Beyond Shocks: How ESG Fundamentals Shape Geopolitical Risk Across Countries

This paper examines the connection between Environmental, Social, and Governance (ESG) factors and the risk of geopolitics, as defined by the Geopolitical Risk (GPR) index. The concept of geopolitical risk is conventionally defined as the direct result of political incidents, war, and international tensions. The current study argues that the concept should be understood in a more structural and sustainable manner, relating to the underlying forces driving geopolitical risk. The main research question is whether and how the three pillars of the ESG factors contribute to the explanation and understanding of cross-country and over-time variations in geopolitical risk. In an effort to avoid the information losses associated with the aggregate nature of the ESG index, the three factors are considered separately and the three pillars are analyzed individually. The empirical context is a balanced cross-country panel data set including 42 countries over the 2000-2023 time period. The data for the three factors is obtained from the World Bank dataset in an effort to standardize and compare the data in a cross-country and cross-time manner. The GPR index is used to measure the level of geopolitical risk and is defined by Dario Caldara and Matteo Iacoviello. The GPR index captures the level of geopolitical tensions based on the analysis of media signals. The combination of the three sources allows for the direct connection and correlation between the three factors and the internationally recognized GPR index. The paper uses an integrated methodological approach that combines the results from three different approaches. The first method uses panel data analysis in an effort to identify the average marginal effects while controlling for unobserved heterogeneity. The second method uses the technique of clustering in an effort to identify structural patterns and divide the countries into groups based on their unique characteristics and risk profiles. The third method uses machine learning regressions and nonparametric analysis in an effort to capture the complex relationships and interactions in the data. The three-step method is used for each pillar in an effort to ensure consistency and comparability. The results suggest that the three factors contribute to the GPR index in a unique manner. The environment and energy structure contribute to the GPR index as a risk multiplier, the social factor is related to the exposure to instability, and the governance factor is a central stabilizing factor. The paper makes a unique contribution to the literature by defining the concept of the three factors and their relationship to the GPR index in a unique and sustainable manner.