




































 
 

 

35 
© 2021 by the authors; licensee Asian Online Journal Publishing Group 
 

Economy 
Vol. 8, No. 2, 35-48, 2021 

ISSN(E) 2313-8181/ ISSN(P) 2518-0118 
DOI: 10.20448/journal.502.2021.82.35.48 

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

    
 

 

 
 
 
Trade Liberalization and Economic Growth: The Scenario of the MINT Economies 

 
Innocent. U. Duru   

 

 
 
Department of Economics, Renaissance University Ugbawka, Enugu State, Nigeria. 
Email: iud3x@yahoo.com Tel: +234-8154827934 

 
Abstract 

This study investigated the impact of trade liberalization on economic growth for Mexico, 
Indonesia, Nigeria and Turkey from 1986 to 2020. The Autoregressive Distributed Lag Bounds 
approach to cointegration and Toda and Yamamoto causality test were utilized for this study. The 
long-run results revealed that there is no relationship between trade liberalization and real gross 
domestic product per capita except for Mexico and in this situation, the significance level was at 
10%. The results of the causality test showed that no causality was detected between real gross 
domestic product per capita and trade liberalization for Mexico and Indonesia. A bidirectional 
causality between real gross domestic product per capita and trade liberalization was found for 
Nigeria whereas a unidirectional causality from trade liberalization to real gross domestic product 
per capita was revealed for Turkey. The no causality results for Mexico and Indonesia means that 
the policy objectives of trade liberalization and economic growth can be pursued independently in 
both economies. In addition, the bidirectional causality detected for Nigeria suggests that the 
policy objectives of trade liberalization and economic growth can be pursued together in Nigeria. 
Furthermore, the unidirectional causality from trade liberalization to real gross domestic product 
per capita found for Turkey implies that she employs trade liberalization policies effectively for 
objectives of economic growth, thus trade liberalization causes economic growth. 

 
Keywords: Trade liberalization, Economic growth, Cointegration, ARDL, Toda and Yamamoto Multivariate Causality, Nigeria. 

JEL Classification: C22; F19; O50. 
 

Citation | Innocent. U. Duru (2021). Trade Liberalization and 
Economic Growth: The Scenario of the MINT Economies. 
Economy, 8(2): 35-48. 
History:  
Received: 19 October 2021 
Revised: 22 November 2021 
Accepted: 14 December 2021 
Published: 27 December 2021  
Licensed: This work is licensed under a Creative Commons 

Attribution 3.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Funding: This study received no specific financial support. 
Competing Interests: The author declares that there are no conflicts of 
interests regarding the publication of this paper. 
Transparency: The author confirms 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. Introduction ...................................................................................................................................................................................... 36 
2. Literature Review and Theoretical Framework ........................................................................................................................ 36 
3. Methodology and Model Specification........................................................................................................................................ 38 
4. Data Presentation, Analysis and Discussion of Results ........................................................................................................... 40 
5. Conclusion and Policy Implications ............................................................................................................................................. 43 
References .............................................................................................................................................................................................. 43 
Appendices ............................................................................................................................................................................................. 44 
 

 
 
 

 

 

 

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https://www.doi.org/10.20448/journal.502.2021.82.35.48
https://orcid.org/0000-0001-8958-0686
https://www.doi.org/10.20448/journal.502.2021.82.35.48
https://orcid.org/0000-0001-8958-0686
https://www.doi.org/10.20448/journal.502.2021.82.35.48
https://orcid.org/0000-0001-8958-0686


Economy, 2021, 8(2): 35-48 

36 
© 2021 by the authors; licensee Asian Online Journal Publishing Group 

 

 

Contribution of this paper to the literature 
This study contributes to existing literature by investigating the impact of trade liberalization 
on economic growth for Mexico, Indonesia, Nigeria and Turkey from 1986 to 2020. 

 
1. Introduction 

The link between trade liberalization and economic growth has attracted extensive research in the parlance of 
international economics. Trade liberalization is believed to be the engine of economic growth and development in 
economies of the world, especially the developing ones. Chile and Talukder (2014) and FAO (2005) maintained that 
liberalized international trade affects long-run economic growth. No wonder, the unrelenting and general 
divergences in the performance of the economy among economies, particularly developing economies has 
heightened the attention given to trade liberalization-economic growth nexus in recent times (Lall, 2004). 

Dollar and Kraay (2001) declared that economies such as China, India, Malaysia and Mexico considered post-
1980 globalizers realized quicker rates of growth than the affluent economies. However, in a similar vein, 
economies that pursued a protectionist regime of trade suffered a declining rate of growth. Evidence from 
numerous literature infers that barriers to trade decreases growth (see, for instance, (Dollar, 1992; Dollar & Kraay, 
2003; Edwards, 1992; Frankel & Romer, 1999; Sachs & Warner, 1995)). However, as a result of the narrow 
openness measure utilized and other methodological deficiencies, Rodriguez and Rodrik (2000) were doubtful about 
the findings. Food and Agriculture Organization (2003) argued that many economies have embarked on trade 
policy reforms to further open their economies and broaden their access to the international market and increase 
the competitiveness of export. 

Trade liberalization campaigners are of the view that it will increase the small domestic market; accelerate the 
transfer of technology; encourage foreign direct investment; generate greater competition; create marketing 
networks; supply technical and managerial skills; increase the flow of knowledge, resources, goods and services 
causing higher growth of the economy (Annabi, 2006; Corbett & Winebrake, 2008; Henry, Kneller, & Milner, 2009; 
McCulloch, Winters, & Cirera, 2003; Stone & Shepherd, 2011; Zhang, 2008). Thus, it is not surprising that Corbett 
and Winebrake (2008) argued that many economies have witnessed shocking economic growth in the recent past as 
a result of their readiness to open their borders and markets to foreign investment and trade. Again, Krueger 
(1997) maintained that the swift industrialization and development in Hong Kong, Singapore, South Korea and 
Taiwan considered as the four East Asian “tigers”,  is a model of beneficiaries of thriving policies of trade 
liberalization since the beginning of the 1960s. 

Among the prominent features of the world economy in the last thirty-six years was that developing economies 
went through fast trade liberalization either individually or as a component of multilateral proposals with the 
World Bank (WB), World Trade Organization (WTO) and the International Monetary Fund (IMF). Hence, in the 
early 1980s, most developing economies were encouraged to effect trade reforms besides the Structural Adjustment 
Programmes (SAPs), imposed by the IMF, the WB and other international organizations, as a necessary step for a 
free-market economy. Thus, in the mid-1980s and early 1990s, trade reforms were effected and trade liberalization 
was entrenched in nearly all the developing economies. 

