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Reserve Bank of India Occasional PapersVol. 32, No. 2, Monsoon 2011

Regional Inequality in Foreign Direct Investment Flows to India:

The Problem and the Prospects

Atri Mukherjee*

Following the liberalisation of the foreign direct investment (FDI) policy in India in the early 1990s, FDI to India has increased significantly in the last decade. However, the growth in FDI flows has been accompanied by strong regional concentration thereby depriving a large number of Indian states from the benefits of a liberalised FDI regime. In view of this, the paper examines what are the major determinants affecting regional distribution of FDI flows in India. The analysis reveals that market size, agglomeration effects and size of manufacturing and services base in a state have significant positive impact on FDI flows. The impact of taxation and cost of labour is negative. While the impact of quality of labour is ambiguous, infrastructure, however, has significant positive influence on FDI flows. With the presence of a strong agglomeration effect, it is essential to have a conscious and coordinated effort at the national and the state government level to make the laggard states more attractive to FDI flows. The efforts may include special thrust on the manufacturing, services and the infrastructure sectors, or direct policy efforts like in the case of China or a combination of both.

JEL Classification : F21, R12, O14, O18,

Keywords : Foreign Direct Investment, Regional Inequality, Manufacturing and Services, Infrastructure

Introduction In the era of globalisation and financial integration, foreigndirect investment (FDI) has emerged as one of the most important forms of capitalflows todeveloping countries.FDI is oftenpreferredoverother formsofcapitalflowsby thepolicymakersas it isconsideredto be of a more stable nature and also it does not form a part of the hostcountry’sexternaldebtstock.Apartfromconstitutingamodeoffinance,FDIalsotendstoenhanceeconomicgrowththroughspillover*AtriMukherjeeisAssistantAdviserintheDepartmentofEconomicandPolicyResearch.Thepaperreflectspersonalviewsoftheauthor.Theauthorisgratefultotheanonymousrefereesfortheirvaluablecomments.

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oftechnologyandknowledgeinthehostcountry.Thereis,however,large inequality in thedistributionofFDIflowswithin theemergingmarketanddevelopingeconomies.Whilesomecountries likeChina,IndiaandBrazilhaveattractedbulkoftheFDIflows,mostoftheothershavefailedtoachievethesame.

FDIflowstoIndiapickedupinthe1990s,aftertheeconomicreformsandliberalisationoftheFDIpolicies.AspertheIMF’sGlobalFinancialStabilityReport,April2012,IndiahasemergedasoneofthemajorrecipientsofFDIflowsamongtheemergingmarketeconomiesinthelastfewyears.CompositionofFDIflowstoIndiarevealsthatoverthe years automatic route has emerged as the most important channel ofFDIflowstoIndia,followedbyreinvestedearningsandacquisitionofshares.FDIthroughgovernmentapprovalroute,ontheotherhand,hasdeclinedovertime,whichisinlinewiththepolicyreforms.ThesectoralcompositionofFDItoIndiahasundergonesignificantchangessince the 1990s.The bulk of the FDI flows in the pre-liberalisationperiodweredirected towards themanufacturing sector. In the recentyears,however,muchoftheFDIflowshavemovedintotheservicessector.MauritiushasemergedasthemostimportantsourceofFDItoIndiaoverthelastdecade.

Destinationwise,economicallyadvancedstateshaveattractedthelion’sshareofFDIflowstoIndia.ThetopsixIndianstates,viz.,Maharashtra, Delhi, Karnataka, Tamil Nadu, Gujarat and AndhraPradesh togetheraccountedforover70percentofFDIequityflowstoIndiaduringtheperiodApril2000toJune2012reflectingdistinctsignsofFDIconcentrationatthestatelevel.TheFDIpolicyinIndiawas liberalised in the early 1990s as a part of economic reforms toattracttheforeigncapitalandalsototakeadvantageofthespilloveroftechnologyandknowledge.Itis,therefore,essentialtoderivemaximumbenefitfromtheFDIflowsandensurethattherisingFDIflowsdonotleadtoanincreaseinregionalinequality.Inviewofthis,anattempthasbeenmadeinthispapertoexaminethemajordeterminantsaffectingregionaldistributionofFDIflowstoIndia.Inlightofthefindings,thepaperalsomakesanattempttolistoutthepossiblepolicyimplicationsforthenationalandthestategovernments.

REGIONALINEquALITyINFOREIGNDIRECTINVESTMENTFLOWS101

Thepaperhasbeenorganisedasfollows:SectionIsetsoutabrief theoreticalbackgroundrelating to the reasons for inter-regionaldifferencesinFDIflows.SectionIIprovidesasurveyofselectempiricalliteratureonthedeterminantsofregionaldistributionofFDIflowsintheinternationalaswellasintheIndiancontext.SectionIIIpresentssomestylisedfactsondistributionofFDIflowsinIndia.SectionIVdescribestherationalebehindselectionofvariables.Themethodologyand theempiricalresultsarefurnishedinSectionV.ThepolicyimplicationsaredrawninSectionVI.

Section IThe Theoretical Background

Traditionally, the FDI has moved from developed to other developedor developing countries preferably in sectors likemining,tea, coffee, rubber, cocoa plantation, oil extraction and refining,manufacturing for home production and exports, etc.Gradually theiroperations have also included services such as banking, insurance,shipping, hotels, etc.As regards location choice, theMultiNationalEnterprises (MNEs) tend to set up their plants in big cities in thedevelopingcountries,whereinfrastructurefacilitiesareeasilyavailable.Therefore,inordertoattractFDIflows,therecipientscountries/regionswererequiredtoprovidebasicfacilitieslikeland,powerandotherpublicutilities, concessions in the form of tax holiday, development rebate, rebateonundistributedprofits,additionaldepreciationallowanceandsubsidised inputs, etc.

Dunning (1998) indicated that the strategies and locationchoiceofMNEshadundergonesignificantchangesbetweenthe1970sand the1990s.He identified somemajordevelopments in theworldeconomywhichhavebeeninstrumentalinchanginglocationdecisionofMNEsduringthisperiod.Thefirstmajordevelopmentisthegrowthof intellectual capital which was reflected in higher expenditure oninformation technology, increase in theknowledgecomponentof themanufacturinggoodsandincreaseintheshareofskilledworkersinthelabourforce.Thegrowingsignificanceofthesenon-materialknowledge-intensiveassetswasledbytremendousgrowthoftheservicessector,particularly knowledge and information oriented services. Secondly,the location of creation and use of these knowledge intensive assets


havebeenincreasinglyinfluencedbythepresenceofimmobileclustersof complementary value-added activities. Spatial bunching of firmsengagedinrelatedactivitieshavebenefitedfromthepresenceofoneanother and of having access to localised support facilities, shared servicecentres,distributionnetworks,customiseddemandpatternsandspecialisedfactorinputs.Thishasgivenriseto“alliancecapitalism”,inwhichthemainshareholdersinthewealthsharingprocessneedtocollaboratemoreactivelyandpurposefullywitheachother.Third,thereis increasing evidence that except for some labour or resource oriented investment in developing countries, MNEs are increasingly seekinglocationswhichofferthebesteconomicandinstitutionalfacilitiesforcorecompetence tobeefficientlyutilized.Fourth, the renaissanceofmarket economy and the consequent changes in themacroeconomicpolicies and macro-organizational strategies of many nationalgovernmentshavealsocontributedsignificantly to theeconomicandpoliticalriskassessmentofFDIbyMNEs.

