This paper studies the influence of the structure of firm R&D, industry R&D spillovers, and plant level physical capital on the factor intensity of production. By the structure of firm R&D we mean its distribution across states and products. By factor intensity we mean the cost shares of variable factors, which in this paper are blue collar labor, white collar labor, and materials. We characterize the effect of the structure of firm R&D on factor intensity using a Translog cost function with quasi-fixed factors. This cost function gives rise to a system of variable cost shares that depends on factor prices, firm and industry R&D, and physical capital.
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The Span of the Effect of R&D in the Firm and Industry
May 1994
Working Paper Number:
CES-94-07
Previous studies have found that the firm's own research and spillovers of research by related firms increase firm productivity. In contrast, in this paper we explore the impact of firm R&D on the productivity of its individual plants. We carry out this investigation of within firm R&D effects using a unique set of Census data. The data, which are from the chemicals industry, are a match of plant level productivity and other characteristics with firm level data on R&D of the parent company, cross-classified by location and applied product field. We explore three aspects of the span of effect of the firm's R&D: (i), the degree to which its R&D is "public" across plants; (ii), the extent of its localization in geographic space, and (iii), the breadth of its relevance outside the applied product area in which it is classified. We find that (i), firm R&D acts more like a private input which is strongly amortized by the number of plants in the firm; (ii), firm R&D is geographically localized, and exerts greater influence on productivity when it is conducted nearer to the plant; and (iii), firm R&D in a given applied product area is of limited relevance to plants producing outside that product area. Moreover, we find that while geographic localization remains significant, it diminishes over time. This trend is consistent with the effect of improved telecommunications on increased information flows within organizations. Finally, we consider spillovers of R&D from the rest of industry, finding that the marginal product of industry R&D on plant productivity, though positive and significant, is far smaller than the marginal product of parent firm's R&D.
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Academic Science, Industrial R&D, and the Growth of Inputs
January 1993
Working Paper Number:
CES-93-01
This paper is a theoretical and empirical investigation of the connection between science, R&D, and the growth of capital. Studies of high technology industries and recent labor studies agree in assigning a large role to science and technology in the growth of human and physical capital, although direct tests of these relationships have not been carried out. This paper builds on the search approach to R&D of Evenson and Kislev (1976) to unravel the complex interactions between science, R&D, and factor markets suggested by these studies. In our theory lagged science increases the returns to R&D, so that scientific advance later feeds into growth of R&D. In turn, product quality improvements and price declines lead to the growth of industry by shifting out new product demand, perhaps at the expense of traditional industries. All this tends to be in favor of the human and physical capital used intensively by high technology industries. This is the source of the factor bias which is implicit in the growth of capital per head. Our empirical work overwhelmingly supports the contention that growth of labor skills and physical capital are linked to science and R&D. It also supports the strong sequencing of events that is a crucial feature of our model, first from science to R&D, and later to output and factor markets.
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Science, R&D, And Invention Potential Recharge: U.S. Evidence
January 1993
Working Paper Number:
CES-93-02
The influence of academic science on industrial R&D seems to have increased in recent years compared with the pre-World War II period. This paper outlines an approach to tracing this influence using a panel of 14 R&D performing industries from 1961-1986. The results indicate an elasticity between real R&D and indicators of stocks of academic science of about 0.6. This elasticity is significant controlling for industry effects. However, the elasticity declines from its level during the 1961-1973 subperiod, when it was 2.2, to 0.5 during the 1974-1986 subperiod. Reasons for the decline include exogenous and endogenous exhaustion of invention potential, and declining incentives to do R&D stemming from a weakening of intellectual property rights. The growth of R&D since the mid-1980s suggests a restoration of R&D incentives in still more recent times.
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Reconciling the Firm Size and Innovation Puzzle
March 2016
Working Paper Number:
CES-16-20RR
There is a prevailing view in both the academic literature and the popular press that firms need to behave more entrepreneurially. This view is reinforced by a stylized fact in the innovation literature that R&D productivity decreases with size. However, there is a second stylized fact in the innovation literature that R&D investment increases with size. Taken together, these stylized facts create a puzzle of seemingly irrational behavior by large firms--they are increasing spending despite decreasing returns. This paper is an effort to resolve that puzzle. We propose and test two alternative resolutions: 1) that it arises from mismeasurement of R&D productivity, and 2) that firm size endogenously drives R&D strategy, and that the returns to R&D strategies depend on scale. We are able to resolve the puzzle under the first tack--using a recent measure of R&D productivity, RQ, we find that both R&D spending and R&D productivity increase with scale. We had less success with the second tack--while firm size affects R&D strategy in the manners expected by theory, there is no strategy whose returns decrease in scale. Taken together, our results are consistent with the Schumpeter view that large firms are the major engine of growth, they both spend more in aggregate than small firms, and are more productive with that spending. Moreover the prescription that firms should behave more entrepreneurially, should be treated with caution--one small firm strategy has lower returns to scale than its large firm counterpart.
