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You're (not) Hired: Artificial Intelligence and Early Career Hiring in the Quarterly Workforce Indicators

April 2026

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Similar Working Papers Similarity between working papers are determined by an unsupervised neural network model know as Doc2Vec.

Doc2Vec is a model that represents entire documents as fixed-length vectors, allowing for the capture of semantic meaning in a way that relates to the context of words within the document. The model learns to associate a unique vector with each document while simultaneously learning word vectors, enabling tasks such as document classification, clustering, and similarity detection by preserving the order and structure of words. The document vectors are compared using cosine similarity/distance to determine the most similar working papers. Papers identified with 🔥 are in the top 20% of similarity.

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  CES-26-56

  How are new college graduates affected by the rise of artificial intelligence, and what can this tell us about the mechanisms behind AI's overall labor market effects? We use administrative records on college graduates to observe how economic outcomes among college majors with differing levels of labor market AI exposure have evolved since large language models became available. We find that post-graduation employment, earnings, and job switching patterns among the most AI-exposed college majors began to diverge immediately following the introduction of ChatGPT in late 2022. In regression-adjusted estimates, the most AI-exposed decile of college majors saw their likelihood of initial employment decline by five percentage points, while full-quarter initial earnings declined by thirteen percent. This earnings decline is comparable in magnitude to the earnings losses associated with graduating into a large recession. Roughly half of the decline in earnings is attributable to lower earnings within the industry sectors that employ these graduates, with the remainder resulting from a shift in the industry mix into lower-wage sectors such as restaurants and retail. The effects attenuate as graduates move further from labor market entry but remain substantial for the most exposed majors.
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• Working Paper

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  AI Exposure and Adoption Among U.S. Firms
  

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  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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  The Microstructure of AI Diffusion: Evidence From Firms, Business Functions, and Worker Tasks
  

  April 2026

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  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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• Working Paper

  
  
  Labor Market Concentration, Earnings Inequality, and Earnings Mobility
  

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• Working Paper

  
  
  Did Timing Matter? Life Cycle Differences in Effects of Exposure to the Great Recession
  

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  Authors: Kevin Rinz

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  College Majors and Earnings Growth
  

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  Authors: Javier Miranda

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  Gender Differences in the Returns to Double Majoring: A Case of Signaling?
  

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