ESG 경영성과와 노동투자 효율성의 비선형성 관계 탐구: 머신러닝 및 패널회귀분석을 기반으로

This study examines the non-linear relationship between ESG management and labor investment inefficiency, extending prior research that predominantly assumes linear effects. While labor is a core production factor, labor investment inefficiency often arises due to information asymmetry and agency problems. We investigate whether ESG management, known to mitigate information asymmetry, influences labor investment inefficiency in a nonlinear manner. Using machine learning techniques, we compare the predictive performance of linear and nonlinear models for labor investment efficiency, and then conduct panel regression analyses to assess the structural relationship between ESG performance and labor investment inefficiency. The results indicate that non-linear models outperform linear models and reveal a statistically significant U-shaped relationship between ESG performance and labor investment inefficiency. These findings suggest that ESG activities improve labor investment efficiency up to an optimal level, beyond which excessive ESG engagement may reduce efficiency. This study contributes to the ESG literature by highlighting the importance of considering nonlinear effects when evaluating ESG strategies and human capital investments.