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Voluntary internalization of speeding externalities with vehicle insurance

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  • Hultkrantz, Lars
  • Nilsson, Jan-Eric
  • Arvidsson, Sara

Abstract

High speed is an important determinant of accidents for speeders as well as for other motorists. This paper develops a framework for analyzing instruments that encourage drivers to internalize the full consequences of their behavior with respect to choice of speed using Pay-As-You-Speed (PAYS) insurance, possibly as an extension of Pay-As-You-Drive (PAYD) insurance. We demonstrate how the combination of a Pigovian taxation scheme and PAYS can be designed in a setting involving two principals (the state and an insurance company) that affect the incentives of commuters to choose between driving and other modes of transport and for those that use the car mode to drive carefully. While the government is assumed to maximize overall social efficiency and therefore wants to implement marginal cost pricing, insurance companies do actuarial pricing, i.e. average cost pricing within risk classes that are homogeneous to the degree that the insurers have information about actual behavior. PAYS insurance improves the insurance industry’s possibility to differentiate premiums according to behavior and therefore to target risk classes in a better way than today. Moreover, since our framework is designed to accomplish differentiation by self-selection, compulsory regulation is not necessary, although there may be reason for the government to facilitate the implementation of the new technology.

Suggested Citation

  • Hultkrantz, Lars & Nilsson, Jan-Eric & Arvidsson, Sara, 2012. "Voluntary internalization of speeding externalities with vehicle insurance," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(6), pages 926-937.
    • Handle: RePEc:eee:transa:v:46:y:2012:i:6:p:926-937
    DOI: 10.1016/j.tra.2012.02.011
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    References listed on IDEAS

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    Cited by:

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    2. Jessica Pesantez-Narvaez & Montserrat Guillen & Manuela Alcañiz, 2019. "Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression," Risks, MDPI, vol. 7(2), pages 1-16, June.
    3. Mohamed Hanafy & Ruixing Ming, 2021. "Machine Learning Approaches for Auto Insurance Big Data," Risks, MDPI, vol. 9(2), pages 1-23, February.
    4. Mercedes Ayuso & Montserrat Guillen & Ana María Pérez-Marín, 2016. "Telematics and Gender Discrimination: Some Usage-Based Evidence on Whether Men’s Risk of Accidents Differs from Women’s," Risks, MDPI, vol. 4(2), pages 1-10, April.
    5. Dementyeva, Maria & Koster, Paul R. & Verhoef, Erik T., 2015. "Regulation of road accident externalities when insurance companies have market power," Journal of Urban Economics, Elsevier, vol. 86(C), pages 1-8.
    6. Donatella Porrini & Giulio Fusco & Cosimo Magazzino, 2020. "Black boxes and market efficiency: the effect on premiums in the Italian motor-vehicle insurance market," European Journal of Law and Economics, Springer, vol. 49(3), pages 455-472, June.
    7. Hsu, Yung-Ching & Shiu, Yung-Ming & Chou, Pai-Lung & Chen, Yen-Ming J., 2015. "Vehicle insurance and the risk of road traffic accidents," Transportation Research Part A: Policy and Practice, Elsevier, vol. 74(C), pages 201-209.
    8. Bian, Yiyang & Yang, Chen & Zhao, J. Leon & Liang, Liang, 2018. "Good drivers pay less: A study of usage-based vehicle insurance models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 107(C), pages 20-34.

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