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A growing attention in the empirical literature has been paid to theincidence of climate shocks and change in migration decisions. Previousliterature leads to different results and uses a multitude of traditionalempirical approaches.  This paper proposes a tree-based Machine Learning (ML) approach to analyzethe role of the weather shocks towards an individual's intention to migrate inthe six agriculture-dependent-economy countries such as Burkina Faso, IvoryCoast, Mali, Mauritania, Niger, and Senegal. We perform several tree-basedalgorithms (e.g., XGB, Random Forest) using the train-validation-test workflowto build robust and noise-resistant approaches. Then we determine the importantfeatures showing in which direction they are influencing the migrationintention. This ML-based estimation accounts for features such as weathershocks captured by the Standardized Precipitation-Evapotranspiration Index(SPEI) for different timescales and various socioeconomic features/covariates.  We find that (i) weather features improve the prediction performance althoughsocioeconomic characteristics have more influence on migration intentions, (ii)country-specific model is necessary, and (iii) international move is influencedmore by the longer timescales of SPEIs while general move (which includesinternal move) by that of shorter timescales.

Impact of weather factors on migration intention using machine learning algorithms