




































  American International Journal of Business and Management Studies  

Vol. 2, No. 2; 2020 

ISSN 2641-4937     E-ISSN 2641-4953 

Published by American Center of Science and Education, USA 

 

11 

 

A Study of Monetary Integration in West Africa and Its 

Implications on Trade on Developing Country (Africa) 

 

 

 

Rabnawaz Khan
 

School of Finance and Economics 
Jiangsu University, Zhenjiang 

Jiangsu, Zhenjiang 212013, People’s Republic of China 

E-mail: khan.rab@stmail.ujs.edu.cn 
 

Jin Xinxin
 

School of Literature art 

Jiangsu University, Zhenjiang 

 Jiangsu, Zhenjiang 212013, People’s Republic of China 

E-mail: 1048186766@qq.com 

 

 

Abstract   
It shows the monetary investigation in west countries the big flow in economy by the gross value change 

effects, also the value of debt policy with debt management strategies to control the budgetary risk of long-term 

economy from sustainability. The intellectual policies of inflation, GDP, trade, and services and merchandise 

trade has affected on the West African country’s monetary policies. The implication of trade by a lag of 

exchange rate indicators has a positive and significant effect. The estimated results reflect the dynamic 

implication of trade with liquidity and proper monitoring policies. The GDP, gross value (GVA), debt policies, 

equity of public administration, trade in service and merchandise trade is positive and significant, all are 
significant. We suggest the optimum control of liquidity with trade service policy recommendations in different 

countries. The research method was based on 5 countries from the 16 countries of western African and 

elaborated by their individual indicators with the least square method. The gross value of debts and public 

administration controlled the development aim of an entire state with strategic and planned environment for 

state and reduce the level of inflation in small and enterprise section and the results analyzed the policy makers 

implement planned in implication of trade with domestic currency and long run endogeneity. The results 

analyzed the monetary policies affecting the level of growth of an individual country.  

 

Keywords: Monetary, West African countries, Trade, Economy.  

1. Introduction 

We have increased the regional interaction the economy by the priority of free trade and growth. According to 

Negotiating Forum (NF) the Continental Free Trade Area (CFTA) is the path of trade and investment which 

convened for CFTA. The investment of incorporate of 53 African countries, represent 1 billion people with $3 

trillion GDP. The policy of implication of west African countries controlled by regional economic community 

(REC’s) likewise West Economic and Monetary Union (WAEMU) is the main building block of achievement of 

free trade and monetary implication.(K. Ahmed, Bhattacharya, Shaikh, Ramzan, & Ozturk, 2017; Aydin, 

2019; Kong & Khan, 2019; MengYunet al., 2018) The highest level of intraregional trade in West African 

countries is low when it compares to the level of a custom union trade in WAEMU and EU’s with 25-60 

percent. Fig 1.   

mailto:khan.rab@stmail.ujs.edu.cn
mailto:1048186766@qq.com


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Figure 1: Intra-regional export for WEMU and ASEAN: Sources: UNCTA, 2015 

Second the monetary policies and implication of trade is satiability of CFA franc zone in African 

countries in the term of macroeconomic. (Bekun, Emir, & Sarkodie, 2019; Grossman G, 1995) The important 

issues some countries unstable and have taking weak attention of historically monetary institutional framework. 

However, the currencies depreciate in an external environment in the region’s stability and legitimate to achieve 

the competitiveness of individual policies. 

 

Figure 2: Franc stability, Sauce: Hallet, 2008 

The global fixed exchange rate has benefited to foreign trade and taxation policies with achieving 

macroeconomic stability.(Abid, 2017; Dong, Wang, & Guo, 2016; J. Du & Zhang, 2018) The research showed 

the competitiveness challenges of GDP, trade services, and merchandise trade. The region economic 

communities in west countries have directly infect the economy by different strategic policies.  

  



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The prior research implication based on economic development and south African country’s economic 

policies and didn’t mention the (Adom & Kwakwa, 2014) strategic policies regarding individual expect of 

foreign exchange rate, federal economic development trade and effected issues of GDP by CFA.(Adom & 

Kwakwa, 2014) Therefore, this research is most import issues weighted and determined the strategic policy with 

CPIA debt policies, inflation, GDP deflator, trade services, trade (GDP),(G. Du, Liu, Lei, & Huang, 2018; Riaz 

et al., 2018) merchandise trade and merchandise export. We base the second section of this research on the 

literature. We base the third section of this research on the method. 4th section showed results and analysis and 

final section held with recommendation and conclusion.   

2. Literature 

We base prior research on implemented policies and strategic changes in sub-Saharan countries 

and highlighted the issues of economic development with individual effects. The convergence member of 

countries showed i.e. inflation, growth, per capita and currency union. (Coleman, 2010; Harvey & Cushing, 

2015)The common stock of macroeconomic policies more in under developing countries, which makes a 

common strategic policy for individual states.  

