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Rising Markups or Changing Technology?
September 2022
Working Paper Number:
CES-22-38R
Recent evidence suggests the U.S. business environment is changing, with rising market concentration and markups. The most prominent and extensive evidence backs out firm-level markups from the first-order conditions for variable factors. The markup is identified as the ratio of the variable factor's output elasticity to its cost share of revenue. Our analysis starts from this indirect approach, but we exploit a long panel of manufacturing establishments to permit output elasticities to vary to a much greater extent - relative to the existing literature - across establishments within the same industry over time. With our more detailed estimates of output elasticities, the measured increase in markups is substantially dampened, if not eliminated, for U.S. manufacturing. As supporting evidence, we relate differences in the markups' patterns to observable changes in technology (e.g., computer investment per worker, capital intensity, diversification to non-manufacturing) and find patterns in support of changing technology as the driver of those differences.
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Advanced Technologies Adoption and Use by U.S. Firms: Evidence from the Annual Business Survey
December 2020
Working Paper Number:
CES-20-40
We introduce a new survey module intended to complement and expand research on the causes and consequences of advanced technology adoption. The 2018 Annual Business Survey (ABS), conducted by the Census Bureau in partnership with the National Center for Science and Engineering Statistics (NCSES), provides comprehensive and timely information on the diffusion among U.S. firms of advanced technologies including artificial intelligence (AI), cloud computing, robotics, and the digitization of business information. The 2018 ABS is a large, nationally representative sample of over 850,000 firms covering all private, nonfarm sectors of the economy. We describe the motivation for and development of the technology module in the ABS, as well as provide a first look at technology adoption and use patterns across firms and sectors. We find that digitization is quite widespread, as is some use of cloud computing. In contrast, advanced technology adoption is rare and generally skewed towards larger and older firms. Adoption patterns are consistent with a hierarchy of increasing technological sophistication, in which most firms that adopt AI or other advanced business technologies also use the other, more widely diffused technologies. Finally, while few firms are at the technology frontier, they tend to be large so technology exposure of the average worker is significantly higher. This new data will be available to qualified researchers on approved projects in the Federal Statistical Research Data Center network.
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High Growth Young Firms: Contribution to Job, Output and Productivity Growth
February 2017
Working Paper Number:
carra-2017-03
Recent research shows that the job creating prowess of small firms in the U.S. is better attributed to startups and young firms that are small. But most startups and young firms either fail or don't create jobs. A small proportion of young firms grow rapidly and they account for the long lasting contribution of startups to job growth. High growth firms are not well understood in terms of either theory or evidence. Although the evidence of their role in job creation is mounting, little is known about their life cycle dynamics, or their contribution to other key outcomes such as real output growth and productivity. In this paper, we enhance the Longitudinal Business Database with gross output (real revenue) measures. We find that the patterns for high output growth firms largely mimic those for high employment growth firms. High growth output firms are disproportionately young and make disproportionate contributions to output and productivity growth. The share of activity accounted for by high growth output and employment firms varies substantially across industries - in the post 2000 period the share of activity accounted for by high growth firms is significantly higher in the High Tech and Energy related industries. A firm in a small business intensive industry is less likely to be a high output growth firm but small business intensive industries don't have significantly smaller shares of either employment or output activity accounted for by high growth firms.
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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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High Growth Young Firms: Contribution to Job, Output and Productivity Growth
January 2016
Working Paper Number:
CES-16-49
Recent research shows that the job creating prowess of small firms in the U.S. is better attributed to startups and young firms that are small. But most startups and young firms either fail or don't create jobs. A small proportion of young firms grow rapidly and they account for the long lasting contribution of startups to job growth. High growth firms are not well understood in terms of either theory or evidence. Although the evidence of their role in job creation is mounting, little is known about their life cycle dynamics, or their contribution to other key outcomes such as real output growth and productivity. In this paper, we enhance the Longitudinal Business Database with gross output (real revenue) measures. We find that the patterns for high output growth firms largely mimic those for high employment growth firms. High growth output firms are disproportionately young and make disproportionate contributions to output and productivity growth. The share of activity accounted for by high growth output and employment firms varies substantially across industries ' in the post 2000 period the share of activity accounted for by high growth firms is significantly higher in the High Tech and Energy related industries. A firm in a small business intensive industry is less likely to be a high output growth firm but small business intensive industries don't have significantly smaller shares of either employment or output activity accounted for by high growth firms.
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IMPROVING THE SYNTHETIC LONGITUDINAL BUSINESS DATABASE
February 2014
Working Paper Number:
CES-14-12
In most countries, national statistical agencies do not release establishment-level business microdata, because doing so represents too large a risk to establishments' confidentiality. Agencies potentially can manage these risks by releasing synthetic microdata, i.e., individual establishment records simulated from statistical models de- signed to mimic the joint distribution of the underlying observed data. Previously, we used this approach to generate a public-use version'now available for public use'of the U. S. Census Bureau's Longitudinal Business Database (LBD), a longitudinal cen- sus of establishments dating back to 1976. While the synthetic LBD has proven to be a useful product, we now seek to improve and expand it by using new synthesis models and adding features. This article describes our efforts to create the second generation of the SynLBD, including synthesis procedures that we believe could be replicated in other contexts.