The “agglomeration” factor has emerged as one of themostimportantdeterminantsofregionaldistributionofFDIflowswithinacountryduringthelasttwodecades.Agglomerationeconomiesemergewhen there are some positive externalities in collocating near othereconomicunitsduetothepresenceofknowledgespillovers,specialisedlabormarketsandsuppliernetwork(Krugman1991).Statisticalresultsfrom several studies focusing on developing economies strongly buttress the argument that foreign investors are inclined to favour such locations that could minimise information costs and offer a variety ofagglomerationeconomies(HeCanfei2002).Acommonfindinginrecentstudiesisthatregionswitharelativelyhigherexistingstockofforeigninvestmentaremorelikelytoattractfurtherinvestments,whichconfirmstheimportanceofpositiveagglomerationexternalities.

Therefore,itemergesthatwhileglobalisationsuggeststhatthelocationandownershipofproduction shouldbecomegeographicallymore dispersed, other economic forces areworking towards amorepronounced geographical concentration of such activity both withinparticular regions and countries. In the above theoretical backdrop,a survey of the empirical literature has been carried out highlighting select country experiences and theexperiencesintheIndiancontext.


Section IISurvey of Select Empirical Literature

Internationally,thereisahostofliteratureanalysingtheinter-country differences in FDI flows. Those studies have identified anumber of factors affecting the location choice of the foreign direct investors.However,manyof thosedeterminants are country-specificandwouldnotapplytostate/provinciallevelmovementofFDIflows.TheliteratureonregionaldistributionofFDIflowswithinacountry,on the other hand, is relatively scarce.Most of the available studiesrelatingtoFDIflowsatthestate/provinciallevelrelatetotheuS,theEuropeanunionandChina.Therearefewanalyticalstudiesoninter-statedifferencesinFDIflowsinIndia.

InthecontextoftheunitedStates,Coughlin,TerzaandAromdee(1989)foundthatthenumberofpotentialsites,statepercapitaincome,manufacturingdensitywithinastate,bettertransportationinfrastructure,higherunemploymentratesandhigherexpenditurestoattractFDIwerepositively linked toFDIflows.On theotherhand,higherwagesandhighertaxrateshadnegativeimpactonFDIflows.FisherandPeters(1998) found that incentivesofferedbyvarious stateshadapositiveimpactoninvestmentflowsto theuS.Incentivesconsideredin theirstudyincludejobcredits,propertytaxabatements,salestaxexemptions,grants, loan guarantees, firm specific job training and infrastructuresubsidies.Within theEuropeanunionmember states, the long termtrends point out the existence of a negative relationship betweentaxationandFDIinflows.Santis,MercuriandVicarelli(2001)foundthatFDIflowswithintheEuropeanunionmemberstatesweremoreinfluencedbythetotalfiscalwedgeonlabourthancorporatetaxrates.Thissuggeststhatmultinationals,whilemakingtheirlocationchoices,focus their attention more to the overall tax burden than on single corporatetaxrates,whichprovideonlypartialinformation.Apartfromtax burden, bilateral degree of trade openness and infrastructure also playanimportantroletoattractFDI.Wolff(2006)foundthatwithinthe European union, the different sub-components of FDI (equity,re-invested profits and other investments) react differently to taxes.Contrarytothepublicbeliefthathighcorporatetaxratesactasthekey


reasons for low investment rates from abroad, the author found thatafter controlling for unobserved country characteristics and common time effects, the top statutory corporate tax rate of both, source and host country,turnedinsignificantfortotalFDIandinvestmentintoequity.Therewere,however,definiteindicationsthathighsourcecountrytaxesincreasedtheprobabilityoffirmstoreinvestprofitsabroad.However,overall experience revealed that global companies give more importance to the simplicity and stability of a country’s tax system than generous tax rebates.Chidlowandyoung(2008)foundthatPolishregionsdifferedsubstantially in attracting foreign capital and the regional characteristics matteredintheselectionoflocation.usingsurveydatafromanonlinequestionnaire and a multinomial logit model incorporating investor specific characteristics, they showed that knowledge-seeking factorsalongsidemarketandagglomerationfactors,actedasthemaindriversof FDI toMazowieckie region (includingWarsaw),while efficiency(low inputcost, availabilityof labourand resources)andgeographicfactorsencouragedFDItotheotherareasofPoland. IntheChinesecontext,basedonpaneldatacovering98hinterlandcitiesofChinafortheyears1999to2005,Luoet al (2008) found that wellestablishedfactorssuchasnaturalresourcesandlowlabourcostswere not important in determining FDI flows to China’s hinterland.Instead,policy incentives and industrial agglomerateswere themostimportant determining factors for FDI flows.using panel dataset oftheareasatprovinciallevelinChinaduringtheperiodof1998-2007,Xu et al (2008) found that agglomeration economies influenced thelocationchoicesofFDIinChina,andcumulativeFDIinanareahadcrucial demonstration effect on thedecisionmakingof thenewFDIentrants.Thestudyalsoindicatedthatalthoughlabourcostscontinuedto remainonecrucial element for locationchoicesofFDI,however,labourqualitywasplayinganincreasinglyimportantroleinattractingFDI from theuS and theEuropean countries.The analysis of core-peripheryframeworksuggestedthatthetwomegacitiesofHongKongandShanghaiasthecoresofagglomerationhadsignificantinfluenceonlocationchoicesofFDIinChina.ForFDIfromdifferentsources,thereexistcountryspecificfeatures.This implies thatpreviouscumulativeforeign investments led to concentration of new investments from


same source country.Boermans et al (2009) found that in linewiththe theoretical predictions, foreign investors preferred to invest more in provinceswithbetterinstitutions,lowerlabourcostandlargermarketsize.The effect ofmarket size onFDIwas larger in provinceswithbetterinstitutions.Sub-samplestudyconfirmedtheexistenceofalargedisparitybetweenEastandWest.Inthepoorerlargewesternprovinces,FDIwasstronglydrivenbythegeographicalfactors,incontrasttotheeastofChina,whereinstitutionsplayedasignificantroletobuildupthe ‘factory of the world’. Robustness tests indicated that two sub-dimensions of institutions, viz., infrastructure and governance, wereimportanttodeterminethelocationchoiceofFDIinChina. Siddharthan (2006) found that the determinants of regionaldistributionofFDIflowsinChinaandIndiawereverysimilartothepattern influencing inter-country FDI flows. In those two countries,muchoftheFDIflowedtorelativelydevelopedregions,whileregionsthatwerepoorinphysical,institutionalandsocialinfrastructurereceivedverylittleFDI.InChina,Easternzoneprovinceswithhighpercapitaincome,bettersocio-economicindicators,betterinfrastructurefacilitiesintermsofelectricity,roadandrailnetworkandhigher internationalorientation in terms of their per capita international trade, also attracted higherFDIflows.Similarly, in India, the stateswithhighper capitaincome, high industrial output, and situated at the coasts attracted high levelsofFDI.Moreover,theregionsthatreceivedlowFDIflowswerealsotheregionsthatattractedlowerdomesticinvestment. IntheIndiancontext,Goldar(2007)foundthatbyandlarge,thesamesetoffactorsinfluencedthelocationdecisionofplantsoflocalcompaniesas thatofforeigncompanies.Hiseconometricanalysisofplant locationacross100 largest cities in17 statesof India revealedthatcity-sizewasanimportantfactorinfluencinglocationdecisionsofindustrial plants.The presence of ametropolitan city in a state alsohad a favourable influence, which probably captured the advantagein‘headquartering’thecountryoperationsoftheMNEs.Thelocationdecisionsofplantsofforeigncompanieswerefoundtobeinfluencedbytheinvestmentclimateandavailabilityofeducatedworkersinthestate,andtheavailabilityofcivicamenitiesinthecities.Morris(2007)argued that in India, the regionswith the metropolitan cities had the


