Nonparametric Testing for Asymmetric Information

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 that the test works reasonably well through Monte Carlo simulations and apply it to an automobile insurance dataset and a long-term care insurance (LTCI) dataset. Our empirical results consolidate Chiappori and Salanié's findings that there is no evidence for the presence of asymmetric information in the French automobile insurance market. While Finkelstein and McGarry found no positive correlation between risk and coverage in the LTCI market in the United States, our test detects asymmetric information using only the information that is available to the insurance company, and our investigation of the source of asymmetric information suggests some sort of asymmetric information that is related to risk preferences as opposed to risk types and thus lends support to Finkelstein and McGarry.