trade openness and gender wage gap: evidence from indonesia

25

Journal of International Cooperation Studies, Vol.26, No.1（2018.7）

Trade Openness and Gender Wage Gap: Evidence from Indonesia

Maryam JAMIELAA*and

Koji KAWABATA**

Ⅰ．Introduction

Manydevelopingcountrieshaveengaged intotheglobalmarketsinceearly1980s.As

acountrybecamemore integrated to themarket, tradevolumeandattractedmany foreign

direct investments inthatcountryalso increasesubstantially.Asaconsequence, thiseconomic

condition impactsmanysectors including itsdomestic labormarket.Accordingto literatures,

tradeliberalizationwillcreatedomesticcompetitionwhichtendstoreducediscriminationinlabor

market,includinggenderwagedisparity（Becker,1957）.

Inpast twodecades, Indonesiahasexperiencedan increase in total tradeandFDI.

Moreover,ascanbeseeninFigure1,theincreasingofFDIvolumeisalsofollowedbyawidening

trendon femaleandmaleearningsgap fromperiod2008-2014.Basedonthe facts, thisstudy

investigatestheimpactoftradeopennessongenderwagegapandhowitsimpactdiffersacross

wagedistribution.

Tradeliberalizationcanaffectgenderwagedifferentials,bothinnegativeorinpositive

ways.Firstly,accordingtoBecker（1957）, inordertobecompetitivewith foreigncompanies,

localfirmsarepushedtobemoreefficient inoperatingtheirproduction.Tobemoreefficient,

localcompanieswilleliminate their “tasteofdiscrimination”andpayworkers’wagebasedon

theirproductivity.Throughthismechanism,female-maleearninggapisbelievedtobedecreased

（ArteconaandCunningham,2002;BlackandBrainerd,2004）.Secondly, indevelopingcountries,

internationaltradewill inducetheirproductionshift into labor intensiveproductionwhichused

low-skilledlaborratherthanhigh-skilledlabor.Sincefemalestereotypeisattachedtolow-skilled

＊ DataandInformationstaff,CenterforStrategicStudies,SecretariatGeneral,MinistryofPublicWorks.＊＊ AssociateProfessor,GraduateSchoolofInternationalCooperationStudiesatKobeUniversity.

26 国　際　協　力　論　集　　第 26 巻第 1 号

labor,genderwagegapwillbereducedthroughthismechanism（Chenetal., 2013）.Another

literaturesuggeststhattradeopennessalsocreatesskilled-biasedtechnicalchange（STBC）which

leadstotheincreaseofdemandforhigh-skilledlabors（Bermanetal.,1994;Juhnetal.,2014;Lee

andWie,2015）.Since female labors fall into low-skilled labor inmostdevelopingcountries,as

aconsequence, thismechanismwilldecreaserelativewage for femaleworkersandwidenthe

earningsdisparity.

Genderinequalityhasbeenanimportantissuethatshouldbesolvedinordertogaina

sustainableeconomicdevelopment,andoneofdimensionsofgender inequality isgenderwage

disparity.Improvementingenderwageequalityishighlycorrelatedwithinclusivegrowth（ILO,

2015）.Byencouragingwomentocontributeineconomicdevelopment,inclusiveeconomicgrowth

canbegainedthroughthreechannelswhichareviaaccumulatedphysicalandhumancapital,

participation in labormarket,and increasedsavings（Arora,2012）.Inadditionto that,women

withhigheraccessto income labortendtouse largeproportionoftheir incomeforhealthand

educationwhichcanincreaseaccumulatedhumancapitalinsociety（Thomas,1997）.

Indonesiangovernmenthasbeenalreadyawaretheimportanceofwomenparticipation

ineconomicdevelopment.Toassureanequalparticipationofwomenandmen ineconomic

development, Indonesiangovernmentputgenderequalityprogramsasmainagendawhich

Figure 1 FDI and Wages （Full-time workers）

Source:StatisticsIndonesia（BPS）

27Trade Openness and Gender Wage Gap: Evidence from Indonesia

written inNationalMedium-TermDevelopmentPlan（RPJMN2015-2019）.However,according

toHumanDevelopmentReport2016,IndonesiaisstilloneofASEANmemberswithhighgender

inequality（0.47）.Intermofwages, femaleworkersstillearnlessthanmale labor,witharatio

ofabout0.80（StatisticsIndonesia,2015）.Intheotherwords,femaleworkersearn20%lessthan

theirmalecounterparts fordoingthesame jobs.Thisdisparity,at theend, leads intoanother

majorproblemwhich is thedecreaseofsharetonational income.Ascanbeenseen inFigure

2,inperiod2008-2014,sharewomentonationalincomestillbelowmen’sandthegapisgetting

wider,which iscausedbyfemale-maleearningsdifferentialsand imbalanceproportionbetween

maleand femaleworkers in labor forceparticipation（IndonesianHumanDevelopmentReport

