THE EXPORTS OF MARINE RESOURCES AND ITS IMPACT ON ECONOMIC DEVELOPMENT IN PAKISTAN: AN ECONOMETRIC ANALYSIS USING VECTOR ERROR CORRECTION MODEL (VECM)
S. Oad^{1}, Q. Jinliang*^{2, }[1], S. S. B. Hussain^{3}, M. Ali^{3,} and Z. U. Jattak^{4 }
^{1}Department of Cultural Industries Management, College of Management, Ocean University of China, Qingdao, 266003, China
^{2}Institute of Maritime Culture Studies, Ocean University of China, Qingdao, 266003, China.
^{3}Department Fisheries Economics & Management, College of Fisheries, Ocean University of China, Qingdao, 266003, China
^{4}Lasbela University of Agriculture, Water and Marine Sciences, Uthal, BalochistanPakistan
*Corresponding Author’s Email: qujinliang@ouc.edu.cn
ABSTRACT
This study examines the link between marine resources exports and their impact on economic development in Pakistan by using Vector ErrorCorrection Models (VECM). The data of 57 years from 1960 to 2017 were used to analyse the impact among variables. The findings of this research were obtained as there is no longrun association discovered between the fisheries exports and economic development. However, we discovered that there is a shortrun relationship sustained between them. In addition, the results from the Ordinary Least Squares (OLS) regression also validates that there is a robust relationship between GDP and exports of fisheries products. The findings indicated that the subsector (Fisheries) of the agriculture sector is playing an extensive role in the economic growth of Pakistan.
Keywords: Marine resources, Fishery sector, GDP, VECM and Pakistan.
http://doi.org/10.36899/JAPS.2022.3.0482 Published first online October 19. 2021
INTRODUCTION
As marine resources can play an important role in economic development. These resources are found in the oceans and are valued, which can be intrinsic or financial value. There are many things that include marine resources such as fisheries and seafood supply, oil and gas, minerals, tourism potential, renewable energy resources and others (Oad et al., 2021; Wood, 2020). Among them, the fishery sector is playing an important role in economic development, mainly in developing countries. Besides export revenues and employment, its production and commercialization make a significant source of good protein (Jaunky, 2011; Thorpe et al. 2005). Alike other countries, the fisheries sector in Pakistan is the major source of livelihood for the coastal population. Moreover, the mosttraded foods in the country are fisheries products. In addition to marine fisheries, inland fisheries found in lakes, ponds, rivers and dams are also significant activity in the country. In Pakistan, the fishery sector is among four subsectors of the agriculture sector (i.e. fisheries, live stocks, forestry and crops) (Anonymous, 2020a; Jawaid et al. 2019).
According to Pakistan Economic Survey 20192020, the agriculture sector and its subsectors witnessed growth. The agriculture sector grew by 2.67% and the livestock sector by 2.58%. Similarly, the fisheries sector recorded a growth of 0.60%, while the forestry sector achieved a growth of 2.29% (Anonymous, 2020a). Pakistan’s coastline is bordering with Arabian Sea. Following the announcement of the Exclusive Economic Zone (EEZ) in 1976, Pakistan added approximately 250,000 square kilometers of coastline to its territory to exploit fisheries (Anonymous, 2014; Jawaid et al., 2019). In Pakistan, marine fisheries are practiced under two separate grounds, namely the coast stretching along the coast of the capital city of Sindh province (Karachi), spreading southeast from Karachi to the border of India and alongside the coastline of Baluchistan province to the border of Iran. China, Thailand, Malaysia, Middle East, Sri Lanka and Japan are Pakistan’s major buyers of fisheries (Anonymous, 2014, 2020a). Though the share of the fisheries sector in agricultural exports and GDP (0.4%), is very small but it has significantly increased the national income through export earnings (Anonymous, 2020a). In the financial year 201718, the country's fish (seafood) exports registered an increase of 27.94% to 198,420 metric tons from total fisheries production. The contribution of the fisheries sector by exports was USD 451.026 million to the national economy. Pakistan’s export value witnessed an increase of 14.57% from USD 393.662 million of 155,091 tons in 201617 (Shah, 2018). Despite its importance and potential, the contribution of the fishery sector to economic development is not visible, identified, and noticed in the literature. Figure 1 demonstrates the growth trend of Fisheries Exports and GDP in Pakistan. It further shows a mixed trend of negative and positive growth in Fisheries Exports over the last 57 years.
Figure 1. Trends of Fisheries Export and GDP in Pakistan (Anonymous, 2020b)
The trade of fisheries can serve as a catalyst for economic development for developing countries with huge fisheries resources (CEMARE, 2000; EU, 2006; FAO, 2003b, 2006). The worldwide fisheries trade in developing countries could add to their economies by giving a significant source of hard cash income (Bostock, 2004; FAO, 2003 a, c). This trade could be important for the economies of developing countries, which often face revenue shortages so that the foreign exchange generation from this trade can fund international debt repayments, import bills and the expenses of domestic governments (FAO, 2005; Thorpe et al., 2004). Apart from this, foreign exchanges earned from fisheries exports could be utilized to import a large quantity of lowcost food for supplying the local markets, consequently contributing to national food security (FAO, 2004, 2006; Valdimarsson and James, 2001). In addition, the fisheries trade ultimately contributes to economic development by supplying employments, increasing incomes within this sector. The effects of the secondary flow also include remittances from migrant workers to families and dependents (FAO, 2005).
