Evolution of Discrimination on Online Platforms

Research on discrimination has predominantly relied on audit studies, which provide clean causal estimates through randomized profile testing but offer only static snapshots of bias. We adopt a dynamic perspective to examine how racial discrimination evolves over time. Using observational panel data from an online educational platform, we first find that African-American teachers receive 30.2% fewer bookings, and their opened classes are 3.3% less likely to be booked compared with their White counterparts during their first week on the platform, a gap that may indicate initial discrimination. Employing individual fixed effects models, we estimate how this gap evolves over time. The results show a striking 1,007% widening of the initial gap over the 12-week period. Our mechanism analysis shows that new students are significantly less likely to book classes with African-American teachers than with White teachers, even when African-American and White teachers have minimal or comparable reputation metrics. As a result, African-American teachers accumulate reputation metrics and customers more slowly than White teachers. Over time, the growing gaps in reputation and customer base further exacerbate the booking gap, driven by both new and repeat students. Our findings suggest that traditional audit studies may significantly underestimate the long-term consequences of early-stage discrimination and highlight how reputation and repeat customer accumulation serve as bias amplifiers on online platforms. We also show that this amplification mechanism operates regardless of whether the initial gap stems from racial discrimination and thus generalizes to other contexts.

This paper was accepted by Srikanth Jagabathula, market design, platform, and demand analytics.

Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2025.02111.