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                       American Economic & Social Review; Vol. 5, No. 1; 2019 

                  ISSN 2576-1269   E-ISSN 2576-1277  

Research Article               Published by Centre for Research on Islamic Banking & Finance and Business, USA 

 

     21 
 

Effect of Trade Diversification on Economic Growth of ECOWAS 

Countries 
 

Ogunyemi Joseph Kayode 

Department of Banking and Finance  

Faculty of Management and Social Sciences  

Adekunle Ajasin University, P.M.B 001  

Akungba Akoko, Ondo State, Nigeria 
 

Dare Funso David 

Ph.D, Department of Banking and Finance  

Faculty of Management and Social Sciences  

Adekunle Ajasin University, P.M.B 001 

Akungba Akoko, Ondo State, Nigeria 

 

Adewole Joseph Adeyinka 

Department of Banking and Finance  

Faculty of Management and Social Sciences  

Adekunle Ajasin University, P.M.B 001  

Akungba Akoko, Ondo State, Nigeria 
E-mail: princeadeyinkaadewolej@gmail.com 

Abstract 

This study investigated the effect of trade diversification on economic growth of ECOWAS countries. The study has 

looked at the determinants of exports and imports in some selected ECOWAS countries. The selected ECOWAS 

countries are Benin, Ghana, and Nigeria. The employed a model form of the GMM estimator was adopted but the 

empirical validation shall be based on ordinary panel regression. The study revealed that the activities of the main 

sector when rated as a percentage of the gross domestic product is a significant factor that influences the exports and 

imports in these selected countries. This means that the activities done in the main sector of these economies have 

significant effect on the value of exports and imports. The activity of the main sector is huge and voluminous 
enough to accommodate some level of significant imports in order to assist production which will also be exported. 

The study has also revealed that the service sector is also a significant factor that influences the exports and imports 

of these selected ECOWAS countries. Many experts are imported into the service sector of these countries and thus 

these served as a significant factor that possess influence on the performance of exports and imports in the countries. 

There is a negative and weak correlation between primary exports and service as a percentage of GDP. Invariably, it 

can be said that the level of primary exports may not be related with the service sector, thus, the association is 

expected. The main sector performance is found to be positively correlated with the primary exports in the selected 

ECOWAS countries. The author then suggested that there is urgent need for ECOWAS states to place more 

emphasis on the exports of manufacturers’ products and make efforts to reduce concentration on exports of primary 

(agriculture and fuel) products. This will help improve their international trade performance especially with respect 

to reducing term of trade losses and unfavorable shocks in foreign earnings. Also, the region should focus on 
production of products for domestic need; in doing this, the ECOWAS states will escape the trap of homogenous 

export and foster more intra-trade links. The region should see production as major objective rather than exports; 

this enhances industrial activities and innovations in the region. This attempt retains economic gains of resource 

within the region and foster economic well-being, the critical mass of ECOWAS challenge is weak productive 

capacity, this has accentuated the progress of the member states and the sole cause of social and economic evils 

within the region. ECOWAS should see exports as originating from domestic sufficiency.  

 

Keywords: Trade Diversification, Economic Growth, ECOWAS Countries. 

1. Introduction 

Many economies of the world are basically interested in measures that can guarantee them viable and robust 

economic statues. This quest is more pronounced among the less developed countries (LDCs) than the developed 

countries (DCs) of the world. To achieve this noble objective, developing economic are constantly implementing 



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policies that would not just increase their output but also, placed them in a very competitive position in the global 

economy. 

Among the English speaking countries in the ECOWAS sub-region in Africa, one of the policies embark upon is the 

management of their exchange rate level to encourage productivity. This step is in line with the understanding that 

exchange rate volatility (ERV) remain a source of concern as currency values partially determine the price paid or 

received for output and, consequently, this affects the profits and welfare of producers and consumers (Choudhri & 

Schembri, 2014). This implies that, ERV can influence the volume of output a country can produce since the cost of 

production is been determined by the cost of production.  

The exchange rate is the domestic price of foreign money. It can be simply viewed as the price of one currency in 

terms of another. In the wake of the recent global financial crisis in 2008/2009, there have been major fluctuations in 

the exchange rates of many countries, resulting in widespread exchange rate misalignments and re-alignments 

among countries. Since the seventies, there has been an increasing importance attached to exchange rate in many 

countries, which could be attributed to the following among other reasons: the floating exchange rate variability and 

volatility as well as the need for foreign exchange risk exposure management; the globalization process and the 

resultant increased rate and volume of fund flows among nations; the trade liberalization undertaken by developing 

countries since 1980s, resulting in opening up their economies; the internationalization of modern business; the 

continuing growth in world trade relative to national economies; the trends towards economic integration in some 

regions; and the rapid pace of change in the technology of money transfer.  

Tarawalie, Sissoho, Conte, and Ahortor (2012) investigate the effects of exchange rate volatility on output growth 

and inflation in the West African Monetary Zone (consisting of Ghana, The Gambia, Guinea, Liberia, Nigeria and 

Sierra Leone) following exchange rate regime shift. Results from their study reveal that while exchange rate 

volatility is inflationary across all the countries, its effect on output growth differ. Specifically, volatility and 

depreciation in particular negatively affects real GDP growth in Liberia and Sierra Leone but positively impacts on 

output in the other countries albeit weakly. The difference in direction and magnitude of effect is not far-fetched 

from the differences in macroeconomic conditions prevailing in each country.  