advantage in ‘headquartering’ the country operations of MNEs andtherefore, attractedbulkof theFDIflows.NunnenkampandStracke(2007) found significant positive correlation of FDI with per capitaincome,populationdensity,percapitabankdeposits,telephonedensity,level of education and per capita net value added in manufacturing inIndia.FDI,on theotherhandwasnegativelycorrelatedwithstatepopulation,andhad insignificantrelation inrespectofavailabilityofelectricityandunemployment rate.Aggarwal (2005) found that rigidlabourmarkets in Indian statesdiscourageFDI.Theeffectof labourmarket rigidities and labour cost, however, was more pronouncedfor the export-oriented as compared to the domesticmarket seekingFDI.Thestudyalsopointedout that thepresenceofEPZworkedasa relevantpull factor forexportorientedFDI.Econometricevidencefound in the study suggested that infrastructure, regional development andhumandevelopmentwerealsokeyfactorsinattractinghigherFDIbothintheexportanddomesticmarketsectors.Inastudyonbusinessenvironment,clusteringandindustrylocationintheIndianCities,usingfirmleveldatacollectedinthe2003roundoftheInvestmentClimateSurvey(ICS)forIndia,LallandMengistae(2005)foundthatthelocalbusiness environment had significant bearing on location decisions.Predatoryenforcementofbusinessregulationsreducedtheprobabilityofabusinesslocatinginacity.Incomparison,betteraccesstofinanceand landandgreateravailabilityof infrastructureattractedfirms toacity.However,firmswerealsoattractedbyagglomerationeconomiesfromclusteringoffirmsintheirownindustry.Thismeansthatnewfirmswillchoose to locateproduction inareas thatarealreadyestablishedcentersintheirlineofbusiness. RamachandranandGoebel(2002)pointedoutthatTamilNaduhad emerged as one of the most favoured investment destination in India on account of a number of advantages viz., strong and stablegovernment with pro-active government policies, investor-friendlyand transparentdecisionmakingprocess,sounddiversified industrialinfrastructure, comfortable power situation, abundant availabilityof skilled manpower, harmonious industrial relations and absenceof labourunrest,highqualityofworkcultureandpeaceful life,bestincentives package in the country, highly cosmopolitan compositionandhighproportionofEnglishspeakingpopulation.FDIinTamilNaduis dominated by investments in theITsector.


Overall, the theory and the empirical literature suggest that the mostimportantdeterminantsoftheregionaldistributionofFDIflowswithinacountryincludethesizeandgrowthofthelocalmarket,thelevelofindustrialactivity,thegrowthoftheservicessector,theavailabilityand quality of physical infrastructure, labour market conditions andquality of labour, policy environment and tax incentives, business climateandthepresenceofagglomerationeconomies.

Section IIIFDI Flows to India: Some Stylised Facts

FDIflowstoIndiahavepickedupsignificantlyintherecentyears.IndiahasemergedasthesecondlargestrecipientofFDIflowsamongtheemergingmarketeconomiesafterChinain2009and2010(Table1).

Table 1: Emerging Market External Equity Financing (inmillionuSdollars)

2008 2009 2010 2011Sub-Saharan Africa 884 1,237 2,841 1,476Central and Eastern Europe 1,105 3,836 7,502 3,733Commonwealth of Independent States 4,087 1,258 6,998 11,164 Russia 2,850 956 5,454 10,794Developing Asia 21,441 61,078 86,923 38,013 China 11,974 39,854 45,448 23,499 India 6,008 16,223 26,179 7,016 Indonesia 2,213 1,286 6,317 2,229 Malaysia 660 3,604 5,818 2,972 Pakistan 109 — 93 — Philippines 125 0 960 596 Thailand 257 111 1,991 1,554Middle East and North Africa 3,832 917 1,695 182Latin America and the Caribbean 12,719 15,416 27,139 18,983 Argentina — — 73 3,576 Brazil 10,435 12,963 24,633 9,029 Chile — 32 1,214 2,340 Colombia — 619 296 3,598 Mexico 2,127 1,567 662 441Total FDI Flows 44,067 83,740 1,33,098 73,552Note: — indicatesthatthefigureiszeroorlessthanhalfofthefinaldigitshownSource:GlobalFinancialStabilityReport,April2012,InternationalMonetaryFund


TheriseinFDIflowstoIndiahasbeenaccompaniedbystrongregionalconcentration(Table2andChart1).Thetopsixstates,viz.,Maharashtra,NewDelhi,Karnataka,Gujarat,TamilNaduandAndhraPradeshaccountedforover70percentoftheFDIequityflowstoIndia

Table 2: FDI Equity Inflows to Indian States 2008-09 2009-10 2010-11 2011-12 2008-09 2009-10 2010-11 2011-12 (uS$million) (PercenttoTotal)Maharashtra 12,431 8,249 6,097 9,553 45.5 31.9 31.4 26.2Delhi 1,868 9,695 2,677 7,983 6.8 37.5 13.8 21.9Karnataka 2,026 1,029 1,332 1,533 7.4 4.0 6.9 4.2Gujarat 2,826 807 724 1,001 10.3 3.1 3.7 2.7TamilNadu 1,724 774 1,352 1,422 6.3 3.0 7.0 3.9AndhraPradesh 1,238 1,203 1,262 848 4.5 4.7 6.5 2.3WestBengal 489 115 95 394 1.8 0.4 0.5 1.1Chandigarh 0 224 416 130 0.0 0.9 2.1 0.4Goa 29 169 302 38 0.1 0.7 1.6 0.1MadhyaPradesh 44 54 451 123 0.2 0.2 2.3 0.3Kerala 82 128 37 471 1.3 0.5 0.2 1.3Rajasthan 343 31 51 33 0.3 0.1 0.3 0.1uttarPradesh 0 48 112 140 0.0 0.2 0.6 0.4Orissa 9 149 15 28 0.0 0.6 0.1 0.1Assam 42 11 8 1 0.2 0.0 0.0 0.0Bihar 0 0 5 24 0.0 0.0 0.0 0.1Region not indicated

4,181 3,148 4,491 12,782 15.3 12.2 23.1 35.0

Total 27,332 25,834 19,427 36,504 100.0 100.0 100.0 100.0Top6States 22,113 21,757 13,444 22,340 80.9 84.2 69.2 61.2Top2States 14,299 17,944 8,774 17,536 52.3 69.5 45.2 48.0Note:1. FDIequityinflowsinclude‘equitycapitalcomponent’only. 2. MaharashtraincludesMaharashtra,Dadra&NagarHaveliandDaman&Diu. 3. DelhiincludesNewDelhiandpartofuPandHaryana. 4. TamilNaduincludesTamilNaduandPondicherry. 5. WestBengalincludesWestBengal,Sikkim,andAndaman&NicobarIslands. 6. ChandigarhincludesChandigarh,Punjab,HaryanaandHimachalPradesh. 7. MadhyaPradeshincludesMadhyaPradeshandChhattisgarh. 8. KeralaincludesKeralaandLakshadweep. 9. uttarPradeshincludesuttarPradeshanduttaranchal. 10. AssamincludesAssam,ArunachalPradesh,Manipur,Meghalaya,Mizoram,Nagaland

andTripura.Source: Department of Industrial Policy and Promotion (DIPP), Ministry of Commerce andIndustry,GovernmentofIndia.


between2008-09and2011-12.Thetoptwostates,i.e.,MaharashtraandDelhiaccountedforover50percentofFDIflowsduringthisperiod.Maharashtraaloneaccountedforover30percentofFDIflowstoIndiaduringthesameperiod.

DespiteimpressivegrowthratesachievedbymostoftheIndianstatesaswellasaggressiveinvestmentpromotionpoliciespursuedbyvariousstategovernments,theconcentrationofFDIflowsacrossafewIndianstatescontinuestoexist.

Section IVSelection of Variables

Market Size

Thetheoryaswellastheempiricalliteraturerevealedthatthesizeofthelocalmarket,generallyrepresentedbythescaleandgrowthof a region, acts as one of the most important determinants of location choice of FDI as it provides an idea about the potential demand for a foreignfirm’soutput.Theattractivenessforlargemarketsisrelatedtolargerpotentialforlocalsales.Localsalesaregenerallymoreprofitablethan exports especially in large countries,where economies of scalemaybeeventuallyreaped.Despitesignificantchangesinthelocationchoice of MNEs in the recent period, large and growing domestic

Chart 1: Cumulative FDI Inflows to Indian States(April 2000-June 2012)

Maharashtra

Delhi

Karnataka

Gujarat

Tamil Nadu

AndhraPradesh

WestBengal

Chandigarh

MadhyaPradesh

Kerala

Goa

Rajasthan

UttarPradesh

Orissa

Assam Bihar

Per cent

0

5

10

15

20

25

30

35


marketcontinuestoremainamajordeterminantofmarket-seekingFDIflows.EmpiricalstudiesconductedinthecontextoftheuS,Europeanunion,ChinaandIndiahavetakenintoaccountanumberofvariablesto represent the market size, viz., GDP, growth rate of GDP, percapita income, personal income, population size, population density,populationgrowth,consumption level,numberofpotential sites inastate, etc.

Inviewofthis,inthisstudy,anattempthasbeenmadetotestthehypothesisthatsizeofthelocalmarkethasimportantimplicationsforregionaldistributionofFDIflowstoIndia.Inthispaper,the‘sizeofthelocalmarket’isrepresentedbytwomostcommonlyusedindicators,viz., per capita net state domestic product (NSDP) and populationdensityofeachstate.

Industrial Linkages

Dunning (1993) suggested that natural resource seeking FDIlooks for foreign locations thatpossessnatural resourcesand relatedtransportandcommunicationinfrastructure, taxandother incentives.Naturalresources includeoil,mineral, rawmaterialsandagriculturalproducts. It isalsooftenarguedthatregionswithamoreestablishedindustrial base are more attractive to foreign investment (Luo et al 2008).IntheIndiancontext,Siddharthan(2006)foundthatthestateswith higher industrial output have attracted high levels of FDI.ThelocationchoicebyMNEsinthe1990s,however,hasbeeninfluencedtoalargeextentbytheavailabilityofnon-materialknowledge-intensiveassetsmainlydrivenbythetremendousgrowthoftheservicessector,particularly knowledge and information oriented services (Dunning,1998).The sectoral break-upofFDIflows in India also reveals thatthe services sector has attracted a large share of FDI flows in therecentperiod(Table3).ItmaybeobservedfromTable3thatfinancialand non-financial services alone accounted for 19 per cent of thecumulativeFDIflows toIndiasinceApril2000.Taking intoaccounttelecommunication,computerhardware&software,constructionandother services activities, overall, the services sector in India has attracted around50percentofFDIflowsduringthesameperiod.


Inviewoftheabove, in this paper, an attempt has been made to testthefollowingthreehypotheses:

• Indian states rich in natural resources are more attractive to FDI flows;

• IndianstateswithstrongindustrialbasetendtoattractmoreFDIflows;

• IndianstateswithhigherservicessectoractivityattractmoreFDIflows.

Theexplanatoryvariablesconsideredinthiscontextarethepercapita mining output, per capita manufacturing output and per capita servicesoutputofeachstate.

Infrastructure

It is commonly argued in the economic literature that development and availability of superior infrastructural facilities have a positive effect on the location choice of FDI firms.As argued byDunning (1998),that though much of the FDI in developing countries is prompted by traditional factors, such as market-size, lower input/labour cost andavailabilityandpricesofnaturalresources,yeteventhere,wherethe

Table 3: Sectoral Orientation of FDI Equity Flows to India 2008-

092009-

102010-

112011-

12 Cumulative

Inflows(April’00–April’12)

PercentageofTotalInflows

(April’00-April’12)

(uS$million)

Services Sector (Financial&non-financial)

6,138 4,353 3,296 5,216 33,428 19

ConstructionDevelopment 1,227 731 21,088 12

Telecommunications 2,558 2,554 1,665 1,997 12,560 7

ComputerSoftware&Hardware 1,677 919 780 796 11,286 6

Drugs&Pharmaceuticals 213 209 3,232 9,659 6

Power 985 1,437 1,272 1,652 7,444 4

AutomobileIndustry 1,152 1,208 1,299 923 6,965 4

Metallurgical Industry 961 407 1,098 1,786 6,374 4

Total 62

Source:DepartmentofIndustrialPolicyandPromotion(DIPP),GovernmentofIndia.


firmshave a choice, physical and human infrastructure,togetherwiththemacroeconomicenvironmentandinstitutionalframeworkofthehostcountrytendtoplayamoredecisiverole.Availabilityoftransportationfacilitiestoreachthenearestportoroutputmarketshavehistoricallybeen considered as an important determinant of setting a business in a particularplace.Mostcommonlyusedvariablestorepresenttransportinfrastructureincludesthepresenceofmajorports,closetothecoastlocation,availabilityandqualityofroadandrailnetwork.Apartfromtransport,physicalinfrastructureintheformofavailabilityofpower,telephonedensity,accesstofinance,availabilityofcivicamenitiesanddegreeofurbanisationwerealsofoundtobeimportantintheempiricalstudies. Inordertotestthehypothesisthat“stateswithbetterinfrastructureattract higher FDI flows compared to others”, two indicators forinfrastructure, viz.,roadroutedensity(roadlengthpersquarekilometerofstatearea)andrailwayroutedensity(railwaylengthpersquarekilometerofstatearea)havebeenconsideredinthisstudy.Labour Conditions The theory suggests that other things being equal, efficiencyseeking foreignfirmsareexpected toprefer lowerwage locations tominimisetheircostofproduction.Overtime,however,foreigninvestorshave started attaching importance to local labour quality. Dunning(1998)indicatedthatwhilelabourcostwasoneofthemajorvariablesinfluencingthelocationofMNEsinthe1970s,itwastheavailabilityand the price of skilled and professional labour that influenced thedecisionmakingoftheMNEsinthe1990s.Sincehigherwagelevelsreflecthigher labourproductivityorhigherqualityofhumancapital,thereforeaninvestingfirmwhichislookingforhighqualityandskilledlabourmaybeattractedbythehigherwagerate.Ithasbeenobservedthat higher the production technology level and technological content intheproduct,labourqualitywouldassumehigherimportance. Inthispaper,wagesperworkerinIndianstateshavebeenusedasanindicatoroflabourcost.qualityoflabourisgenerallyjudgedintermsofeducationalqualificationoftheworkforce.Inordertoassessthe quality of labour, literacy rate and per capita number of educational institutions for higher studies (degree and above) in each state have been considered in theanalysis.


Policy Environment The local policy environment is mainly characterised bypoliciestowardsforeigndirectinvestment,taxstructureandinvestmentincentivesprovidedbythelocalgovernmenttoattractFDI.Overthepastfewdecades,manylocalgovernmentsallovertheworldhavebeenactively involved in improving the policy environment for promoting their countries as attractive destination for foreign investors. Thosegovernments have adopted a host of measures viz., liberalisation oflaws and regulations for the admission and establishment of foreigninvestment projects, provision of guarantees for repatriation ofinvestment andprofits, establishingmechanism for the settlement ofinvestment disputes and extending tax incentives to facilitate and attract foreigninvestmentflowstotheircountries.

In India, as a part of economic reform, many of the states are simplifying the rules and procedures for setting up and operation of the industrial units. SingleWindowSystem is now in existence inmostof thestates. Inaddition,mostof thestatesprovidevariouskindsofincentivesforattractinginvestmentinthenewindustrialunitsaswellastheexistingones.Theincentivesmaybesector-specificorregion-specific.WhileitiscommonamongtheIndianstatestoofferincentivestotheIT/ITeS,biotechnology,tourismandthemicro,smallandmediumenterprise(MSME)sectors,attimesspecialincentivesarealsoofferedinindustriessuchastextile,food,fisheries,film,healthcare,electricitygeneration, etc.Mostofthesector-specificincentivesinIndiataketheform of exemption from stamp duty, registration fee, electricity duty andvarioustypesoftaxes.SpecialEconomicZones(SEZ)alsoenjoyvarious incentives mainly in the form of various duty exemptions.Thedirecttaxbenefitsincludeexemptionfromcommercialtax,salestax, value added tax (VAT), entry tax, special entry tax, luxury tax,entertainment tax, property tax, purchase tax, etc., depending on the industryinconcern.Exemptionofentertainmenttaxesiscommonforthetourismsector.

EmpiricalevidenceinthecontextofEuropeanunionrevealedthat multinationals, whilemaking their location choices, focus theirattention to the overall tax burden rather than on single corporate tax rates, which provide only partial information (Santis, Mercuri and


Vicarelli,2001).Inviewofthis,inthisstudy,thestate’sowntaxrevenueaspercentofNSDPhasbeenusedtoassesstheimpactoftaxstructureonFDIflows.

Agglomeration Economies As countries begin to industrialise, there is a tendency forindustriestoconcentrateinitiallyinareaswherephysicalinfrastructureis readily available and subsequently, for related industries, to gravitate closertogether,therebytakingadvantageofinherentsynergies.Intheprocess, industry clusters are formed, with each geographical areaspecialisingincertainactivities,leadingtospatialdiffusionofindustries.Thisclusteringoffirms,whichisalsoknownasthe“agglomeration”factor has emerged as an important determinant of regional distribution ofFDIflowswithinacountryduringthelasttwodecades.Thereductioninspatialtransactioncostduetoliberalisationofcross-bordermarketand the changing characteristics of the economic activity has favoured thespatialbunchingoffirmsengagedinrelatedactivities,sothateachmaybenefitfromthepresenceof theothers,andofhavingaccess tolocalisedsupportfacilities,sharedservicecenters,distributionnetworks,customised demand pattern and specialised factor inputs (Dunning 1998).Statisticalresultsfromseveralstudiesfocusingondevelopingeconomies strongly buttress the argument that foreign investors are inclined to favor such locations that could minimise information costs andofferavarietyofagglomerationeconomies(HeCanfei2002).Thepresence of agglomeration economies is reflected in terms of priorforeign investment presence, prior concentration of manufacturing plants, number of enterprise in a region, presence of various economic zones(SEZ,EPZetc.),industrialparks,industrialclusters,etc.

In this study, one period lagged value of per capita stock ofFDI in a state has been considered as independent variable to capture these agglomeration effects.A positive and significant coefficient ofthisvariablemeansthepresenceofagglomerationeconomies.

Based on the above analysis, a list of explanatory variablesselectedforthestudyispresentedinTable4.


Table 4: List of Explanatory Variables Selected for the StudyType of factor Variables Expected Sign

A. Marketsize 1. PercapitaNSDP(PCy);2. PopulationDensity(PD):

++

B. IndustrialOrientation 3. Percapitamanufacturingoutput(MANP);4. Percapitaminingoutput(MINP);5. Percapitaservicesoutput(SERP);

+++

C. Infrastructure 6. Roadroutedensity(ROAD);7. Railwayroutedensity(RAIL);

++

D. LabourConditions 8. Wagesperworker(WAGE);9. Literacyrate(LIT);10. Percapitanumberofhighereducational

institutes(EDuP);

-+

+E. PolicyEnvironment 11. State’sowntaxrevenueaspercentof

NSDP(TAX) -F. AgglomerationEffects 12. PercapitaFDIstock(STOCKP) +

In addition to the above, there may be many other factors havinganinfluenceonforeignfirms’investmentdecision.Ithasbeenobserved that multiple factors, viz., pro-active government policies,transparentandinvestmentfriendlydecisionmakingprocess,politicaland legal environment, harmonious industrial relations and the quality of governance institutions together build the investment climate in a state(GlobermanandShapiro,2003;LallandMengistae,2005;Ansariand Ranga, 2010). The “Doing Business” Reports jointly publishedby theWorldBank and InternationalFinancialCorporation considerseven parameters to determine the business environment in a state, viz.,‘easeofstartingbusiness’,‘easeofdealingwithconstructionpermit’,‘ease of registering property’, ‘ ease of paying taxes’, ‘ease of enforcing a contract’, ‘ease of trading across borders’ and ‘ease of closing a business’.Inadditiontothese,thelegalstructure,securityofpropertyrightsandlevelofcorruptioninastate,reflectedintermsofthequalityofjusticemechanismmayalsohavesomeimpactonFDIflows.Theregulation of labour and business is another factor,which is knownto have significant influence on foreign investors’ sentiments. Thenumberofstrikesandindustrialdisputesthattakeplaceintheeconomyportray theamountofcontrolanentrepreneurhasoverhisbusiness.Theprevalenceofstronglabourunionsandlargenumberofindustrialdisputes in thestatesofWestBengalandKerala reflect thestringent


labour lawsandpro-labourgovernmentpolicies in those states.Dueto such industrialdisputes, largenumberofmandaysare lost,whichseriouslyhamperstheprofitabilityofthemanufacturerand,therefore,hasadverseimpactonforeigninvestment.

