collaboration between firm scientists and leading (“star”) academic scientists is a powerful predictor of firms’ success in terms of how many products they have on the market or in development, their sales, and their size and the value of their stock (Darby, 1999).
Donald Siegel of Nottingham University discussed a second important set of effects of innovation, on the workforce itself and how the workforce is managed. There is a strong theoretical case for believing that technological innovation is skill-biased; it increases the demand for highly educated and highly skilled workers because they have a comparative advantage in helping companies implement new technologies effectively (Siegel, 1999). Testing the hypothesis depends critically on data not only on specific technology adoptions but also on labor composition and relative compensation before and after the implementation of new technology. Siegel pointed out that a good deal of research has been on the manufacturing sector and the effects of process innovations, such as computer-aided design and manufacturing, robotics, flexible manufacturing systems, and just-in-time inventory systems. But much work remains to be done on the effects of service sector innovation and in distinguishing types of technologies with different workforce efforts, developing better measures of skill than educational level, and addressing intervening variables such as organizational and human resource management changes.
Workshop participants also observed that data on professionals’ career paths and productivity can supplement other means of evaluating government research and education programs. The link to government education grant, traineeship, fellowship, and research associateship programs is obvious. But federal research grants and contracts also indirectly support a significant share of graduate students in most fields of science and engineering. Government laboratories provide postdoctoral and training to scientists and engineers who leave to work elsewhere, often in industry. As a general matter, linking output and human resource data to government funding data provides an important avenue for measuring government performance. The Academies’ Committee on Science, Engineering, and Public Policy (COSEPUP), in a series of workshops and reports, has discussed the importance of using human resources output as a performance measure for research under the Government Performance and Results Act of 1993 (National Academy of Sciences et al., 1999, 2001).