Forecasting the old‐age dependency ratio to determine a sustainable pension age

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‐age dependency ratio and to other countries.

[1]  Rob J Hyndman,et al.  Forecasting Functions for Time Series and Linear Models [R package forecast version 8.13] , 2020 .

[2]  Kohske Takahashi,et al.  Welcome to the Tidyverse , 2019, J. Open Source Softw..

[3]  Fotios Petropoulos,et al.  forecast: Forecasting functions for time series and linear models , 2018 .

[4]  William Michael Landau,et al.  The drake R package: a pipeline toolkit for reproducibility and high-performance computing , 2018, J. Open Source Softw..

[5]  A. Stephenson,et al.  Household Assets Among Australian Age Pensioners: A preliminary analysis of data from the Department of Human Services , 2017 .

[6]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[7]  Rob J Hyndman,et al.  Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models , 2013, Demography.

[8]  yansui Yang,et al.  The Real Old-Age Dependency Ratio and the Inadequacy of Public Pension Finance in China , 2012 .

[9]  E. Griffey Births , 1915, Veterinary Record.

[10]  Rob J. Hyndman,et al.  Point and interval forecasts of mortality rates and life expectancy: A comparison of ten principal component methods , 2011 .

[11]  Shayne Neumann The intergenerational report , 2010 .

[12]  Wayne Swan,et al.  Australia to 2050: future challenges , 2010 .

[13]  Rob J Hyndman,et al.  Automatic Time Series Forecasting: The forecast Package for R , 2008 .

[14]  Rob J Hyndman,et al.  Stochastic population forecasts using functional data models for mortality, fertility and migration , 2008 .

[15]  P. Apps,et al.  Population Ageing, Taxation, Pensions and Health Costs , 2007 .

[16]  Rob J. Hyndman,et al.  Robust forecasting of mortality and fertility rates: A functional data approach , 2007, Comput. Stat. Data Anal..

[17]  D. Knox Pensions for Longer Life: Linking Australia's Pension Age , 2007 .

[18]  Laurence J. Kotlikoff,et al.  Fertility, Mortality, and the Developed World's Demographic Transition , 2004, SSRN Electronic Journal.

[19]  S. Preston,et al.  Demography: Measuring and Modeling Population Processes , 2000 .

[20]  Ronald Lee,et al.  Modeling and forecasting U. S. mortality , 1992 .

[21]  A. Walker Dependency and Old Age , 1982 .

[22]  I. J. Good,et al.  Some Applications of the Singular Decomposition of a Matrix , 1969 .