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Australia Indonesia Partnershipfor Economic Governance
Women’s Economic Participation in IndonesiaA study of gender inequality in employment, entrepreneurship, and key enablers for change
June 2017
The Australia Indonesia Partnership for Economic Governance (AIPEG) is a
facility to strengthen the evidence-base for economic policy in support of the
Indonesian government. The work is funded by the Australian government as
part of its commitment to Indonesia's growth and development.
This report has been prepared in a collaboration between AIPEG, the Australian
Department of Foreign Affairs and Trade (DFAT) and Monash University’s
Centre for Development Economics and Sustainability (CDES)1.
Women’seconomicparticipationinIndonesia
ExecutiveSummary
The Indonesian economy has undergone dramatic changes over the last few decades. Indonesiaachievedmiddleincomestatusin2004andhighgrowthalsorapidlyreducedpovertyfrom23percentof the population in 1999 to 11 percent in 2016. The share ofmanufacturing and services in theeconomyisgrowing,andagriculturedeclining(althoughstillahighleveloverall).
Yetoneareathathasnotchangedmuchisparticipationofwomeninthelabourmarket.
Thisreportpresentsnewresearchonthelabourmarketsituationforwomenandgenderwagegapsin Indonesia, based on theNational Socio-Economic Survey (Susenas). At 51 percent, Indonesia’sfemalelabourforceparticipationrateiswellbelowthatformales(around80percent)andlowrelativetocountriesatacomparablestageofdevelopment.
ThemaindriversoflowfemalelabourforceparticipationinIndonesiaaremarriage,childrenbelowtwoyearsofageinthehousehold,loweducationalattainment(belowupper-secondaryandtertiarylevels)andchangingeconomicstructure(declineinthefemale-friendlysectorofagricultureduetotransitionsfromruraltourbanareasinparticular).
Onepromisingtrend,however, isthatthepropensityforwomentoparticipate inthe labourforceappears to be increasing among the younger generation, particularly themore educated living inurbanareas.
As a member of the G20 group of the world’s major economies, Indonesia has committed todecreasingthegapbetweenfemaleandmalelabourforceparticipationby25percentby2025.Ourprojectionsshowthatthistargetwillonlybereachedunderthemostoptimisticcircumstances.Underlessoptimistic(andarguablymorerealistic)assumptions,femalelabourforceparticipationmayevendecreaseifthemostrecenttrendscontinue.Policysupport,togetherwithshiftingsocialnormsandpracticesisneeded.
OurresearchalsofindsevidenceofasignificantgenderwagegapinIndonesia.Thegenderwagegapis34percentintheformalsectorand50percentintheinformalsector.Ouranalysisshowsmostofthisgapisnotduetodifferencesinproductivecharacteristicsbutreflectsdiscriminatorypractices.Thereisalsostrongevidenceof‘stickyfloors’intheformalsector–womenatthelowerendofthewagedistribution facingamuchbiggergenderwagegap thanwomen inhigherwage jobs. In theinformalsector (wheremostof thewomenparticipate), thewagegap is largeandconstant forallworkers.
Inanotherareaofeconomicparticipation,entrepreneurship,womentendtobeunder-represented.This is despite the concentration of women in the self-employed informal sector. Lowentrepreneurshipisoftenattributedtowomen’sdifficultyaccessingfinancialresources.Theevidenceonthisishowevermixedandfurtherresearchinthisareaisdesirable.
Women’seconomicparticipationinIndonesia
The report also reviews existing research on key enablers for greater economic equality betweenwomenandmen–education,health, infrastructure, institutionsandlaws. Inparticular,educationequalityisacriticalpathwaytoeconomicopportunitieslaterinlife.OurreviewoftheevidencefindslittleinthewayofgendereducationgapsamongstyoungercohortsinIndonesia.However,theoveralleducationalperformanceofIndonesiansislow.
Onhealthindicators,gendergapsinareassuchaschildmortalityandutilisationofhealthservices,observedinmanydevelopingcountries,arenotapparentinIndonesia.However,maternalmortalityratesarehigherthanincomparablecountries.Equalityinhealthisacriticalareafoundtodeterminehuman capital development (for example, healthier children and adults aremore likely to obtainhighereducationandparticipatemoreinthelabourmarket).
Inadequate transport infrastructure and services are additional barriers towomen’s full economicparticipation.Efficientandsafetransport,inparticularcanassistwomentobetterjuggleworkandfamilyresponsibilities.
Finally,institutionsandlawssignalcommitmenttoimprovinggenderequality.InIndonesia,despitereasonable maternity leave entitlements for formal sector workers, there are several laws thatdiscriminateagainstwomen.This includestaxandinheritancelaws,aswellas lackof legislationorpenaltiestoprotectagainstsexualharassment.
Thereportconcludeswithareasforfurtherresearchincludingwhatdrivesfemaletransitionsinthelabour market. Ultimately, the aim is to provide the evidence base for Indonesia to increasecompetitivenessandgrowththroughwomen’sfulleconomicparticipation.
Women’seconomicparticipationinIndonesia
ListofContents1. Introduction...................................................................................................................................1
2. OverviewofGenderInequality......................................................................................................2
2.1 EducationalInequality............................................................................................................2
2.1.1 Educationattendanceandcompletion..........................................................................2
2.2 LabourMarket........................................................................................................................6
2.2.1 LabourForceParticipation,EmploymentandUnemployment......................................7
2.2.2 EmploymentStatus(Formal/Informal).........................................................................10
2.2.3 IndustrialandOccupationalSegregation.....................................................................11
2.2.4 Workingconditions......................................................................................................12
2.2.5 Wages...........................................................................................................................13
2.2.6 Migration......................................................................................................................16
2.3 Finance&Entrepreneurship................................................................................................17
2.4 Infrastructure.......................................................................................................................19
2.5 Health...................................................................................................................................20
2.6 Institutions&Laws...............................................................................................................22
2.6.1 Lawinrelationtofamilies............................................................................................23
2.6.2 LabourLaws..................................................................................................................23
2.6.3 PropertyRights.............................................................................................................24
2.6.4 PoliticalRepresentation...............................................................................................24
3. StagnationofthefemalelabourforceparticipationinIndonesia:Anageandcohortanalysis..25
3.1 Introduction..........................................................................................................................25
3.2 DataandMethods................................................................................................................26
3.2.1 Descriptiveresults........................................................................................................28
3.3 Generalresults.....................................................................................................................29
3.4 Ageandcohortresults.........................................................................................................31
3.5 FemaleLabourForceParticipationProjection.....................................................................32
3.5.1 ModelPerformance......................................................................................................32
3.5.2 Predictionofdeterminantvariables.............................................................................33
3.5.3 FemaleLabourForceParticipationProjection.............................................................34
3.6 Conclusions...........................................................................................................................35
4. GenderWageGapinIndonesia-adistributionalanalysisoftheformalandinformalsector....36
4.1 Introduction..........................................................................................................................36
4.2 DataandMethod.................................................................................................................36
Women’seconomicparticipationinIndonesia
4.3 DecompositionResults.........................................................................................................42
4.3.1 DecompositionacrosstheWageDistribution..............................................................43
4.3.2 CohortAnalysis.............................................................................................................45
4.4 Conclusions...........................................................................................................................47
5. ConclusionsandFutureResearchAgenda...................................................................................48
References............................................................................................................................................51
Appendix1:Blinder-OaxacaMethodology..........................................................................................54
Appendix2:ProbitestimationofFemaleLabourForceParticipation.................................................55
Appendix3:Projectionsofthedeterminantsoffemalelabourforceparticipation............................56
Appendix4:Genderinequalityinunemploymentrates......................................................................59
A4-1. Dataandmethods............................................................................................................61
A4-2. Results..............................................................................................................................61
A4-3. AgeandCohortEffects.....................................................................................................64
A4-4. Conclusion........................................................................................................................65
A4-5. MethodologicalNoteonthereliabilityoftheSusenasunemploymentrates.................66
Appendix5:Genderwagegapalongthedistributionbystatusofemployment.................................72
Endnotes..............................................................................................................................................80
ListofFigures,TablesandEquations
Figure1Levelofschoolcompletionbyagecohortandgender,2013...................................................3Figure2Yearsofeducationbyagecohortandgender,2013................................................................3Figure3Literacyratesbyregion,genderandagecohort,2013............................................................4Figure4IndonesiaPISA2012testresultsbygender.............................................................................5Figure5TotalEmploymentinAgriculture.............................................................................................6Figure6FemaleLabourForceParticipationbyCountry........................................................................7Figure7Labourforceparticipationbygenderandagegroupin2013..................................................8Figure8FemaleUnemployment............................................................................................................8Figure9UnderemploymentbygenderandUrban/Rural......................................................................9Figure10InformalStatusofEmploymentbyGender..........................................................................11Figure11InformalStatusofEmploymentbyRegionin2013..............................................................11Figure12EmploymentbyIndustry......................................................................................................12Figure13WorkersWageFemale/MaleRatio......................................................................................13Figure14Blinder-OaxacaDecomposition............................................................................................14Figure15Blinder-OaxacaDecompositionbySectorofEmployment..................................................16Figure16Female’sageattheirfirstmarriage,2013............................................................................23Figure17Proportionofseatsheldbywomeninnationalparliaments(%).........................................25Figure18Ageandcohorteffects.........................................................................................................31Figure19ObservedandmodelpredictedFemaleLabourForceParticipation....................................33
Women’seconomicparticipationinIndonesia
Figure20ProjectionofFemaleLabourForceParticipationinIndonesia.............................................35Figure21LogarithmoftheHourlywagesofmaleandfemaleworkers..............................................37Figure22Histogramoftheyearsofexperienceandeducationattainmentbygender......................37Figure23Genderwagegapdecompositionatthemeanbysectorofemployment...........................42Figure24Genderwagegapacrossthewagedistributionbystatusofemployment..........................44Figure25Decompositionoftheexplainedcomponentofthegenderwagegapacrossthewagedistributionbystatusofemployment..................................................................................................44Figure26Genderwagegapacrossthewagedistributionintheformalsectorbyagecohort............45Figure27Decompositionoftheexplainedcomponentofthegenderwagegapacrossthewagedistributionintheformalsectorbyagecohort...................................................................................46Figure28Genderwagegapacrossthewagedistributionintheinformalsectorbyagecohort.........46Figure29Decompositionoftheexplainedcomponentofthegenderwagegapacrossthewagedistributionintheinformalsectorbyagecohort................................................................................47FigureA4-1UnemploymentRateinIndonesia(ModeledILOestimate)............................................59FigureA4-2PredictedprobabilityofyouthunemploymentinIndonesia..........................................64FigureA4-3Predictedprobabilityofyouthunemploymentforruralareas.......................................65FigureA4-4Predictedprobabilityofyouthunemploymentforurbanareas.....................................65FigureA4-5TotalUnemploymentRate(%oftotallabourforce).......................................................67
Table1Enrolmentstatusbygenderforindividualsaged5to18years................................................2Table2TypeofEmployeesbyGenderofOwner.................................................................................18Table3Borrower’scharacteristics,bygender.....................................................................................19Table4SourceofNon-OwnCapitalandAmountBorrowedfromtheBank........................................19Table5Deliveryattendance.................................................................................................................21Table6AverageNumberofChildrenbyagecohort............................................................................22Table7Summarystatisticsoflabourforceparticipationandexplanatoryvariables..........................28Table8Marginaleffectsofpooledsample..........................................................................................30Table9FLFPdeterminatsannualgrowthinpercentagepoints...........................................................33Table10Summarystatisticsofproductivitycharacteristics................................................................39Table11OLSestimatesofWagebygenderandsectorofemployment.............................................40Table12Characteristicscontributiontothetotalwagegapatthemeanbysectorofemployment.43Table13AnAnalysisofFactorsDeterminingLabourMarketGenderInequalityinIndonesia............50TableA4-1UnemploymentDescriptiveStatistics..............................................................................60TableA4-2UnemploymentMarginalEffects-Total..........................................................................62TableA4-3UnemploymentMarginalEffectsRuralandUrban..........................................................63TableA4-4LabourForceQuestionsinSusenasandSakernas...........................................................68TableA4-5YouthDescriptiveStatisticsbyYear.................................................................................69TableA4-6YouthUnemploymentMarginalEffectsbyYear..............................................................70TableA5-1UnconditionalQuantileRegressionCoefficientsbyGenderintheFormalSector...........72TableA5-2UnconditionalQuantileRegressionCoefficientsbyGenderintheInformalSector........74
Women’seconomicparticipationinIndonesia
TableA5-3Decompositionofthegenderwagegapacrosstheearningdistribution........................76TableA5-4Decompositionofthegenderwagegapacrosstheearningdistributionforpeopleaged15to29................................................................................................................................................77TableA5-5Decompositionofthegenderwagegapacrosstheearningdistributionforpeopleaged30to44................................................................................................................................................78TableA5-6Decompositionofthegenderwagegapacrosstheearningdistributionforpeopleaged45to64................................................................................................................................................79
Equation1Labourforceparticipation.................................................................................................27Equation2TrendpredictionofdeterminantsofFLFP.........................................................................33Equation3Wageequation...................................................................................................................38Equation4Blinder-OaxacaDecomposition..........................................................................................54Equation5YouthUnemploymentProbitModel..................................................................................61
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1. IntroductionThis reportpresentsageneraloverviewof the stateofgender inequality in Indonesia.TheGlobalGenderGapReport2(2014)preparedbytheWorldEconomicForumidentifiesinequalityineconomicparticipationandopportunityforwomenasthemostsignificantgenderinequalitychallengeforthecountry. The Indonesian economy has undergone dramatic change over the last few decades.Indonesiaachievedmiddleincomestatusin2004andhighgrowthalsorapidlyreducedpovertyfrom23percentofthepopulationin1999to11percentin2016.Theshareofmanufacturingandservicesintheeconomyisgrowing,andagriculturedeclining(althoughstillatahigh leveloverall).Yetoneareathathasnotchangedistheparticipationofwomeninthelabourmarket.Economicparticipationwillthusbethemainfocusofthisstudy.
