Adverse Selection and Moral Hazard in Insurance: Can Dynamic Data Help to Distinguish?
A standard problem of applied contracts theory is to empirically distinguish between adverse selection and moral hazard. The paper shows that dynamic insurance data allow to distinguish moral hazard from dynamic selection on unobservable. In the presence of moral hazard, experience rating implies negative occurrence dependence: individual claim intensities decrease with the number of past claims. It discusses econometric tests for the various types of data that are typically available. Finally, the paper reveals that dynamic data also allow testing for adverse selection, even if it is based on asymmetric learning.