




































 
 

 

26 
© 2021 by the authors; licensee Asian Online Journal Publishing Group 
 

Economy 
Vol. 8, No. 2, 26-34, 2021 

ISSN(E) 2313-8181/ ISSN(P) 2518-0118 
DOI: 10.20448/journal.502.2021.82.26.34 

© 2021 by the authors; licensee Asian Online Journal Publishing Group 

    
 

 
 
 
Does Terms of Trade Matter for Economic Growth? A Focus on Natural Resource-
Rich Sub-Saharan African Countries 

 
Nzeh Innocent Chile1  

Benedict I. Uzoechina2   
Millicent Adanne Eze3   

Chika P. Imoagwu4   
Ozoh Joan Nwamaka5   

 

 
( Corresponding Author) 

 
1Department of Economics, Renaissance University, Ugbawka, Enugu State, Nigeria. 
1Email: nzechile@yahoo.com Tel: +234-8034222084 
2Department of Economics, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria. 
2Email: ib.uzoechina@unizik.edu.ng Tel: +234-8063304867 
3School of Business and Social Sciences, Abertay University, Dundee, United Kingdom. 
3Email: ezemillicent@gmail.com Tel: (+44)7459452103 
4,5Department of Economics, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria. 
4Email: cp.imoagwu@unizik.edu.ng Tel: +234-8035774516 
5Email: jn.ozoh@unizik.edu.ng Tel: +234-8066678226 

 
Abstract 

The contention that deteriorating terms of trade exists in countries that rely heavily on the 
exploitation and export of natural resources motivated us in this study. We therefore sought to 
investigate the impact of terms of trade on economic growth in natural resource-rich sub-Saharan 
African countries. We carried out the study using annual series that span a period of 1990-2019 
under the framework of panel Random and Fixed effects. Our findings indicate that a long run 
relationship exists between GDP and the explanatory variables used in the study. Results also 
show that, while cross-section random effects indicates that terms of trade positively impacts on 
GDP, period fixed effects shows that terms of trade negatively impacts on GDP even though it is 
not significant.  Results of our study also show that in all the models, labour force total and FDI 
have positive impact on GDP, while trade openness impacts on GDP negatively. We therefore 
recommend that the SSA natural resource-rich countries should diversify their economies away 
from the traditional natural resources base. Also human capital should be improved through 
sound education and training, while all the bottlenecks that constrain the inflow of foreign direct 
investment should be dismantled. 

 
Keywords: Natural resources, GDP, Random effects, Fixed effects, Terms of trade, Labour force, FDI. 

JEL Classification: N57; O40; C33; F10; J21; F21. 
 

Citation | Nzeh Innocent Chile; Benedict I. Uzoechina; Millicent 
Adanne Eze; Chika P. Imoagwu; Ozoh Joan Nwamaka (2021). Does 
Terms of Trade Matter for Economic Growth? A Focus on Natural 
Resource-Rich Sub-Saharan African Countries. Economy, 8(2): 26-
34. 
History:  
Received: 15 September 2021 
Revised: 18 October 2021 
Accepted: 12 October 2021 
Published: 8 December 2021  
Licensed: This work is licensed under a Creative Commons 

Attribution 3.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Acknowledgement: All authors contributed to the conception and design of 
the study. 
Funding: This study received no specific financial support. 
Competing Interests: The authors declare that they have no conflict of 
interests. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study was reported; that no vital 
features of the study have been omitted; and that any discrepancies from the 
study as planned have been explained. 
Ethical: This study follows all ethical practices during writing.   

 

 

Contents 
1. Background to the Study ................................................................................................................................................................ 27 
2. Theoretical Issues Relating to Terms of Trade ........................................................................................................................ 29 
3. Methodology ..................................................................................................................................................................................... 30 
4. Results of Descriptive Statistics ................................................................................................................................................... 31 
5. Conclusion and Recommendations ............................................................................................................................................... 34 
References .............................................................................................................................................................................................. 34 
 

 
 

mailto:nzechile@yahoo.com
mailto:ib.uzoechina@unizik.edu.ng
mailto:ezemillicent@gmail.com
mailto:cp.imoagwu@unizik.edu.ng
mailto:jn.ozoh@unizik.edu.ng
http://creativecommons.org/licenses/by/3.0/
http://creativecommons.org/licenses/by/3.0/
https://www.doi.org/10.20448/journal.502.2021.82.26.34
https://orcid.org/0000-0002-1754-7446
https://orcid.org/0000-0003-4458-7133
https://www.doi.org/10.20448/journal.502.2021.82.26.34
https://orcid.org/0000-0002-1754-7446
https://orcid.org/0000-0003-4458-7133
https://www.doi.org/10.20448/journal.502.2021.82.26.34
https://orcid.org/0000-0002-1754-7446
https://orcid.org/0000-0003-4458-7133
https://www.doi.org/10.20448/journal.502.2021.82.26.34
https://orcid.org/0000-0002-1754-7446
https://orcid.org/0000-0003-4458-7133


Economy, 2021, 8(2): 26-34 

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Contribution of this paper to the literature 
This study contributes to the existing literature by focusing on the link between terms of trade and 
economic growth in natural resource-rich sub-Saharan African countries. 

