Ex Post Moral Hazard in Automobile Insurance Markets with Experience Rating

Accounting for unreported accidents due to ex post moral hazard is important for studying asymmetric information in insurance markets. In this paper, I study ex post moral hazard in an automobile insurance market with experience rating. I develop a dynamic model in which policyholders with private information about their risk types choose accident prevention efforts (ex ante moral hazard) and make claim filing decisions when accidents happen (ex post moral hazard). I then estimate the model using a detailed policy-level panel dataset from China. I find that policyholders do not report 24% of all accidents, which account for about 5% of total monetary losses. The degree of ex post moral hazard varies by experience rating: policyholders with the best rating hide 40% of all accidents. Finally, I use counterfactual experiments to evaluate the welfare implications. I find that experience rating improves policyholder welfare, mainly by inducing higher preventive efforts and reducing accidents. When ex post moral hazard is restricted and policyholders are forced to report all accidents, the benefit from increased accident prevention efforts barely outweighs policyholders’ loss from increased premiums.