تحقیق و توسعه استهلاک، سهام، هزینه های کاربر و رشد بهره وری برای تحقیق و توسعه صنایع ایالات متحده

This paper estimates R&D depreciation rates for U.S. R&D intensive industries. R&D annually depreciates at; 18% for chemical products, 26% for nonelectrical machinery, 29% for electrical products, and 21% for transportation equipment. These depreciation rates lead to new estimates of the marginal (gross of depreciation) returns to R&D capital; 0.25 for chemical products, 0.31 for nonelectrical machinery, 0.34 for electrical products, and 0.27 for transportation equipment. R&D investment significantly contributed to productivity growth; virtually 100% in chemical products, 55% in nonelectrical machinery, 38 percent in electrical products, and 84% in transportation equipment.

The growth of research and development (R&D) capital critically depends on its ‘economically useful’ life. For example, an acceleration in R&D depreciation causes relatively more resources to be devoted to knowledge creating activities in order to sustain a constant knowledge outcome. This resource re-allocation raises the opportunity cost of R&D, and thereby, with all other things constant, reduces the rate of knowledge creation. Thus, estimates of R&D depreciation rates are a critical component for the measurement of R&D capital. The first objective of this paper is to estimate R&D depreciation rates for the major U. S. R&D intensive industries; namely chemical products (SIC 28), nonelectrical machinery (SIC 35), electrical products (SIC 36), and transportation equipment (SIC 37). These four industries account for 78% of manufacturing R&D expenditures, 60% of business sector R&D, and 53% of manufacturing output. The second objective is to compute the R&D capital stocks for these industries, based upon the estimated depreciation rates. As a practical matter, estimates of R&D depreciation rates are potentially useful to the current debate regarding the capitalization of R&D expenditures in the national income and product accounts (NIPA). Presently, NIPA treats R&D expenditures as an intermediate input for business, while for nonprofit institutions and government R&D is treated as current consumption. As a result the contribution of R&D to national savings is underestimated. Thus, capitalizing R&D provides for a more accurate set of national accounts, but requires estimates of R&D depreciation rates (see for instance the discussion in Fraumeni and Okubo, 2004 and Carson et al., 1994). R&D is a durable or capital input, since its productive capability lasts for more than one period. Consequently, accounting for the productive contribution of R&D involves an evaluation of its benefits over several periods. Jorgenson and Griliches, 1967, Diewert, 1980, Hulten and Wykoff, 1981 and Harper et al., 1989, and Hulten (1990) recognize that an appropriate evaluation implies a distinction between the price of using or renting an asset over time, and the price of owning or purchasing it on a particular date. The two prices are related in the following manner; the price of ownership equals the discounted expected stream of future rental payments or user costs that the asset is expected to yield over time. With respect to the measurement of R&D capital, this price distinction creates complications for ‘own use’ situations, as the implicit asset ‘transfer’ between owner and user results in user costs that are not observed in market data. This unobservablity problem is particularly acute for R&D, since typically it is not market-transacted. This issue leads to the third objective of the paper, which is to calculate R&D user costs for the R&D intensive industries, and further with the new user costs, provide new estimates of R&D price elasticities. R&D accumulation is an important determinant of productivity growth (see the survey by Griliches, 1995). Investment in R&D reduces production cost, as inputs are more effectively transformed into outputs, and it alters output characteristics, thereby providing new products to the marketplace. These features enhance productive efficiency, and consequently improve productivity performance. Using the new estimates of R&D stocks and user costs, the last objective of this paper is to determine the contribution of R&D to total factor productivity (TFP) growth for each of the R&D intensive industries. This paper considers R&D depreciation within the context of intertemporal cost minimization, where depreciation rates are estimated simultaneously with other parameters characterizing the overall structure of production. Depreciation rates reflect technical efficiency and indicate the productiveness of ‘old’ capital required to generate the same level of services as ‘new’ capital Jorgenson, 1989 and Hulten and Wykoff, 1996. However, the approach here extends previous empirical literature in the area in a number of directions. Epstein and Denny (1980) use a short-run or static model to estimate physical capital depreciation, while Kollintzas and Choi (1985) estimate physical capital depreciation within a linear model of investment. Nadiri and Prucha (1996) estimate R&D depreciation in a capital adjustment model under the assumptions of both static price expectations, and static non-capital input decisions. The model under consideration is an extension of Bernstein et al. (2004), where production decisions regarding both non-durable, and capital input requirements involve intertemporal considerations. Thus, the model is dynamic, and also results in nonlinear investment and input demand equations. Moreover, since a proper evaluation of the productive contribution of R&D capital involves future prices, price expectations are a potentially important element of the evaluation process. In this paper, in contrast to previous research, price expectation generating processes are jointly estimated with the production structure.1 The paper is organized in the following manner. Section 2 develops the theoretical model providing the framework to estimate R&D depreciation rates. Section 3 contains the discussion on the empirical specification, the regression results, the calculation of R&D capital stocks and user costs. Section 4 addresses the estimated own and cross price elasticities, including those associated with R&D capital. Section 5 develops and decomposes TFP growth rates, and determines the contribution of R&D capital. The last section concludes the paper.

