The Feedback Loop of Statistical Discrimination (SIMONS-ECON)

Jackie Baek (NYU Stern)

Bridging Prediction and Intervention Problems in Social Systems

We study a dynamic model of interactions between a firm and job applicants to identify mechanisms that drive long-term discrimination. In each round, the firm decides which applicants to hire, where the firm's ability to evaluate applicants is imperfect. Each applicant belongs to a group, and central to our model is the idea that the firm becomes better at evaluating applicants from groups in which they have hired from in the recent past. We establish the firm's initial evaluation ability for each group to be a critical factor in determining long-term outcomes. We show that there is a threshold for which if the firm's initial evaluation ability for the group is below the threshold, the group's hiring rate decreases over time and eventually goes to zero. If the group starts above the threshold, then the hiring rate stabilizes to a positive constant. Therefore, even when two groups are identical in size and underlying skill distribution, a marginal difference in the firm's initial evaluation ability can lead to persistent disparities that exacerbate over time through a feedback loop. Importantly, the dynamic nature of our model allows us to assess the long-term impact of interventions, specifically, whether an improvement is sustained even after the intervention is lifted. In this light, we show that drastic short-term interventions are more effective compared to milder long-term interventions. Additionally, we show that smaller groups face inherent disadvantages, requiring a higher initial evaluation ability to achieve a favorable long-term hiring outcome and experiencing lower hiring rates even when they do.