Taxation in Africa

1 Introduction

Information regarding tax revenues in African countries remains a complex subject, characterized by limited data availability and substantial publication delays. The OECD report of 2023 encompasses only 33 countries, with data extending up to the year 2021 (OECD 2023).¹ Some countries have data dating back to the 1990s, and the report provides internationally comparable figures for various taxes, particularly focusing on revenues relative to GDP.

Alternatively, the African Tax Administration Forum (ATAF) offers a databank containing tax data and certain macroeconomic variables (https://www.ataftax.org/), covering the years 2010 through 2022. Tax revenues and GDP are reported in PPP-adjusted US Dollars. In this data story, tax revenues are presented relative to GDP to facilitate comparability with OECD figures.

Given the comprehensive overview provided by the OECD (2023) for countries reporting to the organization, this data narrative emphasizes the comparison of the two distinct data sources and seeks to establish connections between the tax data and information from the Africa Monitor.

Data about tax revenues in African countries is still a challenging topic. Data availability is limited, and publication delays are relatively large. The OECD report from the year 2023 only covers 33 countries with data reaching to the year 2021 (OECD 2023). For some countries data reaches back in the 1990s. Revenues for many different taxes are reported in an internationally comparable manner. In the following, revenues relative to GDP are described.

Table 1: List of countries covered in different data banks

	Africa Monitor	OECD (Taxes)	ATAF (Taxes)
Algeria	DZA
Angola	AGO		AGO
Benin	BEN		BEN
Botswana	BWA	BWA	BWA
Burkina Faso	BFA	BFA	BFA
Burundi	BDI		BDI
Cameroon	CMR	CMR	CMR
Cape Verde	CPV	CPV	CPV
Central African Republic	CAF
Chad	TCD	TCD	TCD
Comoros	COM
Congo - Brazzaville	COG	COG	COG
Congo - Kinshasa	COD	COD	COD
Côte d’Ivoire	CIV	CIV	CIV
Djibouti	DJI
Egypt	EGY	EGY
Equatorial Guinea	GNQ	GNQ
Eritrea	ERI
Eswatini	SWZ	SWZ	SWZ
Ethiopia	ETH
Gabon	GAB	GAB	GAB
Gambia	GMB		GMB
Ghana	GHA	GHA	GHA
Guinea	GIN	GIN	GIN
Guinea-Bissau	GNB		GNB
Kenya	KEN	KEN	KEN
Lesotho	LSO	LSO	LSO
Liberia	LBR		LBR
Libya	LBY
Madagascar	MDG	MDG	MDG
Malawi	MWI	MWI	MWI
Mali	MLI	MLI	MLI
Mauritania	MRT	MRT	MRT
Mauritius	MUS	MUS	MUS
Morocco	MAR	MAR	MAR
Mozambique	MOZ		MOZ
Namibia	NAM	NAM	NAM
Niger	NER	NER	NER
Nigeria	NGA	NGA	NGA
Rwanda	RWA	RWA	RWA
Senegal	SEN	SEN	SEN
Seychelles	SYC	SYC	SYC
Sierra Leone	SLE	SLE	SLE
Somalia	SOM
South Africa	ZAF	ZAF	ZAF
South Sudan	SSD		SSD
Sudan	SDN		SDN
São Tomé & Príncipe	STP
Tanzania	TZA		TZA
Togo	TGO	TGO	TGO
Tunisia	TUN	TUN
Uganda	UGA	UGA	UGA
Western Sahara	ESH
Zambia	ZMB		ZMB
Zimbabwe	ZWE		ZWE
Number	55	33	42

1.1 Similarities and differences between OECD and ATAF data bank

The ATAF data bank, while offering a broader coverage of countries, is characterized by less detailed information, and tax revenues follow different definitions. Table 2 presents an average and a comparison of tax revenues relative to GDP for the years and countries covered by both data banks. Notably, the ATAF data bank provides two distinct items for VAT (domestic and imported), which are aggregated for direct comparison with figures from the OECD data bank.

Table 2: Comparison between data banks

	Total Tax	PIT	CIT	VAT
OECD	15.3	2.8	2.7	4.5
ATAF	14.9	3.0	2.6	5.7

Relative to GDP in %.

Source: ATAF, OECD, own calculation.

Results for total tax revenues, personal income tax (PIT) and corporate income tax (CIT) are quite similar. The discrepancy for value added tax (VAT) is already sizeable. Furthermore, differences in the magnitude and the dynamics in tax revenues can be quite sizeable for single countries, see Figure 1.

Figure 1: Total tax revenues according to both data banks in selected African countries

% relative to GDP.

Source: ATAF, OECD, own calculation.

For some countries and some taxes figures from both data banks are pretty close (ZAF) while for other countries substantial differences show up (CMR).

