causality between trade and growth-evidence from south asian

8/8/2019 Causality Between Trade and Growth-Evidence From South Asian

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Causality between Trade andGrowth: Evidence from South Asian

Countries

A.F.M. Kamrul HassanAssociate Professor

Department of Finance and BankingUniversity of Rajshahi

Rajshahi-6205BANGLADESH

Email: [email protected]
mailto:[email protected]:[email protected]


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Causality Between Trade andGrowth: Evidence from South Asian

Countries

Abstract

This paper investigates the causal relationship between growth rates of

trade openness and real Gross Domestic Product (GDP) in five South

Asian countries, namely Bangladesh, Nepal, Sri Lanka, India and

Pakistan. Standard Granger-causality test in VAR framework is

employed for this purpose. Trade and Growth variables in all countries

are found to be stationary as per Phillips-Peron unit root test. VAR pair-

wise Granger-causality test results suggest causal effect of trade

openness on GDP growth in all the five countries. However, evidence

of Causal effect of GDP growth on trade openness is found in case of

Indian data and our results suggest independence of these two variables

for other countries. Variance decomposition analyses also support the

result obtained from Granger-causality test. Policy implication of the

findings of the study would be to emphasize other sectors of the

economy such as agriculture, industry etc. for economic growth of these

countries as trade is not found to be the engine of growth.

JEL Subject Codes: F15, O40

Key Words: Trade openness, Economic Growth, Granger causality

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plus import as share of GDP, between the period 1990 and 2002. In 32

countries increase in trade openness is associated with decrease in GDP

growth rate and in 14 countries decrease in trade openness is associated

with increase in GDP growth rate. In 13 countries GDP growth rate has

been decreased with decrease in trade openness. Thus out of 106

countries, in 60 (= 47 + 13) countries, i.e. nearly 57 percent of the

countries, trade and GDP growth rates show positive association over the

last couple of decades.

(Insert Table-1 about here)

This proves some support to the claim that there is some causal

relationship between trade and growth. Although the relationship between

trade and growth has been the subject of a voluminous body of literature,

there is a significant amount of disagreement on the direction of causality.

The extent to which international trade engenders economic growth has

been intensely debated in literature. Guillaumet and Richaud (2001)

pointed out that this debate revolves around two main ideas:

1. National development is an indispensable preliminary to openness.

Foreign trade is a step that comes after the agricultural, and in most

cases, the industrial development of the nation.

2. Openness creates an increase in the exchanges, thus creating extra

national wealth. In order to achieve a perfect economic

development, it is imperative to develop the size

of markets.

So it is seen that there is channels through which both trade and

growth can cause each other. This causation has been extensively studied

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between trade openness and economic growth and the objective of this

study is to examine this causality for these five South Asian countries.

The paper is organized as follows: section (II) presents review of

some related literature, section (III) describes the methodology of the

study followed by empirical results in section (IV) and the paper is

concluded in section (V).

II. LITERATURE REVIEW

Studies on the relationship between trade and growth occupy an

important part of international economics. Early studies on the subject

mainly focused on the nature of association between trade and growth.

Michaely (1977) examined empirically the relationship between export

and GDP growth rate and find that in developing countries export, as a

ratio of GDP, has strong positive relationship with GDP growth rate.

Balassa (1978) investigated the correlation between exports and

economic growth for a group of eleven countries for the period 1963-73.

His correlation and regression analysis show that export growth positively

affected the rate of economic growth. Tyler (1981) examined the same

relationship as Balassa (1978) but uses cross-section data of 55 countries

and finds that there is a significant positive association between export

and economic growth.

Ram (1985) employs production function approach to ascertain the

contribution of export to economic growth for two income levels of LDCs

and for two different time periods, i.e. 1960-1970 and 1970-77. He also

arrives at the same conclusion like Balassa (1978) and Tyler (1981) that

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exports contribution to economic growth is significant. Mbaku (1989)

used production function to examine the relationship between exports and

economic growth. He examined the effect of export growth on economic

performance in low and middle-income African countries. He finds that

exports impact on growth was significant, but this impact was stronger in

low-income countries than in middle-income countries. Bhala and Lau

(1991), using annual time series data, also find positive association

between trade openness and economic growth for developing countries.

