EN

Nation

SCIENCE AND ENGINEERING INDICATORS 2010
STEVEN C. BEERING

Chair, President Emeritus, Purdue UniversityPATRICIA D. GALLOWAYVice Chair, Chief Executive Officer, Pegasus Global Holdings, Inc., Cle Elum, WashingtonMARK R. ABBOTTDean and Professor, College of Oceanic and Atmospheric Sciences, Oregon State UniversityDAN E. ARVIZUDirector and Chief Executive, National Renewable Energy Laboratory, Golden, ColoradoBARRY C. BARISHBoard Consultant, Director, Global Design Effort for International Linear Collider, Linde Professor of Physics, Emeritus, California Institute of TechnologyCAMILLA P. BENBOWPatricia and Rodes Hart Dean of Education and Human Development, Peabody College of Education and Human Development, Vanderbilt UniversityRAY M. BOWENPresident Emeritus, Texas A&M UniversityJOHN T. BRUERPresident, The James S. McDonnell Foundation, St. LouisG. WAYNE CLOUGHSecretary, Smithsonian Institution, Washington, DCFRANCE A. CÓRDOVAPresident, Purdue UniversityKELVIN K. DROEGEMEIERAssociate Vice President for Research, Regents’ Professor of Meteorology and Weathernews Chair Emeritus, University of OklahomaJOSÉ-MARIE GRIFFITHSDeputy Director (Biomedical Informatics), TraCS Institute, and Professor, School of Information and Library Science, University of North Carolina at Chapel HillESIN GULARIDean of Engineering and Science

Clemson University

National Science Board

LOUIS J. LANZEROTTIDistinguished Research Professor of Physics, Center for SolarTerrestrial Research, Department of Physics, New Jersey Institute of TechnologyALAN I. LESHNERChief Executive Officer, Executive Publisher, Science, American Association for the Advancement of Science, Washington, DCG. P. “BUD” PETERSONPresident, Georgia Institute of TechnologyDOUGLAS D. RANDALLProfessor and Thomas Jefferson Fellow, University of MissouriARTHUR K. REILLYSenior Director, Strategic Technology Policy, Cisco Systems, Inc., Ocean, New JerseyDIANE L. SOUVAINEProfessor and Chair, Department of Computer Science, Tufts UniversityJON C. STRAUSSInterim Dean, Edward E. Whitacre Jr. College of Engineering, Texas Tech UniversityKATHRYN D. SULLIVANDirector, Battelle Center for Mathematics and Science Education Policy, John Glenn School of Public Affairs, Ohio State UniversityTHOMAS N. TAYLORRoy 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 KansasRICHARD F. THOMPSONKeck Professor of Psychology and Biological Sciences, University of Southern CaliforniaARDEN L. BEMENT, JR.Member ex officio, Director, National Science FoundationCRAIG R. ROBINSONActing Executive Officer, National Science Board, and Director, National Science Board Office
 SCIENCE AND 

ENGINEERING

 INDICATORS 

 2010
Cover Image

The cover design for Science and Engineering Indicators 2010 is based on a computersimulated visualization of Mach 1 homogeneous turbulence. The white regions in the image 

show where the earlier passage of strong shock fronts heated the gas in this turbulent flow. 

Blue regions have the weakest vorticity, and as the vorticity increases in strength, the color 

goes through red to yellow and finally to white. The dynamic visualization was created at the 

Laboratory for Computational Science & Engineering (LCSE), a facility within the University 

of Minnesota’s Digital Technology Center where innovative hardware and system software 

solutions to problems in computational science and engineering can be tested and applied. Work 

in the LCSE has been supported by a series of National Science Foundation equipment grants 

(the most recent is CNS 07-08822). (Credit: Paul Woodward, Laboratory for Computational 

Science and Engineering, University of Minnesota.)

Recommended Citation

National Science Board. 2010. Science and Engineering Indicators 2010. Arlington, VA: 

National Science Foundation (NSB 10-01).
iii
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 F. Lehming, 

Director of the Science and Engineering Indicators Program 

of the Division of Science Resources Statistics (SRS) in the 

Directorate for Social, Behavioral and Economic Sciences: 

Lynda T. Carlson, Division Director; Mary J. Frase, Deputy 

Division Director; Myron P. Gutmann, Assistant Director; 

Judith S. Sunley, Deputy Assistant Director; and David W. 

Lightfoot, former Assistant Director. 

The authors of the manuscript were:

Overview. Rolf F. Lehming, SRS

Chapter 1. Martha Naomi Alt, Xianglei Chen, Leslie Hall, 

MPR Associates; Lawrence Burton, SRS

Chapter 2. Joan S. Burrelli, SRS

Chapter 3. Nirmala Kannankutty, Steven Proudfoot, 

Mark C. Regets, SRS

Chapter 4. Mark Boroush, Francisco A. Moris, 

Raymond M. Wolfe, SRS

Chapter 5. Ronda Britt, Joan S. Burrelli, 

Lawrence Burton, Leslie Christovich, SRS

Chapter 6. Derek Hill, SRS

Chapter 7. Jaquelina C. Falkenheim, SRS

Chapter 8. Paula C. Dunnigan, Taratec Corporation

Acknowledgments

The volume benefited from extensive contributions from 

SRS staff. The division’s senior staff and survey managers 

assured availability of data under often stringent deadlines: 

Richard J. Bennof, Mark K. Fiegener, Susan T. Hill, John E. 

Jankowski, Kelly H. Kang, Nancy Leach, Julia Oliver, and 

Michael Yamaner. Jacqueline Durham assisted in acquiring data from outside sources. Stephen Cohen, Jock Black, 

John E. Jankowski, Jeri M. Mulrow, Emilda B. Rivers, and 

Fan Zhang provided advice with statistical or data presentation issues. Robert Bell and Rolf F. Lehming worked closely 

with authors to develop and to implement plans for several 

chapters in the volume.

John R. Gawalt directed the overall production of the volume. 

Rolfe W. Larson was responsible for editorial and printing 

services. Robin Pentola provided Web direction and support. 

Tanya Gore assisted with the final review of the appendix 

tables, and Rajinder Raut prepared files for review and provided a technical review of the Web site. 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. Overall editing 

of the report was performed by David B. Fialkoff, Janet M. 

McNaughton, and the staff of Lockheed Martin Business 

Process Solutions. The staff of OmniStudio provided composition and production services for the print and electronic 

materials. Many others provided much appreciated advice and 

assistance and are listed under Contributors and Reviewers. 

Overall management of the volume was provided by Rolf F. 

Lehming, Robert Bell, John R. Gawalt and Lynda T. Carlson.

The National Science Board Office provided vital coordination throughout the project. The National Science Board is 

especially grateful to Kyscha Slater for providing operations 

support, Jennifer Lynn Richards for assistance to the S&E 

Indicators Committee, and Jean M. Pomeroy for serving as 

Liaison to the Committee. Robert Bell and Rolf F. Lehming 

were the Executive Secretaries.

iv 

Dr. Steven C. Beering 

Chairman, National Science Board

Dr. Louis J. Lanzerotti

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. 

Craig Abbey, SUNY 

James D. Adams, Rensselaer Polytechnic Institute

David Aldrich, Booz Allen Hamilton

Nick Allum, University of Essex

Bernice T. Anderson, National Science Foundation

Anthony Arundel, UNU-MERIT

Cheikhou Athie, Taratec Corporation

Robert D. Atkinson, Information Technology and Innovation 

Foundation

Laudeline Auriol, Organization for Economic Cooperation 

and Development

Eleanor Babco, Council of Graduate Schools

Roland Bardon, SRI International

Kristina Bartsch, Bureau of Labor Statistics 

William Beckett, Higher Education Statistics Agency, Ltd., 

England

Nathan Bell, Council of Graduate Schools

John Benskin, SRI International

Richard E. Bissell, National Academy of Sciences

Maria Borga, Bureau of Economic Analysis

Prudy Brown, SRI International

Patrick Callan, National Center for Public Policy and Higher 

Education

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

Jill Cape, Taratec Corporation

Ann B. Carlson, National Science Foundation

C. Michael Carolina, Oklahoma Center for the Advancement 

of Science and Technology

Nevin P. Carr, Jr., Office of Naval Research

Susan Choy, MPR Associates

George DeBoer, American Association for the Advancement 

of Science

Connie K. Della-Piana, National Science Foundation

Douglas E. Devereaux, National Institute of Standards and 

Technology

James J. Duderstadt, University of Michigan

Jules Duga, Battelle

Janice M. Earle, National Science Foundation

Ronald Ehrenberg, Cornell University

John H. Falk, Oregon State University

Kaye Husbands Fealing, University of Minnesota

Noah Feinstein, University of Wisconsin at Madison

Michael Finn, Oak Ridge Institute for Science and Education

Barbara Fraumeni, University of Southern Maine

Ron Freedman, The Impact Group, Toronto

Carolyn L. Funk, Virginia Commonwealth University

Patrick Gallagher, National Institute of Standards and 

Technology

Myk Garn, Southern Regional Education Board

Howard Garrison, Federation of American Societies for 

Experimental Biology

Fred Gault, UNU-MERIT

Donna K. Ginther, University of Kansas

Wolfgang Glänzel, University of Leuven, Belgium

Leonid Gokhberg, Higher School of Economics, Russia

Kerry Gruber, National Center for Education Statistics

Gary Guenther, Congressional Research Service

Jong-on Hahm, National Science Foundation

Bronwyn Hall, University of California at Berkeley

Keith Hall, Bureau of Labor Statistics

Kimberly Hamilton, The Patent Board

David Hart, George Mason University

Christopher Hayter, National Governors Association

Robin Henke, MPR Associates

Christopher Hill, George Mason University

Jim Hirabayashi, U.S. Patent and Trademark Office

James Hoehn, EPSCoR/IDeA Foundation

Ned Howenstine, Bureau of Economic Analysis

Robert Ibarra, University of New Mexico

Harold Javitz, SRI International

Jolene Jesse, National Science Foundation

Mark Jones, Higher Education Statistics Agency, Ltd., 

England

Scott Keeter, Pew Research Center

Stuart Kerachsky, National Center for Education Statistics

Cheryl Kerr, Bureau of Labor Statistics

Hoon Kim, Korea Foundation for the Advancement of Science 

& Creativity

Kei Koizumi, Office of Science and Technology Policy

Roslyn Korb, National Center for Education Statistics

Alan B. Kreuger, Department of the Treasury

Jon A. Krosnick, Stanford University

Paris Kucharski, The Patent Board

Jessica Lacasse, Statistics Canada

J. Steven Landefeld, Bureau of Economic Analysis

Julia Lane, National Science Foundation

Charles F. Larson, Innovation Research International 

Cheryl Lloyd, ICF Macro

Susan Carol Losh, Florida State University

Carol Lynch, Council of Graduate Schools

Jacques Maquet, Fundacion BBVA

Ray Mataloni, Bureau of Economic Analysis

Jacqueline Meszaros, National Science Foundation

Mala Mistry, Census Bureau

Sabrina L. Montes, Department of Commerce

Sue Okubo, Bureau of Economic Analysis

Dominic Olivastro, The Patent Board

Rafael Pardo, Fundación BBVA

Jongwon Park, SRI International

Amy P. Patterson, National Institutes of Health

Contributors and Reviewers
Juan Cruz Perusia, UNESCO Institute for Statistics

Melissa F. Pollak, National Science Foundation

Alan Porter, Georgia Institute of Technology

Prem Premakumar, IHS Global Insight

Senta Raizen, Center for Improving Science Education/

WestEd

Lawrence M. Rausch, National Science Foundation

Andrew Reamer, Brookings Institution

E.J. Reedy, Kauffman Foundation 

Caird E. Rexroad, Department of Agriculture

Carol Robbins, Bureau of Economic Analysis

J. David Roessner, SRI International

Philip L. Rones, Bureau of Labor Statistics

Dietram A. Scheufele, University of Wisconsin at Madison

William Schmidt, Michigan State University

Barbara Schneider, Michigan State University

Kazuko Shinohara, National Science Foundation, 

Tokyo Office

Henry Small, Thomson Reuters

Kenneth L. Smith, Office of Naval Research

Shirley F. Smith, Small Business Administration

Thomas Snyder, National Center for Education Statistics

Dixie Sommers, Bureau of Labor Statistics 

George Stamas, Bureau of Labor Statistics 

Paula Stephan, Georgia State University

Carol Stoel, National Science Foundation

Amy Sussman, National Science Foundation

Gregory Tassey, National Institute of Standards 

and Technology 

Michael S. Teitelbaum, Alfred P. Sloan Foundation 

Lori Thurgood, SRI International

Marie Thursby, Georgia Institute of Technology

William Tirre, National Center for Education Statistics

Serge Tran, University of California at Los Angeles

John Tsapogas, National Science Foundation

Andrew Tyndall, Tyndall Report

William Valdez, Department of Energy

Carol Van Hartesveldt, National Science Foundation

Nancy Vincent, Council of Graduate Schools

Kenneth P. Voytek, National Institute of Standards 

and Technology 

Caroline Wagner, SRI International

June Weis, Southern Regional Education Board

Vincent Welch, National Opinion Research Center

Laura Williams, ICF Macro

Michael Wolf, Bureau of Labor Statistics 

Terry S. Woodin, National Science Foundation

Sacha Wunsch-Vincent, Organisation for Economic 

Cooperation and Development

William Zeile, Bureau of Economic Analysis

Klarka Zeman, Statistics Canada 

Robert Zemsky, University of Pennsylvania

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

A Bird’s Eye View of the World’s Changing S&T Picture ....................................................... O-3

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

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

Global Higher Education and Workforce Trends ...................................................................... O-6

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

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

Expanding International Research Collaborations ................................................................... O-10

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

Inventive Activity Shown by Patents ....................................................................................... O-13

Fast-Rising Global Output of Knowledge- and Technology-Intensive Firms ......................... O-14

Booming Global High-Technology Exports Rearranging World Trade Patterns .................... O-16

Big Shifts in World Trade Positions in High-Technology Products ........................................ O-18

Continued Surpluses From U.S. Trade in Knowledge-Intensive Services and 

Intangible Assets ................................................................................................................. O-19

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

Teachers of Mathematics and Science ......................................................................................1-23

Instructional Technology in Education .....................................................................................1-30

Transition to Higher Education .................................................................................................1-34

Conclusion ................................................................................................................................1-38

Notes .........................................................................................................................................1-39

Glossary ....................................................................................................................................1-41

References .................................................................................................................................1-42

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 ...............................2-11

Graduate Education, Enrollment, and Degrees in the United States ........................................2-17

Postdoctoral Education .............................................................................................................2-30

International S&E Higher Education ........................................................................................2-31

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

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

Glossary ....................................................................................................................................2-38

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

Contents

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

Highlights ....................................................................................................................................3-6

Introduction .................................................................................................................................3-9

Scope of the S&E Workforce .....................................................................................................3-9

Employment Patterns ................................................................................................................3-13

Demographics ...........................................................................................................................3-27

S&E Labor Market Conditions .................................................................................................3-37

Global S&E Labor Force ..........................................................................................................3-47

Conclusion ................................................................................................................................3-58

Notes .........................................................................................................................................3-58

Glossary ....................................................................................................................................3-59

References .................................................................................................................................3-60

Chapter 4. Research and Development: National Trends and International Linkages .....4-1

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

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

Trends in National R&D Performance .......................................................................................4-8

Location of R&D Performance .................................................................................................4-16

Business R&D ...........................................................................................................................4-18

Federal R&D .............................................................................................................................4-21

International R&D Comparisons ..............................................................................................4-33

R&D by Multinational Companies ...........................................................................................4-44

Technology and Innovation Linkages .......................................................................................4-50

Conclusion ................................................................................................................................4-57

Notes .........................................................................................................................................4-58

Glossary ....................................................................................................................................4-61

References .................................................................................................................................4-62

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

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

Introduction .................................................................................................................................5-7

Financial Resources for Academic R&D ....................................................................................5-7

Academic R&D Infrastructure ..................................................................................................5-16

Doctoral Scientists and Engineers in Academia .......................................................................5-19

Outputs of S&E Research: Articles and Patents .......................................................................5-29

Conclusion ...............................................................................................................................5-46

Notes .........................................................................................................................................5-47

Glossary ....................................................................................................................................5-51

References .................................................................................................................................5-51

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

Highlights ....................................................................................................................................6-4

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

Knowledge- and Technology-Intensive Industries in the World Economy ................................6-7

Worldwide Distribution of Knowledge- and Technology-Intensive Industries .......................6-14

Trade and Other Globalization Indicators .................................................................................6-23

Innovation-Related Indicators of U.S. and Other Major Economies .......................................6-45

Conclusion ................................................................................................................................6-57

Notes .........................................................................................................................................6-58

Glossary ....................................................................................................................................6-59

References .................................................................................................................................6-60

viii  Contents
Science and Engineering Indicators 2010 ix

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

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

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

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

Public Knowledge About S&T .................................................................................................7-16

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

Public Attitudes About Specific S&T-Related Issues ..............................................................7-36

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

Notes .........................................................................................................................................7-44

Glossary ....................................................................................................................................7-46

References .................................................................................................................................7-47

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

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

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

Elementary/Secondary Education .............................................................................................8-10

Higher Education ......................................................................................................................8-38

Workforce .................................................................................................................................8-62

Financial Research and Development Inputs ............................................................................8-74

R&D Outputs ............................................................................................................................8-90

Science and Technology in the Economy ...............................................................................8-100

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 

ACC American Chemistry Council

ACS American Community Survey

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

AUTM Association of University Technology 

Managers 

BEA Bureau of Economic Analysis 

BLS Bureau of Labor Statistics 

BRDIS Business R&D and Innovation Survey

CATI Cooperative Agreements and Technology 

Indicators

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 

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 EPSCoR Interagency Coordinating 

Committee 

EPA Environmental Protection Agency 

EPSCoR Experimental Program to Stimulate 

Competitive Research 

Esnet DOE’s Energy Sciences Network 

ESP extrasensory perception

EU European Union 

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

 Survey of Graduate Students and 

Postdoctorates in Science and Engineering

GUF general university fund 

HBCU historically black college or university

HHS Department of Health and Human Services 

HS Harmonized Commodity Description and 

Coding System

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 the field 

IRC Internal Revenue Code

IRI Industrial Research Institute

IRS Internal Revenue Service 

ISTE International Society for Technology in 

Education

ITEA International Technology Education 

Association

KEI Knowledge Economy Index

KI knowledge intensive

KTI knowledge- and technology-intensive

LEHD Longitudinal Employer-Household 

Dynamics

LSC Local Systemic Change Through Teacher 

Enhancement

LTT Long-Term Trend

MEP Manufacturing Extension Partnership

MER market exchange rate 

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 

x 
Science and Engineering Indicators 2010 xi

NCRPA National Cooperative Research and 

Production Act

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 

OWH other Western Hemisphere 

PhRMA Pharmaceutical Research and 

Manufacturers of America

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 

RA research assistantship

RDD random direct dialing

RDT research, development, and testing 

S&E science and engineering 

S&T science and technology 

SAR special administrative region

SASS Schools and Staffing Survey 

SBIR Small Business Innovation Research 

SCI Science Citation Index 

SDR Survey of Doctorate Recipients 

SESTAT Scientists and Engineers Statistical Data 

System 

SOI Statistics of Income

SREB Southern Regional Education Board

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

UFO unidentified flying object

UK United Kingdom 

USDA Department of Agriculture 

USDIA Survey of U.S. Direct Investment Abroad 

USGS U.S. Geological Survey

USPTO U.S. Patent and Trademark Office 

USSR Union of Soviet Socialist Republics 

VA Department of Veterans Affairs

VCU Virginia Commonwealth University

WebCASPAR Integrated Science and Engineering 

Resources Data System 

YSD years since highest degree
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 Division of Science Resources Statistics (SRS) 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 seven chapters that follow a generally consistent pattern; an eighth chapter, on state indicators, presented in a unique format; and an overview that precedes 

these eight chapters. The chapter titles are

Elementary and Secondary Education

Higher Education in Science and Engineering

Science and Engineering Labor Force

Research and Development: National Trends and 

International Linkages

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.

A National Science Board policy statement companion 

piece, authored by the Board, draws upon the data in SEI 

and offers 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 Digest of Key Science and Engineering Indicators, 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 2010 and 

associated products. 

The Seven Core Chapters

Each chapter consists of contents and lists of sidebars, 

text tables, and figures; highlights; introduction (chapter 

overview and chapter organization); a narrative synthesis of 

data and related contextual information; conclusion; notes; 

glossary; and references.

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

that summarizes a key point made in the chapter. Bulleted 

points supporting the key point follow.

Introduction. The chapter overview provides a brief explanation of the importance of the topic. It situates the topic 

in the context of major concepts, terms, and developments 

relevant to the data reported. The introduction includes a 

brief narrative account of the logical flow of topics within 

the chapter.

Narrative. The chapter narrative is a descriptive synthesis 

that brings together significant findings. It is also a balanced 

presentation of contextual information that is useful for 
Science and Engineering Indicators 2010 xiii

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.

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. 

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.

The printed version of SEI includes a CD-ROM in PDF format and a packaged set of information cards. The CD-ROM 

contains the complete content of SEI and, as with the online 

version, appendix tables in spreadsheet format. The full set of 

presentation slides is also included. The pocket-sized information cards highlight key patterns and trends. Each card presents 

a selection of figures with captions stating the major point that 

the figure is meant to illustrate.

Overview

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

A Bird’s Eye View of the World’s Changing S&T Picture ........................................................O-3

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

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

Global Higher Education and Workforce Trends .......................................................................O-6

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

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

Expanding International Research Collaborations ....................................................................O-10

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

Inventive Activity Shown by Patents ........................................................................................O-13

Fast-Rising Global Output of Knowledge- and Technology-Intensive Firms ..........................O-14

Booming Global High-Technology Exports Rearranging World Trade Patterns .....................O-16

Big Shifts in World Trade Positions in High-Technology Products .........................................O-18

Continued Surpluses From U.S. Trade in Knowledge-Intensive Services and 

Intangible Assets ...................................................................................................................O-19

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

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

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

List of Figures

Figure O-1. Estimated R&D expenditures worldwide: 1996–2007 ............................................O-4

Figure O-2. R&D expenditures for United States, EU, and Asia: 1996–2007 ...........................O-4

Figure O-3. R&D expenditures as share of economic output of selected countries: 

1996–2007 ..............................................................................................................................O-5

Figure O-4. Average annual growth of R&D expenditures for United States, EU-27, 

and selected Asia-8 economies: 1996–2007 ..........................................................................O-5

Figure O-5. Location of estimated worldwide R&D expenditures: 1996 and 2007 ...................O-6

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

investing region, and performed by foreign affiliates of U.S. multinational companies, 

by host region: 2006 ...............................................................................................................O-6

Figure O-7. Tertiary-educated population 15 years old or older, by country/economy: 

1980 and 2000 ........................................................................................................................O-7

Figure O-8. First university degrees in natural sciences and engineering, selected countries: 

1998–2006 ..............................................................................................................................O-7

Figure O-9. Doctoral degrees in natural sciences and engineering, selected countries: 

1993–2007 ..............................................................................................................................O-8

Figure O-10. Number of researchers in selected regions/countries/economies: 

1995–2007 ..............................................................................................................................O-8

Figure O-11. Average annual growth in number of researchers in selected regions/countries/ 

economies: 1995–2007...........................................................................................................O-9

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

1994, 1999, and 2004 .............................................................................................................O-9

Figure O-13. S&E journal articles produced by selected regions/countries: 1988–2008 .........O-10

Figure O-14. Field shares of research articles for selected countries/economies: 2007 ...........O-10

Figure O-15. Engineering journal articles produced by selected regions/countries: 

1998–2008 ............................................................................................................................O-11

O-1
O-2 Overview

Figure O-16. Engineering article share of total S&E article output for selected regions/ 

countries/economies: 1988–2008 .........................................................................................O-11

Figure O-17. International coauthorship of S&E articles, by region/country: 1988–2007 ......O-12

Figure O-18. Citations in U.S. S&E articles to non-U.S. publications: 1992–2007 .................O-12

Figure O-19. Citations in Asia-10 S&E articles, by cited region/country: 1992–2007 ............O-13

Figure O-20. Citations in China S&E articles, by cited region/country: 1992–2007 ...............O-13

Figure O-21. Share of region’s/country’s papers among world’s most cited S&E 

articles: 2007 ........................................................................................................................O-13

Figure O-22. Share of U.S. patent grants for selected regions/countries: 1995–2008 ..............O-14

Figure O-23. Share of high-value patents, for selected regions/countries: 1997–2006 ............O-14

Figure O-24. Value added of knowledge-intensive and high-technology industries 

as share of region’s/country’s GDP: 1995–2007 .................................................................O-15

Figure O-25. Global value added of knowledge- and technology-intensive industries: 

1995–2007 ............................................................................................................................O-15

Figure O-26. Value added of commercial knowledge-intensive services, 

by selected region/country: 1995–2007 ...............................................................................O-15

Figure O-27. Value added of high-technology manufacturing industries, by selected region/

country: 1995–2007 .............................................................................................................O-16

Figure O-28. Global value added market shares of computer and office machinery 

manufacturing, by region/country: 1995–2007 ....................................................................O-16

Figure O-29. Global high-technology exports as share of production: 1995–2008 .................O-17

Figure O-30. Share of global high-technology exports, by region/country: 1995–2008 ..........O-17

Figure O-31. Global export shares in information and communications technology 

products, by region/country: 1995–2008 .............................................................................O-17

Figure O-32. Selected Asian countries’/economies’ share of high-technology exports 

to United States/EU and China: 1990–2008 ........................................................................O-18

Figure O-33. China’s high-technology exports to selected regions/countries: 1990–2008 ......O-18

Figure O-34. Trade balance in high-technology goods for selected regions/countries: 

1995–2008 ............................................................................................................................O-19

Figure O-35. U.S. imports, exports, and trade balance in commercial knowledge-intensive 

services: 1997–2007 .............................................................................................................O-19
Science and Engineering Indicators 2010 O-3

Introduction

This overview of the National Science Board’s Science 

and Engineering Indicators 2010 brings together 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 2010 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 invite 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 play 

a growing role. 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 (EU), Japan, China, and selected other 

Asian economies (the Asia-9: India, Indonesia, Malaysia, 

the Philippines, Singapore, South Korea, Thailand, Taiwan, 

and Vietnam). 

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 such as 

global R&D expenditures and human resources, including 

researchers. It describes research outputs and their use in 

the form of article citations and patents. It then turns to the 

growth and structural shifts in international high-technology 

markets, trade, and relative trade positions. 

The data available as of this writing do not, for the most 

part, cover the ongoing changes that shook the global economy beginning in 2008. The data therefore cannot accurately 

portray their consequences for the world’s S&T enterprise. 

Thus, the trends discussed here may already be changing in 

unexpected ways. Nevertheless, major patterns and trends 

that have developed over the past decade or more affect, and 

are shaped by, the range of S&T endeavors, from basic research to production and trade of high-technology goods and 

knowledge-intensive services. They are the starting points 

from which to mark any future changes. 

A Bird’s Eye View of the World’s 

Changing S&T Picture

Since the 1990s, a global wave of market liberalization 

has produced an interconnected world economy that has 

brought unprecedented levels of activity and growth, along 

with structural changes whose consequences are not yet fully understood. Governments in many parts of the developing world have come to view science and technology (S&T) 

as integral to economic growth and development, and they 

have set out to build more knowledge-intensive economies 

in which research, its commercial exploitation, and intellectual work would play a growing role. 

To that end, they have taken steps to open their markets to 

trade and foreign investment, develop or recast their S&T infrastructures, stimulate industrial R&D, expand their higher 

education systems, and build indigenous R&D capabilities. 

This has brought a great expansion of the world’s S&T activities and their shift toward developing Asia, where most 

of the rapid growth has occurred. Governments there have 

implemented a host of policies to boost S&T capabilities as 

a means to ensuring their economies’ competitive edge. 

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

continues to maintain a position of leadership but has experienced a gradual erosion of its position in many specific 

areas. Two contributing developments are the rapid increase 

in a broad range of Asian S&T capabilities outside of Japan 

and the fruition 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— 

beyond Japan—is driven by developments in China and 

several other Asian economies (Asia-9).1

 All are seeking to 

boost access to and the quality of higher education and to 

develop world-class research and S&T infrastructures. The 

Asia-9 form a loosely structured supplier zone for China’s 

high-technology manufacturing export industries that increasingly appears to include Japan. Japan, long a preeminent world S&T nation, is holding its own in research and 

some high-value S&T activities but is losing ground to the 

Asia-9 in overall high technology manufacturing and trade. 

India’s high gross domestic product (GDP) growth contrasts 

with a fledgling overall S&T performance.

The EU largely holds 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. 

Multinational companies (MNCs) operating in this changing environment are seeking access to developing markets, 

whose governments provide incentives. Modern communications and management tools support the development of 
O-4 Overview

globally oriented corporations that draw on far-flung, specialized global supplier networks. In turn, host governments 

are attaching conditions to market access and operations 

that, along with technology spillovers, produce new and 

greater indigenous S&T capabilities. Western- and Japanbased MNCs are increasingly joined in world S&T markets 

by newcomers headquartered in developing nations. 

Global Expansion of Research 

and Development Expenditures

In a telling development, the world’s R&D expenditures 

have been on an 11-year doubling path, growing faster than 

total global economic output.2

 This indicator of commitment 

to innovation went from an estimated $525 billion in 1996 

to approximately $1.1 trillion in 2007 (figure O-1). The 

specific data point for each year shown in figure O-1 is an 

imprecise estimate, but the steady and large upward trend 

illustrates the rapidly growing global focus on innovation.3

The United States remained by far the single largest 

R&D-performing country. Its R&D expenditure of $369 

billion in 2007 exceeded the Asian region’s total of $338 

billion and the EU’s (EU-27) $263 billion4

 (figure O-2). 

The U.S. 2007 total broadly matched the combined R&D 

expenditures of the next four largest countries: Japan, China, 

Germany, and France.

If R&D expenditures are long-term investments in innovation, how much of a nation’s economic activity should be 

devoted to them? A U.S. goal in the 1950s was to achieve 

an R&D investment of 1% of GDP by 1957. More recently, 

many governments set their sights at 3% of GDP in pursuit 

of developing knowledge-based economies; the EU formally embraced the 3% goal as its long-term planning target.5

Nearly everywhere, however, decisions affecting the bulk 

of R&D expenditures are made by industry, thus removing 

achievement of such a target from direct government control. In the United States, industry funds about 67% of all 

R&D. For the EU, it is 55%, but with considerable range 

(e.g., nearly 70% for Germany and 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 Asian developing economies 

have exhibited increased R&D/GDP ratios; conversely, 

those in the United States and the EU have broadly held 

steady. Japan’s R&D expenditures amounted to 3.4% of 

GDP in 2007; South Korea’s increased steeply after the 

1990s and reached 3.5% in 2007. 

China’s R&D/GDP ratio more than doubled, from 0.6% 

in 1996 to 1.5% in 2007, 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 China’s R&D volume 

can continue to grow rapidly (figure O-3).

Decade-long R&D growth rates of mature S&T countries 

differ dramatically from those of developing economies. 

Growth of R&D expenditures in the United States, the EU, 

	


	


	
		


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Science and Engineering Indicators 2010 O-5

and Japan averaged about 5%–6% annually, not adjusted for 

inflation. Asian growth ranged from about 9% to 10% for 

India, South Korea, and Taiwan to more than 20% for China. 

Asian R&D growth reflects rising private spending by domestic and foreign firms, as well as increased public R&D 

spending designed to support strategic policies that aim to 

raise economic competitiveness through the development of 

knowledge-based economies (figure O-4).

The relatively greater R&D growth rates in Asia (excluding Japan) resulted in decreases in the percentages of world 

R&D expenditures for the mature S&T establishments—

United States, the EU, and Japan—that were substantial, 

especially in view of the short period and large expenditures involved. The North America region’s (United States, 

Canada, and Mexico) share of estimated world R&D activity 

decreased from 40% to 35%; the EU’s share declined from 

31% to 28%. The Asia/Pacific region’s share increased from 

24% to 31% even with Japan’s comparatively low growth, 

and the share of the rest of the world increased from 5% 

to 6%—still a modest level but a very large relative gain 

that indicates the broadly shared belief in the importance of 

R&D for economic development (figure O-5).

Overseas R&D by Multinational 

Companies

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 by U.S.-based MNCs ($28.5 

billion in 2006) shifted toward emerging Asian markets 

whose combined share, excluding Japan, increased from 5% 

to 14% from 1995 to 2006. This change was driven by U.S. 

affiliates in China, South Korea, and Singapore. In 1995, 

about 90% of all overseas R&D by U.S.-headquartered 

MNCs took place in developed European economies, in 

Canada, and in Japan; by 2006, the combined percentage of 

these economies had declined to 80%. 

In the United States, affiliates of foreign-headquartered 

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O-6 Overview

expenditures represented about 14% of total U.S. business 

R&D performance, up from less than 10% in the 1980s.

Global Higher Education 

and Workforce Trends

No comprehensive measures of the global S&E labor 

force exist, but fragmentary data indicate rapid growth in 

the number of individuals who pursue advanced education, 

especially in developing nations. In recent decades, the increasing number of new S&E degrees, including degrees in 

natural sciences and engineering, awarded in developing 

countries has diminished the advantage that mature countries had held in advanced education.6

Worldwide, the number of persons with a tertiary education continues to grow.7

 Estimates for 1980 and 2000, the 

latest available year, show an increase of about 120 million 

individuals, from 73 million to 194 million (figure O-7). The 

completion of tertiary education expanded most rapidly in 

developing Asian economies, where the combined shares of 

China, India, South Korea, the Philippines, and Thailand increased from 14% to 25% of the world’s total. The number 

of individuals with advanced education in these Asian countries in 2000, 49 million, nearly matched the 2000 U.S. total; 

in 1980, these countries had accounted for less than half. 

	
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Science and Engineering Indicators 2010

NOTE: Preliminary estimates. 

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

Abroad (annual series). See appendix tables 4-32 and 4-34.

Figure O-6

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

foreign affiliates of U.S. multinational companies, by host region: 2006

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Science and Engineering Indicators 2010 O-7

Trends in fragmentary international degree data suggest that 

Asian growth has continued and perhaps accelerated.

Governments in many Western countries and in Japan are 

concerned about lagging student interest in studying natural 

sciences or engineering (NS&E), fields they believe convey 

technical skills and knowledge that are essential for knowledge-intensive economies. In the developing world, the 

number of first university NS&E degrees, broadly comparable to a U.S. baccalaureate, is rising, led by large increases 

in China, from about 239,000 in 1998 to 807,000 in 2006. 

New NS&E degrees earned by Japanese and South Korean 

students combined in 2006 (about 235,000) approximated 

the number earned by U.S. students in that year, even though 

the U.S. population was considerably larger (300 million vs. 

175 million) (figure O-8). 

The expansion of NS&E degrees extends beyond first 

university degrees to degrees certifying completed advanced 

study. Since the early 1990s, the number of NS&E doctorates 

awarded in Japan and India has increased by more than 70%—

to approximately 7,100 and 7,500, respectively. The number 

awarded in South Korea nearly tripled over the same period, 

reaching approximately 3,500. China’s domestic NS&E doctorate awards have increased more than tenfold over the period, to about 21,000 in 2006, nearing the number of NS&E 

doctorates awarded in the United States (figure O-9). 

Most of the post-2002 increase in U.S. NS&E doctorate production reflects degrees awarded to temporary and 

permanent visa holders, who in 2007 earned about 11,600 

of 22,500 U.S. NS&E doctorates.8

 Foreign nationals have 

earned more than half of U.S. NS&E doctorates since 2006. 

Half of these students are from East Asia, mostly from China 

(31%), India (14%), and South Korea (7%). 

For engineering, the numbers are more concentrated. 

Since 1999, the share of U.S. engineering doctorates earned 

by temporary and permanent visa holders has risen from 

51% to 68% in 2007. 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 will stay. 

Sixty percent of temporary visa holders who had earned a 

	
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U.S. S&E doctorate in 1997 were gainfully employed in the 

United States in 2007—the highest 10-year stay rate ever 

observed.9

Expanding Global Researcher Pool

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

R&D and degree data discussed previously. 

The estimated number of researchers grew from nearly 4 

million in 1995 to about 5.7 million in 2007.10 The United 

States and the 27 EU members each accounted for about 1.4 

million researchers—a combined 49% of the total but below 

the 51% share they had held a decade earlier. China’s researchers more than doubled in number, from just over half 

a million to more than 1.4 million, boosting its world share 

from 13% to 25% over the period (figure O-10).

Trends in researcher growth rates vary greatly by country/

region. The United States and the EU had moderate annual 

growth of about 3% between 1995 and 2006. Japan’s rate 

was below 1%. Growth in the Asian region outside Japan 

ranged from 7% to 11%. China, the biggest country, averaged nearly 9% growth, including a brief but sharp break in 

1998–99 that reflected the rapid conversion of state-owned 

to privately owned enterprises as a result of the central government’s policy change. Russia’s researcher growth rate, 

which is now flat, declined over the period (figure O-11).

The contribution of multinational corporations to researcher growth in the overseas markets in which they 

operate is unknown. Data on overseas R&D employment 

of U.S.-based MNCs and their majority-owned affiliates 

are available only every 5 years. The latest data available 

show that their overseas R&D employment increased from 

102,000 in 1994 to 138,000 in 2004. Over the same period, U.S. R&D employment of these MNCs increased from 

625,000 to about 716,000. As a result, the overseas share 

of R&D employment increased from 14% to 16% (figure 

O-12). These data do not include researchers employed 

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Science and Engineering Indicators 2010 O-9

ownership or by firms that perform research under contract 

to MNCs. 

Employment of researchers by foreign-based MNCs in 

other countries is 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. 

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 patent 

applications to the scientific and technical literature indicate 

the linkage between research and practical application. 

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

460,000 in 1988 to an estimated 760,000 in 2008.11 The 

geographical distribution of the authors provides yet another 

indication of the size of a country’s or region’s research enterprise and its ability to produce research results that can 

pass peer review. 

Researchers in the EU and the United States have long 

dominated world article production, but their combined 

world share of published articles decreased steadily from 

69% in 1995 to 59% in 2008 as Asia’s output increased. 

In little more than a decade, Asia’s world article share expanded from 14% to 23%. The increase principally reflected 

China’s output volume, which expanded by about 14% annually over the period. In 2008, China produced about 8% of 

world article output, up from 1% in 1988. By 2007, China’s 

publication volume exceeded Japan’s, moving it into 2nd 

place behind the United States—a distant 2nd place, but up 

from 14th place in 1995. In contrast, India’s output of scientific and technical articles stagnated through the late 1990s 

before beginning to increase, and India’s ranking hardly 

moved, changing from 12th place in 1995 to 11th place in 

2008 (figure O-13).

The distribution of a country’s research publications 

across different fields broadly reflects its research priorities. 

In 2007, more than half of the articles published by U.S. 

researchers reported on work in the biomedical and other 

life sciences, whereas scientists in Asia and some major European countries published a preponderance of articles in the 

physical sciences12 and engineering (figure O-14). Priority 

shifts not evident in figure O-14 include China’s growing focus on chemistry R&D (related articles increased as a share 

of China’s S&E articles from 13% in 1988 to 24% in 2008) 

and declining share of other physical sciences articles (from 

39% to 28%) as well as South Korea’s shift toward greater 

output in biological and medical sciences (from a combined 

17% to 38%). These changes in research portfolios reflect 

government policy choices: China is building up its chemicals industry; South Korea is trying to develop a reputation 

in health sciences.

Worldwide, the number of engineering research articles 

increased substantially faster over the past 20 years than total S&E article production, particularly in Asia, where the 

growth rate (7.8%) in engineering article output exceeded 

that of total S&E article output (6.1%). 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. 

	


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Consequently, the production of engineering research 

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

1988, the U.S. share of engineering articles was 36%; by 

2008, it was 20%. Japan’s share declined from 12% to 7% 

during the same period. Only the EU managed to maintain 

its share at 28%. Asia’s share, excluding Japan, increased 

from 7% to 30%, with China producing nearly half (14%) of 

these articles by 2008 (figure O-15).

This strong and rapidly growing preponderance of engineering articles produced in developing Asian economies 

(figure O-16) is consistent with the region’s emphasis on developing high-technology manufacturing capabilities. The 

Asia-10 region produced more engineering articles than the 

United States starting in 1999 and overtook the EU in 2003. 

In 2005, China overtook Japan in engineering article output 

and moved from ninth place in 1988 to second place. India’s 

relative strength in engineering allowed it to move from seventh place to fifth place in the past 10 years. 

	
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Expanding International 

Research Collaborations

Collaborative research is becoming the norm, as indicated by the increasing coauthorship of journal articles. Articles with authors in two or more countries have increased 

in number faster than any other segment of the S&E literature, indicating growing collaboration across national 

boundaries. In 1988, only 8% of the world’s S&E articles 

	
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Science and Engineering Indicators 2010 O-11

had international coauthors; by 2007, this share had grown 

to 22%.

The United States rate of international collaboration is 

similar to that of Japan and China but lower than that of 

the EU, where explicit EU policies coupled with incentives 

stimulate international, and specifically intra-EU, collaboration (figure O-17). As a result of the large volume of total 

U.S. article output, however, U.S.-based authors appeared 

on 43% of the world’s internationally coauthored articles 

in 2008. 

An index of international collaboration corrects for the effects of unequal size of countries’ research establishments.13 It 

summarizes regional and country coauthorship patterns, with 

values above “1” indicating higher-than-expected, and values 

below 1 indicating lower-than-expected, collaborations. 

U.S. international collaborations measured by this bilateral index were widespread, were generally lower than 

expected, and remained mostly steady over the past decade 

(1998–2008). EU collaborations were equally widespread, 

were generally lower than expected for its large members, 

and increased measurably over the period, quite likely in 

response to explicit EU policies. Unlike the index values for 

established scientific nations, Asia’s index values were substantially higher than expected.

In 2008, U.S. research collaborations were especially 

strong with Canada and Mexico in North America (1.18 and 

1.03), with Israel (1.25), and with South Korea and Taiwan 

in Asia (1.23). U.S. collaborations with China, Japan, and 

India were above the U.S. average.

EU policies to increase intra-European research integration appear to be having their desired effect, as intra-EU 

collaboration index values increased substantially over the 

period, most of them above unity. 

Intraregional collaborations are prevalent in Asia, where 

they have developed even without the integrating framework provided by the EU. Over the 10-year period, high levels of collaboration were evident between China and Japan, 

South Korea, Singapore, and Taiwan, whereas the rate of 

collaboration between China and India diminished noticeably. India, in turn, collaborated more with Japan, South 

Korea, Singapore, and Taiwan. The underlying index values 

	
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O-12 Overview

suggest the genesis of an intra-Asian zone of scientific collaboration that has a counterpart in knowledge- and technology-intensive economic activities.

New Research Patterns 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.14 Citations to nondomestic articles can indicate the 

existence of useful work being done elsewhere. 

Citations in U.S. articles (henceforth, U.S. citations) to 

the domestic literature dropped steadily since 1992 from 

69% to 60% in 2007, attesting to the growth of relevant 

work elsewhere. Figure O-18 shows the regional breakdown 

of nondomestic citations relative to total U.S. citations. 

Most U.S. citations to the nondomestic literature referenced EU publications. In 2007, 23% of total U.S. citations were to EU work, up from about 18% in 1992 but flat 

in recent years. Over the same period, the Japanese share 

gradually declined. Slowly rising citation shares to work 

done in the Asia-8 group remained at a low level, partly because of the overall low level of their publications output, 

but probably also because of language, cultural barriers, and 

research quality. 

EU citation patterns have undergone similar changes, 

with citations to the U.S. literature dropping from 36% to 

28% over the 1992–2007 period. Total citations to Asian articles increased modestly from 5% to 8%, whereas citations 

to the rest of the world increased from 13% to 18%.

Major changes are evident in the Asia-10 group, whose 

internal citations increased from 37% to 41% of the Asia10 total over the 1992–2007 period. Within this group, 

Japan’s share dropped steeply from 31% to 17%, whereas 

China’s share increased from 2% to 12% and the Asia-8’s 

share increased from 5% to 12%. The EU share slowly increased, whereas the U.S. share declined from 36% to 27% 

(figure O-19).

The sheer number of citations by Chinese authors is rising steeply, but in a relative sense, Chinese authors are increasingly citing domestic articles and those by researchers 

in the Asia-8 group but less frequently the work of U.S. scientists. Thus, although the number of citations to U.S. articles increased from about 6,000 in 1992 to approximately 

	
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Science and Engineering Indicators 2010 O-13

82,000 in 2007, the U.S. share contracted from 36% to 25%. 

Japan’s share of citations in the Chinese literature has basically remained unchanged since the early 1990s; the same 

holds for the EU (figure O-20).

Even as global production and citation patterns have 

shifted, the relative quality distribution of worldwide articles, as measured by citations, has changed little. In 2007, 

the United States had consistently higher proportions of its 

articles in the most highly cited categories than the EU or the 

Asia-10 (figure O-21). This broad pattern held for the entire 

1998–2007 period and for all major S&E fields. 

Inventive Activity Shown by Patents

Patents are an indicator of inventive activity. By issuing 

patents that allow the patent owner to demand payment for 

their use, governments 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 because of the sheer volume of U.S. patents and the im-

	
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O-14 Overview

portance of the U.S. market, they are a useful indicator of 

trends in the geographic location of inventive activity.

About half (49%) of the patents granted by the USPTO 

went to U.S.-based inventors in 2008, down from 55% in 

1995 and somewhat below the U.S. share of applications.15

Japan’s share has been a steady 20%–22% over the period, 

above its share of applications; the EU members received 

14%–16%. The Asia-9’s share increased from 3% to 10% 

over the period, mostly on the strength of South Korea and 

Taiwan. China’s share remained in the 1% range in all major 

technology areas. Indigenous inventive activity, a focus of 

government policy, appears elusive, at least as indicated by 

patents filed in a major Western market (figure O-22).

Patents on inventions for which protection is sought in 

the United States, the EU, and Japan require substantial resources for obtaining and maintaining them. This suggests 

that their owners consider them to be valuable. These patents 

are herein treated as an indicator of the distribution of highvalue patenting around the world. 

Just over 30% of high-value patents had U.S. inventors in 

2006, down somewhat from 34% in 1997.16 The EU’s share 

declined somewhat more, to 29% in 2006, followed closely 

by Japan. The Asia-9’s increasing share largely reflects patents with Korean inventors. As with U.S. patents, Chinese 

inventors appeared on only 1% of these high-value patents 

(figure O-23).

Fast-Rising Global Output 

of Knowledge- and 

Technology-Intensive Firms 

Governments in many parts of the world are acting on the 

conviction that knowledge- and technology-intensive economies create well-paying jobs, contribute high-value output, 

and ensure economic competitiveness. In response to changing opportunities, knowledge-intensive (KI) services industries and high-technology (HT) manufacturing industries 

have grown more rapidly than other segments of economic 

activity17 (figure O-24). 

In 2007, these knowledge- and technology-intensive 

(KTI) industries combined contributed just under $16 trillion to global economic output—about 30% of world GDP 

(figure O-25).

Initially these industries were the province of developed 

nations, but they have grown rapidly in developing markets. 

The global value-added volume for the largest aggregate—

commercial knowledge-intensive services—increased from 

$4.5 trillion in 1995 to $9.5 trillion in 2007 (figure O-26). 

The United States, with $3.3 trillion in 2007, produced the 

largest value-added output of these industries, which include 

business services, financial services, and communications. 

	
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Science and Engineering Indicators 2010 O-15

The United States was followed by the EU with $2.9 trillion. 

World shares in these industries fluctuated for the United 

States and the EU, but by 2007 had settled near their 1995 

levels. Increased production by China and the Asia-9 expanded their value-added output of commercial KI services, 

but at about half a trillion dollars each, their world market 

shares remained just below 5%. Flat output growth in Japan 

caused its market share to decline by more than half, to 8%. 

The same pattern is evident in the individual KI service 

sectors: fluctuations in the U.S. and EU shares, steep declines for Japan’s shares, and modest to rapid growth from 

low bases for China and the Asia-9, leading to modest increases in their world shares.

Relative to these KI trends, high-technology manufacturing shows a much stronger world position for the developing Asian economies and much steeper decline for Japan. 

The Asia-9 output was about 10% of the value-added world 

total over the 1995–2007 period, while China’s share increased from 3% to 14%. Japan’s share dropped from 27% 

to 11%. The U.S. and EU shares both showed modest upward movement.

The five HT industries are, in decreasing order of the $1.2 

trillion 2007 global value-added total: communications and 

semiconductors ($445 billion), pharmaceuticals ($319 billion), scientific instruments ($189 billion), aerospace ($153 

	
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O-16 Overview

billion), and computers and office machinery ($114 billion). 

The aggregate distribution by country/economy is shown in 

figure O-27. 

The United States ranked first with 31% of the total, followed by the EU’s 25% share. The United States was the 

world leader in communications and semiconductors (29%), 

pharmaceuticals (32%), and aerospace (52%), and ranked 

behind the EU in scientific instruments (19% vs. 44%). 

However, in computers, the United States (25%), the EU 

(15%), and Japan (5%) all ranked well behind China (39%). 

This category saw a particularly rapid shift in relative world 

value-added positions (figure O-28).

These data obscure a larger dynamic, discussed briefly 

below: the development of a high-technology assembly zone 

in the Asia region, arrayed largely around China. It is likely 

that part of China’s rapid growth of value-added in computer manufacturing reflects the large-scale movement of Taiwanese manufacturing facilities to, and subsequent export 

of computer products from, China. Nevertheless, these data 

highlight the growing concentration of the world’s computer 

and office machinery manufacturing in Asia.

Booming Global High-Technology 

Exports Rearranging World 

Trade Patterns

The total export volume of high-technology products 

increased faster than gross production and pushed exports 

close to 60% of production in 2007, up from 37% in 1995 

(figure O-29). This increase reflects the broadened international base of high-technology manufacturing, the expansion of multinational firms’ overseas production, and a shift 

in the nature of production to increasingly specialized and 

geographically dispersed suppliers. The global economic 

slowdown is mirrored in the greater decline of exports than 

production and the downturn in the 2008 export share.

The global expansion of high-technology trade has made 

China the largest single high-technology exporter and has 

changed the relative positions of the developed and developing countries. China’s share of world high-technology 

exports increased from 6% in 1995 to 20% in 2008, while 

the Asia-9 maintained a 26%–29% share (figure O-30). Japan’s export share eroded from 18% to 8%, the U.S. share 

dropped from 21% to 14%, and the EU maintained a 16%–

18% share.18

	
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The drop in the U.S. share was driven by below-average 

U.S. export growth in computers and information and communications (ICT) products, contrasting with China’s nearly 

twelvefold expansion (figures O-30 and O-31). Since 1995, 

China and the Asia-9 have moved from a combined 42% of 

ICT product exports to 64% of the world’s total, and almost 

70% of computers alone.

	
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O-18 Overview

An Asian high-technology supplier zone appears to be 

developing 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. Chinese high-technology exports to the United States increased 

from $28 billion in 2000 to $112 billion 8 years later, when 

the U.S. recession dampened the pace of increase. Chinese 

exports to the EU increased at a slightly faster pace over the 

period (figures O-32 and O-33).

Big Shifts in World Trade Positions 

in High-Technology Products

In the high-technology goods trade, the United States had 

small trade surpluses during the mid- to late 1990s; these 

turned into a widening deficit after 1998 that has fluctuated 

at about $80 billion since 200519 (figure O-34). The U.S. 

trade deficit in ICT goods—communications and semiconductors and computers—is larger than that. It reached a record $126 billion in 2007 before contracting marginally to 

$119 billion in 2008, reflecting recession-induced lowered 

imports. 

ICT goods have been the major driver behind the overall 

U.S. high-technology trade deficit. The broad shift in the location of production of these goods to Asia coincided with 

growing U.S. demand, which in turn stimulated growing imports. Pharmaceuticals contributed a further $21 billion to 

the 2008 deficit. Aerospace and scientific instruments were 

in surplus, at $50 billion and $9 billion, respectively.

The EU had a relatively stable 1995–2008 trade deficit 

for all high-technology classes combined, smaller than that 

of the United States. However, its ICT deficit was almost 

identical to that of the U.S., reflecting the same dynamic of 

rising domestic demand and relocated production. The EU’s 

aerospace, pharmaceuticals, and scientific instruments trade 

balances were in surplus.

China and the Asia-9 had substantial 2008 high-technology trade surpluses of $129 billion and $221 billion, respectively. Both showed strong increases after 2002. Japan had 

a surplus that fluctuated at about $50 billion for most of the 

period, despite its loss of market share in the production of 

high-technology industries. 

	
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Science and Engineering Indicators 2010 O-19

Continued Surpluses From U.S. Trade 

in Knowledge-Intensive Services 

and Intangible Assets

Unlike U.S. trade in high-technology products, U.S. trade 

in commercial knowledge-intensive services—business, financial, and communications services—has produced a consistent and growing surplus (figure O-35). The trade balance 

widened from $21 billion in 1997 to nearly $50 billion in 

2007, as exports grew faster than imports. Likewise, U.S. 

trade in intangible assets—payments for the use of others’ 

property rights in the production of goods, trademarks, use 

of computer software, books, records, franchise fees, and the 

like—exhibited a similar trend of growing surpluses, which 

reached nearly $60 billion in 2007. 

Conclusion

Science and technology are no longer the province of 

developed nations; they have, in a sense, become “democratized.” Governments of many countries have firmly built 

S&T aspects into their development policies as they vie to 

make their economies more knowledge- and technologyintensive and, thereby, ensure their competitiveness in a globalizing world. These policies include long-term investments 

in higher education to develop human talent, infrastructure 

development, support for research and development, attraction of foreign direct investment and technologically advanced multinational firms, and the eventual development 

of indigenous high-technology capabilities. 

	
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O-20 Overview

The resulting developments open the way for widespread 

international collaboration.20 The broad trend in this direction 

is clearly reflected in the rapid growth of international coauthorships of research articles in the world’s leading journals. 

The developments also carry with them competitive elements. The quest for international talent, once largely limited 

to major Western nations, is now pursued by many, and “brain 

drain” has evolved into cross-national flows of highly trained 

specialists. In S&T, nations are eager to establish specialty 

niches and develop indigenous world-class capacity.

The globalization of the world economy has brought unprecedented levels of growth to many countries, demonstrating that benefits can accrue to all. But the structural changes 

that are part and parcel of rapid growth bring with them painful dislocations, amplified by the uncertainties and potential 

changes fostered by the world-wide recession. How these 

are resolved will inevitably affect the health and development of nations’ S&T systems and their place in the world.

Notes

1. The Asia-9 includes India, Indonesia, Malaysia, the 

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

Vietnam.

2. The World Bank estimates global gross national income to have increased about 80% over the period (current 

PPP dollars).

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 (PPPs), the local costs of a 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 2007 U.S. R&D estimate is $398 

billion; see http://www.nsf.gov/statistics/nsf08318/tables/

tab1.xls. The overview uses the most recent OECD number 

to allow more direct 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. Tertiary education by international convention is 

broadly comparable to at least a U.S. technical school or associate’s degree.

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.

9. Michael Finn, Stay rates of foreign doctorate recipients from U.S. universities, (Oak Ridge, TN: Oak Ridge Institute for Science and Education, forthcoming).

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

11. The database used is Thomson Scientific, Science 

and Social Science Citation Indexes; IpIQ, Inc.; and NSF 

tabulations.

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

atmospheric, and ocean sciences; and astronomy.

13. The index numerator is the percent of country A’s 

international collaborations with country B; the denominator 

is B’s percentage of the world’s international collaborations. 

See appendix table 5-41.

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

and approaches; citation of domestic vs. foreign articles; 

language and cultural barriers; etc. However, when aggregated over many articles, citation indicators carry information about the relative use of articles in subsequent work.

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

16. The geographic distribution is based on location of 

inventor. Multiple-inventor patents are credited fractionally 

to geographic location.

17. These industry groups are defined by the OECD 

and form the basis for databases of economic activity that 

over a large number of the world’s economies. Knowledgeintensive services industries include the commercially tradable business, financial, and communications services, and 

education and health services, which are considered more 

nearly location-bound 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.

18. Internal EU trade was subtracted from both the world 

and EU totals, because the unified EU market structure 

makes trade among its member states akin to trade among 

U.S. states.

19. U.S. trade in advanced technology products shows 

a similar deficit path. These products, defined by the U.S. 

Bureau of the Census, include computer software, advanced 

materials, aerospace, biotechnology, electronics, flexible 

manufacturing, information/communications technology, 

life sciences, nuclear technology, optoelectronics, and weapons. However, data categorized in this fashion are unavailable for most other countries and differ from other trade data 

discussed earlier that are based on OECD definitions.

20. 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).
Science and Engineering Indicators 2010 O-21

Glossary

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

Singapore, South Korea, Taiwan, and Thailand.

Asia-9: Includes India, Indonesia, Malaysia, Philippines, 

Singapore, South Korea, Taiwan, Thailand, and Vietnam

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.

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

Student Learning in Mathematics and Science .........................................................................1-4

Teachers of Mathematics and Science ......................................................................................1-4

Instructional Technology in Education .....................................................................................1-5

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

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

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

Students’ Performance on National Assessments .....................................................................1-8

Students’ Performance on International Assessments of Mathematics and Science ..............1-16

Teachers of Mathematics and Science .......................................................................................1-23

Teacher Quality ......................................................................................................................1-24

Professional Development ......................................................................................................1-27

Teachers’ Salaries ...................................................................................................................1-28

Working Conditions ................................................................................................................1-29

Instructional Technology in Education ......................................................................................1-30

Technology as an Instructional Tool ......................................................................................1-32

Computer Use in Eighth Grade Mathematics and Science .....................................................1-32

Internet Access ........................................................................................................................1-32

Distance Education .................................................................................................................1-33

Transition to Higher Education ..................................................................................................1-34

High School Completion ........................................................................................................1-34

Participation and Performance in the Advanced Placement Program ....................................1-35

Relationship of High School Courses Taken to Postsecondary Success ................................1-36

Immediate Enrollment in Postsecondary Education ...............................................................1-38

Conclusion .................................................................................................................................1-38

Notes ..........................................................................................................................................1-39

Glossary .....................................................................................................................................1-41

References ..................................................................................................................................1-42

List of Sidebars

Homeschooling in the United States ............................................................................................1-8

Mathematics Skills Areas Assessed ...........................................................................................1-10

Development and Content of NAEP Mathematics Assessments ...............................................1-12

NAEP Contrasted With State Achievement Tests .....................................................................1-12

Differences Between TIMSS and PISA Assessments ...............................................................1-16

Sample Items from TIMSS and PISA Assessments ..................................................................1-17

Two States’ Performance on TIMSS: 2007 ...............................................................................1-20

Linking NAEP and TIMSS Results ...........................................................................................1-22

Local Systemic Change Through Teacher Enhancement Program ...........................................1-28

Student and Teacher Technology Literacy ................................................................................1-31

Measuring High School Graduation Rates ................................................................................1-35

Chapter 1

Elementary and Secondary 

Mathematics and 

Science Education

1-1
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. Average mathematics scores of students followed from kindergarten 

through grade 8, by student characteristics: Fall 1998–spring 2007 .......................................1-9

Table 1-3. Highest mathematics skill area in which fall 1998 kindergarteners 

demonstrated proficiency in grade 8, by student characteristics: Spring 2007 ....................1-11

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

by student characteristics and other measures: 1990–2007 and 2000–07.............................1-14

Table 1-5. Average TIMSS mathematics scores of students in grades 4 and 8 

in selected nations, relative to U.S. average: 2007 ...............................................................1-19

Table 1-6. TIMSS mathematics scores at the 90th percentile for students 

in grades 4 and 8, by selected nations: 2007 .........................................................................1-20

Table 1-7. Average TIMSS science scores of students in grades 4 and 8 

in selected nations, relative to U.S. average: 2007 ..............................................................1-21

Table 1-8. Average PISA mathematics and science literacy scores 

of 15-year-old students in selected nations, relative to U.S. average: 2006 .........................1-23

Table 1-9. Public school students in grades 5 and 8 taught mathematics and science 

by teachers with various levels of subject area preparation: 2004 and 2007 ........................1-26

Table 1-10. Public middle and high school teachers of mathematics and science 

with various levels of subject area preparation: 2003 and 2007 ...........................................1-26

Table 1-11. Public school students in grade 5 who were taught mathematics 

by teachers with various qualifications, by student characteristics: 2004 ............................1-27

Table 1-12. Public school students in grade 5 who were taught mathematics and science 

by teachers who had participated in staff development relating to their subject content 

or pedagogy during the past school year: 2004 .....................................................................1-28

Table 1-13. Science and mathematics courses offered at state-sponsored virtual schools, 

by state: 2008 ........................................................................................................................1-34

Table 1-14. Students who took Advanced Placement tests in mathematics and science 

and number and percentage with passing scores, by subject: 1990, 1997, and 2008 ...........1-37

Table 1-15. First-time entry rates into postsecondary education in OECD countries, 

by sex: 2007 ..........................................................................................................................1-39

Table 1-A. State standards and policies regarding K–12 teaching with and learning 

about technology, by academic year: 2004–09 .....................................................................1-31

List of Figures

Figure 1-1. Mathematics achievement scores of kindergartners followed through grade 8, 

by kindergarten mathematics score quartile and mother’s education: 

Fall 1998–spring 2007...........................................................................................................1-10

Figure 1-2. Eighth graders proficient in various mathematics skill areas, by mother’s 

education: Spring 2007 .........................................................................................................1-11

Figure 1-3. NAEP mathematics scores of students in grades 4 and 8 at various percentiles: 

1990–2007 .............................................................................................................................1-13

Figure 1-4. Average NAEP mathematics score of students in grades 4 and 8, 

by race/ethnicity: 1990–2007 ................................................................................................1-15

Figure 1-5. Students meeting various NAEP proficiency levels in mathematics, 

grades 4 and 8: 1990–2007 ...................................................................................................1-15

Figure 1-6. U.S. average TIMSS scores, by grade: 1995, 1999, 2003, and 2007 ......................1-18

Figure 1-7. Changes in selected nations’ performance on TIMSS assessments, 

relative to U.S. performance: 1995 and 2007 .......................................................................1-18

Figure 1-8. Changes in selected nations’ performance on PISA assessments 

of 15-year-olds, relative to U.S. performance: 2000 and 2006 .............................................1-24

Figure 1-9. Students in grade 4 whose teachers participated in various professional 

development activities in mathematics and science in the past 2 years: 2007 ......................1-29
Science and Engineering Indicators 2010 1-3

Figure 1-10. Weekly wages of full-time public school teachers and comparable workers: 

1996–2006 .............................................................................................................................1-29

Figure 1-11. Public school students in grade 5 whose teachers of mathematics and science 

agreed or strongly agreed with selected statements about their schools, 

by student characteristics: 2004 ............................................................................................1-30

Figure 1-12. Schools and classrooms with Internet access, 1994–2005 ....................................1-32

Figure 1-13. On-time graduation rates of U.S. public high school students, 

by race/ethnicity: 2006 ..........................................................................................................1-35

Figure 1-14. High school graduation rates, by OECD country: 2006 .......................................1-36

Figure 1-15. Postsecondary experiences of the class of 2004, by advanced mathematics 

and science credits in high school: 2006 ...............................................................................1-37

Figure 1-16. High school graduates enrolled in college in October after completing high 

school, by sex and type of institution: 1975–2008 ................................................................1-38
1-4 Chapter 1. Elementary and Secondary Mathematics and Science Education

Student Learning in Mathematics 

and Science

Initial disparities in mathematics skills found in the Early Childhood Longitudinal Study, Kindergarten Class of 

1999 (ECLS–K), tended to grow as these students progressed through grade 8.

Kindergarten test scores, along with many demographic 

characteristics, were strong predictors of students’ skills 

in 2007, after 9 years of schooling. Gaps generally grew 

through about grade 3 and thereafter remained stable, 

with some narrowing marginally.

Science understanding was tested in grades 3, 5, and 8; 

gaps between more and less advantaged groups stayed 

about the same over these grades.

Scores on the National Assessment of Educational Progress (NAEP) mathematics test increased among younger 

students through 2008, continuing a steadily rising pattern since 1990.

From 1990 to 2007, fourth graders gained 27 points and 

eighth graders gained 19 points (on a scale of 0–500 for 

both grades).

These increases in performance were shared by boys and 

girls; white, black, and Hispanic students; and students 

from lower- and higher-income families.

The score gaps in 1990 among racial/ethnic groups remained in 2007, but one gap shrank: black fourth graders 

gained enough points to narrow the gap with whites. Between 2000 and 2007, score differences decreased for six 

groups: between black and white students in both grades, 

Hispanic and white students in both grades, and low-income and higher-income students in both grades.

The two younger age groups (9- and 13-year-olds) participating in NAEP Long-Term Trend tests in 2008 had 

higher average mathematics scores than their 1973 peers 

had, but performance among 17-year-olds was flat.

Relatively large score increases among 9-year-olds occurred during the 1980s and early 2000s, while 13-yearolds had steadier (but less steep) gains over the 35 years. 

In each age group, black students narrowed the gaps with 

whites first observed in 1973.

On one recent international assessment, the Trends 

in International Mathematics and Science Study 

(TIMSS:2007), the scores of U.S. fourth and eighth graders were higher than in 1995 in mathematics but not 

in science.

TIMSS exams closely follow the curriculums commonly 

taught in participating countries. U.S. students’ 2007 average mathematics score was higher than their 1995 average score. The U.S. standing among selected comparison 

countries also rose slightly, placing the United States near 

the median of selected nations in both grades.

Scores at the 90th percentile provide information about 

high-achieving students (those who scored higher than 

90% of all test takers). In both fourth and eighth grades, 

the U.S. 90th percentile scores in mathematics were also 

near the median of selected countries on this measure.

Science results were mixed: the average 2007 science 

scores of U.S. fourth and eighth graders had not changed 

measurably from the 1995 average scores. However, the 

U.S. position among selected countries declined in fourth 

grade (two more nations outscored the United States and 

four fewer had lower scores in 2007) and increased slightly in eighth grade (where two fewer scored above and two 

more scored below the United States).

On another international test, the Program for International Student Assessment (PISA:2006), U.S. 15-yearolds scored below most selected nations in 2006, and the 

U.S. standing among selected nations dropped below its 

2000 rank in both mathematics and science. The U.S. 

PISA results in both subjects contrasted sharply with the 

U.S. TIMSS results, particularly in mathematics.

PISA aims to test students’ ability to apply what they have 

learned (e.g., explain answers in mathematical or scientific terms, use logical reasoning, synthesize information). 

Among 19 nations with data available for both years, the 

United States scored below 7 nations in mathematics in 

2000 and below 15 nations in 2006.

In 2006, the average mathematics score of U.S. students 

was lower than scores in 18 comparison nations (out of 

24), and higher than those in 4 other countries—3 of them 

developing economies. The U.S. 90th percentile score 

in mathematics was similarly low relative to scores in 

other nations.

Between 2000 and 2006, the number of countries scoring 

higher than the United States on the PISA science assessment rose from 6 to 12.

Teachers of Mathematics and Science

Most fifth and eighth grade students in public schools 

were taught mathematics and science by teachers with 

basic credentials such as a bachelor’s degree and teaching certificate (but not necessarily in mathematics and 

science). Most students were also taught by teachers with 

more than 3 years of teaching experience.

Virtually all fifth and eighth grade students in public 

schools were taught mathematics and science by teachers who had attained at least a bachelor’s degree, and 

about half of them were taught these two subjects by 

teachers with a master’s or higher degree. More than 

Highlights
80% of students had teachers with a regular or advanced 

teaching certificate.

Forty percent of fifth grade students in 2004 were taught 

mathematics and science by teachers with either a degree 

or certificate in their teaching field (i.e., in-field teachers). In contrast, 54% of fifth graders had teachers of 

mathematics and science with general education preparation. When these students reached eighth grade in 2007, 

80% of them were taught mathematics and science by 

in-field teachers, and the percentage of students taught 

mathematics and science by teachers with general education preparation fell to 9%–10%.

Eighty-two percent or more of fifth and eighth grade 

students in public schools had teachers with more than 3 

years of teaching experience.

Access to better-qualified teachers of mathematics and 

science was not equally distributed among students.

In 2004, black and Hispanic fifth grade students were less 

likely than white students to be taught mathematics by 

teachers with a master’s or advanced degree (39% and 

42% vs. 51%, respectively), a regular or advanced teaching certificate (86% and 85% vs. 92%), and more than 

3 years of experience teaching grade 5 (48% and 58% 

vs. 68%).

Also among fifth graders, a third of those in the lowest 

achievement quartile in grade 3 were taught mathematics 

by a teacher with a degree or certificate in math. In contrast, 42% of fifth graders in the top achievement quartile 

in grade 3 had such teachers.

In 2007, eighth grade students whose mothers had 

not earned a high school diploma were less likely than 

those whose mothers had a bachelor’s or higher degree 

to be taught science by teachers who had a master’s or 

advanced degree (46% vs. 57%), a regular or advanced 

teaching certificate (79% vs. 87%), a degree or certificate in science (84% vs. 93%), and more than 3 years of 

experience teaching science (69% vs. 83%).

Eighth grade students from families with low incomes 

were less likely than those from higher-income families 

to be taught science by teachers with a regular or advanced teaching certificate (79% vs. 86%), a degree or 

certificate in science (84% vs. 89%), and more than 3 

years of experience in teaching science (69% vs. 79%).

About 92% of eighth graders with high achievement in 

fifth grade were taught mathematics by a teacher with a 

degree or certificate in mathematics, compared with 77% 

of those with low fifth grade achievement.

Teacher participation in professional development in 

mathematics and science at the elementary level was not 

as common as that at the middle and high school levels.

While teacher participation in professional development during a school year was almost universal at public 

middle and high schools, in 2004, 47% of public school 

fifth grade students were taught science by teachers who 

reported no staff development in science, and 27% of students were taught mathematics by teachers who reported 

no staff development in mathematics.

On average, teacher participants spent about 14 hours on 

staff development in mathematics and science during the 

entire school year.

Roughly 40% of fifth grade students had teacher participants rating this activity as very useful.

Over the past decade, teachers’ pay increased little after 

adjusting for inflation. Teacher salaries continue to lag 

behind salaries in comparable professions, and the gaps 

have widened in recent years.

In 2006–07, the average salary for all K–12 teachers was 

about $51,000. After adjusting for inflation, teacher salaries grew by 2.8% between 1996-97 and 2006-07.

In 2006, full-time public school teachers earned 86% as 

much in weekly wages as did those in six occupations requiring comparable education and job skills: accountants, 

reporters, registered nurses, computer programmers, 

members of the clergy, and personnel officers. Between 

1996 and 2006, the gap in weekly wages between fulltime teachers and those in these comparable occupations 

widened from $7 to $153 (in constant dollars).

Most public school mathematics and science teachers 

had favorable perceptions of their working conditions. 

However, these positive perceptions were less widely 

held among teachers of disadvantaged and low-achieving 

students.

A majority of public school teachers who taught mathematics and science to fifth and eighth grade students 

expressed positive views of their principal’s leadership, 

their school’s mission and spirit, the efforts of teachers to 

learn new ideas, the relationships among colleagues, and 

parental support. Relatively few of them reported student 

learning and behavioral problems.

These positive perceptions, however, were less widely 

held among teachers of black and Hispanic students, lowachieving students, and students from low-income and 

less-educated families.

Instructional Technology in Education

Access to the Internet in U.S. schools is nearly universal.

In 2005, 94% of classrooms in U.S. public schools had 

computers with Internet access, and the ratio of students 

to instructional computers was 4:1.

Most states have implemented standards for students’ 

understanding of computer technology and teachers’ use 

of technology for instruction.

Science and Engineering Indicators 2010 1-5
All 50 states currently include computer technology in 

their curriculum standards as a subject in which students 

should receive instruction, and 46 include technology in 

their teaching standards.

An increasing number of students have access to and are 

enrolling in distance education opportunities, particularly online courses.

Twenty-five states sponsored virtual schools as of 2007, 

and 57% of secondary schools nationwide provided some 

online learning opportunities to their students in 2005. 

Most state-led programs are at the high school level.

During the 2004−05 school year, there were 506,950 

student enrollments in online courses nationwide, up 

from 317,070 students in 2002−03, but little is known 

about course taking or achievement in science and math 

in particular.

Transition to Higher Education

Most Organisation for Economic Co-operation and Development (OECD) countries outperform the United 

States in terms of secondary school completion.

In 2006, the United States ranked 17th among 23 OECD 

countries with data available on the rate of student secondary school completion.

On-time high school graduation rates have remained 

steady in the United States, and large gaps between racial/ethnic groups persist.

The nationwide on-time graduation rate was 73% in 

2006, and the on-time graduation rate for white students 

was approximately 20 percentage points higher than the 

rate for black and Hispanic students.

Student test taking in Advanced Placement (AP) mathematics and science subjects has increased rapidly 

since 1990.

The number of test takers increased in virtually all AP 

mathematics and science subjects in 2008. In some subjects, the number of test takers increased fivefold or 

more, although participants remain a small proportion of 

the high school population. Fifteen percent of the class of 

2008 earned a score of 3 or higher on at least one AP test 

during high school.

The number of students passing an AP exam is also increasing. Almost 250,000 students passed a mathematics AP exam in 2008, compared with just over 50,000 in 

1990, and more than 200,000 passed a science AP exam 

in 2008, compared with fewer than 50,000 in 1990.

Among high school graduates in 2004, earning credits for 

advanced science and mathematics courses was linked to 

higher rates of postsecondary enrollment at 4-year colleges and lower rates of postsecondary remediation, confirming the results of earlier studies.

Among students with two or more advanced mathematics 

or science credits, 88% and 90%, respectively, enrolled in 

a 4-year college within 2 years of high school graduation, 

compared with only 22% and 12% of students with no 

advanced credits.

More than 40% of students whose highest mathematics 

course was less advanced than algebra II reported taking 

remedial mathematics at the postsecondary level, compared with 17% of students who had taken calculus.

1-6 Chapter 1. Elementary and Secondary Mathematics and Science Education
Science and Engineering Indicators 2010 1-7

Introduction

This chapter describes both inputs and outcomes related 

to K–12 mathematics and science education in the United 

States. The first section focuses on student achievement, including student achievement growth over time and achievement gaps among groups of students. The second section 

focuses on a key determinant of student learning: teachers’ 

qualifications and working conditions. It examines fifth and 

eighth grade teachers’ education, licensure, and working 

conditions. New data permit analysis of how the characteristics of students and their teachers are related, providing 

greater context for understanding students’ achievement. 

This section also presents data on teacher salaries and recent research comparing teacher salaries with the salaries of 

other workers.

The third section describes student access to instructional 

technology, including data on student access to the Internet 

and eighth graders’ use of computers in mathematics and 

science classes. The section also reports on student participation in distance education, a subject new to this volume.

The fourth and final section describes students’ transitions from secondary to postsecondary education—the subject of chapter 2 in this volume. The section begins with 

an update on high school graduation rates and a comparison of U.S. high school graduation rates with those in other 

countries. It next presents data on student participation in 

Advanced Placement (AP) examinations and addresses information on high school graduates’ immediate enrollment 

in postsecondary education. New to this section are data on 

the relationship between students’ high school coursetaking 

and achievement in mathematics and science and enrollment 

and remediation in postsecondary education.

Table 1-1 presents an overview of the topics discussed in 

this chapter and the indicators used to illuminate and flesh 

out the concepts. Whenever a difference or change over time 

is cited in this chapter, it is statistically significant at the 0.05 

probability level.1

Student Learning 

in Mathematics and Science

One of the central goals of educators and legislators is increasing overall student achievement, with a special focus on 

increasing learning by low performers. Concern also centers 

on advancing U.S. performance in relation to that of other 

countries, especially in mathematics, science, and technical 

fields. The most commonly used tools for measuring changes in achievement are standardized assessments. (The terms 

achievement and performance are used interchangeably in 

this section when discussing scores on these tests.)

This section is divided into two parts. The first examines trends in mathematics and science achievement among 

public and private school students in the United States, using two kinds of national data. Longitudinal data follow the 

same group of students over several years, allowing observers to track how individual students learn over time. In some 

Table 1-1

Indicators of elementary and secondary school mathematics and science education

Topic Indicator

Student learning in 

mathematics and science

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Student transitions to 

higher education

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Science and Engineering Indicators 2010
1-8 Chapter 1. Elementary and Secondary Mathematics and Science Education

cases, longitudinal test data may also be linked to teaching 

practices and other factors thought to influence achievement. New test data from the Early Childhood Longitudinal 

Study, Kindergarten Class of 1998–99 (ECLS-K), collected 

in 2007, allow study of performance changes among a kindergarten cohort through eighth grade and of changes over 

time in initial achievement gaps among groups of students.

Cross-sectional data, in contrast to longitudinal data, provide information on particular groups’ performance measured at different points in time. The National Assessment of 

Educational Progress (NAEP) data presented in the first section, for example, examine performance of fourth and eighth 

graders who were sampled in various years between 1990 

and 2007. These data indicate whether and how achievement 

is changing over time for comparable groups of students.

The second part of this section compares student achievement in the United States with that in other countries. The 

latest Trends in International Mathematics and Science 

Study (TIMSS:2007) allows comparisons of U.S. fourth and 

eighth graders with their counterparts in other countries. The 

Program for International Student Assessment (PISA:2006) 

provides test score data for 15-year-olds in the same subjects. These international assessments are both cross-sectional studies.

Students’ Performance 

on National Assessments

Mathematics and Science Performance 

as Students Progress Through Elementary 

and Middle Grades

ECLS-K has followed a group of students who first entered kindergarten in fall 1998 over 9 school years. (The 

mathematics and science education of students who are 

homeschooled is not addressed in this chapter; see sidebar 

“Homeschooling in the United States.”) The study concluded in spring 2007, when most students were in eighth grade.2

The sample used in this analysis included roughly 8,000 students. ECLS-K is unusual among major national and international data collections not only in its focus on the earlier 

years of schooling but also because it allows researchers to 

examine students’ performance in light of variables likely 

to influence learning. Cognitive tests measured students’ 

mathematics knowledge in kindergarten and grades 1, 3, 5, 

and 8 and tracked their science understanding in grades 3, 

5, and 8. The study also collected demographic and family 

information from a parent and surveyed teachers and schools 

for information about school environments, teacher qualifications, and classroom practices.

Gains in Mathematics Test Scores and Gap Changes.

Students begin kindergarten with differing levels of mathematics skills, and researchers have suggested several factors 

that may be related to these initial gaps. A body of research 

has focused in particular on initial gaps between white and 

black children. The early home environment, including how 

well parents prepare children for school (e.g., time spent 

reading to them) plays a role (Magnuson, Rosenbaum, and 

Waldfogel 2008; Jencks and Phillips 1998). Other reasons 

posited include income and education differences among 

parents (Magnuson, Rosenbaum, and Waldfogel 2008; 

Campbell et al. 2008), school segregation (Vigdor and Ludwig 2008), access to effective and well-trained teachers 

(Corcoran and Evans 2008), ability to listen and concentrate, 

and children’s fine motor skills, which need to reach a certain level of development for young children to learn to write 

and draw (Grissmer and Eiseman 2008).

Students’ mathematics achievement was measured on a 

single scale ranging from 0 to 174 throughout the study, allowing the tracking of achievement growth and comparisons 

between groups as children progressed through elementary 

and middle grades. The 1998–99 kindergarten cohort started 

school with an average mathematics score of 26 and gained 

113 points by the spring of eighth grade, to 139 (table 1-2).

For most characteristics, gaps widened during the early 

years of school (when the overall score changes were greater) and then stabilized or even narrowed slightly starting at 

grade 3 or 5, when the rate of overall growth also declined. 

Students’ relative achievement when starting school had an 

influence on growth and eventual grade 8 scores, shown by 

the trajectories of those scoring in the lowest, middle two, 

and highest quartiles in kindergarten (figure 1-1). These 

varying growth patterns were in turn related to demographic 

characteristics. For example, the gap between kindergartners 

Homeschooling 

in the United States

In spring 2007, an estimated 1.5 million students 

ages 5–17 were homeschooled in the United States, accounting for about 3% of the K−12 student population 

at that time (Bielick 2008). Trends from 1999 to 2007 

show a 74% increase in the number of homeschooled 

students over this 8-year period. Homeschooled students in these estimates are defined as those who are 

schooled at home for at least part of their education 

and whose enrollment in public or private school does 

not exceed 25 hours per week.

The decentralized nature of the homeschooled population limits researchers’ ability to collect nationally 

representative data on these students’ achievement 

and other outcomes. A growing number of families choose to homeschool their children, and many 

states are drafting and implementing regulations for 

homeschooling (Belfield 2004; Lines 2003; Lips and 

Feinberg 2008). Thus far, however, no national data 

allow researchers to examine homeschooled students’ 

involvement with mathematics and science courses 

or to compare their achievement with that of students 

who attend public or private schools (Lips and Feinberg 2008).
Science and Engineering Indicators 2010 1-9

whose mothers had no high school diploma versus a bachelor’s or advanced degree was 12 points (20 versus 32) 

(figure 1-1; table 1-2). This initial gap in kindergarten grew 

to 32 points in third grade and then remained at about that 

level. In grade 8, children whose mothers had not finished 

high school reached a score of 123, roughly equivalent to the 

score in grade 5 of those whose mothers had some college.

In another example, white children scored 29 on the test 

given in the fall of their kindergarten year and Asians scored 

30, compared with 22 for both black and Hispanic children. 

The gaps between white and black students and Hispanic 

and Asian students reached a certain point and then stabilized after grade 3.

Gaps based on a few characteristics narrowed a little in 

later grades: English proficiency in kindergarten, primary 

language spoken at home, and the white–Hispanic gap. See 

appendix table 1-2 for score gains by various grade ranges.

Proficiency in Different Skill Areas. The ECLS-K test 

data also indicate whether students were proficient in nine 

mathematics skill areas. (The skills are arranged in a hierarchy such that proficiency in a given area presumes proficiency in the areas below it. See sidebar “Mathematics Skills 

Areas Assessed” for definitions.) By eighth grade, nearly all 

students were proficient in ordinality and sequence, addition 

and subtraction, and multiplication and division (appendix 

table 1-3). Large majorities of eighth graders were also proficient in place value and rate and measurement (89% and 

66%, respectively), which they had studied for several years. 

Only 37% of eighth graders overall were proficient in working with fractions, however, a skill area that the National 

Mathematics Advisory Panel (2008) argues is essential for 

understanding even a first algebra course. Many states and 

districts are debating (and some now require) that all eighth 

graders take algebra (Loveless 2008).

Substantial differences among groups appeared in the three 

highest skill areas—rate and measurement, fractions, and area 

and volume—and differences grew as the difficulty level increased. For example, 63% of students whose mothers had 

a bachelor’s degree were proficient in fractions, compared 

with 16% of students whose mothers had not completed high 

school (figure 1-2). The same pattern was found for proficiency in rate and measurement and area and volume.

Differences by initial math skills in kindergarten were 

also considerable for the highest skill area in which students 

had reached proficiency by eighth grade (table 1-3; appendix 

table 1-4). Kindergarten low achievers were far more likely 

than others to demonstrate no more than low-level skills. For 

example, 21% of low achievers’ highest skill was multiplication and division, versus 0% for high achievers. Also, 39% 

percent of low achievers did not progress beyond proficiency with place value, compared with 7% of high achievers.

Some early low achievers in kindergarten did reach proficiency in high skill areas, however: 24% achieved proficiency with rate and measurement, 7% with fractions, and 

2% with area and volume, the highest skill area assessed. 

Thus, although most initial low-scoring students progressed 

relatively slowly, some managed to overcome obstacles they 

had at school entry.

High mathematics scores in kindergarten were also 

strong predictors of proficiency with higher-level mathematical concepts in eighth grade. By grade 8, 37% of those who 

Table 1-2

Average mathematics scores of students followed from kindergarten through grade 8, by student 

characteristics: Fall 1998–spring 2007

Student characteristic

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grade 8

All students ................................................................... 26 62 99 123 139

Kindergarten mathematics score

ÜiÃÌÊμÕ>ÀÌi ...................................................... 17 46 78 101 120

``iÊÌÜÊμÕ>ÀÌiÃ .............................................. 25 61 98 123 140

}iÃÌÊμÕ>ÀÌi ..................................................... 38 79 123 144 157

,>ViÉiÌVÌÞ

White ..................................................................... 29 66 106 129 145

	>V ..................................................................... 22 52 84 105 123

Ã«>V ................................................................ 22 56 92 118 135

Asian ..................................................................... 30 65 105 133 148

ÌiÀ1⁄2ÃÊi`ÕV>ÌÊÊ£n

Less than high school ........................................... 20 51 82 106 123

}ÊÃVÊ`«> ............................................. 24 58 93 116 133

Some college ........................................................ 26 63 101 125 142

	>ViÀ1⁄2ÃÊÀÊ}iÀÊ`i}Àii .................................. 32 72 114 138 154

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Science and Engineering Indicators 2010
1-10 Chapter 1. Elementary and Secondary Mathematics and Science Education

scored in the highest quartile in kindergarten had achieved 

proficiency in all of the skill areas shown in table 1-3, and 

another 33% had progressed to proficiency with fractions, 

the second highest skill level measured.

Gains in Science Test Scores and Gap Changes. ECLSK science assessments were given in grades 3, 5, and 8 and, 

as with mathematics, were measured on a single scale, in 

this case from 0 to 111. The average science score in grade 

3 was 51 points, increasing to 83 by grade 8. In general, 

growth patterns were similar to those found with mathematics over these higher grades: few changes in gap size, and 

those changes that did occur were minimal (appendix table 

1-5). Exceptions included some racial/ethnic differences. 

White students had the highest science scores in third and 

fifth grades, at 56 and 70, respectively, but by eighth grade 

Asians had closed that gap, with both groups scoring 88–89. 

English language learners (assessed in kindergarten) demonstrated another exception: they were 20 points behind 

English-proficient students on the third grade science assessment but only 15 points behind in eighth grade.

Trends in Mathematics and Science Performance 

in Grades 4 and 8 Through 2007

NAEP includes two assessment programs. The national 

(or 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 standards of what students should 

know and be able to do in various subjects, including science 

and mathematics. Student achievement measured by NAEP 

is documented in an ongoing series of reports, The Nation’s 



	

	




	


	



	
	

	




	

	

	
	
	


	
	



	
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ECLS-K measures student proficiency at nine specific mathematics skill levels. These skill levels, which 

were identified based on frameworks from other national assessments and advice from a panel of education 

experts, represent a progression of mathematics skills 

and knowledge. Levels 6, 7, and 8 were first assessed in 

third grade, and level 9 was first assessed in fifth grade. 

By the fifth grade, levels 1 through 4 were not assessed. 

Each level is labeled by the most sophisticated skill in 

the set (Princiotta, Flanagan, and Germino Hausken 

2006; West, Denton, and Reaney L 2000):

Level 1, Number and shape: Recognize single-digit 

numbers and shapes.

Level 2, Relative size: Count beyond 10, recognize 

the sequence in basic patterns, and compare the relative size and dimensional relationship of objects.

Level 3, Ordinality and sequence: Recognize twodigit numbers, identify the next number in a sequence, identify the ordinal position of an object, 

and solve simple word problems.

Level 4, Add and subtract: Solve simple addition 

and subtraction items and identify relationships of 

numbers in sequence.

Level 5, Multiply and divide: Perform basic multiplication and division and recognize more complex 

number patterns.

Level 6, Place value: Demonstrate understanding 

of place value in integers to the hundreds place.

Level 7, Rate and measurement: Use knowledge of 

measurement and rate to solve word problems.

Level 8, Fractions: Solve problems using fractions.

Level 9, Area and volume: Solve problems using 

area and volume.

Mathematics Skills Areas Assessed
Science and Engineering Indicators 2010 1-11

Report Card, that first began in 1969. A second testing program, the NAEP Long-Term Trend (LTT), is based on nationally representative samples of 9-, 13-, and 17-year-olds. 

The mathematics content framework for NAEP LTT has remained the same since it was first given in 1973, permitting 

analyses of trends over more than three decades.

This section briefly summarizes NAEP science trends—

reported in detail in Science and Engineering Indicators 

2008 (NSB 2008) and then focuses on the new mathematics score data for fourth and eighth graders in 2007 and on 

trends in these scores from 1990 to 2007. New data are neither available for 12th grade mathematics nor for science in 

any grade.3

 The NAEP LTT scores in mathematics are also 

updated through 2008, for three age groups.

NAEP rates students’ performance in two ways: average 

scale scores and the percentage reaching various achievement 

levels. Scale scores place students along a continuous scale 

based on their overall performance on the assessment. A single mathematics scale of 0 to 500 points covers both grades 

4 and 8. See sidebar “Development and Content of NAEP 

Mathematics Assessments” for further information on the 

assessments’ content and design. The NAEP website has 

a searchable database of released NAEP test items (http://

nces.ed.gov/nationsreportcard/itmrls).

Science Performance. No new NAEP science data are 

available for any grade; a science assessment was conducted in early 2009 and data will be available in early 2010, 

too late for inclusion in this volume. As reported in Science 

and Engineering Indicators 2008 (NSB 2008), average 

NAEP science scores increased for 4th graders, held steady 

for 8th graders, and declined for 12th graders between 

	

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Highest mathematics skill area in which fall 1998 kindergarteners demonstrated proficiency in grade 8, 

by student characteristics: Spring 2007 

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All students ........................................ 1 2 8 24 30 20 15

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Science and Engineering Indicators 2010
1-12 Chapter 1. Elementary and Secondary Mathematics and Science Education

NAEP Contrasted With State 

Achievement Tests

Provisions in the No Child Left Behind (NCLB) 

Act require states to test students annually in mathematics, science, and English in grades 3–8 and once 

in high school. These test results, in particular the 

percentage of students reaching the proficient level, 

must be reported to the U.S. Department of Education, 

and schools must show adequate gains every year toward the goal of 100% of students reaching proficient. 

However, states are free to create their own tests and 

set the minimum score for proficient wherever they 

want. The incentives of these accountability systems 

may push policymakers to use easy tests, set the minimum proficient score low, or both (Cronin et al. 2007; 

Peterson and Hess 2008; Loveless 2006). The requirement for steady improvement encourages making the 

tests easier to pass over time, thus boosting apparent 

achievement. State standards vary widely in difficulty, content coverage, and the minimum scores set for 

reaching proficient. This variation has prevented making valid comparisons of student achievement across 

states using state test scores.

To address this problem, a recent study converted 

the proficient cutoff scores that states set for their own 

fourth and eighth grade mathematics tests to the 2005 

National Assessment of Educational Progress (NAEP) 

scale (NCES 2007b). For example, for a state that 

rated 75% of its students proficient on the state’s test, 

researchers assigned that state’s “NAEP-equivalent 

score” as the NAEP score at the 75th percentile on 

NAEP’s test for the same subject and grade level. After converting score data from all available states to 

this NAEP metric, the study found that, in most states, 

students who reached the proficient level on state tests 

had reached NAEP’s basic level, and many states’ average scores were below basic. In addition, the range 

of states’ proficiency standards was 55 NAEP score 

points at grade 4 and 81 at grade 8. To put those ranges 

in context, they are roughly two to three times the difference between black and white students’ scores on 

recent NAEP mathematics assessments.

To document progress in achievement for NCLB, 

a federal regulation issued in late 2008 (Title I—Improving the Academic Achievement of the Disadvantaged, Final Rule (73 Fed. Reg. 64435 [2008]) adds 

another requirement: states must report their students’ 

NAEP test scores along with state test results for the 

same grade and subject. These data will provide information for observers interested in state-by-state comparisons as well as the overall range of achievement 

across states.

Development and Content of 

NAEP Mathematics Assessments

The National Assessment of Educational Progress 

(NAEP) assessments use frameworks developed by 

educators, policymakers, assessment and curriculum 

experts, and skilled practitioners (e.g., mathematicians) in a consensus-oriented process. Frameworks 

define what students should know at a given grade 

level and provide a blueprint for the assessment (Lee, 

Grigg, and Dion 2007). Once developed, the frameworks are reviewed and approved by the National 

Assessment Governing Board (NAGB). NAGB then 

defines three performance levels for each grade. The 

basic level indicates partial mastery of material appropriate for the grade level, proficient indicates solid 

academic performance, and advanced indicates superior performance. (Students in the basic category have 

scores at or above the minimum score for basic but 

lower than the minimum for proficient.) For more detailed definitions of the NAEP proficiency levels, see 

Science and Engineering Indicators 2006, pp. 1-13 

and 1-14 (NSB 2006).

Although experts have approved the NAEP tests as 

measuring achievement with sufficient accuracy (for 

example, see Daro et al. 2007), some have disagreed 

about whether the NAEP proficiency levels are appropriately defined. The National Mathematics Advisory 

Panel concluded in its 2008 study, “On the basis of international performance data, there are indications that 

the NAEP cut scores for the two highest performance 

categories [the proficient and advanced levels] are set 

too high” (NMP 2008). An earlier study commissioned 

by the National Academy of Sciences concluded that 

the process used to set these levels was “fundamentally flawed” (Pellegrino, Jones, and Mitchell 1999). 

NAGB acknowledges the controversy surrounding 

the proficiency levels (Bourque and Byrd 2000) and 

warns data users to interpret findings related to these 

levels with caution (NCES 2006b). Some of the disagreement may stem from different understandings 

of the word proficient, as well as differing expectations about what students should be able to do at particular grade levels. (Mandated statewide achievement 

tests reflect a range of these expectations; see sidebar 

“NAEP Contrasted With State Achievement Tests” 

for more information.)
Science and Engineering Indicators 2010 1-13



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1996 and 2005 (NCES 2006a). Rising scores among lowerperforming and average fourth graders were the primary 

drivers of the increase. The proportion of students reaching 

the proficient level for their grade in science held steady at 

grades 4 and 8, and declined a bit at grade 12. Proficiency 

rates were lower among 12th graders than among students 

in the lower grades.

Mathematics Performance of Fourth and Eighth 

Graders. The upward achievement trends that occurred 

through 2005 on the NAEP fourth and eighth grade mathematics tests continued with the 2007 tests. Between 1990 

and 2007, the average mathematics score for fourth graders 

rose from 213 to 240, and for eighth graders from 263 to 281 

(appendix table 1-6).

At both grade levels, students’ scores increased in each of 

the five content areas tested (number sense, properties, and 

operations; measurement; geometry and spatial sense; data 

analysis, statistics, and probability; and algebra and functions) (Lee, Grigg, and Dion 2007). Performance also improved across the achievement distribution in both grades, 

with scores at five selected percentiles of the score distribution (10th, 25th, 50th, 75th, and 90th) all increasing consistently over these years (figure 1-3; appendix table 1-6). The 

scores of low-achieving fourth graders rose faster than the 

scores of others, reducing the gaps with high achievers (Lee, 

Grigg, and Dion 2007).

Achievement trends for nearly all demographic groups 

reflected the same upward movement (table 1-4). In grades 

4 and 8, both boys and girls increased their scores between 

1990 and 2007. Students in both grades increased their 

scores between 2000 and 2007, regardless of whether they 

were eligible for the subsidized lunch program, an indicator 

of poverty.

The scores of fourth graders in each racial/ethnic group 

with 1990–2007 data available rose consistently over those 

17 years. Black fourth graders had the largest score increase, 

at 34 points (figure 1-4; appendix table 1-6). Similarly, 

white, black, and Hispanic eighth graders scored consistently higher in mathematics. Asians/Pacific Islanders’ performance remained level after 2005, and American Indians/

Alaska Natives showed no change between 2000 (the first 

year with data available) and 2007.

NAEP 2009 results, released as this volume was going to 

press, show that the upward trend in fourth grade mathematics scores has halted, that mathematics scores of eighth graders have continued to improve, and that score gaps among 

racial/ethnic groups are unchanged (NCES 2009a).

Gaps in Mathematics Performance. In most years, 

boys had marginally higher mathematics scores than girls, 

and these gaps remained about equal over the 17-year period 

(appendix table 1-6) (Lee, Grigg, and Dion 2007). Among 

fourth graders, boys performed better than girls in four of the 

five mathematics content areas in 2007; girls scored higher 

only in geometry/spatial sense. Among eighth graders, boys 
1-14 Chapter 1. Elementary and Secondary Mathematics and Science Education

reaching the advanced proficiency level set by the NAEP 

governing board. Students also improved steadily from 

1990 to 2007 on this measure (figure 1-5; appendix table 

1-7), with one exception: fourth graders in 2007 showed no 

change from 2005 in the percentage reaching the advanced

level (Lee, Grigg, and Dion 2007).

Long-Term Trends in Mathematics Performance. The 

NAEP Long-Term Trend assessment program has tested 

students ages 9, 13, and 17 in mathematics for more than 

three decades. LTT assessments differ from the main NAEP 

assessment, whose frameworks and tests are revised over 

time to follow changes in common curriculum at targeted 

grade levels, in that the LTT assessment for each grade level 

has tested the same knowledge and skills over time.4

Since this testing program began, 9- and 13-year-olds 

raised their scores, while 17-year-olds’ scores were essentially flat, with no difference between the first test score 

(304) in 1973 and the most recent (306) in 2008 (appendix 

table 1-8) (Rampey, Dion, and Donahue 2009). Among 

9-year-olds, the average score increased 24 points, to 243 

in 2008; among 13-year-olds, scores increased 15 points, to 

281. The periods of achievement growth differed as well, 

Table 1-4

Changes in NAEP mathematics scores of students in grades 4 and 8, by student characteristics and other 

measures: 1990–2007 and 2000–07

Grade 4 Grade 8

Student characteristic £äqÓääÇ ÓäääqäÇ £äqÓääÇ ÓäääqäÇ

All students ................................................................................... 

-iÝ

Male ....................................................................................... 

i>i ................................................................................... 

,>ViÉiÌVÌÞ

White ..................................................................................... 

	>V ..................................................................................... 

Ã«>V ................................................................................ 

Ã>É*>VvVÊÃ>`iÀa

 ........................................................... NA 

iÀV>Ê`>É>Ã>Ê >ÌÛia

 ............................................ NA NA 

ÀiiÉÀi`ÕVi`«ÀViÊÕVb

Eligible ................................................................................... 

Not eligible ............................................................................ 

Changes in score gaps

Gender gap ............................................................................... 

7ÌiL>VÊ}>« ........................................................................   

7ÌiÃ«>VÊ}>« ..................................................................  

ÜViÊv>ÞÊÛÃ°ÊÌiÀÊv>ÞÊ}>«b ..................................   

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Science and Engineering Indicators 2010

scored higher in three of five content areas—number properties and operations, measurement, and algebra—while girls 

scored higher in one area—data analysis and probability. 

In geometry/spatial sense, boys and girls did not differ in 

performance.

Most gaps among racial/ethnic groups that existed in 

1990 remained in 2007, but some have narrowed, especially 

in recent years. The average score gap between white and 

black fourth graders decreased from 32 to 26 scale points 

between 1990 and 2007. Among eighth graders, the gap increased from 1990 to 2000 but then decreased from 2000 to 

2007. Similarly, the gaps between white and Hispanic students in both grades narrowed from 2000 to 2007.

Score gaps related to family income, as indicated by student eligibility for subsidized lunches, also shrank between 

1996 (the first year available) and 2007, as well as between 

2000 and 2007 for fourth graders. For eighth graders, the 

gap between low-income and other students was about the 

same in 1996 and 2007, with some fluctuations in between. 

It showed a decrease from 2000 to 2007. 

Achievement is also measured in a different way from 

the scale scores discussed above: the percentages of students scoring at or above the basic and proficient levels and 
Science and Engineering Indicators 2010 1-15

with 9-year-olds making larger gains during the 1980s and 

after 1999, and 13-year-olds increasing steadily since 1990.

In each age group, black students gained more points than 

white students over the earlier part of the period, narrowing 

the gaps with whites. The gap between blacks and whites for 

9-year-olds narrowed from 35 points in 1973 to 26 in 2008. 

For 13-year-olds, the gap decreased substantially, from 46 

to 28 points. For both of the younger age groups, this narrowing occurred mainly through 1986; after that, both racial groups increased their scores at roughly similar rates. 

Among 17-year-olds, the 1973 gap between blacks and 

whites of 40 points decreased to 26 points in 2008, with the 

smallest gap appearing in 1990.

Hispanic students at all three ages gained more points 

over time than did whites on the mathematics assessments, 

particularly 13- and 17-year-olds. The score gaps with their 

white peers thus appeared to decrease, but none of those 

changes was significant, in part due to relatively small Hispanic sample sizes in some years.

Parents’ educational attainment, a measure of socioeconomic status, was collected from 13- and 17-year-olds. At 

all levels of parental education, 13-year-olds’ achievement 

increased over the 35 years, while 17-year-olds’ performance 

improved only among students whose parents had not finished 

high school.



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NOTE: Proficient includes advanced.

SOURCE: Lee J, Grigg WS, Dion GS, The Nation’s Report Card: 

Mathematics 2007, NCES 2007-494 (2007).

	

	

1990 1992 1996 2000 2003 2005 2007

0

5

10

15

20

25

30

35

40

Grade 4 proficient

Grade 4 advanced

Grade 8 proficient

Grade 8 advanced
1-16 Chapter 1. Elementary and Secondary Mathematics and Science Education

Students’ Performance on International 

Assessments of Mathematics and Science

Two recent assessments place U.S. student achievement 

in mathematics and science in an international context: the 

Trends in International Mathematics and Science Study and 

the Program for International Student Assessment. TIMSS 

and PISA differ in several fundamental ways; see sidebar 

“Differences Between TIMSS and PISA Assessments.” 

Reports on TIMSS and PISA test results typically compare 

U.S. performance with that of all participating countries 

or with that of all members of the Group of Eight (G-8) or 

Organisation for Economic Co-operation and Development 

(OECD) (Gonzales et al. 2008; Miller et al. 2009; Gonzales 

et al. 2004; Baldi et al. 2007). The differences in the characteristics of countries that participate in these two studies, 

however, confound comparisons between the United States’ 

relative standing on the two assessments.

This section compares U.S. performance to that of a 

subset of nations that either have advanced economies that 

compete globally in fields related to science, technology, 

engineering, and mathematics (STEM) or have developing 

economies with rapidly growing capabilities in these areas. 

Most of the selected countries were included because of 

their current capabilities in science and technology. A few 

Asian countries that are seeking to develop such capacity 

were also included to highlight student performance in these 

highly dynamic countries. (This geographic focus is maintained where possible in the international sections of other 

chapters.) Not all of the 28 selected nations participated in 

each assessment, so the number available for comparison 

with the United States differs by test. Scores for all participating nations are shown in appendix tables.

Results from the two assessments are contradictory: 

U.S. average scores on TIMSS tend to place the United 

States around the middle of the group of selected nations, 

and in mathematics, the United States improved over time. 

In contrast, U.S. scores on PISA were generally near the 

bottom of the group, and the U.S. standing relative to 

other nations declined in both mathematics and science. 

Some of these performance differences may be explained 

by the differences in the tests and which countries participate (see sidebars “Differences Between TIMSS and 

PISA Assessments” and “Sample Items From TIMSS and 

PISA Assessments”).

Several primary differences in the design and purpose 

of these two assessments likely contribute to the differing U.S. results: age of students tested, test content, and 

participating nations.

First, the Trends in International Mathematics and Science Study (TIMSS) tests the mathematics and science 

achievement of students in grades 4 and 8, regardless 

of their age. The Program for International Student Assessment (PISA) assesses the performance of secondary 

school students by sampling 15-year-olds, who are nearing the age when compulsory schooling ends in many 

countries. The divergent international results shown 

here are consistent with differences by age in the main 

National Assessment of Educational Progress (NAEP) 

results: U.S. 12th graders have generally shown flat or 

even declining achievement over time, whereas younger students, particularly 4th graders, have demonstrated 

steadily rising scores (NSB 2008). Similar patterns from 

the NAEP Long-Term Trend assessment are described in 

“Long-Term Trends in Mathematics Performance.”

A second difference between TIMSS and PISA is how 

closely they adhere to the mathematics and science curriculums used for instruction in various countries. TIMSS 

focuses on application of familiar skills and knowledge 

emphasized often in classrooms. (Content experts and 

teachers from various countries select elements of curriculums common to most participating nations.) The PISA 

tests, in contrast, emphasize students’ abilities to apply 

skills and information learned in school (or from life 

experiences) to solve problems or make decisions they 

may face at work or other circumstances. PISA test questions tend to deemphasize factual recall and demand more 

complex reasoning and problem-solving skills than those 

in TIMSS (Neidorf et al. 2006; Loveless 2009), requiring 

students to apply logic, synthesize information, and communicate solutions clearly. Curriculums and teaching 

methods may vary in their emphasis on these skills. (See 

sidebar “Sample Items From TIMSS and PISA Assessments” for examples of science test questions included on 

the two assessments.)

A third main difference between the two assessments 

is the number of participating countries and their levels 

of economic development. Countries participating in 

TIMSS form a large and diverse group: some highly industrialized nations and many developing ones, the latter 

of which have been growing in number over time. In contrast, nearly all countries that participated in PISA were 

members of OECD and thus are economically advanced 

nations. The international comparisons here were limited 

to nations that are current or likely potential competitors 

with the United States in scientific and technical fields, 

however. This restriction increased the overlap between 

nations taking both tests and excluded nearly all developing nations from these analyses. Restricting to this group 

of selected nations prevents the increasing number of developing countries from artificially inflating the United 

States’ standing relative to other nations over time, particularly in TIMSS.

Differences Between TIMSS and PISA Assessments
Science and Engineering Indicators 2010 1-17

Sample Items from TIMSS and PISA Assessments

Sample Science Items from Trends in 

International Mathematics and Science 

Study (TIMSS) Tests (for Eighth Graders)

1) Food and oxygen are produced during photosynthesis in green plants. Chlorophyll is one thing that is 

needed for photosynthesis. Name two more factors that 

are needed for photosynthesis.

Correct answer: Sunlight and carbon dioxide.

Difficulty level: High international benchmark (550)

2a) The diagram shows what happens to three magnets when they are placed close together on a pencil. 

Magnets X and Y move until they touch each other, but 

magnets Y and Z remain separated. Explain why magnets 

X and Y touch each other.

Correct Answer: Because north and south poles were 

facing each other.

2b) Explain why magnets Y and Z remain separated.

Correct answer: Because they may have had south and 

south or north and north facing each other.

Difficulty level: Advanced international benchmark 

(625)

Additional sample questions: http://timss.bc.edu/

TIMSS2007/items.html.

Sample Science Items from Program for 

International Student Assessment (PISA) 

Tests (for 15-Year-Olds)

1) Statues called Caryatids were built on the Acropolis in Athens more than 2,500 years ago. The statues are 

made of a type of rock called marble. Marble is composed of calcium carbonate. In 1980, the original statues 

were transferred inside the museum of the Acropolis and 

were replaced by replicas. The original statues were being eaten away by acid rain.

1a) Normal rain is slightly acidic because it has absorbed 

some carbon dioxide from the air. Acid rain is more acidic 

than normal rain because it has absorbed gases like sulfur 

oxides and nitrogen oxides as well. Where do these sulfur 

oxides and nitrogen oxides in the air come from?

Correct answer: For full credit, students needed to include one or more major sources: car exhausts, factory 

emissions, burning fossil fuels such as oil and coal, gases 

from volcanoes, or “burning of materials that contain 

sulphur and nitrogen.” Answers that mention one actual 

source and one incorrect source (such as nuclear power 

plants) received only partial credit.

Difficulty level: 506

1b) The effect of acid rain on marble can be modeled 

by placing chips of marble in vinegar overnight. Vinegar 

and acid rain have about the same acidity level. When a 

marble chip is placed in vinegar, bubbles of gas form. 

The mass of the dry marble chip can be found before and 

after the experiment. A marble chip has a mass of 2.0 

grams before being immersed in vinegar overnight. The 

chip is removed and dried the next day.

What will the mass of the dried marble chip be?

A. Less than 2.0 grams

B. Exactly 2.0 grams

C. Between 2.0 and 2.4 grams

D. More than 2.4 grams

Correct answer: A

Difficulty level: 460

1c) Students who did this experiment also placed marble chips in pure (distilled) water overnight. Explain why 

the students included this step in their experiment.

Correct Answer: For full credit, students needed to explain that the acid in vinegar dissolves some of the marble 

just like acid in acid rain does, and that distilled water 

does not dissolve marble because it’s much less acidic 

(the water test is a control).

Difficulty level: 717 for full-credit answers, 513 for 

partial credit

Additional sample questions: http://www.pisa.oecd.

org/dataoecd/13/33/38709385.pdf (for science) and 

http://www.oecd.org/dataoecd/14/10/38709418.pdf (for 

mathematics).

Sources: Gonzales et al. 2008; OECD 2007.

Z YX
1-18 Chapter 1. Elementary and Secondary Mathematics and Science Education

Mathematics Performance of U.S. Fourth 

and Eighth Graders on TIMSS

The fourth grade TIMSS mathematics exam covers three 

content areas: number, geometric shapes and measures, 

and data display. The eighth grade assessment addresses 

four content domains: number, algebra, geometry, and data 

and chance.

Performance Trends. Over the 12 years since the first 

TIMSS mathematics assessments in 1995, U.S. fourth and 

eighth graders raised their scores and international ranking 

(Gonzales et al. 2008). The fourth grade average of 529 in 

2007 was 11 points higher than in 1995. For eighth graders, 

the U.S. average of 508 in 2007 reflected a 16-point rise over 

1995’s score (figure 1-6). In addition, while the U.S. eighth 

grade score in 1995 was 8 points below the international 

scale average of 500, in 2007 it was 8 points above, at 508.

Not only did U.S. fourth graders’ mathematics scores 

increase, but the U.S. position relative to selected other nations also shifted upward from 1995 to 2007. Of the selected 

nations whose fourth graders participated in both the 1995 

and 2007 TIMSS, four outscored the United States in 1995, 

compared with three in 2007 (figure 1-7).5 U.S. eighth graders also gained ground over time, outperforming no foreign 



	
	



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Science and Engineering Indicators 2010 1-19

peers in 1995 but two in 2007. Students from eight of the 

selected nations outscored U.S. eighth graders in 1995, compared with four in 2007.

Performance on the 2007 TIMSS Mathematics Tests.

The fourth grade tests focused on three content domains: 

number, geometric shapes and measures, and data display 

(about half the assessment emphasized the number domain, including introductory algebra). For eighth grade, 

the four content domains were number, algebra, geometry, 

and data and chance. The cognitive domains addressed in 

TIMSS are the same for both grades—knowing, applying, 

and reasoning.

U.S. fourth graders’ average score on the 2007 TIMSS 

mathematics assessment (529) was just below the combined 

average for 14 selected nations (534) (table 1-5). (Results for 

all participating nations are available in appendix table 1-9.) 

When those 14 jurisdictions are compared with the United 

States, 6 scored higher, 6 scored lower, and 2 did not differ, 

placing the United States near the middle of the distribution. 

The top scorers—Hong Kong SAR, Singapore, Chinese Taipei, and Japan—each had average scores above 550.

The U.S. eighth grade average mathematics score of 508 

was also below the combined average (514) for 16 selected 

nations and below 5 nations’ individual averages (table 1-5). 

The United States scored higher than 7 nations, putting it 

just above the middle among these 16 nations. The average score of students in Chinese Taipei (the leader) was 90 

points higher than U.S. eighth graders’ average.

Although U.S. students as a whole did not lead the world 

in TIMSS mathematics, two U.S. states that participated individually (Massachusetts and Minnesota) provide examples of 

high performance (see sidebar “Two States’ Performance on 

TIMSS: 2007”). Scores at the 90th percentile present another 

way to examine high-achieving students (those who scored 

higher than 90% of all test takers). In mathematics, the 90th 

percentile score for U.S. fourth graders was 625, lower than 

that of six other nations (table 1-6). U.S. fourth graders scored 

higher than six nations on this measure, and so were at the 

middle. Similarly, the 90th percentile score for U.S. eighth 

graders was 607, lower than the corresponding scores for six 

countries but higher than that of six others. For eighth graders, 

the 90th percentile score gap with top scorers Chinese Taipei 

and the Republic of Korea was more than 100 scale points.

Table 1-5

Average TIMSS mathematics scores of students in grades 4 and 8 in selected nations, relative to U.S. average: 

2007

Nation Grade 4 Nation Grade 8

United States ................................................ 529 United States ................................................ 508

Score higher than U.S. Score higher than U.S.

}Ê}Ê-,a

 ...................................... 607 Chinese Taipei ........................................... 598

Singapore .................................................. 599 ,i«ÕLVÊvÊÀi> ..................................... 597

Chinese Taipei ........................................... 576 Singapore .................................................. 593

Japan ........................................................ 568 }Ê}Ê-,a

 ...................................... 572

,ÕÃÃ>Êi`iÀ>Ì ................................... 544 Japan ........................................................ 570

England ..................................................... 541

-VÀiÊÌÊÃÌ>ÌÃÌV>ÞÊ`vviÀiÌÊvÀÊ1°-°

-VÀiÊÌÊÃÌ>ÌÃÌV>ÞÊ`vviÀiÌÊvÀÊ1°-° Õ}>ÀÞ .................................................... 517

Germany ................................................... 525 England ..................................................... 513

i>À ................................................... 523 ,ÕÃÃ>Êi`iÀ>Ì ................................... 512


âiVÊ,i«ÕLV ......................................... 504

-VÀiÊÜiÀÊÌ>Ê1°-°

Australia .................................................... 516 -VÀiÊÜiÀÊÌ>Ê1°-°

Õ}>ÀÞ .................................................... 510 Australia .................................................... 496

Italy ........................................................... 507 -Üi`i ..................................................... 491

-Üi`i ..................................................... 503 Italy ........................................................... 480


âiVÊ,i«ÕLV......................................... 486 Malaysia .................................................... 474

 ÀÜ>Þ ...................................................... 473 ÀÜ>Þ ...................................................... 469

Thailand .................................................... 441

Indonesia .................................................. 397

TIMSS = Trends in International Mathematics and Science Study

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Science and Engineering Indicators 2010
1-20 Chapter 1. Elementary and Secondary Mathematics and Science Education

Table 1-6

TIMSS mathematics scores at the 90th percentile for students in grades 4 and 8, by selected nations: 2007

Nation Grade 4 Nation Grade 8

United States ................................................ 625 United States ................................................ 607

Score higher than U.S. Score higher than U.S.

Singapore .................................................. 702 Chinese Taipei ........................................... 721

}Ê}Ê-,a

 ...................................... 691 ,i«ÕLVÊvÊÀi> ..................................... 711

Chinese Taipei ........................................... 663 Singapore .................................................. 706

Japan ........................................................ 663 }Ê}Ê-,a

 ...................................... 681

,ÕÃÃ>Êi`iÀ>Ì ................................... 647 Japan ........................................................ 677

England ..................................................... 647 Õ}>ÀÞ .................................................... 624

-VÀiÊÌÊÃÌ>ÌÃÌV>ÞÊ`vviÀiÌÊvÀÊ1°-° -VÀiÊÌÊÃÌ>ÌÃÌV>ÞÊ`vviÀiÌÊvÀÊ1°-°

Australia .................................................... 620 England ..................................................... 618

Õ}>ÀÞ .................................................... 620 ,ÕÃÃ>Êi`iÀ>Ì ................................... 617

Australia .................................................... 600

-VÀiÊÜiÀÊÌ>Ê1°-° 
âiVÊ,i«ÕLVÊ ........................................ 599

i>À ................................................... 611

Germany ................................................... 607 -VÀiÊÜiÀÊÌ>Ê1°-°

Italy ........................................................... 601 -Üi`i ..................................................... 582

-Üi`i ..................................................... 586 Malaysia .................................................... 578


âiVÊ,i«ÕLVÊ........................................ 576 Italy ........................................................... 574

 ÀÜ>Þ...................................................... 566 Thailand .................................................... 562

 ÀÜ>Þ ...................................................... 552

Indonesia .................................................. 509

TIMSS = Trends in International Mathematics and Science Study

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Science and Engineering Indicators 2010

Massachusetts and Minnesota participated in a special 

benchmarking study included in the Trends in International Mathematics and Science Study (TIMSS) 2007, 

along with three Canadian provinces, the city of Dubai, 

and one region of Spain. Results for these entities were 

compared with those for all participating nations. These 

two states, particularly Massachusetts, are among the 

higher-scoring states on the National Assessment of Educational Progress (NAEP), and thus provide some insight 

into how some of the best students in the United States 

compare with their competitors in other nations.

In mathematics, Massachusetts fourth graders scored 

572, far above the scale average of 500, and in third place 

after only two jurisdictions, Hong Kong and Singapore 

(Mullis et al. 2008). Massachusetts’ average score was 

equivalent to scores in Chinese Taipei and Japan. Minnesota 

scored slightly lower (554), below only four Asian leaders 

and on par with Kazakhstan, England, and the Russian 

Federation. At grade 8, both U.S. states (at 547 and 532, 

respectively) scored below the five leading Asian nations 

but above all other participants, including European nations.

In fourth grade science, Massachusetts ranked second 

with its score of 571, after only Singapore (Martin et al. 

2008). Minnesota also performed well (551), bested only 

by Massachusetts and Singapore and scoring on par with 

eight jurisdictions (including the United States overall) 

but above all the rest. Massachusetts eighth graders’ science score (556) was similar to the four leading Asian 

economies’ scores (Singapore, Chinese Taipei, Japan, 

and Republic of Korea) and higher than scores from all 

other participants. At grade 8, Minnesota (at 539) was 

outscored by the four top Asian countries but performed 

similarly to a group that included Hong Kong and several 

high-scoring European nations.

Two States’ Performance on TIMSS: 2007
Science and Engineering Indicators 2010 1-21

Performance on the 2007 TIMSS Science Tests. The 

fourth grade science tests focused on three content areas: 

life, physical, and earth sciences; and on three main skills: 

knowing, applying, and reasoning. At eighth grade, content 

areas expanded to four: biology, chemistry, physics, and 

earth sciences. The cognitive domains underlying test development were the same for both grades: knowing, applying, 

and reasoning. The fourth grade tests emphasize knowing 

more than the eighth grade tests, while reasoning is a greater 

focus in eighth grade.

On the 2007 TIMSS science test for fourth graders, four 

of the comparison nations scored higher and six scored 

lower than the United States, putting the United States just 

above the middle of the group (table 1-7). (Results for all 

participating nations are presented in appendix table 1-10.) 

The four economies that outperformed the United States in 

fourth grade science also had the highest scores on most 

TIMSS tests: Singapore, Chinese Taipei, Hong Kong, and 

Japan. U.S. eighth graders’ average science score of 520 was 

lower than that of eight nations, higher than that of six, and 

Science Performance of U.S. Fourth 

and Eighth Graders: TIMSS

Performance Trends. In contrast to the mathematics 

trends, which showed improvement in both grades, the average scores of U.S. students on the TIMSS science assessment have remained flat since 1995. Fourth graders have 

lost ground internationally, whereas eighth graders slightly 

improved their position relative to other nations (Gonzales 

et al. 2008). At fourth grade, the United States outperformed 

six of seven selected nations in 1995 but only two of them 

in 2007. In addition, the single comparison nation that did 

better than the United States in 1995 (Japan) was joined by 

Singapore and Hong Kong in 2007.

The trend in U.S. standing of eighth graders was slightly 

upward: nations scoring higher than the United States on 

the science assessment dropped from eight in 1995 to six in 

2007. In addition, the United States had not outperformed 

any of the 10 other nations in 1995 but outscored 2 of them 

in 2007 (Sweden and Norway).

Table 1-7

Average TIMSS science scores of students in grades 4 and 8 in selected nations, relative to U.S. average: 2007 

Nation Grade 4 Nation Grade 8

United States ................................................ 539 United States ................................................. 520

Score higher than U.S. Score higher than U.S.

Singapore .................................................. 587 Singapore ................................................... 567

Chinese Taipei ........................................... 557 Chinese Taipei ............................................ 561

}Ê}Ê-,a

 ...................................... 554 Japan ......................................................... 554

Japan ........................................................ 548 ,i«ÕLVÊvÊÀi> ...................................... 553

England ...................................................... 542

-VÀiÊÌÊÃÌ>ÌÃÌV>ÞÊ`vviÀiÌÊvÀÊ1°-° Õ}>ÀÞ ..................................................... 539

,ÕÃÃ>Êi`iÀ>Ì ................................... 546 
âiVÊ,i«ÕLVÊ ......................................... 539

England ..................................................... 542 ,ÕÃÃ>Êi`iÀ>Ì .................................... 530

Õ}>ÀÞ .................................................... 536

Italy ........................................................... 535 -VÀiÊÌÊÃÌ>ÌÃÌV>ÞÊ`vviÀiÌÊvÀÊ1°-°

}Ê}Ê-,a

 ....................................... 530

-VÀiÊÜiÀÊÌ>Ê1°-° Australia ..................................................... 515

Germany ................................................... 528

Australia .................................................... 527 -VÀiÊÜiÀÊÌ>Ê1°-°

-Üi`i ..................................................... 525 -Üi`i ...................................................... 511

i>À ................................................... 517 Italy ............................................................ 495


âiVÊ,i«ÕLVÊ ........................................ 515 ÀÜ>Þ ....................................................... 487

 ÀÜ>Þ...................................................... 477 Malaysia ..................................................... 471

Thailand ..................................................... 471

Indonesia ................................................... 427

TIMSS = Trends in International Mathematics and Science Study

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Science and Engineering Indicators 2010
1-22 Chapter 1. Elementary and Secondary Mathematics and Science Education

equivalent to the remaining two—in other words, slightly 

below the middle (table 1-7).

The U.S. 90th percentile score for fourth graders was 

643, ranking lower than in 2 other nations and higher than 

in 8, or above the midpoint for these 15 nations (Gonzales et 

al. 2008). The difference between Singapore (whose fourth 

graders led all countries) and the United States at the 90th 

percentile was 58 points. In eighth grade, U.S. students at 

the 90th percentile in science scored roughly in the middle 

of the group—lower than in six other nations and higher than 

in five. See sidebar “Linking NAEP and TIMSS Results.”

Mathematics Performance of U.S. 15-Year-Olds: 

PISA

Performance Trends. In contrast to the TIMSS results, 

U.S. 15-year-olds’ performance consistently dropped on the 

PISA tests of mathematical and scientific literacy in relation to student performance in other nations. The U.S. mathematics average of 474 in 2006 is 19 points lower than in 

2000, when the first PISA exams were given, but changes in 

the tests mean that the scores cannot be directly compared 

(OECD 2001; Baldi et al. 2007). While the United States 

scored below 7 nations in 2000, it scored below 15 nations 

in 2006 (of 19 nations with data available for both years).

Performance on the 2006 PISA Mathematics Test.

PISA assesses 15-year-old students in all OECD nations 

and a range of other nations every 3 years on literacy in 

mathematics, science, and reading. The mathematics test 

covers four content areas: space and shape, change and relationships, quantity, and uncertainty. A main mathematics 

skill tested is problem solving (explored in greatest depth in 

2003, when math was PISA’s main focus). Sjøberg (2007) 

and Goldstein (2004) discuss PISA’s content, including 

challenges and critiques.

On the most recent PISA tests, the U.S. score was 474, 

below 18 of the selected nations’ scores (table 1-8). Students 

in the United States demonstrated higher mathematical literacy than students in only four other countries (Italy, Thailand, Indonesia, and Brazil).

The U.S. score at the 90th percentile in mathematics was 

593, lower than that in 18 other nations that participated in 

the PISA exam and higher than in another 3 nations (Thailand, Indonesia, and Brazil) (Baldi et al. 2007). None of the 

OECD member nations had a lower 90th percentile score 

than the United States.

Science Performance of U.S. 15-Year-Olds: PISA

Performance Trends. The U.S. rank among selected nations declined on the PISA scientific literacy test, as on the 

mathematics assessment. In 2000, the United States scored 

below 6 other selected nations (out of 19 participating in both 

years), but in 2006, that number doubled to 12 (figure 1-8).6

Performance on the 2006 PISA Science Test. To measure scientific literacy, PISA includes three skill areas: 

identifying and understanding scientific issues, explaining 

phenomena scientifically, and using scientific evidence. Students were tested on their grasp of essential scientific concepts and theories in four content areas: physical systems, 

living systems, earth and space systems, and technology 

systems. Test items probed whether students understood 

how scientists obtain evidence (scientific means of inquiry) 

and how scientists use data. The test scores range from 1 

to 1,000, and the mean for the 2006 science test was set at 

500. The score scale is divided into six distinct proficiency 

levels that measure competence in science concepts and 

reasoning; each proficiency level encompasses roughly 75 

points (OECD 2007). To put score differences in context, 

the average gain from one grade to the next was 38 points, 

or roughly half a full proficiency level. (This one-grade gain 

was measured using data from nations with sufficient numbers of 15-year-olds in two consecutive grades.)

The science literacy performance of U.S. 15-year-olds in 

2006 placed the United States below 15 of 24 other nations 

and above 4, far below the midpoint (table 1-8; all participating nations’ results are available in appendix table 1-11). 

The U.S. score of 489 fell behind Finland’s (the leading nation) by 74 points.

Linking NAEP and TIMSS Results

To compare the performance of other countries’ 

students with the achievement standards set for the National Assessment of Educational Progress (NAEP), a 

series of studies has used various statistical methods 

to project the results of one assessment into the scale 

of the other (Beaton and Gonzales 1993; Johnson and 

Siegendorf 1998; Johnson et al. 2005; Pashley and 

Phillips 1993). In the most recent of these studies 

(Phillips 2007), scores from the Trends in International Mathematics and Science Study (TIMSS) eighth 

grade mathematics and science assessments in 1999 

and 2003 were translated into the 2000 NAEP eighth 

grade performance levels using data from a sample of 

students who participated in both the 1999 TIMSS and 

2000 NAEP assessments.

NAEP results have long demonstrated that a minority of U.S. students reaches the proficient level of performance as defined in NAEP. The linked TIMSS data 

for both years were not only consistent with this finding in both mathematics and science, but showed that 

few countries’ students met the standard for achievement set by NAEP’s proficient criterion. In math, six 

countries had an average score that met this criterion 

in 1999, and five countries did so in 2003. In science, 

two countries’ average scores fell within the proficient

level in each year.
Science and Engineering Indicators 2010 1-23

The U.S. 90th percentile score in scientific literacy was 

628, below the corresponding score in 10 of the 24 nations 

with data, but above it in 9, putting U.S. top-scoring students just below the middle of the 90th percentile science 

score distribution for these selected nations. Thus, U.S. high 

achievers in science placed in a better position relative to 

other countries than did U.S. students on average.

Teachers of Mathematics and Science

Among the many factors that influence student learning, teacher quality is believed to be one of the most crucial. Studies have found that various aspects of teachers and 

teaching make a significant difference in student performance (Boyd et al. 2008; Clotfelter, Ladd, and Vigdor 2007; 

Croninger et al. 2003; Darling-Hammond et al. 2005; Goe 

2008; Guarino et al. 2006; Hanushek et al. 2005; Harris and 

Sass 2007; Nye, Konstantopoulos, and Hedges 2004; Wayne 

and Youngs 2003; Xue and Meisels 2004). To ensure that 

all classrooms are led by teachers who are effective in promoting student learning, the federal No Child Left Behind 

Act (NCLB) of 2001 mandates that schools and districts hire 

only “highly qualified” teachers, defining “highly qualified” 

in terms of state certification (excluding emergency, provisional, or temporary licenses),7

 a minimum of a bachelor’s 

degree, and demonstrated subject area competence.8

This section examines indicators of teacher preparedness, experience, professional development, salaries, and 

working conditions. The major data source used here is the 

Early Childhood Longitudinal Study, Kindergarten Class of 

Table 1-8

Average PISA mathematics and science literacy scores of 15-year-old students in selected nations, relative to 

U.S. average: 2006

Nation Mathematics Nation Science

United Statesa

 ................................................ 474 United Statesa ................................................ 489

Score higher than U.S. Score higher than U.S.

Chinese Taipei ............................................ 549 >` ....................................................... 563

>` ....................................................... 548 }Ê}Ê-,b ....................................... 542

}Ê}Ê-,b ....................................... 547 Canada ...................................................... 534

,i«ÕLVÊvÊÀi> ...................................... 547 Chinese Taipei ............................................ 532

Netherlands ............................................... 531 Japan ......................................................... 531

-ÜÌâiÀ>` ................................................ 530 Australia ..................................................... 527

Canada ...................................................... 527 Netherlands ............................................... 525

Japan ......................................................... 523 ,i«ÕLVÊvÊÀi> ...................................... 522

Australia ..................................................... 520 Germany .................................................... 516

i>À .................................................... 513 United Kingdom ......................................... 515


âiVÊ,i«ÕLV .......................................... 510 
âiVÊ,i«ÕLV .......................................... 513

Germany .................................................... 504 -ÜÌâiÀ>` ................................................ 512

-Üi`i ...................................................... 502 Ireland ........................................................ 508

Ireland ........................................................ 501 Õ}>ÀÞ ..................................................... 504

À>Vi ........................................................ 496 -Üi`i ...................................................... 503

United Kingdom ......................................... 495

Õ}>ÀÞ ..................................................... 491 -VÀiÊÌÊÃÌ>ÌÃÌV>ÞÊ`vviÀiÌÊvÀÊ1°-°

 ÀÜ>Þ ....................................................... 490 i>À .................................................... 496

À>Vi ........................................................ 495

-VÀiÊÌÊÃÌ>ÌÃÌV>ÞÊ`vviÀiÌÊvÀÊ1°-° Spain .......................................................... 488

Spain .......................................................... 480 ÀÜ>Þ ....................................................... 487

,ÕÃÃ>Êi`iÀ>Ì .................................... 476 ,ÕÃÃ>Êi`iÀ>Ì .................................... 479

-VÀiÊÜiÀÊÌ>Ê1°-° -VÀiÊÜiÀÊÌ>Ê1°-°

Italy ............................................................ 462 Italy ............................................................ 475

Thailand ..................................................... 417 Thailand ..................................................... 421

Indonesia ................................................... 391 Indonesia ................................................... 393

	À>â .......................................................... 370 	À>â .......................................................... 390

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Science and Engineering Indicators 2010
1-24 Chapter 1. Elementary and Secondary Mathematics and Science Education

1998−99.9 This longitudinal study followed students from 

kindergarten through eighth grade and collected data from 

students’ teachers and schools as well as from the students 

and their families. When the cohort was in grades 5 and 8 (in 

2004 and 2007, respectively), ECLS-K collected data from 

their teachers in each of the core academic subjects (i.e., 

reading/language arts, mathematics, and science), allowing 

researchers to distinguish teachers who taught mathematics 

and science from all other teachers.10 Because the teacher 

information in ECLS-K was linked to the sampled students, 

the data enable analysis of whether students from different 

backgrounds and with different levels of prior achievement 

had equal access to high-quality and experienced teachers in 

their fifth and eighth grade mathematics and science classrooms. When possible, comparable data from the 2008 edition of Science and Engineering Indicators are either cited 

or included as complementary information about teachers 

of mathematics and science at the middle school and high 

school levels.

Teacher Quality

Researchers have often relied on indicators such as test 

scores (e.g., Praxis; see Gitomer 2007), education credentials, professional certifications, and teaching experience 

as proxies for teacher quality (Darling-Hammond 2000; 

Wayne and Youngs 2003). These indicators are relatively 

easy to measure and can be readily used to screen prospective candidates. They also align with the requirements for 

highly qualified teachers specified in NCLB. The following 

analysis examines the quality of mathematics and science 

teachers by focusing on the educational attainment, certification status, subject area preparation, and years of teaching 

experience of those who taught mathematics and science in 

public schools to fifth graders in 2004 and eighth graders in 

2007.

Teacher quality is not limited to the characteristics examined here, however; it may include other important elements 

that are difficult or costly to measure, such as teachers’ 

abilities to motivate students, manage the classroom, maximize instruction time, and diagnose and overcome students’ 

learning difficulties. Current research on “teacher quality” 

is designed to yield measurable characteristics of teachers 

that are associated with student learning (Angrist and Guryan 2008; Boyd et al. 2008; Hill, Rowan, and Ball 2005; 

Goe 2008). This work is beginning and is expected to yield 

measures of teacher quality more directly related to student 

achievement than are the indicators examined here.

Formal Preparation

Teachers acquire a significant amount of subject knowledge and teaching skills through formal education and certification. Thus, teachers’ level of educational attainment and 

type of professional certification provide some indication 

of how well teachers are prepared for their work.11 Data on 

teachers’ highest degree and certification status (regardless 

of the field in which the degree/certification was held) indicate that virtually all of them had at least a bachelor’s degree. Nearly half also had a master’s or higher degree, and 

a majority held a regular or advanced teaching certificate 

(NSB 2008).

Similar patterns are observed when the analysis focuses 

on how many students are taught mathematics and science 

by teachers with various levels of educational attainment and 

types of certification. For example, almost all public school 

fifth grade students in 2004 and eighth grade students in 

2007 were taught mathematics and science by teachers who 

had attained a bachelor’s or higher degree (regardless of the 

field in which the degree was earned), and about half of them 

were taught these two subjects by teachers with a master’s 

or higher degree (appendix table 1-12). Furthermore, the 

majority of fifth and eighth grade students (90% and 84%, 

respectively) had teachers of mathematics and science with 

a regular or advanced teaching certificate (regardless of the 

field in which the certification was awarded).

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Subject Area Preparation

Adequate subject matter knowledge and skills are critical 

for teachers to teach their subjects well (The Education Trust 

2008; Goe 2008; Ingersoll 2003). NCLB mandates that all 

students be taught by teachers who not only are fully certified and possess at least a bachelor’s degree, but also demonstrate competence in subject knowledge and teaching. In its 

2007 policy recommendations regarding STEM education, 

the National Science Board (NSB) emphasized that STEM 

teachers should receive adequate STEM content knowledge 

that is aligned with what they are expected to teach (NSB 

2007). Similarly, a report from the National Research Council of the National Academies (2007) advocated that teacher 

preparation and professional development programs focus 

on boosting teachers’ knowledge of science, how students 

learn the subject, and methods and technologies that aid 

science learning for all. However, neither NCLB nor these 

reports’ policy recommendations provide specific guidance 

or criteria regarding “adequate” preparation to teach mathematics and science at various grade levels.

While most states require those who teach mathematics and science at the high school level to have a degree or 

certification in their subject area, state laws and regulations 

regarding preparation of middle school teachers (eighth 

grade teachers fall in this group) vary, with some states allowing general education preparation and others requiring 

subject area preparation. As for elementary school teachers, 

who typically teach multiple subjects, most state policies 

consider teachers with a degree or certification in general 

elementary education to be “qualified” to teach elementary school mathematics and science (and other subjects), 

although some question whether elementary school teachers with general education preparation have sufficiently 

rigorous preparation for teaching mathematics and science 

(Greenberg and Walsh 2008).

Recent research efforts have focused on matching teachers’ formal preparation (as indicated by degree major and 

certification field) with their teaching field to determine 

whether teachers have subject-specific preparation for the 

fields they teach (McGrath, Holt, and Seastrom 2005; Morton et al. 2008). Following this line of research, four levels 

of teachers’ formal preparation for teaching mathematics 

and science at fifth and eighth grade levels were distinguished. In order of decreasing rigor of preparation, they are 

as follows:

In-field: Teachers who taught mathematics and had a 

degree and/or certificate in mathematics or mathematics 

education. Teachers who taught science and had a degree 

major and/or certificate in science or science education.

Related-field: Teachers who taught mathematics and had 

a degree and/or certificate in a field related to their teaching field (such as science, science education, computer 

sciences). This category is omitted for teachers of science 

in ECLS-K because these teachers were not asked about 

their degrees or certificates in specific science fields such 

as physics, chemistry, or biology.

General education: Teachers who taught mathematics or 

science and had a degree and/or certificate in general elementary or secondary education. Such teachers usually 

undergo some pedagogical training in mathematics and 

science.

Other: Teachers who taught mathematics or science but 

did not have a degree or certificate in their teaching field, a 

related field, or general elementary or secondary education.

In-field teaching in mathematics and science was less 

prevalent at lower grade levels than at higher grade levels. 

For example, in 2004, about 40% of fifth grade students in 

public schools were taught mathematics and science by infield teachers (table 1-9; appendix table 1-13). Most students 

at this level (54%) had teachers with general education preparation. When students reached eighth grade in 2007, more 

than 80% of them had in-field teachers in their mathematics 

and science classes, and 9–10% were taught mathematics 

and science by teachers with general education preparation.

Similar patterns were also revealed using the teacher data 

from the 2003–04 and 2007–08 Schools and Staffing Survey (SASS).12 In 2003, 53% of teachers of mathematics and 

67% of teachers of science in public middle schools were 

teaching in field (table 1-10).13 Partly reflecting the impact 

of NCLB on teacher qualifications, in-field mathematics 

teachers in public middle school increased to 64% in 2007, 

representing a significant 11 percentage point increase from 

2003. Seventy percent of teachers of science in public middle schools were teaching in field in 2007, but this does not 

represent a significant increase from the 67% in 2003. In 

both years, between 27% and 38% of middle school teachers 

were teaching mathematics and science with general education preparation.

Moving up to the high school level, in-field teaching became more common. For example, in-field teaching in 2007 

ranged from 82% of teachers of physical sciences and 88% 

of teachers of mathematics to 93% of teachers of biology/

life sciences. The share of teachers with general education 

preparation declined to 3% or lower. Similar percentage 

ranges also were found among public high school mathematics and science teachers in 2003.

Teaching Experience

Experienced teachers are, generally, more effective 

than novices in helping students learn (Boyd et al. 2006; 

Clotfelter, Ladd, and Vigdor 2007; Hanushek et al. 2005; 

Harris and Sass 2008; Rice 2003; Rockoff 2004; Rowan, 

Correnti, and Miller 2002; Wayne and Youngs 2003). 

Overall, teachers with more than 3 years of teaching experience make up a large majority of the mathematics 

and science teaching force in public schools (NSB 2008; 

NCES 2007a). Likewise, between 82% and 88% of public 

school fifth and eighth grade students in 2004 and 2007 

were taught mathematics and science by teachers with more 

than 3 years of teaching experience (appendix table 1-14). 

The majority of these students had teachers with 3 or more 

years of experience in the specific grade level or subject 
1-26 Chapter 1. Elementary and Secondary Mathematics and Science Education

matter in question: 63%−65% of fifth grade students were 

taught mathematics and science by teachers with more than 

3 years of experience in teaching fifth grade classes, and 

76%−78% of eighth grade students had science and mathematics teachers who had taught their respective subject for 

more than 3 years.

Differences in Student Access to Qualified 

Teachers in Science and Mathematics

Access to better-qualified teachers was not equally distributed among students. In general, black and Hispanic students, students from less-educated and low-income families, 

and students with low levels of prior achievement had less 

access to teachers who were highly educated, fully certified, and had more experience and better preparation in the 

subject field than their counterparts (appendix tables 1-12, 

1-13, and 1-14). For example, fifth grade black and Hispanic 

students were less likely than their white peers to be taught 

mathematics by teachers with a master’s or advanced degree 

(39% and 42% vs. 51%, respectively), a regular or advanced 

teaching certificate (86% and 85% vs. 92%), and more than 

3 years of experience in teaching the fifth grade (48% and 

58% vs. 68%) (table 1-11). Students living in low-income 

families were less likely than their peers from higher-income 

families to be taught mathematics by teachers with a master’s or advanced degree (35% vs. 50%, respectively). Also 

among fifth graders, a third of those in the lowest achievement quartile in grade 3 were taught mathematics by in-field 

teachers. In contrast, 41% of fifth graders in the top achievement quartile in grade 3 had such teachers.

Table 1-9

Public school students in grades 5 and 8 taught mathematics and science by teachers with various levels of 

subject area preparation: 2004 and 2007

­*iÀViÌÊ`ÃÌÀLÕÌ®

À>`iÉvi` ÊiÛiÃ Êvi` ,i>Ìi`Êvi` General education Other

À>`iÊxÊ­Óää{®

Mathematics .................................................. 100 38 4 54 4

Science .......................................................... 100 43 na 54 3

À>`iÊnÊ­ÓääÇ®

Mathematics .................................................. 100 85 2 10 4

Science .......................................................... 100 88 na 9 3

na = not applicable

 "/
\Ê*iÀViÌ>}iÃÊ>ÞÊÌÊ>``ÊÌÊ£ää ̄ÊLiV>ÕÃiÊvÊÀÕ`}°

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-\Ê >Ì>Ê
iÌiÀÊvÀÊ
`ÕV>ÌÊ-Ì>ÌÃÌVÃ]Ê
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>ÃÃÊvÊ£nq]ÊÓää{]Ê>`ÊÓääÇÆÊ>`Ê >Ì>Ê

-ViViÊÕ`>Ì]ÊÛÃÊvÊ-ViViÊ,iÃÕÀViÃÊ-Ì>ÌÃÌVÃ°Ê-iiÊ>««i`ÝÊÌ>LiÊ££Î°

Science and Engineering Indicators 2010

Table 1-10

Public middle and high school teachers of mathematics and science with various levels of subject area 

preparation: 2003 and 2007

­*iÀViÌÊ`ÃÌÀLÕÌ®

-VÊiÛiÊ>`ÊÌi>V}Êwi`

2003 2007

ÊiÛiÃ Êwi`

Related 

wi`

General 

education Other Êwi`

Related 

wi`

General 

education Other

Middle school

Mathematics ...................................... 100 53 4 38 5 64 2 31 3

Science .............................................. 100 67 na 29 4 70 na 27 3

}ÊÃV

Mathematics ...................................... 100 87 2 3 7 88 1 3 7

	}ÞÉviÊÃViViÃ .......................... 100 92 4 1 3 93 4 1 2

*ÞÃV>ÊÃViViÃ .............................. 100 78 20 1 1 82 15 1 2

na = not applicable

 "/
\Ê*iÀViÌ>}iÃÊ>ÞÊÌÊ>``ÊÌÊ£ää ̄ÊLiV>ÕÃiÊvÊÀÕ`}°

-"1,

-\Ê >Ì>Ê
iÌiÀÊvÀÊ
`ÕV>ÌÊ-Ì>ÌÃÌVÃ]Ê-VÃÊ>`Ê-Ì>vv}Ê-ÕÀÛiÞ]ÊÓääÎqä{Ê>`ÊÓääÇqänÆÊ>`Ê >Ì>Ê-ViViÊÕ`>Ì]ÊÛÃÊvÊ

-ViViÊ,iÃÕÀViÃÊ-Ì>ÌÃÌVÃ]ÊÃ«iV>ÊÌ>LÕ>ÌÃ°Ê

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 1-27

In eighth grade, students whose mothers had not earned a 

high school diploma were less likely than those whose mothers had a bachelor’s or higher degree to be taught science by 

teachers who had a master’s or advanced degree (46% vs. 

57%, respectively), a regular or advanced teaching certificate (79% vs. 87%), an in-field degree or certificate (84% 

vs. 93%), and more than 3 years of experience in teaching 

science (69% vs. 83%) (appendix tables 1-12, 1-13, and 

1-14). Differences existed when looking at family income 

as well: eighth grade students from families with incomes 

below the poverty threshold were less likely than those 

from higher-income families to be taught science by teachers with a regular or advanced teaching certificate (79% vs. 

86%, respectively), an in-field degree or certificate (84% vs. 

89%), and more than 3 years of experience in teaching science (69% vs. 79%). In addition, 92% of students with high 

achievement in fifth grade were taught mathematics by infield teachers, compared with 76% of those with low fifthgrade achievement who had such teachers.

Professional Development

Teachers rely on professional development to update their 

knowledge, sharpen their skills, and acquire new teaching 

techniques, all of which may enhance the quality of teaching 

and learning (Richardson and Placier 2001; Davis, Petish, 

and Smithey 2006). During the past decade, researchers have 

put significant effort into identifying features of high-quality 

professional development programs (Banilower et al. 2006; 

CCSSO 2008; Clewell et al. 2004; Desimone et al. 2002; 

Garet et al. 2001; Hawley and Valli 2001; Harris and Sass 

2007; Heck, Rosenberg, and Crawford 2006; Penuel et al. 

2007; Porter et al. 2000). They have come to general agreement that professional development is most effective if it

Focuses on subject content

Provides an intensive and sustained approach

Is presented in a format of teacher network, study group, 

mentoring, and coaching as opposed to a traditional 

workshop or conference

Is connected or related to teachers’ daily work

Emphasizes a team approach and collaboration

Provides opportunities for active learning

When professional development is conducted in these ways, 

teachers are more likely to change their instructional practice, 

gain greater subject-matter knowledge, and improve their 

teaching (see, for example, the sidebar “Local Systemic Change 

Through Teacher Enhancement Program”). Consequently, 

there is increased potential for the professional development to 

have an effect on student achievement (Correnti 2007; DarlingHammond and Youngs 2002; Wenglinsky 2002).

Table 1-11

Public school students in grade 5 who were taught mathematics by teachers with various qualifications, 

by student characteristics: 2004

­*iÀViÌ®

Student characteristic

>ÃÌiÀ1⁄2ÃÊÀÊ

higher degree

,i}Õ>ÀÉ>`Û>Vi`Ê

ViÀÌvV>Ìi

i}ÀiiÊÀÊViÀÌvV>ÌiÊ

in mathematics 

­Êvi`®

More than 3 years 

in teaching grade 5

All students ............................................................. 47 90 38 63

,>ViÉiÌVÌÞÊ

7Ìi]ÊÃ«>V ........................................ 51 92 39 68

	>V]ÊÃ«>V ........................................ 39 86 33 48

Ã«>V .......................................................... 42 85 39 58

ÌiÀ1⁄2ÃÊi`ÕV>ÌÊ

Less than high school ..................................... 34 86 37 61

	>ViÀ1⁄2ÃÊÀÊ}iÀÊ`i}Àii ............................ 56 91 34 69

>ÞÊ«ÛiÀÌÞÊÃÌ>ÌÕÃa

	iÜÊ«ÛiÀÌÞÊÌÀiÃ` ................................. 35 88 37 59

LÛiÊ«ÛiÀÌÞÊÌÀiÃ` ................................. 50 90 39 65

ViÛiiÌÊÊ}À>`iÊÎb

Ü .................................................................. 38 84 33 60

} ................................................................. 50 92 41 71

a

i`iÀ>Ê«ÛiÀÌÞÊÌÀiÃ`ÃÊ`iviÊÕÃi`ÃÊLiÜÊ«ÛiÀÌÞÊiÛiÊL>Ãi`ÊÊÕÃi`ÊViÊ>`ÊÕLiÀÊvÊÕÃi`ÊiLiÀÃ°

b
«ÃÌiÊvÊÀi>`}]Ê>Ìi>ÌVÃ]Ê>`ÊÃViViÊ>ViÛiiÌÊÌiÃÌÊÃVÀiÃ°ÊÜÊiÛiÊVÕ`iÃÊÃÌÕ`iÌÃÊÊLÌÌÊμÕ>ÀÌiÊvÊ>ViÛiiÌÊ`ÃÌÀLÕÌÆÊ

}ÊiÛiÊVÕ`iÃÊÌ«ÊμÕ>ÀÌi°

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>ÃÃÊvÊ£nqÊ­

-®]ÊÃ«À}ÊÓää{ÆÊ>`Ê

 >Ì>Ê-ViViÊÕ`>Ì]ÊÛÃÊvÊ-ViViÊ,iÃÕÀViÃÊ-Ì>ÌÃÌVÃ°Ê-iiÊ>««i`ÝÊÌ>LiÃÊ££Ó]Ê££Î]Ê>`Ê££{°

Science and Engineering Indicators 2010
1-28 Chapter 1. Elementary and Secondary Mathematics and Science Education

Evidence from the most recent national teacher survey 

in 2003−04 indicates that almost all mathematics and science teachers in public middle and high schools participated 

in some form of professional development activities during 

the school year (NSB 2008). However, the programs these 

teachers attended consisted mostly of short-term workshops, 

conferences, and training sessions. In general, teachers had 

less exposure to professional development with features 

identified by research as effective in bringing about positive 

changes in teaching practices.

Data from ECLS indicate that in 2004, the percentage of 

fifth graders whose teachers of mathematics and science reported that they had participated in staff development related 

to their subject content or pedagogy during the past school 

year was 73% and 53%, respectively (table 1-12). On average, teacher participants spent about 14 hours on subjectfocused staff development during the entire school year. 

Furthermore, among students whose teachers participated in 

this staff development, about 40% had teachers rating this 

activity as very useful.

The 2007 TIMSS provides further evidence regarding 

the extent to which elementary school teachers participate 

in professional development in mathematics and science 

(Miller et al. 2009). In 2007, the percentage of fourth graders 

whose teachers participated in professional development on 

various aspects of mathematics during the previous 2 years 

ranged from 47% for assessment and 50% for pedagogy/instruction to 60% for mathematics content (figure 1-9). Participation in professional development relating to science 

was even lower among teachers of fourth graders: 24%–42% 

of students had teachers who participated in professional development on science content (42%), pedagogy/instruction 

(29%), and assessment (24%).

Teachers’ Salaries

Adequate pay is important to attracting and retaining 

teachers (Guarino, Santibanez, and Daley 2006). Thus, policymakers often propose increasing teacher salaries to lower 

attrition and improve the quality of the teaching pool, arguing that if teachers could earn higher pay both when entering the profession and over time, stronger candidates would 

be drawn to teaching and more effective teachers might be 

retained (Johnson, Berg, and Donaldson 2005; Loeb and 

Reininger 2004; Stronge, Gareis, and Little 2006).

Table 1-12

Public school students in grade 5 who were taught mathematics and science by teachers who had participated 

in staff development relating to their subject content or pedagogy during the past school year: 2004

­*iÀViÌ®

Subject *>ÀÌV«>Ìi`Ê­ ̄® ÛiÀ>}iÊÕÀÃÊÃ«iÌ

/Õ}ÌÊÃÌ>vvÊ`iÛi«iÌÊ

Ü>ÃÊÛiÀÞÊÕÃivÕÊ­ ̄®

Mathematics .......................................................... 73 14 41

Science .................................................................. 53 14 42

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-ViViÊÕ`>Ì]ÊÛÃÊvÊ-ViViÊ,iÃÕÀViÃÊ-Ì>ÌÃÌVÃ]ÊÃ«iV>ÊÌ>LÕ>ÌÃ°

Science and Engineering Indicators 2010

Local Systemic Change (LSC) Through Teacher Enhancement is a teacher professional development program that aims to improve K–12 instruction in science, 

mathematics, and technology. LSC embraces many characteristics of effective professional development—it requires all mathematics and science teachers from schools 

or districts to participate in a minimum of 130 hours of 

professional development over the course of the project, 

provides ongoing support during the school year, adopts 

a range of formats, emphasizes subject content and pedagogy, offers active learning opportunities, and promotes 

efforts to build a supportive environment for change.

A decade’s worth of data indicate that LSC has had 

a positive impact in many areas, including teachers’ attitudes toward reform-oriented teaching, perceptions of 

their pedagogical preparedness, and adoption of reformoriented teaching practices in the classroom; the quality 

of instruction delivered to students; and student achievement, attitudes, and coursetaking patterns in mathematics 

and science (Banilower et al. 2006; Heck, Rosenberg, and 

Crawford 2006; Shimkus and Banilower 2004). Moreover, 

the program’s impact increased as teachers accrued more 

professional development hours (although there appeared 

to be a limited increase in impact beyond 80 hours).

Local Systemic Change Through Teacher Enhancement Program
Science and Engineering Indicators 2010 1-29

According to the latest annual teacher salary survey conducted by the American Federation of Teachers (AFT),14 the 

average salary for all public K–12 teachers in 2006–07 was 

about $51,000 (AFT 2008). After adjustment for inflation, 

teacher salaries grew by 2.8% from 1996–97 to 2006–07. 

During this 10-year period, 18 states experienced declines in 

inflation-adjusted teacher salaries.

Using data from the Current Population Survey of the Bureau of Labor Statistics, Allegretto, Corcoran, and Mishel 

(2008) compared the weekly wages15 of full-time public 

school teachers with those of people working in occupations 

requiring comparable education and skills, such as accountants, reporters, registered nurses, and computer programmers.16 Their analyses showed that in 2006, full-time public 

school teachers earned 86% as much in weekly wages as did 

those in this set of comparable occupations. Furthermore, 

between 1996 and 2006, the gap in weekly wages between 

full-time teachers and those in comparable occupations widened from $7 to $153, in constant dollars (figure 1-10). A 

similar conclusion has been drawn about mathematics and 

science teachers—that is, their pay fell behind that of many 

professions with comparable educational backgrounds, and 

the gap widened substantially in recent years (NSB 2008).

Working Conditions

Poor working conditions can cause stress and dissatisfaction and may lead teachers to leave the teaching profession 

altogether (Hanushek, Kain, and Rivkin 2004; Hanushek and 

Rivkin 2007; Ingersoll 2001; Johnson, Berg, and Donaldson 

2005). The working conditions that matter most to teachers 

include administrative leadership at their school, working 

relationships among colleagues, level of parental support, 

teaching loads, and student discipline problems (Guarino, 

Santibanez, and Daley 2006).

Most public middle and high school teachers have positive perceptions about their school conditions (NSB 2008). 

Such positive perceptions are also widely held among fifth 

and eighth grade students’ teachers. In 2004, for example, for 

a large majority of fifth grade students (94%), the teachers 

who taught them mathematics and science felt accepted and 

respected by their school colleagues (appendix table 1-15). 

	

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1-30 Chapter 1. Elementary and Secondary Mathematics and Science Education

	



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Large majorities of these students had teachers who believed 

that teachers in their schools were continually learning and 

seeking new ideas (86%);17 that staff members had school 

spirit (81%) and agreed about the central mission of the 

school (75%); that school administrators knew the direction 

of the school and communicated it to staff (81%) and were 

supportive and encouraging (80%); and that parents were 

supportive of school staff (70%). Furthermore, relatively 

few students’ teachers reported various learning and behavioral problems among students (12%−21%). Reports from 

eighth grade teachers were similar (appendix table 1-16), 

although eighth grade teachers were more likely than fifth 

grade teachers to report such problems as student misbehavior interfering with teaching (30% vs. 21%, respectively) and 

many children not being capable of learning (19% vs. 14%).

Positive perceptions of school conditions were less widely held among teachers of minority, socioeconomically disadvantaged, and low-achieving students. For example, fifth 

grade students whose mothers had less than a high school 

education were less likely than students whose mothers had 

a bachelor’s or higher degree to have teachers who described 

parents in their school as “supportive” (61% vs. 82%, respectively) (figure 1-11). About 74% of fifth grade students from 

low-income families, compared with 81% of students from 

more financially advantaged families, had teachers reporting 

supportive school administrators. Compared with 17% of 

fifth grade white students, 33% of black students had teachers whose teaching was interrupted by child misbehavior. 

In addition, compared with 8% of high-achieving students, 

18% of low-achieving students were taught mathematics and 

science by teachers who reported that many of their students 

were not capable of learning the materials they taught.

Instructional Technology in Education

National organizations have endorsed, and federal and 

state policies have encouraged, the incorporation of technology into education. In the context of elementary/secondary 

education, technology is commonly understood to include a 

range of computer applications such as word processing, presentation software, spreadsheets, databases, Internet search 

capability, distance education, virtual schools, interactions 

with simulations and models, and collaboration over local 

and global networks (Dynarski et al. 2007). The Enhancing 

Education Through Technology Act of 2001, a component of 

the No Child Left Behind Act, emphasized developing technology infrastructure within schools, integrating technology 

into curriculums, and training teachers in its use. In 2005, 

the U.S. Department of Education released a National Education Technology Plan, touting the transformative potential 

of technology in teaching and learning and outlining steps to 

incorporate technology into schools (Office of Educational 
Science and Engineering Indicators 2010 1-31

Technology 2004). More recently, the American Recovery 

and Reinvestment Act of 2009 included $650 million for 

additional grants through the Ed-Tech state program, which 

supports various state and local projects related to the use of 

technology in education.

The applications of technology in education are vast, but 

this section focuses specifically on instructional technology—that is, technology products and tools designed to assist 

teaching and learning in elementary and secondary schools. 

(Technology use can be a competency itself, as well as a 

tool toward other knowledge acquisition. For a discussion of 

technology literacy among students and teachers, see sidebar 

“Student and Teacher Technology Literacy.”) It begins by 

discussing recent research on the effectiveness of technology as an instructional tool. In the context of this research, 

it then presents data on eighth graders’ use of instructional 

technology in school, updates national estimates of access to 

computers and the Internet, and examines the prevalence of 

distance education, an instructional application of technology that can potentially increase students’ access to subject 

matter and qualified instructors.

National and international organizations endorse 

technology as both a tool for instruction in various academic subjects and an important area in which K–12 

students should achieve some competency. The National 

Governors Association (NGA) recently argued that the 

prevalence of technology in most professions requires 

all students to have a strong foundation in using technology—along with other science, technology, engineering, 

and mathematics (STEM) competencies—to compete in 

a 21st century economy (NGA 2007). Several organizations, including the International Technology Education 

Association (ITEA), have developed technology standards outlining what students should know about various types of technologies, the concepts behind them, and 

their significance to society (ITEA 2007). The International Society for Technology in Education (ISTE) has 

developed technology standards for teachers and students 

that have been widely adopted by states and districts interested in integrating technology into their educational 

goals (ISTE 2007; Trotter 2009). Beginning in 2012, the 

National Assessment of Educational Progress (NAEP) 

will pilot a computer-based evaluation of students’ understanding of all technologies, including their information technology literacy (Kerr 2008).

State policies also reflect a growing emphasis on K–12 

students’ learning about technology and the use of technology in education (table 1-A; appendix table 1-18). In 

the 2008–09 school year, 46 states included technology in 

their teaching standards, and 21 states required teachers 

to complete technology coursework or to pass a test on 

technology use for initial teacher certification (Editorial 

Projects in Education Research Center 2009a). Ten states 

required technology-related professional development 

for teachers or testing for recertification. Curriculum 

standards in all 50 states included technology as a subject 

in which students should be educated, and 13 tested students on those technology standards, up from just 5 the 

previous year.

Student and Teacher Technology Literacy

Table 1-A

State standards and policies regarding K–12 teaching with and learning about technology, by academic year: 

2004–09

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Science and Engineering Indicators 2010
1-32 Chapter 1. Elementary and Secondary Mathematics and Science Education

Technology as an Instructional Tool

The Internet offers students access to more, and more recent, information than individual schools can provide, but it 

is only one potential application of instructional technology 

to enhance educational outcomes. Computer applications, 

either alone or in concert with traditional instruction, may 

improve achievement by tailoring lessons and skill practice 

to individual students’ needs or by offering students additional opportunities to interact with information. Additionally, computerized assessment may provide more precise and 

efficient feedback on student learning, allowing teachers to 

adapt instruction to student needs (Tucker 2009).

Research on whether and how these tools improve student 

achievement, however, continues to yield mixed results. Some 

computer applications appear to enhance students’ achievement on standardized tests, while others do not (NSB 2006).

An NCLB study of the effectiveness of instructional 

technology failed to find any statistically significant effects of several specific instructional technologies on student achievement (Dynarski et al. 2007). Researchers tested 

three grade 6 math products in 28 schools and three algebra 

products in 23 schools. Teachers in selected schools volunteered to participate and were randomly assigned to use or 

not use the educational software; researchers compared students’ test results and other outcomes. No effects on sixth 

grade mathematics or algebra achievement were observed. 

During the second year of the evaluation, two sixth grade 

math products and two algebra products were tested, and 

again researchers observed no significant effects on student 

achievement (Campuzano et al. 2009). No science products 

were tested.

In contrast, a meta-analysis that used statistical procedures to aggregate the results of 42 studies published in 

peer-reviewed journals found that incorporating instructional technology into teaching and learning had a small, 

positive effect on achievement when compared with instruction without technology (Waxman, Lin, and Michko 2003). 

However, the studies included in the meta-analysis were 

conducted prior to 2003, some based on projects from the 

early 1990s. Given the fast pace of change in instructional 

technology, the results may be less relevant than more recent 

studies. Small-scale recent studies of specific instructional 

technology applications suggest that educational computer 

programs and videogames may promote student engagement and learning when they make use of proven pedagogical techniques (Steinkuehler and Duncan 2008; Ketelhut 

2007; Nelson 2007; Barab et al. 2007; Neulight et al. 2007). 

In its final report, the National Mathematics Advisory Panel (2008) recommended that several types of high-quality 

computer-assisted instruction be considered potentially useful educational tools and that further research be conducted.

Computer Use in Eighth Grade 

Mathematics and Science

According to their teachers, eighth grade students use 

computers in science classes substantially more often than in 

their mathematics classes. More than 70% of students used 

computers in science at least once per month, compared with 

less than 40% in mathematics, according to the 2007 followup of the ECLS-K cohort (appendix table 1-17). Use of 

computers varies with student characteristics, but not consistently. Students with the highest mathematics assessment 

scores were more likely to use computers in science class 

but less likely to use them in mathematics class. Conversely, 

black students were significantly more likely to “never or 

hardly ever” use computers in science class, compared with 

their white and Asian peers, and more likely to use computers “almost every day” in mathematics class.

Until research yields some consensus on the optimal uses 

of computer-based technology for various subjects, grade 

levels, and types of students, its contributions to patterns of 

achievement will remain unclear.

Internet Access

Access to the Internet is nearly universal among public 

elementary and secondary schools in the United States. In 

2005, 100% of public schools had Internet access, and 97% 

of these schools used broadband connections to access the 

Internet (Wells and Lewis 2006). Access was nearly universal not only for schools, but also for classrooms and students: 94% of classrooms in public schools had computers 

with Internet access, and the ratio of students to instructional 

computers was 4:1.18 Moreover, change has been swift: 35% 

of schools and 3% of classrooms had Internet access in 1994 

(figure 1-12), and the student-to-computer ratio was 12:1 in 

1998 (Wells and Lewis 2006).

	
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Schools and classrooms with Internet access, 

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Science and Engineering Indicators 2010 1-33

Furthermore, equity in Internet access appears to have 

been achieved. Since the beginning of the century, public 

schools’ access to the Internet has not varied with minority enrollment or student poverty, and by 2005, neither did 

classrooms’ Internet access (Wells and Lewis 2006).

Distance Education

Technology may provide students with access to courses 

they would otherwise be unable to take by facilitating distance education: instruction in which the teacher and students 

are in different locations. Distance education may include 

videoconferencing and televised or audiotaped courses, but 

Internet courses are the most widespread and fastest-growing 

mode of delivery (Zandberg and Lewis 2008). While distance 

courses preclude some experiential learning (e.g., laboratory 

experiments), well-designed electronic alternatives (e.g., remotely operated laboratories [Nickerson et al. 2007])—may 

be able to fill that gap. Thus far, several meta-analyses have 

found no significant difference between the student learning that occurs in online versus regular classroom instruction (Cavanaugh 2001; Bernard et al. 2004; Cavanaugh et 

al. 2004), suggesting that distance education courses may 

provide students with access to additional courses without 

compromising the quality of instruction.

Goals of Distance Education

NCLB describes distance education as an innovative tool 

to promote access to rigorous academic courses, particularly 

for students in isolated geographic regions (NCLB Title II, 

Part D), and the National Education Technology Plan lists 

distance learning as one of seven major action steps designed 

to improve student achievement through technology. District 

administrators cited “offering courses not otherwise available at the school” as the most important reason to provide 

online learning opportunities (Picciano and Seaman 2007). 

Schools and districts also provide distance learning options 

to ease crowding in schools, increase course access for physically disabled students, allow students to retake courses for 

graduation, expand student access to AP and college-level 

courses, and tutor students for high-stakes graduation exams 

(Education Technology Cooperative 2007; Zandberg and 

Lewis 2008).

Distance education provides one strategy for managing 

shortages of mathematics and science teachers, particularly 

in rural or inner-city areas where attracting and retaining 

them are chronically difficult (Picciano and Seaman 2007). 

For example, Louisiana has implemented a Web-based algebra course targeted at schools with uncertified mathematics 

teachers. The course provides students with access to a certified instructor and offers teachers professional development 

opportunities (Watson, Germin, and Ryan 2008).

Access and Participation in Distance Education

The availability of distance education, most commonly 

provided by postsecondary institutions, independent vendors, states, and school districts (Picciano and Seaman 

2007), has grown substantially over this decade. In 2005, 

57% of secondary schools nationwide provided opportunities for online distance learning to their students (Wells and 

Lewis 2006). In 2008, students in 44 states had access to 

full-time or supplemental online learning opportunities, and 

34 states sponsored programs and initiatives. While stateled programs primarily serve secondary students, opportunities for students in grades K–8 are increasingly common 

(Watson, Germin, and Ryan 2008). Beyond its accessibility, 

however, it is difficult to draw conclusions about nationwide 

distance education because programs and policies vary so 

widely. For example, two states—Michigan and Alabama—

now require students to participate in online learning to 

graduate from high school, while six states do not have online opportunities that are accessible to every student (Watson, Germin, and Ryan 2008). Policies regarding tuition and 

partnerships with private schools and home-school organizations also vary (Watson, Germin, and Ryan 2008; Education Technology Cooperative 2008).

Participation in distance education has increased dramatically. Primary and secondary school enrollment in distance 

education across all subjects grew from 317,070 students in 

2002−03 to 506,950 students in 2004−05, an increase of 60% 

(Zandberg and Lewis 2008).19 In addition, a nationwide survey of school district administrators indicated that approximately 700,000 public school students, about two-thirds of 

whom were in grades 9−12, were enrolled in courses that 

involved a substantial proportion of online learning during the 2005−06 school year (Picciano and Seaman 2007). 

Postsecondary institutions were the leading providers of 

distance education to secondary students in 2005 (Zandberg 

and Lewis 2008), and 12% of 2- and 4-year postsecondary 

institutions reported offering courses, primarily academic 

high school courses, to elementary and secondary students 

in 2006–07 (Parsad and Lewis 2008).

Despite these recent reports, national indicators pointing to elementary and secondary science and mathematics 

education are unavailable. No national data exist on distance 

course taking in math, science, or any particular subject area. 

Likewise, data that identify elementary and secondary students among postsecondary distance education enrollments 

are also as yet unavailable.

Virtual Schools

State-sponsored virtual schools are growing sources for 

distance education. In 1997, five states had virtual school 

programs, which use technology to offer individual courses or supplements to courses taught in traditional schools 

(Tucker 2007). As of 2008, 29 states had established virtual 

school programs (Editorial Projects in Education Research 

Center 2009a). All the members of the Southern Regional 

Education Board (SREB) sponsor virtual schools, and their 

annual surveys indicate that enrollment in these schools has 

been increasing steadily (Education Technology Cooperative 2008).
1-34 Chapter 1. Elementary and Secondary Mathematics and Science Education

Although there are no nationwide data on distance education course offerings or participation in STEM subjects, the 

Education Technology Cooperative at SREB tracks this information for its member states’ virtual schools (table 1-13). 

In the absence of more comprehensive data, it provides an 

indicator of distance education in math and science specifically. These virtual schools provide students with advanced 

courses: 10 of the 14 virtual schools offer several AP courses 

in math or science. Virtual schools offer courses at a range 

of levels, however, including regular and honors levels, and 

offer various electives such as business-focused math, computer science, and specialized sciences (e.g., oceanography). 

In a few states, the virtual school plays a role in remediation 

as well. For example, Alabama offers online remediation for 

the math and science components of the high school graduation exam; more than 1,000 students are enrolled in each 

of those courses, compared with an average of about 200 

students in the other math and science courses offered.

Transition to Higher Education

One role of high school education in the United States is 

to prepare students for further education. This section presents indicators of how well prepared high school graduates 

are, especially in math and science, to engage in postsecondary education.

Although calculating accurate high school graduation rates 

has been a perennial challenge, existing data indicate that less 

than three-quarters of students graduate from high school in 

4 years. On the other hand, a small but growing number of 

students earn college credit during high school by passing 

AP tests. For those students who complete high school, this 

section presents indicators of their movement into postsecondary education. It begins with data on the association of 

students’ high school mathematics and science coursetaking 

and achievement with their postsecondary enrollment and remediation; then it examines long-term trend data on students’ 

immediate enrollment in postsecondary education and presents current data in the context of international rates. Together, these indicators describe high school students’ preparation 

for and transition into postsecondary education.

High School Completion

In 2006, the national on-time high school graduation 

rate—the percentage of entering ninth graders who graduated 4 years later—was 73% (Stillwell and Hoffman 2008). 

About three-quarters of students have completed high 

school on time since 2003. Differences in on-time graduation rates between students in various racial/ethnic groups 

remain large: the graduation rate for white students was approximately 20 percentage points higher than the rates for 

black, Hispanic, and American Indian/Alaska Native students in 2006 (figure 1-13). The Asian/Pacific Islander rate 

was higher than that of all other groups.20

Some students who fail to graduate from high school on 

time eventually earn a high school diploma or alternative 

award such as a General Educational Development (GED) 

Table 1-13

Science and mathematics courses offered at state-sponsored virtual schools, by state: 2008

Mathematics and science courses

State Mathematics Science *a Students enrolledb

Alabama ................................................................................. 11 9 4 x]ÇÎäÊ

À>Ã>Ã ................................................................................. 6 7 0 537

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Georgia ................................................................................... 20 12 6 Ó]ÓÇÈÊ

iÌÕVÞ ................................................................................ 8 11 8 256

Louisiana ................................................................................ 8 8 2 £]£nxÊ

Maryland ................................................................................. 15 9 8 297

Mississippi .............................................................................. 11 11 8 £]ÇxÈÊ

North Carolina ........................................................................ 7 10 5 £]ÈÓ£Ê

">> ............................................................................... 7 8 0 466 

South Carolina ........................................................................ 7 1 0 £]ÓnÇÊ

Tennessee ............................................................................... 7 6 0 871

Virginia .................................................................................... 4 7 7 417

West Virginia ........................................................................... 30 21 7 145

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Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 1-35

credential. In 2006, 88% of 18- through 24-year-olds who 

were not enrolled in high school, institutionalized, or incarcerated had earned a high school diploma or other credential, continuing a rising trend that began in 1980 (Laird et 

al. 2008).21

Graduation Rate Standards

NCLB requires states to set both standards for graduation 

rates and annual improvement targets for schools or groups 

not meeting the standard, but the act provides no minimum 

for either measure, and states’ targets for this measure vary 

considerably. (See sidebar “Measuring High School Graduation Rates.”) Nearly half the states (23) and the District of 

Columbia set graduation rate goals for the class of 2007 at 

or below 75%, and more than half of states defined their improvement targets as “any progress,” or even none, as long 

as their rates did not decline. Thirty-six states had annual 

improvement targets of 0.1% or less in 2008, or less than 

one additional graduate per year for an average-sized high 

school (Alliance for Excellent Education 2008).

Since 2002, states have reported graduation rates disaggregated by racial/ethnic group, family income, disability 

status, and English-language proficiency. Until 2008, however, the determination of whether schools and districts have 

made adequate yearly progress under NCLB rested only on 

overall graduation rates. Regulations issued in 2008 require 

that, beginning in 2011−12, schools and districts must meet 

graduation rate goals for all subgroups to achieve adequate 

yearly progress.22

Graduation Rates in the United States and in 

Other OECD Nations

Difficulties in establishing precise U.S. graduation rates 

notwithstanding, broad comparison can be made of the United States and other OECD member countries. Among the 23 

OECD countries for which graduation data were available, 

the United States ranked 17th in secondary school graduation rates in 2006 (OECD 2008) (figure 1-14).

Participation and Performance in the 

Advanced Placement Program

A relatively small but increasing number of secondary 

students take AP courses, which are designed to be equivalent to some college courses. Students who complete an AP 

course may take the test offered in that subject, and those 

who earn a passing score can earn college credits. Growth 

Measuring High School 

Graduation Rates

Historically, state education agencies have used 

different methods for estimating graduation rates, 

rendering state-by-state comparisons problematic. Experts disagree on the best method to calculate these 

rates, but there is wide consensus that the current calculations are badly flawed (Greene 2002; Swanson 

and Chapman 2003; NGA 2005).

To facilitate comparability, the National Governors Association (NGA) endorsed an adjusted cohort 

method in 2005, and all 50 governors agreed to work 

toward implementing that method (NGA 2005). Using 

this method, the high school graduation rate is calculated 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 

migration into and out of the state over those 4 years.

States require substantial time and funding to develop data collection, storage, and analysis procedures 

before they can use this method. In 2008, 16 states 

were calculating graduation rates using the cohort 

method (NGA 2008). Another 29 states planned to implement data procedures to enable such reporting by 

2012. The remaining 5 states either lacked necessary 

data capacity or had no plans in mid-2008 to calculate 

rates according to this formula.

In 2008, the U.S. Department of Education directed states to use a cohort method that tracks individual 

students, beginning with reports for academic year 

2010−11.*

 The following year, states must include this 

graduation rate as one of the measures used to document adequate yearly progress for schools that include 

12th grade.

*Title I—Improving the Academic Achievement of the Disadvantaged, Final Rule (73 Fed. Reg. 64435 [2008]).

	

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1-36 Chapter 1. Elementary and Secondary Mathematics and Science Education

in the number of students taking AP tests was faster than 

growth in the number of 11th and 12th grade students: 15% 

of the class of 2008 earned a score of 3 or higher on at least 

one AP test during high school, up from 12% in the class of 

2003 (College Board 2009).

The number of students taking AP tests in mathematics 

and science subjects has increased steadily (table 1-14; appendix table 1-19). In most subjects, this increase has been 

substantial, rising by at least fivefold in many subjects since 

1990. The AP statistics test stands out as experiencing especially rapid growth: in 1997 slightly fewer than 7,600 students took the test, rising to more than 106,000 students in 

2008. In sum, proportionately more students are taking tests, 

but the AP program continues to involve a relatively small 

proportion of high school students.

As the number of students taking AP tests has increased, 

so has the number passing each exam (i.e., receiving a score 

of 3, 4, or 5 on a scale of 1–5). Almost 250,000 students 

passed a mathematics AP exam in 2008, compared with a 

little more than 50,000 in 1990. More than 200,000 passed a 

science AP exam in 2008, compared with about 100,000 in 

1997 and fewer than 50,000 in 1990.

While increasing numbers of students are taking and 

passing AP exams, passing rates have declined or remained 

steady in most subjects. The percentage of students passing 

the calculus AB, biology, and chemistry tests dropped by 

at least 9 points between 1990 and 2008, and in only one 

subject, computer science A, did the passing rate increase by 

more than 2 percentage points.

Generally, more students of both sexes and all racial/ethnic groups took AP tests in these subjects in 2008 than in 

1997 (appendix table 1-19). Passing rates did not change by 

more than about 3 percentage points for most of these groups 

in most subjects.

Relationship of High School Courses Taken 

to Postsecondary Success

The rigor of states’ academic standards and graduation 

requirements and student enrollment in advanced mathematics and science courses other than AP courses continue to 

increase.23 The number of students taking advanced math 

and science courses increased on average between 1990 

and 2005, although most of the gains in science leveled off 

after 2000 (NSB 2008). At 29%, precalculus/analysis had 

the highest completion rate among advanced mathematics courses; chemistry was the most commonly completed 

science course at 54%. Overall, state policies have shifted 

to increase the rigor of high school standards and improve 

preparation for college. Twenty states have published definitions of college readiness, and 11 more are working on 

such definitions (Editorial Projects in Education Research 

Center 2009b). In 2009, 23 states had aligned K–12 standards with college and employer expectations, up from only 

four in 2006, according to benchmarks established by the 

American Diploma Project, an initiative that promotes high 

expectations for high school graduates to prepare them for 

college (Achieve, Inc. 2009). Twenty states and the District 

	

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Science and Engineering Indicators 2010 1-37

of Columbia have raised course-taking requirements to meet 

standards consistent with that initiative. Nearly half of states 

required the class of 2008 to pass exit exams, 23 of which 

included math and 12 of which included science, to earn a 

diploma (Editorial Projects in Education Research Center 

2009b). The most recent available data on courses required 

for high school graduation indicate that the majority of states 

require 3–4 math courses (36 states) and 3–4 science courses 

(30 states) to graduate. In addition, 26 states require specific 

math courses and 21 states require specific science courses. 

The most commonly required courses are algebra and biology. However, only a few states require advanced courses, 

for example, nine require algebra II (CCSSO 2009).

Taking certain high school courses, particularly advanced 

mathematics, is linked to postsecondary enrollment and outcomes, as many studies have shown (Adelman, Daniel, and 

Berkovits 2003; Horn 1997; Horn and Kojaku 2001; Horn 

and Nuñez 2000; Laird, Chen, and Levesque 2006; Sadler 

and Tai 2007). Although they do not imply causality, data 

from the high school class of 2004 show that the highest 

course students completed in mathematics and science and 

whether they earned advanced credits in these subjects were 

closely related to whether students had enrolled in a postsecondary institution by 2006 (appendix table 1-20). These 

indicators of better preparation in high school were also associated with a greater likelihood of enrolling in a 4-year 

program. For example, among students whose highest 

mathematics course was below algebra II (the lowest level, 

which also includes students with no high school mathematics course), 9% had enrolled in a 4-year program by 2006, 

compared with 83% of those who studied calculus. Among 

students with more than two advanced mathematics or science credits, 88% and 90%, respectively, had enrolled in a 

4-year college (figure 1-15). High mathematics achievement 

Table 1-14

Students who took Advanced Placement tests in mathematics and science and number and percentage with 

passing scores, by subject: 1990, 1997, and 2008

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Subject 1990 1997 2008 1990 1997 2008 1990 1997 2008

Mathematics

Calculus AB ........................................... ÈÓ]ÈÇÈ £än]{ÎÇ Ó£x]änÈ {{]än È{]ÎÓÓ £Îä]{£Î 71.7 59.3 60.6

Calculus BC ........................................... £Î]äÈ ÓÓ]Î{ ÈÈ]Çnx £ä]ÇÓn £Ç]ÈÎä xÎ]ÎÇä 81.9 78.9 79.9

Statistics ................................................ NA Ç]xx£ £äÈ]xÎ{ NA {]ÈnÈ ÈÓ]Ón NA 62.1 59.1

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Computer science A .............................. NA È]Ó £x]ä£{ NA Î]Ónn n]xÎÇ NA 47.0 56.9

Computer science AB ............................ NA {]ÎÈÇ {]n£x NA Î]£Ó Î]xÓÈ NA 71.7 73.2

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1-38 Chapter 1. Elementary and Secondary Mathematics and Science Education

was also associated with enrollment in a 4-year institution: 

76% of those with scores in the highest mathematics quartile 

enrolled in a 4-year college, compared with 13% of students 

who scored in the lowest quartile.

Conversely, taking lower-level mathematics courses was 

associated with enrolling in a 2-year college. Among students with no advanced mathematics credits, 33% had enrolled in a 2-year college by 2006, compared with 6% of 

students with two or more advanced mathematics credits.

Among 2004 high school graduates who had enrolled in 

postsecondary education by 2006, 30% reported that they had 

taken a remedial course in mathematics at the postsecondary 

level. Students who completed advanced mathematics and 

science courses were less likely to undertake postsecondary 

remediation in mathematics.24 More than 40% of students 

whose highest high school mathematics course was less advanced than algebra II reported taking remedial mathematics 

at the postsecondary level, compared with 17% of students 

who took calculus in high school. Achievement on mathematics assessments was also related to postsecondary remediation rates: 45% of those who scored in the bottom quartile 

of the twelfth grade mathematics test took a remedial mathematics course in college, compared with 18% of those who 

scored in the top quartile.25

Immediate Enrollment in Postsecondary 

Education

Most secondary students expect to attain a postsecondary 

degree. In 2007, 95% of eighth graders expected to attain 

a postsecondary education, and 70% planned to complete 

at least a bachelor’s degree (Walston and Rathburn 2008). 

Not all meet these expectations, however: in 2008, 69% of 

students who completed high school (already a subset of all 

high school students) had enrolled in a postsecondary institution by the October following high school completion (appendix table 1-21). Wide differences in enrollment rates by 

family income, race/ethnicity, and parents’ education persisted. In 2008, females outpaced men in immediate college 

enrollment 72% to 66%; most of this difference in immediate enrollment rates was accounted for by enrollment in 

2-year colleges, where 31% of recently-graduated women 

and 25% of their male counterparts were enrolled in 2008 

(figure 1-16).

Postsecondary Enrollment in an International 

Context

Only broad comparisons of postsecondary enrollment 

rates in the United States and other OECD countries are possible. By one measure, immediate entry rates, U.S. students 

ranked ninth, above the OECD average (table 1-15) (OECD 

2009). In most OECD countries, including the United States, 

female enrollment rates were higher than those of males. 

These comparisons are complicated by differences among 

education systems, types of degrees awarded, and methodological issues with the measure itself.26

Conclusion

Longitudinal data from ECLS-K illustrate gaps among 

groups within one cohort of elementary students: students 

of different racial/ethnic and socioeconomic backgrounds 

began kindergarten with varying levels of achievement, and 

these gaps increased through grade 3 or 5, then stabilized 

through grade 8. The 2007 NAEP mathematics assessments 

indicate improvement in fourth and eighth graders’ mathematics achievement, continuing a trend observed since 

1990. Although some achievement gaps between students 

of different racial/ethnic backgrounds or family income decreased with this assessment, significant gaps remained.

Results of international assessments of student achievement 

in math and science were more mixed. U.S. fourth and eighth 

grade students improved in mathematics on TIMSS: 2007, 

both in their average score and in their standing relative to other nations’ students, although their relative standing rose only 

slightly. In science, U.S. students’ performance on TIMSS did 

not change, but their standing relative to their counterparts in 

other nations did: U.S. fourth graders lost ground to other nations’ fourth graders, while U.S. eighth graders gained slightly. 

Among 15-year-olds, U.S. students’ standing declined rela-

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Science and Engineering Indicators 2010 1-39

tive to that of other nations’ students on the most recent PISA 

assessments in both mathematics and science.

Efforts to improve student achievement include ensuring 

that all students have access to highly qualified teachers, although consensus definitions of “highly qualified” in individual subjects at various grade levels have yet to emerge. From 

the student perspective, while 40% of fifth grade students 

in 2004 were taught mathematics and science by teachers 

with either a degree or certificate in their teaching field (i.e., 

in-field teachers), most students at this level (about 54%) had 

teachers with general education preparation. When these students reached eighth grade in 2007, more than 80% of them 

were taught mathematics and science by in-field teachers, 

and the percentage taught by mathematics and science teachers with general education preparation fell to 9%–10%. Gaps 

in access to teachers with these qualifications were observed, 

however: black and Hispanic students, students from lowincome families, and students whose prior achievement was 

low were less likely than their counterparts to have teachers 

with the highest qualifications or greatest experience.

Teachers’ professional development activities can 

strengthen subject matter knowledge and teaching abilities 

acquired through their formal education. Many fifth graders’ teachers of mathematics and science, however, had not 

participated in professional development in mathematics or 

science. Those who had participated reported that their activities were of relatively short duration, and less than half of 

participants reported their activities as very useful.

Although teachers’ salaries have not kept pace with 

those of employees in occupations with similar training requirements and responsibilities, most teachers of fifth grade 

students had favorable perceptions of their working conditions. Again, however, teachers of some groups of students 

were less likely than others to have such positive perceptions, and the differences fell along student racial/ethnic 

and socioeconomic lines.

By 2005, access to computers and the Internet was virtually universal and did not vary with such student characteristics. Most states include technology in their curriculum 

standards. In addition, increasing numbers of states are providing virtual schools and other opportunities for distance 

education via technology, largely at the secondary level.

In 2006, the on-time (4-year) high school graduation rate 

was 73%, with significant gaps observed by race/ethnicity. 

Looking at secondary school completion in international 

terms, the United States ranked 17th among 23 OECD countries with available data. New federal regulations, effective 

in 2011, will improve accuracy and comparability of data 

concerning on-time graduation among the 50 states.

Rates of immediate entry into postsecondary education 

after high school completion have increased substantially 

since 1975. Longitudinal data indicate that students who 

took more advanced mathematics and science courses in high 

school were more likely than others to undertake postsecondary education within 2 years of completing high school. 

Once enrolled, such students were also less likely to have 

reported taking remedial courses at the postsecondary level.

Notes

1. 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 

Table 1-15

First-time entry rates into postsecondary 

education in OECD countries, by sex: 2007

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Australia .................................. 86 75 96

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Iceland .................................... 73 55 92

-Üi`i .................................. 73 62 85

>` .................................... 71 62 80

 ÀÜ>Þ ................................... 66 52 81

United States .......................... 65 57 72

*ÀÌÕ}> .................................. 64 57 72

Õ}>ÀÞ .................................. 63 55 71

Korea ...................................... 61 63 59

Netherlands ............................ 60 56 65

i>À ................................. 57 45 71

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Ê>ÛiÀ>}i ........................ 56 50 63

United Kingdom ..................... 55 48 63


âiVÊ,i«ÕLV ...................... 54 47 60

Italy ......................................... 53 45 61

Japan ...................................... 46 52 40

Ireland ..................................... 44 41 48

Greece .................................... 43 33 55

Austria .................................... 42 38 45

Spain ...................................... 41 35 48

-ÜÌâiÀ>` ............................. 39 38 40

Germany ................................. 34 34 35

iÝV .................................... 32 32 32

Belgium .................................. 30 29 31

/ÕÀiÞ ..................................... 29 32 26

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Science and Engineering Indicators 2010
1-40 Chapter 1. Elementary and Secondary Mathematics and Science Education

by chance no more than once in 20 samples when there was 

no actual difference between the population means.

2. About 89% of the students were in eighth grade in 

2006–07 because some were held back one or more grades 

and a very small number advanced an extra grade. The cohort members are referred to as eighth graders in 2007, and 

in earlier years by that year’s modal grade, for ease of reading. In subsequent sections of this chapter, students called 

eighth graders (and information about their teachers) are 

restricted to those actually in eighth grade during that year.

3. These recent trends are based on data from the national 

NAEP program. The current national mathematics assessment for grades 4 and 8 was first administered in 1990 and 

was given again in 1992, 1996, 2000, 2003, 2005, and 2007. 

The assessment using the current grade 12 mathematics 

framework has been administered only once, in 2005. Recent trend analyses for grade 12 mathematics are therefore 

not available. The current national science assessment was 

first administered in 1996 and again in 2000, 2005, and the 

spring of 2009. Results from the 2009 administration will be 

available in early 2010.

4. Although the skills and knowledge tested remained 

similar throughout the LTT testing program, some changes 

were made in 2004, including replacing some test questions 

that used out-of-date contexts, adopting some new testing 

procedures, and accommodating some students’ disabilities 

or limited English skills.

5. Trend changes for the United States’ standing are 

shown only in two figures, 1-7 and 1-8. Text and appendix tables show data only for the most recent year for the 

TIMSS and PISA assessments. Thus, fewer nations in each 

category may appear in figures 1-7 and 1-8 compared with 

the relevant tables. In addition, text tables for these international tests show only nations in the selected comparison 

group and, for TIMSS, only those that met all of the testing body’s standards. The corresponding appendix tables 

include all nations in the report and therefore have more nations in each category.

6. See endnote 5.

7. Teaching certification is generally awarded by state 

education agencies to teachers who have completed specific 

requirements. These requirements vary across states but typically include completing a bachelor’s degree, completing a 

period of practice teaching, and passing some type of formal 

tests. States also issue other types of certification besides 

regular or standard certification. For example, probationary 

certification is generally awarded to those who have completed all the requirements except for a probationary teaching period. Provisional or temporary certification is awarded 

to those who still have requirements to meet. Emergency 

certification is issued to those with insufficient teacher preparation who must complete a regular certification program to 

continue teaching (Henke et al. 1997).

8. Specifically, NCLB defines a highly qualified elementary or secondary school teacher as someone who 

holds a bachelor’s degree and full state-approved teaching 

certificate or license (excluding emergency, temporary, and 

provisional certificates) and who demonstrates subject-matter competency in each academic subject taught by having 

an undergraduate or graduate major or its equivalent in the 

subject; passing a test on the subject; holding a full teaching certificate in the subject; or meeting some other state-approved criteria. NCLB requires that newly hired elementary 

school teachers pass tests in subject-matter knowledge and 

teaching skills in mathematics, reading, writing, and other 

areas of the basic elementary school curriculum. Newly 

hired middle and high school teachers must either pass a 

rigorous state test in each academic subject they teach or 

have the equivalent of an undergraduate or graduate major 

or teaching certification in their fields.

9. In previous editions, data from the National Center for 

Education Statistics’ Schools and Staffing Survey (SASS) 

have been used to highlight various aspects of teachers and 

teaching. However, the 2007−08 SASS data were not available for analyses at the time this chapter was prepared.

10. Typically, fifth grade teachers teach not only mathematics and science but also language arts, social studies, and 

other academic subjects and therefore cannot strictly be considered mathematics or science teachers. To refer to teachers who taught mathematics and science more accurately, 

the text uses such phrases as “teachers of mathematics and 

science,” “teachers who taught mathematics and science,” 

or “students who were taught mathematics and science by 

teachers” interchangeably.

11. Alternatives to traditional teacher education have 

increased in number and scope. Among various alternative 

programs, Teach for America (TFA) is the most prominent 

one (Decker, Mayer, and Glazerman 2004). TFA is designed 

to recruit top graduates from some of the most competitive colleges to teach in the most challenging K−12 schools 

throughout the nation. Although TFA has been successful 

in attracting college graduates (e.g., from 2000 to 2003, 

the number of TFA applicants grew almost fourfold, from 

about 4,000 to 16,000) (Decker, Mayer, and Glazerman 

2004), studies that have addressed the effectiveness of TFA 

teachers yielded mixed results from no to significant effects 

(Decker, Mayer, and Glazerman 2004; Kane, Rockoff, and 

Staiger 2006; Xu, Hannaway, and Taylor 2007).

12. SASS teachers responded to a much longer list of 

possible fields for their degree major and certification than 

ECLS teachers, allowing more refined categorization of the 

variable. For more information about the definition of the 

subject area preparation variable in SASS, see the sidebar 

“In-Field and Out-of-Field Teaching” in the 2008 edition of 

Science and Engineering Indicators (NSB 2008).

13. SASS collects data on teachers, whereas ECLS collects data on students, including data about those students’ 

teachers. This difference may contribute to the different 

in-field teaching estimates in the two surveys. Another difference is that SASS includes all middle school teachers, 

whereas ECLS data refer only to those who teach eighth 

grade students.
Science and Engineering Indicators 2010 1-41

14. The Federation collects teacher salary data from each 

state’s department of education.

15. Because teachers’ annual work schedules are different 

from those of other professions, these researchers compared 

wages earned for a week of work, rather than for the entire 

year. Critics of this method (e.g., Podgursky and Tongrut 

2006) argue that the use of weekly wages to compare teachers with other workers may bias teacher earnings downward, 

in that teachers report a weekly wage that may be an annual salary divided over a full year rather than the partial 

year they actually work. To address this concern, Allegretto, 

Corcoran, and Mishel (2008) used several alternative methods to compare the salaries of teachers and other workers in 

their 2008 study and concluded that this bias is small.

16. As part of the National Compensation Survey, the Bureau of Labor Statistics collects specific occupational skill 

information and rates each occupation on the level of skills 

required across 10 different dimensions (e.g., knowledge, 

complexity). Allegretto, Corcoran, and Mishel (2008) used 

the 6 most common occupations among the 16 identified as 

having skill ratings comparable to those of teachers to form 

the group of comparable occupations in their 2008 study. 

These 6 occupations are accountants, reporters, registered 

nurses, computer programmers, members of the clergy, and 

personnel officers, accounting for 83% of the employment 

in 16 occupations with skill ratings comparable to teaching.

17. That is, 86% of fifth grade students had teachers of 

mathematics and science reporting this condition.

18. “Classrooms” includes computer and other labs, library/media centers, regular classrooms, and any other 

rooms used for instructional purposes.

19. These numbers are counts of enrollments in each 

course: if students enroll in more than one course, they are 

counted more than once.

20. There are several other widely accepted estimates 

of on-time graduation rates. Using the Cumulative Promotion Index, the annual Diploma’s Count report reported a 

nationwide rate of 69.2% (Editorial Projects in Education 

Research Center 2009b).

21. This measure of high school completion has its critics, 

however. Some believe that those who earn General Educational Development credentials should not be included with 

high school graduates in measures of graduation rates and 

also object to the base for the calculation. Because the base 

typically excludes incarcerated or institutionalized dropouts, 

critics believe this measure overestimates the high school 

completion rate for young people overall (Heckman and LaFontaine 2008; Pinkus 2006; Greene 2002).

22. Cited in Title I—Improving the Academic Achievement of the Disadvantaged, Final Rule (73 Fed. Reg. 64435 

[2008]).

23. Advanced courses referenced in this section are defined as courses that not all students complete and that are 

not, as a rule, required for graduation. They include trigonometry or algebra III, precalculus or analysis, statistics or 

probability, any calculus, AP/IB calculus, advanced biology, 

chemistry, physics, environmental science, engineering, and 

engineering or science technologies.

24. Postsecondary transcript data are not yet available, 

and so information about taking remedial courses comes 

from student reports, which may be less accurate.

25. While rates of remediation are lower for students who 

achieve on tests and take courses at the highest levels, almost one in five of those students still reports taking remedial math. In part, this may be due to a lack of alignment in 

academic expectations between secondary and postsecondary institutions. The Education Commission of the States 

(ECS) tracks states’ efforts to resolve these discrepancies by 

means of P-16 or P-20 councils, which involve stakeholders 

from early childhood education through college or graduate 

school and work to align expectations and provide seamless 

transitions between levels of education (ECS 2008). As of 

May 2008, 38 states had established a P-16 or P-20 council, 

and 5 more had consolidated agencies or boards that perform essentially the same function. The scope and mission 

of these councils vary widely, however, and there is no nationwide indicator of their accomplishments.

26. OECD measures of enrollment rates shown in table 

1-15 are imperfect and have been criticized by some researchers in the United States and Europe (Wellman 2007; 

Adelman 2008; Kaiser and O’Heron 2005), who argue that 

the OECD methodology—dividing total tertiary enrollment 

by the population at the most common age of entry—penalizes countries, such as the United States, whose population 

is growing. They also contend that the differences between 

education systems and types of degrees awarded across 

countries make international comparisons problematic.

Glossary

Student Learning in Mathematics 

and Science

Eligibility for National School Lunch Program: Student eligibility for this program, which provides free or 

reduced-price lunches, is a commonly used indicator for 

family poverty. Eligibility information is part of the administrative data kept by schools and is based on parentreported family income and family size.

Longitudinal studies: Researchers follow the same group of 

students over a period of years, such as from kindergarten 

through fifth grade. These studies can show achievement 

gains in a particular subject from grade to grade. 

Repeating cross-sectional studies: This type of research 

focuses on how a specific group of students performs in a 

particular year 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 2005.

Scale score: Scale scores place students on a continuous 

achievement scale based on their overall performance on 
1-42 Chapter 1. Elementary and Secondary Mathematics and Science Education

the assessment. Each assessment program develops its 

own scales. 

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.

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. The SASS 

surveys include five types of certification: (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.

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. 

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.

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

Undergraduate Education, Enrollment, and Degrees ...............................................................2-4

Graduate Education, Enrollment, and Degrees .........................................................................2-4

Postdoctoral Education .............................................................................................................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

Online and Distance Education ................................................................................................2-9

For-Profit Institutions .............................................................................................................2-10

Cost of Higher Education .......................................................................................................2-10

Undergraduate Education, Enrollment, and Degrees in the United States ................................2-11

Curricular Reform ...................................................................................................................2-11

Remedial Education ................................................................................................................2-11

Undergraduate Enrollment in the United States .....................................................................2-12

Undergraduate Degree Awards ...............................................................................................2-15

Graduate Education, Enrollment, and Degrees in the United States .........................................2-17

Graduate Enrollment in S&E ..................................................................................................2-17

Financial Support for S&E Graduate Education ....................................................................2-19

Interdisciplinary Education .....................................................................................................2-21

S&E Master’s Degrees ...........................................................................................................2-21

S&E Doctoral Degrees ...........................................................................................................2-24

Postdoctoral Education ..............................................................................................................2-30

International S&E Higher Education .........................................................................................2-31

Higher Education Expenditures ..............................................................................................2-31

Educational Attainment ..........................................................................................................2-33

First University Degrees in S&E Fields .................................................................................2-33

Global Comparison of S&E Doctoral Degrees .......................................................................2-35

Global Student Mobility .........................................................................................................2-36

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

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

Glossary .....................................................................................................................................2-38

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

List of Sidebars

Carnegie Classification of Academic Institutions ........................................................................2-8

Baccalaureate Origins of S&E Doctorate Recipients ..................................................................2-9

Interdisciplinary Dissertation Research .....................................................................................2-22

Professional Science Master’s Degrees .....................................................................................2-22

Doctoral Completion and Attrition ............................................................................................2-25

Globalization and Doctoral Education .......................................................................................2-32

Chapter 2

Higher Education in 

Science and Engineering

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

International Changes in the Ratio of Natural Science and Engineering Degrees to the 

College-Age Population ........................................................................................................2-34

Changes in European Higher Education Since the Bologna Process .......................................2-35

List of Tables

Table 2-1. Postsecondary 2- and 4-year Title IV degree-granting institutions offering 

distance education courses and enrollments in college-level credit-granting distance 

education courses, by level of education and institution type: 2006–07...............................2-10

Table 2-2. Top 10 colleges and universities awarding degrees in computer sciences and 

psychology, by level of degree: 2007 ...................................................................................2-11

Table 2-3. Primary support mechanisms for S&E doctorate recipients, by Carnegie 

classification of doctorate-granting institution: 2007 ...........................................................2-21

Table 2-4. Median number of years from S&E doctorate recipients’ entry to graduate 

school to receipt of doctorate, by Carnegie classification of doctorate-granting 

institution: 1993–2007 ..........................................................................................................2-26

Table 2-5. Foreign recipients of U.S. S&E doctorates, by country/economy of origin: 

1987–2007 .............................................................................................................................2-27

Table 2-6. Asian recipients of U.S. S&E doctorates, by field and country/economy of 

origin: 1987–2007 .................................................................................................................2-27

Table 2-7. European and North American recipients of U.S. S&E doctorates, by field 

and region/country of origin: 1987–2007 .............................................................................2-28

Table 2-8. Source of funding of S&E postdoctoral students: 1993–2006 .................................2-32

List of Figures

Figure 2-1. Enrollment and degrees per thousand population and per billion dollars 

GDP: 1970–2006 ...................................................................................................................2-12

Figure 2-2. Freshmen intending S&E major, by race/ethnicity: 1993–2008 .............................2-14

Figure 2-3. Foreign undergraduate student enrollment in U.S. universities, by top 10 

places of origin and field: April 2009 ...................................................................................2-14

Figure 2-4. Engineering enrollment, by level: 1979–2007 ........................................................2-15

Figure 2-5. S&E bachelor’s degrees, by field: 1993–2007 ........................................................2-16

Figure 2-6. Female share of S&E bachelor’s degrees, by field: 1993–2007 .............................2-16

Figure 2-7. Minority share of S&E bachelor’s degrees, by race/ethnicity: 1995–2007 ............2-17

Figure 2-8. S&E graduate enrollment, by citizenship and race/ethnicity: 1993–2006 ..............2-18

Figure 2-9. Full-time S&E graduate students, by field and mechanism of primary 

support: 2006 .........................................................................................................................2-19

Figure 2-10. Full-time S&E graduate students with primary support from federal 

government, by field: 1996 and 2006 ...................................................................................2-20

Figure 2-11. S&E master’s degrees, by field: 1993–2007 .........................................................2-23

Figure 2-12. S&E master’s degrees, by sex: 1993–2007 ...........................................................2-23

Figure 2-13. S&E master’s degrees, by race/ethnicity and citizenship: 1995–2007 .................2-23

Figure 2-14. S&E doctoral degrees earned in U.S. universities, by field: 1993–2007 ..............2-24

Figure 2-15. S&E doctoral degrees earned by U.S. citizen and permanent resident 

underrepresented minorities, by race/ethnicity: 1995–2007 .................................................2-26

Figure 2-16. S&E doctoral degrees, by sex, race/ethnicity, and citizenship: 1995–2007 .........2-26

Figure 2-17. U.S. S&E doctoral degree recipients, by selected Asian country/economy 

of origin: 1987–2007 .............................................................................................................2-28

Figure 2-18. U.S. S&E doctoral degree recipients, by selected Western European 

country: 1987–2007 ..............................................................................................................2-29

Figure 2-19. U.S. S&E doctoral degree recipients from Europe, by region: 1987–2007 ..........2-29

Figure 2-20. U.S. S&E doctoral degree recipients from Canada and Mexico: 1987–2007 .......2-29

Figure 2-21. Plans of foreign recipients of U.S. S&E doctorates to stay in United 

States: 1987–2007 .................................................................................................................2-30

Figure 2-22. Short-term stay rates of foreign recipients of U.S. S&E doctorates, 

by place of origin: 1996–99 and 2004–07.............................................................................2-30
Science and Engineering Indicators 2010 2-3

Figure 2-23. Postdoctoral students at U.S. universities, by field: 1993–2006 ...........................2-31

Figure 2-24. Postdoctoral students at U.S. universities, by field and citizenship status: 

1993–2006 .............................................................................................................................2-31

Figure 2-25. Attainment of tertiary-type A and advanced research programs, by 

country and age group: 2006 .................................................................................................2-33

Figure 2-26. First university natural sciences and engineering degrees, by selected 

countries: 1998–2006 ............................................................................................................2-34

Figure 2-27. Natural sciences and engineering doctoral degrees, by selected country: 

1993–2006 .............................................................................................................................2-35

Figure 2-28. Foreign students enrolled in tertiary education, by country: 2006 ........................2-36
2-4 Chapter 2. Higher Education in Science and Engineering

Undergraduate Education, Enrollment, and 

Degrees

Enrollment in U.S. higher education is projected to continue rising because of increases in the U.S. college-age 

population.

Reflecting changes in the population of 18-year-olds, 

the number of high school graduates is expected to increase 6% between 2004–05 and 2017–18, a slower rate 

of growth than between 1992–93 and 2004–05 (25%).

Postsecondary enrollment rose from 14.5 million in fall 

1993 to 18.7 million in fall 2006, and is projected to increase to 20.1 million students in 2017.

Postsecondary enrollment of all racial/ethnic groups is 

projected to increase, but the percentage that is white is 

projected to decrease to 61% in 2017, whereas the percentages that are black and Hispanic are projected to increase.

The number of S&E bachelor’s degrees has risen steadily over the past 15 years.

The number of S&E bachelor’s degrees awarded reached 

a new peak of 485,800 in 2007.

Most S&E fields (except computer sciences) experienced 

increases in the number of degrees awarded in 2007. In 

computer sciences, the number of bachelor’s and master’s 

degrees increased sharply from 1998 to 2004 but has decreased since then.

S&E bachelor’s degrees have consistently accounted for 

roughly one-third of all bachelor’s degrees for the past 15 

years.

The share of bachelor’s degrees awarded to women increased in many major S&E fields from 1993 to 2007.

Women have earned 58% of all bachelor’s degrees since 

2002; they have earned about half of all S&E bachelor’s 

degrees since 2000, but major variations persist among 

fields.

In 2007, men earned a majority of bachelor’s degrees 

awarded in engineering, computer sciences, and physics (81%, 81%, and 79%, respectively). Women earned 

half or more of bachelor’s degrees in psychology (77%), 

biological sciences (60%), social sciences (54%), agricultural sciences (50%), and chemistry (50%).

Among fields with notable increases in the proportion of 

bachelor’s degrees awarded to women are earth, atmospheric, and ocean sciences (from 30% to 41%); agricultural sciences (from 37% to 50%); and chemistry (from 

41% to 50%).

Women’s share of bachelor’s degrees in computer sciences, mathematics, and engineering has declined in recent 

years.

The racial/ethnic composition of those earning S&E 

bachelor’s degrees is changing, reflecting both population change and increasing college attendance by members of minority groups.

For all racial/ethnic groups except white, 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 has generally increased since 1995.