 
1. Background to the Study 

The contribution of international trade on economic growth has been emphasized since the days of classical 
economists such as Adam Smith and David Ricardo. By offering opportunities for countries to export their 
products to other countries, international trade enables countries to earn income that is used to facilitate further, 
the development of the domestic economy. The growth effect of international trade is evaluated from, among other 
factors, the terms of trade condition facing the export price of countries and some studies have pointed to a positive 
link between economic growth and terms of trade. For instance, terms of trade improvement has been found to 
encourage higher levels of investment, thus rapid economic growth (Mendoza, 1997). In corroborating this stance, 
Easterly, Kremer, Pritchett, and Summers (1993) find that variability in terms of trade are highly correlated with 
growth and this conforms to findings of studies by Barro and Sala-i-Martin (1995) and Fischer (1993). 

Through international trade, countries can specialize in the production and export of products that they have 
comparative advantage. Notwithstanding the gains inherent with specialization, it comes with its own drawback. 
This limitation puts a constraint on the developing countries whose trade relation with developed countries is 
usually lopsided. As noted in separate studies by Prebisch and Singer, developed countries specialize in the 
production and exports of manufactured products and services that have low substitutes and enjoy stable prices. On 
the other hand, developing countries, with their weak industrial base, specialize in production and exports of 
primary commodities that are highly competitive and which experience volatility in prices. Deterioration in terms 
of trade is therefore among the determinants of widening income gap between developing countries and developed 
countries. Sachs and Warner (2001) observed that specialization in the production and export of primary products 
is a major determinant of economic outcome  

Our study is focused on natural resource-rich sub-Saharan African (SSA) countries which fall under the 
developing countries hypothesized to be likely faced with deteriorating terms of trade. We are motivated by the 
fact that rich natural resource endowments have been found to induce countries endowed with them to abandon 
their critical sectors by failing to diversify their economies and this tendency ends up depressing their GDP. 
Natural resources abundant countries usually face resource-curse and Dutch disease syndrome as they are rigidly 
stuck to the exploitation and exportation of the abundant natural resources. A study by Carmignani and 
Chowdhury (2007) has shown that undue dependence on the export of primary commodities negatively affect 
economic growth in SSA. Irrespective of the fact that a preponderance of studies have been done on the link 
between terms of trade and economic growth, in literature; there is an absence of studies of such in the context of 
natural resource-rich sub-Saharan African countries. Our paper thus aims to fill this gap in literature by 
investigating the nexus between terms of trade and economic growth in SSA countries. 
 

1.1. Stylized Facts on Sub-Saharan African Countries 
By virtue of their huge natural resource deposits, the SSA countries usually export their commodities 

unprocessed. Several theoretical views have been raised concerning the deteriorating export price of commodities 
in relation to industrialized goods. Figure 1 below shows that Congo Democratic Republic and Mauritania are the 
two countries with the highest receipt of natural resources rent respectively and this is followed by Nigeria. South 
Africa and Botswana are the least recipient of natural resources rent within the study period. In 2003, 2009 and 
2011 through 2019 Congo Democratic Republic was on top of other countries in her receipt of natural resources 
rent and in 2008 and 2010, Mauritania got the highest rent. With respect to terms of trade, evidence from Figure 2 
below shows that Nigeria has the highest terms of trade (proxied by net barter term of trade) and this is followed 
by Mauritania and South Africa respectively. It should be noted that the highest recipient of natural resources rent 
are hardly the countries with the highest terms of trade.  A case in point here is Congo Democratic Republic which 
has a high natural resources rent but has a very low terms of trade.  Worthy of note also is the fact that Nigeria 
and South Africa which are the most industrialized countries among the group are rarely the highest natural 
resources rent recipients but their terms of trade is high. The implication of this development is that commodity 
terms of trade in counties with huge natural resources rent deteriorates compared to countries with a relative 
industrial development. The fact that Nigeria has the highest terms of trade within the sample period could be 
because of export from her huge deposit of oil couple with her relative high industrial base.     
 

 
Figure-1. Trend of Natural Resources Rent in Selected SSA Countries. 



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Figure-2. Trend in Net Barter Terms of Trade in Selected SSA countries. 

