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Proceedings of the International Symposium on Emerging Trends in Social Science Research (IS15Chennai Symposium) ISBN: 978-1-941505-23-6

Chennai-India, 3-5 April 2015 Paper ID: C542

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Exports, Imports and Economic Growth in India:

An Empirical Analysis

Sani Hassan Hussaini,

Department of Economics,

SRM University,

Chennai, India.

Email: [email protected]

Bashir Ado Abdullahi,


SRM University,

Chennai, India.


Musa Abba Mahmud,


SRM University,

Chennai, India.


Abstract

This paper is aimed at testing the Export Led Growth Hypothesis for India with annual time

series data from 1980 to 2013. The paper employed several econometric tools which includes

Cointegretion (Johansen method), Vector Error Correction Model (VECM) and causality test.

Johansen cointegretion analysis was employed to find out whether Gross Domestic Product

(GDP), Export and Import are cointegrated. Likewise Vector Error Correction Model was

used to determine whether there is short run and long relationship among the variables.

Granger Causality Test was also used to detect the direction of causality between GDP and

Export. The study found that within the period of 1980 to 2013, the variables are cointegrated

and there exist bidirectional relationship between GDP and Export.

______________________________________________________________________

Key Words: Export, Import, Growth Domestic Product

mailto:[email protected]






1. Introduction

The effects of great depression and Second World War resulted in a hard time for

underdeveloped and developing countries economically (India was not exception). During

that time, many foreign markets were closed and the severity of the economic meltdown

drove many of these countries to look for another solution to development. Most developing

countries in Asia and Latin America adopted import substitution as a strategy for

development. However, during the 1950s and 1960s the Asian countries, like Taiwan, Hong

Kong, Singapore and South Korea, started focusing their development outward, resulting in

an export-led growth strategy. Many of the Latin American countries continued with import

substitution industrialization, just expanding its scope. That is why the strategy of the former

was tagged by some economists as flying Geese while that of later as sitting Ducks. Some

have pointed out that because of the success of the Asian countries, export-led growth should

be considered the best strategy to promote development.

Export Led Growth (ELG) Sometimes Called Export-Oriented Industrialization (EOI),

Export Substitution Industrialization (ESI) or Export Led Industrialization (ELI) is a trade and

economic policy aiming to speed up the industrialization process of a country by opening

domestic markets to foreign competition in exchange for market access in other countries.

Reduced tariff barriers, a floating exchange rate (a devaluation of national currency is

often employed to facilitate exports), and government support for exporting sectors (subsidy)

are all an example of policies adopted to promote EOI and, ultimately, economic

development.

The proponents of Exports led Growth hypothesis (ELG) (Neoclassical school of

economists) postulate that exports make a significant contribution to economic growth.

Enhanced specialization, full capacity utilization of the plant size, getting benefits of the

greater economies of scale, increasing the rate of investment and technological change are

some of the benefits which can be reaped through exports (Krueger, 1978; Balassa, 1978;

Kavoussi, 1984; Ram, 1985). Furthermore, exports can provide foreign exchange that allows

for more imports of intermediate goods, which in turn raises capital formation and thus, 0

economic growth leads to enhancement of skills and technology which increased efficiency,

thereby creating a comparative advantage for the country (Lancaster, 1980; Krugman, 1984;

Arnade and Vasavada, 1995; Fosu, 1996; Thornton, 1996;)

The theoretical relationship between imports and productivity tends to be more

complicated than that between exports and productivity. Increased imports of consumer

products encourage domestic import-substituting firms to innovate and restructure themselves




in order to compete with foreign rivals; therefore, imports enhance productive efficiency.

Under perfect competition in the neoclassical model, an industry reduces factor usage in the

short run when trade barriers are removed and the market is opened up to imports. In the long

run, however, the industry becomes more productive and competitive, and expands its

investments in new technology, resulting in a rightward shift of the industry supply curve. In

general, the effect on productivity of opening the market depends on both market structure

and institutional factors. Under imperfect competition, an import-substituting domestic

market shrinks as imports increase, causing investment to fall and thereby productivity to

eventually fall. Furthermore, higher future expected profits lead to more active research and

development (R&D) investment and innovation efforts, and such R&D may be greater for

exporting firms than for import-substituting firms in light of the large impact of market

opening. Imports of capital goods and intermediate goods that cannot be produced

domestically enable domestic firms to diversify and specialize, further enhancing their

productivity. Finally, there are also theoretical grounds for both positive and negative

causality from productivity to imports (Sangho K. et'al 2007).

