Hostname: page-component-848d4c4894-wzw2p Total loading time: 0 Render date: 2024-06-05T04:48:46.048Z Has data issue: false hasContentIssue false

MODELLING SOCIO-ECONOMIC DIFFERENCES IN MORTALITY USING A NEW AFFLUENCE INDEX

Published online by Cambridge University Press:  17 June 2019

Andrew J.G. Cairns*
Affiliation:
Maxwell Institute for Mathematical Sciences, Edinburgh EH9 3FD, UK Department of Actuarial Mathematics and Statistics Heriot-Watt University, Edinburgh EH14 4AS, UK E-mail: A.J.G.Cairns@hw.ac.uk
Malene Kallestrup-Lamb
Affiliation:
Department of Economics and Business Economics, Center for Research in Econometric Analysis of Time Series (CREATES) Aarhus University, Fuglesangs Allé 4, DK-8210 Aarhus V, Denmark E-mail: mkallestrup@econ.au.dk
Carsten Rosenskjold
Affiliation:
Department of Economics and Business Economics, Center for Research in Econometric Analysis of Time Series (CREATES) Aarhus University, Fuglesangs Allé 4, DK-8210 Aarhus V, Denmark E-mail: mkallestrup@econ.au.dk
David Blake
Affiliation:
Pensions Institute, Cass Business School, City University of London, London EC1Y 8TZ, UK E-mail: D.Blake@city.ac.uk
Kevin Dowd
Affiliation:
Durham University Business School Durham DH1 3LB, UK E-mail: kevin.dowd@durham.ac.uk

Abstract

We introduce a new modelling framework to explain socio-economic differences in mortality in terms of an affluence index that combines information on individual wealth and income. The model is illustrated using data on older Danish males over the period 1985–2012 reported in the Statistics Denmark national register database. The model fits the historical mortality data well, captures their key features, generates smoothed death rates that allow us to work with a larger number of sub-groups than has previously been considered feasible, and has plausible projection properties.