Gender-Based, 2014）.Asimulationconductedby InternationalPovertyCenter showed that

Indonesia lostpotentialrevenueofUS$2.4billionperyearduetothedeclineofwomen labor

participationrateanddisparityinearnings（UnitedNations,2007）.Lookingatmajorimpactsof

female-maleearningsdifferentialsoneconomydevelopment,conductingresearchregardingthis

issueisimportant,especiallyinconditionwhereIndonesiawillfaceagreatforeigncompetitionin

globalizationera.

Figure 2 Female and Male Share to National Income Growth

Source:IndonesiaHumanDevelopmentReport

28 国　際　協　力　論　集　　第 26 巻第 1 号

Furthermore,previousstudiesrelatedtogenderwagedisparity isstillnotabundant.

Mostofstudies tried to identifygenderwagegap in Indonesiawithout take intoaccount the

effectoftradeliberalization.ByconductingOaxaca-BlinderDecomposition,Feridhanusetyawan,

Aswicahyono, andPerdana（2001）,Pirmana（2006）andTaniguchi andTuwo（2014）,used

differentdataset, investigate female-maleearningsdifferentials inruralandurban. Inaddition,

applyingdifferentdecompositionmethod,Sohn（2015）analyzed thegenderwagegapusing

IndonesiaFamilyLifeSurvey（IFLS）dataacrossquantilewagedistributioninIndonesia.Astudy

conductedbyFitrania（2013）isthefirststudytoattempttodrawthelinkbetweentheimpact

ofglobalizationonthegendersalarygap,usinganalternativemeasurement（wagelevelofwage

nets）, inprovincialanalysis.Therefore, tocontributetothe lackof literatureonthis issue, this

studywill focusonthe impactof tradeopennessongenderwagedifferentialsand its impact

acrossquantilewagedistribution.

Toinvestigatetheeffectoftradeopennessandgenderedwages,thisstudyfollowsthe

samemethodologybyBraunsteinandBrenner（2009）,whichconductedanalysisbycombining

micro-data levelandmacro-data level. In thispaper,micro-data level isdrawn fromNational

LaborSurvey（SAKERNAS）fromStatisticsIndonesia,while,macro-levelwhichconsistsofFDI,

tradevolumeandGRDPperprovincelevelisalsogainfromStatisticsIndonesia.Thehypothesis

inthisresearchisthattradeopennessexposurewillaffectwagesformenandwomenworkers

differentlyandthelargerimpactwillbegainedbymaleworkers.Thedatausedinthisresearch

ispoolingcross-sectionaldatacovers34provincesinIndonesiaintheperiod2008-2014.

Therestof thispapercomposes foursections.Section II isa literaturereviewabout

tradeopennessandgenderwagegap. InsectionIII,methodology includingmodelspecification

anddatadescription ispresented.Section IV isdiscussionof theregressionresults.Finally,

sectionVwillpresenttheconclusionandlimitationofstudy.

Ⅱ．Literature Reviews

１．Empirical Evidence on Trade Openness and Female-Male Earnings Differentials SincethediscriminationtheorywasproposedbyGaryBeckerin1957,manyresearchers

attempted toexamine tradeandgenderwage inequality.Thereare several studies showed

thattradeliberalizationexposurebringsanarrowingimpactonthegenderwagegap.Artecona

andCunningham（2002）andBlackandBrainerd（2004）tried to testBecker’sDiscrimination


Theorembycomparingcompetitiveandnon-competitive（concentrated）firms.Arteconaand

Cunningham（2002）useddifference in female-malemean loghourlywagesasameasurement

forthegenderwagegapandfoundthat,inBrazil,womenworkersexperiencedwageincreases

especially therewhoworked inconcentrated industries.Similarly,BlackandBrainerd（2004）

alsodiscoveredthatasimportshareis increased,thegenderwagegaptendstodeclineinU.S.