Figure 2: Personal income tax in selected countries and according to different data banks

% relative to GDP.

Source: ATAF, OECD, own calculation.

Overall, figures in both data banks show relatively strong deviations.

2 Reporting and non-reporting countries

The fact that not all African countries report tax revenues raises the question whether there are circumstances that foster the likelihood to report tax revenues. We do this analysis based on selected variables available in the Africa Monitor. A major result is significant difference in governance indicators for those countries reporting tax revenues to the data banks and those that do not. They are stronger in countries that report taxes to both organizations, OECD and ATAF. With respect to GDP per capita and demographics the results are mixed. It seems that richer and older societies tend to report to the OECD data bank. However, with respect to the ATAF data bank this relation does not hold. The main tendency of these results is valid for observations from the year 2021 (Table 3 and 4) as well as for the whole sample (Table 5 and 6).

Table 3: Differences between countries reporting tax revenues via the OECD and non-reporting countries in 2021

	OECD reporting				OECD non-reporting				Difference
Variable	Obs.	Median	Mean	Std. Dev.	Obs.	Median	Mean	Std. Dev.	Median	Mean	t-value
NGDPD	33	16.1	65.0	120.9	21	7.6	28.7	41.6	8.5	36.3	1.6
NY_GDP_PCAP_KD	33	1610	2928	3345	19	1214	1693	2011	396	1235	1.6
FITB_PA_A	15	4.6	5.3	3.8	5	12.8	11.8	8.4	-8.2	-6.5	-1.7
LUR_PT_A	6	12.3	14.1	7.8	4	5.5	7.7	5.5	6.8	6.4	1.5
GGR_NGDP	33	19.4	20.7	7.8	21	20.1	22.7	12.3	-0.6	-2.0	-0.7
GGX_NGDP	33	25.7	25.6	8.7	21	23.5	24.9	10.3	2.2	0.7	0.3
GGXCNL_NGDP	33	-5.9	-4.9	3.3	21	-3.1	-2.2	4.5	-2.8	-2.7	-2.4
GGXONLB_NGDP	33	-3.6	-2.5	2.5	21	-1.6	-0.7	4.4	-2.0	-1.8	-1.7
GGXWDG_NGDP	33	63.7	63.0	23.8	19	66.6	78.0	42.1	-2.8	-15.1	-1.4
BCA_NGDPD	33	-4.9	-5.3	5.9	21	-3.2	-3.8	10.3	-1.7	-1.5	-0.6
WGI_PC1	33	-1.5	-1.2	1.5	21	-2.2	-2.6	1.3	0.8	1.4	3.7
GE_EST	33	-0.6	-0.5	0.6	21	-1.2	-1.2	0.6	0.6	0.7	4.0
RL_EST	33	-0.4	-0.4	0.6	21	-1.0	-1.1	0.5	0.6	0.7	4.6
RQ_EST	33	-0.6	-0.5	0.6	21	-1.0	-1.2	0.5	0.4	0.6	4.3
VA_EST	33	-0.5	-0.4	0.7	21	-1.0	-0.9	0.6	0.5	0.5	2.6
IEF	33	57.3	58.0	6.0	18	51.7	51.6	6.4	5.7	6.4	3.5
IEF_GI	33	32.2	33.8	12.9	18	27.0	27.3	6.1	5.2	6.6	2.5
IEF_TB	33	77.1	76.1	8.9	18	77.0	76.4	8.6	0.1	-0.3	-0.1
SP_POP_0014_TO_ZS	33	39.4	37.7	7.7	21	41.1	41.0	5.2	-1.7	-3.3	-1.9
SP_POP_65UP_TO_ZS	33	3.2	4.0	2.2	21	3.1	3.3	1.0	0.0	0.7	1.5
SP_URB_TOTL_IN_ZS	33	46.2	47.3	18.7	21	44.6	47.1	19.6	1.6	0.2	0.0
DT_ODA_ODAT_GN_ZS	32	3.5	4.7	4.1	19	10.1	10.3	8.7	-6.7	-5.6	-2.6
DT_ODA_ODAT_PC_ZS	32	57.3	67.2	52.5	21	70.3	91.9	74.6	-13.0	-24.7	-1.3

The variable description can be found in Table A 1.

Source: Africa Monitor, OECD, own calculations.

