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You'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.
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Unemployment 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.
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Expectations versus Reality in Business Formation
February 2026
Working Paper Number:
CES-26-11
Using administrative data on 17 million U.S. business applications linked to outcomes, we compare potential entrants' expectations about employer entry and first-year employment with realizations. On average, applicants overestimate employment, mainly because many expect to enter but do not. Among those who expect and achieve entry, employment is typically underestimated. Expected employment predicts entry and realized employment, but conditional on entry realized employment rises less than one-for-one with expectations. Expectation errors are highly heterogeneous and systematically related to application characteristics and local economic conditions, and they predict near-term employment outcomes. A parsimonious model with heterogeneous priors, learning, and pre-entry selection rationalizes these patterns.
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Positioned at Extremes: Future Job Placements of Immigrant Students at U.S. Colleges
January 2026
Working Paper Number:
CES-26-08
Immigrant students who attend U.S. colleges are disproportionately employed in either large firms'especially multinationals'or small firms and self-employment. Using linked Census and longitudinal employment data, we trace the jobs taken by college students in 2000 during the 2001-20 period and evaluate four mechanisms shaping sector and firm size placement: geographic clustering, degree specialization, firm capabilities/visas, and ethnic self-employment specialization. Degree fields predict large firm and MNE placement, while ethnic specialization explains small firm sorting. Immigrant students who remain in the U.S. earn more than their native peers, suggesting the segmentation reflects productive sorting rather than blocked opportunity.
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Double-Pane Glass Ceiling: Commercial Engagement and the Female-Male Earnings Gap for Faculty
September 2025
Working Paper Number:
CES-25-68
I use administrative data from universities (UMETRICS) linked to the universe of confidential W-2 and 1040-C tax records to measure faculty commercial engagement and its role in female-male earnings gaps. Female faculty are 20 percentage points less likely to engage commercially, with the entire gap driven by self-employment. The raw earnings gap is $63,000 on a base of $162,000 and non-university earnings account for $18,000 (29 percent) of this total. Thus, while university pay explains most of the gap, commercial engagement substantially amplifies it. Earnings gaps appear in all components of non-university pay ' self-employment, and work for incumbent, young/startup, high-tech, and non-high-tech firms ' and remain large, though attenuated, after controlling publications, patents, field, university, scientific resources, age, marital status, childbearing, and demographics. Gaps widen as faculty move up the earnings distribution, and commercial engagement becomes a larger contributor. Men and women engage with similar industries, but men earn more in all shared industries.
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Revisiting the Unintended Consequences of Ban the Box
August 2025
Working Paper Number:
CES-25-58
Ban-the-Box (BTB) policies intend to help formerly incarcerated individuals find employment by delaying when employers can ask about criminal records. We revisit the finding in Doleac and Hansen (2020) that BTB causes statistical discrimination against minority men. We correct miscoded BTB laws and show that estimates from the Current Population Survey (CPS) remain quantitatively similar, while those from the American Community Survey (ACS) now fail to reject the null hypothesis of no effect of BTB on employment. In contrast to the published estimates, these ACS results are statistically significantly different from the CPS results, indicating a lack of robustness across datasets. We do not find evidence that these differences are due to sample composition or survey weights. There is limited evidence that these divergent results are explained by the different frequencies of these surveys. Differences in sample sizes may also lead to different estimates; the ACS has a much larger sample and more statistical power to detect effects near the corrected CPS estimates.
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Startup Dynamics: Transitioning from Nonemployer Firms to Employer Firms, Survival, and Job Creation
April 2025
Working Paper Number:
CES-25-26
Understanding the dynamics of startup businesses' growth, exit, and survival is crucial for fostering entrepreneurship. Among the nearly 30 million registered businesses in the United States, fewer than six million have employees beyond the business owners. This research addresses the gap in understanding which companies transition to employer businesses and the mechanisms behind this process. Job creation remains a critical concern for policymakers, researchers, and advocacy groups. This study aims to illuminate the transition from non-employer businesses to employer businesses and explore job creation by new startups. Leveraging newly available microdata from the U.S. Census Bureau, we seek to gain deeper insights into firm survival, job creation by startups, and the transition from non-employer to employer status.
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Size Matters: Matching Externalities and the Advantages of Large Labor Markets
April 2025
Working Paper Number:
CES-25-22
Economists have long hypothesized that large and thick labor markets facilitate the matching between workers and firms. We use administrative data from the LEHD to compare the job search outcomes of workers originally in large and small markets who lost their jobs due to a firm closure. We define a labor market as the Commuting Zone'industry pair in the quarter before the closure. To account for the possible sorting of high-quality workers into larger markets, the effect of market size is identified by comparing workers in large and small markets within the same CZ, conditional on workers fixed effects. In the six quarters before their firm's closure, workers in small and large markets have a similar probability of employment and quarterly earnings. Following the closure, workers in larger markets experience significantly shorter non-employment spells and smaller earning losses than workers in smaller markets, indicating that larger markets partially insure workers against idiosyncratic employment shocks. A 1 percent increase in market size results in a 0.015 and 0.023 percentage points increase in the 1-year re-employment probability of high school and college graduates, respectively. Displaced workers in larger markets also experience a significantly lower need for relocation to a different CZ. Conditional on finding a new job, the quality of the new worker-firm match is higher in larger markets, as proxied by a higher probability that the new match lasts more than one year; the new industry is the same as the old one; and the new industry is a 'good fit' for the worker's college major. Consistent with the notion that market size should be particularly consequential for more specialized workers, we find that the effects are larger in industries where human capital is more specialized and less portable. Our findings may help explain the geographical agglomeration of industries'especially those that make intensive use of highly specialized workers'and validate one of the mechanisms that urban economists have proposed for the existence of agglomeration economies.
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The Composition of Firm Workforces from 2006'2022: Findings from the Business Dynamics Statistics of Human Capital Experimental Product
April 2025
Working Paper Number:
CES-25-20
We introduce the Business Dynamics Statistics of Human Capital (BDS-HC) tables, a new Census Bureau experimental product that provides public-use statistics on the workforce composition of firms and its relationship to business dynamics. We use administrative W-2 filings to combine population-level worker demographic data with longitudinal business data to estimate the demographic and educational composition of nearly all non-farm employer businesses in the United States between 2006 and 2022. We use this newly constructed data to document the evolution of employment, entry, and exit of employers based on their workforce compositions. We also provide new statistics on the interaction between firm and worker characteristics, including the composition of workers at startup firms. We find substantial changes between 2006 and 2022 in the distribution of employers along several dimensions, primarily driven by changing workforce compositions within continuing firms rather than the reallocation of employment between firms. We also highlight systematic differences in the business dynamics of firms by their workforce compositions, suggesting that different groups of workers face different economic environments due to their employers.
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Workers' Job Prospects and Young Firm Dynamics
January 2025
Working Paper Number:
CES-25-09
This paper investigates how worker beliefs and job prospects impact the wages and growth of young firms, as well as the aggregate economy. Building a heterogeneous-firm directed search model where workers gradually learn about firm types, I find that learning generates endogenous wage differentials for young firms. High-performing young firms must pay higher wages than equally high-performing old firms, while low-performing young firms offer lower wages than equally low-performing old firms. Reduced uncertainty or labor market frictions lower the wage differentials, thereby enhancing young firm dynamics and aggregate productivity. The results are consistent with U.S. administrative employee-employer matched data.
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