It has also been observed that the countries or regions that are politicallyriskywithahistoryofexpropriatingFDI,endemiccorruption,autocraticgovernments,poorlawandordersituationorethnictensiontend to receive lower FDIflows.The Indian experience reveals thatvarious political factors such as political stability of a state or the state government’spoliticalrelationwiththecentralgovernmenthavealsoplayedan important role in attractingFDIflows.Political instabilityresulting from naxalite movements, various corruption and scandals haspreventedFDIflowstocertainstatesofIndiaintherecentperiod.However,intheabsenceofconsistentanduniformcross-sectionalaswellastimeseriesdata,thesefactorshavebeenleftoutoftheempiricalanalysiscarriedoutinthestudy.

Section VThe Methodology and Empirical Results

Theempiricalanalysiscarriedoutinthispaperisbasedonstate-levelpaneldatasetofIndiaovertheperiod2000-01to2010-11covering31statesandunionterritories,viz.,Andaman&NicobarIslands,AndhraPradesh,ArunachalPradesh,Assam,Bihar,Chhattisgarh,Delhi,Goa,Gujarat,Haryana,HimachalPradesh,JammuandKashmir,Jharkhand,Karnataka,Kerala,MadhyaPradesh,Maharashtra,Manipur,Meghalaya,Mizoram,Nagaland,Orissa, Puducherry, Punjab,Rajasthan, Sikkim,TamilNadu,Tripura,uttarPradesh,uttaranchalandWestBengal.ThedependentvariableinthisstudyisthepercapitaannualflowofFDItoeachofthe31Indianstatesduringthe10yearsperiodof2001-02to2010-11.Theannualstate-levelFDIflowsdatareleasedbytheDepartmentofIndustrialPolicyandPromotion(DIPP),MinistryofCommerceandIndustry,GovernmentofIndia,however,hascertainlimitations.First,thestate-levelannualFDIflowsdatapublishedbyDIPPareavailableonlyfrom2008-09onwards.Second,asnotedinTable2,thedataonFDIflowstocertainstatesarenotavailableatthedisaggregatedlevel.Inviewofthis,inthisstudy,thehelpofCentreforMonitoringIndian


Economy(CMIE)databasehasbeentakentocalculatedisaggregatedannualFDIflowsdataforeachofthe31Indianstateswhichisimportantto ensure one-to-one correspondence in definition of a ‘State’ in theFDIstatisticsandtheexplanatoryvariables,whilestudyingtheregionaldeterminants of FDI.The data on annual FDIflows to Indian statesfrom2008-09onwardshavebeendirectlytakenfromtheDIPPdatabasewithCMIEdatausedfordisaggregation.AnnualFDIflowstoIndianstatesduringtheearlieryearshavebeencalculatedbasedonthenewandoutstanding foreign investmentdatabaseof theCMIEconsistentwiththecumulativeFDIflowstothosestatesaspublishedbytheDIPP.Thesampleperiodof2001-02to2010-11hasbeenchosenmainlyonaccountof the fact thatFDIflow to India startedpickinguponly inthe2000sandalsoDIPPhaspublishedregion-wisedataonFDIflows(cumulative)toIndianstatesonlysinceApril2000.TheannualdataonpopulationofeachstatehavebeenworkedoutbasedontheCensusdataonstatepopulationandtheaverageannualexponentialgrowthrateofpopulation.

Multiple sources have been used to obtain the data on the various explanatory variables used for the empirical analysis. Informationon per capita income and variables relating to economic structure is obtained from the NationalAccounts Statistics (NAS) published bytheCentralStatisticsOffice(CSO)oftheGovernmentofIndia(GoI)and theHandbookofStatisticson the IndianEconomypublishedbytheReserveBank of India.The data on the infrastructural variablesareobtainedfromtheCMIEstate-leveldatabase.ThedataonannualwagesperworkerhavebeentakenfromtheAnnualSurveyofIndustries(ASI)publishedbyCSO,GoI.ThedataonliteracyratesandpopulationdensityareworkedoutfromtheCensusofIndia.Thedataonnumberofhigher educational institutes in a state has been compiled from various issuesoftheEconomicSurveyoftheGoIandtheIndianBrandEquityFoundation(IBEF)Reports.ThedataontaxrevenueoftheIndianstateshavebeentakenfromvariousissuesoftheReporton‘StateFinances:AStudyofBudgets’publishedbytheReserveBankofIndia.Thesourcesfor state-level data on FDI stocks, which are measured in terms ofcumulativeFDIflows,aretheDIPPandCMIEstate-leveldatabase.


Table 5: Regional Inequality among India StatesStates Per

CapitaFDI

Flows(Rs)

Area(‘000

Sq Km)

PerCapitaNSDP

(Rs)

PopulationDensity (Personspersq.km)

Rail Route

Density (Kmper1000sq.

km)

LiteracyRate (Per

cent)

AnnualWages

per Worker

(Rs)

State’s OwnTaxRevenue

as per cent to NSDP

2010-11 2011 2010-11 2011 2008-09 2011 2009-10 2010-11A&NIsland 0.0 8.2 76,883 46 0.0 86.3 65,831 NaAndhraPradesh 679.5 275.0 62,912 308 18.9 67.7 61,007 8.9ArunachalPradesh 0.0 83.7 55,789 17 0.0 67.0 Na 2.6Assam 11.9 78.4 30,569 397 29.1 73.2 49,332 6.3Bihar 2.4 94.1 20,708 1,102 37.3 63.8 43,362 5.2Chhattisgarh 0.0 135.1 41,167 189 8.8 71.0 82,983 8.0Delhi 7,274.0 1.4 1,50,653 11,297 123.7 86.3 69,820 6.7Goa 9,424.7 3.7 1,68,572 394 18.7 87.4 1,26,788 7.3Gujarat 545.5 196.0 75,115 308 27.2 79.3 76,316 7.8Haryana 655.6 44.2 94,680 573 35.1 76.6 90,347 7.2HimachalPradesh 0.0 55.6 65,535 123 5.1 83.8 65,255 7.6Jammu&Kashmir 0.0 222.2 37,496 124 1.1 68.7 57,579 8.3Jharkhand 0.0 79.7 29,786 414 24.7 67.6 1,49,847 6.4Karnataka 1,003.3 191.7 60,946 319 15.7 75.6 83,219 10.5Kerala 50.0 38.8 71,434 859 27.0 93.9 54,994 8.9MadhyaPradesh 288.3 308.2 32,222 236 16.1 70.6 82,730 8.8Maharashtra 2,462.3 307.6 83,471 365 18.2 82.9 1,03,406 7.8Manipur 0.0 22.3 29,684 122 0.0 79.9 35,356 3.0Meghalaya 0.0 22.4 50,427 132 16.0 75.5 72,652 3.5Mizoram 0.0 21.0 48,591 52 0.1 91.6 Na 2.1Nagaland 0.0 16.5 52,643 119 0.8 80.1 19,880 2.0Orissa 16.2 155.7 40,412 269 15.3 73.5 91,921 6.3Puducherry 0.0 0.2 98,719 2,598 22.9 86.6 73,191 9.9Punjab 83.0 50.3 69,737 550 42.4 76.7 59,388 8.5Rajasthan 33.5 342.2 42,434 201 17.1 67.1 65,995 6.7Sikkim 0.0 7.1 81,159 86 30.9 82.2 58,900 4.6TamilNadu 847.7 130.0 72,993 555 31.6 80.3 68,422 10.0Tripura 0.0 10.4 44,965 350 14.4 87.8 22,267 3.8uttarPradesh 25.8 240.9 26,355 828 36.1 69.7 68,048 7.7uttarakhand 0.0 53.4 66,368 189 6.5 79.6 78,353 6.6WestBengal 46.6 88.7 48,536 1,029 43.8 77.1 71,626 4.9Note:Naindicatesnotavailable.Source:TheCensusofIndia2011;theCSO,GoI;theDIPP,GoI;theReserveBankofIndia;theCMIE;andtheauthor’sowncalculations.


