Template-Type: ReDIF-Paper 1.0
Title: Learning From Online Ratings
Author-Name: Xiang Hui
Author-Email: 
Author-Name: Tobias J. Klein
Author-Email: 
Author-Name: Konrad Stahl
Author-Email: 

Classification-JEL: D83, L12, L13, L81
Keywords: Online markets, rating, reputation

Abstract: Online ratings play an important role in many markets. However, how fast they can reveal
seller types remains unclear. We propose a simple 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.

Note: 
Length: 81
Creation-Date: 2024-04
Revision-Date: 
File-URL: https://www.crctr224.de/research/discussion-papers/archive/dp532
File-Format: application/pdf
Handle: RePEc:bon:boncrc:CRCTR224_2024_532