Social Learning and Strategic Pricing with Rating Systems
2025-10-28
Rating systems, widely used in online transactions, often reduce buyers' diverse opinions to summary statistics. To explore the consequences of this coarse aggregation, we analyze a dynamic adverse selection model where buyers share anonymous evaluations via a rating system. With heterogeneous buyers, the seller is tempted to secretly lower prices to attract favorable ratings from price-sensitive buyers. That leads to sporadic flash sales. The seller's incentive to manipulate ratings is, however, self-defeating. Our analysis illustrates how the rating system shapes the allocation of surplus and offers insights for platform and product design. (JEL D11, D82, L11, L81)