Accounting for the full distribution of temperature to predict international migration

Evangelina Dardati, Thibault Laurent, Paula Margaretic, Ean Paredes and Christine Thomas-Agnan
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Evangelina Dardati: UCHILE - Universidad de Chile = University of Chile [Santiago]
Thibault Laurent: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
Paula Margaretic: UCHILE - Universidad de Chile = University of Chile [Santiago]
Ean Paredes: UCHILE - Universidad de Chile = University of Chile [Santiago]
Christine Thomas-Agnan: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

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Abstract: 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.

Keywords: Compositional data; Temperature; Migration projections; Climate change (search for similar items in EconPapers)
Date: 2026-03-24
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