Table 4: Differences between countries reporting tax revenues via the ATAF and non-reporting countries in 2021

	ATAF reporting				ATAF non-reporting				Difference
Variable	Obs.	Median	Mean	Std. Dev.	Obs.	Median	Mean	Std. Dev.	Median	Mean	t-value
NGDPD	36	16.7	48.3	97.7	18	12.5	55.9	104.1	4.2	-7.6	-0.5
NY_GDP_PCAP_KD	36	1236	2403	3241	16	1651	2642	2323	-415	-239	-0.3
FITB_PA_A	17	4.6	6.0	5.0	3	12.7	12.0	8.7	-8.1	-6.0	-5.0
LUR_PT_A	6	14.0	14.3	8.3	4	6.7	7.4	3.6	7.3	6.9	2.0
GGR_NGDP	36	18.8	20.4	7.8	18	22.1	23.7	12.6	-3.3	-3.3	-2.5
GGX_NGDP	36	24.9	25.4	8.5	18	24.7	25.4	10.8	0.2	0.0	0.0
GGXCNL_NGDP	36	-5.7	-5.0	2.9	18	-2.7	-1.7	5.0	-3.0	-3.3	-6.7
GGXONLB_NGDP	36	-2.9	-2.5	2.5	18	-1.7	-0.4	4.6	-1.2	-2.1	-5.1
GGXWDG_NGDP	36	66.2	68.8	31.3	16	58.0	67.6	35.0	8.2	1.3	0.2
BCA_NGDPD	36	-4.9	-5.9	7.0	18	-3.0	-2.4	9.1	-1.9	-3.5	-3.0
WGI_PC1	36	-1.4	-1.3	1.5	18	-2.6	-2.7	1.4	1.1	1.4	5.8
GE_EST	36	-0.7	-0.6	0.6	18	-1.0	-1.1	0.7	0.3	0.5	4.7
RL_EST	36	-0.6	-0.5	0.6	18	-1.1	-1.1	0.6	0.5	0.5	5.3
RQ_EST	36	-0.6	-0.6	0.6	18	-1.1	-1.2	0.6	0.5	0.6	6.6
VA_EST	36	-0.4	-0.4	0.7	18	-1.1	-1.1	0.6	0.8	0.7	6.2
IEF	36	57.4	56.6	7.3	15	55.7	53.6	4.9	1.7	3.0	2.5
IEF_GI	36	31.2	32.8	12.3	15	23.6	28.3	8.2	7.6	4.5	2.2
IEF_TB	36	77.7	77.1	9.1	15	74.0	74.1	7.5	3.7	3.0	1.9
SP_POP_0014_TO_ZS	36	41.0	39.4	6.9	18	39.9	38.0	7.2	1.2	1.4	1.2
SP_POP_65UP_TO_ZS	36	3.1	3.5	1.9	18	3.6	4.1	1.8	-0.5	-0.6	-1.9
SP_URB_TOTL_IN_ZS	36	44.0	44.0	17.7	18	51.4	53.8	19.8	-7.4	-9.9	-3.3
DT_ODA_ODAT_GN_ZS	35	5.3	6.7	5.5	16	3.9	7.1	9.1	1.4	-0.4	-0.4
DT_ODA_ODAT_PC_ZS	35	59.3	69.6	50.1	18	70.1	91.4	81.7	-10.8	-21.8	-2.6

The variable description can be found in Table A 1.

Source: Africa Monitor, ATAF, own calculations.

Table 5: Differences between countries reporting tax revenues via the OECD and non-reporting countries in 2010-2021

	OECD reporting				OECD non-reporting				Difference
Variable	Obs.	Median	Mean	Std. Dev.	Obs.	Median	Mean	Std. Dev.	Median	Mean	t-value
NGDPD	396	13.7	55.2	108.5	250	7.6	29.6	45.0	6.1	25.7	4.2
NY_GDP_PCAP_KD	396	1556	2935	3309	230	1072	1716	2102	484	1218	5.6
FITB_PA_A	197	6.7	8.1	5.4	76	12.8	10.1	6.6	-6.1	-2.0	-2.3
LUR_PT_A	151	7.8	9.8	7.0	58	5.5	7.3	5.6	2.2	2.5	2.7
GGR_NGDP	396	19.5	21.4	8.9	248	20.1	21.8	10.4	-0.6	-0.4	-0.5
GGX_NGDP	396	23.1	25.0	9.2	248	23.5	25.4	11.2	-0.3	-0.4	-0.4
GGXCNL_NGDP	396	-3.6	-3.6	4.1	248	-3.1	-3.6	5.8	-0.5	0.0	0.0
GGXONLB_NGDP	396	-1.9	-1.9	3.8	248	-1.6	-2.5	5.8	-0.3	0.7	1.6
GGXWDG_NGDP	396	42.5	46.1	23.3	227	66.6	61.1	44.1	-24.1	-15.0	-4.8
BCA_NGDPD	396	-5.5	-6.2	7.9	248	-3.2	-5.5	12.2	-2.3	-0.7	-0.9
WGI_PC1	396	-1.4	-1.2	-1.4	251	-2.2	-2.6	1.3	0.8	1.4	12.7
GE_EST	396	-0.6	-0.6	-0.6	251	-1.2	-1.2	0.5	0.6	0.6	13.7
RL_EST	396	-0.5	-0.5	-0.6	251	-1.0	-1.1	0.5	0.6	0.6	14.4
RQ_EST	396	-0.5	-0.5	-0.6	251	-1.0	-1.2	0.5	0.5	0.7	15.2
VA_EST	396	-0.4	-0.4	0.7	251	-1.0	-1.0	0.7	0.6	0.5	9.2
IEF	396	56.5	56.6	7.1	212	51.7	50.5	6.8	4.9	6.1	10.3
IEF_GI	396	31.0	33.0	11.3	212	27.0	27.2	6.1	4.0	5.9	8.3
IEF_TB	396	75.7	74.7	9.3	212	77.0	76.0	8.1	-1.3	-1.2	-1.7
SP_POP_0014_TO_ZS	396	41.1	38.8	7.6	252	41.1	42.0	5.0	0.0	-3.2	-6.5
SP_POP_65UP_TO_ZS	396	3.2	3.8	1.7	252	3.1	3.2	0.9	0.0	0.6	5.8
SP_URB_TOTL_IN_ZS	396	43.4	44.8	18.0	252	44.6	44.7	18.9	-1.3	0.1	0.1
DT_ODA_ODAT_GN_ZS	392	3.9	5.2	4.9	232	10.1	10.3	9.9	-6.2	-5.1	-7.4
DT_ODA_ODAT_PC_ZS	392	53.3	69.0	73.7	251	70.3	82.0	69.2	-16.9	-13.0	-2.3

