Does experience rating matter in reducing accident probabilities? A test for moral hazard

Moral hazard and adverse selection are potentially important features of car insurance markets. Interestingly, the empirical literature has found hard to establish the presence of asymmetric information. Such surprising findings are in part due to the lack of extensive longitudinal data.;In the second chapter I examine the empirical importance of moral hazard, adverse selection and true state dependence in the determination of motor vehicle accidents using a longitudinal data set on car insurance policies that I obtained from an Italian insurance company. I develop a dynamic model of driving effort given an insurance contract that allows for moral hazard, for adverse selection and for state dependence. Simulations based on a calibrated version of the structural model suggest that driving effort increases in the marginal premium price increase. From the model I derive an approximate decision rule for driving effort that underlies my empirical specification for the probability of having an accident.;My strategy for disentangling moral hazard from adverse selection makes use of the fact that policy holders in the same experience rating class face the same pricing schedule for future accidents, whereas policy holders belonging to different experience rating classes face different marginal price increases upon having an accident. Under the model, in the presence of moral hazard drivers would take into account marginal price increases when choosing their driving effort.;The estimating equation is a dynamic discrete choice panel data model with unobserved heterogeneity, state dependence and predetermined variables. My unusually rich data allow me to incorporate in a flexible way all these factors into the model. I estimate the model using the semi parametric estimator proposed by Arellano and Carrasco (2003). I find much stronger evidence of moral hazard than in the previous literature. In particular there is negative dependence between accident probabilities and the experience rating class. Thus monetary costs are important determinants of accident probabilities.;In the first chapter of the dissertation I present a survey of both theoretical and empirical studies regarding information asymmetries in car insurance markets.