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

56 Pages Posted: 4 Mar 2021 Last revised: 3 Jan 2023

See all articles by Bar Light

Bar Light

Microsoft Research, NYC

Ramesh Johari

Stanford University

Gabriel Y. Weintraub

Stanford Graduate School of Business, Stanford University

Date Written: January 3, 2021

Abstract

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. An important innovation in our analysis is to transform the platform's information disclosure problem into a constrained price discrimination problem. We leverage this transformation to obtain our structural results.

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.

Keywords: Two-sided markets, market design for platforms, information design, quality selection, price discrimination

Suggested Citation

Light, Bar and Johari, Ramesh and Weintraub, Gabriel Y., Quality Selection in Two-Sided Markets: A Constrained Price Discrimination Approach (January 3, 2021). Available at SSRN: https://ssrn.com/abstract=3759241 or http://dx.doi.org/10.2139/ssrn.3759241

Bar Light (Contact Author)

Microsoft Research, NYC ( email )

NYC, CA
United States

Ramesh Johari

Stanford University ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States

Gabriel Y. Weintraub

Stanford Graduate School of Business, Stanford University ( email )

Stanford, CA 94305
United States

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