This paper studies factor usage in the R&D sector. I show that the usage of non-labor inputs in R&D is significant, and that their usage has grown much more rapidly than the R&D workforce. Using a standard growth decomposition applied to the aggregate idea production function, I estimate that at least 77% of idea growth since the early 1960s can be attributed to the growth of non-labor inputs in R&D. I demonstrate that a similar pattern would hold on the balanced growth path of a standard semi-endogenous growth model, and thus that the decomposition is not simply a by-product of rising research intensity. I then show that combining long-running differences in factor growth rates with non-unitary elasticities of substitution in idea production leads to a slowdown in idea growth whenever labor and capital are complementary. I conclude by estimating this elasticity of substitution and demonstrate that the results favor complimentarities.
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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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Outsourced R&D and GDP Growth
March 2016
Working Paper Number:
CES-16-19
Endogenous growth theory holds that growth should increase with R&D. However coarse comparison between R&D and US GDP growth over the past forty years indicates that inflation scientific labor increased 2.5 times, while GDP growth was at best stagnant. The leading explanation for the disconnect between theory and the empirical record is that R&D has gotten harder. I develop and test an alternative view that firms have become worse at it. I find no evidence R&D has gotten harder. Instead I find firms' R&D productivity declined 65%, and that the main culprit in the decline is outsourced R&D, which is unproductive for the funding firm. This offers hope firms' R&D productivity and economic growth may be fairly easily restored by bringing outsourced R&D back in-house.
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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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Capital Investment and Labor Demand
February 2022
Working Paper Number:
CES-22-04
We study how bonus depreciation, a policy designed to lower the cost of capital, impacted investment and labor demand in the US manufacturing sector. Difference-in-differences estimates using restricted-use US Census Data on manufacturing establishments show that this policy increased both investment and employment, but did not lead to wage or productivity gains. Using a structural model, we show that the primary effect of the policy was to increase the use of all inputs by lowering overall costs of production. The policy further stimulated production employment due to the complementarity of production labor and capital. Supporting this conclusion, we nd that investment is greater in plants with lower labor costs. Our results show that recent policies that incentivize capital investment do not lead manufacturing plants to replace workers with machines.
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The Rise of Specialized Firms
February 2024
Working Paper Number:
CES-24-06
This paper studies firm diversification over 6-digit NAICS industries in U.S. manufacturing. We find that firms specializing in fewer industries now account for a substantially greater share of production than 40 years ago. This reallocation is a key driver of rising industry concentration. Specialized firms have displaced diversified firms among industry leaders'absent this reallocation concentration would have decreased. We then provide evidence that specialized firms produce higher-quality goods: specialized firms tend to charge higher unit prices and are more insulated against Chinese import competition. Based on our empirical findings, we propose a theory in which growth shifts demand toward specialized, high-quality firms, which eventually increases concentration. We conclude that one should expect rising industry concentration in a growing economy.
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Micro Data and the Macro Elasticity of Substitution
March 2012
Working Paper Number:
CES-12-05
We estimate the aggregate elasticity of substitution between capital and labor in the US manufacturing sector. We show that the aggregate elasticity of substitution can be expressed as a simple function of plant level structural parameters and sufficient statistics of the distribution of plant input cost shares. We then use plant level data from the Census of Manufactures to construct a local elasticity of substitution at various levels of aggregation. Our approach does not assume the existence of a stable aggregate production function, as we build up our estimate from the cross section of plants at a point in time. Accounting for substitution within and across plants, we find that the aggregate elasticity is substantially below unity at approximately 0.7. Lastly we assess the sources of the bias of aggregate technical change from 1987 to 1997. We find that the labor augmenting character of aggregate technical change is due almost exclusively to labor augmenting productivity growth at the plant level rather than relative growth in capital intensive plants.
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HOW IMPORTANT ARE SECTORAL SHOCKS
September 2014
Working Paper Number:
CES-14-31
I quantify the contribution of sectoral shocks to business cycle fluctuations in aggregate output. I develop a multi-industry general equilibrium model in which each industry employs the material and capital goods produced by other sectors, and then estimate this model using data on U.S. industries sales, output prices, and input choices. Maximum likelihood estimates indicate that industry-specific shocks account for nearly two-thirds of the volatility of aggregate output, substantially larger than previously assessed. Identification of the relative importance of industry-specific shocks comes primarily from data on industries intermediate input purchases, data that earlier estimations of multi-industry models have ignored.
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Measuring Total Factor Productivity, Technical Change And The Rate Of Returns To Research And Development
May 1991
Working Paper Number:
CES-91-03
Recent research indicates that estimates of the effect of research and development (R&D) on total factor productivity growth are sensitive to different measures of total factor productivity. In this paper, we use establishment level data for the flat glass industry extracted from the Census Bureau's Longitudinal Research Database (LRD) to construct three competing measures of total factor productivity. We then use these measures to estimate the conventional R&D intensity model. Our empirical results support previous finding that the estimated coefficients of the model are sensitive to the measurement of total factor productivity. Also, when using microdata and more detailed modeling, R&D is found to be a significant factor influencing productivity growth. Finally, for the flat glass industry, a specific technical change index capturing the learning-by-doing process appears to be superior to the conventional time trend index.
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The Intangible Divide: Why Do So Few Firms Invest in Innovation?
February 2025
Working Paper Number:
CES-25-15
Investments in software, R&D, and advertising have surged, nearing half of U.S. private nonresidential investment. Yet just a few hundred firms dominate this growth. Most firms, including large ones, regularly invest little in capitalized software and R&D, widening this 'intangible divide' despite falling intangible prices. Using comprehensive US Census microdata, we document these patterns and explore factors associated with intangible investment. We find that firms invest significantly less in innovation-related intangibles when their rivals invest more. One firm's investment can obsolesce rivals' investments, reducing returns. This negative pecuniary externality worsens the intangible divide, potentially leading to significant misallocation.
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Are firm-level idiosyncratic shocks important for U.S. aggregate volatility?
January 2016
Working Paper Number:
CES-16-47
This paper assesses the quantitative impact of firm-level idiosyncratic shocks on aggregate volatility in the U.S. economy and provides a microfoundation for the negative relationship between firm-level volatility and size. I argue that the role of firm-specific shocks through the granular channel plays a fairly limited role in the U.S. economy. Using a novel, comprehensive data set compiled from several sources of the U.S. Census Bureau, I find that the granular com-ponent accounts at most for 15.5% of the variation in aggregate sales growth which is about half found by previous studies. To bridge the gap between previous findings and mine, I show that my quantitative results require deviations from Gibrat's law in which firm-level volatility and size are negatively related. I find that firm-level volatility declines at a substantially higher rate in size than previously found. Hence, the largest firms in the economy cannot be driving a sub-stantial fraction of macroeconomic volatility. I show that the explanatory power of granularity gets cut by at least half whenever the size-variance relationship, as estimated in the micro-level data, is taken into account. To uncover the economic mechanism behind this phenomenon, I construct an analytically tractable framework featuring random growth and a Kimball aggrega-tor. Under this setup, larger firms respond less to productivity shocks as the elasticity of demand is decreasing in size. Additionally, the model predicts a positive (negative) relationship between firm-level mark-ups (growth) and size. I confirm the predictions of the model by estimating size-varying price elasticities on unique product-level data from the Census of Manufactures (CM) and structurally estimating mark-ups using plant-level information from the Annual Survey of Manufactures (ASM).
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