Between 1995 and 2007, the proportion of S&E bachelor’s degrees awarded to Asians/Pacific Islanders increased from 8% to 9%; to black students, from 7% to 

8%; to Hispanic students, from 6% to 8%; and to American Indian/Alaska Native students, from 0.5% to 0.7%, 

although the shares to black and American Indian/Alaska 

Native students have remained fairly flat since 2000. The 

proportion of S&E degrees awarded to white students declined from 73% to 64%.

For white students, 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 remained fairly flat 

from 1995 through 2001 as their numbers in the collegeage population dropped but rose again through 2007.

Students in the United States on temporary visas earned 

only a small share (4%) of S&E bachelor’s degrees in 

2007.

The number of S&E bachelor’s degrees awarded to students on temporary visas increased from about 14,700 

in 1995 to 18,800 in 2004 before declining to 17,400 in 

2007.

Students on temporary visas earned larger shares of 

bachelor’s degrees in certain fields in 2007: 9% of those 

awarded in economics and about 10% of those awarded in 

electrical and industrial engineering.

Graduate Education, Enrollment, and Degrees

S&E graduate enrollment in the United States continued to rise, reaching a new peak of almost 600,000 in fall 

2006.

Following a long period of growth, graduate enrollment 

in S&E declined in the latter half of the 1990s but has 

increased steadily since 1999. First-time full-time enrollment, an indicator of future trends in enrollment, has also 

increased since the late 1990s.

Graduate enrollment in computer sciences and engineering has decreased in recent years, although first-time 

full-time enrollment in these fields increased in 2005 and 

2006.

Highlights
Foreign S&E graduate students in U.S. institutions increased in fall 2006 after 2 years of decline.

S&E graduate students on temporary visas increased 

from 22% to 25% of all S&E graduate students from 

1993 to 2006.

The number of first-time full-time S&E graduate students 

with temporary visas increased in fall 2005 and fall 2006 

after declining 18% from 2001 to 2004. The increases 

(and previous declines) were mainly in computer sciences 

and engineering.

Master’s degrees awarded in S&E fields increased from 

86,400 in 1993 to 121,000 in 2006 but declined in 2007.

Increases occurred in most major science fields, although 

the number of master’s degrees awarded in engineering 

and computer sciences has dropped since 2004.

The number and percentage of master’s degrees awarded 

to women in most major S&E fields have increased since 

1993.

The number of S&E master’s degrees awarded increased 

for all racial/ethnic groups from 1995 to 2007, and the 

percentage awarded to Asians/Pacific Islanders, blacks, 

Hispanics, and American Indians/Alaska Natives increased during that time period.

The number of S&E doctorates awarded by U.S. academic institutions reached a new peak of almost 41,000 

in 2007.

After rising from the mid-1980s through 1998, the number of S&E doctorates declined through 2002 but has 

increased in recent years. The largest increases were in 

engineering and biological/agricultural and medical/other 

life sciences.

The recent growth through 2007 occurred among both 

U.S. citizens/permanent residents and temporary residents.

Foreign students make up a much higher proportion 

of S&E master’s and doctoral degree recipients than of 

bachelor’s degree recipients.

Foreign students received 24% of S&E master’s degrees, 

33% of S&E doctoral degrees, and 4% of S&E bachelor’s 

degrees in 2007.

The number of S&E master’s degrees earned by temporary residents rose from 1995 to 2004 and then dropped 

through 2007.

The number of S&E doctorates earned by temporary residents rose to a new peak of 13,700 in 2007.

Most foreign recipients of U.S. S&E doctorates plan to 

stay in the United States after graduation.

Among 2004–07 graduates, more than three-quarters of 

foreign S&E doctorate recipients with known plans reported they planned to stay in the United States and about 

half had accepted firm offers of employment.

More than 90% of 2004–07 U.S. S&E doctorate recipients from China and 89% of those from India reported 

plans to stay in the United States, and 59% and 62%, respectively, reported accepting firm offers of employment 

or postdoctoral research in the United States.

Between 2000–03 and 2004–07, the percentage reporting definite plans to stay in the United States decreased 

among U.S. S&E doctorate recipients from all of the top 

five countries/economies of origin (China, India, South 

Korea, Taiwan, and Canada). However, for all but Taiwan, 

increases in the number of doctorate recipients more than 

offset declines in the percentage staying.

Postdoctoral Education

The number of doctorate recipients with S&E postdoctoral appointments at U.S. universities increased to almost 50,000 in fall 2006.

More than two-thirds of academic postdoctoral appointments were in biological and medical/other life sciences.

Temporary visa holders accounted for 57% of S&E postdocs in 2006. They accounted for much of the increase in 

the number of S&E postdocs, especially in biological and 

medical sciences.

The number of U.S. citizen and permanent resident S&E 

postdocs at these institutions increased more modestly, 

from approximately 16,700 in 1993 to 21,100 in 2006.

An increasing share of academic S&E postdocs are funded through federal research grants. In fall 2006, 56% of 

S&E postdocs at U.S. universities were funded through 

this mechanism, up from 52% in 1993. Federal fellowships and traineeships funded a declining share of S&E 

postdocs.

International S&E Higher Education

Students in China earned about 21%, those in the 

European Union earned about 19%, and those in the 

United States earned about 11% of the more than 4 

million first university degrees awarded in S&E in 2006.

The number of S&E first university degrees awarded in 

China, Poland, and Taiwan more than doubled between 

1998 and 2006, and those in the United States and many 

other countries generally increased. Those awarded in Japan decreased in recent years.

In China, the number of first university degrees awarded 

in natural sciences and engineering has risen particularly sharply since 2002. In comparison, those awarded in 

Germany, Japan, United Kingdom, and the United States 

have remained relatively flat.

Science and Engineering Indicators 2010 2-5
In the United States, S&E degrees are about one-third of 

bachelor’s degrees and have been for a long time. More 

than half of first degrees were awarded in S&E fields in 

Japan (63%), China (53%), and Singapore (51%).

In the United States, about 5% of all bachelor’s degrees 

are in engineering. In Asia about 20% are in engineering, 

and in China about one-third are in engineering (although 

the percentage has declined in recent years).

In 2006, the United States awarded the largest number of 

S&E doctoral degrees of any individual country, followed 

by China, Russia, Germany, and the United Kingdom.

The numbers of S&E doctoral degrees awarded in China, Italy, and the United States have risen substantially 

in recent years. The numbers of S&E doctoral degrees in 

India, Japan, South Korea, and many European countries 

have risen more modestly.

Women earned 40% of S&E doctoral degrees awarded in 

the United States in 2006, about the same as the percentages earned by women in Australia, Canada, the European Union, and Mexico. The percentage of S&E doctoral 

degrees earned by women ranged from less than 20% in 

some countries to 50% or more in others.

International migration of students and highly skilled 

workers expanded over the past two decades, and countries are increasingly competing for foreign students. In 

particular, migration of students occurred from developing countries to the more developed countries and from 

Europe and Asia to the United States.

Some countries expanded recruitment of foreign students as their own populations of college-age students 

decreased, both to attract highly skilled workers and increase revenue for colleges and universities.

The United States remains the destination of the largest number of foreign students worldwide (undergraduate and graduate), although its share of foreign students 

worldwide decreased from 25% in 2000 to 20% in 2006.

In addition to the United States, other countries that are 

among the top destinations for foreign students include 

the United Kingdom (11%), Germany (9%), and France 

(8%).

2-6 Chapter 2. Higher Education in Science and Engineering
Science and Engineering Indicators 2010 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 S&E education, the research capability necessary for innovation. This chapter focuses on the 

development of human capital by higher education; chapter 

5 focuses on S&E research.

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. After declining in 

the 1990s, the U.S. college-age population is currently increasing and is projected to increase for the next decade. The 

composition of the college-age population 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. Increases in foreign students 

contributed to most of the growth in overall S&E graduate 

enrollment in recent years, but after 11 September 2001, the 

number of foreign students coming to the United States for 

graduate education dropped. In 2006 the number of foreign 

S&E graduate students increased (although they have not yet 

regained earlier levels). Finally, 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 expanding their own higher education systems, 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. 

This chapter does not address the issues of quality of 

higher education or demand for S&E-educated personnel. 

Although the quality of higher education and especially 

the quality of learning outcomes are important, adequate 

national quantitative measures of quality do not yet exist. 

This chapter makes no attempt to determine whether current or future trends in degrees are adequate for the expected 

short- or long-term needs of the labor market. For information on labor market conditions for recent S&E graduates, 

see chapter 3, “Labor Market Conditions for Recent S&E 

Graduates,” particularly the sidebar “Projected Growth of 

Employment in S&E Occupations.” Chapter 5, “Trends in 

Academic Employment of Doctoral Scientists and Engineers,” contains information on academic employment.

Chapter Organization

This chapter describes characteristics of the U.S. higher 

education system and trends in higher education worldwide. 

It begins with characteristics of U.S. higher educational institutions providing S&E education, followed by characteristics of undergraduate education, enrollment, and degrees; 

graduate education, enrollment, and degrees; and postdoctoral education. Trends are discussed by field and demographic group. The chapter highlights the flow of foreign 

students into the United States by country and their intentions to remain in this country. The chapter then presents 

various international higher education indicators, including comparative S&E degree production in several world 

regions and 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’s) Division of 

Science Resources Statistics and the National Center for 

Education Statistics. 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. When sample data are 

used, differences are discussed only if they are statistically 

significant at the 95% confidence level.

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 Obama called for every American to commit to at least 

1 year of postsecondary education. This section discusses 

the characteristics of U.S. higher education institutions providing S&E education as well as trends in and the characteristics of students and degree recipients.

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 

whether public or private and for-profit or nonprofit (NCES 

2008a). The number of these degree-granting institutions 

(including branch campuses) has increased from about 3,000 

in 1975 to about 4,300 in 2007, with most of the growth in 

the 1970s and 1980s, and again from 2000 to 2007. The latter growth occurred largely because of growth in the number of for-profit institutions (NCES 2009b). In 2007, U.S. 

academic institutions awarded more than 2.9 million associate’s, bachelor’s, master’s, and doctoral degrees; 23% of 

these degrees were in S&E (appendix table 2-1).
2-8 Chapter 2. Higher Education in Science and Engineering

Research institutions are the leading producers of S&E 

degrees at the bachelor’s, master’s, and doctoral levels. In 

2007, research institutions (i.e., doctorate-granting institutions with very high research activity) awarded 70% of 

S&E doctoral degrees, 40% of master’s degrees, and 36% 

of bachelor’s degrees in S&E fields. (See sidebar “Carnegie 

Classification of Academic Institutions.”) Master’s colleges 

and universities awarded another 28% of S&E bachelor’s 

degrees and 25% of S&E master’s degrees in 2007. Baccalaureate colleges were the source of relatively few S&E 

bachelor’s degrees (13%) (appendix table 2-1). (See sidebar 

“Baccalaureate-Origins of S&E Doctorate Recipients.”)

Community colleges (also known as 2-year colleges or 

associate’s 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). Thus, 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. Community colleges serve diverse groups of students and offer a more affordable means of participating in postsecondary education. 

Compared with 4-year colleges, community colleges enroll 

greater concentrations of low-income, first-generation, minority, immigrant, part-time, older, and academically underprepared students. The more than 1,000 U.S. community 

colleges enrolled more than 6 million students, or about a 

third of all postsecondary students in the 2006–07 academic year, more than half of whom were enrolled part time 

(NCES 2008a).

Community colleges also act as a bridge between high 

school and college. Dual enrollment programs, which enable 

high school students to take courses that can earn them college credit, are one way to make this link. In 2002–03, 71% 

of U.S. public high schools offered dual credit courses, and 

57% of U.S. postsecondary institutions had high school students taking courses for college credit (NCES 2005a, b). In 

2006, 42 states had dual enrollment policies (WICHE 2006).

Historically, dual enrollment opportunities were offered 

to high-achieving, academically oriented students. However, dual enrollment programs are increasingly viewed as 

means to support postsecondary achievement by averageachieving students and students in career and technical 

education programs. Students enrolled in dual enrollment 

programs can take college courses on a college campus or 

courses taught by high school teachers certified as college 

adjuncts. Courses vary in their eligibility requirements and 

target populations. Dual enrollment programs are presumed 

to have many positive outcomes, including early acclimation 

to postsecondary education, increased high school graduation, decreased need for remediation, and success in postsecondary education. Dual enrollment helps to upgrade career 

and technical education curricula with high-level academic 

and technical experiences (Karp et al. 2008).

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 2005 version of the Carnegie Foundation for the Advancement of Teaching’s 

basic classification scheme for colleges and universities is more complex than previous versions and includes subcategories, new names, and new criteria for 

categories. 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 (96 institutions), high research activity (103 institutions), 

and doctoral/research universities (84 institutions).

Master’s colleges and universities include the 663 

institutions that award at least 50 master’s degrees 

and fewer than 20 doctoral degrees per year.

Baccalaureate colleges include the 767 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 1,814 institutions 

in which all degrees are associate’s degrees or bachelor’s degrees account for less than 10% of all undergraduate degrees.

Special-focus institutions are the 806 institutions in 

which at least 75% of degrees are concentrated in a 

single field or a set of related fields.

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://www.carnegiefoundation.org/classifications/ for more information on the classification 

system and on the methodology used in defining the categories.
Science and Engineering Indicators 2010 2-9

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. Online education is a relatively new phenomenon and online enrollment has grown substantially over the 

past 5 years. Institutions believe that higher fuel costs and 

rising unemployment will drive increased demand for online 

courses in coming years (Allen and Seaman 2008).

About two-thirds of 2-year and 4-year colleges and universities offer distance education courses (table 2-1). Distance education is prevalent in public 2-year colleges (97%) 

and public 4-year colleges and universities (88%). A little 

more than half of private not-for-profit 4-year institutions 

offered online courses. Public 2-year colleges account for 

most of the enrollment (4.8 million), followed by public 

4-year colleges (3.5 million). Private not-for-profit and 

private for-profit 4-year institutions both accounted for a 

little more than 1.8 million enrollments in 2006–07. Most 

(more than 80%) of the 3.9 million students who took at 

least one online course in fall 2007 were undergraduates 

(Allen and Seaman 2008).

Colleges and universities’ most prevalent reasons for offering online courses are meeting students’ need for flexible 

schedules (68%); offering courses to those who would not 

have access because of geographic, family, or work-related 

reasons (about two thirds); making more courses available 

(46%); and seeking to increase student enrollment (45%) 

(NCES 2009b). A smaller percentage of institutions offering programs in engineering (16%) than those offering 

programs in other major disciplines (psychology; social 

sciences and history; computer and information sciences; 

education; health and related sciences; business; and liberal 

arts and sciences, general studies, humanities) (from 24% 

to 33%) offered fully online programs in 2007 (Allen and 

Seaman 2008).

Although baccalaureate colleges produce relatively 

small numbers of undergraduate S&E degree holders 

compared with doctorate- and master’s-granting institutions, they are important contributors to producing future 

S&E doctorate recipients (NSF/SRS 2008). 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. Private institutions, whether research universities or baccalaureate colleges, outperform public 

institutions in the proportion of their bachelor’s degree 

recipients who go on to receive S&E doctorates. The 

number of 1997–2006 S&E doctorate recipients per 

hundred bachelor’s degrees awarded in all fields 9 years 

earlier is higher among private research universities and 

a subset of baccalaureate schools, the Oberlin 50 liberal arts schools.* The Oberlin 50 colleges have a higher 

yield in the social and behavioral sciences and about the 

same yield in the natural sciences but a far lower yield 

in engineering than the private research universities. In 

engineering, the research universities, both public and 

private, yield more future doctorates than either public 

or private baccalaureate colleges. The yield of future 

doctorate recipients is only partly related to the range of 

fields that the various types of institutions offer. Research 

institutions (both public and private) and the Oberlin 50 

schools award more than half of their bachelor’s degrees 

in S&E fields. Baccalaureate and master’s institutions 

(both public and private) award approximately one-third 

of their bachelor’s degrees in S&E fields.

Historically black colleges and universities (HBCUs) 

are important baccalaureate-origin institutions of black 

S&E doctorate recipients. In 2006, about one-third of 

black S&E doctorate recipients received their baccalaureate degrees from HBCUs. When the data were adjusted 

for the number of bachelor’s degrees awarded, HBCUs 

as a group yielded about as many future S&E doctorates 

per thousand bachelor’s degrees awarded as non-HBCU 

institutions. The Oberlin 50 colleges and the private 

research universities yielded the most S&E doctorate 

recipients per thousand black recipients of bachelor’s degrees, but private HBCUs as a group have a yield similar to private baccalaureate colleges and public research 

universities. Similarly, Hispanic-serving institutions are 

important baccalaureate-origin institutions of Hispanic 

S&E doctorate-recipients. Because few tribal colleges or 

universities award bachelor’s degrees in S&E, tribal colleges are not a major source of American Indian S&E 

doctorate recipients (NSF/SRS 2009d).

* The Oberlin 50 institutions are Albion College, Alma College, 

Amherst College, Antioch University, Barnard College, Bates College, 

Beloit College, Bowdoin College, Bryn Mawr College, Bucknell University, Carleton College, Colgate University, Colorado College, Davidson College, Denison University, DePauw University, Earlham College, 

Franklin and Marshall College, Grinnell College, Hamilton College, 

Hampton University, Harvey Mudd College, Haverford College, College of the Holy Cross, Hope College, Kalamazoo College, Kenyon 

College, Lafayette College, Macalester College, Manhattan College, 

Middlebury College, Mount Holyoke College, Oberlin College, Occidental College, Ohio Wesleyan University, Pomona College, Reed 

College, Smith College, St. Olaf College, Swarthmore College, Trinity College (CT), Union College (NY), Vassar College, Wabash College, Wellesley College, Wesleyan University, Wheaton College (IL), 

Whitman College, Williams College, and College of Wooster. Two of 

these institutions (Hampton University and Manhattan College) are now 

Carnegie master’s-granting institutions.

Baccalaureate Origins of S&E Doctorate Recipients
2-10 Chapter 2. Higher Education in Science and Engineering

For-Profit Institutions

The rapid growth of for-profit institutions has been seen 

by some as a competitive threat to public and nonprofit colleges and universities in the United States (Bailey, Badway, 

and Gumport 2001). Over the past 10 years, the number 

of for-profit institutions has grown and the number of degrees that they have awarded has more than doubled (NCES 

2009a; appendix table 2-2). In 2007, about 2,800 academic 

institutions in the United States operated on a for-profit basis. More than half of these institutions offer less-than-2-year 

programs and less than half are degree-granting institutions. 

Of the degree-granting institutions, close to half award associate’s degrees as their highest degree (NCES 2008b).

For-profit academic institutions awarded 2%–3% of S&E 

degrees at the bachelor’s, master’s, and doctoral levels and 

29% of those at the associate’s level in 2007. Computer sciences accounted for 97% of the associate’s degrees and 86% 

of the bachelor’s degrees awarded by for-profit institutions 

in science and engineering fields in 2007 (appendix table 

2-3). For-profit institutions award relatively few S&E master’s and doctoral degrees; those they do award are mainly in 

psychology. For-profit institutions are among the top institutions awarding master’s and doctoral degrees in psychology. In addition, a for-profit institution, the University of 

Phoenix Online Campus, awarded more computer sciences 

bachelor’s degrees than any other academic institution in the 

United States in 2007 (table 2-2).

Cost of Higher Education

Affordability and access to U.S. higher education institutions are perennial concerns (NCPPHE 2008; NSB 2003). 

In the 2008–09 academic year, average tuition and fees at 

4-year colleges rose at a rate greater than inflation. Compared with the previous academic year, average tuition and 

fees rose 6.4% for in-state students at public 4-year colleges, 

5.9% for students in private 4-year colleges, and 4.7% for 

students at public 2-year colleges, while the Consumer Price 

Index increased by 5.6% between July 2007 and July 2008 

(College Board 2008). 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 

3.6% in that year (Commonfund Institute 2008). For students 

at public 4-year colleges, tuition and fee increases over the 

past decade have been larger than in previous decades and 

the net price (that is, the published price minus grant aid and 

tax benefits) has risen since 2003–04 (College Board 2008). 

In the coming years, greater tuition increases may occur in 

response to state reductions in higher education funding as a 

result of the financial downturn that began in 2008.

Table 2-1

Postsecondary 2- and 4-year Title IV degree-granting institutions offering distance education courses and 

enrollments in college-level credit-granting distance education courses, by level of education and institution 

type: 2006–07

Institutions offering college-level creditgranting distance education courses (%)Enrollments in college-level creditgranting distance education courses

Institution type Institutions (n) Either level Undergraduatea

Graduate/ 

first 

professionala Either level Undergraduate

Graduate/

first 

professional

All institutions ....................... 4,200 65 66 60 12,153,000 9,803,000 2,349,900

Public 2-year ..................... 1,000 97 97 na 4,844,000 4,840,000 3,700

Private for-profit 2-year ..... 500 16 16 na 72,000 72,000 na

Public 4-year ..................... 600 88 87 82 3,502,000 2,611,000 890,900

Private not-for-profit 

4-year ............................ 1,500 53 51 46 1,854,000 1,124,000 730,400

Private for-profit 4-year ..... 300 70 70 S 1,869,000 1,144,000 724,800

na = not applicable, 2-year institutions do not offer graduate degrees, although they sometimes offer individual graduate courses; S = suppressed, reporting 

standards not met

a

Based on number of institutions that had undergraduate or graduate/first professional programs in 2006–07.

NOTES: Total includes private not-for-profit 2-year institutions not reported separately. Institutions may offer both undergraduate and graduate/first 

professional courses. Figures rounded to nearest 100. Detail may not add to total because of rounding. Standard errors for data available in source 

publication.

SOURCE: National Center for Education Statistics, Distance Education at Degree-Granting Postsecondary Institutions: 2006-07, NCES 2009-044 (2009).

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 2-11

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, the need for remediation at 

the college level, and recent trends in the number of earned 

degrees in S&E fields.

Curricular Reform

Research on how students learn, as well as concern for 

the number of young people entering S&E, have driven numerous efforts to improve instructional materials and practices and to assess the effectiveness of curricular reforms 

(Fortenberry et al. 2007; Lewis and Lewis 2008; Quitadamo 

et al. 2008). Education innovators in all S&E fields are examining problems in student learning and designing ways to 

address them through integration of disciplinary knowledge 

and education research.

Many of these newer methods involve more interaction between students and faculty, improved technology, 

teamwork, and applications to real-world problems (Brainard 2007). Although research indicates that many of the 

newer methods are effective in improving student understanding of the scientific process and fundamental concepts, 

they are not widely adopted. Universities and departments 

are often unaware of or resistant to new models of instruction, and strong incentives to improve teaching are often 

lacking (Brainard 2007).

Remedial Education

Remedial education (also known as developmental education) offers below-college-level courses or instruction to 

students who enter college without skills in reading, writing, or mathematics adequate for college-level courses. In 

recent years, state university systems have moved remedial 

education out of 4-year colleges and universities and into 

community colleges. In the 2003–04 academic year, about 

29% of community college students had taken at least one 

remedial course in their first year of study (NCES 2008a), 

and nearly 60% of students take at least one remedial course 

at some point during their college education (Attewell et al. 

2006). Mathematics was the most common remedial course 

taken in 2004 (NCES 2008a). Although more than half of 

students pass remedial writing and reading courses, less 

Table 2-2

Top 10 colleges and universities awarding degrees in computer sciences and psychology, by level of degree: 

2007

Degree/academic institution Degrees (n) Degree/academic institution Degrees (n)

Computer sciences associate’s Computer sciences bachelor’s

All institutions ............................................................ 27,680 All institutions ........................................................... 42,596

ECPI College of Technology ................................. 769 University of Phoenix Online Campus .................. 1,993

Full Sail Real World Education .............................. 333 American Intercontinental University Online ........ 867

American Intercontinental University Online ......... 300 Strayer University ................................................. 668

Kaplan University .................................................. 226 University of Maryland University College ........... 646

Anthem College ..................................................... 222 Pennsylvania State University (main campus) ..... 486

Keiser University (Ft. Lauderdale campus)............ 214 Colorado Technical University Online .................. 468

ECPI Technical College (Richmond, VA) ............... 209 DeVry University–Illinois ....................................... 362

Western International University ........................... 187 Rochester Institute of Technology ....................... 355

Technical Career Institutes.................................... 177 University of Maryland Baltimore County ............ 351

Coleman College ................................................... 162 Full Sail Real World Education ............................. 330

Psychology master’s Psychology doctorates

All institutions ............................................................ 18,594 All institutions ........................................................... 4,696

Webster University ................................................ 622 Alliant International University (San Diego, CA) ... 249

Teachers College at Columbia University ............. 365 Capella University ................................................ 131

Troy University ...................................................... 316 Nova Southeastern University .............................. 91

Pepperdine University ........................................... 313 Carlos Albizu University ....................................... 85

Chicago School of Professional Psychology ........ 303 Pacific Graduate School of Psychology ............... 67

Prairie View A&M University .................................. 279 Argosy University Chicago ................................... 59

Nova Southeastern University ............................... 258 Chicago School of Professional Psychology ....... 58

National University ................................................ 246 Argosy University Sarasota .................................. 56

Alliant International University (San Diego, CA) .... 239 Rutgers University New Brunswick ...................... 55

Capella University ................................................. 220 California Institute of Integral Studies .................. 54

SOURCES: National Center for Education Statistics, Integrated Postsecondary Education Data System, Completions Survey; and National Science 

Foundation, Division of Science Resources Statistics, Integrated Science and Engineering Resources Data System (WebCASPAR), http://webcaspar.nsf.gov.

Science and Engineering Indicators 2010
2-12 Chapter 2. Higher Education in Science and Engineering

than half pass their remedial mathematics courses (Attewell 

et al. 2006). (For information on the relationship between 

achievement in science and mathematics courses and postsecondary remediation rates, see chapter 1, “Relationship of 

High School Courses Taken to Postsecondary Success.”)

Some have recently begun to question the effectiveness of 

remedial education (Bailey 2009; Calcagno and Long 2008). 

Fewer than half of students in all remedial classes complete 

the required sequence of remedial courses and few of these 

students go on to college-level courses (Bailey 2009). States 

and individual colleges and universities lack consensus on 

the criteria and cutoff points used for assessing college readiness and on the best strategies to address poor skills. Efforts 

in some colleges are more effective than others. Although 

some students may make progress, they still may not reach 

college-level skills and knowledge. Various efforts have attempted to bridge the gaps in expectations between K–12 

and higher education expectations for achievement (Cohen 

et al. 2006), and several states are currently developing initiatives to improve remedial education. Chapter 1, “Transition to Higher Education,” provides more information on the 

transition from high school to college as well as information 

specifically on states’ efforts to establish standards for transitioning into higher education.

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.”

Trends in Enrollment and Degrees in Light 

of Population and Economic Trends and 

World Events

Trends in the college-age population, economic trends, 

and world events have influenced recent trends in college 

enrollment and degrees. Population trends and, to a lesser extent, economic factors are also used in projections of future 

enrollment and degree trends. Undergraduate enrollment, 

S&E bachelor’s degrees, and graduate S&E enrollment 

have generally risen over time at a faster rate than population growth, reflecting increases in the percentage of the 

population participating in higher education. The greatest 

gains in higher education relative to the population occurred 

in the 1980s (figure 2-1). The college-age (20–24-year-old) 

population in the United States declined through the mid1990s, especially for whites (NSF/SRS 2007). Undergraduate enrollment in all fields and bachelor’s degrees in some 

fields declined in that period, and graduate S&E enrollment 

and S&E doctoral degrees declined a few years later. In contrast to population trends, the economy has generally grown 

faster than higher education enrollment and degrees. That is, 

enrollment and degrees have generally declined over time 

Thousands

Ratio

Undergraduate 

enrollment (thousands)

Graduate enrollment and 

bachelor’s degrees (thousands)

Population ages 20–24 and 25–29 years

Figure 2-1

Enrollment and degrees per thousand population 

and per billion dollars GDP: 1970–2006

GDP = gross domestic product

NOTES: Data on graduate enrollment not available before 1975. Data 

on undergraduate enrollment and bachelor's degrees not available 

for 1999.

SOURCES: Population: Census Bureau, Current Population Reports, 

Series P-25, Nos. 1000, 1022, 1045, 1057, 1059, 1092, and 1095, and 

2000 through 2008 Population Estimates; and National Center for 

Education Statistics (NCES), Digest of Education Statistics 2008 

(NCES 2009-020), http://www.nces.ed.gov/programs/digest/d08/ 

tables/dt08_015.asp, accessed 19 May 2009; Undergraduate 

enrollment and S&E bachelor’s degrees: NCES, Integrated 

Postsecondary Education Data System, Completions Survey; and 

National Science Foundation/Division of Science Resources Statistics, 

WebCASPAR database, http://webcaspar.nsf.gov; Graduate S&E 

enrollment: NSF/SRS, Survey of Graduate Students and Postdoctorates in Science and Engineering, and WebCASPAR database, 

http://webcaspar.nsf.gov. See appendix tables 2-12, 2-14, and 4-1.

Science and Engineering Indicators 2010

1970 1974 1978 1982 1986 1990 1994 1998 2002 2006

0

5

10

15

20

25

20–24 years old 25–29 years old

Enrollment and degrees

1970 1974 1978 1982 1986 1990 1994 1998 2002 2006

0

20

40

60

80

100

Undergraduate enrollment (all fields)

per hundred 20–24 year-olds

Graduate S&E enrollment per 

thousand 25–29-year-olds

S&E bachelor’s degrees

per thousand 20–24-year-olds

Enrollment and degrees per billion dollars GDP

1970 1974 1978 1982 1986 1990 1994 1998 2002 2006

0

50

100

150

200

250 Undergraduate enrollment

(all fields)

20

0

40

60

80

Graduate S&E

enrollment

S&E bachelor’s degrees
Science and Engineering Indicators 2010 2-13

relative to the U.S. economy as measured by gross domestic 

product (GDP). This pattern reverses, however, in economic 

recessions. Upturns in enrollment and degrees relative to 

GDP occurred during recession periods in 1973–75, 1980–

82, 1990–91, and 2001 (figure 2-1). Finally, higher education enrollment and degrees are affected by world events 

in very specific ways. For example the dot-com bust in the 

early 2000s was followed by a precipitous decline in enrollment and degrees in computer sciences, and the 9/11 attacks 

on the United States were followed by a steep drop in international student enrollment through 2004. (See “S&E Bachelor’s Degrees” and “Foreign Undergraduate Enrollment.”)

Overall Enrollment

Enrollment in U.S. institutions of higher education at 

all levels rose from 14.5 million students in fall 1993 to 

18.5 million in fall 2007 (appendix table 2-4). More than 

7 million students (about 38% of all students enrolled in 

higher education institutions in the United States) were enrolled in associate’s colleges in 2007. Research universities 

(doctorate-granting universities with very high research activity) accounted for 13% and master’s-granting universities 

accounted for 22% of all students enrolled (appendix table 

2-4). (See sidebar “Carnegie Classification of Academic 

Institutions” for definitions of the types of academic institutions.) These trends are expected to continue in the near 

future.

Projections of High School Graduation and 

College Enrollment Trends

Because of increases in the population of 18-year-olds 

(rather than changes in graduation rates), the number of high 

school graduates is expected to increase through 2017, although at a lower rate than in the recent past. The number 

of high school graduates is projected to increase 6% between 2004–05 and 2017–18 to 3.3 million graduates. From 

1992–93 to 2004–05, the number of high school graduates 

increased 25% (NCES 2008c). (See chapter 1 for more information on high school graduation rates, course taking, 

and transition from secondary to postsecondary education.)

Among public schools, the number of high school graduates is projected to increase 8% nationally, but large variations exist among the states, with increases projected in 27 

states (mainly in the South and West) and the District of 

Columbia and decreases projected in 23 states (mainly in 

the Midwest and Northeast) (NCES 2008c). Arizona, Georgia, Nevada, Texas, and Utah are projected to have the largest percentage increases. Louisiana, Maine, North Dakota, 

Rhode Island, and Vermont are projected to have the largest 

percentage decreases in public high school graduates.

Similarly, enrollment in higher education is projected to 

increase through 2017. These projections are based primarily on population projections but also incorporate information about household income (a measure of ability to pay) 

and age-specific unemployment rates (a measure of opportunity costs).1

 According to Census Bureau projections, the 

number of college-age (ages 20–24) individuals is expected 

to grow from 21.8 million in 2010 to 28.2 million by 2050 

(appendix table 2-5). From 2010 to 2050, Asians are projected to increase from 4% to 6% and Hispanics are projected 

to increase from 18% to 37% of the college-age population, 

whereas blacks are projected to decrease from 15% to 12% 

and whites are projected to decrease from 60% to 40% of the 

college-age population (NCES 2008c).

Largely because of these demographic changes, postsecondary enrollment is expected to increase 13%, to 20.1 

million students, in 2017 (NCES 2008c). Increased enrollment in higher education is projected to come mainly from 

minority groups, particularly Hispanics. Enrollment of all 

racial/ethnic groups is projected to increase, but the percentage that is white is projected to decrease from 65% 

in 2006 to 61% in 2017, whereas the percentages that are 

black and Hispanic are projected to increase from 13% and 

11%, respectively, to 14% for both groups. (For further 

information on assumptions underlying these projections, 

see “Projection Methodology” in Projections of Education 

Statistics to 2017 [NCES 2008c], http://nces.ed.gov/

programs/projections/projections2017/app_a.asp, accessed 

23 June 2009.)

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 at Los Angeles, has asked 

freshmen at a large number of universities and colleges 

about their intended majors.2

 The data provided a broadly 

accurate picture of degree fields several years later.3

 For at 

least the past two decades, about one-third of all freshmen 

planned to study S&E. In 2008, about one-third of white, 

black, Hispanic, and American Indian freshmen and 47% of 

Asian freshmen reported that they intended to major in S&E 

(figure 2-2). The proportions planning to major in S&E were 

higher for men than for women in every racial/ethnic group, 

with the exception of blacks. In 2007 and 2008, similar percentages of black men and black women planned to major 

in S&E (appendix table 2-6). For most racial/ethnic groups, 

about 10%–16% planned to major in social/behavioral sciences, about 6%–10% in engineering, about 8%–10% in biological/agricultural sciences, 1%–2% in computer sciences, 

2%–3% in physical sciences,4

 and 1% in mathematics or statistics. Higher proportions of Asian freshmen than of those 

from other racial/ethnic groups planned to major in biological/agricultural sciences (18%) and engineering (14%). 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 2-13 and “S&E Bachelor’s 

Degrees” for trends in bachelor’s degrees.) Generally, the 

percentages 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. The percentage earning bachelor’s 

degrees in engineering is smaller than, and the percentage 
2-14 Chapter 2. Higher Education in Science and Engineering

earning bachelor’s degrees in social/behavioral sciences is 

larger than, previous years’ percentages planning to major 

in those fields. (See NSB 2008, pages 2-24 and 2-25, for a 

discussion of longitudinal data on undergraduate attrition in 

S&E.)

The demographic composition of students planning 

S&E majors has become more diverse over time. Women 

increased from 44% of freshmen planning S&E majors in 

1993 to 47% in 2008. White students declined from 79% in 

1993 to 69% in 2008. On the other hand, the proportion of 

Asian students increased from 6% to 12% and the proportion 

of Hispanic students increased from 4% to 12%. American 

Indian students were roughly 2% and black students were 

roughly 11% of freshmen intending to major in S&E in both 

1993 and 2008 (appendix table 2-7).

Foreign Undergraduate Enrollment. The number of 

foreign students enrolled in bachelor’s degree programs in 

U.S. academic institutions rose 5% in the 2007–08 academic 

year to approximately 178,000 (IIE 2008). The increase was 

the second in a row after 4 years of decline, but the number 

of foreign undergraduates in 2007–08 was still 7% below 

the peak in 2001–02. Among new foreign undergraduates, 

enrollment increased 7% in 2007–08, the fourth increase in 

a row, suggesting that enrollment increases are likely to continue. South Korea (almost 33,000), Japan (almost 21,000), 

China (16,500), Canada (13,600), and India (13,600) accounted for the largest numbers of foreign undergraduates 

in the United States in 2007–08. The number of Chinese 

undergraduates increased 65% over the previous year and 

the numbers of South Korean and Indian undergraduates 

increased 17% and 8%, respectively. Among all foreign students (undergraduate and graduate) in 2007–08, the number 

of those studying the physical and life sciences increased 

2%; agricultural sciences, 20%; engineering, 7%; and computer sciences, 4%, compared with the preceding year (IIE 

2008). The number of foreign students studying mathematics decreased 9%.

More recent data from the Bureau of Citizenship and Immigration Services show an 11% increase in undergraduate 

enrollment of foreign students in science and engineering 

from April 2008 to April 2009, mostly in engineering. South 

Korea, China, Japan, Canada, and India were among the top 

countries sending foreign undergraduates in spring 2009 

and were also among the top countries sending foreign S&E 

undergraduates (figure 2-3; appendix table 2-8). Nepal and 

Saudi Arabia, which accounted for fewer total undergraduates in the United States, were also among the top countries 

sending foreign undergraduates in S&E fields, sending more 

than Canada and Japan.

Engineering Enrollment. For the most part, undergraduate enrollment data are not available by field. Students often do not declare majors until their sophomore year; thus, 

	


	


	


	
			



		
							

	 	


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Science and Engineering Indicators 2010 2-15

data by field would include a large proportion of missing 

data. However, because engineering programs generally require students to declare a major in the first year of college, 

engineering enrollment data can serve as early indicators 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 2008).

Undergraduate engineering enrollment declined through 

most of the 1980s and 1990s, rose from 2000 to 2003, declined slightly through 2006, and rose to 431,900 in 2007 

(figure 2-4; appendix table 2-9). The number of undergraduate engineering students in 2007 was the highest it has been 

since the early 1980s. Full-time freshman enrollment followed a similar pattern, reaching 110,600 in 2007, the highest since 1982. These trends correspond with declines in the 

college-age population through the mid-1990s, particularly 

the drop in white 20–24-year-olds, who account for the majority of engineering enrollment (NSF/SRS 2007). Similar 

trends in undergraduate engineering enrollment are reported 

by the American Society for Engineering Education (Gibbons 2008).

Undergraduate Degree Awards

The number of degrees awarded by U.S. academic institutions has been increasing over the past two decades both 

in S&E and non-S&E fields. These trends are expected to 

continue at least through 2017 (NCES 2008c). 

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 people, 

but others continue their education at 4-year colleges or universities and subsequently earn higher degrees.5

 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 2007 (appendix table 2-10).

S&E associate’s degrees from all types of academic institutions rose from 23,400 in 1993 to 62,800 in 2003, before 

declining to 47,500 in 2007. Most of the increase through 

2003, and the subsequent decrease, are attributable to computer sciences, which peaked in 2003. Associate’s degrees 

earned in engineering technology (not included in S&E 

degree totals because of their applied focus) declined from 

more than 40,000 in the early 1990s to 30,100 in 2007 (appendix table 2-10).

Women earned 62% of all associate’s degrees in 2007, 

up from 59% in 1993. They earned a smaller and decreasing 

share of associate’s degrees in S&E: 39% in 2007, down 

from 48% in 1993. Most of the decline is attributable to a 

decrease in women’s share of computer sciences degrees, 

from 51% in 1993 to 26% in 2007 (appendix table 2-10).

Students from underrepresented groups (blacks, Hispanics, and American Indians) earn a higher proportion of 

associate’s degrees than of bachelor’s or more advanced degrees.6

 (See “S&E Bachelor’s Degrees by Race/Ethnicity” 

and “Doctoral Degrees by Race/Ethnicity.”) In 2007, they 

earned 27% of S&E associate’s degrees, more than onethird of all associate’s degrees in social and behavioral sciences, and more than one-quarter of all associate’s degrees 

in biological sciences, computer sciences, and mathematics 

(appendix table 2-11). Since 1995, the number of S&E associate’s degrees earned by these students doubled.

S&E Bachelor’s Degrees

The baccalaureate is the most prevalent S&E degree, accounting for more than 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 the past 15 

years. The number of S&E bachelor’s degrees rose steadily from 366,000 in 1993 to 485,800 in 2007 (appendix 

table 2-12).

Trends in the number of S&E bachelor’s degrees vary 

widely among fields (figure 2-5). The number of bachelor’s 

degrees earned in social and behavioral sciences plateaued 

for much of the 1990s, before rising again through 2007. 

In engineering, mathematics, and physical sciences, the 

number of bachelor’s degrees dropped in the late 1990s, but 

	
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then rose through 2007. In computer sciences, the number 

of bachelor’s degrees increased sharply from 1998 to 2004, 

then dropped sharply through 2007. Except for declines 

from 2000 to 2002, bachelor’s degrees in biological sciences 

have been generally increasing, reaching a new peak in 2007 

(appendix table 2-12).

S&E Bachelor’s Degrees by Sex. Since 1982, women 

have outnumbered men in undergraduate education and have 

earned relatively constant fractions of all bachelor’s and 

S&E bachelor’s degrees for several years. Since 2002, women have earned about 58% of all bachelor’s degrees; since 

2000, they have earned about half of all S&E bachelor’s degrees. Within S&E, men and women tend to study different 

fields. In 2007, men earned a majority of bachelor’s degrees 

awarded in engineering, computer sciences, and physics 

(81%, 81%, and 79%, respectively). Women earned half or 

more of bachelor’s degrees in psychology (77%), agricultural sciences (50%), biological sciences (60%), chemistry 

(50%), and social sciences (54%) (appendix table 2-12).

Although gains in recent years have been more modest 

or nonexistent, the share of bachelor’s degrees awarded 

to women in many major S&E fields has increased (except computer sciences and mathematics) over the past 15 

years (figure 2-6). Among fields with notable increases in 

the proportion of bachelor’s degrees awarded to women are 

earth, atmospheric, and ocean sciences (from 30% to 41%); 

agricultural sciences (from 37% to 50%); and chemistry 

(from 41% to 50%) (appendix table 2-12).

The number of bachelor’s degrees awarded to women in 

S&E and in all fields rose from 1993 through 2007. In contrast, the number of bachelor’s degrees awarded to men in 

S&E and in all fields remained fairly flat in the 1990s but 

increased from 2001 through 2007.7

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.8

 Between 1995 and 2007, the proportion of S&E degrees awarded to white students declined from 73% to 64% 

(appendix table 2-13). The proportion awarded to Asians/

Pacific Islanders increased from 8% to 9%; to black students, from 7% to 8%; to Hispanic students, from 6% to 8%; 

and to American Indian/Alaska Native students, from 0.5% 

to 0.7%, although the shares to black and American Indian/

Alaska Native students have remained fairly flat since 2000 

(figure 2-7). The number of S&E bachelor’s degrees earned 

by white students decreased in the 1990s as their numbers 

in the college-age population dropped but then rose again 

through 2007. The number of S&E bachelor’s degrees 

earned by students of unknown race/ethnicity also increased 

(appendix table 2-13). (See sidebar “Increase in Student 

Nonreporting of Race/Ethnicity” in Science and Engineering Indicators 2008 [NSB 2008].)

	
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Science and Engineering Indicators 2010 2-17

Despite considerable progress over the past couple of decades for underrepresented minority groups earning bachelor’s degrees in any field, the gap in educational attainment 

between young minorities and whites continues to be wide. 

The percentage of the population ages 25–29 with bachelor’s or higher degrees was 20% for blacks, 12% for Hispanics, and 36% for whites in 2007, up from 12%, 9%, and 25%, 

respectively, in 1987 (NCES 2008a). 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 and Hispanics 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; 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 (appendix 

table 2-13).

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 

2007, between 9% and 11% of all baccalaureate recipients in 

each of these racial/ethnic groups earned their degrees in the 

natural sciences,9

 2% to 4% in engineering, and 15% to 18% 

in the social and behavioral sciences. Asian/Pacific Islander 

baccalaureate recipients earned 19% of their bachelor’s degrees in natural sciences and 9% in engineering (appendix 

table 2-13).

For all racial/ethnic groups (except white), 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 (except computer sciences) has generally increased 

since 1995. For white students, 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 

remained fairly flat from 1995 through 2001 but have increased since then (appendix table 2-13).

Bachelor’s Degrees by Citizenship. Since 1995, students on temporary visas in the United States have consistently earned a small share (4%) of S&E degrees at the 

bachelor’s level. These students earned 9% of bachelor’s 

degrees awarded in economics in 2007 and about 10% of 

degrees awarded in electrical and industrial engineering. 

The number of S&E bachelor’s degrees awarded to students 

on temporary visas increased from about 14,700 in 1995 to 

about 18,800 in 2004 before declining to 17,400 in 2007 (appendix table 2-13).

Graduate Education, Enrollment, and 

Degrees in the United States

Graduate S&E educational institutions are a major source 

of both the highly skilled workers of the future and the research needed for a knowledge-based economy. This section 

includes indicators related to graduate enrollment, financial 

support for graduate education, 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

S&E graduate enrollment in the United States reached a 

new peak of 597,600 in fall 2006. Following a long period 

of growth that began in the 1970s (NSB 2008), graduate 

enrollment in S&E declined in the latter half of the 1990s, 

then increased steadily through 2006 (appendix table 2-14). 

Growth occurred through 2006 in most major science and 

engineering fields except agricultural sciences (which remained fairly flat) and computer sciences (which has been 

declining for several years). In engineering, enrollment 

dropped in recent years but rose in 2006. According to more 

recent data from the Engineering Workforce Commission 

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2-18 Chapter 2. Higher Education in Science and Engineering

and the American Society for Engineering Education (Gibbons 2008), graduate engineering enrollment continued to 

rise in 2007. Moreover, the number of full-time engineering 

students reached a new peak in 2007 of 104,900 (figure 2-4; 

appendix table 2-15).

The number of full-time students enrolled for the first 

time in S&E graduate departments offers a good indicator 

of developing trends. The number of first-time full-time 

S&E graduate students also reached a new peak (116,500) 

in 2006. It declined in the mid-1990s in all major S&E fields 

but increased in most fields through 2006 (appendix table 

2-16). Growth was greatest in biological sciences, medical/other life sciences, and social and behavioral sciences. 

After declines in recent years, first-time full-time graduate 

enrollment in engineering and computer sciences increased 

in 2005 and 2006.

Enrollment by Sex

The increase in S&E graduate enrollment occurred across 

all major U.S. citizen and permanent resident demographic 

groups. The number of women enrolled in S&E graduate 

programs has increased steadily since 1993. In contrast, the 

number of men enrolled in S&E graduate programs declined 

from 1993 through the end of that decade before increasing through 2003 and remaining more or less at that level 

through 2006 (appendix table 2-14).

Women’s rising percentages in S&E fields also continued. Women made up 42% of S&E graduate students in 

1993 and 50% in 2006, although large variations among 

fields persist. In 2006, women constituted the majority of 

graduate students in psychology (76%), medical/other life 

sciences (78%), biological sciences (56%), and social sciences (54%). They constituted close to half of graduate students in earth, atmospheric, and ocean sciences (47%) and 

agricultural sciences (48%) and more than one-third of graduate students in mathematics (37%), chemistry (40%), and 

astronomy (34%). Their percentages in computer sciences 

(25%), engineering (23%), and physics (20%) were low in 

2006, although higher than in 1993 (23%, 15%, and 14%, 

respectively) (appendix table 2-14).

Enrollment by Race/Ethnicity 

The proportion of underrepresented minority (black, 

Hispanic, and American Indian/Alaska Native) students in 

graduate S&E programs increased from about 8% in 1993 

to about 11% in 2006.10 Increases occurred in all major science fields and in engineering during that period (appendix 

table 2-17). In 2006, blacks, Hispanics, and American Indians/Alaska Natives as a group made up 6%–7% of graduate 

enrollment in many S&E fields (engineering; mathematics; 

physical sciences; earth, atmospheric, and ocean sciences; 

and computer sciences), 8%–10% of graduate enrollment in 

agricultural and biological sciences, 15% in medical/other 

life sciences, 17% in social sciences, and 19% in psychology. Asians/Pacific Islanders accounted for about 6% of S&E 

graduate enrollment in 2006, up from 5% in 1993.

The number of white S&E graduate students decreased 

from 1994 to 2001 and then increased through 2006, whereas the numbers of Asian, black, Hispanic, and American 

Indian/Alaska Native students increased almost every year 

from 1993 through 2006 (figure 2-8). The rise in the numbers of black, Hispanic, and American Indian/Alaska Native 

graduate students occurred in most S&E fields. The number 

of Asian students increased in most science fields but decreased in engineering and in computer sciences in the past 

3 or 4 years (appendix table 2-17).

Foreign Student Enrollment

Foreign graduate student enrollment in S&E grew from 

110,300 in 1993 to 155,000 in 2003, declined for 2 years, 

and increased slightly in 2006 to 151,000. Foreign students 

increased from 22% to 25% of all S&E graduate students 

from 1993 to 2006 (appendix table 2-17). The concentration of foreign enrollment was highest in engineering (45%), 

computer sciences (44%), physical sciences (40%), mathematics (36%), and economics (52%).11

First-time full-time enrollment of foreign S&E graduate 

students increased in fall 2005 and fall 2006 after declining 18% from 2001 through 2004. The numbers still remain 

slightly below those of 2001 (appendix table 2-18). Declines 

and subsequent increases were concentrated mainly in engineering and computer sciences, fields heavily favored by 

	
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Science and Engineering Indicators 2010 2-19

foreign students. Foreign students’ share of first-time fulltime S&E graduate enrollment dropped from 35% in fall 

2000 to 30% in fall 2006, with most of the decrease in computer sciences (from 71% to 62%) and engineering (from 

61% to 55%) (appendix table 2-18).

According to data collected by the Institute of International Education (IIE 2009), the overall number of foreign 

graduate students in all fields increased 5% from academic 

year 2006–07 to 2007–08, with almost all of the increase 

occurring among master’s degree students. The number of 

foreign doctoral students increased 0.9% to approximately 

109,000, and the number of foreign master’s students increased 9% to approximately 133,700. The number of new 

foreign graduate students rose 8%. India, China, South Korea, Taiwan, and Canada are the top places of origin for foreign graduate students. More than half of all foreign graduate 

students are studying S&E.

More recent data from the Bureau of Citizenship and 

Immigration Services show a continuing increase in foreign graduate students from April 2008 to April 2009, with 

foreign enrollment in S&E fields growing 8% (appendix 

table 2-19). As in the recent past, most of the growth was 

in computer sciences (up 13%) and engineering (up 11%). 

Two countries—India, with 56,680 foreign S&E graduate 

students, and China, with 36,890—accounted for more than 

half of the foreign S&E graduates in the United States in 

April 2009. 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).

Financial Support for S&E Graduate 

Education

More than one-third of all S&E graduate students are 

self-supporting; that is, they rely primarily on loans, their 

own funds, or family funds for financial support. The other 

approximately two-thirds receive primary financial support 

from a variety of sources, including the federal government, 

university sources, employers, nonprofit organizations, and 

foreign governments.

Support mechanisms include research assistantships 

(RAs), teaching assistantships (TAs), fellowships, and traineeships. Sources of funding include federal agency support, 

nonfederal support, and self-support. Nonfederal support includes state funds, particularly in the large public university 

systems; these funds are affected by the condition of overall 

state budgets. Most graduate students, especially those who 

pursue doctoral degrees, are supported by more than one 

source or mechanism during their time in graduate school, 

and some receive support from several different sources and 

mechanisms in any given academic year.

Other than self-support, RAs are the most prevalent primary mechanism of financial support for S&E graduate students. In 2006, a little more than one-fourth of full-time S&E 

graduate students were supported primarily by RAs, 18% 

were primarily supported through TAs, and 12% relied primarily on fellowships or traineeships (appendix table 2-20).

Primary mechanisms of support differ widely by S&E 

field of study (appendix table 2-21). For example, in fall 

2006 full-time students in physical sciences were financially 

supported mainly through RAs (42%) and TAs (38%) (figure 2-9). RAs also were important in agricultural sciences 

(57%); biological sciences (42%); earth, atmospheric, and 

ocean sciences (41%); and engineering (40%). In mathematics, more than half (53%) of full-time students were 

supported primarily through TAs and another 21% were 

self-supported. Full-time students in the social and behavioral sciences were mainly self-supporting (46%) or received 

TAs (20%), and students in medical/other life sciences were 

mainly self-supporting (60%).

The federal government served as the primary source of 

financial support for one-fifth of full-time S&E graduate students in 2006 (appendix table 2-22). The federal government 

plays a substantial role in supporting S&E graduate students 

through some mechanisms and in some fields, and a smaller 

role in others. For example, in 2006 the federal government 

	
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2-20 Chapter 2. Higher Education in Science and Engineering

funded 67% of S&E graduate students on traineeships, 50% 

of those with RAs, and 23% of those with fellowships. Federal financial support for graduate education reaches relatively more students in the biological sciences; the physical 

sciences; 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 2-10). Appendix table 

2-22 provides detailed information by field and mechanism. 

(For information on federal academic R&D funding by discipline, see chapter 5, “Expenditures by Field and Funding 

Source.”)

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 69% of federally supported full-time S&E graduate 

students, up from 66% in 1993. Fellowships and traineeships 

are the means of funding for 21% of the federally funded 

full-time S&E graduate students. The share of federally 

supported S&E graduate students receiving traineeships declined from 15% in 1993 to 12% in 2006, and the share receiving fellowships declined from 11% to 10%. For students 

supported through nonfederal sources in 2006, TAs were the 

most prominent mechanism (39%), followed by RAs (30%) 

(appendix table 2-20).

The National Institutes of Health (NIH) and NSF support 

most of the full-time S&E graduate students whose primary 

support comes from the federal government. In 2006, they 

supported about 27,600 and 20,300 students, respectively. 

Trends in federal agency support of graduate students show 

considerable increases from 1993 to 2006 in the proportion 

of students funded (NIH, from 27% to 33%; NSF, from 20% 

to 24%). Support from the U.S. Department of Defense declined from 14% to 11% of federally supported graduate students (appendix table 2-23).

For doctoral degree students, notable differences exist in 

primary support mechanisms by type of doctorate-granting 

institution. In 2007, the primary support mechanism for S&E 

doctorate recipients from research universities (i.e., doctorategranting institutions with very high research activity, which 

receive the most federal funding) was RAs. For those from 

medical schools, which are heavily funded by NIH, the primary support mechanism was fellowships or traineeships, 

and for those from doctoral/research universities, which receive less federal funding, the primary support mechanism 

was personal funds (table 2-3). These differences by type 

of institution hold for all S&E fields (NSF/SRS 2000). As 

noted earlier in this chapter, about 70% of S&E doctorate 

recipients received their doctorate from research universities 

with very high research activity.

Notable differences also exist for doctoral degree students in primary support mechanisms by sex, race/ethnicity, 

and citizenship. Among U.S. citizens and permanent residents in 2007, men were more likely than women to be supported by RAs (29% compared with 21%) and women were 

more likely than men to support themselves from personal 

sources (21% compared with 13%). 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 (26% and 32%, respectively), whereas underrepresented minorities depended more on fellowships or 

traineeships (35%). The primary source of support for doctoral degree students with temporary visas was an RA (54%) 

(appendix table 2-24).

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. Differences in type of 

support by sex, race/ethnicity, or citizenship remain, however, even accounting for doctorate field (NSF/SRS 2000). 

Although remaining differences in self-support are small 

(2–3 percentage points) in some fields, differences between 

men and women in self-support remain substantial (13–25 

percentage points) in computer and health sciences, and differences between underrepresented minorities and whites in 

RA support remain substantial (15–31 percentage points) in 

	
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Science and Engineering Indicators 2010 2-21

agricultural sciences; computer sciences; earth, atmospheric, 

and ocean sciences; physical sciences; and engineering.

At the time of doctoral degree conferral, 44% of S&E 

doctorate recipients have debt related to their undergraduate or graduate education. More than one-fourth have some 

undergraduate debt and about one-third owe money directly 

related to graduate education. In 2007, 27% of S&E doctorate recipients reported having undergraduate debt and 30% 

reported having graduate debt. For some, debt levels were 

high, especially for graduate debt: 0.3% reported more than 

$70,000 of undergraduate debt and 4% reported more than 

$70,000 of graduate debt (appendix table 2-25).

Levels of debt vary widely by doctorate fields. In 2007, 

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.12 In 2007, 16% of psychology doctoral degree recipients reported graduate debt of more than 

$70,000. Doctorate recipients in engineering; biological sciences; computer sciences; earth, atmospheric, and ocean sciences; mathematics; and physical sciences were least likely 

to report graduate debt. A higher percentage of doctorate recipients in non-S&E fields than those in S&E fields reported 

graduate debt.

Interdisciplinary Education

The scientific community increasingly views interdisciplinary research as critical to innovation and scientific 

advance and as a means to respond to emerging complex 

problems (COSEPUP 1995, 2004; NSF/DGE 2009). Over 

the past decade, academic institutions and federal funding 

agencies have made efforts to promote interdisciplinary 

education and research. Although new programs and efforts have arisen, academic institutions and funding agencies remain for the most part organized around disciplines; 

thus, university structures, evaluation and promotion practices, and funding opportunities often do not facilitate interdisciplinary research (NSF/DGE 2009). Measurement 

of interdisciplinary enrollment and degree attainment also 

remains a challenge, as students often are assigned to only 

one department or program to avoid duplication in records, 

and schools are asked to report the enrollment or degree 

in only one department or program. As interdisciplinary 

degree programs become established and award degrees, 

measurement becomes easier. For example, the number of 

doctoral degrees increased in interdisciplinary fields such 

as neuroscience (from 117 in 1982 to 737 in 2006), materials science (from 147 in 1982 to 582 in 2006), and bioengineering (from 59 in 1982 to 525 in 2006) (NSF/SRS 1993, 

2009c). For information based on students’ own reports of 

their research, see the sidebar “Interdisciplinary Dissertation Research.”

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, 

and in certain others, master’s degrees are awarded when 

students fail to advance to the doctoral level. Professional 

master’s degree programs, which stress interdisciplinary 

Table 2-3

Primary support mechanisms for S&E doctorate recipients, by Carnegie classification of doctorate-granting 

institution: 2007

(Percent distribution)

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) .................. 33,826 24,860 6,045 1,118 1,110 693

All mechanisms .............................. 100.0 100.0 100.0 100.0 100.0 100.0

Fellowship or traineeship ............ 19.4 20.8 13.2 11.3 32.3 15.6

Grant ........................................... 6.2 6.4 3.9 3.0 16.8 5.5

Teaching assistantship ................ 14.5 14.6 18.6 7.8 1.7 5.9

Research assistantship ............... 33.9 37.8 27.1 10.3 22.1 10.4

Other assistantship ..................... 0.6 0.4 1.2 0.8 0.4 0.6

Personal ...................................... 10.4 6.6 17.9 36.2 15.6 30.7

Other ........................................... 3.6 2.9 5.9 4.8 3.1 7.9

Unknown ..................................... 11.5 10.5 12.2 25.8 8.0 23.4

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. Percents may not 

add to 100% because of rounding. 

SOURCE: National Science Foundation, Division of Science Resources Statistics, Survey of Earned Doctorates, special tabulations (2009).

Science and Engineering Indicators 2010
2-22 Chapter 2. Higher Education in Science and Engineering

training, are a relatively new direction in graduate education. 

(See sidebar “Professional Science Master’s Degrees.”)

Master’s degrees in S&E fields increased from 86,400 in 

1993 to 121,000 in 2006 before dropping slightly in 2007 

(appendix table 2-26). Increases occurred in most major science fields. Master’s degrees in engineering and computer 

sciences have dropped since 2004 (figure 2-11).

Master’s Degrees by Sex

The number of S&E master’s degrees earned by women 

rose from about 31,000 in 1993 to about 54,900 in 2007 

(figure 2-12). The number of master’s degrees earned by 

men grew more slowly, from about 55,500 in 1993 to about 

65,400 in 2007, with most of the growth between 2002 

and 2004. The number of S&E master’s degrees earned 

by men declined between 2005 and 2007. As a result, the 

percentage of women earning master’s degrees rose steadily during that time period. In 1993, women earned 36% of 

all S&E master’s degrees; by 2007, they earned 46% (appendix table 2-26).

Professional Science 

Master’s Degrees

Partially in response to the call for more realistic 

programs to serve the nation’s S&E needs and students’ professional goals, a number of universities 

have developed Professional Science Master’s (PSM) 

programs (CGS 2008d; Colwell 2009; NAS 2008; 

NPSMA 2009). These programs are designed to prepare people to work primarily in nonacademic sectors 

as laboratory administrators or project directors in, for 

example, large government or industrial laboratories 

or in small startup companies. They serve people who 

need advanced technical training (beyond the bachelor’s degree) within an S&E field combined with 

knowledge of and skills in business fundamentals, 

management, team building, and communication. Prospective students include people already working as 

S&E professionals and others who feel the “strictly research” approach does not appeal to them. The American Recovery and Reinvestment Act of 2009 (Public 

Law 111-5) includes funds specifically for support of 

such programs.*

Starting from a handful of PSM programs in 1997, 

there are now more than 125 such programs in more 

than 60 institutions in 25 states and the District of Columbia in disciplines such as mathematics, physics, 

biological sciences, computational science, forensics, 

chemistry, and geographical information systems. 

Most PSM programs are interdisciplinary in nature. 

About 2,500 students are enrolled annually, and these 

numbers are increasing. Student enrollment is highest 

in the biological sciences and biotechnology disciplines. More than 2,100 PSM students have graduated 

thus far, and 65% of these graduates have found employment in industry or government (NPSMA 2009). 

Many PSM programs were initiated with startup funds 

from the Sloan Foundation and the Council of Graduate Schools with the intent that they become self-supporting as their value to industry and their students’ 

professional aspirations become apparent. Also of 

note are the growing number of such programs abroad, 

as other nations see the value of preparing an S&Etrained managerial workforce, and the growing interest in them of professional societies and journals (CGS 

2008a; Teitelbaum and Cox 2007).

* See the Joint Explanatory Statement–Division A of the American Recovery and Reinvestment Act of 2009, http://www.rules.

house.gov/bills_details.aspx?NewsID=4149, accessed 12 June 2009.

Interdisciplinary 

Dissertation Research

One indicator of interdisciplinary research is the 

number of doctorate recipients reporting two or more 

dissertation fields. A recent analysis from the Survey 

of Earned Doctorates shows that during the period 

2004–07, the share of doctorate recipients reporting 

more than one dissertation research field fluctuated 

between 28% and 30% (NSF/SRS 2009a).

The report found that interdisciplinary research at 

the dissertation research level occurred mostly within 

the same knowledge domain, whether science (80.2%), 

engineering (58.5%), or non-science and engineering 

(non-S&E, 69.3%). Respondents who reported a primary dissertation field in the sciences most frequently 

reported a secondary research field within the same 

broad field in the sciences. However, this varied considerably by field of primary dissertation research, 

from the biological sciences (81.2%) to computer sciences (11.2%). About half of the doctorate recipients 

who reported a primary dissertation research field in 

the earth, atmospheric, and ocean sciences; the physical sciences; psychology; or the social sciences reported a secondary dissertation research field within the 

same broad field. The biological sciences were also 

the most frequent secondary dissertation research field 

across all the other sciences except the social sciences.

About 29% of mathematics and 11% of computer 

sciences doctorate holders listed a secondary field 

within the same respective major field. Dissertations 

in which the primary research field was computer sciences most frequently had engineering (24.9%) or a 

non-S&E field (20.1%) as the secondary dissertation 

research field. Dissertations with mathematics as the 

primary research field most often had biological sciences (24.6%) or engineering (11.7%) as the secondary field.
Science and Engineering Indicators 2010 2-23

Women’s share of S&E master’s degrees varies by field. 

In 2007, women earned a majority of master’s degrees 

in psychology (79%), biological sciences (60%), social 

sciences (56%), and agricultural sciences (55%). Women 

earned a small share of master’s degrees in engineering, although their share in 2007 (23%) was higher than their share 

in 1993 (15%) (appendix table 2-26). The number of master’s degrees awarded to women in most major S&E fields increased through 2005 but has flattened or declined since then.

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 1995 and 2007, although degrees to 

white students dropped from 1997 to 2002 before increasing 

again (figure 2-13; appendix table 2-27).13

The proportion of master’s degrees in S&E fields earned 

by U.S. citizen and permanent resident racial and ethnic 

minorities increased over the past two decades. Asians/

Pacific Islanders accounted for 8% of S&E master’s 

degrees in 2007, up from 6% in 1995. Blacks, Hispanics, 

and American Indians/Alaska Natives also registered gains 

during this period (from 4% to 7% for blacks, from 3% to 5% 

for Hispanics, and from 0.3% to 0.5% for American Indians/

Alaska Natives). The percentage of S&E master’s degrees 

earned by white students fell from 58% in 1995 to 49% in 

2007 as the percentage of degrees earned by minorities and 

temporary residents increased (appendix table 2-27).

	
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2-24 Chapter 2. Higher Education in Science and Engineering

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 2007, foreign students earned 

24% of S&E master’s degrees. Their degrees are heavily 

concentrated in computer sciences and engineering, where 

they earned 39% and 38%, respectively, of all master’s degrees awarded in 2007 (appendix table 2-27). Within engineering, students on temporary visas earned half of the 

master’s degrees in electrical engineering.

S&E master’s degrees awarded to students on temporary visas rose from approximately 22,200 in 1995 to about 

35,500 in 2004, then declined to 28,700 in 2007. Most of 

the decline in recent years is accounted for by declines in 

computer sciences and engineering.

S&E Doctoral Degrees

Doctoral education in the United States prepares a new 

generation of faculty and researchers in academia, as well as 

a high-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.

After rising from the mid-1980s through 1998, the number of S&E doctorates conferred annually by U.S. universities declined through 2002 but increased in recent years, 

reaching a new peak of almost 41,000 in 2007 (NSB 2008; 

appendix table 2-28). The recent growth through 2007 occurred among both U.S. citizens/permanent residents and 

temporary residents. The largest increases were in engineering, biological/agricultural sciences, and medical/other life 

sciences (figure 2-14). 

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 (NORC 2007). (See sidebar “Doctoral Completion 

and Attrition.”) Time to degree (as measured by time from 

graduate school entry to doctorate receipt) increased through 

the mid-1990s but since then has decreased for S&E fields 

as a whole and for each field (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. In 

2007, the median time to doctorate receipt was 6.4 years in 

physical sciences, 6.9 years in mathematics and biological 

sciences, 7.0 years in engineering, 8.9 years in social sciences, and 9.7 years in medical/other life sciences. From 1995 

to 2007, time to degree shortened in each of these fields. In 

science and engineering as a whole, median time to degree 

decreased from 8.0 to 7.2 years during this period.

Time to degree for doctorate recipients decreased in each 

of the Carnegie types of academic institutions awarding 

doctoral degrees from 1993 to 2007. (See sidebar “Carnegie 

Classification of Academic Institutions.”) The majority of 

S&E doctorates are earned at research universities (i.e., doctorate-granting institutions with very high research activity). 

Time to degree is shortest at these universities: 7.0 years for 

2007, down from 7.8 in 1993. Doctorate recipients at medical schools also finish quickly (7.1 years in 2007). Time to 

degree is longer at research universities with high research 

activity (7.9 years) and longest at doctoral/research universities (9.0 years) (table 2-4).

Doctoral Degrees by Sex

Among U.S. citizens and permanent residents, the proportion of S&E doctoral degrees earned by women has risen 

considerably since 1993, reaching a record high of 55% 

in 2007 (appendix table 2-28). During this period, women 

made gains in most major fields, but considerable differences by field continue. In 2007, women earned half or more of 

doctorates in non-S&E fields, in social/behavioral sciences, 

and in medical/other life sciences, but they earned considerably less than half of doctorates in physical sciences (31%), 

mathematics/computer sciences (26%), and engineering 

(23%) (appendix table 2-28). Although the percentages of 

degrees earned by women in physical sciences and engineer-

	
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Science and Engineering Indicators 2010 2-25

ing are low, they are substantially higher than in 1993 (23% 

and 13%, respectively).

The increase in the proportion of S&E doctoral degrees 

earned by women resulted from both an increase in the number of women and a decrease in the number of men earning 

these degrees. The number of U.S. citizen and permanent 

resident women earning doctorates in S&E increased from 

6,800 in 1993 to 15,000 in 2007 (appendix table 2-28). 

Meanwhile, the number of S&E doctorates earned by U.S. 

citizen or permanent resident men increased from 10,900 

in 1993 to 12,300 in 2007. 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 300 in 1993 to 700 in 2007; 

biological sciences doctorates, from 1,300 to 2,300; physical 

sciences doctorates, from 600 to 700; and social/behavioral 

sciences doctorates, from 3,300 to 4,700. A decrease in the 

number of doctorates earned by men after the mid-1990s 

and through about 2002 to 2004 occurred in non-S&E fields 

as well as in engineering and in most science fields (except 

for biological sciences and medical/other life sciences). In 

recent years, the number of doctorates earned by U.S. citizen and permanent resident men increased in biological sciences; earth, atmospheric, and ocean sciences; computer 

sciences; physical sciences; mathematics; and engineering.

Doctoral Degrees by Race/Ethnicity

The number and proportion of doctoral degrees in 

S&E fields earned by U.S. citizen and permanent resident 

underrepresented minorities has also increased since 1995. 

Blacks earned 1,287, Hispanics earned 1,301, and American 

Indians/Alaska Natives earned 128 S&E doctorates in 2007, 

together accounting for 7% of all S&E doctoral degrees 

earned that year, up from 4% in 1995 (appendix table 2-30). 

Their share of S&E doctoral degrees earned by U.S. citizens 

and permanent residents rose from 6% to 10% 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 2-15). Asian/Pacific Islander 

U.S. citizens and permanent residents earned 6% of all S&E 

doctorates in 2007, down from 7% in 1995. The number 

of S&E doctorates earned by white U.S. citizens and 

permanent residents declined from the mid-1990s to 2002, 

with most of the decrease among white men. The number 

of S&E doctoral degrees earned by white U.S. citizen and 

permanent resident men declined in the late 1990s through 

2003, then gradually increased (figure 2-16). The number 

of degrees earned by white U.S. citizen and permanent 

resident women dropped briefly in 1996 and has increased 

since then. 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 1995 to 49% in 

2007 (appendix table 2-30).

Foreign S&E Doctorate Recipients

Temporary residents earned approximately 13,700 S&E 

doctorates in 2007, up from 8,700 in 1995. Foreign students 

on temporary visas earn a larger proportion of doctoral degrees than master’s, bachelor’s, or associate’s degrees (appendix tables 2-11, 2-13, 2-27, and 2-30). The temporary 

resident share of S&E doctorates rose from 31% in 1995 to 

33% in 2007. Foreign students earn considerable shares of 

doctoral degrees in some fields. In 2007, foreign students 

Doctoral Completion 

and Attrition

An ongoing study by the Council of Graduate Schools (CGS 2008c) collected data on doctoral 

completion and attrition of doctoral students from the 

1992–93 academic year to the 2003–04 academic year 

from about 30 academic institutions for 5 broad fields: 

engineering, mathematics and physical sciences, the 

life sciences, the social sciences, and the humanities. 

The study, which focused on 10-year completion and 

attrition rates, revealed that 57% of doctoral students 

complete their degrees within 10 years (and some are 

likely to complete after that). Completion rates varied 

by field, with higher percentages of students in engineering and the life sciences and lower percentages 

of students in mathematics and the physical sciences, 

social sciences, and humanities completing within 10 

years. Ten-year completion rates varied by subdisciplines within the broader disciplines (e.g., within engineering, 10-year completion rates were higher for 

civil engineering and lower for electrical engineering) 

and also differed between men and women and among 

racial and ethnic groups. Ten-year completion rates 

were higher for men than for women in most fields 

(except the social sciences and humanities) and were 

higher for whites than for all other racial/ethnic groups 

(CGS 2008b).

Exit surveys of doctorate completers conducted as 

part of the study found that financial support, mentoring/advising, and family (nonfinancial) support 

headed the factors reported as influencing doctorate 

completion, with more than half of the respondents reporting each of these as factors in their ability to complete their doctoral programs. The relative prevalence 

of these factors varied by field, although differences 

by broad field of study may reflect differences in the 

demographics of students in the fields (CGS 2009).

The study found that most students who leave doctoral programs leave within the first 4 years. Attrition 

rates have improved over time, with rates of attrition 

lower for later cohorts than for earlier cohorts of students. Attrition was highest in mathematics and the 

physical sciences (CGS 2008a).
2-26 Chapter 2. Higher Education in Science and Engineering

	
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Median number of years from S&E doctorate recipients’ entry to graduate school to receipt of doctorate, by 

Carnegie classification of doctorate-granting institution: 1993–2007

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

1993.......... 7.9 7.8 8.4 10.0 7.9 8.5

1994.......... 8.0 7.8 8.6 9.9 8.0 8.6

1995.......... 8.0 7.8 8.6 10.2 7.9 8.9

1996.......... 7.9 7.7 8.7 9.7 8.0 8.8

1997.......... 7.7 7.5 8.6 10.0 7.9 8.4

1998.......... 7.6 7.4 8.3 9.8 7.2 8.3

1999.......... 7.6 7.4 8.3 9.2 7.0 7.8

2000.......... 7.7 7.5 8.3 9.2 7.2 8.3

2001.......... 7.6 7.4 8.3 9.9 7.3 8.0

2002.......... 7.7 7.5 8.4 10.0 7.1 8.4

2003.......... 7.7 7.5 8.3 10.0 7.1 9.0

2004.......... 7.3 7.1 8.0 9.3 7.0 7.8

2005.......... 7.4 7.3 8.0 9.6 7.1 8.4

2006.......... 7.3 7.1 8.0 8.7 7.0 8.0

2007.......... 7.2 7.0 7.9 9.0 7.1 8.0

SOURCE: National Science Foundation, Division of Science Resources Statistics, Survey of Earned Doctorates, special tabulations (2009).

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 2-27

on temporary visas earned half or more of doctoral degrees 

awarded in engineering, physics, mathematics, computer 

sciences, and economics. They earned considerably lower 

proportions of doctoral degrees in other S&E fields, for example, 30% in biological sciences, 8% in medical/other life 

sciences, and 5% in psychology (appendix table 2-30).

Countries/Economies of Origin

The top 10 foreign countries/economies of origin of foreign S&E doctorate recipients together accounted for 66% 

of all foreign recipients of U.S. S&E doctoral degrees from 

1987 to 2007 (table 2-5). All but 3 of those top 10 countries 

are located in Asia. The major Asian countries/economies 

sending doctoral degree students to the United States have 

been, in descending order, China, India, South Korea, and 

Taiwan.

Asia. From 1987 to 2007, 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 (110,600 of 206,300), almost 

four times more than students from Europe (27,900). Most 

of these degrees were awarded in engineering, biological 

sciences, and physical sciences (table 2-6).

Students from China earned the largest number of U.S. 

S&E doctorates awarded to foreign students during the 

1987–2007 period (50,200), followed by those from India 

(21,400), South Korea (20,500), and Taiwan (18,500) (table 

2-6). The numbers of S&E doctorates earned by students 

from China and India dropped in the late 1990s but have 

been increasing since then (figure 2-17). Over the 20-year 

period, the number of S&E doctorates earned by Chinese nationals increased more than tenfold14 and the number of S&E 

doctorates earned by students from India more than trebled. 

The number of S&E doctoral degrees earned by South Korean students also dipped in the late 1990s and then rose, but 

the number of students did not rise as dramatically as those 

from China and India. In 1987, 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 1987 and 2007, and 

they tended to focus less on engineering than did their Asian 

counterparts (table 2-7). Western European countries whose 

Table 2-5

Foreign recipients of U.S. S&E doctorates, by 

country/economy of origin: 1987–2007

Country/economy Number Percent

All foreign recipients ........ 206,256 100.0

Top 10 total .................. 136,120 66.0

China ........................ 50,220 24.3

India .......................... 21,354 10.4

South Korea ............. 20,549 10.0

Taiwan ...................... 18,523 9.0

Canada ..................... 6,676 3.2

Turkey ....................... 4,575 2.2

Thailand .................... 3,707 1.8

Germany ................... 3,567 1.7

Japan ....................... 3,536 1.7

Mexico ...................... 3,413 1.7

All others ...................... 70,136 34.0

NOTE: Foreign doctorate recipients include permanent and 

temporary residents. 

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Survey of Earned Doctorates, special 

tabulations (2009).

Science and Engineering Indicators 2010

Table 2-6

Asian recipients of U.S. S&E doctorates, by field and country/economy of origin: 1987–2007

Field Asia China India South Korea Taiwan

All fields .................................................................... 168,627 53,665 24,386 26,402 22,577

S&E ....................................................................... 143,927 50,220 21,354 20,549 18,523

Engineering ....................................................... 53,621 16,183 9,419 7,965 8,332

Science ............................................................. 90,306 34,037 11,935 12,584 10,191

Agricultural sciences ..................................... 5,746 1,562 534 807 727

Biological sciences ........................................ 23,637 11,532 3,240 2,386 2,701

Computer sciences........................................ 7,186 2,166 1,791 849 959

Earth, atmospheric, and ocean sciences ...... 2,947 1,461 230 367 319

Mathematics .................................................. 6,888 3,184 641 921 700

Medical/other life sciences ............................ 4,621 992 888 492 819

Physical sciences .......................................... 21,162 10,181 2,606 2,561 2,038

Psychology .................................................... 2,198 350 265 369 308

Social sciences .............................................. 15,921 2,609 1,740 3,832 1,620

Non-S&E ............................................................... 24,700 3,445 3,032 5,853 4,054

NOTE: Foreign doctorate recipients include permanent and temporary residents. 

SOURCE: National Science Foundation, Division of Science Resources Statistics, Survey of Earned Doctorates, special tabulations (2009).

Science and Engineering Indicators 2010
2-28 Chapter 2. Higher Education in Science and Engineering

students earned the largest number of U.S. S&E doctorates from 1987 to 2007 were Germany, the United Kingdom, Greece, Italy, and France, in that order. From 1987 to 

1993, Greece was the primary European country of origin; 

thereafter, its numbers of doctoral degree recipients declined. The numbers of U.S. S&E doctorate recipients from 

Italy and France generally increased over the past two decades. The number of U.S. S&E doctorate recipients from 

the United Kingdom fluctuated mainly between 100 and 150 

over the period, and the number of doctorate recipients from 

Germany declined since 2000 (figure 2-18).

The number of Central and Eastern European students 

earning S&E doctorates at U.S. universities increased from 

55 in 1987 to more than 800 in 2007 (about the same number as those from Western Europe) (figure 2-19). A higher 

proportion of Central and Eastern European U.S. doctorate 

recipients (88%) than of Western European doctorate recipients (73%) earned their doctorates in S&E fields, particularly in mathematics and physical sciences (table 2-7).

North America. The Canadian and Mexican shares of 

U.S. S&E doctoral degrees were small compared with those 

from Asia and Europe. The number of U.S. S&E degrees 

earned by students from Canada increased from about 200 

in 1987 to more than 400 in 2007. The number of doctoral 

degree recipients from Mexico increased from 99 in 1987 

to 187 in 2007 (figure 2-20). A higher proportion of Mexican than of Canadian U.S. doctoral degree recipients earned 

doctorates in science and engineering fields: 85% of Mexican and 64% of Canadian doctoral degree students in U.S. 

universities earned S&E doctorates (table 2-7). In particular, 

higher percentages of Mexican than of Canadian U.S. doctoral degrees were in engineering and agricultural sciences.

Table 2-7

European and North American recipients of U.S. S&E doctorates, by field and region/country of origin: 

1987–2007

Europea North America

Field

All 

countries Western Scandinavia

Central/

Eastern

All 

countries Canada Mexico

All fields ................................................................. 36,073 23,527 2,083 10,463 14,443 10,363 4,028

S&E .................................................................... 27,937 17,168 1,586 9,183 10,111 6,676 3,413

Engineering .................................................... 5,563 3,588 286 1,689 1,725 933 792

Science .......................................................... 22,374 13,580 1,300 7,494 8,386 5,743 2,621

Agricultural sciences .................................. 802 590 61 151 832 264 568

Biological sciences ..................................... 4,121 2,580 237 1,304 1,951 1,378 565

Computer sciences..................................... 1,423 815 74 534 299 211 87

Earth, atmospheric, and ocean sciences ... 1,053 720 83 250 369 229 138

Mathematics ............................................... 2,820 1,284 107 1,429 498 313 184

Medical/other life sciences ......................... 630 488 71 71 603 508 93

Physical sciences ....................................... 5,708 2,902 221 2,585 1,106 823 281

Psychology ................................................. 1,047 803 103 141 912 822 85

Social sciences ........................................... 4,770 3,398 343 1,029 1,816 1,195 620

Non-S&E ............................................................ 8,136 6,359 497 1,280 4,332 3,687 615

a

See figure 2-19 notes for countries included in Western Europe, Scandinavia, and Central/Eastern Europe.

NOTE: Foreign doctorate recipients include permanent and temporary residents.

SOURCE: National Science Foundation, Division of Science Resources Statistics, Survey of Earned Doctorates, special tabulations (2009).

Science and Engineering Indicators 2010

	
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Science and Engineering Indicators 2010 2-29

Stay Rates

Most foreign U.S. doctorate recipients plan to stay in the 

United States after graduation, and although the percentage 

of recipients staying is dropping, the number of recipients 

staying is increasing (figure 2-21). This section examines 

data on foreign S&E doctorate recipients’ plans for staying 

in the United States at the time of doctorate receipt. Chapter 

3 provides data based on examination of Social Security records on the percentage of foreign students with U.S. S&E 

doctorates who remain in the U.S. labor force up to 5 years 

after graduation.

At the time of doctorate receipt, more than three-quarters 

of foreign recipients of U.S. S&E doctorates plan to stay 

in the United States and about half have either accepted an 

offer of postdoctoral study or employment or are continuing employment in the United States (appendix table 2-31). 

Until the early 1990s, 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 postdoctoral 

study or employment (NSB 1998). In the 1990s, however, 

these percentages increased substantially. For example, in 

the period 1996–99, 71% of foreign S&E doctoral degree 

recipients reported plans to remain in the United States after 

receiving their degree and 45% already had firm offers for 

postdoctoral study or employment. In the 2004–07 period, 

77% of foreign doctoral recipients in S&E fields with known 

plans intended to stay in the United States and 51% had firm 

offers to do so (appendix table 2-31). Higher percentages 

of foreign doctorate recipients in physical sciences, biological and agricultural sciences, and mathematics/computer 

	
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2-30 Chapter 2. Higher Education in Science and Engineering

Postdoctoral Education

Typically, postdoctoral fellows (“postdocs”) have temporary appointments involving full-time research or scholarship, the purpose of which is to further their education and 

training. The titles associated with these positions and the 

conditions of employment vary widely. The status of postdoctoral fellows within the academic hierarchy is not well 

defined and varies among institutions, although it is generally accepted that the postdoctoral experience represents 

the last step of a person’s training for becoming an independent investigator and faculty member (COSEPUP 2000). 

Postdoctoral fellows also are important contributors to academic research. They bring a new set of techniques and 

perspectives to the laboratory that broadens research teams’ 

sciences, and lower percentages of foreign doctorate recipients in social/behavioral and health sciences reported definite plans to stay.

Stay rates vary by place of origin. In the period 2004–07, 

more than 90% of U.S. S&E doctoral recipients from China 

and 89% of those from India reported plans to stay in the 

United States, and more than half reported accepting firm 

offers for employment or postdoctoral research in the United 

States (appendix table 2-31). Doctorate recipients from Japan, South Korea, and Taiwan were less likely than those 

from China and India to stay in the United States (figure 

2-22). Among U.S. S&E doctoral degree recipients from Europe, a relatively high percentage from the United Kingdom 

planned to stay, whereas smaller percentages from Greece 

and Spain (compared with other Western European countries) planned to stay after graduation. In North America, 

the percentage of 2004–07 doctoral degree students who 

had definite plans to stay in the United States was higher for 

Canada than for Mexico (appendix table 2-31).

Between 2000–03 and 2004–07, the percentage of U.S. 

S&E doctoral degree recipients from all of the top five 

countries/economies of origin (China, India, South Korea, 

Taiwan, and Canada) reporting definite plans to stay in the 

United States declined. However, for all but Taiwan, increases in the numbers of doctorate recipients more than 

offset declines in the percentage staying. Thus, the numbers 

of U.S. S&E doctoral degree recipients from Canada, China, 

India, and South Korea who had definite plans to stay in the 

United States were larger in the 2004–07 period than in the 

2000–03 period (appendix table 2-31).

	
		

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Science and Engineering Indicators 2010 2-31

experience and can make them more competitive for additional research funding. Chapter 3 provides more detail on 

postdoctoral employment, including reasons for and length 

of postdoc appointments, salaries and subsequent employment. Chapter 5 provides more detail on postdocs in the academic R&D setting.

Since 1993, the number of S&E postdocs at U.S. universities increased from 34,300 to 49,300 in fall 2006 (appendix 

table 2-32). Most of the growth was in biological and medical/other life sciences, accounting for more than two-thirds 

of S&E postdocs (figure 2-23).15

Noncitizens account for much of the increase in the 

number of S&E postdocs, especially in biological sciences 

and medical sciences (figure 2-24). The number of S&E 

postdocs with temporary visas at U.S. universities increased 

from approximately 17,600 in 1993 to 28,200 in 2006. 

The number of U.S. citizen and permanent resident S&E 

postdocs at these institutions increased more modestly, from 

approximately 16,700 in 1993 to 21,100 in 2006 (appendix 

table 2-32). Temporary visa holders accounted for 57% of 

S&E postdocs in 2006.

An increasing share of academic S&E postdocs are 

funded through federal research grants. In fall 2006, 56% 

of S&E postdocs at U.S. universities were funded through 

this mechanism, up from 52% in 1993. Federal fellowships 

and traineeships funded a declining share of S&E postdocs: 

13% in 2006, down from 17% in 1993. In 2006, 31% of 

S&E postdocs were funded through nonfederal sources 

(table 2-8).

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 adopted policies to encourage the 

return of students who studied abroad, to attract foreign 

students, or both. (For information on worldwide trends affecting doctoral education, see sidebar “Globalization and 

Doctoral Education.”)

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 developments. One indicator of the importance of higher education is the percentage of resources 

devoted to higher education, as measured by expenditures 

on tertiary education (education beyond high school) as a 

percentage of gross domestic product (GDP). The United 

States, Canada, and Korea spend the highest percentage of 

GDP on higher education (appendix table 2-33).

An 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 other Organisation for Economic 

Co-operation and Development (OECD) countries between 

	
	

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2-32 Chapter 2. Higher Education in Science and Engineering

Republic, Greece, Iceland, Poland, Portugal, the Slovak Republic, and the United Kingdom, far exceeded the OECD 

average increase in expenditures from 2000 to 2005 (appendix table 2-33). Examination of higher education funding 

over time is complicated by many things, including 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.

1995 and 2000, but less in the United States than in other OECD countries between 2000 and 2005. From 1995 

to 2000, educational expenditures in the United States increased faster than the OECD average and faster than almost 

all of the other OECD countries (except Greece, Ireland, and 

Poland). From 2000 to 2005, educational expenditures in the 

United States increased more slowly than the OECD average but at a similar or faster rate than many countries. (In 

2006, expenditures per student in the U.S. were double the 

OECD average [OECD 2008].) Several countries, the Czech 

Table 2-8

Source of funding of S&E postdoctoral students: 1993–2006

(Percent distribution)

Source 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

All sources ............................ 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Federal fellowships ........... 8.5 8.7 8.8 8.9 9.0 10.2 9.4 9.1 8.3 8.7 7.8 7.9 7.8 7.5

Federal traineeships.......... 8.5 8.1 7.6 7.4 7.2 7.3 6.6 6.0 5.7 5.9 5.7 5.4 5.8 5.5

Federal research grants .... 52.1 51.3 51.9 52.0 51.7 51.2 53.2 54.5 54.7 55.9 56.1 57.9 56.6 55.6

Nonfederal sources ........... 30.9 31.9 31.6 31.6 32.1 31.3 30.7 30.3 31.3 29.4 30.4 28.8 29.8 31.4

NOTE: Percents may not add to 100% because of rounding. 

SOURCE: National Science Foundation, Division of Science Resources Statistics, Survey of Graduate Students and Postdoctorates in Science and 

Engineering, Integrated Science and Engineering Resources Data System (WebCASPAR) database, http://webcaspar.nsf.gov.

Science and Engineering Indicators 2010

With increasing student flows and increasing transnational university partnerships and agreements, doctoral 

education is becoming more global in nature. In addition, 

doctoral education in many countries around the world is 

being shaped by common forces in common ways. Globalization of the economy, shifts to a knowledge-based 

economy, and various policy efforts are transforming 

doctoral education around the world. Nerad and Heggelund (2008) identified several key interrelated dimensions of these global trends in doctoral education:

Across countries, doctoral education is increasingly 

seen as a commodity with a measurable economic 

value.

The market economy is demanding skills in addition to technical skills, that is, skills such as communication, leadership, and team work, which are 

increasingly being incorporated into graduate curricula. Private institutions and for-profit institutions 

are arising where only public institutions used to 

prevail, and tuition is being charged in countries in 

which education used to be free.

Research has shifted from individual curiositydriven research to team research on marketable projects, and research has become more collaborative 

and interdisciplinary.

Developing countries are losing their doctoral students to more developed countries through “brain 

drain.”

Doctoral education around the world is increasingly 

conducted in English, and scholarly papers are written in English.

Doctoral education is becoming more standardized 

in terms of common definitions of degrees, common 

curricular elements, interdisciplinary training, ethics training, relationship between bachelor’s degrees 

and doctorates, and length of time.

Universities are developing and applying quality 

assurance standards and assessment techniques to 

compete more effectively in the global marketplace, 

and new accreditation agencies are being developed.

The Bologna Accords in Europe are moving toward 

harmonization of doctoral education in terms of supervision, length of study, mobility across borders, 

and collaboration.

The nature of the individual university’s responses to 

these changes and the extent to which universities and 

countries embrace or resist them vary.

Globalization and Doctoral Education
Science and Engineering Indicators 2010 2-33

Educational Attainment

Higher education in the United States expanded greatly 

after World War II, and for several decades the United States 

led the world in its population’s educational attainment. In 

the 1990s, many countries in Europe and Asia also began to 

expand their higher education systems. 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, but several other countries have surpassed the 

United States in the percentage of the younger (ages 25–34) 

population with a bachelor’s degree or higher (figure 2-25; 

appendix table 2-34).16

First University Degrees in S&E Fields

More than 12 million students worldwide earned first 

university degrees17 in 2006, with more than 4 million of 

these in S&E fields (appendix table 2-35). These worldwide 

totals include only countries for which relatively recent 

data are available (primarily countries in Asia, Europe, and 

the Americas) and therefore are likely an underestimation. 

Asian universities accounted for 1.8 million of the world’s 

S&E first university degrees in 2006, almost 900,000 of 

these in engineering. Students across Europe (including 

Eastern Europe and Russia) earned more than 1 million S&E 

degrees and students in North and Central America more 

than 600,000 in 2006.

In the United States, S&E degrees are about one-third 

of U.S. bachelor’s degrees and have been for a long time. 

In several countries/economies around the world, the proportion of first university degrees in S&E fields, especially 

engineering, is higher. More than half of first university degrees were in S&E fields in Japan (63%), China (53%), and 

Singapore (51%). China has traditionally awarded a large 

proportion of its first university degrees in engineering, although the percentage has declined in recent years (appendix 

table 2-36). In the United States, about 5% of all bachelor’s 

degrees are in engineering. However, in Asia, about 20% 

are in engineering, and in China about one-third are in engineering (appendix table 2-35). About 12% of all bachelor’s 

degrees in the United States and worldwide are in natural 

sciences (physical, biological, computer, and agricultural 

sciences, and mathematics). See the sidebar “International 

Changes in the Ratio of Natural Science and Engineering 

Degrees to the College-Age Population.”

The number of S&E first university degrees awarded 

in China, Poland, and Taiwan more than doubled between 

1998 and 2006, and those in the United States and many 

other countries generally increased. Those awarded in Japan decreased in recent years (appendix table 2-36). Natural 

sciences and engineering (NS&E) degrees account for most 

of the increase in S&E first university degrees in China. 

The number of NS&E first university degrees in China rose 

sharply from 2002 to 2006 and more than trebled between 

1998 and 2006 (figure 2-26). In comparison, those awarded 

in Germany, Japan, South Korea, the United Kingdom, and 

	
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2-34 Chapter 2. Higher Education in Science and Engineering

the United States remained relatively flat. (For information 

on reforms affecting degree awards in Europe, see sidebar 

“Changes in European Higher Education Since the Bologna 

Process.”)

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 2006, including 

Algeria, Argentina, Canada, Greece, Portugal, Saudi Arabia, 

the United States, and a number of smaller countries. Several 

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 2-37). In Canada, Japan, the 

United States, and many smaller countries, more than half 

of the S&E first university degrees earned by women are 

in the social and behavioral sciences. In South Korea, 45% 

of the S&E first university degrees earned by women are in 

engineering; in Europe, more than 20% are in engineering. 

In the United Kingdom and the United States, 6% of S&E 

first university degrees earned by women are in engineering.

	
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International Changes in the 

Ratio of Natural Science and 

Engineering Degrees to the 

College-Age Population

The ratio of natural sciences and engineering 

(NS&E) degrees to the college-age population is one 

measure of the technical skill level of those entering 

the workforce. Over time, the United States has fallen 

from one of the top countries in terms of its ratio of 

NS&E degrees to the college-age population to near 

the bottom of the 23 countries for which data are available. The ratios of first university degrees in NS&E to 

the college-age population increased substantially in 

recent decades in these 23 countries. In 1975, only Japan had a higher ratio than the United States of NS&E 

degrees per hundred 20–24-year-olds (the college-age 

population). By 1990, a few other countries/economies 

had surpassed the U.S. ratio, and by 2005 nearly all 

had done so. A recent NSF report (NSF/SRS 2009b) 

examined the relative influence on this ratio of increased university degree completion relative to the 

college-age population and NS&E degrees as an increasing share of all degrees. This study examined increases in the ratio of NS&E first university degrees to 

the college-age population in the United States and 22 

other countries/economies for two periods: 1975–90 

and 1990–2005.

The study found that the rising ratio of NS&E degrees to the college-age population in the locations 

compared with the United States can primarily be attributed to increased university degree completion, 

not to an increased emphasis on NS&E education; 

however, the relative importance of these components 

varies substantially by location. In both the 1975–90 

and the 1990–2005 periods, the university degree 

completion component was either the only component 

or the larger component for the majority of countries/

economies for which such data were available. That 

is not to say that increased emphasis on NS&E was 

not an important factor in some countries. The NS&E 

share component was either the only component or the 

larger component for five countries in the 1975–90 period but for no countries in the 1990–2005 period. In 

another eight countries in which the university degree 

completion component was larger, the NS&E share 

component and/or the interaction component was substantial in one or the other period.
Science and Engineering Indicators 2010 2-35

Global Comparison of S&E Doctoral Degrees

Almost 174,000 S&E doctoral degrees were earned 

worldwide in 2006. The United States awarded the largest number of S&E doctoral degrees of any country (about 

30,000),18 followed by China (about 23,000), Russia (almost 20,000), and Germany and the United Kingdom (about 

10,000 each) (appendix table 2-38). More than 52,000 S&E 

doctoral degrees were earned in the European Union.

Women earned 40% of S&E doctoral degrees awarded in 

the United States in 2006, about the same as the percentage 

earned by women in Australia, Canada, the European Union, 

and Mexico. They earned more than half of S&E doctoral 

degrees in Portugal and less than one-quarter of S&E doctoral degrees in the Netherlands, Poland, South Korea, and 

Taiwan (appendix table 2-39).

The number of S&E doctoral degrees awarded in China, 

Italy, and the United States has risen steeply in recent years 

(appendix tables 2-40 and 2-41). The United States awarded 

the largest number of natural sciences and engineering doctoral degrees, but China (as of 2006) was rapidly catching 

up (figure 2-27) and may have since surpassed the United 

States. 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 are in the physical and 

biological sciences. The number of doctoral degrees in those 

	
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Changes in European 

Higher Education Since the 

Bologna Process 

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 by the year 2010 so that 

degrees are comparable, credits are transferable, and 

students, teachers, and researchers can move freely 

from institution to institution across national borders. 

Its aim is to replace the varied degree programs in existence, which typically have taken 5 or more years 

to complete, with a standard 3-year bachelor’s degree 

and a 2-year master’s degree and with a standardized 

credit system. Implementation of these reforms has 

implications for graduate admissions to U.S. academic 

institutions: Will U.S. academic institutions see these 

3-year bachelor’s degrees as equivalent to U.S. 4-year 

bachelor’s degrees? (IIE 2009). The Bologna Process 

is also stimulating discussion about reform of the U.S. 

higher education system (Adelman 2008).

By 2008, higher education reform in Europe had 

been extended to more than 45 countries, but it is still 

in process in many countries and in many fields, and 

the impact is still uncertain. Many countries have established regulations for reform, but implementation 

of changes is ongoing, particularly in some disciplines. In many European countries, law and medicine 

have not moved to the 2-cycle (bachelor’s and master’s) structure. One of the major difficulties countries 

are experiencing in the shift to the 2-cycle structure, 

particularly in law and medicine, is with the bachelorlevel degree’s relevance to the labor market (Huisman, 

Witte, and File 2006). In a few countries, the impact 

of the change on degree trends is already apparent. In 

2001, Italy introduced a 3-year bachelor’s degree in 

accordance with the Bologna guidelines, and the number of students completing an undergraduate degree 

has since increased, particularly from 2004 on (appendix table 2-36). In Germany, implementation was 

later, but evidence of the change is apparent in the 

number of first university degrees in 2006.
2-36 Chapter 2. Higher Education in Science and Engineering

fields stagnated or declined in many of these countries from 

the late 1990s through 2006, although the number of these 

degrees in Italy, Switzerland, and the United States increased 

in recent years. The number of S&E doctoral degrees in Germany has declined slightly since 2000 (appendix table 2-40).

In Asia, China was the largest producer of S&E doctoral 

degrees. The number of S&E doctorates awarded in China 

rose from about 1,900 in 1993 to almost 23,000 in 2006 

(appendix table 2-41). Recently, the Chinese State Council 

Academic Degrees Committee announced that China would 

begin to limit admissions growth in doctoral programs and 

would focus more on quality of graduates (Mooney 2008). 

The number of S&E doctorates awarded in India, Japan, 

South Korea, and Taiwan also rose from 1993 to 2006, 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, most doctorates were awarded in the physical 

and biological sciences (appendix table 2-41).

Global Student Mobility

International migration of students and highly skilled 

workers expanded in the past two decades, and countries are 

increasingly competing for foreign students. In particular, 

students migrated from developing countries to the more developed countries, and from Europe and Asia to the United 

States. Some migrate temporarily for education, whereas 

others remain permanently. Some factors influencing the 

decision to migrate are economic opportunities, research 

opportunities, research funding, and climate for innovation 

in the country of destination (OECD 2004). In recent years, 

many countries, particularly Australia, Canada, the United 

Kingdom, and the United States, have expanded their provision of transnational education, that is, programs for foreign 

students in their home countries (NSB 2008). The rise in 

transnational education, however, has not had much impact 

on foreign student flows (De Wit 2008). The influence of the 

worldwide economic and monetary crises beginning in 2008 

on international flows of students in the future is uncertain.

Some countries expanded recruitment of foreign students 

as their own populations of college-age students decreased, 

both to attract highly skilled workers and increase revenue 

for colleges and universities (OECD 2008). 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, and 

South Korea (appendix table 2-42). The U.S. population of 

20–24-year-olds is projected to increase.

The U.S. share of foreign students worldwide declined in 

recent years, although the United States remains the destination of the largest number of foreign students worldwide 

(both undergraduate and graduate) of all OECD countries 

(figure 2-28). In 2006, the United States received 20% 

of foreign students worldwide, down from 25% in 2000 

	
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Science and Engineering Indicators 2010 2-37

(OECD 2008). Other countries that are among the top destinations for foreign students include the United Kingdom 

(11%), Germany (9%), and France (8%).

Although Australia has a higher percentage of higher education students (undergraduate and graduate) who are foreign (18%) than the United States (3%), it has a lower share 

(6%) of foreign students worldwide. Other countries with 

relatively high percentages of higher education students who 

are foreign include New Zealand (16%), Switzerland and the 

United Kingdom (14%), and Austria (12%). A number of 

countries, including Belgium, Canada, New Zealand, Switzerland, the United Kingdom, and the United States, have 

relatively high percentages (more than 20%) of doctoral students who are foreign (OECD 2008).

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 2007). Foreign 

student enrollment in the United Kingdom is increasing, especially at the graduate level, with increasing flows of students from China and India (appendix table 2-43). In just 5 

years, from 2001 to 2006, foreign students increased from 

35% to 45% of all graduate students studying S&E in the 

United Kingdom. Foreign students now account for more 

than half 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 France, Germany, Ireland, Malaysia, Nigeria, 

Pakistan, and the United States also increased. The number 

of foreign undergraduate students also increased, but not as 

dramatically. Foreign undergraduates accounted for 10% of 

S&E enrollment in the United Kingdom in 2001 and 11% 

in 2006.

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). Almost 60,000 foreign students were enrolled in S&E programs in Japanese 

universities in 2008, up from 42,000 in 2001. Foreign S&E 

student enrollment in Japan is concentrated at the undergraduate level, accounting for 68% of all foreign S&E students. 

Foreign undergraduates account for 3% of undergraduate 

and 14% of graduate S&E students in Japan. Most of the 

foreign students were from Asian countries. China, Indonesia, Malaysia, Myanmar, Nepal, South Korea, Thailand, and 

Vietnam were among the top 10 countries of origin for both 

undergraduates and graduate students. Chinese students accounted for more than half of foreign undergraduate (68%) 

and graduate (54%) S&E students in Japan in 2008 (appendix table 2-44).

Foreign students are an increasing share of enrollment in 

Canadian universities. Foreign S&E students accounted for 

about 6% of undergraduate and 20% of graduate S&E enrollment in Canada in 2006, up from 3% and 17% in 1996, 

although the foreign shares in 2006 were down slightly from 

recent years (NSB 2008). In 2005–06, at both the undergraduate and graduate levels, the highest percentages of foreign 

S&E students were in mathematics/computer sciences and 

engineering. Asian countries/economies were the top places 

of origin of foreign S&E graduate and undergraduate students in Canada. China alone accounted for 18% of foreign 

S&E graduate and undergraduate students in Canada. The 

United States was also among the top countries of origin of 

foreign students, accounting for 6% of foreign S&E graduate students and 12% of foreign S&E undergraduate students 

in Canada (appendix table 2-45).

Although foreign students are a large share of U.S. higher 

education, U.S. students are a relatively small share of foreign students worldwide. About 49,000 U.S. students (in 

all fields) were reported as foreign students by OECD and 

OECD partner countries in 2006, far fewer than the numbers 

of foreign students from China, France, Germany, India, 

Japan, or South Korea. The main destinations of U.S. students were the United Kingdom (14,800), Canada (9,500), 

Germany (3,300), Australia (2,900), France (2,800), Ireland 

(2,100), and New Zealand (2,100)—mainly English speaking countries (OECD 2008).

Approximately 242,000 U.S. students from U.S. universities enrolled in study-abroad programs in the 2006–07 

academic year, up 8.5% from 2005–06 (IIE 2008). A little 

more than one-third were in programs lasting one semester, and more than half were in short-term programs (2–8 

weeks). About 5% 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 math 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 doctoral degrees awarded in all fields and in S&E fields 

continues to rise, having reached new peaks in 2007. Graduate enrollment in S&E fields is also increasing. Most of the 

growth in undergraduate S&E education occurred in science 

fields. In engineering, bachelor’s degrees increased since 

2001 but have not yet attained the levels of the 1980s. Computer sciences degrees dropped precipitously in recent years. 

In doctoral degrees, growth occurred in both science and engineering fields.

Foreign graduate student enrollment in S&E increased in 

2006 after declines in 2004 and 2005. The number of entering foreign students dropped after 11 September 2001 but 

partially rebounded in 2005 and 2006. Students on temporary visas earned 33% of S&E doctorates in the United 

States in 2007 and half or more of doctoral degrees awarded 

in engineering, physics, mathematics, computer sciences, 

and economics. A large fraction of these students stay in the 

United States: more than three-quarters of foreign doctoral 

degree recipients in 2007 planned to stay in the United States 

after graduation.
2-38 Chapter 2. Higher Education in Science and Engineering

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 12 June 2009).

13. Data for racial/ethnic groups are for U.S. citizens and 

permanent residents only.

14. 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 22,953 in 2006.

15. For more information about the distribution of postdoc positions according to sex, see Women, Minorities, and 

Persons with Disabilities in Science and Engineering: 2009

(NSF/SRS 2009d).

16. These data are based on national labor force surveys 

and are subject to sampling error; therefore, small differences between countries may not be meaningful. The standard error for the U.S. percentage of 25–64-year-olds with 

a bachelor’s or higher degree is roughly 0.1, and the standard error for the U.S. percentage of 25–34-year-olds with a 

bachelor’s or higher degree is roughly 0.4.

17. 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, bachelor’s 

degree in the United States and Asian countries).

18. In international comparisons, S&E fields do not include medical or health fields.

Glossary

Baccalaureate-origin institution: The college or university from which an S&E doctorate recipient earned a 

bachelor’s degree. 

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, 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: Those individuals 

who are not citizens of the country in which they study.

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.

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. Universities in several other countries (e.g., Canada, Japan, and the 

United Kingdom) have expanded their enrollment of foreign 

S&E students.

Notes

1. Based on previous projections, NCES has estimated 

that the mean absolute percentage error for bachelor’s degrees projected 9 years out was 8.0 (NCES 2008c).

2. 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 for 

Fall 2008” published by The Cooperative Institutional Research Program of the Higher Education Research Institute, University of California–Los Angeles 

(http://www.gseis.ucla.edu/heri/pr-display.php?prQry=28, 

accessed 23 June 2009). Data reported here are significant 

at the .05 level.

3. 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.

4. The physical sciences include earth, atmospheric, and 

ocean sciences.

5. About 17% of 2001 and 2002 S&E bachelor’s degree 

recipients had previously earned an associate’s degree (NSF/

SRS 2006).

6. Data for racial/ethnic groups are for U.S. citizens and 

permanent residents only.

7. For longer trends in degrees, see the NSF report series 

“Science and Engineering Degrees,” http://www.nsf.gov/

statistics/degrees/, accessed 12 June 2009. For more detail 

on enrollment and degrees by sex and by race/ethnicity, see 

Women, Minorities, and Persons with Disabilities in Science 

and Engineering: 2009 (NSF/SRS 2009d). 

8. Data for racial/ethnic groups are for U.S. citizens and 

permanent residents only.

9. The natural sciences include agricultural; biological; 

computer; earth, atmospheric, and ocean; and physical sciences and mathematics.

10. Data for racial/ethnic groups are for U.S. citizens and 

permanent residents only.

11. See Women, Minorities, and Persons with Disabilities in Science and Engineering: 2009 (NSF/SRS 2009d) for 

more detail on enrollment of foreign students by sex.

12. 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 
Science and Engineering Indicators 2010 2-39

Cohen M, Lengenfelter P, Meredith T, Ward D. 2006. A coordinated effort to prepare students for college. Chronicle of Higher Education 53(17):B20.

College Board. 2008. Trends in College Pricing: 2008. New 

York.

Colwell R. 2009. Graduate education: Professional science master’s programs merit wider support. Science

323(5922):1676–77.

Committee on Science, Engineering, and Public Policy 

(COSEPUP). 1995. Reshaping the Graduate Education of Scientists and Engineers. Washington, DC: National Academies Press. http://www.nap.edu/openbook.

php?record_id=4935. Accessed 22 June 2009.

Committee on Science, Engineering, and Public Policy 

(COSEPUP). 2000. Enhancing the Postdoctoral Experience for Scientists and Engineers. Washington, DC: 

National Academies Press. http://books.nap.edu/catalog.

php?record_id=9831#toc. Accessed 1 June 2007.

Committee on Science, Engineering, and Public Policy 

(COSEPUP). 2004. Facilitating Interdisciplinary Research. Washington, DC: National Academies Press. 

http://www.nap.edu/catalog.php?record_id=11153. Accessed 22 June 2009.

Commonfund Institute. 2008. 2008 HEPI update. http://www.

commonfund.org/Templates/Generic/RESOURCE_ 

REQUEST/target.pdf?RES_GUID=B878F541-69A2-

488A-B649-A575FB597EBB. Accessed 18 February 2009.

Council of Graduate Schools (CGS) 2008a. Global Perspectives on Graduate Education: Proceedings of the Strategic Leaders Global Summit on Graduate Education.

Washington, DC.

Council of Graduate Schools (CGS). 2008b. Ph.D. Completion and Attrition: Analysis of Baseline Demographic Data 

from the Ph.D. Completion Project. Washington, DC.

Council of Graduate Schools (CGS). 2008c. Ph.D. Completion and Attrition: Analysis of Baseline Program Data 

from the Ph.D. Completion Project. Washington, DC.

Council of Graduate Schools (CGS). 2008d. The Professional Science Master’s Degree: Results of a Pilot Survey of Programs. Washington, DC.

Council of Graduate Schools (CGS). 2009. Ph.D. Completion and Attrition: Findings from Exit Surveys of Ph.D. 

Completers. Washington, DC.

De Wit H. 2008. The Dynamics of International Student 

Circulation in a Global Context. Rotterdam: Sense 

Publishers.

Engineering Workforce Commission (EWC). 2008. Engineering & technology enrollments, fall 2007. Washington, DC: American Association of Engineering Societies.

Fortenberry JF, Sullivan P, Jordan N, Knight DW. 2007. 

Engineering education research aids instruction. Science

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ProfileEng.pdf. Accessed 10 April 2009.

Stay rate: The proportion of students on temporary visas 

who have plans to stay in the United States immediately after degree conferral.

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, and have a minimum 

duration of 3 years, although they typically last 4 or more 

years. These 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 correspond to associate’s degrees in 

the United States.

Underrepresented minority: 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

Highlights .....................................................................................................................................3-6

Introduction ..................................................................................................................................3-9

Chapter Overview ....................................................................................................................3-9

Chapter Organization ...............................................................................................................3-9

Scope of the S&E Workforce ......................................................................................................3-9

Measures of the S&E Workforce .............................................................................................3-9

Size of the S&E Workforce ...................................................................................................3-10

Growth of the S&E Workforce ..............................................................................................3-11

Employment Patterns .................................................................................................................3-13

Educational Distribution of Those in S&E Occupations .......................................................3-16

Employment in Non-S&E Occupations .................................................................................3-17

Relationships Between Jobs and Degrees ..............................................................................3-18

Work-Related Training ..........................................................................................................3-20

Who Performs R&D? .............................................................................................................3-21

Employment Sectors ..............................................................................................................3-23

S&E Occupation Density by Industry ....................................................................................3-26

Metropolitan Areas ................................................................................................................3-26

Employer Size ........................................................................................................................3-27

Demographics ............................................................................................................................3-27

Age and Retirement ...............................................................................................................3-27

Women and Minorities in S&E ..............................................................................................3-32

S&E Labor Market Conditions ..................................................................................................3-37

Earnings .................................................................................................................................3-37

Earnings at Different Degree Levels ......................................................................................3-38

Unemployment in S&E Occupations .....................................................................................3-38

Recent S&E Graduates ..........................................................................................................3-40

General Labor Market Indicators for Recent Graduates ........................................................3-41

Recent Doctorate Recipients ..................................................................................................3-42

Postdoc Positions ...................................................................................................................3-44

Global S&E Labor Force ...........................................................................................................3-47

Counts of Global S&E Labor Force .......................................................................................3-48

R&D Employment by Multinational Companies ..................................................................3-49

Migration to the United States ...............................................................................................3-50

Conclusion .................................................................................................................................3-58

Notes ..........................................................................................................................................3-58

Glossary .....................................................................................................................................3-59

References ..................................................................................................................................3-60

List of Sidebars

Scientists Since Babylon ............................................................................................................3-12

Projected Growth of Employment in S&E Occupations ...........................................................3-14

Patenting Activity of Scientists and Engineers ..........................................................................3-21

High-Skill Migration to Japan and the UK ................................................................................3-51

3-1
3-2 Chapter 3. Science and Engineering Labor Force

List of Tables

Table 3-1. Classification of degree field and occupation ..........................................................3-10

Table 3-2. Measures and counts of S&E workforce: 2003 and 2006 ........................................3-11

Table 3-3. Educational background of workers in S&E occupations: 2006 ..............................3-17

Table 3-4. Individuals with highest degree in S&E employed in S&E-related and non-S&E 

occupations, by highest degree and relationship of highest degree to job: 2006 ...................3-19

Table 3-5. Scientists and engineers participating in work-related training, 

by occupation: 2006 ...............................................................................................................3-20

Table 3-6. S&E degree holders with R&D work activities, by occupation: 2006 .....................3-23

Table 3-7. Employment distribution and average earnings of workers in NAICS 4-digit 

industries, by proportion of employment in S&E occupations: 2007 ....................................3-26

Table 3-8. Metropolitan areas with highest percentage of workers in S&E 

occupations: 2007 ..................................................................................................................3-27

Table 3-9. Metropolitan areas with largest number of workers in S&E occupations: 2007 ......3-28

Table 3-10. Workers in S&E and STEM occupations in larger metropolitan areas: 2007 ........3-28

Table 3-11. Labor force participation for individuals with highest degree in S&E, 

by education level and age: 2003 ...........................................................................................3-31

Table 3-12. Proportion of employed S&E doctorate holders who had left full-time 

employment since October 2003, by employment sector and age: April 2006 .....................3-32

Table 3-13. Employment status of retired individuals with highest degree in S&E, 

by education level and age: 2003 ...........................................................................................3-32

Table 3-14. Unemployment rate for individuals in S&E occupations, by sex, race/ethnicity, 

and visa status: 1993, 2003, and 2006 ...................................................................................3-34

Table 3-15. Annual earnings and earnings growth in science and technology and related 

occupations: May 2004–May 2007 ........................................................................................3-38

Table 3-16. Labor market indicators for recent S&E degree recipients 1–5 years after 

receiving degree, by field: 2006 .............................................................................................3-41

Table 3-17. Labor market rates for recent doctorate recipients 1–3 years after receiving 

doctorate, by selected field: 2001, 2003, and 2006 ................................................................3-42

Table 3-18. Doctorate recipients holding tenure and tenure-track appointments at academic 

institutions, by years since receipt of doctorate and selected field: 1993, 2003, 

and 2006 .................................................................................................................................3-43

Table 3-19. Salary of recent doctorate recipients 1–5 years after receiving degree, by degree 

field and percentile: 2006 .......................................................................................................3-44

Table 3-20. Median annual salary of recent doctorate recipients 1–5 years after receiving 

degree, by type of employment: 2006 ....................................................................................3-45

Table 3-21. Postdoc estimates from two NSF/SRS surveys, by place of employment and 

citizen/visa status: Fall 2005 ..................................................................................................3-45

Table 3-22. Salary and benefits of U.S. S&E doctorate holders in postdoc positions: 2006 ....3-47

Table 3-23. Estimates of foreign-born individuals in S&E occupations from NSF/SRS and 

Census Bureau, by educational attainment: 1999, 2000, and 2003 .......................................3-51

Table 3-24. Foreign-born proportion of individuals with highest degree in S&E, by field and 

education level: 2003 .............................................................................................................3-52

Table 3-25. Share of college-educated, foreign-born individuals in United States holding 

foreign degrees, by education level: 2003 .............................................................................3-53

Table 3-26. Average annual salary of new recipients of H-1B temporary work visas, by 

occupation and degree: FY 2006 ...........................................................................................3-57

Table 3-27. Initial applications for student/exchange visitor visas: FY 2001–08 .....................3-57

Table 3-28. Temporary residents who received S&E doctorates in 2002 who 

were in the United States, by program rating: 2003–07 ........................................................3-58

Table 3-A. Growth rates for selected S&E labor force measurements ......................................3-12

Table 3-B. Bureau of Labor Statistics projections of employment and job openings in S&E 

occupations: 2006–16 ............................................................................................................3-15
Science and Engineering Indicators 2010 3-3

List of Figures

Figure 3-1. Employment in S&T occupations: 1950–2007 .......................................................3-13

Figure 3-2 Average annual growth rates of total workforce and workforce in 

S&E occupations: 1960–2007 ...............................................................................................3-13

Figure 3-3. U.S. workforce in S&E occupations: 1983–2007 ...................................................3-13

Figure 3-4. Annual average growth rate of degree production and occupational 

employment, by S&E field: 1980–2000................................................................................3-16

Figure 3-5. Educational attainment by type of occupation: 2007 ..............................................3-16

Figure 3-6. S&E degree background of workers in S&E occupations: 2006 ............................3-17

Figure 3-7. S&E degree holders working in S&E occupations, by degree field: 2006 .............3-18

Figure 3-8. Employed S&E degree holders in jobs related to highest degree, by years 

since degree: 2006 .................................................................................................................3-19

Figure 3-9. S&E bachelor’s degree holders employed in jobs closely related to degree, 

by field and years since degree: 2006 ...................................................................................3-19

Figure 3-10. Intersection of highest degree in S&E and S&E occupation: 2006 ......................3-20

Figure 3-11. Measures of the S&E workforce: 2003 .................................................................3-20

Figure 3-12. Distribution of S&E degree holders with R&D as major work activity, 

by level of education: 2006 ...................................................................................................3-22

Figure 3-13. Distribution of individuals with highest degree in S&E with R&D as 

major work activity, by field of highest degree: 2006 ..........................................................3-22

Figure 3-14. S&E doctorate holders engaged in R&D as major work activity, 

by years since degree: 2006 ..................................................................................................3-23

Figure 3-15. Employment sector for individuals whose highest degree is in S&E 

and for S&E doctorate holders: 2006 ....................................................................................3-24

Figure 3-16. Largest sectors of employment for individuals in S&E occupations: May 2007 ....3-24

Figure 3-17. Self-employment rates of workers whose highest degree is in S&E, 

by degree level and type of self-employment: 2006 .............................................................3-24

Figure 3-18. Self-employment rates of workers whose highest degree is in S&E, 

by degree level and age: 2006 ...............................................................................................3-25

Figure 3-19. Self-employment rates of workers whose highest degree is in S&E, 

by degree level and field: 2006 .............................................................................................3-25

Figure 3-20. Individuals with highest degree in S&E employed in private business, 

by employer size: 2006 .........................................................................................................3-29

Figure 3-21. Age distribution of individuals in labor force with highest degree 

in S&E: 2003 .........................................................................................................................3-29

Figure 3-22. Age distribution of individuals in labor force with highest degree in S&E, 

by degree level: 2003 ............................................................................................................3-30

Figure 3-23. Cumulative age distribution of individuals in labor force whose highest 

degree is in S&E, by degree level: 2003 ...............................................................................3-30

Figure 3-24. Age distribution of S&E doctorate holders in labor force: 1993 and 2003 ..........3-31

Figure 3-25. Employed S&E degree holders older than 50, by selected field of highest 

degree: 2006 ..........................................................................................................................3-31

Figure 3-26. Full-time labor force participation by older individuals with highest degree 

in S&E, by age and degree level: 2006 .................................................................................3-32

Figure 3-27. College-educated women and racial/ethnic minorities in S&E occupations: 

1980, 1990, 2000, and 2007 ..................................................................................................3-33

Figure 3-28. Women and racial/ethnic minority doctorate holders in S&E occupations: 

1990, 2000, and 2007 ............................................................................................................3-33

Figure 3-29. Representation of women among workers whose highest degree is 

S&E bachelor’s, by year of degree: 2006 .............................................................................3-33

Figure 3-30. Representation of women among workers whose highest degree is 

S&E doctorate, by year of doctorate: 2006 ...........................................................................3-33

Figure 3-31. Age distribution of individuals in S&E occupations, by sex: 2003 ......................3-34
3-4 Chapter 3. Science and Engineering Labor Force

Figure 3-32. Age distribution of doctorate holders in S&E occupations, by sex: 2003 ............3-34

Figure 3-33. Age distribution of individuals in S&E occupations, by race/ethnicity: 2003 ......3-35

Figure 3-34. Age distribution of S&E doctorate holders in S&E occupations, 

by race/ethnicity: 2003 ..........................................................................................................3-35

Figure 3-35. Estimated differences in full-time salary between women and men with 

highest degree in S&E, controlling for level of degree and other characteristics: 2006 .......3-36

Figure 3-36. Estimated differences in full-time salary of underrepresented minorities 

versus non-Hispanic whites and Asians with highest degree in S&E, controlling for 

level of degree and other characteristics: 2006 ....................................................................3-36

Figure 3-37. Median salaries for bachelor’s degree holders, by broad field classification and 

years since degree: 2003 .......................................................................................................3-38

Figure 3-38. Salary distribution of S&E degree holders employed full time, by degree 

level: 2003 .............................................................................................................................3-39

Figure 3-39. Median salaries of S&E graduates, by degree level and years since 

degree: 2003 ..........................................................................................................................3-39

Figure 3-40. Unemployment rate, by occupation: 1983–2008 ..................................................3-39

Figure 3-41. Estimated unemployment rates over previous 3 months for workers in S&E 

occupations and selected other categories: March 2008 to September 2009 ........................3-40

Figure 3-42. Unemployment rates for individuals whose highest degree is in S&E, 

by years since degree: 1999 and 2003...................................................................................3-40

Figure 3-43. Involuntarily out-of-field rate of individuals whose highest degree is in S&E, 

by years since degree: 1993 and 2003...................................................................................3-41

Figure 3-44. Doctorate recipients holding tenure and tenure-track appointments at academic 

institutions 4–6 years after degree, by field: 1993–2006 ......................................................3-44

Figure 3-45. Field of doctorate of U.S.-educated S&E doctorate recipients in postdoc 

positions: Fall 2005 ...............................................................................................................3-46

Figure 3-46. U.S. S&E doctorate holders ever holding postdoc, by field and year of 

doctorate: 2006 ......................................................................................................................3-46

Figure 3-47. Former postdocs’ evaluation of how much most recent postdoc position 

enhanced career opportunities, by year of doctorate: 2006 ...................................................3-47

Figure 3-48. Estimated number of researchers in selected regions/countries/economies: 

1995–2007 .............................................................................................................................3-48

Figure 3-49. Tertiary-educated population more than 15 years old, by country: 2000 or most 

recent year .............................................................................................................................3-49

Figure 3-50. Top 11 countries of origin of persons having at least tertiary-level education 

and residing in OECD countries: 2000 .................................................................................3-49

Figure 3-51. R&D employment of U.S. MNCs at their foreign affiliates and foreign MNCs 

at their U.S. affiliates: 1994, 1999, and 2004........................................................................3-49

Figure 3-52. R&D employment of U.S. MNC parent companies in the United States 

and their foreign affiliates: 1994, 1999, and 2004 ................................................................3-50

Figure 3-53. Native-born and foreign-born workers in S&E occupations, by degree level: 

2003 and 2007 .......................................................................................................................3-52

Figure 3-54. Foreign-born individuals with highest degree in S&E living in United States, 

by place of birth: 2003 ..........................................................................................................3-53

Figure 3-55. Foreign-born S&E degree holders whose highest degree is from a foreign 

institution, by year of entry to United States: 2003 ..............................................................3-54

Figure 3-56. Foreign-born S&E degree holders, by citizenship/visa status and year of entry 

to United States: 1980–99 .....................................................................................................3-54

Figure 3-57. Temporary work visas issued in categories that include many high-skilled 

workers: FY 1989–2008 ........................................................................................................3-55 

Figure 3-58. Occupations of new recipients of U.S. H-1B temporary work visas: FY 2006 ....3-55

Figure 3-59. Country of citizenship for new recipients of U.S. H-1B temporary work visas: 

FY 2006 .................................................................................................................................3-56

Figure 3-60. Country of citizenship of doctorate holders who are new recipients of U.S. 

H-1B temporary work visas: FY 2006 ..................................................................................3-56
Science and Engineering Indicators 2010 3-5

Figure 3-61. Five-year stay rates for recipients of U.S. S&E doctorates who have 

temporary U.S. visas, by place of origin and year of doctorate: 1992–2007 ........................3-57

Figure 3-A. Bureau of Labor Statistics projections of increase in employment for S&E and 

selected other occupations: 2006–16 ....................................................................................3-14

Figure 3-B. Bureau of Labor Statistics projections of 2006–16 job openings as percentage of 

2006 employment ..................................................................................................................3-15

Figure 3-C. Patenting activity rate of scientists and engineers, by broad field and level 

of highest degree: 2003 .........................................................................................................3-21

Figure 3-D. Distribution of patenting activity of scientists and engineers, by level 

of highest degree: 2003 .........................................................................................................3-22

Figure 3-E. Entry to Japan of workers with selected classes of high-skilled temporary 

visas: 1990–2005 ...................................................................................................................3-51
3-6 Chapter 3. Science and Engineering Labor Force

The S&E workforce has shown sustained growth for 

over half a century, and growth is projected to continue 

into the future.

The number of workers in S&E occupations grew from 

about 182,000 in 1950 to 5.5 million in 2007. This represents an average annual growth rate of 6.2%, nearly 4 

times the 1.6% growth rate for the total workforce older 

than age 18 during this period.

More recently, from 2004 to 2007, S&E workforce 

growth averaged 3.2% but was still twice as high as that 

of the total U.S. workforce.

The sustained U.S. S&E workforce growth rests largely 

on three factors: increased S&E degree production, immigration of scientists and engineers, and few retirements 

because of the relative youth of the S&E workforce compared to the total U.S. workforce.

Scientists and engineers can be categorized in many 

ways, including by occupation and by degree field.

Defined by occupation, the U.S. S&E workforce totaled 

between 4.3 million and 5.8 million people in 2006.

Individuals with an S&E bachelor’s degree or higher 

(16.6 million) or whose highest degree was in S&E (12.4 

million) substantially outnumbered those working in 

S&E occupations.

The majority of those with an S&E degree but working in 

non-S&E occupations report that their jobs are related to 

their degree.

R&D is an important activity for the S&E workforce.

The majority of S&E degree holders who report R&D 

as a major work activity have bachelor’s degrees as their 

highest degree (53%); only 12% have doctorates.

Engineering degree holders comprise more than one-third 

(36%) of the total R&D workforce; those with degrees in 

computer sciences and mathematics constitute another 17%.

Well above half of doctorate holders in most S&E fields 

report participating in R&D; the exception is those with 

social science doctorates.

Among all scientists and engineers named on patent applications from fall 1998 to fall 2003, 41% held a bachelor’s degree, 31% a master’s degree, and 24% a doctorate.

Scientists and engineers work for all types of employers.

For-profit firms employed 47% of all individuals whose highest degree is in S&E but only 28% of S&E doctorate holders.

Academic institutions employed about 42% of individuals with S&E doctorates, including those in postdocs or 

other temporary positions.

About 17% of employed workers whose highest degree 

was in S&E (1.7 million workers) reported they were 

self-employed in 2006, with two-thirds in incorporated 

businesses.

S&E occupations are found throughout industry.

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.

Small firms are important employers of those with science or engineering degrees. Firms with fewer than 100 

persons employ 36% of them.

Aging and retirement patterns are likely to alter the 

composition of the S&E labor force.

Absent changes in degree production, immigration, and 

retirement patterns, the number of S&E-trained persons 

in the workforce will continue to grow, but at a slowing 

rate, as more S&E workers reach traditional retirement 

age (26% were older than age 50 in 2006).

Across all S&E degree levels, by age 61 about half of 

S&E workers are no longer working full time; for doctorate holders, half no longer work full time by age 66.

A much larger proportion of doctorate holders than those 

with bachelor’s and master’s degrees are near retirement age.

Women remain underrepresented in the S&E workforce, 

although to a lesser degree than in the past.

Women constituted two-fifths (40%) of those with S&E 

degrees in 2006, but their proportion is smaller in most 

S&E occupations.

As more women than men have entered the S&E workforce over the decades, their proportion in S&E occupations rose from 12% in 1980 to 27% in 2007. 

Women in the S&E workforce are on average younger 

than men, suggesting that larger proportions of men than 

of women may retire in the near future, thus changing 

these sex ratios.

The proportion of blacks and Hispanics in the S&E labor 

force is lower than their proportion in the general population; the reverse is true for Asians/Pacific Islanders.

The proportions of blacks and Hispanics in S&E occupations have continued to grow over time. However, these 

groups remain underrepresented relative to their proportions in the total population.

Blacks, Hispanics, and other underrepresented minorities together constitute 24% of the U.S. population, 13% 

of college graduates, and 10% of the college-degreed in 

S&E occupations.

The proportion of blacks in nonacademic S&E occupations was 3% in 1980 and 5% in 2007; that of Hispanics 

was 2% and 4%, respectively.

Highlights
Science and Engineering Indicators 2010 3-7

At the doctoral level, blacks, Hispanics, and American Indians/Alaska Natives combined represented just over 4% 

of employment in nonacademic S&E occupations in 1990 

and 6% in 2007. 

Asian/Pacific Islanders constitute 5% of the U.S. population, 7% of college graduates, and 14% of those in S&E 

occupations; most of them (82%) are foreign born.

Workers with S&E degrees or occupations tend to earn 

more than other comparable workers.

Half of the workers in S&E occupations earned $70,600 

or more in 2007, more than double the median earnings 

($31,400) 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. 

Especially at lower education levels, 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 data through September 2009 illustrate 

the advantages occurring to those whose jobs involve 

S&E: 9.7% unemployment for all workers, 7.6% for 

S&E technicians and computer programmers, 5.4% for 

all bachelor’s degree holders, and 5.5% for those in S&E 

occupations. 

For the 12 months beginning in September 2008, unemployment rates rose sharply for all workers, moving from 

6.1% to 9.7%. Substantial increases occurred for technicians and programmers (4.9 percentage points) and workers in S&E occupations (3.3 percentage points), which 

exceeded those for all bachelor degree holders (2.3 percentage points).

The unemployment rates for S&E doctorate holders are 

generally much lower than for those at other degree levels.

Postdoc positions are increasingly common, but their 

frequency is different in different disciplines.

The total number of postdocs in the United States is unknown. About half of the known postdocs in 2005 are in 

the biological and other life sciences.

The incidence of individuals taking S&E postdoc positions during their careers has risen, from about 31% of 

those with a pre-1972 doctorate to 46% of those receiving 

their doctorate in 2002–05.

A majority of doctorate holders in the life or physical sciences now have a postdoc position as part of their career 

path; so do 30% or more of doctorate holders in mathematics and computer sciences, social sciences, and engineering.

The importance of foreign-born scientists and engineers to 

the S&E enterprise in the United States continues to grow. 

Twenty-five percent of all college-educated workers in 

S&E occupations in 2003 were foreign born, as were 40% 

of doctorate holders in S&E occupations. 

More than 40% of all university-educated foreign-born 

workers had their highest degree from a foreign institution, up from about half that percentage before the 1980s.

From 2003 to 2007, the shares of the foreign born among 

master’s degree and doctorate holders rose 2 percentage 

points each.

About half of all foreign-born scientists and engineers are 

from Asia, including: 16% from India; 11% from China; 4% 

to 6% each from the Philippines, South Korea, and Taiwan. 

More than a third of U.S.-resident doctorate holders come 

from China (22%) and India (14%).

The number of most types of temporary work visas issued 

to high-skilled workers has continued to increase from 

their post-9/11 lows.

More temporary visas are issued than are used.

H-1B temporary work visas are restricted to 65,000 annually, with 20,000 exemptions for students earning U.S. 

master’s degrees or doctorates and further exemptions for 

U.S. academic and research institutions in their own hiring.

Over two-thirds of H-1B visas were issued for S&E occupations, with a large portion of the remainder for closely 

related work.

More than half of all H-1B visa recipients were from India; Asian citizens made up three-quarters of all H-1B 

visa recipients.

Most foreign doctoral students choose to remain in the 

United States after earning their degree.

The 5-year stay rate for foreign doctoral students showed 

a small decline: 62% of 2002 doctorate recipients were in 

the country in 2007, down from 65% for the class of 2000 

but remaining near its record high.

Overall declines in stay rates reflect lower rates for doctorate recipients from some countries (e.g., Taiwan, Japan, 

and India), whereas stay rates for students from other 

countries (e.g., the United Kingdom and Germany) increased.

Tentative evidence suggests that foreign students who receive their doctorates from highly rated departments may 

have long-term (5-year) stay rates that are below the rates 

for those who receive their doctorates from less highly reputed departments.

Science and Engineering Indicators 2010 3-7
3-8 Chapter 3. Science and Engineering Labor Force

The capability to work in science and technology has increased throughout the world. 

There are no comprehensive measures of the global S&E 

labor force, but fragmentary data suggest that the U.S. 

world share is continuing to decline.

Data on the number of researchers compiled by the Organisation for Economic Cooperation and Development 

(OECD) show moderate average growth from 1995 to 

2007 for established scientific nations and regions, in 

contrast to rapid growth in selected developing regions.

Over about a decade, the estimated number of U.S. researchers rose by 40% to about 1.4 million in 2007, that 

of the European Union to 1.4 million, and Japan’s to 

about 710,000.

The number of researchers in China rose to an estimated 

1.4 million, comparable to the estimates for the EU-27 

and the United States.

R&D employment of multinational companies (MNCs) 

has been increasing.

In 2004 U.S.-based MNCs employed about 854,000 research and development (R&D) workers globally, 16% of 

them overseas in majority-owned subsidiaries, compared 

with about 727,000 researchers in 1994 (14% of them 

overseas). 

From 1994 to 2004, R&D employment of foreign-based 

MNCs in the United States rose from about 90,000 to 

129,000.

3-8 Chapter 3. Science and Engineering Labor Force
Science and Engineering Indicators 2010 3-9

Introduction

Chapter Overview

Like most developed economies, the United States increasingly depends on a technically skilled workforce, 

including scientists and engineers. Workers for whom 

knowledge and skill in S&E are central to their jobs have an 

effect on the economy and the wider society that is disproportionate to their numbers: they contribute to research and 

development, increased knowledge, technological innovation, and economic growth. Moreover, the knowledge and 

skills associated with science and engineering have diffused 

across occupations and become more important in jobs that 

are not traditionally associated with S&E. 

Chapter Organization

This chapter has five major sections. The first describes different measures of the U.S. S&E workforce by occupation, education, and technical expertise needed on the job. It also presents 

a discussion of the size and growth of the S&E workforce.

The second section examines employment patterns. This 

includes discussion of the types of jobs that S&E degree 

holders have, where they work, and what they do on the job.

S&E labor force demographics are the subject of the third 

section. Topics include the age distribution and retirement 

patterns of the S&E labor force, trends in the participation 

of women and underrepresented racial/ethnic minorities, 

and the continuing importance of foreign-born, and often 

foreign-educated, scientists and engineers. 

The fourth section presents measures of recent S&E labor market conditions. It includes measures of earnings and 

unemployment, indicators which are applicable to all segments of the labor market. In addition, it reports data on the 

proportion of S&E-trained workers who are involuntarily 

working outside of their field. Because highly educated 

S&E workers often prefer, but cannot always find, work that 

uses knowledge and skills related to their education, variations in this measure can be a valuable indicator of labor 

market conditions for these workers. For recent S&E doctoral recipients, data on academic employment and postdoc 

appointments are also presented.

High-quality data on the global S&E labor force are quite 

sparse. The available data are presented in the final section. 

It includes data on the growth in S&E human capital across 

most of the globe and on the increasing importance of international movements of highly skilled workers to developed 

nations and elsewhere. This section also includes a more detailed discussion of the globalization of the U.S. S&E workforce, about which there are relatively more complete data.

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. 

Occupation

U.S. federal occupation data classify workers by the activities or tasks they primarily perform in their jobs. The Bureau of Labor Statistics’ (BLS’s) Occupational Employment 

Statistics (OES) survey collects data that rely on employers 

to classify their workers using standard occupational definitions. Census Bureau and National Science Foundation 

(NSF) occupational data in this chapter come from surveys 

in which individuals supplied information about job titles 

and/or work activities. This information enables jobs to be 

coded into standard occupational categories. 

Although there is no standard definition of an S&E occupation, 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 level of knowledge and education in S&E fields. A second set of occupations, S&E-related occupations, also require some S&E 

knowledge or training, but not necessarily as a required credential 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 salespeople who sell 

specialized research equipment to chemists and biologists 

and technical writers who edit scientific publications. The 

NSF occupational classification of S&E, S&E-related, and 

non-S&E occupations appears in table 3-1. 

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 meaning.

 In this chapter, the narrow classification of S&E occupations is sometimes expanded to include S&E technicians, 

computer programmers, S&E managers, and a small number of non-health S&E-related occupations such as actuary 

and architect. This broader grouping is referred to here as 

STEM occupations.

Education

The pool of S&E workers could also be identified in 

terms of 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 choose to seek 

such jobs if they do not currently hold them. However, a focus on people with relevant educational credentials includes 

individuals who do not hold jobs that are generally identified with S&E and are not likely to seek them in the future. 
3-10 Chapter 3. Science and Engineering Labor Force

Workers with degrees in S&E may not have maintained current knowledge of 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 (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

Size of the S&E Workforce

Defined by occupation, the U.S. S&E workforce totaled 

between 4.3 million and 5.8 million people in 2006 (table 

3-2). Those in S&E occupations who also had bachelor’s 

degrees were estimated at between 4.3 million (Census Bureau 2007) and 5.0 million (NSF, Division of Science Resources Statistics [SRS], Scientists and Engineers Statistical 

Data System [SESTAT]).3

 SESTAT’s 2006 estimates for 

individuals with an S&E degree at the bachelor’s level or 

higher (16.6 million) or whose highest degree was in S&E 

(12.4 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 or somewhat 

related to their highest degree (1.95 million closely related 

and 2.02 million somewhat related). Counting these people, 

along with those in S&E occupations, as part of the S&E 

workforce increases by 80% the size of the estimate by occupation alone.

The 2003 SESTAT surveys provide the most recent 

estimate for a different subjective assessment of S&E 

Table 3-1 

Classification of degree field and occupation

Classification Degree field

Classification of occupation

Occupation STEM (X) S&T (X)

S&E Computer and mathematical sciences Computer and mathematical scientists X X

Biological, agricultural, and 

 environmental life sciences

Biological, agricultural and 

 environmental life 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 occupation only. For a more detailed classification of occupations and degrees by S&E, S&E-related, 

and non-S&E, see National Science Foundation, Division of Science Resources Statistics (NSF/SRS), 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 2010
Science and Engineering Indicators 2010 3-11

work—whether 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 in S&E occupations and 11.9 million whose highest degree is in an S&E field.

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 6 decades using Census Bureau data. (For a discussion of longer periods, see the sidebar “Scientists Since 

Babylon.”) The number of workers in S&E occupations 

grew from about 182,000 in 1950 to 5.5 million in 2007. 

This represents an average annual growth rate of 6.2%, 

nearly 4 times the 1.6% 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 6.5 

million (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 

and between 2000 and 2007. S&E occupational employment has grown from 2.6% of the workforce in 1983 to 4.3% 

of all employment in 2007 (figure 3-3).

Recent OES employment estimates for workers in S&E 

occupations indicate that the S&E workforce is continuing to grow faster than the total workforce (see table 3A in 

sidebar “Scientists Since Babylon”). The OES estimate was 

5.6 million in May 2007, up 9.9% from the May 2004 total of 5.1 million. This implies an average annual growth 

rate of 3.2%, about double the 1.6% average annual increase 

in employment in all occupations. During the same period, 

the broader STEM aggregate (including technicians, S&E 

managers, etc.) reached 7.6 million in May 2007 but grew at 

an average annual rate of 2.2%—slower than S&E occupations because of employment declines for both technicians/

programmers and S&E managers. OES projections are that 

S&E occupations will continue to 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 pace 

than employment in S&E jobs (figure 3-4; see chapter 2 for a 

Table 3-2

Measures and counts of S&E workforce: 2003 and 2006

Measure Education coverage Data source Number

Occupation

Employment in S&E occupations ....................................... All 2006 BLS OES 4,962,000

Employment in S&E occupations ....................................... Bachelor’s and above 2006 NSF/SRS SESTAT 5,024,000

Employment in S&E occupations ....................................... Bachelor’s and above 2006 Census Bureau ACS 4,262,000

Employment in S&E occupations ....................................... All 2006 Census Bureau ACS 5,771,000

Education

At least one degree in S&E field ......................................... Bachelor’s and above 2006 NSF/SRS SESTAT 16,602,000

Highest degree in S&E field ................................................ Bachelor’s and above 2006 NSF/SRS SESTAT 12,436,000

Employed and job closely related to highest degree ...... Bachelor’s and above 2006 NSF/SRS SESTAT 4,540,000

Job is in S&E ............................................................... Bachelor’s and above 2006 NSF/SRS SESTAT 2,590,000

Job is something other than S&E ................................ Bachelor’s and above 2006 NSF/SRS SESTAT 1,950,000

Employed and job somewhat 

related to highest degree ............................................... Bachelor’s and above 2006 NSF/SRS SESTAT 3,045,000

Job is in S&E ............................................................... Bachelor’s and above 2006 NSF/SRS SESTAT 1,026,000

Job is something other than S&E ................................ Bachelor’s and above 2006 NSF/SRS SESTAT 2,019,000

Employment requires bachelor’s level S&E technical expertise in —

One or more S&E fields....................................................... Bachelor’s and above 2003 NSF/SRS SESTAT and NSCG 12,855,000

Engineering, computer science, math, 

or natural sciences ......................................................... Bachelor’s and above 2003 NSF/SRS SESTAT and NSCG 9,215,000

Social sciences ............................................................... Bachelor’s and above 2003 NSF/SRS SESTAT and NSCG 5,335,000

ACS = American Community Survey; BLS = Bureau of Labor Statistics; OES = Occupational and Employment Statistics; NSF/SRS = National Science 

Foundation, Division of Science Resources Statistics; SESTAT = Scientists and Engineers Statistical Data System; NSCG = National Survey of College 

Graduates

SOURCES: BLS, 2006 OES Survey; Census Bureau, 2006 ACS; and NSF/SRS, 2006 SESTAT integrated file and special analytic file comprising 2003 

SESTAT integrated file and 2003 NSCG.

Science and Engineering Indicators 2010
3-12 Chapter 3. Science and Engineering Labor Force

Scientists Since Babylon

Table 3-A

Growth rates for selected S&E labor force measurements

Measurement Source Years First year Last year

Average annual 

growth rate (%)

Researchers in OECD countries ......................................... OECD 1995–2005 2,815,000 3,880,000 3.3

College graduates in the U.S. in S&E occupations 

 (except postsecondary teachers) .................................... U.S. Census 1990–2005 200,000 390,000 4.6

Doctorate holders in the U.S. in S&E occupations 

 (except postsecondary teachers) .................................... U.S. Census 1990–2005 2,362,000 4,111,000 3.8

Workers with highest degree in S&E who report job 

 related to degree ............................................................. NSF/SRS SESTAT 1993–2006 5,342,000 7,585,000 2.7

S&E doctorate holders in U.S. ............................................ NSF/SRS SESTAT 1993–2006 590,000 803,000 2.4

S&E bachelor’s degree and above holders in U.S. ............ NSF/SRS SESTAT 1993–2006 11,022,000 16,602,000 3.2

Engineers in Japan .............................................................

Statistical 

Yearbook Japan 1980-2000 686,662 1,687,795 4.6

Researchers in China ......................................................... OECD 2000–07 695,000 1,423,400 10.8

NSF/SRS = National Science Foundation, Division of Science Resources Statistics; OECD = Organisation for Economic Co-operation and Development; 

SESTAT = Scientists and Engineers Statistical Data System

SOURCES: NSF/SRS, SESTAT database, 1993 and 2003, http://sestat.nsf.gov; Census Bureau, Public Use Microdata Sample, 1990; American 

Community Survey, 2005; and OECD, Main Science and Technology Indicators (2009/1).

Science and Engineering Indicators 2010

In the early 1960s, a prominent historian of science, 

Derek J. de Solla Price, examined the growth of science 

and the number of scientists over very long periods in 

history and summarized his findings in a book entitled 

Science Since Babylon (1961). Using a number of empirical measures (most over at least 300 years), Price 

found that science, and the number of scientists, tended 

to double about every 15 years, with measures of higher 

quality science and scientists tending to grow slower 

(doubling every 20 years) and measures of lower quality science and scientists tending to grow faster (every 

10 years). 

According to Price (1961), one implication of this 

long-term exponential growth is that “80 to 90% of all 

the scientists that ever lived are alive today.” This insight follows from the likelihood that most of the scientists from the past 45 years (a period of three doublings) 

would still be alive. Price was interested in many implications of these growth patterns, but in particular, he was 

interested in the idea that this growth could not continue 

indefinitely and the number of scientists would reach 

“saturation.” Price was concerned in 1961 that saturation 

had already begun.

How different are the growth rates in the number of 

scientists and engineers in recent periods from what Price 

estimated for past centuries? Table 3-A shows growth 

rates for some measurements of the S&E labor force in 

the United States and elsewhere in the world for a period 

of available data. Of these measures, the number of S&E 

doctorate holders in the United States labor force showed 

the lowest average annual growth of 2.4% (doubling in 

31 years if this growth rate were to continue). The number of doctorate holders employed in S&E occupations in 

the United States showed a faster average annual growth 

of 3.8% (doubling in 20 years if continued). There are no 

global counts of individuals in S&E, but counts of “researchers” in member countries of the Organisation for 

Economic Co-operation and Development (OECD) grew 

at an average annual rate of 3.3% (doubling in 23 years if 

continued). Data on the population of scientists and engineers in most developing countries are very limited, but 

OECD data for researchers in China show a 10.8% average annual growth rate (doubling in 8 years if continued). 

All these numbers are broadly consistent with a continuation of growth in S&E labor exceeding the rate of growth 

in the general labor force. 
Science and Engineering Indicators 2010 3-13

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 total 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 largely made possible by the following 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 education, 

and (3) the relatively small proportion of scientists and engineers leaving the S&E labor force because they had reached 

retirement age. 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 NSB 2003).

Employment Patterns

This section describes the distribution of members of the 

S&E labor force in the economy. In view of the disjunction 

between S&E occupations and S&E degrees, this discussion 

begins with an analysis of data on the educational characteristics of those in S&E occupations and the occupations 

of workers with S&E degrees. It then describes the institutional sectors in which members of the S&E labor force 

are employed and provides industry breakdowns within the 

	


	
	

	







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S&E employees (millions) (bars) Workforce (percent) (line)

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3-14 Chapter 3. Science and Engineering Labor Force

Projected Growth of Employment in S&E Occupations

Projections of employment growth are notoriously 

difficult to make, and the present economic environment makes them even more uncertain. Conceivably, 

the worldwide economic crisis will produce long-term 

changes in employment patterns and trends. The reader 

is cautioned that the assumptions underlying projections such as these, which rely on past empirical relationships, may no longer be valid. 

The most recent BLS occupational projections, for 

the period 2006–16, 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). 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.

S&E occupations are projected to grow by 21.4% 

between 2006 and 2016, while employment in all occupations is projected to grow 10.4% over the same 

period (table 3-B, appendix table 3-2).4 Yet, there are 

challenges to making projections about the S&E workforce. Many corporate and government spending decisions on R&D are difficult or impossible to anticipate. 

In addition, R&D money increasingly crosses borders 

in search of the best place to have particular research 

performed. (The United States may be a net recipient 

of these R&D funds; see the discussion in chapter 4.) 

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.

Approximately 64% of BLS’s projected increase in 

S&E jobs is in computer and mathematical scientist occupations (table 3-B). Apart from these occupations, 

the growth rates projected for physical scientists, life 

scientists, and social scientists are above those for all 

occupations. Engineering occupations, with projected 

growth of 10.6%, are growing at about the same rate 

as all jobs.

Table 3-B also shows occupations that either contain 

significant numbers of S&E trained people or represent 

other career paths for those pursuing graduate training. 

Among these, postsecondary teacher or administrator, 

which includes all fields of instruction, is projected to 

grow faster than computer and mathematical occupations, from 1.8 million to 2.3 million workers over the 

decade between 2006 and 2016—an increase of 31.4%. 

In contrast, BLS projects computer programmers to increase by only 2.0%. 

BLS also projects that job openings in NSF-identified S&E occupations over the 2006–16 period will 

represent a greater proportion of current employment 

than all other occupations—43.9% versus 33.7% (figure 3-B). Job openings include both growth in total employment and openings caused by attrition. 

	
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Science and Engineering Indicators 2010 3-15

Table 3-B

Bureau of Labor Statistics projections of employment and job openings in S&E occupations: 2006–16 

(Thousands)

Occupation 

BLS National 

Employment 

Matrix 2006 

estimate

BLS projected 

2016 

employment

Job openings 

from growth and 

net replacements, 

2006–16

10-year 

growth in total 

employment (%)

10-year job 

openings as 

percent of 2006 

employment

All occupations ................................................. 150,620 166,220 50,732 10.4 33.7

All S&E .......................................................... 5,187 6,296 2,280 21.4 43.9

Computer/mathematical scientists ........... 2,859 3,694 1,466 29.2 51.3

Life scientists ............................................ 258 292 103 12.8 40.0

Physical scientists ..................................... 267 309 109 15.7 41.0

Social scientists/related occupations ....... 291 330 96 13.3 32.9

Engineers .................................................. 1,512 1,671 505 10.6 33.4

S&E-related occupations

S&E managers ........................................... 513 616 200 20.1 39.0

S&E technicians ........................................ 874 986 303 12.8 34.7

Computer programmers ............................ 455 464 117 2.0 25.6

Physicians and surgeons .......................... 633 723 204 14.2 32.3

Health technologists and technicians ....... 2,612 3,094 1,074 18.5 41.1

Selected other occupations

Postsecondary teachers/administrators ... 1,760 2,312 953 31.4 54.1

Lawyers ..................................................... 761 844 228 11.0 29.9

BLS = Bureau of Labor Statistics

NOTES: Estimates of current and projected employment for 2006–16 period 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 derived from multiple sources, data 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 S&E.

SOURCE: BLS, Office of Occupational Statistics and Employment Projections, National Industry-Occupation Employment Projections, 2006–2016 (2007).

Science and Engineering Indicators 2010

	
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3-16 Chapter 3. Science and Engineering Labor Force

private sector, which is the largest employer of individuals 

in S&E occupations. The section also briefly describes the 

metropolitan areas and size of firms in which S&E degree 

holders are found.

Because the workforce’s capacities for R&D, invention, 

and innovation are a continuing focus of policy concern, this 

section also features data on R&D and patenting activities in 

the workforce. Data on work-related training, which can foster innovation through organizational and individual learning, are also presented.

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,5

 2007 ACS data 

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 7% of all S&E workers (except postsecondary teachers) had doctorates. 

Technical issues of 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 

	
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Science and Engineering Indicators 2010 3-17

from technical or vocational school training (with or without earned associate’s degrees) or college courses, and many 

acquire such skills through workforce experience or on-thejob 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 (86%) have 

at least one S&E degree, and 74% have S&E degrees only 

(table 3-3). 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 (37%) and computer and mathematical sciences (21%) (figure 3-6).

Employment 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 (1.3 million workers) or in S&E managerial positions 

(267,000 workers), but they also hold non-S&E jobs such as 

college and precollege teachers in non-S&E areas (622,000 

workers) or work in social services occupations (632,000 

workers). In 2006, 6.2 million workers whose highest degree was in an S&E field did not work in an S&E occupation. Some 1.1 million worked in S&E-related occupations, 

while just over 5.0 million worked in non-S&E jobs. The 

largest category of non-S&E jobs was management and 

management-related occupations, with 1.4 million workers, 

followed by sales and marketing occupations, with 990,000 

workers (NSF/SRS 2006).

Only about 39% 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 (66%) at the top, followed closely 

by computer and mathematical sciences (59%) and physical sciences (55%). Although a smaller percentage (31%) of 

biological/agricultural sciences degree holders work in S&E 

	
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Educational background of workers in S&E 

occupations: 2006

Educational background Number Percent

S&E occupations ..................................... 5,023,635 100.0

At least one S&E degree ...................... 4,294,666 85.5

First bachelor’s degree in S&E ......... 4,023,000 80.1

Highest degree in S&E ..................... 3,929,860 78.2

All degrees in S&E ............................ 3,696,443 73.6

At least one degree in—

 Computer/mathematical 

 sciences ..................................... 1,052,725 21.0

 Life sciences ................................. 576,922 11.5

 Physical sciences .......................... 495,985 9.9

 Social sciences ............................. 651,519 13.0

 Engineering ................................... 1,867,172 37.2

No S&E degrees but at least one 

S&E-related degree ........................... 216,509 4.3

No S&E or S&E-related degrees .......... 512,459 10.2

NOTE: Detail may not add to total because of rounding.

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2006), http://sestat.nsf.gov.

Science and Engineering Indicators 2010
3-18 Chapter 3. Science and Engineering Labor Force

occupations, an additional 25% of persons with degrees in 

these fields work in S&E-related occupations. 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, these field 

differences shrink substantially.

By field, holders of degrees in computer and mathematical sciences and engineering most often work in the broad 

occupation group in which they were trained (51% and 45%, 

respectively). S&E doctorate holders more often work in the 

same broad S&E occupation (64%) compared with individuals whose highest degree is an S&E bachelor’s (24%) (appendix table 3-1). 

Relationships Between Jobs and Degrees

Most individuals with S&E highest degrees who work 

in S&E-related and non-S&E occupations do not see themselves as working entirely outside their field of degree. 

Rather, they indicate that their jobs are either closely (31%) 

or somewhat (32%) related to their degree field (table 3-4). 

Among those in managerial and management-related occupations, for example, 31% characterize their jobs as closely 

related and 41% as somewhat related. Almost half (47%) 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, 74% 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. One to 4 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 72% of bachelor’s degree 

holders (figure 3-8). Even when the fit between an individual’s job and field of degree is assessed using a stricter criterion (“closely related”), the data indicate that many S&E 

bachelor’s degree holders who received their degree 1–4 

years earlier are working in jobs that use skills developed during their college training (figure 3-9). In the natural sciences 

and engineering fields, about half characterized their jobs as 

closely related to their field of degree: 57% in engineering and 

physical sciences, 50% in computer sciences, and 48% in biological/agricultural sciences. The comparable figure for social 

science graduates (30%) was substantially lower.

The stronger relationship between S&E jobs and S&E 

degrees at higher degree levels holds at all career stages, 

as evidenced by comparisons among groups of bachelor’s, 

master’s, and doctoral degree holders at comparable numbers of years since degree award. 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 college training has become obsolete. Against this background, the career-cycle decline in the 

relevance of an S&E degree appears modest. 

Figures 3-10 and 3-11 summarize the loose relationship 

among jobs, degrees, and individuals’ perceptions of the expertise they need to do their work. In figure 3-10, the intersecting area, which shows individuals whose highest degree 

is in S&E who are working in S&E occupations, is less than 

	
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one-third the size of the area comprising individuals with 

only one or the other attribute. Figure 3-11 compares the 

following three groups of individuals who hold at least a 

bachelor’s degree: those in S&E occupations, those whose 

highest degree is in S&E and who say their job is at least 

somewhat related to their degree, and those who say they 

need at least a bachelor’s degree level of S&E expertise to 

perform their job. In 2003, the most recent year in which 

the SESTAT surveys asked about S&E technical expertise, 

about 15 million Americans fell in one or more of these 

categories. Only 21% had all three characteristics, and just 

over half had only one. Even among those in S&E occupations, only about two-thirds 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 

Table 3-4

Individuals with highest degree in S&E employed in S&E-related and non-S&E occupations, by highest degree 

and relationship of highest degree to job: 2006 

(Percent)

Employment

(thousands)

 Degree related to job

Highest degree Closely Somewhat Not

All degree levelsa ................................................ 6,226 31.3 32.4 36.3

Bachelor’s ....................................................... 5,071 28.3 32.3 39.4

Master’s .......................................................... 975 44.9 31.9 23.2

Doctorate ........................................................ 176 41.4 39.0 19.6

aIncludes professional degrees not broken out separately. 

SOURCE: National Science Foundation, Division of Science Resources Statistics, Scientists and Engineers Statistical Data System (SESTAT) (2006), 

http://sestat.nsf.gov.

Science and Engineering Indicators 2010

	
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3-20 Chapter 3. Science and Engineering Labor Force

than half said either that their job was unrelated to their actual degree or that their highest degree was not in S&E.

Work-Related Training

Education for most scientists and engineers does not end 

when they receive their college degree. About two-thirds 

of SESTAT survey respondents (persons who received a 

bachelor’s degree or higher in S&E, or S&E-related fields, 

plus persons holding a non-S&E bachelor’s or higher degree who were employed in an S&E or S&E-related occupation) participated in work-related training in 2006. 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%) 

(table 3-5). 

Most who took training did so to improve skills or knowledge in their current occupational field (56%) (appendix 

table 3-3). Others did so for licensure/certification in their 

current occupational field (21%) or because it was required 

or expected by their employer (14%). 

	
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Table 3-5

Scientists and engineers participating in work-related training, by occupation: 2006

 Participated in training

Occupation All employed Number Percent

All occupations ............................................................................... 18,927,000 12,696,000 67.1

S&E occupations ........................................................................ 5,024,000 3,037,000 60.4

Computer and mathematical scientists .................................. 2,112,000 1,202,000 56.9

Life scientists .......................................................................... 487,000 296,000 60.8

Physical and related scientists ................................................ 334,000 183,000 54.8

Social and related scientists ................................................... 470,000 301,000 64.0

Engineers ................................................................................ 1,621,000 1,056,000 65.1

S&E-related occupations ............................................................ 5,246,000 4,167,000 79.4

Non-S&E occupations ................................................................ 8,657,000 5,492,000 63.4

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 a non-S&E degree 

at bachelor’s level or higher and were employed in an S&E or S&E-related occupation in 2006. Detail may not add to total because of rounding.

SOURCE: National Science Foundation, Division of Science Resources Statistics, Scientists and Engineers Statistical Data System (SESTAT) (2006), 

http://sestat.nsf.gov.

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 3-21

Women participated in work-related training at a higher 

rate than men: 72% compared with 64% of men (appendix 

table 3-4). Smaller percentages of the oldest (aged 65 and 

older) and youngest (24 and under) age groups of workers 

attended training. SESTAT survey respondents at companies of all sizes took work-related training, but more of 

those who worked for larger organizations did so: 58% of 

respondents working in organizations with 10 or fewer people compared with 72% in organizations that employ 500 to 

24,999 people (appendix table 3-5).

Who Performs R&D?

Although individuals with S&E degrees use their knowledge in many ways, there is a special interest in work in 

research and development. R&D creates new knowledge 

and new types of goods and services that fuel economic 

growth. (See sidebar, “Patenting Activity of Scientists and 

Engineers.”) Figure 3-12 shows the distribution of individuals with S&E degrees, by degree level, who report R&D as 

a major work activity—the activity involving the greatest 

or second greatest number of work hours from a list of 14 

choices. 

Individuals with doctorates constitute only 6% of all individuals with S&E degrees but represent 12% of individuals who report R&D as a major work activity. However, the 

majority of S&E degree holders who report R&D as a major 

work activity have only bachelor’s degrees (53%). An additional 31% have master’s degrees and 4% have professional 

degrees, mostly in medicine. 

Figure 3-13 shows the distribution by field of highest degree of individuals whose highest degree is in S&E and who 

reported R&D as a major work activity. Individuals with 

engineering degrees constitute more than one-third (36%) 

of the total R&D workforce, followed by those with social 

science degrees (22%).

Patenting Activity of Scientists and Engineers

The U.S. Patent and Trademark Office (USPTO) 

grants patents to inventions that are new, useful, and 

nonobvious. Thus, 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 

	
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application (appendix table 3-6). This patent activity rate 

was 3.5% for those working in the business/industry sector, 1.7% in the education sector, and 0.9% in the government sector (appendix table 3-7). 

By degree level, S&E doctorate holders have the highest patent activity rate (15.7%), while bachelor’s degree 

holders in S&E-related fields have the lowest (0.7%) 

(figure 3-C). However, there are far fewer doctoral-level 

scientists and engineers, so they account for only about 
3-22 Chapter 3. Science and Engineering Labor Force

Individuals who are in non-S&E occupations do much 

R&D. Table 3-6 shows the occupational distribution of S&E 

degree holders who report R&D as a major work activity. 

Twenty-six percent of those for whom R&D is a major work 

activity are in non-S&E occupations. Among those S&E degree holders whose jobs have them spend at least 10% of 

their time on R&D, 39% are in non-S&E occupations (lawyers or S&E managers, for example).

a quarter of all survey respondents named on a U.S. patent application. Bachelor’s and master’s degree holders 

account for 41% and 31%, respectively, of all patenting 

activity reported in the survey (figure 3-D).

USPTO 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. USPTO 

granted some 1.0 million (although applicants may have 

applied for some of these at an earlier period). (See appendix tables 3-6 through 3-8.) 

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. 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).

	
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Patenting Activity of Scientists and Engineers continued
Science and Engineering Indicators 2010 3-23

Figure 3-14 shows the percentages of S&E doctorate 

holders reporting R&D as a major work activity by field 

of degree and by years since receipt of doctorate. Individuals working in physical sciences and engineering report the 

highest R&D rates over their career cycles, and those in the 

social sciences report the lowest R&D rates. The percentage of doctorate holders engaged in R&D activities declines 

with increasing time since award of the degree. 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.

Employment Sectors

Individuals with S&E degrees are employed in all sectors 

of the U.S. economy. For-profit firms are their largest employer, but substantial numbers work in academia, nonprofit 

organizations, and government, or are self-employed.

For-profit firms employ the greatest number of individuals with S&E degrees (figure 3-15). They employed 47% of 

all individuals whose highest degree is in S&E and 28% of 

S&E doctorate holders. For those with an S&E doctorate, 

4-year colleges and universities are an important but not a 

majority employer (42%). This 42% includes tenured and 

tenure-track faculty, individuals in postdoc and other temporary positions, and individuals with teaching, research, and 

administrative functions.

The OES survey provides more detailed estimates for 

sectors of employment, although it excludes the self-employed and those employed in recent startups (figure 3-16). 

The largest such employment segment for S&E occupations 

was “professional, scientific, and technical services” with 

29%, followed by manufacturing with 17%. Government 

and educational services sectors each had less than 11% of 

total employment in S&E occupations in 2007.

	
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Table 3-6

S&E degree holders with R&D work activities, by occupation: 2006

Employed S&E 

degree holders

R&D as major 

work activity

R&D at least 

10% of work time

Occupation Number Percent Number Percent

R&D activity 

rate (%) Number Percent

R&D activity 

rate (%)

All occupations ................................ 13,752,000 100.0 4,155,000 100.0 30.2 7,369,000 100.0 53.6

S&E occupations ......................... 4,295,000 31.2 2,541,000 61.2 59.2 3,371,000 45.7 78.5

Computer/mathematical 

scientists................................ 1,626,000 11.8 802,000 19.3 49.3 1,171,000 15.9 72.0

 Life scientists ........................... 435,000 3.2 330,000 7.9 75.7 383,000 5.2 88.0

Physical scientists ................... 319,000 2.3 220,000 5.3 68.9 264,000 3.6 82.8

Social scientists ...................... 412,000 3.0 197,000 4.7 47.7 271,000 3.7 65.6

Engineers................................. 1,502,000 10.9 993,000 23.9 66.1 1,282,000 17.4 85.4

S&E-related occupations............. 2,236,000 16.3 524,000 12.6 23.4 1,110,000 15.1 49.6

Non-S&E occupations ................. 7,221,000 52.5 1,090,000 26.2 15.1 2,888,000 39.2 40.0

NOTE: Detail may not add to total because of rounding.

SOURCE: National Science Foundation, Division of Science Resources Statistics, Scientists and Engineers Statistical Data System (SESTAT) (2006), 

http://sestat.nsf.gov.

Science and Engineering Indicators 2010
3-24 Chapter 3. Science and Engineering Labor Force

Self-Employment

More than 1.7 million workers whose highest degree is 

in S&E were self-employed in 2006, 17% of the total (NSF/

SRS 2006). This SESTAT estimate of S&E self-employment is much higher than others that have been published 

elsewhere because it uses a different definition. Most reports 

of federal data on self-employment include only individuals 

whose businesses are unincorporated. While only a minority (33%) of all self-employed workers in the United States 

work in incorporated businesses (Census Bureau 2007), the 

reverse is true for those whose highest degree is in S&E. As 

shown in figure 3-17, adding “incorporated self-employed” 

greatly increases the proportion of workers whose highest 

degree is in S&E who are also self-employed. The rate of incorporated self-employment is much higher for individuals 

with S&E degrees than for the U.S. workforce as a whole, 

where only 11% are self-employed, and only one-third of 

those are incorporated (Census Bureau 2007). Among those 

whose highest degree is in S&E who are also self-employed, 

64% work in incorporated businesses. Similar to other 

types of employment for S&E degree holders, 64% of selfemployed workers whose highest degree is in S&E report 

Figure 3-15

	

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NOTE: Self-employment includes employment at both incorporated 

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SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Scientists and Engineers Statistical Data 

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4-year college/university 9%

Self-employed 17%

Government

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Nonprofit 7%

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SOURCE: Bureau of Labor Statistics, Occupational Employment 

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Educational services 11%

Government 10%

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Science and Engineering Indicators 2010 3-25

that their job is related to the field of their highest degree 

(NSF/SRS 2006).

The proportion of self-employed workers generally decreases by level of degree and increases with age (see figures 

3-17 and 3-18). While 18% of S&E bachelor’s degree holders are self-employed, the proportion falls to 11% for S&E 

doctorate holders. However, self-employment increases 

with age at all degree levels. By age 60–64 self-employment 

reached about 30% for bachelor’s and master’s degree holders and 20% for S&E doctorate holders.

The rates of self-employment are similar across broad 

S&E fields, at the bachelor’s degree level ranging from 

14.8% in computer and mathematical sciences to 20.4% in 

the physical sciences (see figure 3-19). The highest self-employment rate among doctorate holders occurs in the social 

sciences (19%) and the lowest (6%) in computer and mathematical sciences.

Federal S&E Employment

The United States federal government is a major employer of scientists and engineers, 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 210,000 persons in S&E occupations in 2005. Many of these workers were in occupations 

that, nationwide, include relatively large concentrations of 

foreign-born persons, some of whom are non-citizens, rendering them ineligible for many federal jobs. Among federal 

employees, 59% were in science occupations and 41% were 

	
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3-26 Chapter 3. Science and Engineering Labor Force

in engineering occupations. The Department of Defense was 

the largest employer, with nearly 45% of the federal S&E 

workforce (NSF/SRS 2008a).

With regard to gender, the federal S&E workforce (defined by occupation) generally reflects the total S&E workforce. Women make up 26% of all U.S. employees in S&E 

occupations; for federal employees, the comparable proportion is 25%. The number of women in federal S&E positions 

shows a consistent decrease as age increases beyond the 

ages of 40–49; this is also true of the whole S&E workforce. 

The S&E workforce at large is younger than the federal 

S&E workforce. Twenty-eight percent of the general S&E 

workforce is under 35 years of age, with only 15% of those 

in federal S&E occupations in that age group (appendix 

table 3-9).

S&E Occupation Density by Industry

 High-technology employers are not the only companies 

who hire individuals in S&E occupations. As shown in table 

3-7, workers with high-technology knowledge are found in 

industries with very different percentages of S&E occupations as a portion of total employment. Almost 1 million 

workers in S&E jobs are employed in industries whose S&E 

employment component is less than the national average of 

4.2%. These industries employ 79% of all workers and 18% 

of all workers in S&E occupations. Illustrative examples include local government (at 3.0%, with 163,000 S&E jobs), 

hospitals (at 1.4%, with 68,000 S&E jobs), and plastic parts 

manufacturers (at 2.6%, with 16,000 S&E jobs). 

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. 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 ($71,550 versus $36,146).

Metropolitan Areas

The availability of highly skilled workers can be relevant 

to an area’s economic competitiveness. Two measures of 

availability with regard to S&E occupations are (1) the number of workers in S&E occupations and (2) the proportion 

of the entire metropolitan workforce that S&E occupations 

represent. These estimates should be used with care in comparing areas because the geographic scope of a metropolitan 

area varies significantly from city to city. 

The Census Bureau divides some larger metropolitan areas into metropolitan divisions, and these divisions are used 

in comparisons with smaller metropolitan areas. Accordingly, table 3-8 lists metropolitan divisions with the largest 

estimated proportion of the workforce employed in S&E occupations. Table 3-9 lists areas and divisions with the largest 

estimated total number of workers employed in S&E occupations. Table 3-10 presents these data for larger metropolitan areas with multiple metropolitan divisions. These data 

are for May 2007.

The San Jose-Sunnyvale-Santa Clara and Boulder metropolitan areas had 14.3% and 14.2% of their workforces 

employed in S&E occupations, respectively. San Jose-Sunnyvale-Santa Clara had 18.2% of their workers in STEM occupations. No metropolitan areas had higher estimates for 

S&E or STEM occupations. Although the metropolitan areas 

with the highest estimated proportion of S&E employment are 

mainly smaller and perhaps less economically diverse, Washington, DC, Seattle, Boston, San Francisco, and San Jose also 

appear on the list of metropolitan areas with the greatest intensity of S&E occupational employment.

The largest numbers of workers in S&E occupations are 

in the Washington-Arlington-Alexandria, New York-White 

Plains-Wayne, Los Angeles-Long Beach-Glendale, and 

Chicago-Naperville-Joliet metropolitan divisions. These divisions have very large and diverse workforces even after 

being broken off from their larger metropolitan areas. With 

the exception of Washington-Arlington-Alexandria, each of 

Table 3-7

Employment distribution and average earnings of workers in NAICS 4-digit industries, by proportion of 

employment in S&E occupations: 2007

Average annual worker salary ($)

Workers in S&E occupations (%)

All 

occupations

S&E 

occupations

Non-S&E 

occupations

S&E 

occupations

>40 ....................................................................................... 2,456,900 1,150,410 71,550 81,093

20-40 .................................................................................... 3,533,150 952,320 54,039 80,230

10–20 .................................................................................... 10,558,950 1,444,490 56,319 74,833

4.2–10 ................................................................................... 12,158,410 880,540 47,237 68,179

<4.2 (below national average) .............................................. 105,112,220 988,950 36,146 64,961

NAICS = North American Industry Classification System

NOTE: NAICS has hierarchal structure that uses 2 to 4 digits; 4-digit NAICS industries are subsets of 3-digit industries, which are subsets of 2-digit 

sectors.

SOURCE: Bureau of Labor Statistics, Occupational Employment Statistics Survey (May 2007).

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 3-27

these areas has about the same proportion of workers in S&E 

occupations as the national workforce.

Looking just at the larger metropolitan areas, without 

breaking them into divisions, New York-Northern New 

Jersey-Long Island has the largest number (350,670) of individuals employed in S&E occupations but the same proportion (4.2%) as the workforce nationwide (see table 3-10 

and figure 3-3).

Employer Size 

For individuals whose highest degree is in S&E and who 

are employed in business/industry, the distribution of employer size is shown in figure 3-20. Across all degree levels, 

companies with fewer than 100 employees employ 36% of 

S&E degree holders. About 33% work at large firms with 

more than 5,000 employees. In general, there is a similar 

pattern of employment across employer size by degree levels, except that S&E doctorate holders are more concentrated at very small firms.

Demographics

Age and Retirement

The age distribution and retirement patterns of the S&E 

labor force affect its size, productivity, and the opportunities 

it offers for new S&E workers. For many decades, rapid increases in new entries into the workforce created a relatively 

young pool of workers, with only a small percentage near 

traditional retirement age. Now, individuals who earned 

S&E degrees in the late 1960s and early 1970s are moving 

into the later part of their careers. 

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). 

Table 3-8

Metropolitan areas with highest percentage of workers in S&E occupations: 2007

Percentage of workforce Workers employed

Metropolitan area

S&E 

occupations

STEM 

occupations

S&E 

occupations

STEM 

occupations

San Jose-Sunnyvale-Santa Clara, CA........................................ 14.3 18.2 130,180 165,400

Boulder, CO ................................................................................ 14.2 17.4 22,830 28,010

Huntsville, AL .............................................................................. 12.8 16.2 25,680 32,630

Framingham, MA NECTA Division .............................................. 12.7 16.6 19,900 25,940

Durham, NC ................................................................................ 11.1 15.5 29,880 41,560

Lowell-Billerica-Chelmsford, MA-NH NECTA Division ............... 11.1 14.1 13,100 16,580

Washington-Arlington-Alexandria, 

DC-VA-MD-WV Metropolitan Division ...................................... 10.6 12.7 242,350 290,700

Bethesda-Gaithersburg-Frederick, MD Metropolitan Division ... 9.6 12.0 54,370 68,340

Seattle-Bellevue-Everett, WA Metropolitan Division .................. 9.3 11.8 131,620 167,060

Olympia, WA ............................................................................... 8.7 10.1 8,300 9,700

Kennewick-Richland-Pasco, WA ............................................... 8.4 11.2 7,300 9,700

Austin-Round Rock, TX .............................................................. 8.4 11.0 62,270 82,100

Ithaca, NY ................................................................................... 8.0 12.5 4,020 6,270

Bloomington-Normal, IL ............................................................. 8.0 10.1 6,880 8,680

Ann Arbor, MI .............................................................................. 8.0 10.3 15,620 20,250

Boston-Cambridge-Quincy, MA-NH NECTA Division ................ 7.9 10.3 134,190 174,180

Palm Bay-Melbourne-Titusville, FL ............................................ 7.9 10.7 16,210 21,800

Ames, IA ..................................................................................... 7.8 10.7 3,270 4,480

San Francisco-San Mateo-Redwood City, 

CA Metropolitan Division .......................................................... 7.6 9.7 75,700 96,170

Fort Walton Beach-Crestview-Destin, FL ................................... 7.2 8.8 5,860 7,200

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.

SOURCE: Bureau of Labor Statistics, Occupational Employment Statistics Survey (2007).

Science and Engineering Indicators 2010
3-28 Chapter 3. Science and Engineering Labor Force

Table 3-9

Metropolitan areas with largest number of workers in S&E occupations: 2007

Workers employed Percentage of workforce

Metropolitan area

S&E 

occupations

STEM 

occupations

S&E 

occupations

STEM 

occupations

Washington-Arlington-Alexandria, DC-VA-MD-WV Metropolitan 

Division ...................................................................................... 242,350 290,700 10.6 12.7

New York-White Plains-Wayne, NY-NJ Metropolitan Division .... 209,670 279,960 4.1 5.5

Los Angeles-Long Beach-Glendale, CA Metropolitan 

Division ...................................................................................... 160,480 215,970 3.9 5.2

Chicago-Naperville-Joliet, IL Metropolitan Division ................... 156,390 209,890 4.1 5.5

Boston-Cambridge-Quincy, MA-NH NECTA Division ................ 134,190 174,180 7.9 10.3

Seattle-Bellevue-Everett, WA Metropolitan Division .................. 131,620 167,060 9.3 11.8

San Jose-Sunnyvale-Santa Clara, CA........................................ 130,180 165,400 14.3 18.2

Houston-Sugar Land-Baytown, TX ............................................ 128,020 182,920 5.2 7.4

Dallas-Plano-Irving, TX Metropolitan Division ............................ 119,910 161,610 5.8 7.9

Minneapolis-St. Paul-Bloomington, MN-WI ............................... 103,280 137,400 5.8 7.7

Atlanta-Sandy Springs-Marietta, GA .......................................... 102,540 139,350 4.3 5.8

Philadelphia, PA Metropolitan Division ....................................... 94,350 128,750 5.1 6.9

Santa Ana-Anaheim-Irvine, CA Metropolitan Division ................ 80,170 107,300 5.2 7.0

Denver-Aurora, CO ..................................................................... 79,030 99,430 6.4 8.1

San Diego-Carlsbad-San Marcos, CA ....................................... 78,860 105,470 6.0 8.0

Warren-Troy-Farmington Hills, MI Metropolitan Division ............ 76,870 103,390 6.6 8.9

San Francisco-San Mateo-Redwood City, CA Metropolitan 

Division ...................................................................................... 75,700 96,170 7.6 9.7

Phoenix-Mesa-Scottsdale, AZ ................................................... 73,920 107,260 3.9 5.7

Baltimore-Towson, MD ............................................................... 71,660 93,720 5.6 7.3

Oakland-Fremont-Hayward, CA Metropolitan Division .............. 63,540 85,240 6.2 8.3

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.

SOURCE: Bureau of Labor Statistics, Occupational Employment Statistics Survey (2007).

Science and Engineering Indicators 2010

Table 3-10

Workers in S&E and STEM occupations in larger metropolitan areas: 2007

Workers employed Percentage of workforce

Metropolitan area

S&E 

occupations

STEM 

occupations

S&E 

occupations

STEM 

occupations

New York-Northern New Jersey-Long Island, NY-NJ-PA ............ 350,670 474,540 4.2 5.7

Washington-Arlington-Alexandria, DC-VA-MD-WV ..................... 296,720 359,040 10.4 12.6

Los Angeles-Long Beach-Santa Ana, CA ................................... 240,650 323,270 4.2 5.7

Boston-Cambridge-Quincy, MA-NH ........................................... 187,950 244,130 7.6 9.9

Chicago-Naperville-Joliet, IL-IN-WI ............................................ 179,070 241,800 4.0 5.4

Dallas-Fort Worth-Arlington, TX .................................................. 149,470 206,810 5.2 7.1

San Francisco-Oakland-Fremont, CA ......................................... 139,240 181,410 6.9 9.0

Seattle-Tacoma-Bellevue, WA ..................................................... 138,710 177,150 8.2 10.5

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD ..................... 133,990 183,810 4.9 6.7

Detroit-Warren-Livonia, MI .......................................................... 129,550 172,140 6.6 8.8

Miami-Fort Lauderdale-Miami Beach, FL .................................... 68,500 94,400 2.9 4.0

STEM = science, technology, engineering, and mathematics

NOTE: Includes only metropolitan statistical areas with multiple metropolitan divisions. Differences among employment estimates may not be statistically 

significant.

SOURCE: Bureau of Labor Statistics, Occupational Employment Statistics Survey (2007). 

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 3-29

Aside from the possible effects on productivity, early 

career opportunities, and, perhaps, the culture within some 

scientific fields, the age structure of the S&E labor force has 

important implications for its growth rate. This section does 

not attempt to project future S&E labor market trends; however, it posits some general conclusions. Absent changes 

in degree production, retirement patterns, or immigration, 

the number of S&E-trained workers in the labor force will 

continue to grow for some time, but the growth rate may 

slow considerably as an increasing proportion of the S&E 

labor force reaches traditional retirement age. With slowing 

growth, the average age of the S&E labor force will increase.

Age Distribution of the S&E Workforce

Net immigration, morbidity, mortality, and, most of all, 

historical S&E degree production patterns affect the age distribution of scientists and engineers in the workforce. With 

the exception of new fields such as computer sciences (in 

which 56% of degree holders are younger than age 40), the 

greatest population density of individuals with S&E degrees 

occurs between the ages of 40 and 49. Figure 3-21 shows the 

age distribution of the labor force with S&E degrees broken 

out by level of degree. In general, the majority of individuals 

in the labor force with S&E degrees are in their late thirties through their early fifties, with the largest group at ages 

40–44. More than half of workers with S&E degrees are age 

40 or older, and the 40–44 age group is more than twice as 

large as the 60–64 age group.

This general pattern also holds for individuals with S&E 

doctorates. Because of the length of time needed to obtain a 

doctorate, those who hold these degrees are somewhat older 

than individuals who have less advanced S&E degrees. The 

greatest population density of S&E doctorate holders occurs 

between the ages of 40 and 54. This can be seen most easily 

in figure 3-22, which compares the age distribution of S&E 

degree holders in the labor force at each level of degree, and 

in figure 3-23, which shows the cumulative age distribution 

for individuals at each degree level. Even if one takes into 

account the somewhat older retirement ages of doctorate 

holders, a much larger proportion of S&E doctorate holders 

are near traditional retirement ages than are individuals with 

either S&E bachelor’s or master’s degrees.

Percent

Figure 3-20

0	$$ 
!!!
$	
$	
,	.,7%

NOTE: Includes self-employment, employment by noneducation for-profit firms, and by noneducation nonprofit firms.

SOURCE: National Science Foundation, Division of Science Resources Statistics, Scientists and Engineers Statistical Data System (SESTAT) (2006), 

http://sestat.nsf.gov. 

	

	

Employees in company

E+ @*= *6@55 ++@=55 6++@555 %+++@=%555 6%+++@*=%555 *6%+++

+

6

+

6

*+

*6

Bachelor’s Master’s Doctorate All degree levels

Percent of total S&E degree holders

Figure 3-21

$
,
	"	$$	," 
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$	
)

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2003), http://sestat.nsf.gov.

	

	

<25 25–29 30–34 35–39 40–44

Age (years)

45–49 50–54 55–59 60–64 65

0

2

4

6

8

10

12

14

16

Bachelor’s

Master’s

Doctorate
3-30 Chapter 3. Science and Engineering Labor Force

Figure 3-24, which compares the age distributions of 

S&E doctorate holders in 1993 and 2003, highlights the extent of the shift in the age structure of the S&E labor force. 

S&E doctorate holders under age 35 are about the same 

proportion of the S&E doctorate holders in the total labor 

force in both years. However, over the decade, the 35–54 

age group became a much smaller proportion of the doctoral-level S&E labor force. What grew was the proportion of 

S&E doctorate holders age 55 and older.

Across all degree levels and fields, 26.4% of the labor 

force with S&E degrees is older than age 50. The proportion 

ranges from 15% of individuals with their highest degree in 

computer sciences to 41% of individuals with their highest 

degree in geosciences (figure 3-25).

Altogether, the age distribution of S&E-educated individuals suggests the following likely effects on the future of 

the S&E labor force:

Barring large changes in degree production, retirement 

rates, or immigration, the number of trained scientists 

and engineers in the labor force will continue to increase, 

because the number of individuals currently receiving 

S&E degrees exceeds the number of workers with S&E 

degrees nearing traditional retirement age. 

However, unless large increases in degree production occur, the average age of workers with S&E degrees will rise.

Barring large reductions in retirement rates, the total 

number of retirements among workers with S&E degrees 

will increase over the next 20 years. 

Taken together, these factors suggest a slower growing 

and older S&E labor force. Both trends would be accentuated if either new degree production were to drop or immigration were to slow, both concerns raised by a 2003 report 

of the Committee on Education and Human Resources Task 

Force on National Workforce Policies for Science and Engineering of the National Science Board (NSB 2003).

S&E Workforce Retirement Patterns

The retirement behavior of individuals can differ in complex ways. Some individuals retire from one job and continue to work part time or even full time at another position, 

sometimes even for the same employer. Others leave the 

workforce without a retired designation from a formal pension plan. Table 3-11 summarizes three ways of looking at 

changes in workforce involvement for S&E degree holders: 

leaving full-time employment, leaving the workforce, and 

retiring from a particular job.

By age 61, slightly more than 50% of those with an S&E 

bachelor’s degree as their highest degree are no longer 

working full time. The age at which at least half of S&E 

Cumulative percentage

Figure 3-23

8
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.,$

)

NOTE: Age distribution smoothed using kernel density techniques.

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2003), http://sestat.nsf.gov. 

	

	

Bachelor’s

Doctorate

Master’s

Age (years)

21 24 28 32 36 40 767268646056524844

+

*+

=+

:+

<+

++

Density (percent)

Figure 3-22

$
,
	"	$$	," 
!

!!
$	
.,$)

NOTE: Age distribution smoothed using kernel density techniques.

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2003), http://sestat.nsf.gov. 

	

	

0

0.005

0.010

0.015

0.020

0.025

0.030

0.035

Master’s

Bachelor’s Doctorate

Age (years)

21 24 28 32 36 40 767268646056524844
Science and Engineering Indicators 2010 3-31

degree holders no longer work full time increases by degree 

level—to age 62 at the master’s level and age 66 at the doctoral level. Longevity also differs by degree level when measuring the number of individuals who leave the workforce 

entirely: half of all S&E bachelor’s degree holders left the 

workforce entirely by age 65, compared with S&E master’s 

degree and doctorate holders who left the workforce at ages 

66 and 69, respectively. Although many S&E degree holders 

who formally retire from one job continue to work full time 

or part time, formal retirement occurs at similar ages for all 

levels of degree holders: more than 50% of bachelor’s, master’s, and doctoral degree holders have formally retired from 

jobs by age 65, 66, and 67, respectively.

Figure 3-26 shows data on S&E degree holders working 

full time at ages 55–69. For all degree levels, the proportion 

of S&E degree holders who work full time declines fairly 

steadily by age, but after age 55, full-time employment for 

doctorate holders becomes significantly greater than for 

bachelor’s and master’s degree holders. At age 69, 27% of 

doctorate holders work full time, compared with 16% of 

bachelor’s degree recipients.

Table 3-12 shows the rates at which holders of U.S. S&E 

doctorates left full-time employment, by sector of employment, between October 2003 and April 2006. For every age 

group, the retirement rates for S&E doctorate holders were 

slightly higher for those working in the private sector than 

those employed in education or government. Although many 

S&E degree holders who formally retire from one job continue to work full time or part time, this occurs most often 

among individuals younger than age 63 (table 3-13). However, of retired S&E degree holders age 71 to 75, only 12% 

of bachelor’s degree holders keep working either full time 

or part time, 17% of master’s degree holders, and 19% of 

doctorate holders.

Density (percent)

Figure 3-24

$
,
	"
$

!$	,

")	$)

NOTE: Age distribution smoothed using kernel density techniques.

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (1993 and 2003), http://sestat.nsf.gov. 

	

	

0

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

1993

2003

Age (years)

21 24 28 32 36 40 767268646056524844

Figure 3-25

$
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,
$"$"!!
$%

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2006), http://sestat.nsf.gov. 

	

	

Percent

.




7

2




3

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.


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3


7

+ 6 + 6 *+ *6 	+ 	6 =+ =6

Table 3-11

Labor force participation for individuals with 

highest degree in S&E, by education level and 

age: 2003

 Age at which more than half were—

Highest degree

No longer 

employed 

full-time

Not in 

labor force Ever retired

Bachelor’s ................. 61 65 65

Master’s .................... 62 66 66

Doctorate .................. 66 69 67

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 2010
3-32 Chapter 3. Science and Engineering Labor Force

Women and Minorities in S&E

An important part of the growth of the S&E labor force 

comes from the increased presence of women and ethnic 

minorities. In 2006, white males constituted 58% of those 

in the labor force over age 50 whose highest degree was 

in S&E. Among those under age 30, only 35% were white 

males (NSF/SRS 2006). This represents both a change in the 

composition of the total U.S. labor force and a growth in the 

participation of women and minorities in S&E. 

Both women and underrepresented ethnic minorities 

have shown steady growth in their proportion of the S&E 

labor force (see figures 3-27 and 3-28, which look at sex and 

ethnic representation within S&E occupations).

Representation of Women in S&E

Women constituted more than one-fourth (26%) of the 

college-educated workforce in S&E occupations and twofifths (40%) of those with S&E degrees in 2006, according 

to NSF’s SESTAT data. 

Census data on S&E occupations from 1980 to 2007 show 

the number of women in S&E occupations rising from 12% 

to 27% over those 27 years (figure 3-27). Figures 3-29 and 

3-30 show the growth in the number of women with education in S&E for different graduation cohorts and broad fields 

of degree. The notable exception is in computer and mathematical sciences at the bachelor’s degree level, where the 

proportion of women in the workforce is lower for 2002–05 

graduates (27%) than it is for 1972–76 graduates (35%). In 

contrast, the proportion of women in the most recent bachelor’s degree cohorts in both the social sciences and the life 

sciences has risen to above 60%. Among S&E doctorate 

holders in the workforce, the proportion of women is generally higher in more recent cohorts, including the computer 

and mathematical sciences. 

Table 3-12

Proportion of employed S&E doctorate holders 

who had left full-time employment since October 

2003, by employment sector and age: April 2006

(Percent)

October 2003 employment sector

Age (years) All sectors Education Private Government

50–55 ........... 6.7 4.5 9.7 4.4

56–62 ........... 15.0 11.8 18.6 14.9

63–70 ........... 28.0 26.2 31.5 25.2

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2006), http://sestat.nsf.gov.

Science and Engineering Indicators 2010

Table 3-13

Employment status of retired individuals with 

highest degree in S&E, by education level and 

age: 2003

(Percent)

Degree level and 

employment status

Age (years)

50–55 56–62 63–70 71–75

Bachelor’s .............................. 100.0 100.0 100.0 100.0

Part time ............................ 8.2 13.8 10.7 9.0

Full time ............................. 51.1 28.9 9.0 2.6

Not working ....................... 40.7 57.3 80.3 88.4

Master’s ................................. 100.0 100.0 100.0 100.0

Part time ............................ 14.0 15.8 18.3 9.3

Full time ............................. 62.3 35.3 11.8 8.0

Not working ....................... 23.7 48.9 69.9 82.7

Doctorate ............................... 100.0 100.0 100.0 100.0

Part time ............................ 22.6 24.1 21.2 14.7

Full time ............................. 50.6 33.1 12.9 4.7

Not working ....................... 26.8 42.8 65.9 80.6

NOTES: Retired individuals are those who said they had ever retired 

from any job. Percents may not add to 100% because of rounding.

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 2010

	
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Science and Engineering Indicators 2010 3-33

	
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3-34 Chapter 3. Science and Engineering Labor Force

Age Distribution and Experience. On average, women 

in the S&E workforce are younger than men (figures 3-31 

and 3-32). Forty-six percent of women and 31% of men 

employed in science and engineering in 2003 received their 

degrees within the previous 10 years. The difference is even 

more profound at the doctoral level, which has a much greater concentration of women in their late thirties. Consequently, a much larger proportion of male scientists and engineers 

at all degree levels, but particularly at the doctorate level, 

will reach traditional retirement age during the next decade. 

This will affect sex ratios and potentially the number of female scientists in senior-level positions.

Unemployment. Unemployment rates in 2006 were 

somewhat higher for women in S&E occupations than for 

men: 2.2% of men and 2.9% of women were unemployed. In 

contrast, the unemployment rate in 1993 was 2.7% for men 

and 2.1% for women (table 3-14).

Representation of Racial and Ethnic Minorities 

in S&E

With the exception of Asians/Pacific Islanders, racial 

and ethnic minorities represent only a small proportion of 

those employed in S&E occupations in the United States. 

Density (percent)

Figure 3-32

$
,
	"$

!$	



	.,<)

NOTE: Age distribution smoothed with kernel density techniques.

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2003), http://sestat.nsf.gov. 

	

	

Age (years)

21 24 28 32 36 40 767268646056524844

0

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

Women Men

Table 3-14

Unemployment rate for individuals in S&E 

occupations, by sex, race/ethnicity, and visa 

status: 1993, 2003, and 2006

(Percent)

Characteristic 1993 2003 2006

All individuals in S&E 

occupations .......................... 2.6 3.3 2.4

Sex

Male ................................. 2.7 3.3 2.2

Female ............................. 2.1 3.5 2.9

Race/ethnicity

White ............................... 2.4 2.9 2.2

Asian/Pacific Islander ...... 4.0 5.7 2.8

Black ............................... 2.8 4.2 4.4

Hispanic .......................... 3.5 2.5 2.5

Temporary residents ........... 3.4 2.7 2.8

NOTE: 2003 and 2006 data include some individuals with multiple 

races in each category.

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (1993, 2003, and 2006), http://sestat.nsf.gov. 

Science and Engineering Indicators 2010

Density (percent)

Figure 3-31

$
,
	"	$$	

	.

,<)

NOTE: Age distribution smoothed with kernel density techniques.

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2003), http://sestat.nsf.gov. 

	

	

Age (years)

21 24 28 32 36 40 767268646056524844

0

0.005

0.010

0.015

0.020

0.025

0.030

0.035

Women Men
Science and Engineering Indicators 2010 3-35

Collectively, blacks, Hispanics, and other ethnic groups (the 

latter category includes American Indians/Alaska Natives) 

constitute 24% of the total U.S. population, 13% of college 

graduates, and 10% of college-educated individuals employed in S&E occupations. 

Conversely, Asians/Pacific Islanders, despite constituting 

only 5% of the U.S. population, accounted for 7% of college 

graduates and 14% of those employed in S&E occupations 

in 2003. Although most (82%) Asians/Pacific Islanders in 

S&E occupations were foreign born, those born in the United States were also more highly represented in S&E than in 

the total workforce.

Age Distribution. As is the case for women, underrepresented racial and ethnic minorities in the S&E workforce 

are much younger than non-Hispanic whites in the same 

S&E jobs (figure 3-33), and this difference is even more 

pronounced for doctorate holders in S&E occupations (figure 3-34). This finding could point to an upcoming shift in 

the overall composition of the S&E workforce. In the near 

future, a much greater proportion of non-Hispanic white 

doctorate holders in S&E occupations will be reaching traditional retirement ages. This circumstance could signal a 

more rapid increase in the number of non-Hispanic white 

doctorate holders who will retire or otherwise leave S&E 

employment. On the other hand, Asian/Pacific Islander doctorate holders in S&E occupations (measured by race and 

not by place of birth) are on average the youngest racial/

ethnic group, and thus the least likely to have large numbers 

of retirees. 

Salary Differentials for Women and Minorities

Trends in Median Salaries. Women and members of 

underrepresented minority groups have generally lower 

earnings than their male and nonminority counterparts. 

However, differences in average age, work experience, 

fields of degree, sector of employment, and other characteristics can make direct comparison of salary and earnings 

statistics misleading. This section discusses these income 

gaps and explores some of the underlying factors that may 

affect them. 

Factors Influencing Salary Differentials. Regression 

analysis is a statistical method that can be used to examine 

salary and other differences simultaneously.7

 Although this 

type of analysis can provide insight, it cannot give definitive 

answers to questions about the openness of S&E to women 

Density (percent)

Figure 3-33

$
,
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	.

,:
!	
)

NOTES: Age distribution smoothed with kernel density techniques. 

Underrepresented minority includes Hispanic, Black, Native 

Hawaiian/Pacific Islander, American Indian/Alaska Native, and 

multiple race.

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2003), http://sestat.nsf.gov. 

	

	

Age (years)

21 24 28 32 36 40 767268646056524844

0

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

0.045

0.050

Underrepresented minority

White

Asian

Density (percent)

Figure 3-34

$
,
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$

!$	



	.,:
!	
)

NOTES: Age distribution smoothed with kernel density techniques. 

Underrepresented minority includes Hispanic, Black, Native 

Hawaiian/Pacific Islander, American Indian/Alaska Native, and 

multiple race.

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2003), http://sestat.nsf.gov. 

	

	

0

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

0.045

0.050

Underrepresented

minority

White

Asian

Age (years)

21 24 28 32 36 40 767268646056524844
3-36 Chapter 3. Science and Engineering Labor Force

and minorities. The most basic reason is that no labor force 

survey ever captures information on all characteristics that 

may affect compensation. 

Figures 3-35 and 3-36 show estimates of salary differences for different groups after controlling for several individual characteristics. Differences in mean annual salary 

are substantial when comparing all individuals with S&E 

degrees by level of degree only.

In 2006, women with S&E bachelor’s degrees working 

full time had mean salaries that were 36.2% less than those 

of their male counterparts. Likewise, full-time salaries of 

blacks, Hispanics, and individuals in other underrepresented ethnic groups with S&E bachelor’s degrees were 25.8% 

less than those of non-Hispanic whites and Asians/Pacific 

Islanders with S&E bachelor’s degrees.8

 While still substantial, these salary differentials decrease as level of degree 

increases for both women and ethnic minorities, reaching 

21.1% and 15.0% respectively.

Effects of Age and Years Since Degree. On average, 

women and members of underrepresented minority groups 

are younger than their counterparts in most S&E fields. Controlling for differences in both age and years since receipt 

of degree reduces the estimated salary differential for both 

women and minorities at every degree level. 

For women, it reduces salary differentials by about onethird at the bachelor’s and master’s degree levels, and by 

about half at the doctorate level.9

 Statistical controls may 

make less difference at lower degree levels because similar proportions of men and women with S&E degrees are in 

mid-career, but a larger proportion of men are at older ages, 

where salaries begin to decline.

For underrepresented ethnic minorities, controlling for 

age and years since degree produces proportionally larger 

reductions in salary differentials than is the case for women. 

Introducing these controls reduces salary differentials between underrepresented minorities and both non-Hispanic 

whites and Asians/Pacific Islanders by more than half at all 

degree levels.

Figure 3-35



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* = not significantly different from zero at p = .05

NOTES: Salary differentials represent estimated differences in 

full-time salary for women compared to men in regression analyses 

including different characteristics. Regression coefficients are 

estimated using the natural log of full-time annual salary as the 

dependent variable.

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2006), http://sestat.nsf.gov. 

	

	

Negative salary differential (percent)

+ 6 + 6 *+ *6 	+ 	6 =+

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NOTES: Salary differentials represent estimated differences in 

full-time salary for underrepresented ethnic minorities compared to 

non-Hispanic whites and Asians in regression analyses including 

different characteristics. Regression coefficients are estimated using 

the natural log of full-time annual salary as the dependent variable.

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Scientists and Engineers Statistical Data 

System (SESTAT) (2006), http://sestat.nsf.gov. 

	

	

Negative salary differential (percent)

+ 6 + 6 *+ *6 	+

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Science and Engineering Indicators 2010 3-37

Effects of Field of Degree on Salary Differentials. Controlling for field of degree in addition to age and years since 

degree reduces the estimated salary differentials for women 

with S&E degrees to –12.0% at the bachelor’s degree level 

and to –7.6% at the doctorate level.10 These reductions generally reflect the greater concentration of women in the lower-paying social and life sciences as opposed to engineering 

and computer sciences. 

Field of degree is also associated with reduction of estimated salary differentials for underrepresented ethnic 

groups. Controlling for field of degree further reduces salary 

differentials to –9.1% for individuals with S&E bachelor’s 

degrees and to –5.5% for individuals with S&E doctorates. 

At the doctoral level, field of degree, age, and years since 

degree together account for two-thirds of salary differentials 

for underrepresented ethnic groups.

Effects of Occupation and Employer Characteristics 

on Salary Differentials. Occupation and employer characteristics affect compensation.11 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. Other factors affecting 

salary are the sector of the economy, the U.S. region where 

a person works, and whether the person is working in S&E 

or in R&D. However, occupation and employer characteristics may not be determined solely by individual choice; they 

may also in part reflect an individual’s career success.

When comparing women with men and underrepresented 

ethnic groups with non-Hispanic whites and Asians/Pacific 

Islanders, controlling for occupation and employer further 

reduces salary differentials. At the doctoral level, controlling 

for occupation leaves no statistically significant difference 

between the salaries of underrepresented ethnic groups compared with non-Hispanic whites and Asians/Pacific Islanders. 

Effects of Family and Personal Characteristics on Salary Differentials. Marital status, the presence of children, 

parental education, and other personal characteristics are often associated with differences in compensation. Although 

these differences may involve discrimination, they may also 

reflect many subtle individual differences that can affect 

work productivity.12 For example, having highly educated 

parents is associated with higher salaries for individuals 

of all ethnicities and both sexes. It may well be associated 

with greater academic achievement not directly measured in 

these data; alternatively, it may be associated with family 

and personal networks that are conducive to career success. 

In any event, for many individuals in many ethnic groups, 

historical discrimination probably affected parents’ educational opportunities and achievement.

Controlling for these additional characteristics changes 

salary differentials only slightly for each group and degree 

level.13 An additional issue for the wage differentials of 

women, however, is that family and child variables often 

have different effects for men and women. In these estimates, both marriage and children are associated with higher 

salaries for men with S&E degrees at all levels, but have a 

negligible association with women’s earnings. Allowing for 

these differences in sex effects reduces the salary differential 

at the bachelor’s degree level to 4.7% and leaves no statistically significant difference in salary at the master’s degree 

and doctorate levels.

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. In general, holders of S&E 

degrees have higher rates of pay and lower rates of unemployment than other college graduates. However, this does 

not exempt them from unemployment due to overall business cycles or specific events affecting individuals with 

training in their fields. This section looks at both long-term 

and recent trends using NSF, Census Bureau, and BLS data.

Earnings

The estimated annual wages of individuals in S&E occupations, based on BLS’s OES survey, are considerably 

higher than the average of the total workforce. Median annual wages in 2007 (regardless of education level or field) 

in S&E occupations were $70,600, more than double the 

median ($31,410) for total U.S. employment (table 3-15). 

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 ($74,070) than workers in all occupations ($40,690). Mean S&E wages ranged 

from $66,370 for social science occupations to $81,050 for 

engineering occupations. Mean annual wages for technology occupations ranged from $53,165 for technicians and 

programmers to $114,470 for S&E managers.

The 2004–07 growth in mean wages for both the S&E 

and STEM occupation groups (3.4%) was slightly greater 

than that for all workers included in the OES survey (3.2%). 

Among S&E occupations, those in physical S&E occupations experienced the highest wage growth (3.7% average 

annual rate) and those in social science occupations experienced the lowest (3.1% average annual rate).

Workers with S&E degrees also have higher earnings 

than those with degrees in other fields. Figure 3-37 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 non-S&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 at most years since receiving their degree.
3-38 Chapter 3. Science and Engineering Labor Force

Earnings at Different Degree Levels

Figure 3-38 illustrates the distribution of median salaries 

earned by individuals with S&E degrees at various levels. 

(The distributions are heavily skewed, making the median 

a preferred summary statistic.) Not surprisingly, salaries 

are higher for those with more advanced degrees. In 2003, 

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.14

Figure 3-39 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 the formal 

training they received in school. 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 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.

Unemployment in S&E Occupations

Along with higher salaries, relatively low unemployment 

rates are among the labor market rewards of the S&E labor 

force. Historically, unemployment rates in S&E occupations 

have tended to be lower than those for college-educated 

	
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Table 3-15

Annual earnings and earnings growth in science and technology and related occupations: May 2004–May 2007

 Mean Median

Occupation

2007 annual 

earnings ($)

Average annual 

growth rate since 

2004 (%)

2007 annual 

earnings ($)

Average 

annual growth rate 

since 2004 (%)

All U.S. employment ................................................................. 40,690 3.2 31,410 3.0

STEM occupations ............................................................... 72,000 3.4 66,950 3.3

S&E occupations ............................................................... 74,070 3.4 70,600 3.4

Computer/mathematical scientists ................................ 71,940 3.4 68,910 3.5

Life scientists ................................................................. 71,700 3.3 63,170 3.1

Physical scientists ......................................................... 73,720 3.7 67,190 3.9

Social scientists ............................................................. 66,370 3.1 60,380 3.2

Engineers ....................................................................... 81,050 3.7 77,750 3.5

Technology occupations ................................................... 67,870 0.3 NA NA

S&E managers ............................................................... 114,470 4.7 NA NA

S&E technicians/computer programmers ..................... 53,165 2.8 NA NA

S&E-related occupations (not included above) .................... 66,150 4.1 50,540 4.5

Health-related occupations ............................................... 66,000 4.4 55,310 4.8

Other S&E-related occupations ........................................ 73,110 3.3 50,250 3.8

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 2004 and May 2007).

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 3-39

workers generally and much lower than those for workers 

with less than a bachelor’s degree, although the present recession, like that of the early 2000s, is a partial exception 

to these patterns. Unemployment rates in S&E occupations 

are also generally less volatile than unemployment rates 

for these other groups (figure 3-40). The Census Bureau’s 

Current Population Survey data for 1983–2008 indicate that 

the unemployment rate for all individuals in S&E occupations ranged from 1.3% to 4.0%, which contrasted favorably 

with rates for all U.S. workers (ranging from 4.0% to 9.6%) 

Density (percent)

Figure 3-38


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3-40 Chapter 3. Science and Engineering Labor Force

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 5.8%. During most of 

the period, computer programmers had an unemployment 

rate similar to that of S&E occupations, but greater volatility 

(from 1.2% to 6.7%). 

Data on the economic downturn that began in late 2007 

initially fit with long-term trends. 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%). College-educated 

S&E workers had lower unemployment rates (2.1%) than all 

college graduates (2.8%). 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 (7.6%) than that of 

the general labor force (9.7%) (figure 3-41). 

