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What Longevity Predictors Should Be Allowed for When Valuing Pension Scheme Liabilities?

Published online by Cambridge University Press:  22 March 2011

A. M. Madrigal ,
F. E. Matthews ,
D. D. Patel ,
A. T. Gaches  and
S. D. Baxter
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Abstract

Data held within administration records of occupational pension schemes yield a rich source of information on mortality rates and the statistical predictors (covariates) of longevity. In this paper we provide, for the first time, a multivariable analysis of post-retirement mortality using the detailed information held within occupational pension scheme records.

Using the extensive dataset of over one million living pensioners and dependants and 530,000 historic deaths collected by Club Vita, we investigate the importance of factors including gender, affluence and lifestyle on the observed period life expectancy of individuals. We describe one approach to constructing a multivariable model for pensioner baseline mortality, showing how such factors explain a variation in observed period life expectancy in excess of ten years. The relative importance of each factor on mortality is analysed and we describe the interactions between these factors and age, answering questions such as whether the impact of a healthy lifestyle or affluence attenuates with age. Further, we highlight the importance of the choice of affluence measure in analysing mortality, and show that the salary at retirement is a better predictor of longevity than the pension amount for male pensioners.

The results of this paper are directly relevant to any pensions actuary advising on an appropriate baseline assumption (i.e. current mortality rates) for use with occupational pension schemes.

Keywords

LongevityMortalityGeneralised Linear ModellingIncome DifferentialsSocio-EconomicsPensions
Type
Sessional meetings: papers and abstracts of discussions
Information
British Actuarial Journal , Volume 16 , Issue 1 , 23 May 2011 , pp. 1 - 38
Copyright
Copyright © Institute and Faculty of Actuaries 2011

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References

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.CrossRefGoogle Scholar
Agresti, A. (2002). Categorical data analysis, (2nd ed .). Wiley-Interscience.CrossRefGoogle Scholar
Benjamin, B., Pollard, J.H. (1989). The analysis of mortality and other actuarial statistics. Heinemann Professional Publishing.Google Scholar
CACI Ltd (2009). URL http://www.caci.co.uk (accessed May 2009).Google Scholar
Cairns, A.J.G., Blake, D., Dowd, K., Coughlan, G.D., Epstein, D., Ong, A., Balevich, I. (2007). A quantative comparison of stochastic models using data from England & Wales and the United States. Pensions Institute discussion paper PI-0701.Google Scholar
Continuous Mortality Investigation Self Administered Pension Schemes Mortality Committee (CMI) (2007). Working paper 29: An analysis of the results of the mortality of the male and female pensioners of self-administered pension schemes for the period 2000 to 2004 based on data collected by 30 June 2006.Google Scholar
Continuous Mortality Investigation Self Administered Pension Schemes Mortality Committee (CMI) (2009a). Working paper 35: The graduations of the CMI Self-Administered Pension Schemes 2000–2006 mortality experience. Final ‘S1’ series of mortality tables.Google Scholar
Continuous Mortality Investigation Self Administered Pension Schemes Mortality Committee (CMI) (2009b). Draft SAPS working paper (March 2009) Report on the preliminary results of an analysis into the mortality experience of pensioners of self-administered pension schemes for the period 2000 to 2007 based on data collected by 30 June 2008.Google Scholar
Continuous Mortality Investigation (CMI) (2009c). User guide to version 1.1. of the CMI Library of Mortality Projections.Google Scholar
Cox, D.R., Oakes, D.O. (1984). Analysis of survival data. London, Chapman and Hall.Google Scholar
Forfar, D.O., McCutcheon, J.J., Wilkie, A.D. (1988). On graduation by mathematical formula. Journal of the Institute of Actuaries, 115, 1–149, and Transactions of the Faculty of Actuaries, 41, 97–269.CrossRefGoogle Scholar
Gelman, A., Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.Google Scholar
Haberman, S., Renshaw, A. (1996). Generalized linear models and actuarial science. The Statistician, 45, 407.CrossRefGoogle Scholar
Harrell, F.E. (2001). Regression modeling strategies, with applications to linear models, logistic regression and survival analysis. NY, USA, Springer series of statistics.CrossRefGoogle Scholar
Hoffman, R. (2008). Socioeconomic differences in old age mortality. Springer series on demographic methods and population ageing.CrossRefGoogle Scholar
Hosmer, D.W. (1991). The importance of assessing the fit of using logistic regression models: a case study. American Journal of Public Health, 9(10), 1043–1069.Google Scholar
Hosmer, D.W., Lemeshow, S. (1980). Goodness-of-fit tests for the multiple logistic regression model. Communications in Statistics - Theory and Methods, 9(10), 1043–1069.CrossRefGoogle Scholar
Hosmer, D.W., Lemeshow, S. (2000). Applied logistic regression. Wiley Series in probability and statistics.CrossRefGoogle Scholar
Jenne, B., Vaupel, J.W. (editors) (1999). Validation of exceptional longevity. Monographs on Population Ageing, Vol. 6. Odense University Press.Google Scholar
MacDonald, A.S. (1996a). An actuarial survey of statistical models for decrement and transition data I: multiple state, poisson and binomial models. British Actuarial Journal, 2, 129–155.CrossRefGoogle Scholar
MacDonald, A.S. (1996b). An actuarial survey of statistical models for decrement and transition data II: competing risks, non-parametric and regression models. British Actuarial Journal, 2, 429–448.CrossRefGoogle Scholar
MacDonald, A.S. (1996c). An actuarial survey of statistical models for decrement and transition data III: counting process models. British Actuarial Journal, 2, 703–726.CrossRefGoogle Scholar
McCullagh, P., Nelder, J. (1989). Generalized linear models. Chapman & Hall.CrossRefGoogle Scholar
ONS (2007). Trends in ONS longitudinal study estimates of life expectancy, by social class 1972-2005, (available for download from Office of National Statistics (http://www.statistics.gov.uk/downloads/theme_population/Life_Expect_Social_class_1972-05/life_expect_social_class.pdf).Google Scholar
ONS (2008). Life expectancy at birth and at age 65 by local areas in the United Kingdom. (data available to download from http://www.statistics.gov.uk/STATBASE/Product.asp?vlnk=8841)Google Scholar
Perks, W. (1932). On some experiments in the graduation of mortality statistics. Journal of the Institute of Actuaries, 63, 12–40.CrossRefGoogle Scholar
R DEVELOPMENT Core Team (2008). R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.Google Scholar
Richards, S.J., Jones, G.L. (2004). Financial aspects of longevity risk. Paper presented to the Staple Inn Actuarial Society, October 2004.Google Scholar
Richards, S.J. (2007). Modelling pensioner longevity. Life and Pensions Magazine, May 2007.Google Scholar
Richards, S.J. (2008). Applying survival models to pensioner mortality data. British Actuarial Journal, 14, 257–303.CrossRefGoogle Scholar
Thatcher, A.R., Kannisto, V., Vaupel, J.W. (1998). The Force of mortality at ages 80 to 120. Monographs on Population Ageing, 5.Odense University Press.Google Scholar
The Pensions Regulator (2008). Mortality assumptions: good practice when choosing assumptions for defined benefit pension schemes. Guidance document, September 2008.Google Scholar
Therneau, M., Atkinson, E. (1997). An introduction to recursive partitioning using the RPART routines. Technical report, Mayo Foundation.Google Scholar
Veall, M., Zimmermann, K. (1996). Pseudo R2 measures for some common limited dependent variable models. Collaborative Research Center 386, Discussion paper 18, University of Munich.CrossRefGoogle Scholar
Ward, J.H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236–244.CrossRefGoogle Scholar