Optimal Self-Screening and the Persistence of Identity-Driven Choices

I analyze a model in which agents choose whether to undertake a task with an individual-specific probability of success of which they only have a noisy perception. I show how, when agents do not have the tools to correct for noise as a Bayesian would, they can use statistics about the prevalence of their social group among the successful individuals in the task to bias their noisy perception in a direction contingent on their social type and limit the adverse effects of the noise on decision making. This optimal self-screening can improve decision making on average, even when the statistics are irrelevant in a Bayesian sense. Differences in representation across social groups induce differential influences on choice across social types. I show the existence of a stable population equilibrium, not driven by ability differences, in which a priori identical social groups make different choices, fuelling the asymmetries in the representation of social groups among those successful.