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Ex Post Moral Hazard and Bayesian Learning in Insurance

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  • Michael Ludkovski
  • Virginia R. Young

Abstract

We study a dynamic insurance market with asymmetric information and ex post moral hazard. In our model, the insurance buyer's risk type is unknown to the insurer; moreover, the buyer has the option of not reporting losses. The insurer sets premia according to the buyer's experience rating, computed via Bayesian estimation based on buyer's history of reported claims. Accordingly, the buyer has strategic incentive to withhold information about losses. We construct an insurance market information equilibrium model and show that a variety of reporting strategies are possible. The results are illustrated with explicit computations in a two‐period risk‐neutral case study.

Suggested Citation

  • Michael Ludkovski & Virginia R. Young, 2010. "Ex Post Moral Hazard and Bayesian Learning in Insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 77(4), pages 829-856, December.
    • Handle: RePEc:bla:jrinsu:v:77:y:2010:i:4:p:829-856
    DOI: 10.1111/j.1539-6975.2010.01366.x
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    References listed on IDEAS

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    1. Øyvind Kvalnes, 2011. "Blurred Promises: Ethical Consequences of Fine Print Policies in Insurance," Journal of Business Ethics, Springer, vol. 103(1), pages 77-86, April.

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