Impact of Weather Factors on Migration Intention Using Machine Learning Algorithms

John O. R. Aoga (), Juhee Bae (), Stefanija Veljanoska (), Siegfried Nijssen () and Pierre Schaus ()
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John O. R. Aoga: Université d’Abomey-Calavi
Juhee Bae: University of Skövde
Stefanija Veljanoska: Université de Rennes 1, CNRS/CREM-UMR621
Siegfried Nijssen: Université catholique de Louvain
Pierre Schaus: Université catholique de Louvain

SN Operations Research Forum, 2024, vol. 5, issue 1, 1-37

Abstract: Abstract A growing attention in the empirical literature has been paid on the incidence of climate shocks and change on migration decisions. Previous literature leads to different results and uses a multitude of traditional empirical approaches. This paper proposes a tree-based Machine Learning (ML) approach to analyze the role of the weather shocks toward an individual’s intention to migrate in the six agriculture-dependent-economy countries such as Burkina Faso, Ivory Coast, Mali, Mauritania, Niger, and Senegal. We performed several tree-based algorithms (e.g., XGB, Random Forest) using the train-validation-test workflow to build robust and noise-resistant approaches. Then we determine the important features showing in which direction they influence the migration intention. This ML-based estimation accounts for features such as weather shocks captured by the Standardized Precipitation-Evapotranspiration Index (SPEI) for different timescales and various socioeconomic features/covariates. We find that (i) the weather features improve the prediction performance, although socioeconomic characteristics have more influence on migration intentions, (ii) a country-specific model is necessary, and (iii) the international move is influenced more by the longer timescales of SPEIs while general move (which includes internal move) by that of shorter timescales.

Keywords: Migration; Weather shocks; Machine learning; Tree-based algorithms (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s43069-023-00271-y

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