Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-19T06:28:14.137Z Has data issue: false hasContentIssue false
  • English
  • Français

Fitting multi-population mortality models to socio-economic groups

Published online by Cambridge University Press:  14 July 2020

Jie Wen ,
Andrew J.G. Cairns  and
Torsten Kleinow Open the ORCID record for Torsten Kleinow [Opens in a new window]
Jie Wen
Affiliation:
Department of Actuarial Mathematics and Statistics and the Maxwell Institute for Mathematical Sciences, School of Mathematical and Computer Sciences, Heriot-Watt University, EH14 4AS, Edinburgh, UK
Andrew J.G. Cairns
Affiliation:
Department of Actuarial Mathematics and Statistics and the Maxwell Institute for Mathematical Sciences, School of Mathematical and Computer Sciences, Heriot-Watt University, EH14 4AS, Edinburgh, UK
Torsten Kleinow*
Affiliation:
Department of Actuarial Mathematics and Statistics and the Maxwell Institute for Mathematical Sciences, School of Mathematical and Computer Sciences, Heriot-Watt University, EH14 4AS, Edinburgh, UK
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

We compare results for 12 multi-population mortality models fitted to 10 distinct socio-economic groups in England, subdivided using the Index of Multiple Deprivation. Using the Bayes Information Criterion to compare models, we find that a special case of the common age effect (CAE) model fits best in a variety of situations, achieving the best balance between goodness of fit and parsimony. We provide a detailed discussion of key models to highlight which features are important. Group-specific period effects are found to be more important than group-specific age effects, and non-parametric age effects deliver significantly better results than parametric (e.g. linear) age effects. We also find that the addition of cohort effects is beneficial in some cases but not all. The preferred CAE model has the additional benefit of being coherent in the sense of Hyndman et al. ((2013) Demography50(1), 261–283); some of the other models considered are not.

Keywords

Multi-population mortality modelsDeprivationMortality inequalitySocio-economic models
Type
Paper
Information
Annals of Actuarial Science , Volume 15 , Issue 1 , March 2021 , pp. 144 - 172
Copyright
© Institute and Faculty of Actuaries 2020

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

Balia, S. & Jones, A.M. (2008). Mortality, lifestyle and socio-economic status. Journal of Health Economics, 27(1), 1–26.CrossRefGoogle ScholarPubMed
Bennett, J., Li, G., Foreman, K., Best, N., Kontis, V., Pearson, C., Hambly, P. & Ezzati, M. (2015). The future of life expectancy and life expectancy inequalities in england and wales: Bayesian spatiotemporal forecasting. The Lancet, 386(9989), 163–170.CrossRefGoogle ScholarPubMed
Cairns, A.J.G., Blake, D. & Dowd, K. (2006). A two-factor model for stochastic mortality with parameter uncertainty: theory and calibration. Journal of Risk and Insurance, 73(4), 687–718.CrossRefGoogle Scholar
Cairns, A.J.G., Kallestrup-Lamb, M., Rosenskjold, C., Blake, D. & Dowd, K. (2019). Modelling socio-economic differences in mortality using a new affluence index. ASTIN Bulletin, 49(3), 555–590.CrossRefGoogle Scholar
Enchev, V., Kleinow, T. & Cairns, A.J.G. (2015). Multi-population mortality models: fitting, forecasting and comparisons. Scandinavian Actuarial Journal, online, 1–24.Google Scholar
Hyndman, R.J., Booth, H. & Yasmeen, F. (2013). Coherent mortality forecasting: the product-ratio method with functional time series models’, Demography, 50(1), 261–283.CrossRefGoogle Scholar
Kleinow, T. (2015). A common age effect model for the mortality of multiple populations. Insurance: Mathematics and Economics, 63, 147–152. Special Issue: Longevity Nine – the Ninth International Longevity Risk and Capital Markets Solutions Conference.Google Scholar
Kleinow, T., Cairns, A. & Wen, J. (2019). Deprivation and life expectancy in the UK. The Actuary, April 2019.Google Scholar
Lee, R.D. & Carter, L.R. (1992). Modeling and forecasting U.S. mortality. Journal of the American Statistical Association. 87(419), 659–675.Google Scholar
Li, N. & Lee, R. (2005). Coherent mortality forecasts for a group of populations: An extension of the Lee-Carter method. Demography, 42(3), 575–594.CrossRefGoogle ScholarPubMed
Mackenbach, J.P., Kunst, A.E., Cavelaars, A.E., Groenhof, F. & Geurts, J.J. (1997). Socioeconomic inequalities in morbidity and mortality in western Europe. The Lancet, 349(9066), 1655–1659.CrossRefGoogle ScholarPubMed
Plat, R. (2009). On stochastic mortality modeling. Insurance: Mathematics and Economics, 45(3), 393–404.Google Scholar
Renshaw, A. & Haberman, S. (2003). Lee-Carter mortality forecasting with age-specific enhancement. Insurance: Mathematics and Economics, 33(2), 255–272. Papers presented at the 6th IME Conference, Lisbon, 15–17 July 2002.Google Scholar
Smith, T., Noble, M., Noble, S., Wright, G., McLennan, D. & Plunkett, E. (2015a). The English indices of deprivation 2015, research report, Department for Communities and Local Government.Google Scholar
Smith, T., Noble, M., Noble, S., Wright, G., McLennan, D. & Plunkett, E. (2015b). The English indices of deprivation 2015, technical report, Department for Communities and Local Government.Google Scholar
Villegas, A. & Haberman, S. (2014). On the modeling and forecasting of socioeconomic mortality differentials: an application to deprivation and mortality in england. North American Actuarial Journal, 18, 168–193.CrossRefGoogle Scholar
Villegas, A.M., Haberman, S., Kaishev, V.K. & Millossovich, P. (2017). A comparative study of two-population models for the assessment of basis risk in longevity hedges. ASTIN Bulletin, 47, 631–679.CrossRefGoogle Scholar
Wen, J., Kleinow, T. & Cairns, A. J. G. (to appear). Trends in Canadian Mortality by pension level: evidence from the CPP and QPP. North American Actuarial Journal. doi: 10.1080/10920277.2019.1679190.Google Scholar