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Asymmetric information is an important phenomenon in many markets and in particular in insurance markets. Testing for asymmetric information has become a very important issue in the literature in the last two decades. Almost all testing procedures that are used in empirical studies are parametric, which may yield misleading conclusions in the case of misspecification of either functional or distributional relationships among the variables of interest. Motivated by the literature on testing conditional independence, we propose a new nonparametric test for asymmetric information which is applicable in a variety of situations. We demonstrate the test works reasonably well through Monte Carlo simulations and apply it to an automobile insurance data set. Our empirical results consolidate Chiappori and Salanié’s (2000) findings that there is no evidence for the presence of asymmetric information in the French automobile insurance market.


Asymmetric information, Automobile insurance, Conditional independence, Distributional misspecification, Functional misspecification, Nonlinearity, Nonparametric test


Econometrics | Statistics and Probability

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Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.