Exports, Imports and Economic Growth in India.

László Kónya and

Jai Pal Singh

Discussion Paper No. A06.06 ISBN 1 92094 8112 ISSN 1441 3213 December 2006

Exports, Imports and Economic Growth in India

László Kónya1 and Jai Pal Singh2

1 Department of Economics and Finance

La Trobe University, Melbourne, Australia

2 Department of Business Management CCS Haryana Agricultural University, Hisar, India

December 2006

Abstract

With the advent of WTO, India entered into the era of trade reforms in 1991 and is moving gradually towards an open economy. It is widely believed that export and import growth is crucial in providing the impetus for economic growth in developing countries and imports provide the important 'virtuous' link between trade and output growth. Therefore, our aim, here, is to address the export/import-led growth and growth-driven export/import hypotheses for India. In spite of some ambiguity, the results clearly show that exports and imports Granger-cause GDP, both individually and jointly, lending support to the export/import led growth hypotheses. There is also some indication of GDP and exports jointly Granger-causing imports, and GDP and imports jointly Granger-causing exports, but the growth driven export/import hypotheses seem implausible. A possible reason for the results is the favourable trade environment of India. JEL Classification Numbers: C22, C32, F14, F43, O11 Key Words: Export, Import, Economic Growth, India, Unit root, Cointegration, Causality

1. Introduction

Since the early 1960s both policy makers and academics have shown great interest in exploring the possible relationship between international trade and economic growth. The reason is obvious. Nations are concerned about improving the quality of life of their countrymen, which mainly comes from overall, i.e. macroeconomic, development in a highly competitive and globalised world. Thus, creating wealth, increasing GDP is of prime importance for any economy. There are many different approaches to achieve this goal, though not a single foolproof. One possibility is to find new export markets for goods and services, as exports, along with the imports of new technologies, is an important engine of development. This strategy, however, raises the question: should a country promote exports and/or imports to speed up economic development and growth, or should it primarily focus on economic growth to generate international trade?

In the literature there has been considerable debate on the export-led growth (ELG) and growth-driven exports (GDE) hypotheses, with special regard to their implications on development policies and international trade. As reported by Giles and Williams (2000a), the story goes back at least to Nurkse (1961). A large number of empirical studies have focused on this issue, some of them using Indian data. Giles and Williams (2000a, 2000b) and Ahmad (2001) offer almost exhaustive and comprehensive reviews of these studies, highlighting their similarities and differences. In 1991, with the advent of WTO, India has entered into the era of trade reforms and has been moving gradually towards an open economy since then. It is widely believed that exports are crucial in providing the impetus for economic growth in developing countries. Thus, export-led growth has been put forward as an efficient alternative to inward-oriented strategy of development. Outward orientation is said to lead to higher total factor productivity growth (Bhagwati 1978, Krueger 1978, Kavoussi 1984, Ram, 1987) and encourages capital material investment including foreign direct investment. The pressure to compete with the best in the world may lead to better products and service quality and force the domestic producers to reduce inefficiencies. For example, foreign exchange liberalisation, which is an important component of the export-led growth strategy, is likely to reduce the allocation inefficiencies of exchange control. MacDonald (1994) argues that the imports of final and intermediate goods will force domestic producers to innovate and increase their efficiency to compete with foreign imports. Anoruo and Ahmad (2000), referring to Esfahani (1991) and Ram (1990), note that imports have positive influence on economic growth. Imports of capital goods are especially important for developing countries which depend on foreign capital for their economic development programmes. However, to be beneficial, imported capital must be productively engaged in the production of goods and services. Reviewing the relevant literature, Nidugala (2000) provided the following explanations for the importance of the ELG strategy: i) Exports enable an economy to specialise in the production of goods in which it has comparative advantage, resulting in optimal allocation of resources and enhanced overall productivity; ii) Foreign trade can be beneficial through its effect on balance of payments; iii) Trade can expand production possibilities through its effect on competition, access to new technologies and ideas (i.e. through the so-called 'dynamic gains from trade'); iv) Exports enable imports of essential raw materials and capital goods, thus increase the investment in the economy and thereby lead to higher output; v) Foreign

