Endogenous Learning, Persistent Employer Biases, and Discrimination

I present a statistical discrimination model of the labor market in which employers endogenously learn about the productivity of worker groups through their hiring. Previous hiring experiences with groups shape subsequent incentives of profit-maximizing employers to hire from these groups again and learn about their productivity, leading to differential learning across employers and the endogenous development of persistent negatively-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. Discrimination 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