2. Statement of the Problem

The relationship between economic growth and growth in export is still a topic of

discussion and argument by researchers and economists. Some believes that export leads to

economic growth thereby supporting Export Led Growth Hypothesis, while others sees

economics growth leads to growth in export and support Growth Led Export Hypothesis.

Moreover, whether import is positively or negatively related to economic growth is another

issue of discussions.

These divergent views on the relationship between export, import and economic growth

put many developing economies in a dilemma of whether to open up their economies to

promote international trade or whether they should concentrate on economic activities that

will promote international trade.

It is due to these contradicting evidences about the dynamic relationships between

exports import and economic growth that the paper attempts to revisit these relationships in

the case of India for the period 1980 to 2013.

3. Research Questions

1. Whether Export, Import and GDP are cointegrated?

2. Whether their relationships are long run or short run phenomenon?

3. Are there any causal relationships between Export, Import and GDP?

4. What is the direction of the causality? (If any)




4. Research objectives

Base on the above research question, the following research objectives were also designed

1. To determine the relationships between Export, Import and GDP

2. To determine whether the relationships are long run or short run phenomena, or both

3. To determine whether there is any causal relationships between Export, Import and GDP.

4. To determine the directions of the causality.

5. Literature Review

The argument concerning the role of international trade as one of the main deterministic

factor of economic growth is not new. It goes back to the classical economic theories by

Adam Smith and David Ricardo, who argued that international trade plays an important role

in economic growth, and that there are economic gains from specialization. It was also

recognized that exports provide the economy with foreign exchange needed for imports that

cannot be produced domestically. The ELG paradigm has received renewed attention

following the highly successful East Asian export-led growth strategy during the 1970s and

1980s, and especially if compared to the overall failure of import substitution policies in most

of Africa and Latin America (Fouad A. 2005).

An extensive empirical literature exists on the relationship between exports and growth.

In fact, much of the empirical literature on trade and productivity defines trade as exports

rather than imports. Empirical studies have tried to determine whether exports cause

productivity to increase. However, results in this regard seem to depend on both the sample

periods and the countries examined. Some studies find unidirectional causality running from

exports to productivity or from productivity to export while others find bidirectional causality

between the two variables.

Strong economic growth accompanied with robust export performance leads many people

to conclude that export sector of a country has pivotal role in the economic growth of that

country. Export-led growth hypothesis has not only been widely accepted by academics

(Feder 1982, Krueger 1990) and evolved into a new conventional wisdom (Tylor 1981)

(Balassa 1985), but it also has shaped the development of a number of countries as well as the

policies of the World Bank (World Bank 1987, Gazi Salah et’al 2010).

Among earlier empirical studies Emery (1967, 1968), Syron and Walsh (1968), Serven

(1968), Kravis (1970), Michaely (1977), Heller and Porter (1978), Bhagwati (1978) and

Krueger (1978) should be mentioned. This first group of studies explained economic growth

in terms of export expansion alone, in a two-variable framework. That is, they used bivariate




correlation, the Spearman rank correlation test in cross-country format to illustrate the alleged

superior effects of the ELGH (Lussier, 1993, p. 107).

A second group of researchers, which includes Balassa (1978, 1985), Tyler (1981), Feder

(1983), Kavoussi (1984), Ram (1985, 1987) and Moschos (1989), studied the relationship

between export and output performance within a neoclassical framework. The majority of

these investigations aimed at analyzing developing countries by using ordinary least squares

(OLS) on cross-section data and used their results to demonstrate the advantages of the export

promotion strategy in comparison with the import substitution policy. It was not until recently

that this line of research began to focus on country-specific studies, for both industrialized

countries and developing countries.

