Analyzing age-specific mortality, fertility, and migration in subpopulations is a crucial task in demography, with significant policy relevance. In practice, such analysis is challenging when studying numerous subpopulations, due to small sample sizes and demographic heterogeneity. To address this issue, a Bayesian model for the joint analysis of many, potentially small, demographic subgroups is proposed. The model combines three common assumptions about demographic processes in a unified probabilistic framework. The approach provides robust estimates of the demographic process in each subpopulation, allows testing for heterogeneity between subpopulations, and can be used to assess the impact of covariates on the demographic process. This makes the model suitable for probabilistic projection exercises and scenario analysis. An in-depth analysis of age-specific immigration flows to Austria, disaggregated by sex and 155 countries of origin, is used to illustrate the framework. Comparative analysis shows that the model outperforms commonly used benchmark frameworks in both in-sample imputation and out-of-sample prediction exercises.

翻译：分析子群体中的年龄特异性死亡率、生育率和迁移模式是人口学中的一项关键任务，具有重要的政策相关性。在实践中，当研究大量子群体时，由于样本量小和人口异质性，此类分析面临挑战。为解决这一问题，本文提出了一种用于联合分析多个（可能规模较小）人口子群体的贝叶斯模型。该模型在一个统一的概率框架内整合了人口过程中的三种常见假设。该方法能够为每个子群体的人口过程提供稳健估计，允许检验子群体间的异质性，并可评估协变量对人口过程的影响。这使得该模型适用于概率预测练习和情景分析。通过对奥地利按性别和155个来源国分列的年龄特异性移民流入数据进行深入分析，本文展示了该框架的应用。比较分析表明，在样本内插补和样本外预测任务中，该模型的性能优于常用的基准框架。