Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-11T08:09:55.682Z Has data issue: false hasContentIssue false
  • English
  • Français

Getting life expectancy estimates right for pension policy: period versus cohort approach

Published online by Cambridge University Press:  13 May 2020

Mercedes Ayuso ,
Jorge M. Bravo  and
Robert Holzmann
Mercedes Ayuso*
Affiliation:
Department of Econometrics, Statistics and Applied Economy, University of Barcelona, Riskcenter-UB, Barcelona, Spain
Jorge M. Bravo
Affiliation:
Universidade Nova de Lisboa NOVA IMS & MagIC & CEFAGE-UE & Université Paris-Dauphine PSL, Paris, France
Robert Holzmann
Affiliation:
Austrian National Bank, Vienna, Austria Elected Fellow of the Austrian Academy of Sciences, Vienna, Austria
*
*Corresponding author. Email: robert.holzmann@oeaw.ac.at
Get access Rights & Permissions [Opens in a new window]

Abstract

In many policy areas it is essential to use the best estimates of life expectancy, but it is vital to most areas of pension policy. This paper presents the conceptual differences between static period and dynamic cohort mortality tables, estimates the differences in life expectancy for Portugal and Spain, and compares official estimates of both life expectancy estimates for Australia, the United Kingdom, and the United States for 1981, 2010, and 2060. These comparisons reveal major differences between period and cohort life expectancy in and between countries and across years. The implications of using wrong estimates for pension policy, including financial sustainability, are explored.

Keywords

Balancing mechanismcross-country comparisonLee–Carterlife expectancy indexationG23H55J14
Type
Article
Information
Journal of Pension Economics & Finance , Volume 20 , Issue 2 , April 2021 , pp. 212 - 231
Copyright
Copyright © Cambridge University Press 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.)

Footnotes

1

This paper was prepared for the BBVA Expert Forum and profited from many valuable and encouraging discussions and comments from its members.

