FacilitatingDevelopment:TheRoleofBusinessGroupsRAYMONDFISMANColumbiaUniversity,Cambridge,MA,USAandTARUNKHANNA*HarvardBusinessSchool,Boston,MA,USASummary.Adeningcharacteristicofdevelopingcountriesistheinadequacyofbasicservicesnormally required to support organized economic activity. One way in which the private sector actstofacilitatedevelopment isthroughinvestmentsorchestratedbyagglomerationsof rmscalledbusinessgroups. Suchgroupsdominatethelandscapeof virtuallyall developingcountries. Ourstudy of plant location decisions in India shows that group-aliates are more likely to (protably)locateinless-developedstatesthanunaliatedrms; themagnitudeofthisgroup eectislargeand signicant. Furthermore, this result is stronger for more recent location decisions that are lesslikely to have been driven by political economy considerations. We suggest that this is because thescale and scope of groups, and the de facto property rights enforcement within groups inenvironments where legal enforcement is lacking, permit them to overcome some of the dicultiesthatimpairproductioninunderdevelopedregions.2004PublishedbyElsevierLtd.KeywordsAsia,India,businessgroups,locationdecisions1.INTRODUCTIONOneof thedeningcharacteristicsof devel-oping countries is the inadequacy of basicinputsnormallyrequiredtosupportorganizedeconomic activity. Physical infrastructureamenities are either inadequate or lackingentirely,whilesocialservicesareoftenseverelyunderfunded, hamperingthehiringandmain-tenanceof aproductive workforce. Owingtosuchinadequacyofbasicservices,shortagesinancillaryindustriesthatprovideotheressentialinputsarealsocommon. Thesedicultiesareoftensoextremethatitisamysteryhowany-thinggetsdoneat all. Inthispaper, wedrawattentiontotheroleplayedbyagglomerationsof rms inthe private sector, calledbusinessgroups, in ameliorating the costs of suchshortages. Sincebusinessgroupsareapromi-nent feature of the landscape of most, if not all,developingcountries, their role infacilitatingdevelopmentdeservesattention.Economictheorypredictsthatprivateenter-priseshouldstepintoprovidethesebasicser-vices where it is economically ecient to do so.1In most countries where such failures take place,however, private rms are constrained frommakingthisprovision. Inmanycases, govern-ment monopolies control industries such as tele-communications, health care, transportation,www.elsevier.com/locate/worlddevWorldDevelopmentVol.32,No.4,pp.609628,20042004PublishedbyElsevierLtd.PrintedinGreatBritain0305-750X/$-seefrontmatterdoi:10.1016/j.worlddev.2003.08.012*We thank Bharat Anand, Eli Berman, Richard Caves,Alex Dyck, Kei Hirano, Devesh Kapur, JonathanMorduch, Sendhil Mullainathan, AvivNevo, JanRiv-kin, Nicolaj Siggelkow, Steve Spear, Peter Timmer,Catherine Wolfram, Yishay Yafeh, seminar participantsatHIID/MITDevelopmentSeminar,HarvardBusinessSchoolandtheUniversityofWesternOntario,andtheeditor anda referee for many helpful comments anddiscussions. ManythankstoMargaret Schotteforherdiligent andtireless researchassistance, andtoHilahGeer for help with the data. The usual disclaimerapplies.Finalrevisionaccepted:27August2003.609and power; competition from the private sectoris prohibited. Inrecent years, theseindustrieshavebeenopenedtocompetitioninahost ofcountries. Nonetheless, private enterprise hasmade little headway, as government bureau-cracy and regulation often make entry tooexpensive and time-consuming, and weakpropertyrightsandcontract enforcement pre-ventrmsfromcapturingeconomicrentsfromthe goods andservices theyprovide. Finally,government reputationsmay alsoplay aroleoften, regimes are unstable in developingcountries, and the nationalization of infra-structure assets may discourage rms frommakinginvestmentsinsuchprojects.Much time and eort has gone into imputingthecostsof theseinecienciesandevaluatingthe productivity of various infrastructuregoods.2Relativelylittlehas beendone, how-ever,to understand how rmsbehavegiven theconstraints of operating in underservicedregions. Understanding how rms react to suchshortages is essential todetermining the truecosts andbenets of infrastructure provision,andis likelytobeuseful ininformingfuturedevelopmentpolicy.The reality is that rms do exercise choices inthe manner in which they deal with theseshortages. Some rms simply make do with theservices that are publicly provided, acceptinglowproductivityandpoor product qualityasthepriceof doingbusiness inanunderdevel-oped area. One obvious alternative is forcompaniestosupplyinputsfortheirownpri-vate use. Ahost of companies inIndiahaveinvested in V-Sat (very small aperture terminal)connectionstobypassubiquitouscommunica-tions bottlenecks. In Nigeria, where publictelecommunications aresimilarlyunreliable, a1998 World Bank Study found that many rmshadpurchasedradioequipmentforcommuni-cations. The study also found that 92%ofNigerian manufacturers and 64% of Indonesianmanufacturers owned their own electricitygenerators(Anas&Lee, 1998). Firmsinless-developedregionsmaysimilarlyberequiredtoensure their ownsupplies of physical inputs.Forexample, inastudyofIndianautomobileparts manufacturers, it has been found thatplants in less-developed regions keep higherinventories, since most inputs cannot besourcedlocally(Gulyani,1999).Wecontendthat thebusiness groups3thatdominatethelandscapeof virtuallyall devel-oping countries provide an organizationalstructure that is better suitedtodealingwiththe poor availabilityof basic inputs andser-vices. Suchgroupsarecomprisedof adiverseset of businesses, often initiated by a singlefamily, and bound together by equity cross-ownership and common board membership.Group-aliates oftenshareacommonbrandidentity (e.g., SalimGroupinIndonesia, theTataGroupinIndia,SamsunginKorea,etc.),drawona commonlabor pool, andrely oneachother fornancing. Thecommonfamilybondthat oftenruns throughgroup-aliatesacts as a social mechanismthat reduces thelikelihood of reneging of contracts (see, forexample, Granovetter, 1994). Thesecharacter-istics mayput groups inabetter positiontodeal withthe failures innancial, labor, andproduct markets that are ultimately responsiblefor the diculties facing producers in less-developedregions. Asaresult, wemayexpectgroup-aliated rms to be relatively morelikely to locate in such regions. For example, inthe case of infrastructure provision, groupsmay be able to take advantage of economies ofscalebyco-locatinganumber of group-ali-ated plants and sharing the costs ofinfrastructure services. Failures in labor andnancial markets causedbypoor informationows may be similarly mitigated by groupsthroughtheinternalizationofthesemarkets.Inprinciple, manyof theadvantagesaccru-ingtogroup-aliatescanplausiblybearguedto accrue to multiple plants within a single(presumablylarge) rm. It isnot immediatelyconceptually clear whether groups or largermsarebetterabletoorchestrateinvestmentsinunderdevelopedareas.Ontheonehand,theparts of armaremorecloselytiedtogether(by 100% ownership) than are the aliates of agroup, making coordinationmore dicult inthe latter case. On the other hand, the typicallyquiteunrelatedbusinessesinagroupprobablyensure a smoother overall internal cashowthanthat whichis available toarmwhoseactivitiesareconcentratedinasinglebusiness.This maybe of value inthe face of severelyimperfect external capital markets, and mayallowgroups to undertake ambitious invest-ments that a rmneeding access to costlyexternalcapitalmightrationallyforego.In any event, even after allowing for thepossibility that large rms might facilitatelocationinunderdevelopedregions, weexpectthat diversiedgroups may be animportantmeans by which industrial activity is brought toregionswheregovernmentshavefailedtopro-vide basic services. That is, groups may provideWORLDDEVELOPMENT 610one mechanism for providing a partial solutiontooneof thefundamental issuesineconomicdevelopment, withsignicant implications forgovernmentpoliciesindevelopingcountries.Inthispaper, weattempttoinfertheextenttowhichgroup-aliates arebetter positionedto deal with the diculties associated withunderdeveloped areas by looking at rms plantlocationdecisionsinIndia.Consistentwiththeabove arguments, we nd group-aliatedplantsweremorelikelytolocateinless-devel-opedregionsduring197595.Furthermore,wend that this eect is stronger for youngerplants, where the location decision was lesslikely havebeen the result of political economyconsiderations driven by Indias industriallicensing regime. Furthermore, we nd thatgroup rms are more protable (or at leastequally protable) in low-infrastructureregions,relativetounaliatedrms.Ourempiricalestimationfocusesonasinglecountrybecausebothfeaturesthatwewishtoexplorelevel of development and businessgroupsare dicult to measure, makingattentiontodetail important. Membershipofbusiness groups is typically quite secretive.Often rms are members of more than onegroup, raising the potential for specicationerror. There is no universal denition of agroup that adequately captures what itmeans tobe a group-aliate across dierentcountries. Thesefactorsargueagainstacross-countrystudy.India has a number of characteristics thatmake it particularly well suited for such astudy. Alarge proportion of Indian group-aliates are publicly traded, and rms arealiatedwithonlyone group.4Further, thelarge number of groups in India facilitatesstatistical analysis. There is also much variationin the level of