




































American Economic & Social Review; Vol. 4, No. 1; 2018  

ISSN 2576-1269   E-ISSN 2576-1277 

Impact Factor: 3.9 

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

 

 

15 

 

The Dynamics of Exports and Economic Growth: Assessing the 

Evidence from Nigeria 

 

Clement A.U. Ighodaro Ph.D 

Associate Professor and Head  

Department of Economics & Statistics 

University of Benin (UNIBEN) 

Benin City 

Nigeria 

E-mail: clemigho2006@yahoo.com 

 

 

Ovenseri-Ogbomo F. O.   Ph.D (Corresponding Author) 

Senior Lecturer 

Department of Economics, Banking and Finance 

Benson Idahosa University 

Benin City 

Nigeria 

E-mail: fovenseri-ogbomo@biu.edu.ng 

 

 

 

 

Received: November 21, 2018        Accepted: November 28, 2018             Online Published: December 1, 2018        

 

 

Abstract 

The paper empirically examines the dynamics of exports and economic growth in Nigeria using time series data for 

1970 to 2017. The Vector autoregressive model (VAR) was used to investigate the long run and short run 

relationship between exports and economic growth as well as some selected variables. The result shows that there 

exists a stable long run relationship among economic growth, exports, capital expenditure on education and social 

services. Also, the Granger causality results reveal that export Granger causes economic growth and not the other 

way round. This means that an increase in economic growth may result from increase in export, but increase in 

economic growth does not necessarily lead to increase in exports. The Impulse Response Function (IRF) shows that 

a one standard innovation in exports will lead to permanent positive impact on economic growth in Nigeria. This 

therefore supports the exports led growth hypothesis for Nigeria. 

 

Keywords: Economic Growth, Endogeneity, Multivariate and Granger Causality. 

 

1. Introduction 

Since the late 1960s, studies have been conducted to examine the role of export performance in the economic growth 

process. Although, the empirical literature can he considered to be vast, however, its results are clearly contradictory 

for developing countries and industrialized economies, a feature that could explain why this topic is still at the top of 

the agenda for many economists. Most authors as well as multilateral institutions would agree that promoting 

exports and achieving export expansion are beneficial for both developed and developing countries for many reasons 

which according to (World Bank, ‘1993) as documented in Medina Smith and de Carabobo (2000), include the 

following (i) they generate a greater capacity utilization; (ii) they take advantage of economies of scale; (iii) they 

bring about technological progress; (iv) they create employment and increase labour productivity; (v) they improve 

allocation of scarce resources throughout the economy; (vi) they relax the current account pressures for foreign 

capital goods by increasing the country’s external earnings and attracting foreign investment; and (vii) they increase 

the total factor productivity and consequently the well-being of the country. 

mailto:clemigho2006@yahoo.com


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Although, most of the empirical works support the export led economic growth hypothesis, there is no overall 

consensus on this issue. While some economists like (Ram, 1985; Fosu (1990) as well as Salvatore and Hacler, 

1991) seem to generally agree that exports benefit economic growth, others like (Jung and Marshal, 2016 as well as 

Ahmad and Harnhirum, 1995) did not find much support for export led economic growth hypothesis. On the other 

hand, Henriqes and Sadorsky (1996) found that export Granger causes economic growth, Shan and Sun (1998) 

found bi-directional causality between these variables. 

Since there appears to be no consensus on the relationship between export and economic growth in Nigeria and the 

direction of causality, this paper investigates this relationship using data from 1970 to 2003. This paper is organized 

as follows. Section two provides a review of some relevant literature while section three considers methodology and 

empirical analyses and results obtained. Section four deals with conclusion and policy implications. 

2. Review of Relevant Literature 

Although, like several other authors, Caves (1971) stated many decades ago that “Tests of the export-led model, 

then, must intrinsically involve country case studies - present industrial countries in their years of rapid growth or of 

presently underdeveloped countries”. These investigations have examined the connection between export 

performance and the rate of economic growth in particular nations, in some cases using modern time series analysis 

(Khan and Saqib, 2017; Serletis, 1992; Henriques and Sadorsky, 1996 & Al-Yousif 1997). While the results that 

emanate from cross-section studies, based on bi-variate models or ad hoc aggregate production function generally 

agree on the positive relationship between export performance and economic growth, it is odd that the empirical 

results obtained by researchers involved in country case studies strongly differ between nations and periods of time 

studied (Shan & Sun, 2015). This disparity might imply that although cross-section studies are empirically attractive 

for researchers, they could obscure inter-country differences and sacrifice revealing information about the behaviour 

of many countries. 