In connection with researches on the impact of fisheries exports on economic development, literature is very limited that discussed the exports of marine resources like fisheries and their contribution to economies. The study by Jaunky (2011) attempted to explore the underlying association between Fisheries exports growth and economic development for the 23 small Island developing states (SIDS) during 19892002. Various panel unit root and cointegration tests were performed and findings supplied robust proof of the longterm link between Fisheries exports growth and economic development (Jaunky, 2011). Another study by OECD (2008) investigates the link between Fisheries exports and economic development by using panel data of 47 subSaharan countries over the period of 1990–2005. Ordinary least squares (OLS) and analysis of variance (ANOVA) are employed and findings show that there is no relation between Fisheries exports and economic growth (OECD, 2008). The study by Jawaid et al. (2019) explored the relation between fisheries exports and Pakistan's economic development by using annual timeseries data over the period of 1974–2013. The findings from Autoregressive distributed lag and Johansen and Juselius cointegration confirmed the presence of a positive and longrun relation between them. Furthermore, the error correction model discloses that no direct or shortrun relation prevails between fishery exports and economic development (Jawaid et al., 2019). However, some studies were carried out by some scholars on exports and their trends. The research by Kartika (2014) is carried out to evaluate exports of fisheries products and their importance in the ground of Pakistan’s economy. The findings indicate that fisheries products’ exports increased in most years, but witnessed a fluctuation over the previous years (Kartika, 2014). It validates those exports of fisheries products have increased over the years, but have fluctuated over the past few years. By performing an indepth analysis, the study by Ali et al. (2020) explains a comparative analysis of fisheries exports exported from Pakistan to China and around the world (Ali et al., 2020).
There is extensive literature on different variables (other than fisheries exports) contributing to economic growth to the economies in developing countries including Pakistan. Some scholars attempted to explore the relationship among the growth of GDP, growth of export, growth of Labour force, investment and other variables (Abbas, 2012; Amirkhalkhali and Dar, 1995; Bakari, 2017; Bodman, 1996; Darrat, 1987; Ghartey, 1993; Henneberry and Khan, 2000; Jawaid, 2014; Kavoussi, 1984; Khan and Lodhi, 2014; Kunst and Marin, 1989; Moschos, 1989; Ram, 1985; Salvatore and Hatcher, 1991; Sengupta and Espana, 1994; Shahbaz and Rahman, 2014; Tahir et al., 2015; Tyler, 1981; Ullah et al., 2009).
After reviewing the above literature, it was concluded that the relation between exports and their impact on economic developments is considered broadly, but the connection between Fisheries exports and economic development is not much discussed widely and additionally, the connection between Fisheries exports and its impact on economic development is not yet much identified especially in case of Pakistan. Therefore, this study is designed to explore the connection between fisheries exports and GDP (economic development) in Pakistan. This research can help the policymakers and government to pay more attention to expediting the growth of the fisheries sector, if the association between the fisheries sector and GDP was found. The main objective of this research is to answer of following questions:
 Is there any relationship between Marine Resources’ Export and Economic Growth in Pakistan?
 If yes, whether this relation is longrun or shortrun?
MATERIALS AND METHODS
Secondary data was used to evaluate the exports of Marine Resources (fisheries) and their impacts on Economic Growth (GDP) in Pakistan. The data was collected from the official websites of Pakistan, PBS (Pakistan Bureau of Statistics), Marine Fisheries Department Hand Book’s Various Volumes (Anonymous, 2020b) and World Bank. Time series data from 1960 to 2017 (57 years) was employed. The variable “Fisheries exports” was in metric tons, whereas the value of GDP in ‘000’ PKR for the period of 19602017. After the collection of data, the various reports relating to the subject matter were reviewed. Finally, different econometric techniques were applied to analyse the LongRun and Shortrun Relationships. EViews8 software was used for the analysis of the data.
Data Analysis: Two types of tests VECM and OLS were utilized to investigate the relationship between GDP and Exports of fisheries variables.
RESULTS
Regression model: Ordinary Least Squares regression (OLS) is usually called linear regression i.e. simple or multiple that depends on the number of explanatory variables.
GDP = β0 + β_{1}EXP + ε
Whereas GDP is the dependent variable, β0 is an intercept of the model, EXP Independent variables and e is random error.
EXP =Export
GDP = Gross Domestic Product
Null Hypothesis: There relationship is no between GDP and EXP
Alternative Hypothesis: There is a relationship between GDP and EXP
If Probability is lesser than 5%, we will accept an alternative hypothesis and if the probability is greater than 5%, we reject the alternative hypothesis.
After running the OLS regression model (on equation 1), its results are represented in table 1.
Table 1. Results of OLS regression model
Dependent Variable: GDP