development across the regions ofthe country, whichallows us tolookat rmdecisions as a function of development.5Whilethe variance in the level of development is high,theaverage is low.Thisis important,as weareprimarily interested in studying rm reaction todiculties stemming from underdevelopment.6Finally, we note that this studylies at theintersectionof twovast literatures. First, it islinkedtothe literature onindustrial businessgroups (Khanna, 2000) andthe role of suchgroupsinthedevelopmentprocess. Aswearelookingat their particular roleinattenuatinginfrastructure shortages, this paper also tiesinto the broader literature on infrastructuredevelopmentgenerally,anditsroleinpromot-ingeconomicgrowth(seeGramlich, 1994, foranearlierreview, andFernald, 1999, formorerecentworkinthisarea).Weproceedasfollows: section2introducesand describes our data; in section 3, we look atwhy rms might locate in low-infrastructurestates; we describe our main results in section 4;section 5 discusses alternative explanations;section6provides some evidence frominter-viewssupportiveoftheroleofgroupsinfacil-itatingdevelopment;section7concludes.2.DATA(a) Firm-leveldatasourcesThe data are obtained primarily from apublicly available database maintained byCMIE, Centre for Monitoring the IndianEconomy. CMIE is a privately run, 20-year old,Bombay-basedrmthat maintains databasesonprivateandpublicsectoreconomicactivityin India. These databases have become thestandardsourcesof informationonmicroeco-nomic and macroeconomic activity, and arecommonly used by public and private-sectorrms in India, international developmentbanks, andthemedia.7Whilethereareotherpossibledatasources,CMIEdataarethemostreliableandcomprehensive.The database8fromwhich we draw ourinformation contains data similar to thosecontained in Compustat, CRSP and MeadData Centrals Nexis databases, though ourdata are not nearly as comprehensive ordetailed. The database has computerizedinformationdrawnfromannual reports, otherregulatorylings, andpressreleasesofseveralthousand rms operating in India, as wellasdailystockpricedataforstockstradedontheBombayStockExchange. Theaccountinginformationis available fromthe mid-1980s,withcoverageimprovinggreatlyin1989. Theyearforwhichthereisthebestcoverageintheversion of the database to which we have accessis1993.Coveragefor1994and1995issparser,due todelayedrelease of informationbytherms,anddelaysinupdatingthedatabase.Wetherefore use 1993 data in the analyses thatfollow. Themajorityof rms inthedatabaseare public rms (95%). Of these, approxi-matelyhalf are tradedonthe BombayStockExchange, with the remainder traded on severaloftheotherstockexchangesinthecountry.FACILITATINGDEVELOPMENT 611(b) BusinessgroupclassicationThe identication of business groups is ofparticular interest for this study. Betweentheyears1989and1995, thedatabaseidentiesarange of business groups, fromthe smallesttwo-rm groups to the largest Tata Group. Fortheanalysesinthispaper, weadoptthedata-bases classication of rms into groups.9Unlike inavarietyof countries (Goto, 1982;Strachan, 1976), rms are members of only onegroup.Further,thereisvirtuallynomovementofrmsacrossgroups.10KhannaandPalepu(2000) havecarefullyexaminedthequalityoftheCMIEgroupclassication,andfoundthatitpassesanumberofrealitychecks.Our initial sample, consisting of the entirecross-sectionof rms inthe CMIEdatabase,contained4,230rms;manyofthesearemulti-plant rms, yielding a total sample of 5,653observations on location decisions. Since wewereunabletoobtainadequateaggregatedatafor Union Territories (see below), plants locatedintheseregions weredropped(452plant-levelobservations). We also removed government-runrms and foreign-owned rms from thesample,because we feltthat theywere sucientlydier-ent fromother Indianrms as todistort ourresults (217 government-run or joint-sector rmsand90foreignrms dropped).11Firms werethenremovedthat lackedobservations onthevariablesusedinourbasicregression,i.e.,sales(1027),12age (23), and plant location (890).Duringinterviews withbothexecutives andgovernmentocialsinBombayandDelhi, wefoundthere tobe a consensus that, prior to1975, plant location decisions were madeentirely by government bureaucrats rather thanbusiness leaders. This was because a govern-ment licensewas requiredfor anynewenter-prise, and the license was generally givenconditional onthe rmlocatinginthe homestateofthegovernmentocial whomadethelicensing decision(or inthe state of anotherocial towhomapolitical favor was owed).After 1975, a series of reforms andshifts ingovernment steadily eroded the ability ofbureaucrats to control the location of rmsplants. Thedestabilizationofgovernmentthatcame with the Congress Partys fall from powerin1978wasalsorepeatedlycitedassignicant.We outline inthe appendix the evolutionofIndianindustrial reformthat graduallyledtomoreprot-basedplantlocationdecisions. Asindicated there, 1991 is a particularly importantyearinthederegulationprocess.Our claimthat delicensing was a gradualprocess that occurred throughout the post-1975periodisbolsteredbyanexaminationofthenumberoflicensesissued, byyear, follow-ing1974. Whileindustrial productioninIndiagrewat a fairly steady pace of 6%or morethroughout the 1980s, we observe a markeddecline inthe numberoflicensesissued,simplybecause fewer and fewer businesses requiredlicenses to undertake new ventures. In addition,the number of licenses issued (scaled by level ofindustrial production)decreasedfarmorerap-idly in less-developed regions (according todevelopment as dened below), further sug-gestingthat,whiletheIndiangovernmentmayhavebeenforcingrms tolocateinunderde-velopedregionsearlyon, thiseectwasatten-uatedovertime.Recent econometric evidence bolsters ourclaimof a gradual evolution of delicensing.DeLong(2001)showsthatthestructuralbreakinIndiasgrowthrateoccursinresponsetoawave of reformsincluding delicensingin themid-1980s,ratherthaninresponsetothe1991reforms. Indeed, an architect of the Indian 1991deregulationhasrecentlycharacterizedIndiasoverall reformasexhibitingaparticularformofgradualism(Ahluwalia,2002a).Fortheaforementionedreasons, werestrictour sample to rm location decisions that cameafter1975.Weuse1975asacut-odate,asitallows us totakeanextremelyagnostic/unin-formed perspective on the rate of reforms;however, we expect a larger/more signicanteect among younger rms that had greaterfreedominchoosinglocation.13Theexistenceof these gradual reforms provide us withanadditional dimension of variation to furtherexamine our primary hypothesis on plantlocation below. Our best proxy forthe dates oflocation decisions is rm incorporation date, sowe drop all rms with incorporation dates priorto1975.Thisyieldsanalsampleof957rmsand1,193plant-levelobservations.(c) MeasuringdevelopmentIn assessing a states level of development, weattemptedto aggregate a number of distinctelements that wefelt wereimportant for rmlocation decisions. Moreover, we hopedthatusing an aggregate score would allow us to ironoutsomeoftheidiosyncraticcomponentsthatare present in each individual measure. Thefour basic categories included were powergeneration, telecommunications, transporta-WORLDDEVELOPMENT 612tion, andsocial services. Where possible, wetried to obtain proxies that reected the quality,ratherthanthequantity, ofservicesavailable.We also avoided per unit area measures,using instead per capita statistics, since vastundevelopedexpansescouldseriouslybiastheformer type. Telecommunications and trans-portationdataweretakenfromCMIEsInfra-structure in India (Economist IntelligenceService,1996),powergenerationstatisticsweredrawn from CMIEs Indias Energy Sector(Economist Intelligence Service, 1995), andinformationonsocialservicesweretakenfromthe Indian governments Statistical Abstract(1992).14We used three basic measures of energyinfrastructure: decit in production, i.e., thepercentage bywhichdemandexceeds supply;percentagelossin transmission; and generatingcapacitypercapita. All of thesefactorsaectthe likelihoodand frequency of brownouts andblackouts. Moreover, percentagelossintrans-mission is correlatedwith the consistency ofvoltage supplied (i.e., greater losses implyhighervarianceinvoltage),whichisofseriousconcernforrmsusingadvancedtechnologies(Dugger,1999).Ourdataontelecommunicationsandtrans-portationaresomewhatsparser. Toassesstheaccessibility of communications, we used directphone lines per capita and telephone exchangespercapita.15Fortransportation, weusedthepercentageofroadsthataresurfaced.16Finally, for social services, we lookat thepublicprovisionof educationandhealthcare.For education, we use the percentage ofteacherswithformal training, as well astotalexpenditure on education per capita. Ourhealthcareproxiesarehospital bedspercapitaandhealthcareworkerspercapita.Weconverteachofthesevaluesintoanor-malizedscoreusingthefollowing:Tsi Vsi ViVi;where Vsiisthemeasureofamenity iinstate s,andViisthemeanoverstatesforamenityoftype i. This transformation preserves the degreeof dispersion associated with each measure,while normalizing its mean to zero.17The scorefor each type of service is a simple average of itscomponents; our overall measure of develop-ment (DEV) is the sumof these four scores,scaledtomakethelowest scoreequal tozeroandthehighestequal toone. Table1summa-rizesourscoresbystate.Notethatwhilethereare25statesinIndia, welistonly16; therestare omitted from this table because