However, Razaxadehkarsalari, Haghiri and Behrooznia (2011) stress that depreciation of exchange rate through 

external forces from the aspect of government tends to cause a shift from foreign goods to domestic goods. Thereby,  

leads to diversion of income from importing countries to countries exporting through a shift in terms of trade, and 

this tends to have impact on the exporting and importing countries’ economic growth. Razazadehkarsalari, Haghir, 

and Behrooznia (2011) argue further that exchange rate depreciation has a negative effect on developing countries.  

However, the debate on exchange rate volatility and uncertainty has long divided economists as a result of different 

perspectives and methodologies of enquiry. Some studies supported the fixed exchange rate while others argued for 

the floating system. Idika (1998) argues that frequent changes in foreign exchange policies in Nigeria were caused 

by unstable political environment and have prevented these policies from coming full circle within the economy. 

Exchange rate stability which is essential ingredient for growth is influenced greatly by the appropriate policy mix 

by governments in their quest to attain macroeconomic objectives.  Nigeria being an economy that depends majorly 

on revenue from oil would surely feel the impact of exchange rate volatility. Study carried out by World Bank 

(2003), shows that many oil producing nations are exposed to variations in exchange rate due to their large oil 

wealth. However, this variation in exchange rate will then act as tax on investment in traded goods production 

especially agriculture and manufacturing which have an adverse impact on trade performance. Moreover, high 

volatility in exchange rate is harmful to economic growth, and this problem is majorly associated with less 

developed countries (LDCs) like Economic Countries of West Africa State (ECOWAS) countries because of the 

extreme volatility of their income streams and mono-economy.  

The exchange rate directly influences prices and /or profitability of traded and non-traded goods. It is a relative price 

and as such affects the allocation of resources over the short to medium term. The impact of sustained movements of 

the exchange rate on the competitive position of domestic industry vis-à-vis foreign industry in both domestic and 

foreign markets is the key transmission mechanism. In effect, uncertainties resulting from unanticipated changes in 

the domestic and international macroeconomic environments are also key factors. This is more striking in the 

developing countries which depend heavily on external trade: export to earn foreign exchange, imports to purchase 

consumer, intermediate and capital goods as well as external borrowing to finance the foreign exchange gap. 

Therefore, the dependent peripheral structure of these economies is a major factor in the determination of exchange 

rates. 



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Since the introduction of fixed exchange rate regime and adoption of a generalized floating system by the 

industrialized countries in 1973, most developing countries including ECOWAS, have adopted various types of 

exchange rate policies ranging from the peg system, weighted currency basket, managed floating and more recently 

to the monetary zone arrangement (Mordi 2006). The inconsistence in management of  various exchange rate policy 

adopted so far in Nigeria to check the high rate of volatility in exchange rates has jeopardized the overall 

macroeconomic objectives of the government, especially trade performance, since maintaining a relatively stable 

exchange rate within a country enhances and boosts economic growth (Mordi,2006; Mahmood & Ali, 2011; & 

Aliyu 2011).  

Scholars and researchers have put forward suggestions that exchange rate volatility may effects outputs negatively 

or positively. Bundesbank (2010) opined that market agents more than ERV determine the level of output. This 

position is been supported by the view of previous scholars like Cushman (1983) and Lastrapes (1992) who 

maintained that if economic agents are moderately risk averse the impacts of exchange rate volatility on outputs will 

be negative. Additionally, some scholar believed that the negative impact may come directly through uncertainty 

and adjustment costs, and indirectly through its effect on allocation of resources and government policies (Aliyu, 

Yakub, Sanni, & Duke, 2013). Also, some scholars reported the possibility of both positive and negative 

relationships, and some still submitted a no relationship between these variables (Bergvall, 2004; Lama & Medina, 

2010). However, numerous studies still submitted the existence of positive relationship between ERV and output 

(Aron., Elbadawi, & Khan, 1997; Bahmani-Oskooee, 1991; Gbesola & Garba, 2014). 

The overall evidence is best characterized as mixed as the results are sensitive to the choices of proxies for exchange 

rate volatility, sample period, model specification, and countries considered. Nevertheless, the relationship is still 

vital enough to be explored especially for the principal ECOWAS countries namely, Nigeria, Ghana, Gambia, Sierra 

Leones, and Liberia, due to various macroeconomic events, for instance the global financial crisis in 2007/2008. 

Due to these events the relationship between their major trading partners is of interest. More so, for most of these 

countries production activity have been one of the major engines of economic growth. Based on the 

inconclusiveness of previous study in terms of theoretical and empirical findings, this study tries to take a different 

approach in analyzing the relationship. Previous work used autoregressive conditional heteroscedastic (ARCH) and 

generalized autoregressive conditional heteroscedastic (GARCH 1,1) to investigate the long run and the short run 

relationship between exchange rate volatility and output level. The existence of inconclusiveness in the explanation 

of the relationship between exchange volatility and output have led policy makers and researchers to investigate the 

nature and extent of the impact of such movements on volume outputs. However, this study investigate this 

relationship performing Granger causality test in the vector error correction (VECM) framework as in the study of 

Baak (2008). Furthermore, this study looked at the relationship from an aggregate point of view (ECOWAS) not at 

country level. Thus in the light of trade performance, the purpose of this study is to investigate the determinant of 

trade performance in Economic Countries of West Africa State.   