The most recent time series investigations concerning developed countries that have used the econometric 

methodology of cointegration have not been able to establish unequivocally that a robust relationship between these 

variables indeed exists in the long run, namely that the variables are cointegrated as documented in, for example, 

(Islam, 1998). While some have been able to find a long-run relationship, many others have rejected the ELG 

hypothesis i.e. that export expansion causes growth in the long run. Al-Yousif (1997) attempted to remedy the lack 

of empirical evidence related to major oil exporters by analyzing four Arab Gulf countries which are all members of 

OPEC: Saudi Arabia, Kuwait, the United Arab Emirates and Oman. As in other empirical studies in this field, he 

was unable to verify the existence of a long-run relationship between exports and economic growth in the four major 

petroleum exporters of the Persian Gulf. 

A possible justification of the positive results obtained in the investigation conducted by most researchers is that 

these researchers employed population and investment as proxies for the appropriate aggregate inputs, i.e. labour 

force and capital stock. Although they have been widely used in many cross-section growth studies as well as time 

series analysis, many researchers have had serious doubts about them, They thus expressed their suspicion regarding 

studies that have tested the export promotion hypothesis by using, for instance, the investment - output ratio, as 

opposed to capital stock or population instead of labour force, For instance, Alexander (1994) among others rejected 

the use of these proxies in growth studies not only on theoretical grounds but also from an empirical point of view. 

They suggested that if capital stock data are available, they should be used instead of investment because of the 

“significant measurement errors” present in these types of empirical growth studies. However, if data regarding the 

stock of capital are not available, a common recommendation nowadays is to construct a series of capital stock 

(Khan & Saqib, 2017). 

3. Methodology and Empirical Analyses 

In general, previous literature suggests that there are a numbers of factors influencing economic growth generally 

and thus spurious regression will result it only the relationship between export and economic growth is being tested. 

The model used for this study is stated below: 

 

1111 ( XSOSXEDUEXPfGDP  ---------------------------------------------------------------------(1) 

Where; t is time measured chronologically; 

GDP1 = Growth rate of gross domestic product (Proxy for economic growth) 

EXP1= Total exports 

XEDU1= Capital Expenditure on Education 

XSOS1= Capital Expenditure on Social Services 

 



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A priori, it is expected that there will be positive relationship between total exports, capital expenditure on 

education, social services and economic growth. For example, Balassa (1985) argued that in general export 

expansion helps to concentrate investment in those sectors which in turn increase the overall total factor 

productivity. The theory also suggests that causality may run from output to export. For example, Lancaster (1980) 

and Krugrnan (1984) justify one – way causality from output to exports. The data for the study are obtained from the 

Central Bank of Nigeria Statistical Bulletin (2004). 

 

3.1 Stationarity Test 

The Augmented Dickey Fuller (ADF) [Dickey and Fuller, 1979] and Phillips — Perron (PP) [Phillips and Perron, 

1988] tests were used to find out the stationarity of the variables. Consider the equation below: 




 
m

Xaa
11

111111211  --------------------------------------------------------------(2) 

Where: 

1  is our variable of interest ),,,( 111,1 XSOSXEDUEXPGDP  is the difference operator, it is the time trend 

and c is the while noise residual of zero mean and constant mean and constant variance; ),...,,( 121 maa  is a 

set of parameters to be estimated. The null and the alternative hypotheses in the unit root tests are: 

 0:0 H  ( 1  is non — stationary) 

0:1 H  ( 1  is stationary) 

The unit root hypothesis of Dickey- Fuller can be rejected if the t-statistics from these tests is negatively less than 

the critical value. 

