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How (un)Stable Are LLM Occupational Exposure Scores? Evidence from Multi-Model Replication

Author

Listed:
  • Michelle Yin
  • Hoa Vu
  • Claudia Persico

Abstract

A rapidly growing literature estimates AI's labor-market effects using large language models (LLMs) to self-assess occupational exposure. We demonstrate these measures are highly fragile. Replicating the dominant rubric with three frontier models on identical tasks, we find a 3.6-fold divergence in mean exposure with agreement as low as 57%. This measurement instability alters downstream empirical conclusions: in a difference-in-differences framework, individual-level coefficient magnitudes vary 2.4-fold across annotators, and county level estimates flip from a significant negative to an insignificant positive depending on annotators. We formalize this non-classical measurement error, highlighting the risks of treating evolving LLMs as static instruments.

Suggested Citation

  • Michelle Yin & Hoa Vu & Claudia Persico, 2026. "How (un)Stable Are LLM Occupational Exposure Scores? Evidence from Multi-Model Replication," NBER Working Papers 35110, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:35110
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    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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