Forecasting the age structure of the scientific workforce in Australia

 

Planning for a future workforce requires forecasts of age structure changes to inform policy decisions, particularly related to universities and immigration. We propose a new dynamic statistical model for forecasting the age structure of a workforce. Our approach is inspired by a stochastic model used in population forecasting, replacing births with graduate entry, modelling exits through death and retirement, and including a remainder term that captures migration and career changes. Functional data models are used to model age-specific components, while ARIMA models are used for time series components. Simulation is employed to generate forecast distributions, capturing uncertainty from all components. The approach is illustrated using data on Australia’s scientific workforce, allowing us to forecast the age distribution of various scientific disciplines for the next ten years. This analysis was central to an Australian Academy of Science initiative examining the capability of Australia’s science system and identifying workforce gaps.

Cite

@article{asf,
  author  = {Rob J Hyndman and Khuyen Vanh Nguyen},
  doi     = {10.1111/anzs.70043},
  journal = {Australian \& New Zealand J Statistics},
  number  = 1,
  pages   = {1369-1473},
  title   = {Forecasting the age structure of the scientific workforce in Australia},
  url     = {http://robjhyndman.com/publications/asf},
  volume  = 68,
  year    = 2026
}