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Asymmetric Learning in Repeated Contracting: An Empirical Study

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  • Alma Cohen

    (Eitan Berglas School of Economics, Tel Aviv University, the National Bureau of Economic Research, and the Harvard Law School John M. Olin Center for Law, Economics, and Business)

Abstract

This paper uses a unique panel data set of an insurer's transactions with repeat customers. Consistent with the asymmetric learning hypothesis that repeated contracting enables sellers to obtain an informational advantage over their rivals, I find that the insurer makes higher profits in transactions with repeat customers who have a good claims history with the insurer, the insurer reduces the price charged to these repeat customers by less than the reduction in expected costs associated with such customers, and repeat customers with bad claim histories are more likely to flee their record by switching to other insurers. © 2012 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Suggested Citation

  • Alma Cohen, 2012. "Asymmetric Learning in Repeated Contracting: An Empirical Study," The Review of Economics and Statistics, MIT Press, vol. 94(2), pages 419-432, May.
  • Handle: RePEc:tpr:restat:v:94:y:2012:i:2:p:419-432
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    Citations

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    Cited by:

    1. Arvidsson, Sara, 2011. "Predictors of customer loyalty in automobile insurance: The role of private information in risky driving behavior and claim history," Working Papers 2011:2, Swedish National Road & Transport Research Institute (VTI).
    2. Alma Cohen & Peter Siegelman, 2010. "Testing for Adverse Selection in Insurance Markets," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 77(1), pages 39-84.
    3. Arvidsson, Sara, 2011. "Predictors of customer loyalty in automobile insurance - The role of private information in risky driving behavior and claim history," Working papers in Transport Economics 2011:2, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    4. Arvidsson, Sara, 2010. "Reducing asymmetric information with usage-based automobile insurance," Working Papers 2010:2, Swedish National Road & Transport Research Institute (VTI), revised 03 Feb 2011.
    5. Neil A. Doherty & Christian Laux & Alexander Muermann, 2015. "Insuring Nonverifiable Losses," Review of Finance, European Finance Association, vol. 19(1), pages 283-316.
    6. Eling, Martin & Jia, Ruo & Yao, Yi, 2014. "Between-Group Adverse Selection: Evidence from Group Critical Illness Insurance," Working Papers on Finance 1403, University of St. Gallen, School of Finance, revised Oct 2014.
    7. Li, Chu-Shiu & Lin, Chih Hao & Liu, Chwen-Chi & Woodside, Arch G., 2012. "Dynamic pricing in regulated automobile insurance markets with heterogeneous insurers: Strategies nice versus nasty for customers," Journal of Business Research, Elsevier, vol. 65(7), pages 968-976.
    8. Magali Chaudey, 2017. "Why test the theory of incentives in a dynamic framework?," Working Papers 1733, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    9. Shi, Peng & Valdez, Emiliano A., 2011. "A copula approach to test asymmetric information with applications to predictive modeling," Insurance: Mathematics and Economics, Elsevier, vol. 49(2), pages 226-239, September.

    More about this item

    Keywords

    repeat customers; asymmetric information; asymmetric learning; adverse selection; insurance; market power;

    JEL classification:

    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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