 

In terms of export share to GDP, Figure 3 below shows that in all the years, Botswana’s export share to GDP 
is the highest and this is followed by Mauritania and Congo Democratic Republic, respectively. Figure 2 above 
shows that Botswana, even with her high share of export to GDP, is among the countries with the least terms of 
trade. This implies that her terms of trade deteriorates vis-à-vis her trading partners. We also noticed that the two 
countries in the group with the highest GDP, namely: Nigeria and South Africa have a lower export to GDP. With 
a relatively high concentration of business activities in these two countries, the proportion of export to GDP is 
expectedly low as many domestic business activities not meant for export contribute to GDP.  For instance, around 
2014, Nigeria rebased her economy by incorporating the contribution of some sectors such as the 
telecommunication that were hitherto not included in her GDP. Consequently, the country’s GDP expanded and 
became the highest in Africa with the period. 
 

 
Figure-3. Trend in Export in Selected SSA Countries. 

 

With respect to the share of import to GDP, Figure 4 below shows that in 2005 through 2008, Mauritania’s 
import as a percentage of GDP is highest compared to other countries in the sample. In 2009 and 2010 as well as 
2012 through 2015 Botswana’s share of import to GDP was highest. Evidence in Figure 2 above shows that 
Botswana’s term of trade is very low and this is owing to her high import share of GDP. Sierra Leone’s share of 
import to GDP was the highest between 2011 and 2016. In a similar vein, the import share of Sierra Leone to her 
GDP accounts for her low terms of trade within the sample period. We noticed that Nigeria and South Africa 
which are the two countries with the biggest GDP has the least share of import as a percentage of GDP.  This 
shows that import in these countries take only a smaller portion of their relatively huge GDP.    
 



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Figure-4. Trend in Import in Selected SSA Countries. 

 

 

2. Theoretical Issues Relating to Terms of Trade 
Over the years, several theories have been developed to help explain the relationship between countries’ terms 

of trade and their economic performance. In 1950, Raoul Prebisch and Hans Singer raised a popular argument that 
developing countries which exported primary products and imported manufactured products had experienced both 
a decline in their terms of trade and depressing incomes.  Specifically, Prebisch raised the concern over trade 
relationship between developed and developing countries by observing that since developing countries export more 
of primary products that are less competitive at the international market, the commodity terms of trade usually 
deteriorates in relation to industrialized goods. With a low-income elasticity of demand for primary commodities, 
Prebisch observed that the demand for goods of this kind does not keep pace with income. By implication, 
developing countries tend to compete more intensely with each other for markets for their commodities. They go 
about this through reduction in prices; which ultimately affect their terms of trade with developed countries. If a 
country’s terms of trade continues to deteriorate, it implies that this country will experience a decline in the 
purchasing power of these exports vis-à-vis the value of goods and services imported from abroad. This viewpoint 
is in sync with the observation by Hans Singer in a separate study, thus this is referred to in the literature as the 
Prebisch-Singer thesis. 

In another direction, Thirlwall (1979) provided an explanation for why the trade balance is linked to economic 
growth. Thirlwall model was used to explain the export performance and import behavior in determining the long-
term economic growth. It observed that by increasing foreign exchange revenue generated through export sale of 
commodities, such provides avenue to sustainably finance increasing imports arising from increased domestic 
economic activity. Bruno and Sachs (1985) also provided explanation for the nexus between commodity prices and 
economic activity. Two channels were identified by Bruno and Sachs (1985) through which commodity prices can 
influence output. The first is that changes in commodity prices will shift the factor price frontier and the second is 
that commodity prices affect demand through real wages, employment and the world distribution of wealth.  
 

2.1. Empirical Literature 
Over the years, there has been wide interest in the study that investigates the nexus between terms of trade 

and economic growth. It is noteworthy however, that there has never been a uniform outcome of the appropriate 
link between the two as while some studies show a negative link, some indicate that terms of trade is positively 
related to GDP.   

Using time series data from 1990-2008, Fatima (2010) examined terms of trade behavior in Pakistan. The 
paper finds that unfavourable terms of trade has a negative impact on economic growth of Pakistan, as it ultimately 
reduces gross domestic product. In a study involving 94 developed and developing countries over a period of 2004-
2008, Jawaid and Waheed (2011) showed that terms of trade has a significant and positive effect on economic 
growth. In India, Jawaid. and Raza (2012) using annual series spanning a period of 1980-2010, found a significant 
and positive long run relationship between terms of trade and economic growth. The study also found that the 
volatility of terms of trade has negative and significant effect on economic growth. In another cross-country study 
involving sub-Saharan African countries, Mputu (2016) investigated the nexus between terms of trade, trade 
openness and economic growth. Under the framework of Fixed and Random effects models and over a period of 
1980-2011, finding show that terms of trade has a positive relationship with GDP level in SSA countries 

