A multilevel Bayesian approach to climate-fueled migration and conflict

Area of Science:

• Environmental science
• Social science
• Climate change research

Background:

• Climate conditions and extreme events are increasingly studied for their links to conflict and migration.
• Existing research often uses linear fixed-effects models, which may be unreliable due to nonlinearities and spatial heterogeneity.

Purpose of the Study:

• To propose a multilevel Bayesian framework for analyzing climate's impact on social outcomes.
• To address limitations of traditional models in capturing complex relationships and heterogeneity.

Main Methods:

• Developed a multilevel Bayesian framework incorporating identification strategies from natural experiments.
• Applied the framework to analyze temperature anomalies and conflict in Somalia, using outcome distributions suited to event counts.
• Employed partial pooling across regions and time to improve model accuracy and predictive ability.

Main Results:

• The aggregate climate effect on conflict disappeared when accounting for regional heterogeneity.
• Significant regional variations in climate-conflict associations were observed, with positive links only in specific southern regions.
• Extending pooling across time enhanced the model's predictive performance.

Conclusions:

• The multilevel Bayesian framework offers a robust method for studying climate and social outcomes, improving both explanatory and predictive inferences.
• The findings underscore the importance of accommodating heterogeneity in climate-conflict-migration research.
• This approach provides a methodological bridge for interdisciplinary research on climate change impacts.