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tandfonline/action/journalInformation?journalCode=rjte

The Journal of International Trade & Economic

Development

An International and Comparative Review

ISSN: 0963-8199 (Print) 1469-9559 (Online) Journal homepage: tandfonline/loi/rjte

India & South Asia: Geopolitics, regional trade and

economic growth spillovers

Rakesh Kumar

To cite this article: Rakesh Kumar (2019): India & South Asia: Geopolitics, regional trade and

economic growth spillovers, The Journal of International Trade & Economic Development, DOI:

10.1080/09638199.

To link to this article: doi/10.1080/09638199.2019.

Published online: 02 Jul 2019.

Submit your article to this journal

Article views: 89

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####### THE JOURNAL OF INTERNATIONAL TRADE & ECONOMIC DEVELOPMENT

doi/10.1080/09638199.2019.

India & South Asia: Geopolitics, regional trade and

economic growth spillovers

Rakesh Kumar

Department of Management Studies, Deen Dayal Upadhyaya College, University of Delhi, New Delhi,

India

####### ABSTRACT

The South Asian countries formed the regional trade bloc namely South Asian Asso-

ciation for Regional Cooperation (SAARC) with the aim to promote regional economic

cooperation through multilateral engagements. India which comes to be the largest

economy in the SAARC has posted impressive economic growth in the last decades.

As of now India stands major contributor to the exports and imports to/from South

Asia, having trade surplus with all other countries from the region. In this backdrop,

this paper presents the facts on India’s role in the economic development of South Asia

region while testing the potential spillovers of India’s trade and economic growth. We

utilize Autoregressive distributed lag (ARDL) bound test procedure for short and long

run causal relations during the period 1990–2016, hence raising the quality of statis-

tical inference. The results highlight that the economic growth and regional trade of

India are found significant short and long run spillovers on the economic growth of

Bangladesh, Sri Lanka, Nepal and Bhutan. The results are highly insightful for policy

implication which raises the attention towards the greater degree of trade openness

for balanced economic development in the region. India can act as engine of growth,

and thus requires to play key role in pushing forward the SAARC objectives through

political and diplomatic engagements.

KEYWORDS Trade; economic growth; SAARC; SAFTA; ARDL

JEL CLASSIFICATIONS F14, F43, O

ARTICLE HISTORY Received 6 September 2018; Accepted 21 June 2019

1. Introduction

The SAARC countries have rapidly globalized by deregulating their economic structure

to catch up the world economy in the last decades. India which comes to be the mem-

ber of SAARC, continues to top the list of the fastest growing economies in the world

in the last decades. According to World Economic Outlook (2018) India is world’s 7th

largest economy worldwide, with a nominal Gross Domestic Product (GDP) of US$2.

trillion after the United States, China, Japan, Germany, United Kingdom and France,

and is expected to become 5th largest economy in the world by 2022 with GDP worth

of US$ 3 trillion if the growth momentum continues. The International Monetary

CONTACT Rakesh Kumar saini_rakeshindia@yahoo.co Department of Management Studies, Deen Dayal Upadhyaya College, University of Delhi, Sector 3, Dwarka, New Delhi 110078, India

####### THE JOURNAL OF INTERNATIONAL TRADE & ECONOMIC DEVELOPMENT 3

Edwards 1992). They highlight that a country with a higher degree of economic open-

ness tend to grow at faster rate through technology absorption than that of a country

with lower degree of openness. For example, Bhagwati (1988) support the neo clas-

sical arguments that exports from the country promotes economic growth, which in

turn competition promotes human skills as well as technological base of the country.

Romer (1990) provides theoretical base for economic development through trade oppe-

ness which includes spillover effects produced by technological advancement, human

capital and investment in knowledge based sectors.

The spillover effect of trade, investment, and monetary variables are largely stud-

ied in developed countries. For example, Nasseh and Strauss (2000) support the long

run impact of economic activities such as production, business surveys of manufactur-

ing orders, short and long term interest while investigating six European economies.