Thereportcontainsthreemainparts.First,wepresentageneralreviewofdifferentaspectsofgenderinequality.Weexaminethedifferentfacetsofgenderinequalityinthefollowingorder:
i. Educationalinequalityii. Labourmarketinequality
a. Labourforceparticipationb. Employmentstatus(formal/informal)c. Industrialandoccupationalsegregationd. Workingconditionse. Genderwagegapsf. Migration
iii. EntrepreneurshipandFinanceiv. Infrastructurev. Healthinequalityvi. InstitutionsandLaws
Wethenpresenttwopiecesofanalyticalwork.Thefirstfocusesonthemaindriversoffemalelabourforceparticipation(FLFP),exploringthefactorsthathavecontributedtoFLFPremainingunchangedoverthelasttwodecades.Thesecondexaminesthedriversofthegenderwagegapandexamineshowthesedriversdifferacrossthedistributionofwagesintheformalandinformalsectors.
Weconcludewithasectionidentifyingthemostimportantinhibitorsofgenderequalityandsuggestareasforfutureresearch.3
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2. OverviewofGenderInequality
2.1 EducationalInequalityEducationisrecognisedaskeytoreducingpovertyindevelopingcountriesandisasignificantfactorindeterminingwagegapsbetweenmenandwomen.WhileinthepasttherewerevariousreasonsforlowerlevelsoffemaleenrolmentineducationinIndonesia,inparticular,distancefromschoolsandearlymarriage (UN,2003), genderequality ineducation in Indonesiahas increasedmarkedlyoverrecentyearstoapproachparity(ADB2006).
2.1.1 EducationattendanceandcompletionWomen’seducationalachievementinIndonesiahasmadesignificantprogresstowardequalitywithmenatalllevelsofeducation(Buchori&Cameron,2007;UNICEF,2010).Thegapbetweenenrolmentandattainmentbetweenmenandwomenhasnarrowedtothepointofdisappearingandtheredoesnotappeartobeasignificant‘sonpreference’foreducationinIndonesia(Kevane&Levine,2000),althoughthere issomeevidencethat inhardtimefamilieswillcutexpenditureongirls’educationbeforecuttingeducationalexpenditureonboys(L.A.Cameron&Worswick,2001).
Table1presentsfiguresfromthe2013Indonesia’sNationalSocio-EconomicSurvey(Susenas)showingthatthereisverylittledifferencebetweenschoolattendanceforgirlsandboysinbothurbanandruralareas.Girls’attendanceisslightlyhigherthanboys’.
Table1Enrolmentstatusbygenderforindividualsaged5to18years
Source:Authorscalculations.Susenas2013.
This is a relatively recent phenomenon so while for younger women there is very little genderdifferential, older women have lower education levels than their male counterparts. Using theIndonesianFamilyLifeSurveydataandlogisticregressionanalysis,Zhao(2006)foundthatwomeninoldercohortsweresignificantlylesslikelytohaveattendedprimaryschool,butthiswasnotseeninyoungercohorts(bornafter1973).ThelargergendergapineducationamongstoldercohortscanbeseeninFigure1belowwhichpresentsdatafromthe2013Susenas.
Male Female Total Male Female TotalInschool 80% 81% 80% 77% 78% 77%Notcurrentlyattendingschool 9% 8% 8% 12% 11% 11%Neverattendedschooling 11% 11% 11% 11% 11% 11%Total 100% 100% 100% 100% 100% 100%
Urban Rural
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Women’seconomicparticipationinIndonesia
Figure1Levelofschoolcompletionbyagecohortandgender,2013
Source:Susenas2013.The larger gender education gaps in older cohorts can clearly be seen in Figure 2which presentsaverageyearsofeducationbygenderforurbanandruralareasseparately. Inbothurbanareruralareaseducationalparityhasbeenattainedforcohortsaged29andbelow.Figure2Yearsofeducationbyagecohortandgender,2013
Source:Susenas2013.
Theattainmentofgenderequalityineducationisanationwideachievement.ThisistrueevenintheouterregionsofJavaandBaliascanbeseeninFigure3.
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Women’seconomicparticipationinIndonesia
Figure3Literacyratesbyregion,genderandagecohort,2013
Anumberofstudiesreportgendergapsinliteracyrates.Haidi(2004)findsthattherateofilliteracywastwiceashighforwomenthanformen:6.26%comparedto13.85%.Azzizah(2014)alsofindsagap between female and male literacy which varies by region. In their examination of formal
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Women’seconomicparticipationinIndonesia
employmentandliteracy,GallawayandBernasek(2004)concludethatwomenareunderrepresentedin occupations that are correlated with literacy. It seems that the literacy gap is however alsoconstrainedtotheoldercohorts.Figure3aboveshowsthatinallregions,thereisequalityacrossthegenderswithregardtoliteracyintheyoungercohorts.
Azzizah(2014)focusesonthepopulationwhohaveneverattendedschool.Hefindsthatwomenaremorelikelytohaveneverattendedschoolcomparedtomenandthatthisgapisbiggerinruralareasthan in urban areas. Indonesia’s patrilineal system and the emphasis on women’s familyresponsibilitiesisevidentinthereasonsgivenfornotattendingschool,inparticularanemphasisongetting married and a requirement to take care of the family (Azzizah, 2014). Rammohan andRobertson (2012) using the Indonesian Family Life Survey finds female educational outcomes aresignificantlyworse for females from provinceswith patrilocal norms (as opposed tomatrilocal orneolocalnorms).Thesefindingsmayagainreflectpersistinggapsintheoldercohorts.Table1aboveusingthenationallyrepresentativeSusenasdatafindsnogenderdifferencesinhavingneverattendedschoolamongthoseunder25yearsofage.
Anotherimportantaspectofeducationequalityisequalityinthequalityofeducationreceived.OneofthemainchallengesIndonesiafacesintermsofeducationisitslowquality.LookingattheresultofPISAtestscoresin2012,Indonesiawasranked60outof61countriesinmathematics.Comparedtoothercountriesoftheregion,Indonesiaunderperforms.Indonesianchildrenaged15yearshaveanaveragescoreof375comparedtoaveragescoresof573inSingapore;511inVietnam;427inThailand;and421inMalaysia4.Inscienceandreading,Indonesianscoresareverylowaswell,withanaveragescoreof396and382,respectively.
Figure4IndonesiaPISA2012testresultsbygender
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Women’seconomicparticipationinIndonesia
Figure4showsthePISAperformanceofIndonesianstudentsinmaths,scienceandreadingbygenderin2012.Level6isthelevelattainedbytopperformers.Level1signifiesarelativelypoorperformance.Around 75% of Indonesian boys and girls perform at level 1 or lower, representing a very lowachievementforbothgenders.Inscience,thecountry’sperformanceisslightlybetter,againwithnogendergap.Readingskillsiswherethereisthebiggestproportionofchildreninlevel3(closetoanaverage performance), with girls outperforming boys. This gender difference is widely observedaroundtheworldwithwomentendingtoperformbetterthanmeninreadingtests.
2.2 LabourMarketThe Indonesian economy has been growing steadily over the last few decades (with the notableexceptionoftheperiodfollowingthe1997financialcrisis).Economicgrowthhasbeenreflected insignificant changes in the Indonesian labour force. The labour force is now significantly moreurbanised,lessagriculturalandbettereducatedthanitwasthreedecadesago.Forexample,Whilein197026%ofthelabourforcewasinurbanareasand74%inruralareasby2007thecompositionwas41%urbanand59%rural(Chowdhury,Islam,&Tadjoeddin,2009).
Figure5TotalEmploymentinAgriculture
LabourforceparticipationinIndonesiahasincreasedatafasterratethantheworkingagepopulation.Theagecompositionofworkershaschanged–withyoungerworkersnowconstitutingasmallershare,possibly because they are studying for longer. These changes and evolving societal norms haveaffectedtheexperiencesofworkingwomen–theirabilitytofindwork,thetypeofworktheydoandthewagestheyreceive.Inthissection,wefirstexaminefemalelabourforceparticipationovertimeanditsrelationshipwithemployment,unemploymentandunderemployment.Next,welookattheformalityandinformalityofemploymentincludingsomecomparisonsbyindustryandregions.Thenwelookatthegendergapbyindustrialsectorandoccupation.WealsolookatworkingconditionsasthesehavechangedwithIndonesiangrowthandurbanization.Thenwelookatgenderinequalitiesinwages, separating rural and urban areas and examining changes over time. We also present thefindings in the literature fromBlinder-Oaxaca decompositions ofwages that seek to estimate theextenttowhichthegenderwagegapisexplainedbythedifferentcharacteristicsofmenandwomenandtowhatextentitisduetodifferentialtreatmentofthegendersi.e.discrimination.Finally,welookatinternationalmigration.
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2.2.1 LabourForceParticipation,EmploymentandUnemploymentThe 2014 World Development Indicators show 51.4% of Indonesian women aged 15 and aboveparticipating in the labour force (eitherworking or looking forwork). This is low by internationalstandards.Figure6presentsfemalelabourforceparticipationratesforcountriesintheregion(fromCambodiawiththelowestGDP/capitatoMalaysiawiththehighest),andAustralia,theUKandtheUSforcomparison.Vietnam,similarlyalower-middleincomecountry,hasacorrespondingrateof73.0%.Thailand,classifiedasamiddleincomecountry,hasafemalelabourforceparticipationrateof64.3%.The participation of Indonesian women in the labour market is clearly low in relation to similarcountriesanditslevelofdevelopment.
Figure6FemaleLabourForceParticipationbyCountry
Source:WorldBank,2013.
Further,femalelabourforceparticipationhasremainedrelativelystableoverthepasttwodecades.Itincreasedonlyveryslightlyfrom50.2%in1990to51.4%in2013.Maleparticipationincreasedatahigherrateoverthisperiod,from81.1%to84.2%(Chowdhuryetal.,2009).Femaleparticipationislessthantwo-thirdsofthemaleequivalent.
Married women and women with more dependent children have the lowest participation rates(Comola&deMello,2012).Notsurprisingly,women’slabourforceparticipationdeclinesduringtheirmostfertileyears.VanKlaveren,Tijdens,Hughie-Williams,andMartin(2010)showthatwhilemalelabourmarketparticipationishighestintheagerangeof35-49years,forfemalesitishighestinthepost-child-rearingyears(ages45-59).ThisisconsistentwithcalculationsusingdatafromSusenasfrom2013asshowninfigure7.
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Figure7Labourforceparticipationbygenderandagegroupin2013
Cepeda (2013), in an analysis for theWorld Bank, uses information from the IndonesianNationalLabourForceSurvey(Sakernas)2009toshowthatyoungsinglewomenaged15to24havethehighestrateofparticipationcomparedtoothermaritalcategoriesinthisagerange.Theaggregatedropinparticipationonmarriageinthisagerangeisanenormous37.7percentagepoints.Interestinglythebiggest drop is among married women without children, and after the first child the reductiondecreasespereachadditionalchild.Oneofthesuggestedexplanationsforthisisananticipatoryeffect.Aswomengetmarriedtheyexpecttohavechildrenimmediatelysotheystopworkingevenbeforepregnancy.5Fromage25to64,divorcedandwidowedwomenwithchildrenaretheoneswiththehighestlabourforceparticipation.
