Consumer Search on the Internet

This paper analyzes search frictions in online markets using data depicting the web browsing and purchasing behavior of a large panel of consumers. In this data, consumer search behavior is observed prior to a transaction. I use data on consumers shopping for books online to link prices and consumer search patterns at different bookstores, estimating consumer search costs in the context of a fixed-sample search model. The search patterns indicate that consumers visit relatively few firms and exhibit a strong search preference for prominent retailers. I control for search intensities at different retailers during consumers’ search process and find that search cost estimates are lower than when assuming consumers sample equally among alternatives. Accounting for heterogeneity in consumer search intensities across retailers reduces search cost estimates from $2.30 to $1.24 per search. I examine search cost heterogeneity by using a rich set of consumer characteristics and relating them to search patterns and search costs estimates. I use a flexible random effects model in which the number and order of firms visited by the consumer are her optimal ordered choices, allowing search cost cutoffs to depend on regressors. The estimates indicate that consumer search costs are related to their observable characteristics, such as income, where individuals with income greater than $100,000 incur relatively higher search costs. ∗I benefited from comments from Ali Hortaçsu, Jean-Pierre Dubé, Lura Forcum, Jeremy Fox, Matt Gentzkow, Austan Goolsbee, Günter Hitsch, Steven Levitt, Jesse Shapiro, Chad Syverson, José Luis Moraga-González, Matthijs Wildenbeest, as well as seminar participants at the Workshop on Consumer Search, the University of Chicago, Indiana University, University of Michigan, and the Federal Reserve Board of Kansas City. I gratefully acknowledge financial support from the NET Institute (www.netinst.org), the Kauffman Foundation, and CONACYT.