Bayesian probabilistic population projections for all countries

Area of Science:

• Demography
• Statistical Modeling
• Population Studies

Background:

• Deterministic population projections are common but lack uncertainty quantification.
• Probabilistic projections are crucial for robust planning and research.
• Existing UN variants may misrepresent uncertainty for specific demographic groups and country types.

Purpose of the Study:

• To develop and validate a Bayesian method for probabilistic population projections globally.
• To provide more accurate uncertainty estimates for future population figures.
• To assess the accuracy and calibration of the proposed method against historical data.

Main Methods:

• Utilized Bayesian hierarchical models and Markov chain Monte Carlo (MCMC) for probabilistic projection of fertility and life expectancy.
• Integrated projected rates into a cohort-component model for comprehensive population projections.
• Validated the method using out-of-sample prediction from 1950-1990 to 1990-2010.

Main Results:

• The Bayesian method demonstrated reasonable accuracy and calibration for the validation period.
• Current UN variants underestimate uncertainty for the oldest old from 2050 and for high-fertility nations.
• UN variants overstate uncertainty for low-fertility European countries with recovering birth rates.

Conclusions:

• The proposed Bayesian approach offers improved uncertainty assessment for population projections.
• Significant declines in the potential support ratio are projected globally in the coming decades.
• The method provides a more nuanced understanding of future population dynamics across diverse countries.