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competition creates strong pressure for innovation and efficiency in both export and import competing industries, thereby raising the productivity of sectors not directly exposed to foreign competition; vi) Economic success under outward orientation depends on innovation and efficiency, rather than on the directly unproductive profit seeking activities observed under import substitution; vii) Export promotion has increasing marginal returns while import substitution has decreasing marginal returns over time. Piana (2001), while discussing exports, advocates that increasing exports raise production, GDP, and employment. In turn, through the Keynesian multiplier effect, it engenders higher consumption and production, giving rise to a positive feedback loop. Probably, imports will also rise as a consequence. On the other hand, Thangavelu and Rajaguru (2004) suggest that trade has an important impact on productivity and output growth in the economy, however it is imports that provide the important 'virtuous' link between trade and output growth. The current study is a modest attempt to further investigate the relationship between Indian exports, imports and GDP growth, and to re-address the export-led growth (ELG), import-led growth (ILG), and growth driven export/import (GDE/GDI) hypotheses. Specifically, our aim is to study the potentially causal relationship between the logarithms of exports, imports, and GDP (all measured at current prices) in India from 1951/52 to 2003/2004, and to test whether: • Export and/or import and GDP are cointegrated? • Export and/or import Granger cause GDP or vice versa? Since our findings have economic policy implications on the Indian trade policy, this paper is expected to make a useful contribution to the empirical literature. The applied methodology is mainly based on the framework of Kónya (2004a, 2004b) and contributes to the existing literature in the following manner. Firstly, most studies that tested the ELG, ILG and GDE/GDI hypotheses for India, either did not cover the liberalisation (post-1991) period at all, or did so but only for four to five years at most. The current study is based on a reasonably long sample period, spanning from 1950/51 to 2003/2004. This sample period includes 13 years of the post-reform era, making possible to capture the effects of liberalisation on exports, imports and output growth, if there is any effect at all. Secondly, most of the earlier studies have been based on a bivariate framework. An important feature of this paper is the inclusion of imports as an endogenous variable in a vector autoregressive framework, making possible to test the ILG and GDI hypotheses as advocated by Thangavelu and Rajaguru (2004). Thirdly, depending on the time series properties of the data, causality is usually tested with standard Wald tests within vector error correction (VEC) or autoregressive (VAR) models in levels and/or in first-differences. The disadvantage of this strategy is that the final outcome might heavily depend on the preliminary test results which, themselves, are often uncertain and misleading. In this paper, in order to reduce the impact of pre-testing on the conclusions regarding causality, the modified Wald (MWald) test of Toda and Yamamoto (1995) and Dolado and Lütkepohl (1996) is also used in augmented level VAR systems. The advantage of this procedure is that it is valid even under uncertainty about integration and cointegration. In spite of some ambiguity, our results clearly show that in case of India exports and imports Granger-cause GDP, both individually and jointly, lending support to the ELG/ILG hypotheses. There is also some indication of GDP and exports jointly Granger-causing imports, and GDP and imports jointly Granger-causing exports, but the GDE/GDI hypotheses seem implausible.

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A possible explanation of these results is the favourable trade environment of India. Sharma (2000) has noted several factors: real depreciation of exchange rate, liberalisation in investment policy (especially from the early eighties), and provision of export subsidies to reduce the anti-export bias created by the import-substitution policy. A sharp devaluation of rupee since the early nineties has further strengthened export growth. Moreover, in July 1991 India opened up its market by lowering tariff and non-tariff barriers and by liberalizing investment policy. Piana (2001) has noted that in most countries exports have been growing much faster than GDP and, as a consequence, the share of exports in GDP is much larger nowadays than 30 years ago. Piana (2001) also notes that higher exports increase production, GDP, and employment, provided that exports do not simply replace production previously directed to domestic demand. This hypothesis seems to be reasonable in the case of India as the 76% export share in 1997 was due to manufactured goods (Sharma 2000). If exports require raw materials, semi-manufactured goods, and technology, like in India, then export growth is expected to increase imports as well, further inducing GDP growth. The rest of the paper is organized as follows. Section 2 presents a review of studies related to India. The methodological issues are discussed in Section 3, while the sources and properties of the data are described in Section 4. The Granger causality test results are reported and discussed in Section 5. Finally, the summary conclusions and policy implications can be found in Section 6. 2. Review of Studies Related to India

India remained a protected economy for quite a long time that had been described as an ‘import substituting country par excellence’ (Rodrik 1996). Prior to the 1990s, her import regime was dominated by quantitative restrictions on imports and a highly protectionist import tariff structure. The World Bank included India in the list of ‘strongly inward-oriented’ countries, meaning that the overall incentive structure strongly favoured production for the domestic market (Dutta and Ahmed, 2004). Nevertheless, the Indian economy has been undergoing substantial changes since 1991 (Dean et al. 1994). Almost all areas of the economy have been opened to both domestic and foreign private investment, import licensing restrictions on intermediaries and capital goods have been mostly eliminated, tariffs have been significantly reduced, and full convertibility of foreign exchange earnings for current account transactions has been established (Dutta 1998).

Table 1 summarises twenty-nine export-growth time-series studies published between 1978 and 2005 related to India. Using various time-series techniques (unit-root and cointegration tests, single-equation, VAR, EC modelling) they focus either exclusively on India, or on a group of countries, including India. Though the results are sometimes controversial and sensitive to model specification, four studies found support for a significant positive correlation between exports and economic growth, eight for the export-led growth hypothesis, seven for the growth-driven export hypothesis, and two for two-way causality between exports and growth.

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Table 1: Time–Series Studies of Exports and Growth Related to India

Reference Variables Period Conclusion Krueger(1978) Real GNP, real exports relative to average

exports 1954-71 PEG

Schenzler (1982) Real GDP, real exports, export share 1950-79 PEG Jung and Marshall (1985)

Real GDP, GNP, export 1950-81 NC

Ram (1987) Real GDP, exports, % share of changes in exports in GDP

1960-82 No PEG

Nandi and Biswas (1991)

real GDP, export growth 1960-85 ELG

Salvatore and Hatcher (1991)

Real GDP, exports 1963-85, 1963-73, 1973-85

PEG

Hutchison and Singh (1992)

Real GDP, non-export GDP and exports 1950-85 NC in bivariate, GDE in trivariate

Dodaro (1993) Real GDP, real exports of goods and non-factor services

1967-86 NC

Dutt and Ghosh (1994) Real GDP, GNP, exports 1953-91 cointegration Sharma and Dhakal (1994)