 

3.2 Cointegration Test 

To perform cointegration test, the study uses the Johansen and Juselius (1990) multivariate cointegration test by 

formulating the VAR model below: 

PptPt LILLI   111111 )(...)()(  --------------------------------------------------(3) 

where 1 , is ),,,( 1111 XSOSXEDUEXPGDP ; a column vector and I, (L) with I = 1,..., p is a lag operator ɛ is 

the while noise residual of zero mean and constant variance. The order of lag of the model p is determined in 

advanced by Akaike’s Information Criterion (AIC). In the test, the null 

hypothesis of the cointegrating vectors is the alternative of r + 1 cointegrating vectors. 

 

3.3 Granger Causality Test 

Equation (3) can be adapted for the Nigerian economy, particularly in examining the export led growth hypothesis in 

a multivariate framework with the variables as earlier described in equation (1), Equation (3) can be expressed in 

error correction (ECM) form as: 

    ).0()1( 2

141331221110  iidVECMyLXSOSyyLXEDUyLEXPyyyLGDP ttt

        
(5) 

    ).0()1( 2

41331221110  iidUUECMyLXSOSyyLXEDUyLGDPyyyLEXP ttttt

        
(6) 

Where ECM lagged one period is the error correction term derived from the long run cointegrating relationship. 

While the estimated coefficient of ECM ( 4y ) measures the long run relationship, 1y , 2y ,…, 4y  Measures the 

short run causal relation. 

 

3.4 Impulse Response Function (IRF) 

The Generalized Impulse Response Function (GIRF) can be defined as: 

GIRF (n, ε1, ωt-1) = E[yt+n | εj, t, ωt-1] – E[yt+n | ϖt-1] t    (7) 

Where yt a random vector is εt+1 is a random shock, ϖt-1 a specific realization of the information set Ωt-1 and n is the 

forecast horizon. 

 

 



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4. Results and Interpretation 

4.1 Unit Root Tests 

Using the ADF and the PP tests for stationarity, it was found that the series are all 1(1) at both 1% and 5% 

significance level as shown in the table below. 

 

Table 1: Results for Unit Root Tests 

Variables ADF test PP Test 

Intercept Only Trend & 

Intercept 

Result Intercept Only Trend & 

Intercept 

Results 

LGDP -3.843563
***

 

(-3.6576) 

-3.963021
**

 

(-3.5614) 

1(1) -5.685272
*** 

(3.6496) 

-5.768655
***

 

(-4.2712) 

1(1) 

LEXP -5.935315
***

 

(-3.6576) 

-5.846911
***

 

(-4.2826) 

1(1) -8.447214
***

 

(-3.6496) 

-8.342028
***

 

(-4.2712) 

1(1) 

LXEDU -4.753569
***

 

(-3.6576) 

-4.537369
***

 

(-4.2826) 

1(1) -5.632645
***

 

(-3.6496) 

-6.64272
***

 

(-4.2712) 

1(1) 

LXSOS -9.135986
***

 

(-3.6661) 

-4.537369
***

 

(-4.2826) 

1(1) -7.379342
***

 

(-3.6496) 

-7.453436
***

 

(-4.2712) 

1(1) 

 
***

 Significant at 1% level of significance 
 **

 Significant at 5% level of significance 

 

4.2 Results of Cointegration Tests 

Using the Johansen multivariate cointegration technique and based on the Maximum Eigen value, we conclude that 

there exists a single cointegration vector in the model as shown in table two below: 

 

Table 2: Results of Johansen and Juselius Cointegration Results 

Sample: 1970 2017 

Included observations: 48 

Test assumption: Linear deterministic trend in the data 

Series: LGDP LEXP LXEDU LXSOS 

Lags interval: 1 to 1 

 

 

Eigenvalue 

Likelihood 

Ratio 

5 Percent  

Critical Value 

1 Percent  

Critical Value 

Hypothesized  

No. of CE(s) 

0.559463 51.44444 47.21 54.46 None
*
 

0.444390 25.21206 29.68 35.65 At most 1 

0.180025 6.406039 15.41 20.04 At most 2 

0.001706 0.054630 3.76 6.65 At most 3 

  
*
(

**
) denotes rejection of the hypothesis at 5% (1%) significance level 

  L.R. test indicates 1 cointegrating equitation(s) at 5% significance level 

 

The normalized cointegrating coefficients give the long run estimate as below: 

LGDP = – 6.151 – 2.922LEXP + 1.464LXEDU + 3.104LXSOS 

   (1.209)  (1.055)  (1.435)  (7) 

 

Equation (7) indicates that export has negative relationship with economic growth on the long run, though, with a 

high elasticity. The result is in line with the study of Dorado (1993) in a similar study of the Malaysian economy. 

