Executive Summary

Data on scientists, engineers, and other professionals—their training, employment and mobility, structure of work and affiliations, and productivity—are an important but underutilized source of information on industrial innovation, a critical determinant of economic growth and productivity. Human resource (HR) data can supplement or compensate for the limitations of data on research and development expenditures, patents, and other innovation input and output indicators. They can also be used to evaluate government research and education programs. Ultimately, a better understanding of innovation characteristics and how they are changing can inform public policies to stimulate innovation and influence its direction, as well as to enable more people to benefit from it or to moderate its adverse effects on some groups in the population.

The principal official sources of data on professionals involved in the innovation process are surveys of the National Science Foundation (NSF), U.S. Bureau of the Census, and Bureau of Labor Statistics (BLS). Since 1993, several NSF survey data sets have been integrated into Scientists and Engineers Statistical Data (SESTAT), the most comprehensive and easily accessed source of information about the employment, education, and demographic characteristics of U.S.-resident scientists and engineers with at least a bachelor’s degree. BLS and Census survey data are combined to produce the National Industry-Occupation Employment Matrix (NIOEM), which includes establishments in all sectors of the economy and all members of the scientific and technical labor force even below the baccalaureate level, although it does not provide demographic or educational attainment information. Under some circumstances data sets can



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Using Human Resource Data to Track Innovation: Summary of a Workshop Executive Summary Data on scientists, engineers, and other professionals—their training, employment and mobility, structure of work and affiliations, and productivity—are an important but underutilized source of information on industrial innovation, a critical determinant of economic growth and productivity. Human resource (HR) data can supplement or compensate for the limitations of data on research and development expenditures, patents, and other innovation input and output indicators. They can also be used to evaluate government research and education programs. Ultimately, a better understanding of innovation characteristics and how they are changing can inform public policies to stimulate innovation and influence its direction, as well as to enable more people to benefit from it or to moderate its adverse effects on some groups in the population. The principal official sources of data on professionals involved in the innovation process are surveys of the National Science Foundation (NSF), U.S. Bureau of the Census, and Bureau of Labor Statistics (BLS). Since 1993, several NSF survey data sets have been integrated into Scientists and Engineers Statistical Data (SESTAT), the most comprehensive and easily accessed source of information about the employment, education, and demographic characteristics of U.S.-resident scientists and engineers with at least a bachelor’s degree. BLS and Census survey data are combined to produce the National Industry-Occupation Employment Matrix (NIOEM), which includes establishments in all sectors of the economy and all members of the scientific and technical labor force even below the baccalaureate level, although it does not provide demographic or educational attainment information. Under some circumstances data sets can

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Using Human Resource Data to Track Innovation: Summary of a Workshop be linked to enable a simultaneous examination of worker characteristics and firm characteristics. In addition, there are databases such as patent files and indexes to scientific publications that can be searched by individual. Some government research agencies, universities, and professional associations track the employment and work product of individuals affiliated with them; and, of course, scholars construct their own data sets for particular purposes. SESTAT data show several trends in the deployment of PhD scientists and engineers in the United States—the increased role of industry as an employer relative to universities and government, the increased importance of the service sector relative to manufacturing industries, and the movement from the laboratory into non-R&D positions. These trends illustrate broader changes in the innovation process—the reduced role of government-financed R&D, the emergence of the service sector as a locus of much innovation, and the integration of R&D into other functions of the firm—strategy, management, and marketing. Perhaps the best examples of productive uses of HR data are research on the growth of biotechnology, which has no standard industry classification category, and research on alliances among firms and collaborations between firms and university researchers. Much can be done to enhance the utility of human resource data. More precise information on employers and their locations would enable analysts to relate individual and firm characteristics and help compensate for the lack of business unit detail in the national R&D expenditure data. More information on what scientists and engineers do in firms would help illuminate the relationship of research to other functions. Information on scientists’ and engineers’ outputs (e.g., publications, conference proceedings, and patents) would help illuminate R&D spillovers between firms and across industries. And information on people in science and engineering (S&E) occupations who nevertheless lack a baccalaureate degree would help us understand a prevalent pattern in information technology. Important advances in the analysis of innovation would come from linking human resource data with other data sets. For example, matching the name and location of respondents to the NSF surveys with Census establishment data could illuminate the relationship between firm size and innovation and between internal and external sources of innovation. Finally, there is a need for new data on how people actually spend their time. Knowing what share of industrial scientists’ time is spent in the laboratory, managing other employees’ research, assessing the capabilities of other firms, collaborating with professionals outside the firm, or in production engineering would tell us a good deal about how innovation is taking place.