Method: Least Squares

Variable

Coefficient

Std. Error

tStatistic

Prob.

C

748646.2

367956.9

2.034603

0.0466

EXPORT

0.922563

0.034606

26.65895

0

Rsquared

0.92696

Mean dependent var

5062604

Adjusted Rsquared

0.925655

S.D. dependent var

8279832

S.E. of regression

2257596

Akaike info criterion

32.13137

Sum squared resid

2.85E+14

Schwarz criterion

32.20242

Loglikelihood

929.8098

HannanQuinn criter.

32.15905

Fstatistic

710.6998

DurbinWatson stat

0.68269

Prob(Fstatistic)

0


“*”, “**” and “***” indicate that variables are stationary at 1%, 5% and 10% respectively. The level of significance is 5%. We can accept the model because our data is fitted well as the probability is less than 5%. Rsquared is more than 60%, and the probability of the Fstatistic is less than 5%.
Results from table 1, it can be concluded that the alternative hypothesis is accepted because the probability is less than 5%, also Rsquare is greater than 60%, and the probability of Fstatistic is lesser than 5%. So, this indicates that there is a robust relationship between GDP and EXP.
VECM Model: For developing the VECM Model, three steps are involved. These steps include 1^{st} Lag selection, 2^{nd} Johnsen Test for Cointegration and 3^{rd} VECM.
1. Lag selection
The lag value is required in developing the VECM model. Weuse Vector Autoregression Estimates and after this, we employ VAR Lag Order Selection Criteria to take lag.
a. In the first step, Vector Autoregression Estimates is used to select lag
Table 2. Vector Autoregression Estimates
Vector Autoregression Estimates

Sample (adjusted): 1962 2017

Included observations: 56 after adjustments

Standard errors in ( ) & tstatistics in [ ]








GDP

EXPORT







GDP(1)

1.051373

0.648604


(0.19913)

(1.15106)


[ 5.27991]

[ 0.56349]




GDP(2)

0.008170

0.432089


(0.19936)

(1.15240)


[ 0.04098]

[0.37495]




EXPORT(1)

0.070141

0.611154


(0.03444)

(0.19905)


[ 2.03689]

[ 3.07028]




EXPORT(2)

0.027284

0.192222


(0.03025)

(0.17486)


[0.90197]

[ 1.09932]




C

25382.40

528227.8


(65066.4)

(376117.)


[ 0.39010]

[ 1.40443]







Rsquared

0.998200

0.944445

Adj. Rsquared

0.998059

0.940088

Sum sq. resids

6.94E+12

2.32E+14

S.E. equation

368893.7

2132393.

Fstatistic

7069.462

216.7514

Log likelihood

794.6646

892.9161

Akaike AIC

28.55945

32.06843

Schwarz SC

28.74029

32.24927

Mean dependent

5242587.

6523170.

S.D. dependent

8372099.

8711815.







Determinant resid covariance (dof adj.)

2.81E+23

Determinant resid covariance

2.33E+23

Loglikelihood

1665.479

Akaike information criterion

59.83854

Schwarz criterion

60.20021







b. In the 2^{nd} step, we use VAR Lag Order Selection Criteria to lag value.
Table 3. VAR Lag Order Selection Criteria
VAR Lag Order Selection Criteria





Endogenous variables: GDP EXPORT





Exogenous variables: C





Sample: 1960 2017






Included observations: 54



















Lag

LogL

LR

FPE

AIC

SC

HQ















0

1806.855

NA

4.27e+26

66.99463

67.06830

67.02304

1

1611.635

368.7495

3.59e+23

59.91240

60.13339

59.99763

2

1607.864

6.842382

3.62e+23

59.92090

60.28923

60.06295

3

1586.022

38.02180

1.87e+23

59.26008

59.77574*

59.45895

4

1579.810

10.35312*

1.73e+23*

59.17816*

59.84115

59.43385*















* indicates lag order selected by the criterion




LR: sequential modified LR test statistic (each test at 5% level)