In a study for China, Jebran, Iqbal, Bhat, and Ali (2018) using the framework of ARDL over a period of 1980–
2013, revealed that terms of trade significantly and adversely affects economic growth in the short run as well as in 
the long run. The Granger causality results revealed a unidirectional causality running from terms of trade to labor 
force. Another study for Pakistan by Jebran et al. (2018) using the ARDL and  over a period of 1980-2013, showed 
a significant negative long-run and short-run effects of terms of trade on economic growth. This finding finds 
support in Fatima (2010). Dabas and Delbianco (2019) investigates the effects of the ratio of exports/GDP and the 
terms of trade on growth among countries with different level of development and openness and found that terms 
of trade is not favourable to the growth prospects of poor countries. In a cross-country study involving developing 
countries, Jawaid.., Waheed, and Siddiqui (2020) showed a mixed result. While in some countries, terms of trade 
has a positive impact on GDP, in others the impact is negative. Another cross-country study involving some 



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countries in the European Union by Blavasciunaite, Garsviene, and Matuzeviciute (2020) indicated that 
deterioration of trade balance reduces average economic growth. 
 

3. Methodology 
In this study, we estimated the relationship between GDP and the regressors under the framework of the panel 

data models. Panel data model allows a control for variables that cannot be observed or measured such as fiscal or 
exchange rate policies that change over time but not across entities like countries. The model uses subscript i to 
denote individual entities while t refers to time periods. For example, in a panel data model with Yit as the 
dependent variable, this is denoted as i…..1,….,N across all time periods t = 1,…,T. The primary difference 
between panel data models and time series models is that panel data models allow for heterogeneity across groups 
and introduce individual-specific effects. We explored three different models in this study, namely: the pooled OLS, 
fixed effects and random effects by comparing the estimated results of their parameters. The pooled OLS can be 
explained below in Equation 1 as follows:  

                                                           (1) 

In Equation 1 above, x represents the observable characteristics such as income level and   is the coefficient of 

the observable parameter, while  and it  are the intercept term and error term respectively. Under the pooled 

OLS, there are no unobservable individual-specific effects as the intercept captures a uniform effect for all the 
entities.  The panel data can thus be treated as one large, pooled dataset. The implication is that, if we proceed to 

estimate   without accounting for heterogeneity within individual groups, the estimator will be inconsistent and 

biased. 
As a departure from the pooled OLS, the fixed effects model introduces heterogeneity by assuming that 

individual-specific factors may impact or bias the independent variables and as such there is need to control them. 
The assumption is that correlation exists between individual entity’s error term and the independent variables. 
Fixed effects remove the effect of such time-invariant characteristics in a way that it enables one to investigate the 
net effect of the explanatory variables on the dependent variable. Fixed effects model also assumes that those time-
invariant features are unique to the individual entity and should not be correlated with other individual 
characteristics. Each individual entity is unique; hence an error term belonging to any entity as well as the constant 
that captures individual characteristics should not be correlated with the others. If such should occur, the fixed 
effect model is not suitable because any inference drawn with the outcome of its result may be misleading. The 
fixed effect model is specified in Equation 2 below as follows:  

                                               (2) 
where 

 )...1( nii   is the unknown intercept for each entity (n entity-specific intercepts), itY
 
is the dependent variable (i 

= entity and t = time), itX  represents the explanatory variables for each entity at  t, 1  is the coefficient of the 

explanatory variables, it  represents the error term 

Failure to use the fixed effects model, the alternative model should be random effects model. A major 
distinguishing factor behind random effects model is that, unlike the fixed effects model, the changes across 
individual entities is assumed to be random and uncorrelated with the independent variables included in the model. 
The assumption under the random effects that the individual entity’s error term is not correlated with the 
regressors means that time-invariant variables such as fiscal or monetary policies are allowed to play a role as 
explanatory variables. In the fixed effects model these time-invariant variables are absorbed by the intercept. 

With random effect model, one can generalize the inferences beyond the sample used in the model, however; 
the problem with this model is that some variables may not be available such that it may lead to omitted variable 
bias in the model. The random effects model is specified in Equation 3 below as follows: 

                                                                                                           (3) 

where it is between entity error term and it is within entity error term 

Beyond specifying the model linking economic growth and terms of trade, we considered other variables such 
as trade openness, capital and labour which are equally important determinants of economic growth. Our model 
specification is a panel data model modified from Mputu (2016) and Equation 4 below captures the model 

                      (4) 
Where  

itGDP = Gross Domestic Product (A proxy for economic growth) in year t for country i. 