Several theories in literature is showing the impact of policy regarding the monitoring policies, 

which created on asset prices, patents, development and growing of economy. The systematical approach of the 

theories effects on economics variables.(Button, Martini, Scotti, & Volta, 2019; Osabutey & Jackson, 2019) the 

first view is liquidity approach emphasized the increasing liquidity, asset prices increase, and it acts as a link in 

the transmission of liquidity assets on the economic activities with the wide range of development skills and 

determined policies of an individual government.(Bensassi & Jarreau, 2019; Tsao et al., 2019) the other expects 
of low and stable inflation cause of lack of monitoring policies in stabilizing the high level of investment.(Keho, 

2017; Yaya, Ling, Furuoka, Rose Ezeoke, & Jacob, 2019) 2nd the presented a dynamic equilibrium of 

monitoring policies based on the bubble in asset prices, in addition poor monetary policy design such as rate 

rules of sustainable long-term inflation. 3rdthe trend survey of effect on money and monetary policy on asset 

prices including the exchange rate the monetarist theory effects on uncertainty, government policies and 

economic growth. The high-quality boom of assets price, growth of monetary supply and investment. (Asongu, 

Folarin, & Biekpe, 2019; Mikayilov, Hasanov, & Galeotti, 2018; Riaz et al., 2018)Therefore, the prior of 

research implicated the trade in big rule and hold the effect of a portfolio of a financial institution regarding 

huge investment and development policies. 4th the policies of investment in a different channel by self-crating 

the huge gap in monetary policies, where the different price channel has tagging different prices level, credit 

ratio, exchange rate cause of the intellectual policies of inflation, GDP, trade and services and merchandise trade 
has affected on the west African countries monetary policies. The rate of a channel determined the effects on 

price and exchange rate (K. Ahmed, Bhattacharya, M., Shaikh, Z., Ramzan, M., & Ozturk, I  2017; Cham, 

2016). The exchange rate channel, other asset price channels, and the credit channel. Since the present study 

surveys the impact of monetary policy on the exchange rate, it determines the level of intensity in 

financing. (Al-Mulali, Ozturk, & Solarin, 2016; Apergis & Ozturk, 2015) . 

The countries competitiveness will need to ensure the macroeconomic stability, which improved the 

business climate of trade, reduce the hard infrastructure stability and technology with transfer infrastructure 

gap.(Bo, 2015; Schwerhoff & Sy, 2017; Zhao & Kim, 2009) The investment of trade and strategic policies of 

economic development such as skills has increased agriculture as well with adept policies, training and 

extension program and build capabilities of domestic firms.(Acheampong, 2018 Awad & Abugamos, 

2017; Harvey & Cushing, 2015) The structural transformation will require leverage of the ICT sector of 
productivity, financial tie and domestic macroeconomics frameworks.   

 

3. Data, Model and Results 

We base this research paper research method on liner regression between gross value, CPIA debt policies, 

public administration with regional trade, GDP per capita, service in trade, trade of an individual,(Harding, 

2007; Im, Pesaran, & Shin, 2003; Zhang, Liao, & Hao, 2018) merchandise trade and export of low income 

economy. It shows the stability of model the strategic policies,, so the results conducted by the regression. In a 

first step unit root is taking for the stationary and non-stationary level of intimal of 5 countries from the 16 west 

African countries.   

 

 

 



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Table 1. Indicators 

Indicators (Benin, Burkina Faso, Ghana, Guinea, Guinea-

Bissau)  

Indicator WB  Symbol  

Gross value added at basic prices (GVA) (current US$) NY.GDP.FCST.CD GVA 

CPIA debt policy rating (1 = low to 6 = high) IQ.CPA.DEBT.XQ CPIAD 

CPIA quality of public administration rating (1=low to 6=high) IQ.CPA.PADM.XQ CPIAQ 

Inflation, GDP deflator (annual %) NY.GDP.DEFL.KD. ZG IGD 

GDP per capita (current US$) NY.GDP.PCAP.CD GDPPC 

Trade in services (% of GDP) BG.GSR.NFSV.GD.ZS TS 

Trade (% of GDP) NE.TRD.GNFS.ZS TR 

Merchandise trade (% of GDP) TG.VAL.TOTL.GD.ZS MTG 

Merchandise exports to low- and middle-income economies 

within region (% of total merchandise exports) 

TX.VAL.MRCH.WR.ZS MEL 

  

It indicates Table 1 the gross values of different indicators as per indicator codes. The strategic policies 

have been transiting with 9 indicators and individually defined with the period of 1960-2018. However, the 

export level of merchandise. (Perron, 1988; Sinha & Shahbaz, 2018) 

Results and Analysis 
The results and analysis were analyzed using the liner method. In 1st stage mean deviation of individual 

variables have been taking by skewness and Kurtosis, and the deviation analyzed by mean and standard 

deviation. The mean deviation is greater from the standard deviation. The individual indicator shows a 

significant effect on each individual variable(Cheng, Ren, Wang, & Yan, 2019; Im et al., 2003; Saqib, Ahmad, 

& Amezcua-Prieto, 2018; Zhao & Kim, 2009).  

 

Table 2. Mean deviation 

 CPIAD CPIAQ GDPPC GVA IGD MEL MTG TR TS 

 Mean 3.25714

3 

3.08571

4 

422.502

2 

4.85E+

09 

13.9884

1 

18.4213

7 

40.7366

6 

49.1624

2 

12.7690

1 

 Median 3.5 3 341.527

5 

2.36E+

09 

6.38697

4 

12.6578

4 

39.5120

5 

46.9411

1 

12.7851

5 

 Maximum 4.5 3.5 2378.16 5.98E+

10 

123.061

2 

88.2146

8 

93.1964

1 

132.050

2 

27.3494

8 

 Minimum 1 2 68.4247

5 

1.53E+

08 

-

6.34567

7 

0.36662

5 

4.53936

3 

6.32034

3 

4.26148

1 

 Std. Dev. 0.81978 0.40799 356.543 8.64E+ 20.8678 18.5417 15.2425 19.6215 4.24076



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6 5 3 09 8 1 4 3 4 

 Skewness -

0.74088

1 

-

0.64428

6 

2.69987

6 

4.18352

1 

2.62944

2 

1.52885

3 

0.39060

3 

0.86074

1 

0.70782

8 

 Kurtosis 2.59803

2 

2.66245

3 

12.4324

8 

22.1229

5 

10.7893

9 

4.85654

2 

3.59937

4 

4.83443

7 

4.13222

3 

          

 Jarque-

Bera 

6.87516 5.17520

3 

1274.80

8 

4356.94

5 

934.830

9 

136.494

1 

10.4628

6 

68.0331

1 

22.3174

8 

 Probability 0.03214

2 

0.0752 0 0 0 0 0.00534

6 

0 0.00001

4 

          

 Sum 228 216 109428.

1 

1.16E+

12 

3553.05

6 

4715.87 10550.8 12683.9 2081.34

8 

 Sum Sq. 

Dev. 

46.3714

3 

11.4857

1 

3279776

9 

1.78E+

22 

110173.

5 

87667.6

8 

59942.4

6 

98946.1

1 

2913.42 

          

 Observatio

ns 

70 70 259 240 254 256 259 258 163 

  

The given results of analysis have interoperated the distribution t-factors within between valuation.  

  

 



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Table 3. Covariance 

Covariance Analysis: Ordinary        

t-Statistic          

Probability CPIAD  CPIAQ  GDPPC  GVA  IGD  MEL  MTG  TR  TS  

CPIAD  0.685562         

CPIAQ  0.250355 0.153018        

 9.670279 -----         

GDPPC  80.13973 66.1267 176071       

 1.881582 3.493724 -----        

 0.0645 0.0009 -----        

GVA  3.87E+09 3.29E+09 5.63E+12 1.97E+20      

 2.800963 5.934168 25.77055 -----       

 0.0068 0 0 -----       

IGD  -0.379726 1.222435 1808.744 7.36E+10 289.5489     

 -0.214 1.482909 2.078473 2.571124 -----      

 0.8312 0.1431 0.0417 0.0125 -----      

MEL  4.212973 1.085542 1030.478 3.36E+10 -16.18827 302.9732    

 2.426221 1.281843 1.131174 1.101121 -0.434467 -----     



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 0.0181 0.2046 0.2623 0.275 0.6654 -----     

MTG  3.394529 1.364002 1517.276 5.06E+10 23.7466 39.62843 136.398   

 2.975628 2.483048 2.584455 2.575627 0.955278 1.577552 -----    

 0.0041 0.0157 0.0121 0.0124 0.3431 0.1197 -----    

TR  -0.120382 0.812654 2488.352 7.59E+10 63.69064 -17.78276 152.6474 272.7775  

 -0.069875 1.006384 3.053553 2.748993 1.846842 -0.491921 10.27479 -----   

 0.9445 0.3181 0.0033 0.0078 0.0695 0.6245 0 -----   

TS  0.717598 0.461766 965.8295 3.52E+10 3.87425 9.645411 14.46673 14.38365 20.8171 

 1.53567 2.125966 4.637605 5.225046 0.396578 0.971193 2.238991 1.543421 -----  

 0.1296 0.0374 0 0 0.693 0.3352 0.0287 0.1277 -----  

  

 



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Table 2 The significant relationships analyzed by the probability level of linear value, where the highest mean 

deviation is directly affecting the monetary integration and implication of trade in African countries. the 

integration of value has been analyzed by the CPIAD, IGD and TS, where the gross value at the basic rate, 

inflation of GDP with trade service, merchandise trade, export and the quality of public administration in debt 

value and interpreted the monetary policies of Africa and implication on trade.  Table 3 

Figure 3: Mean and covariance 

Table 4. Equity 

Test for Equality of Variances Between Series 

     
Method  df Value Probability 

     
Bartlett  8 59193.83 0 

Levene  (8, 1820) 75.12375 0 

Brown-Forsythe (8, 1820) 43.85331 0 

Category Statistics    

   Mean Abs. Mean Abs. 

Variable Count Std. Dev. Mean Diff. Median Diff. 

CPIAD 70 0.819786 0.706122 0.671429 

CPIAQ 70 0.407995 0.331429 0.314286 



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GDPPC 259 356.5433 234.0249 218.1158 

GVA 240 8.64E+09 4.52E+09 3.82E+09 

IGD 254 20.86788 13.7503 12.14635 

MEL 256 18.54171 13.93338 13.22127 

MTG 259 15.24254 11.83501 11.81549 

TR 258 19.62153 14.58696 14.49539 

TS 163 4.240764 3.173404 3.173305 

All 1829 3.53E+09 5.93E+08 5.02E+08 

     
Bartlett weighted standard deviation:  3.13e+09 

  

Error! Filename not specified. 

Figure 4: Least limitation 

Fig 3 And the highest mean deviation of IGD and TS are indicated the highly effects of domestic trade in 

private sectors, its mean if the investment of individual countries will rise in private sector so effect on the 

monitoring policy.  Table 4  

Table 5.Observation 

Autocorrelation Partial Correlation AC   PAC  Q-Stat  Prob 

       

      **| .    |       **| .    | 1 -0.223 -0.223 3.1455 0.076 

      .*| .    |       .*| .    | 2 -0.116 -0.174 4.0065 0.135 

      **| .    |       **| .    | 3 -0.224 -0.318 7.2805 0.063 

      . |*.    |       . | .    | 4 0.17 -0.003 9.195 0.056 

      . | .    |       .*| .    | 5 -0.052 -0.113 9.376 0.095 

      . | .    |       .*| .    | 6 -0.002 -0.093 9.3762 0.153 

      . | .    |       . | .    | 7 -0.008 -0.014 9.3805 0.226 

      . | .    |       .*| .    | 8 -0.006 -0.079 9.3832 0.311 

      . | .    |       . | .    | 9 -0.003 -0.039 9.3838 0.403 

      . | .    |       . | .    | 10 0.001 -0.028 9.3838 0.496 

      . | .    |       . | .    | 11 -0.003 -0.048 9.3847 0.586 

  



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Table 5-6 is indicated the actual effects of fitted and residual effect by the level of actual intensity. The foreign 

trade computed by the debt policies and quality of public administration which is the part of monitoring 

policies. The trade service and merchandise trade with in economic growth with exports.  

Table 7 is indicated the indicators different policies with sources of inflation and GDP per capita, the 

highlighted part of Trade service and investment, likewise, foreign investment under the stated law implement 

the rules and policies of CPIA quality and debit policies.  Fig 4   

Table 6. Indicated observation 

CPIAD,CPIAQ(-i) CPIAD,CPIAQ(+i) i   lag  lead 

     

         . |******** |          . |******** | 0 0.7564 0.7564 

         . |*******  |          . |*******  | 1 0.6706 0.7213 

         . |******   |          . |*******  | 2 0.581 0.6862 

         . |*****    |          . |*******  | 3 0.5078 0.6511 

         . |****     |          . |******   | 4 0.4219 0.625 

         . |***      |          . |******   | 5 0.3469 0.5988 

         . |***      |          . |******   | 6 0.2846 0.5637 

         . |**       |          . |*****    | 7 0.2368 0.5305 

         . |**       |          . |*****    | 8 0.1927 0.5044 

         . |**       |          . |*****    | 9 0.1576 0.4637 

         . |*.       |          . |****     | 10 0.1315 0.3886 

         . |*.       |          . |***      | 11 0.1053 0.292 

         . |*.       |          . |**       | 12 0.0702 0.2079 

         . | .       |          . |*.       | 13 0.0351 0.0967 

         . | .       |          . | .       | 14 0 0 

         . | .       |          . | .       | 15 0 0 

         . | .       |          . | .       | 16 0 0 

         . | .       |          . | .       | 17 0 0 

         . | .       |          . | .       | 18 0 0 

         . | .       |          . | .       | 19 0 0 

         . | .       |          . | .       | 20 0 0 

         . | .       |          . | .       | 21 0 0 



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         . | .       |          . | .       | 22 0 0 

         . | .       |          . | .       | 23 0 0 

         . | .       |          . | .       | 24 0 0 

         . | .       |          . | .       | 25 0 0 

         . | .       |          . | .       | 26 0 0 

         . | .       |          . | .       | 27 0 0 

         . | .       |          . | .       | 28 0 0 

         . | .       |          . | .       | 29 0 0 

         . | .       |          . | .       | 30 0 0 

         . | .       |          . | .       | 31 0 0 

         . | .       |          . | .       | 32 0 0 

  

Table 4 is indicated the covariance of indicator and their relationship of individual indicators. likewise, GVA, 

CPIAD, GDPPC and TR with MTG have shown the significant effect on investment and implemented policies. 

