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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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The 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.
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Trade and Welfare (across Local Labor Markets)
February 2026
Working Paper Number:
CES-26-16
What are the welfare implications of trade shocks? Theoretically, we provide a sufficient statistic that measures changes in welfare (to a first-order approximation) for the set of workers who start within a region, taking into account adjustment in frictional unemployment, labor force participation, the sectors to which workers apply for jobs, and the regions in which workers choose to live. Our theory is flexible; for instance, it allows for arbitrary heterogeneity in worker productivity and non-pecuniary returns (amenities) across unemployment, labor force non-participation, sectors, and regions. Empirically, we apply these insights to measure changes in welfare between 2000-2007 across workers who start in different commuting zones (CZs) in the U.S. in the year 2000. Finally, we identify the differential impact across CZs of a particular trade shock: granting China permanent normal trade relations.
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College Majors and Earnings Growth
February 2026
Working Paper Number:
CES-26-14
We estimate major-specific earnings profiles using matched American Community Survey (ACS) and Longitudinal Employer-Household Dynamics (LEHD) data. Building on Deming and Noray (2020), we exploit a long earnings panel to overcome key limitations of cross-sectional approaches to lifecycle estimation. We find that engineering and computer science majors experience earnings growth that is comparable to or faster than that of other majors, a category including humanities, education, psychology, and similar fields. In contrast, Deming and Noray (2020) use a crosscohort approach and find that earnings for engineering and computer science majors decline relative to other fields over the lifecycle.
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Life-Cycle Effects of Women's Education on their Careers and Children
January 2026
Working Paper Number:
CES-26-09
We study the causal effect of women's education on their wages, non-wage job amenities, and spillovers to children. Using a regression discontinuity at the school entry birthdate cutoff, we find that women born just before the cutoff are more likely to complete some college, and experience multi-dimensional career gains that grow over the life cycle: greater employment and earnings, as well as more professional and higher-status jobs, more socially meaningful work, and better working conditions. Children's early-life health and prenatal inputs improve in tandem with career improvements, consistent with professional advances spurring'not hindering'infant investments. Career gains are concentrated in jobs that require exactly some college, the same schooling margin shifted by the cutoff, which indicates that increased post-secondary education is the primary channel for these effects. Together, the results show that women's college attendance generates large career returns'from both wages and amenities'that strengthen over time and produce meaningful benefits for children.
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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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Careers of Minimum Wage Workers
January 2026
Working Paper Number:
CES-26-07
We characterize the careers of minimum wage workers by merging SIPP panels covering 1992-2016 into the LEHD. A long-run analysis shows strong earnings growth for these workers in subsequent decades, becoming indistinguishable from peers earning modestly more initially. Most of this growth is due to the steep earnings trajectories of young workers. Older workers earning minimum wages show a modest dip in earnings at that moment compared to earlier and later periods. Increases in state minimum wages do not significantly alter the future careers of workers who are on the minimum wage when the increases occur.
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Trapped or Transferred: Worker Mobility and Labor Market Power in the Energy Transition
December 2025
Working Paper Number:
CES-25-76
Using matched employer-employee data covering 1.35 million US workers separated from the fossil fuel extraction industry between 1999 and 2019, I estimate how local fossil fuel labor demand shocks affect employment and earnings. Employment probabilities fall markedly after exposure, and earnings decline gradually over the first seven years with only partial recovery by ten years since exposure to the shocks. Workers who remain in the fossil fuel sector, disproportionately men in sector-specific roles, experience nearly twice the earnings losses of those who switch sectors, possibly due to limited occupational mobility. Among non-switchers, losses are larger in labor markets with high employer concentration, indicating that scarce outside options translate into lower reemployment wages and weaker bargaining positions. Geographic movers fare worse than stayers, reflecting negative selection (younger, lower-earning) and relocation to metropolitan areas where fossil fuel or low-skilled service sectors remain highly concentrated, leaving monopsony power intact.
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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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LODES Design and Methodology Report: Methodology Version 7
August 2025
Working Paper Number:
CES-25-52
The purpose of this report is to document the important features of Version 7 of the LEHD Origin-Destination Employment Statistics (LODES) processing system. This includes data sources, data processing methodology, confidentiality protection methodology, some quality measures, and a high-level description of the published data. The intended audience for this document includes LODES data users, Local Employment Dynamics (LED) Partnership members, U.S. Census Bureau management, program quality auditors, and current and future research and development staff members.
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