there are normsintheCMIEdatawithplantslocatedinthese states. In addition, thereare two kinds ofadministrative entities in Indiastates andunion territories. The fundamental dierencebetweenthetwoisthatterritoriesarecentrallyadministered, while the states have a greaterdegree of political autonomy. Unfortunately,the data available for territories are incomplete;for this reason, we were unable to includeplantslocatedinunionterritoriesinoursam-ple.Table 1. NormalizedinfrastructurescoresPOWER TELE ROADS SOCIAL DEVAssam )0.23 )1.01 )1.61 )0.47 0.00Bihar 0.39 )1.28 )0.68 )0.46 0.18Gujarat 0.25 1.11 1.51 0.91 1.00Haryana 0.01 0.46 1.80 )0.12 0.77HimachalPradesh )0.43 1.79 )0.24 0.29 0.66Jammu&Kashmir )0.19 )0.37 0.32 0.21 0.46Karnataka 0.56 0.97 0.26 )0.19 0.69Kerala )0.36 0.67 )1.44 0.59 0.39MadhyaPradesh )0.21 )0.42 0.28 )0.53 0.34Maharashtra 0.41 )0.09 1.02 0.71 0.76Orissa )0.11 )0.75 )1.79 )0.44 0.03Punjab 0.72 1.08 1.19 0.36 0.94Rajasthan )0.54 )0.32 )0.06 )0.27 0.30TamilNadu )0.04 0.04 0.65 )0.20 0.53UttarPradesh )0.30 )0.96 )0.22 )0.41 0.20WestBengal )0.29 )1.27 )0.19 )0.37 0.17FACILITATINGDEVELOPMENT 613Wemaybeconcernedabouttheaccuracyofour measurement of thelevel of developmentby state. We do a reality check based onTable 1. Our classications accordquite wellwithcommonconceptions about the level ofdevelopmentbystate. Moreover, ourdevelop-ment score is highly correlated with othermeasures of Indian states development.Perhaps themost relevant alternativescoreisan annual ranking of the Best States to InvestIn publishedbyoneof Indiaspremierbusi-nessnewsmagazines: BusinessToday. Thecor-relationbetweenthis rankordering of statesand our development score is 0.74, and weobtained qualitatively identical results usingthismeasure.18SummarystatisticsofourvariablesfromtheCMIEdatabase are listed in Table 2, Panel A; wealsoinclude statistics brokendownby groupaliation in Table 2, Panel B. Note that the sizedistribution of rms is very dierent for thegroup and nongroup samples. It will therefore beparticularly importantto allowforalotofex-ibility in the sizelocation relationship, which welookat insection6below. Notealsothat themeanofDEVislowerforgroup-aliatesthanfor unaliated rms (with the dierence inmeans signicant at the 5%level), consistent withthe hypothesis that groups should locate more inlow-infrastructure regions.3.WHYLOCATEINALOW-INFRASTRUCTUREREGION?(a) AsimpleillustrativemodelConsider a continuum of identical rms withmeasure1inaneconomywithtworegions,Aand B, and two factors of production, I(infrastructure) and L (labor). Firmsdecide which region to locate in, and how muchlabor tohire. Infrastructure varies across theregions, is givenexogenously, andenters intoproductionas:f L; IX, where X AorB. Allrms produce an identical product, whose priceis dened by Q Dp, and hire laborfrom (immobile) labor pools according toLX SwX, where wXis the wage in eachregion. Assume the f2; f12> 0, and that IA> IB,i.e., regionAishigh-infrastructure. If rmsareprice-takersinproductandlabormarkets,eachrmfaces:Table 2. Summarystatistics(PanelA).SummarystatisticsbyMEMB(PanelB)Mean Std.dev. Min Max #ofObs.PanelAMEMB 0.39 0.49 0.00 1.00 957ASSETS 50.79 155.62 0.34 3,390.11 957SALES 41.65 114.18 0.01 2,984.49 957AGE 12.41 4.38 2.00 21.00 957DEV 0.56 0.30 0 1 1,193PanelBMEMB0ASSETS 23.44 37.21 1.08 530.95 579SALES 22.88 33.26 0.04 306.57 579AGE 12.08 4.33 2.00 21.00 579DEV 0.58 0.30 0 1 694MEMB1ASSETS 92.69 237.44 0.34 3,390.11 378SALES 70.40 173.18 0.01 2,984.49 378AGE 12.93 4.42 3.00 21.00 378DEV 0.53 0.29 0 1 499Variable denitions: MEMBdummy variable denoting group membership; ASSETSBook value of assets, 1993;SALESValueofsales,1993;AGEYearssinceincorporation;DEVLevelofdevelopmentofthestate(s)wherethermsplantsarelocated.Note: the number of observations for DEV is larger since the level of observation is at the plant level, while all othervariablesarerm-level.WORLDDEVELOPMENT 614P MaxLpf L; IX wXL:Sincermsmaylocateineitherregion, wegetthearbitragecondition:pf LA; IA wALA pf LB; IB wBLB;where wiis the market-clearing wage. Sincepf L; IA > pf L; IB for all L, we must havewA> wB, i.e., in order for markets to clear,labor must beless expensiveinthelow-infra-structure region to compensate companieslocating there for the higher costs of productionresultingfromlow-infrastructure.Now, imagine that thereare somerms oftype g (withproductionfunctiong) that arebetter able todeal withinfrastructure short-ages, i.e., gL; IB > f L; IB; g1L; IB > f1L; IB(thoughg12is still positive), while gL; IA f L; IA. Then, inequilibrium, we must haveoneofthefollowingconditionshold:pf LA; IA wALA pf LB; IB wBLB; ipgLA; IA wALA pgLB; IB wBLB: iiIf a rmof type f is making the marginallocationdecision, then(i)will hold; otherwise,(ii)holds.Intherstcase,alltype grmswilllocateinregion B,withtype f rmsbeingsplitbetween the two regions such that marketsclear; type grms earn strictly higher prots inthisscenario.Inthelattercase,type f rmsalllocateinregionA, whiletypegrmsaredis-tributed so as to make markets clear; sincef g in region A, all rms earn the sameprots. Thus, inbothcases, thereis ahigherratiooftype grmsinregion BthaninregionA, andthese rms weaklyearnhigher protsthan type f rms. In all cases, we havewA< wB.The basic point is that rms that are best ableto deal with infrastructure shortages will bemore likely to locate in low-infrastructureregions,asitallowsthemtotakeadvantageofcheapfactorsofproductionthatariseinequi-libriuminorderformarketstoclear.(b) EvidenceThe data provide some casual evidence ofthe costs and benets of locating in a low-infrastructure region. Firms in states withDEV< 0:5 benet from lower wage rates(about 30% less, on average, than wage rates inmoredevelopedstates) andtaxrates(averageof8.2%indeveloped,vs. 7.0%inundevelopedstates), as well as a higher average rate ofgovernment scal benets (0.8% vs. 0.6%).19All of these dierences are signicant at 10% orhigher.204.RESULTS(a) Thebasicresult:dierencesinlocationalchoiceIf it is true that group-aliated rms arebetterabletominimizethecostsoflocatinginless-developedareaswewouldexpect that, allelseequal,thesermswouldbemorelikelytolocate their plants in states with lower values ofDEV. The basic summary statistics suggest thatthis theory is promising: the mean value ofDEVforgrouprmsis0.531,whileitis0.582for nongroup rms.21Figure 1 shows therelationship between a states developmentscore, and the percentage of plants in that statethat are group-aliatedthe patternis quitestriking.OurbasicregressionmodelisgivenbyDEVij a n MEMBi b Xi eij gI;1whereXiis avector of covariates, i is armindex, j indexes the rms plants, andgis avector of industry dummies, indexed by I. DEVijcaptures the level of development of the state inwhichplantjbelongingtormi islocated.22Weallowtheerror structuretobecorrelatedacrossplant-levelobservationswithinthesamerm(i.e.,coveij; eik 6 0).23Controls in the regressions includelog(SALES) as a proxy for rm size, and AGE.Not surprisingly, there is a negative correlationbetweenAGEandDEV: almost bydenition,industry has been later in coming to less-developedregions, soweexpect rmsinsuchregions to be younger on average. The omissionofAGEmaybeparticularlyserious, sinceitisnegatively correlated with MEMB; that is,group-aliatedrmsareolderthannongrouprms. The preceding pair of relationships couldresult inabias awayfromzeroonMEMBscoecientifAGEwereexcluded.The expected sign of the coecient onlog(SALES) is less clearwe might weaklyexpect larger rms to be more likely to locate inless-developed regions for the same reasonsthat larger groups choose to locate in theseareas. Larger rms, however, are generallymore capital intensive, so complementaritiesFACILITATINGDEVELOPMENT 615between public and private capital may result ina higher proportion of large rms in moredeveloped regions. Since group aliation isquite highly correlated with log(SALES), it willbe crucial to control carefully for size eects; wereturntotheseissueslaterinsection6.Finally, we include regressions both with andwithout industry(at the two-digit ISIC-level)xed-eects. It is certainly true that group-aliatedrmsmaydierfromnongrouprmsintheirdistributionacrossindustries. Itisnotclear, however, that we want to absorb theeects resulting fromthese dierential distri-butions. Consider the following: if there arecertainindustries that are more amenable tobeinglocatedinless-developedregions, itmaybe that group-aliates will be more common intheseindustriesbecausetheyarebetterabletolocate inunderdevelopedareas. By includingindustrydummies,thisgroupindustryeectislost.TheresultsfromtheseregressionsarelistedinTable3. Thecoecient onMEMBrangesfrom )0.05to )0.04; thisisbasicallythesameastheunconditionaldierencereportedabove.This dierence is statistically signicant at leastat the 5%level inall regressions. Hence, onaverage, group-aliatedrmsaremorelikely,all else equal, to locate in less-developedregions.Interpretingtheeconomicsignicanceofthecoecient on MEMB is not straightforward, soto illustrate its size, consider the followingthoughtexperiment:armisdecidingwhethertolocateitsplantinanaverageunderdevel-opedregion, say, Rajasthan(DEV 0:30), oranaverage developedregion, say, Haryana(DEV 0:77). Facedwithsuchachoice, ourresults suggest that, all else equal, a group-aliatedrmwouldbemorethan10%pointsmorelikelytolocateinRajasthanthananon-group rm(0.53 vs. 