manufacturingindustries.InIndia,ReillyandDutta（2005）alsodidnotfoundsupportingevidence

thattrade liberalization isassociatedwithhighergenderpaygaps.Moreover,astudybyChen

etal.（2013）whichusedindustry-leveldatahasconductedtoexaminetheimpactofglobalization

ontheChineselabormarketin2004.Theyfoundthatforeignandexportingfirmsemploymore

femaleworkersthandomesticnon-exporters,whichsignificantlyencouragedfemaleemployment

and reduced the genderwage gap in themanufacturing sector.Moreover,Oosterndorp

（2009）analyzedthegenderwagegapusing largecross-countrydatasetby introducinganew

measurementof thegenderwagegap,which isoccupationalgenderwagegap.Hediscovered

thattradeopennesslowersthegenderwagegapinuppermiddleincomecountries,whilethereis

alittleevidencefoundinlowermiddleincomecountries.

Ontheotherhand,inseveraldevelopingcountries,tradeliberalizationincreasesfemale-

maleearningsdifferentials.Seguino（1997）conductedastudy inSouthKoreaand foundthat

women’srelatively lowerwagesandwagediscriminationareassociatedwithweakerwomen’s

fallbackposition in labormarkets. In addition,Berik et al.（2004）compared the impact of

internationaltradeongenderwagegapinTaiwanandSouthKorea.Itappearsthat,incontrast

toneoclassical theory, rising import share ispositivelyassociatedwithwagediscrimination

againstwomeninconcentratedindustries.Moreover,MenonandMeulen-Rodgers（2009）applied

adifferentapproachbyreplacingcross-sectionallong-differencedobservationswithpaneldataset

of28industrieswitha3-digit level.Interestingly,theyfoundanoppositeresultfromReillyand

Dutta’sstudy,anincreaseintradeopennessevenworsenedthegenderwagedifferentialsinthe

industrialsector.

Mostofpreviousstudiesaboveweremore focusingon theeffectof tradeopenness

ongenderearningsdifferentialswithinmanufacturing industriesor firmsdirectlyaffectedby

tradereforms.Thesekindof studieswere typically ignoring thecontributionof individuals’

characteristics,whichusuallyembodied inmicrodata level, that canprovidemorescope for

discoverthemechanismsthroughwhichtradeliberalizationaffectsgenderinequality.Thereare

severalstudiesthatattemptedtocombinemacro-data levelandmicro-data level.Hazarikaand

30 国　際　協　力　論　集　　第 26 巻第 1 号

Otero（2004）conductedaresearchtocomparegenderdifferentialwagesbetweenmaquiladora

andnon-maquiladoraworkersinurbanMexicoi.UsingtheMincerianearningsfunctionandadded

interactiontermsbetweendummyfemaleanddummyformaquiladorasector, they foundthat

genderwagedifferentialdecreasedmoresignificant innon-maquiladora than inmaquiladora.

Inaddition,BraunsteinandBrenner（2007）examinedthe impactofFDIonearningsbetween

femaleandmaleworkersinChinabetweenyear1995and2002bycombiningindividual-datalevel

withFDIandtradeperprovinces.Theresultswerethatwomenexperiencedlargergainsfrom

FDIthanmenin1995,whilemenexperiencedlargerwagegainsfromFDIthanwomenbecause

of industrialupgradingandgender-basedemploymentsegregation in2002.Moreover,arecent

studyconductedbyHanetal.（2012）alsoappliedthesamemethodologyastwopreviousstudies

above tosee the impactof tradereformsonwage inequalityonChina.They found that the

WTOaccessionwassignificantlyassociatedwithrisingwage inequalityandmore importantly

alsorisingthereturnofeducationtocollegeamongChinaworkers.Bycombiningmicrodataand

macrodatalevel,itisexpectedthatinvestigatingtheimpactoftradeopennessongenderwage

gapcanbemorespecificallyandcomprehensive.