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 3-42 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 out of the field (IOF) 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 3-43). These rates ranged from 7% 

to 12% for bachelor’s degree holders in 2003 versus 2% to 

5% for those with doctorates. IOF rates for doctorate holders 

changed little between 1999 and 2003. 

Although S&E qualifications may help workers weather 

recessions, they do not make them immune from adverse 

labor market conditions. 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.

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 

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Science and Engineering Indicators 2010 3-41

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 3-16 summarizes some basic labor market statistics for recent (1–5 years after receipt of degree) recipients 

of S&E degrees. Across all fields of S&E degrees in 2006, 

there was a 3.8% unemployment rate for bachelor’s degree 

holders who received their degrees in the previous 1–5 

years. This ranged from 1.9% for those with engineering degrees to 5.1% for social science degree recipients. Individuals early in their career tend to change jobs more often and 

have higher unemployment, yet most of these values are less 

than the unemployment rate of 4.7% for the full labor force 

in 2006. For doctorate recipients across all fields of degree, 

the unemployment rate was 1.1%. 

A useful but more subjective indicator of labor market 

conditions for recent graduates is the proportion reporting 

that they sought, but could not find, full-time employment 

related to their field of degree. The 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 was 11.0%, but it ranged from 3.6% for recent engineering graduates to 15.7% for recent graduates in the social 

sciences. In all fields of degree, the IOF rate decreases with 

	
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Labor market indicators for recent S&E degree recipients 1–5 years after receiving degree, by field: 2006

 Highest degree field

Indicator and degree All S&E fields

Computer/ 

mathematical 

sciences Life sciences

Physical 

sciences Social sciences Engineering

Percent

Unemployment rate

Bachelor’s .......................... 3.8 4.6 4.6 4.0 5.1 1.9

Master’s ............................. 2.5 3.1 2.9 2.6 4.6 2.5

Doctorate ........................... 1.1 0.6 1.1 1.1 1.9 1.4

Involuntary out-of-field rate

Bachelor’s .......................... 11.0 8.5 9.9 9.4 15.7 3.6

Master’s ............................. 4.2 3.5 4.1 6.4 9.5 2.9

Doctorate ........................... 1.8 1.6 0.6 4.1 4.0 2.5

Dollars

Average salary

Bachelor’s .......................... 39,500 48,600 31,700 35,900 34,400 54,000

Master’s ............................. 55,000 65,000 45,500 44,700 42,100 67,300

Doctorate ........................... 56,000 72,700 54,700 63,300 57,800 75,000

NOTES: Average salary rounded to nearest $100. Unemployment rate for recent S&E degree recipients differs from rate for entire S&E labor force.

SOURCE: National Science Foundation, Division of Science Resources Statistics, Scientists and Engineers Statistical Data System (SESTAT) (2006), 

http://sestat.nsf.gov.

Science and Engineering Indicators 2010
3-42 Chapter 3. Science and Engineering Labor Force

level of education, reaching a low of 1.8% for recent doctorate recipients.

The average salary for recent S&E bachelor’s degree recipients in 2006 was $39,500, ranging from $31,700 in the 

life sciences to $54,000 in engineering. Recent master’s degree recipients had average salaries of $55,000 and recent 

doctorate recipients had salaries yielding only slightly more 

at $56,000. This reflects in part the relatively low postdoc 

salaries of some recent doctorate recipients (see discussion 

in next section) and the greater employment of doctorate 

holders in academia. 

Recent Doctorate Recipients

The career rewards of highly skilled individuals in general, 

and doctorate holders in particular, often extend beyond salary and employment to more personal rewards that come from 

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 available labor market indicators are discussed below, including unemployment rates, 

IOF employment, satisfaction with field of study, employment in academia versus other sectors, employment in postdoc positions, and salaries. Although a doctorate opens career 

opportunities both in terms of salary and type of employment, 

these opportunities come at the price of many years of foregone labor market earnings. Some doctorate holders also face 

an additional period of low earnings while in a postdoc position. In addition, some doctorate holders do not obtain the jobs 

they desire after completing their education.

In 2006, aggregate measures of labor market conditions 

for recent (1–3 years after receipt of degree) recipients of 

U.S. S&E doctorates showed improvement from the already 

generally good conditions found when last measured in 

2003. Unemployment fell from 2.3% to 1.3% and IOF rates 

fell from 3.3% to 1.3% (table 3-17). In addition, the percentage of recent graduates entering tenure-track programs 

at 4-year institutions—a goal of many young doctorate holders—increased, rising from 17.8% in 2003 to 19.2% in 2006 

(table 3-18).

Unemployment

The 1.3% unemployment rate for recent S&E doctorate recipients as of April 2006 was even lower than other 

generally low 2006 unemployment rates. The 2006 unemployment rate for all civilian workers was 4.6%, with lower 

rates of 2.2% for those with a bachelor’s degree or above and 

1.6% for those in S&E occupations (figure 3-40). 

Table 3-17

Labor market rates for recent doctorate recipients 1–3 years after receiving doctorate, by selected field: 

2001, 2003, and 2006 

(Percent)

Unemployment rate Involuntarily out-of-field rate

Field 2001 2003 2006 2001 2003 2006

All S&E ................................................................ 1.3 2.3 1.3 3.4 3.3 1.3

Computer/mathematical sciences .................. 0.3 4.2 0.7 2.4 3.6 2.2

Computer sciences ..................................... 0.4 4.4 1.7 2.3 1.4 2.3

Mathematics ................................................ 0.3 4.0 0.0 2.4 5.6 2.1

Life sciences ................................................... 1.1 2.5 0.9 2.5 1.5 0.3

Agriculture ................................................... 0.3 3.1 0.0 4.1 2.9 1.7

Biological sciences...................................... 1.0 2.6 1.0 2.4 1.3 0.2

Physical sciences ........................................... 1.3 0.9 1.6 5.0 3.6 2.3

Chemistry .................................................... 0.8 1.2 1.9 3.2 4.3 0.9

Geosciences................................................ 1.9 1.5 1.9 3.0 0.0 0.0

Physics/astronomy ...................................... 1.9 0.0 1.0 8.2 4.3 5.9

Social sciences ............................................... 1.3 2.5 1.2 5.1 5.0 1.5

Economics................................................... 2.2 0.3 0.0 2.1 1.9 0.0

Political science .......................................... 0.8 0.0 0.0 8.7 9.0 0.6

Psychology .................................................. 1.4 2.8 1.2 3.8 5.2 1.3

Sociology/anthropology .............................. 1.2 5.0 2.4 6.3 4.5 4.8

Engineering ..................................................... 1.8 2.3 1.9 1.7 3.0 1.5

Chemical ..................................................... 1.6 2.1 0.7 2.0 8.9 9.8

Electrical ...................................................... 0.9 2.3 0.3 1.5 0.8 1.0

Mechanical .................................................. 3.2 5.8 3.0 1.7 2.6 0.0

NOTES: Doctorate recipients in health fields included in life sciences. Rates of 0.0, like other rates in this table, are rounded estimates based on sample 

survey data and do not preclude possibility that some individuals in that field may be unemployed or working involuntarily out of field. Unemployment 

rates for recent doctoral recipients differ from those for the entire S&E labor force.

SOURCE: National Science Foundation, Division of Science Resources Statistics, Survey of Doctorate Recipients (2001, 2003, and 2006), Scientists and 

Engineers Statistical Data System (SESTAT), http://sestat.nsf.gov.

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 3-43

The highest unemployment rates for recent doctorate recipients were in mechanical engineering (3.0%) and sociology/anthropology (2.4%). Unemployment in both fields 

(which also had the highest unemployment rates in 2003) fell 

from 5.8% and 5.0%, respectively, in 2003. The unemployment rate for recent S&E doctorate recipients in computer 

sciences, the field with the third highest unemployment rate 

in 2003, fell from 4.4% to 1.7% in 2006.

Working Involuntarily Outside the Field

In addition to the 1.3% who were unemployed in 2006, 

another 1.3% of recent S&E 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. Comparable figures were 3.4% in 2001 and 3.3% 

in 2003. 

The highest IOF rates were found for recent doctorate recipients in chemical engineering (9.8%), physics/astronomy 

(5.9%), and sociology/anthropology (4.8%). 

Tenure-Track Positions

Many S&E doctorate recipients may aspire to tenuretrack academic appointments, but most will end up working 

in other positions and sectors. Recently, the proportion of 

all recent doctorate recipients entering tenure-track academic jobs has increased, breaking a long-term decline. Such 

increases can be seen between 2001 and 2003, and again 

between 2003 and 2006. As a result, 2006 tenure-track rates 

for those 1–3 years after receiving their degree and those 4–6 

years after receiving their degree were broadly the same as in 

1993 (figure 3-44; table 3-18). From 2003 to 2006, the rate 

for those 1–3 years since receiving their degree rose from 

18% to 19%, and the rate for those 4–6 years since receiving 

their degree increased from 24% to 26%. (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 in 

tenure-track employment occurred in computer sciences, 

from 52% in 1993 to 24% in 2001 despite the difficulties 

computer sciences departments had in finding faculty.

Salaries for Recent S&E Doctorate Recipients

In 2006 for all S&E degree fields, the median annual 

salary for recent doctorate recipients 1–5 years after they 

received their degrees was $52,000. Across various S&E 

fields of degree, median annual salaries ranged from a low 

of $46,000 in the life sciences to a high of $70,000 in engineering (table 3-19). 

By type of employment, salaries for recent doctorate 

recipients ranged from $40,000 for postdoc positions to 

Table 3-18

Doctorate recipients holding tenure and tenure-track appointments at academic institutions, by years since 

receipt of doctorate and selected field: 1993, 2003, and 2006

(Percent)

1993 2003 2006

S&E field 1–3 years 4–6 years 1–3 years 4–6 years 1–3 years 4–6 years

All fields ........................................................ 18.4 26.6 17.8 23.5 19.2 25.8

Computer/mathematical sciences ............ 39.7 54.1 34.5 38.1 36.1 44.0

Computer sciences ............................... 37.1 51.5 30.9 30.3 37.8 36.4

Mathematics .......................................... 41.8 56.0 37.7 43.8 34.7 50.6

Life sciences ............................................. 12.6 24.8 8.0 20.3 13.4 20.8

Agriculture ............................................. 15.6 27.0 23.7 35.1 18.9 30.0

Biological sciences................................ 12.1 24.8 6.5 18.6 13.2 20.6

Physical sciences ..................................... 9.7 18.2 13.7 18.2 10.7 23.8

Chemistry .............................................. 7.7 16.3 14.5 16.0 11.0 22.2

Geosciences.......................................... 12.7 26.2 21.6 35.1 13.9 30.5

Physics/astronomy ................................ 12.0 17.7 9.4 14.5 8.7 22.5

Social sciences ......................................... 26.4 29.2 28.3 31.6 29.6 34.2

Economics ............................................ 46.6 48.6 43.7 32.2 37.4 39.4

Political science .................................... 53.9 47.1 45.0 50.6 45.0 51.3

Psychology ............................................ 12.7 15.5 14.5 21.1 18.7 21.9

Sociology/anthropology ........................ 37.9 46.9 43.3 48.0 62.1 65.0

Engineering ............................................... 16.0 24.6 12.2 16.0 14.7 16.6

Chemical ............................................... 8.1 14.0 4.9 6.0 8.2 9.4

Electrical ................................................ 17.6 26.9 11.6 15.3 18.6 15.4

Mechanical ............................................ 13.5 29.5 11.1 16.0 16.5 14.6

NOTES: Two-year institutions not included. Doctorate recipients in health fields included in life sciences.

SOURCE: National Science Foundation, Division of Science Resources Statistics, Survey of Doctorate Recipients (1993, 2003, and 2006), Scientists and 

Engineers Statistical Data System (SESTAT), http://sestat.nsf.gov.

Science and Engineering Indicators 2010
3-44 Chapter 3. Science and Engineering Labor Force

$80,000 for those employed by private for-profit businesses 

(table 3-20).

Postdoc Positions

The growing number of recent doctorate recipients in 

postdoctoral appointments, generally known as postdocs,15

has become a major issue and 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 a 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?

The total number of postdocs in the United States is unknown; broad estimates depend upon a number of assumptions. NSF’s Survey of Doctorate Recipients (SDR) covers 

U.S. residents with research doctorates in S&E and health 

fields from U.S. universities, but not those with non-U.S. 

doctorates. The NSF Survey of Graduate Students and Postdoctorates in Science and Engineering gathers information 

on postdocs from U.S. academic graduate departments, regardless of where their doctorate was earned. It does not cover people in nonacademic employment, at some university 

research centers, or at academic departments that lack graduate programs. Table 3-21 shows the SDR and GSS estimates 

of the U.S. postdoc population that these surveys cover.

Academic Postdocs. SDR estimates that 22,900 U.S. citizens and permanent residents were in academic postdoc positions in fall 2005, along with 7,700 temporary visa holders.16

The corresponding 2005 GSS estimate is 16,200 U.S. citizens 

and permanent residents but 26,600 temporary visa holders. 

Postdocs in FFRDCs. Many federally funded research 

and development centers (FFRDCs) employ postdocs as part 

of their efforts to assist government agencies with scientific 

research and analysis and to train the country’s researchers 

and scientists. According to NSF’s 2007 Survey of Postdocs 

at FFRDCs, 22 of the 38 FFRDCs on the master government FFRDC list maintained by the NSF reported employing 2,235 postdocs. Of those 2,235 postdocs, 1,336 (about 

60%) were temporary visa holders and 2,030 (about 91%) 

received federal support.

Table 3-19

Salary of recent doctorate recipients 1–5 years 

after receiving degree, by degree field and 

percentile: 2006

(Dollars)

Degree field

25th 

percentile

50th 

percentile

75th 

percentile

All S&E fields .................... 40,000 52,000 74,000

Computer/mathematical 

sciences ...................... 43,500 64,000 84,000

Life sciences ................. 38,000 46,000 65,000

Physical sciences ......... 40,000 53,000 75,600

Social sciences ............. 40,000 51,300 65,000

Engineering ................... 41,000 70,000 87,500

NOTE: Doctorate recipients in health fields included in life sciences.

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Survey of Doctorate Recipients (2006), 

Scientists and Engineers Statistical Data System (SESTAT), http://

sestat.nsf.gov.

Science and Engineering Indicators 2010

	
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Science and Engineering Indicators 2010 3-45

Table 3-20

Median annual salary of recent doctorate recipients 1–5 years after receiving degree, by type of employment: 

2006

(Dollars)

Field All sectors Private

Tenure 

track Postdoc 

Other 

education

Nonprofit/ 

government

All S&E fields .................................................... 52,000 80,000 53,000 40,000 48,500 68,000

Computer/mathematical sciences ................ 64,000 90,000 62,000 48,500 48,000 S

Life sciences ................................................. 42,600 74,000 57,000 40,000 48,000 60,000

Physical sciences ......................................... 53,000 78,000 50,500 42,000 48,000 76,000

Social sciences ............................................. 51,300 65,000 52,000 39,600 50,000 62,000

Engineering ................................................... 70,000 80,000 71,000 40,000 56,000 80,000

S = data suppressed for reasons of reliability 

SOURCE: National Science Foundation, Division of Science Resources Statistics, Survey of Doctorate Recipients (2006), Scientists and Engineers 

Statistical Data System (SESTAT), http://sestat.nsf.gov.

Science and Engineering Indicators 2010

Table 3-21

Postdoc estimates from two NSF/SRS surveys, by place of employment and citizen/visa status: Fall 2005

SDR GSS

Place of employment and citizen/visa status Estimate Percent Estimate Percent

All places of employment

All postdocs ................................................................................. 43,400 100.0 43,100 100.0

U.S. citizens/permanent residents ........................................... 33,400 77.0 16,200 37.5

Temporary visa ......................................................................... 10,000 23.0 27,000 62.5

Higher education institutionsa

All postdocs ............................................................................. 30,500 100.0 26,900 100.0

U.S. citizens/permanent residents........................................ 22,900 74.8 16,200 37.6

Temporary visa ..................................................................... 7,700 25.2 26,900 62.4

All other educational institutions

All postdocs ............................................................................. 1,900 100.0 NA NA

U.S. citizens/permanent residents........................................ 1,600 85.5 NA NA

Temporary visa ..................................................................... 300 14.5 NA NA

Nonprofits/government/industry/all other institutions

All postdocs ............................................................................. 11,100 100.0 NA NA

U.S. citizens/permanent residents........................................ 9,000 81.2 NA NA

Temporary visa ..................................................................... 2,100 18.8 NA NA

NA = not available

GSS = Survey of Graduate Students and Postdoctorates in Science and Engineering; NSF/SRS = National Science Foundation, Division of Science 

Resources Statistics; SDR = Survey of Doctorate Recipients

aFor SDR, individuals reporting postdoc in 4-year U.S. colleges and universities/medical schools/university-affiliated research institutes/unknown 

institution type in fall 2005; for GSS, postdocs in graduate S&E/health departments in U.S. graduate schools (excludes holders of medical and other 

professional degrees, some of whom may also hold doctorates).

NOTES: SDR gathers information from individuals with research doctorates in S&E and health fields earned at U.S. educational institutions. GSS gathers 

information from U.S. educational institutions with programs leading to graduate degrees in S&E/health fields and includes postdocs with doctorates/

equivalent degrees from foreign institutions. Estimates of postdoc status from 2006 SDR constructed from postdoc history module; fall 2005 used rather 

than April 2006 for comparability with GSS data and to capture those who may have left a postdoc position early. Detail may not add to total because of 

rounding.

SOURCES: NSF/SRS, 2006 SDR, Scientists and Engineers Statistical Data System (SESTAT), http://sestat.nsf.gov, and 2005 GSS, Integrated Science 

and Engineering Resources Data System (WebCASPAR), http://webcaspar.nsf.gov.

Science and Engineering Indicators 2010
3-46 Chapter 3. Science and Engineering Labor Force

Other Postdocs. Neither the GSS nor the SDR survey 

includes data on the number of foreign-educated postdocs in 

all sectors. SDR estimates that 29% of U.S.-educated postdocs, 13,000 in all, are in industry, nonprofits, government, 

and other types of educational institutions. Using these data, 

one might estimate as follows:

22,900 U.S. citizens and permanent residents in academic 

postdoc positions (SDR) 

26,900 persons on temporary visas in academic postdoc 

positions (GSS) 

13,000 U.S.-educated persons in postdoc positions not 

covered by GSS (SDR) 

26,500 postdocs on temporary visas in positions not covered by GSS, based on the assumption that proportions of 

temporary visa postdocs in sectors and parts of academia 

not covered by GSS are the same as in the GSS estimates. 

These assumptions yield approximately 89,300 postdocs, 

but other comparably plausible assumptions lead to substantially different totals.

Postdocs by Academic Discipline

About half of all U.S.-educated postdocs in 2005 (49%) 

had doctorates in the biological and other life sciences (figure 3-45). In this field, 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 fields 

of S&E, 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. S&E doctorates received before 

1972,17 31% reported having had a postdoc position earlier 

in their careers (figure 3-46). 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%), 

a majority of 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 

one-third less than the median salary for nonpostdocs 1–3 

years after receiving their doctorates (table 3-22). By broad 

field, this ranges from a 44% pay gap for recent recipients 

of engineering doctorates to a 25% gap for doctorate holders 

in the social sciences. Nonacademic postdocs are better paid 

	
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Science and Engineering Indicators 2010 3-47

than academic postdocs, but their median salary is still 20% 

less than that of nonpostdocs.

Most individuals in postdoc positions in 2006 had employment benefits. Indeed, 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 for Recent Doctorate Recipients

Former postdoc position holders reported reasons for accepting their appointment that are consistent with the traditional intent of a postdoc as a type of apprenticeship, such 

as seeking “additional training in doctorate field” or “training in an area outside of doctorate field.” However, 9% of 

Survey of Doctorate Recipients respondents in a postdoc 

position in April 2006 reported that they took their current 

postdoc position because “other employment not available.” 

This reason was given by 5% of postdocs in the life sciences, 8% in computer and mathematical sciences, 10% in 

the physical sciences, 14% in the social sciences, and 16% 

in engineering. 

Postdoc Outcomes

Most former postdocs report that their most recent postdoctoral appointment enhanced their career opportunities, 

and the proportions who say this are similar for different 

cohorts (figure 3-47). Across all S&E fields and cohorts, 

53%–56% of former postdocs said that their postdoc appointment enhanced their career opportunities to a “great 

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 of Science and Engineering Indicators 2008 (NSB 2008) 

and NSF/SRS (2008b).

Global S&E Labor Force

Science is a global enterprise. The common laws of nature 

cross political boundaries, and the international movement 

of people and knowledge made science global long before 

“globalization” became a label for the increasing interconnections now forming among the world’s economies. The 

rapid development of the capacity to make scientific and 

Table 3-22

Salary and benefits of U.S. S&E doctorate holders in postdoc positions: 2006

Median salary ($) Benefits (%)

Field of doctorate

Academic 

postdoc

Nonacademic 

postdoc

Nonpostdocs 1–3 

years after degree Medical Retirement

All S&E ................................................................. 40,000 48,000 60,000 90.1 48.9

Computer/mathematical sciences ................... 47,000 55,000 72,000 93.0 69.1

Life sciences .................................................... 40,000 44,000 55,000 92.9 47.7

Physical sciences ............................................ 40,000 55,000 63,000 92.7 54.7

Social sciences ................................................ 40,000 50,000 53,000 75.0 44.8

Engineering ...................................................... 40,000 60,000 71,400 92.4 56.2

NOTE: Doctorate recipients in health fields included in life sciences.

SOURCE: National Science Foundation, Division of Science Resources Statistics, Survey of Doctorate Recipients (2006), Scientists and Engineers 

Statistical Data System (SESTAT), http://sestat.nsf.gov. 

Science and Engineering Indicators 2010

Percent

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3-48 Chapter 3. Science and Engineering Labor Force

up to the doctorate. Figure 3-49, based on estimates by Barro 

and Lee (2000), shows the global distribution of tertiary education graduates in 2000 or the most recent available year. 

About one-fourth of the world’s tertiary graduates were in 

the United States; the next three largest countries in terms of 

tertiary education are China, India, and Russia, which are all 

non-OECD members.

Highly Skilled Migrants in OECD Countries

Docquier and Marfouk (2004) made estimates of the highly educated international migrants residing in OECD countries by using data from various national censuses. Based on 

their data, figure 3-50 shows the leading countries of origin of 

non-natives with tertiary-level education who lived in OECD 

countries in 2000. With 1.4 million, the United Kingdom has 

the largest high-skilled diaspora. (Although originally used 

to describe much less voluntary dispersals of population in 

history, the term diaspora is increasingly used to describe the 

internationally mobile portion of a country’s nationals, which 

forms a network for contact and information flow. These networks can provide advantages for a country that help mitigate the loss of human capital through migration.)

The United States, ranking 11th with 448,000 tertiaryeducated citizens who live in other OECD countries, has 

a fairly small high-skilled diaspora compared with its 

technical innovations is creating a new competitive environment. New ways of doing business and performing R&D 

take advantage of gains from new knowledge discovered 

anywhere in the world, from increases in foreign economic 

development, and from the expanding international migration of highly trained scientists and engineers. 

This section begins with an overview of what is known 

about S&E labor forces in advanced countries, which mostly 

concerns researchers and people performing R&D for multinational firms. The remainder of the section deals with 

foreign-born scientists and engineers in the United States.

Other chapters provide indirect indicators on the global 

S&E labor force. Chapter 2 reports on the production of new 

scientists and engineers through university degree programs. 

Chapter 4 provides indicators of R&D performed globally, 

chapter 5 discusses publications output and international 

collaboration, and chapter 6 has information on high-technology activities and global patenting activity.

Counts of Global S&E Labor Force

There are no comprehensive measures of the global S&E 

labor force, but fragmentary data on the global S&E labor 

force suggest that the U.S. world share is continuing to decline, even as U.S. reliance on foreign-born scientists and 

engineers may be near or at a historic high. Data exist within 

some national data systems, and some countries report data 

in standardized form to international agencies such as the 

Organisation for Economic Co-operation and Development 

(OECD). Existing data provide a strong indication of rapid 

growth in the number of individuals who pursue advanced 

education and find employment in technical fields, particularly in developing nations.

OECD collects data on researchers from its member 

countries and selected other countries. Unfortunately, this 

source misses many countries that appear to have high levels 

of S&T activity, including India, Brazil, and Israel. 

Figure 3-48 shows the growth between 1995 and 2007 

in the reported number of researchers in selected countries/

economies. The United States had about the same growth 

of researchers as the EU-27, about 40% each over the time 

period. The number of researchers in Japan rose by just over 

5%. Over the same 12-year period, the reported number of 

researchers in China rose by 173% to more than 1.4 million 

in 2007—close to the estimated U.S. figure and the number 

of the combined EU-27. An important caution in interpreting these data is that although countries used a common definition of “researcher” when reporting their data to OECD, 

there are many judgments necessary to translate from a wide 

variety of national data systems to the OECD definition. 

Tertiary Education

One widely available measure of the education level of 

a country’s population is the number of its residents with 

a tertiary level of education. This is roughly equivalent in 

U.S. terms to individuals who have earned at least a technical associate’s degree, but also includes all higher degrees 

	
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Science and Engineering Indicators 2010 3-49

population, and particularly compared with its number of 

educated workers. 

R&D Employment by Multinational Companies

MNCs perform a substantial proportion of R&D through 

foreign direct investment (FDI) (see chapter 4). Data on 

MNC R&D employment include all employees engaged in 

research and development, including managers, scientists, 

engineers, and other professional and technical employees. 

Data on R&D employment of parent companies of U.S. 

MNCs and their overseas affiliates are available every 5 

years from the Survey of U.S. Direct Investment Abroad 

conducted by the Bureau of Economic Analysis (BEA). Separately, data on R&D employment by foreign-based MNCs 

in the United States are available from BEA’s Survey of Foreign Direct Investment in the United States. 

By definition, FDI does not include external arrangements ranging from R&D contracting to consulting work 

and strategic collaborations.18 Nevertheless, R&D employment by subsidiaries is an important indicator of international R&D activity. 

R&D employment in the United States by foreign firms 

grew slightly faster than R&D employment abroad by U.S. 

firms. R&D employment in the United States by majorityowned affiliates19 of foreign firms rose from 89,800 in 1994 

to 128,500 in 2004, for a 43% increase over the decade (figure 3-51). 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 

SOURCE: Adapted from Barro RJ, Lee J, International data on 

educational attainment: Updates and implications, Center for 

International Development Working Paper No. 042 (2000), 

http://www.cid.harvard.edu/cidwp/042.htm, accessed 9 September 

2009. 

	

	

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China 11%

India 8%

Russia 7% Japan 6%

Philippines 3%

Germany 3%

Brazil 2%

South Korea 2%

United Kingdom 2%

Thailand 2%

Mexico 2%

France 2%

Other 24%

Figure 3-50

	
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SOURCE: Docquier F, Marfouk A, International Migration by 

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3-50 Chapter 3. Science and Engineering Labor Force

U.S. parent company R&D employment of 716,400 workers, U.S. MNCs employed 854,200 R&D workers globally 

(figure 3-52) in 2004.

The average annual growth in R&D employment abroad 

by U.S. firms from 1994 to 2004 was only 3% and did not 

produce a large shift in their overseas employment, which 

rose from 14% to 16% of their total.

The data in both figure 3-51 and figure 3-52 are consistent with two trends discussed in this chapter: rapid growth 

in S&T employment in the United States coinciding with a 

general expansion of the ability to do S&T work throughout 

the world. 

Migration to the United States

The knowledge and specialized skills of scientists and 

engineers can be transferred across national borders through 

the physical movement of people. Governments in many industrialized countries increasingly view the immigration of 

skilled S&E workers as an important contributor to the quality and flexibility of their S&E labor force. Many countries 

have not only increased their research investments, but have 

also made encouraging high-skilled immigration an important part of their national economic strategies. 

The United States has benefited, and continues to benefit, 

from this international flow of knowledge and personnel 

(see Regets 2001 for a general discussion of high-skilled migration). However, competition for skilled labor continues 

to increase. A National Science Board taskforce noted that 

“global competition for S&E talent is intensifying, such that 

the United States may not be able to rely on the international 

S&E labor market to fill unmet skill needs” (NSB 2003). 

(See sidebar “High-Skill Migration to Japan and the UK.”)

Broadly consistent estimates of U.S. reliance on foreignborn scientists and engineers are available from several 

sources. Table 3-23 shows upward trends in the percentage 

of foreign-born individuals in U.S. S&E occupations over 

time. The percentage changes since 2000 may appear small 

but are quite substantial, given the short time span and the 

overall growth of the number of persons in S&E occupations 

from 2000 to 2007: of an estimated 341,000 total increase, 

100,000 were foreign born. 

SESTAT surveys include only individuals who were 

counted in the most recent Decennial Censuses or who received a U.S. S&E degree, thereby missing recently arrived 

foreign-born and foreign-educated scientists and engineers. 

Yet, a large proportion of the foreign-born and foreign-educated members of the S&E labor force are recent arrivals. 

For example, in 2000, about 43% of all college-educated 

foreign-born workers in U.S. S&E occupations reported arriving in the United States after 1990; among doctorate holders 62% reported arriving after this date.

The 2000 census data provide a good estimate of the foreign born who were actually in the United States in April 

2000 but give no information about those performing S&E 

tasks in a wide variety of non-S&E occupations (as discussed earlier in this chapter), nor about which postsecondary teachers are in S&E fields. Within these limitations, the 

Census Bureau’s 2007 American Community Survey permits an analysis of trends in the proportion of the foreign 

born in S&E occupations at each degree level during the current decade. It shows growth of 3 percentage points overall, 

with an extra 4 percentage points each at the master’s degree 

and doctorate levels. 

Between 2003 and 2007, employment of college graduates in nonacademic S&E occupations, as measured by 

the ACS, increased by 345,000: 235,000 U.S. natives and 

110,000 foreign born (figure 3-53). The estimated overall 

proportion of the foreign born rose only slightly over these 4 

years (from 24.6% to 25.2%) but increased by 2 percentage 

points each for those with master’s degrees and doctorates 

in this short span. 

Details on the proportion of foreign-born S&E degree 

holders by field of degree are shown in table 3-24, based on 

2003 SESTAT estimates. At the doctoral level, foreign-born 

individuals constitute about half the total number of workers 

in both engineering (51%) and mathematics/computer sciences (48%), up from 41% and 33% a decade earlier. Only in 

the geosciences and the social sciences are the foreign born 

significantly less than a third of doctorate holders in S&E 

fields. At the bachelor’s degree level, 15% of S&E degree 

holders were foreign born, ranging from 7% of individuals 

in sociology/anthropology to 27% in physics/astronomy and 

28% in electrical engineering. Given the continuing increase 

in foreign participation, it is likely that these 2003-based 

percentages are conservative estimates.

	
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Science and Engineering Indicators 2010 3-51

High-Skill Migration to Japan and the UK

Recent debates and legislative changes in many developed (and sometimes less developed) countries have 

focused on visa programs for temporary high-skilled workers. The United Kingdom and Japan are just two examples 

of countries that have made temporary high-skilled migration important parts of national economic policies. 

A 1989 revision of Japanese immigration laws made it 

easier for high-skilled workers to enter Japan with temporary visas, which allow employment and residence for an 

indefinite period (even though the same visa classes also 

apply to work visits that may last for only a few months). 

In 2005, 169,800 workers entered Japan in selected highskilled temporary visa categories, compared with just 

over 30,000 in 1990 (figure 3-E). For comparison purposes, this equals half the number of Japanese university 

graduates entering the labor force each year and is more 

than the number entering the United States in roughly 

similar categories (H-1B, L-1, TN, O-1, O-2). 

The United Kingdom’s programs for the entry of highskilled workers continue to evolve in ways to encourage 

migration and are currently part of an overall point system. Under the United Kingdom’s recent Highly Skilled 

Migrant Program, admissions grew from 1,197 in 2002 to 

21,939 in 2006. An important note for these numbers is 

that high-skilled EU citizens enter the UK without needing this visa, so actual high-skilled migration to the UK is 

likely to be much larger. During these years, the number 

of U.S. citizens entering the UK as high-skilled migrants 

grew from 273 to a still modest 629 (Salt 2007).

	


	

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Estimates of foreign-born individuals in S&E occupations from NSF/SRS and Census Bureau, by educational 

attainment: 1999, 2000, and 2003 

(Percent)

 2003

Education

1999 NSF/SRS 

SESTAT

2000 Census 

5% PUMS NSF/SRS SESTAT

Census Bureau 

ACS

All college educateda ....................................................... 15.0 22.4 22.5 25.0

Bachelor’s .................................................................... 11.3 16.5 16.3 18.8

Master’s ....................................................................... 19.4 29.0 29.0 32.0

Doctorate ..................................................................... 28.7 37.6 35.6 39.5

ACS = American Community Survey; NSF/SRS = National Science Foundation, Division of Science Resources Statistics; SESTAT = Scientists and 

Engineers Statistical Data System; 5% PUMS = Public Use Microdata Sample with 5% of sample cases

aIncludes 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 2003 

American Community Survey data files. 

SOURCES: NSF/SRS, SESTAT (1999 and 2003), Scientists and Engineers Statistical Data System (SESTAT), http://sestat.nsf.gov; and Census Bureau, 

PUMS (2000) and ACS (2003).

Science and Engineering Indicators 2010
3-52 Chapter 3. Science and Engineering Labor Force

Origins of S&E Immigrants 

Immigrant scientists and engineers come from a broad 

range of countries. Figure 3-54 shows country of birth for 

the 2.2 million foreign-born persons with highest degree in 

S&E in the United States (country details are in appendix table 3-10). Although no one source country dominates, 16% 

came from India and 11% came from China. Source countries for the 276,000 foreign-born holders of S&E doctorates 

are somewhat more concentrated, with China providing 22% 

and India 14%.

Source of Education for S&E Immigrants

The majority of foreign-born scientists and engineers in 

the United States first came to the United States to study, but 

a substantial number came to the United States after receiving their university training abroad. Table 3-25 illustrates the 

various educational routes that highly skilled workers from 

around the world take into the United States workforce and 

indicates how these workers help connect the United States 

to universities and research institutions worldwide.

Across all levels of degree, 42% of the university-educated 

foreign born in the United States had their highest degree 

from a foreign educational institution and 56% had at least 

Table 3-24

Foreign-born proportion of individuals with highest degree in S&E, by field and education level: 2003 

(Percent)

 Highest degree

Field All degree levelsa Bachelor’s Master’s Doctorate

All S&E .......................................................................... 18.8 15.2 27.2 34.6

Computer/mathematical sciences ............................ 25.8 19.3 40.5 47.5

Computer sciences ............................................... 29.9 22.3 46.5 57.4

Mathematics .......................................................... 18.5 14.4 25.5 43.1

Biological/agricultural/environmental life sciences ... 16.6 12.6 21.2 36.2

Agricultural and food sciences .............................. 11.6 8.8 15.9 32.7

Biological sciences................................................ 19.0 14.6 23.9 37.4

Environmental life sciences ................................... 6.6 4.3 13.5 13.3

Physical sciences ..................................................... 22.9 16.9 28.9 36.9

Chemistry .............................................................. 25.3 18.1 42.1 37.0

Geosciences.......................................................... 11.3 8.3 13.0 26.2

Physics/astronomy ................................................ 32.6 27.4 34.4 40.1

Other physical sciences ........................................ 16.3 14.1 11.1 48.7

Social sciences ......................................................... 11.5 10.8 13.3 16.9

Economics ............................................................ 21.7 19.8 30.5 31.5

Political science .................................................... 11.0 9.5 17.1 24.2

Psychology ............................................................ 9.7 10.1 8.5 9.8

Sociology/anthropology ........................................ 7.2 6.7 10.2 13.6

Other social sciences ............................................ 13.0 10.6 18.2 31.3

Engineering ............................................................... 26.8 21.5 38.3 50.6

Aerospace/aeronautical/astronautical ................... 16.4 9.7 29.6 52.6

Chemical ............................................................... 26.0 17.7 49.4 47.0

Civil ........................................................................ 24.9 19.7 39.3 54.2

Electrical ................................................................ 34.1 28.1 45.9 57.5

Industrial ................................................................ 21.5 17.5 33.1 42.0

Mechanical ............................................................ 23.0 19.6 34.3 52.2

Other engineering .................................................. 23.4 18.8 25.8 44.6

aIncludes professional degrees not broken out separately.

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 2010

	
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Science and Engineering Indicators 2010 3-53

one foreign degree. At the highest level of education, 33% of 

foreign-born doctorate holders earned their doctorates from 

a foreign school. 

The prevalence of foreign degrees among foreign-born 

S&E degree holders has been increasing over time (figure 

3-55). Among foreign-born S&E degree holders who entered the United States before 1980, only 20% of doctorate holders and 23% of bachelor’s degree holders had their 

highest degree from a foreign school. These percentages increase for more recent entry cohorts of immigrants. It should 

be noted that some portion of the increase in the most recent 

entry years reflects immigrants who entered during those 

years but have not yet had sufficient time to complete an 

American degree. 

Citizenship and Visa Status of Foreign-Born 

Scientists and Engineers in the United States

The length of time it takes for foreign scientists and engineers to earn U.S. citizenship affects both their decision to 

come to the United States and their subsequent decision to 

stay. As figure 3-56 shows, only about half of foreign S&E 

degree holders who entered the United States in 1991 and 

remained in 2003 had obtained citizenship. Citizenship status may particularly affect the supply of S&T talent available to segments of the U.S. economy that can typically hire 

only citizens: the federal government and private companies 

engaged in defense and other classified research.20 While a 

significant portion of any group of immigrants never seeks 

citizenship, the type of visas that scientists, engineers, and 

other high-skilled workers use for initial entry into the United States affects their path to citizenship. Time spent in the 

United States on a student or temporary work visa does not 

count toward the 5-year waiting period before immigrants 

can apply for citizenship.

Temporary Work Visas

In recent years, policy discussion has focused on the use 

of various forms of temporary work visas by foreign-born 

scientists and other high-skilled workers. The use of these 

temporary visas for high-skilled workers has increased over 

time (as seen in figure 3-57). For all types of temporary 

work visas, the actual number of individuals using them is 

Figure 3-54

9	6,		$$ 
!!!
$	
		&	
$


.,",
!)

UK = United Kingdom

NOTE: Percents may not add to 100% because of rounding.

SOURCE: National Science Foundation, Division of Science Resources Statistics, SESTAT database, 2003, http://sestat.nsf.gov. See appendix table 3-10.

	

	

 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 6%

Former Soviet Union 6%

 Taiwan 4% Canada 4%

 Germany 4%

 South Korea 3%

 Iran 2%

 Japan 2%



$



Other foreign

born 34%

Table 3-25

Share of college-educated, foreign-born 

individuals in United States holding foreign 

degrees, by education level: 2003

(Percent)

Highest degree

Highest 

degree from 

foreign school

Any foreign 

university 

degree

Foreign 

secondary 

school

All college graduates .... 42.4 56.2 70.0

Bachelor’s .................. 50.1 52.1 66.4

Master’s ..................... 27.4 58.7 74.3

Professional ............... 49.4 58.4 63.3

Doctorate .................. 33.1 76.1 87.3

All S&E degree 

 holders ..................... 37.3 55.9 NA

Bachelor’s .............. 45.6 48.0 63.8

Master’s .................. 27.2 63.0 76.9

Professional ........... 28.7 34.6 42.2

Doctorate ............... 34.9 79.7 NA

NA = not available (Data not collected from U.S.-trained S&E 

doctorates.)

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, National Survey of College Graduates (2003), 

Scientists and Engineers Statistical Data System (SESTAT), http://

sestat.nsf.gov.

Science and Engineering Indicators 2010
3-54 Chapter 3. Science and Engineering Labor Force

	
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NOTE: Some data on foreign-born S&E degree holders are available through 2003; however, data after 1999 exclude many individuals with foreign degrees. 

SOURCE: National Science Foundation, Division of Science Resources Statistics, Scientists and Engineers Statistical Data System (SESTAT) (2003), 

http://sestat.nsf.gov.

	

	

Before

1980

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

0

20

40

60

80

100

U.S. citizen

Permanent visa

Temporary visa
Science and Engineering Indicators 2010 3-55

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. 

J-1 Exchange Visas. Of the visa types shown, the J-1 exchange visitor visa is the most issued—more than 350,000 in 

FY 2008. However, many of these visas are given to lower 

skilled workers, and many J-1s are issued for semester or 

summer stays. U.S. Immigration and Customs Enforcement 

(ICE) showed approximately 165,000 J-1 visa holders in the 

United States, of whom 50% were in categories that were 

clearly highly skilled, including nearly 50,000 professors 

and research scholars.

Other Visa Types. There has also been growth in visas 

issued in other high-skilled categories. Between 2003 and 

2008, issuances of L-1 (intracompany transfer) visas grew 

by 47% to 84,000. The smallest series shown in Figure 3-57 

groups together four much smaller high-skilled visa programs: O-1 (a person of outstanding ability), O-2 (an assistant to an O-1, sometimes a postdoc), TN (college-degreed 

citizens of Canada and Mexico), and E-3 (college-degreed 

citizen of Australia). Taken together, these four visa types 

grew by 142% between 2003 and 2008, reaching nearly 

22,000 in the number of visas issued.

H-1B Visas

H-1B temporary work visas are likely to account for a 

larger number of high-skilled workers than other visa classes. The United States typically issues H-1B visas for 3 years 

with the possibility of a 3-year renewal. In October 2003, the 

United States lowered its annual ceiling on admissions from 

195,000 to 65,000, but granted universities and academic research institutions exemptions in their own hiring. In 2005, 

the United States granted an additional 20,000 exemptions 

for students receiving master’s degrees or doctorates from 

U.S. schools.

Although the occupational categories used in H-1B visa 

records do not precisely correspond to the classifications 

used elsewhere in this chapter, it is safe to say that the bulk 

of H-1B visa recipients work in S&E or S&E-related occupations (figure 3-58; table 3-1). 

In 2006, half of new H-1B visa recipients were employed in 

computer-related occupations. This represents a recent increase 

from a low of 25% in 2002. Of those receiving new H-1B visas 

in 2006 who were in computer-related occupations, 44% had 

master’s degrees and just over 1% had doctorates. 

Characteristics of Workers Issued New H-1B Visas

Education Levels. In FY 2006, 57% of new H-1B visa 

recipients had advanced degrees, including 41% with master’s degrees, 5% with professional degrees, and 11% with 

doctorates. This degree distribution differs by occupation, 

with 87% of those holding advanced degrees in math and 

physical sciences occupations (47% with doctorates) and 

89% in life science occupations (61% with doctorates).

Many H-1B visa recipients earned their degrees abroad. 

In FY 2006, 41% of doctorate holders, 79% of professional 

degree holders, and 48% of master’s degree holders who received H-1B visas indicated on their applications that they did 

	
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SOURCE: Department of Homeland Security, U.S. Citizenship and 

Immigration Services; and National Science Foundation, Division of 

Science Resources Statistics, special tabulations.

	

	

Systems

analysis/

programming

46%

Management/administration/other

professionals and technicians 13% 

College/university education 8%

Other engineering/architecture 6%

Medicine/health 5%

Other computer-related 4%

Electrical/electronic engineering 3%

Life sciences 3%

Mathematics/physical sciences 2%

Other education 2%

Social sciences 2% Other 6%
3-56 Chapter 3. Science and Engineering Labor Force

Figure 3-59

8	
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'

56*
 #9A%

SOURCE: Department of Homeland Security, U.S. Citizenship and 

Immigration Services; and National Science Foundation, Division of 

Science Resources Statistics, special tabulations.

	

	

India 54%

China 9%

Canada 3%

South Korea 3%

Philippines 3%

Japan 2%

United Kingdom 2%

Taiwan 1%

Pakistan 1%

Germany 1%

Mexico 1%

Other countries 20%

September 11, 2001 (see table 3-27). F-1 visa applications 

declined from 380,385 in FY 2001 to a low of 282,662 in 

FY 2004. After 2004, the number of applications increased 

each year; the number of F-1 applications was 21% higher in 

FY 2008 than in FY 2001. J-1 visa applications experienced 

smaller declines after September 11, 2001, and were 35% 

higher in FY 2008 than in FY 2001. 

Stay Rates for U.S. Doctorate Recipients with 

Temporary Visas

Many foreign students opt to stay in the United States after earning their degree. As reported in the Survey of Earned 

Doctorates, between 2004 and 2007, 76% to 82% of nonU.S. citizen S&E doctorates had firm commitments for work 

or study in the United States at the time of graduation. The 

rates were slightly lower for temporary visa holders over the 

same time period (75% to 81%) (see chapter 2 for further 

discussion). 

Longer-term stay rates are also high. According to a report by Michael Finn (2009) of the Oak Ridge Institute for 

Science and Education, 62% of 2002 U.S. S&E doctorate 

recipients with temporary visas were in the United States 

in 2007. This is down slightly from a 65% 5-year stay rate 

found in 2005 (figure 3-61), but due to a long upward trend in 

stay rates, this was still higher than any other 5-year stay rate 

estimated between 1992 and 2003. As shown in figure 3-61, 

stay rates differ significantly by country of origin, but have 

generally been increasing for most major source countries. 

New doctorate recipients in 2002 faced relatively poor 

labor market conditions (see discussions earlier in this chapter), and foreign students earning degrees may have also 

been worried about greater difficulties with securing visas 

for themselves and their families. 

Figure 3-60

8	
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'56*
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9A%

UK = United Kingdom

SOURCE: Department of Homeland Security, U.S. Citizenship and 

Immigration Services; and National Science Foundation, Division of 

Science Resources Statistics, special tabulations.

	

	

China 32%

India 13%

Canada 5% South Korea 7%

Germany 3%

UK 3%

Japan 3%

Russia 2%

Taiwan 2%

Turkey 2%

France 2%

Other

countries 26%

not have a graduate degree from a U.S. institution.21 This indicates both the use of the H-1B visa as a way for graduates of 

U.S. schools to continue their careers in the United States and 

the importance of the H-1B visa in bringing foreign-educated 

individuals into the United States (DHS/ICE 2006).

H-1B Country of Citizenship. More than half of recent 

H-1B visa recipients were from India and an additional 9% 

from China. Among doctorate holders, one-third were from 

China and another 13% from India (figures 3-59 and 3-60). 

Altogether, Asian citizens made up three-quarters of all 

H-1B visa recipients; among doctorate holders, they were 

well above half. 

Relatively few doctorate holders from countries with better university systems had U.S. degrees. For example, the 

United Kingdom (21%), Germany (28%), Canada (29%), 

France (30%), and Japan (31%). In contrast, 71% of doctorate holders from China and 59% of doctorate holders from 

India claimed advanced degrees from U.S. institutions on 

their visa applications.

H-1B Salaries. Table 3-26 shows salaries paid to new recipients of H-1B temporary work visas by occupation group 

and level of degree. These starting salary figures, 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-paid 

postdoc fellowships.

Visa Applications and Rejections for Students and 

Exchange Visitors

The F-1 and J-1 visas used by students and exchange 

visitors have recovered from the declines experienced after 
Science and Engineering Indicators 2010 3-57

There was also a geographic pattern to the changes in 

5-year stay rates for foreign S&E doctorate recipients. Stay 

rates actually showed large percentage point increases for 

students from the largest European source countries: the UK 

(+6 percentage points) and Germany (+3 percentage points). 

The overall decline in stay rate between 2005 and 2007 was 

driven largely by decreases in stay rates for several Asian 

source countries: Taiwan (–8 percentage points), Japan (–6 

percentage points), and India (–4 percentage points).

Finn also estimates stay rates for doctorate recipients 

from graduate programs of different quality based on ratings 

Table 3-27

Initial applications for student/exchange visitor 

visas: FY 2001–08

Student (F-1) Exchange visitor (J-1)

Year Applications

Refused 

(%) Applications 

Refused 

(%)

2001... 380,385 22.9 275,959 5.1

2002... 322,644 27.4 270,702 6.2

2003... 288,731 25.3 275,335 7.8

2004... 282,662 22.6 274,789 7.4

2005... 333,161 19.8 311,728 5.8

2006... 385,596 20.1 349,598 5.9

2007... 386,144 24.0 346,946 6.2

2008... 458,406 25.7 371,527 6.6

NOTE: Application counts and refusal rates adjusted for 

reapplications and appeals by same individual.

SOURCE: Department of State, Immigrant Visa Control and 

Reporting Division, administrative data (2001–08).

Science and Engineering Indicators 2010

	
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Table 3-26

Average annual salary of new recipients of H-1B temporary work visas, by occupation and degree: FY 2006

(Dollars)

Occupation All degree levels Bachelor’s Master’s Professional Doctorate

Administrative specializations ........................................... 53,500 49,600 56,200 70,100 85,100

Architecture/engineering/surveying................................... 61,600 58,400 60,000 73,700 73,000

Art ...................................................................................... 44,800 44,500 44,400 na na

Computer-related occupations ......................................... 56,200 56,000 55,600 71,200 80,400

Education .......................................................................... 48,500 36,700 43,800 67,000 51,900

Entertainment/recreation ................................................... 38,900 38,000 40,700 na na

Law/jurisprudence ............................................................. 100,100 63,200 83,200 114,600 na

Life sciences ...................................................................... 45,600 40,400 43,900 47,700 46,700

Managers/officials nec ...................................................... 78,000 70,800 81,500 107,500 105,300

Mathematics/physical sciences ........................................ 60,400 58,500 59,800 60,900 61,400

Medicine/health ................................................................. 72,300 48,100 51,700 86,800 62,700

Miscellaneous professional/technical/managerial............. 64,400 54,800 68,800 na 84,500

Museum/library/archival sciences ..................................... 41,800 39,500 41,300 na na

Religion/theology ............................................................... 37,400 NA 38,500 na na

Social sciences .................................................................. 60,900 54,100 64,000 na 77,600

Writing ............................................................................... 38,200 37,900 37,500 na na

na = not applicable; NA = not available; nec = not elsewhere classified

SOURCE: Department of Homeland Security, U.S. Citizenship and Immigration Services, special tabulations.

Science and Engineering Indicators 2010
3-58 Chapter 3. Science and Engineering Labor Force

of faculty by the publication U.S. News and World Report

and on separate ratings by the National Research Council. 

Finn used these ratings to select 20 to 25 “top-rated” departments in major S&E fields. Doctorate recipients from 

the graduate programs that Finn designated as top rated 

were somewhat less likely to remain in the United States 

than were graduates of other programs (see table 3-28). For 

doctorate recipients, the difference in 1-year stay rates was 

3 percentage points: 67% of those from the top-rated programs and 70% 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 58% 

and 63%, respectively.

Conclusion 

Growth of the U.S. S&E workforce continues to exceed 

that of the overall workforce. However, the 2000–07 period 

showed the smallest growth rate (2.2%) in S&E occupations 

since NSF began tracking these data in the 1950s. Although 

the U.S. recession that began in 2007 affected workers 

across all occupations, S&E occupations appear to be less 

severely affected. The unemployment rate in April 2009 was 

9.0% for all workers, but 4.3% for those working in S&E 

occupations. The influence of the recession on longer-term 

S&E labor force behavior (e.g. retirement rates, part- and 

full-time employment) remains to be seen. 

A large and growing number of Americans hold degrees 

in S&E fields; in 2006, 16.6 million individuals in the U.S. 

workforce held at least one S&E degree. Individuals in S&E 

occupations are highly educated, with more than 70% holding at least a bachelor’s degree in any field; in contrast, less 

than 30% of persons working in all other occupations hold 

a bachelor’s or higher degree. Workers in S&E occupations 

also received higher wages than those in other occupations. 

The globalization of the S&E labor force continues to 

increase. The number of people with S&E is skills rising, 

especially in developing nations, and the location of S&E 

employment is becoming more internationally diverse. S&E 

workers are becoming more internationally mobile. These 

trends reinforce each other: as R&D spending and business 

investment cross national borders in search of available talent, talented people cross borders in search of interesting and 

lucrative work, and employers recruit and move employees 

internationally.

The growth rate of the S&E labor force would be significantly reduced if the United States became less successful in the increasing international competition for scientists 

and engineers. Compared with the United States, many 

other countries are more actively reducing barriers to highly 

skilled immigrants entering their labor markets. Nonetheless, the United States is still an attractive destination for 

many foreign scientists and engineers. 

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 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 

Table 3-28

Temporary residents who received S&E doctorates in 2002 who were in the United States, by program rating: 

2003–07 

(Percent)

Program rating

 Foreign doctorate 

recipients (n) 2003 2004 2005 2006 2007

All programs ...................................................... 7,850 69 66 64 62 62 

Top-rated programs ....................................... 2,611 67 63 61 59 58 

All other programs ......................................... 5,239 70 68 65 64 63 

NOTE: Characterization of programs as “top-rated” by Finn (forthcoming) 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. Oak Ridge, TN: Oak Ridge Institute for Science and Education 

(forthcoming).

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 3-59

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. 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%.

5. Many comparisons using Census Bureau data on occupations are limited to looking at all S&E occupations except 

postsecondary teachers because the current U.S. occupation 

taxonomy does not break out these teachers by field. Only 

NSF surveys of scientists and engineers collect data on postsecondary teachers by field.

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.

7. Specifically presented here are coefficients from linear regressions using the 2003 SESTAT database of individual characteristics on the natural log of reported full-time 

annual salary as of October 2003.

8. Underrepresented ethnic group, as used here, includes 

individuals who reported their race as black, American Indian/Alaska Native, of Hispanic origin, or other ethnicity. 

9. In the regression equation, this is the form: age1

, age2, 

age3

, age4; years since highest degree (YSD)1, YSD2, YSD3, 

YSD4

.

10. The regressions included 20 dummy variables for 

SESTAT field-of-degree categories (out of 21 S&E fields; 

the excluded category was “other social science”).

11. Variables added here include 34 SESTAT occupational groups (excluding “other non-S&E”), whether individuals worked in R&D, the employer’s U.S. census region, 

and the sector of the economy.

12. Variables added here include dummy variables for 

marriage, number of children in the household younger than 

18, whether the father had a bachelor’s degree, whether either 

parent had a graduate degree, citizenship, nativity, and age 

at receipt of first bachelor’s degree minus 20. Sex and ethnic 

minority variables are included in all regression equations.

13. This may be because differences between groups in 

many of these family and personal characteristics are not 

large. It is also possible that variations in these characteristics correlate with those in other controls already in the 

statistical model and in that sense have already been taken 

into account. 

14. Many doctorate holders with salaries at this level are 

postdocs in temporary training positions.

15. Although the formal job title is often postdoctoral 

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 

graduate students take primarily for additional training—a 

period of advanced professional apprenticeship—after completion of a doctorate.

16. Some part of the citizen and permanent resident postdoc population in the fall of 2005 will not be counted even 

in the SDR. Excluded are summer 2005 graduates who may 

be in postdoc positions in the fall of 2005, doctorate holders 

who may have left the country before April 2006, and those 

who have foreign doctorates.

17. Respondents also had to be under age 76 and resident 

in the United States in April 2006. In a similar retrospective 

question on the 1995 SDR, 25% of those earning their doctorates before 1964 reported having had postdoc positions.

18. See section ‘Business-to-business linkages’ in chapter 4 for information on international transactions in R&D 

services and technology alliances.

19. 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%.

20. Outside of government, it is illegal to discriminate 

in employment on the basis of citizenship status. However, 

if the work requires a security clearance, this usually also 

requires citizenship.

21. These figures are likely to somewhat underestimate 

the proportion of H-1B recipients without U.S. graduate degrees. Because a portion of H-1B visas were restricted to applicants with advanced degrees from U.S. institutions, these 

applicants had an incentive to answer the optional question 

about where their degrees were earned; applicants whose 

degrees came exclusively from foreign institutions had no 

reason to answer this question.

Glossary

Career path job: A job that helps graduates fulfill their 

future career plans.

EU-27: The 27 member states of the European Union 

since 2007, 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. 

High-skilled diaspora: Networks of contact and information flow that form among the internationally mobile portion of a country’s nationals. 
3-60 Chapter 3. Science and Engineering Labor Force

Involuntarily out of the 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, or employment part time because full-time work 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. 

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 1–5 years after receiving a doctorate.

Tertiary educated: 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 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.

Barro R, Lee J. 2000. International data on educational attainment: Updates and implications. Cambridge, MA: Center 

for International Development at Harvard University. 

Census Bureau. 2007. American Community Survey, Special tabulations. http://www.census.gov/acs.

Department of Homeland Security, Immigration and Customs Enforcement (DHS/ICE). 2006. Special tabulations.

Docquier F Marfouk A. 2004. International Migration by 

Education Attainment. Bonn, Germany: Institute for the 

Study of Labor. www.iza.org.

Finn M. 2009. Stay rates of foreign doctorate recipients from 

U.S. universities. Oak Ridge, TN: Oak Ridge Institute for 

Science and Education. Forthcoming.

Lowell L, Regets M. 2006. A half-century snapshot of the 

STEM workforce, 1950 to 2000. Washington, DC: Commission on Professionals in Science and Technology.

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. Volume 1, NSB 08-01; volume 2, NSB 08-01A. Arlington, VA: National Science 

Foundation.

National Science Foundation, Division of Science Resources Statistics (NSF/SRS). 2006. Scientists and Engineers 

Statistical Data System (SESTAT), Special tabulations. 

http://sestat.nsf.gov. Accessed 15 October 2009.

National Science Foundation, Division of Science Resources Statistics (NSF/SRS). 2008a. Federal scientists and engineers: 2003-05. NSF 09-302. Arlington, VA.

National Science Foundation, Division of Science Resources Statistics (NSF/SRS). 2008b. Postdoc participation 

of science, engineering, and health doctorate recipients. 

NSF 08-307, Hoffer T, Grigorian K, and Hedberg E. Arlington, VA.

Organisation for Economic Co-operation and Development 

(OECD). 2009. Main Science and Technology Indicators. 

Paris.

Price DJ de Solla. 1961. Science Since Babylon. New Haven: Yale University Press.

Regets MC. 2001. Research and policy issues in high-skilled 

international migration: A perspective with data from the 

United States. Bonn, Germany: Institute for the Study of 

Labor. ftp://ftp.iza.org/dps/dp366.pdf.

Salt, J. 2007. International Migration and the United Kingdom: Report of the United Kingdom SOPEMI Correspondent to the OECD, 2007. London: Migration 

Research United, Department of Geography, University 

College London. http://www2.geog.ucl.ac.uk/mru/docs/

Sop07_20080131.pdf. Accessed 19 October 2009.

Stephan P, Levin S. 1992. Striking the Mother Lode in Science: The Importance of Age, Place, and Time. New 

York: Oxford University Press. 
Chapter 4

Research and Development: 

National Trends and 

International Linkages

4-1

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

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

Location of R&D Performance ................................................................................................4-4

Business R&D ..........................................................................................................................4-4

Federal R&D ............................................................................................................................4-5

Federal R&D Tax Credit ..........................................................................................................4-5

International R&D Comparisons .............................................................................................4-5

R&D by Multinational Companies ..........................................................................................4-6

Technology and Innovation Linkages ......................................................................................4-6

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

Chapter Organization ...............................................................................................................4-8

Trends in National R&D Performance ........................................................................................4-8

Performers of R&D ................................................................................................................4-10

Sources of R&D Funding .......................................................................................................4-13

R&D by Character of Work ...................................................................................................4-14

Location of R&D Performance ..................................................................................................4-16

Distribution of R&D Expenditures Among States .................................................................4-16

Sector Distribution of R&D Performance by State ................................................................4-17

Business R&D in Top States..................................................................................................4-17

Business R&D ............................................................................................................................4-18

Largest R&D Industries .........................................................................................................4-19

Federal R&D ..............................................................................................................................4-21

R&D Funding in Current Federal Budget ..............................................................................4-22

Federal R&D Budget by National Objectives .......................................................................4-23

Federal Spending on R&D by Agency ..................................................................................4-25

Federal Spending on R&D by Performer ...............................................................................4-27

Federal Spending on Research by Field .................................................................................4-28

Federal R&D Tax Credits ......................................................................................................4-31

International R&D Comparisons ...............................................................................................4-33

Global Patterns of R&D Expenditures ...................................................................................4-33

Comparison of Country R&D Intensities ..............................................................................4-35

R&D by Performing Sector and Source of Funds .................................................................4-37

Business Sector ......................................................................................................................4-39

Academic Sector ....................................................................................................................4-42

Government R&D Priorities ..................................................................................................4-42

R&D by Multinational Companies ............................................................................................4-44

U.S. Affiliates of Foreign Companies ....................................................................................4-46

U.S. MNCs and Their Overseas R&D ...................................................................................4-48

Technology and Innovation Linkages ........................................................................................4-50

Business-to-Business Linkages ..............................................................................................4-51

Federal Technology Transfer and Other Innovation-Related Programs ................................4-53

Conclusion .................................................................................................................................4-57

Notes ..........................................................................................................................................4-58

Glossary .....................................................................................................................................4-61

References ..................................................................................................................................4-62
4-2 Chapter 4. Research and Development: National Trends and International Linkages

List of Sidebars

New U.S. Business R&D and Innovation Survey ........................................................................4-7

Definitions of R&D ......................................................................................................................4-8

Unmeasured R&D ......................................................................................................................4-10

The BEA/NSF R&D Satellite Account: R&D and Economic Growth ......................................4-16

Trends in R&D for Industrial Research Institute Members .......................................................4-21

Public Investment in Energy R&D ............................................................................................4-24

Tracking R&D: The Gap Between Performer- and Source-Reported Expenditures .................4-29

Comparing International R&D Expenditures ............................................................................4-32

Global R&D Expenses of Public Corporations .........................................................................4-41

Government Funding Mechanisms for Academic Research .....................................................4-43

Foreign Direct Investment in R&D ............................................................................................4-46

Linking MNC Data From International Investment and Industrial R&D Surveys ....................4-46

Recent Developments in Innovation-Related Metrics ...............................................................4-50

Major Federal Legislation Related to Technology Transfer and Cooperative R&D .................4-54

Federal Technology Transfer: Activities and Metrics ...............................................................4-55

List of Tables

Table 4-1. U.S. R&D expenditures, by performing sector and funding source: 2003–08 ...........4-9

Table 4-2. U.S. R&D expenditures, by character of work, performing sector, and 

funding source: 2008 ..............................................................................................................4-12

Table 4-3. Top 10 states in R&D performance, by sector and intensity: 2007 ..........................4-17

Table 4-4. Top 10 states in business R&D performance and share of R&D, 

by selected industry: 2007 .....................................................................................................4-18

Table 4-5. Business R&D and domestic net sales, by industry: 2006 and 2007 .......................4-19

Table 4-6. Estimated share of computer-related services in company-funded R&D and 

domestic net sales of R&D-performing companies: 1987–2007 ...........................................4-20

Table 4-7. Federal budget authority for R&D and R&D plant: FY 2008–10 ............................4-22

Table 4-8. Federal obligations for research and development, by agency and 

character of work: FY 2008 ...................................................................................................4-26

Table 4-9. Federal obligations for research and development, by agency and performer: 

FY 2008 .................................................................................................................................4-27

Table 4-10. Federal research and experimentation tax credit claims and corporate 

tax returns claiming credit: 1990–2006 .................................................................................4-31

Table 4-11. International comparisons of gross domestic expenditures on R&D and 

R&D share of gross domestic product, by country/economy/region: Most recent year ........4-34

Table 4-12. Gross expenditures on R&D as share of gross domestic product, 

for selected countries: Most recent year ...............................................................................4-37

Table 4-13. Gross expenditures on R&D by performing sector, for selected countries: 

Most recent year .....................................................................................................................4-38

Table 4-14. Gross expenditures on R&D by funding source, for selected countries: 

Most recent year .....................................................................................................................4-38

Table 4-15. Global R&D spending by top 25 corporations: 2006 .............................................4-41

Table 4-16. Share of academic R&D expenditures, by country and S&E field: 

Most recent year ....................................................................................................................4-44

Table 4-17. Government R&D support by major socioeconomic objectives, 

for selected countries: 1981–most recent year .......................................................................4-45

Table 4-18. R&D performed by majority-owned affiliates of foreign companies in 

United States, by selected NAICS industry of affiliate and country/region: 2006 ................4-47

Table 4-19. R&D performed by U.S. multinational companies: 2004–06 ................................4-48

Table 4-20. R&D performed abroad by majority-owned foreign affiliates of U.S. parent 

companies, by selected NAICS industry of affiliate and country/region: 2006 ....................4-49

Table 4-21. U.S. trade in research, development, and testing services: 2001–07 .....................4-52

Table 4-22. Federal laboratory technology transfer activity indicators, by selected 

U.S. agency: FY 2007 ............................................................................................................4-55
Science and Engineering Indicators 2010 4-3

List of Figures

Figure 4-1. U.S. total R&D expenditures: 1953–2008 ..............................................................4-10

Figure 4-2. Shares of national R&D expenditures, by performing and funding 

sectors: 2008 ..........................................................................................................................4-11

Figure 4-3. National R&D, by performing and funding sectors, 1953–2008 ............................4-11

Figure 4-4. National R&D expenditures, by funding source: 1953–2008 .................................4-14

Figure 4-5. National R&D, by character of work, and basic research, by funding and 

performing sectors: 2008 .......................................................................................................4-15

Figure 4-6. R&D performing sectors and funding sources, by character of work: 2008 ..........4-15

Figure 4-7. Federal R&D budget authority, by budget function: FY 1980–2008 .....................4-25

Figure 4-8. Projected federal obligations for R&D, by agency and character of work: 

FY 2008 .................................................................................................................................4-26

Figure 4-9. Federal obligations for R&D, by performing sector: FY 1955–2008 .....................4-28

Figure 4-10. Federal obligations for research, by agency and major S&E field: FY 2008 .......4-30

Figure 4-11. Research and experimentation credit claims as percentage of industryfunded R&D: 1990–2006 .......................................................................................................4-31

Figure 4-12. National R&D expenditures and share of world total, by region: 2007 ................4-33

Figure 4-13. Gross domestic expenditures on R&D by United States, EU-27, OECD, 

and selected other countries: 1981–2007 ...............................................................................4-35

Figure 4-14. Composition of gross domestic product for selected countries, 

by sector: 2008 .......................................................................................................................4-35

Figure 4-15. U.S. R&D share of gross domestic product: 1953–2008 ......................................4-36

Figure 4-16. Gross expenditures on R&D as share of gross domestic product, for 

selected countries: 1981–2007 ...............................................................................................4-36

Figure 4-17. Business R&D financed by foreign sources, for selected countries: 

1981–2007..............................................................................................................................4-39

Figure 4-18. Share of industrial R&D, by industry sector and selected country: 

2005–2007..............................................................................................................................4-40

Figure 4-19. Academic R&D financed by business for selected countries: 1981–2007 ...........4-43

Figure 4-20. R&D performed by U.S. affiliates of foreign companies in United States, 

by investing region, and performed by foreign affiliates of U.S. multinational 

corporations, by host region: 2006 .........................................................................................4-47

Figure 4-21. Regional shares of R&D performed abroad by foreign affiliates of U.S. 

MNCs: 1994–2006 ................................................................................................................4-48

Figure 4-22. R&D contracted out in United States by manufacturing companies as ratio 

of company-funded and -performed R&D: 1993–2007 .........................................................4-52

Figure 4-23. U.S. industrial technology alliances with U.S. and foreign-owned 

companies, worldwide, by country/region of partner: 1990–2006 ........................................4-53

Figure 4-24. SBIR funding, by type: 1983–2006 ......................................................................4-56

Figure 4-A. Energy R&D budgets of national governments of selected IEA members: 

1997–2007..............................................................................................................................4-24

Figure 4-B. Combined energy R&D budgets of IEA members, by technology type: 

1997–2007..............................................................................................................................4-24

Figure 4-C. Discrepancy in U.S. performer-reported and agency-reported federal R&D: 

1980–2007..............................................................................................................................4-29
Trends in National R&D Performance 

U.S. R&D expenditures continued to rise in 2008, outpacing the overall expansion of the nation’s economy. 

NSF estimates that overall spending on R&D conducted 

in the United States was $398 billion (current dollars) in 

2008, up from $373 billion in 2007. This increase represents growth in 2008 of 6.7% over the 2007 level, or 4.5% 

in inflation-adjusted 2000 dollars. However, this 2008 

figure may not fully reflect the effects of the downturn in 

U.S. and global economic conditions that intensified in 

late 2008. 

National R&D spending has increased mostly uninterrupted since 1953. Over the past 20 years, growth in R&D 

spending has averaged 5.6% in current dollars and 3.1% 

in constant dollars—somewhat ahead of the average pace 

of GDP growth over the same period (in both current and 

constant dollars). 

The business sector accounts for most U.S. R&D performance and funding. 

The business sector performed an estimated $289 billion 

of R&D in 2008, or 73% of the U.S. total, drawing on 

both business and federal sources of R&D support. The 

business sector itself provided an estimated $268 billion 

of funding for R&D in 2008, or 67% of the U.S. total; 

almost all of it supported R&D performed by business. 

Over the past 5 years, expanded business spending has 

accounted for much of the nation’s R&D growth.

The academic sector is the second-largest performer of 

U.S. R&D, an estimated $51 billion in 2008, just under 

13% of the U.S. total. 

The federal government is the second-largest funder of 

U.S. R&D, providing an estimated $104 billion, or 26% 

of the U.S. total in 2008.

U.S. R&D is dominated by development expenditures, 

largely performed by the business sector, and most basic 

research is conducted at universities and colleges. 

In 2008, basic research was about 17% ($69 billion) of 

the U.S. total, applied research was about 22% ($89 billion), and development was about 60% ($240 billion). 

Universities and colleges historically have been the main 

performers of U.S. basic research, an estimated 56% of 

total U.S. basic research in 2008. The federal government 

has been the prime source of basic research funding, accounting for 57% of the nation’s total in 2008. 

The business sector, which currently accounts for more 

than half of all U.S. applied research funding, spends 

more than four times as much on applied research as on 

basic research. 

Development in the United States is chiefly a business 

sector activity, which performed 90% of the total development in 2008 and provided 84% of the funding. Most 

of the rest of development funding is provided by the federal government. 

Location of R&D Performance

R&D is geographically concentrated, and states vary significantly in the types of research performed within their 

borders. 

In 2007, the 10 states with the greatest R&D expenditure 

levels accounted for 64% of all U.S. R&D expenditures. 

California alone represented 22% of U.S. R&D—triple 

that of Massachusetts, the next highest state. New Mexico, Massachusetts, and Maryland had the highest R&Dto-GDP ratios in 2006. California ranked seventh in 

R&D/GDP intensity.

Massachusetts, Illinois, California, and Texas accounted 

for about two-thirds of the R&D performed by computer 

and electronics products companies in 2007; New Jersey, 

Connecticut, and Pennsylvania are the leaders in chemicals manufacturing, accounting for 41% of the R&D in 

that industry.

Nationally, small companies (defined as having from 5 to 

499 employees) perform 19% of the nation’s total business R&D. The R&D performance of these small companies is concentrated geographically. Among the top 10 

business R&D-performing states, New York and California had the highest totals of small companies performing 

business R&D, with 23% and 20%, respectively.

Business R&D

Business sector R&D rose to its highest level in 2007. Although 2008 projections show additional growth, they do 

not reflect the effects of the U.S. economic downturn.

R&D performed by the business sector is estimated to 

have reached $269 billion in 2007 and is projected to have 

increased to $289 billion in 2008.

The company-funded R&D-to-sales ratio of companies in 

all industries performing R&D in the United States varied 

between 3.2% and 3.4% during 2003–06; in 2007 it was 

3.5%.

Over three-fourths of business R&D is performed in six 

business sectors. The R&D-to-sales ratio for these sectors 

as a group was 8.0% in 2007, compared with 1.4% for all 

other business sectors.

Highlights

4-4 Chapter 4. Research and Development: National Trends and International Linkages
Federal R&D

Federal R&D spending continued to grow in recently 

proposed and enacted budgets and received further increases through the American Recovery and Reinvestment Act. 

Budget appropriations for federal spending on R&D in 

FY 2009 totaled $147.1 billion (current dollars), an increase of $3.3 billion (or 2.4%) over the enacted FY 2008 

spending level. The proposed overall increase for FY 

2010 is smaller (0.4%).

However, the American Recovery and Reinvestment Act 

(ARRA) of 2009 included a one-time additional increase 

in R&D funding that is estimated to total $18.3 billion in 

FY 2009. 

In the FY 2009 budget, increases in R&D funding were 

greatest for the National Institutes of Health (NIH), the 

Department of Energy (DOE), and the National Science 

Foundation (NSF). Along with the National Aeronautics 

and Space Administration and the National Institute of 

Standards and Technology, these agencies also received 

the largest increases from ARRA.

Defense continues to be the largest function in the federal 

R&D budget. It accounted for 59% of the federal total 

(budget authority) in FY 2008. 

The most dramatic change in national R&D priorities 

over the past 25 years has been the large rise in healthrelated R&D, which grew from 25% of the federal nondefense R&D budget in FY 1980 to 55% in FY 2005. In FY 

2008, health accounted for 52% of the nondefense R&D 

budget.

Federal R&D Tax Credit

Along with direct funding of R&D, the government also 

promotes the conduct of R&D through tax incentives. 

About 11,000 U.S. companies claimed an estimated $7.3 

billion in federal research and experimentation tax credits 

in 2006, compared with $6.4 billion in 2005.

International R&D Comparisons

Many countries conduct R&D, but much of global R&D 

performance continues to be concentrated in a few highincome countries and regions. 

Worldwide R&D expenditures totaled an estimated 

$1.107 trillion in 2007 (the latest year for which data are 

available). The United States accounted for about 33% of 

this total. Japan, the second-largest performer, accounted 

for about 13%. China was third, at about 9%. Germany 

and France, respectively, fourth and fifth (and the largest 

performers in Europe), accounted for 6% and 4%, respectively. The top 10 countries (also including South Korea, 

the United Kingdom (UK), the Russian Federation, Canada, and Italy) account for almost 80% of current global 

R&D performance.

The 27 nations of the European Union (EU-27) accounted 

for about 24% of global R&D. R&D by the EU-27 grew 

at an average annual constant dollar rate of 3.3% between 

1997 and 2007. By comparison, the U.S. pace of growth, 

on the same basis, averaged 3.3%.

Recent growth in R&D expenditures has been most dramatic in China, averaging just above 19% annually in 

inflation-adjusted dollars over the past decade. 

Wealthy economies generally devote larger shares of 

their gross domestic product (GDP) to R&D than do less 

developed economies.

The U.S. R&D/GDP ratio was 2.7% in 2007 and has fluctuated between 2.6% and 2.8% over the past 10 years, 

largely reflecting changes in business R&D spending. In 

2007, the United States ranked eighth among the economies tracked by the OECD; Japan, South Korea, and several smaller developed economies had higher ratios. 

Among the major European R&D-performing countries, 

Italy (2006) and the Russian Federation (2007) had R&D/

GDP ratios of 1.1%. The UK ratio was 1.8% in 2007, and 

those of France and Germany were 2.1% and 2.5%, respectively, in 2007. Canada’s R&D/GDP ratio was 1.9% 

in 2007. Over the past 10 years, these ratios were stable 

or changed only modestly.

R&D/GDP ratios increased substantially in Japan, South 

Korea, and China over the past 10 years. The Japanese 

and South Korean ratios were among the highest in the 

world in 2007, at 3.4% and 3.5% respectively. China’s 

ratio remains relatively low, at 1.5%, but has more than 

doubled from 0.6% in 1996.

Among the countries with the largest R&D expenditures, 

the business sector accounts for the bulk of total R&D 

performance.

Among the top 10 countries for R&D expenditures, the 

business sector is the largest R&D performer, ranging 

from 77% for South Korea and Japan to 49% for Italy. 

No single industry accounted for more than 18% of total 

business R&D in the United States in 2007; many other 

countries displayed much higher industry and sector concentrations.

The pharmaceuticals industry accounts for more than 

25% of business R&D in Denmark and the United Kingdom, and more than 20% in Belgium and Ireland. The 

computers, office and accounting machines industry 

represents only a small share of business R&D in most 

countries; only Japan reports a double-digit concentration 

of business R&D in this industry. The service sector accounted for 30% or more of all business R&D in many 

countries of the Organisation for Economic Co-operation 

and Development (OECD), including the United States.

Science and Engineering Indicators 2010 4-5
R&D by Multinational Companies

Multinational companies (MNCs) represent a substantial 

component of U.S. R&D. Overseas R&D by U.S. MNCs 

reflects gradual changes in their geographic focus. 

Majority-owned affiliates of foreign-based MNCs spent 

$34.3 billion on U.S. R&D in 2006, up from $31.1 billion 

in 2005. Their U.S. R&D expenditures have grown faster 

than total U.S. business R&D and have represented about 

14% of U.S. business R&D since 2003, up from the single 

digits in the early 1990s.

U.S. MNCs performed $216.3 billion in R&D worldwide 

in 2006, including $187.8 billion in the United States 

by parent companies and $28.5 billion by their overseas 

affiliates. The R&D by MNC parents represented 87% 

of their global R&D and about 76% of total U.S. business R&D. Both shares have changed little in recent 

years. However, the geographic distribution of R&D by 

their overseas affiliates is gradually reflecting the role of 

emerging markets.

Europe, Canada, and Japan accounted for a decreasing 

share of R&D by overseas affiliates of U.S. MNCs, representing 90% in 1994 and 80% in 2006. Over the same period, the share performed in Asia (excluding Japan) rose 

from 5.4 % to 13.5%, driven by affiliates’ R&D spending 

in China, Singapore, and South Korea. 

R&D performed by U.S.-owned affiliates located in China and India increased from less than $10 million in each 

country in 1994 to $804 million and $310 million, respectively, in 2006. Although the 2006 levels for China and 

India represented only about 3% and 1%, respectively, 

of total overseas R&D by U.S. MNCs, funding levels in 

some lower cost locations may still be significant from 

the perspective of purchasing power.

Technology and Innovation Linkages

Federal agencies and laboratories continue to engage in 

collaborative and technology transfer activities. Business increased its R&D funding to contractors within the 

United States.

Federal agencies participated in more than 7,000 formal 

Cooperative Research and Development Agreements in 

2007 and more than 9,000 less formally structured collaborative R&D relationships. Federal agencies issued more 

than 1,400 patents in 2007 and held more than 10,000 

active licenses based on their total stock of intellectual 

property.

Businesses in the United States reported contracting out 

an estimated $19.0 billion in R&D to other U.S.-located 

companies in 2007, compared with $12.4 billion in 2006. 

This increased the ratio of contracted-out R&D to company-funded and company-performed R&D from 5.5% 

in 2006 to 7.8% in 2007. For manufacturers, the ratio 

reached 8.5% in 2007, up from 5.7% in 2006.

International trade in R&D services and technology 

alliances indicate the role of external sources and cooperative arrangements aimed at acquiring or jointly 

developing new knowledge.

In 2007, the United States maintained a trade surplus in 

research, development, and testing services of $3.3 billion. Trade within MNCs dominates these statistics—

which is not surprising, given their large role in U.S. 

R&D performance. 

Almost 900 worldwide business technology alliances 

were established in 2006, approximately two-thirds of 

which involved at least one U.S.-owned company regardless of location. Since 1999, the proportion of U.S.-foreign alliances has surpassed U.S.-only alliances, a change 

driven by rapid growth in alliances with European companies. However, in 2006 the number of U.S. alliances 

with Asian non-Japanese partners (50) reached parity 

with U.S.-Japan alliances (54), reflecting growth of the 

former since 1990. 

4-6 Chapter 4. Research and Development: National Trends and International Linkages
Science and Engineering Indicators 2010 4-7

Introduction

As we come to the end of the first decade of the 21st 

century, global economic trends are leading governments 

of most nations to implement financial market support 

measures and economic recovery packages. These policies 

often include measures to stimulate productivity, growth, 

and innovation through support of R&D—widely viewed 

as a long-term contributor to economic growth and national 

competitiveness.

The importance accorded to investment in R&D and 

innovation in public policy discussions is reflected in the 

national and international initiatives that help us better 

understand and measure their results. The America COMPETES Act (Public Law 110-69) and the American Recovery and Reinvestment Act of 2009 (Public Law 111-5) both 

address the importance of the U.S. innovation system for 

national economic growth.

Federal statistical agencies seek to incorporate R&D in 

the system of national accounts to measure, for example, 

its relation to gross domestic product (GDP) and productivity growth. These agencies are also exploring the role of 

cross-border investment in R&D and other intangibles. The 

National Science Foundation (NSF) is conducting a new 

Business R&D and Innovation Survey to collect a broad 

range of indicators that will form a platform for future modules on innovation. (See sidebar “New U.S. Business R&D 

and Innovation Survey.”)

An ongoing project conducted by the Organisation for 

Economic Co-operation and Development (OECD) to design 

an Innovation Strategy examines how changes in the innovation enterprise of OECD member nations may affect their 

ability to achieve certain government and socioeconomic 

goals. Concurrently, the OECD, United Nations Statistical 

Commission, and other international bodies are collaborating to update or develop statistical manuals on intangibles, 

To better understand how R&D is conducted in today’s 

innovation- and global-based economy and to investigate 

ways to improve NSF’s portfolio of R&D measurements, 

NSF commissioned a study by the National Research 

Council’s Committee on National Statistics (CNSTAT) 

in 2004. The committee published its findings in the 2005 

report Measuring Research and Development Expenditures in the U.S. Economy (NRC 2005a). The essence of 

CNSTAT’s concerns and recommendations centered on 

the finding that a new, more comprehensive survey was 

needed to “keep up with the fast-changing environment 

for the conduct and organization of research in the private 

business sector” (NRC 2005a, p 4).

In early 2009, NSF and the U.S. Census Bureau 

launched a new Business R&D and Innovation Survey 

(BRDIS). The survey covers manufacturing and service 

companies and includes questions on a broad range of 

R&D topics (listed below). The survey also begins to 

collect innovation data, with the ultimate objective of 

increasing the number and breadth of innovation-related 

items in the future.

Financial measures of R&D activity:

s Domestic and worldwide sales and revenue

s Detail on domestic and worldwide R&D activity

s Company R&D expense by business segment, type 

of expense, and location (state and country)

s Capital expenditures for R&D (buildings, software, 

equipment)

s Projected R&D expense

Measures of company R&D activity funded by others:

s Funds for worldwide and domestic R&D activity

s R&D funded by others—by business segment, type 

of organization, type of expense, state, and location 

(domestic vs. foreign)

Measures of R&D employment:

s R&D headcount (domestic and worldwide) by occupation and sex

s Number of U.S. R&D employees working under a 

visa (H-1B, L-1, and so on)

s R&D full-time equivalent counts

Measures related to R&D management and strategy:

s R&D partnerships

s Share of R&D for the social sciences, new business 

areas, and specific applications

Measures of intellectual property (IP), technology 

transfer, and innovation:

s Participation in activities to introduce new or significantly improve existing goods, services, methods of production and distribution, or support 

systems

s Patent-related data-number owned or applied for

s Participation in specific technology transfer activities

s Importance of types of IP protection

s Licensing to outside parties

For more information on the new survey, see NSF/

SRS (2008b).

New U.S. Business R&D and Innovation Survey
4-8 Chapter 4. Research and Development: National Trends and International Linkages

national economic accounts, and trade in services. The purpose of these efforts is to better harmonize data that will 

serve as future indicators for measuring innovation.

Chapter Organization

This chapter is organized into seven main sections. An 

overview of national trends in the performance and funding 

of R&D is followed by a discussion of state-level R&D patterns and trends. A third section covers business, the largest 

performer and funder of U.S. R&D. This section is followed 

by a discussion of the patterns of federal government R&D, 

including how those patterns play out in the defense, energy, 

and health arenas, and concludes with federal tax incentives 

for business R&D.

The last three sections of the chapter cover international 

comparisons of R&D, investments by multinational companies (MNCs), and technology and innovation linkages, 

respectively. International comparisons of R&D include national R&D expenditures by performer and source, national 

R&D intensities, and government R&D priorities. The section devoted to MNCs covers overseas investments of U.S. 

MNCs and U.S. R&D by foreign-owned companies. Although global R&D is concentrated in a few developed countries or regions, China and other emerging Asian countries 

have increased their R&D expenditures and have become 

hosts to R&D conducted by U.S. MNCs. The last section 

covers business-to-business external sourcing, technology alliances, and international transactions in R&D services. The 

latter represents the convergence of service-oriented R&D 

and global innovation networking. This section concludes 

with a discussion of innovation-related federal programs and 

activities aimed at technology transfer, R&D, and new technology development and deployment by small firms.

Trends in National R&D Performance

R&D, along with other social, economic, and technological factors, creates new knowledge and contributes to 

innovation and the introduction of new goods, services, 

processes, and managerial practices. Suppliers and users of 

R&D include businesses, educational institutions, not-forprofit research organizations, and governments. Statistics on 

R&D expenditures reported by performing and funding organizations are used as metrics throughout the United States 

and internationally.1

 (See sidebar “Definitions of R&D.”)

NSF estimates indicate that overall spending on R&D 

conducted in the United States was $397.6 billion (current 

dollars) in 2008, up from $372.5 billion in 2007 (table 4-1). 

This represents growth of 6.7%, or 4.5% in inflation-adjusted 

R&D. According to international guidelines for conducting R&D surveys, R&D, 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 the use of this stock of knowledge to devise 

new applications” (OECD 2002).

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 to fields of current or potential interest. This focus is often the case when 

performed by industry or mission-driven federal agencies.

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.

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.

R&D plant. This term 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.

Budget authority. Budget authority is the authority 

provided 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.

For an annotated compilation of definitions of R&D 

by U.S. statistical agencies, tax statutes, accounting bodies, and other official sources, see NSF/SRS (2006).

Definitions of R&D
Science and Engineering Indicators 2010 4-9

(also called constant or real) 2000 dollars.2 The 2008 figures 

are preliminary, however, and may not yet fully reflect the 

effects of the sharp downturn in the U.S. economy and globally beginning in late 2008.

Total estimated R&D expenditures in 2008 were $13.9 

billion higher in real dollars than in 2007 (table 4-1). Most of 

this increase reflected estimated increases in business R&D 

expenditures and funding.

Over the longer term, increases in national R&D spending have been largely uninterrupted since 1953 in both current and real dollars (figure 4-1). The rates of the past several 

years have been above the average annual growth rate over 

the past 20 years (5.6% in current dollars, 3.1% in constant dollars). U.S. R&D spending crossed the $100 billion 

(current dollars) threshold in 1984, passed $200 billion in 

1997, was nearly $300 billion in 2004, and almost reached 

Table 4-1

U.S. R&D expenditures, by performing sector and funding source: 2003–08

Sector 2003 2004 2005 2006 2007 2008

Current $millions

All performing sectors .................................... 288,324 299,201 322,104 347,046 372,527 397,616

Business ..................................................... 200,724 208,301 226,159 247,669 269,267 289,105

Federal government .................................... 35,005 35,632 37,716 38,926 39,897 41,741

Federal intramurala

 .................................. 22,752 22,844 24,470 25,556 25,858 27,000

FFRDCs ................................................... 12,253 12,788 13,246 13,369 14,039 14,741

Industry-administeredb ........................ 2,458 2,485 2,601 3,122 4,839 5,031

U&C-administeredb .............................. 7,301 7,659 7,817 7,306 5,892 6,023

Nonprofit-administered ........................ 2,494 2,644 2,828 2,941 3,308 3,688

Universities and colleges ............................ 40,484 43,128 45,197 46,983 49,021 51,163

Other nonprofit ............................................ 12,111 12,140 13,032 13,469 14,341 15,606

All funding sources ......................................... 288,324 299,201 322,104 347,046 372,527 397,616

Business ..................................................... 186,174 191,376 207,826 227,254 246,927 267,847

Federal government .................................... 83,618 88,766 93,817 98,036 101,764 103,696

Universities and colleges ............................ 7,650 7,937 8,579 9,307 9,993 10,600

Nonfederal government .............................. 2,742 2,883 2,922 3,021 3,249 3,453

Other nonprofit ............................................ 8,140 8,239 8,960 9,429 10,593 12,020

Constant 2000 $millions

All performing sectors ......................................... 270,971 273,335 284,962 297,444 310,913 324,791

Business ........................................................... 188,643 190,294 200,081 212,271 224,732 236,155

Federal government ........................................ 32,898 32,551 33,367 33,362 33,299 34,096

Federal intramurala

 ....................................... 21,383 20,869 21,648 21,904 21,582 22,055

FFRDCs ........................................................ 11,516 11,682 11,719 11,459 11,717 12,042

Industry-administeredb ............................ 2,310 2,270 2,301 2,676 4,039 4,109

U&C-administeredb .................................. 6,861 6,997 6,916 6,262 4,918 4,920

Nonprofit-administered ........................... 2,344 2,415 2,502 2,521 2,761 3,012

Universities and colleges................................. 38,047 39,400 39,986 40,268 40,913 41,792

Other nonprofit ................................................. 11,382 11,090 11,529 11,544 11,969 12,748

All funding sources .............................................. 270,971 273,335 284,962 297,444 310,913 324,791

Business ........................................................... 174,969 174,831 183,862 194,773 206,087 218,790

Federal government ........................................ 78,585 81,092 82,999 84,024 84,933 84,704

Universities and colleges................................. 7,190 7,251 7,589 7,977 8,341 8,658

Nonfederal government .................................. 2,577 2,634 2,585 2,589 2,711 2,821

Other nonprofit ................................................. 7,650 7,527 7,926 8,081 8,841 9,818

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.

bIn June 2006, Los Alamos National Laboratory (approximately $2 billion in annual R&D expenditures in recent years) became industry administered; 

previously, U&C administered. This shift is one reason for change in trends apparent in R&D expenditure figures between 2006 and 2007. 

NOTES: Data for 2008 are preliminary. Data based on annual reports by performers except for nonprofit sector. Expenditure levels for academic and federal 

government performers are calendar-year approximations based on fiscal year data. For federal government expenditures, approximation equal to 75% of 

amount reported in same fiscal year plus 25% of amount reported in subsequent fiscal year. For academic expenditures, respective percentages are 50 and 

50, because those fiscal years generally begin on 1 July instead of 1 October. 

SOURCE: National Science Foundation, Division of Science Resources Statistics, National Patterns of R&D Resources (annual series). See appendix tables 

4-3 and 4-7. 

Science and Engineering Indicators 2010
4-10 Chapter 4. Research and Development: National Trends and International Linkages

$400 billion in 2008. Over the past 20 years, the expansion of U.S. R&D spending has exceeded the pace of GDP 

growth, which averaged 5.3% in current dollars and 2.8% 

in constant dollars, with the difference becoming more substantial in the past few years.

The economic stimulus package enacted in early 2009 

(American Recovery and Reinvestment Act of 2009 [Public Law 111-5]) provided a substantial increase in federal FY 2009 funding for R&D and R&D infrastructure 

($18.3 billion). However, these one-time funds do not enter 

into the federal funding base for subsequent fiscal year budgets, as discussed in the federal R&D section of this chapter.

Estimates of U.S. R&D expenditures are generated by 

adding the annual R&D spending of all sectors of the economy for which expenditures can be reasonably estimated. 

The spending figures come from surveys of organizations 

that historically have performed the vast majority of R&D 

in the United States; however, some components of national 

R&D performance are not reflected in current NSF data, and 

measurement challenges remain. For a further discussion 

of R&D activities not currently captured in NSF’s official 

R&D statistics, see the sidebar “Unmeasured R&D.”

Performers of R&D

NSF tracks the R&D spending patterns of several performers in the overall U.S. R&D system: businesses, the 

intramural R&D activities of federal agencies, federally 

funded R&D centers (FFRDCs),3

 universities and colleges, 

and other nonprofit organizations.

Business Sector

Estimated spending for R&D performed in the United 

States by businesses totaled $289.1 billion (current dollars) 

in 2008 (table 4-1). NSF estimates that business R&D expenditures in 2008 expanded in real terms (constant dollars) by 5.1%, outpacing the real growth of total U.S. R&D 

in the same year (4.5%). Similarly high rates of growth 

prevailed for business R&D in 2005, 2006, and 2007, and 

again, the growth in business R&D outpaced that of total 

U.S. R&D.

The business sector is by far the largest performer of U.S. 

R&D, accounting for 73% of the total in 2008 (figure 4-2). 

The high-water mark of the business sector’s share of U.S. 

R&D to date was 75% in 2000. Over the next 4 years, its 

	


	










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Unmeasured R&D

The estimates of U.S. R&D presented in this volume are derived from surveys of organizations that 

have historically performed the vast majority of R&D 

in the United States. To evaluate U.S. R&D performance over time and in comparison with other countries, however, it is necessary to gauge how much 

R&D goes unmeasured. The following paragraphs describe types of unmeasured R&D performance in the 

United States.

To reduce cost and respondent burden, U.S. industrial R&D estimates are derived from a survey of 

R&D-performing companies with five or more employees. Accordingly, no estimates of R&D performance are available for companies with fewer than 

five employees.

The activity of individuals performing R&D on 

their own time and not under the auspices of a corporation, university, or other organization is similarly 

omitted from official U.S. R&D statistics.

Social science R&D has been excluded from U.S. 

industrial R&D statistics. Also, R&D in the humanities is excluded from 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. 

(The new U.S. Business R&D and Innovation Survey, 

being fielded for the first time in 2009, includes social 

science R&D and will better capture total federally 

funded R&D performed by others. Furthermore, NSF 

is in the process of redesigning its Higher Education 

R&D Survey, which will include non-S&E R&D expenditures in its reported totals.)

NSF has not conducted a survey on R&D performance by nonprofit organizations since 1998, although 

the R&D performance of nonprofits is estimated for 

national R&D totals. NSF and the U.S. Census Bureau 

collected statistics for R&D performance by state governments in the United States for 2006 and 2007, but 

these data have not yet been included in the national 

time series. Data for these performers are discussed in 

“Location of R&D Performance.”
Science and Engineering Indicators 2010 4-11

share declined to about 70% in response to the slowdown 

of the U.S. economy in 2001 and 2002 and the associated 

curtailment of business activities by many R&D-performing 

firms. With the renewal of vigorous business activity thereafter, business spending on R&D moved to a higher-growth 

path. The business sector’s share of R&D rose above 70% in 

2005 and has since continued to increase.

Over the past 5 years, expanded business spending on 

R&D has accounted for much of the growth (in both current and real-dollar terms) in all U.S. R&D spending. The 

most striking trend when contrasting business-sector R&D 

with that of other performers over the past several decades is 

the sustained, far larger real-dollar expansion in the level of 

R&D spending by the business sector (figure 4-3).

As discussed in the section “R&D by Character of Work,” 

three-quarters of the business sector’s R&D performance in 

recent years has been directed toward development activities rather than basic and applied research. Other U.S. R&D 

performers are relatively more active with respect to basic 

and applied research.

The business sector is the chief source of funding 

for its own R&D spending. In 2008, it is estimated that 

$263.3 billion, or 91%, of the business sector’s overall R&D 

expenditures ($289.1 billion) came from the business sector itself (table 4-2), with the balance ($25.8 billion) coming from the federal government. Before the late 1960s, the 

	



 	



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4-12 Chapter 4. Research and Development: National Trends and International Linkages

Table 4-2

U.S. R&D expenditures, by character of work, performing sector, and funding source: 2008

Sector Total Business

Federal 

government

Universities 

and colleges

Other 

nonprofit 

Total 

expenditures 

(% distribution)

R&D .............................................................. 397,616 267,847 103,696 14,053 12,020 100.0

Business ................................................... 289,105 263,310 25,795 * * 72.7

Federal government .................................. 41,741 * 41,741 * * 10.5

Federal intramural ................................. 27,000 * 27,000 * * 6.8

FFRDCs ................................................. 14,741 * 14,741 * * 3.7

Industry-administered ........................ 5,031 * 5,031 * * 1.3

U&C-administered ............................. 6,023 * 6,023 * * 1.5

Nonprofit-administered ...................... 3,688 * 3,688 * * 0.9

Universities and colleges .......................... 51,163 2,908 30,177 14,053 4,024 12.9

Other nonprofit organizations ................... 15,606 1,629 5,982 * 7,995 3.9

Percent distribution by source .................. 100.0 67.4 26.1 3.5 3.0 na

Basic research .......................................... 69,146 12,222 39,379 10,188 7,357 100.0

Business ................................................ 11,907 9,209 2,697 * * 17.2

Federal government .............................. 10,189 * 10,189 * * 14.7

Federal intramural ................................. 4,734 * 4,734 * * 6.8

FFRDCs ................................................. 5,455 * 5,455 * * 7.9

Industry-administered ........................... 2,287 * 2,287 * * 3.3

U&C-administered ................................ 1,736 * 1,736 * * 2.5

Nonprofit-administered ......................... 1,432 * 1,432 * * 2.1

Universities and colleges .......................... 38,822 2,108 23,608 10,188 2,918 56.1

Other nonprofit organizations ................... 8,229 904 2,885 * 4,439 11.9

Percent distribution by source ................. 100.0 17.7 57.0 14.7 10.6 na

Applied research ........................................... 88,578 53,827 28,649 3,169 2,934 100.0

Business .................................................... 61,437 52,758 8,679 * * 69.4

Federal government ................................. 11,599 * 11,599 * * 13.1

Federal intramural ................................. 7,573 * 7,573 * * 8.5

FFRDCs ................................................. 4,026 * 4,026 * * 4.5

Industry-administered ........................ 1,067 * 1,067 * * 1.2

U&C-administered ............................. 1,644 * 1,644 * * 1.9

Nonprofit-administered ...................... 1,315 * 1,315 * * 1.5

Universities and colleges ...................... 10,556 656 5,824 3,169 908 11.9

Other nonprofit organizations ................ 4,985 413 2,546 * 2,026 5.6

Percent distribution by source .............. 100.0 60.8 32.3 3.6 3.3 na

Development ............................................. 239,891 201,798 35,669 696 1,729 100.0

Business ................................................ 215,761 201,342 14,419 * * 89.9

Federal government .............................. 19,953 * 19,953 * * 8.3

Federal intramural ................................. 14,693 * 14,693 * * 6.1

FFRDCs ................................................. 5,260 * 5,260 * * 2.2

Industry-administered ........................ 1,676 * 1,676 * * 0.7

U&C-administered ............................. 2,643 * 2,643 * * 1.1

Nonprofit-administered ......................... 941 * 941 * * 0.4

Universities and colleges .......................... 1,785 144 746 696 199 0.7

Other nonprofit organizations ................... 2,392 312 551 * 1,530 1.0

Percent distribution by source ................. 100.0 84.1 14.9 0.3 0.7 na

* = small to negligible amount, included as part of funding provided by other sectors; na = not applicable

FFRDC = federally funded research and development center; U&C = universities and colleges

NOTES: Data for 2008 are preliminary. Federal intramural includes federal intramural R&D and costs associated with administering extramural R&D. Funding 

for FFRDC performance chiefly federal, but any nonfederal support included in federal figures. State and local government support to industry included 

in industry support for industry performance. State and local government support to universities and colleges ($3,453 million) included in universities and 

colleges support for universities and colleges performance.

SOURCE: National Science Foundation, Division of Science Resources Statistics, National Patterns of R&D Resources (annual series). See appendix tables 

4-3 to 4-10.

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 4-13

federal government was the primary source of funding for 

business R&D.

Note that the decline in federal funding of business R&D, 

as reported by businesses, differs somewhat from the trend 

apparent in R&D spending data collected from federal 

agencies. For details on this discrepancy, see the sidebar 

“Tracking R&D: The Gap Between Performer- and SourceReported Expenditures” later in this chapter.

Universities and Colleges

Universities and colleges performed an estimated 

$51.2 billion of R&D in 2008. The academic sector is the 

second-largest performer of U.S. R&D. It currently represents just below 13% of total U.S. R&D performance, about 

a fifth of the size of business R&D. In the late 1990s and first 

years of the current decade, academic R&D grew faster than 

R&D in any other U.S. sector, with real annual growth rates 

in the range of 6% to 8%. After 2004, however, real growth 

has been much slower, falling to 2.1% in 2008, well below 

the real growth rates for business R&D and total U.S. R&D.

Universities and colleges are estimated to have performed more than half (56%) of the nation’s basic research 

in 2008. (See “R&D by Character of Work.”) They also rely 

much more than the business sector on external R&D funding. In 2008, about 27% of academic R&D was funded by 

the institutions themselves; 59% was funded by the federal 

government; and the balance was funded by state and local 

governments, nonprofits and other types of organizations, 

and private gifts (table 4-2).

Federal Agencies and FFRDCs

R&D performance by the federal government (which 

spans the activities of agency intramural research laboratories, agency planning and administration of both intramural 

and extramural R&D projects, and the FFRDCs) totaled an 

estimated $41.7 billion in 2008, about 11% of all U.S. R&D 

performance. Federal agencies’ intramural R&D activities 

(including the aforementioned planning and administration 

costs) accounted for $27.0 billion (6.8%) of the U.S. total, 

and FFRDCs accounted for $14.7 billion (3.7%). Federal 

agencies’ intramural R&D performance is entirely funded 

by the federal government; FFRDCs also rely chiefly on 

federal funding, with small amounts of nonfederal funds at 

some facilities.

Real expenditures for R&D conducted by federal agencies and FFRDCs combined grew rapidly from 2001 to 

2003, reflecting increased defense spending following 

the terrorist attacks of September 11, 2001. From 2004 to 

2007, federal government R&D performance was essentially flat. It is estimated to have returned to modest growth in 

2008, with increases in both federal intramural and FFRDC 

R&D performance.

The volume of the federal government’s R&D performance is small compared with that of the U.S. business sector. However, the federal sum of $41.7 billion exceeds the 

national R&D expenditures of every country except Japan, 

China, and Germany. Furthermore, this federal expenditure 

does not include sizable government investments in R&D 

infrastructure and equipment. In addition, the federal government maintains research facilities and conducts research 

projects that would be too costly or risky for a single company or university to undertake.

Other Nonprofit Organizations

The figure for R&D performed in the United States by 

other nonprofit organizations in 2008 was an estimated 

$15.6 billion. This amount represents about 4% of all 

U.S. R&D in that year, a share that has been fairly stable 

since 2000.

Sources of R&D Funding

The funding for R&D conducted by organizations in the 

United States comes from a variety of sources, including 

their own funds, as well as contracts and grants from other 

organizations. The funding mix varies across the main performing sectors. Data on the flows of R&D funding within 

sectors, such as between two companies, are limited, but data 

on the flows of R&D between sectors indicate that financial 

relationships between organizations play a significant role in 

the U.S. R&D system.

In 2008, an estimated 19% of U.S. R&D ($74 billion, current dollars) came from funding by an organization in a sector other than the performing sector (table 4-2). Most of this 

between-sector funding comes from the federal government, 

which supports significantly more R&D than it conducts in 

its own laboratories and FFRDCs. In sharp contrast, most 

businesses use a high percentage of their R&D budgets for 

internal projects or to contract for R&D performed by other 

businesses. The small remainder—about 2% of overall business funds for R&D—flows to universities and other nonprofit organizations to support R&D performance.

R&D Funding by the Federal Government

In 2008, according to the reports of R&D performers, 

the federal government funded an estimated $103.7 billion 

(current dollars) of R&D (table 4-1). This amount represented about 26% of all R&D funding in the United States 

(figure 4-2).

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 4-4). But the federal share decreased 

in subsequent years, falling to below 50% in 1979 and to a 

low of 25% in 2000. This declining share of federal R&D 

funding is particularly evident in the business sector. In the 

late 1950s and early 1960s, more than half of the nation’s 

business R&D was funded by the federal government, but by 

2000, less than 10% of business R&D was federally funded 

(appendix table 4-3).

Between 2001 and 2004, however, this decades-long 

trend was attenuated as private investment slowed in the 

face of the 2001–02 recession. In addition, federal R&D 

spending expanded, first in health and then in defense and 

counterterrorism. By 2004, the federal share of the nation’s 
4-14 Chapter 4. Research and Development: National Trends and International Linkages

R&D funding reached 30%, but thereafter it declined again 

to an estimated 26% in 2008.

R&D Funding by Business

The business sector is both the largest performer and the 

largest source of R&D funding in the United States. Business provided an estimated $267.8 billion for R&D in 2008, 

67% of the U.S. total.

The business sector’s share of national R&D funding first surpassed the federal government’s share in 1980 

(figure 4-4). Almost all business funding for R&D is directed toward business R&D, with a small remainder (around 

2%) allocated to academic and other nonprofit performers.

From 1980 to 1985, business support for R&D grew, in 

real dollars, at an average annual rate of almost 8%. From 

1985 to 1994, real growth dropped to 3% per year, before 

expanding to 9% through 2000. Growth declined by 3% a 

year during the 2000–02 recession, was flat in 2003–04, and 

has increased robustly (5% or more real growth annually) 

since 2005. NSF’s preliminary estimate for real growth in 

business-sector R&D funding in 2008 is about 6%.

R&D Funding From Other Sources

R&D funding from other nonfederal sources—academia’s own institutional funds, other nonprofits, and state 

and local governments—is small in comparison to federal 

and business sources, and is estimated to have been below 7% of the total in 2008. Nonetheless, this funding has 

been growing fairly rapidly for some time. From 1998 to 

2008, 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.

Finally, unlike many countries, the United States does not 

currently have data on domestic R&D that is funded by foreign sources. However, NSF has begun to collect these data 

as part of a new business survey. Separately, foreign direct 

investment in R&D, which is measured in the United States, 

provides an indication of international participation in business R&D. However, foreign ownership does not necessarily imply foreign R&D funding, because an affiliate may 

fund activities through its own revenues and other domestic 

sources. (See “R&D by Multinational Companies.”)

R&D by Character of Work

R&D encompasses a wide range of activities, from fundamental research in the physical, life, and social sciences; 

to research addressing such critical issues as global climate 

change, energy efficiency, and health care; to the development of general-purpose technologies and new goods and 

services. Because the activities are so diverse, it helps to 

classify them into distinct categories when analyzing R&D 

expenditures.

Historically, the most common categories used to classify 

R&D are basic research, applied research, and (experimental) development. (See sidebar “Definitions of R&D.”) In 

light of the complex feedback loops involved in knowledge 

creation and exploitation, these categories have been criticized as simplistic and too linear in their implied progression. No alternative measurement frameworks, however, 

have been widely adopted. Accordingly, this chapter relies 

on these longstanding, widely used, and internationally 

comparable categories (OECD 2002) to describe the current 

trends in the character of U.S. R&D expenditures.4

In 2008, the United States performed an estimated 

$69.1 billion of basic research, $88.6 billion of applied research, and $239.9 billion of development (table 4-2). Basic research represented a little more than 17% of the total; 

applied research, 22%; and development, just over 60% 

(figure 4-5).

Historically, the federal government has been the prime 

source of funding for basic research, accounting for an estimated 57% of the nation’s total in 2008 (figure 4-5). The 

share of federal funding to universities and colleges, the nation’s largest performers of basic research, was 61%.

Industry directs only small portions of its R&D funding to 

basic research—an estimated 5% in 2008 (figure 4-6). Many 

businesses believe that basic research involves significant 

uncertainties regarding both the near-term commercial value 

of any discoveries and the firm’s ability to enforce intellectual property rights and earn a return. Some firms, however, view engaging in basic research (whether performed 

internally or in cooperation with other performers) as a way 

to boost human capital resources by attracting and retaining talented scientists and engineers. This can strengthen 

the firm’s capacity for innovation and improve its ability to 

absorb external scientific and technological knowledge. Not 

	
	

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Science and Engineering Indicators 2010 4-15

surprisingly, the industries that invest the most 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.

The business sector currently spends more than four 

times on applied research than basic research, accounting 

for greater than half of U.S. applied research funding. In 

2008, industry invested an estimated $53.8 billion in applied 

research funding, 61% of the U.S. total. Industries that perform a relatively large amount of applied research include 

chemicals, aerospace (mostly funded by the Department of 

Defense [DOD]), and R&D services (where many companies engage in the licensing of technologies).

The bulk of the federal government’s applied research 

funds support work that is performed by the federal agencies 

themselves or by FFRDCs.

	
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4-16 Chapter 4. Research and Development: National Trends and International Linkages

Development expenditures totaled an estimated 

$239.9 billion in 2008, representing 60% of all U.S. R&D 

expenditures.5 The development of new and improved 

goods, services, and processes is dominated by the business 

sector, which funded 84% (an estimated $201.8 billion) 

of all U.S. development in 2008. The federal government 

funded most of the remaining development, totaling 15%, 

or $35.7 billion. Most federal development spending is defense related; this spending includes military aircraft, for 

which the federal government is the main customer. 

The business sector performs a higher share of development activities than it funds, having conducted about 

90% of all U.S. development in 2008. Federal agencies and 

FFRDCs conducted 8%, and all other performers combined 

conducted just below 2%.

R&D expenditures by public and private organizations 

indicate the priority given to the creation of new science 

and technology (S&T)-based knowledge in support of their 

goals. As an input measure, however, R&D expenditures do 

not directly lead to subsequent economic and social outputs. 

For one approach to measuring the role of R&D in economic output and growth, see the sidebar “The BEA/NSF R&D 

Satellite Account: R&D and Economic Growth.”

Location of R&D Performance

More than half of all U.S. R&D is performed in only a 

few states.6

 Nonetheless, patterns of expenditures for R&D 

activities vary among the top R&D-performing states. (For 

a broader range of indicators on state-level S&E activities, 

see chapter 8.)

Distribution of R&D Expenditures Among 

States

In 2007, the 10 states with the greatest R&D expenditure 

levels accounted for about 64% of U.S. R&D expenditures 

that can be allocated to the states. The top 20 states accounted for nearly 85% of the R&D total; the 20 lowest-ranking 

states, around 5%. California alone represented 22% of U.S. 

R&D, exceeding the next-highest state, Massachusetts, by 

more than three times. Appendix table 4-15 provides 2007 

statistics on R&D performers and funders for all the states.

To some degree, state variations in the level of R&D expenditures reflect differences in economic scale. Reporting 

a state’s R&D expenditures as a fraction of its GDP adjusts 

for these differences and is an indicator of R&D intensity at 

the state level.

States with the highest R&D/GDP ratios in 2007 included New Mexico, Massachusetts, and Maryland (table 

4-3). New Mexico is the location of several major government research facilities. Massachusetts benefits from both 

leading research universities and thriving high-technology 

industries. Maryland is the site of many government research facilities and growing research universities. California ranks seventh in R&D intensity. See appendix table 

4-16 for a complete list of states and their corresponding 

R&D intensities.

Measuring R&D as capital investment rather than an 

expense (that is, capitalizing R&D) recognizes that R&D 

has long-term benefits, much as do investments in physical assets. Capitalized R&D has a direct impact on GDP 

because business R&D becomes part of economic output 

instead of an expense. International activities are underway to update systems of national accounts to recognize 

the investment nature of R&D (UNSC 2007). A first step 

in the statistical systems of the United States and other 

OECD countries is to develop R&D satellite accounts, 

that is, supplementary estimates of the GDP and related 

measures that provide greater detail or alternative measurement concepts without changing the core accounts. 

Future research topics include improving the price indexes used to produce inflation-adjusted R&D investment 

figures and measures of the depreciation of R&D as a 

capital asset.

Several U.S. interagency efforts are aimed at identifying improved measures of intangibles, such as R&D, 

and their economic role (Aizcorbe, Moylan, and Robbins 

2009; Jorgenson, Landefeld, and Nordhaus 2006). NSF’s 

Division of Science Resources Statistics, responsible for 

U.S. R&D statistics, and the Bureau of Economic Analysis (BEA), responsible for the U.S. national economic accounts, are jointly developing an R&D Satellite Account 

(Robbins and Moylan 2007). Current plans call for incorporation of R&D capital into the National Income and 

Product Accounts and other core accounts in 2013.

According to BEA preliminary estimates, capitalizing R&D increased the level of current-dollar GDP by 

an average of 2.9% per year between 1959 and 2006. 

Adjusted for inflation, R&D capital would account for 

about 5.1% of real GDP growth between 1959 and 2006. 

This figure compares with a 2.2% share for all business 

investment in commercial and all other types of buildings. During the more recent 1995–2006 period, R&D 

investment accounted for about 7% of real GDP growth, 

with the business sector’s R&D contribution amounting 

to 4.6% percent.

From 1995–2006, the largest estimated contributions 

to real GDP growth came from the pharmaceutical and 

medicine manufacturing industry, which accounted for 

more than 1% of GDP growth. The software publishing 

industry accounted for an additional 0.5%.

The BEA/NSF R&D Satellite Account: R&D and Economic Growth
Science and Engineering Indicators 2010 4-17

Sector Distribution of R&D Performance 

by State

The proportion of R&D performed in each of the major 

R&D-performing sectors (business, universities and colleges, federal intramural facilities and FFRDCs) varies across 

states. States that lead in total R&D tend to be well represented in each of these sectors (table 4-3).

In 2007, business-sector R&D accounted for about 74% 

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, 10th 

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 U.S. 

total, and it also closely follows state total R&D performance. Among the top 10 states in total R&D, only Michigan, New Jersey, and Washington are not also among the 

university R&D top 10, being replaced by North Carolina, 

Ohio, and Florida.

Representing about 11% of the state-distributed U.S. total, federal R&D performance (both intramural and FFRDC) 

is more concentrated geographically than performance in 

other sectors—and the relationship between its geographical 

distribution and that of total R&D is less significant. The top 

four states (Maryland, California, New Mexico, and Virginia) and the District of Columbia represent 64% of all federal 

R&D performance.7

 This figure rises to 78% when the other 

five top 10 states (Massachusetts, Tennessee, Washington, 

Illinois, and Florida) are included.

Federal R&D accounts for 82% of all R&D in New Mexico, home of the nation’s two largest FFRDCs (Los Alamos 

and Sandia National Laboratories). The high figures for 

Maryland (54%), Virginia (38%), and the District of Columbia (74%) reflect the concentration of federal facilities and 

administrative offices in the national capital area. The share 

for Tennessee (32%) reflects the presence of a large federal 

facility, Oak Ridge National Laboratory.

In California, Massachusetts, Washington, and Illinois, 

federal R&D performance accounts for no more than 6% to 

7% of the state R&D totals, even though each state is among 

the top 10 in federal performance. The federal R&D share in 

Florida was 13% in 2007.

Business R&D in Top States

During 2007, companies in the 10 states with the highest business R&D performance reported aggregate R&D 

expenditures of $186.0 billion and accounted for 69% of 

the business R&D performed in the United States. Companies in California alone accounted for 24% of the nation’s 

business R&D. The types of companies that carry out R&D 

vary considerably among these 10 leading states (table 4-4), 

reflecting regional specialization or clusters of business activity. For example, the automotive manufacturing industry 

accounted for 75% of Michigan’s business R&D in 2007, 

although it accounted for only 6% of the nation’s total business R&D.

Table 4-3

Top 10 states in R&D performance, by sector and intensity: 2007

All R&Da Sector ranking R&D intensity (R&D/GDP ratio)

Rank State

Amount 

(current 

$millions) Business

Universities and 

colleges

Federal 

intramural 

and FFRDCb State

R&D/

GDP 

(%)

GDP 

(current 

$billions)

1 California 77,608 California California Maryland New Mexico 7.53 75.2

2 Massachusetts 24,557 Massachusetts New York California Massachusetts 6.97 352.2

3 New Jersey 19,552 New Jersey Texas New Mexico Maryland 5.34 264.4

4 Texas 17,853 Michigan Maryland Virginia Washington 4.85 310.3

5 Michigan 17,402 Texas Pennsylvania District of Columbia Connecticut 4.82 212.3

6 New York 15,939 Washington Massachusetts Massachusetts Michigan 4.58 379.9

7 Washington 15,061 Illinois North Carolina Tennessee California 4.31 1,801.8

8 Illinois 14,287 New York Illinois Washington New Jersey 4.24 461.3

9 Maryland 14,130 Pennsylvania Ohio Illinois District of Columbia 4.17 92.5

10 Pennsylvania 13,510 Connecticut Florida Florida New Hampshire 3.71 57.8

FFRDC = federally funded research and development center; GDP = gross domestic product

a

Includes in-state total R&D performance of business, universities, 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.

NOTE: 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, Division of Science Resources Statistics, Survey of Industrial Research and Development, 2007; Survey of 

Research and Development Expenditures at Universities and Colleges, FY 2008; Survey of Federal Funds for Research and Development, FY 2007-2009; 

Survey of State Research and Development Expenditures, FY 2007. State GDP data are from the U.S. Bureau of Economic Analysis, http://www.bea.gov/

regional/gsp, accessed 29 July 2009. See also appendix tables 4-15 and 4-16.

Science and Engineering Indicators 2010
4-18 Chapter 4. Research and Development: National Trends and International Linkages

The computer and electronic product manufacturing industries performed 22% of the nation’s total business R&D, 

but the shares of this performance were larger in Massachusetts (45%), Illinois (33%), California (33%), and Texas 

(32%). These states have clearly defined regional centers 

of high-technology research and manufacturing, including 

Cambridge and Route 128 in Massachusetts; Champaign 

County, Illinois; Silicon Valley, California; and the Silicon 

Hills of Austin. About two-thirds of R&D performed in the 

United States by computer and electronic product companies in 2007 was located in these four states and accounted 

for 14% of all business R&D nationwide (table 4-4; appendix table 4-11).

R&D performed by chemical manufacturing companies 

remains prominent in New Jersey, Connecticut, and Pennsylvania, all home to the pharmaceuticals and the chemicals 

industries. According to the American Chemistry Council 

(ACC 2009), together these states are host to more than 

2,000 chemical manufacturing establishments, an increase 

of about 500 since 2005, and rank among the top 18 in chemicals industry employment. In 2007, chemical manufacturers 

accounted for 63% of New Jersey’s business R&D, 59% of 

Connecticut’s, and 55% of Pennsylvania’s (table 4-4). These 

three states represented more than 41% of the nation’s R&D 

in this sector.

The R&D and related-services sector, which consists 

largely of biotechnology companies, contract research organizations, and early-stage technology firms, is also geographically concentrated, with California, Massachusetts, 

and New Jersey accounting for more than 42% of R&D. The 

companies in this sector maintain strong ties to the academic 

sector and are often located near large research universities 

(Stuart and Sorenson 2003).

Nationally, small companies (those that have from 5 

to 499 employees8

) performed 19% of total U.S. business 

R&D in 2007 (appendix table 4-11). Among the top 10 business R&D-performing states, New York and California had 

the highest totals of small companies performing business 

R&D, with 23% and 20%, respectively, in each state. Small 

companies in these two states performed 6% of the nation’s 

total business R&D in 2007 (table 4-4).

Business R&D

Businesses perform R&D with a variety of objectives in 

mind, but most business R&D is aimed at developing new 

and improved goods, services, and processes. R&D expenditures, therefore, indicate the level of effort dedicated to 

producing future products and process improvements while 

maintaining current market share and increasing operating 

efficiency. By extension, such expenditures may reflect 

firms’ perceptions of the market’s demand for new and improved technology.

R&D performed by the business sector totaled $269.3 

billion in 2007. The federal government funded 9.9% 

($26.6 billion) of this total, and company funds and other 

private sources financed the remainder (appendix tables 

4-11 to 4-13).9

In addition to absolute levels of R&D expenditures, another indicator in the business sector is R&D intensity—that 

is, R&D relative to production in a company, industry, or 

sector. The measure used most frequently is the ratio of 

company-funded R&D to net sales.10 This statistic provides 

a way to gauge the relative importance of R&D across industries and among firms in the same industry. The companyfunded R&D-to-sales ratio of companies in all industries 

Table 4-4

Top 10 states in business R&D performance and share of R&D, by selected industry: 2007

(Percent)

State

Businessperformed 

R&D (current 

$millions) Chemicals 

Computer and 

electronic 

products

Computerrelated 

services

R&D 

services

Motor 

vehicles

Companies 

with 5–499 

employees

All states ..................... 269,267 20.6 L 21.8 5.4 8.4 6.0 L 18.7

California ................. 64,187 13.9 33.0 14.6 9.5 D 20.2

Massachusetts ........ 19,488 17.4 44.6 5.5 9.9 0.0 18.5

New Jersey ............. 17,892 63.1 6.3 5.2 8.0 0.1 13.4

Michigan ................. 15,736 6.7 1.3 1.9 2.8 74.8 8.5

Texas ....................... 13,889 5.6 32.3 17.8 7.4 0.4 18.6

Washington ............. 12,687 5.2 5.3 2.6 6.5 0.4 12.3

Illinois ...................... 11,362 25.2 32.7 4.3 2.4 1.8 14.1

New York ................. 10,916 30.1 7.8 15.6 4.1 3.0 22.7

Pennsylvania ........... 10,387 55.0 7.3 6.2 5.2 0.8 17.5

Connecticut ............ 9,444 59.0 2.3 2.5 3.2 0.2 8.2

L = lower-bound estimate; D = suppressed to avoid disclosure of confidential information

NOTES: Rankings do not account for margin of error of estimates from sample surveys. Detail does not add to total because not all industries shown.

SOURCE: National Science Foundation, Division of Science Resources Statistics, Survey of Industrial Research and Development.

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 4-19

performing R&D in the United States varied between 3.2% 

and 3.4% during 2003-06; in 2007 it was 3.5% (table 4-5; 

appendix table 4-14).

Largest R&D Industries

Benefits from advances in S&T may be broadly shared 

among industries; however, different industries perform different amounts of R&D.11 Some industries, such as utility,12

finance, insurance, and real estate, have relatively low R&D 

intensities (0.5% or less). Appendix table 4-14 provides data 

on ratios of company-funded R&D to net sales for an array 

of industries.13 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, and R&D 

services)—accounted for 78% of company-funded business 

R&D and 95% of federally funded business R&D in 2007 

(table 4-5).14

Table 4-5

Business R&D and domestic net sales, by industry: 2006 and 2007

(Millions of current dollars)

Businessperformed R&D Federally funded R&D Company-funded R&D

Industry 2006 2007 2006 2007 2006 2007

All .......................................................... 247,669 269,267 24,304 26,585 223,365 242,682

 Highlighted industries ........................ 193,956 L 209,116 L 23,352 L 25,355 L 170,606 183,761

 Chemicals ....................................... 48,913 50,423 662 663 48,251 49,760

 Computer and electronic 

 productsa

 ..................................... 46,329 55,571 L 211 252 L 46,119 55,319

 Software and computer-related 

 servicesb ...................................... 33,831 L 34,079 L 1,048 L 842 32,783 33,237

 Aerospace and defense 

 manufacturingc

 ............................. 27,217 L 30,278 L 15,222 L 16,882 L 11,995 13,397

 R&D and related servicesd .............. 21,104 22,731 6,209 6,716 14,896 16,014

 Automotive manufacturinge

 ............ 16,562 L 16,034 L NA NA 16,562 16,034

 All other ............................................. 53,713 L 60,151 L 952 L 1,230 L 52,759 58,921

Domestic net sales

Businessperformed R&D/ 

sales ratio (%)

Company-funded R&D/ 

sales ratio (%)

2006 2007 2006 2007 2006 2007

All .......................................................... 6,642,500 7,027,049 3.7 3.8 3.4 3.5

 Highlighted industries ........................ 2,530,579 2,602,127 7.7 8.0 6.7 7.1

 Chemicals ....................................... 524,160 589,918 9.3 8.5 9.2 8.4

 Computer and electronic 

 productsa

 ..................................... 612,885 699,520 7.6 7.9 7.5 7.9

 Software and computer-related 

 servicesb ...................................... 376,638 304,952 9.0 11.2 8.7 10.9

 Aerospace and defense 

 manufacturingc

 ............................. 243,110 263,321 11.2 11.5 4.9 5.1

 R&D and related servicesd .............. 86,945 89,166 24.3 25.5 17.1 18.0

 Automotive manufacturinge

 ............ 686,841 655,250 2.4 2.4 2.4 2.4

 All other ............................................. 4,111,921 4,424,922 1.3 1.4 1.3 1.3

L = lower-bound estimate; NA = not available

a

Includes all R&D and domestic net sales 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.

bIncludes R&D and domestic net sales for software (NAICS 5112) and computer systems design and related service industries (NAICS 5415).

c

Includes all R&D for aerospace products and parts (NAICS 3364), plus all federal R&D for navigational, measuring, electromedical, and control 

instruments (NAICS 3345), automotive (NAICS 3361–3363), and other transportation manufacturing industries. Domestic net sales are not included for 

automotive and other transportation manufacturing industries.

dIncludes R&D and domestic net sales for architectural, engineering, and related services (NAICS 5413) and scientific R&D services industries 

(NAICS 5417).

e

Includes all R&D for transportation manufacturing equipment (NAICS 336), except federally funded components that are included in aerospace and 

defense manufacturing sector.

NOTE: Potential disclosure of individual company operations only allows lower-bound estimates for some sectors.

SOURCE: National Science Foundation, Division of Science Resources Statistics, Survey of Industrial Research and Development.

Science and Engineering Indicators 2010
4-20 Chapter 4. Research and Development: National Trends and International Linkages

Chemicals (Including Pharmaceuticals)

Among three-digit North American Industry Classification System (NAICS) codes, the chemicals industry accounted for the largest amount of R&D performed in the 

United States in 2007. Companies in this group performed 

$55.6 billion of R&D, with relatively little of it federally 

funded. Within the chemicals industry, the largest subsector is pharmaceuticals and medicines. In 2007, pharmaceutical companies performed $47.6 billion of company-funded 

R&D, representing 86% of nonfederal R&D funding in the 

chemicals sector (appendix table 4-12).

A related indicator is reported by the Pharmaceutical Research and Manufacturers of America (PhRMA), an industry 

association that represents the country’s leading researchbased pharmaceutical and biotechnology companies. This 

association conducts an annual survey of its members to 

gather information about R&D. In 2007, PhRMA estimated 

that its members invested $35.4 billion in R&D performed in 

the United States and $9.1 billion in R&D performed abroad. 

The total $44.5 billion investment represented 18.7% of domestic sales and 16.4% of global sales (PhRMA 2008a).15

According to PhRMA, U.S. biopharmaceutical research 

companies obtained approval for 26 new medicines in 2007 

from the U.S. Food and Drug Administration. About 75% 

of PhRMA members’ domestic R&D investment supports 

R&D on projects that originate in their own laboratories, and 

25% supports R&D on products licensed from other organizations, notably biotechnology companies, universities, or 

the government (PhRMA 2008b).16

Computer and Electronic Products

Companies in the computer and electronic product manufacturing industry include producers of computers, computer peripherals, communications equipment, and similar 

electronic products and producers of components for such 

products.17 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 product sector.

In 2007, companies in this industry performed 

$50.4 billion of R&D, or 19% of all business R&D (table 

4-5).18 Company and other nonfederal sources funded almost 

all of this R&D. Two of the more R&D-intensive industries, 

communications equipment and semiconductor manufacturing, are included in this group. Both devoted more than 10% 

of sales to R&D in 2007 (appendix table 4-14).

Software and Computer-Related Services

Software and computer-related services industries, such 

as data processing and computer systems design, performed 

approximately $33.2 billion of company-funded R&D in 

2007. The R&D of these industries (14% of the U.S. business sector total), combined with that of the computer and 

electronic product manufacturers, accounted for 34% of 

all industrial R&D in 2007. As computing and information 

technology has become more integrated with every sector of 

the economy, the demand for services associated with these 

technologies has increased.

Between 1987 and 2007, R&D expenditures of companies providing these services grew. In 1987, when an NSF 

survey estimate of software and other computer-related services R&D first became available, companies classified in 

the industry group—computer programming, data processing, other computer-related, engineering, architectural, and 

surveying services—performed $2.4 billion of companyfunded R&D, or 3.8% of all company-funded industrial 

R&D. In 2007, the company-funded R&D of these industries (excluding engineering and architectural services) accounted for 13.7% of all company-funded industrial R&D, 

and these companies accounted for 4.3% of domestic sales 

of R&D-performing companies (table 4-6).19

Table 4-6

Estimated share of computer-related services in 

company-funded R&D and domestic net sales of 

R&D-performing companies: 1987–2007

(Percent)

Year

Company-funded 

R&D

Domestic 

net sales

1987................................ 3.8 1.4

1988................................ 3.6 1.5

1989................................ 3.4 1.4

1990................................ 3.7 1.5

1991................................ 3.6 1.6

1992................................ 4.0 1.6

1993................................ 8.2 1.5

1994................................ 6.6 2.2

1995................................ 8.8 3.3

1996................................ 8.8 2.6

1997................................ 9.1 2.5

1998................................ 9.5 2.2

1999................................ 10.6 2.2

2000................................ 10.9 2.8

2001................................ 13.0 3.5

2002................................ 14.6 5.4

2003................................ 14.3 3.5

2004................................ 14.7 3.0

2005................................ 14.7 3.5

2006................................ 14.7 5.7

2007................................ 13.7 4.3

NOTES: Before 1998 companies classified in Standard Industrial 

Classification (SIC) industries 737 (computer and data processing 

services) and 871 (engineering, architectural, and surveying 

services). After 1998 companies classified in North American 

Industry Classification System (NAICS) industries 5112 (software) 

and 5415 (computer systems design and related services). With SIC 

classification, information technology services share of companyfunded R&D was 10.4% for 1998, indicating SIC-based data may 

overestimate information technology services R&D and net sales 

relative to NAICS-based data.

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Survey of Industrial Research and Development 

(annual series), http://www.nsf.gov/statistics/srvyindustry/, accessed 

6 May 2009.

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 4-21

Aerospace and Defense Manufacturing

Although it is common to refer to the “defense industry,” 

the NAICS industry classification system does not include 

this category. Thus, to approximate the cost of defenserelated R&D, one can focus on aerospace products and 

parts, plus federally funded R&D in the following industries: navigational, measuring, electromedical, and control 

instruments; automotive manufacturing; and other transportation manufacturing industries. Companies in this sector 

perform the majority of DOD’s extramural R&D. In 2007, 

these industries reported performing $16.9 billion of federally funded R&D (table 4-5), about 64% of all federally 

funded industrial R&D. This total accounts for more than 

half of the $30.3 billion that the defense industry as a whole 

spent on R&D, including both federal and nonfederal sources of funds. (See “Federal R&D” for further discussion of 

defense R&D.)

R&D and Related Services

The R&D and related-services category includes companies that provide scientific R&D, engineering, and architectural services to other firms. Also included are businesses 

that conduct R&D for their own use (e.g., biotechnology and 

other firms that conduct R&D in physical, engineering, and 

life sciences) but may not yet have sales. Companies in this 

sector performed $6.7 billion of federally funded R&D in 

2007, the highest figure outside the aerospace and defense 

manufacturing category. Despite the significant amount 

of government-sponsored R&D performed by this sector, 

R&D and related-services companies increasingly rely on 

nonfederal sources of R&D financing. The R&D performed 

by companies in the R&D and related-services sector and 

funded by company and other nonfederal sources has grown 

from $5.8 billion in 1997 to $16 billion in 2007.20 Because 

much of the R&D reported by these companies also appears 

in their reported sales figures, the R&D intensity of this sector is particularly high (26% in 2007).21

Automotive Manufacturing

The sixth-largest business sector in terms of R&D is automotive manufacturing. Companies in this industry reported 

performing $16 billion of company-funded R&D in 2007, 

accounting for 6% of all such R&D performed by businesses 

in the United States. 

In 2007, 15 companies in the automotive manufacturing 

industry reported company-funded R&D expenditures of 

more than $100 million each, collectively representing 83% 

of the industry’s R&D (NSF/SRS 2009). In most industries, 

large companies perform more R&D than small companies, 

but in the automotive manufacturing industry, the distribution of R&D is even more skewed toward large companies, with the R&D activities of General Motors, Ford, and 

DaimlerChrysler dominating the sector. In their reports to 

the Securities and Exchange Commission, these companies 

noted R&D expenses of $20.8 billion in 2006 (IEEE 2009). 

In addition to NSF statistics, other sources of indicators for 

business R&D include surveys conducted by the Industrial 

Research Institute (IRI) and companies’ own annual reports. 

(See sidebar “Trends in R&D for Industrial Research Institute Members.”22)

Federal R&D

The government supports S&T through a number of policy measures, the most direct of which is the conduct and 

funding of R&D that would not or could not be conducted 

or financed in the private sector. This section presents data 

on federally funded R&D activities, on the government’s 

contribution to the U.S. R&D infrastructure, and on federal 

R&D tax credits, which serve as an indirect means of stimulating R&D in the private sector.

Trends in R&D for Industrial 

Research Institute Members

For more than 20 years, the Industrial Research Institute (IRI), a nonprofit association of more than 200 

leading, R&D-performing, manufacturing and service 

companies, has surveyed its U.S.-based members on 

their intentions for the coming year with respect to 

R&D expenditures, focus of R&D, R&D personnel, 

and other items. Because IRI member companies carry 

out a large amount of industrial R&D in the United 

States, the results of these surveys help identify broad 

trends in corporate R&D strategies.

The most recent survey, administered during the 

summer of 2008, suggests that many companies continue to shift the focus of their R&D spending away 

from directed basic research and the support of existing business to new business projects (IRI 2009). 

As reflected in IRI’s Sea Change Index,* IRI survey 

respondents also reported the following plans and expectations for 2009:

Increase outsourcing of R&D to other companies

Increase outsourcing to universities and participation in academic consortia

Increase outsourcing to federal laboratories

Increase participation in alliances and joint R&D 

ventures

Increase acquisition of technological capabilities 

through mergers and acquisitions

Increase spin-offs based on developed technology

Maintain total company expenditures for R&D

Maintain level of technology licensing to others

Overall, these strategic moves are consistent with 

companies’ expectations of flat R&D budgets.

*IRI states that its Sea Change Index likely “understates the absolute value of change,” but the association believes it to be a “good 

indicator of the direction of change.” See IRI (2009) for details.
4-22 Chapter 4. Research and Development: National Trends and International Linkages

R&D Funding in Current Federal Budget

The budget appropriations for federal spending on 

R&D in FY 2009 (signed into law in March 2009) totaled $147.1 billion (table 4-7), an increase of $3.3 billion, or 2.3%, over the enacted FY 2008 spending level of 

$143.7 billion. The president’s proposed FY 2010 budget 

includes requests for spending on R&D of $147.6 billion, 

an increase of $0.6 billion, or 0.4%, over the appropriated 

FY 2009 level.

In addition, a one-time but sizable increase in budget 

authority for federal R&D was provided by the American 

Recovery and Reinvestment Act (ARRA) (Public Law 

111-5) in early 2009. In a preliminary estimate (May 2009), 

the White House’s Office of Science and Technology Policy placed the overall increase of federal R&D and R&D 

infrastructure funding from ARRA at about $18.3 billion in 

FY 2009 (table 4-7).

Adjusted for inflation, the enacted federal budget for 

FY 2009 represents a 0.8% increase in constant dollars. The 

increase proposed by the president for FY 2010 represents a 

constant-dollar decline of 0.6%. The ARRA funding is a sizable increase, whether in current-dollar or inflation-adjusted 

Table 4-7

Federal budget authority for R&D and R&D plant: FY 2008–10

(Millions of current dollars)

Performer/character of work

FY 2008 

Actual

FY 2009 

Enacted

FY 2009 

ARRAa

FY 2010 

Requested 2008–09 2009–10

All R&D, R&D facilities and equipment ................... 143,746 147,065 18,335 147,620 2.3 0.4

DOD (military) ...................................................... 82,278 81,616 300 79,687 –0.8 –2.4

HHS .................................................................... 29,265 30,415 11,103 30,936 3.9 1.7

NIH .................................................................. 28,547 29,748 10,400 30,184 4.2 1.5

All other HHS R&D .......................................... 718 667 703 752 –7.1 12.7

NASA .................................................................. 11,182 10,401 925 11,439 –7.0 10.0

DOE .................................................................... 9,807 10,621 2,446 10,740 8.3 1.1

NSF ..................................................................... 4,580 4,857 2,900 5,312 6.0 9.4

USDA .................................................................. 2,336 2,421 176 2,272 3.6 –6.2

DOC .................................................................... 1,160 1,292 411 1,330 11.4 2.9

NOAA .............................................................. 625 700 1 644 12.0 –8.0

NIST ................................................................ 498 550 410 637 10.4 15.8

VA ........................................................................ 960 1,020 0 1,160 6.3 13.7

DHS .................................................................... 995 1,096 0 1,125 10.2 2.6

DOT ..................................................................... 875 913 0 939 4.3 2.8

DOI ...................................................................... 683 692 74 730 1.3 5.5

USGS .............................................................. 586 611 74 649 4.3 6.2

EPA ..................................................................... 551 580 0 619 5.3 6.7

ED ....................................................................... 313 323 0 384 3.2 18.9

All other ............................................................... 761 818 0 947 7.5 15.8

Research ................................................................ 56,026 58,647 13,285 59,023 4.7 0.6

Basic ................................................................... 28,613 29,881 11,365 30,884 4.4 3.4

Applied ................................................................ 27,413 28,766 1,920 28,139 4.9 –2.2

Development .......................................................... 83,254 83,887 1,408 84,054 0.8 0.2

R&D facilities and equipment ................................. 4,466 4,531 3,642 4,543 1.5 0.3

Defense R&D .......................................................... 84,337 85,426 300 83,760 1.3 –2.0

Nondefense R&D .................................................... 59,409 61,639 18,035 63,860 3.8 3.6

All R&D, R&D facilities and equipment 

(2000 constant $millions) .................................... 117,286 118,267 14,745 117,532 0.8 –0.6

ARRA = American Recovery and Reinvestment Act; DHS = Department of Homeland Security; DOC = Department of Commerce; DOD = Department 

of Defense; DOE = Department of Energy; DOI = Department of the Interior; DOT = Department of Transportation; ED = Department of Education; 

EPA = Environmental Protection Agency; HHS = Department of Health and Human Services; NASA = National Aeronautics and Space Administration; 

NIH = National Institutes of Health; NIST = National Institute of Standards and Technology; NOAA = National Oceanic and Atmospheric Administration; 

NSF = National Science Foundation; USDA = U.S. Department of Agriculture; USGS = U.S. Geological Survey; VA = Department of Veterans Affairs

a

Based on preliminary allocations of ARRA. These figures may change.

bExcludes appropriations from ARRA. Change is FY 2008 actual appropriations to FY 2009 enacted appropriations; FY 2009 enacted appropriations to 

FY 2010 requested appropriations. 

SOURCES: Office of Management and Budget, Budget of the United States Government for Fiscal Year 2010, 7 May 2009; and Office of Science and 

Technology Policy, Executive Office of the President, Federal R&D, Technology, and STEM Education in the 2010 Budget, 7 May 2009.

Science and Engineering Indicators 2010

Annual change (%)b
Science and Engineering Indicators 2010 4-23

terms. The overall effects on the growth of federal R&D 

funding in either year depends on whether added spending 

under ARRA occurs in FY 2009 or FY 2010.

The largest increases among the agencies in the FY 2009 

budget for R&D go to the National Institutes of Health 

(NIH), with an increase of $1.2 billion; the Department of 

Energy (DOE), up $814 million; and NSF, up $277 million 

(table 4-7). These same agencies are also major recipients 

of ARRA funds (table 4-7): $10.4 billion to NIH for added 

biomedical research and laboratory renovation and construction; $2.9 billion to NSF for increased basic research, education and human resources, research facility construction, and 

research instrumentation; and $2.4 billion to DOE for new 

collaborations at the frontiers of energy research and infrastructure investments at the national laboratories. In addition, 

$925 million goes to the National Aeronautics and Space 

Administration (NASA) for accelerated activities in earth 

science climate research missions and the development of a 

next-generation air transport system. Another $410 million 

goes to the National Institute of Standards and Technology 

(NIST) for new standards research, advanced measurement 

equipment, and construction of research facilities.

The president’s FY 2010 proposal for federal R&D notes 

investment priorities in four main areas, as follows:

Sciences for a prosperous America—increased federal 

support for basic research. This focus recognizes that new 

fundamental knowledge and technology have often fueled the creation of new industries with associated hightechnology and high-wage jobs.

A clean energy future—expanded investment in research, 

development, demonstration, and deployment of clean energy technologies to help reduce U.S. dependence on oil, 

create green jobs, and limit the impact of climate change. 

(See sidebar “Public Investment in Energy R&D.”)

Healthy lives for all Americans—increased funding for 

biomedical and health research.

A safe and secure America—development of better science and technology to improve the prediction and prevention of, and the reaction to, destabilizing or paralyzing 

natural and man-made threats; improve capabilities for 

biodefense; and monitor nuclear nonproliferation compliance and prevent the surreptitious entry of weapons of 

mass destruction (OSTP 2009).

Federal R&D Budget by National Objectives

To assist Congress and the president in evaluating and 

setting the federal budget and its components, the Office of 

Management and Budget classifies agency budget requests 

into specific categories called budget functions. Budget 

functions represent a wide range of national objectives that 

the government wants to advance, from defense to health to 

transportation.

Defense-Related R&D

In the FY 2008 budget, defense was the largest budget 

function, accounting for $81.1 billion (current dollars), 

or 59% of the federal R&D budget (appendix table 4-17). 

Nondefense functions totaled $56.9 billion. Defense R&D 

is supported by DOD, DOE, and the Department of Homeland Security (DHS), with DOD accounting for $78 billion 

in FY 2008.

The proportional split between defense and nondefense 

R&D has fluctuated over the past several decades (figure 

4-7). In FY 1980, federal budget authority for defense-related R&D roughly equaled nondefense R&D. During the next 

several years, however, defense R&D expanded rapidly. By 

FY 1985, defense R&D budget authority more than doubled 

that of nondefense R&D. In contrast, between 1986 and 

2001 nondefense surged, and the gap between defense and 

nondefense R&D budgets shrank almost every year. In FY 

2001, the defense budget function represented 53% of the 

federal R&D budget. The trend reversed yet again after September 11, 2001, as defense R&D became more prominent, 

accounting for 59% of the federal R&D budget in FY 2008.

Civilian-Related R&D

The most dramatic change in federal R&D priorities over 

the past 25 years has been the increase in health-related 

R&D (figure 4-7), which rose from 25% of the federal nondefense R&D budget allocation in FY 1980 to 55% in FY 

2005. Growth accelerated after 1998, when policymakers 

set the NIH budget on course to double by FY 2003. In FY 

2008, health-related R&D represented 52% of nondefense 

R&D, even though recent increases have been below the 

level of inflation.

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. The loss of the 

Space Shuttle Columbia and its entire crew in February 2003 

prompted curtailment of manned space missions. In more 

recent years, NASA’s nondefense R&D budget share has increased, growing from 14% in FY 2005 to 17% in FY 2008. 

Nearly 58% of NASA’s $17 billion budget in FY 2008 was 

allocated for R&D; adjusted for inflation, the space-related 

R&D total was higher in FY 2008 than at any time since 

FY 1999.

Federal nondefense R&D classified as general science 

had about a 9% share in the mid 1990s, growing to 14% in 

FY 2008. However, this change reflected chiefly a reclassification of several DOE programs from energy to general 

science in FY 1998.

With respect to the federal budget for basic research, 94% 

of the funding in FY 2008 resided in nondefense budget 

functions (appendix table 4-18). In large part, this 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 for basic research (which is not equivalent to general science R&D) has been flat after adjusting for inflation. 
4-24 Chapter 4. Research and Development: National Trends and International Linkages

International public investment in energy research, 

development, and demonstration (hereafter R&D) has 

grown by about 30% over the 1997–2007 period, from 

$8.6 billion to $11.3 billion in inflation-adjusted dollars 

(figure 4-A). The data reflect annual energy R&D reports, by technology type, submitted by member governments of the International Energy Agency (IEA). These 

data provide insight into governmental R&D priorities in 

this area. The data do not include industry-funded activities in the listed energy types, nor do they cover broader 

activities that seek energy savings or reductions in such 

areas as industrial production and automotive and aircraft design.

The U.S. and Japanese governments reported by far 

the largest energy R&D government funds, fluctuating 

around 30% of the reported IEA total for the United States 

and declining from 38% to 30% for Japan. France, Germany, and the United Kingdom, which are very broadly 

similar in overall R&D spending, committed very different public investments to energy R&D, with France’s 

funding being much larger than expected relative to the 

other two countries. South Korea invested more than the 

combined total of the United Kingdom and Germany.

The biggest energy type is nuclear fission and 

fusion (figure 4-B), which consumed 38% of the 2007 

amount—down from 48% in 1997—and showed share 

losses in many major countries: from 75% to 65% in Japan, from 92% to 60% in France, and from 56% to 33% 

in Germany. In the United States, the energy share of 

nuclear fission/fusion rose from a low of 10% in 2002 

to 18% in 2007—still well below the level of most other 

major countries.

R&D in hydrogen and fuel cell energy is of most recent vintage. It represented about 7% of the IEA 2007 total; Canada stood out with 16% of its energy R&D funds 

in hydrogen and fuel cell technology. R&D in renewable 

energy has slowly risen to about 12% of the total, from 

8% a decade ago; the United Kingdom led in renewable 

energy, with an increase from 9% to 36%; Sweden’s level 

was high at 33%, as was Germany’s at 22%.

The quest for energy efficiency received a fairly steady 

13% of total energy R&D budgets, although the budget 

share was less in Germany, France, and the United Kingdom. All other technologies combined averaged about 

20% but garnered twice that level in the United States 

and much less than the IEA average in Japan and France.

Public Investment in Energy R&D

	
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Science and Engineering Indicators 2010 4-25

In FY 2003, basic research budget authority was $23.8 billion (constant 2000 dollars); in FY 2008, $23.4 billion.

Federal Spending on R&D by Agency

Federal R&D obligations totaled an estimated $114.6 billion in FY 2008 (the most recent year for which complete 

data are available). An additional $1.8 billion was obligated 

for R&D plant (facilities and equipment). Federal obligations for R&D have increased annually on a current-dollar 

basis since the early 1990s, but when adjusted for inflation, 

the increases flatten out after FY 2005 (appendix table 4-19).

More than 20 federal agencies fund R&D in the United 

States. In FY 2008, seven agencies committed more than 

$1 billion each for R&D (figure 4-8; table 4-8; table 4-9; appendix table 4-20). These agencies accounted for about 96% 

of total federal R&D obligations that year.

Department of Defense

DOD funds more than half of all federal R&D, having 

provided an estimated $58.7 billion (51%) in FY 2008. Of 

this total, $51.8 billion, or 88%, went to development, the 

majority ($45.8 billion) being allocated for “major systems 

development,” which includes the primary activities for developing, testing, and evaluating combat systems.

Extramural performers received 71% of DOD’s R&D obligations ($41.8 billion), the bulk going to industrial firms 

($38.6 billion). DOD accounted for about 84% of all federal 

R&D funding to industry in FY 2008. DOD intramural R&D 

accounted for 26%, and FFRDC R&D accounted for 3%.

Department of Health and Human Services

The Department of Health and Human Services (HHS) 

is the primary federal source of funding for health-related 

R&D. In FY 2008, it obligated an estimated $29.7 billion, 

or 26% of all federal R&D, most ($28.5 billion) being R&D 

funding by NIH. HHS R&D funding is almost entirely allocated for research (almost 54% for basic and 46% for applied). Development activities accounted for less than 1% of 

the HHS total.

Extramural performers accounted for 80% ($23.8 billion) of FY 2008 HHS R&D obligations. Universities and 

colleges received $17.1 billion; other nonprofit research organizations, $4.4 billion. HHS provided about 67% of all 

federal R&D funds distributed to universities and colleges 

in FY 2008 and 74% of federal R&D funds distributed to 

nonprofit institutions.

Department of Energy

DOE obligated an estimated $8.2 billion to R&D in 

FY 2008, 7% of the federal R&D total. Research accounted 

for 76% of these obligations (40% for basic and 36% for 

applied). FFRDCs received about 66% of DOE R&D 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 FFRDCs than other agencies. In FY 2008, 

DOE funds accounted for 59% of all federal R&D obligations to FFRDCs.

National Aeronautics and Space Administration

NASA obligated an estimated $6.2 billion to R&D in 

FY 2008, 5% of the federal R&D total. Of this R&D support, 

66% funded development activities; 21%, basic research; 

and 13%, applied research. Extramural R&D (chiefly by 

industry performers) accounted for 64% of NASA’s R&D 

obligations in FY 2008. Agency intramural activities represented 19%—and FFRDC activities, another 17%—of the 

NASA R&D total.

	
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4-26 Chapter 4. Research and Development: National Trends and International Linkages

	


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Table 4-8

Federal obligations for research and development, by agency and character of work: FY 2008

(Millions of current dollars)

Agency

Obligations 

for total R&D 

Basic 

research

Applied 

research Development

Basic 

research (%) 

Applied 

research (%) 

Development 

(%)

All federal government ............ 114,625 27,559 27,538 59,528 24.0 24.0 51.9

DOD ..................................... 58,676 1,510 5,345 51,821 2.6 9.1 88.3

HHS ..................................... 29,657 15,989 13,594 74 53.9 45.8 0.2

DOE ..................................... 8,212 3,243 2,917 2,052 39.5 35.5 25.0

NASA ................................... 6,243 1,298 829 4,117 20.8 13.3 65.9

NSF ...................................... 4,031 3,692 340 0 91.6 8.4 0.0

USDA ................................... 2,357 990 1,197 170 42.0 50.8 7.2

DOC ..................................... 1,062 108 861 93 10.2 81.1 8.8

DOT ...................................... 885 3 638 245 0.3 72.0 27.7

DHS ..................................... 847 191 77 579 22.5 9.1 68.3

DOI ....................................... 625 43 513 68 6.9 82.1 10.9

EPA ...................................... 557 97 379 81 17.4 68.1 14.5

VA ......................................... 480 211 246 23 44.0 51.2 4.8

ED ........................................ 325 4 202 119 1.3 62.0 36.7

Smithsonian Institution ........ 148 148 0 0 100.0 0.0 0.0

AID ....................................... 138 6 132 0 4.1 95.9 0.0

All other ................................ 382 26 270 86 6.9 70.6 22.5

AID = Agency for International Development; DHS = Department of Homeland Security; DOC = Department of Commerce; DOD = Department of 

Defense; DOE = Department of Energy; DOI = Department of the Interior; DOT = Department of Transportation; ED = Department of Education; EPA = 

Environmental Protection Agency; HHS = Department of Health and Human Services; NASA = National Aeronautics and Space Administration; NSF = 

National Science Foundation; USDA = U.S. Department of Agriculture; VA = Department of Veterans Affairs 

NOTES: Table lists all agencies with R&D obligations greater than $100 million in FY 2008. Figures for FY 2008 are preliminary.

SOURCE: NSF, Division of Science Resources Statistics, Federal Funds for Research and Development: Fiscal Years 2007–09.

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 4-27

National Science Foundation

NSF obligated an estimated $4 billion for research in FY 

2008. About 92% of NSF’s support funded basic research, 

and 95% funded extramural performers, chiefly universities 

and colleges ($3.3 billion). NSF is the federal government’s 

primary source of funding for academic, basic S&E research 

and the second-largest federal source (after HHS) of R&D 

funds for universities and colleges.

Department of Agriculture

The U.S. Department of Agriculture (USDA) obligated 

an estimated $2.4 billion for R&D in FY 2008, 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 2008, 

about 64% ($1.5 billion) funded intramural R&D, chiefly 

the Agricultural Research Service.

Department of Commerce

The Department of Commerce (DOC) obligated an estimated $1.1 billion for R&D in FY 2008, mainly for the R&D 

activities of the National Oceanic and Atmospheric Administration and NIST. Research accounted for 91% of the R&D 

for the department as a whole (10% for basic research and 

81% for applied research); 78% of the total was for intramural R&D; and almost 22% supported extramural performers, 

primarily universities and colleges.

Other Agencies

Of the other R&D-funding agencies, eight obligated 

between $100 million and $1 billion for R&D in FY 2008 

(table 4-8). This group included the Departments of Transportation, Homeland Security, the Interior, Veterans Affairs, 

and Education; the Environmental Protection Agency; the 

Smithsonian Institution; and the Agency for International 

Development. These agencies also varied with respect to 

the character of the research and the roles of intramural, 

FFRDC, and extramural performers.

Federal Spending on R&D by Performer

Academia

The federal government has historically been the primary source of funding for R&D performed by universities 

and colleges. Federal obligations for academic R&D in FY 

Table 4-9

Federal obligations for research and development, by agency and performer: FY 2008

(Millions of dollars)

Agency

Obligations 

for total R&D 

Agency 

intramural FFRDCs 

Extramural 

performers

Agency 

intramural 

(%) FFRDCs (%)

Extramural 

performers 

(%)

All federal government ............ 114,625 26,828 9,171 78,627 23.4 8.0 68.6

DOD ..................................... 58,676 15,066 1,770 41,840 25.7 3.0 71.3

HHS ..................................... 29,657 5,287 527 23,843 17.8 1.8 80.4

DOE ..................................... 8,212 678 5,400 2,135 8.3 65.8 26.0

NASA ................................... 6,243 1,198 1,077 3,969 19.2 17.2 63.6

NSF ...................................... 4,032 16 207 3,808 0.4 5.1 94.5

USDA .................................. 2,357 1,497 0 860 63.5 0.0 36.5

DOC ..................................... 1,062 830 1 231 78.2 0.1 21.7

DOT ...................................... 885 250 17 618 28.3 1.9 69.8

DHS ..................................... 847 225 148 475 26.6 17.4 56.0

DOI ....................................... 625 532 0 93 85.2 0.0 14.8

EPA ...................................... 557 407 0 150 73.1 0.0 26.9

VA ......................................... 480 480 0 0 100.0 0.0 0.0

ED ........................................ 325 13 0 312 4.0 0.0 96.0

Smithsonian Institution ........ 148 148 0 0 100.0 0.0 0.0

AID ....................................... 138 17 0 121 12.5 0.0 87.5

All other ................................ 382 183 25 174 47.9 6.5 45.7

AID = Agency for International Development; DHS = Department of Homeland Security; DOC = Department of Commerce; DOD = Department of 

Defense; DOE = Department of Energy; DOI = Department of the Interior; DOT = Department of Transportation; ED = Department of Education; EPA 

= Environmental Protection Agency; FFRDC = federally funded research and development center; HHS = Department of Health and Human Services; 

NASA = National Aeronautics and Space Administration; NSF = National Science Foundation; USDA = U.S. Department of Agriculture; VA = Department 

of Veterans Affairs 

NOTES: Table lists all agencies with R&D obligations greater than $100 million in FY 2008. Figures for FY 2008 are preliminary. Total 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.

SOURCE: NSF, Division of Science Resources Statistics, Federal Funds for Research and Development: Fiscal Years 2007–09.

Science and Engineering Indicators 2010
4-28 Chapter 4. Research and Development: National Trends and International Linkages

2008 totaled an estimated $25.7 billion (current dollars). As 

figure 4-9 illustrates, federal funding for academic R&D 

generally has increased over the long term. In FY 1955, federal obligations for academic R&D stood at $0.8 billion in 

constant 2000 dollars and accounted for 7% of all federal 

R&D funding. In FY 1985, federal obligations to this sector were $9.4 billion, 13% of all federal R&D funding. The 

corresponding figures for FY 2008 were $21.5 billion and 

23%, respectively.

Federal funding of academic R&D grew rapidly after 

FY 1998, the result of a successful bipartisan effort to double 

the budget of NIH from its FY 1998 level over the following 

5 years. Since FY 2004, however, federal R&D obligations to 

universities and colleges have failed to keep pace with inflation. (For additional details on academic R&D, see chapter 5.)

Business

Federal obligations for R&D performed by businesses totaled an estimated $46.0 billion in FY 2008. For decades, the 

business sector has consistently received the bulk of federal 

R&D funds (figure 4-9).

Space program investments in the 1960s fueled the 

growth of federal obligations for business R&D, but after 

the successful Apollo 11 mission to the moon, R&D obligations to industry declined. A decade later, Cold War investments in military technology resulted in a renewed period 

of growth. Similarly, military investment in the aftermath 

of September 11, 2001, has increased the flow of federal 

R&D funding to industry. Adjusting for inflation, federal 

R&D obligations to industry increased by 42% from FY 

2001 to 2008.

The amount of federally funded R&D reported by industry began to diverge from the amount reported by the federal government beginning in FY 1989. For details on this 

discrepancy, see the sidebar “Tracking R&D: The Gap Between Performer- and Source-Reported Expenditures.”

Federal Intramural R&D

Federal obligations for federal intramural R&D totaled an 

estimated $26.8 billion in FY 2008. These funds supported 

R&D performed at federal agencies’ intramural laboratories, 

as well as the costs associated with the planning and administration of both intramural and extramural R&D projects.

Among individual agencies, DOD funds the most intramural R&D, having accounted for 56% of all federal obligations 

for intramural R&D in FY 2008 (table 4-9). DOD’s intramural R&D obligations are almost three times those of HHS, the 

second-largest performer of federal intramural R&D. Only 

two other agencies reported intramural R&D obligations of 

more than $1 billion in FY 2008: NASA and USDA.

FFRDCs

Unique organizations in the federal R&D system, 

FFRDCs were established to help the U.S. government meet 

special long-term research or development needs that could 

not be met as effectively by existing in-house or contractor resources. They were first established during World War 

II to assist DOD and DOE with R&D on nuclear weapons. 

Today, FFRDCs perform R&D for both defense and civilian applications across a broad range of S&E fields. Of the 

37 currently active FFRDCs (appendix table 4-22), 16 are 

sponsored by DOE, the most of any federal agency. These 

16 organizations accounted for about 69% of the R&D obligations of all FFRDCs combined in FY 2007.

Five FFRDCs reported R&D obligations of more than 

$600 million in FY 2007: Los Alamos National Laboratory (DOE), Jet Propulsion Laboratory (NASA), Lawrence 

Livermore National Laboratory (DOE), Sandia National 

Laboratory (DOE), and Oak Ridge National Laboratory 

(DOE). These five accounted for 55% of the FFRDC total 

that year. Los Alamos National Laboratory and Lawrence 

Livermore National Laboratory are the only two laboratories 

in the United States where research on the nation’s nuclear 

stockpile is conducted.

Federal Spending on Research by Field

Federal agencies fund research (that is, basic research 

plus applied research, excluding development) in a wide 

range of S&E fields, from physics and mathematics to aeronautical engineering to sociology. Furthermore, the share of 

funding for research differs by field, as do the trends in funding over time.

	
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Science and Engineering Indicators 2010 4-29

In FY 2008, an estimated $55.1 billion (48%) of the 

$114.6 billion for all R&D supported research. Of this total, $29.7 billion (54%) supported research in the life sciences (figure 4-10; appendix table 4-23). The fields with the 

next-largest amounts were engineering ($9.4 billion, 17%) 

and the physical sciences ($5.2 billion, 10%), followed by 

environmental sciences ($3.3 billion, 6%), and mathematics 

and computer sciences ($3.3 billion, 6%). The balance of 

In some OECD countries, including the United States, 

figures for total government R&D support reported by 

government agencies differ from those reported by performers of R&D work. In keeping with international 

guidance and standards, most countries’ national R&D 

expenditure totals and time series are based primarily on 

data reported by performers (OECD 2002). Differences 

may be expected between funder and performer series for 

many reasons, such as different bases used for reporting 

government obligations (fiscal year) and performance expenditures (calendar year). Nonetheless, the gap between 

the two U.S. R&D series has widened over the past decade or more.

During the mid-1980s, performer-reported federal 

R&D in the United States exceeded federal reports of 

funding by $3 to $4 billion annually (5% to 10% of the 

government total). This pattern reversed itself toward 

the end of the decade; in 1989, the government-reported 

R&D total exceeded performer reports by $1 billion. For 

FY 2007, federal agencies reported obligating $114 billion in total R&D to all R&D performers ($44 billion to 

the business sector), compared with $101 billion in federal 

funding reported by the performers of R&D ($27 billion 

by businesses). In other words, the business-reported total was approximately 40% smaller than the federally reported R&D support to industry in FY 2007 (figure 4-C). 

The difference in federal R&D totals resided primarily in 

DOD funding of development activities by industry.

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 (2005a) noted that comparing federal outlays for R&D 

(as opposed to obligations) to performer expenditures results in a smaller discrepancy. In FY 2007, federal agencies reported total R&D outlays of $109 billion.

Tracking R&D: The Gap Between Performerand Source-Reported Expenditures

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4-30 Chapter 4. Research and Development: National Trends and International Linkages

federal obligations for research in FY 2008 supported the 

social sciences, psychology, and all other sciences ($4.2 billion overall, 8% of the total for research).

HHS, primarily through NIH, accounted for the largest 

share (54%) of federal obligations for research in FY 2008. 

Most of this amount funded research in medical and related life sciences. The five next-largest federal agencies for 

research funding that year were DOD (12%), DOE (11%), 

NSF (7%), USDA (4%), and NASA (4%).

DOD’s research funding emphasized engineering 

($3.7 billion), and mathematics and computer sciences 

($1.2 billion). DOE provided substantial funding for research in the physical sciences ($2.4 billion) and engineering ($2.2 billion), whereas USDA’s research funding was 

chiefly directed at the life sciences ($1.8 billion). NASA’s 

research funding emphasized engineering ($0.8 billion), followed by the physical sciences ($0.6 billion) and environmental sciences ($0.5 billion). NSF, which has a mission to 

“promote the progress of science,” had a relatively balanced 

research portfolio, contributing between $0.6 and $0.8 billion to researchers in each of the following fields: mathematics and computer sciences, physical sciences, engineering, 

environmental sciences, and life sciences.

From 1986 to 2008, real growth in federal obligations for 

research averaged 3.2% per year, increasing from $23.1 billion in 2000 dollars in FY 1986 to $45.0 billion in FY 2008 

(appendix table 4-24). The fields that experienced higherthan-average growth during this period were mathematics 

and computer sciences (5.5% per year in real terms), life 

sciences (4.8%), and psychology (5.8%). Funding for the 

Figure 4-10

Federal obligations for research, by agency and major S&E field: FY 2008

(Billions of current dollars)

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

nec = not elsewhere classified; 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; NSF = National Science Foundation; USDA = Department of 

Agriculture

NOTES: Scale differs for total and HHS compared to the other agencies listed. Figures for FY 2008 are preliminary. Research includes 

basic and applied research.

SOURCE: National Science Foundation, Division of Science Resources Statistics, Federal Funds for Research and Development: Fiscal Years 2007–2009. 

See appendix table 4-23.

Science and Engineering Indicators 2010

0 5 10 15 20 25 30 35
Science and Engineering Indicators 2010 4-31

remaining fields also grew at a faster rate than inflation over 

this period: social sciences (1.9%), engineering (1.8%), and 

environmental sciences (1.3%).

Federal R&D Tax Credits

Background

Contributions of R&D to economic growth and social 

welfare, along with likely underinvestment by private performers, given the difficulty in fully appropriating R&D 

benefits, are often cited as reasons for justifying public support for R&D (NRC 2005b).23 In addition to direct government funding discussed earlier in this chapter, fiscal policy 

tools used to provide such support include tax incentives.24

The federal government offers several corporate tax incentives for qualified R&D expenditures including a deduction 

under Internal Revenue Code (IRC) section 174 (C.F.R. 

Title 26) and a tax credit under section 41. As of 2006, at 

least 32 states also offered credits for company-funded R&D 

(NSB 2008; Wilson forthcoming). This section focuses on 

business R&D tax credits at the federal level.

The research and experimentation (R&E) tax credit, 

established by the Economic Recovery Tax Act of 1981 

(Public Law 97-34), covers R&D activities performed in 

the United States by domestic and foreign-owned firms but 

excludes R&D conducted abroad by U.S. companies. It is 

subject to periodic extensions and, at the time of writing, 

was last renewed by the Emergency Economic Stabilization 

Act of 200825 through 31 December 2009.

The R&E tax credit encompasses a regular credit, as 

well as credits for payments for basic research to qualified 

universities, scientific research organizations, or grant organizations, and for payments to energy research consortia. 

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)).26 Thus, 

the regular credit is characterized as a fixed-base 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. At the same time, the actual or effective credit rate for corporate taxpayers is lower than 20% 

because of limitations involving deductions under IRC section 174 (Guenther 2008).27

Federal Corporate Tax Credit Claims

According to the IRS Statistics of Income Division 

(SOI),28 U.S. companies claimed an estimated $7.3 billion in 

federal R&E tax credits in 2006, involving close to 11,000 

corporate tax returns, compared with $6.4 billion in 2005 

(table 4-10).29 The proportion of R&E credits going to corporations with business receipts of $250 million or more has 

fluctuated narrowly between 75% and 80% since 2003 and 

was 75% in 2006.30

For all industries, the size of R&E claims was about 3.3% 

relative to company-funded R&D in 2006, a proportion that 

has changed little in recent years (figure 4-11). Appendix 

tables 4-25 and 4-26 show data by NAICS industry up to 

2005 (latest available year by detailed industry). Five industries accounted for about three-quarters of R&E credit claims 

in 2005. These industries had much higher ratios of R&E 

claims to industry-funded R&D: computer and electronic 

products (26%); chemicals, including pharmaceuticals and 

Table 4-10

Federal research and experimentation tax credit 

claims and corporate tax returns claiming credit: 

1990–2006

Year

Tax credit claims 

($millions) Tax returns

1990.................. 1,547 8,699

1991.................. 1,585 9,001

1992.................. 1,515 7,750

1993.................. 1,857 9,933

1994.................. 2,423 9,150

1995.................. 1,422 7,877

1996.................. 2,134 9,709

1997.................. 4,398 10,668

1998.................. 5,208 9,849

1999.................. 5,281 10,019

2000.................. 7,079 10,495

2001.................. 6,356 10,389

2002.................. 5,656 10,254

2003.................. 5,488 10,369

2004.................. 5,554 10,244

2005.................. 6,363 11,290

2006.................. 7,311 10,788

SOURCE: Internal Revenue Service, Statistics of Income, 

special tabulations (historical data), http://www.irs.gov/taxstats/

article/0,,id=164402,00.html (2006), accessed 19 June 2009.

Science and Engineering Indicators 2010

	





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4-32 Chapter 4. Research and Development: National Trends and International Linkages

Comparisons of international R&D statistics are hampered by the lack of R&D-specific exchange rates. If 

countries do not share a common currency, some conversion must be made to compare their R&D expenditures. 

Two approaches are commonly used to facilitate international R&D comparisons: (1) normalize national R&D 

expenditures by dividing by GDP, thereby obviating the 

need for currency conversion altogether or (2) convert all 

foreign-denominated expenditures to a single currency, 

resulting in indicators of absolute effort. The first method 

is a straightforward calculation but permits only gross 

national comparisons of R&D intensity. The second 

method permits absolute-level comparisons and analyses 

of countries’ sector- and field-specific R&D, but it entails 

choosing an appropriate method of currency conversion.

Because no widely accepted R&D-specific exchange 

rates exist, the choice is between market exchange rates 

(MERs) and purchasing power parities (PPPs). These 

rates are the only series consistently compiled and available for a large number of countries over an extended period of time.

MERs. At their best, MERs represent the relative 

value of currencies for goods and services that are traded across borders. That is, MERs measure a currency’s 

relative international buying power. Nevertheless, MERs 

may not accurately reflect the true cost of goods or services that are not traded internationally. In addition, 

fluctuations in MERs as a result of currency speculation, 

political events (such as wars or boycotts), and official 

currency intervention greatly impair their statistical utility—despite the fact that such occurrences have little or 

nothing to do with changes in the relative prices of internationally traded goods.

PPPs. PPPs were developed because of the shortcomings of MERs (Ward 1985). PPPs take into account 

the cost differences across countries of buying a similar 

market basket of goods and services in numerous expenditure categories, including nontradables. The PPP basket is thereby assumed to be representative of total GDP 

across countries.

Although the goods and services included in the market basket used to calculate PPP rates differ from the major components of R&D costs (fixed assets, as well as 

wages of scientists, engineers, and support personnel), 

they still result in a more suitable domestic price converter than one based on foreign trade flows. Exchange-rate 

movements bear little relationship to changes in the cost 

of domestically performed R&D. The adoption of the 

euro as the common currency for many European countries provides a useful example: although Germany and 

Portugal now share a common currency, the real costs of 

most goods and services are substantially less in Portugal. 

PPPs are, therefore, the preferred international standard 

for calculating cross-country R&D comparisons wherever possible 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.)

Although PPPs are available for developing countries, 

such as India and China, they may be less useful for converting R&D expenditures in such countries than in more 

developed countries for a number of reasons:

It is difficult or impossible to assess the quality of PPPs 

for some countries, most notably China. Although PPP 

estimates for OECD countries are quite reliable, PPP 

estimates for developing countries are often rough approximations. The latter estimates are based on extrapolations of numbers published by the United Nations 

International Comparison Program and by Professors 

Robert Summers and Alan Heston of the University of 

Pennsylvania and their colleagues.

The composition of the market basket used to calculate 

PPPs likely differs substantially between developing 

and developed countries. The structural differences in 

the economies of developing and developed countries, 

as well as disparities in income, may result in a market 

basket of goods and services in a developing country 

that is quite different from that of a developed country, 

particularly as far as these baskets relate to the various 

costs of R&D.

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.”

Comparing International R&D Expenditures
Science and Engineering Indicators 2010 4-33

medicines (18%); transportation equipment, including motor vehicles and aerospace (13%); information, including 

software (10%); and professional, scientific, and technical 

services, including computer and R&D services (10%). The 

same five industries accounted for 80% of 2005 companyfunded R&D from the NSF/Census Survey of Industrial Research and Development.31

International R&D Comparisons

Data on R&D expenditures can provide a broad picture 

of the changing distribution of R&D activities around the 

world. R&D data available from the OECD cover the organization’s 30 member countries and 9 nonmembers. Data 

from the United Nations Educational, Scientific, and Cultural Organization’s (UNESCO’s) Institute for Statistics are 

used here to supplement OECD statistics in order to cover a 

larger set of countries. Increasingly, these data are collected 

following OECD standards, but the reader should treat them 

as broad indicators of trends and patterns rather than as precise measures.

International comparisons involve currency conversions. 

The discussion here follows the international convention to 

convert 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 in 2007 totaled an estimated $1,107 billion. Although many countries conduct 

R&D, much of global R&D performance continues to be 

concentrated in a few high-income countries and regions.

Three regions predominate (figure 4-12). North America accounts for 35% ($393 billion) of worldwide R&D 

performance; Asia, 31% ($343 billion); and Europe, 28% 

($313 billion). The small remainder, approximately 5%, reflects the R&D of countries in the Latin America/Caribbean, 

Pacific, and Africa/Middle East regions.

The concentration is more apparent when reviewing the 

data of specific countries (table 4-11). By itself, the United 

States accounts for about 33% of the current global R&D total. Japan, the second-largest performer, accounts for about 

13%. China (9%) comes next, followed by Germany (6%) 

and France (4%).

The top two countries thus account for 47% of the 

global R&D total, whereas the top five countries represent 

about 66%. Adding the next 5 countries—South Korea, 

Africa & Middle

East $15 (1.3%)

North America 

$393 (35.5%)

Europe (Western, 

Central, Eastern)

$313 (28.2%)

Asia (East,

South, West)

$343 (31.0%)

Pacific

$18 (1.6%)

World total = $1,107

PPP = purchasing power parity

NOTES: Foreign currencies converted to dollars through purchasing power parities. Sources track R&D for 126 countries. Some country figures are 

estimated.

SOURCES: United Nations Educational, Scientific and Cultural Organization (UNESCO), Institute for Statistics, http://www.uis.unesco.org, accessed 

October 2009; and Organisation for Economic Co-operation and Development, Main Science and Technology Indicators (2009/1).

Science and Engineering Indicators 2010

Figure 4-12

National R&D expenditures and share of world total, by region: 2007

(Billions of U.S. PPP dollars)

Latin America 

& Caribbean

$26 (2.4%)
4-34 Chapter 4. Research and Development: National Trends and International Linkages

the United Kingdom, the Russian Federation, Canada, 

and Italy—increases the total to just below 80%, meaning 

that four-fifths of the world’s R&D is concentrated in just 

10 countries.

With respect to major geopolitical groupings, the R&D 

performance of the 27 nations of the European Union (EU27) currently accounts for about 24% of the global total. 

The Group of Seven (G-7) industrialized countries, of which 

the United States is a member (along with Canada, France, 

Germany, Italy, Japan, and the United Kingdom), account 

for about 65%. The 30 countries constituting the OECD account for about 80% of worldwide R&D. (Among the current major R&D-performing nations, only China is not an 

OECD member.)

U.S. dominance of global R&D performance is notable 

as well with respect to these country groupings. U.S. R&D 

expenditures are currently 40% greater than the total for all 

of the EU-27 countries together. Within the G-7, the United 

States currently accounts for more than half (52%) of the 

R&D total. (The U.S. share was 48% in 1990. It has exceeded 50% since 1997.) Within the OECD, U.S. R&D is 

about 42% of the total.

According to OECD statistics (figure 4-13), total R&D 

by the EU-27 nations has been growing in real dollars over 

the past 10 years at an average annual rate of 3.3%. The pace 

of real growth during the same period for Germany, France, 

and the United Kingdom has been slower: averaging 2.9%, 

1.8%, and 3.0%, respectively. By comparison, the U.S. pace 

of growth, on the same basis, has averaged 3.3%. Growth in 

Japan has been slower, at an annual average rate of 3.0%. 

For the OECD as a whole, real growth in R&D expenditures 

has also expanded on average at a rate of 3.6% annually over 

the past 10 years.

China continues to show the most dramatic growth pattern. The World Bank revised China’s PPP exchange rate in 

late 2007, significantly lowering the dollar value of its R&D 

Country/economy

GERD 

(millions PPP$)

GERD/ 

GDP (%)

Regions/selected countries:

North America

United States (2007) .................. 368,799.0 2.68

Canada (2008) ............................ 23,781.0 1.82

Latin America and Caribbean

Mexico (2005) ............................ 5,919.0 0.46

Argentina (2007) ......................... 2,656.2 0.51

Western Europe

Germany (2007) ......................... 71,860.8 2.54

France (2007) ............................. 43,232.6 2.08

United Kingdom (2007) .............. 38,892.8 1.79

Italy (2006) ................................. 19,678.1 1.13

Spain (2007) ............................... 18,000.3 1.27

Sweden (2007) ........................... 12,076.3 3.60

Netherlands (2007) ..................... 10,949.8 1.70

Austria (2008) ............................. 8,530.1 2.66

Switzerland (2004) ..................... 7,474.3 2.90

Belgium (2007) ........................... 7,028.3 1.87

Finland (2008) ............................ 6,519.7 3.46

Denmark (2007) .......................... 5,008.4 2.55

Norway (2007) ............................ 4,133.0 1.64

Ireland (2008) ............................. 2,855.1 1.45

Portugal (2007) ........................... 2,849.7 1.18

Greece (2007) ............................ 1,828.4 0.58

Luxembourg (2007) .................... 624.0 1.63

Iceland (2008) ............................ 318.2 2.76

Country/economy

GERD 

(millions PPP$)

GERD/ 

GDP (%)

Central and Eastern Europe 

Russian Federation (2007) ......... 23,482.0 1.12

Turkey (2007) .............................. 6,830.0 0.71

Czech Republic (2007) ............... 3,813.8 1.54

Poland (2007) ............................. 3,482.3 0.57

Hungary (2007) .......................... 1,822.9 0.97

Romania (2007) .......................... 1,433.9 0.53

Slovenia (2007) .......................... 828.3 1.53

Slovak Republic (2007) .............. 497.9 0.46

East, South, West Asia

Japan (2007) .............................. 147,800.8 3.44

China (2007) ............................... 102,331.0 1.49

South Korea (2007) .................... 41,741.6 3.47

Taiwan (2007) ............................. 18,324.8 2.63

Singapore (2007) ........................ 5,945.5 2.61

Pacific

Australia (2006) .......................... 14,914.4 2.01

New Zealand (2007) ................... 1,383.7 1.20

Africa and Middle East

Israel (2007) ............................... 8,845.8 4.68

South Africa (2005) .................... 3,654.3 0.92

Selected country groups:

OECD (2007) .................................. 886,347.1 2.29

European Union-27 (2007) ............ 262,985.0 1.77

G-7 countries (2007) ...................... 715,329.6 2.53

EU = European Union; GDP = gross domestic product; GERD = gross domestic expenditure on R&D; OECD = Organisation for Economic Co-operation 

and Development; PPP = purchasing power parity

NOTE: Data for Israel is civilian R&D only.

SOURCE: OECD, Main Science and Technology Indicators (2009/1).

Science and Engineering Indicators 2010

Table 4-11

International comparisons of gross domestic expenditures on R&D and R&D share of gross domestic product, 

by country/economy/region: Most recent year
Science and Engineering Indicators 2010 4-35

expenditures. Nonetheless, the pace of real annual growth 

over the past 10 years in China remains exceptionally high 

at just above 19%.

Finally, both India and Brazil are among the world’s larger R&D performers, although neither has yet become part 

of OECD’s statistical system. According to the UNESCO 

statistics, India performed $15 billion of R&D in 2004 (current U.S. dollars, PPP) and Brazil performed $13 billion 

in 2005. Both figures are about double the levels of R&D 

performance that each country reported in the mid-1990s. 

These levels of R&D expenditures would put both India and 

Brazil in the world’s top 15 R&D performers.

Comparison of Country R&D Intensities

R&D intensity—typically measured as the ratio of a 

country’s national R&D expenditures to GDP for a given 

year—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.

The structure of a national economy—that is, the relative 

prominence of agriculture, manufacturing, services, and so 

on—influences the interpretation of R&D intensity statistics. Businesses and organizations differ widely in their relative need for investment in the latest science and technology, 

and countries whose overall GDP depends considerably on 

industries in the high-technology sector will exhibit higher 

R&D/GDP ratios than other countries.

Figure 4-14 provides background information on the 

GDP composition of the current top 10 R&D-performing 

countries. Agriculture is a comparatively small component 

(4% or less) for 9 of these 10 countries; only China is an 

exception, where agriculture is currently about 11%. For all 

but four of the countries, services account for 70% or more 

of current GDP. In China (49%), South Korea (39%), and 

Russia (41%), industry accounts for a more sizable fraction 

of GDP.

Total R&D/GDP Ratios

The U.S. R&D/GDP ratio was about 2.7% in 2007 (table 

4-11). At this level, the United States is eighth among the 

economies tracked by the OECD. Israel has the highest ratio 

at 4.7%, with Sweden, Finland, Japan, and South Korea all 

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4-36 Chapter 4. Research and Development: National Trends and International Linkages

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 4-15). 

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. Non-federally financed 

R&D increased from about 0.6% of GDP in 1953 to 2.0% of 

GDP in 2007. 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.

Historically, the many peaks and valleys in the U.S. 

R&D/GDP ratio reflect changing federal R&D priorities. 

The ratio’s drop from its peak in 1964 largely resulted from 

federal cutbacks in defense and space R&D programs; from 

1975 to 1979, gains in energy R&D activities kept the ratio 

stable. Beginning in the late 1980s, cuts in defense-related 

R&D kept growth in federal R&D spending below GDP 

growth, while nonfederal growth kept pace with or exceeded 

that of GDP. Since 2000, defense-related R&D spending has 

helped federal R&D spending growth outpace the growth 

of GDP.

Among other top 10 R&D-performing countries, total 

R&D/GDP ratios over the past 10 years show mixed trends 

(figure 4-16). Compared with 1996 R&D/GDP ratios, 2007 

(or 2006) ratios were substantially higher in Japan, China, 

and South Korea; modestly higher for Germany and Canada; somewhat higher for Italy and the United Kingdom; and 

lower for France. Russia’s R&D/GDP ratio grew consistently from the late 1990s but has fallen back to only somewhat 

above its 1996 level in recent years.

	
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Science and Engineering Indicators 2010 4-37

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. Figure 4-16 shows the trends since the 

early 1980s in the nondefense R&D/GDP ratios for 7 of the 

top 10 R&D-performing nations (for which data are available). Although the U.S. ratio (2.3% in 2007) ranks ahead 

of that for the United Kingdom, it lags behind Japan, South 

Korea, and Germany.

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.

Based on the most recent data available, the U.S. basic 

research/R&D ratio is about 0.5% and accounts for less than a 

fifth of the total R&D/GDP ratio (table 4-12). France’s basic 

research ratio is slightly above the U.S. figure but accounts 

for nearly a quarter of its total ratio. South Korea’s basic 

research ratio is close to the U.S. and French figures but 

accounts for less of the total ratio. The basic research ratios for 

Japan, Italy, and especially China are below the U.S. figure.

The following countries have basic research-to-GDP ratios at or above the U.S. level: Switzerland (0.83%), Israel 

(0.78%), Singapore (0.48%), Australia (0.45%), and Denmark (0.44%).

R&D by Performing Sector and Source 

of Funds

In all top 10 countries ranked by R&D expenditures, the 

business sector is currently the largest performer, ranging 

from 77% for South Korea and Japan to 49% for Italy (table 

4-13). Countries with relatively lower business-sector R&D 

Table 4-12

Gross expenditures on R&D as share of gross domestic product, for selected countries: Most recent year 

(Percent)

Country

All 

R&D/GDP

Nondefense 

R&D/GDP Share

Basic 

R&D/GDP Share

United States (2007) ........................... 2.68 2.26 84 0.47 18

Japan (2007) ....................................... 3.44 3.40 99 0.40 12

China (2007) ....................................... 1.49 NA NA 0.05 3

Germany (2006) .................................. 2.54 2.50 98 NA NA

France (2006) ...................................... 2.10 1.94 92 0.50 24

South Korea (2006) ............................. 3.22 3.15 98 0.49 15

United Kingdom (2006) ....................... 1.76 1.60 91 NA NA

Russian Federation (2002) .................. 1.25 0.88 70 0.17 14

Canada (2008) .................................... 1.82 NA NA NA NA

Italy(2005) ........................................... 1.09 1.09 100 0.30 28

Taiwan (2007) ...................................... 2.63 2.60 99 0.26 10

Spain (2003) ....................................... 1.05 1.02 97 0.20 19

Australia (2006) ................................... 2.01 1.93 96 0.45 22

Sweden (2006) .................................... 3.74 3.50 94 NA NA

Israel (2007) ........................................ NA 4.68 NA 0.78 NA

Switzerland (2004) .............................. 2.90 2.88 99 0.83 29

Finland (2007) ..................................... 3.48 3.48 100 NA NA

Denmark (2005) .................................. 2.54 NA NA 0.44 17

Singapore (2007) ................................ 2.61 NA NA 0.45 17

Ireland (2006) ...................................... 1.30 1.30 100 0.31 24

NA = not available

GDP = gross domestic product

NOTES: Top 10 R&D performing countries (United States to Italy) and selected other countries. Figures for Israel are civilian R&D only.

SOURCE: Organisation for Economic Co-operation and Development, Main Science and Technology Indicators (2009/1).

Science and Engineering Indicators 2010
4-38 Chapter 4. Research and Development: National Trends and International Linkages

tend to have greater higher education R&D; these countries 

include Canada, Italy, the United Kingdom, and France. The 

government sector is particularly prominent in the Russian 

Federation, Italy, China, and France.

China’s business R&D sector has spurred much recent 

growth in national R&D expenditures, which rose from 60% 

of the total in 2000 to 72% in 2007. This increase reflects activities by private domestic companies and by multinational 

companies as well as the conversion of government-owned 

enterprises to the private sector.

With respect to R&D funding, the business sector supplies 

66% of total R&D funds in the United States (table 4-14). In 

Japan and South Korea, the business sector supplies higher 

fractions of the total R&D funding than in the United States. 

Germany’s and China’s business sectors provide funding 

shares broadly similar to that of the United States. In France, 

Canada, the United Kingdom, Italy, and the Russian Federation, the business sector provides smaller shares of total R&D 

funding, but the government shares are relatively high. Government support for R&D is particularly low in Japan.

More precise analysis is impeded by the lack of comparable data for foreign-funded R&D in the United States 

(figure 4-17). Russia, the United Kingdom, and Canada 

had the strongest growth in foreign R&D funds during the 

1990s but have recently experienced sharp drops. Foreign 

R&D funding largely comes from foreign companies but 

also includes resources from foreign governments and other overseas organizations. For European countries, growth 

Table 4-14

Gross expenditures on R&D by funding source, for selected countries: Most recent year

(Percent)

Country Business Government Other domestic Abroad

United States (2007) ................................................................ 66.4 27.7 5.8 NA

Japan (2007) ............................................................................ 77.7 15.6 6.3 0.3

China (2007) ............................................................................ 70.4 24.6 NA 1.3

Germany (2006) ....................................................................... 68.1 27.8 0.4 3.8

France (2006) ........................................................................... 52.4 38.4 2.2 7.0

South Korea (2007) .................................................................. 73.7 24.8 1.3 0.2

United Kingdom (2007) ............................................................ 47.2 29.3 5.8 17.7

Russian Federation (2007) ....................................................... 29.4 62.6 0.7 7.2

Canada (2008) ......................................................................... 49.5 31.3 10.3 9.0

Italy (2006) ............................................................................... 40.4 48.3 3.0 8.3

NA = not available

NOTES: Top 10 R&D performing countries. U.S. data on R&D funding from abroad not separately identified but included in sector totals. 

SOURCE: Organisation for Economic Co-operation and Development, Main Science and Technology Indicators (2009/1). 

Science and Engineering Indicators 2010

Table 4-13

Gross expenditures on R&D by performing sector, for selected countries: Most recent year

(Percent)

Country Business Government

Higher 

education

Private 

nonprofit

United States (2007) ................................................................ 71.9 10.7 13.3 4.2

Japan (2007) ............................................................................ 77.9 7.8 12.6 1.7

China (2007) ............................................................................ 72.3 19.2 8.5 0.0

Germany (2007) ....................................................................... 69.9 13.9 16.2 0.0

France (2007) ........................................................................... 63.2 16.5 19.2 1.1

South Korea (2007) .................................................................. 76.2 11.7 10.7 1.4

United Kingdom (2007) ............................................................ 64.1 9.2 24.5 2.1

Russian Federation (2007) ....................................................... 64.2 29.1 6.3 0.3

Canada (2008) ......................................................................... 56.1 9.6 33.8 0.5

Italy (2006) ............................................................................... 48.8 17.2 30.3 3.7

NOTE: Top 10 R&D performing countries.

SOURCE: Organisation for Economic Co-operation and Development, Main Science and Technology Indicators (2009/1). 

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 4-39

in foreign-funded R&D may reflect coordinated European 

Community (EC) efforts to foster cooperative shared-cost 

research through its European Framework Programmes.

Businesses in the United States also receive R&D funding from abroad. However, this funding is not separately reported in U.S. R&D statistics; instead, it is included in the 

figures reported for the business sector.32

Business Sector

The structure of business R&D varies substantially 

among countries in terms of both sector 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 

pharmaceuticals or automotive 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.

Sector Focus for the United States and OECD 

Countries

Using internationally comparable data, no one industry 

accounted for more than 18% of total business R&D in the 

United States in 2007 (figure 4-18; appendix table 4-31) 

(OECD 2009c). This circumstance stems largely from the 

fact that total business R&D expenditures are so large in the 

United States that it is 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 a popular location for R&D performance for a broad range of industries.33

Compared with the United States, many of the other 

countries shown in figure 4-18 display much higher industry sector concentrations. In countries with less business 

R&D, high sector concentrations can result from the activities of one or two large companies. This pattern is notable 

in Finland, where the communication, television, and radio 

equipment industry accounted for more than half of business 

R&D in 2007. This high concentration most likely reflects 

the activities of one company, Nokia, a major manufacturer of mobile phones at the forefront of the convergence of 

communications and the Internet. In contrast, South Korea’s 

high concentration of R&D (48% of all business R&D in 

2006) in this industry is not the result of any one or two 

companies, but reflects the structure of its export-oriented 

economy. South Korea is one of the world’s top producers 

of electronic goods, and among its top export commodities are semiconductors, cellular phones, and computers. In 

the United States, the communication, television, and radio 

equipment industry accounted for 11% of all business R&D 

in 2007.

Other industries also exhibit relatively high concentrations of R&D by country. Automotive manufacturers ranked 

among the largest R&D-performing companies in the world 

in 2006. (See table 4-15 and sidebar “Global R&D Expenses 

of Public Corporations.”) Hence, countries that are home to 

the world’s major automakers also boast the highest concentration of R&D in the automotive manufacturing industry. This industry accounts for 30% of Germany’s business 

R&D, 23% of the Czech Republic’s, and 19% of Sweden’s 

(figure 4-18), reflecting the operations of automakers such 

as Daimler AG and Volkswagen in Germany, Skoda in the 

Czech Republic, and Volvo and Saab in Sweden. Also home 

to large R&D-performing firms in this industry are France 

(18% of all business R&D; PSA Peugeot Citroën, Renault), 

Japan (17%; Toyota, Honda, Nissan), South Korea (15%; 

Hyundai, Kia), and Italy (12%; Fiat). In the United States, 

the automotive manufacturing industry accounted for 6% of 

all business R&D in 2007.

The pharmaceuticals industry is less geographically concentrated than the automotive manufacturing industry but 

is still prominent in several countries. The pharmaceuticals 

industry accounts for more than 27% of business R&D in 

Denmark and the United Kingdom, and more than 20% in 

	
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4-40 Chapter 4. Research and Development: National Trends and International Linkages

Figure 4-18

Share of industrial R&D, by industry sector and selected country: 2005–2007

Science and Engineering Indicators 2010

Pharmaceuticals

Computers, office and

accounting machines

Communication, TV,

and radio equipment

50403020100

Motor vehicles All services Computer and related services

Poland (2006)

Ireland (2005)

Czech Rep (2007)

Norway (2007)

Denmark (2006)

Finland (2007)

Belgium (2006)

Australia (2006)

Netherlands (2006)

Spain (2006)

Sweden (2007)

Italy (2007)

Canada (2006)

UK (2006)

South Korea (2006)

France (2006)

Germany (2006)

Japan (2006)

United States (2007)

(Percent)

Poland (2006)

Ireland (2005)

Czech Rep (2007)

Norway (2007)

Denmark (2006)

Finland (2007)

Belgium (2006)

Australia (2006)

Netherlands (2006)

Spain (2006)

Sweden (2007)

Italy (2007)

Canada (2006)

UK (2006)

South Korea (2006)

France (2006)

Germany (2006)

Japan (2006)

United States (2007)

50403020100

50403020100 50403020100

50403020100 50403020100

UK = United Kingdom

NOTES: Countries listed in descending order by amount of total industrial R&D. Data are for years in parentheses.

SOURCES: Organisation for Economic Co-operation and Development, ANBERD database (2009), http://www.oecd.org/document/17/0,3343,en_2649_

34445_1822033_1_1_1_1,00.html, accessed 15 June 2009; and National Science Foundation, Division of Science Resources Statistics, Survey of Industrial 

Research and Development (2007).
Science and Engineering Indicators 2010 4-41

Table 4-15

Global R&D spending by top 25 corporations: 2006

R&D rank R&D expense ($millions) Sales ($millions)

R&D/sales 

ratio (%)

Company (country) 2006 2005 2006 2005 Change (%) 2006 2005 Change (%) 2006 2005

Toyota Motor (Japan) ............................. 1 4 7,486 6,829 9.6 201,254 176,789 13.8 3.7 3.9

Pfizer (United States) ............................. 2 2 7,423 7,442 –0.3 48,201 51,298 –6.0 15.4 14.5

Ford Motor (United States) .................... 3 1 7,200 8,000 –10.0 160,123 176,896 –9.5 4.5 4.5

Johnson & Johnson (United States) ...... 4 8 7,125 6,312 12.9 53,194 50,434 5.5 13.4 12.5

Microsoft (United States) ....................... 5 7 7,121 6,584 8.2 51,122 44,282 15.4 13.9 14.9

DaimlerChrysler (Germany) ................... 6 3 7,007 7,425 –5.6 199,246 196,863 1.2 3.5 3.8

GlaxoSmithKline (United Kingdom) ....... 7 9 6,611 6,108 8.2 45,263 42,213 7.2 14.6 14.5

Siemens (Germany) ............................... 8 5 6,604 6,776 –2.5 114,779 99,164 15.7 5.8 6.8

General Motors (United States) ............. 9 6 6,600 6,700 –1.5 207,349 190,215 9.0 3.2 3.5

Volkswagen (Germany) .......................... 10 12 6,030 5,364 12.4 137,846 125,219 10.1 4.4 4.3

Samsung Electronics (South Korea) ...... 11 10 5,943 5,765 3.1 91,038 85,927 5.9 6.5 6.7

Intel (United States) ............................... 12 14 5,873 5,145 14.1 35,382 38,826 –8.9 16.6 13.3

Sanofi-Aventis (France) ......................... 13 13 5,823 5,315 9.6 37,293 35,897 3.9 15.6 14.8

International Business Machines 

 (United States) .................................... 14 11 5,682 5,378 5.7 91,424 91,134 0.3 6.2 5.9

Roche Holding (Switzerland) ................. 15 17 5,359 4,640 15.5 34,192 28,882 18.4 15.7 16.1

Novartis (Switzerland) ............................ 16 18 5,349 4,514 18.5 36,031 32,212 11.9 14.8 14.0

Nokia (Finland) ....................................... 17 15 5,122 5,008 2.3 54,049 44,940 20.3 9.5 11.1

Matsushita Electric (Japan) ................... 18 16 4,858 4,746 2.4 76,543 74,746 2.4 6.3 6.3

Honda Motor (Japan) ............................ 19 20 4,638 4,289 8.1 93,174 83,264 11.9 5.0 5.2

Sony (Japan) .......................................... 20 19 4,571 4,469 2.3 69,715 62,822 11.0 6.6 7.1

Robert Bosch GmbH (Germany) ........... 21 21 4,401 4,039 9.0 57,418 54,496 5.4 7.7 7.4

Motorola (United States)........................ 22 24 4,106 3,680 11.6 42,879 36,843 16.4 9.6 10.0

Cisco Systems (United States) .............. 23 30 4,067 3,322 22.4 28,484 24,801 14.9 14.3 13.4

Merck (United States) ............................ 24 22 4,020 3,848 4.5 22,636 22,012 2.8 17.8 17.5

Telefonaktiebolaget LM Ericsson 

 (Sweden) ............................................. 25 25 3,990 3,494 14.2 25,403 21,693 17.1 15.7 16.1

SOURCE: Institute of Electrical and Electronics Engineers (IEEE), IEEE Spectrum Top 100 R&D Spenders, Standard & Poor’s data (2006), http://www.

spectrum.ieee.org/images/dec07/images/12.RDchart.pdf, accessed 5 May 2009.

Science and Engineering Indicators 2010

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. According to information gleaned from public reports, the 25 public corporations with the largest reported 

worldwide R&D expenses spent $143 billion on R&D 

in 2006. The six companies with the largest reported 

R&D expenses—Toyota, Pfizer, Ford Motor Company, 

Johnson & Johnson, Microsoft, and DaimlerChrysler—

each spent between $7 billion and $7.5 billion. The six 

automobile manufacturers on the list reported combined 

spending of $39 billion on R&D (27.3% of the total for 

the top 25) (table 4-15). Eleven companies in the information and communications technologies (ICT) sector spent 

a total of $57.9 billion (40.5% of the total). The remaining eight companies include six pharmaceutical manufacturers and two diversified consumer product–oriented 

manufacturers. The top 25 companies are headquartered 

in 9 countries, with 10 headquartered in the United States. 

The location of a company’s headquarters, however, is 

not necessarily the location of all its R&D activities. Most 

of the companies on this list have manufacturing and research facilities in multiple countries. (For more information, see section “R&D by Multinational Companies.”)

Overall, R&D spending for the top 25 public corporations increased 5.8% in 2006. (The top 25 list was the 

same for 2006 as it was for 2005 except for the addition 

of Cisco Systems, Inc., and the deletion of Nissan Motor Company.) Sales for the group as a whole increased 

6.5%; sales increased 5.2% for the automobile and pharmaceutical manufacturers, 8.9% for the ICT companies 

in the group, and 5.4% for the consumer product manufacturers. R&D expenses increased for the manufacturers 

(pharmaceuticals, 8.5%; automobiles, 0.9%; and consumer products, 11.4%). The ICT companies, representing the sector that has historically had the highest R&D 

intensity, reported a 6.6% increase.

Global R&D Expenses of Public Corporations
4-42 Chapter 4. Research and Development: National Trends and International Linkages

Belgium and Ireland. Denmark, the largest performer of 

pharmaceutical R&D in Europe, is home to Novo Nordisk, a 

world leader in the manufacture and marketing of diabetesrelated drugs, and H. Lundbeck, a research-based company 

specializing in psychiatric and neurological pharmaceuticals. The United Kingdom is the second-largest performer 

of pharmaceutical R&D in Europe and is home to GlaxoSmithKline, which manufacturers medicines and vaccines for 

the World Health Organization’s three priority diseases—

HIV/AIDS, tuberculosis, and malaria. GlaxoSmithKline 

was the third-largest pharmaceuticals company in the world 

in terms of R&D expenditures in 2005 and 2006 (table 4-15). 

In the United States, the pharmaceuticals industry accounted 

for 18% of all business R&D in 2007. U.S.-headquartered 

pharmaceutical companies include Abbott Laboratories, 

Bristol-Myers Squibb, Eli Lilly, Johnson & Johnson, Merck, 

Pfizer, Schering-Plough, and Wyeth.

The computers, office and accounting machines industry represents only a small share of business R&D in most 

countries. Among the OECD countries shown in figure 4-18 

and appendix table 4-31, only Japan reports a double-digit 

concentration of business R&D in this industry, 13% (2006). 

Japan is the home of Fujitsu, Hitachi, and NEC. In the United States, the computers, office and accounting machines 

industry accounted for 3% of all business R&D in 2007. The 

United States is home to Apple, Dell, Hewlett-Packard, Sun 

Microsystems and other companies in this industry.

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 9 of the 19 OECD countries shown in figure 4-18 

and less than 10% in only 4 of the countries. In the United 

States, service industries accounted for 30% of all business 

R&D in 2007.

Other Countries

Internationally comparable data for seven non-OECD 

countries have recently been made available in OECD’s 

Analytical Business Enterprise R&D (ANBERD) database 

(OECD 2009c). Percentage shares of total business R&D 

by industry for Chile, China, Israel, the Russian Federation, 

Singapore, South Africa, and Taiwan are detailed in appendix table 4-31.

Among these countries, the new data show that the communication, television, and radio equipment industry accounts for more than 40% of all business R&D in Singapore 

and Taiwan and more than 15% in Israel and China. Motor vehicle and pharmaceutical R&D account for smaller 

percentages of business R&D than in most of the OECD 

countries. Motor vehicle R&D accounts for 5% or more 

of business R&D in South Africa and China, and the two 

countries with the highest percentages of pharmaceutical 

R&D are Singapore (8%) and China (4%). R&D in the computer, office and accounting machines industry accounts for 

15% of the business R&D performed in Taiwan, the highest 

percentage among the seven nations.

Among the OECD countries shown in figure 4-18, the 

service sector accounts for as little as 7% of business R&D 

in South Korea to as much as 41% in Australia. The newly 

available data show a similar range among the seven nations. 

The percentage of business R&D accounted for by the service sector ranges from 7% in China to more than 60% in 

Israel and the Russian Federation.

Academic Sector

The academic sector’s share of R&D is largest in Canada, 

where it accounted for 36% of national R&D performance in 

2007 (table 4-13). It is lowest in the Russian Federation at 

6%. The academic share in the United States and Japan is in 

the middle at 13%, whereas China is 9%.

Source of Funds

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.”) 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, and almost 7% in 2000 but then fell back to around 

6% in 2006.

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 under 

6% since 2000. Some commentators note with concern this 

recent trend of decline, in light of the significant role that 

academic basic research plays in providing a foundation for 

technological innovation 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 4-19). The highest figures for business support 

of academic R&D are currently in China (35%) and Russia (31%). The figures are also high in Germany (14%) and 

South Korea (14%), whereas Japan, France, and Italy occupy the low end, with figures in the 1% to 3% range.

S&E Fields

Many countries that support a substantial level of academic R&D devote proportionately more of their R&D 

spending to engineering and social science than does the 

United States (table 4-16). The thrust of U.S. academic R&D 

support is more directed at the natural and medical sciences. 

(For a more detailed discussion of S&E field patterns of academic research in the United States and other countries, see 

chapter 5, “Outputs of S&E Research.”)

Government R&D Priorities

Public R&D budget directed toward specific socioeconomic objectives gives insight into government priorities. 

Statistics compiled by the OECD on annual government 
Science and Engineering Indicators 2010 4-43

budget appropriations or outlays for R&D (GBAORD) for 

its members and selected other countries provide a basis for 

such a comparison (table 4-17).

Defense is an objective for government funding of R&D 

for all the top R&D-performing countries, but the share 

varies widely. Defense accounted for 58% of U.S. federal 

R&D support in 2007 but was markedly lower elsewhere: 

a smaller but still significant 29% in France and 28% in the 

United Kingdom, 17% in South Korea, and below 10% 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. It dropped to 52% in 2000 but has risen 

again in the wake of events stemming from September 11, 

2001. 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 

55% of nondefense federal R&D budget support in the United 

States and 26% in the United Kingdom. For both countries, 

the share has expanded dramatically over the prevailing levels several decades ago. The health and environment share is 

currently 17% in South Korea, 13% 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. 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 

Government Funding Mechanisms 

for Academic Research

U.S. universities generally do not maintain data 

on departmental research (i.e., research which 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, usually to address 

the objectives of the federal agencies that provide the 

R&D funds. However, some state government funding 

probably does support departmental research 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 the United States, the federal government is 

much more directly involved in choosing which academic research projects are supported than are national governments in Europe and elsewhere—although 

this is not necessarily the case with state governments. 

In several European G-7 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. These data 

reflect not only the relative international funding 

priorities but also the funding mechanisms and philosophies regarded as the best methods for financing 

academic research.

	
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4-44 Chapter 4. Research and Development: National Trends and International Linkages

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 10% in 2007.34 In the United 

Kingdom, it was 39% in 1981, declining to 7% in 2006. Despite a decline, support for this objective remains substantial 

in some countries: 22% in Germany and 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 accounts for 18% of nondefense 

federal R&D funding in the United States. The share has 

been 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 below 10% for the rest of the OECD countries.

The other purposes objective includes the general advance of knowledge (university research and nonoriented 

government research), education, and other activities directed toward cultural and socioeconomic system purposes. This objective accounts for 17% of nondefense federal 

R&D funding in the United States (table 4-17). The share 

is substantially greater elsewhere: 64% in Germany and the 

United Kingdom, 55% in Japan, and 52% in France. For all 

OECD countries, university research and nonoriented government research constitute the vast majority of the funding 

under this objective.

R&D by Multinational Companies

Foreign direct investment (FDI) refers to the ownership 

of productive assets outside the home country by MNCs. 

(See sidebar “Foreign Direct Investment in R&D.”) FDI and 

international trade are key channels for international knowledge and technology diffusion, which in turn contribute to 

productivity growth (Keller 2004; OECD 2008a). Globalization of R&D through FDI activities by MNCs reflects a 

decentralized model of innovation that takes advantage of 

location-specific skills while seeking to retain the benefits 

Table 4-16

Share of academic R&D expenditures, by country and S&E field: Most recent year 

(Percent distribution)

Field

U.S. 