Real GDP, exports 1960-88 ELG

Bhatt (1995) Export, GDP 1950-93 TWC Rashid (1995) Real GDP, exports 1960-89 No PEG Dutt and Ghosh (1996) Real GDP, GNP, exports 1953-91 No-cointegration Mallick (1996) Real GNP, exports 1951-92 GDE Riezman et al. (1996) GDP, exports 1950-90 ELG in bivariate,

NC in trivariate Xu (1996) Real GDP, exports 1951-90 ELG Dhananjayan and Devi (1997)

Real GNP, total exports, manufactured commodity exports (MCE), MNC as % of total exports

1981-94 PEG

Ghatak and Price (1997)

Real GDP, exports, imports 1960-92 GDE

Rehman and Mustafa (1997)

Real GDP, exports 1965-94 GDE, both in short-run and long-run

Islam (1998) Real GDP, proportion of export earnings in GDP, change in share of non-export in GDP, other variables

1967-91 ELG in multivariate

Asafu-Adjaye and Chakraborty (1999)

Exports, real output 1960-94 NC

Dhawan and Biswal (1999)

Real GDP, exports, terms of trade 1961-93 GDE

Ekanayake (1999) Real GDP, exports 1960-96 TWC, GDE in short-run

Anwer and Sampath (2000)

Real GDP, exports 1960-92 NC

Nidugala (2000) Real GDP, exports, imports and other variables

1960-89 ELG

Kemal et al. (2002) Real GDP, exports 1960-98 ELG in long-run, GDE in short-run

Dutta and Ahmad Real GDP, imports, import price 1971-95 GDI, liberalisation

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(2004) had little impact on import demand

Love and Chandra (2005)

Real GDP, exports 1950-98 ELG

Sharma and Panagiotidis (2005)

Real GDP, exports, imports, gross capital formation, employment

1971-01 NC

Note: PEG = significant positive correlation between exports and economic growth; ELG = export-led growth, GDE= growth-driven export, NC = no-causality between exports and growth, TWC = two-way causality between exports and growth.

3. Methodologies

As mentioned in the Introduction, this paper aims to test the ELG/ILG and GDE/GDI hypotheses for India using the natural logarithms of exports, imports, and GDP measured at current prices between 1951/52 and 2003/2004.1 In order to re-enforce the causality test results, two complementary strategies are applied. The first one, referred to as the indirect approach, assumes that the variables are stationary or can be made so by differencing, and causality is tested with standard Wald tests within VAR (in levels and/or in first-differences) or VEC models. The second strategy suggested by Toda and Yamamoto (1995) and Dolado and Lütkepohl (1996), and referred to as the direct approach, requires less pretesting and is applied in an appropriately augmented level VAR model.

As regards the first strategy, prior to testing for Granger causality, it is important to establish the time-series properties of the data. In particular, the order of integration2 and the existence of common trend(s) are of major importance. Unit Root/Stationarity

To start with, following Engle and Granger (1987), we have tested all three time series for unit roots versus stationarity. This issue is of great importance since, while standard econometric methodologies assume stationarity, many economic time series are non-stationary rendering the usual statistical tests inappropriate and the inferences erroneous and misleading. For instance, in the presence of non-stationary variables the ordinary least squares (OLS) estimation method gives rise to spurious regressions, unless these variables are cointegrated (Granger and Newbold 1974). Due to the generally low power of unit root tests, we have performed five different tests, namely, the augmented Dickey-Fuller (ADF) test, the Dickey-Fuller test with GLS detrending (DF-GLS), the Elliot-Rothenberg-Stock (ERS) point optimal test, the Dickey-Pantula (DP) test for at most two unit roots, and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test for stationarity.3 As our analyses demonstrate, these tests often generate contradicting outcomes and none of them is universally superior.

1 We use logarithms because this transformation is the most commonly used variance stabilizing tool

for variables that have wide range (Weisberg, 1980). 2 Time series Xt is said to be integrated of order d, I(d), if it achieves stationarity after being differenced d times. For d = 0, Xt is stationary in levels and no differencing is necessary, while for d = 1 Xt is non-stationary but its first difference is stationary. 3 About these tests see e.g. Maddala and Kim (1998), Part II.

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The most commonly used unit root test is the ADF test. The simple Dickey-Fuller (DF) test is valid only if the series has been generated by a first-order autoregressive process, while the ADF test, by adding lagged differences of the dependent variable to the right-hand side of the test regression, incorporates a parametric correction for higher-order autocorrelation. The DF-GLS test is a simple modification of ADF test, in which the data are detrended so that all explanatory variables are taken out of the data prior to running the test regression, while the ERS point optimal test is based on a local to unity detrending of the time series. The DP test allows for multiple unit roots. It is a sequential procedure which starts with the supposedly highest order of integration and reduces it gradually. Assuming that there are at the most two unit roots, first we tested for two unit roots against one, and then, provided that the possibility of two unit roots is rejected, we tested for only one unit root against none. Finally, unlike in the ADF, DF-GLS, ERS and DP tests, in the KPSS test tested series is assumed to be trend-stationary under the null hypothesis and integrated of order one under the alternative hypothesis. By exchanging the null and alternative hypotheses, the KPSS test provides a useful comparison to the other four tests. Performing these tests, we faced two practical problems. First, there is the choice of exogenous variables (a constant, a constant and a linear time trend, or neither) in the test regression. Second, we had to specify the lag length, i.e. the number of lagged differences to be added to the test regression.4 Cointegration