FPE: Final prediction error





AIC: Akaike information criterion





SC: Schwarz information criterion





HQ: HannanQuinn information criterion




Findings from table 3: optimum lag would be 4 from the VAR Lag Order Selection Criteria table because the fourth row of this table has the lowest value of LR, FPE, A/C and HQ except SC and these values are also with a star. So, we shall use lag 4 in Johnsen Test for Cointegration and VECM.
 Johnsen Test for Cointegration
After choosing the lag value, we will move to another step Johnsen Test for Cointegration in developing the VECM model. But there is a precondition for employing this test and that is, all variables must be stationary. If they are nonstationary at level, then we have to convert all the variables into first difference and 2^{nd} difference to make them stationary. Once they will become stationary, then we can employ the Johnsen Test for Cointegration. The stationary status of variables shows that all variables are integrated of the same order. So, we use the Augmented Dicky Filler (ADF) test to make the variable stationary.
 Augmented Dicky Filler (ADF) test
Augmented DickeyFuller (ADF) test includes three tests for Unit root.
 At Level
 At first difference
 At second difference
ADF Test’s Hypothesis:
Null Hypothesis: H0: Variable is not stationary
Alternative Hypothesis: H1: Variable is stationary
According to the unit root test, If the probability is less than 5% (if prob< 5%; Result = stationary) we accept alternate hypothesis and reject the null hypothesis, but the probability is more than 5% (if prob> 5%; Result= nonstationary) we reject the alternate hypothesis and accept the null hypothesis. By employing ADF test, we obtain the following results as reported in the table.
Table 4. Results of ADF Test
Variables

On level

Prob

1stDifference

Prob

2ndDifference

Prob

Conclusion

GDP

5.869332

1

1.312651

0.9984***

5.417202

0*

I(2)

EXP

1.212818

0.9979***

9.43883

0*





I(1)

“*”, “**” and “***” indicate that variables are stationary at 1%, 5% and 10% respectively. The level of significance is 5%.
Results: Table 4 demonstrates that variable Export is stationary at the first difference, while variable GDP stationary is at the second difference. Now, two variables are stationary. It indicates that all these variables are integrated of the same order, so we can move to another step in developing the VECM model. Now we easily employ Johnsen Test as its precondition has been fulfilled.
 Use of Johnsen Test for Cointegration
After using the unit root test (ADF) and converting variables into stationary, we employ the Johnsen Test for Cointegration to find the longrun relations between variables GDP and EXPORT.
There are two hypotheses for Johnsen Test for Cointegration
 First Null Hypothesis = None = There is no cointegration among two variables.
In the upper part of the Table, if Trace Statistic value (TSV) is more than Critical Value (CV) and Prob value is less than 5%. So, the null hypothesis would be rejected. In the lower part of a table, if the MaxEigen Statistic value (MESV) is more than CV and the Pvalue is less than 5%. So, the null hypothesis would be rejected.
 Second Null Hypothesis = At most 1= There is at most 1 cointegrated model
In the upper part of a table, if TSV is lesser than CV and Pvalue is more than 5%. So, the null hypothesis can be rejected. In the lower part of a table, if MESV is more than CV and Probvalue is more than 5%. So, the null hypothesis can be rejected.
Table 5. Unrestricted Cointegration Rank Test (Trace)
Sample (adjusted): 1965 2017



Included observations: 53 after adjustments


Trend assumption: Linear deterministic trend (restricted)

Series: GDP EXPORT



Lags interval (in first differences): 1 to 4


Unrestricted Cointegration Rank Test (Trace)












Hypothesized


Trace

0.05


No. of CE(s)

Eigenvalue

Statistic

Critical Value

Prob.**











None *

0.535256

45.73204

25.87211

0.0001

At most 1

0.092080

5.119765

12.51798

0.5793











Trace test indicates 1 cointegrating eqn (s) at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

**MacKinnonHaugMichelis (1999) pvalues


Unrestricted Cointegration Rank Test (Maximum Eigenvalue)











Hypothesized


MaxEigen

0.05


No. of CE(s)

Eigenvalue

Statistic

Critical Value

Prob.**











None *

0.535256

40.61227

19.38704

0.0000

At most 1

0.092080

5.119765

12.51798

0.5793











Maxeigenvalue test indicates 1 cointegrating eqn (s) at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

**MacKinnonHaugMichelis (1999) pvalues


Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):











GDP

EXPORT

@TREND(61)



1.87E06

1.30E06

0.111977



1.23E06

6.66E07

0.054562













Unrestricted Adjustment Coefficients (alpha):












D(GDP)

176696.8

27100.67



D(EXPORT)