References

Alho, J, Bravo, J and Palmer, E (2012) Annuities and life expectancy in NDC. In Holzmann, R, Palmer, E and Robalino, D (eds), Nonfinancial Defined Contribution Pension Schemes in A Changing Pension World, Volume 2 Gender, Politics, and Financial Stability. Washington: World Bank Publications, pp. 395–436.CrossRefGoogle Scholar
Ayuso, M, Bravo, J and Holzmann, R (2015) Population Projections Revisited: Moving Beyond Convenient Assumptions on Fertility, Mortality and Migration. BBVA Working Paper 10. Madrid: Instituto BBVA de Pensiones.Google Scholar
Ayuso, M, Bravo, J and Holzmann, R (2017a) On the heterogeneity in longevity among socioeconomic groups: scope, trends, and implications for earnings-related pension schemes. Global Journal of Human Social Sciences-Economics 17, 33–58, First published as BBVA Working Paper 16.Google Scholar
Ayuso, M, Bravo, J and Holzmann, R (2017b) Addressing longevity heterogeneity in pension scheme design. Journal of Finance and Economics 6, 1–24, First published as BBVA Working Paper 18.CrossRefGoogle Scholar
Blake, D, El Karoui, N, Loisel, S and MacMin, R (2018) Longevity risk and capital markets: the 2015–16 update. Insurance: Mathematics and Economics 78, 157–173. doi: https://doi.org/10.1016/j.insmatheco.2017.10.002.Google Scholar
Board of Trustees (2018) The 2018 Annual Report of the Board of Trustees of the Federal Old-Age and Survivors Insurance and Federal Disability Insurance Trust Funds. Washington, DC: U.S. Government.Google Scholar
Booth, H and Tickle, L (2008) Mortality modelling and forecasting: a review of methods. Annals of Actuarial Science 3, 3–43.CrossRefGoogle Scholar
Booth, H, Maindonald, J and Smith, L (2002) Applying Lee–Carter under conditions of variable mortality decline. Population Studies 56, 325–336.CrossRefGoogle ScholarPubMed
Bravo, J (2007) Period and Prospective Life Tables: Stochastic Models, Actuarial Applications and Longevity Risk Hedging (PhD thesis in economics), University of Évora, Portugal.Google Scholar
Bravo, JM and El Mekkaoui, N (2018) Valuation of longevity-linked life annuities. Insurance: Mathematics and Economics 78, 212–229.Google Scholar
Brouhns, N, Denuit, M and Vermunt, J (2002a) A Poisson Log-bilinear regression approach to the construction of projected life tables. Insurance: Mathematics and Economics 31, 373–393.Google Scholar
Brouhns, N, Denuit, M and Vermunt, J (2002b) Measuring the longevity risk in mortality projections. Bulletin of the Swiss Association of Actuaries 2, 105–130.Google Scholar
Cairns, A, Blake, D and Dowd, K (2006) A two-factor model for stochastic mortality with parameter uncertainty: theory and calibration. The Journal of Risk and Insurance 73, 687–718.CrossRefGoogle Scholar
Cairns, A, Blake, D, Dowd, K, Coughlan, G, Epstein, D, Ong, A and Balevich, I (2009) A quantitative comparison of stochastic mortality models using data from England and Wales and the United States. North American Actuarial Journal 13, 1–35.CrossRefGoogle Scholar
Cairns, A, Blake, D, Dowd, K, Coughlan, G and Khalaf-Allah, M (2011) Bayesian Stochastic mortality modelling for two populations. Astin Bulletin 41, 29–59.Google Scholar
Currie, I (2006) Smoothing and Forecasting Mortality Rates with P-Splines. London: Heriot Watt University.Google Scholar
Currie, I (2016) On fitting generalized linear and non-linear models of mortality. Scandinavian Actuarial Journal 2016, 356–383.CrossRefGoogle Scholar
Currie, I, Durban, M and Eilers, P (2004) Smoothing and forecasting mortality rates. Statistical Modelling 4, 279–298.CrossRefGoogle Scholar
Denuit, M (2007) Distribution of the random future life expectancies in log-bilinear mortality projection models. Lifetime Data Analysis 13, 381–397.CrossRefGoogle ScholarPubMed
Denuit, M and Goderniaux, A-C (2005) Closing and projecting life tables using log-linear models. Bulletin de l'Association Suisse des Actuaries 1, 29–49.Google Scholar
Dowd, K, Cairns, A, Blake, D, Coughlan, G and Khalaf-Allah, M (2011) A gravity model of mortality rates for two related populations. North American Actuarial Journal 15, 334–356.CrossRefGoogle Scholar
Girosi, F and King, G (2007) Understanding the Lee-Carter Mortality Forecasting Method. Working paper. Cambridge, MA, USA: Harvard University.Google Scholar
Goldstein, J and Wachter, K (2006) Gaps and lags: relationships between period and cohort life expectancy. Population Studies 60, 257–269.CrossRefGoogle ScholarPubMed
Goodman, L (1979) Simple models for the analysis of association in cross classifications having ordered categories. Journal of the American Statistical Association 74, 537–552.CrossRefGoogle Scholar
Gourieroux, CC and Lu, Y (2015) Love and death: a Freund model with frailty. Insurance: Mathematics & Economics 63, 191–203.Google Scholar
Gu, D, Pelletier, F and Sawyer, C (2017) Projecting Age-Sex-Specific Mortality: A Comparison of the Modified Lee-Carter and Pattern of Mortality Decline Methods. UN Population Division, Technical Paper No. 6. New York: United Nations.Google Scholar
