The impact of audit committee characteristics on audit fees; evidence from Ghana

Figures & data

Table 1. Summary statistics

Variable	Obs	Mean	Std.Dev.	Min	Max
Dependent variables
AF (GHc)	308	252,891.00	6100.00	2905.00	2,675,000.00
Independent variables
ACS	308	4.6209	1.142	3.00	10.00
ACE	308	2.0141	1.196	1.00	4.00
ACG	308	0.178	0.231	0.00	0.60
ACM	308	4.8072	6.132	4.00	14.00
Control variables
BS	308	8.03	1.813	4	12
BI	308	0.731	0.145	0.29	0.91
F.Age	308	46.54	24.539	3	124
BIG4	308	0.859	0.349	0	1
FS	308	8.522	0.902	6.648	10.12
Lev	308	0.602	0.245	0.022	0.958

AF is audit fees, ACS is audit committee size, ACE is the audit committee financial expertise, ACG is the audit committee gender diversity, BS is the board size, ACM is audit committee meetings, BI is the board independence (non-executive director), Big4 is audit by the big four audit firm, F.Age is firm age, FS is firm size, Lev is firm leverage.

Table 2. Correlation matrix

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)
(1) AF	1.000
(2) ACS	−0.093**	1.000
	(0.043)
(3) ACE	−0.016***	−0.007	1.000
	(0.003)	(0.810)
(4) ACG	−0.290***	−0.023	0.135*	1.000
	(0.000)	(0.467)	(0.000)
(5) ACM	−0.004*	−0.020	−0.036	−0.022	1.000
	(0.079)	(0.515)	(0.246)	(0.475)
(6) BS	0.035*	−0.360*	−0.075*	−0.025	−0.040	1.000
	(0.056)	(0.004)	(0.016)	(0.425)	(0.200)
(7) BI	0.061**	0.008	0.044	−0.006	−0.028	0.069*	1.000
	(0.040)	(0.795)	(0.152)	(0.841)	(0.368)	(0.025)
(8) F.Age	−0.029	0.034	−0.047	0.024	−0.012	−0.046	−0.004	1.000
	(0.355)	(0.269)	(0.127)	(0.446)	(0.693)	(0.138)	(0.895)
(9) BIG4	0.003***	0.036	−0.025	0.019	−0.011	−0.104*	−0.043	−0.016	1.000
	(0.012)	(0.250)	(0.418)	(0.540)	(0.725)	(0.001)	(0.164)	(0.615)
(10) FS	0.139*	0.077*	0.138*	0.053	−0.014	−0.272*	0.028	0.024	0.033	1.000
	(0.000)	(0.013)	(0.000)	(0.085)	(0.642)	(0.000)	(0.371)	(0.435)	(0.291)
(11) Lev	0.028*	−0.122*	−0.142*	0.092*	−0.031	0.649*	0.108*	−0.070*	−0.234*	−0.349*	1.000
	(0.073)	(0.000)	(0.000)	(0.003)	(0.312)	(0.000)	(0.000)	(0.024)	(0.000)	(0.000)

Note: *** p < 0.01, ** p < 0.05, * p < 0.1. AF is audit fees, ACS is audit committee size, ACE is the audit committee financial expertise, ACG is the audit committee gender diversity, BS is the board size, ACM is audit committee meetings, BI is the board independence (non-executive director), Big4 is audit by the big four audit firm, F.Age is firm age, FS is firm size, Lev is firm leverage.

Table 3. Baseline regression results

Audit Fees	Model	Model	Model	Model
	(1)	(2)	(3)	(4)
ACS	−0.530***	−0.241***	−0.196***	−0.191***
	(0.021)	(0.053)	(0.007)	(0.008)
ACE	−0.411***	−0.404***	−0.321***	−0.320***
	(0.004)	(0.062)	(0.002)	(0.004)
ACG	−0.322***	−0.274**	−0.201**	−0.212**
	(0.009)	(0.096)	(0.109)	(0.106)
ACM	0.122***	0.039**	−0.016	−0.028
	(0.029)	(0.013)	(0.018)	(0.025)
BS		0.024***	0.037***	0.131***
		(0.006)	(0.010)	(0.018)
BI		0.036***	0.019**	0.028**
		(0.008)	(0.009)	(0.014)
F.Age		−0.023	−0.032	−0.004
		(0.066)	(0.046)	(0.002)
BIG4		0.406***	0.512**	0.555**
		(0.111)	(0.139)	(0.140)
FS		0.028* (0.015)	0.027** (0.011)	0.024** (0.012)
Lev		0.042***	0.063***	0.115***
		(0.010)	(0.013)	(0.013)
Constant	0.140**	0.472***	0.512***	0.172**
	(0.070)	(0.048)	(0.046)	(0.068)
Obs.	308	308	308	308
R-squared	0.383	0.446	0.587	0.596
Year FE	NO	NO	NO	YES
Firm FE	NO	NO	YES	YES

