Women director characteristics and earnings quality: evidence from banking industry in Indonesia

Figures & data

Table 1. Sample selection process.

Criteria	Total
Banks listed in IDX 2017–2021	46
Syariah banks listed in IDX 2017–2021	(4)
Total banks used as sample	42
Total firm-years sample observations	210

Table 2. Variables operationalization.

Variables	Operationalization
Dependent variable
ALLP	Absolute (Loan loss provisions divided by total initial assets)
Main independent variables
PERCENT_WOMEN	Women directors divided by the total number of directors
TENURE_WOMEN	The average length of time the directors served
Moderating Variables
EDU_WOMEN_Std	A dummy variable, namely code 0 for high school education level, code 1 for diploma education level, code 2 for undergraduate education level, code 3 for master’s education level, and code 4 for doctoral’s education level to measure the educational level of women directors (Fan et al., 2019). Following Kontesa et al. (2020), we standardized educational levels using a ranking methodology based on percentile groups. For instance, the biggest 20th percentile group will score 5, and the smallest 20th percentile group will score 1.
BACK_WOMEN	Dummy variable, namely code 1 for women directors who have an academic background in accounting, economics, and finance, and code 0 otherwise
Control Variables
ROA	Net income divided by total assets
GROWTH	The total assets at the end of year t divided by the total assets at the end of year t-1
AC	Total all members of the audit committee in the company
Variable used to do additional test
DISEXP	Total discretionary expenditures
Variable used to control for endogenity
WOMEN_D	A dummy variable, equal to one if firms have at least one female directors, and zero otherwise.

Table 3. Descriptive statistics.

	Mean	SD	Min	Max
ALLP	0.043	0.120	0.001	1.613
PERCENT WOMEN	0.185	0.190	0	0.750
EDU std	3	1.418	1	5
BACKEDU WOMEN	0.461	0.461	0	1
TENURE WOMEN	6.316	7.670	0	38
ROA	0.004	0.024	−0.181	0.050
GROWTH	0.214	0.834	−0.398	10.863
AC	3.781	1.107	2	8

Note: ALLP = Abnormal Allowance for Earning Assets (Loan Loss Provisions) divided by total initial assets; PERCENT_WOMAN = Proportion of Women Directors; EDU_WOMEN = Education Level of Women Directors; BACKEDU_WOMEN = Women Directors Background; TENURE_WOMEN = Term of Office of Women Directors; ROA = Return On Assets; GROWTH = Asset Growth; AC = Audit Committee.

Table 4. Correlation test results.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
(1) ALLP	1.000
(2) PERCENT_WOMEN	−0.067	1.000
(3) EDU_std	−0.171	0.473	1.000
(4) BACKEDU_WOMEN	−0.042	0.373	0.798	1.000
(5) TENURE_WOMEN	−0.142	0.596	0.456	0.487	1.000
(6) ROA	−0.367	0.152	0.194	0.069	0.101	1.000
(7) GROWTH	0.060	−0.064	−0.136	−0.045	−0.015	−0.008	1.000
(8) AC	−0.065	−0.120	0.116	0.080	−0.081	0.060	−0.094	1.000

Note: ALLP = Abnormal Allowance for Earning Assets (Loan Loss Provisions) divided by total initial assets; PERCENT_WOMAN = Proportion of Women Directors; EDU_WOMEN = Education Level of Women Directors; BACKEDU_WOMEN = Women Directors Background; TENURE_WOMEN = Term of Office of Women Directors; ROA = Return On Assets; GROWTH = Asset Growth; AC = Audit Committee.

Table 5. Multicollinearity test results.

Variable	VIF	1/VIF
EDU_std	3.32	0.301215
BACKEDU_WO ∼ N	3.06	0.326899
TENURE_WOMEN	1.79	0.559726
PERCENT_WO ∼ N	1.76	0.566986
ROA	1.07	0.933331
AC	1.07	0.934983
GROWTH	1.04	0.962463

Table 6. Regression results.

	(1)	(2)
	ALLP	ALLP
PERCENT_WOMEN	−0.653**	−0.549*
	(−2.379)	(−1.843)
EDU_std	0.012	0.016
	(0.649)	(0.788)
PERCENT_WOMEN*EDU_std	0.158*	0.136
	(1.883)	(1.459)
BACKEDU_WOMEN	−0.089	−0.090
	(−0.676)	(−0.651)
PERCENT_WOMEN*BACKEDU_WOMEN	2.162***	2.194***
	(3.093)	(3.103)
EDU_std*BACKEDU_WOMEN	0.016	0.017
	(0.468)	(0.449)
PERCENT_WOMEN*EDU_std*BACKEDU_WOMEN	−0.513***	−0.519***
	(−2.840)	(−2.786)
TENURE_WOMEN	−0.002*	−0.004***
	(-1.793)	(−2.767)
ROA	−1.627***	−1.578***
	(-5.014)	(-4.69)
GROWTH	0.0057	0.004
	(0.630)	(0.505)
AC	−0.005	−0.004
	(−0.639)	(−0.546)
Constant	0.054	0.046
	(1.344)	(0.977)
Observations	210	210
Overall R^2	–	0.2371
Adj R^2	0.2036	–

Note: *, **, and *** are significant at 10%, 5%, and 1% levels, respectively.

Table 7. Regression results.

	(1)	(2)
	ALLP-LOW	ALLP-HIGH
PERCENT_WOMEN	0.012	−0.079
	(0.917)	(−0.111)
EDU_std	0.002**	0.006
	(2.029)	(0.188)
PERCENT_WOMEN*EDU_std	−0.007*	−0.090
	(−1.693)	(−0.385)
BACKEDU_WOMEN	0.021***	−1.342***
	(2.97)	(−4.366)
PERCENT_WOMEN*BACKEDU_WOMEN	−0.086**	7.541***
	(−2.493)	(5.259)
EDU_std*BACKEDU_WOMEN	−0.006***	0.292***
	(−3.279)	(3.786)
PERCENT_WOMEN*EDU_std*BACKEDU_WOMEN	0.024***	−1.616***
	(2.674)	(−4.422)
TENURE_WOMEN	0.001	0.002
	(1.373)	(0.907)
ROA	0.021	−0.870*
	(0.571)	(−1.894)
GROWTH	0.001	0.035
	(0.629)	(1.306)
AC	0.001	−0.012
	(1.630)	(−1.044)
Constant	0.001	0.113*
	(0.134)	(1.697)
Observations	105	105
Overall R^2	0.265	0.443
Adj R^2	0.178	0.377

Note: *, **, and *** are significant at 10%, 5%, and 1% levels, respectively.

Table 8. Real earnings management: discretionary expenses.

	AREM
PERCENT_WOMEN	0.402
	(0.128)
EDU_std	0.481**
	(2.344)
PERCENT_WOMEN*EDU_std	−1.019
	(−1.060)
BACKEDU_WOMEN	−0.458
	(−0.297)
PERCENT_WOMEN*BACKEDU_WOMEN	1.936
	(0.237)
EDU_std*BACKEDU_WOMEN	−0.286
	(−0.703)
PERCENT_WOMEN*EDU_std*BACKEDU_WOMEN	0.400
	(0.190)
TENURE_WOMEN	−0.006
	(−0.397)
ROA	−3.858
	(−1.044)
GROWTH	−0.060
	(−0.581)
AC	−0.214**
	(−2.424)
Constant	0.780
	(1.636)
Observations	201
Adj R^2	0.045

Note: *, **, and *** are significant at 10%, 5%, and 1% levels, respectively.

Table 9. Sub-sample analysis: Covid-19 period excluded.

	Non Covid-19
	ALLP-LOW
PERCENT_WOMEN	−.688*
	(−1.948)
EDU_std	0.036
	(1.475)
PERCENT_WOMEN*EDU_std	0.150
	(1.478)
BACKEDU_WOMEN	0.039
	(0.230)
PERCENT_WOMEN*BACKEDU_WOMEN	2.175**
	(2.501)
EDU_std*BACKEDU_WOMEN	−0.019
	(−0.436)
PERCENT_WOMEN*EDU_std*BACKEDU_WOMEN	−0.519**
	(−2.399)
TENURE_WOMEN	−0.002
	(−1.011)
ROA	−3.587***
	(−6.222)
GROWTH	0.248***
	(5.280)
AC	0.001
	(0.010)
Constant	−0.032
	(−0.547)
Observations	126
Adj R^2	0.479

Note: *, **, and *** are significant at 10%, 5%, and 1% levels, respectively.

