Misperception and informativeness in statistical discrimination
Matteo Escudé, Paula Onuchic, Ludvig Sinander, Quitzé Valenzuela-Stookey
TL;DR
The paper develops a Phelpsian framework to analyze how information quality and ex-ante misperceptions jointly shape labor-market pay gaps. It introduces a two-term decomposition of the payoff change from more informative observables: an instrumental term and a perception-correcting term, and proves that the perception-correcting term is nonnegative under under-perception and nonpositive under over-perception, with the instrumental term always nonnegative. Under certain conditions—such as accurate perceptions or small/near-full information changes—the results imply that increasing information can narrow pay gaps between equally skilled populations, though misperceptions can reverse this outcome. The findings have policy implications for information-related interventions (testing, transparency, algorithmic screening) by showing that informativeness and misperception jointly determine their impact on inequality and efficiency.
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.