Type
Research Article
Copyright
© Astin Bulletin 2019 

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

Blake, D., Cairns, A.J.G., Dowd, K. and Kessler, A. R. (2018) Still living with mortality: The longevity risk transfer market after one decade. British Actuarial Journal, 24, e1, 180.CrossRefGoogle Scholar
Börger, M., Fleischer, D. and Kuksin, N. (2014) Modelling the mortality trend under modern solvency regimes. ASTIN Bulletin, 44, 138.CrossRefGoogle Scholar
Booth, H., Maindonald, J. and Smith, L. (2002) Applying Lee-Carter under conditions of variable mortality decline. Population Studies, 56, 325336.CrossRefGoogle ScholarPubMed
Bound, J., Geronimus, A.T., Rodriguez, J.M. and Waidmann, T.A. (2015) Measuring recent apparent declines in longevity: The role of increasing educational attainment. Health Affairs, 34, 21672173.CrossRefGoogle ScholarPubMed
Brønnum-Hansen, H. and Baadsgaard, M. (2012) Widening social inequality in life expectancy in Denmark. A register-based study on social composition and mortality trends for the Danish population. BMC Public Health, 12, 994.CrossRefGoogle Scholar
Cairns, A.J.G. (2013) Robust hedging of longevity risk. Journal of Risk and Insurance, 80, 621648.CrossRefGoogle Scholar
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, 687718.CrossRefGoogle Scholar
Cairns, A.J.G., Blake, D., Dowd, K., Coughlan, G.D., Epstein, D., Ong, A. and Balevich, I. (2009) A quantitative comparison of stochastic mortality models using data from England & Wales and the United States. North American Actuarial Journal, 13, 135.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, 2959.Google Scholar
Cairns, A.J.G., Blake, D., Dowd, K. and Kessler, A. (2016) Phantoms never die: Living with unreliable population data. Journal of the Royal Statistical Society, Series A, 179, 9751005.CrossRefGoogle Scholar
Cairns, A.J.G. and El Boukfaoui, G. (2017) Basis risk in index based longevity hedges: A guide for longevity hedgers. North American Actuarial Journal (to appear).Google Scholar
Chen, L., Cairns, A.J.G. and Kleinow, T. (2017) Small population bias and sampling effects in stochastic mortality modelling. European Actuarial Journal, 7, 193230.CrossRefGoogle ScholarPubMed
Chetty, R., Stepner, M., Abraham, S., Lin, S., Scuderi, B., Turner, N., Bergeron, A. and Cutler, D. (2016) The association between income and life expectancy in the United States, 2001-2014. Journal of the American Medical Association, 315(16), 17501766.CrossRefGoogle ScholarPubMed
Christensen, K., Davidsen, M., Juel, K., Mortensen, L., Rau, R. and Vaupel, J.W. (2010) The divergent life-expectancy trends in Denmark and Sweden – and some potential explanations. In International Differences in Mortality at Older Ages: Dimensions and Sources (eds. Crimmins, E.M., Preston, S.H., Cohen, B.), pp. 385407. Washington, DC: The National Academies Press.Google Scholar
Coughlan, G.D., Khalaf-Allah, M., Ye, Y., Kumar, S., Cairns, A.J.G., Blake, D. and Dowd, K. (2011) Longevity hedging 101: A framework for longevity basis risk analysis and hedge effectiveness. North American Actuarial Journal, 15, 150176.CrossRefGoogle Scholar
Cristia, J.P. (2009) Rising mortality and life expectancy differentials by lifetime earnings in the United States. Journal of Health Economics, 28, 984995.CrossRefGoogle ScholarPubMed
Dowd, K., Cairns, A.J.G., Blake, D., Coughlan, G.D. and Khalaf-Allah, M. (2010) Backtesting stochastic mortality models: An ex-post evaluation of multi-period-ahead density forecasts. North American Actuarial Journal, 14, 281298.CrossRefGoogle Scholar
Dowd, K., Cairns, A.J.G., Blake, D., Coughlan, G.D., and Khalaf-Allah, M. (2011) A gravity model of mortality rates for two related populations. North American Actuarial Journal, 15, 334356.CrossRefGoogle Scholar
Gavrilov, L.A. and Gavrilova, N.S. (1991) The Biology of Life Span: A Quantitative Approach. New York, NY: Harwood Academic Publisher.Google Scholar
Gilks, W.R., Richardson, S. and Spiegelhalter, D.J. (1996) Markov Chain Monte Carlo in Practice. London: Chapman and Hall.Google Scholar
Hyndman, R., Booth, H. and Yasmeen, F. (2013) Coherent mortality forecasting: The product-ratio method with functional time series models. Demography, 50, 261283.CrossRefGoogle ScholarPubMed
Juel, K. (2008) Middellevetid og dødelighed i Danmark sammenlignet med i Sverige. Hvad betyder rygning og alkohol? [Life expectancy and mortality in Denmark compared to Sweden. What is the effect of smoking and alcohol?]. Ugeskrift Laeger, 170(33), 24232427. (In Danish).Google Scholar
Kallestrup-Lamb, M. and Rosenskjold, C. (2017) Insight into the female longevity puzzle: Using register data to analyse mortality and cause of death behaviour across socioeconomic groups. CREATES Research Paper 2017-08, Department of Economics and Business Economics, Aarhus University.Google Scholar
Kitagawa, E.M. and Hauser, P.M. (1968) Education differentials in mortality by cause of death: United States, 1960. Demography, 5, 318353.CrossRefGoogle Scholar
Kwon, H.-S. and Jones, B. L. (2008) Applications of a multi-state risk factor/mortality model in life insurance. Insurance: Mathematics and Economics, 43, 394402.Google Scholar
Lee, R.D. and Carter, L.R. (1992) Modeling and forecasting U.S. mortality. Journal of the American Statistical Association, 87, 659675.Google Scholar
Lee, R. and Miller, T. (2001) Evaluating the performance of the Lee-Carter method for forecasting mortality. Demography, 38, 537549.CrossRefGoogle ScholarPubMed
Li, N. and Lee, R. (2005) Coherent mortality forecasts for a group of populations: An extension of the Lee-Carter method. Demography, 42, 575594.CrossRefGoogle ScholarPubMed
Mackenbach, J.P., Bos, V., Andersen, O., Cardano, M., Costa, G., Harding, S., Reid, A., Hemström, Ö., Valkonen, T. and Kunst, A.E. (2003) Widening socioeconomic inequalities in mortality in six Western European countries. International Journal of Epidemiology, 32, 830837.CrossRefGoogle ScholarPubMed
Michaelson, A. and Mulholland, J. (2015) Strategy for increasing the global capacity for longevity risk transfer: Developing transactions that attract capital markets investors. Pension and Longevity Risk Transfer for Institutional Investors, 2015(1), 2837.Google Scholar
Office for National Statistics (2011) Trends in life expectancy by National Statistics Socio-economic Classification 1982-2006. Available at: https://www.ons.gov.uk/.Google Scholar
Olshansky, S.J., Antonucci, T., Berkman, L., Binstock, R.H., Boersch-Supan, A., Cacioppo, J.T., Carnes, B.A., Carstensen, L.L., Fried, L.P., Goldman, D.P., Jackson, J., Kohli, M., Rother, J., Zheng, Y. and Rowe, J. (2012). Differences in life expectancy due to race and educational differences are widening, and many may not catch up. Health Affairs, 31(8), 18031813.CrossRefGoogle Scholar
Plat, R. (2009) On stochastic mortality modelling. Insurance: Mathematics and Economics, 45, 393404.Google Scholar
Van Berkum, F., Antonio, K. and Vellekoop, M. (2016) The impact of multiple structural changes on mortality predictions. Scandinavian Actuarial Journal, 2016, 581603.CrossRefGoogle Scholar
Villegas, A. and Haberman, S. (2014) On the modeling and forecasting of socio-economic mortality differentials: An application to deprivation and mortality in England. North American Actuarial Journal, 18, 168193.CrossRefGoogle Scholar
Villegas, A., Haberman, S., Kaishev, V. and Millossovich, P. (2017) A comparative study of two-population models for the assessment of basis risk in longevity hedges. ASTIN Bulletin, 47, 631679.CrossRefGoogle Scholar
Waldron, H. (2013) Mortality differentials by lifetime earnings decile: Implications for evaluations of proposed social security law changes. Social Security Bulletin, 73(1), 137.Google ScholarPubMed