Research Article, ISSN 2304-2613 (Print); ISSN 2305-8730 (Online)

CC-BY-NC 2014

, Asian Business Consortium |

ABR

Page 7

Relationship between Export Revenue and Gross Domestic

Product in Bangladesh: An Econometric Analysis

Sudip Dey

Lecturer, Department of Economics, Premier University, Chittagong, BANGLADESH

E-mail for correspondence:

sudipdey1899@gmail.com

https://doi.org/10.18034/abr.v8i1.2

ABSTRACT

Export revenue is an important issue for Bangladesh. So analysis the relationship between export

revenue and gross domestic product (GDP) is very crucial for the policy makers to develop our

domestic economy as well as to create a good economic relationship with the global economy. The

main object of this article is to investigate the relationship between export revenue and GDP in

Bangladesh. To test stationary correlogram test is used. In this study, I used Granger causality and co-

integration test to test the long-run relationship between GDP and export revenue in Bangladesh from

1981-2015. I found the maximum lag length for the model by using vector autoregressive (VAR) lag

order selection criteria. In the model GDP was dependent variable and three variables (Remittance,

Foreign direct investment, Export revenue) were the independent variables. In correlogram test, I have

seen all of my variables were non-stationary at level, but after taking first difference, they became

stationary. According to Granger causality test, there was bidirectional causality from export revenue

to GDP in Bangladesh. Johansen cointegration test investigated that there was a long-run equilibrium

relationship between export revenue and GDP but by using vector error correction model (VECM) I

have seen there is no statistically significant long-run relationship between export revenue and GDP.

Wald test indicated a statistically significant short-run relationship between the two variables.

Key words: Export revenue, GDP, VAR, Cointegration, Vector Error Correction Model (VECM)

INTRODUCTION

Bangladesh is a developing country. Export can play a

potential rule for development of Bangladesh. Economic

growth is directly related to export. Exports are

component of aggregate demand (AD) and rising export

will help increase AD and cause higher economic growth.

For Bangladesh export can play a significant rule to build

up physical capital, reduce unemployment problem,

develop productive capacity and help integrate the

domestic economy. Readymade garments (RMG) sector is

the main source of our export revenue. 75% of our export

revenue comes from this sector. An estimated 4.2million

people are employed in this sector and most the

employers are women, half of whom come from villages.

By 2013 there were approximately 5000 factories, part of

Bangladesh’s US$19 billion a year export-oriented RMG

industry has revolutionized the country in terms of its

contribution to GDP growth. Bangladesh exports goods

and services to UK, USA, Canada, Japan, Australia, New-

Zealand and Russia etc. Markets are also opening in the

Middle East, Latin America and Africa.

LITERATURE REVIEW

Saaed and Hussain (2015) found that there is

unidirectional causality between exports and economic

growth in Tunisia. These results provide that growth in

Tunisia was propelled by a growth –led import strategy

as well as export-led import and imports are the main

source of economic growth of Tunisia. Quddus and Saeed

(2005) examined if export and GDP are cointegrated by the

using Johansen approach; whether export Granger cause

GDP growth; whether Granger cause investment. A

positive Granger causal relationship running from export

to economic growth is suggested by the test results for the

long-run period. Hussain (2014) found that there is

Granger causality relationship between exports and

economic growth in Pakistan. The relationship between

exports and economic growth has long been a subject of

great interest in the development literature. Javed et al.

(2012) proved that export has a positive and significant

impact on the economy of Pakistan and the results

showed that international trade is an important factor for

Pakistani economy. Akter (2015) revealed that the impact

Dey: Relationship between Export Revenue and Gross Domestic Product in Bangladesh: An Econometric Analysis (7-12)

Page 8 Asian Business Review ● Volume 8 ●Number 1/2018

of export on economic growth is positive but it is negative

for import. Ahmed and Uddin (2009) examined that time

series analysis indicate exports, imports and remittances

cause GDP growth in the short-run but has no long-run

impact. The causal nexus unidirectional long-run GDP

growth causes short run income growth but this affect is

once again unidirectional. By using Johansen’s

multivariate frame work they found real GDP, real

exports, real imports and real remittances were

cointegrated for long-run. Sri Lankan economists

Thirunavukkarasu and Achchuthan investigated that

export and import have positive and significant

relationship for each other and they (export, import) also

have significant impact on GDP. Ismail etal. (2010)

examined a long-run relationship between export and GDP

by using Johansen’s cointegration test and error correction

model was applied to streamline of the variables on

economic growth. Rai and Jhala (2015) found a positive

relationship between growth rate and exports. Zaheer etal.

