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Asymmetric Learning in Repeated Contracting: An Empirical Study

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  • Alma Cohen

Abstract

This paper studies a unique panel dataset of transactions with repeat customers of an insurer operating in a market in which insurers are not required by law or contract to share information about their customers' records. I use this dataset to test the asymmetric learning hypothesis that sellers obtain over time private information that some of their repeat customers have low risk, and that this learning enables sellers to make higher profits in transactions with these repeat customers. Consistent with this hypothesis, I find that the insurer in my dataset makes higher profits in transactions with repeat customers and that these profits are driven by transactions with repeat customers with good past claims history with the insurer; that these higher profits result from repeat customers with good claim history receiving a reduction in premiums that is lower than the reduction in expected costs associated with such customers; and that policyholders with bad claim history are more likely to flee their record by switching to other insurers.

Suggested Citation

  • Alma Cohen, 2008. "Asymmetric Learning in Repeated Contracting: An Empirical Study," NBER Working Papers 13752, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:13752
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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. Paul Kofman & Gregory P. Nini, 2013. "Do Insurance Companies Possess an Informational Monopoly? Empirical Evidence From Auto Insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(4), pages 1001-1026, December.
    3. 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.
    4. 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).
    5. 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.
    6. Neil A. Doherty & Christian Laux & Alexander Muermann, 2015. "Insuring Nonverifiable Losses," Review of Finance, European Finance Association, vol. 19(1), pages 283-316.
    7. 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.
    8. Peng Shi & Wei Zhang, 2016. "A Test of Asymmetric Learning in Competitive Insurance With Partial Information Sharing," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 83(3), pages 557-578, September.
    9. 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.
    10. 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.
    11. 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.
    12. repec:bla:jrinsu:v:84:y:2017:i:2:p:771-809 is not listed on IDEAS

    More about this item

    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
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General

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