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

Michelle Yin, Hoa Vu and Claudia Persico

No 35110, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C81 J23 J24 O33 (search for similar items in EconPapers)
Date: 2026-04
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