Discussion paper
DP17006 Learning from Online Ratings
Online ratings play an important role in many markets. However, how fast they can reveal seller types remains unclear. We propose a new model of rating behavior where learning about the seller type influences the rating decision. We calibrate the model to eBay data and find that ratings can be very informative. After 25 transactions, the likelihood of correctly predicting the seller type is above 95 percent.
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