Earnings management in emerging markets: The COVID-19 and family ownership

Figures & data

Table 1. Measurement variables

Variable	Measurement
EM	Earnings management, either Accruals or real
AEM (DA)	The discretionary accruals measured by Kothari et al. (2005) model
REM	The aggregate value of REM using Roychowdhury’s (2005) model is equal to the sum of ACFO (*-1), ADIE (*-1), and APRC
COVID	indicates “1” for the COVID-19 disease outbreak years 2020 and 2021, and 0 refers to the period before for disease outbreak of COVID-19, from 2017 to 2019 (Aljawaheri et al., 2021; Lassoued & Khanchel, 2021; Rahman et al., 2022).
FOW	At least twenty percent of the company’s total number of shares are owned by members of the same family (Bataineh et al., 2018; Ghaleb et al., 2020).
FSIZE	The natural logarithm of the total assets of the company
LEV	The proportion of aggregate debt to overall assets.
MKTB	The company’s book value/Company’s market value
ROA	Net income/total assets
Industry fixed effects	The industry dummies
Year fixed effects	The year dummies

Table 2. Continuous variables’ descriptive statistics

Variable	Obs	Mean	Std	Min	Max
DA	685	0.0612	0.105	0.001	1.727
REM	685	−2.01e-10	0.202	. −2.158	0. 997
FOW	685	0.203	0.233	0.000	0.852
FSIZE	685	16.969	1.498	12.792	21.132
LEV	685	0.303	0.245	0.001	1.349
MKTB	685	1.037	0.849	0.227	3.536
ROA	685	0.0006	0.0776	−0.541	0.407

Note. DA = abnormal discretionary accruals, REM = the combined values of the three REM models (ACFO*-1 + ADIE*-1 +APRC), FWO = Family ownership concentration, FSIZE = Firm size, LEV = Leverage, MKTB = Percentage of market value/book value, and ROA = Return on assets.

Table 3. Descriptive statistics for dichotomous variables

Variables	Measurement	Fre	Percentage (%)
COVID	“1” for the COVID-19 pandemic period (2020–2021)	274	40.00
	“0’’otherwise (2017–2019)	411	60.00

Table 4. Correlation matrix

Variables	(1)	(2)	(3)	(4)	(5)	(6)		(7)	(8)	VIF
(1) REM	1.000
(2) DA	0.119**	1.000
(3) COVID	0.10**	−0.14**	1.000							1.01
(4) FOW	−0.158***	0.121**	0.007	1.000						1.05
(5) FSIZE	−0.004	−0.101**	−0.017	0.049	1.000					1.47
(6) LEV	0.098	0.007	0.037	−0.144**	0.468***	1.000				1.48
(7) MKTB	−0.218**	0.134**	0.046	0.003	−0.068	0.075		1.000		1.04
(8) ROA	−0.320***	0.162***	−0.044	0.153**	0.196**	−0.165**		0.076	1.00	1.17

Note: *** p<0.01, ** p<0.05, * p<0.1

Table 5. COVID-19 pandemic, family ownership and earnings management by FGLS regression

Variable	DA	REM
COVID	−.004(−2.128) **	.018(2.796) ***
FOW	.009(2.199) **	−.037(−2.581) ***
FSIZE	−.009(−11.058) ***	−.006(−2.472) **
LEV	.015(3.276) ***	.034(2.415) **
MKTB	.005 (3.617) ***	−.021(−5.562) ***
ROA	−0.02 (−1.142)	−.429(−7.923) ***
Year fixed effects	Included	Included
Industry fixed effects	Included	Included
Observation	685	685
Wald chi2	176.695	143.761
Prob > chi2	0.000	0.000

Note: *** p<.01, ** p<.05, * p<.1

Table 6. Trade-off between DA and REM by FGLS Regression

Variable	REM
DA	0.118 (2.308) **
COVID	.012 (3.802) ***
FOW	−.033 (−3.129) ***
FSIZE	.004 (1.742) *
LEV	.034 (3.189) ***
MKTB	−.016 (−6.388) ***
ROA	−.475 (−12.588) ***
Year fixed effects	Included
Industry fixed effects	Included
Observation	685
Wald chi2	137.613
Prob > chi2	0.000

Note: *** p<.01, ** p<.05, * p<.1

Table 7. Alternative Proxies for DA and REM by FGLS regression

Variable	DA jones (1991)	DA Modified Jones (1995)	REM1	REM2
COVID	−.004(−2.258) **	−.004(−2.28) **	.004 (2.153) **	.016(4.177) ***
FOW	.009(2.111) **	.009(2.097) **	−.015(−2.933) ***	−.107(−7.72) ***
FSIZE	−.009(−10.917) ***	−.009 (−10.977) ***	.005(4.417) ***	−.002(.786) ***
LEV	.015(3.368) ***	.015(3.342) ***	.019(2.706) ***	−.007(−.678)
MKTB	.004(3.106) ***	.004(3.206) ***	−.01(−4.487) ***	−.029(− 8.215) ***
ROA	−.012 (−.705)	−.013(−.736)	−.256(−12.819) ***	−.516(−14.543) ***
Year fixed effects	Included	Included	Included	Included
Industry fixed effects	Included	Included	Included	Included
Obser	685	685	685	685
Wald chi2	167.282	171.541	266.569	560.180
Prob > chi2	0.000	0.000	0.000	0.000

Note: *** p < .01, ** p < .05, * p < .1

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