The variable description can be found in Table A 1.

Annual data.

Source: Africa Monitor, ATAF, own calculations.

Table 6: Differences between countries reporting tax revenues via the ATAF and non-reporting countries in 2010-2021

	ATAF reporting				ATAF non-reporting				Difference
Variable	Obs.	Median	Mean	Std. Dev.	Obs.	Median	Mean	Std. Dev.	Median	Mean	t-value
NGDP	402	13.8	48.3	97.7	263	12.5	55.9	104.1	1.4	1.4	0.2
NY_GDP_PCAP_KD	402	1112	2316	3068	224	1694	2795	2799	-581	-480	-1.9
FITB_PA_A	227	7.2	6.0	5.0	49	12.7	12.0	8.7	-5.5	0.3	0.3
LUR_PT_A	154	5.5	14.3	8.3	55	6.7	7.4	3.6	-1.1	-3.4	-3.9
GGR_NGDP	402	18.2	20.4	7.8	261	22.1	23.7	12.6	-3.8	-1.9	-2.4
GGX_NGDP	402	23.0	25.4	8.5	261	24.7	25.4	10.8	-1.8	-1.2	-1.4
GGXCNL_NGDP	402	-3.7	-5.0	2.9	261	-2.7	-1.7	5.0	-0.9	-0.7	-1.6
GGXONLB_NGDP	402	-2.0	-2.5	2.5	261	-1.7	-0.4	4.6	-0.3	0.0	0.0
GGXWDG_NGDP	402	41.4	68.8	31.3	238	58.0	67.6	35.0	-16.6	-11.0	-3.6
BCA_NGDPD	402	-5.4	-5.9	7.0	261	-3.0	-2.4	9.1	-2.4	-2.5	-3.0
WGI_PC1	402	-1.4	-1.3	1.5	245	-2.6	-2.7	1.4	1.2	1.4	13.1
GE_EST	402	-0.7	-0.6	0.6	245	-1.0	-1.1	0.7	0.3	0.5	10.4
RL_EST	402	-0.5	-0.5	0.6	245	-1.1	-1.1	0.6	0.5	0.5	11.7
RQ_EST	402	-0.5	-0.6	0.6	245	-1.1	-1.2	0.6	0.6	0.7	14.8
VA_EST	402	-0.3	-0.4	0.7	245	-1.1	-1.1	0.6	0.9	0.7	14.1
IEF	402	56.6	56.6	7.3	222	55.7	53.6	4.9	0.8	5.5	9.9
IEF_GI	402	30.9	32.8	12.3	222	23.6	28.3	8.2	7.3	5.6	7.5
IEF_TB	402	76.5	77.1	9.1	222	74.0	74.1	7.5	2.5	1.3	1.7
SP_POP_0014_TO_ZS	402	42.9	39.4	6.9	246	39.9	38.0	7.2	3.0	1.5	2.6
SP_POP_65UP_TO_ZS	402	3.1	3.5	1.9	246	3.6	4.1	1.8	-0.5	-0.4	-3.0
SP_URB_TOTL_IN_ZS	402	41.6	44.0	17.7	246	51.4	53.8	19.8	-9.9	-9.2	-6.0
DT_ODA_ODAT_GN_ZS	398	5.4	6.7	5.5	226	3.9	7.1	9.1	1.5	0.6	0.9
DT_ODA_ODAT_PC_ZS	398	58.0	69.6	50.1	245	70.1	91.4	81.7	-12.0	-1.9	-0.3

The variable description can be found in Table A 1.

