Using a large data set on web browsing and purchasing behavior we test to what extent consumers are searching in accordance to various classical search models. We find that the benchmark model of sequential search with a known distributions of prices can be rejected based on the recall patterns we observe in the data. Moreover, we show that even if consumers are initially unaware of the price distribution and have to learn the price distribution, observed search behavior for given consumers over time is more consistent with non-sequential search than sequential search with learning. Our findings suggest non-sequential search provides a more accurate description of observed consumer search behavior. We then utilize the non-sequential search model to estimate the price elasticities and markups of online book retailers.

This article provides a framework for studying price dispersion in markets with product differentiation and search frictions. We show under which assumptions we can obtain an equilibrium in which vertically differentiated firms mix prices over different supports. The model can explain the frequently changing prices reported in several empirical studies, but also why some firms have persistently higher prices than others. We show how to estimate the model by maximum likelihood using only prices. Estimates for grocery items in the United Kingdom reveal that most of the observed price variation is explained by supermarket heterogeneity rather than search frictions, whereas the estimated amount of search is low. * Indiana University; mwildenb@indiana.edu. This article is based on the last chapter of my dissertation. I am grateful to the editor, Philip Haile, and two anonymous referees for comments and suggestions that have substantially improved this article. In addition, I wish to thank José Luis Moraga-González for his valuable comments and suggestions.

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In a recent paper Hong and Shum (2006) present a structural methodology to estimate search cost distributions. We extend their approach to the case of oligopoly and present a new method to estimate search costs by maximum likelihood. We apply our method to a data set of online prices for different computer memory chips. The estimates suggest that on-line consumers have either quite high or quite low search costs so they either search exhaustively in the market or very little, for at most three prices. Search frictions confer a significant amount of market power to the firms: despite that more than 20 firms operate in each of the markets we study, price-cost margins are around 25%. Kolmogorov-Smirnov goodness-of-fit tests suggest we cannot reject the null hypothesis that the observed prices are generated by the model. The paper also illustrates how the structural methodology can be employed to simulate the effects of policy interventions.