,608 1,730,053 37.3 41.2 41.6

Delaware ..................... 111,260 131,287 142,848 402,777 417,196 400,004 27.6 31.5 35.7

District of Columbia .... 133,155 150,461 175,217 291,916 298,611 300,011 45.6 50.4 58.4

Florida ......................... 1,968,126 2,398,022 2,556,593 7,569,406 8,305,281 8,209,092 26.0 28.9 31.1

Georgia ....................... 1,148,814 1,394,550 1,522,467 4,095,362 4,375,178 4,302,039 28.1 31.9 35.4

Hawaii ......................... 184,130 200,132 219,868 584,858 609,835 588,662 31.5 32.8 37.4

Idaho ........................... 149,622 178,690 194,255 632,451 695,428 693,045 23.7 25.7 28.0

Illinois .......................... 1,876,455 2,113,824 2,274,031 6,176,837 6,033,421 5,927,804 30.4 35.0 38.4

Indiana ........................ 672,835 745,940 821,279 3,052,719 3,032,108 2,851,776 22.0 24.6 28.8

Iowa ............................ 351,922 404,729 430,363 1,557,081 1,557,545 1,571,691 22.6 26.0 27.4

Kansas ........................ 385,924 425,214 458,499 1,351,988 1,390,292 1,401,609 28.5 30.6 32.7

Kentucky ..................... 402,094 467,998 528,477 1,866,348 1,875,512 1,857,576 21.5 25.0 28.4

Louisiana ..................... 453,353 496,071 531,693 1,930,662 1,935,850 1,923,739 23.5 25.6 27.6

Maine .......................... 170,334 193,647 205,731 650,385 658,507 641,189 26.2 29.4 32.1

Maryland ..................... 979,588 1,095,665 1,172,521 2,711,382 2,825,040 2,786,271 36.1 38.8 42.1

Massachusetts ............ 1,266,113 1,387,065 1,502,257 3,273,281 3,219,717 3,190,462 38.7 43.1 47.1

Michigan ..................... 1,242,388 1,407,669 1,403,052 4,953,421 4,717,188 4,210,871 25.1 29.8 33.3

Minnesota ................... 783,613 906,335 959,272 2,720,492 2,756,709 2,712,250 28.8 32.9 35.4

Mississippi .................. 256,581 293,533 310,187 1,239,859 1,219,135 1,170,719 20.7 24.1 26.5

Missouri ...................... 695,491 792,737 860,322 2,875,336 2,849,708 2,768,144 24.2 27.8 31.1

Montana ...................... 124,462 139,593 148,144 446,552 463,251 465,220 27.9 30.1 31.8

Nebraska ..................... 230,857 267,867 277,140 923,198 935,447 934,161 25.0 28.6 29.7

Nevada ........................ 206,361 272,492 315,597 1,015,221 1,173,425 1,184,431 20.3 23.2 26.6

New Hampshire .......... 207,431 243,698 246,364 675,541 696,765 698,317 30.7 35.0 35.3

New Jersey ................. 1,510,429 1,734,942 1,785,522 4,130,310 4,207,738 4,116,398 36.6 41.2 43.4

New Mexico ................ 226,334 252,804 263,798 810,024 866,349 876,218 27.9 29.2 30.1

New York ..................... 3,031,927 3,460,430 3,718,473 8,751,441 8,947,069 8,864,298 34.6 38.7 41.9

North Carolina ............. 1,044,025 1,229,917 1,409,863 3,969,235 4,123,857 4,064,521 26.3 29.8 34.7

North Dakota .............. 80,545 95,520 93,818 335,780 343,625 353,008 24.0 27.8 26.6

Ohio ............................ 1,375,311 1,521,816 1,612,549 5,573,154 5,537,419 5,334,774 24.7 27.5 30.2

Oklahoma .................... 383,381 431,778 455,513 1,609,522 1,628,655 1,636,917 23.8 26.5 27.8

Oregon ........................ 488,862 564,786 636,155 1,716,954 1,740,990 1,759,757 28.5 32.4 36.2

Pennsylvania ............... 1,618,658 1,842,351 1,954,078 5,830,902 5,958,238 5,869,594 27.8 30.9 33.3

Rhode Island ............... 156,862 181,553 187,708 520,758 532,961 504,828 30.1 34.1 37.2

South Carolina ............ 454,656 534,821 613,174 1,917,365 1,922,367 1,928,110 23.7 27.8 31.8

South Dakota .............. 89,855 104,555 110,431 397,678 413,819 421,961 22.6 25.3 26.2

Tennessee ................... 649,844 750,100 843,026 2,756,498 2,778,489 2,734,302 23.6 27.0 30.8

Texas ........................... 2,646,909 3,062,665 3,405,108 9,896,002 10,551,547 11,006,179 26.7 29.0 30.9

Utah ............................ 276,360 339,337 387,625 1,097,915 1,230,451 1,285,134 25.2 27.6 30.2

Vermont ....................... 103,476 118,184 116,812 326,742 336,583 335,328 31.7 35.1 34.8

Virginia ........................ 1,232,454 1,438,181 1,537,471 3,502,524 3,783,813 3,895,448 35.2 38.0 39.5

Washington ................. 932,352 1,069,031 1,172,377 2,898,677 3,075,972 3,205,644 32.2 34.8 36.6

West Virginia ............... 157,883 181,476 188,924 764,649 763,696 735,130 20.6 23.8 25.7

Wisconsin ................... 690,065 791,966 834,930 2,894,884 2,890,117 2,829,348 23.8 27.4 29.5

Wyoming ..................... 60,451 68,128 70,705 256,685 267,927 277,669 23.6 25.4 25.5

Puerto Rico ................. 378,586 454,714 495,726 1,162,153 1,250,335 1,126,992 32.6 36.4 44.0

NOTES: Bachelor’s degree holders include those who completed a bachelor’s or higher degree. Workforce represents employed component of civilian 

labor force and reported as annual data not seasonally adjusted.

SOURCES: Census Bureau, 2000 Decennial Census and American Community Survey (various years); Bureau of Labor Statistics, Local Area 

Unemployment Statistics.

Science and Engineering Indicators 2012
8-76  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 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 (BLS) from data provided by state 

workforce agencies. Data on the size of the workforce are BLS estimates and represent the 

employed component of the civilian labor force. In these estimates, workers are assigned to 

a state based on where they live.

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

U In 2010, 4.0% of the U.S. 

workforce (about 5.5 million 

people), worked in occupations 

classified as S&E. This is an 

increase from the 5.0 million 

S&E workers in 2003.

U In 2010, the percentage of the 

workforce engaged in S&E 

occupations ranged from 2.0% 

to 6.6% in individual states.

U The highest percentages of 

S&E occupations were found 

in the District of Columbia and 

the adjacent states of Maryland 

and Virginia as well as in 

Massachusetts, Washington, 

and Colorado in 2010.

Individuals in Science and Engineering Occupations as a Percentage of the 

Workforce

Figure 8-33

Individuals in science and engineering occupations as a percentage of the workforce: 2010

2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 19.5 20.0

CT

MI

NH

NM

TX

UT

FL

IA

KY

ME

OK

SC

SD

TN

GA

HI

IL

IN

MT

ND

NE

WY

CO

WA

AR

LA

MS

NV

WV

AL

AZ

ID

KS

MO

NC

OH

RI

VT

WI

AK

CA

MN

NJ

MA

DE DC MD VA

Percent

1st quartile (4.35%–19.91%)

2nd quartile (3.56%–4.21%)

3rd quartile (2.94%–3.54%)

4th quartile (2.02%–2.92%) 

No data

SOURCES: Bureau of Labor Statistics, Occupational Employment and Wage Estimates 

and Local Area Unemployment Statistics.
Science and Engineering Indicators 2012 8-77

Table 8-33

Individuals in science and engineering occupations as a percentage of the workforce, by state: 2003, 2006, and 2010

S&E occupations Employed workforce

Workforce in S&E 

occupations (%)

State 2003 2006 2010 2003 2006 2010 2003 2006 2010

United States .................. 4,961,550 5,407,710 5,549,980 137,186,622 143,729,350 138,893,366 3.62 3.76 4.00

Alabama ...................... 56,380 66,100 68,450 1,989,784 2,098,462 1,925,064 2.83 3.15 3.56

Alaska ......................... 10,600 10,720 15,430 310,762 326,109 332,403 3.41 3.29 4.64

Arizona ........................ 92,120 98,110 102,870 2,573,137 2,836,638 2,859,967 3.58 3.46 3.60

Arkansas ..................... 21,340 24,860 29,200 1,195,942 1,286,887 1,246,647 1.78 1.93 2.34

California ..................... 676,180 730,010 758,830 16,200,064 16,821,266 15,916,288 4.17 4.34 4.77

Colorado ..................... 124,140 133,730 143,210 2,339,532 2,541,828 2,447,712 5.31 5.26 5.85

Connecticut ................ 81,380 79,380 74,990 1,696,857 1,745,993 1,724,024 4.80 4.55 4.35

Delaware ..................... 17,370 21,550 20,920 403,504 424,618 389,583 4.30 5.08 5.37

District of Columbia .... 54,890 64,120 59,870 285,361 303,791 300,663 19.24 21.11 19.91

Florida ......................... 221,070 246,190 239,600 7,785,547 8,584,095 8,159,147 2.84 2.87 2.94

Georgia ....................... 144,170 136,470 145,220 4,173,787 4,500,150 4,213,719 3.45 3.03 3.45

Hawaii ......................... 16,090 18,940 19,500 592,469 617,807 587,407 2.72 3.07 3.32

Idaho ........................... 22,150 NA 24,130 652,161 718,077 687,321 3.40 NA 3.51

Illinois .......................... 211,230 222,470 197,120 5,916,830 6,225,095 5,964,868 3.57 3.57 3.30

Indiana ........................ 78,410 80,110 90,710 2,997,847 3,080,047 2,822,693 2.62 2.60 3.21

Iowa ............................ 37,320 43,670 44,140 1,537,341 1,595,136 1,568,012 2.43 2.74 2.82

Kansas ........................ 51,970 48,620 48,970 1,364,787 1,403,938 1,396,558 3.81 3.46 3.51

Kentucky ..................... 45,230 44,680 48,790 1,848,059 1,904,467 1,865,961 2.45 2.35 2.61

Louisiana ..................... 41,900 40,180 44,200 1,898,829 1,900,240 1,926,492 2.21 2.11 2.29

Maine .......................... 15,020 15,950 17,470 650,458 665,856 641,978 2.31 2.40 2.72

Maryland ..................... 149,250 159,470 166,700 2,741,325 2,892,733 2,758,219 5.44 5.51 6.04

Massachusetts ............ 184,690 198,670 208,160 3,209,062 3,255,504 3,197,210 5.76 6.10 6.51

Michigan ..................... 182,940 208,520 176,570 4,675,567 4,722,716 4,192,819 3.91 4.42 4.21

Minnesota ................... 117,120 125,930 125,100 2,750,938 2,774,524 2,746,492 4.26 4.54 4.55

Mississippi .................. 22,190 24,910 23,770 1,226,293 1,199,871 1,176,340 1.81 2.08 2.02

Missouri ...................... 84,150 96,420 102,300 2,813,571 2,889,461 2,725,527 2.99 3.34 3.75

Montana ...................... 11,450 13,010 14,620 450,190 476,412 461,337 2.54 2.73 3.17

Nebraska ..................... 30,710 32,500 30,930 931,622 943,176 931,414 3.30 3.45 3.32

Nevada ........................ 22,330 26,930 26,840 1,093,507 1,222,277 1,149,537 2.04 2.20 2.33

New Hampshire .......... 23,430 27,680 29,200 679,420 708,748 698,859 3.45 3.91 4.18

New Jersey ................. 161,420 176,460 185,360 4,108,397 4,257,899 4,076,713 3.93 4.14 4.55

New Mexico ................ 33,600 30,800 36,130 835,835 886,708 873,112 4.02 3.47 4.14

New York ..................... 272,440 306,810 NA 8,703,889 9,062,464 8,806,778 3.13 3.39 NA

North Carolina ............. 132,440 138,790 155,030 3,973,635 4,261,325 4,036,343 3.33 3.26 3.84

North Dakota .............. 8,430 9,360 11,050 336,353 349,368 355,615 2.51 2.68 3.11

Ohio ............................ 177,100 185,190 195,840 5,498,936 5,602,764 5,303,019 3.22 3.31 3.69

Oklahoma .................... 44,360 50,770 44,190 1,598,614 1,650,070 1,630,925 2.77 3.08 2.71

Oregon ........................ 61,230 64,520 NA 1,699,679 1,792,039 1,769,599 3.60 3.60 NA

Pennsylvania ............... 185,560 214,910 NA 5,795,701 6,021,084 5,791,061 3.20 3.57 NA

Rhode Island ............... 18,740 18,060 18,210 533,265 543,973 509,073 3.51 3.32 3.58

South Carolina ............ 48,740 53,230 56,230 1,854,419 1,970,912 1,922,815 2.63 2.70 2.92

South Dakota .............. 9,150 10,120 11,150 408,089 421,799 422,562 2.24 2.40 2.64

Tennessee ................... 63,680 67,040 71,850 2,731,371 2,852,509 2,759,243 2.33 2.35 2.60

Texas ........................... 365,270 408,710 451,390 10,228,640 10,757,510 11,141,903 3.57 3.80 4.05

Utah ............................ 45,570 49,690 50,830 1,139,129 1,285,389 1,262,083 4.00 3.87 4.03

Vermont ....................... 11,420 12,780 12,670 331,292 343,149 338,295 3.45 3.72 3.75

Virginia ........................ 209,280 251,720 255,800 3,647,095 3,862,508 3,896,167 5.74 6.52 6.57

Washington ................. 150,230 171,780 186,210 2,913,230 3,155,384 3,192,117 5.16 5.44 5.83

West Virginia ............... 16,220 17,150 17,070 742,424 777,210 711,068 2.18 2.21 2.40

Wisconsin ................... 93,320 96,860 99,240 2,862,587 2,932,482 2,807,301 3.26 3.30 3.54

Wyoming ..................... 6,130 7,640 8,260 259,489 276,882 273,313 2.36 2.76 3.02

Puerto Rico ................. 19,940 23,850 20,850 1,200,322 1,260,703 1,088,762 1.66 1.89 1.92

NA = not available

NOTES: Workforce represents employed component of civilian labor force and reported as annual data not seasonally adjusted. National total for S&E 

occupations in the United States provided by Occupational Employment Statistics (OES) and includes states with suppressed data. OES estimates for 

2003, 2006, and 2010 S&E occupations based upon May data.

SOURCES: Bureau of Labor Statistics, Occupational Employment and Wage Estimates and Local Area Unemployment Statistics.

Science and Engineering Indicators 2012
8-78  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 above 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

U The number of employed S&E doctorate 

holders in the United States rose from 

517,000 in 1997 to 648,000 in 2008, an 

increase of 25%.

U Overall, the value of this indicator rose 

from 0.39% in 1997 to 0.45% in 2008 

because the number of employed S&E 

doctorate holders nationwide increased 

more rapidly than the size of the 

workforce.

U In 2008, the values for this indicator in 

individual states ranged from 0.18% to 

1.07% of a state’s workforce.

U States in the top quartile tended to be 

home to major research laboratories, 

research universities, or researchintensive industries.

Employed Science and Engineering Doctorate Holders as a Percentage of 

the Workforce

Figure 8-34

Employed science and engineering doctorate holders as a percentage of the workforce: 2008

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 4.1 4.2

CA

CO

HI

NJ

NY

RI

VA

VT

WA DE

AL

FL

KY

LA

MS

NE

NV

OK

WV

WY

AK

AZ

GA

IA

ID

IL

IN

KS

ME

MI

MO

ND

OH

SC

SD

TN

TX

AR WI NM

MN

MT

NC

NH

OR

PA

UT CT MA MD DC

Percent

1st quartile (0.52%–4.19%)

2nd quartile (0.37%–0.50%)

3rd quartile (0.30%–0.36%)

4th quartile (0.18%–0.29%)

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 2012 8-79

Table 8-34

Employed science and engineering doctorate holders as a percentage of the workforce, by state: 1997, 2003, and 2008

Employed S&E 

doctorate holdersa Employed workforce

S&E doctorate holders 

in workforce (%)

State 1997 2003 2008 1997 2003 2008 1997 2003 2008

United States .................. 516,560 590,910 647,800 130,988,267 137,186,622 144,860,347 0.39 0.43 0.45

Alabama ...................... 6,610 5,730 6,000 2,035,156 1,989,784 2,061,601 0.32 0.29 0.29

Alaska ......................... 1,110 1,140 1,200 289,963 310,762 332,389 0.38 0.37 0.36

Arizona ........................ 6,280 7,500 8,800 2,196,901 2,573,137 2,934,136 0.29 0.29 0.30

Arkansas ..................... 2,320 2,790 2,400 1,177,143 1,195,942 1,300,420 0.20 0.23 0.18

California ..................... 70,490 86,550 95,700 14,780,791 16,200,064 16,883,425 0.48 0.53 0.57

Colorado ..................... 10,740 12,220 13,100 2,154,294 2,339,532 2,605,535 0.50 0.52 0.50

Connecticut ................ 8,770 9,780 10,600 1,674,937 1,696,857 1,763,911 0.52 0.58 0.60

Delaware ..................... 3,710 3,000 3,300 378,117 403,504 419,184 0.98 0.74 0.79

District of Columbia .... 11,800 13,800 13,100 262,789 285,361 312,877 4.49 4.84 4.19

Florida ......................... 13,330 16,000 18,600 7,040,660 7,785,547 8,621,454 0.19 0.21 0.22

Georgia ....................... 9,880 12,220 13,500 3,751,699 4,173,787 4,517,730 0.26 0.29 0.30

Hawaii ......................... 2,550 3,040 3,200 566,766 592,469 613,803 0.45 0.51 0.52

Idaho ........................... 2,030 2,450 2,800 598,004 652,161 722,714 0.34 0.38 0.39

Illinois .......................... 21,260 22,400 24,200 5,988,296 5,916,830 6,247,985 0.36 0.38 0.39

Indiana ........................ 7,570 9,590 10,300 3,014,499 2,997,847 3,049,268 0.25 0.32 0.34

Iowa ............................ 4,120 4,660 5,200 1,555,837 1,537,341 1,607,923 0.26 0.30 0.32

Kansas ........................ 3,770 4,060 4,300 1,329,797 1,364,787 1,421,107 0.28 0.30 0.30

Kentucky ..................... 4,110 4,720 4,800 1,809,785 1,848,059 1,898,083 0.23 0.26 0.25

Louisiana ..................... 5,360 5,420 5,200 1,890,102 1,898,829 1,974,881 0.28 0.29 0.26

Maine .......................... 2,150 2,110 2,300 624,410 650,458 665,057 0.34 0.32 0.35

Maryland ..................... 21,020 25,280 28,100 2,646,200 2,741,325 2,900,018 0.79 0.92 0.97

Massachusetts ............ 23,330 30,220 35,000 3,158,851 3,209,062 3,283,147 0.74 0.94 1.07

Michigan ..................... 15,050 17,130 16,700 4,748,691 4,675,567 4,554,464 0.32 0.37 0.37

Minnesota ................... 9,810 11,110 12,600 2,605,673 2,750,938 2,778,500 0.38 0.40 0.45

Mississippi .................. 3,000 3,120 3,300 1,200,845 1,226,293 1,205,464 0.25 0.25 0.27

Missouri ...................... 9,490 9,080 10,000 2,780,185 2,813,571 2,869,569 0.34 0.32 0.35

Montana ...................... 1,690 1,740 2,100 427,504 450,190 485,375 0.40 0.39 0.43

Nebraska ..................... 3,010 2,820 2,800 904,492 931,622 960,438 0.33 0.30 0.29

Nevada ........................ 1,620 2,070 2,800 895,258 1,093,507 1,246,696 0.18 0.19 0.22

New Hampshire .......... 2,230 2,640 2,900 635,469 679,420 716,611 0.35 0.39 0.40

New Jersey ................. 20,440 20,980 21,300 4,031,022 4,108,397 4,256,251 0.51 0.51 0.50

New Mexico ................ 7,480 8,120 7,800 768,596 835,835 909,809 0.97 0.97 0.86

New York ..................... 40,080 44,890 49,000 8,416,544 8,703,889 9,138,034 0.48 0.52 0.54

North Carolina ............. 13,730 17,380 20,100 3,809,601 3,973,635 4,291,565 0.36 0.44 0.47

North Dakota .............. 1,350 1,130 1,300 335,854 336,353 355,622 0.40 0.34 0.37

Ohio ............................ 18,700 20,870 20,800 5,448,161 5,498,936 5,570,514 0.34 0.38 0.37

Oklahoma .................... 4,580 4,640 4,500 1,543,105 1,598,614 1,674,485 0.30 0.29 0.27

Oregon ........................ 6,210 7,830 8,700 1,652,997 1,699,679 1,828,477 0.38 0.46 0.48

Pennsylvania ............... 23,940 27,820 30,000 5,775,178 5,795,701 6,095,678 0.41 0.48 0.49

Rhode Island ............... 2,450 3,170 2,800 504,147 533,265 528,288 0.49 0.59 0.53

South Carolina ............ 4,780 5,210 6,300 1,819,508 1,854,419 1,998,171 0.26 0.28 0.32

South Dakota .............. 1,060 1,020 1,300 383,216 408,089 432,130 0.28 0.25 0.30

Tennessee ................... 8,520 8,840 10,100 2,640,005 2,731,371 2,854,488 0.32 0.32 0.35

Texas ........................... 28,570 33,280 39,900 9,395,279 10,228,640 11,070,779 0.30 0.33 0.36

Utah ............................ 4,800 4,240 5,600 1,034,429 1,139,129 1,324,467 0.46 0.37 0.42

Vermont ....................... 1,750 1,770 1,800 315,806 331,292 342,130 0.55 0.53 0.53

Virginia ........................ 15,250 18,880 21,300 3,323,266 3,647,095 3,954,733 0.46 0.52 0.54

Washington ................. 13,360 15,430 17,700 2,822,223 2,913,230 3,286,973 0.47 0.53 0.54

West Virginia ............... 1,980 1,980 2,000 746,442 742,424 770,845 0.27 0.27 0.26

Wisconsin ................... 8,460 8,390 9,900 2,855,830 2,862,587 2,936,749 0.30 0.29 0.34

Wyoming ..................... 860 650 700 243,944 259,489 286,394 0.35 0.25 0.24

Puerto Rico ................. 660 1,710 2,000 1,132,658 1,200,322 1,208,595 0.06 0.14 0.17

a

Coefficients of variation for estimates of employed S&E doctorate holders presented in appendix table 8-13.

NOTE: Workforce represents employed component of civilian labor force and reported as annual data not seasonally adjusted. 

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.

Science and Engineering Indicators 2012
8-80  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 S&E 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 (BLS) 

from data provided by state workforce agencies. Data on the size of the workforce 

are BLS estimates and represent the employed component of the civilian labor 

force. In these estimates, workers are assigned to a state based on where they live.

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

U In the United States, 1.55 million 

individuals were employed in engineering 

occupations in 2010, an increase from 

the 1.49 million engineers employed 

in 2004. Between 2004 and 2010, the 

percentage of the workforce employed in 

engineering occupations increased from 

1.07% to 1.12%.

U The concentration of engineers in 

individual states ranged from 0.53% to 

1.95% in 2010.

U The states with the highest percentage 

of engineers in their workforces were 

centers of automobile and aircraft 

manufacturing.

U States ranking highest on this indicator 

also ranked high on employment in hightechnology establishments as share of 

total employment.

Engineers as a Percentage of the Workforce

Figure 8-35

Engineers as a percentage of the workforce: 2010

0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3.0 3.3

AR

SD

ID

IL

IN

KS

MN

NH

NJ

OH

OK

OR

PA

RI

SC

TN

UT

VT

WI

WY MI

FL

GA

HI

IA

KY

ME

MO

MT

NC

ND

NE

NV

NY

WV

AL

CA

CT

DE

MD

NM

TX

VA DC

AK

CO

MA

WA

Percent

1st quartile (1.35%–3.04%)

2nd quartile (1.05%–1.26%)

3rd quartile (0.83%–1.04%)

4th quartile (0.53%–0.81%)

No data

SOURCES: Bureau of Labor Statistics, 

Occupational Employment and Wage 

Estimates and Local Area Unemployment 

Statistics.
Science and Engineering Indicators 2012 8-81

Table 8-35

Engineers as a percentage of the workforce, by state: 2004, 2007, and 2010

Engineers Employed workforce

Engineers 

in workforce (%)

State 2004 2007 2010 2004 2007 2010 2004 2007 2010

United States .................. 1,487,810 1,588,350 1,554,780 138,762,591 145,156,139 138,893,366 1.07 1.09 1.12

Alabama ...................... 23,050 27,860 27,480 2,007,153 2,108,873 1,925,064 1.15 1.32 1.43

Alaska ......................... 3,410 3,820 5,600 314,753 329,431 332,403 1.08 1.16 1.68

Arizona ........................ 36,630 35,490 NA 2,650,277 2,903,992 2,859,967 1.38 1.22 NA

Arkansas ..................... 6,760 7,670 6,630 1,221,553 1,293,947 1,246,647 0.55 0.59 0.53

California ..................... 227,310 241,970 222,580 16,354,779 16,970,228 15,916,288 1.39 1.43 1.40

Colorado ..................... 34,390 38,580 37,590 2,392,952 2,598,433 2,447,712 1.44 1.48 1.54

Connecticut ................ 25,960 24,310 23,210 1,703,865 1,761,588 1,724,024 1.52 1.38 1.35

Delaware ..................... 3,950 4,830 5,270 408,266 425,289 389,583 0.97 1.14 1.35

District of Columbia .... 9,760 7,840 9,140 288,397 310,652 300,663 3.38 2.52 3.04

Florida ......................... 61,830 70,400 63,170 7,998,202 8,704,110 8,159,147 0.77 0.81 0.77

Georgia ....................... 30,620 33,080 36,040 4,249,007 4,561,967 4,213,719 0.72 0.73 0.86

Hawaii ......................... 4,770 5,020 5,150 598,175 617,891 587,407 0.80 0.81 0.88

Idaho ........................... 8,830 8,360 7,490 666,080 731,362 687,321 1.33 1.14 1.09

Illinois .......................... 58,180 56,380 58,000 5,968,561 6,323,515 5,964,868 0.97 0.89 0.97

Indiana ........................ 31,160 28,110 30,150 2,997,800 3,081,177 2,822,693 1.04 0.91 1.07

Iowa ............................ 9,820 10,410 11,210 1,534,991 1,601,547 1,568,012 0.64 0.65 0.71

Kansas ........................ 19,080 16,910 14,680 1,381,343 1,415,942 1,396,558 1.38 1.19 1.05

Kentucky ..................... 13,230 13,520 13,530 1,854,703 1,915,131 1,865,961 0.71 0.71 0.73

Louisiana ..................... 15,130 15,170 NA 1,928,464 1,941,642 1,926,492 0.78 0.78 NA

Maine .......................... 4,510 4,000 4,620 653,847 666,305 641,978 0.69 0.60 0.72

Maryland ..................... 36,910 38,730 39,750 2,761,583 2,909,290 2,758,219 1.34 1.33 1.44

Massachusetts ............ 51,450 52,820 49,510 3,203,810 3,280,932 3,197,210 1.61 1.61 1.55

Michigan ..................... 91,000 101,730 81,730 4,686,953 4,680,780 4,192,819 1.94 2.17 1.95

Minnesota ................... 28,990 28,790 28,280 2,752,403 2,775,587 2,746,492 1.05 1.04 1.03

Mississippi .................. 8,320 9,790 NA 1,232,139 1,210,732 1,176,340 0.68 0.81 NA

Missouri ...................... 22,700 25,880 22,910 2,815,878 2,899,695 2,725,527 0.81 0.89 0.84

Montana ...................... 2,600 3,200 3,810 456,385 485,132 461,337 0.57 0.66 0.83

Nebraska ..................... 5,690 5,840 5,940 938,105 953,057 931,414 0.61 0.61 0.64

Nevada ........................ 7,380 8,100 7,840 1,128,223 1,247,491 1,149,537 0.65 0.65 0.68

New Hampshire .......... 8,120 8,140 8,000 687,855 715,310 698,859 1.18 1.14 1.14

New Jersey ................. 38,540 39,960 37,870 4,144,223 4,265,294 4,076,713 0.93 0.94 0.93

New Mexico ................ 10,550 11,290 12,830 849,970 901,704 873,112 1.24 1.25 1.47

New York ..................... 65,710 69,400 65,420 8,816,013 9,112,899 8,806,778 0.75 0.76 0.74

North Carolina ............. 31,030 32,500 32,620 4,031,081 4,321,339 4,036,343 0.77 0.75 0.81

North Dakota .............. 2,100 2,500 2,950 339,541 353,214 355,615 0.62 0.71 0.83

Ohio ............................ 60,790 57,720 56,790 5,502,533 5,626,086 5,303,019 1.10 1.03 1.07

Oklahoma .................... 13,830 13,690 15,350 1,605,641 1,665,819 1,630,925 0.86 0.82 0.94

Oregon ........................ 19,260 18,870 18,320 1,714,447 1,822,010 1,769,599 1.12 1.04 1.04

Pennsylvania ............... 56,950 61,720 63,940 5,859,561 6,054,254 5,791,061 0.97 1.02 1.10

Rhode Island ............... 5,470 5,240 5,230 526,046 545,252 509,073 1.04 0.96 1.03

South Carolina ............ 21,560 22,210 22,570 1,888,050 2,000,185 1,922,815 1.14 1.11 1.17

South Dakota .............. 2,040 NA 2,380 411,708 428,850 422,562 0.50 NA 0.56

Tennessee ................... 22,000 21,940 25,260 2,746,241 2,874,173 2,759,243 0.80 0.76 0.92

Texas ........................... 119,360 130,990 140,560 10,385,318 10,925,311 11,141,903 1.15 1.20 1.26

Utah ............................ 12,050 13,810 14,430 1,179,142 1,319,933 1,262,083 1.02 1.05 1.14

Vermont ....................... 3,730 3,670 3,700 334,188 341,588 338,295 1.12 1.07 1.09

Virginia ........................ 49,810 52,570 53,270 3,715,272 3,926,052 3,896,167 1.34 1.34 1.37

Washington ................. 36,690 NA 54,830 2,999,526 3,235,735 3,192,117 1.22 NA 1.72

West Virginia ............... 4,920 5,340 4,960 746,854 780,869 711,068 0.66 0.68 0.70

Wisconsin ................... 29,170 31,010 28,820 2,868,376 2,951,001 2,807,301 1.02 1.05 1.03

Wyoming ..................... 2,300 2,840 3,140 262,358 283,543 273,313 0.88 1.00 1.15

Puerto Rico ................. 7,370 NA 7,460 1,226,251 1,241,426 1,088,762 0.60 NA 0.69

NA = not available

NOTES: Workforce represents employed component of civilian labor force and reported as annual data not seasonally adjusted. National totals for 

engineers in the United States provided by Occupational Employment Statistics and includes states with suppressed data. 

SOURCES: Bureau of Labor Statistics, Occupational Employment and Wage Estimates and Local Area Unemployment Statistics.

Science and Engineering Indicators 2012
8-82  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 that include agricultural 

and food scientists, biological scientists, conservation scientists and foresters, and medical 

scientists. Physical scientists are identified from standard occupational codes that 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 indicate several scenarios, ranging from a robust cluster of life sciences companies 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 S&E 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 (BLS) from data provided by state 

workforce agencies. Data on the size of the workforce are BLS estimates and represent the 

employed component of the civilian labor force. In these estimates, workers are assigned to 

a state based on where they live.

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 data are collected, faculty teaching in S&E fields are 

not counted as working in S&E occupations. Estimates for states with smaller populations 

are generally less precise than estimates for states with larger populations.

Findings

U About 629,000 individuals 

(0.45% of the workforce) were 

employed as life and physical 

scientists in the United States 

in 2010, an increase from 

the 549,000 life and physical 

scientists employed in 2004, 

which represented 0.40% of 

the workforce.

U In 2010, individual states 

had indicator values ranging 

from 0.25% to 1.18%, which 

showed major differences in 

the concentration of jobs in the 

life and physical sciences.

U States with the highest 

concentrations of life and 

physical scientists in their 

workforces were fairly evenly 

distributed throughout the 

United States.

Life and Physical Scientists as a Percentage of the Workforce

Figure 8-36

Life and physical scientists as a percentage of the workforce: 2010

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.6 1.7

CA

CO

HI

NC

WA

MA

MD

MT

AL

AR

AZ

IA

IL

IN

KY

LA

MO

MS

NH

NV

NY

OH

OK

WV

CT

KS

ME

ND

NE

TX

UT

VA

VT

WI DE

GA

MI

SC

TN

ID

MN

NJ

OR

SD

NM

WY AK DC

Percent

1st quartile (0.60%–1.70%)

2nd quartile (0.43%–0.59%)

3rd quartile (0.34%–0.42%)

4th quartile (0.25%–0.33%)

No data

SOURCES: Bureau of Labor Statistics, 

Occupational Employment and Wage 

Estimates and Local Area Unemployment 

Statistics.
Science and Engineering Indicators 2012 8-83

Table 8-36

Life and physical scientists as a percentage of the workforce, by state: 2004, 2007, and 2010

Life and physical scientists Employed workforce

Life and physical scientists in workforce (%)

State 2004 2007 2010 2004 2007 2010 2004 2007 2010

United States ................. 548,860 602,360 629,280 138,762,591 145,156,139 138,893,366 0.40 0.41 0.45

Alabama ..................... 5,650 7,240 6,200 2,007,153 2,108,873 1,925,064 0.28 0.34 0.32

Alaska ........................ 3,100 3,550 3,930 314,753 329,431 332,403 0.98 1.08 1.18

Arizona ....................... 7,040 7,300 8,950 2,650,277 2,903,992 2,859,967 0.27 0.25 0.31

Arkansas .................... 3,250 3,680 3,760 1,221,553 1,293,947 1,246,647 0.27 0.28 0.30

California .................... 69,820 78,130 95,730 16,354,779 16,970,228 15,916,288 0.43 0.46 0.60

Colorado .................... 11,030 14,800 15,290 2,392,952 2,598,433 2,447,712 0.46 0.57 0.62

Connecticut ............... 8,610 7,330 8,060 1,703,865 1,761,588 1,724,024 0.51 0.42 0.47

Delaware .................... 3,030 3,110 3,650 408,266 425,289 389,583 0.74 0.73 0.94

District of Columbia ... 5,980 6,290 5,100 288,397 310,652 300,663 2.07 2.02 1.70

Florida ........................ 21,620 21,920 NA 7,998,202 8,704,110 8,159,147 0.27 0.25 NA

Georgia ...................... 10,960 8,860 10,520 4,249,007 4,561,967 4,213,719 0.26 0.19 0.25

Hawaii ........................ 3,060 3,810 3,620 598,175 617,891 587,407 0.51 0.62 0.62

Idaho .......................... 3,980 3,500 3,740 666,080 731,362 687,321 0.60 0.48 0.54

Illinois ......................... 19,440 20,540 19,950 5,968,561 6,323,515 5,964,868 0.33 0.32 0.33

Indiana ....................... 9,820 10,990 10,880 2,997,800 3,081,177 2,822,693 0.33 0.36 0.39

Iowa ........................... 4,010 5,660 5,570 1,534,991 1,601,547 1,568,012 0.26 0.35 0.36

Kansas ....................... 4,280 4,900 5,780 1,381,343 1,415,942 1,396,558 0.31 0.35 0.41

Kentucky .................... 4,740 5,350 5,550 1,854,703 1,915,131 1,865,961 0.26 0.28 0.30

Louisiana .................... 6,410 NA 5,720 1,928,464 1,941,642 1,926,492 0.33 NA 0.30

Maine ......................... 2,400 2,750 2,830 653,847 666,305 641,978 0.37 0.41 0.44

Maryland .................... 21,040 21,270 22,190 2,761,583 2,909,290 2,758,219 0.76 0.73 0.80

Massachusetts ........... 20,300 24,030 27,470 3,203,810 3,280,932 3,197,210 0.63 0.73 0.86

Michigan .................... 13,140 NA 12,030 4,686,953 4,680,780 4,192,819 0.28 NA 0.29

Minnesota .................. 11,980 13,550 14,670 2,752,403 2,775,587 2,746,492 0.44 0.49 0.53

Mississippi ................. 4,040 4,460 4,480 1,232,139 1,210,732 1,176,340 0.33 0.37 0.38

Missouri ..................... 10,210 10,960 9,560 2,815,878 2,899,695 2,725,527 0.36 0.38 0.35

Montana ..................... 3,050 NA 3,920 456,385 485,132 461,337 0.67 NA 0.85

Nebraska .................... 4,170 3,550 3,740 938,105 953,057 931,414 0.44 0.37 0.40

Nevada ....................... 3,120 3,490 3,690 1,128,223 1,247,491 1,149,537 0.28 0.28 0.32

New Hampshire ......... 1,880 3,170 2,650 687,855 715,310 698,859 0.27 0.44 0.38

New Jersey ................ 22,420 21,820 24,040 4,144,223 4,265,294 4,076,713 0.54 0.51 0.59

New Mexico ............... 2,040 NA 6,450 849,970 901,704 873,112 0.24 NA 0.74

New York .................... 30,060 30,850 29,560 8,816,013 9,112,899 8,806,778 0.34 0.34 0.34

North Carolina ............ 16,990 19,670 24,300 4,031,081 4,321,339 4,036,343 0.42 0.46 0.60

North Dakota ............. 1,520 1,720 1,620 339,541 353,214 355,615 0.45 0.49 0.46

Ohio ........................... 15,310 18,430 17,940 5,502,533 5,626,086 5,303,019 0.28 0.33 0.34

Oklahoma ................... 6,550 6,710 5,540 1,605,641 1,665,819 1,630,925 0.41 0.40 0.34

Oregon ....................... 8,090 8,530 9,380 1,714,447 1,822,010 1,769,599 0.47 0.47 0.53

Pennsylvania .............. 25,030 26,980 NA 5,859,561 6,054,254 5,791,061 0.43 0.45 NA

Rhode Island .............. 2,810 2,220 NA 526,046 545,252 509,073 0.53 0.41 NA

South Carolina ........... 5,780 5,180 5,290 1,888,050 2,000,185 1,922,815 0.31 0.26 0.28

South Dakota ............. 1,800 NA 2,300 411,708 428,850 422,562 0.44 NA 0.54

Tennessee .................. 6,920 8,180 6,830 2,746,241 2,874,173 2,759,243 0.25 0.28 0.25

Texas .......................... 50,940 52,630 48,850 10,385,318 10,925,311 11,141,903 0.49 0.48 0.44

Utah ........................... 5,630 6,500 5,300 1,179,142 1,319,933 1,262,083 0.48 0.49 0.42

Vermont ...................... 1,370 1,720 1,470 334,188 341,588 338,295 0.41 0.50 0.43

Virginia ....................... 13,200 14,510 15,990 3,715,272 3,926,052 3,896,167 0.36 0.37 0.41

Washington ................ 18,490 NA 22,020 2,999,526 3,235,735 3,192,117 0.62 NA 0.69

West Virginia .............. 3,170 3,010 2,780 746,854 780,869 711,068 0.42 0.39 0.39

Wisconsin .................. 11,970 14,590 12,990 2,868,376 2,951,001 2,807,301 0.42 0.49 0.46

Wyoming .................... 1,960 2,260 2,100 262,358 283,543 273,313 0.75 0.80 0.77

Puerto Rico ................ 4,790 NA 4,210 1,226,251 1,241,426 1,088,762 0.39 NA 0.39

NA = not available

NOTES: Workforce represents employed component of civilian labor force and reported as annual data not seasonally adjusted. National totals for life 

and physical scientists in the United States provided by Occupational Employment Statistics and include states with suppressed data. 