From the mid-1980s research approach to test trade-growth

relationship is shifted to test causal relationship between trade and

growth using causality test developed by Granger (1969). Jung and

Marshall (1985) examined causality between export and growth in

developing countries and find that there is no strong evidence that

exports promote growth. Bi-directional causality is evidenced in some

studies. Chow (1987) investigates the causal relationship between export

growth and industrial development in eight Newly Industrialized Countries

(NICs). By using Sims causality tests he finds that in most NICs, there is a

strong bi-directional causality between the growth of export and industrial

development. Anoruo and Ahmad (2000) examined casual relationship

between trade openness and GDP growth rate in five ASEAN countries,

namely Indonesia, Malaysia, Philippines, Singapore and Thailand over the

period 1960 to 1997. They find that in all five countries trade openness

and GDP growth rates are co-integrated and there is bi-directional

causality between trade openness and GDP growth rate. Frankel, Romer

and Cyrus (1996) adopted a different approach. They employed

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instrumental variable (IV) approach to examine trades impact on ten East

Asian countries, namely Hong Kong, Singapore, Korea, Malaysia, Taiwan,

Philippines, China, Indonesia, Japan and Thailand. They find that in most

cases the contribution of trade openness to growth is a contribution of

trade predicted by gravity model. That means the impact on growth of

trade cannot be attributed to the national policies regarding trade regime.

There are some studies that find independence between export

expansion and economic growth, such as Abhayaratne (1996), Sinha and

Sinha (1996) and Guillaumet and Richaud (2001). Abhayaratne (1996)

studies the relationship between foreign trade and economic growth in Sri

Lanka for the period 1960-1992 and Guillau met and Richaud (2001)

studies trade openness and economic growth in France for the period

1850-2000. Both the studies find that trade and growth are independent.

Similarly Sinha and Sinha (1996) examined the long-run relationship

between trade openness and GDP in India. Although they find a long-run

equilibrium relationship between trade openness and GDP, no causal

relationship between the two is evidenced.

Recently Cuadros, Cuadros, Orts and Alguacil (2004) examined

causality relationship between export, inward foreign direct investment

(FDI) and output using quarterly data for Argentina, Brazil and Mexico for

the period between middle-1970s and 1997. Their findings do not support

export-led growth in these countries; on the contrary, in some cases they

find evidence for a negative causal relationship between domestic income

and export.

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From the above literature review it is clear that there is no unique

answer to the question of causality between trade and economic growth.

All possible ways through which these two may be connected are found in

these studies, that is, trade causes growth, trade and growth are

independent and trade negatively causes growth. This heterogeneity of

findings makes room for further research in this area, especially in

countries like Bangladesh, Nepal, Sri Lanka, India and Pakistan that have

not been subjected to such study before.

III. METHODOLOGY

This paper employs Granger-causality test to examine causal relationship

between growth rates of trade openness and real GDP in five South

Asian countries mentioned above. Accordingly, Granger-causality

test procedure is described first, the issue of stationarity of the

underlying time series is discussed next followed by the discussion

on stability of the estimated VAR and next variance decomposition

is discussed. This section is concluded with the description of data

used in this paper.

(i) Granger-causality

Causality in the sense Granger (1969) is inferred when values of a

variable, say,xt, have explanatory power in a regression ofyt on lagged

values ofytandxt. If lagged values ofxthave no explanatory power for

any of the variables in the system, thenxis viewed as weakly exogenous

to the system. Vector Auto Regression (VAR) of the following forms are

estimated for this purpose

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t

n

i

it

n

i

iitit XYY +++= =

=

1 1

0 (1)

t

n

i

it

n

i

iitit XYX +++= =

=

1 1

0 (2)