Figure8FemaleUnemployment
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Women’s labour forceparticipationdecisionshowever reflecta combinationofmarital and socio-economic status, Alisjahbana and Manning (2006). 6 Poorer married women are more likely toparticipatethanmarriedwomeninnon-poorhouseholds.TaniguchiandTuwo(2014)findthathighereducationalattainmentispositivelyassociatedwiththedecisiontoparticipateinthelabourmarket.
AlthoughfemaleemploymentinIndonesiahasalsobeenslightlyincreasingsince1990,Chowdhuryetal.(2009)showthattheshareoffemaleemploymentintotalemploymenthasdecreasedfrom38.7%to35.1%between1990and2006,comparedtomaleemploymentwhich increasedfrom61.3%to64.9%.7Thisisaresultofgreaterincreasesinmales’labourforceparticipationrelativetofemales’,andfemaleunemploymenthavingincreasedoverthisperiodmorethanmaleunemployment.In2006theunemploymentratewas13.4%forfemalesand8.5%formales.Thishashoweverimprovedsincewith6.7%ofwomenand5.7%ofmenbeingunemployedin2012.8VanKlaverenetal.(2010)showthat unemployment affects mostly young and highly educated females, as presented in Figure 8.Further,AlisjahbanaandManning(2006)findthatbetter-offwomenaremorelikelytobeunemployedwithpoorerwomenbeingmorelikelytobeunderemployed(workingbutwantingtoworkmore).Thisreflectsthatbetteroffwomencanaffordtostayunemployedforlongerperiodswhilepoorerwomenwilltakewhateverworktheycanfind,oftenintheagriculturaland/orinformalsectors.
Figure9showsthatunderemploymentin2013ishigherforwomenthanformeninallgeographicregions.941%ofemployedwomenareunderemployedcomparedto25%ofmen(thiscouldincludevoluntary underemployment) and almost 57% of women in rural non Java-Bali provinces areunderemployed compared to 37% of men. These differences in the number of hours worked bywomenwillconsequentlyaffectaveragemonthlywageincome.Itiscalculatedthatunderemploymentresultsingenderdifferencesinmonthlywageincomeforformalworkersof28.5%andforinformalworkersof50.5%(Cepeda,2013).
Figure9UnderemploymentbygenderandUrban/Rural
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Greaterunderemploymentamongstwomenisalsofoundtobeassociatedwithageandurbanareas.Around20%ofyoung,femaleworkersinpoorurbanhouseholdsworkedlessthan35hoursandweresearching for more work. Taniguchi and Tuwo (2014) examine the relationship betweenunderemployment,maritalstatusandeducation.Theyfindthatmarriagedecreasestheprobabilityofunderemployment.Higher education attainment increases theprobability of full employment andmoderate(asopposedtosevere)underemployment.Thestrongestassociation iswith increases inunderemployment.
2.2.2 EmploymentStatus(Formal/Informal)One reason unemployment rates are relatively low in Indonesia is that unemployment is“unaffordable” to poor households and the informal sector expands to accommodate those whocannotfindformalsectorjobs.Informaljobsarelowaverageproductivityandlowquality(lowpay,nosocialsecurity,lowstabilityandsometimesunsafeconditions).Economicgrowthhasresultedingrowthinformalsectorjobs.Theformalsectorwasestimatedtohavebeengrowingatarateof5.8%prior to the1997 financial crisis. It has sincebeengrowingat a slowerbutnot insubstantial rate.Chowdhuryetal(2009)estimatetheformalsectortohavegrownat2.2%sincethecrisisthroughto2008andtherateofgrowthhasincreasedfurthersincethen.10Wecalculatethatin2013theinformalsectorishoweverstillestimatedtoconstitute75%oftotalemployment.11
Thegenderdifferenceininformalityofemploymenthasshrunkovertime.In1990thepercentageofworkingwomenwhowereemployedintheinformalsectorwas10percentagepointshigherthanformen.Thisgenderdifferencehaddecreasedto7percentagepointsby2006.12Hence,inspiteoftheincreaseineducationlevelsamongstwomen,womencontinuetobeconcentratedininformaljobs.Thisdifferenceisdrivenmainlybytheproportionoffemaleunpaidandcasualworkers,whichis3to1comparedtomales.Marriageanddependentchildrenincreasetheprobabilityofbeinganunpaidfemale worker (relative to being a paid worker in the informal sector) and higher educationalattainmentdecreasestheprobability,ComolaanddeMello(2012).SimilarlyPriebe,Howell,andSari(2014) showthatpoverty isassociatedwiththesectorofemployment.They findthat80%of thewomen inthepooresthouseholdswork in the informalsectorcomparedto34%of thewealthiestwomen.Withinboththepoorestandwealthiestcategoriesmen’sparticipationintheinformalsectorisabout5percentagepointslessthanwomen’s.
Asshowninfigure10,agricultureandfishingisthesectorwiththehighestinformalityforbothmalesandfemales.In2013theagricultural/fisheriessectoraccountedforabout34.9%oftotalemploymentand32.8%oftotalfemaleinformalemployment.Ifwerestrictourattentiontopaidworkersintheinformalsector,womenaremostlikelytobeworkinginhousekeeping,ashomeworkersandinsmallmicroenterprises, where wages, working conditions and job conditions are typically poor (VanKlaverenetal.,2010).
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Figure10InformalStatusofEmploymentbyGender
Asdifferentprovinceshavedifferentlevelsofdevelopmentanddifferentemploymentmarkets,wealsopresent informalitybyprovince inFigure11.BaliandNTB-NTThavethe lowestdifferences ininformalitybygenderacrossallregions.Genderdifferencesintheextentofinformalityarelargerinurbanareas.
Figure11InformalStatusofEmploymentbyRegionin2013
2.2.3 IndustrialandOccupationalSegregationIndonesia’seconomicboominthe1980sand1990sledtoadecreaseinworkforceparticipationintheagriculturesectorfrom66%in1971to41%in1997(Sugiyarto,Oey-Gardiner,&Triaswati,2006).However,womenwereunder-representedintheshiftfromagriculturetomanufacturing.Thiswasmainlydue to the levelofeducationand the typeof skills thatwere required for those jobs. Low
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educationalattainmentoftenexcludedwomenfromaccessingjobsinmanufacturing.However,asthegender gap has been shrinking (as discussed above) andmigration from rural to urban areas hascontinued,thetotalparticipationofwomenandmenintheagriculturesectorhasdecreased,whileparticipationofbothmenandwomenhas increased in themanufacturing sector and the relativeparticipationbygenderhasbecomemoresimilar.SeeFigure12.13
By2007,58.1%and58.9%ofworkingwomenandmen,respectively,wereworkinginnon-agriculturalsectors.Theserviceandtradeandretailsectorshavealsobecomelargerduringthistimewithwomenincreasingtheirshareofemploymentinthesesectors.Cepeda(2013)showsthatadministrativeandmanagerial; and clerical and related occupations are mostly dominated by men. Women’sparticipationinthoseoccupationsis18.0%and40.4%,respectively.Traditionallythoseoccupationsareassociatedwithhigherwages,whichimpliesthattheshareofthewagesinthehandsofwomenisverylow.Otheroccupationswheremaleparticipationisalmostdoubleorhigherareproductionandtransport and agriculture and related activities. Services, professional, technical and sales areoccupationswherebothgendersparticipate.Asmentionedbelow,servicesandsalesareoccupationsthatusuallyareonthelowerrangeofwagesandrequirelonghoursinthejob.
Figure12EmploymentbyIndustry
2.2.4 WorkingconditionsInformationonworking conditions in Indonesia is limited.VanKlaverenet al. (2010) examine thenumberofhoursperweeksworkedbygender,disaggregatedbystatusofemploymentandindustry.Eventhoughtheaveragenumberofweeklyworkinghoursissimilarformalesandfemales,42.8and38.2,a largerproportionofmenworkexcessivehours(definedasmorethan48hoursperweek)-31.8% for males compared to 24.5% for females in 2009.14Economic growth has coincided withshorterworkingdaysand reduced thedifferentialbetweenmen’sandwomen’sworkinghours. In2003around50%ofallmalesworked longerthan48hourscomparedto41%offemales.Averagehoursofworkforfemalesarelessthanmalesacrossallindustries.
Thelongesthoursbeingworkedformenareintheself-employedsector.Thesectorswiththelongestworkinghoursforwomenarehousekeepers;wholesaleandretailtrade;andhotelsandrestaurants.Unlikethenationalaverage, in theseparticular female-dominated industries, thenumberofhoursworkedhasincreasedfrom2000to2008.
13
Women’seconomicparticipationinIndonesia
Working hours are just one facet, and possibly not the most important, of working conditions.PinagaraandBleijenbergh(2010)arguethatwomenfaceadisadvantageinnegotiationinIndonesia.Thisreflectsgenderrolesthataredriventosomeextentbyreligiousviewsandpatriarchalnorms.Perceptions of gender roles affect hiring rates, the potential support women can access and thebargainingpowertheyhavetoadvocateforbetterconditionsatwork.Womengenerallyhavepooreraccesstoworkersunions,fairworkagreementsandcontracts.Forexample,althoughForeignDirectInvestment (FDI) has increased women’s job opportunities particularly in manufacturing, FDI inexport-orientedindustriesprovidesincentivesforemployerstooffermoreprecariousconditionsofemployment inorder to reduce fixed costs and increase international competitiveness (Siegmann,2007). Lackof support structures likechildcare reduces theopportunities forparticipating in thelabourmarket.Womenfacecultural,social,economicandreligiousbarrierstoemploymentandfairconditionsinemployment.Thismayinfluencethewayyoungergenerationsofwomenperceivetheirlabourmarket prospects and affect their educational, occupational and employment choices. Thisreproduces and prolongs the segregation of jobs by gender and results in women being over-representedinlowleveljobswithminimaldecision-makingandfewvisiblesafetymeasuresthatmakewomenmorevulnerablethanmen(AusAid,2012;Blackwood,2008;Elliott,1994).
2.2.5 WagesThemajorityofstudiesthatexaminegenderinequalityinthelabourmarketfocusonwageinequality.There isa largeandgrowingeconomic literature lookingat thecausesof thewagegendergap indifferentcountries.Theindicatorsofgenderinequalitydiscussedabove-labourforceparticipation,employmentandunemploymentgenderratios,sectorandstatusofemployment–alsofeedintothewagegap.Thegenderwagegapultimatelyreflectsdifferencesbetweenmenandwomenineducation,training and skills, experience (reflecting reproductive choices), occupational choice, employmentstatus, labour market choices based on social expectations, and discriminatory hiring and otherpractices.Methodsforestimatingthecontributionofthesedifferentfactorsandstudiesthatdosowillbediscussedbelow.
Figure 13 presents the female/male ratio of average hourly wages. The average hourly wage offemales intheformalsector issomewherebetween70%and80%ofthatofmales.15This lookstohave been improving over time. The figures for the informal sector however show a worseningsituation.Becauseofdatalimitationsintheinformalsector,moststudiesthatattempttoexplaintherawwagegaphavefocusedontheformalsector.
Figure13WorkersWageFemale/MaleRatio
14
Women’seconomicparticipationinIndonesia
Gender wage gaps persist across education levels but are smaller amongst the better educated.Femaleswhodidnotgraduatefromprimaryschoolearnonlyhalfofthatearnedbysimilarmalesandfemaleswhohavegraduatedfromseniorsecondaryearnonaverage79%.Feridhanusetyawanetal.,(2001)findthatthegenderwagegaphasaninvertedUshapewithagereachingthemaximumbyages40to50.Thisislikelytobeduetocumulativedifferencesintheamountofworkexperienceachievedbyfemalescomparedwithmales–asaresultofperiodsoffemalenon-participationinemploymentduetochildbirthandchildcare.
Womenearnlessthanmenacrossalloccupationsandsectors,andthisistrueatalllevelsofeducation,TaniguchiandTuwo(2014).Thewagegapdoesvaryhoweverbyindustrywiththebiggestdifferencesbeingfoundinagricultureandservicesinprivatehouseholdswherewagesarethelowestandwomenearnaround64%ofthemaleaveragewage.Inthehighestpayingsector-finance–wherewagesarealmostdoubletheaveragewage;womenearn6.2%lessthanmen.Inthemostfemale-dominatedsectors-wholesale-retailandhotels-restaurants-eventhoughthereisarelativelyhighaveragelevelofeducation,thehourlywageratesareamongthelowestduetothelongerworkingdays(ADB,2006;VanKlaverenetal.,2010).Consistentwiththesefindings,AlisjahbanaandManning(2006)findthatthe average monthly earning of employed females to males (aged 25-59 years) is lowest for thepooresthouseholds(onehalfcomparedtoanaverageoftwothirdsacrossallsocio-economicgroups).
