Adverse Selection and Moral Hazard in Insurance: Can Dynamic Data Help to Distinguish?
利用动态保险数据，通过经验评级导致的负向发生依赖性来区分道德风险与动态逆向选择，并讨论了适用于不同数据类型的计量检验方法。
A standard problem of applied contracts theory is to empirically distinguish between adverse selection and moral hazard. We show that dynamic insurance data allow to distinguish moral hazard from dynamic selection on unobservables. In the presence of moral hazard, experience rating implies negative occurrence dependence: individual claim intensities decrease with the number of past claims. We discuss econometric tests for the various types of data that are typically available. Finally, we argue that dynamic data also allow to test for adverse selection, even if it is based on asymmetric learning.