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  • Hsin Chung Wang (a1), Jack C. Yue (a2) (a3) and Yi-Hsuan Tsai (a4)


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.



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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.
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.
Camarda, C.G. (2012) MortalitySmooth: An R package for smoothing poisson counts with P-Splines. Journal of Statistical Software, 50 (1), 124.
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.
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.
Gardner, J. and Oswald, A.J. (2004) How is mortality affected by money, marriage, and stress?. Journal of Health Economics, 23 (6), 11811207.
Hu, Y. and Goldman, N. (1990) Mortality differentials by marital status: An international comparison. Demography, 27 (2), 233250.
Jarner, S.F. and Kryger, E.M. (2011) Modelling adult mortality in small populations: The SAINT model. Astin Bulletin, 41 (2), 377418.
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.
Lee, W.C. (2003) A partial SMR approach to smoothing age-specific rates. Annals of Epidemiology, 13 (2), 8999.
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.
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.
Lillard, L.A. and Panis, C.W.A. (1996) Marital status and mortality: The role of health. Demography, 33 (3), 313327.
London, R.L. (1985) Graduation: The revision of estimates. Winsted, CT: ACTEX Publication.
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.
Ministry of Economic Affairs, Taiwan
Ministry of the Interior, Taiwan Government
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.
Schwaiger, E. (2005) Preferred lives-a concept for Taiwan? Underwriting Considerations. Munich Re Group.
Trowbridge, C.L. (1994) Mortality rates by marital status. Transactions, Society of Actuaries, XLVI, 99–122.
Van Den Berg, G.J. and Gupta, S. (2008) Early-life conditions, marital status, and mortality.
Wang, H.C. and Yue, C.J. (2011) Using regular discount sequence to model elderly mortality. Journal of Population Studies, 43, 4073. (In Chinese).
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).
Yue, C.J. (1998) Can marriage extend the life expectancy – an empirical study of Tawian and US. The Insurance Quarterly, 107, 91104. (In Chinese).
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.
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.



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