TheBlinder-Oaxacadecompositionisthemostwidelyusedmethodologyfordetermininghowmuchofthegenderwagegapisduetodifferencesinobservablecharacteristicsbetweenmenandwomen(for example, educational attainment, years of experience, occupation) and how much seems toreflect the mere fact that the worker is female, not male, and which is normally designated asdiscrimination.TheBlinder-Oaxacamethodologyisexplainedinmoredetailinappendix1.Althoughverywidelyusedacrosstheworld,asfarasweareaware,thereareonlyafewstudiesthatattempttodeterminetheportionofthegenderwagegapduetoobservablecharacteristicsandtheportionleftunexplainedintheIndonesiancontext.16
Someoftheexploredexplanationsforthosedifferencesaredifferencesineducation,experience,age,head of household characteristics, industry, poverty level, rural/urban, employment status(formal/informal)andtheeffectofforeigndirectinvestmentandurbanization(Alisjahbana&Manning,2006;Cepeda,2013;Feridhanusetyawan,Aswicahyono,&Perdana,2001;Pirmana,2006;Siegmann,2003,2007;Taniguchi&Tuwo,2014).
Figure14Blinder-OaxacaDecomposition
15
Women’seconomicparticipationinIndonesia
Theresultsofthesestudiessuggestthattherawwagegapisstillhighbuthasdecreasedovertime.The proportion of the gender wage gap that is unexplained (the discrimination component) hashoweverincreasedovertimeaspresentedinFigure14.17Thisfindingemergesfromthecomparisonsof these studies and appears robust even though the studies use different measures of wages,differentspecificationsanddifferentsourcesofinformation.Thesedifferencesexplainsomeofthevariabilityinthesummarypresentedbelow.
Feridhanusetyawanetal.(2001)18isthefirststudyofwhichweareawarethatdecomposesthegenderwagegap.Theyusethe1986and1997Sakernas.andestimatethattherawwagegapintheformalsectortobeabout0.45in1986and0.35in1997.Theypresentestimatesforurbanandruralareasseparately. In 1986 the raw gap was higher in urban areas (0.53) than in rural areas (0.39). Theproportionofthegapattrubutedtodiscriminationwashoweverlowerinurbanareas(46%)versus56%inruralareas.By1997theyestimatethattherawgaphaddecreasedto0.35and0.33forurbanandruralareasrespectivelyandthattheunexplainedproportionrepresentedasmallerportion-30%and42%,respectively.
Pirmana (2006) pools data from the 1996, 1999, 2002 and 2004 Sakernas and calculates a rawdifferencebetweenmaleandfemalerealmonthlywagesof40%.Forty-twopercentofthisdifference(16.8percentagepoints) is found tobe explainedbydifferences in endowments (education level,experience, socio-demographiccharacteristics,economicactivityandsectorand localandregionalcharacteristics)and58%(23.2percentagepoints)isunexplainedorduetodiscrimination.Thatmeansthatawomanwithsimilarcharacteristicstoamanwillonaveragebepaid23%less.19
TaniguchiandTuwo(2014)20usethe2010Sakernasdata.Theyreportarawwagegapof30.8%forworkers with full employment status. They examine the role of age, hours worked, educationalattainment,workoccupation,industryandgeographicallocation.Theyfindthatthevastmajorityofthewagegap93.2%(28.7percentagepoints)isduetodiscriminationwithonly6.8%(2.1percentagepoints)duetodifferencesincharacteristics.Althoughthegenderwagegapishigherinurbanareas,thediscriminationcomponentislargerinruralareas.21
A limitationofthestudiesaboveisthattheyonlyanalysewomenwithformalemploymentstatus.Womencertainlyfacewagediscriminationintheformalsectorbutmostwomenworkintheinformalsector.Cepeda(2013)22istheonlystudythatexaminesthedeterminantsofthewagegapinboththeformaland informalsector. AsshowninFigure15the informalsector is foundtohavenotonlyahigherrawwagegapbutalsoalargerdiscriminationeffect.Thegendergaphasdecreasedovertimeinboththeformalandinformalsectorsbuttheunexplainedproportionofthegaphasincreased.Intheformalsectortheunexplainedproportionhasrisenfrom33%ofthetotalgapof34.9%in2001(11.6 percentage points) to 45% (9.86 percentage points) in 2010 and in the informal sector hasincreasedfrom75%(35.1percentagepoints)to84%(30.5percentagepoints).Whenlookingatthedrivers of those differences, she shows that differences in educational attainment below tertiaryeducation explain a substantial amount of the wage gaps in the formal and informal sector.Additionally,theauthorprovidesevidenceof“stickyfloors”beingafactorinthesettingofwomen’swages.Forboththeformalandinformalsector,thebiggestgenderwagegapwasfoundinthelowesttwodecilesofthewagedistributionandthenitdecreasesovertherestofthedistribution.
16
Women’seconomicparticipationinIndonesia
Figure15Blinder-OaxacaDecompositionbySectorofEmployment
Additionallimitationsofthestudiesdiscussedaboveare1)thattheuseoftheSakernassurveyinmanyofthestudieslimitstheabilitytoexaminetheeffectofhavingchildrenonlabourmarketoutcomesasitdoesnotcontain informationon fertility. It is thusnotpossible toaccuratelycontrol forcareerinterruptionsduetochild-raising.Thiswill leadtoanover-estimateofthegender-wagegapduetodiscrimination23;2)eventhosewagedifferencesduetoobservablese.g.educationalattainment,mayreflect discrimination. For example women may choose to invest less in education because theyanticipatetheywillbepaidlessinthelabourmarket.Similarly,observabledifferencesinexperienceandeducationcanreflectwomen’sreactionstoculturalnormswhichresult inashorterandmorediscontinuousworking life.Occupationalchoicemaysimilarlyreflectthesesocio-cultural factors. Ifthe control variables reflect discrimination, our estimate of the discrimination component willunderestimatethetrueextentofdiscrimination.
2.2.6 MigrationMigrationtoforeigncountriesforworkisanimportantsourceofincomeforwomeninIndonesiaandmigrationrateshaveincreasedoverthelastfewdecades,bothlegalandillegal.24ThemostpopulardestinationsareMalaysiaandtheMiddleEast.OtherAsiancountries,forexample,HongKong,Taiwanand Singapore are also becomingpopular destinations.Most femalemigrantworkerswork in theinformal sectorasdomestichelpers (WorldBank,2010a). In2011womenmadeup to75%of theIndonesian foreignworkers (World Bank, 2014). Thiswas prior to the Indonesia’smoratorium ondomesticworkinSaudiArabiawhichwasimposedin2011andisongoing.Women’sshareoftotalforeignworkershasfallensincebuttheystilloutnumbermen.In2014about54%oftotalIndonesianoverseasmigrantworkerswerefemale.Itisexpectedthattheproportionofwomenintotalmigrationwill decrease further after the government recently announced (May2015) to extend thebanondomesticworkerstotwenty-oneMiddleEasterncountriesfromAugustofthisyear.Mostofthemigrantwomencomefrompoorer,ruralregionsofIndonesia.Womenandmenseektomigrate abroadwith the expectationof earningwages that are not attainable in poor rural areas(AusAid,2012).Ruralwomenaccountfor44%oftotalIndonesianinternationalmigration,althoughtheir sharehasdroppedasa resultof themoratoria (WorldBank,2014).Poverty,unemployment,underemploymentandlackofformaleducation(particularlytrueforolderandpoorerwomen)arethemaindrivingforcesbehindthishighrateofmigration.
17
Women’seconomicparticipationinIndonesia
Protection for femalemigrantworkers (in Indonesia prior to departure and on return, and in thedestination country) is limited. Consequently, mistreatment and even serious physical abuse byemployers is not uncommon.25 There is a real need to formalise, protect and regulate overseasemployment(Silvey,2004).Therehavebeensomeeffortsto improvethesituationbutIndonesia’ssystemforlabourmigrationstillworkspoorlyandchannelsofcoordinationbetweenthegovernment,therecipientgovernmentsandmigrationagenciesarestilltobeimproved(Bazzi&Bintoro,2015).Mostoftheworkerslackaccesstolegalcontracts,financialmarketsandfinancialliteracy,trainingandincountrysupport.26
WhilethemoratoriaondomesticworkintheMiddleEastwasannouncedasameasuretoprotectIndonesianwomenfromexploitationwhileoverseas,inpracticeitwillreducetheopportunitiesforpoorerwomen,particularlyinruralareas,tofindgainfulemploymentandapathoutofpoverty.Itwillrestrict many women to the Indonesian labour market which, as seen above, often discourageswomenfromworkingandtreatstheminequitably.
2.3 Finance&EntrepreneurshipItisestimatedthatinIndonesiaonly23%ofSmallandMedium-SizedEnterprises(SMEs)areownedbywomen (Asia Foundation,2013). Systematicbarriers toentrepreneurshippreventwomen fromeconomic opportunities worldwide. This can not only limit women’s opportunities for startingbusinessesbutcanalsoconfinebusinesseswhichareestablishedtoremainverysmallinscale,oftenoperatingonlyintheinformalsector.
Women’s underrepresentation as entrepreneurs in Indonesia is attributed to various factors.Tambunan(2009)identifiesobstaclessuchaslowlevelsofeducationandfewertrainingopportunitiesfor women, household responsibilities (especially for rural women), legal, cultural or religiousconstraints,andalackofaccesstoformalcreditandfinancialinstitutions.Alackoftimetocompleteincome generating activities due to caring or unpaid roles can also leave women with feweropportunities to develop their own livelihoods and can result in vulnerability to insecure ordiscriminatorysituations.
Usinginformationfromthe2014MicroandSmallManufacturingIndustriesSurvey(IMK)wefindthataround 45% of manufacturing business owners are female. The nature of men’s and women’sbusinesseshowever, appears todifferdramatically. Women’sbusinessesare smaller in scale andmoreinformal.Table2showsthatwhile30%ofbusinessesownedbymenemploypaid(mainlymale)workers,only8%ofwomen’sbusinessesdo.Further,men’sbusinesseshavebeenformalisingatafasterratethanwomen’s(anincreasefrom17%ofmalebusinesseshiringpaidworkersin2009to30%in2014comparedtoanincreasefrom3%to8%forwomenoverthesameperiod).Incontrast,femalebusinesses continue to predominantly be staffed by unpaid female labour. Eighty-four percent ofwomen’s businesses rely on unpaid female workers. These findings are consistent with theobservationthatmanywomenwhodobecomebusinessownersinIndonesiadosooutofnecessityasameanssupplementinghouseholdincomewhenthehusband’sincomeisnotenough,Tambunan(2014).Hence,thereisoftenadifferenceintheaspirationsofmenandwomenfortheirbusinesses,withself-employedfemaleshavingalesserdesiretoexpandand/orformalizetheirbusinesses.
18
Women’seconomicparticipationinIndonesia
Table2TypeofEmployeesbyGenderofOwner
Source:IMK2009and2014.Authors’calculations
Forthosewomenwhodoseektoexpandtheirbusiness,accessto,andcontroloffinancialassetshavebeenshowntobestronglylinkedtowomen’sdecision-makingpowerwithinthehousehold(AusAid,2012). Unlikeinmanyotherdevelopingnations,microcredit inIndonesiahasnotbeenspecificallytargetedtowardswomen(ADB,2006).Aqualitativemeta-analysisconductedbyVongetal. (2013)found that inequality in access tomicrofinance reflects differences in educational attainment andculturalnormsratherthancharacteristicsofmicrofinanceitself.Whileitisoftenassertedthatwomenare less likely than men to use financial institutions, or formal banks in particular, Dames (2012)similarly findsthat it iseducationratherthangenderthat isan importantdeterminantofwhetherIndonesiansaccesscredit.27Thegendergap ineducationhas largelyclosedbuta lackofeducationcould remain a barrier to finance for older women. Many studies link financial participation toeducation,butalsomorespecificallytofinancialeducation.InalaterstudyVongandSong(2015)citedsurveysfindingalmosthalfofIndonesianwomen‘admittedtheyareveryinexperiencedinfinancialservicesandtheirlackofunderstandingoffinancialproductscausesdifficultiesforthemtoformulatesoundfinancialdecisions’.Accesstocredit isnotedtobemoreofan issueforruralwomenand isparticularly related to property ownership rights and, consequently, the ability to offer collateralagainstloans.Thecostofbanktransactionsisalsofoundtoexplainthegapbetweenfemaleandmalefinancialparticipation.A‘one-stopplatform’fortransactionstoreducetheopportunitycostoftimesuchaschildcare, transportationandaccount identificationprocesses’ is recommended,VongandSong(2015).
