A multilevel Bayesian approach to climate-fueled migration and conflict.

ABSTRACT: Do climate conditions and extreme events fuel conflict and migration? This question has been widely studied using causal designs that exploit natural variation in climate variables, often analyzed with linear fixed-effects models. Yet in this setting, nonlinear relationships, distributional features of outcomes, and spatial heterogeneity can cause these models to violate core assumptions and yield unreliable inferences. We propose a multilevel Bayesian framework that accommodates such features while retaining identification strategies from natural experiments. We illustrate its potential with a representative analysis from the literature of the effect of temperature anomalies on conflict in Somalia. When outcome distributions suited to event counts are combined with partial pooling across

SUBMITTER: Palandri C 

PROVIDER: S-EPMC12639035 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature