Custom software is distinct from other types of capital in that it is non-rival'once a firm makes an investment in custom software, it can be used simultaneously across its many establishments. Using confidential U.S. Census data, we document that while firms with more establishments are more likely to invest in custom software, they spend less on it as a share of total capital expenditure. We explain these empirical patterns by developing a model that incorporates the non-rivalry of custom software. In the model, firms choose whether to adopt custom software, the intensity of their investment, and their scope, balancing the cost of managing multiple establishments with the increasing returns to scope from the nonrivalrous custom software investment. Using the calibrated model, we assess the extent to which the decline in the rental rate of custom software over the past 40 years can account for a number of macroeconomic trends, including increases in firm scope and concentration.
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Specialization in a Knowledge Economy
December 2025
Working Paper Number:
CES-25-77
Using firm-level data from the US Census Longitudinal Business Database (LBD), this paper exhibits novel evidence about a wave of specialization experienced by US firms in the 1980s and 1990s. Specifically: (i) Firms, especially innovating ones, decreased production scope, i.e., the number of industries in which they produce. (ii) Innovation and production separated, with small firms specializing in innovation and large firms in production. Higher patent trading efficiency and stronger patent protection are proposed to explain these phenomena. An endogenous growth model is developed with potential mismatches between innovation and production. Calibrating the model suggests that increased trading efficiency and better patent protection can explain 20% of the observed production scope decrease and 108% of the innovation and production separation. They result in a 0.64 percent point increase in the annual economic growth rate. Empirical analyses provide evidence of causality from pro-patent reforms in the 1980s to the two specialization patterns.
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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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AI Exposure and Adoption Among U.S. Firms
September 2026
Authors:
John Haltiwanger,
Lucia Foster,
Martha Stinson,
Emin Dinlersoz,
Cheryl Grim,
Zoltan Wolf,
Sabrina Wulff Pabilonia,
Matthew Dey,
Sean Wang,
Aditya Pande,
Peter B. Meyer
Working Paper Number:
CES-26-61
Measures of exposure to artificial intelligence (AI) are widely used to study where AI is likely to affect firm and worker outcomes, yet limited evidence exists on how closely exposure relates to realized AI adoption at the firm level. We examine this relationship by linking 14 firm-level exposure measures, constructed from occupational exposure estimates in the literature and occupational employment shares from the Bureau of Labor Statistics Occupational Employment and Wage Statistics program, to direct measures of firm AI adoption from the Census Bureau's Business Trends and Outlook Survey. Exposure is positively and statistically significantly associated with adoption, but explains only a modest share of its variation. The strength of the relationship varies considerably across standardized exposure measures: a one-standard-deviation increase in firm-level exposure is associated with a 4'11 percentage point higher firm adoption probability, falling to 1'8 percentage points after controlling for year and sub-sector fixed effects. The exposure'adoption relationship is also heterogeneous across firm-size classes and sectors. Exposure is thus an informative but incomplete signal of adoption. More broadly, the results have implications for the interpretation of exposure-based measures in studies of the economic effects of AI.
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Technology-Driven Market Concentration through Idea Allocation
December 2025
Working Paper Number:
CES-25-78
Using a newly-created measure of technology novelty, this paper identifies periods with and without technology breakthroughs from the 1980s to the 2020s in the US. It is found that market concentration decreases at the advent of revolutionary technologies. We establish a theory addressing inventors' decisions to establish new firms or join incumbents of selected sizes, yielding two key predictions: (1) A higher share of inventors opt for new firms during periods of heightened technology novelty. (2). There is positive assortative matching between idea quality and firm size if inventors join incumbents. Both predictions align with empirical findings and collectively contribute to a reduction in market concentration when groundbreaking technologies occur. Quantitative analysis shows the overall slowdown in technological breakthroughs can capture 95.9% of the rising trend in market concentration and the correlation between the model-generated and the actual detrended market concentration is 0.910.
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What Drives Stagnation: Monopsony or Monopoly?