Thurayia (2004) studied the relationship between exports and economic growth

experience in Saudi Arabia and Sudan. Results showed that the growth rate in total exports in

Saudi Arabia had an active role in achieving economic growth while it had a weak influence

in Sudan. The results of cointegration and error correction models showed a positive effect of

exports on GDP in the short- and long- run, which confirms the validity of the hypothesis of

export-led growth in Saudi Arabia, and Sudan.

Elbeydi, Hamuda and Gazda (2010) investigated the relationship between exports and

economic growth for Libya for the period 1980 to 2007. The findings indicate that there exists

a long-run bi-directional causality between exports and income growth, and thus, the export

promotion policy contributes to the economic growth of Libya. Ullah et al (2009) re-

investigated the export-led growth hypothesis using time series econometric techniques over

the period of 1970 to 2008 for Pakistan. The results reveal that export expansion leads to

economic growth. Rangasamy (2008) examined the exports and economic growth relationship

for South Africa, and provides the evidence that the unidirectional Granger causality runs

from exports to economic growth (Sharma P. K. 2010).

However, many evidences fail to unequivocally support a robust export-economic growth

nexus. The most recent time series investigations concerning DCs that have used the

econometric methodology of cointegration have not been able to establish unequivocally that

a robust relationship between these variables indeed exists in the long term, namely that the

variables are cointegrated (see e.g. Islam, 1998). While some have been able to find a long-

run relationship, many others have rejected the export-led hypothesis i.e. that export

expansion causes growth in the long term. In fact, in most studies the results suggest that this

arises owing to a simple short-term relationship, a feature that is not surprising if we take into

account the fact that the studies that have concentrated their attention on industrialized nations

have also been unable to find a robust relationship between these variables (see e.g. Kugler,




1991). Jung and Marshall (1985), for instance, based on the standard Granger causality tests,

analysed the relationship between export growth and economic growth using time-series data

for 37 developing countries and found evidence for the export-led growth hypothesis in only

four countries. Darrat (1986, 1987) rejects exports-economic growth causality for three out of

four countries included in the sample (Gazi Salah et’al 2010). He detected unidirectional

causality from economic growth to export growth in Taiwan. Mah (2005) studied the long-run

causality between exports and economic growth for China with the help of the significance of

error correction term. This study indicates that export expansion is insufficient to explain the

patterns of real economic growth. Tang (2006) stated that there is no long-run relationship

among exports, real Gross Domestic product, and imports. This study further shows no short-

and long-run causality between export expansion and economic growth in China on the basis

of Granger causality test while economic growth does Granger-cause imports in the short-run.

Amavilah (2003) determined the role of exports in economic growth by analyzing Namibia’s

data from 1968 to 1992. Results explained the general importance of exports, but the study

finds no discernible sign of accelerated growth due to exports.

Many studies on India also show rejection on ELG hypothesis. Dhawan and Biswal

(1999) investigate the ELG hypothesis using a vector autoregressive (VAR) model by

considering the relationship between real GDP, real exports and terms of trade during 1961-

1993. They employ a multivariate framework using Johansen’s co-integration procedure and

find a long-run equilibrium relationship between these three variables and the causal

relationship flows from the growth in GDP and terms of trade to the growth in exports.

However, they conclude that the causality from exports to GDP appears to be a short-run

phenomenon. Asafu-Adjaye et al. (1999) consider three variables: exports, real output and

imports for the period 1960-1994. They do not find any evidence of the existence of a causal

relationship between these variables in case of India and no support for the ELG hypothesis,

which is not too surprising given India’s economic history and trade policies. Anwer and

Sampath (2001) also find evidence against the ELG hypothesis for India. Sharma and

Panagiotidis (2004) re-examines the sources of growth for the period 1971-2001 based upon

Feder’s (1982) model to investigate empirical relationship between export growth and GDP

growth (the export led growth hypothesis). They fail to find support for the hypothesis that

exports Granger cause GDP.

From the review of empirical literature on exports and growth, it is clear that the exports

do not necessarily cause growth. The results reported are clearly sensitive to the variables

employed, theoretical approach used and even on the econometric methodology employed.

