Probabilistic forecasts of international bilateral migration flows

Abstract

We propose a method for forecasting global human migration flows. A Bayesian hierarchical model is used to make probabilistic projections of the 39,800 bilateral migration flows among the 200 most populous countries. We generate out-of-sample forecasts for all bilateral flows for the 2015 to 2020 period, using models fitted to bilateral migration flows for five 5-y periods from 1990 to 1995 through 2010 to 2015. We find that the model produces well-calibrated out-of-sample forecasts of bilateral flows, as well as total country-level inflows, outflows, and net flows. The mean absolute error decreased by 61% using our method, compared to a leading model of international migration. Out-of-sample analysis indicated that simple methods for forecasting migration flows offered accurate projections of bilateral migration flows in the near term. Our method matched or improved on the out-of-sample performance using these simple deterministic alternatives, while also accurately assessing uncertainty. We integrate the migration flow forecasting model into a fully probabilistic population projection model to generate bilateral migration flow forecasts by age and sex for all flows from 2020 to 2025 through 2040 to 2045.

Stochastic evolution model for international migration

“…In contrast, stochastic models assume that migration is a stochastic process, e.g. described as Markov process (Goodman 1961;Rogers 1966) or a posterior distribution within a Bayesian hierarchical model (Azose and Raftery 2015;Azose, Ševčíková, and Raftery 2016;Welch and Raftery 2022). These models are better suited for acknowledging the biases in migration data and the complexity of the migration process as they work with probability distributions and rely on fewer assumptions regarding the migration process itself.…”

Modeling climate migration: dead ends and new avenues

“…In contrast, non-parametric statistical models like the aforementioned random forests and neural networks, which make no prior assumptions about the relationships between relevant driver variables and the resulting mobility outcome but derive these relationships entirely from the training data, require a large number of observations. Models that predict future migration based only on historical migration patterns (i.e., without incorporating exogenous drivers) have established excellent standards for quantifying uncertainties in forecasts (Bijak, 2010;Azose and Raftery, 2015;Azose et al, 2016;Welch and Raftery, 2022). In contrast, models focused on how the interaction of different drivers results in a migration outcome, including the ones discussed here, lag behind these developments (Bijak, 2006).…”

Stochastic evolution model for international migration

“…In contrast, stochastic models assume that migration is a stochastic process, e.g. described as Markov process (Goodman 1961;Rogers 1966) or a posterior distribution within a Bayesian hierarchical model (Azose and Raftery 2015;Azose, Ševčíková, and Raftery 2016;Welch and Raftery 2022).…”