Flexible Modeling of Demographic Transition Processes with a Bayesian Hierarchical B-splines Model

TL;DR

This paper introduces a Bayesian hierarchical B-spline model for capturing demographic transition processes, providing flexible, data-adaptive estimates that outperform traditional parametric models in projections of fertility and contraceptive use.

Contribution

The paper presents the B-spline Transition Model (BTM), a novel approach that models demographic transitions using B-splines within a Bayesian hierarchical framework, improving estimation and projection accuracy.

Findings

BTM generally yields lower projection errors for TFR.

BTM improves out-of-sample predictions for mCPR.

The model adapts flexibly to data, reducing bias in long-term projections.

Abstract

Several demographic and health indicators, including the total fertility rate (TFR) and modern contraceptive use rate (mCPR), evolve similarly over time, characterized by a transition between stable states. Existing approaches for estimation or projection of transitions in multiple populations have successfully used parametric functions to capture the relation between the rate of change of an indicator and its level. However, incorrect parametric forms may result in bias or incorrect coverage in long-term projections. We propose a new class of models to capture demographic transitions in multiple populations. Our proposal, the B-spline Transition Model (BTM), models the relationship between the rate of change of an indicator and its level using B-splines, allowing for data-adaptive estimation of transition functions. Bayesian hierarchical models are used to share information on the transition function between populations. We apply the BTM to estimate and project country-level TFR and mCPR and compare the results against those from extant parametric models. For TFR, BTM projections have generally lower error than the comparison model. For mCPR, while results are comparable between BTM and a parametric approach, the B-spline model generally improves out-of-sample predictions. The case studies suggest that the BTM may be considered for demographic applications.