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A growing body of research investigates how changes in weather shape individual choices about migration, yet highly variable results continue to challenge our understanding of the weather-migration nexus. We use a data-driven approach to identify which weather variables best predicted migration decisions of 54,986 individuals originating in Mexico between 1989 and 2016. Using supervised machine learning, we fit random forests to model migration choices based on individual, household, and community attributes in training data (three-fourths of the sample) from the Mexican Migration Project. We aggregated 36 annual weather variables at the community level and applied k-fold cross-validation to evaluate which models best predicted migration decisions. The top performing models were then applied to the test data (one-fourth of our sample). Three weather variables consistently out-performed others across models: minimum temperature during day, maximum temperature at night, and ‘growing degree days’ – the number of days with optimal growth temperatures for corn (the major crop for most communities). Our results demonstrate that weather is related to individual choices about migration and illustrate the utility of using principled variable selection which revealed that both customized (growing degree days for a particular crop) and generic (max-min temperatures) metrics can be predictive of migration behaviors.

How to model the weather-migration link: a machine-learning approach to variable selection in the Mexico-U.S. context