Audit firm attributes and income smoothing: the moderating influence of audit committee accounting expertise

Figures & data

Table 1. Variable measurements

Variable	Acronym	Definition
Dependent
Income Smoothing	IS	“Alteration in the coefficient variation of profit is divided by the alteration in the coefficient variation of revenue”. If a company’s CVI/CVS ratio is more than 1, it is seen to have smoother income (Bala et al., 2019).
Independent
Big 4 Auditors	BIG 4	Measured as 1 if the Big 4 auditors audited a company and 0 otherwise (Mbir et al., 2020).
Audit fees	AUF	Natural logarithms of audit fees (Bala et al., 2019).
Audit Tenure	AUT	If an audit partner has been hired by a client company for more than seven years, the score is 1; otherwise, it is 0. (Carey & Simnett, 2006; Widyaningsih et al., 2019).
Moderator
AC accounting expertise.	ACAE	Number of AC members with experience in financial accounting as a percentage of all AC members (Bala et al., 2019).
Control
Client-Size	CLSIZ	Natural logarithms of client total assets (Wan Hussin et al., 2018)
Board independence	BI	The ratio of autonomous directors on the board (Waheed et al., 2021).
Firm age	AGE	Calculated as the difference between the observation and listed years (Waheed et al., 2021).
Growth	GROW	Current sales are divided by change in sales (Bala et al., 2019)

Table 2. Tests of Pre-estimation

ADF Test			Link test Direct Model	Link test Moderation Model
Variable	τµ	τT	Hatsq	Hatsq
IS	−27.27***	−26.04***	0.46	0.44
Big 4	−5.74***	−5.49***
AUF	−11.47**	−10.93***
AUT	−8.75***	−7.82***
ACAE	−2.72***	−2.53***

Notes: IS stands for the “income Smoothing Index,” BIG 4 for “Big 4 auditors,” AUF for “Audit Fees,” AUT for “Audit Tenure, ”ACAE for “AC Accounting Expertise,” P values of ***p 0.01.

Table 3. Summary statistics of continuous variables

Variable	Mean	Average	Min.	Max.	Std. Dev.
AUF	13077	34,101	2000	250,000	68,186
AGE	25.00	24.54	1.00	68.00	13.54
ACAE	0.19	0.24	0.00	0.45	0.22
BI	0.71	0.71	0.06	0.92	0.12
CLSIZ	16.27	16.62	12.24	116.90	4.39
GROW	0.00	0.33	−5.26	7.51	2.69

AUF = Audit fees, AGE = firm age, ACAE = AC accounting expertise, BI = board independence, CLSIZ = client size, GROW = firm growth.

Table 4. Summary statistics of dichotomous variables

		Firms	Freq.		Per cent		Aggregate
Variable	Obs.	88	0	1	0	1	%
IS	616	88	241	375	39.12	60.88	100
BIG 4	616	88	266	350	43.18	56.82	100
AUT	616	88	345	271	56.01	43.99	100

Notes: IS = Profit smoothing index, BIG 4 = big 4 auditors, AUT = audit tenure.

Table 5. Correlation

Variable.	IS	BIG 4	AUF	AUT	AGE	ACAE	BI	CLSIZ	GROW
IS.	1.00
BIG 4	0.09*	1.00
AUF	−0.08	0.24***	1.00
AUT	0.09*	−0.04	−0.07	1.00
AGE	0.05	0.08	0.20***	−0.01	1.00
ACAE	−0.05	0.07	0.04	−0.12**	0.03	100
BI	0.13**	0.22***	0.43***	−0.03	0.14***	0.17***	100
CLSIZ	0.01	0.023	0.23***	0.02	0.11**	−0.03	0.20***	100
GROW	0.09*	−0.04	−0.06	0.02	−0.10**	0.02	−0.13**	−0.08*	100

Notes: IS stands for the “income Smoothing Index,” BIG 4 for “Big 4 auditors,” AUF for “Audit Fees,” AUT for “Audit Tenure,” AGE of the firm, ACAE for “AC Accounting Expertise,” BI for “Board Independence,” CLSIZ for “Client Size,” and GROW for “Growth.” P values of ***p 0.01, **p 0.05, and *p 0.1.

