Suyanto Which Firms Benefit from Foreign Direct Investment Empirical Evidence

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JOURNAL OF

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Full Length Articles

Arup Mrtra. Chandan Sharma, Mane-Ange Veganzones-Varoudakis,

Trade liberalization. technology transfer. and hrms productive

performance. The case of lndran manufacturing

Suyanto. Ruhul Salim. Harry Bloch, Which

ｦｾｲｭｳ＠

benefrt from

forergn direct Investment?

ｅｭｰｾｲｩ｣｡ｬ＠

evidence from Indonesian

manufacturing

16

Kazunobu Hayakawa Kenmei Tsubota. Locatron chorce in

low-income countnes Evidence from Japanese rnvestments

in East Asia

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Rashid Ameer, Frnancial constraints and corporate investment rn

Asian countries

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Which

ﬁrms

beneﬁt

from

foreign

direct

investment?

Empirical

evidence

from

Indonesian

manufacturing

Suyanto

a

,

Ruhul

Salim

b,

*

,

Harry

Bloch

b

a

FacultyofEconomics,UniversityofSurabaya,EastJava,Indonesia

b

CurtinBusinessSchool,CurtinUniversity,Perth,WA6845,Australia

1. Introduction

Thespillovereffectsofforeigndirectinvestment(FDI)havebeenamajorconcernforresearchersandpolicymakers

duringthelasttwodecades.AnumberofstudieshaveexaminedthespillovereffectsofFDIondomesticﬁrmproductivity

(Chakraborty&Nunnenkamp,2008;Haddad&Harrison,1993;Hu&Jefferson,2002;Javorcik,2004;Liang,2007;Negara& Firdausy,2011;Takii,2005,2011). Thesestudiesprovidesomeusefulinsightsregarding theevidenceof thespillover

beneﬁtsand offersomerecommendationstomaximizethebeneﬁts.However, mostexistingstudiesexcludetechnical

efﬁciencyandfocusmainlyontechnology,ignoringthattheFDIpresenceinhostcountriesistheimpetusforefﬁciency

improvementthroughcompetitionanddemonstrationeffects(Wang&Blomstrom,1992).AstudyofFDIspillovereffectson

firm-leveltechnicalefficiencyisimportanttoprovideevidenceastowhetherthelargeamountofFDIinflowsgenerate

positiveexternalitiestodomesticﬁrmsthroughefﬁciencyimprovement,thusindicatingwhetherthespilloverhypothesisis

justifiedinthecontextoftechnicalefficiency.SuchastudycanexploretowhatextentFDIcaninduceefficiencyspillovers,

andwhichﬁrmtypesreallybeneﬁtfromthespillovers.

JournalofAsianEconomics33(2014)16–29

ARTICLE INFO

Articlehistory:

Received22November2012 Receivedinrevisedform8May2014 Accepted11May2014

Availableonline19May2014

JELclassiﬁcation:

D24 D29 F23

Keywords:

Foreigndirectinvestment Spillovereffects Technicalefﬁciency Stochasticproductionfrontier Indonesia

ABSTRACT

Despitegrowingconcernregardingtheproductivitybenefitsofforeigndirectinvestment (FDI),veryfewstudieshavebeenconductedontheimpactofFDIonfirm-leveltechnical efficiency.Thisstudyhelpsfillthisgapbyempiricallyexaminingthespillovereffectsof FDIonthetechnicalefficiencyofIndonesianmanufacturingfirms.Apaneldatastochastic productionfrontier(SPF)methodisappliedto3318firmssurveyedovertheperiod1988– 2000. The results reveal evidence of positive FDI spillovers on technical efficiency. Interestingdifferencesemergehoweverwhenthesamplesaredividedintotwoefficiency levels.High-efficiencydomesticfirmsreceivenegativespillovers,ingeneral,while low-efficiencyfirmsgainpositivespillovers.Thesefindingsjustifythehypothesisofefficiency gaps,thatthelargeristheefficiencygapbetweendomesticandforeignfirmstheeasierthe formerextractsspilloverbenefitsfromthelatter.

ß2014ElsevierInc.Allrightsreserved.

*Correspondingauthorat:SchoolofEconomics&Finance,CurtinBusinessSchool,CurtinUniversity,WA6845,Australia.Tel.:+61892664577; fax:+6192663026.

E-mailaddress:Ruhul.Salim@cbs.curtin.edu.au(R.Salim).

ContentslistsavailableatScienceDirect

Journal

of

Asian

Economics

http://dx.doi.org/10.1016/j.asieco.2014.05.003

(11)

Amongthedevelopingeconomies,IndonesiaisparticularlysuccessfulinattractingFDI.NetFDIinﬂowstoIndonesiahave

risenmorethan30timessince1986,reachingarecordlevelofUS$8.3billionin2008(theCentralBankofIndonesia,2011).

However,thereisadearthofresearchonefﬁciencyspilloversinIndonesia.Mostempiricalstudiesexaminespillovereffects

undera frameworkofthelong-runequilibriumproductionfunction,whichassumesthatﬁrmsareproducingata full

efﬁciencylevel.Underthisframework,theFDIspilloversontechnicalefﬁciencyarenotcaptured.

Twopreviousstudiesbytheauthorsfocusontechnicalefﬁciencyusingastochasticproductionfrontierframeworkfor

individual Indonesian manufacturing industries. Suyanto, Salim, and Bloch (2009) examine the pharmaceutical and

chemicalindustries,whileSuyantoetal.(2012)examinetheelectronicandgarmentindustries.However, thereareno

studiesprovidingcomprehensiveresultsforthewholeIndonesianmanufacturingsectorusingastochasticframework.

AstudybyTemenggung(2007)examinesthewholeIndonesianmanufacturingsector.Ourcurrentresearchdiffersfrom

Temenggunginthreeimportantpoints.Firstly,Temenggungappliestheordinaryleastsquared(OLS)regressionmethodfor

paneldata,whichdoesn’tdistinguishbetweenﬁxedeffects(FE)andrandomeffects(FE).Secondly,theclassicalproduction

function,employedinTemenggung(2007),assumesthatallfirmsarefullyefficient,sothatthespillovereffectsofFDIreflect

technological progress. In contrast, the current paper employs the stochastic production frontier, which relaxes the

assumptionoffullefficiencyoffirms,sothatbothtechnologicalprogressandefficiencyimprovementareexamined.Thirdly,

wecalculatethescoresoftechnicalefficiencyofeachfirmandestimatesspillovereffectsseparatelyforhigh-efficiencyand

low-efficiencyfirms,providingausefulinsightintothedifferencesintheabilityofhigh-efficiencyandlow-efficiencyfirms

inabsorbingspillovereffectsfromFDI.

Thisstudycontributestotheexistingliteratureinseveralways.Firstly,itexaminesthespilloverhypothesisbyfocusing

ontechnicalefﬁciency,animportantaspectthatisoftenneglectedinthepreviousstudies.Theadoptionofastochastic

productionfrontierallowstheauthorstoinvestigatetheeffectsofFDIspilloversonﬁrm-leveltechnicalefﬁciency.Secondly,

thisstudycoversalongseriesofsurveyedﬁrms,whichincludesalsotheperiodoftheAsiancrisisonwards.Thirdly,this

study evaluates horizontal, backward, and forward spillovers of FDI. Most importantly, by examining the whole

manufacturing sector,it is possibletoidentifycharacteristics ofindustries that affect thesizeof thetechnology and

efficiencyspilloverstodomesticfirmsfromFDI.Inparticular,wefindevidencethatthesizeofthetechnologygapbetween

foreignanddomesticfirmsiscritical,withlargerefficiencygapsassociatedwithgreaterefficiencyspilloversfromFDI.

Weproceedbyreviewingtheconceptofspillovereffectsinthenextsection.Wethendiscussmethodologyanddata.

EmpiricalresultsarepresentedinSection4andtheconclusionsaregivenintheﬁnalsection.

2. FDI,spillovereffects,andtechnicalefficiency:theoreticalconceptandempiricalevidence

2.1. FDIandspillovereffects

Foreigndirectinvestmentisbelievedtoprovidehostcountrieswithdirectandindirectbeneﬁts.Thedirectbeneﬁtstake

theformsofnewinvestmentsthatboostnationalincome,increasetaxrevenues,andprovidenewemployment;whereasthe

indirectbeneﬁtsareintheformsofexternalitiesthataregeneratedthroughnon-marketmechanismstorecipienteconomies

anddomesticﬁrmswithintheeconomies(Hymer,1960).TheseindirectbeneﬁtsarecommonlyknownasFDIspillovers.

TheliteratureidentiﬁesatleastthreetypesofFDIspillovers.Theseareproductivityspillovers,market-accessspillovers,

and pecuniaryspillovers.Productivityspilloversaredeﬁnedastheexternalities fromFDI thatleadtoincreasesinthe

productivityofdomesticﬁrms(Aitken&Harrison,1999).Market-accessspilloversexistwhenthepresenceofFDIgenerates

anopportunityfordomesticﬁrmstoaccessinternationalmarkets(Blomstrom&Kokko,1998).Pecuniaryspillovershappen

iftheexistenceofFDIaffectstheproﬁtfunctionsofdomesticﬁrmsthroughareductionincostsoranincreaseinrevenues

(Gorg&Strobl,2005).

OfthethreetypesofFDIspillovers,productivityspillovershavebeenaparticularconcernamongpolicymakersand

researchersinthelasttwodecades.VariousincentiveshavebeenprovidedbypolicymakerstoattractFDIandsubstantial

effortshavebeendevotedbyresearcherstoevaluatetheproductivityadvantage.However,theempiricalevidenceismixed

atbest.Somestudiesﬁndevidenceofpositiveproductivityspillovers(Caves,1974;Javorcik,2004;Kugler,2006;Schiff&

Wang,2008;Temenggung,2007),butothersdiscovernonexistentorevennegativespillovers(Aitken&Harrison,1999; Blalock&Gertler,2008;Djankov&Hoekman,2000).Thus,therelationshipbetweenFDIspilloversandﬁrmproductivity

remainsacontroversialissue.

2.2. Spillovereffectsandﬁrm-speciﬁccharacteristics

Some researchersarguethat themixedevidenceintuitively impliesthat thespillover effects arenotan automatic

consequenceoftheforeignpresenceinaneconomy,rathertheydependsigniﬁcantlyonthecharacteristicsofﬁrmsinthe

industries(Gorg&Greenaway,2004;Lipsey&Sjoholm,2005;Smeets,2008).Oneimportantcharacteristicofﬁrmsisthe

technologygapbetweenforeignanddomesticfirms.InastudyonUKmanufacturingfirms,Griffith,Redding,andSimpson

(2002)ﬁndthatthewiderthetechnologygapthelargertheFDIspillovereffectsthatareobtainedbydomesticﬁrms.This

ﬁndingindicatesabeneﬁtofbeinglessadvancedintermsoftechnology,whichsupportsthetheoreticalargumentinFindlay

(1978).AsimilarresultisdiscoveredalsobyCastellaniandZanfei(2003)forFranceandSpain,andbyPeriandUrban(2006)

forItalyandGermany.

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Althoughthereisanadvantageinbeinglessadvanced,thetechnologygapshouldnotbetoowide(Wang&Blomstrom,

1992).Aminimumleveloftechnologyisrequiredfordomesticﬁrmstoabsorbthenewtechnologyfromforeignﬁrms.When

thegapistoowide,thelimitedkindabsorptivecapacityofdomesticﬁrmsmaynotpermitassimilationthenewtechnology

(Glass&Saggi,1998).

2.3. TechnicalefﬁciencygainsfromFDIspillovers

EarlierstudiesonFDIproductivityspilloversfocusontechnologyadvantages(Gorg&Greenaway,2004).Theknowledge

from foreignﬁrms is regarded synonymously withtechnological knowledge, as this is consistent with the useof a

conventionalproductionfunction.Managerialandorganizationalknowledgethatmayleadtoefﬁciencyspilloversarenot

portrayedsinceﬁrmsareassumedtobeproducingatthelong-runequilibriumwithafullefﬁciencycapacity.Thus,the

productivityspilloversintheseearlystudiesareidenticallymeasuredastechnologyspillovers.

Morerecentstudiesfocusonbothefﬁciencyandtechnologyadvantages.Inthesestudies,knowledgeisdeﬁnedbroadlyas

product,process,managerial,andorganizationalknowledge.Hence,productivityspilloversleadtobothtechnologyand

efﬁciencyadvantages.Unfortunately,studiesthatinvestigateefﬁciencyadvantagesarenotplentiful.InastudyonGreek

manufacturingfirms,DimelisandLauri(2002)examinetheeffectofforeignequitysharesonefficiencyandfindapositive

relationshipbetweenthesetwovariables.Also,GhaliandRezgui(2008)analyzetheTunisianmanufacturingsectorandﬁnd

thathigherforeignshareincreaseﬁrmefﬁciency.Addressingthesameissuebutemployingadifferentestimationmethod,

ourstudyinvestigatestheefﬁciencyspilloversinIndonesianmanufacturingﬁrms.WeextendthestudiesbyDimelisand

Lauri(2002)andGhaliandRezgui(2008)byfocusingonverticalspilloversaswellashorizontalspillovers.

3. Methodology,dataset,andvariables

3.1. Methodology

There are twocommonly used methods in measuringefﬁcienciesand productivityat theﬁrm level, namelydata

envelopment analysis(DEA) and stochastic frontieranalysis (SFA).1 _Each_of _the_two _methods _has_its _advantages _and

disadvantages,asexplainedbelow.Thechoicebetweenthesemethodsthusdependsontheobjectiveoftheresearch,the

typeofﬁrmsinthechosenindustry,andthenatureofthedata(Coelli,Rao,O‘Donnell,&Battese,2005;Olesen,Peterson,&

Lovell,1996).

DEAisalinearprogrammingmethodthatobservesproductionpossibilitiesusingthetechniqueofenvelopmentand

measuresefﬁciencyasthedistancetothefrontier(Banker,Charnes,&Cooper,1984;Charnes,Cooper,&Rhodes,1978).This

methodhastheprimaryadvantageofbeingofanon-parametricnatureandhastheabilitytohandlemultipleoutputsand

multipleinputs.2_However,_it_has_the_disadvantage_of_producing_biased_estimates_in_the_presence_of_measurement_error_and

otherstatisticalnoise,asthismethoddoesnotseparatethestochasticrandomnoisefromtheinefﬁciencyeffects(Schmidt,

1985).Hence,theestimationresultsunderthismethodtendtobeverysensitivetosmallchangesinthedata.

Alternatively,thestochasticfrontiermethodisaregression-basedmethodthatassumestwoseparateunobservederror

terms,onerepresentsefﬁciencyandtheotherrepresentsstatisticalnoise(Aigner,Lovell,&Schmidt,1977;Meeusen&van

denBroeck,1977).Ithasachiefadvantageintheabilitytomeasureefﬁciencyinthepresenceofstatisticalnoise.However,

thismethodisparametricandrequiresaspeciﬁcfunctionalformanddistributionalassumptionsfortheerrorterms(Coelli

etal.,2005).

InthisstudythestochasticfrontiermethodisappliedtoanalyzethespillovereffectsfromFDI.Theone-stagestochastic

productionfrontier(SPF)isusedtoestimateaproductionfrontierandatechnicalinefﬁciencyfunctionsimultaneously.As

pointed out by Kumbhakar,Ghosh, and McGuckin(1991) and Wangand Schmidt (2002),the one-stage approach is

preferable than thetwo-stage approach,as thelatter exhibitsat least twolimitations in estimation that can leadto

potentiallyseverebias.Theﬁrstlimitationisthattechnicalefﬁciencymightbecorrelatedwiththeproductioninputs,which

maycauseinconsistentestimatesoftheproductionfrontier.ThesecondlimitationistheOLSmethodinthesecondstageis

inappropriatesincetechnicalefﬁciencydistributionisassumedtobeone-sided.Consideringtheadvantages,thecurrent

studyadoptstheone-stageapproach,followingBatteseandCoelli(1995).

TheBattese–Coelliproductionfrontiercanbeexpressedasfollows:

yit¼ fðxit;t;

b

Þexpð

v

ituitÞ (1)

andtheinefﬁciencyfunctionmaybewrittenas:

uit¼zitdþwit (2)

1

ComprehensivereviewsofthetwomethodsareprovidedbyForsundetal.(1980),Bauer(1990),Bjureketal.(1990),Bravo-UretaandPinheiro(1993),

Greene(1993),Lovell(1993),andCoelli(1995).

2 _The_{non-parametric}_nature_of_DEA_allows_for_measuring_efﬁciency_without_imposing_a_speciﬁc_functional_form_and_a_{distributional}_assumption_on_data.

Suyantoetal./JournalofAsianEconomics33(2014)16–29

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whereyitdenotestheproductionoftheithﬁrm(i=1,2,...,N)inthetthtimeperiod(t=1,2,...,T),xitdenotesa(1k)vector

ofexplanatoryvariables,

b

representsthe(k1)vectorofparameterstobeestimated,expdenotesexponential,

v

_itisthetime

speciﬁcandstochasticerror,withiidN(0,

s

2

v),anduitrepresentstechnicalinefﬁciency,whichisassumedasafunctionofa

(1j)vectorofobservablenon-stochastic explanatoryvariables,zit,anda(j1)vectorofunknownparameterstobe

estimated,

d

,andwitisanunobservablerandomvariable.

TheparametersofEqs.(1)and(2)areestimatedusingthemaximumlikelihoodestimator(MLE)byfollowingthethree

stepsasexplainedinCoelli(1996).Withsimultaneousequationestimation,theMLEestimatesareunbiasedandefﬁcient.

ThevarianceparametersoftheBattese-Coelli’smodelaredeﬁnedas

s

2

sBB

s

2v+

s

2

uand

g

BB

s

2u/

s

2s.3

g

isanimportantparametertodecidewhetherthereistechnicalinefﬁciencyornotinthemodel.Iftheestimatedvalueof

g

isnotstatisticallysigniﬁcant,thereisnotechnicalinefﬁciencyandtheresultsobtainedfromestimatingEq.(1)byordinary

leastsquares(OLS)wouldbeefﬁcient.Incontrast,iftheestimatedvalueof

g

isstatisticallysigniﬁcant,thenthereistechnical

inefﬁciencyandEqs.(1)and(2)shouldbeestimatedsimultaneously.

ThetechnicalefficiencyoftheithfirmcalculatedfromtheEqs.(1)and(2)istheratioofobservedoutputofthefirmtoits

potentialmaximumoutput,whichcanbewrittenas:

TEit¼

yit

yP

it

¼expðuitÞ (3)

FollowingBatteseandCoelli(1988),thebestestimatoroftheexp(uit)isitsconditionalexpectation,E[exp(uit)],so

technicalefﬁciencycanbewrittenas:

TEit¼E½expðuitÞ (4)

Ifitisassumedthattheproductionfrontiertakestheformofalog-linearproductionfunctionandtherearefourinput

variables(labour,capital,material,andenergy)intheproductionprocess,theempiricalmodelcanbeexpressedinnatural

logarithmsofvariablesas:

lnYit¼

b

oþ

b1

lnLitþ

b2

lnKitþ

b3

lnMitþ

b4

lnEitþþ

b5

Tþ

b6

lnFDlSectorþ

v

ituit (5)

whereYisoutput,Lislabour,Kiscapital,Mismaterial,Eisenergy,Tisatime-trendvariablethatincreasesbyoneforeach

year,FDI_SectorisameasureofFDIhorizontalspilloversasexplainedinthenextsectionandtheothervariablesareas

previouslydeﬁned.

TheinefﬁciencyeffectasafunctionofasetofFDIvariables,ayeardummy,anindustrydummy,andaﬁrmdummycanbe

writtenas:

uit¼

d0

þ

d1

FDIFirmitþ

d2

FDISectorjtþ

d3

FDIFirmitFDISectorjtþ

d4

Yearþ

d5

Industryþ

d6

Firmþwit (6)

whereFDI_Firmisadummyvariableforforeigndirectinvestmentthattakesavalueofzeroifaﬁrmhasnoforeignownership

shareandtakesavalueofoneifaforeignﬁrmhasapositiveshare,FDI_Sectorisasdeﬁnedabove,Yearisayeardummy

variable,IndustryisanindustrydummyandFirmisaﬁrmdummy.TheinteractiontermofFDI_FirmFDI_Sectorisincluded

intheinefficiencyequationtoestimatewhetherforeignanddomesticfirmsbenefitequallyfromthepresenceofanew

foreignfirm.Apositive(negative)coefficientontheinteractiontermindicatesless(more)efficiencygainforforeignfirms

thanfordomesticﬁrms.

Eq.(6)isusedtoestimatetheintra-industryspillovers,whichcapturetheeffectsofforeignpresenceonthetechnical

efﬁciencyofﬁrmsinthesameindustry.Theinter-industryspilloversarecommonlyestimatedbyreplacingthe

horizontal-spillovervariable(FDI_Sector)withvertical-spillovervariables.Theinefﬁciencyfunctionfortheinter-industryspilloverscan

beexpressedas:

uit¼

d0

þ

d1

FDIFirmitþ

d2

FDIDownstreamSectorjtþ

d3

FDIFirmitFDIDownstreamSectorjtþ

d4

Yearþ

d5

Industry

þ

d6

Firmþwit (7)

or

uit¼

d0

þ

d1

FDIFirmitþ

d2

FDIUpstreamSectorjtþ

d3

FDIFirmitFDIUpstreamSectorjtþ

d4

Yearþ

d5

Industry

þ

d6

Firmþwit (8)

whereFDI_Downstream_Sector isaproxyfor spillovereffectsfromforeignﬁrmstoforeignand domesticsuppliersand

FDI_Upstream_Sectorisaproxyforspillovereffectsfromforeignﬁrmstoforeignanddomesticbuyers.