So therefore, the relationship of IGD and TS the implanted policies and its implication of trade in Africa.  

Table 7. Maximum factors 

Factor Method: Maximum Likelihood  

 F1 Communality Uniqueness  

CPIAD 0.332776 0.11074 0.88926   

CPIAQ 0.598787 0.358546 0.641454   

GDPPC 0.955697 0.913357 0.086643   

GVA 1 1 0   

IGD 0.308166 0.094966 0.905034   

MEL 0.137412 0.018882 0.981118   

MTG 0.308655 0.095268 0.904732   

TR 0.327268 0.107104 0.892896   

TS 0.549849 0.302334 0.697666   

      

Factor Variance Cumulative Difference Proportion Cumulative 

F1 3.001197 3.001197 --- 1 1 



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Total 3.001197 3.001197  1  

      

 Model Independence Saturated  

Discrepancy 3.877421 7.577025 0   

Chi-square statistic 248.155 484.9296 ---   

Chi-square prob. 0 0 ---   

Bartlett chi-square 230.7066 455.8843 ---   

Bartlett probability 0 0 ---   

Parameters 18 9 45   

Degrees-of-freedom 27 36 ---   

      

Warning: Heywood solution (uniqueness estimates are non-positive). 

Results should be interpreted with caution.  

 

Table 8 shows the padroni test of individual indicators of CPIAD and IGD with GVA within 6-1 ranking, the 

computed results is indicated the stationary issue in nonstationary level. The probability of test is indicated the 

turn over period 1960-2018. Table 9  

 



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Table 8. Correlate 

  

 

 CPIAD CPIAQ GDPPC GVA IGD MEL MTG TR TS 

CPIAD -3.22E-15 0.573707104 -0.087368372 -2.83E-15 -0.129501948 0.246595646 0.248323602 -0.117709958 0.006977153 

CPIAQ 0.573707104 -4.66E-15 -0.169391678 -2.78E-15 -0.000874664 0.077150599 0.113747569 -0.070178457 -0.070515985 

GDPPC -0.087368372 -0.169391678 2.90E-09 3.33E-16 -0.041192064 0.009764396 0.014630985 0.046287995 -0.021006338 

GVA -2.83E-15 -2.78E-15 3.33E-16 -4.44E-16 3.89E-16 -5.55E-17 0 7.22E-16 1.11E-15 

IGD -0.129501948 -0.000874664 -0.041192064 3.89E-16 4.44E-16 -0.097001772 0.024374437 0.12577333 -0.119543142 

MEL 0.246595646 0.077150599 0.009764396 -5.55E-17 -0.097001772 -1.11E-16 0.152526961 -0.106828207 0.045897037 

MTG 0.248323602 0.113747569 0.014630985 0 0.024374437 0.152526961 -2.00E-15 0.690359979 0.101778013 

TR -0.117709958 -0.070178457 0.046287995 7.22E-16 0.12577333 -0.106828207 0.690359979 -6.66E-16 0.010929393 

TS 0.006977153 -0.070515985 -0.021006338 1.11E-15 -0.119543142 0.045897037 0.101778013 0.010929393 1.55E-15 



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Table 9. Fitness summary 

Goodness-of-fit Summary  

Factor: Untitled   

    

 Model Independence Saturated 

Parameters 18 9 45 

Degrees-of-freedom 27 36 --- 

Parsimony ratio 0.75 1 --- 

    

Absolute Fit Indices   

 Model Independence Saturated 

Discrepancy 3.877421 7.577025 0 

Chi-square statistic 248.155 484.9296 --- 

Chi-square probability 0 0 --- 

Bartlett chi-square statistic 230.7066 455.8843 --- 

Bartlett probability 0 0 --- 

Root mean sq. reside. (RMSR) 0.176065 0.362026 0 

Akaike criterion 2.986999 6.352763 0 

Schwarz criterion 2.083792 5.148487 0 

Hannan-Quinn criterion 2.630626 5.877599 0 

Expected cross-validation (ECVI) 4.439921 7.858275 1.40625 

Generalized fit index (GFI) 0.672501 0.488161 1 

Adjusted GFI 0.454168 0.146935 --- 

Non-centrality parameter 221.155 448.9296 --- 

Gamma Hat 0.126405 0.066538 --- 

McDonald Non-centrally 0.17768 0.029979 --- 

Root MSE approximation 0.357747 0.441416 --- 



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Incremental Fit Indices  

 Model   

Bollen Relative (RFI) 0.317688   

Bentler-Bonnet Normed (NFI) 0.488266   

Tucker-Lewis Non-Normed (NNFI) 0.343164   

Bollen Incremental (IFI) 0.517055   

Bentler Comparative (CFI) 0.507373   

  

