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

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  • Georges Dionne
  • Olfa Ghali

Abstract

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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  • Georges Dionne & Olfa Ghali, 2005. "The (1992) Bonus-Malus System in Tunisia: An Empirical Evaluation," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 72(4), pages 609-633.
  • Handle: RePEc:bla:jrinsu:v:72:y:2005:i:4:p:609-633
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    1. Dionne, Georges & Vanasse, Charles, 1989. "A Generalization of Automobile Insurance Rating Models: The Negative Binomial Distribution with a Regression Component," ASTIN Bulletin: The Journal of the International Actuarial Association, Cambridge University Press, vol. 19(02), pages 199-212, November.
    2. Dionne, G & Vanasse, C, 1992. "Automobile Insurance Ratemaking in the Presence of Asymmetrical Information," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(2), pages 149-165, April-Jun.
    3. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Applications to Poisson Models," Econometrica, Econometric Society, vol. 52(3), pages 701-720, May.
    4. Dionne, Georges & Gagne, Robert & Vanasse, Charles, 1998. "Inferring technological parameters from incomplete panel data," Journal of Econometrics, Elsevier, vol. 87(2), pages 303-327, September.
    5. Georges Dionne & Olfa Ghali, 2005. "The (1992) Bonus-Malus System in Tunisia: An Empirical Evaluation," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 72(4), pages 609-633.
    6. 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.
    7. 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 475-488, October.
    8. 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..
    9. 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.
    10. Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
    11. J. Pinquet, 1997. "Experience rating through heterogeneous models," THEMA Working Papers 97-25, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    12. Dominique Henriet & Jean-Charles Rochet, 1986. "La logique des systèmes bonus-malus en assurance automobile: une approche théorique," Annals of Economics and Statistics, GENES, issue 1, pages 133-152.
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    Cited by:

    1. Georges Dionne, 2012. "The Empirical Measure of Information Problems with Emphasis on Insurance Fraud and Dynamic Data," Cahiers de recherche 1233, CIRPEE.
    2. Georges Dionne & Olfa Ghali, 2005. "The (1992) Bonus-Malus System in Tunisia: An Empirical Evaluation," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 72(4), pages 609-633.
    3. Dionne, Georges & Michaud, Pierre-Carl & Pinquet, Jean, 2013. "A review of recent theoretical and empirical analyses of asymmetric information in road safety and automobile insurance," Research in Transportation Economics, Elsevier, vol. 43(1), pages 85-97.
    4. Katja Müller & Hato Schmeiser & Joël Wagner, 2016. "The impact of auditing strategies on insurers’ profitability," Journal of Risk Finance, Emerald Group Publishing, vol. 17(1), pages 46-79, January.
    5. Sharon Tennyson, 2010. "Incentive Effects of Community Rating in Insurance Markets: Evidence from Massachusetts Automobile Insurance," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 35(1), pages 19-46, June.
    6. Ming-Jyh Wang & Chieh-Hua Wen & Lawrence W Lan, 2010. "Modelling Different Types of Bundled Automobile Insurance Choice Behaviour: The Case of Taiwan*," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 35(2), pages 290-308, April.
    7. Sharon Tennyson, 2010. "Rethinking Consumer Protection Regulation in Insurance Markets," NFI Policy Briefs 2010-PB-07, Indiana State University, Scott College of Business, Networks Financial Institute.

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