They use the Variance decomposition method and found the strong explanatory power

of macroeconomic variables in forecasting of stock prices. Darrat and Zhong (2001)

find that multilateral trade arrangements such as NAFTA have promoted stock market

linkages of U., Canada, and Mexico.

Over the last decades several studies offers the results in support of arguments that

economic union converge the economic and financial variables. For example, Kim,

Moshirian, and Wu (2005) finds that the formation of European Union has promoted

the economic integration among the member countries through the convergence of

macroeconomic variables like trade and investment. Liu, Lin, and Lai (2006) investigates

whether the foreign trade differential among the trading countries causes the stock mar-

kets interdependence. The results highlight that the hypothesis is significantly held in the

European countries, but fails to be true in the Asian countries. Further, Shin and Sohan

(2006) examine the impact of trade and financial integration in East Asia in the context of

business cycle, the extent of risk sharing, and price co-movements. They find significant

trade and economic integration in the region. Yartey (2008) underlines the economic

and non economic determinants for economic development by using a panel data of

forty two emerging economies. They find that economic variables–income level, capital

flow, trade, and banking capital in addition to noneconomic variables like political risk,

law and order, and bureaucratic system are the major determinants of development in

emerging economies.

The existing studies offer notable relationship between trade and economic devel-

opment. However, the controversies still exist on the direction of casualty, and thus

find time varying relationship. For example, Ghirmay, Grabowski, and Sharma (2001)

study the cointegration between exports and economic growth in nineteen develop-

ing countries by utilizing multivariate causality analysis. The results support a long-run

relationship between exports and economic growth. Narayan et al. (2007) examined the

export-led growth hypothesis for Fiji and Papua New Guinea by utilizing ARDL frame-

work. Their results support the long run export led growth for Fiji, while short run

relationship for Papua New Guinea. The study of Pistoresi and Rinaldi (2012) inves-

tigates the nexus between exports, imports and GDP in Italy for a period 1863–2004, by

utilizing the different cointegration techniques. The results suggest that the underlying

variables are found cointegrated in the long run while the direction of causality varies in

the short run.

Subsequently, Belloumi (2014) examines the cointegration between foreign direct

investment (FDI), trade openness and economic growth in Tunisia over the period

1970–2008. The study utilizes ARDL bound test procedure. The results highlight the

####### 4 R. KUMAR

significant long run integration between the underlying variables, while no significant

causality is found in the short run. In addition, Further, Pradhan et al. (2017) study this

nexus for nineteen countries falls in Eurozone by using more recent data 1988–2013. The

results show that the financial and economic developments have promoted FDI inflows

in the region in the long run, while FDI propelled economic growth in the short run.

While focusing Asia and specifically South Asia, available studies primarily investi-

gate the spillover of developed countries in the region. For example, Johnson and Soenen

(2002) study the impact of Japanese economy on the twelve Asian economies with a spe-

cial focus on economic factors which causes economic integration. They find that macro

economic variables like exports share and FDI have promoted financial integration

among the Asian markets. Subsequently, Asgharian and Nossman (2013) find significant

economic spillovers from the U. to the China and other Pacific Basin economies while

utilizing stochastic volatility model. The results highlight that the normal and extreme

economic shocks are significant for almost all sample countries except China. Ahmad,

Draz, and Yang (2018) examine the causal relation between exports, FDI and economic

growth among the ASEAN + 5 countries for a period 1981–2013. The results support

the export and FDI led growth in short and long run.

While examining the causal relations between trade, financial development and eco-

nomic growth, existing studies provide notable relations. For example, Liu, Burridge, and

Sinclair (2002) investigate the causal links between trade, economic growth and FDI in

China by utilizing the cointegration techniques. The results support the long run rela-

tionships between the underlying variables and finds bi-directional causality between

economic growth, FDI and exports. Hye, Wizarat, and Lau (2013) examine trade-growth

nexus using data from six Asian countries, by utilizing ARDL approach for a long-run

relationship among exports, imports and economic growth. The results find the export-

led growth model is relevant for all countries except Pakistan, while the import-led

growth model is relevant to all countries. The results points towards the domestic and

overseas demand tend to contribute to economic growth and employment generation in

the sample countries.