The main objective of this study is to examine the effect of trade diversification on economic growth of ECOWAS 

countries from 1980 to 2017. To achieve this, the specific objectives are also to identify determinants of exports and 

imports in selected ECOWAS countries, to determine the impacts of export determinants on export performance in 

selected ECOWAS countries, to analyse the effects of import determinants on import performance in the selected 

ECOWAS countries.  

The hypotheses to be tested in this study are stated in a null form which are (1) Exports and imports have no 

significant impact on trade performance in ECOWAS countries (2) There is no significant impacts of import 

determinants and import performance in Selected ECOWAS countries (3) There is no significant effect of import 

determinants on import performance in ECOWAS countries. 

2. Theoretical Framework  

This presents a straight–ward generalization of the model proposed by Herzer and Nowak-Lehnmann’s (2006) as 

used by Gustavo Ferreira (2009) to test the hypothesis that export diversification has influenced economic growth in 

Costa Rica via externalities of learning-by-exporting and learning-by-doing.  

According to Gustavo Ferreira (2009), the economy is constituted by n sectors from which s are export sectors, thus 

S € n. It also assumed that each i sector is represented by one firm, and that their corresponding output, at a given 

point in time t, is determined by a neoclassical production function:  

Yit = fit (Kit, Lit, Pt) --------------- (1)  



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Where kit and Lit are the standard capital and labour inputs respectively. The input Pt is an index of public 

knowledge and seen as a positive externality in equation (1)  

The knowledge externality has two main properties:  

Knowledge externality is primarily generated by the export sectors as a result of both learning-by-exporting and 

learning-by-doing. Learning-by-exporting arises when an export sector acquires knowledge from their foreign 

purchasers who share part of their know-how and offer advice on productivity enhancement. On the other hand, the 

basic idea behind learning-by-doing is that knowledge creation occurs as a by-product of production and it depends 

on the firm’s cumulative output.  

Hence, firms will increase their stock of knowledge as they expand their exports, and this accumulation process will 

accelerate as a firm exposes itself to competitive international markets.  

Gustavo (2009) assumed that each export sector St produces an equal amount of public knowledge Pt. Hence, a 

nation’s level of aggregated knowledge is given by the following equation  

Pt = St Pet ------------------------ (2)  

Given that Pet is a constant and not directly observable parameter, the level of knowledge in the economy can be 

instead expressed as a function of the number of export sector  

Pt = Z(S)t -------------------------- (3)  

It is assumed that primary goods tend to have a lower potential for learning-by-doing and learning-by-exporting 

comparatively to manufactured goods. Consequently, they hypothesized that the pace of knowledge creation in the 

economy will increase with an increase in the share of manufactured products in total exports.  

Based upon this premise, a new knowledge equation can take the following form  

Pt = Z(St, MXt) --------------------- (4)  

Where the share of manufactured products in total export (MXt) and the number of export sectors (St) are proxies 

for the stock of knowledge in the economy  

The second main property of this model is that knowledge Pt is considered a public good and constant within all 

sectors. By treating Pt as a given, our production function fit has constant-returns-to-scale. It is also assumed that all 

firms operate in perfect competition and are price takes.  

Now, the aggregate production Yt is written as function n  

Yt = ΣYi,t = fi,t (Kit, Lit, Pt) ------------------------------- (5)  

i=1  

Inserting the public knowledge parameter of equations (4) into the production function, we get  

Yt = fit(kit, Lit)(St, MXt) = Kt β Lt δ St ψ MXt γ ---------------------------- (6)  

Where Kt and Lt represent respectively the stock of accumulated capital and labour force of the economy, and 

parameters are constant.  

Inclusion of the number of export sectors and the shares of manufactured exports as explanatory variables to 

equation (6), it is implied that both horizontal and vertical export diversification influence economic growth via 

externalities of learning-by-doing and learning-by-exporting. That is, are greater than zero.  

In order to test the second hypothesis, we made us of dynamic panel growth model based on GMM estimator 

developed by Arellano and Bond (1991)  

To investigate the relationship between export diversification and per capita income, we will therefore use the 

equation of the system GMM estimator similar to Laderman and Maloney (2007).  

Therefore, we estimate a general growth equation of the form:  

Δyi,t = αyi,t-1 + X’i,tβ + yt + ηi + νi,t ----------------------------------- (7)  

Where Δyi,t denotes the log differences of income per capita in period t, yi,t-1 is the log initial income, Xi,t is a 

vector of potential determinants of growth, yt captures sample-wide time effects, ηi are the unobserved time-

invariant country-specific effects, and Vi,t is the residual error component.  

3. Methods  
To empirically justify the research objectives and obtain the long-run relationship between growth and export 

diversification, hereby validating the extent of growth induced by increasing manufacturing export value added and 

its returns to societal development; two models shall be developed to test each of the hypothesis.  



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3.1 ECOWAS Growth-Diversification Model  

To empirically test the long-run relationship between economic growth and increased manufacturing value added 

(diversification) in the ECOWAS region, the equation below is deemed fit  

Yt = ECIt φ, EDIt β --------------------------------- (8)  

Where Yt is real GDP in period t, EDIt is the export diversification index of Ecowas region and ECIt Export 

concentration index.  

