Journal of INTERNATIONAL ECONOMICS Volume 9, No 1, January-June 2018

ISSN 0976-0792

Trade Openness Driver of Economic Growth in BRICS Nations Anju Rani and Amandeep Kaur

An Econometric Analysis of Invisibles in ’s Balance of Payments Raghavender Raju G and Vishwanath Pandit

Impact of Costs on Performance of Ethiopia – A PPML Panel Gravity Equation Approach Benat Mohammedamin Mussa and Gollagari Ramakrishna

Global Integration of Indian Financial System and Vulnerability to External Shocks: Empirical Evidence from Capital Market Nayia Mahajan

A Panel Gravity Model Analysis of India’s Export to Gulf Cooperation Council (GCC) Countries Imran Alam and Shahid Ahmed

Factor Substitution and Returns to Scale in Indian Manufacturing Under R. Bagavathi Muthu and P. Asokan

Book Review Black Money and Havens Author: R Vaidyanathan Reviewer: K. Narendranath Menon

Indexed in: • Ebsco Database • Ulrichsweb • ProQuest • UGC List of Journals • Indian Citation Index (ICI) Aims and Scope Journal of International Economics is devoted to the publication of professional and academic research in all the areas of international economics. It is published in the months of January and July. The journal broadly covers areas such as cross country growth models, population and migration patterns, , trade policy and relations, trade organizations and bodies, foreign investment flows, balance of payments and exchange rate mechanism, multinational corporations and cross border manufacturing, etc.

© Copyright 2018 Institute of Public Enterprise. All Rights Reserved. No part of this publication may be reproduced or copied in any form by any means without prior written permission. The views expressed in this publication are purely personal judgments of the authors and do not reflect the views of the Institute of Public Enterprise. All efforts are made to ensure that the published information is correct. The Institute of Public Enterprise is not responsible for any errors caused due to oversight or otherwise.

Published by: Satyam N Kandula on behalf of Institute of Public Enterprise Owned by: Institute of Public Enterprise Printed by: Satyam N Kandula on behalf of Institute of Public Enterprise Printed at: Wide Reach Advertising Pvt Ltd, 21, Surya Enclave, Trimulgherry, Hyderabad - 500015 Place of Publication: Institute of Public Enterprise, OU Campus, Hyderabad - 500007 Journal of International Economics

Editor-in-Chief Prof RK Mishra, Director, Institute of Public Enterprise, Hyderabad

Editor Dr G Rajesh, Assistant Professor, Institute of Public Enterprise, Hyderabad

Editorial Advisory Board Prof TCA Anant, Chief Statistician of India and Secretary, National Statistical Commission, New Delhi

Prof Rehman Sobhan, Chairman, Center for Policy Dialogue,

Prof NJ Kurian, Visiting Professor, Council for Social Development, New Delhi

Prof S Indrakanth, RBI Chair Professor, Council for Social Development, Southern Regional Centre, Hyderabad

Prof R Nanda Gopal, Director, PSG Institute of Management, Coimbatore

Prof Sam N Basu, Dean, Cotsakos College of Business, William Patterson University, New Jersey, USA

Prof Bhanoji Rao, Visiting Professor, LKY School of Public Policy, National University of , Singapore

Prof BN Swami, Faculty of Business, University of Botswana, Gaborone, Botswana

Editorial Support Mr AV Bala Krishna, Institute of Public Enterprise, Hyderabad Black MoneyandTax Havens Book Review Reviewer: Author: RVaidyanathan R BagavathiMuthu Trade OpennessDriverofEconomicGrowthinBricsNations From theEditor’s Desk Impact ofTrade CostsonExportPerformanceofEthiopia Raghavender RajuG Balance ofPayments An Econometric Analysis ofInvisiblesinIndia’s Anju Rani Manufacturing UnderGlobalization Factor SubstitutionandReturnstoScaleinIndian Imran Alam to GulfCooperationCouncil( A PanelGravityModel Analysis ofIndia’s Export ContentsNayia Mahajan from CapitalMarket Vulnerability toExternalShocks:EmpiricalEvidence Global IntegrationofIndianFinancialSystemand Benat MohammedaminMussa – A PPML PanelGravityEquation Approach KNarendranathMenon Amandeep Kaur and Amandeep Shahid Ahmed and P Asokan and and Vishwanath Pandit

and GollagariRamakrishna Gcc) Countries

101 16 87 72 48 32 3 1

invisibles in balance of payments, trade openness, India’s to Gulf Cooperation economics. international to pertaining as such topics discuss issue this in papers The issues on literature good read to want who those all to addition value a be would that articles those select to attempt an made usual as have we journal the of issue this In Indian economymayfurther witnessthefl ight of capital,whichisnotahappyaugury. continues, trend this If rates. interest its hiking Reserve Federal of wake the in FII of flthe is India for concern of cause a is which issue more One months. coming the ight OPEC has decided to increase the output, it needs to be seen how oil prices will be in All this changed in the past one year, with oil prices again moving northward. Although defi account current infl lesser meant prices. turn oil in falling This of because cit ation. aftermath of OPEC restricting its output. From 2014-16, India witnessed a comfortable For the past few months India also has been rattled by the movement of Oil prices in the sector service being professionals. Indiahasvehementlycriticizedthismove. them of many holders, visa US on restrictions the is India, for concern of matter real the But with Trump well government. down gone not has This motorcycles. Davidson Harley of on tariffsalmonds, imposed as, also India such lentils. products apples, US certain on levies raising by retaliated has It tariffs. India to US, are not very huge. However, India also is not behind in the race to impose from exports aluminum and steel because, much be not would this of impact the that reported is it India, on imposed been have tariffs aluminum and steel although war, trade of case In usual. as concerns many were there Scenario, Indian about Totalk As expected,ChinaandEUretaliatedbytheirownlistoftariffs putting the ideology, being assiduously professed in the era of globalization. worried about. The American trade policy is likely to witness a surge of retaliation thus other countries is well known. It is here, where everyone concerned with global trade is average the cent. Hawley per Smoot 48 that to fact rate The Tariff raised from retaliation by followed was which Tariff Hawley Smoot infamous the of reminiscent is is trade turbulence. serious witnessing Tradethis that remarking of extent the to global went experts the Indeed, WTO. successor its by then and GATT by professed as ideology trade free the threatening step retrograde a as seen being countries other and from aluminum and steel on tariffs impose to decision order.The world liberal the kill tariffswould heavy imposing of policy the that that, opined Critics now.called is it as ‘TrumpDoctrine’, the criticizing, vehemently written being are and matter of heated polemics for the past few months. Articles and editorials were written domain of international economics. The trade wars unleashed by America have been a As this issue of the journal goes into print there are many issues that are in focus in the Editor’s Desk... From the

1 Volume 9, No 1, January-June 2018 2 Journal of International Economics Dr GRajesh talk that books of review about issuespertainingtointernationaleconomics. sending in keep enthusiasm to same request further the We show articles. to contributing continue to contributors regular our request We Money and Tax Havens’. ‘Black titled, book the of review a published also have We topics. other and Council Keywords: International Trade, BRICSNations, Trade Openness,Elasticity for theBRICSnations. paper an attempt has been made to study the effect of openness on economic growth our in so region, the in trade to barriers non-tariff and member tariff all dismantling the by nations across paces desired the at experienced and been intra-regional has both trade in international progress the that so nations these by adopted been has decade half and one last the within Signifi liberalisation reserves. trade foreign cant combined in trillion US$4 estimated and trillion, US$34.415 (PPP) GDP and combined a economies fast-growing with people, large, newlyindustrialised billion 3 their almost or with affairs by signifi global and infl distinguished regional cant all developing on are uence they are but countries, members fi group’s ve the BRICS, as known together are Africa South and China India, , fiBrazil, trade the 2010). of (Sawhney,in eld especially countries, the of future the shaping in role important an playing are groupings fi economic and regional trade nancial The particularly through fl ows. world, the around economies of integration increasing the to refers and and allowing domestic resources to shift from less productive to more productive uses in the economy, enhancing processes already underway due to technological advances and development in a country. It has been identifi ed as a promotersignifi ofmost the structuralof change one growth been economic has of trade determinants Foreign cant Assat rfso, eatet f cnmc, ... at Clee Ku (ata) n cn e ece at reached be can and (Kaithal) Kaul College, Janta B.A.R. Economics, of Department Professor, Assistant 2 Sonepat Kalan Khanpur Vishwavidalaya, Mahila Singh Phool Bhagat Economics, of Department Professor, Assistant 1 Introduction economy, enhancing processes already underway due to technological advances and centuries. Trade openness has been identifi ed as a promoter of structuralInternational trade changehas been inthe main driver the of growth and development in the last few [email protected] and [email protected] “More opencountries...haveexperiencedfasterproductivity growth Trade OpennessDriverofEconomic SN07-72 Volume 9,No.1,January-June2018,pp.3-15 ISSN 0976-0792 Journal ofInternationalEconomics throughout thedecades1960to1990.”-Edwards Growth in Anju Rani 1 Amandeep Kaur andAmandeep Nations BRICS 2

3 Volume 9, No 1, January-June 2018 4 Journal of International Economics td eaie h rltosi o eooi got ad rd oens i BRICS in openness trade Nation ataggregateandDisaggregateLevelduring1991-2016. and growth economic of relationship the examine study by the nations backdrop this member in region, the the in trade to across barriers nontariff and tariff paces all dismantling desired the at experienced been has trade international and intra-regional both in progress the that so nations these by adopted been has decade half and one last the within liberalisation trade Significant nations. economic Asian but growth, their block their importance is expected of to continue to rise in future and may outperformG6 terms in greatly differ economies BRICS The and adopted export led growth which have contributed to their significant growth rates. China and South Africa are together known as BRICS, have opened up their economy of the countries, especially in the field of trade (Sawhney, 2010). Brazil, Russia, India, future the shaping in role important an playing are groupings economic regional The differentamong integration regional with forward carried been has trend This nations. time. of passage with interdependent increasingly become has world develop. The to encouraged them to reduce trade barriers in order has to economies allow emerging for and comparative developing advantages in growth for quest The growth. on impact investment, which extends the market size so trade liberalization process has a positive engine of the growth. fuels Empirical turn studies in have which indicated trade, that promotes trade Liberalization openness 1991). Helpman, leads & to Grossman efficient competition, innovates new products and transfer of new technology (Krugman, 1979; domestic market and might also help the to disseminate extending technological know- for how, channel leads to a provides openness Trade advantage. cost comparative which will increase division of labour, thereby increasing productivity and also provide market the extending in helps it as trade international of benefits the advocated era technology, cheaper flows, imports, capital more and to access larger an have export can markets. they trade that, The indicates both It Classical flows. financial transactions, and and international Neoclassical complete to quicker and easier it made have that advances technological the reflecting 1980s, the since usage common become has openness‘ term the literature, economic In (kaur,2013). trade conventional over dominate that integration economic and openness of concept the a to provided dimension has new capital of mobility The economy. global of integration traditional the the of of modes one as considered is channel trade The flows. financial and trade refers to the increasing integration of economies around the world, particularly through It uses. productive more to productive less from shift to resources domestic allowing motn rl i te ol eooy s rsl o ter ail goig hr in share growing rapidly their of result a as economy world the in role important increasingly their given consider to countries of set interesting particularly a Africa), South and China India, Russia, (Brazil, BRICS the of trade the examines paper The The share of foreign trade in GDP will increase with the foreign trade volume increase. currency revenues and expenditures at the export and import volume increase results. foreign in seen be can increase country,an the of openness the country.With the of The trade. foreign the size of openness on rates indicates the importance level of country the foreign trade for economy the of dependence the indicates also Openness Trade OpennessandBRICSNations Trade PoliciesinBRICSNations openness oneconomicgrowthwillbesearchedforBRICScountries. trade with the US in recent years. Based on these indicators, in our study the effect of in increase an to led for Trade).has General countries’growth These export-oriented Directorate Commission (European respectively euro value million total 340 and a million reached 551 BRICS of the and zone Euro the between exports and imports 2014 In Africa. South and India of China, case the of in deficit case in is the it whilst in Brazil and Russia surplus in is balance trade merchandise The importers. and Russia and India have also entered the list of the world top 20 merchandise exporters then. by goods merchandise of importer largest second the and exporter largest the China made had trade merchandise in growth year-on-year double-digit Aservices. and goods of imports and exports global of respectively 16% and 19% for accounted 2011they by century, and the of turn the of by doubled had 3% share this only but trade, global for accounted BRICS the 1990, In decades. two last the in trade global • • the tradepoliciesofmembercountries. Toknow to important is it growth, and openness between relationship the understand Russia: took place. Presently, Brazil is the member of various regional trading agreements. Free Tradea for negotiations of of Agreement launch (FTAA)the America 1994 In labour gains especially in the case of firms having low productivity (Scholar, 2004). especially NTBs, barriers and trade trade Basic protection played in an important 30%. role Reduction in tariffs.increasing productivity and with about along of down came average restrictions, import high quantitative a at though tariffs, its all bound Brazil Round Uruguay the of end the At 2009). (Sally, agriculture of case the in in 0 to closed and 1997 in 18% to 1987 in 86% from reduced manufacturing in percent 50 than more from (ERP) Protection of Rate Effective these 1995.The in percent 13 almost down to themid1980s to came Due tariff 1994. average and nominal 1991-93 the 1988-89, reforms, in phrases three in reduction tariff Brazil: which resulted in the increasing role of state and trend towards import substitution. policy was trade grow in to started Russia of production domestic when crisis 1998 after experienced shift Another currency. foreign of inflow of source attractive most were countries developed western of Because, most countries. developed with western cooperation and partnership economic on in agreements formalized of were form policies the trade Russian 1990s of mid the By policies. market free liberal to rigid from focus its shifted policy trade Russia’s 1991, 1991. before After changed hardly rate exchange overvalued and prices domestic pegged The Union. Soviet of characteristics main two the were trade foreign on monopoly owned state and protectionism rigid The them. is remove to it difficult granted; very restraints import once because restraint import disparity than better huge always were restraints Firstly, export secondly, reasons. and prices economy international and political domestic between main two to due protection The trade liberalization in Brazil started from late 1980s. Brazil Implemented h Rsin oenet rfre epr rsrit ahr hn import than rather restraint export preferred government Russian The Trade OpennessDriverofEconomicGrowthinBricsNations

5 Volume 9, No 1, January-June 2018 6 Journal of International Economics • • were domestically scarce. The exchange rate and international prices played very played prices international and rate exchange scarce. The domestically were increased the supply it of machinery,that equipment’s, way raw materials and a intermediate goods which such in designed were imports all of percent 90 imports. as well as exports controlled Commission Planning State The planning. economic China: economy withmarketforces. open more to shifted gradually government Indian The programme. liberalization turned this crisis into an opportunity and lunched a comprehensive and systematic government Indian 1991. in crisis B-O-P into resulted expansion fiscal support to expansionary fiscal policy. However, unsustainable internal and external borrowings were freed from industrial licensing. This ad-hoc liberalization was accompanied by been on Liberalization, openness and has export reforms promotion these activity.of focus main By The 1990, rates. 31 tariff the sectors in reduction substantial and restrictions, quantitative of removal procedures, of simplification included economy period Indian the globalize and liberalize adapting to the path to of openness. The major trade policy changes in the reforms post-1991 of series a introduced India of Government the mid-1991, From economy. a diversifying of and needs developing ever-increasing the meet to chemicals and materials, raw products, petroleum goods, capital of mainly consist imports while items, non-traditional and traditional of range wide a cover exports The direction. and composition of growth, terms in change major a undergone has trade foreign India‘s years, sixty last the Over mid-1980s. the in began strategy liberalisation trade External rates. tariff the on concessions large with Ministry the from license a required longer no were list OGL the in items whereby (OGL) Licensing General Open introduced India of Government the 1976, In restrictions. import effectssufferedof adverse industries However, in 1976, the liberalization strategy was initiated again as in the late 1970s, domestic criticism. Thus, policy makers reversed the policy of import liberalization. fetched this but exports on subsidies the in reduction and imports of liberalization the towards steps took again and Rupee Indian devalued India Bank World the of India adopted comprehensive import control until 1966. In 1966, under goods. the consumer pressure on restrictions import complete with tariff high by characterised was policy trade Indian process. liberalization of reversal the for responsible was 1st plan (1951-56). from Nonetheless, the Balance of Payment (B-o-P) crisis liberalization in 1956-57 progressive adopted India independence, after manufactures other and goods consumer light of composed were imports while crops, plantation and other commonwealth countries. Exports consisted chiefly of raw materials and Britain to confined mainly economy.were agricultural relations Tradeof and colonial typical was India of trade foreign 1947, in independence of time the At India: adjust itslawsaccordingtotheWTO standards. to government Russian compelled WTO, the in entry an by followed negotiations, Russian trade. encourage to instruments political and trade existing of range wide goods domestically as well as internationally. Russian government started to apply Russian of competitiveness enhanced which Rubble of devaluation to due is This ro t lt 17s hn’ tae a cmltl dtrie b their by determined completely was trade China’s 1970s late to Prior positive, and statistically significant in line with theoretical expectations. Dashand expectations. Sharma (2008) theoretical has applied Engle with and Granger two-step line co-integration analysis for in the significant statistically was and growth economic positive, on openness of effect the that found and 2010 to 1989 from period the of data annual the using by China analysis data India, panel via Turkey, Russia, BRIC-T) and Brazil, markets; (emerging countries developing rapidly most the for searched was growth economic on openness trade of effect the studied has must be promoted in BRICS nations to enhance economic growth. Mercan of BRICS countries over the sample period 2004-2012 and found that, trade openness between trade openness and long-run economic growth through relationship heterogeneous panel the analysed has (2016), Chakraborty and and Bharali BRICS. in theoretical openness many growth and openness empirical studies have been under-taken to assess the role of foreign trade and trade on literature existing the Reviewing Review ofLiterature • rates. Exportsubsidies,importsurchargesandNTBs werephasedout. ad-valorem to rates tariff non-ad-valorem replaced structure, tariff Africa’s South government the also initiated rounds, to be a part Uruguay of the free trade in agreements. The trade part reforms simplified taking Africa tariff South through With liberalization. openness greater to promotion export from strategy development Africa’sSouth in shift with coincided Africa South in election democratic 1994, In surcharges import Moreover, were also gradually restrictions. removed by 1995 with reduction in the 1980s quantitative quantitative restrictions. During its regime. reduced open Africa more South to countries approach other its some shift to of Africa growth South initiated export-led and reserves gold on dependence than rather to restrictions prior quantitative tariffs. However, decline in the contribution of ISI on strategies towards growth, heavy strategies based was (ISI) ISI during industrialization Protection substitution 1970s. Import Africa’s South responsible was for 1960s and 1950s of attitude pessimistic Export South Africa: in percent 1982 to15percentin2001. 56 almost from reduced regimes was import tariff the statutory transformed average which The completely. 2001 in WTO the of member a became China time the By regimes. rate exchange of policy adopted simultaneously and licenses import removed items, various on restrictions removed list, substitution import of abolition announced officially government Chinese The NTBs. of scope On the other hand, government also took some important steps standard. to international gradually to reduce closer came and system trading liberal a to shifted and tariff in reduction announced China protection. domestic offering time, same the at and, incentives giving by system promotion export through export encouraged of goods, import licensing etc. Thereafter, in early 1990s, the Chinese Government as such tools of array controlling other number of authorized companies adopted to carry out trade, also controlling on range but NTBs and tariff adopted only not composition adversely affected this allocation Hence, of imports. resources and and exports economic China’sgrowth. of China composition determining in role little Trade OpennessDriverofEconomicGrowthinBricsNations et al.,(2013)

7 Volume 9, No 1, January-June 2018 8 Journal of International Economics ewe oens ad cnmc rwh lhuh h sz ad efr gis are gains welfare and different. size the although growth economic and openness between trade openness and growth. Many empirical findings suggested a positive relationship In short review of literature indicates that most of study shows the relationship between openness andgrowthindicatedbi-directionalcausality. results, test causality Granger prediction to According openness. compared trade of and measures several data of panel using growth on openness trade of effect the including indicators studied (1996) Harrison indicators. policy Sachs-Warnertrade the other and indicator existing nine of use the to relationship therobustness openness-growth the of tested (1998) Edwards services. financial and telecommunications basic sectors, service key two for constructed are measures openness Such regime. services country‘s a of openness the of measure outcome-based than rather based Mattoo growth differs from countries. that of liberalization of trade in goods. Second, fordeveloping it suggests a policy- especially et al., (2001) has explains how the impact of liberalization of growth, service sectors on output with associated significantly specifications, most in and, positively are barriers trade that show results estimation The literature. existing the with consistent mostly are ratios intensity trade numerous for a cross section of countries over the last three decades. The regression results for measures openness of number large a using growth with relationship straightforward growth. Yanikkaya (2002) explained that trade liberalization does economic not on impact have positive a has a trade that simple recognized and and 1950-2007 period time Data and Variables: Research Methodology 4. 3. 2. 1. BRICS nationsinpostreformera. The main objective of this study is examined trade openness and economic growth in Objectives ofStudy : The data and other relevant information required for the study have study the for required information relevant other and data The Source ofData: set areconvertedintotheir logforms. For study,the data- in GDP). openness estimations trade the and out GDPdata-set the carrying import/ + (export variable independent the as openness trade and GDP as referred be will it hereafter variable, dependent as product domestic Gross used study growth. The economic on openness trade effectof the analyse to 2016 to 1991 period time the for Africa South and China India, Russia, Brazil, namely 6), 1... (N= To somesuggestionsforfurtherpolicyimplication. provides level inselectedcountries. To analyse the relationship between trade and GDP and at aggregated aggregated and To thetradeopennessindexofBRICSnation’s calculate during1991-2016. disaggregated at gross GDP and import export, disaggregated levelofBRICSnations. of growth the analysis To h aayi i bsd n ae dt fr RC nation BRICS for data panel on based is analysis The

Y= ab trend equationisdefinedas: compound growth rate of trade at aggregated and disaggregated level an exponential an exponential is function fitting to the rate by available data. computed We growth is have the rate The following model growth to rate. compute compound the compound The rate. a growth compound at called grow to said is it annum, per percentage When the time series data relating to a variable increases or decreases by a constant Compound GrowthRate Model Specification is calculated with respect to GDP (Gross Domestic Product) of BRICS Nations in US$. is to analyze the elasticity of trade at aggregated level and disaggregated, so elasticity objective another level. The disaggregated and level aggregated at nations BRICS in trade (import and export at aggregated level) we calculated compound growth of trade of growth in pattern and trend analyse to is objectives our of one Since analysis. our statistical techniques relevant to the data. We have taken absolute time series data for the in applied study is essentially descriptive. method In addition to this, we The have also used econometric Issues). and (Various Journal Economic Trade Foreign Indian Indian and Finance, Review and Currency on Report(s) of and Survey Bulletin “Economic RBI Statistics”, India”, Financial International IMF‘S” been have Data of source main The sources. International and national various the from collected been Ln Yi= bo*+b1LnXi+ui As weknow, Ln e=1 Assume, bo*=Lnbo when variable dependent of independent variableassume zero. value be will what explains which term intercept is bo bo andb1isregressioncoefficient Ui= errortermthatsatisfytheallOLSassumption Xi= GDP (GrossDomesticProduct)inUS$ofBRICSnations Y= Trade ataggregatedanddis-levelofBRICSnations Ln Yi= Ln bo+b1LnXi+ui Taking naturallogbothsides Yi= boXi from 1991to2016. GDP to respect with trade of relationship the check to model following the have We CARG (g%)=(Anti-logb-1).100 following the using by computed formula: is (CARG) growth of rate annual compound The Log Yi= Loga+tb The logarithmictransformationofthisfunctiongives: t= Time Period(1991,1992,19931994,-2016) b= 1+gandgisthecompoundgrowthrate Y= Trade (Import,Export,GDP) t b1 e ui Trade OpennessDriverofEconomicGrowthinBricsNations

9 Volume 9, No 1, January-June 2018 10 Journal of International Economics BRICS nationsandworld. all in digit double to reached is trade of rate growth compound which in period boom is 2001-2010 of decade short In association. this under rate growth highest achieving economies leading two is India and china category.period this entire in For last on is Russia whereas china following growth of top on is India and nation BRICS all in digit China. With this second sub period achieved higher growth rate and reached to double growth of trade is badly hampered in all BRICS nation and reached to negative except period sub third in that show Results nations. BRICS in 1991-2016 i.e., period entire level in three sub period after reform as 1991-2000, 2001-2010 and 2011-2016 and for Table 1 represents the compound growth rate of trade at aggregated and disaggregated Source: Author Calculation Results andDiscussion Source: Author Calculation average. period whole world in twice For than more low. and China by lead very again and significant is is export of it growth India in whereas China and India to except reached but negative export of growth in decrease drastic only not showing is period sub second that reveals also figure this With India. by followed and level disaggregated and aggregated both at nations BRICS among export is of growth China highest with that top on explores Analysis nations. BRICS in 1991-2016 i.e., period entire for and 2011-2016 and 2001-2010 1991-2000, as reform after period sub three in level disaggregated and aggregated at export of Tablerate growth compound the shows 2 Source: Author Calculation Entire Period(1991-2016) Entire Period (1991-2016) Sub Period3(2011-2016) Entire Period(1991-2016) Sub Period3(2011-2016) Sub Period3(2011-2016) Sub- Period2(2001-2010) Sub- Period1(1991-2000) Sub- Period2(2001-2010) Sub- Period1(1991-2000) Sub- Period2(2001-2010) Sub- Period1(1991-2000) Table 1:Compound GrowthRateofTrade (Import&Export)inBRICSNations Year Year Year Table 2:CompoundGrowthRateofExportinBRICSNations Table 3:CompoundGrowthRateofImport inBRICSNations Brazil Brazil Brazil 10.2 10.6 10.0 16.7 16.1 17.5 12.7 -4.7 -5.8 -3.8 9.3 6.2 China China China CompoundGrowthRate(CAGR%) CompoundGrowthRate(CAGR%) CompoundGrowthRate(CAGR %) 17.6 17.4 17.7 21.5 16.6 22.2 17.3 20.6 15.8 4.7 5.4 3.8 India India India 15.6 15.8 15.5 0.04 23.6 22.5 24.5 12.2 11.7 11.1 -1.6 -2.9 Russia Russia Russia 10.7 10.4 19.0 18.6 19.7 11.4 -7.6 -7.6 -7.7 3.4 6.7 2.1 South Africa South Africa South Africa 14.0 12.9 15.2 -5.4 -4.7 -6.0 7.9 8.5 7.4 4.5 3.6 5.4 World World World 11.4 11.6 11.2 -0.8 -0.7 -0.7 7.9 7.9 7.9 6.8 6.8 6.8 with samecompetitor. Whole also. world in even is period is depicting a low positive and significant growth very of import and again china and is on top China and India except all countries in negative three to reached again but rate growth double than more with association of countries all in period sub second in highest reached and time over rising also is for entire period i.e., 1991-2016 in BRICS nations. Figures show that growth of import and 2011-2016 and 2001-2010 1991-2000, as reform after period sub three in level Table 3 provides the compound growth rate of import at aggregated and disaggregated first sub period elasticity of trade is highest in India and South Africa with more than more with South Africa and India in highest is trade of elasticity period sub first in that reveals nation of GDP is change unit one with countries all of trade in change for responsive and the measure 2011-2016elasticity nations. As BRICS and in 1991-2016 i.e., period 2001-2010 entire 1991-2000, as reform after period sub three in level disaggregated and aggregated TableGDPat to respect with trade the reveals 5 2. Value inparenthesisshowsp-value. 1. *Representsthesignificanceofparameterat5%level Source: Author Calculation significant forallBRICSnations. GDP is declining in all three nations whereas except China and Brazil India and by turned to positive followed and growth this in top on is Russia and association of selected nations all in period sub second in highest is GDP of rate growth compound that explore Analysis nations. BRICS in 1991-2016 i.e., period entire for and 2011-2016 and disaggregated level in three sub period after reform as 1991-2000, 2001-2010 and aggregated at Product Domestic Gross of rate growth Tablecompound the reveals 4 Source: Author Calculation Entire Period(1991-2016) Sub-Period3 (2011-2016) Sub-Period1 (1991-2000) Sub-Period2 (2001-2010) Sub-Period1 (1991-2000) Sub-Period2 (2001-2010) Sub-Period3 (2011-2016) Entire Period(1991-2016) Table 4:CompoundGrowthRateofGrossDomesticProductinBRICSNations Year Table 5:ElasticityofTrade (Import&Export)inBRICSNations Year Brazil Brazil (0.001) (0.008) (0.00) (0.00) 18.9 -8.1 7.9 0.7 8.5 0.9 0.7 1.1 China China (0.039) CompoundGrowthRate(CAGR%) (0.00) (0.00) (0.00) 15.1 10.7 19.4 12.6 1.3 1.1 0.5 1.2 Trade OpennessDriverofEconomicGrowthinBricsNations India India (0.15)* (0.00) (0.00) (0.00) 10.1 15.5 -0.5 4.2 6.4 1.7 1.5 1.5 Elasticity Russia Russia (0.001) (0.00) (0.00) (0.7)* -0.08 21.9 -8.2 -7.8 8.6 0.9 0.9 0.9 South Africa South Africa (0.008) (0.01) (0.00) (0.00) 12.9 -6.6 5.6 0.8 1.7 1.0 0.8 1.3 World World (0.29)* (0.00) (0.00) (0.00) 5.5 1.0 8.5 3.6 1.8 1.0 1.1 1.4 11 Volume 9, No 1, January-June 2018 12 Journal of International Economics one tradeelasticityforallnationsofgroupexceptRussia. but insignificant also. Entire period is also revealing positive, significant and more than with lead of India whereas in third sub period India trade elasticity is not only negative nation all in value alpha than less is value p as significant and elasticity one to equal than greater showing is nations all period sub second In Russia. except nations all in (0.05) value alpha to comparison in low is value p as significant and change unit one Source: Author Calculation 1992 1991 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2012 2013 2015 2014 Year 2011 Brazil 0.187138 0.192785 0.159356 0.149005 0.139502 0.152371 0.155985 0.199713 0.210049 0.257138 0.263363 0.282443 0.260414 0.245274 0.264744 0.213691 0.216363 0.227356 0.242464 0.253012 0.268425 0.248351 0.250711 0.16691 0.25002 Table 6:Trade OpennessIndexofBRICSNations China 0.290652 0.309367 0.298485 0.408837 0.384724 0.376949 0.386378 0.360623 0.376433 0.438616 0.426904 0.472073 0.562554 0.648064 0.676062 0.695674 0.670879 0.616667 0.481613 0.543759 0.515428 0.486145 0.470227 0.401026 0.458823 0.191527 0.207368 0.217709 0.238256 0.251474 0.248615 0.252191 0.256987 0.290053 0.281854 0.295686 0.306648 0.352749 0.410097 0.450667 0.439922 0.540615 0.449064 0.471662 0.552847 0.577595 0.555332 0.432748 0.529511 India 0.17626 Federation Russian 0.259861 0.270693 0.303906 0.348723 0.442525 0.486369 0.705412 0.679585 0.612037 0.594448 0.593357 0.565999 0.566673 0.549178 0.519571 0.535937 0.488357 0.505766 0.516329 0.513103 0.510629 0.536534 0.520429 0.47915 0.59849 Africa South 0.380242 0.390801 0.407448 0.435977 0.466092 0.468007 0.489638 0.468616 0.513857 0.550961 0.599898 0.516129 0.528602 0.597491 0.628992 0.719971 0.538643 0.547621 0.600329 0.642324 0.623957 0.511642 0.37534 0.60741 0.64375 World 0.364956 0.367923 0.360058 0.378315 0.408034 0.422552 0.439758 0.437401 0.477173 0.464835 0.464466 0.480839 0.518688 0.543937 0.575825 0.593351 0.622032 0.524614 0.570763 0.607441 0.604378 0.600922 0.555305 0.43958 0.60369 showing self-dependence in terms of trade. Whole period is exhibiting positive and signifipositive exhibiting cantvalueofimportelasticity forallBRICScountries. is period Whole trade. of terms in self-dependence showing insignifibut and elasticity also import cant of value low very only not representing is and all elasticity nations representing import positive and signifihighest with leading cant valueis but inIndia third sub periodperiod China sub second in whereas nations BRICS under value alpha common than more is value p as elasticity import of value signifi showing is cant which Russia except nation all in fi period in sub one rst than trade India period sub third more insignifi also but in is negative elasticity only import not whereas The is also. elasticity cant India of lead with nation all in value than less alpha is value signifi p and as elasticity cant one to equal than greater showing is nations all period sub second In competitor. same with top on is china again and signifi import and of positive growth a cant depicting is period Whole average. world signifiis import of growth period in twice than more and China by lead again and cant whole For India. by followed and period time both of at level disaggregated nations and aggregated BRICS among export of growth highest with top on which and is nations China in BRICS world. all in period digit double boom to reached is is trade 2001-2010 of rate of growth compound decade that explores study of Inferences Conclusions andPolicyImplication more is Africa South in but Brazil in liberalization liberalized intheworld. less is there that shows index openness is increasing in BRICS countries in all over the time period. Trade openness trade of trend The liberalization. of form the in increased period Tradealso openness reform Post the after but more not is openness trade period reform pre the In period. Trade openness index shows the ratio of trade and GDP in BRICS nations in selected 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0