O  = Intercept term 

itNBTOT = Net barter terms of trade ( A proxy for terms of trade) in year t for country i. 

itFDI  = Foreign direct investment in year t for country i. 

itGFCF  = Gross fixed capital formation in year t for country i. 

itLFT  = Labour force total in year t for country i. 

itTOPEN  = Trade openness in year t for country i. 

it = between entity error term. 

it = within entity error term. 

 



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3.1. Data and Sources 
We used annual dataset in this study covering a period of 1990-2019 and the time period considered is 

informed by data availability of some of the variables included in the study. All the data were sourced from the 
World Bank’s World Development Index data bank. The countries selected in our study were informed by the 
grouping done by the Economic Commission for Africa in her 2011 report that grouped sub-Saharan African 
countries on the basis of resource-rich, resource-poor and landlocked countries. The countries that fall under 
resource-rich which we included in our study are: Nigeria, South Africa, Botswana, Congo Democratic Republic, 
Sierra Leone and Mauritania. We used GDP at constant 2010 US$ to proxy economic growth. Following Mputu 
(2016) we used Net barter terms of trade index (2000 = 100) as an indicator for terms of trade. Net inflows at 
current US$ was used to represent foreign direct investment. Gross fixed capital formation (% of GDP) was used to 
represent gross fixed capital formation and trade openness was calculated as the ratio of the sum of export and 
import. We logged GDP and FDI in excel package before including them in the estimation. 
 

4. Results of Descriptive Statistics  
Table 1 below displays the results of descriptive statistics of the variables used in the study. Evidence from the 

results indicate that the mean value of GDP, Net barter terms of trade, Gross fixed capital formation, Labour force 
total, Foreign direct investment and Trade openness are: 10.41935, 104.6052,  23.09278,  6.742246, 8.486756 and 
11.64456, respectively. The variable with the highest mean is Net barter terms of trade with a standard deviation of 
41.44478. On the other hand the variable with the lowest mean value is Labour force total with a standard 
deviation of  0.727383.In terms of the range, the variable that exhibited the highest range is Net barter terms of 
trade, indicating that the variable experiences volatility within the study period. The test of skewness shows that 
all the variables we applied in the study are skewed to the right, while they also exhibited platykurtic in their 
kurtosis. The Jarque Bera statistics show that with probability values of the variable which are less than 5 percent, 
the series are normally distributed. 
 

Table-1. Descriptive Statistics. 

 GDP NBTOT GFCF LFT FDI TOPEN 

Mean 10.41935 104.6052 23.09278 6.742246 8.486756 11.64456 
 Median 10.21180 100.0000 21.31350 7.130176 8.622676 11.82139 
 Maximum 11.67867 224.6432 93.54746 7.800901 9.994977 13.22011 
 Minimum 9.113214 0.000000 0.000000 5.632815 5.020713 9.545307 
Std. Dev. 0.848263 41.44478 12.89481 0.727383 1.000305 1.003765 
 Skewness 0.193410 0.080522 1.061048 -0.057413 -0.942510 -0.382608 
 Kurtosis 1.546182 4.282272 7.097017 1.307929 3.911547 1.966956 
Jarque-Bera 15.27671 11.27355 143.6997 19.41495 29.59348 11.15598 
Probability 0.000482 0.003564 0.000000 0.000061 0.000000 0.003780 
Sum 1687.935 16946.04 3741.030 1092.244 1374.855 1886.419 
 Sum Sq. Dev. 115.8477 276544.9 26770.45 85.18280 161.0982 162.2147 
Observations 162 162 162 162 162 162 

       

4.1. Results of Panel Unit Roots 
In order to confirm the stationarity of the series used in the study so as to avoid the problem of the results 

being spurious, the panel unit root tests we conducted are presented in Table 1 and 2 below. To carry out the tests, 
we utilized the Levin, Lin & Chu (LLC), Im, Pesaran and Shin (IPS), Augment Dickey Fuller-Fisher (ADF-Fisher) 
and Phillp-Perron-Fisher (PP-Fisher) tests in this study. We decomposed the test into two sections. One section 
comprised of IPS, ADF-Fisher and PP-Fisher deals with the null hypothesis of the existence of unit root for 
individual countries and the other section, comprising LLC deals with the existence of unit root for all the 
countries pooled together. In Table 2, the results at level indicate that Gross fixed capital formation, Foreign direct 
investment and Trade openness achieved stationairy at level, ie, I(0) under the LLC. Also, under IPS and ADF-
Fisher, Gross fixed capital formation and Foreign direct investment attained stationarity at level. Lastly, under the 
PP-Fisher, Gross fixed capital formation and Trade openness achieved stationarity at level. The results so far 
indicate that we cannot continue the analysis based on the outcome of unit root test at level. This is because we 
cannot reject the null hypothesis of no unit root on all the variables.     
    