for all possible pairs of ( )YX, series in the group. Where n is the number

of optimum lag length. Optimum lag lengths are determined empirically

by Akaike information criterion ( )AIC . For each equation in the above

VAR , Wald 2 statistics is used to test the joint significance of each of the

other lagged endogenous variables in that equation. The null hypothesis

that tX does not Granger-cause tY is rejected if i in equation (1) is

significantly different from zero. Similarly tY Granger-cause tX if i in

equation (2) is significantly different from zero. If, in equation (1) 0i ;

and in equation (2) =0i ; then there is unidirectional Granger-

causality from tX to tY . Similarly, if in equation (1) 0= i ; and in

equation (2) 0 i ; then there is unidirectional Granger-causality from

tY to tX . Bi-directional Granger-causality is suggested when both i in

equation (1) and i in equation (2) are significantly different from zero.

Finally, independence is suggested if both i

in equation (1) and i

in equation (2) are not significantly different from zero.

(ii) Stationarity of Time Series

The conventional Granger-causality test based on standard VAR is

conditional on the assumption of stationarity of the variables constituting

the VAR. If the time series are non-stationary, the stability condition of

VAR is not met, implying that the 2 (Wald) test statistic for Granger-

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causality is invalid. In this case cointegration and vector error correction

model ( )VECM are recommended to investigate the relationship between

non-stationary variables. Therefore, it is imperative to ensure first that the

underlying data are stationary or I(0). The most widely used unit root test

is Dickey-Fuller (DF) and Augmented Dickey-Fuller ( )ADF test. But many

alternatives to these tests have been suggested, in some cases to

improve on the finite sample properties and in other cases to

accommodate more general modeling framework. One such test is

Phillips-Peron (PP) unit root. The present study makes use of this PP test to

check stationarity of the underlying time series data for its superiority

over DF or ADF tests. Phillips and Peron (1988) propose a non-parametric

method of controlling for higher order serial correlation in a series and is

based on the following first order auto-regressive [AR(1)] process:

ttyay ++= 1 ;

Where; is the first-difference operator, a is the constant, is the

slope and 1ty is the first lag of variable y . The correction for the serial

correlation in is nonparametric since an estimate of the spectrum of

at frequency zero is used that is robust to 11eteroscedasticity and

autocorrelation of unknown form. The Newey and West (1987) method is

used to construct an estimate of the error variance from the estimated

residuals t as follows:

( ) StN

St

t

l

S

N

t

t lsNN

+===

+ ,2

1

111

2

; Where l is a truncation lag parameter and

( )ls, is a window.

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If the underlying series, sayxandy, contain unit root i.e. are not

I(0), but, say, I(1), then the Granger representation theorem requires that

they must be co-integrated that is their linear combination must be I(0). In

the current study, we find that the variables under consideration are

stationary at level, that is they are I(0). So the issue of cointegration is not

addressed here and pair-wise Granger-causality tests between Trade and

Growth for the five countries are carried out in VAR framework.

(iii) Stability of VAR: In order for the conclusions drawn from the VAR, it

is necessary that the VAR be stable or stationary. If the estimated VAR is

stable then the inverse roots of characteristics Autoregressive (AR)

polynomial will have modulus less than one and lie inside the unit circle.

There will be kp roots, where k is the number of endogenous variables

and p is the largest lag.

(iv) Variance Decomposition: One limitation with Granger-causality

test is that the results are valid within the sample, which are useful in

detecting exogeneity or endogeneity of the dependent variable in the

sample period, but are unable to deduce the degree of exogeneity of the

variables beyond the sample period (Narayan and Smyth 2004). To

examine this issue we examine variance decomposition of Trade and

Growth. A shock to the i-th variable not only directly affects the i-th

variable, but is also transmitted to all of the other endogenous variables

through dynamic (lag) structure of the VAR. Variance decomposition

separates the variation in an endogenous variable into the component

shocks to the VAR. Thus variance decomposition provides information

about the relative importance of each random innovation in affecting the

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variables in the VAR. Sims (1980) notes that if a variable is truly

exogenous with respect to other variables in the system, own innovations

will explain all of the variables forecast error variance.

