Hostname: page-component-8448b6f56d-tj2md Total loading time: 0 Render date: 2024-04-24T08:17:18.269Z Has data issue: false hasContentIssue false
  • English
  • Français

THE USE OF ANNUAL MILEAGE AS A RATING VARIABLE

Published online by Cambridge University Press:  18 December 2015

Jean Lemaire ,
Sojung Carol Park  and
Kili C. Wang
Jean Lemaire
Affiliation:
Wharton School, University of Pennsylvania, 459 JMHH, 3730 Walnut Street, Philadelphia, PA 19104-6302, USA E-Mail: lemaire@wharton.upenn.edu, Phone: +1-215-898-7765. Fax: +1-215-898-1280
Sojung Carol Park*
Affiliation:
College of Business Administration, Seoul National University, Republic of Korea
Kili C. Wang
Affiliation:
Research Fellow, Risk and Insurance ResearchCenter, College of Commerce, National Chengchi University, Tamkang University, Taiwan E-Mail: kili@mail.tku.edu.tw
*
E-Mail: park.sojung@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Auto insurance companies must adapt to ever-evolving regulations and technological progress. Several variables commonly used to predict accidents rates, such as gender and territory, are being questioned by regulators. Insurers are pressured to find new variables that predict accidents more accurately and are socially acceptable. Annual mileage seems an ideal candidate. The recent development in new technologies should induce insurance carriers to explore ways to introduce mileage-based insurance premiums. We use the unique database of a major insurer in Taiwan to investigate whether annual mileage should be introduced as a rating variable in auto third-party liability insurance. We find that annual mileage is an extremely powerful predictor of the number of claims at-fault. The inclusion of mileage as a new variable should, however, not take place at the expense of bonus-malus systems; rather, the information contained in the bonus-malus premium level complements the value of annual mileage. An accurate rating system should therefore include annual mileage and bonus-malus as the two main building blocks, possibly supplemented by the use of other variables like age, territory and engine cubic capacity. While Taiwan has specific characteristics (high traffic density, a mild bonus-malus system and limited compulsory auto coverage), our results are so strong that we can confidently conjecture that they extend to all developed nations.

