Statistical Discrimination and Committees

We develop a statistical discrimination model where groups of workers differ in the observability of their productivity signals by the evaluation committee. 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 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. We show that introducing a “temporary” quota allows to reach a steady state equilibrium with a higher welfare when groups have similar size in the population. If instead the discriminated group is underrepresented in the workers’ population (for example because of his race), restoring efficiency requires to implement a “permanent” system of quotas.