SOURCES: Bureau of Labor Statistics, Occupational Employment and Wage Estimates and Local Area Unemployment Statistics.

Science and Engineering Indicators 2012
8-84  Chapter 8. State Indicators

This indicator represents the percent of specialists with advanced computer 

training in a state’s workforce. Computer specialists are identified from 10 

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 utilize computer technology.

 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 self-employed persons and are developed by the Bureau of 

Labor Statistics (BLS) from data provided by state workforce agencies. Data 

on the size of the workforce are BLS estimates and represent the employed 

component of the civilian labor force. In these estimates, workers are assigned 

to a state based on where they live.

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 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

U In the United States, 3.11 million individuals 

(2.24% of the workforce) were employed as 

computer specialists in 2010, an increase from 

the 2.81 million computer specialists employed 

in 2004, which accounted for 2.03% of the 

workforce.

U Individual states showed large differences in 

the intensity of computer-related operations 

in their economies, with 0.71% to 4.51% of 

their workforce employed in computer-related 

occupations in 2010.

U There was a significant concentration of 

computer-intensive occupations 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 scientists and engineers, 

especially computer scientists.

U EPSCoR states tended to have smaller 

percentages of computer specialists in their 

workforces and accounted in total for nearly 

12% of computer specialists nationally.

Computer Specialists as a Percentage of the Workforce

Figure 8-37

Computer specialists as a percentage of the workforce: 2010

0.0 0.9 1.8 2.7 3.6 4.5 5.4 6.3 7.2 8.1 9.0 9.9 10.8

MA

AK

AL

AR

FL

HI

IA

KY

LA

ME

MT

NM

NV

OK

SC

SD

TN

WV

AZ

CA

CT

GA

IL

KS

MI

MO

NC

NE

NH

NY

OH

OR

PA

RI

TX

UT

VT

WI

MS

WY

CO

DE

MD

MN

NJ

WA VA DC

Percent

1st quartile (2.46%–10.10%)

2nd quartile (1.99%–2.35%)

3rd quartile (1.92%–1.40%)

4th quartile (0.71%–1.38%)

No data

SOURCES: Bureau of Labor Statistics, Occupational Employment and Wage Estimates 

and Local Area Unemployment Statistics.
Science and Engineering Indicators 2012 8-85

Table 8-37

Computer specialists as a percentage of the workforce, by state: 2004, 2007, and 2010

Computer specialists Employed workforce

Computer specialists 

in workforce (%)

State 2004 2007 2010 2004 2007 2010 2004 2007 2010

EPSCoR states ................ 285,770 317,290 315,910 22,441,400 23,358,559 22,453,679 1.27 1.36 1.41

Non-EPSCoR states ........ 2,494,820 2,713,430 2,765,040 116,032,794 121,486,928 116,139,024 2.15 2.23 2.38

Average EPSCoR 

 state value .................... na na na na na na 1.32 1.38 1.54

Average non-EPSCoR 

 state value .................... na na na na na na 2.21 2.27 2.49

United States ................... 2,811,480 3,062,930 3,111,330 138,762,591 145,156,139 138,893,366 2.03 2.11 2.24

Alabama ....................... 29,760 33,010 34,450 2,007,153 2,108,873 1,925,064 1.48 1.57 1.79

Alaska .......................... 3,430 3,720 4,770 314,753 329,431 332,403 1.09 1.13 1.44

Arizona ......................... 47,170 54,520 58,490 2,650,277 2,903,992 2,859,967 1.78 1.88 2.05

Arkansas ...................... 12,140a 15,500 18,420 1,221,553 1,293,947 1,246,647 0.99 1.20 1.48

California ...................... 368,000 383,900 408,810 16,354,779 16,970,228 15,916,288 2.25 2.26 2.57

Colorado ...................... 74,450 79,930 84,500 2,392,952 2,598,433 2,447,712 3.11 3.08 3.45

Connecticut ................. 45,030 40,900 40,520 1,703,865 1,761,588 1,724,024 2.64 2.32 2.35

Delaware ...................... 10,240a 11,950 11,680 408,266 425,289 389,583 2.51 2.81 3.00

District of Columbia ..... 30,890 32,210 30,380 288,397 310,652 300,663 10.71 10.37 10.10

Florida .......................... 139,510 141,320 145,710 7,998,202 8,704,110 8,159,147 1.74 1.62 1.79

Georgia ........................ 92,680 86,210 94,050 4,249,007 4,561,967 4,213,719 2.18 1.89 2.23

Hawaii .......................... 7,810 7,840 8,070 598,175 617,891 587,407 1.31 1.27 1.37

Idaho ............................ 8,510 9,410 NA 666,080 731,362 687,321 1.28 1.29 NA

Illinois ........................... 115,550a 137,420 124,300 5,968,561 6,323,515 5,964,868 1.94 2.17 2.08

Indiana ......................... 36,660 39,850 NA 2,997,800 3,081,177 2,822,693 1.22 1.29 NA

Iowa ............................. 22,620 26,400 25,460 1,534,991 1,601,547 1,568,012 1.47 1.65 1.62

Kansas ......................... 20,890 25,750 26,810 1,381,343 1,415,942 1,396,558 1.51 1.82 1.92

Kentucky ...................... 23,170 24,250 26,090 1,854,703 1,915,131 1,865,961 1.25 1.27 1.40

Louisiana ...................... 19,170 16,020 17,420 1,928,464 1,941,642 1,926,492 0.99 0.83 0.90

Maine ........................... 6,890 7,660 8,610 653,847 666,305 641,978 1.05 1.15 1.34

Maryland ...................... 88,260 89,900 94,120 2,761,583 2,909,290 2,758,219 3.20 3.09 3.41

Massachusetts ............. 105,670 111,910 120,720 3,203,810 3,280,932 3,197,210 3.30 3.41 3.78

Michigan ...................... 79,490a 88,980 77,750 4,686,953 4,680,780 4,192,819 1.70 1.90 1.85

Minnesota .................... 66,520 75,230 77,820 2,752,403 2,775,587 2,746,492 2.42 2.71 2.83

Mississippi ................... 8,500 9,290 8,330 1,232,139 1,210,732 1,176,340 0.69 0.77 0.71

Missouri ....................... 57,890 61,000 68,500 2,815,878 2,899,695 2,725,527 2.06 2.10 2.51

Montana ....................... 4,700a 5,170 5,900 456,385 485,132 461,337 1.03 1.07 1.28

Nebraska ...................... 19,520a 20,410 21,360 938,105 953,057 931,414 2.08 2.14 2.29

Nevada ......................... 11,410 12,880 13,870 1,128,223 1,247,491 1,149,537 1.01 1.03 1.21

New Hampshire ........... 14,170 16,780 17,680 687,855 715,310 698,859 2.06 2.35 2.53

New Jersey .................. 111,890 121,690 127,160 4,144,223 4,265,294 4,076,713 2.70 2.85 3.12

New Mexico ................. 8,740a 11,490 13,030 849,970 901,704 873,112 1.03 1.27 1.49

New York ...................... 177,010 200,900 195,990 8,816,013 9,112,899 8,806,778 2.01 2.20 2.23

North Carolina .............. 78,040 81,630 90,640 4,031,081 4,321,339 4,036,343 1.94 1.89 2.25

North Dakota ............... 4,470 3,140 NA 339,541 353,214 355,615 1.32 0.89 NA

Ohio ............................. 95,300 111,160 116,200 5,502,533 5,626,086 5,303,019 1.73 1.98 2.19

Oklahoma ..................... 22,290a 27,600 21,320 1,605,641 1,665,819 1,630,925 1.39 1.66 1.31

Oregon ......................... 33,630 34,980 35,700 1,714,447 1,822,010 1,769,599 1.96 1.92 2.02

Pennsylvania ................ 101,230 115,300 116,600 5,859,561 6,054,254 5,791,061 1.73 1.90 2.01

Rhode Island ................ 9,710a 9,940 10,280 526,046 545,252 509,073 1.85 1.82 2.02

South Carolina ............. 20,670 25,130 25,610 1,888,050 2,000,185 1,922,815 1.09 1.26 1.33

South Dakota ............... 5,000 5,860 5,570 411,708 428,850 422,562 1.21 1.37 1.32

Tennessee .................... 36,670 38,490 38,200 2,746,241 2,874,173 2,759,243 1.34 1.34 1.38

Texas ............................ 204,490 245,730 255,470 10,385,318 10,925,311 11,141,903 1.97 2.25 2.29

Utah ............................. 26,830 30,750 31,090 1,179,142 1,319,933 1,262,083 2.28 2.33 2.46

Vermont ........................ 6,190 5,610 6,340 334,188 341,588 338,295 1.85 1.64 1.87

Virginia ......................... 159,070 171,440 175,640 3,715,272 3,926,052 3,896,167 4.28 4.37 4.51

Washington .................. 85,430 101,030 105,860 2,999,526 3,235,735 3,192,117 2.85 3.12 3.32

West Virginia ................ 6,650 6,900 8,140 746,854 780,869 711,068 0.89 0.88 1.14

Wisconsin .................... 45,730 42,860 55,740 2,868,376 2,951,001 2,807,301 1.59 1.45 1.99

Wyoming ...................... 1,740 1,980 2,160 262,358 283,543 273,313 0.66 0.70 0.79

Puerto Rico .................. 7,840 NA 8,960 1,226,251 1,241,426 1,088,762 0.64 NA 0.82

NA = not available

EPSCoR = Experimental Program to Stimulate Competitive Research

a

Value may be underreported because one or more codes for computer occupations suppressed by state or Bureau of Labor Statistics and not reported 

at state level.

NOTES: Workforce represents employed component of civilian labor force and reported as annual data not seasonally adjusted. For explanation of 

EPSCoR and non-EPSCoR averages, see chapter introduction. 

SOURCES: Bureau of Labor Statistics, Occupational Employment and Wage Estimates and Local Area Unemployment Statistics.

Science and Engineering Indicators 2012
8-86  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 workers’ 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 (BLS) from data provided by state workforce 

agencies and do not include self-employed persons. Data on the size of the 

state workforce are BLS estimates and represent the employed component 

of the civilian labor force.

Situations in which workers live in one state and work in another introduce 

some imprecision into the calculation of this indicator. Estimates for states 

with smaller populations are generally less precise than estimates for states 

with larger populations.

Findings

U Every state’s workforce increased in the use 

of technical workers in the 6 years from 2004 

to 2010. The number of technical workers 

exceeded the number of doctorate holders, 

engineers, or life and physical scientists in the 

workforce during this period.

U Nearly 1.9 million individuals (1.35% of the 

U.S. workforce) were employed as technical 

workers in 2010, an increase from the 1.5 million 

technical workers employed in 2004, which 

accounted for 1.11% of the workforce.

U Individual states showed large differences in 

the percentage of technical workers in their 

workforce, with 0.91% to 2.16% of their 

workforce employed as technical workers 

in 2010.

U EPSCoR states tended to have smaller 

percentages of technical workers in their 

workforces and accounted in total for nearly 

15% of technical workers nationally.

Technical Workers as a Percentage of the Workforce

Figure 8-38

Technical workers as a percentage of the workforce: 2010

0.9 1.2 1.5 1.8 2.1 2.4 2.7 3.0 3.3 3.6 3.9 4.2

AR

FL

GA

HI

IA

KY

MO

MS

NE

NV

OK

SD

TN

VT

AL

AZ

CA

CO

IL

IN

KS

LA

ME

NC

ND

NH

NY

OH

PA

SC

TX

WI

WV

WY ID

CT

DE

MD

MI

MN

MT

NJ

OR

UT

VA

WA DC

AK

MA

NM

Percent

1st quartile (1.56%–3.92%)

2nd quartile (1.30%–1.55%)

3rd quartile (1.18%–1.28%)

4th quartile (0.91%–1.13%)

SOURCES: Bureau of Labor Statistics, Occupational Employment and Wage Estimates 

and Local Area Unemployment Statistics.
Science and Engineering Indicators 2012 8-87

Table 8-38

Technical workers as a percentage of the workforce, by state: 2004, 2007, and 2010

Technical workers Employed workforce

Technical workers 

in workforce (%)

State 2004 2007 2010 2004 2007 2010 2004 2007 2010

EPSCoR states ................ 229,470 295,910 281,200 22,441,400 23,358,559 22,453,679 1.02 1.27 1.25

Non-EPSCoR states ........ 1,299,140 1,747,710 1,583,300 116,032,794 121,486,928 116,139,024 1.12 1.44 1.36

Average EPSCoR 

 state value .................... na na na na na na 1.05 1.33 1.33

Average non-EPSCoR 

 state value .................... na na na na na na 1.14 1.47 1.38

United States ................... 1,535,310 2,054,980 1,876,290 138,762,591 145,156,139 138,893,366 1.11 1.42 1.35

Alabama ....................... 22,380 26,170 25,250 2,007,153 2,108,873 1,925,064 1.12 1.24 1.31

Alaska .......................... 5,120 6,830 6,900 314,753 329,431 332,403 1.63 2.07 2.08

Arizona ......................... 29,030 45,980 37,680 2,650,277 2,903,992 2,859,967 1.10 1.58 1.32

Arkansas ...................... 10,090 13,340 11,910 1,221,553 1,293,947 1,246,647 0.83 1.03 0.96

California ...................... 171,510 251,380 231,630 16,354,779 16,970,228 15,916,288 1.05 1.48 1.46

Colorado ...................... 28,880 39,050 33,070 2,392,952 2,598,433 2,447,712 1.21 1.50 1.35

Connecticut ................. 20,710 30,060 29,140 1,703,865 1,761,588 1,724,024 1.22 1.71 1.69

Delaware ...................... 4,560 8,550 6,750 408,266 425,289 389,583 1.12 2.01 1.73

District of Columbia ..... 6,700 11,360 11,790 288,397 310,652 300,663 2.32 3.66 3.92

Florida .......................... 84,970 97,980 80,170 7,998,202 8,704,110 8,159,147 1.06 1.13 0.98

Georgia ........................ 38,580 51,420 44,830 4,249,007 4,561,967 4,213,719 0.91 1.13 1.06

Hawaii .......................... 4,930 7,260 6,450 598,175 617,891 587,407 0.82 1.17 1.10

Idaho ............................ 7,920 12,880 14,880 666,080 731,362 687,321 1.19 1.76 2.16

Illinois ........................... 59,850 78,540 74,590 5,968,561 6,323,515 5,964,868 1.00 1.24 1.25

Indiana ......................... 30,990 36,080 33,810 2,997,800 3,081,177 2,822,693 1.03 1.17 1.20

Iowa ............................. 12,360 18,850 17,650 1,534,991 1,601,547 1,568,012 0.81 1.18 1.13

Kansas ......................... 13,610 18,420 18,090 1,381,343 1,415,942 1,396,558 0.99 1.30 1.30

Kentucky ...................... 16,410 18,510 17,990 1,854,703 1,915,131 1,865,961 0.88 0.97 0.96

Louisiana ...................... 19,700 24,710 23,730 1,928,464 1,941,642 1,926,492 1.02 1.27 1.23

Maine ........................... 6,770 8,200 7,910 653,847 666,305 641,978 1.04 1.23 1.23

Maryland ...................... 34,990 47,100 45,900 2,761,583 2,909,290 2,758,219 1.27 1.62 1.66

Massachusetts ............. 42,360 63,400 61,110 3,203,810 3,280,932 3,197,210 1.32 1.93 1.91

Michigan ...................... 65,160 73,500 65,340 4,686,953 4,680,780 4,192,819 1.39 1.57 1.56

Minnesota .................... 33,930 47,250 44,080 2,752,403 2,775,587 2,746,492 1.23 1.70 1.60

Mississippi ................... 9,370 13,620 10,920 1,232,139 1,210,732 1,176,340 0.76 1.12 0.93

Missouri ....................... 27,980 34,560 30,930 2,815,878 2,899,695 2,725,527 0.99 1.19 1.13

Montana ....................... 5,740 8,100 8,120 456,385 485,132 461,337 1.26 1.67 1.76

Nebraska ...................... 9,560 11,260 10,500 938,105 953,057 931,414 1.02 1.18 1.13

Nevada ......................... 10,240 12,980 11,300 1,128,223 1,247,491 1,149,537 0.91 1.04 0.98

New Hampshire ........... 7,290 9,760 10,010 687,855 715,310 698,859 1.06 1.36 1.43

New Jersey .................. 52,180 67,710 63,350 4,144,223 4,265,294 4,076,713 1.26 1.59 1.55

New Mexico ................. 12,930 16,540 15,750 849,970 901,704 873,112 1.52 1.83 1.80

New York ...................... 85,140 121,560 115,390 8,816,013 9,112,899 8,806,778 0.97 1.33 1.31

North Carolina .............. 43,300 62,140 52,580 4,031,081 4,321,339 4,036,343 1.07 1.44 1.30

North Dakota ............... 3,440 4,130 5,050 339,541 353,214 355,615 1.01 1.17 1.42

Ohio ............................. 56,900 68,930 63,510 5,502,533 5,626,086 5,303,019 1.03 1.23 1.20

Oklahoma ..................... 15,580 20,250 19,350 1,605,641 1,665,819 1,630,925 0.97 1.22 1.19

Oregon ......................... 21,840 31,980 27,770 1,714,447 1,822,010 1,769,599 1.27 1.76 1.57

Pennsylvania ................ 62,880 81,430 70,910 5,859,561 6,054,254 5,791,061 1.07 1.35 1.22

Rhode Island ................ 4,990 6,920 5,990 526,046 545,252 509,073 0.95 1.27 1.18

South Carolina ............. 21,130 26,970 23,860 1,888,050 2,000,185 1,922,815 1.12 1.35 1.24

South Dakota ............... 3,380 3,790 3,860 411,708 428,850 422,562 0.82 0.88 0.91

Tennessee .................... 27,650 32,890 28,440 2,746,241 2,874,173 2,759,243 1.01 1.14 1.03

Texas ............................ 132,850 181,730 162,150 10,385,318 10,925,311 11,141,903 1.28 1.66 1.46

Utah ............................. 16,360 23,090 19,470 1,179,142 1,319,933 1,262,083 1.39 1.75 1.54

Vermont ........................ 3,800 4,350 4,030 334,188 341,588 338,295 1.14 1.27 1.19

Virginia ......................... 48,320 64,330 59,520 3,715,272 3,926,052 3,896,167 1.30 1.64 1.53

Washington .................. 40,390 55,080 54,330 2,999,526 3,235,735 3,192,117 1.35 1.70 1.70

West Virginia ................ 7,870 8,500 8,550 746,854 780,869 711,068 1.05 1.09 1.20

Wisconsin .................... 30,030 41,690 35,950 2,868,376 2,951,001 2,807,301 1.05 1.41 1.28

Wyoming ...................... 2,660 3,870 4,050 262,358 283,543 273,313 1.01 1.36 1.48

Puerto Rico .................. 9,680 NA 10,400 1,226,251 1,241,426 1,088,762 0.79 NA 0.96

na = not applicable; NA = not available

EPSCoR = Experimental Program to Stimulate Competitive Research

NOTES: Workforce represents employed component of civilian labor force and reported as annual data not seasonally adjusted. For explanation of 

EPSCoR and non-EPSCoR averages, see chapter introduction. 

SOURCE: Bureau of Labor Statistics, Occupational Employment and Wage Estimates and Local Area Unemployment Statistics.

Science and Engineering Indicators 2012
8-88  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 

2009, business performed nearly three-quarters of the total R&D at the national 

level followed by colleges and universities at 13%; followed by government 

facilities, including federally funded R&D centers, and nonprofit institutions.

Both gross domestic product (GDP) and R&D performance are shown 

in current dollars.

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

U The national value of R&D performed as a share 

of gross domestic product (GDP) rose slightly 

between 2000 and 2008, from 2.48% to 2.61%.

U In 2008, state values for this indicator ranged 

from 0.40% to 7.65%, indicating large 

differences in the geographic concentration of 

R&D activity.

U New Mexico has large federal R&D activities and 

a relatively small GDP giving it the highest value 

for this indicator.

U States with high rankings on this indicator also 

tended to rank high on S&E doctorate holders as 

a share of the workforce.

U The total R&D performed in states in the 

EPSCoR group was approximately one-tenth 

of that performed in states in the non-EPSCoR 

group.

R&D as a Percentage of Gross Domestic Product

Figure 8-39

R&D as a percentage of gross domestic product: 2008

0.0 0.7 1.4 2.1 2.8 3.5 4.2 4.9 5.6 6.3 7.0 7.7

AK

LA

NV

OK

SD

WY

AR

FL

GA

HI

KY

ME

MS

MT

NE

SC

WV

AL

VA

IA

IL

KS

MO

ND

NY

TN

TX

WI MI

CA

NH

NJ

CT

MA

WA

DC

MD NM

AZ

CO

DE

ID

IN

MN

NC

OH

OR

PA

RI

UT

VT

Percent

1st quartile (2.86%–7.39%)

2nd quartile (2.07%–2.82%)

3rd quartile (1.12%–1.89%)

4th quartile (0.44%–1.04%)

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 2012 8-89

Table 8-39

R&D as a percentage of gross domestic product, by state: 2000, 2004, and 2008

R&D performed ($millions) State GDP ($millions) R&D performed/GDP (%)

State 2000 2004 2008 2000 2004 2008 2000 2004 2008

EPSCoR states ................ 18,730 25,969 32,822 1,368,845 1,684,709 2,079,663 1.37 1.54 1.58

Non-EPSCoR states ........ 223,829 255,087 333,891 8,457,057 10,026,090 12,093,434 2.65 2.54 2.76

Average EPSCoR 

 state value .................... na na na na na na 1.55 1.71 1.71

Average non-EPSCoR 

 state value .................... na na na na na na 2.47 2.56 2.79

United States ................... 244,855 283,439 372,660 9,884,171 11,788,910 14,270,458 2.48 2.40 2.61

Alabama ....................... 1,730 2,760 4,870 116,014 142,086 169,694 1.49 1.94 2.87

Alaska .......................... 196 271 269 25,913 34,408 49,186 0.76 0.79 0.55

Arizona ......................... 3,107 3,544 7,010 161,901 201,287 260,454 1.92 1.76 2.69

Arkansas ...................... 454 514 747 68,146 83,551 99,497 0.67 0.62 0.75

California ...................... 55,093 59,607 81,323 1,317,343 1,571,198 1,911,741 4.18 3.79 4.25

Colorado ...................... 4,230 5,497 5,810 171,930 201,656 254,218 2.46 2.73 2.29

Connecticut ................. 4,888 7,881 11,322 163,943 188,576 225,958 2.98 4.18 5.01

Delaware ...................... 1,532 1,182 1,594 40,957 51,282 58,674 3.74 2.30 2.72

District of Columbia ..... 2,296 2,383 5,946 58,269 78,111 97,361 3.94 3.05 6.11

Florida .......................... 4,663 5,409 6,515 481,115 621,251 747,770 0.97 0.87 0.87

Georgia ........................ 2,796 3,655 5,232 294,479 343,158 405,269 0.95 1.07 1.29

Hawaii .......................... 291 490 663 41,372 52,185 66,119 0.70 0.94 1.00

Idaho ............................ 1,434 1,006 1,375 36,091 44,050 55,212 3.97 2.28 2.49

Illinois ........................... 12,767 11,300 11,961 474,444 546,661 637,037 2.69 2.07 1.88

Indiana ......................... 3,252 5,130 6,111 198,020 231,961 263,616 1.64 2.21 2.32

Iowa ............................. 1,017 1,625 2,136 93,287 115,993 134,959 1.09 1.40 1.58

Kansas ......................... 1,420 2,169 2,029 85,742 99,974 125,333 1.66 2.17 1.62

Kentucky ...................... 866 1,006 1,463 113,108 131,655 155,592 0.77 0.76 0.94

Louisiana ...................... 627 972 1,193 131,430 171,848 213,441 0.48 0.57 0.56

Maine ........................... 319 384 516 36,395 44,342 49,972 0.88 0.87 1.03

Maryland ...................... 8,634 14,341 16,605 182,953 232,215 281,659 4.72 6.18 5.90

Massachusetts ............. 13,004 15,987 20,090 272,680 310,476 365,623 4.77 5.15 5.49

Michigan ...................... 18,892 16,722 15,507 336,786 365,189 375,436 5.61 4.58 4.13

Minnesota .................... 4,299 5,992 6,697 188,449 227,321 262,758 2.28 2.64 2.55

Mississippi ................... 513 651 808 65,615 77,617 96,713 0.78 0.84 0.84

Missouri ....................... 2,583 3,038 3,884 180,982 208,763 241,344 1.43 1.46 1.61

Montana ....................... 170 295 401 21,629 27,863 35,838 0.79 1.06 1.12

Nebraska ...................... 439 740 988 57,233 69,615 84,884 0.77 1.06 1.16

Nevada ......................... 377 623 913 75,907 100,677 132,270 0.50 0.62 0.69

New Hampshire ........... 775 1,665 2,496 44,067 51,293 58,780 1.76 3.25 4.25

New Jersey .................. 13,133 12,460 20,713 349,334 410,176 483,560 3.76 3.04 4.28

New Mexico ................. 3,085 5,114 5,906 50,262 64,208 77,168 6.14 7.97 7.65

New York ...................... 13,556 13,113 16,486 770,621 893,399 1,109,080 1.76 1.47 1.49

North Carolina .............. 5,045 6,491 8,612 281,418 327,547 403,927 1.79 1.98 2.13

North Dakota ............... 146 558 511 18,250 23,335 31,677 0.80 2.39 1.61

Ohio ............................. 7,662 7,816 10,164 381,175 428,974 470,640 2.01 1.82 2.16

Oklahoma ..................... 660 814 1,030 91,292 112,444 151,850 0.72 0.72 0.68

Oregon ......................... 2,116 3,664 4,802 112,974 137,341 174,454 1.87 2.67 2.75

Pennsylvania ................ 9,842 10,813 13,068 395,811 462,280 545,198 2.49 2.34 2.40

Rhode Island ................ 1,501 1,840 1,233 33,522 42,933 47,378 4.48 4.29 2.60

South Carolina ............. 1,126 1,599 2,086 115,392 134,765 159,500 0.98 1.19 1.31

South Dakota ............... 85 149 254 24,009 30,588 38,293 0.35 0.49 0.66

Tennessee .................... 2,057 3,180 3,871 177,582 213,888 247,796 1.16 1.49 1.56

Texas ............................ 11,552 14,266 20,316 732,987 906,893 1,202,104 1.58 1.57 1.69

Utah ............................. 1,361 1,602 2,522 69,483 82,616 112,353 1.96 1.94 2.24

Vermont ........................ 465 546 546 18,033 21,909 24,636 2.58 2.49 2.22

Virginia ......................... 5,069 7,345 11,472 261,894 329,927 402,853 1.94 2.23 2.85

Washington .................. 10,516 10,936 16,696 227,828 258,069 334,477 4.62 4.24 4.99

West Virginia ................ 457 523 778 41,419 48,785 59,039 1.10 1.07 1.32

Wisconsin .................... 2,693 3,675 4,967 177,638 209,275 239,150 1.52 1.76 2.08

Wyoming ...................... 61 98 154 17,047 23,296 38,917 0.36 0.42 0.40

Puerto Rico .................. NA NA NA 69,208 82,809 95,708 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, business, universities, other nonprofit organizations, and state agencies. R&D and GDP 

reported in current dollars. For explanation of EPSCoR and non-EPSCoR averages, see 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; United Nations Statistics Division.

Science and Engineering Indicators 2012
8-90  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.

Federal obligations for R&D come from the National Center for Science and Engineering Statistics and cover data reported by 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

U Federal R&D obligations have increased 

appreciably from about $74 billion in 2000 to 

about $125 billion in 2008, an increase of 76% in 

current dollars.

U In 2008, federal R&D obligations per civilian 

worker were concentrated in a few states; only 

10 states and the District of Columbia exceeded 

the national average of $862 per worker.

U Federal R&D obligations in 2008 varied greatly 

among the states, ranging from $112 to $4,591 

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 8-40

Federal R&D obligations per employed worker: 2008

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 56,000 56,500

MA

VA

AK

AZ

HI

MN

NJ

NY

PA

TN

UT

AL

CA

CO

CT

RI

WA NM

AR

DE

FL

GA

IA

ID

IL

IN

KS

KY

LA

ME

MI

MO

MS

MT

NC

ND

NE

NH

NV

OH

OK

OR

SC

SD

TX

VT

WI

WV

WY MD DC

Dollars

1st quartile ($668–$56,052)

2nd quartile ($410–$592)

3rd quartile ($253–$395)

4th quartile ($112–$251)

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 2012 8-91

Table 8-40

Federal R&D obligations per employed worker, by state: 2000, 2004, and 2008

Federal R&D obligations 

($thousands) Employed workers

Federal R&D obligations/ 

employed worker ($)

State 2000 2004 2008 2000 2004 2008 2000 2004 2008

United States ................. 74,074,333 109,498,570 124,844,682 136,955,714 138,762,591 144,860,347 541 789 862

Alabama ..................... 1,670,058 3,035,516 2,782,193 2,067,147 2,007,153 2,061,601 808 1,512 1,350

Alaska ........................ 155,155 461,308 222,163 299,324 314,753 332,389 518 1,466 668

Arizona ....................... 1,151,925 2,643,735 2,449,176 2,404,916 2,650,277 2,934,136 479 998 835

Arkansas .................... 116,333 146,682 145,265 1,207,352 1,221,553 1,300,420 96 120 112

California .................... 14,819,382 19,259,424 19,938,653 16,024,341 16,354,779 16,883,425 925 1,178 1,181

Colorado .................... 1,398,887 2,068,038 2,668,121 2,300,192 2,392,952 2,605,535 608 864 1,024

Connecticut ............... 806,228 2,122,540 1,815,401 1,697,670 1,703,865 1,763,911 475 1,246 1,029

Delaware .................... 69,867 89,747 144,314 402,777 408,266 419,184 173 220 344

District of Columbia ... 2,443,108 3,329,397 17,537,244 291,916 288,397 312,877 8,369 11,544 56,052

Florida ........................ 2,286,464 3,265,014 2,290,401 7,569,406 7,998,202 8,621,454 302 408 266

Georgia ...................... 2,654,039 1,906,523 1,386,021 4,095,362 4,249,007 4,517,730 648 449 307

Hawaii ........................ 234,291 488,209 347,753 584,858 598,175 613,803 401 816 567

Idaho .......................... 217,349 288,538 355,408 632,451 666,080 722,714 344 433 492

Illinois ......................... 1,496,751 1,880,072 2,168,530 6,176,837 5,968,561 6,247,985 242 315 347

Indiana ....................... 512,847 556,743 672,029 3,052,719 2,997,800 3,049,268 168 186 220

Iowa ........................... 275,560 538,270 634,853 1,557,081 1,534,991 1,607,923 177 351 395

Kansas ....................... 224,013 287,446 300,406 1,351,988 1,381,343 1,421,107 166 208 211

Kentucky .................... 253,526 251,763 252,464 1,866,348 1,854,703 1,898,083 136 136 133

Louisiana .................... 249,045 446,722 406,131 1,930,662 1,928,464 1,974,881 129 232 206

Maine ......................... 249,812 210,258 168,009 650,385 653,847 665,057 384 322 253

Maryland .................... 8,778,541 12,608,595 13,312,685 2,711,382 2,761,583 2,900,018 3,238 4,566 4,591

Massachusetts ........... 4,552,289 5,963,595 6,883,096 3,273,281 3,203,810 3,283,147 1,391 1,861 2,096

Michigan .................... 1,005,043 1,117,762 1,866,491 4,953,421 4,686,953 4,554,464 203 238 410

Minnesota .................. 797,334 831,259 1,393,037 2,720,492 2,752,403 2,778,500 293 302 501

Mississippi ................. 413,820 1,598,715 435,207 1,239,859 1,232,139 1,205,464 334 1,298 361

Missouri ..................... 905,875 3,058,821 1,111,285 2,875,336 2,815,878 2,869,569 315 1,086 387

Montana ..................... 105,359 188,774 159,461 446,552 456,385 485,375 236 414 329

Nebraska .................... 104,079 210,458 204,579 923,198 938,105 960,438 113 224 213

Nevada ....................... 273,344 554,983 312,624 1,015,221 1,128,223 1,246,696 269 492 251

New Hampshire ......... 357,828 422,144 294,312 675,541 687,855 716,611 530 614 411

New Jersey ................ 1,979,346 2,273,723 2,192,726 4,130,310 4,144,223 4,256,251 479 549 515

New Mexico ............... 2,210,494 3,363,175 3,502,888 810,024 849,970 909,809 2,729 3,957 3,850

New York .................... 2,989,719 4,505,321 4,651,187 8,751,441 8,816,013 9,138,034 342 511 509

North Carolina ............ 1,062,536 1,683,581 1,772,567 3,969,235 4,031,081 4,291,565 268 418 413

North Dakota ............. 64,051 108,573 98,326 335,780 339,541 355,622 191 320 276

Ohio ........................... 2,164,645 2,794,181 2,580,353 5,573,154 5,502,533 5,570,514 388 508 463

Oklahoma ................... 232,217 498,478 260,753 1,609,522 1,605,641 1,674,485 144 310 156

Oregon ....................... 468,167 504,810 581,074 1,716,954 1,714,447 1,828,477 273 294 318

Pennsylvania .............. 2,396,146 3,731,084 3,279,378 5,830,902 5,859,561 6,095,678 411 637 538

Rhode Island .............. 418,037 642,064 643,721 520,758 526,046 528,288 803 1,221 1,219

South Carolina ........... 249,938 384,307 453,003 1,917,365 1,888,050 1,998,171 130 204 227

South Dakota ............. 38,803 70,036 76,379 397,678 411,708 432,130 98 170 177

Tennessee .................. 824,300 1,381,987 1,689,925 2,756,498 2,746,241 2,854,488 299 503 592

Texas .......................... 3,021,013 5,506,721 5,029,588 9,896,002 10,385,318 11,070,779 305 530 454

Utah ........................... 305,980 1,183,694 699,928 1,097,915 1,179,142 1,324,467 279 1,004 528

Vermont ...................... 72,030 417,091 120,985 326,742 334,188 342,130 220 1,248 354

Virginia ....................... 4,961,535 7,298,269 9,282,197 3,502,524 3,715,272 3,954,733 1,417 1,964 2,347

Washington ................ 1,345,649 2,311,260 4,339,544 2,898,677 2,999,526 3,286,973 464 771 1,320

West Virginia .............. 235,677 315,693 224,575 764,649 746,854 770,845 308 423 291

Wisconsin .................. 420,839 645,875 660,915 2,894,884 2,868,376 2,936,749 145 225 225

Wyoming .................... 35,059 47,596 47,358 256,685 262,358 286,394 137 181 165

Puerto Rico ................ 81,016 100,904 84,929 1,162,153 1,226,251 1,208,595 70 82 70

NOTES: Only 11 agencies 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 employed component 

of civilian labor force and reported as annual data not seasonally adjusted. Federal R&D obligations 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.

Science and Engineering Indicators 2012
8-92  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.

Federal obligations for R&D come from the National Center for Science and Engineering Statistics and include data reported by 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 self-employed 

persons and are developed by the Bureau of Labor Statistics (BLS) from data provided 

by state workforce agencies. Due to 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.

Estimates for states with smaller populations are generally less precise than estimates 

for states with larger populations.

Findings

U The federal government obligated 

approximately $125 billion for R&D 

in 2008—more than $20,000 for 

each person employed in an S&E 

occupation.

U Federal R&D obligations per person 

employed in an S&E occupation 

ranged across the states from 

$4,347 to $101,360 in 2008.

U The distribution for this indicator 

was highly skewed in 2008, with 

only 10 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.

U The 7 lowest ranking states are 

EPSCoR states.