Further, Hye and Lau (2015) investigate the nexus between trade openness besides

physical and human capital on economic growth as measured by GDP growth rate in

India by utilizing ARDL model and other multivariate causality techniques. The results

report that trade openness index negatively impacts on economic growth in the long

run, while the impact of physical and human capital is found positive. The result of

granger causality test reports the positive impact of trade openness and human capi-

tal are found positive. Vithessonthi and Kumarasinghe (2016) investigate the impact of

financial development and international trade integration on stock markets integration,

by using the data of fifteen Asian countries over the period 1985–2013. They find that

trade integration is not linked with financial development and stock markets integration.

Adeel-Farooq, Bakar, and Raji (2017) investigate the impact of trade openness, finan-

cial development and human capital on economic development for two largest South

Asian countries (India and Pakistan). They find significant cointegration between the

underlying variables and economic growth of both the countries.

The recent studies have documented the significant spillover growth among the

regional trade partners for having pro trade policies. For example, Wang and Chen

(2016) show the growth pattern of Chinese economy and the way it has affected the eco-

nomic performance of twenty five emerging economies. The results stress on the deeper

trade interdependency between those economies in order to boost the economic growth

####### 6 R. KUMAR

Table 1. Key economic indicators.

Country 1990 1998 2006 2016 Average 1990–

Per Capita Income (current US $) India 363 409 792 1717 791. Pakistan 371 470 873 1442 771. Sri Lanka 463 850 1437 3857 1640. Bangladesh 297 395 494 1358 566. Nepal 193 212 348 730 375. Bhutan 557 695 1335 2782 1317.

GDP growth rate (%) India 5 6 9 7 6. Pakistan 4 2 6 5 4. Sri Lanka 6 4 7 4 5. Bangladesh 5 5 6 7 5. Nepal 4 3 3 0 4. Bhutan 10 5 6 8 6.

Trade as percentage of GDP (%) India 15 23 46 40 32. Pakistan 38 34 35 25 33. Sri Lanka 68 78 71 50 68. Bangladesh 18 27 38 37 32. Nepal 32 56 44 48 48. Bhutan 57 82 113 77 89.

Trade Intensity Index for South Asia India 1 2 1 1 1. Pakistan 2 3 4 2 3. Sri Lanka 5 6 10 6 7. Bangladesh 6 11 5 3 6. Nepal 11 30 40 28 27. Bhutan 9 59 45 37 45.

Source: The data is compiled by the author from World development Indicators and Asian Development Bank database.

Union are highest integrated worldwide, while the South Asian countries fail to achieve

the full potential of regional integration. In fact, there is highly mismatch between trade

and financial openness in South Asia as compared to other Asian counterparts (see Ding

and Masha 2012).

At the interregional, the biggest trade partners of India are ASEAN + 3 with trade

share of 25%, followed by Middle East with trade share of 24% and East Asia with

trade share of 16% (see, Asian Development Bank database 2016). In the last decades,

India has constructively worked on India’s ‘Look East Policy’ to shift focus from the

western countries to the East Asian countries with the objective to promote economic

and political ties. Until the last decades, European Union was the second largest trade

partner of India with a trade share of 19%, replaced by the Middle East.

2. Geopolitics and regional trade linkages

In the last decades, trade and investment in South Asian countries grew faster than any

part of the economy in the world. These countries introduced major changes on the pol-

icy part of foreign trade and investment at the intra and interregional levels as per WTO

charter. At regional level, the first step was the formation of trade bloc namely SAARC

in 1980s by seven countries from the South Asia region (India, Pakistan, Bangladesh,

Bhutan, Maldives, Nepal, and Sri Lanka). It was the believed to be the top multilateral

####### THE JOURNAL OF INTERNATIONAL TRADE & ECONOMIC DEVELOPMENT 7

agreement despite of political indifferences. In the 13th summit of SAARC at Dhaka,

Afghanistan was inducted as 8th member.