There seems to be a disconnect between the findings of small scale studies which documentdisadvantage forwomen in accessing finance in Indonesia, and the findings ofmuch larger,morerepresentativesurveyswhichfindlittleevidenceongenderdifferentials,makingthisanareaworthyoffurtherwork.AusAid(2012)emphasisestheimportanceofgenderconsiderationswhenformulatingfinancialinclusionpolicy,butWorldBank(2010b)notestheyhaveobservedfewsignificantdifferencesingender-disaggregatedindicatorsrelatedtofinancialinclusion,suchasinformalsavingsandhavingabankaccount.WorldBank(2010b),usingdatafromtwosurveys28onaccesstofinancialservices,alsofindsfewsignificantgenderdifferencesinaccesstofinancialservices.Therewerenosignificantgenderdifferencesinborrowers’characteristicsortheinstitutiontheychoosetoborrowfrom(seeTable3).Theydidhoweverfindgenderdifferencesinthereasonsgivenforhavingabankaccount.Womenweremorelikelytohaveabankaccountinordertosaveforfutureneeds,whereasmenweremoreconcernedabouttheirabilitytoobtainaformalloan.
2009 2014 2009 2014 2009 2014ProportionofpaidMales 8% 14% 14% 24% 1% 2%proportionofpaidFemales 3% 6% 3% 6% 2% 6%ProportionofunpaidMales 35% 32% 57% 52% 9% 7%ProportionofunpaidFemales 54% 48% 26% 18% 88% 84%
Total Ownerisamale Ownerisafemale
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Women’seconomicparticipationinIndonesia
Table3Borrower’scharacteristics,bygender
Source:WorldBank(2010b).
MostfemaleentrepreneursinIndonesiausepersonalandfamilysavingsasthemostcommonsourceofcapital,AsiaFoundation(2013).Thisishoweveralsotrue,althoughslightlylessso,ofmales.The2014IMKdatashowthat88%ofwomenfinancedtheirbusinesswiththeirowncapitalcomparedto82%ofmen.Table4showsthatforownerswhousedothersourcesforcapital,37%ofmalesusedabankloancomparedtoonly12%offemales.Furthermore,ifwomendoborrowfortheirbusinesses,theamountborrowedissmaller.Ofrespondentswhodidnotuseabankloanasasourceofcapital,62%ofthewomenreportedthatthemainreasonwasthattheywerenot interested inborrowing(comparedto45%ofmen).29
Table4SourceofNon-OwnCapitalandAmountBorrowedfromtheBank
Source:IMK2009and2014.Authors’calculations
TheIMKdatadodetectagendergapinaccesstofinance,althoughmaybenotaslargeasmayhavebeenexpected,at leastinthemanufacturingsector.However, it is importanttonotethattheIMKsample provides information only on peoplewho have amanufacturing business and somay notpresentacompletepictureofaccesstofinanceinIndonesia.Forexample,wedonotknowhowmanywomen(andmen)wishedtostartabusinessbutcouldnotdosoduetoalackofcapital.Totheextentthatmorewomenthanmencouldnotstartabusiness,theIMKdatawillunder-representtheextentofinequalityinaccesstofinance.
2.4 InfrastructureTheprovisionofinfrastructuredeterminestheabilityofbothmenandwomentoproduceoutputandaccessjobs.Thisistrueoftheprovisionofenergy-electricityandgas–whichcanbenecessarytorunsmallbusinesses,andalsotransportinfrastructure.Thegenderedimpactoftheprovisionoftransportinfrastructureandservicesisanunderstudiedareabutonewhichisstartingtoreceivemoreattention.Anumberof studieshavedocumentedhowwomen’s transportneedsdiffer fromthoseofmen.30Womenhavebeenfoundtobemoredependentonpublictransportthanmenasmen,asthemainbreadwinners,aretheoneswhomostoftenhaveprimaryaccesstoanyhouseholdvehiclee.g.motorcycles, leaving the women in the household to travel on foot or by public transport. Women’s
Male Female Male FemaleBank 25% 4% 37% 12%Cooperative 3% 3% 4% 6%finacialinstitution(nobank) 2% 1% 3% 3%VentureCapital 0% 0% 0% 0%BorrowingfromPartners - - 7% 16%Borrowingforpeople 44% 42% 31% 38%Family 10% 6% 10% 6%Other 16% 44% 8% 18%Total 100% 100% 100% 100%
2009 2014
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Women’seconomicparticipationinIndonesia
transport needs also differ because they often have responsibility in the household for shopping,caringforchildrenandtheelderly,whilealsopossiblyworkingtogeneratean income.Thus,whilemen’stransportneedsarewellmetbytransportaimedatgettingpeopletoandfrombusinesscentresinpeakperiods,women’sneedsdiffer.Womenrequireservicesdistributedmoreevenlythroughoutthedaywithroutingandticketingthatallowsformorestopsandstops inareasthatallowforthecarryingoutofhouseholdshoppingandotherchores.
Security on public transport is also of greater concern towomen. The provision ofwomen’s onlycarriages goes some way towards reducing the opportunities for sexual harassment on publictransportbutothermeasures,suchasprovidingsafe,well-litandvisiblewaitingareasalsomakepublictransport more female-friendly. Like most places, the transport industry is male-dominated inIndonesia, often leading towomen’s needsbeingoverlooked. There is a growing recognition thatgreater gender balance is needed amongst transport planners and engineers if we are to see atransportsystemthatbalancestheneedsofmenandwomen.
Inadequatetransportinfrastructurelimitstherangeofemploymentopportunitiespeoplecanaccessandislikelytohaveadisproportionatelylargeeffectonwomengiventheirgreaterrelianceonpublictransportandtheneedforsafeandreliabletransportthatenablesthemtofulfiltheiremploymentandhousehold responsibilities.Althoughgenderdifferences in transportneedshave started toberecognised (particularly in relation to urban transport), there is very little work on the impact oftransportinfrastructureandfemalelabourmarketparticipation.Byprovidingphysicalaccesstojobsand markets, transport infrastructure can play a potentially important role in boosting women’seconomicparticipation31.
2.5 HealthGenderdifferencesinhealthstatusareimportantintheirownrightandalsoaffectwomen’sabilitytoparticipateinthelabourmarketandtheirproductivity.Alackofinvestmentinwomen’shealthhaslonglastingconsequences,affectingcognitivedevelopment,schoolprogressionandlabourincome.Gendergapsinhealthearlyinlifearelikelytowidenfromchildhoodtoadulthood.Gendergapsinhealth–forexampleininfantandchildmortalityandmorbidity–althoughfoundinmanydevelopingcountries,arenotevidentinIndonesia.Thereissomeevidencethatwomenhavemorelimitedaccesstocurativemedical treatmentthanmen(reflectingtheir lowercapacity forpayment) (ADB,2006).Thereishowevernotmuchinformationonwomen’saccesstogeneralhealthservices.Mentalhealthis one area where women seem to be more in need than men. Using information from theIndonesian Family Life Survey (IFLS) Friedman and Thomas (2009) find thatwomen aremorelikely thanmento report feelingsadandanxiousandhavingdifficulty sleeping.Theyarealsomorelikelytoreportbeinginpoorhealth.13.6%ofwomen15-49yearssufferfromachroniclackofproteinandanaemia(JICA,2011).
Indonesiadoesnotperformparticularlywell in respect to reproductivehealth.Maternalmortalityratesarehigh(althoughdecreasing)relativetosimilarcountries.The2013maternalmortalityratewas190per100,000livebirths(fallingfrom450in1986and307in2000(ADB,2006))whileothermembersoftheregionlikeVietnamregisteronly47per100,000livebirths.32MaternalhealthservicesinIndonesiaaregenerallyoflowquality.
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Women’seconomicparticipationinIndonesia
Table5presentsthepersonwhoattendedthefirstandthelastdeliveryofwomenovertime.Whenexaminingthedecisionsofthesamewomanacrosstime,wecanseethatfromthefirsttothelastdelivery more women were going to see a doctor or midwives and also fewer women went totraditionalhealersorfamilymembers.Thispatternisapparentinboth,ruralandurbanareas.Whencomparingacrossyears,wecanseethattheproportionofwomenwhoselastdeliverywasattendedbyamedicalprofessionalincreasedfrom73%in2007to85%in2013.Thisincreaseismuchstrongerinruralareaswheretheprofessionalassistanceatbirthincreased16percentagepoints,comparedto4percentagepointsinurbanareas.Althoughdeliveriesassistedbytrainedpersonnelhaveincreased,thematernalmortalityrateispersistentlyhighinIndonesia.Thepoorqualityofcareisthelikelyculprit.Non-professionalassistancestillaccountsfor23%ofcareinruralareas.
Accesstocontraceptionisalsoverylimitedfornon-marriedwomen.Althoughthefertilityratehasbeendecreasingovertime(Table6),in2004thecontraceptiveprevalenceratewasonly60%in2004(JICA,2011)33andmostof themethodswerewomenbiased, forexampleoral contraceptivesandinjectables,asopposedtocondoms.
Afurtherareawherelittleisknown,isthehealthstatusofelderlywomen.Lifeexpectancyatbirthis5yearshigher forwomenthanmen(68.8 formenand72.7 forwomen).Thiscanbeofparticularimportanceasagingrequiresspecifichealthcareandasfemalelifeexpectancyis5yearshigherthanmen’s,womenareatgreaterriskofalackofprovisionofservicesinoldage.Thisisparticularlytrueforwomeninthepooresthouseholds,where10%ofhouseholdshaveafemalehouseholdheadandtheaverageageis55.
Table5Deliveryattendance
Personwhoattendedthedelivery
Firstdelivery
Lastdelivery
Firstdelivery
Lastdelivery
Firstdelivery
Lastdelivery
TotalDoctor 12.32 13.64 15.44 16.88 16.61 18.21Midwife 53.96 58 61.85 63.71 65.04 66.02Paramedic 0.52 0.89 0.39 0.66 0.42 0.53TraditionalHealer 30.27 25.31 19.79 17.34 15.46 13.79Family 2.69 1.91 2.33 1.24 2.34 1.37Other 0.24 0.25 0.2 0.16 0.13 0.08Total 100 100 100 99.99 100 100UrbanDoctor 20.71 22.25 23.24 24.87 24.42 26.01Midwife 64.25 65.81 65.82 65.48 66.89 66.44Paramedic 0.39 0.64 0.32 0.54 0.45 0.39TraditionalHealer 13.4 10.51 9.68 8.74 7.29 6.7Family 1.12 0.66 0.83 0.28 0.84 0.39Other 0.13 0.13 0.12 0.1 0.11 0.07Total 100 100 100.01 100 100 100RuralDoctor 6.11 7.27 7.9 9.15 9.12 10.73Midwife 46.35 52.22 58.02 62 63.27 65.62Paramedic 0.61 1.07 0.47 0.78 0.4 0.66TraditionalHealer 42.76 36.27 29.55 25.66 23.29 20.58Family 3.86 2.83 3.79 2.18 3.78 2.32Other 0.32 0.34 0.27 0.23 0.15 0.09Total 100.01 100 100 100 100.01 100Source:Susenas
2007 2011 2013
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Women’seconomicparticipationinIndonesia
Table6AverageNumberofChildrenbyagecohort
Year 2007 2011 2013
Age
15-19 0.5 0.5 0.5 (0.6) (0.6) (0.5)20-29 1.3 1.2 1.2 (0.9) (0.9) (0.8)30-39 2.5 2.3 2.3 (1.4) (1.3) (1.3)40-49 3.5 3.3 3.2 (2.0) (1.9) (1.8)50-59 4.4 4.1 4.1 (2.5) (2.3) (2.3)60-69 5.1 4.9 4.8 (3.0) (2.8) (2.7)70ormore 5.3 5.2 5.3 (3.2) (3.1) (3.1)
Source:SUSENAS2007,2011and2013.StandardDeviationinparenthesis.
2.6 Institutions&LawsCommitmentstoimprovingandachievinggenderequalitycanbedemonstratedthroughlaws,nationalandregionalpoliciesaswellasinstitutions.IndonesiaratifieditscommitmenttotheUNConventionontheEliminationofAllFormsofDiscriminationagainstWomen(CEDAW)in1984andhassubsequentlyreconfirmeditspositionthroughitssupportofsubsequentdeclarationssuchastheBeijingDeclaration(UN,2003).
