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Testing for Asymmetric Information in Insurance with Deep Learning

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  • Maliar, Serguei
  • Salanié, Bernard

Abstract

The positive correlation test for asymmetric information developed by Chiappori and Salanié (2000) has been applied in many insurance markets. Most of the literature focuses on the special case of constant correlation; it also relies on restrictive parametric specifications for the choice of coverage and the occurrence of claims. We relax these restrictions by estimating conditional covariances and correlations using deep learning methods. We test the positive correlation property by using the intersection test of Chernozhukov, Lee, and Rosen (2013) and the 'sorted groups' test of Chernozhukov, Demirer, Duflo, and Fernandez-Val (2023). Our results confirm earlier findings that the correlation between risk and coverage is small. Random forests and gradient boosting trees produce similar results to neural networks.

Suggested Citation

  • Maliar, Serguei & Salanié, Bernard, 2024. "Testing for Asymmetric Information in Insurance with Deep Learning," CEPR Discussion Papers 19105, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:19105
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    More about this item

    Keywords

    Insurance; Asymmetric information; Machine learning;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

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