(v) Data: Data used for the analyses are growth rates of trade openness

and real GDP. Causality is examined between these two variables in three

South Asian countries, namely, Bangladesh, Nepal, Sri Lanka, India and

Pakistan. The study uses annual data on GDP, export and imports.

Depending on the availability of data, time period covered by the analyses

is different for the five countries. They are as follows:

Bangladesh: 1974-2003

Nepal: 1972-2003

Sri Lanka: 1961-2003

India: 1961-2002

Pakistan: 1961-2004

Trade openness is the measure of the degree of a countrys integration

with rest of the world through its export and import. Different policies

such as reduction of import tariff, providing export subsidies etc. are

taken to increase this integration. A suitable proxy for trade openness is

the volume of foreign trade as compared to GDP. Thus, it is measured by

the ratio of the sum of export and import to GDP, that is,

( )100

Imx

GDP

portExport +. Growth rates are calculated by the transformation

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( )100

1

1 xX

XX

t

tt

, where X represents trade openness and real GDP. Real

GDP is calculated as 100min

xrGDPDeflato

alGDPNo

. All data are collected from

International Financial Statistics (IFS)-2004, CD-ROM version.

Econometrics computer program Eviews-4 has been used for all

econometric estimation purposes.

IV. EMPIRICAL RESULTS

This section presents results of empirical analyses of the paper.

Stationarity of data is examined first, then before proceeding to Granger-

causality test results, stability of the estimated VAR is examined. Next

Granger-causality test results are presented followed by the results of

variance decomposition.

(i) Unit Root Test: PP unit root test results for Trade and Growth

variables of Bangladesh, Nepal and Sri Lanka are presented in Table-2.


PP test results show that for Trade and Growth variables in all three

countries the null hypothesis of non-stationarity is rejected at 1% or 5%

significance level in all five countries. That is the variables under study do

not contain unit root, they are stationary or I(0) processes.

(ii) Granger-causality Test

Unit root test results reported in Table-2 satisfy the condition of

stationarity of data for Granger-causality test in a system of VAR. So next

pair-wise Granger causality tests are performed in VAR. However, before

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analyzing Granger-causality test results, it is necessary to examine

whether the estimated VAR is stable. Table-3 reports inverse roots of

characteristics Autoregressive (AR) polynomial for five countries.

Optimum lag order for Bangladesh is found to be three and one for other

four countries. As there are two endogenous variables in the system,

number of roots for Bangladesh is six and two for other countries. From

Table-4 it is seen that the inverse roots of characteristics Autoregressive

(AR) polynomial have modulus less than one and lie inside the unit circle

in all cases. So the VARs are stable.


Pair-wise Granger-causality test results are reported in Table-4. Granger

causality test results show that the Wald 2 statistic fails to reject the null

hypothesis that Trade does not Granger cause Growth in all five countries.

Test statistic also fails to reject the other null hypothesis that Growth does

not Granger cause Trade in four countries, except India. In case of India

the null hypothesis is rejected at 10% significance level. The results

suggest that only in case of India there is unidirectional Granger causality

from Growth to Trade. In all other cases no evidence is found in favor of

causality between Trade and Growth in Granger sense.


(iii) Variance Decomposition

Variance decomposition analysis is used to supplement the Granger-

causality test results obtained in the previous section to examine the out-

of-sample causality or non-causality. These results are summarized in

Table-5(a) through Table-5(e) for a 15-year period. Results show that the

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causality or non-causality between variables in the sample period is also

valid for out of sample period.

[Insert Table-5(a) through Table-5(e) about here]

According to the test results reported in Table-5(a) to 5(e), a high

proportion of Growths shocks are explained by its own innovations in

each country. At the end of 15 years the forecast error variances for

Growth in Bangladesh, Nepal, Sri Lanka, India and Pakistan explained by

its own innovations are 95.14 percent, 97.33 percent, 98.68 percent,

95.37 percent and 99.99 percent respectively. Trades contribution in

explaining the variance of Growth is almost nothing.