The view on whether trade liberalization is a prerequisite for fast and sustained economic growth or not 
remained mixed. While some authors believe that trade liberalization is a must for fast and sustained economic 
growth (Berg & Krueger, 2003; Edwards, 1993; Edwards, 1997; Edwards, 1998; Krueger, 1990; Krueger, 1998; 
Winters, McCulloch, & McKay, 2004), others contest this assertion claiming that there is small evidence indicating 
that trade liberalization is meaningfully related to economic growth  (Harrison & Hanson, 1999; Rodriguez & 
Rodrik, 2001). Regardless of the programmes applied to boost openness to trade in the four developing market 
economies of Mexico, Indonesia, Nigeria and Turkey, regarded as MINT economies, trade barriers still exist. 
Irrespective of important trade reforms in MINT economies, some disagreements concerning the role of trade 
liberalization in them persist. 

In light of the above, this study seeks to examine the impact of trade liberalization from the perspective of the 
MINT economies. This research question was addressed in this study: What is the causal link between trade 
liberalization and economic growth in MINT countries? The general objective of this study is to investigate the 
causal link between trade liberalization and economic growth in the MINT countries. The rest of the paper is 
structured as follows. Section 2 focuses on the literature review and theoretical framework. Section 3 discusses the 
methodology. Section 4 dwells on data presentation, analysis and discussion of results while the conclusion and 
policy implications are presented in section 5. 
 

2. Literature Review and Theoretical Framework 
2.1. Empirical Literature 

An extensive body of theoretical and empirical literature has investigated the relationship between trade 
liberalization and economic growth with mixed results. For instance, Nduka (2013) employed the Ordinary Least 
Square (OLS) methodology from 1970-2008 to examine empirically the nexus between trade openness and 
economic growth in Nigeria. The findings of the test of cointegration indicated the presence of a long-run 
equilibrium link between the variables. The findings indicated that trade openness had a positive and significant 
relationship with economic growth in Nigeria. In a similar vein, Mercan, Gocer, Bulut, and Dam (2013) applied the 
panel data technique to investigate the impact of openness on economic growth for the fast-emerging economies of 
Brazil, Russia, India, China and Turkey considered as BRIC-T countries from 1989-2010.  The results revealed a 
positive and statistically significant relationship between openness and economic growth according to apriori 
expectations.  

In addition, Dao (2014) applied the panel data techniques and pooled OLS regression to examine the link 
between trade openness and economic growth for a panel of 71 countries over the globe from 1980 to 2010. The 
results revealed a positive and significant link between trade openness and economic growth. Hamad, Burhan, and 
Stabua (2014) used the OLS methodology to investigate the impact of trade liberalization on economic growth in 



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Tanzania using annual time series data from 1970 to 2010. This period was decomposed into an era of the closed 
economy (1970-1985) and an era of the open economy (1986-2010). The results showed that openness to trade had 
a positive and significant impact on economic growth in Tanzania. However, this impact was comparatively greater 
in the closed economy era than the era of the open economy.  

Equally, Manwa (2015) applied the Autoregressive Distributed Lag (ARDL) methodology and fixed effects 
panel data estimations from 1980 to 2011 to empirically examine the nexus between trade liberalization and 
economic growth in Southern African Customs Union (SACU) economies of Namibia, Swaziland, Botswana, South 
Africa and Lesotho.  The findings revealed that trade liberalization measured through adjusted trade ratios, tariffs, 
the real effective exchange rate and trade ratios exerted an insignificant effect on economic growth in Swaziland. 
Botswana, Namibia and Lesotho. However, in the case of South Africa, the results unearthed that trade 
liberalization exerted an impact on economic growth consistently. 

In another similar study, Qazi (2015) employed the ARDL methodology on data from 1971-2013 to examine 
the effect of financial and trade liberalization on economic growth in Pakistan through the conduits of private 
saving and investment. The determinants of capital account liberalization were investigated in the study as well. 
The findings based on the equation of economic growth revealed that the index of financial liberalization, capital 
stock and labour force had a positive relationship with economic growth. However, the index of financial openness 
and openness to trade had a negative relationship with economic growth. In addition, the results showed that real 
deposit rate, per capita real private income, public saving and index of financial liberalization had a positive 
relationship with private saving in the long run. On the other hand, capital account liberalization, openness to trade 
and index of financial openness had a negative relationship with private saving in the long run.  

Furthermore, the findings revealed that per capita real private income, index of financial liberalization and 
public investment had a positive relationship with private investment in the long run. However, real interest rates 
and openness to trade had a negative relationship with private investment in the long run. Finally, the findings of 
the effect of trade liberalization/openness on capital account liberalization/openness highlighted a positive 
relationship between trade openness and capital account liberalization. Additionally, the findings further revealed 
that trade liberalization and openness to trade have a positive association with financial openness. Summing up, the 
general results revealed that the index of financial liberalization had a positive relationship with economic growth, 
investment and private saving. 

Kalu, Nwude, and Nnenna (2016) as well utilized the Classical Linear Regression Model (CLRM) from 1991-
2013 to analyze the effect of trade openness on economic growth in Nigeria. The findings showed that export and 
net export both had positive and significant links with economic growth. On the other hand, imports exerted a 
positive and significant impact on economic growth. In another similar study, (Hozouri, 2016) employed the 
dynamic panel data methodology and data from 2000 to 2013 to examine the effect of trade liberalization on 
economic growth in 17 Middle East and North Africa (MENA) countries of Iran, Yemen, Algeria, United Arab 
Emirates (UAE), Bahrain, Tunisia, Djibouti, Syria, Egypt, Saudi Arabia,  Jordan, Qatar, Kuwait, Oman, Lebanon, 
Morocco and Libya. The findings revealed that the sensitivity of economic growth has a significant and negative 
link with tariff changes, however, its link with the volume of trade was positive. Keho (2017) used the ARDL 
methodology and the Granger causality test proposed by Toda and Yamamoto in another related study to 
investigate the effect of openness to trade on economic growth in Cote d‟Ivoire from 1965-2014. The results 
revealed that openness to trade had a positive impact on economic growth in the short-run and long-run 
respectively. Furthermore, the findings suggest a positive and strong balancing link between trade openness and 
capital formation in stimulating economic growth. 

Employing the Error Correction Model (ECM) methodology and time-series data from 1980-2016, Bekele 
(2017) investigated the link between trade liberalization and economic growth in Ethiopia. The findings showed 
that trade openness exerted a positive and significant impact on economic growth. In addition, Moyo and Khobai 
(2018) employed the ARDL approach to cointegration and the Pooled Mean Group (PMG) model in a similar 
study to investigate the link between trade openness and economic growth in Southern African Development 
Cooperation (SADC) economies of Botswana, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, 
South Africa, Swaziland, Tanzania and Zambia using data from 1990-2016. The results showed that trade openness 
had a negative effect on economic growth in the long run.  