(2014) indicated that exports and imports have significant

relationship economic growth rate. They also suggested

that government should move towards more exchange rate

liberalization policy for increasing economic growth. In this

paper, I want to investigate the relationship between export

revenue and GDP in Bangladesh using the time series

analysis with different kinds of econometric models.

METHODOLOGY

We know causality is the foundation of any study to examine

an economic relationship. So I started the empirical analysis

with Granger causality test to examine if export revenue

Granger causes GDP and / or inversely GDP Granger causes

export revenue. Correlogram test is used for testing the time

series data are stationary or not. For optimal lag length

selection, I used Vector Autoregressive (VAR), model. To test

the long-run relationship between GDP and export revenue

Johansen cointegration test is run. Vector error correction

model has used whether the variables have a long-run

significant relationship or not. All of the econometric tests

are done by Eviews-7 and SPSS-20.

DATA SOURCES

Time series data are used for the model over the 1981-2015

periods in Bangladesh, which are collected from various

primary sources. Data on GDP is taken from World Bank.

Data on remittances and export revenue are collected from

Bangladesh Economic Review, and data on foreign direct

investment (FDI) is taken from Bangladesh Bureau of

Statistics.

MODEL SPECIFICATION

To examine the relationship between export revenue and

GDP, I have specified following the econometric model

where GDP is dependent variable and remittance, FDI

and export revenue are independent variables. The model

is stated as follows:

GDP = f (Remittance, FDI, Export revenue)

ttttt

UExrFDImGDP 



Where, GDP = Gross Domestic Product, Rem =

Remittances, FDI = Foreign Direct Investment, Exr =

Export revenue. All the variables are counted in Million

$US, É, Ê, Î, Ý =parameters to be estimate, U = Stochastic

term, and t =1, 2, 3,……..,35 (time period from 1981-2015).

RESULTS AND DISCUSSION

Correlogram Test

Correlogram test is used to check the variables are

stationary or not. The results have shown that all the

variables are non-stationary at level. But when these

variables are tested at first difference, then the null

hypothesis is accepted, and the alternative hypothesis is

rejected. Because all variables’ p-values > 0.05 (5%). That

means all variables are stationary at first difference and

their integrated order is one or I (1).

Optimal Lag Length Selection

After the correlogram test, I got the maximum lag length

by running vector autoregressive (VAR) lag order

selection. From table 1 the maximum lag length is 3,’ and

it is chosen on different criterions’ minimum value. All

criteria are asking to take 3lag. So my optimum lag would

be 3,’ and I will use it in Johansen cointegration test and

vector error correction model.

Table 1: VAR Lag Order Selection Criteria

Endogenous variables: GDP REM FDI EXR

Exogenous variables: C; Date: 01/05/17 Time: 7:37

Sample: 1 35; included observations: 32

Lag

LogL

FPE

AIC

-1159.305

4.43e+26

72.70658

72.88979

72.76731

-1049.686

184.9827

1.29e+24

66.85537

67.77145

67.15902

-1017.410

46.39651

4.90e+23

65.83813

67.48708

66.38471

-948.0863

82.32190*

2.01e+22*

62.50539*

64.88721*

63.29490*

* 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: Hannan-Quinn information criterion

Granger causality test

First I used Granger causality test to examine the

relationship between GDP and export revenue for

Bangladesh from 1981to 2015. It is a technique to consider

both lagged and endogenous relationship. The results of

causality between GDP, Rem, FDI, and Exr are contained

in table 2. The results show a bidirectional relationship

between GDP and Export revenue in Bangladesh. The

results of the test are given in the table 2.

Research Article, ISSN 2304-2613 (Print); ISSN 2305-8730 (Online)

CC-BY-NC 2014

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Table 2: Pair wise Granger Causality Tests

Date: 01/05/17 Time: 11:58

Sample: 1 35; Lags: 3

Null Hypothesis:

Obs

F-Statistic

Prob.

