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8/8/2019 Causality Between Trade and Growth-Evidence From South Asian
1/26
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]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
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.
(Insert Table-2 about here)
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.
(Insert Table-3 about here)
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.
(Insert Table-4 about here)
(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
Growth Does not Granger CauseTrade
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
Growth Does not Granger CauseTrade
0.314498 0.5749
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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
Period Variance decompositionof Growth
Variancedecomposition of
TradeGrowth Trade Growth Trade
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
Period Variance decompositionof Growth
Variancedecomposition of
TradeGrowth Trade Growth Trade
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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
Period Variance decompositionof Growth
Variancedecomposition of
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