applied research, and development—when much of the engine of innovation stems from the intersection of these components, or in the details of each. Public policy attention to early-stage technology development, the Advanced Technology Program, and process innovation requires data beyond these basic components of R&D. Similarly, the data are sometimes used to measure the output of R&D, when, in reality, in measuring expenditures, they reflect only one of the inputs to innovation and economic growth. It would be desirable to devise, test and, if possible, implement survey tools that more directly measure the economic output of R&D in terms of short-term and long-term innovation. Finally, the structure of the data collection is tied to models of R&D performance that are increasingly unrepresentative of the whole of the R&D enterprise. The growth of the service sector, the growing recognition of the role of small firms in R&D, the shift in funding from manufacturing R&D to health-related R&D, changes in geographic location, and the globalization of R&D have all served to challenge the current system for depicting the amount and character of R&D in today’s economy. New forms of conducting R&D in collaborative environments, using joint ventures or outsourcing arrangements, working through alliances, and outsourcing R&D to foreign affiliates are just a few of the emerging ways of conducting research and development that are not well measured by the traditional R&D surveys.

At the same time that the foundation of R&D statistics is coming under increasing pressure, the league of uses and users continues to expand. The National Science Board continues to make sophisticated use of these data in producing the comprehensive volume, Science and Engineering Indicators, every 2 years, which places additional stress on the data in terms of quality and timeliness. The data are used by the administration, particularly the U.S. Office of Management and Budget (OMB) and the Office of Science and Technology Policy, to paint a complete picture of federal and nonfederal investment in R&D. Congress not only relies on the NSF data but also has directed collection of data necessary for evaluating the need for public investment in R&D. New uses of the data for purposes for which they were not originally intended are springing up. The inclusion of R&D investment in national income and product accounts, as well as in estimates of multifactor productivity, are two examples of the emerging uses that refocus attention on these data sources.

Finally, as the data have come under increasing use, they have come under increasing scrutiny. Some users are deeply troubled by the apparent discrepancy between reports of federal spending on R&D and the amounts that academia and industry report that they have received from the federal government. This large discrepancy casts doubt on the reliability of some of the data sources.

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