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Search with Learning

Author

Listed:
  • Babur De los Santos

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

  • Ali Hortacsu

    (University of Chicago and NBER)

  • Matthijs R. Wildenbeest

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

Abstract

This paper provides a method to estimate search costs in an environment in which consumers are uncertain about the price distribution. Consumers learn about the price distribution by Bayesian updating their prior beliefs. The model provides bounds on the search costs that can rationalize observed search and purchasing behavior. Using individual-specific data on web browsing and purchasing behavior for electronics sold online we show how to use these bounds to estimate search costs. Estimated search costs are sizable and are found to relate to consumer characteristics in intuitive ways. The model outperforms a standard sequential search model in which the price distribution is known to consumers.

Suggested Citation

  • Babur De los Santos & Ali Hortacsu & Matthijs R. Wildenbeest, 2012. "Search with Learning," Working Papers 2012-03, Indiana University, Kelley School of Business, Department of Business Economics and Public Policy.
    • Handle: RePEc:iuk:wpaper:2012-03
    as

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    File URL: http://kelley.iu.edu/riharbau/RePEc/iuk/wpaper/bepp2012-03-DelosSantos-Hortacsu-Wildenbeest.pdf
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    References listed on IDEAS

    as
    1. Rothschild, Michael, 1974. "Searching for the Lowest Price When the Distribution of Prices Is Unknown," Journal of Political Economy, University of Chicago Press, vol. 82(4), pages 689-711, July/Aug..
    2. Stigler, George J., 2011. "Economics of Information," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 5, pages 35-49.
    3. 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.
    4. Matthijs R. Wildenbeest, 2011. "An empirical model of search with vertically differentiated products," RAND Journal of Economics, RAND Corporation, vol. 42(4), pages 729-757, December.
    5. Bikhchandani, Sushil & Sharma, Sunil, 1996. "Optimal search with learning," Journal of Economic Dynamics and Control, Elsevier, vol. 20(1-3), pages 333-359.
    6. J. J. McCall, 1970. "Economics of Information and Job Search," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 84(1), pages 113-126.
    7. Philip A. Haile & Han Hong & Matthew Shum, 2003. "Nonparametric Tests for Common Values at First-Price Sealed-Bid Auctions," NBER Working Papers 10105, National Bureau of Economic Research, Inc.
    8. 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.
    9. Dana, James D, Jr, 1994. "Learning in an Equilibrium Search Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 745-771, August.
    10. Moraga-González, José Luis & Wildenbeest, Matthijs R., 2008. "Maximum likelihood estimation of search costs," European Economic Review, Elsevier, vol. 52(5), pages 820-848, July.
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    13. Rosenfield, Donald B. & Shapiro, Roy D., 1981. "Optimal adaptive price search," Journal of Economic Theory, Elsevier, vol. 25(1), pages 1-20, August.
    14. Michael Rothschild, 1974. "Searching for the Lowest Price When the Distribution of Prices Is Unknown: A Summary," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 1, pages 293-294, National Bureau of Economic Research, Inc.
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    16. 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.
    17. De los Santos, Babur, 2018. "Consumer search on the Internet," International Journal of Industrial Organization, Elsevier, vol. 58(C), pages 66-105.
    18. Sergei Koulayev, 2009. "Estimating demand in search markets: the case of online hotel bookings," Working Papers 09-16, Federal Reserve Bank of Boston.
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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Ali Hortaçsu & Seyed Ali Madanizadeh & Steven L. Puller, 2017. "Power to Choose? An Analysis of Consumer Inertia in the Residential Electricity Market," American Economic Journal: Economic Policy, American Economic Association, vol. 9(4), pages 192-226, November.
    2. José Luis Moraga-González & Zsolt Sándor & Matthijs R. Wildenbeest, 2015. "Consumer Search and Prices in the Automobile Market," Tinbergen Institute Discussion Papers 15-033/VII, Tinbergen Institute.
    3. Jason R. Blevins & Garrett T. Senney, 2019. "Dynamic selection and distributional bounds on search costs in dynamic unit‐demand models," Quantitative Economics, Econometric Society, vol. 10(3), pages 891-929, July.
    4. Janssen, Maarten C.W. & Parakhonyak, Alexei & Parakhonyak, Anastasia, 2017. "Non-reservation price equilibria and consumer search," Journal of Economic Theory, Elsevier, vol. 172(C), pages 120-162.

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    More about this item

    Keywords

    consumer search; learning; electronic commerce;
    All these keywords.

    JEL classification:

    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets

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