Transforming equation (7) into a log-linear regular form, we have  

LogYt = α + φlogECIt + βlogEDIt+ υt ------------------- (9)  

Where log is the natural logarithm of the variable, and estimates φ, β represent elastisities. The error term υt is 

assumed to be white-noise (random walk) normally and identically distributed.  

Equation (9) will be subjected to empirical scrutiny and the model will test the diversification-led growth hypothesis 

for the manufacturing sector in Ecowas states.  

Ho :φ, β = 0  

H1 :φ, β > 0  

It is hypothesized that estimates φ, β are positive and statistically significant, thus confirming the diversification-led 

growth.  

3.2 Panel Per capita trade growth model  

In the testing the country specific diversification induced growth in the Ecowas region, a model form of the GMM 

estimator will be adopted but the empirical validation shall be based on ordinary panel regression.  

Δyi,t = αyi,t-1 + X’i,tβ + νi,t ------------------------- (10)  

Where Δyi,t denotes log difference of income per capita in period t, yi,t-1 is the log initial income, Xi,t is a vector of 

potential determinants of growth and Vi,t is the residual error component.  

X’i,t = IVtα1, AGRtα2, MFRtα3, SEVtα4, SPEtα5 ------------------------- (11)  

Where IVt is investment, AGRt is the share of agriculture contribution to GDP (agricultural value added), MFRt is 

the share of the manufacturing sector to GDP (Manufacturing value added), SEVt is the share of the service sector to 

GDP (services value added), and SPEt is the percentage share of primary export.  

logyi,t = α0 + α1logyi,t-1 + α2logIVt + α3logAGRt + α4logMFRt + α5logSEVt + α6logSPEt+ Vt ----------------------

--- (12)  

It is hypothesized that the estimates α1, α2, α3, α4, α5, α6, α7 are positive and statistically significant but a greater 

magnitude is expected from α4, α5 in order to appropriately validate the bases of the research.  

Ho: α1, α2, α3, α4, α5 = 0  

H1: α1, α2, α3, α4, α5 > 0  

3.3 Econometric Approach  

The study relies on secondary data; the 3 three member states of ECOWAS including Benin, Ghana, and Nigeria. 

The required data set on the variables to be tested in the models adapted for the study were drawn from these 

countries.  

In performing the empirical analysis, the first step is to examine the time series properties of all the variables. For 

proper model specification, the unit root and co-integration test shall be conducted.  

The Augmented Dickey-fuller (ADF) root tests for determining variables orders of integration shall be presented. 

The test for the order of stationary has led to the development of the Dickey- Fuller (1979) set of unit root tests. We 

test the null hypothesis of a difference stationary against the alternative hypothesis of a level stationary. That is:  

H0: Yt = I(1)  

H1: Yt =I(0)  

With critical values which are all negative and larger (in absolute terms) than ADF statistics; if the null hypothesis 

cannot be rejected then Yt cannot be stationary. It may be I(1) or I(2) or have an even higher order of integration. 

The test for unit root is pertinent because it has been observed that, very often time series data are non-stationary. In 

such cases, the residuals of these time series are correlated with their own lagged values, thereby violating one of the 

standard Ordinary Least Square assumptions; hereby making estimates biased and inconsistent with standard errors 

generally underestimated.  



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In determining the long-run relationship of the variables, the study shall adopt a co-integration test based on the 

approach of Johansen (1988) and Juselius (1990) for testing the long-run dynamic behaviour of variables under 

study. The vector error correction model shall be adopted for testing the short-run dynamics and guarantee 

successful correction of errors generated in each period within the model, the Johansen procedure unlike the Engel 

and Granger two steps static procedure allows the simultaneous evaluation of multiple relationships and imposes no 

prior restrictions on the co-integration space.  

From the foregoing econometrical analyses, it is established that before regression analysis of equation model can be 

made, it is essential to identify the order of integration of each time series provided that the variable can be 

transformed into a stationary variable through differencing, concerning the dynamic growth model in equation above 

which is rewritten below.  

LOG(Y) = α0 + φLOG(ECI) + βLOG(EDI)  

The differenced model can be written as  

LOG(DY) = α0 + φLOG(DEDI) + βLOG(DECI)  

Most studies assume that time series data are stationary. However, it has been argued that this assumption is not 

appropriate for most economic variables and that these variables are better modeled as integrated of order one I(1) 

processes, that is, non-stationary and needs to be differenced once to become stationary.  

A non-stationary series can be reviewed as a testable hypothesis by performing unit root test. A test for unit root has 

its origin in the work of Fuller (1976) and Dickey and Fuller (1979, 1981). The theory of co-integration arises out of 

the need to ensure the long run equilibrium or relationship of the observed variables. The theoretical stages involved 

are as follows.  

3.4 Testing for the order of the integration of the series  

The test for the order of stationary has led to the development of the Dickey- Fuller (1979) set of unit root tests. We 

test the null hypothesis of a difference stationary against the alternative hypothesis of a level stationary. That is:  

H0: Yt = I(1)  

H1: Yt =I(0)  

With critical values which are all negative and larger (in absolute terms) than ADF statistics; if the null hypothesis 

cannot be rejected then Yt cannot be stationary. It may be I(1) or I(2) or have an even higher order of integration.  

