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The (1992) Bonus-Malus System in Tunisia: An Empirical Evaluation

  • Georges Dionne
  • Olfa Ghali

The objective of this study is to assess empirically what impact introduction of the bonus-malus system (BMS) has had on road safety in Tunisia. The results of the Tunisian experiment are of particular importance since, during the last decade, many European countries decided to eliminate their mandatory bonus-malus scheme. These results indicate that the BMS reduced the probability of reported accidents for good risks but had no effect on bad risks. Moreover, the reform's overall effect on reported accident rates is not statistically significant, but the exit variable is positive in explaining the number of reported accidents. To avoid any potential selectivity bias, we also made a joint estimate of the reported accident and selection equations. The reform has a positive effect on the exit variable but still does not affect the accidents reported. This indicates that policyholders who switch companies are those attempting to skirt the imposed incentive effects of the new rating policy. Some of the control variables are statistically significant in explaining the number of reported accidents: the vehicle's horsepower, the policyholder's place of residence, and the coverages for which policyholders are underwritten. Copyright The Journal of Risk and Insurance.

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Article provided by The American Risk and Insurance Association in its journal The Journal of Risk and Insurance.

Volume (Year): 72 (2005)
Issue (Month): 4 ()
Pages: 609-633

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Handle: RePEc:bla:jrinsu:v:72:y:2005:i:4:p:609-633
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  1. Gourieroux Christian & Monfort Alain & Trognon A, 1982. "Pseudo maximum lilelihood methods : applications to poisson models," CEPREMAP Working Papers (Couverture Orange) 8203, CEPREMAP.
  2. Dionne, G. & Vanasse, C., 1988. "A Generalization Of Automobile Insurance Rating Models: The Negative Binomial Distribution With A Regression Component," Cahiers de recherche 8833, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  3. J. Pinquet, 1997. "Experience rating through heterogeneous models," THEMA Working Papers 97-25, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
  4. Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
  5. Dionne, G. & Gane, R. & Vanasse, C., 1995. "Infessing Technological Parameters from Incomplete Panel Data," Cahiers de recherche 9537, Universite de Montreal, Departement de sciences economiques.
  6. Dionne, G. & Maurice, M. & Pinquet, J. & Vanasse, C., 2001. "The Role of Memory in Long-Term Contracting with Moral Hazard: Empirical Evidence in Automobile Insurance," Ecole des Hautes Etudes Commerciales de Montreal- 01-05, Ecole des Hautes Etudes Commerciales de Montreal-Chaire de gestion des risques..
  7. Cameron, A Colin & Trivedi, Pravin K, 1986. "Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators and Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 1(1), pages 29-53, January.
  8. Lechner, Michael, 1995. "Some Specification Tests for Probit Models Estimated on Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(4), pages 475-88, October.
  9. Dionne, G. & Vanasse, C., 1988. "Automobile Insurance Ratemaking in the Presence of Asymmetric Information," Cahiers de recherche 8834, Universite de Montreal, Departement de sciences economiques.
  10. Guilkey, David K. & Murphy, James L., 1993. "Estimation and testing in the random effects probit model," Journal of Econometrics, Elsevier, vol. 59(3), pages 301-317, October.
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