Accounting for the full distribution of temperature to predict international migration

This paper evaluates the role of climate variables in predicting international migration by proposing two alternative modeling approaches: scalar-on-composition and scalar-on-density regressions. We compare them with the standard scalar-on-scalar approach. Although most studies rely on annual averages of daily temperatures, focusing solely on central measures can mask essential details, such as nonlinearities and threshold effects. Using the full temperature distribution, either by binning or smoothing, the proposed models achieve improved predictive performance out-of-sample. These gains highlight the importance of properly handling the compositional nature of daily temperature bin data to avoid misleading interpretation of the estimates and flawed inferences. Finally, we demonstrate how incorporating complete temperature distributions into alternative climate scenarios can substantially affect projected outmigration.