２． Indonesia-specific Research Studies on Trade Openness and Female-Male

Earnings Differentials PreviousstudiesinIndonesiarelatedtotradeliberalizationandthegenderwagegapare

notabundant.Feridhanusetyawanetal.（2001）, investigatedthegenderwagegap inIndonesia

usingmicro-leveldatafrom1986-1997,theresultsshowedthatthegenderwagegapstillexists

inIndonesiaeventhoughthetrendisdeclining.Pirmana（2006）usesanewdataset1996-2004,

foundresultcontradictingwithpreviousstudies.Hereportedthatthereisanincreaseingender

earningsinequalitiesinIndonesia,whichisattributed41.6%toendowmentdifferencesand58.4%

tounobservedandunexplained factors. Inaddition,TaniguchiandTuwo（2014）, foundnew

evidencethattheurbangenderwagegapislessthanthegapinruralareas,moreover,unlikein

ruralareas,theurbangenderwagegapiswideramongyoungeragecohorts.Anotherstudywas

conductedbySohn（2015）using2007IndonesiaFamilyLifeSurvey（IFLS）dataacrossquantile

wagedistributioninIndonesia.Byapplyingquantileregression,hepointedoutthattheexplained

gapremainssimilaracrosswagedistribution,while,unexplainedfactorsdecreaseinthetotalgap.

A studybyFitrania（2013） is the only research that tried todrawa relationship

betweenglobalizationand thegenderwagegapacrossprovinces in Indonesia.Following the


samemethodasOosterndrop（2009）,sheusedtheoccupationalgenderwagegapandclassified

provinces into low-andhigh incomeprovinces and occupations into low-skill andhigh-skill

occupations.Shedrewtheconclusion that inadevelopingcountry like Indonesia,globalization

mainlyreducesthegenderwagegapinlow-skilloccupations.

３．Research Contribution Since the literature ongenderwage inequality and trade liberalization is still not

abundant in Indonesia, thisstudyattemptstocontributeto thisfield.Therearetwoapparent

differencesbetween this studyandotherprevious studies.First, followingBraunstein and

Brenner（2009）andHazarikaandOtero（2004）, thisresearch focusesonthe impactof trade

openness at a household-level perspective by combiningmacro-data andmicro-data level.

AccordingtoBraunsteinandBrenner（2009）,bymergingmacro-dataandmicro-datalevels,the

effectsofFDIonfemale-maleearningsdisparitycanbeexaminedasawholewithoutabandoning

the individual’s characteristicvariables,whichcan impairapurelymacroeconomicapproach.

Secondly,sincemanyotherpreviousstudiesonlyfocusonexamininggenderpaygaponaverages

without taking into account the inequality acrosswagedistribution, this papergobeyond

averages toseeacompleteunderstandingabout thedifferenteffectof trade liberalizationon

genderearningsdifferentialsacrosswagedistributionusingthesamemethodasHanetal.（2012）.

Ⅲ．Methodology and Data

１．Econometric Model and Estimation Methods FollowingHazarikaandOtero（2004）andHanetal.（2012）,thisresearchusesextended

Micherianwagemodelbyaddinginteractiontermbetweendummyvariablegenderandvariable

tradeopenness.Thebaselinespecificationasexpressedasfollows:

Where:

=individual; =province; =timeperiod（year）.

isanaturallogarithmofrealhourlywageforeachworker.

isavariabletomeasuretradeopennessexposureineachprovince.

32 国　際　協　力　論　集　　第 26 巻第 1 号

isafemaledummy.

iscontrolvariablesthatcaptureindividualcharacteristicsandtheeconomicenvironment.

Followingprevious studiesbyHazarikaandOtero（2004）,BraunsteinandBrenner

（2007）andHanetal.（2012）,themodelspecificationabove isestimatedusingOrdinaryLeast

Square（OLS）estimationmethod.Furthermore,quantileregressionisalsoappliedinthisstudy

toinvestigatetheimpactoftradeopennessacrosswagedistribution.

２．Data Description Thisstudyusessecondarydata fromNationalLaborSurvey（SAKERNAS）published

byStatisticsIndonesia.SAKERNAScovers200,000householdsandonlyincludesfamilymembers

over10yearsold.Thisstudyusesthedataofindividualswhoarebetween15and65yearsold

andwhoearnedwage in2008-2014.Furthermore, this studyalsousesmacroeconomicdata,

whichareprovincial tradeopennessand the relatedvariables.Thesedataaredrawn from

InvestmentCoordinatingBoard（BKPM）andStatistics Indonesia. Finally,weconductall the

dataintopoolingcross-sectionaldata.

This studyuses logarithm formof real hourlywageas adependentvariable. It is

becausehourlywagegivesanappropriatecomparableunitofanalysis forwagesince female

workersusuallyworkshorterthanmaleworkerinIndonesia.SAKERNASprovidesonlymonthly

wagedata,soweconvertedmonthlynominalwageintohourlyrealwage.Moreover,thisstudy

usestwotypesofmeasurementfortradeopenness:theratiooftradetoGRDPandtheratioof

FDItoGRDP,foreachprovince.Itisexpectedthattradeliberalizationdoesstructurallyimpact

labormarkets inwaysbeyond individualcharacteristicsofworkers（BraunsteinandBrenner,

2007）.

