Separating Moral Hazard from Adverse Selection in Automobile Insurance: Longitudinal Evidence from France

This paper uses longitudinal data to perform tests of asymmetric information in the French automobile insurance market for the 1995-1997 period. This market is characterized by the presence of a regulated experience-rating scheme (bonus-malus). We demonstrate that the result of the test depends crucially on how the dynamic process between insurance claims and contract choice is modelled. We apply a Granger causality test controlling for the unobservables. We find evidence of moral hazard which we distinguish from adverse selection using a multivariate dynamic panel data model. Experience rating appears to lead high risk policyholders to choose contracts that involve less coverage over time. These policyholders respond to contract changes by increasing their unobservable efforts to reduce claims.Automobile insurance, road safety, asymmetric information, experience rating, moral hazard, adverse selection, dynamic panel data models, Granger causality test

Separating Moral Hazard from Adverse Selection and Learning in Automobile Insurance: Longitudinal Evidence from France

The identification of information problems in different markets is a challenging issue in the economic literature. In this paper, we study the identification of moral hazard from adverse selection and learning within the context of a multi-period dynamic model. We extend the model of Abbring et al. (2003) to include learning and insurance coverage choice over time. We derive testable empirical implications for panel data. We then perform tests using longitudinal data from France during the period 1995-1997. We find evidence of moral hazard among a sub-group of policyholders with less driving experience (less than 15 years). Policyholders with less than 5 years of experience have a combination of learning and moral hazard, whereas no residual information problem is found for policyholders with more than 15 years of experience.Moral hazard, adverse selection, learning, dynamic insurance contracting, panel data, empirical test

Separating Moral Hazard from Adverse Selection and Learning in Automobile Insurance: Longitudinal Evidence from France

The identification of information problems in different markets is a challenging issue in the economic literature. In this paper, we study the identification of moral hazard from adverse selection and learning within the context of a multi-period dynamic model. We extend the model of Abbring et al. (2003) to include learning and insurance coverage choice over time. We derive testable empirical implications for panel data. We then perform tests using longitudinal data from France during the period 1995-1997. We find evidence of moral hazard among a sub-group of policyholders with less driving experience (less than 15 years). Policyholders with less than 5 years of experience have a combination of learning and moral hazard, whereas no residual information problem is found for policyholders with more than 15 years of experience

LES ASSUREURS FRANÇAIS ONT-ILS INTÉRÊT À UTILISER LES POINTS DE PERMIS POUR TARIFER L’ASSURANCE AUTOMOBILE ?

Le permis à points est en vigueur dans la majorité des pays industrialisés. C’est le cas de la France depuis le 1er juillet 1992. Cet article présente la genèse et les caractéristiques du système français et les compare à celles de quelques pays étrangers. L’objectif principal est de vérifier si les retraits de points de permis, qui sont des mesures directes des infractions commises par les conducteurs, sont significatifs pour prédire le risque d’accident. Pour ce faire, nous avons estimé des modèles à partir de données françaises issues de l’enquête Parc Automobile de la Sofres et contenant pour l’année 1998 l’information sur les retraits de points de permis des automobilistes interrogés. Les résultats des modèles estimés montrent que les retraits de points de l’année (t-1) expliquent significativement le risque d’accident à l’année t. En outre, nous vérifions que le risque d’être impliqué dans un accident est croissant en fonction du nombre de points retirés. En plus de leur intérêt indéniable pour des mesures efficaces en matière de securité routière, les résultats présentés ici fournissent une preuve de l’intérêt qu’ont les assureurs français à avoir accès aux infractions au code de la route pour les utiliser à des fins de tarification a priori et a posteriori.The driving license based on demerit points system is in use in almost all industrialized countries. This is the case in France since 1st July 1992. The purpose of this article is to present the French system and to verify whether convictions and offenses are significant factors to explain traffic accidents. The data comes from a survey called Parc Automobile conducted by Sofres in France. It contains the information about points lost by drivers in 1998 and before. The results from the regressions show that points lost in period (t-1) are significant for predicting accidents in period t. Furthermore, we show that the risk of being involved in an traffic accident in the current year is a raising function of the points lost in the previous year. These results show clearly that the informational content of the French demerit points system which is a direct measure of the driver’s convictions and offenses can be useful for road safety management and for assessing risks in automobile insurance. Consequently, they can be used for both risk classification and experience rating