Annual data.

Source: Africa Monitor, ATAF, own calculations.

In the following, the results will be based on the OECD data bank.

3 Comparison with tax revenues in OECD countries

Roughly half of the countries exhibit tax revenues of less than 15% relative to GDP, while South Africa, Morocco, Tunisia, and the Seychelles surpass the 20% mark. The overall landscape of taxation across African countries is notably heterogeneous. However, certain countries show patterns akin to the OECD average, particularly evident in the personal income tax (1100) for Lesotho, Namibia, and South Africa.

In African nations, the value-added tax (5111) tends to be lower than in the OECD, although some countries reach comparable levels. Despite its lower position, it remains a relatively significant tax for many African countries. Social security contributions (2000) yield weak revenues across the board, with only Tunisia and Morocco boasting substantial income from this source.

Across almost all tax categories, revenues relative to GDP in Africa are lower than those in the OECD. Notably, taxes on corporate income (1200) are on par with OECD levels. Customs and import duties (5123) are even higher in many African countries and play a more significant role relative to total tax revenues compared to the OECD.

Interestingly, for some countries, tax revenues are not the primary source of government income; international grants hold more prominence.

Figure 8: Tax revenues in African Countries in comparison to OECD figures (in %)

Relative to GDP in per cent. Average for the period 2010 to 2021.

Source: OECD, own calculations.

4 Tax revenue trends

There is a moderately increasing trend of tax revenues relative to GDP over the whole period. In the first half of the 2010s the share of PIT and VAT increased. In sum, there is less dynamics in recent years compared to the first half of the 2010s (Figure 4).

Figure 4: Tax revenues relative to GDP for all reporting African countries (in %)

PIT: Personal income tax; CIT: Corporate income tax; VAT: values added tax; Other taxes: total taxes minus PIT, CIT, and VAT.

Source: OECD, own calculations.

The increasing trend holds for many countries. Only in few African countries the tax revenues relative to GDP were substantially smaller in 2021 compared to the year 2010 or the average between 2010 and 2020. Examples are Equatorial Guinea and Gabon (Figure 5).

Figure 5: Total tax revenues relative to GDP (in %)

Source: OECD, own calculations.

Recently, some countries showed up with substantially higher relative tax revenues from direct taxes. However, the upward trend does not hold for all.

Figure 6: Direct tax revenues (PIT+CIT) relative to GDP (in %)

Source: OECD, own calculations.

Results for the value added tax are even more divers (Figure 7). The Seychelles exhibit strong increases, while in Cape Verde or Niger there are downward tendencies.

Figure 7: Value added tax revenues relative to GDP (in %)

Source: OECD, own calculations.

In sum, the overall upward trend is not substantial. Tax revenues do not converge to the level of taxes in advanced economies at a fast path. For more details on see also OECD (2023).

5 Which tax under which circumstances?

To get a first impression about the roots of the heterogeneity in African taxation patterns the correlation between tax revenues and other economic variables are calculated. Strongest correlations can be found between indicators measuring the quality of governance and overall tax revenues. On the other hand, there is negative correlation between the share of young people and tax revenues. The cross-sectional correlation between GDP per capita and tax revenues is quite strong for some taxes, but not overwhelming.

Table 7: Correlation between tax revenues rel. to GDP and selected variables of the Africa Monitor (country specific means)