SignificantregionalinequalityacrosstheIndianstatesmaybeobservedintermsofpercapitaFDIflowsandvariousgeographicandsocio-economicindicatorsconsideredinthestudy(Table5).Thelandarea across the states varies from 3,42,240 square km in the largeststateofRajasthantoonlyaround300squarekmintheunionterritoryof Puducherry. Population density in the national capital region ofDelhiisashighas11,297personspersquarekmascomparedtoonly17personspersquarekminthenortheasternhillstateofArunachalPradesh.PercapitaNSDPvariesbetweenRs.1,68,572inGoaandRs.20,708inBiharreflectingwideregionaldisparityinincome.Keralahasthehighestliteracyrateof94percent,whereasBiharhasaliteracyrateofonly64percent.WhileDelhihasthebestrailconnectivityinIndiafollowedbyWestBengal, there ishardlyanyrailwaynetworkin thenortheasternhillstatesofIndiaandtheAndamanandNicobarIsland.WageratesalsovarysubstantiallyacrossthestateswithannualwagesperworkerbeingthehighestinJharkhand(Rs.1,49,847)andthelowestinNagaland(Rs.19,880).Thereisalsosignificantdifferenceacrossthestatesintermsoftaxation.TheState’sowntaxrevenueasapercentofNSDPisthehighestforKarnatakaat10.55percentandthelowestforNagalandat2.03percent.

Theestimationmethodusedinthisstudyisfixedeffectpooledleast squares. Four model specifications have been considered inthis study and the estimation results are reported in Table 6. In allthemodels, thedependentvariable ispercapitaFDIflows to Indianstates.Allregionalcharacteristicsasexplainedintermsofexplanatoryvariablesarelaggedbyoneyear,giventhereasoningthatFDIflowsinparticular year is determined by the economic conditions prevailed in thepreviousyear.

The estimation results indicate that the signs of estimatedcoefficients formost of the explanatory variables are in accordancewith thea prioriexpectationwithonlyafewexceptions.Asregardsthemarket size, the coefficient of state per capitaNSDP is positiveandsignificantat1percentlevelinModel1.PercapitaNSDPhasanexplanatorypowerbothasanindicatorofregionalpurchasingpower


and the levelofeconomicdevelopment inastate.Thecoefficientofpopulation density is positive and significant at 1 per cent level inModel 1,Model 2 andModel 3.This clearly indicates that the FDIflowstoIndiaaremarketseekinginnature.Thisisinconfirmationwith

Table 6: Regression ResultsExplanatoryVariables

ModelSpecification1

ModelSpecification2

ModelSpecification3

ModelSpecification4

C -597.49(-0.58)

-3249.58(-1.98)

** -510.27(-0.13)

-5380.34(-4.59)

***

PCy 0.05(4.23)

***

PD 4.02(5.86)

*** 3.83(5.79)

*** 4.98(8.04)

***

MANP 0.14(2.69)

***

MINP 0.09(1.58)

SERP 0.11(7.02)

***

ROAD 0.01(0.02)

RAIL 128.86(2.4)

** 260.86(4.99)

***

WAGE -0.03(-2.54)

** -0.04(-3.97)

*** -0.01-0.99

LIT -21.11(-0.41)

EDuP 0.08(0.05)

TAX -294.01(-2.39)

** -286.44(-2.42)

** -296.15(-2.30)

**

STOCKP 0.27(17.83)

***

Totalpool(balanced) observations

310 310 310 310

R-squared 0.56 0.60 0.55 0.73

Adjusted R–squared

0.50 0.55 0.49 0.70

Note: Figuresintheparenthesesrepresenttherespectivetvalues.***,**and*denotesignificanceat1%,5%and10%level,respectively.


theresultsofearlierstudiesbyKumar(2002),Banga(2003),Goldar(2007), Nunnenkamp and Stracke (2007) and Dhingra and Sidhu(2011),wheremarketsizewasfoundtobeanimportantdeterminantofFDIflowstoIndia.

The estimation results confirm the hypothesis that economicstructure of a state reflected in terms of industrial orientation playsan important role in attracting FDI flows. For example, per capitamanufacturingoutput,whichisan indicatorof the levelof industrialactivityinastate,hasastrongpositiveinfluenceonFDIflows(Model3). This supports the view that new investments move to regionswithstrongindustriallinkages.Similarly,thecoefficientofpercapitaservicesoutputispositiveandsignificantat1percentlevelinModel2 indicating stateswhich have higher services sector activity attracthigher FDI flows. This is in confirmation with the trend observedin the sectoral distributionofFDIflows to India.The impact of percapitaminingoutputonFDIflowsis,however,insignificantthoughitscoefficientispositiveinModel4.

TheimpactofinfrastructureonFDIflowstoIndiaispositive.TherailwayconnectivityhasastrongpositiveimpactonFDIflowsinModel2andModel4.Thepositivecontributionofroadtransportation,however, lacks statistical significance in Model 1. The level ofinfrastructure was found to play an important role by some of theearlier studies, viz.,Kumar (2002),MukimandNunnenkamp (2010)andDhingraandSindhu(2011).

As regards labourconditions,wagesseem tohaveanegativeimpactonFDIflows,thecoefficientofannualwagesperworkerbeingsignificantinModel1andModel2.Thisisinlinewiththetheoreticalexpectation that FDI flows are attracted by lower cost of labour. Incomparison to cost of labour, the impact of quality of labour on FDI flowsseemstobelessimportant.Thevariablerepresentingpercapitanumber of higher educational institutes in a state has a positive impact onFDIflowsbutlacksstatisticalsignificance(Model3).Inthesamemodel,thecoefficientofliteracyrateisnegative,indicatingthelevelofbasiceducationinastatehaslittleroletoplayinattractingFDIflows.This reflects the fact that some of the stateswith very high literacy


rates viz.,Andaman&NicobarIslands,HimachalPradesh,Mizoram,Puducherry,SikkimandTripuradonotattractmuchFDIflows.

