Price Recommendations and the Value of Data: A Mechanism Design Approach

Abstract

I study how a platform can affect its sellers’ pricing decisions by using price recommendations. Sellers are too small to take account of network effects in their pricing decisions. To alleviate this problem, the platform influences seller prices by strategically disclosing demand information. I then study how the platform values additional information to improve price recommendations. I identify distortions in the way the platform uses and collects information and relate the nature and extent of these distortions with its business model. A platform that makes profits on both sides of the market uses information efficiently, but values information less to what is socially desirable. A platform that makes profits only on the seller side uses information to help sellers extract buyers surplus, which is inefficient. Further, such platforms value different types of information than the social planner.