Table-2. Result of Panel Unit Root at Level. 

 Common Unit Root Individual Unit Root 

Series LLC IPS ADF-Fisher PP-Fisher 

GDP 1.65685(0.9512) 4.80080(1.0000) 0.69790(1.0000) 0.16116(1.0000) 
NBTOT 0.56658(0.7145) 0.84922(0.8021) 6.88317(0.8652) 8.56876( 0.7393) 
GFCF -2.11267(0.0173)* -2.20526(0.0137)* 22.7924(0.0295)* 35.0049(0.0005)* 
LFT 1.50505(0.9338) 3.91597(1.0000) 1.44229(0.9999) 3.95019(0.9843) 
FDI -3.24064(0.0006)* -2.01848(0.0218)* 23.1447(0.0265)* 17.7510(0.1235) 

TOPEN -1.94754(0.0257)* 1.22607(0.8899) 6.30656(0.8998) 18.8968( 0.0910)** 
Note: * and ** signify significant levels at both the 5% and 10% respectively. 

 
Since not all the series achieved stationarity at level, we proceeded to investigate their stationarity at first 

difference as shown in Table 3 below. From the results, only labour force total did not achieve stationarity under 
LLC, while other series became stationary at the 5 percent level. Under the IPS, ADF-Fisher and PP-Fisher, all the 
series achieved stationarity at the 5 percent level. Therefore, after first difference, all the series we employed in the 
study became I(1). 

 
 



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Table-3. Result of panel unit root at first difference. 

 Common Unit Root Individual Unit Root 

 LLC IPS ADF Fisher PP-Fisher 

∆GDP -3.72235(0.0001)* -4.68957( 0.0000)* 45.6192( 0.0000)* 57.0196( 0.0000)* 

∆NBTOT -5.00232(0.0000)* -6.55118(0.0000)* 63.6629(0.0000)* 111.065(0.0000)* 
∆GFCF -6.92952( 0.0000)* -7.65763( 0.0000)* 76.5362( 0.0000)* 104.383( 0.0000)* 
∆LFT -1.23676(0.1081) -2.56349( 0.0052)* 27.1787(0.0073)* 49.0163(0.0000)* 
∆FDI -4.67977(0.0000)* -7.08865( 0.0000)* 69.4274( 0.0000)* 189.935( 0.0000)* 

∆TOPEN -7.02833(0.0000)* -6.09969(0.0000)* 59.0359(0.0000)* 87.9075(0.0000)* 
Note: * and ** signify significant levels at both the 5% and 10% respectively. 

 

4.2. Panel Co-Integration Test 
Having confirmed that the series do not have unit root, we went ahead to conduct a co-integration test to 

ascertain whether there is a long-run association or equilibrium among the series. We employed the Kao co-
integration test to achieve this. The Kao test is based on the test of significant of the residual under the null 
hypothesis of no co-integration. From the result in Table 4, the coefficient of the residual is -0.107656 with a t-
statistic of -2.680084 and p-value of 0.0083 which is lower than 5 percent. Consequently, we cannot accept the null 
hypothesis, thus meaning that a long-run equilibrium exists among the variables. 
 

Table-4. Kao Co-integration Result. 
Kao Residual Co-integration Test 
Series: GDP NBTOT FDI GFCF LFT TOPEN  
Dependent Variable: D(RESID) 
Sample: 1990 2019 
Null Hypothesis: No co-integration 

Variable Coefficient Std. Error t-Statistic Prob. 

RESID(-1) -0.107656 0.040169 -2.680084 0.0083 

 

4.3. Results of Panel Models  
With the result showing that the series are co-integrated, we present and discuss the estimated results of the 

coefficients of the parameters in all the models. We decided to estimate a pooled Ordinary Least Square (OLS) 
which we present for comparison sake. Next we present the panel fixed and random effects results. However, before 
that, we have to decide on which panel estimation technique is preferable. That is, between random and fixed 
effects techniques which one is more appropriate? To evaluate this, we employed the Hausman’s chi-square 
statistic. The Hausman test is conducted under the assumption that the random effect is being uncorrelated with 
the explanatory variables. Under the null hypothesis that the error term does not have any correlation with the 
explanatory variables, the random effect is more appropriate if it passes this test. As a guide to this test, we tested 
for both the period random effect and the cross-section random effect. These tests are displayed in Table 5 below 
and the results show that under the cross-section test, random effect is more appropriate as we cannot reject the 
null hypothesis of the error terms not being correlated with the explanatory variables. However, at the 5 percent 
level and under the period effect, fixed effect technique is more appropriate since we have every reason to reject the 
null hypothesis. Thus, we can estimate a cross-section random effect model to obtain the parameter estimates.  
 

