IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v40y2021i5p871-899.html
   My bibliography  Save this article

Estimation of Preference Heterogeneity in Markets with Costly Search

Author

Listed:
  • Ilya Morozov

    (Northwestern University, Evanston, Illinois 60208)

  • Stephan Seiler

    (Imperial College London, London SW7 2BU, United Kingdom; Centre for Economic Policy Research, London EC1V 0DX, United Kingdom)

  • Xiaojing Dong

    (Santa Clara University, Santa Clara, California 95053)

  • Liwen Hou

    (Shanghai Jiao Tong University, 200240 Shanghai, China)

Abstract

We study the estimation of preference heterogeneity in markets in which consumers engage in costly search to learn product characteristics. Costly search amplifies the way consumer preferences translate into purchase probabilities, generating a seemingly large degree of preference heterogeneity. We develop a search model that allows for flexible preference heterogeneity and estimate its parameters using a unique panel data set on consumers’ search and purchase behavior. The results reveal that when search costs are ignored, the model overestimates standard deviations of product intercepts by 53%. We show that the bias in heterogeneity estimates leads to incorrect inference about price elasticities and seller markups and has important consequences for personalized pricing.

Suggested Citation

  • Ilya Morozov & Stephan Seiler & Xiaojing Dong & Liwen Hou, 2021. "Estimation of Preference Heterogeneity in Markets with Costly Search," Marketing Science, INFORMS, vol. 40(5), pages 871-899, September.
    • Handle: RePEc:inm:ormksc:v:40:y:2021:i:5:p:871-899
    DOI: 10.1287/mksc.2021.1287
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.2021.1287
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.2021.1287?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yuxin Chen & Song Yao, 2017. "Sequential Search with Refinement: Model and Application with Click-Stream Data," Management Science, INFORMS, vol. 63(12), pages 4345-4365, December.
    2. Dan Yavorsky & Elisabeth Honka & Keith Chen, 2021. "Consumer search in the U.S. auto industry: The role of dealership visits," Quantitative Marketing and Economics (QME), Springer, vol. 19(1), pages 1-52, March.
    3. Raluca M. Ursu, 2018. "The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions," Marketing Science, INFORMS, vol. 37(4), pages 530-552, August.
    4. V. Joseph Hotz & Robert A. Miller, 1993. "Conditional Choice Probabilities and the Estimation of Dynamic Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 497-529.
    5. 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.
    6. Monica Giulietti & Michael Waterson & Matthijs Wildenbeest, 2014. "Estimation of Search Frictions in the British Electricity Market," Journal of Industrial Economics, Wiley Blackwell, vol. 62(4), pages 555-590, December.
    7. Jidong Zhou, 2017. "Competitive Bundling," Econometrica, Econometric Society, vol. 85, pages 145-172, January.
    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. Simon P. Anderson & Regis Renault, 1999. "Pricing, Product Diversity, and Search Costs: A Bertrand-Chamberlin-Diamond Model," RAND Journal of Economics, The RAND Corporation, vol. 30(4), pages 719-735, Winter.
    10. 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.
    11. Daniel Ackerberg, 2009. "A new use of importance sampling to reduce computational burden in simulation estimation," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 343-376, December.
    12. Elisabeth Honka & Pradeep Chintagunta, 2017. "Simultaneous or Sequential? Search Strategies in the U.S. Auto Insurance Industry," Marketing Science, INFORMS, vol. 36(1), pages 21-42, January.
    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. Weitzman, Martin L, 1979. "Optimal Search for the Best Alternative," Econometrica, Econometric Society, vol. 47(3), pages 641-654, May.
    15. Sergei Koulayev, 2014. "Search for differentiated products: identification and estimation," RAND Journal of Economics, RAND Corporation, vol. 45(3), pages 553-575, September.
    16. Andrés Elberg & Pedro M. Gardete & Rosario Macera & Carlos Noton, 2019. "Dynamic effects of price promotions: field evidence, consumer search, and supply-side implications," Quantitative Marketing and Economics (QME), Springer, vol. 17(1), pages 1-58, March.
    17. 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.
    18. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
    19. 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.
    20. Jean-Pierre Dubé & Günter J. Hitsch & Peter E. Rossi & Maria Ana Vitorino, 2008. "Category Pricing with State-Dependent Utility," Marketing Science, INFORMS, vol. 27(3), pages 417-429, 05-06.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Adam N. Smith & Stephan Seiler & Ishant Aggarwal, 2023. "Optimal Price Targeting," Marketing Science, INFORMS, vol. 42(3), pages 476-499, May.
