Papers Containing Tag(s): 'North American Industry Classification System'
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Viewing papers 1 through 10 of 387
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Working PaperWhat Happens to Contractors After States Ban Affirmative Action?
July 2026
Working Paper Number:
CES-26-41
Using restricted Census business records, I explore how banning affirmative action in state contracting affects minority- and women-owned business enterprises (MWBEs). I find that ending affirmative action led MWBE contractors to gradually downsize, with the most pronounced reductions in force experienced by Black-owned businesses and larger MWBEs. Despite these workforce changes, existing MWBEs were no more likely to shut down than other businesses. New MWBEs were relatively less common after a state's ban, highlighting how bans can shift the demographic composition of new contractors. A calibrated model suggests bans are equivalent to considerable reductions in MWBE productivity and scrap values.View Full Paper PDF
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Working PaperTip of the Iceberg: How Much Do Tips Bunch at Reporting Thresholds?
June 2026
Working Paper Number:
CES-26-40
We study the importance of bunching in the context of tip-income reporting by workers at full-service, single-unit restaurants in the United States. Using tax reports at both the individual and the employer levels, we show that reported tip income varies with minimum-wage laws that provide an incentive for tipped workers to report some, but not necessarily all, of their tips. As a result, reported tips bunch at the minimum required threshold. We quantify missing tips due to bunching at nearly $63 million per year in 2018 dollars, on average over the period 2005-2018. Bunching is stronger for jobs at small employers and in the earlier part of the time series and declined monotonically from 2010 to 2018. Using restaurant-level revenue data, we also estimate the total value of unreported tips assuming an average tip rate of 12%. We find that tips are missing throughout the distribution. All told, missing tips exceed $4 billion per year, implying that bunching explains only 1.5% of all missing tips.View Full Paper PDF
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Working PaperNew 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.View Full Paper PDF
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Working PaperEmployees in the US Nonprofit Sector
May 2026
Working Paper Number:
CES-26-33
The nonprofit sector employs roughly 10% of the American workforce, making it the third largest workforce behind the retail and manufacturing sectors. Despite this, relatively little is known about its employees. This paper is the first to use comprehensive administrative tax data, covering the near-universe of workers in the US, to quantify and explain the causes of the nonprofit pay differential. Unconditionally, we find the nonprofit earnings penalty to be 12% relative to for-profit workers. Estimating an 'AKM' worker-firm job ladder model, we show that most of the penalty is causal and not driven by selection. We also document considerable heterogeneity across industries, both in terms of earnings premia/penalties and worker selection, and show that nonprofit and for-profit earnings have been converging over time.View Full Paper PDF
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Working PaperThe Real Effects of Bankruptcy Forum Shopping
May 2026
Working Paper Number:
CES-26-29
Many non-Delaware firms strategically file for bankruptcy in Delaware. Should this "forum shopping" be allowed? This question has motivated nine proposed congressional bills over decades of policy debate. Using a novel natural experiment and Census-Bureau microdata, we inform this debate. Comparing similar firms within a Delaware-adjacent state, we show that proximity to Delaware predicts forum shopping. Instrumenting with proximity, we find that forum shopping causally: (i) prevents closures'and liquidations, (ii) shortens bankruptcies, (iii) boosts creditor recovery, and (iv) increases post-bankruptcy employment by 24.8%. Proximity to Delaware is uncorrelated with growth for not-yet-bankrupt or never-bankrupt firms, validating the exclusion restriction.View Full Paper PDF
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Working PaperThe Adoption of Non-Rival Inputs and Firm Scope
April 2026
Working Paper Number:
CES-26-28
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.View Full Paper PDF
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Working PaperYou're (not) Hired: Artificial Intelligence and Early Career Hiring in the Quarterly Workforce Indicators
April 2026
Working Paper Number:
CES-26-27
Using detailed tabulations from matched employer-employee administrative data, I document evidence of an immediate, sizable, and persistent decrease in the level of early career (22-24 year old) hires following introduction of ChatGPT within the industry-state cells that are most exposed to AI. The decline in hires is the primary cause of large observed declines in employment over the subsequent period. Regressionadjusted employment of early career workers in the most AI-exposed quintile of industry-state cells declined by 12% over the 10 quarters following the introduction of ChatGPT, even as employment in lessexposed industries has remained stable. The rate of hiring largely recovered by early 2025, attributable to a smaller employment base. Earnings growth of early career workers in the most exposed industries slowed slightly relative to those in less exposed industries. Although the most AI-exposed quintile of detailed industries is dominated by a handful of industry sectors, I find that the association of higher AI exposure with reduced early career employment