Learning from Online Ratings

83 Pages Posted: 26 Jun 2024

See all articles by Xiang Hui

Xiang Hui

Washington University in St. Louis

Tobias J. Klein

Tilburg University

Konrad O. Stahl

University of Mannheim - Department of Economics; Centre for Economic Policy Research (CEPR)

Date Written: 2024

Abstract

Online ratings play an important role in many markets. However, how fast they can reveal seller types remains unclear. To study this question, we propose a new model in which a buyer learns about the seller’s type from previous ratings and her own experience and rates the seller if she learns enough. We derive two testable implications and verify them using administrative data from eBay. We also show that alternative explanations are unlikely to explain the observed patterns. After having validated the model in that way, we calibrate it to eBay data to quantify the speed of learning. We find that ratings can be very informative. After 25 transactions, the likelihood of correctly predicting the seller type is above 95 percent.

Keywords: online markets, rating, reputation, Bayesian learning

JEL Classification: D830, L120, L130, L810

Suggested Citation

Hui, Xiang and Klein, Tobias J. and Stahl, Konrad O., Learning from Online Ratings (2024). CESifo Working Paper No. 11171, Available at SSRN: https://ssrn.com/abstract=4875548 or http://dx.doi.org/10.2139/ssrn.4875548

Xiang Hui (Contact Author)

Washington University in St. Louis ( email )

Tobias J. Klein

Tilburg University ( email )

P.O. Box 90153
Tilburg, DC Noord-Brabant 5000 LE
Netherlands

Konrad O. Stahl

University of Mannheim - Department of Economics ( email )

D-68131 Mannheim
Germany
+49 621 181 1875 (Phone)
+49 621 181 1874 (Fax)

Centre for Economic Policy Research (CEPR)

London
United Kingdom

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