Impact of weather factors on migration intention using machine learning algorithms

TL;DR

This study employs machine learning algorithms to analyze how weather shocks influence migration intentions in six African countries, revealing that weather features enhance prediction accuracy and that migration patterns vary with timescales and country specifics.

Contribution

It introduces a machine learning framework to assess weather impacts on migration, emphasizing the importance of country-specific models and timescale effects.

Findings

Weather features improve migration prediction accuracy.

Country-specific models are essential for accurate analysis.

Longer timescale SPEIs influence international migration more.

Abstract

A growing attention in the empirical literature has been paid to the incidence of climate shocks and change in 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 towards 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 perform 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 are influencing 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) weather features improve the prediction performance although socioeconomic characteristics have more influence on migration intentions, (ii) country-specific model is necessary, and (iii) international move is influenced more by the longer timescales of SPEIs while general move (which includes internal move) by that of shorter timescales.