Estimating Search with Learning
23 Pages Posted: 20 Oct 2008 Last revised: 13 Nov 2009
Date Written: September 1, 2009
In this paper we estimate a structural model of search for differentiated products, using a unique dataset of consumer online search for hotels. We propose and implement an identification strategy that allows us to separately estimate consumer's beliefs, search costs and preferences. Learning plays an essential role in this strategy: it creates variation of posterior beliefs across consumers that is orthogonal to the variation in search costs. We show that ignoring both the limited nature and endogeneity of choice sets due to search may lead to significant biases in estimates of consumer demand: from 70 percent to more than twice depending on information assumptions. Second, the median search cost is about 25 dollars per 15 hotels; there is also a significant heterogeneity of search costs among the population. We perform a statistical test between models of search from known (Stigler 1967) and from unknown (Rothschild 1974) distribution and find that our data favors the latter: consumers learn about the price-quality relationship while searching.
Keywords: consumer search, online markets, structural estimation, maximum likelihood
JEL Classification: C14, D43, D83, L13
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