Keywords

Auto liability insuranceRating variablesAnnual mileage
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 46 , Issue 1 , January 2016 , pp. 39 - 69
Copyright
Copyright © Astin Bulletin 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allison, P.D. (2005) Fixed Effects Regression Methods for Longitudinal Data Using SAS. Cary, NC: SAS Institute.Google Scholar
American Academy of Actuaries. (1980) Risk Classification: Statement of Principles. Washington DC.Google Scholar
Avraham, R., Logue, K. and Schwarcz, D. (2013) Understanding Insurance Anti-Discrimination Laws. University of Michigan Law School Scholarship Repository, Ann Arbor, MI, Art 52.Google Scholar
Ayuso, M., Guillen, M. and Perez-Marin, M. (2014) Time and distance to first accident and driving patterns of young drivers with pay-as-you-drive insurance. Accident Analysis and Prevention, 73, 125–131.CrossRefGoogle ScholarPubMed
Bailey, R. and Simon, L. (1960) Two studies in automobile insurance ratemaking. ASTIN Bulletin, 1 (4), 192–217.CrossRefGoogle Scholar
Bickel, J.E. (2007) Some comparisons among quadratic, spherical, and logarithmic scoring rules. Decision Analysis, 4 (2), 49–65.CrossRefGoogle Scholar
Blackmon, G. and Zeckhauser, R. (1991) Mispriced equity: Regulated rates for auto insurance in Massachusetts. American Economic Review, 81 (2), 65–69.Google Scholar
Boucher, J.P. and Guillen, M. (2009) A survey on models for panel count data with applications to insurance, RACSAM-Revista de la Real Academic de Ciencas Exactas, Fisicos y Naturales. Serie A. Matematicas, 103 (2), 277–294.Google Scholar
Boucher, J.P. and Inoussa, R. (2014) A posteriori ratemaking with panel data. ASTIN Bulletin, 44 (3), 587–612.CrossRefGoogle Scholar
Boucher, J.P., Denuit, M. and Guillen, M. (2008) Models of insurance claim counts with time dependence based on generalization of Poisson and negative binomial distribution. Variance, 2 (1), 135–162.Google Scholar
Breusch, T.S. and Pagan, A.R. (1979) A simple test for heteroscedasticity and random coefficient variation. Econometrica, 47 (5), 1287–1294.CrossRefGoogle Scholar
Brown, R., Charters, D., Gunz, S. and Haddow, N. (2007) Colliding interest – age as an automobile insurance rating variable: Equitable rate-making or unfair discrimination? Journal of Business Ethics, 72 (2), 103–114.CrossRefGoogle Scholar
Butler, P. (2006) Driver negligence vs. Odometer miles: Rival theories to explain 12 predictors of auto insurance claims, American Risk & Insurance Association Annual Meeting, Washington, D.C.Google Scholar
CIA. (2015) The World Factbook. www.cia.gov/library/publications/the-world-factbook/geos/tw.html.Google Scholar
Derrig, R. and Tennyson, S. (2011) The impact of rate regulation on claims: Evidence from Massachusetts automobile insurance. Risk Management and Insurance Review, 14 (2), 173–199.CrossRefGoogle Scholar
Ferreira, J. and Minikel, E. (2010) Pay-As-You-Drive auto insurance in Massachusetts. Conservation Law Foundation and Environmental Insurance Agency. http://www.clf.org/wp-content/uploads/2010/12/CLF-PAYD-Study_November-2010.pdf.Google Scholar
Ferreira, J. and Minikel, E. (2013) Measuring per mile risk for Pay-As-You-Drive auto insurance. Transportation Research Record: Journal of the Transportation Research Board, 2297 (10), 97–103.CrossRefGoogle Scholar
Gaulding, J. (1995) Race, sex, and genetic discrimination in insurance: What's Fair? Cornell Law Review, 80, 1646–1694.Google Scholar
Geisser, S. (1993) Predictive Inference. New York, NY: Chapman and Hall.CrossRefGoogle Scholar
Gourieroux, C and Jasiak, J. (2004) Heterogeneous INAR (1) model with application to car insurance. Insurance: Mathematics and Economics, 34 (2), 177–192.Google Scholar
Green, W.H. (1994) Accounting for excess zeroes and sample selection in Poisson and negative binomial regression models. Department of Economics, Stern school of Business, New York University. https://archive.nyu.edu/bitstream/2451/26263/2/94-10.pdf.Google Scholar
Harrington, S. (1991) Auto insurance in Michigan: Regulation, no-fault, and affordability. Journal of Insurance Regulation, 91 (10), 144–183.Google Scholar
Hausman, J., Hall, B. and Griliches, Z. (1984) Econometric models for count data with application to the patents-R and D relationship. Econometrica, 52 (4), 909–938.CrossRefGoogle Scholar
Jaffe, D. and Russell, T. (2001) The regulation of automobile insurance in California. In Deregulating Property-Liability Insurance: Restoring Competition and Increasing Market Efficiency (ed. Cummins, D.), Washington, DC: American Enterprise Institute – Brookings Institution Joint Center for Regulatory Studies, 195–236.Google Scholar
Janke, M. (1991) Accidents, mileage, and the exaggeration of risk. Accident Analysis and Prevention, 23 (2–3), 183–188.CrossRefGoogle ScholarPubMed
Jovanis, P. and Chang, H. (1986) Modeling the relationship of accidents to miles traveled. Transportation Research Record, 1068, 42–51.Google Scholar
Kelly, M. and Nielson, N. (2006) Age as a variable in insurance pricing and risk classification. The Geneva Papers: Issues and Practice, 31 (2), 212–232.Google Scholar
Kohavi, R. (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. The International Joint Conference on Artificial Intelligence, 14 (2), 1137–1145.Google Scholar
Langford, J., Koppel, S.McCarthy, D. and Srinivasan, S. (2008) In defence of the “low-mileage bias”. Accident Analysis and Prevention, 40 (6), 1996–1999.CrossRefGoogle ScholarPubMed
Lemaire, J. (1985) Automobile Insurance: Actuarial Models. Boston: Kluwer Nijhoff Publishing.CrossRefGoogle Scholar
Lemaire, J. and Zi, H. (1994) A comparative analysis of 30 bonus-malus systems. ASTIN Bulletin, 24 (2), 287–309.CrossRefGoogle Scholar
Litman, T. (2011) Distance-based vehicle insurance feasibility. Costs and Benefits. Victoria Transport Policy Institute. www.vtpi.org/dbvi_com.pdf.Google Scholar
Lourens, P., Vissers, J. and Jesserun, M. (1999) Annual mileage, driving violations, and accident involvement in relation to drivers' sex, age, and level of education. Accident Analysis and Prevention, 31 (5), 593–597.CrossRefGoogle ScholarPubMed
Paefgen, J., Staake, T. and Thiesse, F. (2013) Evaluation and aggregation of pay-as-you-drive insurance rate factors: A classification analysis approach. Decision Support Systems, 56, 192–201.CrossRefGoogle Scholar
Paefgen, J., Staake, T. and Thiesse, F. (2014) Multivariate exposure modeling of accident risk: Insights from Pay-as-you-drive insurance data. Transportation Research, Part A: Policy and Practice, 61, 27–40.Google Scholar
Progressive Insurance. (2005) Texas Mileage Study: Relationship between Annual Mileage and Insurance Losses. http://www.nctcog.org/trans/air/programs/payd/PhaseI.pdf.Google Scholar
Regan, L., Weiss, M. and Tennyson, S. (2008) The relationship between auto insurance rate regulation and insured loss costs: An empirical analysis. Journal of Insurance Regulation, 27 (2), 23–46.Google Scholar
Sass, J. and Siefried, F. (2012) Insurance markets and unisex tariffs: Is the European court of justice improving or destroying welfare? Scandinavian Actuarial Journal, online, http://dx.doi.org/10.1080/03461238.2012.683450.CrossRefGoogle Scholar
Schwarze, R. and Wein, T. (2005) Is the market classification of risk always efficient? Evidence from German third party motor insurance. ESRC Centre for Analysis of Risk and Regulation, London School of Economics and Political Science, Discussion Paper 32.Google Scholar
Staplin, L., Gish, K. and Joyce, J. (2008) “Low mileage bias” and related policy implications – A cautionary note, Accident Analysis and Prevention, 40 (3), 1249–1252.CrossRefGoogle ScholarPubMed
Taiwan Insurance Institute. (2014) Insurance statistics. www.tii.org.tw/econtent/statistics/statistics01.asp.Google Scholar
Taiwan Insurance Institute. (2015) Insurance laws and regulations database. http://law.tii.org.tw/Eng/new01.asp?LSID=FL006900&ldate=20131227.Google Scholar
Weiss, M., Tennyson, S. and Regan, L. (2010) The effects of automobile insurance rate regulation on loss costs and claim frequency: An empirical analysis. Journal of Risk and Insurance, 77 (3), 597–624.CrossRefGoogle Scholar