Table-5. The Hausman Test. 

Hausman Test  for Cross-Section Random  Effect 

Test Summary Chi-Sq. Statistic Chi-Sq. d.f Prob 

Cross-section random 5.210925 5 0.3907 
 Hausman Test  for Period Random Effect 
Test Summary Chi-Sq. Statistic Chi-Sq. d.f Prob 
Period random 9 .840208 5 0.0799 

                                        
Since the result of the Hausman test for period random effect indicates that we cannot accept the null 

hypothesis, we have to consider estimating a fixed effect model. However, in order to apply the appropriate model 
under the fixed effect, we have to conduct a joint significance test of the fixed effects estimates in least squares 
specifications. This test is based on the null hypothesis that there is no presence of cross-section fixed and period 
fixed effects. If the chi-square is significant, we reject the null hypothesis that the effects are redundant. From the 
result in Table 6 below, we cannot reject the hypothesis of non-existence of period effect as the p-value of the 
estimate is 0.9731 which is higher than the 5 percent level. Consequently, we are going to estimate period fixed 
effects in order to obtain the parameter estimates. 
 

Table-6. Fixed Effect Tests. 

Cross-section Fixed Effects Test 

Effects Test Statistic d.f. Prob. 

Cross-section F 836.145048 (5,151) 0.0000 
Cross-section Chi-square 543.743498 5 0.0000 

Period fixed Effects 

Effects Test Statistic d.f. Prob. 
Period F 0.460889 (29,127) 0.9913 
Period Chi-square 16.210469 29 0.9731 

 
The results presented in Table 7 below show that under the pooled OLS, net barter terms of trade negatively 

and significantly influences GDP. We also find that gross fixed capital formation, labour force total and foreign 
direct investment are all positively related to GDP, even though the effect of gross fixed capital formation is not 



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significant. Trade openness is however found to negatively influence economic growth. Theoretically, most of the 
pooled OLS results certify the apriori expectations, however; since the model does not accommodate the 
heterogeneity associated with the individual cross-sections, these results cannot be used for policy formulation. 
 

Table-7. Pooled  OLS Results. 

Variables Coefficient Std. Error t-Statistic Prob. 

Constant 4.974968 0.335272 14.83861 0.0000 
NBTOT -0.000134 0.000793 -0.169252 0.8658 
GFCF 0.003117 0.002184 1.427330 0.1555 
LFT 1.198399 0.060505 19.80652 0.0000 
FDI 0.311343 0.037975 8.198546 0.0000 
TOPEN -0.458218 0.043779 -10.46654 0.0000 
R2 0.865834    
F-Stat. 201.3470    
Prob.(F-statistic) 0.000000    

DW2 0.265694    

 
Under the cross-section random effect results in Table 8 below, net barter terms of trade positively and 

significantly impact on GDP. This result is a complete departure from the result of the pooled OLS result. The 
implication of the result is that favourable terms of trade lead to improvement in the economic growth of a country. 
Thus, as the SSA natural resource-rich countries export their abundant natural resources and other commodities, 
their terms of trade improves which leads to improvement in their GDP. This result finds support in the finding by 
Mputu (2016). Finding also indicates that Gross fixed capital formation is significant and negatively influences 
GDP. The pooled OLS result for gross fixed capital formation shows that it does not have significant impact on 
GDP. The negative link between gross fixed capital formation and GDP finds support in a separate study by Nzeh 
(2020) for Nigeria which is one of the biggest economies in the group. One plausible reason why gross fixed capital 
formation influences GDP negatively could be owing to the fact that the countries comprising this group are less 
industrialized such that the capacity utilization of these fixed inputs is negligible. We found labour force total to 
positively influence GDP, but FDI does not have a significant effect on GDP even though the link is positive. The 
result of labour force finds conformity in Mputu (2016) and Ayadi (2017) and it shows the importance of labour 
productivity on economic growth. We guess that the non-significance of FDI on GDP could be due to the fact that 
these countries are less developed industrially as they rely mainly in the export of primary products. Also, as noted 
by Ayadi (2017) the hostile business environment which deters the inflow of investment could be the reason. Trade 
openness is found to have a negative impact on GDP and this finds support in Mputu (2016) and Ayadi (2017). As 
observed by Mputu (2016) resource curse hypothesis can be validated by this result as the countries hardly 
diversify their economies but only rely on export of abundant natural resources. 

