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Borrowing Constraints, Markups, and Misallocation
December 2025
Working Paper Number:
CES-25-75
We document new facts that link firms' markups to borrowing constraints: (1) less constrained firms within an industry have higher markups, especially in industries where assets are difficult to borrow against and firms rely more on earnings to borrow; (2) markup dispersion is also higher in industries where firms rely more on earnings to borrow. We explain these relationships using a standard Kimball demand model augmented with borrowing against assets and earnings. The key mechanism is a two-way feedback between markups and borrowing constraints. First, less constrained firms charge higher markups, as looser constraints allow them to attain larger market shares. Second, higher markups relax borrowing constraints when firms rely on earnings to borrow, as those with higher markups have higher earnings. This two-way feedback lowers TFP losses from markup dispersion, particularly when firms rely on earnings to borrow.
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Measurement Matters: Financial Reporting and Productivity
December 2025
Working Paper Number:
CES-25-72
We examine how differences in financial reporting practices shape firm productivity. Leveraging new audit questions in the U.S. Census Bureau's 2021 Management and Organizational Practices Survey (MOPS), and complementary tax return data from the Internal Revenue Service (IRS) and detailed financial records from Sageworks, we find that (i) variation in reporting quality explains 10-20 percent of intra-industry total factor productivity dispersion, and (ii) evidence of complementarity between the effects of financial audits and management practices driving firm productivity. We then examine the underlying mechanisms. First, audits function as a managerial technology, improving the precision of internal information and raising efficiency, with stronger effects in competitive, low-margin industries and among younger firms. Second, exploiting cross-state variation in tax incentives, we show that audits constrain underreporting and mitigate the downward bias in measured productivity. Together, these results highlight the underrated importance of financial reporting quality driving firm productivity.
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Manufacturing Dispersion: How Data Cleaning Choices Affect Measured Misallocation and Productivity Growth in the Annual Survey of Manufactures
September 2025
Working Paper Number:
CES-25-67
Measurement of dispersion of productivity levels and productivity growth rates across businesses is a key input for answering a variety of important economic questions, such as understanding the allocation of economic inputs across businesses and over time. While item nonresponse is a readily quantifiable issue, we show there is also misreporting by respondents in the Annual Survey of Manufactures (ASM). Aware of these measurement issues, the Census Bureau edits and imputes survey responses before tabulation and dissemination. However, edit and imputation methods that are suitable for publishing aggregate totals may not be suitable for estimating other measures from the microdata. We show that the methods used dramatically affect estimates of productivity dispersion, allocative efficiency, and aggregate productivity growth. Using a Bayesian approach for editing and imputation, we model the joint distributions of all variables needed to estimate these measures, and we quantify the degree of uncertainty in the estimates due to imputations for faulty or missing data.
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National Chains and Trends in Retail Productivity Dispersion
September 2025
Working Paper Number:
CES-25-64
Productivity dispersion within an industry is an important characteristic of the business environment, potentially reflecting factors such as market structure, production technologies, and reallocation frictions. The retail trade sector saw significant changes between 1987 and 2017, and dispersion statistics can help characterize how it evolved over this period. In this paper, we shed light on this transformation by developing public-use Dispersion Statistics on Productivity (DiSP) data for the retail sector for 1987 through 2017. We find that from 1987 through 2017, dispersion increased between retail stores at the bottom and middle of the productivity distribution. However, when we weight stores by employment dispersion, the middle of the distribution is lower initially and decreases over time. These patterns are consistent with a retail landscape featuring more and more activity taking place in chain stores with similar productivity. Firm-based dispersion measures exhibit a similar pattern. Further investigation reveals that there is substantial heterogeneity in dispersion levels across industries.
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Job Tasks, Worker Skills, and Productivity
September 2025
Authors:
John Haltiwanger,
Lucia Foster,
Cheryl Grim,
Zoltan Wolf,
Cindy Cunningham,
Sabrina Wulff Pabilonia,
Jay Stewart,
Cody Tuttle,
G. Jacob Blackwood,
Matthew Dey,
Rachel Nesbit
Working Paper Number:
CES-25-63
We present new empirical evidence suggesting that we can better understand productivity dispersion across businesses by accounting for differences in how tasks, skills, and occupations are organized. This aligns with growing attention to the task content of production. We link establishment-level data from the Bureau of Labor Statistics Occupational Employment and Wage Statistics survey with productivity data from the Census Bureau's manufacturing surveys. Our analysis reveals strong relationships between establishment productivity and task, skill, and occupation inputs. These relationships are highly nonlinear and vary by industry. When we account for these patterns, we can explain a substantial share of productivity dispersion across establishments.
