IDEAS home Printed from https://ideas.repec.org/a/tpr/restat/v94y2012i2p419-432.html

Asymmetric Learning in Repeated Contracting: An Empirical Study

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.mitpressjournals.org/doi/pdf/10.1162/REST_a_00181
    File Function: link to full text PDF
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or

    for a different version of it.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Neil A. Doherty & Christian Laux & Alexander Muermann, 2015. "Insuring Nonverifiable Losses," Review of Finance, European Finance Association, vol. 19(1), pages 283-316.
    2. 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.
    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, March.
    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 2011:2, Swedish National Road & Transport Research Institute (VTI).
    5. 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.
    6. 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-Etienne (GATE Lyon St-Etienne), Université de Lyon.
    7. 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.
    8. 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).
    9. Martin Eling & Ruo Jia & Jieyu Lin & Casey Rothschild, 2022. "Technology heterogeneity and market structure," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(2), pages 427-448, June.
    10. 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.
    11. Georges Dionne & Nathalie Fombaron & Neil Doherty, 2012. "Adverse selection in insurance contracting," Working Papers 12-8, HEC Montreal, Canada Research Chair in Risk Management.
    12. Georges Dionne & Nathalie Fombaron & Wanda Mimra, 2025. "Adverse Selection in Insurance," Springer Books, in: Georges Dionne (ed.), Handbook of Insurance, edition 0, pages 165-221, Springer.
    13. Zifeng Zhao & Peng Shi & Xiaoping Feng, 2021. "Knowledge Learning of Insurance Risks Using Dependence Models," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 1177-1196, July.
    14. Ruo Jia & Zenan Wu, 2019. "Insurer commitment and dynamic pricing pattern," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 44(1), pages 87-135, March.
    15. 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.
    16. Ruo Jia & Zenan Wu, 2019. "Insurer commitment and dynamic pricing pattern," The Geneva Papers on Risk and Insurance Theory, Springer;International Association for the Study of Insurance Economics (The Geneva Association), vol. 44(1), pages 87-135, March.
    17. Martin Eling & Ruo Jia & Yi Yao, 2017. "Between-Group Adverse Selection: Evidence From Group Critical Illness Insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 84(2), pages 771-809, June.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tpr:restat:v:94:y:2012:i:2:p:419-432. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: The MIT Press (email available below). General contact details of provider: https://direct.mit.edu/journals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.