(2007)

Japan 

(2006)

Germany 

(2002)

Russia 

(2007)

Canada 

(2005)

Taiwan 

(2006)

Spain 

(2006)

Australia 

(2006)

Sweden 

(2005) 

Academic R&D expenditure (PPP $billions) ... 50.24 17.62 9.64 1.59 8.16 2.03 4.31 3.83 2.34

Academic R&D ............................................... 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

NS&E ........................................................... 91.2 67.4 78.8 81.4 80.3 86.3 63.1 74.0 78.9

Natural sciences ...................................... 37.6 11.6 29.2 29.4 NA 17.9 23.1 29.9 19.2

Engineering ............................................. 15.0 24.5 20.3 46.8 NA 40.9 23.4 11.7 22.8

Medical sciences ..................................... 32.9 26.8 25.2 3.0 NA 19.9 14.1 26.9 31.8

Agricultural sciences ............................... 5.8 4.6 4.1 2.2 NA 7.6 2.5 5.4 5.1

Social sciences and humanities ................. 6.7 32.6 20.7 18.6 19.7 13.7 36.9 26.0 19.5

Social sciences ....................................... 6.5 NA 8.4 13.5 NA 10.8 22.3 20.5 13.1

Humanities .............................................. 0.1 NA 12.4 5.1 NA 2.9 14.6 5.5 6.3

Academic R&D nec ..................................... 2.1 NA 0.4 NA NA NA NA NA 1.6

Academic NS&E 

NS&E ........................................................... 100.0 100.0 100.0 100.0 NA 100.0 100.0 100.0 100.0

Natural sciences ...................................... 41.2 17.2 37.0 36.2 NA 20.8 36.6 40.5 24.3

Engineering ............................................. 16.4 36.3 25.8 57.5 NA 47.4 37.2 15.8 28.9

Medical sciences ..................................... 36.0 39.7 32.0 3.6 NA 23.0 22.3 36.4 40.3

Agricultural sciences ............................... 6.3 6.8 5.2 2.7 NA 8.9 3.9 7.3 6.5

NA = not available

nec = not elsewhere classified; NS&E = natural sciences and engineering; PPP = purchasing power parity

NOTES: Data for following top 10 R&D-performing countries not available: China, France, South Korea, United Kingdom, and Italy. Additional countries 

included in top 15 of R&D-performance. Detail may not add to total because of rounding.

SOURCES: National Science Foundation, Division of Science Resources Statistics, Academic Research and Development Expenditures: FY 2007 (2009); 

and Organisation for Economic Co-operation and Development (OECD), OECD.Stat database, accessed 6 March 2009.

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 4-45

Table 4-17

Government R&D support by major socioeconomic objectives, for selected countries: 1981–most recent year

(Percent)

GBAORD 

budget shares Nondefense R&D budget shares

Country/economy and year

GBAORD (current 

US$millions, PPP) Defense Nondefense

Health and 

environment

Economic 

development 

programs

Civil 

space

Other 

 purposes

OECD

 1981.............................................. 68,423.0 37.7 62.3 17.8 37.7 10.2 34.3

 1990.............................................. 135,732.9 39.3 60.7 18.8 28.9 11.8 40.5

 2000.............................................. 196,850.7 28.2 71.8 22.3 23.0 10.0 44.7

 Most recent (2006) ....................... 287,197.0 33.2 66.8 25.3 21.6 9.3 43.8

United States

 1981.............................................. 33,735.0 56.4 45.4 31.2 36.1 20.3 12.4

 1990.............................................. 63,781.0 62.6 37.4 40.2 22.2 24.2 13.4

 2000.............................................. 83,612.5 51.6 48.4 49.9 13.4 20.9 15.8

 Most recent (2007) ....................... 141,890.3 57.8 42.2 54.7 10.3 18.4 16.6

European Union-27

 1981.............................................. na na na na na na na

 1990.............................................. na na na na na na na

 2000.............................................. 75,267.4 13.1 86.9 11.6 22.7 6.1 59.6

 Most recent (2006) ....................... 96,995.8 13.3 86.7 13.7 22.3 5.4 58.6

Germany

 1981.............................................. 8,572.5 8.9 91.1 9.6 34.9 4.6 50.9

 1990.............................................. 13,269.1 13.5 86.5 10.8 25.9 6.8 56.5

 2000.............................................. 16,787.5 7.8 92.2 9.4 21.6 4.9 64.1

 Most recent (2007) ....................... 20,837.7 6.1 93.9 9.7 21.6 5.0 63.7

France

 1981.............................................. 7,211.8 38.4 61.6 13.3 37.9 6.7 42.1

 1990.............................................. 13,738.9 40.1 60.0 9.3 32.8 13 44.9

 2000.............................................. 14,721.6 21.4 78.6 9.7 17.7 14.2 58.4

 Most recent (2007) ....................... 15,538.5 28.8 71.2 13.2 21.9 12.6 52.3

United Kingdom

 1981.............................................. 6,791.4 46.3 53.7 13.1 38.5 3.8 44.6

 1990.............................................. 8,154.7 43.5 56.5 18.1 31.9 5.5 44.5

 2000.............................................. 10,346.0 36.2 63.8 28.3 12.1 3.7 55.9

 Most recent (2006) ....................... 14,768.8 28.3 71.7 25.8 7.1 3.0 64.1

Japan

 1981.............................................. NA NA NA NA NA NA NA

 1990.............................................. 10,255.4 5.4 94.6 4.5 33.9 6.9 54.7

 2000.............................................. 21,196.9 4.1 95.9 6.6 33.4 5.8 54.2

 Most recent (2007) ....................... 29,184.8 4.5 95.5 7.1 30.6 7.3 55.0

South Korea

 1981.............................................. NA NA NA NA NA NA NA

 1990.............................................. NA NA NA NA NA NA NA

 2000.............................................. 5,007.2 20.5 79.5 14.8 53.4 3.1 28.7

 Most recent (2007) ....................... 10,831.9 16.6 83.4 17.1 52.4 4.6 25.9

na = not applicable; NA = not available 

GBAORD = government budget appropriations or outlays for R&D; OECD = Organisation for Economic Co-operation and Development; 

PPP = purchasing power parity

NOTES: Nondefense R&D classified as other purposes consists primarily of general advancement of knowledge (university funds and nonoriented 

research programs). GBAORD figures not currently available for China and incomplete for Russian Federation. See appendix table 4-30.

SOURCE: OECD, Main Science and Technology Indicators (2008/2).

Science and Engineering Indicators 2010
4-46 Chapter 4. Research and Development: National Trends and International Linkages

of common ownership and control.35 Overseas locations 

may also facilitate networking opportunities with foreign 

companies, research centers, and universities. Thus, R&D 

by MNCs complements activities with external parties, such 

as R&D contracting, technology alliances, and international 

exchanges of R&D services, discussed later in this chapter. 

As a whole, these activities reflect a collaborative and global 

framework for creating and exploiting new knowledge by 

leveraging internal and external capabilities (OECD 2008a).

As described below, according to Bureau of Economic 

Analysis (BEA) data, the majority of R&D by U.S. MNCs 

continues to be performed in the United States. Western 

Europe has attracted the majority of U.S.-owned overseas 

R&D, followed by the Asia-Pacific region.36 Likewise, 

foreign-owned companies continue to invest in R&D in the 

United States.

U.S. Affiliates of Foreign Companies

R&D performed by majority-owned affiliates of foreign 

companies located in the United States (U.S. affiliates) 

reached $34.3 billion in 2006, compared with $31.1 billion in 2005 (appendix table 4-32).37 R&D expenditures by 

these companies grew at an annual average rate of 11.3% 

(8.6% after adjusting for inflation) from 1987 to 2006, more 

than double the 5.3% (2.8%) rate for total business R&D 

performed in the United States. This faster growth rate increased their share in total business R&D from the single 

digits in the early 1990s to 14.8% in 2003; their share has 

hovered near 14% since then. Details on the R&D character of work for MNCs are under development. (See sidebar 

“Linking MNC Data From International Investment and Industrial R&D Surveys.”)

Since the late 1980s, European subsidiaries have performed about three-fourths of all U.S. affiliates’ R&D 

($25.8 billion in 2006) (figure 4-20). The share of Japaneseowned companies grew from the single digits in the late 

1980s to between 10% and 12% since 1996. European and 

Japanese subsidiaries combined have accounted for more 

than 85% of these expenditures since 2001.

In 2006, manufacturing accounted for about three-quarters of U.S. affiliates’ R&D, including 37% in chemicals 

(of which pharmaceuticals was 90%), 12% in transportation equipment, and 9% in computer and electronic products 

(table 4-18; appendix table 4-33). These three industries also 

topped overall U.S. business R&D.

Foreign Direct Investment in R&D

Direct investment is defined as ownership or control 

of 10% or more of the voting securities of a business 

(affiliate) in another country. As with other overseas 

activity, the geographic distribution of affiliates’ 

R&D varies by investing country and industry (OECD 

2007). FDI in R&D is driven by factors ranging from 

costs and long-term market and technological opportunities to infrastructure and policy considerations, 

such as availability of appropriately trained human 

resources and intellectual property protection (Niosi 

1999; Thursby and Thursby 2006; von Zedtwitz and 

Gassmann 2002).

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, can 

be obtained from BEA’s Survey of Foreign Direct 

Investment in the United States (FDIUS) and BEA’s 

Survey of U.S. Direct Investment Abroad (USDIA). 

BEA data used in this section cover nonbank companies.* Affiliate data 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. Available 

data do not, however, allow for distinguishing among 

these alternative investments.†

*Nonbank data exclude activities by companies classified in depository credit intermediation, which comprises commercial banks, 

savings institutions, credit unions, bank holding companies, and financial holding companies. †

For detailed methodology, see http://www.bea.gov/international/

usdia2004f.html (USDIA) and http://www.bea.gov/scb/pdf/internat/

fdinvest/meth/FDIUS2002Benchmark.pdf (FDIUS).

Linking MNC Data 

From International Investment 

and Industrial R&D Surveys

An ongoing data development project aims to integrate the statistical information from BEA’s international investment surveys with the NSF/Census Survey 

of Industrial Research and Development. Combining 

technological and investment data from these complementary sources will facilitate a better assessment of 

globalization trends in R&D and technological innovation. The initial methodological study demonstrated 

the feasibility and utility of such a linkage.

A combined preliminary data set provided information for the first time on R&D expenditures by 

U.S. and foreign MNCs by character of work (basic 

research, applied research, and development). The 

study has also produced tangible benefits for the participating agencies, including improvements in survey sampling and the quality of reported data. These 

promising initial results have prompted the three participating agencies to continue work in this area. For 

more information, see NSF/SRS (2007b) and Census 

Bureau et al. (2005).
Science and Engineering Indicators 2010 4-47

Science and Engineering Indicators 2010

NOTES: Preliminary estimates. 

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

Abroad (annual series). See appendix tables 4-32 and 4-34.

Figure 4-20

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

foreign affiliates of U.S. multinational corporations, by host region: 2006

$1.3 $0.04 

$0.9

$2.5 $1.6 

$4.6

$5.6

$25.8

$0.2

$18.6

(Billions of current U.S. dollars)

$0.8

 $0.07 

Table 4-18

R&D performed by majority-owned affiliates of foreign companies in United States, by selected NAICS industry 

of affiliate and country/region: 2006

(Millions of current U.S. dollars)

Manufacturing Nonmanufacturing

Country/region

All 

industries Total Chemicals Machinery

Computer 

and 

electronic 

products

Electrical 

equipment

Transportation 

equipment Information

Professional, 

technical, 

scientific 

services

All countries .................... 34,257 25,035 12,750 789 3,072 1,329 4,198 967 1,879

Canada ....................... 1,586 295 D 11 D D D D 83 e

Europe ........................ 25,803 22,121 12,168 637 2,568 1,226 3,697 592 729

France ..................... 3,335 2,978 D D 575 D 180 e 165 D

Germany .................. 6,742 5,880 1,761 99 121 D 2,812 D D

Netherlands ............. 1,562 D D D D 2 D 0 4

Switzerland .............. 5,039 4,483 4,248 44 D D 5 2 D

United Kingdom ...... 6,801 6,357 3,836 45 e 1,682 28 491 D 110 e

Asia/Pacific ................. 4,589 1,475 409 D 380 39 e D D 986

Japan ...................... 3,995 1,258 396 58 295 38 e 262 18 e 819

Latin America/OWH .... D 920 2 e D D D 4 e 3 D

Middle East ................. D 161 D 1 D 0 9 D 0

Africa ........................... 35 D D 0 0 0 0 D 0

D = suppressed to avoid disclosure of confidential information; e = >50% of value for data cell estimated to account for data not reported by respondents

NAICS = North American Industry Classification System; OWH = other Western Hemisphere

NOTES: Preliminary 2006 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 6 May 2009.

Science and Engineering Indicators 2010
4-48 Chapter 4. Research and Development: National Trends and International Linkages

Statistics from 2006 indicate that affiliates from different 

countries emphasized R&D in different industries. Germanowned affiliates located in the United States spent more than 

40% of their $6.7 billion R&D in transportation equipment 

(table 4-18). British-owned companies accounted for more 

than half of affiliates’ R&D in computers and electronic 

products. Japanese-owned firms accounted for 44% of affiliates’ R&D expenditures in professional, scientific, and 

technical services. Swiss-owned firms performed a third of 

affiliates’ R&D in pharmaceuticals.

U.S. MNCs and Their Overseas R&D

U.S. MNCs (parent companies and their foreign affiliates) performed $216.3 billion in R&D worldwide in 2006 

(table 4-19). Parents of U.S. MNCs performed $187.8 billion in R&D, compared with $177.6 billion in 2005, a 2.5% 

increase on an inflation-adjusted basis.38 The 2006 R&D 

by MNC parents represented 87% of global R&D by U.S. 

MNCs and about 76% of U.S. business R&D. Both shares 

have changed little since 2004.39

Overseas R&D performed by majority-owned foreign affiliates (henceforth, foreign affiliates) reached $28.5 billion 

in 2006, compared with $27.7 billion in 2005 (essentially 

unchanged on an inflation-adjusted basis). However, since 

1999 foreign affiliates’ R&D expenditures increased at a 

4.0% annual average rate after adjusting for inflation, and 

have increased at a 5.0% annual average rate since 1994.

In 2006, affiliates located in Europe accounted for 65% 

($18.6 billion of $28.5 billion) of overseas affiliates’ R&D, 

of which the United Kingdom and Germany combined 

represented more than half ($10.3 billion) (appendix table 

4-34).40 Europe’s share, however, is down from 73% in 1994 

(figure 4-21).

Indeed, the geographic distribution of R&D by overseas 

affiliates of U.S. MNCs is gradually reflecting the role of 

emerging markets in global R&D (figure 4-21).41 In particular, major developed economies or regions (Canada, Europe, 

and Japan) account for a decreasing share of the overseas 

R&D investments of U.S. MNCs, declining from 90% in 

1994 to 80% in 2006. Over the same period, the share of the 

region termed Asia except Japan more than doubled, from 

Table 4-19

R&D performed by U.S. multinational companies: 

2004–06

R&D performed 

(current US$millions) Shares of MNC (%)

Year

U.S. 

parents MOFAs

Total 

MNCs

U.S. 

parents MOFAs

2004..... 164,189 25,840 190,029 86.4 13.6

2005..... 177,598 27,653 205,251 86.5 13.5

2006..... 187,813 28,484 216,297 86.8 13.2

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). See appendix tables 

4-34 and 4-36.

Science and Engineering Indicators 2010

	



	 	
	
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Science and Engineering Indicators 2010 4-49

5.4% to 13.5%, driven by the R&D spending of U.S.-owned 

affiliates in China, Singapore, and South Korea.

On an individual country basis, changes proved more 

modest in terms of global shares, although funding levels 

in some lower-cost locations may still be significant from 

the perspective of purchasing power. R&D performed by 

U.S.-owned affiliates in China and India increased from less 

than $10 million in each country in 1994 to $804 million 

in China and $310 million in India in 2006, but these levels represented only about 3% and 1%, respectively, of total 

overseas R&D by U.S. MNCs. In the Middle East, Israel 

accounted for virtually all R&D by affiliates of U.S. MNCs, 

with about 3% of the global share. Brazil represented twothirds of Latin America’s U.S.-owned affiliates’ R&D and a 

2% global share.

In 2006, manufacturing affiliates accounted for 83% of 

overseas affiliates’ R&D, including 68% by three manufacturing industries: transportation equipment (29%), chemicals (including pharmaceuticals) (22%), and computer and 

electronic products (17%) (table 4-20). More than half of 

R&D by U.S.-owned affiliates in Europe was performed 

by affiliates classified in transportation equipment (35%) 

and chemicals (21%). Affiliates classified in transportation equipment also performed half of U.S.-owned R&D in 

Table 4-20

R&D performed abroad by majority-owned foreign affiliates of U.S. parent companies, by selected NAICS 

industry of affiliate and country/region: 2006

(Millions of current U.S. dollars)

Manufacturing Nonmanufacturing

Country/region

All 

industries Total Chemicals Machinery

Computer 

and 

electronic 

products

Electrical 

equipment, 

appliances, 

and componentsTransportation 

equipment Information

Professional, 

technical, 

scientific 

services

All countries .................... 28,484 23,638 6,166 1,128 4,874 651 8,342 1,014 2,688

Canada ........................... 2,503 1,766 759 37 260 14 587 271 415

Europe ............................ 18,628 15,635 3,882 830 1,976 503 6,460 374 1,790

Belgium ....................... 948 699 D 15 D D 26 3 226

France ......................... 1,447 1,287 313 110 206 28 392 29 78

Germany ..................... 4,919 4,754 253 279 609 245 2,888 22 100

Ireland ......................... 848 538 234 0 225 4 5 204 D

Italy ............................. 689 587 274 84 21 42 86 * 84

Netherlands ................ 486 426 184 26 35 D D 8 38

Sweden ....................... 1,536 1,512 72 8 68 20 D 1 19

Switzerland ................. 933 501 254 52 61 4 6 D D

United Kingdom .......... 5,378 4,296 1,412 200 632 71 1,582 77 862

Asia and Pacific .............. 5,575 4,680 1,233 D 2,105 129 849 D D

Australia ...................... 596 536 162 D D 1 D 1 28

China ........................... 804 675 30 15 541 35 30 D D

Hong Kong .................. 105 47 D 0 16 D 0 4 50

India ............................ 310 106 8 13 D * 20 D 108

Japan .......................... 1,739 1,560 893 10 397 D 92 111 16

Korea .......................... 729 704 34 15 201 D D D 1

Malaysia ...................... 249 248 3 * 241 1 0 0 *

Singapore .................... 850 634 D * 564 2 D D D

Latin America/OWH ........ 865 811 242 50 27 6 419 * 20

Brazil ........................... 571 539 136 48 18 4 307 0 11

Middle East ..................... 847 693 29 D 506 0 0 D D 

Israel ........................... 846 693 29 D 506 0 0 D D 

Africa .............................. 65 53 21 1 0 * 26 2 *

South Africa ................ 52 42 19 1 0 * D 2 *

D = suppressed to avoid disclosure of confidential information; * = $500,000

NAICS = North American Industry Classification System; OWH = other Western Hemisphere

NOTES: Preliminary 2006 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 exclue 

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 6 May 2009.

Science and Engineering Indicators 2010
4-50 Chapter 4. Research and Development: National Trends and International Linkages

Brazil. Affiliates classified in chemicals performed half of 

R&D by U.S.-owned companies in Japan.

Reflecting the increasing global linkages in information 

technology production and development, affiliates classified in computer and electronic product manufacturing performed the majority of U.S.-owned R&D in some emerging 

markets: Malaysia (97%), China (67%), Singapore (66%), 

and Israel (60%) (table 4-20). In terms of service industries, 

affiliates classified in the information industry (which includes software and Internet publishers and telecommunications) performed about one-fourth of U.S.-owned R&D in 

Ireland. Finally, 35% of R&D by U.S.-owned affiliates in 

India was performed by those classified in professional, scientific, and technical services (which includes computer and 

scientific R&D services).42 Nevertheless, European-located 

affiliates performed two-thirds of the $2.7 billion in overseas, U.S.-owned R&D in this industry.

Technology and Innovation Linkages

Increasingly, R&D and innovation are pursued in a collaborative and interactive environment, often embedded 

in global supply, production, and distribution networks 

(Dahlander and Gann 2007; Howells 2008; OECD 2008a). 

This section presents indicators on two types of innovation 

linkages: (1) business-to-business interactions in the form 

of contracted-out R&D, international transactions in R&D 

services, and global technology alliances, and (2) publicprivate collaborations. Overall, these indicators illustrate a 

variety of intra- and cross-organizational arrangements intended to absorb, manage, and exploit external and/or jointly 

developed knowledge (Chesbrough, Birkinshaw, and Teubal 

2006; Ozman 2009). For ongoing development activities related to innovation indicators, see the sidebar “Recent Developments in Innovation-Related Metrics.”

Interest in R&D and innovation-related metrics by 

governments, academic researchers, and businesses has 

a long history (Earl and Gault 2006) but has intensified 

in recent years in the United States (DOC 2008; Mandel 

2008; McKinsey & Company 2008; Moris, Jankowski, 

and Perolle 2008; NRC 2005a; NSF/TCB 2008) and 

elsewhere (Gault and von Hippel 2009; OECD 2008c). 

Recent developments in innovation-related metrics are 

driven by a number of factors, including:

The rapidly evolving nature of innovation in terms of 

joint production and exploitation of knowledge, userbased innovation, new business models, entrepreneurship, and global linkages

Advances in social, behavioral, economic, and management theories of creativity, productivity, and innovation

Developments in national standards on statistical quality and protection of confidentiality

Emerging international accounting standards and official statistics guidance on intangibles and other nonfinancial assets

Advances in data development, integration, and empirical research methodology involving microdata 

sets, administrative data, data enclaves, and new computing and visualization tools

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 the marketplace (OECD/

Eurostat 2005, p 146). R&D is only one of many possible knowledge inputs driving innovation. For example, 

innovation may result from the integration of existing 

technology or from a new business model. Enhanced international guidance and ongoing methodological studies 

to better capture statistics on nontechnological innovation, innovation linkages, and service-sector activities are 

driving development of metrics across OECD countries.

Part of the challenge in developing new metrics resides 

in the broad scope of innovation activities covering inputs, processes, cross-sector linkages, immediate outputs 

(for example, products or patents), long-term socioeconomic impacts, and infrastructure or system-wide variables (such as policy incentives or technology standards). 

Accordingly, data development spans multiple strategies, 

including surveys and data linking and integration, as 

well as non-survey-based methods, such as case studies, 

administrative databases, and Web-based information—

pursued in parallel or in combination (NSF/SRS 2007a; 

NSF/TCB 2008). The following describes selected activities in the development of these indicators.

Intangible Investment and GDP

Treating spending on intangibles, such as software and 

R&D, as investment in the national income and product 

accounts (NIPA) (which include GDP and other U.S. 

economic accounts) recognizes intangible capital, along 

with other forms of investment inputs, in the production 

of economic output (Corrado, Hulten, and Sichel 2006; 

UNSC 2007). International statistical manuals are being 

updated or developed to provide guidance for comparable 

measures in this area, including an updated manual for the 

United Nations System of National Accounts (SNA) and 

a new OECD handbook covering intangibles and national 

accounts (Aspden 2008). Software has been considered an 

Recent Developments in Innovation-Related Metrics

(continued on next page)
Science and Engineering Indicators 2010 4-51

Business-to-Business Linkages

Technology and innovation linkages vary by type of partner or knowledge source and level of interaction (OECD/Eurostat 2005). Knowledge sources range from academic papers, 

conference proceedings, and reports from government laboratories to information from commercial sources, such as marketing and management consultants, patent licensors, R&D 

contractors, and technology vendors. This section examines 

indicators related to business transactions and organizational 

arrangements to acquire or jointly develop new knowledge.

Contract R&D Expenses Within the United States

Increasingly, companies that perform R&D in the United 

States contract out these activities. These companies reported 

an estimated $19 billion in R&D performed by external organizations located in the United States43 in 2007, compared 

with $12.4 billion in 2006 (appendix tables 4-39 and 4-40).44

The all-industries ratio of contracted-out R&D to companyfunded, company-performed R&D increased from 5.5% in 

2006 to 7.8% in 2007.45 For manufacturers, the ratio reached 

8.5% in 2007, up from 5.7% in 2006 (figure 4-22).

Across R&D-intensive industries, pharmaceuticals had the 

highest ratio of contracted-out R&D (21%) in 2007. The ratio for 

automotive manufacturers was 7.3%, and for navigational, measuring, electromedical, and control instruments (a subsector of 

the computer and electronic products industry) was 3.8%. Within services, the contracted-out R&D ratio was 13.8% for scientific R&D services and 8.3% for telecommunications in 2007.

Exports and Imports of R&D Services

Across OECD countries, international trade in services, 

especially those involving intangibles and knowledge-based 

assets, presents unique measurement challenges for both business accounting and official statistics (OECD 2008b; Reinsdorf and Slaughter 2009; Yorgason 2007). An indicator in 

investment in U.S. NIPA since 1999, and BEA and NSF 

continue to work on an R&D satellite account, as described 

earlier in this chapter. BEA plans to incorporate both R&D 

and spending on artistic and literary originals as intangible 

investment in the core economic accounts in 2013 and is 

considering an expanded satellite account that would contain experimental statistics for other intangibles (Aizcorbe, 

Moylan, and Robbins 2009).

Science of Science and Innovation Policy 

Program, Research Data Infrastructure, and 

Advanced Computing Tools

The NSF Science of Science and Innovation Policy 

(SciSIP) program supports research designed to advance 

the scientific basis of science and innovation policy. Research funded by the program is aimed at developing, 

improving, and expanding models, analytical tools, data, 

and metrics. An area of interest is the development of 

data infrastructure to support empirical research on innovations within organizations (NSF/TCB 2008). Other 

efforts focus on cyber-infrastructure research; advanced 

computing and Web-based tools to protect, archive, link, 

mine, and analyze data (Lane, Heus, and Mulcahy 2008); 

and advanced visualization and analytical tools for document-based information, such as patents and bibliographic entries (Börner, Chen, and Boyack 2003).

Entrepreneurship and Business Dynamics

Two National Research Council (NRC) reports cite 

the need to leverage business data collected by statistical agencies for research and policymaking purposes 

by more effectively integrating data sets (NRC 2006, 

2007b). Data sets for the study of business dynamics include the Census Bureau’s Business Dynamics Statistics 

(BDS) (Census Bureau 2009) and Longitudinal Employer-Household Dynamics (LEHD) program (Abowd, 

Haltiwanger, and Lane 2004), along with the Business 

Employment Dynamics published by the Bureau of Labor 

Statistics (BLS 2009). Research topics of interest include 

technology adoption, innovation, outsourcing, globalization, market entry and exit by companies, and new or 

small technology-based firms. Indeed, entrepreneurship 

has been extensively researched as a vehicle for transferring and exploiting new knowledge from public or private sources (Audretsch 2009). In the United States, the 

Kauffman Foundation funds research in entrepreneurship 

and innovation (Kauffman 2008) and sponsors a social 

longitudinal survey on young firms (Kauffman 2009).

OECD Innovation Microdata Project and EU 

Community Innovation Surveys

The OECD innovation microdata project aims at exploiting microdata from the EU Community Innovation 

Surveys (CIS) for economic analysis. In recent years, 

research teams from different OECD countries have collaborated in applying similar methodologies to their national CIS. Expected products include analytical studies 

and new innovation indicators covering, for example, international technology transfer, nontechnological innovation, and intellectual property rights (OECD 2009a).

The project is part of a larger OECD Innovation Strategy initiative established in 2007; the objective is to 

explore strategies to harness the potential of innovation 

based on a better understanding of innovation. Research 

is focusing on markets and governance, human capital, 

global dimensions, and the changing nature of innovation, along with measurement, reporting, and assessment 

of innovation (OECD 2009b)
4-52 Chapter 4. Research and Development: National Trends and International Linkages

this area is international trade in research, development, and 

testing (RDT) services, including transactions among unaffiliated or independent companies (unaffiliated trade) and trade 

within MNCs (affiliated trade). These data are part of balance-of-payment statistics and complement other fee-based 

transactions (such as royalties and licensing), as well as performance and funding information from R&D surveys (Moris 

2009). U.S. data for total RDT trade have been available since 

2001 from BEA’s international transaction surveys.46

In 2007, total U.S. exports (affiliated and unaffiliated) of 

RDT services reached a record $14.7 billion, compared with 

record imports of $11.4 billion, resulting in a trade surplus of 

$3.3 billion. Affiliated trade dominates these U.S. RDT statistics (table 4-21)—which is not surprising, given the large role 

of MNCs (including U.S. parents and foreign-owned companies) in R&D performance. (See “R&D by Multinational 

Companies.”) Affiliated trade in RDT has recorded between 

$4 billion and $4.5 billion in annual trade surpluses since 

2001, compared with diminishing balances for unaffiliated 

trade (table 4-21). With affiliated transactions, U.S. trade surplus in RDT services is driven not by U.S. MNC parents but 

by the relatively high level of exports from U.S. affiliates of 

foreign MNCs to their foreign parents and other foreign affiliates of the parent companies (Moris 2009).

Newly available country detail shows that 62.8% of U.S. 

RDT exports in 2007 were purchased by European businesses and another 12.2% by Japanese businesses (appendix 

table 4-41). European countries accounted for virtually the 

same share of RDT import transactions (62.1%) in 2007, 

whereas Japan accounted for 5.6%. Several emerging markets appear as sources of U.S. RDT imports, namely Israel 

(6.2%) and India (5.3%).

International Technology Alliances

Interfirm R&D alliances, partnerships, and networks add 

an element of R&D co-production compared with R&D contracts or technology licensing.47 R&D alliances may be defined as domestic or international cooperative arrangements 

that combine resources aimed at shared R&D objectives 

(Hagedoorn, Link, and Vonortas 2003).48 U.S. restrictions 

on multifirm cooperative research were loosened by the 

1984 National Cooperative Research Act (Public Law 98-

462), followed by the 1993 National Cooperative Research 

and Production Act (NCPRA) (Public Law 103-42), as a 

way of addressing concerns about the technological leadership and international competitiveness of American firms in 

the early 1980s (Scott 2008).

This section features data from the Cooperative Agreements and Technology Indicators (CATI) database, which 

collects data on worldwide business technology partnerships.49 It is based on public announcements and includes 

business alliances with an R&D or technology component, 

such as joint research or development agreements, R&D 

contracts, and equity joint ventures. The database contains 

counts dating back to 1980.50

Table 4-21 

U.S. trade in research, development, and testing services: 2001–07

(Millions of dollars)

Exports Imports Trade balance

Year Total Affiliated Unaffiliated Total Affiliated Unaffiliated Total Affiliated Unaffiliated

2001.......................... 7,610 6,564 1,046 3,389 2,664 725 4,221 3,900 321

2002.......................... 8,678 7,536 1,142 4,063 3,035 1,028 4,615 4,501 114

2003.......................... 9,467 8,291 1,176 5,071 3,761 1,310 4,396 4,530 –134

2004.......................... 9,563 8,275 1,288 5,778 3,816 1,962 3,785 4,459 –674

2005.......................... 10,431 9,135 1,296 7,239 4,950 2,289 3,192 4,185 –993

2006.......................... 12,821 11,165 1,655 9,429 6,726 2,702 3,392 4,439 –1,047

2007.......................... 14,698 12,686 2,012 11,437 8,364 3,073 3,261 4,322 –1,061

SOURCE: Bureau of Economic Analysis, U.S. International Services, http://www.bea.gov/international/intlserv.htm, accessed 6 May 2009.

Science and Engineering Indicators 2010

	



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According to CATI, in 2006 (the latest available year), 

about 900 new worldwide business technology alliances 

were formed, approximately two-thirds of which involved at 

least one U.S.-owned company regardless of location. Close 

to 60% of the worldwide total focused on biotechnology, 

and 23% focused on information technology (appendix table 

4-42). Other areas include materials research and engineering, 

aerospace, automotive, and chemicals. In terms of ownership, 

the 2006 counts can be grouped into alliances involving only 

U.S.-owned companies (249), U.S. and foreign-owned companies (356), and only foreign-owned companies (293).

Since 1999, the proportion of U.S.-foreign alliances annually has surpassed U.S.-only alliances, driven by rapid 

growth in U.S. alliances with European-owned companies 

(figure 4-23). The U.S.-Europe alliances increased 141% 

from 1990 to 2006, compared with about an 80% increase in 

U.S.-only alliances. The predominance of U.S. and European 

companies in CATI technology agreements is consistent with 

rankings of global R&D by major pharmaceutical, biotechnology, software, and automotive MNCs (UK DIUS 2008). 

At the same time, the number of U.S.-Japan alliances in 2006 

(54) effectively reached parity with U.S. alliances with other 

Asia-Pacific countries (50), (reflecting the rapid growth of 

the latter since 1990, albeit from relatively low levels (figure 

4-23). The 50 U.S. alliances with Asia-Pacific companies, 

excluding Japan, were driven by collaborative agreements 

with companies headquartered in India (15), China (12), and 

South Korea (11). This pattern reflects the increasing if still 

modest role of these countries as hosts for U.S.-owned R&D 

discussed earlier in this chapter. Of course, noting simple 

frequencies of international collaborative agreements is only 

a first step in tracking the economic and policy relevance for 

participating companies and their home and host countries 

(Bozeman and Dietz 2001; Siegel 2003).51

Federal Technology Transfer and Other 

Innovation-Related Programs

This section reviews two sets of indicators on publicprivate collaboration supporting technology transfer and 

innovation (for academic patents and related knowledge 

diffusion indicators, see chapter 5).52 The first set includes 

federal programs for technology transfer from R&D funded 

and performed by government agencies and laboratories. 

The second set includes federal programs that support new 

or small U.S. companies in R&D or technology deployment 

activities 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 

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 a brief overview of these 

	
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4-54 Chapter 4. Research and Development: National Trends and International Linkages

initiatives, see the sidebar “Major Federal Legislation Related to Technology Transfer and Cooperative R&D.”

Federal Technology Transfer

Federal technology transfer refers to processes through 

which the knowledge and capabilities of federal intramural 

laboratories and other research facilities can be directed to 

the R&D needs of outside public or private organizations—

and through which the inventions and other intellectual assets arising from federal laboratory R&D can be conveyed 

to outside parties for development and commercialization 

(FLC 2006). Since the Stevenson-Wydler Act of 1980, all 

federal labs have been required to have technology transfer offices (Office of Research and Technology Applications [ORTA]) to assist in identifying transfer opportunities 

and establishing appropriate arrangements for relationships 

with external parties.53 Indicators on these activities illustrate a diverse range of mechanisms used in federal technology transfer.54 For background information, see the sidebar 

“Federal Technology Transfer: Activities and Metrics.”

Table 4-22 shows total technology transfer activity statistics for FY 2007, as well as statistics for six agencies that 

account for the majority of this activity. In 2007, federal 

laboratories participated in 7,327 cooperative research and 

development agreements (CRADAs) with businesses and 

organizations, compared with 7,271 in 2006 and 5,949 in 

2005 (appendix table 4-43). Federal labs also participated 

in 9,445 non-CRADA collaborative R&D relationships in 

2007. Agencies issued more than 1,400 patents in 2007 

and held 10,347 active licenses, including just below 4,000 

Technology Innovation Act of 1980 (StevensonWydler 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 governmentoperated 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 Stevenson-Wydler 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.

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.

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 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)—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.

Major Federal Legislation Related 

to Technology Transfer and Cooperative R&D
Science and Engineering Indicators 2010 4-55

Federal technology transfer can take a variety of forms 

(FLC 2006), including the following:

Commercial transfer. Movement of knowledge or technology is developed by a federal lab and transferred to 

private organizations in the commercial marketplace.

Scientific dissemination. Publications, conference 

papers, and working papers are distributed, and other 

forms of data dissemination are employed.

Export of resources. Federal lab personnel are made available to outside organizations with R&D needs through 

collaborative agreements or other service mechanisms.

Import of resources. The federal lab brings in outside 

technology or expertise to enhance the lab’s existing 

capabilities.

Dual use. Technologies, products, or families of products with both commercial and federal applications are 

developed.

Most federal labs engage in all of these forms of 

technology transfer to some extent. The emphases and 

relative levels of each vary widely across the federal 

agencies, depending on the parent agency’s mission, the 

lab’s main areas of scientific and technological interest, 

typical clients, prevailing scientific/technical culture, and 

any special transfer authorities the agency may have been 

granted. For some agencies and their labs, the principal 

technology transfer thrust is patents, patent licenses, and 

material transfer agreements. Others emphasize traditional public dissemination of new scientific or technical 

knowledge and cooperative R&D relationships with outside organizations—with patenting and licensing activity 

taking place only when it is determined that private-sector 

investment in a new technology is needed to achieve development and commercialization goals.

Several metrics illustrate activities and agency priorities among three main classes of mechanisms. The invention disclosure and patenting category involves 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 patent awards received. The licensing

category covers federal lab licensing of federal intellectual property, such as patents or copyrights, to outside 

parties to enable further development and commercialization, usually through the technology transfer office. 

For example, in recent years, DOE’s government-owned, 

contractor-operated laboratories have increasingly used 

their special authority to transfer software technology 

through relatively quickly executed copyright license 

mechanisms. The third main category is collaborative relationships for R&D, including CRADAs.

Federal Technology Transfer: Activities and Metrics

Table 4-22

Federal laboratory technology transfer activity indicators, by selected U.S. agency: FY 2007

Technology transfer activity indicator Total DOD HHS DOE NASA USDA DOC

Invention disclosures and patenting

Inventions disclosed .............................. 4,486 838 447 1,575 1,268 126 32

Patent applications filed ........................ 1,824 597 261 693 105 114 7

Patents issued ....................................... 1,406 425 379 441 93 37 4

Licensing

All licenses, total active ......................... 10,347 460 1,418 5,842 1,883 339 217

Invention licenses ............................... 3,935 460 915 1,354 461 339 217

Other intellectual property licenses .... 6,405 0 460 4,488 1,422 0 0

Collaborative relationships for R&D

CRADAs, total active ............................. 7,327 2,971 285 697 1 230 2,778

Traditional CRADAs ............................ 3,117 2,383 206 697 1 184 154

Other collaborative R&D relationships ... 9,445000 2,666 4,084 2,695

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 total: Department of the Interior, Department of Transportation, Department of Veterans Affairs, 

and Environmental Protection Agency. Department of Homeland Security expected to provide technology transfer statistics starting in FY 2008. Invention 

licenses refers to inventions that are/could be patented. Other intellectual property refers to intellectual property protected through mechanisms other 

than a patent, e.g., copyright. Total active CRADAs refers to agreements executed under CRADA authority (15 U.S.C. 3710a). Traditional CRADAs are 

collaborative R&D partnerships between a federal laboratory and one or more nonfederal organizations. Federal agencies have varying authorities for 

other kinds of collaborative R&D relationships. 

SOURCE: National Institute of Standards and Technology, Federal Laboratory Technology Transfer, Fiscal Year 2007, Summary Report to the President 

and the Congress, January 2009, http://patapsco.nist.gov/ts/220/external/index.htm, accessed 6 May 2009. See appendix table 4-43.

Science and Engineering Indicators 2010
4-56 Chapter 4. Research and Development: National Trends and International Linkages

invention licenses, based on their total stock of intellectual 

property. Appendix table 4-43 provides data for all agencies 

for FY 2000–07.

Small Business Innovation–Related Programs

This section reviews federal programs that support new 

or small U.S. companies in R&D or technology deployment activities. These programs include the Small Business 

Innovation Research (SBIR) program, the Small Business 

Technology Transfer (STTR) program, the Technology Innovation Program (TIP), and the Hollings Manufacturing 

Extension Partnership (MEP). The first three programs provide early-stage technology financing, whereas the last one 

provides technical assistance to small and medium-sized 

manufacturers.

The SBIR program was created by the Small Business Innovation Development Act of 1982. According to the SBIR 

statute, federal agencies with extramural R&D obligations 

exceeding $100 million must set aside a fixed percentage of 

such obligations for projects involving small business (those 

with 500 or fewer employees). This set-aside has been 2.5% 

since FY 1997. The program has multiple objectives, namely stimulating technological innovation, fostering the use 

of small business to meet federal R&D needs, encouraging 

participation by minority and disadvantaged persons in technological innovation, and increasing private-sector commercialization of innovation derived from federal R&D. SBIR’s 

sister program, the STTR program, was created in 1992 to 

stimulate cooperative R&D and technology transfer involving small businesses and nonprofit organizations, including 

universities and FFRDCs. Both of these programs are coordinated by the Small Business Administration (SBA). In 

FY 2007, SBIR and STTR combined awarded $2.3 billion 

(SBA 2009).55

In FY 2006, 11 federal agencies awarded a total of 

$1.9 billion to about 5,900 SBIR projects (appendix tables 

4-44 and 4-45). Funded technology areas include computers 

and electronics, information services, materials, energy, and 

life science applications. DOD represented just below 50% 

of total SBIR funds, whereas HHS represented 30%, consistent with its 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 (NRC 2008). 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 federal funding.56 Over the life of the program, 

the share of phase II funding has increased from about twothirds in the mid-1980s to more than three-fourths (figure 

4-24). Bridge funding and other support for startups beyond 

phase II were found to be critical for successful commercialization by a recent NRC study (NRC 2008, p 209). Some 

agencies have implemented “phase IIB” or “phase II+” 

matching funds and/or technical and business support for 

qualified awardees (NRC 2008, pp 209–16).

The STTR program is also structured in three phases and 

involves R&D performed jointly by small businesses and 

nonprofit research organizations. Federal agencies with extramural R&D budgets exceeding $1 billion participate in 

the STTR program. Starting in FY 2004, the required setaside doubled to 0.3%, compared with the 2.5% set-aside for 

SBIR. In FY 2006, DHS participated for the first time, along 

with DOD, NSF, DOE, NASA, and HHS. From FY 1994 to 

2006, STTR awarded $1.3 billion to about 6,000 projects, 

including $226 million to 878 projects in FY 2006 (appendix 

tables 4-44 and 4-46).

Figure 4-24

$/0
	
-		!	+)

Dollars (millions)

SBIR = Small Business Innovation Research Program 

SOURCE: Small Business Administration, Small Business Innovation Research Program Annual Report (various years). See appendix table 4-45. 

	

	

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1



)

)

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+4:

+4::
Science and Engineering Indicators 2010 4-57

According to SBA, small businesses interested in participating in the STTR program must find a research institution 

that meets the program’s definition and develop a working 

agreement before competing for an STTR award. Universities are active as STTR partners. For example, in FY 2004, 

at least 200 universities, many with multiple awardees, partnered with small companies under STTR; 15 FFRDCs also 

collaborated with awardees (SBA 2005).

Established by the America COMPETES Act of 2007 

and administered by NIST,57 TIP 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.”58

The new program replaces the Advanced Technology Program (ATP). From FY 1990 to 2007, ATP awarded funds 

for 824 projects with a combined funding of $4.6 billion, 

about equally split between the program and its participants 

(appendix table 4-47). The first TIP competition focused on 

advanced sensors to support monitoring and assessment of 

civil infrastructure, such as water pipelines, roads, bridges, 

and tunnels (appendix table 4-48).

A national system of affiliated manufacturing extension 

centers, MEP is also housed at NIST. It was established by the 

Omnibus Trade and Competitiveness Act of 1988 to enhance 

the productivity and technological performance of small and 

medium-sized U.S. manufacturers (15 U.S.C. 278(k)).

MEP centers receive federal funding on a competitive basis for their development and operations. Nonfederal funding is required for 50% or more of the centers’ capital and 

annual operating funds. Companies receive technical and 

managerial assistance, generally on a reimbursable basis, 

but receive no direct federal funding (Schacht 2008). Federal funding for MEP reached $106.8 million in FY 2007 

and $91 million in FY 2008 (appendix table 4-49). Activities included technology deployment and technical services 

involving advanced manufacturing systems and engineering 

services, as well as business services such as management 

and strategy development, marketing, and training. For nontechnical services, MEP centers generally partner with commercial and academic consultants and government agencies 

(Shapira 2001, pp 983–84).59

Conclusion

U.S. spending on R&D reached an estimated $397.6 billion in 2008, a 6.7% increase (or 4.5% in inflation-adjusted 

dollars) from the 2007 total. This 2008 figure is preliminary, 

however, and may not fully reflect the effects of the downturn in U.S. and worldwide economic conditions that took 

place in the latter half of the year.

In 2008, businesses performed an estimated $289.1 billion (current dollars), or 73%, of the total U.S. R&D. The academic sector is the second-largest performer of U.S. R&D, 

with estimated expenditures of $51.2 billion, or just below 

13% of the U.S. total. Federal agencies, their laboratories, 

and FFRDCs accounted for an estimated $41.7 billion, or 

about 11% of the total. Nonprofit organizations performed 

the remainder, $15.6 billion, or about 4%.

Business and the federal government are the two largest 

sources of R&D funding. The business sector provided an 

estimated $267.8 billion (current dollars) of funding for R&D 

in 2008, 67% of the U.S. total. The federal government funded an estimated $103.7 billion of R&D, or 26% of the total. 

Over the past 5 years, expanded business spending on R&D 

has accounted for much of the growth (in both current and 

real-dollar terms) in total U.S. R&D spending. Federal funding overall has been flat or declining on a real-dollar basis. At 

the time of this writing, the impact of the current economic 

slowdown in U.S. R&D expenditures remains uncertain.

Historically, the federal government has been the prime 

source of funding for basic research, accounting for an estimated 57% of the nation’s total in 2008. Moreover, in the 

same year, the federal government funded 61% of the basic 

research performed by universities and colleges, the nation’s 

largest performers of basic research.

The budget appropriations for federal spending on R&D 

in FY 2009 totaled $147.1 billion, an increase of $3.3 billion, or 2.4%, over the FY 2008 spending level. The president’s proposed budget for FY 2010 requests federal R&D 

spending of $147.6 billion, an increase of $0.6 billion, or 

0.4%, over the appropriated FY 2009 level. Furthermore, the 

American Recovery and Reinvestment Act, enacted in early 

2009, provided a one-time increase in funding for federal 

R&D and R&D infrastructure, estimated to total $18.3 billion in FY 2009.

Globally, R&D expenditures totaled an estimated 

$1,107 billion in 2007, the most recent year for which internationally comparable data are available. R&D is concentrated regionally in North America (35%), Asia (31%), and 

Europe (28%) and is further concentrated within a relatively 

few countries. According to OECD statistics, the United 

States (with 33% of the world total) and Japan (13%) account for almost half of global R&D. That figure increases 

to two-thirds by adding the next three countries on the list, 

China (9%), Germany (6%), and France (4%). Adding South 

Korea, the United Kingdom, the Russian Federation, Canada, and Italy completes the top 10 countries, accounting for 

about 80% of global R&D.

China, which ranks third globally in R&D spending, continues to exhibit the most dramatic growth pattern. Its real 

R&D growth over the past decade has averaged just over 

19% annually. Both India and Brazil also are among the 

world’s larger and faster-growing R&D performers, according to UNESCO statistics. India performed about $15 billion of R&D in 2004, and Brazil about $13 billion in 2005 

(both figures are the most recent available data). The totals 

reported for both countries are about double the levels of 

R&D performance each reported in the mid-1990s. Comparability of these figures to the OECD statistics is unclear, but 

such levels of R&D expenditures would rank both India and 

Brazil among the world’s top 15 R&D-performing nations.
4-58 Chapter 4. Research and Development: National Trends and International Linkages

Another dimension of the international character of R&D 

performance is the activities by U.S. MNCs overseas. More 

than 85% of annual global R&D expenditures by U.S. MNCs 

are made in the United States ($187.8 billion of $216.3 billion in 2006); however, the geographic distribution of R&D 

expenditures outside the United States by the overseas affiliates of U.S. MNCs ($28.5 billion in 2006) is gradually 

shifting to reflect the role of emerging markets. In particular, major developed economies or regions (Canada, Japan, 

and Europe) account for a decreasing share of the overseas 

R&D investments of U.S. MNCs, declining from 90% in 

1994 to 80% in 2006. Over the same period, the share of 

the Asia region (excluding Japan) more than doubled, from 

5.4% to 13.5%, driven by the R&D spending of U.S.-owned 

affiliates in China, Singapore, and South Korea. Among individual countries, R&D performed by U.S.-owned affiliates in China and India increased from less than $10 million 

in each country in 1994 to $804 million and $310 million, 

respectively, in 2006. The 2006 levels for China and India 

represented about 3% and 1%, respectively, of total overseas 

R&D by U.S. MNCs.

The increasing role of services and international collaboration in R&D and innovation is reflected in statistics 

on trade in R&D services. In 2007, total U.S. exports of research, development, and testing services reached a record 

$14.7 billion, compared with record imports of $11.4 billion, resulting in a trade surplus of $3.3 billion. Trade within 

MNCs dominates these statistics—which is not surprising, 

given the significant role of MNCs in R&D performance.

Rapid changes in the collaborative and global nature of 

R&D and the increasing role of services and nontechnological innovation call for continuous enhancements in the 

indicators of these activities and their impact. U.S. federal 

statistical agencies continue to collaborate domestically and 

internationally to facilitate improved and comparable data. 

Ongoing U.S. data development projects featured in this 

chapter include the new Business R&D and Innovation Survey, the R&D NIPA satellite account, exploratory work on 

intangibles and innovation accounts, and efforts in the area 

of research data infrastructure by NSF’s Science of Science 

and Innovation Policy Program.

Notes

1. As financial input indicators, statistics on expenditures 

in and of themselves do not indicate the extent to which 

R&D efforts are effective or successful.

2. 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 research. See appendix table 4-1 for GDP deflators used in this chapter.

3. FFRDCs are R&D-performing organizations that are 

exclusively or substantially financed by the federal government. They operate 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.

4. The topic of R&D categories is also part of ongoing 

survey redesign and methodological studies in the United 

States and internationally.

5. 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.

6. The latest data available on the distribution of U.S. 

R&D performance by state are for 2007. All U.S. R&D expenditures that year were estimated at $372.5 billion. Of this 

total, $359.7 billion could be attributed to expenditures in the 

50 states and the District of Columbia. The state-attributed 

totals differ from the U.S. total for a number of reasons: some 

industry 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 2007) could not be allocated by state; state-level university R&D data have not been adjusted for double-counting of 

R&D passed from one academic institution to another; state 

agency intramural R&D (collected by NSF starting in 2006 

[see NSF/SRS 2008a]) are not included in the U.S. total; and 

state-level university and federal R&D performance data are 

not converted from fiscal to calendar years.

7. 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 explains the large amount of federal intramural 

R&D reported for the District of Columbia.

8. For most manufacturing industries, the Small Business Administration has established a size threshold of 500 

employees to define small companies. The NSF Survey of 

Industrial Research and Development does not sample companies with fewer than five employees because of concerns 

about respondent burden.

9. These estimates were derived from the NSF-Census 

Bureau’s annual Survey of Industrial Research and Development, which collects financial data related to R&D activities from companies performing R&D in the United States. 

These data provide a basis for analyzing R&D investment of 

the business sector and are the official source for U.S. busi-
Science and Engineering Indicators 2010 4-59

ness R&D estimates. (See sidebar “New U.S. Business R&D 

and Innovation Survey.”)

10. A similar measure of R&D intensity is the ratio of 

R&D to value-added (gross output minus cost of intermediate inputs). Value-added is often used in studies of productivity because it allows analysts to focus on the economic 

output attributable to the specific industrial sector in question by subtracting inputs produced in other sectors.

11. Industry-level analyses are complicated by the fact 

that each company’s R&D is reported in only one industry.

12. In the North American Industry Classification System 

(NAICS), the utility industry comprises establishments engaged in the provision of electric power, natural gas, steam, 

and water, as well as the removal of sewage. Establishments 

that provide telephone and other communication services 

are included in other NAICS industries.

13. Because federal R&D funding is concentrated among 

a few companies in a small number of industries, the potential for disclosing information about a particular company is 

high; therefore, these data are often suppressed. This situation prevents the precise tabulation of total R&D performance and the calculation of R&D-to-net-sales ratios for 

many industries.

14. For a recent study on the role of service industries in 

R&D and innovation, see Gallaher, Link, and Petrusa (2006).

15. Methodological differences between the PhRMA 

Annual Membership Survey and the NSF Survey of Industrial Research and Development make it difficult to directly compare estimates from the two surveys. For example, 

the PhRMA survey definition of R&D includes phase IV 

clinical trials (trials conducted after a drug is licensed and 

available for doctors to prescribe), whereas the NSF survey 

definition does not. In addition, NSF survey sales data may 

contain income from sources not related to the production of 

drugs and medicines.

16. In NSF’s Survey of Industrial Research and Development, companies that predominantly license their technology rather than manufacture finished products are often 

classified in the scientific R&D service industry. Therefore, 

a sizable amount of biotechnology R&D that serves the 

pharmaceutical industry is reported in the R&D service sector. (See “R&D and Related Services.”)

17. Data 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.

18. Suppression of federal R&D funding information prohibits the precise tabulation of total R&D performance for 

some industries. Lower-bound analyst estimates are given 

in cases where the potential disclosure of company-reported 

data or classification issues prevents the publication of total 

estimates from survey data.

19. The introduction of a more refined industry classification scheme in 1999 allowed more detailed reporting in nonmanufacturing industries. For the cited statistics, the R&D 

expenditures of companies in the software, other information, 

and computer system design and related service industries 

were combined. These three industries provided the closest 

approximation to the broader category cited for earlier years 

without exceeding the coverage of the broader category.

20. NAICS-based R&D estimates are available only to 

1997. Estimates for 1997 and 1998 were bridged from a different industry classification scheme. Total R&D for this 

sector has grown from $9.2 billion in 1997 to $16.0 billion 

in 2007.

21. 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. For a related discussion, see “Technology and Innovation Linkages.”

22. Because R&D expenses reported on financial documents differ from the data reported on the NSF Survey of 

Industrial Research and Development, direct comparisons of 

these sources are not possible. For an explanation of the differences between the two, see Shepherd and Payson (1999).

23. Support for private R&D can be studied along several dimensions, including the immediate effect in stimulating R&D relative to costs (e.g., tax expenditures [forgone 

public revenues]) and administrative expenses), longer-term 

impacts (e.g., growth and employment), and relationship to 

other policy tools. See Atkinson (2007) and Tassey (2007) 

for recent discussions on the U.S. tax credit, Wilson (forthcoming) and Wu (2004) for empirical studies on state R&D 

credits, and Bloom, Griffith, and Van Reenen (2002) and 

OECD (2003) for country-level empirical studies on the effectiveness of R&D tax credits.

24. Tax incentives include tax allowances, exemptions, 

or deductions (reductions in taxable income) and tax credits 

(reductions in tax liability). Each of these tax incentives may 

be designed with different features, such as eligibility criteria, allowable expenses, and level versus incremental bases 

(OECD 2003).

25. H.R.1424, Public Law No. 110-343 (Division C, 

Title III, Section 301).

26. Qualified R&D costs include company-funded expenses for wages paid, supplies used in the conduct of qualified research, and certain contract expenses. For tax purposes, 

R&D expenses are restricted to the somewhat narrower concept of R&E expenditures. Qualified expenses must satisfy 

tests involving the experimental and technological nature of 

activities (26 CFR 1.41-2). Research in the social sciences 

or humanities is excluded. See NSF/SRS (2006) for details.

27. One of two forms of alternative credit formulas may 

be used in lieu of regular credit provisions: an alternative 
4-60 Chapter 4. Research and Development: National Trends and International Linkages

incremental R&E tax credit (AIRC), enacted in 1996, and 

a simplified alternative credit (ASIC), established in 2006. 

Companies may select only one of these three credit types 

on a permanent basis unless the IRS authorizes a change. 

The 2008 bill extending the overall R&E credit increased the 

statutory rate for the ASIC from 12% to 14% and repealed 

the AIRC for the 2009 tax year only (Guenther 2008).

28. The target population for SOI’s corporate statistics 

consists of returns of active corporations required to file one 

of nine 1120 IRS tax forms, where corporations refers to 

for-profit corporations, joint-stock companies, and certain 

unincorporated associations. Estimates on corporate tax statistics are based on a stratified probability sample of unaudited active returns. Active returns include returns having 

current income or deductions. IRS data are for tax years, 

which cover accounting periods ending any month between 

July of the calendar year of reference through June of the 

following calendar year. Estimates are subject to sampling 

and nonsampling errors. For SOI statistical methodology, 

see section 3 in IRS (2009).

29. Actual credit amounts are lower than claims because 

of limits on overall or general business credits. Corporations 

requesting the R&E credit must complete IRS Form 6765 

(the latest form is available at http://www.irs.gov/pub/irs-pdf/

f6765.pdf). SOI tax credit estimates reported in this section 

cover only C corporations. In particular, data excludes passthrough entities (those that pay little or no federal income 

tax at the corporate level but are instead required by law to 

pass any profits or losses to their shareholders, where they 

are taxed at the individual rate) such as S corporations (IRS 

form 1120S), real estate investment trusts (1120-REIT), and 

regulated investment companies (1120-RIC).

30. See figure C in http://www.irs.gov/taxstats/article/ 

0,,id=164402,00.html (accessed 19 June 2009). This source 

also has data by type of R&E credit and related IRS/SOI 

publications.

31. See appendix table 4-12. Although both IRS and NSF/

Census statistics use NAICS as the underlying industry classification system, comparisons of R&D-related estimates at 

the industry level are problematic because of differences in 

classification and company consolidation procedures. For 

example, industry codes for tax purposes are based on gross 

receipts, whereas the classification in the NSF/Census survey is based on dollar payrolls.

32. Accordingly, the business share of R&D funding for 

the United States in table 4-14 is overstated—specifically 

in comparison with the business-sector shares for countries 

where foreign sources of R&D funding are reported separately from domestic sources. R&D investments by foreign 

MNCs (discussed later in this chapter) provide an indication 

of international participation in U.S. business R&D. However, foreign ownership does not necessarily imply foreign 

funding, given that an affiliate may fund activities through 

domestic sources.

33. For discussions of R&D diversity measurement, see 

Archibugi and Pianta (1992, 1996).

34. 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.

35. For international intra-MNC transactions in R&D 

services, see “Technology and Innovation Linkages.” See 

chapter 3 for MNC R&D employment and chapter 6 for FDI 

financial flows.

36. Western Europe and Asia have also attracted the 

majority of FDI financial flows by U.S. MNCs (Sethi et 

al. 2003).

37. For these data, the United States includes the 50 

states; Washington, DC; Puerto Rico; and all U.S. territories 

and possessions.

38. BEA defines a parent company of a U.S. MNC as an 

entity (individual, branch, partnership, or corporation), resident in the United States, that owns or controls at least 10% 

of the voting securities, or equivalent, of a foreign business 

enterprise. Data are for nonbank U.S. MNC parent companies. Affiliate data cover majority-owned, nonbank foreign 

affiliates of nonbank U.S. parents. For selected NSF data on 

overseas R&D funded by companies with R&D activities in 

the 50 states and Washington, DC, see appendix tables 4-37 

and 4-38.

39. Data on parents’ R&D for 2004 and later are not fully 

comparable with earlier data because of improvements in 

statistical coverage. The improvements resulted from comprehensive information on parent R&D activities obtained 

from the Bureau of Economic Analysis 2004 Benchmark 

Survey of U.S. Direct Investment Abroad and from new information obtained through an ongoing interagency statistical project (see NSF/SRS [2007b]).

40. In comparison, the share of value-added (gross product) by affiliates located in Europe was 54.3% in 2006. Affiliates in the United Kingdom and Germany also reported 

the largest value-added figures over this period (BEA 2009).

41. See “International R&D Comparisons.”

42. See Branstetter and Foley (2007, pp 15–21), NSF/

SRS (2004), OECD (2008d), and von Zedtwitz, (2004) for 

FDI R&D and technology alliances in China. For information on India and other emerging markets, see Arora and 

Gambardella (2004) and NRC (2007a).

43. Outside organizations include independent companies, universities, nonprofit organizations, and government, 

but the majority of this R&D is performed by companies. 

See appendix table 4-40 for industry-specific data.

44. Data are for R&D contract expenditures paid by U.S. 

industrial R&D performers (using company and other nonfederal R&D funds) to other domestic performers. In this 

section, contract R&D refers to a transaction with external 

parties involving R&D payments or income, regardless of its 

legal form. Transactions by companies that do not perform 
Science and Engineering Indicators 2010 4-61

internal R&D in the United States are excluded, as are R&D 

activities contracted out to companies located overseas.

45. Company-funded is shorthand for “company and other nonfederal.”

46. RDT is part of the larger category of business, professional, and technical services. RDT services include 

commercial and noncommercial research as well as product 

development and testing services. The latter component includes non-R&D testing services. RDT covers services by all 

companies regardless of industry classification, not just activities of companies or establishments classified in NAICS 

5417. Starting with 2006 data, new BEA survey forms BE120 (benchmark) and BE-125 (quarterly) collect both affiliated and unaffiliated transactions. For further methodological 

information, see http://www.bea.gov/surveys/iussurv.htm.

47. In practice, these activities may be part of a given 

business arrangement or innovation project. Furthermore, 

technology alliances may or may not be part of larger agreements involving manufacturing, marketing, and other business functions.

48. Drivers for R&D collaboration include reduction in 

costs and/or time to market, sharing of instrumentation and 

other infrastructure, technology diversification (exploration and experimentation across multiple technology platforms), and long-term learning (Cantwell, Gambardella, and 

Granstrand 2004; Ozman 2009). The policy environment, 

especially antitrust regulation and intellectual property protection, is also critical to the incidence of these drivers and 

their economy-wide impact (Scott 2008).

49. For data from the Cooperative Research (CORE) database, based on Department of Justice registrations required 

by NCRPA, see NSF/SRS (2006, p 4-34).

50. CATI is a literature-based database that draws on 

sources such as newspapers, journal articles, books, and 

specialized journals that report on business events. Agreements involving small or startup firms are likely to be underrepresented. Another limitation is that the database draws 

primarily from English-language materials. Data on alliance 

structure, size, duration, or outputs are not available. For 

studies combining CATI and other data sources, see papers 

and references in Hagedoorn, Link, and Vonortas (2003).

51. For an overview of indicator development in this area, 

see Jankowski, Link, and Vonortas (2001) and Hagedoorn, 

Link, and Vonortas (2003).

52. 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). See also PCAST 

(2008) and chapters 8 and 9 in Link and Siegel (2007).

53. Federal agencies frequently cited in government reports on federal technology transfer include the Departments 

of Agriculture, Commerce, Defense, Energy, Health and 

Human Services, Homeland Security, the Interior, Transportation, and Veterans Affairs; the Environmental Protection 

Agency; and the National Aeronautics and Space Administration. Data include both federal intramural laboratories 

and FFRDCs.

54. Notably missing among these indicators are technical articles published in professional journals, conference 

papers, and other kinds of scientific communications; however, few labs regularly tabulate and report this information.

55. FY 2007 figures are preliminary. As this volume was 

going to press, the House and Senate agreed to the latest in a 

series of short-term extensions of these programs.

56. 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.

57. See Section 3012 of the America COMPETES Act 

(Public Law 110-69), enacted 9 August 2007. Final rules 

prescribing TIP procedures were released 25 June 2008 (15 

C.F.R. Part 296). The first competition was announced in 

July 2008, and the first awards were made in January 2009.

58. Public Law 110-69, Section 3012.

59. For example, beginning in 2006, MEP began collaborating to connect small manufacturers with trade promotion 

specialists of DOC’s International Trade Administration and 

its export assistance centers in specific industry sectors, such 

as machinery and microelectronics (GAO 2007, p 20). For 

MEP impact studies, see http://blue.nist.gov/sshome.

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.
4-62 Chapter 4. Research and Development: National Trends and International Linkages

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.

EU-27: 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. And, in January 2007, Bulgaria and Romania were 

added, bringing the total of 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 either by an industrial firm, a university, or a 

nonprofit institution. 

Foreign affiliate: Company located overseas but owned 

by a U.S. parent.

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.

G-7 countries: The Group of Seven industrialized nations includes Canada, France, Germany, Italy, Japan, the 

United Kingdom, and the United States. 

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 

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. 

Intellectual property: Any product of the human intellect—such as an invention, discovery, technology, creation, 

development, or other form of expression of an idea—regardless of whether the subject matter is protectable under 

the laws governing the different forms of intellectual property. The most common forms of intellectual property protection include patents, copyrights, trademarks, and trade 

secrets.

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: 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 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. 

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), industryuniversity 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.

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, company-funded 

R&D to net sales ratio, and R&D per employee.

R&D plant: Expenditures in the acquisition of, construction of, major repairs to, or alterations in structures, works, 

equipment, facilities, or land for use in R&D activities.

Technology alliance: Cooperative arrangement aimed 

at co-development of new products or capabilities through 

R&D and other technical collaboration.

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

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

Financial Resources for Academic R&D .................................................................................5-4

Academic R&D Infrastructure ..................................................................................................5-4

Doctoral Scientists and Engineers in Academia .......................................................................5-4

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

Introduction ..................................................................................................................................5-7

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

Chapter Organization ...............................................................................................................5-7

Financial Resources for Academic R&D .....................................................................................5-7

Academic R&D Within the National R&D Enterprise .............................................................5-9

Federal Support of Higher Education R&D .............................................................................5-9

Other Sources of Funding .......................................................................................................5-10

Expenditures by Field and Funding Source ...........................................................................5-12

Non-S&E R&D Expenditures .................................................................................................5-13

Academic R&D by Institution ................................................................................................5-13

Academic R&D Equipment ...................................................................................................5-15

Academic R&D Infrastructure ...................................................................................................5-16

Cyberinfrastructure: Networking ...........................................................................................5-16

Bricks and Mortar ...................................................................................................................5-17

Doctoral Scientists and Engineers in Academia ........................................................................5-19

Trends in Academic Employment of Doctoral Scientists and Engineers ...............................5-19

Recent S&E Doctorate Holders ..............................................................................................5-25

Government Support of Academic Doctoral Researchers .....................................................5-26

Collaborative Research ...........................................................................................................5-28

Outputs of S&E Research: Articles and Patents ........................................................................5-29

S&E Article Output ................................................................................................................5-29

Coauthorship and Collaboration .............................................................................................5-33

Trends in Output and Collaboration Among U.S. Sectors .....................................................5-38

Trends in Citation of S&E Articles .......................................................................................5-40

Academic Patents, Licenses, Royalties, and Startups ............................................................5-42

Patent-to-Literature Citations ................................................................................................5-45

Conclusion ................................................................................................................................5-46

Notes ..........................................................................................................................................5-47

Glossary .....................................................................................................................................5-51

References ..................................................................................................................................5-51

List of Sidebars

Data Sources for Financial Resources for Academic R&D ........................................................5-8

Congressional Earmarks ............................................................................................................5-10

EPSCoR: The Experimental Program to Stimulate Competitive Research ...............................5-11

Women Faculty at Research Universities ..................................................................................5-24

Bibliometric Data and Terminology ..........................................................................................5-30

S&E Publishing Trends in Iran ..................................................................................................5-33

5-1
5-2 Chapter 5. Academic Research and Development

Can Bibliometric Data Provide Accurate Indicators of Interdisciplinary Research? ................5-35

Publications and Resource Inputs ..............................................................................................5-47

List of Tables

Table 5-1. R&D expenditures in non-S&E fields at universities and colleges: FY 2007–08 ...5-14

Table 5-2. Bandwidth to commodity Internet (Internet1) and Internet2 at academic 

institutions: FY 2005–08 ......................................................................................................5-17

Table 5-3. Highest internal network speeds, by highest degree granted: FY 2003–08 ............5-17

Table 5-4. S&E research space in academic institutions, by field: FY 1988–2007 ...................5-18

Table 5-5. New construction of S&E research space in academic institutions, by field 

and time of construction: FY 2002–07..................................................................................5-19

Table 5-6. S&E doctorate holders employed in academia, by position: Selected years, 

1973–2006 .............................................................................................................................5-20

Table 5-7. S&E doctorate holders employed in academia with research or teaching as 

primary or secondary work activity, by degree field: 1973 and 2006 ..................................5-21

Table 5-8. Average annual growth rate for employment of S&E doctorate holders in academia

 reporting research as a primary or secondary work activity, by degree field: 1973-2006 ....5-21

Table 5-9. Women as percentage of S&E doctorate holders, by position: Selected 

years, 1973–2006 ..................................................................................................................5-22

Table 5-10. Minorities as percentage of S&E doctorate holders, by position: Selected 

years, 1973–2006 ..................................................................................................................5-24

Table 5-11. S&E doctorate holders employed in academia and full-time S&E faculty 

reporting research as primary or secondary work activity, by field and years since 

doctorate: 2006 ......................................................................................................................5-27

Table 5-12. S&E doctorate holders employed in academia with federal support, by 

degree field, years since doctorate, and position: 2006 ........................................................5-28

Table 5-13. S&E doctorate holders employed in academia 4–7 years after receiving 

degree reporting receipt of federal support in previous year, by degree field: 

Selected years, 1973–2006 ....................................................................................................5-28

Table 5-14. S&E articles in all fields, by country/economy: 1995 and 2007 ............................5-31

Table 5-15. S&E research portfolios of selected regions/countries, by field: 2007 ..................5-34

Table 5-16. Authors per S&E article, by field: 1988, 1993, 1998, 2003, and 2008 ..................5-34

Table 5-17. Interregional collaboration on S&E articles: 1998 and 2008 .................................5-36

Table 5-18. International collaboration on S&E articles, by selected country/economy: 

1998 and 2008 .......................................................................................................................5-37

Table 5-19. International coauthorship of S&E articles with the United States, by 

selected country/economy: 1998 and 2008 ...........................................................................5-38

Table 5-20. Index of international collaboration on S&E articles, by selected country/

economy pair: 1998 and 2008 ...............................................................................................5-39

Table 5-21. U.S. article coauthorship, by sector, foreign coauthorship, and U.S. 

coauthor sector: 1998 and 2008 ............................................................................................5-41

Table 5-22. S&E articles, citations, and international citations, by selected region/

country: 1998 and 2008 .........................................................................................................5-42

Table 5-A. EPSCoR and EPSCoR-like program budgets, by agency: FY 1998–2008 .............5-11

Table 5-B. International coauthorship with Iran, by top five coauthoring countries: 2008 .......5-33

List of Figures

Figure 5-1. Academic R&D, basic and applied research, and basic research as share of 

U.S. total from all performing sectors in each category: 1970–2008 .....................................5-9

Figure 5-2. Sources of academic R&D funding: 1972–2008 ....................................................5-10

Figure 5-3. Federal and nonfederal academic R&D expenditures: 1996–2008 .........................5-12

Figure 5-4. Academic R&D expenditures, by field: 1998–2008 ...............................................5-12
Science and Engineering Indicators 2010 5-3

Figure 5-5. Changes in share of academic R&D in selected S&E fields: 1990–2000 and 

2000–08 .................................................................................................................................5-13

Figure 5-6. Federally financed academic R&D expenditures, by agency and field: FY 2008 ..5-13

Figure 5-7. Sources of academic R&D funding for public and private institutions: 2008 ........5-14

Figure 5-8. Components of institutional R&D expenditures for public and private 

academic institutions: 1998–2008 .........................................................................................5-14

Figure 5-9. Academic R&D, by rank of university and college academic R&D 

expenditures: 1988–2008 ......................................................................................................5-15

Figure 5-10. Total and federally funded academic R&D pass-throughs: FY 2000–08 .............5-15

Figure 5-11. Current fund expenditures for research equipment at academic 

institutions, by field: 1998–2008...........................................................................................5-16

Figure 5-12. S&E doctorate holders employed in academia, by degree field: 1973–2006 .......5-20

Figure 5-13. S&E doctorate holders, by type of academic appointment: 1973–2006 ...............5-20

Figure 5-14. Women as percentage of S&E doctorate holders employed in academia 

with research as a primary or secondary work activity, by position: Selected years, 

1973–2006 .............................................................................................................................5-23

Figure 5-15. Women as percentage of full-time S&E research faculty, by field: 

Selected years, 1973–2006 ....................................................................................................5-23

Figure 5-16. Minorities as percentage of S&E doctorate holders, by position: 2006 ................5-25

Figure 5-17. Recent S&E doctorate holders employed in academia, by position: 

1979–2006 .............................................................................................................................5-26

Figure 5-18. S&E doctorate holders employed in academia 4–7 years after receiving 

degree, by position: 1979–2006 ............................................................................................5-27

Figure 5-19. S&E doctorate holders employed in academia with federal support, by 

years since doctorate: 1973–2006 .........................................................................................5-27

Figure 5-20. S&E article output, by major S&E publishing region/country: 1995–2007 .........5-32

Figure 5-21. Share of world S&E articles coauthored domestically and internationally: 

1988–2008 .............................................................................................................................5-36

Figure 5-22. S&E article output of U.S. nonacademic sectors: 1996–2008 ..............................5-39

Figure 5-23. Total, domestic, and international citations: 1992–2008 ......................................5-42

Figure 5-24. United States, EU, China, Japan, and Asia-8 share of cited papers, by 

citation percentile: 2008 ........................................................................................................5-43

Figure 5-25. Index of highly cited articles, by selected field and region/country: 1998 

and 2008 ................................................................................................................................5-44

Figure 5-26. U.S. academic patents, by selected technology area: 1988–2008 .........................5-45

Figure 5-27. Citations in U.S. patents to S&E articles, by selected article field: 1998–2008 ...5-45

Figure 5-28. Academic share of U.S. patent citations to U.S. articles, by selected field: 

1998, 2003, 2008 ...................................................................................................................5-46

Figure 5-A. Share of Iran’s S&E articles coauthored domestically and internationally: 

1988–2008 .............................................................................................................................5-33

Figure 5-B. U.S. academic S&E article output, S&E article author names, academic 

R&D expenditures, and academic R&D workforce: 1988–2007..........................................5-47
5-4 Chapter 5. Academic Research and Development Chapter 5. Academic Research and Development

Financial Resources for Academic R&D

In 2008, U.S. academic institutions spent $52 billion on 

R&D, and the higher education sector continues to account for the majority of basic research performed in the 

United States. 

Academic performers are estimated to account for 55% 

of U.S. basic research ($69 billion), 31% of total (basic 

plus applied) research ($157 billion), and 13% of all 

R&D ($395 billion) estimated to have been conducted in 

the United States in 2008.

Higher education’s share of total U.S. research expenditures increased by 11 percentage points between 1982 

and 2002 (from 24% to 35%), but has since declined to 

an estimated 31% in 2008.

Support from the federal government decreased in recent years with no funding growth for 3 straight years. 

The federal government provided 60% ($31.2 billion) 

of funding for academic R&D expenditures in 2008. In 

inflation-adjusted dollars, this represents a 0.2% increase 

from FY 2007 and follows decreases of 1.6% in FY 2007 

and 0.2% in FY 2006. 

According to the federal agencies providing the funding, 

total federal obligations for academic R&D peaked in 

2004 at $22.1 billion (in constant 2000 dollars) and have 

since declined by almost 7% to an estimated $20.7 billion 

in FY 2009. 

Higher education R&D funding from all nonfederal 

sources combined has grown steadily since FY 2004.

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 funding. 

After a 3-year decline between 2001 and 2004 (low of $2.1 

billion), industry funding of academic R&D increased for 

the fourth year in a row, to $2.9 billion in 2008. 

The distribution of academic R&D expenditures across 

the various broad S&E fields has remained relatively 

constant since 1990, with the life sciences receiving the 

most funding. 

In 2008, the life sciences continued to receive the largest share of investment in academic R&D, accounting for 

roughly 60% of all expenditures.

Over the past two decades, the broad field of life sciences 

was the only field to experience any meaningful increase 

in its share of total academic R&D, rising more than 4 

percentage points since 1998.

In 2008, about $1.9 billion was spent for academic research equipment. This represents a real increase of 

1.0% from FY 2007, but a decline of more than 10% 

from the 2004 level. 

About 80% of FY 2008 equipment expenditures were 

concentrated in three fields: the life sciences (43%), engineering (23%), and the physical sciences (16%). 

After a period of steady growth between 2001 and 2004, 

equipment expenditures in the physical sciences, medical 

and biological sciences, and engineering have all declined 

since 2005. 

Academic R&D Infrastructure

Research-performing colleges and universities continued to expand their physical resources for conducting 

research. However, while cyberinfrastructure capabilities continued to expand significantly, the expansion of 

traditional “bricks and mortar” infrastructure slowed.

A large majority of institutions now have connections to 

high-speed networks; 25% of institutions have more than 

one connection.

By FY 2007, 74% of all institutions had internal network 

distribution speeds of at least 1 gigabit.

For the first time in 20 years, almost half of all S&E fields 

experienced a decline in their research space.

Doctoral Scientists and Engineers in 

Academia

The size of the doctoral academic S&E workforce reached 

an estimated 272,800 in 2006 but grew more slowly than 

the number of S&E doctorate holders in other employment sectors from 1973 to 2006. Full-time faculty positions, although still the predominant type of employment, 

increased more slowly than postdoc and other full- and 

part-time positions, especially at research universities. 

The share of all S&E doctorate holders employed in academia dropped from 55% in 1973 to 45% in 1991 and has 

remained at about that level through 2006. 

Among S&E doctorate holders in academia, full-time 

faculty declined continually from 88% in the early 1970s 

to 72% in 2006. 

Postdocs and others in full-time nonfaculty positions 

constitute an increasing percentage of academic S&E 

employment, having grown from 10% in 1973 to 22% 

in 2006. This change was especially pronounced in 

the 1990s. 

The share of part-time positions was roughly 2% to 4% 

from 1973 through 1999, but has risen since then to 6% 

in 2006. 

Highlights
Science and Engineering Indicators 2010 5-5

The number of academic S&E doctorate holders reporting research as their primary or secondary work activity showed greater growth from 1973 to 2006 than the 

number reporting teaching as their primary or secondary activity. 

The number of researchers grew 2.5% per year (from 

82,300 to 183,700) between 1973 and 2006, and the 

number of teachers grew 1.7% per year (from 94,900 to 

163,300). 

About two-thirds of doctoral scientists and engineers employed in academic institutions are engaged in research as 

either a primary or secondary work activity.

Life scientists accounted for more than one-third of academic doctorate holders reporting research as a primary 

or secondary work activity in 2006. Life scientists also accounted for most of the growth in academic researchers. 

The number of academic researchers in the physical sciences and mathematics grew more slowly, at average annual growth rates of 1.1% and 1.6%, respectively, from 

1973 to 2006. Growth rates for academic researchers in 

all fields were greatest in the 1980s. 

The number of full-time faculty in the life sciences has 

risen, but the percentage of full-time faculty in the life 

sciences who are tenured or on the tenure track has declined because the number of tenured and tenure-track life 

scientists has remained fairly stable since the late 1980s. 

The demographic composition of academic researchers 

changed substantially between 1973 and 2006. 

Women increased from 6% to 29% of full-time doctoral 

S&E research faculty from 1973 to 2006. 

Underrepresented minorities (blacks, Hispanics, and 

American Indians/Alaska Natives) increased from about 

2% to about 8% of full-time doctoral S&E research faculty. 

The Asian/Pacific Islander share of full-time doctoral 

S&E research faculty increased substantially, from 4% 

to 13%. 

The share of whites among full-time doctoral S&E research faculty fell from 92% to 79% during the period. 

In most fields, the percentage of full-time doctoral S&E 

faculty with federal support for their work was about the 

same in 2006 as it was in the late 1980s. 

A little less than half (46%) of full-time doctoral S&E 

faculty received federal support in both 1987 and in 2006. 

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 2.5% between 1995 and 2007. The U.S. growth 

rate was much lower, at 0.7%.

The United States accounted for 28% of the world total 

S&E articles in 2007, down from 34% in 1995. The share 

of the European Union also declined, from 35% in 1995 

to 32% in 2007.

In Asia, average annual growth rates were high—for 

example, 17% in China and 14% in South Korea. As a 

result, in 2007 China moved past the United Kingdom, 

Germany, and Japan to rank as the world’s 2nd-largest 

producer, up from 5th place in 2005 and 14th place in 

1995.

The research portfolios of the top article-producing 

countries, as indicated by publication of S&E articles, 

varied widely. China, Japan, and eight other Asian countries (the “Asia-8”) emphasized the physical sciences 

more than the United States and the European Union.

In 2007, S&E research articles in chemistry and physics accounted for just under one-half of China’s total 

article production, 36% of Japan’s, and 37% of the Asia8’s. These two fields accounted for 17% of the total for 

the United States and 25% of the total for the European Union.

Articles in the life sciences (biological, medical, agricultural, and related sciences) accounted for 57% of all 

U.S. S&E articles, compared with 49% for the European 

Union, 25% for China, 45% for Japan, and 34% for the 

Asia-8.

Country research portfolios also differed in their emphasis on engineering, with the Asian countries more heavily 

concentrated in this broad field (China at 16%, Japan at 

11%, and the Asia-8 at 19%) than the U.S. or the European Union (7%–8%).

S&E research articles continue to indicate increasing 

collaboration across institutions in the United States and 

internationally. 

Coauthored articles grew from 40% of the world’s total S&E 

articles in 1988 to 64% in 2008. Coauthored articles listing only authors from different institutions in the same 

country increased from 32% of all articles in 1988 to 42% 

in 2008. Articles listing authors from institutions in more 

than one country grew from 8% to 22% over the same period.

Within-sector coauthorship increased in all U.S. sectors, 

growing, for example, from 38% of academic S&E article 

output in 1998 to 45% in 2008. Cross-sector coauthorship 

increased generally, mainly due to an increase of 7–10 

percentage points in each nonacademic sector’s coauthorScience and Engineering Indicators 2010 5-5
5-6 Chapter 5. Academic Research and Development

ship with academia. U.S. sector coauthorship with foreign 

authors grew in all sectors by 7–10 percentage points. 

The U.S. share of world article output and article citations has declined but not the influence of U.S. research 

articles, as indicated by the percentage of U.S. articles 

that are among the most highly cited worldwide. 

Between 1998 and 2008, the U.S. share of world articles 

declined from 34% in 1998 to 29% in 2008, while its 

share of total citations in S&E articles declined from 47% 

to 38%. Over the same period, China’s share of publications increased from 2% to 6%, and its share of citations 

from 1% to 4%. 

The percentage of U.S.-authored S&E articles receiving 

the highest number of citations—an indicator of research 

quality and high impact on subsequent research—has 

changed little. Between 1998 and 2008, the U.S. index 

of highly cited articles declined from 1.83 to 1.78 and remained well above the expected index value of 1. Indexes 

of the European Union, China, Japan, and the Asia-8 all 

increased but remained below 1.

Indicators of academic patenting are mixed. U.S. Patent 

and Trademark Office (USPTO) data show that patent grants to U.S. universities declined to about 3,000 in 

2008. Other indicators relating to academic patenting 

suggest increasing activity.

According to USPTO data, patent grants to universities 

and colleges increased sharply from 1988 to about 1999, 

when they peaked at just under 3,700 patents, and then 

fell to about 3,000 in 2008. Three technology areas have 

dominated these patent awards (chemistry, biotechnology, and pharmaceuticals), accounting for 45% of the total 

patents awarded to U.S. universities in 2008.

Data from another source show that invention disclosures filed with university technology management offices grew from 13,700 in 2003 to 17,700 in 2007 and 

that patent applications filed by reporting universities and 

colleges increased from 7,200 in 2003 to almost 11,000 

in 2007.

5-6 Chapter 5. Academic Research and Development
Science and Engineering Indicators 2010 5-7

Introduction

America’s academic institutions play a pivotal role in the 

U.S. system for conducting R&D and fostering innovation. 

They conduct the bulk (55%) of U.S. basic research and in the 

process train the nation’s new researchers. U.S. universities 

have also become active participants in turning new researchbased knowledge into innovative products and processes and 

in broader regional economic development activities. This 

chapter analyzes available data bearing on these points. (For 

the key output of trained personnel, see chapter 2.)

Chapter Overview 

U.S. universities and colleges carry out the majority of 

basic research activity (55%) and a substantial portion of all 

R&D in the United States. The federal government has been 

and continues to be the major financial supporter of academic R&D, providing more than 60% of the funding in 2007. 

Other major funding sources are the institutions themselves, 

industry, and state and local government. 

Over the past two decades, the shares of funding allocated 

to the various S&E fields1

 have changed, with the share going to medical sciences growing substantially and the share 

going to physical sciences and engineering declining.

Academic R&D is conducted largely by doctoral scientists and engineers. Over time, universities and colleges 

have relied less on full-time tenure-track faculty and more 

on postdocs and other nonfaculty to conduct research; in 

addition, a steady percentage of full-time graduate students 

has been supported by research assistantships. The demographic composition of academic researchers is changing, 

with increasing numbers of women and minorities, especially among the younger age groups, and increasing numbers of 

foreign-born scientists and engineers. 

A measure of research output, the number of U.S. S&E 

articles published in the world’s leading S&E journals, recently began to increase after remaining flat for almost a decade, concurrent with strong growth in the European Union 

and several Asian countries. However, the U.S. share of the 

world’s S&E article output has declined since the early 1970s. 

The U.S. share of the world’s influential—i.e., most highly 

cited—articles has declined, though U.S. scientific publications remain highly influential relative to those of other countries. Article output by the academic sector, which publishes 

most U.S. research articles, mirrored the overall U.S. trend, 

even as research inputs (specifically, academic R&D expenditures and research personnel) continued to increase. 

Both domestic and international R&D collaboration have 

increased significantly over the past two decades. U.S. scientists and engineers in all sectors collaborated extensively 

with colleagues in other U.S. sectors and abroad. The results 

of academic research increasingly extend beyond articles 

to patents, which are an indicator of academic institutions’ 

efforts to protect the intellectual property derived from 

their inventions, and to technology transfer, universityindustry collaboration, and other related activities such as 

revenue-generating licenses and formation of startup companies that emanate from their institution.

This chapter addresses key aspects of the academic R&D 

enterprise, including the level, field allocation, and institutional distribution of academic R&D funds; the state of 

research equipment and facilities at academic institutions; 

trends in the number and composition of the academic S&E 

doctoral labor force; and indicators of research outputs. 

Chapter Organization 

The first section of this chapter discusses the role of 

academia within the national R&D enterprise. This discussion is followed by an examination of trends in the financial 

resources provided for academic R&D, including identification of key funders and allocations of funds across both 

academic institutions and S&E fields. Because the federal 

government has been the primary source of support for academic R&D for more than half a century, the importance 

of selected agencies to both overall support and support for 

individual fields is explored in some detail. This section also 

presents data on changes in the distribution of funds among 

academic institutions, on the number of academic institutions that receive federal R&D support, and on equipment. 

The next section examines the status of the physical 

infrastructure necessary to conduct university research activities. Data are presented on both the traditional research 

infrastructure such as research space, and on infrastructure 

resulting from technological changes such as networking.

The third section discusses trends in employment of academic doctoral scientists and engineers, especially those engaged in research. Major trends examined include numbers 

and characteristics of academic doctoral scientists and engineers, the types of positions they hold, their research activities, and the federal support for their research. The section 

also examines reported collaboration among researchers.

The chapter concludes with an analysis of trends in two 

types of research outputs: S&E articles, as measured by data 

from a set of journals covered by the Science Citation Index 

(SCI) and the Social Sciences Citation Index (SSCI), and 

patents issued to U.S. universities. (A third major output of 

academic R&D, educated and trained personnel, is discussed 

in chapter 2 and in the preceding section of this chapter.) 

This section looks specifically at the volume of research (article counts), collaboration in the conduct of research (joint 

authorship), use in subsequent scientific activity (citation 

patterns), and use beyond science (citations to the literature 

that are found in patents). It concludes with a discussion of 

academic patenting and some returns to academic institutions from their patents and licenses.

Financial Resources for Academic R&D

Academic R&D is a significant part of the national R&D 

enterprise.2

 Academic scientists and engineers conduct the 

bulk of the nation’s basic research, about one-third of its 
5-8 Chapter 5. Academic Research and Development

Data Sources for Financial Resources for Academic R&D 

The data used to describe financial and infrastructure 

resources for academic R&D are derived from three National Science Foundation (NSF) surveys. These surveys 

use similar but not always identical definitions, and the 

nature of the respondents also differs across the surveys. 

The three main surveys are as follows: 

Survey of Federal Funds for Research and Development 

Survey of Research and Development Expenditures at 

Universities and Colleges 

Survey of Science and Engineering Research Facilities 

The first survey collects data from federal agencies, whereas the last two collect data from universities and colleges. 

Data presented in the first part of this section, “Academic R&D Within the National R&D Enterprise,” are derived 

from the NSF series National Patterns of R&D Resources, 

which sums results from several NSF surveys of the various sectors of the U.S. economy (for example, universities, 

businesses, and the federal government) so that the components of the overall R&D effort are placed in a national 

context. These data are reported on a calendar-year basis, 

and the data for 2008 are preliminary. Since 1998, the series has also attempted to eliminate double counting in the 

academic sector by subtracting current fund expenditures 

for separately budgeted S&E R&D that are passed through 

to other institutions via subcontracts and similar collaborative research arrangements. 

Data in subsequent portions of the section derive from 

the Survey of Research and Development Expenditures 

at Universities and Colleges (Academic R&D Expenditures Survey). They are reported on an academic fiscalyear basis (e.g., FY 2008 covers July 2007 to June 2008 

for most institutions) and do not net out the funds passed 

through to other institutions; therefore, they differ from 

those reported earlier. Data on major funding sources, 

funding by institution type, distribution of R&D funds 

across academic institutions, and expenditures by field 

and funding source are also derived from this survey. 

The data on “Top Agency Supporters” and “Agency 

Support by Character of Work” in the “Federal Support 

of Higher Education R&D” section come from NSF’s 

Survey of Federal Funds for Research and Development. 

This survey collects data on R&D obligations for each 

federal fiscal year (e.g., FY 2008 covers October 2007 

through September 2008) from 30 federal agencies. Data 

for FY 2008–09 are preliminary estimates. The amounts 

reported for FY 2008–09 are based on administration 

budget proposals and do not necessarily represent actual 

appropriations. It should be noted that federal obligation 

data (e.g., $25.7 billion in federal FY 2008) do not match 

the federally funded expenditures data reported by academic institutions ($31.2 billion in academic FY 2008) 

for several reasons. First, the period covered by the two 

surveys is slightly different; second, there is necessarily a lag between the obligation date and the beginning 

of project expenditures and some awards span multiple 

years; and third, some of the expenditures data double 

count federal R&D awards that are reported both by the 

primary institution receiving the funds and again by an 

academic subrecipient to whom funds are passed through 

(about $1.5 billion in FY 2008). 

Data on research equipment are taken from the Survey 

of Research and Development Expenditures at Universities and Colleges. Data on research facilities and cyberinfrastructure are taken from the Survey of Science and 

Engineering Research Facilities and are also reported by 

academic fiscal year. The population for this survey is a 

subset of the population for the Academic R&D Expenditures Survey and includes all institutions reporting $1 

million or more in current fund expenditures for R&D. 

The Facilities survey was broadened starting in FY 2003 

to include data on computing and networking capacity. 

Although terms are defined specifically in each survey, 

in general, are classified as capital projects, are fixed items such as buildings, often cost 

millions of dollars, and are not included in R&D expenditures as reported here. Research equipment, however, 

is purchased with current funds (those in the yearly operating budget for ongoing activities) and is included 

within R&D expenditures. Because the categories are not 

mutually exclusive, some large instrument systems could 

be classified as either facilities or equipment. Generally, 

academic institutions account separately for capital projects and current fund expenditures.

Redesign of the Survey of R&D 

Expenditures at Universities and Colleges

The Survey of Research and Development Expenditures at Universities and Colleges has been conducted 

annually since 1972. In 2007, NSF began an intensive 

3-year effort to evaluate and redesign the survey. The 

goals of the redesign were to (1) update the survey instrument to reflect current accounting principles in order 

to obtain more valid and reliable measurements of the 

amount of academic R&D spending in the United States, 

(2) expand the current survey items to collect the additional detail most often requested by data users, and (3) 

evaluate the feasibility of expanding the scope of data 

collected beyond that of R&D expenditures. 

As part of the redesign effort, NSF held data user workshops and expert panel meetings, worked with accounting 

and survey methodology experts, and visited more than 40 

institutions to receive input on possible changes to the survey. A pilot test of the redesigned survey was administered 
Science and Engineering Indicators 2010 5-9

total (basic plus applied) research, and 13% of its total R&D. 

To carry out world-class research and advance the scientific 

knowledge base, U.S. academic researchers require adequate 

and stable financial resources and the research facilities and 

instrumentation that facilitate high-quality work. For a discussion of the sources of the data used in this section, see sidebar, 

“Data Sources for Financial Resources for Academic R&D.” 

Academic R&D Within the National R&D 

Enterprise

Universities and colleges play an important role in the nation’s overall R&D effort, especially by contributing to the 

generation of new knowledge through basic research.3

 Since 

1998, basic research performed within institutions of higher 

education has accounted for more than half of the basic research performed in the United States.

In 2008, U.S. universities and colleges spent $52 billion 

($42 billion in constant 2000 dollars) on R&D. Higher education’s prominence as an R&D performer increased slightly during the past three decades, rising from about 10% of 

all R&D performed in the United States in the early 1970s 

to an estimated 13% in 2008 (figure 5-1). For a comparison 

with other countries, see “International R&D Comparisons” 

in chapter 4.

Academic R&D involves mostly basic and applied research and little development activity.4

 In 2008, an estimated 

96% of academic R&D expenditures went for research (76% 

for basic and 21% for applied) and 4% for development (appendix table 5-1). Universities and colleges accounted for an 

estimated 31% of the U.S. basic and applied research total in 

2008, down from a high of 35% in 2002 but still above the 

levels prevalent until then (figure 5-1). Higher education’s 

share of total U.S. research expenditures had previously increased by 11 percentage points between 1982 and 2002. In 

terms of basic research alone, the higher education sector is 

the country’s largest performer, currently accounting for an 

estimated 55% of the national total. 

Federal Support of Higher Education R&D

Higher education R&D relies heavily on federal support, 

along with a variety of other funding sources. The federal government has consistently contributed the majority of the funds 

(figure 5-2).5

 It accounted for about 60% of the $51.9 billion 

of R&D funds expended by universities and colleges in FY 

2008 (appendix table 5-2).6

 In current dollars, federally funded 

to 40 institutions during the fall of 2009, and full implementation of the redesigned survey is planned for 

the fall of 2010. 

The new survey, now titled the “Higher Education 

R&D Survey,” will continue to capture core information on R&D expenditures by sources of funding and 

field. In addition, it will include the following data:

Total R&D expenditures funded by nonprofit institutions (previously included under “Other sources”)

Total R&D expenditures funded from all types of 

foreign sources 

Total R&D expanded to include R&D expenditures 

in both S&E and non-S&E fields as well as clinical 

trial expenditures

Detail by field (both S&E and non-S&E) for R&D 

expenditures from each source of funding (federal, state/local, institution, industry, nonprofit, and 

other)

Total R&D expenditures from projects within university interdisciplinary research centers (test metric on interdisciplinary R&D)

Total R&D expenditures by direct cost categories 

(salaries, software, equipment, etc.)

Counts of proposals submitted during the fiscal year

Counts and dollar amounts of R&D awards during 

the fiscal year, with a breakout of stimulus awards

In addition to these changes, NSF has also been 

working with data users and experts to explore the 

feasibility of collecting systematic data on both R&D 

personnel and intellectual property and commercialization within universities and colleges. It is expected 

that additional questions on these topics will be added 

to the Higher Education R&D Survey in future years.

	


	

				
		

									

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5-10 Chapter 5. Academic Research and Development

Top Agency Supporters

Six agencies are responsible for most of the federal obligations for higher education R&D, providing an estimated 

97% of the $25.7 billion obligated in FY 2009 (appendix 

table 5-3).8

 NIH was by far the largest funder, providing an 

estimated 65% of total federal academic R&D obligations in 

FY 2009. The National Science Foundation (NSF) provided 

an additional 15%, the Department of Defense (DOD) 8%, 

the Department of Energy (DOE) 4%, the National Aeronautics and Space Administration (NASA) 2%, and the U.S. 

Department of Agriculture (USDA) 2%. 

Agency Support by Character of Work

More than 56% of federal obligations from FY 2007 

through FY 2009 funded basic research projects (appendix 

table 5-4). The two agencies funding the majority of academic basic research were NIH and NSF. More than onethird of federal obligations for academic R&D from 2007 

through 2009 funded applied research, with NIH providing 

the vast majority of funds in that category as well. About 

5% of R&D obligations went toward development during 

2007–09. DOD and NASA were responsible for more than 

80% of the small amount of federal academic R&D funds 

spent on development.

Other Sources of Funding

In contrast to the recent trend in federal R&D funding, 

higher education R&D funding from nonfederal sources has 

grown steadily since FY 2004, and grew by 8% (6% in inflation-adjusted terms) between 2007 and 2008 (figure 5-3). 

Institutional funds. In FY 2008, institutional funds from 

universities and colleges constituted the second largest 

source of funding for academic R&D, accounting for 

academic R&D expenditures rose 2.5% between FY 2007 and 

FY 2008 to $31.2 billion. After adjustment for inflation, this 

represents a 0.2% increase from FY 2007 and follows 2 years 

of slight declines in constant dollars since FY 2005. 

Another look at recent trends is provided by federal 

agency-reported inflation-adjusted obligations for academic 

R&D—funds going to academic institutions in a given fiscal year, to be spent over the current and succeeding years. 

In constant 2000 dollars, federal academic R&D obligations 

peaked in FY 2004 at $22.1 billion and have since declined 

by almost 7% to an estimated $20.7 billion in FY 2009 (appendix table 5-3). Constant dollar federal R&D obligations 

had grown more than 10% each year between FY 1998 and 

FY 2001, largely reflecting a commitment to double the R&D 

budget of the National Institutes of Health (NIH) over 5 years. 

Consequently, between 1998 and 2004, NIH’s share of federal academic R&D funding increased from 57% to 63%. 

The American Recovery and Reinvestment Act, signed 

into law by President Obama on February 17, 2009, provides 

an additional $18.3 billion in appropriations for federal 

R&D and R&D facilities and equipment in FY 2009. (See 

“Federal R&D” in chapter 4.)7

The federal government’s overall contribution is the 

combined result of numerous discrete funding decisions 

made by several R&D-supporting agencies with differing 

missions and purposes, which in turn affect research priorities in the academic sector. Most of the federal R&D funding 

to the higher education sector is allocated through competitive peer review (see sidebar, “Congressional Earmarks”). 

Examining and documenting the funding patterns of the 

key funding agencies is important to understanding both 

their roles and that of the federal government overall. For a 

discussion of a major federal program with the objective of 

improving the geographical distribution of federal obligations for academic R&D, see sidebar, “EPSCoR: The Experimental Program to Stimulate Competitive Research.”

Congressional Earmarks

Academic earmarking is the congressional practice 

of providing federal funds to educational institutions for 

facilities or projects without merit-based peer review. 

Obtaining exact figures for either the amount of funds 

or the number of projects earmarked for universities and 

colleges, overall or for research, is difficult. There is no 

accepted definition of an earmark, and funding legislation is often obscure in its description of the earmarked 

projects. Broad estimates using a consistent approach in 

compiling these data are as follows.

Academic earmarks stood at an estimated $2.3 billion in FY 2008 (Brainard and Hermes 2008), a 15% 

increase over an estimated $2.0 billion reported last in 

FY 2003 in the Chronicle of Higher Education (Brainard and Borrego 2003). Approximately two-thirds ($1.6 

billion) of the FY 2008 funds and $1.4 billion of FY 

2003 funds were for R&D projects, R&D equipment, or 

construction or renovation of R&D laboratories.

	


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Science and Engineering Indicators 2010 5-11

EPSCoR: The Experimental Program to Stimulate Competitive Research

EPSCoR, the Experimental Program to Stimulate 

Competitive Research, originated as a response to a 

number of stat ed federal objectives. Section 3(e) of the 

National Sci ence 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, 

through out the United States, and to avoid undue concentration of such research and education.” 

In 1978, Congress authorized NSF to implement EPSCoR in response to broad public concerns about the extent of geographical concentration of federal funding for 

R&D. Eligibility for EPSCoR participation was lim ited 

to those jurisdictions that historically had received lesser 

amounts of federal R&D funding and had demon strated 

a commitment to develop their research bases and to improve the quality of S&E research conducted at their universities and colleges.

The success of the NSF EPSCoR programs during the 

1980s subsequently prompted the creation of EPSCoR and 

EPSCoR-like programs in six other federal agen cies: the 

Departments of Energy, Defense, and Agricul ture; 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 by the federal 

agencies with EPSCoR or EPSCoR-like programs. The 

major objectives of the EICC focused on improving coordination among and be tween the federal agencies in implementing EPSCoR and EPSCoR- like programs consistent 

with the policies of the participating agencies. 

EPSCoR seeks to increase the R&D competitiveness of 

an eligible state through the development and utiliza tion of 

the science and technology (S&T) resources residing in its 

colleges and universities. It strives to achieve this objective 

by (1) stimu lating 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 2008, the seven ElCC agencies invested a total of 

$419 million on EPSCoR and EPSCoR-like programs, up 

from approximately $97 million in 1999 (see table 5-A). 

The Environmental Protection Agen cy discontinued issuing separate EPSCoR program solici tations in FY 2006. 

Table 5-A

EPSCoR and EPSCoR-like program budgets, by agency: FY 1998–2008

(Millions of dollars)

Agency 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

All agencies .......................... 74.1 96.7 139.8 225.3 288.9 358.0 353.3 367.4 367.1 363.1 418.9

DOD .................................. 18.0 19.0 24.0 18.7 15.7 15.7 8.4 11.4 11.5 9.5 17.0

DOE .................................. 6.8 6.8 6.8 7.7 7.7 11.7 7.7 7.6 7.3 7.3 14.7

EPA ................................... 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.4 0.0 0.0 0.0

NASA ................................ 5.0 10.0 10.0 10.0 10.0 10.0 10.0 12.0 12.5 12.8 15.5

NIH .................................... 5.0 10.0 40.0 100.0 160.0 210.0 214.0 222.0 220.0 218.0 223.6

NSF ................................... 36.8 48.4 51.3 74.8 79.3 88.8 93.7 93.4 97.8 101.5 120.0

USDA ................................ NA NA 5.2 11.6 13.7 19.3 17.0 18.6 18.0 14.0 28.1

NA = not available 

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, May 2009.

Science and Engineering Indicators 2010

20% ($10.4 billion) of the total (appendix table 5-2). Institutional funds encompass (1) institutionally financed 

research expenditures and (2) unrecovered indirect costs 

and cost sharing. They exclude departmental research, 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 has since 

remained at roughly that level. Funds for institutionally 

financed R&D may derive from general-purpose state or 

local government appropriations; general-purpose awards 

from industry, foundations, or other outside sources; endowment income; and gifts. Universities may also use income from patents, licenses, or patient care revenues to 

support R&D. (See section “Patent-Related Activities and 

Income” later in this chapter for a discussion of patent 

and licensing income.) 
5-12 Chapter 5. Academic Research and Development

State and local government funds. State and local governments provided 7% ($3.4 billion) of higher education 

R&D funding in FY 2008. Even though their absolute 

funding total continues to rise annually, the nonfederal 

government share has declined since its peak of 10.2% 

in the early 1970s. 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.9

They exclude any general-purpose state or local government appropriations that academic institutions designate 

and use to fund separately budgeted research or pay for 

unrecovered indirect costs; such funds are categorized as 

institutional funds.

10 (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 (6%), and support of 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 increased for the fourth year in a row, to $2.9 billion in FY 

2008. (See appendix table 4-5 for time-series data on 

industry-reported R&D funding.) 

Other sources of funds. In FY 2008, other sources of 

support accounted for 8% ($4.0 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 and all other sources 

not included in the other categories. 

Expenditures by Field and Funding Source 

Investment in academic R&D historically has been concentrated in a few individual S&E fields. The life sciences 

have for decades accounted for more than half of all academic R&D expenditures. In FY 2008, they accounted for 

approximately 60% of both the federal and nonfederal totals 

(appendix table 5-5). Within the life sciences, the medical 

sciences accounted for 33% of all academic R&D expenditures and the biological sciences accounted for another 19% 

(appendix table 5-5).11

Between 1998 and 2008, R&D expenditures in the medical sciences almost doubled, from $7.7 billion to $14.1 billion 

in constant 2000 dollars (figure 5-4), changing the distribution of academic R&D expenditures across the various broad 

S&E fields. The life sciences gained 4 percentage points over 

the period, driven by a 4-percentage-point rise in the share 

of medical sciences, from 29% to 33% of the total (appendix table 5-6). The physical sciences lost 2 percentage points, 

from 10% to 8% of the total. Figure 5-5 shows share gains and 

losses in both the 1990–2000 and 2000–08 periods.

Of the $31.2 billion in academic R&D expenditures funded 

by the federal government, R&D projects in the life sciences accounted for $18.7 billion (60%) in FY 2008 (appendix table 5-7). 

	
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Science and Engineering Indicators 2010 5-13

The Department of Health and Human Services (HHS), notably 

NIH, contributes the majority of this life science funding (83%). 

Although their share of total academic R&D funding is much 

smaller, DOD, DOE, NASA, and NSF have more diversified 

funding patterns (figure 5-6). In FY 2008, NSF was the lead 

federal funding agency for academic research in the physical 

sciences (29% of federally funded R&D expenditures); mathematical sciences (47%); computer sciences (42%); and environmental sciences (34%). DOD was the lead funding agency in 

engineering (32%). 

The proportion of academic R&D expenditures funded 

by the federal government also varies significantly by field 

(appendix table 5-8). The field with the largest proportion 

of federal funding in FY 2008 was atmospheric sciences, 

at 80%, followed by physics (76%), mathematical sciences 

(72%), and aeronautical/astronautical engineering (72%). 

The fields with the smallest percentages of federal funding 

in FY 2008 were economics (32%), political science (37%), 

and agricultural sciences, which received less than 30% of 

their funds from federal sources. 

Between 1975 and 1990, the federally financed proportion of R&D spending declined in all of the broad S&E fields 

(appendix table 5-8).12 Since 1990, those declines have either stabilized or reversed, and the federal share reported in 

FY 2008 was higher than the 1990 share for all fields except 

mathematical sciences and physical sciences. 

Non-S&E R&D Expenditures

Academic institutions spent a total of $2.2 billion on 

R&D in non-S&E fields in FY 2008 (table 5-1).13 This represents an increase of 9% over the $2.1 billion spent in FY 

2007.14 This $2.2 billion is in addition to the $51.9 billion 

expended on S&E R&D. The largest amounts reported for 

R&D in non-S&E fields were for education ($880 million), 

business and management ($325 million), and humanities 

($254 million). The federal government funds smaller proportions of R&D in non-S&E than in S&E fields: 37% of the 

$2.2 billion in non-S&E R&D in FY 2008.

Academic R&D by Institution

The previous sections examined R&D for the entire academic sector. This section looks at some of the differences 

across institution types. 

	
	

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5-14 Chapter 5. Academic Research and Development

Funding for Public and Private Universities 

and Colleges 

Public and private universities rely on the same major 

sources to fund their R&D projects, but the relative contribution of those sources differs substantially (figure 5-7; appendix table 5-9). In FY 2008, the federal government provided 

72% of the R&D funds spent by private institutions, compared with 55% for public institutions. Conversely, public 

institutions received approximately 9% of their $35.3 billion in R&D expenditures from state and local governments, 

compared with 2% of private institutions’ $16.6 billion. 

Public academic institutions also supported a larger portion of their R&D from their own sources (24% versus 12% at 

private institutions). Their larger proportion of institutional 

R&D funds may reflect general-purpose state and local government funds that public institutions have directed toward 

R&D.15 Private institutions in turn report a larger proportion of unrecovered indirect costs (53% of their institutional 

total in 2008, versus 42% for public institutions). For both 

types of institutions, these shares have declined over the past 

decade, from 64% to 53% for private institutions and from 

46% to 42% for public institutions (figure 5-8).

Both public and private institutions received approximately 6% of their R&D support from industry in FY 2008. 

Table 5-1

R&D expenditures in non-S&E fields at universities and colleges: FY 2007–08

(Millions of current dollars)

2007 2008

Field

Total 

expenditures

Federal 

expenditures

Total 

expenditures

Federal 

expenditures

All non-S&E fields ............................................................................... 2,051 802 2,241 838

Business and management ............................................................ 273 52 325 65

Communication, journalism, and library science ............................ 90 31 89 29

Education ........................................................................................ 899 471 880 451

Humanities ...................................................................................... 241 60 254 63

Law ................................................................................................. 73 29 88 28

Social work ..................................................................................... 93 40 124 59

Visual and performing arts .............................................................. 46 4 59 4

Other non-S&E fields ...................................................................... 335 116 422 139

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, Division of Science Resources Statistics, Survey of Research and Development Expenditures at Universities and 

Colleges, FY 2008. 

Science and Engineering Indicators 2010

	
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Science and Engineering Indicators 2010 5-15

The share of total R&D expenditures funded by all other 

sources was also comparable, at 7% and 9%, respectively. 

Distribution of R&D Funds Across Academic 

Institutions 

Academic R&D expenditures are concentrated in a relatively small number of institutions. In FY 2008, 679 institutions reported spending at least $150,000 on S&E R&D. 

Of these, the top-spending 20 accounted for 30% of total 

academic R&D spending and the top 100 for 80% of all 

academic R&D expenditures. Appendix table 5-10 presents 

the detailed distribution among the top 100 institutions. The 

concentration of academic R&D funds among the top 100 

institutions has stayed constant over the past two decades 

(figure 5-9), as have the shares held by both the top 10 and 

the top 20 institutions. 

It should be noted that the composition of the universities in each of these groups varies 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 of funds is found among university performers of non-S&E R&D. The top 20 performers 

accounted for 36% of the total non-S&E R&D expenditures 

in FY 2008 (appendix table 5-11). 

R&D Collaboration Between Higher Education 

Institutions

One way to measure the extent of collaboration among academic institutions is to examine how much of their total R&D 

expenditures was passed through to other academic institutions or received by institutions as subrecipient funding. R&D 

funds for joint projects that were passed through universities 

to other university subrecipients more than doubled from FY 

2000 to FY 2008, from $699 million to $1.7 billion (figure 

5-10; appendix table 5-12). This amount represents 3.3% of 

total academic R&D expenditures in FY 2008, compared with 

2.3% of the total in FY 2000. In FY 2008, 90% ($1.5 billion) 

of these pass-through funds came from federal sources.

Not coincidentally, universities receiving pass-through 

funds from other universities likewise reported a rapid increase in subrecipient R&D expenditures between FY 2000 

and FY 2008, from $669 million to $1.7 billion. More than 

90% ($1.6 billion) of these subrecipient funds originated 

from federal sources.16

Overall, $3.5 billion was passed through institutions to all 

types of subrecipients in FY 2008 (including both academic 

and nonacademic institutions), and $3.9 billion was received 

as subrecipient funding from all types of pass-through entities (appendix table 5-12). Again, the majority of these funds 

were from federal sources (87% of pass-through funds and 

90% of subrecipient expenditures).

Academic R&D Equipment 

Research equipment is an integral component of the academic R&D enterprise. This section examines expenditures 

for moveable research equipment necessary for the conduct 

of organized research projects (e.g., computers, telescopes) 

and the federal role in funding these expenditures. 

	
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5-16 Chapter 5. Academic Research and Development

In FY 2008, about $1.9 billion in current funds was spent 

for academic research equipment (appendix table 5-13).17 In 

constant dollars, this represents an increase of 1.0% from FY 

2007 but a decline of more than 10% from the 2004 level. 

Overall, expenditures for R&D equipment have risen 61% in 

real dollars since 1985. About 80% of FY 2008 expenditures 

were concentrated in three fields: the life sciences (43%), engineering (23%), and the physical sciences (16%) (appendix 

table 5-13). After a period of steady growth between 2001 

and 2004, equipment expenditures in the physical sciences, 

medical and biological sciences, and engineering have declined since FY 2005 (figure 5-11). 

Federal funds for research equipment are generally received as part of research grants or as separate equipment 

grants. The share of federal funding for research equipment 

varies significantly by field (appendix table 5-14). The field 

of atmospheric sciences had the largest proportion of federally 

funded R&D equipment (85%) in FY 2008. The overall share 

of research equipment funded by the federal government fluctuated between 56% and 64% over the past two decades. 

Academic R&D Infrastructure

Physical infrastructure is an essential resource for the conduct 

of R&D. Not long ago, R&D capital infrastructure primarily 

consisted of instruments and research space such as laboratories 

and computer rooms. Consequently, the principal indicators of 

the state of research infrastructure have been square footage of 

designated research space and counts of instruments. 

Over the past two decades, technological advances have 

brought fundamental changes not only in the methods of scientific research but also in the infrastructure necessary to 

conduct R&D. The infrastructure resulting from these technological innovations is often called cyberinfrastructure. 

Cyberinfrastructure may involve single resources such as a 

network used to transfer data, or it may involve a complex 

interaction of numerous resources resulting in sophisticated 

capabilities such as high-performance computation or remote use of scientific instrumentation. Regardless of how 

simple or complex this infrastructure may be, cyberinfrastructure has become an essential resource for science. 

Cyberinfrastructure: Networking 

Networking is a critical component of academic cyberinfrastructure that facilitates many research-related activities such as 

communication, data transfer, high-performance computation, 

and remote use of instrumentation.18 In FY 2007, networking infrastructure was pervasive on many academic campuses 

and rapidly expanding in capability and coverage. Researchperforming institutions19 had greater numbers of 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 wireless coverage on campus.

External Bandwidth

Early in the decade, some institutions reported no Internet1 

connections of any kind, but by mid-decade, all institutions 

had connections and their bandwidths significantly increased. 

Between FY 2005 and FY 2007, the number of institutions 

with total Internet1 and Internet2 bandwidth of more than 100 

megabits increased almost 30% (table 5-2). At the same time, 

the number of institutions with the fastest bandwidth speeds 

also continued to expand. The percentage of institutions with 

total Internet1 and Internet2 bandwidth of 1 gigabit or faster 

rose by more than 50% in FY 2007, reaching 34% of all institutions. If institutional estimates are realized, the percent 

of institutions with total bandwidth of 1 gigabit or faster will 

double between FY 2005 and FY 2008 to 42%.

Bandwidth capability increased across different types of 

academic institutions. However, the colleges and universities 

with the fastest bandwidths were dominated by doctorategranting institutions. In FY 2007, all but one institution with 

total Internet1 and Internet2 bandwidth greater than 2.4 gigabits granted doctorates. Of all institutions with bandwidth of 

at least 1 gigabit, 83% were doctorate granting. This trend is 

likely to continue into FY 2008 and beyond. If institutions 

	


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Science and Engineering Indicators 2010 5-17

achieve their estimates for FY 2008, there will be a 70% increase in the number of institutions with bandwidths of greater than 2.4 gigabits. All but two will be doctorate granting. 

Part of the increase in total bandwidth speed can be at least 

partially attributed to an increase in the number of connections to high-performance networks. The number of connections to Internet2 grew gradually over the decade, and by the 

end of FY 2007, a large majority (70%) of institutions had 

Internet2 connections. Between FY 2005 and FY 2007, the 

percentage of institutions with connections to the National 

Lambda Rail (NLR) increased 150% to approximately 25% 

of all institutions. After holding steady since the beginning 

of the decade, the number of institutions anticipating connections to federal government high-performance networks 

such as the Department of Energy’s ESnet increased in FY 

2007. Institutions have also begun connecting to more than 

one high-performance network. For example, in FY 2007, 

25% had connections to both Internet2 and NLR.

Internal Institutional Networks

Similar to the trend of increased external bandwidth speed, 

internal network distribution speeds at academic institutions 

increased considerably. Since early in the 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 of less than 1 gigabit; by the end of FY 2007, only 25% 

did (table 5-3). In FY 2003, no institutions had distribution 

speeds faster than 2.5 gigabits, but by FY 2007, 13% of academic institutions did. By FY 2007, the large majority (74%) 

of institutions had distribution speeds of 1 gigabit or faster. 

In FY 2007, all but one academic institution 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 

2007, the comparable figure was 59%.

Bricks and Mortar

Research Space

Research-performing colleges and universities continued 

a two-decade trend of increasing the amount of research 

space at their institutions.20 By FY 2007, academic institutions had 192 million net assignable square feet (NASF) of 

research space (table 5-4). In recent years though, the rate 

of increase in research space has begun to slow. During 

Table 5-3

Highest internal network speeds, by highest 

degree granted: FY 2003–08

(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

 D10 mb ............ 001

 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

 D10 mb ............ 111

 11–999 mb ...... 24 18 39

 1–2.4 gb .......... 61 63 55

 2.5–9 gb .......... 2 2 1

 10 gb ............... 10 14 3

 >10 gb ............. 1 2 0

 Other ............... 1 1 1

FY 2008 .................. 100 100 100

 D10 mb ............ 111

 11–999 mb ...... 20 14 34

 1–2.4 gb .......... 51 51 51

 2.5–9 gb .......... 4 4 2

 10 gb ............... 21 26 10

 >10 gb ............. 2 3 1

 Other ............... 1 1 1

* = >0 but <0.5%

gb = gigabits per second; mb = megabits per second

NOTE: FY 2008 data estimated. Percents may not add to 100% 

because of rounding.

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Survey of Science and Engineering Research 

Facilities, Fiscal Years 2003–07.

Science and Engineering Indicators 2010

Highest degree granted

Table 5-2

Bandwidth to commodity Internet (Internet1) and 

Internet2 at academic institutions: FY 2005–08 

(Percent distribution)

Bandwidth FY 2005 FY 2007 FY 2008

All bandwidth ...................... 100 100 100

No bandwidth ................. 0 0 0

10 mb ............................ 6 32

11–100 mb ...................... 42 33 24

101–999 mb .................... 30 31 30

1–2.5 gb .......................... 15 23 26

>2.5 gb ............................ 6 10 16

Other ............................... * 1 1

 * = >0 but <0.5%

gb = gigabits/second; mb = megabits/second

NOTES: 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. FY 2008 

data estimated. Percents may not add to 100% because of rounding. 

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Survey of Science and Engineering Research 

Facilities, Fiscal Years 2005 and 2007.

Science and Engineering Indicators 2010
5-18 Chapter 5. Academic Research and Development

FY 2005–07, institutions increased their research space at 

the slowest rate (3.7%) since FY 1998–99 (table 5-4). The 

rate of increase peaked in FY 2001–03 at 11%. Since then, 

the rate of increase has gradually declined. 

Although the stock of research space increased overall in 

FY 2005–07, for the first time in 20 years, almost half of the 

S&E fields21 experienced a decline in their research NASF. 

Particularly notable is that for the first time since FY 1988, 

the health and clinical sciences experienced an actual loss in 

research space of 7%. This decrease followed some of the 

largest increases in research space in any S&E field since the 

beginning of decade. 

Even with the decline in FY 2007 though, the health 

and clinical sciences still had the second largest amount 

of research NASF (37 million) of all S&E fields. Only the 

biological and biomedical sciences had a greater amount of 

square footage (46 million), having increased 18% from the 

amount reported in FY 2005. In addition to the health and 

clinical sciences, the social sciences; the physical science 

subfields of earth, atmospheric and ocean sciences and astronomy, chemistry, and physics; and the fields classified as 

“other” experienced losses in research space. Only the earth, 

atmospheric, and ocean sciences had experienced a loss in 

the previous biennial period.

Continuing a trend that began in FY 2001, research space 

in the computer sciences once again experienced the largest rate of increase when compared with all other fields. In 

FY 2007, research space in the computer sciences increased 

20% to approximately 5 million NASF.

New Construction

In conjunction with the slowdown in the increase in research space, the total amount of newly constructed research 

space also began to slow at the beginning of the decade 

(table 5-5). Since FY 2002–03, the total amount of new research space constructed declined by approximately 45%. 

However, within this overall broad decline, the amount and 

direction of change in new construction varied significantly 

by S&E field.

During FY 2006–07, initiation of construction of new research space was greatest in the biological and biomedical 

sciences (3 million NASF), the health and clinical sciences 

(2 million NASF), and engineering (1 million NASF). Relative to previous years, however, these three fields experienced 

a decline in the amount of new construction. The amount 

of newly constructed research space in engineering and in 

the biological and biomedical sciences declined from FY 

2002–03 to FY 2004–05 and again, to a lesser extent, in FY 

2006–07. In the health and clinical sciences, the amount of 

newly constructed research space declined 34% from FY 

2002–03 to FY 2004–05. From FY 2004–05 to FY 2006–07, 

it declined another 48%.

Three fields of science reversed the decline in the amount 

of newly constructed space from earlier in the decade: the 

physical, computer, and agricultural sciences. The amount 

of newly constructed research space in the physical sciences 

increased 25% between FY 2004–05 and FY 2006–07, with 

the majority of new space located in astronomy, chemistry, 

and physics.

Table 5-4

S&E research space in academic institutions, by field: FY 1988–2007

(Millions of net assignable square feet)

Field 1988 1990 1992 1994 1996 1998 1999 2001 2003 2005 2007

All fields ............................................................. 112 116 122 127 136 143 148 155 172.7 185.1 191.9

Agricultural and natural resources ................. 18 21 20 20 22 25 24 27 26.4 26.8 28.4

Biological and biomedical sciences .............. 24 27 28 28 30 31 32 33 36.0 38.5 45.6

Computer and information sciences.............. 1 1 2 2 2 2 2 2 3.1 4.1 4.9

Engineering .................................................... 16 17 18 21 22 23 24 26 27.4 28.9 30.2

Health and clinical sciences .......................... 19 20 22 23 25 25 26 28 34.9 39.7 37

Mathematics and statistics ............................ 1 1 1 1 1 1 1 1 1.5 1.6 1.7

Physical sciences .......................................... 22 22 23 24 25 26 27 27 29.3 29.6 29.3

Earth, atmospheric, and ocean sciences ... 6 6 7 7 7 8 8 8 8.9 8.6 8.5

Astronomy, chemistry, and physics............ 16 16 16 17 18 18 19 19 20.4 21.0 20.8

Psychology .................................................... 3 3 3 3 3 3 3 4 4.4 4.8 5.0

Social sciences .............................................. 3 3 3 3 4 5 5 5 5.7 6.3 6.2

Other sciences ............................................... 4 2 2 2 2 3 3 3 3.8 4.9 3.7

 

Research animal space .................................. na na 9 11 12 12 13 na 16.7 16.5 18.3

na = not applicable, question not asked

NOTES: National Science Foundation, Division of Science Resources Statistics (NSF/SRS) bases S&E fields used in its Survey of Science and Engineering 

Research Facilities on those in National Center for Education Statistics (NCES) Classification of Instructional Programs (CIP). NCES updates CIP every 10 

years. S&E fields used in FY 2007 Survey of Science and Engineering Research Facilities reflect NCES 2000 CIP update. For comparison of subfields in 

FY 2005 and FY 2007 surveys, see S&E Research Facilities: FY 2007, detailed statistical tables. Detail may not add to total because of rounding. Research 

animal space listed separately and also included in individual field totals. 

SOURCE: NSF/SRS, Survey of Science and Engineering Research Facilities, Fiscal Years 1987–2007.

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 5-19

In FY 2006–07, the funding of newly constructed research space returned to the pattern prevalent since the mid1990s. Institutions use one or more sources to fund their 

capital projects, including the federal government, state or 

local governments, and the institutions’ own funds. In the 

previous biennial period, the proportion of new construction 

costs funded by state and local governments dropped significantly relative to other funding sources, to 22%. Concurrently, funding by the institutions’ own sources rose to 70% of 

all new construction funds ($5.8 billion).22 In FY 2006–07, 

state and local governments returned to funding about onethird of new construction, while institutional internal sources funded about 60%. The remaining funding came from the 

federal government. The federal proportion of funding has 

remained relatively stable and small over time.

Doctoral Scientists 

and Engineers in Academia

The role of research in U.S. universities is both to create 

new knowledge and to educate students who will become 

the future generations of researchers and teachers (Association of American Universities 2006). Doctoral scientists and 

engineers in academia, and in particular faculty in U.S. colleges and universities, are an important aspect of academic 

R&D, as they generally engage in both research and teaching. The focus of this section is on the research aspects of 

doctoral scientists and engineers in academia. 

This section examines trends in employment and labor 

market conditions of doctoral scientists and engineers in U.S. 

universities and colleges, with a particular focus on research activity. Trends in and characteristics of S&E doctoral researchers, including young investigators, are discussed as well as 

trends in government support for research. Chapter 3 provides 

more information on the workforce as a whole, and chapter 2 

provides information on the output of students and degrees.

Trends in Academic Employment of Doctoral 

Scientists and Engineers

Academic employment of doctoral scientists and engineers grew over the past three decades, although growth 

was slower than in the business or government sectors. As 

a result, the share of all S&E doctorate holders employed in 

academia dropped from about 55% to 45% during the 1973–

2006 period (NSB 2008).23 The number of S&E doctorate 

holders employed in academia grew from 118,000 in 1973 to 

272,800 in 2006 (table 5-6). Mirroring trends in R&D funding, life scientists accounted for much of the growth in academic employment. In engineering and many science fields, 

growth in academic employment slowed in the early 1990s 

but increased in recent years (figure 5-12).

Although full-time faculty positions continue to be the 

norm in academic employment, S&E doctorate holders increasingly are employed in part-time, postdoc, or full-time 

nonfaculty positions. From 1973 to 2006, the share of S&E 

doctorate holders employed in full-time faculty positions 

Table 5-5

New construction of S&E research space in academic institutions, by field and time of construction: FY 2002–07

(Millions of net assignable square feet)

Field

Started in FY 2002 

or FY 2003

Started in FY 2004 

or FY 2005

Started in FY 2006 

or FY 2007

All fields .................................................................................................. 16.2 10.2 8.9

Agricultural and natural resources ...................................................... 0.8 0.4 0.5

Biological and biomedical sciences ................................................... 4.0 3.2 3.0

Computer and information sciences................................................... 1.0 0.3 0.6

Engineering ......................................................................................... 2.2 1.5 1.3

Health and clinical sciences ............................................................... 5.0 3.3 1.7

Mathematics and statistics ................................................................. * * *

Physical sciences ............................................................................... 2.1 0.8 1.0

Earth, atmospheric, and ocean sciences ........................................ 0.6 0.3 0.3

Astronomy, chemistry, and physics................................................. 1.5 0.5 0.7

Psychology ......................................................................................... 0.2 0.2 0.1

Social sciences ................................................................................... 0.2 0.1 0.1

Other sciences .................................................................................... 0.7 0.3 0.7

 

Research animal space ....................................................................... 1.4 1.2 1.0

* = >0 but <50,000 net assignable square feet

NOTES: National Science Foundation, Division of Science Resources Statistics (NSF/SRS) bases S&E fields used in its Survey of Science and Engineering 

Research Facilities on those in National Center for Education Statistics (NCES) Classification of Instructional Programs (CIP). NCES updates CIP every 10 

years. S&E fields used in FY 2007 Survey of Science and Engineering Research Facilities reflect NCES 2000 CIP update. For comparison of subfields in 

FY 2005 and FY 2007 surveys, see S&E Research Facilities: FY 2007, detailed statistical tables. Detail may not add to total because of rounding. Research 

animal space listed separately and also included in individual field totals. 

SOURCE: NSF/SRS, Survey of Science and Engineering Research Facilities, Fiscal Years 2003–2007.

Science and Engineering Indicators 2010
5-20 Chapter 5. Academic Research and Development

decreased while the share employed in postdoc or other fulland part-time positions increased (table 5-6 and figure 5-13). 

The full-time faculty share was 72% of all academic employment in 2006, down from 88% in the early 1970s. The 

full-time nonfaculty share rose from 6% in 1973 to 13% in 

2006. Part-time positions accounted for only a small share 

(between 2% and 4%) of all academic S&E doctoral employment throughout most of the period before rising to almost 6% in 2006. Postdocs rose from 4% in 1973 to 9% of 

all academically employed S&E doctorate holders in 2006. 

The lack of growth in the number of tenured and tenuretrack positions in the life sciences, concurrent with increasing numbers of new doctorate holders, has been a subject 

of much focus in recent years (Benderly 2004, NRC 2005, 

Check 2007, Garrison and McGuire, 2008). Although the 

number of tenured full-time faculty in all fields increased 

from 90,700 in 1979 to 122,500 in 2006, their percentage of 

the academic workforce decreased from 69% to 62% (appendix table 5-15). This decline is largely accounted for by 

decreases in the life sciences (from 65% to 56%) and physical sciences (from 74% to 65%). Despite large increases in 

Table 5-6

S&E doctorate holders employed in academia, 

by position: Selected years, 1973–2006

(Percent)

Position 1973 1983 1993 2006

All positions (number in 

thousands) ....................... 118.0 176.1 213.8 272.8

Full-time professors ....... 36.1 39.7 37.4 31.3

Full-time associate 

professors ................... 26.6 26.0 22.7 19.8

Full-time junior faculty .... 24.8 18.6 20.5 21.2

Full-time nonfaculty ....... 6.4 7.6 10.4 13.4

Postdocs ........................ 3.6 4.7 6.2 8.5

Part-time positions ........ 2.5 3.4 2.8 5.8

NOTES: Academic employment limited to U.S. doctorate holders 

employed at 2- or 4-year colleges or universities. Full-time junior 

faculty includes assistant professors and instructors, and full-time 

nonfaculty includes positions such as research associates, adjunct 

appointments, lecturers, and administrative positions. Total excludes 

those employed part time because they are students or retired. 

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Survey of Doctorate Recipients, special 

tabulations. 

Science and Engineering Indicators 2010

	


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Science and Engineering Indicators 2010 5-21

academic R&D expenditures (appendix table 5-6) and in the 

number of doctorates granted in the life sciences (appendix 

table 2-26), the number of tenured and tenure-track life scientists has remained fairly stable since the late 1980s (appendix 

table 5-15). 

Research as Either Primary or Secondary 

Work Activity 

About two-thirds of doctoral scientists and engineers 

employed in academic institutions are engaged in research 

as either a primary or secondary work activity. From 1973 

to 2006, the number of academic S&E doctorate holders 

reporting research as a primary or secondary work activity 

showed greater growth than the number reporting teaching 

as a primary or secondary activity (table 5-7). On average, the 

number of researchers grew 2.5% per year and the number 

of teachers grew 1.7% per year. 

The life sciences accounted for much of this trend, with 

the number of life science researchers growing from 26,000 

to 66,700, an average annual growth rate of 2.9% (table 

5-8 and appendix table 5-16). Life scientists accounted for 

more than one-third of academic doctorate holders reporting 

research as a primary or secondary work activity in 2006. 

The number of researchers in computer sciences grew the 

fastest, at 16.3% from 1979 to 2006, although from a small 

base. The number of academic researchers in the physical 

sciences and mathematics grew more slowly, at average annual growth rates of 1.1% and 1.6%, respectively, from 1973 

to 2006. Growth rates for academic researchers in all fields 

were greatest in the 1980s. From 1979 to 1989, the average 

Table 5-7

S&E doctorate holders employed in academia with research or teaching as primary or secondary work activity, by 

degree field: 1973 and 2006

(Thousands)

1973 2006 Growth rate 1973–2006 (%)

Degree field Research Teaching Research Teaching Research Teaching

All fields ............................................ 82.3 94.9 183.7 163.3 2.5 1.7

Physical sciences ......................... 18.8 20.2 26.9 24.1 1.1 0.5

Mathematics ................................. 6.8 8.8 11.4 13.8 1.6 1.4

Computer sciences ....................... NA NA 4.1 4.0 NA NA

Life sciences ................................. 26.0 25.3 66.7 45.4 2.9 1.8

Psychology ................................... 7.3 9.8 20.5 21.1 3.2 2.3

Social sciences ............................. 14.3 20.2 32.8 37.9 2.5 1.9

Engineering ................................... 9.0 10.5 21.4 17.1 2.7 1.5

NA = not available

NOTES: Physical sciences include earth, atmospheric, and ocean sciences. Academic employment limited to U.S. doctorate holders employed at 2- or 

4-year colleges or universities. Total excludes those employed part time because they are students or retired. Research includes basic or applied research, 

development, or design. Because individuals may select both a primary and a secondary work activity, they can be counted in both groups. 

SOURCE: National Science Foundation, Division of Science Resources Statistics, Survey of Doctorate Recipients, 1973 and 2006, special tabulations. 

Science and Engineering Indicators 2010

Table 5-8

Average annual growth rate for employment of S&E doctorate holders in academia reporting research as a 

primary or secondary work activity, by degree field: 1973–2006

(Percent)

Degree field 1973–2006 1973–79 1979–89 1989–99 1999–2006

All fields ................................................................ 2.5 1.5 5.4 1.0 1.3

Physical sciences ............................................. 1.1 –0.5 3.5 0.6 –0.2

Mathematics ..................................................... 1.6 0.2 4.0 –0.4 2.0

Computer sciences ........................................... NA NA 34.4 7.1 6.4

Life sciences ..................................................... 2.9 3.6 4.9 1.6 1.3

Psychology ....................................................... 3.2 2.1 5.7 1.8 2.5

Social sciences ................................................. 2.5 0.4 7.6 0.1 0.8

Engineering ....................................................... 2.7 1.5 6.0 1.0 1.4

NA = not available

NOTES: Physical sciences include earth, atmospheric, and ocean sciences. Academic employment limited to U.S. doctorate holders employed at 2- or 

4-year colleges or universities and excludes those employed part time because they are students or retired. Research includes basic or applied research, 

development, and design. 

SOURCE: National Science Foundation, Division of Science Resources Statistics, Survey of Doctorate Recipients, special tabulations.

Science and Engineering Indicators 2010
5-22 Chapter 5. Academic Research and Development

annual growth rate for doctoral scientists and engineers with 

research as a primary or secondary work activity was more 

than 5% per year, compared with less than 2% per year in the 

1970s, 1990s, and 2000s.

Demographic Characteristics of Academic 

Researchers 

The demographic composition of doctoral S&E researchers has changed over the past three decades, reflecting changes in the student population (see chapter 2 and 

appendix table 5-17). As women, minorities, and foreignborn researchers became an increasing share of academic 

researchers over the past two decades, men, and particularly 

white men, became a decreasing share.

Women in the Doctoral S&E Workforce. In 2006, 33% 

of all S&E doctorate holders employed in academia were 

women, up from 9% in 1973 (table 5-9). This rise reflects the 

increase in the proportion of women among recent S&E doctorate holders. Women hold a larger share of junior faculty 

positions than positions at either the associate or full professor rank (figure 5-14). In 2006, women constituted 25% of 

full-time senior faculty (full and associate professors) and 

42% of full-time junior faculty (assistant professors and lecturers), the latter slightly higher than their share of recently 

earned S&E doctorates (table 5-9; see also “Doctoral Degrees by Sex” in chapter 2). However, their share of both junior and senior faculty positions rose substantially between 

1973 and 2006. Although women are growing numbers of 

full-time faculty, they constitute more than half of academic 

S&E doctorate holders employed part time. 

The percentage of women among full-time doctoral 

S&E research faculty is similar to the percentage of women 

among all S&E doctorate holders employed in academia. 

Women increased from 6% to 29% of full-time doctoral 

S&E research faculty from 1973 to 2006 (appendix table 

5-16). Women’s representation in some fields is higher than 

in others. Women make up almost half of full-time faculty 

researchers in psychology, about one-third of those in life 

sciences and social sciences,24 and 11% of those in engineering (figure 5-15 and table 5-9). 

Women are also a growing percentage of faculty at research institutions—up from 8% in 1977 to 23% in 2003—

yet they remain less well represented at these institutions 

than at freestanding medical schools or at master’s granting 

institutions (NSF/SRS 2008). (See sidebar “Women Faculty 

at Research Universities.”) For a more complete discussion 

of the role of women, see Women, Minorities, and Persons 

with Disabilities in Science and Engineering: 2009 (NSF/

SRS 2009b).

Racial/Ethnic Groups in the Academic Doctoral Workforce. Asians and underrepresented minorities (blacks, Hispanics, and American Indians/Alaska Natives) constitute 

increasing shares of the academic doctoral workforce (table 

5-10 and figure 5-16).25 Between 1973 and 2006, the percentages of Asians and underrepresented minorities in the 

S&E academic doctoral workforce increased from 4% to 

14% and 2% to 8%, respectively. These changes reflect increases in these groups’ shares of recently earned doctorates. 

See “Doctoral Degrees by Race/Ethnicity” in chapter 2 for 

trends in doctoral degrees.

Table 5-9

Women as percentage of S&E doctorate holders, by position: Selected years, 1973–2006

Position and degree field 1973 1983 1993 2006

All positions ............................................................................... 9.1 15.0 21.9 33.0

Full-time senior faculty........................................................... 5.8 9.3 14.2 25.0

Full-time junior faculty ........................................................... 11.3 23.5 32.2 42.3

Full-time faculty researchers .................................................. 6.3 11.4 17.7 28.6

Physical sciences ............................................................... 3.0 4.6 7.7 15.2

Mathematics ....................................................................... 4.7 7.4 9.0 18.8

Computer sciences ............................................................ NA 9.2 15.8 22.9

Life sciences ...................................................................... 7.9 12.6 21.5 32.9

Psychology ......................................................................... 13.1 23.8 35.4 48.9

Social sciences .................................................................. 8.2 16.3 21.5 33.7

Engineering ........................................................................ 0.3 2.0 4.1 11.2

Full-time nonfaculty ............................................................... 14.5 23.1 30.2 36.2

Postdocs ................................................................................ 14.3 30.1 30.8 40.8

Part-time positions ................................................................ 48.3 41.7 61.0 51.5

NA = not available

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; and full-time nonfaculty includes positions such as research associates, adjunct 

appointments, lecturers, and administrative positions. Total excludes those employed part time because they are students or retired. Physical sciences 

include earth, atmospheric, and ocean sciences. Research includes basic or applied research, development, and design. 

SOURCE: National Science Foundation, Division of Science Resources Statistics, Survey of Doctorate Recipients, special tabulations. 

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 5-23

Among full-time doctoral research faculty, Asians increased from 4% to 13% from 1973 to 2006, and underrepresented minorities increased from 2% to 8%. Because of 

these increases, the proportion of full-time doctoral research 

faculty who are white decreased from 92% in 1973 to 79% 

in 2006 (appendix table 5-17). 

Underrepresented minorities constituted a smaller share 

of employment at research universities than other racial/ethnic groups. In 2006, 35% of underrepresented minority S&E 

doctorate holders employed in academia were employed 

in research institutions, compared with 51% of Asian and 

42% of white S&E doctorate holders employed in academia 

(NSB 2008). Notably, in 2003, the percentage of black S&E 

faculty employed at research universities (28%) was lower 

than the percentage employed at comprehensive universities 

(31%), largely because of this group’s prevalence in historically black colleges and universities, most of which are comprehensive institutions (NSF/SRS 2006).26

	
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5-24 Chapter 5. Academic Research and Development

The distribution of racial/ethnic groups within S&E fields 

differs (appendix table 5-18). The percentage of underrepresented minorities among full-time faculty researchers ranges 

from about 5% in the physical sciences to about 10% in the 

social sciences. Asians are more heavily represented in engineering and computer sciences (where they constitute 26% 

and 37% of full-time faculty researchers, respectively) and 

represented at very low levels in psychology (4%) and social 

sciences (8%). For a more complete discussion of the role 

of Asians and underrepresented minorities, see Women, Minorities, and Persons with Disabilities in Science and Engineering: 2009 (NSF/SRS 2009b). 

Women Faculty at Research Universities

Table 5-10

Minorities as percentage of S&E doctorate holders, by position: Selected years, 1973–2006

1973 1983 1993 2006

Position and degree field

Underrepresented 

minority Asian

Underrepresented 

minority Asian

Underrepresented 

minority Asian

Underrepresented 

minority Asian

All positions ........................................... 2.0 4.2 3.7 6.7 5.0 9.8 8.2 14.1

Full-time faculty ................................. 1.9 3.9 3.6 6.1 5.0 8.6 7.9 11.7

Full-time faculty researchers .............. 1.7 4.3 3.2 7.4 4.8 10.1 7.6 13.5

Physical sciences ........................... 1.8 4.0 3.0 7.7 4.5 10.9 4.6 11.5

Mathematics ................................... 1.8 4.4 2.8 10.0 3.3 14.0 7.5 19.3

Computer sciences ........................ NA NA NA 20.7 7.3 36.3 6.6 36.8

Life sciences .................................. 2.0 3.6 2.8 6.5 3.7 8.2 7.3 13.2

Psychology ..................................... 1.7 S 3.1 1.9 5.7 2.2 8.5 4.3

Social sciences .............................. 1.7 4.3 4.8 5.4 7.0 6.2 9.9 8.1

Engineering .................................... 0.9 9.5 2.7 14.8 3.9 21.6 7.3 25.7

Postdocs ............................................ 2.4 11.9 4.8 13.3 4.5 27.1 7.5 35.4

Other positions .................................. 2.9 5.7 4.1 8.2 5.3 8.9 9.3 13.7

NA = not available; S = data suppressed for reasons of confidentiality and/or reliability

NOTES: Underrepresented minority includes blacks, Hispanics, and American Indians/Alaska Natives. Asian includes Pacific Islanders through 1999, but 

excludes in 2001 through 2006. 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 nonfaculty positions such as research associates, 

adjunct appointments, lecturers, and administrative positions. Total excludes those employed part time because they are students or retired. Physical 

sciences include earth, atmospheric, and ocean sciences. Research includes basic or applied research, development, and design. 

SOURCE: National Science Foundation, Division of Science Resources Statistics, Survey of Doctorate Recipients, special tabulations. 

Science and Engineering Indicators 2010

In a congressionally mandated study of women faculty 

in research universities, the National Research Council 

(2009) found that women faculty do as well as or better 

than men in hiring, promotions, and access to university 

resources. The study focused on tenured or tenure-track 

faculty in 6 disciplines (biology, chemistry, civil engineering, electrical engineering, mathematics, and physics) at 89 research universities. Women constituted 12% 

of the faculty in the disciplines and universities studied. The study found that in these research universities, 

women were a lower percentage of applicants for tenured 

or tenure-track positions than they were of recent doctorates, especially in chemistry and biology. However, 

women were a higher percentage of interviewees than 

of applicants and a higher percentage of those hired than 

of interviewees. The study also found that women constituted a lower percentage of tenure candidates than of 

assistant professors, but that among those up for tenure 

review, women were more likely than men to receive tenure. The study found little difference in lab space, equipment, or percentage of time teaching or doing research 

and little difference in outcomes (e.g., honors, funding, 

salaries) of tenured or tenure-track faculty, with a few 

exceptions, including salaries of full professors and publications. Because of its specific mandate, the report did 

not address women who did not apply to research universities or those who left research universities, but noted 

the need for further research in these areas. By necessity, 

it did not address other types of academic employment, 

other types of academic institutions, or other issues affecting women’s employment in academia, including 

dual careers, effects of children and family obligations, 

or institutional climate. Many other studies of women in 

academia address some of these issues (e.g., Long 2001; 

COSEPUP 2007; NSF 2004; Ginther 2001; Hosek et al. 

2005; Rosser, Daniels, and Wu 2006; Fox 2005).
Science and Engineering Indicators 2010 5-25

Foreign-Born Doctorate Holders in the Academic Doctoral S&E Workforce. Foreign-born S&E doctorate holders contribute substantially to academic R&D in the United 

States. Reliance by U.S. colleges and universities on foreign 

talent increased during the 1990s. Chapter 3 discusses more 

fully trends in immigration and employment characteristics 

of foreign-born scientists and engineers.

Approximately 31,400 noncitizens (permanent residents 

and temporary visa holders) and 31,300 naturalized U.S. citizens with a U.S. S&E doctorate were employed in academia 

in 2006 (appendix table 5-19). In addition, U.S. universities 

and colleges employ an unknown but probably large number 

of foreign-born S&E doctorate holders with doctorates from 

foreign universities.27 (Chapter 3 of Science and Engineering Indicators 2008 [NSB 2008] estimated that about 36% 

of foreign-born S&E doctorate holders had foreign-earned 

doctorates.) The discussion in this section is limited to U.S. 

doctorate holders. 

Foreign-born S&E doctorate holders (both noncitizens 

and naturalized citizens) with U.S. S&E doctorates were 

23% of the total academic doctoral S&E workforce in 2006 

and close to half (47%) of academic postdocs (appendix 

table 5-19). Foreign-born S&E doctorate holders constitute 

a higher percentage of researchers than of all academically 

employed S&E doctorate holders. In 2006, they represented 

27% of all academic researchers, regardless of rank or type 

of position, 24% of full-time faculty researchers, and 20% of 

all full-time faculty. U.S. S&E doctorate holders with temporary or permanent visas increased from about 4% of fulltime faculty researchers with U.S. doctorates in 1973 to 10% 

in 2006 (appendix table 5-17).

Foreign-born S&E doctorate holders with U.S. doctorates are more heavily concentrated in computer sciences, 

mathematics, and engineering than in other fields. These 

foreign-born doctorate holders account for more than half of 

all academic researchers and of full-time faculty researchers in computer sciences and for 39%–48% of all academic 

researchers and full-time faculty researchers in mathematics 

and engineering. In contrast, they represent 27% or less of 

all academic researchers and 21% or less of full-time faculty 

researchers in the life sciences, the physical sciences, psychology, and the social sciences (appendix table 5-19). 

Recent S&E Doctorate Holders

Many doctoral candidates aspire to an academic, tenured 

faculty position, even though nonacademic employment has 

for many years exceeded that in universities and colleges, 

and the composition of academic hiring has changed as relatively fewer full-time faculty and relatively more part-time 

and nonfaculty are hired. Nevertheless, the relative availability of faculty positions is thought to have provided market signals to aspiring graduate students. 

Over the past three decades, the share of recent doctorate 

holders (i.e., those with doctorates earned within 3 years of 

the survey) in full-time tenured or tenure-track faculty positions decreased and the prevalence of postdoc positions 

increased (figure 5-17). Between 1979 and 2006, the share 

of recent doctorate holders hired into full-time tenured or 

tenure-track faculty positions fell from 42% to 29%. Conversely, the overall share of recent S&E doctorate holders 

who reported being in postdoc positions rose from 25% to 

45% during that period. (See the discussion of postdocs in 

chapter 3, “Science and Engineering Labor Force,” for more 




	



	
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5-26 Chapter 5. Academic Research and Development

information, including reasons for accepting a postdoc position and short-term career trajectory.) The share employed 

in part-time positions also rose in the 1970s and early 1980s 

but remained at roughly 5% from 1985 through 2006. The 

share employed in other full-time positions (e.g., adjunct 

faculty, lecturers, research associates, administrators) remained fairly stable over the period except for decreases 

from 1979 to 1981 and from 2003 to 2006.

The percentage of recent S&E doctorate holders and recent full-time S&E doctoral faculty engaged in research is 

higher than is the case for those who have had their doctorate 

for 12 or more years (table 5-11).28 In some fields (e.g., computer sciences and engineering), research is a more prevalent 

activity among those who have had their doctorate for less 

than 8 years. In the life sciences, although research is most 

prevalent within 1 to 3 years of award of the doctorate, a 

relatively high percentage of doctorate holders remain in research, even among those with more experience. 

Young Doctorate Holders With a Track Record 

For those employed in academia 4–7 years after earning 

their doctorate, the trends are quite similar to those for doctorate holders who have had their degree for 1–3 years, although the former group’s percentage employed in postdoc 

positions is much smaller and their percentage employed in 

faculty positions much larger. About half of S&E doctorate 

holders who have had their degree for 4–7 years had fulltime tenured or tenure-track faculty positions in 2006, down 

from 64% in 1979 (figure 5-18). The percentage in postdoc 

positions rose from 6% to 15%, and the percentage in parttime positions rose from 3% to 6%. The percentage employed in full-time, non-tenure-track, nonfaculty positions 

changed little over time. 

Government Support of Academic Doctoral 

Researchers 

Academic researchers rely on the federal government 

for a majority of their overall research support. This section 

presents data from reports by S&E doctorate holders in academia about the presence or absence of federal support for 

their work.29 However, nothing is known about the amount 

of funds received by individual researchers. 

In 2006, 46% of full-time S&E doctoral faculty reported 

federal government support, about the same percentage as 

was the case in the late 1980s and only slightly higher than 

in 1973 (figure 5-19). As appendix table 5-20 shows, the 

percentage of S&E doctorate holders in academia who received federal support and the percentage of full-time S&E 

faculty who received federal support differed greatly across 

the S&E fields. In 2006, more than half of full-time S&E 

doctoral faculty in the physical sciences, the life sciences, 

and engineering and less than half of those in mathematics, computer sciences, psychology, and the social sciences 

received federal support. The percentage receiving federal 

support was lowest in social sciences (24%). 

The percentage with federal support was higher among 

S&E doctorate holders in research universities (64%) and 

medical schools (70%) and lower among those employed in 

doctoral/research universities (28%), master’s-granting universities (21%), and baccalaureate colleges (22%) (appendix 

table 5-20). 

Federal Support of Young S&E Doctorate Holders 

in Academia

Early receipt of federal support is viewed as critical to 

launching a promising academic research career, yet federal 

support is less available to young S&E faculty with doctorates than to more established faculty, and the percentage of 

young S&E faculty with federal support is declining. Among 

full-time faculty, the percentage reporting federal support in 

2006 was lower for those with recently earned doctorates 

than for all full-time faculty. Moreover, for younger faculty 

as well as all faculty, the percentage reporting federal support was lower in 2006 than in 1991 (a peak year) (figure 

5-19). It should be pointed out that these data provide no 

information about whether an individual reporting federal 

support is being supported as a principal investigator on a research project or is participating in a more dependent status 

rather than as an independent researcher.

Among S&E doctorate holders with recently earned doctorates, those in full-time faculty positions were less likely 

to receive federal support than those in postdoc or other 

	
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Science and Engineering Indicators 2010 5-27

Table 5-11

S&E doctorate holders employed in academia and full-time S&E faculty reporting research as primary or 

secondary work activity, by field and years since doctorate: 2006

(Percent)

Group and years since doctorate All fields

Physical 

sciences

Mathematics 

and statistics

Computer 

and 

information 

sciences

Life 

sciences Psychology

Social 

sciences Engineering

S&E doctorate holders employed 

in academia

All .................................................. 67.3 68.0 65.7 70.5 69.8 58.5 65.4 72.4

1–3 ............................................. 78.9 84.5 78.9 79.6 80.0 67.8 71.5 88.3

4–7 ............................................. 72.0 76.2 78.2 79.7 73.0 59.7 68.4 80.7

8–11 ........................................... 69.3 69.9 73.5 63.5 72.7 56.8 69.1 73.1

E12 ............................................ 63.2 62.7 59.6 65.0 65.8 56.6 62.9 65.5

Full-time S&E faculty

All .................................................. 68.6 66.5 68.4 74.0 69.9 63.6 68.8 70.9

1–3 ............................................. 73.5 64.4 71.1 83.9 66.6 69.0 79.9 82.0

4–7 ............................................. 74.0 73.9 81.5 85.1 70.1 68.1 73.1 85.6

8–11 ........................................... 73.4 74.7 80.3 68.1 75.1 64.0 74.1 73.3

E12 ............................................ 66.0 64.0 63.4 68.0 69.1 61.8 65.4 66.3

NOTES: Academic employment limited to U.S. doctorate holders employed at 2- or 4-year colleges or universities and excludes those employed part time 

because they are students or retired. Faculty includes full, associate, and assistant professors plus instructors. Physical sciences include earth, atmospheric, 

and ocean sciences. Research includes basic or applied research, development, and design. 

SOURCE: National Science Foundation, Division of Science Resources Statistics, Survey of Doctorate Recipients, special tabulations. 

Science and Engineering Indicators 2010

	


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5-28 Chapter 5. Academic Research and Development

full-time positions in 2006 (table 5-12). Almost half of those 

with recently earned doctorates reported receiving federal 

support, with 30% of those in full-time faculty positions, 

49% in other full-time positions, and 69% in postdoc positions receiving federal support. Over the past three decades, 

the percentage of recent S&E doctorate holders in full-time 

faculty positions who have federal support remained fairly 

constant (except in the life sciences, where it declined), but 

the percentage in postdocs and in full-time nonfaculty positions with federal support declined (NSB 2008). The share 

of recent doctorate holders with federal support was relatively low in the social sciences and higher in the life and 

physical sciences and in engineering (table 5-12). 

Among full-time faculty and postdocs in 2006, those 

who had received their doctorate 4–7 years earlier were 

more likely to receive federal support than those with recently earned doctorates (table 5-12). However, those who 

had received their doctorate 4–7 years earlier were also less 

likely to receive support in 2006 than in 1991 (figure 5-19 

and table 5-13). 

Collaborative Research

Research in many fields has increasingly involved collaboration of researchers, whether on large or small projects. 

Funding entities often encourage collaborative research, 

which can bring together people of different disciplines, 

different types of institutions, different economic sectors, 

and different countries. As noted in the section “R&D Collaborations Between Higher Education Institutions,” R&D 

funds for joint projects that were passed through academic 

institutions to other institutions increased from FY 2000 to 

FY 2008, and most of the funds were from federal sources. 

This section explores S&E doctorate holders’ reports of their 

collaboration with others. Information on trends in and the 

Table 5-12

S&E doctorate holders employed in academia with federal support, by degree field, years since doctorate, and 

position: 2006

(Percent)

Years since doctorate and position All fields

Physical 

sciences

Mathematics 

and statistics

Computer 

and 

information 

sciences

Life 

sciences Psychology

Social 

sciences Engineering

1–3 years since doctorate

All positions .................................. 49.1 66.1 36.6 37.4 55.9 45.2 18.8 58.9

Full-time faculty ......................... 29.5 39.2 18.4 23.7 31.6 38.1 16.5 42.5

Other full-time positions ............ 48.7 64.7 S 64.3 56.2 32.2 32.9 50.7

Postdocs ................................... 68.6 77.8 60.5 100.0 67.7 66.0 27.0 74.4

4–7 years since doctorate

All positions .................................. 47.3 58.2 32.0 45.3 57.5 35.7 21.5 63.9

Full-time faculty ......................... 41.7 52.8 29.7 43.0 49.9 36.6 21.9 61.3

Other full-time positions ............ 43.4 59.2 29.5 49.9 49.4 31.0 22.5 62.6

Postdocs ................................... 76.0 72.2 79.5 S 77.4 88.8 S 87.9

S = suppressed, too few cases

NOTES: Academic employment limited to U.S. doctorate holders employed at 2- or 4-year colleges or universities. Recent doctorate holders earned 

doctorate within either 3 years or 4–7 years preceding survey. Full-time faculty includes full, associate, and assistant professors plus instructors. Other 

full-time positions include nonfaculty appointments such as research associates, adjunct appointments, lecturers, and administrative positions. Physical 

sciences include earth, atmospheric, and ocean sciences. Total includes part-time positions not shown separately but excludes those employed part time 

because they are students or retired. 

SOURCE: National Science Foundation, Division of Science Resources Statistics, Survey of Doctorate Recipients, special tabulations. 

Science and Engineering Indicators 2010

Table 5-13

S&E doctorate holders employed in academia 4–7 

years after receiving degree reporting receipt of 

federal support in previous year, by degree field: 

Selected years, 1973–2006

(Percent)

Degree field 1973 1983 1991 2006

All fields 47.1 50.1 57.4 47.3

Physical sciences ..................... 44.8 66.2 67.2 58.2

Mathematics ............................. 29.0 39.8 28.3 32.0

Computer sciences ................... NA 43.5 66.2 45.3

Life sciences ............................. 59.7 67.1 70.6 57.5

Psychology ............................... 37.8 32.3 38.8 35.7

Social sciences ......................... 29.0 28.1 36.6 21.5

Engineering ............................... 50.7 64.3 73.2 63.9

NA = not available

NOTES: 1991 used because 1993 not comparable with other years 

and understates degree of federal support by asking whether work 

performed during week of April 15 supported by government. In 

other years, question pertains to work conducted over course of year. 

Physical sciences include earth, atmospheric, and ocean sciences. 

Academic employment limited to U.S. doctorate holders employed at 

2- or 4-year colleges or universities and excludes those employed part 

time because they are students or retired. 

SOURCE: National Science Foundation, Division of Science 

Resources Statistics, Survey of Doctorate Recipients, special 

tabulations. 

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 5-29

extent of coauthorship and collaboration using indicators developed from the research literature can be found later in this 

chapter under “Coauthorship and Collaboration.”

In 2006, close to 70% of S&E full-time research faculty 

employed in academic institutions reported working in an 

immediate work group or team (appendix table 5-21).30

Seventy-five percent worked with others elsewhere in the 

same organization, 58% worked with individuals in other 

organizations in the United States, and 29% worked with 

individuals located in other countries. Team work is most 

common among life scientists, physical scientists, and engineers (80%, 72%, and 77%, respectively) and least common 

among mathematicians (49%) and social scientists (50%). 

The percentage of full-time research faculty engaged in international collaboration was higher among those who were 

born outside the United States (34%) than among those born 

in the United States (28%). Differences between foreign 

and native-born researchers were even more pronounced 

in some fields, such as mathematics (36% compared with 

21%), psychology (39% compared with 24%), and social 

sciences (41% compared with 28%). Although the differences in computer sciences appear large, they are not statistically significant. 

Among full-time S&E research faculty, much of the international collaboration was by e-mail or telephone (98%), 

52% involved travel abroad, 54% involved travel to the 

United States, and 38% involved Web-based or virtual technology (appendix table 5-22). Web-based or virtual technology was used far more by computer scientists (56%) than by 

other scientists and engineers engaged in international collaboration (38% overall). In many fields, a higher percentage 

of foreign-born than of U.S.-born research faculty travelled 

abroad for collaboration. More information on collaboration 

in scientific articles can be found in the next section.

Outputs of S&E Research: 

Articles and Patents

Chapter 2 of this volume discusses the human capital outputs of higher education in S&E. The present chapter focuses 

on the S&E functions of U.S. colleges and universities, including funding and performance, physical infrastructure, and 

human capital devoted to R&D. This section examines the 

intellectual output of academic S&E research using indicators 

derived from formal research articles and U.S. patent data.

Researchers have traditionally published the results of 

their work in the world’s peer-reviewed S&E journals,31 and 

article-level data from these journals are used here to explore total S&E research output by countries and—within 

the United States—by sectors of the economy.32 These socalled bibliometric data are also useful for tracking trends 

in S&E research collaboration using coauthorship measures 

between and among departments, institutions, sectors, and 

countries. (See sidebar “Bibliometric Data and Terminology.”) Finally, citations in more current research articles 

to previous research offer insight into the importance and 

impact of previous research.

The 2008 edition of Indicators (NSB 2008) focused attention throughout these bibliometric indicators on three large 

geographic units: the United States, the 27 members of the 

European Union, and a group of 10 fast-growing countries in 

Asia. This edition adjusts that particular organization of the 

data to focus instead on five S&E article-producing countries/regions that together account for more than four-fifths 

of the world total: the United States, the European Union, 

China, Japan, and eight countries/economies together referred to as the “Asia-8” (India, Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan, and Thailand). 

S&E researchers publish the results of their work in the 

peer-reviewed literature, and symbolic payment for their 

work is a citation to their article when it is used by future 

researchers (see Merton 1973). This recognition is uniquely 

valuable inside the scientific community, where it can be critical to career advancement, and does not necessarily reflect 

the value society might place on particular scientific findings.

 In contrast, when researchers file for patent protection 

for a practical advance over “prior art” and the claim is 

granted in a successful patent, the patent owner obtains certain rights to the potential value of the advance. This chapter 

uses the patenting activities of U.S. academic institutions as 

another type of indicator of the outputs of academic S&E 

research. (Chapter 6, “Industry, Technology, and the Global 

Marketplace,” discusses patenting by other sectors in “Global Trends in Patenting.”) Because citations to the S&E literature in successful patents indicate the use of past research in 

practical advances, literature/patent linkage data illuminate 

patterns of the impacts of academic S&E research on potential technological development. 

S&E Article Output

Between 1995 and 2007, the total world S&E article output as contained in the journals tracked by the Science Citation Index (SCI) and Social Sciences Citation Index (SSCI), 

which are analyzed in this chapter, grew at an average annual rate of 2.5% (table 5-14). Scientists and engineers in 

institutions in the member countries of the European Union 

authored or coauthored 32% of the world total in 2007,33 

followed by the United States with 28%. China, Japan, and 

the Asia-8 accounted for another 22% of the world total (appendix table 5-25).34

Growth in S&E article output across these five countries/

regions has been uneven. Twelve-year growth rates in the 

mature economies of the U.S. (0.7%), Japan (1.0%), and the 

European Union (1.9%) have been lower than in the rapidly 

growing economies of the Asia-8 (9.0%) and China (16.5%) 

(appendix table 5-25; figure 5-20). 

Exactly 200 countries or other entities35 receive credit 

for publishing S&E articles (appendix table 5-23). A small 

number account for most of the publications.36 Thus, table 

5-14 shows that five countries (the U.S., China, Germany, 
5-30 Chapter 5. Academic Research and Development

Bibliometric Data and Terminology

The article counts, coauthorship data, 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 Reuters in the Science 

Citation Index (SCI) and Social Sciences Citation Index 

(SSCI) (http://thomsonreuters.com/products_services/science/). The data exclude 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. The data are refined 

in a database prepared for NSF by The Patent BoardTM, 

formerly CHI Research, Inc., under a license agreement 

between The Patent BoardTM and Thomson Reuters. 

Journal Selection. Since Science and Engineering Indicators 2004, this section has used a changing set of journals that reflects the current mix of journals and articles in 

the world, rather than a fixed journals set. 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 portions of the Web of 

Science, a digital data product. Using citation data, Thomson Reuters then creates subsets of the SCI and SSCI that 

are available on CD-ROM and in print. These published 

data files are notable for the relatively high citation rank 

of the journals within their corresponding S&E subfields 

and the exclusion of journals of only regional interest, especially in the social sciences. Likewise, a declining citation rank can result in the removal of a journal from these 

highly selective data products. 

Using the CD-ROM data, the Patent BoardTM updates 

the NSF master file of journals; the number of journals 

analyzed by NSF from SCI/SSCI was 4,093 in 1988 and 

5,266 in 2008. These journals give good coverage of a 

core set of internationally recognized peer-reviewed scientific journals. The coverage extends to electronic-only 

journals and print journals with electronic versions. In 

the period 1995–2008, the database contained 9,358,420 

S&E notes, reviews, and articles. 

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. 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 as 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 

multiauthor 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.

Several changes introduced in this edition of Indicators inhibit comparison with data from the same source 

used in previous editions.

Previous editions reported data based on the year an 

article entered the database (tape year), not on the year 

it was published (publication year). NSF analysis has 

shown that, for the U.S. data, each new tape year file 

fails to capture from 10% to 11% of articles that will 

eventually be reported for the most current publication year; for some countries, the discrepancy is much 

larger. Here, data in the first section only (“S&E Article Output”) are reported by publication year through 

2007, which contains virtually complete data for this 

and prior publication years.

Publication data in the remaining bibliometrics sections (“Trends in Output and Collaboration Among 

U.S. Sectors,” “Coauthorship and Collaboration,” and 

“Trends in Citation of S&E Articles”) are reported by 

tape year through 2008.

The regions and countries/economies included in the 

bibliometric data are listed in appendix table 5-23. Data reported in this section are grouped into 13 broad S&E fields 

and 125 subfields, which are listed in appendix table 5-24. 

Japan, and the United Kingdom) accounted for more than 

50% of the total world S&E article output in 2007. The 45 

countries in table 5-14—less than one-quarter of the countries in the data—produced 98% of the world total of S&E 

articles. Nevertheless, the data are constantly evolving to reflect changes in the makeup of nations around the world or 

the sudden appearance of an author from a heretofore nonpublishing country.37

Trends in Country and Regional Authorship

Steadily increasing investments in S&E education and 

research infrastructure in many countries, especially in 

Asia, have led to increased R&D in those countries and 

laid the groundwork for increased research productivity. 

As a result, scientists and engineers in those countries are 

increasingly prominent contributors to international peerreviewed journals. 
Science and Engineering Indicators 2010 5-31

Table 5-14

S&E articles in all fields, by country/economy: 1995 and 2007

Rank Country 1995 2007

Average annual 

change (%)

Percent of total 

(2007)

2007 cumulative 

world total (%)

na World ........................... 564,645 758,142 2.5 na na

1 United States ........... 193,337 209,695 0.7 27.7 27.7

2 China ........................ 9,061 56,806 16.5 7.5 35.2

3 Japan ....................... 47,068 52,896 1.0 7.0 42.1

4 United Kingdom ....... 45,498 47,121 0.3 6.2 48.3

5 Germany .................. 37,645 44,408 1.4 5.9 54.2

6 France ...................... 28,847 30,740 0.5 4.1 58.3

7 Canada .................... 23,740 27,799 1.3 3.7 61.9

8 Italy .......................... 17,880 26,544 3.3 3.5 65.4

9 Spain ........................ 11,316 20,981 5.3 2.8 68.2

10 South Korea ............. 3,803 18,467 14.1 2.4 70.6

11 India ......................... 9,370 18,194 5.7 2.4 73.0

12 Australia ................... 13,125 17,831 2.6 2.4 75.4

13 Netherlands ............. 12,089 14,210 1.4 1.9 77.3

14 Russia ...................... 18,603 13,953 -2.4 1.8 79.1

15 Taiwan ...................... 4,759 12,742 8.6 1.7 80.8

16 Brazil ........................ 3,436 11,885 10.9 1.6 82.3

17 Sweden .................... 9,287 9,914 0.5 1.3 83.6

18 Switzerland .............. 7,220 9,190 2.0 1.2 84.9

19 Turkey ...................... 1,715 8,638 14.4 1.1 86.0

20 Poland ...................... 4,549 7,136 3.8 0.9 86.9

21 Belgium .................... 5,172 7,071 2.6 0.9 87.9

22 Israel ........................ 5,741 6,622 1.2 0.9 88.7

23 Denmark .................. 4,330 5,236 1.6 0.7 89.4

24 Finland ..................... 4,077 4,989 1.7 0.7 90.1

25 Greece ..................... 2,058 4,980 7.6 0.7 90.8

26 Austria ...................... 3,425 4,825 2.9 0.6 91.4

27 Iran ........................... 279 4,366 25.7 0.6 92.0

28 Mexico ..................... 1,937 4,223 6.7 0.6 92.5

29 Norway ..................... 2,920 4,079 2.8 0.5 93.1

30 Singapore ................. 1,141 3,792 10.5 0.5 93.6

31 Czech Republic ........ 1,955 3,689 5.4 0.5 94.0

32 Portugal ................... 990 3,424 10.9 0.5 94.5

33 Argentina .................. 1,967 3,362 4.6 0.4 94.9

34 New Zealand ............ 2,442 3,173 2.2 0.4 95.4

35 South Africa ............. 2,351 2,805 1.5 0.4 95.7

36 Ireland ...................... 1,218 2,487 6.1 0.3 96.1

37 Hungary ................... 1,764 2,452 2.8 0.3 96.4

38 Egypt ........................ 1,388 1,934 2.8 0.3 96.6

39 Ukraine ..................... 2,516 1,847 -2.5 0.2 96.9

40 Chile ......................... 889 1,740 5.8 0.2 97.1

41 Thailand ................... 340 1,728 14.5 0.2 97.3

42 Slovenia ................... 434 1,280 9.4 0.2 97.5

43 Romania ................... 678 1,252 5.2 0.2 97.7

44 Croatia ..................... 600 1,102 5.2 0.1 97.8

45 Serbia ....................... NA 1,057 NA 0.1 98.0

na = not applicable; NA = not available

NOTES: Countries shown produced >1,000 articles in 2007. Countries ranked on 2007 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 countries omitted.

SOURCES: Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/; The Patent BoardTM; and National Science 

Foundation, Division of Science Resources Statistics. See appendix table 5-25.

Science and Engineering Indicators 2010
5-32 Chapter 5. Academic Research and Development

Differences in recent rates of growth in article production 

are striking. In the major Asian countries, average annual 

growth rates between 1995 and 2007 were highest in China, 

at 17%. Across the Asia-8 countries, growth rates have been 

9% annually for the same period (appendix table 5-25), led 

by Thailand (15%), South Korea (14%), Singapore (11%), 

and Taiwan (9%) (table 5-14). These growth rates mirror 

those in R&D expenditures and number of researchers. Japan’s growth in article output averaged a modest 1% annually between 1995 and 2007. China’s rapid growth rate in 

S&E article output propelled it in 2007 past the United Kingdom, Germany, and Japan to rank as the world’s secondlargest producer, up from 5th place in 2005 and 14th place in 

1995 (appendix table 5-25).38 During the same period, South 

Korea went from 22nd to 10th place. 

These high rates of growth in S&E article authorship 

in Asia contrast with much slower rates for the world as a 

whole (2.5%), for countries with mature S&E infrastructures such as the United States (0.7%), and for the European 

Union (1.9%) (appendix table 5-25). Russia’s article output 

decreased over the period, from 18,600 in 1995 to 14,000 

in 2007, as did Ukraine’s, from 2,500 to 1,800. Many of the 

other former republics of the Union of Soviet Socialist Republics (USSR) experienced negative growth on this indicator as well. 

Countries within the European Union showed different 

trends in S&E article output between 1995 and 2007. Growth 

rates below 3% were common, for example, in Austria, Belgium, Denmark, Germany, and the Netherlands. Among 

the lowest rates of growth on this indicator were the United 

Kingdom (0.3%), France (0.5%), and Sweden (0.5%). Relatively high growth was experienced by the Czech Republic 

(5.4%), Greece (7.6%), Ireland (6.1%), Portugal (10.9%), 

and Spain (5.3%). Although not a member of the European 

Union, Turkey experienced one of the fastest growth rates in 

S&E article output in the world: 14.4% annually (from 1,700 

articles in 1995 to 8,600 in 2007 (appendix table 5-25).

The countries in Central and South America together increased their S&E article output between 1995 and 2007 at 

an annual rate of 7.8%. Among the Central and South American countries that had more than 1,000 articles in 2007, Brazil had the highest growth rate (10.9%), followed by Mexico 

(6.7%), Chile (5.8%), and Argentina (4.6%). Brazil is also 

steadily increasing in rank among the world’s S&E article 

producers: it was 23rd in 1995 and 16th in 2007 (table 5-14).

Across North Africa and the Middle East, only Egypt 

(2.8% annual growth since 1995), Israel (1.2%), and Iran 

(25.7%) produced substantial numbers of S&E articles in 

2007. Iran’s growth rate was the fastest of all nations (see 

sidebar “S&E Publishing Trends in Iran”).

Research Portfolios of Top Article-Producing 

Countries/Regions

The S&E article output of the United States, the European Union, China, Japan, and the Asia-8 together accounted 

for 82% of the world total in 2007 (appendix table 5-25). A 

field-by-field comparison across these five countries/regions 

provides a snapshot of their research portfolios, and strong 

differences are evident. China, Japan, and the Asia-8 emphasize the physical sciences more than the United States and 

the European Union. China’s S&E research articles in chemistry and physics accounted for almost one-half of its total 

article production in 2007 (table 5-15). In Japan, these two 

fields accounted for 36%, and in the Asia-8, 37%, compared 

with 17% for the United States and 25% for the European 

Union. The proportions of all five portfolios in astronomy 

(1.5%) and the geosciences (4.0%–5.5%) were similar.

These portfolios also vary in their emphasis on the life sciences (the biological, medical, agricultural, and other life sciences): the U.S. output in these fields accounted for 57% of its 

total, compared with 49% for the European Union, 25% for 

China, 45% for Japan, and 34% for the Asia-8 (table 5-15). 

A third strong contrast across the five countries/regions 

is the emphasis on engineering: S&E research publications 

with authors in Asia are relatively more heavily concentrated in engineering (China at 16%, Japan at 11%, and the 

Asia-8 at 19%) than those with authors in the United States 

(7%) or the European Union (8%).

	


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Science and Engineering Indicators 2010 5-33

S&E Publishing Trends in Iran

Iran-based authors produced 4,400 articles in 2007, 

and Iran’s S&E publication growth rate has been the fastest in the world. Growth in publications has been strong 

across many fields, resulting in a 2007 publication portfolio weighted toward chemistry (30% of the total), engineering (15%), the medical sciences (14%), the biological 

sciences (14%), and physics (14%) (appendix tables 5-25 

through 5-38). 

Iran has an evolving science policy framework and a 

growing number of research institutions to carry out the 

framework (UNCTAD 2005). The country has a growing 

	
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International coauthorship with Iran, by top five 

coauthoring countries: 2008

 Articles

Country Number Percent

World ........................................... 1,514 100.0

United States ........................... 346 22.9

United Kingdom ....................... 232 15.3

Canada .................................... 217 14.3

Germany .................................. 129 8.5

France ...................................... 113 7.5

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 countries on basis of institutional addresses listed on articles. 

Articles on whole count basis, i.e., each collaborating country credited 

one count. 

SOURCES: Thomson Reuters, SCI and SSCI, http://thomsonreuters. com/

products_services/science/; The Patent BoardTM; and National Science 

Foundation, Division of Science Resources Statistics, special tabulations. 

Science and Engineering Indicators 2010

adult literacy rate (77% in 2003), but its economy is dominated by extraction and export of oil and gas. Current 

policy envisions a more diversified economy and a transition to development and production of petrochemicals 

and other high-technology products.

Iran’s pattern of international coauthorship has mirrored that of other countries with immature S&E capabilities: they coauthor internationally at very high rates, 

but these rates decline as domestic capacity builds. Iran’s 

rate of international coauthorship was 42% in 1988 but 

had declined to 25% in 2008, near the world average of 

22% (figure 5-A; see also figure 5-21). 

Despite a declining rate of international coauthorship 

Iran’s total number of international coauthorships has been 

growing steadily, and coauthorships with each of its main 

foreign coauthor countries have also been growing. Table 

5-B shows Iran’s top five coauthoring countries in 2008.

Coauthorship and Collaboration

Coauthored, collaborative articles with authors from different institutions and different countries have continued to 

increase, indicating increasing knowledge transfer or knowledge sharing among institutions and across national boundaries.39, 40 The discussion begins with consideration of broad 

trends in coauthorship for the world as a whole, moves to 

regional patterns, and ends with an examination of countrylevel trends, including selected country-to-country coauthorship patterns and indexes of international collaboration. 

(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.” 

Indicators of collaboration using different data are discussed 

earlier in this chapter under “Collaborative Research” in the 

“Doctoral Scientists and Engineers in Academia” section. 

For a consideration of the limitations of bibliometric techniques in identifying interdisciplinary S&E research, see the 

sidebar “Can Bibliometric Data Provide Accurate Indicators 

of Interdisciplinary Research?”

Article Author Names and Institutions 

Between 1988 and 2005, the number of S&E articles, 

notes, and reviews grew by 60%, while the number of institutions and the number of author names on them both grew 

by more than 100% (NSB 2008, 08-01, figure 5-29). The 

trend continued in 2008. In all broad fields, the number of 
5-34 Chapter 5. Academic Research and Development

author names per article increased (table 5-16). The average number of authors per paper was more than five in astronomy, physics, the biological sciences, and the medical 

sciences. Growth in the average number of coauthors was 

slowest in the social sciences (from 1.4 authors per paper in 

1988 to 1.9 in 2008) and in mathematics (from 1.5 in 1988 to 

2.0 in 2008). Unpublished NSF analyses show that in 2008, 

90% of all S&E articles had at least two author names.

A closely related indicator, coauthored articles (i.e., articles with authors in different departments or institutions), 

has also increased steadily. Coauthored articles grew from 

40% of the world’s total S&E articles in 1988 to 64% in 

2008 (figure 5-21). This growth has two parts. Coauthored 

articles that list only domestic institutions in the byline grew 

from 32% of all articles in 1988 to 42% in 2008. Articles that 

list institutions from more than one country, that is, internationally coauthored articles (which also may have multiple 

domestic institutional authors) grew from 8% to 22% over 

the same period. The remainder of this section focuses on 

these internationally coauthored articles.

International Coauthorship From a Regional 

Perspective

From the perspective of large article-producing countries/

regions, interregional coauthorship has increased steadily.41

From 1998 to 2008, interregional coauthorship increased as 

a percentage of the total article output of the United States 

(from 20% to 30%), the European Union (from 21% to 29%), 

Japan (17% to 26%), and the Asia-8 (22% to 27%) (table 

5-17). Notably, China failed to increase on this indicator: as 

Table 5-15

S&E research portfolios of selected regions/countries, by field: 2007

(Percent)

Field U.S. EU China Japan Asia-8

All articles (n) .................................................. 209,695 245,852 56,806 52,896 56,123

Engineering ................................................. 6.9 8.1 16.0 11.1 19.1

Astronomy ................................................... 1.4 1.5 0.7 0.8 0.5

Chemistry .................................................... 7.5 12.0 24.5 16.1 18.0

Physics ....................................................... 9.3 13.4 24.0 19.7 18.9

Geosciences ............................................... 5.5 5.3 4.3 4.0 4.0

Mathematics ............................................... 1.9 2.7 3.3 1.3 1.5

Computer sciences ..................................... 1.1 0.9 1.3 0.4 1.4

Agricultural sciences ................................... 1.8 2.4 2.0 2.2 2.4

Biological sciences ..................................... 25.1 20.6 14.0 21.4 16.5

Medical sciences ........................................ 27.8 25.2 8.4 21.3 14.9

Other life sciences ...................................... 2.0 0.9 0.2 0.1 0.4

Psychology ................................................. 4.3 2.6 0.3 0.7 0.6

Social sciences ........................................... 5.4 4.3 0.8 1.0 1.6

EU = European Union

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 on basis of institutional address(es) listed on article. Articles on fractional-count basis, i.e., for articles with collaborating 

institutions from multiple countries, each country receives fractional credit on basis of proportion of its participating institutions. See appendix table 5-23 for 

countries/economies included in EU and Asia-8. Percents may not add to 100% because of rounding.

SOURCES: Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/; The Patent BoardTM; and National Science 

Foundation, Division of Science Resources Statistics. See appendix tables 5-25 through 5-38.

Science and Engineering Indicators 2010

Table 5-16

Authors per S&E article, by field: 1988, 1993, 1998, 

2003, and 2008

Field 1988 1993 1998 2003 2008

All fields ............................. 3.1 3.4 3.8 4.2 4.7

Engineering .................... 2.5 2.8 3.1 3.4 3.8

Astronomy ...................... 2.5 3.2 3.6 4.5 5.9

Chemistry ....................... 3.1 3.3 3.6 3.9 4.3

Physics .......................... 3.3 3.8 4.2 4.7 5.3

Geosciences .................. 2.4 2.7 3.2 3.5 4.0

Mathematics .................. 1.5 1.6 1.8 1.9 2.0

Computer sciences ........ 1.9 2.0 2.3 2.6 3.0

Agricultural sciences ...... 2.7 2.9 3.3 3.8 4.3

Biological sciences ........ 3.3 3.7 4.2 4.6 5.3

Medical sciences ........... 3.6 4.1 4.5 5.0 5.6

Other life sciences ......... 2.0 2.1 2.4 2.9 3.2

Psychology .................... 2.0 2.2 2.5 2.8 3.2

Social sciences .............. 1.4 1.5 1.6 1.8 1.9

NOTE: Articles classified by year they entered database rather than 

year of publication.

SOURCES: Thomson Reuters, Science Citation Index and Social 

Sciences Citation Index, http://thomsonreuters.com/products_

services/science/; The Patent BoardTM; and National Science 

Foundation, Division of Science Resources Statistics, special 

tabulations.

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 5-35

Can Bibliometric Data Provide Accurate Indicators 

of Interdisciplinary Research?

To address the need for indicators of interdisciplinary 

research (IDR), NSF/SRS commissioned a panel of researchers* to review recent attempts to measure the growth 

of interdisciplinary S&E research. The panel reviewed 74 

publications dealing with IDR. It concluded that, despite 

increased study of IDR in the literature, existing indicators 

of IDR based solely on bibliometric data were unsatisfactory for management and policy purposes and relied on an 

overly simplistic concept of IDR (Wagner, Roessner, and 

Bobb 2009; Wagner et al. 2009). The panel also found that 

problems with current data sources and analytical techniques raise questions about the validity of these measures. 

The panel concluded that conceptualization of IDR involves both the outputs of research and research processes: 

it stressed that both social developments (e.g., new S&E 

working relationships, new career trajectories, new institutions) and cognitive developments (e.g., new theory, new 

ways of using existing data, new problem frameworks) are 

essential markers of IDR. Bibliometric data alone do not 

capture these dimensions of IDR. 

The panel identified an emerging consensus that studies 

of IDR need measures of knowledge integration that could 

be applied to the work of either a team of researchers or 

an individual. However, they found limited agreement on 

what such integration entails and even less agreement on 

what would count as evidence of it. 

The panel also assessed the limitations of current attempts at measurement of IDR, most of which use Thomson Reuters data products. These are organized into a 

structure based on the discipline of the journal in which 

articles are published. Studies then measure the “cognitive distance” reflected by the diversity of citations in their 

target data (authors, articles, journals) from the Thomson 

Reuters journal structure and treat this distance as the measure of IDR. 

Alternative analytical techniques are under development. These use statistical and visualization techniques 

that seek to detect certain hidden structures in the data 

that may indicate IDR. However, these techniques still 

require validation. Bibliometric measures will also need 

to be supplemented by survey data, ethnographic studies, 

expert review, and other evidence to confirm the degree 

of interdisciplinarity in research output. Indicators of IDR 

may also vary depending on user needs. For example, measurements of IDR appropriate for projects, programs, and 

nations are likely to be different. The panel summarized 

its conclusions as follows (Wagner, Roessner, and Bobb 

2009, p 9-10, 16):

The Panel’s consensus...is that it is premature to 

identify one or a small set of indicators or measures of 

interdisciplinary research...in part, because of a lack 

of understanding of how current attempts to measure 

IDR conform to the actual process and practice of interdisciplinary research, and the outcomes resulting 

from that practice.

The literature is rich and maturing, but has not 

reached a point that permits meaningful assessment of 

IDR, especially for public policy and research management purposes.

 ____________________

*The assessment was performed by three researchers at SRI International, Caroline S. Wagner, J. David Roessner, and Kamau Bobb, working with the following experts on interdisciplinarity and visualization: 

Katy Börner, Indiana University; Kevin W. Boyack, SciTech Strategies, 

Inc.; Joann Keyton, North Carolina State University; Julie Thompson 

Klein, Wayne State University; and Ismael Rafols, University of Sussex. These eight researchers are referred to as the “panel” in this sidebar.

a percentage of its total S&E article output, China’s interregionally coauthored articles declined from 26% in 1998 to 

25% in 2008. 

As a percentage of the world’s interregionally coauthored 

articles, the shares of articles with a U.S., European Union, 

or Japanese institutional author declined slightly, giving way 

to a rise in the share of articles with an institutional author 

from China (from 6% to 13%) or the Asia-8 (from 9% to 

14%). These changes in share of the world’s interregional 

articles are similar to the changes in each region’s share of 

all the world’s articles.

The other regions identified in table 5-17 tend to have a 

less-developed S&E infrastructure, and scientists and engineers in those regions tend more often to coauthor articles 

with colleagues in the more scientifically advanced regions/

countries. For example, in 2008, 41% of all S&E articles 

with an institutional author from the Near East/North Africa 

(which includes Israel) had an author from another region, 

as did 61% of S&E articles with an institutional author from 

Sub-Saharan Africa (which includes South Africa). The following sections look more closely at coauthorship patterns 

of specific countries and country pairs.

International Coauthorship Patterns From a 

Country Perspective

When the region-level data discussed in the previous section are disaggregated to the country level, a richer picture of 

international S&E article coauthorship emerges. Table 5-18 

displays the international coauthorship rates of countries 

that had institutional authors on at least 5% or more of the 

world’s internationally coauthored S&E articles in 2008 (see 

also appendix tables 5-39 and 5-40). 

The sheer volume of U.S. internationally coauthored articles dominates these measures: 30% of U.S. articles in 2008 
5-36 Chapter 5. Academic Research and Development

were internationally coauthored, and U.S.-based researchers were coauthors of 43% of the world’s total internationally coauthored articles. The next highest percentages of the 

world’s coauthored articles were held by Germany and the 

United Kingdom, each at 19% of the world total. 

Even higher rates of international coauthorship are evident among the countries of the European Union and in 

Switzerland. Both Japan’s and the Asia-8’s international coauthorship rates have increased over the past 10 years. 

What accounts for specific coauthorship relationships? 

Narin and colleagues (1991) concluded that “the direction of 

international coauthorship is heavily dependent on linguistic 

and historical factors.” Coauthorship data suggest that geography, cultural relations, and the language of particular 

pairs or sets of countries play a role (Glänzel and Schubert 

2005; Schubert and Glänzel 2006), and these preferences 

have been evolving over time (Glänzel 2001). In more recent 

years, European Union policies and incentives that foster 

intra-European Union, cross-border collaboration are also 

partly responsible for some high rates of coauthorship. The 

discussion below in the section “International Collaboration 

in S&E” identifies strong coauthorship relations in specific 

country pairs across the world, based on the strength of their 

coauthorship rates. 

International Coauthorship With the United States

Table 5-19 lists the 31 countries whose institutions appeared on at least 1% of U.S. internationally coauthored articles in 2008. U.S. authors are most likely to coauthor with 

colleagues from the United Kingdom (13.9%), Germany 

(12.7%), Canada (12.0%), and China (10.4%—up from 

3.5% in 1998). 

Table 5-18 shows that the rate at which U.S. researchers participate in international collaboration is below that of 

many countries with smaller science establishments. However, because of the size of the S&E establishment in the 

United States, the share of U.S. internationally coauthored 

articles that were coauthored with any other country is lower 

than the share of the other country’s internationally coauthored articles that were coauthored with U.S. researchers 

(table 5-19). For example, 3% of U.S. scientists who coauthored internationally in 2008 collaborated with Israeli 

Table 5-17

Interregional collaboration on S&E articles: 1998 

and 2008

(Percent)

Share of 

region’s/ 

country’s total 

article output

Share of world’s 

interregional 

articles

Region/country 1998 2008 1998 2008

United States .............. 20 30 57 55

EU ............................... 21 29 66 61

Other Western 

Europe ....................... 43 48 12 12

Asia ............................. 18 23 25 34

China ....................... 26 25 6 13

Japan ...................... 17 26 13 11

Asia-8 ...................... 22 27 9 14

Other Asia ............... 62 65 1 1

Other former USSR .... 31 42 11 7

Near East/ 

North Africa ............... 39 41 7 7

Central/ 

South America .......... 39 40 9 9

Sub-Saharan Africa .... 47 61 3 4

Other ........................... 31 43 20 21

EU = European Union; USSR = Union of Soviet Socialist Republics

NOTES: Interregionally coauthored articles have at least one 

collaborating institution from indicated region/country and an institution 

from outside that region/country. 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 region/country on basis 

of institutional address(es) listed on article. Articles on whole-count 

basis, i.e., each collaborating region/country credited one count. See 

appendix table 5-23 for countries/economies included in each region. 

Detail adds to more than 100% because articles may have authors 

from more than two countries/economies. Asia computed as sum of 

collaboration among China, Japan, Asia-8, and Other Asia.

SOURCES: Thomson Reuters, SCI and SSCI, http://thomsonreuters.

com/products_services/science/; The Patent BoardTM; and National 

Science Foundation, Division of Science Resources Statistics, special 

tabulations.

Science and Engineering Indicators 2010

	


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Science and Engineering Indicators 2010 5-37

counterparts; the corresponding figure for Israel, with its 

much smaller scientific infrastructure, is 52%. Again, 51% 

of scientists and engineers in Canada who coauthored internationally collaborated with U.S. colleagues, but only 12% 

of U.S. international coauthorship was with colleagues at 

Canadian institutions;42 linguistic, geographic, and other ties 

combine to facilitate these collaborations. 

Notable changes in these patterns of U.S. international coauthorship parallel changes in other indicators discussed 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 6 percentage points over the past decade, and China’s share of U.S. 

internationally coauthored articles increased almost 7 percentage points (table 5-19). U.S. scientists and engineers lost relative share of international coauthorship with some countries/

economies, notably India, South Korea, Taiwan, and Japan, as 

their counterparts in those countries/economies broadened the 

geographic scope of their collaborative relations. 

International Collaboration in S&E

In developing indicators of international collaboration 

between countries and across regions, researchers have developed statistical techniques that account for unequal sizes 

in countries’ S&E article output and coauthorship patterns 

(Glänzel and Schubert 2004). One of the simplest is the index of international collaboration (table 5-20), which is defined as the ratio of country A’s rate of collaboration with 

country B divided by country B’s rate of total international 

coauthorship (Narin, Stevens, and Whitlow 1991). Indexes 

above 1 represent rates of coauthorship that are higher than 

expected, and indexes below 1 indicate rates of coauthorship 

that are lower than expected. For example, if country B produces 12% of internationally coauthored articles, and 12% 

of country A’s coauthored articles are with country B, the 

index of international collaboration is 12%/12% = 1.0. The 

indexes for all pairs of countries that produced more than 1% 

of all internationally coauthored articles in 2008 are shown 

in appendix table 5-41.

Table 5-20 lists the international collaboration index 

for selected pairs of countries. In North America, the Canada-United States index shows a rate of collaboration that 

is slightly greater than would be expected based solely on 

the number of internationally coauthored articles shared by 

these two countries, and the index has changed little over the 

past decade. The Mexico-United States index is just about as 

would be predicted and is also stable. 

Collaboration indexes between pairs of countries on 

opposite sides of the North Atlantic are all low and have 

changed little over the decade. In Europe, collaboration patterns are mixed, but most have increased over the decade, 

indicating growing integration across the European Union in 

terms of S&E article publishing. Among the large publishing countries of Germany, the United Kingdom, and France, 

collaboration was less than expected but grew in all three 

countries over the decade. 

The Scandinavian countries43 increased their coauthorship indexes with many countries in Europe (appendix table 

5-41), and within Scandinavia, the indexes are among the 

highest in the world and, overall, have been growing stronger (table 5-20).

Cross-Pacific collaboration patterns are mixed. JapanUnited States collaboration fell below the expected value 

over the decade, while the China-United States index rose 

to near 1. U.S. collaboration indexes with South Korea 

and Taiwan declined but remained higher than expected 

in both cases. Canadian scientists and engineers were less 

likely than their U.S. neighbors to have coauthored with colleagues in Asia. Mexico’s collaboration with Argentina is 

almost four times higher than expected, at 3.74 in 2008. In 

South America, the collaboration index of Argentina-Brazil, 

at 5.32, is one of the highest in the world.

Table 5-18

International collaboration on S&E articles, by 

selected country/economy: 1998 and 2008

(Percent)

Share of country’s/ 

economy’s total 

article output

Share of world’s 

internationally 

co authored 

articles

Country/economy 1998 2008 1998 2008

United States ................ 20 30 44 43

EU

France ....................... 38 52 16 14

Germany ................... 36 51 20 19

Italy ........................... 38 45 10 9

Netherlands .............. 40 52 7 6

Spain ......................... 34 45 6 8

United Kingdom ........ 32 49 19 19

Other Western Europe

Switzerland ............... 50 65 6 6

Asia

China ......................... 26 25 4 11

Japan ........................ 17 26 10 9

Asia-8 ........................ 23 30 7 12

Other

Australia .................... 31 45 5 7

Canada ..................... 34 46 10 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 2008 

international total omitted. See appendix table 5-23 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: Thomson Reuters, SCI and SSCI, http://thomsonreuters.

com/products_services/science/; The Patent BoardTM; and National 

Science Foundation, Division of Science Resources Statistics, special 

tabulations.

Science and Engineering Indicators 2010
5-38 Chapter 5. Academic Research and Development

 Collaboration indexes within Asia and across the South 

Pacific between the large article producers are generally higher than expected but with only minor changes over the past 

decade. Australia’s coauthorships are strongly linked to New 

Zealand, at nearly four times the expected rate of coauthorship. Two strongly coauthoring pairs are South Korea-Japan 

and Australia-Singapore. India’s collaboration index with 

South Korea grew from 1.61 to 2.19 over the past decade. 

Trends in Output and Collaboration Among 

U.S. Sectors

In the U.S. innovation system, ties between and among 

universities, industry, and government have been 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 accounted for 

Table 5-19

International coauthorship of S&E articles with the United States, by selected country/economy: 1998 and 2008

(Percent)

1998 2008

Country/economy

U.S. share 

of country’s/ 

economy’s 

international articles

Country/ 

economy share of 

U.S.’s international 

articles

U.S. share 

of country’s/ 

economy’s 

international articles

Country/ 

economy share of 

U.S.’s international 

articles

World ............................................ 43.9 na 43.3 na

United Kingdom ........................ 29.6 12.5 32.0 13.9

Germany ................................... 29.9 13.7 29.7 12.7

Canada ..................................... 53.2 11.6 51.2 12.0

China ......................................... 35.8 3.5 42.1 10.4

France ....................................... 24.7 8.7 26.0 8.3

Japan ........................................ 45.2 10.4 38.7 7.9

Italy ........................................... 31.8 7.0 32.5 7.1

Australia .................................... 35.1 4.3 33.6 5.2

South Korea .............................. 60.6 2.9 53.5 5.0

Spain ......................................... 24.9 3.4 27.1 4.8

Netherlands .............................. 29.9 4.4 30.5 4.5

Switzerland ............................... 31.2 4.2 31.6 4.3

Sweden ..................................... 29.0 3.6 29.2 3.2

Israel ......................................... 55.1 3.9 52.3 2.8

Brazil ......................................... 38.1 2.3 38.5 2.8

Russia ....................................... 24.7 3.9 27.3 2.7

Taiwan ....................................... 63.4 1.7 53.4 2.5

Belgium ..................................... 23.1 2.1 24.4 2.3

India .......................................... 40.6 1.9 34.3 2.3

Denmark ................................... 30.5 2.2 30.2 1.9

Mexico ...................................... 44.5 1.6 44.8 1.7

Poland ....................................... 25.0 1.8 26.4 1.7

Austria ....................................... 25.3 1.5 24.5 1.6

Norway ...................................... 29.1 1.2 29.6 1.4

Finland ...................................... 27.5 1.5 27.1 1.4

Greece ...................................... 29.6 0.9 33.4 1.3

New Zealand ............................. 34.7 1.0 34.9 1.2

Turkey ....................................... 38.0 0.6 41.7 1.2

Singapore .................................. 28.7 0.4 30.3 1.1

Argentina ................................... 35.1 0.9 34.9 1.1

South Africa .............................. 31.1 0.7 34.8 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 2008; countries/economies with less than 1% of U.S.’s 2008 international articles omitted.

SOURCES: Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/; The Patent BoardTM; and National Science 

Foundation, Division of Science Resources Statistics. See appendix tables 5-39 and 5-40.

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 5-39

more than 70% of all U.S. articles (76% in 2008) (appendix 

table 5-42). This section contrasts U.S. academic authorship 

with nonacademic authorship, including output trends by sector and the extent of 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 

50,000 to 55,000 articles44 per year between 1995 and 2008 

(appendix table 5-42). The number of articles produced by 

scientists and engineers in the federal government and in industry was more than 15,000 in 1995 but slowly declined to 

range between 13,000 and 14,000 each through 2008 (figure 

5-22). State and local government authorship, dominated 

by articles in the medical and biological sciences, remained 

constant across the decade. Between 1995 and 2008, scientists and engineers in the private nonprofit sector increased 

their output from about 15,000 to about 18,000.

Federally funded research and development centers 

(FFRDCs) are research institutions 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 

Table 5-20

Index of international collaboration on S&E articles, 

by selected country/economy pair: 1998 and 2008

International 

collaboration index

Country/economy pair 1998 2008

North/South America

Canada–U.S. ......................... 1.21 1.18

Mexico–U.S. .......................... 1.01 1.03

U.S.–Brazil .............................. 0.87 0.89

Argentina–Brazil ..................... 4.33 5.32

Mexico–Argentina .................. 2.99 3.74

North Atlantic

UK–U.S. ................................. 0.67 0.74

Germany–U.S. ....................... 0.68 0.68

France–U.S. ........................... 0.56 0.60

Canada–France ...................... 0.63 0.74

Europe

France–Germany.................... 0.74 0.91

France–UK ............................. 0.73 0.87

Germany–UK ......................... 0.68 0.86

Belgium-Netherlands ............. 2.50 2.68

Italy–Switzerland .................... 1.51 1.38

Poland–Czech Republic......... 2.15 3.48

Hungary–Germany ................. 1.23 1.34

Germany–Czech Republic ..... 1.27 1.46

Scandinavia

Finland–Sweden .................... 3.39 3.98

Norway–Sweden .................... 4.10 3.96

Sweden–Denmark .................. 2.88 3.38

Finland–Denmark ................... 2.36 3.15

Pacific Rim

Japan–U.S. ............................ 1.03 0.89

China–U.S. ............................. 0.82 0.97

South Korea–U.S. .................. 1.38 1.23

Taiwan–U.S. ........................... 1.44 1.23

China–Canada ....................... 0.66 0.73

Japan–Canada ....................... 0.59 0.55

Asia/South Pacific

China–Japan .......................... 1.53 1.38

South Korea–Japan ............... 1.99 1.90

Australia–Singapore ............... 1.93 1.70

Australia–China ...................... 1.05 1.14

Australia–New Zealand .......... 4.28 3.80

India–Japan ............................ 0.94 1.12

India–South Korea ................. 1.61 2.19

UK = United Kingdom

NOTES: International collaboration index is 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: Thomson Reuters, SCI and SSCI, http://thomsonreuters.

com/products_services/science/; The Patent BoardTM; and National 

Science Foundation, Division of Science Resources Statistics. See 

appendix table 5-41.

Science and Engineering Indicators 2010 

	


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5-40 Chapter 5. Academic Research and Development

instruments not otherwise available in other research venues. 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 

69% of publication by this sector in 2008, reflecting its specialized research programs. Physics articles accounted for 

39% of the FFRDC total (9% of the total for all sectors); 

engineering articles for 15% (7% of the total for all sectors); 

and chemistry articles for 16% (8% of the total for all sectors 

(appendix table 5-42).

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 2009a).

In contrast, articles published by authors in the private 

nonprofit sector are primarily in the medical sciences (55% 

of the sector’s articles in 2008) and biological sciences 

(25%) (appendix table 5-42). Federal government authors 

show a similar pattern, with 30% in the biological sciences 

and 27% in the medical sciences. 

Trends in Sector Coauthorship

This section considers coauthorship data as an indicator 

of collaboration at the sectoral level between U.S. institutional authors and between U.S. sectors and foreign institutions.45 These data show that the growing integration of 

R&D activities, as measured by coauthorship, is occurring 

across the full range of R&D-performing institutions internationally as well as domestically.

Between 1998 and 2008, coauthorship within sectors increased for all U.S. sectors.46 Coauthorship within academia 

rose from 38% in 1998 to 45% in 2008. FFRDC-FFRDC 

coauthorship increased 5 percentage points (table 5-21). 

U.S. cross-sectoral coauthorships show a mixed pattern 

between 1998 and 2008. Coauthorship between FFRDCs 

and industry decreased. (Articles authored by industry physicists have been declining gradually across the period. Since 

a strong emphasis of FFRDC-authored articles is in physics 

(39%), it may be that fewer and fewer physicists are available in industry for potential coauthorship with physicists in 

FFRDCs.)47 The largest gains in all sectors (6.8–9.8 percentage points) were with coauthors in academia, by far the largest sector with the largest pool of potential S&E coauthors. 

Cross-sector coauthorship with academic authors was higher 

in 2008 (54%–74%) than intrasector coauthorship within academia (45%), and cross-sector coauthorship with academia 

was higher in all sectors than any intrasector coauthorship 

(table 5-21). 

Except for the decline in coauthorship between FFRDCs 

and industry, the indicators presented in this section hint 

at increasing integration between and among the different 

types of U.S. institutions that publish the results of R&D in 

the scientific and technical literature. Growth in coauthorship has been particularly strong between U.S. authors in 

academia and in all other sectors. 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 the integration of S&E research across 

institutions, both nationally and internationally. 

International collaboration increased rapidly in the United 

States. International coauthorship rates rose by 7–10 percentage points between 1998 and 2008 (table 5-21). Authors at 

FFRDCs reached the highest rate of collaboration with foreign 

authors, at 42%, followed by industry and academia at 29% 

each. Astronomers in most U.S. sectors increased their rates 

of international coauthorship the most rapidly, and geoscientists, mathematicians, and physicists in most U.S. sectors also 

increased their collaboration with international colleagues at 

a higher-than-average pace (NSB 2008, 08-01A, p A5-66). 

Trends in Citation of S&E Articles 

Citations indicate influence. 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. Like the indicators of international coauthorship discussed above, cross-national citations are evidence 

that S&E research is increasingly international in scope. Between 1992 and 2008, international citations grew faster than 

total citations: 5.8% annually versus 4.6% (figure 5-23). By 

2008, international citations were two-thirds of all citations.48

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. Appendix table 5-43 shows, for example, 

that between 1994–96 and 2004–06, the U.S. share of world 

S&E articles declined from 34% to 29% across all fields;49

the U.S. share declined in every broad field, although the 

decline varied in size. Table 5-22 shows the parallel trends 

for the U.S. share of citations and indicates an even larger 

decline, from 47% to 38%. 

China’s share of both 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, China’s share of international citations decreased between 1998 and 2008, from 
Science and Engineering Indicators 2010 5-41

64% to 51%, 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. 

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 5-43 shows citation percentiles for 1998 

and 2008 by field for the top five S&E article-producing 

countries/regions.50 In that table, a country whose research 

influence was disproportionate to its output would have 

higher numbers of articles in higher citation percentiles, 

whereas a country whose influence was less than its output 

would suggest would have higher numbers of articles in 

lower citation percentiles. In other words, a country whose 

research is highly influential would have higher shares of 

articles in higher citation percentiles.

This is the case in every field for U.S. articles. In both 

1998 and 2008, as displayed in appendix table 5-43, 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 

2008, U.S. articles represented 29% of the world total of 2 

million articles in the cited period shown; the U.S. authored 

52% of the rare 19,500 articles in the 99th percentile and 

25% of the 1.2 million articles below the 50th percentile. 

This broad pattern was unchanged from the 1998 pattern. 

Citations to the European Union’s S&E articles displayed 

a different pattern: it had higher percentages of articles in the 

lower percentiles across all fields of S&E except in the agricultural sciences (appendix table 5-43). Figure 5-24 displays 

these relationships for all five countries/regions. 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 from the 

European Union, China, Japan, and the Asia-8 are more 

Table 5-21

U.S. article coauthorship, by sector, foreign coauthorship, and U.S. coauthor sector: 1998 and 2008

(Percent)

U.S. coauthor sector

Year/sector

Foreign 

coauthor FFRDCs

Federal 

government

State/local 

government

Academic 

institutions Industry

Private 

nonprofit

1998

Federal government ....................................... 19.7 3.2 17.4 2.0 54.4 9.0 8.5

Industry .......................................................... 19.7 3.3 9.6 1.4 44.8 14.1 8.9

Academic ....................................................... 19.6 2.6 7.8 1.4 37.7 6.0 8.6

FFRDCs ......................................................... 32.9 12.6 8.6 0.2 48.5 8.4 3.4

Private nonprofit ............................................ 17.8 1.2 8.0 2.3 57.1 8.0 24.3

State/local government.................................. 10.9 0.5 13.9 13.5 64.2 8.8 16.2

2008

Federal government ....................................... 27.4 4.0 20.5 2.9 62.0 9.5 12.9

Industry .......................................................... 29.2 3.5 10.5 1.9 53.8 17.9 13.5

Academic ....................................................... 28.8 3.2 8.1 1.5 45.2 6.4 10.5

FFRDCs ......................................................... 41.6 17.6 9.7 0.2 58.3 7.6 5.0

Private nonprofit ............................................ 27.6 1.6 10.3 2.7 63.9 9.7 28.8

State/local government.................................. 17.8 0.6 18.3 15.6 74.0 10.8 21.7

1998–2008 change (percentage points)

Federal government ....................................... 7.8 0.8 3.1 0.9 7.6 0.5 4.5

Industry .......................................................... 9.5 0.2 0.8 0.5 9.0 3.8 4.6

Academic ....................................................... 9.2 0.6 0.3 0.2 7.5 0.3 1.9

FFRDCs ......................................................... 8.7 5.0 1.1 * 9.8 -0.7 1.6

Private nonprofit ............................................ 9.7 0.4 2.2 0.5 6.8 1.8 4.5

State/local government.................................. 6.9 0.1 4.4 2.1 9.8 2.1 5.5

* = rounds to zero

FFRDC = federally funded research and development center

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. Detail may add to more than 100% 

because articles may have authors from more than two sectors.

SOURCES: Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/; The Patent BoardTM; and National Science 

Foundation, Division of Science Resources Statistics, special tabulations. 

Science and Engineering Indicators 2010
5-42 Chapter 5. Academic Research and Development

often found in the lower citation percentiles. These data are 

summarized in appendix table 5-44. As the U.S. share of all 

articles produced declined between 1998 and 2008, 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. 

