Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-11T07:44:47.247Z Has data issue: false hasContentIssue false

MARITAL STATUS AS A RISK FACTOR IN LIFE INSURANCE: AN EMPIRICAL STUDY IN TAIWAN

Published online by Cambridge University Press:  22 February 2016

Hsin Chung Wang
Affiliation:
Department of Finance and Actuarial Science, Aletheia University, New Taipei City, Taiwan, Republic of Chinaau4369@mail.au.edu.tw
Jack C. Yue
Affiliation:
Department of Statistics, National Chengchi University, Taipei, Taiwan, Republic of China Risk and Insurance Research Center, National Chengchi University, Taipei, Taiwan, Republic of Chinacsyue@nccu.edu.tw
Yi-Hsuan Tsai
Affiliation:
Department of Risk Management and Insurance, National Chengchi University, Taipei, Taiwan, Republic of Chinaisuan.tsai@gmail.com

Abstract

Gender and age are the top two risk factors considered in pricing life insurance products. Although it is believed that mortality rates are also related to other factors (e.g. smoking, overweight, and especially marriage), data availability and marketing often limit the possibility of including them. Many studies have shown that married people (particularly men) benefit from the marriage, and generally have lower mortality rates than unmarried people. However, most of these studies used data from a population sample; their results might not apply to the whole population. In this study, we explore if mortality rates differ by marital status using mortality data (1975–2011) from the Taiwan Ministry of the Interior. In order to deal with the problem of small sample sizes in some marital status groups, we use graduation methods to reduce fluctuations in mortality rates. We also use a relational approach to model mortality rates by marital status, and then compare the proposed model with some popular stochastic mortality models. Based on computer simulation, we find that the proposed smoothing methods can reduce fluctuations in mortality estimates between ages, and the relational mortality model has smaller errors in predicting mortality rates by marital status. Analyses of the mortality data from Taiwan show that mortality rates differ significantly by marital status. In some age groups, the differences in mortality rates are larger between marital status groups than between smokers and non-smokers. For the issue of practical consideration, we suggest modifications to include marital status in pricing of life insurance products.

Type
Research Article
Copyright
Copyright © Astin Bulletin 2016 

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

Cairns, A.J.G., Blake, D. and Dowd, K. (2006) A two-factor model for stochastic mortality with parameter uncertainty: Theory and calibration. Journal of Risk and Insurance, 73 (4), 687718.CrossRefGoogle Scholar
Cairns, A.J.G., Blake, D., Dowd, K., Coughlan, G.D. and Khalaf-Allah, M. (2011) Bayesian stochastic mortality modelling for two populations. Astin Bulletin, 41 (1), 2959.Google Scholar
Camarda, C.G. (2012) MortalitySmooth: An R package for smoothing poisson counts with P-Splines. Journal of Statistical Software, 50 (1), 124.CrossRefGoogle Scholar
Debón, A., Montes, F., Mateu, J., Porcu, E. and Bevilacqua, M. (2008) Modelling residuals dependence in dynamic life tables: A geostatistical approach. Computational Statistics and Data Analysis, 52 (6), 31283147.CrossRefGoogle Scholar
Dowd, K., Blake, D., Cairns, A.J.G., Coughlan, G.D. and Khalaf-Allah, M. (2011) A gravity model of mortality rates for two related populations. North American Actuarial Journal, 15 (2), 334356.CrossRefGoogle Scholar
Gardner, J. and Oswald, A.J. (2004) How is mortality affected by money, marriage, and stress?. Journal of Health Economics, 23 (6), 11811207.CrossRefGoogle Scholar
Hu, Y. and Goldman, N. (1990) Mortality differentials by marital status: An international comparison. Demography, 27 (2), 233250.CrossRefGoogle ScholarPubMed
Jarner, S.F. and Kryger, E.M. (2011) Modelling adult mortality in small populations: The SAINT model. Astin Bulletin, 41 (2), 377418.Google Scholar
Lee, R.D. and Carter, L. (1992) Modeling and forecasting the time series of U.S. mortality. Journal of the American Statistical Association, 87 (419), 659675.Google Scholar
Lee, W.C. (2003) A partial SMR approach to smoothing age-specific rates. Annals of Epidemiology, 13 (2), 8999.CrossRefGoogle ScholarPubMed
Lewis, E.B. (1982) Control of body segment differentiation in drosophila by the bithorax gene complex. In Embryonic Development, Part A: Genetics Aspects Edited by Burger, M.M. and Weber, R.New York: Alan R. Liss, Inc. 269288.Google Scholar
Li, N. and Lee, R. (2005) Coherent mortality forecasts for a group of populations: An extension of the lee-carter method. Demography, 42 (3), 575594.CrossRefGoogle ScholarPubMed
Lillard, L.A. and Panis, C.W.A. (1996) Marital status and mortality: The role of health. Demography, 33 (3), 313327.CrossRefGoogle ScholarPubMed
London, R.L. (1985) Graduation: The revision of estimates. Winsted, CT: ACTEX Publication.Google Scholar
Martikainen, P., Martelin, T., Nihtilä, E., Majamaa, K. and Koskinen, S. (2005) Differences in mortality by marital status in Finland from 1976 to 2000: Analyses of changes in marital-status distributions, socio-demographic and household composition, and cause of death. Population Studies, 59 (1), 99115.CrossRefGoogle ScholarPubMed
Ministry of the Interior, Taiwan Government http://www.moi.gov.tw/statGoogle Scholar
Renshaw, A.E. and Haberman, S. (2006) A cohort-based extension to the lee-carter model for mortality reduction factors. Insurance: Mathematics and Economics, 38 (3), 556570.Google Scholar
Schwaiger, E. (2005) Preferred lives-a concept for Taiwan? Underwriting Considerations. Munich Re Group.Google Scholar
Trowbridge, C.L. (1994) Mortality rates by marital status. Transactions, Society of Actuaries, XLVI, 99–122.Google Scholar
Van Den Berg, G.J. and Gupta, S. (2008) Early-life conditions, marital status, and mortality. http://www.iza.org/conference_files/SUMS2007/gupta_s3359.pdfGoogle Scholar
Wang, H.C. and Yue, C.J. (2011) Using regular discount sequence to model elderly mortality. Journal of Population Studies, 43, 4073. (In Chinese).Google Scholar
Wang, H.C., Jin, S. and Yue, C.J. (2012) A simulation study of small area mortality projection. Journal of Population Studies, 45, 77110. (In Chinese).Google Scholar
Yue, C.J. (1998) Can marriage extend the life expectancy – an empirical study of Tawian and US. The Insurance Quarterly, 107, 91104. (In Chinese).Google Scholar
Yue, C.J. and Huang, H. (2011) A study of incidence experience for taiwan life insurance. Geneva Papers on Risk and Insurance - Issues and Practice, 36 (4), 718733.CrossRefGoogle Scholar
Zhou, R., Li, J.S.H. and Tan, K.S. (2013) Pricing mortality risk: A two-population model with transitory jump effects. Journal of Risk and Insurance, 80 (3), 733774.CrossRefGoogle Scholar