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RESEARCHQuantitative Marketing and Economics (2026) 24:9 https://doi.org/10.1007/s11129-026-09313-1We are grateful to many employees and executives at eBay without whom this research could not have been possible. We thank Dominic Coey, Andrei Hagiu, Ali Hortaçsu, Dimitriy Masterov, Carl Mela, and Glen Weyl for very helpful comments. Steven Tadelisstadelis@berkeley.eduChris Noskocnosko@gmail.com1 Amazon.com Inc., Seattle, USA2 University of California, Berkeley, Berkeley, CA, USA3 National Bureau of Economic Research, Cambridge, MA, USAReceived: 24 June 2025 / Accepted: 14 July 2026© The Author(s) 2026The limits of reputation in platform markets: An empirical analysis and fi eld experimentChris Nosko 1  · Steven Tadelis 2,3AbstractReputation 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, pub-lic 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 pur-chasing, even after controlling for reputation measures visible to buyers. We then evaluate a fi eld 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 incorpo-rate reputational externalities across sellers.Keywords Reputation mechanisms · Platform markets · Online marketplaces · Reputational externalities · Search rankingJEL Classif i cation D47 · D82 · L15 · L21 · L86