*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. robust errors are in parenthesis. AF is audit fees, ACS is audit committee size, ACE is the audit committee financial expertise, ACG is the audit committee gender diversity, BS is the board size, ACM is audit committee meetings, BI is the board independence (non-executive director), Big4 is audit by the big four audit firm, F.Age is firm age, FS is firm size, Lev is firm leverage.

Table 4. Robustness test results- mitigation of endogeneity concerns

Audit Fees	Model (1)	Model (2)
	IV-2SLS	GMM
ACS	−0.055***	−0.051***
	(0.010)	(0.007)
ACE	−0.021***	−0.034**
	(0.003)	(0.016)
ACG	−0.020**	−0.013**
	(0.010)	(0.005)
ACM	−0.038**	−0.036**
	(0.008)	(0.008)
LAG_AF		0.357***
		(0.096)
BS	0.011**	0.043**
	(0.005)	(0.019)
BI	0.018***	0.021***
	(0.002)	(0.006)
F.Age	−0.015**	−0.013**
	(0.006)	(0.006)
BIG4	0.086***	0.071***
	(0.013)	(0.017)
FS	0.048***	0.053***
	(0.016)	(0.013)
Lev	0.021***	0.036***
	(0.002)	(0.006)
Constant	0.234***	0.343***
	(0.060)	(0.058)
Obs.	308	308
R-squared	0.579	0.581
Specification test
Durbin-Wu-Hausman test	0.000	-
AR (1)	-	0.0310
AR (2)	-	0.8972
Hansen test	0.2611	0.2530
Difference-Sargan tests	0.1301	0.2322

*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. standard errors are in parenthesis. AF is audit fees, ACS is audit committee size, ACE is the audit committee financial expertise, ACG is the audit committee gender diversity, BS is the board size, ACM is audit committee meetings, BI is the board independence (non-executive director), Big4 is audit by the big four audit firm, F.Age is firm age, FS is firm size, Lev is firm leverage.

Table 5. Further test-sensitivity analysis based on financial and non-financial firms

	Financial firms		Non-financial firms
	IV-2SLS	GMM	IV-2SLS	GMM
	(1)	(2)	(1)	(2)
ACS	-0.048**	-0.041**	-0.021**	-0.032**
	(0.023)	(0.019)	(0.010)	(0.015)
ACE	-0.071***	-0.066***	-0.064**	-0.058**
	(0.011)	(0.018)	(0.028)	(0.053)
ACG	-0.023**	-0.033**	-0.021*	-0.029*
	(0.009)	(0.014)	(0.011)	(0.015)
ACM	-0.018**	-0.021**	-0.014**	-0.017**
	(0.009)	(0.010)	(0.007)	(0.008)
CONTROLS	YES	YES	YES	YES
Obs.	111	111	197	197
R-squared	0.422	0.475	0.401	0.444
Specification test
Durbin-Wu-Hausman test	0.000	--	0.000	--
AR (1)		0.0220		0.0280
AR (2)		0.7361		0.7012
Hansen test	0.2314	0.2613	0.2361	0.2510
Difference-Sargan tests	0.2243	0.2561	0.2402	0.2684
Coefficient difference test between the financial and non-financial firms
ACS chi2 (1) = 4.021 Prob > chi2 = 0.0341**
ACE chi2 (1) = 6.031 Prob > chi2 = 0.0124**
ACG chi2 (1) = 5.036 Prob > chi2 = 0.0214**
ACM chi2 (1) = 3.033 Prob > chi2 = 0.0424**

*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. Standard errors in parenthesis. The difference in coefficient test was reported only for the gmm estimation.