and fewer hires is observed across most sectors of the economy. Timing of effects in event studies is consistent with an immediate effect on hiring following introduction of ChatGPT. However, triple difference estimates provide some evidence of earlier trend shifts on employment, hiring, and separations around the onset of the COVID pandemic. I discuss potential explanations, including the increase in remote work and increased educational attainment among workers in AI-exposed occupations. Nonetheless, job gains to early career workers and backfill hires show evidence of discontinuous decline at the time of ChatGPT's release in comparison to older workers in the same industries. A local projections analysis at the NAICS industry group level shows that industries with high AI exposure are not particularly sensitive to unexpected fluctuations in monetary policy on average relative to other industries in employment, hiring, or separations. A historical decomposition suggests that up to one quarter of relative early career employment declines through 2025q2 may be attributable to monetary policy shocks through 2023, but the analysis does not find evidence that these shocks can explain the rapid decline in hires at the most AI-exposed firms in comparison to others.View Full Paper PDF
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Working PaperAllocating Misallocation: Decomposing Measures of Aggregate Allocative Efficiency
April 2026
Working Paper Number:
CES-26-26
We explore sources of measured misallocation using establishment data from U.S. manufacturing industries. We decompose standard revenue productivity dispersion statistics into contributions by dispersion in revenue margins over costs and dispersion in input cost shares across plants. We establish a formal link between these components and measured allocative efficiency. The results indicate the components contribute similarly to apparent rising misallocation in US manufacturing. We use the mapping between distortions that influence these distinct components to explore the relationship between inferred distortions and mechanisms that influence one or both sources of revenue productivity dispersion. Finally, we show rising misallocation in the US manufacturing sector in the last several decades is pervasive, and yet a few industries account for over half of the aggregate decline.View Full Paper PDF
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Working PaperThe Microstructure of AI Diffusion: Evidence From Firms, Business Functions, and Worker Tasks
April 2026
Working Paper Number:
CES-26-25
Using novel, nationally representative data from the 2026 AI supplement to the U.S. Census Bureau's Business Trends and Outlook Survey (BTOS), we characterize AI diffusion across three interconnected layers: overall firm use, deployment across business functions, and worker-task use. This multi-layered approach provides a nuanced picture of business AI adoption. During the supplement reference period (Nov 2025-Jan 2026), 18% of firms used AI in a business function, rising to 32% on an employment-weighted basis; adoption is expected to reach 22% within six months. AI use is substantially higher in large firms and knowledge-intensive sectors, with use rates reaching 50%-60% (60%-70%, employment-weighted) for very large firms in the Information, Professional Services, and Finance sectors. Among adopting firms, the scope of use remains limited: 57% of users integrate AI in three or fewer business functions, most commonly Sales and Marketing (52%), Strategy and Business Development (45%), and IT (41%). In 23% (41%, employment-weighted) of firms, workers use AI in work-related tasks. Writing, document analysis, and information search are the leading Generative AI use in tasks, though 65% of firms limit use to three or fewer tasks. The evidence points to both top-down and bottom-up diffusion channels: worker task use sometimes occurs without formal firm-level adoption, and firm-level adoption sometimes occurs without worker task use. Most users (66%) rely on AI solely to augment tasks, while AI-related employment decreases are rare, occurring in only 2% of firms. Regression analysis shows a robust positive correlation between firm commercial performance and the breadth of AI integration, including functional deployment, task-level use, and operational investment. A distinct divergence emerges, however, with respect to labor outcomes. Functional breadth and operational investment are positively associated with employment decreases, whereas worker-task integration shows no significant link to headcount reduction once functional integration and operational investment are taken into account.View Full Paper PDF
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Working PaperUnemployment Insurance Extensions, Labor Market Concentration, and Match Quality
April 2026
Working Paper Number:
CES-26-24
I investigate whether the effects of UI extensions are different for workers exposed to higher levels of local labor market concentration, a potential source of employer market power. I exploit measurement error in state unemployment rates that led to quasi-random assignment of UI durations in the U.S. during the Great Recession. Using matched employer-employee data from the Longitudinal Employer-Household Dynamics program, I find that UI extensions lengthen nonemployment durations by one week and cause economically meaningful but not statistically significant increases in earnings. The UI-earnings effect is significantly lower at higher levels of concentration, while there is no difference in the UI-duration effect. The lower UI-earnings effect is driven by the extremes of the distribution of concentration. My results suggest that match improvements from UI are attenuated at higher levels of concentration.View Full Paper PDF