	1000	1100	1200	2000	3000	4000	5000	5111	5121	5123	5124	5200	Total Tax
NGDPD	-0.43	0.33	-0.34	0.21	0.06	0.29	0.30	0.43	0.30	0.18	0.43	-0.21	0.47
NY_GDP_PCAP_KD	0.48	0.22	0.64	0.06	0.07	0.35	0.35	0.24	0.53	0.28	0.24	0.55	0.49
FITB_PA_A	-0.29	-0.29	-0.19	-0.12	-0.24	0.49	-0.49	-0.63	-0.16	0.08	-0.63	0.40	-0.49
LUR_PT_A	0.70	0.69	0.41	0.10	-0.10	0.28	-0.01	0.35	-0.05	0.35	0.35	0.08	0.41
GGR_NGDP	0.72	0.70	0.38	0.03	0.17	0.34	0.28	0.55	0.18	0.35	0.55	0.18	0.54
GGX_NGDP	0.77	0.76	0.39	0.07	0.21	0.37	0.29	0.59	0.22	0.38	0.59	-0.15	0.58
GGXCNL_NGDP	-0.25	-0.29	-0.08	-0.15	-0.16	-0.13	-0.04	-0.17	-0.18	0.14	-0.17	0.11	-0.21
GGXONLB_NGDP	-0.11	-0.21	-0.13	-0.05	-0.13	-0.04	-0.14	-0.10	-0.11	0.16	-0.10	0.25	-0.01
GGXWDG_NGDP	0.12	0.18	-0.12	0.21	-0.07	0.48	0.54	-0.43	0.41	0.40	-0.43	0.18	0.44
BCA_NGDPD	-0.04	-0.06	-0.03	0.02	-0.22	0.00	-0.30	0.14	0.27	-0.31	0.14	0.08	-0.11
GE_EST	0.57	-0.52	0.28	-0.11	0.12	0.50	0.61	0.66	0.64	0.13	0.66	0.56	0.72
RL_EST	0.53	0.55	-0.17	0.06	0.18	-0.46	0.54	0.68	0.50	0.11	0.68	0.44	0.64
RQ_EST	0.45	-0.46	0.08	-0.03	0.08	-0.48	0.54	0.64	-0.51	-0.13	0.64	0.52	0.60
VA_EST	0.38	0.48	-0.07	0.10	0.05	0.47	0.53	0.63	0.37	0.08	0.63	0.30	0.51
IEF	-0.25	0.22	0.00	0.00	0.05	-0.36	0.41	0.48	0.39	-0.10	0.48	0.47	-0.41
IEF_GI	0.57	0.57	-0.24	0.06	0.14	0.45	0.55	0.68	-0.48	0.14	0.68	0.43	0.66
IEF_TB	0.29	0.28	0.24	0.09	0.09	0.09	0.05	0.09	0.26	0.00	0.09	0.32	0.12
IC_BUS_EASE_DFRN_XQ_DB1719	0.57	0.52	0.33	0.24	-0.06	0.45	0.50	0.57	0.58	-0.21	0.57	0.44	0.67
SP_POP_0014_TO_ZS	-0.58	-0.49	-0.42	-0.39	0.07	-0.64	-0.57	-0.61	-0.67	-0.22	-0.61	-0.51	-0.77
SP_POP_65UP_TO_ZS	0.42	0.37	0.36	0.45	0.05	0.65	0.61	0.59	0.71	-0.17	0.59	0.58	0.74
SP_URB_TOTL_IN_ZS	0.19	0.01	0.29	0.28	0.16	-0.32	0.07	0.08	0.02	0.03	0.08	0.04	0.25
DT_ODA_ODAT_GN_ZS	-0.32	-0.06	-0.48	-0.23	-0.14	0.24	-0.01	-0.03	-0.05	0.35	-0.03	0.22	-0.26
DT_ODA_ODAT_PC_ZS	-0.19	-0.26	0.08	0.09	-0.14	0.33	-0.61	-0.45	-0.40	0.46	-0.45	0.30	-0.44

The variable descriptions can be found in Table A1 and Table A2 (taxes). Red marking hints to correlations with absolute values above 0.5.

Source: Africa Monitor, OECD, own calculations.

Table 8: Number of observations corresponding to the results in Table 7

	1000	1100	1200	2000	3000	4000	5000	5111	5121	5123	5124	5200	Total Tax
NGDPD	33	32	32	28	33	29	33	33	33	33	33	33	33
NY_GDP_PCAP_KD	33	32	32	28	33	29	33	33	33	33	33	33	33
FITB_PA_A	17	17	17	16	17	13	17	17	17	17	17	17	17
LUR_PT_A	32	31	31	27	32	27	32	32	32	32	32	32	32
GGR_NGDP	33	32	32	28	33	29	33	33	33	33	33	33	33
GGX_NGDP	33	32	32	28	33	29	33	33	33	33	33	33	33
GGXCNL_NGDP	33	32	32	28	33	29	33	33	33	33	33	33	33
GGXONLB_NGDP	33	32	32	28	33	29	33	33	33	33	33	33	33
GGXWDG_NGDP	33	32	32	28	33	29	33	33	33	33	33	33	33
BCA_NGDPD	33	32	32	28	33	29	33	33	33	33	33	33	33
GE_EST	33	32	32	28	33	29	33	33	33	33	33	33	33
RL_EST	33	32	32	28	33	29	33	33	33	33	33	33	33
RQ_EST	33	32	32	28	33	29	33	33	33	33	33	33	33
VA_EST	33	32	32	28	33	29	33	33	33	33	33	33	33
IEF	33	32	32	28	33	29	33	33	33	33	33	33	33
IEF_GI	33	32	32	28	33	29	33	33	33	33	33	33	33
IEF_TB	33	32	32	28	33	29	33	33	33	33	33	33	33
IC_BUS_EASE_DFRN_XQ_DB1719	33	32	32	28	33	28	33	33	33	33	33	33	33
SP_POP_0014_TO_ZS	33	32	32	28	33	29	33	33	33	33	33	33	33
SP_POP_65UP_TO_ZS	33	32	32	28	33	29	33	33	33	33	33	33	33
SP_URB_TOTL_IN_ZS	33	32	32	28	33	29	33	33	33	33	33	33	33
DT_ODA_ODAT_GN_ZS	33	32	32	28	33	29	33	33	33	33	33	33	33
DT_ODA_ODAT_PC_ZS	33	32	32	28	33	29	33	33	33	33	33	33	33

The variable descriptions can be found in Table A1 and Table A2 (taxes).