3

Thecompletederivationthelog-likelihoodfunctionoftheBattese-CoellimodelanditsrelatedvarianceparametersarediscussedinBatteseandCoelli (1993).

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3.2. Dataanddatasetconstruction

Theprimarydataforourstudyaretheannualsurveysofmediumand largemanufacturingestablishments(Survey

TahunanStatistikIndustriorSI)conductedbytheIndonesianCentralBoardofStatistics(BadanPusatStatistikorBPS).These

annualsurveyscoverawiderangeofinformationfromeachsurveyedestablishment.Thebasicinformationincludesyearof

starting production, industrial classification, location, and the specific identification code. There is also information

regardingownership,whichincludesforeignanddomesticownership,andinformationrelatedtoproduction,suchasgross

output, number of workers in production and non-production, value of ﬁxed capital, material usage, and energy

consumption.

Theannualsurveyshavebeenconductedsince1975andthemostrecentavailabledatarelatestotheyear2007.However,

thisstudyusesthedatafrom1988to2000.Theyear1988ischosenasastartingyearsinceitistheﬁrstyearthatthe

replacementvalueofﬁxedassets,whichisusedasameasureforcapital,isavailable.Theyear2000isselectedasthelastyear

becausetheBPSchangedthespecificidentificationcodein2001tothenewidentificationcode(KIPN)withoutprovidinga

concordancetabletotheprevioususedidentiﬁcationcode(PSID).Effortstomatchtheobservationsintheyears2001–2005

totheyears1988–2000usingoutputvaluesandlabourdonotyieldconsistentresults.Therefore,thelongestpossibleperiod

forthisstudyis1988–2000.

Inconstructingaconsistentdataset,severaladjustmentsareconducted.Theseincludeadjustmentforindustrialcode,

adjustmentforvariabledeﬁnitions,cleaningfornoiseandtypologicalerrors,backcastingmissingvaluesofcapital,matching

ﬁrmsforabalancedpanel,andchoosingindustrieswithforeignﬁrms.Thebalancedpaneldataarepreferableinthisstudy

duetotwoadvantages:(1)itenablestracingthetechnicalefﬁciencyscoresofeachobservedﬁrmduringtheperiodofstudy;

(2)itremovestheinﬂuenceofaﬁrmthatappearsonlyinoneortwoyears,whiletheperiodofestimationisfor13years.The

detailsofadjustmentsarepresentedinAppendix1.Aftertheadjustments,theﬁnalbalancedpanelofdataconsistsof3318

establishmentswith43,225observations.

Toshowtheinﬂuenceoftheconstructionofthebalancedpaneldataset,thedescriptivestatisticsoftherelatedvariables

arecalculatedforthebalancedpaneldataandfortheoriginaldatabeforetheadjustmentprocess.Theoriginaldataconsistof

establishmentsthatdonotreportcompleteinformationon output,labour,capital,material,orenergy.Thereforethese

establishmentsarenotincludedinthecalculationofthedescriptivestatisticsfororiginaldata.FollowingTakii(2005),(1)0.5

percentobservationswiththelowestvaluesofoutputand1.5percentobservationswiththehighestvaluesofoutputare

removed.Afterthesedeletions,thedescriptivestatisticsfortheoriginaltotaldata,aspresentedinTable1,consistsof24,188

establishmentsforanunbalancedpanelof238,628observations.

Table1showsthattheminimumvaluesofvariableslnY,lnL,lnK,lnM,lnEfortheoriginaldataarelowerifcomparedtothe

minimumvaluesofthosevariablesfromthebalancedpanel.Thismakessenseasthebalancedpaneldataremovessome

observations duringthe adjustment process. The maximum values of those variables are higher in theoriginal data

comparedtothoseinbalancedpaneldata.Themeanvaluesoftheseﬁvevariablesarehigherinthebalancedpaneldata

comparedtothoseinoriginaldata,whilethestandarddeviationsoftheseﬁvevariablesarelowerinbalancedpanelwhen

comparedtothoseinoriginaldata.

ForFDI_Firm,theminimumvalueiszeroandthemaximumvalueisonebothfororiginaldataandthebalancedpanel

data,becausethisvariableisadummyvariable.Further,theminimumvalueandthemaximumvalueofvariablesFDI_Sector,

Table1

Descriptivestatisticsfortheoriginaldataandthebalancedpaneldata.

Originaldataa

Balancedpaneldata

Min Max Mean SD Min Max Mean SD

ProductionFrontier

lnY 6.461 20.980 12.514 2.256 6.591 20.761 13.964 2.006

lnL 2.398 10.649 4.079 1.327 2.639 10.292 4.702 1.088

lnK 4.105 23.398 12.308 2.268 4.220 23.106 13.152 2.245

lnM 3.871 20.033 11.765 2.418 4.239 19.454 12.164 2.221

lnE 1.791 16.583 9.377 2.221 1.882 15.836 9.587 2.077

FDI_Sector 0 1.492 0.208 0.218 0 1.492 0.234 0.209

InefﬁciencyFunction

FDI_Firm 0 1 0.064 0.273 0 1 0.072 0.258

FDI_Sector 0 1.492 0.208 0.218 0 1.492 0.234 0.209

FDI_Downstream_Sector 0.002 5.443 0.176 0.212 0.002 5.443 0.176 0.204

FDI_Upstream_Sector 0 0.921 0.160 0.181 0 0.921 0.160 0.174

NumberofEstablishments 24,188 24,188 24,188 24,188 3318 3318 3318 3318 NumberofObservation 231,064 231,064 231,064 231,064 43,225 43,225 43,225 43,225

Source:Authors’calculationsfromtheannualsurveysoftheIndonesianCentralBoardofStatistics(BadanPusatStatistikorBPS).

Y=output,L=labour,K=capital,M=materialandE=energy.

a_The_original_data_in_this_table_exclude:₍₁₎_the_{establishments}_that_do_not_report_information_on_output,_labour,_capital,_material,_or_energy;₍₂₎_1.5

percentobservationswiththelowestvaluesofoutputand1.5percentobservationsthehighestvaluesofoutput.

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FDI_Backward,andFDI_Forwardarethesamefororiginaldataandforthebalancedpanel,asthecalculationofthese

inter-industryvariablesisbasedonallﬁrmsintheoriginaldataasinBlalockandGertler(2008).Themeanvaluesofthesethree

spillovervariablesarehigherinthebalancedpanelcomparedtothoseintheoriginaldata,whereasthestandarddeviations

arelowerinbalancedpanel.FromthedescriptivestatisticsinTable1,theauthorsconcludethatthereisnosubstantialbiasin

theadjustmentprocesssincethereisnosubstantialdifferenceinthemaximumvalue,minimumvalue,meanvalue,and

standarddeviation.

3.3. Measurementofvariables

Therearetwosetsofvariablesincludedinthisstudy:productionvariablesandinefﬁciencyvariables.Theproduction

variables consistofoutput,labour,capital,material,energy,time trendandFDI_Sector,whiletheinefﬁciencyvariables

includeFDIvariables(FDI_Firm,FDI_Sector,FDI_Upstream_Sector,andFDI_Downstream_Sector),ayeardummy,anindustry

dummy,andaﬁrmdummy.TheprecisedeﬁnitionofeachvariableisgiveninAppendix2.

Inthisstudy,grossoutputisusedasthemeasureforoutput(y).Itreferstothetotalvalueofoutputproducedbyaﬁrm.

