IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp11512.html
   My bibliography  Save this paper

Getting Life Expectancy Estimates Right for Pension Policy: Period versus Cohort Approach

Author

Listed:
  • Ayuso, Mercedes

    (University of Barcelona)

  • Bravo, Jorge Miguel

    (Universidade Nova de Lisboa)

  • Holzmann, Robert

    (University of New South Wales)

Abstract

In many policy areas it is essential to use the best estimates of life expectancy, but such estimates are vital to most areas of pension policy – from indexed access age and the calculation of initial benefits to the financial sustainability of pension schemes and the operation of their balancing mechanism. This paper presents the conceptual differences between static period and dynamic cohort mortality tables, estimates the differences in life expectancy between both tables using data from 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. This comparison reveals major differences between period and cohort life expectancy in and between countries and across years. Using measures of period instead of cohort life expectancy creates an implicit subsidy for individuals of 30 percent or more, with potentially stark consequences on the financial sustainability of pension schemes. These and other implications for pension policy are explored and next steps suggested.

Suggested Citation

  • Ayuso, Mercedes & Bravo, Jorge Miguel & Holzmann, Robert, 2018. "Getting Life Expectancy Estimates Right for Pension Policy: Period versus Cohort Approach," IZA Discussion Papers 11512, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp11512
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp11512.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Andrew Cairns & David Blake & Kevin Dowd & Guy Coughlan & David Epstein & Alen Ong & Igor Balevich, 2009. "A Quantitative Comparison of Stochastic Mortality Models Using Data From England and Wales and the United States," North American Actuarial Journal, Taylor & Francis Journals, vol. 13(1), pages 1-35.
    2. Booth, H. & Tickle, L., 2008. "Mortality Modelling and Forecasting: a Review of Methods," Annals of Actuarial Science, Cambridge University Press, vol. 3(1-2), pages 3-43, September.
    3. Hyndman, Rob J. & Shahid Ullah, Md., 2007. "Robust forecasting of mortality and fertility rates: A functional data approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4942-4956, June.
    4. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
    5. Frank Denton & Christine Feaver & Byron Spencer, 2005. "Time series analysis and stochastic forecasting: An econometric study of mortality and life expectancy," Journal of Population Economics, Springer;European Society for Population Economics, vol. 18(2), pages 203-227, June.
    6. Brouhns, Natacha & Denuit, Michel & Vermunt, Jeroen K., 2002. "A Poisson log-bilinear regression approach to the construction of projected lifetables," Insurance: Mathematics and Economics, Elsevier, vol. 31(3), pages 373-393, December.
    7. Jarner, Søren Fiig & Kryger, Esben Masotti, 2011. "Modelling Adult Mortality in Small Populations: The Saint Model," ASTIN Bulletin, Cambridge University Press, vol. 41(2), pages 377-418, November.
    8. Edward Whitehouse, 2007. "Life-Expectancy Risk and Pensions: Who Bears the Burden?," OECD Social, Employment and Migration Working Papers 60, OECD Publishing.
    9. Ayuso, Mercedes & Bravo, Jorge Miguel & Holzmann, Robert, 2016. "On the Heterogeneity in Longevity among Socioeconomic Groups: Scope, Trends, and Implications for Earnings-Related Pension Schemes," IZA Discussion Papers 10060, Institute of Labor Economics (IZA).
    10. Ronald Lee & Timothy Miller, 2001. "Evaluating the performance of the lee-carter method for forecasting mortality," Demography, Springer;Population Association of America (PAA), vol. 38(4), pages 537-549, November.
    11. Holzmann, Robert & Alonso-García, Jennifer & Labit-Hardy, Heloise & Villegas, Andres M., 2017. "NDC Schemes and Heterogeneity in Longevity: Proposals for Redesign," IZA Discussion Papers 11193, Institute of Labor Economics (IZA).
    12. Gourieroux, Christian & Lu, Yang, 2015. "Love and death: A Freund model with frailty," Insurance: Mathematics and Economics, Elsevier, vol. 63(C), pages 191-203.
    13. Andrew J. G. Cairns & David Blake & Kevin Dowd, 2006. "A Two‐Factor Model for Stochastic Mortality with Parameter Uncertainty: Theory and Calibration," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 73(4), pages 687-718, December.