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Technifying Ventures
July 2025
Working Paper Number:
CES-25-49
How do advanced technology adoption and venture capital (VC) funding impact employment and growth? An analysis of data from the US Census Bureau suggests that while both advanced technology use and VC funding matter on their own for firm outcomes, their joint presence is most strongly correlated with higher employment levels. VC presence is linked with a high increase in employment, though primarily among a limited subset of firms. In contrast, technology adoption is associated with a smaller rise in employment, yet it influences a considerably larger number of firms. A model of startups is created, focusing on decisions to use advanced technology and seek VC funding. The model is compared with firm-level data on employment, advanced technology use, and VC investment. Several thought experiments are conducted using the model. Some experiments assess the importance of advanced technology and VC in the economy. Others examine the reallocation effects across firms with different technology choices and funding sources in response to shifts in taxes and subsidies.
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Private Equity and Workers: Modeling and Measuring Monopsony, Implicit Contracts, and Efficient Reallocation
June 2025
Working Paper Number:
CES-25-37
We measure the real effects of private equity buyouts on worker outcomes by building a new database that links transactions to matched employer-employee data in the United States. To guide our empirical analysis, we derive testable implications from three theories in which private equity managers alter worker outcomes: (1) exertion of monopsony power in concentrated markets, (2) breach of implicit contracts with targeted groups of workers, including managers and top earners, and (3) efficient reallocation of workers across plants. We do not find any evidence that private equity-backed firms vary wages and employment based on local labor market power proxies. Wage losses are also very similar for managers and top earners. Instead, we find strong evidence that private equity managers downsize less productive plants relative to productive plants while simultaneously reallocating high-wage workers to more productive plants. We conclude that post-buyout employment and wage dynamics are consistent with professional investors providing incentives to increase productivity and monitor the companies in which they invest.
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Firm Heterogeneity, Misallocation, and Trade
May 2025
Working Paper Number:
CES-25-33
To what extent do domestic distortions influence the gains from trade? Using data from Chinese manufacturing surveys and U.S. census records, I document two novel stylized facts: (1) Larger producers in China exhibit lower revenue productivity, whereas larger producers in the U.S. exhibit higher revenue productivity. (2) Larger exporters in China exhibit lower export intensity, whereas larger exporters in the U.S. exhibit higher export intensity. A model of heterogeneous producers shows that only the U.S. patterns are consistent with an efficient allocation. To reconcile the observed patterns in China, I introduce producer- and destination-specific subsidies and estimate the model without imposing functional form assumptions on the joint distribution of productivity and subsidy rates. Accounting for distortions in China leads to substantially smaller estimated gains from trade.
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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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The Rise of Industrial AI in America: Microfoundations of the Productivity J-curve(s)
April 2025
Working Paper Number:
CES-25-27
We examine the prevalence and productivity dynamics of artificial intelligence (AI) in American manufacturing. Working with the Census Bureau to collect detailed large-scale data for 2017 and 2021, we focus on AI-related technologies with industrial applications. We find causal evidence of J-curve-shaped returns, where short-term performance losses precede longer-term gains. Consistent with costly adjustment taking place within core production processes, industrial AI use increases work-in-progress inventory, investment in industrial robots, and labor shedding, while harming productivity and profitability in the short run. These losses are unevenly distributed, concentrating among older businesses while being mitigated by growth-oriented business strategies and within-firm spillovers. Dynamics, however, matter: earlier (pre-2017) adopters exhibit stronger growth over time, conditional on survival. Notably, among older establishments, abandonment of structured production-management practices accounts for roughly one-third of these losses, revealing a specific channel through which intangible factors shape AI's impact. Taken together, these results provide novel evidence on the microfoundations of technology J-curves, identifying mechanisms and illuminating how and why they differ across firm types. These findings extend our understanding of modern General Purpose Technologies, explaining why their economic impact'exemplified here by AI'may initially disappoint, particularly in contexts dominated by older, established firms.
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