Endogenous Learning, Persistent Employer Biases, and Discrimination

Abstract

I present a statistical discrimination model of the labor market in which employers are initially uncertain about the productivity of worker groups and endogenously learn about it through their hiring. Previous hiring experiences with groups shape subsequent incentives of employers to hire from these groups again and learn more about their productivity, leading to differential learning across employers and biased beliefs about the productivity of groups. Given a market-clearing wage, optimal hiring follows a cutoff rule in posterior beliefs: employers with sufficiently negative experiences with workers from a group stop hiring from the group, preserving negative biases and leading to a negatively-skewed distribution of beliefs about their productivity. When employers have noisier initial information on the productivity of one worker group, discrimination against the group can arise and persist without productivity differentials or prior employer biases, with market competition, and with or without worker signaling or investment decisions. The model generates steady state predictions analogous to the Becker (1957) taste-based model, in a statistical framework with beliefs replacing preferences, explaining apparent prejudice as the result of "incorrect" statistical discrimination. The model also generates additional predictions with policy implications that contrast with traditional models of discrimination.