780920.3

241990.0













1 Cointegrating Equation(s):

Log likelihood

1528.559












Normalized cointegrating coefficients (standard error in parentheses)

GDP

EXPORT

@TREND(61)



1.000000

0.694043

59747.49




(0.09847)

(12997.2)



Adjustment coefficients (standard error in parentheses)


D(GDP)

0.331161





(0.05353)




D(EXPORT)

1.463584





(0.30855)














Results of Table 5:
First Null Hypothesis: TSV is more than CV and Prob value is less than 5%. So, we can reject the null hypothesis. MESV is more than CV and Prob value is less than 5%. So, the null hypothesis can be rejected.
Second Null Hypothesis = TSV is lesser than CV and Prob value is more than 5%. So, the null hypothesis can be rejected. MESV is smaller than CV and. Prob value is more than 5%. So, the null hypothesis can be rejected.
The results show that there is a relationship and there is at most 1 cointegrated model.
However, the Unrestricted Cointegration Rank Test (Trace) and Unrestricted Cointegration Rank Test (Maximum Eigenvalue) are telling the same thing.
So, these variables are cointegrated and have longrun associationship. In other words, it can be said that in the longrun, they moved together.
If variables are cointegrated and have a longrun associationship, then restricted VAR i.e. VECM Model can be employed. But if variables are not cointegrated, then unrestricted VAR can be used rather than the VECM model.
 Vector Error Correction Estimates (VECM)
VECM supplies facts about the long and shortrun relationship among variables. This model will help to find two things. 1. Longrun causality 2. Shortrun causality
Table 6. Vector Error Correction Estimates
Vector Error Correction Estimates


Sample (adjusted): 1965 2017

Included observations: 53 after adjustments

Standard errors in ( ) & tstatistics in [ ]







CointegratingEq:

CointEq1








GDP(1)

1.000000





EXPORT(1)

0.311630



(0.06747)



[4.61896]





C

2949342.








Error Correction:

D(GDP)

D(EXPORT)







CointEq1

0.392438

1.383551


(0.08933)

(0.51479)


[ 4.39336]

[ 2.68761]




D(GDP(1))

0.112307

0.397497


(0.15046)

(0.86712)


[0.74642]

[ 0.45841]




D(GDP(2))

0.304646

3.428441


(0.27123)

(1.56314)


[ 1.12319]

[ 2.19331]




D(GDP(3))

1.673472

9.059342


(0.37507)

(2.16153)


[4.46180]

[4.19117]




D(GDP(4))

0.753153

5.903302


(0.35802)

(2.06327)


[2.10369]

[2.86114]




D(EXPORT(1))

0.201273

0.225012


(0.04376)

(0.25222)


[ 4.59898]

[ 0.89213]




D(EXPORT(2))

0.127851

0.107580


(0.05082)

(0.29288)


[ 2.51575]

[ 0.36732]




D(EXPORT(3))

0.291565

1.010892


(0.05867)

(0.33810)


[ 4.96991]

[ 2.98995]




D(EXPORT(4))

0.198000

0.656913


(0.04778)

(0.27537)


[ 4.14387]

[ 2.38558]




C

1222979.

4340487.


(269651.)

(1554019)


[ 4.53541]

[ 2.79307]







Rsquared

0.941893

0.692492

Adj. Rsquared

0.929732

0.628129

Sum sq. resids

2.43E+12

8.06E+13

S.E. equation

237501.7

1368738.

Fstatistic

77.44654

10.75928

Log likelihood

725.6931

818.5210

Akaike AIC

27.76200

31.26494

Schwarz SC

28.13376

31.63670

Mean dependent

602496.6

517468.2

S.D. dependent

895955.4

2244525.







Determinant resid covariance (dof adj.)

8.59E+22

Determinant resid covariance

5.65E+22

Loglikelihood

1538.717

Akaike information criterion

58.89500

Schwarz criterion

59.71285







Results: According to table 6, we don’t have sufficient information to predict the short run as well as the longrun relationship of variables because table 5 lacks the pvalue which is the core heart of the interpretation, therefore to get the pvalue we run the following test and to find short and longrun associations between variables.
 LongRun Analysis or causality
The longterm and shortterm associations’ interpretation is depending on the criteria of C1 to C10.C1 represents the associationship of longrun between the dependent variable (GDP) and independent variable (Export). So, the criteria of longrun causality are that C1’s coefficient should be significant and negative. (Probability should also be less than 5%).
Table 7. Results of Least Square by Dependent Variable: D(GDP)
Dependent Variable: D(GDP)



Method: Least Squares



Sample (adjusted): 1965 2017



Included observations: 53 after adjustments


D(GDP) = C(1)*( GDP(1)  0.311630249078*EXPORT(1)  2949342.49225

) + C(2)*D(GDP(1)) + C(3)*D(GDP(2)) + C(4)*D(GDP(3)) + C(5)

*D(GDP(4)) + C(6)*D(EXPORT(1)) + C(7)*D(EXPORT(2)) + C(8)

*D(EXPORT(3)) + C(9)*D(EXPORT(4)) + C(10)












Coefficient

Std. Error

tStatistic

Prob.