For example, cross-section studies are more likely to corroborate a positive relationship




between exports and growth, while the results of time series studies depend substantially on

the countries analyzed, the period chosen and the econometric methods used. In addition,

since cross-section studies can obscure particularities of developing countries, especially,

those that are low-income countries, the correct strategy to follow from an empirical point of

view is to address the issue in a single country framework, using as much as possible the

recent developments in time series analysis (Narayan C. P. 2010).

6. Data Sources and Methodology

The objective of this paper is to investigate the dynamics of the relationship between

exports, imports and economic growth in India using the annual data for the period 1980 to

2013. All necessary data for the sample period are obtained from IECONOMICS (Trading

Economics) and Ministry for Commerce and Industry, Government of India.

In this study, the variables are Total Exports by India (EXP), total Import (IMP) and

Economic Growth (GDP) i.e. Gross Domestic Product (GDP) is used as the proxy for

economic growth in India. All the variables are taken in their raw data.

7. Stationary vs Non stationary of a Data

The most commonly point to begin with a time series analysis is the test of stationarity,

because regressing nonstationary time series data on one or more nonstationary time series

data will result in spurious regression. That is why researchers when dealing with time series

data always begin with unit root test, a test of stationarity that has become widely popular

over the past several years (Gujarati 2012). Test for units are performed on single or

individual time series.

However, it is important to note that not all nonstationary regressions are spurious. This is

quite possible when the two (or more) time series share same trend so that the regression of

one on the other(s) will not be necessary spurious (Gujarati 2012). To be specific, we use the

following equation:-

Yt = β1 + β2Xt + ut (1)

Where Yt is our dependent variable and Xt is our independent variable, β1 and β2 are the

constant and slope respectively. ut is the error term. Both Yt and Xt are I(1); that is they

contains stochastic trend. In other words they are nonstationary at level. Let us rewrite the

equation as

ut = Yt - β1 - β2Xt (2)




If ut is subjected to unit root analysis and found to be a stationary; that is, it is I(0),

although both dependent and independent variables are individually I(1), that is they have

stochastic trend, their liner combination is I(0). Thus, the linear combination cancels out the

stochastic trends in the two series. In this case we say that the variables are cointegrated

(Gujarati 2012). A test of cointegretion can be thought of as a pre-test to avoid spurious

regression situations (C. W. J. Granger, 1986).

In short provided we check that the residuals from nonstationary regression are I(0) or

stationary, the traditional regression methodology (including the t and F tests) is applicable

also to data involving nonstationary time series (Gujarati 2012).

In the language of co-integration theory, a regression such equation (1) is known as a co-

integrating equation and the slope parameter is known as co-integrating parameter. The

concept of co-integration can be extended to a regression model containing k regressors. In

this case we will have k co-integrating parameters.

The above procedure of co-integration test was introduced by Engle and Granger in 1987.

In other words, it is known as Engle and Granger Test of co-integration. The major drawback

of this method is that it only allows for a single co-integrating equation, and many variables

are used in a regression equation there are possibility of more than one co-integration

equation i.e. when we have k variables in an equation, there is possibility of k – 1 number of

co-integration equations. This gave birth to Johansen Co-integration Test. Which is going to

be used in this study, since our variables are three, which means there could be two co-

integration vectors?

At this juncture the co-integration equation to be used in this study is

GDPt = β1 + β2 IMP + β3 EXP + ut (3)

Where GDP is the growth domestic product taken as proxy of economic growth of India

and a dependent variable, while IMP and EXP are the total imports and exports of India

respectively, taken as independent variables. The sign of the parameters is taken from

Correlation Test which shows that both Export and Import are positively related to GDP.

8. Test of Co-integration

Economically speaking two or more variables are said to be cointegrated when they have

a log-run equilibrium relationship between them (Gujarati 2012). To this effect, the study

employed Johansen's framework. The details of this approach can be found in Johansen

(1988) and Johansen and Julius (1990). Subsequently, the study will employed Error

Correction Model to test whether there is a short run relationship between the variables as




well as the speed of adjustment towards long run equilibrium. Finally, causality test will also

be applied to check causation as well as the direction of the causation amount the variables

under study. Meanwhile, other statistical test such as Autocorrelation, Heteroskedasticity and

Normality test will be applied.