4. Conclusion 

The above research is proved that investment in these western African countries not only to develop the individual 

region or society, it creating effect on the entire African state with huge monitoring polices. The gross value of debts 

and public administration is controlled the development objective of entire state with strategic and planned 

environment for state and reduce the level of inflation in small and enterprise section. The monitoring policies in 

developing countries is one of the important issues and influence factor in each individual state. Therefore, the 
impact of monetary policies on GDP per capita reflect the exchange rate in developing countries and generalized 

method of covariance by the probability level of linear value, where the highest mean deviation is directly affecting 

the monetary integration and implication of trade in African countries. The integration of value has been analyzed 

by the CPIAD, IGD and TS. The above results estimated the coefficient of all indicators with 95% confidence 

interval. In addition, Wald test confirm the validity of the instrument by absence of serial autocorrelation in 1st order. 

The estimation shows the monetary integration in African states. Therefore, the functioning of the foreign exchange 

and their implication in trade constantly shows the significant effects on proxy monitoring policies. The results 

reflect the debt policies, public administration with regional trade, GDP per capita, service in trade, trade of 

individual, merchandise trade, and export of low income economy. The stability of model is indicated the strategic 

policies so the results is conducting by the regression. It has necessary for the individual state to reduce the economy 

dependency in export and oil sources, to prevent the equity of public administration, trade in service and 
merchandise trade. The fluctuation of monetary policies and implication review the sector of currency policies in 

trade and investment. Furthermore, the above per-capita results are heighted the trade services and merchandise 

trade with different level of GDP. The gross development product need strategic techniques for development and 

entire export so therefore the one corner has been solving with monitoring policies and premeditated planed.  

 

References 

Abid, M. (2017). Does economic, financial and institutional developments matter for environmental quality? A 

comparative analysis of EU and MEA countries. Journal of Environmental Management, 188, 183-194. 

doi:https://doi.org/10.1016/j.jenvman.2016.12.007 

Acheampong, A. O. (2018). Economic growth, CO2 emissions and energy consumption: What causes what and 

where? Energy Economics, 74, 677-692. doi:https://doi.org/10.1016/j.eneco.2018.07.022 

Adom, P. K., & Kwakwa, P. A. (2014). Effects of changing trade structure and technical characteristics of the 
manufacturing sector on energy intensity in Ghana. Renewable and Sustainable Energy Reviews, 35, 475-

483. doi:https://doi.org/10.1016/j.rser.2014.04.014 

Ahmed, K., Bhattacharya, M., Shaikh, Z., Ramzan, M., & Ozturk, I. (2017). Emission intensive growth and trade in 

the era of the Association of Southeast Asian Nations (ASEAN) integration: An empirical investigation 

from ASEAN-8. Journal of Cleaner Production, 154, 530-540. 

doi:https://doi.org/10.1016/j.jclepro.2017.04.008 

Ahmed, K., Bhattacharya, M., Shaikh, Z., Ramzan, M., & Ozturk, I  (2017). Emission intensive growth and trade in 

the era of the Association of Southeast Asian Nations (ASEAN) integration: an empirical investigation 

https://doi.org/10.1016/j.jenvman.2016.12.007
https://doi.org/10.1016/j.eneco.2018.07.022
https://doi.org/10.1016/j.rser.2014.04.014
https://doi.org/10.1016/j.jclepro.2017.04.008


www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 2, No. 2; 2020 

26 

 

from ASEAN-8. Journal of Cleaner Production, 154, 530-540. 

doi:https://doi.org/10.1016/j.jclepro.2017.04.008 

Al-Mulali, U., Ozturk, I., & Solarin, S. A. (2016). Investigating the environmental Kuznets curve hypothesis in 

seven regions: The role of renewable energy. Ecological Indicators, 67, 267-282. 

doi:https://doi.org/10.1016/j.ecolind.2016.02.059 

Apergis, N., & Ozturk, I. (2015). Testing Environmental Kuznets Curve hypothesis in Asian countries. Ecological 
Indicators, 52, 16-22. doi:https://doi.org/10.1016/j.ecolind.2014.11.026 

Asongu, S. A., Folarin, O. E., & Biekpe, N. (2019). The long run stability of money demand in the proposed West 

African monetary union. Research in International Business and Finance, 48, 483-495. 

doi:https://doi.org/10.1016/j.ribaf.2018.11.001 

Awad, A., & Abugamos, H. (2017 ). Income-carbon Emissions Nexus for Middle East and North Africa Countries: 

A Semi-parametric Approach,. International Journal of Energy Economics and Policy, 7(2).  