Federal R&D Obligations per Individual in Science and Engineering Occupation

Figure 8-41

Federal R&D obligations per individual in science and engineering occupation: 2008

0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 100,000 110,000 270,000 280,000

AL

AK

CO

HI

IA

ID

MN

MO

MS

NC

ND

NH

NJ

NV

NY

OH

PA

TX

UT

WV

AZ

CA

CT

TN MD

AR

DE

FL

GA

IL

IN

KS

LA

ME

MI

NE

OK

OR

SC

SD

VT

WI

WY

MA

RI

VA NM DC

Dollars

1st quartile ($18,150–$276,783)

2nd quartile ($10,844–$16,742)

3rd quartile ($7,841–$10,542)

4th quartile ($4,947–$7,398)

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 and Wage 

Estimates.
Science and Engineering Indicators 2012 8-93

Table 8-41

Federal R&D obligations per individual in science and engineering occupation, by state: 2003, 2005, and 2008

Federal R&D obligations 

($millions) Individuals in S&E occupations

Federal R&D 

obligations/individual in 

S&E occupation ($)

State 2003 2005 2008 2003 2005 2008 2003 2005 2008

United States ................. 100,982 116,331 124,842 4,961,550 5,233,520 5,781,460 20,353 22,228 21,594

Alabama ..................... 3,212 3,108 2,782 56,380 62,790 68,580 56,971 49,498 40,566

Alaska ........................ 399 495 222 10,600 11,230 13,260 37,642 44,078 16,742

Arizona ....................... 2,385 2,959 2,449 92,120 96,410 102,100 25,890 30,692 23,986

Arkansas .................... 145 165 145 21,340 24,660 29,310 6,795 6,691 4,947

California .................... 20,170 19,964 19,939 676,180 716,530 791,750 29,829 27,862 25,183

Colorado .................... 1,735 2,265 2,668 124,140 126,110 147,000 13,976 17,961 18,150

Connecticut ............... 2,068 2,400 1,815 81,380 83,930 80,290 25,412 28,595 22,606

Delaware .................... 95 94 144 17,370 18,010 22,330 5,469 5,219 6,449

District of Columbia ... 2,986 4,162 17,537 54,890 63,410 63,360 54,400 65,636 276,783

Florida ........................ 2,854 2,590 2,290 221,070 241,000 248,200 12,910 10,747 9,226

Georgia ...................... 2,133 2,182 1,386 144,170 137,580 147,380 14,795 15,860 9,404

Hawaii ........................ 414 600 348 16,090 17,460 18,830 25,730 34,364 18,481

Idaho .......................... 218 290 355 22,150 23,880 23,310 9,842 12,144 15,230

Illinois ......................... 1,935 2,128 2,169 211,230 221,630 224,370 9,161 9,602 9,667

Indiana ....................... 574 567 672 78,410 79,910 90,840 7,320 7,095 7,398

Iowa ........................... 500 488 635 37,320 40,300 46,180 13,398 12,109 13,751

Kansas ....................... 269 358 300 51,970 51,630 54,260 5,176 6,934 5,529

Kentucky .................... 247 296 252 45,230 44,530 NA 5,461 6,647 NA

Louisiana .................... 453 444 406 41,900 41,030 41,790 10,811 10,821 9,715

Maine ......................... 167 292 168 15,020 15,500 17,000 11,119 18,839 9,882

Maryland .................... 8,027 12,501 13,313 149,250 160,120 167,070 53,782 78,073 79,685

Massachusetts ........... 5,492 6,592 6,883 184,690 193,180 217,310 29,736 34,124 31,674

Michigan .................... 1,693 1,177 1,866 182,940 192,150 204,290 9,254 6,125 9,134

Minnesota .................. 866 768 1,393 117,120 120,930 134,440 7,394 6,351 10,361

Mississippi ................. 1,181 438 435 22,190 23,480 27,270 53,222 18,654 15,952

Missouri ..................... 1,350 4,202 1,111 84,150 92,260 105,390 16,043 45,545 10,542

Montana ..................... 131 182 159 11,450 11,940 NA 11,441 15,243 NA

Nebraska .................... 168 193 205 30,710 31,530 31,820 5,471 6,121 6,442

Nevada ....................... 419 593 313 22,330 24,400 27,300 18,764 24,303 11,465

New Hampshire ......... 512 514 294 23,430 26,840 29,150 21,852 19,151 10,086

New Jersey ................ 2,088 2,525 2,193 161,420 174,270 198,060 12,935 14,489 11,072

New Mexico ............... 3,090 3,593 3,503 33,600 32,530 34,560 91,964 110,452 101,360

New York .................... 4,383 5,320 4,651 272,440 289,010 326,510 16,088 18,408 14,245

North Carolina ............ 1,617 1,806 1,773 132,440 134,290 153,680 12,209 13,449 11,537

North Dakota ............. 107 118 98 8,430 9,070 9,450 12,693 13,010 10,370

Ohio ........................... 2,967 2,962 2,580 177,100 180,900 206,320 16,753 16,374 12,505

Oklahoma ................... 570 401 261 44,360 46,370 48,900 12,849 8,648 5,337

Oregon ....................... 514 650 581 61,230 62,030 70,070 8,395 10,479 8,292

Pennsylvania .............. 3,989 3,677 3,279 185,560 204,270 227,170 21,497 18,001 14,434

Rhode Island .............. 566 889 644 18,740 18,080 18,090 30,203 49,170 35,600

South Carolina ........... 454 493 453 48,740 50,460 57,770 9,315 9,770 7,841

South Dakota ............. 55 70 76 9,150 9,460 11,870 6,011 7,400 6,403

Tennessee .................. 1,131 1,426 1,690 63,680 66,390 72,760 17,761 21,479 23,227

Texas .......................... 5,414 5,187 5,030 365,270 389,550 463,850 14,822 13,315 10,844

Utah ........................... 803 1,058 700 45,570 45,110 52,570 17,621 23,454 13,316

Vermont ...................... 201 263 121 11,420 12,770 12,360 17,601 20,595 9,790

Virginia ....................... 6,709 8,747 9,282 209,280 236,650 259,280 32,058 36,962 35,799

Washington ................ 2,442 2,641 4,340 150,230 160,960 NA 16,255 16,408 NA

West Virginia .............. 383 808 225 16,220 16,040 17,000 23,613 50,374 13,235

Wisconsin .................. 658 652 661 93,320 93,590 101,680 7,051 6,967 6,501

Wyoming .................... 43 38 47 6,130 7,350 8,850 7,015 5,170 5,311

Puerto Rico ................ 112 101 85 19,940 20,950 22,970 5,617 4,821 3,700

NA = not available

NOTES: Only 11 agencies 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 reported in current dollars. National total for S&E occupations in the United States provided by Occupational Employment Statistics (OES). 

OES estimates for 2003 S&E occupations based upon November data; estimates for remaining years based upon 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 and Wage Estimates.

Science and Engineering Indicators 2012
8-94  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 some expenditures for nonR&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 previous 

NSF surveys on state government R&D are not comparable. Data for 

the value of gross domestic product (GDP) and for R&D expenditures 

are shown in current dollars.

Findings

U Nationally, state government agencies spent a total 

of $1.2 billion on R&D in 2007. This represented 

$88 for each $1 million of a state’s gross domestic 

product (GDP).

U State agency R&D expenditures accounted for less 

than one-half of 1 percent of total R&D expenditures 

in 2007; most R&D was funded by nonstate sources.

U In 2007, the state values for this indicator ranged 

from $9 to $579 per $1 million of state GDP.

U Nine 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.

U State R&D totals display considerable volatility 

between FY 2006 and FY 2007. Four states (Florida, 

Indiana, New Jersey, and Rhode Island) included 

new agencies in their reporting from 2006 to 2007.

State Agency R&D Expenditures per $1 Million of Gross Domestic Product

Figure 8-42

State agency R&D expenditures per $1 million of gross domestic product: 2007

0 60 120 180 240 300 360 420 480 540 600

AL

CA

CO

DC

DE

GA

IA

ID

LA

MA

MN

MS

NE

NH

NM

NV

OR

RI

TN

TX

UT

VA

WI

CT

FL

IN

MD

NJ

SD

HI

ND

AK

MT

PA

SC WV

ME

OH WY

Dollars

AR

AZ

IL

KS

KY

MI

MO

NC

NY

OK

VT

WA

1st quartile ($147–$579)

2nd quartile ($67–$132)

3rd quartile ($40–$63)

4th quartile ($9–$37)

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 2012 8-95

Table 8-42

State agency R&D expenditures per $1 million of gross domestic product, by state: 2006 and 2007

State agency R&D 

expenditures ($)

State GDP 

($millions)

State agency R&D ($)/ 

$1 million GDP 

State 2006 2007 2006 2007 2006 2007

EPSCoR states .......................................... 195,833,394 232,177,940 1,917,410 2,014,616 102 115

Non-EPSCoR states .................................. 824,010,424 989,262,653 11,306,386 11,862,368 73 83

Average EPSCoR state value .................... na na na na 140 157

Average non-EPSCoR state value ............. na na na na 94 107

United States ............................................. 1,021,016,894 1,223,449,593 13,310,942 13,969,326 77 88

Alabama ................................................. 7,269,319 7,340,365 159,263 165,981 46 44

Alaska .................................................... 10,019,060 9,526,100 41,820 44,587 240 214

Arizona ................................................... 37,151,471 20,442,635 246,837 260,122 151 79

Arkansas ................................................ 4,869,648 7,658,199 93,518 97,187 52 79

California ................................................ 107,793,045 91,842,652 1,800,779 1,874,783 60 49

Colorado ................................................ 8,997,236 11,924,981 230,206 242,900 39 49

Connecticut ........................................... 19,209,064 29,285,710 210,278 222,498 91 132

Delaware ................................................ 2,812,102 2,611,108 56,660 60,108 50 43

District of Columbia ............................... 1,173,076 2,009,000 87,146 92,342 13 22

Florida .................................................... 42,329,624 96,968,573 730,191 759,572 58 128

Georgia .................................................. 10,620,188 4,886,946 381,500 400,331 28 12

Hawaii .................................................... 12,067,849 22,643,330 61,194 64,212 197 353

Idaho ...................................................... 2,280,873 2,739,006 50,526 54,344 45 50

Illinois ..................................................... 37,184,281 41,974,809 602,147 629,379 62 67

Indiana ................................................... 6,220,575 40,534,381 249,209 262,596 25 154

Iowa ....................................................... 13,564,062 6,790,053 124,319 134,410 109 51

Kansas ................................................... 14,348,384 11,752,696 112,207 121,268 128 97

Kentucky ................................................ 17,558,997 11,960,634 147,177 151,506 119 79

Louisiana ................................................ 11,216,568 6,587,314 204,861 205,758 55 32

Maine ..................................................... 17,509,051 27,525,552 47,688 49,195 367 560

Maryland ................................................ 24,945,119 40,298,691 261,076 273,693 96 147

Massachusetts ....................................... 10,729,419 5,600,189 337,723 353,329 32 16

Michigan ................................................ 75,016,589 32,849,159 376,610 387,086 199 85

Minnesota .............................................. 6,219,201 10,529,048 246,012 254,567 25 41

Mississippi ............................................. 2,744,882 2,893,892 86,089 93,194 32 31

Missouri ................................................. 18,465,303 15,567,277 223,716 233,008 83 67

Montana ................................................. 8,606,319 8,200,230 32,256 35,100 267 234

Nebraska ................................................ 5,602,163 4,043,480 76,547 82,185 73 49

Nevada ................................................... 1,397,463 1,748,776 124,191 133,782 11 13

New Hampshire ..................................... 2,040,544 1,685,178 56,071 57,856 36 29

New Jersey ............................................ 25,900,482 59,747,701 454,978 472,000 57 127

New Mexico ........................................... 3,105,000 672,921 71,478 74,393 43 9

New York ................................................ 103,597,135 128,361,166 1,032,879 1,085,225 100 118

North Carolina ........................................ 14,344,310 37,607,109 379,050 397,975 38 94

North Dakota ......................................... 21,062,090 9,908,722 26,068 28,552 808 347

Ohio ....................................................... 55,068,629 114,086,509 454,145 468,707 121 243

Oklahoma ............................................... 8,922,036 10,731,050 131,904 140,183 68 77

Oregon ................................................... 7,382,722 7,389,914 160,019 167,016 46 44

Pennsylvania .......................................... 117,320,158 103,973,448 507,275 532,117 231 195

Rhode Island .......................................... 150,000 1,771,949 46,449 47,334 3 37

South Carolina ....................................... 22,427,746 31,493,843 149,285 158,041 150 199

South Dakota ......................................... 5,791,586 5,473,603 32,451 35,082 178 156

Tennessee .............................................. 5,355,000 4,549,998 236,554 242,678 23 19

Texas ...................................................... 28,019,645 29,650,947 1,055,959 1,147,970 27 26

Utah ....................................................... 3,214,170 2,752,228 100,466 108,815 32 25

Vermont .................................................. 1,680,533 1,529,805 23,651 24,093 71 63

Virginia ................................................... 11,579,623 15,486,526 375,090 389,319 31 40

Washington ............................................ 22,834,218 23,333,431 300,225 325,112 76 72

West Virginia .......................................... 6,024,577 22,179,830 55,334 57,001 109 389

Wisconsin .............................................. 10,949,155 12,828,572 229,143 237,160 48 54

Wyoming ................................................ 6,326,604 19,500,357 30,722 33,674 206 579

Puerto Rico ............................................ 1,458,790 2,326,241 88,902 93,263 16 25

na = not applicable

EPSCoR = Experimental Program to Stimulate Competitive Research; GDP = gross domestic product

NOTES: For explanation of EPSCoR and non-EPSCoR averages, see chapter introduction. R&D and GDP reported in current dollars.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, Survey of State Research and Development 

Expenditures (FY 2006 and FY 2007); Bureau of Economic Analysis, Gross Domestic Product data; United Nations Statistics Division.

Science and Engineering Indicators 2012
8-96  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 non-R&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

U In 2007, state government agency R&D 

expenditures averaged $8.43 per employed 

civilian worker nationwide.

U State agency R&D funding per civilian worker 

across the United States was approximately 

1% of the $764 in federal R&D obligations per 

worker in 2007.

U State agency R&D spending per civilian worker 

varied greatly among the states in 2007, ranging 

from a low of $0.75 to a high of $68.77.

U Eight EPSCoR states are among those with the 

highest values for this indicator.

State Agency R&D Expenditures per Employed Worker

Figure 8-43

State agency R&D expenditures per employed worker: 2007

0 7 14 21 28 35 42 49 56 63 70

AL

AR

CA

CO

DC

DE

GA

IA

ID

IL

KY

LA

MA

MN

MO

MS

NE

NH

NM

NV

OK

OR

RI

TN

TX

UT

VA

VT

WI

CT

MT

NJ

NY

OH

PA

SC

HI

ME WY

AK

ND

WV

Dollars

AZ

FL

IN

KS

MD

MI

NC

SD

WA

1st quartile ($14.01–$68.77)

2nd quartile ($6.25–$13.85)

3rd quartile ($3.75–$6.14)

4th quartile ($0.75–$3.48)

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 2012 8-97

Table 8-43

State agency R&D expenditures per employed worker, by state: 2006 and 2007

State agency R&D 

expenditures ($) Employed workers

State agency R&D 

expenditures/employed 

worker ($)

State 2006 2007 2006 2007 2006 2007

United States ........................... 1,021,016,894 1,223,449,593 143,729,350 145,156,139 7.10 8.43

Alabama ............................... 7,269,319 7,340,365 2,098,462 2,108,873 3.46 3.48

Alaska .................................. 10,019,060 9,526,100 326,109 329,431 30.72 28.92

Arizona ................................. 37,151,471 20,442,635 2,836,638 2,903,992 13.10 7.04

Arkansas .............................. 4,869,648 7,658,199 1,286,887 1,293,947 3.78 5.92

California .............................. 107,793,045 91,842,652 16,821,266 16,970,228 6.41 5.41

Colorado .............................. 8,997,236 11,924,981 2,541,828 2,598,433 3.54 4.59

Connecticut ......................... 19,209,064 29,285,710 1,745,993 1,761,588 11.00 16.62

Delaware .............................. 2,812,102 2,611,108 424,618 425,289 6.62 6.14

District of Columbia ............. 1,173,076 2,009,000 303,791 310,652 3.86 6.47

Florida .................................. 42,329,624 96,968,573 8,584,095 8,704,110 4.93 11.14

Georgia ................................ 10,620,188 4,886,946 4,500,150 4,561,967 2.36 1.07

Hawaii .................................. 12,067,849 22,643,330 617,807 617,891 19.53 36.65

Idaho .................................... 2,280,873 2,739,006 718,077 731,362 3.18 3.75

Illinois ................................... 37,184,281 41,974,809 6,225,095 6,323,515 5.97 6.64

Indiana ................................. 6,220,575 40,534,381 3,080,047 3,081,177 2.02 13.16

Iowa ..................................... 13,564,062 6,790,053 1,595,136 1,601,547 8.50 4.24

Kansas ................................. 14,348,384 11,752,696 1,403,938 1,415,942 10.22 8.30

Kentucky .............................. 17,558,997 11,960,634 1,904,467 1,915,131 9.22 6.25

Louisiana .............................. 11,216,568 6,587,314 1,900,240 1,941,642 5.90 3.39

Maine ................................... 17,509,051 27,525,552 665,856 666,305 26.30 41.31

Maryland .............................. 24,945,119 40,298,691 2,892,733 2,909,290 8.62 13.85

Massachusetts ..................... 10,729,419 5,600,189 3,255,504 3,280,932 3.30 1.71

Michigan .............................. 75,016,589 32,849,159 4,722,716 4,680,780 15.88 7.02

Minnesota ............................ 6,219,201 10,529,048 2,774,524 2,775,587 2.24 3.79

Mississippi ........................... 2,744,882 2,893,892 1,199,871 1,210,732 2.29 2.39

Missouri ............................... 18,465,303 15,567,277 2,889,461 2,899,695 6.39 5.37

Montana ............................... 8,606,319 8,200,230 476,412 485,132 18.06 16.90

Nebraska .............................. 5,602,163 4,043,480 943,176 953,057 5.94 4.24

Nevada ................................. 1,397,463 1,748,776 1,222,277 1,247,491 1.14 1.40

New Hampshire ................... 2,040,544 1,685,178 708,748 715,310 2.88 2.36

New Jersey .......................... 25,900,482 59,747,701 4,257,899 4,265,294 6.08 14.01

New Mexico ......................... 3,105,000 672,921 886,708 901,704 3.50 0.75

New York .............................. 103,597,135 128,361,166 9,062,464 9,112,899 11.43 14.09

North Carolina ...................... 14,344,310 37,607,109 4,261,325 4,321,339 3.37 8.70

North Dakota ....................... 21,062,090 9,908,722 349,368 353,214 60.29 28.05

Ohio ..................................... 55,068,629 114,086,509 5,602,764 5,626,086 9.83 20.28

Oklahoma ............................. 8,922,036 10,731,050 1,650,070 1,665,819 5.41 6.44

Oregon ................................. 7,382,722 7,389,914 1,792,039 1,822,010 4.12 4.06

Pennsylvania ........................ 117,320,158 103,973,448 6,021,084 6,054,254 19.48 17.17

Rhode Island ........................ 150,000 1,771,949 543,973 545,252 0.28 3.25

South Carolina ..................... 22,427,746 31,493,843 1,970,912 2,000,185 11.38 15.75

South Dakota ....................... 5,791,586 5,473,603 421,799 428,850 13.73 12.76

Tennessee ............................ 5,355,000 4,549,998 2,852,509 2,874,173 1.88 1.58

Texas .................................... 28,019,645 29,650,947 10,757,510 10,925,311 2.60 2.71

Utah ..................................... 3,214,170 2,752,228 1,285,389 1,319,933 2.50 2.09

Vermont ................................ 1,680,533 1,529,805 343,149 341,588 4.90 4.48

Virginia ................................. 11,579,623 15,486,526 3,862,508 3,926,052 3.00 3.94

Washington .......................... 22,834,218 23,333,431 3,155,384 3,235,735 7.24 7.21

West Virginia ........................ 6,024,577 22,179,830 777,210 780,869 7.75 28.40

Wisconsin ............................ 10,949,155 12,828,572 2,932,482 2,951,001 3.73 4.35

Wyoming .............................. 6,326,604 19,500,357 276,882 283,543 22.85 68.77

Puerto Rico .......................... 1,458,790 2,326,241 1,260,703 1,241,426 1.16 1.87

NOTE: R&D expenditures reported in current dollars.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, Survey of State Research and Development 

Expenditures (FY 2006 and FY 2007); Bureau of Labor Statistics, Local Area Unemployment Statistics.

Science and Engineering Indicators 2012
8-98  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 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 and 

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

U Nationally, state government agencies 

spent about $1.2 billion for R&D in 

2007. By comparison, the federal 

government obligated more than $111 

billion for R&D in 2007.

U In 2007, the average state agency 

R&D expenditure per person 

employed in an S&E occupation was 

$219, compared to about $20,000 

the federal government averaged for 

each person employed in an S&E 

occupation.

U State agency R&D funding per person 

employed in an S&E occupation 

ranged from $20 to $2,404 to per 

state in 2007.

U Several EPSCoR 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 8-44

State agency R&D expenditures per individual in science and engineering occupation: 2007

0 240 480 720 960 1,200 1,440 1,680 1,920 2,160 2,400 2,640

AL

AZ

CA

CO

DC

DE

GA

IA

ID

IL

KS

LA

MA

MI

MN

MO

MS

NE

NH

NM

NV

OK

OR

RI

TN

TX

UT

VA

VT

WA

WI

AR

CT

FL

KY

MD

NC

NJ

NY

PA

SD ND

IN

MT

OH

SC

HI

AK WV ME WY

Dollars

1st quartile ($398–$2,404)

2nd quartile ($155–$397)

3rd quartile ($96–$152)

4th quartile ($20–$86)

SOURCES: National Science Foundation, 

National Center for Science and Engineering Statistics, Survey of State Research and 

Development Expenditures; Bureau of Labor 

Statistics, Occupational Employment and 

Wage Estimates.
Science and Engineering Indicators 2012 8-99

Table 8-44

State agency R&D expenditures per individual in science and engineering occupations, by state: 2006 and 2007

State agency R&D 

expenditures ($) Individuals in S&E occupations

State agency R&D 

expenditures/individual in 

S&E occupation ($)

State 2006 2007 2006 2007 2006 2007

United States ........................... 1,021,016,894 1,223,449,593 5,382,290 5,591,990 190 219

Alabama ............................... 7,269,319 7,340,365 66,100 69,650 110 105

Alaska .................................. 10,019,060 9,526,100 10,720 11,990 935 795

Arizona ................................. 37,151,471 20,442,635 98,110 102,380 379 200

Arkansas .............................. 4,869,648 7,658,199 24,860 28,460 196 269

California .............................. 107,793,045 91,842,652 730,010 753,570 148 122

Colorado .............................. 8,997,236 11,924,981 133,730 138,990 67 86

Connecticut ......................... 19,209,064 29,285,710 79,380 80,280 242 365

Delaware .............................. 2,812,102 2,611,108 21,550 22,140 130 118

District of Columbia ............. 1,173,076 2,009,000 64,120 63,150 18 32

Florida .................................. 42,329,624 96,968,573 246,190 244,140 172 397

Georgia ................................ 10,620,188 4,886,946 136,470 136,880 78 36

Hawaii .................................. 12,067,849 22,643,330 18,940 18,740 637 1,208

Idaho .................................... 2,280,873 2,739,006 NA 24,330 NA 113

Illinois ................................... 37,184,281 41,974,809 222,470 225,180 167 186

Indiana ................................. 6,220,575 40,534,381 80,110 83,080 78 488

Iowa ..................................... 13,564,062 6,790,053 43,670 45,430 311 149

Kansas ................................. 14,348,384 11,752,696 48,620 50,040 295 235

Kentucky .............................. 17,558,997 11,960,634 44,680 49,030 393 244

Louisiana .............................. 11,216,568 6,587,314 40,180 38,450 279 171

Maine ................................... 17,509,051 27,525,552 15,950 15,960 1,098 1,725

Maryland .............................. 24,945,119 40,298,691 159,470 162,540 156 248

Massachusetts ..................... 10,729,419 5,600,189 198,670 205,610 54 27

Michigan .............................. 75,016,589 32,849,159 208,520 212,040 360 155

Minnesota ............................ 6,219,201 10,529,048 125,930 129,840 49 81

Mississippi ........................... 2,744,882 2,893,892 24,910 25,520 110 113

Missouri ............................... 18,465,303 15,567,277 96,420 102,170 192 152

Montana ............................... 8,606,319 8,200,230 13,010 13,240 662 619

Nebraska .............................. 5,602,163 4,043,480 32,500 31,420 172 129

Nevada ................................. 1,397,463 1,748,776 26,930 26,920 52 65

New Hampshire ................... 2,040,544 1,685,178 27,680 28,450 74 59

New Jersey .......................... 25,900,482 59,747,701 176,460 186,120 147 321

New Mexico ......................... 3,105,000 672,921 30,800 33,440 101 20

New York .............................. 103,597,135 128,361,166 306,810 322,520 338 398

North Carolina ...................... 14,344,310 37,607,109 138,790 142,970 103 263

North Dakota ....................... 21,062,090 9,908,722 9,360 9,660 2,250 1,026

Ohio ..................................... 55,068,629 114,086,509 185,190 196,390 297 581

Oklahoma ............................. 8,922,036 10,731,050 50,770 51,430 176 209

Oregon ................................. 7,382,722 7,389,914 64,520 67,890 114 109

Pennsylvania ........................ 117,320,158 103,973,448 214,910 218,890 546 475

Rhode Island ........................ 150,000 1,771,949 18,060 18,400 8 96

South Carolina ..................... 22,427,746 31,493,843 53,230 54,120 421 582

South Dakota ....................... 5,791,586 5,473,603 10,120 11,550 572 474

Tennessee ............................ 5,355,000 4,549,998 67,040 70,820 80 64

Texas .................................... 28,019,645 29,650,947 408,710 441,410 69 67

Utah ..................................... 3,214,170 2,752,228 49,690 51,340 65 54

Vermont ................................ 1,680,533 1,529,805 12,780 12,760 131 120

Virginia ................................. 11,579,623 15,486,526 251,720 254,710 46 61

Washington .......................... 22,834,218 23,333,431 171,780 183,900 133 127

West Virginia ........................ 6,024,577 22,179,830 17,150 16,560 351 1,339

Wisconsin ............................ 10,949,155 12,828,572 96,860 99,380 113 129

Wyoming .............................. 6,326,604 19,500,357 7,640 8,110 828 2,404

Puerto Rico .......................... 1,458,790 2,326,241 23,850 NA 61 NA

NA = not available

NOTES: R&D expenditures reported in current dollars. National total for S&E occupations in the United States provided by Occupational Employment 

Statistics (OES) and includes states with suppressed data. OES estimates for 2006 and 2007 S&E occupations based on May data.

SOURCES: National Science Foundation, National Center for Science and Engineering Statistics, Survey of State Research and Development 

Expenditures (FY 2006, FY 2007); Bureau of Labor Statistics, Occupational Employment and Wage Estimates.

Science and Engineering Indicators 2012
8-100  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 

indicates that the businesses within a state are making a large investment 

in their R&D activities.

R&D is geographically concentrated and states vary in the type of 

research performed. The indicator reflects state differences in industrial 

structure as well as the behavior or priorities of individual businesses.

Private-industry output is the portion of state gross domestic product 

contributed by state businesses. Data are presented in current dollars.

Estimates for states with smaller economies are generally less precise 

than those for states with larger economies.

Findings

U The amount of R&D performed by business rose 

from nearly $192 billion in 2000 to about $267 

billion in 2008, an increase of 39% (in current 

dollars).

U The value of this indicator exhibited little overall 

change between 2000 and 2008.

U Business performed R&D as a share of private 

industry output varied greatly among states in 

2008, ranging from 0.17 to 5.18.

U Business R&D was concentrated in a few 

states—only 11 states had indicator values that 

exceeded the national average in 2008.

Business-Performed R&D as a Percentage of Private-Industry Output

Figure 8-45

Business-performed R&D as a percentage of private-industry output: 2008

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

AK

LA

MS

MT

OK

SD

WY

AR

DC

FL

GA

HI

KY

ME

NE

NV

SC

TN

WV

AL

AZ

ID

IN

MN

UT

IA

KS

ND

NM

NY

RI OR CA

MA

WA

MI

NH

NJ CT

CO

IL

MD

NC

OH

PA

TX

VA

VT

WI

Percent

1st quartile (2.02%–5.18%)

2nd quartile (1.31%–2.00%)

3rd quartile (0.64%–1.26%)

4th quartile (0.17%–0.57%)

No data

SOURCES: National Science Foundation, 

National Center for Science and Engineering 

Statistics, Survey of Industrial Research and 

Development and Business R&D and Innovation Survey (2008); Bureau of Economic 

Analysis, Gross Domestic Product data.
Science and Engineering Indicators 2012 8-101

Table 8-45

Business-performed R&D as a percentage of private-industry output, by state: 2000, 2004, and 2008

Business-performed 

R&D ($millions)

Private-industry output 

($millions)

Business-performed 

R&D/private-industry 

output (%)

State 2000 2004 2008 2000 2004 2008 2000 2004 2008

United States ................... 192,197 201,131 267,373 8,736,085 10,360,070 12,513,954 2.20 1.94 2.14

Alabama ....................... 821 i 1,227 3,099 98,017 120,036 141,959 0.84 1.02 2.18

Alaska .......................... 48e 35e 69 20,604 27,762 40,851 0.23 0.13 0.17

Arizona ......................... 2,182 i 2,570 5,232 142,165 175,425 226,566 1.53 1.47 2.31

Arkansas ...................... 400 287 443 59,543 72,483 85,589 0.67 0.40 0.52

California ...................... 45,455 46,614 67,532 1,178,200 1,394,822 1,690,730 3.86 3.34 3.99

Colorado ...................... 3,143 4,008 4,019 152,673 176,819 223,771 2.06 2.27 1.80

Connecticut ................. 4,132 i 7,177 10,518 149,857 171,687 202,965 2.76 4.18 5.18

Delaware ...................... 1,468 i 1,059 NA 37,496 46,886 53,116 3.92 2.26 NA

District of Columbia ..... 196e 182e 571 38,028 51,268 65,387 0.52 0.35 0.87

Florida .......................... 3,773 i 3,486 4,178 422,803 546,699 656,807 0.89 0.64 0.64

Georgia ........................ 2,159 i 2,160 3,344 259,656 299,108 350,732 0.83 0.72 0.95

Hawaii .......................... 93e 131 269 32,634 40,639 51,153 0.28 0.32 0.53

Idaho ............................ 1,363 681 961 31,101 37,741 47,506 4.38 1.80 2.02

Illinois ........................... 8,393 i 8,554 8,900 430,158 490,751 575,417 1.95 1.74 1.55

Indiana ......................... 2,888 i 4,208 4,991 178,905 209,833 236,860 1.61 2.01 2.11

Iowa ............................. 762 963 1,509 82,694 103,266 119,473 0.92 0.93 1.26

Kansas ......................... 1,327 i 1,804 i 1,600 74,127 85,461 107,298 1.79 2.11 1.49

Kentucky ...................... 762 565 933 97,524 112,663 131,054 0.78 0.50 0.71

Louisiana ...................... 364e 311 411 114,856 150,660 191,186 0.32 0.21 0.21

Maine ........................... 255 213 308 31,480 38,279 42,842 0.81 0.56 0.72

Maryland ...................... 2,213 3,826 4,333 151,696 192,127 231,981 1.46 1.99 1.87

Massachusetts ............. 10,857 11,819 15,028 249,074 283,069 332,816 4.36 4.18 4.52

Michigan ...................... 17,489 i 15,170 13,742 302,201 325,281 330,225 5.79 4.66 4.16

Minnesota .................... 3,971 5,199 5,728 169,289 204,355 235,450 2.35 2.54 2.43

Mississippi ................... 242e 160 252 54,551 63,966 79,700 0.44 0.25 0.32

Missouri ....................... 1,978 2,151 NA 160,270 184,238 211,133 1.23 1.17 NA

Montana ....................... 78e 70 148 17,880 23,201 30,161 0.44 0.30 0.49

Nebraska ...................... 335e 383 561 49,625 60,247 73,777 0.68 0.64 0.76

Nevada ......................... 433 417 677 68,368 90,497 118,834 0.63 0.46 0.57

New Hampshire ........... 722 1,330 2,169 40,297 46,535 52,702 1.79 2.86 4.12

New Jersey .................. 10,580 10,993 19,054 315,519 367,791 431,293 3.35 2.99 4.42

New Mexico ................. 1,203 i 450 735 40,561 51,351 62,598 2.97 0.88 1.17

New York ...................... 11,622 8,793 11,455 691,330 795,998 991,767 1.68 1.10 1.16

North Carolina .............. 4,535 4,565 6,246 246,974 284,688 346,083 1.84 1.60 1.80

North Dakota ............... 83e 379 i 303 15,279 19,494 27,627 0.54 1.94 1.10

Ohio ............................. 6,245 5,516 7,405 341,648 382,095 417,814 1.83 1.44 1.77

Oklahoma ..................... 463 410 595 75,743 93,288 126,536 0.61 0.44 0.47

Oregon ......................... 1,533 3,057 4,074 98,912 119,455 153,690 1.55 2.56 2.65

Pennsylvania ................ 8,473 8,005 9,735 357,944 416,222 491,213 2.37 1.92 1.98

Rhode Island ................ 1,167 i 1,320 i 538 29,270 37,672 41,163 3.99 3.50 1.31

South Carolina ............. 1,059 961 1,221 97,797 113,129 131,991 1.08 0.85 0.93

South Dakota ............... 89e 72 133 20,827 26,707 33,859 0.43 0.27 0.39

Tennessee .................... 1,644 1,630 1,608 158,028 190,752 218,374 1.04 0.85 0.74

Texas ............................ 10,048 10,992 16,166 651,993 805,767 1,071,943 1.54 1.36 1.51

Utah ............................. 1,063 1,089 1,945 59,897 70,844 97,181 1.77 1.54 2.00

Vermont ........................ 389 423 422 15,846 19,025 21,161 2.45 2.22 1.99

Virginia ......................... 2,683 4,006 6,142 216,942 271,629 329,458 1.24 1.47 1.86

Washington .................. 8,235 i 8,840 i 13,876 197,965 221,032 286,249 4.16 4.00 4.85

West Virginia ................ 329 202 334 34,494 40,074 48,549 0.95 0.50 0.69

Wisconsin .................... 2,415 2,645 3,798 158,975 187,471 213,141 1.52 1.41 1.78

Wyoming ...................... 37e 23 63 14,369 19,782 34,223 0.26 0.12 0.18

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: R&D expenditures reported in current dollars.

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 2012
8-102  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 a little over 

half of the U.S. basic research, about a third of total research 

(basic plus applied), and roughly 10% of all R&D conducted 

in the U.S. 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 over $150,000 in R&D 

expenditures.

Data for the value of gross domestic product (GDP) by state 

and for R&D expenditures are shown in current dollars.

Findings

U Expenditures for research performed in academic institutions 

have almost doubled in a decade, rising from $30.0 billion in 

2000 to $54.8 billion in 2009 (in current dollars).

U 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 29% between 2000 and 

2009. Most of this change occurred between 2000 and 2005.

U In 2009, the value of this indicator ranged from $1.46 to 

$10.60 across states.

U The largest percentage increase in academic R&D as a share 

of GDP occurred in South Dakota, an EPSCoR state, where 

the value of this indicator more than doubled between 2000 

and 2009.

U While the average indicator value for both EPSCoR and 

non-EPSCoR states increased over the period 2000 to 2009, 

non-EPSCoR states continued to have nearly 6.5 times the 

amount of spending academic S&E R&D as EPSCoR states.