In addition, using the ECM and data from 1980 to 2016, Elijah and Musa (2019) explored the dynamic impact 
of trade openness on economic growth in Nigeria. The findings of the short-run and long-run indicated that 
openness to trade is harmful to economic growth in Nigeria. Additionally, Ajayi and Araoye (2019) employed the 
Vector Error Correction Model (VECM) to analyze the effect of openness to trade on economic growth in Nigeria 
from 1970 to 2016. The findings of the test of cointegration indicated the presence of a long-run link between the 
variables. The results showed that openness to trade exerted a negative impact on Nigeria‟s economic growth. 
Furthermore, using the ARDL methodology, Duru, Okafor, Adikwu, and Njoku (2020) examined empirically the 
causal relationship between trade liberalization and economic growth in Nigeria from 1981 to 2018. The results 
showed a unidirectional causality running from trade liberalization to economic growth (trade-led growth) during 
the study period. 

Evidence from the past works reviewed revealed that irrespective of many works that tried to shed light on the 
impact of trade liberalization on economic growth, the argument, among economists, researchers, policymakers, 
academics within the realm of policymaking and academia on the relationship between trade liberalization and 
growth is still open. Based on the results of empirical studies, economies that are more outward-oriented record 
better performance in terms of economic growth in the long run ((Dollar & Kraay, 2004; Edwards, 1998; Frankel & 
Romer, 1999; Lee, Ricci, & Rigobon, 2004; Sachs, Warner, Åslund, & Fischer, 1995), among others). However, 
Rodriguez and Rodrik (2001) argued that these results had been questioned continuously based on at least two 
limitations: The first flaw is associated with the measurement technique of countries‟ openness to trade. On the 
other hand, the second flaw has to do with the methodology of estimation. 

Furthermore, evidence shows that there is no macroeconomic study that had separated the MINT countries 
and explored the impact of trade liberalization on their economies from the context of general equilibrium. Past 



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researches merely confronted this debate from a cross-country context. This part of the investigation is novel. 
Thus, the nexus between trade liberalization and economic growth to the best of my knowledge has been generally 
unnoticed and unstudied for the MINT countries. This study fills this gap in knowledge by investigating the 
impact of trade liberalization on economic growth for the MINT economies. The dependence of MINT countries 
on trade, the dearth of research on the issue in the context of MINT economies and the methodological flaws 
previously spotted provide the rationalizations for embarking on this study. In addition, the study made a 
methodological contribution through the use of the Autoregressive Distributed Lag methodology that makes it 
possible for the short-run and long-run effects of trade liberalization on economic growth to be ascertained. 
 

2.2. Theoretical Framework 
In the contention of  Thindwa and Seshamani (2014), three theories account for the gains of trade liberalization 

to countries. These are the comparative advantage theory proposed by David Ricardo, Heckscher-Olin (H-O) 
neoclassical factor endowment theory of trade and the endogenous growth theory. The endogenous growth theory 
would form the theoretical foundation for this study. This is based on the premise that the new growth theory or 
the endogenous growth theory proposed by Romer (1986); Lucas (1988); Romer (1990); Grosman and Helpman 
(1991) and Barro and Sala-i-Martin (1997) is the main theoretical platform employed by academics for analyzing 
the link between trade liberalization and economic growth.  

The comparative advantage theory proposed that there would be the realization of benefits from trade if every 
country concentrates on the production of the goods in which it has a comparative advantage (Salvatore, 2007). 
The benefits from trade are decomposed into static and dynamic gains. In the opinion of Thindwa and Seshamani 
(2014), „‟static gains from trade stem from the fact that countries have different factor endowments and therefore, 
the opportunity cost of production varies between countries‟‟ (p.964). However, dynamic gains from trade stem 
from the increased output of resources. Labour is the only input of production in the Ricardian comparative 
advantage theory. The right of entry to export markets is increased through international trade, and economies 
would benefit if increasing returns are presumed to hold. These benefits from trade could accrue to the MINT 
economies under the platform of trade liberalization through the enhanced right of entry to international markets. 
The foreign reserves of the MINT countries and by implication its import cover could be improved through the 
foreign exchange gotten from trade. Improved output of labour, attainment of ground-breaking knowledge and 
technology could spur dynamic gains. The main limitation of this classical model is that it is a static model based 
on one input of production. Thus, it is subjected to restrictions on how the economy operates today. 

The H-O model builds upon the Ricardian model of comparative advantage. However, it added extra input of 
production. The land was incorporated as a second input of production to reflect the endowment of resources. It 
believes that economies can embark on international trade by exporting products in which they have a comparative 
advantage. In the contention of this model, comparative advantage is expressed in terms of factor abundance and 

intensity in a given country. Thindwa and Seshamani (2014) maintained that „‟a country has a comparative 
advantage if it has a particular resource in abundance and if the ratio of that resource to others is high in 

production (factor intensity)‟‟ (p.965). Hence, Salvatore (2007) claimed that a state has a comparative advantage in 
manufacturing a product that employs the resource that shows these features. Trade between economies and 
resources concentration in the manufacturing of comparatively few products breeds a high standard of living for 
the economies concerned. The MINT economies are labour-abundant countries as most emerging economies with 
an endowment of natural resources. Based on these resources, the MINT countries have engaged formal and 
informal labours together to work on these resources in developing their economies. 

The endogenous growth theory postulates that growth results from domestic factors in an economy such as 
innovation, knowledge and investment in human capital.   Investment in these internal factors would be of great 
benefit to economies of the world. This is partly based on the premise that economies of scale in production can 
stem from such decisions. Reduction in the alterations of prices paves way for effective distribution of internal 
resources to different sectors of the economy. However, Howitt (1998) maintained that externalities resulting from 
the engagement of advanced technology in manufacturing in economies of the globe are responsible for economic 
development.   

The MINT economies employ resources internal to their economies for the promotion of economic growth. 
Hence, on the ground of theory, the MINT countries are expected to realize gains from trade in particular and 
attain economic growth and development in general as a result of trade liberalization and openness. However, the 
condition in these MINT countries may not be in line with theoretical expectations. This is based on the premise 
that there are other determinants of economic growth like trade liberalization. Thus, from the standpoint of 
macroeconomics, trade liberalization may not be the significant element influencing economic growth. However, a 
principal limitation of the endogenous growth theory is that it has continued to rely on some long-established 
neoclassical assumptions that are repeatedly unsuitable for emerging economies (Todaro & Smith, 2015). Despite 
this limitation, it had continued to be the main model of choice among academics in the analysis of the link between 
trade liberalization and economic growth. 
 

