AI and Coder Employment: Compiling the Evidence

Leland Crane and Paul E. Soto
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Paul E. Soto: https://www.federalreserve.gov/econres/paul-e-soto.htm

No 2026-018, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

Abstract: We evaluate whether LLMs have had any discernible impact on the aggregate labor market so far. We focus on occupations that are computer programming-intensive, motivated by data showing that coding is one of the most LLM-exposed tasks. Linking O*NET to CPS we find that aggregate employment of coders has decelerated sharply since the introduction of ChatGPT. Using a novel control variable for industry-level shocks we show that the deceleration is not attributable to the exposure of coders to slowing industries, suggesting instead that coders experienced an occupation-specific shock around the introduction of ChatGPT. Coder employment has continued to grow in recent years, though much more slowly than it did pre-2022. We validate the industry-level control variable by examining historical examples of occupations that experienced either occupation-specific or industry-level shocks. We also provide statistics on the agreement rates between different measures of AI exposure.

Keywords: Labor demand; Machine learning; Shocks (search for similar items in EconPapers)
JEL-codes: J23 J24 O33 (search for similar items in EconPapers)
Pages: 57 p.
Date: 2026-03-23
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:102997

DOI: 10.17016/FEDS.2026.018

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