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arXiv · 2508.20053

Misperception and informativeness in statistical discrimination

Matteo Escudé·Paula Onuchic·Ludvig Sinander·Quitzé Valenzuela-Stookey
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Abstract

We study the interplay of information and prior (mis)perceptions in a Phelps-Aigner-Cain-type model of statistical discrimination in the labor market. We decompose the effect on average pay of an increase in how informative observables are about workers' skills into a non-negative instrumental component, reflecting increased surplus due to better matching of workers with tasks, and a perception-correcting component capturing how extra information diminishes the importance of prior misperceptions about the distribution of skills in the worker population. We sign the perception-correcting term: it is non-negative (non-positive) if the population was ex-ante under-perceived (over-perceived). We then consider the implications for pay gaps between equally-skilled populations that differ in information, perceptions, or both, and identify conditions under which improving information narrows pay gaps.

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Matteo Escudé, Paula Onuchic, Ludvig Sinander, Quitzé Valenzuela-Stookey. 2026-06-18. Misperception and informativeness in statistical discrimination. https://arxiv.org/abs/2508.20053

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