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Semi-Nonparametric Estimation of Consumer Search Costs

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Author Info

  • Jose Luis Moraga-Gonzalez

    (University of Groningen and CESifo)

  • Zsolt Sandor

    (Universidad Carlos III de Madrid)

  • Matthijs R. Wildenbeest

    (Department of Business Economics and Public Policy, Indiana University Kelley School of Business)

Abstract

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 di erent valuations or selling costs. We show that pooling data from di erent markets increases the number of estimated critical search cost cuto s 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 nd 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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File URL: http://www.bus.indiana.edu/riharbau/RePEc/iuk/wpaper/bepp2007-20-moraga-sandor-wildenbeest.pdf
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Bibliographic Info

Paper provided by Indiana University, Kelley School of Business, Department of Business Economics and Public Policy in its series Working Papers with number 2007-20.

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Date of creation: Dec 2007
Date of revision: Jun 2010
Handle: RePEc:iuk:wpaper:2007-20

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Keywords: consumer search; oligopoly; search costs; semi-nonparametric estimation;

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