The common problems such as low per capita income, low infrastructure, low social

services and high incidence of poverty led the common action programmes to promote

the regional trade. The outcome was the signing of South Asian Preferential Trading

Arrangement (SAPTA) in 1995. The objective was to promote economic cooperations

by providing preferential treatment by way of reducing import tariffs on eligible items

and other economic barriers. Further, a more liberalized agreement namely South Asia

Free Trade Agreement (SAFTA) was made in January 2004 which became effective in

January 2006. The objective was to initiate structural reforms in the process of intrare-

gional trade. The arrangement provided special and the most favourable treatment to

the least developed countries (LDCs) in the region.

As per SAFTA, the non least developing countries (NLDCs) in South Asia (India, Pak-

istan and Sri Lanka) required to bring their duties down to 20% in the first phase of the

two-year period ending in 2007. In the final five-year phase ending 2013, the 20% duty

would be reduced to zero in a series of annual cuts. While, the LDCs (Nepal, Bhutan,

Bangladesh, Afghanistan and Maldives) had an additional three years time frame to

reduce tariffs to zero by 2016. It is noted that smaller economies such as Nepal, Bhutan,

and Maldives are more dependent on intraregional trade than the larger economies, and

thus will benefit more from trade liberalization.

The intraregional capital flows in the South Asian region are much more limited

than intraregional trade. The man made trade barriers, mutual trust deficit, the actual

and perceived threats are big barriers which kept South Asian countries from deeper

intraregional trade connectivity. It is evident with the fact that despite of reciprocal

most favoured nation (MFNs) status among the SAARC (except Pakistan which does

not give to India), intra regional trade share stands under 6 percent as of now. India

which is the largest economy in the region, has total regional trade under 4 percent.

India and Pakistan have witnessed low trade relations in last decades because of trust

deficit and conflicting political relations on account of proxy terrorism and unresolved

territorial disputes. The 19th SAARC summit which was scheduled to be held in Pak-

istan on November 2016, was cancelled after India announced its boycott in the protest

of terrorist attack on the Indian military establishment. Later, Bangladesh, Afghanistan,

Bhutan also withdrew their participation from the summit, leaving in an indefinite

postponement of the summit.

The total trade shares and trade volume of India with the SAARC countries is

summarized in Figure 1. Some of the important observations can be made are: (1)

Bangladesh is the largest trade partner of India (trade volume (% trade share) increased

from US$312 million (0%) during 1990 to US$6423 million (1%) dur-

ing 2016) (2) Bhutan stands the least trade partner of India with zero trade during

1990, reach to the level of US$649 million (with 0% trade share) during 2016.

(3) It is noted that total trade volume of India with other countries from the region

has increased largely especially after 2000. This means that India being the largest

economy in the region, has maintained a favourable trade balance in the region. (4)

Pakistan which comes to be the 2nd largest economy in the region, maintains poor

trade relations with India despite of MFN status by the later. It is noted that the total

trade volume has increased from US$88 during to US$2447 dur-

ing 2016, while the India’s trade share has increased very marginal during the same

period.

####### THE JOURNAL OF INTERNATIONAL TRADE & ECONOMIC DEVELOPMENT 9

shown as:

Y t = c 0 + c 1 EXt+c 2 IMt+c 3 Xt + εt (1)

where, Yt denotes the GDP growth rate of dependent country.

IMt denotes import share of India with dependent country

EXt denotes export share of India with dependent country.

Xt denotes GDP growth rate of India.

εt denotes error term with zero mean and constant variance.

To analyse the long run and short run relationship among the variables, we utilize

the ARDL procedure of vector autoregressive (VAR) model with lag order p and q in Z t,

where Zt is a column vector composed of four variables Z t = (Yt, IMt, EXt, Xt) where

Y t is dependent variables, and IMt, EXt and Xt are independent regressors. We apply

ARDL model to test the long and short run causality in preference to other cointegration

techniques proposed by Engle and Granger (1987), Johansen and Juselius (1990) because

of its three merits. Firstly, the ARDL does not require all the variables to be stationary

at same level and can be applied when the variables are stationary at level or at first

difference, or there should be mixed order. Secondly, the ARDL is more efficient in case

of small sample data set. Thirdly, the ARDL model provides unbiased estimates of short

and long run causality simultaneously.