1991 1992 Figure 1:Trade OpennessTrend inBRICSNations 1993 1994 1995 1996 Brazil South Africa India

1997 Trade Openness Trends 1998 1999 2000 2001 2002

2003 Trade OpennessDriverofEconomicGrowthinBRICSNations 2004

World 2005 China Russian Federation 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 13 Volume 9, No 1, January-June 2018 14 Journal of International Economics Edwards, S. (1993). Openness, trade liberalization, and growth in developing countries, Eckhard, Siggel & Agrawal, P. (2009). The Impact of Economic Reforms on Indian on Reforms Economic of Impact The P.(2009). Agrawal, & Siggel Eckhard, Know? WeReally Do What Growth: and Productivity Openness, (1998). S. Edwards, Dash, R. K & Sharma, C. (2008). Government Expenditure and Economic Growth in Growth Economic and Expenditure Government (2008). C. Sharma, & K R. Dash, Narrative, Growth Analytic an Independence, Since India (2004). B. long, De the for Prepared Paper Growth”. Kraay,Tradeand Institutions & Dollar,(2002). D. A. hn R, atn L & oya NV (09. pnes a b Go fr Growth: for Good be Can Openness (2009). N.V. Loayza, & L. Kaltani R., Chang Anwer, M. S. & Sampath, R.R. (2001). Exports and Economic Growth, Greater to Led Reforms Policy Trade Have (1997). J. Nash, & S. Andriamananjara, huai, ..20) Eooi rfrs n ni sne 91 Hs Gradualism Has 1991: since India in reforms Economic M.S.(2002). Ahluwalia, Anthony P., Thirlwall (2000). Trade, Trade Liberalisation and Economic Growth: Theory References protected previously for industries. imports from competition increased brings also openness potentially can economy reduce poverty through world the creation of new jobs the in export industries. However, with greater integration increased The economy. the in growth high achieving in nations BRICS to helped certainly has era liberalization post Policy ImplicationoftheStudy- The policy implication of this study also indicates that rr, . Gmadla 20) Te lblzto o te otae Industry: Software the of Globalization The (2004). Gambardella & A. Arora, Productivity,F.Tradeand (2004). Alcala, Ciccone & Journal ofEconomicLiterature, auatrr: vdne rm Sal ape Survey, Sample Small a from Evidence Manufacturers: The EconomicJournal, India, IUP JournalofPublicFinance, pp: 184-204. D. Rodrik (ed.) “In Search of Prosperity”, Princeton, NJ, Princeton University press, D.C. Carnegie- Rochester Conference Series on Public Policy, World Bank, Washington pp: 33-49. The Role of Policy Complementarities, Complementarities, Policy of Role The Journal Washington DC: The World Bank. Countries?, Developing in Openness August 2009. Worked? JournalofEconomicPerspectives and Evidence,EconomicResearchPapersNo.63. esetvs n Opruiis o Dvlpd n Dvlpn Country, Working PaperNo.10538 Developing and Developed for Opportunities and Perspectives 119(2), pp:613-46. , 47(3),pp:79-88. 108 (1),pp:383-398. . 31(2), pp:1358-1393. 6(4),pp:60-69. 90(8), Economics, Development of Journal ol Bn Wrig ae N. 1730 No. Paper Working Bank World , 16(3),pp:67-78. Quarterly Journal of Economics of Journal Quarterly 37, Profile, Asian Indian Economic NBER , ,

rud C & oay B (08. rd, euain, n Income, and Regulations, Trade, (2008). B. Bolaky, & C. Freund, Romer, D. (1993). Openness and Inflation: Theory and Evidence, and Openness Trade (2015). Lau Wee-Yeap & Hye Adnan, Muhammad, Qazi, Openness Trade of Impact (2016). Chakraborty Kumar Deb & Bharali Priyanka, Development, Economic of Mechanics the On (1988). Robert Lucas, 1980-2005, India: in Growth Economic of Politics (2006). A. Kohli, Economic and Openness Trade Spending, Government (2008). Mallick Hrushikesh, Sachs, Jeffrey D. & Andrew, Warner (1995). Economic Reform and the Process of Process the and Reform Economic (1995). Warner Andrew, & D. Jeffrey Sachs, Monetary Economics and Management, India, from evidence Empirical Growth: Economic BRICS, of Commerce, 21(2),pp:67-74. Case The Growth: Economic on Political Weekly, 6(2),pp:45-52. Growth inIndia: A Time Series Analysis, WorkingPaper403. Development Economics Economics Global Integration, , 108(5),pp:869-903. 16(1),pp:45-56. Brookings PapersonEconomic Activity, pp:1-18. , 22(1),pp:3-12. , 87(1),pp:309-321. Trade OpennessDriverofEconomicGrowthinBricsNations iysgr nvriy ora of Journal University Vidyasagar Journal of Business Economics Business of Journal Quarterly Journal of cnmc and Economic Journal of Journal of Journal 15 Volume 9, No 1, January-June 2018 16 Journal of International Economics Imports, DomesticandWorld GDP Keywords: InvisibleReceipts,Payments,Balanceof Exports, our tradedefi cit. at CAD maintain to payments sustainable of level, but also balance reduces our India’sdependences on external in assistance to fi role important nance an play only not traditional and transfers private invisible that concludes paper the Invisibles, of role visible the observing By services. to compare disturbances external the to sensitive more are income Investment and services Modern that found is It balance. Invisibles the in growth stable fi a global experienced post crisis,India that nancial observe level, we disaggregate more in analysis descriptive employing After regime. economic attempts to highlight the role of Invisibles in Indian Balance of Payment under the new an important mitigation to avoid the current account risk of the Indian BOP. This paper structural reforms in the year 1991 there was huge growth in Invisibles which became fito account capital on depend to risky very is it Hence defi trade the nance to Due cit. which are highly volatile in nature and opportunistic as experienced in 1991 and 2009. Payments. The capital account balance which mainly comprises of foreign investment to fi nance the trade defi cit is not a healthy and long term solution for Indian Balance of by the capital account surplus. The heavy dependence on the capital account surplus India faced the the huge trade provided defi has cit which and current receipts, account defi invisible sustained support to the current account balance. in Due to higher imports than citexports, which growth was always strong by fi nanced marked been have During the last two and half decades, the developments in India’s Balance of Payments Professor, Department of Economics, Sri Sathya Sai Institute of Higher 2 Learning, Prasanthi Nilayam, Puttaparthy, Head, Department of Economics, Sri Sathya Sai Institute of Higher Learning, Prasanthi Nilayam, Puttaparthy, AP1 place took which crisis the fact In trade. with along service International the of growth rapid the was 1990’s the in economy Indian the of features striking most the of One Introduction AP andcanbe [email protected] and [email protected] An EconometricAnalysisofInvisiblesin Journal ofInternationalEconomics SN07-72 Volume 9,No.1,January-June2018,pp.16-31 ISSN 0976-0792 Raghavender RajuG India’s BalanceofPayments 1 andVishwanath Pandit 2 h cret con dfct pr fo fnnig h tae eii ad CAD,italso and deficit the trade provides ample opportunities for the job creation. It financing was only after the from 2 Apart deficit. account current the financing of source save and alternative an became receipts invisible of surplus huge The surplus. Invisible of growth rapid and sustain was there 1991 in economy the of major part of the trade deficit by the capital account of BOP. With the structural change the finance to used we when risk of lots invite used Traditionallywe account. current of deficit trade the financing in role excellent an playing are balance invisible overall the receipts, income Investment and exports software of rate growth in fall of Despite recession intheworldeconomygeneral. and particular in economy US of down slow to due difficulties growth of facing 30% maintain started to present even at 2005-06, in growth of 60% around were which services The States. United by consumed were services modern Indian the of 60% around side other the On reserves. in fall to due heavily affected income investment reserves which came down by almost US $ 50 billion after the global crisis. Growth of Forex of amount the by determined primarily were receipts income Investment rate. East the Asian after crisis i.e., (1998-1990). Both rate services growth and negative the of income year experienced one the volatile only growth across came which stable payments. Invisible in Observing at a disaggregate rise level it is found that growth in the transfers receipts quick were no was there way same the In tourism. especially the main sources of the invisible receipts were private remittances and traditional items in software industry it started increasing heavily. and Prior to the growth aggregate of modern services at both reforms. First 5 years there was a payments smooth growth of invisible receipts, but and due to the boom after immediately rise receipts sudden no was there that observed Invisibles we level disaggregate of data the at Looking came downto1.7%oftheGDP form4.7%in2012-13. thelast In CAD which to due deficit trade of 78% covered account. Invisible also 2013-14 i.e., year fiscal trade on deficit the exceeded Invisibles from earning because notonlycovered it surplus in 2001-04 also was account current our years From three these during but deficit trade the tradedeficit. the of surplus theInvisibles 82 percentage 2000-01 year the financed fiscal the In deficit. account current the curtail and deficit trade the finance to crisis the after since helping is balance Invisible the in transfers, there was the consistent huge surplus in the Invisibles balance. This surplus Due to the rapid growth in IT sector, software and communication along with the private the contrast in while in manufacture sectorhasbeenlessreboots. imbalance trade high the of despite services enable IT the was the dynamism of the service sector particularly in the information technologyand phenomena of the high growth rate (nearly double digit) prior to world economic crisis important the of One reforms. economic the by performance this in aided agreeable out into one of the growing economics in the world over the last two and half decades, in the year 1991 was boom in disguise for the Indian economy. Our country has turned reforms, theroleofinvisible becamevisible. An Econometric Analysis ofInvisiblesinIndia’s BalanceofPayments nd generation of 17 Volume 9, No 1, January-June 2018 18 Journal of International Economics effect is obtained even when exports of merchandise is replaced with total trade in trade total with replaced is merchandise of exports when even obtained is effect similar services, in export to networks its use can market goods in penetration with high country a wherein effects network to due a exports service exert on exports influence positive merchandise that suggested They exports. service of externalities particularly exports merchandise manufacturing exports, and exports of services, trade in exports of goods between has positive complementarity the about talks (2012) Gupta and Eichengreen experts service of growth the explaining with connection In to healthservices. equity, access for efficiency, and trade quality this of implications negative as well as positive the and trade, this in players global main the traded, be can which services health in ways various the highlights also study The context. this in multilateral and regional national, the from learnt be can that lesson the and services health in trade macroeconomics and health (CMH) provides an overview of the nature of International loss of investor’s confidence. Chanda (2001) trade in health services of commission on 1980’sthe over imbalances sufferedalso a it to due problem account capital the from and he found that the economic crisis of the 1991 was primarily due to the large fiscal (2013) Mathew made was BOP India’s the of challenges and trends the on study A India’s BalanceofPayments. in deficit the curtailing in trade Invisibles in growth of importance the explains also He i.e., ever since independence India has never come reforms across such surplus in Invisibles. economic the before that found and (2011) Limba by highlighted was BOP India’s in Invisibles of trends The deficit. account current Managing the controlling by payments economy: globalizing a in challenges India’s external sector has highlighted the overcoming role of invisibles in balancing the his Balance topic of in the (2005) Reddy on economy. global lecture service-oriented new the of miracle a like is Payments, of Balance which is built India’sup of a large trade deficit the maintained by large positive invisibles receipts, of condition the crisis though the (2001) after Venkataramanandecade a within experienced county which growth the identifying In are regime economic new included here. the under payments of Balance Indian the in Invisible’s ofthe review the Here i.e., study present the to related worthy. closely are which scholar’s that praise by done studies few really are field this in experts and scholar’s Though the study in the invisibles are limited but the contribution made by the various Accumulated Wisdom • • • Objective oftheStudy disaggregate level. and aggregate at payments and receipts invisible of determinants the Toidentify receipts andpaymentsatadisaggregatelevel. Invisible in appears which item various of rate growth and trends the Toanalyse of Balance Indian in payments and receipts payments. Invisibles of role the highlight To competitiveness ofallfirms inopeneconomics,nomatterwhattheyproduce. growth for some countries, but more important is that of they are engine a be key determinant can of the Services wide-growth. economy and trade, of effects distributional the volumes, trade of determinant and important an sectors, be may service policies, service the the on thus of performance the and gain, welfare potential of major source of a source is liberalization services increasing the in Evidence agreements. the gains potential examines the that effortand services, from greatertrade trade through liberalization such to achieve tocooperate in contributions investment the and trade on international of centres determinants (1997) Hoekman items such on Disusing role. important very a play etc., services health services, financial services, consultancy services, in trade which in part miscellaneous of consist also Invisibles and industryexperts. academicians makers, policy the of attention the caught has recession global of face India’s IT industry has in India’s technology based economic development even in the growth are confined to the developed world. Mohapatra (2003) explains that the role of realization of the potential that IT offers for human welfare, IT- induced productivity and the revenue declines in their country. receipts Joseph (2002) explains that service there is an non-factor increasing especially software The and IT and technology.enables accounted gems 60 percent fall informational from textiles, due the and to with chemical along jewellery, suffered also services enables IT and software sectors, oriented of export Specially, Balance (2010). India’s Viswanthan by of examined items was Invisibles payments the on affects its and crisis economic World increase inmigrationandtotalearningsofmigrants. the by explained be can time over growth their that finds and India to remittances of determinants the analyses Gupta (2006). Gupta Poonam by undertaken recently was the is improvement in it the balance of payment situation in India. One of the pioneering study that opines the article to contributors main are components his which remittance private and In exports services all in surges (2012) credit, Acharya terms. and net loans in determined foreign being on payments service including income investment and services non-factor transfers, private into disaggregated been have eclectic with side supply up and factors. In the “IEG-DSE” structural model for supply India by Krishnamurthi (2004) come Invisibles both combining they invisibles, (2009) for functions Ghosh import and and exports Sachdeva by examined and was exports and this imports invisibles between relation complementarity exists There can alsobesuccessfullyappliedtotradeinservices. trade merchandise of model conventional that show studies most that expound They and 2008. to 1970 from side period the for services of demand exports of growth in factors the side supply by played role the Explore (2011) Kumar and Banga goods. and liabilities, property and labour, and cross-border transfers, comprising both public transactions of effect combined assets non-resident with associated income services, in trade international to relating the represents BOP the of account invisibles The Analysis ofInvisiblesin India’s Bop An Econometric Analysis ofInvisiblesinIndia’s BalanceofPayments 19 Volume 9, No 1, January-June 2018 20 Journal of International Economics Invisibles withthesimplegraph. a miracle of the new service-oriented global economy. We can show the visible role of up of a huge trade deficit is mainly sustained by large positive invisible inflows, is truly i.e., 2% of GDP for nearly two decades. The India’s balance of level payments, threshold which below is deficit built account current the minimize to country the helped also has Invisibles the from earning high persistent The Privatization). and Globalization reserves at around $ 318 billion under the new economic policy exchange regime (liberalization, foreign with 2011-12 & 2008-09, 1995-96, in except surpluses persistent and large the recorded has Payments of Balance Indian the Invisibles the of support has provided which the receipts, sustained invisibles support in to growth the strong current by account market position. been In have fact developments it was the constant and private transfers. In the last two and half decades the India’s Balance of Payments buoyant inflows of private transfers. The receipts in the nonfactor-service increased nonfactor-service the in receipts The transfers. private of inflows buoyant the Business service receipts reflecting the high skillof intensity of the Indian workforce and prominence rising mainly exports, services Non-factor the in increase the contributed by largely is invisibles threshold Net the below in CAD increase GDP. the The the back of 1.7% bring i.e., to level able were we billion 115 $ to billion 107 $ the from invisibles net the in increase the with Again Invisibles. the by covered was GDP the of deficit trade 58.8% year that since words other GDP In reforms. of economic the 4.2% after of CAD high historical the Net witnesses the we by and supported surplus was invisibles GDP of 6% this of GDP,out of 10.2% $189 was was which deficit billion trade the that Before Invisibles. in surplus milliondue million $-88163 107493 $ only the was to account thecurrent but million $-195656 was deficit trade the 2012-13 year the In receipts. huge the by CAD sustainable the maintain to cushion a provided which support visible Invisibles was it But CAD. high the for way which thehugetrade by visible much caused paved turn in which positive never payment was trade our that shows graph very above deficit. The theexternal was of Invisibles risk of the minimizes contribution significantly the 1991 the from Since Source: RBIHandbookofStatisticsonIndianEconomy2015-16. Figure 1:India’s Trade, InvisibleandCurrent Account Balances In US Million $ -250000 -200000 -150000 -100000 100000 150000 -50000 50000 0

1990-91

Trade Balance 1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 INVISIBLES 1998-99 1999-00 2000-01 2001-02 YEAR 2002-03

CURRENT ACCOUNT BALANCE 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 shows thecontributionofinvisiblesinIndianbalancepayments. clearly table 2012-13. The in billion 80 $ to 2013-14 in billion 78 $ from payments the in fall was there and 2012-13 in billion 145 $ from 2013-14 year the in billion $151 to level. Even though the CAD has crossed the 3% threshold level in year 2011-12for year in level threshold 3% the crossed has CAD the though Even threshold level. the above was CAD the 2012-13 and 2011-12 year the in billion 195 and billion 189 of deficit trade huge this of Because history. independent post- India’s in level unprecedented an 2012-13, in 10.4% to 2002-03 in GDP of 2.1% from steadily widened India of deficit trade merchandise the time same the GDPat But 2012-13. in Asthe deficit. trade Table worsening clearly shows increase in invisibles to steady from the 2.1% of GDP in due 2000-01 to 6.2% of widening been have deficits account current trade, Invisibles in surpluses significant of Despite surplus. invisibles the by covered was deficit trade the of 82% 2001-02 year the In 2000-01. from i.e., reforms economic the of generation second the rapidly grew invisibles net the in Surplus The what wehadduringthetimeofBOP crisis(1991). is turning worst and in 2011-12 it not only cross the threshold level but was more than year the CAD was 0.4% of the G.D.P. This Since years. then the current account situation of India 3 these in invisibles next very the the in But surplus. capital the by accompanied was surplus account current of earning strong the to due mainly was This • • • result intheyear2001-02,2002-03&2003-04wehadsurpluscurrentaccount. a as account trade on deficit the exceeded even invisibles from earning This deficit. account current the finance to capital the on less depend to country the made which invisibles the by covered was deficit trade the of % 71.4 1993-94 year trade the In the depict. of percentage large the covering by CAD the curtail to role vital a playing is Invisibles reforms economic the from right but 2012-13 year fiscal the in only Not Source: RBIHandbookofStatisticsonIndianEconomy2015-16. In the year 2003-04 current accountwas2.3%oftheGDP.In theyear2003-04current accountwas1.2%oftheGDP.In theyear2002-03current accountwas0.7%oftheGDP.In theyear2001-02current 2015-16 2014-15 2013-14 2012-13 2000-01 1999-00 1994-95 1993-94 2011-12 2010-11 Year Table 1:Contribution ofInvisiblestoTrade Deficit Percentage ofTrade DeficitCoveredbyInvisibles An Econometric Analysis ofInvisiblesinIndia’s BalanceofPayments 59 65 82 81 78 55 82 74 63 71 21 Volume 9, No 1, January-June 2018 22 Journal of International Economics in thepaymentsofInvisibleswaschieffactorsforhighcurrentaccountdeficit. the from billion 107 $ the $ to 111decrease invisibles billion in net the the previous 2012-13 year 2011-12.year the So In along with CAD. growing trade deficit, increasing high the to leads also but surpluses invisibles net the reduces only not invisibles the same magnitude to reduce the net invisibles surplus. The increase in the payments of $7.4 billion in the year 1990-91 to $ 233 billion the payments is also shooting up at the and growth of the services. Though the Invisibles receipts has sharply increased from invisibles receipt is mainly contributed by the huge private remittances from the abroad of the GDP in 2013-14 compare to 4.7% of GDP as previous year. The increase in this 1.7% the to down came CAD the billion 233 $ to billion 224 $ from receipts invisibles the in increase the to in due But cent 2012-13. in cent per per 4.7 4.2 of peak historic from a 2011-12to widened CAD the GDP, of Proportion a As 2012-13. in billion netinvisibles 2011-1288.2 somewhat in US$ to billion 78.2 US$ from widened CAD andwidening the down, going balance beelevated to continuing deficit trade With Invisibles ReceiptsandPayments safe inbothlongandshortterm. also but reliable only not is Tradeof deficit problem the solve to earning Invisibles on the context Depending past. this the in done has In it as saviour a elucidation. be can invisibles the long-term of role growing and healthy a not is account capital and reserves on dependence of much too that us tells country the of experience past the Hence flows. capital term short volatile highly of up made primarily is reserves our of part sad the increasing. And are goods of bill imports which at rate the of view the in further,especially deplete to reserves India’s by offered coverage import the for time much take not will it as action immediate demand situation the Therefore months. 7 able to cover the import bill of the country for 16 months in 2003-04 came down to the sizeable reserves to cover the import bill for nearly 7 months. The reserves which were the first time since after the BOP crisis, the only difference in 2012 is that we have the Source: RBIHandbookofStatistics onIndianEconomy2015-16.