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 is seen to 

have changed little over the past 10 years. Between 1998 and 

2008, the U.S. index of highly cited articles barely changed 

(from 1.83 to 1.78) (figure 5-25; appendix table 5-44) and 

remained well above the expected index value of 1. During the same period, the European Union increased its index 

from 0.73 to 0.89, and China, Japan, and the Asia-8 increased 

their index values but remained below their expected values. 

In other words, the United States had 78% more articles than 

expected in the 99th percentile of cited articles in 2008, and 

the European Union had 11% fewer than expected. China 

had 58% fewer articles in the 99th percentile than expected 

in 2008, and Japan 42% fewer. 

The United States experienced notable gains on the index of highly cited articles in engineering and computer sciences (although with relatively low counts in the latter) and 

a decline in chemistry (appendix table 5-44). The European 

Union reached its expected value in chemistry, physics, and 

the agricultural sciences. China achieved an index value of 

1 in engineering and mathematics. Japan did not achieve its 

expected value in any broad field. 

Academic Patents, Licenses, Royalties, 

and Startups 

Other indicators of academic R&D outputs reflect universities’ efforts to capitalize on their intellectual property 

in the form of patents and associated activities. The majority 

Table 5-22

S&E articles, citations, and international citations, by selected region/country: 1998 and 2008

(Percent)

Share of world articles Share of world citations

Share of world/country/economy 

citations that are international

Country/region 1998 2008 1998 2008 1998 2008

World .................................... 100.0 100.0 100.0 100.0 60.2 66.3

United States .................... 34.0 28.9 46.9 38.3 46.9 51.8

EU ..................................... 34.6 33.1 32.4 33.2 43.7 49.4

China ................................. 1.6 5.9 0.6 4.3 63.6 51.0

Japan ................................ 8.5 7.8 6.8 6.3 60.7 68.6

Asia-8 ................................ 3.6 6.8 1.5 4.6 61.8 65.3

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 5-23 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 1998 are references made in articles in 1998 data tape 

to articles in 1994–96 data tapes; data shown are for the 3 years in cited year window. 

SOURCES: Thomson Reuters, SCI and SSCI, http://thomsonreuters.com/products_services/science/; The Patent BoardTM; and National Science 

Foundation, Division of Science Resources Statistics, special tabulations.

Science and Engineering Indicators 2010

	
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Science and Engineering Indicators 2010 5-43

of U.S. universities did not become actively involved in the 

management of their own intellectual property until late in 

the 20th century, although some were granted patents much 

earlier.51 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 (AUTM 2009). 

Efforts to encourage links between university-based research and commercial exploitation of the results of that 

research have been widely studied by researchers. Mowery 

(2002) notes the strong growth in funding by NIH and the 

predominance of biomedically related patenting by universities in the 1990s. Branstetter and Ogura (2005) identify 

a “bio-nexus” in patent-to-paper citations, and Owen-Smith 

and Powell (2003) explore the effects of an academic medical center as part of the “scientific capacity” of a research 

university. In a qualitative study of two research universities 

that would appear to have similar capacities, Owen-Smith 

and Powell (2001) examine the very different rates of invention disclosure of the two campuses. Stephan and colleagues 

(2007) found strong differences in patenting activity among 

university scientists by field of science; a strong relationship 

between publication activity and patenting by individual researchers; and patenting by university researchers in only a 

small proportion of the potential population.

The following sections discuss overall trends in university patenting and related indicators through 2007–08.

University Patenting Trends

U.S. Patent and Trademark Office (USPTO) data show 

that annual patent grants to universities and colleges ranged 

from 2,950 to 3,700 between 1998 and 2008 (appendix table 

5-45). In 2008, just over 3,000 patents were awarded to colleges and universities in the United States.52 (Data in the next 

section on invention disclosures and applications suggest that 

patent grants to academic institutions may increase in the 

coming years.)

The top 200 R&D-performing institutions, with 96% 

of the total patents granted to U.S. universities during the 

1998–2008 period, dominate among universities and university systems receiving patent protection.53 College and 

university patents as a percentage of U.S. nongovernmental 

patents fell from 5.2% in 1998 to 4.3% in 2008. Among the 

top R&D-performing institutions that received patents between 1998 and 2008, 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 North Carolina). 

Between 1998 and 2008, three technology areas dominated 

U.S. university patenting: chemicals (19%), biotechnology 

(15%), and pharmaceuticals (14%) (appendix table 5-46). In 

	
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5-44 Chapter 5. Academic Research and Development

numbers of patents, all three of these technology areas have 

declined from previous highs (figure 5-26). The next three 

highest technology areas over the period were semiconductors 

and electronics (6%), measurement and control equipment 

(5%), and computers and peripherals (5%), each accounting 

for about 200 patents in 2008 (appendix table 5-46). 

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 13,700 in 2003 to 17,700 in 2007 

(notwithstanding a small decline in institutions responding 

to the AUTM survey over the same period) (appendix table 5-47). Likewise, new U.S. patent applications filed by 

AUTM respondents also increased, from 7,200 in 2003 to 

10,900 in 2007. The AUTM survey respondents reported 

348 startup companies formed in 2003 and 510 in 2007. The 

AUTM 2007 survey also found 3,148 cumulative, operational startup firms associated with U.S. university patenting 

and licensing activities (AUTM 2009).

Most royalties from licensing agreements accrue to relatively few patents and the universities that hold them, and 

many of the AUTM respondent offices report negative income. (Thursby and colleagues [2001] note that the objectives of university technology management offices include 

more than royalty income.) At the same time, large onetime payments to a university can affect the overall trend 

	
	

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Science and Engineering Indicators 2010 5-45

in university licensing income. In 2007, the 161 institutions 

that responded to the AUTM survey reported a total of $1.9 

billion in net royalties from their patent holdings (appendix 

table 5-47). 

Between 2003 and 2007, the inventory of revenue-generating licenses and options across all AUTM respondent 

institutions increased from 9,000 to 12,500 (appendix table 

5-47). New licenses and options executed grew over the period from about 3,900 in 2003 to 4,400 in 2007.

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 innovation.54 This indicator of 

science linkage to practical advance increased sharply in the 

late 1980’s and early 1990’s (Narin, Hamilton, and Olivastro 1997), due at least 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 2008, growth on this indicator was 

much slower. Of utility patents awarded to both U.S. and 

foreign assignees, the number citing S&E articles (11% of 

total utility patents awarded in 2008) grew 1.4% annually 

over the 10-year period, compared with 0.7% annually for 

all utility patents (appendix table 5-48). Much of the growth 

in S&E citing patents was in patents awarded to non-U.S. 

assignees: these grew 3.1% annually.

Five broad S&E fields (the biological sciences, the medical sciences, chemistry, physics, and engineering) accounted 

for 97% of the total citations in these patents (appendix table 

5-49 and figure 5-27). Citations to the biological sciences 

have decreased from their high of 58,000 in 1998 and 1999 

but have more recently stabilized at around 50,000 per year. 

Citations to the medical sciences have increased since 2005 

to about 26,000 in 2008.

The data discussed in the previous three paragraphs were 

heavily influenced by U.S. patents awarded to foreign assignees and references in those patents to non-U.S. S&E 

articles. Considering only citations to U.S. articles, overall 

growth in citations has been flat over the past 10 years (appendix table 5-48). Change in citations to articles authored in 

	
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5-46 Chapter 5. Academic Research and Development

both the private nonprofit and government sectors has been 

negative over the period. Growth in citations to academic 

papers (0.9% annually) and to FFRDC papers (4.6% annually) shows that citations to papers in these two sectors have 

been replacing declining citations to articles in other sectors. Citations to academic articles account for most of this 

replacement, despite the slower rate of growth in these citations. Of total citations to U.S. articles in 2008, 64% were 

to academic articles, compared with 2% to FFRDC articles.

Figure 5-28 summarizes the increasing role of citations to 

U.S. academic articles in the science linkage to U.S. patents. 

Across all fields, academic articles made up 58% of total citations to U.S. articles in 1998 and 64% in 2008. Of the five 

broad fields of S&E that accounted for 97% of all patent citations to U.S. academic articles, increased shares of academic 

citations were notable in engineering (from 46% to 59%) and 

physics (from 43% to 65%). These increasing shares of patent citations to U.S. academic S&E articles are parallel to the 

increasing shares of academic S&E articles as compared with 

other sectors, as discussed above in the section “Trends in 

Output and Collaboration Among U.S. Sectors.”

Conclusion 

U.S. universities and colleges continue to be important 

performers of U.S. R&D, and particularly basic research. 

Both the overall academic S&E doctoral workforce and the 

academic research workforce have continued to increase. Citation data indicate that U.S. scientific publications remain 

highly influential relative to those of other countries. While 

the United States continues to produce more S&E articles 

than any other country, its share of total world articles has 

declined due to high publication growth rates elsewhere, notably China.

Although funding for academic R&D has been increasing, a number of shifts in funding sources have occurred. 

After increasing between 2000 and 2004, the federal government’s share of funding for academic R&D decreased 

from 2005 through 2008. In addition, federal funding for 

academic R&D has either declined in constant dollars or remained flat since 2005. Industry support for academic R&D 

declined from 2002 to 2004 but rose again through 2008. 

The state and local government share of support for academic R&D has been generally declining since 1972, and 

the university share of support for academic R&D has been 

generally increasing. 

The structure and organization of academic R&D have 

also changed. (See sidebar “Publications and Resource Inputs.”) Research-performing colleges and universities continued to expand their stock of research space, particularly in 

the biological and medical sciences. The number of academic S&E doctoral researchers has grown over the past couple 

of decades, with the life sciences accounting for much of the 

trend. Life scientists accounted for more than a third of academic S&E doctoral researchers in 2006. Increasingly, these 

researchers are employed in postdoc or other nonfaculty positions. Particularly among life scientists, the number of new 

doctorate holders has increased greatly while the numbered 

	


								
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Science and Engineering Indicators 2010 5-47

Publications and Resource Inputs

The publication of research results in peer-reviewed 

scientific journals is a key output of scientific research. 

In the early 1990s, the number of S&E articles published 

by U.S. academic scientists and engineers in the world’s 

major peer-reviewed journals plateaued while resource 

inputs—funds and personnel—kept increasing (figure 

5-B). With some variations, this trend occurred across 

different types of institutions and different fields, despite 

increases in research inputs such as funds and personnel 

(NSF/SRS 2007).

An examination of relationships among publications, 

resource inputs, and institutional characteristics in the 

top 200 academic R&D institutions found that, with 

the possible exception of S&E faculty and the number 

of S&E doctoral recipients, inflation-adjusted resources 

for publications have increased faster than the number 

of publications. From 1990 to 2001, resource inputs increased per publication, with about 29% more resources 

consumed per fractional count publication in 2001 than 

in 1990. This pattern of increasing inputs required to 

yield the same quantity of publication outputs occurred 

across the entire U.S. academic system. Possible reasons 

for the increasing inputs per article include a rise in the 

complexity of research required for publication; costs for 

faculty, postdocs, S&E doctoral recipients, and research 

materials and equipment that are increasing faster than 

the gross domestic product implicit price deflator; and 

increased communication costs for collaboration (NSF/

SRS 2010, forthcoming). In figure 5-B, the steadily rising number of total author names on articles with at least 

one academic author is another indicator of the strong 

growth in research collaboration and article coauthorship 

noted elsewhere in this chapter.

	
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of tenured or tenure-track positions has remained relatively 

constant since the late 1980s.

Academic R&D is also becoming more international in 

a number of ways. U.S. academic scientists and engineers 

are collaborating extensively with colleagues in other countries: In 2008, 29% of journal articles with a U.S. academic 

author also had at least one coauthor from abroad. The intimate linkage 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 after graduation. Academia has also been able to 

attract many talented foreign-born scientists and engineers 

into its workforce, with the percentage of foreign-born fulltime doctoral S&E faculty in research institutions approaching half the total in some fields. 

Notes

1. See appendix table 5-6 for the fields and subfields included in science and engineering in this section.

2. The academic R&D totals presented here exclude expenditures at federally funded research and development centers 

(FFRDCs) associated with universities. Those expenditures 

are tallied separately and are examined in greater detail in 

chapter 4. FFRDCs and other national laboratories (including 

federal intramural laboratories) also play an important role in 

academic research and education, providing research opportunities for both students and faculty at academic institutions 

and highly specialized shared research facilities.

3. For this discussion, the terms universities and colleges, higher education, and academic institutions are used 

interchangeably and include only those schools that grant a 
5-48 Chapter 5. Academic Research and Development

bachelor’s or higher degree in science or engineering and 

spend at least $150,000 for separately budgeted R&D in 

S&E.

4. For the definitions used in NSF surveys and a fuller 

discussion of these concepts, see chapter 4 sidebar, “Definitions of R&D.”

5. The discussion of federal support for academic R&D 

includes both obligation data from the funding source (federal agencies) and expenditures data from the performer 

(universities and colleges). 

6. 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.

7. This funding was required to be obligated by the end of 

FY 2009; however, the expenditures for these projects will 

span several years.

8. Performing and funding series may differ for many 

reasons. For a more detailed discussion of the differences 

between these two sources, see chapter 4 sidebar, “Tracking R&D: Gap Between Performer- and Source-Reported 

Expenditures.”

9. Federal grants and 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.

10. This follows a standard of reporting that assigns funds 

to the entity that determines how they are to be used rather 

than to the one that necessarily disburses the funds.

11. The medical sciences include fields such as pharmacy, neuroscience, oncology, and pediatrics. The biological sciences include fields such as microbiology, genetics, 

epidemiology, and pathology. These distinctions may be 

blurred at times because boundaries between fields often are 

not well defined.

12. In this section of the chapter and the section “Doctoral Scientists and Engineers in Academia,” the broad S&E 

fields refer to the computer sciences, environmental sciences 

(sometimes referred to as “earth, atmospheric, and ocean 

sciences”), life sciences, mathematical sciences, physical 

sciences, psychology, social sciences, other sciences (those 

not elsewhere classified), and engineering. The more disaggregated S&E fields are referred to as “subfields.” The 

fourth section of the chapter, “Outputs of S&E Research: 

Articles and Patents,” groups the broad fields and subfields 

slightly differently (see sidebar “Bibliometric Data and Terminology” and appendix table 5-24).

13. Data reported on non-S&E R&D expenditures are 

slightly lower-bound estimates for the national totals because 

NSF did not attempt to estimate for the 2.7% nonresponse 

rate on this 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.

14. Data on non-S&E R&D expenditures have only been 

collected since FY 2003, and response rates for years prior 

to 2006 make trend data unreliable. 

15. Another hypothesis is that some of the difference may 

be due to many public universities not having the incentive 

to negotiate full recovery of indirect costs of research because such funds are frequently returned to the state treasury 

rather than the institution.

16. Amounts reported as passed through to higher education subrecipients do not precisely equal amounts reported 

as received by those subrecipients due to differences in timing and in the item response rates for these two survey questions each year.

17. Because of rising capitalization thresholds, the dollar 

threshold for inclusion in the equipment category has likely 

changed over time. Generally, university equipment costing 

less than $5,000 would be classified under the cost category 

of “supplies.”

18. The “bricks and mortar” section of the Survey of Science and Engineering Research Facilities asks institutions 

to report their research space only. The reported figures 

therefore 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.

19. Research-performing academic institutions are defined as colleges and universities that grant degrees in science or engineering 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.

20. 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), 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 at least partially 

used for research is prorated to reflect the proportion of time 

and use devoted to research.

21. 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). NCES updates CIP every 

10 years. The S&E fields used in the FY 2007 Survey of Science and Engineering Research Facilities reflect the NCES 

2000 CIP update. For a comparison of the subfields in the 

FY 2005 and FY 2007 surveys, see the S&E Research Facilities: FY 2007 detailed statistical tables.

22. Institutional funds may include operating funds, endowments, tax-exempt bonds and other debt financing, indirect costs recovered from federal grants/contracts, and 

private donations.
Science and Engineering Indicators 2010 5-49

23. 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 employed in the business sector, and 

the United States had one of the smallest fractions employed 

in government (OECD 2009).

24. Psychology (also called a behavioral science) is discussed and broken out separately in tables from the social 

sciences because trends over time and characteristics of doctorate holders in psychology and social sciences differ. 

25. The inclusion or exclusion of those on temporary and 

permanent visas has little impact on the analysis. Data on 

Pacific Islanders were not collected separately from Asians 

before 2001. From 1975 to 1999, the Asian category includes Pacific Islanders, but from 2001 to 2006 it does not. 

In 2006, approximately 200 Pacific Islander doctoral S&E 

researchers were employed in academia. If combined with 

Asians, they would constitute less than 1% of the combined 

category.

26. The Carnegie classification used in that report was 

the 1994 version.

27. The switch to the American Community Survey as 

the sampling frame for the National Survey of College Graduates in 2010 and beyond may improve estimates of nonU.S.-trained doctorate holders in future years.

28. Among all S&E doctorate holders employed in academia, this is the case both in fields in which postdocs are 

prevalent (such as physical sciences and life sciences) and 

fields in which postdocs are less prevalent (such as computer 

sciences and mathematics).

29. 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 1 

month. 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 directly with results for the earlier years or with those 

from the 1999 through 2006 surveys, which again used an 

entire reference year.

The discussion in this edition of Indicators generally 

compares data for 2006 with data for 1991. All calculations 

express the proportion of those 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 the trends in the proportion of all 

academic researchers supported by federal funds occurred 

against a background of rising overall numbers of academic 

researchers.

30. 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?”

31. 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.

32. The U.S. sector identification in this chapter is quite 

precise; to date, sector identification has not been possible 

for other countries. 

33. European Union data include all member states as of 

2007 (see appendix table 5-23 for a list of member countries). 

34. The Asia-8 includes India, Indonesia, Malaysia, Philippines, Singapore, South Korea, Thailand, and Taiwan.

35. 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 5-23 for a list of the countries represented in the data.

36. Distributions of data in which a small percentage of 

cases accounts 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). Other phenomena with such distributions include, 

e.g., earthquakes (among a large number of earthquakes 

only a few 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).

37. For example, Montenegro appeared in the data in 2006 

for the first time as an independent country; the tiny Pacific island nation of Niue appeared in 2007 for the first time because 

a coauthor from that country appeared in the data.

38. See also NSB 2008, table 5-21, for detail on field 

level ranks and changes in rank since 1995.

39. Coauthorship data are 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, decreasing 

costs of travel and communication, national policies, and 

so forth (NSB 2006). Katz and Martin (1997), Bordons and 

Gómez (2000), and Laudel (2002) analyze limitations of coauthorship as an indicator of research collaboration. Other 

researchers have continued using these data (Adams et al. 

2005; Gómez, Fernández, and Sebastián 1999; Lundberg et 

al. 2006; Wuchty, Jones, and Uzzi 2007; Zitt, Bassecoulard, 

and Okubo 2000). 
5-50 Chapter 5. Academic Research and Development

40. 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.

41. The coauthorship data discussed in this paragraph are 

restricted to coauthorship across the regions/countries identified in table 5-17; that is, collaboration between or among 

countries of the European Union, e.g., is ignored. Intraregional coauthorship is discussed in the following sections.

42. 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. 

43. Finland is included here as one of the Scandinavian 

countries; Iceland is not.

44. 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.

45. Identification of the sector of the non-U.S. institution 

is not possible with the current data set.

46. Readers are reminded that coauthors from different departments in an institution are coded as different institutions.

47. Referring to the declining share of industry’s basic research articles in physics, the National Science Board (NSB) 

noted, “Most of this decline is accounted for by widespread 

restructuring of a few large corporations during this period, including closure, downsizing, or reorientation of large 

central research laboratories. Increased globalization, intensified competition, and commercial priorities may have contributed to the decline in publishing by companies and their 

researchers” (NSB 2008, p 6-36).

48. This chapter uses the convention of a 3-year citation 

window with a 2-year lag, e.g., 2008 citation rates are from 

references in articles contained in the tape for 2008 to articles contained in the 2004, 2005, and 2006 tapes 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.

49. 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.

50. Percentiles are specified percentages below which 

falls the remainder of the articles, e.g., 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 2004 to 2006 failed to receive at least 22 citations in 2008. 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 5-43 and 5-44 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 in the identified percentile. For example, using this method of counting, the 

75th percentile for engineering contained articles with three 

to four citations in 2004 through 2006, whereas the 75th 

percentile for the biological sciences contained articles with 

five to eight citations.

51. For an overview of these developments in the 20th 

century, see Mowery (2002).

52. It is unclear whether the recent downturn in patents 

granted to universities/colleges is a result of changes in 

USPTO processing. For example, in its Performance and 

Accountability Report Fiscal Year 2008, USPTO reported 

an increase in average processing time (“patent average total pendency”) from 29.1 months in 2005 to 32.2 months in 

2008 (USPTO 2008).

53. The institutions listed in appendix table 5-45 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.

54. 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. 
Science and Engineering Indicators 2010 5-51

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 “Financial Resources 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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Highlights .....................................................................................................................................6-4

Knowledge- and Technology-Intensive Industries in the World Economy ............................6-4

Trends in Knowledge- and 

Technology-Intensive Industries ..........................................................................................6-4

Information and Communications Services and Manufacturing .............................................6-4

U.S. and Global Trade in Knowledge- and Technology-Intensive Goods and Services .........6-4

U.S. Trade Positions ................................................................................................................6-5

Foreign Direct Investment .......................................................................................................6-5

Trade in Intangible Assets .......................................................................................................6-5

Patents ......................................................................................................................................6-5

Angel and Venture Capital Funding in the United States ........................................................6-5

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

Chapter Overview ....................................................................................................................6-7

Chapter Organization ...............................................................................................................6-7

Data Sources, Definitions, and Methodology ..........................................................................6-7

Knowledge- and Technology-Intensive Industries in the World Economy .................................6-7

Worldwide Distribution of Knowledge- and Technology-Intensive Industries ........................6-14

Health and Education Services ..............................................................................................6-15

Commercial Knowledge-Intensive Service Industries ...........................................................6-16

High-Technology Manufacturing Industries ..........................................................................6-17

Information and Communications Technology Industries ....................................................6-21

Industries That Are Not Knowledge or Technology Intensive .............................................6-22

Trade and Other Globalization Indicators ..................................................................................6-23

Trade of High-Technology Goods .........................................................................................6-25

U.S. Trade in Advanced Technology Products ......................................................................6-34

Globalization of Knowledge-Intensive Service Industries ....................................................6-39

U.S. Multinationals in Knowledge- and Technology-Intensive Industries ............................6-39

U.S. and Foreign Direct Investment in Knowledge- and Technology-Intensive Industries .....6-42

Innovation-Related Indicators of U.S. and Other Major Economies ........................................6-45

U.S. Trade in Intangible Assets .............................................................................................6-45

U.S. Trade in Industrial Processes .........................................................................................6-45

Global Trends in Patenting ....................................................................................................6-46

U.S. High-Technology Small Businesses ..............................................................................6-49

Innovation and Knowledge-Based Economic Growth .........................................................6-56

Conclusion .................................................................................................................................6-57

Notes ..........................................................................................................................................6-58

Glossary .....................................................................................................................................6-59

References ..................................................................................................................................6-60

List of Sidebars

Comparison of Data Classification Systems Used .......................................................................6-8

Industry Data and Terminology ...................................................................................................6-9

Consumption of High-Technology Manufactured Goods .........................................................6-18

Tracing the Geography of the Value Chain of Products ............................................................6-21

Trends in Industries Not Classified as Services or Manufacturing ............................................6-24

Product Classification and Determination of Country of Origin of Trade Goods .....................6-27

U.S. Patents Granted, by Type of Ownership ............................................................................6-48

Chapter 6

Industry, Technology, and 

the Global Marketplace

6-1
6-2 Chapter 6. Industry, Technology, and the Global Marketplace

List of Tables

Table 6-1. Global value added for selected service industries, by region/country/

economy: Selected years, 1995–2007 ....................................................................................6-25

Table 6-2. Global value added for manufacturing industries, by selected technology 

level and region/country/economy: Selected years, 1995–2007 ............................................6-26

Table 6-3. Exports of manufactured products, by selected technology level region/

country/economy: Selected years: 1995–2008 ......................................................................6-34

Table 6-4. U.S. exports and imports of commercial knowledge-intensive services, 

by region/country/economy: 2007 ........................................................................................6-40

Table 6-5. Stock of U.S. direct investment abroad and foreign direct investment 

in United States, by selected industry/service: 2000 and 2008 ..............................................6-43

Table 6-6. Stock of U.S. direct investment abroad and of foreign direct investment 

in United States, by selected industry and region/country/economy: 2000 and 2008 ...........6-44

Table 6-7. Firms and employment in U.S. small businesses versus all businesses: 2006 .........6-52

Table 6-8. Leading types of employers among high-technology small businesses, 

by industry: 2006 ...................................................................................................................6-53

Table 6-9. Average investment of angel and venture capital per business: 2002–08 ................6-55

Table 6-10. Investors and employees of firms receiving angel capital investment: 2001–07 ....6-56

Table 6-A. Contribution of value capture for Apple iPod and HP laptop computer, 

by country/economy of origin: 2005 ......................................................................................6-22

Table 6-B. Region/country/economy share of global value added for selected 

industries: Selected years, 1995–2007 ...................................................................................6-24

List of Figures

Figure 6-1. Global output of knowledge- and technology-intensive industries as a share 

of global GDP: 1992–2007 ....................................................................................................6-10

Figure 6-2. Output of knowledge- and technology-intensive industries as a share of 

GDP, by selected region/country: 1992-2007 ........................................................................6-10

Figure 6-3. Output of commercial knowledge-intensive services as a share of GDP, by 

selected region/country: 1992-2007 .......................................................................................6-11

Figure 6-4. Output of high-technology manufacturing and ICT industries as a share of 

GDP, by selected region/country:1992–2007 ........................................................................6-11

Figure 6-5. Macroeconomic indicators, by region/country/economy: 1992–2007 ....................6-12

Figure 6-6. GDP per capita for developing economies: 1992–2007 .........................................6-13

Figure 6-7. GDP per employed person, by region/country/economy: 1992–2007 ....................6-13

Figure 6-8. Indicators of ICT adoption and intensity: 2005 ......................................................6-14

Figure 6-9. Global value added of knowledge- and technology-intensive 

industries: 1995–2007 ............................................................................................................6-15

Figure 6-10. Global value added of education and health services, by selected region/

country/economy: 1995–2007 ...............................................................................................6-15

Figure 6-11. Value added of commercial knowledge-intensive services, by selected 

region/country/economy: 1995–2007 ....................................................................................6-16

Figure 6-12. Global value added of commercial knowledge-intensive services, by 

selected region/country/economy: 1995–2007 ......................................................................6-17

Figure 6-13. Value added of high-technology manufacturing industries, by selected 

region/country/economy: 1995–2007 ....................................................................................6-18

Figure 6-14. Global value added of selected high-technology manufacturing industries, 

by selected region/country/economy: 1995–2007 .................................................................6-20

Figure 6-15. Value added of ICT industries, by selected region/country/economy: 

1995–2007..............................................................................................................................6-23

Figure 6-16. Global production of high-technology manufacturing industries and 

exports of high-technology manufacturing products: 1995–2008 .........................................6-27

Figure 6-17. Distribution of global exports of high-technology products: 2008 .......................6-27

Figure 6-18. Global exports of high-technology products, by selected region/country/

economy: 1995–2008 .............................................................................................................6-28
Science and Engineering Indicators 2010 6-3

Figure 6-19. Region/country/economy share of global exports of selected hightechnology products: 1995–2008 ...........................................................................................6-29

Figure 6-20. Trade balance of high-technology and ICT products, by selected region/

country/economy: 1995–2008 ...............................................................................................6-30

Figure 6-21. United States’, EU’s, and Japan’s imports of ICT products, by share of 

selected region/country/economy of origin: 1995–2008 .......................................................6-30

Figure 6-22. China’s imports of ICT products, by share of selected region/country/

economy of origin: 1995–2008 ..............................................................................................6-31

Figure 6-23. Japan’s exports of ICT products, by share of selected region/country/

economy of destination: 1995–2008 ......................................................................................6-31

Figure 6-24. Asia-9, Philippines, and Singapore’s exports of ICT products, by share of 

selected region/country/economy of destination: 1995–2008 ...............................................6-32

Figure 6-25. South Korea’s, Malaysia’s, and Taiwan’s exports of ICT products, by 

share of selected region/country/economy of destination: 1995–2008 ..................................6-33

Figure 6-26. Asia-9’s, Malaysia’s, and Singapore’s imports of ICT products, by share 

of selected region/country/economy of origin: 1995–2008 ...................................................6-33

Figure 6-27. U.S. advanced technology product trade: 1992–2008 ..........................................6-35

Figure 6-28. U.S. advanced technology product trade balance and trade-weighted 

exchange rate: 1992–2008 .....................................................................................................6-36

Figure 6-29. U.S. advanced technology product trade, by selected technology and 

region/country/economy: 2000–08 ........................................................................................6-36

Figure 6-30. U.S. advanced technology product trade balance, by selected technology 

and region/country/economy: 2000–08 .................................................................................6-37

Figure 6-31. Advanced technology product share of U.S. merchandise trade, by region/

country/economy: 2008 .........................................................................................................6-37

Figure 6-32. U.S. trade in commercial knowledge-intensive services: 1997-2007 ...................6-39

Figure 6-33. U.S. trade balance of selected commercial knowledge-intensive services, 

by selected industry: 1992–2007 ...........................................................................................6-40

Figure 6-34. Globalization indicators of U.S. multinational corporations in commercial 

knowledge-intensive services: 1999 and 2006 ......................................................................6-41

Figure 6-35. Globalization indicators of U.S. multinational corporations in selected 

high-technology manufacturing industries: 1999 and 2006 ...................................................6-41

Figure 6-36. U.S. share of output of U.S. ICT multinational corporations, by industry: 

1999 and 2006 ........................................................................................................................6-42

Figure 6-37. U.S. trade in intangible assets: 1992–2007 ...........................................................6-45

Figure 6-38. U.S. trade in industrial processes, by region/country/economy: 2007 ..................6-46

Figure 6-39. Region/country/economy share of USPTO patent applications and grants: 

1995–2008..............................................................................................................................6-47

Figure 6-40. USPTO patent grants, by selected technology area: 1995–2008 ..........................6-49

Figure 6-41. USPTO patents granted in information and communication technology, 

by selected region/country/economy: 1995–2008 .................................................................6-50

Figure 6-42. USPTO patents granted in selected technologies, by selected region/

country/economy: 1995–2008 ...............................................................................................6-51

Figure 6-43. Global triadic patent families, by selected region/country/economy: 1997–2006 ...6-52

Figure 6-44. Distribution of U.S. angel investment, by financing stage: 2002–08 ...................6-53

Figure 6-45. U.S. venture capital investment, by share of investment stage: 1996–2008 .........6-54

Figure 6-46. U.S. angel and venture capital investment: 2001–08 ............................................6-54

Figure 6-47. U.S. angel capital investment, by selected technology areas: 2006–08 ................6-55

Figure 6-48. U.S. venture capital investment, by share of selected technology area: 

1999–2008..............................................................................................................................6-56

Figure 6-49. World Bank Knowledge Economic Index, by selected region/country/

economy: 1995 and 2005 .......................................................................................................6-57

Figure 6-A. Global apparent consumption of high-technology manufacturing industry 

output, by selected region/country/economy: 1995–2007 .....................................................6-19

Figure 6-B. Components of value added and value capture ......................................................6-21

Figure 6-C. USPTO patents granted, by ownership type: 1992, 1999, and 2008 ......................6-48
6-4 Chapter 6. Industry, Technology, and the Global Marketplace

Knowledge- and Technology-Intensive 

Industries in the World Economy 

The U.S. economy had the highest concentration 

among major economies of knowledge- and technologyintensive (KTI) industries, a key part of the global 

economy.

KTI industries, including knowledge-intensive (KI) service and high-technology (HT) manufacturing industries, 

have become a major part of the global economy, providing 

almost 30% of global economic output in 2007.

The U.S. economy had the highest concentration of KTI 

industries among major economies. These industries accounted for 38% of U.S. gross national product (GDP) in 

2007. China’s KTI industries created 23% of GDP in 2007, 

up from 21% in 1992.

Labor productivity growth has been higher in China and 

the Asia-9 than in the developed economies. (The Asia-9 

includes India, Indonesia, Malaysia, the Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam.) Despite China’s 8% annual growth over the past 15 years and 

4% growth in the countries and economies of the Asia-9, 

their absolute productivity levels remain far below those of 

their developed counterparts.

U.S. per capita income in 2007 was about 25% higher than 

that of Japan and 40% higher than the European Union 

(EU) average. The per capita income of China and the 

Asia-9 has grown far faster than that of the three developed 

economies—the United States, the EU, and Japan. 

Trends in Knowledge- and 

Technology-Intensive Industries

The United States is the largest producer of privately 

provided KTI service and manufacturing industries.

KTI industries contributed $15.7 trillion to the world 

economy: $5.0 trillion in largely location-bound education and health services, $9.5 trillion in tradable services, 

and $1.2 trillion in HT manufacturing.

The United States is the largest provider of commercial 

KI service industries (business, financial, and communications). The U.S. world share edged up from 32% in 

1995 to 34% in 2007. 

China’s share of global commercial KI service industries 

rose from 2% in 1995 to 5% in 2007, led by nearly 20% 

average annual growth of its communications industry. 

India’s communications industry also grew rapidly.

In HT manufacturing industries, 90% of global value 

added was accounted for by the United States (30%), the 

EU (25%), China (14%), and Japan and the Asia-9 (about 

10% each).

China’s share of HT manufacturing industries more than 

quadrupled, rising from 3% in 1997 to 14% in 2007, surpassing the Asia-9 in 2006 and Japan in 2007. 

Information and Communications Services 

and Manufacturing

The United States is the largest provider of information 

and communications technology (ICT) service and manufacturing industries.

The United States and the EU are the largest producers 

of ICT service and manufacturing industries (27% share 

each of global value added). 

China’s share of ICT global value added rose sharply 

from 4% to 12% between 1995 and 2007. Japan’s share 

declined steeply from 22% to 9% over the decade. 

U.S. and Global Trade in Knowledge- and 

Technology-Intensive Goods and Services

The United States lost market share in global HT exports, whereas China became the largest single country 

exporting HT goods.

The U.S. share of global HT exports declined from 21% 

in 1995 to 14% in 2008, largely because of a fall in ICT 

goods exports. 

China’s share of global HT goods exports more than 

tripled, from 6% in 1995 to 20% in 2008, making it the 

single largest exporting country for HT products.

The U.S. trade balance of HT products shifted from surplus to deficit, starting in the late 1990s. In 2000, the deficit was $32 billion in current dollars; in 2008, the deficit 

widened to $80 billion. The deficit in ICT goods alone 

was almost $120 billion in 2008. 

China’s trade position in HT products moved from balance to surplus, starting in 2001, and rapidly increased 

from less than $13 billion in 2003 to almost $130 billion 

in 2008, driven by trade in ICT goods. The Asia-9’s trade 

surplus also increased over the past decade from less than 

$50 billion to more than $220 billion, an increase entirely 

due to an expansion of its surplus in information technology (IT) goods. 

China’s rise as the world’s major assembler and exporter 

of many electronic goods is reflected by a sharp increase 

in China’s share of imports of ICT goods from the United 

States, the European Union, and Japan. 

Trade data indicate that assembly of ICT goods has 

shifted to China and that the Asia-9 has become a major supplier of components and inputs. Its share of China’s ICT imports jumped from 40% to 70% in a decade; 

China’s share of the Asia-9’s exports nearly quadrupled, 

Highlights
intra-Asia trade is up, and Japan’s export data also show a 

pronounced shift toward China. 

U.S. Trade Positions

The United States has maintained a surplus in trade 

of commercial KI services, but its surplus in advanced 

technology products turned into a deficit earlier in this 

decade.

U.S. trade in commercial KI service industries has been 

in surplus for the past decade and grew from $21 billion 

in 1997 to $47 billion in 2007. 

U.S. trade in advanced technology products generated an 

initial deficit in 2002 that widened to $56 billion by 2008. 

The deficit in the manufacturing component of ICT alone 

reached more than $100 billion, with smaller deficits in 

the life sciences and optoelectronics. Aerospace and electronics generated surpluses of $55 billion and $25 billion, 

respectively. 

The largest U.S. trade deficit in advanced technology 

products was $66 billion with China, its largest trading 

partner country, followed by $19 billion with the Asia-9 

and $8 billion with Japan. ICT deficits were higher: $75 

billion with China, $44 billion with the Asia-9, and $9 

billion with Japan.

The United States had a $7-billion surplus with the EU in 

2008; aerospace, the life sciences, and ICT manufacturing constituted the largest share of advanced technologies 

trade with this region.

Foreign Direct Investment

U.S. overseas investment in KTI industries was more 

than $900 billion, and direct investment in the United 

States in these industries was almost $600 billion.

U.S. overseas investment in commercial KI service industries stood at $834 billion and HT manufacturing industries at $121 billion by 2008. 

Financial services had the largest share of commercial 

KI service industries by far (76%), followed by business 

services (22%) and communications (2%). Among HT 

manufacturing industries, communications and semiconductors (44%) and pharmaceuticals (30%) had the largest 

shares. 

Direct investments in the United States in commercial KI 

service industries stood at $390 billion in 2008; direct investment in U.S. HT manufacturing industries stood at 

$187 billion.

Financial services had the largest share (64%) of foreign 

direct investment in commercial KI service industries, 

followed by business services (23%) and communications (13%). Among HT manufactures, the largest shares 

were in pharmaceuticals and in communications and 

semiconductors.

Trade in Intangible Assets

The United States runs a surplus with the rest of the 

world in trade of intangible assets, including patent licensing fees and use of trade secrets.

Investment and trade in intangible assets such as copyrights, trademarks, and patents is sizeable. In 2007, the 

United States had a surplus of nearly $60 billion in trade 

of intangible assets, which has grown steadily over the 

past two decades.

An important component of the surplus in U.S. intangible 

assets is generated by industrial processes ($19 billion), 

which include licensing fees for patents and use of trade 

secrets. U.S. exports in this category were $37 billion in 

2007.

The EU is the United States’ largest trading partner for industrial processes (nearly 50% share), followed by Japan 

(19%). More than half of the U.S. surplus is with the EU 

($10 billion), and it has smaller surpluses with the Asia9, China, and Latin America. The U.S. has a deficit of $3 

billion with Japan. 

Patents

The United States, the EU, and Japan have similar shares 

of economically valuable patents, accounting for a combined 90% share of the total. 

Inventions for which patent protection is sought in three 

of the world’s largest markets—the United States, the EU, 

and Japan—are presumed to be of higher-than-average 

value. The United States, the EU, and Japan have similar 

shares of high-value patents, accounting for nearly 90% 

of the total. The Asia-9’s share increased from 1% in 1997 

to 6% in 2006, accounted for almost entirely by South 

Korea. 

The United States is the leading source of U.S. Patent and 

Trademark Office (USPTO) patent applications; however, foreign-based inventors, attracted by the size and 

openness of the U.S. market, have traditionally provided 

almost half of these applications. 

In 2008 the U.S. share of patent applications declined to 

51%, with gains for second- and third-ranked Japan and 

the EU. The Asia-9’s share in 2008 was flat at 10% compared to 2007, but double its level of a decade ago, driven 

by growth in applications from South Korea and Taiwan. 

India’s and China’s patent applications grew but remained 

modest, with India’s share below and China’s share barely 

above 1%. Trends are similar in patents granted.

Angel and Venture Capital Funding in the 

United States

Investment in angel and venture capital, an important 

source of financing for HT small businesses, fell in 2008 

after several years of increases.

Science and Engineering Indicators 2010 6-5
Angel investors provided $19 billion in financing in 

2008, compared with $26 billion in 2007—the first decline since 2002. Health services received the largest 

share of investment (16%), followed by software (13%), 

retail (12%), and biotechnology (11%). 

U.S. venture capitalists invested $28.1 billion in 2008—an 

8% decline, compared with the level in 2007. Computer 

software had the largest share of investment from 2007 

to 2008 (18%), followed by biotechnology (16%) and industrial/energy (13%), possibly reflecting opportunities in 

green and renewable energy. 

6-6 Chapter 6. Industry, Technology, and the Global Marketplace
Science and Engineering Indicators 2010 6-7

Introduction

Chapter Overview

Economists increasingly emphasize the central role of 

knowledge, particularly R&D and other activities to promote science and technology, in a country’s economic success. Information and communications technology (ICT), 

for example, is widely regarded as a transformative technology that has altered lifestyles and the way business is conducted across a wide range of sectors. 

This chapter examines some of the downstream effects of 

R&D on the United States and the global marketplace. One 

key area is the creation of knowledge- and technology-intensive (KTI) industries and the diffusion and application of 

new technologies throughout other industries. Technologyintensive manufacturing and knowledge-intensive service 

industries have become an important and growing part of 

the United States’ and other economies. 

The globalization of the world economy and the vigorous 

pursuit of national innovation policies by developing countries have led to the rise of new centers of high-technology 

manufacturing and knowledge-intensive service industries. 

The United States continues to be a world leader in both, but 

Asian and other developing countries have become major 

producers and exporters and are building up their indigenous 

capability. The rise of these new centers of activity and the 

increasing fragmentation of production across borders and 

firms have stimulated foreign investment and trade. 

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 firms, and investment in intangible goods and services.

Chapter Organization

This chapter is organized into four 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 a set of emerging Asian countries/economies (the Asia-9).1

 The time span starts in the early 1990s, 

roughly from 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-high-technology 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 high-technology manufacturing industries, 

with a special focus on the Asian region. 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 last section presents innovation indicators and examines U.S. trade in intangible goods. It next examines patterns in country shares of high-value patents. A discussion 

of U.S. high-technology small businesses includes data on 

the number of high-technology small business startups and 

existing firms, employment, and venture and angel capital 

investment by industry. The last section also presents World 

Bank indicators of the knowledge capability of the United 

States and other major economies, which may have bearing 

on their current ability and future capacity to innovate. 

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 in tabular format. 

Knowledge- and Technology-Intensive 

Industries in the World Economy

Science and technology are widely regarded as important 

for the growth and competitiveness of all industries and for 

national economic growth. The Organisation for Economic 

Co-operation and Development (OECD 2001 and 2007) has 

identified 10 categories of service and manufacturing industries—collectively referred to as KTI industries—that have a 

particularly strong link to science and technology.2 Although 

a number of other taxonomies exist, they do not allow examination of worldwide production and trade data. 

Five knowledge-intensive service industries incorporate 

high technologies either in their services or in the delivery of their services. They include financial, business, 

and communications services (including computer software development and R&D), which are generally commercially traded. They also include education and health 

services, which are primarily government provided and 

location bound. 

The five high-technology manufacturing industries include aerospace, pharmaceuticals, computers and office 
6-8 Chapter 6. Industry, Technology, and the Global Marketplace

Comparison of Data Classification Systems Used

System Type of data Basis Coverage Data source Data preparation

High-technology 

manufacturing 

industries

Production and value 

added 

Industry by 

International 

Standard Industrial 

Classification

Aerospace, pharmaceuticals, 

office and computing 

equipment, communications 

equipment, scientific 

instruments

United Nations 

Commodity Trade 

Statistics and IHS 

Global Insight 

IHS Global 

Insight, 

proprietary 

special 

tabulations

Knowledgeintensive service 

industries

Industry production 

(revenues from services), 

in current dollars

Industry by 

International 

Standard Industrial 

Classification

Business, financial, 

communications, health, and 

education services

United Nations 

Commodity Trade 

Statistics and IHS 

Global Insight 

IHS Global 

Insight, 

proprietary 

special 

tabulations

Trade in 

high-technology 

products

Product exports and 

imports, in current 

dollars 

Product by 

technology area, 

harmonized code, 

country of origin 

and destination

Aerospace, pharmaceuticals, 

office and computing 

equipment, communications 

equipment and scientific 

instruments

United Nations 

Commodity Trade 

Statistics and IHS 

Global Insight

IHS Global 

Insight, 

proprietary 

special 

tabulations

U.S. trade 

in advanced 

technology 

products

U.S. product exports 

and imports, in current 

dollars

Product by 

technology area, 

harmonized code, 

country of origin 

and destination

Biotechnology, life sciences, 

optoelectronics, information 

and communications, 

electronics, flexible 

manufacturing, advanced 

materials, aerospace, 

weapons, nuclear technology, 

software

U.S. Census 

Bureau, Foreign 

Trade Division

U.S. Census 

Bureau, 

Foreign Trade 

Division, special 

tabulations

U.S. trade in 

commercial 

knowledgeintensive 

services

U.S. exports and 

imports, in current 

dollars

Type of service, 

country of origin

Business, financial, and 

communications services

U.S. Bureau 

of Economic 

Analysis

U.S. Bureau 

of Economic 

Analysis

Globalization of 

U.S. 

multinationals

Value added and direct 

investment position, in 

current dollars

North American 

Industry 

Classification, in 

country of origin 

and destination

Business, financial, and 

communications services, 

aerospace, pharmaceuticals, 

office and computing 

equipment, communications 

equipment, scientific 

instruments manufacturing

U.S. Bureau 

of Economic 

Analysis

U.S. Bureau 

of Economic 

Analysis

U.S. trade 

in intangibles

U.S. receipts 

and payments, 

in current dollars

Type of intangibles 

and industrial 

processes

Total intangibles and 

industrial processes

U.S. Bureau 

of Economic 

Analysis

U.S. Bureau 

of Economic 

Analysis

Patents Number of patents 

for inventions, triadic 

patents (invention 

with patent granted 

or applied for in 

U.S., European, and 

Japanese patent offices)

Technology class, 

country of origin

More than 400 U.S. patent 

classes, inventions classified 

according to technology 

disclosed in application

U.S. Patent and 

Trademark Office 

(USPTO) and 

Organisation for 

Economic Cooperation and 

Development 

(OECD)

USPTO, The 

Patent Board, 

and OECD

Angel capital Funds invested by U.S. 

angel investors

Technology Biotechnology, electronics, 

financial services, health care, 

industrial/energy, information 

technology, media, 

telecommunications

Center for 

Venture 

Research, 

University of New 

Hampshire 

Center for 

Venture 

Research, 

University of 

New Hampshire 

Venture capital Funds invested by U.S. 

venture capital funds

Technology area 

defined by data 

provider

Biotechnology, 

communications, computer 

hardware, consumer related, 

industrial/energy, medical/

health, semiconductors, 

computer software, Internet 

specific

National 

Venture Capital 

Association

Thomson 

Financial 

Services, 

special 

tabulations
Science and Engineering Indicators 2010 6-9

machinery, communications equipment, and scientific 

(medical, precision, and optical) instruments.3

 These 

industries spend a high proportion of their revenues on 

R&D, and their products contain or embody technologies developed from R&D. Aerospace comparisons will 

reflect, in part, government funding for military aircraft, 

missiles, and spacecraft and differences in national commercial and civilian flight regulations. Global comparisons of pharmaceuticals gross domestic product (GDP) 

shares or market revenues may be influenced by differing 

national regulations covering foreign pharmaceuticals. 

Information and communications technology (ICT) is 

a subset of KTI industries. It consists of two high-technology manufacturing industries—(1) computers and office machinery and (2) communications equipment and 

semiconductors—and two knowledge-intensive service 

industries—(1) communications and (2) computer services—that are classified under business services. ICT is 

used in a wide variety of economic sectors and is considered an important driver of economic growth. 

The OECD classification of knowledge-intensive service 

and high-technology manufacturing industries is an imprecise measure for a number of reasons. For example, hightechnology manufacturing and knowledge-intensive service 

industries may produce non-high-technology products or 

non-knowledge-intensive services, and technologically advanced manufacturing industries are excluded if they do not 

spend a high proportion of their revenues on R&D. 

This section examines the prominence of KTI industries 

in the global economy. The value added of these industries 

as a share of GDP is presented as an indicator of their relative 

importance in the major and world economies (see sidebar, 

“Industry Data and Terminology,” for a discussion of value 

added and other measures). Selected data are presented on 

the economic wealth and productivity growth of these economies, with particular focus on the United States and other 

economies that are knowledge and technology intensive. 

KTI industries have become a major part of the global 

economy. Value added of these industries was almost $16 

trillion in 2007, representing 29% of world GDP compared 

with a 26% share 15 years ago (figure 6-1; appendix tables 

The industry production and trade data used in this 

chapter come from a proprietary data set developed by 

IHS Global Insight that covers a consistent set of industries across 70 countries. IHS Global Insight’s data set 

uses data from the United Nations, the Organisation for 

Economic Co-operation and Development, and other 

sources, combined with IHS Global Insight’s proprietary 

forecasting and estimation for missing data in some developing countries. 

Two measures of industry activity are used in this 

chapter: value added and exports and imports. Value 

added and exports and imports are expressed in current 

(not adjusted for inflation) dollars. These measures are 

not compatible with past editions of this chapter, which 

expressed value added and exports and imports in constant (adjusted for inflation) dollars. 

Value added, a measure of industry production, 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 added is credited to regions or countries on the 

basis of where the company reported the activity. This 

is likely to be an imperfect measure because globalization and fragmentation of production may mean that the 

activity occurred in a different region or country than was 

reported by the company. In addition, companies have 

different reporting and accounting conventions for crediting and allocating production performed by their subsidiaries or companies in foreign countries. 

Value added of a company’s activity is assigned to a 

single manufacturing or service industry on the basis of 

the largest share of the company’s shipment of goods or 

delivery of services. This method of categorizing company activity is imperfect because an industry classified 

as manufacturing may include services, and a company 

classified as being within a service industry may include 

manufacturing or directly serve a manufacturing company. Furthermore, the single-industry classification is not a 

good measure for companies that have diversified activities in many categories of industry. 

Exports and imports are valued as the sum (gross) of 

value added contributed by all countries, firms, or other 

entities involved in production. This measure is not compatible 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. This is an imperfect measure for exports produced in multiple countries because the assigned country 

may not be the same location where the most value added 

took place. 

Exports and imports are assigned to a single product 

category by the exporter or customs agent on the basis 

of the primary content of the good. This method is imperfect because the product may contain other products. 

The trade product classification is not directly compatible 

with the industry classification of company production. 

For example, exports classified as semiconductor products may have originated from a company classified as 

being in the computer industry.

Industry Data and Terminology
6-10 Chapter 6. Industry, Technology, and the Global Marketplace

6-1 and 6-2). The share increased during the past decade 

before leveling off in 2002. The increase in the worldwide 

share of KTI industries was concentrated in five regional/

national economies, which conduct nearly 90% of global 

R&D—the United States, the EU, Japan, the Asia-9, and 

China.4

 

The United States had the highest concentration of KTI 

industries (38% of GDP in 2007), 4 percentage points higher 

compared with its level in 1992 (figure 6-2; appendix tables 

6-1 and 6-2). The percentage point increase in the corresponding shares of the EU and Japan was similar, reaching 

30% and 28%, respectively, in 2007. 

China’s KTI industries increased their share of GDP from 

21% to 23% (figure 6-2; appendix tables 6-1 and 6-2). The 

Asia-9’s share climbed from 19% to 22% during this period. 

The shares of three Asia-9 countries/economies—the Philippines, South Korea, and Taiwan—rose by about 10 percentage points, reaching a 25% to 30% share of their GDP in 

Figure 6-1

Global output of knowledge- and technologyintensive industries as a share of global GDP: 

1992–2007

Percent

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 knowledgeintensive services and high-technology manufacturing industries 

classified by Organisation for Economic Co-operation and 

Development (OECD). 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. Information 

and communications technology, classified by OECD, includes two 

knowledge-intensive services—communications services and 

computer and related services (part of business services)—and two 

high-technology manufacturing industries—communications and 

semiconductors and computers and office machinery.

SOURCE: IHS Global Insight, World Industry Service database, 

special tabulations (2009).

Science and Engineering Indicators 2010

0

5

10

15

20

25

30

35

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Public knowledge-intensive services

High-technology manufacturing industries

Information and communications technology

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2007, significantly higher than the Asia-9 average. India’s 

share was 18% in 2007, 3 percentage points higher than it 

was 15 years ago. 

An increase in the intensity of the Asia-9 and China’s 

KTI industries coincided with liberalization of their economies, increases in R&D expenditures, and adoption of policies to encourage high-technology industry production and 

trade. The KTI shares of other developing economies in 

Latin America, Africa, Central Europe/Asia, and the Middle 

East have grown little or have stagnated and are comparatively low (appendix tables 6-1 and 6-2).

Value added of commercial knowledge-intensive services amounted to $10 trillion in 2007, representing about 60% 

of the value added of all KTI industries (appendix table 6-3). 

Commercial knowledge-intensive services increased their 

share of world economic activity from 15% to 17% over 

the 15-year period, driving the increase in the KTI share of 

world GDP (figure 6-1; appendix tables 6-2 and 6-3). Value 

added of U.S. commercial knowledge-intensive services increased from 19% of U.S. GDP to 24%, the highest share of 

the knowledge-based economies (figure 6-3). The EU and 

Japan experienced a similar percentage point increase in the 

commercial knowledge-intensive share of their GDP. The 

share for China and the Asia-9 increased by 1 to 2 percentage 

points, reaching 14% and 13%, respectively, in 2007. Their 

considerably lower shares reflect their stage of development. 

As a share of the global economy, ICT value added rose 

from 4% in 1992 to 5% in 2007 (figure 6-1; appendix tables 

6-2 and 6-4). ICT shares in the developed economies edged 

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6-12 Chapter 6. Industry, Technology, and the Global Marketplace

share of its GDP doubled, climbing from 5% in 1992 to 9% 

in 2007. The Asia-9’s share was steady at 5% during this 

period. A major factor in the rise of China’s ICT intensity 

is that it became a major world exporter of ICT goods. The 

trend of the high-technology manufactures’ share in the five 

economies was similar to that for ICT (figure 6-4; appendix 

table 6-5). 

The relatively high and growing intensity of KTI industries in the United States, the EU, Japan, China, and the Asia9 coincided with elevated living standards, as measured by 

GDP per capita. The United States, the EU, and Japan account for about half of the world’s economic activity and 

also have the highest living standards (figure 6-5; appendix 

table 6-6). The United States has the highest per capita income among these economies ($31,260 in 1990 purchasing power parity [PPP]5

), 26% higher than Japan and 40% 

higher than the EU. The Asia-9 and China, each with economic production approximately the size of Japan’s, have 

far lower per capita incomes. However, per capita income 

varies widely in the Asia-9. The per capita income of India, 

Indonesia, the Philippines, and Vietnam is less than $4,500 

(1990 PPP), whereas South Korea, Singapore, and Taiwan 

have standards of living similar to that of the EU. 

China and the Asia-9 have made remarkable progress in 

raising their living standards over the past decade and a half. 

China’s per capita income grew at an annual average rate 

of almost 8%, resulting in per capita income more than tripling since 1992 (figure 6-6; appendix table 6-6). The Asia-9 

economies grew at an annual average rate of 4%, resulting 

in almost a doubling of per capita income. Singapore, South 

Korea, and Taiwan grew slightly faster than the Asia-9 average, resulting in living standards rising from middle to high 

income. India’s per capita income doubled from $1,300 to 

$2,800, propelled by 5% growth during this period. The per 

capita income of other developing economies has grown at 

half the rate (or less) of the Asia-9. 

Many economists and policymakers regard productivity 

growth as the single most important factor in maintaining 

and advancing living standards. Standard productivity measures, such as labor or multifactor output per hour, are not 

available for many countries. A proxy measure—GDP per 

employed person—is used here, spanning 1992 to 2007 (appendix table 6-7).6

Labor productivity growth was much lower for the developed economies than the developing economies, but 

productivity levels were much higher (appendix table 6-7). 

Labor productivity growth rates for the United States, the 

EU, and Japan averaged less than 2% annually (1.7%, 1.8%, 

and 1.3%, respectively) (figure 6-5). In contrast, China’s 

labor productivity grew at an estimated 8% annual rate. 

	
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Productivity growth of the Asia-9 economies averaged 

roughly 4%, ranging from India’s 5% to 3-4% for Singapore, South Korea, and Taiwan.

Despite impressive gains, productivity levels in China 

and the Asia-9 remain far below those of the United States, 

the EU, and Japan (figure 6-7; appendix table 6-7). China’s 

gap with the United States decreased by 10 percentage points 

from 1992 to 2007 but remains at one-fifth the U.S. level. 

The Asia-9’s gap narrowed slightly to 16% (from 12%) of 

the U.S. level. However, the labor productivity levels of Singapore, South Korea, and Taiwan are equivalent to those of 

the EU and Japan.

ICT has been identified by many economists and policymakers as vital for national economic growth and the competitiveness of all industries.7

 Bresnahan and Trajtenberg 

(1995) and others have identified ICT as a “general-purpose 

technology” that has the potential for pervasive use in a wide 

range of sectors because (1) it can be used with a variety of 

inputs and technologies and (2) it is subject to falling prices 

that stimulate further demand and use.8

 ICT is regarded as 

crucial for the growth of today’s knowledge-based economies in much the same way that earlier general-purpose 

technologies (the steam engine, metal forging, and automatic machinery) were crucial for growth during the Industrial 

Revolution. Thus, adoption and diffusion of ICT may be 

an important indicator of future economic and productivity 

growth and of a country’s capacity to innovate. 

Three ICT indicators are presented here:

ICT intensity: ICT spending as a share of GDP

The World Bank’s Knowledge Economy Index (KEI): 

a measure of per capita diffusion and adoption of ICT9

	
	







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6-14 Chapter 6. Industry, Technology, and the Global Marketplace

The relatively low standing of China and the Asia-9 in 

the KEI index, despite their relatively high share of global 

ICT spending, may be due to China’s and India’s very large 

populations and because China and some Asia-9 countries/

economies are net exporters of ICT goods. The benefit that 

China and some of the Asia-9 derive from ICT exports may 

come at the cost of not using cheaper and more powerful ICT 

products throughout their domestic economy and populace.10

Worldwide Distribution of Knowledgeand Technology-Intensive Industries

As national and regional economies change, the worldwide centers of KTI industries shift in importance. Shifts take 

place both for this entire group of industries and for individual service and manufacturing industries within the group. 

The global value-added output of knowledge-intensive 

service industries and high-technology manufacturing 

National share of global ICT spending: a measure of 

the scale of the economy’s demand for global ICT products and services. 

The United States ranks highest in the share of global 

ICT spending, scores highest in the KEI index, and ties with 

China in having the highest ratio of ICT spending to GDP 

(figure 6-8). The EU and Japan score nearly as high in the 

KEI index but have a lower intensity of ICT spending than 

the United States. China and the Asia-9 have greater ICT intensity and a higher share of global ICT spending than other 

developing regional/national economies. However, China 

and the Asia-9 score lower in the KEI index compared with 

Latin America, the Middle East, and Central Europe/Asia. 

ICT index scores vary widely within the Asia-9: The developed economies score at the same level as the United States, 

but India and other developing economies score at only half 

the Asia-9 average. 

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Science and Engineering Indicators 2010 6-15

industries accounted for an estimated $15.7 trillion in 2007, 

of which $5.0 trillion was for the largely location-bound, 

publicly funded knowledge-intensive services: $2.8 trillion for health and $2.2 trillion for education (figure 6-9; 

appendix tables 6-8 and 6-9). The total for tradable knowledge-intensive services and high-technology manufactures 

amounted to $10.7 trillion—$9.5 trillion for services and 

$1.2 trillion for manufacturing—out of an estimated total 

world economic output of $54.8 trillion (IMF 2009). 

Health and Education Services

The health and education sectors generated an estimated 

global value added of $2.8 and $2.2 trillion, respectively, in 

2007 (appendix tables 6-8 and 6-9). International comparison 

of these two sectors is complicated by variations in the size 

and distribution of each country’s population 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 health sector of the United States, which has more 

private sector involvement than many countries, is the second largest in the world as measured by share of global value 

added (35%), behind the EU’s 37% share (figure 6-10; appendix table 6-9). The U.S. and EU shares fluctuated considerably over the past decade but were roughly stable at 

	
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6-16 Chapter 6. Industry, Technology, and the Global Marketplace

the beginning and end of the period. Japan’s world share 

fell from 12% in 1995 to 7% in 2007. China’s share was 

stable and the Asia-9’s share rose from 3% to 4% during 

this period.

The United States is also the second largest provider of education at a 32% share, placing it behind the EU’s 34% share, 

with little change in these shares over the period (figure 6-10; 

appendix table 6-8). Third-ranked Japan’s share declined 

from 15% in 1995 to 6% in 2007, China’s share rose from 

2% to 5%, and the Asia-9’s share rose from 4% to 5%, largely 

because of strong growth in education spending in India, the 

Philippines, South Korea, Taiwan, and Thailand. Gains by 

China, India, and other Asian countries 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 trends in S&E higher 

education in Asia and other regions/countries/economies.) 

Commercial Knowledge-Intensive Service 

Industries

Business services is the largest of the three commercial 

knowledge-intensive service industries ($5.1 trillion value 

added); it includes computer and data processing services 

and commercial R&D services (appendix table 6-10). Financial services, the next largest industry, generated $3.2 trillion 

(appendix table 6-11). Communications ($1.3 trillion), the 

smallest of the knowledge-intensive industries, is arguably 

the most technology driven of the commercial knowledgeintensive services (appendix table 6-12). 

Worldwide, the volume of commercial knowledge-intensive services more than doubled over a decade, from $4.5 

trillion in 1995 to $9.5 trillion in 2007 (appendix table 6-3). 

The United States remains the largest provider of commercial knowledge-intensive services, with $3.3 trillion of the 

value added globally in 2007 (figure 6-11). The EU maintained second place at $2.9 trillion, trailed by Japan with 

$0.8 trillion. The volume of value added for commercial 

knowledge-intensive services in China and the Asia-9 is 

growing but remains low, at half a trillion dollars each. 

Three distinct growth patterns marked the commercial 

knowledge-intensive service industries of these regions. 

However, trends in these services are probably influenced 

by the level and growth of per capita income and changing consumption patterns of these economies rather than by 

advances in technology. The United States, the EU, and the 

Asia-9 grew at a pace similar to the world average (appendix table 6-3). (Fluctuations in growth for the U.S. and the 

EU during the past decade may partially reflect fluctuations 

in the dollar/euro exchange rate.11) Japan’s output stagnated 

over the decade, causing its world share to drop from 17% in 

1995 to 8% in 2007 (figure 6-11). China’s output expanded 

more than two times the world’s average growth rate but 

began from a low base, reaching 5% of the 2007 world total.

The same patterns can be seen in the individual service 

industries, with the shares for the United States and the 

EU consistently near 25% of global value added, steeply 

	
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Science and Engineering Indicators 2010 6-17

declining shares for Japan, and modest to rapid growth from 

low bases for China (moving from 2%–3% to 4%–7% of 

the world total over the decade) and the Asia-9, depending 

on the industry (figure 6-12; appendix tables 6-10 through 

6-12). Within the EU, the Eastern European countries and 

Ireland generally grew at least twice as fast as the EU average in all three industries. Among the Asia-9 countries/

economies, India was the second largest producer behind 

South Korea; its share rose from 0.8% to 1.4% as a result of 

strong growth in all three industries.

In other developing regions, Central Europe/Asia’s commercial knowledge-intensive services expanded more than 

twice as fast as the world’s average growth rate, led by 

growth in Russia and Turkey (appendix table 6-3). Its share 

of global value added increased from 1% to 3% because of 

strong growth in business and financial service industries. 

The Middle East expanded slightly faster than the world 

average rate, led by very rapid growth by Iran. Although 

Latin America grew at the world average, Mexico’s output 

expanded 50% faster than the world average and Brazil’s 

output more than doubled between 2003 and 2007 because 

of strong growth in business services and communications 

(appendix tables 6-10 through 6-12).

High-Technology Manufacturing Industries

Five manufacturing industries constitute the high-technology manufacturing sector, as defined by the OECD. In 

decreasing order of 2007 global value added, they are communications and semiconductors ($445 billion), pharmaceuticals ($319 billion), scientific instruments ($189 billion), 

aerospace ($153 billion), and computers and office machinery ($114 billion) (appendix tables 6-13 through 6-17).

The United States, the EU, Japan, China, and the Asia9 dominate high-technology manufacturing industries. 

In 2007, their collective shares accounted for 90% of the 

$1.2 trillion global total (figure 6-13; appendix table 6-5). 

U.S. high-technology manufacturers continued to rank first 

with $374 billion value added, followed by the EU at $306 

billion and China at $167 billion. However, the EU ranks 

first in domestic consumption of high-technology manufactured goods, followed by the United States (see sidebar, 

“Consumption of High-Technology Manufactured Goods”). 

Since 1995, the high-technology share of total U.S. manufacturing has increased modestly from 17% to 21% (appendix tables 6-5 and 6-18). In contrast, for all manufacturing 

industries, the EU is the global leader (29% of value added) 

and the United States ranks second (20%).

From 1995 to 2007, high-technology manufacturing output rose faster (69%) than total manufacturing (59%) (appendix tables 6-5 and 6-18). The United States, the EU, and 

the Asia-9 experienced growth in high-technology manufacturing close to the world average, whereas Japan’s output 

declined, resulting in a drop in its world share from 27% to 

11% (figure 6-13). China’s growth in high-technology manufacturing output greatly exceeded the world average, expanding ninefold over the decade, from $19 billion to $167 

	
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6-18 Chapter 6. Industry, Technology, and the Global Marketplace

billion, and its world share more than quadrupled from 3% 

to 14%. The high-technology share of the Chinese manufacturing sector jumped from 7% to 13% during this period. 

These country patterns were broadly similar to the output 

growth trends in domestic consumption of high-technology 

manufactured goods and knowledge-intensive services (figures 6-11 and 6-A). 

In 2007, the United States was the world leader in global 

value added in three high-technology manufacturing industries: communications and semiconductors (29%), pharmaceuticals (32%), and aerospace (52%) (figure 6-14; appendix 

tables 6-13, 6-14, and 6-16). The United States ranked behind the EU in scientific instruments (19% vs. 44%) and 

well behind China in computers and office machinery (25% 

vs. 39%) (appendix tables 6-15 and 6-17).

The U.S. share of global value-added in high-technology manufacturing remained roughly stable over the decade 

(figure 6-13; appendix tables 6-5 and 6-18). (Fluctuations 

in U.S. growth may be partially due to changes in the value 

of the U.S. dollar.) The U.S. share of global value added 

was relatively stable in the aerospace, communications and 

	
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Production of high-technology goods feeds both domestic demand 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 different concepts: consumption of final goods and capital investment for further production 

(intermediate goods). Available data series do not permit 

the examination of these two concepts separately.

Patterns of the world’s use of high-technology manufactures have changed considerably over the past decade. 

The U.S. share of domestic use, so defined, fell from 28% 

in 1995 to 25% in 2004 and has largely stayed at that level 

(figure 6-A). The EU’s share stayed broadly the same at 

26%–27% over the decade; it overtook the United States 

in 2003 to become the leading consumer of high-technology goods. Japan’s share declined by more than half, 

from 21% to 8%; the Asia-9’s share stayed essentially 

stable at 10% during this period.

China’s share surged from 4% in 1995 to 16% in 2007. 

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 for further assembly and ultimate export. 

Consumption of High-Technology 

Manufactured Goods
Science and Engineering Indicators 2010 6-19

semiconductors, computers and office machinery, and pharmaceutical industries (figure 6-14; appendix tables 6-14 

through 6-17). The U.S. share in scientific instruments, however, fell significantly from 29% to 19% during this period. 

Anecdotal evidence suggests that assembly of computers 

and semiconductors shifted from the United States to China 

and other Asian countries, contributing to China’s vigorous 

expansion of its output in these industries. However, U.S.-

based firms such as Dell and Apple continued to grow and 

to be highly profitable, deriving much of their profits from 

high-value activities such as logistics, design, and marketing 

that remained in the United States (see Dedrick, Kraemer, 

and Linden 2008, and sidebar, “Tracing the Geography of 

the Value Chain of Products”).

The EU’s share stayed roughly stable in three industries: 

pharmaceuticals (31%), communications and semiconductors (15%), and aerospace (27%) (figure 6-14; appendix 

tables 6-13, 6-14, and 6-16). The EU increased its share of 

scientific instruments by 6 percentage points to 44% over 

the decade but experienced a significant decline in computers and office machinery (appendix tables 6-15 and 6-17). 

Output of several Eastern European member countries—the 

Czech Republic, Hungary, Poland, and the Slovak Republic—grew much more rapidly in these industries than output 

of other member countries. This is consistent with evidence 

that these countries have become assembly centers for hightechnology industries based in more developed EU economies (Kaminski and Ng 2001). 

The communications and semiconductors and computers and office machinery industries drove China’s rapid expansion of high-technology manufacturing, coinciding with 

China becoming the world’s low-cost assembler and exporter of these goods. China’s communications and semiconductors industry grew nearly sixfold over the decade, its world 

share climbing from 4% to 15% (figure 6-14 and appendix 

table 6-13). Its computer industry grew at 45% annually between 1995 and 2007; its world share jumped from 1% to 

almost 40% over the same period (appendix table 6-17). 

China’s growth in other high-technology industries was 

also rapid—China at least quadrupled its world share in 

pharmaceuticals, scientific instruments, and aerospace (figure 6-14 and appendix tables 6-14, 6-15, and 6-16).

Japan’s share loss, driven primarily by the communications and semiconductors and the computers and office 

machinery industries, also extended to the other three hightechnology industries (figure 6-14 and appendix tables 6-13 

through 6-17). This broad downward trend may reflect its 

lengthy economic stagnation and the shift of production 

to China and other Asian economies. The Asia-9’s share 

of global value added edged up from 9% to 10%, reaching 

parity with Japan in 2007 (figure 6-13; appendix table 6-5). 

South Korea had very strong growth in communications 

and semiconductors, moving its share of global value added 

from 4% to 10% (appendix table 6-13). 

India has a very limited high-technology manufacturing 

industry, but its value added grew more than twice as fast 

as the Asia-9’s average (appendix table 6-5). India’s growth 

was concentrated in pharmaceuticals, with gains in scientific instruments—industries in which the United States and 

other multinationals have established a presence in India 

(appendix tables 6-14 and 6-15). 

In other developing regions, high-technology manufacturing output in Central Europe/Asia grew more than 

twice the world average over the 1995–2007 period, led 

by growth in Russia and Turkey (appendix table 6-5). The 

Middle East also gained, driven by Israel and Iran. Growth 

in both of these regions was led by scientific instruments 

and pharmaceuticals; communications and semiconductors 

also contributed to the Middle East’s gain (appendix tables 

6-13, 6-14, and 6-15). Latin America grew at a rate near 

the world average, the second slowest of the developing regions. However, Mexico, an important assembly center for 

high-technology goods, grew two times faster than the world 

average during this period, led by pharmaceuticals and 

communications and semiconductors. Brazil’s growth was 

stagnant between 1995 and 2003; however, it has grown rapidly since 2003, surpassing Mexico in 2005 to become the 

	
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Science and Engineering Indicators 2010 6-21

largest Latin American producer. Brazil’s aerospace industry grew by sevenfold and its computer industry registered 

strong gains.

Information and Communications 

Technology Industries

ICT as discussed here comprises both the communications 

and computer services industries and the computer, communications, and semiconductors manufacturing industries. 

In 2007, ICT generated an estimated total of $2.6 trillion in 

global value—$2.0 trillion in communications and computer 

services, and $0.6 trillion in the manufacturing industries (appendix table 6-4).

In 2007, the United States and the EU tied as the largest 

ICT producers (about $700 billion), followed by secondranked China ($315 billion). Japan and the Asia-9 converged 

in a range of approximately $205–$230 billion (figure 6-15; 

appendix table 6-4). 

The U.S. and EU shares fluctuated over the decade but 

showed little change in 2007 compared with a decade ago 

(figure 6-15; appendix table 6-4). Japan’s share fell steeply 

during this period, mirroring its downward trends in share 

in both high-technology and knowledge-intensive industries. China’s share tripled from 4% to 12% because of 

strong gains across all ICT industries. The Asia-9’s share 

was flat during this period, although India’s share rose 

Tracing the Geography of the Value Chain of Products

Several studies sponsored by the Sloan Foundation have attempted to estimate the value-added contribution of countries involved in the production of 

several electronic goods, including the Apple iPod and 

the Hewlett-Packard laptop computer. These studies essentially show that the big 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 value 

capture of these goods. Value capture does not count the 

cost of direct labor (figure 6-B) which, when included, 

could raise a country’s share (if direct labor was performed in the country) or lower it (if direct labor was performed in another country). Thus, the estimates shown 

must be regarded as broadly indicative only.

The Apple iPod study estimates that the United States 

receives the largest share of value capture based on the 

factory price (39%), largely reflecting Apple’s gross 

profit (36%) (table 6-A). The study sorts iPod components into key and low-cost generic items. Key inputs account for 37% of the wholesale price, and value capture 

is estimated for their manufacturers. The estimated U.S. 

share is 3%, raising the total U.S. share to 39%. Asia’s 

key inputs share is estimated at 14%, with Japan capturing 12% because of the expensive hard drive manufactured by Toshiba. (If direct labor costs were available, 

Japan’s share of value added would be arguably lower 

because Toshiba manufactures its hard drives in China 

and the Philippines.) The value capture of the generic inputs is estimated at 10%, of which 3% is estimated as the 

value capture from manufacture of these components. 

China, the location of final assembly, receives an estimated 2% share of the Apple iPod’s value capture (table 

6-A). The study estimates that China’s value capture is 

very small because final assembly of an iPod requires 

about 10 minutes and the minimum monthly wage for a 

worker is about $100. Because final assembly of the iPod 

and other electronic goods yields little value for China, 

the authors claim that trade statistics are misleading because the U.S. trade deficit with China increases by about 

$150 plus the cost of shipping for every iPod sold in the 

United States, whereas the value added by China is estimated at only a few dollars. Table 6-A summarizes similar data for the Hewlett-Packard laptop computer.

Figure 6-B

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 & 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/papers.asp., accessed 7 November 2009.

Science and Engineering Indicators 2010

(continued on next page)
6-22 Chapter 6. Industry, Technology, and the Global Marketplace

Non-Knowledge-Intensive Commercial Services

Commercial services not classified as knowledge intensive include the wholesale and retail, restaurant and hotel, 

transportation and storage, and real estate industries. The 

United States leads the EU by a slim margin, as measured by 

share of global value added (29%) in the wholesale and retail 

industry—the largest of these industries ($5.9 trillion)—and 

is the second-ranked provider in the other three industries 

(table 6-1). Allowing for fluctuations, the national/regional 

shares remained stable or showed slight upward trends except for Japan, whose shares fell in all of these industries.

Non-High-Technology Manufacturing Industries

Non-high-technology manufacturing industries are divided into three categories, as classified by the OECD: 

medium-high technology, medium-low technology, and 

low technology. These industries include motor vehicle 

manufacturing and chemicals production, excluding pharmaceuticals (medium-high technology), rubber and plastic 

production and basic metals (medium-low technology), and 

paper and food product production (low technology). 

The share trends in all of these industry segments are the 

same as for high technology—share losses for the United 

States and larger share losses for Japan, stable or slight declines for the EU, stable or slight increases for the Asia-9, 

and strong share gains across all segments for China.

from 0.5% to 1.1%, driven by gains in communications and 

computer services.

In other developing regions, Central Europe/Asia and 

Latin America increased their world share by 1 percentage 

point over the decade, reaching 4% and 5%, respectively, 

in 2007 because of strong growth in ICT service industries 

(appendix table 6-4). 

Industries That Are Not Knowledge 

or Technology Intensive 

Science and technology are used in many industries besides high-technology manufacturing and services. Services 

not classified as knowledge intensive incorporate technology in their services or in the delivery of their services but 

at a lower intensity compared with the knowledge-intensive 

services discussed above. Manufacturing industries not classified as high technology by the OECD use advanced manufacturing techniques, incorporate technologically advanced 

inputs in manufacture, and/or perform or rely on R&D in applicable scientific fields. In addition, some industries not classified as either manufacturing or services use or incorporate 

science and technology to varying degrees in their products 

and processes (see sidebar, “Trends in Industries Not Classified as Services or Manufacturing”).

Table 6-A

Contribution of value capture for Apple iPod and HP laptop computer, by country/economy of origin: 2005

(Percent)

Product, country/economy, 

and manufacturer Activity

 Share

factory price

Apple video iPod

U.S. .................................................. Design/marketing, manufacturing of components 38.7

Apple (gross profit) ....................... Design/marketing 35.7

U.S. contract manufacturer .......... Manufacturing of components 3.0

Japan ............................................... Manufacturing of components 12.0

South Korea ..................................... Manufacturing of components 0.4

Taiwan .............................................. Manufacturing of components 2.0

China ................................................ Final assembly 1.8

Hewlett-Packard laptop computer

U.S. .................................................. Design/marketing, operating system/chip, manufacturing of components 47.0

HP (gross profit) ........................... Design/marketing 28.0

Microsoft and Intel ....................... Operating system and chip 18.0

U.S. contract manufacturer .......... Manufacturing of components 1.0

Japan ............................................... Manufacturing of components 7.0

South Korea ..................................... Manufacturing of components 1.0

Taiwan .............................................. Manufacturing of components 2.0

NA .................................................... Final assembly NA

NA = not available

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 Center, University of California–Irvine (2008), http://pcic.merage.uci.edu/papers.asp., accessed 27 May 2009.

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 6-23

Medium-high-technology industries: These industries 

produced $2.1 trillion in global value added in 2007. The 

U.S. share fell from 23% to 17% between 1995 and 2007 

(table 6-2), and the EU share remained roughly stable 

(32%). Japan’s share fell from 24% to 13%, China’s more 

than quadrupled from 3% to 13%, and the Asia-9’s share 

rose from 7% to 9%. 

Medium-low-technology industries: The U.S. and EU 

shares of these industries ($2.5 trillion global value added) fell 3 percentage points each over the decade, reaching 16% and 28%, respectively (table 6-2). Japan’s share 

fell from 24% to 10%, its steepest loss among these three 

segments.

Low-technology industries: These industries produced 

$2.6 trillion in global value added in 2007. The U.S. and 

EU shares fell slightly (table 6-2). The Asia-9’s share remained stable, as opposed to its small gains in the other 

two segments.

Trade and Other 

Globalization Indicators

In the modern world economy, production is more often 

globalized (i.e., value is added to a product in more than one 

nation) than in the past and less often vertically integrated 

(i.e., conducted under the auspices of a single company and 

its subsidiaries). These trends have affected all industries, 

but their impact has been particularly strong in electronic, 

ICT, and other KTI manufacturing and service industries. 

The broader context is the rapid expansion of these industrial and services capabilities in many developing countries, 

both for export and internal consumption.

Global high-technology trade volume has risen faster 

than global production, indicating the growing importance 

of international suppliers of intermediate goods that are then 

used in the assembly of the final products purchased by the 

consumer. Data on multinational companies and cross-border investment likewise indicate growing interconnection 

among the world’s economies.

This discussion of trade trends in high-technology manufactured products focuses on the United States, the EU, Japan, the Asia-9, and China. Europe and East Asia have a 

substantial volume of intraregional trade that is treated differentially in this section. Intra-EU exports are excluded 

because the EU is an integrated trading bloc with common 

external trade tariffs and few restrictions on intra-EU trade. 

Trade between China and Hong Kong is excluded because it 

is essentially intracountry trade. The substantial intra-Asia-9 

trade is included because the group is not an integrated economy. Analytically, this allows delineation of a developing 

Asia-9/China supplier and manufacturing zone of high-technology goods that are largely destined for export to the EU, 

the United States, and Japan.

Trade data are an imperfect indicator of where value is 

added to a product. When the United States imports an ICT 

	
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6-24 Chapter 6. Industry, Technology, and the Global Marketplace

Trends in Industries Not Classified as Services or Manufacturing

Table 6-B

Share of global value added for selected industries, by region/country/economy: Selected years, 1995–2007

(Percent distribution)

Industry and region/country/economy 1995 1997 1999 2001 2003 2005 2007

Agriculture

Global value added (current $billions) .................. 1,113.3 1,150.1 1,033.4 1,003.9 1,167.6 1,390.0 1,835.8

All countries .......................................................... 100.0 100.0 100.0 100.0 100.0 100.0 100.0

United States .................................................... 8.4 9.6 9.1 9.8 9.8 9.6 9.1

EU ...................................................................... 21.9 19.4 19.5 18.1 18.7 16.8 15.8

Japan ................................................................ 9.2 6.6 7.9 6.9 6.1 5.0 3.6

China ................................................................ 13.1 15.2 17.3 19.0 18.0 20.3 21.3

Asia-9 ................................................................ 19.4 19.3 19.3 18.8 18.9 18.9 19.5

All other countries ............................................. 28.0 30.0 26.9 27.4 28.5 29.5 30.7

Construction

Global value added (current $billions) .................. 1,626.4 1,587.7 1,591.6 1,607.5 1,846.3 2,311.3 2,775.2

All countries .......................................................... 100.0 100.0 100.0 100.0 100.0 100.0 100.0

United States .................................................... 17.6 21.3 25.5 29.2 26.9 26.2 22.0

EU ...................................................................... 30.0 27.5 28.3 26.8 31.7 31.9 34.3

Japan ................................................................ 26.8 21.6 21.1 18.2 15.1 12.5 9.4

China ................................................................ 3.2 4.1 4.5 4.9 5.2 5.6 6.7

Asia-9 ................................................................ 7.9 8.6 6.2 6.0 6.7 7.5 8.4

All other countries ............................................. 14.6 16.9 14.4 14.9 14.3 16.3 19.3

Mining

Global value added (current $billions) .................. 469.7 552.0 462.6 600.4 748.3 1,305.1 1,695.3

United States .................................................... 15.8 16.8 18.5 19.8 19.2 17.1 16.2

EU ...................................................................... 15.4 12.2 13.0 11.5 10.5 8.5 7.6

Japan ................................................................ 1.9 1.2 1.2 0.9 0.6 0.3 0.3

China ................................................................ 7.2 9.4 11.6 10.3 10.8 9.6 10.2

Asia-9 ................................................................ 7.9 7.6 7.9 7.6 7.4 6.8 7.0

All other countries ............................................. 51.9 52.8 47.8 49.9 51.5 57.6 58.6

Utilities

Global value added (current $billions) .................. 718.9 693.8 700.1 687.3 795.7 961.7 1,149.7

All countries .......................................................... 100.0 100.0 100.0 100.0 100.0 100.0 100.0

United States .................................................... 25.2 25.9 26.5 29.4 27.6 24.9 24.5

EU ...................................................................... 26.6 25.6 24.5 21.2 25.2 25.7 26.0

Japan ................................................................ 25.4 21.8 23.4 22.4 19.3 16.3 12.6

China ................................................................ 2.7 3.9 5.0 5.5 6.0 9.2 10.3

Asia-9 ................................................................ 5.1 5.7 5.5 5.9 6.1 5.8 6.1

All other countries ............................................. 15.0 17.1 15.1 15.6 15.8 18.1 20.5

EU = European Union

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-9 includes India, Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. 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, special tabulations (2009).

Science and Engineering Indicators 2010

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 are dependent on or use science and technology. For example, 

agriculture relies on breakthroughs in biotechnology, 

construction uses knowledge from materials science, 

mining is dependent on earth sciences, and utilities rely 

on advances in energy science.

The United States ranks first in mining, second in 

construction and utilities behind the EU, and fourth in 

agriculture as measured by share of global value added 

among the five major economies (table 6-B). The U.S. 

share rose from 18% to 22% in construction over the decade, and its share in the other three industries remained 

stable. The EU’s share rose or 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, particularly agriculture and utilities. The Asia-9’s shares were stable or 

slightly higher. 
Science and Engineering Indicators 2010 6-25

good that is assembled in China from components that, in 

turn, are imported from other Asian economies, China’s 

value added may be small because its contribution is limited to final assembly of the good (Koopman, Wang, and 

Wei 2008). Much of the value added may originate from 

Asian, EU, or U.S. firms that manufactured the components 

or conducted design, marketing, software development, and 

other activities. The factory price and shipping cost of the 

good, however, would be fully credited to China’s exports 

and U.S. imports. Accurately apportioning value added is 

fraught with difficulties (see sidebar, “Tracing the Geography of the Value Chain of Products”). 

Trade of High-Technology Goods

A country’s success in exporting its goods to other countries is one measure of its comparative economic advantage—

the goods it produces are provided not just to its local market 

but are also competitive in a world market. 

The gross value of global exports of high-technology 

products—communications and semiconductors, computers and office machinery, pharmaceuticals, scientific instruments, and aerospace—reached $2.9 trillion in 2008, up from 

$915 billion in 1995 (appendix table 6-19).12 (See sidebar, 

“Product Classification and Determination of Country of 

Origin of Trade Goods” for discussion on how trade goods 

Table 6-1

Global value added for selected service industries, by region/country/economy: Selected years, 1995–2007

(Percent distribution)

Service industry and region/country/

economy 1995 1997 1999 2001 2003 2005 2007

Wholesale and retail

Global value added (current $billions) ..... 3,575.8 3,601.9 3,683.1 3,717.3 4,242.7 5,020.3 5,899.8

All countries ............................................. 100.0 100.0 100.0 100.0 100.0 100.0 100.0

United States ....................................... 27.2 30.4 32.9 34.9 32.7 30.8 28.8

EU ......................................................... 27.0 25.7 25.9 24.4 27.8 27.7 28.4

Japan ................................................... 23.5 18.9 18.7 16.5 14.5 13.4 10.8

China .................................................... 2.2 2.9 3.1 3.6 3.9 4.1 4.6

Asia-9 ................................................... 5.8 6.2 5.6 5.9 6.1 6.8 7.6

All other countries ................................ 14.3 15.9 13.8 14.6 15.1 17.2 19.9

Real estate

Global value added (current $billions) ..... 2,570.2 2,606.8 2,755.7 2,889.0 3,371.9 3,929.9 4,623.7

All countries ............................................. 100.0 100.0 100.0 100.0 100.0 100.0 100.0

United States ....................................... 31.7 34.8 36.9 40.5 37.8 36.3 34.3

EU ......................................................... 31.6 30.0 29.3 26.9 31.4 32.6 35.0

Japan ................................................... 21.9 17.7 18.1 16.8 15.3 13.9 11.3

China .................................................... 1.4 1.7 1.9 2.2 2.4 2.7 3.3

Asia-9 ................................................... 3.2 3.5 3.1 3.1 3.1 3.3 3.5

All other countries ................................ 10.2 12.2 10.7 10.6 10.0 11.2 12.5

Transport and storage

Global value added (current $billions) ..... 1,207.6 1,206.2 1,237.8 1,255.9 1,452.9 1,775.4 2,147.4

All countries ............................................. 100.0 100.0 100.0 100.0 100.0 100.0 100.0

United States ....................................... 18.7 21.0 23.2 23.6 21.8 20.5 19.0

EU ......................................................... 29.9 29.1 29.7 28.0 32.1 31.7 32.3

Japan ................................................... 23.2 17.4 17.1 15.4 13.8 11.8 9.4

China .................................................... 3.8 4.7 5.5 7.0 6.6 7.4 8.1

Asia-9 ................................................... 6.6 7.0 6.4 6.5 6.9 7.6 8.3

All other countries ................................ 17.8 20.8 18.0 19.5 18.9 21.0 22.9

Restaurants and hotels

Global value added (current $billions) ..... 706.8 734.1 787.1 806.2 934.3 1,116.1 1,336.3

All countries ............................................. 100.0 100.0 100.0 100.0 100.0 100.0 100.0

United States ....................................... 26.3 29.4 31.0 33.0 31.2 29.9 28.4

EU ......................................................... 29.4 28.7 29.4 27.8 32.1 32.6 33.7

Japan ................................................... 21.6 17.4 17.0 15.2 14.0 12.5 10.4

China .................................................... 2.7 3.5 3.7 4.3 4.2 4.8 5.6

Asia-9 ................................................... 6.1 6.3 5.2 5.5 5.6 5.9 6.6

All other countries ................................ 13.8 14.7 13.7 14.2 12.9 14.2 15.3

EU = European Union

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-9 includes India, Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. 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, special tabulations (2009).

Science and Engineering Indicators 2010
6-26 Chapter 6. Industry, Technology, and the Global Marketplace

are classified.) Removing intrabloc (within the EU) and intracountry (China to Hong Kong) exports reduces these totals to 

$732 billion in 1995 and an estimated $2.3 trillion in 2008—

base figures for the analyses that follow (figure 6-16). Among 

the five high-technology products, the world export value was 

greatest in communications and semiconductors (45% of the 

total) followed by computers (20%), giving the ICT products 

about two-thirds of the total (figure 6-17; appendix tables 

6-20 through 6-24).

The threefold increase in exports was greater than the 

rise in global production of these industries over the period, 

from $2.0 trillion to $4.0 trillion (figure 6-16). This probably 

reflects the broadened geographic base of high-technology 

manufacturing overall, the expansion of multinational firms’ 

production to overseas venues, and the shift of production 

from vertically integrated firms to greater reliance on international external suppliers.

Global Trade Balance Trends in High-Technology 

Manufactures

The expansion of high-technology trade has led to changes in the relative positions of the developed and developing 

countries (figure 6-18; appendix table 6-19). Measured in 

relative volume of exports, the U.S. position has declined 

from 21% in 1995 to 14% in 2008, reflecting broad drops 

in exports of U.S. ICT goods (communications and semiconductors and computers and office machinery), which 

account for nearly 45% of the nation’s high-technology exports (figure 6-19; appendix tables 6-19 through 6-21). (See 

sidebar, “Product Classification and Determination of Country of Origin of Trade Goods,” for discussion of how exports 

are credited to countries.) Japan’s share declined steadily 

over the period, from 18% to 8%, again largely because of 

declining exports of ICT goods. The EU’s high-technology 

export share remained approximately stable at 16%–18%. 

Amidst a great increase in world exports, China’s share 

surged from 6% to 20% over little more than a decade, 

Table 6-2

Global value added for manufacturing industries, by selected technology level and region/country/economy: 

Selected years, 1995–2007

(Percent distribution)

Manufacturing technology level and region/

country/economy 1995 1997 1999 2001 2003 2005 2007

Medium high

Global value added (current $billions) ..... 1,394.7 1,343.5 1,313.1 1,251.6 1,462.2 1,747.8 2,127.1

All countries ............................................. 100.0 100.0 100.0 100.0 100.0 100.0 100.0

United States ....................................... 22.5 24.9 26.7 26.6 23.3 19.3 17.4

EU ......................................................... 31.5 31.0 31.2 29.7 32.7 31.9 32.3

Japan ................................................... 23.9 20.4 20.6 19.0 17.5 16.9 13.3

China .................................................... 2.7 3.6 3.5 5.0 7.1 10.2 13.4

Asia-9 ................................................... 6.5 6.7 5.9 6.7 7.2 8.1 8.6

All other countries ................................ 12.9 13.4 12.1 13.1 12.3 13.7 15.0

Medium low

Global value added (current $billions) ..... 1,352.5 1,325.3 1,277.5 1,257.9 1,464.9 1,981.5 2,518.8

All countries ............................................. 100.0 100.0 100.0 100.0 100.0 100.0 100.0

United States ....................................... 19.1 22.0 23.8 24.0 20.7 19.3 16.1

EU ......................................................... 31.2 29.5 30.2 28.3 30.3 28.0 28.1

Japan ................................................... 23.8 19.9 18.9 17.5 15.6 13.5 9.8

China .................................................... 3.5 3.9 4.1 5.6 7.4 10.0 14.2

Asia-9 ................................................... 7.6 8.2 7.4 7.6 8.1 9.0 9.5

All other countries ................................ 14.8 16.5 15.5 17.0 17.8 20.1 22.3

Low 

Global value added (current $billions) ..... 1,809.3 1,766.6 1,792.2 1,743.3 1,942.2 2,229.1 2,549.7

All countries ............................................. 100.0 100.0 100.0 100.0 100.0 100.0 100.0

United States ....................................... 24.7 26.9 30.2 31.3 28.7 26.1 23.0

EU ......................................................... 31.5 30.2 29.5 27.3 30.4 29.7 29.8

Japan ................................................... 18.8 14.8 15.0 13.9 12.1 10.7 8.3

China .................................................... 2.9 4.1 4.0 5.1 6.3 8.7 11.9

Asia-9 ................................................... 6.0 6.3 5.7 5.7 5.8 6.0 6.5

All other countries ................................ 16.1 17.7 15.7 16.7 16.8 18.7 20.4

EU = European Union

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-9 includes India, Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. 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, special tabulations (2009).

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 6-27

making it the largest single exporting country for high-technology manufactured goods (figure 6-18; appendix table 

6-19). The Asia-9 region has maintained its position at more 

than a quarter of the total. However, this largely reflects the 

rise of a manufacturing supplier zone around China that is 

focused on ICT goods (see “Trends in the Geographic Distribution of Bilateral High-Technology Trade,” later in this 

chapter).

Notable differences are apparent in the export performance of these countries and regions for the five products (figure 6-19; appendix tables 6-20 through 6-25). The 

United States and Japan have been losing export shares in 

most industries, with the exception of the U.S. aerospace 

share, which has fluctuated at about 50%. EU shares have 

held approximately steady, with strong market shares for 

pharmaceuticals, aerospace, and scientific instruments. 

China’s market shares have grown substantially since 2000, 

Product Classification 

and Determination of Country 

of Origin of Trade Goods

The characteristics of goods in international trade 

are determined from a product perspective. Data on 

product trade are first recorded at the country’s ports 

of entry. Each type of product is assigned a product 

trade code by the customs agent according to the harmonized system.*

 Exporters generally identify the 

product being shipped and include its proper code. 

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 

value of products entering or exiting U.S. ports may 

include the value of components, inputs, or services 

classified in different product categories or originating 

from other countries 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. For example, a 

General Motors car that was assembled in the United 

States with components imported from Germany and 

Japan and that is destined for export to Canada will be 

labeled “Made in the USA.” The country where the 

product was “substantially transformed” may not be 

the location where the most value was added. 

*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 previously adhered-to schedules in more than 50 

countries, including the United States.

	
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contributed a further $21 billion to the overall 2008 deficit 

(appendix table 6-23). 

U.S. trade in aerospace products registered a trade surplus 

of $50 billion in 2008, continuing its trend of surpluses for 

the past two decades; trade in scientific instruments added a 

smaller surplus of $9 billion (appendix tables 6-22 and 6-24). 

The EU high-technology trade balance remained roughly 

stable, with a deficit of about $20 to $50 billion over this 

period (figure 6-20; appendix table 6-19). However, the EU 

ICT deficit grew from $38 billion in 1995 to $117 billion in 

2008, reflecting the same underlying structural shift (appendix table 6-26). Rising surpluses in aerospace, pharmaceuticals, and scientific instruments offset the increasing ICT 

deficit (appendix tables 6-22 through 6-24).

The trade positions of China and the Asia-9 also changed 

substantially. China’s trade position, which had been in balance for much of the 1980s and 1990s, moved to a surplus 

after 2001 (figure 6-20; appendix table 6-19) and rapidly increased from less than $13 billion in 2003 to almost $130 

billion in 2008, driven by the ICT goods trade (appendix 

table 6-26). The Asia-9’s trade surplus grew from about $50 

billion to more than $220 billion over the past decade, entirely due to an expansion of its surplus in ICT goods (however, see the next section). Japan’s surplus showed little 

change, despite its loss of market share in production of 

high-technology industries.

Geographic Distribution of Bilateral HighTechnology Trade

The shift in trade in global high-technology manufactures 

over the past decades (i.e., the shift away from the developed regional/national economies to China and the Asia-9) 

was accompanied by a pronounced shift in the distribution 

of bilateral trade among these and the three other economies—the United States, the EU, and Japan. Trade in ICT 

goods, the largest single category of high-technology industry goods, illustrates these shifting patterns.

Final assembly of ICT goods and components shifted—

from the United States, the EU, Japan, and developed economies among the Asia-9—toward China early in this decade, 

and some assembly work has subsequently shifted from 

China to the less-developed Asia-9 economies (Athukorala 

and Yamashita 2006, Ng and Yeats 2003, Rosen and Wing 

2005). This discussion examines trends in bilateral trade distribution of ICT goods.

The rise of China as the world’s major assembler and exporter of many electronic goods is reflected by a sharp increase in China’s share of ICT imports in the United States, 

the European Union, and Japan (figure 6-21; appendix tables 

6-27 through 6-29). China’s share of these economies’ ICT 

imports was 40%–50% in 2008, compared with 10% or less 

in 1995. Data on China’s bilateral exports show that about 

65% of its ICT exports were shipped to the United States, Japan, and the EU, suggesting that most of China’s exports are 

finished products destined primarily for developed countries 

(figure 6-22; appendix table 6-30). The trends for China’s 

capturing almost 40% of the world export market in computers and office machinery and showing strong growth in 

semiconductors export shares (but negligible shares in aerospace and pharmaceuticals). The Asia-9 region accounts for 

large shares of semiconductor and computer exports and, 

together with China, captured more than 60% of the world 

export market share in these industries.

Throughout the 1980s and into the mid-1990s, the United 

States consistently exported more high-technology products 

than it imported, in contrast to deficits recorded for other 

U.S. manufacturing products.13 A growing U.S. import volume in the late 1990s shifted the U.S. high-technology trade 

balance from surplus to deficit (figure 6-20 and appendix 

table 6-19). In 2000, the deficit was $32 billion in current 

dollars; by 2008, the deficit had widened to $80 billion. 

ICT goods are driving the U.S. high-technology trade 

deficit: In 2008, the ICT industries ran a deficit of almost 

$120 billion in current dollars (figure 6-20; appendix table 

6-26). The emergence of large deficits in these products 

reflected rising domestic demand, which coincided with a 

broad shift in location of the production of ICT goods to developing countries, largely in Asia. This, in turn, stimulated 

imports of ICT goods from these countries. Pharmaceuticals 

	
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Science and Engineering Indicators 2010 6-29

ICT exports by share of developed economy showed little 

change over time.

Trends in data regarding China’s imports and the Asia-9’s 

exports of ICT goods suggest that much of final assembly 

has shifted to China, with the Asia-9 acting as key suppliers 

of components and inputs. The Asia-9’s share of China’s 

ICT imports rose from 40% in 1995 to 71% in 2008 (figure 

6-22; appendix table 6-30). Imports from Taiwan increased 

	
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Science and Engineering Indicators 2010 6-31

the most, from 11% to 23% of China’s total ICT imports. 

South Korea’s and the Philippines’ shares also increased 

by about 5 percentage points each, reaching 14% and 

5%, respectively; Singapore’s share was stable. However, 

Japan’s share of China’s imports fell from 30% to 16%.

Japan’s ICT export data show a pronounced shift toward 

China, rising from a 10% share of its ICT export goods to 

28% since 1995 (figure 6-23; appendix table 6-29). The 

share of Japanese exports to the United States fell sharply 

over the period, from 36% to 15%; its share to the Asia-9 

	


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6-32 Chapter 6. Industry, Technology, and the Global Marketplace

was steady at about 25% (figure 6-21; appendix tables 6-27 

and 6-31). These patterns are consistent with reports that 

Japanese (and Taiwanese) manufacturers began exporting 

components for assembly in plants they established in China. U.S. purchases of ICT goods from Japan may have been 

supplanted by goods assembled in and shipped from China 

for Japanese and Taiwanese firms.

The Asia-9’s bilateral export data are consistent with China’s import data showing the rise of the Asia-9 as a major 

supplier to China’s ICT manufacturing industries. China’s 

share of the Asia-9’s exports nearly quadrupled from 8% 

to 31% over the decade (figure 6-24; appendix table 6-31). 

China’s share growth was strongest in the exports of South 

Korea (from 8% to 30%), Taiwan (from 12% to 43%), Singapore (from 10% to 29%), and the Philippines (from 5% to 

38%) (figures 6-24 and 6-25; appendix tables 6-32 through 

6-35). The share of Asia-9’s ICT exports going directly to 

the United States or the EU fell sharply during this period 

(appendix tables 6-27 and 6-28). 

The data indicate that the Asia-9 countries/economies 

have come to be assemblers and exporters of both intermediate and finished ICT goods, the former going to China 

and other Asia-9 destinations, the latter largely to the United States, the EU, and other developed nations. The intraAsia-9 share of Asia-9 ICT imports rose from 36% to 46% 

over the past decade (figure 6-26; appendix table 6-31), coinciding with a sharp increase (from 7% to 26%) in imports 

from China. This is consistent with the Asia-9 countries/

economies importing components from China for final or 

intermediate assembly and re-exporting them back to China 

for final assembly and export. 

The Asia-9 countries/economies—particularly Malaysia, South Korea, Taiwan, and Singapore—remain substantial suppliers of ICT goods to the EU and the United States 

(about 30% each) and to Japan (39%) (figure 6-24; appendix 

tables 6-31 and 6-33 through 6-36). 

Exports of Medium- and Low-Technology 

Manufactured Products

The U.S. export performance in products associated with 

less knowledge intensity and less use of R&D provides a 

context for its high-technology status. In these industries, the 

United States has historically had lower world export shares, 

although some convergence, which largely reflects declines 

in the U.S. high-technology share, has been evident since 

the late 1990s. 

The U.S. share of world exports in medium-high-technology products (i.e., motor vehicles, chemicals, railroad 

equipment) was 14% in 2008, which was equal to its share 

in high-technology industries (table 6-3) and which placed it 

fourth behind the EU (24%, excluding intra-EU trade) and 

Japan and the Asia-9 (15% each). The U.S. and EU shares 

have remained stable over the past decade, whereas Japan’s 

share has fallen from 22% to 15%. China, ranked fifth, has 

rapidly expanded its share of global exports from 4% to 13% 

(excluding trade between China and Hong Kong). 

	
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6-34 Chapter 6. Industry, Technology, and the Global Marketplace

The United States also ranks fourth (8%) in share of world 

exports in medium-low-technology products (table 6-3), behind the EU and the Asia-9 (16% and 18%, respectively) and 

China (13%). U.S. export share in low-technology products 

in 2008 (at 12%) also placed it fourth behind China (22%), 

the EU (18%), and the Asia-9 (16%). 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 hightechnology exports.

U.S. Trade in Advanced Technology Products

The Census Bureau has developed a classification system for internationally traded products that embody new or 

leading-edge technologies. This classification system has 

significant advantages for determining whether an industry and its products are high technology 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 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 civil 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 

Table 6-3

Exports of manufactured products, by selected technology level and region/country/economy: 

Selected years: 1995–2008

(Percent distribution)

Manufacturing technology level and region/country/economy 1995 1998 2001 2004 2006 2008

Medium high

Global exports (current $billions) .......................................... 630.4 697.0 805.7 1,171.6 1,477.8 1,812.0

All countries .......................................................................... 100.0 100.0 100.0 100.0 100.0 100.0

United States .................................................................... 17.3 18.0 16.8 14.0 14.2 14.1

EU ...................................................................................... 25.5 25.2 24.4 24.6 23.4 23.7

Japan ................................................................................ 21.7 19.2 17.4 17.2 16.0 15.2

China ................................................................................. 3.9 4.9 6.5 8.8 10.8 12.8

Asia-9 ................................................................................ 11.3 10.6 12.2 14.9 15.1 15.2

All other countries ............................................................. 20.2 22.2 22.6 20.6 20.5 19.0

Medium low

Global exports (current $billions) .......................................... 396.2 413.4 480.5 816.2 1,258.6 1,769.3

All countries .......................................................................... 100.0 100.0 100.0 100.0 100.0 100.0

United States .................................................................... 10.8 12.3 11.8 8.3 8.2 8.4

EU ...................................................................................... 20.7 20.1 17.7 16.9 16.1 15.8

Japan ................................................................................ 13.2 11.2 9.1 8.2 6.9 6.6

China ................................................................................. 5.2 6.0 7.1 9.4 10.5 12.7

Asia-9 ................................................................................ 15.8 17.2 16.9 18.5 18.6 18.0

All other countries ............................................................. 34.3 33.2 37.6 38.7 39.6 38.5

Low

Global exports (current $billions) .......................................... 559.7 561.0 626.4 818.9 993.3 1,235.7

All countries .......................................................................... 100.0 100.0 100.0 100.0 100.0 100.0

United States .................................................................... 14.5 14.7 14.0 11.8 11.9 12.0

EU ...................................................................................... 20.7 20.5 19.1 19.7 18.4 18.3

Japan ................................................................................ 3.8 3.8 3.6 3.0 2.7 2.5

China ................................................................................ 12.1 13.6 15.3 17.9 20.3 21.8

Asia-9 ................................................................................ 20.1 18.3 18.5 16.8 16.5 16.2

All other countries ............................................................. 28.8 29.1 29.5 30.9 30.1 29.3

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-9 includes India, Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. 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, special tabulations (2009).

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 6-35

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, video conferencing, 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 advanced technology products is an important component of overall U.S. trade, accounting for about 

one-fifth of total trade volume for the past two decades. In 

2008, U.S. exports of advanced technology products were 

$276 billion (nearly 21% of goods exports) and imports 

were $331 billion (16% of total goods imports) (figures 6-27 

and 6-28 and appendix table 6-37). As with high-technology 

industries trade accounts, imports of advanced technology 

products grew faster than exports since the early 1990s, 

sending the U.S. trade balance in these products into deficit in 2002 (figure 6-28). By 2008, the deficit reached $56 

billion, comprising 7% of the total U.S. goods trade deficit 

($816 billion). 

Changes in exchange rates may have been a contributing 

factor to these trends because the U.S. dollar’s value against 

a basket of its major trading partners’ currencies appreciated 

more than 60% between the early 1990s and 2002, coinciding with the shift from surplus to deficit (figure 6-28). However, the dollar depreciated about 20% through 2008, and 

the deficit continued to widen. 

It is likely that the growing deficit was affected by changing world production and trade patterns, adoption of new 

business and production processes, establishment of productive capacity abroad, and the emergence of export-oriented 

high-technology industries in Asia and other regions and 

countries.

U.S. Advanced Technology Product Trade, by 

Technology 

Five technology areas—information and communications, aerospace, electronics, the life sciences, and optoelectronics—accounted for a combined share of about 90% of 

U.S. advanced technology product trade in 2008 (figure 6-29; 

appendix tables 6-38 through 6-47). Information and communications had the largest single share (43%), followed by 

aerospace (21%), electronics (13%), the life sciences (11%), 

and optoelectronics (5%). Three of these technologies have 

generated substantial trade deficits: information and communications ($104 billion), optoelectronics ($21 billion), 

and the life sciences ($15 billion) (figure 6-30). The rapid 

rise in the overall deficit between 2002 and 2008 was driven 

by the deficit in ICT, widening from $48 billion to more than 

	
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in 2008 ($28 billion more than in 2000), as exports jumped 

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Science and Engineering Indicators 2010 6-37

imports and exports fell during the period, but imports declined more steeply. 

U.S. Advanced Technology Trade, by Region and 

Country

The majority of U.S. advanced technology trade occurs 

with six regions/countries: the EU (24%), the Asia-9 (21%), 

China (19%), Latin America (15%), Japan (7%), and Canada 

(7%) (figure 6-29 and appendix table 6-37). U.S. trade with 

Asia (Asia-9, China, and Japan) accounts for nearly half of 

total U.S. advanced technology trade. U.S. merchandise trade 

with Asia also contains a higher-than-average share of advanced technology goods. This share in 2008 was twice the 

U.S. average for exports to the Asia-9 (35%) and 27% for China. Japan’s 22% share equaled that of the EU (figure 6-31). 

China and Japan. China exported $92 billion of advanced technology products to the United States (about onefourth of U.S. imports) and imported $26 billion in 2008. The 

United States has the largest deficit with China, which is its 

third largest trading partner among the six regions/countries 

	
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6-38 Chapter 6. Industry, Technology, and the Global Marketplace

and the largest single country (figure 6-30; appendix table 

6-37). ICT goods account for nearly 90% of U.S. imports of 

advanced technology products from China (appendix table 

6-40). U.S. exports of advanced technology goods include 

aerospace, electronics, and information and communications 

(appendix tables 6-38 through 6-40). 

The volume of U.S.-China advanced technology trade 

more than quadrupled over this decade, and in 2003 China 

surpassed Japan as the United States’ single largest country 

partner in these goods (appendix table 6-37). U.S. imports 

from China have increased much faster than its exports to 

China, pushed by a rising trade volume in ICT technologies. 

The steep rise in imports and flat export growth widened the 

U.S. deficit with China in information and communications 

from $6 billion to $75 billion (figure 6-30; appendix table 

6-40).

Japan was the largest trading country partner with the 

United States until it was overtaken by China in 2003 (appendix table 6-37). Information and communications technology constituted nearly half of all U.S. imports from Japan 

in 2008, similar to its prevalence in imports from China (appendix table 6-40). Among advanced technology exports 

to Japan, aerospace accounted for the largest share (42%); 

information and communications products ranked second 

(18%) (appendix table 6-38). 

The Asia-9. The Asia-9’s trade was one-fifth of total advanced technology trade volume in 2008 (figure 6-29), with 

exports of $73 billion to the United States and imports of 

$54 billion (figure 6-30; appendix table 6-37). Malaysia, 

Singapore, South Korea, and Taiwan are the Asia-9’s major 

U.S. trading partners. The $19-billion U.S. deficit with the 

Asia-9 consists of a $12-billion deficit with Malaysia and 

smaller deficits with South Korea, Taiwan, and Thailand 

(and a small surplus with Singapore). 

As with China, ICT products constituted the largest share 

of total U.S. advanced technology trade with the Asia-9. 

Important suppliers are Malaysia ($17 billion), South Korea ($13 billion), and Taiwan ($8 billion) (appendix table 

6-40). U.S. imports of $52 billion and exports of $9 billion 

produced a deficit of more than $40 billion in ICT products 

in 2008. 

The Asia-9 ICT deficit in information and communications was partly offset by a $24-billion combined surplus in 

aerospace, electronics, and flexible manufacturing products 

(appendix tables 6-38, 6-39, and 6-45). Combined U.S. exports of these technologies were $41 billion in 2008, 76% 

of total U.S. exports to the Asia-9. Important customers of 

these three technologies were South Korea, Singapore, and 

Taiwan (in all three categories), India (aerospace), and Malaysia and the Philippines (electronics).

The U.S. trade position in advanced technology goods 

with the Asia-9 has been relatively stable over this decade. 

This may reflect the migration of final assembly of many 

ICT goods from the Asia-9 to China, coinciding with a widening deficit of ICT trade with China. 

The European Union. Trade with the EU accounts for 

nearly one-fourth of U.S. advanced technology product 

trade (figure 6-29; appendix table 6-37). The EU exported 

$69 billion and imported $76 billion, resulting in a $7-billion 

surplus in 2008 (figure 6-30). Five EU members—France, 

Germany, Ireland, the Netherlands, and the United Kingdom—accounted for nearly 80% of total U.S.-EU trade in 

these goods. Aerospace, the life sciences, and ICT had a 

combined 77% share of the volume of U.S.-EU advanced 

technology product trade in 2008 (appendix tables 6-38, 

6-40, and 6-41). 

The United States had substantial surpluses with the EU 

in aerospace ($13 billion) and ICT goods ($9 billion) (appendix tables 6-38 and 6-40). Important EU customers of 

aerospace and ICT are France, Germany, and the UK; the 

Netherlands purchases the most U.S. ICT goods of the EU 

countries. 

The life sciences produced a $15-billion deficit (appendix 

table 6-41). Ireland was by far the largest EU supplier of life 

sciences products, accounting for more than half of the EU’s 

$27 billion in exports to the United States in 2008. Other 

substantial suppliers were Belgium, France, Germany, and 

the UK. 

The U.S. trade surplus with the EU narrowed from $22 

billion in 2000 to $7 billion in 2008 (figure 6-30), reflecting the deficit in life sciences rising from $6 billion to $16 

billion due to much more rapid growth of imports (appendix 

tables 6-37 and 6-41).

Latin America and Canada. U.S. advanced technology 

trade with Latin America amounted to 15% of total U.S. 

advanced trade in 2008 (figure 6-29; appendix table 6-37). 

Mexico is by far the largest trading partner in Latin America 

(10% share of U.S. advanced technology trade), followed 

by distant-second Brazil (2%). ICT products accounted for 

half of Latin America’s total U.S. trade in these products 

(appendix table 6-40).

Strong growth in U.S. aerospace and ICT exports was 

more than offset by large import increases in optoelectronics 

and ICT (appendix tables 6-38, 6-40, and 6-42). Mexico was 

the main supplier of optoelectronic imports, which rose from 

$0.5 billion to $15 billion. The United States also had a substantial deficit with Mexico in ICT goods ($10 billion). The 

U.S.-Mexico trade deficit in these goods reflects, in part, 

Mexico’s duty-free imports of U.S. components and their 

assembly and re-export to the United States.

U.S. advanced technology trade with Canada amounted 

to 7% of total trade in 2008 (figure 6-29; appendix table 

6-37). Canada exported $17 billion and imported $28 billion, resulting in a surplus of $11 billion (figure 6-30; appendix table 6-37). ICT and aerospace constituted three-quarters 

of this bilateral trade (appendix tables 6-38 and 6-40). The 

United States had a $9-billion surplus with Canada in ICT 

goods and a $2-billion deficit in aerospace products.
Science and Engineering Indicators 2010 6-39

U.S. Multinationals 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 strictly comparable 

with the world industry data. However, the BEA data do provide useful information on the globalization of activity and 

the employment of U.S. multinationals in these industries. 

Commercial Knowledge-Intensive Service 

Industries

U.S. multinationals in commercial knowledge-intensive 

service industries generated $720 billion in value added in 

2006, of which more than 80% ($602 billion) occurred in 

the United States, according to BEA data (figure 6-34; appendix table 6-50). Financial services ranked first by value 

added ($270 billion), followed by business services ($239 

billion) and communication services ($212 billion).14 The 

proportion of value added from their U.S. operations was 

highest in communications (94%), followed by financial services (86%) and business services (71%). The distribution of 

Globalization of Knowledge-Intensive 

Service Industries

Services have historically been more local and insulated 

from global competition than manufactured goods because 

they were less easily traded and often had to be located near 

the consumer. However, rapid growth of new international 

markets, increased competition, and advances in communications and other enabling technologies have ushered in 

the globalization of services. Tradable knowledge-intensive 

services include three commercial services: business, financial, and communications. Education and health have also 

become globalized but to a much lesser extent than the commercial knowledge-intensive services. Overall, the current 

extent of globalization of knowledge-intensive services is 

less than that of high-technology manufacturing industries.

The volume of U.S. trade in commercial knowledgeintensive services is lower than trade in high-technology 

manufactured goods but is producing increased surpluses. 

Commercial knowledge-intensive service industries are a 

key component of the overall U.S. trade in private services, 

accounting for 40% of the total (appendix table 6-49). U.S. 

exports of (receipts for) commercial knowledge-intensive 

service industries were $185 billion in 2007 (nearly 40% 

of total private services exports), and imports (payments) 

were $138 billion (again, 40% of the total) (figure 6-32). 

The resulting surplus, $47 billion, accounted for one-third 

of the overall surplus in private services trade ($139 billion) 

in 2007. 

Business, professional, and technical services, the category that includes R&D and computer services, is the largest 

component of trade in commercial knowledge-intensive service industries (55%) (table 6-4; appendix table 6-49) (See 

“Business to Business Linkages, Exports, and Imports of 

R&D Services” in chapter 4 for discussion of trends in U.S. 

trade in R&D services, a component of business services). 

Finance is the second-largest component (40%), with communications being much smaller (5%). 

U.S. trade in commercial knowledge-intensive services 

has been in surplus for the past 10 years (figure 6-33), in 

contrast to deficits in U.S. trade of high-technology goods. 

Business services produced a $39-billion surplus in 2007, 

out of a total of $47 billion (table 6-4; appendix table 6-49). 

Financial services gained a small surplus, and telecommunications services trade is balanced.

The bulk of U.S. trade in commercial knowledge-intensive service industries was with the EU (42%), with business 

services as the largest component (table 6-4). The next-largest trade partner was Latin America (21%), with a relatively 

large share in financial services that may, in part, reflect offshore banking in the Caribbean. The Asia-9’s share of trade 

in commercial knowledge-intensive services was much 

smaller than in high-technology products.

	
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6-40 Chapter 6. Industry, Technology, and the Global Marketplace

value added between U.S. and foreign affiliates showed little 

change between 1999 and 2006. 

The U.S. multinationals in commercial knowledge-intensive service industries employed 3.7 million workers in the 

United States in 2006, of whom about 40% were employed 

in business services and about 30% each in communications 

and financial services (appendix table 6-50). Business and financial services firms employed 0.9 million and 0.5 million, 

respectively, at their foreign affiliates (data are not available 

for communications services). From 1999 to 2006, the foreign 

employment shares rose from 19% to 28% in financial services and from 36% to 38% in business services (figure 6-33).

High-Technology Manufacturing Industries

BEA data show that U.S. multinationals in four of these 

five industries generated more than $300 billion worldwide 

in value added in 2006, of which about two-thirds originated 

in the United States (appendix table 6-50). Production in the 

computer industry was the most globalized, as measured 

by the distribution between U.S. and foreign value added, 

with 48% of value added originating from the United States 

in 2006, down from 64% in the late 1990s (figure 6-35). 

The U.S. value added in the communications and semiconductors industry also showed a substantial shift to foreign 

Table 6-4

U.S. exports and imports of commercial 

knowledge-intensive services, by region/country/

economy: 2007 

(Billions of dollars)

Service and region/country/

economy Exports Imports Balance

All commercial knowledgeintensive services

All countries ....................... 184.5 137.8 46.7

Asia-9 ............................. 12.7 12.2 0.5

Canada ........................... 14.6 10.8 3.8

China .............................. 7.8 4.1 3.7

EU ................................... 75.7 60.2 15.5

Japan ............................. 12.2 5.6 6.6

Latin America ................ 34.1 34.1 0.0

All others ........................ 27.4 10.9 16.6

Financial services

All countries .................... 68.6 61.7 6.9

Asia-9 ......................... 2.9 1.2 1.7

Canada ....................... 5.7 1.9 3.9

China .......................... 2.6 1.1 1.5

EU ............................... 27.6 26.8 0.8

Japan .......................... 4.1 1.6 2.5

Latin America ............. 18.1 19.5 -1.4

All others ..................... 7.5 9.7 -2.1

Telecommunications

All countries .................... 8.3 7.3 0.9

Asia-9 ......................... 0.6 0.8 -0.2

Canada ....................... 0.7 0.5 0.2

China .......................... 0.2 0.3 -0.1

EU ............................... 2.7 2.5 0.2

Japan .......................... 0.3 0.2 0.1

Latin America ............. 2.8 2.1 0.7

All others ..................... 1.0 0.9 0.1

Business, professional, 

and technical services

All countries .................... 107.7 68.8 38.9

Asia-9 ......................... 9.2 10.2 -1.0

Canada ....................... 8.1 8.4 -0.2

China .......................... 5.0 2.7 2.3

EU ............................... 45.4 30.9 14.5

Japan .......................... 7.9 3.9 4.0

Latin America ............. 13.2 4.8 8.4

All others ..................... 18.9 8.0 10.9

EU = European Union

NOTES: Knowledge-intensive services classified by Organisation 

for Economic Co-operation and Development and include business, 

financial, communications, education, and health. Commercial 

knowledge-intensive services exclude education and health. 

Business, professional and technical services classified as part 

of business services. China includes Hong Kong. Latin America 

includes Argentina, Bermuda, Brazil, Chile, Mexico, and Venezuela. 

Detail may not add to total because of rounding. 

SOURCE: Bureau of Economic Analysis, International Economic 

Accounts, U.S. International Services: Cross-Border Trade 1986–

2007, and Services Supplied Through Affiliates, 1986–2006, http://

www.bea.gov/international/intlserv.htm, accessed 15 September 

2009.

Science and Engineering Indicators 2010

	
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Science and Engineering Indicators 2010 6-41

production, from 77% to 63%. The U.S. share was relatively 

stable in pharmaceuticals and scientific instruments. 

U.S. multinationals in high-technology manufacturing 

employed 1.3 million workers in the United States in 2006 

(appendix table 6-50). Employee data for foreign affiliates, 

available for three of the four industries, show that nearly 

half of the total workforce for pharmaceuticals and computers is employed abroad, along with one-third of the scientific 

instruments workforce. The distribution between U.S. and 

foreign employment showed little change in pharmaceuticals and computers from 1999 to 2006. However, the U.S. 

	
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6-42 Chapter 6. Industry, Technology, and the Global Marketplace

employment share in scientific instruments fell from 72% to 

66% over this period (figure 6-35).

Information and Communications Technology 

Services and Manufacturing

U.S. multinationals in the ICT industries generated 

more than $400 billion worldwide in value added in 2006, 

of which 70% was attributable to ICT services and 30% to 

ICT manufacturing (appendix table 6-50). U.S. ICT multinationals generated most (75%) of their production from their 

headquarters and other U.S. locations, and the remainder 

from their foreign affiliates (figure 6-36). 

However, the distribution of value added between U.S. 

and foreign affiliates varies widely by industry. The U.S. 

share of value added in ICT services was highest in telecommunications (97%), about average in information and 

data processing services (77%), and considerably lower in 

computer systems design (58%) (figure 6-36; appendix table 

6-50). In the two ICT manufacturing industries, the domestic 

value-added portion is below the overall ICT average: 64% 

in communications and semiconductors and 48% in computers and office machinery. 

Globalization of ICT, as measured by the U.S. and foreign shares of value added, has increased in this decade. The 

U.S. share dropped from 81% of value added to 75% because of substantial declines in the two ICT manufacturing 

industries, whereas the U.S. share of value added remained 

stable in the ICT service industries (figure 6-36; appendix 

table 6-50). (Employment data for foreign affiliates for 2006 

are not available for four of the five ICT industries.) 

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 2009). 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 in Knowledge- and 

Technology-Intensive Industries

According to data from the BEA, the stock of U.S. direct investment abroad had reached $121 billion in hightechnology manufactures and $834 billion in commercial 

knowledge-intensive service industries by 2008 (table 6-5; 

appendix table 6-51).15 This represented one-quarter of the 

stock of all U.S. direct overseas investment in all manufacturing industries ($0.5 trillion) and about one-third of U.S. 

direct overseas investment in all services ($2.5 trillion). 

The stock of U.S. foreign direct investment abroad in 

high-technology manufacturing industries increased from 

$87 billion in 2000 to $121 billion in 2008 (table 6-5; appendix table 6-51). Communications and semiconductors 

increased from $42 billion to $54 billion, pharmaceuticals 

	
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Science and Engineering Indicators 2010 6-43

from $25 billion to $37 billion, aerospace from $3 billion 

to $11 billion, and scientific instruments from $3 billion to 

$10 billion. However, the investment stock of the computer 

industry dropped by 36%, from $14 billion to $9 billion, and 

its share of all high-technology manufacturing industries fell 

by half, from 16% to 7%.

The stock of U.S. direct investment abroad in commercial 

knowledge-intensive service industries was $834 billion in 

2008, one-third of the stock of total U.S. direct investment 

abroad in all services (table 6-5; appendix table 6-51). Financial services dominated commercial knowledge-intensive 

services investments at $634 billion (76%), up from $217 

billion in 2000. Business services grew from $61 billion in 

2000 to $185 billion in 2008. However, the stock of U.S. 

FDI in communications fell from $27 billion to $15 billion. 

Geographic data on U.S. FDI investments in high-technology industries is limited to computer and electronic 

products, which includes computers, communications and 

semiconductors, and scientific instruments. For these products, the EU was the largest recipient with $27 billion (35% 

share in 2008), followed by $23 billion in the Asia-9 (30%) 

(table 6-6). Investments in Canada, China, and Japan were 

4%–13% of the total. There was little change in these shares 

from 2000 to 2008. 

The largest foreign destinations for U.S. direct investment 

in financial services are the EU ($314 billion in 2008) and 

Latin America ($195 billion), for a combined 80% of the total 

(table 6-6). The Asia-9, Canada, China, and Japan have 5% 

or less of the total. The EU was the largest recipient at $78 

billion (64% share) of investment in information services, 

which includes communications. Investments in Asia were 

smaller, with 2% in China and 5% each in the Asia-9 and 

Japan. 

Data on professional, scientific, and technical services, a 

component of business services, show that the EU had $53 

billion of the $81 billion in stock of worldwide U.S. FDI in 

this industry in 2008 (table 6-6). The Asia-9, Canada, and 

China were the next-largest recipients with shares of 5%–

10%. The shares of these regions/countries shifted between 

2000 and 2008. Canada’s share increased from 6% to 10% 

and China’s share increased from 2% to 5%. Japan’s share 

fell sharply from 16% to 3%. 

Foreign Direct Investment in U.S. Knowledge- and 

Technology-Intensive Industries

According to BEA data, the stock of FDI in U.S. hightechnology manufacturing industries stood at $187 billion 

in 2008, up from $133 billion in 2000 and above the stock 

of $128 billion in U.S. investment abroad (table 6-5; appendix table 6-51). The FDI stock in the U.S. pharmaceuticals 

industry was about $125 billion in 2008, and the stock in 

communications and semiconductors was $25 billion, for a 

combined share of 80% of FDI stock in U.S. high-technology industries. The share of pharmaceuticals doubled, from 

34% to 67%, and the share of communications and semiconductors fell from 47% to 13%. 

FDI stock in U.S. commercial knowledge-intensive service industries was $390 billion in 2008, compared with 

Table 6-5

Stock of U.S. direct investment abroad and foreign direct investment in United States, by selected industry/

service: 2000 and 2008

(Billions of dollars)

Industry/service

U.S. direct investment abroad Foreign direct investment in U.S.

2000 2008 2000 2008

All industries .................................................................................... 1,316.2 3,162.0 1,256.9 2,278.9

Manufacturing .............................................................................. 343.9 512.3 480.6 795.3

High-technology manufacturing ............................................... 87.3 120.8 132.5 186.8

Aerospace ............................................................................. 2.9 11.3 4.5 10.5

Communications and semiconductors ................................. 41.9 53.7 61.7 24.9

Computers and peripheral equipment .................................. 14.1 8.6 2.5 6.5

Pharmaceuticals ................................................................... 25.3 37.1 44.7 124.8

Scientific and measuring equipment .................................... 3.1 10.1 19.0 20.1

All services ................................................................................... 874.6 2,486.1 735.9 1,285.0

Commercial KI services............................................................ 305.0 834.1 NA 389.5

Business services ................................................................. 61.0 185.2 47.0 91.0

Communications .................................................................. 26.9 14.9 NA 49.7

Finance ................................................................................. 217.1 634.0 167.0 248.9

NA = not available

NOTES: High-technology manufacturing industries and commercial knowledge-intensive services classified by Organisation for Economic Co-operation 

and Development. 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 September 2009.

Science and Engineering Indicators 2010
6-44 Chapter 6. Industry, Technology, and the Global Marketplace

$729 billion in the stock of U.S. investment abroad in these 

industries (table 6-5; appendix table 6-51). The largest industry was financial services ($249 billion), followed by 

$91 billion in business services and $50 billion in communications. FDI stock in U.S. financial services increased by 

nearly 50% (from $167 to $249 billion) and nearly doubled 

in business services (from $47 billion to $91 billion). (Data 

for communications services are not available for 2000.)

Limited data on geographic origin show that the EU 

and Japan are the largest sources of foreign direct investment in U.S. computer and electronic products industries, 

which comprised more than 90% of the stock of worldwide 

Table 6-6

Stock of U.S. direct investment abroad and of foreign direct investment in United States, by selected industry and 

region/country/economy: 2000 and 2008

(Billions of dollars)

Industry/service and region/country/economy

U.S. direct investment abroad Foreign direct investment in U.S.

2000 2008 2000 2008

Computers and electronic products

All regions/countries/economies ................................................. 59.9 76.5 92.8 63.3

Asia-9 ....................................................................................... 20.0 22.9 NA NA

Canada ..................................................................................... 4.9 4.6 27.1 -0.3

China ........................................................................................ 5.1 9.9 0.2 NA

EU ............................................................................................. 23.3 26.7 40.4 40.1

Japan ....................................................................................... 3.6 3.3 17.3 19.0

Latin America ........................................................................... 0.7 1.4 2.8 1.5

All others .................................................................................. 2.3 7.7 NA NA

Financial services

All regions/countries/economies ................................................. 217.1 634.0 167.0 248.9

Asia-9 ....................................................................................... 6.2 21.6 NA NA

Canada ..................................................................................... 26.3 32.7 19.9 62.1

China ........................................................................................ 6.7 13.4 NA 0.0

EU ............................................................................................. NA 314.1 94.8 146.0

Japan ....................................................................................... 22.9 28.0 14.1 21.7

Latin America ........................................................................... 73.7 195.1 12.7 -19.8

All others .................................................................................. NA 29.1 NA NA

Information services

All regions/countries/economies ................................................. 52.3 121.9 146.9 158.0

Asia-9 ....................................................................................... 1.1 6.5 NA NA

Canada ..................................................................................... 2.3 4.1 12.9 11.8

China ........................................................................................ 0.7 1.1 0.3 NA

EU ............................................................................................. 33.7 77.5 98.6 126.1

Japan ....................................................................................... 2.5 5.6 NA 1.8

Latin America ........................................................................... 6.9 8.6 13.3 0.7

All others .................................................................................. 5.2 18.5 NA NA

Professional, scientific, and technical services

All regions/countries/economies ................................................. 32.9 81.2 30.5 62.1

Asia-9 ....................................................................................... 1.5 5.2 NA NA

Canada ..................................................................................... 1.9 8.2 1.2 2.2

China ........................................................................................ 0.8 3.7 NA NA

EU ............................................................................................. 16.0 52.8 27.7 45.1

Japan ....................................................................................... 5.4 2.8 0.8 5.0

Latin America ........................................................................... 3.5 2.1 0.5 1.8

All others .................................................................................. 3.8 6.4 NA NA

NA = not available

EU = European Union

NOTES: Regions/countries/economies are destination of U.S. direct investment abroad and source/origin of foreign direct investment in U.S. industries. 

Asia-9 includes India, Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. China includes Hong Kong. EU data 

for 2000 exclude Bulgaria, Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Slovakia, and Slovenia. Latin America 

includes Argentina, Bermuda, Brazil, Chile, Mexico, and Venezuela. EU data for 2008 include all 27 member countries.

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 September 2009.

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 6-45

investment in these U.S. industries ($63 billion) in 2008 (table 6-6). The EU’s investment stayed constant at about $40 

billion between 2000 and 2008. However, its share increased 

from 44% to 63% because of a $30-billion decline in the 

stock of total inward investment in this industry during this 

period. Japan’s investment rose from $17 billion in 2000 to 

$19 billion in 2008. Canada’s investment fell sharply from 

$27 billion (29% share) to a slight negative position ($0.3 

billion).16

In commercial knowledge-intensive service industries, 

the two largest sources of FDI in U.S. financial services are 

the EU and Canada, which provided more than 80% of the 

$264 billion in stock of worldwide investment in this industry in 2008 (table 6-6). The EU had the largest share (80%) of 

the $146 billion in investment stock in the U.S. information 

services industry in 2008. Its share increased 13 percentage 

points between 2000 and 2008. Latin America’s share fell 

from 9% to less than 1%. The EU was also the largest investor in professional, scientific, and technical services, with 

a share of 73% ($45 billion of inward investment in 2008). 

The EU’s share, however, fell almost 20 percentage points 

between 2000 and 2008. Japan’s share of investment in this 

industry more than doubled, from 3% to 8%. 

Innovation-Related Indicators of U.S. 

and Other Major Economies 

Innovation—the creation of new or significantly improved products or processes, along with novel marketing 

activities and organizational methods—is widely recognized 

as instrumental to the realization of commercial value in the 

marketplace and as a driver of economic growth.17 ICT technologies, for example, have stimulated innovation of new 

products, services, and industries that have transformed the 

world economy over the past several decades. However, direct measures of innovation for the United States and many 

other regional/national economies remain limited. (See the 

section on intangible assets in this chapter and sidebar, “Developments in Innovation-Related Metrics,” in chapter 4.)

U.S. Trade in Intangible Assets

Intangible assets are those that embody knowledge content, for example, patents, trademarks, and licensing of computer software (Idris 2003). These can be traded (licensed for 

use). The United States has a longstanding surplus in trade 

of intangible assets with the rest of the world (figure 6-37). 

U.S. receipts for exports of intangible assets were $83 billion in 2007, 14% higher than in 2006 (figure 6-37; appendix 

table 6-52).18 U.S. imports (payments) were $25 billion (up 

by 5%), producing a $58-billion surplus. U.S. exports and 

imports of intangible assets have grown every year but one 

between 1992 and 2007, and the surplus has widened over 

the period. 

About three-quarters of the intangible assets trade 

involved exchanges between multinationals and their 

affiliates, either with U.S. parents and their foreign affiliates 

or with foreign parents and their U.S. affiliates (appendix table 6-52).19 Firms with marketable industrial processes may 

prefer affiliated over unaffiliated transactions to exercise 

greater control over the distribution and use of this property, 

especially when the intellectual property is instrumental to 

the firm’s competitive position in the marketplace (Branstetter, Fishman, and Foley 2006). Differential tax policies may 

also affect a firm’s choice of transaction mechanisms.

Despite the greater value of transactions among affiliated 

companies, both affiliated and unaffiliated transactions have 

grown at the same pace over the past two decades. These 

trends suggest a greater internationalization of U.S. business 

activity and a growing reliance on intellectual property and 

other intangible assets developed overseas.20

U.S. Trade in Industrial Processes

A major component of U.S. intangible assets trade is 

industrial processes—the use of patents, trade secrets, and 

other proprietary rights. These data are used as approximate 

	
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indicators of relative comparative advantage in the creation 

of industrial technology and its subsequent diffusion. 

Comparable data on trade in industrial processes are 

available only for 2006 and 2007. These data include the 

combined transactions among affiliated firms (i.e., among 

firms that are tied to one another by ownership rights) and 

unaffiliated ones. 

U.S. exports of industrial processes were $37 billion in 

2007, 45% of total intellectual property exports; U.S. imports were $18 billion, 72% of total intangible assets imports 

(figure 6-38). The resulting surplus, $19 billion, accounted 

for one-third of the overall surplus in U.S. trade in intangible 

assets. 

The EU had the largest share of any economy (45%) in 

U.S. trade in industrial processes, followed by Japan (19%). 

Latin America, the Asia-9, and China had shares below 10% 

(figure 6-38). More than half of the U.S. surplus in 2007 

was with the EU ($10.2 billion). The United States ran a 

surplus of $3–$4 billion with the Asia-9 and Latin America, 

and nearly a $1-billion surplus with China. These surpluses 

were partially offset by a $2.8-billion deficit with Japan. 

Global Trends in Patenting

To foster inventiveness, 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.21 

Inventors obtain patents from government-authorized agencies for inventions judged to be “new...useful...and...

nonobvious.”22

Patenting is an intermediate step toward innovation, and 

patent data provide indirect and partial indicators of innovation. Not all inventions are patented, and the propensity 

to patent differs by industry and technology area. Not all 

patents are of equal value; 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.23 These market 

attributes make U.S. patenting data useful for identifying 

trends in global inventiveness. 

This section also deals with a subset of patents that their 

owners presume to be of sufficient economic value to warrant the high costs associated with patent filing and maintenance in three of the world’s largest markets: the United 

States, the EU, and Japan.24

Trends in Applications for USPTO Patents

Data on patent filings provide a more current look at inventiveness trends than do data on patents granted because 

of the long lead times.25 As it turns out, trends in patent applications are a reasonable proxy for later trends in patents 

granted.

Inventors filed 456,000 patent applications with USPTO 

in 2008, unchanged from 2007, but nearly double the number 

a decade ago (figure 6-39; appendix tables 6-53 and 6-54). 

The strong growth of U.S. patent applications between the 

mid-1990s and 2007 coincided with a strengthening of the 

patent system and the extension of patent protection into 

new technology areas through policy changes and judicial 

decisions during the 1980s and 1990s (NRC 2004). The 

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flattening of growth in 2008 may reflect the onset of the 

global financial crisis and economic downturn in 2008.

Inventors residing in the United States filed 232,000 of 

these applications in 2008, about 9,000 less than in 2007 and 

the first yearly decline since 1996 (figure 6-39; appendix 

tables 6-53 and 6-54).26 The U.S. resident share continued to 

fall, dropping from 53% in 2007 to 51% in 2008, and down 

from 55% in 1996, which may be indicative of increased 

globalization and increased recognition by developing countries of the potential value of intellectual property. Most 

USPTO patents credited to the United States are owned by 

corporations (see sidebar, “U.S. Patents Granted, by Type of 

Ownership”).27

Japan, the EU, and the Asia-9 are the main sources of 

inventors outside of the United States who file U.S. patent 

applications (figure 6-39; appendix table 6-54). Japan-based 

inventors filed 82,000 applications (18%) in 2008, followed 

by 65,000 by EU inventors (14%) and 46,000 (10%) by 

Asia-9 inventors, mostly from South Korea and Taiwan. 

China is ranked a distant fifth with a 1% share. The majority 

of applications from other regions originate from advanced 

countries, including Australia, Canada, and Switzerland. 

The number of patent applications from Japan and the EU 

grew more slowly from 1995 to 2008 than those originating elsewhere (appendix tables 6-53 and 6-54). The Asia-9’s 

number of applications rose at more than twice the average 

rate, driven by increases in South Korea and Taiwan, and 

increased the Asia-9 share from 5% to 10% (figure 6-39). 

Growth in the number of applications from India and China 

accelerated during this period but from very low levels. The 

location of China-based inventors shifted from Hong Kong 

(64% of China’s patent applications in 1997) to mainland 

China (81% of China’s patent applications in 2008).

USPTO patents granted among these five major world regions/countries reveal trends very similar to those observed 

for patent applications through 2008 (figure 6-39; appendix 

tables 6-56 and 6-57). However, the U.S. share edged down 

from 50% in 2007 to 49% in 2008, the first time the U.S. 

share has been less than half for the past four decades (USPTO 2008). The Asia-9’s share rose from 9% to 10% and the 

shares of the EU, Japan, and China remained steady.

USPTO Patents Granted, by Technology Area

This section discusses trends in several technology areas. 

The biggest—information and communications technologies—accounts for nearly 40% of all USPTO patents (figure 

6-40 and appendix table 6-60). Two smaller technology areas, 

aerospace and pharmaceuticals, are closely associated with 

their respective high-technology industries. Measurement 

and control equipment is linked with scientific instruments 

industries. Biotechnology, medical equipment, and medical 

electronics are important technologies for health care. 

ICT Patenting. Patents in the largest single patent 

group, ICT—computers, semiconductors, and telecommunications—have risen rapidly and accounted for 65,000 

	
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6-48 Chapter 6. Industry, Technology, and the Global Marketplace

(41% share) of the 158,000 patents granted in 2008, up from 

22,000 (21%) in 1995 (figure 6-40 and appendix tables 6-58 

and 6-60). The U.S. share of ICT patents (48%) was identical to its total share of patents; it was higher in computers (55%) and substantially lower in semiconductors (37%) 

(figures 6-39 and 6-41).

Japan ranked second in ICT patents (23% in 2008) (figure 

6-41; appendix table 6-59). This area of strength, relative 

to its average share of 21%, reflects a higher-than-average 

share in semiconductors (29%) (figure 6-39; appendix table 6-62). Nevertheless, Japan’s overall ICT share declined 

steeply during the decade, from 36% in 1995 to 23% in 

2008, reflecting declining shares in all three ICT technologies (appendix tables 6-61 through 6-63). 

The EU, fourth-ranked in ICT, was relatively weaker in 

these technologies compared with its overall share (figures 

6-39 and 6-41; appendix tables 6-59 and 6-61 through 6-63). 

Its share has been roughly flat in all three ICT technology 

areas. 

The Asia-9’s share of ICT patents more than doubled, 

from 5% in 1995 to 13% in 2008, because of strong growth 

in all three technology areas (figure 6-41; appendix tables 

6-59, 6-61, 6-62, 6-63). The Asia-9 surpassed the EU in 

2007 and ranked third in ICT patents. The majority of patents fueling this growth originated from South Korea and 

Taiwan. China’s share of USPTO ICT patents was small 

(1%), but strong growth from a low base in computer and 

semiconductor patents was evident over the decade. 

Patents in Other Technology Areas. The United States 

has a comparatively higher-than-average share of patents in 

aerospace and four technology areas connected with health: 

pharmaceuticals, biotechnology, medical equipment, and 

medical electronics (figures 6-39 and 6-42; appendix tables 

6-64 through 6-68). Its share of aerospace patents fluctuated 

U.S. Patents Granted, by Type of Ownership

	
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Corporations own the majority of patents granted to 

U.S. entities, and their share has been steadily increasing 

since the early 1990s (figure 6-C). In 2008, U.S. corporations owned 88% of patents issued to U.S. inventors, 

with individuals owning 11%; in 1992, the respective 

shares were 74% and 24%. The U.S. Patent and Trademark Office defines the corporate sector as including 

U.S. corporations, small businesses, and educational institutions. U.S. universities and colleges owned about 4% 

of U.S. utility patents granted to corporations in 2005. 

(For a further discussion of academic patenting, see 

“Academic Patents, Licenses, Royalties, and Startups” in 

chapter 5.)

Corporations also own the majority of U.S. patents issued to the rest of the world; that share has also been increasing over the past decade. The individual ownership 

share of patents issued to the rest of the world (which is 

about half the level in the United States) has fallen since 

the early 1990s.
Science and Engineering Indicators 2010 6-49

broadly above 60%. In the health-related areas, the U.S. 

share stayed above 60% in medical equipment and medical 

electronics, and was just below 60% in pharmaceutical and 

biotechnology patents.

The EU’s patents position is relatively strong in aerospace, pharmaceuticals, biotechnology, measurement and 

control equipment, and medical electronics (figure 6-42; appendix tables 6-64, 6-66, 6-68, and 6-69). Its share of patents 

in these technologies is about 20% compared with its 14% 

overall share (figure 6-39). Its share in medical equipment 

patents is close to its overall share.

As a group, the Asia-9 is relatively weaker in these technologies, as indicated by its patent shares in each technology 

area, which are half or less of the overall Asia-9 share; the 

exception is measurement and control equipment, which is 

near the average (7%) (figures 6-39 and 6-42; appendix tables 6-64 through 6-69). The Asia-9 share has risen over the 

past decade in measurement and control equipment, pharmaceuticals, and biotechnology. Its share has remained roughly 

stable in the other technologies. 

China’s share in pharmaceuticals, biotechnology, and 

measurement and control equipment is the same as its overall share (figures 6-39 and 6-42; appendix tables 6-65, 6-66, 

and 6-69). Its shares in aerospace, medical equipment, and 

medical electronics are 0.5%, significantly below its overall 

share (1%) (appendix tables 6-64, 6-67, and 6-68). 

Patenting of 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. That is, 

they are deemed to be substantially economically valuable.

The number of such “triadic” patents was estimated at 

about 51,600 in 2006 (the last year for which these data 

are available), up from 41,500 in 1997, and showing little 

growth after 2004. The United States, the EU, and Japan 

held basically equal shares (figure 6-43; appendix table 

6-70),28 and their nearly identical positions in triadic patents 

contrast with a far greater gap between them in USPTO patent applications and grants. 

The United States, the EU, and Japan together accounted 

for more than 93% of triadic patents in 1997, but that share 

dropped to 87% by 2006 (figure 6-43; appendix table 6-70). 

The Asia-9’s corresponding share increase from 1% in 1997 

to 6% in 2006 was almost entirely driven by increasing 

South Korean high-value filings. Taiwan had much lower 

activity in triadic patent filings than in total USPTO applications and grants, and high-value patent filings by China and 

India, though increasing, remain minuscule. 

U.S. High-Technology Small Businesses

Many of the new technologies and industries seen as critical 

to U.S. economic growth are also closely identified with small 

businesses, that is, those employing fewer than 500 people. 

Biotechnology, the Internet, and computer software are examples of industries built around new technologies in whose 

initial commercialization small businesses played an essential 

role. 

This section covers patterns and trends that characterize 

small businesses operating in high-technology industries. It is 

based on data from the Census Bureau. Two sources of financing for high-technology small businesses are examined, using 

	
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6-50 Chapter 6. Industry, Technology, and the Global Marketplace

data from the National Venture Capital Association and the 

University of New Hampshire’s Center for Venture Research. 

Employment in High-Technology Small 

Businesses

Small firms (those with fewer than 500 employees) employed about one-third of all workers in industries classified 

by the Bureau of Labor Statistics (BLS) as high technology. 

In contrast, small firms accounted for slightly more than half 

of total employment in all industries29 in 2006 (table 6-7). 

About one-half million small businesses operating in hightechnology industries employed 5 million workers in 2006 

(appendix table 6-71).30

In 2006, most workers in these high-technology small 

businesses (68%) were in the service sector (table 6-8; appendix table 6-71), concentrated in six BLS high-technology 

categories: architecture, computer systems design, consulting, management, commercial equipment and services, and 

R&D. These service industries collectively employed more 

than 85% of workers employed by all small businesses in 

high-technology service industries in 2006. The manufacturing sector employs most of the remaining workers in hightechnology small businesses (30% in 2006).

Small business employment in high-technology manufacturing is similarly concentrated within a relatively small 

number of industries: motor vehicle parts, metal working, 

semiconductors, other machinery, fabricated metals, and 

navigational and measurement tools (table 6-8; appendix 

table 6-71). These six industries collectively employed more 

than half of all workers in all manufacturing high-technology small businesses and 15% of the entire high-technology 

small business labor force in 2006.

	
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6-52 Chapter 6. Industry, Technology, and the Global Marketplace

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. Two types of financing, called angel investment and venture capital investment, 

are often critical to financing nascent and growing hightechnology and entrepreneurial businesses. (In this section, 

business denotes anything from an entrepreneur with an idea 

to a legally established operating company.) 

Angel investors tend to be wealthy individuals who invest their own funds in entrepreneurial businesses, either 

individually or through informal networks, usually in exchange for ownership equity. 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 get to participate 

in managerial decisions. 

Venture capital firms have categorized their investments 

into four broad financing stages, which are also relevant for 

discussion of angel investment:

Seed and startup supports proof-of-concept development (seed) and initial product development and marketing (startup).

Early funds support the initiation of commercial manufacturing and sales.

Expansion financing 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 funds include 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.

Angel investor funds are concentrated in the seed-startup 

and early stages. During the 2007–08 period, they provided 

80% of investment for these stages, compared with 20% in 

	
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Table 6-7

Firms and employment in U.S. small businesses versus all businesses: 2006

Business

All technologies High technology

Firms 

(thousands)

Employment 

(millions)

Firms 

(thousands)

Employment

 (millions)

All businesses .................................................................................. 6,022 120.0 519 15.4

Small businesses (number) .......................................................... 6,004 60.2 504 5.3

Small businesses (%) ................................................................... 99.7 50.2 97.1 34.4

NOTES: Small businesses are firms with <500 employees. Firms 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. High-technology small business employment is lower bound 

estimate because employment not available for a few industries due to data suppression. 

SOURCES: Census Bureau, Statistics of U.S. Businesses, http://www.census.gov/csd/susb/susb06.htm, accessed 1 June 2009; and Hecker DE. 2006. 

High-technology employment: A NAICS-based update. Monthly Labor Review 128(7):57–72, http://www.bls.gov/opub/mlr/2005/07/art6full.pdf, accessed 

1 June 2009.

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 6-53

later stages (figure 6-44). Venture capital, however, is provided primarily for expansion and later-stage funding (figure 6-45; appendix table 6-72). 

This section examines angel and venture capital investment patterns in the United States, focusing on the period 

from 2001 to 2008. The section examines (1) changes in the 

overall level of investment, (2) investment by stage of financing, and (3) the technology areas that U.S. angel and 

venture capitalists find attractive. 

U.S. Angel Investment. According to data from the Center for Venture Research, angel investors provided $19 billion in financing in 2008, a sharp drop from $26 billion in 

2007 following 5 consecutive years of increases (figure 6-46; 

appendix table 6-73).31 An estimated 55,000 businesses received financing from angel investors in 2008, 1,600 fewer 

than in 2007 but 4,500 more than in 2006 (table 6-9). The 

average investment per business fell from about $455,000 in 

2007 to $346,000 in 2008.

Although angel investors continue to concentrate on the 

riskiest stage of business development, they have become 

more conservative in their investment patterns. The share of 

angel funding going to seed-startup was 42% in the 2007–08 

period compared with 47% in the 2002–04 period (figure 

6-44). 

	
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Leading types of employers among high-technology small businesses, by industry: 2006

Industry

Employment 

(thousands)

 Percent

distribution

All industries ............................................................................................................................................. 5,275 100.0

Service industries ................................................................................................................................. 3,599 68.2

Top six combined .............................................................................................................................. 3,085 58.5

Architectural, engineering, and related services............................................................................ 923 17.5

Computer systems design and related services ........................................................................... 667 12.6

Management, scientific, and technical consulting services .......................................................... 637 12.1

Management of companies and enterprises ................................................................................. 352 6.7

Professional and commercial equipment and supplies merchant wholesalers ............................. 311 5.9

Scientific research and development services .............................................................................. 194 3.7

All others ........................................................................................................................................... 514 9.7

Manufacturing ....................................................................................................................................... 1,554 29.5

Top six combined .............................................................................................................................. 800 15.2

Motor vehicle parts manufacturing ................................................................................................ 163 3.1

Metalworking machinery manufacturing ....................................................................................... 139 2.6

Semiconductor and other electronic component manufacturing .................................................. 136 2.6

Other general purpose machinery manufacturing ......................................................................... 135 2.6

Other fabricated metal product manufacturing ............................................................................. 127 2.4

Navigational, measuring, electromedical, and control instruments manufacturing ...................... 100 1.9

All others ........................................................................................................................................... 754 14.3

Other ..................................................................................................................................................... 122 2.3

NOTES: Small businesses are firms with <500 employees. Firms include those reporting no employees on their payroll. Firm is an entity that is either a 

single location with no subsidiary or branches or is 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. High-technology small business employment is lower bound 

estimate because employment not available for a few industries due to data supression. Other includes agriculture, mining, and utilities.

SOURCES: Census Bureau, Statistics of U.S. Businesses, http://www.census.gov/csd/susb/susb06.htm, accessed 1 June 2009; and Hecker DE. 2006. 

High-technology employment: A NAICS-based update. Monthly Labor Review 128(7):57–72, http://www.bls.gov/opub/mlr/2005/07/art6full.pdf, accessed 

1 June 2009.

Science and Engineering Indicators 2010
6-54 Chapter 6. Industry, Technology, and the Global Marketplace

Changes in the technology areas that attract angel investment may indicate changes in the parts of the economy that 

offer future growth opportunities. Healthcare services received the largest share of angel investment in 2008 (16%), 

5 percentage points lower than its 2006 share (figure 6-47). 

Software received 13% of total angel investment in 2008, 

5 percentage points lower than its 2006 share. Biotechnology received 11% of total investment in 2008, 7 percentage points lower than its 2006 share. The share of industrial/

energy increased from 6% in 2006 to 8% in 2008, possibly 

reflecting opportunities that angel investors see in green and 

clean energy technologies. 

Businesses receiving angel investment in 2007 employed 

about 200,000 workers (table 6-10). This figure is about the 

same as employment in the 2005–06 period. Each business 

employed an average of 3.5 workers in 2007, slightly lower 

than the average in 2005–06.

U.S. Venture Capital Investment. U.S. venture capitalists invested $28.1 billion in 2008, an 8% decline compared 

with the level in 2007 and the first decline since 2003 (figure 6-46; appendix table 6-72). The amounts of angel and 

venture capital investment have been very similar for the 

past 5 years. Since declining sharply in 2001 following the 

end of the dot.com boom, angel and venture capital investments have generally been strengthening, but in 2008 they 

remained well below their previous peaks.

Venture capitalists financed 3,300 firms in 2007, far fewer than the number of businesses financed by angel investors 

in the same year (57,000) (table 6-9; appendix table 6-72). 

Average venture capital investment has been about $8.5 million per firm for the past several years, much larger than the 

corresponding figure for angel investment. 

The number of businesses funded by venture capital and 

the average amount of investment have been increasing during the past several years. The number of businesses was 

about 3,300 in 2007–08, one-quarter higher than the average 

for the 2002–05 period (table 6-9; appendix table 6-72). The 

average investment per business in 2008 ($8.6 million) was 

about $675,000 lower (not inflation adjusted) than that in 

2007 but approximately $650,000 higher than the average 

for the 2002–03 period. 

	
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Science and Engineering Indicators 2010 6-55

Venture capital investment has become generally more 

conservative than angel investment, and venture capital investments have more often been made in the later stages of 

business development. Capital provided for expansion and 

later-stage financing accounted for a combined share of 75% 

or more from 2002 to 2008 (figure 6-44; appendix table 

6-72). Expansion financing accounted for half or more of 

all venture capital investment from 1996 through 2004, after 

which its share declined to 37% in 2007–08 as later-stage 

investments rose to 39%.

Venture capitalists have largely abandoned the seedstartup stage, which was 9% in the 1996–98 period, declined 

to 2% in the 2002–04 period, and recently recovered to a 

modest 5% (figure 6-44; appendix table 6-72). The factors 

behind the downturn are thought to be the desire for lowered 

investment risk, a shorter time horizon for realizing gains, 

and an increase in venture capital companies’ base level for 

investment, which has come to exceed the amounts typically required for the earliest stages. The recent increase is 

thought to reflect the emergence of promising new investment opportunities after the closeout of holdings in mature 

companies (NVCA 2007a). 

Venture Capital Financing, by Industry. Computer 

software had the largest share of venture capital funding of 

any industry in 2007–08 (18%) but registered a 5-percentagepoint decline from 1999–2001 levels (figure 6-48; appendix 

table 6-72). Likewise, the share of telecommunications declined to 7% in 2007–08, about half of its 1999–2001 level. 

Biotechnology received the second highest share of venture capital funding in 2007–08 (16%), slightly below the 

2002–06 level but more than triple its share during the 1999–

2001 period (figure 6-48; appendix table 6-72). The trend 

in medical devices and equipment was similar. Its share 

quadrupled from 3% during the 1999–2001 period to 13% 

in 2007–08.

Industrial/energy’s share more than doubled from 6% in 

2005–06 to 13% in 2007–08, similar to the trend in angel 

investment and thought to reflect investor interest in renewable and clean energy (figure 6-48; appendix table 6-72). 

Likewise, investments in clean technologies—a cross-cutting category of green and renewable energy—increased 

from a 9% share of venture investment in 2007 to a 15% 

share in 2008. 

	
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Average investment of angel and venture capital per business: 2002–08

Year

 Angel capital Venture capital

Businesses

(n)

Total

investment 

($billions)

Average 

investment/

business 

($thousands)

Businesses

(n)

Total

investment 

($billions)

Average 

investment/

business 

($thousands)

2002............................ 36,000 15.7 436 2,634 21.3 8,087

2003............................ 42,000 18.1 431 2,461 19.3 7,842

2004............................ 48,000 22.5 469 2,625 22.1 8,419

2005............................ 49,500 23.1 467 2,708 22.9 8,456

2006............................ 51,000 25.6 502 3,089 26.3 8,514

2007............................ 57,120 26.0 455 3,301 30.6 9,270

2008............................ 55,480 19.2 346 3,262 28.1 8,614

NOTE: Business includes anything from an entrepreneur with an idea to a legally established operating company.

SOURCES: Jeffrey Sohl, Analysis Reports, Center for Venture Research, University of New Hampshire, http://wsbe.unh.edu/analysis-reports-0; and 

National Venture Capital Association and Price Waterhouse Coopers, Money Tree Report, https://www.pwcmoneytree.com/MTPublic/ns/index.jsp, 

accessed 15 March 2009.

Science and Engineering Indicators 2010
6-56 Chapter 6. Industry, Technology, and the Global Marketplace

Innovation and Knowledge-Based Economic 

Growth 

The World Bank developed its Knowledge Economy 

Index (KEI) to show the potential of countries to adopt, 

generate, diffuse, and harness knowledge in economic 

development. Knowledge is regarded as an important 

factor of innovation, given the shift of economic activity toward KTI industries and the growing importance of 

intangible assets. 

The KEI is a simple average of four indicator scores that 

measure countries’ relative standing in ICT, innovation, 

education, and economic incentive and institutional regime. In turn, the four component indicators are composed 

of several variables each. Countries are ranked in order of 

their scores on each variable, and scores are normalized on 

a scale of 0 to 10 compared with all countries: The top 10% 

of performers get a normalized score between 9 and 10, the 

next decile receives normalized scores between 8 and 9, and 

so on. 

The 2005 KEI scores of the United States, Japan, and the 

EU were the highest among the major regions/countries/

economies, followed by those of Taiwan, Singapore, and 

South Korea (figure 6-49; appendix table 6-74). Over a decade (1995–2005), the KEI scores of the United States, the 

EU, and Japan declined somewhat (figure 6-49; appendix 

table 6-74). The U.S. score fell largely because of a decline 

in the ICT sector, whose index value dropped from 9.8 to 

8.9, and also because of weakness in the education sector. 

Japan’s lowered KEI score reflected a decline in Japan’s 

economic incentive regime; the EU’s score was reduced because of a lowered education sector score. 

Among the developing countries/economies, China, Taiwan, and Vietnam showed considerable improvement over 

the decade, albeit from very different levels (figure 6-49; 

appendix table 6-74). China improved its scores in all four 

component indicators, with the largest gains in the ICT and 

innovation scores. Although China’s gap with the developed economies narrowed, its KEI score remains well below 

those of the developed economies. 

Among the Asia-9, Taiwan and Vietnam showed solid 

increases (figure 6-49; appendix table 6-74). India’s KEI 

index remained unchanged, thus widening the gap with 

China. India’s modest score gains in innovation and economic incentive regime values were offset by weaknesses 

in ICT and education indicators, which remained in the 20% 

percentile range.

Among other developing countries, Brazil, Croatia, and 

Sri Lanka showed solid gains (appendix table 6-74). The improvement in Brazil’s score reflected a large increase in its 

education score and a rise in its ICT score.

Table 6-10

Investors and employees of firms receiving angel capital investment: 2001–07

Year

Businesses receiving 

investment

Angel

investors Total employees

Average employees 

per business receiving 

investment

2001............................ NA NA NA NA

2002............................ 36,000 200,000 NA NA

2003............................ 42,000 220,000 NA NA

2004............................ 48,000 225,000 141,200 2.9

2005............................ 49,500 227,000 198,000 4.0

2006............................ 51,000 234,000 201,400 3.9

2007............................ 57,120 258,200 200,000 3.5

NA = not available

NOTE: Business includes anything from an entrepreneur with an idea to a legally established operating company.

SOURCE: Jeffrey Sohl, Analysis Reports, Center for Venture Research, University of New Hampshire, http://wsbe.unh.edu/analysis-reports-0.

Science and Engineering Indicators 2010

	
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Science and Engineering Indicators 2010 6-57

Conclusion

The U.S. economy continues to be a leading global 

economy and competitor in technology-based industries as 

measured by its overall performance, market position in KTI 

industries, and position in patenting and other measures of 

technological capability. The U.S. economy has grown relatively rapidly and become more productive while sustaining 

a high and rising per capita income. 

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 knowledge-intensive 

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 knowledgeintensive services and licensing of patents and trade secrets.

Although the United States remains a leader in many KTI 

industries, its market position in most of these industries 

has either flattened or slipped. The historically strong U.S. 

trade position in advanced technology products has shifted 

to deficit because of the faster growth of imports. This shift 

is due in part to U.S. companies moving assembly and other 

routine activities to China and other East Asian countries. 

However, the U.S. deficit also reflects the development of 

indigenous capability of East Asian countries in high-technology manufacturing industries. 

China and other emerging Asian economies are showing rapid progress in their overall economic progress and 

	
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6-58 Chapter 6. Industry, Technology, and the Global Marketplace

technological capabilities. Their market positions in KTI 

industries—particularly high-technology manufacturing industries—have strengthened, and their shares of U.S. and 

economically valuable patents have risen, led by South 

Korea and Taiwan. World Bank indicators of innovative 

capacity also show that these emerging Asian economies 

are converging with the United States or are making rapid 

progress. 

China has become a leading global producer and exporter 

of high-technology manufacturing goods by becoming the 

world’s major assembly center, supplied by components and 

inputs from East Asian economies. However, China’s rapid 

progress in other indicators of technological capability and 

the nascent rise of globally competitive Chinese companies 

suggest that China is moving to more technologically challenging and higher end manufacturing activities.

The EU’s position is similar to that of the United States—

relatively strong economic performance with flat or slight 

declines in its market position of KTI industries. 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 high-technology manufacturing industries is due, 

in part, to Japanese companies shifting production to China 

and other Asian economies. 

The severe downturn of the global economy, starting in 

2008, has interrupted these trends observed over the past decade. The United States, the EU, and other developed economies have experienced sharp declines in their commercial 

knowledge-intensive service industries. The steep drop in 

exports of high-technology manufacturing goods has adversely affected many Asian economies and slowed China’s 

growth. Whether the global downturn will lead to fundamental changes in the market positions of the United States 

and other major economies in the production and trade of 

KTI industries remains uncertain.

Notes

1. The Asia-9 includes India, Indonesia, Malaysia, the 

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

Vietnam.

2. See OECD (2001) for a discussion of classifying economic activities according to degree of “knowledge intensity.” A different, product-based classification of the Census 

Bureau is used in part of the discussion on trade.

3. In designating these high-technology manufacturing 

industries, OECD took into account both the R&D done 

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 intensities were calculated as the ratio of 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 inputoutput 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 equal or greater R&D intensities. For additional perspectives on OECD’s methodology, 

see Godin (2004).

4. The combined estimated R&D expenditures of these 

regions/countries were $969 billion (2007 purchasing power 

parity) of an estimated $1.1 trillion in global R&D expenditures in 2007. 

5. Purchasing power parity is the exchange rate required 

to purchase an equivalent market basket of goods.

6. This is an imprecise measure for comparing productivity growth, especially between developed and developing 

economies. One reason is that productivity is more difficult 

to measure in the service sector, and services typically have 

a far larger part of GDP in developed compared with developing economies. 

7. See Atkinson and McKay (2007:16–17), for a discussion and references to the impact of IT on economic growth 

and productivity. 

8. See Bresnahan and Trajtenberg (1995) and DeLong 

and Summers (2001) for a discussion of ICT and generalpurpose technologies. 

9. This index is composed of three measures: telephones 

per 1,000 people, computers per 1,000 people, and Internet 

users per 10,000 people. Country scores on measures are 

normalized on a scale of 1–10, with 10 being equivalent to 

the highest score received by a country.

10. See Mann (2006:90–92), for a discussion of the economic benefits of importing versus exporting ICT. 

11. The U.S. dollar strengthened about 30% in value 

between 1995 and 2001 against a trade-weighted basket of 

European currencies (1995–98) and the euro (1999–2001) 

and subsequently lost more than 50% in value against the 

euro between 2001 and 2007. This exchange-rate movement 

lowered European industry output measured in U.S. current 

dollars between 1995 and 2001 and raised it between 2001 

and 2007. 

12. IHS Global Insight data as of July 2009.

13. 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.

14. 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. 

15. 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 
Science and Engineering Indicators 2010 6-59

a different country. For more information, see Ibarra and 

Koncz (2008). 

16. In these data, BEA values foreign direct investment 

(FDI) at historical cost. According to BEA, a negative FDI 

position in the United States occurs when total claims of 

U.S. subsidiaries on their foreign multinational parent companies (MNCs) exceed the foreign MNCs’ investment in the 

United States, which typically results when U.S. affiliates 

are net lenders to their foreign parents. 

17. There are widely different definitions of innovation, 

but common to these definitions is the commercialization of 

something that did not previously exist.

18. Earlier data are not comparable because of a change 

in the data collected.

19. An affiliate is a business enterprise located in one 

country that is directly or indirectly owned or controlled by 

an entity in another country. The controlling interest for an 

incorporated business is 10% or more of its voting stock; for 

an unincorporated business, it is an interest equal to 10% of 

voting stock. 

20. In addition, data on the destination of multinational 

corporate sales to foreign affiliates also suggest that market 

access is an important factor in the firms’ decisions to locate 

production abroad. See Borga and Mann (2004).

21. 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. 

22. 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 [so] 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?,” at http://www.uspto.gov/web/

offices/pac/doc/general/index.html#patent. Accessed 19 

June 2009. 

23. The Japan Patent Office (JPO) is also a major patent office but has a much smaller share of foreign patents 

compared with the USPTO and the European Patent Office 

(EPO). 

24. 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.

25. USPTO reports that average time to process an application (pendancy) was 31.1 months for utility, plant, and 

reissue patent applications in FY 2006, compared with 18.3 

months in FY 2003. Applications for utility patents account 

for the overwhelming majority of these requests. EPO reports that the average pendancy was 45.3 months in 2005.

26. Unless otherwise noted, USPTO patents are assigned 

to countries on the basis of the residence of the first-named 

inventor. 

27. U.S. patenting data by type of ownership and by state 

are available only for U.S. patents granted. 

28. 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.

29. The high-technology definition used here is from the 

Bureau of Labor Statistics and differs from that used in earlier sections.

30. See Hecker (2005) for a definition and methodology 

for determining high-technology industries. Several industries identified by BLS as high technology before 2003 are 

not covered in the Census Bureau’s data.

31. Comparable data on angel capital investment before 

2001 are not available.

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-9: India, Indonesia, Malaysia, the Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. 

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-15: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden, and the UK. 

EU (EU-27): Current member countries of the European 

Union are 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 UK. 

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. 
6-60 Chapter 6. Industry, Technology, and the Global Marketplace

Gross domestic product (GDP): The market value of all final goods and services produced within a country within 

a given period of time. 

Harmonized code, harmonized system (HS): Developed 

by the Customs Cooperation Council, the Harmonized 

System, or Harmonized Commodity Description and 

Coding System, is used to classify goods in international 

trade. 

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 knowledge intensive service industries, communications and 

computer services, which is a subset of business services.

Intellectual property: Intangible property resulting from 

creativity that is protected in the form of patents, copyrights, trademarks, and trade secrets. 

Intra-EU exports: Exports from 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. 

Small business: A company or firm with less than 500 

employees. 

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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Athukorala P-C, Yamashita N. 2006. Production fragmentation and trade integration: East Asia in a global context. North American Journal of Economics and Finance 

17:233–56.

Borga M, Mann M. 2004. U.S. international services: Crossborder trade in 2003 and sales through affiliates in 2002. 

Survey of Current Business, October:25–31.

Branstetter LG, Fisman R, Foley CF. 2006. Do stronger intellectual property rights increase international technology transfer? Empirical evidence from U.S. firm-level 

panel data. Quarterly Journal of Economics 121:321–49.

Bresnahan T, Trajtenberg M. 1995. General purpose technologies: “Engines of growth”? Journal of Econometrics 

65:83–108. 

Cohen W, Nelson R, Walsh J. 2000. Protecting Their Intellectual Assets: Appropriability Conditions and Why U.S. 

Manufacturing Firms Patent (or Not). National Bureau of 

Economic Research (NBER) Working Paper No. 7552. 

http://www.nber.org/papers/w7552. Accessed 15 June 

2009.

Dedrick J, Kraemer KL, Linden G. 2008. Who profits from 

innovation in global value chains? A study of the iPod and 

Notebook PCs. University of California, Irvine. http://

pcic.merage.uci.edu/papers/2008/WhoProfits.pdf. Accessed 15 June 2009.
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DeLong JB, Summers LH. 2001. How important will the 

information economy be? Some simple analytics. University of California, Berkeley, and National Bureau of 

Economic Research. http://econ161.berkeley.edu/Econ_

Articles/summers_jh_2001/jh_analytics.pdf. Accessed 19 

October 2009.

Godin B. 2004. The new economy: What the concept owes 

to the OECD. Research Policy 33:679–90.

Hecker D. 2005. High-technology employment: A NAICSbased update. Monthly Labor Review July:57–72. http://

www.bls.gov/opub/mlr/2005/07/art6full.pdf. Accessed 

15 June 2009.

Ibarra M, Koncz J. 2008. Direct investment positions 

for 2008: Country and industry detail. Survey of Current Business 89(7):20–34. http://www.bea.gov/scb/

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2009.

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Exports Is Really Made in China? Assessing Domestic 

Value Added When Processing Trade is Pervasive. National Bureau of Economic Research (NBER) Working 

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Reserve Bank of Dallas. http://dallasfed.org/research/

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Mann C, Kirkegaard JF. 2006. Accelerating the Globalization of America: The Role for Information Technology. 

Washington, DC: Institute for International Economics. 

National Research Council (NRC). 2004. A Patent System 

for the 21st Century. Merrill SA, Levin RC, Myers MB, 

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Knowledge-Based Economy. Washington, DC: National 

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National Venture Capital Association (NVCA). 2007. National Venture Capital Association Yearbook 2007. 

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What Are Their Implications for Regional Cooperation and Growth? Policy Research Working Paper No. 

3084. The World Bank. http://econ.worldbank.org/external/default/main?pagePK=64165259&piPK=64165

421&theSitePK=469372&menuPK=64216926&entity

ID=000094946_03071704242197. Accessed 4 August 

2009.

Organisation for Economic Co-operation and Development 

(OECD). 2001. Knowledge-Based Industries. Paris: Directorate for Science, Technology, and Industry/ Economic Analysis Statistics.

Organisation for Economic Co-operation and Development (OECD). 2007. Science, Technology and Industry 

Scoreboard 2007, Annex 1. Paris: Directorate for Science, 

Technology, and Industry. http://masetto.sourceoecd.org/

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2009. 

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Strategic Studies, and American Enterprise Institute for 

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28 August 2009.

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 ..................................................................................................................................7-7

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

Chapter Organization ................................................................................................................7-7

A Note About the Data and the Terminology ...........................................................................7-7

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

S&T Information Sources .........................................................................................................7-7

Public Interest in S&T ............................................................................................................7-12

Involvement ............................................................................................................................7-15

Public Knowledge About S&T ..................................................................................................7-16

Understanding Scientific Terms and Concepts .......................................................................7-17

Reasoning and Understanding the Scientific Process .............................................................7-23

Understanding of Statistics and Charts ...................................................................................7-25

Pseudoscience .........................................................................................................................7-25

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

Promise and Reservations .......................................................................................................7-28

Federal Funding of Scientific Research ..................................................................................7-29

Confidence in the Science Community’s Leadership .............................................................7-31

Influence on Public Issues ......................................................................................................7-31

What Makes an Activity Scientific .........................................................................................7-34

Views of S&E Occupations ....................................................................................................7-35

Public Attitudes About Specific S&T-Related Issues ...............................................................7-36

Environment and Climate Change ..........................................................................................7-37

Nuclear Power ........................................................................................................................7-39

Biotechnology and Its Medical Applications .........................................................................7-39

Genetically Modified Food .....................................................................................................7-40

Nanotechnology ......................................................................................................................7-40

Stem Cell Research and Human Cloning ...............................................................................7-41

Science and Mathematics Education ......................................................................................7-42

Scientific Research on Animals ..............................................................................................7-42

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

Notes ..........................................................................................................................................7-44

Glossary .....................................................................................................................................7-46

References ..................................................................................................................................7-47

List of Sidebars

Survey Data Sources ....................................................................................................................7-8

Scientific Research in the Media in Europe ...............................................................................7-15

Asset-Based Models of Knowledge ...........................................................................................7-18

Chapter 7

Science and Technology: 

Public Attitudes and 

Understanding

7-1
7-2 Chapter 7. Science and Technology: Public Attitudes and Understanding

New Science Knowledge Questions ..........................................................................................7-20

Knowledge Difference or Measurement Error? .........................................................................7-25

Public Expectations About Technological Advances ................................................................7-30

“Climate Change” Versus “Global Warming” ..........................................................................7-38

List of Tables

Table 7-1. News followed “very closely” by American public: 1996–2008 .............................7-13

Table 7-2. Leading nightly news story lines on science and technology, by topic area: 

2007 and 2008 .......................................................................................................................7-14

Table 7-3. Visits to informal science and other cultural institutions, by country/region: 

Most recent year ....................................................................................................................7-17

Table 7-4. Correct answers to scientific literacy questions, by sex: 2001, 2004, 

2006, and 2008 ......................................................................................................................7-19

Table 7-5. Adult and student correct answers to factual knowledge questions .........................7-24

Table 7-6. Correct answers to questions about charts and statistics, reasoning/life 

sciences, and understanding of experiment/controlling variable by sex: 2008 .....................7-27

Table 7-7. Adult and student correct answers to scientific process questions ..........................7-28

Table 7-8. Public confidence in institutional leaders: 2008 .......................................................7-32

Table 7-9. Preferred groups for influencing decisions about public issues: 2006 .....................7-33

Table 7-10. Perceived understanding of public issues by various groups: 2006 .......................7-33

Table 7-11. Perceived scientific consensus on public issues: 2006 ...........................................7-34

Table 7-12. Prestige of various occupations: Selected years, 1977–2008 .................................7-36

Table 7-13. Public approval of specific environmental proposals: 2001–07 .............................7-38

Table 7-14. Public opinion on medical technologies derived 

from stem cell research: Most recent year .............................................................................7-42

Table 7-A. Public expectations for future scientific achievements within next 25 years: 

1979 and 1981 .......................................................................................................................7-30

List of Figures

Figure 7-1. Primary source of information, by use: 2008 ..........................................................7-10

Figure 7-2. Primary source of information about current events, science and technology, 

and specific scientific issues: 2001–08 .................................................................................7-11

Figure 7-3. Primary source of current news events and science and technology 

information: 2008 ..................................................................................................................7-11

Figure 7-4. Public interest in selected issues: 2008 ...................................................................7-12

Figure 7-5. Public interest in selected science-related issues: 1979–2008 ................................7-12

Figure 7-6. Network nightly news coverage of science and technology: 1988–2008 ...............7-14

Figure 7-7. Attendance at informal science and other cultural institutions, by institution 

type and education level: 2008 ..............................................................................................7-16

Figure 7-8. Correct answers to factual knowledge of science questions: 1992–2008 ...............7-18

Figure 7-9. Correct answers to nanotechnology questions, by factual knowledge of 

science: 2008 .........................................................................................................................7-20

Figure 7-10. Correct answers to new factual knowledge questions, by trend factual 

knowledge questions: 2008 ...................................................................................................7-23

Figure 7-11. Correct answers to scientific literacy questions, by country/region: 

Most recent year ....................................................................................................................7-26

Figure 7-12. Correct answers to four questions testing concept of experiment/controlling 

variable, by sex: 2008 ...........................................................................................................7-28

Figure 7-13. Public assessment of scientific research: 1979–2008 ...........................................7-29

Figure 7-14. Public attitudes toward government funding of scientific research: 

Selected years, 1981–2008 ....................................................................................................7-31

Figure 7-15. Importance of scientific process, research credentials, and external 

validation to public’s belief that something is scientific, by education level: 2006 .............7-35

Figure 7-16. Worry about quality of environment: 2001–08 .....................................................7-37
Science and Engineering Indicators 2010 7-3

Figure 7-17. Public priorities for environmental protection versus economic growth: 

1984–2009 .............................................................................................................................7-38

Figure 7-18. European attitudes towards energy production by nuclear power: 2008 ..............7-39

Figure 7-19. Public attitudes toward stem cell research: 2001–08 ............................................7-41

Figure 7-20. Public attitudes toward education in America: Selected years, 1981–2008 .........7-43

Figure 7-21. Public attitudes toward conducting human health research that may inflict 

pain or injury to animals: Selected years, 1988–2008 ..........................................................7-43
Information Sources, Interest, and 

Involvement

Television and the Internet are the primary sources 

Americans use for science and technology (S&T) information. The Internet is the main source of information 

for learning about specific scientific issues such as global 

climate change or biotechnology.

More Americans select television as their primary source 

of S&T information than any other medium.

The Internet ranks second among sources of S&T information, and its margin over other sources is large and has 

been growing.

Internet users do not always assume that online S&T information is accurate. About four out of five have checked 

on the reliability of information at least once.

Continuing a long-standing pattern, Americans consistently express high levels of interest in S&T in surveys. 

However, other indicators, such as the types of news they 

follow closely, suggest a lower level of interest.

High levels of interest in S&T are part of a long-standing 

trend, with more than 80% of Americans reporting they 

were “very” or “moderately” interested in new scientific 

discoveries. But relative to other news topics, interest in 

S&T is not particularly high.

As with many news topics, the percentage of Americans 

who say they follow “science and technology” news 

“closely” has declined over the last 10 years.

Recent surveys in other countries, including South Korea, 

China, and much of Europe, indicate that the overall level 

of public interest in “new scientific discoveries” and “use 

of new inventions and technologies” tends to be higher in 

the United States.

Interest in “environmental pollution” or “the environment” is similarly high in the U.S., Europe, South Korea, 

and Brazil. About 9 in 10 respondents in each country 

expressed interest in this topic.

In 2008, a majority of Americans said they had visited 

an informal science institution such as a zoo or a natural 

history museum within the past year. This proportion is 

generally consistent with results from surveys conducted 

since 1979, but slightly lower than the proportion recorded in 2001.

Americans with more formal education are much more 

likely to engage in informal science activities.

Compared with the United States, visits to informal science institutions tend to be less common in Europe, Japan, China, Russia, and Brazil.

Public Knowledge About S&T

Many Americans do not give correct answers to questions about basic factual knowledge of science or the scientific inquiry process.

Americans’ factual knowledge about science is positively 

related to their formal education level, income level, the 

number of science and math courses they have taken, and 

their verbal ability.

People who score well on long-standing knowledge measures that test for information typically learned in school 

also appear to know more about new science related 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 in Japan, China, or Russia.

In the United States, levels of factual knowledge of science have been stable; Europe shows evidence of recent 

improvement in factual knowledge of science.

In European countries, China, and Korea demographic 

variations in factual knowledge are similar to those in the 

United States.

Compared to the mid-1990s, Americans show a modest 

improvement in understanding the process of scientific 

inquiry in recent years.

Americans’ understanding of scientific inquiry is strongly associated with their factual knowledge of science and 

level of education.

Americans’ scores on questions measuring their understanding of the logic of experimentation and controlling 

variables do not differ by sex. In contrast, men tend to 

score higher than women on factual knowledge questions 

in the physical sciences.

Public Attitudes About S&T in General

Americans in all demographic groups consistently endorse the past achievements and future promise of S&T.

In 2008, 68% of Americans said that the benefits of 

scientific research have strongly outweighed the harmful results, and only 10% said harmful results slightly or 

strongly outweighed the benefits.

Nearly 9 in 10 Americans agree with the statement “because of science and technology, there will be more opportunities for the next generation.”

Americans also express some reservations about science. 

Nearly half of Americans agree that “science makes our 

way of life change too fast.”

Highlights

7-4 Chapter 7. Science and Technology: Public Attitudes and Understanding
Americans tend to have more favorable attitudes about 

the promise of S&T than Europeans, Russians, and the 

Japanese. Attitudes about the promise of S&T in China 

and South Korea are as positive as those in the United 

States and in some instances even more favorable. However, residents of China and Korea are more likely than 

Americans to think that “science makes our way of life 

change too fast.”

Support for government funding of scientific research is 

strong.

In 2008, 84% of Americans expressed support for government funding of basic research.

More than one-third of Americans (38%) said in 2008 

that the government spends too little on scientific research and 11% said the government spends too much. 

Other kinds of federal spending such as health care and 

education generate stronger public support.

The public expresses confidence in science leaders.

In 2008, more Americans expressed a “great deal” of 

confidence in scientific leaders than in the leaders of any 

other institution except the military.

Despite a general decline in confidence in institutional 

leaders that has spanned more than three decades, confidence in science leaders has remained relatively stable. 

The proportion of Americans indicating “a great deal of 

confidence” in the scientific community oscillated between 35% and 45% in surveys conducted since 1973. In 

every survey, the scientific community has ranked either 

second or third among institutional leaders.

On science-related public policy issues (including global climate change, stem cell research, and genetically 

modified foods), Americans believe that science leaders, compared with leaders in other sectors, are relatively 

knowledgeable and impartial and should be relatively influential. However, they also perceive a considerable lack 

of consensus among scientists on these issues.

Over half of Americans (56%) accord scientists “very 

great prestige.” Ratings for engineers are lower (40% indicate “very great prestige”), but nonetheless better than 

those of most other occupations.

In 2008, scientists ranked higher in prestige than 23 other 

occupations surveyed, a ranking similar to that of firefighters.

Between 2007 and 2008, engineers’ rating of “very high 

prestige” increased from 30% of survey respondents 

to 40%.

Public Attitudes About Specific S&T Issues

Americans have recently become more concerned about 

environmental quality. However, concern about the environment is outranked by concern about the economy, 

unemployment, and the war in Iraq.

Between 2004 and 2008, the proportion of Americans expressing “a great deal” or “a fair amount” of worry about 

the quality of the environment increased from 62% to 

74%. Nonetheless, when asked to name the country’s top 

problem in early 2009, only about 2% mentioned environmental issues.

In 2008, 67% of Americans believed that the government was spending too little to reduce pollution and 7% 

thought it was spending too much.

The trend in support for environmental protection is less 

evident when Americans are asked about trade-offs between environmental protection and economic growth. In 

March 2009, 51% of all Americans indicated that economic growth should take precedence over the environment.

Americans support the development of alternative 

sources of energy.

A majority of Americans favor government spending to 

develop alternate sources of fuel for cars (86%), to develop solar and wind power (79%), and to enforce environmentally friendly regulations such as setting higher 

emissions and pollution standards for business and industry (84%).

Since the mid-1990s, American public opinion on nuclear energy has been evenly divided, but the proportion of 

Americans favoring the use of nuclear power as one of 

the ways to provide electricity for the U.S. increased from 

53% in 2007 to 59% in 2009.

Europeans are divided on nuclear energy, but support is 

on the rise. The proportion of Europeans who said they 

favored energy production by nuclear power stations increased from 37% in 2005 to 44% in 2008, while the proportion opposing it decreased from 54% in 2005 to 45% 

in 2008. Support for nuclear energy varies a great deal 

among countries in this region. Citizens in countries that 

have operational nuclear power plants are more likely to 

support nuclear energy than those in other countries.

Despite the increased funding of nanotechnology and 

growing numbers of nanotechnology products in the 

market, Americans remain largely unfamiliar with this 

technology.

Even among respondents who had heard of nanotechnology, knowledge levels were not high.

When nanotechnology is defined in surveys, Americans 

express favorable attitudes overall.

Science and Engineering Indicators 2010 7-5
A majority of Americans favor medical research that 

uses stem cells from human embryos. However, Americans are overwhelmingly opposed to reproductive cloning and wary of innovations using “cloning technology.”

Support for embryonic stem cell research is similar to 

previous years. In 2008, 57% of Americans favored 

embryonic stem cell research while 36% opposed it. A 

higher proportion (70%) favors stem cell research when it 

does not involve human embryos.

More than three-quarters of Americans oppose human 

cloning.

7-6 Chapter 7. Science and Technology: Public Attitudes and Understanding
Science and Engineering Indicators 2010 7-7

Introduction

Chapter Overview

Science and technology (S&T) affect all aspects of 

American life. As workers, Americans use technology to improve productivity in ways that could not even be imagined 

a generation ago, applying recently invented tools and applications. As consumers, they entertain themselves with high 

technology electronic products; make friends, communicate, 

and keep informed about the world through the Internet; and 

benefit from advances in medical technologies. As citizens, 

they may engage in discussions on climate change, stem cell 

research, and deficit spending—issues about which atmospheric scientists, microbiologists, and macroeconomists 

have formal training and expertise.

It is increasingly difficult for Americans to be competent 

as workers, consumers, and citizens without some degree 

of competence in S&T. Because competence begins with 

understanding, this chapter presents indicators about news, 

information, and knowledge of S&T. How the American 

citizenry collectively deals with public issues that involve 

S&T may, in turn, affect what kinds of S&T development 

America will support. Thus the chapter includes indicators 

of people’s attitudes about S&T-related issues. To put U.S. 

data in context, this chapter examines trend indicators for 

past years and comparative indicators for other countries.

Chapter Organization

The chapter is divided into four major 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, it compares American 

adults’ understanding of science to that of American students. The third and fourth sections of the chapter describe 

public attitudes toward S&T. The third section contains 

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 in public affairs. The fourth section addresses public attitudes on issues 

in which S&T plays an important role, such as the environment, 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 nuclear power, biotechnology, genetically modified (GM) food, nanotechnology, stem 

cell research, and cloning.

A Note About the Data and the Terminology

This chapter emphasizes trends over time, patterns of 

variation within the U.S. population, and international patterns. It gives less weight to the specific percentages of 

survey respondents who gave particular answers to the questions posed to them. Although, inevitably, the chapter reports 

these percentages, they are subject to numerous sources of 

error and should be treated with caution. 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. In contrast to specific percentages, consistent and substantial trends and patterns warrant 

greater confidence (see sidebar, “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 the 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 the term “S&T” is used in the parts of the chapter 

discussing these data. Survey questions asking about confidence in institutional leaders, prestige of occupations, and 

views of different disciplines use terms such as “scientific 

community,” “scientists,” “researchers,” or “engineers,” so 

“S&E” is used in sections examining issues related to occupations, careers, and fields of research. Although science 

and engineering are distinct fields, national data that make 

this distinction are scarce.

Information Sources, Interest, 

and Involvement

Because S&T are relevant to so many aspects of daily 

life, 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.

S&T Information Sources

U.S. Patterns and Trends

More Americans get most of their information about current news events from television than from any other source. 

When asked “Where do you get most of your information 

about current news events?,” 47% say television, with substantial percentages also reporting the Internet (22%) and 

newspapers (20%) as their main source (figure 7-1; appendix table 7-1). Since the 1990s, the proportion of Americans 

getting information about current news events from the 
7-8 Chapter 7. Science and Technology: Public Attitudes and Understanding

Survey Data Sources

National 

scope

Sponsoring 

organization Title Years used Information used

Data collection 

method

Number of respondents/

margin of error of general 

population estimates

United States National Science 

Foundation (NSF)

Public Attitudes Toward 

and Understanding of 

Science and Technology 

(1979–2001); includes 

University of Michigan 

Survey of Consumer 

Attitudes 2004 

1979–2001, 

2004

Information sources, 

interest, informal 

science institution visits, 

government spending, 

general attitudes, science/

math education and animal 

research attitudes

Random direct 

dialing (RDD) 

computer-assisted 

telephone survey 

n = ~1,600–2,000 + 2.47% 

– + 3.03%

National Opinion 

Research Center 

(NORC) at the 

University of 

Chicago

General Social Survey 

(GSS) 

1973–2008 Government spending, 

confidence in institutional 

leaders

Face-to-face 

interviews

Government spending: 

n = 1,574–2,992 

+ 2.1% – + 3.5% 

Confidence in institutional 

leaders: 

n = 876–1,989 

+ 2.6% – + 3.8%

NORC at the 

University of 

Chicago

GSS S&T module 2006, 2008 Information sources, 

interest, informal 

science institution visits, 

government spending, 

general attitudes, science/

math education and 

animal research attitudes, 

nanotechnology awareness 

and attitudes

Face-to-face 

interviews

n = 1,864 (2006) 

+ 2.68% 

n = 1,505 (2008) 

+ 2.98%

Gallup Organization Various ongoing surveys 1984, 

1990–1992, 

1995, 

1997–2009 

Environment, stem cell 

research, nuclear power 

attitudes

RDD n = ~1,000 

+3.0%

Virginia 

Commonwealth 

University (VCU) 

Center for Public 

Policy

VCU Life Sciences 

Survey 

2001–08 S&T interest, general 

attitudes, stem cell 

research and animal 

research attitudes 

RDD n = ~1,000 

+ 3.0% (2006 and 2007) 

+ 3.8% (2008)

Department 

of Education, 

National Center for 

Education Statistics 

(NCES)

National Assessment 

of Education Progress 

(NAEP)

2000 

(8th graders); 

2005 

(4th and 

8th graders)

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)

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)

Pew Research 

Center for the 

People 

& the Press

Biennial News 

Consumption Survey 

1996–2008 Information sources, 

interest

RDD n = 3,615 (2008) + 2.0%

Pew Research 

Center for the 

People 

& the Press

News Interest Index 2007–2008 Information sources, 

interest

RDD n = ~1,000 

+ 3.5%

Pew Internet & 

American Life 

Project

Pew Internet & American 

Life Project Survey

2006 Information sources, 

interest, involvement

RDD n = 2,000 

+ 3.0%

Harris Interactive The Harris Poll 1977–2008 Occupational prestige 

attitudes, internet use

RDD Occupational prestige: n = 

~1,000 

(~500 asked about each 

occupation)

Internet use n = ~2,020

CBS News/ 

New York Times

CBS News/New York 

Times Poll

2008 Genetically modified food 

awareness and attitudes

RDD n = 1,065 

+ 3.0%

Woodrow Wilson 

International Center 

for Scholars

Project on Emerging 

Nanotechnologies (2008)

2008 Nanotechnology 

awareness and attitudes 

Telephone 

interviews

n = 1,003 

+ 3.1%
Science and Engineering Indicators 2010 7-9

Survey Data Sources

National 

scope

Sponsoring 

organization Title Years used Information used

Data collection 

method

Number of respondents/

margin of error of general 

population estimates

International European 

Commission

Special Eurobarometer 

224/Wave 63.1: 

Europeans, Science 

and Technology (2005); 

Special Eurobarometer 

282/Wave 67.2: 

Scientific Research in the 

Media (2007); Special 

Eurobarometer 297/

Wave 69.1: Attitudes 

Towards Radioactive 

Waste (2008); Special 

Eurobarometer 300/

Wave 69.2: Europeans’ 

Attitudes Towards 

Climate Change (2008)

1992, 2005, 

2007, 2008

Knowledge, trust in 

scientists and public 

support for basic research 

attitudes, among others

Face-to-face 

interviews

n = 32,897 total:~1,000 for 

27 countries, ~500 for 4 

countries (2005) 

n = 26,717 total: :~1,000 

for 24 countries, ~500 for 3 

countries (2007)

n = 26,746 total:~1,000 for 

24 countries, ~500 for 3 

countries (2008)

n=30,170 total:~1,000 for 

27 countries, ~500 for 4 

countries (2008) 

+ 1.9% – + 3.1%

Canadian 

Biotechnology 

Secretariat

Canada-U.S. Survey on 

Biotechnology 

2005 Biotechnology, 

nanotechnology, and 

other technology attitudes 

(includes U.S. data on 

specific issues)

RDD Canada: n = 2,000 

+ 2.19% 

U.S.: n = 1,200 + 2.81%

British Council, 

Russia 

Russian Public Opinion 

of the Knowledge 

Economy (2004) 

1996, 2003 Various knowledge and 

attitude items

Paper 

questionnaires

n = 2,107 (2003)

Chinese Ministry 

of Science and 

Technology

China Science and 

Technology Indicator 

2002 (2002)

2001 Various knowledge and 

attitude items

Information not 

available 

n = 8,350

China Research 

Institute for Science 

Popularization 

(CRISP)

Chinese Public 

Understanding of Science 

and Attitudes towards 

Science and Technology, 

2007 (2008)

2007 Various knowledge and 

attitude items

Face-to-face 

interviews

n = 10,059 (2007) + 3.0%

Japan National 

Institute of Science 

and Technology 

Policy

The 2001 Survey of 

Public Attitudes Toward 

and Understanding of 

Science & Technology 

in Japan

2001 Various knowledge and 

attitude items

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 Tech-nology 2004, 

2008

2004, 2006, 

2008

Interest, informal science 

institution visits, various 

knowledge and attitude 

items 

Face-to-face 

interviews

n = 1,000 

+ 3.1%

Malaysian Science 

and Technology 

Information Centre 

Public Awareness of 

Science and Technology 

Malaysia 2004 (2005)

2004 Various knowledge and 

attitude items

Face-to-face 

interviews

n = 6,896 

+ 2.0%

India National 

Council of Applied 

Economic Research

India Science Survey 

2004

2004 Various knowledge and 

attitude items

Face-to-face 

interviews

n = 30,255

Department of 

Education, NCES

Trends in International 

Mathematics and Science 

Study (TIMSS)

2003 

(8th grade)

Science knowledge Paper 

questionnaires

U.S.: n = 8,912 

+ 1.4% (for all TIMSS 

questions)

Other 44 countries: n = 

2,943–8,952+ 1.0% – 2.4% 

(for all TIMSS questions)

BBVA Foundation BBVA Foundation 

International Study on 

Attitudes Towards Stem 

Cell Research and Hybrid 

Embryos (2008)

2007/2008 

combined

Knowledge, awareness, 

and attitudes on stem cell 

research

Face-to-face 

interviews

n = 1,500 in each of 15 

countries 

+ 2.6%

Ministry of Science 

and Technology of 

Brazil

Public Perceptions of 

Science and Technology

(2007)

2006 Interest, informal science 

institution visits

Face-to-face 

interviews

n = 2,004 

+ 2.2%

Samuel Neaman 

Institute for 

Advanced Studies 

in Science and 

Technology

Science and Technology 

in the Israeli 

Consciousness (2006)

2006 Prestige of science careers Telephone 

interviews

n = 490

NOTES: All surveys are national in scope. 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

Internet has increased considerably and the proportion using newspapers for current events has declined (figure 7-2).1 

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 another (Pew Research Center for the People and the Press 2008).

Americans report a somewhat different pattern of primary sources for S&T information than for information about 

current news events (figure 7-3; appendix tables 7-1 and 

7-2). For both kinds of information, more Americans select 

television as their primary source than any other medium, 

followed by the Internet. The Internet, magazines, and books 

or other printed material are more widely used as primary 

information sources for S&T than for current news; the opposite is true for television, newspapers, and radio (figure 

7-3). The proportion of Americans who said the Internet was 

their primary source for S&T news grew from 22% in 2006 

to 28% in 2008. Since 2001, this proportion has more than 

tripled (figure 7-2).

When asked, “If you wanted to learn about scientific issues such as global warming or biotechnology, where would 

you get information?,” 54% of Americans choose the Internet even though almost one out of five Americans cannot 

access the Internet at home, work, schools, libraries, and 

other locations (Harris Interactive 2008a). Television (21%) 

ranked as a distant second (figure 7-1; appendix table 7-3). 

Reliance on the Internet, which grew substantially over the 

past decade, is still growing but shows signs of leveling off 

(figure 7-2).

In general, use of the Internet for news and information, 

including S&T information, is higher among younger audiences and increases with education and income. (Access to 

high-speed Internet connections is also associated with more 

time online and more extensive reliance on the Internet for 

news and information [Cole 2007; Horrigan 2006].) Conversely, the use of television decreases with education and 

income and increases with age (appendix tables 7-1 and 7-2). 

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). Younger generations 

that grow up relying more exclusively on the Internet are not 

likely 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 

(Horrigan 2006) examined how Americans use the Internet 

to acquire information about science. 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 

	


	




	



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Science and Engineering Indicators 2010 7-11

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 

“compare different or opposing scientific theories” (37%). 

How skillfully or how often Americans engage in the search 

for scientific information, whether on the Internet or elsewhere, remains unknown.

Using information effectively involves more than 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 ten 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 for the People and the Press 2008).

Evidence about how Americans judge the credibility of 

S&T information in the media is scant. Pew’s 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 in different ways, either by comparing it to 

other information they found online, comparing it to offline 

sources (science journals, encyclopedia), or looking up the 

original source of the information (Horrigan 2006; for additional details see NSB 2008).

	
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7-12 Chapter 7. Science and Technology: Public Attitudes and Understanding

International Comparisons

As in the United States, data collected between 2001 and 

2008 in other countries, including the European Union (EU) 

states, Japan, Russia, and China, uniformly identify television as the leading source of S&T news and information. In 

a 2008 South Korean survey, more respondents named the 

Internet (28%) as their primary source of S&T information 

than named newspapers (16%) (KOFAC 2009). In most other countries, however, newspapers generally ranked second 

and relatively few survey respondents cited the Internet as 

an important source of S&T information. This may be due to 

differences in the availability of Internet access across countries (Internet World Statistics 2009). National differences in 

how questions were asked make precise comparisons among 

different countries impossible.

More recent data on S&T for the other countries do not 

exist; further details on these older data are presented in the 

2006 edition of Science and Engineering Indicators (NSB 

2006). 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 nonprinted sources.

Public Interest in S&T

U.S. Patterns and Trends

High levels of self-reported interest in S&T are part of 

a long-standing pattern, as shown in the results of 12 surveys funded by the National Science Foundation (NSF). 

More than 80% of Americans report they are interested in 

new scientific discoveries (figure 7-4). When asked in the 

General Social Survey (GSS) in 2008 about their interest 

in new scientific discoveries, 86% reported that they are 

either “very” or “moderately” interested (appendix table 

7-4). The proportion of respondents expressing interest in 

new scientific discoveries decreased slightly between 2001 

and 2008 (figure 7-5), but this decline might have resulted 

from a difference in the surveys’ data collection over that 

period.2

 Comparable data from the 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” or “some” interest in new scientific discoveries fluctuated between 83% and 87% (VCU Center for Public Policy 

2006; see NSB 2008). Interest in new scientific discoveries 

increases with education and the number of mathematics and 

science courses people have taken (appendix table 7-5).

Relative to interest in other topics, however, interest in 

S&T in the GSS was not particularly high (figure 7-4). Interest in “new scientific discoveries” and “use of new inventions 

and technologies” ranked in the middle among the 10 areas 

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most frequently listed in the surveys: above space exploration, agriculture and farming, and international and foreign 

policy; below new medical discoveries, environmental pollution, economic issues and business conditions, and about 

the same as military and defense policy and local schools. Of 

course, a more inclusive concept of S&T might treat several 

of the topics in this list, such as space exploration and new 

medical discoveries, as part of the S&T category; furthermore, other topics often include substantial S&T content.3

Survey responses about the types of news Americans follow raise questions about how interested Americans really 

are in S&T. For more than 10 years, Pew (Pew Research 

Center for the People and the Press 2008) has collected 

data on categories of news that Americans follow “very 

closely.” In 2008, 13% of the public followed S&T news 

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 

say they follow S&T closely has declined between 1996 and 

2008. S&T’s relative standing in the list of topics has also 

slipped; it ranked ahead of seven topics in 1996, but ahead 

of only two of the same topics in 2008.

Since 1986, the Pew Research Center for the People and 

the Press has maintained a news interest index that tracks 

individual stories that make headlines. The index is based 

on frequent surveys that record the proportion of Americans 

who, when asked about a news story, say they are following it “very closely.” Stories that attract considerable public 

interest are often included in several surveys, and the same 

story may appear several times in the news interest index. In 

2007, stories that dominated the list of the public’s top news 

stories included the rising price of gasoline, the war in Iraq, 

and human and natural disasters (such as the Virginia Tech 

University shootings, the Minneapolis bridge collapse, and 

the California wildfires) (PEJ 2008). In 2008, stories about 

the condition of the U.S. economy, rising gas prices, the debate over a Wall Street bailout, the 2008 presidential election, major drops in the U.S. stock market, and the impact of 

Hurricane Ike appeared near the top of the list (PEJ 2009). 

Interest in S&T does not appear to have been the central 

factor motivating the public’s interest in these stories rather 

than others.

A different kind of news indicator is the amount of coverage news organizations devote to S&T. This indicator 

can involve either sheer quantity (e.g., broadcast time) or 

prominence (e.g., lead stories). For 20 years, the Tyndall 

Report has tracked the time that the three major broadcast 

networks devoted to 18 categories of news on their nightly 

newscasts (Tyndall Report 2009). Two categories with large 

science, engineering, and technology components are “science, space, and technology,” and “biotechnology and basic 

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.

SOURCES: Pew Research Center for the People and the Press, Online papers modestly boost newspaper readership: Maturing Internet news 

audience broader than deep (30 July 2006), Biennial News Consumption Survey (27 April–22 May 2006), http://people-press.org/reports/display.

php3?ReportID=282, accessed 26 April 2007 (1996–2006); 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 2010
7-14 Chapter 7. Science and Technology: Public Attitudes and Understanding

medical research. “Science, space and technology” includes 

stories on manned and unmanned space flight, astronomy, 

scientific research, computers, the Internet, and telecommunications media technology. It excludes forensic science, 

and telecommunications media content. “Biotechnology and 

basic medical research” includes stem cell research, genetic 

research, cloning, and agribusiness bioengineering and excludes clinical research and medical technology. Stories often do not fall neatly into a single category or theme.

Neither category has ever occupied a large percentage of 

the approximately 15,000 minutes of annual nightly weekday newscast coverage on the networks. “Science, space, 

and technology,” the larger of the two categories, garnered 

752 minutes in its peak year (1999) (figure 7-6).4

 The time 

devoted to “science, space, and technology” coverage in the 

network nightly news has been on a downward trend since 

2003, while the time devoted to “biotechnology and basic 

medical research,” though considerably lower, has been on 

the rise in the same period.

Trends in the “science, space, and technology” category, 

along with recent annual lists of leading individual stories 

in that category, suggest that developments in the nation’s 

space program and new ways to use cellular phones and the 

Internet received the largest amount of news coverage (table 

7-2). In the “biotechnology and basic medical research” category, the war on cancer, the use of genetic testing to predict 

disease, and stem cell research received the largest amount 

of news coverage. Time devoted to cancer research coverage is greater than for any other story. The importance of 

Table 7-2

Leading nightly news story lines on science and technology, by topic area: 2007 and 2008

(Annual minutes of coverage)

Topic area/leading story line 2007 Topic area/leading story line 2008

Science, space, and technology Science, space, and technology

NASA Space Shuttle program ............................................ 39 Mars astronomy: NASA rovers search for water ................ 19

International space station construction ............................ 31 Spy satellite falls out of orbit, shot down ........................... 18

NASA astronaut love triangle ............................................. 21 Mathematics education in schools ..................................... 11

NASA astronauts suspected of drunken space flights ....... 18 High-technology multitasking is distracting ....................... 11

Cellular telephone computer combo invented: iPhone ...... 14 Cellular telephone extras: ringtones, wallpaper.................. 7

Videostreams shared online in viral networks: YouTubea

 .... 12 Internet search engine Yahoo! takeover bid ....................... 7

Internet used by teens for social networking: Facebook .... 12 International space station construction ............................ 6

High school science fair competitions held for students.... 10 Physicists build supercollider in Switzerland ...................... 5

Mathematics education in schools ..................................... 8 Inventions and innovations in technology surveyed ........... 5

Inventions and innovations in technology surveyed ........... 7 China censors Internet access and e-mail traffic ............... 5

Biotechnology/basic medical research Biotechnology/basic medical research

War on cancer/research efforts .......................................... 70 War on cancer/research efforts .......................................... 69

Human embryo stem cell biotechnology research ............. 27 Genetic DNA biotech analysis predicts diseases ............... 29

Organs may be grown in laboratory for implant ................. 12

Surgery improved by minimally invasive techniques .......... 11

Animal cloning in agriculture safety research ..................... 6

a

Refers to the rise of YouTube as a video file-sharing technology.

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 2007 and 2008. Excluded from science, 

space, and technology are stories on forensic science and media content. Excluded from biotechnology and basic medical research are stories on clinical 

research and medical technology. 

SOURCE: Tyndall Report, special tabulations (January 2009), http://www.tyndallreport.com, accessed 23 September 2009.

Science and Engineering Indicators 2010

	
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competing stories, such as the economic crisis, plays a role 

in deciding what news is covered.

International Comparisons

Using identical questions, recent surveys conducted in 

other countries indicate that the overall level of self-reported 

public interest in S&T is lower than in the United States. Between 75% and 80% of survey respondents in South Korea, 

China, and Europe said they were “very” or “moderately” 

interested in “new scientific discoveries” and “use of new 

inventions and technologies” compared to 86% and 88% respectively of Americans in the 2008 GSS, respectively (appendix table 7-4) (KOFAC 2009; CRISP 2008; EC 2005). 

Using slightly different questions, about three-quarters of 

Brazilians said they were “very interested” or “a little interested” in “science and technology” (MCT of Brazil 2006). 

In Malaysia, 58% of the respondents said they were “interested” or “very interested” in the “latest inventions in new 

technology” and 51% in the “latest inventions in science” 

(MASTIC 2004).