3. Methodology and Model Specification 
This study used annual data from 1986-2020 to examine the nexus between trade liberalization and economic 

growth in MINT countries. The data were obtained from World Bank World Development Indicators (WDI) 
database. The datasets are depicted in Table 1, Table 2, Table 3, and Table 4 respectively in the appendix. The 
dependent variable is the real Gross Domestic Product (GDP) per capita in constant 2015 US$. The time series 
characteristics of the variables were checked for unit root using the Augmented Dickey-Fuller (ADF) test. The 
goodness of fit and model adequacy of our specification was checked through diagnostic and stability tests. The 
Autoregressive Distributed Lag (ARDL) Bounds test to cointegration suggested first by Pesaran and Shin (1999) 
and advocated by Pesaran, Shin, and Smith (2001) was utilized to estimate the economic growth equation. 
Following Hozouri (2016) with modifications, the economic growth equation that would be estimated to establish 
the association between trade liberalization and economic growth in MINT economies is stated in Equation 1 as: 



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                                                        (1) 

       
                                       

                                         
                                              

                                       
 

   
            

                                                             

                                         

              

 
Based on economic theory, gross fixed capital formation and openness to trade are expected to be positive. 

However, general government final consumption expenditure is expected to be negative. Iterating Equation 1 into 
the ARDL framework yields: 

              ∑    

 

   

             ∑    

 

   

         ∑    

 

   

        ∑    

 

   

           

                                                              
Where p denotes the lag length, Δ represents the difference operator, α0 is the drift, ɛt is the error term, ø1, ø2, ø3 

and ø4 are coefficients of short-run dynamics while  1,  2,  3 and  4 are coefficients of the long-run relationship. 
Thus, Equation 2 is the base equation for measuring the short-run and long-run links among the variables.  The 
technique of the bounds test requires employing the F-test to ascertain the existence of a long-run link in levels 
between RGDPPCAP and its determining factor. The specification of this test is as follows:  

                 
(absence of long-run relationship among the variables) against the parameters: 

                 
(presence of long-run relationship among the variables) 

The hypothesis stated above is judged through asymptotic critical value bounds of the F-statistic proposed by 
Pesaran et al. (2001). For instance, if the calculated F–statistic is lower than the I(0) critical values bound, the null 
hypothesis of the absence of a long-run link between RGDPPCAP and its determinants cannot be rejected. On the 
other hand, If the calculated F–statistic is greater than the I(1) critical values bound, the alternative hypothesis of 
the presence of a long-run link in levels between the dependent variable and the independent variables will be 
accepted. Additionally, if the F–statistic falls between the I(0) and I(1) critical values bounds, the test is 
inconclusive. 

If the presence of a long-run relationship in levels between the dependent variable and independent variables 
was not established, the process terminates. However, if the presence of a long-run relationship was established 
between the variables in the model, the short-run and long-run estimates of the economic growth model would be 
measured. Based on Equation 3, the long-run elasticities can be calculated using OLS. 

             ∑    

 

   

             ∑    

 

   

         ∑  

 

   

         ∑  

 

   

           

        
The last step would be the estimation of short-run elasticities. The estimates of the short-run were obtained 

through an ECM.  

           
   ∑     

 
                ∑     

 
            ∑   

 
            ∑   

 
               

                    ) 
Where ø1, ø2, ø3 and ø4 in Equation 4 are the parameters of the short-run dynamics,  π is the speed of 

adjustment and ecmt-1 is the error correction term. The parameter π is expected to be negative and significant to 
corroborate the long-run link between the dependent variable and the independent variables.  
 

Table-1. ADF Test Results. 

Country Variable Augmented Dickey-Fuller (ADF) 

  Level First Difference I(d) 

Mexico RGDPPCAP -5.0764*** - I (0) 

 GFCF -2.4626 -6.2528*** I (1) 
 OPEN -0.6965 -4.9125*** I (1) 
 GGOVFCE -0.5261 -5.0627*** I (1) 

Indonesia RGDPPCAP -3.9512*** - I (0) 
 GFCF -1.7972 -3.4376** I (1) 
 OPEN -2.6699 -8.7883*** I (1) 
 GGOVFCE -2.5230 -6.1109*** I (1) 

Nigeria RGDPPCAP -3.7055*** - I (0) 
 GFCF -1.5707 -4.6706*** I (1) 
 OPEN -3.5378*** - I (0) 
 GGOVFCE -0.7973 -4.9806*** I (1) 

Turkey RGDPPCAP -6.1037*** - I (0) 
 GFCF -1.7972 -3.4376** I (1) 
 OPEN -2.6699 -8.7883*** I (1) 
 GGOVFCE -2.2298 -6.1676*** I (1) 

Note: ** and *** indicate statistical significance at 1% and 5% levels. 



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4. Data Presentation, Analysis and Discussion of Results 
4.1. Results of Augmented Dickey-Fuller (ADF) Test. 

The ADF unit root results are depicted in Table 1. The results revealed that most of the series were stationary 
in first differences. On the other hand, some series such as RGDPPCAP for all MINT economies and OPEN for 
Nigeria were stationary at levels. Evidence from Table 1 shows that the application of the ARDL methodology is 
justified since the variables of interest were integrated at different orders. 
 

Table-2. Diagnostic Results 

Country Test Type of Statistic Test Statistic P-value 

Mexico Breusch-Godfrey Serial Correlation LM Test 
Ramsey RESET test 
Jarque-Bera normality test 
Heteroskedasticity Test: ARCH 

𝜒2 
F 

𝜒2 

𝜒2 

0.4763 
16.1527 
7.1447 
9.7094 

0.7881 
0.0007 
0.0281 
0.6414 

Indonesia Breusch-Godfrey Serial Correlation LM Test 
Ramsey RESET test 
Jarque-Bera normality test 
Heteroskedasticity Test: ARCH 

𝜒2 
F 

𝜒2 

𝜒2 

7.8331 
33.5222 
2.5720 
13.7343 

0.0199 
0.0000 
0.2764 
0.3180 

Nigeria Breusch-Godfrey Serial Correlation LM Test 
Ramsey RESET test 
Jarque-Bera normality test 
Heteroskedasticity Test: ARCH 

𝜒2 
F 

𝜒2 

𝜒2 

0.6018 
0.2083 
4.3560 
9.8542 

0.7402 
0.6533 
0.1133 
0.6287 

Turkey Breusch-Godfrey Serial Correlation LM Test 
Ramsey RESET test 
Jarque-Bera normality test 
Heteroskedasticity Test: ARCH 

𝜒2 
F 

𝜒2 

𝜒2 

10.4453 
0.8033 
0.8882 
15.8064 

0.0054 
0.3813 
0.6414 
0.2003 

 