In Us Million $ 100000 150000 200000 250000 Figure 2:India’s InvisibleReceiptsandPayments 50000 0

1990-91 1991-92 1992-93 INVISIBLE receipts 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01

YEAR 2001-02 2002-03 invisible payments 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16

much stronger role in India’s BoP, the invisible receipts need to grow at a constant growth rate. a at grow to need receipts invisible BoP,the India’s in role stronger much a play to invisibles the For receipts. the than volatile more are Payments general. in As shown in figure the growth rates of both invisible receipts and payments are volatile Invisible ReceiptsandPaymentsGrowthRates income investment the of payments the components oftheinvisibles. in rise the by caused mainly is Invisibles payments the in increase This payments. Invisibles in increase the for reason main licenses fees, office expense advertising, and out go on royalties account of the technological payment service, was management service, financial for Payment 1999-00. in billion 17.39 $ payments from 22.66 $ Grossinvisibles to 2000-01 in 32% fees. about of increase sharp recorded licenses advertising, royalties payments, and services, interest of financial account on 2013-14.This payments in in year rise reasonable the the in to billion due $34 was further and 1998-99 in billion 16.56 $ from Invisible Payment recorded a sensible increase of about 5% in 199-00 to $17.39 billion Source: RBIHandbookofStatistics onIndianEconomy2015-16. Source: RBIHandbookofStatisticsonIndianEconomy2015-16. 2010-16 2005-10 2000-05 1995-00 1990-95 Year Figure 3:GrowthRatesofIndia’s InvisibleReceiptsandPayments

In Percent -20 -10 10 20 30 40 50 60 0 Table 2:CompositionsoftheInvisiblesReceipts 1991-92

Non-Factor Services 1992-93 1993-94 1994-95 1995-96 61.92 53.12 44.88 48.34 65.11 1996-97 1997-98 1998-99 1999-00 Series 1 2000-01

2001-02 An Econometric Analysis ofInvisiblesinIndia’s BalanceofPayments YEAR 2002-03 2003-04

Series 2 2004-05 2005-06 2006-07 Income 4.73 8.38 7.73 6.70 4.23 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 Transfers 2015-16 30.16 29.70 39.15 48.42 47.44 23 Volume 9, No 1, January-June 2018 24 Journal of International Economics Non-factor services. of components the of done been has analysis average year five a receipts invisibles total to receipts services miscellaneous the Non-factor of contribution the Tosee the invisibles. the among And of receipts the increase transfers. to lot a contributed has the services miscellaneous the services surpassing up picking services Non-factor started enabled IT receipts the and services software the in growth the to due reforms economic the of generation second the after But 44.8%. services Non-factor to compare invisibles total the of 48% the than more with more was receipts transfer mainly private transfers till the late 1990’s. In fact from the 1995-00 the contribution of transfers the from more was receipts invisibles India depicts, clearly table above The of time but as a contributor to the total invisibles payments its share decreased from decreased share its payments invisibles total the to contributor a as but time of period the over rapidly rose income the in payment the Though 2010-14. year the in 71.42 to increased It services. Non-factor the from were 51.46 payments, invisibles total the of out 1990-95 year the rapidly.In increased has services payments Non-factor the the in receipts as same times of period the over that depicts find table The Source: RBIHandbookofStatisticsonIndianEconomy2015-16. their receiptsfromthe34%in1990-95to73.2%2010-14. in increase rapid the with more contributing started services software business agency,and services news fees, license and copyright royalties services, construction services compromising financial services, communication services, software services, miscellaneous the the by Travel.1995 contributed the was after receipts But services the above graph we can see that in the year In 1990-95 about exports. 40 skill-intensive % IT-relatedof other the total and non-factor exports service software like activities newer from earnings India’s in jump second unprecedented an the witnessed earnings, 1990s the tourism of by half dominated was 1980s the to up period the While Source: RBIHandbookofStatisticsonIndianEconomy2015-16. 1990-95 2000-05 1995-00 2005-10 2010-16 Year 2010-16 2005-10 2000-05 1995-00 1990-95 Year Travel 39.37 17.42 27.35 12.05 12.66 Table 3:CompositionsoftheNon-FactorReceipts Table 4:CompositionsoftheInvisiblesPayments Non-Factor Services Transportation 71.42 70.70 67.65 64.02 51.46 23.47 17.75 12.07 11.77 11.04 Insurance 2.54 2.05 1.78 1.63 1.65 Income 25.46 26.47 30.21 35.61 48.31 G.n.ie 0.57 2.90 1.69 0.41 0.38 Transfers 3.12 2.83 2.14 0.37 0.23 34.05 49.95 Misc. 67.33 74.87 73.23 five yearaverageanalysishasbeendoneonthecomponentsofNon-factorservices. a payments invisibles total to as payments services Non-factor the in Torise the see in 2010-14 was largely because of the significant rise in the official transfer payments. main The reason for payments. the increase total in the the transfer to payments from increased 0.23% in also 1990-95 to has 3.12% share transfers the on payments the low because its receipt is only 4.73 % in 2010-14. Like the further down non-factor come to services has decrease payment this But 2010-14. in 25.46% to 1990-95 in 48.31 each series. for out carried been has test root unit the mind in this Keeping tests. root unit using stationarity for data series time Tothe check to necessary is it results, spurious avoid Unit roottests Empirical Analysis demand fortheserviceitemsandothercommoditieswhicharelinkedtoit. growing to due is This service. miscellaneous the like high that not was travel the of travel the of share the increased only from 12% in non-factor 1990-95 in service 15.2% in total 2010-14. But the miscellaneous increase the in the payments with of out Along payments. 2010-14 in receipts 63.1% to 1990-95 in significantly 45.8% has from services increased miscellaneous of payments the of share the receipts the payments. with Non-factor Along in payments total of out 35.27% i.e., more also was also shows that the in 1990-95 along with the miscellaneous, services transportation payments miscellaneous the on payments the table services. The non-factor the of components other the to compare as more were that infer can we table above the In Source: RBIHandbookofStatisticsonIndianEconomy2015-16. LnWGDP LnSERRECP LnIMPO LnINVPAY LnEXPO LnINVREC LnGDP 2010-16 2005-10 2000-05 1995-00 1990-95 Year Variable Table 6:UnitRootTests withTrend andIntercept:(1990–2016) Travel 15.28 17.06 20.00 16.30 12.03 Table 5:CompositionsoftheNon-FactorPayments Transportation -1.515 -1.521 -2.269 -2.391 -1.943 -0.621 -3.210 Level 18.81 21.73 19.06 27.01 35.27 Nonstationary Nonstationary Nonstationary Nonstationary Nonstationary Nonstationary Nonstationary An Econometric Analysis ofInvisiblesinIndia’s BalanceofPayments Inference Insurance 1.80 2.15 2.15 1.58 3.47 1 st Difference -3.567 -3.660 -3.892 -4.958 -3.596 -3.596 -5.105 G.n.ie 1.01 1.08 1.61 2.55 3.35 Stationary Stationary Stationary Stationary Stationary Stationary Stationary Inference 63.10 57.97 57.18 52.55 45.89 Misc. 25 Volume 9, No 1, January-June 2018 26 Journal of International Economics most of the variables are stationary at the first difference, i.e., these variables are variables these i.e., difference, integrated oforderone. first the at stationary are variables the of that see most we Thus stationary. be to out to turn variables the all differencing, first the at levels, since, all are nonstationary at levels, we have gone for first differencing. After From the above table, we infer that all the variables used in the models are nonstationary 1% criticalvalue Source: Basedon Authors Calculation. (R D(LnINVPAY) =0.020+0.608*D(LnGDP) +0.299*D(LnIMPO(-1))0.196*DUM Equation 2: Invisible Payments (R D(LnINVREC) =0.076+0.955*D(LnWGDP)0.211*D(LnEXPO) +0.077*DUM Equation 1: Invisible Receipts Aggregate Invisibles thus asexportsrise,theinvisiblereceiptsontheseexport earningsalsorise. services which are linked to exports such as insurance, transportation would also rise, we thus services, the rises, exports our our of volume as of Similarly two. the between most relation positive a see use or hire to prefer would foreigners the increase, are income world receipts As exports. our of invisible volume and income total world of level The the by explained receipts. income receipts, services investment the and include receipts would transfer receipts invisible total the level aggregate At LnFORINV LnEXTDEBT LnINVINCPAY LnUSTBRATE LnFCA LnINVINCREC LnROI LnOPECGDP LnPVTTRANRECP LnSERPAYM Variable InvisiblePayments=GrossDomesticProduct+Imports InvisibleReceipts=World Income(World GDP asProxy)+Exports (0.81) (3.42) * =–4.416,5%criticalvalue-3.622,10%--3.248 2 2 =0.73 =0.56 -3.145 -0.986 -2.391 -1.230 -1.548 -1.003 -1.533 -1.660 -3.156 -1.973 Level (3.06) (1.92) D.W: 2.59) D.W: 1.55) Nonstationary Nonstationary Nonstationary Nonstationary Nonstationary Nonstationary Nonstationary Nonstationary Nonstationary Nonstationary Inference (3.11) (1.06) 1 st Difference -4.310 -4.958 -3.880 -5.062 -6.044 -4.274 -3.649 -6.499 -5.609 -1.27 Stationary Stationary Stationary Stationary Stationary Stationary Stationary Stationary Stationary Stationary Inference (R D(LnSERRECP) =0.040+0.809*D(LnWGDP)0.480*D(LnEXPO)0.199*DUM Equation 3: Services Receipts Dis-Aggregate Invisibles Around 70percentofvariationinthepaymentsareexplainedbythesevariables. payments made on investment income. Both the explanatory variables are significant. and payments services various the include would payments invisible Essentially two. the between relation positive a is there thus rise, also would variables, payments two invisible these the in rise a is there When by imports. of measured volume level, and GDP income domestic domestic the by explained are payments invisible The (R D(LnPVTTRANRECP) = 0.085 + 0.953*D(LnOPECGDP) + 0.083*D(LnROI) + 0.283*DUM of Interest Rate Domestic + Countries OPEC of GDP = Receipts Transfer Private Equation 5: Private Transfer Receipts (R D(LnSERPAYM) =0.060+0.655*D(LnGDP(-1))0.326*D(LnIMP)0.252*DUM Equation 4: Services Payments payments isexplainedbythesetwoexplanatoryvariables. services increase. in would variation services of hire cent to per desire 80 the Around rises, level income the as general, in because, true is This significant. more is which is which India, GDP the is it variables, in two the Of payments. service the explaining in significant are level income the variables these Both imports. by India’s of volume the and GDP explained domestic by measured are India in payments Service significant. Both the variables have positive coefficient. Of the two variables, the world GDP is not country as tourists and as a result our earnings increase in the form of travel receipts. our visit to foreigners the for opportunity the in increase to leads income world the in Increase insurance. and transportation of form the in earn we receipts the be would higher increase, volume export the As income. world and world of rest the to export we that exports merchandise the by influenced like are insurance) services transportation, (include travel, side receipts on items of rest include which receipts Invisible Services Receipts=World GrossDomesticProduct+Volume ofExports ServicesPayments=GrossDomesticProduct+Volume ofImports (2.21) (2.93) (2.44) 2 2 2 =0.72 =0.78 =0.83 (2.15) (1.82) (3.18) D.W: 2.16) D.W: 2.23) D.W: 1.57) An Econometric Analysis ofInvisiblesinIndia’s BalanceofPayments (2.86) (0.57) (2.62) 27 Volume 9, No 1, January-June 2018 28 Journal of International Economics (R D(LnINVINCREC) =0.054+0.993*D(LnFCA(-1))0.309*D(LnUSTBRATE) Bill Rate TreasuryUS + Currency Assets Foreign = Receipts Income Equation 6: Investment Investment IncomeReceipts the D.W. Statis2.16 either,autocorrelation of as problem no is equation estimated above the In variables. explanatory two these by explained being is receipts transfer in variation of percent receipts. We can infer that interest rate is not that influencing the transfers. Around 70 transfer private of inflows the in increase an expect we increases, activity gulf the As interest. Of of these two variables the rate significant variable turnsdomestic out to be theand OPEC GDP.proxy) as OPEC of (GDP activity gulf by determined are India The private transfer receipts i.e., remittances from Indian workers working abroad into are basedondynamicsimulation. Forecasts equations. estimated the for forecasting in-sample by undertaken is This test. validation a to it subject we are equations estimated the good how Toexamine Forecasting fromtheEquations (R D(LnINVINCPAY) =0.039+0.672*D(LnEXTDEBT)0.018*D(LnFORINV(-1)) Equation 7: Investment IncomePayments is observesthat,externaldebtmoresignificantthan theforeigninvestments. more the investment, It foreign investment. these on returns the for pay to need of we because payments investment inflow the higher Similarly payment. higher increases, the debt be external would of volume the As investment. foreign the and debt external of amount the by influenced are dividend and profits, interest, of form the in The investment income payment dealing with servicing of capital account transactions are assets currency more significantininfluencingthereceipts. Foreign income. more earn and market bond US the in invest to us for motivation the higher increases, rate bond the As income. earn and assets the investing for opportunity the be would higher assets, currency foreign of level the Higher variable. dependent the to positively related are variables the Both rate. bond of the RBI are dependent upon the level of foreign currency assets and US government The investment income receipts i.e., earnings on deployment of foreign currency assets InvestmentIncomePayments=ExternalDebt+ForeignInvestments + 0.427*DUM (1.34) + 0.184*DUM (1.77) 2 2 =0.71 =0.79 (6.47) (3.32) D.W: 2.39) D.W: 2.42) (4.80) (1.03) eis n oes r vr coe o h ata sre ad hr i n systematic no is there and series actual the to tendencies toover/underestimatetheactualdata. close very are models in series the that us suggests and predictive performance low of very the models really are very is satisfactory. which Implying 0.016, that the of forecasted maximum is coefficient inequality the Theil equations, our in 1, to 0 be should coefficient inequality The Theil is model forecasted the that satisfactory. tells which percent, 0.24 error than squared less is mean equations Root all the for model our in Here percent. two than less be always and Theil inequality coefficient. For a good model the Root mean squared error should error squared mean Root calculating by model the of accuracy the testing for go We Source: Basedon Authors Calculation. china $ 66 billion. Besides this, the invisibles receipts have also provided the degree of 2013-14. India was the largest recipient of private remittances in the world followed by to 2003-04 from billion $70 to billion 22 $ increased it decade one of period the Over abroad. from remittances of workers’ the case are contributor the main the In transfers appreciate. private the of place the enjoys still services Software payments, to got decreased by 50%. Despite the fall in its growth rate due to high receipts compare growth its recently globe the across recession and economy US in fall the to due But growth of software services that it wants to make India IT super power in near Future. the regarding optimistic so is government The force. work Indian the of intensity skill three last in exports, years business service service has played an the effective because with of its rising Along performance, billion. high 69.3 $ the to billion 12.8 $ from increased exports 2011-12software to 2003-04 years, nine just of period the in fact In services. software of exports the in increase rapid the been has development main riseof factor services and private sustained transfers. As far as bythe the non-factor service is concerned, the minimized issignificantly Invisibles. The main contributing factor to the rise in payment the invisibles receipts are the external non- the to deficit positive the trade account. The high BOP The risk of the country caused by the huge merchandise 1991. since payments of balance earning Indian of the invisibles was always the there since from the in 1991 to cover heavy deficit invisibles of the of role After doing rigorous descriptive analysis of the invisibles, it brings clearly the impressive Conclusion Eq. 1 7 6 5 4 3 2 Root MeanSquaredError(RMSE) Table 7:RMSE andTICValues forEstimatedEquations 0.245 0.070 0.142 0.164 0.168 0.166 0.082 An Econometric Analysis ofInvisiblesinIndia’s BalanceofPayments Theil InequalityCoefficient(TIC) 0.003 0.008 0.008 0.008 0.008 0.003 0.011 29 Volume 9, No 1, January-June 2018 30 Journal of International Economics it demandsgrowthofsurplusinvisiblebalance. payments, of balance healthy the maintain to that conclude can we Lastly payments. income investment of form the in over account current for burden become spill will it otherwise investment foreign called also is it then programme promotion export the services exportsisbyalso influenced the foreigninvestment. If this capitalis usedfor exports of the country. In fact in the above the analysis promote we will found turn that the in growth this in infrastructure; modern of development for flows capital the using suggest studies point economy.this of At growth the for role important an plays also of the sudden fall of capital account from $107 billion to $7 billion. But the capital flows because BOP deficit the with across came we 2008-09 year the In nature. in that volatile infer highly can is account we capital whereas sustainable balance very is invisible balance invisible in and growth the account capital both of trend the Looking invisibles butalsotothevisibleitems. the current account surplus. In fact it is the infrastructure which is not only affecting the to led turn in which facilitate, be should imports and exports of insurance proper with relative stability. So in order to boost this invisibles surplus the sound transport system stability to the current account receipts as the invisible balances always witnessed the Mehta, Mehernosh. (2014). Effect of India’s current account deficit on external on deficit account evolving current India’s its of Effect and (2014). Mehernosh. Industry Mehta, Software Indian The (2005). S. Suma Atherye, References Deshpande, Atul R. (2013). Is India’s current account deficit Problem? Truly Global, Truly Problem? deficit account current India’s Is (2013). R. Atul Deshpande, Revisiting (2005). Aggarwal. Suresh and Erumban Azeez Abdul Das, Kusum Deb Chanda, Rupa. (2005). Trade in financial services: India’s opportunities and constraints. Banga, Rashmi. (2005). Critical Issues in India’s service-led growth. Indian council for policy the and India on impact its Crisis, Financial Global (2011). Nirupam. Bajpai, ut Poa ad ae Gro. 20) Udrtnig h Ida Software Indian the Understanding (2004). Gordon. James and Poonam Gupta Industry practice, Software : Through Working (2005). Kyle. Eischen, et ad oeg ecag rates, exchange foreign and debts pp: 319-418. published, Access Advance change, corporate and Industrial capability, service Journal ofEconomicDevelopment growth. IARIW-UNSW special conferenceonproductivitymanagement,Sydney. productivity sector service India’s Understanding India in Growth Service-led the Paper Working relations economic Series, No.152. International on research for council Indian research onInternationaleconomicrelationsWorking PaperSeriesNo.171. response. ColumbiaGlobalCentres. pp: 54-65. Revolution, organization, IMF workingpaper WP/04/171 CGIRS WorkingPaperSeries No.111. . OR ora o Eoois n Finance and Economics of Journal IOSR . ,

ektrmnn S (01. niils n ni’ Blne f amns Te Hindu The Payments. of Balance India’s in Invisibles (2001). S. Venkitaramanan, ad, uha rvd ad ei rsd 21) Mcocnmc eemnns of determinants Macroeconomic (2013). Prasad Devi and Trivedi Pushpa Panda, Payments, of Balance Indian in Invisibles Of Analysis An (2011). S. Goud Limba, Jadhav, Narendra. (2007). Maximising Developmental Benefits of Migrant Remittances, India’s as Sector Service The (2010). Barry. Eichengreen and Pooonam Gupta, Korattysswaroopam, John Pucher and Nisha. (2004). The Crisis of Public Transport in Jhamb, Rajesh Kumar. (2011). Contribution of the software Industry in the growth of the group ofpublication. private transferstoIndia,JournalofQuantitativeEconomics, International Journal of Business Economics and Management Research pp: 1-20. and ManagementResearch, Journal ofEconomicDevelopment Relations, Economic International on research for council Indian Growth. Economic to Road India: Overwhelming Needs but Limited Resources, decade, last the in economy Indian Working PaperSeriesNo.249. pp:97-111. . International Journal of Business Economics Business of Journal International An Econometric Analysis ofInvisiblesinIndia’s BalanceofPayments Journal of Public Transportation, pp:51-76. , pp:1-11.

31 Volume 9, No 1, January-June 2018 32 Journal of International Economics Keywords: Ethiopia,Exports,Trade Costs,GravityModel,PPML Estimation harmonization andsimplifiavailable, information cationofdocumentshelpreducetradecosts. that trade making recommends as such also measures study facilitation trade The on focusing them. with linkages regional in participates and suggested that the country would be better off if it exports to its neighbouring countries fithese of view is In it countries. ndings, coastal than countries landlocked with more Ethiopia that suggests also result empirical The exports. its on impact no had it if off better be sends its will exports to neighbouring countries. Ethiopia In contrast, tariff that rate and GDP implying of Ethiopia Ethiopia, of exports on impact negative signifia had distance by proxied costs trade that indicate results cant The estimation. Pseudo Maximum Likelihood (PPML) estimation procedure has been employed in the Poisson used,the been of have fi model exporter effects partners and xed importers and trading model major 10 of sample A Ethiopia has 2010-2015. been used in the analysis. Whilst two types of panel of data models: pooled period the for data gathered panel balanced a using model gravity panel a estimated have We Ethiopia. of performance export on costs trade of impact the examine to attempt an is paper This Facultymember, University, EthiopianCivilService Addis Ababa [email protected] 2 Facultymember, University, EthiopianCivilService Addis Ababa [email protected] 1 signifi improved cantly have imports and increasing exports been both has of performance value trade The external substantially. Ethiopia’s result, a As exports. of these countries (IMF, form 2001). Ethiopia is also following liberal trade policies to promote the in growth promoting for in factors important the of one trade as seen is exports international encouraging Liberalized poverty. alleviate and development Developing countries including Ethiopia have been striving hard to promote economic Introduction Impact of Trade Costs on Export Performance Impact ofTradeCostsonExport of Ethiopia–A Benat MohammedaminMussa Journal ofInternationalEconomics SN07-72 Volume 9,No.1,January-June2018,pp.32-47 ISSN 0976-0792 PPML Approach Panel GravityEquation 1 andGollagariRamakrishna 2 transportation and communication which makes trade between countries very simple. According to WTO (2008) there have been considerably large reductions in the cost of Contrary to this, there are some studies that show the reduction of trade costs globally. factors forthelowcompetitivenessofEthiopia’s productsintheworldmarket. cost of export per container was $2380 in 2014. in $2380 was container per export of cost importing key inputs (Aschenaki, 2004). According to the World Bank (2016) as Ethiopia’s well as products its exporting in costs trade high considerably from suffers it sea, the accessing relies for Kenya and and Djibouti country particularly are countries landlocked neighbouring that on a heavily is countries Ethiopia for As higher countries. coastal much than becomes landlocked costs trade of effect the Moreover, good itself(AndersonandWincoop,2004). the of cost production the than other user final a to good a getting in incurred costs all include to defined broadly costs. Tradeare trade costs as termed are costs These are that costs the goods, exported related to sending goods of abroad have become one of volume the major concerns for the Ethiopia. in increase such with However, contributed tothisgrowth. exports its of rise the and conditions external positive that mentioned also report The Ethiopia has been one of the world’s fastest growing economies over the past decade. (2014) Bank World the to According 2014). (Kebede, 2004/05 in (PASDEP) Poverty End to Development Sustained for and Plan Accelerated of implementation the since 2 costs as“all 1 costs trade defined broadly authors These 2004). and Wincoop Van 2003, and (2001, Anderson by conducted been have costs trade in studies Many Review ofTheoreticalandEmpiricalEvidences policy implicationandsuggestionarepresentedinthe conclusionsection. Finally, section. succeeding the in presented are findings empirical and analysis Data of review presents section sources. next data and specification model by followed the evidence empirical and theoretical follows: as organized is paper the of rest The of exports determines Ethiopia? And dotradecostshaveanyeffect ontheexportperformanceofEthiopia? what questions; research following the assesses paper this study, the of objective the address to order in particular, In not. or export Ethiopia’s for setback a been have costs trade whether explores paper this these, with line In the risingcostsoftradeactuallymatterforcountriesexportornotcouldberaised. whether of question the positive, remains trade of benefit net the given regard, this In integration (WTO, 2008). more efficiently and exchange information between potential buyers and sellers which lowered the costs of international goods exchange to companies allowed have interaction internet to telephone from telecommunications, efficient More while itwasonly$885forDjiboutiinthe sameperiod. $1850 was Eritrea of cost export instance, For countries. neighbouring other to compared higher much is figure This Impact of Trade CostsonExportPerformanceofEthiopia– A PPML PanelGravityEquation Approach 1 This could possibly be one of the of one be possibly could This 2

33 Volume 9, No 1, January-June 2018 34 Journal of International Economics to 44percent. effects border unexplained McCallum’s reduced and puzzle border larger explain to factors. resistance multilateral other including by model gravity managed authors The Van Wincoop (2003) have tried to solve the ‘border puzzle’ using McCallum’s data via and Subsequently,Anderson costs. trade about unexplained large lefts study,which the analyse to used measures empirical the and costs trade to regard with questions numerous raised has study McCallum’s However,(NAFTA). Agreement Trade Free American North the border through integrated the highly are that behind countries for than even costs higher trade are costs trade border the beyond that found study to the losses incurred when products cross the provincial borders within Canada. The comparison a makes and Canada to US from shipped are goods when volume trade on the effect of trade costs on exports of goods. McCallum (1995) estimated the loss of There are several empirical evidences that have been provided by different researchers investment verycostly(Porturgal&Wilson,2008). on business competition, and weak governance make international trade (export) and combination with other factors like corruption, underdeveloped institutions, constraints poor,in are countries some why guarantee not may costs trade although that, of top denying firms the access to high quality of foreign inputs (Portugal & Wilson, 2008). On abroad. From producer’s side, trade costs matter because they obstruct production by costs hampers their and ability to take advantages of comparatively low priced goods from onconsumers trade high to due goods of both price high the effect side, consumer’s From significant welfare. producers have they because matter costs Trade (Anderson andWincoon,2001). irregularity interacting with the most efficient transport and communication technology non-border costs are largely natural trade costs that arise from distance and geological while regulations and customs adjusting foreign with consistent them make and to designs product languages, foreign learning laws, foreign with familiar lawyers hiring regulations, foreign about information gathering as such resources, real involve that tradecosts. costs trade generate and borders andnon-border national to costs related are that costs are costs border Border to two: in classified be can costs Trade include costsrelatedtoborderprocedures,transportationandlogistics(WT of destination.Trade costs (in the absence of information) are also narrowly defined to country the in consumers’price and origin of country the in producers’price between Wincoon, and (Anderson 2004). regulatory Similarly, retail)” Ali (2015) defined trade and costs to include and all factors that drive a wedge (wholesale legal costs currencies, distribution different local and of costs, use the with associated costs costs, enforcement contract costs, information barriers), non-tariff and barriers(tariffs policy costs), time and costs freight (both costs transportation includes this others Among incurred in getting a good to a final user other than the production cost of the good itself. O, 2015). Ethiopian exportandtrade agreementforpreferentialtrade. on impact between negative relationship negative a significant found also had had study The trade. rate bilateral Ethiopia’s exchange nominal and distance as such gravity model over augmented the nine years panel data (2004 -2012). The author found that trade costs an using export Ethiopia’s of determinants the analysed (2014) Studying the bilateral trade of Ethiopia and east African community countries, Tebekew factors ofEthiopia’s export. side supply of determinant important an be to found been had countries importing of policy trade and infrastructure transport, internal hand, other the On country. the of effect exports significant bilateral exert to on position a in not are rates exchange real and distance like cost that confirmed trade result The estimation. the in included also explain were policy trade importers that variables addition, In Ethiopia. of export on rate exchange real of impact the studied has (2011) Bekele Similarly, countries. neighbouring to exports it if off better be would Ethiopia that implying negative was variable distance of coefficient the of result estimation The variables explanatory the of one as incorporated been had partners) trading its and Ethiopia between distance by (proxied costs trade study this In model. gravity using export its on membership COMESA Ethiopia’s of impact the studied (2008) Mohammed context, Ethiopian In and thetendencyofhighexport. growth productivity strong relatively exhibited costs trade in experience declines large that relatively industries that indicate results Their transportation. and rates tariff of U.S. manufacturing industries and plants to changes in trade costs on industry-level 2 and 6 per cent, respectively. Likewise Bernard about by trade bilateral increase would each percent, 10 by costs transport and tariff momentum of reduction the that high confirmed author The costs. trade of gaining level low of because is partly Asia in trade that found (2007) De inPakistan Similarly, countries. ofexports partner its thegrowth in costs trade of reduction the to due mainly was 2004 and 1994 between that confirmed findings Their variables. gravity traditional model the to addition in rates exchange bilateral and rate tariff study includes their in analysis The Pakistan. in costs trade of impact the examine to model in exports of countries products. For instance, Khan and Kalirajan (2011) used gravity increase to lead costs trade of reduction the show studies some hand, other the On the southernmarketwascrucial. to proximity as market southern the than north to exports India’s affected negatively more had distance as such costs confirmed trade that study,found that was In it income. capita findings per in Their difference and similarity GDP,GDP model. total by India’s explained was gravity export India’s that of augmented an determinants using the markets countries) studied have (2014) (developed northern and countries) (developing southern its to exports manufactured Aswal and Suresh Similarly, Impact of Trade CostsonExportPerformanceofEthiopia– A PPML PanelGravityEquation Approach et al., (2006) examined the response 35 Volume 9, No 1, January-June 2018 36 Journal of International Economics h ipc o tae ot o epr o Ehoi b etnig h gaiy oe to model gravity the extending by Ethiopia include theeffect ofotherfactors. of export on costs trade of impact the incorporated and model gravity applied study our regard, this In Ethiopia. of exports on costs trade of impact the address specifically growth or to attempt an is toexport study present this isrelated it rather way specific determinants of exports which includes trade a costs as one of the variables. Therefore, in costs trade of effect the address not did Ethiopia of context the in literature reviewed the hand, other the On these variablesinordertoreachaconclusion. of some involving Ethiopia of exports on costs trade of impact the assess empirically to important is it integration. Therefore, regional and agreements trade preferential to due lower be may they or network communication and road infrastructure, of lack like certain country may be significantly higher than others dueto specificbilateralfactors of level and a study.to under exports country of the costs of the development token, period, same the By study variables, of choice techniques, estimation in difference to trade due is reduced probably to This costs. due communications and exports transportation of specifically costs volume costson of trade increase of the that effect found negative others the export, find studies some While mixed. rather have evidences been empirical the that concluded be can it discussion, above the From concept of gravity model was originally introduced by Tinbergen in 1962 analogous 1962 in Tinbergen by model.The introduced originally was basic gravity model gravity the of concept from emanates study this in specification model The Model Specification the for data of availability on included countries. based chosen also is period time The chosen. been data. After listing 20 trading partners of Ethiopia, 10 countries with available data have chosen on the basis of are their share countries in Ethiopia’s The trade and estimation. on the model availability of the required for variables the on data of availability and exports in importance their on based 2010-2015 of period the over ) and Sudan India, Italy, Kingdom, United Emirates, , Arab United (China, Arabia, Ethiopia Saudi ,of partners trading major 10 for data panel used have We Likelihood Maximum (PPML) Pseudo estimation method which Poisson has not been used used by the we earlier studies on Ethiopia. model, the the get To of partners. estimates trading parameter its to country home from function export of estimation Defining Ethiopia as a single ‘home country’, the analysis is based on an econometric way.specific more a in studied be can model gravity the in relationships the that so panel data sets, this study aims at the analysis of one-way trade flow of home country trade huge a studies usually which trade bilateral or models multi-country on based is approach traditional the Although, approach. equation gravity the using explored The determinants of international trade of a country in relation to its partners are usually Model SpecificationandData gravity modelfollowsfromtheabovementionedequationinalinearoutline section datamightbeavoidedwithpanel(Baltagi, 1995,Hsiao,2014). Moreover,cross- from arises sometimes that multicollinearity potential of problem the factors. population-invariant or cultural economic, institutional, like effects country or time specific the identify to easier it makes and heterogeneity of types general more researchers have turned towards panel data, which allows taking into consideration of which in turn can lead to an estimation bias (Kareem, 2013). To alleviate countries this problem, among heterogeneity for account sufficiently not can which data sectional The empirical analysis of gravity equation has traditionally been analysed using cross- Econometric Strategy specific for test effects. to on so and language same sharing the speaking agreement, border, trade land common a a of member a being like variables dummy of number a ico (03icue to diinl aibe, aey otad n inward and outward namely, variables, resistance multilateral additional two (2003)included and Anderson Wincoop variables. other including by costs trade explain to tried economists various Consequently, facilities. environmental and policy of terms in costs several However, distance is found to be a poor proxy of trade costs because trade encompasses gravity. of law Newton’s to 5 4 3 This canbeexplainedasfollows: heterodscedasticity becauseofJensen’s inequality on the logarithmic transformed model could be significantly misleading in the presence of based results estimation the because is This methods. estimation data panel with even However, the logarithmic transformation of the model for its estimation still causes problems Where X typically takethefollowingform: related to the distance between them. This forms the basis of gravity model and would two countries is directly related with their economic sizes (GDP/GNP) and is inversely product of the respective countries, Dis countries, respective the of product expected value. Jensen’s inequality states that the expected value of a logarithm of random variables does not equal to the logarithm of home countrydoesnothaveanycontrol. which on rate exchange and tariffs from arises resistance outward the and country home in rigidities and inefficiencies According to these authors the inward multilateral resistance emanates from the existing infrastructural and institutional their GDPsanddiminisheswithdistance (Krugmanet.al.2012). product of their masses and diminishes with distance, trade between any two countries is proportional to the product of the to proportional is objects two any between attraction gravitational the states that gravity of law Newton’s like Just ij = trade (export) from country i to j, β = constant, GDP Impact of Trade CostsonExportPerformanceofEthiopia– A PPML PanelGravityEquation Approach 4 . Likewise, most studies estimate the gravity model by adding by model gravity the estimate studies most Likewise, . 3 In this traditional gravity model, trade (exports) between (exports) trade model, gravity traditional this In

ij = distance between country i &j. An intuitive An &j. i country between distance = 5 (Silva& Tenryero, 2016). i