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How Firms Respond to Business Cycles: The Role of Firm Age and Firm Size
June 2013
Working Paper Number:
CES-13-30
There remains considerable debate in the theoretical and empirical literature about the differences in the cyclical dynamics of firms by firm size. This paper contributes to the debate in two ways. First, the key distinction between firm size and firm age is introduced. The evidence presented in this paper shows that young businesses (that are typically small) exhibit very different cyclical dynamics than small/older businesses. The second contribution is to present evidence and explore explanations for the finding that young/small businesses were hit especially hard in the Great Recession. The collapse in housing prices accounts for a significant part of the large decline of young/small businesses in the Great Recession.
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The Cyclicality of Productivity Dispersion
May 2011
Working Paper Number:
CES-11-15
Using plant-level data, I show that the dispersion of total factor productivity in U.S. durable manufacturing is greater in recessions than in booms. This cyclical property of productivity dispersion is much less pronounced in non-durable manufacturing. In durables, this phenomenon primarily reflects a relatively higher share of unproductive firms in a recession. In order to interpret these findings, I construct a business cycle model where production in durables requires a fixed input. In a boom, when the market price of this fixed input is high, only more productive firms enter and only more productive incumbents survive, which results in a more compressed productivity distribution. The resulting higher average productivity in durables endogenously translates into a lower average relative price of durables. Additionally, my model is consistent with the following business cycle facts: procyclical entry, procyclical aggregate total factor productivity, more procyclicality in durable than non-durable output, procyclical employment and countercyclicality in the relative price of durables and the cross section of stock returns.
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Towards Unrestricted Public Use Business Microdata: The Synthetic Longitudinal Business Database
February 2011
Working Paper Number:
CES-11-04
In most countries, national statistical agencies do not release establishment-level business microdata, because doing so represents too large a risk to establishments\' confidentiality. One approach with the potential for overcoming these risks is to release synthetic data; that is, the released establishment data are simulated from statistical models designed to mimic the distributions of the underlying real microdata. In this article, we describe an application of this strategy to create a public use file for the Longitudinal Business Database, an annual economic census of establishments in the United States comprising more than 20 million records dating back to 1976. The U.S. Bureau of the Census and the Internal Revenue Service recently approved the release of these synthetic microdata for public use, making the synthetic Longitudinal Business Database the first-ever business microdata set publicly released in the United States. We describe how we created the synthetic data, evaluated analytical validity, and assessed disclosure risk.
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How Does Venture Capital Financing Improve Efficiency in Private Firms? A Look Beneath the Surface
June 2008
Working Paper Number:
CES-08-16
Using a unique sample from the Longitudinal Research Database (LRD) of the U.S. Census Bureau, we study several related questions regarding the efficiency gains generated by venture capital (VC) investment in private firms. First, does VC backing improve the efficiency (total factor productivity, TFP) of private firms, and are certain kinds of VCs (higher reputation versus lower reputation) better at generating such efficiency gains than others? Second, how are such efficiency gains generated: Do venture capitalists invest in more efficient firms to begin with (screening) or do they improve efficiency after investment (monitoring)? Third, how are these efficiency gains spread out over rounds subsequent to VC investment? Fourth, what are the channels through which such efficiency gains are generated: increases in product market performance (sales) or reductions in various costs (labor, materials, total production costs)? Finally, how do such efficiency gains affect the probability of a successful exit (IPO or acquisition)? Our main findings are as follows. First, the overall efficiency of VC backed firms is higher than that of non-VC backed firms. Second, this efficiency advantage of VC backed firms arises from both screening and monitoring: the efficiency of VC backed firms prior to receiving financing is higher than that of non-VC backed firms and further, the growth in efficiency subsequent to receiving VC financing is greater for such firms relative to non-VC backed firms. Third, the above increase in efficiency of VC backed firms relative to non-VC backed firms increases over the first two rounds of VC financing, and remains at the higher level till exit. Fourth, while the TFP of firms prior to VC financing is lower for higher reputation VC backed firms, the increase in TFP subsequent to financing is significantly higher for the former firms, consistent with higher reputation VCs having greater monitoring ability. Fifth, the efficiency gains generated by VC backing arise primarily from improvement in product market performance (sales); however for higher reputation VCs, the additional efficiency gains arise from both an additional improvement in product market performance as well as from reductions in various input costs. Finally, both the level of TFP of VC backed firms prior to receiving financing and the growth in TFP subsequent to VC financing positively affect the probability of a successful exit (IPO or acquisition).
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