C(1)

0.392438

0.089325

4.393361

0.0001

C(2)

0.112307

0.150461

0.746416

0.4595

C(3)

0.304646

0.271234

1.123188

0.2676

C(4)

1.673472

0.375066

4.461805

0.0001

C(5)

0.753153

0.358016

2.103687

0.0413

C(6)

0.201273

0.043765

4.598983

0.0000

C(7)

0.127851

0.050820

2.515753

0.0157

C(8)

0.291565

0.058666

4.969910

0.0000

C(9)

0.198000

0.047781

4.143874

0.0002

C(10)

1222979.

269651.3

4.535411

0.0000











Rsquared

0.941893

Mean dependent var

602496.6

Adjusted Rsquared

0.929732

S.D. dependent var

895955.4

S.E. of regression

237501.7

Akaike info criterion

27.76200

Sum squared resid

2.43E+12

Schwarz criterion

28.13376

Log likelihood

725.6931

HannanQuinn criter.

27.90496

Fstatistic

77.44654

DurbinWatson stat

1.693747

Prob(Fstatistic)

0.000000














In Table 7, we can accept the model because our data is fitted well as the probability is less than 5%, Rsquared is more than 60%, and the probability of Fstatistic is less than 5%.
Results: In table 7, the coefficient of C (1) is not negative but significant. It shows that there is no longterm relationship.
 ShortRun Analysis
To find a shortrun associationship between GDP and Export, the Wald test can be used.
Null Hypothesis: C (6) = C(7) = C(8)= C(9)= 0
Where C (6) , C(7), C(8) and C(9)represents Export
Table 8. Results of Wald Test
Wald Test:



Equation: Untitled










Test Statistic

Value

Df

Probability









Fstatistic

14.69401

(4, 43)

0.0000

Chisquare

58.77605

4

0.0000









Null Hypothesis: C(6)=C(7)=C(8)=C(9)=0

Null Hypothesis Summary:










Normalized Restriction (= 0)

Value

Std. Err.









C(6)

0.201273

0.043765

C(7)

0.127851

0.050820

C(8)

0.291565

0.058666

C(9)

0.198000

0.047781









Restrictions are linear in coefficients.