/GDP

j = Gross Domestic ...... 1 ...... 3 ...... 2 37 Volume 9, No 1, January-June 2018 38 Journal of International Economics The expectedvalueoftheabovelog-linearizedequationwouldbe: 7 6 X Pseudo Poison using equation gravity Maximum Likelihood(PPML). the of form The multiplicative model. the of appropriate estimation an not direct the in is lies then model linearized model log- of estimation linearized the to approach alternative log- facts, above the to Due Since lnE[εij] improper becauselogarithmofzeroisundefined then is case this in transformation logarithmic itself. The data the of nature the to due estimates. is misrepresented and estimation through OLS will result in misleading and inconsistent (PPML) estimator. The equationcanbewrittenasinthefollowingfunction: Likelihood Maximum Pseudo Poisson the applies paper this model, the of estimates parameter the measure justifications. To above the of gravity account of to in estimation taking the equation for used been has model data panel a study, this in Thus issue inthisprocess. an more no is flows trade zero handling of problem the level, in measured is variable the and variable dependent the of transformation logarithmic the undertaking of need method natural no most is heteroscedasticity.there of Since pattern the on the information further any without is this that underlined (2006) Tenreyro and Silva Santos ijt observations whichareprobablynon-randomly distributed. flows trade zero-valued omitted by caused bias selection sample a sufferfrom likely very will estimation the (2009) al., Burger to according Besides, 1996). Irwin, & (Eichengreen data deleted the in encompassed are that information of losing like well as problems serious number causes observations small the a omitting hand, such other the of On 1982). chosen & Aitkin, (Flowerdew the with highly varies estimation resulting the value, small some adding of case the 2008).However,in (Mohammed, literature the in suggested been have matrix trade the from observations zero-valued To the of rid get or observations all to values positive small some adding flows, trade valued zero- of problem the solve estimates distribution” its inconsistent of moments to order leads higher- Tenreyro,on 2006,p.653). heteroskedasticity depends variable of random presence the of the value expected in the model because empirical the of linearization log “the =β 0 GDP 6 On the other hand, the data of export may involve zero or missing values missing or zero involve may export of data the hand, other the On it β1 GDP E[ln(ε jt β2 Dis ij )] (which is Jensen’s inequality), the conditional distribution of X ij β3 DGDPPC ijt β4 TR jt β5 e θj e θi ε 7 (Westerlund &Wilhelmsson,2009). ijt

...... 5 ...... 4 ...... 7 ...... 6 (Sillva & (Sillva et ij

to bestationaryinlevels. found are variables all indicates, above output the root. As unit a contains series the We have employed the Levin- Lin- Chu(2002) panel unit root test to examine whether Source: Computed for each variable beforehand, so as to avoid the spurious correlation problems, if any. It is very important to test the existence of unit root and examine the order of integration Panel UnitRootTest Model EstimationandFindings GDP DGDPPC Ho: paneldatahasunitroot(notstationary)Ha: has notunitroot(stationary) X TR off Comlang_ Contig Landlocked (Dis Distance Variables ijt jt ij ) ln_DGDPPC ijt Variables ln_Export ln_GDPj ln_GDPi ln_TR ijt countries attimet. Denotes thetariff rateofimporting two countries. The percapitaGDP differential between importing countries.(GDP Denotes GDP ofexportingand country i(Ethiopia)tojattimet. Denotes thetotalvalueofexportsfrom Comlang_off border ornot. whether countryiandjshareacommon Contig isadummyvariablesignifying landlocked ornot. whether theimportingcountryjis Landlocked common languageornot. signifies whethercountryiandjsharea demand siderespectively. are usedtocontrolforthesupplyand partners. Measures thedistancebetweentrading Impact of Trade CostsonExportPerformanceofEthiopia– A PPML PanelGravityEquation Approach Summary Statistic isanotherdummyshowing Table 2:Test ResultforPanelUnitRoot isadummyvariablethat Definition -51.4163 -72.1493 Table 1:Definition of Variables -3.4963 -3. 894 -2.531 it ) and(GDP jt p-values ) 0.000 0.000 0.000 0.000 0.000 Expected sign +/ - + + + - - - www.distancecalculator.net World Bank,WDIdatabase World Bank,WDIdatabase Test forunitrootinlevel Ethiopian Revenuesand World Bankdatabase Customs Authority Source I(0) I(0) I(0) I(0) I(0) 39 Volume 9, No 1, January-June 2018 40 Journal of International Economics • • Diagnostic Tests effect modelistheappropriatemodel. result we above the From reject the null hypothesis since the isappropriate. probability is less than 0.05 and conclude model that fixed effect fixed that states hypothesis The null hypothesis states that random effect model is appropriate and the alternative Source: Computed between theunobservedcountry-specificrandomeffects andtheregressors. correlation significant a is there whether answers test This study. this in used been In order to choose between fixed and random effect models Hausman (1978) test has Choosing betweenFixedandRandomEffectModel Source: Computed e(V) ln_GDPj ln_GDPi Landlock Comlang_off Contig ln_TR ln_DGDPPC ln_Dis _cons Appropriate model Prob. Chi-sq statistic(4) obe fet f needn vral fo te oe, utcliert ts has test multicollinearity been conductedinthisstudy. model, the from variable independent of effect double the avoid to and variables explanatory between correlation the identify to order In Test forMulticollinearity sectional dependence. we cannot reject the null hypotheses. Therefore, we conclude that there is no cross means which percent 5 than greater is which 0.5483 is test this for probability The Average theoff-diagonal valueof absolute elements=0.339 Pesaran’s Pr=0.5483 testofcrosssectionalindependence=0.600, whether thereiscrosssectionaldependenceornot. The resultsareasfollows: test to (2004) test CD Pesaran used we study this In correlated. not are residuals are residuals the tests whether results (also called contemporaneous correlation). The null hypothesis is that assess in bias to to lead can dependence sectional Cross dependence not. or entities across correlated sectional cross a test We Test ForCrossSectionalDependence Table 3:Test forChoosingbetweenFixedandRandomEffectModel ln_GDPi -0.4826 1.0000 -0.2713 -0.0805 -0.2031 0.4896 0.1832 0.4976 -0.5960 ln_GDPj 1.0000 0.5850 0.1334 0.3340 -0.0979 -0.0979 -0.8583 -0.1821 ln_Dis -0.5375 -0.1807 -0.0741 0.4115 0.1694 1.0000 -0.1790 Table 4:CorrelationMatrix ln_DGD~C -0.2641 0.3932 0.3684 0.5530 1.0000 -0.4643 Test summary ln_TR -0.1659 0.0267 -0.0797 1.0000 -0.5988 Contig 0.1147 0.2999 1.0000 -0.3506 Fixed effect model 0.0000 31.06 Comlan~f 0.1106 1.0000 -0.0853 Landlock 1.0000 -0.0309 _cons 1.0000 • test we first predicted the fitted values for each specification and then we included a we included then and specification each for values fitted the predicted first we test weusedthe models, pooled traditional RESET (Ramsey Regression Equation Specification Error and Test) test. In this effects fixed of specification correct the verify To PPML. specific country of bunch dummy variables. With this a assumption, we have estimated the by gravity equation using proxied be could countries partner’s of effects fixed the that concept the on based is estimator.procedure appropriate This possible a as effects exporter and importers effects with fixed model as regression variable dummy estimation. the during Tovariables these omitting a used have we problem, this solve sets. Therefore, using the traditional fixed effects method of estimation would result in data the in variation within no manifest locked land and languages common border, common distance, as such variables model our in instance, For variables. invariant The econometric model of gravity equation contains many time invariant or nearly time- Estimation ResultsandInterpretation • multicollinearity problem. Hailer to data. According the in problem multicollinearity of non-existence the indicating weak; As indicated in the correlation matrix the correlations among explanatory variables are F (1,9) H0: nofirst-orderautocorrelation Wooldridge testforautocorrelationinpaneldata: serially is term correlated. ForthispurposewehaveusedWooldridge test. error the whether verify to performed is test autocorrelation The Test for Autocorrelation homoskedasticity issatisfied. of assumption hence;the homoskedasticity of hypothesis null the reject to fail We we cannotrejectthenullhypothesesimplyingthatthere isnoautocorrelation. From the test, it is clear that the probability of F statistic Prob.> F is greater than 0.05. Therefore, Prob.>chi2 chi2 (1) Variables: valuesofln_export fitted Ho: Constantvariance /Cook-WeisbergBreusch-Pagan resultsareasfollows: testforheteroskedasticity hypothesis isthatthereheteroskedasticiy. alternative the and error) of variance constant (or homoskedasticity is hypothesis In this study we have tested for heteroskedaticity using Breuch pagan test. The null Test forHeteroscedasticity (2006) correlation coefficient below 0.9 may not cause serious notcause may 0.9 below coefficient correlation (2006) al., et = = = = 3.861 0.0810 0.7979 0.07 Impact of Trade CostsonExportPerformanceofEthiopia– A PPML PanelGravityEquation Approach 41 Volume 9, No 1, January-June 2018 42 Journal of International Economics R-squared: .78607811 Pseudo log-likelihood:-558660.61 Number ofobservationsdropped:0 Number ofobservations:60 Number ofparameters:9 In thisstudywefirstestimatethepooledmodelusingPPML methodasbelow. Once the pre estimation tests are accomplished the next step is estimation the model. Pooled ModelEstimationusingPPML fitted valuestermwouldbecomeinsignificant. higher order of fitted valuesinto the regression.If the model is correctly specified,the chi2 (1)= specified. correctly is model Prob.> the chi2 = 0.52 which implies that adding whether other variables to the model is verify irrelevant. to used been has test RESET The RESET Test that arelandlockedtrademorethanthecoastalcountries. countries that Ethiopia’s means This impact. positive impact significant statistically a has to it as exports, able is Landlocked variable the However, Ethiopia. of exports isalso difference, tariff rate and common border language are found to be insignificant in explaining common of coefficient The positively. Ethiopia of affects export and significant statistically GDPPC effects percent. of The 3 about by export decreases 1 percent partner trading its and at Ethiopia between distance in increase percent 1 a level. Thus, significant and negative is hand other the on coefficient Distance The GDP ofEthiopiaisfoundtobeinsignificantinexplainingitsexport. in the importers GDP increases export of Ethiopia by 1.5 per cent. On the other hand, increase percent 1 a as: goes interpretation The level. cent per 1 at significant is and GDP ofimporters the coefficient table the above see in can As we sign apositive has The *,**Indicatesoneandfivepercentstatisticalsignificantrespectively Source: Computed Cons Comlang_off Landlocked Contig ln_TRj ln_DGDPPCij ln_Disij ln_GDPj ln_GDPi Variables Prob.> chi2= 0.41 Table 5:RegressionResultsofPooledModelUsingPPML Coefficients .2858186** -3.005792* -.5198697 1.851038* 1.582369* -14.06297 .2858186 .2938851 .1358125 (Std. Err. adjustedfor10clustersinDis) 0.5238 10.04258 .0565518 .7584001 .1687998 .2967988 .4243438 .3270501 .2091069 .457262 S.E Z –test -1.40 -1.52 -4.65 4.24 2.21 0.66 1.25 4.69 0.46 Prob. 0.161 0.129 0.000 0.027 0.210 0.000 0.000 0.647 0.511 R-squared: .92051004 Pseudo log-likelihood:-179579.2 Number ofobservationsdropped:0 Number ofobservations:60 Number ofparameters:14 Now let’s add exporters and importers fixed effect and estimate our model as itis ourmodel shown intable6below: estimate and effect fixed importers and exporters add let’s Now Exporter andImportersFixedEffectModelEstimationusingPPML asperour level per cent 1 at expectation. andsignificant positive becomes language official Common of coefficient the Furthermore, level. cent per 1 at significant become have border common and Ethiopia of GDP rate, tariff except variables the of coefficients are shown in table 6.The estimation results are better than the PPML model as all the that results the presented model the into effect fixed exporter and importers Adding onre. n pt o is motne ls atnin a be pi o is mat on impact its on paid been exports in the literature has with regard between to Ethiopia. It attention is in occurs this context, less the present study that is importance, its trade of of spite level In the countries. determining in role key a play costs Trade Conclusion Conclusion, PolicyImplicationandSuggestions not relevant. is model the to variables additional including Therefore, specified. correctly is model The RESET test for the PPML with importers and exporters fixed effect shows that the chi2 (1)= RESET Test The *and**Indicatesonefivepercentstatisticalsignificant respectively Source: Computed Cons Comlang_off Landlocked Contig ln_TRj ln_DGDPPCij ln_Disij ln_GDPj ln_GDPi Table 6:RegressionResultsofExporterandImportersFixedEffectModelUsingPPML Prob.> chi2= Variable 1.25 -1.632555* Coefficients .1996714* 4.607744* 2.036159* -20.42435 0.2640 -2.00359* .3127604 .3186194 Impact of Trade CostsonExportPerformanceofEthiopia– A PPML PanelGravityEquation Approach -1.36363 (Std. Err. adjustedfor10clustersinDis) 4.191719 .0565518 .7584001 .1687998 .2967988 .4243438 .3270501 .2091069 .457262 S.E Z –test 10.08 -4.87 -1.80 -5.50 -4.72 3.53 1.85 6.23 1.52 Prob. 0.000 0.000 0.000 0.072 0.064 0.000 0.000 0.000 0.128 43 Volume 9, No 1, January-June 2018 44 Journal of International Economics included in the sample have low tariff rates compared to other countries. Therefore, countries. other to compared countries rates tariff landlocked low have the sample the that in included fact the to due be might This countries. coastal than landlocked are that countries with more trades country the that implying Ethiopia exports of explaining in significant statistically and positive is variable landlocked The Suggestion governance andimpartialityhelpsreducetradecosts toagreaterextent. good all, of Most applications. their in non-discriminatory and predictable consistent, and procedures, and regulations their in transparent be should policies Finally,trade products abroadmightalsoreducetradecosts. ship to necessary are that infrastructures Improving documents. of simplification and that help reduce trade costs such as making trade information available, harmonization Furthermore, it is important to focus on the most significant measures unions andexportsitsproductstothoseunions. trade regional joins it if off better be will Ethiopia members, and their among tariffs costs reduce trade to is agreements trade Regional of purpose the Since smoothly. land-linked economy along with its neighbouring countries in order to ship goods more Besides, the country should participate in regional linkages to shift from landlocked to related other and transportation of effect negative hindrances. the reduce to as so countries neighbouring its with trade to Ethiopia for better is it Therefore distant. relatively are that most of the trading partners of Ethiopia are fact countries from Europe the and to Asia which due be could This exports. on effect negative significant absolute an has costs trade of component distance The Ethiopia. of exports have on costs influence trade significant that evidence provided has paper this in out carried analysis The Policy Implications and common languages have inconsistent outcomes in terms of sign and significance. partners trading the among difference border,GDPPC common variables; other The other the On hand, sign. Tariff unexpected rate its is found from to be apart statistically insignificant models in be explaining both exports to of in Ethiopia. found significant statistically is variable landlocked Similarly, models. both in negatively Ethiopia of be to found is statistically Ethiopia insignificant in explaining its of exports. The Distance GDP variable affects exports whereas, positively Ethiopia of exports affects and The empirical results indicate that GDP of importing countries is statistically significant effects fixed exporter and model usingthePoissonpseudomaximumlikelihood(PPML)estimationtechnique. importer and model pooled models: data panel of types model has been found to be appropriate and the study has provided estimation of two any have costs trade impact on do the export And performance Ethiopia? of Ethiopia? of Based on the exports Hausman test, the fixed effect determines what questions; research following the addressed paper 2015.The – 2010 of period the for data panel using Ethiopia of exports on costs trade of impact the address specifically to pursued I. U. Khan & K. Kalirajan (2011). The impact of trade costs on exports: An empirical An exports: on costs trade of impact (2011).The Kalirajan K. & Khan U. I. Its on Membership COMESA Ethiopia’s of Impact The (2008). Mohammed Husen Hausman, J. A. (1978). Specification Tests in Econometrics, in Tests Specification (1978). A. J. Hausman, of Commodities Export Major of Contribution The (2013). Mulugeta Zewdu Fitsum the in Equation Gravity of Estimation and (2013).Modelling Kareem Olanike Fatima Welfare, and Trade Borders, (2003). Wincoop van Eric & J.E. Anderson, TradeBrookings Forum.DOI: Welfare,Trade, Borders, and (2001). James Anderson. Andrew B., Bernard et.al. (2006). Trade costs, firms and productivity, and firms costs, Trade (2006). et.al. Bernard B., Andrew Ethiopia: in Trade Foreign of Potential and (2014).Determinants Kebede Alekaw References is needed. research further where area the is this Nevertheless, country. this to products its send to Ethiopia encourages still and this compensate may rate tariff low the costs, even though being landlocked increases the cost of trade in the form of transportation Flowerdew, R. & M. Aitkin (1982). A Method of Fitting the Gravity Model Basedonthe ERCA(2016) Annual Report. T.Simply Bayoumi, Regionalism A& Evidence Is B. Diversion? (1995). Eichengreen, Cambridge York: New Edition, Third Data, Panel of Analysis (2014). Hsiao Cheng Burger M. F. van Oort & G.J. Linders (2009). On the Specification of the Gravity Model Bemnet Aschenaki (2004). Transport costinEthiopia: An ImpedimenttoExport? Baltagi, B.H.(1995).Econometric Analysis ofPanelData,Chichester. Anderson, J.E. & Eric van Wincoop (2004). Trade Costs,Journal of Economic Literature, modelling, Exports: An Augmented GravityModelling Approach. pp: 1251-71. Ethiopia totheVolatility oftheCountry’s ExportEarnings. Presence ofZero Trade: A Validation ofHypothesesUsing Africa’s Trade Data. 10.1353/btf.2001.0002, BrookingsInstitutionPress. Monetary Economics. A GravityModel Analysis, EthiopianDevelopmentResearchInstitute. Poisson Distribution, from theEvolutionofECandEFTA, CentreforEconomic PolicyResearch. University Press. Estimation, Analysis Zero-Inflated and Zeros Excess Zeros, Trade: of Vol. 42pp:691-751. Trade Forum,2001,pp:207-243. , 4(2),pp:167-90. Economic Modelling Impact of Trade CostsonExportPerformanceofEthiopia– A PPML PanelGravityEquation Approach Journal ofRegionalScience 28,pp:1341-1347. , 22,pp:191-202. Econometrica pta Economic Spatial Journal of Brookings 46(6), ,

45 Volume 9, No 1, January-June 2018 46 Journal of International Economics Westerlund J, Wilhmsson F. (2009). Estimating the gravity model without gravity using Westerlund J, Wilhelmsson F (2009) Estimating the gravity model without gravity using Westerlund J, Wilhelmsson F (2009) Estimating the gravity model without gravity using Westerlund J, Wilhelmsson F (2009) Estimating the gravity model without gravity using Exports Manufactured India’s of Determinants (2014). NeerajAswal and G K Suresh Silva, J. M. C. & S. Tenreyro (2006). The log of gravity, The Review of Economics and from Evidence Firm-level Composition: Trade and Costs Trade (2015). Ali Salamat Asian from Evidence Empirical Trade: on Costs Trade of Impact (2007). De. Prabir and Barriers Africa: in Costs Trade (2008). Wilson S. J. & A. Portugal-Perez, eaa, . . 20) Gnrl igotc et o Cos eto Dpnec in Dependence Section Cross for Test Diagnostic General (2004). H. M. Pesaran, National BankofEthiopia(2015). Annual Report. McCallum, J. (1995). National Borders Matter: Canada-U.S. Regional Trade Patterns, Levin, A., C.-F. Lin, & C.-S. J. Chu. (2002).Unit root tests in panel data: Asymptotic and Ethiopia, in Diversification Export for Lakew,Prospects (2003). B. Economics International (2012). Melitz J. Marc & Maurice Obstfeld R., Paul Krugman AExports?, Ethiopia’s for Matter Rate Exchange Real Does (2011). Bekele Kebede IMF (2001).Global Trade LiberalizationandtheDevelopingCountries. panel data, panel data. Appl Econ43: 641-649. panel data. Appl Econ43: 641-649. panel data. Appl Econ43: 641-649. and FinancialIssues, to SouthandNorth: Economics Aof GravityModel Journal Analysis, International Statistics Forthcoming. Pakistan, Workingpaper Series No.27. Paper Trade,Working on Network Training and Research Asia-Pacific Countries. Research DevelopmentGroup Trade Team. Reform, for Opportunities Panels,Working Paper American EconomicReview finite-sample properties, Paper, ERD/SWP/007/2003. Theory &Policy, Ninthedition,PearsoneducationInc. Model Analysis. Gravity Appl Econ 4(1),pp:144-151. , 43,pp:64-649. , UniversityofCambridge&USC. , InternationalGrowthCenter. Journal ofEconometrics oiy eerh okn Ppr 4619 Paper Working Research Policy , 85(3),pp:615-23. , 108,pp:1-24. NBE Staff Working Staff NBE , World Bank www.distancecalculator.net (accessedon24 April, 2017) WTO (2015).Whytradecostsmatterforinclusive,sustainablegrowth. of Organization Industrial the and Production of Location the Trade, (2008). WTO World Bank(2017).World DevelopmentIndicators. Sheet Data Integrated Document, Information World(2016).Project Bank World Bank(2014).UnleashingthePotentialofEthiopia’s ExportIndustry. Ethiopia of Trade Bilateral for Analysis Data Panel ATebekew (2014). Wondwosen, Firms. (PID/ISDS). and East African Community countries: The Gravity Model Approach. Addis Ababa. Impact of Trade CostsonExportPerformanceofEthiopia– A PPML PanelGravityEquation Approach 47 Volume 9, No 1, January-June 2018 48 Journal of International Economics Keywords: Volatility Spillover, StockMarket,Globalization,FinancialCrisis the increase always linkages global exposure toexternalshocks. strong because studied been also has crises inflfiThe 2008 others. of than uence nancial India with integrated more are these of it was found that national markets including India are not independent ones and some market in the form of stock return and volatility spillovers. While taking G-20 countries, fiinternational with India in market equity of integration of (degree) extent and nancial fistability in domestic maintaining markets. In this paper attempt has been made to measure nature nancial for mechanism is important transmission of nature fi markets the of nancial comprehending integration for and linkages international of study A Assistant Professor, P.G. Department of Economics, * Doaba College, Jalandhar and can be reached at nayiamahajan02@ in literature of body fast-growing the to contributes study present The approaches. non-linear various using spillover volatility and return stock of form the in integration measuring international financial for investigated focused markets studies capital have These fi integration. global international etc) nancial the around (2013) spillovers Greenidge volatility and and return on Grosvenor (2012), Lagesh (2011), Jung and Dimpfl and (2010), Padhi e Prasarnsith (2009), Nikolova Yimaz and and Bhar Diebold (2006), (2007), Dibooglu (Fratzscher and studies Chancharoenchai Many volatility. (2005), and Baele returns (2001), of terms in both others, in effects spillover create fi market global one and in relationships changes integration, nancial cross-border increasing the With 2014). (Prashant, country any for policy monetary of both global and local nature. This interdependence limits the scope for independent shocks external to markets emerging fi the of exposure the increase global linkages nancial Strong 2013). (Louzis, markets domestic fi in maintaining stability for nancial mechanism is important transmission their fi and of fi crises markets nature nancial of the nancial comprehending integration for and linkages international of study A Introduction gmail.com and Vulnerability toExternalShocks:Empirical Global IntegrationofIndianFinancialSystem Journal ofInternationalEconomics SN07-72 Volume 9,No.1,January-June2018,pp.48-71 ISSN 0976-0792 Evidence fromCapitalMarket Nayia Mahajan* where lnisfornaturallogarithm discussion. whole the concludes Suggestions’ and Implications Policy ‘Conclusion, titled section the Finally, functions. response impulse and analysis decomposition variance using models ‘Variancespillover volatility the section of dynamics run following short reveals Analysis’Decomposition The Model)’. (Spillover Analysis Bivariate of Estimates spillover and vulnerability to financial crisis in volatility 2008 and return return is of form presented the the in in integration all financial theglobal of sectionMeasurement in series. ‘Empirical present volatility of nature the examines and countries considered the all of series return stock the for analysis univariate presents Analysis’Univariate methodology.and of database Estimates with ‘Empirical along titled data section The of properties econometric other and statistics descriptive Methodology’analyses and ‘Database titled section The sections. four comprises This 2008. of shock external of light the in market financial international with India in market equity of integration of market integration. An attempt has been made to measure nature and extent (degree) financial international of investigation the to dedicated economics financial empirical 2 1 countries G20 other 16 and India of those are investigated markets stock The Database global economycanbebestrepresentedbyG-20countries. Thus, countries. these by produced is (GDP) Product Domestic Gross world of 80% than Europe. more as Also, well South as and East America, North Middle from Asia, nations includes it as diversification geographical sufficient contributes emerging and as markets well The as developed selected. both of been mix a has represents group countries the that is G-20 rationale of group market, global the represent To Sample Selection Database andMethodology from the major stock exchanges of these countries these of exchanges stock major the from obtained indices stock to benchmark comprises analysis 1997 for dataset 1, The 2014. 2, July June from prices stock Friday) to (Friday closing weekly the covers data R formula. log returns for the stock prices of all the countries are calculated with help of following weekly Friday-to-Friday utilized. been has day working previous the on data closing Friday,on trading for closed was market the of any if that such sampled are data The traced. adequately be could economy an in movements market large caused that factors or data instead of world indices (to represent international market) is that specific events t =ln(P in Appendix A. The list of all the indices used for domestic as well as foreign markets along with symbols and data range has been given of data. Three (Soudi Arabia, South Africa and Turkey) out of G-20 countries are not included in the sample due to non availability Global IntegrationofIndianFinancialSystemandVulnerability toExternalShocks:EmpiricalEvidencefromCapitalMarket t ) –ln(P t-1 ) 2 . The advantage of using country using of advantage The . 1 . The . 49 Volume 9, No 1, January-June 2018 50 Journal of International Economics X Mean Specification in general,twodistinctspecificationsformeanandvarianceareasfollows: estimate volatilities in returns of individual markets. Under the E-GARCH methodology, Asymmetric GARCH (E-GARCH) model proposed by Nelson (1991) has been used to Univariate E-GARCHModel Indian of integration global measure to financial system. order in spillover volatility and return and Following models have been used to estimate conditional volatility, volatility clustering, series. financial the of most in present found is which distribution the in asymmetry for allow GARCH model with asymmetric extensions. Asymmetric GARCH (E-GARCH) models bivariate (ii) and model GARCH univariate (i) using on based is countries G20 other In the present study, estimation of volatility in returns and its co-movements in time-varying India and of existence the test to volatility andspillovereffects inreturnsandvolatilityacrossmarkets. used models of variety a in are patterns There these volatility. predict and depict to models various developed have series time financial in Researchers returns. stock of properties clustering and varying time like volatility in patterns important and interesting the capture not does and unconditional is deviation standard or variance by represented volatility of measure traditional The Modeling ConditionalVolatility or unitrootispresentintheseries. have been utilized to discern whether stock-return series of all countries are stationary, details) for (2007) Gujrati (see tests Philip-Perron and Fuller Both Dickey Augmented Unit RootTest Methodology Used Variance Equation leverage effect and it is expected to be negative implying that bad news has a bigger a has news bad that implying negative be to expected is it effectand leverage how volatility is affected by current and past information respectively”. γ measures the indicate or respectively “Basically,news old effectsand the new measure of GARCH and ARCH study, the per As 2012). Mahajan, and (Verma positive versa” further vice and with changes associated are changes price positive that implying clustering volatility indicates β positive “A variance. periods’ previous of impact the of newsabout measures effect the thatmeasures term volatility from the previous period the ARCH on current period volatility.is β is the α GARCH term that specification, this In log t = c+μ ( σ 2 t ) = t ω + ∑ i = p 1 α i µ t − i / σ t − i + ∑ j m = 1 γ j µ t − j / σ t − j + ∑ K q = 1 β k log ( σ 2 t − k )