Thenumberofemployeesdirectlyandindirectlyengagedinproductionisusedforthemeasureoflabour(L).Asameasureof

capital(K),thisstudyusesthereplacementvalueofcapital,whilematerial(M)ismeasuredusingthetotalvalueofrawand

intermediatematerialsandenergy(E)ismeasuredasthesumofelectricityandfuelexpenses.

FDI_Firmismeasuredbyadummyofforeigndirectinvestment,whichtakesavalueofoneifaﬁrmhasapositiveforeign

ownershipandtakesavalueofzeroifotherwise.AsameasurefortheFDIhorizontalspillovers,thisstudyusestheshareof

foreignﬁrmoutputtothetotaloutputattheﬁve-digitISICsectorallevel,whichisexpressedasinAitkenandHarrison

(1999):

FDISectorjt¼

P

i8i2jFDIFirmityit P

i8i2jyit

(9)

Eq.(9)capturestheeffectofFDIatthesectorallevelonproductivityattheﬁrmlevel.Itshowsthespillovereffectsof

foreignpresenceondomesticﬁrmsinthesameﬁve-digitISICindustry.

TwoalternativemeasuresofFDIspilloversinthisstudyaremeasuresofinter-industryspillovers.Thepresenceofforeign

firmsin certainfive-digit ISICindustriesmay createproductivityexternalities forfirmsin upstreamanddownstream

industries.Thisstudymeasurestheinter-industryspilloversbyusingvariablesthatreﬂecttheextentofbackwardand

forward linkages between industries. Following Javorcik(2004), themeasure for FDI spillovers fromforeign ﬁrmsin

industriesk(k6¼j)thatarebeingsuppliedbydomesticﬁrmsinindustriesjis:

FDIDownstreamSectorjt¼

X

kifk6¼j

ajk

FDISectorkt (10)

where

a

jkistheproportionofsectorj’soutputsuppliedtosectork,whichistakenfromtheinput–output(IO)matrixof

four-digitindustries.4Similarly,themeasureforFDIspilloversfromforeignﬁrmsinindustriesmwhoseproductsarebought

bydomesticﬁrmsinindustriesnis:

FDIUpstreamSectormt¼

X

nifn6¼m

g

mnFDISectornt (11)

where

g

mnistheproportionofinputspurchasedbyindustrynfromindustrymintotalinputsourcedbyindustryn,which

istakenfromtheinput–output(IO)matrixoffour-digitindustries.

Atime-trendvariableisincorporatedintheproductionfunctiontomeasuretechnicalchange.Thetime-trendvariable

takesavalueofonefortheyear1988,avalueoftwofortheyear1989,andsoon.Anindustrydummycaptureseffectsspeciﬁc

toaparticularindustryand hasa valueofoneforanindustry foranobservationofthatindustryand avalueofzero

otherwise.Asimilarprocedureisalsoappliedtotheﬁrmdummyandyeardummyvariables.

4. Empiricalresults

Weestimatea stochasticfrontierestimationandﬁrsttestforconstantreturnstoscaletocheckwhethertheCobb–

Douglasproductionfrontierisbestsuitedtothedata.FollowingtheprocedureofjointrestrictiontestinBaltagi(2011,p.80),

thetestofconstantreturnstoscaleisconductedunderthenullhypothesisthatthesumoftheestimatedparameters(

b

i)in

4_During_the_selected_period_in_this_study,_there_are_four_available_IO_matrixes,_which_were_published_in_1990,_1993,_1995,_and_2000._This_study_uses_these

fourinput-outputmatrixesforcalculatingthebackwardcoefﬁcientajk.Thefollowingistheprocedureforobtainingvaluesofajk.Valuesofajkbeforeand

including1990aretakenfromthe1990IOmatrix.Valuesofajkfor1991and1992arelinearlyinterpolatedfromthe1990and1993IOmatrixes.Valuesof

ajkfor1993aretakenfromthe1993IOmatrix.Valuesofajkfor1994arecalculatedfromthelinearinterpolationofthe1993and1995IOmatrixes.Valuesof

ajkfor1995aretakenfromthe1995IOmatrix.Valuesofajkfrom1996to1999arelinearlyinterpolatedfromthe1995andthe2000IOmatrixes.Finally,

valuesofajkfor2000aretakenfromthe2000IOmatrix.

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productionfrontierinEq.(5)isequaltoone.Theregressionsumofsquaresforunrestrictedmodel(RSSU)is39,631.63,

whereastheregressionsumofsquaredforrestrictedmodel(RSSR)is25,549.50.TheF-statisticsis392.52,suggestingthatthe

nullhypothesisisrejected.ThisresultconﬁrmsthattheCobb–Douglasproductionfrontierisnotthebestsuitedmodelfor

thestochasticfrontierestimation.Rather,asthesumofthecoefﬁcients oftheinputvariablesis greaterthanone,the

unrestrictedmodelwithvariablereturnstoscaleisappropriateandisusedbelow

4.1. Intra-industryspillovers

Webeginwithestimationofintra-industryspillovers.UsingEqs.(5)and(6),theproductionfrontierandtheinefﬁciency

functionare estimatedsimultaneouslyfor observing theeffects offoreign investmenton theproduction frontierand

technicalefficiencyoffirms.Fortheinefficiencyfunction,thetechnicalefficiencyvariable(uit)isspecifiedasafunctionofa

foreignsharedummy(FDI_Firm),theshareofforeignﬁrms’outputsovertotaloutputsinthefour-digitindustry(FDI_Sector),

andaninteractingtermbetweenFDI_FirmandFDI_Sector.Whenforeigninvestmentincreasestheﬁrm’stechnicalefﬁciency,

thecoefﬁcientofFDI_Firmisnegative.5_When_technology_spills_over_from_ﬁrms_with_foreign_direct_investment_to_purely

domesticﬁrmsinthesameindustry,thecoefﬁcientofFDI_Sectorisnegative.Asfortheinteractionterm,thesignofthe

coefficientshowswhetherornotforeigndirectinvestmentaffectsthefirm’sabilitytobenefitfromspilloversoriginating

fromotherforeign-ownedﬁrmsinthesameindustry.

Weestimatefouralternativemodelsinordertotesttherobustnessoftheestimatedparameters.Intheﬁrstmodel,ayear

dummyandanindustrydummyareincludedintheinefﬁciencyequation.Theestimatedparametersarepresentedinthe

Model(1)columnofTable2.Theresultsfromtheproductionfrontiershowthatthefourinputvariablescontributepositively

andsigniﬁcantlytooutput,suggestingapositiveelasticityofeachinputonoutput.Thereisalsoapositiveandstatistically

signiﬁcantcoefﬁcientof thetime-trendvariableindicating that technicalchangecontributespositivelytooutput.The

positiveandstatisticallysigniﬁcantcoefﬁcientofFDI_Sectorsuggestshorizontalspilloversfromintra-industryforeigndirect

investmentincreasetheproductionfrontierforallﬁrms.

Fromtheestimatesoftheinefﬁciencyfunction,whichisthemainfocusofthisstudy,thecoefﬁcientofFDI_Firmis

negativeandhighlysignificant,indicatingthatforeigndirectinvestmentdecreasesthefirm’stechnicalinefficiency.This

suggeststhatfirmswithforeignownershipare,onaverage,moreefficientthanpurelydomesticfirms.Thisfindingconfirms

theargumentinCaves(1971)andDunning(1988)thatforeignﬁrmsaremorelikelytooperateontheproductionfrontier.

