IFRS adoption and tax revenue performance in Africa: does Africa need tax-targeted IFRS reforms?

Figures & data

Table 1. Sample size selection

Countries	Number
African Countries	54
Non-IFRS Adopters	(34)
IFRS Adopters	21
Partial Adopters (Adopters with alternative standards or limit to some firms)	(13)
Full Adopters	8
Early Adopters	(2)
Sample Required	6

Source: Developed from Various Sources (Deloitte, 2021; Tawiah, 2019)

Table 2. Measurement of variables and Apriori

Variables	Measurement	Apriori
Dependent Variable
Tax Revenue Performance (TRP)	Tax Revenue to GDP Ratio	n/a
Independent Variable
IFRS Adoption (IFRS)	Binary score: 1 adopt else 0	-
Control Variables		n/a
Inflation (INF)	Consumer Price Index	-
Interest Rate (INT)	Average Banks’ Lending Rate	-
Exchange Rate (EXR)	Local Currency to USD	-

Source: Authors’ Construct: Developed from Varying Literature (Birungi, 2015; Hung, 2017; Mahzar and Meon, 2016; Reiss, 2015; Shivanda & Obwogi, 2018)

Table 3. Descriptive properties of the variables

Variables	TAXGDP	IFRS	INT	EXR	INF
Mean	19.35828	0.391026	18.17029	668.9441	10.66652
Median	19.37056	0.000000	16.89862	8.609181	8.214918
Maximum	38.40000	1.000000	47.00000	10026.52	72.83550
Minimum	3.372540	0.000000	6.288882	0.119914	−3.290000
Std. Dev.	9.120442	0.489552	7.396702	1738.405	9.626862
Observations	156	156	156	156	156

Source: Authors’ Estimation from Eviews 9.0 Package

Table 4. Correlation matrix

Variables	TAXGDP	IFRS	INT	EXR	INF
TAXGDP	1.0000
IFRS	−0.3616	1.0000
INT	−0.5005	−0.22753	1.0000
EXR	−0.3686	0.19002	0.22143	1.0000
INF	−0.30828	−0.18110	0.5380	0.0759	1.000

Source: Authors’ Estimation from Eviews 9.0 Package

Table 5. Results of stationarity analysis

Variables	ADF	PP	LLC	Order
TAXGDP	27.5214***	29.9791***		I(0)
IFRS	2.2714	2.3462		I(1)
INF	49.7452***	61.6067***		I(0)
INT	20.8993**	10.0689	−1.4095*	I(0)
EXR	1.4382	1.1616		I(1)

Source: Authors’ Estimation from Eviews 9.0 Package

Table 6. Pooled mean group and panel DOLS empirical estimation results

	PMG		Panel DOLS
Variable	Coefficient	Std. Errors	Coefficient	Std. Errors
Long Run Estimates
IFRS	−0.1253**	0.0688	−0.1341***	0.0527
EXR	0.1826***	0.0563	−0.1791***	0.0664
INF	−0.1394**	0.0653	−0.1421**	0.0402
INT	0.7552***	0.1594	0.7921***	0.1323
Short Run Estimates
COINTEQ01	−0.4164**	0.2145
D(IFRS)	0.1210**	0.0571
D (EXR)	0.3664	0.2657
D (INF)	0.0348**	0.0176
D (INT)	−0.7620*	0.4470
C	0.076228	0.3747

Notes: * (**) *** indicate significance at 10%, 5%, and 1%, respectively. D is the first difference operator

Source: Authors’ Estimation from Eviews 9.0 Package

Figure 1. Tax revenue performance trend (1996–2020).

Deloitte. (2021) Cyprus Tax News [ Accessed on 02/05/2023 from https://www2.deloitte.com/content/dam/Deloitte/cy/Documents/tax/taxalerts/CY_TaxAlerts_28_05_21EN_Noexp.pdf]

 Google Scholar

Tawiah, V. (2019). The state of IFRS in Africa. Journal of Financial Reporting and Accounting, 17(4), 635–649. https://doi.org/10.1108/JFRA-08-2018-0067

 Web of Science ®Google Scholar

Birungi, J. M. (2015). The effect of selected macroeconomic variables on government revenues in Rwanda (Doctoral dissertation, University of Nairobi).

 Google Scholar

Hung, F. S. (2017). Explaining the nonlinearity of inflation and economic growth: The role of tax evasion. International Review of Economics & Finance, 52, 436–445. https://doi.org/10.1016/j.iref.2017.03.008

 Web of Science ®Google Scholar

Reiss, M. (2015). Effects of fiscal policy on the real economy ( Doctoral dissertation, uniwien).

 Google Scholar

Shivanda, A. R., & Obwogi, J. (2018). Effect of macroeconomic variables on tax revenue in Kenya.

 Google Scholar