Measuring AI exposure in U.S. agri-food labor markets

Becatien Yao and Aleksan Shanoyan

No 404319, 2026 Annual Meeting, July 26 - 28, 2026, Kansas City, Missouri from Agricultural and Applied Economics Association

Abstract: In many U.S. rural counties, agriculture remains a central economic activity, with jobs, incomes, and local public revenues closely tied to agri-food production. As generative artificial intelligence (AI) transforms workplaces across the economy, understanding how exposure varies across agri-food labor markets is an important first step toward evaluating potential implications for rural communities. Existing exposure measures often rely on detailed occupation data that are not consistently available at the county level, particularly in rural areas. This paper develops an occupation-based framework for measuring AI exposure using broad occupation groups reported in the American Community Survey and applies it to all U.S. counties. Exposure scores decline with rurality and are generally lower in farming, mining, and manufacturing-dependent counties. Comparison with an established task-based measure yields a state-level correlation of 0.93, indicating strong consistency between the two approaches. Using Quarterly Workforce Indicators data, the paper also examines employment trends across counties with different exposure levels. In highly exposed urban counties, employment growth among younger workers weakens relative to older workers after 2022. Similar patterns are not evident in the broader sample of rural counties and are less pronounced in farming-dependent counties. The results suggest that occupational composition remains an important source of variation in AI exposure and that the early labor market changes associated with generative AI may differ across local labor markets.

Keywords: Agribusiness (search for similar items in EconPapers)
Pages: 26
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:ags:aaea26:404319

DOI: 10.22004/ag.econ.404319

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