National Patterns of R&D Resources: 1998

Why Statistics on R&D Expenditures are Collected and Analyzed

Economic growth is widely viewed as a key factor that influences the well-being of individuals and societies. In broad terms, it is attributable to two processes: growth in economic resources—natural resources, labor, and physical capital—and improvements in quality and productivity—producing more and/or better products from the same resources. The first of these, while an important source of growth, is often limited by basic physical constraints. For example, a nation may experience economic growth through an expanded labor force, but economic output per person may remain unchanged. More natural resources may be exploited, but often at the expense of limiting their availability for future use. In contrast, the accumulation of physical capital, i.e., structures and equipment, is more commonly welcomed as a reflection of economic progress. Such accumulation, though, also drains additional resources, either directly through additional consumption of fuel and materials, or indirectly through depreciation and its associated replacement costs.[1]

The second causal factor of economic growth—improvements in quality and productivity—need not involve the kinds of trade-offs associated with the growth of economic resources. Through improvements in human capital, physical capital, and organizational operations, advances in science and engineering can offer more and/or better products without consumption of additional resources. Such advances, or technological changes, may not always be beneficial, as adverse consequences sometimes lead to the realization that not all new technologies are worthwhile. Nevertheless, knowledge is usually cumulative, and as societies learn from their mistakes, people and nations might continue to benefit from scientific and engineering accomplishments.

It follows that economic growth, especially in the long run, is highly dependent on the R&D activities of scientists and engineers. However, the precise relationship between R&D and improvements in quality and productivity (such as the time lag between R&D and its economic effects) has been extremely difficult for economists to identify and measure, and that relationship varies greatly by the types of products and services developed.[2]

Moreover, like expenditures on anything, expenditures on R&D may tell little about the ultimate quality or value of what is received from the money being spent. This is especially the case when one is examining individual projects, where any assessment of the true value of an endeavor is confounded by its interaction with other R&D projects. In the aggregate, this interaction among industrial sectors, "or interindustry technology flows" has, itself, become a topic of research and analysis.[3]

As an example of the complexity of R&D analysis, even if a project is deemed a complete failure, its failure might provide researchers with the knowledge that the particular path undertaken had been wrong, thereby helping to steer future R&D endeavors in the right direction. In addition, philosophical and cultural issues could arise in any assessment of a project. For instance, basic research enhances fundamental knowledge, which in turn enhances applied knowledge. Nevertheless, whether, or to what extent, fundamental knowledge is a desired end in itself would be determined, in part, by societal values, rather than economic analysis alone.

Yet, despite the uncertainties about the meaning and value of information on R&D expenditures, such information is collected extensively by the United States and many other nations, and it is disseminated and studied worldwide by analysts in a wide variety of fields. One reason for this broad interest is that aggregate R&D expenditure data is a measure of the level of economic purchasing power that has been devoted to R&D projects as opposed to alternative economic activities. More precisely, industrial (private-sector) funding of R&D, which represents most of R&D expenditure in the United States, may be interpreted as an economic measure of how important R&D is to U.S. companies, which could have easily devoted those funds to any number of other purposes. Likewise, government support for R&D reflects government and society's commitment to scientific and engineering advancement, which is an objective that, of course, competes for dollars against other functions served by discretionary government funding. The same basic notion holds as well for the other sectors that fund R&D—universities and colleges, and other nonprofit organizations.

In effect, in broader terms R&D expenditures measure the perceived economic importance of R&D relative to all other economic activities. Because institutions invest in R&D without knowing the outcome (if they did know the outcome, then it would not be R&D), the amount they devote will be based on their perception, rather than their knowledge, of R&D's value. As already argued, that value is relative because it competes with other forms of investment.

Such information about R&D's perceived relative value is extremely useful for economic decisionmaking. For example, if R&D in a particular field of study increases, this may reflect an increase in demand for scientists and engineers to study and work in that field. An increase in R&D in a particular industrial sector could be among the first signs that the sector is about to expand with new lines of products or services. Of course, R&D data alone would not be enough to accurately analyze the future growth of a field of study or an industrial sector, but it may well be an important input into any such analysis.

In conclusion, the R&D data presented in this report provide important information for economic and social decision making, and may even provide clues into our future as a society. We provide these data for this very reason—to encourage and facilitate useful analyses of the nation's economic and social conditions. As mentioned above, we are now soliciting your feedback on the details of how our data have already been used successfully in published studies. As we acquire this kind of information, we will tabulate and summarize it in future reports, in addition to using it in our continual efforts to improve our data.


[1] Economists familiar with this topic might criticize this perspective as simplistic, because productivity increases may be embodied in the quantity of measured capital. (See, for example, Griliches, Z., "Hedonic Price Indexes and the Measurement of Capital and Productivity: Some Historical Reflections," in Fifty Years of Economic Measurement: The Jubilee of the Conference on Research in Income and Wealth. E. Berndt and J. Triplett, eds., University of Chicago Press, Chicago, 1990; and Payson, S. "The Difficulty of Measuring Capital, Revisited," Technological Forecasting and Social Change, Vol. 56, No. 2, October 1997.)

[2] For recent analyses of the relationship between R&D and economic growth, see, for example: Griliches, Zvi, "Productivity, R&D, and the Data Constraint." American Economic Review Vol. 84: 1--23, 1994; Nordhaus, William D. (1994) "Do Real Output and Real Wage Measures Capture Reality? The History of Lighting Suggests Not." Cowles Foundation Discussion Paper No. 1078, September, 1994; Payson, S., "Quality Improvement Versus Cost Reduction: A Broader Perspective on Evolutionary Economic Change," Technology Analysis & Strategic Management, Vol. 10, No. 1, 69--88, 1998; and Rosenberg, N., and R. Nelson. "American Universities and Technical Advance in Industry." Research Policy Vol. 23: 323--348, 1994.

[3] See, for example, Schnabl, H., "The Subsystem-MFA: A Qualitative Method for Analyzing National Innovation Systems-The Case of Germany," Economic Systems Research, Vol. 7, No. 4, 1995.

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