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Endogenous personalized pricing in the Hotelling model

Author

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  • Houba, Harold
  • Motchenkova, Evgenia
  • Wang, Hui

Abstract

We study endogenous personalized pricing in Hotelling’s model with vertically differentiated products, cost asymmetries and linear adjustment costs. We characterize the equilibrium and its welfare consequences. Adopting personalized pricing is the dominant strategy for both companies. The less efficient company never gains compared to competition in uniform pricing. We clarify the conditions under which the more efficient company can gain from competition in personalized pricing. Aggregate consumer surplus increases, but personalized pricing can harm consumers who purchase from the more-efficient (high-quality) firm.

Suggested Citation

  • Houba, Harold & Motchenkova, Evgenia & Wang, Hui, 2023. "Endogenous personalized pricing in the Hotelling model," Economics Letters, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:ecolet:v:225:y:2023:i:c:s0165176523000629
    DOI: 10.1016/j.econlet.2023.111037
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    References listed on IDEAS

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    1. Greg Shaffer & Z. John Zhang, 2002. "Competitive One-to-One Promotions," Management Science, INFORMS, vol. 48(9), pages 1143-1160, September.
    2. Qihong Liu & Konstantinos Serfes, 2004. "Quality of Information and Oligopolistic Price Discrimination," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 13(4), pages 671-702, December.
    3. Armstrong, Mark & Vickers, John, 2001. "Competitive Price Discrimination," RAND Journal of Economics, The RAND Corporation, vol. 32(4), pages 579-605, Winter.
    4. Thisse, Jacques-Francois & Vives, Xavier, 1988. "On the Strategic Choice of Spatial Price Policy," American Economic Review, American Economic Association, vol. 78(1), pages 122-137, March.
    5. Toshihiro Matsumura & Noriaki Matsushima, 2015. "Should Firms Employ Personalized Pricing?," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 24(4), pages 887-903, October.
    6. Anindya Ghose & Ke‐Wei Huang, 2009. "Personalized Pricing and Quality Customization," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 18(4), pages 1095-1135, December.
    7. Esteves, Rosa-Branca, 2022. "Can personalized pricing be a winning strategy in oligopolistic markets with heterogeneous demand customers? Yes, it can," International Journal of Industrial Organization, Elsevier, vol. 85(C).
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    Cited by:

    1. Zhao, Dan & Wang, Jian & Li, Ye-kai & Tang, Jin-huan & Zhang, Shui-wang, 2024. "How to promote the transition of fuel vehicle enterprises under dual credit policy? An improved tripartite evolutionary game analysis with time delay," Energy, Elsevier, vol. 293(C).
    2. Stefano Colombo & Clara Graziano & Aldo Pignataro, 2023. "Personalized Pricing with Imperfect Customer Recognition," CESifo Working Paper Series 10455, CESifo.
    3. Qiuyu Lu & Noriaki Matsushima & Shiva Shekhar, 2024. "Welfare Implications of Personalized Pricing in Competitive Platform Markets: The Role of Network Effects," CESifo Working Paper Series 10994, CESifo.

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    More about this item

    Keywords

    Personalized prices; Hotelling model; Technology adoption; Oligopoly; Profit paradox;
    All these keywords.

    JEL classification:

    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • L4 - Industrial Organization - - Antitrust Issues and Policies

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