Academic Science and Engineering R&D per $1,000 of Gross Domestic 

Product

Figure 8-46

Academic science and engineering R&D per $1,000 of gross domestic product: 2009

0.9 1.8 2.7 3.6 4.5 5.4 6.3 7.2 8.1 9.0 9.9 10.8

NV

AR

DE

FL

ID

ME

NJ

OK

SD

VA

WY

AL

HI

MI

MT

NC

NE

NH

PA

RI

UT

VT

WI

AK

AZ

CT

DC

IL

KY

LA

MN

TN

TX

WA

WV

ND

NM MA MD

CA

CO

GA

IA

IN

KS

MO

MS

NY

OH

OR

SC

Dollars

1st quartile ($4.59–$10.60)

2nd quartile ($3.85–$4.57)

3rd quartile ($2.89–$3.80)

4th quartile ($1.46–$2.86)

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 2012 8-103

Table 8-46

Academic science and engineering R&D per $1,000 of gross domestic product, by state: 2000, 2005, and 2009

Academic S&E R&D 

($thousands) State GDP ($millions)

Academic R&D ($)/ 

$1,000 GDP

State 2000 2005 2009 2000 2005 2009 2000 2005 2009

EPSCoR states ................ 3,949,767 6,161,002 7,236,610 1,368,845 1,809,030 2,042,476 2.89 3.41 3.54

Non-EPSCoR states ........ 25,784,807 39,205,177 47,239,938 8,457,057 10,662,668 11,873,474 3.05 3.68 3.98

Average EPSCoR 

 state value .................... na na na na na na 2.95 3.66 3.67

Average non-EPSCoR 

 state value .................... na na na na na na 3.25 3.87 4.16

United States ................... 29,980,402 45,669,100 54,802,274 9,884,171 12,554,535 14,014,842 3.03 3.64 3.91

Alabama ....................... 428,122 589,860 761,982 116,014 151,096 166,819 3.69 3.90 4.57

Alaska .......................... 107,417 153,721 132,554 25,913 37,824 45,861 4.15 4.06 2.89

Arizona ......................... 465,777 720,184 873,063 161,901 222,968 249,711 2.88 3.23 3.50

Arkansas ...................... 130,894 209,518 239,593 68,146 88,227 98,795 1.92 2.37 2.43

California ...................... 4,053,042 6,264,908 7,406,053 1,317,343 1,691,991 1,847,048 3.08 3.70 4.01

Colorado ...................... 544,204 825,048 1,058,283 171,930 217,412 250,664 3.17 3.79 4.22

Connecticut ................. 468,435 669,923 752,793 163,943 197,055 227,550 2.86 3.40 3.31

Delaware ...................... 78,126 115,751 133,810 40,957 54,749 60,660 1.91 2.11 2.21

District of Columbia ..... 245,828 302,921 325,726 58,269 82,837 98,892 4.22 3.66 3.29

Florida .......................... 851,932 1,448,634 1,663,542 481,115 680,277 732,782 1.77 2.13 2.27

Georgia ........................ 926,749 1,274,410 1,565,574 294,479 363,154 394,117 3.15 3.51 3.97

Hawaii .......................... 161,300 240,247 300,302 41,372 56,869 65,428 3.90 4.22 4.59

Idaho ............................ 73,726 119,871 120,721 36,091 48,675 53,661 2.04 2.46 2.25

Illinois ........................... 1,170,625 1,771,107 2,113,124 474,444 569,544 631,970 2.47 3.11 3.34

Indiana ......................... 509,141 759,622 1,005,216 198,020 239,575 259,894 2.57 3.17 3.87

Iowa ............................. 418,263 548,301 562,569 93,287 120,258 136,062 4.48 4.56 4.13

Kansas ......................... 258,336 348,751 441,321 85,742 105,164 122,544 3.01 3.32 3.60

Kentucky ...................... 274,238 452,265 540,295 113,108 139,336 155,789 2.42 3.25 3.47

Louisiana ...................... 399,411 579,476 670,995 131,430 197,163 205,117 3.04 2.94 3.27

Maine ........................... 57,753 96,569 128,434 36,395 45,587 50,039 1.59 2.12 2.57

Maryland ...................... 1,507,549 2,356,905 3,021,052 182,953 248,139 285,116 8.24 9.50 10.60

Massachusetts ............. 1,485,792 2,079,548 2,463,395 272,680 323,301 360,538 5.45 6.43 6.83

Michigan ...................... 995,756 1,456,218 1,742,051 336,786 375,260 369,671 2.96 3.88 4.71

Minnesota .................... 416,411 558,259 757,745 188,449 238,367 258,499 2.21 2.34 2.93

Mississippi ................... 217,064 353,078 416,804 65,615 81,500 94,406 3.31 4.33 4.42

Missouri ....................... 614,101 893,013 1,008,901 180,982 216,633 237,955 3.39 4.12 4.24

Montana ....................... 99,069 170,791 181,649 21,629 30,088 34,999 4.58 5.68 5.19

Nebraska ...................... 208,480 360,148 393,611 57,233 72,504 86,411 3.64 4.97 4.56

Nevada ......................... 106,340 178,492 182,016 75,907 114,771 125,037 1.40 1.56 1.46

New Hampshire ........... 150,982 287,472 298,298 44,067 53,653 59,086 3.43 5.36 5.05

New Jersey .................. 567,666 865,641 913,835 349,334 429,985 471,946 1.62 2.01 1.94

New Mexico ................. 246,258 361,466 435,375 50,262 67,776 76,871 4.90 5.33 5.66

New York ...................... 2,290,812 3,610,287 4,224,536 770,621 961,941 1,094,104 2.97 3.75 3.86

North Carolina .............. 1,040,017 1,655,844 2,160,505 281,418 354,973 407,032 3.70 4.66 5.31

North Dakota ............... 67,406 149,994 185,708 18,250 24,672 31,626 3.69 6.08 5.87

Ohio ............................. 918,500 1,531,614 1,895,074 381,175 444,715 462,015 2.41 3.44 4.10

Oklahoma ..................... 252,419 291,697 335,840 91,292 120,662 142,388 2.76 2.42 2.36

Oregon ......................... 346,149 536,228 636,594 112,974 143,349 167,481 3.06 3.74 3.80

Pennsylvania ................ 1,549,050 2,367,837 2,722,278 395,811 482,324 546,538 3.91 4.91 4.98

Rhode Island ................ 129,697 199,709 246,322 33,522 44,169 47,470 3.87 4.52 5.19

South Carolina ............. 294,184 487,776 611,539 115,392 141,929 158,786 2.55 3.44 3.85

South Dakota ............... 27,269 67,012 102,299 24,009 31,641 38,255 1.14 2.12 2.67

Tennessee .................... 405,013 726,078 832,991 177,582 224,522 243,849 2.28 3.23 3.42

Texas ............................ 2,039,642 3,073,724 3,984,258 732,987 970,997 1,146,647 2.78 3.17 3.47

Utah ............................. 308,059 400,276 500,421 69,483 90,748 111,301 4.43 4.41 4.50

Vermont ........................ 64,762 117,400 125,023 18,033 22,773 24,625 3.59 5.16 5.08

Virginia ......................... 587,718 910,163 1,088,367 261,894 356,852 409,732 2.24 2.55 2.66

Washington .................. 642,934 901,558 1,083,799 227,828 279,405 331,639 2.82 3.23 3.27

West Virginia ................ 73,420 146,489 174,486 41,419 51,964 61,043 1.77 2.82 2.86

Wisconsin .................... 661,470 999,847 1,203,919 177,638 218,923 239,613 3.72 4.57 5.02

Wyoming ...................... 43,094 83,449 77,633 17,047 26,238 36,760 2.53 3.18 2.11

Puerto Rico .................. 74,529 100,235 105,330 69,208 86,157 NA 1.08 1.16 NA

na = not applicable; NA = not available

EPSCoR = Experimental Program to Stimulate Competitive Research; GDP = gross domestic product

NOTES: Academic R&D reported for institutuons with R&D over $150,000. GDP reported in current dollars. For explanation of EPSCoR and non-EPSCoR 

averages, see 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.

Science and Engineering Indicators 2012
8-104  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 excludes those above 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

U In 2008, just over 33,000 S&E 

doctorates were awarded by 

U.S. academic institutions, 

approximately 26% more than 

in 1997.

U The value of this indicator in 2008 is 

back to the level it was in 1997 after 

a decline early in the decade and a 

slow increase to the current level.

U 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 EPSCoR states tend to 

fall into the former category.

Science and Engineering Doctorates Conferred per 1,000 Employed S&E 

Doctorate Holders

Figure 8-47

Science and engineering doctorates conferred per 1,000 employed S&E doctorate holders: 2008

14 21 28 35 42 49 56 63 70 77 84

AK

ME

NM

MD

SD

WA

AR

CA

KS

MA

MO

NH

NY

PA

TX

UT

DE

MT

NJ

NV

OR

VA

AL

AZ

KY

MN

ND

NE

OH

WV

GA

IL

LA

MI

RI

WI

FL

IN

WY IA

CO

CT

HI

MS

NC

OK

SC

TN

Degrees

DC

ID

VT

1st quartile (59.8–81.2)

2nd quartile (51.4–59.3)

3rd quartile (38.5–50.2)

4th quartile (17.5–38.1)

SOURCES: National Science Foundation, 

National Center for Science and Engineering 

Statistics, Survey of Earned Doctorates and 

Survey of Doctorate Recipients.
Science and Engineering Indicators 2012 8-105

Table 8-47

Science and engineering doctorates conferred per 1,000 employed S&E doctorate holders, by state: 1997, 2003, 

and 2008

 S&E doctorates conferred

Employed S&E 

doctorate holdersa

S&E doctorates 

conferred/1,000 employed 

S&E doctorate holders

State 1997 2003 2008 1997 2003 2008 1997 2003 2008

United States ................. 26,392 25,329 33,145 516,560 590,910 647,800 51.1 42.9 51.2

Alabama ..................... 283 293 356 6,610 5,730 6,000 42.8 51.1 59.3

Alaska ........................ 15 31 21 1,110 1,140 1,200 13.5 27.2 17.5

Arizona ....................... 457 417 526 6,280 7,500 8,800 72.8 55.6 59.8

Arkansas .................... 68 74 120 2,320 2,790 2,400 29.3 26.5 50.0

California .................... 3,352 3,768 4,800 70,490 86,550 95,700 47.6 43.5 50.2

Colorado .................... 603 555 589 10,740 12,220 13,100 56.1 45.4 45.0

Connecticut ............... 396 402 479 8,770 9,780 10,600 45.2 41.1 45.2

Delaware .................... 117 84 128 3,710 3,000 3,300 31.5 28.0 38.8

District of Columbia ... 318 345 352 11,800 13,800 13,100 26.9 25.0 26.9

Florida ........................ 768 943 1,387 13,330 16,000 18,600 57.6 58.9 74.6

Georgia ...................... 519 632 929 9,880 12,220 13,500 52.5 51.7 68.8

Hawaii ........................ 139 105 152 2,550 3,040 3,200 54.5 34.5 47.5

Idaho .......................... 58 69 78 2,030 2,450 2,800 28.6 28.2 27.9

Illinois ......................... 1,499 1,373 1,690 21,260 22,400 24,200 70.5 61.3 69.8

Indiana ....................... 638 622 755 7,570 9,590 10,300 84.3 64.9 73.3

Iowa ........................... 357 274 422 4,120 4,660 5,200 86.7 58.8 81.2

Kansas ....................... 246 244 240 3,770 4,060 4,300 65.3 60.1 55.8

Kentucky .................... 194 191 281 4,110 4,720 4,800 47.2 40.5 58.5

Louisiana .................... 307 263 333 5,360 5,420 5,200 57.3 48.5 64.0

Maine ......................... 42 33 43 2,150 2,110 2,300 19.5 15.6 18.7

Maryland .................... 620 578 840 21,020 25,280 28,100 29.5 22.9 29.9

Massachusetts ........... 1,490 1,345 1,891 23,330 30,220 35,000 63.9 44.5 54.0

Michigan .................... 951 943 1,099 15,050 17,130 16,700 63.2 55.0 65.8

Minnesota .................. 504 474 739 9,810 11,110 12,600 51.4 42.7 58.7

Mississippi ................. 139 130 156 3,000 3,120 3,300 46.3 41.7 47.3

Missouri ..................... 430 476 521 9,490 9,080 10,000 45.3 52.4 52.1

Montana ..................... 57 52 80 1,690 1,740 2,100 33.7 29.9 38.1

Nebraska .................... 148 164 159 3,010 2,820 2,800 49.2 58.2 56.8

Nevada ....................... 58 75 98 1,620 2,070 2,800 35.8 36.2 35.0

New Hampshire ......... 117 122 149 2,230 2,640 2,900 52.5 46.2 51.4

New Jersey ................ 691 607 757 20,440 20,980 21,300 33.8 28.9 35.5

New Mexico ............... 161 136 143 7,480 8,120 7,800 21.5 16.7 18.3

New York .................... 2,445 2,099 2,713 40,080 44,890 49,000 61.0 46.8 55.4

North Carolina ............ 678 681 925 13,730 17,380 20,100 49.4 39.2 46.0

North Dakota ............. 53 64 74 1,350 1,130 1,300 39.3 56.6 56.9

Ohio ........................... 1,173 937 1,248 18,700 20,870 20,800 62.7 44.9 60.0

Oklahoma ................... 210 172 208 4,580 4,640 4,500 45.9 37.1 46.2

Oregon ....................... 299 274 335 6,210 7,830 8,700 48.1 35.0 38.5

Pennsylvania .............. 1,279 1,248 1,594 23,940 27,820 30,000 53.4 44.9 53.1

Rhode Island .............. 166 147 190 2,450 3,170 2,800 67.8 46.4 67.9

South Carolina ........... 206 191 288 4,780 5,210 6,300 43.1 36.7 45.7

South Dakota ............. 36 32 38 1,060 1,020 1,300 34.0 31.4 29.2

Tennessee .................. 372 320 449 8,520 8,840 10,100 43.7 36.2 44.5

Texas .......................... 1,575 1,426 2,166 28,570 33,280 39,900 55.1 42.8 54.3

Utah ........................... 253 211 288 4,800 4,240 5,600 52.7 49.8 51.4

Vermont ...................... 35 27 50 1,750 1,770 1,800 20.0 15.3 27.8

Virginia ....................... 697 636 868 15,250 18,880 21,300 45.7 33.7 40.8

Washington ................ 426 409 553 13,360 15,430 17,700 31.9 26.5 31.2

West Virginia .............. 55 95 121 1,980 1,980 2,000 27.8 48.0 60.5

Wisconsin .................. 631 498 671 8,460 8,390 9,900 74.6 59.4 67.8

Wyoming .................... 61 42 53 860 650 700 70.9 64.6 75.7

Puerto Rico ................ 65 96 214 660 1,710 2,000 98.5 56.1 107.0

a

Coefficients of variation for estimates of employed S&E doctorate holders provided in appendix table 8-13.

NOTE: Data on U.S. S&E doctorate holders 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 2012
8-106  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. Research is more 

central to the mission of some of these institutions than others.

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 computer sciences; mathematics; the biological, agricultural, or environmental life sciences; physical sciences; social sciences; psychology; 

engineering; and health fields. S&E doctorate data are estimates and exclude those with 

doctorates from foreign institutions and those above 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 regardless of residence.

Findings

U Between 1997 and 2008, the number 

of scientific and engineering articles 

published by academia increased 

from 138,000 to 168,000 and the 

number of S&E doctorate holders in 

academia increased from 246,000 to 

290,000.

U In 2008, the value of this indicator 

ranged from 238 S&E articles per 

1,000 doctorate holders in academia 

to 812 across the states.

U 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.

U The average indicator value for 

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 8-48

Academic science and engineering article output per 1,000 S&E doctorate holders in academia: 2008

180 240 300 360 420 480 540 600 660 720 780 840

ME

ID

SD

AR

KS

KY

MS

ND

NH

MT

WV

DC

GA

MN

NJ

TN

UT

VT

WA

WY

AL

AZ

CO

FL

IA

LA

MO

NC

NY

OH

OR

RI

TX

WI

CT

IN

MI

NE

PA

CA

IL

MD

DE

MA

AK

HI

NM

NV

OK

SC

VA

Articles

1st quartile (585–812)

2nd quartile (528–582)

3rd quartile (427–527)

4th quartile (238–418)

SOURCES: The Patent BoardTM, special 

tabulations (2011) 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 2012 8-107

Table 8-48

Academic science and engineering article output per 1,000 S&E doctorate holders in academia, by state: 1997, 

2003, and 2008

 Academic S&E article output S&E doctorate holders in academiaa

Academic articles/ 

1,000 academic 

doctorate holders

State 1997 2003 2008 1997 2003 2008 1997 2003 2008 

EPSCoR states ................ 16,096 17,479 19,506 41,750 42,890 43,300 386 408 450

Non-EPSCoR states ........ 120,251 129,972 146,975 201,710 232,390 244,100 596 559 602

Average EPSCoR 

 state value .................... na na na na na na 372 394 445

Average non-EPSCoR 

 state value .................... na na na na na na 585 546 583

United States ................... 137,597 148,722 167,852 245,670 277,970 289,500 560 535 580

Alabama ....................... 1,838 1,851 1,974 4,640 3,240 3,500 396 571 564

Alaska .......................... 160 195 285 450 600 700 356 325 407

Arizona ......................... 2,133 2,152 2,455 3,050 3,660 4,200 699 588 585

Arkansas ...................... 575 664 716 1,520 1,850 1,500 379 359 477

California ...................... 16,862 18,744 21,001 26,050 29,830 30,500 647 628 689

Colorado ...................... 2,408 2,615 2,855 4,550 5,320 5,200 529 492 549

Connecticut ................. 2,692 2,748 3,070 4,000 4,490 5,100 673 612 602

Delaware ...................... 476 580 650 750 800 800 635 725 812

District of Columbia ..... 1,083 1,061 1,106 2,210 2,690 2,100 490 394 527

Florida .......................... 3,976 4,551 5,678 6,850 8,710 9,700 580 523 585

Georgia ........................ 3,076 3,640 4,299 5,780 7,240 8,100 532 503 531

Hawaii .......................... 531 572 697 1,380 1,910 1,900 385 299 367

Idaho ............................ 287 305 360 780 1,190 1,400 368 257 257

Illinois ........................... 6,469 6,959 7,662 10,620 10,930 11,500 609 637 666

Indiana ......................... 2,862 3,022 3,645 4,680 5,810 5,900 612 520 618

Iowa ............................. 2,130 2,220 2,232 3,100 3,390 3,900 687 655 572

Kansas ......................... 1,134 1,235 1,292 2,260 2,380 2,700 502 519 478

Kentucky ...................... 1,320 1,434 1,604 3,040 3,320 3,400 434 432 472

Louisiana ...................... 1,810 1,759 1,753 3,580 3,570 3,100 506 493 565

Maine ........................... 238 267 285 1,340 1,150 1,200 178 233 238

Maryland ...................... 4,259 4,946 5,453 6,400 7,060 8,200 666 700 665

Massachusetts ............. 8,762 9,445 10,834 11,810 14,630 13,800 742 646 785

Michigan ...................... 4,620 5,071 5,804 7,850 9,050 9,000 589 560 645

Minnesota .................... 2,300 2,287 2,634 4,490 5,600 5,400 512 408 488

Mississippi ................... 583 710 840 1,940 2,060 1,900 301 345 442

Missouri ....................... 3,032 3,122 3,443 5,770 5,770 5,800 526 541 594

Montana ....................... 256 363 396 1,020 1,090 1,200 251 333 330

Nebraska ...................... 983 991 1,115 2,360 1,880 1,800 417 527 619

Nevada ......................... 352 458 571 980 1,260 1,500 359 364 381

New Hampshire ........... 579 627 683 1,130 1,360 1,600 512 461 427

New Jersey .................. 2,952 3,150 3,326 5,290 6,160 6,300 558 511 528

New Mexico ................. 782 792 835 2,450 2,960 2,300 319 268 363

New York ...................... 11,781 12,179 13,378 20,900 22,360 23,100 564 545 579

North Carolina .............. 4,762 5,321 6,170 7,740 9,650 10,600 615 551 582

North Dakota ............... 262 315 411 900 740 900 292 426 457

Ohio ............................. 4,900 5,088 5,635 9,750 10,620 9,900 503 479 569

Oklahoma ..................... 853 933 1,081 2,680 2,900 3,000 318 322 360

Oregon ......................... 1,550 1,648 1,972 2,690 3,690 3,300 576 447 598

Pennsylvania ................ 7,756 8,260 9,419 12,150 15,650 15,200 638 528 620

Rhode Island ................ 828 871 1,020 1,730 2,180 1,800 479 399 567

South Carolina ............. 1,155 1,428 1,587 3,230 3,000 3,800 358 476 418

South Dakota ............... 136 165 202 700 670 700 194 246 289

Tennessee .................... 2,123 2,310 2,826 4,720 5,210 5,500 450 443 514

Texas ............................ 8,415 9,423 10,755 13,760 15,240 18,400 612 618 585

Utah ............................. 1,492 1,538 1,786 3,080 2,770 3,400 485 555 525

Vermont ........................ 369 383 475 1,140 1,100 900 324 349 528

Virginia ......................... 2,822 2,991 3,593 5,830 7,630 8,800 484 392 408

Washington .................. 3,091 3,412 3,605 5,410 6,740 7,300 571 506 494

West Virginia ................ 400 375 417 1,190 1,190 1,200 336 315 348

Wisconsin .................... 3,025 3,129 3,445 5,390 5,180 6,000 561 604 574

Wyoming ...................... 189 204 255 560 490 500 337 417 510

Puerto Rico .................. 167 212 265 640 1,360 1,300 261 156 204

na = not applicable

EPSCoR = Experimental Program to Stimulate Competitive Research

a

Coefficients of variation for estimates of S&E doctorate holders in academia presented in appendix table 8-14.

NOTE: For explanation of EPSCoR and non-EPSCoR averages, see chapter introduction. 

SOURCES: The Patent BoardTM, special tabulations (2011) 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.

Science and Engineering Indicators 2012
8-108  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

U From 2000 to 2009, the number of academic 

S&E publications rose from about 137,000 to 

about 163,000—an increase of 19% that may 

reflect both an increase in publications and an 

increase in the size of the journal set.

U In 2009, academic researchers produced an 

average of 3.0 publications per $1 million of 

academic R&D, compared with 4.6 in 2000. This 

partly reflects the effect of general price inflation 

but may also indicate rising academic research 

costs.

U The value of this indicator ranged from 1.70 to 

4.64 across the states in 2009.

U Between 2000 and 2009, the value for this 

indicator decreased in all states except Alaska 

and by 35% nationwide.

Academic Science and Engineering Article Output per $1 Million of Academic 

S&E R&D

Figure 8-49

Academic science and engineering article output per $1 million of academic S&E R&D: 2009

1.5 1.8 2.1 2.4 2.7 3.0 3.3 3.6 3.9 4.2 4.5 4.8

MD MA

AK

MS

MT

ND

NM

SD

AR

AZ

CA

CO

ID

KS

KY

NC

NV

OH

OR

WI

WY

HI

ME

NH

NY

OK

TN

VA

WA

DC

FL

IL

IN

MI

MN

MO

NJ

PA

UT

VT IA

CT

RI DE

AL

GA

LA

NE

SC

TX

WV

Articles

1st quartile (3.35–4.64)

2nd quartile (2.91–3.33)

3rd quartile (2.55–2.84)

4th quartile (1.70–2.46)

SOURCES: The Patent BoardTM, special 

tabulations (2011) from Thomson Reuters, 

Science Citation Index and Social Sciences 

Citation Index; National Science Foundation, 

National Center for Science and Engineering 

Statistics, Academic Research and Development Expenditures.
Science and Engineering Indicators 2012 8-109

Table 8-49

Academic science and engineering article output per $1 million of academic S&E R&D, by state: 2000, 2004, and 2009

 Academic S&E article output

Academic S&E R&D 

($millions)

Academic articles/ 

$1 million academic R&D

State 2000 2004 2009 2000 2004 2009 2000 2004 2009

United States ................. 137,088 142,535 162,969 29,980 43,143 54,802 4.57 3.30 2.97

Alabama ..................... 1,714 1,760 1,833 428 572 762 4.00 3.08 2.41

Alaska ........................ 167 180 256 107 146 133 1.55 1.23 1.93

Arizona ....................... 2,059 2,092 2,478 466 651 873 4.42 3.21 2.84

Arkansas .................... 543 626 710 131 183 240 4.15 3.42 2.96

California .................... 16,962 17,873 20,722 4,053 6,013 7,406 4.19 2.97 2.80

Colorado .................... 2,381 2,360 2,900 544 771 1,058 4.38 3.06 2.74

Connecticut ............... 2,659 2,718 3,006 468 649 753 5.68 4.19 3.99

Delaware .................... 492 560 621 78 115 134 6.29 4.87 4.64

District of Columbia ... 1,074 999 1,083 246 303 326 4.37 3.30 3.32

Florida ........................ 4,029 4,503 5,604 852 1,307 1,664 4.73 3.45 3.37

Georgia ...................... 3,072 3,570 4,210 927 1,222 1,566 3.31 2.92 2.69

Hawaii ........................ 528 538 680 161 241 300 3.27 2.23 2.27

Idaho .......................... 265 304 351 74 117 121 3.59 2.60 2.91

Illinois ......................... 6,466 6,682 7,513 1,171 1,713 2,113 5.52 3.90 3.56

Indiana ....................... 2,871 2,854 3,507 509 841 1,005 5.64 3.39 3.49

Iowa ........................... 2,094 2,033 2,109 418 532 563 5.01 3.82 3.75

Kansas ....................... 1,219 1,118 1,283 258 333 441 4.72 3.36 2.91

Kentucky .................... 1,282 1,356 1,500 274 424 540 4.67 3.20 2.78

Louisiana .................... 1,716 1,689 1,718 399 559 671 4.30 3.02 2.56

Maine ......................... 255 251 287 58 99 128 4.41 2.53 2.24

Maryland .................... 4,457 4,728 5,127 1,508 2,268 3,021 2.96 2.08 1.70

Massachusetts ........... 8,957 9,249 10,490 1,486 2,000 2,463 6.03 4.62 4.26

Michigan .................... 4,609 4,870 5,888 996 1,397 1,742 4.63 3.49 3.38

Minnesota .................. 2,147 2,238 2,519 416 535 758 5.16 4.18 3.32

Mississippi ................. 614 689 859 217 348 417 2.83 1.98 2.06

Missouri ..................... 2,933 2,940 3,376 614 842 1,009 4.78 3.49 3.35

Montana ..................... 295 323 380 99 155 182 2.98 2.08 2.09

Nebraska .................... 934 1,035 1,002 208 325 394 4.48 3.19 2.55

Nevada ....................... 400 393 536 106 164 182 3.76 2.40 2.95

New Hampshire ......... 558 656 661 151 277 298 3.70 2.37 2.22

New Jersey ................ 2,856 2,860 3,195 568 805 914 5.03 3.55 3.50

New Mexico ............... 765 757 791 246 304 435 3.11 2.49 1.82

New York .................... 11,535 11,820 13,019 2,291 3,352 4,225 5.04 3.53 3.08

North Carolina ............ 4,851 5,133 5,989 1,040 1,447 2,161 4.66 3.55 2.77

North Dakota ............. 236 323 345 67 152 186 3.50 2.13 1.86

Ohio ........................... 4,801 4,976 5,364 919 1,320 1,895 5.23 3.77 2.83

Oklahoma ................... 849 897 1,101 252 283 336 3.36 3.17 3.28

Oregon ....................... 1,600 1,637 1,860 346 505 637 4.62 3.24 2.92

Pennsylvania .............. 7,649 7,870 9,071 1,549 2,208 2,722 4.94 3.56 3.33

Rhode Island .............. 826 838 982 130 192 246 6.37 4.36 3.99

South Carolina ........... 1,219 1,364 1,507 294 456 612 4.14 2.99 2.46

South Dakota ............. 131 149 207 27 59 102 4.80 2.52 2.02

Tennessee .................. 2,141 2,249 2,717 405 658 833 5.29 3.42 3.26

Texas .......................... 8,433 8,924 10,335 2,040 2,879 3,984 4.13 3.10 2.59

Utah ........................... 1,491 1,427 1,723 308 407 500 4.84 3.51 3.44

Vermont ...................... 390 383 426 65 116 125 6.02 3.30 3.41

Virginia ....................... 2,845 2,835 3,439 588 849 1,088 4.84 3.34 3.16

Washington ................ 3,153 3,168 3,386 643 897 1,084 4.90 3.53 3.12

West Virginia .............. 359 352 470 73 135 174 4.89 2.60 2.69

Wisconsin .................. 2,845 2,967 3,318 661 957 1,204 4.30 3.10 2.76

Wyoming .................... 173 202 232 43 60 78 4.01 3.36 2.98

Puerto Rico ................ 191 215 282 75 NA 105 2.56 NA 2.67

NA = not available

NOTE: Academic R&D expenditures reported in current dollars.

SOURCES: The Patent BoardTM, special tabulations (2011) 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 

Expenditures.

Science and Engineering Indicators 2012
8-110  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 universityaffiliated 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. Changes in the number 

of patents assigned to academic institutions may occur for a given year when the assignee is 

changed or when the list of approved academic institutions is modified, as occurred in 2005.

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 above 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

U Throughout the United States, the 

number of patents assigned to 

academic institutions increased 

from about 2,400 in 1997 to about 

2,800 in 2008, an increase of 15%; 

the number of academic S&E 

doctorate holders rose by 18% 

during the same period.

U In 2008, states varied widely on 

this indicator, with values ranging 

from 0 to 17.3 patents per 1,000 

S&E doctorate holders employed 

in academia, possibly indicating a 

difference in patenting philosophy 

or the mix of industries with which 

these academic institutions deal.

U California showed the highest level 

of both academic patenting and 

venture capital investment.

U 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 8-50

Academic patents awarded per 1,000 science and engineering doctorate holders in academia: 2008

0.0 1.8 3.6 5.4 7.2 9.0 10.8 12.6 14.4 16.2 18.0

AK

DC

HI

ID

IN

MO

ND

NV

OK

RI

TN

VA

WY

AR

IA

MN

NE

NY

UT

AL

AZ

CO

CT

ME

MT

OH

VT

WA IL

FL

MI

MA

MD

NJ

WI CA

DE

GA

KY

LA

MS

NC

NH

NM

OR

PA

TX

Patents

KS

SC

SD

WV

1st quartile (9.3–17.3)

2nd quartile (6.8–9.0)

3rd quartile (4.7–6.7)

4th quartile (0.0–4.4)

SOURCES: U.S. Patent and Trademark Office, Technology Assessment and Forecast 

Branch, U.S. Colleges and Universities—Utility Patent Grants, Calendar Years 1969–2008; 

National Science Foundation, National Center 

for Science and Engineering Statistics, Survey of Doctorate Recipients.
Science and Engineering Indicators 2012 8-111

Table 8-50

Academic patents awarded per 1,000 science and engineering doctorate holders in academia, by state: 1997, 

2003, and 2008

 Patents awarded to 

 academic institutions

S&E doctorate holders 

in academiaa

Academic patents/1,000 

academic S&E doctorate holders

State 1997 2003 2008 1997 2003 2008 1997 2003 2008

United States ................. 2,443 3,256 2,818 245,670 277,970 289,500 9.9 11.7 9.7

Alabama ..................... 23 41 19 4,640 3,240 3,500 5.0 12.7 5.4

Alaska ........................ 2 3 0 450 600 700 4.4 5.0 0.0

Arizona ....................... 22 22 25 3,050 3,660 4,200 7.2 6.0 6.0

Arkansas .................... 8 24 14 1,520 1,850 1,500 5.3 13.0 9.3

California .................... 428 664 527 26,050 29,830 30,500 16.4 22.3 17.3

Colorado .................... 37 32 32 4,550 5,320 5,200 8.1 6.0 6.2

Connecticut ............... 37 44 28 4,000 4,490 5,100 9.3 9.8 5.5

Delaware .................... 5 7 7 750 800 800 6.7 8.8 8.8

District of Columbia ... 4 3 2 2,210 2,690 2,100 1.8 1.1 1.0

Florida ........................ 91 130 129 6,850 8,710 9,700 13.3 14.9 13.3

Georgia ...................... 49 76 68 5,780 7,240 8,100 8.5 10.5 8.4

Hawaii ........................ 6 7 7 1,380 1,910 1,900 4.3 3.7 3.7

Idaho .......................... 8 7 7 780 1,190 1,400 10.3 5.9 5.0

Illinois ......................... 79 105 131 10,620 10,930 11,500 7.4 9.6 11.4

Indiana ....................... 39 23 22 4,680 5,810 5,900 8.3 4.0 3.7

Iowa ........................... 44 56 35 3,100 3,390 3,900 14.2 16.5 9.0

Kansas ....................... 7 20 8 2,260 2,380 2,700 3.1 8.4 3.0

Kentucky .................... 15 21 25 3,040 3,320 3,400 4.9 6.3 7.4

Louisiana .................... 25 29 24 3,580 3,570 3,100 7.0 8.1 7.7

Maine ......................... 1 4 7 1,340 1,150 1,200 0.7 3.5 5.8

Maryland .................... 71 128 119 6,400 7,060 8,200 11.1 18.1 14.5

Massachusetts ........... 177 208 222 11,810 14,630 13,800 15.0 14.2 16.1

Michigan .................... 97 115 122 7,850 9,050 9,000 12.4 12.7 13.6

Minnesota .................. 50 68 55 4,490 5,600 5,400 11.1 12.1 10.2

Mississippi ................. 6 11 17 1,940 2,060 1,900 3.1 5.3 8.9

Missouri ..................... 37 41 27 5,770 5,770 5,800 6.4 7.1 4.7

Montana ..................... 4 4 7 1,020 1,090 1,200 3.9 3.7 5.8

Nebraska .................... 28 17 17 2,360 1,880 1,800 11.9 9.0 9.4

Nevada ....................... 2 9 6 980 1,260 1,500 2.0 7.1 4.0

New Hampshire ......... 7 11 14 1,130 1,360 1,600 6.2 8.1 8.8

New Jersey ................ 71 118 99 5,290 6,160 6,300 13.4 19.2 15.7

New Mexico ............... 46 90 18 2,450 2,960 2,300 18.8 30.4 7.8

New York .................... 204 236 217 20,900 22,360 23,100 9.8 10.6 9.4

North Carolina ............ 97 121 94 7,740 9,650 10,600 12.5 12.5 8.9

North Dakota ............. 3 4 4 900 740 900 3.3 5.4 4.4

Ohio ........................... 70 85 58 9,750 10,620 9,900 7.2 8.0 5.9

Oklahoma ................... 17 14 14 2,680 2,900 3,000 6.3 4.8 4.7

Oregon ....................... 18 19 25 2,690 3,690 3,300 6.7 5.1 7.6

Pennsylvania .............. 131 161 131 12,150 15,650 15,200 10.8 10.3 8.6

Rhode Island .............. 10 7 7 1,730 2,180 1,800 5.8 3.2 3.9

South Carolina ........... 14 23 13 3,230 3,000 3,800 4.3 7.7 3.4

South Dakota ............. 1 0 2 700 670 700 1.4 0.0 2.9

Tennessee .................. 28 42 26 4,720 5,210 5,500 5.9 8.1 4.7

Texas .......................... 123 166 155 13,760 15,240 18,400 8.9 10.9 8.4

Utah ........................... 39 25 32 3,080 2,770 3,400 12.7 9.0 9.4

Vermont ...................... 2 7 6 1,140 1,100 900 1.8 6.4 6.7

Virginia ....................... 55 62 46 5,830 7,630 8,800 9.4 8.1 5.2

Washington ................ 39 52 50 5,410 6,740 7,300 7.2 7.7 6.8

West Virginia .............. 2 11 3 1,190 1,190 1,200 1.7 9.2 2.5

Wisconsin .................. 59 80 93 5,390 5,180 6,000 10.9 15.4 15.5

Wyoming .................... 5 3 2 560 490 500 8.9 6.1 4.0

Puerto Rico ................ 0 5 2 640 1,360 1,300 0.0 3.7 1.5

a

Coefficients of variation for estimates of S&E doctorate holders in academia presented in appendix table 8-14.

SOURCES: U.S. Patent and Trademark Office, Technology Assessment and Forecast Branch, special tabulations of U.S. Colleges and Universities—Utility 

Patent Grants, Calendar Years 1969–2008; National Science Foundation, National Center for Science and Engineering Statistics, Survey of Doctorate 

Recipients (various years).

Science and Engineering Indicators 2012
8-112  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

U About 108,000 utility patents were 

awarded to inventors residing in the 

United States in 2010, an increase 

from the 88,000 utility patents 

awarded in 2003.

U In 2010, the national average for this 

indicator was 19.4 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 1.8 to 

50.7 patents per 1,000 individuals in 

S&E occupations in 2010.

U More than 25% of all 2010 U.S. utility 

patents were awarded to residents 

of California. Texas and New York 

were each awarded over 7,000 utility 

patents in 2010, representing nearly 

14% of the total.

