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Working PaperAI Exposure and Adoption Among U.S. Firms
September 2026
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.View Full Paper PDF
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Working PaperGender Differences in the Returns to Double Majoring: A Case of Signaling?
September 2026
Working Paper Number:
CES-26-60
Over 15 percent of college graduates in the United States graduate with more than one major. While much research exists on the returns to different individual majors, less is known about the causal effects of double majoring. This study provides novel estimates on the returns to those who choose to double major as an undergraduate. We improve on previous studies that rely on controls for observable characteristics by addressing selection concerns in two notable ways. First, we control for institution-specific differences that may influence both the decision to double major and subsequent earnings using proprietary data by including institution fixed effects. Second, we adopt a partial identification approach to address non-random selection into double majoring within institutions. Results broadly align with estimates from prior studies at the aggregate level, but reveal notable gender differences not previously detected. Women experience an earnings return to double majoring of approximately 5 percent, while the return for men is statistically negligible. Our analysis suggests that this discrepancy is consistent with double majors serving as a signaling effect in the labor market, which may help offset the gender pay gap.View Full Paper PDF
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Working PaperResilience After Climate Shocks: Evidence From SBA Disaster Loans
September 2026
Working Paper Number:
CES-26-58
We show that natural disasters generate persistent declines in U.S. county employment and net job creation. We then examine whether emergency credit can mitigate these effects. Linking the universe of SBA disaster-loan applications to Census Bureau records, we exploit a credit-score screening rule introduced in 2016 and implement a fuzzy regression discontinuity design around the score cutoff. Firms just above the threshold are substantially more likely to receive government-backed loans, and approval increases employment relative to declined applicants, with effects persisting over subsequent years. The adjustment operates primarily through lower job destruction, while reduced firm exit and stronger job creation also contribute. Overall, SBA lending preserved an estimated 9.8% of the jobs that would otherwise have been lost in disaster-affected counties.View Full Paper PDF
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Working PaperHeritability and Social Interaction in Labor Market Outcomes: Evidence From the Population of Twins in the United States
September 2026
Working Paper Number:
CES-26-57
We use data on 44,000 twin pairs observed as adolescents in the 2000 Census, linked to their earnings 20 years later, to study the heritability of labor market outcomes. We extend the Classical Twins Design (CTD), which identifies heritability from contrasts between monozygotic and dizygotic twins, to settings where an analyst does not observe zygosity but can measure outcomes for same-sex and opposite-sex twins. We also allow for the presence of a family- and sex-specific component affecting same-sex siblings but only partly shared by opposite-sex siblings. Our extended model identifies heritability from the difference-in-differences of same-sex vs. opposite-sex twins vs. siblings. The estimates indicate strong heritability (h2'''0.36) of log earnings.''Using an AKM (Abowd et al., 1999) decomposition that separates the person-specific component of earnings from employer-specific pay premiums, we find that both components are heritable. The heritability of employer pay premiums, however, is mainly driven by same-sex twins who work at the same firm, suggesting that social interactions may lead to an over-estimate of the genetic component in earnings. Excluding siblings who work together leads to estimates of the heritability of log earnings that are 15% lower. Similar biases may be present in CTD-based estimates of heritability for other social outcomes like education.View Full Paper PDF
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Working PaperGraduating Into Disruption: Labor Market Outcomes for AI-Exposed College Majors
September 2026
Working Paper Number:
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.View Full Paper PDF