Summary of VECM model: Pvalue of Chisquare in Table 8 is lesser than 5% and null hypothesis would be rejected. It validates the shortrun associationship between Exports and GDP. So, there is no longterm causality but a shortterm associationship between Exports and GDP. We can say there is no longrun relationship or associationship among variables but there is shortrun relation between these two variables.
DISCUSSION
We can conclude from the results of the OLS model presented in Table 1 that we accept the alternative hypothesis because the probability is less than 5%, also Rsquare is more than 60%, and the probability of Fstatistic is less than 5%. So, this indicates that there is a robust relationship between GDP and EXP.
After finding the relationship as presented in Table 1, we employed another model i.e. VECM to further investigate the relations between GDP and EXP. We need to involve steps to develop the VECM model.
First, we have to find lag value and then Johnsen Test for Cointegration and at the end; we will run VECM to find relations between variables.
In table 3, the optimum lag would be 4 from the VAR Lag Order Selection Criteria table because the fourth row of this table has the lowest value of LR, FPE, A/C and HQ except SC and these values are also with a star. So, we shall use lag 4 in Johnsen Test for Cointegration and VECM. Before employing, Johnsen Test for Cointegration and VECM, we make variables stationary. Table 4 demonstrates that variable Export is stationary at first difference and while, variable GDP is stationary at the second difference. Both variables are not stationary at the level. Now, two variables are stationary. It indicates that all these variables are integrated of the same order, so we can move to another step in developing the VECM model. Now we easily employ Johnsen Test as its precondition has been fulfilled.
Results of Table 5, show that TSV is more than CV and Prob value is less than 5%. So, we can reject the null hypothesis. MESV is more than CV and Prob value is smaller than 5%. So, the null hypothesis would be rejected. TSV is lesser than CV and Prob value is more than 5%. So, the null hypothesis cannot be rejected. MESV is lesser than CV and. Prob value is more than 5%. So, the null hypothesis cannot be rejected. The results show that there is a relationship and there is at most 1 cointegrated model.
However, the Unrestricted Cointegration Rank Test (Trace) and Unrestricted Cointegration Rank Test (Maximum Eigenvalue) are telling the same thing. So, these variables are cointegrated and have longrun associationship. In other words, it can be said that in the longrun, they moved together.
If variables are cointegrated or have a longterm associationship, then restricted VAR i.e. VECM Model can be used. But if variables are not cointegrated, then the VECM model cannot be employed. Rather unrestricted VAR would be used. So, the VECM model is employed.
Results presented in table 6 don’t show enough information to forecast the shortterm and longterm associationship among variables because table 5 does not have the pvalue that is coreheart for interpretation of relationships among variables, so another test is employed to get the pvalue.
The Pvalue of Chisquare in Table 8 is less than 5% which shows that the null hypothesis can be rejected. It shows there is short term connection between Exports and GDP. There is no longterm associationship but shortrun relation between Exports and GDP. We can say there is no longrun relationship or associationship among variables but there is shortrun relation between these two variables.
So, there is a strong relationship between GDP and Exports of fisheries but the relation is shortterm as presented by VECM’s model.
Conclusion: At the end, we conclude that the marine resources (Fisheries Export) have a positive impact on GDP. The results of OLS regression show that fisheries export has a significant relationship with GDP. It confirms that if exports of marine resources (Fisheries Exports) increase and GDP also rise. In addition to this VECM model also validates that there is no longrun relation but shortrun relations between GDP and Fisheries Exports. The results from the cointegrating equations or model indicate that cointegration exists between fisheries’ exports and its economic growth in Pakistan. The shortrun relation between GDP and Exports of fisheries shows that there is no sustainable policy in the country to maintain or increase the quantity of fisheries products. There are certain issues obstructing the growth of exports such as lack of labour skills, outdated infrastructure i.e. rusted Jattis, no fibreglass, nonimplementation of HACCP, nonimplementation of safety, cleanness and sanitation standards. Because of these issues, European countries banned fisheries exports from Pakistan in 2006. Moreover, Pakistan is exporting 2530% fisheries of the total production of fisheries in the country and it has the capacity to export more to earn more foreign exchange. So, Pakistan should make a more effective policy to attract the International Markets to increase the exports of marine resources especially fisheries. Some of them include marketing skills, technology skills, managerial skills, etc. The increase in fisheries exports would contribute more to economic growth in the country.
REFERENCES
 Amirkhalkhali, S., and A. A. Dar, (1995). A varyingcoefficients model of export expansion, factor accumulation and economic growth: Evidence from crosscountry, time series data. Econ Model. 12(4): 435441.
 , (2014). Economic Survey, Economic Affairs Division, Govt. Pakistan., Islamabad.
 , (2020a). Economic Survey, Economic Affairs Division, Govt. Pakistan., Islamabad.
