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:|
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- Babur De los Santos, 2008.
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08-15, NET Institute, revised Sep 2008.
- Babur De los Santos & Ali Hortacsu & Matthijs R. Wildenbeest, 2009.
"Testing Models of Consumer Search using Data on Web Browsing and Purchasing Behavior,"
2009-05, Indiana University, Kelley School of Business, Department of Business Economics and Public Policy.
- Babur De Los Santos & Ali Hortacsu & Matthijs R. Wildenbeest, 2012. "Testing Models of Consumer Search Using Data on Web Browsing and Purchasing Behavior," American Economic Review, American Economic Association, vol. 102(6), pages 2955-80, October.
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- Han Hong & Matthew Shum, 2006. "Using price distributions to estimate search costs," RAND Journal of Economics, RAND Corporation, vol. 37(2), pages 257-275, 06.
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