Misperception and informativeness in statistical discrimination
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.
Cite
@article{arxiv.2508.20053,
title = {Misperception and informativeness in statistical discrimination},
author = {Matteo Escudé and Paula Onuchic and Ludvig Sinander and Quitzé Valenzuela-Stookey},
journal= {arXiv preprint arXiv:2508.20053},
year = {2026}
}