Abstract Reputation systems are central to online marketplace design, but they can fail in two related ways. First, buyers may generalize from individual transactions to the platform as a whole, creating reputational externalities across sellers. Second, public reputation measures may be biased or too coarse to let buyers accurately infer seller quality. We study these problems using detailed transaction-level data from eBay. We construct a measure of seller quality based on the share of transactions that generate positive feedback, including transactions for which buyers leave no feedback. Our measure predicts poor transaction outcomes and subsequent purchasing, even after controlling for reputation measures visible to buyers. We then evaluate a field experiment in which eBay incorporated this measure into its search-ranking algorithm. The treatment shifted buyers toward higher-quality sellers and increased subsequent purchasing without reducing contemporaneous conversion. The results highlight the need for richer models of platform markets that incorporate reputational externalities across sellers.
Nosko et al. (Tue,) studied this question.