4.2. Results of Diagnostic Tests 
Even though there was no cointegration among the variables for Nigeria and Turkey, diagnostic tests were 

executed based on the estimated short-run models. However, for Mexico and Indonesia that we considered both 
long run and short run models based on the results of the bounds tests, diagnostic tests were also executed. All the 
results were reported in Table 2. Based on the results, none of the models had problems of heteroscedasticity. 
Except for Mexico's economic growth model, the Jarque-Bera normality test revealed that the residuals were 
normally distributed for the growth models of Indonesia, Nigeria and Turkey. The Histogram for the normality of 
residuals for the MINT economies are depicted in Figure 1a, Figure 1b, Figure 1c, and Figure 1d respectively in 
the appendix. In addition, the results revealed that the growth models for Mexico and Nigeria had no problems of 
serial correlation. However, the economic growth models for Indonesia and Turkey had problems of serial 
correlation. Furthermore, the results of the Ramsey Reset test shows the possibility of the growth models not 
being correctly specified for Mexico and Indonesia. This is because the probability values of 0.0007 and 0.0000 
against the Ramsey Regression Equation Specification Error Test (RESET) test for Mexico and Indonesia 
respectively were less than the suggested 5 per cent level of significance. Hence, the null hypothesis that the model 
was correctly specified was rejected. However, the economic growth models for Nigeria and Turkey were correctly 
specified.   
 

Table-3. Bound Test Results. 

Country F-statistics Significance 
Level 

Lower Critical Value 
Bound I(0) 

Upper Critical Value 
Bound I(1) 

Mexico 3.9648 1% 
5% 

10% 

5.17 
4.01 
3.47 

6.36 
5.07 
4.45 

Indonesia 4.4956 1% 
5% 

10% 

5.17 
4.01 
3.47 

6.36 
5.07 
4.45 

Nigeria 1.3850 1% 
5% 

10% 

5.17 
4.01 
3.47 

6.36 
5.07 
4.45 

Turkey 2.2671 1% 
5% 

10% 

5.17 
4.01 
3.47 

6.36 
5.07 
4.45 

Note: Critical value bounds for the F-statistic from Pesaran et al. (2001). 

 

4.3. Results of Bound Test 
Table 3 depicts the results of the bounds F-test. For Mexico and Indonesia, the F-values falls between the 

lower critical value bound I(0) and the upper critical value bound I(1). Thus, the test was considered inconclusive.  
However, considering the empirical illustration as depicted in Table 2, the F-statistic obtained for Mexico (3.9648) 
and Indonesia (4.4956) falls between the lower critical value bound I(0) and the upper critical value bound I(1). 
Hence, we considered both long-run and short-run models for Mexico and Turkey. On the other hand, the 
calculated F statistics for Nigeria and Turkey falls below the lower critical value bound I(0). This implies the 
absence of a long-run relationship or cointegration among variables. Hence, there was no estimation of the ARDL-
ECM for Nigeria and Turkey as a result of the absence of cointegration among the variables. However, the short-
run model was estimated for Nigeria and Turkey due to no cointegration among variables. 
 
 



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

 

 

Table-4. Results of short-run estimates for the short-run model. 

Country: Nigeria 

Dependent Variable: RGDPPCAP 

Variable Coefficient Std. Error t-Statistic Prob. 

RGDPPCAP(-1) 0.2539 0.2085 1.2177 0.2375 
RGDPPCAP(-2) 0.2117 0.2192 0.9661 0.3455 

GFCF -0.3211 0.2129 -1.5080 0.1472 
GFCF(-1) 0.2466 0.3262 0.7560 0.4585 
GFCF(-2) -0.3519 0.2883 -1.2208 0.2364 

OPEN 0.0349 0.0914 0.3812 0.7071 
OPEN(-1) 0.1498 0.1030 1.4542 0.1614 
OPEN(-2) -0.1546 0.0897 -1.7246 0.1000 

GGOVFCE -0.4772 0.6641 -0.7185 0.4807 
GGOVFCE(-1) 0.5943 0.8779 0.6769 0.5062 
GGOVFCE(-2) -0.4240 0.6371 -0.6655 0.5133 

C 22.2635 14.7968 1.5046 0.1481 
R-squared  0.4962                                                                                                   Adjusted R-squared 0.1940  
Schwarz Criterion 6.2160                                                                                       F-statistic 1.6418  
Durbin-Watson Stat 1.8389                                                                                   Prob (F-statistic) 0.1579 

Country: Turkey 
RGDPPCAP(-1) 0.0057 0.2270 0.0251 0.9802 
RGDPPCAP(-2) -0.0165 0.2103 -0.0785 0.9382 

GFCF -0.5234 0.8925 -0.5865 0.5641 
GFCF(-1) 1.7294 1.2253 1.4115 0.1735 
GFCF(-2) -1.0741 0.6625 -1.6213 0.1206 

OPEN -0.0660 0.1237 -0.5335 0.5996 
OPEN(-1) -0.0643 0.2269 -0.2833 0.7798 
OPEN(-2) 0.1848 0.1704 1.0849 0.2909 

GGOVFCE -2.5242 1.7138 -1.4728 0.1564 
GGOVFCE(-1) 1.7028 2.2886 0.7441 0.4655 
GGOVFCE(-2) -0.1501 1.4973 -0.1002 0.9212 

C 6.7454 20.7135 0.3257 0.7481 
R-squared  0.3017                                                                                                  Adjusted R-squared -0.1173 
Schwarz Criterion 6.7699                                                                                      F-statistic 0.7200  
Durbin-Watson Stat 2.1620                                                                                  Prob (F-statistic) 0.7167 

Note: ***, ** and * denote significance at 1%, 5% and 10% respectively.   

 

4.4. Results of the Short-Run Model 
Because there was no cointegration among the variables for Nigeria and Turkey, the short-run model was 

estimated for both countries. The estimated coefficients for real GDP per capita at first lag were positive and 
statistically insignificant for both Nigeria and Turkey. The insignificance of this estimate means that the real GDP 
of the previous year does not impact on real GDP of the current year in the short run. In addition, the estimates of 
gross fixed capital formation and general government final consumption expenditure for Nigeria and Turkey were 
negative and statistically insignificant. The insignificance nature of these estimates suggests that both gross fixed 
capital formation and general government final consumption expenditure do not impact economic growth in the 
short run. Based on the results of the short-run model reported in Table 4, openness to trade utilized as a proxy for 
trade liberalization had a positive and insignificant impact on economic growth for Nigeria in the short run. The 
insignificant estimate implies that it does not have an impact on Nigeria's economic growth in the short run. 
However, in the case of Turkey, openness to trade exerted a negative and insignificant relationship with economic 
growth in the short run.  
 

Table-5. Long-run estimates for economic growth model. 

Country GFCF OPEN GGOVFCE C 

Mexico 0.4828 
[0.8912] 
(0.3833) 

0.2771 
[1.7578*] 
(0.0941) 

0.5150 
[0.5168] 
(0.6109) 

-19.1853 
[-1.1727] 
(0.2547) 

Indonesia 0.0076 
[0.0375] 
(0.9705) 

0.0374 
[0.3002] 
(0.7672) 

1.1617 
[1.2604] 
(0.2220) 

-7.5152 
[-0.5173] 
(0.6106) 

Note: Probability Values are in bracket - ( ). 
t-statistics are in [] 
* denote significance at 10% level. 