It is well documented in the literature that most economic variables are non-stationary in their levels but stationary in their first differences. If individual time series turn out to be non-stationary in their levels i.e. contain stochastic trends, it is still possible that these stochastic trends are common across the series, leading to one or more stationary combinations of the level series. The existence of a long-run equilibrium (stationary) relationship among economic variables is referred to in the economic literature as cointegration. In the presence of integrated variables, a necessary pre-condition to test for causality is to check whether the variables are cointegrated. Granger (1986), Engle and Granger (1987), and Engle and Yoo (1987) have investigated the relationship between variables when common trend exists between them. In particular, Engle and Granger (1987) have shown that if two series, say {yt} and {xt}, are both I(d), then their linear combination, zt = yt - αxt, will, in general, be I(d). However, if there is a constant α where {zt} is I(d-b), and b > 0, then yt and xt are said to be cointegrated, denoted as CI(d, b). Specifically, if yt and xt are CI(1,1), then they share the same stochastic trend and thus have a

4 We decided to include enough number of lags to remove serial correlation in the residuals. In particular, in the ADF, DF-GLS, ERS and DP regressions the optimum lag lengths were primarily selected by minimising the modified Akaike and Schwarz information criterion. Subsequently, the residuals were tested for autocorrelation using the Breusch-Godfrey LM and the Ljung-Box portmanteau tests, and when autocorrelation was detected, the lag length was increased (up to a maximum of 8 years) in order to whiten the residuals. In the KPSS test we used the Bartlett spectral window kernel-based estimator to obtain a consistent estimate of the variance and selected the bandwidth by using the Newey-West method.

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long-run equilibrium relationship. Moreover, as Engle and Granger (1987) have shown, there must be either unidirectional or bi-directional Granger causality between them. 5 To this end, we have applied two maximum likelihood tests, the trace and maximum eigenvalue tests, advocated by Johansen (1988) and Johansen and Juselius (1990). The first tests the null hypothesis of at most r cointegrating vectors against the alternative hypothesis of more than r cointegrating vectors, while the second tests the null hypothesis of exactly r cointegrating vectors against the alternative hypothesis of r + 1 cointegrating vectors. Granger Causality

The concept of Granger causality, more precisely precedence, is based on the idea that a cause cannot come after its effect. More precisely, variable X is said to Granger-cause an other variable, Y, if the future value of Y (yt+1) is conditional on the past values of X (xt-1, xt-2, ... , x0) and thus the history of X is likely to help predict Y.

The application of the indirect approach to testing for Granger causality requires us to distinguish three possibilities. Assuming that both variables are stationary, Granger causality between them can be tested within the following level VAR model:

1 1, , 1, ,1 1

2 2, , 2, ,1 1

L L

t l i t l l i t l tl l

L L

t l i t l l i t ll l

y y x

x y x

1,

2,t

α β γ ε

α β γ

− −= =

− −= =

= + + +

= + + +

∑ ∑

∑ ∑ ε (1)

where index t refers to the time period (t = 1, ..., T ), l to the lag, and 1, , 2, ,,i t i tε ε are supposed to be white-noise errors (i.e. they have zero means, constant variances and are individually serially uncorrelated) that may be correlated with each other. With respect to this system, there is one-way Granger causality running from X to Y if in the first equation not all 1,lγ 's are zero but in the second all 2,lβ 's are zero, there is one-way Granger causality from Y to X if in the first equation all 1,lγ 's are zero but in the second not all

2,lβ 's are zero, there is two-way Granger causality between Y and X if neither all 2,lβ 's nor all 1,lγ 's are zero, and there is no Granger causality between Y and X if all 2,lβ 's and 1,lγ 's are zero. If {yt} and {xt}, are both I(1) but are not cointegrated, then their first differences are stationary, hence Granger causality between them can be tested within a VAR model in first differences:

5 To be cointegrated, Xt and Yt must have the same order of integration (Engle and Granger, 1987; Granger, 1986). However, in case of more than two variables cointegration might occur even if the variables are integrated of different orders. For example, two I(2) and a third I(1) variable can be cointegrated if the former two has an I(1) combination which in turn is cointegrated with the third variable.

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1 1, , 1, ,1 1

2 2, , 2, ,1 1

L L

t l i t l l i t l tl l

L L

t l i t l l i t l tl l

y y x

x y x

1,

2,

α β γ ε

α β γ

− −= =

− −= =

∆ = + ∆ + ∆ +

∆ = + ∆ + ∆ +

∑ ∑

∑ ∑ ε

,

(2)

Finally, if {yt} and {xt}, are CI(1,1), then they have an error-correction (EC) representation

1 1 1 1 1, , 1, , 1,1 1

2 2 1 1 2, , 2, , 21 1

( )

( )

L L

t t t l i t l l i t l tl l

L L

t t t l i t l l i t l tl l

y y x y x

x y x y x

α λ δ β γ ε

α λ δ β γ

− − − −= =

− − − −= =

∆ = + − + ∆ + ∆ +

∆ = + − + ∆ + ∆ +

∑ ∑

∑ ∑ ε

1)

