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American Journal of  Environmental
Economics (AJEE) 

Natural Resource Endowment, Human Capital Development, and Economic Growth in  
Selected African Countries

Kehinde John Akomolafe1*

Volume 3 Issue 1, Year 2024
ISSN: 2833-7905 (Online)

DOI: https://doi.org/10.54536/ajee.v3i1.3966
https://journals.e-palli.com/home/index.php/ajee

Article Information ABSTRACT

Received: October 13, 2024

Accepted: November 22, 2024

Published: December 07, 2024

Policymakers, particularly in Africa, have continued to be preoccupied with the issue of  the 
resource curse and how to address it. This study was conducted to investigate the moderating 
effect of  human capital development in the resource-growth relationship in six chosen 
African countries  from 1992 to 2019. Panels Corrected Standard Errors was used as the 
main estimator. The results confirm the Dutch disease by showing that natural resources had 
a negative impact on economic growth,while human capital had a beneficial influence. It was 
also shown that the development of  human capital could not mitigate the detrimental impact 
of  natural resources on economic growth. Hence, African countries should make efforts to 
reduce their dependence on natural resources and focus on the development of  other sectors 
of  the economy as a way to solve the problem of  the resource curse.

Keywords

Africa, Economic Growth, Human 
Capital, PCSE, Resource Curse

1 Department of  Economics, Afe Babaola University, Ado Ekiti, Nigeria
* Corresponding author’s e-mail: akjohn@abuad.edu.ng

INTRODUCTION
An essential component of  a nation’s wealth is its 
natural resources. Many nations possess abundant 
natural resources that might lead to economic growth. 
The problem, however, is how to turn these resources 
into sustainable development and economic growth 
(Zallé, 2019). Discussions concerning the role of  
natural resources in economic growth have occupied the 
literature since the ground-breaking work of  Auty (1997) 
on the curse of  natural resources. The study argued 
that when compared to nations without such resources, 
nations with abundant resources experienced slower rates 
of  economic growth. The resource curse states that there 
is an adverse relationship between economic growth and 
natural resources (Dou et al., 2022). The rate of  economic 
growth decreases as the quantity of  natural resources 
rises. This pattern defies logic as economic theory states 
that natural resources increase an economy’s capacity 
for production and, hence, its potential for economic 
expansion. 
However, economic stagnation does not result from 
the simple existence of  natural resources. Instead, 
the availability of  natural resources creates economic 
distortions that act as transmission channels and 
subsequently impact economic growth negatively. Various 
factors have been investigated in the literature to explain 
this phenomenon. These include factors such as  political 
factors, institutional, and even environmental factors 
(Destek et al., 2023; Leonard, et al., 2022; Mlambo, 2022).
In recent times, the discussion has focused on the role of  
human capital in explaining the resource curse (Ozcan et 
al., 2023; Tian et al., 2024). Zafar et al. (2019) contends that 
without knowledgeable and competent human capital, 
sustainable utilization of  natural resources is impossible. 
Societies’ willingness to adopt environmentally friendly 

and energy-efficient technologies is stimulated by human 
capital. Effective extraction, processing, and management 
of  natural resources depend on knowledgeable and skilled 
workers. The potential economic gains are hampered by 
the likelihood of  resource waste or mismanagement in 
the absence of  human capital.
Africa is a country blessed with abundant natural resources, 
ranging from oil, gas, solid minerals, fertile land, and water 
resources. According to African Development Bank 
(AFDB, 2016), the proven oil reserves on the continent 
make up 8% of  global reserves, while natural gas reserves 
make up to 7%. Additionally, according to AFDB (2016) 
estimates, over USD 30 billion in government revenue 
might come from Africa’s extractive resources annually 
over the next 20 years.
The continent has failed to convert its resource abundance 
into long-term economic growth and development in spite 
of  these advantages. 32 of  the world’s 46 least developed 
nations are in Africa, and the majority of  them are 
endowed with natural resources (United Nations, 2022). 
According to Ahmed et al. (2020), this has been ascribed 
to problem of  human capital development. Education, 
health care, and skill development are not adequately 
funded in many African countries. As a result, there is a 
shortage of   knowledgeable and experienced workforce 
required for the efficient management and use of  natural 
resources (Chikoko & Mthembu, 2020).  If  human capital 
holds the secret to the relationship between natural 
resources and economic growth, investing in human 
capital would be the right path for African countries.
Hence, this study examines the role of  human capital in 
resource-growth nexus in selected African countries. 

