This paper studies the estimation of the cost of non-sequential search. We provide a new method based on semi-nonparametric (SNP) estimation that allows us to pool price data from different consumer markets with the same underlying search cost distribution but different valuations or selling costs. We show that pooling data from different markets increases the number of estimated critical search cost cutoffs at all quantiles of the search cost distribution, which increases the precision of the estimates. A Monte Carlo study shows that the method works well in small samples. We apply our method to a data set of online prices for memory chips and find that the search cost density is essentially bimodal such that a large fraction of consumers searches very little, while a smaller fraction of consumers samples a relatively large number of stores.

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