Statistical Discrimination and the Efficiency of Quotas

We develop a statistical discrimination model a la Cornel and Welch (1996) where groups of workers (males-females) differ in the observability of their productivity signals. We assume that the informativeness of the productivity signals depends on the match between the potential worker and the interviewer: when both parties have similar backgrounds, the signal is likely to be more informative. Under this “homo-accuracy” bias, the group that is most represented in the evaluation committee generates more accurate signals, and, consequently, has a greater incentive to invest in human capital. This generates a discrimination trap. If, for some exogenous reason, one group is initially poorly evaluated (less represented into the evaluation committee), this translates into lower investment in human capital of individuals of such group, which leads to lower representation in the evaluation committee in the future, generating a persistent discrimination process. We explore this dynamic process and show that quotas may be effective to deal with this discrimination trap. In particular, we show that introducing a quota allows to reach a steady state equilibrium with a higher welfare than the one obtained in the decentralized equilibrium in which talented workers of the discriminated group decide not to invest in human capital.