ESG Determinants of Financial Development: Integrating Econometrics and Machine-Learning Evidence

The objective of this research is to analyze the relationship between ESG factors and financial development, measured by Domestic Credit to the Private Sector by Banks (DCB). The empirical analysis employs a balanced panel of 82 countries for the years 2016 to 2022, obtained from the World Bank database. The proposed econometric model incorporates multiple ESG factors, including environmental (E), social (S), and governance (G). The list of econometric models under consideration includes fixed effects, random effects, WLS (weighted least squares), dynamic panel, and fixed effects with HAC estimation. Based on the conducted tests, the fixed effects estimation method has been chosen because the presence of serial correlation, heteroskedasticity, and cross-sectional dependence suggests that other methods will not provide an adequate model. As a result, fixed effects enable obtaining reliable estimates regarding the relationships between ESG factors and DCB. In addition, a KNN (K-Nearest Neighbors) regression was used to analyze potential nonlinear effects of the factors. The results show the strong positive relationship between ESG factors and financial development. More specifically, the presence of clean energy sources is associated with a positive DCB, and the depletion of natural resources is negatively associated with DCB. Moreover, social and governance factors are positively associated with financial development.