(3)

and the inclusion of the EC term 1( t ty xδ− −− provides an additional channel through which the variables can Granger-cause each other. Since in each of these cases all the variables included in the system are stationary, the direct approach to testing for Granger causality is based on standard Wald tests for zero restrictions on the coefficients of the appropriate VAR or VEC model. In the presence of I(1) variables, the Wald tests are likely to have non-standard asymptotic properties. However, as Toda and Yamamoto (1995) and Dolado and Lütkepohl (1996) pointed out, Wald tests that do not restrict the coefficients of all lagged terms under the null hypothesis still have their usual chi-square distribution. Consequently, given that all variables are I(0) or I(1), the direct approach to testing for Granger causality is performed by a so-called modified Wald (MWald) procedure, where a level VAR model augmented by an extra, redundant lag (VARAL) is estimated and a Wald test is performed on the first L non-redundant lags (i.e. t-1, t-2, …, t-L). 4. Data and its Properties Data Sources

Annual data for fifty-four years ranging from 1950/51 to 2003/04 were used for testing the various hypotheses. The data on Indian exports, imports and gross domestic product (GDP) are measured in current prices and in local currency (Indian rupees). The data were collected from several publications and websites, such as the Directorate General of Commercial Intelligence and Statistics (Ministry of Commerce, Government of India), National Accounts Statistics (Central Statistical Organisation, Ministry of Statistics and Programme implementation, Government of India), Planning Commission of India, Reserve Bank of India, and various issues of Economic Surveys. The natural logarithms of these time series, denoted as LNGDP, LNEXP and LNINMP, respectively, are plotted in Figure 1.

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5

6

7

8

9

10

11

12

13

14

15

1950 1960 1970 1980 1990 2000

LNGDP LNEXPORT LNIMPORT

Figure 1: The Logarithms of Indian GDP, Exports and Imports Unit-root / Stationarity Test Results

Unit-root / stationarity tests have been performed on the levels and on the first (also on the second, if necessary) differences of LNGDP, LNEXP and LNINMP. Since each series exhibits a trend, the level series were tested assuming that the data generating processes have a linear trend component, while in case of the first differences only drift terms were used. The results are summarized in Tables 2 trough 4.

Starting with Table 2, the DF-GLS (with lags 1 and 3), the ERS (with lags 0 and 1), the DP (with lag 1) and the KPSS tests (with lag 5) all suggest that LNGDP has a unit root, implying non stationarity in levels. However, the ADF test (with lags 0 and 1) suggests that LNGDP is (trend) stationary, i.e. it is I(0), while the DP test with 2 lags provides no evidence against two unit roots in LNGDP, i.e. it might be I(2). The results are unambiguous for LNEXP and LNIMP; all tests suggest that they are non-stationary because they have a unit root. Unit root and stationarity tests for the first-differences of the logarithms of GDP (∆LNGDP), export (∆LNEXP) and import (∆LNIMP) are displayed in Table 3. The results are again mixed. The ADF and ERS tests with zero lag and the DF-GLS test suggest that ∆LNGDP is stationary, i.e. LNGDP is at most I(1). However, the ADF and ERS tests with 2 lags and the KPSS test imply that ∆LNGDP is not stationary, i.e. LNGDP is at least I(1). As regards the other two series, all tests but KPSS reject the presence of a unit root in ∆LNEXP and ∆LNIMP, so LNEXP and LNIMP are likely I(1).

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Table 2: Unit-Root and Stationarity Tests for the Logarithms of GDP, Exports and Imports

Variables

LNGDP LNEXP LNIMP Unit-Root / Stationarity Tests

Lag Test stat. Lag Test stat. Lag Test stat. ADF 0

1 -3.250*

-3.323*0 -2.686 1 -2.908

DF-GLS 1 3

-1.032 -1.310

0 2

-0.849 -0.945

1 -1.706

ERS 0 1

160.4 158.9

0 144.1 1 45.46

DP step 1 1 2

-5.328***

-2.568 0 1

-5.215***

-3.540**0 -6.153***

step 2 1 1.769 0 1

2.377 1.402

0 1.851

KPSS 5 0.246*** 5 0.230*** 5 0.236***

Notes: a) ADF: Augmented Dickey-Fuller test; DF-GLS: Dickey-Fuller test with GLS detrending; ERS: Elliot-Rothenberg-Stock Point Optimal test; KPSS: Kwiatkowski-Phillips-Schmidt-Shin test; DP: Dickey-Pantula test for at most two unit roots.

b) In all tests except KPSS, lag refers to lags of the first differences. Allowing for a maximum lag length of 10 years, the lag lengths are selected by minimizing the Akaike and Schwarz Information Criteria. In KPSS, lag denotes the bandwidth selected on the basis of the Newey-West method using Bartlett kernel.

c) *, **, and *** indicate significance at the 10, 5, and 1% levels. d) Each test equation has a deterministic linear trend.

Table 3: Unit-Root and Stationarity Tests for the First-Difference of the

Logarithms of GDP, Exports, and Imports

Variables

∆LNGDP ∆LNEXP ∆LNIMP Unit-Root / Stationarity Tests

Lag Test stat. Lag Test stat. Lag Test stat. ADF 0

2 -5.328***

-2.568 0 1

-5.215***

-3.540**0 -6.153***

DF-GLS 2 -1.950** 0 1

-5.073***

-3.579**0 -3.928***

ERS 0 2

1.490***

4.865 0 1

1.096***

2.090**0 2.656*

KPSS 5 0.604** 4 0.633** 0 0.507**

Notes: See notes a-c, Table 2. d) Each test equation has a constant term.