3.5 Co-integration Representation  

After determining the order of integration as established in the first stage, the second stage proceeds to obtain the co-

integrating vector in the regression equation. This is conducted using the Johansen procedure, the indication of one 

(unique) co-integrating vector at appropriate lag that ensure non serial correlation confirms the convergence of the 

estimated variables.  

3.6 Error Correction Modeling Representation  

Having established the long run series convergence, and that the variables are co-integrated, the third stage proceeds 

to estimate the error correction representation. The ECM incorporates the full (short run) dynamics of the model 

specified above. The theory of error correction model arises out of the need to integrate short run dynamics with 

long run equilibrium. At this stage all the conventional statistical tests of significance are considered to be 

appropriate including the diagnostic tests for the assessment of the adequacy of the model. Co-integration is a 

necessary condition for error correction model to hold.  

The purpose of the ECM is to switch to a short run model. Allowance is made for any short run divergence, in a 

corrective mechanism by which previous disequilibria in the relationship between the level of money balance and 

the level of one or more of its determinants, are permitted to affect the current change in money holdings.  

Theory expects that the ECM be negative and highly significant implying that an error in the current period is being 

corrected in the previous period.  

In estimating the model two, a panel least square analytical procedure is attempted to sieve the effects of the trade 

composition indicators on per capita income. Afterwards, the model estimated the country and period fixed effects.  

 

 

 



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4. Results 

4.1 Descriptive Statistic  

Descriptive statistics in this study considers important elements such as the mean, standard deviation, skewness and 

kurtosis for the variables used in the study where the interaction of data are described as given thus. 

Table 1 Summary of Descriptive Statistics 

Variables  OBS Mean Std. Dev Min Max 

PEXP 81 55.36 18.76 12.40 81.29 

AGDP 81 26.97 5.40 21.34 36.57 

SGDP 81 45.39 6.31 35.91 54.36 

MSCT 81 13.71 4.91 7.42 21.19 

Source: Researchers Computation, 2019 

The above presented table 1 presents the summary of the descriptive statistics for the parameters used specifically 

agricultural produce as a percentage of GDP (AGDP) is the dependent variable, while primary exports, Service as a 

percentage of Gross Domestic Product (SGDP), and Main Sector Performance (MSCT) as the independent 

variables. 

Primary export for the period of study and for the three countries namely Benin Republic, Nigeria, and Ghana has an 

average value of 55.36. The primary exports of these countries deviated by 18.76 while its minimum value stood at 

12.40 and its maximum value stood at 81.29. Agricultural produce as a percentage of gross domestic product has an 

average percentage of 26.97%, it was highest at 36.57%, lowest at 21.35%, and its standard deviation was 5.40%. 

Service as a percentage of gross domestic product has an average value of 45.39%, its standard deviation stood at 

6.31%, the maximum value is 54.36% and the minimum value is 35.91. The main sector performance has an average 

value of 13.71, it deviated by 4.91, the minimum value stood at 7.42, and the maximum value is 21.19. 

4.2 Correlation Matrix 

Table 2 shows the correlation values between the dependent and independent variable and covariance matrix 

(amongst themselves). Correlation matrix depicts the level of association between and among all pairs of variables 

given the level of significance.  

Table 2 correlation matrix 

Variables  PEXP AGDP SGDP MSCT 

PEXP 1.0000    

AGDP 0.2567 1.0000   

SGDP -0.1225 0.7024 1.0000  

MSCT 0.1043 -0.7450 -0.9862 1.0000 

Source: Researchers Computation, 2019 

From the presented result, primary exports have a positive but weak association with agricultural produce as a 

percentage of gross domestic products. This implies that as the level of agricultural produce increases, primary 

exports also increase. The correlation coefficient between primary exports and service as a percentage of gross 

domestic products is -0.1225. This implies that there is a negative and weak correlation between primary exports and 

service as a percentage of GDP. Invariably, it can be said that the level of primary exports may not be related with 

the service sector, thus, the association is expected. The main sector performance is found to be positively correlated 

with the primary exports in the selected ECOWAS countries. It therefore means that primary export is expected to 

move in the same direction in which the main sector performance moves. Although the correlation is found to be 

weak but it is a positive correlation. 

Agricultural produce as a percentage of GDP is found to be positively correlated with service sector as a percentage 

of GDP. This may be explained as seeing more expertise in the agricultural sector due to the involvement of 

technical know-how of those in the service sector. The correlation coefficient between agricultural gross domestic 

product and service as a percentage of GDP stood at 0.7024. A negative correlation and strong one is found to exist 

between agriculture as a percentage of GDP and the main sector performance. The correlation coefficient is found to 

be -0.7450 implying that as one variable increases, the other is on the decrease. 

A negative and strong correlation coefficient is found between the service sector as a percentage of GDP and main 

sector performance in the selected ECOWAS countries. The correlation coefficient stood at -0.9862 implying that 

the main sector performance would reduce should the service sector performance as a percentage of GDP increases. 



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4.3 Pre-Test Statistics 

4.3.1 Panel Data Unit Root Test 

Table 3 below shows the summary of the panel data unit root analysis using the Levin, Lin and Chu method at level 

and first difference. The result of this method is also backed up by the result of the Augmented Dickey Fuller (ADF) 

method as well as the Phillip Perron (PP) method. 

