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Costly search and consideration sets in storable goods markets

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  • Tiago Pires

    (University of North Carolina)

Abstract

Costly search can result in consumers restricting their attention to a subset of products–the consideration set–before making a final purchase decision. The search process is usually not observed, which creates econometric challenges. I show that inventory and the availability of different package sizes create new sources of variation to identify search costs in storable goods markets. To evaluate the importance of costly search in these markets, I estimate a dynamic choice model with search frictions using data on purchases of laundry detergent. My estimates show that consumers incur significant search costs, and ignoring costly search overestimates the own-price elasticity for products more often present in consideration sets and underestimates the elasticity of frequently excluded products. Firms employ marketing devices, such as product displays and advertising, to influence consideration sets. These devices have direct and strategic effects, which I explore using the estimates of the model. I find that using marketing devices to reduce a product’s search cost during a price promotion has modest effects on the overall category revenues, and decreases the revenues of some products.

Suggested Citation

  • Tiago Pires, 2016. "Costly search and consideration sets in storable goods markets," Quantitative Marketing and Economics (QME), Springer, vol. 14(3), pages 157-193, September.
  • Handle: RePEc:kap:qmktec:v:14:y:2016:i:3:d:10.1007_s11129-016-9169-2
    DOI: 10.1007/s11129-016-9169-2
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    as
    1. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
    2. Christine Boizot & Jean-Marc Robin & Michael Visser, 2001. "The demand for food products," Post-Print hal-03416605, HAL.
    3. Igal Hendel & Aviv Nevo, 2006. "Measuring the Implications of Sales and Consumer Inventory Behavior," Econometrica, Econometric Society, vol. 74(6), pages 1637-1673, November.
    4. Michael Dinerstein & Liran Einav & Jonathan Levin & Neel Sundaresan, 2018. "Consumer Price Search and Platform Design in Internet Commerce," American Economic Review, American Economic Association, vol. 108(7), pages 1820-1859, July.
    5. Tülin Erdem & Susumu Imai & Michael Keane, 2003. "Brand and Quantity Choice Dynamics Under Price Uncertainty," Quantitative Marketing and Economics (QME), Springer, vol. 1(1), pages 5-64, March.
    6. Igal Hendel & Aviv Nevo, 2006. "Sales and consumer inventory," RAND Journal of Economics, RAND Corporation, vol. 37(3), pages 543-561, September.
    7. 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.
    8. Stephan Seiler, 2013. "The impact of search costs on consumer behavior: A dynamic approach," Quantitative Marketing and Economics (QME), Springer, vol. 11(2), pages 155-203, June.
    9. Sergei Koulayev, 2014. "Search for differentiated products: identification and estimation," RAND Journal of Economics, RAND Corporation, vol. 45(3), pages 553-575, September.
    10. 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.
    11. Elisabeth Honka, 2014. "Quantifying search and switching costs in the US auto insurance industry," RAND Journal of Economics, RAND Corporation, vol. 45(4), pages 847-884, December.
    12. Kfir Eliaz & Ran Spiegler, 2011. "Consideration Sets and Competitive Marketing," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 78(1), pages 235-262.
    13. Christine Boizot & Jean-Marc Robin & Michael Visser, 2001. "The demand for food products," Post-Print hal-03416604, HAL.
    14. Hauser, John R & Wernerfelt, Birger, 1990. "An Evaluation Cost Model of Consideration Sets," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 16(4), pages 393-408, March.
    15. Michelle Sovinsky Goeree, 2008. "Limited Information and Advertising in the U.S. Personal Computer Industry," Econometrica, Econometric Society, vol. 76(5), pages 1017-1074, September.
    16. Boizot, Christine & Robin, Jean-Marc & Visser, Michael, 2001. "The Demand for Food Products: An Analysis of Interpurchase Times and Purchased Quantities," Economic Journal, Royal Economic Society, vol. 111(470), pages 391-419, April.
    17. 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.
    18. 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.
    19. Wesley R. Hartmann & Harikesh S. Nair, 2010. "Retail Competition and the Dynamics of Demand for Tied Goods," Marketing Science, INFORMS, vol. 29(2), pages 366-386, 03-04.
    20. Jean‐Pierre Dubé & Günter J. Hitsch & Peter E. Rossi, 2010. "State dependence and alternative explanations for consumer inertia," RAND Journal of Economics, RAND Corporation, vol. 41(3), pages 417-445, September.
    21. 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.
    22. Martin Pesendorfer, 2002. "Retail Sales: A Study of Pricing Behavior in Supermarkets," The Journal of Business, University of Chicago Press, vol. 75(1), pages 33-66, January.
    23. Stephan Seiler, 2013. "The impact of search costs on consumer behavior: A dynamic approach," Quantitative Marketing and Economics (QME), Springer, vol. 11(2), pages 155-203, June.
    24. Igal Hendel & Aviv Nevo, 2006. "Sales and Consumer Inventory," RAND Journal of Economics, The RAND Corporation, vol. 37(3), pages 543-561, Autumn.
    25. Draganska, Michaela & Klapper, Daniel, 2010. "Choice Set Heterogeneity and the Role of Advertising: An Analysis with Micro and Macro Data," Research Papers 2063, Stanford University, Graduate School of Business.
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    11. David Ronayne, 2020. "The Only Dance in Town: Unique Equilibrium in a Generalized Model of Price Competition," Economics Series Working Papers 874, University of Oxford, Department of Economics.
    12. Johannes Johnen & David Ronayne, 2021. "The only Dance in Town: Unique Equilibrium in a Generalized Model of Price Competition," Journal of Industrial Economics, Wiley Blackwell, vol. 69(3), pages 595-614, September.
    13. Donna, Javier D. & Pereira, Pedro & Pires, Tiago & Trindade, Andre, 2018. "Measuring the Welfare of Intermediaries in Vertical Markets," MPRA Paper 90465, University Library of Munich, Germany.
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    17. José L Moraga-González & Zsolt Sándor & Matthijs R Wildenbeest, 2021. "Simultaneous Search for Differentiated Products: The Impact of Search Costs and Firm Prominence," The Economic Journal, Royal Economic Society, vol. 131(635), pages 1308-1330.
    18. Fabio Antoniou & Raffaele Fiocco, 2023. "Storable Good Market With Intertemporal Cost Variations," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(1), pages 361-385, February.
    19. Pires, Tiago, 2018. "Measuring the effects of search costs on equilibrium prices and profits," International Journal of Industrial Organization, Elsevier, vol. 60(C), pages 179-205.
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    23. Avery Haviv, 2022. "Consumer Search, Price Promotions, and Counter-Cyclic Pricing," Marketing Science, INFORMS, vol. 41(2), pages 294-314, March.

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    More about this item

    Keywords

    Search costs; Consideration set; Information; Storable goods; Dynamic discrete-choice models;
    All these keywords.

    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce

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