The ARDL framework involves estimation of unrestricted error correction model,

where the short run effects can be estimated directly, and long run relationship can be

estimated indirectly. The ARDL model as proposed by Pesaran and Shin (1999); Pesaran

et al., (2001) essentially model the dependent variable to be function of lagged variable of

dependent variable, and current and lagged variable of independent variables as shown

below:

y t = α 0 +

∑

i= 1

βi yt−i +

∑

i= 0

∅i x t−i + εt (2)

Where, ytand x t are two time series variable, while εtis stochastic error term. By re-

parameterization equation (2) can be written as:

Yt = a 0 + λ 1 Y t− 1 + λ 2 EXt− 1 + λ 3 IMt− 1 + λ 4 Xt− 1 +

∑ n 1

i= 1

biYt−i

+

∑ n 2

i= 0

ciEXt−i +

∑ n 3

i= 0

diIMt−i +

∑ n 4

i= 0

eiXt−i + εt (3)

Where, a 0 is a drift, λ 1... .λ 4 are long run multipliers, is first difference operator,

while εt is error term with normally distribution. The null hypothesis i. λ 1 = λ 2 =

λ 3 = λ 4 = 0 of long run cointegration is tested against the alternative hypothesis i.

λ 1 = λ 2 = λ 3 = λ 4 = 0.

The ARDL procedure runs in two stages. The first stage involves the bound test

for testing the long run cointegrating relationship between the variables. While the

second stage estimates the long relationship as depicted equation (1) and short run

causal relation in error correction term (ECT) framework. The ECT integrates the

short-run dynamics with the long-run equilibrium without losing long-run informa-

tion. Researchers widely utilize ARDL model to examine the nexus between economic

####### 10 R. KUMAR

variables in the small data set (see, Ghirmay, Grabowski, and Sharma 2001; Narayan et al.

2007; Mamun and Nath 2004; Pistoresi and Rinaldi 2012; Hye, Wizarat, and Lau 2013;

Belloumi 2014; Kumar 2019).

4. Empirical findings

4. Descriptive statistics

The descriptive statistics are presented in Table 2, highlights the stochastic properties

of data. It is note that the GDP growth rate of Bhutan is found highest to 17% in

2007 followed by India 10% in 2010. The same two countries have also reported

highest variation in GDP growth rates during the study period with standard deviation

of 3 and 2 respectively. Bangladesh followed by Sri Lanka are the largest exports

partners of India with average export shares of 2% and 1% respectively, while

Bhutan is the least with 0% average export shares. Nepal and Sri Lanka come to be the

largest imports partners of India with average import shares each of 0% and 0%

respectively, while Bhutan is the least import partner with average import share of 0%.

4. Unit root test

We began our econometric analysis with tracing of integration order in given data set.

The application of ARDL bound test requires the variables to be stationary either of

same order or of mix of order. The stationary level is determined by using the unit root

test. In finance literature, researchers commonly use Augmented Dickey-Fuller (ADF) at

Table 2. Descriptive statistics.

Average Maximum Minimum Std. dev Skewness Kurtosis

India X 6 10 1 2 −0 2.

India’s export (EX) and import (IM) share with Bangladesh Y 5 7 3 0 −0 2. EX 2 3 1 0 0 2. IM 0 0 0 0 0 3.

India’s export (EX) and import (IM) share with Pakistan Y 4 7 1 1 0 2. EX 0 1 0 0 0 2. IM 0 0 0 0 2 10.

India’s export (EX) and import (IM) share with Sri Lanka Y 5 9 −1 2 −1 6. EX 1 2 0 0 −0 3. IM 0 0 0 0 1 3.

India’s export (EX) and import (IM) share with Nepal Y 4 8 0 1 −0 5. EX 0 1 0 0 0 2. IM 0 0 0 0 1 4.

