Using Human Resource Data to Track Innovation: Summary of a Workshop (2002)

In discussing directions for human resource data development, workshop participants pointed out that there are undoubtedly numerous productive uses of the current SESTAT, BLS, and Census data. For some purposes, however, existing indicators are inadequate and new data are needed. For example, despite the extensive data on the production of degrees earned in science and engineering fields at the bachelor’s, master’s, and doctoral levels, surprisingly little is known about what they do in their jobs every day and over their careers, especially if those careers are in industry. In innovative sectors such as information technology technical work is often performed by people without formal science or engineering educations or without bachelor’s degrees. For other purposes, the data exist but are not being fully exploited. There are also opportunities to link data collected by public and private institutions in more productive ways. But there are limits including budget constraints and privacy protections that will shape choices among potential improvements in human resource data collection and analysis.

Charlotte Kuh of the National Research Council staff commented that these choices deserved more systematic thought in light of the considerable cost of assembling large data sets and the constraints entailed in protecting the confidentiality of individually identifiable information. What do we really want to know about the relationship of human resources to innovation? What are the important questions? What reasonably strong conceptual models need testing? How can data systems be made sufficiently flexible to address policy issues that may arise in the future? The suggestions offered by workshop participants are simply candidates for

further evaluation. They do not reflect any coherent intellectual framework for priority setting.

Several workshop participants perceived a need to derive more information on subjects of current surveys, particularly scientists and engineers working in industry:

• More precise information on employers and their locations (as is available on academic scientists and engineers) would enable analysts to relate individual and firm characteristics. In particular, if SDR respondents employed in companies were asked to indicate the industrial classification of their establishment (plant, laboratory, etc.) this would help overcome the lack of business unit level R&D expenditure data.¹

• More information on what scientists and engineers do in firms would help illuminate the relationship of research to other functions—strategy, finance, production, and marketing—highly relevant to successful innovation. (See below for a suggestion for obtaining even more detailed information on activities.)

• Information on scientists’ and engineers’ outputs and public activity (publications, conference presentations, involvement in consortia or other collaborations, etc.) would help illuminate R&D spillovers among firms, between industries, and across sectors. The 1995 NSCG and SDR contained a module on patenting and publishing (Morgan et al., 2001). Such a module, modified to reflect changing patterns of publishing, patenting, and collaboration, might be included periodically in the SESTAT surveys.

• Data on stock options, which are prevalent in high technology industries, would fill a growing gap in the information on professionals’ nonwage compensation.

Some workshop participants maintained that it is desirable to expand the NSF definition of the S&E workforce and the information obtained about certain categories of scientists and engineers, although the costs of such steps would have to be considered. In particular,

• Very little information is available about the work of scientists and engineers who occupy managerial positions that nonetheless use knowledge and skills from post-secondary scientific and technical training to direct or influence innovation. Examples are plant manager, division executive, strategic planner, patent attorney, and business developer.

• People in S&E positions who nevertheless lack an S&E baccalaureate degree are not included in the SESTAT surveys. This is presumed to be prevalent in information technology fields (e.g., computer programming and network administration); but a workshop participant familiar with many biotechnology start-up companies said that it is also the case in that industry that a significant share of the technical workforce lacks a BA or BS degree.

• Finally, it was observed that the NSF SESTAT data on the web and on compact disc should be constructed to permit longitudinal analysis, which is not currently the case.

Workshop participants generally agreed that more important advances in analysis of innovation would come from linking human resource data with other data sets. Paula Stephan cited the example of matching the name and location of respondents to the SDR and SED with Census establishment data providing detailed information on characteristics of the firms. Such a link could illuminate the relationship between firm size and innovation as well as the internal versus external sources of innovation by small firms. The lack of information from HR data alone about the resources available to individual scientists and engineers could be overcome by linking the SDR data with firm R&D expenditures or with databases such as the CRISP file of National Institutes of Health research grants. Other promising linkages are between SDR data and publication data in the International Scientific Index and between SDR and patent data.

In the judgment of a number of researchers at the workshop, the creation of the regional Census Bureau centers at which qualified investigators can access confidential microdata has been crucial to efforts to link economic datasets and their expansion should be considered. Their utility would be greater, however, if they provided access to OES and other Bureau of Labor Statistics data sets.

Jim Adams observed that one of the most important but least well understood questions about innovation processes is how people actually

spend their time. What percentage of industrial scientists’ time is spent conducting research or development, managing others’ R&D research, assessing the R&D and technological capabilities of other firms (e.g. acquisition on collaboration candidates interacting with customers), collaborating and communicating with professionals outside the firm, or in production engineering? How much time is devoted to continuing education or simply keeping up with the research field? Time-use surveys, in which individuals are interviewed over a period of time or asked to maintain diaries, are an accepted way of addressing such questions and have been used in a variety of economic contexts such as to measure unpaid work as input for satellite accounts to national economic accounts and to help evaluate income and welfare policies (National Research Council, 2000a). This research method has not been used to better understand the innovation process, however. Some form of time-use survey is a candidate for a SESTAT special module.

A final suggestion was that federal agencies consider sponsoring additional targeted surveys of key professional groups of interest—for example, biotechnologists—collecting information on activities, output, relationships, and compensation well beyond that solicited in the NSF surveys of scientists and engineers.