In the 2005 European survey (called the 2005 “Eurobarometer”), there was considerable variation among different 

countries in self-reported interest in S&T-related issues, and 

the overall level of interest was down from the most recent 

survey in 1992. In both the United States and in Europe, men 

showed more interest in S&T than women. For more recent 

European data on interest in scientific research in general, 

see sidebar “Scientific Research in the Media in Europe.”5

Interest in environmental issues is similarly high in the 

United States, Europe, South Korea, and Brazil—about 9 in 

10 respondents in each country or region expressed interest 

in this topic, although slight variations in survey terminology should be taken into account.6

 In Malaysia, interest in 

“environmental pollution” was lower (61% said they were 

“interested” or “very interested” in this issue).

Like Americans, Europeans and Brazilians are more interested in medicine than in S&T in general. In the United 

States, nearly everyone was interested in new medical discoveries (94%); in Brazil, most people (91%) were interested in “medicine and health” issues. In Europe, South Korea, 

and China, interest in new medical discoveries seemed to 

be lower—between 77% and 83% said they were “very” or 

“moderately” interested in this issue. In Malaysia, 59% indicated they were “interested” or “very interested” in the “latest inventions in the field of medicine.”7

Involvement

Involvement with S&T outside the classroom in informal, voluntary, and self-directed settings—such as museums, science centers, zoos, and aquariums—is an indicator 

of interest in S&T.8 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.

In the 2008 GSS, 59% of Americans indicated that they 

had visited an informal science venue during the previous 

In 2007, the European Commission conducted a survey to learn how to motivate European citizens to become 

more involved in science, research, and innovation. Faceto-face interviews were conducted in people’s homes, in 

their national language, in the European Union’s (EU) 27 

member states (EC 2007).

The survey shows that the majority of Europeans 

(57%) are “very” and “fairly” interested in scientific research. Interest is much higher in the EU-15 (62%) than 

in the 12 countries that recently joined (38%). The countries most interested in scientific research were Sweden, 

Denmark, France, Luxembourg, the Netherlands, Belgium, and Finland. Men and more highly educated individuals expressed more interest in this subject. Medicine 

attracted the highest degree of public interest (62%), followed by the environment (43%).

Television is the most popular medium for information and also the medium with the widest reach. The majority of EU citizens (61%) watch television programs 

about scientific research regularly or occasionally, nearly 

half read scientific articles in general newspapers and 

magazines, and 28% look at information on scientific issues on the Internet. Television is also the most trusted 

medium for obtaining science information, ranking first 

in trustworthiness in 25 out of the 27 EU member states.

Overall, EU-27 citizens are satisfied with media coverage of scientific research, in particular those who are 

interested in this subject. The majority believe the coverage devoted to scientific research in the media is sufficient, but about one-third believes that it is not given 

enough importance. Most European citizens view science 

media coverage as reliable, objective, useful, varied, and 

sufficiently visual. However, they also express that science media coverage is difficult to understand, removed 

from their actual concerns, and not entertaining. More 

highly educated respondents are more likely to view media coverage of scientific information as more useful, understandable, entertaining, and not too far removed from 

citizen concerns.

Europeans tend to prefer to receive short news reports 

about scientific research on a regular basis (43%) rather 

than occasional in-depth information (34%). In addition, 

they prefer to restrict public scientific debates to scientists and experts rather than to actively participate themselves, and they would prefer that scientists rather than 

journalists present scientific information.

Scientific Research in the Media in Europe
7-16 Chapter 7. Science and Technology: Public Attitudes and Understanding

year9 (appendix table 7-6). Half said they had visited a zoo 

or aquarium and over one-quarter had visited a “natural history museum” (27%) or a “science and technology museum” 

(26%). 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 

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 attending the 

three institutions (zoo/aquarium, S&T museum, and public 

library) is down slightly from the last time these questions 

were asked in 2001. However, these differences may be due 

to changes in the data collection methods over this period 

discussed earlier in the chapter, rather than to actual changes 

in attendance.

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 7-7; appendix table 7-7). Those in higher income 

brackets are more likely to have attended a zoo or an aquarium, a natural history or an S&T museum, or an art museum, 

but just as likely as those in the lowest income bracket to 

have visited a public library. In general, visits to informal 

science institutions are lower 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 use of 

these different sources of exposure to science information 

complement, rather than replace, one another.

Fewer Europeans report visits to informal science institutions (EC 2005). In the EU-25, about 27% of adults said 

they had visited a zoo or aquarium, 16% said they had visited a “science museum or technology museum or science 

centre,” and 8% said they had attended a “science exhibition or science ‘week.’” As in the United States, older and 

less-educated Europeans reported less involvement in these 

activities. In addition, European adults in households with 

more inhabitants more often reported informal science activities. Insofar as household size indicates the presence of 

minor children, this probably indicates another parallel with 

the United States. One demographic pattern is notably different between Europe and the United States: where European 

men (19%) are much more likely than women (13%) to visit 

informal science or technology museums and centers, these 

gender differences do not exist in the United States (appendix table 7-7). (For additional details on the comparison with 

European data, see NSB 2008.)

Compared with the United States, visits to natural history 

and science and technology museums are less common in 

Japan, South Korea, China, Brazil, and Russia (table 7-3). 

The proportion of respondents who indicated they had visited a zoo/aquarium is similar in the U.S., China, and Japan. 

Unmeasured differences in the prevalence and accessibility 

of informal science learning opportunities across countries 

make it difficult to attribute different visit patterns to differences in interest.

Public Knowledge About S&T

Scientific literacy can be relevant to the public policy and 

personal choices that people make. In developing measures 

for scientific literacy across nations, the Organisation for 

Economic Co-operation and Development (OECD) (2003) 

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 

	
	

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Science and Engineering Indicators 2010 7-17

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 evidencebased 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)

As the reference to changes made through human activity 

makes clear, the OECD definition implies an understanding 

of technology. The OECD takes the view that literacy is a 

matter of degree and that people cannot simply be classified 

as either literate or not literate.

A good understanding of basic scientific terms, concepts, 

and facts; an ability to comprehend how science generates 

and assesses evidence; and a capacity to distinguish science 

from pseudoscience are widely used indicators of scientific 

literacy. (For a different perspective on scientific literacy, 

see sidebar, “Asset-Based Models of Knowledge.”)

U.S. survey data indicate that many Americans cannot 

provide correct 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, perform no better, on balance, when asked similar questions. However, 

compared to middle-school students, American adults perform relatively well. In light of the limitations of using a 

small number of questions largely keyed to knowledge taught 

in school, generalizations about American’s knowledge of 

science should be made cautiously.

Understanding Scientific Terms and Concepts

U.S. Patterns and Trends

U.S. data show that the public’s level of factual knowledge about science has not changed much over time. Figure 

7-8 shows average numbers of correct answers to a series 

of mostly true-false science questions in different years for 

which fully comparable data were collected (appendix table 

7-8).10 Although performance on individual questions varies 

somewhat over time (appendix table 7-9), overall scores are 

relatively similar.

Factual knowledge of science is positively related to people’s level of formal schooling, income level, and the number of science and math courses they have taken. Factual 

knowledge is also positively related to scores on a 10-item 

vocabulary test included in the GSS, which scholars in many 

disciplines have often used to assess verbal skills (Malhotra, 

Krosnick, and Haertel 2007).11 In the factual questions included in NSF surveys since 1979, which allow for the observation of trends over time (referred to as “trend factual 

questions” below), men score higher on the questions in the 

physical sciences and women score higher on those in the 

biological sciences (table 7-4).12

Table 7-3

Visits to informal science and other cultural institutions, by country/region: Most recent year

(Percent)

Institution

United 

States,

2008

South 

Korea,

2008

China,

2007

Brazil,

2006

EU,

2005

Russia,

2003

Japan,

2001

Zoo/aquariuma

 ...................................... 50 36 52 28 27 9 43

Natural history museum ....................... 27 NA 14 NA NA NA 20

Science/technology museumb .............. 26 11 17 4 16 1 13

Public library ......................................... 64 34 41 25 34 16 47

Art museum .......................................... 32 34 18 12 23 7 35

NA = not available, question not asked

EU = European Union

a “Zoo, botanic garden, or environmental park” for Brazil, “Zoo, aquarium, or botanic garden” for China, “Zoo” for Russia.

b “Science museums or technology museums or science centers” for EU.

NOTES: Responses to (United States, Japan, 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); (EU, Russia, 

China, Brazil) Which of the following have you visited in the last twelve months (Multiple answers possible). 

SOURCES: (United States) University of Chicago, National Opinion Research Center, General Social Survey (2008); 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); Chinese Ministry of Science and Technology, Chinese Public Understanding of Science and Attitudes towards Science and 

Technology, 2007 (2008); (Brazil) Ministry of Science and Technology, Public Perceptions of Science and Technology (2007); (EU) Eurobarometer 224/

Wave 63.1: Europeans, Science and Technology (2005); (Russia) British Council, Russian Public Opinion of the Knowledge Economy (2004); Japan 

National Institute of Science and Technology Policy, The 2001 Survey of Public Attitudes Toward and Understanding of Science & Technology in Japan 

(2001). See appendix table 7-6 for U.S. trends.

Science and Engineering Indicators 2010
7-18 Chapter 7. Science and Technology: Public Attitudes and Understanding

Respondents 65 and older are less likely than others to answer the questions correctly (appendix tables 7-8 and 7-10). 

An analysis of surveys conducted between 1979 and 2006 

concluded that generational experiences are more important 

than cognitive declines associated with aging in explaining 

these differences (Losh 2009, 2010).

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 emerged after 

they completed their schooling.

In 2008, the GSS asked Americans questions that tested 

their knowledge of a topic that has not been central to the 

standardized content of American science education: nanotechnology. Survey respondents who scored relatively well 

overall on the questions that were asked repeatedly over the 

years also exhibited greater knowledge of this topic (figure 

7-9).13 Likewise, the educational and demographic characteristics associated with higher scores on the trend factual 

knowledge questions are also associated with higher scores 

for this new topic (appendix table 7-11). These data suggest 

that the knowledge items used to measure trends, although 

focused on the kind of factual knowledge learned in school, 

are a reasonable indicator of factual science knowledge in 

general, including knowledge that is acquired later in life.

Similarly, national standards for what students should 

know reflect new science concepts beyond those covered 

by the long-standing questions that measure trends in public knowledge of science. In 2008, the GSS included questions on science and mathematics knowledge that were more 

closely aligned with national standards for what students 

should know. The questions were selected from three national exams administered to students and Project 2061, an initiative by the American Association for the Advancement of 

Science (AAAS) that develops assessment materials aligned 

Asset-Based Models of Knowledge

Many researchers and educators interested in the 

public’s understanding of science advocate studying the skills people bring to bear on scientific issues 

that they deal with in their daily lives (e.g., gardening, bird-watching). Because individuals encounter 

S&T in different ways, they acquire different S&T 

knowledge “assets,” which they then can use to make 

sense of unfamiliar issues (National Research Council 2009). For researchers and educators who favor 

an asset-based model of scientific literacy, public understanding of science is less a “generalized body of 

knowledge and skills that every citizen should have 

by a certain age” than “a series of specific sets of 

only moderately overlapping knowledge and abilities 

that individuals construct over their lifetimes” (Falk, 

Storksdieck, and Dierking 2007). In education, assetbased perspectives on knowledge have been useful in 

helping teachers build on children’s existing strengths 

to improve their performance.

Generalized assessments of S&T knowledge may 

underestimate the assets available to individuals when 

they deal with S&T matters of greater interest and 

consequence to them, because these types of assessments ask questions on topics of little interest to many 

respondents. In contrast, a knowledge assessment that 

is tailored to an S&T domain with which an individual 

is familiar might yield very different results. In addition, because people often use their knowledge assets 

in group interactions, such as a nature outing, some researchers question the value of individual assessments 

in a test or survey (Roth and Lee 2002).

Researchers have developed measures of adult science understanding to assess how people make sense 

of specific experiences or scientific materials (Friedman 2008). National indicators that evaluate domainspecific knowledge or group problem-solving are not 

practical, but a perspective on scientific literacy that 

stresses domain-specific or group assets is useful, because it points to a significant limitation of generalized 

indicators of individual scientific literacy.

	
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Science and Engineering Indicators 2010 7-19

Table 7-4

Correct answers to scientific literacy questions, by sex: 2001, 2004, 2006, and 2008

(Percent)

Question 2001 2004 2006 2008

Physical science

The center of the Earth is very hot. (True)

Male ......................................................................................................................................... 85 86 85 88

Female ..................................................................................................................................... 76 72 75 80

All radioactivity is man-made. (False)

Male ......................................................................................................................................... 81 82 77 74

Female ..................................................................................................................................... 71 66 64 67

Lasers work by focusing sound waves. (False)

Male ......................................................................................................................................... 61 59 62 64

Female ..................................................................................................................................... 30 28 32 34

Electrons are smaller than atoms. (True)

Male ......................................................................................................................................... 52 52 61 59

Female ..................................................................................................................................... 43 39 48 47

The continents have been moving their location for millions of years and will continue to 

move. (True)

Male ......................................................................................................................................... 83 85 85 82

Female ..................................................................................................................................... 74 71 75 73

Does the Earth go around the Sun, or does the Sun go around the Earth? (Earth around Sun)

How long does it take for the Earth to go around the Sun? (One year)

Malea

 ....................................................................................................................................... 66 NA 66 58

Femalea

 ................................................................................................................................... 42 NA 46 44

Biological science

It is the father’s gene that decides whether the baby is a boy or a girl. (True)

Male ......................................................................................................................................... 58 51 55 53

Female ..................................................................................................................................... 72 70 72 71

Antibiotics kill viruses as well as bacteria. (False)

Male ......................................................................................................................................... 46 49 50 47

Female ..................................................................................................................................... 55 58 61 60

A doctor tells a couple that their genetic makeup means that they’ve got one in four chances 

of having a child with an inherited illness. (1) Does this mean that if their first child has the 

illness, the next three will not? (No); (2) Does this mean that each of the couple’s children will 

have the same risk of suffering from the illness? (Yes) 

Male ......................................................................................................................................... 68 67 72 66

Female ..................................................................................................................................... 67 62 67 63

Two scientists want to know if a certain drug is effective against high blood pressure. The first 

scientist wants to give the drug to 1,000 people with high blood pressure and see how many 

of them experience lower blood pressure levels. The second scientist wants to give the drug 

to 500 people with high blood pressure and not give the drug to another 500 people with 

high blood pressure, and see how many in both groups experience lower blood pressure 

levels. Which is the better way to test this drug? Why is it better to test the drug this way? 

(The second way because a control group is used for comparison) 

Maleb ....................................................................................................................................... 39 49 42 37

Femaleb ................................................................................................................................... 38 43 41 39

NA = not available 

a Data represent composite of correct responses to both questions. Second question only asked if first question answered correctly. No composite 

percentage computed for 2004 because second question not asked.

b Data represent a composite of correct responses to both questions. 

SOURCES: National Science Foundation, Division of Science Resources Statistics, Survey of Public Attitudes Toward and Understanding of 

Science and Technology (2001); University of Michigan, Survey of Consumer Attitudes (2004); and University of Chicago, National Opinion 

Research Center, General Social Survey (2006, 2008). 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 2010
7-20 Chapter 7. Science and Technology: Public Attitudes and Understanding

with current curricular standards.14 This battery of questions 

included nine factual questions, two questions measuring 

chart reading and understanding of the statistical concept of 

“mean,” and five questions that tested reasoning and understanding of the scientific process. Two out of the 16 questions were open-ended and the rest were multiple-choice 

(see sidebar, “New Science Knowledge Questions”).15

The results show that survey respondents who answered 

the additional factual knowledge questions correctly also 

tended to provide correct answers to the trend factual knowledge questions (figure 7-10; appendix tables 7-10 and 7-12). 

This suggests again that the trend factual questions are a 

reasonable indicator of the type of knowledge students are 

tested on in national assessments.

Out of seven factual science knowledge questions where 

comparison scores with fourth and eighth grade students 

were possible, adult Americans received a higher or similar score in five of them (table 7-5). 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 on paper and 

self-administered, whereas the majority of respondents in 

the GSS answered orally to an interviewer. Elementary and 

middle school students had an advantage over adults in that 

classroom preparation preceded their tests.

New Science Knowledge 

Questions

These questions were included in the 2008 General 

Social Survey to assess different aspects of science and 

technology knowledge. Answers are bold. The factual 

knowledge questions (questions 1, 3–5, and 7–11) are 

combined into scale 2 in some figures and appendix 

tables. Other questions test a person’s knowledge of 

charts and statistics (questions 12 and 13), reasoning/

life sciences (questions 2 and 14), and experiment/

controlling variables (questions 6 and 14–16). Note 

that the correct answer for question 14 can be reached 

by using reasoning skills, knowledge in the life sciences, or understanding of the experiment/controlling 

variables concept.

Opening script: Now, we are going to do some 

more detailed questions on science and technology. 

Scientists and educators are interested in how familiar adults are with the things being taught in today’s 

schools. Many of these questions are likely to concern 

things that weren’t taught or emphasized when you 

were in school. Some of the questions involve pictures 

or graphs.

1. What property of water is most important for living organisms?

A) It is odorless.

B) It does not conduct electricity.

C) It is tasteless.

D) It is liquid at most temperatures on Earth.

2. Please look at Card 1. The two objects shown 

there have the same mass, but object B loses heat more 

quickly than object A.

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Which combination of bodily features would be 

BEST suited to a small animal that lives in a cold climate and needs to minimize heat loss?

A) Long ears and a long body.

B) Small ears and a short tail.

C) A long nose and a long tail.

D) A short nose and large ears.

E) A long tail and a short nose.

	
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Science and Engineering Indicators 2010 7-21

3. Which of the following is a key factor that enables 

an airplane to lift?

A) Air pressure beneath the wing is greater than 

that above the wing.

B) Pressure within the airplane is greater that that of 

the outside.

C) Engine power is greater than that of friction.

D) The plane’s wing is lighter than air.

4. Lightning and thunder happen at the same time, but 

you see the lightning before you hear the thunder. Explain why this is so.

A correct response indicates that light travels faster than sound so the light gets to your eye before the 

sound reaches your ear.

5. 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.

A correct response refers to a neutralization or a 

chemical reaction that results in products that do not 

react with litmus paper.

6. Please look at Card 2. A student wants to find out 

if temperature affects the behavior of goldfish. He has 4 

fish bowls and 20 goldfish. Which of the experiments on 

Card 2 should he do? Correct answer: A.

7. 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?

A) Yes, because there is plenty of ocean water.

B) Yes, because ocean water has many natural 

fertilizers.

C) No, because ocean water is too salty for plants 

grown on land.

D) No, because ocean water is much more polluted 

than rainwater.

8. Which one of the following is NOT an example of 

erosion?

A) The wind in the desert blows sand against a rock.

B) A glacier picks up boulders as it moves.

C) A flood washes over a riverbank, and the water 

carries small soil particles downstream.

D) An icy winter causes the pavement in a road 

to crack.

9. Traits are transferred from generation to generation 

through the...

A) sperm only.

B) egg only.

C) sperm and egg.

D) testes.

10. How do most fish get the oxygen they need to 

survive?

A) They take in water and break it down into hydrogen and oxygen.

B) Using their gills, they take in oxygen that is dissolved in water.

C) They get their oxygen from the food they eat.

D) They come to the surface every few minutes to 

breathe air into their lungs.

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New Science Knowledge Questions continued
7-22 Chapter 7. Science and Technology: Public Attitudes and Understanding

11. For which reason may people experience shortness of breath more quickly at the top of a mountain than 

along a seashore?

A) A slower pulse rate.

B) A greater gravitational force on the body.

C) A lower percent of oxygen in the blood.

D) A faster heartbeat.

E) A slower circulation of blood.

12. Please look at Card 3. Day-night rhythms dramatically affect our bodies. Probably no body system is more 

influenced than the nervous system. The figure on Card 3 

illustrates the number of errors made by shift workers in 

different portions of the 24-hour cycle.

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which of these time periods did the most errors occur?

A) 2 A.M. to 4 A.M.

B) 8 A.M. to 10 A.M.

C) 12 P.M. to 2 P.M.

D) 2 P.M. to 4 P.M.

E) 8 P.M. to 10 P.M.

13. As part of a laboratory experiment, five students 

measured the weight of the same leaf four times. They 

recorded 20 slightly different weights. All of the work 

was done carefully and correctly. Their goal was to be as 

accurate as possible and reduce error in the experiment 

to a minimum.

Which of the following is the BEST method to report 

the weight of the leaf?

A) Ask the teacher to weigh the leaf.

B) Report the first measurement.

C) Average all of the weights that were recorded.

D) Average the highest and lowest weights recorded.

E) Discard the lowest five weights.

14. Please look at Card 4. A gardener has an idea that 

a plant needs sand in the soil for healthy growth. In order 

to test her idea she uses two pots of plants. She sets up 

one pot of plants as shown on the top part of the card. 

Which one of the pictures on the bottom part of the card 

shows what she should use for the second pot? Correct 

answer is E.



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Which ONE of the following should she use for the 

second pot of plants?

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Science and Engineering Indicators 2010 7-23

items vary from country to country, and no country consistently outperforms the others (figure 7-11). For the questions 

reported in figure 7-11, knowledge scores are relatively low 

in China, Russia, and Malaysia. Compared with the United 

States and the highly developed countries in Europe, Japanese scores are also relatively low.16

Science knowledge scores vary considerably across the 

EU-25 countries, with northern European countries, led by 

Sweden, recording the highest total scores on a set of 13 

questions. For a smaller set of 4 items that were administered in both 1992 and 2005 in 12 European countries, each 

country performed better in 2005. In contrast, the 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. (For more details on scientific 

literacy in individual countries in Europe, see NSB 2008.)

Reasoning and Understanding the Scientific 

Process

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 principles of probabilistic reasoning to a series of questions about a couple whose children 

The variation patterns on these items were similar to the 

trend factual questions. However, men scored higher than 

women in all but one of the additional factual knowledge 

questions included in the 2008 GSS (appendix tables 7-10 

and 7-12).

International Comparisons

Adults in different countries and regions have been asked 

identical or substantially similar questions to test their factual knowledge of science. (For an examination of how 

question wording is related to international differences in 

knowledge measures, see sidebar, “Knowledge Difference 

or Measurement Error?”) Knowledge scores for individual 

15. Please look at Card 5. What is the scientist trying to find out from this experiment?

A) If the number of fish in the fish bowl affects 

the behavior of the fish.

B) If the temperature of the fish bowl affects the 

behavior of the fish.

C) If the temperature and the amount of light affect the behavior of the fish.

D) If the number of fish, the temperature, and the 

amount of light affect the behavior of the fish.

16. Why did you choose that answer?

A) Because I already know what affects the behavior of fish.

B) Because that is what is allowed to change in 

 this experiment.

C) Because that is what stays the same in this 

 experiment.

D) Because that is what the scientist decided to 

 include in this experiment.

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Table 7-5

Adult and student correct answers to factual knowledge questions

(Percent correct)

Factual questions Field of study Concepts measured

U.S. adult Student

2008 General 

Social Survey 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

86 61 NA NAEP 2005, 

4th grade

2.Traits are transferred from 

generation to generation 

through the... 

Life sciences Reproduction and 

heredity

80 86 74 TIMSS 

Science 2003, 

8th grade

3. How do most fish get 

the oxygen they need to 

survive? 

Life sciences Change and evolution; 

adaptation and natural 

selection

76 78 NA NAEP 2005, 

8th grade

4. What property of water is 

most important for living 

organisms? 

Physical 

sciences

Matter and its 

transformations

69 76 NA NAEP 2000, 

8th grade

5. Which 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

55 37 NA NAEP 2005, 

8th grade

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

45 36 NA NAEP 2005, 

8th grade

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, 

8th grade

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. Original sources of questions are NAEP and TIMSS. For complete questions, see sidebar: 

“New Science Knowledge Questions.”

SOURCES: University of Chicago, National Opinion Research Center, General Social Survey (2008), see appendix table 7-12; NAEP, http://nces.ed.gov/

nationsreportcard/itmrls/startsearch.asp, accessed 22 September 2009; and TIMSS, http://nces.ed.gov/timss/results03.asp, accessed 22 September 2009. 

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 7-25

have a one-in-four chance of suffering from an inherited 

disease.17 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. 

An 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.

In 2008, 65% of Americans responded correctly to the 

two questions about probability, 38% to the questions testing the concept of experiment, and 22% to the questions testing the concept of scientific study. Scores on the probability 

questions fluctuate each year but are relatively stable over 

time; however, between 2006 and 2008 the combined scores 

of the two probability questions slightly declined. Scores in 

the other scientific process questions were generally higher 

than they were in the mid-1990s, but decreased somewhat in 

2008 (appendix table 7-13). Performance on these questions 

is strongly associated with the different measures of science knowledge and education (appendix table 7-14). Older 

Americans and those with lower incomes, two groups that 

tend to have less education in the sciences, also score lower 

on the inquiry measures. Men and women obtain similar 

scores on these questions (tables 7-4 and 7-6).

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 and benchmark their scores to national results 

of middle school students. From 29% to 57% of Americans 

responded correctly to questions measuring the concepts of 

scientific experiment and controlling variables (appendix 

tables 7-13 and 7-15). However, only 12% of Americans 

responded correctly to all the questions on this topic and 

nearly 20% of Americans did not respond correctly to any of 

them (figure 7-12).18 These data suggest that relatively few 

Americans have a generalized understanding of experimental design that they can reliably apply to different situations.

The proportion of Americans with a strong grasp of experimental design does not vary by sex. However, Americans who answered at least three of four experimental 

knowledge questions correctly were more likely to have 

a college education or higher, have taken more courses in 

math and science, and have a clear understanding of the scientific method. They are also more likely to be in the top 

income bracket and to respond correctly to factual science 

knowledge and probability questions.

Adults’ scores in the experimental knowledge questions 

are similar to middle school students in one question (question 2 in table 7-7) but lower in two others, out of the three 

questions where the comparison was possible.

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). The results from the 2008 GSS show that 77% 

of Americans can read a simple chart correctly and 66% 

understand the concept of “mean” in statistics. Understanding these two concepts is associated with formal education, 

the number of math and science courses taken, income, and 

verbal ability. Older respondents were less likely to respond 

correctly to these two questions (appendix table 7-15).

Pseudoscience

The results of 13 NSF-funded surveys conducted between 

1979 and 2008 show a trend toward fewer Americans seeing astrology as scientific. In the 2008 GSS 63% of Americans indicated they believed that astrology was “not at all 

Surveys from different countries have tried to measure 

public knowledge about how children inherit the chromosomes that determine their sex. The data appear to indicate that Americans understand this topic better than 

their counterparts in other countries. The true-false question asked in the United States is “It is the father’s gene 

that decides whether the baby is a boy or a girl.” (True.) 

Europeans and Chinese have been asked the same question about the mother’s gene. (False.) Although a knowledgeable survey respondent would treat these questions 

as equivalent, research on how people answer surveys 

suggests that they may not be. Survey methodologists 

have found that many respondents exhibit an acquiescence bias—a tendency to give a positive answer (e.g., 

true, yes, agree) to questions, independent of their content 

(Holbrook, Green, and Krosnick 2003; Krosnick 2000). 

Accordingly, respondents will seem more knowledgeable 

when the correct answer to a question is “true.”

The 2008 GSS included an experiment to test whether observed national differences on this topic are real 

knowledge differences or are products of acquiescence 

bias. Some respondents were asked the usual U.S. question, while others got the international variant. The experiment indicated that the national differences result from 

knowledge differences and not from acquiescence bias. 

A larger proportion of respondents (71%) answered correctly when the right answer was false than when it was 

true (62%) (appendix tables 7-9 and 7-10).

Knowledge Difference or Measurement Error?
7-26 Chapter 7. Science and Technology: Public Attitudes and Understanding

	




	
	
	

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scientific” and 28% said that it was only “sort of scientific.” 

Respondents with more years of formal education were less 

likely to perceive astrology to be at all scientific. In 2008, 

78% of college graduates indicated that astrology was “not 

at all scientific,” compared with 60% of high school graduates. Those who scored highest on the factual knowledge 

measures were less likely to perceive astrology to be at all 

scientific (78%) than those who scored lowest (45%). Respondents who correctly understood the concept of scientific 

inquiry were more likely to say that astrology was not at all 

scientific (74%) than those who did not understand the concept (57%). However, the youngest age group (18–24) was 

less likely to say astrology is “not at all scientific” (49%) and 

more likely to say it was “sort of scientific” (44%) (appendix 

table 7-16).19

Public Attitudes About S&T in General

Generalized public support for S&T can make a difference in many ways. Public openness to technological change 

gives U.S. businesses opportunities to build a domestic 

customer base, create a foundation for worldwide technical 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, enables 

ambitious proposals for sustained federal S&T investments 

to reach fruition. Public confidence that S&E community 

leaders are trustworthy, S&E research findings are reliable, 

and S&E experts bring valuable judgment and knowledge to 

bear on public issues permits scientific knowledge to have 

influence over practical affairs. In addition, positive public 

perceptions of S&E occupations encourage young people to 

pursue S&E careers.

To be sure, claims of scientific and technological progress should be evaluated critically. But widespread public 

skepticism about S&T, going beyond the reasoned examination of particular cases, would represent a consequential 

change in American public opinion. Changing public opinion could affect national strategies that link progress in S&T 

to overall national progress.

Table 7-6

Correct answers to questions about charts and statistics, reasoning/life sciences, and understanding of 

experiment/controlling variable by sex: 2008

(Percent)

Question

Both 

sexes Men Women

1. Please look at card 3. Day-night rhythms dramatically affect our bodies. Probably no body system is 

more influenced than the nervous system. The figure on card 3 illustrates the number of errors made 

by shift workers in different portions of the 24-hour cycle. Based on the data illustrated in the figure, 

during which of these time periods did the most errors occur? ........................................................................ 77 80 73

2. As part of a laboratory experiment, five students measured the weight of the same leaf four times. They 

recorded 20 slightly different weights. All of the work was done carefully and correctly. Their goal was 

to be as accurate as possible and reduce error in the experiment to a minimum. Which of the following 

is the BEST method to report the weight of the leaf?........................................................................................ 66 70 63

3. Please look at card 1. The two objects shown there have the same mass, but object B loses heat more 

quickly than object A. Which combination of bodily features would be BEST suited to a small animal 

that lives in a cold climate and needs to minimize heat loss?a

 .......................................................................... 51 54 49

4. Please look at card A. A gardener has an idea that a plant needs sand in the soil for healthy growth. In 

order to test her idea she uses two pots of plants. She sets up one pot of plants as shown on the top 

part of the card. Which one of the pictures on the bottom part of the card shows what she should use 

for the second pot?b .......................................................................................................................................... 51 49 53

5. Please look at card 2. A student wants to find out if temperature affects the behavior of goldfish. He 

has four fish bowls and 20 goldfish. Which of the experiments on card 2 should he do? ................................ 57 59 56

6. Combined responses to two interrelated questions: (Question 1) What is the scientist trying to find out 

from this experiment? (Question 2, follow-up) Why did you choose that answer?c

 .......................................... 29 30 28

a

 Respondent can reach correct answer through both reasoning and knowledge of life sciences.

b Respondent can answer this question by using knowledge of experiment/controlling variable or knowledge in the life sciences.

c

 Data represent a composite of correct responses to both questions.

NOTE: For complete questions, see sidebar: “New Science Knowledge Questions Included in the General Social Survey: 2008.”

SOURCE: University of Chicago, National Opinion Research Center, General Social Survey (2008). See appendix table 7-15. Questions 1, 2, and 3 

originally from American Council on Education, GED Testing Service, Science Official GED Practice Test (2006). Question 4 originally from Trends in 

International Mathematics and Science Study (TIMMS), Complete TIMSS 8 Science Concepts and Items 4, http://nces.ed.gov/timss/pdf/TIMSS8_

Science_Items.pdf, accessed 22 September 2009. Questions 5 and 6 originally from American Association for the Advancement of Science, AAAS 

Project 2061, http://www.project2061.org/publications/2061Connections/2007/media/controlling_variables_poster.pdf, accessed 22 September 2009.

Science and Engineering Indicators 2010
7-28 Chapter 7. Science and Technology: Public Attitudes and Understanding

This section presents general indicators of public attitudes and orientations toward S&T in the United States and 

in other countries. It covers views of the promise of S&T 

and reservations about science, overall support for government funding of research, confidence in the leadership of the 

scientific community, perceptions of the proper influence of 

scientists over controversial public issues about which the 

research community claims expertise, perceptions about 

what it means to be scientific and which disciplines and 

practices are scientific, and views of S&E as occupations.

Promise and Reservations

NSF surveys dating back to 1979 show that Americans 

endorse the past achievements and future promise of S&T. 

In practically any major American social grouping, few individuals express serious doubt about the promise of science. 

In 2008, 43% of GSS respondents said that the benefits of 

scientific research strongly outweighed the harmful results 

and substantial percentages said that benefits either slightly 

outweighed harms (25%) or volunteered that the two were 

about equal (16%). Only 10% of respondents said that the 

harms either slightly or strongly outweighed benefits and the 

remainder said that they did not know. These numbers were 

generally consistent with those from earlier surveys (figure 7-13; appendix tables 7-17 and 7-18). Americans overwhelmingly agree that S&T will foster “more opportunities 

Table 7-7

Adult and student correct answers to scientific process questions 

(Percent correct)

Process question Field of study

U.S. adult Student

2008 General 

Social Survey United States International

Question 

source

1. Please look at Card A. A gardener has an idea 

that a plant needs sand in the soil for healthy 

growth. In order to test her idea she uses two 

pots of plants. She sets up one pot of plants as 

shown on the top part of the card. Which one 

of the pictures on the bottom part of the card 

shows what she should use for the second pot?a

 ....... Life sciences 51 70 58

TIMSS 

Science 2003, 

8th grade

2. Please look at Card 5. What is the scientist trying 

to find out from this experiment? ................................. Life sciences 40 38 NA

AAAS Project 

2061

3. (Follow-up to question 2) Why did you choose 

that answer? ................................................................ Life sciences 38 46 NA

AAAS Project 

2061

NA = not available, question not asked

AAAS = American Association for the Advancement of Science; TIMSS = Trends in International Mathematics and Science Study.

a

 Respondent can answer this question by using knowledge of experiment/controlling variable or knowledge in the life sciences.

NOTES: Questions appeared in 2008 General Social Survey. Original sources of questions are TIMSS and AAAS Project 2061. For complete questions, 

see sidebar: “New Science Knowledge Questions Included in the General Social Survey: 2008.”

SOURCES: University of Chicago, National Opinion Research Center, General Social Survey (2008), see appendix table 7-15; TIMSS, http://nces.ed.gov/

timss/results03.asp; Deboer GE, Gogos A. Unpublished results of national field test assessing middle school students’ understanding of controlling 

variables, AAAS Project 2061. 

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for the next generation,” with about 89% expressing agreement in the 2008 GSS (appendix table 7-19). Agreement 

with this statement has been increasing moderately for over 

a decade.20

Eight annual Virginia Commonwealth University (VCU) 

Life Sciences Surveys show similar results. The percentage 

of Americans who agreed that “developments in science 

helped make society better” ranged between 83% and 90% 

(VCU Center for Public Policy 2006 and 2008). Similarly, between 2002 and 2008 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%. During the same 

period, between 88% and 92% agreed that “new technology 

used in medicine allows people to live longer and better.”

Americans who have more years of formal education 

and score higher on measures of science knowledge express 

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). Optimism about science among the most interested and knowledgeable public, 

however, may not necessarily correspond with accurate expectations about the speed of scientific progress (see sidebar, “Public Expectations About Technological Advances”).

Although data from other countries are not entirely comparable, they appear to indicate that Americans have 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 U.S., and on some 

questions attitudes are even more favorable, but their reservations about science are somewhat higher (appendix table 

7-18). 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.

Both in the United States and abroad, respondents also 

express reservations about S&T. For eight years (2001–08), 

VCU Life Sciences Surveys have asked respondents whether they agree that “scientific research these days doesn’t pay 

enough attention to the moral values of society.” Each year, 

a majority has agreed; however, the percentage that agreed 

has dropped substantially, from 73% in 2001 to 56% in 2008. 

In the 2008 GSS, large minorities of survey respondents registered agreement with other statements expressing reservations about science, such as “science makes our way of life 

change too fast” (47% agree, 51% disagree). The proportion 

that agrees with this statement decreases with education, 

family income, and factual knowledge of science (appendix 

table 7-20). The question has been asked in numerous other 

countries (appendix table 7-18). Although levels of agreement with this statement in the United States appear to be 

similar to those in Russia, surveys in other countries record 

much higher levels of agreement.21

Federal Funding of Scientific Research

U.S. public opinion consistently and strongly supports 

federal spending on basic research. NSF surveys have repeatedly 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.” Agreement with this statement 

has increased slightly since the early 1990s, with 84% favoring federal support in 2008 and only 12% opposing it (appendix tables 7-21 and 7-22).

Responses to a GSS question about federal spending on 

scientific research provide further evidence of increasing 

public support for federal spending on scientific research. 

Since 1981, the proportion of Americans who thought the 

Figure 7-13

		
		

	

	
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NOTE: Includes all years for which data collected.

SOURCES: National Science Foundation, Division of Science 

Resources Statistics, Survey of Public Attitudes Toward and 

Understanding of Science and Technology (1979–2001); University 

of Michigan, Survey of Consumer Attitudes (2004); and University of 

Chicago, National Opinion Research Center, General Social Survey 

(2006, 2008). See appendix tables 7-17 and 7-18. 

	

	

Cumulative percent

0 20 40 60 80 100

Benefits of scientific research strongly outweigh harmful results

Benefits of scientific research slightly outweigh harmful results

Benefits of scientific research are about equal to harmful results

Harmful results of scientific research slightly outweigh benefits

Harmful results of scientific research strongly outweigh benefits

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7-30 Chapter 7. Science and Technology: Public Attitudes and Understanding

government was spending too little on scientific research has 

increased, fluctuating between 29% and 34% in the 1980s, 

between 30% and 37% in the 1990s, and between 34% and 

41% since 2001. In 2006 and 2008, only about 11% said that 

the government was spending too much in this area, the lowest levels registered since 1981 (figure 7-14; appendix tables 

7-23 and 7-24).

Although support for federal research investment is at 

historically high levels, other kinds of federal spending generate even stronger public support. Support for increased 

spending is greater in numerous program areas, including 

health care (75%), education (74%), assistance to the poor 

(69%), environmental protection (66%), social security 

(59%), and mass transportation (46%). Still, based on the 

proportion of the U.S. population favoring increased spending, scientific research (38%) ranks well ahead of spending 

in national defense (24%), space exploration (14%), and assistance to foreign countries (11%).22

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 2005, 76% of Europeans agreed that “even 

if it brings no immediate benefits, scientific research which 

adds to knowledge should be supported by government,” 

and only 7% disagreed. In 2007, 74% of Chinese agreed 

to a similar statement. Because both the European and the 

Chinese survey offered a middle option (“neither agree nor 

disagree”), these percentages are lower than figures for the 

Public Expectations About Technological Advances

In the late 1970s and early 1980s, Jon Miller surveyed 

Americans about the technological breakthroughs they 

did and did not expect in the next 25 years and looked at 

the differences in the expectations of three different segments of the public with regard to S&T: the attentive, the 

interested, and the nonattentive (Miller 1983).

The attentive public included those citizens who were 

at least moderately interested and knowledgeable about 

S&T issues and remained informed in these areas. The interested public included individuals who were interested 

in S&T matters and perceived themselves to be at least 

moderately well informed, but were not very knowledgeable and did not keep up with information in these areas. 

The nonattentive public had little interest in, or knowledge about, S&T issues.

The findings showed that majorities of the attentive 

public, and to a large extent the interested public, thought 

it was “very likely” that within 25 years science would 

discover ways to accurately predict earthquakes, to economically desalinate seawater for human consumption, 

and to find more efficient cheap energy sources and a 

cure for common forms of cancer. In contrast, Americans 

who were not attentive to S&T issues leaned toward the 

“possible but not likely” answer (see table 7-A below).

At present seismologists can provide broad forecasts, 

but cannot yet accurately predict when and where earthquakes will happen. The cost of seawater desalination has 

become more competitive than in the past, but it is still 

not economically viable on a broad scale. Early detection, innovative surgery techniques, and new therapies 

have improved the prognosis for many types of cancers, 

but no cure has been found. Miller’s survey data suggest 

that the attentive and interested publics were more optimistic than the nonattentive, but also, in these instances, 

less accurate in their expectations about the speed of scientific progress.

Table 7-A

Public expectations for future scientific achievements within next 25 years: 1979 and 1981

(Percent)

How likely do you think it is that researchers 

will achieve... in the next 25 years or so?

Attentive public Interested public Nonattentive public

1979

(n = 289)

1981

(n = 637)

1979

(n = 292)

1981

(n = 617)

1979

(n = 839)

1981

(n = 1,940)

Percent responding “very likely”

A way to predict when and where earthquakes will occur ...... 72 63 54 63 46 NA

More efficient sources of cheap energy .................................. 81 74 60 65 50 NA

A cure for the common forms of cancer ................................. 58 59 48 61 43 NA

A way to put communities in outer space ............................... 28 21 18 23 13 NA

New ways of effectively reducing the crime rate ..................... 14 NA 17 NA 14 NA

A way to economically desalinate seawater for human 

consumption .......................................................................... 64 63 47 63 39 NA

An economic theory to control inflation and reduce 

unemployment ....................................................................... NA 20 NA 28 NA NA

NA = not available, question not asked

SOURCE: Miller JD, The American People and Science Policy: The Role of Public Attitudes in the Policy Process, New York: Pergamon Press, Inc. (1983).

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 7-31

United States, where no middle category was offered (appendix table 7-21). Agreement in South Korea, Malaysia, 

Japan, and Brazil reaches levels comparable to those in the 

United States and Europe.

Support for increased government spending on scientific 

research is relatively common in Europe as well. Over half 

of Europeans agreed in 2005 that their “government should 

spend more money on scientific research and less on other 

things.” Although this proportion is nominally higher than 

the percentage of Americans who support more government spending, numerous context and wording differences 

between the questions leave responses open to substantially 

differing interpretations.23 Public support for increased 

spending on scientific research was substantially greater in 

South Korea (67% in 2004) than in the United States (Korea 

Science Foundation 2004).

Confidence in the Science Community’s 

Leadership

For the science-related decisions that citizens face, a 

comprehensive understanding of the relevant scientific 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 must consult the judgments of leaders and other 

experts whom they believe can speak authoritatively about 

the scientific knowledge that is relevant to an issue.

Public confidence in the 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 

2008, the percentage of Americans expressing “a great deal 

of confidence” in leaders of the scientific community (39%) 

was the same as those expressing “a great deal of confidence” 

in leaders of the medical community (39%) and higher than 

for all other institutions except the military (51%).

Conversely, the percentage expressing “hardly any confidence at all” was lower for scientific leaders than for leaders 

of any other institution about which this question was asked 

(table 7-8). Throughout the entire period in which this question has been asked, the percentage of Americans expressing a great deal of confidence in the leaders of the scientific 

community has fluctuated within a relatively narrow range, 

hovering between 35% and 45% (appendix table 7-25). 

In contrast, for some other institutions (e.g., the military), 

confidence has shown more variability over the past three 

decades.

Science usually ranks second or third in the public confidence surveys, with medicine or the military ranking 

first. The consistently high confidence in the leadership of 

the scientific community contrasts with a general decline 

in confidence in other institutional leaders over the years. 

The medical community, for example, has seen a long-term 

decline in confidence. Over half of Americans expressed a 

great deal of confidence in medical leaders in the mid-1970s, 

compared with about 40% in recent years. Thirty years ago 

confidence in the medical community was higher than confidence in scientific leaders. However, since 2002 science has 

scored as well as or better than medicine on this indicator, 

although the scores for the two fields remain very close.

Influence on Public Issues

Government support for scientific research derives partly 

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7-32 Chapter 7. Science and Technology: Public Attitudes and Understanding

making 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, the GSS asked about the appropriate influence 

of science on four public policy issues to which scientific 

research might be considered relevant—global climate 

change, research using human embryonic stem cells, federal 

income taxes, and genetically modified (GM) foods. Survey respondents were asked how much influence a group of 

scientists with relevant expertise (e.g., medical researchers, 

economists) should have in deciding about each issue, how 

well the scientists understood the issue, and to what extent 

the scientists 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). Respondents were also asked 

a question about their perception of the level of consensus 

among the scientists regarding a largely factual aspect of the 

issue (e.g., “the existence and causes of global warming” or 

“the importance of stem cell research”) and a question that 

probed their attitude regarding each issue.

The GSS data indicate that Americans believe that scientists should have a relatively large amount of influence on 

public decisions concerning these issues (table 7-9). For the 

four issues, the percentage who said that scientists should 

have either “a great deal” or “a fair amount” of influence 

ranged from 85% (“global warming”) to 72% (“income taxes”). For each issue, the percentage was greater for scientists 

than for either of the other leadership groups. The contrast 

among the groups was more pronounced for the three issues 

that dealt with biological or geophysical phenomena than for 

income taxes, where elected officials ranked closely behind 

economists.

Americans also give scientists relatively high marks for 

understanding the four issues (table 7-10). The GSS asked 

respondents to rate each leadership group’s understanding 

of a largely factual aspect of each issue on a five-point scale 

ranging from “very well” to “not at all.” For the three issues dealing with biological or geophysical phenomena, 

the differences in perceived understanding were large: between 64% and 74% of the public placed the relevant scientists in one of the top two categories, whereas only 9% to 

14% placed any of the other groups in those categories. The 

contrast among groups was smaller for the tax issue, with 

economists (52%) ranking ahead of business leaders (44%) 

and elected officials (28%).

Patterns for the question about which groups would “support what is best for the country as a whole versus what 

serves their own narrow interests” were similar. For each 

issue, Americans placed the scientific group in one of the top 

two categories much more often than they placed either of 

the other leadership groups in those categories.

One factor that may limit the influence of scientific 

knowledge and the scientific community over 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 four issues using a five-point scale ranging from “near 

complete agreement” to “no agreement at all.” The degree 

of perceived consensus of medical researchers on “the importance of stem cells for research” was the only item for 

Table 7-8

Public confidence in institutional leaders: 2008

(Percent)

Type of institution

Level of confidence in leaders Don’t 

A great deal Some Hardly any know

Military .................................................................................................. 51 37 10 1

Medicine ............................................................................................... 39 50 11 *

Scientific community ............................................................................ 39 51 6 4

U.S. Supreme Court ............................................................................. 31 53 14 2

Education ............................................................................................. 29 54 15 1

Organized religion................................................................................. 20 53 25 2

Banks and financial institutions ............................................................ 19 60 21 1

Major companies .................................................................................. 16 66 16 2

Organized labor .................................................................................... 12 57 27 4

Congress .............................................................................................. 10 51 37 2

Executive branch of federal government ............................................. 10 49 38 3

Television .............................................................................................. 9 51 39 1

Press ..................................................................................................... 9 45 45 1

* = <0.5% responded

NOTE: Detail may not add to total because of rounding.

SOURCE: University of Chicago, National Opinion Research Center, General Social Survey (2008). See appendix table 7-25.

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 7-33

Table 7-9

Preferred groups for influencing decisions about public issues: 2006

(Percent)

Public issue/group

Preferred degree of influence

A great deal A fair amount A little None at all Don’t know

Global warming 

Environmental scientists ......................................... 47 38 7 3 4

Elected officials ....................................................... 17 33 33 13 4

Business leaders ..................................................... 10 22 38 25 5

Stem cell research

Medical researchers ................................................ 39 41 11 4 5

Elected officials ....................................................... 11 35 32 15 6

Religious leaders..................................................... 8 21 36 29 6

Federal income taxes

Economists ............................................................. 21 51 18 4 6

Elected officials ....................................................... 21 40 24 11 4

Business leaders ..................................................... 9 37 36 13 4

Genetically modified foods

Medical researchers ................................................ 41 40 10 3 5

Elected officials ....................................................... 7 30 37 21 5

Business leaders ..................................................... 3 16 41 35 5

NOTES: Responses to: How much influence should each of the following groups have in deciding: global warming policy; government funding for stem 

cell research; reducing federal income taxes; restricting sale of genetically modified foods? Detail may not add to total because of rounding. 

SOURCE: University of Chicago, National Opinion Research Center, General Social Survey (2006). See appendix table 7-21 in National Science Board, 

Science and Engineering Indicators 2008 (NSB 08-01A) (2008).

Science and Engineering Indicators 2010

Table 7-10

Perceived understanding of public issues by various groups: 2006

(Percent)

Degree of understanding (on scale of 1 to 5)

Very well

5432

Not at all

1

Don’t 

Public issue/group know

Global warming

Environmental scientists ...................................... 44 22 22 4 4 4

Business leaders .................................................. 4 8 30 32 22 4

Elected officials .................................................... 5 7 31 29 24 4

Stem cell research

Medical researchers ............................................. 50 24 15 3 3 6

Religious leaders.................................................. 6 8 26 29 25 6

Elected officials .................................................... 3 7 35 26 22 6

Federal income taxes

Economists .......................................................... 33 19 29 7 7 5

Business leaders .................................................. 15 29 33 12 6 4

Elected officials .................................................... 10 18 34 19 15 5

Genetically modified foods

Medical researchers ............................................. 32 32 18 8 5 6

Business leaders .................................................. 4 7 24 31 28 6

Elected officials .................................................... 3 6 24 33 29 5

NOTES: Responses to: How well do the following groups understand: causes of global warming; importance of stem cell research; effects of reducing 

federal income taxes; risks posed by genetically modified foods? Detail may not add to total because of rounding. 

SOURCE: University of Chicago, National Opinion Research Center, General Social Survey (2006). See appendix table 7-22 in National Science Board, 

Science and Engineering Indicators 2008 (NSB 08-01A) (2008).

Science and Engineering Indicators 2010
7-34 Chapter 7. Science and Technology: Public Attitudes and Understanding

which more than half of respondents (52%) chose one of the 

two points near the “complete agreement” end of the scale 

(table 7-11). In the case of the perceived consensus of environmental scientists on “the existence and causes of global 

warming,” 42% chose one of these two points denoting a 

high degree of consensus. 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” (28%) or economists on “the 

effects of reducing federal income taxes” (20%).

With a few exceptions, responses to these questions do 

not differ markedly among demographic groups. Americans 

with higher incomes, more education, and more science 

knowledge tend to have more favorable perceptions of the 

knowledge, impartiality, and level of agreement among scientists. For a more detailed presentation of these data and 

further discussion of this subject, see NSB 2008.

What Makes an Activity Scientific

The label “scientific” is usually considered a favorable

one. When research studies claim to be scientific, they claim 

to produce valid knowledge; when occupations claim to be 

scientific, they claim their practitioners have systematic expertise. It is important for the public to be able to scrutinize 

these claims critically and use reasonable criteria to judge 

them, because not all claims that an activity is scientific are 

equally warranted.

In 2006, the GSS included two batteries of questions that 

probed what characteristics Americans associate with scientific studies and what disciplines and practices Americans 

consider scientific. These indicators provide insight into 

how Americans discriminate between more and less scientific endeavors. (Data from these questions are reported in 

greater detail in NSB 2008.)

Attributes That Make Something Scientific

One group of questions asked how important each of 

eight characteristics is in “making something scientific.” 

These characteristics can be divided into three groups:

Features of the research process:

s The conclusions are based on solid evidence.

s The researchers carefully examine different interpretations of the results, even ones they disagree with.

s Other scientists repeat the experiment and find similar 

results.

Aspects of the credentials and institutional settings that 

lend credibility to the research:

s The people who do the research have advanced degrees in their field.

s The research is done by scientists employed in a university setting.

s The research takes place in a laboratory.

External validation by other belief systems:

s The results of the research are consistent with common sense.

s The results of the research are consistent with religious beliefs.

Americans were most likely to consider features of the 

research process to be very important. Over two-thirds said 

that “conclusions based on solid evidence” (80%), “carefully examin[ing] different interpretations of the results” 

(73%), and “replication of results by other scientists” (67%) 

were very important in making something scientific.

Americans thought that researcher qualifications were almost as important, with 62% classifying “the people who do 

it have advanced degrees in their field” as very important. 

Table 7-11

Perceived scientific consensus on public issues: 2006

(Percent)

Degree of consensus (on scale of 1 to 5)

Group/public issue

Near 

complete 

agreement

5432

No 

agreement 

at all 

1

Don’t 

know

Medical researchers on importance 

of stem cells for research ..................................... 19 33 29 4 5 9

Environmental scientists on existence 

and causes of global warming .............................. 14 28 35 9 6 9

Medical researchers on risks and benefits 

of genetically modified foods ............................... 9 19 41 11 7 13

Economists on effects of reducing federal 

income taxes ........................................................ 5 15 40 14 13 13

NOTES: Responses to: To what extent do [people in group] agree on [public issue]? Detail may not add to total because of rounding. 

SOURCE: University of Chicago, National Opinion Research Center, General Social Survey (2006). See appendix table 7-24, National Science Board, 

Science and Engineering Indicators 2008 (NSB 08-01A) (2008).

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 7-35

Institutional settings often associated with research, such as 

laboratories (41%) and universities (33%), ranked lower. 

Respondents viewed these settings as similar in importance 

to having results that were “consistent with common sense.” 

Most Americans viewed consistency with religion as either 

not at all important (39%) or not too important (31%) to 

making something scientific.

Response patterns for this group of questions are related 

to the respondent’s education level (figure 7-15). Although 

Americans at all levels of education rated research process 

characteristics as most important, more highly educated 

Americans gave these characteristics the highest ratings. In 

contrast, individual credentials, institutional auspices, and 

consistency with other beliefs were seen as less important 

among more highly educated respondents than among others. As a result of these divergent patterns, the gap in importance between process characteristics and other attributes is 

wide at higher levels of education but relatively narrow for 

people with less schooling (figure 7-15). (For more details, 

see NSB 2008.)

Which Fields Are Scientific

The 2006 GSS asked Americans about eight fields of research or practice and whether they were “very scientific,

pretty scientific, not too scientific, or not scientific at all.”

Practically all Americans (98%) perceived medicine as 

“very” or “pretty” scientific, even though it is focused more

on practical service delivery and less on research than other 

listed fields, including biology and physics. Nonetheless,

both of these disciplines were also overwhelmingly seen 

as either “very” or “pretty” scientific (94% for biology and 

90% for physics). Americans with more years of education 

and more classroom exposure to science and mathematics 

were more likely to believe that these two fields were relatively scientific, particularly physics.

Engineering, a discipline which like medicine involves 

the application of science and mathematics to develop solutions to practical problems, ranked below the other three 

fields on this measure; 77% perceived engineering as “very” 

or “pretty” scientific.

About 50% of Americans said that the two social science disciplines on the list (economics and sociology) were 

“very” or “pretty” scientific. Accounting and history were 

less likely to be placed at the scientific end of the scale; respondents with less education were more likely than others 

to classify history as relatively scientific. A similar question on the 2005 Eurobarometer about an overlapping set of 

fields produced generally similar results (EC 2005).

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 over 30 years, the Harris Poll (Harris Interactive 

2008b) has asked about the prestige of a large number of 

occupations, including scientists and engineers (table 7-12). 

In 2008, 56% of Americans said that scientists had “very 

great prestige,” and 40% expressed this view about engineers. Most occupations in the surveys ranked well below 

engineers.24

Between 1977 and 2008, the percentage of survey respondents attributing “very great prestige” to scientists has 

fluctuated between 51% and 66%. There has not been a clear 

trend over the years. The comparable score for engineers increased from 30% in 2007 to 40% in 2008, the highest level 

in thirteen surveys since the question was first asked in 1977.

Scientists ranked higher in prestige than almost all occupations in the Harris surveys. In recent years, their ranking was comparable with that of nurses, doctors, firefighters, 

and teachers and ahead of military and police officers. Engineers’ standing is high and comparable to occupations 

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7-36 Chapter 7. Science and Technology: Public Attitudes and Understanding

clustered just below the top group (including clergy, military 

officers, and police officers).

Prestige appears to reflect perceived service orientation 

and public benefit more than high income or celebrity; for 

instance, the proportions of respondents who attributed 

“very great prestige” to entertainers or actors were 15% and 

16%, respectively (table 7-12). Americans are more likely to 

trust people in prestigious occupations (including scientists) 

to tell the truth (Harris Interactive 2006).

Some evidence suggests that Americans rate scientific 

careers more positively than is the case in at least some other 

countries. In 2004, a little over 50% of South Koreans said 

they would feel happy if their son or daughter wanted to 

become a scientist. Among Chinese, science (40%) ranked 

close to medicine (41%) and teaching (43%) as an occupation that survey respondents hoped their children will pursue 

(CRISP 2008). In the United States, 80% of those surveyed 

in 2001 expressed positive views regarding their children 

becoming scientists.

In 2006, the majority of Israelis said they would be 

pleased if their children became scientists (77%), engineers 

(78%), or physicians (78%) (Yaar 2006).

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 science 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 impact on innovation. The climate of opinion concerning new research areas 

could 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.

For these reasons, survey responses about policy controversies involving science, specific research areas, and 

emerging technologies are relevant. In addition, responses 

about relatively specific matters provide a window into the 

Table 7-12

Prestige of various occupations: Selected years, 1977–2008

(Percent)

Occupation 1977 1982 1992 1997 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008

Firefighter .............................. NA NA NA NA NA NA NA NA 55 48 56 63 61 57

Scientist ................................. 66 59 57 51 55 56 53 51 57 52 56 54 54 56

Doctor .................................... 61 55 50 52 61 61 61 50 52 52 54 58 52 53

Nurse ..................................... NA NA NA NA NA NA NA NA 47 44 50 55 50 52

Teacher .................................. 29 28 41 49 53 53 54 47 49 48 47 52 54 52

Military officer ........................ NA 22 32 29 34 42 40 47 46 47 49 51 52 46

Police officer .......................... NA NA 34 36 41 38 37 40 42 40 40 43 46 46

Farmer ................................... NA NA NA NA NA NA NA NA NA NA NA 36 41 41

Priest/minister/clergy ............. 41 42 38 45 46 45 43 36 38 32 36 40 42 40

Engineer ................................ 34 30 37 32 34 32 36 34 28 29 34 34 30 40

Member of Congress ............. NA NA 24 23 25 33 24 27 30 31 26 28 26 28

Architect ................................ NA NA NA NA 26 26 28 27 24 20 27 27 23 28

Lawyer ................................... 36 30 25 19 23 21 18 15 17 17 18 21 22 24

Athlete ................................... 26 20 18 21 20 21 22 21 17 21 23 23 16 20

Journalist ............................... 17 16 15 15 15 16 18 19 15 14 14 16 13 18

Union leader .......................... NA NA 12 14 16 16 17 14 15 16 15 12 13 18

Business executive ................ 18 16 19 16 18 15 12 18 18 19 15 11 14 17

Actor ...................................... NA NA NA NA NA NA NA NA 13 16 16 12 9 16

Entertainer ............................. 18 16 17 18 19 21 20 19 17 16 18 18 12 15

Accountant ............................ NA 13 14 18 17 14 15 13 15 10 13 17 11 15

Banker ................................... 17 17 17 15 18 15 16 15 14 15 15 17 10 15

Stockbroker ........................... NA NA NA NA NA NA NA NA 8 10 8 11 12 10

Real estate agent/broker ....... NA NA NA NA NA NA NA NA 6 5 9 6 5 6

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: Prestige Paradox: High Pay Doesn’t Necessarily Equal High Prestige: Teachers’ Prestige Increases the Most Over 30 Years, Harris Poll, Harris 

Interactive (5 August 2008), http://www.harrisinteractive.com/harris_poll/index.asp?PID=939, accessed 22 September 2009.

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 7-37

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 scientific and technological 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).

Attitudes toward policy issues always involve a multitude 

of factors and 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 begins with data on environmental issues, 

including global climate change and nuclear power. It then 

covers attitudes toward recent and novel technologies, including medical biotechnology, agricultural biotechnology 

(i.e., GM food), and nanotechnology. Data on cloning and 

stem cell research follow, and the section concludes with recent data on attitudes toward science and mathematics education and toward scientific research on animals.

Environment and Climate Change

The Gallup Organization’s annual survey on environmental issues indicates that Americans have become somewhat more concerned about environmental quality in the 

last 4 years (figure 7-16). Between 2004 and 2008, the percentage of Americans expressing “a great deal” or “a fair 

amount” of worry about the “quality of the environment” 

rose from 62% to 74%, returning approximately to its 2001 

level (Saad 2008b).

Despite the rise in “worry” about the environment, concern about this issue barely registers when surveys ask 

Americans to name the country’s top problem. In surveys 

conducted in the first quarter of 2009, 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?” (The Gallup 

Organization 2009a, 2009b). In close-ended questions, worry 

about the environment ranked lower than worry about the 

economy (90%), the availability and affordability of either 

energy (82%) or healthcare (81%), and crime and violence 

(80%). The proportion of Americans worried about the quality of the environment was similar to the proportion worried 

about Social Security (75%), future terrorist attacks (73%), 

and hunger and homelessness (73%) and higher than the percentage worried about illegal immigration (70%), unemployment (68%), drug use (67%), and race relations (45%).

In the 2008 GSS, the majority of Americans (66%) believed that the government is spending too little to reduce 

pollution and only a handful thought it spent too much (8%, 

appendix table 7-23). The proportion who believed that the 

government is spending too little in this policy area has fluctuated between 60% and 67% since 1997 and is still lower 

than it was in 1988 and 1990 (76% for both years). The trend 

in support for environmental protection was less evident 

when Americans were asked about tradeoffs between environmental protection and economic growth (figure 7-17). 

In March 2009, only 42% of Americans indicated that the 

protection of the environment should take precedence over 

economic growth (down from about 70% in 1990–91 and 

in 2000).

However, when asked about various proposals to protect 

the environment in Gallup surveys conducted between 2001 

and 2007 (table 7-13), strong majorities endorsed government spending to develop alternate sources of fuel for automobiles and to develop solar and wind power. Majorities 

also favored different environmentally friendly measures 

such as setting higher emissions and pollution standards for 

business and industry and enforcing federal environmental 

regulations more strongly. Lower proportions favored expanding the use of nuclear energy and opening up the Arctic 

National Wildlife Refuge in Alaska for oil exploration (The 

Gallup Organization 2009a).

National data on the use of biofuels for energy consumption is scarce, but one survey found that 70% of Americans 

thought using ethanol was “mostly a good idea” (Broder and 

Connelly 2007).

Climate change, sometimes referred to as global warming 

(see sidebar “‘Climate Change’ Versus ‘Global Warming’”), 

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7-38 Chapter 7. Science and Technology: Public Attitudes and Understanding

has recently become more prominent among environmental issues for the American public. Since 2000, Gallup has 

asked Americans how much they personally worry about 

eight environmental issues. The percentage of Americans 

who said they worried “a great deal” about “global warming” decreased from 40% in 2000 to 26% in 2004, but 

increased to 34% in 2008 (Saad 2009). Even with this increase, “global warming” still ranked eighth among these issues. The percentage of Americans worrying “a great deal” 

about this issue was lower than the percentage of Americans 

worrying “a great deal” about water-related environmental 

issues such as “pollution of drinking water” (59%), “pollution of rivers, lakes, and reservoirs” (52%), “contamination 

of soil and water by toxic waste” (52%), “maintenance of the 

“Climate Change” Versus 

“Global Warming”

The terms “climate change” and “global warming” 

are often used interchangeably. Scientists increasingly 

prefer the term “climate change,” which conveys the 

idea that more than a rise in temperatures is occurring 

(National Academies 2008b). However, most survey 

data registers opinion about global warming, not about 

climate change.

Limited research in the United States and Europe 

suggests that variations in terminology do not significantly affect survey responses on this issue. A large 

sample of voluntary survey respondents in the United 

States, randomly divided into two groups, was asked 

“If nothing is done to reduce climate change/global 

warming in the future, how serious a problem do you 

think it will be?” The two groups responded similarly, 

regardless of which term was used (Villar and Krosnick 2009).

Two similar European experiments also showed 

that the two terms made little or no difference in perceptions of the problem. In one, respondents were 

asked to identify “the most serious problem currently 

facing the world as a whole” from a list that included 

either “global warming” or “climate change.” In the 

other, the choice of term did not affect how Europeans 

rated the seriousness of the problem “at this moment” 

(EC 2008b).

Table 7-13

Public approval of specific environmental proposals: 2001–07

(Percent)

Environmental proposal 2001 2002 2003 2006 2007

Spending government money to develop alternate sources of fuel for automobiles .................... NA NA NA 85 86

Setting higher emissions and pollution standards for business and industry ................................ 81 83 80 77 84

More strongly enforcing federal environmental regulations ........................................................... 77 78 75 79 82

Spending more government money on developing solar and wind power .................................... 79 NA NA 77 81

Setting higher auto emissions standards for automobiles ............................................................. 75 72 73 73 79

Imposing mandatory controls on carbon dioxide emissions and other greenhouse gases ........... NA NA 75 75 79

Expanding use of nuclear energy ................................................................................................... 44 45 43 55 50

Opening Arctic National Wildlife Refuge in Alaska for oil exploration ............................................ 40 40 41 49 41

NA = not available, question not asked

NOTES: Responses to: I am going to read some specific environmental proposals. For each one, please say whether you generally favor or oppose it. 

How about...? Data reflect responses of “favor.” Table includes all years for which data collected; question asked in March of each year. 

SOURCE: Gallup’s Pulse of Democracy: The Environment, http://www.gallup.com/poll/1615/Environment.aspx, accessed 22 September 2009.

Science and Engineering Indicators 2010

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Science and Engineering Indicators 2010 7-39

nation’s fresh water supply for household needs” (49%), and 

also “air pollution” (45%). Response categories in surveys, 

however, are not always distinct and may evoke overlapping 

associations in respondents. “Air pollution,” for example, is 

related to carbon emissions and climate change.

Recent data show additional signs that awareness of climate change is increasing. Since 2004, Gallup surveys 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 80% in 2008 

(The Gallup Organization 2009a). In addition, the number 

of Americans who say that the effects of “global warming” 

have already begun to occur has been steadily increasing 

since 2004 and was at an all time high in 2008 at 61%. The 

percentage of Americans who believe that most scientists 

think “global warming” is occurring has also been rising for 

over a decade. Most Americans think that “the increases in 

the Earth’s temperature over the last century” are largely the 

result of human activities rather than natural changes; that 

percentage has been stable since 2001, hovering between 

58% and 61% (The Gallup Organization 2009a).

Nuclear Power

In the debate over America’s sources of energy, nuclear 

power has been a controversial subject. On the one hand, 

nuclear power is an appealing option to meet energy needs 

due to its low emissions of greenhouse gases and other atmospheric pollutants. On the other hand, there are serious concerns about this technology, such as risks in the operation 

of nuclear plants, the disposal of nuclear waste, and nuclear 

proliferation.

Overall, support for nuclear power is lower than for 

conservation-based energy strategies (table 7-13), but it has 

grown in the last 2 years. American public opinion has been 

fairly evenly divided since the mid-1990s, but the proportion 

of Americans who favor the use of nuclear power as one of 

the ways to provide electricity for the U.S. increased from 

53% in 2007 to 59% in 2009 (Jones 2009). A substantial 

minority of Americans (42%) thinks nuclear power plants 

are not safe and prior surveys indicate that three out of five 

Americans oppose the construction of a nuclear energy plant 

in their local communities.25

Despite some differences in wording between the Eurobarometer and the U.S. questions, a 2008 report shows that 

European public opinion on nuclear energy is divided but 

support for energy production by nuclear power stations 

has grown since 2005 (EC 2008a). Support for nuclear energy varies a great deal among countries in this region. In 

general, citizens of countries that have operational nuclear 

power plants are considerably more likely to support nuclear 

energy than citizens of other countries (figure 7-18).26

Biotechnology and Its Medical Applications

Recent advances in recombinant DNA technology enable the manipulation of genetic material to produce plants 

and animals with desirable characteristics. The most recent 

American data on attitudes in this area are from 2005. They 

show that Americans, Canadians, and Europeans have 

similarly favorable attitudes toward biotechnology in general and medical applications in particular. A study that collected U.S. and Canadian data found that about two-thirds 

	
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7-40 Chapter 7. Science and Technology: Public Attitudes and Understanding

of survey respondents in each country registered favorable 

attitudes (Canadian Biotechnology Secretariat 2005).27

Few Americans (about 1 in 10) consider themselves 

“very familiar” with biotechnology and Canadians report 

slightly less familiarity. Without a strong knowledge base 

to use in evaluating information, their assessment of the 

credibility of information sources is an important element 

in forming their judgments about information on this topic. 

In both the United States and Canada, scientific journals and 

government-funded scientists were the top-rated institutions 

that could provide information about biotechnology. Conversely, privately owned mass media, biotechnology company executives, and religious and political leaders ranked 

near the bottom in both countries. (For more detail on this 

subject, see NSB 2008.)

Genetically Modified Food

Although the introduction of GM crops has provoked 

much less controversy in the United States than in Europe, 

U.S. popular support for this application of biotechnology 

is limited. According to a 2008 CBS/New York Times poll, 

44% of Americans indicated they had not heard much about 

GM ingredients added to foods to make them taste better and 

last longer. However, 87% believed that these foods should 

be labeled and 53% expected that it was “not very likely” or 

“not at all likely” that they would buy food that was 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 

favored “the introduction of genetically modified foods into 

the U.S. food supply” (Mellman Group, Inc. 2006). The proportion 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 expressed 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 in the 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).

Data from the 2008 GSS indicated that overall familiarity 

with nanotechnology is similar to its 2006 level. The proportion of Americans who had heard “a lot” or “some” about 

nanotechnology remained virtually unchanged (5% and 15% 

in both 2006 and 2008), but the proportion of those who had 

heard “a little” or “nothing at all” declined slightly (appendix table 7-26). These numbers are similar to those reported 

by the Project on Emerging Nanotechnologies based on a 

national survey conducted in August 2008 (Peter D. Hart 

Research Associates 2008). While the questions asked are 

not strictly comparable, familiarity with nanotechnology in 

the 2006 GSS was similar to that in Europe in 2005, in which 

44% of survey respondents said they had heard of it (Gaskell 

et al. 2006).

Despite increased federal funding and more than 600 

nanotechnology products already on the market (The National Academies 2008a),28 nanotechnology knowledge levels were not high (appendix table 7-11) and remained similar 

to 2006, even among the minority of GSS respondents who 

had heard of nanotechnology. In 2008, 63% of the respondents who had heard at least a little about this technology 

correctly indicated that the statement “nanotechnology involves manipulating extremely small units of matter, such as 

individual atoms, in order to produce better materials” was 

true, but many (29%) said they did not know, and a few (8%) 

thought this statement was false. Almost half (47%) did not 

know whether the statement “the properties of nanoscale 

materials often differ fundamentally and unexpectedly from 

the properties of the same materials at larger scales” was 

true, while 41% correctly answered true and the remaining 

12% answered false. A third of the respondents answered 

both questions correctly.

When nanotechnology is defined in surveys, Americans express favorable expectations for it. After receiving 

a brief explanation of nanotechnology, GSS respondents 

were asked about the likely balance between the benefits 

and harms of nanotechnology. Similar to 2006, in 2008 

38% said the “benefits will outweigh the harmful results” 

and only 9% expected the harms to predominate (appendix 

table 7-27). In 2008, however, the proportion of Americans 

who said they did not know whether the benefits of nanotechnology would outweigh the harmful results or vice versa 

increased, and the proportion who expected the benefits to 

be equal to the harmful results decreased. The fact that about 

half of respondents either gave a neutral response (12%) or 

said they did not know (40%) suggests that these opinions 
Science and Engineering Indicators 2010 7-41

are open to change as Americans become more familiar with 

this technology.

Favorable expectations for nanotechnology are associated with more education, greater science knowledge, and 

greater familiarity with nanotechnology. Men are also more 

likely to have favorable expectations than women (appendix 

table 7-27). In these aspects, patterns are similar to those for 

responses concerning S&T in general.

In the GSS data, favorable attitudes toward nanotechnology are also associated with greater familiarity with it. 

That is, Americans who say they are more familiar with 

nanotechnology are more likely to believe that its benefits 

will outweigh the risks. However, this association does not 

mean that when people become more familiar their attitudes necessarily become positive. Some data suggest that 

when individuals who report knowing little or nothing about 

nanotechnology hear a balanced statement of its risks and 

benefits, they develop less favorable opinions of it (Peter D. 

Hart Research Associates, Inc. 2008). Furthermore, recent 

research suggests that attitudes toward nanotechnology are 

likely to vary depending on the context in which it is applied, with energy applications viewed much more positively than those in health and human enhancements (Pidgeon 

et al. 2009).

Stem Cell Research and Human Cloning

Unlike most issues involving scientific research, studies using embryonic stem cells have generated considerable 

public controversy. In the case of stem cell research, strongly held views about moral fundamentals determine many 

people’s attitudes. There is less reason to believe that this is 

the case for certain other S&T issues, such as nuclear power.

Although a majority of the public supports such research, 

a substantial minority is opposed to it. When surveys ask 

about medical technologies that could be derived from embryonic stem cell research in the context of expected health 

benefits, public response is relatively positive. But technologies that involve cloning human embryos evoke consistently 

strong and negative responses.

Since 2004, the majority of the American public has favored “medical research that uses stem cells from human 

embryos” (VCU Center for Public Policy 2008). Support 

grew continuously from 2002 (35% in favor) to 2005 (58% 

in favor) and remained at a similar level in 2008 (figure 

7-19). In eight annual Gallup surveys between 2002 and 

2009, the percentage of Americans who found such research 

“morally acceptable” ranged from 52% to 64%, and the percentage saying it was “morally wrong” from 30% to 39% 

(Saad 2008a; The Gallup Organization 2009c). Similarly, in 

five Pew surveys conducted between August 2004 and August 2007, a consistent but slim majority agreed that it was 

“more important to continue stem cell research that might 

produce new medical cures than to avoid destroying the human embryos used in the research” while about a third said 

not destroying embryos was more important (Pew Forum on 

Religion and Public Life 2008).

Support for stem cell research is higher when the question 

inquires about research that uses stem cells from sources that 

do not involve human embryos. Seven out of ten respondents favored this type of research in 2008, down slightly 

from 75% in 2007 (VCU Center for Public Policy 2008). 

Support also increased when the question was 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 only 21% do not (VCU Center for 

Public Policy 2006). Responses become more mixed when 

questions mention “cloning technology” that is used only to 

help medical research develop new treatments for disease. 

However, opinion is decidedly negative when the question 

asks about cloning or genetically altering animals without 

mention of a medical purpose (table 7-14).

Americans are overwhelmingly opposed to human cloning. In a 2008 VCU survey, the idea of cloning or genetically altering humans was rejected by 78% of Americans 

(VCU Center for Public Policy 2008). The specter of reproductive cloning can generate apprehension about therapeutic 

cloning. Asked how concerned they were that “the use of 

	
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7-42 Chapter 7. Science and Technology: Public Attitudes and Understanding

human cloning technology to create stem cells for human 

therapeutic purposes will lead to a greater chance of human 

reproductive cloning,” over two-thirds of Americans said 

they were either very (31%) or somewhat (37%) concerned 

(VCU Center for Public Policy 2006).

In 2008, about two-thirds of Americans were “very clear” 

(23%) or “somewhat clear” (41%) about the difference between stem cells that come from human embryos, stem cells 

that come from adults, and stem cells that come from other 

sources (VCU Center for Public Policy 2008). However, 

public attitudes toward cloning technology are not grounded 

in a strong grasp of the difference between reproductive and 

therapeutic cloning (see glossary for the definitions). Most 

Americans (64%) said they were not clear (“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, despite, or perhaps because of, the increased visibility 

of stem cell research as a public issue.

Support for stem cell research is strongest among people 

with more years of formal education. Americans who are 

more religious and more politically conservative are more 

likely to oppose such research (VCU Center for Public Policy 2008).

A recent 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 are more aware 

of stem cell research than residents of most other countries 

and more often respond correctly to knowledge questions 

on this subject. Americans are somewhat more likely than 

residents of several countries in Europe to believe that stem 

cell research is immoral (appendix table 7-28).

Science and Mathematics Education

In much 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, majorities of Americans in all demographic groups agreed that the quality of science and mathematics education in American schools is inadequate. Their 

level of agreement increases with education, science knowledge, income, and age (appendix table 7-29). Dissatisfaction 

with the quality of math and science education increased 

from 63% in 1985 to 70% in 2008, but is still below its peak 

in 1992 (75%) (figure 7-20; appendix table 7-30).

In addition, the proportion of Americans who indicated 

they believe the government is spending too little money in 

improving education in the biannual GSS surveys has been 

consistently over 70% since the early 1980s. Along with improving health care, this is one of the two top areas where the 

public feels government spending is too low (figure 7-20, 

appendix table 7-23).

Scientific Research on Animals

The medical research community conducts experimental 

tests on animals in order 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 kinds of animal 

research. Nearly two-thirds said they favored “using animals in medical research” (VCU Center for Public Policy 

Table 7-14

Public opinion on medical technologies derived from stem cell research: 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, strongly oppose) .................................................... 70 22

3. 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) .................. 52 45

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) ........ 17 78

NOTES: Question 1 asked 7–21 November 2006. Questions 2, 3, 4 asked 24 November–7 December 2008. Detail does not add to total because “don’t 

know” responses not shown. 

SOURCE: Virginia Commonwealth University (VCU), Center for Public Policy, VCU Life Sciences Survey (question 1, 2006; questions 2, 3, 4, 2008), http://

www.vcu.edu/lifesci/centers/cen_lse_surveys.html, accessed 22 September 2009.

Science and Engineering Indicators 2010
Science and Engineering Indicators 2010 7-43

2007). According to a different survey conducted by Gallup, 

the 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).

However, opposition 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 in the 

early 1990s disagreed. This proportion increased to 51% in 

2001 and 58% in 2008 (figure 7-21, appendix tables 7-31 

and 7-32).29 Annual surveys conducted by Gallup since 2001 

show a similar pattern. While a majority of Americans say 

that “medical testing on animals is morally acceptable,” this 

percentage decreased from 65% in 2001 to 56% in 2008 

(Saad 2008a). Men are more likely than women to approve 

this kind of research (appendix table 7-31).

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 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).

While recent comparable international data are lacking, 

a survey conducted by Gallup 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 U.S. 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).

	
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7-44 Chapter 7. Science and Technology: Public Attitudes and Understanding

Conclusion

In assessing public knowledge and attitudes concerning 

S&T, two kinds of standards for judgment are possible. One 

standard involves comparing a country’s knowledge and attitudes with those recorded in the past or in other countries. 

The second 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.

By the first standard, the survey data provide little or no 

evidence of declining knowledge or increasingly negative 

attitudes. Relative to Americans in the recent past, today’s 

Americans score as well on knowledge measures and tend to 

be more skeptical about scientific claims for pseudoscience, 

such as astrology. In addition, three decades of U.S. data 

consistently show that Americans endorse the past achievements and future promise of S&T, are optimistic about new 

technologies, and are favorably predisposed to increasing 

government investment in science. When Americans compare science with other institutions, science’s relative ranking is as or more favorable than in the past. In addition, the 

prestige of the engineering profession grew in the last year.

When the data are examined using other countries as a 

benchmark, the United States compares favorably. Compared with adult residents of other developed countries, 

Americans appear to know as much or more about science, and they express as much or more optimism about 

technology.

By the second standard, trend data show that significant 

minorities of Americans cannot answer relatively simple 

knowledge questions about S&T, they express basic misconceptions about emerging technologies such as biotechnology 

and nanotechnology, and they believe that relatively great 

scientific uncertainty surrounds the existence and causes 

of global climate change. Sizable parts of the population 

express reservations about how the speed of technological 

change affects our way of life or the use of animals in medical research.

Regardless of the standard used in assessing public 

knowledge and attitudes, one pattern in the data stands out: 

more highly educated Americans tend to know more about 

S&T, express more favorable attitudes about S&T, and make 

discriminations that are more consistent with those likely to 

be made by scientists and engineers themselves. Thus, for 

example, they focus more heavily on process criteria when 

evaluating whether something is scientific, and their classification of fields as more and less scientific more closely 

resembles a classification that would be found in a university catalog. Whether this association is causal is uncertain. 

Although greater knowledge may affect attitudes and perspectives, pre-existing attitudes and perspectives may affect 

whether or not people acquire the kinds of knowledge available to them in school.

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 has come 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 (Pew Research 

Center for the People and the Press 2008).

2. 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 public opinion account for the decline 

in interest observed between 2001 and 2008. In interviews 

conducted over the phone, respondents may be more likely 

to respond to questions in a socially desirable way (Holbrook, Green, and Krosnick 2003). In addition, a single 

purpose survey may suggest to respondents that science and 

technology are important.

3. 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.

4. The peak in the coverage of the category “Science, space, 

and technology” in 1999 illustrated in figure 7-6 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.

5. The question on interest in new scientific discoveries included in the 2005 Eurobarometer (EC 2005) was the 

same three-category question asked in the United States between 1979 and 2001 and in 2008 (“very interested,” “moderately interested,” and “not at all interested”). The question 

asked in the 2007 Eurobarometer (EC 2007) was different 

because it asked about interest in “scientific research” rather 

than “new scientific discoveries” and gave respondents four 

options (“very interested,” “fairly interested,” “not very interested,” and “not at all interested”). Thus, the data in this 

sidebar are not strictly comparable to earlier Eurobarometer 

surveys or to the U.S. data question on interest.

6. In Brazil the survey asked respondents about their interest in “medicine and health” issues and “environmental 

issues” and the question categories included “very interested,” “a little interested,” and “not at all interested.”

7. In the past, interest in space exploration has consistently ranked low both in the United States and around the 

world, relative to other S&T topics. Surveys in Russia, China, and Japan have documented this general pattern. However, though there are new U.S. data on this subject, there 

have been no recent surveys documenting interest in space 

exploration in other countries.

8. People can become involved with S&T through many 

other nonclassroom activities. Examples of such activities 
Science and Engineering Indicators 2010 7-45

include participating in government policy processes, going to movies that feature S&T, bird-watching, and building computers. Nationally representative data on this sort of 

involvement with S&T are unavailable.

9. In the 2008 GSS, respondents received two similar introductions to this question. Response patterns did not vary 

depending on which introduction was given.

10. 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.

11. Formal schooling and verbal skills are positively associated. In the 2008 GSS data, verbal skills contributed to 

factual knowledge even when controlling for education.

12. Among respondents with comparable formal education, attending informal science institutions was associated 

with greater knowledge.

13. 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.

14. 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.

15. 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:

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.”

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 

which is faster. For example, “One goes at the speed of 

light and the other at the speed of sound.”

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:

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:

The response referred explicitly to the formation of 

water (and salt) from the neutralization reaction (e.g., 

“Hydrochloric acid and sodium hydroxide will mix together to form water and salt, which is neutral.”)

The response referred to neutralization (or the equivalent) even if the specific reaction is not mentioned 

(e.g., “The mixed solution is neutral, so litmus paper 

does not react.”)

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 

(e.g., “The acid and base react, and the new chemicals 

do not react with litmus paper.”)

Partially correct: The response mentioned only that acids 

and bases are “balanced,” “opposites,” “cancel each other 

out,” or only 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.”

Incorrect: The response did not mention any of the above 

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.”

16. 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).

17. Early NSF surveys used additional questions to measure understanding of probability. Through a process similar 

to that described in note 12, Bann and Schwerin (2004) identified a smaller number of questions that could be administered to develop a comparable indicator. These questions 

were administered in 2004 and 2006, and appendix tables 

7-13 and 7-14 record combined probability responses using 

these questions; appendix table 7-13 also shows responses to 

individual probability questions in each year.
7-46 Chapter 7. Science and Technology: Public Attitudes and Understanding

18. Figure 7-12 includes four questions on experimental design included in appendix tables 7-13 and 7-15. Two 

of these four questions include a question on experimental 

design and a follow-up question asking “why”; correct responses to these two questions represent the combined responses and are incorporated into the figure as one question.

19. 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.

20. 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.

21. There are large differences among European countries. The lowest support for this statement is found in Iceland, with 38% expressing agreement. Other countries where 

less than half of residents agree include Ireland (42%), Finland (44%), Denmark (44%), the United Kingdom (45%), 

and the Netherlands (47%).

22. 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.

23. Unlike the U.S. question, the European question joins 

two logically independent ideas—more spending on science 

and less spending on other priorities. In addition, because 

nations begin from different levels of spending, survey responses cannot be read as indicating different views about 

the proper level of spending in this area, nor do they indicate 

the strength of sentiment in different countries. Differences 

in the connotations of questions posed in different languages 

add further complexities. Perhaps for some or all of these 

reasons, variations among European countries in responses 

to this question are large, with about two-thirds of respondents agreeing in Italy, Spain, and France, but less than onethird in Finland and the Netherlands.

24. There are many different types of specializations 

within occupations and prestige may well vary within the 

same occupation or industry.

25. The two questions from the 2009 Gallup survey were 

each asked to half the sample (N=500).

26. 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-27 average. An 

earlier Eurobarometer study showed that the Spaniards and 

the Romanians were less aware of the fact that their countries 

have nuclear power plants than respondents in other countries 

with nuclear plants in operation. 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.

27. A 2006 Canadian survey showed little or no change 

from 2005 (Decima Research 2006).

28. According to a recent 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, 2008a).

29. 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 discussed above.

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-15: Austria, Belgium, Denmark, Finland, France, 

Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, 

Portugal, Spain, Sweden, United Kingdom.

EU-25: Austria, Belgium, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, 

Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, United Kingdom.

EU-27: 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 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 
Science and Engineering Indicators 2010 7-47

warming can be considered part of climate change along 

with changes in precipitation, sea level, etc.

Nanotechnology: Manipulating matter at unprecedentedly small scales to create new or improved products that 

can be used in a wide variety of ways.

Reproductive cloning: Technology used to generate genetically identical individuals with the same nuclear DNA as 

another individual.

Therapeutic cloning: Use of cloning technology in medical research to develop new treatments for diseases; differentiated from human reproductive cloning.

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Chapter 8

State Indicators

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

Chapter Overview ....................................................................................................................8-6

Types of Indicators ..................................................................................................................8-6

Data Sources and Considerations .............................................................................................8-6

Key Elements for Indicators ....................................................................................................8-7

High-Technology Industries ....................................................................................................8-8

Appendix Tables ......................................................................................................................8-8

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

Elementary/Secondary Education

Fourth Grade Mathematics Performance ...................................................................................8-10

Fourth Grade Mathematics Proficiency .....................................................................................8-12

Fourth Grade Science Performance ...........................................................................................8-14

Fourth Grade Science Proficiency .............................................................................................8-16

Eighth Grade Mathematics Performance ...................................................................................8-18

Eighth Grade Mathematics Proficiency .....................................................................................8-20

Eighth Grade Science Performance ...........................................................................................8-22

Eighth Grade Science Proficiency .............................................................................................8-24

Public School Teacher Salaries ..................................................................................................8-26

Elementary and Secondary Public School Current Expenditures as Share of Gross 

Domestic Product ...................................................................................................................8-28

Current Expenditures per Pupil for Elementary and Secondary Public Schools .......................8-30

Share of Public High School Students Taking Advanced Placement Exams ............................8-32

Share of Public High School Students Scoring 3 or Higher on at Least One Advanced 

Placement Exam .....................................................................................................................8-34

High School Graduates or Higher Among Individuals 25–44 Years Old .................................8-36

Higher Education

Bachelor’s Degrees Conferred per 1,000 Individuals 18–24 Years Old ...................................8-38

Bachelor’s Degrees in Natural Sciences and Engineering Conferred per 1,000 

Individuals 18–24 Years Old .................................................................................................8-40

S&E Degrees as Share of Higher Education Degrees Conferred ..............................................8-42

Natural Sciences and Engineering Degrees as Share of Higher Education Degrees Conferred ...8-44

S&E Graduate Students per 1,000 Individuals 25–34 Years Old ..............................................8-46

Advanced S&E Degrees as Share of S&E Degrees Conferred ..................................................8-48

Advanced Natural Sciences and Engineering Degrees as Share of Natural Sciences and 

Engineering Degrees Conferred .............................................................................................8-50

Average Undergraduate Charge at Public 4-Year Institutions ..................................................8-52

Average Undergraduate Charge at Public 4-Year Institutions as Share of Disposable 

Personal Income .....................................................................................................................8-54

State Expenditures on Student Aid per Full-Time Undergraduate Student ...............................8-56

Associate’s Degree Holders or Higher Among Individuals 25–44 Years Old ..........................8-58

Bachelor’s Degree Holders or Higher Among Individuals 25–44 Years Old ...........................8-60

8-1
8-2 Chapter 8. State Indicators

Workforce

Bachelor’s Degree Holders Potentially in the Workforce .........................................................8-62

Individuals in S&E Occupations as Share of Workforce ...........................................................8-64

Employed S&E Doctorate Holders as Share of Workforce .......................................................8-66

Engineers as Share of Workforce ..............................................................................................8-68

Life and Physical Scientists as Share of Workforce ..................................................................8-70

Computer Specialists as Share of Workforce ............................................................................8-72

Financial Research and Development Inputs

R&D as Share of Gross Domestic Product ................................................................................8-74

Federal R&D Obligations per Civilian Worker .........................................................................8-76

Federal R&D Obligations per Individual in S&E Occupation ..................................................8-78

State Agency R&D Expenditures per $1 Million of Gross Domestic Product ..........................8-80

State Agency R&D Expenditures per Civilian Worker .............................................................8-82

State Agency R&D Expenditures per Individual in S&E Occupation ......................................8-84

Business-Performed R&D as Share of Private-Industry Output ................................................8-86

Academic R&D per $1,000 of Gross Domestic Product ...........................................................8-88

R&D Outputs

S&E Doctorates Conferred per 1,000 Employed S&E Doctorate Holders ...............................8-90

Academic S&E Article Output per 1,000 S&E Doctorate Holders in Academia ......................8-92

Academic S&E Article Output per $1 Million of Academic R&D ...........................................8-94

Academic Patents Awarded per 1,000 S&E Doctorate Holders in Academia ..........................8-96

Patents Awarded per 1,000 Individuals in S&E Occupations ...................................................8-98

Science and Technology in the Economy

High-Technology Share of All Business Establishments ........................................................8-100

Net High-Technology Business Formations as Share of All Business Establishments ...........8-102

Employment in High-Technology Establishments as Share of Total Employment ................8-104

Average Annual Federal SBIR Funding per $1 Million of Gross Domestic Product .............8-106

Venture Capital Disbursed per $1,000 of Gross Domestic Product ........................................8-108

Venture Capital Deals as Share of High-Technology Business Establishments .....................8-110

Venture Capital Disbursed per Venture Capital Deal ..............................................................8-112

List of Tables

Table 8-1. Average fourth grade mathematics performance, by state: 2000, 2005, and 2007 ......8-11

Table 8-2. Students reaching proficiency in fourth grade mathematics, by state: 

2000, 2005, and 2007 .............................................................................................................8-13

Table 8-3. Average fourth grade science performance, by state: 2000 and 2005 ......................8-15

Table 8-4. Students reaching proficiency in fourth grade science, by state: 2000 and 2005 .....8-17 

Table 8-5. Average eighth grade mathematics performance, by state: 2000, 2005, and 2007 .....8-19

Table 8-6. Students reaching proficiency in eighth grade mathematics, by state: 

2000, 2005, and 2007 .............................................................................................................8-21

Table 8-7. Average eighth grade science performance, by state: 2000 and 2005 .....................8-23

Table 8-8. Students reaching proficiency in eighth grade science, by state: 2000 and 2005 ....8-25

Table 8-9. Public school teacher salaries, by state: 1997, 2002, and 2007 ................................8-27

Table 8-10. Elementary and secondary public school current expenditures as share of 

gross domestic product, by state: 1997, 2002, and 2007 .......................................................8-29

Table 8-11. Current expenditures per pupil for elementary and secondary public schools, 

by state: 1997, 2002, and 2007 ..............................................................................................8-31
Science and Engineering Indicators 2010 8-3

Table 8-12. Share of public high school students taking Advanced Placement Exams, 

by state: 2000, 2004, and 2008 ..............................................................................................8-33

Table 8-13. Share of public high school students scoring 3 or higher on at least one 

Advanced Placement Exam, by state: 2000, 2004, and 2008 ................................................8-35

Table 8-14. High school graduates or higher among individuals 25–44 years old, 

by state: 2000, 2003, and 2007 ..............................................................................................8-37

Table 8-15. Bachelor’s degrees conferred per 1,000 individuals 18–24 years old, 

by state: 1997, 2002, and 2007 ..............................................................................................8-39

Table 8-16. Bachelor’s degrees in natural sciences and engineering conferred per 1,000 

individuals 18–24 years old, by state: 1997, 2002, and 2007 ................................................8-41

Table 8-17. S&E degrees as share of higher education degrees conferred, by state: 

1997, 2002, and 2007 .............................................................................................................8-43

Table 8-18. Natural sciences and engineering degrees as share of higher education 

degrees conferred, by state: 1997, 2002, and 2007 ................................................................8-45

Table 8-19. S&E graduate students per 1,000 individuals 25–34 years old, by state: 

1997, 2002, and 2007 .............................................................................................................8-47

Table 8-20. Advanced S&E degrees as share of S&E degrees conferred, by state: 

1997, 2002, and 2007 .............................................................................................................8-49

Table 8-21. Advanced natural sciences and engineering degrees as share of natural 

sciences and engineering degrees conferred, by state: 1997, 2002, and 2007 ........................8-51

Table 8-22. Average undergraduate charge at public 4-year institutions, by state: 

1998, 2003, and 2008 .............................................................................................................8-53

Table 8-23. Average undergraduate charge at public 4-year institutions as share of 

disposable personal income, by state: 1998, 2003, and 2008 ................................................8-55

Table 8-24. State expenditures on student aid per full-time undergraduate student, by 

state: 1997, 2002, and 2007 ...................................................................................................8-57

Table 8-25. Associate’s degree holders or higher among individuals 25–44 years old, 

by state: 2000, 2003, and 2007 ..............................................................................................8-59

Table 8-26. Bachelor’s degree holders or higher among individuals 25–44 years old, 

by state: 2000, 2003, and 2007 ..............................................................................................8-61

Table 8-27. Bachelor’s degree holders potentially in the workforce, by state: 2000, 2003, 

and 2007 .................................................................................................................................8-63

Table 8-28. Individuals in S&E occupations as share of workforce, by state: 2004, 2006, 

and 2008 .................................................................................................................................8-65

Table 8-29. Employed S&E doctorate holders as share of workforce, by state: 

1997, 2001, and 2006 .............................................................................................................8-67

Table 8-30. Engineers as share of workforce, by state: 2004, 2006, and 2008 .........................8-69

Table 8-31. Life and physical scientists as share of workforce, by state: 2004, 2006, 

and 2008 .................................................................................................................................8-71

Table 8-32. Computer specialists as share of workforce, by state: 2004, 2006, and 2008 ........8-73

Table 8-33. R&D as share of gross domestic product, by state: 1998, 2002, and 2007 ............8-75

Table 8-34. Federal R&D obligations per civilian worker, by state: 1997, 2002, and 2007 .....8-77

Table 8-35. Federal R&D obligations per individual in S&E occupation, by state: 

2003, 2005, and 2007 .............................................................................................................8-79

Table 8-36. State agency R&D expenditures per $1 million of gross domestic product, 

by state: 2006 and 2007 .........................................................................................................8-81

Table 8-37. State agency R&D expenditures per civilian worker, by state: 2006 and 2007 .....8-83

Table 8-38. State agency R&D expenditures per individual in S&E occupation, 

by state: 2006 and 2007 .........................................................................................................8-85

Table 8-39. Business-performed R&D as share of private-industry output, by state: 

1998, 2002, and 2007 .............................................................................................................8-87
8-4 Chapter 8. State Indicators

Table 8-40. Academic R&D per $1,000 of gross domestic product, by state: 1998, 2003, 

and 2008 .................................................................................................................................8-89

Table 8-41. S&E doctorates conferred per 1,000 employed S&E doctorate holders, 

by state: 1997, 2001, and 2006 ..............................................................................................8-91

Table 8-42. Academic S&E article output per 1,000 S&E doctorate holders in academia, 

by state: 1997, 2003, and 2008/06 .........................................................................................8-93

Table 8-43. Academic S&E article output per $1 million of academic R&D, by state: 

1998, 2003, and 2008 .............................................................................................................8-95

Table 8-44. Academic patents awarded per 1,000 S&E doctorate holders in academia, 

by state: 1997, 2001, and 2006 ..............................................................................................8-97

Table 8-45. Patents awarded per 1,000 individuals in S&E occupations, by state: 2004, 

2006, and 2008 .......................................................................................................................8-99

Table 8-46. High-technology share of all business establishments, by state: 2003, 2004, 

and 2006 ...............................................................................................................................8-101

Table 8-47. Net high-technology business formations as share of all business 

establishments, by state: 2004 and 2006 ..............................................................................8-103

Table 8-48. Employment in high-technology establishments as share of total employment, 

by state: 2003, 2004, and 2006 ............................................................................................8-105

Table 8-49. Average annual federal SBIR funding per $1 million of gross domestic 

product, by state: 1998–2000, 2002–04, and 2006–08 ........................................................8-107

Table 8-50. Venture capital disbursed per $1,000 of gross domestic product, by state: 

1998, 2003, and 2008 ...........................................................................................................8-109

Table 8-51. Venture capital deals as share of high-technology business establishments, 

by state: 2003, 2004, and 2006 ............................................................................................8-111

Table 8-52. Venture capital disbursed per venture capital deal, by state: 1998, 2003, 

and 2008 ...............................................................................................................................8-113

Table 8-A. 2002 NAICS codes that constitute high-technology industries .................................8-9

List of Figures

Figure 8-1. Average fourth grade mathematics performance: 2007 ..........................................8-10

Figure 8-2. Students reaching proficiency in fourth grade mathematics: 2007 .........................8-12

Figure 8-3. Average fourth grade science performance: 2005 ..................................................8-14

Figure 8-4. Students reaching proficiency in fourth grade science: 2005 .................................8-16

Figure 8-5. Average eighth grade mathematics performance: 2007 ..........................................8-18

Figure 8-6. Students reaching proficiency in eighth grade mathematics: 2007 .........................8-20

Figure 8-7. Average eighth grade science performance: 2005 ..................................................8-22

Figure 8-8. Students reaching proficiency in eighth grade science: 2005 .................................8-24

Figure 8-9. Public school teacher salaries: 2007 .......................................................................8-26

Figure 8-10. Elementary and secondary public school current expenditures as share of 

gross domestic product: 2007 ................................................................................................8-28

Figure 8-11. Current expenditures per pupil for elementary and secondary public 

schools: 2007 .........................................................................................................................8-30

Figure 8-12. Share of public high school students taking Advanced Placement Exams: 2008 ....8-32

Figure 8-13. Share of public high school students scoring 3 or higher on at least one 

Advanced Placement Exam: 2008 .........................................................................................8-34

Figure 8-14. High school graduates or higher among individuals 25–44 years old: 2007 ........8-36

Figure 8-15. Bachelor’s degrees conferred per 1,000 individuals 18–24 years old: 2007 ........8-38

Figure 8-16. Bachelor’s degrees in natural sciences and engineering conferred per 1,000 

individuals 18–24 years old: 2007 .........................................................................................8-40

Figure 8-17. S&E degrees as share of higher education degrees conferred: 2007 ....................8-42
Science and Engineering Indicators 2010 8-5

Figure 8-18. Natural sciences and engineering degrees as share of higher education 

degrees conferred: 2007 .........................................................................................................8-44

Figure 8-19. S&E graduate students per 1,000 individuals 25–34 years old: 2007 ...................8-46

Figure 8-20. Advanced S&E degrees as share of S&E degrees conferred: 2007 ......................8-48

Figure 8-21. Advanced natural sciences and engineering degrees as share of natural 

sciences and engineering degrees conferred: 2007 .................................................................8-50

Figure 8-22. Average undergraduate charge at public 4-year institutions: 2008 .......................8-52

Figure 8-23. Average undergraduate charge at public 4-year institutions as share of 

disposable personal income: 2008 .........................................................................................8-54

Figure 8-24. State expenditures on student aid per full-time undergraduate student: 2007 ......8-56

Figure 8-25. Associate’s degree holders or higher among individuals 25–44 years old: 2007 .....8-58

Figure 8-26. Bachelor’s degree holders or higher among individuals 25–44 years old: 2007 ......8-60

Figure 8-27. Bachelor’s degree holders potentially in the workforce: 2007 .............................8-62

Figure 8-28. Individuals in S&E occupations as share of workforce: 2008 ..............................8-64

Figure 8-29. Employed S&E doctorate holders as share of workforce: 2006 ...........................8-66

Figure 8-30. Engineers as share of workforce: 2008 .................................................................8-68

Figure 8-31. Life and physical scientists as share of workforce: 2008 ......................................8-70

Figure 8-32. Computer specialists as share of workforce: 2008 ................................................8-72

Figure 8-33. R&D as share of gross domestic product: 2007 ....................................................8-74

Figure 8-34. Federal R&D obligations per civilian worker: 2007 .............................................8-76

Figure 8-35. Federal R&D obligations per individual in S&E occupation: 2007 .....................8-78

Figure 8-36. State agency R&D expenditures per $1 million of gross domestic product: 2007 ....8-80

Figure 8-37. State agency R&D expenditures per civilian worker: 2007 ..................................8-82

Figure 8-38. State agency R&D expenditures per individual in S&E occupation: 2007 ..........8-84

Figure 8-39. Business-performed R&D as share of private-industry output: 2007 ...................8-86

Figure 8-40. Academic R&D per $1,000 of gross domestic product: 2008 ..............................8-88

Figure 8-41. S&E doctorates conferred per 1,000 employed S&E doctorate holders: 2006 .....8-90

Figure 8-42. Academic S&E article output per 1,000 S&E doctorate holders in 

academia: 2008/06 .................................................................................................................8-92

Figure 8-43. Academic S&E article output per $1 million of academic R&D: 2008 ................8-94

Figure 8-44. Academic patents awarded per 1,000 S&E doctorate holders in academia: 2006 ...8-96

Figure 8-45. Patents awarded per 1,000 individuals in S&E occupations: 2008 .......................8-98

Figure 8-46. High-technology share of all business establishments: 2006 ..............................8-100

Figure 8-47. Net high-technology business formations as share of all business 

establishments: 2006 ............................................................................................................8-102

Figure 8-48. Employment in high-technology establishments as share of total 

employment: 2006 ...............................................................................................................8-104

Figure 8-49. Average annual federal SBIR funding per $1 million of gross domestic 

product: 2006–08 .................................................................................................................8-106

Figure 8-50. Venture capital disbursed per $1,000 of gross domestic product: 2008 .............8-108

Figure 8-51. Venture capital deals as share of high-technology business establishments: 

2006......................................................................................................................................8-110

Figure 8-52. Venture capital disbursed per venture capital deal: 2008 ...................................8-112

Figure 8-A. U.S. map and list of abbreviations ...........................................................................8-7
8-6 Chapter 8. State Indicators

 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 52 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 neither ranked 

with the states nor assigned a quartile value that could be 

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 for fewer than one-quarter of the indicators 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 provided to identify meaningful trends. However, consistent data were not always available for the 10-year period, 

and data are given only for the years in which comparisons 

are appropriate. Most indicators contain data for 2006–07; 

some contain data for 2008.

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-two 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. Instead, mastery of collegelevel 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. New 

indicators in this edition focus on state government agency 

funding for R&D. 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 second 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.

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 

Introduction
Science and Engineering Indicators 2010 8-7

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. The sample map below shows the outline of 

each state. 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. Crosshatching indicates states for which no data are available.

The second element is a quartiles table. States are listed 

alphabetically by quartile. The range of indicator values for 

that quartile is shown at the top of the column. Ties at quartile breaks were resolved by moving the tied states into one 

quartile. 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.

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.

 The quartiles table identifies states in the EPSCoR 

group—those identified as eligible for EPSCoR-like programs by 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, 

AK

Figure 8-A

U.S. map and list of abbreviations

HI

WA

OR

CA

NV

ID

MT

WY

UT

CO

AZ NM

TX

ND

SD

NE

KS

OK

LA

AR

MO

IA

MN

WI

MI

IL IN

KY

TN

MS AL GA

FL

SC

NC

VA

OH

WV

PA

NY

ME NH VT

MA

CT

RI

NJ

DE

MD

DC

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
8-8 Chapter 8. State Indicators

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 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 three years of data and the calculated values of the indicator are presented for each state, the District 

of Columbia, and Puerto Rico. Puerto Rico is included in the 

data table only when data are available.

For selected indicators, the data table has been expanded 

to include the average data and indicator value for the 50 

states and the District of Columbia, and the averages for the 

EPSCoR and non-EPSCoR states. These averages have been 

calculated in two ways. The first two lines, “EPSCoR states” 

and “Non-EPSCoR states,” treat each group as a single geographical unit, ignoring the division of that unit into separate 

states. The ratio for the group is calculated by totaling the 

numerator value of each of the states in the group and the 

denominator value of each of the states in the group and dividing to compute an average. For example, the EPSCoR 

states average of R&D by gross domestic product by state is 

calculated by summing the R&D of all the EPSCoR states, 

summing the gross domestic product of these states, and dividing to compute an average. States with more R&D and a 

larger gross domestic product affect this average more than 

smaller ones do, just as data on California affect U.S. totals 

more than data on Wyoming do.

The third and fourth lines, “Average EPSCoR state value” and “Average non-EPSCoR state value,” represent the 

average of the individual state ratios for an indicator. The 

average EPSCoR state value for R&D by gross domestic 

product by state is calculated by summing the ratios for the 

24 EPSCoR states and dividing by 24. All state ratios count 

equally in this computation.

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 

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 (NAEP) 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 2010 8-9

Table 8-A

2002 NAICS codes that constitute high-technology industries

NAICS code Industry

1131, 32.................................. 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 2010
8-10 Chapter 8. State Indicators

Fourth Grade Mathematics Performance

1st quartile (252–244) 2nd quartile (243–240) 3rd quartile (239–236) 4th quartile (235–214)

Indiana Colorado Alaska ‡

 Alabama ‡

Kansas ‡ Connecticut Arkansas ‡ Arizona

Massachusetts Delaware ‡ Illinois California

Minnesota Florida Michigan District of Columbia

Montana ‡

 Idaho ‡ Missouri Georgia

New Hampshire ‡

 Iowa Nebraska ‡ Hawaii ‡

New Jersey Maine ‡ Oklahoma ‡ Kentucky ‡

North Dakota ‡ Maryland Oregon Louisiana ‡

Ohio New York Rhode Island ‡ Mississippi ‡

Pennsylvania North Carolina South Carolina ‡ Nevada ‡

Vermont ‡ South Dakota ‡ Utah New Mexico ‡

Virginia Texas West Virginia ‡ Tennessee

Wisconsin Washington

Wyoming ‡

‡ EPSCoR state

NOTE: See figure 8-2 text for explanation of achievement levels.

SOURCE: National Center for Education Statistics, National Assessment of Educational Progress. See table 8-1.

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 2007.

Student performance is presented in terms of average scores on a scale from 0 to 500. Higher scores 

indicate that fourth graders are demonstrating a stronger 

foundation for adult mathematics competency. An average score designated as NA (not available) indicates that 

the state either did not participate in the assessment or 

did not have a representative sample of fourth graders 

that was large enough for reporting state-level results.

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.

Figure 8-1

Average fourth grade mathematics performance: 2007

1st quartile

2nd quartile

3rd quartile

4th quartile

Findings

U In 2007, the nationwide average mathematics score of fourth grade 

public school students was 239, a significant increase from 224 in 

2000.

UÊ ÀÊÌiÊ{£ÊÕÀÃ`VÌÃÊÌ>ÌÊ«>ÀÌV«>Ìi`ÊÊLÌÊÌiÊÓäääÊ>`ÊÓääÇÊ

mathematics assessments, the average score for public school fourth 

graders showed a statistically significant increase between 2000 and 

2007. Only the District of Columbia reported an average score in 2007 

that was below the 2000 national average of 224.

UÊ /iÊiÌÀiÊvÕÀÌÊ}À>`iÊÃÌÕ`iÌÊÃ>«i]ÊVÕ`}ÊÃÌÕ`iÌÃÊ«iÀvÀ}Ê>ÌÊ

the 10th, 25th, 50th, 75th, and 90th percentiles, demonstrated statistically 

significant gains in mathematics scores between 2000 and 2007.

UÊ /iÊÃÌ>ÌiÃÊÜÌÊÌiÊ}iÃÌÊ>ÛiÀ>}iÊ>Ìi>ÌVÃÊÃVÀiÃÊvÀÊvÕÀÌÊ

graders were concentrated in the northern United States. A number of 

EPSCoR states were included in this group.

UÊ /iÊ}>«ÃÊÊ>Ìi>ÌVÃÊÃVÀiÃÊLiÌÜiiÊÜÌiÊvÕÀÌÊ}À>`iÀÃÊ>`Ê

black or Hispanic fourth graders narrowed significantly between 

2000 and 2007. The gender gap in fourth grade mathematics scores, 

although much smaller, showed no significant change between 2000 

and 2007.

Achievement levels

(Scores range from 0–500)

Advanced ..... 282–500

Proficient ...... 249–281

Basic ............. 214–248
Science and Engineering Indicators 2010 8-11

Table 8-1

Average fourth grade mathematics performance, by state: 2000, 2005, 

and 2007

(Score out of 500)

State 2000 2005 2007

United States ................................................................... 224* 237* 239

Alabama ....................................................................... 217* 225* 229

Alaska .......................................................................... NA 236 237

Arizona ......................................................................... 219* 230 232

Arkansas ...................................................................... 216* 236 238

California ...................................................................... 213* 230 230

Colorado ...................................................................... NA 239 240

Connecticut ................................................................. 234* 242 243

Delaware ...................................................................... NA 240* 242

District of Columbia ..................................................... 192* 211* 214

Florida .......................................................................... NA 239* 242

Georgia ........................................................................ 219* 234 235

Hawaii .......................................................................... 216* 230* 234

Idaho ............................................................................ 224* 242 241

Illinois ........................................................................... 223* 233* 237

Indiana ......................................................................... 233* 240* 245

Iowa ............................................................................. 231* 240* 243

Kansas ......................................................................... 232* 246 248

Kentucky ...................................................................... 219* 231* 235

Louisiana ...................................................................... 218* 230 230

Maine ........................................................................... 230* 241 242

Maryland ...................................................................... 222* 238 240

Massachusetts ............................................................. 233* 247* 252

Michigan ...................................................................... 229* 238 238

Minnesota .................................................................... 234* 246 247

Mississippi ................................................................... 211* 227 228

Missouri ....................................................................... 228* 235* 239

Montana ....................................................................... 228* 241* 244

Nebraska ...................................................................... 225* 238 238

Nevada ......................................................................... 220* 230 232

New Hampshire ........................................................... NA 246* 249

New Jersey .................................................................. NA 244* 249

New Mexico ................................................................. 213* 224* 228

New York ...................................................................... 225* 238* 243

North Carolina .............................................................. 230* 241 242

North Dakota ............................................................... 230* 243* 245

Ohio ............................................................................. 230* 242 245

Oklahoma ..................................................................... 224* 234* 237

Oregon ......................................................................... 224* 238 236

Pennsylvania ................................................................ NA 241* 244

Rhode Island ................................................................ 224* 233 236

South Carolina ............................................................. 220* 238 237

South Dakota ............................................................... NA 242 241

Tennessee .................................................................... 220* 232 233

Texas ............................................................................ 231* 242 242

Utah ............................................................................. 227* 239 239

Vermont ........................................................................ 232* 244* 246

Virginia ......................................................................... 230* 240* 244

Washington .................................................................. NA 242 243

West Virginia ................................................................ 223* 231* 236

Wisconsin .................................................................... NA 241* 244

Wyoming ...................................................................... 229* 243 244

Puerto Rico .................................................................. NA NA NA

*significantly different (pÊÊ°äx®ÊvÀÊÓääÇÊÜiÊÞÊiÊÕÀÃ`VÌÊÀÊÌiÊ>ÌÊÃÊLi}ÊiÝ>i`ÆÊ

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 2010
8-12 Chapter 8. State Indicators

Fourth Grade Mathematics Proficiency

1st quartile (58%–44%) 2nd quartile (43%–40%) 3rd quartile (39%–33%) 4th quartile (32%–14%)

Connecticut Colorado Alaska ‡

 Alabama ‡

Indiana Delaware ‡ Arkansas ‡ Arizona

Kansas ‡

 Idaho ‡ Florida California

Massachusetts Iowa Hawaii ‡ District of Columbia

Minnesota Maine ‡

 Illinois Georgia

Montana ‡

 Maryland Michigan Kentucky ‡

New Hampshire ‡ New York Missouri Louisiana ‡

New Jersey North Carolina Nebraska ‡ Mississippi ‡

North Dakota ‡ South Dakota ‡ Oklahoma ‡ Nevada ‡

Ohio Texas Oregon New Mexico ‡

Pennsylvania Virginia Rhode Island ‡ Tennessee

Vermont ‡

 South Carolina ‡

Washington Utah

Wisconsin West Virginia ‡

Wyoming ‡

‡ EPSCoR state

SOURCE: National Center for Education Statistics, National Assessment of Educational Progress. See table 8-2.

Figure 8-2

Students reaching proficiency in fourth grade mathematics: 2007

­*iÀViÌÆÊÃVÀiÃÊvÊÓ{Ê>`Ê>LÛiÊ`V>ÌiÊ«ÀwViVÞ®

1st quartile

2nd quartile

3rd quartile

4th quartile

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 of Educational Progress (NAEP) provides 

a federally authorized measure of student performance in mathematics. 

The National Assessment Governing Board sets performance standards 

that provide a context for interpreting NAEP results. The standards 

define “proficiency” as well as performance levels that indicate greater 

(“advanced”) and lesser (“basic”) accomplishment. For the fourth 

grade, the proficient level (scores 249–281) represents solid academic 

performance and demonstrates competency over challenging subjectmatter knowledge, its application to real-world situations, and mastery 

of appropriate analytical skills. 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 198,000 fourth grade students in 7,800 schools participated in the 2007 NAEP mathematics assessment. A designation of 

NA (not available) indicates that the state either did not participate in 

the assessment or did not have a representative sample of fourth graders 

that was large enough for reporting state-level results.

Findings

U In 2007, 39% 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. Several states 

more than doubled the percentage of their students 

performing at or above the proficient level.

UÊ "vÊÌiÊ{£ÊÕÀÃ`VÌÃÊÌ>ÌÊ«>ÀÌV«>Ìi`ÊÊLÌÊÌiÊ

2000 and 2007 assessments, all showed significant 

increases in mathematics proficiency levels for public 

school fourth graders in 2007.

UÊ The states with the highest percentages of fourthgraders who were proficient in mathematics are 

concentrated in the northern United States. A number 

of EPSCoR states were included in this group.

UÊ Substantial differences in mathematics proficiency 

exist among racial/ethnic groups of fourth graders. 

The gaps increased between 2000 and 2007 as 

blacks and Hispanics failed to match the gains made 

in mathematics proficiency by whites. The gender gap 

among fourth graders is much smaller and remained 

unchanged between 2000 and 2007.

Achievement levels

(Scores range from 0–500)

Advanced ..... 282–500

Proficient ...... 249–281

Basic ............. 214–248
Science and Engineering Indicators 2010 8-13

Table 8-2

Students reaching proficiency in fourth grade mathematics, by state: 2000, 

2005, and 2007

(Percent)

State 2000 2005 2007

United States .................................................................. 22* 35* 39

Alabama ...................................................................... 13* 21* 26

Alaska ......................................................................... NA 34 38

Arizona ........................................................................ 16* 28 31

Arkansas ..................................................................... 14* 34 37

California ..................................................................... 13* 28 30

Colorado ..................................................................... NA 39 41

Connecticut ................................................................ 31* 43 45

Delaware ..................................................................... NA 36* 40

District of Columbia .................................................... 5* 10* 14

Florida ......................................................................... NA 37* 37

Georgia ....................................................................... 17* 30 32

Hawaii ......................................................................... 14* 27* 33

Idaho ........................................................................... 20* 41 40

Illinois .......................................................................... 20* 32* 36

Indiana ........................................................................ 30* 38* 46

Iowa ............................................................................ 26* 37* 43

Kansas ........................................................................ 29* 47 51

Kentucky ..................................................................... 17* 26* 31

Louisiana ..................................................................... 14* 24 24

Maine .......................................................................... 23* 39 42

Maryland ..................................................................... 21* 38 40

Massachusetts ............................................................ 31* 49* 58

Michigan ..................................................................... 28* 37 37

Minnesota ................................................................... 33* 47 51

Mississippi .................................................................. 9* 19 21

Missouri ...................................................................... 23* 31* 38

Montana ...................................................................... 24* 38* 44

Nebraska ..................................................................... 24* 36 38

Nevada ........................................................................ 16* 26* 30

New Hampshire .......................................................... NA 47* 52

New Jersey ................................................................. NA 46* 52

New Mexico ................................................................ 12* 19* 24

New York ..................................................................... 21* 36* 43

North Carolina ............................................................. 25* 40 41

North Dakota .............................................................. 25* 40* 46

Ohio ............................................................................ 25* 43 46

Oklahoma .................................................................... 16* 29 33

Oregon ........................................................................ 23* 37 35

Pennsylvania ............................................................... NA 41* 47

Rhode Island ............................................................... 22* 31* 34

South Carolina ............................................................ 18* 36 36

South Dakota .............................................................. NA 40 41

Tennessee ................................................................... 18* 28 29

Texas ........................................................................... 25* 40 40

Utah ............................................................................ 23* 37 39

Vermont ....................................................................... 29* 44* 49

Virginia ........................................................................ 24* 40 42

Washington ................................................................. NA 42 44

West Virginia ............................................................... 17* 25* 33

Wisconsin ................................................................... NA 40* 47

Wyoming ..................................................................... 25* 42 44

Puerto Rico ................................................................. NA NA NA

*significantly different (pÊÊ°äx®ÊvÀÊÓääÇÊÜiÊÞÊiÊÕÀÃ`VÌÊÀÊÌiÊ>ÌÊÃÊLi}ÊiÝ>i`ÆÊ

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 2010
8-14 Chapter 8. State Indicators

Fourth Grade Science Performance

1st quartile (161–158) 2nd quartile (157–152) 3rd quartile (151–148) 4th quartile (147–133) No data

Kentucky ‡

 Colorado Florida Alabama ‡ Alaska ‡

Maine ‡ Connecticut Georgia Arizona District of Columbia

Massachusetts Delaware ‡

 Illinois Arkansas ‡ Iowa

Missouri Idaho ‡

 Maryland California Kansas ‡

Montana ‡ Indiana North Carolina Hawaii ‡ Nebraska ‡

New Hampshire ‡ Michigan Oklahoma ‡ Louisiana ‡ New York

North Dakota ‡

 Minnesota Oregon Mississippi ‡ Pennsylvania

South Dakota ‡ New Jersey South Carolina ‡

 Nevada ‡

Vermont ‡ Ohio Tennessee New Mexico ‡

Virginia Utah Texas Rhode Island ‡

Wisconsin Washington West Virginia ‡

 Wyoming ‡

‡ EPSCoR state

NOTE: See figure 8-4 text for explanation of achievement levels.

SOURCE: National Center for Education Statistics, National Assessment of Educational Progress. See table 8-3.

Figure 8-3

Average fourth grade science performance: 2005

1st quartile

2nd quartile

3rd quartile

4th quartile

No data

This indicator represents each state’s average score on 

the National Assessment of Educational Progress (NAEP) in 

science for its fourth grade students in public schools. The 

NAEP science assessment is a federally authorized measure of 

student performance in which 44 states participated in 2005.

Student performance is presented in terms of average scores 

on a scale from 0 to 300. Higher scores indicate that fourth graders are demonstrating a stronger foundation for adult science 

competency. An average score designated as NA (not available) 

indicates that the state either did not participate in the assessment 

or did not have a representative sample of fourth graders that 

was large enough for reporting state-level results.

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 2005, the nationwide average science score of fourth grade 

public school students was 149, an increase from 145 in 2000. 

UÊ "vÊÌiÊÎÈÊÃÌ>ÌiÃÊÌ>ÌÊ«>ÀÌV«>Ìi`ÊÊLÌÊÌiÊÓäääÊ>`ÊÓääxÊ

science assessments, 20 reported increases in average 

scores of their public school fourth graders, but only 9 of 

these increases were statistically significant. 

UÊ -ÌÕ`iÌÃÊ«iÀvÀ}Ê>ÌÊÌiÊ£äÌ]ÊÓxÌ]Ê>`ÊxäÌÊ«iÀViÌiÃÊ

demonstrated gains in science scores between 2000 and 

2005, whereas students performing at the 75th and 90th 

percentiles showed no statistically significant change in 

average score.

UÊ /iÊÃÌ>ÌiÃÊÜÌÊÌiÊ}iÃÌÊ>ÛiÀ>}iÊÃViViÊÃVÀiÃÊvÀÊvÕÀÌ

graders were concentrated in the northern United States. A 

number of EPSCoR states were included in this group.

UÊ /iÊ}>«ÃÊÊÃViViÊÃVÀiÃÊLiÌÜiiÊÜÌiÊvÕÀÌÊ}À>`iÀÃÊ

and black or Hispanic fourth graders narrowed significantly 

between 2000 and 2005. The gender gap in fourth grade 

science scores, although much smaller, remained unchanged 

between 2000 and 2005.

Achievement levels

(Scores range from 0–300)

Advanced ..... 205–300

Proficient ...... 170–204

Basic ............. 138–169
Science and Engineering Indicators 2010 8-15

Table 8-3

Average fourth grade science performance, by state: 2000 and 2005

(Score out of 300)

State 2000 2005

United States ..................................................................... 145* 149

Alabama ......................................................................... 143 142

Alaska ............................................................................ NA NA

Arizona ........................................................................... 140 139

Arkansas ........................................................................ 145 147

California ........................................................................ 129* 137

Colorado ........................................................................ NA 155

Connecticut ................................................................... 156 155

Delaware ........................................................................ NA 152

District of Columbia ....................................................... NA NA

Florida ............................................................................ NA 150

Georgia .......................................................................... 142* 148

Hawaii ............................................................................ 136* 142

Idaho .............................................................................. 152 155

Illinois ............................................................................. 150 148

Indiana ........................................................................... 154 152

Iowa ............................................................................... 159 NA

Kansas ........................................................................... NA NA

Kentucky ........................................................................ 152* 158

Louisiana ........................................................................ 139 143

Maine ............................................................................. 161 160

Maryland ........................................................................ 145* 149

Massachusetts ............................................................... 161 160

Michigan ........................................................................ 152 152

Minnesota ...................................................................... 157 156

Mississippi ..................................................................... 133 133

Missouri ......................................................................... 157 158

Montana ......................................................................... 160 160

Nebraska ........................................................................ 150 NA

Nevada ........................................................................... 142 140

New Hampshire ............................................................. NA 161

New Jersey .................................................................... NA 154

New Mexico ................................................................... 140 141

New York ........................................................................ 148 NA

North Carolina ................................................................ 147 149

North Dakota ................................................................. 160 160

Ohio ............................................................................... 155 157

Oklahoma ....................................................................... 151 150

Oregon ........................................................................... 148 151

Pennsylvania .................................................................. NA NA

Rhode Island .................................................................. 148 146

South Carolina ............................................................... 140* 148

South Dakota ................................................................. NA 158

Tennessee ...................................................................... 145* 150

Texas .............................................................................. 145* 150

Utah ............................................................................... 154 155

Vermont .......................................................................... 160 160

Virginia ........................................................................... 155* 161

Washington .................................................................... NA 153

West Virginia .................................................................. 149 151

Wisconsin ...................................................................... NA 158

Wyoming ........................................................................ 156 157

Puerto Rico .................................................................... NA NA

IÃ}vV>ÌÞÊ`vviÀiÌÊvÀÊÓääxÊÜiÊÞÊiÊÕÀÃ`VÌÊÀÊÌiÊ>ÌÊÃÊLi}ÊiÝ>i`ÆÊ

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 2010
8-16 Chapter 8. State Indicators

Fourth Grade Science Proficiency

1st quartile (40%–35%) 2nd quartile (33%–27%) 3rd quartile (26%–25%) 4th quartile (24%–12%) No data

Kentucky ‡

 Colorado Florida Alabama ‡ Alaska ‡

Maine ‡ Connecticut Georgia Arizona District of Columbia

Massachusetts Delaware ‡

 North Carolina Arkansas ‡ Iowa

Missouri Idaho ‡

 Oklahoma ‡ California Kansas ‡

Montana ‡ Illinois Oregon Hawaii ‡ Nebraska ‡

New Hampshire ‡ Indiana South Carolina ‡ Louisiana ‡ New York

North Dakota ‡

 Maryland Tennessee Mississippi ‡ Pennsylvania

Ohio Michigan Texas Nevada ‡

South Dakota ‡ Minnesota New Mexico ‡

Vermont ‡ New Jersey Rhode Island ‡

Virginia Utah West Virginia ‡

Wisconsin Washington

 Wyoming ‡

‡ EPSCoR state

SOURCE: National Center for Education Statistics, National Assessment of Educational Progress. See table 8-4.

Figure 8-4

Students reaching proficiency in fourth grade science: 2005

­*iÀViÌÆÊÃVÀiÃÊvÊ£ÇäÊ>`Ê>LÛiÊ`V>ÌiÊ«ÀwViVÞ®

1st quartile

2nd quartile

3rd quartile

4th quartile

No data

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 of Educational Progress (NAEP) provides a 

federally authorized measure of student performance in science. The National 

Assessment Governing Board sets performance standards that provide a context for interpreting NAEP results. The standards define “proficiency” as well 

as performance levels that indicate greater (“advanced”) and lesser (“basic”) 

accomplishment. For the fourth grade, the proficient level (scores 170–204) 

represents solid academic performance and demonstrates competency over 

challenging subject-matter knowledge, its application to real-world situations, 

and mastery of appropriate analytical skills. The advanced level (205–300) signifies superior performance. The basic level (138–169) denotes partial mastery 

of knowledge and skills that are prerequisite for proficient work. A National 

Academy of Sciences panel evaluated the process used to establish the standards 

for the science assessment and urged that they be considered developmental 

and interpreted with caution.

Approximately 147,700 fourth grade students in 8,500 schools participated 

in the 2005 NAEP science assessment. A designation of NA (not available) 

indicates that the state either did not participate in the assessment or did not 

have a representative sample of fourth graders that was large enough for reporting state-level results.

Findings

U In 2005, 27% of fourth grade public school 

students nationwide performed at or 

above the proficient level in science, which 

is not significantly different from 26% 

in 2000.

UÊ Of the 36 states that participated in both 

the 2000 and 2005 science assessments, 

only 4 states showed increases that were 

statistically significant.

UÊ The states with the highest percentage of 

fourth graders who demonstrated science 

proficiency are located in the northern 

United States. A number of EPSCoR 

states were included in this group.

UÊ Substantial differences in science 

proficiency exist between racial/ethnic 

groups of fourth graders. The gender gap 

is much smaller and remained unchanged 

between 2000 and 2005.

Achievement levels

(Scores range from 0–300)

Advanced ..... 205–300

Proficient ...... 170–204

Basic ............. 138–169
Science and Engineering Indicators 2010 8-17

Table 8-4

Students reaching proficiency in fourth grade science, by state: 2000 

and 2005

(Percent)

State 2000 2005

United States ..................................................................... 26 27

Alabama ......................................................................... 22 21

Alaska ............................................................................ NA NA

Arizona ........................................................................... 22 18

Arkansas ........................................................................ 23 24

California ........................................................................ 13* 17

Colorado ........................................................................ NA 32

Connecticut ................................................................... 35 33

Delaware ........................................................................ NA 27

District of Columbia ....................................................... NA NA

Florida ............................................................................ NA 26

Georgia .......................................................................... 23 25

Hawaii ............................................................................ 16 19

Idaho .............................................................................. 29 29

Illinois ............................................................................. 31 27

Indiana ........................................................................... 32 27

Iowa ............................................................................... 36 NA

Kansas ........................................................................... NA NA

Kentucky ........................................................................ 28* 36

Louisiana ........................................................................ 18 20

Maine ............................................................................. 37 36

Maryland ........................................................................ 24 27

Massachusetts ............................................................... 42 38

Michigan ........................................................................ 32 30

Minnesota ...................................................................... 34 33

Mississippi ..................................................................... 13 12

Missouri ......................................................................... 34 36

Montana ......................................................................... 36 37

Nebraska ........................................................................ 26 NA

Nevada ........................................................................... 19 17

New Hampshire ............................................................. NA 37

New Jersey .................................................................... NA 32

New Mexico ................................................................... 17 18

New York ........................................................................ 24 NA

North Carolina ................................................................ 23 25

North Dakota ................................................................. 36 36

Ohio ............................................................................... 31 35

Oklahoma ....................................................................... 26 25

Oregon ........................................................................... 27 26

Pennsylvania .................................................................. NA NA

Rhode Island .................................................................. 25 23

South Carolina ............................................................... 20* 25

South Dakota ................................................................. NA 35

Tennessee ...................................................................... 24 26

Texas .............................................................................. 23 25

Utah ............................................................................... 31 33

Vermont .......................................................................... 38 38

Virginia ........................................................................... 32* 40

Washington .................................................................... NA 28

West Virginia .................................................................. 24 24

Wisconsin ...................................................................... NA 35

Wyoming ........................................................................ 31 32

Puerto Rico .................................................................... NA NA

IÃ}vV>ÌÞÊ`vviÀiÌÊvÀÊÓääxÊÜiÊÞÊiÊÕÀÃ`VÌÊÀÊÌiÊ>ÌÊÃÊLi}ÊiÝ>i`ÆÊ

NA = not available

NOTE: 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 2010
8-18 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 2007.

Student performance is presented in terms of average 

scores on a scale from 0 to 500. Higher scores indicate 

that eighth graders are demonstrating a stronger foundation for adult mathematics competency. An average score 

designated as NA (not applicable) indicates that the state 

either did not participate in the assessment or did not have 

a representative sample of eighth graders that was large 

enough for reporting state-level results.

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 2007, the nationwide average mathematics score of eighth 

grade public school students was 280, a statistically significant 

increase from 272 in 2000.

UÊ "vÊÌiÊ{äÊÕÀÃ`VÌÃÊÌ>ÌÊ«>ÀÌV«>Ìi`ÊÊLÌÊÌiÊÓäääÊ>`ÊÓääÇÊ

mathematics assessments, 35 reported statistically significant 

increases.

UÊ The entire eighth grade student sample, including students 

performing at the 10th, 25th, 50th, 75th, and 90th percentiles, 

demonstrated statistically significant gains in mathematics scores 

between 2000 and 2007.

UÊ States with high average mathematics scores for eighth graders 

frequently included those in the New England and North Central 

regions of the United States. A number of EPSCoR states were 

included in this group.

UÊ The gaps in mathematics scores between white eighth graders and 

black or Hispanic eighth graders narrowed significantly between 

2000 and 2007. The gender gap in eighth grade mathematics 

scores, although much smaller, remained unchanged between 

2000 and 2007.

Eighth Grade Mathematics Performance

1st quartile (298–286) 2nd quartile (285–283) 3rd quartile (282–275) 4th quartile (274–248)

Colorado Alaska ‡

 Arizona Alabama ‡

Kansas ‡ Delaware ‡ Connecticut Arkansas ‡

Maine ‡ Idaho ‡ Florida California

Maryland Indiana Georgia District of Columbia

Massachusetts Iowa Illinois Hawaii ‡

Minnesota Nebraska ‡ Kentucky ‡ Louisiana ‡

Montana ‡ North Carolina Michigan Mississippi ‡

New Hampshire ‡ Ohio Missouri Nevada ‡

New Jersey Oregon New York New Mexico ‡

North Dakota ‡ Washington Oklahoma ‡ Tennessee

Pennsylvania Rhode Island ‡

 West Virginia ‡

South Dakota ‡ South Carolina ‡

Texas Utah

Vermont ‡

Virginia

Wisconsin

Wyoming ‡

‡ EPSCoR state

NOTE: See figure 8-6 text for explanation of achievement levels.

SOURCE: National Center for Education Statistics, National Assessment of Educational Progress. See table 8-5.

Figure 8-5

Average eighth grade mathematics performance: 2007

1st quartile

2nd quartile

3rd quartile

4th quartile

Achievement levels

(Scores range from 0–500)

Advanced ..... 333–500

Proficient ...... 299–332

Basic ............. 262–298
Science and Engineering Indicators 2010 8-19

Table 8-5

Average eighth grade mathematics performance, by state: 2000, 2005, 

and 2007

(Score out of 500)

State 2000 2005 2007

Average EPSCoR state value ......................................... 270 275 278

Average non-EPSCoR state value .................................. 274 280 282

United States .................................................................. 272 * 278* 280

Alabama ...................................................................... 264 262 266

Alaska ......................................................................... NA 279* 283

Arizona ........................................................................ 269 * 274 276

Arkansas ..................................................................... 257 * 272 274

California ..................................................................... 260 * 269 270

Colorado ..................................................................... NA 281* 286

Connecticut ................................................................ 281 281 282

Delaware ..................................................................... NA 281* 283

District of Columbia .................................................... 235 * 245* 248

Florida ......................................................................... NA 274 277

Georgia ....................................................................... 265 * 272 275

Hawaii ......................................................................... 262 * 266* 269

Idaho ........................................................................... 277 * 281* 284

Illinois .......................................................................... 275 * 278 280

Indiana ........................................................................ 281 * 282* 285

Iowa ............................................................................ NA 284 285

Kansas ........................................................................ 283 * 284* 290

Kentucky ..................................................................... 270 * 274* 279

Louisiana ..................................................................... 259 * 268* 272

Maine .......................................................................... 281 * 281* 286

Maryland ..................................................................... 272 * 278* 286

Massachusetts ............................................................ 279 * 292* 298

Michigan ..................................................................... 277 277 277

Minnesota ................................................................... 287 * 290 292

Mississippi .................................................................. 254 * 262 265

Missouri ...................................................................... 271 * 276* 281

Montana ...................................................................... 285 286 287

Nebraska ..................................................................... 280 * 284 284

Nevada ........................................................................ 265 * 270 271

New Hampshire .......................................................... NA 285* 288

New Jersey ................................................................. NA 284* 289

New Mexico ................................................................ 259 * 263* 268

New York ..................................................................... 271 * 280 280

North Carolina ............................................................. 276 * 282 284

North Dakota .............................................................. 282 * 287* 292

Ohio ............................................................................ 281 * 283 285

Oklahoma .................................................................... 270 * 271* 275

Oregon ........................................................................ 280 282 284

Pennsylvania ............................................................... NA 281* 286

Rhode Island ............................................................... 269 * 272* 275

South Carolina ............................................................ 265 * 281 282

South Dakota .............................................................. NA 287 288

Tennessee ................................................................... 262 * 271* 274

Texas ........................................................................... 273 * 281* 286

Utah ............................................................................ 274 * 279 281

Vermont ....................................................................... 281 * 287* 291

Virginia ........................................................................ 275 * 284* 288

Washington ................................................................. NA 285 285

West Virginia ............................................................... 266 * 269 270

Wisconsin ................................................................... NA 285 286

Wyoming ..................................................................... 276 * 282* 287

Puerto Rico ................................................................. NA NA NA

*significantly different (pÊÊ°äx®ÊvÀÊÓääÇÊÜiÊÞÊiÊÕÀÃ`VÌÊÀÊÌiÊ>ÌÊÃÊLi}ÊiÝ>i`ÆÊ

 ÊrÊÌÊ>Û>>LiÆÊ

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 2010
8-20 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 of Educational Progress (NAEP) provides a 

federally authorized measure of student performance in mathematics. The 

National Assessment Governing Board sets a performance standard that 

provides a context for interpreting NAEP results. The standards define 

“proficiency” as well as performance levels that indicate greater (“advanced”) and lesser (“basic”) accomplishment. For the eighth grade, the 

proficient level (scores 299–332) represents solid academic performance 

and demonstrates competency over challenging subject-matter knowledge, 

its application to real-world situations, and mastery of appropriate analytical skills. 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 153,000 eighth grade students in 6,900 schools participated in the 2007 NAEP mathematics assessment. A designation of 

NA (not available) indicates that the state either did not participate in the 

assessment or did not have a representative sample of eighth graders that 

was large enough for reporting state-level results.

Findings

U In 2007, 31% 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.

UÊ Of the 39 states that participated in both the 2000 

and 2007 assessments, 30 showed statistically 

significant increases in mathematics proficiency 

among public school eighth graders in 2007.

UÊ States with the highest percentages of 

eighth graders demonstrating proficiency in 

mathematics were located in the North Central 

and mid-Atlantic regions. A number of EPSCoR 

states were included in this group.

UÊ Substantial differences in mathematics 

proficiency exist among racial/ethnic groups of 

eighth graders, and the gaps between whites and 

blacks or Hispanics increased slightly between 

2000 and 2007. The gender gap in mathematics 

proficiency among eighth graders is much smaller 

and increased slightly between 2000 and 2007.

Eighth Grade Mathematics Proficiency

1st quartile (51%–37%) 2nd quartile (36%–34%) 3rd quartile (32%–25%) 4th quartile (24%–8%)

Colorado Connecticut Alaska ‡

 Alabama ‡

Kansas ‡ Idaho ‡ Arizona Arkansas ‡

Maryland Indiana Delaware ‡ California

Massachusetts Iowa Florida District of Columbia

Minnesota Maine ‡

 Georgia Hawaii ‡

Montana ‡ Nebraska ‡ Illinois Louisiana ‡

New Hampshire ‡ North Carolina Kentucky ‡ Mississippi ‡

New Jersey Ohio Michigan Nevada ‡

North Dakota ‡ Oregon Missouri New Mexico ‡

Pennsylvania Texas New York Oklahoma ‡

South Dakota ‡ Washington Rhode Island ‡ Tennessee

Vermont ‡

 Wyoming ‡ South Carolina ‡ West Virginia ‡

Virginia Utah

Wisconsin

 

‡ EPSCoR state

SOURCE: National Center for Education Statistics, National Assessment of Educational Progress. See table 8-6.

Figure 8-6

Students reaching proficiency in eighth grade mathematics: 2007

­*iÀViÌÆÊÃVÀiÃÊvÊÓÊ>`Ê>LÛiÊ`V>ÌiÊ«ÀwViVÞ®

1st quartile

2nd quartile

3rd quartile

4th quartile

Achievement levels

(Scores range from 0–500)

Advanced ..... 333–500

Proficient ...... 299–332

Basic ............. 262–298
Science and Engineering Indicators 2010 8-21

Table 8-6

Students reaching proficiency in eighth grade mathematics, by state: 

2000, 2005, and 2007

(Percent)

State 2000 2005 2007

United States .................................................................. 25* 28* 31

Alabama ...................................................................... 16 15 18

Alaska ......................................................................... NA 29 32

Arizona ........................................................................ 20* 26 26

Arkansas ..................................................................... 13* 22 24

California ..................................................................... 17* 22* 24

Colorado ..................................................................... NA 32* 37

Connecticut ................................................................ 33 35 35

Delaware ..................................................................... NA 30 31

District of Columbia .................................................... 6 7 8

Florida ......................................................................... NA 26 27

Georgia ....................................................................... 19* 23 25

Hawaii ......................................................................... 16* 18* 21

Idaho ........................................................................... 26* 30* 34

Illinois .......................................................................... 26* 29 31

Indiana ........................................................................ 29* 30* 35

Iowa ............................................................................ NA 34 35

Kansas ........................................................................ 34* 34* 40

Kentucky ..................................................................... 20* 23* 27

Louisiana ..................................................................... 11* 16 19

Maine .......................................................................... 30 30* 34

Maryland ..................................................................... 27* 30* 37

Massachusetts ............................................................ 30* 43* 51

Michigan ..................................................................... 28 29 29

Minnesota ................................................................... 39 43 43

Mississippi .................................................................. 9* 14 14

Missouri ...................................................................... 21* 26* 30

Montana ...................................................................... 36 36 38

Nebraska ..................................................................... 30* 35 35

Nevada ........................................................................ 18* 21 23

New Hampshire .......................................................... NA 35 38

New Jersey ................................................................. NA 36* 40

New Mexico ................................................................ 12* 14* 17

New York ..................................................................... 24* 31 30

North Carolina ............................................................. 27* 32 34

North Dakota .............................................................. 30* 35* 41

Ohio ............................................................................ 30* 34 35

Oklahoma .................................................................... 18 20 21

Oregon ........................................................................ 31 33 35

Pennsylvania ............................................................... NA 31* 38

Rhode Island ............................................................... 22* 24* 28

South Carolina ............................................................ 17* 30 32

South Dakota .............................................................. NA 36 39

Tennessee ................................................................... 16* 21 23

Texas ........................................................................... 24* 31* 35

Utah ............................................................................ 25* 30 32

Vermont ....................................................................... 31* 38* 41

Virginia ........................................................................ 25* 33 37

Washington ................................................................. NA 36 36

West Virginia ............................................................... 17 17 19

Wisconsin ................................................................... NA 36 37

Wyoming ..................................................................... 23* 29* 36

Puerto Rico ................................................................. NA NA NA

*significantly different (pÊÊ°äx®ÊvÀÊÓääÇÊÜiÊÞÊiÊÕÀÃ`VÌÊÀÊÌiÊ>ÌÊÃÊLi}ÊiÝ>i`ÆÊ

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 2010
8-22 Chapter 8. State Indicators

This indicator represents each state’s average score on 

the National Assessment of Educational Progress (NAEP) in 

science for its eighth grade students in public schools. The 

NAEP science assessment is a federally authorized measure of 

student performance in which 44 states participated in 2005.

Student performance is presented in terms of average 

scores on a scale from 0 to 300. Higher scores indicate that 

eighth graders are demonstrating a stronger foundation for 

adult science competency. An average score designated as 

NA (not applicable) indicates that the state either did not 

participate in the assessment or did not have a representative 

sample of eighth graders that was large enough for reporting 

state-level results.

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 that offered 

these accommodations.

Findings

U Of the 36 states that participated in both the 2000 and 2005 

science assessments, 10 states reported 2005 average 

scores that were significantly higher than those in 2000 and 4 

states reported average scores that were significantly lower.

UÊ States with the highest average science scores for eighth 

graders were concentrated in the northern United States. A 

number of EPSCoR states were included in this group.

UÊ The gaps in science scores between white eighth graders and 

black or Hispanic eighth graders did not change significantly 

between 2000 and 2005.

Eighth Grade Science Performance

Figure 8-7

Average eighth grade science performance: 2005

1st quartile (163–158) 2nd quartile (155–152) 3rd quartile (150–144) 4th quartile (143–132) No data

Idaho ‡

 Colorado Arkansas ‡ Alabama ‡ Alaska ‡

Maine ‡ Connecticut Georgia Arizona District of Columbia

Massachusetts Delaware ‡

 Illinois California Iowa

Minnesota Kentucky ‡

 Indiana Florida Kansas ‡

Montana ‡ Michigan Maryland Hawaii ‡ Nebraska ‡

New Hampshire ‡ Missouri North Carolina Louisiana ‡ New York

North Dakota ‡

 New Jersey Oklahoma ‡ Mississippi ‡ Pennsylvania

South Dakota ‡

 Ohio Rhode Island ‡ Nevada ‡

Vermont ‡ Oregon South Carolina ‡ New Mexico ‡

Wisconsin Utah Tennessee Texas

Wyoming ‡ Virginia West Virginia ‡

 Washington

‡ EPSCoR state

NOTE: See figure 8-8 text for explanation of achievement levels.

SOURCE: National Center for Education Statistics, National Assessment of Educational Progress. See table 8-7.

1st quartile

2nd quartile

3rd quartile

4th quartile

No data

Achievement levels

(Scores range from 0–300)

Advanced ..... 208–300

Proficient ...... 170–207

Basic ............. 143–169
Science and Engineering Indicators 2010 8-23

Table 8-7

Average eighth grade science performance, by state: 2000 and 2005

(Score out of 300)

State 2000 2005

United States ..................................................................... 148 147

Alabama ......................................................................... 143* 138

Alaska ............................................................................ NA NA

Arizona ........................................................................... 145* 140

Arkansas ........................................................................ 142 144

California ........................................................................ 129* 136

Colorado ........................................................................ NA 155

Connecticut ................................................................... 153 152

Delaware ........................................................................ NA 152

District of Columbia ....................................................... NA NA

Florida ............................................................................ NA 141

Georgia .......................................................................... 142 144

Hawaii ............................................................................ 130* 136

Idaho .............................................................................. 158 158

Illinois ............................................................................. 148 148

Indiana ........................................................................... 154* 150

Iowa ............................................................................... NA NA

Kansas ........................................................................... NA NA

Kentucky ........................................................................ 150* 153

Louisiana ........................................................................ 134* 138

Maine ............................................................................. 158 158

Maryland ........................................................................ 146 145

Massachusetts ............................................................... 158* 161

Michigan ........................................................................ 155 155

Minnesota ...................................................................... 159 158

Mississippi ..................................................................... 134 132

Missouri ......................................................................... 154 154

Montana ......................................................................... 164 162

Nebraska ........................................................................ 158 NA

Nevada ........................................................................... 141* 138

New Hampshire ............................................................. NA 162

New Jersey .................................................................... NA 153

New Mexico ................................................................... 139 138

New York ........................................................................ 145 NA

North Carolina ................................................................ 145 144

North Dakota ................................................................. 159* 163

Ohio ............................................................................... 159 155

Oklahoma ....................................................................... 149 147

Oregon ........................................................................... 154 153

Pennsylvania .................................................................. NA NA

Rhode Island .................................................................. 148 146

South Carolina ............................................................... 140* 145

South Dakota ................................................................. NA 161

Tennessee ...................................................................... 145 145

Texas .............................................................................. 143 143

Utah ............................................................................... 154 154

Vermont .......................................................................... 159* 162

Virginia ........................................................................... 151* 155

Washington .................................................................... NA 154

West Virginia .................................................................. 146 147

Wisconsin ...................................................................... NA 158

Wyoming ........................................................................ 156* 159

Puerto Rico .................................................................... NA NA

IÃ}vV>ÌÞÊ`vviÀiÌÊvÀÊÓääxÊÜiÊÞÊiÊÕÀÃ`VÌÊÀÊÌiÊ>ÌÊÃÊLi}ÊiÝ>i`ÆÊ

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 2010
8-24 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 of Educational Progress (NAEP) provides a federally 

authorized measure of student performance in science. The National Assessment 

Governing Board sets performance standards that provide a context for interpreting 

NAEP results. The standards define “proficiency” as well performance levels that 

indicate greater (“advanced”) and lesser (“basic”) accomplishment. For the eighth 

grade, the proficient level (scores 170–207) represents solid academic performance 

and demonstrates competency over challenging subject-matter knowledge, its application to real-world situations, and mastery of appropriate analytical skills. The 

advanced level (208–300) signifies superior performance. The basic level (143–169) 

denotes partial mastery of knowledge and skills that are prerequisite for proficient 

work. A National Academy of Sciences panel evaluated the process used to establish 

the standards for the science assessment and urged that they be considered developmental and interpreted with caution.

Approximately 143,400 eighth grade students in 6,400 schools participated in 

the 2005 NAEP science assessment. A designation of NA (not available) indicates 

that the state either did not participate in the assessment or did not have a representative sample of eighth graders that was large enough for reporting state-level results.

Findings

U Of the 36 states that participated in both 

the 2000 and 2005 science assessments 

for public school eighth graders, 4 states 

showed significant increases in science 

proficiency in 2005.

UÊ Unlike math proficiency, science 

proficiency did not increase between 

2000 and 2005.

UÊ The states with the highest percentages 

of eighth graders who were proficient in 

science are concentrated in the northern 

United States. A number of EPSCoR 

states were included in this group.

UÊ The nationwide percentage of students 

who performed at or above the proficient 

level in science was identical for fourth 

and eighth graders in 2005.

Eighth Grade Science Proficiency

Figure 8-8

Students reaching proficiency in eighth grade science: 2005

­*iÀViÌÆÊÃVÀiÃÊvÊ£ÇäÊ>`Ê>LÛiÊ`V>ÌiÊ«ÀwViVÞ®

1st quartile (43%–35%) 2nd quartile (34%–31%) 3rd quartile (29%–23%) 4th quartile (22%–14%) No data

Colorado Connecticut Arkansas ‡

 Alabama ‡ Alaska ‡

Idaho ‡ Kentucky ‡ Delaware ‡ Arizona District of Columbia

Massachusetts Maine ‡

 Georgia California Iowa

Michigan Missouri Illinois Florida Kansas ‡

Minnesota New Jersey Indiana Hawaii ‡ Nebraska ‡

Montana ‡ Oregon Maryland Louisiana ‡ New York

New Hampshire ‡ Utah Oklahoma ‡ Mississippi ‡ Pennsylvania

North Dakota ‡

 Washington Rhode Island ‡ Nevada ‡

Ohio South Carolina ‡ New Mexico ‡

South Dakota ‡ Tennessee North Carolina

Vermont ‡

 Texas

Virginia West Virginia ‡

Wisconsin

Wyoming ‡

‡ EPSCoR state

SOURCE: National Center for Education Statistics, National Assessment of Educational Progress. See table 8-8.

1st quartile

2nd quartile

3rd quartile

4th quartile

No data

Achievement levels

(Scores range from 0–300)

Advanced ..... 208–300

Proficient ...... 170–207

Basic ............. 143–169
Science and Engineering Indicators 2010 8-25

Table 8-8

Students reaching proficiency in eighth grade science, by state: 2000 

and 2005

(Percent)

State 2000 2005

United States ..................................................................... 29 27

Alabama ......................................................................... 23 19

Alaska ............................................................................ NA NA

Arizona ........................................................................... 23 20

Arkansas ........................................................................ 22 23

California ........................................................................ 14* 18

Colorado ........................................................................ NA 35

Connecticut ................................................................... 35 33

Delaware ........................................................................ NA 29

District of Columbia ....................................................... NA NA

Florida ............................................................................ NA 21

Georgia .......................................................................... 23 25

Hawaii ............................................................................ 14 15

Idaho .............................................................................. 37 36

Illinois ............................................................................. 29 27

Indiana ........................................................................... 33 29

Iowa ............................................................................... NA NA

Kansas ........................................................................... NA NA

Kentucky ........................................................................ 28 31

Louisiana ........................................................................ 18 19

Maine ............................................................................. 35 34

Maryland ........................................................................ 27 26

Massachusetts ............................................................... 39 41

Michigan ........................................................................ 35 35

Minnesota ...................................................................... 41 39

Mississippi ..................................................................... 15 14

Missouri ......................................................................... 33 33

Montana ......................................................................... 44 42

Nebraska ........................................................................ 38 NA

Nevada ........................................................................... 22 19

New Hampshire ............................................................. NA 41

New Jersey .................................................................... NA 33

New Mexico ................................................................... 20 18

New York ........................................................................ 28 NA

North Carolina ................................................................ 25 22

North Dakota ................................................................. 38* 43

Ohio ............................................................................... 39 35

Oklahoma ....................................................................... 25 25

Oregon ........................................................................... 34 32

Pennsylvania .................................................................. NA NA

Rhode Island .................................................................. 27 26

South Carolina ............................................................... 20 23

South Dakota ................................................................. NA 41

Tennessee ...................................................................... 24 25

Texas .............................................................................. 23 23

Utah ............................................................................... 34 33

Vermont .......................................................................... 39 41

Virginia ........................................................................... 29* 35

Washington .................................................................... NA 33

West Virginia .................................................................. 24 23

Wisconsin ...................................................................... NA 39

Wyoming ........................................................................ 34* 37

Puerto Rico .................................................................... NA NA

IÃ}vV>ÌÞÊ`vviÀiÌÊvÀÊÓääxÊÜiÊÞÊiÊÕÀÃ`VÌÊÀÊÌiÊ>ÌÊÃÊLi}ÊiÝ>i`ÆÊ

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 2010
8-26 Chapter 8. State Indicators

This indicator measures the average base salary of all fulltime public school teachers. The year is the end date of the 

academic year. For example, 2007 data represent salaries for the 

2006–07 academic year. The figures (given in current dollars) 

include salaries for teachers with varying amounts of teaching 

experience and various kinds and levels of formal education.

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 During the 2006–07 academic year, salaries for public school 

teachers nationwide averaged $50,816, ranging from a state 

low of $35,378 to a high of $63,640.

UÊ Between 1997 and 2007, average teacher salaries across 

the nation rose by 32% in terms of current dollars. Average 

teacher salaries remained essentially flat when expressed in 

constant dollars.

UÊ The highest salaries for teachers are found in states with a 

high cost of living.

UÊ Teachers in EPSCoR states tended to receive lower average 

salaries, placing them predominantly in the bottom half of the 

state rankings.

UÊ High salaries for public school teachers do not necessarily 

correspond to high student achievement scores on the NAEP 

mathematics and science tests.

Public School Teacher Salaries

1st quartile ($63,640–$54,658) 2nd quartile ($51,937–$45,941) 3rd quartile ($45,833–$42,816) 4th quartile ($42,798–$35,378)

Alaska ‡

 Arizona Alabama ‡ Idaho ‡

California Georgia Arkansas ‡ Maine ‡

Connecticut Hawaii ‡ Colorado Mississippi ‡

Delaware ‡ Indiana Florida Missouri

District of Columbia Minnesota Iowa Montana ‡

Illinois New Hampshire ‡ Kansas ‡ Nebraska ‡

Maryland North Carolina Kentucky ‡ New Mexico ‡

Massachusetts Ohio Louisiana ‡ North Dakota ‡

Michigan Oregon Nevada ‡ Oklahoma ‡

New Jersey Vermont ‡ South Carolina ‡ South Dakota ‡

New York Washington Tennessee Utah

Pennsylvania Wisconsin Texas West Virginia ‡

Rhode Island ‡ Wyoming ‡ Virginia

‡ EPSCoR state

SOURCE: National Center for Education Statistics, Digest of Education Statistics. See table 8-9.

Figure 8-9

Public school teacher salaries: 2007

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-27

Table 8-9

Public school teacher salaries, by state: 1997, 2002, and 2007

(Dollars)

State 1997 2002 2007

United States .......................................................................................... 38,509 44,604 50,816

Alabama .............................................................................................. 32,549 39,268 43,389

Alaska ................................................................................................. 50,647 49,418 54,658

Arizona ................................................................................................ 33,350 36,966 45,941

Arkansas ............................................................................................. 29,975 35,389 44,245

California ............................................................................................. 43,474 53,870 63,640

Colorado ............................................................................................. 36,175 40,222 45,833

Connecticut ........................................................................................ 50,426 54,300 60,822

Delaware ............................................................................................. 41,436 48,363 54,680

District of Columbia ............................................................................ 45,012 47,049 59,000

Florida ................................................................................................. 33,881 38,719 45,308

Georgia ............................................................................................... 36,042 44,073 49,905

Hawaii ................................................................................................. 35,842 41,951 51,922

Idaho ................................................................................................... 31,818 37,482 42,798

Illinois .................................................................................................. 42,679 50,000 58,246

Indiana ................................................................................................ 38,575 44,195 47,831

Iowa .................................................................................................... 33,275 38,230 43,130

Kansas ................................................................................................ 35,837 36,673 43,334

Kentucky ............................................................................................. 33,950 37,847 43,646

Louisiana ............................................................................................. 28,347 35,437 42,816

Maine .................................................................................................. 33,800 37,100 41,596

Maryland ............................................................................................. 41,148 46,200 56,927

Massachusetts .................................................................................... 43,806 50,293 58,624

Michigan ............................................................................................. 44,251 52,037 54,895

Minnesota ........................................................................................... 37,975 43,330 49,634

Mississippi .......................................................................................... 27,720 32,800 40,182

Missouri .............................................................................................. 34,342 37,695 41,839

Montana .............................................................................................. 29,950 34,379 41,225

Nebraska ............................................................................................. 31,768 36,236 42,044

Nevada ................................................................................................ 37,340 41,524 45,342

New Hampshire .................................................................................. 36,867 38,911 46,527

New Jersey ......................................................................................... 49,349 54,575 59,920

New Mexico ........................................................................................ 29,715 36,490 42,780

New York ............................................................................................. 49,560 53,081 58,537

North Carolina ..................................................................................... 31,225 42,959 46,410

North Dakota ...................................................................................... 27,711 31,709 38,822

Ohio .................................................................................................... 38,831 44,492 51,937

Oklahoma ............................................................................................ 29,270 35,412 42,379

Oregon ................................................................................................ 40,900 43,886 50,911

Pennsylvania ....................................................................................... 47,429 50,599 54,970

Rhode Island ....................................................................................... 43,019 49,758 55,956

South Carolina .................................................................................... 32,659 38,943 44,133

South Dakota ...................................................................................... 26,764 31,295 35,378

Tennessee ........................................................................................... 33,789 38,554 43,816

Texas ................................................................................................... 32,644 39,293 44,897

Utah .................................................................................................... 31,750 37,414 40,566

Vermont ............................................................................................... 37,200 38,802 48,370

Virginia ................................................................................................ 35,837 41,262 44,727

Washington ......................................................................................... 37,860 43,483 47,882

West Virginia ....................................................................................... 33,159 36,751 40,531

Wisconsin ........................................................................................... 38,950 43,114 47,901

Wyoming ............................................................................................. 31,721 37,841 50,692

Puerto Rico ......................................................................................... NA NA NA

NA = not available

NOTES: National average for United States is reported value in 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 2010
8-28 Chapter 8. State Indicators

This indicator measures the relative amount of resources that local, state, 

and federal governments direct toward public education in prekindergarten 

through grade 12. It is calculated by dividing the current expenditures of 

elementary and secondary public schools by the state’s gross domestic product (GDP). Current expenditures include instruction and instruction-related 

costs, student support service, 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.

Financial data on public elementary and secondary education are reported 

by the National Center for Education Statistics, Department of Education. 

These data are part of the National Public Education Financial Survey and are 

included in the 2007 Common Core of Data, a comprehensive annual national 

statistical database that covers approximately 97,000 public elementary and 

secondary schools and 18,000 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 2007 

represent expenditures for the 2006–07 academic year. GDP data refer to 

the 2007 calendar year.

Findings

U The 2007 national average for spending on 

elementary and secondary education was 

3.48% of the GDP, a slight increase from 

3.28% in 1997. Among individual states, the 

value for this indicator ranged from 2.34% to 

5.28% of the state’s GDP in 2007, indicating 

that some states were directing a much 

higher percentage of their resources toward 

elementary and secondary education.

UÊ Spending for elementary and secondary 

public education as a share of the state’s GDP 

decreased in 16 states and the District of 

Columbia during the 1997–2007 period.

UÊ States that spent the highest percentage 

of their GDP on elementary and secondary 

education are located in the eastern region of 

the United States. A number of EPSCoR states 

were included in this group.

Elementary and Secondary Public School Current Expenditures as Share of 

Gross Domestic Product

1st quartile (5.28%–3.87%) 2nd quartile (3.86%–3.54%) 3rd quartile (3.52%–3.14%) 4th quartile (3.09%–1.22%)

Arkansas ‡

 Alabama ‡ Arizona Colorado

Maine ‡

 Alaska ‡ California Delaware ‡

Michigan Connecticut Hawaii ‡ District of Columbia

Mississippi ‡

 Georgia Idaho ‡ Florida

New Hampshire ‡

 Indiana Illinois Louisiana ‡

New Jersey Kansas ‡ Iowa Nevada ‡

New York Kentucky ‡ Minnesota North Carolina 

Ohio Maryland Missouri North Dakota ‡

Rhode Island ‡ Massachusetts Nebraska ‡ South Dakota ‡

South Carolina ‡ Montana ‡ Oklahoma ‡ Tennessee

Vermont ‡

 New Mexico ‡ Oregon Utah

West Virginia ‡

 Pennsylvania Texas Washington

Wisconsin Wyoming ‡

 Virginia

‡ EPSCoR state 

-"1,

-\Ê >Ì>Ê
iÌiÀÊvÀÊ
`ÕV>ÌÊ-Ì>ÌÃÌVÃÊ­ 

-®]Ê 

-Ê
Ê
ÀiÊvÊ>Ì>]Ê >Ì>Ê*ÕLVÊ
`ÕV>ÌÊ>V>Ê-ÕÀÛiÞÆÊ>`Ê	ÕÀi>ÕÊvÊ

Economic Analysis, Gross Domestic Product data. See table 8-10.

Figure 8-10

Elementary and secondary public school current expenditures as share of gross domestic product: 2007

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-29

Table 8-10

Elementary and secondary public school current expenditures as share of gross domestic product, by state: 

1997, 2002, and 2007

Public school expenditures 

(current $thousands) State GDP (current $millions)

School expenditures/

GDP (%)

State 1997 2002 2007 1997 2002 2007 1997 2002 2007

United States ................... 270,151,583 368,499,139 476,825,866 8,237,996 10,398,403 13,715,741 3.28 3.54 3.48

Alabama ....................... 3,436,406 4,444,390 6,245,031 102,433 123,805 164,524 3.35 3.59 3.80

Alaska .......................... 1,069,379 1,284,854 1,634,316 25,028 29,186 44,887 4.27 4.40 3.64

Arizona ......................... 3,527,473 5,499,645 7,815,720 127,370 171,942 245,952 2.77 3.20 3.18

Arkansas ...................... 2,074,113 2,822,877 3,997,701 59,182 72,203 95,116 3.50 3.91 4.20

California ...................... 29,909,168 46,265,544 57,352,599 1,019,150 1,340,446 1,801,762 2.93 3.45 3.18

Colorado ...................... 3,577,211 5,151,003 6,579,053 132,881 182,154 235,848 2.69 2.83 2.79

Connecticut ................. 4,522,716 6,031,062 7,855,459 137,698 166,073 212,252 3.28 3.63 3.70

Delaware ...................... 788,715 1,072,875 1,437,707 35,488 45,324 61,545 2.22 2.37 2.34

District of Columbia ..... 632,952 912,432 1,130,006 50,368 67,717 92,516 1.26 1.35 1.22

Florida .......................... 12,018,676 15,535,864 22,887,024 391,451 522,719 741,861 3.07 2.97 3.09

Georgia ........................ 7,230,405 10,853,496 14,828,715 237,468 306,680 391,241 3.04 3.54 3.79

Hawaii .......................... 1,057,069 1,348,381 1,998,913 37,546 43,476 62,019 2.82 3.10 3.22

Idaho ............................ 1,090,597 1,481,803 1,777,491 28,510 36,651 52,110 3.83 4.04 3.41

Illinois ........................... 11,720,249 16,480,787 20,326,591 403,982 487,129 617,409 2.90 3.38 3.29

Indiana ......................... 6,055,055 7,704,547 9,497,077 168,115 205,015 249,229 3.60 3.76 3.81

Iowa ............................. 2,885,943 3,565,796 4,231,932 81,923 97,356 129,911 3.52 3.66 3.26

Kansas ......................... 2,568,525 3,450,923 4,339,477 72,071 89,573 116,986 3.56 3.85 3.71

Kentucky ...................... 3,382,062 4,268,608 5,424,621 105,725 120,726 152,099 3.20 3.54 3.57

Louisiana ...................... 3,747,507 4,802,565 6,040,368 113,261 134,308 207,407 3.31 3.58 2.91

Maine ........................... 1,351,500 1,812,798 2,258,764 30,873 38,625 48,021 4.38 4.69 4.70

Maryland ...................... 5,529,309 7,480,723 10,198,084 154,139 204,120 264,426 3.59 3.66 3.86

Massachusetts ............. 6,846,610 9,957,292 12,453,611 221,827 284,386 352,178 3.09 3.50 3.54

Michigan ...................... 11,686,124 14,975,150 17,013,259 298,994 349,837 379,934 3.91 4.28 4.48

Minnesota .................... 5,087,353 6,586,559 8,060,410 155,938 198,558 252,472 3.26 3.32 3.19

Mississippi ................... 2,035,675 2,642,116 3,692,358 57,954 68,144 87,652 3.51 3.88 4.21

Missouri ....................... 4,775,931 6,491,603 7,957,705 158,203 188,351 229,027 3.02 3.45 3.47

Montana ....................... 902,252 1,073,005 1,320,112 19,142 23,560 34,266 4.71 4.55 3.85

Nebraska ...................... 1,707,455 2,206,946 2,825,608 50,542 59,934 80,360 3.38 3.68 3.52

Nevada ......................... 1,434,395 2,169,000 3,311,471 59,917 81,274 129,314 2.39 2.67 2.56

New Hampshire ........... 1,173,958 1,641,378 2,246,692 36,569 46,188 57,820 3.21 3.55 3.89

New Jersey .................. 11,771,941 15,822,609 22,448,262 300,910 372,754 461,295 3.91 4.24 4.87

New Mexico ................. 1,557,376 2,204,165 2,904,474 47,442 52,510 75,192 3.28 4.20 3.86

New York ...................... 24,237,291 32,218,975 43,679,908 654,750 821,577 1,105,020 3.70 3.92 3.95

North Carolina .............. 5,964,939 8,550,546 11,248,336 228,864 296,435 390,467 2.61 2.88 2.88

North Dakota ............... 577,498 711,437 838,221 16,316 19,880 28,518 3.54 3.58 2.94

Ohio ............................. 10,948,074 14,774,065 18,251,361 332,124 389,773 462,506 3.30 3.79 3.95

Oklahoma ..................... 2,990,044 3,875,547 4,750,536 78,019 97,170 136,374 3.83 3.99 3.48

Oregon ......................... 3,184,100 4,214,512 5,039,632 96,591 117,131 158,268 3.30 3.60 3.18

Pennsylvania ................ 12,820,704 15,550,975 20,404,304 343,368 423,110 533,212 3.73 3.68 3.83

Rhode Island ................ 1,151,888 1,533,455 2,039,633 28,506 36,909 46,699 4.04 4.15 4.37

South Carolina ............. 3,296,661 4,744,809 6,023,043 97,397 121,582 151,703 3.38 3.90 3.97

South Dakota ............... 627,109 819,296 977,006 19,804 26,416 35,211 3.17 3.10 2.77

Tennessee .................... 4,145,380 5,511,452 6,975,099 153,405 191,525 245,162 2.70 2.88 2.85

Texas ............................ 20,167,238 28,191,128 36,105,784 599,492 783,480 1,148,531 3.36 3.60 3.14

Utah ............................. 1,822,725 2,374,702 2,987,810 56,590 72,665 105,574 3.22 3.27 2.83

Vermont ........................ 718,092 992,149 1,300,149 15,167 19,553 24,627 4.73 5.07 5.28

Virginia ......................... 6,343,766 8,718,554 12,465,858 211,921 285,759 384,132 2.99 3.05 3.25

Washington .................. 5,587,808 7,103,721 8,752,007 178,334 231,463 310,279 3.13 3.07 2.82

West Virginia ................ 1,847,560 2,219,013 2,742,344 38,795 45,032 57,877 4.76 4.93 4.74

Wisconsin .................... 5,975,122 7,592,176 9,029,660 151,549 188,600 233,406 3.94 4.03 3.87

Wyoming ...................... 591,488 761,830 1,124,564 14,904 19,619 31,544 3.97 3.88 3.57

Puerto Rico .................. 1,796,077 2,152,724 3,268,200 48,187 71,624 NA 3.73 3.01 NA

NA = not available

GDP = gross domestic product

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Science and Engineering Indicators 2010
8-30 Chapter 8. State Indicators

Current Expenditures per Pupil for Elementary and Secondary 

Public Schools

Figure 8-11

Current expenditures per pupil for elementary and secondary public schools: 2007

1st quartile ($16,163–$11,060) 2nd quartile ($11,037–$9,102) 3rd quartile ($9,080–$8,391) 4th quartile ($8,286–$5,706)

Alaska ‡

 Georgia Alabama ‡ Arizona

Connecticut Illinois Arkansas ‡

 Colorado

Delaware ‡

 Kansas ‡ California Idaho ‡

District of Columbia Michigan Florida Kentucky ‡

Hawaii ‡ Minnesota Indiana Mississippi ‡

Maine ‡ Montana ‡ Iowa Nevada ‡

Maryland Nebraska ‡ Louisiana ‡ North Carolina

Massachusetts New Hampshire ‡ Missouri Oklahoma ‡

New Jersey Ohio New Mexico ‡ South Dakota ‡

New York Pennsylvania North Dakota ‡ Tennessee

Rhode Island ‡

 Virginia Oregon Texas

Vermont ‡

 West Virginia ‡ South Carolina ‡ Utah

Wyoming ‡

 Wisconsin Washington

‡ EPSCoR state 

SOURCES: National Center for Education Statistics (NCES), NCES Common Core of Data, State Nonfiscal Survey of Public Elementary/Secondary 


`ÕV>ÌÆÊ>`Ê >Ì>Ê*ÕLVÊ
`ÕV>ÌÊ>V>Ê-ÕÀÛiÞ°ÊSee table 8-11.

1st quartile

2nd quartile

3rd quartile

4th quartile

This indicator measures 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 represent 

amounts spent on the day-to-day operations of schools and school districts. 

They 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.

During the 2006–07 school year, 65.8% of current expenses were used for 

instructional costs, 5.3% for student support services, 10.8% for administrative 

costs, and 18.0% for operational costs.

The number of pupils enrolled in prekindergarten through grade 12 is determined during the fall of the academic year. All figures represent actual spending 

and have not been adjusted for inflation. The year is the end date of the academic 

year. For example, data for 2007 represent costs for the 2006–07 academic year.

Current expenditures per pupil do not take into account the cost of living in a 

state, which could affect the amount of goods and services that can be purchased.

Findings

U Per-pupil spending on day-to-day operations 

grew nationwide from $5,923 in 1997 to 

$9,683 in 2007, an increase of 63% in 

Õ>`ÕÃÌi`Ê`>ÀÃ°Ê/ÃÊÜ>ÃÊiμÕÛ>iÌÊÌÊ>Ê

VÀi>ÃiÊvÊ>««ÀÝ>ÌiÞÊÎä ̄Ê>vÌiÀÊ>`ÕÃÌ}Ê

for inflation.

UÊ In 2007, all states showed substantial 

increases in per-pupil spending relative to 

1997, and only 1 state did not exceed the 

1997 national average, compared with 31 

states in 1997.

UÊ Per-pupil spending in individual states varied 

widely, ranging from a high of $16,163 to a 

low of $5,706 in 2007.

UÊ No direct correlation can be made between 

spending and academic performance. 

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
Science and Engineering Indicators 2010 8-31

Table 8-11

Current expenditures per pupil for elementary and secondary public schools, by state: 1997, 2002, and 2007

Public school expenditures 

($thousands) Student enrollment

Per-pupil 

expenditures ($)

State 1997 2002 2007 1997 2002 2007 1997 2002 2007

United States ................. 270,151,583 368,499,139 476,825,866 45,611,046 47,647,972 49,245,840 5,923 7,734 9,683

Alabama ..................... 3,436,406 4,444,390 6,245,031 747,932 737,190 743,632 4,595 6,029 8,398

Alaska ........................ 1,069,379 1,284,854 1,634,316 129,919 134,358 132,608 8,231 9,563 12,324

Arizona ....................... 3,527,473 5,499,645 7,815,720 799,250 922,180 1,065,082 4,413 5,964 7,338

Arkansas .................... 2,074,113 2,822,877 3,997,701 457,349 449,805 476,409 4,535 6,276 8,391

California .................... 29,909,168 46,265,544 57,352,599 5,686,198 6,223,821 6,406,821 5,260 7,434 8,952

Colorado .................... 3,577,211 5,151,003 6,579,053 673,438 742,145 794,026 5,312 6,941 8,286

Connecticut ............... 4,522,716 6,031,062 7,855,459 527,129 570,228 575,100 8,580 10,577 13,659

Delaware .................... 788,715 1,072,875 1,437,707 110,549 115,560 122,254 7,135 9,284 11,760

District of Columbia ... 632,952 912,432 1,130,006 78,648 75,392 72,850 8,048 12,103 15,511

Florida ........................ 12,018,676 15,535,864 22,887,024 2,242,212 2,500,478 2,671,513 5,360 6,213 8,567

Georgia ...................... 7,230,405 10,853,496 14,828,715 1,346,761 1,470,634 1,629,157 5,369 7,380 9,102

Hawaii ........................ 1,057,069 1,348,381 1,998,913 187,653 184,546 180,728 5,633 7,306 11,060

Idaho .......................... 1,090,597 1,481,803 1,777,491 245,252 246,521 267,380 4,447 6,011 6,648

Illinois ......................... 11,720,249 16,480,787 20,326,591 1,973,040 2,071,391 2,118,276 5,940 7,956 9,596

Indiana ....................... 6,055,055 7,704,547 9,497,077 982,876 996,133 1,045,940 6,161 7,734 9,080

Iowa ........................... 2,885,943 3,565,796 4,231,932 502,941 485,932 481,368 5,738 7,338 8,791

Kansas ....................... 2,568,525 3,450,923 4,339,477 466,293 470,205 469,506 5,508 7,339 9,243

Kentucky .................... 3,382,062 4,268,608 5,424,621 656,089 654,363 683,173 5,155 6,523 7,940

Louisiana .................... 3,747,507 4,802,565 6,040,368 793,296 731,328 675,851 4,724 6,567 8,937

Maine ......................... 1,351,500 1,812,798 2,258,764 213,593 205,586 193,986 6,327 8,818 11,644

Maryland .................... 5,529,309 7,480,723 10,198,084 818,583 860,640 851,640 6,755 8,692 11,975

Massachusetts ........... 6,846,610 9,957,292 12,453,611 933,898 973,140 968,661 7,331 10,232 12,857

Michigan .................... 11,686,124 14,975,150 17,013,259 1,685,714 1,730,668 1,714,709 6,932 8,653 9,922

Minnesota .................. 5,087,353 6,586,559 8,060,410 847,204 851,384 840,565 6,005 7,736 9,589

Mississippi ................. 2,035,675 2,642,116 3,692,358 503,967 493,507 495,026 4,039 5,354 7,459

Missouri ..................... 4,775,931 6,491,603 7,957,705 900,517 909,792 899,426 5,304 7,135 8,848

Montana ..................... 902,252 1,073,005 1,320,112 164,627 151,947 143,624 5,481 7,062 9,191

Nebraska .................... 1,707,455 2,206,946 2,825,608 291,967 285,095 280,647 5,848 7,741 10,068

Nevada ....................... 1,434,395 2,169,000 3,311,471 282,131 356,814 424,240 5,084 6,079 7,806

New Hampshire ......... 1,173,958 1,641,378 2,246,692 198,308 206,847 203,551 5,920 7,935 11,037

New Jersey ................ 11,771,941 15,822,609 22,448,262 1,227,832 1,341,656 1,388,850 9,588 11,793 16,163

New Mexico ............... 1,557,376 2,204,165 2,904,474 332,632 320,260 328,220 4,682 6,882 8,849

New York .................... 24,237,291 32,218,975 43,679,908 2,843,131 2,872,132 2,809,649 8,525 11,218 15,546

North Carolina ............ 5,964,939 8,550,546 11,248,336 1,210,108 1,315,363 1,427,880 4,929 6,501 7,878

North Dakota ............. 577,498 711,437 838,221 120,123 106,047 96,670 4,808 6,709 8,671

Ohio ........................... 10,948,074 14,774,065 18,251,361 1,844,698 1,830,985 1,836,096 5,935 8,069 9,940

Oklahoma ................... 2,990,044 3,875,547 4,750,536 620,695 622,139 639,391 4,817 6,229 7,430

Oregon ....................... 3,184,100 4,214,512 5,039,632 537,854 551,480 562,574 5,920 7,642 8,958

Pennsylvania .............. 12,820,704 15,550,975 20,404,304 1,804,256 1,821,627 1,871,060 7,106 8,537 10,905

Rhode Island .............. 1,151,888 1,533,455 2,039,633 151,324 158,046 151,612 7,612 9,703 13,453

South Carolina ........... 3,296,661 4,744,809 6,023,043 652,816 676,198 703,119 5,050 7,017 8,566

South Dakota ............. 627,109 819,296 977,006 143,331 127,542 121,158 4,375 6,424 8,064

Tennessee .................. 4,145,380 5,511,452 6,975,099 904,818 924,899 978,368 4,581 5,959 7,129

Texas .......................... 20,167,238 28,191,128 36,105,784 3,828,975 4,163,447 4,599,509 5,267 6,771 7,850

Utah ........................... 1,822,725 2,374,702 2,987,810 481,812 484,677 523,586 3,783 4,900 5,706

Vermont ...................... 718,092 992,149 1,300,149 106,341 101,179 95,399 6,753 9,806 13,629

Virginia ....................... 6,343,766 8,718,554 12,465,858 1,096,093 1,163,091 1,220,440 5,788 7,496 10,214

Washington ................ 5,587,808 7,103,721 8,752,007 974,504 1,009,200 1,026,774 5,734 7,039 8,524

West Virginia .............. 1,847,560 2,219,013 2,742,344 304,052 282,885 281,939 6,076 7,844 9,727

Wisconsin .................. 5,975,122 7,592,176 9,029,660 879,259 879,361 871,027 6,796 8,634 10,367

Wyoming .................... 591,488 761,830 1,124,564 99,058 88,128 84,770 5,971 8,645 13,266

Puerto Rico ................ 1,796,077 2,152,724 3,268,200 618,861 604,177 544,138 2,902 3,563 6,006

SOURCES: National Center for Education Statistics (NCES), NCES Common Core of Data, State Nonfiscal Survey of Public Elementary/Secondary 


`ÕV>ÌÊ­Û>ÀÕÃÊÞi>ÀÃ®ÆÊ>`Ê >Ì>Ê*ÕLVÊ
`ÕV>ÌÊ>V>Ê-ÕÀÛiÞÊ­Û>ÀÕÃÊÞi>ÀÃ®°

Science and Engineering Indicators 2010
8-32 Chapter 8. State Indicators

1st quartile (37.2%–28.4%) 2nd quartile (27.6%–21.2%) 3rd quartile (21.1%–15.3%) 4th quartile (15.0%–8.4%)

Arkansas ‡

 Delaware ‡ Alaska ‡ Alabama ‡

California District of Columbia Hawaii ‡ Arizona

Colorado Illinois Indiana Idaho ‡

Connecticut Minnesota Kentucky ‡ Iowa

Florida Nevada ‡

 Michigan Kansas ‡

Georgia New Jersey Montana ‡ Louisiana ‡

Maine ‡ New Mexico ‡ New Hampshire ‡ Mississippi ‡

Maryland Oregon Ohio Missouri

Massachusetts South Carolina ‡

 Oklahoma ‡ Nebraska ‡

New York Texas Pennsylvania North Dakota ‡

North Carolina Utah South Dakota ‡ Rhode Island ‡

Vermont ‡ Washington Tennessee Wyoming ‡

Virginia Wisconsin West Virginia ‡

‡ EPSCoR state 

SOURCE: College Board, Advanced Placement Report to the Nation. See table 8-12.

1st quartile

2nd quartile

3rd quartile

4th quartile

Share of Public High School Students Taking Advanced Placement Exams

Figure 8-12

Share of public high school students taking Advanced Placement Exams: 2008

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 measures the percentage of students in 

the graduating class who have taken one or more AP Exams. This percentage is calculated by dividing the number of students in the graduating class 

who have taken at least one AP Exam by the total number of students in the 

graduating class.

Throughout the United States, nearly 758,000 public school students from 

the class of 2008 took nearly 2.2 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.

In the 50 states and the District of Columbia, students from the class of 

2008 attended 12,323 U.S. public schools that participated in the AP program. 

This represented 79% of the public high schools in the United States. 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 25.0% of the 

class of 2008.

UÊ The percentage of public school students 

taking an AP Exam varied greatly among 

states and ranged from 8.4% to 37.2% of the 

class of 2008. Thirty-six states and the District 

of Columbia exceeded the 2000 national 

average in 2008, compared with 15 states and 

the District of Columbia that exceeded the 

national average in 2000.

UÊ AP participation levels were higher for all 

ÕÀÃ`VÌÃÊÊÓäänÊÌ>ÊÊÓäää°ÊÀ>Ã>ÃÊ

showed the largest increase, with the class of 

2008 exceeding the participation of the class 

of 2000 by more than 25 percentage points. 

Many of the EPSCoR states had the lowest 

AP Exam participation rates.
Science and Engineering Indicators 2010 8-33

Table 8-12

Share of public high school students taking Advanced Placement 

Exams, by state: 2000, 2004, and 2008

(Percent)

State 2000 2004 2008

United States ............................................. 15.9 20.9 25.0

Alabama ................................................. 7.2 8.8 13.5

Alaska .................................................... 15.4 16.7 20.3

Arizona ................................................... 11.3 12.9 14.0

Arkansas ................................................ 8.1 13.0 33.3

California ................................................ 22.2 28.5 30.8

Colorado ................................................ 18.6 25.3 30.5

Connecticut ........................................... 19.1 24.6 29.0

Delaware ................................................ 13.3 19.6 26.8

District of Columbia ............................... 17.3 23.1 26.3

Florida .................................................... 22.7 33.5 34.0

Georgia .................................................. 17.2 21.5 30.3

Hawaii .................................................... 10.6 14.8 16.6

Idaho ...................................................... 9.6 12.5 14.5

Illinois ..................................................... 13.4 18.6 22.8

Indiana ................................................... 11.9 15.5 19.8

Iowa ....................................................... 6.9 10.0 12.6

Kansas ................................................... 7.0 9.2 13.7

Kentucky ................................................ 10.6 15.5 19.8

Louisiana ................................................ 3.2 5.0 8.4

Maine ..................................................... 14.8 19.9 31.9

Maryland ................................................ 20.2 29.2 37.2

Massachusetts ....................................... 19.6 25.3 29.2

Michigan ................................................ 13.9 16.8 20.2

Minnesota .............................................. 13.4 16.4 22.5

Mississippi ............................................. 5.6 7.0 12.6

Missouri ................................................. 5.5 8.1 10.8

Montana ................................................. 10.1 13.0 15.9

Nebraska ................................................ 5.0 6.3 10.7

Nevada ................................................... 15.1 19.8 24.6

New Hampshire ..................................... 13.3 16.0 21.1

New Jersey ............................................ 17.9 21.3 24.2

New Mexico ........................................... 11.1 17.0 21.5

New York ................................................ 27.3 32.4 35.4

North Carolina ........................................ 19.7 26.9 28.4

North Dakota ......................................... 5.9 8.4 10.4

Ohio ....................................................... 11.3 15.2 17.6

Oklahoma ............................................... 9.5 17.0 20.2

Oregon ................................................... 10.5 13.6 21.2

Pennsylvania .......................................... 12.4 14.9 17.9

Rhode Island .......................................... 10.7 12.1 14.9

South Carolina ....................................... 17.7 19.2 23.1

South Dakota ......................................... 9.6 13.5 16.0

Tennessee .............................................. 10.4 13.6 16.5

Texas ...................................................... 16.6 23.2 27.5

Utah ....................................................... 24.5 27.6 27.6

Vermont .................................................. 16.6 21.2 29.0

Virginia ................................................... 25.0 28.1 34.1

Washington ............................................ 11.5 18.5 25.0

West Virginia .......................................... 8.4 13.0 15.3

Wisconsin .............................................. 15.2 20.0 24.2

Wyoming ................................................ 6.1 11.2 15.0

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 2010
8-34 Chapter 8. State Indicators

Share of Public High School Students Scoring 3 or Higher on at Least One 

Advanced Placement Exam

1st quartile (23.4%–17.3%) 2nd quartile (16.6%–13.0%) 3rd quartile (11.9%–8.6%) 4th quartile (8.3%–3.7%)

California Alaska ‡

 Arkansas ‡ Alabama ‡

Colorado Delaware ‡ Idaho ‡ Arizona

Connecticut Georgia Indiana District of Columbia

Florida Illinois Kansas ‡

 Hawaii ‡

Maine ‡ Michigan Kentucky ‡ Iowa

Maryland Minnesota Montana ‡

 Louisiana ‡

Massachusetts Nevada ‡ New Mexico ‡ Mississippi ‡

New Jersey New Hampshire ‡ Ohio Missouri

New York Oregon Oklahoma ‡

 Nebraska ‡

North Carolina South Carolina ‡ Pennsylvania North Dakota ‡

Utah Texas Rhode Island ‡ West Virginia ‡

Vermont ‡ Washington South Dakota ‡ Wyoming ‡

Virginia Wisconsin Tennessee

‡ EPSCoR state 

SOURCE: College Board, Advanced Placement Report to the Nation. See table 8-13.

Figure 8-13

Share of public high school students scoring 3 or higher on at least one Advanced Placement Exam: 2008

1st quartile

2nd quartile

3rd quartile

4th quartile

This indicator provides a measure of the extent to which 

high school students are successfully demonstrating mastery 

of college-level material. It 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 grades of 3 or higher. A high value on this indicator shows the extent to which students have been offered 

access to rigorous coursework and successfully mastered its 

requirements.

A total of 37 different AP Exams are offered each spring 

by the College Board. The exams are scored on a scale of 

1 to 5, with 3 representing work equivalent to college-level 

performance ranging from midlevel B to midlevel C. 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 is considered one of 

the best predictors of success in college by many colleges 

and universities.

Findings

U Nationally, 15.2% of public school students in the class of 2008 

demonstrated the ability to do college-level work by obtaining 

a score of 3 or higher on at least one AP Exam, a substantial 

increase over the 10.2% achieved by the class of 2000.

UÊ Students from all states demonstrated greater success on AP 

Exams in 2008 than in 2000, but this success was not evenly 

distributed. In 2008, 20 states and the District of Columbia 

had percentages below the 2000 national average of 10.2% 

V«>Ài`ÊÜÌÊÎnÊÕÀÃ`VÌÃÊÊÓäää°

UÊ The percentage of students who are successful on AP 

Exams varies widely among states. For the class of 2008, 

this percentage ranged from a high of 23.4% to a low of 

3.7% across states. Some of this variation occurs because 

opportunities for advanced work are more readily available to 

students in certain states.

UÊ 6>ÕiÃÊvÊÌÃÊ`V>ÌÀÊÜiÀiÊ}iÀÊvÀÊ>ÊÕÀÃ`VÌÃÊÊÓäänÊ

than in 2000. Although two of the three states with the largest 

increase in performance for the class of 2008 were EPSCoR 

states, most of the EPSCoR states ranked in the lower quartiles.
Science and Engineering Indicators 2010 8-35

Table 8-13

Share of public high school students scoring 3 or higher on at least one 

Advanced Placement Exam, by state: 2000, 2004, and 2008

(Percent)

State 2000 2004 2008

United States ................................................... 10.2 13.2 15.2

Alabama ....................................................... 3.9 5.0 6.8

Alaska .......................................................... 10.1 10.8 13.3

Arizona ......................................................... 7.2 8.0 7.9

Arkansas ...................................................... 4.3 6.1 10.6

California ...................................................... 15.0 18.7 20.2

Colorado ...................................................... 12.2 16.2 19.0

Connecticut ................................................. 13.6 17.6 21.0

Delaware ...................................................... 7.6 11.1 13.8

District of Columbia ..................................... 6.6 8.2 6.9

Florida .......................................................... 13.5 19.2 18.2

Georgia ........................................................ 9.7 12.0 16.3

Hawaii .......................................................... 5.8 7.7 8.0

Idaho ............................................................ 6.5 8.1 9.5

Illinois ........................................................... 9.9 13.3 15.2

Indiana ......................................................... 6.0 7.7 10.0

Iowa ............................................................. 4.9 6.6 8.3

Kansas ......................................................... 4.4 6.3 8.6

Kentucky ...................................................... 5.5 7.7 10.0

Louisiana ...................................................... 1.9 2.5 3.7

Maine ........................................................... 10.1 12.8 19.3

Maryland ...................................................... 14.1 19.4 23.4

Massachusetts ............................................. 14.5 18.1 20.8

Michigan ...................................................... 8.8 10.9 13.0

Minnesota .................................................... 8.1 10.6 14.2

Mississippi ................................................... 2.3 2.9 3.9

Missouri ....................................................... 3.7 5.3 6.5

Montana ....................................................... 6.8 8.8 10.6

Nebraska ...................................................... 3.2 4.0 6.5

Nevada ......................................................... 9.1 12.4 13.5

New Hampshire ........................................... 9.2 10.9 15.5

New Jersey .................................................. 12.9 15.5 17.3

New Mexico ................................................. 6.1 8.1 9.9

New York ...................................................... 17.9 21.2 23.3

North Carolina .............................................. 11.3 15.8 17.3

North Dakota ............................................... 4.4 5.7 6.9

Ohio ............................................................. 7.1 9.4 10.8

Oklahoma ..................................................... 5.4 8.3 9.8

Oregon ......................................................... 7.1 8.8 13.1

Pennsylvania ................................................ 8.3 10.1 11.9

Rhode Island ................................................ 6.9 7.8 9.5

South Carolina ............................................. 10.0 11.2 13.8

South Dakota ............................................... 5.9 8.3 9.7

Tennessee .................................................... 6.2 7.9 9.2

Texas ............................................................ 9.9 13.1 14.5

Utah ............................................................. 17.4 19.3 18.9

Vermont ........................................................ 11.5 14.0 19.8

Virginia ......................................................... 15.9 17.7 21.3

Washington .................................................. 7.6 11.6 15.5

West Virginia ................................................ 4.6 6.4 6.9

Wisconsin .................................................... 10.5 13.7 16.6

Wyoming ...................................................... 3.8 6.7 7.5

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 2010
8-36 Chapter 8. State Indicators

This indicator represents the percentage of the early–midcareer population that has earned at least a high school credential. The indicator displays results based on where high school 

graduates live rather than where they were educated. High 

values indicate a resident population and potential workforce 

with widespread basic education credentials.

Estimates of educational attainment have been developed 

by the Census Bureau, which bases them on the 2000 Decennial Census and the American Community Survey (ACS). The 

census is conducted every 10 years, but ACS provides annual 

data on the characteristics of the population and where they live.

In 2005, ACS became the largest household survey in the 

United States, with an annual sample size of about 3 million 

addresses. Estimates of population are developed by the Census 

Bureau through the Population Estimates Program, which is also 

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, 86.7% of the early–mid-career population had at 

least a high school credential in 2007, an increase from the 

85.0% who held such a credential in 2000.

UÊ ÀÌÞÃÝÊÃÌ>ÌiÃÊ>`ÊÌiÊÃÌÀVÌÊvÊ
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an increase in the percentage of their early–mid-career 

population with at least a high school credential between 

2000 and 2007. Ten states had 2007 values below the 2000 

national average of 85.0%, compared with 17 states and the 

District of Columbia in 2000.

UÊ ÊÓääÇ]ÊÌiÊi>ÀÞq`V>ÀiiÀÊ««Õ>ÌÊÜÌÊ>ÌÊi>ÃÌÊ>Ê}

school credential varied greatly among states, ranging from 

80.8% to 95.8%. States in close proximity to the southern 

border of the United States tended to rank lowest on this 

indicator.

High School Graduates or Higher Among Individuals 25–44 Years Old

Figure 8-14

High school graduates or higher among individuals 25–44 years old: 2007

1st quartile (95.8%–91.0%) 2nd quartile (90.7%–89.3%) 3rd quartile (89.2%–86.2%) 4th quartile (85.8%–80.8%)

Connecticut Alaska ‡

 Colorado Alabama ‡

Hawaii ‡ Delaware ‡ Florida Arizona

Iowa District of Columbia Illinois Arkansas ‡

Maine ‡ Idaho ‡ Indiana California

Massachusetts Kansas ‡

 Kentucky ‡ Georgia

Minnesota Maryland Missouri Louisiana ‡

Montana ‡ Michigan New York Mississippi ‡

New Hampshire ‡ Nebraska ‡ Oklahoma ‡ Nevada ‡

New Jersey Ohio Oregon New Mexico ‡

North Dakota ‡ South Dakota ‡ Rhode Island ‡ North Carolina

Pennsylvania Virginia South Carolina ‡

 Tennessee

Vermont ‡ Washington Utah Texas

Wisconsin Wyoming ‡

 West Virginia ‡

‡ EPSCoR state 

-"1,

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1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-37

Table 8-14

High school graduates or higher among individuals 25–44 years old, by state: 2000, 2003, and 2007

Graduates 25–44 years old Population 25–44 years old

Graduates/population 

25–44 years old (%)

State 2000 2003 2007 2000 2003 2007 2000 2003 2007

United States ................... 72,241,876 71,684,426 72,358,114 85,040,251 84,216,990 83,483,659 85.0 85.1 86.7

Alabama ....................... 1,064,945 1,027,964 1,036,147 1,288,527 1,241,184 1,234,350 82.6 82.8 83.9

Alaska .......................... 186,160 167,805 176,847 203,522 194,823 197,222 91.5 86.1 89.7

Arizona ......................... 1,232,818 1,286,915 1,462,672 1,511,469 1,599,029 1,781,045 81.6 80.5 82.1

Arkansas ...................... 622,698 608,116 637,072 750,972 738,579 755,981 82.9 82.3 84.3

California ...................... 8,286,071 8,529,909 8,581,683 10,714,403 10,832,873 10,581,536 77.3 78.7 81.1

Colorado ...................... 1,242,919 1,239,272 1,283,903 1,400,850 1,417,501 1,448,632 88.7 87.4 88.6

Connecticut ................. 926,614 903,677 847,351 1,032,689 999,800 925,266 89.7 90.4 91.6

Delaware ...................... 207,799 204,842 207,161 236,441 233,356 230,359 87.9 87.8 89.9

District of Columbia ..... 157,077 160,782 173,875 189,439 188,758 192,511 82.9 85.2 90.3

Florida .......................... 3,840,710 3,924,625 4,166,135 4,569,347 4,676,558 4,812,179 84.1 83.9 86.6

Georgia ........................ 2,238,995 2,280,061 2,404,616 2,652,764 2,723,720 2,834,749 84.4 83.7 84.8

Hawaii .......................... 333,762 316,491 334,898 362,336 352,806 353,251 92.1 89.7 94.8

Idaho ............................ 316,815 323,260 362,468 362,401 370,690 401,380 87.4 87.2 90.3

Illinois ........................... 3,265,416 3,267,787 3,188,986 3,795,544 3,727,314 3,600,910 86.0 87.7 88.6

Indiana ......................... 1,567,100 1,494,212 1,528,485 1,791,828 1,748,331 1,734,016 87.5 85.5 88.1

Iowa ............................. 740,397 709,299 697,233 808,259 775,320 753,784 91.6 91.5 92.5

Kansas ......................... 687,268 675,316 656,852 769,204 743,961 727,170 89.3 90.8 90.3

Kentucky ...................... 1,009,246 1,013,026 1,023,568 1,210,773 1,182,970 1,188,087 83.4 85.6 86.2

Louisiana ...................... 1,044,255 1,014,054 949,440 1,293,128 1,230,819 1,159,582 80.8 82.4 81.9

Maine ........................... 339,227 325,208 312,986 370,597 358,691 337,652 91.5 90.7 92.7

Maryland ...................... 1,487,216 1,454,663 1,409,457 1,664,677 1,641,907 1,568,230 89.3 88.6 89.9

Massachusetts ............. 1,795,438 1,763,262 1,651,537 1,989,783 1,922,446 1,794,769 90.2 91.7 92.0

Michigan ...................... 2,630,713 2,551,652 2,421,941 2,960,544 2,840,435 2,683,585 88.9 89.8 90.3

Minnesota .................... 1,395,170 1,374,938 1,330,265 1,497,320 1,465,370 1,423,704 93.2 93.8 93.4

Mississippi ................... 650,242 645,671 629,509 807,170 782,327 766,714 80.6 82.5 82.1

Missouri ....................... 1,426,806 1,399,485 1,397,472 1,626,302 1,587,931 1,579,645 87.7 88.1 88.5

Montana ....................... 225,105 213,382 216,166 245,220 232,735 235,309 91.8 91.7 91.9

Nebraska ...................... 441,527 432,446 415,179 487,107 471,024 457,810 90.6 91.8 90.7

Nevada ......................... 508,173 538,622 624,384 628,572 679,392 761,550 80.8 79.3 82.0

New Hampshire ........... 350,744 340,140 326,094 381,240 373,644 351,263 92.0 91.0 92.8

New Jersey .................. 2,313,820 2,254,281 2,184,317 2,624,146 2,578,072 2,400,533 88.2 87.4 91.0

New Mexico ................. 425,745 400,847 433,949 516,100 506,956 516,167 82.5 79.1 84.1

New York ...................... 4,926,064 4,912,059 4,698,849 5,831,622 5,667,484 5,383,101 84.5 86.7 87.3

North Carolina .............. 2,117,289 2,096,022 2,187,835 2,500,535 2,507,025 2,552,793 84.7 83.6 85.7

North Dakota ............... 164,893 157,062 148,753 174,891 160,522 155,217 94.3 97.8 95.8

Ohio ............................. 2,965,744 2,840,789 2,754,008 3,325,210 3,172,294 3,054,756 89.2 89.5 90.2

Oklahoma ..................... 836,030 796,708 827,697 975,169 946,358 955,471 85.7 84.2 86.6

Oregon ......................... 861,602 880,905 907,879 997,269 1,003,698 1,034,933 86.4 87.8 87.7

Pennsylvania ................ 3,136,195 2,966,827 2,895,587 3,508,562 3,343,434 3,182,590 89.4 88.7 91.0

Rhode Island ................ 265,033 262,340 248,540 310,636 306,459 281,590 85.3 85.6 88.3

South Carolina ............. 990,207 1,002,730 1,026,577 1,185,955 1,167,347 1,185,520 83.5 85.9 86.6

South Dakota ............... 188,052 182,643 177,251 206,399 197,386 197,197 91.1 92.5 89.9

Tennessee .................... 1,439,729 1,446,735 1,480,790 1,718,428 1,684,796 1,725,854 83.8 85.9 85.8

Texas ............................ 5,115,457 5,136,496 5,598,948 6,484,321 6,644,003 6,926,932 78.9 77.3 80.8

Utah ............................. 555,513 602,199 672,439 626,600 648,111 753,898 88.7 92.9 89.2

Vermont ........................ 162,109 153,679 147,144 176,456 168,392 157,657 91.9 91.3 93.3

Virginia ......................... 1,962,040 1,911,347 1,971,608 2,237,655 2,227,978 2,204,242 87.7 85.8 89.4

Washington .................. 1,617,766 1,607,576 1,637,950 1,816,217 1,803,610 1,834,696 89.1 89.1 89.3

West Virginia ................ 420,900 400,998 411,722 501,343 479,781 473,410 84.0 83.6 87.0

Wisconsin .................... 1,429,331 1,369,084 1,369,475 1,581,690 1,537,180 1,499,802 90.4 89.1 91.3

Wyoming ...................... 126,931 116,217 122,414 138,619 131,810 135,059 91.6 88.2 90.6

Puerto Rico .................. 794,579 NA 874,101 1,049,995 1,069,617 NA 75.7 NA NA

NA = not available

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Science and Engineering Indicators 2010
8-38 Chapter 8. State Indicators

Earning a bachelor’s degree gives people greater opportunities to work in 

higher-paying jobs than are generally available to those with less education. It 

also prepares them for advanced education. In addition, the presence of higher 

education institutions that produce such degrees may generate resources for the 

state. The cohort 18–24 years old was chosen to approximate the age range of 

most students who are pursuing an undergraduate degree.

Although the number of bachelor’s degrees awarded is based on an actual 

count, the population ages 18–24 years is an estimate developed by the Census 

Bureau in the Population Estimates Program, which relies on the Decennial 

Census. 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, however, may 

make this indicator less meaningful in predicting the qualifications of a state’s future 

workforce. A state’s value for this indicator may also be high when its higher education system draws a large percentage of out-of-state students, a situation that sometimes occurs in states with small resident populations and the District of Columbia.

Findings

UÊ ÊÓääÇ]Ê£°xÊÊL>ViÀ1⁄2ÃÊ`i}ÀiiÃÊ

were conferred nationally in all fields, 

which is up from 1.2 million in 1997 

and represents an increase of 27%. 

Between 1997 and 2007, the number 

of bachelor’s degrees conferred per 

1,000 individuals 18–24 years old in 

the population has increased by 8% 

nationwide.

UÊ In 2007, state values on this indicator 

varied greatly. They ranged from 89.9 to 

20.7 bachelor’s degrees conferred per 

1,000 individuals 18–24 years old.

UÊ Ê£ÓÊÃÌ>ÌiÃÊ>`ÊÌiÊÃÌÀVÌÊvÊ

Columbia, fewer bachelor’s degrees 

were conferred per 1,000 individuals 

18–24 years old in 2007 than in 1997.

