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Corresponding Author:
Zhao Shuai, Shenzhen University, Shenzen, China - Department of Government and International Affairs, Lingnan University, Hong Kong, China

Coauthors:
Shalendra Sharma, Lingnan University, Hong Kong, China
Peiqing Lu, Boston University, Boston, USA

Political Leadership, Institutions, and Economic Growth: Evidence from Machine Learning

September 14, 2026
JEL classification: C55; D72; O11; O43
Keywords: Political Leadership; Economic Growth; Institutional Constraints; Leadership Tenure; Regime Context; Machine Learning; SHAP

Abstract

This study examines how political leadership and institutions jointly influence economic growth. Using a global dataset of 1,447 leader-term observations from 135 countries between 1945 and 2015, we employ Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to uncover nonlinear and context-dependent relationships that conventional regression models may overlook. The results show that the economic impact of political leadership varies substantially across institutional settings. Leadership tenure exhibits a nonlinear relationship with growth, with the strongest predictive influence occurring during the early years of office, followed by a decline and a modest recovery over longer tenures. In contrast, regime type contributes relatively little explanatory power once institutional quality is taken into account. The findings highlight the critical role of institutions in shaping how leadership characteristics affect economic performance. By integrating interpretable machine learning with political economy, this study provides new evidence on the interaction between leaders and institutions and demonstrates the value of machine-learning methods for advancing research on the determinants of long-run economic growth.

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Political Leadership, Institutions, and Economic Growth: Evidence from Machine Learning

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Institute for International Economics of the Genoa Chamber of Commerce

Institute for International Economics
of the Genoa Chamber of Commerce