Search with Learning
This paper provides a method to estimate search costs in an environment in which consumers are uncertain about the price distribution. Consumers learn about the price distribution by Bayesian updating their prior beliefs. The model provides bounds on the search costs that can rationalize observed search and purchasing behavior. Using individual-specific data on web browsing and purchasing behavior for electronics sold online we show how to use these bounds to estimate search costs. Estimated search costs are sizable and are found to relate to consumer characteristics in intuitive ways. The model outperforms a standard sequential search model in which the price distribution is known to consumers.
|Date of creation:||Aug 2012|
|Date of revision:|
|Contact details of provider:|| Postal: 1309 East Tenth Street, Room 451, Bloomington, IN 47405-1701|
Web page: http://kelley.iu.edu/bepp/
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