LITERATURE REVIEW
Various attempts have been made to examine the 



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relationship between natural resource and economic 
growth in the literature. For instance studies such as 
Asiedu  et al., 2021; Atil et al., 2020; Epo & Nochi Faha, 
2020;  Khan et al., 2022; Kwakwa et al., 2022; Inuwa et al., 
2022; Jie  & Lan, 2024; Hayat & Tahir, 2021; Lee & He, 
2022; Nawaz et al., 2022;  Shabbir et al., 2020; Tabash et al., 
2022; Wang et al., 2023) have examined the relationship 
between natural resources and economic growth. Asiedu 
et al. (2021) investigate how institutional quality mediates 
the link between economic growth and the endowment 
of  natural resources. The study  discover that only 
when institutional frameworks guarantee openness and 
effectiveness in resource management do resource-rich 
nations see growth. Also, Atil et al. (2020) look into how 
the extraction of  natural resources affects environmental 
deterioration and how it affects economic growth. The 
results show that by making the environment more 
vulnerable, unsustainable resource exploitation might 
impede long-term growth.
Similarly, Epo and Nochi Faha (2020) focus on how 
resource richness and human capital development interact 
in African economies. The study comes to the conclusion 
that resource riches frequently does not transfer into 
sustainable growth in the absence of  large investments in 
human capital.Furthermore, Khan et al. (2022) examine 
resource-dependent economies’ macroeconomic stability. 
The findings draw attention to resource revenue volatility 
and its detrimental impacts on inflation, exchange rates, 
and fiscal stability—all of  which have a deleterious 
influence on economic growth. Kwakwa et al. (2022) 
stress the value of  economic diversification in nations 
with abundant natural resources. The study concludes 
that nations that rely primarily on natural resources 
experience growth stagnation, but diversification reduces 
risks and boosts resilience.
Inuwa et al. (2022) investigate the connection between 
growth, economic inequality, and natural resources. 
According to the findings, an unequal allocation of  
resource earnings can worsen societal tensions and might 
impede growth. Additionally, Jie and Lan (2024) evaluate 
how green technology is being adopted in economies 
that rely on natural resources. The study concludes that 
in order to achieve sustainable growth, environmental 
policy and human capital development are essential.
According to Hayat and Tahir (2021), nations that 
prioritize R&D get greater returns from their resource 
riches than those that do not. According to Lee and 
He (2022), manufacturing sectors are adversely affected 
by resource dependency, which frequently results in 
a “Dutch disease” effect. The relationship between 
governance and corruption and resource growth is 
examined by Nawaz et al. (2022). The study concludes 
that the benefits of  resource riches on economic growth 
are greatly reduced by corruption. Shabbir et al. (2020) 
look into the connection between poverty alleviation 
and resource exports. Although resource exports boost 
GDP, the results show that their ability to reduce poverty 
is restricted because of  inadequate income transfer 

mechanisms.
In the same vein, Tabash et al. (2022) examine how FDI 
affects the link between growth and resources. According 
to their findings, when there is a supportive policy 
environment, FDI increases the benefits of  resource 
riches. Environmental sustainability is the main emphasis 
of  Wang et al. (2023) as a mediator in the resource-growth 
relationship. According to the findings, countries that 
prioritize sustainability see long-term growth, while those 
that disregard environmental issues see diminishing gains. 
The empirical research shows how intricate the connection 
is between economic growth and natural resources. By 
analyzing the moderating role of  human capital in the 
resource-growth nexus, this study contributes to the 
existing studies.

MATERIALS AND METHODS
Two models were used for the analysis. The first was used 
to examine the  effect of  natural resource and human capital 
on economic growth, while the second model was used 
to examine the moderating effect of  human capital in the  
resource-growth relationship in selected African countries.

Model One
E C O G D P i t = γ 1 + δ 1 H U M A N D E X i t + δ 2 
N A T U R E E N D i t + δ 3 G L O B A L I Z A i t + δ 4 
F I N D E X O N E i t + δ 5 E X C H A N G E i t + δ 6 
OPENNESSit+∈it             (3.1)

Model Two
ECOGDPit=γ1+δ1HUMANATUit+δ2GLOBALIZAit 
+ δ 4 F I N D E X O N E i t + δ 5 E X C H A N G E i t + δ 6 
OPENNESSit+∈it             (3.9)
Where ECOGDP is the log of  gross domestic products,  
NATUREEND  is  the log of  natural resources rent, 
GLOBALIZA is the log of  globalization index,  and 
FINDEXONE is the log of  financial development, 
HUMANDEX is the log of  human capital, OPENNESS 
is the log of  trade openness, HUMANATU is the 
moderating variable  (HUMANDEX*NATUREEND) 
between natural resources and human capital.
Economic growth was measured using GDP per capital 
in dollar, Human capital index was measured using years 
of  schooling education, Globalization was measured using 
globalization index, Natural resources endowment was 
measured using natural resources rent as %of  GDP, Trade 
Openness was measured as the ratio of  total trade to GDP.
The countries  selected for the study are Algeria, Gabon, 
Mauritius, Egypt, South Africa, Tunisia.  They were 
selected because they were the countries with highest 
human capital development in Africa (World Bank, 
2020). The time series version of  the data is from 1992 to 
2019. They were sourced from World Bank, (2022) and 
Feenstra et al. (2022).  Variance Inflation Factor was used 
to check for multicollinearity in the models. The test for 
heteroskdesticity was followed using modified wald test. 
The  Pesaran Cross-Sectional Dependence test  was used 
to test for cross-sectional dependence. The panel unit root 



