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Adverse Selection and Moral Hazard In Insurance: Can Dynamic Data Help to Distinguish?

Author

Listed:
  • Jaap H. Abbring

    (Free University,)

  • James J. Heckman

    (University of Chicago,)

  • Pierre-André Chiappori

    (University of Chicago,)

  • Jean Pinquet

    (Université Paris X-Nanterre,)

Abstract

A standard problem of applied contracts theory is to empirically distinguish between adverse selection and moral hazard. We show that dynamic insurance data allow to distinguish moral hazard from dynamic selection on unobservables. In the presence of moral hazard, experience rating implies negative occurrence dependence: individual claim intensities decrease with the number of past claims. We discuss econometric tests for the various types of data that are typically available. Finally, we argue that dynamic data also allow to test for adverse selection, even if it is based on asymmetric learning. (JEL: D82, G22, C41, C14) Copyright (c) 2003 The European Economic Association.

Suggested Citation

  • Jaap H. Abbring & James J. Heckman & Pierre-André Chiappori & Jean Pinquet, 2003. "Adverse Selection and Moral Hazard In Insurance: Can Dynamic Data Help to Distinguish?," Journal of the European Economic Association, MIT Press, vol. 1(2-3), pages 512-521, 04/05.
    • Handle: RePEc:tpr:jeurec:v:1:y:2003:i:2-3:p:512-521
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    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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