Bachelor’s Degrees Conferred per 1,000 Individuals 18–24 Years Old

Figure 8-15

Bachelor’s degrees conferred per 1,000 individuals 18–24 years old: 2007

1st quartile

2nd quartile

3rd quartile

4th quartile

1st quartile (139.6–63.2) 2nd quartile (62.2–54.4) 3rd quartile (54.0–45.6) 4th quartile (44.1–20.7)

Arizona Colorado Alabama ‡

 Alaska ‡

District of Columbia Connecticut Idaho ‡ Arkansas ‡

Indiana Delaware ‡ Illinois California

Iowa Kansas ‡

 Kentucky ‡ Florida

Massachusetts Maine ‡

 Louisiana ‡ Georgia

Missouri Michigan Maryland Hawaii ‡

Nebraska ‡ Minnesota North Carolina Mississippi ‡

New Hampshire ‡ Montana ‡ Oklahoma ‡ Nevada ‡

North Dakota ‡ New York Oregon New Jersey

Pennsylvania Ohio South Carolina ‡

 New Mexico ‡

Rhode Island ‡ South Dakota ‡ Tennessee Texas

Vermont ‡

 Utah Virginia Wyoming ‡

West Virginia ‡ Wisconsin Washington

‡ EPSCoR state 

-"1,

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and Census Bureau, Population Estimates Program. See table 8-15.
Science and Engineering Indicators 2010 8-39

Table 8-15

Bachelor’s degrees conferred per 1,000 individuals 18–24 years old, by state: 1997, 2002, and 2007

Bachelor’s degrees Population 18–24 years old

Degrees/1,000 

individuals 

18–24 years old

State 1997 2002 2007 1997 2002 2007 1997 2002 2007

United States ................... 1,171,891 1,288,674 1,490,321 24,980,036 28,419,861 29,407,260 46.9 45.3 50.7

Alabama ....................... 20,638 20,223 21,762 433,513 448,685 446,162 47.6 45.1 48.8

Alaska .......................... 1,473 1,377 1,512 66,729 64,031 72,877 22.1 21.5 20.7

Arizona ......................... 20,029 26,553 41,640 430,444 548,385 589,973 46.5 48.4 70.6

Arkansas ...................... 9,214 10,078 11,421 248,415 270,578 262,261 37.1 37.2 43.5

California ...................... 108,255 126,507 145,083 3,050,146 3,564,996 3,797,718 35.5 35.5 38.2

Colorado ...................... 20,213 22,577 25,677 362,705 458,430 457,831 55.7 49.2 56.1

Connecticut ................. 13,684 14,820 18,524 258,320 290,083 319,957 53.0 51.1 57.9

Delaware ...................... 4,295 4,894 5,093 65,110 80,006 83,909 66.0 61.2 60.7

District of Columbia ..... 7,869 8,564 10,261 43,082 71,859 73,516 182.7 119.2 139.6

Florida .......................... 47,428 53,542 65,682 1,183,286 1,439,912 1,588,214 40.1 37.2 41.4

Georgia ........................ 27,396 29,820 36,564 736,994 876,433 900,438 37.2 34.0 40.6

Hawaii .......................... 4,702 4,757 5,486 117,605 122,728 124,276 40.0 38.8 44.1

Idaho ............................ 4,509 4,913 7,894 134,718 145,770 146,117 33.5 33.7 54.0

Illinois ........................... 51,742 57,862 63,214 1,108,589 1,246,753 1,303,052 46.7 46.4 48.5

Indiana ......................... 30,477 33,918 38,369 566,940 625,957 600,127 53.8 54.2 63.9

Iowa ............................. 17,939 19,255 20,538 270,541 304,469 304,047 66.3 63.2 67.5

Kansas ......................... 14,739 15,135 17,372 254,180 286,702 288,019 58.0 52.8 60.3

Kentucky ...................... 14,705 16,419 19,125 394,870 409,809 381,413 37.2 40.1 50.1

Louisiana ...................... 17,506 20,216 21,428 463,579 491,999 469,512 37.8 41.1 45.6

Maine ........................... 5,565 5,787 6,859 110,057 109,039 111,798 50.6 53.1 61.4

Maryland ...................... 21,391 23,161 26,513 427,282 486,971 539,344 50.1 47.6 49.2

Massachusetts ............. 40,378 43,069 47,567 501,116 600,205 656,481 80.6 71.8 72.5

Michigan ...................... 44,427 47,538 52,625 916,990 971,354 967,733 48.4 48.9 54.4

Minnesota .................... 22,594 24,475 29,044 426,154 492,940 503,943 53.0 49.7 57.6

Mississippi ................... 10,252 11,899 12,052 296,825 315,700 303,351 34.5 37.7 39.7

Missouri ....................... 27,994 31,990 35,127 498,637 557,922 555,959 56.1 57.3 63.2

Montana ....................... 4,752 5,277 5,217 86,917 90,990 93,761 54.7 58.0 55.6

Nebraska ...................... 9,871 10,646 12,065 163,298 181,923 185,182 60.4 58.5 65.2

Nevada ......................... 3,669 4,244 5,568 140,784 191,087 207,957 26.1 22.2 26.8

New Hampshire ........... 7,581 7,260 8,274 93,994 110,493 117,693 80.7 65.7 70.3

New Jersey .................. 24,845 28,376 32,695 667,162 702,715 759,003 37.2 40.4 43.1

New Mexico ................. 6,088 5,823 6,815 171,641 193,224 203,225 35.5 30.1 33.5

New York ...................... 96,193 101,741 117,274 1,588,411 1,836,834 1,977,437 60.6 55.4 59.3

North Carolina .............. 34,202 36,132 40,920 694,894 821,050 857,552 49.2 44.0 47.7

North Dakota ............... 4,627 4,810 5,543 66,864 77,340 82,096 69.2 62.2 67.5

Ohio ............................. 49,163 52,934 58,813 1,046,134 1,092,489 1,074,846 47.0 48.5 54.7

Oklahoma ..................... 15,116 16,005 18,553 330,430 376,524 368,779 45.7 42.5 50.3

Oregon ......................... 13,194 13,955 17,270 295,027 342,012 332,599 44.7 40.8 51.9

Pennsylvania ................ 62,482 69,542 81,168 1,021,108 1,133,927 1,192,303 61.2 61.3 68.1

Rhode Island ................ 8,409 9,038 10,215 82,236 112,316 113,670 102.3 80.5 89.9

South Carolina ............. 15,177 17,294 20,092 379,854 419,038 430,733 40.0 41.3 46.6

South Dakota ............... 4,390 4,477 5,104 74,361 80,949 82,097 59.0 55.3 62.2

Tennessee .................... 21,147 23,330 26,877 509,421 564,930 548,165 41.5 41.3 49.0

Texas ............................ 71,409 79,556 94,601 1,979,779 2,335,170 2,421,150 36.1 34.1 39.1

Utah ............................. 15,606 17,876 19,655 277,479 340,031 328,226 56.2 52.6 59.9

Vermont ........................ 4,299 4,642 5,060 51,147 59,065 61,388 84.1 78.6 82.4

Virginia ......................... 30,207 32,819 39,151 648,469 711,752 762,960 46.6 46.1 51.3

Washington .................. 22,846 24,172 28,500 521,036 594,975 596,815 43.8 40.6 47.8

West Virginia ................ 8,172 9,022 10,543 183,414 173,637 157,857 44.6 52.0 66.8

Wisconsin .................... 27,380 28,699 32,229 487,388 543,276 550,539 56.2 52.8 58.5

Wyoming ...................... 1,652 1,655 1,687 51,961 52,398 53,199 31.8 31.6 31.7

Puerto Rico .................. 14,107 16,464 16,989 NA NA NA NA NA NA

NA = not available

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Science and Engineering Indicators 2010
8-40 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 exclude social sciences and psychology. The ratio of new NS&E 

bachelor’s degrees to the population ages18–24 years indicates the extent to which a 

state prepares young people to enter the types of technology-intensive occupations 

that are fundamental to a knowledge-based, technology-driven economy. In addition, 

the presence of higher education institutions that produce such degrees may generate 

resources for the state. The cohort 18–24 years old was chosen to approximate the age 

range of most students who are pursing an undergraduate degree.

Although the number of NS&E bachelor’s degrees awarded is based on an actual 

count, the population ages 18–24 years is an estimate developed by the Census Bureau 

in the Population Estimates Program, which relies on the Decennial Census. 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 percentage 

of out-of-state students to study in NS&E fields, a situation that sometimes occurs in 

states with small resident populations and the District of Columbia.

Findings

U Between 1997 and 2007, the value of this 

indicator did not change appreciably.

UÊ In 2007, the value of this indicator ranged 

from 15.4 to 3.7 bachelor’s degrees 

conferred per 1,000 individuals 18–24 

years old for individual states.

UÊ States that ranked in the top two 

quartiles on this indicator were generally 

the same as those in the top two 

quartiles for the number of bachelor’s 

degrees conferred per 1,000 individuals 

18–24 years old.

UÊ EPSCoR states were uniformly 

distributed throughout the four quartiles 

of the state ranking, indicating that in 

spite of the lack of a large, federally 

supported research structure, institutions 

in these states do provide college-level 

training in NS&E fields.

Bachelor’s Degrees in Natural Sciences and Engineering Conferred per 

1,000 Individuals 18–24 Years Old

Figure 8-16

Bachelor’s degrees in natural sciences and engineering conferred per 1,000 individuals 18–24 years old: 2007

1st quartile

2nd quartile

3rd quartile

4th quartile

1st quartile (19.8–10.4) 2nd quartile (10.2–8.7) 3rd quartile (8.6–6.9) 4th quartile (6.8–3.7)

Colorado Arizona Alabama ‡

 Alaska ‡

District of Columbia Delaware ‡ California Arkansas ‡

Iowa Idaho ‡ Connecticut Florida

Maine ‡

 Indiana Illinois Georgia

Maryland Kansas ‡

 Louisiana ‡ Hawaii ‡

Massachusetts Michigan New York Kentucky ‡

Montana ‡ Minnesota North Carolina Mississippi ‡

North Dakota ‡ Missouri Ohio Nevada ‡

Pennsylvania Nebraska ‡ Oklahoma ‡ New Jersey

Rhode Island ‡

 New Hampshire ‡ South Carolina ‡ New Mexico ‡

South Dakota ‡ Oregon Virginia Tennessee

Vermont ‡

 Utah Washington Texas

Wisconsin West Virginia ‡

 Wyoming ‡

‡ EPSCoR state 

-"1,

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and Census Bureau, Population Estimates Program. See table 8-16.
Science and Engineering Indicators 2010 8-41

Table 8-16

Bachelor’s degrees in natural sciences and engineering conferred per 1,000 individuals 18–24 years old, by 

state: 1997, 2002, and 2007

 NS&E bachelor’s degrees Population 18–24 years old

Degrees/1,000 individuals 

18–24 years old

State 1997 2002 2007 1997 2002 2007 1997 2002 2007

EPSCoR states ................ 33,748 35,339 37,632 4,462,502 4,864,031 4,847,332 7.6 7.3 7.8

Non-EPSCoR states ........ 164,878 178,965 197,718 20,474,452 23,483,971 24,486,412 8.1 7.6 8.1

Average EPSCoR 

 state value .................... na na na na na na 8.6 8.1 8.6

Average non-EPSCoR 

 state value .................... na na na na na na 8.6 8.2 8.7

United States ................... 199,868 216,028 236,802 24,980,036 28,419,861 29,407,260 8.0 7.6 8.1

Alabama ....................... 3,460 3,333 3,728 433,513 448,685 446,162 8.0 7.4 8.4

Alaska .......................... 301 229 267 66,729 64,031 72,877 4.5 3.6 3.7

Arizona ......................... 2,773 3,715 6,034 430,444 548,385 589,973 6.4 6.8 10.2

Arkansas ...................... 1,462 1,656 1,525 248,415 270,578 262,261 5.9 6.1 5.8

California ...................... 21,476 23,435 26,086 3,050,146 3,564,996 3,797,718 7.0 6.6 6.9

Colorado ...................... 4,477 4,993 5,046 362,705 458,430 457,831 12.3 10.9 11.0

Connecticut ................. 1,954 1,904 2,333 258,320 290,083 319,957 7.6 6.6 7.3

Delaware ...................... 693 717 734 65,110 80,006 83,909 10.6 9.0 8.7

District of Columbia ..... 1,242 1,724 1,452 43,082 71,859 73,516 28.8 24.0 19.8

Florida .......................... 6,642 7,808 9,226 1,183,286 1,439,912 1,588,214 5.6 5.4 5.8

Georgia ........................ 4,546 5,509 5,984 736,994 876,433 900,438 6.2 6.3 6.6

Hawaii .......................... 582 686 683 117,605 122,728 124,276 4.9 5.6 5.5

Idaho ............................ 871 1,008 1,324 134,718 145,770 146,117 6.5 6.9 9.1

Illinois ........................... 8,428 9,559 9,441 1,108,589 1,246,753 1,303,052 7.6 7.7 7.2

Indiana ......................... 5,170 5,378 5,860 566,940 625,957 600,127 9.1 8.6 9.8

Iowa ............................. 2,956 3,140 3,237 270,541 304,469 304,047 10.9 10.3 10.6

Kansas ......................... 2,312 2,482 2,506 254,180 286,702 288,019 9.1 8.7 8.7

Kentucky ...................... 2,177 2,312 2,419 394,870 409,809 381,413 5.5 5.6 6.3

Louisiana ...................... 3,061 3,594 3,381 463,579 491,999 469,512 6.6 7.3 7.2

Maine ........................... 991 1,110 1,185 110,057 109,039 111,798 9.0 10.2 10.6

Maryland ...................... 4,389 4,862 5,585 427,282 486,971 539,344 10.3 10.0 10.4

Massachusetts ............. 7,115 7,394 7,564 501,116 600,205 656,481 14.2 12.3 11.5

Michigan ...................... 8,324 8,609 9,240 916,990 971,354 967,733 9.1 8.9 9.5

Minnesota .................... 3,717 4,205 4,722 426,154 492,940 503,943 8.7 8.5 9.4

Mississippi ................... 1,707 1,753 1,767 296,825 315,700 303,351 5.8 5.6 5.8

Missouri ....................... 4,525 5,310 5,054 498,637 557,922 555,959 9.1 9.5 9.1

Montana ....................... 1,135 1,197 1,133 86,917 90,990 93,761 13.1 13.2 12.1

Nebraska ...................... 1,477 1,412 1,733 163,298 181,923 185,182 9.0 7.8 9.4

Nevada ......................... 503 556 785 140,784 191,087 207,957 3.6 2.9 3.8

New Hampshire ........... 1,265 1,133 1,127 93,994 110,493 117,693 13.5 10.3 9.6

New Jersey .................. 4,654 5,392 5,147 667,162 702,715 759,003 7.0 7.7 6.8

New Mexico ................. 1,124 1,129 1,251 171,641 193,224 203,225 6.5 5.8 6.2

New York ...................... 14,300 15,173 16,436 1,588,411 1,836,834 1,977,437 9.0 8.3 8.3

North Carolina .............. 6,541 6,313 6,754 694,894 821,050 857,552 9.4 7.7 7.9

North Dakota ............... 843 876 898 66,864 77,340 82,096 12.6 11.3 10.9

Ohio ............................. 7,980 8,002 8,432 1,046,134 1,092,489 1,074,846 7.6 7.3 7.8

Oklahoma ..................... 2,473 2,514 2,583 330,430 376,524 368,779 7.5 6.7 7.0

Oregon ......................... 2,073 2,483 2,984 295,027 342,012 332,599 7.0 7.3 9.0

Pennsylvania ................ 11,287 12,425 13,953 1,021,108 1,133,927 1,192,303 11.1 11.0 11.7

Rhode Island ................ 1,134 1,287 1,571 82,236 112,316 113,670 13.8 11.5 13.8

South Carolina ............. 2,821 2,798 3,073 379,854 419,038 430,733 7.4 6.7 7.1

South Dakota ............... 991 1,009 1,056 74,361 80,949 82,097 13.3 12.5 12.9

Tennessee .................... 3,455 3,380 3,617 509,421 564,930 548,165 6.8 6.0 6.6

Texas ............................ 11,292 12,014 14,487 1,979,779 2,335,170 2,421,150 5.7 5.1 6.0

Utah ............................. 2,714 3,037 3,338 277,479 340,031 328,226 9.8 8.9 10.2

Vermont ........................ 754 809 944 51,147 59,065 61,388 14.7 13.7 15.4

Virginia ......................... 5,529 5,875 6,562 648,469 711,752 762,960 8.5 8.3 8.6

Washington .................. 3,860 4,053 4,614 521,036 594,975 596,815 7.4 6.8 7.7

West Virginia ................ 1,141 1,296 1,562 183,414 173,637 157,857 6.2 7.5 9.9

Wisconsin .................... 4,701 4,997 5,982 487,388 543,276 550,539 9.6 9.2 10.9

Wyoming ...................... 470 443 397 51,961 52,398 53,199 9.0 8.5 7.5

Puerto Rico .................. 2,771 3,074 2,787 NA NA NA NA NA NA

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Science and Engineering Indicators 2010
8-42 Chapter 8. State Indicators

This indicator is a measure of the extent to which a state’s 

higher education programs are concentrated in S&E fields. The 

indicator is expressed as the percentage of higher education 

degrees that were conferred in S&E fields.

S&E fields include the physical, earth, ocean, atmospheric, 

biological, agricultural, 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 excluded.

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 2007 represent degrees conferred 

during the 2006–07 academic year.

Findings

U In 2007, nearly 686,000 S&E bachelor’s, master’s, and 

doctoral degrees were conferred nationwide, an increase of 

nearly 27% during the past decade.

UÊ The proportion of S&E degrees as a share of total degrees 

conferred did not show any substantial changes between 

1997 and 2007.

UÊ There are noteworthy differences in the proportions of 

technical higher education degrees conferred in different 

states. In some states, more than 40% of higher education 

degrees were awarded in technical fields. In others, only 

about 20% of higher education degrees were awarded in 

technical fields.

UÊ States in which the highest percentages of higher education 

degrees were conferred in technical fields tended to be 

located in the western United States.

UÊ 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.

1st quartile (53.3%–34.5%) 2nd quartile (34.3%–30.9%) 3rd quartile (30.8%–28.0%) 4th quartile (27.9%–18.0%)

California Alaska ‡

 Alabama ‡ Arizona

Colorado Connecticut Delaware ‡

 Arkansas ‡

District of Columbia Georgia Florida Kansas ‡

Maryland Hawaii ‡ Idaho ‡ Kentucky ‡

Montana ‡ Maine ‡ Illinois Louisiana ‡

New Jersey Massachusetts Indiana Minnesota

North Carolina Nevada ‡

 Iowa Mississippi ‡

Oregon New Hampshire ‡ Michigan Missouri

South Dakota ‡

 New Mexico ‡ New York Nebraska ‡

Utah Pennsylvania Ohio North Dakota ‡

Vermont ‡ Texas Oklahoma ‡ Tennessee

Virginia Wisconsin Rhode Island ‡

 West Virginia ‡

Washington South Carolina ‡

Wyoming ‡

‡ EPSCoR state 

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education Data System. See table 8-17.

S&E Degrees as Share of Higher Education Degrees Conferred

Figure 8-17

S&E degrees as share of higher education degrees conferred: 2007

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-43

Table 8-17

S&E degrees as share of higher education degrees conferred, by state: 1997, 2002, and 2007

 S&E degrees All higher education degrees

S&E/higher education 

degrees (%)

State 1997 2002 2007 1997 2002 2007 1997 2002 2007

United States ................... 538,702 597,517 685,914 1,636,726 1,811,202 2,138,003 32.9 33.0 32.1

Alabama ....................... 7,801 7,747 9,920 28,678 29,034 32,207 27.2 26.7 30.8

Alaska .......................... 684 561 750 2,009 1,828 2,261 34.0 30.7 33.2

Arizona ......................... 7,244 8,667 13,463 30,255 42,697 74,778 23.9 20.3 18.0

Arkansas ...................... 2,930 3,385 3,440 11,577 12,740 14,835 25.3 26.6 23.2

California ...................... 66,347 77,904 89,947 151,485 176,513 204,838 43.8 44.1 43.9

Colorado ...................... 11,427 13,308 13,729 28,304 31,388 35,981 40.4 42.4 38.2

Connecticut ................. 7,153 7,294 9,052 21,300 22,939 27,781 33.6 31.8 32.6

Delaware ...................... 2,005 2,084 2,325 5,660 6,583 7,642 35.4 31.7 30.4

District of Columbia ..... 7,068 7,555 8,287 15,872 16,468 20,489 44.5 45.9 40.4

Florida .......................... 17,551 20,934 27,510 65,429 73,988 91,561 26.8 28.3 30.0

Georgia ........................ 10,725 13,605 16,566 37,793 42,480 49,495 28.4 32.0 33.5

Hawaii .......................... 2,128 2,129 2,511 6,533 6,211 7,330 32.6 34.3 34.3

Idaho ............................ 2,038 2,305 2,859 5,656 6,245 9,614 36.0 36.9 29.7

Illinois ........................... 22,100 25,141 30,055 80,243 89,307 101,537 27.5 28.2 29.6

Indiana ......................... 12,127 12,833 14,442 39,188 44,005 51,564 30.9 29.2 28.0

Iowa ............................. 6,704 6,879 7,893 21,926 23,706 25,698 30.6 29.0 30.7

Kansas ......................... 6,065 6,388 6,552 20,074 20,728 23,943 30.2 30.8 27.4

Kentucky ...................... 5,299 6,154 7,218 19,669 21,825 27,152 26.9 28.2 26.6

Louisiana ...................... 6,843 7,322 7,767 23,565 26,491 28,224 29.0 27.6 27.5

Maine ........................... 2,236 2,408 2,733 6,686 7,145 8,532 33.4 33.7 32.0

Maryland ...................... 12,177 13,859 16,932 32,362 35,601 41,936 37.6 38.9 40.4

Massachusetts ............. 22,537 24,538 26,363 66,594 71,260 78,421 33.8 34.4 33.6

Michigan ...................... 19,809 20,938 23,006 62,669 70,717 75,304 31.6 29.6 30.6

Minnesota .................... 9,619 11,243 12,571 29,990 33,450 45,085 32.1 33.6 27.9

Mississippi ................... 3,602 3,887 4,294 13,823 15,619 16,438 26.1 24.9 26.1

Missouri ....................... 10,841 12,813 13,515 39,902 46,834 53,828 27.2 27.4 25.1

Montana ....................... 2,240 2,327 2,450 5,706 6,340 6,509 39.3 36.7 37.6

Nebraska ...................... 3,420 3,663 4,115 12,629 14,190 15,765 27.1 25.8 26.1

Nevada ......................... 1,266 1,503 2,267 4,726 5,489 7,279 26.8 27.4 31.1

New Hampshire ........... 3,212 3,626 3,725 9,772 9,486 11,207 32.9 38.2 33.2

New Jersey .................. 13,725 16,083 16,851 34,345 39,686 46,676 40.0 40.5 36.1

New Mexico ................. 2,981 2,724 3,302 8,835 8,402 9,748 33.7 32.4 33.9

New York ...................... 48,004 50,423 55,360 146,034 155,906 185,736 32.9 32.3 29.8

North Carolina .............. 15,028 16,470 19,022 43,477 46,754 55,071 34.6 35.2 34.5

North Dakota ............... 1,587 1,570 1,731 5,417 5,777 7,042 29.3 27.2 24.6

Ohio ............................. 20,918 21,879 24,410 69,154 73,784 82,584 30.2 29.7 29.6

Oklahoma ..................... 6,541 6,808 7,442 20,001 21,531 24,244 32.7 31.6 30.7

Oregon ......................... 6,359 6,828 8,387 17,733 19,139 23,655 35.9 35.7 35.5

Pennsylvania ................ 27,575 30,717 35,314 84,854 95,111 113,396 32.5 32.3 31.1

Rhode Island ................ 3,132 3,430 3,875 10,558 11,359 12,724 29.7 30.2 30.5

South Carolina ............. 6,550 7,255 7,649 20,179 22,023 25,841 32.5 32.9 29.6

South Dakota ............... 2,128 1,962 2,204 5,512 5,493 6,386 38.6 35.7 34.5

Tennessee .................... 8,172 8,425 9,272 28,733 32,004 36,576 28.4 26.3 25.3

Texas ............................ 29,744 33,844 40,387 97,090 108,049 130,830 30.6 31.3 30.9

Utah ............................. 6,984 7,793 8,787 18,920 21,496 23,993 36.9 36.3 36.6

Vermont ........................ 2,183 2,264 2,880 5,517 6,003 7,042 39.6 37.7 40.9

Virginia ......................... 16,270 17,956 20,679 42,316 44,555 53,981 38.4 40.3 38.3

Washington .................. 10,761 12,292 14,026 30,499 31,971 37,541 35.3 38.4 37.4

West Virginia ................ 2,629 2,927 3,239 10,548 11,413 13,707 24.9 25.6 23.6

Wisconsin .................... 10,986 11,684 13,691 34,805 37,284 41,842 31.6 31.3 32.7

Wyoming ...................... 1,247 1,183 1,149 2,124 2,155 2,154 58.7 54.9 53.3

Puerto Rico .................. 4,896 5,715 5,620 15,811 19,433 22,734 31.0 29.4 24.7

NOTES: S&E degrees include bachelor’s, master’s, and doctorate. S&E degrees include physical, computer, agricultural, biological, earth, atmospheric, 

Vi>]Ê>`ÊÃV>ÊÃViViÃÆÊ«ÃÞV}ÞÆÊ>Ìi>ÌVÃÆÊ>`Êi}iiÀ}°ÊoÊ}iÀÊi`ÕV>ÌÊ`i}ÀiiÃ»ÊVÕ`iÃÊL>ViÀ1⁄2Ã]Ê>ÃÌiÀ1⁄2Ã]Ê>`Ê`VÌÀ>Ìi°Ê

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education Data System (various years).

Science and Engineering Indicators 2010
8-44 Chapter 8. State Indicators

This indicator is a measure of 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, earth, ocean, atmospheric, biological, agricultural, 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 excluded.

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 2007 represent degrees conferred 

during the 2006–07 academic year.

Findings

U In 2007, nearly 376,000 NS&E bachelor’s, master’s, and 

doctoral degrees were conferred nationwide, an increase of 

nearly 25% during the past decade.

UÊ The proportion of NS&E degrees as a share of total degrees 

conferred showed an 8% decline between 2002 and 2007.

UÊ There are noteworthy differences in the proportions of natural 

science or engineering higher education degrees conferred in 

different states. In 2007, the proportions ranged between 35% 

and 11%.

UÊ 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 Share of Higher Education 

Degrees Conferred

Figure 8-18

Natural sciences and engineering degrees as share of higher education degrees conferred: 2007

1st quartile (34.7%–19.6%) 2nd quartile (19.4%–17.2%) 3rd quartile (17.0%–15.2%) 4th quartile (15.1%–11.5%)

Alabama ‡

 Alaska ‡ Florida Arizona

California Idaho ‡

 Illinois Arkansas ‡

Colorado Iowa Indiana Connecticut

Georgia New Jersey Kansas ‡

 Delaware ‡

Maryland North Dakota ‡ Louisiana ‡ District of Columbia

Michigan Ohio Maine ‡

 Hawaii ‡

Montana ‡ Oklahoma ‡ Massachusetts Kentucky ‡

New Mexico ‡ Oregon Mississippi ‡ Minnesota

North Carolina Pennsylvania Nebraska ‡ Missouri

South Dakota ‡

 Rhode Island ‡ Nevada ‡ New York

Utah South Carolina ‡

 New Hampshire ‡ Tennessee

Virginia Texas West Virginia ‡

Washington Vermont ‡

Wisconsin

Wyoming ‡

‡ EPSCoR state 

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education Data System. See table 8-18.

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-45

Table 8-18

Natural sciences and engineering degrees as share of higher education degrees conferred, by state: 1997, 2002, 

and 2007

NS&E degrees All higher education degrees

NS&E/higher education 

degrees (%)

State 1997 2002 2007 1997 2002 2007 1997 2002 2007

United States ................... 300,380 346,643 375,931 1,636,726 1,811,202 2,138,003 18.4 19.1 17.6

Alabama ....................... 4,971 4,842 6,550 28,678 29,034 32,207 17.3 16.7 20.3

Alaska .......................... 440 338 438 2,009 1,828 2,261 21.9 18.5 19.4

Arizona ......................... 4,379 5,713 8,569 30,255 42,697 74,778 14.5 13.4 11.5

Arkansas ...................... 1,751 2,276 2,112 11,577 12,740 14,835 15.1 17.9 14.2

California ...................... 35,241 40,070 42,934 151,485 176,513 204,838 23.3 22.7 21.0

Colorado ...................... 6,625 8,100 7,506 28,304 31,388 35,981 23.4 25.8 20.9

Connecticut ................. 3,196 3,296 4,206 21,300 22,939 27,781 15.0 14.4 15.1

Delaware ...................... 1,006 996 1,126 5,660 6,583 7,642 17.8 15.1 14.7

District of Columbia ..... 2,963 3,701 3,061 15,872 16,468 20,489 18.7 22.5 14.9

Florida .......................... 9,334 11,944 14,494 65,429 73,988 91,561 14.3 16.1 15.8

Georgia ........................ 6,366 8,673 10,230 37,793 42,480 49,495 16.8 20.4 20.7

Hawaii .......................... 899 976 1,027 6,533 6,211 7,330 13.8 15.7 14.0

Idaho ............................ 1,298 1,564 1,846 5,656 6,245 9,614 22.9 25.0 19.2

Illinois ........................... 12,308 15,361 17,106 80,243 89,307 101,537 15.3 17.2 16.8

Indiana ......................... 7,219 7,967 8,546 39,188 44,005 51,564 18.4 18.1 16.6

Iowa ............................. 3,966 4,209 4,739 21,926 23,706 25,698 18.1 17.8 18.4

Kansas ......................... 3,572 3,875 3,638 20,074 20,728 23,943 17.8 18.7 15.2

Kentucky ...................... 2,960 3,694 4,039 19,669 21,825 27,152 15.0 16.9 14.9

Louisiana ...................... 4,249 4,828 4,730 23,565 26,491 28,224 18.0 18.2 16.8

Maine ........................... 1,220 1,331 1,403 6,686 7,145 8,532 18.2 18.6 16.4

Maryland ...................... 6,869 8,144 9,891 32,362 35,601 41,936 21.2 22.9 23.6

Massachusetts ............. 11,411 12,634 12,988 66,594 71,260 78,421 17.1 17.7 16.6

Michigan ...................... 12,698 13,977 14,726 62,669 70,717 75,304 20.3 19.8 19.6

Minnesota .................... 4,997 6,909 6,773 29,990 33,450 45,085 16.7 20.7 15.0

Mississippi ................... 2,342 2,528 2,773 13,823 15,619 16,438 16.9 16.2 16.9

Missouri ....................... 6,020 7,367 7,301 39,902 46,834 53,828 15.1 15.7 13.6

Montana ....................... 1,504 1,669 1,571 5,706 6,340 6,509 26.4 26.3 24.1

Nebraska ...................... 2,178 2,350 2,533 12,629 14,190 15,765 17.2 16.6 16.1

Nevada ......................... 740 887 1,234 4,726 5,489 7,279 15.7 16.2 17.0

New Hampshire ........... 1,833 2,167 1,726 9,772 9,486 11,207 18.8 22.8 15.4

New Jersey .................. 7,441 9,098 8,513 34,345 39,686 46,676 21.7 22.9 18.2

New Mexico ................. 1,877 1,886 2,201 8,835 8,402 9,748 21.2 22.4 22.6

New York ...................... 23,908 26,586 27,486 146,034 155,906 185,736 16.4 17.1 14.8

North Carolina .............. 8,388 9,629 10,947 43,477 46,754 55,071 19.3 20.6 19.9

North Dakota ............... 1,053 1,115 1,208 5,417 5,777 7,042 19.4 19.3 17.2

Ohio ............................. 12,290 13,512 14,240 69,154 73,784 82,584 17.8 18.3 17.2

Oklahoma ..................... 3,688 3,862 4,170 20,001 21,531 24,244 18.4 17.9 17.2

Oregon ......................... 3,014 3,470 4,220 17,733 19,139 23,655 17.0 18.1 17.8

Pennsylvania ................ 15,787 18,537 20,083 84,854 95,111 113,396 18.6 19.5 17.7

Rhode Island ................ 1,691 2,017 2,231 10,558 11,359 12,724 16.0 17.8 17.5

South Carolina ............. 4,121 4,598 4,486 20,179 22,023 25,841 20.4 20.9 17.4

South Dakota ............... 1,336 1,371 1,405 5,512 5,493 6,386 24.2 25.0 22.0

Tennessee .................... 4,638 4,651 5,157 28,733 32,004 36,576 16.1 14.5 14.1

Texas ............................ 18,346 21,547 24,382 97,090 108,049 130,830 18.9 19.9 18.6

Utah ............................. 3,749 4,380 4,707 18,920 21,496 23,993 19.8 20.4 19.6

Vermont ........................ 964 1,045 1,320 5,517 6,003 7,042 17.5 17.4 18.7

Virginia ......................... 8,669 9,768 10,747 42,316 44,555 53,981 20.5 21.9 19.9

Washington .................. 5,897 7,277 7,364 30,499 31,971 37,541 19.3 22.8 19.6

West Virginia ................ 1,516 1,817 2,067 10,548 11,413 13,707 14.4 15.9 15.1

Wisconsin .................... 6,634 7,232 8,433 34,805 37,284 41,842 19.1 19.4 20.2

Wyoming ...................... 818 859 748 2,124 2,155 2,154 38.5 39.9 34.7

Puerto Rico .................. 3,439 4,082 3,636 15,811 19,433 22,734 21.8 21.0 16.0

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, 

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SOURCE: National Center for Education Statistics, Integrated Postsecondary Education Data System (various years).

Science and Engineering Indicators 2010
8-46 Chapter 8. State Indicators

Graduate students in S&E fields may become the technical 

leaders of the future. The ratio of S&E graduate students to a 

state’s population ages 25–34 years old is a relative measure of 

a state’s population with graduate training in S&E. Graduate 

students are counted on the basis of their university enrollment and include state residents, residents of other states, 

and noncitizens. The cohort 25–34 years old was chosen to 

approximate the age of most graduate students. This population cohort includes all state residents ages 25–34 and does 

not distinguish between citizens and noncitizens.

Data on S&E graduate students were collected by surveying 

all academic institutions in the United States that offer doctoral 

or master’s degree programs in any S&E field, including the 

physical, earth, ocean, atmospheric, biological, agricultural, 

computer, and social sciences; mathematics; engineering; and 

psychology. Graduate students enrolled in schools of nursing, 

public health, dentistry, veterinary medicine, and other healthrelated disciplines are not included.

Findings

U The number of S&E graduate students in the United States 

grew from approximately 405,000 in 1997 to 499,000 in 2007, a 

23% increase.

UÊ Individual states provided graduate level S&E training to varying 

proportions of the population, with 2.9% to 0.5% of individuals 

25–34 years old pursuing S&E graduate studies in 2007.

UÊ Changes in the value of this indicator between 1997 and 2007 

may reflect shifts in population, significant changes in S&E 

graduate education, or a combination of both.

UÊ 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, Florida, and 

Minnesota.

S&E Graduate Students per 1,000 Individuals 25–34 Years Old

Figure 8-19

S&E graduate students per 1,000 individuals 25–34 years old: 2007

1st quartile

2nd quartile

3rd quartile

4th quartile

1st quartile (88.9–14.2) 2nd quartile (13.4–11.2) 3rd quartile (10.8–8.9) 4th quartile (8.3–5.1)

Connecticut California Alabama ‡

 Alaska ‡

Delaware ‡ Colorado Arizona Arkansas ‡

District of Columbia Indiana Florida Georgia

Illinois Michigan Hawaii ‡

 Kentucky ‡

Iowa Montana ‡ Idaho ‡ Maine ‡

Kansas ‡ Nebraska ‡ Louisiana ‡ Mississippi ‡

Maryland New Jersey Missouri Nevada ‡

Massachusetts Ohio New Hampshire ‡ Oregon

Minnesota Utah North Carolina South Carolina ‡

New Mexico ‡ Virginia Oklahoma ‡ Tennessee

New York Wisconsin South Dakota ‡ Washington

North Dakota ‡

 Wyoming ‡ Texas

Pennsylvania Vermont ‡

Rhode Island ‡ West Virginia ‡

‡ EPSCoR state 

SOURCES: National Science Foundation, Division of Science Resources Statistics, Survey of Graduate Students and Postdoctorates in Science and 


}iiÀ}ÆÊ>`Ê
iÃÕÃÊ	ÕÀi>Õ]Ê*«Õ>ÌÊ
ÃÌ>ÌiÃÊ*À}À>°ÊSee table 8-19.
Science and Engineering Indicators 2010 8-47

Table 8-19

S&E graduate students per 1,000 individuals 25–34 years old, by state: 1997, 2002, and 2007

S&E graduate students Population 25–34 years old

S&E graduate 

students/1,000 

individuals 25–34 years old

State 1997 2002 2007 1997 2002 2007 1997 2002 2007

United States ................... 405,307 451,387 499,024 39,558,339 39,698,462 40,590,926 10.2 11.4 12.3

Alabama ....................... 5,288 5,704 6,162 625,515 587,727 603,903 8.5 9.7 10.2

Alaska .......................... 749 736 832 79,364 89,170 100,168 9.4 8.3 8.3

Arizona ......................... 6,468 7,153 8,157 654,684 781,407 915,523 9.9 9.2 8.9

Arkansas ...................... 1,853 1,932 2,649 335,763 351,068 378,494 5.5 5.5 7.0

California ...................... 51,176 58,469 66,792 5,279,694 5,271,205 5,179,032 9.7 11.1 12.9

Colorado ...................... 8,371 9,620 9,749 545,166 691,900 727,990 15.4 13.9 13.4

Connecticut ................. 5,562 7,026 8,643 488,806 431,725 399,838 11.4 16.3 21.6

Delaware ...................... 1,413 1,564 1,777 119,512 105,900 106,957 11.8 14.8 16.6

District of Columbia ..... 7,843 7,783 9,549 104,953 103,017 107,374 74.7 75.6 88.9

Florida .......................... 13,978 17,465 20,741 1,970,764 2,106,930 2,276,893 7.1 8.3 9.1

Georgia ........................ 8,481 10,092 11,435 1,217,255 1,315,540 1,374,703 7.0 7.7 8.3

Hawaii .......................... 1,598 1,657 1,858 160,985 171,531 177,096 9.9 9.7 10.5

Idaho ............................ 1,426 1,691 1,853 150,552 175,200 207,897 9.5 9.7 8.9

Illinois ........................... 21,857 24,813 25,281 1,783,545 1,800,397 1,766,903 12.3 13.8 14.3

Indiana ......................... 8,309 8,840 9,791 854,925 821,411 850,340 9.7 10.8 11.5

Iowa ............................. 4,617 4,928 5,146 374,757 356,050 363,669 12.3 13.8 14.2

Kansas ......................... 5,817 6,202 5,638 358,134 346,195 360,118 16.2 17.9 15.7

Kentucky ...................... 3,507 4,389 4,391 556,431 559,574 586,067 6.3 7.8 7.5

Louisiana ...................... 5,362 5,928 5,391 601,194 581,665 579,336 8.9 10.2 9.3

Maine ........................... 584 639 764 172,945 152,953 149,052 3.4 4.2 5.1

Maryland ...................... 9,163 10,164 11,357 814,881 731,189 719,905 11.2 13.9 15.8

Massachusetts ............. 19,274 22,006 23,604 1,014,531 895,476 821,184 19.0 24.6 28.7

Michigan ...................... 14,708 16,706 15,595 1,423,309 1,321,563 1,256,377 10.3 12.6 12.4

Minnesota .................... 6,435 7,248 12,733 669,827 667,582 683,505 9.6 10.9 18.6

Mississippi ................... 2,686 2,776 3,142 382,623 374,687 382,061 7.0 7.4 8.2

Missouri ....................... 5,760 6,828 7,645 754,942 733,432 774,880 7.6 9.3 9.9

Montana ....................... 1,168 1,328 1,502 98,004 101,874 116,754 11.9 13.0 12.9

Nebraska ...................... 2,368 2,633 2,874 217,666 220,620 228,308 10.9 11.9 12.6

Nevada ......................... 1,466 1,662 2,262 253,645 323,849 381,263 5.8 5.1 5.9

New Hampshire ........... 1,192 1,444 1,493 185,759 156,636 150,642 6.4 9.2 9.9

New Jersey .................. 10,537 11,613 12,468 1,169,347 1,155,785 1,058,894 9.0 10.0 11.8

New Mexico ................. 2,970 3,374 3,819 221,752 234,601 261,877 13.4 14.4 14.6

New York ...................... 38,481 40,114 45,887 2,780,058 2,675,696 2,522,113 13.8 15.0 18.2

North Carolina .............. 9,810 11,271 13,170 1,143,569 1,212,062 1,215,863 8.6 9.3 10.8

North Dakota ............... 847 1,108 1,485 84,358 73,534 78,531 10.0 15.1 18.9

Ohio ............................. 16,921 17,159 18,982 1,600,802 1,476,170 1,461,238 10.6 11.6 13.0

Oklahoma ..................... 3,763 4,484 4,604 433,901 451,368 489,871 8.7 9.9 9.4

Oregon ......................... 3,805 4,304 4,300 432,385 485,187 523,985 8.8 8.9 8.2

Pennsylvania ................ 18,640 19,786 20,643 1,663,839 1,499,828 1,450,797 11.2 13.2 14.2

Rhode Island ................ 1,554 1,768 1,961 155,539 137,322 128,693 10.0 12.9 15.2

South Carolina ............. 3,562 3,313 3,303 570,554 555,147 572,462 6.2 6.0 5.8

South Dakota ............... 851 1,028 891 90,549 90,023 98,407 9.4 11.4 9.1

Tennessee .................... 6,151 6,136 6,200 797,574 804,714 843,811 7.7 7.6 7.3

Texas ............................ 26,779 31,264 35,100 2,857,237 3,248,635 3,505,689 9.4 9.6 10.0

Utah ............................. 3,908 4,321 4,850 292,936 343,479 432,390 13.3 12.6 11.2

Vermont ........................ 569 601 630 86,613 72,309 70,148 6.6 8.3 9.0

Virginia ......................... 11,380 12,805 14,000 1,097,057 1,028,882 1,048,130 10.4 12.4 13.4

Washington .................. 5,841 6,129 6,414 817,042 849,669 900,709 7.1 7.2 7.1

West Virginia ................ 1,974 2,320 2,276 229,861 224,782 231,369 8.6 10.3 9.8

Wisconsin .................... 7,639 8,244 8,415 728,952 691,683 710,393 10.5 11.9 11.8

Wyoming ...................... 846 819 820 54,283 60,113 69,597 15.6 13.6 11.8

Puerto Rico .................. 2,256 3,371 3,280 NA NA NA NA NA NA

NA = not available

NOTE: S&E graduate students include students pursuing graduate degrees in physical, computer, agricultural, biological, earth, atmospheric, ocean, and 

ÃV>ÊÃViViÃÆÊ«ÃÞV}ÞÆÊ>Ìi>ÌVÃÆÊ>`Êi}iiÀ}°

SOURCES: National Science Foundation, Division of Science Resources Statistics, Survey of Graduate Students and Postdoctorates in Science and 


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ÃÌ>ÌiÃÊ*À}À>Ê­Û>ÀÕÃÊÞi>ÀÃ®°

Science and Engineering Indicators 2010
8-48 Chapter 8. State Indicators

This indicator shows 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, earth, ocean, atmospheric, biological, agricultural, 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.

Findings

U In 2007, more than 150,000 advanced S&E degrees were 

awarded nationwide. This total represented approximately 

26% more degrees than were awarded in 1997. However, the 

share of advanced degrees remained stable as a percentage 

of all S&E degrees conferred.

UÊ In 2007, some states provided more extensive graduate-level 

technical training, with nearly 34% of their S&E graduates 

V«iÌ}ÊÌÀ>}Ê>ÌÊÌiÊ>ÃÌiÀ1⁄2ÃÊÀÊ`VÌÀ>ÊiÛiÆÊÌiÀÊ

states had much smaller graduate S&E programs, with values 

as low as 8%.

UÊ The largest absolute increases in the production of advanced 

S&E degree holders between 1997 and 2007 occurred in 

California, Illinois, Texas, and New York.

UÊ 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 S&E graduate 

programs

Advanced S&E Degrees as Share of S&E Degrees Conferred

Figure 8-20

Advanced S&E degrees as share of S&E degrees conferred: 2007

1st quartile

2nd quartile

3rd quartile

4th quartile

1st quartile (45.1%–25.3%) 2nd quartile (25.0%–22.0%) 3rd quartile (21.9%–18.4%) 4th quartile (18.3%–8.0%)

Alabama ‡

 California Florida Arizona

Alaska ‡

 Colorado Indiana Arkansas ‡

Delaware ‡ Connecticut Louisiana ‡ Idaho ‡

District of Columbia Georgia Minnesota Iowa

Illinois Hawaii ‡

 Montana ‡ Maine ‡

Maryland Kansas ‡ Nebraska ‡ New Hampshire ‡

Massachusetts Kentucky ‡ Nevada ‡ Rhode Island ‡

Michigan Mississippi ‡ North Carolina South Carolina ‡

New Jersey Missouri North Dakota ‡ Utah

New Mexico ‡

 Ohio Oklahoma ‡ Washington

New York Pennsylvania Oregon West Virginia ‡

Texas Vermont ‡ South Dakota ‡ Wisconsin

Wyoming ‡

 Virginia Tennessee

‡ EPSCoR state

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education Data System. See table 8-20.
Science and Engineering Indicators 2010 8-49

Table 8-20

Advanced S&E degrees as share of S&E degrees conferred, by state: 1997, 2002, and 2007

 Advanced S&E degrees All S&E degrees

 Advanced S&E/ 

 all S&E degrees (%)

State 1997 2002 2007 1997 2002 2007 1997 2002 2007

United States ................... 119,428 122,569 150,127 503,939 533,788 626,200 23.7 23.0 24.0

Alabama ....................... 1,863 1,991 2,282 7,199 7,160 8,271 25.9 27.8 27.6

Alaska .......................... 199 156 235 679 559 725 29.3 27.9 32.4

Arizona ......................... 1,787 1,612 1,930 6,743 7,538 11,915 26.5 21.4 16.2

Arkansas ...................... 414 476 552 2,917 3,096 3,226 14.2 15.4 17.1

California ...................... 12,988 14,942 19,027 56,483 65,685 78,329 23.0 22.7 24.3

Colorado ...................... 2,906 3,002 3,209 11,306 12,344 13,320 25.7 24.3 24.1

Connecticut ................. 1,729 1,752 2,136 6,973 7,008 8,586 24.8 25.0 24.9

Delaware ...................... 432 389 547 1,929 2,003 2,154 22.4 19.4 25.4

District of Columbia ..... 3,036 3,030 3,558 6,566 7,074 7,889 46.2 42.8 45.1

Florida .......................... 3,926 4,454 5,525 17,252 19,736 25,785 22.8 22.6 21.4

Georgia ........................ 2,279 2,767 3,272 10,415 12,278 14,646 21.9 22.5 22.3

Hawaii .......................... 536 443 552 2,046 2,032 2,385 26.2 21.8 23.1

Idaho ............................ 305 306 481 1,677 1,811 2,649 18.2 16.9 18.2

Illinois ........................... 6,466 6,786 9,205 21,877 23,368 27,328 29.6 29.0 33.7

Indiana ......................... 2,455 2,324 2,880 11,590 11,682 13,541 21.2 19.9 21.3

Iowa ............................. 1,137 1,089 1,256 6,560 6,604 7,364 17.3 16.5 17.1

Kansas ......................... 1,187 1,135 1,325 5,295 5,424 6,005 22.4 20.9 22.1

Kentucky ...................... 972 997 1,601 5,020 5,344 6,470 19.4 18.7 24.7

Louisiana ...................... 1,519 1,453 1,672 6,721 7,073 7,629 22.6 20.5 21.9

Maine ........................... 221 175 212 2,172 2,300 2,662 10.2 7.6 8.0

Maryland ...................... 3,483 3,927 4,886 11,867 13,135 15,800 29.4 29.9 30.9

Massachusetts ............. 6,496 6,760 7,906 21,879 23,196 25,519 29.7 29.1 31.0

Michigan ...................... 4,621 4,977 5,557 18,631 19,139 21,374 24.8 26.0 26.0

Minnesota .................... 1,665 1,813 2,252 9,265 9,642 11,534 18.0 18.8 19.5

Mississippi ................... 730 727 838 3,447 3,558 3,817 21.2 20.4 22.0

Missouri ....................... 2,751 2,971 3,176 10,603 11,988 12,741 25.9 24.8 24.9

Montana ....................... 378 364 443 2,128 2,110 2,291 17.8 17.3 19.3

Nebraska ...................... 546 612 742 3,084 3,090 3,793 17.7 19.8 19.6

Nevada ......................... 284 278 393 1,218 1,335 2,021 23.3 20.8 19.4

New Hampshire ........... 445 440 472 2,953 2,883 3,321 15.1 15.3 14.2

New Jersey .................. 2,974 3,258 3,957 12,792 14,316 15,624 23.2 22.8 25.3

New Mexico ................. 868 708 854 2,764 2,438 2,863 31.4 29.0 29.8

New York ...................... 11,419 11,511 13,543 44,186 44,595 51,276 25.8 25.8 26.4

North Carolina .............. 2,477 2,694 3,421 14,878 15,132 17,325 16.6 17.8 19.7

North Dakota ............... 238 210 312 1,556 1,487 1,644 15.3 14.1 19.0

Ohio ............................. 5,370 4,489 5,405 19,687 18,920 21,640 27.3 23.7 25.0

Oklahoma ..................... 1,557 1,698 1,357 5,625 5,861 6,185 27.7 29.0 21.9

Oregon ......................... 1,284 1,175 1,514 6,145 6,535 7,986 20.9 18.0 19.0

Pennsylvania ................ 5,385 5,614 7,193 26,023 27,674 32,641 20.7 20.3 22.0

Rhode Island ................ 549 566 648 2,914 3,067 3,597 18.8 18.5 18.0

South Carolina ............. 1,004 981 1,079 5,999 6,170 6,981 16.7 15.9 15.5

South Dakota ............... 382 314 416 2,012 1,779 2,029 19.0 17.7 20.5

Tennessee .................... 1,633 1,386 1,620 8,048 8,013 8,802 20.3 17.3 18.4

Texas ............................ 7,072 7,164 9,413 27,060 28,592 36,053 26.1 25.1 26.1

Utah ............................. 1,077 1,006 1,308 6,434 6,739 7,999 16.7 14.9 16.4

Vermont ........................ 334 287 632 2,035 2,119 2,747 16.4 13.5 23.0

Virginia ......................... 3,423 3,156 4,126 15,366 15,694 18,369 22.3 20.1 22.5

Washington .................. 2,032 1,903 2,310 10,014 10,350 12,632 20.3 18.4 18.3

West Virginia ................ 472 492 573 2,559 2,732 3,142 18.4 18.0 18.2

Wisconsin .................... 1,852 1,608 2,117 10,390 10,555 12,763 17.8 15.2 16.6

Wyoming ...................... 270 201 207 957 825 812 28.2 24.4 25.5

Puerto Rico .................. 498 784 1,133 4,358 5,044 5,202 11.4 15.5 21.8

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SOURCE: National Center for Education Statistics, Integrated Postsecondary Education Data System (various years).

Science and Engineering Indicators 2010
8-50 Chapter 8. State Indicators

This indicator shows 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, earth, ocean, atmospheric, biological, agricultural, and computer sciences; mathematics; and engineering. 

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.

Findings

U In 2007, nearly 94,000 advanced NS&E degrees were 

awarded nationwide. This total represented approximately 

26% more than were awarded in 1997, but the share of 

advanced degrees remained stable as a percentage of all 

NS&E degrees conferred.

UÊ In 2007, some states provided more extensive graduate-level 

training in NS&E, with nearly 37% of their NS&E graduates 

V«iÌ}ÊÌÀ>}Ê>ÌÊÌiÊ>ÃÌiÀ1⁄2ÃÊÀÊ`VÌÀ>ÊiÛiÆÊÌiÀÊ

states had much smaller graduate NS&E programs, with 

values as low as 12%.

UÊ The largest absolute increases in the production of advanced 

NS&E degree holders between 1997 and 2007 occurred in 

California and Texas. 

UÊ 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 NS&E graduate 

programs.

Advanced Natural Sciences and Engineering Degrees as Share of Natural 

Sciences and Engineering Degrees Conferred

Figure 8-21

Advanced natural sciences and engineering degrees as share of natural sciences and engineering degrees 

conferred: 2007

1st quartile

2nd quartile

3rd quartile

4th quartile

1st quartile (42.3%–26.8%) 2nd quartile (25.9%–22.3%) 3rd quartile (22.1%–19.8%) 4th quartile (19.5%–12.1%)

Alaska ‡

 Florida Idaho ‡ Alabama ‡

California Georgia Indiana Arizona

Colorado Hawaii ‡

 Iowa Arkansas ‡

Connecticut Kentucky ‡ Kansas ‡ Maine ‡

Delaware ‡ Louisiana ‡ Montana ‡ Minnesota

District of Columbia Mississippi ‡

 Nebraska ‡ North Dakota ‡

Illinois Missouri New Hampshire ‡ Rhode Island ‡

Maryland Nevada ‡ Oklahoma ‡ South Carolina ‡

Massachusetts New Mexico ‡ Oregon South Dakota ‡

Michigan North Carolina Tennessee Washington

New Jersey Ohio Utah Wisconsin

New York Pennsylvania West Virginia ‡

 Wyoming ‡

Texas Vermont ‡

 Virginia

‡ EPSCoR state

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education Data System. See table 8-21.
Science and Engineering Indicators 2010 8-51

Table 8-21

Advanced natural sciences and engineering degrees as share of natural sciences and engineering degrees 

conferred, by state: 1997, 2002, and 2007

Advanced NS&E degrees NS&E degrees conferred

Advanced NS&E degrees/ 

NS&E degrees conferred (%)

State 1997 2002 2007 1997 2002 2007 1997 2002 2007

United States ................... 74,427 77,551 93,952 300,380 346,643 375,931 24.8 22.4 25.0

Alabama ....................... 949 931 1,181 4,971 4,842 6,550 19.1 19.2 18.0

Alaska .......................... 134 107 146 440 338 438 30.5 31.7 33.3

Arizona ......................... 1,277 1,079 1,258 4,379 5,713 8,569 29.2 18.9 14.7

Arkansas ...................... 276 331 378 1,751 2,276 2,112 15.8 14.5 17.9

California ...................... 7,205 8,907 11,496 35,241 40,070 42,934 20.4 22.2 26.8

Colorado ...................... 2,028 2,147 2,090 6,625 8,100 7,506 30.6 26.5 27.8

Connecticut ................. 1,140 1,182 1,535 3,196 3,296 4,206 35.7 35.9 36.5

Delaware ...................... 237 198 323 1,006 996 1,126 23.6 19.9 28.7

District of Columbia ..... 1,243 1,514 1,296 2,963 3,701 3,061 42.0 40.9 42.3

Florida .......................... 2,412 2,944 3,562 9,334 11,944 14,494 25.8 24.6 24.6

Georgia ........................ 1,544 1,907 2,348 6,366 8,673 10,230 24.3 22.0 23.0

Hawaii .......................... 293 242 261 899 976 1,027 32.6 24.8 25.4

Idaho ............................ 235 243 380 1,298 1,564 1,846 18.1 15.5 20.6

Illinois ........................... 3,817 4,108 4,988 12,308 15,361 17,106 31.0 26.7 29.2

Indiana ......................... 1,568 1,471 1,892 7,219 7,967 8,546 21.7 18.5 22.1

Iowa ............................. 876 798 977 3,966 4,209 4,739 22.1 19.0 20.6

Kansas ......................... 717 691 789 3,572 3,875 3,638 20.1 17.8 21.7

Kentucky ...................... 540 612 901 2,960 3,694 4,039 18.2 16.6 22.3

Louisiana ...................... 1,069 992 1,216 4,249 4,828 4,730 25.2 20.5 25.7

Maine ........................... 165 116 170 1,220 1,331 1,403 13.5 8.7 12.1

Maryland ...................... 2,241 2,618 3,312 6,869 8,144 9,891 32.6 32.1 33.5

Massachusetts ............. 3,792 4,050 4,814 11,411 12,634 12,988 33.2 32.1 37.1

Michigan ...................... 3,343 3,748 4,073 12,698 13,977 14,726 26.3 26.8 27.7

Minnesota .................... 942 1,121 1,206 4,997 6,909 6,773 18.9 16.2 17.8

Mississippi ................... 510 515 645 2,342 2,528 2,773 21.8 20.4 23.3

Missouri ....................... 1,300 1,283 1,637 6,020 7,367 7,301 21.6 17.4 22.4

Montana ....................... 283 273 324 1,504 1,669 1,571 18.8 16.4 20.6

Nebraska ...................... 372 388 529 2,178 2,350 2,533 17.1 16.5 20.9

Nevada ......................... 192 185 283 740 887 1,234 25.9 20.9 22.9

New Hampshire ........... 322 336 349 1,833 2,167 1,726 17.6 15.5 20.2

New Jersey .................. 2,120 2,262 2,543 7,441 9,098 8,513 28.5 24.9 29.9

New Mexico ................. 573 499 571 1,877 1,886 2,201 30.5 26.5 25.9

New York ...................... 6,801 6,546 7,685 23,908 26,586 27,486 28.4 24.6 28.0

North Carolina .............. 1,736 1,997 2,513 8,388 9,629 10,947 20.7 20.7 23.0

North Dakota ............... 179 156 236 1,053 1,115 1,208 17.0 14.0 19.5

Ohio ............................. 3,369 2,789 3,361 12,290 13,512 14,240 27.4 20.6 23.6

Oklahoma ..................... 728 798 894 3,688 3,862 4,170 19.7 20.7 21.4

Oregon ......................... 808 777 925 3,014 3,470 4,220 26.8 22.4 21.9

Pennsylvania ................ 3,409 3,649 4,586 15,787 18,537 20,083 21.6 19.7 22.8

Rhode Island ................ 339 374 408 1,691 2,017 2,231 20.0 18.5 18.3

South Carolina ............. 774 741 771 4,121 4,598 4,486 18.8 16.1 17.2

South Dakota ............... 256 198 243 1,336 1,371 1,405 19.2 14.4 17.3

Tennessee .................... 1,059 864 1,079 4,638 4,651 5,157 22.8 18.6 20.9

Texas ............................ 4,925 5,089 6,545 18,346 21,547 24,382 26.8 23.6 26.8

Utah ............................. 719 666 956 3,749 4,380 4,707 19.2 15.2 20.3

Vermont ........................ 106 121 294 964 1,045 1,320 11.0 11.6 22.3

Virginia ......................... 2,309 2,041 2,530 8,669 9,768 10,747 26.6 20.9 23.5

Washington .................. 1,313 1,299 1,381 5,897 7,277 7,364 22.3 17.9 18.8

West Virginia ................ 319 332 409 1,516 1,817 2,067 21.0 18.3 19.8

Wisconsin .................... 1,392 1,180 1,527 6,634 7,232 8,433 21.0 16.3 18.1

Wyoming ...................... 171 136 136 818 859 748 20.9 15.8 18.2

Puerto Rico .................. 204 354 462 3,439 4,082 3,636 5.9 8.7 12.7

NS&E = natural sciences and engineering

 "/
-\Êo -E
Ê`i}ÀiiÃÊVviÀÀi`»ÊVÕ`iÃÊL>ViÀ1⁄2Ã]Ê>ÃÌiÀ1⁄2Ã]Ê>`Ê`VÌÀ>ÌiÆÊo>`Û>Vi`Ê -E
Ê`i}ÀiiÃ»ÊVÕ`iÃÊÞÊ>ÃÌiÀ1⁄2ÃÊ>`Ê`VÌÀ>Ìi°Ê -E
Ê

`i}ÀiiÃÊVÕ`iÊ«ÞÃV>]ÊV«ÕÌiÀ]Ê>}ÀVÕÌÕÀ>]ÊL}V>]Êi>ÀÌ]Ê>ÌÃ«iÀV]Ê>`ÊVi>ÊÃViViÃÆÊ>Ìi>ÌVÃÆÊ>`Êi}iiÀ}°

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education Data System (various years).

Science and Engineering Indicators 2010
8-52 Chapter 8. State Indicators

Average Undergraduate Charge at Public 4-Year Institutions

Figure 8-22

Average undergraduate charge at public 4-year institutions: 2008

1st quartile

2nd quartile

3rd quartile

4th quartile

No data

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 2008 represent costs for 

the 2007–08 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 2008, the total annual nominal charge for a full-time 

undergraduate student to attend a public 4-year institution 

averaged $13,424 nationally, an increase of 76% during the 

past decade in current dollars. This was equivalent to an 

VÀi>ÃiÊvÊ>««ÀÝ>ÌiÞÊ{ä ̄Ê>vÌiÀÊ>`ÕÃÌ}ÊvÀÊv>Ì°

UÊ ÊÃÌ>ÌiÃÊÃÜi`Ê>ÀÊVÀi>ÃiÃÊÊÕ`iÀ}À>`Õ>ÌiÊV>À}iÃÊ

at public institutions in 2008, as compared with 1998. In 

several states, undergraduate charges more than doubled 

during this period.

UÊ In 2008, the state average for a year of undergraduate 

education at a public 4-year institution ranged from a low of 

$9,479 to a high of $19,548.

UÊ Tuition and required fees averaged 44% of the total charges 

at public 4-year institutions in 2008, but individual states had 

different cost structures.

1st quartile ($19,548–$15,089) 2nd quartile ($14,893–$12,367) 3rd quartile ($12,289–$10,984) 4th quartile ($10,889–$9,479) No data

Connecticut California Alabama ‡

 Arkansas ‡ District of Columbia

Delaware ‡

 Colorado Alaska ‡ Florida

Illinois Indiana Arizona Idaho ‡

Maryland Iowa Georgia Louisiana ‡

Massachusetts Kentucky ‡ Hawaii ‡ Mississippi ‡

Michigan Maine ‡ Kansas ‡ New Mexico ‡

New Hampshire ‡ Minnesota Montana ‡ North Carolina

New Jersey Missouri Nebraska ‡

 Oklahoma ‡

Ohio New York Nevada ‡ South Dakota ‡

Pennsylvania Oregon North Dakota ‡ Utah

Rhode Island ‡

 Texas Tennessee Wyoming ‡

South Carolina ‡ Virginia West Virginia ‡

Vermont ‡ Washington Wisconsin

‡

 EPSCoR state

SOURCE: National Center for Education Statistics, Integrated Postsecondary Education Data System. See table 8-22.
Science and Engineering Indicators 2010 8-53

Table 8-22

Average undergraduate charge at public 4-year institutions, by state: 

1998, 2003, and 2008

(Dollars)

State 1998 2003 2008

United States ............................................. 7,628 9,828 13,424

Alabama ................................................. 6,354 7,931 11,035

Alaska .................................................... 7,131 9,457 11,719

Arizona ................................................... 6,669 8,797 12,289

Arkansas ................................................ 5,890 7,791 10,598

California ................................................ 8,491 10,849 14,893

Colorado ................................................ 7,552 9,179 13,314

Connecticut ........................................... 9,652 11,805 16,263

Delaware ................................................ 9,165 11,523 16,165

District of Columbia ............................... NA NA NA

Florida .................................................... 6,890 8,762 10,709

Georgia .................................................. 6,924 8,749 10,984

Hawaii .................................................... NA 8,242 12,202

Idaho ...................................................... 6,074 7,585 9,871

Illinois ..................................................... 8,537 11,027 16,795

Indiana ................................................... 8,494 10,655 14,096

Iowa ....................................................... 6,426 9,185 13,191

Kansas ................................................... 6,098 7,791 11,338

Kentucky ................................................ 5,662 7,691 12,641

Louisiana ................................................ 5,710 6,922 9,479

Maine ..................................................... 8,576 10,329 14,791

Maryland ................................................ 9,717 12,332 15,644

Massachusetts ....................................... 8,894 10,818 16,159

Michigan ................................................ 8,947 11,408 16,003

Minnesota .............................................. 7,617 9,983 14,188

Mississippi ............................................. 5,534 8,039 10,776

Missouri ................................................. 7,520 9,395 13,385

Montana ................................................. 6,855 8,966 11,609

Nebraska ................................................ 6,100 8,408 11,852

Nevada ................................................... 7,295 9,001 12,168

New Hampshire ..................................... 9,846 9,415 18,293

New Jersey ............................................ 10,235 13,937 19,548

New Mexico ........................................... 5,459 7,979 10,610

New York ................................................ 9,460 10,984 14,140

North Carolina ........................................ 5,919 8,350 10,889

North Dakota ......................................... 6,264 7,388 11,134

Ohio ....................................................... 9,022 12,260 16,354

Oklahoma ............................................... 5,301 6,832 10,600

Oregon ................................................... 8,394 10,548 13,868

Pennsylvania .......................................... 9,769 12,944 17,187

Rhode Island .......................................... 9,962 12,266 15,775

South Carolina ....................................... 7,160 11,139 15,089

South Dakota ......................................... 5,993 7,724 10,522

Tennessee .............................................. 5,788 8,349 11,340

Texas ...................................................... 6,313 8,661 12,367

Utah ....................................................... 5,953 7,410 9,706

Vermont .................................................. 11,469 14,016 18,245

Virginia ................................................... 8,627 9,538 13,928

Washington ............................................ 7,704 10,816 13,478

West Virginia .......................................... 6,558 8,175 11,426

Wisconsin .............................................. 6,409 8,204 11,747

Wyoming ................................................ 6,450 7,977 10,068

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 

Üi}Ìi`ÊLÞÊÕLiÀÊvÊvÕÌiiμÕÛ>iÌÊÕ`iÀ}À>`Õ>ÌiÃÊLÕÌÊÌÊ>`ÕÃÌi`ÊÌÊÀiviVÌÊÃÌÕ`iÌÊ

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 2010
8-54 Chapter 8. State Indicators

Average Undergraduate Charge at Public 4-Year Institutions as Share of

Disposable Personal Income

Figure 8-23

Average undergraduate charge at public 4-year institutions as share of disposable personal income: 2008

1st quartile

2nd quartile

3rd quartile

4th quartile

No data

This indicator provides 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 2008 represent costs for the 2007–08 

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 2008, a year of undergraduate education at a state 

institution would have consumed, on average, 38.4% of a 

resident’s disposable income, an increase from the 32.9% it 

would have consumed a decade earlier.

UÊ 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 2008.

UÊ Although a year of undergraduate education at a public 

institution became more expensive for residents in most 

states within the past decade, affordability improved in one 

state during the past decade as its per capita disposable 

personal income rose appreciably.

UÊ ,iÃ`iÌÃÊÊÃÝÊÃÌ>ÌiÃÊiÝ«iÀiVi`Ê>ÀÊVÀi>ÃiÃÊÊÌiÊ

cost of a year of undergraduate education relative to their 

purchasing power (in excess of 10% of their per capita 

disposable income) between 1998 and 2008.

1st quartile (52.8%–43.8%) 2nd quartile (43.4%–37.4%) 3rd quartile (36.7%–34.8%) 4th quartile (34.5%–23.1%) No data

Delaware ‡

 Arizona Alabama ‡ Alaska ‡ District of Columbia

Illinois Arkansas ‡

 Colorado Florida

Indiana California Connecticut Hawaii ‡

Kentucky ‡ Iowa Georgia Idaho ‡

Maine ‡ Maryland Nebraska ‡ Kansas ‡

Michigan Massachusetts New Mexico ‡ Louisiana ‡

New Hampshire ‡ Minnesota New York Nevada ‡

New Jersey Mississippi ‡ North Carolina North Dakota ‡

Ohio Missouri Tennessee Oklahoma ‡

Oregon Montana ‡ Texas South Dakota ‡

Pennsylvania Rhode Island ‡ Utah Wyoming ‡

South Carolina ‡ Virginia Washington

Vermont ‡

 West Virginia ‡ Wisconsin

‡

 EPSCoR state

-"1,

-\Ê >Ì>Ê
iÌiÀÊvÀÊ
`ÕV>ÌÊ-Ì>ÌÃÌVÃ]ÊÌi}À>Ìi`Ê*ÃÌÃiV`>ÀÞÊ
`ÕV>ÌÊ>Ì>Ê-ÞÃÌiÆÊ>`Ê	ÕÀi>ÕÊvÊ
VVÊ>ÞÃÃ]Ê-Ì>ÌiÊ>`ÊV>Ê

Personal Income data. See table 8-23.
Science and Engineering Indicators 2010 8-55

Table 8-23

Average undergraduate charge at public 4-year institutions as share of disposable personal income, 

by state: 1998, 2003, and 2008

Average undergraduate 

charge ($)

Per capita disposable 

personal income ($)

Undergraduate 

charge/disposable 

personal income (%)

State 1998 2003 2008 1998 2003 2008 1998 2003 2008

United States ................. 7,628 9,828 13,424 23,163 28,028 34,949 32.9 35.1 38.4

Alabama ..................... 6,354 7,931 11,035 19,500 23,969 30,297 32.6 33.1 36.4

Alaska ........................ 7,131 9,457 11,719 24,401 29,748 39,458 29.2 31.8 29.7

Arizona ....................... 6,669 8,797 12,289 20,250 24,368 29,391 32.9 36.1 41.8

Arkansas .................... 5,890 7,791 10,598 18,146 22,214 28,270 32.5 35.1 37.5

California .................... 8,491 10,849 14,893 24,258 29,457 37,041 35.0 36.8 40.2

Colorado .................... 7,552 9,179 13,314 24,565 30,331 37,039 30.7 30.3 35.9

Connecticut ............... 9,652 11,805 16,263 30,068 36,379 46,775 32.1 32.5 34.8

Delaware .................... 9,165 11,523 16,165 23,933 29,605 35,880 38.3 38.9 45.1

District of Columbia ... NA NA NA 30,608 40,583 56,245 NA NA NA

Florida ........................ 6,890 8,762 10,709 22,728 27,495 34,880 30.3 31.9 30.7

Georgia ...................... 6,924 8,749 10,984 21,792 25,581 30,082 31.8 34.2 36.5

Hawaii ........................ NA 8,242 12,202 22,967 27,168 35,939 NA 30.3 34.0

Idaho .......................... 6,074 7,585 9,871 19,192 23,111 28,638 31.6 32.8 34.5

Illinois ......................... 8,537 11,027 16,795 25,103 30,025 37,298 34.0 36.7 45.0

Indiana ....................... 8,494 10,655 14,096 21,572 25,950 30,437 39.4 41.1 46.3

Iowa ........................... 6,426 9,185 13,191 21,725 25,866 32,919 29.6 35.5 40.1

Kansas ....................... 6,098 7,791 11,338 22,171 26,803 33,642 27.5 29.1 33.7

Kentucky .................... 5,662 7,691 12,641 19,218 23,137 28,424 29.5 33.2 44.5

Louisiana .................... 5,710 6,922 9,479 19,385 23,647 32,651 29.5 29.3 29.0

Maine ......................... 8,576 10,329 14,791 20,576 25,791 31,593 41.7 40.0 46.8

Maryland .................... 9,717 12,332 15,644 25,610 32,470 41,325 37.9 38.0 37.9

Massachusetts ........... 8,894 10,818 16,159 26,916 34,112 43,134 33.0 31.7 37.5

Michigan .................... 8,947 11,408 16,003 23,077 27,936 31,719 38.8 40.8 50.5

Minnesota .................. 7,617 9,983 14,188 24,649 30,169 37,300 30.9 33.1 38.0

Mississippi ................. 5,534 8,039 10,776 17,593 21,281 27,077 31.5 37.8 39.8

Missouri ..................... 7,520 9,395 13,385 21,683 26,159 31,339 34.7 35.9 42.7

Montana ..................... 6,855 8,966 11,609 18,738 23,965 30,627 36.6 37.4 37.9

Nebraska .................... 6,100 8,408 11,852 22,392 27,866 33,678 27.2 30.2 35.2

Nevada ....................... 7,295 9,001 12,168 24,576 28,473 35,768 29.7 31.6 34.0

New Hampshire ......... 9,846 9,415 18,293 25,403 31,090 38,304 38.8 30.3 47.8

New Jersey ................ 10,235 13,937 19,548 28,914 34,714 43,921 35.4 40.1 44.5

New Mexico ............... 5,459 7,979 10,610 18,382 22,631 28,922 29.7 35.3 36.7

New York .................... 9,460 10,984 14,140 26,461 31,053 40,254 35.8 35.4 35.1

North Carolina ............ 5,919 8,350 10,889 21,400 24,935 30,311 27.7 33.5 35.9

North Dakota ............. 6,264 7,388 11,134 20,620 26,469 35,824 30.4 27.9 31.1

Ohio ........................... 9,022 12,260 16,354 22,405 26,477 31,370 40.3 46.3 52.1

Oklahoma ................... 5,301 6,832 10,600 19,161 23,950 33,143 27.7 28.5 32.0

Oregon ....................... 8,394 10,548 13,868 21,951 26,218 31,643 38.2 40.2 43.8

Pennsylvania .............. 9,769 12,944 17,187 23,301 28,433 35,413 41.9 45.5 48.5

Rhode Island .............. 9,962 12,266 15,775 23,111 29,022 36,336 43.1 42.3 43.4

South Carolina ........... 7,160 11,139 15,089 19,440 23,449 28,556 36.8 47.5 52.8

South Dakota ............. 5,993 7,724 10,522 21,251 27,253 34,216 28.2 28.3 30.8

Tennessee .................. 5,788 8,349 11,340 21,452 26,133 31,327 27.0 31.9 36.2

Texas .......................... 6,313 8,661 12,367 22,282 26,865 34,850 28.3 32.2 35.5

Utah ........................... 5,953 7,410 9,706 18,937 22,742 26,641 31.4 32.6 36.4

Vermont ...................... 11,469 14,016 18,245 21,515 27,250 34,634 53.3 51.4 52.7

Virginia ....................... 8,627 9,538 13,928 23,662 29,787 37,194 36.5 32.0 37.4

Washington ................ 7,704 10,816 13,478 24,615 29,992 38,009 31.3 36.1 35.5

West Virginia .............. 6,558 8,175 11,426 18,068 22,117 27,926 36.3 37.0 40.9

Wisconsin .................. 6,409 8,204 11,747 22,382 27,318 32,835 28.6 30.0 35.8

Wyoming .................... 6,450 7,977 10,068 21,613 29,691 43,607 29.8 26.9 23.1

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 

­Ài«ÀÌi`ÊÊVÕÀÀiÌÊ`>ÀÃ®°Ê/ÕÌÊ>`ÊviiÃÊÜi}Ìi`ÊLÞÊÕLiÀÊvÊvÕÌiiμÕÛ>iÌÊÕ`iÀ}À>`Õ>ÌiÃÊLÕÌÊÌÊ>`ÕÃÌi`ÊÌÊÀiviVÌÊÃÌÕ`iÌÊÀiÃ`iVÞ°Ê,Ê

and board based on full-time students. National value for disposable personal income is value reported by Bureau of Economic Analysis.

-"1,

-\Ê >Ì>Ê
iÌiÀÊvÀÊ
`ÕV>ÌÊ-Ì>ÌÃÌVÃ]ÊÌi}À>Ìi`Ê*ÃÌÃiV`>ÀÞÊ
`ÕV>ÌÊ>Ì>Ê-ÞÃÌiÊ­Û>ÀÕÃÊÞi>ÀÃ®ÆÊ>`Ê	ÕÀi>ÕÊvÊ
VVÊ>ÞÃÃ]Ê

State and Local Personal Income data.

Science and Engineering Indicators 2010
8-56 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 2007 represent costs for the 2006–07 academic year.

Findings

U The total amount of state financial aid from 

grants provided to undergraduates rose 

nationwide from $3.0 billion in 1997 to $7.3 

billion in 2007.

UÊ On a per-student basis, state funding for 

student grants across the United States 

increased from $568 per undergraduate in 

1997 to $1,029 per undergraduate in 2007 

(in current dollars).

UÊ The amount of financial assistance 

provided by states and the District of 

Columbia varied greatly in 2007. Nine 

ÕÀÃ`VÌÃÊ>ÛiÀ>}i`ÊiÃÃÊÌ>Êf£ääÊ«iÀÊ

undergraduate student, while 16 provided 

more than $1,000 per student. Four states 

reported spending less, in current dollars, 

per student for student financial aid in 

2007 than in 1997, even though the cost 

of undergraduate education rose rapidly 

during this period. Three of these four states 

provided less than $100 per student.

State Expenditures on Student Aid per Full-Time Undergraduate Student

1st quartile ($2,821–$1,222) 2nd quartile ($1,192–$660) 3rd quartile ($586–$226) 4th quartile ($189–$17)

California District of Columbia Arkansas ‡

 Alabama ‡

Florida Indiana Colorado Alaska ‡

Georgia Louisiana ‡ Connecticut Arizona

Illinois Maryland Delaware ‡

 Hawaii ‡

Kentucky ‡ Michigan Iowa Idaho ‡

New Jersey Minnesota Kansas ‡ Montana ‡

New Mexico ‡ Nevada ‡ Maine ‡ Nebraska ‡

New York North Carolina Massachusetts New Hampshire ‡

Pennsylvania Ohio Mississippi ‡ North Dakota ‡

South Carolina ‡ Oklahoma ‡ Missouri South Dakota ‡

Tennessee Texas Oregon Utah

Washington Vermont ‡

 Rhode Island ‡ Wyoming ‡

West Virginia ‡ Virginia Wisconsin

‡

 EPSCoR state

-"1,

-\Ê >Ì>ÊÃÃV>ÌÊvÊ-Ì>ÌiÊ-ÌÕ`iÌÊÀ>ÌÊ>`Ê`Ê*À}À>Ã]ÊÕ>Ê-ÕÀÛiÞÊ,i«ÀÌÆÊ>`Ê >Ì>Ê
iÌiÀÊvÀÊ
`ÕV>ÌÊ-Ì>ÌÃÌVÃ]ÊÌi}À>Ìi`

Postsecondary Education Data System. See table 8-24.

Figure 8-24

State expenditures on student aid per full-time undergraduate student: 2007

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-57

Table 8-24

State expenditures on student aid per full-time undergraduate student, by state: 1997, 2002, and 2007

State expenditures on 

student aid (current $thousands)

Undergraduate enrollment 

at 4-year institutions

State expenditures 

on student aid/ 

undergraduate ($)

State 1997 2002 2007 1997 2002 2007 1997 2002 2007

United States ................. 3,014,185 4,999,047 7,339,605 5,302,145 6,156,202 7,133,213 568 812 1,029

Alabama ..................... 8,163 7,335 8,772 94,855 103,215 115,282 86 71 76

Alaska ........................ 213 NA 587 10,411 10,899 11,617 20 NA 51

Arizona ....................... 2,748 2,812 13,231 75,904 118,138 284,069 36 24 47

Arkansas .................... 13,725 37,897 30,212 51,954 60,282 67,132 264 629 450

California .................... 257,544 514,348 763,008 454,713 551,489 620,961 566 933 1,229

Colorado .................... 39,446 60,013 67,300 96,815 108,137 137,785 407 555 488

Connecticut ............... 20,299 45,175 41,717 58,564 70,650 79,591 347 639 524

Delaware .................... 1,183 1,626 13,489 18,892 22,309 23,026 63 73 586

District of Columbia ... 939 1,321 33,666 30,793 36,261 39,588 30 36 850

Florida ........................ 109,048 321,447 484,227 177,504 247,696 347,718 614 1,298 1,393

Georgia ...................... 185,867 362,201 480,730 142,198 162,595 198,074 1,307 2,228 2,427

Hawaii ........................ 379 531 408 19,770 22,630 24,216 19 23 17

Idaho .......................... 977 4,810 5,750 26,751 40,276 43,060 37 119 134

Illinois ......................... 298,993 407,622 444,348 208,464 237,251 285,235 1,434 1,718 1,558

Indiana ....................... 79,149 126,390 200,237 157,992 178,153 202,564 501 709 989

Iowa ........................... 42,411 51,668 55,535 77,563 87,498 102,432 547 591 542

Kansas ....................... 10,235 13,099 16,498 63,212 70,546 72,929 162 186 226

Kentucky .................... 28,902 86,325 184,399 84,044 91,423 103,830 344 944 1,776

Louisiana .................... 16,705 104,117 120,305 116,082 122,627 113,090 144 849 1,064

Maine ......................... 6,636 12,021 15,556 24,556 28,255 31,378 270 425 496

Maryland .................... 42,188 51,910 95,089 80,058 94,159 104,236 527 551 912

Massachusetts ........... 57,477 114,600 83,649 182,142 194,274 217,010 316 590 385

Michigan .................... 85,872 106,244 206,242 194,897 228,848 245,394 441 464 840

Minnesota .................. 92,746 130,408 162,919 103,063 125,283 136,720 900 1,041 1,192

Mississippi ................. 590 21,481 22,588 50,169 54,737 58,369 12 392 387

Missouri ..................... 26,654 43,488 59,768 115,631 133,642 148,534 231 325 402

Montana ..................... 314 2,810 4,563 27,175 27,882 27,665 12 101 165

Nebraska .................... 3,211 7,380 10,388 48,119 49,914 55,104 67 148 189

Nevada ....................... 3,707 19,899 38,353 16,920 25,774 42,335 219 772 906

New Hampshire ......... 679 3,075 3,727 31,568 34,996 38,418 22 88 97

New Jersey ................ 161,033 212,195 279,219 104,728 124,387 135,340 1,538 1,706 2,063

New Mexico ............... 19,565 39,395 70,518 30,975 35,763 39,322 632 1,102 1,793

New York .................... 633,902 699,481 861,448 421,585 484,218 525,797 1,504 1,445 1,638

North Carolina ............ 46,248 134,196 221,632 157,278 175,306 206,979 294 765 1,071

North Dakota ............. 2,454 1,776 2,238 23,553 27,535 27,994 104 64 80

Ohio ........................... 128,652 194,039 255,593 235,206 261,671 285,339 547 742 896

Oklahoma ................... 22,046 31,464 66,075 71,838 86,968 92,355 307 362 715

Oregon ....................... 16,241 19,866 33,383 55,434 68,061 72,865 293 292 458

Pennsylvania .............. 241,296 337,014 457,980 298,877 339,385 374,628 807 993 1,222

Rhode Island .............. 5,699 6,077 13,021 37,890 43,202 48,036 150 141 271

South Carolina ........... 21,540 102,039 271,239 74,790 86,528 96,153 288 1,179 2,821

South Dakota ............. 346 NA 2,140 23,204 25,328 25,849 15 NA 83

Tennessee .................. 19,364 37,915 231,287 112,152 124,302 143,843 173 305 1,608

Texas .......................... 42,761 199,523 374,730 306,560 361,336 411,776 139 552 910

Utah ........................... 2,170 4,069 8,566 73,490 89,795 98,342 30 45 87

Vermont ...................... 11,318 15,636 17,189 21,890 22,208 26,048 517 704 660

Virginia ....................... 80,064 110,467 149,296 142,237 158,773 195,135 563 696 765

Washington ................ 59,631 102,458 184,942 84,623 97,843 121,059 705 1,047 1,528

West Virginia .............. 10,527 21,054 77,883 51,761 56,387 56,805 203 373 1,371

Wisconsin .................. 52,168 68,167 93,802 133,295 147,367 164,355 391 463 571

Wyoming .................... 160 163 163 NA NA 7,831 NA NA 21

Puerto Rico ................ 23,824 35,602 33,444 112,666 127,677 134,046 211 279 249

NA = not available

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Statistics, Integrated Postsecondary Education Data System (various years).

Science and Engineering Indicators 2010
8-58 Chapter 8. State Indicators

This indicator represents the percentage of the early- to 

mid-career population that has earned at least a college degree. That degree may be an associate’s, bachelor’s, master’s, 

or doctoral degree. The indicator represents where college 

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 college program.

Estimates of educational attainment are developed by the 

Census Bureau based on the 2000 Decennial Census and the 

American Community Survey (ACS). The census is conducted 

every 10 years, but ACS provides annual data on the characteristics of population and housing. In 2005, ACS became the 

largest household survey in the United States, with an annual 

sample size of about 3 million addresses. Estimates of population are taken from the Census Bureau’s Population Estimates 

Program, which is also based on the 2000 Decennial Census.

Findings

U The early- to mid-career population with at least an 

associate’s degree was 38.3% nationwide in 2007, which 

represents an increase from 34.7% in 2000.

UÊ Between 2000 and 2007, all states showed an increase in the 

percentage of their early- to mid-career population with at 

least an associate’s degree.

UÊ In 2007, the percentage of this cohort with at least an 

associate’s degree varied greatly among states, ranging from 

51.8% to 26.9%.

UÊ States that ranked highest on this indicator tended to be 

located in the northern United States.

UÊ States with the lowest cost of living tended to rank lowest on 

this indicator.

Associate’s Degree Holders or Higher Among Individuals 25–44 Years Old

Figure 8-25

Associate’s degree holders or higher among individuals 25–44 years old: 2007

1st quartile

2nd quartile

3rd quartile

4th quartile

1st quartile (59.1%–43.2%) 2nd quartile (43.0%–37.4%) 3rd quartile (37.2%–33.3%) 4th quartile (32.8%–26.9%)

Colorado California Alaska ‡

 Alabama ‡

Connecticut Delaware ‡ Florida Arizona

District of Columbia Hawaii ‡

 Georgia Arkansas ‡

Maryland Illinois Idaho ‡ Kentucky ‡

Massachusetts Iowa Indiana Louisiana ‡

Minnesota Kansas ‡ Michigan Mississippi ‡

Nebraska ‡ Maine ‡ Missouri Nevada ‡

New Hampshire ‡ Oregon Montana ‡ New Mexico ‡

New Jersey Pennsylvania North Carolina Oklahoma ‡

New York Rhode Island ‡ Ohio Tennessee

North Dakota ‡

 South Dakota ‡ South Carolina ‡ Texas

Vermont ‡

 Utah Wyoming ‡ West Virginia ‡

Virginia Washington

 Wisconsin

‡

 EPSCoR state

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Science and Engineering Indicators 2010 8-59

Table 8-25

Associate’s degree holders or higher among individuals 25–44 years old, by state: 2000, 2003, and 2007

Associate’s degree holders or higher 

 25–44 years old Population 25–44 years old

Associate’s degree 

holders/individuals 

25–44 years old (%)

State 2000 2003 2007 2000 2003 2007 2000 2003 2007

United States ................. 29,471,612 30,738,684 31,935,182 85,040,251 84,216,990 83,483,659 34.7 36.5 38.3

Alabama ..................... 370,196 381,050 388,036 1,288,527 1,241,184 1,234,350 28.7 30.7 31.4

Alaska ........................ 61,646 58,059 65,635 203,522 194,823 197,222 30.3 29.8 33.3

Arizona ....................... 472,901 498,703 583,947 1,511,469 1,599,029 1,781,045 31.3 31.2 32.8

Arkansas .................... 177,657 187,589 207,170 750,972 738,579 755,981 23.7 25.4 27.4

California .................... 3,670,622 3,918,228 3,958,150 10,714,403 10,832,873 10,581,536 34.3 36.2 37.4

Colorado .................... 596,036 623,279 635,206 1,400,850 1,417,501 1,448,632 42.5 44.0 43.8

Connecticut ............... 443,608 447,818 431,258 1,032,689 999,800 925,266 43.0 44.8 46.6

Delaware .................... 84,170 90,649 86,106 236,441 233,356 230,359 35.6 38.8 37.4

District of Columbia ... 90,097 100,283 113,684 189,439 188,758 192,511 47.6 53.1 59.1

Florida ........................ 1,513,345 1,616,842 1,781,351 4,569,347 4,676,558 4,812,179 33.1 34.6 37.0

Georgia ...................... 884,108 929,979 1,016,236 2,652,764 2,723,720 2,834,749 33.3 34.1 35.8

Hawaii ........................ 136,758 132,630 149,667 362,336 352,806 353,251 37.7 37.6 42.4

Idaho .......................... 112,690 121,592 142,792 362,401 370,690 401,380 31.1 32.8 35.6

Illinois ......................... 1,444,942 1,487,189 1,522,302 3,795,544 3,727,314 3,600,910 38.1 39.9 42.3

Indiana ....................... 537,644 543,808 590,086 1,791,828 1,748,331 1,734,016 30.0 31.1 34.0

Iowa ........................... 289,740 294,559 324,136 808,259 775,320 753,784 35.8 38.0 43.0

Kansas ....................... 282,475 307,608 294,750 769,204 743,961 727,170 36.7 41.3 40.5

Kentucky .................... 317,109 335,263 359,918 1,210,773 1,182,970 1,188,087 26.2 28.3 30.3

Louisiana .................... 316,348 346,949 312,253 1,293,128 1,230,819 1,159,582 24.5 28.2 26.9

Maine ......................... 122,958 128,525 131,939 370,597 358,691 337,652 33.2 35.8 39.1

Maryland .................... 672,460 714,825 701,070 1,664,677 1,641,907 1,568,230 40.4 43.5 44.7

Massachusetts ........... 942,748 970,834 929,041 1,989,783 1,922,446 1,794,769 47.4 50.5 51.8

Michigan .................... 982,169 1,026,212 992,470 2,960,544 2,840,435 2,683,585 33.2 36.1 37.0

Minnesota .................. 631,677 668,668 680,415 1,497,320 1,465,370 1,423,704 42.2 45.6 47.8

Mississippi ................. 208,866 214,703 227,816 807,170 782,327 766,714 25.9 27.4 29.7

Missouri ..................... 517,750 541,597 567,002 1,626,302 1,587,931 1,579,645 31.8 34.1 35.9

Montana ..................... 81,428 85,047 86,169 245,220 232,735 235,309 33.2 36.5 36.6

Nebraska .................... 185,090 187,939 203,440 487,107 471,024 457,810 38.0 39.9 44.4

Nevada ....................... 152,536 167,370 214,807 628,572 679,392 761,550 24.3 24.6 28.2

New Hampshire ......... 156,434 163,231 159,905 381,240 373,644 351,263 41.0 43.7 45.5

New Jersey ................ 1,076,450 1,105,776 1,090,780 2,624,146 2,578,072 2,400,533 41.0 42.9 45.4

New Mexico ............... 149,398 142,448 157,903 516,100 506,956 516,167 28.9 28.1 30.6

New York .................... 2,359,507 2,432,498 2,465,176 5,831,622 5,667,484 5,383,101 40.5 42.9 45.8

North Carolina ............ 844,019 892,169 949,768 2,500,535 2,507,025 2,552,793 33.8 35.6 37.2

North Dakota ............. 71,509 70,144 75,163 174,891 160,522 155,217 40.9 43.7 48.4

Ohio ........................... 1,075,353 1,107,195 1,115,946 3,325,210 3,172,294 3,054,756 32.3 34.9 36.5

Oklahoma ................... 276,525 275,638 294,617 975,169 946,358 955,471 28.4 29.1 30.8

Oregon ....................... 333,963 355,143 393,990 997,269 1,003,698 1,034,933 33.5 35.4 38.1

Pennsylvania .............. 1,230,548 1,243,379 1,291,414 3,508,562 3,343,434 3,182,590 35.1 37.2 40.6

Rhode Island .............. 117,758 128,487 118,325 310,636 306,459 281,590 37.9 41.9 42.0

South Carolina ........... 357,570 370,577 411,754 1,185,955 1,167,347 1,185,520 30.2 31.7 34.7

South Dakota ............. 73,128 76,724 81,602 206,399 197,386 197,197 35.4 38.9 41.4

Tennessee .................. 489,940 511,871 529,569 1,718,428 1,684,796 1,725,854 28.5 30.4 30.7

Texas .......................... 1,973,279 2,059,427 2,244,095 6,484,321 6,644,003 6,926,932 30.4 31.0 32.4

Utah ........................... 222,534 247,337 289,026 626,600 648,111 753,898 35.5 38.2 38.3

Vermont ...................... 70,277 68,018 69,117 176,456 168,392 157,657 39.8 40.4 43.8

Virginia ....................... 874,239 904,354 951,423 2,237,655 2,227,978 2,204,242 39.1 40.6 43.2

Washington ................ 693,591 721,329 772,894 1,816,217 1,803,610 1,834,696 38.2 40.0 42.1

West Virginia .............. 115,337 123,752 130,375 501,343 479,781 473,410 23.0 25.8 27.5

Wisconsin .................. 566,244 566,942 596,698 1,581,690 1,537,180 1,499,802 35.8 36.9 39.8

Wyoming .................... 44,235 44,448 48,790 138,619 131,810 135,059 31.9 33.7 36.1

Puerto Rico ................ 358,595 NA 417,208 1,049,995 1,069,617 NA 34.2 NA NA

NA = not available

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Science and Engineering Indicators 2010
8-60 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. That degree may be at the bachelor’s, master’s, or doctoral level. 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 

Census Bureau based on the 2000 Decennial Census and the 

American Community Survey (ACS). The census is conducted 

every 10 years, but ACS provides annual data on the characteristics of population and housing. In 2005, ACS became the 

largest household survey in the United States, with an annual 

sample size of about 3 million addresses. Estimates of population are taken from the Census Bureau’s Population Estimates 

Program, which is also based on the 2000 Decennial Census.

Findings

U The early- to mid-career population with at least a bachelor’s 

degree was 29.8% nationwide in 2007, which represents an 

increase from 26.8% in 2000.

UÊ All states showed an increase in the percentage of their earlycareer population with at least a bachelor’s degree between 

2000 and 2007.

UÊ In 2007, the percentage of the early-career population with at 

least a bachelor’s degree varied among states, ranging from 

44.2% to 19.4%. The highest percentages tended to be found 

in the New England and Middle Atlantic states.

UÊ States with the lowest cost of living tended to rank lowest on 

this indicator.

UÊ EPSCoR states tended to be clustered in the lower quartiles 

for this indicator. However, several northern EPSCoR states 

showed high values.

Bachelor’s Degree Holders or Higher Among Individuals 25–44 Years Old

Figure 8-26

Bachelor’s degree holders or higher among individuals 25–44 years old: 2007

1st quartile

2nd quartile

3rd quartile

4th quartile

1st quartile (56.4%–33.5%) 2nd quartile (32.3%–28.3%) 3rd quartile (28.1%–24.5%) 4th quartile (24.4%–19.4%)

Colorado California Arizona Alabama ‡

Connecticut Delaware ‡ Florida Alaska ‡

District of Columbia Georgia Idaho ‡ Arkansas ‡

Illinois Hawaii ‡ Indiana Kentucky ‡

Maryland Iowa Michigan Louisiana ‡

Massachusetts Kansas ‡ Missouri Mississippi ‡

Minnesota Maine ‡ Montana ‡ Nevada ‡

New Hampshire ‡ Nebraska ‡ North Carolina New Mexico ‡

New Jersey North Dakota ‡ Ohio Oklahoma ‡

New York Oregon South Carolina ‡ Tennessee

Rhode Island ‡

 Pennsylvania Texas West Virginia ‡

Vermont ‡ South Dakota ‡ Wyoming ‡

Virginia Utah

 Washington

 Wisconsin

‡

 EPSCoR state

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Science and Engineering Indicators 2010 8-61

Table 8-26

Bachelor’s degree holders or higher among individuals 25–44 years old, by state: 2000, 2003, and 2007

Bachelor’s degree holders 

25–44 years old Population 25–44 years old

Bachelor’s degree 

holders/individuals 

25–44 years old (%)

State 2000 2003 2007 2000 2003 2007 2000 2003 2007

EPSCoR states ................ 3,004,954 3,180,725 3,264,082 13,582,778 13,252,012 13,214,968 22.1 24.0 24.7

Non-EPSCoR states ........ 19,692,206 20,707,252 21,483,835 71,268,034 70,776,220 70,076,180 27.6 29.3 30.7

Average EPSCoR 

 state value .................... na na na na na na 23.4 25.4 26.3

Average non-EPSCoR 

 state value .................... na na na na na na 28.0 29.7 31.2

United States ................... 22,781,996 23,984,096 24,856,576 85,040,251 84,216,990 83,483,659 26.8 28.5 29.8

Alabama ....................... 275,759 282,805 290,288 1,288,527 1,241,184 1,234,350 21.4 22.8 23.5

Alaska .......................... 45,560 44,868 48,098 203,522 194,823 197,222 22.4 23.0 24.4

Arizona ......................... 355,836 374,059 435,697 1,511,469 1,599,029 1,781,045 23.5 23.4 24.5

Arkansas ...................... 136,883 149,619 151,406 750,972 738,579 755,981 18.2 20.3 20.0

California ...................... 2,882,717 3,134,086 3,171,265 10,714,403 10,832,873 10,581,536 26.9 28.9 30.0

Colorado ...................... 480,984 513,973 518,478 1,400,850 1,417,501 1,448,632 34.3 36.3 35.8

Connecticut ................. 362,272 380,576 356,702 1,032,689 999,800 925,266 35.1 38.1 38.6

Delaware ...................... 65,811 73,052 65,103 236,441 233,356 230,359 27.8 31.3 28.3

District of Columbia ..... 84,836 96,119 108,659 189,439 188,758 192,511 44.8 50.9 56.4

Florida .......................... 1,081,551 1,159,165 1,285,284 4,569,347 4,676,558 4,812,179 23.7 24.8 26.7

Georgia ........................ 718,591 766,181 815,246 2,652,764 2,723,720 2,834,749 27.1 28.1 28.8

Hawaii .......................... 99,378 97,202 104,925 362,336 352,806 353,251 27.4 27.6 29.7

Idaho ............................ 80,235 88,937 102,126 362,401 370,690 401,380 22.1 24.0 25.4

Illinois ........................... 1,149,688 1,191,554 1,225,024 3,795,544 3,727,314 3,600,910 30.3 32.0 34.0

Indiana ......................... 397,050 404,241 431,559 1,791,828 1,748,331 1,734,016 22.2 23.1 24.9

Iowa ............................. 202,004 200,579 225,941 808,259 775,320 753,784 25.0 25.9 30.0

Kansas ......................... 223,467 243,308 230,048 769,204 743,961 727,170 29.1 32.7 31.6

Kentucky ...................... 234,921 247,142 268,167 1,210,773 1,182,970 1,188,087 19.4 20.9 22.6

Louisiana ...................... 256,363 283,161 242,865 1,293,128 1,230,819 1,159,582 19.8 23.0 20.9

Maine ........................... 86,989 92,827 95,436 370,597 358,691 337,652 23.5 25.9 28.3

Maryland ...................... 566,294 600,135 587,903 1,664,677 1,641,907 1,568,230 34.0 36.6 37.5

Massachusetts ............. 773,569 820,821 793,674 1,989,783 1,922,446 1,794,769 38.9 42.7 44.2

Michigan ...................... 719,607 764,082 753,761 2,960,544 2,840,435 2,683,585 24.3 26.9 28.1

Minnesota .................... 476,707 506,833 512,435 1,497,320 1,465,370 1,423,704 31.8 34.6 36.0

Mississippi ................... 144,488 149,176 156,955 807,170 782,327 766,714 17.9 19.1 20.5

Missouri ....................... 407,449 424,660 443,268 1,626,302 1,587,931 1,579,645 25.1 26.7 28.1

Montana ....................... 62,682 63,186 64,466 245,220 232,735 235,309 25.6 27.1 27.4

Nebraska ...................... 134,516 138,152 147,777 487,107 471,024 457,810 27.6 29.3 32.3

Nevada ......................... 111,517 128,178 160,041 628,572 679,392 761,550 17.7 18.9 21.0

New Hampshire ........... 114,745 121,639 123,284 381,240 373,644 351,263 30.1 32.6 35.1

New Jersey .................. 899,016 932,505 922,809 2,624,146 2,578,072 2,400,533 34.3 36.2 38.4

New Mexico ................. 110,360 106,530 116,114 516,100 506,956 516,167 21.4 21.0 22.5

New York ...................... 1,817,661 1,885,493 1,967,978 5,831,622 5,667,484 5,383,101 31.2 33.3 36.6

North Carolina .............. 636,799 682,432 712,815 2,500,535 2,507,025 2,552,793 25.5 27.2 27.9

North Dakota ............... 46,291 49,712 49,433 174,891 160,522 155,217 26.5 31.0 31.8

Ohio ............................. 806,803 835,693 840,228 3,325,210 3,172,294 3,054,756 24.3 26.3 27.5

Oklahoma ..................... 209,025 211,507 223,073 975,169 946,358 955,471 21.4 22.3 23.3

Oregon ......................... 257,875 278,460 307,422 997,269 1,003,698 1,034,933 25.9 27.7 29.7

Pennsylvania ................ 938,930 959,366 989,049 3,508,562 3,343,434 3,182,590 26.8 28.7 31.1

Rhode Island ................ 88,647 101,468 94,366 310,636 306,459 281,590 28.5 33.1 33.5

South Carolina ............. 259,773 279,322 297,357 1,185,955 1,167,347 1,185,520 21.9 23.9 25.1

South Dakota ............... 51,213 52,989 56,132 206,399 197,386 197,197 24.8 26.8 28.5

Tennessee .................... 380,929 393,328 411,337 1,718,428 1,684,796 1,725,854 22.2 23.3 23.8

Texas ............................ 1,571,951 1,623,020 1,761,431 6,484,321 6,644,003 6,926,932 24.2 24.4 25.4

Utah ............................. 162,495 174,787 213,575 626,600 648,111 753,898 25.9 27.0 28.3

Vermont ........................ 52,787 53,121 53,807 176,456 168,392 157,657 29.9 31.5 34.1

Virginia ......................... 722,081 750,953 788,015 2,237,655 2,227,978 2,204,242 32.3 33.7 35.7

Washington .................. 520,382 553,669 577,393 1,816,217 1,803,610 1,834,696 28.7 30.7 31.5

West Virginia ................ 83,441 92,148 91,998 501,343 479,781 473,410 16.6 19.2 19.4

Wisconsin .................... 402,965 396,601 435,546 1,581,690 1,537,180 1,499,802 25.5 25.8 29.0

Wyoming ...................... 30,103 30,676 30,817 138,619 131,810 135,059 21.7 23.3 22.8

Puerto Rico .................. 245,975 NA 291,498 1,049,995 1,069,617 NA 23.4 NA NA

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EPSCoR = Experimental Program to Stimulate Competitive Research

NOTE: For explanation of EPSCoR and non-EPSCoR averages, see chapter introduction.

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Science and Engineering Indicators 2010
8-62 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.

Degree data are based on the U.S. Census Bureau’s 2000 Decennial Census 

and American Community Survey 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. Civilian workforce data are Bureau of Labor Statistics estimates of employed persons based on Local Area Unemployment Statistics. 

Estimates for jurisdictions with smaller populations are generally less precise 

than estimates for jurisdictions with larger populations.

Findings

U In 2007, 47 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 30.8% 

in 2007. This ratio varied considerably among the 

states, ranging from 22.2% to 42.6% in 2007.

UÊ The value of this indicator increased in most 

ÕÀÃ`VÌÃÊLiÌÜiiÊÓäääÊ>`ÊÓääÇ°Ê/ÃÊ

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.

UÊ The geographic distribution of bachelor’s degree 

holders bears little resemblance to any of the 

degree production indicators, which may be 

indicative of the considerable mobility of the 

college-educated population in the United States.

1st quartile (51.1%–32.7%) 2nd quartile (32.6%–28.4%) 3rd quartile (28.3%–26.0%) 4th quartile (25.8%–22.2%)

Colorado California Alaska ‡

 Alabama ‡

Connecticut Delaware ‡ Arizona Arkansas ‡

District of Columbia Florida Idaho ‡ Indiana

Hawaii ‡

 Georgia Missouri Iowa

Illinois Kansas ‡

 Nebraska ‡ Kentucky ‡

Maryland Maine ‡ New Mexico ‡ Louisiana ‡

Massachusetts Michigan North Dakota ‡ Mississippi ‡

New Hampshire ‡ Minnesota Ohio Nevada ‡

New Jersey Montana ‡ Oklahoma ‡ South Dakota ‡

New York North Carolina South Carolina ‡ Tennessee

Vermont ‡ Oregon Utah West Virginia ‡

Virginia Pennsylvania Wisconsin Wyoming ‡

Washington Rhode Island ‡

 Texas

‡

 EPSCoR state

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Statistics. See table 8-27.

Bachelor’s Degree Holders Potentially in the Workforce

Figure 8-27

Bachelor’s degree holders potentially in the workforce: 2007

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-63

Table 8-27

Bachelor’s degree holders potentially in the workforce, by state: 2000, 2003, and 2007

Bachelor’s degree holders 

25–64 years old Employed workforce

Bachelor’s degree 

holders/workforce (%)

State 2000 2003 2007 2000 2003 2007 2000 2003 2007

United States .................. 39,078,598 43,038,717 47,027,346 136,940,378 137,418,377 152,650,836 28.5 31.3 30.8

Alabama ...................... 479,734 532,098 557,588 2,067,147 2,000,039 2,182,779 23.2 26.6 25.5

Alaska ......................... 87,739 91,931 99,542 299,324 308,523 352,304 29.3 29.8 28.3

Arizona ........................ 638,515 689,950 847,406 2,404,916 2,565,030 3,029,090 26.6 26.9 28.0

Arkansas ..................... 247,079 276,084 303,203 1,207,352 1,199,379 1,367,801 20.5 23.0 22.2

California ..................... 4,960,210 5,611,074 5,925,276 16,024,341 16,226,987 18,188,055 31.0 34.6 32.6

Colorado ..................... 819,906 901,534 988,923 2,300,192 2,323,554 2,705,557 35.6 38.8 36.6

Connecticut ................ 633,867 695,356 706,405 1,697,670 1,704,693 1,865,483 37.3 40.8 37.9

Delaware ..................... 111,260 126,828 125,749 402,777 403,504 442,692 27.6 31.4 28.4

District of Columbia .... 133,155 148,230 166,334 291,916 283,736 325,562 45.6 52.2 51.1

Florida ......................... 1,968,126 2,266,930 2,596,402 7,569,406 7,811,887 9,147,797 26.0 29.0 28.4

Georgia ....................... 1,148,814 1,266,705 1,486,341 4,095,362 4,180,568 4,814,831 28.1 30.3 30.9

Hawaii ......................... 184,130 196,970 215,385 584,858 588,880 649,080 31.5 33.4 33.2

Idaho ........................... 149,622 172,807 199,003 632,451 652,627 754,136 23.7 26.5 26.4

Illinois .......................... 1,876,455 2,032,846 2,190,396 6,176,837 5,942,720 6,697,382 30.4 34.2 32.7

Indiana ........................ 672,835 707,713 801,095 3,052,719 3,011,436 3,211,461 22.0 23.5 24.9

Iowa ............................ 351,922 366,596 415,765 1,557,081 1,543,507 1,660,979 22.6 23.8 25.0

Kansas ........................ 385,924 434,766 445,743 1,351,988 1,364,410 1,478,781 28.5 31.9 30.1

Kentucky ..................... 402,094 435,777 498,060 1,866,348 1,851,017 2,043,770 21.5 23.5 24.4

Louisiana ..................... 453,353 512,319 480,089 1,930,662 1,899,642 1,997,873 23.5 27.0 24.0

Maine .......................... 170,334 193,729 207,589 650,385 655,561 704,693 26.2 29.6 29.5

Maryland ..................... 979,588 1,083,343 1,141,741 2,711,382 2,750,040 2,980,353 36.1 39.4 38.3

Massachusetts ............ 1,266,113 1,370,101 1,450,619 3,273,281 3,211,853 3,408,197 38.7 42.7 42.6

Michigan ..................... 1,242,388 1,378,696 1,435,481 4,953,421 4,681,180 5,019,984 25.1 29.5 28.6

Minnesota ................... 783,613 891,852 938,351 2,720,492 2,765,997 2,930,553 28.8 32.2 32.0

Mississippi .................. 256,581 279,111 297,806 1,239,859 1,228,526 1,314,811 20.7 22.7 22.7

Missouri ...................... 695,491 776,798 834,879 2,875,336 2,819,935 3,031,187 24.2 27.5 27.5

Montana ...................... 124,462 130,542 144,380 446,552 447,679 501,349 27.9 29.2 28.8

Nebraska ..................... 230,857 244,248 273,457 923,198 932,870 983,438 25.0 26.2 27.8

Nevada ........................ 206,361 241,719 309,343 1,015,221 1,092,651 1,335,852 20.3 22.1 23.2

New Hampshire .......... 207,431 226,741 252,305 675,541 684,348 738,314 30.7 33.1 34.2

New Jersey ................. 1,510,429 1,639,510 1,744,741 4,130,310 4,126,674 4,466,275 36.6 39.7 39.1

New Mexico ................ 226,334 232,196 259,419 810,024 832,639 943,062 27.9 27.9 27.5

New York ..................... 3,031,927 3,275,249 3,561,887 8,751,441 8,713,529 9,519,301 34.6 37.6 37.4

North Carolina ............. 1,044,025 1,155,486 1,324,014 3,969,235 3,965,695 4,519,186 26.3 29.1 29.3

North Dakota .............. 80,545 91,105 96,019 335,780 335,453 365,598 24.0 27.2 26.3

Ohio ............................ 1,375,311 1,480,377 1,598,059 5,573,154 5,502,110 5,976,510 24.7 26.9 26.7

Oklahoma .................... 383,381 414,535 451,047 1,609,522 1,597,338 1,732,703 23.8 26.0 26.0

Oregon ........................ 488,862 533,853 613,549 1,716,954 1,704,397 1,927,802 28.5 31.3 31.8

Pennsylvania ............... 1,618,658 1,736,241 1,896,406 5,830,902 5,818,296 6,287,116 27.8 29.8 30.2

Rhode Island ............... 156,862 185,148 184,117 520,758 535,458 576,987 30.1 34.6 31.9

South Carolina ............ 454,656 521,905 575,269 1,902,029 1,868,309 2,136,516 23.9 27.9 26.9

South Dakota .............. 89,855 95,907 108,977 397,678 405,840 442,555 22.6 23.6 24.6

Tennessee ................... 649,844 719,592 784,298 2,756,498 2,720,676 3,036,736 23.6 26.4 25.8

Texas ........................... 2,646,909 2,892,917 3,278,378 9,896,002 10,260,318 11,492,422 26.7 28.2 28.5

Utah ............................ 276,360 292,932 374,739 1,097,915 1,132,948 1,361,768 25.2 25.9 27.5

Vermont ....................... 103,476 113,291 121,452 326,742 333,788 353,861 31.7 33.9 34.3

Virginia ........................ 1,232,454 1,361,804 1,505,597 3,502,524 3,646,114 4,054,199 35.2 37.3 37.1

Washington ................. 932,352 1,037,358 1,131,129 2,898,677 2,916,045 3,408,191 32.2 35.6 33.2

West Virginia ............... 157,883 179,117 186,093 764,649 742,990 808,840 20.6 24.1 23.0

Wisconsin ................... 690,065 732,493 829,718 2,894,884 2,866,994 3,089,321 23.8 25.5 26.9

Wyoming ..................... 60,451 64,307 67,782 256,685 259,987 287,743 23.6 24.7 23.6

Puerto Rico ................. 378,586 NA 485,235 1,162,153 1,200,322 1,393,808 32.6 NA 34.8

NA = not available

NOTES: Bachelor’s degree holders include those who completed a bachelor’s or higher degree. Workforce represents employed component of civilian 

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Unemployment Statistics.

Science and Engineering Indicators 2010
8-64 Chapter 8. State Indicators

This indicator shows the extent to which a state’s workforce is employed in S&E 

occupations. A high value for this indicator shows 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. They 

exclude managers, technicians, elementary and secondary schoolteachers, and 

medical personnel.

State data on individuals in S&E occupations come from the Occupational 

Employment Statistics (OES) survey, which surveys states’ workplaces and assigns 

workers to a state based on where they work. The survey is conducted as part of a 

cooperative program between the Bureau of Labor Statistics (BLS) and state employment security agencies. State data on the size of the civilian workforce are BLS 

estimates based on the Current Population Survey, which assigns workers 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. Because OES data do not classify postsecondary teachers by field, in these data, faculty teaching in S&E fields are not counted 

as working in S&E occupations. Estimates for jurisdictions with smaller populations 

are generally less precise than estimates for jurisdictions with larger populations.

Findings

U In 2008, 3.8% of the U.S. workforce 

(about 5.8 million people) worked in 

occupations classified as S&E.

UÊ In 2008, the percentage of the 

workforce engaged in S&E 

occupations ranged from 2.0% to 

6.4% in individual states.

UÊ The highest percentages of S&E 

occupations were found in the 

District of Columbia and the 

>`>ViÌÊÃÌ>ÌiÃÊvÊ>ÀÞ>`Ê>`Ê

Virginia as well as in Massachusetts 

and Colorado in 2008.

UÊ EPSCoR states tended to cluster in 

the lower quartiles of this indicator, 

indicating that their workforces 

contained a smaller percentage of 

individuals in S&E occupations.

Individuals in S&E Occupations as Share of Workforce

Figure 8-28

Individuals in S&E occupations as share of workforce: 2008

1st quartile (19.04%–3.96%) 2nd quartile (3.95%–3.37%) 3rd quartile (3.35%–2.80%) 4th quartile (2.76%–1.99%) No data

California Alaska ‡

 Alabama ‡ Arkansas ‡ Kentucky ‡

Colorado Kansas ‡ Arizona Florida Montana ‡

Connecticut Missouri Georgia Iowa Washington

Delaware ‡

 New Hampshire ‡ Hawaii ‡ Louisiana ‡

District of Columbia New Mexico ‡ Idaho ‡ Maine ‡

Maryland New York Illinois Mississippi ‡

Massachusetts North Carolina Indiana Nevada ‡

Michigan Ohio Nebraska ‡ North Dakota ‡

Minnesota Oregon Oklahoma ‡ South Carolina ‡

New Jersey Pennsylvania Rhode Island ‡ South Dakota ‡

Texas Utah Wisconsin Tennessee

Virginia Vermont ‡

 Wyoming ‡ West Virginia ‡

‡

 EPSCoR state

-"1,

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1st quartile

2nd quartile

3rd quartile

4th quartile

No data
Science and Engineering Indicators 2010 8-65

Table 8-28

Individuals in S&E occupations as share of workforce, by state: 2004, 2006, and 2008

S&E occupations Employed workforce

Workforce in S&E 

occupations (%)

State 2004 2006 2008 2004 2006 2008 2004 2006 2008

United States .................. 5,085,740 5,407,710 5,781,460 139,213,523 144,581,912 153,999,337 3.65 3.74 3.75

Alabama ...................... 57,560 66,100 68,580 2,014,678 2,120,573 2,162,479 2.86 3.12 3.17

Alaska ......................... 10,660 10,720 13,260 312,922 323,531 357,136 3.41 3.31 3.71

Arizona ........................ 95,380 98,110 102,100 2,649,243 2,854,381 3,132,667 3.60 3.44 3.26

Arkansas ..................... 22,150 24,860 29,310 1,228,163 1,292,886 1,370,259 1.80 1.92 2.14

California ..................... 693,670 730,010 791,750 16,444,457 17,029,307 18,391,844 4.22 4.29 4.30

Colorado ..................... 126,280 133,730 147,000 2,384,562 2,537,037 2,730,447 5.30 5.27 5.38

Connecticut ................ 82,820 79,380 80,290 1,714,758 1,765,075 1,876,125 4.83 4.50 4.28

Delaware ..................... 17,980 21,550 22,330 408,022 424,506 442,902 4.41 5.08 5.04

District of Columbia .... 57,750 64,120 63,360 285,567 296,957 332,703 20.22 21.59 19.04

Florida ......................... 229,950 246,190 248,200 8,056,259 8,692,761 9,231,462 2.85 2.83 2.69

Georgia ....................... 141,710 136,470 147,380 4,257,465 4,522,025 4,847,650 3.33 3.02 3.04

Hawaii ......................... 16,360 18,940 18,830 597,147 628,277 654,261 2.74 3.01 2.88

Idaho ........................... 22,310 NA 23,310 670,746 723,621 754,879 3.33 NA 3.09

Illinois .......................... 219,530 222,470 224,370 6,012,320 6,315,715 6,697,335 3.65 3.52 3.35

Indiana ........................ 79,120 80,110 90,840 3,017,271 3,108,806 3,230,367 2.62 2.58 2.81

Iowa ............................ 39,280 43,670 46,180 1,542,342 1,602,849 1,675,981 2.55 2.72 2.76

Kansas ........................ 52,020 48,620 54,260 1,378,713 1,400,169 1,496,943 3.77 3.47 3.62

Kentucky ..................... 44,350 44,680 NA 1,859,902 1,922,163 2,042,915 2.38 2.32 NA

Louisiana ..................... 42,230 40,180 41,790 1,926,594 1,910,348 2,078,935 2.19 2.10 2.01

Maine .......................... 15,160 15,950 17,000 661,163 678,843 706,829 2.29 2.35 2.41

Maryland ..................... 154,310 159,470 167,070 2,766,653 2,892,620 2,997,709 5.58 5.51 5.57

Massachusetts ............ 186,260 198,670 217,310 3,204,653 3,234,860 3,424,018 5.81 6.14 6.35

Michigan ..................... 183,140 208,520 204,290 4,694,981 4,730,291 4,935,584 3.90 4.41 4.14

Minnesota ................... 119,380 125,930 134,440 2,781,744 2,822,297 2,932,961 4.29 4.46 4.58

Mississippi .................. 23,190 24,910 27,270 1,234,167 1,218,664 1,314,444 1.88 2.04 2.07

Missouri ...................... 87,200 96,420 105,390 2,821,802 2,885,857 3,012,126 3.09 3.34 3.50

Montana ...................... 11,390 13,010 NA 456,624 478,162 506,159 2.49 2.72 NA

Nebraska ..................... 31,720 32,500 31,820 940,047 945,270 995,635 3.37 3.44 3.20

Nevada ........................ 23,980 26,930 27,300 1,134,550 1,240,868 1,373,462 2.11 2.17 1.99

New Hampshire .......... 24,350 27,680 29,150 693,648 711,512 738,858 3.51 3.89 3.95

New Jersey ................. 165,150 176,460 198,060 4,177,841 4,309,021 4,496,727 3.95 4.10 4.40

New Mexico ................ 33,500 30,800 34,560 850,164 895,623 959,458 3.94 3.44 3.60

New York ..................... 272,930 306,810 326,510 8,810,155 9,072,733 9,679,617 3.10 3.38 3.37

North Carolina ............. 135,380 138,790 153,680 4,028,598 4,250,619 4,543,754 3.36 3.27 3.38

North Dakota .............. 8,420 9,360 9,450 338,221 346,359 369,671 2.49 2.70 2.56

Ohio ............................ 180,360 185,190 206,320 5,507,404 5,609,056 5,971,874 3.27 3.30 3.45

Oklahoma .................... NA 50,770 48,900 1,608,849 1,650,877 1,748,416 NA 3.08 2.80

Oregon ........................ 62,570 64,520 70,070 1,722,058 1,796,165 1,957,953 3.63 3.59 3.58

Pennsylvania ............... 195,730 214,910 227,170 5,889,957 6,009,858 6,394,884 3.32 3.58 3.55

Rhode Island ............... 19,660 18,060 18,090 531,121 547,618 567,597 3.70 3.30 3.19

South Carolina ............ 51,030 53,230 57,770 1,900,122 1,988,378 2,152,965 2.69 2.68 2.68

South Dakota .............. 9,420 10,120 11,870 409,263 417,100 444,890 2.30 2.43 2.67

Tennessee ................... 65,120 67,040 72,760 2,733,793 2,835,530 3,041,276 2.38 2.36 2.39

Texas ........................... 383,180 408,710 463,850 10,456,224 10,921,673 11,701,585 3.66 3.74 3.96

Utah ............................ 43,030 49,690 52,570 1,169,163 1,272,801 1,383,743 3.68 3.90 3.80

Vermont ....................... 11,770 12,780 12,360 337,709 348,026 355,432 3.49 3.67 3.48

Virginia ........................ 220,180 251,720 259,280 3,704,593 3,878,988 4,124,766 5.94 6.49 6.29

Washington ................. 154,610 171,780 NA 3,008,352 3,160,350 3,476,766 5.14 5.44 NA

West Virginia ............... 16,100 17,150 17,000 744,034 767,134 806,152 2.16 2.24 2.11

Wisconsin ................... 95,230 96,860 101,680 2,871,034 2,918,155 3,084,130 3.32 3.32 3.30

Wyoming ..................... 6,760 7,640 8,850 263,705 275,617 292,606 2.56 2.77 3.02

Puerto Rico ................. 20,410 23,850 22,970 1,226,251 1,260,703 1,366,307 1.66 1.89 1.68

NA = not available

 "/
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Ê

occupations in the United States provided by Occupational Employment Statistics (OES). OES estimates for 2004, 2006, and 2008 S&E occupations 

based on May data.

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Science and Engineering Indicators 2010
8-66 Chapter 8. State Indicators

This indicator shows 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 derive from 

the National Science Foundation’s Survey of Doctorate Recipients, which 

excludes individuals with doctorates from foreign institutions and those 

above the age of 75. The Survey of Doctorate Recipients is a sample survey. 

Estimates for states with smaller populations 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.

Civilian workforce data are Bureau of Labor Statistics estimates 

from the Local Area Unemployment Statistics, which bases location on 

residence. Workforce data represent annual estimates of the employed 

civilian labor force; estimates are not seasonally adjusted.

Findings

U The number of employed S&E doctorate holders 

in the United States rose from 517,000 in 1997 to 

618,000 in 2006, an increase of 20%.

UÊ Overall, the value of this indicator rose from 

0.39% in 1996 to 0.43% in 2006 because the 

number of employed S&E doctorate holders 

nationwide increased more rapidly than the size of 

the workforce.

UÊ In 2006, the values for this indicator in individual 

states ranged from 0.20% to 1.00% of a state’s 

workforce.

UÊ States in the top quartile tended to be home to 

>ÀÊÀiÃi>ÀVÊ>LÀ>ÌÀiÃ]ÊÀiÃi>ÀVÊÕÛiÀÃÌiÃ]Ê

or research-intensive industries.

UÊ EPSCoR states tended to be clustered in the 

lower two quartiles for this indicator, reflecting the 

v>VÌÊÌ>ÌÊÜiÀÊiÛiÃÊvÊvi`iÀ>ÊÃÕ««ÀÌÊÀi`ÕViÊLÊ

opportunities for S&E doctorate holders.

Employed S&E Doctorate Holders as Share of Workforce

Figure 8-29

Employed S&E doctorate holders as share of workforce: 2006

1st quartile

2nd quartile

3rd quartile

4th quartile

1st quartile (4.49%–0.49%) 2nd quartile (0.48%–0.37%) 3rd quartile (0.35%–0.29%) 4th quartile (0.28%–0.20%)

California Hawaii ‡

 Alaska ‡ Alabama ‡

Colorado Idaho ‡ Arizona Arkansas ‡

Connecticut Illinois Georgia Florida

Delaware ‡

 Michigan Indiana Kentucky ‡

District of Columbia Minnesota Iowa Mississippi ‡

Maryland Montana ‡ Kansas ‡ Nevada ‡

Massachusetts New Jersey Louisiana ‡ Oklahoma ‡

New Mexico ‡ North Carolina Maine ‡ South Dakota ‡

New York North Dakota ‡ Missouri West Virginia ‡

Rhode Island ‡ Ohio Nebraska ‡ Wyoming ‡

Vermont ‡ Oregon New Hampshire ‡

Virginia Pennsylvania South Carolina ‡

Washington Utah Tennessee

 Texas

 Wisconsin

‡

 EPSCoR state

-"1,

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Area Unemployment Statistics. See table 8-29.
Science and Engineering Indicators 2010 8-67

Table 8-29

Employed S&E doctorate holders as share of workforce, by state: 1997, 2001, and 2006

Employed S&E 

doctorate holdersa Employed workforce

S&E doctorate holders 

in workforce (%)

State 1997 2001 2006 1997 2001 2006 1997 2001 2006

United States .................. 516,560 572,800 618,370 130,988,267 137,115,199 144,581,912 0.39 0.42 0.43

Alabama ...................... 6,610 5,330 5,900 2,035,156 2,034,909 2,120,573 0.32 0.26 0.28

Alaska ......................... 1,110 1,200 1,110 289,963 301,694 323,531 0.38 0.40 0.34

Arizona ........................ 6,280 7,070 8,410 2,196,901 2,453,453 2,854,381 0.29 0.29 0.29

Arkansas ..................... 2,320 2,560 2,840 1,177,143 1,194,024 1,292,886 0.20 0.21 0.22

California ..................... 70,490 80,870 87,370 14,780,791 16,220,033 17,029,307 0.48 0.50 0.51

Colorado ..................... 10,740 11,780 13,150 2,154,294 2,303,494 2,537,037 0.50 0.51 0.52

Connecticut ................ 8,770 9,490 10,330 1,674,937 1,700,046 1,765,075 0.52 0.56 0.59

Delaware ..................... 3,710 3,540 3,110 378,117 404,135 424,506 0.98 0.88 0.73

District of Columbia .... 11,800 14,200 13,330 262,789 286,649 296,957 4.49 4.95 4.49

Florida ......................... 13,330 15,740 17,630 7,040,660 7,624,718 8,692,761 0.19 0.21 0.20

Georgia ....................... 9,880 11,990 12,940 3,751,699 4,112,868 4,522,025 0.26 0.29 0.29

Hawaii ......................... 2,550 2,580 2,850 566,766 589,216 628,277 0.45 0.44 0.45

Idaho ........................... 2,030 2,230 2,840 598,004 644,816 723,621 0.34 0.35 0.39

Illinois .......................... 21,260 22,110 24,110 5,988,296 6,113,536 6,315,715 0.36 0.36 0.38

Indiana ........................ 7,570 9,580 9,870 3,014,499 3,020,985 3,108,806 0.25 0.32 0.32

Iowa ............................ 4,120 4,390 4,890 1,555,837 1,568,638 1,602,849 0.26 0.28 0.31

Kansas ........................ 3,770 3,970 4,250 1,329,797 1,347,715 1,400,169 0.28 0.29 0.30

Kentucky ..................... 4,110 4,590 4,990 1,809,785 1,852,056 1,922,163 0.23 0.25 0.26

Louisiana ..................... 5,360 5,290 5,470 1,890,102 1,922,110 1,910,348 0.28 0.28 0.29

Maine .......................... 2,150 1,990 2,350 624,410 650,699 678,843 0.34 0.31 0.35

Maryland ..................... 21,020 22,730 26,220 2,646,200 2,712,268 2,892,620 0.79 0.84 0.91

Massachusetts ............ 23,330 29,100 32,360 3,158,851 3,275,343 3,234,860 0.74 0.89 1.00

Michigan ..................... 15,050 17,380 17,900 4,748,691 4,876,338 4,730,291 0.32 0.36 0.38

Minnesota ................... 9,810 11,410 11,850 2,605,673 2,755,808 2,822,297 0.38 0.41 0.42

Mississippi .................. 3,000 3,170 3,310 1,200,845 1,229,884 1,218,664 0.25 0.26 0.27

Missouri ...................... 9,490 9,280 9,230 2,780,185 2,867,853 2,885,857 0.34 0.32 0.32

Montana ...................... 1,690 1,440 1,990 427,504 447,827 478,162 0.40 0.32 0.42

Nebraska ..................... 3,010 2,890 2,970 904,492 925,783 945,270 0.33 0.31 0.31

Nevada ........................ 1,620 2,030 2,620 895,258 1,042,182 1,240,868 0.18 0.19 0.21

New Hampshire .......... 2,230 2,470 2,440 635,469 680,706 711,512 0.35 0.36 0.34

New Jersey ................. 20,440 22,740 20,840 4,031,022 4,117,543 4,309,021 0.51 0.55 0.48

New Mexico ................ 7,480 7,750 8,330 768,596 821,003 895,623 0.97 0.94 0.93

New York ..................... 40,080 43,980 45,840 8,416,544 8,743,924 9,072,733 0.48 0.50 0.51

North Carolina ............. 13,730 16,760 18,880 3,809,601 3,929,977 4,250,619 0.36 0.43 0.44

North Dakota .............. 1,350 1,080 1,380 335,854 336,228 346,359 0.40 0.32 0.40

Ohio ............................ 18,700 20,070 20,540 5,448,161 5,566,735 5,609,056 0.34 0.36 0.37

Oklahoma .................... 4,580 4,360 4,420 1,543,105 1,614,627 1,650,877 0.30 0.27 0.27

Oregon ........................ 6,210 7,040 8,280 1,652,997 1,711,041 1,796,165 0.38 0.41 0.46

Pennsylvania ............... 23,940 26,140 29,090 5,775,178 5,874,153 6,009,858 0.41 0.45 0.48

Rhode Island ............... 2,450 2,640 3,020 504,147 520,677 547,618 0.49 0.51 0.55

South Carolina ............ 4,780 5,130 5,920 1,819,508 1,842,291 1,988,378 0.26 0.28 0.30

South Dakota .............. 1,060 1,000 1,050 383,216 400,352 417,100 0.28 0.25 0.25

Tennessee ................... 8,520 8,980 9,980 2,640,005 2,728,523 2,835,530 0.32 0.33 0.35

Texas ........................... 28,570 32,490 35,970 9,395,279 9,991,920 10,921,673 0.30 0.33 0.33

Utah ............................ 4,800 4,820 5,540 1,034,429 1,108,547 1,272,801 0.46 0.43 0.44

Vermont ....................... 1,750 1,750 1,700 315,806 330,099 348,026 0.55 0.53 0.49

Virginia ........................ 15,250 17,460 19,790 3,323,266 3,537,719 3,878,988 0.46 0.49 0.51

Washington ................. 13,360 14,760 16,920 2,822,223 2,863,705 3,160,350 0.47 0.52 0.54

West Virginia ............... 1,980 1,890 2,020 746,442 758,904 767,134 0.27 0.25 0.26

Wisconsin ................... 8,460 8,720 9,500 2,855,830 2,897,937 2,918,155 0.30 0.30 0.33

Wyoming ..................... 860 840 730 243,944 259,508 275,617 0.35 0.32 0.26

Puerto Rico ................. 660 1,410 1,690 1,132,658 1,133,988 1,260,703 0.06 0.12 0.13

a

Coefficients of variation for estimates of employed S&E doctorate holders presented in appendix table 8-13.

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Area Unemployment Statistics.

Science and Engineering Indicators 2010
8-68 Chapter 8. State Indicators

This indicator shows the representation of trained engineers in a state’s 

workforce. Engineers design and operate production processes and create new 

products and services. The indicator encompasses 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.

State data on individuals in S&E occupations come from the Occupational 

Employment Statistics (OES) survey, which surveys states’ workplaces and assigns 

workers to a state based on where they work. The survey is conducted as part of 

a cooperative program between the Bureau of Labor Statistics (BLS) and state 

employment security agencies. State data on the size of the civilian workforce are 

BLS estimates based on the Current Population Survey, which assigns workers 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. Because OES data do not classify 

postsecondary teachers by field, in these data, 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 In the United States, 1.63 million 

individuals were employed in 

engineering occupations in 2008, an 

increase over the 1.48 million engineers 

employed in 2004. Between 2004 and 

2008, the percentage of the workforce 

employed in engineering occupations 

remained unchanged at 1.06%.

UÊ The concentration of engineers in 

individual states ranged from 0.54% to 

1.87% in 2008.

UÊ The states with the highest percentage 

of engineers in their workforces were 

centers of automobile and aircraft 

manufacturing.

UÊ States ranking highest on this indicator 

also ranked high on employment in 

high-technology establishments as 

share of total employment.

Engineers as Share of Workforce

Figure 8-30

Engineers as share of workforce: 2008

1st quartile

2nd quartile

3rd quartile

4th quartile

1st quartile (2.47%–1.20%) 2nd quartile (1.16%–0.99%) 3rd quartile (0.96%–0.75%) 4th quartile (0.74%–0.54%)

Alabama ‡

 Arizona Florida Arkansas ‡

Alaska ‡ Delaware ‡ Hawaii ‡ Georgia

California Idaho ‡

 Illinois Iowa

Colorado Kansas ‡

 Indiana Kentucky ‡

Connecticut Minnesota Louisiana ‡ Maine ‡

District of Columbia New Hampshire ‡ Mississippi ‡ Montana ‡

Maryland Ohio Missouri Nebraska ‡

Massachusetts Pennsylvania New Jersey Nevada ‡

Michigan South Carolina ‡ New York North Carolina

New Mexico ‡

 Utah Oklahoma ‡ North Dakota ‡

Texas Vermont ‡ Oregon South Dakota ‡

Virginia Wisconsin Rhode Island ‡ West Virginia ‡

Washington Wyoming ‡ Tennessee 

‡

 EPSCoR state

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Science and Engineering Indicators 2010 8-69

Table 8-30

Engineers as share of workforce, by state: 2004, 2006, and 2008

Engineers Employed workforce

Engineers 

in workforce (%)

State 2004 2006 2008 2004 2006 2008 2004 2006 2008

United States .................. 1,480,520 1,535,620 1,626,330 139,213,523 144,581,912 153,999,337 1.06 1.06 1.06

Alabama ...................... 22,170 26,210 26,430 2,014,678 2,120,573 2,162,479 1.10 1.24 1.22

Alaska ......................... 3,480 3,330 4,450 312,922 323,531 357,136 1.11 1.03 1.25

Arizona ........................ 36,180 35,630 35,850 2,649,243 2,854,381 3,132,667 1.37 1.25 1.14

Arkansas ..................... 5,900 7,210 7,340 1,228,163 1,292,886 1,370,259 0.48 0.56 0.54

California ..................... 220,120 231,480 240,860 16,444,457 17,029,307 18,391,844 1.34 1.36 1.31

Colorado ..................... 34,370 37,040 41,130 2,384,562 2,537,037 2,730,447 1.44 1.46 1.51

Connecticut ................ 26,160 24,070 23,920 1,714,758 1,765,075 1,876,125 1.53 1.36 1.27

Delaware ..................... 3,810 4,810 5,120 408,022 424,506 442,902 0.93 1.13 1.16

District of Columbia .... 10,490 8,920 8,220 285,567 296,957 332,703 3.67 3.00 2.47

Florida ......................... 59,070 67,810 69,040 8,056,259 8,692,761 9,231,462 0.73 0.78 0.75

Georgia ....................... 30,550 30,170 36,020 4,257,465 4,522,025 4,847,650 0.72 0.67 0.74

Hawaii ......................... 4,560 5,380 5,020 597,147 628,277 654,261 0.76 0.86 0.77

Idaho ........................... 8,250 9,270 7,870 670,746 723,621 754,879 1.23 1.28 1.04

Illinois .......................... 59,010 57,270 55,840 6,012,320 6,315,715 6,697,335 0.98 0.91 0.83

Indiana ........................ 30,380 28,380 30,780 3,017,271 3,108,806 3,230,367 1.01 0.91 0.95

Iowa ............................ 9,900 10,420 10,270 1,542,342 1,602,849 1,675,981 0.64 0.65 0.61

Kansas ........................ 19,020 17,480 16,930 1,378,713 1,400,169 1,496,943 1.38 1.25 1.13

Kentucky ..................... 12,870 12,950 13,880 1,859,902 1,922,163 2,042,915 0.69 0.67 0.68

Louisiana ..................... 15,790 15,250 18,270 1,926,594 1,910,348 2,078,935 0.82 0.80 0.88

Maine .......................... 4,830 4,230 4,480 661,163 678,843 706,829 0.73 0.62 0.63

Maryland ..................... 33,190 36,880 39,390 2,766,653 2,892,620 2,997,709 1.20 1.27 1.31

Massachusetts ............ 50,370 51,750 54,330 3,204,653 3,234,860 3,424,018 1.57 1.60 1.59

Michigan ..................... 91,600 99,680 92,190 4,694,981 4,730,291 4,935,584 1.95 2.11 1.87

Minnesota ................... 30,370 28,280 29,490 2,781,744 2,822,297 2,932,961 1.09 1.00 1.01

Mississippi .................. 8,140 9,830 10,160 1,234,167 1,218,664 1,314,444 0.66 0.81 0.77

Missouri ...................... 21,070 22,870 25,950 2,821,802 2,885,857 3,012,126 0.75 0.79 0.86

Montana ...................... 2,580 2,840 3,570 456,624 478,162 506,159 0.57 0.59 0.71

Nebraska ..................... 5,810 5,820 6,350 940,047 945,270 995,635 0.62 0.62 0.64

Nevada ........................ 7,190 7,960 7,870 1,134,550 1,240,868 1,373,462 0.63 0.64 0.57

New Hampshire .......... 7,890 8,090 7,870 693,648 711,512 738,858 1.14 1.14 1.07

New Jersey ................. 37,850 38,130 40,720 4,177,841 4,309,021 4,496,727 0.91 0.88 0.91

New Mexico ................ 12,170 10,870 11,500 850,164 895,623 959,458 1.43 1.21 1.20

New York ..................... 64,920 68,540 74,570 8,810,155 9,072,733 9,679,617 0.74 0.76 0.77

North Carolina ............. 31,400 30,040 33,400 4,028,598 4,250,619 4,543,754 0.78 0.71 0.74

North Dakota .............. 2,230 2,520 2,530 338,221 346,359 369,671 0.66 0.73 0.68

Ohio ............................ 62,560 57,810 60,120 5,507,404 5,609,056 5,971,874 1.14 1.03 1.01

Oklahoma .................... 12,520 13,840 14,040 1,608,849 1,650,877 1,748,416 0.78 0.84 0.80

Oregon ........................ 18,500 NA 18,740 1,722,058 1,796,165 1,957,953 1.07 NA 0.96

Pennsylvania ............... NA 61,620 63,340 5,889,957 6,009,858 6,394,884 NA 1.03 0.99

Rhode Island ............... 5,270 5,430 5,150 531,121 547,618 567,597 0.99 0.99 0.91

South Carolina ............ 21,260 22,460 22,750 1,900,122 1,988,378 2,152,965 1.12 1.13 1.06

South Dakota .............. 2,050 2,210 2,440 409,263 417,100 444,890 0.50 0.53 0.55

Tennessee ................... 21,100 21,230 23,130 2,733,793 2,835,530 3,041,276 0.77 0.75 0.76

Texas ........................... 120,810 123,990 146,520 10,456,224 10,921,673 11,701,585 1.16 1.14 1.25

Utah ............................ 11,560 13,090 14,350 1,169,163 1,272,801 1,383,743 0.99 1.03 1.04

Vermont ....................... 3,440 3,780 3,790 337,709 348,026 355,432 1.02 1.09 1.07

Virginia ........................ 47,180 50,780 54,280 3,704,593 3,878,988 4,124,766 1.27 1.31 1.32

Washington ................. 45,140 49,840 55,490 3,008,352 3,160,350 3,476,766 1.50 1.58 1.60

West Virginia ............... 4,920 5,230 5,320 744,034 767,134 806,152 0.66 0.68 0.66

Wisconsin ................... 29,590 30,990 32,010 2,871,034 2,918,155 3,084,130 1.03 1.06 1.04

Wyoming ..................... 2,290 2,570 3,260 263,705 275,617 292,606 0.87 0.93 1.11

Puerto Rico ................. 7,290 8,280 7,990 1,226,251 1,260,703 1,366,307 0.59 0.66 0.58

NA = not available

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Science and Engineering Indicators 2010
8-70 Chapter 8. State Indicators

This indicator shows a state’s ability to attract and retain life and physical scientists. 

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.

State data on individuals in S&E occupations come from the Occupational Employment Statistics (OES) survey, which surveys states’ workplaces and assigns workers 

to a state based on where they work. The survey is conducted as part of a cooperative 

program between the Bureau of Labor Statistics (BLS) and state employment security 

agencies. State data on the size of the civilian workforce are BLS estimates based on the 

Current Population Survey, which assigns workers 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. Because OES data do not classify postsecondary teachers by field, in these data, faculty teaching in S&E fields are not counted 

as working in S&E occupations. Estimates for jurisdictions with smaller populations 

are generally less precise than estimates for jurisdictions with larger populations.

Findings

U About 621,000 individuals (0.40% 

of the workforce) were employed 

as life and physical scientists in 

the United States in 2008, similar 

to the 546,160 life and physical 

scientists employed in 2004, which 

represented 0.39% of the workforce.

UÊ In 2008, individual states had 

indicator values ranging from 0.20% 

ÌÊ£°ä{ ̄]ÊÜVÊÃÜi`Ê>ÀÊ

differences in the concentration 

vÊLÃÊÊÌiÊviÊ>`Ê«ÞÃV>Ê

sciences.

UÊ States with the highest concentrations 

of life and physical scientists in 

their workforces were uniformly 

distributed throughout the United 

States.

UÊ EPSCoR states appeared to be fairly 

evenly distributed throughout all four 

quartiles for this indicator.

Life and Physical Scientists as Share of Workforce

Figure 8-31

Life and physical scientists as share of workforce: 2008

1st quartile

2nd quartile

3rd quartile

4th quartile

No data

1st quartile (1.70%–0.50%) 2nd quartile (0.48%–0.40%) 3rd quartile (0.39%–0.35%) 4th quartile (0.33%–0.20%) No data

Alaska ‡

 Connecticut Alabama ‡ Arizona Kentucky ‡

California Idaho ‡ Indiana Arkansas ‡ Louisiana ‡

Colorado Kansas ‡ Iowa Florida Michigan

Delaware ‡

 Minnesota Maine ‡ Georgia Montana ‡

District of Columbia North Carolina Mississippi ‡ Illinois Washington

Hawaii ‡

 North Dakota ‡ Missouri Nevada ‡

Maryland Oregon Nebraska ‡ New York

Massachusetts Pennsylvania New Hampshire ‡

 Ohio

New Jersey Texas Rhode Island ‡

 Oklahoma ‡

New Mexico ‡ Utah Virginia South Carolina ‡

South Dakota ‡ Vermont ‡ West Virginia ‡ Tennessee

Wyoming ‡

 Wisconsin

‡

 EPSCoR state

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Science and Engineering Indicators 2010 8-71

Table 8-31

Life and physical scientists as share of workforce, by state: 2004, 2006, and 2008

Life and physical scientists Employed workforce

Life and physical scientists in workforce (%)

State 2004 2006 2008 2004 2006 2008 2004 2006 2008

United States ................. 546,160 577,890 621,020 139,213,523 144,581,912 153,999,337 0.39 0.40 0.40

Alabama ..................... 5,630 5,690 7,570 2,014,678 2,120,573 2,162,479 0.28 0.27 0.35

Alaska ........................ 3,090 3,010 3,720 312,922 323,531 357,136 0.99 0.93 1.04

Arizona ....................... 6,940 6,460 7,660 2,649,243 2,854,381 3,132,667 0.26 0.23 0.24

Arkansas .................... 2,890 2,880 3,180 1,228,163 1,292,886 1,370,259 0.24 0.22 0.23

California .................... 68,020 72,590 92,000 16,444,457 17,029,307 18,391,844 0.41 0.43 0.50

Colorado .................... NA 14,130 15,040 2,384,562 2,537,037 2,730,447 NA 0.56 0.55

Connecticut ............... 8,460 7,750 7,550 1,714,758 1,765,075 1,876,125 0.49 0.44 0.40

Delaware .................... 3,100 2,940 3,420 408,022 424,506 442,902 0.76 0.69 0.77

District of Columbia ... 5,860 6,370 5,650 285,567 296,957 332,703 2.05 2.15 1.70

Florida ........................ 20,490 22,100 22,280 8,056,259 8,692,761 9,231,462 0.25 0.25 0.24

Georgia ...................... 13,090 9,820 9,610 4,257,465 4,522,025 4,847,650 0.31 0.22 0.20

Hawaii ........................ 2,400 3,390 3,570 597,147 628,277 654,261 0.40 0.54 0.55

Idaho .......................... 9,930 3,860 3,100 670,746 723,621 754,879 1.48 0.53 0.41

Illinois ......................... 19,390 22,650 20,370 6,012,320 6,315,715 6,697,335 0.32 0.36 0.30

Indiana ....................... NA 10,350 11,530 3,017,271 3,108,806 3,230,367 NA 0.33 0.36

Iowa ........................... NA 5,390 5,900 1,542,342 1,602,849 1,675,981 NA 0.34 0.35

Kansas ....................... 4,640 NA 6,010 1,378,713 1,400,169 1,496,943 0.34 NA 0.40

Kentucky .................... 5,300 4,990 NA 1,859,902 1,922,163 2,042,915 0.28 0.26 NA

Louisiana .................... 6,130 6,090 NA 1,926,594 1,910,348 2,078,935 0.32 0.32 NA

Maine ......................... 2,430 2,650 2,750 661,163 678,843 706,829 0.37 0.39 0.39

Maryland .................... 18,150 19,930 22,630 2,766,653 2,892,620 2,997,709 0.66 0.69 0.75

Massachusetts ........... 20,700 23,260 26,930 3,204,653 3,234,860 3,424,018 0.65 0.72 0.79

Michigan .................... 10,340 12,940 NA 4,694,981 4,730,291 4,935,584 0.22 0.27 NA

Minnesota .................. 11,700 13,450 13,990 2,781,744 2,822,297 2,932,961 0.42 0.48 0.48

Mississippi ................. 4,540 4,490 4,890 1,234,167 1,218,664 1,314,444 0.37 0.37 0.37

Missouri ..................... 9,920 10,190 10,620 2,821,802 2,885,857 3,012,126 0.35 0.35 0.35

Montana ..................... 3,050 3,450 NA 456,624 478,162 506,159 0.67 0.72 NA

Nebraska .................... 4,280 4,350 3,580 940,047 945,270 995,635 0.46 0.46 0.36

Nevada ....................... 3,210 3,460 3,400 1,134,550 1,240,868 1,373,462 0.28 0.28 0.25

New Hampshire ......... 1,870 2,250 2,690 693,648 711,512 738,858 0.27 0.32 0.36

New Jersey ................ 19,710 NA 25,170 4,177,841 4,309,021 4,496,727 0.47 NA 0.56

New Mexico ............... 7,550 5,380 6,870 850,164 895,623 959,458 0.89 0.60 0.72

New York .................... NA 31,280 28,460 8,810,155 9,072,733 9,679,617 NA 0.34 0.29

North Carolina ............ 19,190 NA 21,860 4,028,598 4,250,619 4,543,754 0.48 NA 0.48

North Dakota ............. 1,570 1,610 1,650 338,221 346,359 369,671 0.46 0.46 0.45

Ohio ........................... 15,020 17,320 19,040 5,507,404 5,609,056 5,971,874 0.27 0.31 0.32

Oklahoma ................... NA 7,010 5,720 1,608,849 1,650,877 1,748,416 NA 0.42 0.33

Oregon ....................... 7,990 NA 9,170 1,722,058 1,796,165 1,957,953 0.46 NA 0.47

Pennsylvania .............. 25,460 NA 28,610 5,889,957 6,009,858 6,394,884 0.43 NA 0.45

Rhode Island .............. 2,790 2,120 2,080 531,121 547,618 567,597 0.53 0.39 0.37

South Carolina ........... 5,190 5,680 5,220 1,900,122 1,988,378 2,152,965 0.27 0.29 0.24

South Dakota ............. 1,770 1,900 2,350 409,263 417,100 444,890 0.43 0.46 0.53

Tennessee .................. 7,380 7,680 7,920 2,733,793 2,835,530 3,041,276 0.27 0.27 0.26

Texas .......................... 47,540 50,040 46,710 10,456,224 10,921,673 11,701,585 0.45 0.46 0.40

Utah ........................... 5,820 6,330 6,520 1,169,163 1,272,801 1,383,743 0.50 0.50 0.47

Vermont ...................... 1,250 1,480 1,460 337,709 348,026 355,432 0.37 0.43 0.41

Virginia ....................... NA 15,370 14,810 3,704,593 3,878,988 4,124,766 NA 0.40 0.36

Washington ................ NA 20,590 NA 3,008,352 3,160,350 3,476,766 NA 0.65 NA

West Virginia .............. 2,850 3,230 2,890 744,034 767,134 806,152 0.38 0.42 0.36

Wisconsin .................. 11,660 13,000 14,580 2,871,034 2,918,155 3,084,130 0.41 0.45 0.47

Wyoming .................... 1,840 2,070 2,320 263,705 275,617 292,606 0.70 0.75 0.79

Puerto Rico ................ 4,840 5,470 5,380 1,226,251 1,260,703 1,366,307 0.39 0.43 0.39

NA = not available

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with the exception of Puerto Rico reflect revised population controls and model reestimation.

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Science and Engineering Indicators 2010
8-72 Chapter 8. State Indicators

This indicator shows the extent to which a state’s workforce makes use 

of specialists with advanced computer training. 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.

State data on individuals in S&E occupations come from the Occupational Employment Statistics (OES) survey, which surveys states’ workplaces 

and assigns workers to a state based on where they work. The survey is 

conducted as part of a cooperative program between the Bureau of Labor 

Statistics (BLS) and state employment security agencies. State data on the 

size of the civilian workforce are BLS estimates based on the Current Population Survey, which assigns workers 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. Because OES 

data do not classify postsecondary teachers by field, in these data, 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 In the United States, 3.20 million individuals 

(2.08% of the workforce) were employed as 

computer specialists in 2008, similar to the 2.81 

million computer specialists employed in 2004, 

which accounted for 2.02% of the workforce.

UÊ Individual states showed large differences in 

the intensity of computer-related operations 

in their economies, with 0.68% to 4.16% of 

their workforce employed in computer-related 

occupations in 2008.

UÊ There was a significant concentration of 

computer-intensive occupations in the District of 


ÕL>Ê>`ÊÌiÊ>`>ViÌÊÃÌ>ÌiÃÊvÊ>ÀÞ>`Ê

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.

UÊ EPSCoR states tended to have smaller 

percentages of computer specialists in their 

workforces, suggesting that their economies are 

less technically oriented in this respect.

1st quartile (9.68%–2.10%) 2nd quartile (2.09%–1.74%) 3rd quartile (1.72%–1.20%) 4th quartile (1.19%–0.68%)

Colorado Arizona Alabama ‡

 Alaska ‡

Connecticut California Florida Arkansas ‡

Delaware ‡ Georgia Hawaii ‡ Kentucky ‡

District of Columbia Illinois Idaho ‡ Louisiana ‡

Maryland Michigan Indiana Maine ‡

Massachusetts Missouri Iowa Mississippi ‡

Minnesota Nebraska ‡ Kansas ‡ Montana ‡

New Hampshire ‡ New York New Mexico ‡ Nevada ‡

New Jersey North Carolina Oklahoma ‡ North Dakota ‡

Texas Ohio South Dakota ‡ South Carolina ‡

Utah Oregon Tennessee West Virginia ‡

Virginia Pennsylvania Vermont ‡ Wyoming ‡

Washington Rhode Island ‡ Wisconsin

‡

 EPSCoR state

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Computer Specialists as Share of Workforce

Figure 8-32

Computer specialists as share of workforce: 2008

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-73

Table 8-32

Computer specialists as share of workforce, by state: 2004, 2006, and 2008

Computer specialists Employed workforce

Computer specialists 

in workforce (%)

State 2004 2006 2008 2004 2006 2008 2004 2006 2008

EPSCoR states ................ 276,210 310,990 317,290 22,500,274 23,256,125 24,693,283 1.23 1.34 1.28

Non-EPSCoR states ........ 2,476,430 2,613,670 2,713,430 116,427,682 121,028,830 128,973,351 2.13 2.16 2.10

Average EPSCoR 

 state value .................... na na na na na na 1.25 1.39 1.32

Average non-EPSCoR 

 state value .................... na na na na na na 2.17 2.21 2.15

United States ................... 2,806,910 2,960,460 3,198,050 139,213,523 144,581,912 153,999,337 2.02 2.05 2.08

Alabama ....................... 28,320 32,720 33,010 2,014,678 2,120,573 2,162,479 1.41 1.54 1.53

Alaska .......................... 3,320 3,810 3,720 312,922 323,531 357,136 1.06 1.18 1.04

Arizona ......................... 45,930 49,180 54,520 2,649,243 2,854,381 3,132,667 1.73 1.72 1.74

Arkansas ...................... 12,470a 13,360 15,500 1,228,163 1,292,886 1,370,259 1.02 1.03 1.13

California ...................... 370,180 380,040 383,900 16,444,457 17,029,307 18,391,844 2.25 2.23 2.09

Colorado ...................... 74,940 76,200 79,930 2,384,562 2,537,037 2,730,447 3.14 3.00 2.93

Connecticut ................. 44,120 44,160 40,900 1,714,758 1,765,075 1,876,125 2.57 2.50 2.18

Delaware ...................... 8,730a 11,930 11,950a 408,022 424,506 442,902 2.14 2.81 2.70

District of Columbia ..... 28,040 31,810 32,210 285,567 296,957 332,703 9.82 10.71 9.68

Florida .......................... 137,740 143,450 141,320 8,056,259 8,692,761 9,231,462 1.71 1.65 1.53

Georgia ........................ 94,080 89,390 86,210 4,257,465 4,522,025 4,847,650 2.21 1.98 1.78

Hawaii .......................... 7,440 8,140 7,840 597,147 628,277 654,261 1.25 1.30 1.20

Idaho ............................ 8,710 10,180 9,410a 670,746 723,621 754,879 1.30 1.41 1.25

Illinois ........................... 114,860a 129,880 137,420 6,012,320 6,315,715 6,697,335 1.91 2.06 2.05

Indiana ......................... 37,540 37,230 39,850 3,017,271 3,108,806 3,230,367 1.24 1.20 1.23

Iowa ............................. 22,650 24,940 26,400 1,542,342 1,602,849 1,675,981 1.47 1.56 1.58

Kansas ......................... 20,850 24,110 25,750 1,378,713 1,400,169 1,496,943 1.51 1.72 1.72

Kentucky ...................... 23,800 23,510 24,250 1,859,902 1,922,163 2,042,915 1.28 1.22 1.19

Louisiana ...................... 18,500 17,090 16,020 1,926,594 1,910,348 2,078,935 0.96 0.89 0.77

Maine ........................... 6,860 7,640 7,660 661,163 678,843 706,829 1.04 1.13 1.08

Maryland ...................... 92,450 91,040 89,900 2,766,653 2,892,620 2,997,709 3.34 3.15 3.00

Massachusetts ............. 103,280 109,430 111,910 3,204,653 3,234,860 3,424,018 3.22 3.38 3.27

Michigan ...................... 74,600a 89,280 88,980 4,694,981 4,730,291 4,935,584 1.59 1.89 1.80

Minnesota .................... 67,600 71,930 75,230 2,781,744 2,822,297 2,932,961 2.43 2.55 2.56

Mississippi ................... 8,770 8,510 9,290 1,234,167 1,218,664 1,314,444 0.71 0.70 0.71

Missouri ....................... 56,460 61,120 61,000a 2,821,802 2,885,857 3,012,126 2.00 2.12 2.03

Montana ....................... 4,500a 5,790 5,170a 456,624 478,162 506,159 0.99 1.21 1.02

Nebraska ...................... 15,890a 20,030 20,410 940,047 945,270 995,635 1.69 2.12 2.05

Nevada ......................... 11,540 12,940 12,880 1,134,550 1,240,868 1,373,462 1.02 1.04 0.94

New Hampshire ........... 13,180 16,390 16,780 693,648 711,512 738,858 1.90 2.30 2.27

New Jersey .................. 114,370 116,290 121,690 4,177,841 4,309,021 4,496,727 2.74 2.70 2.71

New Mexico ................. 9,720a 11,060 11,490 850,164 895,623 959,458 1.14 1.23 1.20

New York ...................... 170,140 188,620 200,900a 8,810,155 9,072,733 9,679,617 1.93 2.08 2.08

North Carolina .............. 77,240 80,150 81,630 4,028,598 4,250,619 4,543,754 1.92 1.89 1.80

North Dakota ............... 4,250 4,650 3,140a 338,221 346,359 369,671 1.26 1.34 0.85

Ohio ............................. 93,300 99,960 111,160 5,507,404 5,609,056 5,971,874 1.69 1.78 1.86

Oklahoma ..................... 21,600a 26,200 27,600 1,608,849 1,650,877 1,748,416 1.34 1.59 1.58

Oregon ......................... 29,120 33,960 34,980 1,722,058 1,796,165 1,957,953 1.69 1.89 1.79

Pennsylvania ................ 102,590 110,090 115,300 5,889,957 6,009,858 6,394,884 1.74 1.83 1.80

Rhode Island ................ 7,150a 9,490 9,940a 531,121 547,618 567,597 1.35 1.73 1.75

South Carolina ............. 20,730 23,070 25,130 1,900,122 1,988,378 2,152,965 1.09 1.16 1.17

South Dakota ............... 5,090 5,160 5,860 409,263 417,100 444,890 1.24 1.24 1.32

Tennessee .................... 36,870 36,570 38,490 2,733,793 2,835,530 3,041,276 1.35 1.29 1.27

Texas ............................ 209,360 224,330 245,730 10,456,224 10,921,673 11,701,585 2.00 2.05 2.10

Utah ............................. 25,340 30,060 30,750 1,169,163 1,272,801 1,383,743 2.17 2.36 2.22

Vermont ........................ 5,810 5,920 5,610 337,709 348,026 355,432 1.72 1.70 1.58

Virginia ......................... 151,810 169,830 171,440 3,704,593 3,878,988 4,124,766 4.10 4.38 4.16

Washington .................. 83,480 80,140 101,030 3,008,352 3,160,350 3,476,766 2.77 2.54 2.91

West Virginia ................ 7,230 7,250 6,900 744,034 767,134 806,152 0.97 0.95 0.86

Wisconsin .................... 46,380 46,400 42,860 2,871,034 2,918,155 3,084,130 1.62 1.59 1.39

Wyoming ...................... 1,750 2,040 1,980 263,705 275,617 292,606 0.66 0.74 0.68

Puerto Rico .................. 7,380 9,050 8,750 1,226,251 1,260,703 1,366,307 0.60 0.72 0.64

na = not applicable

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.

 "/
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with the exception of Puerto Rico reflect revised population controls and model reestimation. For explanation of EPSCoR and non-EPSCoR averages, see 

chapter introduction. 

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Science and Engineering Indicators 2010
8-74 Chapter 8. State Indicators

This indicator shows 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 refers 

to R&D activities performed by federal and state agencies, 

businesses, universities, and nonprofit organizations. In 2007, 

business performed 72.0% of total R&D at the national level 

followed by colleges and universities at 13.3%; government 

facilities, including federally funded R&D centers, at 10.5%; 

and nonprofit institutions at 4.2%. Data for the value of gross 

domestic product (GDP) and for R&D expenditures 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. State-level R&D data 

from years before 1998 are not comparable.

Findings

U The national value of R&D expenditures as a share of GDP rose 

slightly between 1998 and 2007, from 2.47% to 2.62%.

UÊ In 2007, state values for this indicator ranged from 0.41% 

to 7.53%, indicating large differences in the geographic 

concentration of R&D activity.

UÊ New Mexico, which has large federal R&D activities and a 

relatively small GDP, is an outlier on this indicator.

UÊ States with high rankings on this indicator also tended to rank 

high on S&E doctorate holders as a share of the workforce.

UÊ The total R&D performed in states in the EPSCoR group was 

approximately one-tenth of that performed in states in the nonEPSCoR group. EPSCoR state values on this indicator were 

more concentrated toward the low end of the distribution than 

comparable values for the ratio of academic R&D to state GDP.

R&D as Share of Gross Domestic Product

Figure 8-33

R&D as share of gross domestic product: 2007

1st quartile (7.53%–2.74%) 2nd quartile (2.61%–2.04%) 3rd quartile (2.00%–1.12%) 4th quartile (1.01%–0.41%)

California Arizona Alabama ‡

 Alaska ‡

Colorado Delaware ‡ Georgia Arkansas ‡

Connecticut Idaho ‡ Iowa Florida

District of Columbia Illinois Kansas ‡

 Hawaii ‡

Maryland Indiana Missouri Kentucky ‡

Massachusetts Montana ‡ Nebraska ‡ Louisiana ‡

Michigan North Carolina New York Maine ‡

Minnesota Ohio North Dakota ‡ Mississippi ‡

New Hampshire ‡ Pennsylvania South Carolina ‡ Nevada ‡

New Jersey Rhode Island ‡ Tennessee Oklahoma ‡

New Mexico ‡ Utah Texas South Dakota ‡

Oregon Vermont ‡ West Virginia ‡ Wyoming ‡

Washington Virginia Wisconsin

‡

 EPSCoR state

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VVÊ

Analysis, Gross Domestic Product data. See table 8-33.

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-75

Table 8-33

R&D as share of gross domestic product, by state: 1998, 2002, and 2007

R&D performed ($millions) State GDP ($millions) R&D performed/GDP (%)

State 1998 2002 2007 1998 2002 2007 1998 2002 2007

EPSCoR states ................ 18,832 24,247 29,581 1,231,448 1,451,648 1,981,871 1.53 1.67 1.49

Non-EPSCoR states ........ 193,314 228,752 326,296 7,396,530 8,879,038 11,641,354 2.61 2.58 2.80

Average EPSCoR 

 state value .................... na na na na na na 1.79 1.83 1.64

Average non-EPSCoR 

 state value .................... na na na na na na 2.49 2.53 2.84

United States ................... 214,752 255,705 359,739 8,679,660 10,398,403 13,715,741 2.47 2.46 2.62

Alabama ....................... 1,926 2,323 3,289 106,656 123,805 164,524 1.81 1.88 2.00

Alaska .......................... NA 308 311 23,165 29,186 44,887 NA 1.06 0.69

Arizona ......................... 2,318 4,096 5,006 137,581 171,942 245,952 1.68 2.38 2.04

Arkansas ...................... 283 427 632 61,861 72,203 95,116 0.46 0.59 0.66

California ...................... 43,919 51,388 77,608 1,085,884 1,340,446 1,801,762 4.04 3.83 4.31

Colorado ...................... 4,565 4,218 6,828 143,160 182,154 235,848 3.19 2.32 2.90

Connecticut ................. 3,559 6,774 10,228 145,373 166,073 212,252 2.45 4.08 4.82

Delaware ...................... 2,556 1,319 1,607 36,831 45,324 61,545 6.94 2.91 2.61

District of Columbia ..... 2,606 2,706 3,862 51,682 67,717 92,516 5.04 4.00 4.17

Florida .......................... 4,773 5,498 7,158 417,169 522,719 741,861 1.14 1.05 0.96

Georgia ........................ 2,492 3,935 4,425 255,612 306,680 391,241 0.97 1.28 1.13

Hawaii .......................... 242 456 592 37,549 43,476 62,019 0.64 1.05 0.95

Idaho ............................ 1,127 1,370 1,115 29,800 36,651 52,110 3.78 3.74 2.14

Illinois ........................... 8,830 10,190 14,287 423,855 487,129 617,409 2.08 2.09 2.31

Indiana ......................... 3,089 4,326 5,980 178,909 205,015 249,229 1.73 2.11 2.40

Iowa ............................. 1,054 1,346 1,882 83,665 97,356 129,911 1.26 1.38 1.45

Kansas ......................... 1,518 1,865 1,697 76,005 89,573 116,986 2.00 2.08 1.45

Kentucky ...................... 645 1,128 1,406 108,813 120,726 152,099 0.59 0.93 0.92

Louisiana ...................... 542 858 1,073 118,085 134,308 207,407 0.46 0.64 0.52

Maine ........................... 159 429 485 31,731 38,625 48,021 0.50 1.11 1.01

Maryland ...................... 8,019 9,030 14,130 161,954 204,120 264,426 4.95 4.42 5.34

Massachusetts ............. 13,382 14,316 24,557 236,079 284,386 352,178 5.67 5.03 6.97

Michigan ...................... 13,655 15,082 17,402 309,431 349,837 379,934 4.41 4.31 4.58

Minnesota .................... 3,818 5,247 7,533 164,897 198,558 252,472 2.32 2.64 2.98

Mississippi ................... 366 691 838 60,513 68,144 87,652 0.61 1.01 0.96

Missouri ....................... 1,868 2,478 3,754 164,267 188,351 229,027 1.14 1.32 1.64

Montana ....................... 191 236 859 19,884 23,560 34,266 0.96 1.00 2.51

Nebraska ...................... 315 663 900 52,076 59,934 80,360 0.60 1.11 1.12

Nevada ......................... 571 524 794 63,635 81,274 129,314 0.90 0.64 0.61

New Hampshire ........... 1,340 1,435 2,146 39,102 46,188 57,820 3.43 3.11 3.71

New Jersey .................. 11,368 13,020 19,552 314,117 372,754 461,295 3.62 3.49 4.24

New Mexico ................. 3,032 4,689 5,663 45,918 52,510 75,192 6.60 8.93 7.53

New York ...................... 13,731 13,354 15,939 686,906 821,577 1,105,020 2.00 1.63 1.44

North Carolina .............. 4,560 5,135 9,204 242,904 296,435 390,467 1.88 1.73 2.36

North Dakota ............... 119 295 327 16,936 19,880 28,518 0.71 1.48 1.15

Ohio ............................. 6,970 8,310 10,041 348,723 389,773 462,506 2.00 2.13 2.17

Oklahoma ..................... 513 793 921 79,341 97,170 136,374 0.65 0.82 0.68

Oregon ......................... 1,910 2,892 4,333 100,951 117,131 158,268 1.89 2.47 2.74

Pennsylvania ................ 8,762 9,763 13,510 361,800 423,110 533,212 2.42 2.31 2.53

Rhode Island ................ 1,677 1,639 1,081 29,537 36,909 46,699 5.68 4.44 2.32

South Carolina ............. 989 1,668 2,291 102,945 121,582 151,703 0.96 1.37 1.51

South Dakota ............... 60 111 240 20,771 26,416 35,211 0.29 0.42 0.68

Tennessee .................... 2,503 2,568 3,659 160,872 191,525 245,162 1.56 1.34 1.49

Texas ............................ 10,774 14,223 17,853 629,209 783,480 1,148,531 1.71 1.82 1.55

Utah ............................. 1,495 1,572 2,337 60,168 72,665 105,574 2.48 2.16 2.21

Vermont ........................ 175 398 534 15,935 19,553 24,627 1.10 2.04 2.17

Virginia ......................... 4,934 5,895 9,473 226,569 285,759 384,132 2.18 2.06 2.47

Washington .................. 8,466 10,511 15,061 195,794 231,463 310,279 4.32 4.54 4.85

West Virginia ................ 421 542 650 39,500 45,032 57,877 1.07 1.20 1.12

Wisconsin .................... 2,501 3,585 4,555 160,681 188,600 233,406 1.56 1.90 1.95

Wyoming ...................... 65 80 129 14,859 19,619 31,544 0.44 0.41 0.41

Puerto Rico .................. NA NA NA 54,086 71,624 NA NA NA NA

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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. 

-"1,

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Science and Engineering Indicators 2010
8-76 Chapter 8. State Indicators

This indicator shows how federal R&D funding is disbursed geographically relative to the size of a state’s 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 Science 

Foundation Survey of Federal Funds for Research and Development and include data reported by 11 federal agencies. The 

Department of Defense (DOD) disburses most—approximately 

50%—federal R&D funding. The geographic distribution of 

DOD development funding 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 size of a state’s civilian workforce is a nonseasonally 

adjusted annual estimate of employed workers based on the 

Bureau of Labor Statistics Current Population Survey, which 

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 

$68 billion in 1997 to about $111 billion in 2007, an increase of 

63% in current dollars.

UÊ In 2007, federal R&D obligations per civilian worker were 

VViÌÀ>Ìi`ÊÊ>ÊviÜÊÃÌ>ÌiÃÆÊÞÊ£ÓÊÃÌ>ÌiÃÊ>`ÊÌiÊÃÌÀVÌÊvÊ

Columbia exceeded the national average of $764 per worker.

UÊ Federal R&D obligations in 2007 varied greatly among the states, 

ranging from $4,029 to $115 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.

UÊ EPSCoR states tended to be concentrated in the lower two 

quartiles of this indicator, showing that many EPSCoR states 

receive smaller amounts of federal R&D funding than would have 

been anticipated based on the size of their civilian workforce.

1st quartile ($13,453–$805) 2nd quartile ($756–$424) 3rd quartile ($397–$276) 4th quartile ($235–$115)

Alabama ‡

 Alaska ‡ Delaware ‡ Arkansas ‡

Arizona Hawaii ‡ Florida Indiana

California Maine ‡

 Georgia Kansas ‡

Colorado Minnesota Idaho ‡ Kentucky ‡

Connecticut New Hampshire ‡ Illinois Louisiana ‡

District of Columbia New Jersey Iowa Nebraska ‡

Maryland New York Michigan Nevada ‡

Massachusetts North Carolina Mississippi ‡ Oklahoma ‡

Montana ‡ Ohio Missouri South Carolina ‡

New Mexico ‡ Pennsylvania North Dakota ‡ South Dakota ‡

Rhode Island ‡ Tennessee Oregon Wisconsin

Virginia Texas Vermont ‡

 Wyoming ‡

Washington Utah West Virginia ‡

‡

 EPSCoR state

-"1,

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Statistics, Local Area Unemployment Statistics. See table 8-34.

Federal R&D Obligations per Civilian Worker

Figure 8-34

Federal R&D obligations per civilian worker: 2007

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-77

Table 8-34

Federal R&D obligations per civilian worker, by state: 1997, 2002, and 2007

Federal R&D obligations 

(current $thousands) Civilian workers

Federal R&D obligations/ 

civilian worker ($)

State 1997 2002 2007 1997 2002 2007 1997 2002 2007

United States ................. 68,364,994 83,629,201 111,210,200 130,988,267 136,781,272 145,595,173 522 611 764

Alabama ..................... 2,213,683 2,704,834 2,203,409 2,035,156 1,994,748 2,105,951 1,088 1,356 1,046

Alaska ........................ 99,928 273,779 249,799 289,963 305,112 330,587 345 897 756

Arizona ....................... 732,065 2,057,261 2,345,659 2,196,901 2,512,714 2,913,695 333 819 805

Arkansas .................... 95,709 141,267 166,582 1,177,143 1,204,467 1,293,852 81 117 129

California .................... 13,731,238 15,686,055 21,313,080 14,780,791 16,180,799 17,208,903 929 969 1,238

Colorado .................... 1,340,231 1,608,971 2,625,822 2,154,294 2,304,109 2,602,015 622 698 1,009

Connecticut ............... 846,458 1,916,926 1,987,490 1,674,937 1,700,949 1,780,473 505 1,127 1,116

Delaware .................... 48,964 78,846 127,615 378,117 401,301 427,766 129 196 298

District of Columbia ... 2,232,284 2,849,531 4,130,639 262,789 284,615 307,049 8,495 10,012 13,453

Florida ........................ 3,326,418 2,300,550 2,715,997 7,040,660 7,662,511 8,779,299 472 300 309

Georgia ...................... 3,919,868 2,019,248 1,326,052 3,751,699 4,135,381 4,602,947 1,045 488 288

Hawaii ........................ 150,722 375,159 343,581 566,766 584,354 631,911 266 642 544

Idaho .......................... 205,660 230,910 281,334 598,004 646,142 733,652 344 357 383

Illinois ......................... 1,140,163 1,693,942 2,115,154 5,988,296 5,969,393 6,361,750 190 284 332

Indiana ....................... 410,646 525,745 572,444 3,014,499 3,002,515 3,065,590 136 175 187

Iowa ........................... 228,180 404,545 602,947 1,555,837 1,567,836 1,598,261 147 258 377

Kansas ....................... 255,490 290,516 246,693 1,329,797 1,350,960 1,418,666 192 215 174

Kentucky .................... 91,291 321,284 221,222 1,809,785 1,838,495 1,932,028 50 175 115

Louisiana .................... 211,036 431,989 418,396 1,890,102 1,892,636 1,921,343 112 228 218

Maine ......................... 68,683 254,518 377,059 624,410 650,943 671,337 110 391 562

Maryland .................... 7,328,937 7,192,243 11,578,771 2,646,200 2,733,103 2,873,512 2,770 2,632 4,029

Massachusetts ........... 3,437,962 4,658,616 6,741,246 3,158,851 3,243,409 3,255,611 1,088 1,436 2,071

Michigan .................... 735,221 1,244,244 1,709,933 4,748,691 4,724,998 4,659,927 155 263 367

Minnesota .................. 609,395 1,150,839 1,383,666 2,605,673 2,749,525 2,796,737 234 419 495

Mississippi ................. 289,791 622,714 423,671 1,200,845 1,214,631 1,231,743 241 513 344

Missouri ..................... 1,130,148 1,202,671 1,142,325 2,780,185 2,829,985 2,878,399 407 425 397

Montana ..................... 79,347 112,924 652,591 427,504 445,281 485,615 186 254 1,344

Nebraska .................... 82,981 144,671 187,239 904,492 921,201 953,769 92 157 196

Nevada ....................... 295,042 335,989 298,924 895,258 1,066,477 1,271,472 330 315 235

New Hampshire ......... 278,697 296,575 317,036 635,469 679,818 712,048 439 436 445

New Jersey ................ 1,318,793 2,021,450 2,141,156 4,031,022 4,117,265 4,276,561 327 491 501

New Mexico ............... 1,933,123 2,746,139 3,201,259 768,596 823,191 909,968 2,515 3,336 3,518

New York .................... 2,471,213 3,746,837 5,061,983 8,416,544 8,721,428 9,087,278 294 430 557

North Carolina ............ 900,947 1,390,440 1,824,802 3,809,601 3,930,736 4,308,624 236 354 424

North Dakota ............. 53,015 102,136 113,203 335,854 333,605 354,003 158 306 320

Ohio ........................... 1,879,784 2,103,409 2,602,628 5,448,161 5,503,109 5,640,081 345 382 461

Oklahoma ................... 160,356 271,565 225,577 1,543,105 1,602,118 1,657,964 104 170 136

Oregon ....................... 319,587 502,284 504,150 1,652,997 1,704,131 1,827,285 193 295 276

Pennsylvania .............. 1,893,867 3,162,026 3,277,761 5,775,178 5,869,224 6,013,406 328 539 545

Rhode Island .............. 403,844 501,299 627,538 504,147 525,721 547,927 801 954 1,145

South Carolina ........... 166,607 371,006 417,465 1,819,508 1,840,598 2,011,255 92 202 208

South Dakota ............. 41,955 58,679 61,925 383,216 402,397 429,495 109 146 144

Tennessee .................. 566,242 961,149 1,658,662 2,640,005 2,714,992 2,893,748 214 354 573

Texas .......................... 3,640,162 3,374,405 5,426,669 9,395,279 10,115,299 10,992,828 387 334 494

Utah ........................... 319,826 408,747 765,138 1,034,429 1,113,645 1,325,480 309 367 577

Vermont ...................... 49,885 136,374 107,999 315,806 331,763 340,073 158 411 318

Virginia ....................... 4,849,753 5,756,339 8,753,773 3,323,266 3,588,079 3,930,984 1,459 1,604 2,227

Washington ................ 1,226,154 1,998,915 4,708,365 2,822,223 2,877,022 3,253,475 434 695 1,447

West Virginia .............. 193,061 254,239 216,621 746,442 749,164 771,837 259 339 281

Wisconsin .................. 332,214 594,816 670,963 2,855,830 2,860,915 2,937,903 116 208 228

Wyoming .................... 28,368 39,585 36,187 243,944 258,462 279,090 116 153 130

Puerto Rico ................ 58,943 135,294 85,850 1,132,658 1,169,760 1,241,426 52 116 69

NOTES: Only 11 agencies required to report federal R&D obligations: Departments of Agriculture, Commerce, Defense, Energy, Health and Human Services, 

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National Science Foundation. These obligations represent approximately 98% of total federal R&D obligations. Civilian workers represent employed component 

vÊVÛ>Ê>LÀÊvÀViÊ>`ÊÀi«ÀÌi`Ê>ÃÊ>Õ>Ê`>Ì>ÊÌÊÃi>Ã>ÞÊ>`ÕÃÌi`°Ê

-"1,

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Labor Statistics, Local Area Unemployment Statistics.

Science and Engineering Indicators 2010
8-78 Chapter 8. State Indicators

This indicator describes 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 Science Foundation’s 

Survey of Federal Funds for Research and Development and include data reported by 11 federal agencies. The Department of Defense (DOD) disburses 

most—approximately 50%—federal R&D funding. The geographic distribution 

of DOD development funding to industry 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. They 

exclude managers, technicians, elementary and secondary schoolteachers, and 

medical personnel. State data on individuals in S&E occupations come from 

the Occupational Employment Statistics (OES) survey, which surveys states’ 

workplaces and assigns workers to a state based on where they work. Because 

OES data do not classify postsecondary teachers by field, in these data, faculty 

teaching in S&E fields are not counted as working 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 $111 billion for R&D in 2007—

about $20,000 for each person employed in an 

S&E occupation.

UÊ The state distribution of federal R&D 

obligations per person employed in an S&E 

occupation ranged from $95,724 to $4,394 in 

2007.

UÊ The distribution for this indicator was highly 

skewed in 2007, with only 14 states and 

the District of Columbia above the national 

average. High values were reported in the 

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defense contractors are located.

UÊ EPSCoR states tended to rank in the lower 

two quartiles for this indicator, showing that 

many EPSCoR states receive smaller amounts 

of federal R&D funding than would have been 

anticipated based on the number of S&E 

workers in the state.

1st quartile ($95,724–$23,426) 2nd quartile ($22,915–$12,295) 3rd quartile ($11,698–$8,065) 4th quartile ($7,705–$4,394)

Alabama ‡

 Alaska ‡ Florida Arkansas ‡

California Arizona Georgia Delaware ‡

Connecticut Colorado Idaho ‡ Indiana

District of Columbia Hawaii ‡

 Illinois Kansas ‡

Maine ‡ Iowa Louisiana ‡ Kentucky ‡

Maryland Mississippi ‡ Michigan Nebraska ‡

Massachusetts New York Minnesota Oklahoma ‡

Montana ‡ North Carolina Missouri Oregon

New Mexico ‡

 Ohio Nevada ‡ South Carolina ‡

Rhode Island ‡ Pennsylvania New Hampshire ‡ South Dakota ‡

Tennessee Texas New Jersey Wisconsin

Virginia Utah North Dakota ‡

 Wyoming ‡

Washington West Virginia ‡ Vermont ‡

‡

 EPSCoR state

-"1,

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Statistics, Occupational Employment and Wage Estimates. See table 8-35.

Federal R&D Obligations per Individual in S&E Occupation

Figure 8-35

Federal R&D obligations per individual in S&E occupation: 2007

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-79

Table 8-35

Federal R&D obligations per individual in S&E occupation, by state: 2003, 2005, and 2007

Federal R&D obligations 

(current $millions) Individuals in S&E occupations

Federal R&D 

obligations/individual 

in S&E occupation ($)

State 2003 2005 2007 2003 2005 2007 2003 2005 2007

United States ................. 91,247 106,743 111,212 4,961,540 5,233,520 5,591,990 18,391 20,396 19,888

Alabama ..................... 2,933 2,800 2,203 56,380 62,790 69,650 52,020 44,596 31,630

Alaska ........................ 246 234 250 10,600 11,230 11,990 23,210 20,796 20,851

Arizona ....................... 1,857 2,674 2,346 92,120 96,410 102,380 20,156 27,741 22,915

Arkansas .................... 140 154 167 21,340 24,660 28,460 6,547 6,255 5,868

California .................... 17,410 19,380 21,313 676,180 716,530 753,570 25,748 27,046 28,283

Colorado .................... 1,612 2,037 2,626 124,140 126,110 138,990 12,985 16,150 18,893

Connecticut ............... 2,068 2,154 1,987 81,380 83,930 80,280 25,411 25,658 24,751

Delaware .................... 91 94 128 17,370 18,010 22,140 5,261 5,228 5,781

District of Columbia ... 2,916 3,993 4,131 54,890 63,410 63,150 53,127 62,978 65,416

Florida ........................ 2,522 2,198 2,716 221,070 241,000 244,140 11,408 9,120 11,125

Georgia ...................... 1,514 1,707 1,326 144,170 137,580 136,880 10,503 12,411 9,687

Hawaii ........................ 350 384 344 16,090 17,460 18,740 21,731 22,016 18,356

Idaho .......................... 216 273 281 22,150 23,880 24,330 9,757 11,436 11,550

Illinois ......................... 1,900 1,983 2,115 211,230 221,630 225,180 8,996 8,946 9,392

Indiana ....................... 561 554 572 78,410 79,910 83,080 7,158 6,928 6,885

Iowa ........................... 465 448 603 37,320 40,300 45,430 12,466 11,108 13,273

Kansas ....................... 190 198 247 51,970 51,630 50,040 3,656 3,835 4,936

Kentucky .................... 232 263 221 45,230 44,530 49,030 5,131 5,901 4,507

Louisiana .................... 442 402 418 41,900 41,030 38,450 10,547 9,799 10,871

Maine ......................... 145 240 377 15,020 15,500 15,960 9,650 15,473 23,622

Maryland .................... 7,804 12,211 11,579 149,250 160,120 162,540 52,291 76,264 71,238

Massachusetts ........... 5,157 5,702 6,741 184,690 193,180 205,610 27,920 29,516 32,785

Michigan .................... 1,673 1,105 1,710 182,940 192,150 212,040 9,146 5,752 8,065

Minnesota .................. 861 758 1,384 117,120 120,930 129,840 7,354 6,270 10,659

Mississippi ................. 1,174 424 424 22,190 23,480 25,520 52,900 18,062 16,614

Missouri ..................... 1,270 4,040 1,142 84,150 92,260 102,170 15,091 43,793 11,177

Montana ..................... 130 177 653 11,450 11,940 13,240 11,314 14,811 49,320

Nebraska .................... 146 145 187 30,710 31,530 31,420 4,765 4,603 5,952

Nevada ....................... 409 382 299 22,330 24,400 26,920 18,330 15,675 11,107

New Hampshire ......... 363 364 317 23,430 26,840 28,450 15,498 13,574 11,142

New Jersey ................ 1,786 2,344 2,141 161,420 174,270 186,120 11,063 13,451 11,503

New Mexico ............... 2,850 3,279 3,201 33,600 32,530 33,440 84,823 100,808 95,724

New York .................... 3,973 4,956 5,062 272,440 289,010 322,520 14,583 17,147 15,695

North Carolina ............ 1,611 1,791 1,825 132,440 134,290 142,970 12,163 13,340 12,765

North Dakota ............. 102 105 113 8,430 9,070 9,660 12,070 11,589 11,698

Ohio ........................... 2,396 2,370 2,603 177,100 180,900 196,390 13,529 13,100 13,254

Oklahoma ................... 274 254 226 44,360 46,370 51,430 6,185 5,469 4,394

Oregon ....................... 480 557 504 61,230 62,030 67,890 7,843 8,987 7,424

Pennsylvania .............. 3,788 3,235 3,278 185,560 204,270 218,890 20,413 15,835 14,976

Rhode Island .............. 523 572 628 18,740 18,080 18,400 27,927 31,651 34,130

South Carolina ........... 412 408 417 48,740 50,460 54,120 8,447 8,094 7,705

South Dakota ............. 55 70 62 9,150 9,460 11,550 5,988 7,398 5,368

Tennessee .................. 1,039 1,293 1,659 63,680 66,390 70,820 16,320 19,474 23,426

Texas .......................... 4,757 4,989 5,427 365,270 389,550 441,410 13,023 12,806 12,295

Utah ........................... 650 814 765 45,570 45,110 51,340 14,268 18,043 14,901

Vermont ...................... 182 171 108 11,420 12,770 12,760 15,926 13,371 8,464

Virginia ....................... 6,213 8,214 8,754 209,280 236,650 254,710 29,687 34,711 34,368

Washington ................ 2,292 2,388 4,708 150,230 160,960 183,900 15,257 14,834 25,601

West Virginia .............. 367 773 217 16,220 16,040 16,560 22,651 48,163 13,104

Wisconsin .................. 657 648 671 93,320 93,590 99,380 7,042 6,926 6,752

Wyoming .................... 41 34 36 6,130 7,350 8,110 6,704 4,564 4,439

Puerto Rico ................ 112 101 86 19,940 20,950 NA 5,628 4,842 NA

NOTES: Only 11 agencies required to report federal R&D obligations: Departments of Agriculture, Commerce, Defense, Energy, Health and Human 

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Ê

occupations in the United States provided by Occupational Employment Statistics (OES). OES estimates for 2003 S&E occupations based on November 

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Bureau of Labor Statistics, Occupational Employment and Wage Estimates.

Science and Engineering Indicators 2010
8-80 Chapter 8. State Indicators

This indicator measures the ratio between the amount of state agency R&D 

funding and the size of a state’s economy. 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 funding come from National Science Foundation 

(NSF) surveys of state agencies covering 2006 and 2007 expenditures. The 

data cover funding administered by state government departments, agencies, 

independent commissions, and other state-run entities. They exclude staterun 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. Although the surveys are limited to R&D 

activities, the data may include some expenditures for non-R&D activities 

such as commercialization, environmental testing, and routine survey work.

Because of differences in the survey populations, definition of covered 

R&D activities, and collection methods, the results of 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 $89 for each $1 million of a 

state’s GDP.

UÊ State agency R&D expenditures accounted 

for 0.34% of total R&D expenditures in 2007, 

indicating that most R&D was funded by 

nonstate sources.

UÊ In 2007, the state values for this indicator 

ranged from $618 per $1 million to $9 per 

$1 million of state GDP, reflecting varying 

approaches to the role of state government 

agencies in the funding of R&D.

UÊ A substantial number of EPSCoR states are 

among those with the highest values for this 

indicator, suggesting that there is a statelevel effort to improve R&D infrastructure in 

ÌiÃiÊÃÌ>ÌiÃ]ÊÌÊÕÃÌÊ>Êvi`iÀ>ÊivvÀÌ°

State Agency R&D Expenditures per $1 Million of Gross Domestic Product

1st quartile ($618.20–$152.40) 2nd quartile ($137.98–$67.99) 3rd quartile ($67.97–$40.32) 4th quartile ($37.94–$8.95)

Alaska ‡

 Arizona Alabama ‡ District of Columbia

Hawaii ‡

 Arkansas ‡ California Georgia

Indiana Connecticut Colorado Louisiana ‡

Maine ‡ Florida Delaware ‡ Massachusetts

Maryland Illinois Idaho ‡

 Mississippi ‡

Montana ‡ Kansas ‡ Iowa Nevada ‡

North Dakota ‡ Kentucky ‡ Minnesota New Hampshire ‡

Ohio Michigan Missouri New Mexico ‡

Pennsylvania New Jersey Nebraska ‡ Rhode Island ‡

South Carolina ‡ New York Oregon Tennessee

South Dakota ‡ North Carolina Vermont ‡

 Texas

West Virginia ‡

 Oklahoma ‡ Virginia Utah

Wyoming ‡

 Washington Wisconsin

‡

 EPSCoR state

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Ý«i`ÌÕÀiÃÆÊ>`Ê	ÕÀi>ÕÊ

of Economic Analysis, Gross Domestic Product data. See table 8-36.

Figure 8-36

State agency R&D expenditures per $1 million of gross domestic product: 2007

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-81

Table 8-36

State agency R&D expenditures per $1 million of gross domestic product, by state: 2006 and 2007

State agency R&D 

expenditures (current $)

State GDP 

(current $millions)

State agency R&D($)/ 

$1 million GDP 

State 2006 2007 2006 2007 2006 2007

EPSCoR states .......................................... 195,833,394 232,177,940 1,898,423 1,981,871 103 117

Non-EPSCoR states .................................. 824,010,424 989,262,653 11,133,340 11,641,354 74 85

Average EPSCoR state value .................... na na na na 141 160

Average non-EPSCoR state value ............. na na na na 96 111

United States ............................................. 1,021,016,894 1,223,449,593 13,119,937 13,715,741 78 89

Alabama ................................................. 7,269,319 7,340,365 158,566 164,524 46 45

Alaska .................................................... 10,019,060 9,526,100 43,117 44,887 232 212

Arizona ................................................... 37,151,471 20,442,635 237,397 245,952 157 83

Arkansas ................................................ 4,869,648 7,658,199 90,864 95,116 54 81

California ................................................ 107,793,045 91,842,652 1,742,172 1,801,762 62 51

Colorado ................................................ 8,997,236 11,924,981 226,266 235,848 40 51

Connecticut ........................................... 19,209,064 29,285,710 204,964 212,252 94 138

Delaware ................................................ 2,812,102 2,611,108 59,589 61,545 47 42

District of Columbia ............................... 1,173,076 2,009,000 88,174 92,516 13 22

Florida .................................................... 42,329,624 96,968,573 716,505 741,861 59 131

Georgia .................................................. 10,620,188 4,886,946 376,410 391,241 28 12

Hawaii .................................................... 12,067,849 22,643,330 58,676 62,019 206 365

Idaho ...................................................... 2,280,873 2,739,006 48,441 52,110 47 53

Illinois ..................................................... 37,184,281 41,974,809 583,990 617,409 64 68

Indiana ................................................... 6,220,575 40,534,381 238,693 249,229 26 163

Iowa ....................................................... 13,564,062 6,790,053 121,945 129,911 111 52

Kansas ................................................... 14,348,384 11,752,696 110,645 116,986 130 100

Kentucky ................................................ 17,558,997 11,960,634 146,415 152,099 120 79

Louisiana ................................................ 11,216,568 6,587,314 203,167 207,407 55 32

Maine ..................................................... 17,509,051 27,525,552 46,340 48,021 378 573

Maryland ................................................ 24,945,119 40,298,691 257,577 264,426 97 152

Massachusetts ....................................... 10,729,419 5,600,189 335,313 352,178 32 16

Michigan ................................................ 75,016,589 32,849,159 375,759 379,934 200 86

Minnesota .............................................. 6,219,201 10,529,048 242,095 252,472 26 42

Mississippi ............................................. 2,744,882 2,893,892 84,586 87,652 32 33

Missouri ................................................. 18,465,303 15,567,277 220,092 229,027 84 68

Montana ................................................. 8,606,319 8,200,230 31,994 34,266 269 239

Nebraska ................................................ 5,602,163 4,043,480 75,290 80,360 74 50

Nevada ................................................... 1,397,463 1,748,776 123,054 129,314 11 14

New Hampshire ..................................... 2,040,544 1,685,178 56,073 57,820 36 29

New Jersey ............................................ 25,900,482 59,747,701 448,426 461,295 58 130

New Mexico ........................................... 3,105,000 672,921 72,161 75,192 43 9

New York ................................................ 103,597,135 128,361,166 1,028,320 1,105,020 101 116

North Carolina ........................................ 14,344,310 37,607,109 380,932 390,467 38 96

North Dakota ......................................... 21,062,090 9,908,722 25,851 28,518 815 347

Ohio ....................................................... 55,068,629 114,086,509 451,600 462,506 122 247

Oklahoma ............................................... 8,922,036 10,731,050 130,094 136,374 69 79

Oregon ................................................... 7,382,722 7,389,914 150,984 158,268 49 47

Pennsylvania .......................................... 117,320,158 103,973,448 508,769 533,212 231 195

Rhode Island .......................................... 150,000 1,771,949 45,733 46,699 3 38

South Carolina ....................................... 22,427,746 31,493,843 146,211 151,703 153 208

South Dakota ......................................... 5,791,586 5,473,603 32,008 35,211 181 155

Tennessee .............................................. 5,355,000 4,549,998 235,753 245,162 23 19

Texas ...................................................... 28,019,645 29,650,947 1,068,119 1,148,531 26 26

Utah ....................................................... 3,214,170 2,752,228 97,963 105,574 33 26

Vermont .................................................. 1,680,533 1,529,805 23,628 24,627 71 62

Virginia ................................................... 11,579,623 15,486,526 368,604 384,132 31 40

Washington ............................................ 22,834,218 23,333,431 291,298 310,279 78 75

West Virginia .......................................... 6,024,577 22,179,830 56,016 57,877 108 383

Wisconsin .............................................. 10,949,155 12,828,572 223,394 233,406 49 55

Wyoming ................................................ 6,326,604 19,500,357 29,904 31,544 212 618

Puerto Rico ............................................ 1,458,790 2,326,241 NA NA NA NA

>ÊrÊÌÊ>««V>LiÆÊ ÊrÊÌÊ>Û>>Li


*-
,ÊrÊ
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«iÌÌÛiÊ,iÃi>ÀVÆÊ*ÊrÊ}ÀÃÃÊ`iÃÌVÊ«À`ÕVÌ

NOTES: For explanation of EPSCoR and non-EPSCoR averages, see chapter introduction. 

SOURCES: National Science Foundation, Division of Science Resources Statistics, Survey of State Research and Development Expenditures (FY 2006 

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Science and Engineering Indicators 2010
8-82 Chapter 8. State Indicators

This indicator measures the extent of R&D activity funded by state government 

agencies relative to the size of the state’s 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 funding come from National Science Foundation surveys of state 

agencies covering 2006 and 2007 expenditures. The data cover funding administered 

by state government departments, agencies, independent commissions, and other staterun 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. Although the surveys are limited to R&D activities, 

the data may include some expenditures for non-R&D activities such as commercialization, environmental testing, and routine survey work.

The size of a state’s civilian workforce is a nonseasonally adjusted annual estimate of 

employed workers based on the Bureau of Labor Statistics Current Population Survey, 

which 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 In 2007, state government 

agency R&D expenditures 

averaged $8.42 per civilian 

worker nationwide.

UÊ 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.

UÊ State agency R&D spending per 

civilian worker varied greatly 

among the states in 2007, 

ranging from a high of $69.87 to 

a low of $0.74.

UÊ A number of EPSCoR states had 

high values for this indicator.

1st quartile ($69.87–$14.02) 2nd quartile ($13.97–$6.19) 3rd quartile ($6.10–$3.73) 4th quartile ($3.49–$0.74)

Alaska ‡

 Arizona Arkansas ‡ Alabama ‡

Connecticut District of Columbia California Georgia

Hawaii ‡

 Florida Colorado Louisiana ‡

Maine ‡ Illinois Delaware ‡ Massachusetts

Maryland Indiana Idaho ‡

 Mississippi ‡

Montana ‡ Kansas ‡ Iowa Nevada ‡

New York Kentucky ‡ Minnesota New Hampshire ‡

North Dakota ‡ Michigan Missouri New Mexico ‡

Ohio New Jersey Nebraska ‡ Rhode Island ‡

Pennsylvania North Carolina Oregon Tennessee

South Carolina ‡

 Oklahoma ‡ Vermont ‡ Texas

West Virginia ‡

 South Dakota ‡ Virginia Utah

Wyoming ‡

 Washington Wisconsin

‡

 EPSCoR state

-"1,

-\Ê >Ì>Ê-ViViÊÕ`>Ì]ÊÛÃÊvÊ-ViViÊ,iÃÕÀViÃÊ-Ì>ÌÃÌVÃ]Ê-ÕÀÛiÞÊvÊ-Ì>ÌiÊ,iÃi>ÀVÊ>`ÊiÛi«iÌÊ
Ý«i`ÌÕÀiÃÆÊ>`Ê	ÕÀi>ÕÊ

of Labor Statistics, Local Area Unemployment Statistics. See table 8-37.

State Agency R&D Expenditures per Civilian Worker

Figure 8-37

State agency R&D expenditures per civilian worker: 2007

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-83

Table 8-37

State agency R&D expenditures per civilian worker, by state: 2006 and 2007

State agency R&D 

expenditures (current $) Civilian workers

State agency R&D 

expenditures/civilian 

worker ($)

State 2006 2007 2006 2007 2006 2007

United States ........................... 1,022,475,684 1,225,775,834 144,581,912 145,595,173 7.07 8.42

Alabama ............................... 7,269,319 7,340,365 2,120,573 2,105,951 3.43 3.49

Alaska .................................. 10,019,060 9,526,100 323,531 330,587 30.97 28.82

Arizona ................................. 37,151,471 20,442,635 2,854,381 2,913,695 13.02 7.02

Arkansas .............................. 4,869,648 7,658,199 1,292,886 1,293,852 3.77 5.92

California .............................. 107,793,045 91,842,652 17,029,307 17,208,903 6.33 5.34

Colorado .............................. 8,997,236 11,924,981 2,537,037 2,602,015 3.55 4.58

Connecticut ......................... 19,209,064 29,285,710 1,765,075 1,780,473 10.88 16.45

Delaware .............................. 2,812,102 2,611,108 424,506 427,766 6.62 6.10

District of Columbia ............. 1,173,076 2,009,000 296,957 307,049 3.95 6.54

Florida .................................. 42,329,624 96,968,573 8,692,761 8,779,299 4.87 11.05

Georgia ................................ 10,620,188 4,886,946 4,522,025 4,602,947 2.35 1.06

Hawaii .................................. 12,067,849 22,643,330 628,277 631,911 19.21 35.83

Idaho .................................... 2,280,873 2,739,006 723,621 733,652 3.15 3.73

Illinois ................................... 37,184,281 41,974,809 6,315,715 6,361,750 5.89 6.60

Indiana ................................. 6,220,575 40,534,381 3,108,806 3,065,590 2.00 13.22

Iowa ..................................... 13,564,062 6,790,053 1,602,849 1,598,261 8.46 4.25

Kansas ................................. 14,348,384 11,752,696 1,400,169 1,418,666 10.25 8.28

Kentucky .............................. 17,558,997 11,960,634 1,922,163 1,932,028 9.14 6.19

Louisiana .............................. 11,216,568 6,587,314 1,910,348 1,921,343 5.87 3.43

Maine ................................... 17,509,051 27,525,552 678,843 671,337 25.79 41.00

Maryland .............................. 24,945,119 40,298,691 2,892,620 2,873,512 8.62 14.02

Massachusetts ..................... 10,729,419 5,600,189 3,234,860 3,255,611 3.32 1.72

Michigan .............................. 75,016,589 32,849,159 4,730,291 4,659,927 15.86 7.05

Minnesota ............................ 6,219,201 10,529,048 2,822,297 2,796,737 2.20 3.76

Mississippi ........................... 2,744,882 2,893,892 1,218,664 1,231,743 2.25 2.35

Missouri ............................... 18,465,303 15,567,277 2,885,857 2,878,399 6.40 5.41

Montana ............................... 8,606,319 8,200,230 478,162 485,615 18.00 16.89

Nebraska .............................. 5,602,163 4,043,480 945,270 953,769 5.93 4.24

Nevada ................................. 1,397,463 1,748,776 1,240,868 1,271,472 1.13 1.38

New Hampshire ................... 2,040,544 1,685,178 711,512 712,048 2.87 2.37

New Jersey .......................... 25,900,482 59,747,701 4,309,021 4,276,561 6.01 13.97

New Mexico ......................... 3,105,000 672,921 895,623 909,968 3.47 0.74

New York .............................. 103,597,135 128,361,166 9,072,733 9,087,278 11.42 14.13

North Carolina ...................... 14,344,310 37,607,109 4,250,619 4,308,624 3.37 8.73

North Dakota ....................... 21,062,090 9,908,722 346,359 354,003 60.81 27.99

Ohio ..................................... 55,068,629 114,086,509 5,609,056 5,640,081 9.82 20.23

Oklahoma ............................. 8,922,036 10,731,050 1,650,877 1,657,964 5.40 6.47

Oregon ................................. 7,382,722 7,389,914 1,796,165 1,827,285 4.11 4.04

Pennsylvania ........................ 117,320,158 103,973,448 6,009,858 6,013,406 19.52 17.29

Rhode Island ........................ 150,000 1,771,949 547,618 547,927 0.27 3.23

South Carolina ..................... 22,427,746 31,493,843 1,988,378 2,011,255 11.28 15.66

South Dakota ....................... 5,791,586 5,473,603 417,100 429,495 13.89 12.74

Tennessee ............................ 5,355,000 4,549,998 2,835,530 2,893,748 1.89 1.57

Texas .................................... 28,019,645 29,650,947 10,921,673 10,992,828 2.57 2.70

Utah ..................................... 3,214,170 2,752,228 1,272,801 1,325,480 2.53 2.08

Vermont ................................ 1,680,533 1,529,805 348,026 340,073 4.83 4.50

Virginia ................................. 11,579,623 15,486,526 3,878,988 3,930,984 2.99 3.94

Washington .......................... 22,834,218 23,333,431 3,160,350 3,253,475 7.23 7.17

West Virginia ........................ 6,024,577 22,179,830 767,134 771,837 7.85 28.74

Wisconsin ............................ 10,949,155 12,828,572 2,918,155 2,937,903 3.75 4.37

Wyoming .............................. 6,326,604 19,500,357 275,617 279,090 22.95 69.87

Puerto Rico .......................... 1,458,790 2,326,241 1,260,703 1,241,426 1.16 1.87

SOURCES: National Science Foundation, Division of Science Resources Statistics, Survey of State Research and Development Expenditures (FY 2006 

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Science and Engineering Indicators 2010
8-84 Chapter 8. State Indicators

This indicator measures the extent of the R&D activity funded by a state’s government 

agencies relative to the number of individuals engaged in S&E occupations in the state.

Data on state R&D funding come from National Science Foundation surveys of state 

agencies covering 2006 and 2007 expenditures. The data cover funding administered 

by state government departments, agencies, independent commissions, and other staterun 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. Although the surveys are limited to R&D 

activities, the data may include some expenditures for non-R&D activities such as 

commercialization, environmental testing, and routine survey work.

The denominator of this indicator measures individuals with bachelor’s or higher 

degrees who work in S&E occupations. S&E occupations are defined by standard occupational codes. They include engineers and computer, mathematical, life, physical, 

and social scientists. They exclude managers, technicians, elementary and secondary 

schoolteachers, and medical personnel. State data on individuals in S&E occupations 

come from the Occupational Employment Statistics (OES) survey, which surveys states’ 

workplaces and assigns workers to a state based on where they work. Because OES 

data do not classify postsecondary teachers by field, in these data, faculty teaching in 

S&E fields are not counted as working in S&E occupations.

State Agency R&D Expenditures per Individual in S&E Occupation

Figure 8-38

State agency R&D expenditures per individual in S&E occupation: 2007

1st quartile

2nd quartile

3rd quartile

1st quartile ($2,404–$398) 2nd quartile ($397–$155) 3rd quartile ($152–$96) 4th quartile ($86–$20)

Alaska ‡

 Arizona Alabama ‡ Colorado

Hawaii ‡

 Arkansas ‡ California District of Columbia

Indiana Connecticut Delaware ‡

 Georgia

Maine ‡

 Florida Idaho ‡ Massachusetts

Montana ‡

 Illinois Iowa Minnesota

New York Kansas ‡

 Mississippi ‡ Nevada ‡

North Dakota ‡ Kentucky ‡ Missouri New Hampshire ‡

Ohio Louisiana ‡ Nebraska ‡ New Mexico ‡

Pennsylvania Maryland Oregon Tennessee

South Carolina ‡

 Michigan Rhode Island ‡ Texas

South Dakota ‡

 New Jersey Vermont ‡ Utah

West Virginia ‡ North Carolina Washington Virginia

Wyoming ‡

 Oklahoma ‡ Wisconsin

‡

 EPSCoR state

-"1,

-\Ê >Ì>Ê-ViViÊÕ`>Ì]ÊÛÃÊvÊ-ViViÊ,iÃÕÀViÃÊ-Ì>ÌÃÌVÃ]Ê-ÕÀÛiÞÊvÊ-Ì>ÌiÊ,iÃi>ÀVÊ>`ÊiÛi«iÌÊ
Ý«i`ÌÕÀiÃÆÊ>`Ê	ÕÀi>ÕÊ

of Labor Statistics, Occupational Employment and Wage Estimates. See table 8-38.

4th quartile

Findings

U In 2007, the average state agency 

R&D expenditure per person 

employed in an S&E occupation 

was $219, indicating that state 

agency funding for R&D was a very 

small fraction of total R&D funding.

UÊ 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, about $20,000 for each 

person employed in an S&E 

occupation.

UÊ State agency R&D funding per 

person employed in an S&E 

occupation ranged from $2,404 to 

$20 per state in 2007.

UÊ Several EPSCoR states had the 

highest state agency R&D spending 

per S&E worker.
Science and Engineering Indicators 2010 8-85

Table 8-38

State agency R&D expenditures per individual in S&E occupation, by state: 2006 and 2007

State agency R&D 

expenditures (currrent $) Individuals in S&E occupations

State agency R&D 

expenditures/individual in 

S&E occupation ($)

State 2006 2007 2006 2007 2006 2007

United States ........................... 1,022,475,684 1,225,775,834 5,407,710 5,591,990 189 219

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: National total for S&E occupations in the United States provided by Occupational Employment Statistics (OES). OES estimates for 2006 and 

2007 S&E occupations based on May data.

SOURCES: National Science Foundation, Division of Science Resources Statistics, Survey of State Research and Development Expenditures (FY 2006, 

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ÃÌ>ÌiÃ°

Science and Engineering Indicators 2010
8-86 Chapter 8. State Indicators

This indicator measures the role of R&D in a state’s 

business activity. Business R&D focuses on projects that 

are expected to yield new or improved products, processes, 

or services and to bring direct benefits to a company. A high 

value for this indicator shows that the businesses within a state 

are making a significant investment in their R&D activities.

Because industries differ in their reliance on R&D, the 

indicator reflects state differences in industrial structure as 

much as the behavior or priorities of individual businesses. 

Estimates for states with smaller economies are generally less 

precise than those for states with larger economies.

The methodology for making state-level assignments of 

the business R&D reported by companies with operations in 

multiple states changed in 1998. Therefore, pre-1998 data on 

the amount of R&D performed by industry in states are not 

comparable.

Business-Performed R&D as Share of Private-Industry Output

Figure 8-39

Business-performed R&D as share of private-industry output: 2007

1st quartile (6.08%–2.16%) 2nd quartile (2.03%–1.30%) 3rd quartile (1.28%–0.61%) 4th quartile (0.52%–0.14%)

California Arizona Alabama ‡

 Alaska ‡

Colorado Idaho ‡ District of Columbia Arkansas ‡

Connecticut Illinois Florida Hawaii ‡

Delaware ‡ Kansas ‡ Georgia Louisiana ‡

Indiana Maryland Iowa Mississippi ‡

Massachusetts Missouri Kentucky ‡ Montana ‡

Michigan North Carolina Maine ‡ Nevada ‡

Minnesota Ohio Nebraska ‡ North Dakota ‡

New Hampshire ‡ Texas New Mexico ‡ Oklahoma ‡

New Jersey Utah New York South Dakota ‡

Oregon Vermont ‡ Rhode Island ‡ West Virginia ‡

Pennsylvania Virginia South Carolina ‡ Wyoming ‡

Washington Wisconsin Tennessee

‡

 EPSCoR state

-"1,

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Economic Analysis, Gross Domestic Product data. See table 8-39.

1st quartile

2nd quartile

3rd quartile

4th quartile

Findings

U The amount of R&D performed by business rose from nearly 

$164 billion in 1998 to about $266 billion in 2007, an increase 

of more than 60% (in current dollars).

UÊ The value of this indicator exhibited little overall change 

between 1998 and 2007.

