Globally increasing migration pressures call for new modelling approaches in order to design effective policies. It is important to have not only efficient models to predict migration flows but also to understand how specific parameters influence these flows. In this paper, we propose an artificial neural network (ANN) to model international migration. Moreover, we use a technique for interpreting machine learning models, namely Partial Dependence Plots (PDP), to show that one can well study the effects of drivers behind international migration. We train and evaluate the model on a dataset containing annual international bilateral migration from $1960$ to $2010$ from $175$ origin countries to $33$ mainly OECD destinations, along with the main determinants as identified in the migration literature. The experiments carried out confirm that: 1) the ANN model is more efficient w.r.t. a traditional model, and 2) using PDP we are able to gain additional insights on the specific effects of the migration drivers. This approach provides much more information than only using the feature importance information used in previous works.
Migration patterns, friendship networks, and the diaspora: the potential of Facebook Social Connectedness Index to anticipate displacement patterns induced by Russia invasion of Ukraine in the European Union
Lyles School of Civil Engineering, Purdue University（普渡大学莱尔斯土木工程学院）；Agronomy Department, Purdue University（普渡大学农学系）；Institut de Physique Théorique, CEA, CNRS-URA 2306（法国原子能和替代能源委员会/法国国家科学研究中心理论物理研究所）；Centre d’Analyse et de Mathématique Sociales, CNRS/EHESS（法国国家科学研究中心/高等社会科学学院社会分析与数学中心）