Table1showseachvariabledescriptionandTable2shows thesummarystatistics,

respectivelyii.

Table 1 Variable Description

Variable Descriptions

wage RealwageperhourinIDR

trade RatiooftradetoGRDPinprovince

FDI RatioofFDItoGRDPinprovince

female =1iffemale,0otherwise


age Worker’sage

tenure Workingyearsincurrentjob

workhours（perweek） Workinghoursperweek

urban =1ifurban,0otherwise

married =1ifmarried,0otherwise

educ_elementary =1iffinishedelementaryschool,0otherwise

educ_junior =1iffinishedjunior-highschool,0otherwise

educ_senior =1iffinishedsenior-highschool,0otherwise

educ_college =1iffinishedcollegeandmore,0otherwise

sector_manufacture =1ifworkingatmanufacturing,0otherwise

sector_service =1ifworkingatservice,0otherwise

GRDP ProvincialGDP（trillionIDR）

Table 2 Summary Statistics

Variable Mean S.D. Min Max

wage（hourly） 7,440 11,627 36 2,010,417

trade 0.331 0.374 0.001 3.060

FDI 0.025 0.035 0.000 0.414

female 0.344 0.475 0 1

age 36.641 11.408 15 65

tenure 8.122 8.632 0 60

workhours（perweek） 43.826 15.538 1 98

urban 0.589 0.492 0 1

married 0.712 0.453 0 1

educ_elementary 0.195 0.396 0 1

educ_junior 0.170 0.376 0 1

educ_senior 0.340 0.474 0 1

educ_college 0.189 0.391 0 1

sector_manufacture 0.139 0.346 0 1

sector_nonmanufacture 0.701 0.458 0 1

GRDP（trillionIDR） 345 478 4 5,920

n=971,044

34 国　際　協　力　論　集　　第 26 巻第 1 号

Ⅳ．Results and Discussion

Theregressionresultsarepresented inTable3.Mostof theestimatedcoefficientsof

keyvariables,suchas trade, FDI, femaleandthe interactionterms,havesimilarandconsistent

signswiththepreviousstudies.

Theestimatedcoefficientof trade fromOLSresult ispositiveandsignificant,while

thevalueoftheestimatedcoefficientoftradeisbiggerinhighquantilewagegroupthaninlow

quantilewagegroup.Assuming thatworkers inhighquantilewagegroupareworkerswith

high-skilledoccupations, it indicates that tradeshareofGRDP increaseswages forhigh-skilled

labormorethanlow-skilled labor.Thisfindingis in linewithapreviousstudybyLeeandWie

（2015）.TheyfoundthatinIndonesia,tradecauseddemandtoshifttowardmoreskilledlaborand

increasedtheirwages.

TheestimatedcoefficientsofFDI, fromOLSresult andquantile regression results,

arepositiveandsignificant.Moreover, thevaluesarequite largerthanthevaluesof tradeand

arealmostthesameamongallquantilewagegroups,unliketheestimatedcoefficientsoftrade.

TheresultofFDIisnotinlinewithpreviousstudiesbyLeeandWie（2015）andFeenstraand

Hanson（1997）focusingonMexicoduringthe1980s.

Fromoverall results,dummyvariable femalearenegativeandsignificant. Incolumn

OLS, thecoefficient isnegativeandsignificantwithvalue-0.3094. Inotherwords,onaverage,

afemaleworker’shourlywageis26.6%belowacomparablemaleworker’shourlywageiii.This

finding is in linewith theresultofHazarikaandOtero（2004） in thecaseofMexico.From

quantileregressionresult, itappears that thevalueofestimatedcoefficientof female in10th

quantilewagedistributionislowerthanin90thquantilewagedistribution.Itimpliesthatfemale-

malewagedifferentialsarelargerinlowerwagegroupthaninhigherwagegroup.

