Examination of executive compensation determinants in the hospitality industry: A quantile regression approach

Abstract

Scope and Method of Study: This study has investigated the determinants of executive compensation in the hospitality industry with selected variables from both the pay-for-performance rule and the managerial power approach, using two multiple regression analysis methods: OLS regression and quantile regression. OLS regression analysis was performed first to identify the determinants of executive compensation in the hospitality industry on the basis of both the pay-for-performance rule and the managerial power approach. At the second stage of analysis, quantile regression analysis was adopted to examine whether the independent variables were differently related to different levels of executive compensation in the hospitality industry.

Findings and Conclusions: Both the OLS regression analysis and the quantile regression analysis supported that the pay-for-performance rule and the managerial power approach influence executive compensation in the hospitality industry. The results of OLS regression analysis for the hospitality industry revealed that three financial variables, firm debt ratio, firm size, and EPS, and two managerial power variables, PDIR (current director or not) and executive stock ownership, were positively related to executive compensation with statistical significance. In addition, the quantile regression analysis showed that the level of executive compensation was differently related to each independent pay-for-performance and managerial power variable. In other words, the different measures could be utilized to determine different levels of executive compensation.