However, in the short run, the estimated error correction term for D(LGDP) is statistically significant at 1%. Also, 

the speed of adjustment to long run is high at (-0673). The short run dynamics for the log of the first difference of 

gross domestic product reveals that all the variables have the expected sign except first lag of exports as well as first 

and second lags of capital expenditure on social services. 

It is worthy to note that in the short run, an increase in exports only increase economic growth in the previous two 

years. Among the variables used, capital expenditure on education appears to be the most significant determinant of 

economic growth in Nigeria. It is also found that though the separate effects of the variables may be insignificant by 

the t-statistics, the joint effect of the variables will significantly influence economic growth based on the F-statistics. 



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In the dynamics of the first difference of exports, it was found that the second lag of capital expenditure on 

education is the most important variables. In the short run dynamics of capital expenditure on education, the 

estimated error correction term has a contrary sign with a very low coefficient. On the other hand, capital 

expenditure on social services appears to be the most important variable in the short run dynamics of D (LXSOS) as 

shown in table three below. 

 

Table 3: Vector Error Correction Result 

Sample (adjusted): 1973 2017 

Included observations: 48 after adjusting endpoints 

Standard error & t-statistics in parentheses 

 

Cointegrating Eq: CointEg1    

LGDP(-1) 1.000000    

     

LEXP(-1) -2.217615    

 (0.41061)    

 (-5.40084)    

     

LXEDU(-1) 2.984899    

 (0.45394)    

 (6.57559)    

     

LXSOS(-1) 0.499324    

 (0.34344)    

 (1.45390)    

     

C -6.137688    

Error Correction: D(LGDP) D(LEXP) D(LXEDU) D(LXSOS) 

CointEq1 -0.673358 

(-4.89664) 

0.179768 

(1.96920) 

0.014576 

(0.21659) 

-0.046042 

(-0.41356) 

     

D(LGDP(-1)) 0.122411 

(0.59406) 

0.035560 

(0.25996) 

0.111561 

(1.10625) 

0.187664 

(1.12492) 

     

D(LGDP(-2)) 0.391359 

(1.84529) 

-0.092342 

(-0.65587) 

-0.117404 

(-1.13111) 

-0.115013 

(-0.66983) 

     

D(LEXP(-1)) -0.853806 

(-1.42424) 

0.214416 

(0.53185) 

-0.143186 

(0.73002) 

0.674154 

(1.38904) 

     

D(LEXP(-2)) 0.149918 

(0.34010) 

-0.214416 

(-0.73271) 

-0.143186 

(-0.66372) 

-0.267756 

(-0.75028) 

     

D(LXEDU(-1)) 1.306010 

(2.47897) 

-0.393377 

(-1.12476) 

-0.062907 

(-024398) 

0.393694 

(0.92303) 

     

D(LXEDU(-2)) 1.362213 

(2.68709) 

-0.745703 

(-2.21579) 

-0.271001 

(-1.09230) 

 

-0.154769 

(-0.37710) 

D(LXSOS(-1)) -0.212143 

(-0.86316) 

-0.022299 

(-0.13667) 

0.072594 

(0.60353) 

-0.412545 

(-2.07332) 

     

D(LXSOS(-2)) -0.444017 0.358784 0.102615 0.260889 



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(-1.82284) (2.21875) (0.86079) (1.32294) 

     

C -0.062120 

(-0.63310) 

0.154324 

(2.36916) 

0.080948 

(1.68567) 

0.049992 

(0.62932) 

R-squared  0.643745         0.4317077     0.173714  0.440659 

 

4.3 Pairwise Granger Causality Test 

The pair wise Granger causality tests reveal that there is unilateral relationship between exports and economic 

growth with the causality running from export to economic growth. The result further revealed that exports Granger 

causes capital expenditure on education and social services. The result did not show any evidence of reverse 

causality which means that there is no problem of endogeneity. The result is shown in table for below: 