D(REM) does not Granger Cause D(GDP)

1.30757

0.2949

D(GDP) does not Granger Cause D(REM)

15.5761

8.E-06

D(FDI) does not Granger Cause D(GDP)

1.59136

0.2175

D(GDP) does not Granger Cause D(FDI)

8.83668

0.0004

D(EXR) does not Granger Cause D(GDP)

38.1572

3.E-09

D(GDP) does not Granger Cause D(EXR)

26.6660

8.E-08

D(FDI) does not Granger Cause D(REM)

4.22304

0.0156

D(REM) does not Granger Cause D(FDI)

5.44588

0.0053

D(EXR) does not Granger Cause D(REM)

9.77387

0.0002

D(REM) does not Granger Cause D(EXR)

6.24278

0.0028

D(EXR) does not Granger Cause D(FDI)

5.64386

0.0045

D(FDI) does not Granger Cause D(EXR)

19.8485

1.E-06

Johansen Test of Cointegration

The precondition for Johansen cointegration test is, the

variables must be non-stationary at level but when we

convert all the variables into the first difference, and then

they will become stationary. Only then we can run the

Johansen cointegration test. Here all of my variables are

stationary at first difference, and we can run the

cointegration test. Table 3 shows the presence of

cointegration for the variables adopted in this study, where

it is statistically valid. This implies that there is a long-run

relationship amongst GDP, remittance, FDI and export

revenue. Max Eigenvalue test indicates 1 cointegrating

equation at the 0.05 level. Trace indicates 1 cointegrating

equation at the 0.05 level. * denotes rejection of the

hypothesis at the 0.05 level. The results of the Trace tests

indicate the presence that the two variables are cointegrated

vectors.

Table 3: Unrestricted Cointegration Rank Test (Trace)

Date: 12/30/16 Time: 04:56

Sample (adjusted): 5 35

Included observations: 31 after adjustments

Trend assumption: Linear deterministic trend

Series: GDP REM FDI EXR

Lags interval (in first differences): 1 to 3

Hypothesized

Trace

0.05

No. of CE(s)

Eigenvalue

Statistic

Critical Value

Prob.**

None *

0.953238

118.9662

47.85613

0.0000

At most 1

0.433196

24.02277

29.79707

0.1995

At most 2

0.153862

6.422759

15.49471

0.6455

At most 3

0.039319

1.243513

3.841466

0.2648

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

* denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized

Max-Eigen

0.05

No. of CE(s)

Eigenvalue

Statistic

Critical

Value

Prob.**

None *

0.953238

94.94344

27.58434

0.0000

At most 1

0.433196

17.60001

21.13162

0.1455

At most 2

0.153862

5.179246

14.26460

0.7190

At most 3

0.039319

1.243513

3.841466

0.2648

Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values

Vector Error Correction Model

Since four variables are cointegrated, I can run VECM

model. In the previous test, I have seen that the variables

are cointegrated and there is a long-run relationship

among the variables. So, in this case, I can run VECM. In

this paper I used a multivariate framework, which is given

below:

















itit

tGDPt

FDIimiGDPieGDP

11110

)(Re)()(



tGDP

Exri

)(











...........................(1)

Table 4: Dependent Variable: D(GDP)

Method: Least Squares

Date: 12/30/16 Time: 04:59

Sample (adjusted): 5 35

Included observations: 31 after adjustments

D(GDP) = C(1)*( GDP(-1) - 26.5640629593*REM(-1) +

229.12319484*FDI(-1) - 4.70907382612*EXR(-1) + 2237.39742401 ) +

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

C(5)*D(REM(-1)) + C(6)*D(REM(-2)) + C(7)*D(REM(-3)) +

C(8)*D(FDI(-1)) + C(9)*D(FDI(-2)) + C(10)*D(FDI(-3)) +

C(11)*D(EXR(-1)) + C(12)*D(EXR(-2)) + C(13)*D(EXR(-3)) + C(14)

Coefficient

Std. Error

t-Statistic

Prob.