AnumberoflabourlawsinIndonesiadealdirectlywithgenderequality.Forexample,lawsgoverningmaternityandmenstruationleave.Otherstargettheoverallpopulationliketheestablishmentofminimumwages.Manyoftheselawsarenotenforced.Thosethatareenforced,havesometimesproventohaveunforeseennegativeconsequencesforwomen.Forexample,Suryahadi,Widyanti,Perwira,andSumarto(2003)findthattheimpositionofminimumwagesbetween1998and2000hadanegativeeffectontheemploymentoflow-skilledwomenfrompoorerhouseholds.Similarly,ithasbeensuggestedthatthematernityleaveprovisionsenshrinedinIndonesianlawactasadisincentiveforemployerstoformallyhirewomen.TheeffectoflawsorlawschangesisanunderstudiedareainIndonesiathatcouldshedlightonhowtopromotegenderequalityinthelabourmarket.Forexample,lookingattheeffectofminimumwages;menstrual,miscarriageandmaternityleaveprovisions;ortheEqualEmploymentOpportunitystrategyimplementedin2003onFLFP,statusofemploymentandwagegaps.
Despitesupportforinternationalconventions,somelawsinIndonesiadonothaveequalimpactsonwomenandmen.Somelawsactivelylimitwomen’sindependence.Forexample,Indonesiantaxregulationsrequiremarriedwomentousethesametaxfilenumberastheirhusband(ADB2006),makingitmoredifficultformarriedwomentomakeindependentfinancialdecisions.Additionally,theCivilCoderequireshusbandstoassistwomeninsigningcontracts,removingwomen’scontrol
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overtheirownfinancialtransactions.Finally,thereisalackoflegislationandhenceprotectionforwomenagainstsexualharassment(WorldBank,2016).
2.6.1 LawinrelationtofamiliesDocuments proving head of the household status are required for female-headed households toaccessgovernmentpovertyreliefprogramsandotherentitlementprogramsaswellasprocuringbirthcertificatesfortheirchildren,whicharerequiredforstateschoolenrolment.Womeninpoorfamilieshoweveroftenlacksuchlegaldocuments(suchasdivorcecertificates,Alfitri(2012);oridentificationcards,Lockley,Tobias,andBah(2013)).ExtensivelegalreforminIndonesiahastakenplacetoincreasewomen’saccesstothereligiouscourts inordertoformallydocumenttheirroleastheheadofthehousehold(Alfitri,2012;WorldBank,2011).Suchlegalreformswererequiredasthecostofcourtfeesandtransportationtoaccessthecourtswas,andinsomecasesremains,beyondthemeansofthepoor(WorldBank,2011).
AlthoughthelegalageformarriageinIndonesiais21yearsold,withparentalpermissionwomencanbemarriedasyoungas16yearsold,ADB(2006).Earlymarriagecan leadto leavingschoolbeforefinishing,asmanyeducationalestablishmentswillnotacceptmarriedwomen,aswellasadolescentpregnancy and its associated risks (ADB 2006). Figure 16 presents the distribution of age at firstmarriage. Itshowsasignificantproportionofgirlsgetmarriedbeforetheageof18,particularly inruralareas.Theaverageageofmarriagehasbeenincreasingveryslowlyfrom19.5inruralareasin2007to19.77in2013.
Figure16Female’sageattheirfirstmarriage,2013
Source:Susenas2013.
2.6.2 LabourLawsLabourlawsareanother,unintendedsourceofdiscriminationagainstwomen.ADB(2006)suggeststhatthehighunemploymentrateamongstIndonesiawomenmaybetheresultoflabourlawswhich
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provideprovisionsforwomensufferingfrommenstrualpaintotakeleaveaswellasmaternityandmiscarriageleave.Maternityleaveprovisionsstatethatafemaleworkershallreceiveherwagesinfullfor theperiodofmaternity leave (VanKlaverenetal., 2010) yet inpracticewomenaredismissedratherthanaffordedtheirthreemonthsmaternityleave(ADB2006).Elliott(1994)arguesthateventhoughmaternityleaveprovisionsarestrong,duetothenatureoftheworkwomenareemployedin- lowpaid,unskilled,andseasonal–andtheoversupplyof labour for thesepositions,womencaneasilybedismissed.
In thecreationof labour lawsafter independence, Indonesiabasedmanyof their lawsoncoloniallegislationandinsomecasesenforcedtraditionalgenderideologies(Elliott,1994).Inparticular,theauthorcitestheexclusionofwomenfromnight-timeworkasoneoftheprohibitiveaspectsoftheLabourAct(1948).Therehavebeenlegalattemptstopromoteequalityofremunerationbygender;suchastheEqualEmploymentOpportunitystrategyimplementedin2003.Howevertheenforcementofthoseregulationsisnotstrongenoughtobeeffective(Pinagara&Bleijenbergh,2010).Indonesiaalsolackslegislationtoprotectagainstsexualharassmentinemployment(WorldBank,2016).
2.6.3 PropertyRightsAccesstolandandpropertyrightsformtheproductivebasisofmanyhouseholdsinpartsofIndonesiawheresmall-scaleagricultureisoftentheprimaryfoodandincomesource.Women’saccesstolandorregistrationofthetitle intheirnameisuncommon,withthemajorityofmaritalpropertybeingregisteredinthehusband’sname(ADB2006).Althoughjointlandownershipisformallyadoptedinthelawandco-ownershipisinformallyrecognised,fewlandtitlesareheldjointly.
InheritedpropertyinIndonesiaisallocatedpredominatelyaccordingtoIslamiclaw(ADB2006),whichallocatesgreaterproportions tosonsrather thandaughters;althoughtherearesomeregions thatemphasise gender equality in inheritance as per adat traditions (as shown by responses to theIndonesian Family Life Survey), Kevaneand Levine (2000). Furthermore, thereare regions, Java inparticular,wheretheyoungestdaughterfulfilsthecaregivingroletoolderparentsandassuchinheritsthe parental home. In regions with matrilineal traditions the property is passed from mother todaughter(Kevane&Levine,2000),howeverthisisnotparticularlycommon.
2.6.4 PoliticalRepresentationFemalepoliticalrepresentationisoftenseenasawaytoensurepolicydecisionsaremadewithgenderequalityinmind.TheGovernmentofIndonesiahassettargetsforwomen’sparticipationinparliament,political parties and decision-making institutions, with legislation mandating 30 percent femalerepresentation(JICA,2011).However,theselevelshavenotbeenachievedandarenowatargetofthe current National Mid-Term Development Plan. The Constitution of Indonesia promises equalprotectiontoallcitizens,buttheIndonesianlocalgovernmentsaremale-dominated(Kevane&Levine,2000). Figure17showsthat,although increasing, thenumberofseats isbelowthegovernment’stargetandalsolowbyinternationalstandards.OthercountrieslikeEastTimor,thePhilippinesandVietnamhave38.5%,27.7%and24.3%femalerepresentationinparliament,respectively.
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Figure17Proportionofseatsheldbywomeninnationalparliaments(%)
Women’svoicesarealsounderrepresentedincorporateboardrooms(WorldPolicyAnalysisCentre(2015);CreditSuisse(2014)).In2013,only5%ofpositionsoncompanyboardsinIndonesiaareheldbywomen.Thiscomparesunfavourablywiththeglobalaverageof12.7%and,incomparisontoothercountriesintheregionlikeMalaysia(11%)andthePhilippines(12%).
Political power was radically decentralised in Indonesia in 2001, with significant decision-makingpowersbeingtransferredfromthenationalgovernmenttodistrictgovernments.Thereintroductionoftraditionallawsandinstitutionshasbeenthemostcitedeffectofdecentralisationonwomen(Mahy,2012).Theambitioustransitiontoadecentralisedsystemofgovernancehas“unintentionallymadewayforanumberoflocalgovernmentstoadvancetheiraspirationofpublicpoliciesbasedonShari’aorIslamiclaw,”(ADB2006,p28).Mahy(2012)explainsthatsomeoftheselocallawsareparticularlydiscriminatorytowardswomeninparticular,notrecognisingtherightofwomentoownpropertyorearnanindependentincome.Siahaan(2003)echoesthisfindingontheeffectofdecentralisationonwomen,notingthatalthoughithasincreasedparticipation‘ithasbeenlessencouragingtowomen’sparticipationand[political]representationatthelocallevel’.
3. StagnationofthefemalelabourforceparticipationinIndonesia:Anageandcohortanalysis34
3.1 IntroductionIndonesianowhasthelargesteconomyintheAssociationofSoutheastAsianNationsandthe16thworldwide (ADB,2015). The continuedeconomicdevelopmenthasmeant rising average incomes,changesinthesectoralstructureoftheeconomy(fromagriculturetomanufacturingandservices)andincreasingindustrializationandurbanization(Elias&Noone,2011).Indonesiaachievedmiddleincomestatusin2004andhighgrowthalsorapidlyreducedpovertyfrom23percentofthepopulationin1999to11percentin2016.Inspiteofthesignificantchanges,theimpactontheexperiencesofwomeninthe labour market appears to be rather muted. The 2014 World Development Indicators show51.4percentofIndonesianwomenaged15andaboveparticipatinginthelabourforce(eitherworkingorlookingforwork).Thishasremainedlargelyunchangedoverthepasttwodecadeswhichhasmeantthatthelargegapbetweenfemaleandmaleparticipationcontinuesandfemaleparticipationremainslowrelativetocountriesatacomparablestageofdevelopmentintheregion(seealsoADB,ILO,andIDB(2010)).
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Insection2,wereviewedstudiesthatwerelargelyconsistentinidentifyingthemaindriversoffemaleparticipation.Theseincludemaritalstatus,prevailingeconomicconditionsandthelevelofeducationalattainment.Themainaimof thissectionof thereport is todisentanglehowthedriversof femalelabourforceparticipationinIndonesiahavecontributedtokeepingfemalelabourforceparticipationunchangedover theperiod1996to2013.Wedothisbyseparating labour forceparticipation intocomponents due to factors on the supply and demand side of the labour market – educationalattainment, marital status, fertility, household structure, distance to urban centres, main localindustries–andimplementingacohortanalysiswhichseparatesouttheeffectof life-cyclefactors(age)onwomen’slabourmarketparticipationandcohorteffects(changesinparticipationovertime).
Understandingtheconstraintsthatwomenfaceinthelabourmarketisessentialininformingpoliciesaimedataddressingtheseconstraints.Previousstudiesattributethistogenderdifferencesinfamilyroles, child-caring and also cultural norms in relation towomen’s traditional roles (Jayachandran,2014).Increasesinparticipationarelikelytohaveflowoneffectsthroughfemaleempowermentandmayaffectother facetsof thegenderdivide (e.g.political representation,havinggreater sayoverhouseholddecisionsandbeinglessacceptingofspousalviolence).ImprovingfemaleparticipationisalsoimportanttohelptheIndonesianeconomyshiftfromapatternofeconomicgrowthdrivenbyresourcesandcheaplabourandcapitaltogrowthbasedonhighproductivityandinnovation(ADB,2015). This could help Indonesia avoid the middle-income trap and continue its economicdevelopmentintothefuture.
3.2 DataandMethodsThedatausedinthissectionisfromtwosources-theNationalSocioeconomicSurvey(SUSENAS)andtheVillagePotentialStatistics(PODES).
The SUSENAS is a nationally representative survey conducted annually and typically composed ofabout200,000households.Eachsurveycontainsacorequestionnairewhichconsistsofinformationon all household members listing their sex, age, marital status, and educational attainment andinformationonlabourmarketactivity,healthandfertility.
The Susenas allows us to explore the role of child-raising in the decision to participate and theavailabilityofalternativechild-carersinthehousehold(primarilygrandparentsandotherwomenwhocouldactasbabysitters).WesupplementtheSusenasdatawithdatafromthePODES.Thisisacensusof all villages across Indonesia (approximately 65,000).Weuse the PODES for somedemand sidecharacteristicsofthelabourmarketsuchasthedistancetothenearestdistrictoffice(toactasaproxyforaccesstojobs)andthemainsourceofincomeofthevillage.
• At the individual level,wecontrol for if the individual is thehouseholdhead, theirmaritalstatus(e.g.married,divorced,widowedorsingle)andthelevelofeducationachievedbytheindividualasmeasuredbytheirreceiptofcertificate(e.g.iftheindividualcompletedprimaryschool,lowersecondaryschool,uppersecondaryschool,ortertiaryeducation).