(1) (2) market toforeign(individualcountries),followingmodelhasbeendeveloped. Indian from spillover volatility and return measure to attempt an In objective. desired epciey n h dmsi mre. While market. domestic the in respectively variance. However, term log(σ feature attained by E-GARCH additional an is This magnitude. same the of news good the than volatility on impact 4 3 specified. been have equations variance and mean following the markets, foreign to India from In a similar but separate model, which captures the transmission of return and volatility in themodelforcapturingeffect of2008crisisonreturnandvolatilityspillover. incorporated been has (DUM) variable Dummy markets. foreign to respect with India ploe vrals rm h frin (j foreign the from variables spillover n h aoe model, above the In In present study bivariate E-GARCH bivariate study present In account forconditionalHeteroskedasticityisthroughclassofGARCHModels”. effectspillover and transmission information capture to researchers by employed that markets. According to Kanas other of co-volatility the to regard with market one of volatility the model to is models Bauwens et. al. (2006) Bivariate E-GARCH(SpilloverModel) ARCH effect, βforleverage(asymmetric)effect andγforGARCHeffect. domestic market. Moreover, R equations financial meltdown. in augmented been has far 2008 based U.S. of impact examine to order in (Dum) variable Dummy by (4) and (3) so discussed model E-GARCH basic The The model used in present study is given below with mean and variance specifications. basis of Akaike InformationCriterion(AIC)”. In above model, R model, above In it Bivariate E-GARCH model is one version ofmultivariateGARCH model. Bivariate E-GARCHmodelisoneversion For moreinformationonNelson’s (1991)E-GARCH modelseeEviewsUserGuidelines,Version 7) =c = = 2 t i ) = + Global IntegrationofIndianFinancialSystemandVulnerability toExternalShocks:EmpiricalEvidencefromCapitalMarket + R +α t-1 + i |μ t-1 t-1 Dum+µ + /σ is used to find impact of previous period returns, and α is for is α and returns, period previous of impact find to used is

t-1 suggested suggested that “the most appropriate use of multivariate GARCH + | +β and i (μ it (2000) “The multivariate econometric technique normally 3

+ is to capture the impact of returns of previous period in model. The values of p, m and q are determined on the , t-1 + /σ , t-1 log r te em fr -eid eun n volatility and return t-period for terms the are ) +γ 4 models have been formulated for fulfilling the fulfilling for formulated been have models depict the properties of conditional volatilities in th mre i trs f eun n conditional and return of terms in market ) i log(σ + + 2 t-1 ) + DUM + and Dum +ω +

DUM + r contemporaneous are it

(5) (3) (6) (4)

51 Volume 9, No 1, January-June 2018 52 Journal of International Economics condition ofstationaritytoperformfurtheranalysis. applied. been have tests Phillip-Perron Results of both the tests and are reported in TableADF 1. both All the return series are series, satisfying the return in root unit or when there, actually integration of order find to order in (1989)). Thus (Perron data not the in breaks are there are that roots unit discover to tends test ADF regular The Unit RootTest Results data comprising thesefeatures. with dealing of capable are models GARCH (2013), Glies and Li to According that data. the in dependencies nonlinear found substantial as well as linear of been presence is there has It compiled. returns stock of tendencies the know to analyzed return series from the seventeen markets of of G20 countries, Q-statistic including India, have and been kurtosis skewness, variance, mean, as such properties basic The indices observed minimumreturns. the of most when Brothers, Lehmen of collapse the after point 2008) a 6, at (October considered been has data the in break structural a Therefore, countries. shown insignificant effect. It may be because of the reason that markets ofU.S. that reason of the bebecause may It effect. insignificant shown has break this itself, U.S in But, 2008. in crisis by affected were economies these of China, Indonesia, Mexico, Canada, Russia, South Korea, Brazil, U.K signify Argentina, that volatility India, in the stock of markets case in equation variance coefficients in significant dummy However,for crisis. subprime by affected were only countries of returns these mean that suggests which U.S. and U.K except countries, the all of 5 (θ efficient the of returns U.S, and U.K Russia, period. present the on impact significant India, making not are periods previous co- Another except that, clear of estimates is by it Given equation, 2. Table in mean given are countries sample the properties all clustering in examine volatility of to model univariate of results estimation Parameter Empirical EstimatesofUnivariate Analysis crisis occurred financial crisis the U.S. the when in 2008 in included trough suffered countries index each that seventeen found was in it study, prices stock closing weekly plotting While Preliminary Analysis run dynamics. decomposition variance short for VARthe used (Vector under been and have analysis framework Autoregressive) function response impulse models, above from Apart The troughsinthe indices ofU.Kand Argentina arenot soobvious. = = + ) in mean equation for dummy variable is insignificant for the return series return the for insignificant is variable dummy for equation mean in 5 . During this period, minimum returns have been observed in all the all in observed been have returns minimum period, this During . + + + + log + DUM + ) +

+ DUM +

(8) (7) aus of values Empirical EstimatesofBivariate Analysis (SpilloverModel) worldwide 2008, mid-September after India But, including economy.markets the in crunch investors financial of the detection among early to due volatility high experiencing already were 7 6 Table,the In Tablein presented 3. are 6 and 5 equations in given model the for results Estimated is (and viceversa)usingE-GARCHmodel. it Therefore, countries. economies all other to India from of spillover volatility of extent returns the measure to worthwhile stock the in present is clustering of properties volatility that, clear is it analysis univariate from Since (2013)). Greenidge and Grosvenor (2012), Lagesh and Padhi (2011), Jung and Dimpfle (2010), Prasarnsith (2007), Nikolova and (Bhar interdependence or integration financial international of Transmission of return and volatility around the globe is one of the measures of degree f eky odtoa volatility conditional weekly of series Therefore, series. return concerned the in volatility conditional of presence the indicates which significant are countries the all for (β) Leverage and (γ) GARCH (α), ofARCH coefficients series, return the all of analysis univariate of estimates the per urudn te olpe f emn rtes n etme 20 wih clearly which 2008 September on Brothers justifies theincorporationofstructuralbreakatgivenpoint. Lehman of collapse the surrounding period the during maximum or periods) other to (compared higher either is volatility model for all the countries and it is univararite observed the that from in derived all series the countries conditional (except these of Argentina), graphs presents B Appendix univariate modelgiveninequations3and4. significant which suggests the foreign influence on mean returns of India. of returns mean on influence foreign the suggests which significant markets in terms of return and volatility respectively. All of these three coefficients are ttsi sos ht tnadzd eiul dd o ehbt diinl RH effect. ARCH additional exhibit Thus, indicatingthatvariance equationsarewellspecified. not did residuals standardized that test shows ARCH-LM insignificant statistic the Moreover, sample. the in included countries the all on impact contagion had 2008 in crisis that signifies variable dummy of coefficients relatively the on influence greater smaller markets. The negative and significant (except for Argentina and South Korea) exert to likely are markets dominant that theory markets.most influential the are Canada U.S and Japan, Australia, with Thus,itgoes suggesting that Indian financial sector is integrated with global financial system where thereby Canada, of case in 42.5040 and U.S, of case in 76.1899 by followed Japan, for 78.2965 is persistence volatility cross of (for coefficient 3.4757 This Argentina). to Germany, France, Italy and U.K). The magnitude of spillover (E.U, varies from 142.09 (for Australia) countries European and Brazil except countries all from significant is coefficient that shows volatility spillover from foreign markets to domestic market which been used. spillover,volatility has of model model GARCH bivariate univariate subsequent from the derived In volatility conditional from itsequitymarketandsharpdepreciation ofrupeeinforeignexchangemarket(seeV In India, the most immediate and considerable effect of crisis was seen as an outflow of foreigninstitutional investment Global IntegrationofIndianFinancialSystemandVulnerability toExternalShocks:EmpiricalEvidencefromCapitalMarket and shows the effect of last period’s return on current period whereas period current on return period’s last of effect the shows are the contemporaneous spillover coefficients from foreign from coefficients spillover contemporaneous the are 6 ol hv bre h but f hs cie. oevr as Moreover, crises. these of brunt the borne have could 7 o al h rtr sre hv be cmue from computed been have series return the all for erma andMahajan,2012). is the 53 Volume 9, No 1, January-June 2018 54 Journal of International Economics shows different trends. The coefficient The trends. different shows 8, equation by given model spillovers volatility the spillovers, shock the to contrast In relationships (bidirectional)exists affects stock market Indian in returns in Argentina negatively. Thus, it is the only case of volatility Argentina in which two way toohave signifies which that coefficient ofArgentina (-6.8802) is that negative case significant only The case. this in relation unidirectional the conveys that market Indian in volatility by affected getting not are returns markets’ foreign concerned, is transmission volatility as far market. As Indian in return to respond Korea) South and Indonesia China, Argentine, (except markets foreign all in returns that found been has it 7, equation of estimates per markets. As system. Table 4 shows the results for return and volatility financial spillover Indian from India to to markets foreign foreign from effects spillover unidirectional the giving is it words, other In scenario. whole the of facet one the give far so discussed results The 10 9 8 stock marketsofallcountrieshasbeenanalyzedinVAR system order to know such dynamic interactions, the structure of interdependence among the In markets. these between exists interaction multi-lateral which to extent the analyse to need is there relationships, national these of dynamics various transmission For markets. financial among exist inter-relationships the models, above by depicted As VarianceDecomposition Analysis amount ofinteractionisdetected betweenIndiaandothernationalmarkets. substantial a but, only market domestic in innovation by explained is market Indian in variations of proportion major the Although, system. financial foreign Indian (through the in influence markets) of channels main the gives 5 Table (weeks). periods 10 are caused by the innovations either in domestic or in foreign markets after 1 to 5 and In Tablewhich given are fractions) (into of variations of decomposition 6 details 5 and rm nin akt o te ntoa mres Te eut pit u significant out point Italy markets and China foreign results France, Brazil, all U.S., to except The market Indian markets. from effects national asymmetric or spillovers other volatility to market Indian from these markets. aremore andIndonesia and Indian between (transmission) flow information is there Japan U.S, and India with integrated Canada, Australia, then integration financial It, thus, can be concluded that if volatility spillover is taken as a mechanism to measure in thepast. to guide their investment related decisions but they must take into consideration news foreign) should not follow information on only current local or international movements forall as well as (domestic term markets the all in investors that suggests countries the (almost) andleverage GARCH ofARCH, coefficients significant the addition, In 20.6938. i.e., E.U for is parameter this and by assumed value least (73.57). The Australia (75.9510), Japan (77.8702), Canada by followed (82.9787), Indonesia of case used toanalyzecertain aspectsoftherelationshipsbetween thesetofvariables often are these Therefore, variables. of set a among correlations the represents model (VAR)Vector Autoregressive Italyarethecountriesforwhichthiscoefficientisinsignificant inpreviousvolatilityspillovermodel. France, Braziland The impactofvolatilityonreturnsfor Argentina issignificantinthepreviousmodel. 8 . in this model shows the extent of spillovers of extent the shows model this in 9 . Moreover, this coefficient is highest in highest is coefficient this Moreover, . 10 . one intheinternationalfinancialsystemshortperiodtoo. (considered in the study) are not independent and Indian market markets is not the national exogenous that concluded be may it Thus, period. short the in markets Indian in innovation by explained are others among Argentina and China U.K, Japan, but, market foreign the enough explaining national not is other market in stock Indian variations Although markets? for accounted is market Indian much how i.e., markets foreign the on innovations domestic of influence the tells hand, other the on Table6 (2013) etc with results of many studies like Baele (2005), Bhar and Nikolova (2007), Li and Giles less developed markets are influenced by dominant market and this is also not in align comparatively that theory with well go not does which substantially market Indian the by the markets of Argentina, Canada, E.U and Germany. But U.S. seems not to explain explained respectively.enough week, are tenth market Moreover,Indian in variations and fifth on 12.04% 12.11%and and week second on 13.12% is which maximum, is Australia by explained variance error of percentage However, period. first in market Indian in variation (2.39%) maximum explaining is market Brazilian that reveals Data 11 the from derived been have univariate analysis(usingE-GARCH model)ofallthecountries. volatilities conditional attempt, subsequent a In data. the in root unit and of properties root unit and volatility non-normality, conditional the of markets stock the from seventeen of G-20 countries, first preliminaryreturns analysis has been made which unravels the using While spillover. volatility of form the in The study examines the nature and extent of global integration of Indian capital market Conclusion, PolicyImplicationsandSuggestions getting affected bydomesticmarket(India) inshortperiodtoo. markets dieoutafterfifthweek. foreign the all of responses that Tableobserved from is made It been 8. has markets Further, examination of responses negligible. of almost Indian foreign are markets these on to that shocks after to impact and innovations weeks in Their five Indian till long. observed been has for returns persist not do responses and weak generally are markets persist to impact the returns in India. It is observed that foreign market shocks national these of each in originating innovations long how after Tablethat presents 7 of responses and markets other others toinnovationinIndianstockmarketarepresented to Tables 7and8. given shock the to India of responses impulse been examined using the simulated responses under VAR framework. The normalized each of the seventeen markets to the shocks given in Indian market and vice versa has Following the analysis of variance decomposition, the pattern of dynamic responses of Impulse ResponseFunction

Bala and Premaratne (2003) in contrast to these, evidenced the volatility spillover from smaller to dominant market dominant to smaller from spillover which iscontradictory tootherstudies. volatility the evidenced these, to contrast in (2003) Premaratne and Bala Global IntegrationofIndianFinancialSystemandVulnerability toExternalShocks:EmpiricalEvidencefromCapitalMarket Thus, it can be concluded that all the considering markets are affecting and affecting are markets considering the all that concluded be can it Thus, 11 . 55 Volume 9, No 1, January-June 2018 56 Journal of International Economics Bhar, R. (2001). Return and Volatility Dynamics in the Spot and Futures Markets in Markets Futures and Spot the in Dynamics Volatility and Return (2001). R. Bhar, AModels: GARCH Multivariate (2006). Rombouts V.K. J. Laurent, S. L., Bauwens, rwh n saiiy hc cn e eie fo getr nerto o international of integration greater financial markets,appropriateandprecisemeasures needtoundertake. from desired be can which stability and growth the policies tend to work. To exploit the potential benefits, in terms of macro-economic To conclude with it is suggested that one needs to take more cautious view of the way no Therefore, shocks from foreignmarketsareincorporated. uncertain analysis. of responses present the until fruitful by be will conformed policy monetary independent are is which any nation market of international in market policies financial and meltdown financial financial like changes integrated external to vulnerable always Globally economy. domestic the of makers policy the for implications significant have study the from found results The with India. relations should be maintained with the countries which are financial found prominent less more that or suggested not is it integrated growth, economic hence and efficiency benefits of portfolio diversification gets reduced. Thus, for achieving the higher level of It is well established in literature that with greater degree of market integration, potential not independentonesintheshortperiodalso. are market Indian including markets capital national that exhibits function impulse response and analysis decomposition variance both of results empirical the Further, with thesecountries. and U.K thereby suggests that contagion impact was there on the relationship of India run. Korea South Russia, Indonesia, China, long Brazil, for Mexico, significant Australia, found in India been has with model considered the in re1ation meltdown financial U.S 2008 bidirectional for variable Dummy having markets other the are Korea South and Russia Moreover, market. 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Source: Authors’ Calculations Note: p-valuesinparentheses.‘*’ indicatessignificantvalue ofteststatistics U.S U.K South Korea Russia Mexico Japan Italy Indonesia Germany France E.U China Canada Brazil Australia Argentina India Return Series Table 1:Unit RootTest ResultsfortheReturnSeriesofRespectiveCountries Global IntegrationofIndianFinancialSystemandVulnerability toExternalShocks:EmpiricalEvidencefromCapitalMarket -31.8378* (0.0000) -30.7993* (0.0000) -32.5151* (0.0000) -17.9891* (0.0000) -30.7896* (0.0000) -30.4569* (0.0000) -29.3347* (0.0000) -30.8131* (0.0000) -30.6030* (0.0000) -31.9361* (0.0000) -31.5708* (0.0000) -28.1704* (0.0000) -32.6662* (0.0000) -19.2375* (0.0000) -30.7022* (0.0000) -28.0824* (0.0000) -28.7509* (0.0000) ADF Statistic (At Level) Philip-Perron Test statistics -31.0465* (0.0000) -32.4024* (0.0000) -27.9063* (0.0000) -30.7885* (0.0000) -30.4476* (0.0000) -29.3473* (0.0000) -30.9967* (0.0000) -30.5935* (0.0000) -31.9072* (0.0000) -31.5397* (0.0000) -28.5808* (0.0000) -32.5301* (0.0000) -31.3779* (0.0000) -30.7167* (0.0000) -31.8411* (0.0000) -28.1944*(0.0000) -28.7847*(0.0000) (At Level) Stationary Stationary Stationary Stationary Stationary Stationary Stationary Stationary Stationary Stationary Stationary Stationary Stationary Stationary Stationary Stationary Stationary Result 59 Volume 9, No 1, January-June 2018 60 Journal of International Economics Source: Authors’ Calculations Note: p-valuesintheparentheses.‘*’ indicatessignificanceat10%level. log(σ R Argentina Indonesia Germany Australia Country Canada t Mexico Russia France =c+ Japan Korea South China Brazil India Italy U.K U.S E.U 2 t ) = R -0.0021* (0.0948) (0.2736) (0.8784) (0.0526) (0.0002) (0.4257) (0.0420) (0.8515) (0.6205) (0.1376) (0.5496) (0.5693) (0.3182) (0.0071) (0.9539) (0.1107) (0.1132) 0.0038* 0.0035* 0.0027* 0.0035* -0.0009 -0.0012 -0.0001 +α|μ 0.0017 0.0001 0.0007 0.0006 0.0013 0.0029 0.0006 0.0006 0.0011 t-1 c +θDumµ Table 2:Estimates forE-GARCHModelallcountries t-1 -0.0789* (0.0036) (0.2038) (0.0191) (0.4766) (0.6882) (0.0265) (0.5512) (0.4886) (0.2140) (0.3779) (0.8522) (0.3403) (0.2291) (0.9166) (0.4410) (0.1823) (0.0112) 0.0933* 0.0858* 0.0778* -0.0441 -0.0254 -0.0174 -0.0488 -0.0291 -0.0067 -0.0356 -0.0469 -0.0037 -0.0273 /σ 0.0158 0.0233 0.0469 t-1 | +β(μ (0.0020) (0.5763) (0.0095) (0.3292) (0.1976) (0.3058) (0.5862) (0.2374) (0.3162) (0.9193) (0.5908) (0.7394) (0.8356) (0.7385) (0.5227) (0.7933) (0.1141) 0.0079* 0.0024* -0.0030 -0.0024 -0.0019 -0.0039 -0.0007 -0.0006 -0.0005 -0.0011 -0.0011 t 0.0022 0.0047 0.0001 0.0004 0.0006 0.0010 θ t-1 /σ t-1 -6.8289* -0.5359* -0.6706* -0.3342* -0.3288* -2.2269* -0.3062* -0.2072* -0.3335* -0.1796* -0.4979* -0.7739* -0.3893* -0.4280* -0.7547* -0.9003* -0.5055* (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0033) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) ) +γlog(σ (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0014) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0001) (0.0000) (0.0000) 1.1039* 0.2595* 0.1967* 0.2132* 0.1544* 0.2031* 0.1636* 0.0681* 0.2719* 0.2161* 0.2026* 0.1359* 0.1697* 0.1980* 0.3227* 0.2331* 0.1165* α 2 t-1 ) + -0.0863* -0.2047* -0.0754* -0.0974* -0.2153* -0.0582* -0.0864* -0.0152* -0.1330* -0.1468* -0.1384* -0.2019* -0.0826* -0.1366* (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0003) (0.0000) (0.0000) (0.0986) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0002) (0.0000) -0.1142* 0.2792* 0.0799* Dum +ω β (0.0681) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) 0.9467* 0.9316* 0.9702* 0.9689* 0.7076* 0.9730* 0.9753* 0.9679* 0.9861* 0.9556* 0.9233* 0.9605* 0.9586* 0.9146* 0.8977* 0.9556* 0.0711* γ t -0.0608* -0.0215* -0.0315* -0.0262* -0.0729* -0.0212* -0.0338* -0.0882* (0.0001) (0.0007) (0.1426) (0.0340) (0.0017) (0.4059) (0.0068) (0.0036) (0.0000) (0.0025) (0.0038) (0.1949) (0.4393) (0.1809) (0.2369) (0.0001) (0.5120) 0.3271* -0.0246 -0.0091 -0.0214 -0.025* 0.0314 0.0201 0.0111 -0.019 ARCH-LM Test 0.1682 (0.6817) 0.1679 (0.6821) 0.1527 (0.6960) 0.1523 (0.6964) 4.5873 (0.3322) 4.6009 (0.3322) 1.8679 (0.1717) 1.8677 (0.1721) 0.5009 (0.4791) 0.5001 (0.4797) 0.4363 (0.5095) 0.4355 (0.5095) 0.0036 (0.9520) 0.0036 (0.9520) 0.0891 (0.7653) 0.0889 (0.7656) 0.2619 (0.6088) 0.2614 (0.6093) 0.9357 (0.3334) 0.9345 (0.3340) 0.1089 (0.7414) 0.0000 (0.9965) 0.0000 (0.9965) 0.6456 (0.4217) 0.6446 (0.4223) 0.7395 (0.3898) 0.7384 (0.3904) 0.0176 (0.8946) 0.0175 (0.8948) 0.0432 (0.8354) 0.0431 (0.8356) 1.3162 (0.2516) 0.1092 (0.7411) 1.3173 (0.2511) Observations F-Statistics and N* = + + + DUM +

= + + + log + + DUM + Global IntegrationofIndianFinancialSystemandVulnerability toExternalShocks:EmpiricalEvidencefromCapitalMarket

Table 3: Estimates of Bivariate E-GARCH Model (Spillover Effect from Foreign Countries to India) ARCH-LM Test Country F-statistics and N*Obs U.S. 0.0102* 0.0364 0.0736* -13.6989* -0.0028 -0.7017* 0.1861* -0.0721* 0.9248* 76.1899* -0.0456* 0.1604 (0.6889) (0.0000) (0.3084) (0.0643) (0.0000) (0.1464) (0.0000) (0.0000) (0.0015) (0.0000) (0.0002) (0.0079) 0.1607 (0.6885) Australia 0.0100* 0.0433 0.0809* -24.5289* 0.0018 -0.6737* 0.1718* -0.0771* 0.9274* 142.09* -0.0743* 0.1601 (0.6891) (0.0000) (0.2314) (0.0934) (0.0000) (0.3726) (0.0000) (0.0000) (0.0003) (0.0000) (0.0018) (0.0014) 0.1605 (0.6887) Argentina 0.0059* 0.0883* 0.0096 -0.3341* -0.0046* -0.6215* 0.2167* -0.0867* 0.9381* 3.4757* -0.0071 0.0867 (0.7685) (0.0048) (0.0133) (0.4245) (0.0864) (0.0387) (0.0000) (0.0000) (0.0000) (0.0000) (0.0114) (0.6825) 0.0869 (0.7681) Brazil 0.0078* 0.0421 0.3178* -2.9596* -0.0034* -0.3043* 0.1255* -0.0508* 0.9720** 8.1494 -0.0205 2.0743 (0.1501) (0.0009) (0.1673) (0.0000) (0.0028) (0.0962) (0.0002) (0.0000) (0.0037) (0.0000) (0.1721) (0.0156) 2.0742 (0.1498) Canada 0.0040* 0.0747* 0.6561* -5.0549* -0.0009 -0.4360* 0.1185* -0.0480* 0.9536* 42.5040* -0.0389* 0.4647 (0.4956) (0.0103) (0.0206) (0.0000) (0.0090) (0.5813) (0.0010) (0.0000) (0.0161) (0.0000) (0.0189) (0.0025) 0.4655 (0.4951) China 0.0070 0.0749* 0.0471 -3.9447* -0.0029 -0.4237* 0.1591* -0.0720* 0.9604* 30.2179* -0.0255* 0.0040 (0.9494) (0.1055) (0.0321) (0.1329) (0.0975) (0.1562) (0.0000) (0.0000) (0.0002) (0.0000) (0.0025) (-0.0249) 0.0040 (0.9493) E.U. 0.0045* 0.0661* 0.4460* -2.1690* -0.0008 -0.2576* 0.1275* -0.0305* 0.9774* 5.7769 -0.0255* 0.8215 (0.3650) (0.0035) (0.0394) (0.0000) (0.0487) (0.6619) (0.0020) (0.0000) (0.0547) (0.0000) (0.4236) (0.0255) 0.8226 (0.3644) France 0.0047* 0.0719* 0.4488* -2.3035* -0.0009 -0.2692* 0.1301* -0.0339* 0.9762* 8.2234 -0.0289* 0.5115 (0.4747) (0.0029) (0.0229) (0.0000) (0.0379) (0.5855) (0.0007) (0.0000) (0.0318) (0.0000) (0.3005) (0.0133) 0.5115 (0.4741) Germany 0.0047* 0.4770 0.4352* -2.1231* -0.0017 -0.2451* 0.1114* -0.0259* 0.9777* 6.2926 -0.0229* 0.7853 (0.3758) (0.0031) (0.1480) (0.0000) (0.0153) (0.3529) (0.0019) (0.0000) (0.0763) (0.0000) (0.3611) (0.0104) 0.7864 (0.3752) Indonesia 0.0055* 0.0664* 0.0337 -1.5741 -0.0036 -0.3173* 0.1382* -0.0494* 0.9717* 11.4665* -0.0140 0.0581 (0.8096) (0.0131) (0.0602) (0.2699) (0.1413) (0.1089) (0.0000) (0.0000) (0.0029) (0.0000) (0.0513) (0.1539) 0.0582 (0.8093) Italy 0.0039* 0.0689* 0.3919* -1.3508 0.0002 -0.3737* 0.1522* -0.0378* 0.9641* 12.6191 -0.0407* 0.2049 (0.6509) (0.0075) (0.0355) (0.0000) (0.1308) (0.9283) (0.0007) (0.0000) (0.0474) (0.0000) (0.1139) (0.0248) 0.2053 (0.6505) Japan 0.0127* 0.0559 0.0686* -10.4808* -0.0014 -0.5346* 0.1854* -0.0431* 0.9530* 78.2965* -0.0412* 0.0692 (0.7926) (0.0000) (0.1036) (0.0320) (0.0000) (0.4811) (0.0000) (0.0000) (0.0424) (0.0000) (0.0028) (0.0013) 0.0693 (0.7923) Mexico 0.0047* 0.0702* 0.4239* -3.1191* -0.0018 -0.4755* 0.0904* -0.0274 0.9469* 32.6545* -0.0269* 1.7563 (0.1854) (0.0229) (0.0269) (0.0000) (0.0309) (0.3549) (0.0013) (0.0016) (0.1666) (0.0000) (0.0145) (0.0098) 1.7568 (0.1850) Russia 0.0049* 0.0664* 0.0977* -0.6928* -0.0029 0.4496* 0.1499* -0.0728* 0.9541* 5.5914* -0.0213* 0.0789 (0.7787) (0.0033) (0.0570) (0.0000) (0.0447) (0.1256) (0.0000) (0.0000) (0.0001) (0.0000) (0.0272) (0.0567) 0.0792 (0.7784) South 0.0109* 0.0548 -0.0199 -4.1969* -0.0077* -0.8038* 0.2349* -0.0808* 0.9188* 33.5385* -0.0000 0.0002 (0.9878) Korea (0.0000) (0.1229) (0.4274) (0.0000) (0.0003) (0.0000) (0.0000) (0.0006) (0.0000) (0.0003) (0.9987) 0.0002 (0.9878) United 0.0029* 0.0768* 0.1442* -0.0062 -0.0009 -0.2493* 0.1389* -0.0421* 0.9787* 0.0416 -0.0229* 0.0063 (0.9366) Kingdom (0.0428) (0.0236) (0.0000) (0.9653) (0.6356) (0.0000) (0.0000) (0.0071) (0.0000) (0.8956) (0.0035) 0.0063 (0.9365) Note: Figures in the parentheses are p-values. ‘*’ indicates significance at 10% level. Source: Authors’ Calculations. 61 Volume 9, No 1, January-June 2018 62 Journal of International Economics