Under the period fixed effect result in Table 8, we find net barter terms of trade to negatively impact on GDP 
even though this impact is not significant. This result is similar to the result of pooled OLS, except that the size of 
the coefficient differs. The result shows the effect of period of time on the link between terms of trade and GDP. It 
is a common knowledge that primary export products which mainly account for the export of the countries in our 
sample usually experience volatility from time to time. It is the exogenous shocks in the price of these commodities 
that usually leads to the unfavourable terms of trade. We find gross fixed capital formation to insignificantly 
impact on GDP which is in line with the pooled OLS result. Results for labour force and FDI follow theoretical 
expectation as they impact positively on GDP. Also, result of trade openness is in conformity with findings by 
Ayadi (2017) and Mputu (2016) as it negatively impacts on GDP. What is perhaps revealing in the results is that 
the results for gross fixed capital formation and net terms of trade in both period fixed effects and pooled OLS are 
similar except that only the size of the coefficients of their parameter estimates differs. We found that the 
coefficients of the parameter estimates under the pooled OLS are higher than that of the period fixed effects.  
 

Table-8. Results of Cross-section and Fixed Effects: GDP as the Dependent Variable. 

Cross-Section Random Effect Results   

Variables Coefficient Std. Error t-Statistic Prob. 

Constant 0.468267 0.667024 0.702025 0.4837 
NBTOT 0.000467 0.000196 2.381779 0.0184 

GFCF -0.001113 0.000658 -1.691667 0.0927 
LFT 1.487516 0.127710 11.64762 0.0000 

FDI 0.002909 0.009883 0.294384 0.7689 

TOPEN -0.011371 0.029759 -0.382102 0.7029 
R2 0.848083    

F-Stat. 174.1756    
Prob(F-statistic) 0.000000    

DW2 0.157922    
 Period Fixed Effect Results  

Variables Coefficient Std. Error t-Statistic Prob. 

Constant 5.116940 0.444825 11.50327 0.0000 
NBTOT -0.000197 0.001023 -0.192922 0.8473 

GFCF 0.002374 0.002548 0.931740 0.3532 
LFT 1.193007 0.100098 11.91842 0.0000 

FDI 0.329744 0.051529 6.399170 0.0000 
TOPEN -0.478658 0.061924 -7.729717 0.0000 

R2 0.878609    
F-Stat. 27.03548    

Prob.(F-statistic) 0.000000    

DW2 0.291087    

 



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5. Conclusion and Recommendations 
In this study, we investigated the nexus between terms of trade and economic growth in sub-Saharan African 

natural resource-rich countries over a period of 1990-2019. We employed random and fixed effects panel models in 
the study along with pooled OLS which we used for the sake of comparison. Findings of our results indicate that a 
long run relationship exists between GDP and the explanatory variables used in the study. We also noted that the 
models used in the study do not exhibit similar results. For instance, while cross-section random effect indicates 
that net barter terms of trade positively impact on GDP, result of period fixed effect  and pooled OLS show that net 
barter terms of trade negatively impacts on GDP even though it is not significant. Also, gross fixed capital 
formation is positively linked to GDP both under the pooled OLS and period fixed effect even though the result is 
not significant. Results of our study also show that in all the models, labour force total and FDI have positive link 
with GDP while trade openness negatively influences GDP. The positive impact of net barter terms of trade on 
GDP under the cross-section random effect is in tune with the export-led growth hypothesis, while the negative 
link between net barter terms of trade and GDP under the period fixed effects shows the existence of resource-
curse and Dutch disease hypotheses. The implication of the period fixed effects is that period volatility in the 
export price of SSA countries usually deteriorates their terms of trade. The period fixed effects result that shows a 
negative link between terms of trade and GDP finds support in the result of the link between trade openness and 
GDP which indicates negative link in all the models. 

With respect to our findings, we recommend that the SSA resource-rich countries should diversify their 
economies away from the traditional natural resources base. This will assist these countries in effectively utilizing 
their fixed capital to enhance productivity. Human capital has been found to impact positively on GDP as evidence 
of our study has supported this with labour force total found to positively impact on GDP. On grounds of this, we 
recommend that efforts should be geared towards improving the labour force through sound education and 
training. Foreign direct investment has been found not to contribute to growth; we advise that conducive 
environment necessary for attracting foreign investment should be put in place. This requires institutional 
upgrading to remove all bottlenecks that hamper FDI inflows. In another dimension, we recommend a gradual 
migration from the production and export of consumer products to capital goods which are more competitive in the 
international market. This will help in improving the terms of trade of these countries and thus reduces the impact 
of exogenous shocks in the prices of the export of primary products. In terms of modeling the link between GDP 
and terms of trade in these countries, both period and cross-section fixed and random effects should be explored in 
order to get better results. 
 

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