  Online platforms collect rich information about participants and then share
some of this information back with them to improve market outcomes. In this
paper we study the following information disclosure problem in two-sided
markets: If a platform wants to maximize revenue, which sellers should the
platform allow to participate, and how much of its available information about
participating sellers' quality should the platform share with buyers? We study
this information disclosure problem in the context of 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. The platform's
information disclosure problem naturally transforms into a constrained price
discrimination problem where the constraints are determined by the equilibrium
outcomes of the specific two-sided market model being studied. We analyze this
constrained price discrimination problem to obtain our structural results.