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Table 4: Unit-Root and Stationarity Tests for the Second-Difference of the Logarithm of GDP

∆2LNGDP Unit-Root /

Stationarity Tests Lag Test stat. ADF 1 -9.532***

DF-GLS 10 -1.037

ERS 1 0.795 KPSS 0 0.014

Notes: See notes a-c, Table 2. d) Each test equation has a constant term.

Finally, we also performed the unit-root / stationarity tests on the second difference of the log of GDP (∆2LNGDP). This time the ADF and KPSS tests indicate stationarity, but the other three tests fail to reject the unit-root null hypothesis implying non-stationarity in the second differences (Table 4). However, the fact that the alleged optimal lag length reached the fairly generous a priori upper limit, i.e. 10 years, casts some doubt on the DF-GLS result.

All things considered we conclude that all three variables can be modelled as I(1), i.e. first-difference stationary series. Cointegration Test Results

The next step is to search for cointegration between LNGDP, LNEXP and LNIMP in order to find out whether they share the same stochastic trend(s). Following the recommendation of Riezmann et. al (1996) that imports are an important variable while considering causality between exports and growth, and that the omission of imports could lead to biased results, we tested for cointegration both within bivariate (LNGDP – LNEXP, LNGDP – LNIMP) and trivariate (LNGDP – LNEXP – LNIMP) frameworks. We have applied two cointegration tests, namely Johansen’s trace and maximum eigenvalue tests. The results are summarised in Table 5.

Both tests performed on the bivariate and trivariate systems strongly reject the null hypothesis of r = 0 (no-cointegration between the variables) in favour of cointegration between LNGDP and LNIMP, and between LNGDP, LNEXP and LNIMP, irrespective of the lag structure. In the trivariate system the null hypothesis of r = 1 can also be firmly rejected, while r = 2 is maintained by the tests. Consequently, LNGDP, LNEXP and LNIMP share two cointegrating relations. However, in the LNGDP – LNIMP bivariate system there is some evidence for a second cointegrating relation at zero lag, suggesting that for LNGDP and LNIMP a level VAR model might be appropriate. The tests for LNGDP and LNEXP show mixed evidence, the outcomes depend on the lag structure. Both tests reject null of r = 0 for zero lag, but neither rejects it for two lags. In brief, the Johansen cointegration tests imply long-run association between (the logarithms of) GDP and imports in a bivariate framework and between GDP, export and import in a trivariate framework, while the results are ambiguous for GDP and export.

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Table 5: Cointegration Tests for the Logarithms of GDP, Exports and Imports

Cointegration Tests Variables

LNGDP LNEXP

LNGDP LNIMP

LNGDP LNEXP LNIMP

Lag H0 Test stat. Lag H0 Test stat. Lag H0 Test stat. JT 0

r = 0 r ≤ 1

28.010***

0.711

0

r = 0 r ≤ 1

17.747***

2.772*

0

r = 0 r ≤ 1 r ≤ 2

43.6(19**)*

14.094**

1.438 2

r = 0 r ≤ 1

10.813 (0.062)

6

r = 0 r ≤ 1

19.698***

0.033 10 r = 0

r ≤ 1 r ≤ 2

109.243***

37.795***

0.695 JME 0

r = 0 r = 1

27.299***

0.711

0

r = 0 r = 1

14.974***

2.772*

0

r = 0 r = 1 r = 2

29.525***

12.656**

1.438 2

r = 0 r = 1

10.751 (0.062)

6

r = 0 r = 1

19.955***

0.033 10 r = 0

r = 1 r = 2

71.448***

37.146***

0.649

Notes: a) JT: Johansen’s Trace test; JME: Johansen’s Maximum Eigenvalue test.

b) Lag refers to lag of the first differences. Allowing for a maximum lag length of 10 years, the lag lengths are selected by minimising the Akaike and Schwarz Information Criteria.

c) *, **, and *** indicate significance at the 10, 5, and 1% levels. d) The level data are assumed to have linear trends but the cointegrating equations have

only intercepts (Case 3 in EViews 5). 5. Granger Causality Test Results

As stated earlier, this paper follows a similar approach to Kόnya (2004a, 2004b) to investigate the ELG, ILG and GDE/GDI hypotheses within bivariate and trivariate frameworks. In particular, we applied two strategies for testing causality. First, allowing for the existence and lack of cointegration alike, we tested for Granger causality between LNGDP and LNEXP; LNGDP and LNIMP; LNGDP, LNEXP and LNIMP by applying Wald tests within finite-order vector error correction models (VECM) and first-difference vector autoregressive (VARD) models. Second, we also performed a modified Wald (MWald) procedure advocated by Dolado and Lütkepohl (1996) and Toda and Yamamota (1995) in augmented level vector autoregressive (VARAL) models, which is valid for both cointegrated and non-cointegrated variables as. The Granger causality tests results under these two strategies are shown in Tables 6 through 9.

The results in Table 6 are based on the first strategy when both series are I(1) but not cointegrated (VARD) and when they are cointegrated (VECM) (Kόnya 2004a, pp. 79; 2004b, pp. 84). They imply two-way causality between LNGDP and LNEXP, and between LNGDP and LNIMP.

