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Accurate estimates of historic migration trends and forecasts of future trends are essential to crafting effective migration policies. Recent methodological advances made it possible to generate plausible estimates of international migration flows at a global scale; however, flow forecasting method development lags progress in estimation.

In this talk, I propose a Bayesian hierarchical model for forecasting global bilateral human migration flows. I show that this model produces well calibrated out-of-sample forecasts of bilateral flows, as well as total country-level inflows, outflows, and net flows. I adapt a fully probabilistic population projection model to generate bilateral migration flow forecasts by age and sex for the 200 most populous countries from 2020 through 2045.