Hanewald, K (2011) Explaining mortality dynamics: the role of macroeconomic fluctuations and cause of death trends. North American Actuarial Journal 15, 290–314.CrossRefGoogle Scholar
Holzmann, R, Palacios, R and Zviniene, A (2001) On the economics and scope of implicit pension debt: an international perspective. Empirica 28, 97–129.CrossRefGoogle Scholar
Holzmann, R, Alonso-García, J, Labit-Hardy, H and Villegas, AM (2019) NDC Schemes and heterogeneity in longevity: Proposals for redesign. In Holzmann, R, Palmer, E, Palacios, R and Sacchi, S (eds), Progress and Challenges of Nonfinancial Defined Contribution Pension (NDC) Schemes, Volume 1: Addressing Marginalization, Polarization, and the Labor Market. Washington, DC: World Bank, Chapter 14, pp. 307–332.CrossRefGoogle Scholar
Human Mortality Database (2017) University of California, Berkeley (United States), and Max Planck Institute for Demographic Research (Germany). Available at www.mortality.org.Google Scholar
Hunt, A and Blake, D (2014) A general procedure for constructing mortality models. North American Actuarial Journal 18, 116–138.CrossRefGoogle Scholar
Hunt, A and Blake, D (2015) On the Structure and Classification of Mortality Models. London: The Pensions Institute – Cass Business School.CrossRefGoogle Scholar
Hyndman, R and Ullah, S (2007) Robust forecasting of mortality and fertility rates: a functional data approach. Computational Statistics & Data Analysis 51, 4942–4956.CrossRefGoogle Scholar
Jarner, S and Kryger, E (2011) Modelling adult mortality in small populations: the SAINT model. Astin Bulletin 41, 377–418.Google Scholar
Lee, R and Carter, L (1992) Modeling and forecasting U.S. Mortality. Journal of the American Statistical Association 87, 659–671.Google Scholar
Lee, R and Miller, T (2001) Evaluating the performance of the Lee–Carter method for forecasting mortality. Demography 38, 537–549.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, 575–594.CrossRefGoogle ScholarPubMed
Missov, T and Lenart, A (2011) Linking period and cohort life-expectancy linear increases in Gompertz proportional hazards models. Demographic Research 24, 455–468.CrossRefGoogle Scholar
Oeppen, J and Vaupel, J (2002) Broken limits to life expectancy. Science (New York, N.Y.) 296, 1029–1031.CrossRefGoogle ScholarPubMed
Organisation of Economic Co-operation and Development (OECD) (2012) Putting pensions on auto-pilot: automatic-adjustment mechanisms and financial sustainability of retirement-income systems. In OECD (ed) OECD Pensions Outlook 2012. Paris: OECD Publishing. 45–76. http://dx.doi.org/10.1787/9789264169401-5-en.CrossRefGoogle Scholar
Organisation of Economic Co-operation and Development (OECD) (2015) Pensions at A Glance 2015: OECD and G20 Indicators. Paris: OECD Publishing. http://dx.doi.org/10.1787/pension_glance-2015-en.Google Scholar
Organisation of Economic Co-operation and Development (OECD) (2017) Pensions at A Glance 2017: OECD and G20 Indicators. Paris: OECD Publishing. http://dx.doi.org/10.1787/pension_glance-2017-en.Google Scholar
Palmer, E (2012) Generic NDC: Equilibrium, valuation, and risk sharing with and without NDC bonds. In Holzmann, R, Palmer, E and Robalino, D (eds), Nonfinancial Defined Contribution Pension Schemes in A Changing Pension World, Volume 2 Gender, Politics, and Financial Stability. Washington: World Bank Publications, pp. 309–333.CrossRefGoogle Scholar
Palmer, E, Alho, J and Zhao de Gosson de Varennes, Y (2018) Projecting Cohort Life Expectancy from the Changing Relationship Between Period and Cohort Mortalities. Uppsala: University of Uppsala/University of Helsinki (mimeo).Google Scholar
Plat, R (2009) On stochastic mortality modeling. Insurance: Mathematics and Economics 45, 393–404.Google Scholar
Productivity Commission (2013) An Ageing Australia: Preparing for the Future. Commission Research Paper. Canberra: Productivity Commission.Google Scholar
Renshaw, A and Haberman, S (2003) Lee–Carter mortality forecasting with age-specific enhancement. Insurance: Mathematics and Economics 33, 255–272.Google Scholar
Renshaw, A and Haberman, S (2006) A cohort-based extension to the Lee–Carter model for mortality reduction factors. Insurance: Mathematics and Economics 38, 556–570.Google Scholar
United Nations (UN) (2017) World Population Prospects: The 2017 Revision, Methodology of the United Nations Population Estimates and Projections. Department of Economic and Social Affairs, Population Division 2017, Working Paper No. ESA/P/WP.250. New York: United Nations.Google Scholar
Whitehouse, E (2007) Life-Expectancy Risk and Pensions: Who Bears the Burden? OECD Social, Employment and Migration Working Papers, No. 60. Paris: OECD Publishing. http://dx.doi.org/10.1787/060025254440.Google Scholar
Zhu, W, Tan, K and Wang, C-W (2017) Modeling multicountry longevity risk with mortality dependence: a Lévy subordinated hierarchical Archimedean copulas approach. The Journal of Risk and Insurance 84(S1), 477–494.CrossRefGoogle Scholar