C(1)

-0.022020

0.116494

-0.189026

0.8523

C(2)

0.144955

0.194823

0.744033

0.4670

C(3)

0.067075

0.226821

0.295719

0.7710

C(4)

0.046506

0.588714

0.078996

0.9380

C(5)

-4.582433

4.269568

-1.073278

0.2981

C(6)

-4.023298

4.945088

-0.813595

0.4271

C(7)

-0.416980

4.623882

-0.090180

0.9292

C(8)

0.746194

24.52954

0.030420

0.9761

C(9)

0.862665

16.92070

0.050983

0.9599

C(10)

-15.02848

17.49347

-0.859091

0.4022

C(11)

1.408499

2.245559

0.627238

0.5388

C(12)

-0.400249

1.750651

-0.228629

0.8219

C(13)

10.42977

2.043848

5.103004

0.0001

C(14)

-660.5104

1303.875

-0.506575

0.6190

R-squared

0.938037

Mean dependent var

5536.903

Adjusted R-squared

0.890654

S.D. dependent var

11442.86

S.E. of regression

3783.867

Akaike info criterion

19.61733

Sum squared resid

2.43E+08

Schwarz criterion

20.26494

Log likelihood

-290.0687

Hannan-Quinn criter.

19.82844

F-statistic

19.79682

Durbin-Watson stat

1.946862

Prob(F-statistic)

0.000000

The other three equations in the ECM model system are:



















tmt

FDIimiGDPiem )(Re)()(

211Re20



tmit

Exri

)(Re

)(











…………………….(2)

Dey: Relationship between Export Revenue and Gross Domestic Product in Bangladesh: An Econometric Analysis (7-12)

Page 10 Asian Business Review ● Volume 8 ●Number 1/2018



















tFDIt

FDIimiGDPieFDI )(Re)()(

31130



tFDIit

Exri

)(











…………………..…..(3)



















tExrt

FDIimiGDPieExr )(Re)()(

41140



tExrit

Exri

)(











………………………(4)

t

Is the error- correction term,



is the adjustment

coefficient, and



is the white-noise disturbance terms.

If the variables have long-run relationship, the coefficient



must be statistically significant. In table 4 C (1) is

the speed of adjustment towards long- run equilibrium

but it must be significant, and the sign must be negative.

From our results (Table-4) we can see that C (1) is negative

(-0.0220220) but the p-value, (0.8523)> 0.05. So, there is no

long-run significant causality from the three independent

variables (Rem, FDI, Exr). Meaning that Rem, FDI, and Exr

have no statistically significant influence on the

dependent variable GDP in the long- run. In other words,

there is no statistically significant long-run causality

running from Rem, FDI, and Exr to GDP. The results are

given in the table 4 above.

Wald test

I used Wald Statistics to check the short run causality.

Here, the null hypothesis Ho: C (11)=C(12)=C(13)=0 (

There is no short-run causality from export revenue to

GDP). According to test results (Table-5), we can reject the

Null hypothesis, because of our p-value (0.000) < 0.05. So

there is short-run causality from export revenue to GDP.

The results of the test are given below:

Table 5: Wald Test

Equation: Untitled

Test Statistic

Value

Probability

F-statistic

13.11311

(3, 17)

0.0001

Chi-square

39.33934

0.0000

Null Hypothesis: C(11)=C(12)=C(13)=0

Null Hypothesis Summary:

Normalized

Restriction (= 0)

Value

Std. Err.

C(11)

1.408499

2.245559

C(12)

-0.400249

1.750651

C(13)

10.42977

2.043848

Restrictions are linear in coefficients.

FINDINGS AND CONCLUSION

The main objective of this study was to investigate the

relationship between export revenue and GDP in

Bangladesh. We know export revenue is an important

factor for economic progress and GDP is a good criterion

for measuring this progress. From Granger causality test I

have seen, there is a bidirectional relationship between

GDP and export revenue, which means that export

revenue is the source of GDP in Bangladesh. In Johansen

cointegration test there was a long-run relationship

between GDP and export revenue. But in VECM there is

no long-run significant relationship between the variables

and the short-run relationship was checked by Wald test.

It means that export should be limited. For attracting our

goods and services to foreign countries, we should make

the variation in goods and services and ensure the quality

of goods and services. We should innovate new

technology and create high skill labor force. Since

Bangladesh exports a lot of garment products every year

hence products quality must be outstanding. The

government needs to support the garments industry by

giving loan so that they can invest a lot.

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