9. Result Analysis

The study begins with subjecting the error term into a unit root test to find out whether it

is stationary or not using Augmented Dickey-Fuller Unit Root Test (ADF) and Phillips-

Perron Test. As stated earlier, until the error is stationary, we cannot apply co-integration test

for nonstationary time series without subjecting the series individually into a unit root test.

The table below shows the outcome of both the ADF and PP Tests of unit root.

Table 1: Unit Root Test - ADF (5%)

Variable Test

Statistic Critical Value P value Shape

Ս -4.544451 -2.967767 0.0012 Constant

-4.511152 -3.574244 0.0063 Constant &

Trend

The above table shows how our error term was subjected into unit root tests. Both

Augmented Dickey-fuller and Philips Perron have three different shapes. With constant, with

constant and trend and also with no trend no constant. This paper is restricted to only the first

two in both ADF and PP. furthermore; the study uses only 5% level of significance. The

information presented in table 1 shows the values of test statistics, critical value as well as the

probability value in both constant and trend shapes of ADF.

The null hypothesis is that the variable has unit root. That is it nonstationary data. There

are two ways to make a decision. First, if the test statistic is more than critical value, at the

null hypothesis is rejected. Second, if the probability value is less than 5% we can reject the

null hypothesis. The above table shows that ADF test with both constant as well as constant

and trend the Test Statistics (in absolute term) is greater than the critical value, therefore we

can reject the null hypothesis of unit root in our error term. Meanwhile, the P value in both

cases of ADF test are less than 5%. Therefore we reject the null hypothesis of unit root in our

error term, which means our error term is stationary.




Table 2: Unit Root Test – Phillips-Perron (5%)

Variable Test

Statistic Critical

Value P value Shape

Ս -4.532244 -2.967767 0.0012 Constant

-4.553421 -3.574244 0.0057 Constant &

Trend

The PP test of unit root further support the information given by the ADF test that the

error term is stationary, since the P-value of both constant and constant and trend are 0.12%

and 0.57% respectively. This implies that we can reject the null hypothesis at 5% level.

Meanwhile, the Test Statistic is also more than the critical value which further suggests the

rejection of the null hypothesis, which also reaffirm that our error term is stationary.

This outcome suggests that the variables are cointegrated under the Engle and Granger

framework. The next section of the empirical study investigates whether the series under

scrutiny are cointegrated under the Johansen frame work, so that a well defined linear

relationship exists among them in the long run. Thus, we proceed to test for the long run

association ship between the variables on levels using the Trace Statistic and Maximum Eigen

Statistic tests, all of which are based on the ‘null hypothesis of no co-integration.

Table 3: Johansen Test Of Co-integration

Hypothesized

Number of Co-

integrating

Equations

Eigen Value Trace

Statistics

Critical Value at 5%

Level (P Value)

Maximum

Eigen Statistic

Critical Value at

5% Level

(P Value)

None* 0.588232 37.70652 29.79707(0.0050) 26.61887 21.13162(0.0076)

At most 1 0.286290 11.08765 15.49471(0.2062) 10.11834 14.26460(0.2043)

At most 2 0.031794 0.969309 3.841466(0.3249) 0.969309 3.841466(0.3249)

Source: Author’s own calculations

*denotes rejections of hypothesis at 5% level

The null hypothesis of no co-integrating equation is strongly rejected with a probability of

0.5 percent. Also, the critical value at 5 percent level is less than the Trace Statistic. In the

same vain, the Maximum Eigen Statistic rejects the null hypothesis of no co-integration

equation among the variables under study.

Both the Trace Statistics and Maximum Eigen Statistics accept the null hypothesis of at

most 1. That is, there is existence of at most one co-integrating equation among the variables.