13

Table 6: WALD Granger Causality Tests for the Logarithms of GDP, Exports and Imports in VEC and First-Difference VAR Models of Two Endogenous Variables

Models

VECM VARD Null hypothesis Rank

Lag t-stat χ2-stat Lag χ2-stat 0 1

2 4.137**

3.026 LNGDP →⁄ LNEXP

1 0 2

5.203***

3.120***

0.083

0 1 2

8.313***

13.323***LNEXP →⁄ LNGDP

1 0 2

3.731***

1.306

12.806***

0 1 4

4.589**

4.756 LNGDP →⁄ LNIMP

1

0 6

3.196***

3.305***

14.133**

0 1 4

2.981*

13.776***LNIMP →⁄ LNGDP

1

0 6

3.236*** -0.418

17.656***

Notes: a) x →⁄ y indicates no Granger causality from x to y. b) Rank refers to the cointegrating rank and lag to lags of the first

differences. The optimal lag length is selected by minimising the Akaike and Schwarz Information Criteria in level VAR (for VECM) or in first-difference VAR (for VARD), allowing for a maximum lag length of 10 years. If the residuals generated by the optimal lag length are autocorrelated, heteroscedastic or non-normal, the lag length is gradually increased.

c) The models were subjected to three residual tests: Breusch-Godfrey LM test for general, higher order (up to order 10) ARMA errors, White test for heteroscedasticity (without cross terms) and Jarque-Bera test for normality. At the 5% significance level each model passed these tests, except the VARD(1) models.

d) In VECM Granger causality is tested by the t-statistic (which is asymptotically normal) on the speed of adjustment coefficient (α) and by a Wald (χ2) test on the coefficients of the lagged first-differences of the hypothesized causal variable. In VARD Granger causality is tested by a Wald (χ2) test on the coefficients of the lagged first-differences of the hypothesized causal variable.

e) *, **, and *** indicate significance at the 10, 5, and 1% levels. f) The level data are assumed to have linear trends but the cointegrating equations have

only intercepts (i.e. Case 3 in EViews 5). Next, we tested causality based on the direct approach with MWald test (Kόnya 2004a, pp. 80, 88), i.e. following the second strategy, which is based on an augmented vector autoregressive model in levels (VARAL). The results, shown in Table 7 suggest one-way causality from LNEXP to LNGDP and from LNIMP to LNGDP. Table 7: MWALD Granger Causality Tests for the Logarithms of GDP, Exports and Imports

in Augmented Level VAR Models of Two Endogenous Variables

14

Model

VARAL Null hypothesis

Lag Order χ2-stat 1

1 2

0.516 0.445

LNGDP →⁄ LNEXP

3 1 2

1.899 1.234

1

1 2

3.677*

4.680**LNEXP →⁄ LNGDP

3 1 2

9.708**

7.123*

1

1 2

1.087 1.498

LNGDP →⁄ LNIMP

7 1 2

11.624 9.593

1

1 2

3.334*

4.591**LNIMP →⁄ LNGDP

7 1 2

20.840***

17.227**

Notes: a) x →⁄ y indicates no Granger causality from x to y. b) Lag is the optimal lag order of an augmented level VAR (VARAL)

chosen by minimising the Akaike and Schwarz Information Criteria (p) and order is the highest degree of integration in the system (d). The order of VARAL is p+d. If the residuals generated by the optimal lag length are autocorrelated, heteroscedastic or non-normal, p is gradually increased.

c) The models were subjected to three residual tests: Breusch-Godfrey LM test for general, higher order (up to order 10) ARMA errors, White test for heteroscedasticity (without cross terms) and Jarque-Bera test for normality. At the 5% significance level each model passed these tests.

d) Granger causality is tested by a Wald (χ2) test on the coefficients of the first p lags of the hypothesized causal variable.

e) *, **, and *** indicate significance at the 10, 5, and 1% levels. f) The level data are assumed to have linear trends.

In Table 8 the Wald tests on the coefficients of the lagged first-differences of individual causal variables indicate one-way causality from LNEXP to LNGDP and from LNIMP to LNGDP. Interestingly, the Wald tests on the coefficients of the lagged first-differences of two causal variables at a time also suggest that LNGDP and LNIMP jointly Granger-cause LNEXP, LNGDP and LNEXP jointly cause LNIMP, and LNEXP, LNIMP jointly Granger-cause LNGDP.

15

Table 8: WALD Granger Causality Tests for the Logarithms of GDP, Exports and Imports in VEC Models of Three Endogenous Variables

Model

VECM Null hypothesis Rank

Lag t-stat χ2-stat

LNGDP →⁄ LNEXP 2 6 5.118 LNEXP →⁄ LNGDP 2 6 16.676**

LNIMP →⁄ LNGDP 2 6 17.679***

LNGDP →⁄ LNIMP 2 6 9.269

LNGDP, LNIMP →⁄ LNEXP 2 0

6

5.506***

-1.536 2.865***

-2.013**

15.050

LNGDP, LNEXP →⁄ LNIMP 2 0

6

3.088***

1.927*

1.395 0.828

12.101

LNEXP, LNIMP →⁄ LNGDP 2 0

6

3.335***

-1.925*

-0.829 -1.256

39.608***

Notes: a) x →⁄ y indicates no Granger causality from x to y. b) Rank refers to the cointegrating rank and lag to lags of the first

differences. The optimal lag length is selected by minimising the Akaike and Schwarz Information Criteria in level VAR (for VECM) or in first-difference VAR (for VARD), allowing for a maximum lag length of 6 years.

