executive pay respectively. The coefficients of the number of contestants are insignificant in Models 3A and 3B therefore suggesting that executive pay gap is not related to the number of contestants (the results for executive and non-executives combined show a significant result at the 5% level in Model 3A). The coefficient for the interaction term (con*gov) is negative and significant (at the 1% level) in both models. Therefore, the results provide mixed support for H3: H3a is not supported but H3b is strongly supported: higher state ownership weakens the relationship between executive pay gap and number of contestants. Executive pay gap is negatively related to board size, but only significant at the 10% level for Model 3A: each additional executive reduces the pay gap by nearly 1.4%. The pay gap is positively related to duality in both models (1% sig. level): the pay gap is 3.4% bigger if the HPE1 is CEO/Deputy CEO and Chair/Deputy Chair. Further, the pay gap is significantly positively related to foreign ownership and negatively related to years of IPO. 5.2.2. Executive pay dispersion and firm performance H4a predicts that corporate performance is positively related to the executive pay gap, and H4b states that this is especially the case in less government-controlled companies. Model 4 tests these relationships. INSERT TABLE 7 HERE Table 7 reports the estimates of the relationship between executive pay differences (measured by WD) and firm performance. The dependent variables are three measures of firm performance: ROA, EPS, and Q. All the models are significant at the 1% Tobin’s level. Using OLS regression, models 4A, 4B and 4C explain 2.0%, 14.6% and 3.8% of the total variances of firm performance respectively. Models 4D, 4E and 4F were tested using robust regression with standard errors clustered by firm, and explain 2.4%, 15% and 4.2% of the total variance of firm performance respectively. For Models 4A, 4C, 4D and 4F, both executive pay differences (WD) and the interaction term WG are not significant. In contrast, Models 4B and 4E show that after controlling for the average level of executive pay, firm characteristics and industry effects, there is a significant (at the 5% level) and positive relationship between executive pay differences
executive pay respectively. The coefficients of the number of contestants are insignificant in Models 3A and 3B therefore suggesting that executive pay gap is not related to the number of contestants (the results for executive and non-executives combined show a significant result at the 5% level in Model 3A). The coefficient for the interaction term (con*gov) is negative and significant (at the 1% level) in both models. Therefore, the results provide mixed support for H3: H3a is not supported but H3b is strongly supported: higher state ownership weakens the relationship between executive pay gap and number of contestants. Executive pay gap is negatively related to board size, but only significant at the 10% level for Model 3A: each additional executive reduces the pay gap by nearly 1.4%. The pay gap is positively related to duality in both models (1% sig. level): the pay gap is 3.4% bigger if the HPE1 is CEO/Deputy CEO and Chair/Deputy Chair. Further, the pay gap is significantly positively related to foreign ownership and negatively related to years of IPO. 5.2.2. Executive pay dispersion and firm performance H4a predicts that corporate performance is positively related to the executive pay gap, and H4b states that this is especially the case in less government-controlled companies. Model 4 tests these relationships. INSERT TABLE 7 HERE Table 7 reports the estimates of the relationship between executive pay differences (measured by WD) and firm performance. The dependent variables are three measures of firm performance: ROA, EPS, and Tobin’s Q. All the models are significant at the 1% level. Using OLS regression, models 4A, 4B and 4C explain 2.0%, 14.6% and 3.8% of the total variances of firm performance respectively. Models 4D, 4E and 4F were tested using robust regression with standard errors clustered by firm, and explain 2.4%, 15% and 4.2% of the total variance of firm performance respectively. For Models 4A, 4C, 4D and 4F, both executive pay differences (WD) and the interaction term WG are not significant. In contrast, Models 4B and 4E show that after controlling for the average level of executive pay, firm characteristics and industry effects, there is a significant (at the 5% level) and positive relationship between executive pay differences
Power_%26_tournament_theories_final_14_02_2011.docx