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Consumer Search on the Internet

Author

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  • Babur de los Santos

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

Abstract

This paper uses consumer search data to explain search frictions in online markets, within the context of an equilibrium search model. I use a novel dataset of consumer online browsing and purchasing behavior, which tracks all consumer search prior to each transaction. Using observed search intensities from the online book industry, I estimate search cost distributions that allow for asymmetric consumer sampling. Research on consumer search often assumes a symmetric sampling rule for analytical convenience despite its lack of realism. Search behavior in the online book industry is quite limited: in only 25 percen of the transactions did consumers visit more than one bookstore's website. The industry is characterized by a strong consumer preference for certain retailers. Accounting for unequal consumer sampling halves the search cost estimates from 1.8 to 0.9 dollars per search in the online book industry. Analysis of time spent online suggests substitution between the time consumers spend searching and the relative opportunity cost of their time. Retired people, those with lower education levels, and minorities (with the exception of Hispanics) spent significantly more time searching for a book online. There is a negative relationship between income levels and time spent searching.

Suggested Citation

  • Babur de los Santos, 2008. "Consumer Search on the Internet," Working Papers 2008-06, Indiana University, Kelley School of Business, Department of Business Economics and Public Policy.
  • Handle: RePEc:iuk:wpaper:2008-06
    as

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    File URL: http://kelley.iu.edu/riharbau/RePEc/iuk/wpaper/bepp2008-06-delossantos.pdf
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    References listed on IDEAS

    as
    1. Clay, Karen & Krishnan, Ramayya & Wolff, Eric, 2001. "Prices and Price Dispersion on the Web: Evidence from the Online Book Industry," Journal of Industrial Economics, Wiley Blackwell, vol. 49(4), pages 521-539, December.
    2. Clay, Karen, et al, 2002. "Retail Strategies on the Web: Price and Non-price Competition in the Online Book Industry," Journal of Industrial Economics, Wiley Blackwell, vol. 50(3), pages 351-367, September.
    3. Kathy Baylis & Jeffrey Perloff, 2002. "Price Dispersion on the Internet: Good Firms and Bad Firms," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 21(3), pages 305-324, November.
    4. Maarten C. W. Janssen & José Luis Moraga-González, 2004. "Strategic Pricing, Consumer Search and the Number of Firms," Review of Economic Studies, Oxford University Press, vol. 71(4), pages 1089-1118.
    5. Jeffrey R. Brown & Austan Goolsbee, 2002. "Does the Internet Make Markets More Competitive? Evidence from the Life Insurance Industry," Journal of Political Economy, University of Chicago Press, vol. 110(3), pages 481-507, June.
    6. 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.
    7. MacMinn, Richard D, 1980. "Search and Market Equilibrium," Journal of Political Economy, University of Chicago Press, vol. 88(2), pages 308-327, April.
    8. Michael R. Baye & John Morgan, 2009. "Brand and Price Advertising in Online Markets," Management Science, INFORMS, vol. 55(7), pages 1139-1151, July.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Jason R. Blevins & Garrett T. Senney, 2014. "Dynamic Selection and Distributional Bounds on Search Costs in Dynamic Unit-Demand Models," Working Papers 14-02, Ohio State University, Department of Economics.
    2. Mark Armstrong & Jidong Zhou, 2016. "Search Deterrence," Review of Economic Studies, Oxford University Press, vol. 83(1), pages 26-57.
    3. 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.
    4. Castilla, Carolina & Haab, Timothy C., 2010. "Asymmetric Search and Loss Aversion: Choice Experiment on Consumer Willingness to Search in the Gasoline Retail Market," 2010 Annual Meeting, July 25-27, 2010, Denver, Colorado 61672, Agricultural and Applied Economics Association.
    5. Armstrong, Mark & Zhou, Jidong, 2010. "Exploding offers and buy-now discounts," MPRA Paper 22531, University Library of Munich, Germany.
    6. Rauh, Michael T., 2009. "Strategic complementarities and search market equilibrium," Games and Economic Behavior, Elsevier, vol. 66(2), pages 959-978, July.
    7. Babur De los Santos & Ali Hortacsu & Matthijs R. Wildenbeest, 2009. "Testing Models of Consumer Search Using Data on Web Browsing Behavior," Working Papers 09-23, NET Institute, revised Aug 2009.
    8. Gal OEstreicher-Singer & Barak Libai, 2011. "Assessing Value in Product Networks," Working Papers 11-29, NET Institute, revised Sep 2011.
    9. 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.
    10. Wilson, Chris M, 2009. "Market Frictions: A Unified Model of Search and Switching Costs," MPRA Paper 13672, University Library of Munich, Germany.
    11. Backus, Matthew R. & Podwol, Joseph Uri & Schneider, Henry S., 2014. "Search costs and equilibrium price dispersion in auction markets," European Economic Review, Elsevier, vol. 71(C), pages 173-192.
    12. Sergei Koulayev, 2009. "Estimating demand in search markets: the case of online hotel bookings," Working Papers 09-16, Federal Reserve Bank of Boston.
    13. 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.
    14. Sergei Koulayev, 2014. "Search for differentiated products: identification and estimation," RAND Journal of Economics, RAND Corporation, vol. 45(3), pages 553-575, September.
    15. Sergei Koulayev, 2008. "Estimating search with learning," Working Papers 08-29, NET Institute, revised Oct 2008.

    More about this item

    Keywords

    consumer search; internet; search costs;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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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