Flexible Bayesian Modelling of Age-Specific Counts in Many Demographic Subpopulations

Abstract

Analysing age-specific mortality, fertility, and migration patterns is a crucial task in demography with significant policy relevance. In practice, such analysis is challenging when studying a large number of subpopulations, due to small observation counts within groups and increasing demographic heterogeneity between groups. This article proposes a Bayesian model for the joint analysis of age-specific counts in many, potentially small, demographic subpopulations. The model utilizes smooth latent factors to capture common age-specific patterns across subpopulations and facilitates additional information sharing through a hierarchical prior. It provides smoothed estimates of the latent age pattern in each subpopulation, allows testing for heterogeneity, and can be used to assess the impact of covariates on the demographic process. An in-depth case study of age-specific immigration flows to Austria, disaggregated by sex and 155 countries of origin, is discussed. Comparative analysis demonstrates that the model outperforms commonly used benchmark frameworks in both in-sample imputation and out-of-sample predictive exercises.