The catalogue of uses and directory of users of the research and development expenditure data of the NSF Science Resources Statistics (SRS) Division has grown by leaps and bounds over its history. Today, SRS data are widely relied on to perform their historical role in measuring federal R&D funding and R&D performance, but increasingly they are asked to serve purposes never envisioned when the data series were initiated. This growth in the community of users and in the variety of uses has outstripped the capacity of the SRS program to provide all the data needed by all users in a fast-changing world. The limitations of the data have prevailed despite the fact that, over the years, the measures have expanded to sharpen the focus on R&D funding in colleges and universities and in the service sectors of the economy.

In order to be more responsive, NSF has attempted to expand the data in depth and detail, but the agency has been only partially successful. For example, the industrial R&D sample was increased to better capture R&D performance in small and nonmanufacturing firms. The addition of state breakdowns is another example. These refinement and catch-up efforts are generally considered to have fallen short of keeping pace with the expansion of requirements for R&D data in the recent past.

At their most basic level, and to a large extent, R&D expenditure data are important in and of themselves. The time series and periodic special studies are critical to the understanding of trends by source of funding, performer of research and development activity, type of R&D (usually following a taxonomy of basic research, applied research, and development), field of science or engineering, and geographic area (Jaffe, 1996). However, they are incomplete. They measure input, rather than output, of R&D.

Of growing importance, however, are ancillary purposes to which the SRS data have been enlisted. For example, R&D expenditure data today are a key factor in understanding significant processes for which they were not initially designed, such as innovation. Innovation has been defined as the invention, commercialization, and diffusion of new products, processes, and services; these, in turn, are taken to be an important determinant of economic growth, productivity, and welfare (National Research Council, 2001a). The recent NRC report Using Human Resource Data to Track Innovation (2001a) points out that R&D expenditure data are often taken as the best surrogate indicator of innovation, in part because of the high degree of industry and firm detail and wide industry coverage and in part because they are the most consistently collected data with annual time series that extend back for decades. Thus, they are taken to represent the best time series related to innovation. Other series, such as counts of pat-

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