 , (2020b). The Fisheries Statistics, Marine fisheries Deptt. Of Ministry of Maritime Affairs, Govt. Pakistan., Islamabad.
 Bakari, S., (2017). The long run and short run impacts of exports on economic growth: Evidence from Gabon. Report (published). Deptt. of Econ. Sci., Univ. of Tunis El Manar, Tunisia.
 Bodman, P. M., (1996). On export‐led growth in Australia and Canada: cointegration, causality and structural stability. Aust Econ Pap. 35(67): 282299.
 Bostock, T., P. Greenhalgh, and U. Kleih, (2004). Policy research implications of liberalization of fish trade for developing countries. Synthesis report (published). Nat. Resour. Inst., Univ. of Greenwich, UK.
 , (2000). Fishing Agreement: Trade and Fisheries Management, Overcapacity, Overcapitalization and Subsidies in European Fisheries. In Hatcher, A. and D. Tingley (Eds.). Portsmouth, United Kingdom.
 Darrat, A. F., (1987). Are exports an engine of growth? Another look at the evidence. Econ. 19(2): 277283.
 EU C., (2006). https://ec.europa.eu/comm/fisheries/doc_et_publ/factsheets/facts/en/pcpa42.html ; accessed 05 Jan 2020.
 , (2003a). Fish for the poor in a globalized economymacro benefits vs micro impacts. Presentation given at the expert consultation on international fish trade and food security. Rome, Italy.
 , (2003b). Globalisation, Industry Structure, Market Power and Impact of Fish Trade: Opportunities and Challenges for Developed (OECD) Countries. FAO Industry and Expert Consultation on International Trade. Rio de Janeiro, Brazil.
 , (2003c). International Fish Trade: Presentation Given at the Expert Consultation on International Fish Trade and Food Security. FAO Fisheries Report No.708. Casablanca, Morocco.
 , (2004). The State of World Fisheries and Aquaculture. FAO Fisheries Deptt. Rome, Italy.
 , (2005). Responsible Fish Trade and Food Security. FAO Fisheries Technical Paper No. 456. Rome, Italy.
 , (2006). The State of World Fisheries and Aquaculture. FAO Fisheries Deptt. Rome, Italy.
 Ghartey, E. E., (1993). Causal relationship between exports and economic growth: some empirical evidence in Taiwan, Japan and the US. Econ. 25(9): 11451152.
 Henneberry, S. R., and M. E. Khan, (2000). An analysis of the linkage between agricultural exports and economic growth in Pakistan. Int. Food Agribus. Mark. 10(4): 1329.
 Jaunky, V. C., (2011). Fish exports and economic growth: the case of SIDS. Manage. 39(4): 377395.
 Kartika, S., (2014). A study on Exports of Fish and Fish products and their Role in Economic Growth of Pakistan. J. Mar. Sci. 4(64): 14.
 Kavoussi, R. M., (1984). Export expansion and economic growth: Further empirical evidence. Dev. Econ. 14(1): 241250.
 Kunst, R. M., and D. Marin, (1989). On exports and productivity: a causal analysis. Econ. Stat. 71(4): 699703.
 Ali, M., M. Yongtong, S. Oad, S. B. H. Shah, M. T. Kalhoro, and G.M. Lahbar, (2020). A comparative analysis on expansion of Pakistan fisheries trade: World & China. Indian J. GeoMar. Sci. 49(10): 16431650.
 Shahbaz, M., and M. M. Rahman, (2014). Exports, financial development and economic growth in Pakistan. J. Dev. Iss. 13(2): 155–170.
 Khan, M. Z., and A. S. Lodhi, (2014). Nexus between financial development, agriculture raw material exports, trade openness and economic growth of Pakistan. J. Commer. Soc. Sci. 8(3): 629639.
 Moschos, D., (1989). Export expansion, growth and the level of economic development: an empirical analysis. Dev. Econ. 30(1): 93102.
 Oad, S., Q. Jinliang and S. B. H Shah, (2021). Tourism: economic development without increasing CO2 emissions in Pakistan. Dev. Sustain, 124.
 , (2008). Global Change in African Fish Trade: Engine of Development or Threat to Local Food Security? OECD Food, Agriculture and Fisheries Working Papers No. 10. Paris, France.
 Ram, R., (1985). Exports and economic growth: Some additional evidence. Dev. Cult. Change. 33(2): 415425.
 Abbas, S., (2012). Causality between exports and economic growth: Investigating suitable trade policy for Pakistan. Eurasian J. Bus. Econ. (5): 9198.
 T. Jawaid., (2014). Trade openness and economic growth: A lesson from Pakistan. Foreign Trade Rev. 49(2): 193212.
 Jawaid, S. T., M. H. Siddiqui, Z. Atiq, and U. Azhar, (2019). Fish Exports and Economic Growth: The Pakistan’s Experience. Global Bus. Rev. 20(2): 279296.
 Ullah, S., B. Zaman, M. Farooq, and A. Javid, (2009). Cointegration and causality between exports and economic growth in Pakistan. J. Soc. Sci. 10(2): 264272.
 Salvatore, D., and T. Hatcher, (1991). Inward oriented and outward oriented trade strategies. Dev. Stud. 27(3): 725.
 Sengupta, J. K., and J. R. Espana, (1994). Exports and economic growth in Asian NICs: an econometric analysis for Korea. Econ. 26(1): 4151.
 Shah, S., (2018). Pakistan fish exports up 27.94pc, netting $451.026 million in FY18. Daily The News, Pakistan.
 Tahir, M., H. Khan, M. Israr, and A. Qahar, (2015). An analysis of export led growth hypothesis: Cointegration and causality evidence from Sri Lanka. Econ. Bus. 3(2): 6269.
 Thorpe, A., C. Reid, V. R. Anrooy, and C. Brugère, (2004). African poverty reduction strategy programmes and the fisheries sector: Current situation and opportunities. Dev. Rev. 16(2): 328–350.
 Thorpe, A., C. Reid, V. R. Anrooy, and C. Brugere, (2005). Integrating fisheries into the national development plans of Small Island Developing States: Ten years on from Barbados. Resour. Forum. 29(1): 5169.
 Tyler, W. G., (1981). Growth and export expansion in developing countries: Some empirical evidence. Dev. Econ. 9(1): 121130.
 Valdimarsson, G., and D. James, (2001). World fisheries utilisation of catches. Coast. Manage. 44(910): 619633.
 Wood, D., (2020). Marine Resources: Characteristics, Formation & Management https://study.com/academy/lesson/marineresourcescharacteristicsformationmanagement.html ; accessed 01 Jan 2020.
