Adverse selection and moral hazard in insurance: can dynamic data help to distinguish?
Abstract
A standard problem of applied contract theory is to empirically distinguish between adverse selection and moral hazard. We show that dynamic insurance data allows to distinguish moral hazard from dynamic selection on unobservables. In the presence of moral hazard, experience rating implies negative occurence dependence: individual intensities decrease with the number of past claims. We discuss econometric tests for the various type of data that are typically available. Finaly, we argue that dynamic data also allow to test for adverse selection, even if it is based on asymmetric learning.