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Telematics and Gender Discrimination: Some Usage-Based Evidence on Whether Men’s Risk of Accidents Differs from Women’s

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  • Mercedes Ayuso

    (Riskcenter, Universidad de Barcelona, Av. Diagonal, 690, Barcelona 08034, Spain)

  • Montserrat Guillen

    (Riskcenter, Universidad de Barcelona, Av. Diagonal, 690, Barcelona 08034, Spain)

  • Ana María Pérez-Marín

    (Riskcenter, Universidad de Barcelona, Av. Diagonal, 690, Barcelona 08034, Spain)

Abstract

Pay-as-you-drive ( PAYD ), or usage-based automobile insurance (UBI), is a policy agreement tied to vehicle usage. In this paper we analyze the effect of the distance traveled on the risk of accidents among young drivers with a PAYD policy. We use regression models for survival data to estimate how long it takes them to have their first accident at fault during the coverage period. Our empirical application with real data is presented and shows that gender differences are mainly attributable to the intensity of use. Indeed, although gender has a significant effect in explaining the time to the first crash, this effect is no longer significant when the average distance traveled per day is introduced in the model. This suggests that gender differences in the risk of accidents are, to a large extent, attributable to the fact that men drive more often than women. Estimates of the time to the first accident for different driver risk types are presented. We conclude that no gender discrimination is necessary if telematics provides enough information on driving habits.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jrisks:v:4:y:2016:i:2:p:10-:d:67819
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    References listed on IDEAS

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    Cited by:

    1. Guillen, Montserrat & Bermúdez, Lluís & Pitarque, Albert, 2021. "Joint generalized quantile and conditional tail expectation regression for insurance risk analysis," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 1-8.
    2. David A. Cather, 2020. "Reconsidering insurance discrimination and adverse selection in an era of data analytics," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 45(3), pages 426-456, July.
    3. Montserrat Guillen & Ana M. Pérez-Marín & Mercedes Ayuso & Jens Perch Nielsen, 2018. "“Exposure to risk increases the excess of zero accident claims frequency in automobile insurance”," IREA Working Papers 201810, University of Barcelona, Research Institute of Applied Economics, revised May 2018.
    4. Francis Duval & Jean‐Philippe Boucher & Mathieu Pigeon, 2023. "Enhancing claim classification with feature extraction from anomaly‐detection‐derived routine and peculiarity profiles," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 90(2), pages 421-458, June.
    5. Denuit, Michel & Guillen, Montserrat & Trufin, Julien, 2018. "Multivariate credibility modeling for usage-based motor insurance pricing with behavioral data," LIDAM Discussion Papers ISBA 2018032, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Gao, Guangyuan & Wüthrich, Mario V. & Yang, Hanfang, 2019. "Evaluation of driving risk at different speeds," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 108-119.

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