Patents Awarded per 1,000 Individuals in Science and Engineering 

Occupations

Figure 8-51

Patents awarded per 1,000 individuals in science and engineering occupations: 2010

0 5 10 15 20 25 30 35 40 45 50 55

AK

AR

DC

AZ

DE

IA

IL

IN

NC

OH

RI

TX

WI

AL

HI

LA

MD

MO

MS

MT

ND

NE

SC

SD

VA

WV

WY

MA

MI

NH

NJ

NV

UT

CT

MN

WA ID CA VT

CO

FL

GA

KS

KY

ME

NM

OK

TN

Patents

1st quartile (20.0–50.7)

2nd quartile (13.1–19.2)

3rd quartile (9.2–12.9)

4th quartile (1.4–7.2)

No data

SOURCES: U.S. Patent and Trademark Office, Electronic Information Products Division/

Patent Monitoring Branch, Patent Counts by 

Country/State and Year, Utility Patents, January 1, 1963–December 31, 2009; Bureau of 

Labor Statistics, Occupational Employment 

and Wage Estimates.
Science and Engineering Indicators 2012 8-113

Table 8-51

Patents awarded per 1,000 individuals in science and engineering occupations, by state: 2003, 2006, and 2010

Patents awarded Individuals in S&E occupations

Patents/1,000 

individuals in 

 S&E occupations

State 2003 2006 2010 2003 2006 2010 2003 2006 2010

United States ................. 87,864 89,795 107,765 4,961,550 5,407,710 5,549,980 17.7 16.6 19.4

Alabama ..................... 397 357 444 56,380 66,100 68,450 7.0 5.4 6.5

Alaska ........................ 37 36 28 10,600 10,720 15,430 3.5 3.4 1.8

Arizona ....................... 1,584 1,705 1,976 92,120 98,110 102,870 17.2 17.4 19.2

Arkansas .................... 152 138 144 21,340 24,860 29,200 7.1 5.6 4.9

California .................... 19,688 22,275 27,337 676,180 730,010 758,830 29.1 30.5 36.0

Colorado .................... 2,069 2,118 2,135 124,140 133,730 143,210 16.7 15.8 14.9

Connecticut ............... 1,667 1,652 1,875 81,380 79,380 74,990 20.5 20.8 25.0

Delaware .................... 346 357 367 17,370 21,550 20,920 19.9 16.6 17.5

District of Columbia ... 49 63 82 54,890 64,120 59,870 0.9 1.0 1.4

Florida ........................ 2,563 2,600 2,978 221,070 246,190 239,600 11.6 10.6 12.4

Georgia ...................... 1,333 1,487 1,905 144,170 136,470 145,220 9.2 10.9 13.1

Hawaii ........................ 75 84 121 16,090 18,940 19,500 4.7 4.4 6.2

Idaho .......................... 1,803 1,663 1,095 22,150 NA 24,130 81.4 NA 45.4

Illinois ......................... 3,296 3,294 3,611 211,230 222,470 197,120 15.6 14.8 18.3

Indiana ....................... 1,385 1,165 1,492 78,410 80,110 90,710 17.7 14.5 16.4

Iowa ........................... 665 666 763 37,320 43,670 44,140 17.8 15.3 17.3

Kansas ....................... 428 492 615 51,970 48,620 48,970 8.2 10.1 12.6

Kentucky .................... 439 413 536 45,230 44,680 48,790 9.7 9.2 11.0

Louisiana .................... 390 321 304 41,900 40,180 44,200 9.3 8.0 6.9

Maine ......................... 150 142 211 15,020 15,950 17,470 10.0 8.9 12.1

Maryland .................... 1,453 1,410 1,578 149,250 159,470 166,700 9.7 8.8 9.5

Massachusetts ........... 3,908 4,011 4,923 184,690 198,670 208,160 21.2 20.2 23.7

Michigan .................... 3,857 3,758 3,823 182,940 208,520 176,570 21.1 18.0 21.7

Minnesota .................. 2,953 2,957 3,597 117,120 125,930 125,100 25.2 23.5 28.8

Mississippi ................. 162 119 145 22,190 24,910 23,770 7.3 4.8 6.1

Missouri ..................... 823 721 975 84,150 96,420 102,300 9.8 7.5 9.5

Montana ..................... 121 121 105 11,450 13,010 14,620 10.6 9.3 7.2

Nebraska .................... 185 186 214 30,710 32,500 30,930 6.0 5.7 6.9

Nevada ....................... 389 386 540 22,330 26,930 26,840 17.4 14.3 20.1

New Hampshire ......... 677 602 725 23,430 27,680 29,200 28.9 21.7 24.8

New Jersey ................ 3,522 3,172 3,874 161,420 176,460 185,360 21.8 18.0 20.9

New Mexico ............... 390 344 434 33,600 30,800 36,130 11.6 11.2 12.0

New York .................... 6,234 5,627 7,082 272,440 306,810 NA 22.9 18.3 NA

North Carolina ............ 1,871 1,974 2,636 132,440 138,790 155,030 14.1 14.2 17.0

North Dakota ............. 55 66 107 8,430 9,360 11,050 6.5 7.1 9.7

Ohio ........................... 3,183 2,630 3,230 177,100 185,190 195,840 18.0 14.2 16.5

Oklahoma ................... 516 544 516 44,360 50,770 44,190 11.6 10.7 11.7

Oregon ....................... 1,665 2,060 2,040 61,230 64,520 NA 27.2 31.9 NA

Pennsylvania .............. 3,182 2,842 3,351 185,560 214,910 NA 17.1 13.2 NA

Rhode Island .............. 266 269 276 18,740 18,060 18,210 14.2 14.9 15.2

South Carolina ........... 571 577 517 48,740 53,230 56,230 11.7 10.8 9.2

South Dakota ............. 80 74 70 9,150 10,120 11,150 8.7 7.3 6.3

Tennessee .................. 797 669 925 63,680 67,040 71,850 12.5 10.0 12.9

Texas .......................... 6,029 6,308 7,545 365,270 408,710 451,390 16.5 15.4 16.7

Utah ........................... 638 684 1,017 45,570 49,690 50,830 14.0 13.8 20.0

Vermont ...................... 429 437 642 11,420 12,780 12,670 37.6 34.2 50.7

Virginia ....................... 1,110 1,094 1,587 209,280 251,720 255,800 5.3 4.3 6.2

Washington ................ 2,285 3,286 5,258 150,230 171,780 186,210 15.2 19.1 28.2

West Virginia .............. 139 103 118 16,220 17,150 17,070 8.6 6.0 6.9

Wisconsin .................. 1,787 1,688 1,814 93,320 96,860 99,240 19.1 17.4 18.3

Wyoming .................... 71 48 82 6,130 7,640 8,260 11.6 6.3 9.9

Puerto Rico ................ 27 25 24 19,940 23,850 20,850 1.4 1.0 1.2

NA = not available

NOTES: Origin of utility patent determined by residence of first-named inventor. National totals for S&E occupations in the United States provided by 

Occupational Employment Statistics (OES) and include states with suppressed data. OES estimates for 2003, 2006, and 2010 S&E occupations 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, January 1, 1963–December 31, 2009; Bureau of Labor Statistics, Occupational Employment and Wage Estimates.

Science and Engineering Indicators 2012
8-114  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 does not take into account establishment size. Each establishment 

with an employer identification number is counted without regard to 

the number of its employees.

The data pertaining to establishments for the years 2003 and later 

are based on their classification according to the 2002 edition of the 

North American Industry Classification System (NAICS). See table 

8-A in the “Introduction” for a list of the 46 industries (by 4-digit NAICS code) that are defined as high technology. Data for years prior to 

2003 are not directly comparable.

Findings

U The number of establishments in high-technology 

industries rose from about 590,000 in 2003 to more 

than 646,000 in 2008, an increase of 56,000 or 9%.

U The percentage of U.S. establishments in hightechnology industries went from 8.17% to 8.52% of 

the total business establishments during the 2003–08 

period, and most states showed an upward trend 

in the percentage of their establishments in hightechnology industries.

U Between 2003 and 2008, the largest growth in 

the number of establishments in high-technology 

industries occurred in California and Florida, which 

added 8,700 and 6,200 establishments, respectively.

U The state distribution of this indicator is similar to that 

of three other indicators: bachelor’s degree holders, 

S&E doctoral degree holders, and S&E occupations, 

all expressed as a share of the workforce.

U 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 8-52

High-technology establishments as a percentage of all business establishments: 2008

5 6 7 8 9 10 11 12 13 14 15

IA

MS

ND

SD

WV

AK

AR

KS

LA

MI

NC

NM

NY

OH

OR

PA

VT

WY

MA

MD

NJ

AZ

CT

FL

MN

OK

WA

CO

VA DE DC

CA

GA

IL

NH

NV

TX

UT

Percent

AL

HI

ID

IN

KY

ME

MO

MT

NE

RI

SC

TN

WI

1st quartile (9.28%–14.78%)

2nd quartile (7.66%–9.10%)

3rd quartile (6.68%–7.64%)

4th quartile (5.19%–6.55%)

SOURCE: Census Bureau, special tabulations 

(2011) of 1989–2008 Business Information 

Tracking Series.
Science and Engineering Indicators 2012 8-115

Table 8-52

High-technology establishments as a percentage of all business establishments, by state: 2003, 2006, and 2008

 High-technology 

 establishments All business establishments

High-technology/business 

establishments (%)

State 2003 2006 2008 2003 2006 2008 2003 2006 2008

EPSCoR states ................ 83,464 88,790 91,126 1,202,246 1,255,900 1,261,851 6.94 7.07 7.22

Non-EPSCoR states ........ 504,364 541,875 551,958 6,001,637 6,308,168 6,304,793 8.40 8.59 8.75

Average EPSCoR 

 state value .................... na na na na na na 7.08 7.17 7.31

Average non-EPSCoR 

 state value .................... na na na na na na 8.23 8.40 8.57

United States ................... 590,417 633,727 646,195 7,223,240 7,585,035 7,587,695 8.17 8.35 8.52

Alabama ....................... 6,347 6,613 6,754 99,453 103,236 103,690 6.38 6.41 6.51

Alaska .......................... 1,345 1,494 1,489 19,037 19,838 19,870 7.07 7.53 7.49

Arizona ......................... 10,433 11,942 12,269 120,966 137,532 139,876 8.62 8.68 8.77

Arkansas ...................... 4,012 4,373 4,675 64,058 66,647 66,558 6.26 6.56 7.02

California ...................... 77,614 85,514 86,312 822,751 875,682 876,984 9.43 9.77 9.84

Colorado ...................... 15,532 17,259 18,047 143,398 154,254 156,426 10.83 11.19 11.54

Connecticut ................. 7,827 7,810 7,736 91,207 93,232 92,428 8.58 8.38 8.37

Delaware ...................... 3,964 3,700 3,407 24,739 25,563 25,134 16.02 14.47 13.56

District of Columbia ..... 2,589 3,062 3,111 19,357 20,967 21,051 13.38 14.60 14.78

Florida .......................... 38,118 43,678 44,285 458,823 516,185 506,466 8.31 8.46 8.74

Georgia ........................ 18,820 20,825 21,402 208,350 225,577 227,233 9.03 9.23 9.42

Hawaii .......................... 2,097 2,325 2,294 30,950 33,063 32,862 6.78 7.03 6.98

Idaho ............................ 2,515 2,912 3,083 39,582 45,599 46,133 6.35 6.39 6.68

Illinois ........................... 27,606 28,821 29,265 310,589 320,756 321,441 8.89 8.99 9.10

Indiana ......................... 9,626 10,158 10,132 147,073 151,024 149,891 6.55 6.73 6.76

Iowa ............................. 4,316 4,548 4,659 80,745 82,542 82,207 5.35 5.51 5.67

Kansas ......................... 5,716 6,035 6,004 74,637 76,261 75,958 7.66 7.91 7.90

Kentucky ...................... 5,453 5,769 5,893 90,358 92,700 92,471 6.03 6.22 6.37

Louisiana ...................... 7,218 7,439 7,670 101,933 101,647 103,877 7.08 7.32 7.38

Maine ........................... 2,466 2,612 2,642 40,519 41,941 41,683 6.09 6.23 6.34

Maryland ...................... 13,428 14,632 15,009 132,782 140,021 138,416 10.11 10.45 10.84

Massachusetts ............. 17,183 17,107 17,434 177,910 174,997 173,933 9.66 9.78 10.02

Michigan ...................... 16,937 17,049 16,773 236,221 235,245 228,890 7.17 7.25 7.33

Minnesota .................... 12,834 13,348 13,257 145,364 150,896 148,641 8.83 8.85 8.92

Mississippi ................... 3,269 3,336 3,469 59,565 60,442 60,793 5.49 5.52 5.71

Missouri ....................... 9,562 10,130 10,178 149,753 154,177 152,165 6.39 6.57 6.69

Montana ....................... 2,108 2,415 2,564 33,616 36,550 37,228 6.27 6.61 6.89

Nebraska ...................... 2,797 3,072 3,269 50,213 51,822 52,064 5.57 5.93 6.28

Nevada ......................... 5,387 5,975 6,024 53,080 61,061 61,721 10.15 9.79 9.76

New Hampshire ........... 3,511 3,554 3,603 38,119 39,273 38,812 9.21 9.05 9.28

New Jersey .................. 24,286 24,534 24,307 237,097 242,649 238,080 10.24 10.11 10.21

New Mexico ................. 3,322 3,553 3,635 43,386 45,814 46,091 7.66 7.76 7.89

New York ...................... 35,926 37,346 38,308 500,559 514,992 517,873 7.18 7.25 7.40

North Carolina .............. 14,869 16,908 17,582 207,500 221,898 224,757 7.17 7.62 7.82

North Dakota ............... 964 1,035 1,117 20,371 21,286 21,514 4.73 4.86 5.19

Ohio ............................. 19,875 20,347 20,127 269,202 269,398 263,353 7.38 7.55 7.64

Oklahoma ..................... 6,859 7,301 7,536 85,633 89,440 91,186 8.01 8.16 8.26

Oregon ......................... 7,500 8,083 8,525 102,462 110,317 111,266 7.32 7.33 7.66

Pennsylvania ................ 22,266 23,486 23,930 297,040 303,507 302,568 7.50 7.74 7.91

Rhode Island ................ 1,976 2,059 2,076 29,172 30,322 29,713 6.77 6.79 6.99

South Carolina ............. 5,869 6,551 6,978 98,735 105,060 106,501 5.94 6.24 6.55

South Dakota ............... 1,206 1,266 1,397 24,314 25,419 25,624 4.96 4.98 5.45

Tennessee .................... 8,196 8,772 8,882 129,458 134,776 136,321 6.33 6.51 6.52

Texas ............................ 45,062 47,520 49,419 481,804 508,092 521,383 9.35 9.35 9.48

Utah ............................. 5,474 6,531 6,913 60,011 68,612 71,301 9.12 9.52 9.70

Vermont ........................ 1,453 1,535 1,548 21,747 22,261 22,067 6.68 6.90 7.01

Virginia ......................... 18,868 21,678 22,482 182,783 196,849 197,376 10.32 11.01 11.39

Washington .................. 13,171 14,411 15,116 166,229 179,368 181,688 7.92 8.03 8.32

West Virginia ................ 2,257 2,308 2,343 40,225 40,480 39,579 5.61 5.70 5.92

Wisconsin .................... 9,035 9,438 9,609 141,560 145,590 143,830 6.38 6.48 6.68

Wyoming ...................... 1,353 1,558 1,656 18,804 20,175 20,722 7.20 7.72 7.99

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 explanation of EPSCoR and non-EPSCoR averages, see chapter introduction.

SOURCE: Census Bureau, special tabulations (2011) of 1989–2008 Business Information Tracking Series.

Science and Engineering Indicators 2012
8-116  Chapter 8. State Indicators

The business base of a state is constantly changing as new businesses 

form and others cease to function. 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 are 

based on their classification according to the 2002 edition of the North American Industry Classification System (NAICS). See table 8-A in the “Introduction” for a list of the 46 industries (by 4-digit 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

U In 2008, about 2,800 more businesses in 

high-technology industries ceased operations 

than were formed in the United States. From 

a base of approximately 7.6 million total 

business establishments, 76,033 new business 

establishments were formed in high-technology 

industries and 78,801 ceased operations in 

those same industries.

U The effect of the business downturn became 

evident in 2008 as 30 states plus the District 

of Columbia had more businesses in hightechnology industries ceasing operations than 

were being formed.

U The 7 top-ranking states on this indicator were 

EPSCoR states. However, the largest numbers 

of net new businesses were formed in Texas and 

Pennsylvania.

Net High-Technology Business Formations as a Percentage of All Business 

Establishments

Figure 8-53

Net high-technology business formations as a percentage of all business establishments: 2008

–0.7 –0.6 –0.5 –0.4 –0.3 –0.2 –0.1 0.0 0.1 0.2 0.3

DE

AK

AZ

CA

CT

DC

MI

MN

NJ

RI

AL

FL

GA

HI

IA

ID

IN

KS

MA

MD

NC

NV

OH

TN

UT

VA

VT

WI

CO

IL

KY

MO

MS

NE

NH

NM

NY

OK

OR

PA

SC

TX

WA

WV

LA

ME

SD

AR

MT

ND

WY

Percent

1st quartile (0.05% to 0.26%)

2nd quartile (–0.02% to 0.04%)

3rd quartile (–0.08% to –0.04%)

4th quartile (–0.61% to –0.10%)

SOURCE: Census Bureau, special tabulations 

(2011) of 1989–2008 Business Information 

Tracking Series.
Science and Engineering Indicators 2012 8-117

Table 8–53

Net high-technology business formations as a percentage of all business establishments, by state: 2004, 2006, 

and 2008

Net high-technology 

business formations All business establishments

High-technology 

formations/business 

establishments (%)

State 2004 2006 2008 2004 2006 2008 2004 2006 2008

United States ................. 11,598 14,031 –2,768 7,366,978 7,585,035 7,587,695 0.16 0.18 –0.04

Alabama ..................... 63 134 –65 100,521 103,236 103,690 0.06 0.13 –0.06

Alaska ........................ 22 66 –32 19,309 19,838 19,870 0.11 0.33 –0.16

Arizona ....................... 357 446 –236 125,330 137,532 139,876 0.28 0.32 –0.17

Arkansas .................... 123 98 131 65,127 66,647 66,558 0.19 0.15 0.20

California .................... 1,099 2,633 –973 838,615 875,682 876,984 0.13 0.30 –0.11

Colorado .................... 490 509 148 146,937 154,254 156,426 0.33 0.33 0.09

Connecticut ............... –47 44 –124 92,710 93,232 92,428 –0.05 0.05 –0.13

Delaware .................... –52 –78 –153 25,344 25,563 25,134 –0.21 –0.31 –0.61

District of Columbia ... 66 195 –25 19,503 20,967 21,051 0.34 0.93 –0.12

Florida ........................ 1,743 1,009 –322 483,693 516,185 506,466 0.36 0.20 –0.06

Georgia ...................... 642 734 –228 214,200 225,577 227,233 0.30 0.33 –0.10

Hawaii ........................ 51 90 –14 31,538 33,063 32,862 0.16 0.27 –0.04

Idaho .......................... 54 151 –10 41,205 45,599 46,133 0.13 0.33 –0.02

Illinois ......................... 452 243 65 315,093 320,756 321,441 0.14 0.08 0.02

Indiana ....................... 208 164 –78 149,050 151,024 149,891 0.14 0.11 –0.05

Iowa ........................... 12 150 –14 81,334 82,542 82,207 0.01 0.18 –0.02

Kansas ....................... 160 114 –31 75,600 76,261 75,958 0.21 0.15 –0.04

Kentucky .................... 116 42 46 91,598 92,700 92,471 0.13 0.05 0.05

Louisiana .................... –38 195 121 102,866 101,647 103,877 –0.04 0.19 0.12

Maine ......................... 81 31 49 41,131 41,941 41,683 0.20 0.07 0.12

Maryland .................... 475 278 –56 135,699 140,021 138,416 0.35 0.20 –0.04

Massachusetts ........... 156 193 –141 175,426 174,997 173,933 0.09 0.11 –0.08

Michigan .................... 44 27 –408 237,392 235,245 228,890 0.02 0.01 –0.18

Minnesota .................. 185 39 –278 148,276 150,896 148,641 0.12 0.03 –0.19

Mississippi ................. 7 83 52 60,364 60,442 60,793 0.01 0.14 0.09

Missouri ..................... 195 279 –7 153,584 154,177 152,165 0.13 0.18 0.00

Montana ..................... 108 98 80 34,570 36,550 37,228 0.31 0.27 0.21

Nebraska .................... 64 98 32 50,803 51,822 52,064 0.13 0.19 0.06

Nevada ....................... 169 207 –32 55,713 61,061 61,721 0.30 0.34 –0.05

New Hampshire ......... 30 13 0 38,707 39,273 38,812 0.08 0.03 0.00

New Jersey ................ –80 38 –393 240,013 242,649 238,080 –0.03 0.02 –0.17

New Mexico ............... 37 98 –1 44,071 45,814 46,091 0.08 0.21 0.00

New York .................... 702 274 20 509,873 514,992 517,873 0.14 0.05 0.00

North Carolina ............ 514 692 –53 212,457 221,898 224,757 0.24 0.31 –0.02

North Dakota ............. –1 34 43 20,763 21,286 21,514 0.00 0.16 0.20

Ohio ........................... 204 111 –273 271,078 269,398 263,353 0.08 0.04 –0.10

Oklahoma ................... 75 236 22 87,180 89,440 91,186 0.09 0.26 0.02

Oregon ....................... 156 141 93 104,966 110,317 111,266 0.15 0.13 0.08

Pennsylvania .............. 474 278 177 300,832 303,507 302,568 0.16 0.09 0.06

Rhode Island .............. 67 8 –54 29,900 30,322 29,713 0.22 0.03 –0.18

South Carolina ........... 175 230 25 100,947 105,060 106,501 0.17 0.22 0.02

South Dakota ............. 16 9 42 24,693 25,419 25,624 0.06 0.04 0.16

Tennessee .................. 39 372 –75 131,355 134,776 136,321 0.03 0.28 –0.06

Texas .......................... 401 1,221 231 489,782 508,092 521,383 0.08 0.24 0.04

Utah ........................... 283 382 –16 62,644 68,612 71,301 0.45 0.56 –0.02

Vermont ...................... 42 22 –4 22,072 22,261 22,067 0.19 0.10 –0.02

Virginia ....................... 845 986 –162 188,533 196,849 197,376 0.45 0.50 –0.08

Washington ................ 346 476 52 170,848 179,368 181,688 0.20 0.27 0.03

West Virginia .............. 16 –13 19 40,732 40,480 39,579 0.04 –0.03 0.05

Wisconsin .................. 215 66 –12 143,739 145,590 143,830 0.15 0.05 –0.01

Wyoming .................... 37 85 54 19,262 20,175 20,722 0.19 0.42 0.26

Puerto Rico ................ NA NA NA NA NA NA NA NA NA

NA = not available

SOURCE: Census Bureau, special tabulations (2011) of 1989–2008 Business Information Tracking Series.

Science and Engineering Indicators 2012
8-118  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 are based on their classification 

according to the 2002 edition of the North American Industry Classification 

System (NAICS). See table 8-A in the “Introduction” for a list of the 46 

industries (by 4-digit NAICS code) that are defined as high technology. Data 

on total employment and NAICS industry establishment employment are 

provided by the 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, public administration, or 

most government employees.

Findings

U Employment in high-technology industries in the 

United States increased slightly from 13.6 million 

in 2003 to 13.9 million in 2008.

U Nationwide, the value of this indicator declined 

from 11.96% in 2003 to 11.47% in 2008.

U On this indicator, states varied greatly in 2008, 

ranging from 5.5% to 16.0% of their workforce 

employed in high-technology industries.

U During the 2003–08 period, Michigan and New 

York recorded the largest net losses of jobs in 

high-technology industries, while Texas, Virginia, 

Florida, and Georgia posted the largest net gains 

of jobs in high-technology industries.

U States were distributed similarly on the hightechnology employment and high-technology 

establishment indicators.

Employment in High-Technology Establishments as a Percentage of Total 

Employment

Figure 8-54

Employment in high-technology establishments as a percentage of total employment: 2008

5.5 6.6 7.7 8.8 9.9 11.0 12.1 13.2 14.3 15.4 16.5

HI

MT

NV

FL

IA

IN

KY

ME

NE

WY

MI

MO

NM

OR

UT

AL

AR

LA

RI

TN

AK

CA

CT

GA

IL

MN

TX

CO

DC

KS

NH

MA

MD

NJ

WA VA

AZ

DE

ID

NC

ND

NY

OH

OK

PA

SC

VT

WI

Percent

MS

SD

WV

1st quartile (12.64%–15.95%)

2nd quartile (10.77%–12.40%)

3rd quartile (8.69%–10.53%)

4th quartile (5.50%–8.34%)

SOURCE: Census Bureau, special tabulations 

(2011) of 1989–2008 Business Information 

Tracking Series.
Science and Engineering Indicators 2012 8-119

Table 8-54

Employment in high-technology establishments as share of total employment, by state: 2003, 2006, and 2008

 Employment in high-technology 

 establishments Total employment

High-technology/ 

Total employment (%)

State 2003 2006 2008 2003 2006 2008 2003 2006 2008

United States ................. 13,563,122 13,733,632 13,870,679 113,373,663 119,892,505 120,933,454 11.96 11.45 11.47

Alabama ..................... 152,879 162,197 162,713 1,597,265 1,713,185 1,714,898 9.57 9.47 9.49

Alaska ........................ 21,851 27,306 30,794 216,707 241,568 248,274 10.08 11.30 12.40

Arizona ....................... 234,603 246,648 243,159 1,997,990 2,334,665 2,334,785 11.74 10.56 10.41

Arkansas .................... 95,180 93,648 93,991 988,822 1,041,868 1,025,872 9.63 8.99 9.16

California .................... 1,781,830 1,826,638 1,785,871 12,986,496 13,830,274 13,746,493 13.72 13.21 12.99

Colorado .................... 274,979 272,952 291,442 1,883,883 2,018,905 2,121,945 14.60 13.52 13.73

Connecticut ............... 210,114 198,450 200,106 1,550,615 1,585,660 1,551,373 13.55 12.52 12.90

Delaware .................... 52,349 47,749 42,178 385,098 388,178 389,927 13.59 12.30 10.82

District of Columbia ... 54,314 57,297 61,916 422,912 446,502 466,152 12.84 12.83 13.28

Florida ........................ 576,274 618,540 614,504 6,548,276 7,534,165 7,367,042 8.80 8.21 8.34

Georgia ...................... 413,384 428,272 448,230 3,386,590 3,622,522 3,633,902 12.21 11.82 12.33

Hawaii ........................ 25,777 28,848 28,500 458,952 512,488 518,052 5.62 5.63 5.50

Idaho .......................... 55,706 59,082 56,624 466,379 546,108 537,706 11.94 10.82 10.53

Illinois ......................... 646,285 619,777 662,391 5,204,887 5,356,504 5,463,437 12.42 11.57 12.12

Indiana ....................... 219,598 224,644 227,568 2,540,554 2,672,558 2,619,567 8.64 8.41 8.69

Iowa ........................... 102,387 96,190 105,099 1,232,709 1,295,143 1,317,765 8.31 7.43 7.98

Kansas ....................... 155,023 146,849 169,118 1,109,699 1,142,487 1,185,876 13.97 12.85 14.26

Kentucky .................... 121,838 125,204 129,162 1,471,622 1,551,791 1,570,686 8.28 8.07 8.22

Louisiana .................... 137,029 143,846 145,702 1,603,492 1,592,682 1,654,417 8.55 9.03 8.81

Maine ......................... 35,184 37,934 40,028 488,788 508,061 508,905 7.20 7.47 7.87

Maryland .................... 315,887 326,546 333,584 2,088,552 2,231,888 2,235,033 15.12 14.63 14.93

Massachusetts ........... 460,984 496,630 464,759 2,974,164 3,043,643 3,076,457 15.50 16.32 15.11

Michigan .................... 499,133 475,350 424,066 3,884,881 3,817,762 3,637,690 12.85 12.45 11.66

Minnesota .................. 315,994 329,927 318,203 2,381,860 2,475,859 2,517,847 13.27 13.33 12.64

Mississippi ................. 66,566 64,558 64,420 912,004 940,329 944,776 7.30 6.87 6.82

Missouri ..................... 254,299 263,494 272,023 2,387,245 2,467,626 2,472,861 10.65 10.68 11.00

Montana ..................... 20,296 26,958 23,651 302,932 342,461 359,721 6.70 7.87 6.57

Nebraska .................... 68,975 64,779 66,152 774,858 789,117 805,633 8.90 8.21 8.21

Nevada ....................... 61,847 66,875 72,942 970,678 1,165,243 1,156,305 6.37 5.74 6.31

New Hampshire ......... 63,264 64,914 79,105 540,132 577,322 595,473 11.71 11.24 13.28

New Jersey ................ 550,224 550,515 556,378 3,578,674 3,644,967 3,646,897 15.38 15.10 15.26

New Mexico ............... 60,399 68,627 72,838 571,057 628,472 641,010 10.58 10.92 11.36

New York .................... 823,992 790,696 758,087 7,415,430 7,531,772 7,622,956 11.11 10.50 9.94

North Carolina ............ 349,424 358,501 370,028 3,337,552 3,523,954 3,585,005 10.47 10.17 10.32

North Dakota ............. 20,584 22,450 33,096 258,878 278,395 304,892 7.95 8.06 10.85

Ohio ........................... 531,491 518,835 514,408 4,769,406 4,824,859 4,728,989 11.14 10.75 10.88

Oklahoma ................... 132,887 141,575 137,334 1,184,312 1,276,743 1,335,467 11.22 11.09 10.28

Oregon ....................... 152,140 161,641 166,086 1,338,380 1,461,339 1,482,627 11.37 11.06 11.20

Pennsylvania .............. 566,406 549,180 564,569 5,028,650 5,189,349 5,233,871 11.26 10.58 10.79

Rhode Island .............. 35,806 41,020 42,046 427,369 440,715 433,626 8.38 9.31 9.70

South Carolina ........... 163,373 170,200 167,198 1,550,227 1,631,690 1,654,494 10.54 10.43 10.11

South Dakota ............. 18,890 20,202 22,551 299,723 325,045 337,830 6.30 6.22 6.68

Tennessee .................. 219,898 245,517 225,724 2,298,836 2,472,939 2,493,070 9.57 9.93 9.05

Texas .......................... 1,158,481 1,144,997 1,210,285 8,049,300 8,709,575 9,232,889 14.39 13.15 13.11

Utah ........................... 99,856 114,815 124,399 900,331 1,038,879 1,114,776 11.09 11.05 11.16

Vermont ...................... 29,402 27,001 29,372 256,401 263,759 272,847 11.47 10.24 10.77

Virginia ....................... 459,017 502,890 508,097 2,932,471 3,173,767 3,186,112 15.65 15.85 15.95

Washington ................ 401,413 347,710 387,407 2,292,462 2,420,633 2,536,196 17.51 14.36 15.28

West Virginia .............. 46,635 45,284 45,280 561,317 583,033 592,356 8.31 7.77 7.64

Wisconsin .................. 233,967 253,499 259,072 2,382,979 2,481,998 2,496,839 9.82 10.21 10.38

Wyoming .................... 15,008 16,375 18,423 180,866 204,058 221,835 8.30 8.02 8.30

Puerto Rico ................ NA NA NA NA NA NA NA NA NA

NA = not available

SOURCE: Census Bureau, special tabulations (2011) of 1989–2008 Business Information Tracking Series.

Science and Engineering Indicators 2012
8-120  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. All data are expressed in 

current dollars. A high value indicates that small business firms in a state 

are doing cutting-edge development work that attracts federal support.

Findings

U The SBIR program remained unchanged in size 

at about $1.2 billion both in 2000-2002 and in 

2008–10 despite significantly higher spending in 

2004–2006.

U Over the 3 year period of 2008–2010, SBIR 

funds were concentrated in relatively few states; 

the total of annual state awards range from less 

than $1 million to $246 million.

U 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.

U 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 8-55

Average annual federal Small Business Innovation Research funding per $1 million of gross domestic

product: 2008–10

0 50 100 150 200 250 300 350 400 450

AK

DC

FL

GA

IA

ID

IL

IN

KS

KY

LA

MO

MS

ND

NE

NV

OK

SC

SD

TN

TX

WV

WY

CO

MD

NM

CA

MT

OR

UT NH MA

AL

VA

VT

AR

AZ

CT

DE

HI

ME

MI

MN

NC

NJ

NY

OH

PA

RI

WA

WI

Dollars

1st quartile ($93–$448)

2nd quartile ($59–$89)

3rd quartile ($28–$52)

4th quartile ($8–$27)

SOURCES: Small Business Administration, 

Office of Technology, SBIR program statistics 

(various years); Bureau of Economic Analysis, 

Gross Domestic Product data; Government 

of Puerto Rico, Office of the Governor.
Science and Engineering Indicators 2012 8-121

Table 8-55

Average annual federal Small Business Innovation Research funding per $1 million of gross domestic product, 

by state: 2000–02, 2004–06, and 2008–10

Average SBIR funding 

($thousands)

Average state GDP 

($millions)

SBIR funding ($)/ 

$1 million GDP

State 2000–02 2004–06 2008–10 2000–02 2004–06 2008–10

 2000– 

02

2004– 

06

2008– 

10

United States ................. 1,234,995 1,904,499 1,258,785 10,224,860 12,551,462 14,279,027 121 152 88

Alabama ..................... 23,221 39,143 26,688 120,421 150,815 169,693 193 260 157

Alaska ........................ 480 345 793 27,516 38,017 48,056 17 9 16

Arizona ....................... 25,485 33,842 19,436 169,727 223,697 254,591 150 151 76

Arkansas .................... 1,663 7,103 5,156 71,008 88,432 100,286 23 80 51

California .................... 255,855 391,456 245,846 1,347,048 1,687,989 1,886,626 190 232 130

Colorado .................... 61,513 85,830 58,409 179,766 216,425 254,174 342 397 230

Connecticut ............... 19,923 26,823 18,742 167,173 198,636 230,256 119 135 81

Delaware .................... 4,636 6,662 3,556 42,926 54,230 60,538 108 123 59

District of Columbia ... 5,069 3,725 1,472 63,259 82,698 99,847 80 45 15

Florida ........................ 26,348 41,292 30,486 507,821 677,240 742,762 52 61 41

Georgia ...................... 12,353 18,234 14,148 304,529 362,604 400,819 41 50 35

Hawaii ........................ 3,716 7,796 4,921 42,830 56,749 66,102 87 137 74

Idaho .......................... 2,365 3,994 1,252 36,692 47,750 54,769 64 84 23

Illinois ......................... 17,782 24,985 21,848 486,319 572,784 640,175 37 44 34

Indiana ....................... 6,048 15,759 11,505 202,128 240,248 266,395 30 66 43

Iowa ........................... 2,985 4,091 3,034 95,298 120,190 137,906 31 34 22

Kansas ....................... 3,808 5,359 2,569 88,939 105,782 125,016 43 51 21

Kentucky .................... 3,280 4,406 5,886 116,894 139,389 158,217 28 32 37

Louisiana .................... 2,585 5,002 2,669 136,238 191,291 212,470 19 26 13

Maine ......................... 3,098 9,264 2,607 38,156 45,872 50,551 81 202 52

Maryland .................... 60,481 97,376 64,011 195,088 247,143 287,360 310 394 223

Massachusetts ........... 181,889 250,946 165,114 280,962 323,833 368,297 647 775 448

Michigan .................... 21,110 43,732 27,485 341,731 372,353 376,426 62 117 73

Minnesota .................. 18,182 24,292 19,635 194,424 237,233 263,765 94 102 74

Mississippi ................. 2,524 3,364 755 67,551 81,735 96,193 37 41 8

Missouri ..................... 4,967 9,959 5,725 186,134 216,371 241,105 27 46 24

Montana ..................... 6,847 8,460 4,539 22,839 30,069 35,635 300 281 127

Nebraska .................... 2,259 4,003 2,404 59,339 72,889 87,027 38 55 28

Nevada ....................... 4,315 6,847 1,608 79,282 113,213 127,652 54 60 13

New Hampshire ......... 15,483 22,097 16,847 45,109 53,672 59,383 343 412 284

New Jersey ................ 35,400 48,903 32,895 363,315 431,713 480,947 97 113 68

New Mexico ............... 19,955 24,172 17,663 51,974 67,821 77,906 384 356 227

New York .................... 48,783 84,178 72,144 801,591 962,740 1,120,908 61 87 64

North Carolina ............ 16,220 32,246 30,515 291,887 353,857 411,965 56 91 74

North Dakota ............. 1,900 1,861 1,253 19,258 24,692 32,663 99 75 38

Ohio ........................... 50,697 79,947 43,551 387,263 442,611 470,118 131 181 93

Oklahoma ................... 3,718 7,918 4,797 95,791 121,670 147,260 39 65 33

Oregon ....................... 14,792 28,485 18,417 114,930 146,903 172,029 129 194 107

Pennsylvania .............. 42,299 79,624 44,611 408,829 483,960 553,805 103 165 81

Rhode Island .............. 4,297 7,803 4,458 35,746 44,517 48,027 120 175 93

South Carolina ........... 5,327 6,527 4,355 119,900 141,993 160,910 44 46 27

South Dakota ............. 1,184 503 475 25,587 31,560 38,814 46 16 12

Tennessee .................. 9,971 10,435 8,364 184,826 224,988 248,817 54 46 34

Texas .......................... 45,318 87,373 46,517 761,387 977,950 1,185,415 60 89 39

Utah ........................... 11,446 14,424 11,255 72,145 91,277 112,731 159 158 100

Vermont ...................... 2,956 5,061 3,823 18,822 22,778 24,960 157 222 153

Virginia ....................... 73,712 100,364 75,508 277,582 353,956 412,148 266 284 183

Washington ................ 30,253 49,405 29,865 231,737 279,233 335,525 131 177 89

West Virginia .............. 2,292 5,009 1,655 43,004 52,028 61,575 53 96 27

Wisconsin .................. 11,944 21,382 16,402 183,789 219,114 242,343 65 98 68

Wyoming .................... 2,262 2,695 1,116 18,348 26,752 38,068 123 101 29

Puerto Rico ................ 219 453 NA NA NA NA NA NA NA

NA = not available

GDP = gross domestic product; SBIR = Small Business Innovation Research

NOTE: GDP reported in current dollars.

SOURCES: Small Business Administration, Office of Technology, SBIR program statistics (various years); Bureau of Economic Analysis, Gross Domestic 

Product data; Government of Puerto Rico, Office of the Governor.

Science and Engineering Indicators 2012
8-122  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, IPOs, 

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

U The total amount of venture capital invested in 

the United States has been highly volatile during 

the past decade. In 2000, it was at $105 billion, 

decreasing to $22 billion in 2010. The average 

value for this indicator for the United States was 

$10.59 in 2000 and $1.50 in 2010.

U Venture capital investment is concentrated in 

relatively few states. Companies in California 

received 50% of the total venture capital 

disbursed in the United States in 2010, followed 

by companies in Massachusetts with 11%. Four 

states reported no venture capital investment in 

2010.

U In 2010, the value of this indicator across states 

ranged from $0.0 to $6.26.

U The average indicator value for EPSCoR 

states was substantially lower than that for 

non-EPSCoR states. The state distribution of 

venture capital was similar to indicators of hightechnology business activity.

