Bayesian Population Projections for the United Nations

TL;DR

This paper introduces a Bayesian hierarchical approach to generate probabilistic population projections for countries, providing uncertainty estimates and addressing limitations of traditional deterministic methods used by the UN.

Contribution

It develops a Bayesian framework for population projections, incorporating uncertainty and extending current models for fertility and life expectancy.

Findings

The method produces probabilistic projections with uncertainty quantification.

It improves upon deterministic models by addressing long-term fertility assumptions.

The approach is implemented in R packages for practical use.

Abstract

The United Nations regularly publishes projections of the populations of all the world's countries broken down by age and sex. These projections are the de facto standard and are widely used by international organizations, governments and researchers. Like almost all other population projections, they are produced using the standard deterministic cohort-component projection method and do not yield statements of uncertainty. We describe a Bayesian method for producing probabilistic population projections for most countries which are projections that the United Nations could use. It has at its core Bayesian hierarchical models for the total fertility rate and life expectancy at birth. We illustrate the method and show how it can be extended to address concerns about the UN's current assumptions about the long-term distribution of fertility. The method is implemented in the R packages bayesTFR, bayesLife, bayesPop and bayesDem.