October 2022
Working Paper Number:
CES-22-45
Wages for the vast majority of workers have stagnated since the 1980s while productivity
has grown. We investigate two coexisting explanations based on rising market power: 1. Monopsony, where dominant firms exploit the limited mobility of their own workers to pay lower wages; and 2. Monopoly, where dominant firms charge too high prices for what they sell, which lowers production and the demand for labor, and hence equilibrium wages economy-wide. Using establishment data from the US Census Bureau between 1997 and 2016, we find evidence of both monopoly and monopsony, where the former is rising over this period and the latter is stable. Both contribute to the decoupling of productivity and wage growth, with monopoly being the primary determinant: in 2016 monopoly accounts for 75% of wage stagnation, monopsony for 25%.
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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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Automation and the Workforce: A Firm-Level View from the 2019 Annual Business Survey
April 2022
Authors:
John Haltiwanger,
Lucia Foster,
Emin Dinlersoz,
Nikolas Zolas,
Daron Acemoglu,
Catherine Buffington,
Nathan Goldschlag,
Zachary Kroff,
David Beede,
Gary Anderson,
Eric Childress,
Pascual Restrepo
Working Paper Number:
CES-22-12R
This paper describes the adoption of automation technologies by US firms across all economic sectors by leveraging a new module introduced in the 2019 Annual Business Survey, conducted by the US Census Bureau in partnership with the National Center for Science and Engineering Statistics (NCSES). The module collects data from over 300,000 firms on the use of five advanced technologies: AI, robotics, dedicated equipment, specialized software, and cloud computing. The adoption of these technologies remains low (especially for AI and robotics), varies substantially across industries, and concentrates on large and young firms. However, because larger firms are much more likely to adopt them, 12-64% of US workers and 22-72% of manufacturing workers are exposed to these technologies. Firms report a variety of motivations for adoption, including automating tasks previously performed by labor. Consistent with the use of these technologies for automation, adopters have higher labor productivity and lower labor shares. In particular, the use of these technologies is associated with a 11.4% higher labor productivity, which accounts for 20'30% of the difference in labor productivity between large firms and the median firm in an industry. Adopters report that these technologies raised skill requirements and led to greater demand for skilled labor, but brought limited or ambiguous effects to their employment levels.
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Market Power And Wage Inequality
September 2022
Working Paper Number:
CES-22-37
We propose a theory of how market power affects wage inequality. We ask how goods and labor market power jointly affect the level of wages, the Skill Premium, and wage inequality. We then use detailed microdata from the US Census between 1997 and 2016 to estimate the parameters of labor supply, technology and the market structure. We find that a less competitive market structure lowers the wage level, contributes 7% to the rise in the Skill Premium and accounts for half of the increase in between-establishment wage variance.
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Who Hires Whom? Entrepreneurial Backgrounds and Labor Market Opportunities
July 2026
Working Paper Number:
CES-26-45
What are the implications of unequal access to entrepreneurial careers for labor markets? Using data from the U.S. Census and LinkedIn profiles, we document that entrepreneurs are significantly more likely to hire workers from similar social backgrounds (gender, race, age, education, etc.). These effects are quantitatively large across several demographic dimensions. For example, female employee share at female-founded startups is 36.4pp higher after controlling for industry-by-metro area-by-cohort fixed effects, with corresponding estimates of 51.2pp for Blacks, 37.3pp for Hispanics, and 11.3pp for non-college individuals. Large effects are present in high-growth startups, across industries and occupations, and remain stable across new firm cohorts. In addition, we find that these differences persist out to at least 20 years. We use wage data and an AKM research design to untangle whether the relative differences are driven by labor demand or labor supply effects. We find that demand drives the differences: group-specific wage decompositions show that new firms pay higher relative wages to individuals from similar backgrounds to the entrepreneur. Using these estimates, we calibrate a model of entrepreneurship with heterogeneous ability and production functions, and assess the impacts on relative wage from reducing access barriers to entrepreneurship.
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New U.S. Business Establishments: Surging or Stalling?
June 2026
Working Paper Number:
CES-26-36
Since the 1990s, the Bureau of Labor Statistics (BLS) has reported much more rapid growth in U.S. private sector employer establishments than has the Census Bureau' the gap reached roughly 1.6 million by 2023. Using linked BLS-Census microdata, we document two main drivers. First, a large and growing number of employers providing services to the elderly and persons with disabilities are in scope for the BLS frame but not the Census Bureau's. Second, many firms appear with substantially more establishments in the BLS frame. These discrepancies substantially affect the measured establishment size distribution and quantitative policy analysis.
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