    14. Nan Li & Ronald Lee, 2005. "Coherent mortality forecasts for a group of populations: An extension of the lee-carter method," Demography, Springer;Population Association of America (PAA), vol. 42(3), pages 575-594, August.
    15. Trifon Missov & Adam Lenart, 2011. "Linking period and cohort life-expectancy linear increases in Gompertz proportional hazards models," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 24(19), pages 455-468.
    16. Katja Hanewald, 2011. "Explaining Mortality Dynamics," North American Actuarial Journal, Taylor & Francis Journals, vol. 15(2), pages 290-314.
    17. Renshaw, A. E. & Haberman, S., 2003. "Lee-Carter mortality forecasting with age-specific enhancement," Insurance: Mathematics and Economics, Elsevier, vol. 33(2), pages 255-272, October.
    18. Robert Holzmann & Robert Palacios & Asta Zviniene, 2001. "On the Economics and Scope of Implicit Pension Debt: An International Perspective," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 28(1), pages 97-129, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cláudia Simões & Luís Oliveira & Jorge M. Bravo, 2021. "Immunization Strategies for Funding Multiple Inflation-Linked Retirement Income Benefits," Risks, MDPI, vol. 9(4), pages 1-28, March.
    2. Bravo, Jorge M. & Ayuso, Mercedes & Holzmann, Robert & Palmer, Edward, 2021. "Addressing the life expectancy gap in pension policy," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 200-221.
    3. Jorge Miguel Bravo & Mercedes Ayuso & Robert Holzmann & Edward Palmer, 2021. "Intergenerational Actuarial Fairness when Longevity Increases: Amending the Retirement Age," CESifo Working Paper Series 9408, CESifo.
    4. Fabrizio Culotta, 2021. "Life Expectancy Heterogeneity and Pension Fairness: An Italian North-South Divide," Risks, MDPI, vol. 9(3), pages 1-22, March.
    5. Holzmann, Robert & Alonso-García, Jennifer & Labit-Hardy, Heloise & Villegas, Andres M., 2017. "NDC Schemes and Heterogeneity in Longevity: Proposals for Redesign," IZA Discussion Papers 11193, Institute of Labor Economics (IZA).
    6. Bernd Genser & Robert Holzmann, 2019. "National Pension Policy and Globalization: A New Approach to Strive for Efficient Portability and Equitable Taxation," Working Paper Series of the Department of Economics, University of Konstanz 2019-04, Department of Economics, University of Konstanz.
    7. Samuel Asante Gyamerah & Janet Arthur & Saviour Worlanyo Akuamoah & Yethu Sithole, 2023. "Measurement and Impact of Longevity Risk in Portfolios of Pension Annuity: The Case in Sub Saharan Africa," FinTech, MDPI, vol. 2(1), pages 1-20, January.
    8. Keivan Diakite & Pierre Devolder, 2021. "Progressive Pension Formula and Life Expectancy Heterogeneity," Risks, MDPI, vol. 9(7), pages 1-19, July.
    9. Bravo, Jorge Miguel & Ayuso, Mercedes & Holzmann, Robert, 2019. "Making Use of Home Equity: The Potential of Housing Wealth to Enhance Retirement Security," IZA Discussion Papers 12656, Institute of Labor Economics (IZA).
    10. Strozza, Cosmo & Vigezzi, Serena & Callaway, Julia & Kashnitsky, Ilya & Aleksandrovs, Aleksandrs & Vaupel, James W, 2022. "Socioeconomic inequalities in survival to retirement age or shortly afterwards: a register-based analysis," OSF Preprints 8wbdv, Center for Open Science.
    11. József Banyár, 2021. "The Outlines of a Possible Pension System Funded with Human Capital," Risks, MDPI, vol. 9(4), pages 1-32, April.
    12. Jesús-Adrián Álvarez & Malene Kallestrup-Lamb & Søren Kjærgaard, 2020. "Linking retirement age to life expectancy does not lessen the demographic implications of unequal lifespans," CREATES Research Papers 2020-17, Department of Economics and Business Economics, Aarhus University.
    13. Mercedes Ayuso & Jorge M. Bravo & Robert Holzmann & Edward Palmer, 2021. "Automatic Indexation of the Pension Age to Life Expectancy: When Policy Design Matters," Risks, MDPI, vol. 9(5), pages 1-28, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Blake, David & Cairns, Andrew J.G., 2021. "Longevity risk and capital markets: The 2019-20 update," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 395-439.
    2. Blake, David & El Karoui, Nicole & Loisel, Stéphane & MacMinn, Richard, 2018. "Longevity risk and capital markets: The 2015–16 update," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 157-173.
    3. Bravo, Jorge M. & Ayuso, Mercedes & Holzmann, Robert & Palmer, Edward, 2021. "Addressing the life expectancy gap in pension policy," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 200-221.