The main issue addressed in this paper relates to the estimation of R&D depreciation rates for R&D intensive industries. The results show that R&D depreciates at an annual rate of; 18% for chemical products, 26% for nonelectrical machinery, 29% for electrical products, and 21% for transportation equipment. R&D capital appears to depreciate in about 3–5 years, at rates that are 3.5–6 times faster than the observed rate for physical capital, and 1.2–2.2 faster than physical capital’s estimated economic depreciation rate. With respect to the R&D capital stocks based on the estimated depreciation rates, over the period from 1955 to 1999 the stocks grew by; 143.42% for chemical products at an annual rate of 3.26%, 106.58% for nonelectrical machinery at an annual rate of 2.42%, and 86.64% for electrical products at an annual rate of 1.97%. Due to the cyclical nature of R&D activity in transportation equipment, R&D capital grew by only 8.44%, with an annual rate of 0.19%. With the estimated depreciation rates and price expectations generating processes, R&D user costs were calculated. In 1996, which is the reference time period, the user costs were; 0.246 for chemical products, 0.313 for nonelectrical machinery, 0.342 for electrical products, and 0.273 for transportation equipment. These results imply that the (gross of depreciation) marginal returns to R&D capital in nonelectrical machinery and electrical products are equal and exceed the marginal returns in chemical products and transportation equipment. Estimates of factor price elasticities demonstrated that generally R&D is price inelastic. The pattern of cross price elasticities showed that R&D capital and labor inputs are substitutes, while in industries where R&D and physical capital are complements, R&D and intermediate inputs are also complementary inputs. This is the case for nonelectrical machinery and transportation equipment. Conversely, where R&D and intermediate inputs are substitutes, then the two capital inputs are substitutes, as in the chemical and electrical products industries. These effects have potentially important implications for the evaluation of tax policies on R&D investment and production. First, the existence of complements and substitutes to R&D means that R&D tax incentives can generate significant effects on investment in machinery and equipment and on employment. Second, tax policies aimed at physical capital, such as investment tax credits, and policies targeted to labor, such as payroll taxes, affect R&D investment, especially in R&D intensive industries. These ‘cross’ effects must be considered for a proper evaluation of tax policies. R&D investment clearly generated significant productivity gains in R&D intensive industries. Specifically for chemical products, essentially all of observed productivity growth can be accounted for by R&D. R&D contributed 55% to annual productivity growth in nonelectrical machinery, 38% in electrical products, and approximately 84% to productivity gains in transportation equipment. Our results also contribute to the debate about the potential secular decline in the return to R&D towards generating productivity gains. Our findings suggest that there has been a resurgence in R&D’s contribution to productivity growth, and so past declines do not represent a secular trend. Furthermore, a novel aspect of the framework developed in this paper is the consideration of the relative roles of factor efficiency and R&D capital in productivity decomposition. The results indicate that R&D capital accumulation offsets the deceleration in the growth of factor efficiency, and therefore substitutes for input-augmenting efficiency in improving productivity performance. This paper has provided a framework to estimate R&D depreciation rates within the context of intertemporal production decisions. These rates then enable R&D capital, and its user cost to be more accurately calculated, which in turn fosters the determination of R&D related effects, such as its contribution to productivity growth.