Table 6. Robust Logistic Regression of the relationship between AUFA and IS (Direct model)

Variable	Dy/Dx	Delta-method Std. Err.	Z-Val.	P-Val.
BIG 4	0.09	0.04	2.39**	0.02
AUF	−0.08	0.02	−4.41***	0.00
AUT	0.07	0.04	1.90**	0.06
AGE	0.00	0.00	1.60	0.11
ACAE	−0.33	0.16	−2.06**	0.04
Big 4* ACAE	−0.53	0.26	−2.05**	0.04
AUF* ACAE	−0.10	0.04	−2.26**	0.02
AUT* ACAE	−0.48	0.20	−2.44**	0.02
BI	0.62	0.13	4.94***	0.00
CLSIZ	0.00	0.00	0.49	0.62
GROW	0.02	0.01	2.90***	0.00
Pseud. R^2		0.06
x^2		47.93
Prob.		0.00
Estimation Test: Link test (Hatsq)		0.46
Gof Test Probability x^2		0.54
Correct classification		64.77%

Notes: IS stands for the “income Smoothing Index,” BIG 4 for “Big 4 auditors,” AUF for “Audit Fees,” AUT for “Audit Tenure,” AGE of the firm, ACAE for “AC Accounting Expertise,” BI for “Board Independence,” CLSIZ for “Client Size,” and GROW for “Growth.” P values of ***p 0.01 and **p 0.05.

Table 7. Pooled logit estimation of the link between AUFA and IS in high and low ACAE firms

High ACAE firms					Low ACAE firms
Variable	(coeff.)	(Std. Errs.)	t-val.	p-val.		(coeff.)	(Std. Errr.)	t-val.	p-val.
BIG 4	−2.59	1.17	−2.21**	0.03		0.29	0.19	1.57	0.12
AUF	−0.99	0.44	−2.24**	0.03		−0.27	0.10	2.86***	0.01
AUT	−1.75	0.84	−2.08**	0.04		0.21	0.19	1.16	0.25
AGE	0.02	0.03	0.76	0.45		0.01	0.01	1.37	0.17
ACAE	−5.30	2.82	−1.88*	0.06		−1.26	0.79	−1.60	0.11
BI	5.16	3.03	1.71*	0.09		2.52	0.64	3.94***	0.00
CLSIZ	−0.61	0.24	−2.51***	0.01		0.01	0.02	0.59	0.56
GROW	0.03	0.12	0.23	0.82		0.12	0.04	3.18***	0.00
Cons	18.40	4.83	3.81***	0.00		2.07	0.99	2.08**	0.04
Pseud. R^2	0.38					0.06
x^2	21.76					29.74
Prob.	0.01					0.03
Estimation Test: Link test (Hatsq)	0.68					0.62
Gof Test Probability x^2	4.67 0.93					8.33 0.22
Correct classification	71.08					63.82
Observation	398					218

Notes: IS stands for the “income Smoothing Index,” BIG 4 for “Big 4 auditors,” AUF for “Audit Fees,” AUT for “Audit Tenure,” AGE of the firm, ACAE for “AC Accounting Expertise,” BI for “Board Independence,” CLSIZ for “Client Size,” and GROW for “Growth.” P values of ***p 0.01, **p 0.05, and *p 0.1.

Table 8. Robust logit estimation of the link between AUFA and IS “moderation model”

Variable	Dy/Dx	Delta-method Std. Err.	Z-Val.	P- Val.
BIG 4	0.45	0.18	2.47***	0.01
AUF	−0.01	0.04	−0.37	0.71
AUT	0.39	0.14	2.81***	0.01
Big 4* ACAE	−0.53	0.26	−2.05**	0.04
AUF* ACAE	−0.10	0.04	−2.26**	0.02
AUT* ACAE	−0.48	0.20	−2.44**	0.02
AGE	0.00	0.00	1.68*	0.09
ACAE	1.11	0.49	2.26**	0.02
BI	0.56	0.13	4.34***	0.00
CLSIZ	0.00	0.00	0.24	0.81
GROW	0.02	0.01	2.87***	0.00
Pseud. R^2		0.08
x^2		57.91
Prob.		0.00
Estimation Test: Link test (Hatsq)		0.44
Gof Test Probability x^2		0.32
Correct classification		65.91%