As we can see, the Critical values of both Trace Statistic and Maximum Eigen Statistic are

greater than their respective values and the probability in both cases are greater than 5 per




cent. Thus, the variables under study have long run relationship among them which answer

the first research question. But in the short-run there may be deviations from this equilibrium,

and it is required to verify whether such disequilibrium converges on the long-run equilibrium

or not. Thus, Vector Error Correction Model is used to generate such short-run dynamics.

Error correction mechanism provides a means whereby a proportion of the disequilibrium

is corrected in the next period. So, error correction mechanism is a means to reconcile the

short-run and long run behavior. The estimation of a Vector Error Correction Model (VECM)

requires selection of an appropriate lag length. The number of lags in the model is determined

according VAR Lag order Selection Criteria, which include Akaike Information Criterion

(AIC); Schwarz Information Criterion (SIC) and Hannan-Quinn Information Criterion (HQ).

All the aforementioned lag selection criteria unanimously adopted 3 lags for the model. Thus,

an error correction model using 3 lags is estimated and the results shows that the overall

goodness of fit of the model shown by the R2 is fairly good which means the independent

variables are able to explain almost 70% (68.4%) of the changes in the dependent variable.

This is true because in India, there are other variables apart from import and export

(international trade) that influence GDP. The F – statistic is high accounted for 4.11, and its

probability is highly statistically significance which suggests that the model is well fit. The

Durbin – Watson statistic which shows the presence or absence of autocorrelation among the

residuals is accounted for 1.7. This could be further checked using other test of correlation to

ensure consistency.

To obtain the probability value for proper decision making about our VECM, the system

equation was generated and the target model was further analyzed. The estimated coefficient

of co-integrating model (EC1) which is also the speed of adjustment toward long run

equilibrium is not only negative but its corresponding P value is also significance at 5 percent

level. This further explains that there exist long run influences of both the independent

variables on the dependent variable. The EC1 which is accounted for 0.83, with its probability

of 0.271, indicate that more than 80% of the deviation in the short run is corrected each

period.

To find whether there exist a short run influence of export and import on the economic

growth of India, Wald Test is employed by this study where each of the independent variables

were tested against the dependent variable. Table 4 shows the short run influence between

GDP and Export.




Table 4: Wald Test GDP and Export

Test Statistic Value Probability

F-statistic 6.657602 0.0029

Chi-square 19.97281 0.0002

Null hypothesis : no influence in the short run

Sources: Authors’ calculations using E-views

The null hypothesis is rejected since the P value of the Chi-square value is significance.

Therefore the study concludes that there is short run influence of export on Economic growth

in India within the period of study. Thus with respect to Export variable there are both long

run and short run influence.

Table 5: Wald Test GDP and Import

Test Statistic Value Probability

F-statistic 3.206795 0.0465

Chi-square 9.620384 0.0221

Null hypothesis : no influence in the short run

Sources: Authors’ calculations using E-views

The null hypothesis of no short run influence between GDP and Import is also rejected.

Compare to Export, the influence of Import to economic growth is less (the P value of Export

is 0.02% while that of Export is 2.21%). This goes in line with literature that certain level of

Import negatively affect economic growth. Therefore India has to put more effort with

regards to goods and services imported to ensure that only those items that will positively

affect growth are imported.

Meanwhile, the second research question which stated that whether the relationship

between the variables is a short run or long run phenomenon has now been answered. The

VECM and subsequent Wald Test confirmed that the relationships between Export, Import

and Economic Growth in India during the period under study are both short run as well as

long run phenomena.

The third research objective is to determine whether there is causal relationship between

the variables. This is tackled using Granger Test of Causality. The study performs this test

using bivariate autoregressive process for the variables. The essence is first, to find whether

there exist causal relationships among the variables, second, to find the direction of the

causation (if any). This will help us to confirm or reject ELG Hypothesis with respect to India

during the period under study.