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tested using Breitung and Dias panel unit root test. The 
Panel co-integration test was done using  the  Pedroni co-
integration test,  while Panels Corrected Standard Errors 
was used as the primary estimator.

RESULTS AND DISCUSSIONS
Testing for Multicollinearity 

The test for multicollinearity was examined using the 
variance inflation factor (VIF) and tolerance factor, 
The VIF should be less than 10 as a general guideline 
(Thompson et al., 2017). Table 1 outcome reveals that 
none of  the variables has VIF that is up to 8, while the 
tolerance factor is above 0.1. This suggests that there is 
no multicolinearity in the models.

Table 1: The Results Of  The Multicollinearity  Test
Variable Model One Model Two

Variance Inflation Tolerance Factor Variance Inflation Tolerance Factor
FINDEXONE 7.24 0.138048 4.88 0.204864
EXCHANGE 5.10 0.196068 2.57 0.389130
HUMANDEX 3.09 0.324032
NATUREEND 2.86 0.349573
HUMANATU 2.28 0.438871
GLOBALIZA 2.69 0.371270 1.95 0.512788
OPENNNESS 1.72 0.582524 1.65 0.607811

Source: Computed by the Author

Table 2: The Results of  the Multicollinearity Test
Model One Model Two
F(1, 5) 147.340 F(1, 5) 75.515
Prob  0.0001 Prob  0.0003

N.B: ***,**,*, indicates significance at 1%, 5%, and 10% respectively
Source: Computed by the Author

Table 3: Results of  Modified Wald Test for Groupwise 
heteroskedasticity
Model One Model Two
chi2 (6)  173.16*** chi2 (6)  200.07***
Prob  0.0000 Prob  0.0000

N.B: ***,**,*, indicates significance at 1%, 5%, and 10% respectively
Source: Computed by the Author

Testing for Auto-Correlation
The Wooldridge test for auto-correlation was used to test 
the auto-correlation in the models. The probability values 
in Table 2 are less than 5% in the two models, indicating 
that they are significant at that level.This suggests that the 
models have serial correlation.

Testing for Heteroskedasticity
Modified Wald test for groupwise heteroskedasticity was used 
to examine  heteroskedasticity in the models. The result shows 
that the probability values in Table 3 are less than 0.05. The 
implication is that heteroskedasticity issue exists in  the model.

Testing for Slope Heterogeneity
Pesaran and Yamagata (2008) was used to test for the 
slope heterogeneity in the model. The probability value of  
the test in  models is less than 0.05, which means that the 
null hypothesis that slope coefficients are homogeneous 
is rejected, as shown in Table 4. The implication is that 
the  model is heterogeneous.

Table 4: Results of  Pesaran and Yamagata  Test
Variables Model One Model Two
 Delta P-value Delta P-value

9.884*** 0.000 12.067*** 0.000
adj. 11.695*** 0.000 13.934*** 0.000

N.B: ***,**,*, indicates significance at 1%, 5%, and 10% 
respectively
Source: Computed by the Author

Table 5: The Results of  the  Cross-Section Dependence 
Test
Variable Model One Model two

CD-test P- 
Value

CD-test P- 
Value

ECOGDP +7.443 0.000 +7.443 0.000
EXCHANGE +14.851 0.000 +14.851 0.000
HUMANDEX + 20.3 0.000
NATUREEND +10.781 0.000
HUMANATU +10.912 0.000
GLOBALIZA +17.595 0.000 +17.595 0.000
FINDEXONE +7.048 0.000 +7.048 0.000
OPENNNESS +-2.233 0.026 +-2.233 0.026

N.B: ***,**,*, indicates significance at 1%, 5%, and 10% 
respectively
Source: Computed by the Author

Testing for Cross-Section Dependence
The cross-sectional dependence test in this study was 
conducted using Pesaran’s (2004) CD test. As shown 
in Table 5, all the variables in the two models have 
probability values that are less than 0.05 which indicate 
that cross-sectional dependence is present in the models.



