In this section, I report and review the results of OLS regressions. This is followed by sensitivity tests. The first sets of tests are based on OLS Model 1 (Tables 6 and 7).
Model 1 examines the audit fee determinants using both the experimental and the control variables. Table 7 provides the results for OLS tests conducted using Industry and Year as dichotomous control variables. Table 8 provides the results for OLS tests conducted using Years and Audit firms as dichotomous control variables. The reason for keeping industry and audit firm separate is the high association between certain industries and certain auditors, but this is not an issue when auditor reduces to a single BIG4 variable. The F scores of the OLS tests are significant (p<0.01, two tail) in both Tables 7 and 8.
7.1.3.1 Experimental Variables
7.1.3.1.1 Audit Committee Independence
In Tables 7 and 8, the coefficients of both ACINDPERt and ACEXPPERt are positive but not significant. The result of ACINDPERt does not support either the demand-side or the supply-side arguments that independent directors in the audit committee demand better audit quality and that audit firms view high percentage of independent directors as a factor that can reduce their audit risk.
105 The result of ACEXPPERt also does not support either the demand or the supply-side arguments that financial experts in the audit committee demand better audit quality or that audit firms view a high percentage of financial experts as a risk mitigating factor. It is also possible that there is little variability across firms as audit committee independence and expertise are required by SOX. The results do not support H1a and H1b.
7.1.3.1.2 Institutional Ownership
The coefficient of INSTt is negative and significant in both Table 7 (p<0.05, one tail), and in Table 8 (p<0.01, one tail). This result supports the supply-side argument that audit firms reduce their audit fees due to the presence of high institutional ownership. Therefore, H2 is supported.
7.1.3.1.3 Executive Compensation
In Table 7, the coefficient of STIPt is positive and significant (p<0.01, two tail), and the coefficient of STOPt is also positive and significant (p<0.01, two tail), However, the coefficient of LTIPt is positive but not significant. In Table 8, the coefficient on STIPt is positive and significant (p<0.01, two tail), and the coefficient on STOPt is positive and significant (p<0.01, two tail). The coefficient on LTIPt is once again positive but not significant.
For short-term incentives and stock options, the results support the supply- side argument that incentives paid to CEOs in cash, equity or stock options increases audit risk leading to higher audit fees. Audit firms are aware that managers may engage in risk-taking behaviour to enhance personal wealth from incentive pay, and that equity incentives could lead to earnings management.
These results do not support H3a and H3c but support H3b, which are based on the opposing demand and supply-side effects leading to no systematic variations in audit fees. However, the results of H3a and H3b support the supply-side argument that short-term and equity incentives could act as audit risk indicators. For LTIPt, the percentile distribution (Table 3, Panel A) shows that most of the
106 firms do not reward their CEOs with long-term incentives. Only the firms in the last quartile (75% to 100%) offer LTIPt. Figure 5a and 5b clearly show that LTIPt is almost zero for most SmallCap and MidCap firms.
The results of STIPt and STOPt tend to suggest that CEOs’ short-term incentives and stock options are considered high-risk factors in setting the audit fee by the audit firms.
7.1.3.2 Control Variables
The results of the control variables reported in Tables 7 and 8 are discussed here. The coefficient of BSEGt (Table 8) and GSEGt are positive and significant (p<0.01, one tail), indicating that an audit firm’s perceived risk increases with the number of business segments resulting in increased audit fees being charged. This supports both the demand and supply-side arguments that more business segments require more audit effort, and, because auditors perceive higher audit risks associated with an increasing number of segments, charge higher audit fees. Of the INDSi dummy variable, the mining and construction, textile, printing and publishing, extractive, transportation, utilities and retail industries have a negative and significant effect (p<0.01, two tail) on audit fees whereas the services and computers industries have a positive and significant (p<0.05, two tail) effect on audit fees (Table 7). The coefficients of ARINVt are positive and significant (p<0.01, one tail) in both Tables 7 and 8 indicating that audit firms view the accounts receivable and inventory as risk factors, which leads to higher audit fees. The coefficients of LOSSt are positive and significant (p<0.01, one tail) suggesting that the reporting of financial losses in any two consecutive years increases audit risk, thereby increasing the audit fee. The coefficient of BIG4 are negative and significant (p<0.01, one tail) in both Table 7 and 8.
The coefficients of INDSP are positive and significant (p<0.01, one tail) indicating that the industry specialist audit firms (BIG4) are able to charge a premium audit fee for their industrial expertise. The coefficients of BDSIZEt are negative and significant (p<0.01, two tail). The coefficients of ACSIZEt are negative
107 and significant (p<0.05, two tail). The coefficients of LOGMBt and LEVERAGEt are negative and significant (p<0.01, two tail) suggesting higher growth and debt reduce audit fees.
The coefficient of NAFt is positive and significant (p<0.01, two tail) suggesting that incumbent auditors still provide similar amounts of audit and non- audit services.
Year 2004 to 2006 seem to be significantly associated with audit fees confirming that audit fees did rise in the initial years after the implementation of SOX. Year 2008 is negatively associated with audit fees.
(Insert Tables 7 and 8 here)
7.1.3.3 Multicollinearity
Gujarati (2003) and Hair et al. (1995) regard a bivariate correlation of 0.80 as the threshold at which multicollinearity concerns may threaten the Ordinary Least Squares (OLS) regression analysis. None of the significant bivariate correlations of Table 6 were that high.
Further, variance inflation factor (VIF) values greater than 10 may be a cause for concern of multicollinearity, which could bias the parameter estimates (Myers 2001). VIF in the multivariate regression results (Tables 7 and 8) are well below 10, in most of cases, less than three, ruling out the effects of multicollinearity on hypothesis testing. Since the data involve similar companies over a period of five years, I run the time series tests for auto-correlation and, find that the Durbin- Watson coefficients are above two suggesting that the data is not auto-correlated. Therefore, I find no strong evidence of multicollinearity or auto-correlation.