Towards the Ranking of the Importance of Revolutionary Destabilization Factors in Asian and African Countries Using Machine Learning Methods

Abstract

This study investigates which structural factors most strongly predict armed and unarmed revolutionary destabilization across Sub-Saharan Africa (SSA), the Middle East and North Africa (MENA), as well as Asia using country–year data for 1950–2022 and a set of economic, demographic, political, and climatic indicators. It employs an interpretable machine learning framework (CatBoost with SHAP values and permutation importance), which allows it to relax functional form assumptions, systematically compare predictor importance across outcomes and regions, and uncover nonlinear and interaction effects. Armed revolutionary destabilization is shown to be most strongly associated with prior conflict, economic contraction, natural-resource dependence, and weak state capacity, particularly in SSA and MENA, while in Asia it is driven more by domestic political-economic dynamics. Unarmed revolutionary destabilization is linked to institutional legitimacy, corruption vulnerability, population density, and external economic shocks, with export shocks most salient in Asia, incumbent duration in SSA, and trade integration in MENA.

Keywords

revolutions machine learning CatBoost feature importance political instability armed conflict mass protest Sub-Saharan Africa MENA Asia

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