Source: Africa Monitor, OECD, own calculations

6 Tax revenues and the business cycle

An initial assessment of the impact of business cycle fluctuations on tax revenues involves examining the correlation between real GDP growth and changes in tax revenues relative to GDP. If tax revenues are directly proportional to GDP, the corresponding correlation will be zero. A positive correlation may result from a progressive tax system (acting as an automatic stabilizer) or an active fiscal policy. Conversely, a negative correlation could stem from reliance on taxes less tied to economic activity or during periods of fiscal stress when austerity measures are implemented by the government.

For various countries, the correlation is notably positive, indicating progressive taxation and stabilizing fiscal policies (see Table 9). Particularly noteworthy is the case of Botswana, where a 1-percentage-point increase in GDP corresponds to a 0.2-percentage-point rise in the tax share (refer to Figure 9). A more substantial positive correlation is evident in South Africa, influenced in part by the impact of the COVID-19 outbreak. In contrast, Sierra Leone exhibits a markedly negative correlation, primarily attributed to data from the years of the Ebola outbreak.

These examples highlight that the relationship between economic fluctuations and taxation can be influenced by specific events. A more in-depth, country-specific analysis is warranted.

Table 9: Correlation between real GDP growth and tax revenues relative to GDP (2011-2021)

	1000	1100	1200	2000	3000	4000	5000	5111	5121	5123	5124	5200	Total Tax
BFA	0.84	0.00	0.79	-0.04	–	0.15	0.12	0.26	0.17	0.72	0.26	-0.36	0.49
BWA	0.81	–	–	–	–	0.20	0.19	-0.05	0.49	–	-0.05	0.02	0.84
CIV	-0.40	–	-0.39	0.72	0.24	0.46	0.75	0.74	0.38	0.59	0.74	0.14	0.56
CMR	0.19	0.38	0.08	-0.22	0.01	0.07	0.51	0.56	-0.36	0.42	0.56	0.53	0.57
COD	0.64	0.35	0.57	-0.03	0.18	-0.18	-0.13	-0.04	0.32	0.55	-0.04	0.31	0.29
COG	0.05	0.00	0.09	–	-0.19	0.27	-0.07	0.01	-0.26	0.10	0.01	–	0.03
CPV	0.02	-0.56	0.38	–	–	0.00	0.17	0.02	0.10	0.02	0.02	–	0.11
EGY	-0.08	0.10	-0.14	0.04	–	–	0.55	0.56	0.21	0.49	0.56	-0.30	0.31
GAB	0.52	0.42	0.49	-0.20	–	0.01	0.15	0.28	0.20	-0.09	0.28	0.33	0.49
GHA	0.58	0.40	0.51	-0.09	–	–	-0.26	0.06	-0.43	0.23	0.06	0.10	0.09
GIN	0.03	0.48	-0.11	–	0.24	0.07	0.07	0.14	-0.25	-0.05	0.14	–	0.09
GNQ	0.23	-0.25	0.26	-0.13	–	–	-0.40	-0.37	-0.08	-0.23	-0.37	0.71	0.22
KEN	-0.53	-0.17	-0.24	0.32	–	–	0.46	0.39	0.35	0.38	0.39	0.23	-0.01
LSO	0.30	0.25	0.13	–	–	–	0.01	0.24	-0.29	–	0.24	–	0.30
MAR	-0.48	-0.17	-0.47	-0.88	–	0.24	0.22	0.15	-0.14	-0.19	0.15	-0.06	-0.45
MDG	0.54	-0.02	0.56	-0.41	–	–	0.76	0.62	0.06	0.59	0.62	0.07	0.74
MLI	-0.24	-0.06	-0.24	-0.03	-0.12	0.19	0.17	0.11	-0.03	0.25	0.11	0.25	0.05
MRT	-0.19	0.01	-0.27	0.14	–	-0.12	0.38	0.49	-0.16	0.34	0.49	-0.15	0.21
MUS	-0.30	-0.43	-0.02	-0.11	-0.04	-0.01	0.42	0.02	0.53	-0.14	0.02	0.41	0.18
MWI	0.52	0.56	0.16	–	–	–	0.58	0.49	0.33	0.52	0.49	–	0.57
NAM	0.07	-0.07	0.09	-0.34	–	0.01	0.42	0.43	–	-0.26	0.43	-0.07	0.50
NER	0.04	-0.03	0.01	-0.20	0.00	0.02	-0.04	0.11	-0.34	-0.51	0.11	-0.30	-0.03
NGA	0.19	-0.17	0.19	0.15	–	–	0.00	-0.37	-0.27	0.11	-0.37	-0.47	0.18
RWA	0.05	0.23	-0.07	-0.37	–	-0.88	0.51	0.41	0.31	0.41	0.41	0.10	0.34
SEN	0.37	0.07	0.25	0.36	-0.24	0.13	0.25	0.40	0.07	0.21	0.40	-0.19	0.32
SLE	-0.07	0.04	-0.19	–	–	–	-0.44	-0.73	0.17	-0.57	-0.73	–	-0.50
SWZ	-0.11	-0.37	0.09	-0.36	–	0.39	-0.13	0.27	0.22	–	0.27	0.03	-0.24
SYC	0.10	-0.09	0.24	-0.22	–	-0.16	0.30	0.34	0.03	-0.01	0.34	0.01	0.15
TCD	-0.39	0.40	-0.43	–	0.35	0.30	-0.27	-0.40	-0.16	-0.11	-0.40	–	-0.37
TGO	0.37	0.09	0.41	–	0.39	–	0.30	0.36	-0.29	0.13	0.36	–	0.34
TUN	-0.05	-0.23	0.06	-0.76	0.00	–	0.40	0.44	-0.07	0.18	0.44	0.02	-0.08
UGA	0.54	0.07	0.36	–	–	–	0.60	0.40	0.27	0.46	0.40	0.46	0.64
ZAF	0.82	0.35	0.60	0.89	0.90	-0.35	0.57	0.12	0.69	0.73	0.12	0.44	0.87

