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Forecasting the Old-Age Dependency Ratio to Determine a Sustainable Pension Age

Author

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  • Rob J Hyndman
  • Yijun Zeng
  • Han Lin Shang

Abstract

We forecast the old-age dependency ratio for Australia under various pension age proposals, and estimate a pension age scheme that will provide a stable old-age dependency ratio at a specified level. Our approach involves a stochastic population forecasting method based on coherent functional data models for mortality, fertility and net migration, which we use to simulate the future age-structure of the population. Our results suggest that the Australian pension age should be increased to 68 by 2030, 69 by 2036, and 70 by 2050, in order to maintain the old-age dependency ratio at 23%, just above the 2018 level. Our general approach can easily be extended to other target levels of the old-aged dependency ratio and to other countries.

Suggested Citation

  • Rob J Hyndman & Yijun Zeng & Han Lin Shang, 2020. "Forecasting the Old-Age Dependency Ratio to Determine a Sustainable Pension Age," Monash Econometrics and Business Statistics Working Papers 31/20, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2020-31
    as

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    File URL: https://www.monash.edu/business/ebs/research/publications/ebs/wp31-2020.pdf
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    References listed on IDEAS

    as
    1. Patricia Apps & Ray Rees, 2007. "Population Ageing, Taxation, pensions and Health Costs," Australian Journal of Labour Economics (AJLE), Bankwest Curtin Economics Centre (BCEC), Curtin Business School, vol. 10(2), pages 79-97.
    2. 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.
    3. Hyndman, Rob J. & Booth, Heather, 2008. "Stochastic population forecasts using functional data models for mortality, fertility and migration," International Journal of Forecasting, Elsevier, vol. 24(3), pages 323-342.
    4. 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.
    5. 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.
    6. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    7. Han Lin Shang & Heather Booth & Rob Hyndman, 2011. "Point and interval forecasts of mortality rates and life expectancy: A comparison of ten principal component methods," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 25(5), pages 173-214.
    8. Patricia Apps & Ray Rees & Margi Wood, 2007. "Population ageing, taxation, pensions and health costs, CHERE Working Paper 2007/10," Working Papers 2007/10, CHERE, University of Technology, Sydney.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    coherent forecasts; demographic components; functional time series; pension age;
    All these keywords.

    JEL classification:

    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • J14 - Labor and Demographic Economics - - Demographic Economics - - - Economics of the Elderly; Economics of the Handicapped; Non-Labor Market Discrimination
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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