0.425). The dierence,giventhemultitudeoffactorsthatgoesintoarmslocationaldecision,isverylarge.As outlined in section 2, reforms in Indiathroughout the sample period graduallyincreasedthe abilityof rms tochoose plantlocations without government interference.Thiswouldhaveresultedinashiftawayfrompoliticaleconomy-basedlocationalchoicesandtoward location decisions dictated by economiceciency. To the extent that groups were betterable to locate eciently in less-developedregions,weexpect thecoecientonMEMBtobelarger for younger rms. Weexaminethisprediction empirically by adding the interactiontermMEMB AGEtoourbasicspecication.Theseresultsarereportedincolumns(4) and(5) of Table 3. The coecient on MEMB is nowfarhigher()0.095); thismaybeinterpretedasthe eect of group membership on new rms(i.e., AGE 0, incorporation date of 1993). Weconclude that younger group-aliates are morelikelytolocateinless-developedregions, con-sistent with our thesis. Since 1991 is a landmarkderegulationdate, werunasimilarregression,0.150.30.450.60.750 0.2 0.4 0.6 0.8 1DEVMean(MEMB)Figure 1.Relationshipbetweenstatesdevelopmentscoreandthepercentageofgroup-aliatedplantsinthestate.WORLDDEVELOPMENT 616proxying for age using a dummy D1991,denedas one if incorporationdate is onorafter1991. Similarresults, reportedincolumn(6) ensue: thecoecient onMEMBis)0.04,andthe coecient onthe interactiontermis)0.08(thoughit is not signicant at conven-tionallevels).(b) NonlinearitiesinthesaleslocationrelationshipIn theory, our regressions control for sizeeectsbyincludinglog(SALES)asaregressor.Note that the distributionof SALESis sub-stantially dierent for group vs. nongroup rms(seeTable2, Panel B). Our specications arereasonableaslongastherelationshipbetweenSALESandDEVdoesnotdeviatetoodrasti-callyfromtheloglinearspecicationsthatweuse.Ifthisisnotso,however,ourresultsmaybedrivensimplybyourassumptionsof func-tionalform.Toexaminethisfurther,werunasplineregression, lettingtheslopeonSALESchange every 20 units, i.e., we use the followingmodel:DEVi a bXi cMEMBiX5m1fMISALESiP20 M SALESi ei;whereISALES Px 0; if SALES< x;1; if SALES Px:

Inthisspecication, thecoecientonMEMBis almost identical to that obtained in ouroriginalregressions.(c) UsingperformanceasadependentvariableTheabovemodels assumeahighdegreeofeconomic rationality in the location deci-sions of rms. But, these decisions likelyinvolve a lot of unobservable, noneconomic,considerations. Forexample, manygroupsarefamily,ratherthanprofessionally,managed;insuchcases, close proximitytofamilial homestends to dominate locational choices. More-over,issuesofendogeneityarisewithanyrm-level characteristics that we might use asTable 3. Theeectofgroupaliationonlocationalchoice(1) (2) (3) (4) (5) (6) (7)DependentVar:DEVMEMB )0.051)0.044)0.043)0.040)0.095)0.115)0.042(0.018)a(0.019) (0.020) (0.018) (0.039) (0.019) (0.016)log(SALES) )0.008 )0.006 )0.004 )0.004 )0.001 )0.007(0.006) (0.007) (0.006) (0.012) (0.011) (0.013)AGE )0.002 )0.002 )0.005)0.001(0.002) (0.002) (0.003) (0.003)MEMB AGE 0.005(0.003)D1991 )0.109(0.036)MEMB D1991 0.079(0.063)CAPIN 0.012(0.078)MEMB CAPIN )0.24(0.11)Constant 0.583 0.603 0.618 0.053 0.607 0.047 0.62(0.011) (0.019) (0.027) (0.008) (0.032) (0.007) (0.11)IndustryFE No No No Yes Yes Yes NoR20.007 0.009 0.009 0.077 0.012 0.076 0.018Observations 1,193 1,193 1,193 1,193 1,193 1,193 1,193aHeteroskedasticity-correctedstandarderrorsinparentheses,allowingforrm-levelclustering.Signicantat10%.Signicantat5%.Signicantat1%.FACILITATINGDEVELOPMENT 617regressors. Using locational choice as thedependentvariablemaythusintroducealotofunnecessarynoiseandpotential biases. Whileany observational study encounters suchproblems, they would be reduced here bylooking at a purer economic outcome as thedependentvariable. Asourmodel insection3(aswell ascommonsense) suggests, if group-aliates are better able to minimize the costs oflocating in less-developed regions, then theyshould be (weakly) more protable in theseregionsrelativetonongrouprms.It mayalsobe the case that, while groupsfacilitatedevelopment bylocatinginunderde-velopedregions, theydonot dosoeciently.For example, it may be that, in the extreme, thecompany towns describedbelow(Pirojshana-gar, Tatanagar, Birlagrametc.) arenot mani-festations of any particular eciency, butrather monuments to the family that con-structed them. By looking at economic out-comes, weseektoverify, at aminimum, thatgroups are at least no worse than nongroups inbringing development to underdevelopedregions.Weuseanapproximationof TobinsQ, aswell asaccountingprots(returnonassets, orROA) as our measures of performance.24There are disadvantages associatedwitheachmeasure of protability, which is why we reportboth sets of results. By using ROA, we arerelyingonaccountingdata, whichmaynotbereliableandarecertainlyverynoisy.Therearealsodiculties withusingTobins Q: due todata limitations, we may only use a coarseapproximationofthetruevalueof Q;also,thelackof ecient equitymarkets adds alot ofnoisetoanymeasurebasedonstockprice.In spite of these problems, the raw data seemto bear out our hypothesismean ROAishigherforgrouprmsinundevelopedregions(0.091 vs. 0.087 in developed regions), andmean Qdiersonlyslightly(1.56indevelopedvs. 1.57inundeveloped). Fornongrouprms,however,bothmeanROAand Qarehigherindeveloped states (0.096 vs. 0.093 for ROA; 1.48vs. 1.35 for Q). Note, that neither of thesedierence indierences (i.e., the dierencebetweengroupsandnongroups, inthechangeinperformanceinmovingfromdevelopedtoundevelopedstates)isstatisticallysignicant.Multivariate analyses also weakly supportthe hypothesis that group-aliated rms arebetter able to protably locate in low-infra-structure regions than nongroup rms. Ourbasicregressionsmodelisgivenby:ROAi a b1 MEMBi b2 DEVib3 MEMBi DEVi b4 XigI eij:AsimilarsetofregressionswererunwithQas the dependent variable. The coecient ontheMEMB*DEVinteractiontermis negativein both the ROAandQregressions. In theROAregression, the coecient on MEMB*DEV equals )0.023; the mean of ROA isapproximately0.095, implyingthat ashift tolocating in a low-infrastructure region (say,DEV 0:25)fromahighinfrastructureregion(say, DEV 0:75) will reduce the ROAof anongrouprmby about 12%relative totheeectofsuchashiftonagroup-aliatedrm.The comparable value when Q is the dependentvariable is also 12%. While these eects are notsignicant at conventional levels (t-statisticswereintherangeof1.21.5), weinterprettheresultsasindicatingthatgroupsareatleastnoless ecient than nongroups in bringingindustrialactivitytounderdevelopedregions.5.ALTERNATIVEEXPLANATIONS(a) RespondingtoregulatorydistortionsandgovernmentbureaucracyRegulatory distortions relevant to plantlocation decisions decreased following 1975 butdid not completely cease until 1991. A dierentexplanationfor our results havingtodowithresponses toexisting regulations canthus beconstructed. It may be that groups wererestrictedfromplant expansions becauseof asocietal bias against large organizations,spawned by a socialist regime in India (andenshrined in the Monopoly and RestrictiveTrade Practices ActMRTP). If the MRTPwas relaxed for groups only for plants inunderdeveloped regions, and if nongroupsfacednosuchconstraints, wemight expect toseeexactlythepatternsthatwedond.The results of two tests lead us to believe thatthis explanation cannot completely account forthepatternsinthedata.First,wereferbacktothe results in columns (4)(6) in Table 3. If thisalternativeexplanationis correct, andgroupswere forced into underdeveloped regions purelybecause that was the only place they wereallowedtoinvest,weshouldseetheeectthatweidentifybecomeweakerastheageofrmsdecreases. Ourresultsontheinteractionterm,WORLDDEVELOPMENT 618MEMB*AGE, go in the opposite direction,undermining the credibility of this politicaleconomy-basedexplanation.Thesecondtestexploitsinterindustryvaria-tion in capital intensity. Under our positedexplanation,grouplocationinunderdevelopedregions should be especially pronounced forcapital-intensiveindustries, becauseweconjec-ture that (capital) market failures will mattermost here. Under thealternativeexplanation,thereisnoclearreasonwhythepropensitytolocate inunderdevelopedregions shouldvarywithcapital intensity. We denedthe capitalintensity of a rmtobe givenby(Property,Plant, andEquipment)/Sales; inother words,the physical assets requiredper unit of sales.We then calculated the capital intensity of eachindustry (CAPIN) by taking an average over allrms intheindustry.25For easeof interpre-tation, these values werethen scaled to take onvaluesbetweenzero(services)andone(cementandglass). Theresults, listedinthenal col-umn of Table 3, are consistent with ourhypothesis. The coecient onthe interactiontermMEMB*CAPINis negative, signicant,andverylarge()0.23),i.e.,incapital-intensiveindustries, theeectofgroupaliationissig-nicantlystronger.It is, of course, possible that governmentintervention in the location process might workinmyriadotherways.Itmaybethatunderde-veloped regions are particularly prone to behighlypoliticizedandthatgroupsareuniquelyable to do business in such environments,perhaps duetosuperior political connections.Ourdatasuggestthatthisconjectureisworthprobingbecausethescalbenets(normalizedby sales) grantedby governments torms isindeed higher in underdeveloped regions forgrouprmsbutlowerfornongrouprmsthanit is in developed regions. Accordingly, we