Emotional Intelligence as Human Capital: A Behavioral Economic Perspective on Productivity, Well-Being and Sustainable Economic Growth

Abstract

Economic sciences have traditionally explained labor productivity and employment outcomes through education, skills, and technological inputs, often overlooking the emotional and psychological dimensions that shape real-world economic behavior. This study advances a human-centered economic framework by examining the influence of emotional intelligence and personality traits on labor productivity, employment quality and economic resilience, with psychological well-being and work engagement serving as key mediating mechanisms. Work environment and digital/AI intensity are incorporated as contextual moderators to reflect contemporary labor market conditions. Using a machine learning based analytical approach, the study captures complex, nonlinear relationships among emotional, psychological and economic variables. The results reveal that emotional intelligence is a dominant predictor of labor productivity, outperforming personality traits, AI literacy and work environment factors. Psychological well-being and work engagement significantly mediate these relationships, indicating that productivity gains are realized through sustained mental health and active work involvement rather than isolated skill acquisition. Contextual and technological factors enhance, but do not substitute for human-centered capabilities. Methodologically, ensemble machine learning models outperform traditional approaches highlighting their value for behavioral and labor economics research. The findings extend human capital theory by integrating emotional and psychological dimensions and reinforce behavioral economics perspectives on bounded rationality and adaptive performance. Policy implications emphasize the importance of well-being-centered education, workforce development and sustainable growth strategies aligned with the Sustainable Development Goals.