Impact of weather factors on migration intention using machine learning
algorithms
- URL: http://arxiv.org/abs/2012.02794v1
- Date: Fri, 4 Dec 2020 16:59:15 GMT
- Title: Impact of weather factors on migration intention using machine learning
algorithms
- Authors: John Aoga, Juhee Bae, Stefanija Veljanoska, Siegfried Nijssen, Pierre
Schaus
- Abstract summary: This paper proposes a tree-based Machine Learning approach to analyze the role of the weather shocks towards an individual's intention to migrate.
We perform several tree-based algorithms using the train-validation-test workflow to build robust and noise-resistant approaches.
We find that weather features improve the prediction performance although socioeconomic characteristics have more influence on migration intentions.
- Score: 17.012869982527725
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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