• At the household level,we control for the number of people living in the household, thenumberof femalesagedbetween45and65years in thehousehold (excludingthe femalerespondent)whoarepotentialbabysitters,thenumberofelderly(definedasgreaterthanorequalto65yearsofage)femalesormalesinthehouseholdandthenumberofchildreninthehouseholdbyage(theagegroupingsare0to2yearsofage,3to6,7to11,and12to17).
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• Atthevillagelevel,wecontrolfordistancetothenearestdistrictofficeandthemainsourceofvillage income.Wealsocontrol forprovincialunemployment rates (calculated fromtheSusenas)toactasaproxyfortheunderlyingeconomicconditionsatthattime.
AdisadvantageoftheSusenasisthatitiscross-sectionalsowecannotobservethesameindividualsorhouseholdsacrosstime.ButbyusingtheSusenasfrom1996,2000,2007,2011and2013surveyyears,wecanobservehowtheparticipationofdifferentbirthcohorts(groupsofpeopleborninthesameyears)changeovertime.Usingdatacoveringsuchalongtimeperiodallowsacloseexaminationoflifecycle(age)effectsandtrendsovertime(cohorteffects)onfemaleparticipation.
Toestimatethedeterminantsoffemalelabourforceparticipationweregresswhetheranindividualparticipatesinthelabourforceornot(yi)onasetofpotentialdrivers(xi)usingabinaryprobitmodel.Thatis,weestimate:
Equation1Labourforceparticipation
yi=β0+ !"#$"%& i+εi,y=1[y*>0],
Thevectorofpotentialdrivers(xi)includesthosediscussedabove.Onthesupplysideofthelabourmarketwecontrolformaritalstatus,iftheindividualisthehouseholdheadandthehighestlevelofeducationachieved,household size, thepresenceofababysitterorelderlymenorwomen in thehouseholdandthenumberofchildrenatcertainages.Onthedemandside,weincludedistancetothenearestdistrictofficeandthemainsourceofincomeinthevillage.Wealsocontrolforgeographicdifferencesusingprovincedummiesandtheunemploymentrateforeachprovince.
Intuitively, theregression identifiestherelationshipbetweenthecontrolvariableand labour forceparticipation.Themagnitudeoftheeffectiscapturedbythecoefficientonthecontrolvariable(β).
Dummyvariablesarealsoincludedfortheageoftheindividualatthetimeofthesurveyandtheiryear of birth. The age and cohort analysis will establish whether the younger cohorts behavedifferently in relation to labour force participation compared to their older counterparts and theextenttowhichthepropensitytoparticipateinthelabourmarkethaschangedovertime.
Thecoefficients(andassociatedmarginaleffects)ontheagedummiescapturehowanindividual’slikelihoodofparticipatingvariesacrossthelife-cycle,irrespectiveoftheiryearofbirthaftercontrollingforothercharacteristics.Thecoefficientsontheyearofbirthdummyvariablesallowsustocomparepeople born in different years and so identifywhether the younger cohorts behave differently inrelationtolabourforceparticipationthantheiroldercounterparts.35
Weestimateequation(1)separatelyformenandwomenanddisaggregatedbyruralandurbanandJava-Baliandnon-Java-Balitogiveusanunderstandingofthemaindriversoffemaleparticipation.
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3.2.1 DescriptiveresultsTable7presentsthesummarystatisticsoflabourforceparticipationdtheexplanatoryvariablesforruralandurbanareas.
Table7Summarystatisticsoflabourforceparticipationandexplanatoryvariables
Urban RuralVariables Male Female Male FemaleIndividualcharacteristics: Labourforceparticipation 81.2% 47.3% 88.5% 56.1%Householdhead 57.3% 7.5% 62.1% 6.7%Maritalstatus:Single 37.1% 71.2% 30.8% 19.9%Maritalstatus:Married 61.1% 63.4% 67.0% 71.8%Maritalstatus:Divorced 0.9% 2.6% 1.0% 2.6%Maritalstatus:Widowed 00.9% 5.2% 1.2% 5.7%Education:Atleastprimary 90.8% 86.0% 75.4% 67.2%Education:Atleastlowersecondary 69.5% 62.3% 38.5% 30.8%Education:Atleastuppersecondary 22.1% 18.5% 8.1% 6.2%Education:Atleasttertiary 10.5% 9.5% 2.8% 2.5%Householdcharacteristics: Householdsize 4.8 4.7Babysitter 0.3 0.3Numberofelderlyfemales 0.1 0.1Numberofelderlymales 0.1 0.1Numberofchildren:0to2yearsold 0.2 0.2Numberofchildren:3to6yearsold 0.3 0.4Numberofchildren:7to11yearsold 0.4 0.5Numberofchildren:12to17yearsold 0.7 0.7Villagecharacteristics: Distancetonearestdistrictoffice('100km) 0.5 0.8Mainincome:Agriculture 0.3 0.961Mainincome:Mining/quarrying 0.01 0.01Mainincome:Processing/industry 0.1 0.01Mainincome:Largetrading/retail 0.2 0.01Mainincome:Servicesotherthantrade 0.35 0.02Unemployment 0.06 0.06Observations 469,157 481,751 681,427 691,280Source:Author’scalculationsusingSusenasandPODES.
Asdescribedabove,thereisasubstantialgapbetweenfemaleandmalelabourforceparticipation–female labourforceparticipationisonaverage40percent lessthanmaleparticipation(85percentcompared to 52percent). The participation rates are higher for men and women in rural areascomparedtourbanareas.Mosthouseholdheadsaremales,andmostfemalesandmalesaremarried.Therearemorepotentialbabysittersinurbanhouseholds,possiblyduetohigherhousingprices.Atthe village level, the distance to nearest district office is unsurprisingly less in urban areas andagriculture ismostprevalent inruralareaswhileservicesand largetrading/retailare large incomesourcesinurbanareas.
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3.3 GeneralresultsTable8presentstheresultsofestimatingequation(1)formenandwomenbyruralandurbanstatus.Maritalstatusisakeydriveroflabourforceparticipationforwomen.Amarriedwomaninruralareasis11percentagepoints less likelytobeworkingor lookingforworkthanasinglewomanandthisdifferenceisstatisticallysignificant.Theimpactismorepronouncedformarriedwomeninurbanareasastheyare25percentagepointslesslikelytobeparticipatingthansinglewomen.
Beingahouseholdheadforbothmenandwomenincreasesthe likelihoodofparticipation inbothurbanandruralareas.Butthemagnitudeoftheimpactformenissubstantiallysmallerbecausemenaregenerally theprimary incomeearners sowork irrespectiveofwhether theyare thehouseholdhead or not. The level of educational attainment is also a strong driver of female labour forceparticipation.Forwomen,completinguppersecondaryschoolincreasesthelikelihoodofparticipationcompared to someonewho only completed lower secondary by 19percent in rural areas and by22percentinurbanareasrespectively.Themagnitudeoftheimpactincreasesfurtherifwomenattaintertiaryeducation.Butformenthereislittlevariationintheprobabilityofparticipatingwithdifferentlevelsofeducation.Men,asthemainbreadwinnersinIndonesiansocietytendtowork,regardlessoftheirlevelofeducation.
Householdsizedecreasesparticipationforwomeninruralareas–anincreaseinhouseholdsizeofonedecrease the likelihood of participation by nearly 2 percentage points. But the magnitude of theimpactforurbanfemalesandmalesaremuchclosertozero.Thepresenceofapotentialbabysitter,elderlyfemaleormaleinthehouseholdsignificantlyincreasethelikelihoodoffemaleparticipationbyaround1to3percentagepoints.Thismayreflecttheabilityofthewomantoleavechildrenathomewith thebabysitter or theelderly relative. Themagnitudeof the impact of thesepotential child-minders is much higher for women than men (the effect is negligible for men). The presence ofchildreninthehouseholdalsohasmarkedlydifferenteffectsformenandwomen.Forwomen,thepresenceofyoungchildrenhasanegativeeffectonthelikelihoodofparticipating.Thepresenceofachildundertwoyearsofagedecreasestheprobabilityofparticipationby8percentagepointsbuthasonlyasmall(andpositive)effectonmen’slabourmarketactivity.
Onthedemandsideofthelabourmarket,wehypothesisedthatthecoefficientfordistancetothenearestdistrictofficewouldbenegativeasitwasintendedtocapturedistancetoanactivelabourmarket,however,thecoefficientispositive,albeitsmall.Thevariablecouldbepositivelycorrelatedwithagriculturalemploymentinruralareas,withthepositivecoefficientreflectingwomen’sgreaterinvolvement in agriculture. The villages’ main sources of income variables show that femaleparticipationishighestinareaswithagricultureandindustry(whichincludesmanufacturing).Butastheeconomymovesfurtherawayfromagriculturetoothersectors,femaleparticipationdrops.
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Table8Marginaleffectsofpooledsample
VariablesRural Urban
Female Male Female MaleHouseholdhead 0.2115*** 0.0563*** 0.1191*** 0.0385*** (0.0031) (0.0015) (0.0040) (0.0021)Maritalstatus:Single(omitted) Maritalstatus:Married -0.1129*** 0.0758*** -0.2508*** 0.1579*** (0.0025) (0.0016) (0.0028) (0.0025)Maritalstatus:Divorced 0.0057 0.0089*** 0.0059 0.0304*** (0.0050) (0.0019) (0.0058) (0.0033)Maritalstatus:Widowed -0.1629*** 0.0146*** -0.1626*** 0.0491*** (0.0046) (0.0016) (0.0047) (0.0025)Education:Noschooling(omitted) Education:Atleastprimary -0.0297*** 0.0018** -0.0226*** 0.0175*** (0.0016) (0.0007) (0.0026) (0.0021)Education:Atleastlowersecondary -0.0652*** -0.0404*** -0.0608*** -0.0632*** (0.0017) (0.0007) (0.0020) (0.0011)Education:Atleastuppersecondary 0.1257*** 0.0169*** 0.1609*** 0.0588*** (0.0032) (0.0009) (0.0027) (0.0012)Education:Atleasttertiary 0.2516*** 0.0062*** 0.2038*** -0.0085*** (0.0040) (0.0019) (0.0034) (0.0024)Householdsize -0.0158*** -0.0049*** 0.0046*** -0.0040*** (0.0006) (0.0002) (0.0006) (0.0004)Babysitter 0.0177*** 0.0049*** 0.0134*** -0.0059*** (0.0020) (0.0005) (0.0022) (0.0011)Numberofelderlyfemales 0.0315*** 0.0036*** 0.0106*** -0.0039** (0.0025) (0.0009) (0.0029) (0.0017)Numberofelderlymales 0.0252*** 0.0088*** 0.0209*** 0.0060*** (0.0024) (0.0009) (0.0030) (0.0019)Numberofchildren:0to2yearsold -0.0792*** 0.0105*** -0.0754*** 0.0183*** (0.0016) (0.0007) (0.0020) (0.0014)Numberofchildren:3to6yearsold 0.0055*** 0.0084*** -0.0251*** 0.0170*** (0.0013) (0.0006) (0.0016) (0.0011)Numberofchildren:7to11yearsold 0.0251*** 0.0088*** -0.0043*** 0.0152*** (0.0012) (0.0004) (0.0014) (0.0009)Numberofchildren:12to17yearsold 0.0223*** 0.0073*** 0.0041*** 0.0115*** (0.0011) (0.0004) (0.0012) (0.0007)Distancetooffice('100km) 0.0011 -0.0036 0.0159*** 0.0005 (0.0009) (0.0028) (0.0016) (0.0019)Mainincome:Agriculture(omitted) Mainincome:Mining/quarrying -0.1260*** -0.0289*** -0.0695*** -0.0172*** (0.0103) (0.0034) (0.0077) (0.0014)Mainincome:Processing/industry -0.0191*** -0.0325*** 0.0047 -0.0307*** (0.0071) (0.0025) (0.0030) (0.0013)Mainincome:Largetrading/retail -0.0942*** 0.0244*** -0.0180*** 0.0499*** (0.0069) (0.0006) (0.0021) (0.0018)Mainincome:Servicesotherthantrade -0.1307*** 0.0366*** -0.0389*** 0.0756*** (0.0048) (0.0004) (0.0020) (0.0011)Unemployment -0.0027*** 0.007*** -0.0070*** -0.0015*** (0.0002) (0.0059) (0.0002) (0.0120)Observations 691,280 681,427 481,751 469,157
Source:AuthorscalculationsusingSusenasandPODES.*Themarginaleffectsforprovinceandagedummiescanbeprovidedonrequest.Significancelevels***p<0.01,**p<0.05,*p<0.1
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3.4 AgeandcohortresultsThedescriptive results showed that the raw female labour forceparticipation figureshave largelyremainedunchangedoverthesurveyyears.Thissectionexaminestheresultsbyageandcohorttoenableustounderstandtheextentofchangesinparticipationacrossthelife-cycleand/orchangingattitudesbyyoungercohortstowardsparticipationthatmaykeeptheaggregatefiguresunchanged.