Figure 9: GDP growth rates and the change in tax revenues

 

GDP: real GDP growth rates in percent. Total Tax: change in tax revenues relative to GDP in percentage points.

7 Appendix: Variable lists

Table A1: Variables used in this data story taken from the Africa Monitor.

NGDPD	Gross Domestic Product, Current Prices: U.S. Dollars (Billions)
NY_GDP_PCAP_KD	GDP per Capita (Constant 2015 US$)
NGDP_RPCH	Gross Domestic Product, Constant Prices: Percent Change
FITB_PA_A	Government Securities, Treasury Bill Rate: Percent per Annum
LUR_PT_A	Unemployment Rate: Percent
GGR_NGDP	General Government Revenue: Percent of GDP
GGX_NGDP	General Government Total Expenditure: Percent of GDP
GGXCNL_NGDP	General Government Net Lending/Borrowing: Percent of GDP
GGXONLB_NGDP	General Government Primary Net Lending/Borrowing: Percent of GDP
GGXWDG_NGDP	General Government Gross Debt: Percent of GDP
BCA_NGDPD	Current Account Balance (WEO): Percent of GDP
WGI_PC1	First Principal Component of 6 WGI Indicator Estimates
GE_EST	Government Effectiveness: Estimate (-2.5 - 2.5)
RL_EST	Rule of Law: Estimate (-2.5 - 2.5)
RQ_EST	Regulatory Quality: Estimate (-2.5 - 2.5)
VA_EST	Voice and Accountability: Estimate (-2.5 - 2.5)
IEF	Index of Economic Freedom: Overall Score (0-100)
IEF_GI	Index of Economic Freedom: Government Integrity (0-100)
IEF_TB	Index of Economic Freedom: Tax Burden (0-100)
IC_BUS_EASE_DFRN_XQ_DB1719	Ease of Doing Business: Score (0-100)
SP_POP_0014_TO_ZS	Population Ages 0-14 (% of Total Population)
SP_POP_65UP_TO_ZS	Population Ages 65 and above (% of Total Population)
SP_URB_TOTL_IN_ZS	Urban Population (% of Total Population)
DT_ODA_ODAT_GN_ZS	Net ODA Received (% of GNI)
DT_ODA_ODAT_PC_ZS	Net ODA Received per Capita (Current US$)

Table A2: Numbering of different taxes according to the OECD data set

1000	Taxes on income, profits and capital gains
1100	Taxes on income, profits and capital gains of individuals
1200	Taxes on income, profits and capital gains of corporates
2000	Social security contributions (SSC)
3000	Taxes on payroll and workforce
4000	Taxes on property
5000	Taxes on goods and services
5111	Value added taxes
5121	Excises
5123	Customs and import duties
5124	Taxes on exports
5200	Taxes on use of goods and perform activities

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1. OECD (2023). Revenue Statistics in Africa. Via internet: https://www.oecd-ilibrary.org/taxation/revenue-statistics-in-africa-2023_15bc5bc6-en-fr↩︎