When the variances of Trade are considered, except India, above 90

percent of it variances are explained by itself. After 15 years period,

variances of Trade explained by itself in Bangladesh, Nepal, Sri Lanka, and

Pakistan are 96.96 percent, 97.54 percent, 92.44 percent and 90.73

percent. Only in case of India 83.03 of Trade variance after 15 years is

explained by itself and 16.96 percent by Growth. Growths contribution in

explaining relatively larger proportion of Trades variance is consistent

with the Granger-causality findings that in India Growth Granger cause

Trade.

V. CONCLUSION

This paper examines the causal relationship in Granger sense between the

growth rates of trade openness and real GDP in five South Asian countries,

namely Bangladesh, Nepal, Sri Lanka, India and Pakistan within VAR

framework. Variance decomposition analysis is carried out to examine the

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consistency of within-sample Granger-causality result with out-of-sample

causality. Econometric estimation procedure starts with the examination

of stationarity property of the variables under consideration. Phillips-Peron

method is employed for this purpose and it is found that growth rates of

trade openness and real GDP are stationary at 5% level. Except

Bangladesh, one lag is found to be appropriate for VAR and for

Bangladesh the lag order of three is found to be appropriate. The modulus

of inverse roots of characteristics Autoregressive (AR) polynomial for all

VARs are found less than one and lie within the unit circle implying the

stability of the estimated VARs. Wald 2 statistic for pair-wise Granger

causality tests fail to reject both null hypothesis that Trade does not

Granger-cause Growth in all five countries under study. However, Wald

2 statistic fail to reject the other null hypothesis that Growth does not

Granger-cause in Bangladesh, Nepal, Sri Lanka and Pakistan, but reject in

case of India at 10% level. Variance decompositions also confirm these

results. A very high proportion of forecast error variances of real GDP

growth rates in all five countries are explained by their own innovations.

In case of the growth rate of trade openness in India, relatively larger

proportion of its variance is explained by real GDP growth rate. In all

other four countries, almost all of variances of growth rate of trade

openness are explained by itself.

This result supports the findings of Abhayaratnes (1996) study on

Sri Lanka, but contradicts with Sinha and Sinhas (1996) study on India.

This contradiction may be attributed to the fact that since independence

India followed a planned economy model with strictly controlled external

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trade. Indias fascination with the planned economy model began to

wither since the mid-1980s (Bhattacharyya 2004). So Sinha and Sinhas

study do not cover sufficient time period to capture a causal relationship

between trade and GDP. This finding is in line with the view of Guillaumet

and Richaud (2001) that national development is an indispensable

preliminary to openness. Foreign trade is a step that comes after the

agricultural, and in most cases, the industrial development of the nation.

Absence of causal effect from trade to growth implies that domestic

demand is the main source of economic growth of these countries. In such

a situation overemphasizing international trade as an engine for growth

may cause policymakers to overlook other sources of growth. Policy

implication to boost economic growth would be to prioritize other

development agendas like agricultural development, industrial

development by adopting import-substitution strategy and protecting

domestic industries from foreign competition and pay attention to create

domestic market to boost domestic demand. Another implication of this

study is that trade openness does not has any role in reducing poverty in

these countries as it does not cause growth. However, absence of trades

causal effect on growth may also stem from the fact that the trade regime

of South Asian countries has not been truly liberal (Geest, 2004). If this

were the case then the policy implication would be to adopt truly liberal

outward looking trade policies so that trades impact on growth is exerted

properly in these countries.

Although this study establishes non-causality between growth rates

of trade openness and real GDP in Bangladesh, Nepal, Sri Lanka and

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Pakistan and unidirectional causality from growth rate of trade openness

to GDP growth, still there is room for further research, such as, impact of

import and export on growth may be examined separately; trades impact

on growth may be examined through gravity model and relationship

between trade and industrial production may also be examined. In

addition, impact of trade reform measures on growth-trade relationship

may well be a prospective field of future research.

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TABLES

Table-1: Association between Trade openness and GDP growth

rate

(Figures represent number of countries)

Growth

Increase DecreaseTrade Increas

e47 32

Decrease

14 13

Source: World Development Indicator 2004.

