# Misperception and informativeness in statistical discrimination

## TL;DR

This paper examines how information and misperceptions influence statistical discrimination and pay gaps in the labor market, highlighting the roles of information informativeness and perception correction in wage outcomes.

## Contribution

It decomposes the effects of increased informativeness into surplus and perception-correcting components, providing new insights into the impact on pay gaps and discrimination.

## Key findings

- Perception-correcting component is non-negative if the population was under-perceived.
- Improving information can narrow pay gaps under certain conditions.
- The model links information quality, perceptions, and wage disparities.

## 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.