Aydin, M. (2019). The effect of biomass energy consumption on economic growth in BRICS countries: A country-

specific panel data analysis. Renewable Energy, 138, 620-627. 

doi:https://doi.org/10.1016/j.renene.2019.02.001 

Bekun, F. V., Emir, F., & Sarkodie, S. A. (2019). Another look at the relationship between energy consumption, 

carbon dioxide emissions, and economic growth in South Africa. Science of The Total Environment, 655, 

759-765. doi:https://doi.org/10.1016/j.scitotenv.2018.11.271 
Bensassi, S., & Jarreau, J. (2019). Price discrimination in bribe payments: Evidence from informal cross-border 

trade in West Africa. World Development, 122, 462-480. 

doi:https://doi.org/10.1016/j.worlddev.2019.05.023 

Bo, N. A. C. B. P. S. (2015). Factors in the cross-cultural adaptation of African students in Chinese 

universities. Journal of Research in International Education, 14(2), 98-113.  

Button, K., Martini, G., Scotti, D., & Volta, N. (2019). Airline regulation and common markets in Sub-Saharan 

Africa. Transportation Research Part E: Logistics and Transportation Review, 129, 81-91. 

doi:https://doi.org/10.1016/j.tre.2019.07.007 

Cham, T. (2016). Does monetary integration lead to an increase in FDI flows? An empirical investigation from the 

West African Monetary Zone (WAMZ). Borsa Istanbul Review, 16(1), 9-20. 

doi:https://doi.org/10.1016/j.bir.2016.01.002 
Cheng, C., Ren, X., Wang, Z., & Yan, C. (2019). Heterogeneous impacts of renewable energy and environmental 

patents on CO2 emission - Evidence from the BRIICS. Science of The Total Environment, 668, 1328-

1338. doi:https://doi.org/10.1016/j.scitotenv.2019.02.063 

Coleman, S. (2010). Inflation persistence in the Franc zone: Evidence from disaggregated prices. Journal of 

Macroeconomics, 32(1), 426-442. doi:https://doi.org/10.1016/j.jmacro.2009.08.002 

Dong, B., Wang, F., & Guo, Y. (2016). The global EKCs. International Review of Economics & Finance, 43, 210-

221. doi:https://doi.org/10.1016/j.iref.2016.02.010 

Du, G., Liu, S., Lei, N., & Huang, Y. (2018). A test of environmental Kuznets curve for haze pollution in China: 

Evidence from the penal data of 27 capital cities. Journal of Cleaner Production, 205, 821-827. 

doi:https://doi.org/10.1016/j.jclepro.2018.08.330 

Du, J., & Zhang, Y. (2018). Does One Belt One Road initiative promote Chinese overseas direct investment? China 

Economic Review, 47, 189-205. doi:https://doi.org/10.1016/j.chieco.2017.05.010 
Grossman G, K. A. (1995). Economic growth and the environment. Quarterly Journal of Economics, 100(2), 353-

377.  

Harding, J. W. a. J. M. H. (2007). Generalized Linear Models  and Extensions (2nd ed.). 

Harvey, S. K., & Cushing, M. J. (2015). Is West African Monetary Zone (WAMZ) a common currency 

area? Review of Development Finance, 5(1), 53-63. doi:https://doi.org/10.1016/j.rdf.2015.05.001 

Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of 

Econometrics, 115(1), 53-74. doi:https://doi.org/10.1016/S0304-4076(03)00092-7 

Keho, Y. (2017). Revisiting the Income, Energy Consumption and Carbon Emissions Nexus: New Evidence from 

Quantile Regression for Different Country Groups. International Journal of Energy Economics and 

Policy, 7(3), 356-363.  

Kong, Y., & Khan, R. (2019). To examine environmental pollution by economic growth and their impact in an 
environmental Kuznets curve (EKC) among developed and developing countries. PloS one, 14(3). 