splitour sample into those rms that did not receiveanyscal benets (893of 1,193observations)andthosethat did. Ourresults, inbothmag-nitudesandstatisticalsignicance,arequalita-tivelyidentical inbothsubsamples. Therefore,whiletheremaybeagroupeectinobtainingsuchbenets, it doesnot seemtoaccount forourresults.Another proxy for the degree of bureaucraticcorruptioncomes fromthe results of a 1997survey on corruption conducted by a news-magazine, IndiaToday. Inthissurvey, respon-dents were asked to rate the three most corruptstatesinIndiainorder. Theywerethenaskedto do likewisefor thethree least corruptstates.Based on the survey results, each state wasassignedascalarcorruptionscore (CORR).This measure was onlymoderatelycorrelatedwithDEV q 0:32.Moreover,CORRandMEMB were almost completely (thoughslightly negatively) uncorrelated. There wassimilarly almost no relationship betweenCORRand MEMBin multivariate analyses.Thus, the bureaucratic corruptionhypothesisdoesnotseemtoexplainourobservedpatterninlocationalchoice.Finally,wemakenoteofaconceptualargu-mentthatisindependentofderegulation. ThefallofthedominantCongressPartyinthelate1970s, afterthreedecadesofsingle-partyrule,signaledtheonsetofpoliticalinstability,intheformof a changed opposition in Parliamentandinthe formof routine coalitiongovern-ments. Conceptually, as we detail in theappendixitismuchmoredicultforbargainsto be struck between politicians and rms whenthetenureof politicians becamesomuchlesssecure.(b) IntrastateheterogeneityManyofIndiasstatesarevast, bothinsizeandpopulation, andthere is goodreasontobelievethat therewouldbeconsiderablevari-ationindevelopmentwithinstates. Whileitisnotclearthatthisshouldbethesourceofanysystematic bias, replicating our ndings withsomedistrict-leveldatawouldcertainlybolsterour condenceintheresults. Whilethereareobvious benets frommovingtomore disag-gregateddata,thereisacostaswell.Ouronlydistrict-level data come fromthe Census ofIndia, 1981, which provides much weakerinfrastructureproxiesthanareavailableatthestatelevel.Weobtaineddistrict-level dataforGujarat,Karnataka, Maharashtra, Tamil Nadu, andWest Bengal.26As our measure of telecom-municationsdevelopment, weusethepercent-age of villages withaccess totelephones. Wesimilarly dene measures for other develop-mentamenitiesbylookingatthepercentageofvillages with the following: availability ofpower (Power); access tothe village via per-manent road (Transportation); access toprimary schools and medical services (SocialServices). We aggregate these items in the samemanner as with the state-level data, and ran ouranalyses with this newdistrict-level develop-mentmeasure.OurresultswereverysimilarinFACILITATINGDEVELOPMENT 619terms of the magnitude of the impliedeect,thoughadmittedlysomewhat noisier(p-valuesranging from 0.01 to 0.15). We attribute this tothe extremely coarse nature of our district-leveldevelopment proxies. It is alsointeresting tonote that most of the variation between districtsisabsorbedbystatexed-eects. That is, ouruseof thestateas thelevel of analysis seemsjustiedbydistrict-leveldata.(c) FirmdiusionSuppose that the rst few plants of anyorganizationaremorelikelytobelocatedinamoredevelopedregionduetotheproximityofproductmarketsandinput suppliers. Supposefurther that the constructionof future plantsdiuses outward to take advantage of newopportunities in other areas. Under this processof plant location, a larger organization willhave a larger proportion of plants in less-developedregionssimplybecauseofthisdiu-sion process, rather than any productiveadvantages.27This story, however, yields aseparateprediction: theplantsthatanorgani-zationbuildsearlyonshouldbemorelikelytobe located in a more developed region thanthosethat it buildslater. Totest forthispos-sibility, werepeatedthebasicanalysesof thispaper, adding a counter designating theorder inwhichagroups rms wereincorpo-rated. So, agroups rst rmwas assignedavalue of one, the second rm to be incorporatedwas assigned a two, and so on. When thisvariable was added to our regressions, itscoecientwasextremelyclosetozero. Hence,we donot ndevidence tosupport the rmdiusionhypothesis.6.THEROLEOFGROUPSINLESS-DEVELOPEDREGIONSTogetabetterunderstandingofthefactorsthat cause groups tolocate inless-developedregions, we conducted a series of interviewswithIndianmanagers andexecutives at bothgroupandnongrouprms.Thedominantrea-sonsaredescribedbelow. All butthelasttwoareconsistentwithourhypothesisthatgroup-aliatesaremorelikelytolocateinunderde-velopedregionsbecausetheyarebetterabletomitigatetheeects of market failures. Unfor-tunately, we are unable to test the relativeimportance of these explanations with ourcurrent data, but we hope to pursue this line ofresearchinthefuture.(a) DirectinfrastructuresubstitutionBecause of the high degree of xedness in thecosts of the infrastructure investments descri-bedintheintroduction,itisecienttospreadtheseexpensesoveralargevolumeofassets.28Capital sharing across unaliated rms may bedicult because of government regulations andrestrictions, andbecausetheabsenceof prop-erty rights reduces the incentive toinvest.29Agglomerationwithina single rmis alsoapossibility, but this may not always be desirableorpractical.An alternative solution to the problemofscale is the sharing of capital within closely knitsets of companies aliated with a single group.This sharing of resources is observed in varyingdegreesthroughoutIndia, butismoststrikingat the self-contained industrial cities con-structedbyanumberofIndiaslargestgroups.These facilities include Jamshedpur (or Tata-nagar, aliatedwiththe TataGroup), Piroj-shanagar (Godrej Group), Birlagram (BirlaGroup), and Modinagar (Modi Group). Ineachcase, the groupprovides suchbasic ser-vicesaspowergeneration, roads, schools, andemployee housingfor the joint use of its co-locatedcompanies.(b) HumanresourceconsiderationsThelowerqualityofschoolsandhealthcare,aswellasthelackofculturalamenitiesinless-developed areas, makes it a serious challenge torecruit competent managers andengineers towork in these regions. Skilled workers are oftenmuch sought after in the labor market, andhave ample opportunities available to remain inmore cosmopolitan areas. Because of theshared infrastructure facilities listed above,groupsareoftenabletomakelifeinbackwardareas more palatable for skilled employees.Another advantage was describedbyaman-agerfromtheTataGroup,whoexplainedthatgroups often recruit young managers to spend afew years at a facility in a less-developed region,with the promise that a job would later beavailablewiththeGroupinamajorcenter.30Moreover,sincemanygroupshavelargepoolsof shared labor resources, they are abletorotate skilledworkers throughfacilities inless-developed areas on shorter-termassign-ments.WORLDDEVELOPMENT 620(c) SupplyofinputsSincetheconcentrationofindustriesisrela-tively sparse inless-developedareas, there isusually insucient demand to stimulate thedevelopment of supporting industries. Basicinputs and repair services must therefore besourced fromrelatively distant suppliers. Apotential producerwithonlyalocal operationmay have trouble establishing and coordinatingtheserelationships, particularlygiventhepoorquality of communications in less-developedareas.Grouprms,ontheotherhand,alreadyhave well-established supplier networks, andareabletocoordinatethedeliveryofmaterialsthroughacorporate headquarters. Often, thesupplies will come fromacompanywhichisitself agroup-aliate. Forexample, whentheGodrejGroupopenedanewfactoryinGoa,ithadmaterials shippedtoit fromtheGroupsprincipalfacilitynearBombayfortherstfewyearsofitsoperation.(d) FinancingOne particularly important input that may bedicult to access from less-developed regions isbank nancing. Indias nancial institutions areconcentrated in the countrys major urbancenters, particularlyBombay; becauseof de-cient contract enforcement, reputations estab-lished via word-of-mouth contact are veryimportantinbankingrelationships.Theestab-lishment and maintenance of the necessaryreputation requires a strong presence in a majorcenter,whichmakesitparticularlydicultforindependent rms in less-developed areas toobtainnancing. Incontrast, agroupislikelytohaveaheadoceinamajorcitythatmayhandlenancingonbehalfofitsruralsubsidi-aries.Manygroupsalsoreportedlyutilizeinternalcapital markets, eventhoughthere are somelegal restrictions on borrowing and lendingamong the publicly tradedgroupcompanies.Thismaybeyet anotheravenuefornancingthatisnotavailabletounaliatedrms.(e) RiskanddiversicationLocating a production facility in a regionwithoutsupportingservicesismorerisky,sim-plybecausetherearemorethingsthatcangoseriouslywrong. Groups aremorecapableofbearingthis riskbecause of their greater sizeanddiversication.(f) Land-intensiveprojectsgroupsandthegovernmentbureaucracyWhilemanyaspectsof theIndianeconomyhavebeenliberalizedsince1975, landacquisi-tion and land use are still highly regulated. Forsmaller plots of land, there are few bureaucraticbarrierstobeovercome.Ifaprojectrequiresalot of land, however, it must be rezonedforindustrial use. Thisprocessmaytakeyearstoworkitswaythroughthebureaucracy, result-inginpotentiallycostlyproject delays. Thereexist considerable economies of scale in dealingwith the Indian bureaucracymany largegroupsreportedlymaintainindustrial embas-sies inNewDelhi whose sole purpose is tohandle group relations with the government(Encarnation, 1989). Large groups are thusoftenabletoexpeditetherezoningprocessduetosuperiorpoliticalcontacts.Now, it is usuallypreferabletolocatepro-jectswithsignicantlandrequirementsinless-developed regions, where property costs arelower.31Hence, land-intensive projects, forwhichgroup-aliatedrms are at anadvan-tage, will be more commoninless-developed(i.e.,lowpropertycost)regions.