 

Table 4: Granger Causality Results 

Pairwise Granger Causality Tests 

Sample: 1970 2017 

Lags: 2 

Null Hypothesis: Obs F-Statistic Probability 

LEXP does not Granger Cause LGDP 

LGSP does not Granger Cause LEXP 

48 

 

2.95462 

0.70405 

0.06912 

0.50343 

LXEDU does not Granger Cause LGDP 

LGDP does not Granger Cause LXEDU 

48 1.43754 

0.38559 

0.25512 

0.68374 

LXSOS does not Granger Cause LGDP 

LGDP does not Granger Cause LXSOS 

48 0.49497 

1.50237 

0.61501 

0.24062 

LXEDU does not Granger Cause LEXP 

LEXP does not Granger Cause LXEDU 

48 0.35426 

3.33236 

0.70491 

0.05088 

LXSOS does not Granger Cause LEXP 

LEXP does not Granger Cause LXSOS 

48 1.19172 

10.8496 

0.31917 

0.00035 

LXSOS does not Granger Cause LXEDU 

LXEDU does not Granger Cause LXSOS 

48 0.79310 

5.40114 

0.46270 

0.01064 

 

4.4 Results of Impulse Response Function (IRFs) and Variance Decomposition 

The impulse response function as shown in figure one reveals that a one standard deviation shock on economic 

growth induces more economic growth in the first years and after the second year, began to fall and in the third year 

became negative throughout the ten years period. On the other hand, a one standard deviation shock on total exports 

induces more economic growth throughout the ten years period. This was the contrary with a one standard deviation 

shock on capital expenditure on education and social services. These impacted negatively on economic growth as 

shown in figure one below. 

 

Figure One: Impulse Response Function (IFRS) 

Response to One S.D. Innovations 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Response of LGDP to LXEDU 
0.4 

 
 

0.2 

 
 

0.0 
 

 
-0.2 

 

 

-0.4 

1      2      3      4      5      6      7      8      9      10 

0.4 

 
 

0.2 

 
 

0.0 
 

 
-0.2 

 

 

-0.4 

1      2      3      4      5      6      7      8      9      10 

Response of LGDP to LGDP 



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4.5 Variance Decomposition 

The result shows that at the end of ten years, all the variables are strongly endogenous except the variable 

representing economic growth. Specifically, the forecast error variance for LGDP, LEXP, LXEDU and LXSOS are 

10.13%, 70.47%, 91.77% and 73.91% respectively as shown in table five below: 

 

Table 5: Variance Decomposition 

Period LGDP LEXP LXEDU LXSOS 

1 100.000 54.01572 99.93407 93.90853 

2 75.31464 63.43650 96.24512 69.67855 

3 47.13629 59.45352 94.55185 74.71588 

4 27.86811 62.75408 93.26340 73.19829 

5 20.08576 64.88225 92.39210 72.87481 

6 16.26251 66.85111 92.19956 73.60076 

7 13.99194 67.93567 91.99272 73.55004 

8 12.39418 68.83610 91.98258 73.84307 

9 11.12299 69.72068 91.88635 73.87481 

10 10.13392 70.47443 91.76646 73.90597 

           Ordering: LGDP LEXP LXEDU LXSOS  

 

5. Conclusion and Policy Implications 

The main objective of the paper is to empirically examine the dynamics of exports and economic growth in Nigeria 

using time series data for 1970 to 2017. The Vector autoregressive model (VAR) was applied to investigate the long 

run and short run relationship between exports and economic growth as well as some selected variables. The result 

shows that there exists a stable long run relationship among economic growth, exports, capital expenditure on 

education and social services. Also, the Granger causality results reveal that export Granger causes economic growth 

and not the other way round. This means that an increase in economic growth may result from increase in export, 

but increase in economic growth does not necessarily lead to increase in exports. The impulse response function 

shows that a one standard innovation in exports will lead to permanent positive impact on economic growth in 

Nigeria. This therefore supports the exports led growth hypothesis for Nigeria. In general, exports are a viable 

variable that determines economic growth in Nigeria. 

 

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