    4. Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather, 2023. "Thirty years on: A review of the Lee–Carter method for forecasting mortality," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1033-1049.
    5. Norkhairunnisa Redzwan & Rozita Ramli, 2022. "A Bibliometric Analysis of Research on Stochastic Mortality Modelling and Forecasting," Risks, MDPI, vol. 10(10), pages 1-17, October.
    6. Katrien Antonio & Anastasios Bardoutsos & Wilbert Ouburg, 2015. "Bayesian Poisson log-bilinear models for mortality projections with multiple populations," BAFFI CAREFIN Working Papers 1505, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    7. Apostolos Bozikas & Georgios Pitselis, 2018. "An Empirical Study on Stochastic Mortality Modelling under the Age-Period-Cohort Framework: The Case of Greece with Applications to Insurance Pricing," Risks, MDPI, vol. 6(2), pages 1-34, April.
    8. Jaap Spreeuw & Iqbal Owadally & Muhammad Kashif, 2022. "Projecting Mortality Rates Using a Markov Chain," Mathematics, MDPI, vol. 10(7), pages 1-18, April.
    9. Guibert, Quentin & Lopez, Olivier & Piette, Pierrick, 2019. "Forecasting mortality rate improvements with a high-dimensional VAR," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 255-272.
    10. Jorge Miguel Bravo & Mercedes Ayuso & Robert Holzmann & Edward Palmer, 2021. "Intergenerational Actuarial Fairness when Longevity Increases: Amending the Retirement Age," CESifo Working Paper Series 9408, CESifo.
    11. David Blake & Marco Morales & Enrico Biffis & Yijia Lin & Andreas Milidonis, 2017. "Special Edition: Longevity 10 – The Tenth International Longevity Risk and Capital Markets Solutions Conference," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 84(S1), pages 515-532, April.
    12. Colin O’hare & Youwei Li, 2017. "Modelling mortality: are we heading in the right direction?," Applied Economics, Taylor & Francis Journals, vol. 49(2), pages 170-187, January.
    13. Colin O’hare & Youwei Li, 2017. "Models of mortality rates – analysing the residuals," Applied Economics, Taylor & Francis Journals, vol. 49(52), pages 5309-5323, November.
    14. Kenneth Wong & Jackie Li & Sixian Tang, 2020. "A modified common factor model for modelling mortality jointly for both sexes," Journal of Population Research, Springer, vol. 37(2), pages 181-212, June.
    15. Lenny Stoeldraijer & Coen van Duin & Leo van Wissen & Fanny Janssen, 2013. "Impact of different mortality forecasting methods and explicit assumptions on projected future life expectancy: The case of the Netherlands," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(13), pages 323-354.
    16. Rachel WINGENBACH & Jong-Min KIM & Hojin JUNG, 2020. "Living Longer in High Longevity Risk," JODE - Journal of Demographic Economics, Cambridge University Press, vol. 86(1), pages 47-86, March.
    17. Marie-Pier Bergeron-Boucher & Søren Kjærgaard & James E. Oeppen & James W. Vaupel, 2019. "The impact of the choice of life table statistics when forecasting mortality," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(43), pages 1235-1268.
    18. Mercedes Ayuso & Jorge M. Bravo & Robert Holzmann & Edward Palmer, 2021. "Automatic Indexation of the Pension Age to Life Expectancy: When Policy Design Matters," Risks, MDPI, vol. 9(5), pages 1-28, May.
    19. Shang, Han Lin & Haberman, Steven, 2017. "Grouped multivariate and functional time series forecasting:An application to annuity pricing," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 166-179.
    20. Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather, 2022. "Thirty years on: A review of the Lee-Carter method for forecasting mortality," SocArXiv 8u34d, Center for Open Science.

    More about this item

    Keywords

    cross-country comparison; Lee-Carter; life expectancy indexation; balancing mechanism;
    All these keywords.

    JEL classification:

    • D9 - Microeconomics - - Micro-Based Behavioral Economics
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • H55 - Public Economics - - National Government Expenditures and Related Policies - - - Social Security and Public Pensions
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth
    • J14 - Labor and Demographic Economics - - Demographic Economics - - - Economics of the Elderly; Economics of the Handicapped; Non-Labor Market Discrimination
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iza:izadps:dp11512. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Holger Hinte (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.