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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
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    References listed on IDEAS

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    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. 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.
    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.

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