Furthermore,theinteractiontermbetweentradeandfemale（trade*female）isexpected

toexplaintheimpactoftradeliberalizationongenderwagegap.Fromquantileregressionresults,

theestimatedcoefficientoftrade*female ispositiveandsignificant,exceptinhighquantilewage

group. It implies that tradehelps toreducethewagegapbetween femaleandmaleworkers

in lowandmiddle-quantilewagegroup.Meanwhile, inhighquantilewagegroup, tradehasa

wideningeffectonthegenderwagegap.ThesefindingsaresimilarwiththeresultsofFitrania

（2013）,which foundthat the impactofglobalizationongenderpaygapreduction is larger in

low-skilledoccupationthan inhigh-skilledoccupation.Thenarrowingeffectof tradeon female-


maleearningsdifferentialsinlowquantilewagedistributionscanbeexplainedbytheHeckscher-

Ohlin（H-O）tradetheorythat thepresenceof international trade induceshigherdemandand

increasesrelativewagesforlow-skilledworkersinmostdevelopingcountries.Ontheotherhand,

awidening impactof tradeongenderearningsdifferentialmightbecausedby thepresence

of technologicalchangeswhichaffectsrelativewagesbyshiftingdemandforhigh-skilled labor

（LeeandWie,2015）.Anupgradingtechnologyalsoinducestheneedtoemploybetter-qualified

workerswhich ismorebeneficial formaleworkers. It isbecause femaleworkers in thesame

occupationaresubjecttostereotypesthatharmtheirpositioninthelabormarket（Oostendorp,

2009）.

Moreover, the estimated coefficient of interaction termbetweenFDI anddummy

variablefemale（FDI*female）showthesamepatternlikevariables trade*female,whichishigher

estimatedvalueoflowwagegroupthanthatofhighwagegroup.Theseresultsindicatethatboth

tradeandFDIcanhelptoreducegenderwagegap.Ontheotherhand,theapparentdifference

is that thevaluesofcoefficients forFDI*femalearehigher thanthevaluesof trade*female. It

indicatesthatthemagnitudeofFDI’seffectonfemale-maleearningsdifferentials is largerthan

trade’seffectonthegenderpaygap.

For thecontrolvariables,almostall thesignsof theestimatedcoefficientsare in line

with theory.Variables, suchasage, tenure,urban,married,andeducation,exceptworkhour,

significantlyincreaseindividual’swage.

Table 3 OLS and Quantile Regression Results

DependentVariable:lnwage 　　　　　　　OLS Quantile

IndependentVariables Overall 10th 50th 90thtrade 0.0491 *** 0.0047 0.0430 *** 0.1013 ***

（0.0025）（0.0050）（0.0026）（0.0036）

FDI 1.9604 *** 2.1612 *** 2.0096 *** 2.1892 ***（0.0268）（0.0540）（0.0281）（0.0385）

female -0.3094 *** -0.4169 *** -0.2773 *** -0.2479 ***（0.0023）（0.0047）（0.0025）（0.0034）

trade*female 0.0331 *** 0.1256 *** 0.0201 *** -0.0326 ***（0.0040）（0.0082）（0.0043）（0.0059）

FDI*female 0.5334 *** 0.6490 *** 0.5522 *** 0.5409 ***（0.0475）（0.0891）（0.0464）（0.0636）

age 0.0399 *** 0.0466 *** 0.0359 *** 0.0329 ***（0.0005）（0.0009）（0.0005）（0.0007）

36 国　際　協　力　論　集　　第 26 巻第 1 号

Ⅴ．Conclusion

Thepurposeofthisstudyistoinvestigatetheimpactoftradeopennessongenderwage

gapacrosswagedistributioninIndonesia.UsingtheIndonesianHouseholdSurveydatafor2008-

2014,weexaminetheimpactbyquantileregressionanalysiswithanextendedMincermodel.

TheregressionresultsindicatethatfemaleworkersinIndonesiastillearnlessthanmale

workers,onaverage,especiallyinlowerwageclasses.Moreover,itisfoundthattradeopenness

agesq -0.0004 *** -0.0005 *** -0.0004 *** -0.0003 ***（0.0000）（0.0000）（0.0000）（0.0000）

tenure 0.0177 *** 0.0183 *** 0.0174 *** 0.0131 ***（0.0001）（0.0002）（0.0001）（0.0002）

lnwork_hour -0.6084 *** -0.4777 *** -0.6336 *** -0.7581 ***（0.0020）（0.0036）（0.0019）（0.0025）

urban 0.0576 *** 0.0746 *** 0.0350 *** 0.0401 ***（0.0016）（0.0033）（0.0017）（0.0024）

married 0.1281 *** 0.1381 *** 0.1228 *** 0.1152 ***（0.0020）（0.0040）（0.0021）（0.0028）

