IDEAS home Printed from https://ideas.repec.org/a/eee/ecmode/v52y2016ipbp749-763.html
   My bibliography  Save this article

Retirement planning in the light of changing demographics

Author

Listed:
  • Wang, Hong
  • Koo, Bonsoo
  • O'Hare, Colin

Abstract

With increasing longevity and decreasing fertility rates, governments and policy makers are increasingly engaged in the question of long term retirement planning. In many cases this has included emphasising the need for individuals to take more responsibility for their own retirement planning through tax incentives, compulsion and changes to the age at which state retirement benefits become available. In the case of Australia, as is considered here, long term retirement planning has been focused around the development of a compulsory defined contribution (DC) superannuation system. Here we investigate the interaction between population ageing and the sustainability of the superannuation system by modelling a general superannuation scheme to compare the adequacy of retirement funds under a number of alternative scenarios. The model incorporates stochastic longevity forecasts and provides insight into the sufficiency of compulsory retirement saving both now and future. We find that the current pension scheme is more robust to longevity improvements for mid-class individuals however significant gaps arise for low-income individuals as longevity improves. Without addressing these issues, government expenditure is expected to increase substantially.

Suggested Citation

  • Wang, Hong & Koo, Bonsoo & O'Hare, Colin, 2016. "Retirement planning in the light of changing demographics," Economic Modelling, Elsevier, vol. 52(PB), pages 749-763.
  • Handle: RePEc:eee:ecmode:v:52:y:2016:i:pb:p:749-763
    DOI: 10.1016/j.econmod.2015.10.014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0264999315003004
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.econmod.2015.10.014?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Creedy, John & Gemmell, Norman & Scobie, Grant, 2015. "Pensions, savings and housing: A life-cycle framework with policy simulations," Economic Modelling, Elsevier, vol. 46(C), pages 346-357.
    2. Creedy, John & Gemmell, Norman & Scobie, Grant, 2015. "Pensions, savings and housing: A life-cycle framework with policy simulations," Economic Modelling, Elsevier, vol. 46(C), pages 346-357.
    3. 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.
    4. O’Hare, Colin & Li, Youwei, 2012. "Explaining young mortality," Insurance: Mathematics and Economics, Elsevier, vol. 50(1), pages 12-25.
    5. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    6. Plat, Richard, 2009. "On stochastic mortality modeling," Insurance: Mathematics and Economics, Elsevier, vol. 45(3), pages 393-404, December.
    7. Rob Hyndman & Heather Booth & Farah Yasmeen, 2013. "Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models," Demography, Springer;Population Association of America (PAA), vol. 50(1), pages 261-283, February.
    8. 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.
    9. Marcin Bielecki & Krzysztof Makarski & Joanna Tyrowicz & Marcin Waniek, 2015. "In the search for the optimal path to establish a funded pension system," Working Papers 2015-22, Faculty of Economic Sciences, University of Warsaw.
    10. 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.
    11. Koka, Katerina & Kosempel, Stephen, 2014. "A life-cycle analysis of ending mandatory retirement," Economic Modelling, Elsevier, vol. 38(C), pages 57-66.
    12. Orazio Attanasio & James Banks & Matthew Wakefield, 2004. "Effectiveness of tax incentives to boost (retirement) saving: theoretical motivation and empirical evidence," IFS Working Papers W04/33, Institute for Fiscal Studies.
    13. 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.
    14. Renshaw, A.E. & Haberman, S., 2006. "A cohort-based extension to the Lee-Carter model for mortality reduction factors," Insurance: Mathematics and Economics, Elsevier, vol. 38(3), pages 556-570, June.
    15. 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.
    16. 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.
    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. Elena V. Chistova, 2016. "Possibilities for Increasing the Retirement Age in Russia in Response to Population Ageing," Montenegrin Journal of Economics, Economic Laboratory for Transition Research (ELIT), vol. 12(3), pages 127-138.
    2. Chen, An & Li, Hong & Schultze, Mark B., 2023. "Optimal longevity risk transfer under asymmetric information," Economic Modelling, Elsevier, vol. 120(C).
    3. 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.
    4. Pengkun Wu & Yuanyuan Wu & Chong Wu, 2018. "Research on Fertility Policy in China: The Relative Necessity for Reform Among the Different Provinces," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(2), pages 751-767, January.

    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. 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.
    2. 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.
    3. Li, Han & O’Hare, Colin, 2017. "Semi-parametric extensions of the Cairns–Blake–Dowd model: A one-dimensional kernel smoothing approach," Insurance: Mathematics and Economics, Elsevier, vol. 77(C), pages 166-176.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Han Lin Shang & Steven Haberman, 2020. "Retiree Mortality Forecasting: A Partial Age-Range or a Full Age-Range Model?," Risks, MDPI, vol. 8(3), pages 1-11, July.
    13. O'Hare, Colin & Li, Youwei, 2014. "Identifying structural breaks in stochastic mortality models," MPRA Paper 62994, University Library of Munich, Germany.
    14. 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.
    15. 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.
    16. Jaap Spreeuw & Iqbal Owadally & Muhammad Kashif, 2022. "Projecting Mortality Rates Using a Markov Chain," Mathematics, MDPI, vol. 10(7), pages 1-18, April.
    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. Han Li & Colin O’Hare, 2019. "Mortality Forecasting: How Far Back Should We Look in Time?," Risks, MDPI, vol. 7(1), pages 1-15, February.
    19. Ufuk Beyaztas & Hanlin Shang, 2022. "Machine-Learning-Based Functional Time Series Forecasting: Application to Age-Specific Mortality Rates," Forecasting, MDPI, vol. 4(1), pages 1-15, March.
    20. 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.

    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:eee:ecmode:v:52:y:2016:i:pb:p:749-763. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30411 .

    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.