This study explores systemic disparities in CEO compensation, emphasizing the influence of demographic factors, company characteristics, and ethnicity. Despite efforts to increase diversity in corporate leadership, inequalities persist, particularly affecting women and racial minorities. Drawing on data from Wharton Research Data Services (WRDS) and prior studies, this research incorporates advanced machine learning techniques, including the Name-Ethnicity-Classifier, to improve ethnicity classification and analysis. Using clustered regression analysis, we evaluated predictors of CEO compensation, controlling for demographic variables (e.g., age, gender), company financial metrics (e.g., revenue, sales, number of employees), and ethnicity. Results reveal that older CEOs and those in firms with more directors earn significantly higher compensation. Female CEOs were found to receive slightly lower pay than male CEOs. Ethnicity also plays a significant role, with East Asian and Hispanic CEOs earning higher compensation compared to their White counterparts. However, no significant differences were observed for South Asian or Black CEOs. This study underscores the systemic and nuanced factors shaping executive pay, highlighting disparities influenced by both gender and ethnicity. By integrating machine learning for detailed ethnicity classification and employing robust statistical methods, the research contributes actionable insights for promoting diversity, equity, and inclusion in corporate leadership and compensation practices.