educ_elementary 0.1307 *** 0.1657 *** 0.1222 *** 0.1006 ***（0.0027）（0.0056）（0.0029）（0.0040）

educ_junior 0.2988 *** 0.3289 *** 0.2804 *** 0.2707 ***（0.0028）（0.0060）（0.0031）（0.0043）

educ_senior 0.4858 *** 0.4940 *** 0.4760 *** 0.4769 ***（0.0028）（0.0058）（0.0030）（0.0041）

educ_college 0.9473 *** 0.9427 *** 0.9318 *** 0.8700 ***（0.0036）（0.0072）（0.0038）（0.0052）

sector_manufacture -0.0957 *** -0.1140 *** -0.0948 *** -0.0902 ***（0.0065）（0.0151）（0.0079）（0.0108）

sector_service -0.1259 *** -0.2038 *** -0.1133 *** -0.0652 ***（0.0063）（0.0146）（0.0076）（0.0104）

lnGRDP 0.0535 *** 0.0612 *** 0.0505 *** 0.0607 ***（0.0007）（0.0015）（0.0008）（0.0011）

_cons 9.1046 *** 7.7397 *** 9.4274 *** 10.2288 ***（0.0188）（0.0391）（0.0203）（0.0279）

regiondummies yes 　 yes 　 yes 　 yes 　occupationdummies yes yes yes yesobs. 971,044 　 971,044 　 971,044 　 971,044 　R-squared 0.3996PseudoR-sq. 　　 0.1521 　 0.263 　 0.2889 　

Robuststandarderrorsinparentheses（Clusteredonprovince）.

***1%significance


contributestoreducinggenderwagegapasthelowerwagelevel,andthattheimpactofFDIon

wageislargerthanthatoftrade.

Fromthe findingsabove, it showed that tradeopennesscanchange thedynamicof

genderwage in local labormarket.Theresults indicate thepossibilityapolicy intervention

canreducegenderwagegap.Namely, if thegovernmentandpolicymakerscan formulatethe

appropriatesocialandlaborpoliciesandlinkthemwithpoliciesthatpromoteinternationaltrade

andinvestment,genderwagegapcanbereduced.Thisissuealsoindicatesthatthereisroomfor

policyintervention.Inotherwords,governmentsandpolicymakerscanencouragecompaniesto

provideequalvocationaltrainingandvocationaleducationprogramsinordertoreducethegap

betweentheskillsofwomenandmen’sworkers.Ultimately,thispolicycanincreasethechances

ofdevelopingwomen’sabilitytoacquirebroademploymentandraisingwomen’srelativewage.

Asalimitationofthestudy,theremaybeareversecausalitybetweenFDIandwagesin

spitethatFDIisusedasameasurementoftradeopennessinthisstudy.Oneofthemethodsfor

thisproblemisInstrumentalVariable（IV）approach.BraunsteinandBrenner（2007）suggested

infrastructureorgeography factorsasseveralpotential instrumentvariables.However,dueto

thelackofdataavailabilityofthosevariables,IVapproachdoesnotaddressinthisstudy.This

pointisforfurtherresearch.

Appendix: Table A1 Variable Description for Region and Occupation Dummy

Variable Description

regiondummies 　

reg_sumatera =1ifSumatera,0otherwise

reg_kalimantan =1ifKalimantan,0otherwise

reg_sulawesi =1ifSulawesi,0otherwise

reg_maluku =1ifMaluku,0otherwise

reg_java =1ifJava,0otherwise

occupationdummies

j_prof =1ifprofessional,1otherwise

j_manager =1ifmanager,0otherwise

j_admin =1ifadministrationstaff,0otherwise

j_sales =1ifsalesclerk,0otherwise

j_services =1ifservices,0otherwise

j_farmer =1iffarmer,0otherwise

j_prod =1ifproduction,0otherwise

38 国　際　協　力　論　集　　第 26 巻第 1 号

ReferencesArtecona,R.,&W.Cunningham（2002）Effects of Trade Liberalization on The Gender Wage Gap in Mexico,

WashingtonD.C.:TheWorldBank.Becker,G.（1957）The Economics of Discrimination,Chicago:TheUniversityofChicagoPress.Berik, G.,Y.V.MeulenRodgers,& J. Zveglich, Jr.（2004） “InternationalTrade andGenderWage

Discrimination:EvidencefromEastAsia,”Review of Development Economics,8（2）:237-254.Berman,E.,J.Bound,&M.Stephen（1998）“ImplicationsofSkill-BiasedTechnologicalChange:International

