Estimation of Preference Heterogeneity in Markets with Costly Search

We study the estimation of preference heterogeneity in markets where consumers engage in costly search to learn product characteristics. Costly search amplifies the way consumer preferences translate into purchase probabilities, generating a seemingly large degree of preference heterogeneity. We develop a search model that allows for flexible preference heterogeneity and estimate its parameters using a unique panel dataset on the search and purchase behavior of consumers. The results reveal that when search costs are ignored, the model overestimates standard deviations of product intercepts by 53%. We show that the bias in heterogeneity estimates leads to incorrect inference about price elasticities and seller markups and has important consequences for personalized pricing.