Table 3 Summary of Panel Unit Root at Level 

Var t-test p-val 

PEXP 0.02341 0.5093 

AGDP -0.53009 0.2980 

SGDP -0.44049 0.3298 

MSCT -0.37367 0.3543 

Source: Researcher Computation (2019) 

Table 4 Panel Unit Root at First Difference 

Var t-test p-val Remarks 

PEXP -1.73730 0.0412 I(1) 

AGDP -5.77450 0.0000 I(1) 

SGDP -3.87605 0.0001 I(1) 

MSCT -5.88380 0.0000 I(1) 

Source: Researcher Computation (2019) 

From the result of the panel data unit root reported in the above table, it was discovered that all the variables are 

stationary at first difference. As such, the stationary level of the variables is employed in the analysis of the study. 

The pooled ordinary least square, fixed effect and random effect will be examined at first difference in order to 

avoid or correct the likely problem of autocorrelation which may occur while using the variables even while they are 

not stationary.  

4.3.2 Hausman Specification Test 

Table 5 summarized the Hausman specification test for random effect. 

Test Summary Chi-Sq. 

Statistic 

Chi-Sq. d.f. Prob.  

Period random 1.531497 3 0.6750 

** WARNING: estimated period random effects variance is zero. 

 

Period random effects test comparisons 

 

Variable Fixed   Random  Var(Diff.)  Prob.  

D(MSCT) -1.248366 -1.207472 0.012895 0.7188 

D(PEXP) 0.054854 0.030956 0.000482 0.2762 

D(SGDP) -0.446702 -0.432045 0.000760 0.5951 

Source: Researcher Computation (2019) 

The Hausman specification test is conducted to select between the fixed effect and random effect. The null 

hypothesis is that random effect is appropriate while the alternative hypothesis states that fixed effect is appropriate. 

From the result of the Hausman test, the probability value is 0.6750 which signifies that the test is not significant at 

5% level of significance. Therefore, the study makes use of the random effect because the test tells that the random 

effect is appropriate since the test is not significant at 5%. 

The pooled regression result is summarized in Table 6 below. This result will assist in empirically verifying the first 

hypothesis of the study. The first hypothesis states that exports and imports have no significant impact on trade 

performance in ECOWAS countries. 

Table 6 Summary of Pooled OLS Result 

Variable Coefficient Std. Error t-Statistic Prob.   

C -0.207084 0.248105 -0.834665 0.4068 

D(MSCT) -1.244974 0.192367 -6.471874 0.0000 

D(PEXP) 0.054640 0.034682 1.575438 0.1197 



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D(SGDP) -0.443001 0.055950 -7.917787 0.0000 

 Effects Specification   

Cross-section fixed (dummy variables)  

Period fixed (dummy variables)  

R-squared 0.693244     Mean dependent var -

0.486876 

Adjusted R-squared 0.510969     S.D. dependent var 3.711734 

S.E. of regression 2.595643     Akaike info criterion 5.026878 

Sum squared resid 464.8779     Schwarz criterion 6.052106 

Log likelihood -236.9918     Hannan-Quinn criter. 5.442783 

F-statistic 3.803285     Durbin-Watson stat 1.931303 

Prob(F-statistic) 0.000001    

Source: Researcher Computation (2019) 

The result of the pooled ordinary least square above depicts that main sector and service sector performance as a 

percentage of GDP are found to be significant factors that influence the level of trade performance in the ECOWAS 

countries. 

Main sector performance is found to exert a negative relationship with trade performance in the selected ECOWAS 

countries with a coefficient of -1.24, implying that as main sector performance increases by one percent, trade 

performance reduces by 1.24 percent. Also, service sector performance as a percentage of GDP is found to be 

negatively related with trade performance in the selected ECOWAS countries. This implies that a percentage 

increase in the performance of the service sector will lead to a 0.44 percent decrease in trade performance in the 

ECOWAS countries. 

The study therefore rejects the first hypothesis that states that there are no significant determinants of trade 

performance in the ECOWAS countries as the main sector performance as well as the service sector performance are 

found to be significant factor that influences trade performance in the selected ECOWAS countries. 

Furthermore, in achieving the second and third objectives of the study, the fixed effect result of the ordinary least 

square is presented below. 

Table 7 Summary of the Random Effect OLS 

Variable Coefficient Std. Error t-Statistic Prob.   

C -0.219775 0.245051 -0.896854 0.3718 

D(MSCT) -1.207472 0.152748 -7.905006 0.0000 

D(AGDP) 0.030956 0.026356 1.174554 0.2428 

D(SGDP) -0.432045 0.047518 -9.092210 0.0000 

 Effects Specification   

   S.D.   Rho   

Period random  0.000000 0.0000 

Idiosyncratic random 2.568947 1.0000 

 Weighted Statistics   

R-squared 0.542008     Mean dependent var -

0.486876 

Adjusted R-squared 0.529167     S.D. dependent var 3.711734 

S.E. of regression 2.546890     Sum squared resid 694.0716 

F-statistic 42.20950     Durbin-Watson stat 1.952857 

Prob(F-statistic) 0.000000    

     
Source: Researcher Computation (2019) 

The second hypothesis of the study states that there is no significant impact of the determinants of trade performance 

on exports performance in the selected ECOWAS countries. The result presented in Table 7 above indicates that 

main sector performance and service sector performance influence export performance in the selected ECOWAS 

countries. 