Venture Capital Disbursed per $1,000 of Gross Domestic Product

Figure 8-56

Venture capital disbursed per $1,000 of gross domestic product: 2010

0.0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 5.7 6.0 6.3

MD

RI

UT

VT

CO

WA

AZ

DE

IA

KS

MI

MN

OH

WI

CT

GA

IL

PA

TX

VA

AK

AL

AR

FL

HI

ID

IN

KY

LA

ME

MO

MS

MT

ND

NE

NM

NV

OK

SC

SD

TN

WV

WY

DC

NC

NH

NJ

NY

OR CA MA

Dollars

1st quartile ($0.93–$6.26)

2nd quartile ($0.33–$0.92)

3rd quartile ($0.13–$0.29)

4th quartile ($0.00–$0.09)

SOURCE: PricewaterhouseCoopers and 

National Venture Capital Association, special 

tabulations (2011) of MoneyTreeTM Survey, 

Bureau of Economic Analysis, Gross Domestic Product data.
Science and Engineering Indicators 2012 8-123

Table 8-56

Venture capital disbursed per $1,000 of gross domestic product, by state: 2000, 2005, and 2010

Venture capital disbursed 

($millions) State GDP ($millions)

Venture capital 

($)/$1,000 GDP

State 2000 2005 2010 2000 2005 2010 2000 2005 2010

United States ................. 104,678 23,115 21,799 9,884,171 12,554,535 14,551,782 10.59 1.84 1.50

Alabama ..................... 266 20 1 116,014 151,096 172,567 2.30 0.13 0.00

Alaska ........................ 4 0 0 25,913 37,824 49,120 0.14 0.00 0.00

Arizona ....................... 34 13 83 161,901 222,968 253,609 0.21 0.06 0.33

Arkansas .................... 626 123 5 68,146 88,227 102,566 9.18 1.40 0.05

California .................... 43,034 11,011 10,978 1,317,343 1,691,991 1,901,088 32.67 6.51 5.77

Colorado .................... 4,165 644 468 171,930 217,412 257,641 24.22 2.96 1.81

Connecticut ............... 1,509 202 200 163,943 197,055 237,261 9.21 1.02 0.84

Delaware .................... 135 7 31 40,957 54,749 62,280 3.29 0.13 0.50

District of Columbia ... 478 28 96 58,269 82,837 103,288 8.21 0.33 0.93

Florida ........................ 2,687 329 186 481,115 680,277 747,735 5.58 0.48 0.25

Georgia ...................... 2,325 254 333 294,479 363,154 403,070 7.90 0.70 0.83

Hawaii ........................ 203 12 11 41,372 56,869 66,760 4.91 0.21 0.17

Idaho .......................... 20 8 8 36,091 48,675 55,435 0.54 0.16 0.14

Illinois ......................... 2,358 277 575 474,444 569,544 651,518 4.97 0.49 0.88

Indiana ....................... 269 104 69 198,020 239,575 275,676 1.36 0.43 0.25

Iowa ........................... 31 32 52 93,287 120,258 142,698 0.33 0.27 0.36

Kansas ....................... 265 2 42 85,742 105,164 127,170 3.09 0.02 0.33

Kentucky .................... 202 32 12 113,108 139,336 163,269 1.78 0.23 0.07

Louisiana .................... 113 4 18 131,430 197,163 218,853 0.86 0.02 0.08

Maine ......................... 140 5 2 36,395 45,587 51,643 3.85 0.10 0.04

Maryland .................... 1,820 488 357 182,953 248,139 295,304 9.95 1.97 1.21

Massachusetts ........... 10,312 2,583 2,373 272,680 323,301 378,729 37.82 7.99 6.26

Michigan .................... 337 81 152 336,786 375,260 384,171 1.00 0.22 0.39

Minnesota .................. 1,039 242 140 188,449 238,367 270,039 5.51 1.02 0.52

Mississippi ................. 20 10 0 65,615 81,500 97,461 0.30 0.12 0.00

Missouri ..................... 590 56 61 180,982 216,633 244,016 3.26 0.26 0.25

Montana ..................... 17 27 2 21,629 30,088 36,067 0.77 0.91 0.05

Nebraska .................... 143 13 12 57,233 72,504 89,786 2.50 0.18 0.13

Nevada ....................... 31 159 29 75,907 114,771 125,650 0.41 1.38 0.23

New Hampshire ......... 751 92 57 44,067 53,653 60,283 17.03 1.72 0.94

New Jersey ................ 3,290 887 451 349,334 429,985 487,335 9.42 2.06 0.92

New Mexico ............... 21 76 23 50,262 67,776 79,678 0.42 1.13 0.29

New York .................... 6,835 1,127 1,339 770,621 961,941 1,159,540 8.87 1.17 1.15

North Carolina ............ 1,825 395 456 281,418 354,973 424,935 6.49 1.11 1.07

North Dakota ............. 6 0 0 18,250 24,672 34,685 0.33 0.00 0.00

Ohio ........................... 976 140 157 381,175 444,715 477,699 2.56 0.31 0.33

Oklahoma ................... 53 0 13 91,292 120,662 147,543 0.58 0.00 0.09

Oregon ....................... 793 134 174 112,974 143,349 174,151 7.02 0.94 1.00

Pennsylvania .............. 2,873 482 508 395,811 482,324 569,679 7.26 1.00 0.89

Rhode Island .............. 75 76 65 33,522 44,169 49,234 2.23 1.73 1.32

South Carolina ........... 448 3 21 115,392 141,929 164,445 3.88 0.02 0.13

South Dakota ............. 0 0 0 24,009 31,641 39,893 0.01 0.00 0.00

Tennessee .................. 453 89 52 177,582 224,522 254,806 2.55 0.39 0.20

Texas .......................... 6,094 1,175 891 732,987 970,997 1,207,494 8.31 1.21 0.74

Utah ........................... 674 192 143 69,483 90,748 114,538 9.69 2.12 1.25

Vermont ...................... 46 35 33 18,033 22,773 25,620 2.57 1.55 1.28

Virginia ....................... 3,310 526 375 261,894 356,852 423,860 12.64 1.47 0.89

Washington ................ 2,790 837 613 227,828 279,405 340,460 12.25 3.00 1.80

West Virginia .............. 5 11 4 41,419 51,964 64,642 0.11 0.20 0.06

Wisconsin .................. 192 69 122 177,638 218,923 248,265 1.08 0.31 0.49

Wyoming .................... 0 4 10 17,047 26,238 38,527 0.00 0.15 0.26

Puerto Rico ................ NA NA 4 69,208 86,157 NA NA NA NA

NA = not available

GDP = gross domestic product

NOTE: GDP reported in current dollars. 

SOURCES: PricewaterhouseCoopers and National Venture Capital Association, special tabulations (2011) of MoneyTreeTM Survey; Bureau of Economic 

Analysis, Gross Domestic Product data; United Nations Statistics Division.

Science and Engineering Indicators 2012
8-124  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, IPOs, 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 and later are based on their classification according to the 

2002 edition of the North American Industry Classification System (NAICS). 

See table 8-A in the “Introduction” for a list of the 46 industries (by 4-digit 

NAICS code) that are defined as high technology. Data for years prior to 2003 

are not directly comparable.

Findings

U The number of venture capital deals that 

involved U.S. companies increased from about 

2,900 deals in 2003 to more than 3,800 deals in 

2008.

U In 2008, venture capital deals were concentrated 

in only a few states. Indicator values ranged 

from a low of zero to a high of 2.32% with a 

median value of 0.24%.

U Companies in high-technology industries located 

in Massachusetts were the most successful in 

accessing venture capital investments in 2008, 

with a 2.32% rate. California companies in hightechnology industries obtained venture capital 

investment at a rate of 1.80% and Washington 

companies attained a rate of 1.08%. No other 

states reached a rate of 1.00%.

U In 2008, companies in 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 8-57

Venture capital deals as a percentage of high-technology business establishments: 2008

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4

AK

AL

AR

AZ

DE

FL

IA

ID

IN

KY

LA

ME

MS

MT

ND

NE

NV

OK

SC

SD

WV

WY

MD

NH

NY

PA WA CA MA

Percent

DC

GA

HI

IL

KS

MI

MN

MO

NC

NJ

OH

TN

TX

VA

WI

CO

CT

NM

OR

RI

UT

VT

1st quartile (0.44%–2.32%)

2nd quartile (0.24%–0.41%)

3rd quartile (0.12%–0.23%)

4th quartile (0.00%–0.11%)

SOURCES: PricewaterhouseCoopers, and 

National Venture Capital Association, special 

tabulations (2011) of MoneyTreeTM Survey; 

Census Bureau, special tabulations (2011) 

of 1989–2008 Business Information Tracking 

Series.
Science and Engineering Indicators 2012 8-125

Table 8-57

Venture capital deals as a percentage of high-technology business establishments, by state: 2003, 2006, and 2008

Venture capital deals High-technology establishments

Venture capital deals/

high-technology 

establishments (%)

State 2003 2006 2008 2003 2006 2008 2003 2006 2008

United States ................. 2,903 3,672 3,806 590,417 633,727 646,195 0.49 0.58 0.59

Alabama ..................... 9 7 8 6,347 6,613 6,754 0.14 0.11 0.12

Alaska ........................ 0 0 0 1,345 1,494 1,489 0.00 0.00 0.00

Arizona ....................... 16 29 20 10,433 11,942 12,269 0.15 0.24 0.16

Arkansas .................... 3 6 0 4,012 4,373 4,675 0.07 0.14 0.00

California .................... 1,122 1,549 1,552 77,614 85,514 86,312 1.45 1.81 1.80

Colorado .................... 72 98 100 15,532 17,259 18,047 0.46 0.57 0.55

Connecticut ............... 34 30 34 7,827 7,810 7,736 0.43 0.38 0.44

Delaware .................... 1 3 6 3,964 3,700 3,407 0.03 0.08 0.18

District of Columbia ... 6 8 11 2,589 3,062 3,111 0.23 0.26 0.35

Florida ........................ 61 56 36 38,118 43,678 44,285 0.16 0.13 0.08

Georgia ...................... 55 81 80 18,820 20,825 21,402 0.29 0.39 0.37

Hawaii ........................ 6 10 6 2,097 2,325 2,294 0.29 0.43 0.26

Idaho .......................... 5 1 6 2,515 2,912 3,083 0.20 0.03 0.19

Illinois ......................... 58 55 67 27,606 28,821 29,265 0.21 0.19 0.23

Indiana ....................... 8 15 16 9,626 10,158 10,132 0.08 0.15 0.16

Iowa ........................... 1 2 5 4,316 4,548 4,659 0.02 0.04 0.11

Kansas ....................... 2 7 23 5,716 6,035 6,004 0.03 0.12 0.38

Kentucky .................... 3 7 10 5,453 5,769 5,893 0.06 0.12 0.17

Louisiana .................... 1 3 10 7,218 7,439 7,670 0.01 0.04 0.13

Maine ......................... 2 4 4 2,466 2,612 2,642 0.08 0.15 0.15

Maryland .................... 84 110 97 13,428 14,632 15,009 0.63 0.75 0.65

Massachusetts ........... 378 395 405 17,183 17,107 17,434 2.20 2.31 2.32

Michigan .................... 17 18 43 16,937 17,049 16,773 0.10 0.11 0.26

Minnesota .................. 58 39 47 12,834 13,348 13,257 0.45 0.29 0.35

Mississippi ................. 4 1 0 3,269 3,336 3,469 0.12 0.03 0.00

Missouri ..................... 23 13 24 9,562 10,130 10,178 0.24 0.13 0.24

Montana ..................... 1 0 2 2,108 2,415 2,564 0.05 0.00 0.08

Nebraska .................... 2 3 3 2,797 3,072 3,269 0.07 0.10 0.09

Nevada ....................... 6 7 6 5,387 5,975 6,024 0.11 0.12 0.10

New Hampshire ......... 32 21 28 3,511 3,554 3,603 0.91 0.59 0.78

New Jersey ................ 88 94 90 24,286 24,534 24,307 0.36 0.38 0.37

New Mexico ............... 5 9 19 3,322 3,553 3,635 0.15 0.25 0.52

New York .................... 119 209 235 35,926 37,346 38,308 0.33 0.56 0.61

North Carolina ............ 76 62 51 14,869 16,908 17,582 0.51 0.37 0.29

North Dakota ............. 2 0 2 964 1,035 1,117 0.21 0.00 0.18

Ohio ........................... 25 41 52 19,875 20,347 20,127 0.13 0.20 0.26

Oklahoma ................... 2 6 5 6,859 7,301 7,536 0.03 0.08 0.07

Oregon ....................... 21 31 35 7,500 8,083 8,525 0.28 0.38 0.41

Pennsylvania .............. 90 128 171 22,266 23,486 23,930 0.40 0.55 0.71

Rhode Island .............. 10 7 10 1,976 2,059 2,076 0.51 0.34 0.48

South Carolina ........... 4 3 11 5,869 6,551 6,978 0.07 0.05 0.16

South Dakota ............. 1 1 1 1,206 1,266 1,397 0.08 0.08 0.07

Tennessee .................. 22 11 21 8,196 8,772 8,882 0.27 0.13 0.24

Texas .......................... 165 188 146 45,062 47,520 49,419 0.37 0.40 0.30

Utah ........................... 22 39 33 5,474 6,531 6,913 0.40 0.60 0.48

Vermont ...................... 6 9 9 1,453 1,535 1,548 0.41 0.59 0.58

Virginia ....................... 80 89 81 18,868 21,678 22,482 0.42 0.41 0.36

Washington ................ 81 143 164 13,171 14,411 15,116 0.61 0.99 1.08

West Virginia .............. 5 3 1 2,257 2,308 2,343 0.22 0.13 0.04

Wisconsin .................. 8 20 19 9,035 9,438 9,609 0.09 0.21 0.20

Wyoming .................... 1 1 1 1,353 1,558 1,656 0.07 0.06 0.06

Puerto Rico ................ 1 2 NA NA NA NA NA NA NA

NA = not available

SOURCES: PricewaterhouseCoopers and National Venture Capital Association, special tabulations (2011) of MoneyTreeTM Survey; Census Bureau, 

special tabulations (2011) of 1989–2008 Business Information Tracking Series.

Science and Engineering Indicators 2012
8-126  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, IPOs, 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 states reported fewer than 10 venture capital deals in 

2010. In such states, a single large or small venture capital investment can 

substantially affect the value of this indicator.

Findings

U In 2010, the size of the average venture capital 

investment in the United States was about 

$6.7 million per deal. This is a decline from $13 

million per deal in 2000 expressed in current 

dollars. The size of venture capital deals has not 

changed appreciably since 2005.

U After a high venture capital investment in 2000 of 

over 7,800 deals, the total number of deals has 

remained essentially constant for the second 

half of the decade at 3,152 in 2005 and 3,266 

in 2010.

U In 2010, the state distribution on this indicator 

was skewed from a high value of $26 million 

per deal to a low of zero, with a median value 

of about $5 million per deal. The value of this 

indicator continued to show a high level of 

variability from year to year and among states.

Venture Capital Disbursed per Venture Capital Deal

Figure 8-58 

Venture capital disbursed per venture capital deal: 2010

0.0 2.6 5.2 7.8 10.4 13.0 15.6 18.2 20.8 23.4 26.0

AK

AL

HI

ID

KS

KY

ME

MS

MT

ND

NM

SC

SD

WV

CO

GA

LA

MA

MN

NH

NJ

OK

OR

TX

UT

VA

VT

WA

WI

CA

DC

IL

NC

NV

WY IA

Dollars (millions)

AR

AZ

CT

DE

FL

IN

MD

MI

MO

NE

NY

OH

PA

RI

TN

1st quartile ($6.23–$25.75)

2nd quartile ($4.99–$6.07)

3rd quartile ($2.30–$4.90)

4th quartile ($0.00–$1.94)

SOURCE: PricewaterhouseCoopers and 

National Venture Capital Association, special 

tabulations (2011) of MoneyTreeTM Survey.
Science and Engineering Indicators 2012 8-127

Table 8-58

Venture capital disbursed per venture capital deal, by state: 2000, 2005, and 2010

Venture capital disbursed 

($millions) Venture capital deals

Venture capital/deal 

($millions)

State 2000 2005 2010 2000 2005 2010 2000 2005 2010

United States ................. 104,678 23,115 21,799 7,873 3,152 3,266 13.30 7.33 6.67

Alabama ..................... 266 20 1 28 3 2 9.51 6.73 0.30

Alaska ........................ 4 0 0 1 0 0 3.50 0.00 0.00

Arizona ....................... 626 123 83 64 25 17 9.77 4.94 4.88

Arkansas .................... 34 13 5 5 2 1 6.86 6.30 5.00

California .................... 43,034 11,011 10,978 2,938 1,310 1,289 14.65 8.40 8.52

Colorado .................... 4,165 644 468 218 76 77 19.10 8.47 6.07

Connecticut ............... 1,509 202 200 116 32 52 13.01 6.30 3.85

Delaware .................... 135 7 31 4 3 9 33.68 2.40 3.45

District of Columbia ... 478 28 96 44 11 11 10.87 2.52 8.77

Florida ........................ 2,687 329 186 185 55 39 14.52 5.98 4.76

Georgia ...................... 2,325 254 333 223 63 63 10.43 4.04 5.29

Hawaii ........................ 203 12 11 3 4 5 67.67 2.98 2.30

Idaho .......................... 20 8 8 4 2 4 4.88 4.00 1.94

Illinois ......................... 2,358 277 575 199 54 60 11.85 5.14 9.59

Indiana ....................... 269 104 69 26 10 14 10.35 10.36 4.90

Iowa ........................... 31 32 52 4 4 2 7.70 8.03 25.75

Kansas ....................... 265 2 42 22 4 36 12.04 0.43 1.16

Kentucky .................... 202 32 12 14 3 14 14.41 10.67 0.85

Louisiana .................... 113 4 18 15 4 3 7.51 1.00 5.98

Maine ......................... 140 5 2 15 2 5 9.35 2.25 0.44

Maryland .................... 1,820 488 357 175 101 70 10.40 4.84 5.10

Massachusetts ........... 10,312 2,583 2,373 766 366 351 13.46 7.06 6.76

Michigan .................... 337 81 152 53 19 33 6.36 4.25 4.59

Minnesota .................. 1,039 242 140 109 43 26 9.53 5.64 5.37

Mississippi ................. 20 10 0 3 2 0 6.50 5.00 0.00

Missouri ..................... 590 56 61 49 10 14 12.05 5.60 4.35

Montana ..................... 17 27 2 3 2 2 5.57 13.70 0.96

Nebraska .................... 143 13 12 10 3 3 14.31 4.37 3.83

Nevada ....................... 31 159 29 10 9 3 3.08 17.61 9.53

New Hampshire ......... 751 92 57 56 24 10 13.40 3.85 5.69

New Jersey ................ 3,290 887 451 185 74 71 17.79 11.99 6.35

New Mexico ............... 21 76 23 8 15 13 2.64 5.09 1.78

New York .................... 6,835 1,127 1,339 605 129 266 11.30 8.74 5.03

North Carolina ............ 1,825 395 456 153 51 57 11.93 7.74 8.00

North Dakota ............. 6 0 0 1 0 0 6.10 0.00 0.00

Ohio ........................... 976 140 157 77 38 52 12.67 3.68 3.02

Oklahoma ................... 53 0 13 9 0 2 5.83 0.00 6.50

Oregon ....................... 793 134 174 68 26 33 11.66 5.17 5.26

Pennsylvania .............. 2,873 482 508 254 99 153 11.31 4.86 3.32

Rhode Island .............. 75 76 65 9 13 13 8.29 5.87 4.99

South Carolina ........... 448 3 21 13 1 8 34.43 2.70 2.59

South Dakota ............. 0 0 0 1 0 0 0.30 0.00 0.00

Tennessee .................. 453 89 52 45 21 18 10.07 4.22 2.89

Texas .......................... 6,094 1,175 891 466 168 143 13.08 6.99 6.23

Utah ........................... 674 192 143 61 28 25 11.04 6.86 5.72

Vermont ...................... 46 35 33 4 5 6 11.60 7.04 5.47

Virginia ....................... 3,310 526 375 275 87 51 12.03 6.04 7.36

Washington ................ 2,790 837 613 254 126 116 10.98 6.64 5.29

West Virginia .............. 5 11 4 2 5 4 2.25 2.10 0.94

Wisconsin .................. 192 69 122 21 16 19 9.13 4.28 6.41

Wyoming .................... 0 4 10 0 4 1 0.00 1.00 10.00

Puerto Rico ................ NA NA 4 10 1 1 NA NA 4.49

NA = not available

NOTE: Venture capital amounts reported in current dollars.

SOURCE: PricewaterhouseCoopers and National Venture Capital Association, special tabulations (2011) of MoneyTreeTM Survey.

Science and Engineering Indicators 2012

Appendix 

Methodology and Statistics

A-1

Introduction

Science and Engineering Indicators (SEI) contains data 

compiled from a variety of sources. The purpose of this appendix is to explain 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 

some basic information about how statistical procedures and 

reasoning are applied.

The first section describes the statistical considerations 

that are part of the selection process for data sets to be included in SEI. The next section discusses the different types 

of data (e.g., sample surveys, censuses, and administrative 

records) used in the report and provides some information 

about each type. A section on data accuracy follows, discussing factors that can affect accuracy at all stages of the 

survey process. The last section discusses the statistical testing employed to determine whether differences between 

sample survey-based 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 sources 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 

that 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 comprise it.

For data collected by governments in other countries and 

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 in developing 

data quality standards and improving international comparability for key data, and methodological information about 

the data generated by this international statistical system is 

relatively complete.

Methodological information about data from nongovernmental sources and from governmental agencies outside the 

international statistical system is often less well documented. These data are evaluated and must meet basic scientific 

standards for representative sampling of survey respondents 

and adequate and unbiased coverage of the population under 

study, and 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 some segment of the S&E enterprise are not cited in 

SEI because their coverage of the United States as a nation 

is partial in terms of geography, incomplete in terms of segments of the population, or otherwise not representative. For 

example, data may be available only for 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:

 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 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 Survey of Recent College Graduates, which gathers data on individuals who recently received bachelor’s or 

master’s degrees in science, engineering, and 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, once 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 Survey of Research and Development 

Expenditures at Universities and Colleges. 

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. 

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. 

Surveys are conducted using a variety of modes (e.g., 

mail, telephone, the Internet, or in person). They can be self 

or interviewer administered. Many surveys are conducted in 

more than one mode. For example, the Survey of Graduate Students and Postdoctorates in Science and Engineering, 

a census of establishments (university departments) from 

which students earn S&E graduate degrees, collects most 

of its data via a Web-based questionnaire but also allows 

respondents to answer a paper questionnaire. The National 

Survey of College Graduates, a longitudinal sample survey 

that collects data on individuals with S&E-related degrees 

and/or occupations, is initially conducted by sending a paper 

questionnaire by mail. Later, potential participants who did 

not respond to the questionnaire are contacted via telephone 

or in person. 

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 
Science and Engineering Indicators 2012  A-3

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 

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. Each stage 

of the survey process is a potential source of nonsampling 

error. For most types, there is no practical method of measuring the extent of nonsampling error. 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 

would 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. Sampling error is not a function of the percentage of the population in the sample (when the population is large) or the 

population size but 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 is measured by the standard error of the 

estimate, sometimes called the “margin of error.” The standard error of an estimate measures how closely the estimate 

from a particular sample approximates the average result of 

all possible samples. The standard error of the estimate is 

expressed as a range in the size of the difference (e.g., ±2%) 

between the sample estimate and the average result of all 

possible samples. 
A-4  Appendix. Methodology and Statistics

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 

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 

.05 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 5%. 

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. 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 and/ 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. 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 
Science and Engineering Indicators 2012  A-5

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. 

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 http://www.nsf.gov/statistics/indicators/appendix/.

Chapter 1. Elementary and Secondary Mathematics and Science Education

1-1 Average NAEP mathematics scores of students in grades 4, 8, and 12, by student and school characteristics: 

1990–2009

1-2 Students in grades 4, 8, and 12 scoring at or above NAEP’s proficient level in mathematics for their grade, by 

student and school characteristics: 1990–2009

1-3 Average NAEP science scores of students in grades 4, 8, and 12, by student and school characteristics: 2009

1-4 Students in grades 4, 8, and 12 scoring at or above NAEP’s proficient level in science, by student and school 

characteristics: 2009

1-5 Ninth-graders proficient in various algebra skill areas, by selected student and school characteristics: 2009

1-6 Average PISA mathematics and science literacy scores of 15-year-old students, by country: 2009 

1-7 Advanced mathematics and science credits earned by high school graduates, by student and school 

characteristics: Selected years, 1990–2009

1-8 High school graduates completing advanced mathematics courses, by student and school characteristics and 

subject: Selected years, 1990–2009

1-9 High school graduates completing advanced science and engineering courses, by student and school 

characteristics and subject: Selected years, 1990–2009

1-10 Public middle and high school mathematics and science teachers, by minority enrollment and school poverty 

level: Academic year 2007–08

1-11 Highest degree attainment of public middle and high school mathematics and science teachers, by minority 

enrollment and school poverty level: Academic years 2003–04 and 2007–08

1-12 Certification of public middle and high school mathematics and science teachers, by minority enrollment, 

school poverty level, and type of certification: Academic years 2003–04 and 2007–08

1-13 Public middle and high school mathematics and science teachers who entered teaching through an alternative 

certification program, by minority enrollment and school poverty level: Academic year 2007–08

1-14 Participation in practice teaching by new public middle and high school mathematics and science teachers, by 

preparation for first-year teaching activities: Academic year 2007–08

1-15 Duration of practice teaching by new public middle and high school mathematics and science teachers, by 

minority enrollment and school poverty level: Academic years 2003–04 and 2007–08

1-16 Pathway to teaching for public middle and high school mathematics and science teachers, by participation in 

practice teaching: Academic year 2007–08

1-17 Preparedness for first-year teaching tasks of new public middle and high school mathematics and science 

teachers, by minority enrollment, school poverty level, and teaching tasks: Academic years 2003–04 and 

2007–08

1-18 Years of teaching experience of public middle and high school mathematics and science teachers, by minority 

enrollment and school poverty level: Academic years 2003–04 and 2007–08

1-19 Preparation of public middle and high school mathematics and science teachers for teaching in their field, by 

minority enrollment and school poverty level: Academic years 2003–04 and 2007–08

1-20 Participation in induction program during first year of teaching among new public middle and high school 

mathematics and science teachers, by minority enrollment and school poverty level: Academic years 2003–04 

and 2007–08

1-21 Support received by new public middle and high school mathematics and science teachers during their first year 

teaching, by support type: Academic year 2007–08

1-22 Participation in and duration of professional development on various topics by public middle and high school 

mathematics and science teachers during the past 12 months, by minority enrollment and school poverty level: 

Academic year 2007–08

1-23 Topics rated as top priority for additional professional development by public middle and high school 

mathematics and science teachers, by minority enrollment and school poverty level: Academic year 2007–08

B-1
B-2  List of Appendix Tables

1-24 Average salaries of public middle and high school mathematics and science teachers and percentage who were 

satisfied with their salaries, by minority enrollment and school poverty level: Academic year 2007–08

1-25 Perceptions of working conditions of public middle and high school mathematics and science teachers, by 

minority enrollment and school poverty level: Academic years 2003–04 and 2007–08

1-26 Current occupational status of former public school mathematics and science teachers: Academic year 2008–09

1-27 Serious student problems reported by public middle and high school mathematics and science teachers, by 

minority enrollment and school poverty level: Academic years 2003–04 and 2007–08

1-28 High school graduates enrolled in college in October after completing high school, by demographic 

characteristics and institution type: 1975–2009

1-29 First-time entry rates into university-level education in OECD countries, by sex: 2008

Chapter 2. Higher Education in Science and Engineering

2-1 S&E degrees awarded, by degree level, Carnegie institution type, and field: 2009

2-2 Degrees awarded by private for-profit academic institutions, by broad field and degree level: 2000–09

2-3 Degrees awarded by private for-profit academic institutions, by field and degree level: 2009

2-4 Undergraduate students in postsecondary institutions taking distance education courses, by control and level of 

institution: 2007–08

2-5 Full-time S&E graduate students, by field and mechanism of primary support: 2009

2-6 Full-time S&E graduate students primarily supported by federal government, by field and mechanism of 

primary support: 2009

2-7 Full-time S&E graduate students primarily supported by federal government, by field and agency: 2009

2-8 Primary support mechanisms for S&E doctorate recipients, by citizenship, sex, and race/ethnicity: 2009

2-9 Amount of undergraduate and graduate debt of S&E doctorate recipients, by field: 2009

2-10 Enrollment in higher education, by Carnegie institution type: 1994–2009

2-11 Projections of U.S. population ages 20–24 years, by sex and race/ethnicity: 2010–50

2-12 Freshmen intending S&E major, by field, sex, and race/ethnicity: 1995–2010

2-13 Freshmen intending to major in selected S&E fields, by sex and race/ethnicity: 1995–2010

2-14 Foreign undergraduate student enrollment in U.S. universities, by field and selected places of origin: November 

2009 and 2010

2-15 Undergraduate enrollment in engineering and engineering technology programs: 1995–2009

2-16 Earned associate’s degrees, by sex and field: 2000–09

2-17 Earned associate’s degrees, by citizenship, field, and race/ethnicity: 2000–09

2-18 Earned bachelor’s degrees, by sex and field: 2000–09

2-19 Earned bachelor’s degrees, by citizenship, field, and race/ethnicity: 2000–09

2-20 S&E graduate enrollment, by sex and field: 2009

2-21 S&E graduate enrollment, by citizenship, field, and race/ethnicity: 2009

2-22 Engineering enrollment, by enrollment level and attendance: 1989–2009

2-23 First-time full-time S&E graduate students, by citizenship and field: 2009

2-24 Foreign graduate student enrollment in U.S. universities, by field and selected places of origin: November 2009 

and 2010

2-25 Earned master’s degrees, by sex and field: 2000–09

2-26 Earned master’s degrees, by citizenship, field, and race/ethnicity: 2000–09

2-27 Earned doctoral degrees, by citizenship, field, and sex: 2000–09

2-28 Earned doctoral degrees, by citizenship, field, and race/ethnicity: 2000–09

2-29 Median number of years from S&E doctorate recipients’ entry to graduate school to receipt of doctorate, by 

field: 1979–2009

2-30 Expenditures on tertiary education as a percentage of GDP and change in expenditures: 1995, 2000, and 2007

2-31 Tertiary-type A, advanced research programs, and tertiary education, by age group and country: 2008

2-32 First university degrees, by selected region and country/economy: 2008 or most recent year

2-33 S&E first university degrees, by selected Western or Asian country/economy and field: 2000–08

2-34 First university degrees, by field, sex, and region/country/economy: 2008 or most recent year

2-35 Earned S&E doctoral degrees, by selected region/country/economy and field: 2008 or most recent year

2-36 Earned S&E doctoral degrees, by sex, selected region/country/economy, and field: 2008 or most recent year

2-37 S&E doctoral degrees in the United States and selected European countries, by field: 2000–08

2-38 S&E doctoral degrees, by selected Asian country/economy and field: 1994–2008
Science and Engineering Indicators 2012 t B-3

2-39 Trends in population ages 20–24 years, by selected country and region: 2000–50

2-40 Foreign S&E student enrollment in UK universities, by enrollment level, place of origin, and field: Academic 

years 1998–99 and 2008–09

2-41 Foreign S&E student enrollment in Japanese universities, by enrollment level, place of origin, and field: 2004 and 

2010

2-42 S&E student enrollment in Canadian universities, by enrollment level, top place of origin, and field: 1999 and 

2008

Chapter 3. Science and Engineering Labor Force

3-1 Bureau of Labor Statistics projections of occupational employment: 2008–18

3-2 Scientists and engineers, by occupation and degree field: 2008

3-3 Occupation of employed S&E degree holders: 2008

3-4 Employment and wages, by broad occupational category, state, and selected metropolitan statistical areas (May 

2010)

3-5 Employment sector of S&E highest degree holders and S&E doctorate holders: 2008

3-6 Primary reason for scientists and engineers to participate in work-related training, by labor force status: 2008

3-7 Scientists and engineers participating in work-related training, by employment status, highest degree level, and 

sex: 2008

3-8 Alternate rates of labor underutilization for S&E, STEM, and all occupations: March 2008–September 2011

3-9 Workers in S&E occupations, by sex and occupation: 2008

3-10 Race/ethnicity of individuals in S&E occupations, by sex and broad occupation: 2008

3-11 Employed S&E highest degree holders, by sex and field of degree: 2008

3-12 Employed S&E highest degree holders, by sex, race/ethnicity, degree level, field of highest degree, and broad 

occupation category: 2008

3-13 S&E workers, by race/ethnicity and occupation: 2008

3-14 Employed S&E highest degree holders, by race/ethnicity and field of degree: 2008

3-15 Estimate and median salary of full-time workers with highest degree in S&E field, by sex and occupation: 2008

3-16 Median salary among scientists and engineers employed full time, by race/ethnicity and occupation: 2008 

(corrected February 2012)

3-17 Full-time S&E workers who are foreign-born, by occupation and highest degree level: 2003 and 2008

3-18 Place of birth and proportion with U.S. highest degrees of non-U.S.-born S&E highest degree holders: 2003 and 

2008

3-19 Occupations of new H-1B visa recipients: FY 2009

3-20 Plans of foreign recipients of U.S. S&E doctorates to stay in the United States, by field and place of origin: 

1998–2009

3-21 Employed S&E highest degree holders, by degree level and age category: 2008

3-22 R&D personnel in selected regions/countries: 1995–2009

3-23 Researchers as a share of total employment in regions/countries/economies with 25,000 or more researchers in 

2009: 1995, 2003, 2009

3-24 Worldwide, domestic, and foreign R&D employment, by selected characteristics: 2009

3-25 S&E workforce international interaction, by employment and respondent characteristic: 2006

Chapter 4. Research and Development: National Trends and International Comparisons

4-1 Gross domestic product and implicit price deflators: 1953–2010

4-2 Purchasing power parity, market exchange rate, ratio of market exchange rate to purchasing power parity, for 

selected countries: 1981–2009

4-3 U.S. research and development expenditures, by performing sector and source of funds: 1953–2009

4-4 U.S. basic research expenditures, by performing sector and source of funds: 1953–2009

4-5 U.S. applied research expenditures, by performing sector and source of funds: 1953–2009

4-6 U.S. development expenditures, by performing sector and source of funds: 1953–2009

4-7 U.S. research and development expenditures, by source of funds and performing sector: 1953–2009

4-8 U.S. basic research expenditures, by source of funds and performing sector: 1953–2009

4-9 U.S. applied research expenditures, by source of funds and performing sectors: 1953–2009
B-4  List of Appendix Tables

4-10 U.S. development expenditures, by source of funds and performing sector: 1953–2009

4-11 U.S. R&D expenditures, by state, performing sector, and funding sector: 2008

4-12 U.S. R&D and gross domestic product, by state: 2008

4-13 Worldwide R&D expense for R&D performed by the company, by industry and company size: 2008

4-14 Domestic and foreign R&D performed by the company, by industry and company size: 2008

4-15 Sources of funds for domestic R&D performed by the company, by industry and company size: 2008

4-16 Domestic sales, domestic R&D performed and paid for by the company, and R&D intensity, by industry and 

company size: 2008

4-17 Worldwide R&D expense and domestic R&D performance paid for by the company and by others, by business 

activity: 2008

4-18 R&D expenditures by majority-owned affiliates of foreign companies in United States, by region/country/

economy of ultimate beneficial owner: 1997–2008

4-19 R&D performed by majority-owned affiliates of foreign companies in United States, by NAICS industry of 

affiliate: 1997–2001

4-20 R&D performed by majority-owned affiliates of foreign companies in United States, by NAICS industry of 

affiliate: 2002–06

4-21 R&D performed by majority-owned affiliates of foreign companies in United States, by NAICS industry of 

affiliate: 2007–08

4-22 R&D performed abroad by majority-owned foreign affiliates of U.S. parent companies, by region/country/

economy: 1997–2008

4-23 R&D performed abroad by majority-owned foreign affiliates of U.S. parent companies, by selected NAICS 

industry of affiliate: 1999–2003

4-24 R&D performed abroad by majority-owned foreign affiliates of U.S. parent companies, by selected NAICS 

industry of affiliate: 2004–08

4-25 R&D performed in United States by U.S. multinational company parent companies, by NAICS industry: 

1999–2003

4-26 R&D performed in United States by U.S. multinational company parent companies, by NAICS industry: 

2004–08

4-27 U.S. trade in research, development, and testing services, by affiliation and by selected region/country/

economy: 2006–09

4-28 Federal budget authority for R&D, by budget function: FY 1980–2010

4-29 Federal budget authority for basic research, by budget function: FY 1980–2010

4-30 Federal obligations for research and development, by character of work and R&D plant: FY 1953–2009

4-31 Estimated federal obligations for R&D and R&D plant, by selected agency, performer, and character of work: 

FY 2009

4-32 Discrepancy between federal R&D support, as reported by performers and federal agencies: 1985–2009

4-33 Federally funded research and development centers, R&D expenditures: FY 2008 and FY 2009

4-34 Federal obligations for research, by agency and S&E field: FY 2009

4-35 Federal obligations for research, by detailed S&E field: Selected years, FY 1989–2009

4-36 Federal research and experimentation tax credit claims, by NAICS industry: 1998–2008

4-37 Corporate tax returns claiming the federal research and experimentation tax credit, by NAICS industry: 1998–

2008

4-38 Federal technology transfer activity indicators, U.S. agencies with federal labs: FY 2001–09

4-39 SBIR and STTR awards, by type of award: FY 1983–2008

4-40 SBIR award funding, by type of award and federal agency: FY 1983–2008

4-41 Small Business Technology Transfer Program award funding, by type of award and federal agency: FY 1994–

2008

4-42 Technology Innovation Program summary data: FY 2008–10

4-43 Gross expenditures for R&D and expenditures for R&D as share of gross domestic product, for selected 

countries: 1981–2009

4-44 Gross expenditures on R&D, by performing and funding sectors, for selected countries: 2008

4-45 Government budget appropriations or outlays for R&D, by socioeconomic objectives, selected countries: 2009

4-46 Share of business expenditures for R&D, by industry and selected country/economy: 2007–10
Science and Engineering Indicators 2012  B-5

Chapter 5. Academic Research and Development

5-1 R&D expenditures at academic institutions and academic share of U.S. total R&D performance, distributed by 

character of work: 1970–2009

5-2 Academic R&D expenditures, by source of funding: 1972–2009

5-3 Federal obligations for academic R&D, by agency: 1970–2009

5-4 Federal obligations for academic R&D, by character of work: 2007–09

5-5 R&D expenditures at academic institutions, by S&E field and source of funds: 2009

5-6 R&D expenditures at academic institutions, by S&E field: Selected years, 1975–2009

5-7 Federally financed R&D expenditures at academic institutions, by S&E field and agency: FY 2009

5-8 Academic R&D funds provided by federal government, by S&E field: Selected years, 1975–2009