Table 6: Granger Causality Test between GDP, Export and Import

Lags: 3

Null Hypothesis: Obs F-Statistic Probability

EXP does not Granger Cause GDP

GDP does not Granger Cause EXP

31

31

3.04076

3.11781

0.0484

0.0449

IMP does not Granger Cause GDP

GDP does not Granger Cause IMP

31

31

3.08469

7.05595

0.0464

0.0015

EXP does not Granger Cause IMP

IMP does not Granger Cause EXP

31

31

6.97740

1.79323

0.0015

0.1754

Source: Authors Calculations using E-views

The null hypotheses of no causality between GDP and EXP running from both directions

were rejected. This confirmed that between the periods under study there exists bidirectional

causality between GDP and Export. The results implies that export of goods and services

from India to international community help the economy to earn more foreign currency for

further investment, which in turn creates more income and employment. On the other hand,

economic growth helps in allocation of resources for both domestic export purposes (virtuous

cycle of wealth).

The study also noticed that the relationship between import and GDP during this period is

also bidirectional. That is, GDP causes Import and vice vasa. Thus, increase in income

stimulates the demand of foreign goods and services to Indians. On the other hand, importing

of machines and equipment’s especially those that cannot be produced in India help in

increasing the production capacity of the country.

The study further discovered a relationship between Export and Import. The null

hypothesis of Export does not Granger Because Import is strongly rejected with a probability

of 0.15 per cent. This shows that the proceeds from export help India to settle most of its

import. Thus, the more the proceeds from export the more likely import to change. On the

other hand, the null hypothesis of no causality running from import to export is accepted.

The outcomes of causality test answer our third and fourth questions. The third question

is to check the existence of causal relationship especially between GDP and Export which

stand to be our most focal point, while the fourth questions is to find out the direction(s) of

causality. As it was stated earlier, there exist a bidirectional relationship between GDP and

Export in India during the period 1980 – 2013. This result supports the Export Led Growth

Hypothesis in the case of India.




Before Granger Causality Test was carried out, the study employed other residuals

diagnostic check to ensure consistency.

First, the study checked whether the residuals are normally distributed using Normality

Test (Jarque-Bera approach). The null hypothesis of normally distributed of the residuals

should only be accepted if the probability is greater than 5%.

Table 7: Normality Test

0

2

4

6

8

10

-100 -75 -50 -25 0 25 50 75 100 125

Series: ResidualsSample 1984 2013Observations 30

Mean 2.18e-14Median -7.865257Maximum 121.2954Minimum -78.16211Std. Dev. 49.08152Skewness 0.735463Kurtosis 3.112597

Jarque-Bera 2.720379Probability 0.256612

The study accepts the null hypothesis of normal distribution of the residual since the

probability of Jacque-Bera is more than 5 percent.

Second, since the study is dealing with time series, the possibility of autocorrelation is

high. Therefore there is need to test the residuals for autocorrelation. The study employed LM

Test of residuals serial autocorrelations adopted by Breusch-Godfrey.

Table 8: Breusch-Godfrey Serial Correlation LM Test

F–Statistic 1.243527 Probability 0.3268

Observations R-Squared 5.672282 Probability 0.1287

Null hypothesis: No serial correlation

Source: Author’s calculations

The null hypothesis of no serial correlations is accepted with the probability of observed

R-Squared of 12 percent (Which is more than 5% state by the thumb rule of the LM test).

This shows that the study is not suffering from autocorrelation.




Third, to ensure consistency, the study further employed Breusch-Pagan-Godfrey

Heteroskedastic Test which is shown in table 9.

Table 9: Heteroskedasticity Test: Breusch-Pagan-Godfrey

F–Statistic 0.803520 Probability 0.6441

Observations R-Squared 10.85746 Probability 0.5412

Null hypothesis: No Heteroskedasticity

The probability of the observations R-Squared also implies that the null hypothesis of no

Heteroskedasticity is accepted since it is more than 5 percent. Thus the study is not suffering

from Heteroskedasticity.

10. Conclusion and Policy Implications

The purpose of this study is to test the applicability of Export Led Growth Hypothesis

(ELG) with respect to India during the period of 1980-2013. The variables used represent the

economic growth and international trade.

The results of Johansen Cointegretion Test shows that during the period under study, the

variables are cointegrated and VECM further indicate both short run and long run influence of

Import and Export on the GDP.

