Corruption, Taxation of Natural Resources and Sustainable Development in Africa

Abstract

African nations grapple with balancing socio-economic growth and environmental preservation amid resource extraction. Taxation is advocated for revenue and environmental control. However, corruption poses a significant hurdle among duty bearers and citizens. Using a multidimensional regression strategy, this research explores corruption’s interplay with taxation policies and their impact on African sustainable development. The findings stress anti-corruption measures’ critical role in enabling effective taxation strategies for sustainable resource management across diverse African nations. Firstly, they indicate that robust anti-corruption measures significantly shape successful taxation policies for sustainable resource management. Moreso, the study underscores a strong link between reduced corruption, enhanced taxation strategies, and improved prospects for sustainable development across Africa’s sectors. Finally, it highlights the urgent need for cohesive policies and robust enforcement mechanisms to drive sustainable development in Africa’s natural resource sector.

Appendices

Appendix 1: Description of Variables

Number	Variable type	Variable name	Abbreviation	Description	References	Source
1	Dependent	Proxy for Sustainable development	SD	Sustainable development index	Badur et al. (2023), Nyantakyi et al. (2023), Quayson et al. (2023), Arora and Mishra (2019)	Sustainable development index (Retrieved from https://www.sustainabledevelopmentindex.org/)
2	Dependent and Independent	Tax	TAX	Sum of Corporate income tax and Royalty on gross turnover	Ajeigbe et al. (2023)	Taxation of mining industries (Retrieved from https://fiscalite-miniere.ferdi.fr/en/download/tax-systems)
3	Control	Corruption score	CS	Corruption score	Badur et al. (2023), Hope (2022), Rubusundram and Rasiah (2019), Kalkuhl et al. (2018)	Transparency International
4	Control	Trade openness	TOS	Sum of imports and exports of goods and services as a percentage of the country’s GDP divided by two	Badur et al. (2023), Tiba and Belaid (2021), Asongu et al. (2021)	World Development Index
5	Control	Urban population	URP	People living in urban areas as a percentage of the entire country’s population	Hove et al. (2018), Ogwu (2019)	World Development Index
6	Control	Population growth rate	PG	Population growth rate	Arora and Mishra (2019), Ogwu (2019)	World Development Index
7	Control	GDP per capita growth	GDP	Annual percentage growth rate of GDP per capita	Hope (2022), Halim and Rahman (2022), Ajeigbe et al. (2023), Asongu et al.(2021)	World Development Index
8	Control	Political Stability and Absence of Violence/Terrorism	PSA	Perceptions of the likelihood of political instability and/or politically motivated violence, including terrorism	Kunawotor et al. (2020), Appiah-Kubi et al. (2021)	World Development Index
9	Control	Human capital index (HCI)	HCI	Contributions of health and education to worker productivity ranges from 0 to 1	Fhima et al. (2023), Oyinlola and Adedeji (2022)	World Development Index
10	Control	Gross fixed capital formation	GFCF	Gross outlays by the private sector and its fixed domestic assets as a percentage of GDP	Tiba and Belaid (2021), Christoforidis et al. (2021)	World Development Index
11	Control	Government spending	GS	Gross national expenditure is the sum of household final consumption expenditure, general government final consumption expenditure, and gross capital formation	Cockx and Francken (2016)	World Development Index
12	Control	Unemployment	UEM	Unemployment refers to the share of the labor force that is without work but available for and seeking employment. Definitions of labor force and unemployment differ by country	Akinyele et al. (2023), Carraro et al. (1996)	World Development Index
13	Control	Institutional quality	INS	Principal component analysis of the six institutional quality measures (control of corruption, government effectiveness, political stability and absence of violence, regulatory quality, rule of law, voice and accountability)	Botlhole et al. (2012)	World Governance Index
14	Control	Human capital index (HCI)	HCI	Contributions of health and education to worker productivity ranges from 0 to 1	Fhima et al. (2023), Oyinlola and Adedej, (2022)	World Development Index
15	Control	Gross fixed capital formation	GFCF	Gross outlays by the private sector and its fixed domestic assets as a percentage of GDP	Tiba and Belaid (2021), Christoforidis et al. (2021)	World Development Index

Preliminary Diagnostic Checks

In this section, we discuss the outcome of the diagnostic test conducted and the determination of the correlation matrix prior to the test for the presence of cross-sectional dependence. For panel models, the absence of multicollinearity is relevant in obtaining reliable estimates. In Table 4, we find no evidence of multicollinearity in all the variables used for estimating the model. The variance inflation factor estimates in Appendix 2 support the absence of multicollinearity in the data. From Appendix 2 Wooldridge’s (2002) test for autocorrelation and the Breusch-Pagan/Cook-Weisberg test for heteroskedasticity show evidence of serial correlation and heteroskedasticity, respectively. However, the Pesaran cross-sectional dependence test in the residuals shows no evidence of temporal and spatial dependence in the cross-sectional residuals. Nonetheless, the series exhibits strong cross-sectional dependence. In the absence of endogeneity, the Hausmann (1978) test shows that a fixed effect model is appropriate for the panel data. To account for the cross-sectional dependence in the series, we apply the Driscoll-Kraay standard error correctional model. Hoechle (2007) has argued that temporal and spatial dependence in the series and the residuals make estimates from panel regression unreliable. Also, assuming cross-sectional correlation is constant across series will lead to misspecification errors.