Furthermore,thenegativeand statisticallysigniﬁcantestimateofFDI_Sectorsuggeststhat knowledgespillsoverfrom

foreign-ownedfirmsincreasesthetechnicalefficiencyofallfirmsintheindustry.Thisresultisinlinewiththeargumentin

WangandBlomstrom(1992)andﬁndingsinGhaliandRezgui(2008).ThisresultisalsoconsistentwithﬁndingsinTakii (2005),Temenggung(2007)andBlalockandGertler(2008),whichusedifferentmethodsofanalysis.

Thepositivesignificantestimateofinteractingtermmeansthat,althoughtheforeign-ownedfirmsalsobenefitfromthe

presenceofotherforeigninvestmentintheindustry,thebeneﬁtissmallerthanfordomesticﬁrms.Giventhattheestimated

coefficientofFDI_FirmandtheestimatedcoefficientofFDI_Sectorarenegativeandstatisticallysignificant,thepositive

coefﬁcient of the interaction term means that uit/FDI_Firm=0.5763+0.0330FDI_Sector and that uit/FDI_Sector=

0.2224+0.0330FDI_Firm.AsbothFDI_FirmandFDI_Sectorareeachalwayslessthanorequaltoonebyconstruction,

theneteffectof FDI_Sectoris negativefor allforeignﬁrmsaswellasdomestic ﬁrms.However,themagnitudeofthe

improvementinefficiencyfromhavingforeignfirmsintheindustryisalwaysgreaterfordomesticfirmsthanforforeign

ﬁrms.

Inaddition,weconductjointsigniﬁcancetest(F-test)onthemagnitudeofspilloversforforeignestablishmentsinorder

tochecksigniﬁcanceofthedirecteffectandtheinteractingeffectofspilloversonforeignﬁrms.6_The_value_of

F-statisticis

calculatedfromthelog-likelihoodvalueoftheunrestrictedmodelandtheloglikelihoodvalueoftherestrictedmodel(when

boththecoefﬁcientofFDI_SectorandthecoefﬁcientofinteractingvariableFDI_FirmFDI_Sectorequaltozero).Thevalueof

loglikelihoodfortheunrestrictedmodelis7704.48,whereasthevalueofloglikelihoodfortherestrictedmodelis7643.00,

Sothat,theF-statisticis13.22,whichsuggeststhattheunrestrictedmodel(byincludingvariablesFDI_Sectorandinteracting

variableFDI_FirmFDI_Sector)isthecorrectmodelandthetwovariablesarejointlysigniﬁcantaffectingspilloverson

foreignestablishmentsat1%level.

Theestimatedcoefficientofyeardummyisnotstatisticallysignificant,suggestingthatonaveragethereisnosignificant

differenceintechnicalinefficiencyscoresoffirmsacrossthesampleyears.Thestatisticallysignificantestimatedcoefficient

ofindustrydummysuggeststhatthereisasignificantdifferenceininefficiencyscoresacrossfive-digitindustries.

Thehighlysigniﬁcantestimateofgammaimplicatesthatestimationofstochasticfrontiershouldincludeaninefﬁciency

effect. This ﬁnding provides the justiﬁcation for the simultaneous estimation of stochastic production frontier and

inefﬁciencyequation.Inotherwords,themodelisappropriatelyrepresentingtheobservedﬁrms.

Inthesecondmodel,industrydummiesarereplacedbyﬁrmdummies,inordertocontrolforﬁrmheterogeneityacross

thesample.TheresultsaregivenintheModel(2)columnofTable2.Thesignandsigniﬁcanceofestimatesaresimilarto

5

Thedependentvariablefortheinefficiencyfunctionistechnicalinefficiency.ThenegativecoefficientofFDI_Firmindicatesthatforeigninvestment decreasesinefficiency,whichimpliesanincreaseinthefirm’sefficiency.

6 _We_are_grateful_to_one_of_the_reviewers_for_suggesting_this_point.

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thoseintheﬁrstmodel.Thenotabledifferenceisonlyinthemagnitudeoftheestimates.FocusingontheFDIvariables,the

magnitudesofcoefﬁcientsaresmallerinthissecondmodelcompared tothose intheﬁrstmodel. Inotherwords,the

inclusionofﬁrmdummyandtheexclusionofindustrydummyinthesecondestimation(Model2)resultsinasmallereffect

ofFDIspilloversontechnicalinefficiency.Thisisnotsurprising.Firm-specificeffectsarelargelycapturedbythefirmdummy,

whichremovesapotentialsourceofbiasintheestimatesofothercoefﬁcients.Notably,theresultsregardingthedirectionof

spillovereffectsarethesameasinthefirstmodel,asthecoefficientofFDI_Sectorisnegativeandstatisticallysignificantin

bothmodels.

Forthethirdmodel,onlyatimedummyisincludedasacontrollingvariableintheestimation.Theresultingestimates,

whicharepresentedintheModel(3)column,areverycomparablewiththeresultsinModel(1)andModel(2).Similar

ﬁndingsarealsoobservedinModel(4),whenthetimedummy,industrydummyandﬁrmdummyandareallexcludedfrom

estimation.TheresultsfromthesefourmodelsconﬁrmtherobustnessoftheestimatesofthepositivespilloversfromFDIon

thetechnicalefﬁciencyofdomesticﬁrms.

4.2. Inter-industryspillovers

Besidestheeffectsondomesticﬁrmsinthesameindustry,FDIcanalsogeneratespilloversondomesticﬁrmsinother

industries.Weestimatesixmodelsoftheinter-industryspillovers,andtheresultsofeachmodelarepresentedinTable3.

Theﬁrstthreemodelsareestimatedonthefullsampleandthelastthreemodelsareestimatedonthesub-sampleofonly

domesticﬁrms.Inthethreefull-samplemodels,theﬁrstmodelistocapturethesimultaneouseffectofthethreespillover

variablesontechnicalinefﬁciency.ThesecondandthethirdmodelfocusontheindividualeffectofeachoftheverticalFDI

spillovers(i.e.thedownstreamspilloverandtheupstreamspillover).Thesamestructureisalsoappliedtothesub-sampleof

onlydomesticﬁrms,withModel4capturesthesimultaneouseffectofthethreespillovervariables,Model5capturesthe

downstreameffectonly,andmodel6capturesonlytheupstreameffect.

In theﬁrstmodel (theﬁrst resultscolumnof Table3), thethree proxiesofspillover variables areincludedin the

estimations.Theresultsshowthatthehorizontalspillovervariable(FDI_Sector)hasanegativeandstatisticallysigniﬁcant

coefficient,suggestingthatanincreaseintheshareofforeignfirmoutputdecreasestechnicalinefficiencyacrossfirmsinthe

industry.Similarly,thespilloversfromFDIindownstreamindustriesalsodecreaseinefﬁciencyofsuppliers,asdemonstrated

bythenegativeandhighlysigniﬁcantcoefﬁcientofthebackwardspillovervariable(FDI_Downstream_Sector).Inaddition,

the coefﬁcientof the forwardspillover variable (FDI_Upstream_Sector) is negativeand highly signiﬁcant,indicating a

negativerelationshipbetweenFDIinsupplierindustriesandtheindustry’sowntechnicalinefﬁciency.Althoughweemploy

adifferentmethodologyanduseadifferentdataset,theﬁndingsaresimilartothoseinLiang(2007).