5-9 Sources of R&D funds at private and public academic institutions: 1989, 1999, and 2009

5-10 Top 100 academic institutions in S&E R&D expenditures, by source of funds: 2009

5-11 Top 50 academic institutions in non-S&E R&D expenditures, by non-S&E field: 2009

5-12 Expenditures for academic R&D passed through to and received by subrecipients: FY 2000–09

5-13 S&E research space in academic institutions, by field: FY 1988–2009

5-14 Current expenditures for research equipment at academic institutions, by S&E field: Selected years, 1985–2009

5-15 Federal share of current funding for research equipment at academic institutions, by S&E field: Selected years, 

1985–2009

5-16 SEH doctorate holders employed in academia, by type of position and degree field: 1973–2008

5-17 SEH doctorate holders employed in academia, by type of position, sex, and degree field: 1973–2008

5-18 SEH doctorate holders employed in academia, by type of position, degree field, and race/ethnicity: 1973–2008

5-19 SEH doctorate holders employed in academia, by type of position, degree field, and citizenship: 2008

5-20 SEH doctorate holders employed in academia, by research priority, type of position, and degree field: 1973–

2008

5-21 Early career SEH doctorate holders employed in academia, by Carnegie institution type, years since doctorate, 

and type of position: 1973–2008

5-22 Academic SEH doctorate holders with federal support, by degree field, research activity, and type of position: 

1973–2008

5-23 SEH doctorate holders and full-time faculty with federal support, by degree field and Carnegie classification of 

employer: 2008

5-24 SEH doctorate holders employed in academia with federal support, by degree field, years since doctorate, and 

type of position: 1973–2008

5-25 Regions and countries/economies in S&E publications data

5-26 Fields and subfields of S&E publications data

5-27 S&E articles in all fields, by region/country/economy: 1995–2009

5-28 S&E articles in agricultural sciences, by region/country/economy: 1995–2009

5-29 S&E articles in astronomy, by region/country/economy: 1995–2009

5-30 S&E articles in biological sciences, by region/country/economy: 1995–2009

5-31 S&E articles in chemistry, by region/country/economy: 1995–2009

5-32 S&E articles in computer sciences, by region/country/economy: 1995–2009

5-33 S&E articles in engineering, by region/country/economy: 1995–2009

5-34 S&E articles in geosciences, by region/country/economy: 1995–2009

5-35 S&E articles in mathematics, by region/country/economy: 1995–2009

5-36 S&E articles in medical sciences, by region/country/economy: 1995–2009

5-37 S&E articles in other life sciences, by region/country/economy: 1995–2009

5-38 S&E articles in physics, by region/country/economy: 1995–2009

5-39 S&E articles in psychology, by region/country/economy: 1995–2009

5-40 S&E articles in social sciences, by region/country/economy: 1995–2009

5-41 Internationally coauthored S&E articles, by selected country/economy pairs: 1995 and 2010

5-42 Indexes of internationally coauthored S&E articles, by selected country/economy pairs: 1995 and 2010

5-43 U.S. S&E articles, by field and sector: 1995–2010

5-44 S&E articles, by field, citation percentile, and region/country/economy of institutional author: 2000 and 2010

5-45 Share of all S&E articles and top 1% of cited articles and index of highly cited articles, by field and selected 

region/country/economy: 2000 and 2010

5-46 U.S. utility patent awards, by selected characteristics of patent owner: 1998–2010

5-47 U.S. university patents awarded, by technology area: 1990–2010
B-6  List of Appendix Tables

5-48 Academic patenting and licensing activities: 1999–2009

5-49 U.S. utility patents citing S&E literature, by patent assignee sector, article author sector, and patent issue year: 

1998–2010

5-50 Citation of S&E articles in USPTO patents, by cited field and cited country/sector: 1998–2010

Chapter 6. Industry, Technology, and the Global Marketplace

6-1 Value added of knowledge- and technology-intensive industries, by region/country/economy: 1990–2010

6-2 Nominal GDP, by region/country/economy: 1995–2010

6-3 Value added of commercial knowledge-intensive services, by region/country/economy: 1990–2010

6-4 Value added of education services, by region/country/economy: 1990–2010

6-5 Value added of health and social services, by region/country/economy: 1990–2010

6-6 Value added of business services, by region/country/economy: 1990–2010

6-7 Value added of computer programming and related services, by region/country/economy: 1990–2010

6-8 Value added of R&D services, by region/country/economy: 1990–2010

6-9 Value added of financial services, by region/country/economy: 1990–2010

6-10 Value added of communications services, by region/country/economy: 1990–2010

6-11 Value added of high-technology manufacturing industries, by region/country/economy: 1990–2010

6-12 Value added of all manufacturing industries, by region/country/economy: 1990–2010

6-13 Value added of ICT industries, by region/country/economy: 1990–2010

6-14 Real GDP per employed person, by region/country/economy: 1990–2009

6-15 Real GDP per capita, by region/country/economy: 1990–2009

6-16 Value added of pharmaceuticals, by region/country/economy: 1990–2010

6-17 Value added of semiconductors, by region/country/economy: 1990–2010

6-18 Value added of medical, precision, and optical equipment, by region/country/economy: 1990–2010

6-19 Value added of medical and measuring equipment, by region/country/economy: 1990–2010

6-20 Value added of communications equipment, by region/country/economy: 1990–2010

6-21 Value added of aircraft and spacecraft, by region/country/economy: 1990–2010

6-22 Value added of computers and office machinery, by region/country/economy: 1990–2010

6-23 Regions and countries/economies in world industry data

6-24 Exports and imports of high-technology goods, by region/country/economy: Selected years, 1995–2010

6-25 Exports and imports of all manufactured goods, by region/country/economy: Selected years, 1995–2010

6-26 Exports and imports of communications goods, by region/country/economy: Selected years, 1995–2010

6-27 Exports and imports of semiconductor goods, by region/country/economy: Selected years, 1995–2010

6-28 Exports and imports of computer and office machinery goods, by region/country/economy: Selected years, 

1995–2010

6-29 Exports and imports of scientific instruments and measuring equipment goods, by region/country/economy: 

Selected years, 1995–2010

6-30 Exports and imports of pharmaceutical goods, by region/country/economy: Selected years, 1995–2010

6-31 Exports and imports of aerospace goods, by region/country/economy: Selected years, 1995–2010

6-32 Regions and countries/economies in world trade data

6-33 Trade in communications and computer and office machinery goods, by selected region/country/economy of 

destination and origin: Selected years, 1995–2010

6-34 Trade in semiconductors, by selected region/country of destination and origin: Selected years, 1995–2010

6-35 Trade in aerospace goods, by selected region/country of destination and origin: Selected years, 1995–2010

6-36 Trade in pharmaceuticals, by selected region/country of destination and origin: Selected years, 1995–2010

6-37 U.S. trade in advanced-technology products, by region/country/economy: 2000–10

6-38 U.S. trade in information and communications products, by region/country/economy: 2000–10

6-39 U.S. trade in aerospace products, by region/country/economy: 2000–10

6-40 U.S. trade in electronics products, by region/country/economy: 2000–10

6-41 U.S. trade in life-sciences products, by region/country/economy: 2000–10

6-42 U.S. trade in optoelectronics products, by region/country/economy: 2000–10

6-43 U.S. trade in advanced materials, by region/country/economy: 2000–10

6-44 Companies in the United States reporting innovation activities, by industry: 2006–08

6-45 USPTO patent grants, by region/country/economy: 1995–2010

6-46 U.S. patent applications and grants, by industry: 2009
Science and Engineering Indicators 2012  B-7

6-47 USPTO patents granted in information and communications technology, by region/country/economy: 1995–

2010

6-48 USPTO patents granted in networking technology, by region/country/economy: 1995–2010

6-49 USPTO patents granted in information processes technology, by region/country/economy: 1995–2010

6-50 USPTO patents granted in telecommunications technology, by region/country/economy: 1995–2010

6-51 USPTO patents granted in automation and control technology, by region/country/economy: 1995–2010

6-52 USPTO patents granted in medical electronics technology, by region/country/economy: 1995–2010

6-53 USPTO patents granted in semiconductor technology, by region/country/economy: 1995–2010

6-54 USPTO patents granted in optics technology, by region/country/economy: 1995–2010

6-55 USPTO patents granted in measuring techniques and instrumentation technology, by region/country/economy: 

1995–2010

6-56 USPTO patents granted in pharmaceuticals technology, by region/country/economy: 1995–2010

6-57 USPTO patents granted in materials technology, by region/country/economy: 1995–2010

6-58 USPTO patents granted in aerospace and defense technology, by region/country/economy: 1995–2010

6-59 USPTO patents granted in computer systems technology, by region/country/economy: 1995–2010

6-60 USPTO patents granted in biotechnology, by region/country/economy: 1995–2010

6-61 USPTO patents granted in medical equipment technology, by region/country/economy: 1995–2010

6-62 Regions and countries/economies in USPTO patent data

6-63 Triadic patent families, by region/country/economy: 1999–2008

6-64 USPTO trademark applications, by region/country/economy: 1998–2008

6-65 U.S. venture capital investment, by financing stage and industry/technology: 1995–2010

6-66 USPTO patents granted in clean energy and pollution control technologies, by region/country/economy: 

1995–2010

6-67 USPTO patents granted in alternative energy, by region/country/economy: 1995–2010

6-68 USPTO patents granted in energy storage, by region/country/economy: 1995–2010

6-69 USPTO patents granted in smart grid technology, by region/country/economy: 1995–2010

6-70 USPTO patents granted in pollution mitigation, by region/country/economy: 1995–2010

6-71 USPTO patents granted in fuel cells, by region/country/economy: 1995–2010

6-72 USPTO patents granted in nuclear energy, by region/country/economy: 1995–2010

6-73 USPTO patents granted in air pollution control, by region/country/economy: 1995–2010

6-74 USPTO patents granted in water pollution control, by region/country/economy: 1995–2010

6-75 USPTO patents granted in recycling, by region/country/economy: 1995–2010

6-76 USPTO patents granted in bioenergy, by region/country/economy: 1995–2010

6-77 USPTO patents granted in solar energy, by region/country/economy: 1995–2010

6-78 USPTO patents granted in hybrid and electric vehicles, by region/country/economy: 1995–2010

6-79 USPTO patents granted in wind energy, by region/country/economy: 1995–2010

6-80 USPTO patents granted in battery technology, by region/country/economy: 1995–2010

6-81 USPTO patents granted in hydrogen production and storage, by region/country/economy: 1995–2010

6-82 USPTO patents granted in cleaner coal technology, by region/country/economy: 1995–2010

6-83 USPTO patents granted in capture and storage of carbon and other greenhouse gases, by region/country/

economy: 1995–2010

6-84 USPTO patents granted in solid waste control, by region/country/economy: 1995–2010

Chapter 7. Science and Technology: Public Attitudes and Understanding

7-1 Primary source of information about current news events, by respondent characteristic: 2010

7-2 Primary source of information about science and technology, by respondent characteristic: 2010

7-3 Primary source of information about specific scientific issues, by respondent characteristic: 2010

7-4 Public interest in selected issues: 1981–2010

7-5 Public interest in selected issues, by respondent characteristic: 2010

7-6 Visitors to informal science and other cultural institutions: 1981–2008

7-7 Visitors to informal science and other cultural institutions, by respondent characteristic: 2008

7-8 Correct answers to trend factual knowledge of science questions, by respondent characteristic: 1992–2010

7-9 Correct answers to factual knowledge questions in physical and biological sciences: 1985–2010

7-10 Correct answers to factual knowledge questions in physical and biological sciences, by respondent 

characteristic: 2010

7-11 Correct answers to nanotechnology questions, by respondent characteristic: 2010
B-8  List of Appendix Tables

7-12 Correct answers to polar questions, by respondent characteristic: 2006 and 2010

7-13 Correct answers to scientific process questions: 1990–2010

7-14 Correct answers to scientific process questions, by respondent characteristic: 2010

7-15 Correct answers to questions about charts and statistics, reasoning/life sciences, and understanding of 

experiment/controlling variables, by respondent characteristic: 2008

7-16 Comparison of correct answers given by adults and students to scientific process questions

7-17 New science knowledge questions

7-18 Correct answers to scientific terms and concept questions in “factual knowledge of science, scale 2,” by 

respondent characteristic: 2008

7-19 Public assessment of astrology, by respondent characteristic: 1979–2010

7-20 Public assessment of benefits and harms of scientific research, by respondent characteristic: 2010

7-21 Public assessment of whether science and technology result in more opportunities for next generation, by 

respondent characteristic: 2010

7-22 Public assessment of whether science makes life change too fast, by respondent characteristic: 2010

7-23 Attitudes toward science and technology, by region/country: Most recent year

7-24 Public opinion on whether federal government should fund basic scientific research: 1985–2010

7-25 Public opinion on whether federal government should fund basic research, by respondent characteristic: 2010

7-26 Public assessment of federal government spending, by policy area: 1981–2010

7-27 Public confidence in institutional leaders: 1973–2010

7-28 Public preferences about various groups’ influence on decisions about public issues: 2006 and 2010

7-29 Public perceptions of various groups’ understanding of public issues: 2006 and 2010

7-30 Public perceptions of various groups’ impartiality in making policy recommendations about public issues: 2006 

and 2010

7-31 Public perceptions of scientific consensus on public issues: 2006 and 2010

7-32 Awareness, knowledge, and attitudes about stem cell research in the United States, Europe, Japan, and Israel: 

2008

7-33 Familiarity with nanotechnology, by respondent characteristic: 2010

7-34 Public assessment of benefits and harms of nanotechnology, by respondent characteristic: 2010

7-35 Public attitudes toward conducting human health research that may inflict pain or injury to animals: 1988–2008

7-36 Public attitudes toward conducting human health research that may inflict pain or injury to animals, by 

respondent characteristic: 2008

7-37 Public assessment of whether the quality of science and mathematics education in American schools is 

inadequate: 1985–2008

7-38 Public assessment of whether the quality of science and mathematics education in American schools is 

inadequate, by respondent characteristic: 2008

Chapter 8. State Indicators

8-1 Average mathematics NAEP scores and achievement-level results for grades 4 and 8 white students in public 

schools, by state: 2000 and 2009

8-2 Average science NAEP scores and achievement-level results for grades 4 and 8 white students in public 

schools, by state: 2009

8-3 Average mathematics NAEP scores and achievement-level results for grades 4 and 8 black students in public 

schools, by state: 2000 and 2009

8-4 Average science NAEP scores and achievement-level results for grades 4 and 8 black students in public schools, 

by state: 2009

8-5 Average mathematics NAEP scores and achievement-level results for grades 4 and 8 Hispanic students in public 

schools, by state: 2000 and 2009

8-6 Average science NAEP scores and achievement-level results for grades 4 and 8 Hispanic students in public 

schools, by state: 2009

8-7 Average mathematics NAEP scores and achievement-level results for grades 4 and 8 Asian students in public 

schools, by state: 2000 and 2009

8-8 Average science NAEP scores and achievement-level results for grades 4 and 8 Asian students in public 

schools, by state: 2009

8-9 Average mathematics NAEP scores and achievement-level results for grades 4 and 8 female students in public 

schools, by state: 2000 and 2009
Science and Engineering Indicators 2012  B-9

8-10 Average science NAEP scores and achievement-level results for grades 4 and 8 female students in public 

schools, by state: 2009

8-11 Average mathematics NAEP scores and achievement-level results for grades 4 and 8 male students in public 

schools, by state: 2000 and 2009

8-12 Average science NAEP scores and achievement-level results for grades 4 and 8 male students in public schools, 

by state: 2009

8-13 Coefficient of variation for estimates of employed S&E doctorate holders, by state: 1997, 2003, and 2008

8-14 Coefficient of variation for estimates of science and engineering doctorate holders in academia, by state: 1997, 

2003, and 2008
A

Academic research and development. See also Research and 

development (R&D)

 article output per $1 million of, 8.108f, 8.109t

 as share of GDP, 8.102f, 8.103t

 bricks and mortar infrastructure for, 5.15–18

 by institution, 5.13–15

 collaborative, 5.27

 congressional earmarks for, 5.10

 cyberinfrastructure for, 5.18–19

 demographics of researchers in, 5.22–25

 Department of Agriculture in, 5.12t

 Department of Defense in, 5.12t

 Department of Energy in, 5.12t

 doctoral scientists and engineers in, 5.25–27

 employment trends in, 5.19–31, 5.32f, 5.32t

 Environmental Protection Agency in, 5.12t

 expenditures

 by field, 5.11–13

 by funding source, 5.7–11

 in agricultural and natural resources, 5.16f

 in biological and biomedical sciences, 5.16f

 in computer and information sciences, 5.16f

 in engineering, 5.16f

 in medical sciences, 5.16f

 in physical sciences, 5.16f

 federal support of, 5.9–10

 top agencies in, 5.10

 financial resources for, 5.7–15, 5.12t

 government support of academic doctoral researchers, 5.28–31

 in computer sciences, 5.13f

 in engineering, 5.13f

 in environmental sciences, 5.13f

 in life sciences, 5.13f

 in mathematics, 5.13f

 in physical sciences, 5.13f

 in psychology, 5.13f

 in social sciences, 5.13f

 industry funding for, 5.11

 infrastructure, 5.15–19

 institutional funds for, 5.10

 interdisciplinary, 5.53, note 31

 internal institutional networks in, 5.19

 Internet access and, 5.18–19

 life sciences, 5.13f

 National Aeronautics and Space Administration in, 5.12t

 National Institutes of Health in, 5.12t

 National Science Foundation in, 5.12t

 non-science and engineering, 5.13

 output of, 5.32–50

 racial/ethnic groups in, 5.23–25

 recent doctorate holders in, 3.34–35, 5.21–26, 5.28

 space for, 5.16–17

 by field, 5.17t

 in agricultural sciences, 5.17t

 in biological sciences, 5.17t

 in computer sciences, 5.17t

 in mathematics, 5.17t

 in physical sciences, 5.17t

 in psychology, 5.17t

 in social sciences, 5.17t

 new construction of, 5.16–17

 state and local government funding for, 5.10–11

 USDA in, 5.12t

 within national research and development enterprise, 5.7

 women in, 5.22–23

Achievement gaps, in mathematics, 1.13, 1.13t

Aerospace

 patents, 6.51f

 value added in, 6.24f

Agency for International Development (AID), 4.32t, 4.35t

Agriculture, value added in, 6.27t

AID. See Agency for International Development (AID)

Alabama. See Chapter 8

Alaska. See Chapter 8

Angel investment, 6.57–58, 6.58f, 6.59f

Animals, research on, public attitudes about, 7.43–44

Apple iPad, 6.30, 6.30t

Argentina

 Articles coauthored with United States, 5.39t

 international collaboration on articles in, 5.38t

 journal articles from, 5.34t

 research and development expenditures as share of GDP, 4.45t

Arizona. See Chapter 8

Arkansas. See Chapter 8

Asia. See also specific countries

 article collaboration in, 5.38t

 ascent of, O.3

 business services in, 6.24f

 citation of papers from, 5.44f

 citations in articles from, O.14f

 communications equipment in, 6.24f

 computer and office machinery manufacturing in, O.16f

 doctorate recipients from, 2.29, 2.29f, 2.29t

 education services in, 6.13t

 exports of high-technology products, 6.35f

 financial services in, 6.33f

 gross domestic product (GDP)

 per capita, 6.16f

 per employed person, 6.15f

 health services in, 6.13t

 high-technology manufacturing, O.16, 6.22f, 6.24f

 consumption of high-technology products, 6.23, 6.23f

 growth of, 6.20f

 value added in, O.16f, 6.22f, 6.24f

 high-value patents from, O.14f

 highly cited works from, 5.46f

 information and communication technology

 exports, O.17, 6.35f, 6.36t

 imports, 6.37f

 output of, 6.13f

 value added, 6.21f, 6.24f

 journal articles produced in, O.10f, 5.32–41

 in engineering, O.10f

 knowledge- and technology-intensive industry in, 6.12f, 6.13f

 knowledge-intensive industry as share of GDP, O.15f

 manufacturing value added, 6.29t

 research and development expenditures, O.4f, O.5f, 4.40–52

 trade balance in, O.19f, 6.29–42

 U.S. advanced technology trade with, 6.34

 U.S. patent grants to, O.14f

 value of knowledge-intensive services in, O.15f

Australia

 article collaboration in, 5.38t

Index

I-1
I-2  Index

 broadband penetration in, 6.17f

 coauthorship from, with United States, 5.39t

 educational attainment in, 2.33f

 foreign students in, 2.36f

 industrial research and development in, 4.45t

 international collaboration on articles in, 5.38t

 journal articles from, 5.34t

 research and development by U.S. companies in, 4.29t

 research and development expenditures as share of GDP, 4.45t, 4.46f

Austria

 coauthorship from, with United States, 5.39t

 educational attainment in, 2.33f

 foreign students in, 2.36f

 journal articles from, 5.34t

 research and development expenditures as share of GDP, 4.45t

B

Belarus, research and development expenditures as share of GDP, 4.45t

Belgium

 coauthorship from, with United States, 5.39t

 educational attainment in, 2.33f

 foreign students in, 2.36f

 industrial research and development in, 4.45t

 international collaboration on articles in, 5.38t

 journal articles from, 5.34t

 research and development by U.S. companies in, 4.29t

 research and development expenditures as share of GDP, 4.45t

Bibliometric data. See Literature, scientific and technical

Biotechnology

 patents, 6.53

 public attitudes about, 7.40–41

Bologna Process, 2.32, 2.34

Brazil

 coauthorship from, with United States, 5.39t

 international collaboration on articles in, 5.38t

 journal articles from, 5.34t

 research and development by U.S. companies in, 4.29t

 tertiary education achievement in, O.7f

Broadband penetration, in selected region/country, 6.17f

C

California. See Chapter 8

 research and development in, 4.12t

Canada

 article collaboration in, 5.38t

 broadband penetration in, 6.17f

 coauthorship from, with United States, 5.39t

 doctorate recipients from, 2.29t, 2.31f

 educational attainment in, 2.33f

 enrollment in U.S. undergraduate programs, 2.19f

 foreign students in, 2.36f

 GDP in, by sector, 4.44f

 H-1B holders from, 3.51f

 immigrants from, education of, 3.53f

 industrial research and development in, 4.45t

 international collaboration on articles in, 5.38t

 journal articles from, 5.34t

 research and development by U.S. companies in, 4.29t

 research and development expenditures as share of GDP, 4.45t, 4.46f

 stay rates of doctorate recipients from, 3.53f

 U.S. advanced technology trade with, 6.34

Carnegie Classification of Institutions of Higher Education, 2.8

Charts, understanding of, 7.26

Chile

 educational attainment in, 2.33f

 foreign students in, 2.36f

 journal articles from, 5.34t

 research and development expenditures as share of GDP, 4.45t

China

 article collaboration in, 5.39t, 5.38t

 citation of papers from, O.14f, 5.44f

 citation patterns, O.14f

 coauthorship from, with United States, 5.39t

 commercial knowledge-intensive services, 6.24f

 communications equipment manufacturing in, 6.24f

 computer and office machinery manufacturing in, O.16f

 doctoral degrees in, O.8f

 doctorate recipients from, 2.29t, 2.29f

 education services in, 6.13t

 enrollment in U.S. undergraduate programs, 2.19f

 exports of commercial knowledge-intensive services, O.17f

 exports of high-technology products, 6.35f

 exports to selected countries, O.18f

 financial services in, 6.33f

 gross domestic product

 by sector, 4.44f

 per capita, 6.16f

 per employed person, 6.15f

 H-1B holders from, 3.51f

 health services in, 6.13t

 high-technology manufacturing

 consumption of high-technology products, 6.23f

 value added in, O.16, 6.22f, 6.24f

 high-value patents from, O.14f

 highly cited works from, 5.46f

 immigrants from, education of, 3.53f

 information and communication technology

 export share, O.17f

 imports, 6.37f, 6.39f

 output of, 6.13f

 value added, 6.21f, 6.24f

 international collaboration on articles in, 5.38t

 journal articles from, O.10f, 5.34t

 in engineering, O.10f, O.11f

 knowledge- and technology-intensive industry output in, 6.12f, 6.13f

 knowledge-intensive industry as share of GDP, O.15f

 manufacturing value added, 6.24f, 6.29t

 patent trends in, 6.50

 research and development by U.S. companies in, 4.29t

 research and development expenditures, O.4, O.5f

 as share of GDP, 4.45t, 4.46f

 researcher numbers in, O.9–10, O.9f

 South Korea exports to, O.18f

 stay rates of doctorate recipients from, 3.53f

 supercomputers in, 6.25, 6.25f

 Taiwan exports to, O.18f

 tertiary education achievement in, O.7f

 trade balance in, O.19f

 U.S. advanced technology trade with, 6.41–42

 U.S. patent grants to, O.14f

 value of knowledge-intensive services in, O.15f

Climate change, public attitudes about, 7.36–38

Cloning, public attitudes about, 7.40–41

Colorado. See Chapter 8

Commercial knowledge-intensive services industries, 6.11–12, 6.20f

Commercial services, non-knowledge-intensive, 6.26

Common Core State Standards, 1.18

Computer specialists, as share of workforce, 8.84f, 8.85t

Connecticut. See Chapter 8

 research and development in, 4.12t

Construction, value added in, 6.27f

Croatia
Science and Engineering Indicators 2012 I-3

 journal articles from, 5.34t

 research and development expenditures as share of GDP, 4.45t

Cuba, international mobility of students, 2.36f

Czech Republic

 educational attainment in, 2.33f

 foreign students in, 2.36f

 high school graduation rate in, 1.33f

 industrial research and development in, 4.45t

 international collaboration on articles in, 5.38t

 journal articles from, 5.34t

 research and development expenditures as share of GDP, 4.45t

D

Delaware. See Chapter 8

Denmark

 coauthorship from, with United States, 5.39t

 educational attainment in, 2.33f

 foreign students in, 2.36f

 high school graduation rate in, 1.33f

 international collaboration on articles in, 5.38t

 journal articles from, 5.34t

 research and development expenditures as share of GDP, 4.45t, 4.46f

Department of Agriculture (USDA), 4.32t, 4.33, 4.35t, 4.36f, 4.37f, 5.12t

Department of Commerce (DOC), 4.32t, 4.33, 4.35t, 4.36f, 4.37f

Department of Defense (DOD), 4.31, 4.32t, 4.35t, 4.36f, 4.37f, 5.12t

Department of Education (ED), 4.32t, 4.35t

Department of Energy (DOE), 4.32t, 4.32–33, 4.35t, 4.36f, 4.37f, 

5.12t

Department of Health and Human Services (HHS), 4.31–32, 4.32t, 

4.35t, 4.36f, 4.37f

Department of Homeland Security (DHS), 4.32t, 4.33, 4.35t, 4.36f

Department of the Interior (DOI), 4.32t, 4.35t

Department of Transportation (DOT), 4.32t, 4.35t

DHS. See Department of Homeland Security (DHS)

District of Columbia. See Chapter 8

 research and development in, 4.12t

DOC. See Department of Commerce (DOC)

DOD. See Department of Defense (DOD)

DOE. See Department of Energy (DOE)

DOI. See Department of the Interior (DOI)

DOT. See Department of Transportation (DOT)

E

ED. See Department of Education (ED)

Education. See also Academic research and development; Students

 Advanced Placement program, 8.34f, 8.35t, 8.36f, 8.37t, 8.38f, 8.39t

 associate’s degrees

 in science and engineering, 2.20

 or higher among 25–44-year-olds, 8.70f, 8.71t

 bachelor’s degrees, 2.20–23

 by citizenship, 2.22

 by field, 2.20f

 by race/ethnicity, 2.21–22, 2.22f

 female share of, 2.21f

 holders potentially in workforce, 8.74, 8.74f, 8.75t

 minority share of, 2.22f

 or higher among 25–44-year-olds, 8.72f, 8.73t

 per 1,000 18–24-year-olds, 8.42f, 8.43t

 in science and engineering, 8.44f, 8.45t

 in natural sciences and engineering, 8.46f, 8.47t

 in charter schools, 1.11

 Carnegie Classification of Institutions of Higher Education, 2.8

 Common Core State Standards in, 1.18

 community colleges, 2.8–9

 distance, 2.10

 doctoral degrees, O.8f, 2.26–31

 article output per 1,000 holders of, 8.106f, 8.107t

 by citizenship, 2.29f, 2.30f

 by country/economy of origin, 2.29–2.31, 2.29t, 2.30t, 2.31t

 by field, 2.27f

 by race/ethnicity, 2.27–28, 2.28f, 2.29f

 by sex, 2.27

 completion and attrition, 2.27

 conferred in S&E per 1,000 employed S&E doctorate holders, 

8.104f, 8.105t

 employed holders of, as share of workforce, 8.78f, 8.79t

 foreign recipients, 2.28, 2.29–31, 2.29f, 2.29t, 2.30t, 2.31t

 global comparison of, 2.34

 globalization and, 2.34–37

 labor market for, 3.33–40

 patents per 1,000 holders of science and engineering, 8.110f, 

8.111t

 salaries for holders of, 3.36

 stay rates, 3.50–52, 3.53f

 tenure-track positions for holders of, 3.35–36

 time to completion, 2.27, 2.28t

 unemployment among holders of, 3.35

 expenditures, U.S.

 as share of GDP, 8.30f, 8.31t

 per pupil, 8.32f, 8.33t

 financial aid for, 2.11–16, 8.66f, 8.67t

 graduate, in United States, 2.24–31

 in science and engineering per 1,000 25–34 year olds, 8.52f, 8.53t

 graduation rates, 1.30–31, 1.32t, 1.33f

 high school completion, 1.30–31, 8.40f, 8.41t

 higher

 advanced science and engineering degrees as share of total 

science and engineering degrees, 8.54f, 8.55t, 8.56f, 8.57t

 associate’s degrees, 2.20

 bachelor’s degrees, 2.20–23

 by country, O.7f

 cost of, 2.11, 2.11f

 distance, 2.10

 for-profit institutions, 2.9

 immediate enrollment in, 1.30

 online, 2.10

 overview of U.S., 2.7–15

 transition to, 1.30–34

 workforce trends and, O.7–8

 international expenditures on higher, 2.32

 international mobility of students, 2.34–37

 master’s degrees, 2.25–26

 by citizenship, 2.26

 by field, 2.25f

 by race/ethnicity, 2.26, 2.26f

 by sex, 2.25, 2.25f

 professional, 2.25

 mathematics (precollege)

 eighth grade performance in, 1.9–12 1.11f, 1.12t, 1.13t, 8.20f, 8.21t

 eighth grade proficiency in, 8.22f, 8.23t

 elementary student performance in, 1.8–12, 1.10t

 fourth grade performance in, 1.9–12 1.11f, 1.12t, 1.13t, 8.12f, 8.13t

 fourth grade proficiency in, 8.14f, 8.15t

 gap changes in, 1.13, 1.13t

 international assessments of, 1.14–15

 middle grade student performance in, 1.8–12, 1.10t

 proficiency in different skill areas, 1.13–14, 1.14f

 public attitudes about, 7.44

 race/ethnicity and achievement in, 1.10t

 skills areas, 1.14

 national assessments, 1.8–14

 of immigrants to United States, 3.48
I-4  Index

 relationship of employment and, 3.16–17

 science (precollege)

 achievement gaps in, 1.13

 and engineering degrees as share of total degrees, 8.48f, 8.49t, 

8.50f, 8.51t

 eighth grade performance in, 8.24f, 8.25t

 eighth grade proficiency in, 8.26f, 8.27t

 fifteen-year-olds’ performance in, 1.15

 fourth grade performance in, 8.16f, 8.17t

 fourth grade proficiency in, 8.18f, 8.19t

 public attitudes about, 7.44

 rising performance in, 1.13

 state achievement tests, 1.23

 teachers (precollege)

 attrition, 1.28–29, 1.29f

 certification of, 1.22–1.24

 experience of, 1.25

 formal preparation of, 1.22–25

 professional development of, 1.26–28, 1.27f, 1.28f

 quality of, 1.22–25

 salaries of, 1.28, 1.30f, 8.28f, 8.29t

 subject area preparation of, 1.25–26, 1.26t

 working conditions, 1.28–30, 1.31f

 undergraduate

 average cost of, 8.60f, 8.61t

 as share of disposable income, 8.62f, 8.63t

 degree awards, 2.20–22

 in United States, 2.16–23

Egypt

 journal articles from, 5.34t

 research and development expenditures as share of GDP, 4.45t

Employment. See also Workforce, science and engineering

 in high technology as share of total, 8.118f, 8.119t

Energy

 investment in, 6.60–68

 patents, 5.48–50, 6.65–69

Engineers, as share of workforce, 8.80f, 8.81t

Environment, public attitudes about, 7.36–40

Environmental Protection Agency (EPA), 4.32t, 4.35t, 5.12t

EPA. See Environmental Protection Agency (EPA)

EPSCoR. See Experimental Program to Stimulate Competitive 

Research (EPSCoR)

Estonia, educational attainment in, 2.33f

EU. See European Union (EU)

European Union (EU)

 article collaboration in, 5.38t

 broadband penetration in, 6.17f

 China exports to, O.18f

 citation of papers from, 5.44f

 communications equipment manufacturing in, 6.24f

 computer and office machinery manufacturing market shares, 

O.16f

 doctorate recipients from, 2.30, 2.30f

 education services in, 6.13t

 export share, knowledge-intensive services, O.17f

 exports of high-technology products, 6.35f

 financial services in, 6.12f

 gross domestic product, per employed person, 6.15f

 health services in, 6.13t

 high-technology manufacturing

 consumption of high-technology products, 6.23f

 value added in, O.16, 6.22f, 6.24f

 high-value patents from, O.14f

 highly cited works from, 5.46f

 knowledge- and technology-intensive industries

 exports, O.17f

 output of, 6.12f

 trade balance in, O.19f

 value added, 6.21f, 6.24f

 journal articles produced by, O.10f

 in engineering, O.10f, O.11f

 knowledge- and technology-intensive industry output in, 6.12f, 

6.13f

 knowledge-intensive services in, O.15f

 manufacturing value added, 6.24f, 6.29t

 research and development expenditures, O.4f, O.5f

 as share of GDP, 4.45t

 researcher numbers in, O.9–10, O.9f

 South Korea exports to, O.18f

 Taiwan exports to, O.18f

 trade balance in, O.19f

 U.S. advanced technology trade with, 6.34

 U.S. patent grants to, O.14f

 value added of knowledge-intensive services in, O.15f

Evolution 

 public attitudes about teaching of, 7.37, 7.41–42

 public knowledge about, 7.20

Experimental Program to Stimulate Competitive Research (EPSCoR), 

5.11, 5.12, 8.8–9. See also Chapter 8

Exports. See also Globalization; Trade

 of knowledge-intensive services, O.17f

 of high-technology products by selected region/country/economy, 

6.35f

 of medium- and low-technology products, 6.39

 trade patterns and, O.17–18

 valuation of, 6.11

F

Federal government, U.S.

 as research and development funding source, 4.13–15

 as research and development performers, 4.12

 employment by, 3.24

 in research and development, 4.28–37

 by agency, 4.31–33, 4.31f

 by field, 4.33–35, 4.37f

 by national objective, 4.28–30

 by performer, 4.31–33

 civilian-related, 4.30

 defense-related, 4.28–30

 in federal budget, 4.28–30, 4.31f

 obligations per civilian worker, 8.90f, 8.91t

 obligations per individual in science and engineering 

occupation, 8.92f, 8.93t

 tax credits, 4.35–37

 public opinion on funding of scientific research by, 7.29–32

 research and development by, 4.28–37

 technology transfer by, 4.38, 4.39, 4.40

Financial services, 6.33f

Finland

 coauthorship from, with United States, 5.39t

 high school graduation rate in, 1.33f

 industrial research and development in, 4.45t

 international collaboration on articles in, 5.38t

 international mobility of students from, 2.36f

 journal articles from, 5.34t

 research and development expenditures as share of GDP, 4.45t

Florida. See Chapter 8

Foreign direct investment (FDI)

 in knowledge- and technology-intensive industries, 6.45–46

 in research and development, 4.25

France

 article collaboration in, 5.38t
Science and Engineering Indicators 2012 I-5

 broadband penetration in, 6.17f

 coauthorship from, with United States, 5.39t

 doctoral degrees, 2.30f

 educational attainment in, 2.33f

 first university degrees in, O.8f

 GDP in, by sector, 4.44f

 H-1B holders from, 3.51f

 industrial research and development in, 4.45t

 international collaboration on articles in, 5.38t, 5.41t

 international mobility of students from, 2.36f

 journal articles from, 5.34t

 research and development by U.S. companies in, 4.29t

 research and development expenditures as share of GDP, 4.45t, 

4.46f, 4.47t

G

GDP. See Gross domestic product (GDP)

Genetically modified (GM) food, public attitudes about, 7.42

Georgia. See Chapter 8

Germany

 academic research and development expenditures in, 4.52f

 article collaboration in, 5.38t

 broadband penetration in, 6.17f

 coauthorship from, with United States, 5.39t

 doctorate recipients from, 2.29t

 educational attainment in, 2.33f

 first university degrees in, O.8f

 foreign students in, 2.36f

 GDP in, by sector, 4.44f

 H-1B holders from, 3.51f

 high school graduation rate in, 1.33f

 immigrants from, education of, 3.53f

 industrial research and development in, 4.45t, 4.50f

 international collaboration on articles in, 5.38t

 journal articles from, 5.34t

 research and development by U.S. companies in, 4.29t

 research and development expenditures as share of GDP, 4.45t, 4.46f

 stay rates of doctorate recipients from, 3.53f

 tertiary education achievement in, O.7f

Global warming. See Climate change

Globalization. See also Exports; Trade

 doctoral education and, 2.34–37

 of knowledge-intensive services industries, 6.29–46

 value chain and, 6.30–31

GM. See Genetically modified (GM) food

Greece

 coauthorship from, with United States, 5.39t

 educational attainment in, 2.33f

 first-time entry rates into postsecondary education, 1.39t

 foreign students in, 2.36f

 high school graduation rate in, 1.33f

 journal articles from, 5.34t

 research and development expenditures as share of GDP, 4.45t

Gross domestic product (GDP)

 academic research and development as share of, 8.102f, 8.103t

 comparison of, for selected countries by sector, 4.44f

 education expenditures as share of, U.S., 8.30f, 8.31t

 information and communication technology as share of, 6.13f

 knowledge-intensive industry output as share of, 6.12f

 research and development as share of, O.4–5, O.5f, 8.88f, 8.89t

 from state agencies, 8.94f, 8.95t

 research and development ratio with, in U.S. states, 4.11, 4.12t

 technology manufacturing as share of, 6.13f

H

H-1B visas, 3.49–50, 3.51f, 3.52t

Hawaii. See Chapter 8

Health services, 6.12–13, 6.13t

HHS. See Department of Health and Human Services (HHS)

