This paper estimates a simple model of innovation in the U.S. manufacturing sector and derives summary indicators of product and process innovation. Our empirical work reveals that the drivers of innovation extend well beyond business research and development (R & D) spending. Capital investment, cutting-edge scientific output from academic institutions, and the growth of the science and engineering workforce all matter. The dynamic structure of our model suggests that policymakers must look beyond the short-term when developing and evaluating innovation initiatives because there can be lags of several years before inputs are fully realized in innovative performance. But simple simulations conducted with our equations indicate that even a modest increase in key innovation inputs reaps significant benefits.
Innovation is the holy grail of prosperity and economic growth. Ever since Robert Solow's (1957) path-breaking work on the sources of growth, policymakers and businessmen have sought to understand what Solow labeled as "technological change"--the large proportion of economic growth that could not be explained by growth in labor, capital, and raw material inputs. This "residual" growth, he hypothesized, must be due to innovations that allow companies to use their capital, labor, and materials more efficiently. Jorgenson (2001) estimates that the residual accounts for 20 percent of GDP growth and nearly 30 percent of labor productivity growth.
Economists have learned a great deal about the "Solow residual" in the ensuing half-century. But innovation remains something of a mystery. We have come to understand that it is important for improving business competitiveness, economic growth, and living standards. But there is still much to learn about the innovative process itself and how we can measure and encourage innovation.
John Marburger, President Bush's top science adviser, has voiced frustration about our weak understanding of the roots of innovation. In an interview with Manufacturing & Technology News in the fall of 2005, he discussed one important implication of this knowledge gap: the dearth of yardsticks by which to measure the effectiveness of federal science and technology policy and to evaluate the likely effects of alternative policy initiatives (Jacobson, 2005). The oft-cited data about degrees being earned, patents filed, or papers published have meaning, in Marburger's view, only in the context of a model of "how the world works." (1)
Clearly, national policymakers and business leaders are in need of models that provide better guidance than is currently available on how the United States should invest its limited science and technology dollars and on what set of innovation-related decisions by individual corporations would yield optimal individual and social returns. An increasing number of countries are grappling with these issues. And, more generally, Stern, Porter, and Furman (2000) note that four countries that have increased their level of innovative capacity over the last quarter century--Japan, Sweden, Finland, and Germany--have done so as a result of active policy programs that have included human capital investment, R & D tax credits, and the opening of markets to international competition.
The purpose of this paper, which we intend as a response to...