Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/301297 
Year of Publication: 
2024
Series/Report no.: 
CESifo Working Paper No. 11171
Publisher: 
CESifo GmbH, Munich
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.
Subjects: 
online markets
rating
reputation
Bayesian learning
JEL: 
D83
L12
L13
L81
Document Type: 
Working Paper
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