Online platforms collect rich information about participants, and then share this information back with participants to improve market outcomes. In this paper we study the following information disclosure problem of a two-sided market: how much of its available information about sellers’ quality should the platform share with buyers to maximize its revenue?
One key innovation in our analysis is to reduce the study of optimal information disclosure policies to a {\em constrained price discrimination} problem. The information shared by the platform induces a “menu” of equilibrium prices and sellers’ expected qualities. Optimization over feasible menus yields a price discrimination problem. The problem is constrained because feasible menus are only those that can arise in the equilibrium of the two sided-market for some information disclosure policy.
We analyze this constrained price discrimination problem, and apply our insights to two distinct two-sided market models: one in which the platform chooses prices and sellers choose quantities (similar to ride-sharing), and one in which sellers choose prices (similar to e-commerce). We provide conditions under which a simple information structure of banning a certain portion of sellers from the platform, and not sharing any information about the remaining participating sellers maximizes the platform’s revenue.