(g) AccountingprotsFirms locating in backward areas receive,amongother benets, ave-year taxholiday.Sincethese areas areoverwhelminglyconcen-tratedinless-developedstates,32rmsinthesestates will, on average, pay lower taxes on theirprots(seesection3foraconrmationofthisfact). As an accountant with a large Indiangrouppointedout, this provides anincentiveforrmstolocatetheirmostprotableopera-tions in less-developed states. This eect will beparticularly strong for vertically integratedgroupsforthefollowingreason: agroupmaybuildan upstreamplant ina backwardarea,and sell its output to a group-aliate that doesnotenjoythesametaxadvantagesatanina-tedtransferprice. Thiswill articiallyincreasethe prots of the untaxed operation at theexpense the more heavily taxed operation,thereby lowering the total taxes paidby thegroupasawhole.7.DISCUSSIONIn this paper, we have shown that rmsassociated with business groups are more likelyFACILITATINGDEVELOPMENT 621to locate in less-developed regions. We interpretthis as evidence that business groups arelocatingintheseareasbecausetheyarebetterabletocopewiththeshortagesassociatedwithless-developedregions.This result has important implications forevaluatingboththe development process andthevalueofgroups.Ifwearetoevaluatefullythe costs of underdevelopment, we need to rstunderstandhowrmsaredealingwithcurrentshortages, and how they might react to changesinthe provisionof basic services. Until now,there has been very little work done on thebehaviorof rms(andindustrial organizationingeneral)indevelopingcountries.33(a) Privatevs.publicsectordevelopmentOf course, an alternative mechanism forstimulating development in underdevelopedregions is for government-owned rms todeliberately locate their plants in less-developedareas. Whenwe revisitedthe sample of gov-ernment-owned companies that we discardedfor our main analyses (a total of 235 plants, 217rms), we found a mean level of DEV for theserms of 0.47, considerably lower than theaverage for any class of private rms. Thisbasic nding held after we controlled forindustryandsizeeects.Thisbegsthequestionofwhoismoreeec-tive at bringing industry to underdevelopedareas: government rmsorgrouprms. If wetakeprotabilityasameasureofperformance,then the basic statistics suggest that govern-ment rms arenot doingaverygoodjobofanythingatalloperatingROAisonly0.4%,andnetROAiswellbelowzero.Whileprotsmaynotbeafairbenchmarkforanalyzingtheperformanceofgovernment-runentities,whichprobably have other primary objectives, Indiangovernment run rms are notorious enough fortheir operating ineciency (Kelkar & Rao,1996) that it would be a stretch to imagine thatthey could facilitate development more e-cientlythanprivate-sectorgroupaliates.34(b) Foreignvs.domesticinfrastructuredevelopmentJust as business groups might use cross-industry(butwithin-country)internal marketstosubstitutefor unavailableexternal marketstolocateinunderdevelopedregions,onemightexpect that multinationals couldsimilarlyusecrosscountry (but usually within-industry)internal markets (Foley, 2003). Onthe otherhand, multinationals are less likely to be able toget aroundproblems of contract enforcementthanaredomesticbusinessgroups,makingtheissue of multinational location in under-developedregionsanempirical one. Whenweinclude foreign rms in our sample, we nd thatthey are unambiguously more likely to locate indevelopedregions, andthatourresult regard-ing group-aliateslocation in underdevelopedregionsisstrengthened.(c) TheeectofgroupplantlocationondevelopmentGodrejs experiences at Pirojshanagar high-lightanimportantroleofgroupsinthedevel-opment process. Soonafter thetownshipwasbuilt, small-scale industries sprang upin thesurrounding areas to serve the needs of thefactories and their employees. Eventually, basicinfrastructure was built toserve the needs ofthisnewpopulation. Thus, withbasicservicesand ancillary industries present, other enter-prises were able to locate in the area. Therefore,Godrejs original decisiontolocateat Vikrolisubsequentlyspurreddevelopment inthesur-rounding area.35The Tata Groups steelfacilityatJamshedpur(sometimescalledTata-nagar) isanotherparticularlystrikingillustra-tion of this phenomenon. Jamshedpur began asaself-containedfacilityinthejunglesofBihar,but has grown into a major city of nearly700,000inhabitants.Discussions with Indian managers suggestthat this is part of amore general phenome-nonbeingthe rst tolocate inaless-devel-opedregionisdicult becauseoftheabsenceof infrastructure andancillaryindustries. Butanenterprise of sucient scale will spur thedevelopment of these services, whichinturnmakes the area more attractive as a site forother industries. Our discussions suggest thatgroup rms often play this leading role.Unfortunately, ourcurrent datadonot allowustotestthishypothesisdirectly,butwehopethatitwillbeatopicforfutureresearch.Recognition of the role of business groups infacilitatingdevelopmentisimportantinevalu-atingthe eects of groups, particularlygiventhat they are so often portrayed as rent-seekingmonopolists. We are certainly not claiming thatthisisthereasonfortheexistenceof businessgroupscertainly, there are many (see, forexample, Amsden&Hikino, 1994; Ghemawat&Khanna, 1998; Granovetter, 1994; Khanna,WORLDDEVELOPMENT 6222000; Le, 1976). But, giventhe presence ofgroupsindevelopingcountries, understandingtheir role in the development process isimportant in evaluating the implications oftheirexistence.Finally, wenotethatunderstandingtheroleof groups (andof rms, more generally) canfurther our understanding of some of thequandaries inthe literature oninfrastructure.Forexample, Gramlichs(1994) reviewpointsout that muchattentionhas beendevotedtoanalyzingtheextentofinfrastructureshortfall.Answering this question completely wouldrequireappreciatinghowrmsinvestprivatelyinresponsetolackofpublicinfrastructure, soas to partly alleviate de facto scarcity. Gramlichalso points out that there are numerousempirical quandaries regarding the eects ofpublic infrastructure. Whereas he focuses onmeasurement-relatedissues that fuel this puz-zle, our paper suggests that the eects of publicinfrastructure might wellbe conditioned by theextent towhichrms privately substitute forpublicinfrastructure.NOTES1. Thiswould,atleast,bethepredictionoftheCoasetheorem. But, infrastructure has a number of publicgoodscharacteristicsthatmightmakeprivateprovisiondicult.Thisisimpliedbysomeofwhatfollows.2. See Jimenez (1995) for acomprehensive literaturesurvey; seeMorrisonandSchwartz(1996)forarecentcontributiontotheliteratureonphysicalinfrastructure;seePsacharopoulos(1994)forareviewoftheliteratureontheproductivityofeducationalexpenditure.3. These take dierent names in dierent countries,e.g., grupos in Latin America, chaebol in Korea, businesshousesinIndia.4. Inmost developingcountries, group-aliates are,forthemostpart,privatelyheld.Forexample,Indone-siasSalimGrouphasanestimated300400rms, butonly ve of these are publicly listed. In addition, rms inIndonesia, Central America and other countries areoften partially held by several groups, making itdicult to determine the group membership of aparticularrm.5. As a headline in an Indian news magazine pro-claimed in February 1996, Indias infrastructure isstretchedbeyondcapacityandwill soonhavedomesticproduction . . . in a hopeless bind. The article citeschronic shortages of electricity, communications, andtransportation facilities as among the most seriousproblemsfacingIndianindustry.6. Other countries satisfy the low level of developmentandhighvarianceinlevel of development criteria, forexample, China, but the lackof reliable measures ofgroupaliationmakes theseless appropriatesites forouranalysis.Moreover,wehavebeenunabletoobtainplantlocationdataforthesecountries.7. Asarecentexample, AhujaandMajumdar(1998)use these datatoexamine the performance of Indianstate-ownedenterprises,and Khanna andPalepu (2000)use these data to showthat the performance conse-quences of diversication in India are dierent fromthoseinadvancedeconomies.8. CMIE database: Computerized Information onMagneticMedium.9. While a group is not a legal construct, CMIE uses avarietyofsourcestoclassifyrmsintogroups.Priortothe repeal of the MRTP(Monopolies andRestrictiveTrade Practices) Act in1991, acomprehensive list ofrms belonging to Large Industrial Houses waspublished by the government. This forms a startingpoint fortheCMIEclassicationforanumberof thegroups. Other signicantsources of information includethe following: (a) identifyingthe promoters of armwhen it was rst started and tracing whether the originalowners retained their aliation with the rm, (b)announcement by individual rms of the groups ofwhichthey are a part, andby the groups of lists ofaliated rms. Such announcements appear periodicallyin annual reports, advertisements, or at the time ofpublic oerings and news releases about the groups andrms future plans, and (c) identifying the interest that agrouphasinaparticularrmthroughthemembershipofitsboardofdirectors.CMIEalsoregularlymonitorschanges