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

Résumé

Using an information design framework, I study how platforms can affect the pricing decisions of their sellers by using price recommendations. Sellers are too small to take into account their influence on the total mass of buyers visiting the platform in their pricing decisions. By contrast, the platform's preferred transaction price should capture network effects. Taking advantage of its superior information about demand, the platform persuades sellers to set lower prices on average. I show that, under some conditions, the platform can achieve its optimal pricing policy only using price recommendations. Then, I analyze how a platform collects data by studying how its profit varies with respect to its information structure. I compare this behaviour to the socially optimal way of collecting data. Unlike a social planner, the platform does not internalize consumer surplus nor the sellers' costs but both aim at collecting biased information to reduce the sellers' prices.