AI Exposure and the Labor Market: Measurement, Mechanisms, and Adaptive Capacity

• In Event: Organizations, Occupations, and Work Section Roundtables
  In Refereed Roundtable (90 minute): Table 1: Artificial Intelligence and Work

Abstract

How does artificial intelligence reshape the current workforce in the United States, and how should we measure its reach at the individual level? This paper synthesizes the rapidly evolving literature on AI exposure—the degree to which occupational tasks overlap with AI capabilities—and its labor market consequences. We first provide a taxonomy of measurement approaches, distinguishing potential exposure indices based on task-capability overlap from realized adoption measures that track internal usage data and actual firm-level integration. Then, we outline a novel measurement strategy that leverages industry and occupation write-in responses from the American Community Survey (ACS). These self-reported write-ins in free-text format can be scored for AI exposure at the individual level, capturing within-occupation heterogeneity that standardized taxonomies miss. The empirical findings describe which subgroups defined by sex, education, and geography are disproportionately affected by AI.

Authors

• Jiahui Xu, Pennsylvania State University

• Lynda L. Laughlin, U.S. Census Bureau