in group structure. Shifts in group aliation areextremely rare, but when they do occur, they arereectedinthedatabase. Notethat thedatabasedoesnotcontainahistorical recordofeachrmsaliationFACILITATINGDEVELOPMENT 623with dierent groups; rather the group membershipvariablereectsthemostcurrentaliation.ThereisnoambiguitybetweenCMIEs classicationof rms intogroups andthose attemptedby other sources againstwhichwe crosscheckedthe data. For example, list oftop100rms areoftenpublishedbyIndiasleadingbusiness magazines. When their group aliation isincluded,itdoesnotcontradictthatfromoursource.10. Inthecaseoffamily-controlledgroups,successionfromone generation to another often results in thegroup being split into multiple parts. We identiedseveral prominent groups that hadgonethroughsuchperiods of succession in the past 20 years, and checked tosee that CMIEhad indeed classied each subgroupseparately. Thus, theBirlagroupisclassiedinseveraldierent parts, as is the group originally run by theGoenkas.11. The case for excluding government-run rms isstrong, astheydiermarkedlyfromotherrmsinoursample alongmanyimportant observable dimensions,forexample,earningnegativeprotsonaverage.More-over, itisvery likely that thelocational choice decisionsof government-run rms would be made based onentirelydierent criteriafromprivate rms. Similarly,weexpect thatforeignrmswouldusedierentcriteria,thoughtheir inclusionactually strengthens our result(seebelow).12. Number of observations dropped are listed inbrackets; theattritionisdonesequentially. Firmswithzero sales were also omitted, since this is often entered ifdata are unavailable. For these observations, noaccountingdatawereavailable.Wemightbeconcernedthat this would cause the following selection bias:supposethat datawereunavailableforlesswell-estab-lishedrms. Thenwe might expect that the droppedobservations wouldbeexcessivelyweightingnongrouprms, withaparticularlystrongbias inless-developedareas.Thecombinationofthesetwobiasescouldmeanthatourresultsarebiasedupward.Whiletheformerisdenitely the case (76%of the rms dropped werenongroup rms), the latter does not seem to be true (theaveragevalueofDEVforthesermsis0.62,somewhathigher than the full sample average of 0.55). Thissuggests that the bias is more likely to run the other way.Whenwe lookat some basic regressions without sizecontrols, wedoinfactndthatthegroup eectforthissubsetofrmsislargerthanthefullsamplevalue.13. Astheappendiximplies, defactodelicensingonlybeganintheearly1980s; if weuse1983asourcut-odate, the groupeect reported belowis considerablystronger. We are, ineect, stacking the deckagainstourselvesbyusingsuchaconservativestartingpoint.14. Whilethesedataaremorerecentthanmostoftheplant location decisions in our sample, this should not beproblematic. Whileindustrial reformsdidindeedbegininthe 1980s, reformof the infrastructure sector is amuch more recent development. Furthermore, sinceinfrastructure is a stock (rather than ow) variable,requiringsubstantial capital outlays, changes ininfra-structurelevelsareveryslow-moving.ThisisconrmedbyAhluwalias(2002b) reportingof indicesthat assessthequalityofinfrastructureprovisioninthe14largestIndianstatesforthreetimeperiods: 198081; 199192;and199697. Thecorrelation ofthe199697 indexwiththat of 199192 and 198081 is 0.996 and 0.968respectively.15. Unfortunately,wehavenotyetfoundmoredirectmeasures of telecommunications quality. It is of someconsolation, however, to note that in crosscountry data,telephone lines per capita is highly correlated with morequality oriented measures, including telecom investment(0.76),overallqualityasassessedbytheWorldCompet-itivenessReport(0.65),andfaultsper100calls()0.52).16. It has been pointed out that the quality of surfacedroads varies drastically; this is only a problemif webelieve that surfaced road quality is negatively related tothepercentageofroadsthataresurfaced.Conventionalwisdom,however,wouldarguefortheopposite.17. With this measure, the magnitudes of the dierencesin each measure matters. We also ran our analyses usinga rank order statistic to measure the various componentsof development. This resultedina measure that washighly correlated with the one used in our analysesbelow. Not surprisingly, using this rank order-basedmeasure yielded almost identical results to those wereporthere.Another concern might be the weighting that thevariousmeasuresareaccorded. Tocheckofthedegreetowhichthismightbedrivinghowstatesareclassied,we repeatedour classicationalgorithm, adoptingthe(nonagricultural) weights used by Shah (1970) in hisassessment of infrastructure in India. This had almost noeectontheorderingofstatesinterms ofdevelopment,andthusdidnotsubstantiallyaectourresults.18. NotethattheBusinessTodayscoreisnotidealforourpurposes,sinceittakesintoaccountsuchfactorsasnancial investment incentives that are relevant for acompanys location decision, but do not reect thestateslevelofeconomicdevelopment.WORLDDEVELOPMENT 62419. CMIE describes scal benets as benets given bythe government to companies operating in certainindustriesand/ortopromotespecicobjectives. Fertil-izer companies receive subsidies under the retentionpricesupportsystem. Teacompaniesreceivereplantingsubsidies. Similarly, exporting companies receive sub-sidies under the duty-drawback scheme or the cashcompensatory scheme or the international price re-imbursementscheme.20. The dierences remainif we blockonassets orindustry.21. While locational choices are observed that theplant level, these decisions are probably made at therm-level. Thereisthereforelikelytobesomecorrela-tionamong a rms locational choices. Todeal withmultiplant rms, plant location observations areweighted by 1=n, where n is the number of plants ownedbythe rm. This eectivelyassumes ahighdegree ofcorrelation among the rms decisions, collapsing nobservations into one; this weighting is used for allnonregression calculations below. An alternativeapproachwouldbe toassume complete independenceamong locational choices for each rm. This corre-sponds to using the unweighted data in the calculations;thisapproachyieldedalmostidenticalresults.22. Note that jdiers across rms, since eachrm hasadierentnumberofplants.23. We repeated these analyses allowing for correlationamong observations within the same group; thisincreased our standard errors a very small amount,anddidnotsubstantivelyaectthefollowingresults.24. Weapproximate QbyMarket Value ofEquity Book Value ofPfd:StockBook Value ofDebtBook Value ofTotal Assets;wheremarketvalueisbasedontheclosingpriceonthelast trading day of 1993. Data availability considerationspreclude us from computing a better approximation to Q(see Lindenberg & Ross, 1981). The relevant measure ofreturns is operating income (see Khanna &Palepu,2000). Wethereforeusetax-adjustedROA, denedasROA{Net Prot +(1)Tax Rate) Interest/Assets.The results are similar (thoughsomewhat stronger) ifweusenonadjustedROA(i.e.,(NetProt)/Assets).25. Incalculating this average, we omittedobserva-tionsgreaterthan10andlessthan0.10.26. These states were chosen because they had thelargest number of plant observations; we didnot useUttarPradesh simply becauseitisdivided intosomanydistricts that entering data onall of its districts wasimpractical.27. Note that the basic argument made in this paper isconsistentwith(thoughdoesnotnecessarilyimply)thepresence of a diusion eect. That is, group-aliates areabletodiuseoutwardbecauseoftheadvantagestheyhaveinproducinginless-developedregions.28. As a result, smaller rms may bear a greaterburden where public infrastructure is lackingtheWorldBankstudydescribedabove foundthat, whilelarger Nigerian rms spent around 10% of theirmachinery and equipment budget on infrastructureexpenditures, smaller rms were spending closer to25%. In the case of Indonesia, smaller rms werespendingupto25times morefor privatepower thanlargercompanies.29. For example, Nigerianrms areprohibitedfromsellinganyexcesspowercapacity.30. In a world of perfect labor markets, this would notbe an important considerationan unaliated companyinaless-developedregioncouldrecruitskilledworkerswhowouldbeabletosimplyndanewjobinamoredeveloped area after a few years. But, given theimperfectinformationowsthatexistinreality,ndinga job in Bombay if one is working in rural Orissa wouldbe a tremendous challenge. There are also signalingconsiderationsif aTataemployeeinBiharwishes tolookforajobinanotherstate,hehastheadvantageofthe Tata reputation, which is transferable across regions.Thesamecannotbesaidofalocalrmsreputation.31. Unfortunately, we have not been able to obtain anydatatoconrmthese assertions; this sectionis basedpurelyoninterviewswithIndianexecutives,anddiscus-sionswithD.Kapur.32. For rms locatedinstates withDEV< 0:5, thepercentage ofrmslocatedinbackward areasis64%ascomparedwith38%forrmsinstateswith DEV> 0:5.33. It is worth noting that private rms have often hadtocompensateforinfrastructuredecienciesinhistoryandcurrently. Someexamplesof companiesthat havedone so protably (or, at least, with no obviousimpairment of productive eciency) include Indiasleadingsoftware companytoday(Infosys), one of theFACILITATINGDEVELOPMENT 625worldsmostcelebratedfurnitureretailers,Ikea(invest-mentsinAlmhult,itsSwedishhometown).34. Evidence fromother countries like China alsosuggest that government, through state-owned