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Predicted risk perception and risk-taking behavior: The case of impaired driving

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Abstract

Research suggests an association between risk perception and risk-taking behavior in a variety of contexts. There is empirical evidence that perceived risk is generally biased and that perception of risk influences behavior. Perception of risk can be endogenous. It is therefore more appropriate to instrument risk perception. This article studies the perception of the risks associated with impaired driving and the relation between predicted risk perception and driving behavior. We survey a sample of license-holders, half of whom are drivers with a past conviction for impaired driving, the other half or control group without such conviction. Predicted perceptual biases are shown to influence actual driving behavior.

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Notes

  1. For an assessment, see Benson and Rasmussen (1999), Young and Likens (2000), and Eisenberg (2003).

  2. See Arrow (1982) for a review of the first generation of studies and a discussion of the relation between learning processes and the rationality of behaviors.

  3. In the Phelps study, the perceived relative risk of DUI versus driving while sober ranges from 1.47 to 7.43, depending on the number of drinks considered. In Levitt and Porter (see their Table 7 for drivers under 25 years old), the estimated relative risk for drivers in that age group is between 3.9 and 4.7.

  4. See also the recent study of Lundborg (2007).

  5. We assume that individuals truthfully answer when evaluating different risks which does not necessarily eliminate endogenous problems and perception biases. Indeed, we do not see why there should be any personal advantage to lie when responding to questions about risk perception.

  6. Polinsky and Shavell (2000) present a general review of policies of dissuasion; Zaal (1999) gives a summary of the literature on measures encouraging compliance with the Safety Code. See also Paternoster (1987).

  7. Detailed analysis of the survey procedure is presented in Appendix 1 on the web site.

  8. Details are given in Appendix 1 on the web site.

  9. For details, see Table 17 in Appendix 2 on the web site.

  10. For details, see Table 18 in Appendix 2 on the web site.

  11. We decided to have the same number of potential drivers for all questions. We choose 20,000 in order to have a feasible interval for bodily injury accidents, as discussed later.

  12. The reestimated equations are in Table 1A of Appendix 3.1 on the web site. We did also use all the variables in Table 2 and obtained the same results. They are available from the authors. Which methodology dominates is still an open question in the literature. We did a paired t-test on the difference between the two predictions (with all variables or with only the significant ones). We did not reject the null hypothesis that the difference between the two predictions is 0. Details are available.

  13. Complete regressions are available in Appendix 3.2 on the web site.

  14. We used the estimations presented in Table 2A on the web site.

  15. The predicted perceptions come from Table 3A on the web site.

  16. The estimated perceptions are computed from regressions in Tables 4A and 5A on the web site.

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Acknowledgements

This research was financed by the Société de l’assurance automobile du Québec, the Québec Department of Transport, and the Fonds pour la formation et l’aide à la recherche in the FCAR-MTQ-SAAQ program on road safety. Previous versions have been presented at the École Nationale des Arts et Métiers, Paris, at the Risk Attitude Conference, Montpellier, France, and at the FUR XII Conference, Roma. We thank Jean Boudreault, Andrée Brassard, and Lyne Vézina for their collaboration at various stages of this project. Stéphane Messier made an excellent contribution to the preparation and the management of the survey and Claire Boisvert improved significantly the presentation of the original manuscript. We thank Michèle Cohen, the editor, and an anonymous referee for very useful comments.

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Authors and Affiliations

  1. HEC Montréal, CIRPÉE and CIRRELT, 3000, Chemin de la Cote-Ste-Catherine, room 4454, Montreal, H3T 2A7, Canada

    Georges Dionne

  2. UQAM, CIRPÉE and CIRRELT Département des sciences économiques, Université du Québec à Montréal, C.P. 8888, Suc. Centre-Ville, Montréal, H3C 3P8, Canada

    Claude Fluet

  3. CIRRELT, Université de Montréal, C.P. 6128, Succ. Centre-Ville, Montréal, H3C 3J7, Canada

    Denise Desjardins

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  1. Georges Dionne
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Correspondence to Georges Dionne.

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Dionne, G., Fluet, C. & Desjardins, D. Predicted risk perception and risk-taking behavior: The case of impaired driving. J Risk Uncertainty 35, 237–264 (2007). https://doi.org/10.1007/s11166-007-9023-8

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