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Returns to Consumer Search: Evidence from eBay

Author

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  • Thomas Blake
  • Chris Nosko
  • Steven Tadelis

Abstract

A growing body of empirical literature finds that consumers are relatively limited in how much they search over product characteristics. We assemble a dataset of search and purchase behavior from eBay to quantify the returns, and thus implied costs, to consumer search on the internet. The extensive nature of the eBay data allows us to examine a rich and detailed set of questions related to search in a way that previous structural models cannot. In contrast to the literature, we find that consumers search a lot: on average 36 times per purchase over 3 (distinct) days, with most sessions ending in no purchase. We find that search costs are relatively low, in the region of 25 cents per search page. We pursue the analysis further by, i) examining how users refine their search, ii) how search behavior spans multiple search sessions, and iii) how the amount of search relates to finding lower prices.

Suggested Citation

  • Thomas Blake & Chris Nosko & Steven Tadelis, 2016. "Returns to Consumer Search: Evidence from eBay," NBER Working Papers 22302, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:22302
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    References listed on IDEAS

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    Cited by:

    1. Susan Athey & Michael Luca, 2019. "Economists (and Economics) in Tech Companies," Journal of Economic Perspectives, American Economic Association, vol. 33(1), pages 209-230, Winter.
    2. Ben Casner, 2021. "Learning while shopping: an experimental investigation into the effect of learning on consumer search," Experimental Economics, Springer;Economic Science Association, vol. 24(1), pages 238-273, March.
    3. Donna, Javier D. & Schenone, Pablo & Veramendi, Gregory F., 2020. "Networks, frictions, and price dispersion," Games and Economic Behavior, Elsevier, vol. 124(C), pages 406-431.
    4. Tesary Lin & Sanjog Misra, 2022. "Frontiers: The Identity Fragmentation Bias," Marketing Science, INFORMS, vol. 41(3), pages 433-440, May.
    5. Daron Acemoglu & Ali Makhdoumi & Azarakhsh Malekian & Asuman Ozdaglar, 2017. "Fast and Slow Learning From Reviews," NBER Working Papers 24046, National Bureau of Economic Research, Inc.
    6. Raluca M. Ursu & Qingliang Wang & Pradeep K. Chintagunta, 2020. "Search Duration," Marketing Science, INFORMS, vol. 39(5), pages 849-871, September.
    7. Casner, Ben, 2020. "Seller curation in platforms," International Journal of Industrial Organization, Elsevier, vol. 72(C).
    8. Kevin Ducbao Tran, 2020. "Partitioned Pricing and Consumer Welfare," Discussion Papers of DIW Berlin 1888, DIW Berlin, German Institute for Economic Research.
    9. Tom Blake & Sarah Moshary & Kane Sweeney & Steve Tadelis, 2021. "Price Salience and Product Choice," Marketing Science, INFORMS, vol. 40(4), pages 619-636, July.
    10. Tesary Lin & Sanjog Misra, 2020. "The Identity Fragmentation Bias," Papers 2008.12849, arXiv.org, revised Feb 2021.

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    More about this item

    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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