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

Georges Dionne (), Pierre-Carl Michaud and Maki Dahchour

Cahiers de recherche from CIRPEE

Abstract: 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.

Keywords: Automobile insurance; road safety; asymmetric information; experience rating; moral hazard; adverse selection; dynamic panel data models; Granger causality test (search for similar items in EconPapers)
JEL-codes: C23 D80 G22 L51 (search for similar items in EconPapers)
Date: 2004
New Economics Papers: this item is included in nep-cfn, nep-ias and nep-mic
References: Add references at CitEc
Citations: View citations in EconPapers (11)

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http://www.cirpee.org/fileadmin/documents/Cahiers_2004/CIRPEE04-20.pdf (application/pdf)

Related works:
Working Paper: Separating moral hazard from adverse selection in automobile insurance: Longitudinal evidence from France (2004) Downloads
Working Paper: Separating Moral Hazard from Adverse Selection in Automobile Insurance: Longitudinal Evidence from France (2004) Downloads
Working Paper: Separating Moral Hazard from Adverse Selection in Automobile Insurance: Longitudinal Evidence from France (2004) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:lvl:lacicr:0420

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