 

4.5. Results of Estimated Long-Run Coefficients 
The estimated long-run coefficients for gross fixed capital formation was positive and statistically insignificant 

for both Mexico and Indonesia. This results contravenes the findings of Duru and Ezenwe (2020); Duru et al. 
(2020) and Duru et al. (2021). The coefficient for trade openness used as a proxy for trade liberalization had a 
positive and significant relationship with economic growth for Mexico. This implies that trade liberalization 
contributes to economic growth in Mexico in the long run. This result is in line with the submissions of Nduka 
(2013); Mercan et al. (2013); Dao (2014); Hamad et al. (2014) and Bekele (2017). However, it is contrary to the 
submissions of Duru et al. (2020). However, trade liberalization had a positive and insignificant impact on economic 
growth in Indonesia. The insignificance of this estimate implies that it does not impact economic growth in the 
long run. This result is contrary to the submissions of Duru et al. (2021). Furthermore, the coefficients of general 



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government final consumption expenditure for both Mexico and Indonesia was positive and insignificant as 
reported in Table 5. This result finds an advocate in Duru et al. (2020). 
  

Table 6: Results of estimated short-run error correction model. 

Country: Mexico 

Dependent Variable: RGDPPCAP 

Variable Coefficient Std. Error t-Statistic Prob. 

Δ(RGDPPCAP(-1)) -0.0037 0.1597 -0.0235 0.9815 

Δ(GFCF) 1.8696 0.3566 5.2433*** 0.0000 

Δ(GFCF(-1)) 0.0847 0.5704 0.1486 0.8834 

Δ(OPEN) 0.1223 0.1361 0.8989 0.3794 

Δ(OPEN(-1)) -0.1443 0.1567 -0.9206 0.3682 

Δ(GGOVFCE) -2.2661 1.0936 -2.0721** 0.0514 

Δ(GGOVFCE(-1)) -0.0969 1.1515 -0.0841 0.9338 
ECMt-1 -1.1413 0.2958 -3.8578*** 0.0010 

ECM = RGDPPCAP + 0.4828*GFCF + 02771*OPEN + 0.5150*GGOVFCE – 19.1853*C – 0.5417*D 

Country: Indonesia 

Δ(RGDPPCAP(-1)) 0.4643 0.2201 2.1096** 0.0477 

Δ(GFCF) 0.5511 0.4227 1.3037 0.2072 

Δ(GFCF(-1)) 0.2538 0.3800 0.6680 0.5117 

Δ(OPEN) -0.3020 0.0626 -4.8237*** 0.0001 

Δ(OPEN(-1)) 0.0162 0.1016 0.1598 0.8746 

Δ(GGOVFCE) -2.0284 1.1360 -1.7856* 0.0893 

Δ(GGOVFCE(-1)) 0.2400 1.1596 0.2069 0.8382 
ECMt-1 -0.9966 0.2461 -4.0494*** 0.0006 

ECM = RGDPPCAP + 0.0076*GFCF + 0.0374*OPEN + 1.1617*GGOVFCE – 7.5152*C – 0.0823*D 
Note: ***, ** and * denote significance at 1%, 5% and 10% respectively.   

 
The results of the short-run dynamic model are illustrated in Table 6. For Mexico, change in gross fixed 

capital formation and change in general government final consumption expenditure was merely the variables that 
had a statistically significant effect on the change in real GDP per capita in the short run. Thus, the change in the 
gross fixed capital formation of the previous year exerted a positive and significant effect on economic growth. In 
addition, change in general government final consumption expenditure had a negative and significant effect on 
economic growth. Furthermore, change in trade openness used as a proxy for trade liberalization had a positive and 
insignificant impact on economic growth. However, in the case of Indonesia, the first lag of the dependent variable, 
change in trade openness and change in general government final consumption expenditure were merely the 
variables that had a statistically significant effect on the change in real GDP per capita in the short run. Hence, the 
change in the real GDP per capita of the previous year exerted a positive and significant impact on economic 
growth.  

Also, changes in trade liberalization exerted a negative and significant impact on economic growth. This means 
that trade liberalization does not contribute to economic growth in the short run. Furthermore, change in general 
government final consumption expenditure had a negative and significant effect on economic growth. The findings 
showed that all the Error Correction Terms (ECT) were negative and statistically significant. The ECT measures 
the speed of adjustment back to long-run equilibrium as a result of a shock. For instance, for Mexico, the coefficient 
of the lagged ECT in the growth equation model was -1.1413. It is negative and statistically significant. Its 
coefficient of -1.1413 means that 114% of the previous shock to equilibrium in the long-run economic growth was 
corrected by it within one year. However, in the case of Indonesia, the ECT coefficient of -0.9966 means that 100% 
of the previous shock to equilibrium in the long-run economic growth was corrected by it within one year. The 
ECT coefficient was also negative and statistically significant. 
         

Table-7. Results of the Granger Causality Test (TY Augmented Lags Methods). 

 Sources of Causation 

Country/Dependent Variable RGDPPCAP 

𝜒2 

OPEN 

𝜒2 

Mexico 
RGDPPCAP 
OPEN 

 
- 

2.7046 

 
3.4141 

- 
Indonesia 
RGDPPCAP 
OPEN 

 
- 

2.3573 

 
4.9858 

- 
Nigeria 
RGDPPCAP 
OPEN 

 
- 

10.9709*** 

 
9.2840*** 

- 
Turkey 
RGDPPCAP 
OPEN 

 
- 

1.9548 

 
7.9540** 

- 
Note: *** and ** Indicate significance at the 1 per cent and 5 per cent levels respectively. 

 

4.6. The Results of Toda and Yamamoto Multivariate Causality Test 
The Toda and Yamamoto causality test was employed to find the causality between real Gross Domestic 

Product (GDP) per capita and trade openness. The results of the causality test are illustrated in Table 7. In the case 
of Mexico, the findings revealed that there was no causal relationship between real GDP per capita and openness to 



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trade. Thus, the findings implied that there is no causal link between trade liberalization and real GDP per capita 
in Mexico. Just like Mexico, there was no causal relationship between trade liberalization and real GDP per capita 
for Indonesia. Unlike Indonesia and Mexico, a bi-directional causal relationship between real GDP per capita and 
trade openness was detected for Nigeria. For Turkey, a unidirectional causal relationship from trade openness to 
real GDP per capita was identified. This shows that trade liberalization promotes economic growth in Turkey. Our 
causality results for Nigeria is in line with the submissions of Nwinee and Olulu-Briggs (2016) and Nduka, 
Chukwu, Ugbor, and Nwakaire (2013). However, it contradicts the findings of Yakubu and Akanegbu (2018) and 
Tyopev (2019).  
 