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Main sector is found to influence exports performance in these selected countries negatively such that a percentage 

change in the performance of the main sector will lead to a decrease in export performance by 1.20 percent. The 

study found an indirect and significant relationship between main sector performance and export performance in the 

selected ECOWAS countries. It was also discovered in the result of the random effect OLS above that primary 

export does not significantly influence the performance of exports in the selected ECOWAS countries. This however 

does not follow theoretical backings. There is a positive relationship but the relationship is found to be insignificant 

and as such, the effect of primary exports on export performance can be disregarded. 

Furthermore, it was discovered in the study that service sector performance as a percentage of GDP influences a 

negative effect on exports performance in the selected ECOWAS countries such that a percentage increase in the 

service sector performance as a percentage of GDP will lead to a 0.43 percentage decrease in export performance. 

4.4 Coefficient of Determination (R2) 

The coefficient of determination is used to measure the strength of the relationship between the dependent variable 

and the independent variables. It measures the extent to which variations in the independent variables explain the 

variations in the dependent variable. From the result of the test above, the coefficient of determination is found to be 

0.542008 implying that 54.20 percent of the variations in the export performance is explained by variations in main 

sector performance, primary exports, and service sector as a percentage of GDP while the remaining 44.80 

percentage are explained by factors which are not included in the model. 

4.5 F-Statistics 

The F-statistics is used to measure the joint significance of the independent variables on the dependent variable. The 

F-statistics value of the study stood at 42.20950 with probability of 0.0000 implying that the independent variables 

significantly influence the performance of exports in the selected ECOWAS countries. It therefore implies that main 

sector as a percentage of GDP, primary exports and service sector as a percentage of GDP jointly have significant 

influence on export performance in the selected ECOWAS countries. 

4.6 Residual Test  

4.6.1 Durbin Watson Test 
The Durbin Watson test is used to understand if there is the problem of autocorrelation in the residuals of the 

variables employed in the study. The DW value of the result is 1.952857. Using the rule of thumb, the study can be 

said to be free from the problem of autocorrelation since the DW value is very close to 2. Another proof is that the 

DW value is greater than the R2 value, therefore it can be said that the model is free from the problem of 

autocorrelation. 

4.6.2 Normality Test and Stability of the Model 

Figure 1 

0

5

10

15

20

25

30

35

-12 -10 -8 -6 -4 -2 0 2 4 6 8

Series: Standardized Residuals

Sample 1981 2017

Observations 111

Mean       0.000000

Median   0.210791

Maximum  8.692923

Minimum -11.39175

Std. Dev.   2.511920

Skewness  -1.154540

Kurtosis   10.48012

Jarque-Bera  283.4388

Probability  0.000000

Source: Author Computation (2019) 

The Jarque-Bera test is used to check for the normality of the variables and also the stability of the model. The 

Jarque-Bera value is 283.4388 with the probability value of 0.0000. The null hypothesis is that the variables are not 

normally distributed. The null hypothesis is therefore rejected since the test is significant at 5% level of significance 

and thus it can be concluded that the model is normally distributed. 

 



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5. Discussion and Implications of Findings 

The study has looked at the determinants of exports and imports in some selected ECOWAS countries. The selected 

ECOWAS countries are Benin, Ghana, and Nigeria. The study revealed that the activities of the main sector when 

rated as a percentage of the gross domestic product is a significant factor that influences the exports and imports in 

these selected countries. This means that the activities done in the main sector of these economies have significant 

effect on the value of exports and imports. The activity of the main sector is huge and voluminous enough to 

accommodate some level of significant imports in order to assist production which will also be exported. The study 

has also revealed that the service sector is also a significant factor that influences the exports and imports of these 

selected ECOWAS countries. Many experts are imported into the service sector of these countries and thus these 

served as a significant factor that possess influence on the performance of exports and imports in the countries. 

The study also revealed that the primary export is not significant enough to influence the level of imports and 

exports in the selected ECOWAS countries. This finding is against the findings of Aissata, Siba and Hady (2018); 

Santos-Paulino (2000) which found that potential trade was significant in the European Union and that exports react 

negatively to an increase in relative prices. 

5. Conclusion 

In this study, an attempt was made to determine the effect of trade diversification on economic growth of ECOWAS 

countries. West African countries are growing at a satisfactory rate, but it does not change the fact that many are not 

only on a Less Developed Countries status, still fail to diversify to promote a more inclusive growth. Africa is still 

lagging when it comes to regional, trade integration and seems to rely more on the rest of the world than their closest 

neighbors. Policies with new approaches and techniques tailored to the African market must be enacted and 

implemented to drift away from superficial cooperation agreements of simple deregulation and trade liberalization 
and dive into deeper levels of integration. In order to do so, regional and trade integration should be a priority.  

Main sector performance is found to exert a negative relationship with trade performance in the selected ECOWAS 

countries with a coefficient of -1.24, implying that as main sector performance increases by one percent, trade 

performance reduces by 1.24 percent. Also, service sector performance as a percentage of GDP is found to be 

negatively related with trade performance in the selected ECOWAS countries. This implies that a percentage 

increase in the performance of the service sector will lead to a 0.44 percent decrease in trade performance in the 

ECOWAS countries. 