Table 10. Propensity score matching.

Gaussian Kernel: Bootstrapped 100 replications
	Coeff	z-stat
WOMEN_D	−0.558**	−2.54
Control variables	Yes
Wald chi2(1)	6.46
Prob > chi^2	0.011
Replications	100
Adj. R^2	0.043

Note: *, **, and *** are significant at 10%, 5%, and 1% levels, respectively.

Table

No	CODE	BANK NAME
1.	AGRO	Bank Rakyat Indonesia Agroniaga Tbk PT
2.	AGRS	Bank IBK Indonesia Tbk PT
3.	AMAR	Bank Amar Indonesia PT
4.	ARTO	Bank Jago Tbk PT
5.	BABP	Bank MNC Internasional Tbk PT
6.	BACA	Bank Capital Indonesia Tbk PT
7.	BBCA	Bank Central Asia Tbk PT
8.	BBHI	Bank Harda Internasional Tbk PT
9.	BBKP	Bank KB Bukopin Tbk PT
10.	BBMD	Bank Mestika Dharma Tbk PT
11.	BBNI	Bank Negara Indonesia (Persero) Tbk PT
12.	BBRI	Bank Rakyat Indonesia (Persero) Tbk PT
13.	BBSI	Bank Bisnis Internasional PT
14.	BBTN	Bank Tabungan Negara (Persero) Tbk PT
15.	BBYB	PT Bank Neo Commerce Tbk PT
16.	BCIC	Bank JTrust Indonesia Tbk PT
17.	BDMN	Bank Danamon Indonesia Tbk PT
18.	BEKS	Bank Pembangunan Daerah Banten Tbk PT
19.	BGTG	Bank Ganesha Tbk PT
20.	BINA	Bank Ina Perdana Tbk PT
21.	BJBR	Bank Pembangunan Daerah Jawa Barat dan Banten Tbk PT
22.	BJTM	Bank Pembangunan Daerah Jawa Timur Tbk PT
23.	BKSW	Bank QNB Indonesia Tbk PT
24.	BMAS	Bank Maspion Indonesia Tbk PT
25.	BMRI	Bank Mandiri (Persero) Tbk PT
26.	BNBA	Bank Bumi Arta Tbk PT
27.	BNGA	Bank CIMB Niaga Tbk PT
28.	BNII	Bank Maybank Indonesia Tbk PT
29.	BNLI	Bank Permata Tbk PT
30.	BSIM	Bank Sinarmas Tbk PT
31.	BSWD	Bank Of India Indonesia Tbk PT
32.	BTPN	Bank BTPN Tbk PT
33.	BVIC	Bank Victoria International Tbk PT
34.	DNAR	Bank Oke Indonesia Tbk PT
35.	INPC	Bank Artha Graha Internasional Tbk PT
36.	MAYA	Bank Mayapada Internasional Tbk PT
37.	MCOR	Bank China Construction Bank Indonesia Tbk PT
38.	MEGA	Bank Mega Tbk PT
39.	NISP	Bank OCBC NISP Tbk PT
40.	NOBU	Bank National NOBU Tbk PT
41.	PNBN	Bank Pan Indonesia Tbk PT
42.	SDRA	Bank Woori Saudara Indonesia 1906 Tbk PT

Fan, Y., Jiang, Y., Zhang, X., & Zhou, Y. (2019). Women on boards and bank earnings management: From zero to hero. Journal of Banking & Finance, 107, 105607. https://doi.org/10.1016/j.jbankfin.2019.105607

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Kontesa, M., Lako, A., & Wendy, W. (2020). Board capital and earnings quality with different controlling shareholders. Accounting Research Journal, 33(4/5), 593–613. https://doi.org/10.1108/ARJ-01-2020-0017

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Data availability statement

Data derived from public domain resources. The data supporting this study’s findings are available in the bank’s annual financial report published on its website or derived from the Indonesia Stock Exchange at https://www.idx.co.id/id. The list of banks included as data samples in this study is in the appendix attached.