= + + + DUM +

= + + + log ) + + DUM +

Table 4: Estimates of Bivariate E-GARCH Model (Return and Volatility Spillover Effect from Domestic Country to Foreign Countries) ARCH LM(1) Country F-statistics N*Obs U.S. -0.0008 -0.1087* 0.0648* 0.4554 0.0032* -0.8035* 0.1684* -0.2159* 0.9155* 23.7081 -0.0198 3.7096 (0.0544) (0.6795) (0.0019) (0.0018) (0.7288) (0.0454) (0.0000) (0.0000) (0.0000) (0.0000) (0.2226) (0.2877) 3.7024 (0.0543) Australia 0.0021 -0.0301 0.0413* -0.9569 -0.0006 -1.6434* 0.2044* -0.1865* 0.8282* 73.5700* 0.0746* 0.0014 (0.9704) (0.1186) (0.4322) (0.0247) (0.3515) (0.6590) (0.0001) (0.0000) (0.0000) (0.0000) (0.0207) (0.0174) 0.0014 (0.9703) Argentina 0.0072 -0.0029 -0.0051 -6.8802* 0.0023 -0.5591* 0.2779* 0.0715* 0.9363* 48.4641* -0.0990* 0.3318 (0.5647) (0.1958) (0.9363) (0.9317) (0.0647) (0.5997) (0.0000) (0.0000) (0.0017) (0.0000) (0.0001) (0.0000) 0.3325 (0.5642) Brazil -0.0016 -0.0936* 0.5160* 1.7471 -0.0011 -0.1770* 0.0402* -0.0955* 0.9782* 6.0709 -0.0274* 2.3142 (0.1286) (0.5855) (0.0003) (0.0000) (0.3630) (0.6617) (0.0003) (0.0271) (0.0000) (0.0000) (0.3512) (0.0083) 2.3135 (0.1283) Canada 0.0008 -0.1011* 0.2724* -0.4282 0.0003 -1.0215* 0.1806* -0.1074* 0.8995* 77.8702* -0.0181 0.0514 (0.8207) (0.6186) (0.0027) (0.0000) (0.7312) (0.8339) (0.0000) (0.0000) (0.0000) (0.0000) (0.0019) (0.2719) 0.0515 (0.8204) China -0.0011 0.0272 0.0354 1.1694 -0.0010 -0.2278* 0.1323* -0.0163 0.9812* 1.6776 -0.0229* 0.7283 (0.3937) (0.6599) (0.4280) (0.2262) (0.4726) (0.6396) (0.0042) (0.0000) (0.1555) (0.0000) (0.7991) (0.0072) 0.7293 (0.3931) E.U. -0.0016 -0.0968* 0.3264* 1.0172 0.0008 -0.3537* 0.1100* -0.0927* 0.9671* 20.6938* 0.0013 0.2329 (0.6295) (0.4530) (0.0047) (0.0000) (0.4974) (0.6689) (0.0001) (0.0000) (0.0000) (0.0000) (0.0284) (0.8949) 0.2333 (0.6291) France -0.0007 -0.1094* 0.3054* 0.3771 0.0010 -0.3522* 0.1330* -0.0814* 0.9695* 19.5316 -0.0198 0.2325 (0.6298) (0.7356) (0.0027) (0.0000) (0.8056) (0.5792) (0.0004) (0.0000) (0.0000) (0.0000) (0.2226) (0.2877) 0.2329 (0.6294) Germany 0.0007 -0.0561 0.3402* -0.5539 0.0014 -0.7841* 0.2083* -0.1487* 0.9184* 27.7300* -0.0098 0.1596 (0.6897) (0.7408) (0.1006) (0.0000) (0.7192) (0.4640) (0.0000) (0.0000) (0.0000) (0.0000) (0.0790) (0.5912) 0.1599 (0.6893) Indonesia 0.0053* -0.0315 0.0221 -1.7049 -0.0013 -1.3183* 0.2935* -0.1059* 0.8473* 82.9787* -0.1046* 0.0586 (0.8088) (0.0405) (0.3850) (0.4957) (0.4001) (0.4955) (0.0000) (0.0000) (0.0000) (0.0000) (0.0013) (0.0003) 0.0587 (0.8085) Italy -0.0012 0.0007 0.2894* 0.0463 0.0011 -0.4532* 0.1942* -0.1153* 0.9621* 15.2517 0.0178 1.4529 (0.2284) (0.5214) (0.9829) (0.0000) (0.9736) (0.9468) (0.0001) (0.0000) (0.0000) (0.0000) (0.2290) (0.3066) 1.4538 (0.2279) Japan -0.0001 0.0078 0.0678* -1.2093 0.0029 -2.6589* 0.2018* -0.2189* 0.6600* 75.9510* 0.0387 0.4444 (0.5052) (0.9563) (0.8440) (0.0112) (0.4378) (0.1841) (0.0000) (0.0000) (0.0000) (0.0000) (0.0461) (0.3969) 0.4452 (0.5046) Mexico 0.0031 -0.0972* 0.3712* -0.9231 -0.0019 -0.3881* 0.1118* -0.0839* 0.9614* 25.8090* -0.0284* 0.7676 (0.3812) (0.1443) (0.0040) (0.0000) (0.5749) (0.2816) (0.0012) (0.0001) (0.0001) (0.0000) (0.0793) (0.0106) 0.7686 (0.3806) Russia 0.0025 0.0804* 0.2225* 0.1666 -0.0014 -0.6415* 0.2242* -0.1083* 0.9290* 43.6490* -0.0279* 0.7415 (0.3894) (0.4783) (0.0204) (0.0000) (0.9531) (0.6616) (0.0000) (0.0000) (0.0000) (0.0000) (0.0493) (0.0615) 0.7426 (0.3888) South -0.0013 -0.0388 -0.0068 2.6464 0.0004 -0.7858* 0.2387* -0.1035* 0.9126* 37.0309* -0.0940* 0.0577 (0.8103) Korea (0.6522) (0.2818) (0.8396) (0.2311) (0.8631) (0.0000) (0.0000) (0.0000) (0.0000) (0.0911) (0.0002) 0.0578 (0.8100) United -0.0039 0.0737* 0.2136* 1.4923 0.0076* -7.5633* 0.9517* 0.3330* 0.0293 44.3893* 0.2011* 0.0937 (0.7596) Kingdom (0.1215) (0.0702) (0.0000) (0.4272) (0.0027) (0.0000) (0.0000) (0.0000) (0.5067) (0.0000) (0.0461) 0.0939 (0.7596) Note: Figures in the parentheses are p-values. ‘*’ indicates significance at 10% level. Source: Authors’ Calculations. Source: Authors’ Calculations Source: Authors’ calculations India Market Explained Argentina Argentina Australia Australia Brazil Brazil Canada Canada China China E.U E.U France France Germany Germany Indonesia Indonesia Italy Italy Japan Japan Mexico Mexico Russia Russia South Korea South Korea U.K U.K U.S U.S Standard Error Table 5:Variance DecompositionofIndianReturn bytheInnovationsinotherMarkets Table 6:Variance DecompositionofForeignMarketsbyInnovationsinIndianmarket Innovations in Global IntegrationofIndianFinancialSystemandVulnerability toExternalShocks:EmpiricalEvidencefromCapitalMarket Period I 0.0879 93.91

0.20 0.01 2.39 1.75 0.07 1.36 0.29 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Innovations inIndianmarket Period II Period I 0.0896 76.03 13.38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.12 0.03 0.19 0.27 0.07 0.03 0.63 0.00 2.96 2.06 1.43 0.01 1.09 0.24 0.03 0.00 0.15 0.03 0.36 0.02 0.23 0.03 1.79 Market Explained:India Period III 0.0910 70.64 12.45 3.31 1.93 1.79 0.46 1.70 0.23 1.17 0.03 0.16 0.53 0.50 2.60 0.59 0.19 1.71 Period II Period IV 0.55 0.09 0.16 0.07 0.28 0.12 0.03 0.03 0.25 0.04 0.74 0.19 0.10 0.09 0.52 0.06 0.0913 66.63 12.16 3.15 1.82 1.73 1.04 1.60 0.22 0.07 0.15 4.37 0.49 2.54 0.99 0.18 1.73 1.11 Period V 0.0913 65.49 12.11 3.12 1.88 1.72 1.22 1.59 0.22 1.14 0.10 0.15 4.32 0.51 2.53 1.96 0.19 1.75 Period X 0.55 0.10 0.17 0.10 0.28 0.05 0.07 0.06 0.25 0.07 0.74 0.21 0.10 0.09 0.53 0.07 Period X 0.0913 65.09 12.04 3.12 1.88 1.72 1.25 1.58 0.24 1.41 0.18 4.53 0.52 2.53 0.19 1.77 0.11 2.11 63 Volume 9, No 1, January-June 2018 64 Journal of International Economics Source: Authors’ Calculations Source: Authors’ Calculations U.S U.K South Korea Russia Mexico Japan Italy Indonesia Germany France E.U China Canada Brazil Australia Argentina India Argentina Australia Canada Brazil China Germany E.U France Indonesia Japan Italy Mexico Russia South Korea U.K U.S Responses in Table 8:ImpulseResponsesofForeignMarketstotheUnitShockIndianMarket A UnitShock Table 7:Impulse ResponsesofIndianMarkettotheUnitShockOtherMarkets Impulse given to Period I -0.00004 Period I -0.0032 -0.0007 -0.0013 0.0015 0.0015 0.0013 0.0004 -0.0016 0.2889 0.0014 0.0003 0.0039 0.0046 0.0008 0.0001 0.0035 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Impulse ResponsesinIndianMarket Unit ShockGiventoIndianMarket Period II Period II -0.00007 0.00003 -0.0003 -0.0004 -0.0006 -0.0004 -0.0006 -0.0001 -0.0003 -0.0002 -0.0027 0.00009 0.0002 0.0013 0.0014 0.0007 0.0002 -0.0012 -0.0004 -0.0008 -0.0013 -0.0004 -0.0016 0.0056 0.0121 0.0012 0.0005 0.0003 0.0002 0.0006 0.0019 0.0006 0.0044 Period III Period III -0.00009 0.00006 0.00007 0.00008 -0.0019 -0.0003 -0.0005 -0.0002 -0.0037 -0.0006 -0.0014 0.0004 0.0007 0.0003 0.0003 0.0006 0.0016 0.0062 0.0011 0.0011 0.0011 0.0008 0.0026 0.0023 0.0002 0.0028 0.0028 0.0004 0.0024 0.0055 0.0021 0.0014 0.0007 Period IV Period IV -0.000006 0.000003 0.000006 -0.00007 -0.00009 -0.00002 0.00005 0.00019 0.00002 -0.0008 -0.0002 -0.0003 0.0002 0.0003 0.0003 0.0003 0.0004 0.0008 0.0002 0.0004 0.0001 0.0004 0.0002 0.0006 0.0023 0.0028 0.0006 0.0069 0.0004 0.0010 0.0024 0.0002 0.0013 Period V Period V 0.000001 -0.00007 -0.0003 -0.0002 -0.0001 -0.0005 -0.0002 -0.0006 -0.0014 -0.0003 -0.0008 -0.0003 -0.0004 -0.0007 0.0010 0.0002 0.0004 0.0007 0.0005 0.0004 0.0004 0.0003 0.0004 0.0004 0.0004 0.0003 0.0016 0.0007 0.0006 0.0007 0.0007 0.0035 0.0011 0.0000003 -0.000006 Period X Period X 0.000009 0.000002 0.000009 0.000006 0.000002 -0.00002 -0.00002 -0.00001 -0.00001 -0.00002 -0.00002 -0.00002 -0.00002 -0.00001 -0.00001 -0.00002 -0.00002 -0.00002 -0.00002 0.00002 0.00003 0.00001 0.00004 0.00007 0.00002 0.00001 0.00002 0.00002 0.00001 0.00009 0.00004 Plots ofConditionalVolatility inReturnSeries Range List ofStockIndicesusedalongwithRespectiveSymbolsandData Unites Kingdom European Union United States South Korea Russia Mexico Japan Italy Indonesia Germany France China Canada Brazil Australia Argentina India Country Global IntegrationofIndianFinancialSystemandVulnerability toExternalShocks:EmpiricalEvidencefromCapitalMarket S&P/BSE-Sensex Composite Index .000 .001 .002 .003 .004 .005 .006 ESTX50EURP S&P/ASX200 MERVAL25 RTSI Index IBOVESPA Nikkei 225 FTSE 100 FTSEMIB S&P/TSX S&P 500 SSE C.I CAC40 KOSPI Index DAX IPC 100 200 Appendix B 300 Appendix A FTSEMIB.MI ^STOXX50E 000001.SS ^BSESN IM25.BA HXU.TO RTS.RS Symbol ^GDAXI ^GSPC ^BVSP ^AXJO ^FTSE ^JKSE ^N225 ^FCHI ^MXX ^KSII C India o 400 n d i t i o n a l 500 v a r i a n c e November 10,1997toJune2,2014 600 January 5,1998toJune2,2014 July 7,1997toJune2,2014 July 7,1997toJune2,2014 July 7,1997toJune2,2014 July 7,1997toJune2,2014 July 7,1997toJune2,2014 July 7,1997toJune2,2014 July 7,1997toJune2,2014 July 7,1997toJune2,2014 July 7,1997toJune2,2014 July 7,1997toJune2,2014 July 7,1997toJune2,2014 July 7,1997toJune2,2014 July 7,1997toJune2,2014 July 7,1997toJune2,2014 July 7,1997toJune2,2014 700 800 Data used 65 Volume 9, No 1, January-June 2018 66 Journal of International Economics .000 .001 .002 .003 .004 .005 .006 .007 .008 .009 .000 .001 .002 .003 .004 .005 .00 .01 .02 .03 .04 .05 .06 .07 100 100 100 200 200 200 300 300 300 Argentina Australia C Brazil C C o 400 o o 400 400 n n n d d d i t i i t t i o i i o o n n n a a a l 500 l l 500 500 v v v v a a a r r r i a i i a a n n n c c c e e e 600 600 600 700 700 700 800 800 800 Global IntegrationofIndianFinancialSystemandVulnerability toExternalShocks:EmpiricalEvidencefromCapitalMarket .0000 .0005 .0010 .0015 .0020 .0025 .0030 .0035 .0040 .000 .001 .002 .003 .004 .005 .006 .007 .008 .009 .000 .001 .002 .003 .004 .005 .006 .007 .008 100 100 100 200 200 200 European Union 300 300 300 Canada China C C C o o 400 400 o n n 400 n d d d i i t t i i i t o o i o n n n a a a l l 500 500 l v v 500 v a a a r r r i i a a i a n n n c c c e e e 600 600 600 700 700 700 800 800 800 67 Volume 9, No 1, January-June 2018 68 Journal of International Economics .000 .002 .004 .006 .008 .010 .012 .014 .000 .002 .004 .006 .008 .010 .012 .014 .016 .000 .002 .004 .006 .008 .010 100 100 100 200 200 200 300 300 300 Indonesia Germany France C C C o o o 400 400 400 n n n d d d i i i t t t i i i o o o n n n a a a l l l 500 500 500 v v v a a a r r r i i i a a a n n n c c c e e e 600 600 600 700 700 700 800 800 800 Global IntegrationofIndianFinancialSystemandVulnerability toExternalShocks:EmpiricalEvidencefromCapitalMarket .000 .001 .002 .003 .004 .005 .006 .007 .000 .004 .008 .012 .016 .020 .000 .002 .004 .006 .008 .010 .012 .014 100 100 100 200 200 200 300 300 300 Mexico Japan C C C 400 Italy o o o 400 400 n n n d d d i i i t t t i i i o o o n n n a a a 500 l l l 500 500 v v v a a a r r r i i i a a a n n n c c c e e e 600 600 600 700 700 700 800 800 800 69 Volume 9, No 1, January-June 2018 70 Journal of International Economics .000 .002 .004 .006 .008 .010 .012 .014 .016 .000 .005 .010 .015 .020 .025 .030 .035 0 1 2 3 4 5 6 100 100 100 200 200 200 United Kingdom 300 300 300 South korea Russia C C o o 400 400 n n d d i i t t i i o o n n 500 a a v l l 500 500 a v v r a a i a r r i i n a a c n n e c c 600 e e 600 600 700 700 700 800 800 800 Global IntegrationofIndianFinancialSystemandVulnerability toExternalShocks:EmpiricalEvidencefromCapitalMarket

.000 .002 .004 .006 .008 .010 .012 100 200 300 United States C o 400 n d i t i o n a l 500 v a r i a n c e 600 700 800 71 Volume 9, No 1, January-June 2018 72 Journal of International Economics Simulation Keywords: India-GCCExport,GravityModel,PanelData,ExportPotential, agreement (FTA) willbewin-winsituationforbothside. trade free India-GCC proposed that concluded paper end, the At countries. GCC all of tariff simulation suggest that tariff potential reduction will improve India’s export potential export with of Result country. signifihas importing India that shows Further, countries. results six all with potential by export cant imposed tariff and them between distance by determined negatively is it while Diaspora, and colony common namely variables binary two and openness trade economies, of size by determined positively is export bilateral India-GCC that show Results done. been also has simulation tariff section, last the In 2015. i.e., year latest the for calculated been has countries GCC coeffiof help six trade all of with value potential cient trade India’s determinants, fl ow gravity model. Panel data has been used for the period 2001 to 2015. Further with the paper present augmented context, of flhelp export in this the India-GCC with of ow determinants defiinvestigated has So, in cit. been have countries GCC with trade India’s decade last the in and economy an in role crucial very a played have Exports Assat rfso, eatet f cnmc, ai Mli Ilma Nw eh ad a b rahd at reached be can and Delhi New Islamia, Millia Professor & HoD, Department of Economics, Jamia Millia Jamia Islamia, New Delhi 2 and can be reached at [email protected] Economics, of Department Professor, Assistant 1 trade liberalisationperiod. post- the for India for valid is (ELGH) Hypothesis Growth Led Exports that suggested improve foreign trade as well as export activities. Empirical study of P. Agrawal (2014) to measures of number adopted and economy its opened also India 1991, in reform economic adopting After (ELGH). Hypothesis Growth Led Exports as known also is It growth. its to related positively and directly is export of country,performance a for Exports have played a very crucial role in an economy. Many research suggested that Introduction [email protected] A Panel to GravityModelAnalysisofIndia’sExport Gulf CooperationCouncil( Journal ofInternationalEconomics SN07-72 Volume 9,No.1,January-June2018,pp.72-86 ISSN 0976-0792 Imran Alam 1 Shahid Ahmed and GCC ) Countries 2 Tariff especially from GCC countries. Trade balance was in surplus till 2005-06 and then went indeficitand2015-16itwasUS$14,111.76 basket and million. 2005-06 imports till surplus India’s in in was balance role Trade countries. crucial GCC from very especially a plays oils and market international in trade balance became negative after 2005-06. This So, is because imports. of increase of in volume oil prices than less was exports of volume 2005-06, after but rise, has imports and exports Volumeof 1). table (see 20015-16 year the in million 97,469.19 US$ to 2001-02 in 5,485.01million US$ from risen has countries GCC the with trade Brief Profile of India-GCC Trade Performance – maximum remittancefromtheseregions. o ntd ain, . mlin nin (ags eptit cmuiy n vr GCC every in community countries) expatriate (largest Indians million 8.2 Nations, United to according Further, world. the in remittance of source main are countries GCC while countries, receipt remittance of list the on place first on stand India Nations, United to According relation. India-GCC of aspect another is remittance and migration Labour to explorethepossibilitiesoffreetradeagreement(Alam& Ahmed, 2017). Policy’ to improve their economic relation with them. Recently, the both the sides acknowledge are trying to side, other growing importance of the Asian economies, GCC countries On have also initiated ‘Look East Policy’. GCC West ‘Look with launched relation India the nations improve to 2005, In opportunities. investment and ventures joint trade, growing of form the in stronger becoming started relation This remittance. and migration labour imports, gas and oil of forms the in height new a reached had it 1970s and 1960s in oil-discovery After system. barter of time the since ever a existed is which Countries, UAE and Qatar,Saudi (GCC) Arabia Oman, Kuwait, Bahrain, namely countries Council six of group Cooperation Gulf with relation trade India’s 1 trade how that know to important very is it context; this in So, negative. in always is global trade. Further, it also revealed that from 2005-06 onwards; India’s trade balance India’s in role important very a playing is region GCC that show clearly figures Above Source: DGCIS,MinistryofCommerce,GovernmentIndia,2017 2007-08 2005-06 2003-04 2001-02 2009-10 2015-16 2013-14 2011-12 Destination andOrigin by Migrants Stock: Migrant International Trendsin Social (2015). Affairs and Economic of Department Nations, United Year 1 21,760.24 30,479.97 45,360.29 41,678.72 48,221.20 are working in GCC countries. As a result of this migration, India receives India migration, this of result countries. a GCC As in working are 11,775.30 7,067.03 3,798.06 Export Table 1:India’s Trade withGCCCountries(inUS$million) (% share of GCC) Export 13.34 17.05 14.83 15.89 15.34 11.42 11.07 A PanelGravityModel Analysis ofIndia’s ExporttoGulfCooperationCouncil(GCC)Countries 8.67 101,799.42 102,181.94 45,089.79 53,497.43 55,790.47 Import 7,805.04 1,686.95 3,252.53 (% share of GCC) Import 17.92 18.55 14.64 22.61 20.88 5.23 3.28 4.16 During the last decade, India’s total 150,020.62 147,542.23 19,580.34 10,319.56 66,850.03 83,977.40 97,469.19 Trade 5,485.01 Total Trade (% share of GCC) 16.12 19.63 18.55 17.98 15.15 8.41 7.27 5.76 -23,329.55 -53,578.22 -56,821.65 -23,017.46 -14,111.76 Balance 3,970.26 3,814.50 Trade 2,111.11 73 Volume 9, No 1, January-June 2018 74 Journal of International Economics rd fo fo Tre ad te epres f asn o h cutis thatbuyit. countries major six the to applied is model” tothecountries “econometric The process. this raisin in used of been has model gravity exporters Augmented and other Turkey from the flow inspect trade to was research the of objective main The (2013): al., Bulent Miranet. positive impacttoenhanceintra-regionaltradeamongSAARCcountries. GDP, that Further,region. South that shows result Free Asia Trade depicted (SAFTA)Agreement a made Result (1985-2011).this among trade years determine which variable crucial are 27 tariff and distance population, for used been has data Panel model. gravity panel of use by factors countries the Asian all among out flow find trade influence to which was paper this of objective (2015): Ahmed S. and Kumar S. and potentials. this. Both cross- sectional and panel data has been used to analyse trade determinants with the help of . There are numerous literatures which deal with potentials trade and determinants export analyse to is paper present of theme Main Literature Review India-GCC export. in tariff reduction of impact the see will we section, final the In calculated. be also will countries GCC six all with potential trade India’s results, model gravity augmented of help India-GCC the with Later, affect model. gravity which panel augmented of determinants help the the with export bilateral out find to try will paper present Hence, deficit has to improve or how export performance with GCC countries has to increase. 2 the growth in economy.in growth the for demand increased an in results population increased This diminished exports. This can be explained in a way that population also increases with in results relation proportional inversely country.The the of Product Domestic Gross the in increase capita per the analyzes one when turns table the However, exports. the in increase exact) be to (5.42% 5% a around to leads Egypt of GDP the in 1% of relation related. exponentially is Aexports The agricultural the rise GDPand national the between trade. of model gravity of use by (1994-2008) for years fifteen market of international period a the in goods agricultural of export the affected that factors Assem Abu Hatab transportation ofgoods,itwillbeaboonfortheraisin trade. variety of raisin like raisin grown organically. Additionally, if the sea routes are used for innovative and alluring prices to enhance the exports. They can even introduce newer on findingfocus the opportunitiesmust in theirraisin neighbouring regions.of Also, theytrade need totheir come upenhance with to want that countries the So, exporter. and the lag in time. This is ensured by countries importing it from the nearest possible volume. In the raisin trade, exporter there is trade a requirement the to reduce affects the the distance transportation charges The volume. between trade the determining distance in vital is geographical importer and that The shows globe. the observation throughout the raisin of of export result the of cent per 90 than more of boasts The sixcountriesareUS, Turkey, South Africa, Iran,GreeceandChile. et. al., (2010): main aim of this paper was to evaluate the important 2 under discussion. These six nations combined nations six These discussion. under a panel data model. The study takes care of the trade within the GAFTAthe also within It trade area. the of care takes study The model. data panel a GAFTA the way the agreement has reviews affected trade. paper This study This is built (2008): on the Peridy gravity equation, and Nicolas estimates and Abedini Javad an with future hopeful a of vision enhanced traderelationshipintheGCCregion. a have agreements trade signed newly the that suggested is It research. this for undertaken period time the during factions two the between arrangement trade formal any exist not does there that say to This EU. and dismal performance in trade with West, there exist an unofficial intensive trade with US West even when GAFTA was implemented about a decade ago. The exception to the has East the with Trade GCC. the for volume predicted the of reached not yet has it whereas prediction the surpassed formation the since decade a about in potential possible the exploring to matured has it that said be may It time. over considerable not is intra-trade GCC the in change the of level the that noting worth is it However, research this through opined is that it surpass the expectations if the major it factors affecting the trade are considered. Still, terms. complete the in minor is share trade intra- GCC the that fact known a is It trade. in partners their regions’with the trade of data series-cross-sectional time pooled on based used was model trade gravity The of the old and the emerging trade preferential arrangements in the region of MENA of region the in arrangements preferential trade emerging the and old the of context the within fall GCC. countries to These belonging states the of potential trade the analyze to was research this of objective main The (2008): Boughanmi Houcine means viawhichmigrationcanaffect thetrade. sector economy. The results show that the structure of the market explains the various multi- a mathematically,with estimated is model” account. A“monopolistic into taken how the trade of Switzerland was affected by migration. The goods differentiation was migration and trade and their market structure. The research demonstrated statistically, Silvio H. T. Tai (2009): The objective of the analysis was to search for the link between liberalization canhelpinenhancingthetradepotentialperiodofcrisis. tariff and facilitation trade research, the to according Nonetheless, demand. global in slowdown of condition the in even opportunities major and scopes huge are there as player a as emerge can still India world, the of part large a In period. post-crisis the in China with expansion trade Africa for scope of with Latin lot and However,a America. is there volume trade good a has also India region. Asia-Pacific the the in with countries potential trade possible maximum of the potential exploring trade is India global that of suggest estimates India The model. gravity of trade help India’s on the crisis with global potential of impact the estimates paper This (2010): De Prabir a have to found are stimulates distance, negative influenceonagriculturalexports. partners of its proxy of costs, Transportation currencies exports. the agricultural against Pound Egyptian in depreciation that indicating coefficient, positive significant a has volatility exchange The reduced exports. to leads growth domestic Hence, country. the within goods exportable the 3 It stands forMiddleEastandNorth African Countries. A PanelGravityModel Analysis ofIndia’s ExporttoGulfCooperationCouncil(GCC)Countries 3 . 75 Volume 9, No 1, January-June 2018 76 Journal of International Economics given aboosttothetraderelationship. obstacle for trade in Vietnam before 1990 has lost its importance and has consequently an as posed that barriers artificial well. The as here applicable are trade international factors (Gross National Product, per capita GNP and geographical distance) affecting traditional common The “average”. of level a reach to “under-performer” of status the of period a five year in lot a improved has Vietnam seventeen that and highlight results Vietnam The between nations. APEC partnership trade the measure to used is Thegravity model APEC. and with ASEAN of Vietnam trade the influencing determinants Le Quoc-Phuong levels ofGreekimportsfromtheBalkansthisresultis notsurprising. deplorable as the actual trade is only 2% of the calculated potential. Given the very is low It under-traded. extremely are imports The potential. estimated the below is actual the means which one, than less is cases all in volume trade potential the and volume trade Balkans. actual the of with ratio trade The their in Greece for potential great still is there that conclusion a reach to used is method Regression” Unrelated “Seemingly former.of case the in heightened is affect the EU, and Greek between, compared is exists for the exports of the European Union nations. However, when this phenomenon them. from imports the overtake countries) Balkan (to Greek from scenario same The trade between the member nations of EU and the Balkan states. However, the exports trade is under-explored in the region. Also, there is still great amount of possibilities of The researchers have used the gravity model. The results exhibits that the potential of nations. Balkan nine and Greece between potential estimated the and potential trade Dionysios Chionis as inabroadagreementincludingSouthandEast Asian countries. agreements, it will give regional a boost to in the export potentials, with participates respect to China ASEAN as if well that deduced been has It language. common a and barriers, trade existing the countries, the of growth by determined actually is volume Nations has yet to affect the exports in the Asia-Pacific region appreciably. The export Asian Southeast of Association and (APEC) Cooperation Economic Asia-Pacific that concludes It equation. gravity is used model The (1992-2000). years nine of period a for is collected data The region. Asia-Pacific from countries twenty-three by equating widened is research this of scope The liberalization. trade RTA’sand light various the of in partners trading to regional its was with paper performance trade this China’s of the out objective find main The (2005): Hove van Jan and Abraham Filip that regional trade has increased by 20 per cent since GAFTA has been implemented. has drastically affected the trade effects. The calculation of gross shows expectations, sunk costs and border effects. Also, it is found that the GAFTAlike issues new as well as trade international of GDP) the and distance (geographical agreement factors traditional of importance that shows analysis this from inference The 2005). (1988- years eighteen of period a for countries reference other thirty-five considers i.e., 1989 to 1994. On a global scale, it can be said that it has improved from 19) Te i o ti rsac ws o cn h major the scan to was research this of aim The (1996): al., et. et. al., (2002): The main aim of the study was to analyze the actual GDP log(Trade logarithms. Sothenewequationwillbe- taking after form linear into transform often (1) equation analysis regression the For α isconstant Where, Trade of basicgravitymodeltradewillbe- will also increase and that’s impact will be negative in bilateral trade. So, the equation of proxy cost trade increase will as regions two between distance the as taken means which cost trade is distance Here regions. two these between distance to related proportional to multiplication of economic size (often using GDP or GNP) and inversely gravity directly is which regions two between flows model, trade bilateral predict also trade of model gravity universal Newtonian Like 1962. in Tinbergen J. by used first in all over the world including international trade. In international trade this model was discipline of number in used also is law this them. of between Application distance to square the to related inversely and masses their of multiplication the to proportional directly is which force a with particle another to attract particle every universe the in that states which (1687) gravitation universal of law Newton’s Isaac from derived is trade of Basically, model regions. gravity two between trade bilateral influence which This paper is based on gravity model of trade which is used to find out the determinants Methodology andDataSource export flowandIndia’s exportpotentialwithallsixGCCcountries. India-GCC of determinants investigate will study present pattern, trade global India’s in role countries GCC of mind the of Toavailable. in is keep case study little very in India-GCC knowledge, my per as But potential. trade and flow the trade out of find determinant to used widely is trade of model gravity that clear is it above, the From trade flow by using gravity data. But cross-sectional data creates biased gravity model Traditionally, many researchers have used cross-sectional data to find out the bilateral basic gravityequationoftrade topredictabilateralflowbetweentwosides. Trade Where, Distance GDP j i is GrossDomesticProductofcountryj is GrossDomesticProductofcountryi ij ij isbilateraltradebetweencountry = α α, β α, ij isdistancebetweencountry ij ) =α+β 1, β 2 and 1 β log (GDP 3 are coefficient and coefficient are A PanelGravityModel Analysis ofIndia’s ExporttoGulfCooperationCouncil(GCC)Countries

i ) +β 2 log (GDP i andcountryj i andcountryj e ijt j ) +β is error term. This equation is known as known is equation This term. error is 3 log (DIST ij ) +e ij .....(1) ...(2) 77 Volume 9, No 1, January-June 2018 78 Journal of International Economics if countryexport islessthanexpectedsign ofpopulationcoefficient willbenegative. expected sign of population will be positive. On the other side, due to absorption effect negative. If a country has big population and they enjoy economies of scale effect than World Bank. Expected sign of coefficient of population (β population of coefficient of sign Expected Bank. World country of data population – (Population) POP export will also increase. So, expected sign of coefficient of GDP for both country (β country both for GDP of coefficient of sign expected So, increase. also will export World Bank in US Dollar at constant price 2010. As the GDP of a country will increase, WDI, from taken been has GDPdata So, 2015. to 2001 for available not is GNPdata here But taken. has GNP or GDP either country a of size economic the measuring of purpose the for trade, of model gravity in Basically - Product) Domestic (Gross GDP price 2010.GDP deflatordatahasbeentakenfromWDI, World Bank. constant at Dollar US in data trade real into converted is deflator,GDP it of help with taken from World Integrated Trade Solution (WITS), UNCOMTRADE Database. Then j EX (bilateral Export) – In this model real bilateral export between country Where ireferscountryi,jandttimeperiod(year). (DIASPORA β log (EX countries India-GCC of Estimation 2015. bilateral exporthasbeencalculatedwithfollowingaugmentedgravitymodel: to 2001 of period the for countries GCC six all with export bilateral India’s for model gravity augmented estimates study This indicated thatrandomeffect isappropriate. the Further,probability (Prob. > chosen. chi2) of LM been is 0.000 (result has of Breusch–Pagan model Lagrange multiplier) effect random have we So, variables. variant time with along variables dummy and distance like variable invariant time of impact invariant variables (Ozdeser & Ertac, 2010). In this study, gravity model will check the time and variant time (both of impact the check to want we if model effect fixed over both time variant and time invariant variables. of So, effect the random see effectcan model effectmodel random will while only be effect variables preferred variant time for model effect fixed used is effectmodel Fixed common. technique, most are (REF) effectmodel random and (FEM) estimation data panel of number various the Among the relevantrelationshipsamongvariablesovertime(Kumar&Ahmed,2015). capture can data panel Munir,that & is 2015). (Sultan advantage freedom Another of estimates by reducing collinearity among independent variables through large degree individual heterogeneity. Panel data framework increases the efficiency of econometric shows many advantages over cross-sectional and time series data due to its control for estimates due to heterogeneity (Chang & Wall, 2005). However, panel data estimation and β at time t 5 o (DIST log 2 ) ispositive. ijt ) =β is dependent variable. Bilateral export data in US Dollar at current price has ij + β + ) ij ) +e 0 + β ijt 6 o (TAR log