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The Demand for Human Capital: A Microeconomic Approach
December 2001
Working Paper Number:
CES-01-16
We propose a model for explaining the demand for human capital based on a CES production function with human capital as an explicit argument in the function. The resulting factor demand model is tested with data on roughly 6,000 plants from the Census Bureau's Longitudinal Research Database. The results show strong complementarity between physical and human capital. Moreover, the complementarity is greater in high than in low technology industries. The results also show that physical capital of more recent vintage is associated with a higher demand for human capital. While the age of a plant as a reflection of learning-by-doing is positively related to the accumulation of human capital, this relation is more pronounced in low technology industries.
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Learning by Doing and Plant Characteristics
August 1996
Working Paper Number:
CES-96-05
Learning by doing, especially spillover learning, has received much attention lately in models of industry evolution and economic growth. The predictions of these models depend on the distribution of learning abilities and knowledge flows across firms and countries. However, the empirical literature provides little guidance on these issues. In this paper, I use plant level data on a sample of entrants in SIC 38, Instruments, to examine the characteristics associated with both proprietary and spillover learning by doing. The plant level data permit tests for the relative importance of within and between firm spillovers. I include both formal knowledge, obtained through R&D expenditures, and informal knowledge, obtained through learning by doing, in a production function framework. I allow the speed of learning to vary across plants according to characteristics such as R&D intensity, wages, and the skill mix. The results suggest that (a) Ainformal@ knowledge, accumulated through production experience at the plant, is a much more important source of productivity growth for these plants than is Aformal@ knowledge gained via research and development expenditures, (b) interfirm spillovers are stronger than intrafirm spillovers, (c) the slope of the own learning curve is positively related to worker quality, (d) the slope of the spillover learning curve is positively related to the skill mix at plants, (e) neither own nor spillover learning curve slopes are related to R&D intensities. These results imply that learning by doing may be, to some extent, an endogenous phenomenon at these plants. Thus, models of industry evolution that incorporate learning by doing may need to be revised. The results are also broadly consistent with the recent growth models.
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A Guide To R&D Data At The Center For Economic Studies U.S. Bureau Of THe Census
August 1994
Working Paper Number:
CES-94-09
The National Science Foundation R&D Survey is an annual survey of firms' research and development expenditures. The survey covers 3000 firms reporting positive R&D. This paper provides a description of the R&D data available at the Center for Economic Studies (CES). The most basic data series available contains the original survey R&D data. It covers the years 1972-92. The remaining two series, although derived from the original files, specialize in particular items. The Mandatory Series contains required survey items for the years 1973-88. Items reported at firms' discretion are in the Voluntary Series, which covers the years 1974-89. Both of the derived series incorporate flags that track quality of the data. Both also include corrections to the data based on original hard copy survey evidence stored at CES. In addition to describing each dataset, we offer suggestions to researchers wishing to use the R&D data in exploring various economic issues. We report selected response rates, discuss the survey design, and provide hints on how to use the data.
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The Rising Returns to R&D: Ideas Are Not Getting Harder to Find
May 2025
Working Paper Number:
CES-25-29
R&D investment has grown robustly, yet aggregate productivity growth has stagnated. Is this because 'ideas are getting harder to find'? This paper uses micro-data from the US Census Bureau to explore the relationship between R&D and productivity in the manufacturing sector from 1976 to 2018. We find that both the elasticity of output (TFP) with respect to R&D and the marginal returns to R&D have risen sharply. Exploring factors affecting returns, we conclude that R&D obsolescence rates must have risen. Using a novel estimation approach, we find consistent evidence of sharply rising technological rivalry. These findings suggest that R&D has become more effective at finding productivity-enhancing ideas but these ideas may also render rivals' technologies obsolete, making innovations more transient.
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Computer Investment, Computer Networks and Productivity
January 2005
Working Paper Number:
CES-05-01
Researchers in a large empirical literature find significant relationships between computers and labor productivity, but the estimated size of that relationship varies considerably. In this paper, we estimate the relationships among computers, computer networks, and plant-level productivity in U.S. manufacturing. Using new data on computer investment, we develop a sample with the best proxies for computer and total capital that the data allow us to construct. We find that computer networks and computer inputs have separate, positive, and significant relationships with U.S. manufacturing plant-level productivity. Keywords: computer input; information technology; labor productivity
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A Flexible Test for Agglomeration Economies in Two U.S. Manufacturing Industries
August 2004
Working Paper Number:
CES-04-14
This paper uses the inverse input demand function framework of Kim (1992) to test for economies of industry and urban size in two U.S. manufacturing sectors of differing technology intensity: farm and garden machinery (SIC 352) and measuring and controlling devices (SIC 382). The inverse input demand framework permits the estimation of the production function jointly with a set of cost shares without the imposition of prior economic restrictions. Tests using plant-level data suggest the presence of population scale (urbanization) economies in the moderate- to low-technology farm and garden machinery sector and industry scale (localization) economies in the higher technology measuring and controlling devices sector. The efficiency and generality of the inverse input demand approach are particularly appropriate for micro-level studies of agglomeration economies where prior assumptions regarding homogeneity and homotheticity are less appropriate.
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