enter-prises, takesonthetaskof providingcradletogravebenets (housing, healthcare, childcare, retirementincome, disabilityinsurance, andunemployment insur-ance)whenthesearenotgenerallyavailable(Steinfeld,1998). Recent press articles point to the great ineciencywith which these services are provided, however (see, forexample, Full speed ahead, p. 56, Far EasternEconomic Review, October 7, 1999). Itis very importanttonotethatabigdierencebetweenChineseSOEsandIndianbusinessgroupsistheabsenceofahard-budgetconstraintintheformer; thelatterareentirelyprivate-sectorrms,thatliveanddiebytheruleofthemarket.35. Ironically,thishasresultedinanescalationinthecosts that Godrej had hoped to avoid by buildingPirojshanagar. The octroi surtax nowapplies to rawmaterials coming into Vikroli, while property prices andlabor costs have skyrocketed. Because of its investmentsininfrastructure, aswell asIndianregulations prohib-iting the dismissal of employees, Godrej has faced manyobstaclesinmovingonceagaintoalow-costregion.REFERENCESAhluwalia, M. S. (2002a). Economic reforms inIndiasince 1991: Has gradualism worked? Journal ofEconomicPerspectives,16(3),6788.Ahluwalia, M. S. (2002b). State-level performance undereconomic reforms in India. In A. Krueger (Ed.),Economic policy reforms and the Indian economy.Chicago,IL:UniversityofChicagoPress.Ahuja, G., & Majumdar, S. (1998). An assessment of theperformance of Indian state-owned-enterprises. Jour-nalofProductivityAnalysis,9(2),113132.Amsden, A., &Hikino, T. (1994). Project executioncapability, organizational know-howandconglom-erate corporate growth in late industrialization.industrialandCorporateChange,3,111148.Anas,A.,&Lee,L.S.(1998).ManufacturersresponsestoinfrastructuredecienciesinNigeria:Privatealter-natives and policy options. Report INU98, TheWorldBank,Washington,DC.DeLong, B. (2001). India since independence: An analyticgrowthnarrative. Unpublishedmanuscript, Depart-ment of Economics, University of California, Berke-ley.Dugger, C. (1999). Indias high-tech, and sheepish,capitalism.TheNewYorkTimes,December15.Economist Intelligence Service (1995). Indias energysector. Bombay: Centre for Monitoring IndianEconomy.Economist IntelligenceService(1996). InfrastructureinIndia (Vol. 2). Bombay: Centre for MonitoringIndianEconomy.Encarnation, D. (1989). Dislodging multinationals: In-dias comparative perspective Ithaca. NY: CornellUniversityPress.Fernald, J. (1999). Roads toprosperity? Assessingthelinkbetweenpubliccapital andproductivity. Amer-icanEconomicReview,89,619638.Foley, F. (2003). The eects of having an Americanparent: An analysis of the growth of U.S. multi-national aliates. AnnArbor: Universityof Michi-ganPress.Ghemawat, P., &Khanna, T. (1998). The nature ofdiversiedbusiness groups: Aresearchdesignandtwo case studies. Journal of Industrial Economics,46(1),3561.Goto,A.(1982).Businessgroupsinamarketeconomy.EuropeanEconomicReview,19,5370.Gramlich, E. (1994). Infrastructure investment: A reviewessay. Journal of EconomicLiterature, 32(3), 11761196.Granovetter, M. (1994). Business groups. In N. J.Smelser &R. Swedberg (Eds.), The handbook ofeconomic sociology (pp. 453475). Princeton, NJ:PrincetonUniversityPress.Gulyani, S. (1999). Innovating with infrastructure: HowIndiaslargest carmakercopeswithpoorelectricitysupply. World Development, 27(10), 17491768.Joshi, V., &Little, I. M. D. (1997). Indias economicreforms:19912001.Delhi:OxfordUniversityPress.Jimenez,E.(1995).Humanandphysicalinfrastructure:Investmentandpricingpoliciesindevelopingcoun-tries. InJ. Behrman&T. Srinivasan(Eds.), Hand-book of development economics (Vol. IIIB).Amsterdam:Elsevier.Kelkar,V.,&Rao,V.V.B.(1996).India:Developmentpolicy imperatives. NewDelhi: Tata McGraw-HillPublishingCompanyLimited.Khanna, T. (2000). Business groups and social welfare inemerging markets: Existing evidence and unansweredquestions.European Economic Review,44(46),748761.Khanna, T., &Palepu, K. (2000). Is groupaliationprotableinemergingmarkets?Anempirical anal-ysisofdiversiedIndianbusinessgroups.JournalofFinance,55(2),867891.Le, N. (1976). Capital markets intheless developedcountries: The group principle. In R. McKinnon(Ed.), Money and nance in economic growth anddevelopment.NewYork:MarcelDekker.Lindenberg, E. B., & Ross, S. A. (1981). Tobins q ratioand industrial organization. Journal of Business,54(1),132.Psacharopoulos, G. (1994). Returns toInvestment ineducation: Aglobal update. World Development,22(9),13251343.Morrison, C. J., & Schwartz, A. E. (1996). Stateinfrastructure andproductive performance. Ameri-canEconomicReview,86(6),10951111.WORLDDEVELOPMENT 626Shah, N.(1970). Infrastructurefor theIndian economy.In V. Dagli (Ed.), Infrastructure for the Indianeconomy.Bombay:Vora&Co.StatisticalAbstract,India(1992).IndiaCentralStatisti-cal Organization, NewDelhi: Manager of Publica-tions.Steinfeld, E. (1998). Forging reform in China: The fate ofstate-owned industry. New York: Cambridge Univer-sityPress.Strachan,H.(1976).Familyandotherbusinessgroupsineconomicdevelopment: Thecaseof Nicaragua. NewYork:Praeger.APPENDIXA.EVOLUTIONOFINDIANINDUSTRIALPOLICYThehistoryofIndianindustrialregulationissuciently convoluted and bizarre that itwould be impossible to capture its manynuances andsubtleties inashort description.We tryhere togive onlyabrief overviewtoillustratethepoint that rms weregivenverylittle discretion in plant location decisionsduring 195075, andthat beginning in1975,therehasbeenafairlysteadyprocessofliber-alizationwhichcontinuestothepresent.Thereareseveral sourcesofinformationfromwhichthis sectiondraws, includingKelkar andRao(1996)andJoshiandLittle(1997).(a)IndustriesAct,1951In 1948, the Industrial Policy Resolution wasdraftedasaprogrammeofplanneddevelop-ment in order to secure the most eective use ofscarce resources, bothdomestic andexternal,for promoting balanced economic growth.This resolution was given legislative backingwiththeIndustries(DevelopmentandRegula-tion) Act of 1951, whichessentiallygave thegovernment complete control over Indiasindustrial output. TheAct createdalicensingsystemunder which government permissionwasrequiredforanynewenterprise,aswellasany increase in the output of an existingenterprise. Thegovernmentwasactuallygivendirect control over far more than just totaloutput. For example, the degree of productdierentiationwas, strangelyenough, dictatedby government bureaucrats. Since imports werealsoseverelylimited,anyrmthatwasabletoprocure a license enjoyed a considerable degreeof market power. This lack of competitionmeant that acompanycouldinvest heavilyinpolitical rent-seeking, run a relatively inecientoperation, andstill be highlyprotable. Onecomponentofthebargainstruckbetweenpol-iticiansandprospectiveenterpriseswasusuallythatthelocationofanynewproductionfacili-tiesbechosenat thediscretionof thelicense-grantinggovernmentocial.Sincethistypeofrent-seeking was less overt (and therefore easierto engineer) than outright bribery, it wasalmostalwaysapartoftheinformal licens-ingagreement.GivenIndiasdemocraticpolit-ical structure, the incentives for license-grantingocials are obvious: enterprises were oftenrequiredtolocateintheocials homestates,orinthestatesof otherocialstowhomthedecision-maker owed political favors. Thus, thehome states of top ministers received whatwas perceived to be a disproportionatelylarge volume of newinvestment throughout195175.(b)1975NoticationsBeginning in 1973, the Indian governmentbegan to recognize that growth in certainindustries was necessary in order to provide fora steadily growing national population (andeconomy). In 1975, the government announcedthat anannual capacityincreaseof 5%wouldautomaticallybeallowedin15industriesthatweredeemedtobeimportantfromthepointof view of the national economy or are engagedin the production of articles of mass con-sumption.(c)1977ElectionsFromindependenceuntil themid-1970s, theCongresspartycontrolledboththefederalandstate governments, and did not face any seriousthreatstoitsrule. Thisstabilityfacilitatedthekinds of political deal-makingthat ledtothepoliticizationof plant locationdecisions out-linedabove. Thesituationchangeddrasticallywhen the Janata party took control of thefederal government in 1977; the governmenthas seen much fracture and instability sincethen.Thishasmeantthatgovernmentocialsover the past two decades have been drawnfromdierent political parties; also, a politi-cians tenure has alsobeenmade less secure,which makes it less likely that he will be able topayback anypolitical favors. Bothoftheseeects havemadepolitical deal-makingmuchmoredicult.FACILITATINGDEVELOPMENT 627(d)Industrialreformsduringthe1980sIndiasindustrial policywassteadilyliberal-ized throughout the 1980s. The IndustrialLicensing Policies of 1980 and 1982 furtherexpandedthelistofindustriesthatweregivenautomaticcapacityexpansion,andtheamountof expansionwasincreased; someeortsweremade at streamlining the licensing process. Thedelicensingofspecicindustriesbeganin1983,whennineindustriesofnationalimportancewerepartiallydelicensed.Thiswasfollowedbytheannouncementofamajorliberalizationin1985,when22industriesweredelicensed.Overthe next few years, a number of other industriesweredelicensed, leadinguptothemuch-cele-bratedindustrialpolicyreformsof1991,whichended industrial licensing in all industries,except ahandful deemedtobeimportant fornationalsecurity.WORLDDEVELOPMENT 628