UÊ Business R&D was concentrated in a few states—only 12 

states had indicator values that met or exceeded the national 

average in 2007.
Science and Engineering Indicators 2010 8-87

Table 8-39

Business-performed R&D as share of private-industry output, by state: 1998, 2002, and 2007

Business-performed R&D 

(current $millions)

Private-industry output 

(current $millions)

Business-performed R&D/ 

private-industry output (%)

State 1998 2002 2007 1998 2002 2007 1998 2002 2007

United States ................. 163,658 185,505 265,919 7,652,500 9,131,170 12,064,609 2.14 2.03 2.20

Alabama ..................... 845 846 1,771 89,994 104,211 138,392 0.94 0.81 1.28

Alaska ........................ 37 51 58 18,175 23,302 36,914 0.20 0.22 0.16

Arizona ....................... 1,801 3,201 3,846 120,484 150,429 215,329 1.49 2.13 1.79

Arkansas .................... 213 225 339 54,258 62,883 82,061 0.39 0.36 0.41

California .................... 32,856 42,177 64,187 965,937 1,184,559 1,596,045 3.40 3.56 4.02

Colorado .................... 3,180 2,823 5,223 126,013 160,289 207,494 2.52 1.76 2.52

Connecticut ............... 3,346 6,077 9,444 132,955 150,755 192,695 2.52 4.03 4.90

Delaware .................... 1,356 1,219 1,472 33,652 41,196 56,314 4.03 2.96 2.61

District of Columbia ... D 194 379 32,710 43,937 62,311 1.83 0.44 0.61

Florida ........................ 3,265 3,707 4,569 365,813 459,933 655,459 0.89 0.81 0.70

Georgia ...................... 1,617 2,107 2,788 224,870 267,441 339,136 0.72 0.79 0.82

Hawaii ........................ 55 103 218 29,201 33,619 47,923 0.19 0.31 0.45

Idaho .......................... 1,103 992 726 25,510 31,197 45,004 4.32 3.18 1.61

Illinois ......................... 7,318 7,616 11,362 384,342 438,363 558,823 1.90 1.74 2.03

Indiana ....................... 2,922 3,572 4,939 161,797 184,923 224,499 1.81 1.93 2.20

Iowa ........................... 750 753 1,202 73,908 85,652 114,859 1.01 0.88 1.05

Kansas ....................... 1,384 1,427 1,304 65,697 77,183 100,600 2.11 1.85 1.30

Kentucky .................... 606 656 890 94,081 103,514 128,916 0.64 0.63 0.69

Louisiana .................... 377 248 373 103,343 116,505 184,848 0.36 0.21 0.20

Maine ......................... 137 250 265 27,363 33,121 41,206 0.50 0.75 0.64

Maryland .................... 1,905 3,800 3,665 133,482 168,770 216,069 1.43 2.25 1.70

Massachusetts ........... 10,367 10,609 19,488 214,890 258,688 320,565 4.82 4.10 6.08

Michigan .................... 12,554 13,565 15,736 278,874 313,384 337,072 4.50 4.33 4.67

Minnesota .................. 3,367 4,460 6,636 148,057 177,427 226,097 2.27 2.51 2.94

Mississippi ................. 183 224 279 50,894 56,215 72,521 0.36 0.40 0.38

Missouri ..................... 1,505 1,592 2,736 146,453 166,436 200,977 1.03 0.96 1.36

Montana ..................... 63 66 134 16,607 19,565 28,927 0.38 0.34 0.46

Nebraska .................... 195 342 489 44,485 50,901 69,174 0.44 0.67 0.71

Nevada ....................... 476 339 567 56,995 72,826 116,816 0.84 0.47 0.49

New Hampshire ......... 1,138 1,153 1,814 35,812 41,991 52,268 3.18 2.75 3.47

New Jersey ................ 11,107 11,566 17,892 282,938 335,111 413,706 3.93 3.45 4.32

New Mexico ............... 1,450 331 568 37,455 41,702 62,107 3.87 0.79 0.91

New York .................... 10,283 9,234 10,916 614,396 736,066 993,104 1.67 1.25 1.10

North Carolina ............ 3,483 3,704 6,829 212,790 259,825 338,159 1.64 1.43 2.02

North Dakota ............. 46 154 126 14,277 16,671 24,359 0.32 0.92 0.52

Ohio ........................... 5,742 6,230 7,265 312,647 346,524 410,857 1.84 1.80 1.77

Oklahoma ................... 369 412 527 65,997 80,492 114,350 0.56 0.51 0.46

Oregon ....................... 1,345 2,320 3,629 88,532 100,222 138,781 1.52 2.31 2.61

Pennsylvania .............. 7,393 7,064 10,387 324,847 381,405 480,942 2.28 1.85 2.16

Rhode Island .............. 1,332 1,121 411 25,892 32,294 40,846 5.14 3.47 1.01

South Carolina ........... 996 1,054 1,426 87,771 102,565 126,253 1.13 1.03 1.13

South Dakota ............. 40 53 132 17,932 23,084 30,910 0.22 0.23 0.43

Tennessee .................. 2,440 1,289 1,638 142,438 169,564 218,172 1.71 0.76 0.75

Texas .......................... 8,984 10,744 13,889 558,165 691,968 1,026,886 1.61 1.55 1.35

Utah ........................... 1,119 1,116 1,764 51,610 61,934 91,319 2.17 1.80 1.93

Vermont ...................... 114 286 413 13,976 16,974 21,249 0.82 1.68 1.94

Virginia ....................... 2,540 2,920 4,840 186,444 235,685 314,689 1.36 1.24 1.54

Washington ................ 7,072 8,579 12,687 168,427 198,461 266,138 4.20 4.32 4.77

West Virginia .............. D 264 233 33,440 37,308 47,466 1.00 0.71 0.49

Wisconsin .................. 1,929 2,649 3,411 143,368 167,489 207,614 1.35 1.58 1.64

Wyoming .................... 20 21 37 12,506 16,611 27,388 0.16 0.13 0.14

Puerto Rico ................ NA NA NA NA NA NA NA NA NA

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Bureau of Economic Analysis, Gross Domestic Product data.

Science and Engineering Indicators 2010
8-88 Chapter 8. State Indicators

This indicator measures the extent of spending on academic 

research performed in a state relative to the size of the state’s 

economy. Academic R&D is more basic and less productoriented than R&D performed by business. It can be a valuable 

basis for future economic development. In this indicator, data 

for Maryland exclude expenditures by the Applied Physics 

Laboratory at the Johns Hopkins University.

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 doubled in a decade, rising from $25.8 billion in 1998 to 

$51.8 billion in 2008 (in current dollars).

UÊ In the United States, growth in academic research increased 

more rapidly than GDP, causing the value of this indicator 

to increase by 23% between 1998 and 2008. Most of this 

V>}iÊVVÕÀÀi`ÊLiÌÜiiÊ£nÊ>`ÊÓääÎÆÊÌiÀiÊÜ>ÃÊÊ

significant change between 2003 and 2008.

UÊ The largest percentage increases in academic R&D as a share 

of GDP occurred in two EPSCoR states, where the value of 

this indicator more than doubled between 1998 and 2008.

UÊ The EPSCoR states were concentrated in the highest and 

lowest quartiles of the state ranking for this indicator, showing 

considerable variation in the amount of academic R&D being 

conducted in each EPSCoR state relative to the size of the 

state’s economy.

Academic R&D per $1,000 of Gross Domestic Product

Figure 8-40

Academic R&D per $1,000 of gross domestic product: 2008

1st quartile

2nd quartile

3rd quartile

4th quartile

1st quartile ($10.05–$4.43) 2nd quartile ($4.37–$3.68) 3rd quartile ($3.53–$2.66) 4th quartile ($2.65–$1.45)

Maryland Alabama ‡

 Arizona Alaska ‡

Massachusetts California Connecticut Arkansas ‡

Mississippi ‡ Colorado Illinois Delaware ‡

Montana ‡ District of Columbia Kansas ‡ Florida

Nebraska ‡

 Georgia Kentucky ‡ Idaho ‡

New Hampshire ‡ Hawaii ‡ Louisiana ‡ Maine ‡

New Mexico ‡ Indiana Minnesota Nevada ‡

North Carolina Iowa New York New Jersey

North Dakota ‡

 Michigan Tennessee Oklahoma ‡

Pennsylvania Missouri Texas South Dakota ‡

Rhode Island ‡ Ohio Washington Virginia

Vermont ‡

 Oregon West Virginia ‡ Wyoming ‡

Wisconsin South Carolina ‡

 Utah

‡

 EPSCoR state

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Economic Analysis, Gross Domestic Product data. See table 8-40.
Science and Engineering Indicators 2010 8-89

Table 8-40

Academic R&D per $1,000 of gross domestic product, by state: 1998, 2003, and 2008

Academic R&D 

(current $thousands) State GDP (current $millions)

Academic R&D ($)/ 

$1,000 GDP

State 1998 2003 2008 1998 2003 2008 1998 2003 2008

EPSCoR states ................ 3,441,370 5,430,010 6,948,937 1,231,448 1,540,024 2,068,625 2.79 3.53 3.36

Non-EPSCoR states ........ 22,097,707 34,288,279 44,466,163 7,396,530 9,274,429 11,999,705 2.99 3.70 3.71

Average EPSCoR 

 state value .................... na na na na na na 2.84 3.69 3.51

Average non-EPSCoR 

 state value .................... na na na na na na 3.18 3.90 3.90

United States ................... 25,771,999 39,999,163 51,784,120 8,679,657 10,886,172 14,165,565 2.97 3.67 3.66

Alabama ....................... 442,088 550,756 707,801 106,656 130,210 170,014 4.14 4.23 4.16

Alaska .......................... 76,358 142,413 111,418 23,165 31,219 47,912 3.30 4.56 2.33

Arizona ......................... 405,999 617,978 831,192 137,581 182,011 248,888 2.95 3.40 3.34

Arkansas ...................... 117,108 183,908 246,786 61,861 75,685 98,331 1.89 2.43 2.51

California ...................... 3,392,094 5,357,900 7,026,354 1,085,884 1,406,511 1,846,757 3.12 3.81 3.80

Colorado ...................... 489,419 694,862 924,073 143,160 187,397 248,603 3.42 3.71 3.72

Connecticut ................. 406,618 594,507 731,711 145,373 169,885 216,174 2.80 3.50 3.38

Delaware ...................... 72,779 104,650 133,231 36,831 48,587 61,828 1.98 2.15 2.15

District of Columbia ..... 232,922 280,874 369,020 51,682 71,719 97,235 4.51 3.92 3.80

Florida .......................... 712,704 1,204,592 1,591,774 417,169 559,021 744,120 1.71 2.15 2.14

Georgia ........................ 804,151 1,176,523 1,521,486 255,612 317,922 397,756 3.15 3.70 3.83

Hawaii .......................... 148,007 184,602 278,751 37,549 46,441 63,847 3.94 3.97 4.37

Idaho ............................ 72,395 105,039 113,482 29,800 38,148 52,747 2.43 2.75 2.15

Illinois ........................... 1,030,955 1,614,270 1,972,752 423,855 510,296 633,697 2.43 3.16 3.11

Indiana ......................... 426,328 725,752 954,188 178,909 215,434 254,861 2.38 3.37 3.74

Iowa ............................. 358,613 498,669 527,769 83,665 102,210 135,702 4.29 4.88 3.89

Kansas ......................... 213,250 310,111 403,512 76,005 93,560 122,731 2.81 3.31 3.29

Kentucky ...................... 241,520 377,635 506,057 108,813 124,892 156,436 2.22 3.02 3.23

Louisiana ...................... 354,011 514,403 660,139 118,085 146,726 222,218 3.00 3.51 2.97

Maine ........................... 35,265 83,935 128,090 31,731 40,152 49,709 1.11 2.09 2.58

Maryland ...................... 1,330,288 2,040,747 2,747,001 161,954 213,306 273,333 8.21 9.57 10.05

Massachusetts ............. 1,348,220 1,821,924 2,271,757 236,079 293,840 364,988 5.71 6.20 6.22

Michigan ...................... 882,700 1,390,083 1,593,654 309,431 359,030 382,544 2.85 3.87 4.17

Minnesota .................... 367,779 517,912 698,920 164,897 208,179 262,847 2.23 2.49 2.66

Mississippi ................... 152,683 324,236 406,459 60,513 72,259 91,782 2.52 4.49 4.43

Missouri ....................... 484,502 807,075 960,171 164,267 195,547 237,797 2.95 4.13 4.04

Montana ....................... 76,655 141,220 185,791 19,884 25,526 35,891 3.86 5.53 5.18

Nebraska ...................... 186,320 300,540 376,092 52,076 64,628 83,273 3.58 4.65 4.52

Nevada ......................... 83,888 154,515 190,893 63,635 87,828 131,233 1.32 1.76 1.45

New Hampshire ........... 117,323 252,210 302,008 39,102 48,198 60,005 3.00 5.23 5.03

New Jersey .................. 484,942 754,426 876,698 314,117 389,077 474,936 1.54 1.94 1.85

New Mexico ................. 228,740 306,636 416,991 45,918 57,469 79,901 4.98 5.34 5.22

New York ...................... 1,929,694 3,078,092 4,044,815 686,906 850,243 1,144,481 2.81 3.62 3.53

North Carolina .............. 899,507 1,397,859 1,980,833 242,904 306,018 400,192 3.70 4.57 4.95

North Dakota ............... 56,945 133,615 180,764 16,936 21,672 31,208 3.36 6.17 5.79

Ohio ............................. 810,225 1,268,397 1,827,042 348,723 402,399 471,508 2.32 3.15 3.87

Oklahoma ..................... 208,873 295,098 333,230 79,341 103,452 146,448 2.63 2.85 2.28

Oregon ......................... 314,355 436,958 594,945 100,951 121,638 161,573 3.11 3.59 3.68

Pennsylvania ................ 1,348,936 2,014,842 2,604,118 361,800 440,704 553,301 3.73 4.57 4.71

Rhode Island ................ 111,979 187,131 236,627 29,537 39,357 47,364 3.79 4.75 5.00

South Carolina ............. 248,474 435,328 576,219 102,945 127,885 156,384 2.41 3.40 3.68

South Dakota ............... 25,474 49,977 91,797 20,771 27,418 36,959 1.23 1.82 2.48

Tennessee .................... 346,742 600,004 787,122 160,872 200,279 252,127 2.16 3.00 3.12

Texas ............................ 1,697,344 2,764,769 3,744,182 629,209 828,797 1,223,511 2.70 3.34 3.06

Utah ............................. 249,147 385,158 425,683 60,168 75,428 109,777 4.14 5.11 3.88

Vermont ........................ 58,585 106,581 117,210 15,935 20,575 25,442 3.68 5.18 4.61

Virginia ......................... 497,209 776,067 1,052,601 226,569 302,540 397,025 2.19 2.57 2.65

Washington .................. 543,239 871,113 1,058,170 195,794 240,813 322,778 2.77 3.62 3.28

West Virginia ................ 64,150 125,417 170,869 39,500 46,452 61,652 1.62 2.70 2.77

Wisconsin .................... 535,997 877,800 1,117,152 160,681 195,904 240,429 3.34 4.48 4.65

Wyoming ...................... 48,500 60,054 74,720 14,859 21,685 35,310 3.26 2.77 2.12

Puerto Rico .................. 87,592 78,410 100,401 54,086 74,827 NA 1.62 1.05 NA

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NOTES: Academic R&D reported for institutions with R&D over $150,000. For Maryland, academic R&D excludes R&D performed by Applied Physics 

Laboratory at Johns Hopkins University. For explanation of EPSCoR and non-EPSCoR averages, see chapter introduction.

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Science and Engineering Indicators 2010
8-90 Chapter 8. State Indicators

This indicator provides a measure of 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 in 

computer sciences; mathematics; the biological, agricultural, or environmental life sciences; physical sciences; social sciences; psychology; engineering; and health fields. 

S&E doctorate data derive from the National Science Foundation’s Survey of Doctorate Recipients, which excludes individuals with doctorates from foreign institutions 

and those above the age of 75. The Survey of Doctorate Recipients is a sample survey. 

Estimates for states with smaller populations are generally less precise than estimates 

for states with larger populations. Data for doctorates conferred are presented by the 

location where the doctorate was earned; data for S&E doctorate holders are presented 

by employment location regardless of residence.

The indicator does not take into account the postgraduation mobility of recent S&E 

doctorate recipients to their place of employment. Graduate students with temporary 

visas may decide to return home after graduation to begin their careers. The indicator 

also does not take into account individuals with non-U.S. S&E doctorates who are 

working in the United States.

Findings

U In 2006, about 29,000 S&E 

doctorates were awarded by 

U.S. academic institutions, 

approximately 19% more than in 

2001 and 12% more than in 1997.

UÊ Nationwide, the value of this 

indicator declined between 1997 

and 2006, reflecting an increase 

in the stock of employed S&E 

doctorate holders in the United 

States.

UÊ 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.

1st quartile (85.5–56.9) 2nd quartile (56.5–46.9) 3rd quartile (43.4–35.6) 4th quartile (34.3–11.5)

Alabama ‡

 Kentucky ‡ Arkansas ‡ Alaska ‡

Arizona Massachusetts California District of Columbia

Florida Minnesota Colorado Hawaii ‡

Georgia Mississippi ‡ Connecticut Idaho ‡

Illinois Missouri Delaware ‡ Maine ‡

Indiana Nebraska ‡ Nevada ‡ Maryland

Iowa New York New Hampshire ‡

 Montana ‡

Kansas ‡ North Carolina North Dakota ‡ New Jersey

Louisiana ‡

 Oklahoma ‡ Oregon New Mexico ‡

Michigan Pennsylvania South Carolina ‡ South Dakota ‡

Ohio Texas Tennessee Vermont ‡

Rhode Island ‡ West Virginia ‡ Utah Washington

Wisconsin Wyoming ‡

 Virginia

‡

 EPSCoR state

SOURCES: National Science Foundation, Division of Science Resources Statistics, Survey of Earned Doctorates and Survey of Doctorate Recipients. See

table 8-41.

S&E Doctorates Conferred per 1,000 Employed S&E Doctorate Holders

Figure 8-41

S&E doctorates conferred per 1,000 employed S&E doctorate holders: 2006

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-91

Table 8-41

S&E doctorates conferred per 1,000 employed S&E doctorate holders, by state: 1997, 2001, and 2006

 S&E doctorates conferred

Employed S&E 

doctorate holdersa

S&E doctorates 

conferred/1,000 employed 

S&E doctorate holders

State 1997 2001 2006 1997 2001 2006 1997 2001 2006

United States ................. 25,948 24,466 29,015 516,560 572,800 618,370 50.2 42.7 46.9

Alabama ..................... 339 311 359 6,610 5,330 5,900 51.3 58.3 60.8

Alaska ........................ 18 25 18 1,110 1,200 1,110 16.2 20.8 16.2

Arizona ....................... 444 382 498 6,280 7,070 8,410 70.7 54.0 59.2

Arkansas .................... 70 68 111 2,320 2,560 2,840 30.2 26.6 39.1

California .................... 3,289 3,095 3,748 70,490 80,870 87,370 46.7 38.3 42.9

Colorado .................... 546 479 473 10,740 11,780 13,150 50.8 40.7 36.0

Connecticut ............... 362 339 438 8,770 9,490 10,330 41.3 35.7 42.4

Delaware .................... 118 106 121 3,710 3,540 3,110 31.8 29.9 38.9

District of Columbia ... 255 238 272 11,800 14,200 13,330 21.6 16.8 20.4

Florida ........................ 815 773 1,033 13,330 15,740 17,630 61.1 49.1 58.6

Georgia ...................... 555 604 771 9,880 11,990 12,940 56.2 50.4 59.6

Hawaii ........................ 108 91 86 2,550 2,580 2,850 42.4 35.3 30.2

Idaho .......................... 58 53 64 2,030 2,230 2,840 28.6 23.8 22.5

Illinois ......................... 1,276 1,238 1,372 21,260 22,110 24,110 60.0 56.0 56.9

Indiana ....................... 669 636 745 7,570 9,580 9,870 88.4 66.4 75.5

Iowa ........................... 409 366 418 4,120 4,390 4,890 99.3 83.4 85.5

Kansas ....................... 279 264 270 3,770 3,970 4,250 74.0 66.5 63.5

Kentucky .................... 211 177 268 4,110 4,590 4,990 51.3 38.6 53.7

Louisiana .................... 328 338 324 5,360 5,290 5,470 61.2 63.9 59.2

Maine ......................... 38 27 27 2,150 1,990 2,350 17.7 13.6 11.5

Maryland .................... 714 707 876 21,020 22,730 26,220 34.0 31.1 33.4

Massachusetts ........... 1,355 1,370 1,607 23,330 29,100 32,360 58.1 47.1 49.7

Michigan .................... 943 881 1,040 15,050 17,380 17,900 62.7 50.7 58.1

Minnesota .................. 485 466 557 9,810 11,410 11,850 49.4 40.8 47.0

Mississippi ................. 145 133 187 3,000 3,170 3,310 48.3 42.0 56.5

Missouri ..................... 444 430 512 9,490 9,280 9,230 46.8 46.3 55.5

Montana ..................... 57 39 64 1,690 1,440 1,990 33.7 27.1 32.2

Nebraska .................... 176 153 155 3,010 2,890 2,970 58.5 52.9 52.2

Nevada ....................... 44 49 95 1,620 2,030 2,620 27.2 24.1 36.3

New Hampshire ......... 88 73 93 2,230 2,470 2,440 39.5 29.6 38.1

New Jersey ................ 560 557 611 20,440 22,740 20,840 27.4 24.5 29.3

New Mexico ............... 148 135 178 7,480 7,750 8,330 19.8 17.4 21.4

New York .................... 2,160 1,985 2,350 40,080 43,980 45,840 53.9 45.1 51.3

North Carolina ............ 719 722 886 13,730 16,760 18,880 52.4 43.1 46.9

North Dakota ............. 51 41 50 1,350 1,080 1,380 37.8 38.0 36.2

Ohio ........................... 1,220 1,057 1,213 18,700 20,070 20,540 65.2 52.7 59.1

Oklahoma ................... 221 224 220 4,580 4,360 4,420 48.3 51.4 49.8

Oregon ....................... 287 246 311 6,210 7,040 8,280 46.2 34.9 37.6

Pennsylvania .............. 1,273 1,210 1,415 23,940 26,140 29,090 53.2 46.3 48.6

Rhode Island .............. 148 139 205 2,450 2,640 3,020 60.4 52.7 67.9

South Carolina ........... 231 238 257 4,780 5,130 5,920 48.3 46.4 43.4

South Dakota ............. 37 33 36 1,060 1,000 1,050 34.9 33.0 34.3

Tennessee .................. 395 374 428 8,520 8,980 9,980 46.4 41.6 42.9

Texas .......................... 1,620 1,575 1,924 28,570 32,490 35,970 56.7 48.5 53.5

Utah ........................... 275 243 240 4,800 4,820 5,540 57.3 50.4 43.3

Vermont ...................... 35 52 47 1,750 1,750 1,700 20.0 29.7 27.6

Virginia ....................... 615 571 705 15,250 17,460 19,790 40.3 32.7 35.6

Washington ................ 474 454 545 13,360 14,760 16,920 35.5 30.8 32.2

West Virginia .............. 79 66 112 1,980 1,890 2,020 39.9 34.9 55.4

Wisconsin .................. 648 508 569 8,460 8,720 9,500 76.6 58.3 59.9

Wyoming .................... 59 33 38 860 840 730 68.6 39.3 52.1

Puerto Rico ................ 55 92 73 660 1,410 1,690 83.3 65.2 43.2

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, Division of Science Resources Statistics, Survey of Earned Doctorates and Survey of Doctorate Recipients.

Science and Engineering Indicators 2010
8-92 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, 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 Scientific 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 derive from the National Science Foundation’s Survey of Doctorate Recipients, which excludes those with doctorates from foreign 

institutions and those above the age of 75. The Survey of Doctorate Recipients is a sample 

survey. Estimates for states with smaller populations 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 During the decade beginning in 

1997, the number of scientific 

and technical articles published 

and number of S&E doctorate 

holders increased proportionally, 

resulting in no significant change 

in the nationwide value of this 

indicator.

UÊ 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.

UÊ In the most recent data, the 

states with the highest values for 

this indicator were distributed 

across the nation.

UÊ The average indicator value for 

EPSCoR states was considerably 

lower than the average indicator 

value for non-EPSCoR states.

Academic S&E Article Output per 1,000 S&E Doctorate Holders in Academia

Figure 8-42

Academic S&E article output per 1,000 S&E doctorate holders in academia: 2008/06

1st quartile (723–592) 2nd quartile (589–505) 3rd quartile (501–427) 4th quartile (424–236)

Arizona Alabama ‡

 Alaska ‡ Arkansas ‡

California Colorado District of Columbia Hawaii ‡

Connecticut Georgia Kansas ‡ Idaho ‡

Delaware ‡ Indiana Kentucky ‡ Maine ‡

Florida Louisiana ‡ Minnesota Mississippi ‡

Illinois Nebraska ‡ Rhode Island ‡ Montana ‡

Iowa New Hampshire ‡ South Carolina ‡ Nevada ‡

Maryland New Jersey Tennessee New Mexico ‡

Massachusetts New York Utah North Dakota ‡

Michigan Ohio Vermont ‡ Oklahoma ‡

Missouri Oregon Virginia South Dakota ‡

North Carolina Pennsylvania Washington West Virginia ‡

Texas Wisconsin Wyoming ‡

‡

 EPSCoR state

-"1,

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1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-93

Table 8-42

Academic S&E article output per 1,000 S&E doctorate holders in academia, by state: 1997, 2003, and 2008/06

 Academic S&E article output S&E doctorate holders in academiaa

Academic articles/ 

1,000 academic 

doctorate holders

State 1997 2003 2008 1997 2003 2006 1997 2003 2008/06

EPSCoR states ................ 16,096 17,479 19,506 41,750 42,890 44,410 386 408 439

Non-EPSCoR states ........ 120,252 129,972 146,975 201,710 232,390 243,740 596 559 603

Average EPSCoR 

 state value .................... na na na na na na 372 394 430

Average non-EPSCoR 

 state value .................... na na na na na na 585 546 582

United States ................... 137,598 148,722 167,852 245,670 277,970 290,730 560 535 577

Alabama ....................... 1,838 1,851 1,974 4,640 3,240 3,430 396 571 576

Alaska .......................... 160 195 285 450 600 580 356 325 492

Arizona ......................... 2,133 2,152 2,455 3,050 3,660 4,010 699 588 612

Arkansas ...................... 575 664 716 1,520 1,850 1,940 379 359 369

California ...................... 16,862 18,744 21,001 26,050 29,830 29,070 647 628 722

Colorado ...................... 2,408 2,615 2,855 4,550 5,320 5,540 529 492 515

Connecticut ................. 2,692 2,748 3,070 4,000 4,490 4,770 673 612 644

Delaware ...................... 476 580 650 750 800 950 635 725 684

District of Columbia ..... 1,083 1,061 1,106 2,210 2,690 2,580 490 394 429

Florida .......................... 3,976 4,551 5,678 6,850 8,710 9,590 580 523 592

Georgia ........................ 3,076 3,640 4,299 5,780 7,240 7,750 532 503 555

Hawaii .......................... 531 572 697 1,380 1,910 1,670 385 299 417

Idaho ............................ 287 305 360 780 1,190 1,490 368 257 242

Illinois ........................... 6,469 6,959 7,662 10,620 10,930 11,860 609 637 646

Indiana ......................... 2,862 3,022 3,645 4,680 5,810 6,190 612 520 589

Iowa ............................. 2,130 2,220 2,232 3,100 3,390 3,530 687 655 632

Kansas ......................... 1,134 1,235 1,292 2,260 2,380 2,600 502 519 497

Kentucky ...................... 1,320 1,434 1,604 3,040 3,320 3,610 434 432 444

Louisiana ...................... 1,810 1,759 1,753 3,580 3,570 3,470 506 493 505

Maine ........................... 238 267 285 1,340 1,150 1,210 178 233 236

Maryland ...................... 4,259 4,946 5,453 6,400 7,060 7,590 666 700 718

Massachusetts ............. 8,762 9,445 10,834 11,810 14,630 14,980 742 646 723

Michigan ...................... 4,620 5,071 5,804 7,850 9,050 9,410 589 560 617

Minnesota .................... 2,300 2,287 2,634 4,490 5,600 5,730 512 408 460

Mississippi ................... 583 710 840 1,940 2,060 2,020 301 345 416

Missouri ....................... 3,032 3,122 3,443 5,770 5,770 5,750 526 541 599

Montana ....................... 256 363 396 1,020 1,090 1,230 251 333 322

Nebraska ...................... 983 991 1,115 2,360 1,880 1,900 417 527 587

Nevada ......................... 352 458 571 980 1,260 1,620 359 364 352

New Hampshire ........... 579 627 683 1,130 1,360 1,270 512 461 538

New Jersey .................. 2,952 3,150 3,326 5,290 6,160 6,500 558 511 512

New Mexico ................. 782 792 835 2,450 2,960 2,220 319 268 376

New York ...................... 11,781 12,179 13,378 20,900 22,360 23,110 564 545 579

North Carolina .............. 4,762 5,321 6,170 7,740 9,650 10,310 615 551 598

North Dakota ............... 262 315 411 900 740 970 292 426 424

Ohio ............................. 4,900 5,088 5,635 9,750 10,620 10,620 503 479 531

Oklahoma ..................... 853 933 1,081 2,680 2,900 2,890 318 322 374

Oregon ......................... 1,550 1,648 1,972 2,690 3,690 3,620 576 447 545

Pennsylvania ................ 7,756 8,260 9,419 12,150 15,650 16,210 638 528 581

Rhode Island ................ 828 871 1,020 1,730 2,180 2,040 479 399 500

South Carolina ............. 1,155 1,428 1,587 3,230 3,000 3,720 358 476 427

South Dakota ............... 136 165 202 700 670 690 194 246 293

Tennessee .................... 2,123 2,310 2,826 4,720 5,210 5,640 450 443 501

Texas ............................ 8,415 9,423 10,755 13,760 15,240 17,170 612 618 626

Utah ............................. 1,492 1,538 1,786 3,080 2,770 3,580 485 555 499

Vermont ........................ 369 383 475 1,140 1,100 1,050 324 349 453

Virginia ......................... 2,822 2,991 3,593 5,830 7,630 8,050 484 392 446

Washington .................. 3,091 3,412 3,605 5,410 6,740 7,190 571 506 501

West Virginia ................ 400 375 417 1,190 1,190 1,320 336 315 316

Wisconsin .................... 3,025 3,129 3,445 5,390 5,180 5,970 561 604 577

Wyoming ...................... 189 204 255 560 490 520 337 417 490

Puerto Rico .................. 167 212 265 640 1,360 1,270 261 156 209

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.

NOTES: For explanation of EPSCoR and non-EPSCoR averages, see chapter introduction. Workforce represents employed component of civilian labor force 

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Science and Engineering Indicators 2010
8-94 Chapter 8. State Indicators

This indicator shows the relationship between the number of academic S&E publications and expenditures for academic R&D. A high value for this indicator means 

that the S&E publications output of a state’s academic institutions is high relative to 

their R&D spending. Academic institutions include 2-year colleges, 4-year colleges and 

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 Scientific 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.

In this indicator, Maryland data exclude expenditures by the Applied Physics 

Laboratory (APL) at the Johns Hopkins University. APL employs more than 3,000 

workers and supports the Department of Defense, the National Aeronautics and Space 

Administration, and other government agencies. It does not focus on academic research.

Findings

U From 1998 to 2008, the number 

of academic S&E publications 

rose from about 138,000 to about 

168,000—an increase of 22% that 

may reflect both an increase in 

publications and an increase in the 

ÃâiÊvÊÌiÊÕÀ>ÊÃiÌ°

UÊ In 2008, academic researchers 

produced an average of 3.2 

publications per $1 million of 

academic R&D, compared with 

5.4 in 1998. This partly reflects the 

effect of general price inflation but 

may also indicate rising academic 

research costs.

UÊ Between 1998 and 2008, the 

value for this indicator decreased 

in all states but one and by 40% 

nationwide.

UÊ EPSCoR states tended to cluster in 

the lower quartiles for this indicator.

Academic S&E Article Output per $1 Million of Academic R&D

Figure 8-43

Academic S&E article output per $1 million of academic R&D: 2008

1st quartile (4.88–3.62) 2nd quartile (3.59–3.11) 3rd quartile (3.09–2.75) 4th quartile (2.66–1.98)

Connecticut Florida Alabama ‡

 Alaska ‡

Delaware ‡ Idaho ‡ Arizona Hawaii ‡

Illinois Kansas ‡ Arkansas ‡ Louisiana ‡

Indiana Kentucky ‡ California Maine ‡

Iowa Missouri Colorado Maryland

Massachusetts New York District of Columbia Mississippi ‡

Michigan North Carolina Georgia Montana ‡

Minnesota Oklahoma ‡ Nebraska ‡ New Hampshire ‡

New Jersey Oregon Nevada ‡ New Mexico ‡

Pennsylvania Tennessee Ohio North Dakota ‡

Rhode Island ‡ Virginia South Carolina ‡ South Dakota ‡

Utah Washington Texas West Virginia ‡

Vermont ‡ Wyoming ‡ Wisconsin

‡

 EPSCoR state

SOURCES: Thomson Reuters, Science Citation Index and Social Sciences Citation Index, http://thomsonreuters.com/products_services/scienceÉÆÊ/iÊ*>ÌiÌÊ

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Ý«i`ÌÕÀiÃ°Ê-iiÊÌ>LiÊn{Î°

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-95

Table 8-43

Academic S&E article output per $1 million of academic R&D, by state: 1998, 2003, and 2008

 Academic S&E article output

Academic R&D 

(current $millions)

Academic articles/ 

$1 million academic R&D

State 1998 2003 2008 1998 2003 2008 1998 2003 2008

United States ................. 138,147 148,722 167,852 25,772 39,999 51,784 5.36 3.72 3.24

Alabama ..................... 1,829 1,851 1,974 442 551 708 4.14 3.36 2.79

Alaska ........................ 157 195 285 76 142 111 2.05 1.37 2.56

Arizona ....................... 1,960 2,152 2,455 406 618 831 4.83 3.48 2.95

Arkansas .................... 568 664 716 117 184 247 4.85 3.61 2.90

California .................... 17,056 18,744 21,001 3,392 5,358 7,026 5.03 3.50 2.99

Colorado .................... 2,467 2,615 2,855 489 695 924 5.04 3.76 3.09

Connecticut ............... 2,793 2,748 3,070 407 595 732 6.87 4.62 4.20

Delaware .................... 499 580 650 73 105 133 6.86 5.54 4.88

District of Columbia ... 1,089 1,061 1,106 233 281 369 4.68 3.78 3.00

Florida ........................ 4,085 4,551 5,678 713 1,205 1,592 5.73 3.78 3.57

Georgia ...................... 3,061 3,640 4,299 804 1,177 1,521 3.81 3.09 2.83

Hawaii ........................ 511 572 697 148 185 279 3.45 3.10 2.50

Idaho .......................... 273 305 360 72 105 113 3.77 2.91 3.17

Illinois ......................... 6,399 6,959 7,662 1,031 1,614 1,973 6.21 4.31 3.88

Indiana ....................... 2,884 3,022 3,645 426 726 954 6.77 4.16 3.82

Iowa ........................... 2,147 2,220 2,232 359 499 528 5.99 4.45 4.23

Kansas ....................... 1,117 1,235 1,292 213 310 404 5.24 3.98 3.20

Kentucky .................... 1,245 1,434 1,604 242 378 506 5.16 3.80 3.17

Louisiana .................... 1,794 1,759 1,753 354 514 660 5.07 3.42 2.66

Maine ......................... 242 267 285 35 84 128 6.87 3.19 2.23

Maryland .................... 4,412 4,946 5,453 1,330 2,041 2,747 3.32 2.42 1.98

Massachusetts ........... 8,722 9,445 10,834 1,348 1,822 2,272 6.47 5.18 4.77

Michigan .................... 4,610 5,071 5,804 883 1,390 1,594 5.22 3.65 3.64

Minnesota .................. 2,279 2,287 2,634 368 518 699 6.20 4.42 3.77

Mississippi ................. 613 710 840 153 324 406 4.01 2.19 2.07

Missouri ..................... 3,037 3,122 3,443 485 807 960 6.27 3.87 3.59

Montana ..................... 299 363 396 77 141 186 3.91 2.57 2.13

Nebraska .................... 1,002 991 1,115 186 301 376 5.38 3.30 2.96

Nevada ....................... 358 458 571 84 155 191 4.27 2.97 2.99

New Hampshire ......... 594 627 683 117 252 302 5.07 2.49 2.26

New Jersey ................ 2,813 3,150 3,326 485 754 877 5.80 4.17 3.79

New Mexico ............... 724 792 835 229 307 417 3.17 2.58 2.00

New York .................... 11,977 12,179 13,378 1,930 3,078 4,045 6.21 3.96 3.31

North Carolina ............ 4,803 5,321 6,170 900 1,398 1,981 5.34 3.81 3.11

North Dakota ............. 263 315 411 57 134 181 4.62 2.36 2.28

Ohio ........................... 4,902 5,088 5,635 810 1,268 1,827 6.05 4.01 3.08

Oklahoma ................... 871 933 1,081 209 295 333 4.17 3.16 3.24

Oregon ....................... 1,531 1,648 1,972 314 437 595 4.87 3.77 3.31

Pennsylvania .............. 7,839 8,260 9,419 1,349 2,015 2,604 5.81 4.10 3.62

Rhode Island .............. 814 871 1,020 112 187 237 7.27 4.65 4.31

South Carolina ........... 1,170 1,428 1,587 248 435 576 4.71 3.28 2.75

South Dakota ............. 127 165 202 25 50 92 5.00 3.30 2.20

Tennessee .................. 2,176 2,310 2,826 347 600 787 6.28 3.85 3.59

Texas .......................... 8,388 9,423 10,755 1,697 2,765 3,744 4.94 3.41 2.87

Utah ........................... 1,511 1,538 1,786 249 385 426 6.07 3.99 4.20

Vermont ...................... 359 383 475 59 107 117 6.13 3.60 4.06

Virginia ....................... 2,891 2,991 3,593 497 776 1,053 5.81 3.85 3.41

Washington ................ 3,049 3,412 3,605 543 871 1,058 5.61 3.92 3.41

West Virginia .............. 396 375 417 64 125 171 6.17 2.99 2.44

Wisconsin .................. 3,059 3,129 3,445 536 878 1,117 5.71 3.56 3.08

Wyoming .................... 192 204 255 49 60 75 3.95 3.40 3.41

Puerto Rico ................ 189 212 265 88 78 100 2.16 2.70 2.64

NA = not available

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Science and Engineering Indicators 2010
8-96 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 academic patents awarded. Academic patenting is highly 

concentrated and partly reflects the resources devoted to institutional patenting offices.

This indicator is an approximate measure of the degree to which results with 

perceived economic value are generated by the doctoral academic workforce. Academia includes 2-year colleges, 4-year colleges and universities, medical schools, 

and university-affiliated research centers. Utility patents—commonly known as 

patents for inventions—include any new, useful, or improved method, process, 

machine, device, manufactured item, or chemical compound, and represent a key 

measure of intellectual property.

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 derive from the 

National Science Foundation’s Survey of Doctorate Recipients, which excludes 

those with doctorates from foreign institutions and those above the age of 75. 

The Survey of Doctorate Recipients is a sample survey. Estimates for states with 

smaller populations 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 awarded to 

academic institutions increased from 

about 2,500 in 1997 to about 3,400 in 

ÓääÈ]Ê>ÊVÀi>ÃiÊvÊÎÈ ̄ÆÊÌiÊÕLiÀÊ

of academic S&E doctorate holders 

rose by 18% during the same period.

UÊ In 2006, 11.6 academic patents were 

produced nationally for each 1,000 

S&E doctorate holders employed in 

academia, slightly higher than the 10.1 

patents produced in 1997.

UÊ In 2006, states varied widely on this 

indicator, with values ranging from 0 to 

24.7 patents per 1,000 S&E doctorate 

holders employed in academia, 

indicating a difference in patenting 

philosophy or mix of industries with 

which these academic institutions deal.

UÊ California showed the highest level of 

both academic patenting and venture 

capital investment.

Academic Patents Awarded per 1,000 S&E Doctorate Holders in Academia

Figure 8-44

Academic patents awarded per 1,000 S&E doctorate holders in academia: 2006

1st quartile (24.7–11.1) 2nd quartile (10.8–7.3) 3rd quartile (6.9–5.2) 4th quartile (4.9–0.0)

Arkansas ‡

 Alabama ‡ Colorado Alaska ‡

California Arizona Delaware ‡ Kansas ‡

Florida Connecticut District of Columbia Maine ‡

Iowa Georgia Hawaii ‡ Montana ‡

Maryland Illinois Idaho ‡ Nevada ‡

Massachusetts Kentucky ‡ Indiana New Mexico ‡

Michigan Minnesota Louisiana ‡ North Dakota ‡

New Hampshire ‡ Missouri Mississippi ‡ Rhode Island ‡

New Jersey Nebraska ‡ Oregon South Dakota ‡

New York Ohio South Carolina ‡ Vermont ‡

North Carolina Oklahoma ‡ Tennessee West Virginia ‡

Texas Pennsylvania Virginia

Wisconsin Utah Washington

 Wyoming ‡

‡

 EPSCoR state

-"1,

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i}iÃÊ>`Ê1ÛiÀÃÌiÃ1ÌÌÞÊ*>ÌiÌÊÀ>ÌÃÆÊ>`Ê >Ì>Ê

Science Foundation, Division of Science Resources Statistics, Survey of Doctorate Recipients. See table 8-44.

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-97

Table 8-44

Academic patents awarded per 1,000 S&E doctorate holders in academia, by state: 1997, 2001, and 2006

Patents awarded to 

academic institutions

S&E doctorate holders 

in academiaa

Academic patents/1,000 

academic S&E doctorate 

holders

State 1997 2001 2006 1997 2001 2006 1997 2001 2006

United States ................. 2,482 3,282 3,382 245,670 261,780 290,730 10.1 12.5 11.6

Alabama ..................... 23 40 27 4,640 3,050 3,430 5.0 13.1 7.9

Alaska ........................ 2 0 0 450 530 580 4.4 0.0 0.0

Arizona ....................... 21 17 34 3,050 3,340 4,010 6.9 5.1 8.5

Arkansas .................... 8 28 25 1,520 1,640 1,940 5.3 17.1 12.9

California .................... 436 693 719 26,050 26,790 29,070 16.7 25.9 24.7

Colorado .................... 32 31 29 4,550 5,120 5,540 7.0 6.1 5.2

Connecticut ............... 34 37 51 4,000 4,420 4,770 8.5 8.4 10.7

Delaware .................... 4 5 5 750 840 950 5.3 6.0 5.3

District of Columbia ... 28 13 15 2,210 2,840 2,580 12.7 4.6 5.8

Florida ........................ 96 106 171 6,850 8,250 9,590 14.0 12.8 17.8

Georgia ...................... 45 75 80 5,780 6,450 7,750 7.8 11.6 10.3

Hawaii ........................ 6 4 10 1,380 1,570 1,670 4.3 2.5 6.0

Idaho .......................... 6 6 10 780 980 1,490 7.7 6.1 6.7

Illinois ......................... 81 109 122 10,620 11,090 11,860 7.6 9.8 10.3

Indiana ....................... 39 17 32 4,680 5,710 6,190 8.3 3.0 5.2

Iowa ........................... 51 67 52 3,100 3,220 3,530 16.5 20.8 14.7

Kansas ....................... 7 18 4 2,260 2,270 2,600 3.1 7.9 1.5

Kentucky .................... 16 20 28 3,040 3,240 3,610 5.3 6.2 7.8

Louisiana .................... 26 42 23 3,580 3,470 3,470 7.3 12.1 6.6

Maine ......................... 0 2 5 1,340 1,200 1,210 0.0 1.7 4.1

Maryland .................... 66 114 136 6,400 6,100 7,590 10.3 18.7 17.9

Massachusetts ........... 188 218 234 11,810 13,390 14,980 15.9 16.3 15.6

Michigan .................... 104 105 120 7,850 8,820 9,410 13.2 11.9 12.8

Minnesota .................. 50 65 62 4,490 5,540 5,730 11.1 11.7 10.8

Mississippi ................. 6 12 14 1,940 2,000 2,020 3.1 6.0 6.9

Missouri ..................... 40 55 42 5,770 5,710 5,750 6.9 9.6 7.3

Montana ..................... 4 4 6 1,020 810 1,230 3.9 4.9 4.9

Nebraska .................... 27 21 19 2,360 1,960 1,900 11.4 10.7 10.0

Nevada ....................... 2 4 4 980 1,260 1,620 2.0 3.2 2.5

New Hampshire ......... 3 10 18 1,130 1,240 1,270 2.7 8.1 14.2

New Jersey ................ 52 81 80 5,290 5,860 6,500 9.8 13.8 12.3

New Mexico ............... 19 17 9 2,450 2,910 2,220 7.8 5.8 4.1

New York .................... 224 282 284 20,900 21,770 23,110 10.7 13.0 12.3

North Carolina ............ 96 148 131 7,740 9,050 10,310 12.4 16.4 12.7

North Dakota ............. 5 4 4 900 660 970 5.6 6.1 4.1

Ohio ........................... 75 93 86 9,750 9,920 10,620 7.7 9.4 8.1

Oklahoma ................... 17 22 27 2,680 2,800 2,890 6.3 7.9 9.3

Oregon ....................... 27 23 20 2,690 3,250 3,620 10.0 7.1 5.5

Pennsylvania .............. 138 213 147 12,150 13,590 16,210 11.4 15.7 9.1

Rhode Island .............. 9 19 9 1,730 1,730 2,040 5.2 11.0 4.4

South Carolina ........... 14 14 22 3,230 3,030 3,720 4.3 4.6 5.9

South Dakota ............. 2 2 2 700 640 690 2.9 3.1 2.9

Tennessee .................. 25 42 37 4,720 4,800 5,640 5.3 8.8 6.6

Texas .......................... 125 155 191 13,760 14,270 17,170 9.1 10.9 11.1

Utah ........................... 38 48 35 3,080 3,100 3,580 12.3 15.5 9.8

Vermont ...................... 3 3 4 1,140 1,050 1,050 2.6 2.9 3.8

Virginia ....................... 49 41 48 5,830 7,180 8,050 8.4 5.7 6.0

Washington ................ 42 56 42 5,410 6,390 7,190 7.8 8.8 5.8

West Virginia .............. 2 4 2 1,190 1,150 1,320 1.7 3.5 1.5

Wisconsin .................. 65 74 102 5,390 5,210 5,970 12.1 14.2 17.1

Wyoming .................... 4 3 3 560 570 520 7.1 5.3 5.8

Puerto Rico ................ 0 5 2 640 1,070 1,270 0.0 4.7 1.6

a

Coefficients of variation for estimates of S&E doctorate holders in academia presented in appendix table 8-14.

SOURCES: Patent and Trademark Office, Technology Assessment and Forecast Branch, U.S. Colleges and Universities—Utility Patent Grants, Calendar 

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Science and Engineering Indicators 2010
8-98 Chapter 8. State Indicators

This indicator shows state patent activity normalized to the number of 

employees in S&E occupations. People in S&E occupations include computer, 

mathematical, life, physical, and social scientists; engineers; and postsecondary 

teachers in any of these fields. Managers, elementary and secondary schoolteachers, and medical personnel are excluded.

This indicator includes 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 residence of the firstnamed inventor. Only U.S.-origin patents are included. State data on individuals 

in S&E occupations come from the Occupational Employment Statistics (OES) 

survey, which surveys states’ workplaces and assigns workers to a state based 

on where they work. 

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. Because OES data do not classify 

postsecondary teachers by field, faculty teaching in S&E fields are not counted as 

working in S&E occupations. Estimates for jurisdictions with smaller populations 

are generally less precise than estimates for jurisdictions with larger populations.

Findings

U In 2008, about 77,000 utility patents 

were awarded to inventors residing in the 

United States, a decline from the 84,000 

utility patents awarded in 2004.

UÊ In 2008, the national average for this 

indicator was 13.4 patents, which was 

lower than the average of 16.6 in 2004. 

This decline may have been due to 

fewer patent applications being filed, a 

reduced capacity of USPTO to process 

applications and award patents, and/or 

an increase in the number of individuals 

working in S&E occupations.

UÊ Idaho typically reports the highest values 

for this indicator, reflecting the presence 

of the Department of Energy’s highpatenting Idaho National Laboratory. 

Values for the remaining states ranged 

from 35.4 to 1.5.

UÊ Nearly 25% of all 2008 U.S. utility patents 

were awarded to residents of California.

Patents Awarded per 1,000 Individuals in S&E Occupations

Figure 8-45

Patents awarded per 1,000 individuals in S&E occupations, by state: 2008

1st quartile

2nd quartile

3rd quartile

4th quartile

No data

1st quartile (49.8–14.6) 2nd quartile (13.7–10.8) 3rd quartile (10.6–6.2) 4th quartile (6.0–1.1) No data

Arizona Colorado Florida Alabama ‡

 Kentucky ‡

California Illinois Georgia Alaska ‡ Montana ‡

Connecticut Indiana Kansas ‡ Arkansas ‡ Washington

Delaware ‡

 Iowa Louisiana ‡ District of Columbia

Idaho ‡

 Nevada ‡ Maine ‡ Hawaii ‡

Massachusetts New Jersey Maryland Mississippi ‡

Michigan North Carolina New Mexico ‡ Missouri

Minnesota Ohio North Dakota ‡

 Nebraska ‡

New Hampshire ‡ Rhode Island ‡ Oklahoma ‡ South Dakota ‡

New York Texas Pennsylvania Virginia

Oregon Utah South Carolina ‡

 West Virginia ‡

Vermont ‡ Wisconsin Tennessee Wyoming ‡

‡

 EPSCoR state

SOURCES: Patent and Trademark Office, Electronic Information Products Division/Patent Technology Monitoring Branch, Patent Counts by Country/State 

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Science and Engineering Indicators 2010 8-99

Table 8-45

Patents awarded per 1,000 individuals in S&E occupations, by state: 2004, 2006, and 2008

Patents awarded Individuals in S&E occupations

Patents/1,000 

individuals in 

 S&E occupations

State 2004 2006 2008 2004 2006 2008 2004 2006 2008

United States ................. 84,249 89,795 77,479 5,085,740 5,407,710 5,781,460 16.6 16.6 13.4

Alabama ..................... 375 357 279 57,560 66,100 68,580 6.5 5.4 4.1

Alaska ........................ 39 36 20 10,660 10,720 13,260 3.7 3.4 1.5

Arizona ....................... 1,621 1,705 1,584 95,380 98,110 102,100 17.0 17.4 15.5

Arkansas .................... 132 138 108 22,150 24,860 29,310 6.0 5.6 3.7

California .................... 19,488 22,275 19,181 693,670 730,010 791,750 28.1 30.5 24.2

Colorado .................... 2,099 2,118 1,622 126,280 133,730 147,000 16.6 15.8 11.0

Connecticut ............... 1,577 1,652 1,356 82,820 79,380 80,290 19.0 20.8 16.9

Delaware .................... 342 357 325 17,980 21,550 22,330 19.0 16.6 14.6

District of Columbia ... 75 63 68 57,750 64,120 63,360 1.3 1.0 1.1

Florida ........................ 2,456 2,600 2,046 229,950 246,190 248,200 10.7 10.6 8.2

Georgia ...................... 1,326 1,487 1,344 141,710 136,470 147,380 9.4 10.9 9.1

Hawaii ........................ 76 84 77 16,360 18,940 18,830 4.6 4.4 4.1

Idaho .......................... 1,785 1,663 1,162 22,310 NA 23,310 80.0 NA 49.8

Illinois ......................... 3,162 3,294 2,741 219,530 222,470 224,370 14.4 14.8 12.2

Indiana ....................... 1,280 1,165 985 79,120 80,110 90,840 16.2 14.5 10.8

Iowa ........................... 658 666 561 39,280 43,670 46,180 16.8 15.3 12.1

Kansas ....................... 448 492 425 52,020 48,620 54,260 8.6 10.1 7.8

Kentucky .................... 407 413 413 44,350 44,680 NA 9.2 9.2 NA

Louisiana .................... 343 321 260 42,230 40,180 41,790 8.1 8.0 6.2

Maine ......................... 134 142 113 15,160 15,950 17,000 8.8 8.9 6.6

Maryland .................... 1,313 1,410 1,232 154,310 159,470 167,070 8.5 8.8 7.4

Massachusetts ........... 3,672 4,011 3,516 186,260 198,670 217,310 19.7 20.2 16.2

Michigan .................... 3,756 3,758 2,996 183,140 208,520 204,290 20.5 18.0 14.7

Minnesota .................. 2,754 2,957 2,535 119,380 125,930 134,440 23.1 23.5 18.9

Mississippi ................. 136 119 102 23,190 24,910 27,270 5.9 4.8 3.7

Missouri ..................... 768 721 615 87,200 96,420 105,390 8.8 7.5 5.8

Montana ..................... 119 121 91 11,390 13,010 NA 10.4 9.3 NA

Nebraska .................... 191 186 191 31,720 32,500 31,820 6.0 5.7 6.0

Nevada ....................... 410 386 375 23,980 26,930 27,300 17.1 14.3 13.7

New Hampshire ......... 626 602 477 24,350 27,680 29,150 25.7 21.7 16.4

New Jersey ................ 2,957 3,172 2,722 165,150 176,460 198,060 17.9 18.0 13.7

New Mexico ............... 370 344 280 33,500 30,800 34,560 11.0 11.2 8.1

New York .................... 5,846 5,627 4,885 272,930 306,810 326,510 21.4 18.3 15.0

North Carolina ............ 1,794 1,974 1,841 135,380 138,790 153,680 13.3 14.2 12.0

North Dakota ............. 53 66 63 8,420 9,360 9,450 6.3 7.1 6.7

Ohio ........................... 2,889 2,630 2,227 180,360 185,190 206,320 16.0 14.2 10.8

Oklahoma ................... 447 544 417 NA 50,770 48,900 NA 10.7 8.5

Oregon ....................... 1,725 2,060 1,781 62,570 64,520 70,070 27.6 31.9 25.4

Pennsylvania .............. 2,883 2,842 2,414 195,730 214,910 227,170 14.7 13.2 10.6

Rhode Island .............. 309 269 218 19,660 18,060 18,090 15.7 14.9 12.1

South Carolina ........... 524 577 395 51,030 53,230 57,770 10.3 10.8 6.8

South Dakota ............. 82 74 54 9,420 10,120 11,870 8.7 7.3 4.5

Tennessee .................. 681 669 586 65,120 67,040 72,760 10.5 10.0 8.1

Texas .......................... 5,930 6,308 5,712 383,180 408,710 463,850 15.5 15.4 12.3

Utah ........................... 683 684 642 43,030 49,690 52,570 15.9 13.8 12.2

Vermont ...................... 400 437 437 11,770 12,780 12,360 34.0 34.2 35.4

Virginia ....................... 1,077 1,094 1,030 220,180 251,720 259,280 4.9 4.3 4.0

Washington ................ 2,221 3,286 3,517 154,610 171,780 NA 14.4 19.1 NA

West Virginia .............. 100 103 74 16,100 17,150 17,000 6.2 6.0 4.4

Wisconsin .................. 1,658 1,688 1,349 95,230 96,860 101,680 17.4 17.4 13.3

Wyoming .................... 52 48 35 6,760 7,640 8,850 7.7 6.3 4.0

Puerto Rico ................ 19 25 NA 20,410 23,850 22,970 0.9 1.0 NA

NA = not available

NOTES: Origin of utility patent determined by residence of first-named inventor. National total for S&E occupations in the United States provided by 

Occupational Employment Statistics (OES). OES estimates for 2004, 2006, and 2008 S&E occupations based on May data.

SOURCES: Patent and Trademark Office, Electronic Information Products Division/Patent Technology Monitoring Branch, Patent Counts by Country/State 

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Science and Engineering Indicators 2010
8-100 Chapter 8. State Indicators

This indicator measures 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. 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 2003, 2004, and 

2006 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 earlier years are not directly comparable.

Findings

U The number of establishments in high-technology industries 

rose from about 590,000 in 2003 to about 634,000 in 2006, 

an increase of 43,000, or 7%.

UÊ The percentage of U.S. establishments in high-technology 

industries grew from 8.17% to 8.35% of the total business 

establishments during the 2003–06 period, and most 

states showed an increase in the percentage of their 

establishments in high-technology industries.

UÊ Between 2003 and 2006, the largest growth in the number 

of establishments in high-technology industries occurred 

in California and Florida, which added 7,900 and 5,560 

establishments, respectively.

UÊ 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.

UÊ EPSCoR states tended to cluster in the lower quartiles 

and exhibited a lower group average, indicating that these 

states had a business mix with a smaller percentage of 

establishments in high-technology industries.

High-Technology Share of All Business Establishments

Figure 8-46

High-technology share of all business establishments: 2006

1st quartile (14.60%–9.05%) 2nd quartile (8.99%–7.55%) 3rd quartile (7.53%–6.51%) 4th quartile (6.48%–4.86%)

California Arizona Alaska ‡

 Alabama ‡

Colorado Connecticut Arkansas ‡ Idaho ‡

Delaware ‡ Florida Hawaii ‡ Iowa

District of Columbia Illinois Indiana Kentucky ‡

Georgia Kansas ‡ Louisiana ‡ Maine ‡

Maryland Minnesota Michigan Mississippi ‡

Massachusetts New Mexico ‡ Missouri Nebraska ‡

Nevada ‡ North Carolina Montana ‡ North Dakota ‡

New Hampshire ‡ Ohio New York South Carolina ‡

New Jersey Oklahoma ‡ Oregon South Dakota ‡

Texas Pennsylvania Rhode Island ‡ West Virginia ‡

Utah Washington Tennessee Wisconsin

Virginia Wyoming ‡

 Vermont ‡

‡

 EPSCoR state

SOURCE: Census Bureau, 1989–2004 Business Information Tracking Series, special tabulations. See table 8-46.

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-101

Table 8-46

High-technology share of all business establishments, by state: 2003, 2004, and 2006

 High-technology 

 establishments All business establishments

High-technology/business 

establishments (%)

State 2003 2004 2006 2003 2004 2006 2003 2004 2006

EPSCoR states ................ 83,464 84,985 88,790 1,202,246 1,224,016 1,255,900 6.94 6.94 7.07

Non-EPSCoR states ........ 504,364 515,962 541,875 6,001,637 6,123,459 6,308,168 8.40 8.43 8.59

Average EPSCoR 

 state value .................... na na na na na na 7.08 7.07 7.17

Average non-EPSCoR 

 state value .................... na na na na na na 8.23 8.25 8.40

United States ................... 590,417 603,642 633,727 7,223,240 7,366,978 7,585,035 8.17 8.19 8.35

Alabama ....................... 6,347 6,407 6,613 99,453 100,521 103,236 6.38 6.37 6.41

Alaska .......................... 1,345 1,358 1,494 19,037 19,309 19,838 7.07 7.03 7.53

Arizona ......................... 10,433 10,901 11,942 120,966 125,330 137,532 8.62 8.70 8.68

Arkansas ...................... 4,012 4,142 4,373 64,058 65,127 66,647 6.26 6.36 6.56

California ...................... 77,614 79,288 85,514 822,751 838,615 875,682 9.43 9.45 9.77

Colorado ...................... 15,532 16,027 17,259 143,398 146,937 154,254 10.83 10.91 11.19

Connecticut ................. 7,827 7,794 7,810 91,207 92,710 93,232 8.58 8.41 8.38

Delaware ...................... 3,964 3,907 3,700 24,739 25,344 25,563 16.02 15.42 14.47

District of Columbia ..... 2,589 2,695 3,062 19,357 19,503 20,967 13.38 13.82 14.60

Florida .......................... 38,118 40,165 43,678 458,823 483,693 516,185 8.31 8.30 8.46

Georgia ........................ 18,820 19,424 20,825 208,350 214,200 225,577 9.03 9.07 9.23

Hawaii .......................... 2,097 2,152 2,325 30,950 31,538 33,063 6.78 6.82 7.03

Idaho ............................ 2,515 2,582 2,912 39,582 41,205 45,599 6.35 6.27 6.39

Illinois ........................... 27,606 28,200 28,821 310,589 315,093 320,756 8.89 8.95 8.99

Indiana ......................... 9,626 9,858 10,158 147,073 149,050 151,024 6.55 6.61 6.73

Iowa ............................. 4,316 4,324 4,548 80,745 81,334 82,542 5.35 5.32 5.51

Kansas ......................... 5,716 5,900 6,035 74,637 75,600 76,261 7.66 7.80 7.91

Kentucky ...................... 5,453 5,585 5,769 90,358 91,598 92,700 6.03 6.10 6.22

Louisiana ...................... 7,218 7,192 7,439 101,933 102,866 101,647 7.08 6.99 7.32

Maine ........................... 2,466 2,541 2,612 40,519 41,131 41,941 6.09 6.18 6.23

Maryland ...................... 13,428 13,974 14,632 132,782 135,699 140,021 10.11 10.30 10.45

Massachusetts ............. 17,183 17,305 17,107 177,910 175,426 174,997 9.66 9.86 9.78

Michigan ...................... 16,937 16,988 17,049 236,221 237,392 235,245 7.17 7.16 7.25

Minnesota .................... 12,834 13,055 13,348 145,364 148,276 150,896 8.83 8.80 8.85

Mississippi ................... 3,269 3,274 3,336 59,565 60,364 60,442 5.49 5.42 5.52

Missouri ....................... 9,562 9,745 10,130 149,753 153,584 154,177 6.39 6.35 6.57

Montana ....................... 2,108 2,229 2,415 33,616 34,570 36,550 6.27 6.45 6.61

Nebraska ...................... 2,797 2,864 3,072 50,213 50,803 51,822 5.57 5.64 5.93

Nevada ......................... 5,387 5,493 5,975 53,080 55,713 61,061 10.15 9.86 9.79

New Hampshire ........... 3,511 3,559 3,554 38,119 38,707 39,273 9.21 9.19 9.05

New Jersey .................. 24,286 24,256 24,534 237,097 240,013 242,649 10.24 10.11 10.11

New Mexico ................. 3,322 3,385 3,553 43,386 44,071 45,814 7.66 7.68 7.76

New York ...................... 35,926 36,706 37,346 500,559 509,873 514,992 7.18 7.20 7.25

North Carolina .............. 14,869 15,426 16,908 207,500 212,457 221,898 7.17 7.26 7.62

North Dakota ............... 964 972 1,035 20,371 20,763 21,286 4.73 4.68 4.86

Ohio ............................. 19,875 20,120 20,347 269,202 271,078 269,398 7.38 7.42 7.55

Oklahoma ..................... 6,859 6,965 7,301 85,633 87,180 89,440 8.01 7.99 8.16

Oregon ......................... 7,500 7,659 8,083 102,462 104,966 110,317 7.32 7.30 7.33

Pennsylvania ................ 22,266 22,796 23,486 297,040 300,832 303,507 7.50 7.58 7.74

Rhode Island ................ 1,976 2,043 2,059 29,172 29,900 30,322 6.77 6.83 6.79

South Carolina ............. 5,869 6,048 6,551 98,735 100,947 105,060 5.94 5.99 6.24

South Dakota ............... 1,206 1,234 1,266 24,314 24,693 25,419 4.96 5.00 4.98

Tennessee .................... 8,196 8,226 8,772 129,458 131,355 134,776 6.33 6.26 6.51

Texas ............................ 45,062 45,522 47,520 481,804 489,782 508,092 9.35 9.29 9.35

Utah ............................. 5,474 5,716 6,531 60,011 62,644 68,612 9.12 9.12 9.52

Vermont ........................ 1,453 1,498 1,535 21,747 22,072 22,261 6.68 6.79 6.90

Virginia ......................... 18,868 19,758 21,678 182,783 188,533 196,849 10.32 10.48 11.01

Washington .................. 13,171 13,480 14,411 166,229 170,848 179,368 7.92 7.89 8.03

West Virginia ................ 2,257 2,259 2,308 40,225 40,732 40,480 5.61 5.55 5.70

Wisconsin .................... 9,035 9,249 9,438 141,560 143,739 145,590 6.38 6.43 6.48

Wyoming ...................... 1,353 1,396 1,558 18,804 19,262 20,175 7.20 7.25 7.72

Puerto Rico .................. NA NA NA NA NA NA NA NA NA

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EPSCoR = Experimental Program to Stimulate Competitive Research

NOTE: For explanation of EPSCoR and non-EPSCoR averages, see chapter introduction.

SOURCE: Census Bureau, 1989–2006 Business Information Tracking Series, special tabulations.

Science and Engineering Indicators 2010
8-102 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 hightechnology 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 2002 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 2006, about 14,000 net new businesses in 

high-technology industries were formed in the 

United States. From a base of approximately 7.6 

million total business establishments, 84,777 

new business establishments were formed in 

high-technology industries and 70,746 ceased 

operations in those same industries.

UÊ Almost all states showed more establishments 

beginning operations in high-technology 

industries than ceasing operations in 2006.

UÊ Utah and Virginia showed the highest rates of net 

high-technology business formation in 2006. 

However, the largest numbers of net new businesses 

were formed in California, Texas, and Florida.

UÊ EPSCoR states tended to be distributed 

throughout the state ranking, indicating that the 

rate of net high-technology business formation 

in many of these states was comparable to that 

in non-EPSCoR states.

Net High-Technology Business Formations as Share of All Business

Establishments

Figure 8-47

Net high-technology business formations as share of all business establishments: 2006

1st quartile (0.93%–0.28%) 2nd quartile (0.27%–0.18%) 3rd quartile (0.16%–0.05%) 4th quartile (0.04% to –0.31%)

Alaska ‡

 Florida Alabama ‡ Delaware ‡

Arizona Hawaii ‡

 Arkansas ‡ Michigan

California Iowa Connecticut Minnesota

Colorado Louisiana ‡ Illinois New Hampshire ‡

District of Columbia Maryland Indiana New Jersey

Georgia Missouri Kansas ‡

 Ohio

Idaho ‡

 Montana ‡ Kentucky ‡ Rhode Island ‡

Nevada ‡

 Nebraska ‡ Maine ‡ South Dakota ‡

North Carolina New Mexico ‡ Massachusetts West Virginia ‡

Tennessee Oklahoma ‡

 Mississippi ‡

Utah South Carolina ‡ New York

Virginia Texas North Dakota ‡

Wyoming ‡ Washington Oregon

 Pennsylvania

 Vermont ‡

 Wisconsin

‡

 EPSCoR state

SOURCE: Census Bureau, 1989–2004 Business Information Tracking Series, special tabulations. See table 8-47.

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-103

Table 8-47

Net high-technology business formations as share of all business establishments, by state: 2004 and 2006

 Net high-technology 

 business formations

 All business 

 establishments

High-technology 

formations/business 

establishments (%)

State 2004 2006 2004 2006 2004 2006

United States ........................... 11,598 14,031 7,366,978 7,585,035 0.16 0.18

Alabama ............................... 63 134 100,521 103,236 0.06 0.13

Alaska .................................. 22 66 19,309 19,838 0.11 0.33

Arizona ................................. 357 446 125,330 137,532 0.28 0.32

Arkansas .............................. 123 98 65,127 66,647 0.19 0.15

California .............................. 1,099 2,633 838,615 875,682 0.13 0.30

Colorado .............................. 490 509 146,937 154,254 0.33 0.33

Connecticut ......................... –47 44 92,710 93,232 –0.05 0.05

Delaware .............................. –52 –78 25,344 25,563 –0.21 –0.31

District of Columbia ............. 66 195 19,503 20,967 0.34 0.93

Florida .................................. 1,743 1,009 483,693 516,185 0.36 0.20

Georgia ................................ 642 734 214,200 225,577 0.30 0.33

Hawaii .................................. 51 90 31,538 33,063 0.16 0.27

Idaho .................................... 54 151 41,205 45,599 0.13 0.33

Illinois ................................... 452 243 315,093 320,756 0.14 0.08

Indiana ................................. 208 164 149,050 151,024 0.14 0.11

Iowa ..................................... 12 150 81,334 82,542 0.01 0.18

Kansas ................................. 160 114 75,600 76,261 0.21 0.15

Kentucky .............................. 116 42 91,598 92,700 0.13 0.05

Louisiana .............................. –38 195 102,866 101,647 –0.04 0.19

Maine ................................... 81 31 41,131 41,941 0.20 0.07

Maryland .............................. 475 278 135,699 140,021 0.35 0.20

Massachusetts ..................... 156 193 175,426 174,997 0.09 0.11

Michigan .............................. 44 27 237,392 235,245 0.02 0.01

Minnesota ............................ 185 39 148,276 150,896 0.12 0.03

Mississippi ........................... 7 83 60,364 60,442 0.01 0.14

Missouri ............................... 195 279 153,584 154,177 0.13 0.18

Montana ............................... 108 98 34,570 36,550 0.31 0.27

Nebraska .............................. 64 98 50,803 51,822 0.13 0.19

Nevada ................................. 169 207 55,713 61,061 0.30 0.34

New Hampshire ................... 30 13 38,707 39,273 0.08 0.03

New Jersey .......................... -80 38 240,013 242,649 –0.03 0.02

New Mexico ......................... 37 98 44,071 45,814 0.08 0.21

New York .............................. 702 274 509,873 514,992 0.14 0.05

North Carolina ...................... 514 692 212,457 221,898 0.24 0.31

North Dakota ....................... –1 34 20,763 21,286 0.00 0.16

Ohio ..................................... 204 111 271,078 269,398 0.08 0.04

Oklahoma ............................. 75 236 87,180 89,440 0.09 0.26

Oregon ................................. 156 141 104,966 110,317 0.15 0.13

Pennsylvania ........................ 474 278 300,832 303,507 0.16 0.09

Rhode Island ........................ 67 8 29,900 30,322 0.22 0.03

South Carolina ..................... 175 230 100,947 105,060 0.17 0.22

South Dakota ....................... 16 9 24,693 25,419 0.06 0.04

Tennessee ............................ 39 372 131,355 134,776 0.03 0.28

Texas .................................... 401 1,221 489,782 508,092 0.08 0.24

Utah ..................................... 283 382 62,644 68,612 0.45 0.56

Vermont ................................ 42 22 22,072 22,261 0.19 0.10

Virginia ................................. 845 986 188,533 196,849 0.45 0.50

Washington .......................... 346 476 170,848 179,368 0.20 0.27

West Virginia ........................ 16 -13 40,732 40,480 0.04 -0.03

Wisconsin ............................ 215 66 143,739 145,590 0.15 0.05

Wyoming .............................. 37 85 19,262 20,175 0.19 0.42

Puerto Rico .......................... NA NA NA NA NA NA

NA = not available

SOURCE: Census Bureau, 1989–2006 Business Information Tracking Series, special tabulations.

Science and Engineering Indicators 2010
8-104 Chapter 8. State Indicators

This indicator measures 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.

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 for years prior to 

2002 are not directly comparable.

Findings

U Employment in high-technology industries in the United 

States remained fairly steady between 2003 and 2006, at 13 

million.

UÊ Nationwide, the value of this indicator declined about 4%, 

from 11.96 in 2003 to 11.45 in 2006, as total employment 

grew during this period.

UÊ On this indicator, states varied greatly in 2006, ranging 

from 5.6% to 16.3% of their workforce employed in hightechnology industries.

UÊ During the 2003–06 period, Washington, New York, Illinois, 

>`ÊV}>ÊÀiVÀ`i`ÊÌiÊ>À}iÃÌÊiÌÊÃÃiÃÊvÊLÃÊÊ}

technology industries, while California, Virginia, Florida, and 

>ÃÃ>VÕÃiÌÌÃÊ«ÃÌi`ÊÌiÊ>À}iÃÌÊiÌÊ}>ÃÊvÊLÃÊÊ}

technology industries.

UÊ States were distributed similarly on the high-technology 

employment and high-technology establishment indicators. 

EPSCoR states tended to have smaller percentages of their 

workforces employed in high-technology industries.

Employment in High-Technology Establishments as Share of Total

Employment

Figure 8-48

Employment in high-technology establishments as share of total employment: 2006

1st quartile (16.32%–12.45%) 2nd quartile (12.30%–10.58%) 3rd quartile (10.56%–8.21%) 4th quartile (8.07%–5.63%)

California Alaska ‡

 Alabama ‡ Hawaii ‡

Colorado Delaware ‡ Arizona Iowa

Connecticut Georgia Arkansas ‡

 Kentucky ‡

District of Columbia Idaho ‡ Florida Maine ‡

Kansas ‡ Illinois Indiana Mississippi ‡

Maryland Missouri Louisiana ‡ Montana ‡

Massachusetts New Hampshire ‡ Nebraska ‡ Nevada ‡

Michigan New Mexico ‡ New York North Dakota ‡

Minnesota Ohio North Carolina South Dakota ‡

New Jersey Oklahoma ‡ Rhode Island ‡ West Virginia ‡

Texas Oregon South Carolina ‡ Wyoming ‡

Virginia Pennsylvania Tennessee

Washington Utah Vermont ‡

 Wisconsin

‡

 EPSCoR state

SOURCE: Census Bureau, Business Information Tracking Series, special tabulations. See table 8-48.

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-105

Table 8-48

Employment in high-technology establishments as share of total employment, by state: 2003, 2004, and 2006

 Employment in high-technology 

 establishments All employment

High-technology/ 

all employment (%)

State 2003 2004 2006 2003 2004 2006 2003 2004 2006

United States ................. 13,563,122 13,356,596 13,733,632 113,373,663 115,049,548 119,892,505 11.96 11.61 11.45

Alabama ..................... 152,879 158,927 162,197 1,597,265 1,628,733 1,713,185 9.57 9.76 9.47

Alaska ........................ 21,851 22,107 27,306 216,707 223,099 241,568 10.08 9.91 11.30

Arizona ....................... 234,603 238,462 246,648 1,997,990 2,043,729 2,334,665 11.74 11.67 10.56

Arkansas .................... 95,180 101,124 93,648 988,822 1,007,283 1,041,868 9.63 10.04 8.99

California .................... 1,781,830 1,767,202 1,826,638 12,986,496 13,260,306 13,830,274 13.72 13.33 13.21

Colorado .................... 274,979 265,613 272,952 1,883,883 1,908,126 2,018,905 14.60 13.92 13.52

Connecticut ............... 210,114 204,107 198,450 1,550,615 1,537,160 1,585,660 13.55 13.28 12.52

Delaware .................... 52,349 54,164 47,749 385,098 391,647 388,178 13.59 13.83 12.30

District of Columbia ... 54,314 57,250 57,297 422,912 436,791 446,502 12.84 13.11 12.83

Florida ........................ 576,274 587,452 618,540 6,548,276 6,863,196 7,534,165 8.80 8.56 8.21

Georgia ...................... 413,384 411,977 428,272 3,386,590 3,451,802 3,622,522 12.21 11.94 11.82

Hawaii ........................ 25,777 26,203 28,848 458,952 473,181 512,488 5.62 5.54 5.63

Idaho .......................... 55,706 53,738 59,082 466,379 488,557 546,108 11.94 11.00 10.82

Illinois ......................... 646,285 617,306 619,777 5,204,887 5,216,180 5,356,504 12.42 11.83 11.57

Indiana ....................... 219,598 219,694 224,644 2,540,554 2,586,282 2,672,558 8.64 8.49 8.41

Iowa ........................... 102,387 96,100 96,190 1,232,709 1,241,688 1,295,143 8.31 7.74 7.43

Kansas ....................... 155,023 153,046 146,849 1,109,699 1,115,930 1,142,487 13.97 13.71 12.85

Kentucky .................... 121,838 119,167 125,204 1,471,622 1,489,285 1,551,791 8.28 8.00 8.07

Louisiana .................... 137,029 129,722 143,846 1,603,492 1,623,431 1,592,682 8.55 7.99 9.03

Maine ......................... 35,184 36,221 37,934 488,788 494,165 508,061 7.20 7.33 7.47

Maryland .................... 315,887 323,966 326,546 2,088,552 2,151,093 2,231,888 15.12 15.06 14.63

Massachusetts ........... 460,984 455,749 496,630 2,974,164 2,979,251 3,043,643 15.50 15.30 16.32

Michigan .................... 499,133 486,706 475,350 3,884,881 3,895,217 3,817,762 12.85 12.49 12.45

Minnesota .................. 315,994 309,303 329,927 2,381,860 2,392,481 2,475,859 13.27 12.93 13.33

Mississippi ................. 66,566 61,858 64,558 912,004 928,181 940,329 7.30 6.66 6.87

Missouri ..................... 254,299 257,290 263,494 2,387,245 2,420,994 2,467,626 10.65 10.63 10.68

Montana ..................... 20,296 20,452 26,958 302,932 314,806 342,461 6.70 6.50 7.87

Nebraska .................... 68,975 69,724 64,779 774,858 774,187 789,117 8.90 9.01 8.21

Nevada ....................... 61,847 64,648 66,875 970,678 1,021,842 1,165,243 6.37 6.33 5.74

New Hampshire ......... 63,264 63,907 64,914 540,132 550,869 577,322 11.71 11.60 11.24

New Jersey ................ 550,224 558,921 550,515 3,578,674 3,609,297 3,644,967 15.38 15.49 15.10

New Mexico ............... 60,399 61,149 68,627 571,057 580,443 628,472 10.58 10.53 10.92

New York .................... 823,992 798,462 790,696 7,415,430 7,431,893 7,531,772 11.11 10.74 10.50

North Carolina ............ 349,424 345,316 358,501 3,337,552 3,365,050 3,523,954 10.47 10.26 10.17

North Dakota ............. 20,584 20,176 22,450 258,878 265,632 278,395 7.95 7.60 8.06

Ohio ........................... 531,491 512,352 518,835 4,769,406 4,761,492 4,824,859 11.14 10.76 10.75

Oklahoma ................... 132,887 133,871 141,575 1,184,312 1,194,830 1,276,743 11.22 11.20 11.09

Oregon ....................... 152,140 147,549 161,641 1,338,380 1,355,101 1,461,339 11.37 10.89 11.06

Pennsylvania .............. 566,406 551,971 549,180 5,028,650 5,106,171 5,189,349 11.26 10.81 10.58

Rhode Island .............. 35,806 36,577 41,020 427,369 434,600 440,715 8.38 8.42 9.31

South Carolina ........... 163,373 164,035 170,200 1,550,227 1,560,401 1,631,690 10.54 10.51 10.43

South Dakota ............. 18,890 19,897 20,202 299,723 307,944 325,045 6.30 6.46 6.22

Tennessee .................. 219,898 217,191 245,517 2,298,836 2,346,903 2,472,939 9.57 9.25 9.93

Texas .......................... 1,158,481 1,101,175 1,144,997 8,049,300 8,116,465 8,709,575 14.39 13.57 13.15

Utah ........................... 99,856 101,547 114,815 900,331 934,939 1,038,879 11.09 10.86 11.05

Vermont ...................... 29,402 27,572 27,001 256,401 256,040 263,759 11.47 10.77 10.24

Virginia ....................... 459,017 489,703 502,890 2,932,471 3,054,221 3,173,767 15.65 16.03 15.85

Washington ................ 401,413 329,698 347,710 2,292,462 2,268,155 2,420,633 17.51 14.54 14.36

West Virginia .............. 46,635 46,172 45,284 561,317 568,581 583,033 8.31 8.12 7.77

Wisconsin .................. 233,967 245,257 253,499 2,382,979 2,434,580 2,481,998 9.82 10.07 10.21

Wyoming .................... 15,008 14,820 16,375 180,866 187,318 204,058 8.30 7.91 8.02

Puerto Rico ................ NA NA NA NA NA NA NA NA NA

NA = not available

SOURCE: Census Bureau, 1989–2006 Business Information Tracking Series, special tabulations.

Science and Engineering Indicators 2010
8-106 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 (up 

to $100,000) and to develop the technology to a point where it 

can be commercialized (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 average annual SBIR award dollars won by small businesses in a state are divided by the average 

annual gross domestic product. A high value indicates that small 

business firms in a state are doing cutting-edge development work 

that attracts federal support.

Findings

U Significant growth has occurred in the SBIR program in 

recent years as total annual awards increased from about 

$1.1 billion in 1998–2000 to about $1.7 billion in 2006–08.

UÊ -	,ÊvÕ`ÃÊ>ÀiÊVViÌÀ>Ìi`ÊÊÀi>ÌÛiÞÊviÜÊÃÌ>ÌiÃÆÊÌiÊ

total of annual state awards may range from less than $1 

million to more than $300 million.

UÊ 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.

UÊ States with a high ranking on this indicator also tended 

to rank high on the high-technology and venture capital 

indicators.

Average Annual Federal SBIR Funding per $1 Million of Gross Domestic 

Product

Figure 8-49

Average annual federal SBIR funding per $1 million of gross domestic product: 2006–08

1st quartile ($626–$148) 2nd quartile ($147–$88) 3rd quartile ($76–$37) 4th quartile ($33–$12)

Alabama ‡

 Arizona Arkansas ‡ Alaska ‡

California Connecticut Florida District of Columbia

Colorado Delaware ‡

 Georgia Iowa

Maine ‡

 Hawaii ‡ Idaho ‡ Louisiana ‡

Maryland Michigan Illinois Mississippi ‡

Massachusetts Minnesota Indiana Nebraska ‡

Montana ‡ New Jersey Kansas ‡ Nevada ‡

New Hampshire ‡ North Carolina Kentucky ‡ North Dakota ‡

New Mexico ‡ Pennsylvania Missouri South Carolina ‡

Ohio Rhode Island ‡ New York South Dakota ‡

Oregon Utah Oklahoma ‡

Vermont ‡ Washington Tennessee

Virginia Wisconsin Texas

 West Virginia ‡

 Wyoming ‡

‡

 EPSCoR state

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VVÊ>ÞÃÃ]ÊÀÃÃÊiÃÌVÊ

Product data. See table 8-49.

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-107

Table 8-49

Average annual federal SBIR funding per $1 million of gross domestic product, by state: 1998–2000, 2002–04, 

and 2006–08

Average SBIR funding 

(current $thousands)

Average state GDP 

(current $millions)

SBIR funding ($)/ 

$1 million GDP

State 1998–2000 2002–04 2006–08 1998–2000 2002–04 2006–08

1998–

2000

2002– 

04

2006– 

08

United States ................. 1,060,758 1,725,643 1,731,667 9,209,969 10,963,871 13,676,177 115 157 127

Alabama ..................... 19,304 33,144 38,477 111,052 131,847 164,792 174 251 233

Alaska ........................ 318 495 707 24,840 31,836 45,182 13 16 16

Arizona ....................... 20,428 28,534 28,187 148,211 182,467 244,438 138 156 115

Arkansas .................... 1,443 3,240 6,631 64,759 76,675 94,855 22 42 70

California .................... 217,278 360,660 340,849 1,184,540 1,422,133 1,800,632 183 254 189

Colorado .................... 55,158 80,814 79,329 157,102 188,960 237,064 351 428 335

Connecticut ............... 20,674 29,454 22,096 152,037 172,690 212,468 136 171 104

Delaware .................... 3,814 4,156 6,172 39,247 48,739 60,512 97 85 102

District of Columbia ... 3,675 5,840 3,011 55,596 72,450 93,076 66 81 32

Florida ........................ 21,897 37,526 42,914 443,689 563,008 731,715 49 67 59

Georgia ...................... 11,278 16,484 16,813 274,527 321,024 390,223 41 51 43

Hawaii ........................ 3,583 5,772 7,581 38,792 46,777 61,352 92 123 124

Idaho .......................... 1,001 3,664 3,164 32,481 39,142 50,779 31 94 62

Illinois ......................... 15,985 22,500 27,053 443,933 510,618 609,086 36 44 44

Indiana ....................... 5,068 10,689 16,932 186,355 216,259 246,664 27 49 69

Iowa ........................... 1,561 4,875 4,290 86,655 103,834 128,891 18 47 33

Kansas ....................... 3,374 4,938 4,326 79,160 93,853 116,894 43 53 37

Kentucky .................... 2,298 4,237 5,602 111,398 125,786 152,345 21 34 37

Louisiana .................... 1,453 3,126 4,326 124,551 148,154 213,844 12 21 20

Maine ......................... 1,948 5,604 7,094 33,545 40,656 48,052 58 138 148

Maryland .................... 51,664 93,258 77,717 171,231 215,216 266,532 302 433 292

Massachusetts ........... 164,078 242,323 219,333 254,548 295,018 350,605 645 821 626

Michigan .................... 21,486 33,544 43,965 324,273 357,314 380,089 66 94 116

Minnesota .................. 13,374 24,805 24,991 174,288 210,064 253,304 77 118 99

Mississippi ................. 1,546 3,232 1,040 62,605 72,301 88,305 25 45 12

Missouri ..................... 5,721 7,342 8,645 169,985 196,271 229,120 34 37 38

Montana ..................... 3,622 7,045 8,278 20,552 25,513 34,046 176 276 243

Nebraska .................... 1,641 2,998 2,438 53,653 64,322 79,552 31 47 31

Nevada ....................... 1,830 7,772 3,364 68,732 89,770 127,167 27 87 26

New Hampshire ......... 12,602 22,174 22,501 40,944 48,606 57,806 308 456 389

New Jersey ................ 30,790 47,712 40,718 328,735 390,642 462,949 94 122 88

New Mexico ............... 19,136 21,965 22,293 48,547 57,810 76,080 394 380 293

New York .................... 42,872 75,707 79,435 731,452 856,081 1,091,942 59 88 73

North Carolina ............ 13,936 22,739 35,124 259,759 308,945 393,523 54 74 89

North Dakota ............. 1,110 1,960 918 17,180 21,430 28,261 65 91 32

Ohio ........................... 44,859 67,535 70,856 360,448 405,302 463,139 124 167 153

Oklahoma ................... 3,054 6,630 6,820 84,106 104,044 138,622 36 64 49

Oregon ....................... 14,627 19,556 26,881 105,886 123,868 156,930 138 158 171

Pennsylvania .............. 36,393 64,864 77,650 375,843 441,249 531,060 97 147 146

Rhode Island .............. 2,353 7,783 6,753 31,330 39,446 46,666 75 197 145

South Carolina ........... 2,296 7,397 4,630 108,041 127,106 151,808 21 58 30

South Dakota ............. 1,244 1,291 493 21,815 27,785 34,300 57 46 14

Tennessee .................. 8,342 9,411 12,256 168,457 202,218 243,916 50 47 50

Texas .......................... 39,651 71,023 77,916 675,146 837,983 1,144,532 59 85 68

Utah ........................... 9,621 14,299 15,315 63,857 76,327 104,466 151 187 147

Vermont ...................... 3,317 4,808 4,760 16,835 20,656 24,538 197 233 194

Virginia ....................... 60,913 98,276 96,609 243,330 304,390 382,864 250 323 252

Washington ................ 26,052 46,713 45,128 210,710 241,841 308,449 124 193 146

West Virginia .............. 1,306 5,719 2,153 40,694 47,063 58,460 32 122 37

Wisconsin .................. 8,724 17,592 24,686 168,477 196,807 232,039 52 89 106

Wyoming .................... 1,060 2,418 2,447 16,040 21,575 32,243 66 112 76

Puerto Rico ................ 236 216 8 57,876 75,220 NA 4 3 NA

NA = not available

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Science and Engineering Indicators 2010
8-108 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 shows 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.

Findings

U The total amount of venture capital invested in the United 

States increased from about $21 billion in 1998 to about 

$28 billion in 2008, an overall increase of 34%. However, 

this was a decade of great volatility, as U.S. venture capital 

investment peaked at $106 billion in 2000.

UÊ Venture capital is concentrated in relatively few states. 

Companies in California received 50% of the total venture 

capital disbursed in the United States in 2008, followed by 

companies in Massachusetts with 11%.

UÊ The distribution of venture capital is becoming more 

concentrated, making it more difficult for companies in many 

states to access this type of financing. Thirty-five states 

reported lower values for this indicator in 2008 than in 1998.

UÊ The average indicator value for EPSCoR states was 

substantially lower than that of non-EPSCoR states. The 

state distribution of venture capital was similar to indicators 

of high-technology business activity.

Venture Capital Disbursed per $1,000 of Gross Domestic Product

Figure 8-50

Venture capital disbursed per $1,000 of gross domestic product: 2008

1st quartile ($8.15–$1.15) 2nd quartile ($1.14–$0.48) 3rd quartile ($0.43–$0.19) 4th quartile ($0.17–$0.00)

California Arizona District of Columbia Alabama ‡

Colorado Connecticut Florida Alaska ‡

Maryland Delaware ‡ Idaho ‡ Arkansas ‡

Massachusetts Georgia Iowa Hawaii ‡

Minnesota Illinois Kansas ‡ Louisiana ‡

New Hampshire ‡ Indiana Kentucky ‡ Mississippi ‡

New Jersey Michigan Maine ‡ Nevada ‡

North Carolina New Mexico ‡ Missouri North Dakota ‡

Pennsylvania New York Montana ‡ Oklahoma ‡

Utah Ohio Nebraska ‡ South Carolina ‡

Vermont ‡ Oregon Tennessee South Dakota ‡

Virginia Rhode Island ‡ West Virginia ‡ Wyoming ‡

Washington Texas Wisconsin

‡

 EPSCoR state

SOURCES: PricewaterhouseCoopers, Venture Economics, and National Venture Capital Association, MoneyTree SurveyTM]ÊÃ«iV>ÊÌ>LÕ>ÌÃÆÊ>`Ê	ÕÀi>ÕÊvÊ

Economic Analysis, Gross Domestic Product data. See table 8-50.

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-109

Table 8-50

Venture capital disbursed per $1,000 of gross domestic product, by state: 1998, 2003, and 2008

Venture capital disbursed 

(current $millions) State GDP (current $millions)

Venture capital 

($)/ $1,000 GDP

State 1998 2003 2008 1998 2003 2008 1998 2003 2008

United States ................. 21,037 19,776 28,284 8,679,660 10,886,172 14,165,565 2.42 1.82 2.00

Alabama ..................... 82 30 24 106,656 130,210 170,014 0.77 0.23 0.14

Alaska ........................ 0 0 0 23,165 31,219 47,912 0.00 0.00 0.00

Arizona ....................... 210 73 208 137,581 182,011 248,888 1.53 0.40 0.84

Arkansas .................... 7 1 0 61,861 75,685 98,331 0.11 0.01 0.00

California .................... 7,955 8,564 14,264 1,085,884 1,406,511 1,846,757 7.33 6.09 7.72

Colorado .................... 726 628 813 143,160 187,397 248,603 5.07 3.35 3.27

Connecticut ............... 373 212 127 145,373 169,885 216,174 2.57 1.25 0.59

Delaware .................... 0 0 63 36,831 48,587 61,828 0.00 0.01 1.01

District of Columbia ... 47 56 31 51,682 71,719 97,235 0.91 0.78 0.32

Florida ........................ 552 309 240 417,169 559,021 744,120 1.32 0.55 0.32

Georgia ...................... 431 295 426 255,612 317,922 397,756 1.68 0.93 1.07

Hawaii ........................ 4 13 7 37,549 46,441 63,847 0.11 0.28 0.11

Idaho .......................... 30 52 12 29,800 38,148 52,747 1.02 1.37 0.23

Illinois ......................... 429 377 444 423,855 510,296 633,697 1.01 0.74 0.70

Indiana ....................... 39 25 124 178,909 215,434 254,861 0.22 0.11 0.48

Iowa ........................... 9 0 40 83,665 102,210 135,702 0.11 0.00 0.30

Kansas ....................... 10 25 46 76,005 93,560 122,731 0.14 0.27 0.37

Kentucky .................... 38 5 30 108,813 124,892 156,436 0.34 0.04 0.19

Louisiana .................... 68 1 8 118,085 146,726 222,218 0.58 0.01 0.04

Maine ......................... 62 1 20 31,731 40,152 49,709 1.94 0.02 0.41

Maryland .................... 328 348 477 161,954 213,306 273,333 2.03 1.63 1.74

Massachusetts ........... 2,009 2,744 2,974 236,079 293,840 364,988 8.51 9.34 8.15

Michigan .................... 124 80 246 309,431 359,030 382,544 0.40 0.22 0.64

Minnesota .................. 361 233 491 164,897 208,179 262,847 2.19 1.12 1.87

Mississippi ................. 4 1 0 60,513 72,259 91,782 0.06 0.01 0.00

Missouri ..................... 611 78 87 164,267 195,547 237,797 3.72 0.40 0.36

Montana ..................... 0 0 16 19,884 25,526 35,891 0.00 0.00 0.43

Nebraska .................... 29 205 16 52,076 64,628 83,273 0.56 3.17 0.19

Nevada ....................... 24 40 13 63,635 87,828 131,233 0.38 0.46 0.10

New Hampshire ......... 185 154 181 39,102 48,198 60,005 4.73 3.20 3.02

New Jersey ................ 476 886 708 314,117 389,077 474,936 1.52 2.28 1.49

New Mexico ............... 8 4 69 45,918 57,469 79,901 0.17 0.06 0.87

New York .................... 1,299 660 1,299 686,906 850,243 1,144,481 1.89 0.78 1.14

North Carolina ............ 327 387 459 242,904 306,018 400,192 1.34 1.26 1.15

North Dakota ............. 1 15 0 16,936 21,672 31,208 0.03 0.67 0.01

Ohio ........................... 309 179 248 348,723 402,399 471,508 0.89 0.44 0.53

Oklahoma ................... 101 31 17 79,341 103,452 146,448 1.28 0.30 0.12

Oregon ....................... 54 108 176 100,951 121,638 161,573 0.53 0.88 1.09

Pennsylvania .............. 562 499 693 361,800 440,704 553,301 1.55 1.13 1.25

Rhode Island .............. 26 66 39 29,537 39,357 47,364 0.88 1.66 0.83

South Carolina ........... 137 14 26 102,945 127,885 156,384 1.33 0.11 0.17

South Dakota ............. 0 4 1 20,771 27,418 36,959 0.00 0.13 0.01

Tennessee .................. 108 84 65 160,872 200,279 252,127 0.67 0.42 0.26

Texas .......................... 1,171 1,250 1,283 629,209 828,797 1,223,511 1.86 1.51 1.05

Utah ........................... 117 107 194 60,168 75,428 109,777 1.94 1.41 1.76

Vermont ...................... 1 5 43 15,935 20,575 25,442 0.09 0.25 1.69

Virginia ....................... 766 413 484 226,569 302,540 397,025 3.38 1.37 1.22

Washington ................ 736 464 955 195,794 240,813 322,778 3.76 1.92 2.96

West Virginia .............. 2 13 24 39,500 46,452 61,652 0.05 0.27 0.39

Wisconsin .................. 90 38 75 160,681 195,904 240,429 0.56 0.19 0.31

Wyoming .................... 0 0 2 14,859 21,685 35,310 0.00 0.00 0.04

Puerto Rico ................ NA NA NA 54,086 74,827 NA NA NA NA

NA = not available

GDP = gross domestic product

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Science and Engineering Indicators 2010
8-110 Chapter 8. State Indicators

This indicator provides a measure of 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. In most cases, 

a company will not receive more than one infusion of venture 

capital in a given year.

Venture capital investment can bring needed capital and 

management expertise that can help to grow a high-technology 

company. High values indicate that high-technology companies 

in a state are frequently using venture capital to facilitate their 

growth and development.

Findings

U The number of venture capital deals that involved U.S. 

companies increased from about 2,900 deals in 2003 to 

about 3,700 deals in 2006.

UÊ In 2006, venture capital deals were concentrated in only a 

few states. Indicator values ranged from a high of 2.31% to 

a low of zero with a median value of 0.20%.

UÊ Companies in high-technology industries located in 

Massachusetts were the most successful in accessing 

venture capital investments in 2006, with a 2.31% rate. 

California companies in high-technology industries obtained 

venture capital investment at a rate of 1.81%. No other 

states reached a rate of 1.00%.

UÊ In 2006, 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 Share of High-Technology Business Establishments

Figure 8-51

Venture capital deals as share of high-technology business establishments: 2006

1st quartile (2.31%–0.40%) 2nd quartile (0.39%–0.19%) 3rd quartile (0.15%–0.10%) 4th quartile (0.08%–0.00%)

California Arizona Alabama ‡

 Alaska ‡

Colorado Connecticut Arkansas ‡ Delaware ‡

Hawaii ‡ District of Columbia Florida Idaho ‡

Maryland Georgia Indiana Iowa

Massachusetts Illinois Kansas ‡

 Louisiana ‡

New Hampshire ‡ Minnesota Kentucky ‡ Mississippi ‡

New York New Jersey Maine ‡ Montana ‡

Pennsylvania New Mexico ‡ Michigan North Dakota ‡

Texas North Carolina Missouri Oklahoma ‡

Utah Ohio Nebraska ‡ South Carolina ‡

Vermont ‡ Oregon Nevada ‡ South Dakota ‡

Virginia Rhode Island ‡ Tennessee Wyoming ‡

Washington Wisconsin West Virginia ‡

‡

 EPSCoR state

SOURCES: PricewaterhouseCoopers, Venture Economics, and National Venture Capital Association, MoneyTree SurveyTM]ÊÃ«iV>ÊÌ>LÕ>ÌÃÆÊ>`Ê
iÃÕÃÊ

Bureau, Business Information Tracking Series, special tabulations. See table 8-51.

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-111

Table 8-51

Venture capital deals as share of high-technology business establishments, by state: 2003, 2004, and 2006

Venture capital deals High-technology establishments

Venture capital deals/

high-technology 

establishment (%)

State 2003 2004 2006 2003 2004 2006 2003 2004 2006

United States ................. 2,903 3,036 3,672 590,417 603,642 633,727 0.49 0.50 0.58

Alabama ..................... 9 5 7 6,347 6,407 6,613 0.14 0.08 0.11

Alaska ........................ 0 0 0 1,345 1,358 1,494 0.00 0.00 0.00

Arizona ....................... 16 12 29 10,433 10,901 11,942 0.15 0.11 0.24

Arkansas .................... 3 1 6 4,012 4,142 4,373 0.07 0.02 0.14

California .................... 1,122 1,225 1,549 77,614 79,288 85,514 1.45 1.55 1.81

Colorado .................... 72 75 98 15,532 16,027 17,259 0.46 0.47 0.57

Connecticut ............... 34 32 30 7,827 7,794 7,810 0.43 0.41 0.38

Delaware .................... 1 1 3 3,964 3,907 3,700 0.03 0.03 0.08

District of Columbia ... 6 8 8 2,589 2,695 3,062 0.23 0.30 0.26

Florida ........................ 61 57 56 38,118 40,165 43,678 0.16 0.14 0.13

Georgia ...................... 55 73 81 18,820 19,424 20,825 0.29 0.38 0.39

Hawaii ........................ 6 4 10 2,097 2,152 2,325 0.29 0.19 0.43

Idaho .......................... 5 2 1 2,515 2,582 2,912 0.20 0.08 0.03

Illinois ......................... 58 51 55 27,606 28,200 28,821 0.21 0.18 0.19

Indiana ....................... 8 9 15 9,626 9,858 10,158 0.08 0.09 0.15

Iowa ........................... 1 4 2 4,316 4,324 4,548 0.02 0.09 0.04

Kansas ....................... 2 9 7 5,716 5,900 6,035 0.03 0.15 0.12

Kentucky .................... 3 5 7 5,453 5,585 5,769 0.06 0.09 0.12

Louisiana .................... 1 3 3 7,218 7,192 7,439 0.01 0.04 0.04

Maine ......................... 2 3 4 2,466 2,541 2,612 0.08 0.12 0.15

Maryland .................... 84 85 110 13,428 13,974 14,632 0.63 0.61 0.75

Massachusetts ........... 378 365 395 17,183 17,305 17,107 2.20 2.11 2.31

Michigan .................... 17 19 18 16,937 16,988 17,049 0.10 0.11 0.11

Minnesota .................. 58 47 39 12,834 13,055 13,348 0.45 0.36 0.29

Mississippi ................. 4 5 1 3,269 3,274 3,336 0.12 0.15 0.03

Missouri ..................... 23 10 13 9,562 9,745 10,130 0.24 0.10 0.13

Montana ..................... 1 0 0 2,108 2,229 2,415 0.05 0.00 0.00

Nebraska .................... 2 0 3 2,797 2,864 3,072 0.07 0.00 0.10

Nevada ....................... 6 5 7 5,387 5,493 5,975 0.11 0.09 0.12

New Hampshire ......... 32 23 21 3,511 3,559 3,554 0.91 0.65 0.59

New Jersey ................ 88 88 94 24,286 24,256 24,534 0.36 0.36 0.38

New Mexico ............... 5 8 9 3,322 3,385 3,553 0.15 0.24 0.25

New York .................... 119 149 209 35,926 36,706 37,346 0.33 0.41 0.56

North Carolina ............ 76 57 62 14,869 15,426 16,908 0.51 0.37 0.37

North Dakota ............. 2 1 0 964 972 1,035 0.21 0.10 0.00

Ohio ........................... 25 32 41 19,875 20,120 20,347 0.13 0.16 0.20

Oklahoma ................... 2 11 6 6,859 6,965 7,301 0.03 0.16 0.08

Oregon ....................... 21 27 31 7,500 7,659 8,083 0.28 0.35 0.38

Pennsylvania .............. 90 92 128 22,266 22,796 23,486 0.40 0.40 0.55

Rhode Island .............. 10 7 7 1,976 2,043 2,059 0.51 0.34 0.34

South Carolina ........... 4 5 3 5,869 6,048 6,551 0.07 0.08 0.05

South Dakota ............. 1 3 1 1,206 1,234 1,266 0.08 0.24 0.08

Tennessee .................. 22 23 11 8,196 8,226 8,772 0.27 0.28 0.13

Texas .......................... 165 162 188 45,062 45,522 47,520 0.37 0.36 0.40

Utah ........................... 22 27 39 5,474 5,716 6,531 0.40 0.47 0.60

Vermont ...................... 6 4 9 1,453 1,498 1,535 0.41 0.27 0.59

Virginia ....................... 80 73 89 18,868 19,758 21,678 0.42 0.37 0.41

Washington ................ 81 114 143 13,171 13,480 14,411 0.61 0.85 0.99

West Virginia .............. 5 3 3 2,257 2,259 2,308 0.22 0.13 0.13

Wisconsin .................. 8 10 20 9,035 9,249 9,438 0.09 0.11 0.21

Wyoming .................... 1 2 1 1,353 1,396 1,558 0.07 0.14 0.06

Puerto Rico ................ 1 1 2 NA NA NA NA NA NA

NA = not available

SOURCES: PricewaterhouseCoopers, Venture Economics, and National Venture Capital Association, MoneyTreeTMÊ-ÕÀÛiÞ]ÊÃ«iV>ÊÌ>LÕ>ÌÃÆÊ>`Ê

Census Bureau, 1989–2006 Business Information Tracking Series, special tabulations.

Science and Engineering Indicators 2010
8-112 Chapter 8. State Indicators

This indicator provides a measure of 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.

This indicator provides some measure of the magnitude of 

investment that developing companies in a specific 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 year-toyear basis. Eighteen states reported fewer than 10 venture capital 

deals in 2008. In such states, a single large or small venture capital 

investment can significantly affect the value of this indicator.

Findings

U In 2008, the size of the average venture capital investment 

in the United States was about $7 million per deal. This 

represented an increase in investment size from about $5 

million per deal in 1998 but a decline from $13 million per 

deal in 2000 expressed in current dollars.

UÊ The total number of venture capital deals has risen slightly, 

increasing from 3,632 deals in 1998 to 3,806 in 2008.

UÊ In 2008, the state distribution on this indicator was skewed 

from a high value of $24 million per deal to a low of zero, 

with a median value of $5.03 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 8-52

Venture capital disbursed per venture capital deal: 2008

(Millions of dollars)

1st quartile ($24.00–$7.34) 2nd quartile ($6.65–$4.91) 3rd quartile ($4.77–$2.79) 4th quartile ($2.38–$0.00)

Arizona Florida Alabama ‡

 Alaska ‡

California Georgia Connecticut Arkansas ‡

Colorado Illinois District of Columbia Hawaii ‡

Delaware ‡ Maine ‡ Kentucky ‡ Idaho ‡

Indiana Maryland Missouri Kansas ‡

Iowa Michigan New Mexico ‡ Louisiana ‡

Massachusetts Nebraska ‡ Ohio Mississippi ‡

Minnesota New Hampshire ‡ Oklahoma ‡ Nevada ‡

Montana ‡ New York Pennsylvania North Dakota ‡

New Jersey Oregon Rhode Island ‡ South Carolina ‡

North Carolina Utah Tennessee South Dakota ‡

Texas Virginia Vermont ‡ Wyoming ‡

West Virginia ‡ Washington Wisconsin

‡

 EPSCoR state

SOURCE: PricewaterhouseCoopers, Venture Economics, and National Venture Capital Association, MoneyTree SurveyTM, special tabulations. See table 8-52.

1st quartile

2nd quartile

3rd quartile

4th quartile
Science and Engineering Indicators 2010 8-113

Table 8-52

Venture capital disbursed per venture capital deal, by state: 1998, 2003, and 2008

Venture capital disbursed 

(current $millions) Venture capital deals

Venture capital/deal 

(current $millions)

State 1998 2003 2008 1998 2003 2008 1998 2003 2008

United States ................. 21,037 19,776 28,284 3,632 2,943 3,806 5.79 6.72 7.43

Alabama ..................... 82 30 24 15 9 8 5.49 3.32 3.01

Alaska ........................ 0 0 0 0 0 0 0.00 0.00 0.00

Arizona ....................... 210 73 208 36 16 20 5.84 4.58 10.40

Arkansas .................... 7 1 0 2 3 0 3.45 0.37 0.00

California .................... 7,955 8,564 14,264 1,388 1,135 1,552 5.73 7.55 9.19

Colorado .................... 726 628 813 122 70 100 5.95 8.97 8.13

Connecticut ............... 373 212 127 74 34 34 5.05 6.24 3.74

Delaware .................... 0 0 63 0 1 6 0.00 0.40 10.45

District of Columbia ... 47 56 31 3 6 11 15.63 9.35 2.79

Florida ........................ 552 309 240 62 62 36 8.90 4.99 6.65

Georgia ...................... 431 295 426 98 58 80 4.39 5.09 5.33

Hawaii ........................ 4 13 7 3 5 6 1.40 2.56 1.20

Idaho .......................... 30 52 12 3 5 6 10.10 10.44 1.98

Illinois ......................... 429 377 444 70 56 67 6.13 6.73 6.63

Indiana ....................... 39 25 124 8 8 16 4.88 3.06 7.73

Iowa ........................... 9 0 40 7 0 5 1.26 0.00 8.04

Kansas ....................... 10 25 46 3 11 23 3.47 2.26 1.98

Kentucky .................... 38 5 30 16 3 10 2.34 1.80 2.95

Louisiana .................... 68 1 8 11 1 10 6.18 1.20 0.82

Maine ......................... 62 1 20 11 2 4 5.59 0.45 5.05

Maryland .................... 328 348 477 54 87 97 6.08 4.00 4.91

Massachusetts ........... 2,009 2,744 2,974 391 381 405 5.14 7.20 7.34

Michigan .................... 124 80 246 32 16 43 3.88 5.01 5.71

Minnesota .................. 361 233 491 79 58 47 4.57 4.02 10.44

Mississippi ................. 4 1 0 2 4 0 1.75 0.20 0.00

Missouri ..................... 611 78 87 19 19 24 32.16 4.13 3.60

Montana ..................... 0 0 16 0 0 2 0.00 0.00 7.80

Nebraska .................... 29 205 16 5 3 3 5.82 68.20 5.33

Nevada ....................... 24 40 13 10 6 6 2.42 6.70 2.10

New Hampshire ......... 185 154 181 26 32 28 7.11 4.82 6.47

New Jersey ................ 476 886 708 76 90 90 6.27 9.85 7.87

New Mexico ............... 8 4 69 4 5 19 1.93 0.72 3.65

New York .................... 1,299 660 1,299 193 118 235 6.73 5.59 5.53

North Carolina ............ 327 387 459 82 77 51 3.98 5.02 9.00

North Dakota ............. 1 15 0 1 2 2 0.50 7.25 0.20

Ohio ........................... 309 179 248 63 28 52 4.91 6.39 4.77

Oklahoma ................... 101 31 17 11 2 5 9.22 15.55 3.46

Oregon ....................... 54 108 176 18 21 35 2.97 5.12 5.03

Pennsylvania .............. 562 499 693 138 97 171 4.07 5.15 4.05

Rhode Island .............. 26 66 39 5 10 10 5.20 6.55 3.92

South Carolina ........... 137 14 26 16 4 11 8.56 3.58 2.38

South Dakota ............. 0 4 1 0 1 1 0.00 3.50 0.50

Tennessee .................. 108 84 65 24 23 21 4.48 3.67 3.10

Texas .......................... 1,171 1,250 1,283 188 168 146 6.23 7.44 8.79

Utah ........................... 117 107 194 33 22 33 3.54 4.84 5.87

Vermont ...................... 1 5 43 2 6 9 0.70 0.87 4.77

Virginia ....................... 766 413 484 102 82 81 7.51 5.04 5.97

Washington ................ 736 464 955 109 83 164 6.75 5.58 5.82

West Virginia .............. 2 13 24 1 5 1 2.00 2.52 24.00

Wisconsin .................. 90 38 75 16 8 19 5.64 4.70 3.96

Wyoming .................... 0 0 2 0 0 1 0.00 0.00 1.50

Puerto Rico ................ NA NA NA NA NA NA NA NA NA

NA = not available

SOURCE: PricewaterhouseCoopers, Venture Economics, and National Venture Capital Association, MoneyTreeTM Survey, special tabulations.

Science and Engineering Indicators 2010

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 selfor 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 2010 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 give a known, nonzero probability of 

selection to each member of a target population. The advantage of probabilistic sampling methods is that sampling 

error can be calculated. Such methods include random 

sampling, systematic sampling, and stratified sampling. 
Science and Engineering Indicators 2010 A-5

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/ and on CD.

Chapter 1. Elementary and Secondary Mathematics and Science Education

1-1 Average mathematics scores of students followed from kindergarten through grade 8, by student characteristics: 

Fall 1998–spring 2007

1-2 Mathematics test score gains of students followed from kindergarten through grade 8, by student characteristics: 

Fall 1998–spring 2007

1-3 Proficiency of 1998–99 kindergartners in various mathematics skill areas in grade 8, by student characteristics: 

Spring 2007

1-4 Highest mathematics skill area in which 1998–99 kindergarteners demonstrated proficiency in grade 8, by 

student characteristics: Spring 2007

1-5 Average science scores of students followed from kindergarten through grade 8, by student characteristics: 

Spring 2002–spring 2007

1-6 Average NAEP mathematics scores of students in grades 4 and 8, by student characteristics: 1990–2007 

1-7 Students in grades 4 and 8 scoring at or above NAEP’s proficient level in mathematics for their grade, by 

student characteristics: 1990–2007

1-8 Average mathematics scores of 9-, 13-, and 17-year-olds on the NAEP Long-Term Trend tests, by student 

characteristics: 1973–2008

1-9 Average TIMSS mathematics scores of students in grades 4 and 8 in all participating nations, relative to U.S. 

average scores, by nation: 2007

1-10 Average TIMSS science scores of students in grades 4 and 8 in all participating nations, relative to U.S. average 

scores, by nation: 2007

1-11 Average PISA mathematics and science literacy scores of 15-year-old students in all participating nations, 

relative to U.S. average scores: 2006

1-12 Public school students in grades 5 and 8 who were taught mathematics and science by teachers with a bachelor’s 

or higher degree and a regular/advanced teaching certificate, by student characteristics: 2004 and 2007

1-13 Public school students in grades 5 and 8 who were taught mathematics and science by teachers with various 

levels of subject area preparation, by student characteristics: 2004 and 2007

1-14 Public school students in grades 5 and 8 who were taught mathematics and science by teachers with more than 3 

years of teaching experience, by student characteristics: 2004 and 2007

1-15 Public school students in grade 5 whose teachers of mathematics and science agreed or strongly agreed with 

various statements about their schools, by student characteristics: 2004

1-16 Public school students in grade 8 whose mathematics and science teachers agreed or strongly agreed with 

various statements about their schools, by student characteristics: 2007

1-17 Frequency that students in grade 8 use computers during mathematics and science class, by student 

characteristics: 2007

1-18 State standards and policies regarding K–12 teaching with and learning about technology: 2008–09

1-19 Students who took Advanced Placement tests in mathematics and science and number and percentage with 

passing scores, by sex and race/ethnicity: 1997 and 2008

1-20 Postsecondary experiences of the class of 2004, by mathematics and science preparation and skills in high 

school: 2006

1-21 High school graduates enrolled in college in October after completing high school, by family income, race/

ethnicity, and parents’ education: Selected years, 1975–2008

Chapter 2. Higher Education in Science and Engineering

2-1 S&E degrees awarded, by degree level, Carnegie institution type, and field: 2007

2-2 Degrees awarded by private for-profit academic institutions, by broad field and degree level: 1998–2007

2-3 Degrees awarded by private for-profit academic institutions, by field and degree level: 2007

B-1
B-2 List of Appendix Tables

2-4 Enrollment in higher education, by Carnegie institution type: 1993–2007

2-5 Projections of U.S. population ages 20–24 years, by sex and race/ethnicity: 2010–50

2-6 Freshmen intending S&E major, by field, sex, and race/ethnicity: 1993–2008

2-7 Freshmen intending to major in selected S&E fields, by sex and race/ethnicity: 1993–2008

2-8 Foreign undergraduate student enrollment in U.S. universities, by field and selected places of origin: April 2008 

and 2009

2-9 Undergraduate enrollment in engineering and engineering technology programs: 1993–2007

2-10 Earned associate’s degrees, by sex and field: 1993–2007

2-11 Earned associate’s degrees, by citizenship, field, and race/ethnicity: 1995–2007

2-12 Earned bachelor’s degrees, by sex and field: 1993–2007

2-13 Earned bachelor’s degrees, by citizenship, field, and race/ethnicity: 1995–2007

2-14 S&E graduate enrollment, by sex and field: 1993–2006

2-15 Engineering enrollment, by enrollment level and attendance: 1987–2007

2-16 First-time full-time S&E graduate students, by field: 1993–2006

2-17 S&E graduate enrollment, by citizenship, field, and race/ethnicity: 1993–2006

2-18 First-time full-time S&E graduate students, by citizenship and field: 2000–06

2-19 Foreign graduate student enrollment in U.S. universities, by field and selected places of origin: April 2008 and 

2009

2-20 Full-time S&E graduate students, by source and mechanism of primary support: 1993–2006

2-21 Full-time S&E graduate students, by field and mechanism of primary support: 2006

2-22 Full-time S&E graduate students primarily supported by federal government, by field and mechanism of 

primary support: 2006

2-23 Full-time S&E graduate students primarily supported by federal government, by agency: 1993–2006

2-24 Primary support mechanisms for S&E doctorate recipients, by citizenship, sex, and race/ethnicity: 2007

2-25 Amount of undergraduate and graduate debt of S&E doctorate recipients, by field: 2007

2-26 Earned master’s degrees, by sex and field: 1993–2007

2-27 Earned master’s degrees, by citizenship, field, and race/ethnicity: 1995–2007

2-28 Earned doctoral degrees, by citizenship, field, and sex: 1993–2007

2-29 Median number of years from S&E doctorate recipients’ entry to graduate school to receipt of doctorate, by 

field: 1977–2007

2-30 Earned doctoral degrees, by citizenship, field, and race/ethnicity: 1995–2007

2-31 Plans of foreign recipients of U.S. S&E doctorates to stay in United States, by field and place of origin: 1996–

2007

2-32 Postdocs at U.S. universities, by field and citizenship: 1993–2006

2-33 Expenditures on tertiary education as a percentage of GDP and change in expenditures: 1995, 2000, and 2005

2-34 Tertiary-type A, advanced research programs, and tertiary education, by age group and country: 2006

2-35 First university degrees, by selected region and country/economy: 2006 or most recent year

2-36 S&E first university degrees, by selected Western or Asian country/economy and field: 1998–2006

2-37 First university degrees, by field, sex, and region/country/economy: 2006 or most recent year

2-38 Earned S&E doctoral degrees, by selected region/country/economy and field: 2006 or most recent year

2-39 Earned S&E doctoral degrees, by sex, selected region/country/economy, and field: 2006 or most recent year

2-40 S&E doctoral degrees, by selected Western industrialized country and field: 1998–2006

2-41 S&E doctoral degrees, by selected Asian country/economy and field: 1993–2006

2-42 Trends in population ages 20–24 years, by selected country and region: 2000–50

2-43 Foreign S&E student enrollment in UK universities, by enrollment level, place of origin, and field: 2001–02 and 

2006–07

2-44 Foreign S&E student enrollment in Japanese universities, by enrollment level, place of origin, and field: 2001 

and 2008

2-45 S&E student enrollment in Canadian universities, by enrollment level, top place of origin, and field: 1995–96 

and 2005–06

Chapter 3. Science and Engineering Labor Force

3-1 Occupation of employed S&E degree holders: 2006

3-2 Bureau of Labor Statistics projections of occupational employment: 2006–16

3-3 Primary reason for scientists and engineers to participate in work-related training: 2006
Science and Engineering Indicators 2010 B-3

3-4 Scientists and engineers participating in work-related training, by sex: 2006

3-5 Scientists and engineers participating in work-related training, by employer size: 2006

3-6 Patenting indicators on scientists and engineers, by broad field and level of highest degree: 1998–2003

3-7 Patenting indicators on scientists and engineers, by sector of employment: 1998–2003

3-8 Patenting indicators on scientists and engineers, by field of highest degree: 1998–2003

3-9 Federal scientists and engineers compared with all S&E occupations, by age and sex: 2005 and 2006

3-10 U.S. residents with S&E as highest degree receiving degrees from non-U.S. institutions, by degree level and 

place of birth: 2003

Chapter 4. Research and Development: National Trends and International Linkages

4-1 Gross domestic product and implicit price deflators: 1953–2008

4-2 Purchasing power parity, market exchange rate, and market exchange rate/purchasing power parity ratios, by 

selected countries: 1981–2007

4-3 U.S. R&D expenditures, by performing sector and funding source: 1953–2008

4-4 U.S. basic research expenditures, by performing sector and funding source: 1953–2008

4-5 U.S. applied research expenditures, by performing sector and funding source: 1953–2008

4-6 U.S. development expenditures, by performing sector and funding source: 1953–2008

4-7 U.S. R&D expenditures, by funding source and performing sector: 1953–2008

4-8 U.S. basic research expenditures, by funding source and performing sector: 1953–2008

4-9 U.S. applied research expenditures, by funding source and performing sector: 1953–2008

4-10 U.S. development expenditures, by funding source and performing sector: 1953–2008

4-11 Total (federal plus company and other) funds for industrial R&D performance in United States, by industry and 

company size: 2003–07

4-12 Company and other nonfederal funds for industrial R&D performance in United States, by industry and 

company size: 2003–07

4-13 Federal funds for industrial R&D performance in United States, by industry and company size: 2003–07

4-14 Company and other (nonfederal) R&D fund share of net sales in R&D-performing companies, by industry and 

company size: 2003–07

4-15 R&D expenditures, by state, funding source, and performing sector: 2007

4-16 Total R&D and gross domestic product, by state: 2007

4-17 Federal R&D budget authority, by budget function: FY 1980–2008

4-18 Federal basic research budget authority, by budget function: FY 2000–08

4-19 Federal obligations for research and development, by character of work and R&D plant: FY 1955–2008

4-20 Estimated federal obligations for R&D and R&D plant, by selected agency, performer, and character of work: 

FY 2008

4-21 Discrepancy in U.S. performer-reported and agency-reported federal R&D: 1980–2007

4-22 R&D expenditures at federally funded research and development centers: FY 2007

4-23 Estimated federal obligations for research, by agency and S&E field: FY 2008

4-24 Federal obligations for research, by detailed S&E field: FY 1986–2008

4-25 Federal research and experimentation tax credit claims, by selected NAICS industry: 2001–05

4-26 Federal research and experimentation tax credit: Number of corporate tax returns claiming credit, by NAICS 

industry: 2001–2005

4-27 Gross expenditures for R&D and R&D as share of gross domestic product, by selected country: 1981–2007

4-28 Nondefense gross expenditures on R&D and nondefense R&D as share of gross domestic product, for selected 

countries: 1981–2007

4-29 Gross expenditures on R&D, by performing sector and funding source, for selected countries: Most recent year

4-30 Government budget appropriations or outlays, by selected country and socioeconomic objective: Most recent 

year

4-31 Share of business expenditures for R&D, by industry and selected country/economy: 2005–07

4-32 R&D expenditures by majority-owned affiliates of foreign companies in United States, by country/region of 

ultimate beneficial owner: 1997–2006

4-33 R&D performed by majority-owned affiliates of foreign companies in United States, by NAICS industry of 

affiliate: 1997–2006
B-4 List of Appendix Tables

4-34 R&D performed abroad by majority-owned foreign affiliates of U.S. parent companies, by country/economy/

region: 1997–2006

4-35 R&D performed abroad by majority-owned foreign affiliates of U.S. parent companies, by selected NAICS 

industry of affiliate: 1999–2006

4-36 R&D performed in United States by U.S. multinational company parent companies, by NAICS industry: 

1999–2006

4-37 Company and other nonfederal funds for industrial R&D performed abroad, by industrial sector: 1987–2007

4-38 Company and other nonfederal funds for industrial R&D performed abroad, by NAICS industry: 2003–07

4-39 Company-funded and -performed R&D expenditures and R&D contracted out to other organizations in United 

States: 1993–2007

4-40 R&D expenditures contracted out in United States, by NAICS industry: 2005–07

4-41 U.S. trade in research, development, and testing services, by affiliation and by selected region/country: 2006–07

4-42 Industrial technology alliances classified by country of ultimate parent company, by technology and type 

(equity/nonequity): 1980–2006

4-43 Federal technology transfer activity indicators for U.S. agencies with federal laboratories: FY 2001–07

4-44 SBIR and STTR awards, by type of award: FY 1983–2006

4-45 SBIR award funding, by type of award and federal agency: FY 1983–2006

4-46 Small Business Technology Transfer Program award funding, by type of award and federal agency: FY 1994–

2006

4-47 Advanced Technology Program projects, number of participants, and funding: FY 1990–2007

4-48 Technology Innovation Program summary data: FY 2008

4-49 Hollings Manufacturing Extension Partnership, federal obligations: FY 1988–2008

Chapter 5. Academic Research and Development

5-1 Academic R&D expenditures directed to basic research, applied research, and development: 1970–2008

5-2 Support for academic R&D, by sector: 1972–2008

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 Federal and nonfederal R&D expenditures at academic institutions, by field and source of funds: 2008

5-6 Expenditures for academic R&D, by field: Selected years, 1975–2008

5-7 Federally financed academic R&D expenditures, by S&E field and agency: FY 2008

5-8 Academic R&D funds provided by federal government, by field: Selected years, 1975–2008

5-9 Sources of R&D funds at private and public institutions: 1988, 1998, and 2008

5-10 Top 100 academic institutions in R&D expenditures, by source of funds: 2008

5-11 Top 50 academic institutions in non-S&E R&D expenditures, by non-S&E field: 2008

5-12 Expenditures for academic R&D passed through to and received by subrecipients: FY 2000–08

5-13 Current expenditures for research equipment at academic institutions, by field: Selected years, 1985–2008

5-14 Federal share of current funding for research equipment at academic institutions, by field: Selected years, 

1985–2008

5-15 Full-time faculty with S&E doctorates employed in academia, by tenure status and degree field: 1979–2006

5-16 S&E doctorate holders employed in academia reporting research as primary or secondary work activity, by type 

of position, sex, and degree field: 1973–2006

5-17 Characteristics of full-time doctoral research faculty: 1973–2006

5-18 S&E doctorate holders employed in academia reporting research as primary or secondary work activity, by type 

of position, race/ethnicity, and degree field: 1973–2006

5-19 S&E doctorate holders employed in academia, by type of position, work activity, and citizenship: 2006

5-20 S&E doctorate holders and full-time faculty with federal support, by degree field and Carnegie classification of 

employer: 2006

5-21 S&E doctorate holders employed in academia reporting working in teams, by position, type of teamwork, place 

of birth, and degree field: 2006

5-22 S&E doctorate holders employed in academia reporting international collaboration, by type of position, means 

of collaboration, and degree field: 2006

5-23 Regions and countries/economies in S&E publications data

5-24 Fields and subfields of S&E publications data

5-25 S&E articles in all fields, by region/country/economy: 1995–2007
Science and Engineering Indicators 2010 B-5

5-26 S&E articles in agricultural sciences, by region/country/economy: 1995–2007

5-27 S&E articles in astronomy, by region/country/economy: 1995–2007

5-28 S&E articles in biological sciences, by region/country/economy: 1995–2007

5-29 S&E articles in chemistry, by region/country/economy: 1995–2007

5-30 S&E articles in computer sciences, by region/country/economy: 1995–2007

5-31 S&E articles in engineering, by region/country/economy: 1995–2007

5-32 S&E articles in geosciences, by region/country/economy: 1995–2007

5-33 S&E articles in mathematics, by region/country/economy: 1995–2007

5-34 S&E articles in medical sciences, by region/country/economy: 1995–2007

5-35 S&E articles in other life sciences, by region/country/economy: 1995–2007

5-36 S&E articles in physics, by region/country/economy: 1995–2007

5-37 S&E articles in psychology, by region/country/economy: 1995–2007

5-38 S&E articles in social sciences, by region/country/economy: 1995–2007

5-39 Internationally coauthored S&E articles, by selected country/economy pairs: 1998

5-40 Internationally coauthored S&E articles, by selected country/economy pairs: 2008

5-41 Indexes of internationally coauthored S&E articles, by selected country/economy pairs: 1998 and 2008

5-42 U.S. S&E articles, by field and sector: 1995–2008

5-43 S&E articles, by field, citation percentile, and country/region of institutional author: 1998 and 2008

5-44 Share of all S&E articles and top 1% of cited articles and index of highly cited articles by field and selected 

country/region: 1998 and 2008

5-45 U.S. utility patent awards, by selected characteristics of patent owner: 1998–2008

5-46 U.S. university patents awarded, by technology area: 1988–2008

5-47 Academic patenting and licensing activities: 1991–2007

5-48 U.S. utility patents, utility patents citing S&E literature, cited articles, and total citations, by patent assignee 

sector and article sector: 1998-2008

5-49 Citation of U.S. S&E articles in U.S. patents, by cited field and cited sector: 1998–2008

Chapter 6. Industry, Technology, and the Global Marketplace

6-1 Value added of knowledge- and technology-intensive industries, by region/country/economy: 1985–2007

6-2 Nominal GDP, by region/country/economy: 1992–2007

6-3 Value added of commercial knowledge-intensive services, by region/country/economy: 1985–2007

6-4 Value added of ICT industries, by region/country/economy: 1985–2007

6-5 Value added of high-technology manufacturing industries, by region/country/economy: 1985–2007

6-6 Real GDP per capita, by region/country/economy: 1990–2007

6-7 Real GDP per employed person, by region/country/economy: 1990–2007

6-8 Value added of education services, by region/country/economy: 1985–2007

6-9 Value added of health services, by region/country/economy: 1985–2007

6-10 Value added of business services, by region/country/economy: 1985–2007

6-11 Value added of financial services, by region/country/economy: 1985–2007

6-12 Value added of communications services, by region/country/economy: 1985–2007

6-13 Value added of communications and semiconductors, by region/country/economy: 1985–2007

6-14 Value added of pharmaceuticals, by region/country/economy: 1985–2007

6-15 Value added of scientific instruments, by region/country/economy: 1985–2007

6-16 Value added of aerospace industry, by region/country/economy: 1985–2007

6-17 Value added of computers and office machinery, by region/country/economy: 1985–2007

6-18 Value added of all manufacturing industries, by region/country/economy: 1985–2007

6-19 Exports and imports of high-technology goods, by region/country/economy: 1995–2008

6-20 Exports and imports of communications and semiconductors goods, by region/country/economy: 1995–2008

6-21 Exports and imports of computer and office machinery goods, by region/country/economy: 1995–2008

6-22 Exports and imports of scientific instruments and measuring equipment goods, by region/country/economy: 

1995–2008

6-23 Exports and imports of pharmaceutical goods, by region/country/economy: 1995–2008

6-24 Exports and imports of aerospace goods, by region/country/economy: 1995–2008

6-25 Regions and countries/economies in bilateral trade data

6-26 Exports and imports of information and communications technology goods, by region/country/economy: 

1995–2008
B-6 List of Appendix Tables

6-27 U.S. exports and imports of information and communications technology goods, by region/country/economy of 

origin and destination: 1995–2008

6-28 EU exports and imports of information and communications technology goods, by region/country/economy of 

origin and destination: 1995–2008

6-29 Japan exports and imports of information and communications technology goods, by region/country/economy 

of origin and destination: 1995–2008

6-30 China and Hong Kong exports and imports of information and communications technology goods, by region/

country/economy of origin and destination: 1995–2008

6-31 Asia-9 exports and imports of information and communications technology goods, by region/country/economy 

of origin and destination: 1995–2008

6-32 Philippines exports and imports of information and communications technology goods, by region/country/

economy of origin and destination: 1995–2008

6-33 Singapore exports and imports of information and communications technology goods, by region/country/

economy of origin and destination: 1995–2008

6-34 South Korea exports and imports of information and communications technology goods, by region/country/

economy of origin and destination: 1995–2008

6-35 Taiwan exports and imports of information and communications technology goods, by region/country/economy 

of origin and destination: 1995–2008

6-36 Malaysia exports and imports of information and communications technology goods, by region/country/

economy of origin and destination: 1995–2008

6-37 U.S. trade in advanced-technology products, by region/country/economy: 2000–08

6-38 U.S. trade in aerospace products, by region/country/economy: 2000–08

6-39 U.S. trade in electronics products, by region/country/economy: 2000–08

6-40 U.S. trade in information and communications products, by region/country/economy: 2000–08

6-41 U.S. trade in life sciences products, by region/country/economy: 2000–08

6-42 U.S. trade in optoelectronics products, by region/country/economy: 2000–08

6-43 U.S. trade in advanced materials, by region/country/economy: 2000–08

6-44 U.S. trade in biotechnology products, by region/country/economy: 2000–08

6-45 U.S. trade in flexible manufacturing products, by region/country/economy: 2000–08

6-46 U.S. trade in nuclear products, by region/country/economy: 2000–08

6-47 U.S. trade in weapons products, by region/country/economy: 2000–08

6-48 Regions and countries/economies in U.S. advanced technology product trade data

6-49 U.S. trade in private service industries, by industry: 1997–2007

6-50 Domestic and foreign value added and employment of U.S. multinationals, by selected industry: 1999 and 2006

6-51 U.S. direct investment abroad and foreign direct investment in United States, by selected industry: 2000–08

6-52 U.S. receipts and payments of royalties and fees associated with affiliated and unaffiliated foreign companies: 

1992–2007

6-53 USPTO patent applications, by region/country/economy: 1992–99

6-54 USPTO patent applications, by region/country/economy: 2000–08

6-55 Regions and countries/economies in USPTO patent data

6-56 USPTO patent grants, by region/country/economy: 1992–99

6-57 USPTO patent grants, by region/country/economy: 2000–08

6-58 USPTO patents granted, by technology area: 1995–2008

6-59 USPTO patents granted in information and communications technology, by region/country/economy: 1995–

2008

6-60 Regions and countries/economies in USPTO patent technology data

6-61 USPTO patents granted in computer technology, by region/country/economy: 1995–2008

6-62 USPTO patents granted in semiconductor technology, by region/country/economy: 1995–2008

6-63 USPTO patents granted in telecommunications technology, by region/country/economy: 1995–2008

6-64 USPTO patents granted in aerospace technology, by region/country/economy: 1995–2008

6-65 USPTO patents granted in pharmaceuticals technology, by region/country/economy: 1995–2008

6-66 USPTO patents granted in biotechnology, by region/country/economy: 1995–2008

6-67 USPTO patents granted in medical equipment, by region/country/economy: 1995–2008

6-68 USPTO patents granted in medical electronics, by region/country/economy: 1995–2008

6-69 USPTO patents granted in measurement and control equipment technology, by region/country/economy: 

1995–2008
Science and Engineering Indicators 2010 B-7

6-70 Triadic patent families, by region/country/economy: 1993–2006

6-71 Firms and employment in U.S. high-technology small businesses, by industry: 2006

6-72 U.S. venture capital investment, financing stage, industry, and number of companies: 1995–2008

6-73 U.S. angel capital investment, number of businesses, investors, and employment: 2001–08

6-74 World Bank knowledge development indexes by region/country/economy: 1995 and 2005

Chapter 7. Science and Technology: Public Attitudes and Understanding

7-1 Primary source of information about current news events, by respondent characteristic: 2008

7-2 Primary source of information about science and technology, by respondent characteristic: 2008

7-3 Primary source of information about specific scientific issues, by respondent characteristic: 2008

7-4 Public interest in selected issues: 1979–2008

7-5 Public interest in selected issues, by respondent characteristic: 2008

7-6 Visits to informal science and other cultural institutions: 1981–2008

7-7 Visits to informal science and other cultural institutions, by respondent characteristic: 2008

7-8 Correct answers to scientific terms and concept questions in “factual knowledge of science, scale 1,” by 

respondent characteristic: 1992–2008

7-9 Correct answers to scientific terms and concept questions: 1985–2008

7-10 Correct answers to scientific terms and concept questions, by respondent characteristic: 2008

7-11 Correct answers to nanotechnology questions, by respondent characteristic: 2008

7-12 Correct answers to scientific terms and concept questions in “factual knowledge of science, scale 2,” by 

respondent characteristic: 2008

7-13 Correct answers to scientific process questions: 1988–2008

7-14 Correct answers to scientific process questions, by respondent characteristic: 2008

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 Public assessment of astrology, by respondent characteristic: 1979–2008

7-17 Public assessment of benefits and harms of scientific research, by respondent characteristic: 2008

7-18 Attitudes toward science and technology, by region/country: Most recent year

7-19 Public assessment of whether science and technology result in more opportunities for next generation, by 

respondent characteristic: 2008

7-20 Public assessment of whether science makes life change too fast, by respondent characteristic: 2008

7-21 Public opinion on whether federal government should fund basic scientific research: 1985–2008

7-22 Public opinion on whether federal government should fund basic research, by respondent characteristic: 2008

7-23 Public assessment of federal government spending, by policy area: 1981–2008

7-24 Public assessment of federal government spending, by policy area and respondent characteristic: 2008

7-25 Public confidence in institutional leaders: 1973–2008

7-26 Familiarity with nanotechnology, by respondent characteristic: 2008

7-27 Public assessment of benefits and harms of nanotechnology, by respondent characteristic: 2008

7-28 Awareness, knowledge, and attitudes about stem cell research in the United States, Europe, Japan, and Israel: 

2008

7-29 Public assessment of whether the quality of science and mathematics education in American schools is 

inadequate, by respondent characteristic: 2008

7-30 Public assessment of whether the quality of science and mathematics education in American schools is 

inadequate: 1985–2008

7-31 Public attitudes toward conducting human health research that may inflict pain or injury to animals, by 

respondent characteristic: 2008

7-32 Public attitudes toward scientific research on animals: 1988–2008

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 2007

8-2 Average science NAEP scores and achievement-level results for grades 4 and 8 white students in public 

schools, by state: 2000 and 2005

8-3 Average mathematics NAEP scores and achievement-level results for grades 4 and 8 black students in public 

schools, by state: 2000 and 2007
B-8 List of Appendix Tables

8-4 Average science NAEP scores and achievement-level results for grades 4 and 8 black students in public schools, 

by state: 2000 and 2005

8-5 Average mathematics NAEP scores and achievement-level results for grades 4 and 8 Hispanic students in public 

schools, by state: 2000 and 2007

8-6 Average science NAEP scores and achievement-level results for grades 4 and 8 Hispanic students in public 

schools, by state: 2000 and 2005

8-7 Average mathematics NAEP scores and achievement-level results for grades 4 and 8 Asian students in public 

schools, by state: 2000 and 2007

8-8 Average science NAEP scores and achievement-level results for grades 4 and 8 Asian students in public 

schools, by state: 2000 and 2005

8-9 Average mathematics NAEP scores and achievement-level results for grades 4 and 8 female students in public 

schools, by state: 2000 and 2007

8-10 Average science NAEP scores and achievement-level results for grades 4 and 8 female students in public 

schools, by state: 2000 and 2005

8-11 Average mathematics NAEP scores and achievement-level results for grades 4 and 8 male students in public 

schools, by state: 2000 and 2007

8-12 Average science NAEP scores and achievement-level results for grades 4 and 8 male students in public schools, 

by state: 2000 and 2005

8-13 Coefficient of variation for estimates of employed S&E doctorate holders, by state: 1997, 2001, and 2006

8-14 Coefficient of variation for estimates of S&E doctorate holders in academia, by state: 1997, 2001, and 2006
A

Academic research and development. See also Research and 

development (R&D)

article output per $1 million of, 8.94f, 8.95t

as share of GDP, 8.88f, 8.89t

bricks and mortar infrastructure in, 5.17–19

by institution, 5.13–16

collaborative, 5.28–29

congressional earmarks for, 5.10

cyberinfrastructure for, 5.16–17

demographics of researchers in, 5.22–25

Department of Agriculture in, 5.11t

Department of Defense in, 5.11t

Department of Energy in, 5.11t

doctoral scientists and engineers in, 5.19–29

employment trends in, 5.19–25

Environmental Protection Agency in, 5.11t

expenditures

by field, 5.12–13

by funding source, 5.12–13

in biological sciences, 5.16f

in computer sciences, 5.16f

in engineering, 5.16f

in environmental sciences, 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, 5.7–16

federal government as, 5.14f

industry as, 5.14f

state and local government as, 5.14f

government support of academic doctoral researchers, 5.26–28

in computer sciences, 5.13f

in engineering, 5.13f

in environmental sciences, 5.13f

in mathematics, 5.13f

in physical sciences, 5.13f

in psychology, 5.13f

in social sciences, 5.13f

industry funding for, 5.12

infrastructure, 5.16–19

institutional funds for, 5.10–11

interdisciplinary, 5.35

internal networks in, 5.17

Internet access and, 5.16–17

life sciences, 5.13f

National Aeronautics and Space Administration in, 5.11t

National Institutes of Health in, 5.11t

National Science Foundation in, 5.11t

non-science and engineering, 5.13

output of, 5.29–46

portfolios of top article-producing countries/regions, 5.32, 5.34t

racial/ethnic groups in, 5.22–25

recent doctorate holders in, 5.25–26

space for, 5.17–18

by field, 5.18t

in agricultural sciences, 5.18t

in biological sciences, 5.18t

in computer sciences, 5.18t

in mathematics, 5.18t

in physical sciences, 5.18t

in psychology, 5.18t

in social sciences, 5.18t

new construction of, 5.18–19

state and local government funding for, 5.12

USDA in, 5.11t

within national research and development enterprise, 5.9

women in, 5.22

Achievement gaps

in mathematics, 1.8, 1.9t, 1.13–14

in science, 1.10

Aerospace

patents, 6.51f

value added of, 6.20f

Africa, gross domestic product of, as share of global, 6.12f

Agency for International Development (AID), 4.26t, 4.27t

Agriculture industry, value added, 6.24f

AID. See Agency for International Development (AID)

Alabama. See also Chapter 8

science and mathematics courses offered in, 1.34t

Alaska. See Chapter 8

Angel investment, 6.53–54, 6.55f, 6.56t

Animals, research on, public attitudes about, 7.42–43

Apple iPod, 6.22f

Argentina

coauthorship from, with U.S., 5.38t

international collaboration on articles in, 5.39t

journal articles from, 5.31t

research and development expenditures as share of GDP, 4.34t

stay rates of doctorate recipients from, 2.30f

Arizona. See Chapter 8

Arkansas. See also Chapter 8

science and mathematics courses offered in, 1.34t

Asia. See also specific countries

article collaboration in, 5.36t

ascent of, O.3

business services value added in, 6.17f

China exports to, O.18f

citation of papers from, 5.43f

citations in articles from, O.13f

commercial knowledge-intensive services value added, 6.17f

communication services value added in, 6.17f

computer and office machinery manufacturing market shares, 

O.16f

doctorate recipients from, 2.27, 2.28f

education services value added, 6.15f

exports of high-technology products, 6.28f, 6.29f

financial services value added in, 6.17f

gross domestic product

as share of global, 6.12f

per capita, 6.13f

per employed person, 6.13f

health services value added, 6.15f

high-technology manufacturing 

consumption in, 6.19f

value added in, 6.18f, 6.20f

value in, O.16f

high-value patents from, O.14f

highly cited works from, 5.44f

information and communication technology 

adoption and intensity, 6.14f

export share, O.17f

exports, 6.32f

Index

I-1
I-2 Index

imports, 6.30f, 6.33f

output of, 6.11f

spending, 6.14f

value added, 6.23f

journal articles produced by, O.10f

in engineering, O.11f

knowledge- and technology-intensive industry output in, 6.10f,

6.11f

knowledge-intensive industry as share of GDP, O.15f

manufacturing value added, 6.26t

research and development expenditures, O.4f, O.6f

research portfolios in, 5.34t

trade balance in, O.19f

U.S. advanced technology trade with, 6.38

U.S. patent grants from, O.14f

value of knowledge-intensive services in, O.15f

Australia

academic research and development expenditures in, 4.44t

article collaboration in, 5.37t

coauthorship from, with U.S., 5.38t

educational attainment in, tertiary, 2.33f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

industrial research and development in, 4.40f

international collaboration on articles in, 5.39t

journal articles 

by field, O.10f

from, 5.31t

PISA math and science literacy scores in, 1.23t

research and development by U.S. companies in, 4.49t

research and development expenditures as share of GDP, 4.34t,

4.37f

stay rates of doctorate recipients from, 2.30f

TIMSS mathematics scores in, 1.19t, 1.20

TIMSS science scores in, 1.21t

Austria

coauthorship from, with U.S., 5.38t

educational attainment in, tertiary, 2.33f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

journal articles from, 5.31t

research and development expenditures as share of GDP, 4.34t

B

Belgium

coauthorship from, with U.S., 5.38t

educational attainment in, tertiary, 2.33f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

industrial research and development in, 4.40f

international collaboration on articles in, 5.39t

journal articles from, 5.31t

research and development by U.S. companies in, 4.49t

research and development expenditures as share of GDP, 4.34t

Bibliometric data, 5.35

Biotechnology

patents, 6.51f

public attitudes about, 7.39–40

Bologna Process, 2.35

Brazil

coauthorship from, with U.S., 5.38t

international collaboration on articles in, 5.39t

journal articles 

by field, O.10f

from, 5.31t

PISA math and science literacy scores in, 1.23t

research and development by U.S. companies in, 4.49t

stay rates of doctorate recipients from, 2.30f

tertiary education achievement in, O.7f

Business

high-technology share of, 8.100f, 8.101t

net formations of high technology, 8.102f, 8.103t

research and development, 4.18–21, 4.39–42

as share of private-industry output, 8.86f, 8.87t

in top states, 4.17–18

C

California. See also Chapter 8

research and development in, 4.17t

business, 4.18t

Canada

academic research and development expenditures in, 4.44t

article collaboration in, 5.37t

coauthorship from, with U.S., 5.38t

doctorate recipients from, 2.27t, 2.29f

educational attainment in, tertiary, 2.33f

energy research and development in, 4.24f

enrollment in U.S. undergraduate programs, 2.14f

foreign students in tertiary education in, 2.36f

GDP in, by sector, 4.35f

H-1B holders from, 3.56f

immigrants from, education of, 3.53f

industrial research and development in, 4.40f

international collaboration on articles in, 5.39t

journal articles 

by field, O.10f

from, 5.31t

PISA math and science literacy scores in, 1.23t

research and development by U.S. companies in, 4.49t

research and development expenditures as share of GDP, 4.34t,

4.37f

stay rates of doctorate recipients from, 2.30f

U.S. advanced technology trade with, 6.38

Carnegie Classification of Institutions of Higher Education, 2.8

Charts, understanding of, 7.25–27

Chile

educational attainment in, tertiary, 2.33f

foreign students in tertiary education in, 2.36f

journal articles from, 5.31t

China

article collaboration in, 5.36t, 5.37t

business services value added in, 6.17f

citation of papers from, 5.43f

citations in articles from, O.13f

coauthorship from, with U.S., 5.38t

commercial knowledge-intensive services value added, 6.17f

communication services value added in, 6.17f

computer and office machinery manufacturing market shares, 

O.16f

doctoral degrees in, O.8f

doctorate recipients from, 2.27t, 2.28f

education services value added, 6.15f

enrollment in U.S. undergraduate programs, 2.14f

export share, high-technology, O.17f

exports of high-technology products, 6.28f, 6.29f

exports to selected countries, O.18f

financial services value added in, 6.17f

gross domestic product

as share of global, 6.12f

by sector, 4.35f

per capita, 6.13f

per employed person, 6.13f
Science and Engineering Indicators 2010 I-3

H-1B holders from, 3.56f

health services value added, 6.15f

high-technology manufacturing 

consumption in, 6.19f

value added in, 6.18f, 6.20f

value in, O.16f

high-value patents from, O.14f

highly cited works from, 5.44f

immigrants from, education of, 3.53f

information and communication technology 

adoption and intensity, 6.14f

export share, O.17f

imports, 6.30f, 6.31f

output of, 6.11f

spending, 6.14f

value added, 6.23f

international collaboration on articles in, 5.39t

Japan exports to, O.18f

journal articles 

by field, O.10f

from, 5.31t

produced by, O.10f

in engineering, O.11f

knowledge- and technology-intensive industry output in, 6.10f,

6.11f

knowledge-intensive industry as share of GDP, O.15f

manufacturing value added, 6.26t

research and development by U.S. companies in, 4.49t

research and development expenditures, O.4, O.5f

as share of GDP, 4.34t, 4.37f

research portfolios in, 5.34t

researcher numbers in, O.8f, O.9f

South Korea exports to, O.18f

stay rates of doctorate recipients from, 2.30f

Taiwan exports to, O.18f

tertiary education achievement in, O.7f

trade balance in, O.19f

U.S. advanced technology trade with, 6.37–38

U.S. patent grants from, O.14f

value of knowledge-intensive services in, O.15f

Chinese Taipei

PISA math and science literacy scores in, 1.23t

TIMSS mathematics scores in, 1.19t, 1.20

TIMSS science scores in, 1.21t

Climate change

global warming vs. as term, 7.38

public attitudes about, 7.37–39

Cloning, public attitudes about, 7.41–42

Colorado. See Chapter 8

Commercial knowledge-intensive services industries, 6.16–17, 6.39f,

6.40f, 6.41f

Commercial services, non-knowledge-intensive, 6.22

Computer specialists, as share of workforce, 8.72f, 8.73t

Connecticut. See also Chapter 8

research and development in, 4.17t

business, 4.18t

Construction, value added for, 6.24f

Croatia, journal articles from, 5.31t

Cuba, immigrants from, education of, 3.53f

Czech Republic

educational attainment in, tertiary, 2.33f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

high school graduation rate in, 1.36f

industrial research and development in, 4.40f

international collaboration on articles in, 5.39t

journal articles from, 5.31t

PISA math and science literacy scores in, 1.23t

research and development expenditures as share of GDP, 4.34t

TIMSS mathematics scores in, 1.19t, 1.20

TIMSS science scores in, 1.21t

D

de Solla Price, Derek J., 3.12

Delaware. See Chapter 8

Denmark

coauthorship from, with U.S., 5.38t

educational attainment in, tertiary, 2.33f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

high school graduation rate in, 1.36f

industrial research and development in, 4.40f

international collaboration on articles in, 5.39t

journal articles from, 5.31t

PISA math and science literacy scores in, 1.23t

research and development expenditures as share of GDP, 4.34t,

4.37f

TIMSS mathematics scores in, 1.19t, 1.20

TIMSS science scores in, 1.21t

Department of Agriculture (USDA), 4.26f, 4.26t, 4.27, 4.27t, 4.30f,

5.11t

Department of Commerce (DOC), 4.26f, 4.26t, 4.27, 4.27t, 4.30f

Department of Defense (DOD), 4.25, 4.26f, 4.26t, 4.27t, 4.30f, 5.11t

Department of Education (ED), 4.26t, 4.27t

Department of Energy (DOE), 4.25, 4.26f, 4.26t, 4.27t, 4.30f, 5.11t

Department of Health and Human Services (HHS), 4.25, 4.26f, 4.26t,

4.27t, 4.30f

Department of Homeland Security (DHS), 4.26t, 4.27t

Department of Interior (DOI), 4.26t, 4.27t

Department of Transportation (DOT), 4.26t, 4.27t

DHS. See Department of Homeland Security (DHS)

District of Columbia. See also Chapter 8

research and development in, 4.17t

DOC. See Department of Commerce (DOC)

DOD. See Department of Defense (DOD)

DOE. See Department of Energy (DOE)

DOI. See Department of 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, 1.35–36, 1.37f, 8.32f, 8.33t,

8.34f, 8.35t

associate’s degrees

in science and engineering, 2.15

or higher among 25-44 year olds, 8.58f, 8.59t

bachelor’s degrees, 2.15–17

by citizenship, 2.17

by field, 2.16f

by race/ethnicity, 2.16, 2.17f

female share of, 2.16f

holders potentially in workforce, 8.62, 8.63t

minority share of, 2.17f

or higher among 25–44-year-olds, 8.60f, 8.61t

per 1,000 18–24-year-olds, 8.38f, 8.39t

in natural sciences and engineering, 8.40f, 8.41t

Carnegie Classification of Institutions of Higher Education, 2.8

community colleges, 2.8

curricular reform, 2.11

distance, 1.33–34, 2.9

doctoral degrees, 2.24–30, O.8f
I-4 Index

article output per 1,000 holders of, 8.92f, 8.93t

baccalaureate origins of recipients, 2.9

by citizenship, 2.26f

by country/economy of origin, 2.27–2.28, 2.28t

by field, 2.24f

by race/ethnicity, 2.25, 2.26f

by sex, 2.24–2.25, 2.26f

completion and attrition, 2.25

conferred in S&E per 1,000 employed S&E holders of, 

8.90f, 8.91t

employed holders of, as share of workforce, 8.66f, 8.67t

foreign recipients, 2.25, 2.27, 2.27t

global comparison of, 2.35–36

globalization and, 2.32

labor market for, 3.42–44

patents per 1,000 science and engineering, 8.96f, 8.97t

salaries for, 3.43–44

stay rates, 2.29–30, 2.30f

tenure-track positions for, 3.43

time for completion, 2.24, 2.26t

unemployment of, 3.42–43

expenditures, U.S.

as share of GDP, 8.28f, 8.29t

per pupil, 8.30f, 8.31t

financial aid for, state expenditure per student, 8.56f, 8.57t

graduate, in U.S., 2.17–30

in science and engineering per 1,000 25-34 year olds, 8.46f,

8.47t

graduation rates, 1.35, 1.35f, 1.36f

high school completion, 1.34–35

high school or higher level achieved, in U.S., 8.36f, 8.37t

higher

advanced science and engineering degrees as share of total 

science and engineering degrees, 8.48f, 8.49t,

8.50f, 8.51t

associate’s degrees, 2.15

bachelor’s degrees, 2.15–17

by country, O.7f

cost of, 2.10

distance, 2.9, 2.10t

for-profit institutions, 2.10

immediate enrollment in, 1.38

online, 2.9

overview of U.S., 2.7–10

transition to, 1.34

workforce trends and, O.6–8

homeschooling, in U.S., 1.8

instructional technology in, 1.30–34

international expenditures on higher, 2.31–32

Internet access in, 1.32–33

master’s degrees, 2.21–24

by citizenship, 2.24

by field, 2.23f

by race/ethnicity, 2.23, 2.23f

by sex, 2.22–23, 2.23f

professional, 2.22

mathematics

eighth grade performance in, 1.13, 1.13f, 1.14t, 8.18f, 8.19t

eighth grade proficiency in, 8.20f, 8.21t

elementary student performance in, 1.8–10, 1.9t

fourth grade performance in, 1.13, 1.13f, 1.14t, 8.10f, 8.11t

fourth grade proficiency in, 8.12f, 8.13t

gap changes in, 1.8–9

international assessments of, 1.16–23

long-term trends in performance, 1.14–15

middle grade student performance in, 1.8–10

mother’s education and student achievement in, 1.10f, 1.11f

proficiency in different skill areas, 1.9–1.10, 1.11f, 1.11t

public attitudes about, 7.42

race/ethnicity and achievement in, 1.15f

skills areas, 1.10

national assessments, 1.8–15

of immigrants to U.S., 3.52–53

postdoctoral, 2.30–31

relationship of employment and, 3.18–20

remedial, 2.11–12

science

achievement gaps in, 1.10

degrees as share of total degrees, 8.42f, 8.43t

eighth grade performance in, 8.22f, 8.23t

eighth grade proficiency in, 8.24f, 8.25t

fifteen-year-olds’ performance in, 1.22

fourth grade performance in, 8.14f, 8.15t

fourth grade proficiency in, 8.16f, 8.17t

performance trends, 1.21

public attitudes about, 7.42

rising performance in, 1.11–13

TIMSS test scores in, 1.21–1.22

state achievement tests, 1.12

student mobility and, 2.36–37

teachers

experience of, 1.25–26

formal preparation of, 1.24–25

professional development of, 1.27–28, 1.28t

quality of, 1.24–27

salaries of, 1.28–1.29, 1.29t

student access to qualified, 1.26, 1.27t

subject area preparation of, 1.25, 1.26t

technology literacy of, 1.31

working conditions, 1.29–30, 1.30f

undergraduate

average cost of, 8.52f, 8.53t

as share of disposable income, 8.54f, 8.55t

degree awards, 2.15–17

in U.S., 2.11–17

virtual schools, 1.33–34

Egypt, journal articles from, 5.31t

Employment. See also Workforce, science and engineering

by small businesses, 6.50, 6.52t, 6.53t

in high technology as share of total, 8.104f, 8.105t

Engineers, as share of workforce, 8.68f, 8.69t

Environment, public attitudes about, 7.37–39

Environmental Protection Agency (EPA), 4.26t, 4.27t, 5.11t

EPA. See Environmental Protection Agency (EPA)

EPSCoR. See Experimental Program to Stimulate Competitive 

Research (EPSCoR)

Estonia

educational attainment in, tertiary, 2.33f

foreign students in tertiary education in, 2.36f

EU. See European Union (EU)

European Union (EU)

article collaboration in, 5.36t

business services value added in, 6.17f

China exports to, O.18f

citation of papers from, 5.43f

commercial knowledge-intensive services value added, 6.17f

communication services value added in, 6.17f

computer and office machinery manufacturing market shares, 

O.16f

doctorate recipients from, 2.27–28, 2.29f

education services value added, 6.15f

export share, high-technology, O.17f
Science and Engineering Indicators 2010 I-5

exports of high-technology products, 6.28f, 6.29f

financial services value added in, 6.17f

gross domestic product

as share of global, 6.12f

per employed person, 6.13f

health services value added, 6.15f

high-technology manufacturing 

consumption in, 6.19f

value added in, 6.18f, 6.20f

value in, O.16f

high-value patents from, O.14f

highly cited works from, 5.44f

information and communication technology 

adoption and intensity, 6.14f

export share, O.17f

output of, 6.11f

spending, 6.14f

value added, 6.23f

journal articles produced by, O.10f

in engineering, O.11f

knowledge- and technology-intensive industry output in, 6.10f,

6.11f

knowledge-intensive industry as share of GDP, O.15f

manufacturing value added, 6.26t

research and development expenditures, O.4f, O.5f

as share of GDP, 4.34t

research portfolios in, 5.34t

researcher numbers in, O.8f, O.9f

scientific research in media in, 7.15

South Korea exports to, O.18f

Taiwan exports to, O.18f

trade balance in, O.19f

U.S. advanced technology trade with, 6.38

U.S. patent grants from, O.14f

value of knowledge-intensive services in, O.15f

Experimental Program to Stimulate Competitive Research (EPSCoR). 

See also State indicators in Chapter 8, 5.11, 8.8

Exports

as share of production, O.17f

information and communication technology, O.17f

of high-technology products by selected region/country/

economy, 6.28f, 6.29f

of medium- and low-technology products, 6.32–34

share of, by region/country, O.17f

trade patterns and, O.16–18

valuation of, 6.9

F

Federal government, U.S.

as research and development funding source, 4.13–14

as research and development performers, 4.13

employment by, 3.25–26

in research and development, 4.21–33

by agency, 4.25–27, 4.30f

by field, 4.28–31, 4.30f

by national objective, 4.23–25

by performer, 4.27–28

civilian-related, 4.23–25

defense-related, 4.23

in federal budget, 4.22–23, 4.22t

obligations per civilian worker, 8.76f, 8.77t

obligations per individual in science and engineering 

occupation, 8.78f, 8.79t

tax credits, 4.31–33

public opinion on funding of scientific research by, 7.29–31

research and development by, 4.21–33

Financial services, 6.17f

Finland

coauthorship from, with U.S., 5.38t

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

high school graduation rate in, 1.36f

industrial research and development in, 4.40f

international collaboration on articles in, 5.39t

journal articles from, 5.31t

PISA math and science literacy scores in, 1.23t

research and development expenditures as share of GDP, 4.34t,

4.37f

Florida. See also Chapter 8

science and mathematics courses offered in, 1.34t

Foreign direct investment, in knowledge- and technology-intensive 

industries, 6.43–45

France

article collaboration in, 5.37t

coauthorship from, with U.S., 5.38t

educational attainment in, tertiary, 2.33f

energy research and development in, 4.24f

foreign students in tertiary education in, 2.36f

GDP in, by sector, 4.35f

H-1B holders from, 3.56f

industrial research and development in, 4.40f

international collaboration on articles in, 5.39t

journal articles 

by field, O.10f

from, 5.31t

PISA math and science literacy scores in, 1.23t

research and development by U.S. companies in, 4.49t

research and development expenditures as share of GDP, 4.34t,

4.37f

tertiary education achievement in, O.7f

G

GDP. See Gross domestic product (GDP)

Genetically modified (GM) food, public attitudes about, 7.40

Georgia. See also Chapter 8

science and mathematics courses offered in, 1.34t

Germany

academic research and development expenditures in, 4.44t

article collaboration in, 5.37t

coauthorship from, with U.S., 5.38t

doctoral degrees in, O.8f

doctorate recipients from, 2.27t

educational attainment in, tertiary, 2.33f

energy research and development in, 4.24f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

GDP in, by sector, 4.35f

H-1B holders from, 3.56f

high school graduation rate in, 1.36f

immigrants from, education of, 3.53f

industrial research and development in, 4.40f

international collaboration on articles in, 5.39t

journal articles 

by field, O.10f

from, 5.31t

PISA math and science literacy scores in, 1.23t

research and development by U.S. companies in, 4.49t

research and development expenditures as share of GDP, 4.34t,

4.37f

stay rates of doctorate recipients from, 2.30f

tertiary education achievement in, O.7f

TIMSS mathematics scores in, 1.19t, 1.20

TIMSS science scores in, 1.21t
I-6 Index

Global warming. See also Climate change

climate change vs., as term, 7.38

Globalization

doctoral education and, 2.32

of knowledge-intensive services industries, 6.39

production and, 6.23

GM. See Genetically modified (GM) food

Greece

coauthorship from, with U.S., 5.38t

educational attainment in, tertiary, 2.33f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

high school graduation rate in, 1.36f

journal articles from, 5.31t

research and development expenditures as share of GDP, 4.34t

Gross domestic product (GDP)

academic research and development as share of, 8.88f, 8.89t

comparison of, for selected countries by sector, 4.35f

education expenditures as share of, U.S., 8.28f, 8.29t

global, 6.12f

information and communication technology as share of, 6.11f

knowledge-intensive industry as share of, O.15f

knowledge-intensive industry output as share of, 6.10f

research and development as share of, 8.74f, 8.75t, O.4–5, O.5f

from state agencies, 8.80f, 8.81t

research and development ratio with, in U.S. states, 4.16, 4.17t

technology manufacturing as share of, 6.11f

undergraduate enrollment in terms of, U.S., 2.12f

H

H-1B visas, 3.55–56

Hawaii. See Chapter 8

Health services, 6.15–16

Hewlett-Packard laptop computer, 6.22f

HHS. See Department of Health and Human Services (HHS)

Homeschooling, in U.S., 1.8

Hong Kong

PISA math and science literacy scores in, 1.23t

research and development by U.S. companies in, 4.49t

TIMSS mathematics scores in, 1.19t, 1.20

TIMSS science scores in, 1.21t

Hotels, value added for, 6.25f

Human cloning, public attitudes about, 7.41–42

Hungary

educational attainment in, tertiary, 2.33f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

high school graduation rate in, 1.36f

journal articles from, 5.31t

PISA math and science literacy scores in, 1.23t

research and development expenditures as share of GDP, 4.34t

TIMSS mathematics scores in, 1.19t, 1.20

TIMSS science scores in, 1.21t

I

Iceland

educational attainment in, tertiary, 2.33f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

high school graduation rate in, 1.36f

research and development expenditures as share of GDP, 4.34t

ICT. See Information and communications technology (ICT)

Idaho. See Chapter 8

Illinois. See also Chapter 8

research and development in, 4.17t

business, 4.18t

Imports, valuation of, 6.9

India

coauthorship from, with U.S., 5.38t

doctoral degrees in, O.8f

doctorate recipients from, 2.27t, 2.28f

enrollment in U.S. undergraduate programs, 2.14f

H-1B holders from, 3.56f

immigrants from, education of, 3.53f

international collaboration on articles in, 5.39t

journal articles 

by field, O.10f

from, 5.31t

in engineering, O.11f

research and development by U.S. companies in, 4.49t

research and development expenditures, O.5f

stay rates of doctorate recipients from, 2.30f

tertiary education achievement in, O.7f

Indiana. See Chapter 8

Indonesia

PISA math and science literacy scores in, 1.23t

TIMSS mathematics scores in, 1.19t, 1.20

TIMSS science scores in, 1.21t

Industrial processes, U.S. trade in, 6.45–46, 6.46f

Industrial Research Institute (IRI), 4.21

Information and communications technology (ICT). See also

Knowledge- and technology-intensive (KTI) industries

adoption and intensity of, 6.14f

as share of GDP, 6.11f

China imports of, 6.31f

exports, from Asia, 6.32f

importance of, 6.13

imports of, 6.30f

indicators, 6.13–14

industries in, 6.9

Japan exports of, 6.31f

manufacturing and, 6.42

output in, as share of GDP, 6.11f

patenting, 6.47–48, 6.50f

spending, by region/country, 6.14f

trade balance of, 6.30f

value added of, 6.21–22, 6.23f

Innovation-related metrics, 4.50–51, 6.45–56

Intangible assets, U.S. trade in, 6.45, 6.45f

Interdisciplinary research, 5.35

Internet access

academic research and development and, 5.16–17

in education, 1.32–33

Iowa. See Chapter 8

iPod, 6.22f

Iran

immigrants from, education of, 3.53f

journal articles from, 5.31t

publishing trends in, 5.33

stay rates of doctorate recipients from, 2.30f

Ireland

educational attainment in, tertiary, 2.33f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

high school graduation rate in, 1.36f

industrial research and development in, 4.40f

journal articles from, 5.31t

PISA math and science literacy scores in, 1.23t

research and development by U.S. companies in, 4.49t

research and development expenditures as share of GDP, 4.34t,

4.37f

IRI. See Industrial Research Institute (IRI)
Science and Engineering Indicators 2010 I-7

Israel

coauthorship from, with U.S., 5.38t

educational attainment in, tertiary, 2.33f

journal articles 

by field, O.10f

from, 5.31t

research and development by U.S. companies in, 4.49t

research and development expenditures as share of GDP, 4.34t,

4.37f

Italy

article collaboration in, 5.37t

coauthorship from, with U.S., 5.38t

educational attainment in, tertiary, 2.33f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

GDP in, by sector, 4.35f

high school graduation rate in, 1.36f

industrial research and development in, 4.40f

international collaboration on articles in, 5.39t

journal articles by field, O.10f

journal articles from, 5.31t

PISA math and science literacy scores in, 1.23t

research and development by U.S. companies in, 4.49t

research and development expenditures as share of GDP, 4.34t,

4.37f

TIMSS mathematics scores in, 1.19t, 1.20

TIMSS science scores in, 1.21t

J

Japan

academic research and development expenditures in, 4.44t

article collaboration in, 5.36t, 5.37t

business services value added in, 6.17f

China exports to, O.18f

citation of papers from, 5.43f

coauthorship from, with U.S., 5.38t

commercial knowledge-intensive services value added, 6.17f

communication services value added in, 6.17f

computer and office machinery manufacturing market shares, 

O.16f

doctoral degrees in, O.8f

doctorate recipients from, 2.27t

education services value added, 6.15f

educational attainment in, tertiary, 2.33f

energy research and development in, 4.24f

enrollment in U.S. undergraduate programs, 2.14f

export share, high-technology, O.17f

exports of high-technology products, 6.28f, 6.29f

exports to China, O.18f

exports to U.S., O.18f

financial services value added in, 6.17f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

gross domestic product

as share of global, 6.12f

by employed person, 6.13f

by sector, 4.35f

H-1B holders from, 3.56f

health services value added, 6.15f

high school graduation rate in, 1.36f

high-technology manufacturing 

consumption in, 6.19f

value added in, 6.18f, 6.20f

value in, O.16f

high-value patents from, O.14f

highly cited works from, 5.44f

immigrants from, education of, 3.53f

industrial research and development in, 4.40f

information and communication technology 

adoption and intensity, 6.14f

export share, O.17f

exports, 6.31f

imports, 6.30f

output of, 6.11f

spending, 6.14f

value added, 6.23f

international collaboration on articles in, 5.39t

journal articles 

by field, O.10f

from, 5.31t

produced by, O.10f

in engineering, O.11f

knowledge- and technology-intensive industry output in, 6.10f,

6.11f

knowledge-intensive industry as share of GDP, O.15f

manufacturing value added, 6.26t

migration to, 3.51

PISA math and science literacy scores in, 1.23t

research and development by U.S. companies in, 4.49t

research and development expenditures, O.5f

as share of GDP, 4.34t, 4.37f

research portfolios in, 5.34t

researcher numbers in, O.8f, O.9f

stay rates of doctorate recipients from, 2.30f

tertiary education achievement in, O.7f

TIMSS mathematics scores in, 1.19t, 1.20

TIMSS science scores in, 1.21t

trade balance in, O.19f

U.S. advanced technology trade with, 6.37–38

U.S. patent grants from, O.14f

value of knowledge-intensive services in, O.15f

Japan, R&D expenditures, O.4

Journal articles

as research output, O.9–10

author names in, 5.33–34

by country/economy, 5.31t

citations in

research patterns and, O.12–13

trends in, 5.40–42

coauthorship of, 5.33–38, O.12f

collaboration on, 5.33–38

engineering, in selected regions/countries, O.11f

field shares of, O.10f

highly cited, 5.41–42, 5.44f

in Iran, trends in, 5.33

international coauthorship of, with U.S., 5.38t

output by sector, 5.39–40

patent citations to, 5.45–46

per $1 million of academic research and development, 8.94f,

8.95t

per 1,000 science and engineering doctorate holders, 8.92f, 8.93t

K

Kansas. See Chapter 8

KEI. See Knowledge Economy Index (KEI)

Kentucky. See also Chapter 8

science and mathematics courses offered in, 1.34t

Knowledge Economy Index (KEI), 6.13–14, 6.56, 6.57f

Knowledge- and technology-intensive (KTI) industries

commercial service, 6.17–17

data and terminology in, 6.9

education sector in, 6.15–16
I-8 Index

foreign direct investment in, 6.43–45

global output of, 6.10f

globalization and, 6.23–45

health sector in, 6.15–16

in world economy, 6.7–14

investment in, 6.42–45

macroeconomic indicators, 6.12f

multinational companies in, 6.39–42

output of, by selected region/country, 6.11f

trade and, 6.23–45

value added of, global, 6.15f

worldwide distribution of, 6.14–23

Knowledge-intensive firms, rising output of, O.14–16

Korea. See South Korea

KTI. See Knowledge- and technology-intensive (KTI) industries

L

Leadership, public confidence in scientific, 7.31, 7.32t

Life scientists, as share of workforce, 8.70f, 8.71t

Literature, scientific and technical

as research output, O.9–10

author names in, 5.33–34

by country/economy, 5.31t

citations in

research patterns and, O.12–13

trends in, 5.40–42

coauthorship of, 5.33–38, O.12f

collaboration on, 5.33–38

engineering, in selected regions/countries, O.11f

field shares of, O.10f

highly cited, 5.41–42, 5.44f

in Iran, trends in, 5.33

international coauthorship of, with U.S., 5.38t

output by sector, 5.39–40

patent citations to, 5.45–46

per $1 million of academic research and development, 8.94f,

8.95t

per 1,000 science and engineering doctorate holders, 8.92f, 8.93t

Local Systemic Change (LSC) Through Teacher Enhancement, 1.28

Louisiana. See also Chapter 8

science and mathematics courses offered in, 1.34t

LSC. See Local Systemic Change (LSC) Through Teacher 

Enhancement

Luxembourg

educational attainment in, tertiary, 2.33f

foreign students in tertiary education in, 2.36f

high school graduation rate in, 1.36f

research and development expenditures as share of GDP, 4.34t

M

Maine. See Chapter 8

Malaysia

enrollment in U.S. undergraduate programs, 2.14f

information and communication technology 

exports, 6.33f

imports, 6.33f

research and development by U.S. companies in, 4.49t

research and development expenditures, O.5f

TIMSS mathematics scores in, 1.20

TIMSS science scores in, 1.21t

Manufacturing

computer and office machinery, market shares of, O.16f

high-technology, 6.17–21

consumption of, 6.18, 6.19f

multinational companies in, 6.40–42

value added of selected industries, by selected region/

country/economy, 6.20f

non-high-technology, 6.22–23

trade balance trends in, 6.26–28

value added for, 6.26t

value chain geography of, 6.21

value of high-technology, by selected region/country, O.16f

Maryland. See also Chapter 8

research and development in, 4.17t

science and mathematics courses offered in, 1.34t

Massachusetts. See also Chapter 8

mathematics scores in, 1.20

research and development in, 4.17t

business, 4.18t

Mathematics

eighth grade performance in, 1.13, 1.13f, 1.14t, 1.18–1.19, 8.18f,

8.19t

eighth grade proficiency in, 8.20f, 8.21t

elementary student performance in, 1.8–10, 1.9t

fifteen-year-olds’ performance in, 1.22

fourth grade performance in, 1.13, 1.13f, 1.14t, 1.18–1.19, 8.10f,

8.11t

fourth grade proficiency in, 8.12f, 8.13t

gap changes in, 1.8–9

international assessments of, 1.16–23

long-term trends in performance, 1.14–15

middle grade student performance in, 1.8–10

mother’s education and student achievement in, 1.10f, 1.11f

proficiency in different skill areas, 1.9–1.10, 1.11f, 1.11t

public attitudes about education in, 7.42

race/ethnicity and achievement in, 1.15f

skills areas, 1.10

Mexico

coauthorship from, with U.S., 5.38t

doctorate recipients from, 2.27t, 2.29f

educational attainment in, tertiary, 2.33f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

H-1B holders from, 3.56f

high school graduation rate in, 1.36f

international collaboration on articles in, 5.39t

journal articles 

by field, O.10f

from, 5.31t

research and development expenditures as share of GDP, 4.34t

stay rates of doctorate recipients from, 2.30f

tertiary education achievement in, O.7f

Michigan. See also Chapter 8

research and development in, 4.17t

business, 4.18t

Migration

education of, 3.52–53

origins of, 3.52, 3.53f

to Japan, 3.51

to U.K., 3.51

to U.S., 3.50–58

work visas and, 3.53–56

Mining, value added for, 6.24f

Minnesota. See also Chapter 8

mathematics scores in, 1.20

Minorities

bachelor’s degree attainment by, 2.16, 2.17f

doctoral degree attainment by, 2.25, 2.26f

in academic research and development, 5.22–25

master’s degree attainment by, 2.23, 2.23f
Science and Engineering Indicators 2010 I-9

mathematics achievement by, 1.15f

Mississippi. See also Chapter 8

science and mathematics courses offered in, 1.34t

Missouri. See Chapter 8

MNCs. See Multinational companies

Montana. See Chapter 8

Multinational companies (MNCs)

in knowledge- and technology -intensive industries, 6.39–42

research and development by

employment, O.9f

overseas, O.5–6

research and development employment by, 3.49–50

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, 7.40–41

public knowledge of, 7.20f

NASA. See National Aeronautics and Space Administration (NASA)

National Aeronautics and Space Administration (NASA), 4.25, 4.26f,

4.26t, 4.27t, 4.30f, 5.11t

National Assessment Governing Board (NAGB), 1.12

National Assessment of Educational Progress (NAEP) assessments, 

1.12, 1.16

National Institutes of Health (NIH), 5.11t

National Mathematics Advisory Panel, 1.12

National Science Foundation (NSF), 4.26f, 4.26t, 4.27, 4.27t, 4.30f,

5.11t

NCLB. See No Child Left Behind (NCLB) Act

Nebraska. See Chapter 8

Nepal, enrollment in U.S. undergraduate programs, 2.14f

Netherlands

article collaboration in, 5.37t

coauthorship from, with U.S., 5.38t

educational attainment in, tertiary, 2.33f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

industrial research and development in, 4.40f

international collaboration on articles in, 5.39t

journal articles 

by field, O.10f

from, 5.31t

PISA math and science literacy scores in, 1.23t

research and development by U.S. companies in, 4.49t

research and development expenditures as share of GDP, 4.34t

Nevada. See Chapter 8

New Hampshire. See also Chapter 8

research and development in, 4.17t

New Jersey. See also Chapter 8

research and development in, 4.17t

business, 4.18t

New Mexico. See also Chapter 8

research and development in, 4.17t

New York. See also Chapter 8

research and development in, 4.17t

business, 4.18t

New Zealand

coauthorship from, with U.S., 5.38t

educational attainment in, tertiary, 2.33f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

high school graduation rate in, 1.36f

international collaboration on articles in, 5.39t

journal articles from, 5.31t

research and development expenditures as share of GDP, 4.34t

Nigeria

enrollment in U.S. undergraduate programs, 2.14f

stay rates of doctorate recipients from, 2.30f

NIH. See National Institutes of Health (NIH)

No Child Left Behind (NCLB) Act, 1.12, 1.33

North American Industry Classification System (NAICS) codes, 8.9t

North Carolina. See also Chapter 8

research and development in, 4.17t

science and mathematics courses offered in, 1.34t

North Dakota. See Chapter 8

Norway

coauthorship from, with U.S., 5.38t

educational attainment in, tertiary, 2.33f

energy research and development in, 4.24f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

high school graduation rate in, 1.36f

industrial research and development in, 4.40f

journal articles from, 5.31t

PISA math and science literacy scores in, 1.23t

research and development expenditures as share of GDP, 4.34t

TIMSS mathematics scores in, 1.19t, 1.20

TIMSS science scores in, 1.21t

NSF. See National Science Foundation (NSF)

Nuclear power, public attitudes about, 7.39

O

Ohio. See Chapter 8

Oklahoma. See also Chapter 8

science and mathematics courses offered in, 1.34t

Oregon. See Chapter 8

P

Pakistan, H-1B holders from, 3.56f

Patents

as research output, O.9–10

by ownership type, 6.48

by scientists and engineers, 3.21–22

by technology area, 6.47–49, 6.49f, 6.51f

citations to literature in, 5.45–46

global trends in, 6.46–49

high-value, for selected regions/countries, O.14f

in information and communication technology, 6.47–48, 6.50f

inventive activity shown by, O.13–14

per 1,000 individuals in science and engineering occupations, 

8.98f, 8.99t

per 1,000 science and engineering doctorate holders, 8.96f, 8.97t

related activities and income, 5.44–45

share of U.S. grants for selected regions/countries, O.14f

triadic, 6.49, 6.52f

university trends and, 5.43–44

Pennsylvania. See also Chapter 8

research and development in, 4.17t

business, 4.18t

Pharmaceuticals

exports of, 6.29f

patents, 6.51f

value added of, 6.20f

Philippines

H-1B holders from, 3.56f

immigrants from, education of, 3.53f

information and communication technology exports, 6.32f

tertiary education achievement in, O.7f
I-10 Index

Physical scientists, as share of workforce, 8.70f, 8.71t

PISA. See Program for International Student Assessment (PISA)

Poland

coauthorship from, with U.S., 5.38t

educational attainment in, tertiary, 2.33f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

high school graduation rate in, 1.36f

industrial research and development in, 4.40f

international collaboration on articles in, 5.39t

journal articles 

by field, O.10f

from, 5.31t

research and development expenditures as share of GDP, 4.34t

Portugal

educational attainment in, tertiary, 2.33f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

journal articles from, 5.31t

research and development expenditures as share of GDP, 4.34t

Program for International Student Assessment (PISA), 1.16, 1.17, 

1.22, 1.23t, 1.24f

Pseudoscience, 7.25–27

Publishing

as research output, O.9–10

author names in, 5.33–34

by country/economy, 5.31t

citations in

research patterns and, O.12–13

trends in, 5.40–42

coauthorship in, 5.33–38, O.12f

collaboration in, 5.33–38

engineering, in selected regions/countries, O.11f

field shares of, O.10f

highly cited works, 5.41–42, 5.44f

in Iran, trends in, 5.33

international coauthorship in, with U.S., 5.38t

output by sector, 5.39–40

patent citations, 5.45–46

per $1 million of academic research and development, 8.94f,

8.95t

per 1,000 science and engineering doctorate holders, 8.92f, 8.93t

Puerto Rico. See Chapter 8

R

Race/ethnicity

bachelor’s degree attainment by, 2.16, 2.17f

doctoral degree attainment by, 2.25, 2.26f

in academic research and development, 5.22–25

master’s degree attainment by, 2.23, 2.23f

mathematics achievement by, 1.15f

Real estate, value added for, 6.25f

Republic of Korea. See South Korea

Research

applied, 4.8

basic, 4.8

citations and, O.12

collaboration, expansion of, O.10–12

institutions, in higher education system, 2.8

interdisciplinary dissertation, 2.22

on animals, public attitudes about, 7.42–43

output, O.9–10

Research and development (R&D). See also Academic research and 

development

academic sector, 4.42

government funding mechanisms for, 4.43

aerospace and defense, 4.19t, 4.21

as share of GDP, 8.74f, 8.75t, O.4–5, O.5f

automotive manufacturing, 4.19t, 4.21

budget authority, 4.8

business, 4.18–21, 4.39–42

as share of private-industry output, 8.86f, 8.87t

in top states, 4.17–18

by character of work, 4.14–16, 4.15f

by Industrial Research Institute members, 4.21

by multinational companies, 4.44–50

by performing sector, 4.37–39

by source of funds, 4.37–39

chemical, 4.19t, 4.20

China, O.4, O.5f

classification of, 4.14

computers and electronics, 4.19t, 4.20

definition of, 4.8

definitions, 4.8

economic growth and, 4.16

employment

by multinational companies, 3.49–50

of U.S.-based multinational corporations, O.9f

energy, 4.24

expenditures

as share of GDP, 4.36f, O.5f

Asia, O.4f

by character of work, 4.12t

by performing sector and funding source, 4.9t, 4.11f, 4.12t

by state agencies

per $1 million of GDP, 8.80f, 8.81t

per civilian worker, 8.82f, 8.83t

per individual in science and engineering occupation, 

8.84f, 8.85t

by top corporations, 4.41t

China, O.4, O.5f

comparing international, 4.32

distribution of, among states, 4.16, 4.17t

European Union, O.4f, O.5f

global expansion of, O.4–5

global patterns of, 4.33–35

growth in, O.5f

India, O.5f

Japan, O.4, O.5f

location of, O.6f

Malaysia, O.5f

performer vs. source reported, 4.29

Singapore, O.5f

South Korea, O.4, O.5f

Taiwan, O.5f

Thailand, O.5f

total U.S., 4.10f

United States, O.4f, O.5f

worldwide, O.4f

EPSCoR and, 5.11

exports and imports of services in, 4.51–52

federal, 4.21–33

by agency, 4.25–27, 4.30f

by field, 4.28–31, 4.30f

by national objective, 4.23–25

by performer, 4.27–28

civilian-related, 4.23–25

defense-related, 4.23

in federal budget, 4.22–23, 4.22t

obligations per civilian worker, 8.76f, 8.77t

obligations per individual in science and engineering 

occupation, 8.78f, 8.79t

tax credits, 4.31–33

federal legislation related to, 4.54
Science and Engineering Indicators 2010 I-11

foreign direct investment in, 4.46

funding sources, 4.13–14

business as, 4.14

federal government as, 4.13–14

government priorities, 4.42–44

in business sector, 4.10–13

in federal agencies, 4.13

in research and development, 4.19t, 4.21

in universities and colleges, 4.13

industries in, largest, 4.19–21

international, comparisons of, 4.33–44

location of performance, 4.16–18

obligations, 4.8

outlays, 4.8

overseas, by multinational companies, 4.48–50, O.5–6

performers of, 4.10–13

plant, 4.8

sector distribution, by U.S. state, 4.17

software, 4.19t, 4.20

trends, 4.8–16

unmeasured, 4.10

workforce performing, 3.21–23

Researchers

expansion of global pool, O.8–9

growth in numbers of, O.9f

numbers of, O.8f

Restaurants, value added for, 6.25f

Retail, value added for, 6.25f

Rhode Island. See Chapter 8

Romania

journal articles from, 5.31t

research and development expenditures as share of GDP, 4.34t

Russia

academic research and development expenditures in, 4.44t

coauthorship from, with U.S., 5.38t

GDP in, by sector, 4.35f

H-1B holders from, 3.56f

journal articles 

by field, O.10f

from, 5.31t

researcher numbers in, O.8f, O.9f

tertiary education achievement in, O.7f

Russian Federation

educational attainment in, tertiary, 2.33f

foreign students in tertiary education in, 2.36f

PISA math and science literacy scores in, 1.23t

research and development expenditures as share of GDP, 4.34t,

4.37f

TIMSS mathematics scores in, 1.19t, 1.20

TIMSS science scores in, 1.21t

S

Salaries

age and, 3.36

at different degree levels, 3.38, 3.39f

differentials in, of minorities and women, 3.35–36

employer characteristics and, 3.37

family characteristics and, 3.37

field of degree and, 3.37

for doctorate recipients, 3.43–44

of H-1B holders, 3.56, 3.57t

personal characteristics and, 3.37

teacher, 1.28–1.29, 1.29t, 8.26f, 8.27t

Saudi Arabia, enrollment in U.S. undergraduate programs, 2.14f

SBIR. See Small Business Innovation Research (SBIR)

Science

achievement gaps in, 1.10

eighth grade performance in, 8.22f, 8.23t

eighth grade proficiency in, 8.24f, 8.25t

fifteen-year-olds’ performance in, 1.22

fourth grade performance in, 8.14f, 8.15t

fourth grade proficiency in, 8.16f, 8.17t

performance trends, 1.21

public attitudes about education in, 7.42

rising performance in, 1.11–13

TIMSS test scores in, 1.21–1.22

Science and engineering (S&E)

advanced degrees in, share of, 8.48f, 8.49t, 8.50f, 8.51t

associate’s degrees, 2.15

bachelor’s degrees, 2.15–17

by citizenship, 2.17

by field, 2.16f

by race/ethnicity, 2.16, 2.17f

female share of, 2.16f

minority share of, 2.17f

per 1,000 18–24-year-olds, 8.40f, 8.41t

degrees as share of total degrees, 8.42f, 8.43t, 8.44f, 8.45t

doctoral degrees, 2.24–30

article output per 1,000 holders of, 8.92f, 8.93t

by citizenship, 2.26f

by country/economy of origin, 2.27–2.28, 2.28t

by field, 2.24f

by race/ethnicity, 2.25, 2.26f

by sex, 2.24–2.25, 2.26f

completion and attrition, 2.25

conferred per 1,000 employed holders of, 8.90f, 8.91t

foreign recipients, 2.25, 2.27, 2.27t

global comparison of, 2.35–36

labor market for, 3.42–44

patents per 1,000, 8.96f, 8.97t

salaries for, 3.43–44

stay rates, 2.29–30, 2.30f

tenure-track positions for, 3.43

time for completion, 2.24, 2.26t

unemployment of, 3.42–43

first university degrees in, 2.33–34

graduate education

enrollment in, 2.17–19

by race/ethnicity, 2.18, 2.18f

by sex, 2.18

foreign students, 2.18–19

financial support for, 2.19–21, 2.19f, 2.21t

interdisciplinary, 2.21

per 1,000 25–34-year-olds, 8.46f, 8.47t

international education, 2.31–32

master’s degrees, 2.21–24

by citizenship, 2.24

by field, 2.23f

by race/ethnicity, 2.23, 2.23f

by sex, 2.22–23, 2.23f

professional, 2.22

postdoctoral education, 2.30–31

public views on occupations in, 7.35–36

ratio of degrees in, to college-age population, 2.34

reasoning and understanding of scientific process, 7.23–25

undergraduate enrollment in, U.S., 2.13–15

workforce

age, 3.27–30, 3.29f, 3.30f, 3.31f, 3.32f

demographics, 3.27–37

earnings, 3.37–41

at different degree levels, 3.38, 3.39f

growth, 3.38f

education classification, 3.9–10, 3.10t

educational distribution of, 3.16–17
I-12 Index

employer sizes, 3.27, 3.29f

employment growth, 3.14, 3.15f

employment patterns, 3.13–27

employment sectors, 3.23–26

federal employment of, 3.25–26

global, 3.47–58

counts of, 3.48

migration of, to U.S., 3.50–58

growth of, 3.11–13, 3.12t, 3.13f

higher education and trends in, O.6–8

history of, 3.12

in academic research and development, 5.19–29

in metropolitan areas, 3.26–27, 3.27t, 3.28t

in research and development, 3.21–23

labor market conditions, 3.37–47

minorities in, 3.34–37

age distribution of, 3.35, 3.35f

salary differentials of, 3.35–36

non-S&E occupation employment of, 3.17–18

occupation classification, 3.9

occupation density by industry, 3.26

patenting activity of, 3.21–22

postdoc positions, 3.44–47, 3.45t, 3.47f, 3.47t

recent graduates in, 3.40–41

doctorate recipients, 3.42–44

labor market indicators for, 3.41–42

relationship of education and employment of, 3.18–20

retirement patterns, 3.30–31

self-employment in, 3.24–25

size of, 3.10–11, 3.11t

technical expertise classification, 3.10

tenure-track positions, 3.43

training, 3.20–21

unemployment, 3.38–39, 3.39f

of doctorate recipients, 3.42–43

women in, 3.32–37, 3.33f

Science and technology (S&T)

asset-based models of knowledge of, 7.18

confidence in leadership in, 7.31, 7.32t

current events primary sources on, 7.11f

influence of, on public issues, 7.31–34

information sources, 7.7–12

involvement, 7.15–16

issues in, public attitudes about, 7.36–43

literacy, by sex, 7.19t

network news coverage of, 7.14f, 7.14t

promise of, 7.28–29

public attitudes about, 7.27–36

public expectations about advances in, 7.30

public interest in, 7.12–15

public knowledge about, 7.16–27

reservations about, 7.28–29

statistics and charts understanding, 7.25–27

understanding of terms and concepts in, 7.17–23

Scientific, as label, 7.34–35

Serbia, journal articles from, 5.31t

Singapore

coauthorship from, with U.S., 5.38t

information and communication technology 

exports, 6.32f

imports, 6.33f

international collaboration on articles in, 5.39t

journal articles 

by field, O.10f

from, 5.31t

in engineering, O.11f

research and development by U.S. companies in, 4.49t

research and development expenditures, O.5f

as share of GDP, 4.34t, 4.37f

researcher numbers in, O.8f, O.9f

TIMSS mathematics scores in, 1.19t, 1.20

TIMSS science scores in, 1.21t

Slovak Republic

educational attainment in, tertiary, 2.33f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

high school graduation rate in, 1.36f

research and development expenditures as share of GDP, 4.34t

Slovenia

educational attainment in, tertiary, 2.33f

foreign students in tertiary education in, 2.36f

journal articles from, 5.31t

research and development expenditures as share of GDP, 4.34t

Small business

angel investment in, 6.53–54, 6.55f, 6.56t

employment in, 6.50, 6.52t, 6.53t

financing of, 6.52–55

leading types, 6.53t

venture capital investment in, 6.54–55, 6.55f

Small Business Innovation Research (SBIR), funding per $1 million 

of GDP, 8.106f, 8.107t

Smithsonian Institution, 4.26t, 4.27t

South Africa

coauthorship from, with U.S., 5.38t

journal articles 

by field, O.10f

from, 5.31t

research and development by U.S. companies in, 4.49t

research and development expenditures as share of GDP, 4.34t

South Carolina. See also Chapter 8

science and mathematics courses offered in, 1.34t

South Dakota. See Chapter 8

South Korea

coauthorship from, with U.S., 5.38t

doctoral degrees in, O.8f

doctorate recipients from, 2.27t, 2.28f

educational attainment in, tertiary, 2.33f

energy research and development in, 4.24f

enrollment in U.S. undergraduate programs, 2.14f

exports to China, O.18f

exports to U.S., O.18f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

GDP in, by sector, 4.35f

H-1B holders from, 3.56f

high school graduation rate in, 1.36f

immigrants from, education of, 3.53f

industrial research and development in, 4.40f

information and communication technology exports, 6.33f

international collaboration on articles in, 5.39t

journal articles 

by field, O.10f

from, 5.31t

engineering, O.11f

PISA math and science literacy scores in, 1.23t

research and development by U.S. companies in, 4.49t

research and development expenditures, O.4, O.5f

as share of GDP, 4.34t, 4.37f

researcher numbers in, O.8f, O.9f

stay rates of doctorate recipients from, 2.30f

tertiary education achievement in, O.7f

TIMSS mathematics scores in, 1.19t

Spain

academic research and development expenditures in, 4.44t
Science and Engineering Indicators 2010 I-13

article collaboration in, 5.37t

coauthorship from, with U.S., 5.38t

educational attainment in, tertiary, 2.33f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

high school graduation rate in, 1.36f

industrial research and development in, 4.40f

journal articles 

by field, O.10f

from, 5.31t

PISA math and science literacy scores in, 1.23t

research and development expenditures as share of GDP, 4.34t,

4.37f

State achievement tests, 1.12

State indicators. See Chapter 8

Statistics, understanding of, 7.25–27

Stem cell research, public attitudes about, 7.41–42

Storage, value added for, 6.25f

Students. See also Education

access to qualified teachers, 1.26, 1.27t

homeschooling of, in U.S., 1.8

mathematics performance

by race/ethnicity, 1.15f

eighth grade, 1.13, 1.13f, 1.14t, 1.18–1.19

elementary, 1.8–10, 1.9t

fifteen-year-olds, 1.22

fourth grade, 1.13, 1.13f, 1.14t, 1.18–1.19

gap changes, 1.8–9

long-term trends, 1.14–15

middle grade, 1.8–10

mother’s education and, 1.10f, 1.11f

proficiency in different skill areas, 1.9–1.10, 1.11f, 1.11t

skills areas, 1.10

mobility of, 2.36–37

national assessment performance on, 1.8–15

science performance

achievement gaps in, 1.10

fifteen-year-olds, 1.22

performance trends, 1.21

rising, 1.11–13

TIMSS test scores in, 1.21–1.22

technology literacy of, 1.31

Sweden

academic research and development expenditures in, 4.44t

coauthorship from, with U.S., 5.38t

educational attainment in, tertiary, 2.33f

energy research and development in, 4.24f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

high school graduation rate in, 1.36f

industrial research and development in, 4.40f

international collaboration on articles in, 5.39t

journal articles from, 5.31t

PISA math and science literacy scores in, 1.23t

research and development by U.S. companies in, 4.49t

research and development expenditures as share of GDP, 4.34t,

4.37f

TIMSS mathematics scores in, 1.19t, 1.20

TIMSS science scores in, 1.21t

Switzerland

article collaboration in, 5.37t

coauthorship from, with U.S., 5.38t

educational attainment in, tertiary, 2.33f

foreign students in tertiary education in, 2.36f

high school graduation rate in, 1.36f

journal articles 

by field, O.10f

from, 5.31t

PISA math and science literacy scores in, 1.23t

research and development by U.S. companies in, 4.49t

research and development expenditures as share of GDP, 4.34t,

4.37f

T

Taiwan

academic research and development expenditures in, 4.44t

coauthorship from, with U.S., 5.38t

doctorate recipients from, 2.27t, 2.28f

exports to China, O.18f

exports to EU, O.18f

exports to U.S., O.18f

H-1B holders from, 3.56f

immigrants from, education of, 3.53f

information and communication technology exports, 6.33f

journal articles 

by field, O.10f

from, 5.31t

in engineering, O.11f

research and development expenditures, O.5f

as share of GDP, 4.34t, 4.37f

researcher numbers in, O.8f, O.9f

stay rates of doctorate recipients from, 2.30f

Tax credits, federal research and development, 4.31–33

Teachers

experience of, 1.25–26

formal preparation of, 1.24–25

professional development of, 1.27–28, 1.28t

quality of, 1.24–27

salaries of, 1.28–1.29, 1.29t, 8.26f, 8.27t

student access to qualified, 1.26, 1.27t

subject area preparation of, 1.25, 1.26t

technology literacy of, 1.31

working conditions, 1.29–30, 1.30f

Technology. See also Knowledge- and technology-intensive (KTI) 

industries; Science and technology (S&T)

in education, 1.30–34

trade of, 6.25–34

Technology-intensive firms. See also Knowledge- and technologyintensive (KTI) industries

rising output of, O.14–16

Tennessee. See also Chapter 8

science and mathematics courses offered in, 1.34t

Texas. See also Chapter 8

research and development in, 4.17t

business, 4.18t

Thailand

doctorate recipients from, 2.27t

journal articles from, 5.31t

PISA math and science literacy scores in, 1.23t

research and development expenditures, O.5f

tertiary education achievement in, O.7f

TIMSS mathematics scores in, 1.19t, 1.20

TIMSS science scores in, 1.21t

TIMSS. See Trends in International Mathematics and Science Study 

(TIMSS)

Trade

balance in selected regions/countries, O.19f

exports and patterns in, O.16–18

geographic distribution of bilateral high-technology, 6.28–32

knowledge- and technology-intensive industries and, 6.23–45

of high-technology goods, 6.25–34

product classification in, 6.27
I-14 Index

shifts in positions, O.18

surpluses in U.S., O.19

Transport, value added for, 6.25f

Trends in International Mathematics and Science Study (TIMSS), 

1.16, 1.17

Turkey

coauthorship from, with U.S., 5.38t

doctorate recipients from, 2.27t

educational attainment in, tertiary, 2.33f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

H-1B holders from, 3.56f

high school graduation rate in, 1.36f

journal articles 

by field, O.10f

from, 5.31t

research and development expenditures as share of GDP, 4.34t

stay rates of doctorate recipients from, 2.30f

U

U.S. Patent and Trademark Office (USPTO), 6.46–48

Ukraine, journal articles from, 5.31t

United Kingdom

article collaboration in, 5.37t

coauthorship from, with U.S., 5.38t

doctoral degrees in, O.8f

educational attainment in, tertiary, 2.33f

energy research and development in, 4.24f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

GDP in, by sector, 4.35f

H-1B holders from, 3.56f

high school graduation rate in, 1.36f

immigrants from, education of, 3.53f

industrial research and development in, 4.40f

international collaboration on articles in, 5.39t

Japan exports to, O.18f

journal articles 

by field, O.10f

from, 5.31t

migration to, 3.51

PISA math and science literacy scores in, 1.23t

research and development by U.S. companies in, 4.49t

research and development expenditures as share of GDP, 4.34t,

4.37f

stay rates of doctorate recipients from, 2.30f

tertiary education achievement in, O.7f

TIMSS mathematics scores in, 1.19t, 1.20

TIMSS science scores in, 1.21t

United States

academic research and development expenditures in, 4.44t

article collaboration in, 5.36t, 5.37t

business services value added in, 6.17f

China exports to, O.18f

citation of papers from, 5.43f

commercial knowledge-intensive services trade, 6.39f, 6.40f

commercial knowledge-intensive services value added, 6.17f

communication services value added in, 6.17f

computer and office machinery manufacturing market shares, 

O.16f

doctoral degrees in, O.8f

education services value added, 6.15f

educational attainment in, tertiary, 2.33f

energy research and development in, 4.24f

export share, high-technology, O.17f

exports of high-technology products, 6.28f, 6.29f

financial services value added in, 6.17f

first-time entry rates into postsecondary education, 1.39t

foreign students in tertiary education in, 2.36f

graduate education in, 2.17–30

gross domestic product

as share of global, 6.12f

by sector, 4.35f

health services value added, 6.15f

high school graduation rate in, 1.36f

high-technology manufacturing 

consumption in, 6.19f

value added in, 6.18f, 6.20f

value in, O.16f

high-value patents from, O.14f

higher education in, overview of, 2.7–10

highly cited works from, 5.44f

homeschooling in, 1.8

industrial processes trade in, 6.45–46, 6.46f

industrial research and development in, 4.40f

information and communication technology 

adoption and intensity, 6.14f

export share, O.17f

output of, 6.11f

spending, 6.14f

value added, 6.23f

intangible assets trade, 6.45, 6.45f

international collaboration on articles in, 5.39t

investment in knowledge- and technology-intensive industries, 

6.42–43

journal articles 

by field, O.10f

from, 5.31t

produced by, O.10f

in engineering, O.11f

knowledge- and technology-intensive industry output in, 6.10f,

6.11f

manufacturing value added, 6.26t

migration to, 3.50–58

PISA math and science literacy scores in, 1.23t

public understanding of scientific terms and concepts in, 

7.17–23

research and development expenditures, O.4f, O.5f

as share of GDP, 4.34t, 4.37f

researcher numbers in, O.8f, O.9f

science and technology patterns and trends in, 7.7–11

South Korea exports to, O.18f

Taiwan exports to, O.18f

tertiary education achievement in, O.7f

TIMSS mathematics scores in, 1.19, 1.20

TIMSS science scores in, 1.21t

trade balance in, O.19f

trade in advanced technology products, 6.34–38

trade surpluses in, O.19

undergraduate education in, 2.11–17

value of knowledge-intensive services in, O.15f

Universities, patenting trends, 5.43–44

USDA. See Department of Agriculture (USDA)

USPTO. See U.S. Patent and Trademark Office (USPTO)

Utah. See Chapter 8

Utilities, value added for, 6.24f

V

VA. See Veterans Administration

Value added

definition of, 6.9

of commercial knowledge-intensive services, 6.17f
Science and Engineering Indicators 2010 I-15

of education and health services, 6.15f

of information and communication technology industries, 

6.21–22, 6.23f

of knowledge- and technology-intensive industries

global, 6.15f

Venture capital

by industry, 6.55, 6.56f

by share of investment stage, 6.54f

deals as share of high-technology business establishments, 

8.110f, 8.111t

disbursed per venture capital deal, 8.112f, 8.113t

in small businesses, 6.54–55

per $1,000 of GDP, 8.108f, 8.109t

Vermont. See Chapter 8

Veterans Administration (VA), 4.26t, 4.27t

Vietnam

enrollment in U.S. undergraduate programs, 2.14f

immigrants from, education of, 3.53f

Virginia. See also Chapter 8

science and mathematics courses offered in, 1.34t

Virtual schools, 1.33–34

Visas, work, 3.53–56

W

Washington. See also Chapter 8

research and development in, 4.17t

business, 4.18t

West Virginia. See also Chapter 8

science and mathematics courses offered in, 1.34t

Wisconsin. See Chapter 8

Women

as faculty at research universities, 5.24

first university degrees by, 2.34

in academic research and development, 5.22

in S&E workforce, 3.32–37, 3.33f

age distribution of, 3.34

salary differentials of, 3.35–36

unemployment among, 3.34

share of S&E bachelor’s degrees, 2.16f

Workforce

bachelor’s degree holders potentially in, 8.62, 8.63t

computer specialists as share of, 8.72f, 8.73t

employed science and engineering holders as share of, 8.66f,

8.67t

engineers as share of, 8.68f, 8.69t

life scientists as share of, 8.70f, 8.71t

physical scientists as share of, 8.70f, 8.71t

science and engineering

age, 3.27–30, 3.29f, 3.30f, 3.31f, 3.32f

as share of total workforce, 8.64f, 8.65t

demographics, 3.27–37

earnings, 3.37–41

at different degree levels, 3.38, 3.39f

growth, 3.38f

education classification, 3.9–10, 3.10t

educational distribution of, 3.16–17

employer sizes, 3.27, 3.29f

employment growth, 3.14, 3.15f

employment patterns, 3.13–27

employment sectors, 3.23–26

federal employment of, 3.25–26

global, 3.47–58

counts of, 3.48

migration of, to U.S., 3.50–58

growth of, 3.11–13, 3.12t, 3.13f

higher education and trends in, O.6–8

history of, 3.12

in academic research and development, 5.19–29

in metropolitan areas, 3.26–27, 3.27t, 3.28t

in research and development, 3.21–23

labor market conditions, 3.37–47

minorities in, 3.34–37

age distribution of, 3.35, 3.35f

salary differentials of, 3.35–36

non-S&E occupation employment of, 3.17–18

occupation classification, 3.9

occupation density by industry, 3.26

patenting activity of, 3.21–22

postdoc positions, 3.44–47, 3.45t, 3.47f, 3.47t

recent graduates in, 3.40–41

doctorate recipients, 3.42–44

labor market indicators for, 3.41–42

relationship of education and employment of, 3.18–20

retirement patterns, 3.30–31

self-employment in, 3.24–25

size of, 3.10–11, 3.11t

technical expertise classification, 3.10

tenure-track positions, 3.43

training, 3.20–21

unemployment, 3.38–39, 3.39f

of doctorate recipients, 3.42–43

women in, 3.32–37, 3.33f

Working conditions, teacher, 1.29–30, 1.30f

Wyoming. See Chapter 8

STEVEN C. BEERING

Chair, President Emeritus, Purdue UniversityPATRICIA D. GALLOWAYVice Chair, Chief Executive Officer, Pegasus Global Holdings, Inc., Cle Elum, WashingtonMARK R. ABBOTTDean and Professor, College of Oceanic and Atmospheric Sciences, Oregon State UniversityDAN E. ARVIZUDirector and Chief Executive, National Renewable Energy Laboratory, Golden, ColoradoBARRY C. BARISHBoard Consultant, Director, Global Design Effort for International Linear Collider, Linde Professor of Physics, Emeritus, California Institute of TechnologyCAMILLA P. BENBOWPatricia and Rodes Hart Dean of Education and Human Development, Peabody College of Education and Human Development, Vanderbilt UniversityRAY M. BOWENPresident Emeritus, Texas A&M UniversityJOHN T. BRUERPresident, The James S. McDonnell Foundation, St. LouisG. WAYNE CLOUGHSecretary, Smithsonian Institution, Washington, DCFRANCE A. CÓRDOVAPresident, Purdue UniversityKELVIN K. DROEGEMEIERAssociate Vice President for Research, Regents’ Professor of Meteorology and Weathernews Chair Emeritus, University of OklahomaJOSÉ-MARIE GRIFFITHSDeputy Director (Biomedical Informatics), TraCS Institute, and Professor, School of Information and Library Science, University of North Carolina at Chapel HillESIN GULARIDean of Engineering and Science

Clemson University

National Science Board

LOUIS J. LANZEROTTIDistinguished Research Professor of Physics, Center for SolarTerrestrial Research, Department of Physics, New Jersey Institute of TechnologyALAN I. LESHNERChief Executive Officer, Executive Publisher, Science, American Association for the Advancement of Science, Washington, DCG. P. “BUD” PETERSONPresident, Georgia Institute of TechnologyDOUGLAS D. RANDALLProfessor and Thomas Jefferson Fellow, University of MissouriARTHUR K. REILLYSenior Director, Strategic Technology Policy, Cisco Systems, Inc., Ocean, New JerseyDIANE L. SOUVAINEProfessor and Chair, Department of Computer Science, Tufts UniversityJON C. STRAUSSInterim Dean, Edward E. Whitacre Jr. College of Engineering, Texas Tech UniversityKATHRYN D. SULLIVANDirector, Battelle Center for Mathematics and Science Education Policy, John Glenn School of Public Affairs, Ohio State UniversityTHOMAS N. TAYLORRoy 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 KansasRICHARD F. THOMPSONKeck Professor of Psychology and Biological Sciences, University of Southern CaliforniaARDEN L. BEMENT, JR.Member ex officio, Director, National Science FoundationCRAIG R. ROBINSONActing Executive Officer, National Science Board, and Director, National Science Board Office
EN

Nation

SCIENCE AND ENGINEERING INDICATORS 2010