Quality Selection in Two-Sided Markets: A Constrained Price Discrimination Approach

Abstract

Online platforms collect rich information about participants and then share some of this information with their participants to improve market outcomes. In this paper we study the following information disclosure problem in a two-sided market: which sellers should the platform allow to participate and how much of its available information about participant sellers' quality should the platform share with buyers to maximize its own revenue. To study this information disclosure problem, we introduce two distinct two-sided market models: one in which the platform chooses prices and the sellers choose quantities (similar to ride-sharing), and one in which the sellers choose prices (similar to e-commerce). Our main results provide conditions under which simple information structures commonly observed in practice, such as banning certain sellers from the platform while not distinguishing between participating sellers, maximize the platform's revenue. An important innovation in our analysis that we leverage to obtain our structural results is to transform the study of the two-sided market platform's optimal information disclosure policy into a constrained price discrimination problem.