1 o (GDP log .... (3) jit + β + ) it + β + ) 7 o (IO log 2 o (GDP log jt + β + ) 8 jt o (LANG log + β + ) i n country j and 3 o (POP log 3 and ij + β + ) β 4 it s ae fo WDI, from taken is ) is either positive or positive either is ) 9 + β + ) o (COL log 4 o (POP log i and country ij + β + ) 10 jt + ) log 1 restrictiveness, so,expectedsignofcoefficientvaluetariff (β trade of form Tariffanother Database. is UNCOMTRADE of help the with IDB WTO: TAR (Tariff) –Here applied tariff data imposed by country by imposed data tariffTAR (Tariff)applied –Here defines India’s export potential with GCC countries. In others words, if P/A value is value P/A if words, others In countries. GCC with potential export India’s defines (A) countries GCC and India between value export actual and (P) model augmented period latest the for potentials export the coefficient value from augmented gravity model. using The study has estimated by the total calculated been has Potential Export India-GCC sides. two between flows trade future predict to is model gravity of aspect useful Export Potential - Another Totalannual. are data All 2015). observation inthisdatasetis180.SoftwareStata14 has usedforallcalculation. to (2001 year 15 of period the for countries GCC six all with export bilateral India’s for model gravity augmented estimates study This Etudes D’ Centre is source common Data colony ispositive. of sign zero. Expected (CEPII). otherwise Internationales Informations one D’ et be Prospectives will it than colonizer source is Centre D’ Etudes Prospectives et D’ Informations Internationales (CEPII). Internationales Informations D’ et Prospectives Etudes D’ Centre is source DIST (Distance) – it is distance between trade centre of country of centre trade between distance is it – (Distance) DIST 5 4 division, UnitedNation(2015). affairs,social and population economic of department is data migrant of Source zero. country i So, in this context a dummy Indian variable is developed of by us. If number average numbers of large country that region. Gulf to export bilateral India’s in impact positively suggested countries gulf in immigrants (2007) Kayail B. S. of study – DIASPORA positive. Comlang increase. So, expected sign of coefficient of Import Openness for countries for Openness Import of coefficient of sign expected will So, trade economy,increase. its opens or restriction trade removes country WDI, a is If source Bank. Data World ratio. GDP import as known also is it – Openness) (Import IO of distance(β two between distance the coefficient of sign as expected So, increase. also So, will cost export increase cost. will countries trade of proxy a as taken is distance Here Comcol (common colony) – if country if – colony) (common Comcol transaction cost.So,expectedsignofcommonlanguageispositive. reduce will it further and negotiation trade improve to help will language common that Centre D’ Etudes Prospectives et D’ Informations Internationales (CEPII). It is expected (official or commercial) and ethnicity than it will be one otherwise zero. Data source is isoa ouain n country in population Diaspora for theyearof2000(inplace2001), 2005,2010and2015hastaken. population migrant of average Diaspora, variable dummy of calculation the for So, gap. year five with available is Data www.cepii.fr/CEPII/en/publications/wp/abstract.asp?NoDoc=3877 j

for the period of 2001 to 2015, dummy value will be one otherwise it will be will it otherwise one be will value dummy 2015, to 2001 of period the for cmo lnug) i country if – language) (common 5 ) isnegative. A PanelGravityModel Analysis ofIndia’s ExporttoGulfCooperationCouncil(GCC)Countries 5 Expected sign of Diaspora ispositive. ExpectedsignofDiaspora s oe hn n pr et f oa pplto of population total of cent per one than more is j 2015. Ratio of computed export value from value export computed of Ratio 2015. i.e., and country and i n country and i were colonies with the same the with colonies were j j j on country on hr cmo language common share 6 ) willbenegative. i j. country and i is taken from taken is j (β j Data 7 ) is 4

79 Volume 9, No 1, January-June 2018 80 Journal of International Economics level and 10 per cent level respectively.level of cent coefficient per The 10 and level constant. are things other the all if cent per 0.75 by exports bilateral in decrease to lead will i) cent. Here coefficient value is less than one, which means with the increase of increase the with means which one, than less is value coefficient Here cent. country with exports bilateral total its in increase to leads in increase cent per 1 constant; are things other the all if that in figure 1. Here a bilateral export is dependent variable. The goodness of fit of the (R model of fit of goodness The variable. dependent is export bilateral a Here 1. figure in of India-GCCExport–Result Determinants of augmented gravity model is displayed Empirical Results (P) and actualexportvalue(A) value export computer between difference number, the absolute see To country.in that potential export with potential export has India means it one, than greater constant. Further, coefficient of Further,coefficient constant. are things other the all if rate decreasing with but increase will exports bilateral total population of country of population ofcountry’s value Coefficient in cent increase 1 per means is 0.88,which jpopulation trade. for opportunities more creates population of size higher means Which effect. country’sof country of population while absorption effect it will be negative. Here population of country a country enjoys economies of scale effect than it will be positive otherwise in case of As earlier discussed, coefficient of population could be positive or negative. In case of between country are constant; 1 per cent increase in GDP of country The estimated coefficient of coefficient estimated The variables aresignificantwithexpectedsign. binary and variables explanatory other all Remaining insignificant. positively also is Population of country value of value Tariffcountry by imposed are constant. things other all if cent per 2.98 i.e., cent per three decrease approximately will trade bilateral sides total two these between distance in increase It cent -2.98. per i.e., 1 high that very implies is coefficient distance of Size level. cent per 1 at significant negatively is cost trade of proxy is which nations two between distance of Coefficient j willalsoincreasebutatdecreasingrate. say that with the increase of country cent if all other things are constant. Here coefficient value is less than one, so we can Tariff 2 ) is 0.86. Result shows that all independent variables sign are as expected. as are sign variables independent all that shows Result 0.86. is ) population coefficient indicates that country that indicates coefficient population j ji is -0.75. It means 1 per cent increase in tariff (imposed by country by (imposed tariff in increase cent per 1 means It -0.75. is i andjbyapproximately0.46percent. i is positively insignificant and binary variable common language would increase total bilateral trade between trade bilateral total increase would j is negatively significant at 1 per cent level. Coefficient level. cent per 1 at significant negatively is j oni i.e., P-A hasalsobeencalculated. is positively significant at 1 per cent level. Positive sign Positive level. cent per 1 at significant positively is j GDP GDP i and j j is 0.46, which means that if all the other things other the all if that means which 0.46, is population bilateral trade between country GDP j is positively significant at the 1 per cent per 1 the at significant positively is j will increase total bilateral exports enjoys economies of scale of economies enjoys j by approximately 0.84 per 0.84 approximately by j GDP i is positively insignificant i is 0.84, which means which 0.84, is of country of GDP i and by 0.88 per 0.88 by j GDP i and i will j on i, R G c R

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81 Volume 9, No 1, January-June 2018 82 Journal of International Economics countries. GCC six all with potential exports has India that Tableshows countries. clearly GCC India-GCC Export Potential – Table 2 shows the exports potential of India with all six Diaspora. All thevariablesarepositivelysignificantexcepttariff anddistance. j, tariff imposed by country both sides, distance between them, Population of of country GDP are countries GCC and India between exports bilateral for determinants So, imposed onallGCCcountries liesinbetweenninetoelevenpercent. tariff (2015), increased. present has At countries GCC all with trade India-total that is result and India by reduced were rate tariff year the over that shows Figure 2015. to Table 3 shows applied tariff rate imposed by India on all six GCC countries from 2001 potential. Then we will analyse the impact of 100 per cent of tariff reduction (no tariff) on exports potential. exports on tariffreduction of cent per 50 of impact the analyse will we First, the impact of tariff on bilateral exports and imports potentials between analyse these will two we sides. section, present the in So, cases. exports India-GCC of determinant important an is tariff that saw we section, first the Result ofTariff Simulation – In million andBahrainwithUS$253million. Qatar with US$ 1,061 million, with US$ 779 million, Oman with US$ 549 by followed than million 1,504 US$ with position second at came UAE million. 2,996 US$ Kuwait with potential exports maximum has India numbers, absolute of case In exports potentialtoincreasewithIndia. respectively, which means UAE has 5 per cent and Saudi Arabia has only one per cent Saudi Arabia has very little exports potential with India whose P/A vale is 1.05 and and UAE countries, 1.01 trade GCC all Oman. Among with India’s cent per 24 that increase could potential implies which 1.24, value P/A Bahrain. with with position cent fourth at per came 42 Oman increase could potential trade India’s that implies which P/A1.42, with value Bahrain is country Next Oman. potential with cent per 140 increase trade could India’s Qatar means which this, 2.40, value After P/A Bahrain. with position with second cent at comes per 225 increase means It could 3.25. potential is trade value P/AIndia’s where Kuwait with potential exports highest has India Source: calculation Author’s Saudi Arabia Bahrain Kuwait Oman Qatar UAE Table 2:India-GCCExportPotential($millions) 34,353 7,713 2,543 2,797 4,326 P 855 j on i and two binary variables namely common colony and 32,849 7,635 1,061 2,248 1,331 A 602 1,504 1,482 2,996 (P-A) 779 549 253 j, import openness of country ((P-A)/A)*100 140 225 24 42 1 5 P/A 1.01 2.40 3.25 1.05 1.24 1.42 will increase up to $ 2436 million from actual exports which means it will increase 230 potential exports India-Qatar exports. actual from cent per 71 increase will it means which exports actual from million 1598 $ to up increase will potential exports Oman India- exports. actual from cent per 347 increase will it means which exports actual from from million 4617 cent $ to per up increase 95 will potential increase exports India-Kuwait will exports. actual it means which exports actual from million 574 $ up to increase will potential exports India-Bahrain numbers, absolute In increase. will countries GCC all with potential India’stariffexports reduction; cent per 50 of result a we compare this result with applied tariff rate result (Table 2), we came to know that as Table 5 shows the result of exports potential with 50 per cent reduction in applied rate. If tariff onexports,byapplyingthesimplemathematics;wemultipliedP valueby1.375. of reduction cent per 50 of impact the check to So, constant. are things other the all if cent, per 0.75 by exports bilateral in increase to leads will tariff in decrease cent per 1 means It determinants. exports of case the in 0.75 - tariff is of value coefficient that section first the in saw already potential. we exports As on tariff of reduction cent per Results of 50 percentTariff Reduction – In this section, we will see the impact of 50 Source: WTO: IDB five percent,whileincaseofIndiaitisapproximatelyninetoelevencent. to four between in lies countries GCC all tariffby (2015), India present on At imposed Another increased. has countries GCC India. to all tariffcompared less imposed have countries GCC all that is thing noticeable with trade India-total that is result and countries GCC six all by reduced rate tariff year the over that shows Figure 2015. to Table 4 shows applied tariff rates imposed were by GCC countries on India from 2001 Source: WTO: IDB Saudi Arabia Saudi Arabia Bahrain Bahrain Kuwait Kuwait Oman Oman Qatar Qatar UAE UAE Table 4:GCCcountriesimposingtariffonIndia(AppliedRate) Table 3:India imposingtariffonGCCcountries(AppliedRate) 29.72 26.67 32.04 31.46 28.49 29.13 2001 12.26 A PanelGravityModel Analysis ofIndia’s ExporttoGulfCooperationCouncil(GCC)Countries 2001 4.88 3.86 5.43 3.44 7.52 16.05 15.24 17.75 15.12 16.31 2005 2005 17.3 4.88 4.75 4.91 5.06 6.08 4.7 10.72 10.37 11.88 2010 9.74 9.18 9.96 2010 4.75 4.75 5.13 4.65 4.93 5.3 10.23 11.23 2015 2015 8.38 8.96 9.26 4.55 5.08 4.88 4.54 4.56 4.54 9.1 83 Volume 9, No 1, January-June 2018 84 Journal of International Economics that reductionintariff willboostIndia-GCC exports. say can we So, exports. actual from cent per 44 increase will it means which exports actual from million 14386 $ to up increase will potential exports India-UAE exports. actual from cent per 39 increase will it means which exports actual from million 2970 per cent from actual exports. India-Saudi Arabia exports potential will increase up to $ So, wecansaythatreduction in100percenttariff willboostIndia-GCCexports. million from actual exports which means it will increase 83 per cent from actual exports. 27,269 $ to up increase will potential exports India-UAE exports. actual from cent per 77 increase will it means which exports actual from million 5863 $ to up increase will potential exports India-Saudi Arabia exports. actual from cent per 319 increase will it means which exports actual from million 3389 India-Qatar $ to up exports. increase will actual potential exports from cent per 118 increase will it means which exports actual actual from from million 2647 cent $ to per up increase 469 will potential increase exports India-Oman will exports. it means which exports 6240 $ actual to from up million increase will potential exports India-Kuwait exports. actual from cent per 149 increase will it means which exports actual from million 894 $ to up increase GCC countries will increase. In absolute numbers, India-Bahrain exports potential will all with potential exports India reduction tariff cent per 100 of result a as that know to came we than 2), (table result rate tariff applied with result this compare will If applied rate. in reduction cent per 100 with potential exports of result the shows Table6 mathematics; wemultipliedP valueby1.75. simple the applying by exports, on tariff) (no tariff of reduction cent per 100 of impact the check to So, constant. are things other the all if cent, per 0.75 by exports bilateral in increase to leads will tariff in decrease cent per 1 of means case It determinants. the exports in - 0.75 is tariff of value coefficient we that As section potential. first the exports in on saw tariff) already (no tariff of reduction cent per 100 of impact the see will we section, this Results of100percentTariff Reduction(notariff)–In Source: calculation Author’s Saudi Arabia Bahrain Kuwait Oman Qatar UAE Table 5:Export Potential-50%ReductioninTariff ($millions) 5948.25 3845.88 10605.4 47235.4 3496.63 1175.63 P A 32,849 1,331 2,248 7,635 1,061 602 (P-A) 4617.25 573.625 1597.88 2970.38 14386.4 2435.63 ((P-A)/A)*100 346.90 229.56 95.29 71.08 38.90 43.80 P/A 4.47 1.95 1.71 1.39 1.44 3.30 gaa, . 21) h Rl o Eprs n ni’ Eooi Growth, Economic India’s in Exports of Role The (2014). P. Agrawal, be win-winsituationforbothsides. (FTA)will agreement trade free India-GCC propose export. we context this in So, India-GCC countries. boost to factor key GCC all with a potential export India’s improve will reduction any that suggested Paper also is Tariff countries. GCC in their workers of care extra take should government Indian Hence, variable. crucial very a is trade barriers.In case of India-GCC exportaswellremittance is concern Diaspora So, both sides should open their economy as much as possible and remove all kind of variables; import openness, trade Diaspora and tariff are key of variables which effect India-GCC model export. gravity tradition except that suggest paper this end, the It GCC six all with potential export countries. India’s improve will reduction) tariff cent per 100 and reduction tariff cent per (50 scenarios both that shows simulation tariff of result Further, Arabia. Saudi and UAE Oman, Bahrain, Qatar, by followed and Kuwait with Result of export potential for year 2015 shows that India has maximum export potential variables arepositivelysignificantexcepttariff anddistance. country between them, Population of country distance sides, both of GDP and countries GCC and India between exports bilateral with the help of augmented gravity model of trade. Result shows that determinants for determinants export India-GCC out find paper present facts, these of light the in So countries. GCC with negative in been has balance trade India’s 2005-06; since side, other the On trade. global India’s in role important very a playing are countries GCC Conclusion Source: calculation Author’s bdn, . Prd, . 20) Te rae Aa Fe Tae ra GFA: an (GAFTA): Area Trade Free Arab Greater The (2008). N. Péridy, & J. Abedini, References Abraham, F. & Hove, J. (2005). The Rise of China: Prospects of Regional Trade Policy, Saudi Arabia paper number345. Estimation of Its Trade Effects, Review ofWorldEconomics Bahrain Kuwait Oman Qatar UAE j on Table 6:Export Potential-100%ReductioninTariff ($millions) i and two binary variables namely common colony and Diaspora. All the Diaspora. All and colony common namely variables binary two and P A PanelGravityModel Analysis ofIndia’s ExporttoGulfCooperationCouncil(GCC)Countries 1496.25 4450.25 4894.75 13497.8 60117.8 7570.5 , 141(3),pp:486-509. Journal of j, import openness of country 32,849 A 1,061 2,248 1,331 7,635 602 (P-A) 3389.25 2646.75 27268.8 5862.75 894.25 6239.5 ((P-A)/A)*100 , 23(4), pp:848-872. j, tariff imposed by 148.55 319.44 468.78 117.74 83.01 76.79 E working IEG P/A 2.49 4.19 2.18 5.69 1.83 1.77 85 Volume 9, No 1, January-June 2018 86 Journal of International Economics Tai, S.H.T. (2009). Market Structure and the Link between Migration and Trade, Pakistan: of Potential TotalTradeand Import Export, (2015). K. Munir, & M. Sultan, and Potential TradeIndia’s Crisis: Financial and Economic Global (2010). De, Prabir Acountries: zone euro with potential trade Turkey’s (2010). D. Ertac, & H. Ozdeser, July 14 (Retrieved Mathematica. Principia Naturalis Philosophiæ (1687). I. Newton, Karayil, S.B. (2007). Does Migration Matter in Trade? A Study of India’s Exports to the Agricultural Egyptian of Determinants (2010). X. Huo, & E. Roamstad, A.A., Hateb, Le, Q.P., Nguyen, D.T. & Bandara, J.S. (1996). Vietnam’s Foreign Trade in the Context Data Panel Approach: Empirical by An Model Gravity (2015). Kumar,& S. S. Ahmed, Miran, B., Atis, E., Bektas, Z., Salali, E. & Cankurt, M. (2013). An Analysis of International Tinbergen, J. (1962). Shaping the world economy: Suggestions for an international an for Suggestions economy: world the Shaping (1962). J. Tinbergen, hoi, . Lagvs P & ais G (02. rees rd Wt Te Balkan The With Trade Greece’s (2002). G. Zanias, & P. Liargovas, D., Chionis, trade of models gravity in heterogeneity for Controlling (2005). Wall & I.H., Cheng. Countries Cooperation Gulf Arab the of Potential Trade The (2008). H. Boughanmi, empirical an relations: trade India-GCC of Prospects (2017). S. Ahmed, & I. Alam, Gaiy oe Apoc, Rtivd 4 a 21 from 2017 May muenchen.de/66621/) 14 (Retrieved Approach, Model Gravity A Integration Integration, Regional Asian for Implications and Prospects, gravity study, EuropeanJournalofScientificResearch from Retrieved Library. http://cudl.lib.cam.ac.uk/view/PR-ADV-B) Digital Cambridge–Cambridge of University from 2014 GCC Countries,South Asia EconomicJournal Exports: A GravityModel Approach, ModernEconomy Bulletin Approach, A Gravity Region: Asia-Pacific the and ASEAN of 50(4), pp:233–249. Countries, Asian South for Implications with Application edu/record/152200/files/SP%20Miran.pdf at: (Available Approach. Model Gravity Trade:A Raisin economic policy, New York: Twentieth CenturyFund. of WorldEconomics,Vol. 145,No.2,225-249 Countries: IsIt Too Little?,JournalofEconomicIntegration integration, 56. (GCC): Model AApproach, Gravity investigation, , 13(2),pp:185-199. , Vol. 25,No.1,pp.32-68 Federal ReserveBankofSt.LouisReview Foreign Trade Review, 52(2),pp:106–117. 23(1), pp:42- 23(1), Integration, Economic of Journal ) (Accessedon14May2017) , 8(1). , 1,pp:134-143 , 43(1),15–23. , 87(1),pp:49-63. http://ageconsearch.umn. oeg Tae Review Trade Foreign , 17(3),pp:608-622. Journal of Economic of Journal https://mpra.ub.uni- SA Economic ASEAN Review , Substitution (CES)ProductionFunction Keywords: ReturnstoScale,ElasticityofSubstitution,Constant Elasticityof of substitutionbetweenlabourandcapitalduring2001-13. elasticity of terms in standard the to up been not has and appreciable been has scale This study concludes that the 1980-13. performance Indian manufacturing study in terms of returns of to period the during India like economy surplus labour in distressed a exposed has substitution of elasticity The 2001-13. during manufacturing Indian in change signifi technical a labour-saving to cant due was This reforms. economic of consolidation of period a during manufacturing Indian in unity than less or unity either be to found been has substitution of elasticity The level. aggregate at and states the of most in cheerful been has as 2001-13 reforms economic of consolidation of period of Indian manufacturing in terms of returns to scale has been found credible during the performance The 2012-13). to (2001-02 reforms economic of consolidation of period to a and 2000-01) (1980-81 to (1991-92 policy changes economic in changes policy major of hoc phase a 1990-91), ad and piecemeal of phase a as sub-periods into trifurcated been has 2012-13) to (1980-81 period study The industry. manufacturing Indian in scale to returns and substitution factor estimate to used been has function production (CES) Substitution of Elasticity Constant selected. been have Rajasthan and Punjab Pradesh, Madhya Haryana, Pradesh, Andhra Karnataka, Pradesh, Uttar Nadu, Tamil Gujarat, Maharashtra, namely India of states major ten the imbalance, regimes since 1980. This study used ASI data for the period 1980-13. To study regional labour and returns to scale in Indian manufacturing across states under various policy and capital between substitution of elasticity estimate to attempt an makes study The Ascae rfso, eatet f cnmc, haaaa Clee Mdri Tml au n cn e ece at reached be can and Nadu Tamil Madurai, College, Thiagarajar Economics, of Department Professor, Associate 2 at reached be can and Nadu Tamil Madurai, College, Thiagarajar Economics, of Department Professor, Assistant 1 [email protected] [email protected] SN07-72 Volume 9,No.1,January-June2018,pp.87-100 ISSN 0976-0792 Journal ofInternationalEconomics to ScaleinIndianManufacturing Factor SubstitutionandReturns R. BagavathiMuthu Under Globalization 1 P. and Asokan 2 87 Volume 9, No 1, January-June 2018 88 Journal of International Economics factor havinglowproductivity(Goldar for substituted be may productivity higher having factor the or factor growing slow for substituted be may factor growing fast the since making policy in use their including Malaysia. in growth applicability wide has elasticity substitution factor of estimate The economic could and state sector steady path, growth particular balanced unemployment, the production, affect in labour and how capital between examined substitutability (2010) inputs Ling the Kiew Pui (2003). Irmen and (2003) Papageorgiou and Miyagiwa by tested empirically also been has assessment worker.This per output of steady-state level with thetransitionandahigher growthratealong leads toahigher labor and capital between substitution of elasticity higher a that sense the in growth function production economic of CES engine powerful a is substitution using factor of degree the growth that conclude and economic and substitution of elasticity the between link the investigated to attempted have (2000) Grandville La de and Klump and (1989) Grandville La de literature. growth the in attention much received not has although the that argued also He importance of elasticity has long growth. been recognized in several on economic branches of economics, it effect first-order a has function production but the of parameter second-order a is labour and capital between economics. industrial to According substitution of elasticity the (2010), Irmen Andreas Elasticity of substitution between the inputs plays a key role in the realm of research in Introduction nin cnm mr ueu: a i wl hl i udrtnig h ted i income in trends the share of understanding labour and capital, and (b) a fresh set of estimates of in elasticity of substitution help will it (a) useful: more economy Indian study.to the for substitution of elasticity of study the make factors other following The important it making growth, economic of sustainability the for parameter crucial a is labour and capital between substitution of elasticity the Thus, production. of intensity capital increasing shaping of face the in even growth economic of rate high relatively meaning high, is labour thereby that labour and can easily be substituted by capital, capital it may be possible to sustain between a substitution of elasticity the If labour. and capital between substitution of elasticity the on crucially depends problem this of magnitude The growth. of sustainability the to input capital to returns diminishing by challenge trigger may which input labour in rate growth the above well commonly is In rapidly developing countries such as India and China, the growth rate in capital input capital andlabour. between substitution of elasticity the on crucially depends be, to out turn will problem this serious How growth. of sustainability the to challenge a pose may input capital to returns diminishing that implies ratio capital-labour rising steeply a by characterized commonly is countries developing rapidly in growth economic that fact capita The income. per in differences explaining in efficiency technical and factors productive of role relative the and change, technical biased the of impact state, the capital, steady on return of the rate to convergence of speed the capita, per of income level state and steady rate growth the decline, or growth perpetual of possibility the affects It Many important growth issues depend on the precise value of elasticity of substitution. et al.,2013). Indian manufacturingfortheperiodof1980-2013. therefore makes an attempt to analyze elasticity of substitution and returns to scale in the assess to essential performance of and Indian manufacturing industry useful under various policy is regimes. The it study Hence, India. Innovative to India Incredible from move can India that so development and growth expedite could which and process andglobalization productivity privatization enhance through to industries manufacturing aim Indian 1990s in and efficiency 1980s the in initiated measures The Ideas’ long runeconomicgrowth. and ‘Growth book (2005) Jones’s investigates between that there is direct relationship I. between economies of scale and Charles policy. major public provide scale.The they for to and implications pre-requisite ofreturns the is concepts concepts these the of by understanding clear influenced as well as levels output and size of firms which designs and determines the structure of industries and their prices equilibrium and optimal the Aincreases. production of scale the where forecast outcome that the terms interrelated economics. are inindustrial scale of research diseconomies and of scale field of the Economies in role vital a plays scale to Returns equilibrium modelsfortheIndianeconomy. general computable building for parameters useful provide would differentsectors for industries, elasticity of substitution between labour and capital is relatively low which low relatively is capital and labour between substitution of elasticity industries, function by SURE production and CES ADRLof regression basis methods. the In on a India majority in of industries Indian manufacturing manufacturing 22 for substitution in Goldar substitution manufacturing. of Indian elasticity analyze to attempted been have studies of number The Review ofLiterature Indian manufacturingacrossstatesundervariouspolicy regimessince1980. in scale to returns and labour and capital between substitution of elasticity estimate to attempt an makes study the So, globalization. and further of policies 1980, subsequent of Policy liberalization mild of introduction the under since adopted India policies in various States across industry manufacturing in capital and labour between substitution of elasticity of magnitude actual the is what that Economics Industrial of realm the in researchers all of minds the in arises question the Naturally industries. Indian in production of factors the among relationship the of composition dynamic the that indicate to due was industries. This across elasticity of magnitude clearly the in variation exists there industries manufacturing Indian in labour and capital between possibilities substitution on ] (2007) Sarma and Seshaiah Venkata (2009), Upender productivity higher having factor may be the substituted for factor having or low productivity. factor The studies [Goldar growing slow factor for growing substituted fast be the may since making policy in use has their elasticity including substitution applicability factor wide of estimate the that out point (2013) al., et Goldar Statement oftheProblem (2013) have attempted to estimate elasticity of elasticity estimate to attempted have (2013) al., et Factor SubstitutionandReturnstoScaleinIndianManufacturingUnderGlobalization et al.,(2013), 89 Volume 9, No 1, January-June 2018 90 Journal of International Economics manufacturing for the period 1965-95 by using developed specification of CES of of elasticity of estimate specification substitution hasbeenhigh inaggregateIndianmanufacturing. the that developed observed study using The (1989). by Bairam by 1965-95 production Indian in period substitution the of elasticity for analyzed manufacturing to attempted has (2001) Kumar Sunil the underlyinggeneralequilibriummodel capital-labor ratio grows is not a property of the Solow growth model but a property of the is accumulation capital which economy’s an as changes how AES that is belief in our for basis The growth. of engine models, growth other with robust growth and Solow the model of independent are which results the conjecture: following the to found that AES is positively related to the level of economic development which leads linear model. The results support the ACMS conjecture generally. More importantly, it is applied new modeling and then numerical has approximation study The techniques grows. to economy solve the this as AES varies highly which and non- determined in endogenously growth, is economic of model multi-sector a constructed has study the aggregate the that idea economic development goes back to Arrow The manufacturing. of process the with evolves labor Indian and capital between (AES) substitution of elasticity in Substitution of Elasticity Kaz Miyagiwa and Chris Papageorgiou (2006) have analyzed Endogenous Aggregate during thepost-liberalizationperiodcomparedtopre-liberalizationperiod. increased has labour and capital between possibility substitution the that indicates It respectively.periods overall and post-liberalization pre-liberalization, during 1.28 and elasticity of The substitution between labour and determinants. capital has been observed input that 0.84, technology,1.30 the are for penetration) proxy technology restrictions, (a trade reduced index liberalization and materials energy, labour,which in capital, function cost translog a estimating by out carried is liberalization analysis (1970-1990) This periods. pre as well as (1991-2003) post the for out has carried analysis been Separate 1970-2003. of period the for sector manufacturing Indian structure of cost the analyze to attempted have (2007) Sarma and Seshaiah Venkata industries. the that fact the evincing unity than substitution possibilities are relatively more in favour more of labour across the major Indian is data section cross on based function production substitution of elasticity constant from obtained substitution of elasticity of capital across twenty six major industries in India during 2004-05. The numerical value and labour between possibilities substitution of extent the examined (2009) Upender Cobb-Douglas the than rather production function. function production CES the with change technical industries. different This across study concludes that the substitution manufacturing industry has of been adopting labour saving elasticity in variation substantial to leads et. al., (1961). To evaluate this conjecture, parameter and distribution parameter respectively and elasticity of substitution σ = f icma ad d hoc ad and piecemeal of the study period (1980-81 to 2012-13) has been trifurcated into sub-periods of a phase a as prices Research KLEMS India Teamof engaged study the on Based input. labour of measure (2014), constant persons of at number total added and output (CSO), value of measure gross a Organization as used (2004-05=100) Statistical has study Central This by India. of published Government (ASI) Industries of Survey The data of the present study have been collected from the various volumes of Annual Sources ofData Methodology oftheStudy h etmto eovd y mna 16) prxmto t te E production CES the to approximation (1967) function isasfollows: Kmenta by evolved estimation The by linearestimationtechniques. often therefore is function CES approximated by the so-called Kmenta (1967) approximation, The which can be estimated techniques. estimate estimation to linear possible usual not the is with it it and parameters in non-linear is function CES the As 0< denoted by is function CES production Indianmanufacturing in the scale of to specification andreturns The industry. substitution factor out find to used been has function production (CES) Substitution of Elasticity Constant following The Model usedforestimatingFactorSubstitutionandReturnstoscaleparameter year ofthestudyperiod. every in added value gross manufacturing registered Indian of cent per 60 than more Maharashtra, contribution their of basis the on selected been have Rajasthan and Punjab Pradesh, namely India of Madhya Haryana, Pradesh, states Andhra Karnataka, Pradesh, major Uttar TamilNadu, Gujarat, ten the imbalance, regional study To been collectedfromvariousissuesofEconomicSurveyIndia. has industry tool machine and machine of index price sale whole series, input capital Economic Advisor, Ministry of Industry, and . For constructing the whole sale price index for manufactured products was collected from the Office of the output, on data reported the to corrections price making for context, this In sources. Besides the ASI data, the required data have been procured from the other secondary of consolidation of economic reforms(2001-02to2012-13). period a and 2000-01) to (1991-92 policy economic in changes < 1, m and