Inthesecondandthethirdmodels(thesecondandthethirdcolumnsofTable3),theimpactsofbackwardspillover

variableandtheforwardspillovervariableareestimatedseparately.Ineachmodel,themagnitudeofthecoefﬁcientofthe

includedspilloversvariableislargerthaninModel1,butneitherthesignnorthestatisticalsigniﬁcanceofthecoefﬁcient

changes.Clearly,thereismulti-colinearityamongthespilloversvariablesthatmakestheidentiﬁcationofseparateeffects

Table2

Estimatingintra-industryspillovers.

Variables Model(1) Model(2) Model(3) Model(4)

Productionfrontier

lnL 0.2227***

(0.0033) 0.2256***

(0.0031) 0.2197***

(0.0030) 0.2167***

(0.0031)

lnK 0.1018***_(0.0019) _0.1043***_(0.0017) _0.1023***_(0.0018) _0.1097***_(0.0012)

lnM 0.6263***_(0.0018) _0.6218***_(0.0018) _0.6223***_(0.0017) _0.6191***_(0.0022)

lnE 0.1128***_(0.0017) _0.1160***_(0.0017) _0.1165***_(0.0017) _0.1176***_(0.0016)

T 0.0007*

(0.0005) 0.0039**

(0.0006) 0.0066***

(0.0028) 0.0012***

(0.0003)

FDI_Sector 0.1224***

(0.0055) 0.2044***

(0.0065) 0.2687***

(0.0096) 0.1577***

(0.0065)

Inefﬁciencyfunction

FDI_Firm 0.5763***

(0.0264) 0.1550***

(0.0018) 0.1960***

(0.0104) 0.2362***

(0.0092)

(0.0896) 0.2000***

(0.0149) 0.1780***

(0.0027) 0.1819***

(0.0034)

FDI_FirmFDI_Sector 0.0330***_(0.0028) _0.0460***_(0.0036) _0.1035**_(0.0184) _0.0673***_(0.0086)

YearDummy 0.0002(0.0031) 0.0010(0.0009) 0.0010(0.0019) – IndustryDummy 0.0039*

(0.0008) – – –

FirmDummy – 0.0001**

(0.0000)a

– –

Sigma-squared 0.0416***

(0.0010) 0.0416***

(0.0005) 0.0413***

(0.0003) 0.0418***

(0.0003) Gamma 0.0380***

(0.0038) 0.0224***

(0.0083) 0.0086***

(0.0002) 0.0151***

(0.0020) Log-likelihood 7704.484 7759.086 7618.974 7572.755 NumberofObservations 43,225 43,225 43,225 43,225

Source:Authors’calculations.

Notes:Y=output,L=labour,K=capital,M=material,E=energy,T=timetrend.Standarderrorsareinparentheses.

a

Theestimatedstandarderroris0.000009. * Signiﬁcantatthe10%level.

** Signiﬁcantatthe5%level. *** Signiﬁcantatthe1%level.

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Table 3

Estimating inter-industry spillovers.

Variables Full sample (1) Full sample (2) Full sample (3) Domestic sample (4) Domestic sample (5) Domestic sample (6)

Production frontier

lnL 0.2264***

(0.0030) 0.2209***

(0.0030) 0.2197***

(0.0029) 0.2258***

(0.0012) 0.2238***

(0.0033) 0.2256***

(0.0033)

lnK 0.1007***

(0.0018) 0.1023***

(0.0018) 0.1019***

(0.0018) 0.0986***

(0.0018) 0.0999***

(0.0022) 0.0981***

(0.0019)

lnM 0.6255***_(0.0018) _0.6271***_(0.0018) _0.6268***_(0.0017) _0.6225***_(0.0014) _0.6236***_(0.0020) _0.6229***_(0.0017)

lnE 0.1117***_(0.0017) _0.1144***_(0.00170) _0.1159***_(0.0016) _0.1217***_(0.0014) _0.1226***_(0.0018) _0.1227***_(0.0018)

T 0.0002**

(0.0000)a

0.0028* (0.0013) 0.0004***

(0.0001) 0.0009**

(0.0006) 0.0021**

(0.0001) 0.0010***

(0.0002)

(0.0013) 0.0308***

(0.0038) 0.0217***

(0.0007) 0.0056***

(0.0007) 0.0572***

(0.0035) 0.0323***

0.0064

Inefﬁciency function

FDI_Firm 0.2945***

(0.0137) 0.3920***

(0.0393) 0.1257***

(0.0130) – – –

(0.0061) – – 0.2766***

(0.0275) – –

FDI_Downstream_Sector 0.0216***_(0.0021)

0.0715***_(0.0043) _–

0.0279***_(0.0047)

0.0548***_(0.0027) _–

FDI_Upstream_Sector 0.0462***_(0.0060)

0.1842***_(0.0097)

0.0682***_(0.0175) _–

0.3067***_(0.0214)

Year Dummy 0.0018*

(0.0006) 0.0050*

(0.0017) 0.0017**

(0.0003) 0.0011***

(0.0002) 0.0046**

(0.0005) 0.0002***

(0.0010) Firm Dummy 0.0000b***

(0.0000)c

0.0000d***

(0.0000)e

0.0000f***

(0.0000)g 0.0001*** (0.0000)h 0.0001** (0.0000)i 0.0001***

(0.0000)j*

(0.0003) 0.0416***

(0.0003) 0.0405***

(0.0003) 0.0411***

(0.0007) 0.0418***

(0.0001) 0.0405***

(0.0004) Gamma 0.0194***

(0.0013) 0.0417***

(0.0040) 0.0124***

(0.0008) 0.0612***

(0.0111) 0.0709***

(0.0019) 0.0561***

(0.0045) Log-likelihood 7849.487 7668.081 7750.109 8118.497 8001.479 8040.274 Number of Observations 43,225 43,225 43,225 40,042 40,042 40,042

Source: Authors’ calculations.

Notes:Y= output,L= labour,K= capital,M= material,E= energy,T= Time trend. Actual estimates area

0.00004,b 0.000034,c 0.0000017,d 0.000034,e 0.0000019,f 0.000034,g 0.0000014,h 0.0000024,i 0.000012,j

0.0000035. Standard errors are in parentheses. * Signiﬁcant at the 10% level.

** Signiﬁcant at the 5% level. *** Signiﬁcant at the 1% level.

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difficult.ThecoefficientoftheFDI_Downstream_Sectorbeingnegativeandstatisticallysignificantatthe1%levelinboth

Model1andModel2,indicatesarobustﬁndingthattheforeignentryinathree-digitindustrydecreasesthetechnical

inefﬁciencyofdomestic suppliers(i.e.positivebackwardspillovers).Similarly, thenegativeandstatisticallysigniﬁcant

coefﬁcientoftheFDI_Upstream_SectorinbothModel1andModel3indicatesarobustﬁndingthatthepresenceofforeign

ﬁrmsinathree-digitindustrydecreasestheinefﬁciencyofdomesticbuyers(i.e.positiveforwardspillovers).

