Factors Affecting ESG Performance: A Panel Quantile Regression Approach

Environmental, social, and governance (ESG) performance has gained increasing importance as a key indicator of sustainability in global economies. In this context, accurately identifying the determinants of ESG performance and designing appropriate policies accordingly is crucial. Therefore, this study constructs ESG scores for 141 countries over the period 2000-2021 using principal component analysis (PCA), and classifies countries into three homogeneous clusters—low, medium, and high ESG scores—by employing the K-means clustering algorithm, which groups countries with similar characteristics. Subsequently, for each cluster, the method-of-moments quantile regression technique introduced by Machado and Santos-Silva (2019) is applied to analyse the impacts of economic growth, foreign direct investment (FDI), trade openness, financial development, and government effectiveness on ESG performance across different quantiles. Empirical findings reveal that ESG scores are quantile-dependent with respect to the explanatory variables, regardless of cluster separation. Furthermore, in the cluster with the highest ESG scores, the ESG determinants are ranked by their positive impact, such as government effectiveness, financial expansion, GDP, and trade openness, whereas FDI exerts a negative effect. For countries with medium ESG scores, the determinants are GDP, financial development, trade openness, and government effectiveness, whereas FDI does not exhibit a statistically significant effect. In the third cluster, which consisted of the lowest ESG scores, GDP, financial development, government effectiveness, and FDI exert positive effects on ESG, whereas trade openness has a negative impact. Overall, the results indicate that improving ESG performance requires sustained economic growth and comprehensive structural and institutional policy measures.