Hong Kong, research and development by U.S. companies in, 4.29t

Human cloning, public attitudes about, 7.40–41

Hungary

 educational attainment in, 2.33f

 foreign students in, 2.36f

 high school graduation rate in, 1.33f

 journal articles from, 5.34t

 research and development expenditures as share of GDP, 4.45t

I

Iceland

 educational attainment in, 2.33f

 high school graduation rate in, 1.33f

 research and development expenditures as share of GDP, 4.45t

ICT. See Information and communications technology (ICT)

Idaho. See Chapter 8

Illinois. See Chapter 8

 research and development in, 4.11, 4.12t

Imports, valuation of, 6.11

India

 coauthorship from, with United States, 5.39t

 doctoral degrees in, O.8f

 doctorate recipients from, 2.29t, 2.29f

 enrollment in U.S. undergraduate programs, 2.19f

 H-1B holders from, 3.51f

 immigrants from, education of, 3.53f

 international collaboration on articles in, 5.38t

 journal articles

 from, 5.34t

 in engineering, O.11f

 patent trends in, 6.50

 research and development by U.S. companies in, 4.29t

 stay rates of doctorate recipients from, 3.53f

 tertiary education achievement in, O.7f, 2.33f

Indiana. See Chapter 8

Indonesia, tertiary education achievement in, O.7f

Information and communications technology (ICT). See also

Knowledge- and technology-intensive (KTI) industries

 as share of GDP, 6.13f

 China imports of, 6.37f

 exports, from Asia, 6.36t, 6.37f

 importance of, 6.14

 imports of, 6.37f, 6.39f

 indicators, 6.14–15

 industries in, 6.14–15

 Japan exports of, 6.36t

 manufacturing and, 6.44–45

 output in, as share of GDP, 6.13f

 patenting, 6.51–53, 6.52t

 spending, by region/country, 6.13f

 trade balance of, 6.34f

 value added of, 6.21f, 6.24f, 6.25–26

Innovation-related metrics, 4.18, 6.46–60

Interdisciplinary research, 5.53, note 31

Internet access

 academic research and development and, 5.18–19

 broadband penetration and, 6.17f

Iowa. See Chapter 8

iPad, 6.30f

Iran

 immigrants from, education of, 3.53f

 journal articles from, 5.34t

 research and development expenditures as share of GDP, 4.45t

 stay rates of doctorate recipients from, 3.53f

Ireland
I-6  Index

 educational attainment in, 2.33f

 high school graduation rate in, 1.33f

 industrial research and development in, 4.45t

 international mobility of students from, 2.36f

 journal articles from, 5.34t

 research and development by U.S. companies in, 4.29t

 research and development expenditures as share of GDP, 4.45t, 4.46f

Israel

 coauthorship from, with United States, 5.39t

 educational attainment in, 2.33f

 journal articles from, 5.34t

 research and development by U.S. companies in, 4.29t

 research and development expenditures as share of GDP, 4.45t, 4.46f

Italy

 article collaboration in, 5.38t

 coauthorship from, with United States, 5.39t

 educational attainment in, 2.33f

 foreign students in, 2.36f

 GDP in, by sector, 4.44f

 high school graduation rate in, 1.33f

 industrial research and development in, 4.45t

 international collaboration on articles in, 5.38t

 journal articles from, 5.34t

 research and development by U.S. companies in, 4.29t

 research and development expenditures as share of GDP, 4.45t, 4.46f

J

Japan

 academic research and development expenditures in, 4.52f

 article collaboration in, 5.39t, 5.38t

 broadband penetration in, 6.17f

 business services in, 6.33f

 China exports to, O.18f

 citation of papers from, 5.44t

 coauthorship from, with United States, 5.39t

 commercial knowledge-intensive services in, 6.24f

 communication services in, 6.24f

 computer and office machinery manufacturing in, O.16f

 doctoral degrees in, O.8f

 doctorate recipients from, 2.29t

 education services in, 6.13t

 educational attainment in, tertiary, 2.33f

 enrollment in U.S. undergraduate programs, 2.19f

 export share, high-technology, O.17f

 exports of high-technology products, 6.35f

 exports to China, O.18f

 exports to U.S., O.18f

 financial services in, 6.33f

 foreign students in, 2.36f

 gross domestic product

 by employed person, 6.15f

 by sector, 4.44f

 H-1B holders from, 3.51f

 health services in, 6.13t

 high school graduation rate in, 1.33f

 high-technology manufacturing

 consumption of high-technology products, 6.23f

 value added in, O.16, 6.22f, 6.24f

 high-value patents from, O.14f

 highly cited works from, 5.46f

 immigrants from, education of, 3.53f

 industrial research and development in, 4.45t

 information and communication technology

 exports, O.17f, 6.36t

 imports, 6.37f

 output of, 6.13f

 value added, 6.21f, 6.24f

 international collaboration on articles in, 5.38t

 journal articles produced in, O.10, 5.32–41

 in engineering, O.11f

 knowledge- and technology-intensive industry output in, 6.12f, 6.13f

 knowledge-intensive services in, O.15f

 manufacturing value added, 6.29t

 research and development by U.S. companies in, 4.29t

 research and development expenditures, O.5f, 4.40–52

 as share of GDP, 4.45t, 4.46f

 researcher numbers in, O.9, O.9f

 stay rates of doctorate recipients from, 3.53f

 tertiary education achievement in, O.7f, 2.33f

 trade balance in, O.19f, 6.29–42

 U.S. advanced technology trade with, 6.41–42, 6.43f

 U.S. patent grants to, O.14f

 value added of knowledge-intensive services in, O.15f, 6.20f

Journal articles, O.9–11, 5.32–45

 author names in, 5.35–36

 by country/economy, 5.34t

 citations in

 research patterns and, O.12

 trends in, 5.43–45

 coauthorship of, O.11–12, O.11f, 5.35–40, 

 collaboration on, 5.35–40

 engineering, in selected regions/countries, O.11f

 highly cited, 5.43–45, 5.46f

 international coauthorship of, with United States, 5.39t

 output by sector, 5.41–43

 patent citations to, 5.48–50

 per $1 million of academic research and development, 8.108f, 

8.109t

 per 1,000 science and engineering doctorate holders, 8.106f, 8.107t

K

Kansas. See Chapter 8

Kentucky. See Chapter 8

Knowledge- and technology-intensive (KTI) industries

 commercial service, 6.11–12

 data and terminology in, 6.11

 foreign direct investment in, 6.45–46

 global output of, 6.12f

 globalization and, 6.29–46

 in education sector, 6.12–13

 in health sector, 6.12–13

 in world economy, 6.10–17

 investment in, 6.45–46

 multinational companies in, 6.42–45

 output of, by selected region/country, 6.13f

 trade and, 6.29–46

 value added of, global, 6.12f

 worldwide distribution of, 6.17–28

Knowledge-intensive firms, rising output of, O.15–16

Korea. See South Korea

KTI. See Knowledge- and technology-intensive (KTI) industries

L

Leadership, public confidence in scientific, 7.31–32, 7.31t

Literature, scientific and technical

 as research output, O.9–11, O.10f

 author names in, 5.35–36

 bibliometric terminology, 5.33

 by country/economy, 5.34t

 citations in, O.12, 5.43–45

 coauthorship of, O.11f, 5.35–40

 collaboration on, 5.35–41
Science and Engineering Indicators 2012 I-7

 engineering, in selected regions/countries, O.11f

 highly cited, 5.43–45, 5.46f

 international coauthorship of, with United States, 5.38–39, 5.39t

 output by sector, 5.41–42

 patent citations to, 5.48–50

 per $1 million of academic research and development, 8.108f, 8.109t

 per 1,000 science and engineering doctorate holders, 8.106f, 8.107t

Louisiana. See Chapter 8

Luxembourg

 educational attainment in, 2.33f

 high school graduation rate in, 1.33f

 research and development expenditures as share of GDP, 4.45t

M

Maine. See Chapter 8

Malaysia

 enrollment in U.S. undergraduate programs, 2.19f

 information and communication technology

 exports, 6.35f, 6.36t

 imports, 6.37f

 research and development by U.S. companies in, 4.29t

 research and development expenditures, O.4f

Manufacturing

 computer and office machinery, value added, O.16f

 high-technology, O.15–16

 by selected region/country, O.16f

 consumption of products of, 6.23, 6.23f

 multinational companies in, 6.44–45

 value added of selected industries, by selected region/country/

economy, 6.24f

 non-high-technology, 6.26–27

 trade balance trends in, 6.26–38

 value added for, 6.29t

 value chain geography of, 6.30

 value added of high-technology manufacturing, O.16f

Maryland. See Chapter 8

 research and development in, 4.12t

Massachusetts. See Chapter 8

 research and development in, 4.12t

Mathematics (precollege)

 achievement gaps, 1.13, 1.13t

 achievement in charter schools, 1.11, 1.11f

 eighth grade performance in, 1.8–12 1.11f, 1.12t, 1.13t, 8.20f, 8.21t

 eighth grade proficiency in, 8.22f, 8.23t

 elementary student performance in, 1.8–12, 1.10t

 fifteen-year-olds’ performance in, 1.15

 fourth grade performance in, 1.8–12 1.11f, 1.12t, 1.13t, 8.12f, 8.13t

 fourth grade proficiency in, 8.14f, 8.15t

 international assessments of, 1.14–15

 middle grade student performance in, 1.8–12

 proficiency in different skill areas, 1.13–14, 1.15f

 race/ethnicity and achievement in, 1.10t

 skills areas, 1.14

Mexico

 coauthorship from, with United States, 5.39t

 doctorate recipients from, 2.29t, 2.31f

 educational attainment in, 2.33f

 first university degrees in, O.7f

 H-1B holders from, 3.51f

 high school graduation rate in, 1.33f

 international collaboration on articles in, 5.38t

 journal articles from, 5.34t

 research and development expenditures as share of GDP, 4.45t

 stay rates of doctorate recipients from, 3.53f

Michigan. See Chapter 8

 research and development in, 4.12t

Migration. See Workforce, science and engineering, immigrants in

Mining, 6.27f

Minnesota. See Chapter 8

Minorities. See also Race/ethnicity

 bachelor’s degree attainment by, 2.21–22, 2.22f

 doctoral degree attainment by, 2.27–28, 2.28f, 2.29f

 in academic research and development, 5.23–24, 5.24t

 in S&E workforce, 3.43–47

 master’s degree attainment by, 2.26, 2.26f

 mathematics achievement by, 1.10t

Mississippi. See Chapter 8

Missouri. See Chapter 8

MNCs. See Multinational companies (MNCs)

Montana. See Chapter 8

Morocco, research and development expenditures as share of GDP, 

4.45t

Multinational companies (MNCs)

 employment in, 3.58–60

 in knowledge- and technology-intensive industries, 6.42–45

 research and development by, 4.25–27

 employment, O.9f

 overseas, O.5–6

 research and development employment by, O.9f, 3.58–60

N

NAEP. See National Assessment of Educational Progress (NAEP) 

assessments

NAGB. See National Assessment Governing Board (NAGB)

NAICS. See North American Industry Classification System (NAICS) 

codes

Nanotechnology

 public attitudes about, 7.21, 7.23

 public knowledge of, 7.23f

NASA. See National Aeronautics and Space Administration (NASA)

National Aeronautics and Space Administration (NASA), 4.26f, 4.32t, 

4.33, 4.35t, 4.36f, 4.37f, 5.12t

National Assessment Governing Board (NAGB), 1.8

National Assessment of Educational Progress (NAEP) 1.8–14, 1.10t, 

1.11f, 1.12t, 1.13t

National Institutes of Health (NIH), 5.12t

National Mathematics Advisory Panel, 1.8

National Science Foundation (NSF), 4.32t, 4.33, 4.35t, 4.36f, 4.37f, 5.12t

NCLB. See No Child Left Behind (NCLB) Act

Nebraska. See Chapter 8

Nepal, enrollment in U.S. undergraduate programs, 2.19f

Netherlands

 article collaboration in, 5.38t

 broadband penetration in, 6.17f

 coauthorship from, with United States, 5.39t

 educational attainment in, 2.33f

 foreign students in, 2.36f

 industrial research and development in, 4.45t

 international collaboration on articles in, 5.38t

 journal articles from, 5.34t

 research and development by U.S. companies in, 4.29t

 research and development expenditures as share of GDP, 4.45t

Nevada. See Chapter 8

New Hampshire. See Chapter 8

 research and development in, 4.12t

New Jersey. See Chapter 8

 research and development in, 4.12t

New Mexico. See Chapter 8

 research and development in, 4.12t

New York. See Chapter 8

 research and development in, 4.12t

New Zealand
I-8  Index

 coauthorship from, with United States, 5.39t

 educational attainment in, 2.33f

 foreign students in, 2.36f

 high school graduation rate in, 1.33f

 international collaboration on articles in, 5.38t

 journal articles from, 5.34t

 research and development expenditures as share of GDP, 4.45t

Nigeria, enrollment in U.S. undergraduate programs, 2.19f

NIH. See National Institutes of Health (NIH)

No Child Left Behind (NCLB) Act, 1.7, 1.21, 1.25, 1.30

North American Industry Classification System (NAICS) codes, 8.11t

North Carolina. See Chapter 8

 research and development in, 4.12t

North Dakota. See Chapter 8

Norway

 coauthorship from, with United States, 5.39t

 educational attainment in, O.7f, 2.33f

 foreign students in, 2.36f

 high school graduation rate in, 1.33f

 industrial research and development in, 4.45t

 journal articles from, 5.34t

 research and development expenditures as share of GDP, 4.45t

NSF. See National Science Foundation (NSF)

Nuclear power, public attitudes about, 7.40

O

Ohio. See Chapter 8

Oklahoma. See Chapter 8

Oregon. See Chapter 8

P

Pakistan

 H-1B holders from, 3.51f

 journal articles from, 5.34t

Patents

 as research output, O.9–11

 by scientists and engineers, 3.28–29

 by technology area, 6.51–53, 6.53f, 6.51f

 citations to literature in, 5.48–50

 clean energy, 5.48–50, 6.65–69

 global trends in, 6.47–51

 high-value, for selected regions/countries, O.14f

 in information and communication technology, 6.51–53, 6.52f

 inventive activity shown by, O.12–14

 legislation, 6.49

 per 1,000 individuals in science and engineering, 8.112f, 8.113t

 per 1,000 science and engineering doctorate holders, 8.110f, 8.11t

 related activities and income, 5.45–46

 share of U.S. grants for selected regions/countries, O.14f

 triadic, 6.53–54, 6.53f

 university trends and, 5.45

Pennsylvania. See Chapter 8

 research and development in, 4.12t

Pharmaceuticals

 exports of, 6.36t, 6.40f

 innovation in, 6.47f

 investment in, 6.46t

 patents, 6.51f, 6.52t

 value added of, 6.24f

Philippines

 H-1B holders from, 3.51f

 immigrants from, education of, 3.53f

 information and communication technology exports, 6.36t, 6.37f

 tertiary education achievement in, O.7f

PISA. See Program for International Student Assessment (PISA)

Poland

 broadband penetration in, 6.17f

 coauthorship from, with United States, 5.39t

 educational attainment in, 2.33f

 foreign students in, 2.36f

 high school graduation rate in, 1.33f

 industrial research and development in, 4.45t

 international collaboration on articles in, 5.38t

 journal articles from, 5.34t

 research and development expenditures as share of GDP, 4.45t

Portugal

 educational attainment in, 2.33f

 foreign students in, 2.36f

 journal articles from, 5.34t

 research and development expenditures as share of GDP, 4.45t

Program for International Student Assessment (PISA), 1.14–15, 1.17f, 

Pseudoscience, 7.27

Publishing. See also Literature, scientific and technical

 as research output, O.9–11

 author names in, 5.35–36

 by country/economy, 5.34t

 citations in

 research patterns and, O.12

 trends in, 5.43–45

 coauthorship in, O.11f, 5.35–40

 collaboration in, 5.35–40

 engineering, in selected regions/countries, O.11f

 highly cited works, 5.43–45, 5.46f

 international coauthorship in, with United States, 5.39t

 output by sector, 5.41–42

 patent citations, 5.48–50

 per $1 million of academic research and development, 8.108f, 

8.109t

 per 1,000 science and engineering doctorate holders, 8.106f, 8.107t

Puerto Rico. See Chapter 8

R

Race/ethnicity. See also Minorities

 bachelor’s degree attainment by, 2.21–22, 2.22f

 doctoral degree attainment by, 2.27–28, 2.27f, 2.28f

 in academic research and development, 5.23–25

 master’s degree attainment by, 2.26, 2.26f

 mathematics achievement by, 1.10t

Republic of Korea. See South Korea

Research. See also Academic research and development; Research 

and development

 applied, 4.15

 basic, 4.15

 citations and, O.12

 collaboration, expansion of, O.11–12

 institutions, in higher education system, 2.7

 on animals, public attitudes about, 7.43–44

 output, O.9–11

Research and development (R&D). See also Academic research and 

development

 academic sector, 4.52

 government funding mechanisms for, 4.52

 aerospace and defense, 4.21, 4.22t

 as share of GDP, O.4–5, O.5f , 8.88f, 8.89t

 automotive manufacturing, 4.21, 4.22t

 budget authority, 4.28– 4.30, 4.31f

 business, 4.17–24, 4.48–52

 as share of private-industry output, 8.100f, 8.101t

 in top states, 4.12t

 by character of work, 4.15–16, 4.17f
Science and Engineering Indicators 2012 I-9

 by multinational companies, 4.25–27

 by performing sector, 4.44–46

 by source of funds, 4.44–46

 chemical, 4.21, 4.22t

 China, O.4–5, O.5f

 classification of, 4.15–16

 clean energy, 6.64–65

 computers and electronics, 4.21, 4.22t

 economic growth and, 4.17

 employment

 by multinational companies, 3.58–60

 of U.S.-based multinational corporations, O.9f

 expenditures

 as share of GDP, O.5f, 4.46f

 Asia, O.4f

 by character of work, 4.14t, 4.16t, 4.17t, 4.35t, 4.46f

 by performing sector and funding source, 4.9t, 4.13f, 4.35t

 by state agencies

 per $1 million of GDP, 8.94f, 8.95t

 per civilian worker, 8.96f, 8.97t

 per individual in science and engineering occupation, 8.98f, 8.99t

 by top corporations, 4.51t

 China, O.4–5, O.5f

 distribution of, among states, 4.11, 4.12t

 European Union, O.4–5, O.4f, O.5f

 global expansion of, O.4–5

 global patterns of, 4.40–42

 growth in, O.5f

 India, O.5f

 international comparisons, 4.40–52

 Japan, O.4–5, O.5f

 location of, O.6f

 Malaysia, O.5f

 performer vs. source reported, 4.34

 Singapore, O.5f

 South Korea, O.4, O.5f

 Taiwan, O.5f

 total U.S., 4.10f

 United States, O.4–5, O.4f, O.5f

 worldwide, O.4f

 EPSCoR and, 5.11, 5.12

 exports and imports of services in, 4.27–28

 federal, 4.28–37

 by agency, 4.31–33, 4.32f

 by field, 4.33–35, 4.37f

 by national objective, 4.28–30

 by performer, 4.31–33

 civilian-related, 4.30

 defense-related, 4.28–30

 in federal budget, 4.28–30, 4.31f

 obligations per civilian worker, 8.90f, 8.91t

 obligations per individual in science and engineering 

occupation, 8.92f, 8.93t

 tax credits, 4.35–37

 federal legislation related to, 4.39

 foreign direct investment in, 4.25

 funding sources, 4.12–15

 business as, 4.12–13

 federal government as, 4.13–15

 government priorities, 4.46–48

 in business sector, 4.9–11

 in federal agencies, 4.12

 in universities and colleges, 4.11

 industries in, largest, 4.19–23

 international comparisons, 4.40–52

 location of performance, O.6, 4.11

 obligations, 4.30–33, 4.32t, 4.33f, 4.35t, 4.36f, 4.37f

 outlays, 4.30, 4.34, 4.34f

 overseas, by multinational companies, O.5–6, 4.25

 performers of, 4.8–12

 plant, 4.30

 social science, 4.8

 software, 4.21, 4.22t

 trends, 4.7–17

 unmeasured, 4.8

 workforce performing, 3.25–27

Researchers

 expansion of global pool, O.8–9, 3.56–57

Rhode Island. See Chapter 8

Romania

 journal articles from, 5.34t

 research and development expenditures as share of GDP, 4.45t

Russia

 coauthorship from, with United States, 5.39t

 educational attainment in, 2.33f

 first university degrees in, O.7f

 foreign students in, 2.36f

 GDP in, by sector, 4.44f

 H-1B holders from, 3.51f

 journal articles from, 5.34t

 patent trends in, 6.50

 research and development expenditures as share of GDP, 4.45t, 4.46f

 researcher numbers in, O.9f

S

Salaries

 at different degree levels, 3.32–33, 3.34f

 differentials in, of minorities and women, 3.45–47

 employer characteristics and, 3.46–47

 family characteristics and, 3.47

 field of degree and, 3.46–47

 for doctorate recipients, 3.36

 of H-1B visa holders, 3.50, 3.52t

 personal characteristics and, 3.46–47

 teacher (precollege), 1.28, 1.30f, 8.28f, 8.29t

Saudi Arabia, enrollment in U.S. undergraduate programs, 2.19f

SBIR. See Small Business Innovation Research (SBIR)

Science (precollege)

 eighth grade performance in, 8.24f, 8.25t

 eighth grade proficiency in, 8.26f, 8.27t

 fifteen-year-olds’ performance in, 1.15

 fourth grade performance in, 8.16f, 8.17t

 fourth grade proficiency in, 8.18f, 8.19t

 public attitudes about education in, 7.44

 rising performance in, 1.13

Science and engineering (S&E)

 advanced degrees in, share of, 8.54f, 8.55t, 8.56f, 8.57t

 associate’s degrees, 2.20

 bachelor’s degrees, 2.20–22

 by citizenship, 2.22

 by field, 2.18f

 by race/ethnicity, 2.21–22, 2.22f

 female share of, 2.21f

 minority share of, 2.22f

 per 1,000 18–24-year-olds, 8.44f, 8.45t

 in charter schools, 1.11

 degrees as share of total degrees, 8.48f, 8.49t, 8.50f, 8.51t

 doctoral degrees, O.8f, 2.26–31

 article output per 1,000 holders of, 8.106f, 8.107t

 by citizenship, 2.29f

 by country/economy of origin, 2.29–31, 2.30f, 2.30t, 2.31t

 by field, 2.27f
I-10  Index

 by race/ethnicity, 2.27–28, 2.28f

 by sex, 2.27

 completion and, 2.27

 conferred per 1,000 employed holders of, 8.104f, 8.105t

 foreign recipients, 2.28, 2.29–31, 2.30t, 2.31t

 global comparison of, 2.34

 labor market for, 3.34–36

 patents per 1,000, 8.110f, 8.111t

 salaries for, 3.36

 stay rates, 3.50–52, 3.53f

 tenure-track positions for, 3.35–36

 time for completion, 2.27, 2.28t

 unemployment of, 3.35

 first university degrees in, 2.32–34

 graduate education

 enrollment in, 2.24–25

 by race/ethnicity, 2.26, 2.26f

 by sex, 2.25

 foreign students, 2.24–25

 financial support for, 2.13–15, 2.13t, 2.14f, 2.15t

 interdisciplinary, 2.25

 per 1,000 25–34-year-olds, 8.52f, 8.53t

 international education, 2.32–37

 master’s degrees, 2.25–26

 by citizenship, 2.26

 by field, 2.25f

 by race/ethnicity, 2.26, 2.26f

 by sex, 2.25, 2.25f

 professional, 2.25

 public views on occupations in, 7.33–34

 ratio of degrees in, to college-age population, 2.32

 reasoning and understanding of scientific process, 7.23–26

 undergraduate enrollment in, U.S., 2.16–19

 workforce. See also Workforce 

 age, 3.52–56, 3.54f, 3.55f

 demographics, 3.40–56

 earnings, 3.32–33

 at different degree levels, 3.32–33, 3.34f

 growth, 3.33t

 education classification, 3.8, 3.9t

 educational distribution of, 3.14–15

 employer sizes, 3.19–20, 3.20f

 employment growth, 3.12, 3.12f

 employment patterns, 3.17–29

 employment sectors, 3.18–19, 3.22–25

 federal employment of, 3.24

 global, 3.56–61

 counts of, 3.56–57

 migration of, 3.57–58

 growth of, 3.10–13, 3.13t, 3.14f

 higher education and trends in, O.7–8

 immigrants in, 3.47–52, 3.57–58

 in academic research and development, 5.19–25

 in metropolitan areas, 3.21–22, 3.21t, 3.22t

 in research and development, 3.25–27

 labor market conditions, 3.29–40

 minorities in, 3.43–3.47

 age distribution of, 3.41f, 3.44

 salary differentials of, 3.45–3.47

 non-S&E occupation employment of, 3.15–16

 occupation classification, 3.7

 occupation density by industry, 3.20

 patenting activity of, 3.28

 postdoc positions, 3.36–40, 3.38f, 3.39t

 recent graduates in, 3.33

 doctorate recipients, 3.34–36

 labor market indicators for, 3.33–34

 relationship of education and employment of, 3.16–17

 retirement patterns, 3.52–53

 self-employment in, 3.23–24

 size of, 3.10, 3.10t

 technical expertise classification, 3.8, 3.9

 tenure-track positions, 3.35–36

 training, 3.29

 unemployment, 3.29–31, 3.32f

 of doctorate recipients, 3.35

 women in, 3.40–43, 3.40f

Science and technology (S&T)

 attitudes about specific issues in, 7.34–44

 general attitudes about, 7.27–34

 confidence in leadership in, 7.31–32, 7.31t

 influence on public issues of experts in, 7.32–33

 promise of, 7.28–29

 reservations about, 7.28–29

 public interest in, 7.12–14

 public involvement in informal learning, 7.16–18

 public knowledge about, 7.18–27

 sex differences in, 7.21t

 statistics and charts, understanding of, 7.26

 terms and concepts, understanding of, 7.19–23

 sources of public’s information about, 7.6–18

 blending of print and online coverage of, 7.11, 7.11t

 current events primary sources on, 7.10f

Serbia, journal articles from, 5.34t

Singapore

 coauthorship from, with United States, 5.39t

 information and communication technology

 exports, 6.35f

 imports, 6.37f

 international collaboration on articles in, 5.38t

 journal articles from, 5.34t

 in engineering, O.11f

 research and development by U.S. companies in, 4.29t

 research and development expenditures, O.5f

 as share of GDP, 4.45t

 researcher numbers in, O.9f

Slovak Republic

 educational attainment in, 2.33f

 high school graduation rate in, 1.33f

 research and development expenditures as share of GDP, 4.45t

Slovenia

 educational attainment in, 2.33f

 journal articles from, 5.34t

 research and development expenditures as share of GDP, 4.45t

Small business

 angel investment in, 6.57–58, 6.58f, 6.59f

 employment in, 3.19–20

 federal programs, 4.38–40

 financing of, 6.56–60

 leading types, 6.56t

 venture capital investment in, 6.58–60, 6.59f

Small Business Innovation Research (SBIR), 4.38–39

 funding per $1 million of GDP, 8.120f, 8.121t

Smithsonian Institution, 4.32t, 4.35t

South Africa

 coauthorship from, with United States, 5.39t

 journal articles from, 5.34t

 research and development by U.S. companies in, 4.29t

 research and development expenditures as share of GDP, 4.45t

South Carolina. See Chapter 8

South Dakota. See Chapter 8

South Korea
Science and Engineering Indicators 2012 I-11

 broadband penetration in, 6.17f

 coauthorship from, with United States, 5.39t

 doctoral degrees in, O.8f

 doctorate recipients from, 2.29t, 2.29f

 educational attainment in, 2.33f

 enrollment in U.S. undergraduate programs, 2.19f

 exports to China, O.18f

 exports to United States, O.18f

 first university degrees in, O.7f

 foreign students in tertiary education in, 2.36f

 GDP in, by sector, 4.44f

 H-1B visa holders from, 3.51f

 high school graduation rate in, 1.33f

 immigrants from, education of, 3.53f

 industrial research and development in, 4.45t

 information and communication technology exports, 6.35f

 international collaboration on articles in, 5.38t

 journal articles

 from, 5.34t

 engineering, O.11f

 research and development by U.S. companies in, 4.29t

 research and development expenditures, O.4, O.5f

 as share of GDP, 4.45t, 4.46f

 researcher numbers in, O.9f

 stay rates of doctorate recipients from, 3.53f

Spain

 article collaboration in, 5.38t

 coauthorship from, with United States, 5.39t

 educational attainment in, 2.33f

 foreign students in, 2.36f

 high school graduation rate in, 1.33f

 industrial research and development in, 4.45t

 journal articles from, 5.34t

 research and development expenditures as share of GDP, 4.45t

State achievement tests, 1.23

State indicators. See Chapter 8

Statistics, public understanding of, 7.26

Stem cell research, public attitudes about, 7.40–41

Students (precollege). See also Education

 access to qualified teachers, 1.26, 1.26t

 in charter schools, in United States, 1.11

 mathematics performance

 achievement gaps, 1.13, 1.13t

 by race/ethnicity, 1.10t

 eighth grade, 1.9–12 1.10f, 1.10t, 1.11f, 1.12t, 1.13t, 8.20f, 8.21t

 elementary, 1.8–12, 1.10t

 fifteen-year-olds, 1.15

 fourth grade, 1.9–12 1.10f, 1.10t, 1.11f, 1.12t, 1.13t, 8.12f, 8.13t

 middle grade, 1.8–12

 proficiency in different skill areas, 1.15–16, 1.15f

 skills areas, 1.14

 national assessment performance on, 1.7–15

 science performance

 achievement gaps in, 1.13

 eighth grade, 8.24f, 8.25t

 fifteen-year-olds, 1.15

 rising, 1.13

 tracking systems, 1.33

Supercomputers, in China, 6.25, 6.25f

Sweden

 coauthorship from, with United States, 5.39t

 educational attainment in, 2.33f

 high school graduation rate in, 1.33f

 industrial research and development in, 4.45t

 international collaboration on articles in, 5.38t

 journal articles from, 5.34t

 research and development by U.S. companies in, 4.29t

 research and development expenditures as share of GDP, 4.46f, 4.47t

Switzerland

 article collaboration in, 5.38t

 coauthorship from, with United States, 5.39t

 educational attainment in, 2.33f

 foreign students in, 2.36f

 high school graduation rate in, 1.33f

 journal articles from, 5.34t

 research and development by U.S. companies in, 4.29t

T

Taiwan

 coauthorship from, with United States, 5.39t

 doctorate recipients from, 2.29t, 2.29f

 exports to China, O.18f

 exports to EU, O.18f

 exports to United States, O.18f

 H-1B visa holders from, 3.51f

 information and communication technology exports, 6.35f

 journal articles

 from, 5.34t

 in engineering, O.11f

 research and development expenditures, O.5f

 as share of GDP, 4.47t

 researcher numbers in, O.9, O.9f

 stay rates of doctorate recipients from, 3.53f

Tax credits, federal research and development, 4.35–37

Teachers (precollege)

 attrition of, 1.29

 certification of, 1.22–24

 experience of, 1.25

 formal preparation of, 1.22–25

 professional development of, 1.26–28, 1.27f, 1.28f

 quality of, 1.22–25

 salaries of, 1.28, 1.30f, 8.28f, 8.29t

 subject area preparation of, 1.25–26, 1.26t

 working conditions, 1.28–30, 1.31f

Technology. See Knowledge- and technology-intensive (KTI) 

industries; Science and technology (S&T)

Technology-intensive firms. See also Knowledge- and technologyintensive (KTI) industries

 rising output of, O.15–16

Texas. See Chapter 8

 research and development in, 4.12t

Thailand

 doctorate recipients from, 2.29t

 first university degrees in, O.7f

 journal articles from, 5.34t

Trade

 balance in selected regions/countries, O.19f

 exports and patterns in, O.17–18

 knowledge- and technology-intensive industries and, 6.29–46

 of high-technology goods, 6.34–36

 product classification in, 6.38

 shifts in positions, O.17–18

 surpluses in U.S., O.18–19

Tunisia

 journal articles from, 5.34t

Turkey

 coauthorship from, with United States, 5.39t

 doctorate recipients from, 2.29t

 educational attainment in, 2.33f

 foreign students in, 2.36f

 H-1B visa holders from, 3.51f

 high school graduation rate in, 1.33f

 journal articles from, 5.34t

 stay rates of doctorate recipients from, 3.53f
I-12  Index

U

U.S. Patent and Trademark Office (USPTO), 6.48–53

Ukraine

 journal articles from, 5.34t

United Kingdom

 article collaboration in, 5.38t

 broadband penetration in, 6.17f

 coauthorship from, with United States, 5.39t

 educational attainment in, 2.33f

 first university degrees in, O.7f

 foreign students in, 2.36f

 GDP in, by sector, 4.44f

 H-1B visa holders from, 3.51f

 high school graduation rate in, 1.33f

 industrial research and development in, 4.45t

 international collaboration on articles in, 5.38t

 journal articles from, 5.34t

 research and development by U.S. companies in, 4.29t

 research and development expenditures as share of GDP, 4.46f, 4.47

 stay rates of doctorate recipients from, 3.53f

Universities, patenting trends, 5.45

USDA. See Department of Agriculture (USDA)

USPTO. See U.S. Patent and Trademark Office (USPTO)

Utah. See Chapter 8

V

VA. See Veterans Administration

Value added

 definition of, 6.11

 of commercial knowledge-intensive services, 6.24f

 of education and health services, 6.13t

 of information and communication technology industries, 6.21f, 

6.24f, 6.25–26

 of knowledge- and technology-intensive industries, global, 6.12f

Venture capital

 by industry, 6.58, 6.59f

 by share of investment stage, 6.59–60, 6.59f

 deals as share of high-technology business, 8.124f, 8.135t

 disbursed per venture capital deal, 8.126f, 8.127t

 in small businesses, 6.58–60

 per $1,000 of GDP, 8.122f, 8.123t

Vermont. See Chapter 8

Veterans Administration (VA), 4.32t, 4.35t

Vietnam

 enrollment in U.S. undergraduate programs, 2.19f

Virginia. See Chapter 8

Visas, work, 3.49–50, 3.52t

W

Washington. See Chapter 8

 research and development in, 4.12t

West Virginia. See Chapter 8

Wisconsin. See Chapter 8

Women

 as faculty at research universities, 5.22, 5.23t

 first university degrees by, 2.33–34

 in academic research and development, 5.22–23

 in S&E workforce, 3.40–43, 3.40f

 age distribution of, 3.41t

 salary differentials of, 3.45–46

 unemployment among, 3.42

 share of S&E bachelor’s degrees, 2.21f

Workforce. See also Science and engineering, workforce

 bachelor’s degree holders potentially in, 8.74f, 8.75t

 computer specialists as share of, 8.84f, 8.85t

 employed science and engineering degree holders as share of, 8.78f, 8.79t

 engineers as share of, 8.80f, 8.81t

 life scientists as share of, 8.82f, 8.83t

 physical scientists as share of, 8.82f, 8.83t

 science and engineering

 age, 3.52–56, 3.54f, 3.55f

 as share of total workforce, 8.76f, 8.77t

 demographics, 3.40–56

 earnings, 3.32–35

 at different degree levels, 3.32–33, 3.34f

 growth, 3.33t

 education classification, 3.8, 3.9t

 educational distribution of, 3.14–15

 employer sizes, 3.19–20, 3.20f

 employment growth, 3.12, 3.12f

 employment patterns, 3.17–29

 employment sectors, 3.18–19, 3.22–25

 federal employment of, 3.24

 global, 3.56–61

 counts of, 3.56–57

 migration of, to U.S., 3.57–58

 growth of, 3.10–13, 3.13t, 3.14f

 higher education and trends in, O.7–8

 in academic research and development, 5.19–25

 in metropolitan areas, 3.21–22, 3.21t, 3.22t

 in research and development, 3.25–27

 labor market conditions, 3.29–40

 minorities in, 3.43–47

 age distribution of, 3.41f, 3.44

 salary differentials of, 3.45–47

 non-S&E occupation employment of, 3.15–16

 occupation classification, 3.7

 occupation density by industry, 3.20

 patenting activity of, 3.28

 postdoc positions, 3.36–40, 3.38f, 3.39t

 recent graduates in, 3.33

 doctorate recipients, 3.34–36

 labor market indicators for, 3.33–34

 relationship of education and employment of, 3.16–17

 retirement patterns, 3.52–53

 self-employment in, 3.23–24

 size of, 3.10, 3.10t

 technical expertise classification, 3.8, 3.9

 tenure-track positions, 3.35–36

 training, 3.29

 unemployment, 3.29–32, 3.30f

 of doctorate recipients, 3.35

 women in, 3.40–43, 3.40f

Work visas, 3.49–50, 3.52t

Wyoming. See Chapter 8
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