doi:https://doi.org/10.1371/journal.pone.0209532 

https://doi.org/10.1016/j.jclepro.2017.04.008
https://doi.org/10.1016/j.ecolind.2016.02.059
https://doi.org/10.1016/j.ecolind.2014.11.026
https://doi.org/10.1016/j.ribaf.2018.11.001
https://doi.org/10.1016/j.renene.2019.02.001
https://doi.org/10.1016/j.scitotenv.2018.11.271
https://doi.org/10.1016/j.worlddev.2019.05.023
https://doi.org/10.1016/j.tre.2019.07.007
https://doi.org/10.1016/j.bir.2016.01.002
https://doi.org/10.1016/j.scitotenv.2019.02.063
https://doi.org/10.1016/j.jmacro.2009.08.002
https://doi.org/10.1016/j.iref.2016.02.010
https://doi.org/10.1016/j.jclepro.2018.08.330
https://doi.org/10.1016/j.chieco.2017.05.010
https://doi.org/10.1016/j.rdf.2015.05.001
https://doi.org/10.1016/S0304-4076(03)00092-7
https://doi.org/10.1371/journal.pone.0209532


www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 2, No. 2; 2020 

27 

 

MengYun, W., Imran, M., Zakaria, M., Linrong, Z., Farooq, M. U., & Muhammad, S. K. (2018). Impact of 

terrorism and political instability on equity premium: Evidence from Pakistan. Physica A: Statistical 

Mechanics and its Applications, 492, 1753-1762. doi:https://doi.org/10.1016/j.physa.2017.11.095 

Mikayilov, J. I., Hasanov, F. J., & Galeotti, M. (2018). Decoupling of CO2 emissions and GDP: A time-varying 

cointegration approach. Ecological Indicators, 95, 615-628. 

doi:https://doi.org/10.1016/j.ecolind.2018.07.051 
Osabutey, E. L. C., & Jackson, T. (2019). The impact on development of technology and knowledge transfer in 

Chinese MNEs in sub-Saharan Africa: The Ghanaian case. Technological Forecasting and Social Change, 

148, 119725. doi:https://doi.org/10.1016/j.techfore.2019.119725 

Perron, P. (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75(2), 335-346.  

Riaz, A., Husain, S., Yousafzai, M. T., Nisar, I., Shaheen, F., Mahesar, W., . . . Ali, A. (2018). Reasons for non-

vaccination and incomplete vaccinations among children in Pakistan. Vaccine, 36(35), 5288-5293. 

doi:https://doi.org/10.1016/j.vaccine.2018.07.024 

Saqib, S. E., Ahmad, M. M., & Amezcua-Prieto, C. (2018). Economic burden of tuberculosis and its coping 

mechanism at the household level in Pakistan. The Social Science Journal, 55(3), 313-322. 

doi:https://doi.org/10.1016/j.soscij.2018.01.001 

Schwerhoff, G., & Sy, M. (2017). Financing renewable energy in Africa – Key challenge of the sustainable 

development goals. Renewable and Sustainable Energy Reviews, 75, 393-401. 
doi:https://doi.org/10.1016/j.rser.2016.11.004 

Sinha, A., & Shahbaz, M. (2018). Estimation of Environmental Kuznets Curve for CO2 emission: Role of 

renewable energy generation in India. Renewable Energy, 119, 703-711. 

doi:https://doi.org/10.1016/j.renene.2017.12.058 

Tsao, L., Slater, S. E., Doyle, K. P., Cuong, D. D., Khanh, Q. T., Maurer, R., . . . Krakauer, E. L. (2019). Palliative 

Care–Related Knowledge, Attitudes, and Self-Assessment Among Physicians in Vietnam. Journal of Pain 

and Symptom Management. doi:https://doi.org/10.1016/j.jpainsymman.2019.08.001 

Yaya, O. S., Ling, P. K., Furuoka, F., Rose Ezeoke, C. M., & Jacob, R. I. (2019). Can West African countries catch 

up with Nigeria? Evidence from smooth nonlinearity method in fractional unit root 

framework. International Economics, 158, 51-63. doi:https://doi.org/10.1016/j.inteco.2019.02.004 

Zhang, Q., Liao, H., & Hao, Y. (2018). Does one path fit all? An empirical study on the relationship between 
energy consumption and economic development for individual Chinese provinces. Energy, 150, 527-543. 

doi:https://doi.org/10.1016/j.energy.2018.02.106 

Zhao, X., & Kim, Y. (2009). Is the CFA Franc Zone an Optimum Currency Area? World Development, 37(12), 

1877-1886. doi:https://doi.org/10.1016/j.worlddev.2009.03.011 

  

 

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https://doi.org/10.1016/j.physa.2017.11.095
https://doi.org/10.1016/j.ecolind.2018.07.051
https://doi.org/10.1016/j.techfore.2019.119725
https://doi.org/10.1016/j.vaccine.2018.07.024
https://doi.org/10.1016/j.soscij.2018.01.001
https://doi.org/10.1016/j.rser.2016.11.004
https://doi.org/10.1016/j.renene.2017.12.058
https://doi.org/10.1016/j.jpainsymman.2019.08.001
https://doi.org/10.1016/j.inteco.2019.02.004
https://doi.org/10.1016/j.energy.2018.02.106
https://doi.org/10.1016/j.worlddev.2009.03.011