    2. Pedro M. Gardete & Megan Hunter, 2024. "Multiattribute Search: Empirical Evidence and Information Design," Marketing Science, INFORMS, vol. 43(5), pages 1052-1080, September.
    3. Robert Donnelly & Ayush Kanodia & Ilya Morozov, 2024. "Welfare Effects of Personalized Rankings," Marketing Science, INFORMS, vol. 43(1), pages 92-113, January.
    4. Elisabeth Honka & Stephan Seiler & Raluca Ursu, 2023. "Consumer Search: What Can We Learn from Pre-Purchase Data?," CESifo Working Paper Series 10786, CESifo.
    5. Honka, Elisabeth & Seiler, Stephan & Ursu, Raluca, 2024. "Consumer search: What can we learn from pre-purchase data?," Journal of Retailing, Elsevier, vol. 100(1), pages 114-129.
    6. Sven Heim, 2021. "Asymmetric cost pass-through and consumer search: empirical evidence from online platforms," Quantitative Marketing and Economics (QME), Springer, vol. 19(2), pages 227-260, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Raluca M. Ursu & Qianyun Zhang & Elisabeth Honka, 2023. "Search Gaps and Consumer Fatigue," Marketing Science, INFORMS, vol. 42(1), pages 110-136, January.
    2. Raluca Ursu & Stephan Seiler & Elisabeth Honka, 2023. "The Sequential Search Model: A Framework for Empirical Research," CESifo Working Paper Series 10264, CESifo.
    3. Raluca M. Ursu & Qingliang Wang & Pradeep K. Chintagunta, 2020. "Search Duration," Marketing Science, INFORMS, vol. 39(5), pages 849-871, September.
    4. Rafael P. Greminger, 2022. "Optimal Search and Discovery," Management Science, INFORMS, vol. 68(5), pages 3904-3924, May.
    5. Navid Mojir & K. Sudhir, 2014. "Price Search Across Time and Across Stores," Cowles Foundation Discussion Papers 1942R, Cowles Foundation for Research in Economics, Yale University, revised Jul 2019.
    6. Avery Haviv, 2022. "Consumer Search, Price Promotions, and Counter-Cyclic Pricing," Marketing Science, INFORMS, vol. 41(2), pages 294-314, March.
    7. Navid Mojir & K. Sudhir, 2014. "A Model of Multi-pass Search: Price Search across Stores and Time," Cowles Foundation Discussion Papers 1942R2, Cowles Foundation for Research in Economics, Yale University, revised Feb 2020.
    8. Hana Choi & Carl F. Mela, 2019. "Monetizing Online Marketplaces," Marketing Science, INFORMS, vol. 38(6), pages 948-972, November.
    9. Stephan Seiler & Fabio Pinna, 2017. "Estimating Search Benefits from Path-Tracking Data: Measurement and Determinants," Marketing Science, INFORMS, vol. 36(4), pages 565-589, July.
    10. Jiarui Liu, 2021. "Sequential Search Models: A Pairwise Maximum Rank Approach," Papers 2104.13865, arXiv.org, revised Nov 2021.
    11. Andrés Elberg & Pedro M. Gardete & Rosario Macera & Carlos Noton, 2019. "Dynamic effects of price promotions: field evidence, consumer search, and supply-side implications," Quantitative Marketing and Economics (QME), Springer, vol. 17(1), pages 1-58, March.
    12. Bart J. Bronnenberg & Jun B. Kim & Carl F. Mela, 2016. "Zooming In on Choice: How Do Consumers Search for Cameras Online?," Marketing Science, INFORMS, vol. 35(5), pages 693-712, September.
    13. Anocha Aribarg & Thomas Otter & Daniel Zantedeschi & Greg M. Allenby & Taylor Bentley & David J. Curry & Marc Dotson & Ty Henderson & Elisabeth Honka & Rajeev Kohli & Kamel Jedidi & Stephan Seiler & X, 2018. "Advancing Non-compensatory Choice Models in Marketing," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 82-92, March.
    14. Matsumoto, Brett & Spence, Forrest, 2016. "Price beliefs and experience: Do consumers’ beliefs converge to empirical distributions with repeated purchases?," Journal of Economic Behavior & Organization, Elsevier, vol. 126(PA), pages 243-254.
    15. 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.
    16. Dan Yavorsky & Elisabeth Honka & Keith Chen, 2021. "Consumer search in the U.S. auto industry: The role of dealership visits," Quantitative Marketing and Economics (QME), Springer, vol. 19(1), pages 1-52, March.
    17. Robert Donnelly & Ayush Kanodia & Ilya Morozov, 2024. "Welfare Effects of Personalized Rankings," Marketing Science, INFORMS, vol. 43(1), pages 92-113, January.
    18. Honka, Elisabeth & Seiler, Stephan & Ursu, Raluca, 2024. "Consumer search: What can we learn from pre-purchase data?," Journal of Retailing, Elsevier, vol. 100(1), pages 114-129.
    19. Rafael P. Greminger, 2022. "Heterogeneous Position Effects and the Power of Rankings," Papers 2210.16408, arXiv.org, revised Dec 2023.
    20. Mantian (Mandy) Hu & Chu (Ivy) Dang & Pradeep K. Chintagunta, 2019. "Search and Learning at a Daily Deals Website," Marketing Science, INFORMS, vol. 38(4), pages 609-642, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:40:y:2021:i:5:p:871-899. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.