Notes: IS stands for the “income Smoothing Index,” BIG 4 for “Big 4 auditors,” AUF for “Audit Fees,” AUT for “Audit Tenure,” AGE of the firm, ACAE for “AC Accounting Expertise,” BI for “Board Independence,” CLSIZ for “Client Size,” and GROW for “Growth.” P values of ***p 0.01 and **p 0.05”.

Table 9. Complementary measurements of outcome variables “direct model”

Variable	Dy/Dx	Delta-method. Std. Errs.	Z-Val.	P- Val.
BIG 4	0.02	0.04	0.60	0.55
AUF	−0.06	0.02	−2.89***	0.00
AUT	0.03	0.04	0.88	0.38
AGE	0.00	0.00	2.29**	0.02
ACAE	−0.08	0.16	−0.51	0.61
BI	0.62	0.13	4.84***	0.00
CLSIZ	0.00	0.00	0.13	0.89
GROW	0.03	0.01	3.54***	0.00
Pseud. R^2		0.06
x^2		48.01
Prob.		0.00
Estimation Test: Link test (Hatsq)		0.93
Gof Test Probability x^2		0.80
Correct classification		63.80%

Notes: IS stands for the “income Smoothing Index,” BIG 4 for “Big 4 auditors,” AUF for “Audit Fees,” AUT for “Audit Tenure,” AGE of the firm, ACAE for “AC Accounting Expertise,” BI for “Board Independence,” CLSIZ for “Client Size,” and GROW for “Growth.” P values of ***p 0.01 and **p 0.05.

Table 10. Complementary measurements of outcome variables “moderation model”

Variable	Dy/Dx	Delta-method Std. Errs.	Z-Val.	P- Val.
BIG 4	0.18	0.16	1.15	0.25
AUF	−0.02	0.03	−0.72	0.47
AUT	0.31	0.14	2.27**	0.02
Big 4* ACAE	−0.23	0.22	−1.07	0.29
AUF* ACAE	−0.05	0.03	−2.09**	0.04
AUT* ACAE	−0.41	0.19	−2.13**	0.03
AGE	0.00	0.00	2.40**	0.02
ACAE	−0.77	0.33	−2.31**	0.02
BI	0.57	0.13	4.43***	0.00
CLSIZ	0.00	0.00	−0.01	0.99
GROW	0.03	0.01	3.52***	0.00
Pseud. R^2		0.06
x^2		57.91
Prob.		0.00
Estimation Test: Link test (Hatsq)		0.56
Gof Test Probability x^2		0.28
Correct classification		65.58%

Notes: IS stands for the “income Smoothing Index,” BIG 4 for “Big 4 auditors,” AUF for “Audit Fees,” AUT for “Audit Tenure,” AGE of the firm, ACAE for “AC Accounting Expertise,” BI for “Board Independence,” CLSIZ for “Client Size,” and GROW for “Growth.” P values of ***p 0.01 and **p 0.05.

Table 11. IS model: “Dynamic panel data estimation GMM”

Variable	Coeff.	Std. Errs.	Z-Val.	P-Val.
IS
L1.	−0.35	0.05	−7.02***	0.00
BIG 4	0.14	0.07	1.92*	0.06
AUF	−0.12	0.04	−3.36***	0.00
AUT	0.08	0.13	0.66	0.51
AGE	0.01	0.01	2.27**	0.03
ACAE	−0.85	0.57	−1.49	0.14
BI	0.54	0.25	2.19**	0.03
CLSIZ	0.02	0.03	0.73	0.47
GROW	0.02	0.01	1.39	0.17
CONS	1.56	0.44	3.53***	0.00
Wald x^2	371.71
Probability.	0.00
AR I: Probability.	0.00
AR II: Probability.	0.575
Sargan test: Probability.	0.08
Hansen test: Probability.	0.54

Notes: IS stands for the “income Smoothing Index,” BIG 4 for “Big 4 auditors,” AUF for “Audit Fees,” AUT for “Audit Tenure,” AGE of the firm, ACAE for “AC Accounting Expertise,” BI for “Board Independence,” CLSIZ for “Client Size,” and GROW for “Growth.” P values of ***p 0.01 and **p 0.05.