Thecoefficientofstate’sowntaxrevenueaspercentofNSDPisnegativeandsignificant inModel1,Model2andModel3,whichsupportstheargumentthatFDIpreferstateswithlowertaxrates.EarlierKumar(2002)foundthatacountry’sabilitytoattractFDIisaffectedby policy factors such as tax rates, investment incentives, performance requirements, etc.EmpiricalevidenceinthecontextoftheuSandtheEualso revealed that the regionswithhigher tax rates attract lowerFDIflows(Coughlin,TerzaandAromdee,1989;MercuriandVicarelli,2001).

OneperiodlaggedvalueofpercapitaFDIstockhasastrongpositiveimpactonFDIflows,indicatingtheimportanceofagglomerationeffects (Model4).This confirms thehypothesis that cumulativeFDIflowsinastatehasimportantdemonstrationeffectondecisionmakingofnewFDIentrants, i.e.,newforeign investment tends toenter intoareaswithalreadyhighlevelsofFDIflows.Thereare,however,cases,whereMNEshaveshowninvestmentinterestinstateswithlowerFDIpenetration,suchas,POSCOandArcelor-MittalinOrissaandBhatindarefinery (a joint venture of Hindustan Petroleum Corp (HPCL) andMittalEnergyInvestmentPteLtd)inPunjab.

Section VIPolicy Implications

FDI to India has increased significantly in the last decade.However, the growth inFDIflows has been accompanied by strongregional concentration.The findings of the study reveal thatmarketsize,agglomerationeffectsandsizeofmanufacturingandservicesbaseinastatehavesignificantpositiveimpactontheregionaldistributionofFDIflowsinIndia.Theimpactoftaxationandcostoflabourisnegative.While the impact of quality of labour is ambiguous, infrastructure,however,hasasignificantpositiveimpactonFDIflows.MininghasapositiveinfluenceonFDIflows,butlacksstatisticalsignificance.

Thepresenceofstrongagglomerationeffectindicatesthatthestates already rich inFDIflows tend to receivemoreof themwhichmakeitmoredifficultfortheotherstatestoattractfreshinvestments.In


viewofthisdifficulty,aconscious and coordinated effort at the national andthestategovernmentlevelswouldbeessentialtomakethelaggardstatesmoreattractivetoFDIflows.ThedirectmethodtoachievethisobjectivemaybetodesignthenationalFDIpolicyinsuchawaythatasizableportionofFDIflowstoIndiamoveintothelaggardstates.Theindirectwayis toprovideaboost to theoveralleconomyof the lessadvancedstates,withspecialthrustonthemanufacturing,servicesandthe infrastructure sectors so that they themselves become attractive to foreigninvestors.

First, as regards the direct method, it has been observed in the Chinese context, that after liberalising the FDI flows in the 1970s,ChinafacedwithsomewhatsimilarsortofexperiencelikeIndia.SincetheintroductionofChina’scoastalpreferenceopendoorpolicyin1978,theregionaldisparitybetweenthecoastalbeltandChina’sinteriorhadincreased(Luoet al2008).Thisresulted intoconcentrationofafewworldclassindustrialclusterslocatedinfivecoastalChineseprovincesat the expense of the Chinese hinterland. Subsequent FDI to Chinahasfavouredregions thatwereopenedearlierover thehinterland.Inview of this, one important policy changes enacted by the Chinesegovernmentwas to raise the entry requirements forFDI into coastalbelt designed to secure high value investments, while encouraginglabour intensive investments in the interior. Accordingly, since thelate1990s,mostMNEsinChinahavemadefundamentalchanges totheirbusinessstrategiesandoperationalpoliciestoadjusttochangesinpolicy,marketconditionsandtheregulatoryenvironment.InviewoftheChineseexperience,similarsetofpoliciesmaybeconsideredintheIndiancontexttodirectpartoftheFDIflowstothestates,whicharenotreceivingmuchofFDIflowsatpresent.

Second, as regards the indirect method, it has been observed thatsizeofthemanufacturingsectorhasasignificantpositiveimpactonFDIflows.Thisimpliesforeigninvestors’preferenceforstateswithastrongindustrialbase.Therefore,itisessentialforthelessindustriallydevelopedstatestocatchupwiththedevelopedonestoattractlargershareofFDIflows.TheNationalManufacturingPolicy(NMP),recentlyannouncedbytheGovernmentofIndiaisawelcomestepandmayhelp


in this direction if properlyimplemented.TheequityanddistributivejusticewouldbebestfulfilledifundertheNMP,theGovernmentgivestopprioritytothestateswithlowerindustrialbasetogivethemachanceofcatchingupwiththeothers.

Third,theservicessectorhasattractedalargeshareofFDIflowsto India in the recent period. The econometric analysis also revealsthatservicessectorhasasignificantpositiveimpactonFDIflows.Inaddition,growthoftheservicessectorcancreatemoreemploymentforskilled,semi-skilledandunskilledpeople.Ithasbeenobservedthatintherecentperiod,itistheIT/BPOserviceswhichhascreatedthelargestjobopportunityinIndiaandnotthemanufacturingindustries.Therefore,apart from providing a boost to the manufacturing sector, it is equally important to provide a boost to the services sector,which spans thevaluechainfromlow-endlocalisedservicestothemostsophisticatedglobally-competitiveintellectualpropertybasedservices.Accordingly,the manufacturing policy in India needs to be complemented by a compatibleservicespolicy.

Fourth,theimpactoftheminingsectoronFDIflowswasfoundtobelessimportantinthestudy.FDIinminingintherecentperiodhasconfrontedwithanumberofsocio-economicproblems.TheoperationsoftwoofthemegaFDIsteelprojects-POSCOIndiaandArcelorMittalhave been delayed due to seemingly intractable problems, mostly surroundingsocio-economicissueslikeacquisitionofland,forestandenvironmentclearances,rehabilitationandresettlementoftheproject-affectedpeople,NaxalitemovementsinChhattisgarh,Jharkhand,OrissaandWestBengal,non-allocationofadequatecaptivemines,andsupplyofrawmaterials.GiventhelargepotentialforFDIinminingduetotheCentralGovernment’sthrusttowardsdevelopmentoftheinfrastructuresector,andwithanumberoflargeFDIprojectsinmininginthepipeline(POSCOIndiasteelprojects inOrissaandKarnataka,ArcelorMittalsteelprojectsinOrissa,JharkhandandKarnataka,BP-Relianceoilandgasproject inAndhraPradesh,Lafaragecementproject inHimachalPradeshetc.), it is essential for the central and the state governments totakecoordinatedpolicyeffortstowardscreatingamorefavourablepolicy environment by simplifying the land acquisition procedure and reducing the delayintheapprovalmechanism.


Finally, of late, there has been a lot of debate about the merits and demerits in liberalising FDI in retail, insurance, pension and aviationsectorsinIndia.WiththeissueofFDIstillhot,itisimportantforthegovernmenttotakeduecareinformulatingitsFDIpoliciessoastoreducetheregionaldisparityratherthanaggravatingit.

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