Evidence,”The Quarterly Journal of Economics,113（4）:1245-1279.Black,S.,&E.Brainerd（2004）“ImportingQuality?TheImpactofGlobalizationonGenderDiscrimination,”

Industrial and Labor Relations Review,57（4）:540-559.Braunstein,E.,&M.Brenner（2007）“ForeignDirect InvestmentandGenderedWages inUrbanChina,”

Feminist Economics,13（3-4）:213-237.Chen,Z.,Y.Ge,H.Lai,&C.Wan（2013）“Globalization andGenderWage Inequality inChina,”World

Development,44:256-266.Dollar,D.,&R.Gatti（1999）“GenderInequality, IncomeandGrowth:AreGoodTimesGood forWomen?”

PolicyResearchReportonGenderandDevelopmentWorkingPaperSeries,No.1.Driffield,N.,&K.Taylor（2000）“FDIandTheLaborMarket:AReviewofTheEvidenceandPolicy

Implications,”Oxford Review of Economic Policy,16（3）:90-103.Feenstra,R.C.,&G.H.Hanson（1997）“ForeignDirect InvestmentandRelativeWages:Evidence from

Mexico’sMaquiladoras,”Journal of International Economics,42（3-4）:371-394.Feridhanusetyawan,T.,H.Aswicahyono,&A.A.Perdana（2001）“TheMale-FemaleWageDifferentials in

Indonesia,”CSISWorkingPaperSeries,No.59,CenterforStrategicandInternationalStudies（CSIS）.Fitrania,S.（2013）“The ImpactofGlobalizationonGenderWageGap in Indonesia:ASub-NationalLevel

Analysis,”MasterThesis,TilburgUniversity.Fussell,E.（2000）“MakingLaborFlexible:TheRecompositionofTijuana’sMaquiladoraFemaleLaborForce,”

Feminist Economics,6（3）:59-79.Han,J.,R.Liu,&J.Zhang（2012）“GlobalizationandWageInequality:EvidencefromUrbanChina,”Journal of

International Economics,87（2）:288-297.Hazarika,G.,&R.Otero（2004）“ForeignTradeandtheGenderEarningsDifferential inUrbanMexico,”

Journal of Economic Integration,19（2）:353-373.Juhn,C.,G.Ujhelyi,&C.Villegas-Sanchez（2014）“Men,Women,andMachines:HowTradeImpactsGender

Inequality,”Journal of Development Economics,106:179-193.Lee, J.-W.,&D.Wie,（2015）“TechnologicalChange,SkillDemand,andWage Inequality:Evidence from

Indonesia,”World Development,67:238-250.Menon,N.,&Y.V.Rodgers（2009）“InternationalTradeandtheGenderWageGap:NewEvidence from

India’sManufacturingSector,”World Development,37（5）:965-981.Oostendorp,R.H.（2009）“GlobalizationandtheGenderWageGap,”The World Bank Economic Review,23（1）:

141-161.Pirmana,V.（2006）“EarningsDifferentialbetweenMale-Female in Indonesia:Evidence fromSAKERNAS

Data,”WorkingPaperinEconomicsandDevelopmentStudies,No.200608,PadjajaranUniversity.Reilly,B.,&P.V.Dutta（2005）“TheGenderPayGapandTradeLiberalisation:EvidenceforIndia,”PRUS

WorkingPaper,No.32,UniversityofSussex.Seguino,S.（1997）“Genderwageinequalityandexport-ledgrowthinSouthKorea,”The Journal of Development

Studies,34:102-132.Sohn,K.（2015）“GenderDiscrimination inEarnings in Indonesia:FullerPicture,”Bulletin of Indonesian

Economic Studies,51（1）:95-121.Taniguchi,K.,&A.Tuwo（2014）“NewEvidenceontheGenderWageGap inIndonesia,”ADBEconomics

WorkingPaperSeries,No.404.UnitedNationsDevelopmentProgramme（2016）Human Development Report 2016: Human Development for

Everyone,NewYork:UNDP.


Notesi Maquiladora industry isan industry forexportspecializedmanufacturingunits inMexico

whichestablishedundertheagreementwithUnitedStatescalledBraceroProgram.ii SeealsovariabledescriptionforregionandoccupationdummyinTableA1inAppendix.iii （e-0.3094-1）× 100%=-26.6%

trade openness and gender wage gap: evidence from indonesia

Documents