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Contracting in a market with differential information

Author

Listed:
  • Marta Rocha

    (Nova School of Business and Economics, Universidade Nova de Lisboa)

  • Thomas Greve

    (Faculty of Economics, University of Cambridge)

Abstract

This Consider an oligopolistic industry where two firms have access to the same technology and compete in prices, but one firm has access to better information about the customers in the market. We assume that better information allows the better informed firm to attract specific customers. The better informed firm obtains a first customer contact advantage, whereas the uninformed firm can only offer a menu of prices without being able to pre-identify the types of customers. We show that better information does not lead to higher profit.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Marta Rocha & Thomas Greve, 2016. "Contracting in a market with differential information," Working Papers EPRG 1624, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
  • Handle: RePEc:enp:wpaper:eprg1624
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    References listed on IDEAS

    as
    1. Rodrigo Montes & Wilfried Sand-Zantman & Tommaso Valletti, 2019. "The Value of Personal Information in Online Markets with Endogenous Privacy," Management Science, INFORMS, vol. 65(3), pages 1342-1362, March.
    2. Yuxin Chen & Ganesh Iyer, 2002. "Research Note Consumer Addressability and Customized Pricing," Marketing Science, INFORMS, vol. 21(2), pages 197-208, November.
    3. Drew Fudenberg & Jean Tirole, 2000. "Customer Poaching and Brand Switching," RAND Journal of Economics, The RAND Corporation, vol. 31(4), pages 634-657, Winter.
    4. Zhijun Chen & Chongwoo Choe & Noriaki Matsushima, 2020. "Competitive Personalized Pricing," Management Science, INFORMS, vol. 66(9), pages 4003-4023, September.
    5. Rodrigo Montes & Wilfried Sand-Zantman & Tommaso Valletti, 2015. "The Value of Personal Information in Markets with Endogenous Privacy," CEIS Research Paper 352, Tor Vergata University, CEIS, revised 05 Aug 2015.
    6. Esteves, Rosa-Branca, 2010. "Pricing with customer recognition," International Journal of Industrial Organization, Elsevier, vol. 28(6), pages 669-681, November.
    7. Taylor, Curtis & Wagman, Liad, 2014. "Consumer privacy in oligopolistic markets: Winners, losers, and welfare," International Journal of Industrial Organization, Elsevier, vol. 34(C), pages 80-84.
    8. Vives, Xavier, 1990. "Information and competitive advantage," International Journal of Industrial Organization, Elsevier, vol. 8(1), pages 17-35.
    9. Paul Belleflamme & Wing Man Wynne Lam, & Wouter Vergote, 2020. "Competitive Imperfect Price Discrimination and Market Power," Marketing Science, INFORMS, vol. 39(5), pages 996-1015, September.
    10. Alessandro Acquisti & Curtis Taylor & Liad Wagman, 2016. "The Economics of Privacy," Journal of Economic Literature, American Economic Association, vol. 54(2), pages 442-492, June.
    11. Yiquan Gu & Leonardo Madio & Carlo Reggiani, 2019. "Exclusive Data, Price Manipulation and Market Leadership," CESifo Working Paper Series 7853, CESifo.
    12. Vidyanand Choudhary & Anindya Ghose & Tridas Mukhopadhyay & Uday Rajan, 2005. "Personalized Pricing and Quality Differentiation," Management Science, INFORMS, vol. 51(7), pages 1120-1130, July.
    13. Alberto Cambini & Laura Martein, 2009. "Generalized Convexity and Optimization," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-540-70876-6, December.
    14. Einy, Ezra & Moreno, Diego & Shitovitz, Benyamin, 2002. "Information Advantage in Cournot Oligopoly," Journal of Economic Theory, Elsevier, vol. 106(1), pages 151-160, September.
    15. Jamasb,Tooraj & Pollitt,Michael G. (ed.), 2011. "The Future of Electricity Demand," Cambridge Books, Cambridge University Press, number 9781107008502.
    16. Chokler, Adi & Hon-Snir, Shlomit & Kim, Moshe & Shitovitz, Benyamin, 2006. "Information disadvantage in linear Cournot duopolies with differentiated products," International Journal of Industrial Organization, Elsevier, vol. 24(4), pages 785-793, July.
    17. Brophy Haney, A. & Jamasb, T. & Pollitt, M.G., 2009. "Smart Metering and Electricity Demand: Technology, Economics and International Experience," Cambridge Working Papers in Economics 0905, Faculty of Economics, University of Cambridge.
    18. Thomas Gehrig & Oz Shy & Rune Stenbacka, 2012. "A Welfare Evaluation of History-Based Price Discrimination," Journal of Industry, Competition and Trade, Springer, vol. 12(4), pages 373-393, December.
    19. Yu‐Hung Chen & Baojun Jiang, 2019. "Effects of Monitoring Technology on the Insurance Market," Production and Operations Management, Production and Operations Management Society, vol. 28(8), pages 1957-1971, August.
    20. Jentzsch, Nicola, 2016. "State-of-the-Art of the Economics of Cyber-Security and Privacy," EconStor Research Reports 126223, ZBW - Leibniz Information Centre for Economics.
    21. Ramon Casadesus-Masanell & Andres Hervas-Drane, 2015. "Competing with Privacy," Management Science, INFORMS, vol. 61(1), pages 229-246, January.
    22. 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.
    23. Rodrigo Montes & Wilfried Sand-Zantman & Tommaso Valletti, 2015. "The Value of Personal Information in Markets with Endogenous Privacy," CEIS Research Paper 352, Tor Vergata University, CEIS, revised 05 Aug 2015.
    24. Eid, Cherrelle & Koliou, Elta & Valles, Mercedes & Reneses, Javier & Hakvoort, Rudi, 2016. "Time-based pricing and electricity demand response: Existing barriers and next steps," Utilities Policy, Elsevier, vol. 40(C), pages 15-25.
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    More about this item

    Keywords

    price competition; personalised payment; differential information.;
    All these keywords.

    JEL classification:

    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets

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