India’s export (EX) and import (IM) share with Bhutan Y 6 17 −0 3 1 5. EX 0 0 0 0 0 2. IM 0 0 0 0 −0 2.

Note: All the variables are defined in Section 3. Source: Author’s own.

####### 12 R. KUMAR

Table 3. ADF test (first difference) with structural break: 2006.

With intercept With intercept and slope

Country GDP EX IM GDP EX IM

IN −4*** – – −4*** – – BAN −7*** −5*** −4*** −7*** −4** −2. PAK −2 −5*** −4*** −4*** −2 −5*** SRI −4*** −3* −2 −4*** −4*** −3* NPL −5*** −4*** −3** −3** −3** −3** BHU −5*** −3* −3** −5*** −3* −4***

Notes: All the variables are defined in Section 3. Critical value at 1% level of significance is −4. Critical value at 5% level of significance is −3. Critical value at 10% level of significance is −3. ***denotes significant at 1% level of significance. **denotes significant at 5% level of significance. *denotes significant at 10% level of significance. Source: Author’s own.

of F-statistic calls the rejection of null hypothesis of no cointegration among the under-

lying variables. Following the Pesaran, Shin, and Smith (2001), the null hypothesis is

rejected if estimated F-statistic comes to be higher than the upper bound value, while

it is accepted if the F-statistic comes to be lower bound critical value. Other ways, the

cointegration test is inconclusive.

It is noted from Table 4 that the F-statistic for four countries (Bangladesh, Sri Lanka,

Bhutan, and Nepal) are found significantly greater than the upper bound value, high-

lighting the fact of significant long run cointegration between the underlying variables.

This implies that the long run causality exists among the economic growth rate, export

and import shares of India when growth rates of host countries are taken as depen-

dent variable. However, the economic growth rate of Pakistan does not respond to

economic growth rate, and export and import shares with India, highlighting the fact of

no spillovers of Indian growth story to Pakistan. Overall, the total trade between India

and Pakistan has declined from the maximum trade volume of US$ 2 billion in 2014

to US$ 2 billion in the 2016–17. In 2016, the total trade registered a decline of 14%

over the previous year (see, ADB database). The results for Pakistan are consistent with

the earlier empirical studies which does not support export led growth for Pakistan (see,

Love and Chandra 2005; Hye, Wizarat, and Lau 2013).

As shown in the Table 4, the best fit ARDL models for Bangladesh (2,3,3,3), for Sri

Lanka (1,0,0,1), for Nepal (2,0,1,0) and for Bhutan (1,1,1,1) highlights the number of lags

of underlying variables as given in equation 1. The bound test results are in the line of

Table 4. ARDL bound test.

ARDL F statistics Remarks F(PAK/IN) 1,0,1,0 2 Not cointegrated F(BAN/IN) 2,3,3,3 7*** Cointegrated F(SRI/IN) 1,0,0,1 7*** Cointegrated F(NPL/IN) 2,0,1,0 11*** Cointegrated F(BHU/IN) 1,1,1,1 13*** Cointegrated Notes: Bound critical values at 1% level of significance: I(0)-I(1):4–5. Bound critical values at 5% level of significance: I(0)-I(1):3–4. ***denotes significant at 1% level of significance. Source: Author’s own.

####### THE JOURNAL OF INTERNATIONAL TRADE & ECONOMIC DEVELOPMENT 13

past studies which support the significant cointegration between economic growth and

foreign trade (see, Ghirmay, Grabowski, and Sharma 2001; Pistoresi and Rinaldi 2012;

Wizarat and Lau 2013; Belloumi 2014; Kumar 2019).

4. Long and short run causality

Having established the cointegration among the variables for four countries, we proceed

to estimate the coefficients for long and short run causality by applying ARDL in error

correction (ECT) framework. The bound test is estimated with the assumption that all

the variables included in the ARDL model are stationary at level i. I(0) or at first differ-

ence i. I(1), while the second level is estimated on the assumption that all the variables

are stationary at first difference. The results of long and short run causality for four coun-

tries are presented in Tables 5–8 in ECT framework. The ECT integrates the short-run

causality with the long-run equilibrium without losing long-run information. The value

of the ECT should be statistical significant with negative sign. If the ECT coefficient falls

between −1 and −2, then it produces the dampened fluctuations around the long run

equilibrium which allows to restore the long run equilibrium rapidly (e. Narayan and

Smyth 2006).

