Probabilistic Models for Human Migration Forecasting and Residency Imputation

Abstract

I develop probabilistic models to enhance the estimation and forecasting of human migration flows and residency. Using a Bayesian hierarchical approach, I first propose a model for forecasting global bilateral migration flows among the 200 most populous countries, producing well-calibrated projections that reduce error rates compared to existing methods. This model is integrated into a population projection framework to forecast migration flows by age and sex, providing the first probabilistic forecasts of international bilateral migration flows through 2045. Next, I address the influence of age structure on much longer-term migration forecasts by introducing the Migration Age Structure Index (MASI) that adjusts net migration rates, offering narrower prediction intervals and more accurate projections of population change, especially for aging populations. Finally, I improve the Person-Place Model (PPM), a key tool used by countries without population registers for census and intercensal population estimation, by developing the Bayes PPM—a Bayesian hierarchical model that refines residency estimates from administrative records. This model eliminates crude approximations to a well-defined statistical model currently in use, enhancing the accuracy of uncertainty intervals in demographic estimates. Collectively, these contributions offer more capable tools for forecasting migration and demographic changes, supporting policymakers in navigating complex global migration dynamics.