The directions of causality were detected using Granger Test of causality which indicates

bidirectional causality between GDP and Export. This shows that export causes economic

growth which also turns around to cause export. By implication the ELG Hypothesis can said

to be applicable here in India during the period under study. Although the focal point of the

study is GDP and Export (components of ELG) it is important to report that GDP also has

bidirectional causality with import. This indicates that some of the goods and services

imported such as oil, machines, engines and other electronic equipment, plastic, organic

chemicals, ships boats etc stimulate economic growth. On the other hand, increase in income

increase the need for foreign goods especially those that cannot be produced domestically.

The policy implication of the positive association between exports and economic growth

reveals that economic reform policies and the shift towards a free market helped the economy

to reallocate its resources to productive uses. But that does not means there are no other issues

that need to be addressed. These include further trade liberalization, further tariff revisions,

non- tariff barriers, exchange rate policies, the building up of an efficient service

infrastructure, improve in technology through research and development, balance growth

path, international jealous and so on.




Recently, a lot of expensive foreign super markets, restaurant etc are increasing all over

the country and they imports their products which in most cases can be produced

domestically. This instigates conspicuous consumptions, reduce the level of saving and

investments, and increase the volume of import and perhaps affect economic growth.

The outcome of this study goes in line with earlier empirical studies such as Emery (1967,

1968), Syron and Walsh (1968), Seven (1968), Kravis (1970), Michaely (1977), Heller and

Porter (1978), Bhagwati (1978) and Krueger (1978) Thurayia (2004), and Elbeydi, Hamuda

and Gazda (2010). Other studies about India that support ELG include, Ghatak and Price

(1997), Nidugala (2001), Dash (2009); Narayan Chandra Pradhan (2010). However, there are

number of studies that oppose to these findings such as Mallick (1996), Asafu-Adjaye et al.

(1999), Dhawan and Biswal (1999).

References

Arif Billah Dar Niyati Bhanja Amaresh Samantaraya, 2013, Export Led Growth or Growth

Led Export Hypothesis in India.

Emilio J. Medina-Smith, 2001, Is the Export-Led Growth Hypothesis Valid for Developing

Countries? A Case Study of Costa Rica, study series No 7.

Fouad Abou-Stait 2005, Are Exports the Engine of Economic Growth? Applications of

Cointegration and Causality. Economic Research working Paper. No 76.

Gujarati D.N, Porter D.C, Gunasekar S, 2013, Basic Econometrics, fifth edition, McGraw Hill

Education (India) Pvt Limited, New Delhi.

Gujarati Damodar, 2011, Econometrics By Example, Replika Press Pvt Ltd.

Haydory A.A, Salahuddin G, 2009, Export, Import, Remittance and Growth in Bangladesh:

An Empirical Analysis. Trade and Development Reviews vol. 2, (2): 79-92.

Ministry for Commerce and Industry, Government of India.

Mishra P.K, 2010, A Dynamics of Relationship Between Exports and Economic Growth in

India, IJESAR vol. 4 (2): 53-70.

Narayan Chandra P. 2010, Export and Economic Growth: An Examination of ELG

Hypothesis for India, RBI occasional papers vol. 31 (3).

Per-Ola Maneschiöld, 2008, A Note on the Export-Led Growth Hypothesis: A Time series

Approach. Cuadernos De Economia, Vol. 45, pp. 293-302.

Priyanka Sahni, Atri V. N. 2012, Export-Led Growth in India: An Empirical Investigation.

IJM vol. 2(7).

Sangho Kim, Hyunjoon Lim, and Donghyun Park, 2007, Could Import be Beneficial for

Economic Growth: Some Evidence from Republic of Korea. Working paper series no 103.




Shirazi N. S, Abdulmanap T. A. 2005, Export-Led Growth Hypothesis: Further Econometric

Evidence from South Asia.

Trading Economics.com.

Uddin, M.G.S., Khan, S.A., and Alam, M.M. 2010. An Empirical Study on Export, Import

and Economic Growth in Bhutan, Indian Development Review, Vol. 8(1), pp. 95-104. (ISSN

0972-9437; Publisher- Serials Publications, India; Indexed in EconLit).

Wikipeadia, the Free Encyclopedia.

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