Appendix 2: Variance Inflation Factor

Independent variables	VIF	1/VIF
TAX	2.714	0.368
CRS	1.552	0.644
TOS	2.181	0.458
URP	2.190	0.457
PGT	1.836	0.545
GDP	2.147	0.466
PSA	1.435	0.697
HCI	1.094	0.914
GFC	2.784	0.359
Mean VIF	1.993

1. The abbreviation TAX stands for sum of corporate income tax and royalty on gross turnover, CRS stands for corruption score, TOS denotes trade openness, URP stands for urban population, PGT denotes population growth, GDP stands for gross domestic product capita per growth, PSA stands for political stability and absence of violence, HCI denote human capital development, and GFC means gross fixed capital formation

Appendix 3: Cross-Sectional Dependence Test in the Series

Variable	Statistic
ID	14.509**
CRS	14.686**
TAX	−0.860
TOS	−1.360
URP	15.873**
PGT	3.764**
GDP	−0.281
PSA	−0.378
GFC	−0.378
HCI	15.872**

1. Note ** Denotes statistical significance at 5%. The abbreviation CRS stands for Corruption score, TAX stands for the sum of corporate income tax and royalty on gross turnover, TOS denotes trade openness, URP stands for urban population, PGT denotes population growth, GDP stands for gross domestic product per capita per growth, PSA stands for political stability and absence of violence, HCI denotes human capital development, and GFC means gross fixed capital formation

Appendix 4: Diagnostic Tests

Test	Statistic	P-value
Wooldridge test for autocorrelation	1540.053	0.000
Breusch-Pagan/Cook-Weisberg test for heteroscedasticity	9.090	0.003
Hausman (1978)	895.210	0.000
Pesaran CD test in the residuals	−1.132	0.258

Appendix 5: Country-Level Descriptive Statistics Averaged for the Period 2011–2019

The table presents country-level descriptive statistics averaged for the period 2011–2019. To examine the impact of corruption score and natural resources taxes on sustainable development in Africa, this study uses the sustainability development indicator, corruption score index and tax on natural resources as it main dependent and independent variables respectively. Trade openness, Urban population, GDP per capita growth, Political Stability and Absence of Violence/Terrorism, Human capital index, and Gross fixed capital formation are incorporated as controls in the study.

Panel A

	SDI	CS	TAX	TOS	URP
Angola	0.609	18.111	23.914	36.147	63.406
Burkina Faso	0.455	35.556	11.541	29.839	27.554
Congo, Republic	0.603	19.467	23.548	57.387	65.543
Ghana	0.638	39.433	17.579	36.877	54.071
Madagascar	0.561	24.222	17.833	30.295	35.205
Mali	0.453	28.422	11.547	30.431	39.981
South Africa	0.672	39.122	33.444	27.967	64.817

Panel B

	PGT	GDP	PSA	HCI	GFC
Angola	3.617	−1.628	−0.375	0.080	17.563
Burkina Faso	2.915	2.693	−0.827	0.083	0.000
Congo, Republic	2.526	−3.092	−0.500	0.093	38.572
Ghana	2.323	4.199	0.032	0.098	0.000
Madagascar	2.629	0.533	−0.486	0.084	16.609
Mali	3.122	1.080	−1.742	0.071	11.119
South Africa	0.809	0.316	−0.147	0.092	14.133

1. The abbreviation SDI is Sustainable development indicator, CS denotes the corruption score, TAX is the tax on natural resources, TOS is trade openness, URP is urban population, PGT is population growth, GDP is the gross domestic product per capita, PSA is the Political Stability and Absence of Violence/Terrorism, HCI is the human capital index and GFC is gross fixed capital formation (private sector)

The country-level descriptive statistics of the study variables are presented in Table 1. Among the seven sampled African countries, the average sustainable development indicator ranges between 0.453 and 0.672. Mali shows the lowest sustainable development indicator, 0.453 while South Africa shows the highest, 0.672. That implies high levels of sustainable development in South Africa as compared to Mali. The average corruption score ranges between 18.111 and 39.122. This implies that the highest corruption score is 39.122 and the lowest corruption score is 18.111. Unlike the other variables, a high value signifies a positive whereas a lower value signifies a negative. According to Transparency International, a high corruption score, 100, signifies a very clean country, a country with no corruption whereas a low corruption score signifies a highly corrupt. From the table, Angola is highly corrupt with a corruption score of 18.111 followed by Congo, Republic, 19.467. South Africa on the other hand, has the highest corruption score, hence a less corrupt country.

The average tax on natural resources range between 11.541 and 33.444. This implies that relatively low taxes are paid on natural resources among the sample African countries. From the table, the average GDP trade for the period 2011 to 2019 ranges between 27.967 and 57.387. Unlike the main independent and dependent variables, Congo, Republic recorded the highest GDP trade over the sample period. Urban population has relatively high averages as compared to the other variables. South Africa has the highest percentage of its population living in the urban areas, 64.817. Burkina Faso, on the other hand, has the least percentage of its population living in urban areas, 27.554.

The mean level of population growth ranges between 0.809 and 3.617. On average, South Africa’s population increased at the lesser rate while Angola’s population increased at a very faster rate. The average GDP per capita ranges between −3.092 and 4.199. Among the sampled African countries, Ghana has the highest annual percentage growth rate of GDP per capita while Congo, Republic has the least. The average human capital index is relatively low, ranging between 0.071 and 0.093. The average contribution of health and education to the productivity of workers of the sampled countries below 0.1. Gross fixed capital formation for private sectors has an average range of 0.000 and 38.572. Among the sampled countries, Burkina Faso and Ghana has the no gross outlays from private sectors whereas Congo, Republic has the highest outlays from private sectors.