The study therefore rejects the first hypothesis that states that there are no significant determinants of trade 

performance in the ECOWAS countries as the main sector performance as well as the service sector performance are 

found to be significant factor that influences trade performance in the selected ECOWAS countries. Based on the 

findings in this study, the following recommendations are suggested; (1) There is urgent need for ECOWAS states 

to place more emphasis on the exports of manufacturers’ products and make efforts to reduce concentration on 

exports of primary (agriculture and fuel) products. This will help improve their international trade performance 

especially with respect to reducing term of trade losses and unfavourable shocks in foreign earnings. (2) Also, the 

region should focus on production of products for domestic need; in doing this, the ECOWAS states will escape the 

trap of homogenous export and foster more intra-trade links. The region should see production as major objective 

rather than exports; this enhances industrial activities and innovations in the region. This attempt retains economic 

gains of resource within the region and foster economic well-being, the critical mass of ECOWAS challenge is weak 

productive capacity, this has accentuated the progress of the member states and the sole cause of social and 

economic evils within the region. ECOWAS should see exports as originating from domestic sufficiency. (3) 

ECOWAS needs to concentrate efforts on exports of commodities that attract more stable earnings by increasing the 

value-added of its major exports and encouraging investment into viable sectors. Continue dependence on primary 

commodities would only diverge the region from achieving stationary stage of income and perpetually place the 

region in income trap; concentration of efforts on quick supply response and highly dynamic products which attract 

higher price in the international market would revive the economies of the region. (4) Also, the region should 

attempt a horizontal diversification in the short-run by expanding the variety of usefulness of its export basket. For 

instance, products such as Cocoa, Crude, Coffee etc can be processed into their various useful-ness before exporting 

rather than exporting in crude form. Industries should be developed to explore and handle several components of 

these products. Crude for instance, at processing stage can be sieved into several components such as gases, 

kerosene, diesel, motor oil, petroleum jelly, etc; this implies that several industries can be developed from crude 

resource. (5) The ECOWAS member states should concentrate on mass injection of capital investment in viable 



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sectors of their economies, the region should invest heavily in developing sectors that are capable of generating 

spill-over and export discovery that would enhance comparative advantage along a new export cluster and facilitate 

the emergence of new exports. This would accentuate the level of human capital utilization, societal advancement 

and global trade penetration of the region. 

6. References 

Aron, J., Elbadawi, I. and Khan, B. (1997). Determinants of real exchange rate in South Africa. Working Paper for 

Centre for Studies of African Economics and Statistics, Oxford University, 16, 45–62. 

Aliyu, S. R.(2011). Impact of oil price shock and exchange rate volatility on economic growth in Nigeria: An 
empirical investigation. Research journal of international studies,4(11),220-231. 

Aliyu, S., Yakub, M., Sanni, G., and Duke, O. (2013). Exchange Rate Pass-through in Nigeria : Evidence from a 

Vector Error Correction Model. EconPaper, 1071–1084. 

Bundesbank, D. (2010). Nominal and real exchange rate movements during the financial crisis. Deutsche 

Bundesbank Monthly Report, (July), 39–55. 

Bahmani-Oskooee, M. (1991). Is there a long-run relation between the trade balance and the real effective exchange 

rate of LDCs? Economics Letters, 36, 403–407. 

Bahmani-OskooeeM, Economidou C, and Goswami G, G., (2005). Export-led growth hypothesis revisited: a panel 

cointegration approach. Scientific Journal of Administrative Development 3:40–55. 

Choudhri, E. and Schembri, L. (2014). Productivity, commodity prices and the real exchange rate: The long-run 

behavior of the Canada–US exchange rate. International Review of Economics & Finance, 29, 537–551. 

Gbesola, A. and Garba, T. (2014). Trend of the Nigeria exchange rate from 1970-2013 (pp.32–43). 
Idika, K.U. (1998). Nigeria foreign exchange markets. Management and Development. Spectrum Book Ltd. Ibadan. 

Nigeria International Monetary Fund (1984). Exchange rate variability and world trade, IMF Occasional 

Paper 28 

Martin, W., 2001. “Trade policies, developing countries and globalization”. Development Research Group. 

Washington DC: World Bank.  

Mordi, C. N. (2006). Challenges of exchange rate volatility in economics management in Nigeria. Central Bank of 

Nigeria Bullion. 30(3) 

Mahmood, I. & Ali, S. Z. (2011). Impact of Exchange Rate Volatility on Macroeconomic performance of Pakistan, 

International Research Journal of Finance and Economics, issue 64. pp. 1450-2887. 

Medina-Smith, E. J. 2001. “Is the Export Led Growth Hy-pothesis Valid for the Developing Countries? A Case 

Study for Costa Rica,” Policy Issues in International Trade and Commodities, Study Series Number 7, 
United Nations Con-ference on Trade and Development.  

Tarawalie, A. B., Sissoho, M., Conte, M., and Ahortor, C. R. (2012). Exchange Rate, Inflation and Macroeconomic 

Performance in the West African Monetary Zone (WAMZ). WAMI Occasional Paper Series No. 2  

 

 

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