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Testing for the Unit Roots in the Panel
Due to the model’s cross-sectional dependence, which was 
demonstrated in the preceding section,  Breitung and Dias 
panel unit root test. The test was initially run using the 
variables at level form. The p-value of  the lambda statistics 

for each variable indicates that it is higher than 5%. This 
suggests that the unit root null hypothesis about the series 
is not rejected. The p-values are all below 5% when the first 
difference was taken for each variable. Thus, it is said that 
all of  the variables are first-order integrated.

Table 6: Breitung and Dias Panel Unit Root Test Results
Variable At Level First Difference

lambda P-value lambda P-value
ECOGDP 1.1396 0.8728 -3.4873 0.0002
EXCHANGE 0.2156 0.5853 -2.4334 0.0075
HUMANDEX -0.4863 0.3134 -1.7191 0.0428
NATUREEND -0.2854 0.3877 -5.3850 0.0000
GLOBALIZA 1.3119 0.9052 -5.9925 0.0000
FINDEXONE -1.0392 0.1494 -7.0062 0.0000
OPENNNESS 0.4316 0.6670 -6.4102 0.0000
HUMANATU -0.0227 0.4909 -7.4031 0.0000

N.B: ***,**,*, indicates significance at 1%, 5%, and 10% respectively
Source: Computed by the Author

Table 7: The Results Of  Pedroni Panel Co-Integration Test
Variable Model One Model Two

Statistics P-value Statistics P-value
Modified Phillips-Perron 3.3050 0.0005 2.7898 0.0026
Phillips-Perron t 2.3445 0.0095 1.9817 0.0238
Augmented Dickey-Fuller 2.8575 0.0021 2.3220 0.0101

N.B: ***,**,*, indicates significance at 1%, 5%, and 10% respectively
Source: Computed by the Author

Testing for Panel Co-integration
Using the Pedroni panel co-integration test, the panel co-
integration test was carried out. The test provides three 
statistics for analysis. All the three  statistics reported 

significant results at 1% level of  significance as shown 
in Table 7. As a result, the  null hypothesis of  no co-
integration among the variables  is rejected.

The Result of  the  Panel-Corrected Standard Errors 
(PCSE)
Given the results of  the preliminary tests, PCSE was used 
for the analysis. Table 8 shows that in the first model, 
there is a positive relationship between human capital 
index and economic growth. The result shows that a 1% 
increase in the human capital index is associated with 
0.7% increase in the log of  GDP which is the proxy for 
economic growth. However, the result of  natural resource 
endowment shows a negative relationship with the log of  
GDP.This implies that an increase in natural resource 
endowment by 1% is associated with 0.1% decrease in the 
log of  GDP, and consequently, economic growth. The 
result confirms a case of  Dutch disease. 
In term of  the moderating effects of  human capital, the 
result of  the second model shows that there is a negative 

relationship between moderated  human capital index and 
the log of  GDP. By implication, the moderated human 
capital index exerts a negative influence on economic 
growth.
The implication is that the interaction between human 
capital development with natural resources does not 
make the countries avoid the Dutch disease. This shows 
that the solution to the problem of  Dutch  disease goes 
beyond having more human capital development.  
The results of  the control variables show that there is a 
positive relationship between exchange rate depreciation 
and economic growth. Also, a positive relationship was 
found between financial development and economic 
growth. Similarly, trade openness was also found to have 
a positive effect on economic growth, while globalization 
was found to be insignificant.

Table 8: Effects of  Human Capital and Natural Resources Endowment  on Economic Growth
Coef. P>z Coef. P>z

EXCHANGE .1119741*** 0.000 .1464998*** 0.000



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CONCLUSION
The study was done to examine the moderating role 
of  human capital development in the  resource-growth 
nexus in selected countries in Africa. The findings show 
that the individual effect of  human capital on economic 
growth is positive but that of  natural resource was found 
to be negative, confirming the Dutch disease. Also, it 
was also found that human capital development could 
not moderate the negative effect of  natural resource 
on economic growth. Given the conclusions of  this 
study, it is recommended African governments should 
work to improve human capital development in the 
continent. African countries should tailor human capital 
development towards the development of  their natural 
resources.  African countries should make efforts to 
reduce their dependence on natural resources and also 
focus on the development of  other sectors of  the 
economy 

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HUMANDEX .7154225*** 0.000
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FINDEXONE .0714025*** 0.007 .1098935*** 0.000
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_CONS 7.347609*** 0.000 6.959705*** 0.000
Wald chi2(6)  183.15***  147.62***
Prob > chi2 0.0000 0.0000

N.B: ***,**,*, indicates significance at 1%, 5%, and 10% respectively
Source: Computed by the Author



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