Table 5 highlights the results for Bangladesh. It shows that the economic growth

of India has significant short and long run positive impact on economic growth of

Table 5. Short and long run relationship for Bangladesh.

Long run relationship Short run relationship

Regressor Coefficient Regressor Coefficient

EX −0 Y(− 1 ) 0* (0) (0) IM 17 EX 1*** (0) (0) X 1** EX(− 1 ) −0. (0) (0) Constant −2 EX(− 2 ) 0. (0) (0) IM 0. (0) IM(− 1 ) −4. (0) IM(− 2 ) −7* (0) X 0*** (0) X(− 1 ) −0. (0) X(− 2 ) 0. (0) ECT(− 1 ) −0** (0) Notes: R square: .92. F-statistics: 7* (0). All the variables are defined in Section 3. Probability values are in brackets. Stands for first difference operator. *** denotes significant at 1% level of significance. ** denotes significant at 5% level of significance.

denotes significant at 10% level of significance. Source: Author’s own.

####### THE JOURNAL OF INTERNATIONAL TRADE & ECONOMIC DEVELOPMENT 15

Table 8. Short and long run relationship for Bhutan.

Long run relationship Short run relationship

Regressor Coefficient Regressor Coefficient

EX −55*** EX 25. (0) (0) IM 106*** IM 47. (0) (0) X 0** X 0** (0) (0) Constant −1 ECT(− 1 ) −1*** (0) (0) Notes: R square: .87. F-statistics: 16* (0). All the variables are defined in Section 3. Probability values are in brackets. stands for first difference operator. *** denotes significant at 1% level of significance. ** denotes significant at 5% level of significance.

denotes significant at 10% level of significance. Source: Author’s own.

long run. The trade between India and Sri Lanka is limited to agriculture products and

intermediate goods. The top five items in the export basket of India to Sri Lanka are

(1) coffee, tea, mate, and spices (2) salt, sulphar, stones, plaster, lime, and cement (3)

pharmaceutical products (4) articles of apparel, accessories, not knit (5) vehicle other

than railway and tramway. While the top five items in the import basket of India from

Sri Lanka are (1) consumer goods (2) vegetables (3) raw materials (4) textile and cloth-

ing (5) food products. The ECT term which stands −1, highlighting the fact that Sri

Lankan economy tends to restore the long run equilibrium before the year following the

deviations of independent variables.

Table 7 reports the results for Nepal, highlighting the significant negative impact of

import share of India on economic growth of Nepal in the long run, while the impact

of other sample variables are found non significant. The India’s export share with Nepal

stands 1% of the total exports during 2015. The engineering products contributed

around 36% of India’s exports to Nepal in 2016–17 while mineral products (mainly

petroleum products) contributed 23% of exports. The top five items in the export basket

of India to Nepal are (1) mineral fuels, oils, distillation products (2) Iron and steel (3)

machinery, nuclear reactors, boilers (4) vehicles other than railway, tramway (5) electri-

cal & electrical equipment. While the top five imported items from Nepal are (1) Waters,

including minerals waters (2) Plastic and made of plastics (3) footwear, leather or com-

position of leather (4) tubes, pipes and hollow profiles of iron and steel (5) cotton yarn,

synthetic staple fibres. The total trade between India and Nepal has increased by 32%

to US$ 5 billion during 2016 as compared to last year. The India’s exports to Nepal have

increased by 37% to US$ 5 billion in 2016 from USD 3 billion in the previous year,

across most products (see, MoCI, Government of India). The India’s import share from

Nepal comes to be 0% of the total imports during 2016. The ECT which comes to be

−1, highlighting that Nepal economy approaches to the equilibrium rapidly before

the year following the deviations of export and import share and economic growth of

India.