are efficiency parameter, Substitution parameter, returns to scale parameter, parameter, to efficiency Substitution returns are oiy hne (908 t 19-1, pae f major of phase a 1990-91), to (1980-81 changes policy Where, Q = output, K= capital and L= labour, A>0, labour, L= and capital K= output, = Q Where, Factor SubstitutionandReturnstoScaleinIndianManufacturingUnderGlobalization

91 Volume 9, No 1, January-June 2018 92 Journal of International Economics coefficients, substitutionanddistributionparametershavebeenestimated by the actual duration of shift and then aggregating such products across factories. across products such aggregating then and shift of duration actual the by both and eight by shift a in workers of number the multiplying by out carried is in ASI man-hours of consumption the that out pointed however been has working day.It as a in well hours as workers of number includes it as measure better a often as is regarded worked’ or ‘man-hours of man-hours use of The employed. number persons of total number the average the of terms in measured generally is input Labour Measurement ofLabour gross of value the from output atconstantprices. prices constant at input gross of value the deducting from obtained been has prices constant at added value later,the the in While index. price wholesale respective the by gross deflated is value of the then and that prices, constant at from output addedat materials raw value subtracting the by obtained former, been Inthe has prices constant method. deflation double and method deflation single namely added value real of figures the get to approaches distinct two are Here gross valueaddedatconstantprices(2004-05=100)asameasureofoutput. case such used has study value. This In of terms in only measured be could cumbersome. output of aggregation is output physical of terms in output measuring the So overall of output share and separate relative price the indices of which are basis needed the for on adverse set computed of are products. Weights weights. appropriate by aggregated be can products differentdissimilar in and expressed units is output each and Generally output. one added than more produce industries the of most because practicable, value between choices not important is this But output. of measure best the is output physical which the of output physical output, of measurement the In Measurement ofOutput to related issues and measurement ofoutputandinputs. problems conceptual the discusses section This inputs. and output of measurement and specification of that is researchers the by encountered problem major efficiency, the and productivity of measures of estimation empirical In Variable Construction where It canbewrittenas, ,

Based on the above, the labour coefficients and capital and coefficients labour the above, the on Based ,

and σ = . I The grossrealinvestmentI Five percenthasbeentakenasannualrateofdiscardcapitalinthepresentstudy d = Annual rateofdiscardcapital. K D I K the in computed been following way. has This cumulatively. added been have prices constant at capital has been deflated by gross fixed capital formation index and gross investment value of fixed capital. For obtaining estimate of fixed capital, bench mark year of fixed replacement the of estimate rough a as taken been has the year base twice the of value analysis, book present the in Hence, new. was it when equipment of value the half exactly be would composition age balanced a of equipment finished of value the the For In order to construct the time series of gross stock. fixed capital stock, the study assume that capital measuring for 1973-74. as taken been has year mark bench the series, stock used capital of construction been has method inventory Perpetual in production. stock capital fixed gross by rendered services or stock capital net or gross use to not or whether is question important measurement. An a common finding of difficulty the tools, buildings and other stock, structure. The rolling machinery,heterogeneous nature equipment, of the productive variables of creates up made is Capital literature. the in controversial been has and difficult inherently is stock capital of measurement The Measurement ofCapital persons engagedhasbeenusedasthemeasureoflabour. So, the resultant series do not measure the actual man-hours worked. Total number of across the States. The performance of returns to scale in Indian manufacturing has manufacturing Indian in scale to returns of performance The States. the across seen been has scale to returns in variation substantial with level aggregate the at 13 1980- during 1.78 been has scale to returns the that observed is it 1, table the From Manufacturing: 1980-13 Returns to Scale and Factor Substitution between Labour and Capital In Indian Results andDiscussion P B t t = (B = Grossrealinvestmentinfixedcapitalduringtheyeart;and t t t t t =Priceindexofgrossfixedcapitalformationat2004-05 prices. = Bookvalueoffixedcapitalintheyeart; = Grossfixedcapitalat2004-05pricesbytheendofyeart; = K = Depreciationintheyeart;and t t-1 –B + I t-1 t - d.K + D t t-1 ) /P , where t where t iscomputedbyfollowingexpression: Factor SubstitutionandReturnstoScaleinIndianManufacturingUnderGlobalization . 93 Volume 9, No 1, January-June 2018 94 Journal of International Economics auatrn drn etr pro o suy n em o rtrs o cl ws found was scale to encouraging. returns of terms in study of period entire during manufacturing Indian study.of the performance of The period entire during Pradesh Madhya except states ten the all in found been has scale to Returns Increasing period. study entire the during (1.32) Gujarat by followed and (0.17) Pradesh Madhya in lowest the been has it while (2.13) TamilNadu and (2.41) Haryana by followed (2.70), Karnataka in has scale highest to the been returns has scale to study. increasing Returns of period as whole the during study found been of period entire the during healthy been h feil tcnlg hs en dpe ol i Mhrsta as thesubstitution inMaharashtra only adopted been has technology flexible the that indicates This period. study entire the during (1.00) Pradesh Madhya and (1.01) of Elasticity by followed Pradesh (3.71), Andhra Maharashtra in Maharashtra. highest the been has substitution except study under states the has all substitution in low of very elasticity found as been study of period entire the during one unhealthy an the States. The performance of elasticity of substitution in Indian across manufacturing has been witnessed been has substitution level of aggregate elasticity in the variation at substantial 1980-13 and during 0.47 been has substitution of elasticity The Source: Computedusing ASI Data Rajasthan Punjab Haryana Madhya Pradesh Karnataka Andhra Pradesh Uttar Pradesh Tamil Nadu Gujarat Maharashtra All India State Parameters Table 1:EstimatedParametersofCESProductionFunction:1980-13 Model: Returns toscale 1.61 1.98 2.41 0.17 2.70 1.69 1.58 2.13 1.32 1.47 1.78 Substitution Parameter 0.003 -0.01 -0.71 -0.73 0.36 0.15 0.32 0.00 0.42 0.81 1.11 substitution Elasticity of σ = 0.93 0.86 0.75 1.00 0.70 0.55 0.99 1.01 3.57 3.71 0.47 states inIndianmanufacturing. the of most in lower been has capital and labour between possibility substitution the surplus economy like India during a phase of piecemeal and ad hoc state of Maharashtra. The elasticity of substitution has exposed a distressed in labour manufacturing the in technology appropriate been has production Cobb-Douglas the The elasticity of substitution has been found 1.00 in Maharashtra which indicates that hoc ad and piecemeal of phase the during the substitution possibility between labour and capital has been greater in these states flexible technology has been followed hoc only in Gujarat, Karnataka ad and Uttar Pradesh and as piecemeal of phase the during (1.36) Pradesh Uttar and (3.39) Karnataka by followed (7.46), Gujarat in highest the been has substitution of Elasticity level. aggregate the at high very found been has hoc ad and piecemeal of phase the during joyful been has manufacturing Indian in substitution of elasticity of performance The level aggregate the at States. the across seen been has substitution of elasticity in variation 1980-91 substantial and during 3.21 been has substitution of elasticity The revealed enjoyable duringthephaseofpiecemealandadhoc has scale study.to returns the of terms of in manufacturing period Indian of entire performance The during (0.93) Maharashtra by Madhya followed in lowest (0.75), the been Pradesh has scale to Returns study. the of period entire during Pradesh Madhya and Maharashtra except states the all in found been has scale to hoc ad and piecemeal of phase the during (1.96) Haryana and (1.97) Gujarat (2.19), TamilNadu by followed (3.00), Pradesh Uttar in phase of piecemeal and ad hoc the during picture distressing a presented Pradesh Madhya and Maharashtra namely Pradesh, Karnataka, Haryana, Punjab and Rajasthan while in the remaining two states Pradesh, TamilAndhra Gujarat, Uttar namely Nadu, study the for selected states ten the of states eight in encouraging been of has manufacturing performance Indian in scale The to States. returns the across seen been has scale to returns in variation The returns to scale has been 1.86 during 1980-91 at the aggregate Manufacturing 1980-91 level with substantial Returns to Scale and Factor Substitution between Labour and Capital In Indian 1980-13. study of period the during India like economy surplus labour in distressed a exposed oga pouto hs en prpit tcnlg i te auatrn states manufacturing the Pradesh Andhra Gujarat, in of technology appropriate been has production Douglas Cobb- the that shows This 1980-13. period the during respectively Pradesh Madhya Pradesh Andhra Gujarat, in 1.00 and 1.01 0.99, found been has substitution of possibility between labour and capital has been found low in other states. The elasticity

and Madhya Pradesh. The elasticity of substitution has substitution of elasticity The Pradesh. Madhya and Factor SubstitutionandReturnstoScaleinIndianManufacturingUnderGlobalization policy changes. Returns to scale has been the highest policy changes during the period 1980-91. period the during changes policy policy changes as elasticity of substitution of elasticity as changes policy oiy hne.Ti idcts ht the that indicates This changes. policy policy changes. Increasing Returns Increasing changes. policy policychanges. policy changes as

and 95 Volume 9, No 1, January-June 2018 96 Journal of International Economics followed by Karnataka (2.74), Andhra Pradesh (2.34) and Tamil Nadu (1.84) during (1.84) (4.12), TamilNadu and Haryana (2.34) Pradesh in Andhra (2.74), highest Karnataka by the followed been has substitution of Elasticity level. aggregate the at unity found been has substitution of elasticity as policy economic in changes major of phase the during supporting been has manufacturing Indian in substitution of elasticity of performance The States. the across found being variation substantial The elasticity of substitution has been 1.00 during 1991-01 at the aggregate level and phase ofmajorchangesineconomicpolicy. a during states manufacturing five in and level aggregate at found been has scale to Returns However, Increasing states. five remaining the in depressing and states five in encouraging been has manufacturing Indian in scale to returns as 1991-01 policy returns of to scale terms remained inconclusive in during the manufacturing phase of Indian major changes of in economic performance efficiency The policy. economic in lowest in Punjab (0.45), followed by Gujarat (0.62) during the phase of major changes policy.economic in changes of major the phase during the been has Returnstoscale to scale has been the highest in Karnataka (2.08), followed by Andhra Pradesh (1.35), policy.Returns economic in changes major of phase a during Rajasthan and Punjab and depressing in The the remaining fiveHaryana states namely States. and Gujarat, UttarPradesh Pradesh, Karnataka, Madhya the Pradesh, Andhra Nadu, across TamilMaharashtra, namely seen and been level has aggregate scale the performance of returns to to scale in Indian manufacturing at has been robust in five states returns 1991-01 during in 1.38 variation been substantial has scale to returns The Manufacturing: 1991-01 Returns to Scale and Factor Substitution between Labour and Capital In Indian Source: Computedusing ASI Data Rajasthan Punjab Haryana Madhya Pradesh Karnataka Andhra Pradesh Uttar Pradesh Tamil Nadu Gujarat Maharashtra All India State Parameters Table 2:Estimated ParametersofCESProductionFunction:1980-91 Model: Returns toscale 1.60 1.48 1.96 0.75 1.16 1.38 3.00 2.19 1.97 0.93 1.86 Substitution Parameter 0.327 -0.70 -0.27 -0.87 -0.05 -0.68 0.29 0.09 0.22 0.40 1.45 substitution Elasticity of σ = 0.79 0.91 0.81 0.71 0.40 0.75 3.39 1.36 7.46 1.06 3.21 manufacturing. Indian in states the of most in unity than more been has capital and labour between possibility substitution the that showing policy economic in the changes major during of India phase like economy surplus labour in encouraging of been elasticity has The substitution states manufacturing these in technology appropriate be to found been has production Cobb-Douglas the that indicates Pradesh Madhya and Pradesh in economic policy. The elasticity of substitution has been found 1.00 in Gujarat, Uttar between possibility substitution the labour and capital has been as higher in these states during the phase of major changes manufacturing Indian in adopted been has technology flexible the that indicates This states. nine in unity than more be to found the phase of major changes in economic policy. The elasticity of substitution has been been found credible during the period of consolidation of economic reforms 2001-13 reforms economic of consolidation of period the during credible found been has scale to returns of terms in manufacturing Indian of performance efficiency The reforms. economic of consolidation of Andhra period the by during (0.60) Haryana followed by followed (2.42), Karnataka in highest the Pradesh (2.41) and Rajasthan (1.94) been while it is at the has lowest in Madhya Pradesh (0.61) scale to Returns Madhya Pradesh and Haryana during the period of consolidation of economic reforms. and increasing returns to as scale has been healthy found at found aggregate level level The and been all the has states States. except manufacturing aggregate Indian the in scale across the to returns found of been performance 2001-13at has during scale to 2.09 returns in been deviation considerable has scale to returns The Manufacturing: 2001-13 Returns to Scale and Factor Substitution between Labour and Capital In Indian Source: Computedusing ASI Data Rajasthan Punjab Haryana Madhya Pradesh Karnataka Andhra Pradesh Uttar Pradesh Tamil Nadu Gujarat Maharashtra All India State Parameters Table 3:EstimatedParametersofCESProductionFunction:1991-01 Model: Returns toscale 0.95 0.45 2.08 1.18 0.98 1.35 0.71 1.04 0.62 1.18 1.38 Factor SubstitutionandReturnstoScaleinIndianManufacturingUnderGlobalization Substitution Parameter -0.34 -0.75 -0.07 -0.63 -0.57 -0.03 -0.45 -0.11 -0.11 0.03 0.00 substitution Elasticity of σ = 0.96 1.53 4.12 1.07 2.74 2.34 1.03 1.84 1.01 1.13 1.00 97 Volume 9, No 1, January-June 2018 98 Journal of International Economics at aggregatelevel. and states the of most in strong been has manufacturing Indian in scale to returns as piecemeal and ad hoc of phase the and study of period entire the during healthy be to found been has scale to returns of terms in manufacturing Indian of performance The 1980. since regimes labour and returns to scale in Indian manufacturing across states under various policy and capital between substitution of elasticity estimate to attempt an makes study The Policy Implications manufacturing. Indian in states the of most in unity than more been has capital and labour between possibility substitution the that showing policy economic in the changes major during of India phase like economy surplus labour in encouraging been has substitution of elasticity The periods. period sub other the compared during reforms economic lower of consolidation relatively of been has capital and labour between possibility of either period one or less than one in the Indian manufacturing. This indicates that during the substitution (1.07) Pradesh Uttar consolidation of economic reforms. The elasticity of substitution has been found to be by followed (1.15) Pradesh Madhya in been found lower at the aggregate level. Elasticity of substitution has been the highest has substitution of elasticity that reason the for reforms economic of consolidation of period the during mark the to up not been has manufacturing Indian in substitution of elasticity of performance The States. the across found been has deviation significant The elasticity of substitution has been 0.77 during 2001-13 at the aggregate level and Source: Computedusing ASI Data Rajasthan Punjab Haryana Madhya Pradesh Karnataka Andhra Pradesh Uttar Pradesh Tamil Nadu Gujarat Maharashtra All India State Parameters Table 4:Estimated ParametersofCESProductionFunction:2001-13 Model: Returns toscale policy changes. Increasing Returns to scale has been found at 1.94 1.73 0.60 0.61 2.42 2.41 1.76 1.34 1.46 1.50 2.09 Substitution Parameter -0.01 -0.13 -0.06 -0.01 0.23 3.90 0.35 0.14 0.02 0.28 0.11 substitution Elasticity of σ = 0.80 0.20 0.74 0.87 0.98 0.77 1.01 1.15 1.07 0.89 1.01 ut (02. mat f lsiiis f usiuin ehia Cag, n Labour and Change, Technical Substitution, of Elasticities of Impact (2012). Gupta Diamond Slutsky the of Quest In (1989). O. Grandville, La de Ideas, and Growth (2005). Jones, I. Charles from theexistingcapital-intensivetechniquestolabourintensivetechniques. appropriate make changes should in factor prices for government removal of distortions Further,in factor markets to tempt production. a reinstate of techniques labour intensive appropriate of diffusion and invention financially the support for development should technological the government industries, Indian in technologies intensive labour of adoption the promote to order In endowments. factor domestic to apt which manufacturing Indian in technologies intensive labour promoting should of policy planners the stress the that suggested study present Thus India. of labour country the abundant in problem unemployment further to lead may technology labour-saving the that is result this of implication policy appropriately.The prices factor changes to The low 2001-13. elasticity of substitution period implies a low the absorption of surplus labour during in response manufacturing Indian in change technical labour-saving significant a to due was This 1991. since industries Indian in intensive-liberalization manufacturing and observed to be relatively low in all the states during the process of economic of aggregate in falling been consolidation has substitution of elasticity of of degree the is, period That reforms. the during manufacturing Indian in unity than less the or unity either be to that found been has it and manufacturing seeing Indian in states the of policy economic most in in unity than more been changes has capital and labour between major possibility substitution of phase the showing been during has It cheerful 1980-13. study of period surplus entire the labour during in India condition like economy distressed a exposed has substitution of elasticity The of economicreforms2001-13. credible in most of the states and at aggregate level during the period of consolidation states revealed been has five scale to in returns The states. impressed five remaining the been in depressing and has scale to returns as 1991-01 policy economic in changes major of phase the during inconclusive been has it while policy economic in changes major of phase the during states manufacturing five in and level aggregate Bishwanath Goldar, Basantha K. Pradhan & Akhilesh K. Sharma (2013). Elasticity of Elasticity (2013). Sharma K. & Akhilesh Pradhan K. Goldar,Basantha Bishwanath In Elasticity Demand Labour and Liberalization Trade (2008). Goldar Bishwanath References Review euain o Lbu Wlae n nin Industries, Indian in 2012/10. Welfare Labour on Regulations 1B. EditedbyPhilippe Aghion &StevenN.Durlauf,Elsevier B.V. Indian Economy, TheJournalofIndustrialstatistics the of Industries Manufacturing in Inputs Labour and Capital between Substitution Indian Manufacturing, , 79(3),pp:468-481. JournalofInternationalEconomics Factor SubstitutionandReturnstoScaleinIndianManufacturingUnderGlobalization Handbook of Economic Growth, VolumeEconomic of Handbook , 2(2),pp:169-194. SR Wrig Paper Working ASARC , 55(2),pp:391-409. , American Economic American 99 Volume 9, No 1, January-June 2018 100 Journal of International Economics mna (97. n siain f h CS rdcin Function, Production CES the of Estimation On (1967). J Kmenta of Elasticity the and Growth Economic (2000). Grandville La de O. and R., Klump, Pui Kiew Ling (2010). Elasticity of Substitution and Economic Growth, Economic and Substitution of Elasticity (2010). Ling Kiew Pui Growth: and Substitution of Elasticity (2003). Papageorgiou C. and K., Miyagiwa, Kaz Miyagiwa and Chris Papageorgiou (2006). Endogenous Aggregate Elasticity of Elasticity Aggregate Endogenous (2006). Papageorgiou Chris and Miyagiwa Kaz oe, .. 18) Icesn rtrs n ln-u growth, long-run and returns Increasing (1986). P.M. Romer, pne, (09. lsiiy f usiuin ewe Lbu ad aia across Capital and Labour between Substitution of Elasticity (2009). M Upender, Sunil Kumar (2001). Kumar Sunil 90(1), pp:282-291. Substitution: Two Theorems andSomeSuggestions, University. Normalized CESintheDiamondModel, Economic Review Substitution, PERKEM V, JILID2,15-17October, pp:392-420. Econometrics andQuantitativeStudies Twenty Six Major Industries in India during 2004-05 Deep andPublicationsPrivateLimited,NewDelhi. Economy, 94,October, pp:1002-1037. Working paper 2006-06 paper Working Productivity andFactorSubstitution: Theoryand Analysis, , 8,pp:180-189. , Department of Economics, Louisiana State Louisiana Economics, of Department , 6(1),pp:101-111. Economic Theory , International Journal of Applied American EconomicReview, , 21(1),pp:155-165. ora o Political of Journal PROSIDING International Prof RVaidyanathan Author Black MoneyandTax Havens Book Review , extortion, cheating and fiand cheating and extortion, printing smuggling, trade, narcotics illicit frauds, nancial goods, ofexpenses counterfeit of manufacturing profiillicit as such activities and criminal or of inflhost ation a ts through incomes of reporting non to due money black the modus of generating black money that include ‘corruption of public resources, readers trade based enlightens book The money.as it black eliminate generates not invariably if corruption money black of scourge the reduce to taken be to need steps elevating and global life investments is impinged by corruption. Therefore public to attain those twin objectives in probity establishing that states Vaidyanathan Professor Indian Expressand The HinduBusinessLine. New The including publications numerous in years several over penned had author the articles numerous the from rework Taxand a Money essentially ‘Black havens’is tax havensfromchapter3isinexplicable. and the Panama Papers in chapter 11. Why the author chose to wade in to and out of 6 chapter in studies case Indian 5, chapter in featured Money Blood with havens, tax the nature and magnitude of black money, the remaining chapters, (3 to 12) deal with discusses book fi the the of While chapters proclaims. two title rst the what chapters Taxand Money Havens’. Published by Westland publications, the book lays out in most of ‘Black the twelve out turned author an as avatar his in has IRDA and PFRDA Bangalore SEBI, IIM RBI, as at such bodies regulatory experience of member a teaching been had who of individual an and decades three than more with person a companies, several of director a as Vaidyanathan,serves R. who Professor person a Plumbing theWorld ofBlackMoneyandTax Havens SN07-72 Volume 9,No.1,January-June2018,pp.101-102 ISSN 0976-0792 Journal ofInternationalEconomics Institute ofPublicEnterprise, K NarendranathMenon Hyderabad Professor Reviewer 101 Volume 9, No 1, January-June 2018 102 Journal of International Economics of smoke and mirrors and cloak and dagger. Do read it to unravel the mystery and mystery recipetostymie themountainofblackmoney.know theauthor’s the unravel to it read Do dagger. and cloak and mirrors and smoke of find it a useful read, given that the theme, thanks to novels and movies, evokes visuals black money and tax havens. Besides management students, the lay public too would of features diverse the know to wishing anyone for primer a as serve could book The developing countriesillegally. of out flowed dollars ten obtained, countries developing that dollar one every for told, plays. money Truthillicit that drakes and ducks be the of sense a gets one and billion and the developing countries during the same period (2003-2012) which was health a mere $809 as such services $6.6 trillion in illicit lost outflows. Juxtapose this with the totalhave official development assistance to social alone countries developing basic the 2012, to and 2003 Between justice education. and public security fund to from used been services otherwise have would resources the because so is This worrying. particularly is illegally countries developing of out of sums huge Transferof $18 trillion,roughlyathirdoftheglobalGDP. around be to money black global estimates IMF challenge. the to approach global a deleterious consequencesthat it can create has made governments think of adopting it on made been not the and generated been has has that money black of assault amount mindboggling The hitherto. serious a why brittle wonder appear one surprisingly makes that exhibit), chains, (see in ensconced HAVENS’ is TAX turn AND MONEY in ‘BLACK which words emblazoned, the with book the of page cover The per centduringthesameperiod. 1.4 measly a was average world the whereas 3.3% at grew economy Irish the 1999, a country that has immensely benefited by serving as a tax haven. Between 1982 and of example an activity.is economic Ireland stimulates it that is haven tax a as serves that country the to money illicit such of flow the of side positive the However funds. of tripping’ ‘round comprehend to reader the helps route’ ‘Mauritius the as known be to came what operations. of domestic Aexplanation to vivid investment direct foreign or markets share through tripped round is it unless purposes domestic for leveraged be cannot money Such people’. its and stability its India, on confidence of lack ‘a of The author holds that illicit money stashed abroad by Indians in tax havens is indicative made toensurethatnomoneytrailisleft. is attempt Every authorities. tax the and systems electronic by same the of capture fearing banks through transactions formal avoid to tries it that and economy cash the on dependent primarily is it that avers author the money’, black ‘domestic on Writing explosives’ (page4). and ammunition arms, in trade and manufacturing illicit currency, fake of circulation Common Subscription Form To The Publications Division, Institute of Public Enterprise, OU Campus, Hyderabad - 500 007 Telangana, India.

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