Table 12. IS model: “Dynamic panel data estimation GMM”

Variable	Coeff.	Std. Err.	Z-Val.	P-Val.
IS
L1.	−0.20	0.07	−3.03***	0.00
BIG 4	0.67	0.69	0.97	0.34
AUF	−0.03	0.06	−0.50	0.62
AUT	0.89	0.46	1.96**	0.05
Big 4* ACAE	−0.80	0.08	−1.76*	0.08
AUF* ACAE	−0.14	0.05	−2.58***	0.01
AUT* ACAE	−1.31	0.62	−2.12***	0.04
AGE	0.01	0.01	1.75*	0.08
ACAE	−1.45	1.26	−1.15	0.25
BI	0.35	0.28	1.27	0.21
CLSIZ	0.00	0.01	0.33	0.74
GROW	0.02	0.01	1.65*	0.10
CONS	0.30	0.90	0.33	0.74
Probability.	392.71
AR I: Probability.	0.00
AR II: Probability.	0.00
Sargan test: Probability.	0.575
Hansen test: Probability.	0.11
Probability.	0.96

Notes: IS stands for the “income Smoothing Index,” BIG 4 for “Big 4 auditors,” AUF for “Audit Fees,” AUT for “Audit Tenure,” AGE of the firm, ACAE for “AC Accounting Expertise,” BI for “Board Independence,” CLSIZ for “Client Size,” and GROW for “Growth.” P values of ***p 0.01, **p 0.05, and *p 0.1.

Bala, H., Amran, N. A., & Shaari, H. (2019). Audit committee attributes and cosmetic accounting in Nigeria: The moderating effect of audit price. Managerial Auditing Journal, 35(2), 177–206. https://doi.org/10.1108/MAJ-06-2018-1897

 Web of Science ®Google Scholar

Mbir, D. E. G., Agyemang, O. S., Tackie, G., Abeka, M. J., & Ntim, C. G. (2020). IFRS compliance, corporate governance, and financial reporting quality of GSE-listed nonfinancial firms. Cogent Business & Management, 7(1), 1759856. https://doi.org/10.1080/23311975.2020.1759856

 Web of Science ®Google Scholar

Carey, P., & Simnett, R. (2006). Audit partner tenure and audit quality.Accounting Review. 81(3), 653–676. https://www.jstor.org/stable/4093109

 Google Scholar

Widyaningsih, I. A., Harymawan, I., Mardijuwono, A. W., Ayuningtyas, E. S., Larasati, D. A., & Ntim, C. G. (2019). Audit firm rotation and audit quality: Comparison before vs after the elimination of audit firm rotation regulations in Indonesia. Cogent Business & Management, 6(1), 1–15. https://doi.org/10.1080/23311975.2019.1695403

 Web of Science ®Google Scholar

Wan Hussin, W. N., Bamahros, H. M., & Shukeri, S. N. (2018). Lead engagement, partner workload, partner-client tenure, and audit reporting lag: Evidence from Malaysia. Managerial Auditing Journal, 33(3), 246–266. https://doi.org/10.1108/MAJ-07-2017-1601

 Web of Science ®Google Scholar

Waheed, A., Hussain, S., Hanif, H., Mahmood, H., Malik, Q. A., & Ntim, C. G. (2021). Corporate social responsibility and firm performance: The moderation of investment horizon and corporate governance. Cogent Business & Management, 8(1), 1938349. https://doi.org/10.1080/23311975.2021.1938349

 Web of Science ®Google Scholar