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The (Short-Term) Effects of Large Language Models on Unemployment and Earnings

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Listed:
  • Danqing Chen
  • Carina Kane
  • Austin Kozlowski
  • Nadav Kunievsky
  • James A. Evans

Abstract

Large Language Models have spread rapidly since the release of ChatGPT in late 2022, accompanied by claims of major productivity gains but also concerns about job displacement. This paper examines the short-run labor market effects of LLM adoption by comparing earnings and unemployment across occupations with differing levels of exposure to these technologies. Using a Synthetic Difference in Differences approach, we estimate the impact of LLM exposure on earnings and unemployment. Our findings show that workers in highly exposed occupations experienced earnings increases following ChatGPT's introduction, while unemployment rates remained unchanged. These results suggest that initial labor market adjustments to LLMs operate primarily through earnings rather than worker reallocation.

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  • Danqing Chen & Carina Kane & Austin Kozlowski & Nadav Kunievsky & James A. Evans, 2025. "The (Short-Term) Effects of Large Language Models on Unemployment and Earnings," Papers 2509.15510, arXiv.org.
    • Handle: RePEc:arx:papers:2509.15510
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    File URL: http://arxiv.org/pdf/2509.15510
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