Toisolatethespillovereffectsononlydomesticﬁrms,weestimatetheModels1through3forthesub-sampleofonly

domesticﬁrms.TheestimationresultsarepresentedinthefourththroughsixthresultcolumnsinTable3.Theresultsare

similartothoseforthefullsampleoffirmsintermsofthesignsandsignificanceofthecoefficients.However,itisnotable

thatthecoefﬁcientsforthespilloversvariablesinthedomesticﬁrmsamplearegenerallyoflargermagnitudethanthe

correspondingcoefﬁcientsforthefullsample.ThisprovidesfurtherevidencetosupportthatfromtheresultsinTable2

showingthatspilloversfromforeignfirmsaremorebeneficialforpurelydomesticownedfirmsthanforfirmswithdirect

foreigninvestment.

GiventheresultsfromTable3,weconcludethatthespillovereffectsfromFDIdecreasetechnicalinefﬁciencyofdomestic

firms inupstreamand downstream industries.Thesefindings confirmtheargumentin Javorcik(2004) thata foreign

presenceinadomesticmarketmaygeneratenotonlyspillovereffectsondomesticﬁrmsinthesameindustrybutalso

providespilloverbeneﬁtstodomesticﬁrmsintheupstreamanddownstreamindustries.

4.3. Spillovereffectsandtheleveloftechnicalefﬁciency

Sofar,theanalysispoolstogetherallﬁrmswithdifferentlevelsofefﬁciency.Ithasadvantageofshowingtheaverage

effectofFDIspilloversonaﬁrm’stechnicalefﬁciency.However,ithasadisadvantageinthatthespillovereffectsareassumed

tobeuniformforallﬁrms.Thus,theanalysisdoesnotclearlydistinguishwhichﬁrmsgainthemostspillovereffectfromFDI.

Inthissection,theanalysisisextendedtoansweraquestionofwhetherthelevelofefﬁciencyinﬂuencestheabilityof

firmsinabsorbingspilloverbenefits.Thefirmsaredividedintotwogroups:firmswithahigh-efficiencylevelandthosewith

alow-efficiencylevel.Theproceduretogroupthefirmsisbysortingthefirmsfromtheonewiththehighesttechnical

efficiencyleveltothefirmwiththelowestefficiencylevel,andthenthesortedfirmsaredividedintotwo.Theupperhalfof

thedataiscategorizedasthehigh-efficiencyfirmsandthelowerhalfisthelow-efficiencyfirms.Theestimationresultsfor

thesetwogroupsofﬁrmsarepresentedinTable4.Weestimateresultsforthefullsampleofﬁrmsaswellasforthe

sub-sampleofonlydomesticﬁrms.

Startingfromthefullsampleestimations,thecoefﬁcientofFDI_Firmisnegativeandstatisticallysigniﬁcantbothamong

high-efficiencyfirms(column1ofTable4)andamonglow-efficiencyfirms(column2),suggestingthatforeign-ownedfirms

have a higher technical efficiencylevel in both groups offirms. The positive and significantcoefficient ofFDI_Sector

demonstratesthatspilloversattheindustriallevelincreasetheinefficiencyofthefirms(i.e.anegativeefficiencyspillover).

Incontrast,thelow-efficiencyfirmsexperienceadecreaseintechnicalinefficiencywhenforeignfirmsaremoreimportantin

theindustry(i.e.apositiveefficiencyspillover),asindicatedbyanegativeandhighlysignificantcoefficientofFDI_Sector

(column2).

Table4

Estimatingintra-industryspilloversinhigh-efficiencyandlow-efficiencyfirms.

Variables Fullsample Domesticsample

High-efficiencyfirms(1) Low-efficiencyfirms(2) High-efficiencyfirms(3) Low-efficiencyfirms(4)

Productionfrontier

lnL 0.2049***_(0.0047) _0.2258***_(0.0040) _0.2372***_(0.0018) _0.2012***_(0.0038)

lnK 0.1080***_(0.0032) _0.0985***_(0.0024) _0.1025***_(0.0024) _0.0911***_(0.0021)

lnM 0.6038***

(0.0023) 0.6634***

(0.0027) 0.5883***

(0.0036) 0.6900***

(0.0026)

lnE 0.1316***

(0.0027) 0.0835***

(0.0023) 0.1429***

(0.0013) 0.0791***

(0.0018)

T 0.0021**

(0.0009) 0.0001**

(0.0000)b

0.0022***

(0.0004) 0.0064***

(0.0003)

(0.0058) 0.0492**

(0.0141) 0.0849***

(0.0032) 0.0727**

(0.0133)

Inefﬁciencyfunction

FDI_Firm 0.0617***_(0.0088)

0.0096*_(0.0063) _– _–

FDI_Sector 0.0742***_(0.0062)

0.0556***_(0.0035) _0.0657***_(0.0038)

0.0660***_(0.0115)

YearDummy 0.0020*

(0.0014) 0.0027***

(0.0007) 0.0029***

(0.0004) 0.0015***

(0.0001) FirmDummy 0.0001***

(0.0000)a

0.0001***

(0.0000)c

0.0001***

(0.0000)d

0.0000e**

(0.0000)f

(0.0004) 0.0382***

(0.0004) 0.0414***

(0.0005) 0.0341***

(0.0006) Gamma 0.0369***

(0.0043) 0.0151***

(0.0023) 0.0540***

(0.0036) 0.0746***

(0.0019) Log-likelihood 3493.823 4697.164 3597.36 5417.533 NumberofObservations 21,612 21,613 20,021 20,021

Source:Authors’calculations.

Notes:Y=output,L=labour,K=capital,M=material,E=energyandT=timetrendActualestimatesare:a

0.0000042,b

0.000037c

0.000005d

0.0000076,e

0.000018,f

0.0000066.Standarderrorsareinparentheses. * Signiﬁcantatthe10%level.

** Signiﬁcantatthe5%level. *** Signiﬁcantatthe1%level.

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ThecoefﬁcientsofFDI_Sectorforthesub-sampleofonlydomesticﬁrms(columns3and4)areofthesamesignand

signiﬁcanceasinthecorrespondingfullsampleestimation,butthemagnitudeofimpactissomewhatlowerinthedomestic

firmsub-sample.ThissuggeststhatFDIspillovershavesmallerimpactondomesticfirmsthanonforeignfirmsinindustries

withlargetechnologygaps.

TheresultsinTable4demonstratethatﬁrmswithdifferentefﬁciencylevelsmayreceivedifferenteffectsofFDIspillovers.

High-efficiencyfirmstend toobtainnegativespillover effects,while low-efficiencyfirmsexperience positive spillover

effects.Thesefindingsconfirmtheargumentthatthereisadvantagefrombeinglessadvancedintermsofefficiencyinterms

ofbeneﬁttingfromspillovers(Glass&Saggi,1998;Wang&Blomstrom,1992)andareconsistentwiththeresultsinGrifﬁth

etal.(2002),CastellaniandZanfei(2003),andPeriandUrban(2006).

5. Conclusion

ThisarticleempiricallyexaminesthespillovereffectsofFDIonﬁrmtechnicalefﬁciencyintheIndonesianmanufacturing

sectorfortheperiodbetween1988and2000.UsingtheframeworkofBatteseandCoelli’s(1995)stochasticproduction

frontier,wefindevidenceofapositivespillovereffectofFDItofirmsinthesameindustry(competitors),firmsinanupstream

industry(suppliers),andﬁrmsinadownstreamindustry(buyers).Thepositivespillovereffectis observedinboththe

estimationforthefullsampleofﬁrmsandtheestimationforthesub-sampleofonlydomesticﬁrms.Notably,theeffectson

domesticﬁrmsaregenerallymorepowerfulthan