5. Conclusion and Policy Implications 
This study investigated the link between trade liberalization and economic growth in the MINT economies 

using time series data from 1986-2020. The ARDL methodology and the Toda and Yamamoto Multivariate 
Causality test were utilized in this study. The long-run results revealed that trade openness had no impact on real 
GDP per capita except for Mexico. Even in this situation, trade openness became significant at the 10% level of 
significance. The results of the causality test showed that no causality was detected between real gross domestic 
product per capita and trade liberalization for Mexico and Indonesia. A bidirectional causality between real gross 
domestic product per capita and trade liberalization was found for Nigeria whereas a unidirectional causality from 
trade liberalization to real gross domestic product per capita was revealed for Turkey.  

The no causality results for Mexico and Indonesia means that the policy objectives of trade liberalization and 
economic growth can be pursued independently in both countries. In addition, the bidirectional causality detected 
for Nigeria suggests that the policy objectives of trade liberalization and economic growth can be pursued together 
in Nigeria. Furthermore, the unidirectional causality from trade liberalization to real gross domestic product per 
capita found for Turkey implies that she employs trade liberalization policies effectively for objectives of economic 
growth, thus trade liberalization causes economic growth. 
 

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Appendices 
Appendix 1: Data set on real gross domestic product per capita of MINT economies. 
 

Table-1. Data on RGDPPCAP of MINT Economies. 

Year Mexico Indonesia Nigeria Turkey 

1986 -5.0968586 3.7422332 -2.5099486 4.888763818 
1987 -0.3338109 2.891931495 0.52584855 7.402176226 
1988 -0.7083912 3.80170995 4.5469365 0.447775659 
1989 2.10829938 5.507034964 -0.7088532 -1.486309099 
1990 3.20338291 5.3479457 8.93068727 7.383071281 
1991 2.30201264 5.07105896 -2.164465 -0.967257707 
1992 1.68311479 4.711831054 2.02582456 3.324226368 
1993 0.15792316 4.760224439 -4.4570781 5.931883178 
1994 3.15906427 5.839520704 -4.2328183 -6.175858761 
1995 -7.8317514 6.562205374 -2.5300523 6.17718775 
1996 5.07244944 6.217928008 1.63459401 5.686793303 
1997 5.193558 3.189871378 0.40682595 5.886121916 
1998 3.58158308 14.35055602 0.05719452 0.806059059 
1999 1.24783802 0.605436819 -1.8957202 -4.750586281 
2000 3.44097633 3.482210606 2.4191326 5.32213582 
2001 -1.7929026 2.235179698 3.29057075 -7.147845597 
2002 -1.4029391 3.090635928 12.4574682 4.890330717 
2003 0.07226822 3.376532827 4.65778629 4.252446053 
2004 2.49483102 3.630908791 6.48960368 8.28603301 
2005 0.86952153 4.289591484 3.72162394 7.559697386 
2006 2.98443254 4.107514355 3.32621788 5.620645339 
2007 0.78256904 4.946468138 3.8220723 3.797987497 
2008 -0.357633 4.620033675 3.97251049 -0.382922616 
2009 -6.6741654 3.247328238 5.19795441 -6.027912669 
2010 3.61719033 4.812273068 5.15854535 6.919606567 



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2011 2.22716348 4.748318533 2.52532223 9.509983157 
2012 2.24713983 4.606485522 1.47285123 3.093101823 
2013 0.0294528 4.151428229 3.85372268 6.664883623 
2014 1.54403511 3.639072303 3.51397656 3.168090824 
2015 2.01903688 3.555062495 -0.0292823 4.328127771 
2016 1.40302067 3.758837332 -4.1683884 1.642463409 
2017 0.93145654 3.841197264 -1.7888176 5.794264413 
2018 1.05031157 3.987824861 -0.6797247 1.428914877 
2019 -1.1393691 3.871444142 -0.3797524 -0.40134604 
2020 -9.2054 -3.10714219 -4.2601131 0.659370381 

Source: World Bank, World Development Indicators Database. 

 
Appendix 2. Data Set on Gross Fixed Capital Formation of MINT Economies. 
 

Table-2. Data on GFCF of MINT Economies. 

Year Mexico Indonesia Nigeria Turkey 

1986 18.72587 25.551 54.94827 25.551 
1987 17.54044 25.17103 50.04989 25.17103 
1988 18.67873 26.99156 43.75477 26.99156 
1989 17.37131 28.53473 52.48744 28.53473 
1990 17.97946 30.55156 53.12219 30.55156 
1991 18.73103 29.67142 48.40018 29.67142 

1992 19.6226 28.00259 43.77439 28.00259 
1993 20.84942 26.28067 44.47636 26.28067 
1994 21.67319 27.57069 42.06784 27.57069 
1995 16.35404 28.42981 37.20593 28.42981 
1996 18.41382 29.60236 36.58167 29.60236 
1997 19.84665 28.30768 38.42226 28.30768 
1998 21.08837 25.42951 40.5534 25.42951 
1999 21.11672 20.13876 38.278 20.13876 
2000 21.48889 19.85085 34.04928 19.85085 
2001 19.9337 19.67266 30.03794 19.67266 
2002 19.26942 19.42916 26.76866 19.42916 
2003 19.7779 19.50606 28.3709 19.50606 
2004 20.47646 22.44862 26.06325 22.44862 
2005 20.70371 23.64051 24.96612 23.64051 
2006 21.54334 24.13099 26.1665 24.13099 
2007 21.94191 24.94694 20.18004 24.94694 
2008 23.16439 27.69859 18.85977 27.69859 
2009 22.12647 31.11477 21.11545 31.11477 
2010 21.5827 30.99941 16.81501 30.99941 
2011 22.27391 31.30745 15.67631 31.30745 
2012 22.84044 32.71963 14.21112 32.71963 
2013 21.25247 31.96578 14.16873 31.96578 
2014 20.99786 32.51674 15.08353 32.51674 
2015 22.43071 32.81193 14.82718 32.81193 
2016 22.80226 32.57773 14.72496 32.57773 
2017 22.09217 32.16064 14.71562 32.16064 
2018 22.0425 32.2885 19.01838 32.2885 
2019 20.66706 32.34713 24.62523 32.34713 
2020 18.78994 31.73343 28.64594 31.73343 

Source: World Bank, World Development Indicators Database 
 
Appendix 3. Data Set on Trade Openness of MINT Economies 
 

Table-3. Data on OPEN of MINT Economies. 

