Contribution of Scientific Knowledge to Productivity Growth

As already mentioned, productivity effects are changes in output that are not explained by changes in inputs. In this context, productivity is measured as a "residual effect" - something that is left-over, or unexplained by standard factors. Some economists have explored more direct approaches for understanding technological change. However, because technological change is so broad and diverse, studies are generally unable to characterize aggregate productivity enhancements in terms of a small number of specific factors. Consequently, many economists who are interested in the overall effects of R&D on productivity have focused on relatively broad factors associated with the behavior of firms and the mechanisms through which they evolve via the acquisition of knowledge, e.g., Nelson and Winter (1982) and Rosenberg (1994).

Adams (1990) performed a study combining the two approaches of aggregate productivity measurement and knowledge acquisition by firms. His study used a production function, as in other productivity studies, but with two added inputs: knowledge acquired by firms, and spillovers of knowledge between firms of different industries. As a proxy for knowledge, Adams used the number of articles appearing in technical journals in nine fields: agriculture, biology, chemistry, computer science, engineering, geology, mathematics, medicine, and physics. He attributed these quantities of knowledge to scientific and engineering personnel in those fields, and used data on personnel counts by industry to approximate levels of knowledge by industry. He measured spillover effects among industries based on the commonality of their scientific personnel. He observed that these knowledge inputs explained part of the "residual" defining productivity, and found them to be "sizable determinants of productivity growth" (p. 698).

The principal findings to emerge from Adam's study are that lags in time exist between the appearance of research in the academic community and their effect on productivity. More precisely, Adams (p. 676) found that knowledge strongly contributes to growth, but lags 20 years behind the first appearance of research in the science community. The lag for interindustry spillovers is approximately 30 years. However, computer science and engineering have shorter lags of approximately 10 years.

In analyzing broad categories of goods or services, useful results are often difficult to acquire because of the variation that exists within a category. If a specific product, rather than a category, is considered, then the interrelationship between scientific discoveries and economic effects can be identified more easily. In 1990, Trajtenberg performed such an analysis in one of the most rigorous and sophisticated studies of its kind. He investigated computed tomography (CT) scanners, which are often used in medical diagnosis and had been proven to be superior to X-ray machines. Through detailed collection of expenditure data on R&D by U.S. firms, and complicated analysis of the economic and social benefits that result from different CT scanners, Trajtenberg approximates the annual social rate of return to R&D to be 270 percent.

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