Search with Learning

Abstract

This paper provides a method to estimate search costs in a differentiated product environment in which consumers are uncertain about the utility distribution. Consumers learn about the utility distribution by Bayesian updating their Dirichlet process prior beliefs. The model provides expressions for bounds on the search costs that can rationalize observed search and purchasing behavior. Using individual-specific data on web browsing and purchasing behavior for MP3 players sold online we show how to use these bounds to estimate search costs as well as the parameters of the utility distribution. Our estimates indicate that search costs are sizable. We show that wrongfully assuming consumers are not learning while searching can lead to severely biased search cost and elasticity estimates.

Keywords: consumer search, learning, electronic commerce, consumer behavior

JEL Classification: D43, D83, L13