Table-2: Phillips-Peron (PP) Unit Root Test Results

Country Variable Test StatisticsIntercept without

trend1Intercept with

trend2

Bangladesh

Trade -7.859815* -7.810545*Growth -8.422294* -8.217761*

Nepal Trade -4.140993* -4.121130**Growth -7.168909* -7.095871*

Sri Lanka Trade -5.771675* -5.684842*

Growth -6.768331* -6.810528*India Trade -5.352499* -5.362619*

Growth -6.837627* -7.479622*Pakistan Trade -6.955309* -6.943345*

Growth -5.989897* -5.921164*Note: 1. * and ** indicate significant at 1% and 5% levels.

2. Critical values at 1%, 5% and 10% are 3.6752, -2.9665 and 2.6220 respectively

Critical values at 1%, 5% and 10% are 4.3082, -3.5731 and 3.2203respectively.

Table-3: Inverse roots of characteristics Autoregressive (AR)

polynomial

Country Root Modulus

Bangladesh

-0.484950 0.552584i

0.735205

-0.484950 +0.552584i

0.735205

0.635853 0.635853

-0.205599 0.446404i

0.491475

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-0.205599 +0.446404i

0.491475

0.229979 0.229979Nepal -0.299552 0.299552

0.244419 0.244419

Sri Lanka 0.261132 0.261132-0.079302 0.079302

India 0.027991 0.245645i

0.247235

0.027991 +0.245645i

0.247235

Pakistan 0.108287 0.108287-0.051522 0.051522

Table-4: VAR Pair-wise Granger causality Test

Null Hypothesis Wald 2 Statistic

Probability

BangladeshTrade Does not Granger CauseGrowth

2.550878 0.6355

Growth Does not Granger CauseTrade

1.208525 0.7510

NepalTrade Does not Granger CauseGrowth

1.017115 0.6014

Growth Does not Granger CauseTrade 0.019207 0.8898

Sri-LankaTrade Does not Granger CauseGrowth

0.131640 0.7167


1.545707 0.2138

IndiaTrade Does not Granger Cause

Growth

2.232065 0.1352

Growth Does not GrangerCause Trade

2.836867 0.0921

PakistanTrade Does not Granger CauseGrowth

2.70E-05 0.9959


0.314498 0.5749

24


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Table-5(a): Variance Decomposition of Growth and Trade:

Bangladesh

Period Variance decomposition

of Growth

Variance

decomposition ofTrade

Growth Trade Growth Trade1 100.0000 0.000000 0.000000 100.00005 96.19975 3.800249 2.782175 97.2178210 95.30688 4.693120 3.009622 96.9903815 95.14507 4.854928 3.033490 96.96651

Table-5(b):Variance Decomposition of Growth and Trade: Nepal

Period Variance decompositionof Growth

Variancedecomposition of

TradeGrowth Trade Growth Trade

1 100.0000 0.000000 2.588591 97.411415 97.34140 2.658597 2.458831 97.5411710 97.33664 2.663356 2.458829 97.5411715 97.33662 2.663378 2.458829 97.54117

Table-5: Variance Decomposition of Growth and Trade: SriLanka




1 100.0000 0.000000 1.694817 98.305185 99.68657 0.313431 7.553472 92.4465310 99.68656 0.313435 7.553606 92.44639

15 99.68656 0.313435 7.553606 92.44639

Table-5(d): Variance Decomposition of Growth and Trade: India




25


26/26

1 100.0000 0.000000 9.758107 90.241895 95.34160 4.658398 16.96902 83.0309810 95.34155 4.658448 16.96911 83.0308915 95.34155 4.658448 16.96911 83.03089

Table-5(e): Variance Decomposition of Growth and Trade:

Pakistan



Trade

Growth Trade Growth Trade1 100.0000 0.000000 8.293249 91.706755 99.99993 6.57E-05 9.263508 90.7364910 99.99993 6.57E-05 9.263508 90.7364915 99.99993 6.57E-05 9.263508 90.73649

causality between trade and growth-evidence from south asian

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