c) The models were subjected to three residual tests: Breusch-Godfrey LM test for general, higher order (up to order 10) ARMA errors, White test for heteroscedasticity (without cross terms) and Jarque-Bera test for normality. At the 5% significance level VECM(0) suffers from heteroscedasticity and VECM(6) has non-normal errors.

d) In VECM Granger causality is tested by a t-statistic (which is asymptotically normal) on the speed of adjustment coefficients (α1, α2) and by a Wald (χ2) test on the coefficients of the lagged first-differences of the hypothesized causal variable(s).

e) *, **, and *** indicate significance at the 10, 5, and 1% levels. f) The level data are assumed to have linear trends but the cointegrating equations have

only intercepts (i.e. Case 3 in EViews 5). Finally, the results in Table 9, based on the modified Wald (MWald) test, also indicate that LNEXP and LNIMP Granger-cause LNGDP both individually and jointly, while LNGDP does not cause either LNEXP or LNIMP.

16

Table 9: MWALD Granger Causality Tests for the Logarithms of GDP, Exports and Imports in Augmented Level VAR Models of Three Endogenous Variables

Model

VARAL(p+d) Null hypothesis

Lag Order χ2-stat 1 1

2 0.864 0.775

LNGDP →⁄ LNEXP

6 1 2

3.743 2.238

1 1 2

1.671 1.877

LNEXP →⁄ LNGDP

6 1 2

29.160***

21.859***

1 1 2

0.649 2.153

LNGDP →⁄ LNIMP

6 1 2

10.266 9.148

1 1 2

1.066 2.828*

LNIMP →⁄ LNGDP

6 1 2

24.083***

15.013**

1 1 2

0.974 1.317

LNGDP, LNIMP →⁄ LNEXP

6 1 2

8.703 7.652

1 1 2

1.931 4.950*

LNGDP, LNEXP →⁄ LNIMP

6 1 2

16.390 14.449

1 1 2

4.887*

7.546**LNEXP, LNIMP →⁄ LNGDP

6 1 2

58.943***

49.454***

Notes: a) x →⁄ y indicates no Granger causality from x to y. b) Lag is the optimal lag order of a level VAR chosen by minimising the

Akaike and Schwarz Information Criteria (p) and order is the highest degree of integration in the system (d).

c) The models were subjected to three residual tests: Breusch-Godfrey LM test for general, higher order (up to order 10) ARMA errors, White test for heteroscedasticity (without cross terms) and Jarque-Bera test for normality. At the 5% significance level VARAL(7) and VARAL(8) have non-normal errors.

d) Granger causality is tested by a Wald (χ2) test on the coefficients of the first p lags of the hypothesized causal variable.

d) *, **, and *** indicate significance at the 10, 5, and 1% levels. e) The level data are assumed to have linear trends.

17

Summing up, on the basis of the Wald and MWald tests performed in both bivariate and trivariate frameworks, there is strong evidence in favour of the ELG and ILG hypotheses, but not for the GDE/GDI hypotheses. 6. Conclusions and Policy Implications Summary Conclusions

In this study we aimed at exploring whether India experienced export/import-led growth, or growth-driven export/import, both or none, during the fifty-four years period spanning over 1951/52-2003/2004. Our data series are reasonably long and cover the pre- and post liberalisation periods alike, making it possible to capture the effects of measures to promote exports and output growth. We studied Granger causality between the logarithms of exports, imports and GDP. In order to re-enforce the results, we applied two complementary strategies. The first, indirect, approach assumes that the variables are stationary or can be made stationary by differencing. It makes use of pre-testing for unit roots and cointegration and, depending on the outcomes, testing for causality is carried out with Wald tests in VAR and/or VEC models in levels and/or first differences. The second, direct, approach is based on a modified Wald test without need to pre-test for unit roots and cointegration.

Based on these strategies, we are of the opinion that exports and imports, both jointly and individually, Granger-cause GDP (supporting the ELG and ILG hypotheses), as confirmed by the Wald and MWald tests in the bivariate and trivariate frameworks alike. There is also some evidence of GDP and exports jointly Granger-causing imports, and GDP and imports jointly Granger-causing exports, while the growth driven export/import (GDE/GDI) hypotheses seem implausible in case of India. Policy Implications for India

This study confirms that export and import growth has been instrumental in accelerating economic growth in India. The evidence of causality from exports to economic growth implies that exports can have positive effect on economic growth. Exports, for example, can boost output growth in the short-run by allowing the use of excess capacity in cases where domestic demand requires less than full capacity production. Based on outcome similar to ours, Nidugala (2000) favours the ongoing reforms regarding openness for faster economic growth and higher GDP in India. By further opening up her market and continuing the ongoing trade (export/import) promotion policy reforms, India can not only boost its economic growth further but can also fuel growth in the entire South Asian region. As suggested by Kemal et al. (2002), in the long-run exports can have beneficial effect on economic growth in a variety of ways. First, export production allows economies with narrow domestic markets to overcome size limitations and to reap economies of scale. Second, by relaxing the foreign exchange constraints, higher exports can permit higher imports of capital goods thereby strengthening the productive capacity of the economy. Third, exports lead to an improvement in economic efficiency by enhancing the degree of competition. Fourth, exports contribute to productivity gains through diffusion of technical knowledge and learning by doing. Further, export-oriented production and investment tend to take place in the most efficient sectors of the economy fostering a pattern of production that is consistent with a country's comparative advantages. Specialisation in these sectors improves productivity in the

18

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