The results for Bhutan are presented in Table 8. It highlights that export share of India

has significant negative long run impact on the economic growth of Bhutan, while the

####### 16 R. KUMAR

impact of economic growth rate and import share of India are found significantly pos-

itive. India stands as the top trading partner of Bhutan, as India served as destination

for 90% of Bhutan’s total exports and a source of 82% of Bhutan’s total imports during

2016. There is free trade between the two countries with no Basic Custom Duty on the

imports from, and exports to Bhutan as per Indo-Bhutan Trade Agreements. The exports

basket of India to Bhutan includes the engineering sector which constitutes 56% of total

exports to Bhutan in 2016. The other principal products of export are mineral prod-

ucts including petroleum products (26% of exports), and prepared foodstuffs including

dairy products. While the principal items in the imports basket of India from Bhutan is

Electricity which constitutes 56% of the total imports in 2016. The other major imports

are base metals and metal products – mainly iron and steel products (28% of imports).

The total trade between the two countries has expanded by 7% to US$ 808 mil-

lion in 2016. The India’s exports to Bhutan have increased to US$ 509 million in

2016–17 from US$ 468 million in 2015, while the imports has increased by 6% to

US$ 299 million during the same time period due to pro trade policies (see, MoCI,

Government of India). The ECT of Bhutan is found significantly negative, highlight-

ing the speed of recovery in the economy caused by the deviations of trade share and

economic growth of India.

The direct observations are made from the results that the exports of India to all

the four countries have non significant negative impact on the economic growth rates.

However, the imports by India from the three countries (Bangladesh, Sri Lanka, and

Bhutan) are found positive impact on the economic growth rates of these countries. The

export and imports coefficients are significant in case of Bhutan, while non significant for

other three countries. These results support findings of earlier studies which propose the

export led growth hypothesis (see Ghirmay, Grabowski, and Sharma 2001; Mamun and

Nath. 2004; Narayan et al. 2007; Hye, Wizarat, and Lau 2013). Further it is observed that

the economic growth rate of India holds significant positive impact on the growth rates

of Bangladesh, Sri Lanka and Bhutan while significant negative impact on the growth

rate of Nepal.

Figure 2 summarizes the overall finding of long and short run spillovers of trade

(exports plus imports) and economic growth rate of India. The observations can be

made that the maximum spillover can be observed for Bhutan in the long run and

Figure 2. Short and long run spillovers impact of export, import and growth rate of India. Source: Author’s own.

####### 18 R. KUMAR

be pursued by unlocking regional sources of growth like promotion of regional trade

and cutting down the restrictions on capital flows with the objective to gain in the long

run. India which comes to be the largest economy in the South Asia can act as engine

of growth by promoting the trade with the neighbouring countries. If trade barriers are

lowered, trade within South Asia can increase three-fold, from US $23 billion to US $

billion, as pointed by World Bank report (see, Kathuria 2018). India has the potential to

triple its trade to US $62 billion from the current level of US$22 billion, while Sri Lanka

has the potential to double its trade to South Asia, the report further added.

As shown in section 4, the export and import basket of India with respect to other

South Asian countries are found diverse. The maximum share in exports which India

exports to South Asia are of engineering products (33% of total exports), followed by

textile and textile products (20% of total exports), and minerals (14% of total exports).

While import basket of India from South Asia includes mainly agriculture products (32%

of the total imports) followed by textiles and textile products (21% of total imports),

minerals (16% of total imports) and engineering products (13% of total imports). Having

diverse trade pattern, there is need to strengthen bilateral trade agreements like Indo-

Sri Lanka free trade agreement (FTA), and Indo-Bhutan Free Trade Agreement for the

promotion of bilateral trade.

The promotion of trade should be viewed an opportunity to correct regional eco-

nomic growth imbalances. The economic integration in South Asia should proceed with

higher intra-regional trade, which allow cross border investments. This is strongly nec-

essary to realize development objectives and sustainable development of the region.

Having the dominant size on the part of economy and other resources, India requires

to play key role in pushing forward the SAARC process in attaining its objectives.

Disclosure statement

No potential conflict of interest was reported by the author.

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