Year Mexico Indonesia Nigeria Turkey 

1986 29.60622 41.00954 9.135846 41.00954 

1987 31.26232 46.97425 19.49534 46.97425 
1988 38.79034 47.25456 16.94061 47.25456 
1989 38.32965 49.08188 34.18262 49.08188 
1990 38.5197 52.89186 30.92474 52.89186 
1991 35.78654 54.83956 37.0216 54.83956 
1992 35.5535 57.42743 38.22739 57.42743 
1993 27.82791 50.52339 33.71975 50.52339 
1994 30.70997 51.8771 23.05924 51.8771 
1995 46.32102 53.95859 39.52838 53.95859 
1996 50.4192 52.26474 40.25773 52.26474 
1997 48.77736 55.99386 51.46101 55.99386 
1998 50.99612 96.18619 39.27861 96.18619 
1999 50.61797 62.94391 34.45783 62.94391 
2000 52.43268 71.43688 48.9956 71.43688 
2001 47.16607 69.79321 49.6805 69.79321 
2002 46.69791 59.07946 40.03517 59.07946 
2003 50.20569 53.61649 49.33496 53.61649 



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2004 53.48615 59.76129 31.89587 59.76129 
2005 53.93813 63.98794 33.05946 63.98794 
2006 56.09272 56.65713 42.56657 56.65713 
2007 56.79528 54.82925 39.33693 54.82925 
2008 57.77703 58.5614 40.79684 58.5614 
2009 55.96777 45.51212 36.05871 45.51212 
2010 60.76032 46.70127 43.32076 46.70127 
2011 63.46968 50.18001 53.27796 50.18001 
2012 65.76725 49.5829 44.53237 49.5829 
2013 63.76488 48.63737 31.04886 48.63737 
2014 64.92536 48.08018 30.88519 48.08018 
2015 71.08909 41.93764 21.33265 41.93764 
2016 76.06221 37.42134 20.72252 37.42134 
2017 77.11574 39.3555 26.3476 39.3555 
2018 80.5633 43.07431 33.00783 43.07431 
2019 77.91529 37.44878 34.02388 37.44878 
2020 77.98212 33.19059 25.39979 33.19059 

Source: World Bank, World Development Indicators Database. 

 
Appendix 4. Data Set on General Government Final Consumption Expenditure of MINT Economies 
 

Table-4. Data on GGOVFCE of MINT Economies. 

Year Mexico Indonesia Nigeria Turkey 

1986 8.756565 11.24248 1.929236 7.588005 
1987 8.358128 9.424605 1.632709 7.822874 
1988 8.484909 8.976333 1.552698 7.612643 
1989 8.327433 9.389376 1.315222 9.343364 
1990 8.430745 9.534441 1.220141 10.96102 
1991 9.115903 9.138088 1.220982 12.41925 
1992 9.942875 9.516266 2.047629 12.92502 
1993 9.114783 9.023312 2.148452 12.89387 
1994 9.466826 8.114181 1.769021 11.65737 
1995 8.55116 7.829064 1.166196 10.7858 
1996 8.119714 7.566959 0.911235 11.57077 
1997 8.261602 6.842805 0.912571 12.25939 
1998 8.543129 5.693508 1.375668 10.61322 
1999 9.178512 6.604457 1.383378 12.66856 
2000 9.515219 6.531995 2.123442 11.92866 
2001 9.881126 6.889059 1.990621 12.64592 
2002 10.37772 7.257458 1.340488 12.83567 
2003 11.03379 8.129486 0.951747 12.59042 
2004 10.48729 8.321868 4.787637 12.3152 
2005 10.52171 8.109508 4.544547 12.18597 
2006 10.34442 8.627169 5.125842 12.90753 
2007 10.40538 8.34647 9.44834 13.38969 
2008 10.72952 8.423781 9.428957 13.59629 
2009 11.91696 9.589178 8.649948 15.65786 
2010 11.77136 9.005915 8.8481 14.87456 
2011 11.77708 9.058677 8.572152 13.60036 
2012 11.94673 9.248788 8.228178 14.12612 
2013 12.19139 9.51772 7.155219 14.01838 
2014 12.19496 9.425026 6.464486 14.01998 
2015 12.3172 9.749414 5.935159 13.80517 
2016 12.01041 9.52781 5.384282 14.73321 
2017 11.6166 9.120571 4.403315 14.38026 
2018 11.57089 9.021232 5.604329 14.68642 
2019 11.35833 8.809677 5.572002 15.52729 
2020 12.75341 9.289052 8.707691 15.24316 

Source: World Bank, World Development Indicators Database 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 



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Appendix 5a: Graph of Diagnostic Test for Mexico 
 

0

1

2

3

4

5

6

7

8

-5 -4 -3 -2 -1 0 1 2 3

Series: Residuals

Sample 1988 2020

Observations 33

Mean       3.42e-15

Median   0.459148

Maximum  2.600107

Minimum -4.535895

Std. Dev.   1.776660

Skewness  -1.067945

Kurtosis   3.796305

Jarque-Bera  7.144671

Probability  0.028090

 
Figure-1a. Histogram for normality of residuals for Mexico. 

Source: Extract from E-views econometric software. 

 
Appendix-5b. Graph of Diagnostic Test for Indonesia. 
 

0

1

2

3

4

5

6

7

-4 -3 -2 -1 0 1 2 3

Series: Residuals

Sample 1988 2020

Observations 33

Mean      -2.56e-15

Median   0.171742

Maximum  2.808352

Minimum -4.472006

Std. Dev.   1.727399

Skewness  -0.675743

Kurtosis   3.209787

Jarque-Bera  2.571969

Probability  0.276378

 
Figure-1b. Histogram for normality of residuals for Indonesia. 

Source: Extract from E-views econometric software. 

 
Appendix-5c. Graph of Diagnostic Test for Nigeria. 
 

0

1

2

3

4

5

6

7

-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9

Series: Residuals

Sample 1988 2020

Observations 33

Mean       3.35e-15

Median  -0.216977

Maximum  8.592356

Minimum -4.120166

Std. Dev.   2.761376

Skewness   0.706850

Kurtosis   4.081396

Jarque-Bera  4.355951

Probability  0.113271

 
Figure-1c. Histogram for normality of residuals for Nigeria. 

Source: Extract from E-views econometric software 

 
 
 
 
 
 
 
 
 



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Appendix-5d. Graph of Diagnostic Test for Turkey. 
 

0

1

2

3

4

5

6

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7

Series: Residuals

Sample 1988 2020

Observations 33

Mean      -2.79e-15

Median   0.433369

Maximum  6.621809

Minimum -9.331744

Std. Dev.   3.642632

Skewness  -0.399565

Kurtosis   3.085635

Jarque-Bera  0.888172

Probability  0.641410

 
Figure-1d. Histogram for Normality of Residuals for Turkey. 

Source: Extract from E-views econometrics software. 

 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  

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