Workshop participants offered a variety of suggestions to the National Science Foundation (NSF) and other federal agencies to improve the usefulness and relevance of data on industrial research and innovation, as well as to increase efficiency in collecting and processing the data. The suggestions dealt with the need to (1) clarify policy information needs; (2) improve the quality, coverage, and collection of existing data items; and (3) identify and collect new types of data. The following is a brief summary of ideas presented by individual participants. It is not a list of consensus recommendations of the meeting and does not represent judgments of the Board on Science, Technology and Economic Policy (STEP) or the National Research Council arrived at after careful consideration of the costs, benefits, and trade-offs. Subsequent sections of this report elaborate on these points.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 4
--> 2 Summary of Suggestions Workshop participants offered a variety of suggestions to the National Science Foundation (NSF) and other federal agencies to improve the usefulness and relevance of data on industrial research and innovation, as well as to increase efficiency in collecting and processing the data. The suggestions dealt with the need to (1) clarify policy information needs; (2) improve the quality, coverage, and collection of existing data items; and (3) identify and collect new types of data. The following is a brief summary of ideas presented by individual participants. It is not a list of consensus recommendations of the meeting and does not represent judgments of the Board on Science, Technology and Economic Policy (STEP) or the National Research Council arrived at after careful consideration of the costs, benefits, and trade-offs. Subsequent sections of this report elaborate on these points. Clarify Policy Information Needs and Federal Role in Data Collection Identify innovation data needs for policy. Public programs generate many specific needs for information on industrial innovation and technology use. Much of this information is collected and used only on an ad hoc basis by the agencies. This is in part an inevitable consequence of changing public policies. Nevertheless, greater coordination among agencies involving program managers in identifying innovation-related data needs could improve the quality and coverage of data available to the agencies. Clarify federal role. A core set of innovation indicator data items is best collected and reported by the federal government, including, for example,
OCR for page 4
--> periodic census-type activities. The federal portfolio is not static, however, and should be reconsidered from time to time. To derive maximum value from this national aggregate statistical program, a premium should be placed on the ability to link these data with other innovation and economic data and on consistency over time to produce useable time series and longitudinal data. Draw on Outside Expertise and Capacities to Improve Data Programs Explore public-private partnerships. The use of public-private partnerships and cooperative agreements for data collection and reporting should be explored, particularly in areas where program-specific data or exploratory, developmental efforts are needed. The advantages and disadvantages of such agreements and obstacles to them need to be studied. Form a consultative body. There is no standing mechanism for the NSF, Department of Commerce, and other agencies to acquire expert advice on issues related to innovation data collection, integration, and reporting. A specific suggestion was made that a technical advisory committee to the Science Resources Studies Division of the NSF should be reinstituted. Improve Existing Data Broaden industry coverage. Research and innovation databases, such as the NSF's R&D survey, should incorporate emerging, high-growth, technology-intensive industries such as telecommunications and biotechnology and industries across the service sector—financial services, transportation, and retailing, among others. Although the NSF has taken constructive steps to adjust its data collection to changes in the structure of the economy, additional analysis of innovation processes in these industries is needed to inform the development of appropriate survey instruments. Collect business segment detail. Existing survey programs, such as the NSF R&D survey, should collect information at the business unit level of corporate activity rather than for the firm as a whole. In an economy in which large, complex, multiproduct firms perform most R&D activities, the business unit represents a more homogeneous set of activities, whereas combining responses on a range of variables for a variety of products and processes will obscure significant industry-specific conditions that affect technological innovation. Moreover, managers at the business unit level are likely to be better informed about innovation-related investments and performance measures than are corporate headquarters officials.
OCR for page 4
--> Collect geographic detail. Geographic location detail should be collected in surveys of R&D and other innovation-related data. These data are increasingly important in light of the role of local regions in innovation performance and domestic economic development. It should be possible to link information at the county or Statistical Metropolitan Area (SMA) level, not merely the state level. Link databases. Greater integration of government data on R&D and other science and technology and innovation-related information and the improved linkage of these data to other economic data such as census establishment data would enhance the usefulness of innovation-related information. Consider industry use of data. Additional effort should be devoted to ascertaining the usefulness of industrial innovation survey data in the private-sector where benchmarking and other assessment activities are increasingly common. For example, information relating activities such as R&D and patenting to performance is useful to decision makers in both the public and private-sectors, although more for large than for small firms and at different levels of data aggregation ranging from the plant and the firm to the industry and the economy as a whole. The responsiveness of firms to inquiries, and especially their cooperation in providing new information, depends on managers' perceptions of the direct utility to the firm of the collected data as well as the firm's stake in better-informed public policy. Moreover, to the degree that public and private interests in better indicators and innovation analysis overlap, there may be opportunities for public-private partnerships in data collection and dissemination. Care would have to be taken not to exclude small firms from such arrangements. Develop New Data Items Develop new output measures. A high priority is the development of new and more direct measures of innovation output, both at the micro and the macro levels in the national income and product accounts. An example of a promising approach is the development of financial market valuations of firms' R&D activities. This is especially important for understanding how innovation occurs in new emerging industries. Conduct innovation surveys. In other industrialized countries, government-sponsored surveys are collecting much policy-relevant information on the characteristics of firms' innovation activities including their means of appropriating returns to R&D. Such surveys may track a number of variables over time, but they also enable analysts to probe new issues
OCR for page 4
--> arising from changes occurring in the processes of industrial innovation. In Europe this activity takes the form of the European Union-wide Community Innovation Survey; in Canada it takes the form of a series of more narrowly focused surveys. The NSF should consider collecting this type of information regularly, building upon its 1994 pilot effort and drawing on the experience of other governments and international scholars in designing such a program. Conduct technology use surveys. U.S. and other national surveys of manufacturing technology adoption and application have produced enterprise level data useful in understanding the relationships among new technology applications, firm performance and productivity, and workforce characteristics such as skill levels, wages, and job turnover. The federal government should consider undertaking periodic surveys of technology adoption and use in both the manufacturing and service sectors.