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Search for differentiated products: identification and estimation

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  • Sergei Koulayev

Abstract

type="main"> When consumers search for differentiated products, a given search decision can be explained either by low search cost or by low tastes for the set of products already found. We propose an identification strategy that allows to estimate the search cost distribution in the presence of unobserved tastes. The required data takes the form of conditional search decisions: observations of search actions combined with previously observed product displays. We develop an application using clickstream data from a hotel search platform. Estimates of price elasticity of demand in the search model differ from those in the static model, reflecting the bias due to endogeneity of search-generated choice sets.

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  • Sergei Koulayev, 2014. "Search for differentiated products: identification and estimation," RAND Journal of Economics, RAND Corporation, vol. 45(3), pages 553-575, September.
    • Handle: RePEc:bla:randje:v:45:y:2014:i:3:p:553-575
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    1. Han Hong & Matthew Shum, 2006. "Using price distributions to estimate search costs," RAND Journal of Economics, RAND Corporation, vol. 37(2), pages 257-275, June.
    2. Eric J. Johnson & Wendy W. Moe & Peter S. Fader & Steven Bellman & Gerald L. Lohse, 2004. "On the Depth and Dynamics of Online Search Behavior," Management Science, INFORMS, vol. 50(3), pages 299-308, March.
    3. Michael D. Smith & Erik Brynjolfsson, 2001. "Consumer Decision-making at an Internet Shopbot: Brand Still Matters," NBER Chapters, in: E-commerce, pages 541-558, National Bureau of Economic Research, Inc.
    4. Michael D. Smith & Erik Brynjolfsson, 2001. "Consumer Decision-making at an Internet Shopbot: Brand Still Matters," NBER Chapters, in: E-commerce, pages 541-558, National Bureau of Economic Research, Inc.
    5. De los Santos, Babur, 2018. "Consumer search on the Internet," International Journal of Industrial Organization, Elsevier, vol. 58(C), pages 66-105.
    6. Babur De Los Santos & Ali Hortacsu & Matthijs R. Wildenbeest, 2012. "Testing Models of Consumer Search Using Data on Web Browsing and Purchasing Behavior," American Economic Review, American Economic Association, vol. 102(6), pages 2955-2980, October.
    7. Sergei Koulayev, 2013. "Search With Dirichlet Priors: Estimation and Implications for Consumer Demand," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 226-239, April.
    8. Ali Hortaçsu & Chad Syverson, 2004. "Product Differentiation, Search Costs, and Competition in the Mutual Fund Industry: A Case Study of S&P 500 Index Funds," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(2), pages 403-456.
    9. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, January.
    10. Alan T. Sorensen, 2000. "Equilibrium Price Dispersion in Retail Markets for Prescription Drugs," Journal of Political Economy, University of Chicago Press, vol. 108(4), pages 833-862, August.
    11. Nitin Mehta & Surendra Rajiv & Kannan Srinivasan, 2003. "Price Uncertainty and Consumer Search: A Structural Model of Consideration Set Formation," Marketing Science, INFORMS, vol. 22(1), pages 58-84, June.
    12. Babur De los Santos & Sergei Koulayev, 2012. "Optimizing Click-through in Online Rankings for Partially Anonymous Consumers," Working Papers 2012-04, Indiana University, Kelley School of Business, Department of Business Economics and Public Policy.
    13. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
    14. Steven T. Berry, 1994. "Estimating Discrete-Choice Models of Product Differentiation," RAND Journal of Economics, The RAND Corporation, vol. 25(2), pages 242-262, Summer.
    15. Jun B. Kim & Paulo Albuquerque & Bart J. Bronnenberg, 2010. "Online Demand Under Limited Consumer Search," Marketing Science, INFORMS, vol. 29(6), pages 1001-1023, 11-12.
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