Abstract
We propose a general framework that can be used to analyse the mortality experience of a large portfolio of lives. The objective of the framework is to provide a firm evidence base to support the setting of future mortality assumptions for the portfolio as a whole or subgroup-by-subgroup. The framework is developed in tandem with an analysis of the mortality of pensioners in the Universities Superannuation Scheme (USS), the largest funded pension scheme in the UK and one with a highly educated and very homogeneous membership. The USS experience was compared with English mortality subdivided into deprivation deciles using the Index of Multiple Deprivation (IMD). USS was found to have significantly lower mortality rates than even IMD-10 (the least deprived of the English deciles), but with similar mortality improvement rates to that decile over the period 2005–2016. Higher pensions were found to predict lower mortality, but only weakly so, and only for persons who retired on the first day of a month (mostly from active service). We found that other potential covariates derived from an individual’s post/zip code (geographical region and the IMD associated with their local area) typically had no explanatory power. This lack of dependence is an important conclusion of the USS-specific analysis and contrasts with others that consider the mortality of more heterogeneous scheme memberships. Although the key findings are likely to be particular to USS, we argue that our analytical framework will be useful for other large pension schemes and life annuity providers.
Original language | English |
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Pages (from-to) | 381-415 |
Number of pages | 35 |
Journal | European Actuarial Journal |
Volume | 12 |
Issue number | 1 |
Early online date | 29 Apr 2022 |
DOIs | |
Publication status | Published - Jun 2022 |
Keywords
- Age standardised mortality rate
- Graphical diagnostics
- Index of multiple deprivation
- Longevity risk
- Occupation
- Pensioners’ mortality
ASJC Scopus subject areas
- Statistics and Probability
- Economics and Econometrics
- Statistics, Probability and Uncertainty