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Fairness and annuity divisors for notional defined contribution pension schemes

Published online by Cambridge University Press:  29 October 2020

María del Carmen Boado-Penas*
Affiliation:
Institute for Financial and Actuarial Mathematics (IFAM), University of Liverpool, UK
Steven Haberman
Affiliation:
Faculty of Actuarial Science and Insurance, Cass Business School, City, University of London, UK
Poontavika Naka
Affiliation:
Department of Statistics, Chulalongkorn Business School, Chulalongkorn University, Thailand
*
*Corresponding author. Email: carmen.boado@liverpool.ac.uk
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Abstract

The use of a gender-neutral annuity divisor introduces an intra-generational redistribution from short-lived towards long-lived individuals; this entails a transfer of wealth from males to females and from low socioeconomic groups to high socioeconomic groups. With some subpopulations consisting of females from low socioeconomic groups (or males from high groups), the net effect of the redistribution is unclear. The study aims to quantify the lifetime income redistribution of a generic NDC system using two types of divisor – the demographic and the economic – to compute the amount of an initial pension. With this in mind, the redistribution (actuarial fairness) among subpopulations is assessed through the ratio between the present value of expected pensions received and contributions paid. We find that all subgroups of women and men with high educational attainment benefit from the use of the unisex demographic divisor. This paper also shows that the value of the economic divisor depends markedly on population composition. When mortality differentials by gender and level of education are considered, economic divisors are mostly driven by the longevity effect corresponding to gender.

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Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press
Figure 0

Figure 1. Age-earnings profile estimates during working life, age 25–69 by gender and level of education (in EUR).Source: Authors' calculations derived from Eurostat Database (2017).

Figure 1

Figure 2. Evolution of mortality rates across gender and three levels of education.Source: Authors' calculations derived from Human Mortality Database (2017).

Figure 2

Figure 3. Annuity divisors under the baseline assumptions by gender and education.Source: Authors' calculations.

Figure 3

Figure 4. PVR across subpopulations in the cases of notional capital including and excluding survivor dividend.Source: Authors' calculations.

Figure 4

Table 1. PVR for different subgroups when only gender differences in mortality taking into account in the calculation of the divisors

Figure 5

Table 2. PVR for different subgroups when all individuals are considered to have the same education level (low, medium or high education) for the calculation of the economic divisor

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Table 3. PVR for different subgroups when six different combinations of population are considered for the calculation of the economic divisor

Figure 7

Figure 5. PVR when using unisex demographic divisor with different retirement ages in the case of including and excluding survivor dividend.Source: Authors' calculations.Note: Results are based on the main assumptions, but retirement ages vary between 61 and 70.

Figure 8

Table 5. PVR with different discount rates and % relative change compared to the case of using demographic divisor under the main assumptions with 1.6% discount rate

Figure 9

Table 4. PVR with different notional rates and % of relative change compared to the case of using demographic divisor under the main assumptions with 1.6% of notional rate

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Table 6. PVR with different indexation rates and % relative change compared to the case of using demographic divisor under the main assumptions with 1.6% of indexation rate

Figure 11

Figure A1. Parameter estimates of the ‘stratified Lee–Carter’ model for unisex, male and female.Source: Authors' calculations derived from Human Mortality Database (2017) and Eurostat Database (2017).

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Table A1. Parameters estimation of the ARIMA(p, d, q)model

Figure 13

Figure A2. Forecasts of the mortality index Kt with 95% confident interval.Source: Authors' calculations.Note: The prediction intervals are not used in our analysis but we are aware that these are very narrow in some cases. This is a feature of some of the ARIMA models selected and it would be improved by considering the effect of parameter uncertainty through the use of boot-strapping.

Figure 14

Table A2. One-year probabilities of death in a dynamic context