TheresultsformalesandfemalesareshownbelowinFigure18.Theresultsoftheageanalysisarelargelyasanticipated. It shows female labour forceparticipation increasesquicklyupuntil around25yearsofagebeforeslowingovertheagestypicallyassociatedwithchildbearing.Itpeaksataround50yearsofagebeforestartingtodecline.Thecontrastwithmalesshowstheextentofthedisparityacross these years. Men’s participation rises sharply to almost 100percent once the period ofeducationalattainmentisoverandremainsconstantbeforestartingtodecreasefromage50.
The cohort analysis reveals some interesting findings. It shows that femaleparticipationhasbeenincreasingfromaround40percentforthoseborninthe1940stoaround60percentforthoseborninthe1980s.Malelabourforceparticipationhasremainedatabout95percentacrossthecohorts.
Figure18Ageandcohorteffects
Source:Author’scalculationsusingSusenasandPODES
Theanalysisthusrevealsalargeincreaseintheunderlyingpropensityforwomentoparticipate,whichmayreflectchangingculturalnorms. Ifthistrendcontinuesovertime,astheoldercohortsexitthelabourmarketwewouldexpecttoseeanincreaseintotalfemaleparticipation.
Therearesomedifferencesbetweenruralandurbanareas.Theageprofileforyoungerurbanfemalesis lower than their rural counterparts. This probably reflects thehigher educational attainment inurbanareasdelayingtheirentryintothelabourmarket.Theyoungerfemalecohortsinurbanareashavealso improved theirparticipation themost compared to theiroldercounterparts.The labourforceparticipationoftheoldercohortsinurbanareasisestimatedataround20percentandnearlytriplesto60percentfortheyoungestcohorts.Theincreaseinruralareasismuchsmallerbutstartsfromahigherbase(increasingfrom40percentto60percent).Thisisagainconsistentwithchangingculturalnormsandwomenbeginningtobeacceptedintonon-agriculturalemploymentinurbanareas.
0
0.2
0.4
0.6
0.8
1
15 20 25 30 35 40 45 50 55 60
Part
icipa
tion
Prob
abili
ty
Age
Ageeffect
Female Male
1943 1953 1963 1973 1983YearofBirth
Cohorteffect
Females Males
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Women’seconomicparticipationinIndonesia
In furtherresultsnotreportedherewefindthatyoungercohorts forbothmarriedandunmarriedfemalesincreasetheirlabourforceparticipationcomparedtotheiroldercounterparts.Thissuggeststhatthechangeinattitudestowardsfemaleparticipationisnothinderedbytraditionalroleslinkedtomaritalstatus.YoungercohortsacrossalllevelsofeducationattainmenthaveimprovedtheirlabourforceparticipationcomparedtotheiroldercohortsTherearealsosimilar increases in labourforceparticipation forwomen inyoungercohortsacrosshouseholdswithdifferentagesof childrenandvillageswithprocessing/industry, large trading/retail and services as theirmain sourceof income.Youngercohortsfromagriculturalvillageshavealsoincreasedtheirlabourforceparticipationbutnottothesameextentastheothersectorsgiventhatfemalelabourforceparticipationwasalreadyquitehighintheoldercohortsinagriculturalvillages.
InAppendix4weexaminegenderdifferencesinyouthunemployment.WedothisasthehighrateofyouthunemploymentisapressingpolicyconcerninIndonesia.Youthunemploymentishigheracrossthewholepopulationandhigheramongthebettereducated.Wehoweverfind little intermsofagenderwagegapinyouthunemployment.Youthunemploymentisslightlyhigheramongwomeninruralareas,withnogenderdifferenceapparentinurbanareas.Hence,youngmenandwomenseemtofacesimilarchallengesintermsoffindingemploymentearlyintheirworkinglives.
3.5 FemaleLabourForceParticipationProjectionTheG20countries’commitmenttoincreasethefemale/malelabourmarketparticipationgapin2014by25%by2025,meansthatIndonesiawillneedtoincreaseitsfemalelabourforceparticipation(FLFP)to58.5%inthenext10years.Thisgoalwillbechallengingtoachievegiventhatwomen’slabourforceparticipationinIndonesiahasremainedconstantatjustover50%forthelasttwodecades.However,ourworkaboveidentifiedanincreasingunderlyingpropensityforwomentoparticipateinthelabourmarketonceotherfactors,suchaschangesinurbanization,educationandhouseholdcomposition,arecontrolledfor.ThissectionpresentsprojectionsofFLFPto2025.
WebuildontheestimationpresentedintheanalysisoftheFLFPinSection3.3,usingSUSENASdatafrom1996,2000,2007,2011and2013.Inthissection,wefirstexaminehowwellthemodelpredictsFLFPbycomparingthevaluespredictedbythemodelwiththeobservedlevelsintherawdata.WethenestimatetherateofgrowthofeachofthevariablesthatdetermineFLFPinourmodelandusethese to project FLFP through to 2025.36We examine the sensitivity of our results to alternativescenariosandthenconclude.
3.5.1 ModelPerformanceUsingtheestimatedcoefficientsinequation1insection337,wecalculatethepredictedvaluesofFLFPwithinthesampleperiodandcomparetheresulttotheobservedvalues.Figure19showstheresult.Themodelperformsrelativelywellwiththepredictedvaluebeingclosetotheobservedvalue,exceptin2000wheretheactualvaluedipsfromtrend.Weobservethatthepredictedtrendbetween1996and2007issteeperthatthetrendafter2007.
33
Women’seconomicparticipationinIndonesia
Figure19ObservedandmodelpredictedFemaleLabourForceParticipation
3.5.2PredictionofdeterminantvariablesInordertopredictthevaluesofFLFPupto2025,weneedtomakeassumptionsaboutthevaluesofthevariables’thatdetermineFLFP(e.g.levelofeducation,urbanization,agecomposition).Weuseaverysimpletrend-timeseriesmodeltopredictthevalueofallthedeterminants(')upto20yearsaheadfollowingequation2whichweestimateusingdatafrom1996to2013.
Equation2TrendpredictionofdeterminantsofFLFP
'" = )* + )&, + -"
Where,takesthevalueof1in1996,5in2000,12in2007,16in2011and18in2013;and-istherandomerrorterm.Table9showstheestimatedpercentagepointgrowthforeachofthevariablesandthetrajectoriesareshowninappendix3.Intermsofeducation,thismodelpredictsthateachyeartheproportionofwomenwithatleastprimaryschooleducationwillgrow0.008percentagepointswhiletheproportionofwomenwithtertiaryeducationormorewillincreaseby0.0032annually.Theproportionofpeoplelivinginurbanareasisforecasttoincreaseby0.0073percentagepointseachyear.
In order to apply the estimated life cycle effects (coefficients on age groups)we also project thedistributionofwomenacrossagegroups.38Weassumethattheproportionofpeoplelivingineachprovinceremainsconstantatthemean.
30%
40%
50%
60%
70%
1996 2000 2007 2011 2013
FemalePredicted FemaleObserved
34
Women’seconomicparticipationinIndonesia
Table9FLFPdeterminatsannualgrowthinpercentagepoints
VARIABLES TimetrendHouseholdhead 0.0020Maritalstatus:Married 0.0022Maritalstatus:Divorced 0.0000Maritalstatus:Widowed 0.0017Education:Primary 0.0080Education:Lowersecondary 0.0122Education:Uppersecondary 0.0042Education:Tertiary 0.0032Householdsize -0.0273Numberofelderlyfemales -0.0004Numberofelderlymales -0.0002Presenceofapotentialbabysitter 0.0019Numberofchildren:0to2yearsold 0.0004Numberofchildren:3to6yearsold -0.0028Numberofchildren:7to11yearsold -0.0063Numberofchildren:12to17yearsold -0.0154Urban 0.0073Distancetonearestdistrictoffice('100km) 0.0063Mainincome:Mining/quarrying 0.0004Mainincome:Processing/industry 0.0007Mainincome:Largetrading/retail -0.0011Mainincome:Servicesotherthantrade -0.0023Unemployment# -0.1431
3.5.2 FemaleLabourForceParticipationProjectionAccording to the predicted model, the target of decreasing the female to male labour forceparticipation gap by 25% in 2025 will not be achieved under current trends. We present twoprojections. The most optimistic projection assumes that trends in underlying variables observedbetween1996and2013willcontinue.Thesecond,morepessimisticprojection,reflectsthefactthegrowthinFLFPflattensoffafter2007(seefigure1),andsousesonlydatafrom2007to2013toprojectintothefuture.
Figure 20 presents the results of both scenarios. The red line between 1996 and 2015 shows theobservedlevels.ThegreentrianglesshowtheofficialBPSestimatedfigures.Theorangedottedlinerepresentstheoptimisticscenarioandthebluedashedlinerepresentsthepessimisticscenario.
35
Women’seconomicparticipationinIndonesia
Figure20ProjectionofFemaleLabourForceParticipationinIndonesia
UndertheoptimisticscenarioFLFPjustreachesthe58.5%targetby2025.ItisforecastthatFLFPwillreach59%by2025.UnderthelessoptimisticscenariotheFLFPwillremainalmostconstantthroughto2025withFLFPdecreasingslightlyby2025.39
3.6 ConclusionsFemalelabourforceparticipationinIndonesiahasremainedrelativelyconstantfrom1996to2013eveninthefaceofdramaticeconomicchange.Ourfindingshoweversuggestthatonceyoucontrolforindividual,householdandvillagecharacteristics,therearesignsthattheunderlyingpropensityforwomentoparticipateinthelabourforcehasbeenincreasing,particularlyinurbanareas.Thisisaninterestingresultandisconsistentwithchangesinsocietalattitudestowardsfemalesinthelabourmarket.Offsettingthissecular increase inwomen’s labour forceparticipationhasbeendecreasingparticipationasaresultof the lesser importanceofagriculture. If this trendcontinuesthenastheoldercohortsexitthelabourmarket,femalelabourforceparticipationwilleventuallyincrease.
WehoweverfindthattheG20targetof58.5%femalelabourforceparticipationby2025willonlybereachedunderourmostoptimisticscenario.Thelessoptimistic(andarguablymorerealistic)scenariosuggeststhattheFLFPmayevendecreaseifthemostrecenttrendscontinue.Ourresultshaveseveralpolicyimplications.Thatmaritalstatusandthepresenceofyoungchildrenhavesuchalargenegativeimpactonfemalelabourforceparticipationsuggeststhatpoliciestargetedat providing some form of child-care for women with young children may be effective. Policiesensuringthatwomenhaveaccesstothehigherlevelsofeducation,particularlyinruralareaswhereeducational attainment is lower, could also be useful. That the cohort analysis finds that theunderlying propensity for women to participate in the labour markets is increasing is promising.However,theongoingmovementoftheIndonesianeconomyawayfromtheagriculturalsector,giventheimportanceoftheagriculturalsectortofemaleemployment,willcontinuetooffsetthiseffect.Policiesdesignedtoprovidewomenwithaccesstoemploymentinnon-traditionalindustrialsectors,forexample,throughtheprovisionofsubsidisedvocationaleducationand/orcampaignsthatprovideandpromoteopportunitiesforwomeninthesesectors,arealsoworthyofattention.
30% 35% 40% 45% 50% 55% 60% 65% 70%
1996
2000
2007
2011
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
ProjectionFLFP target2025 ProjectionFLFP(07-13) BPSOfficial
36
Women’seconomicparticipationinIndonesia
4. GenderWageGapinIndonesia-adistributionalanalysisoftheformalandinformalsector40
4.1 IntroductionIndonesia has a large genderwage gapwithwomen being paid around 30% less than a similarlyqualifiedman.InFigure13insection2wepresentedthefemale/maleratioofaveragehourlywagesfor the formal and informal sectors separately. Theaveragehourlywageof females in the formalsectorissomewherebetween70%and80%ofthatofmales.Thislookstohavebeenimprovingovertime.Thefiguresfortheinformalsectorhowevershowaworseningsituation.
Genderwagegapsultimatelyreflectdifferencesbetweenmenandwomenineducation,trainingandskills,experience (including reproductivechoices),occupational choice,employmentstatus, labourmarketchoicesbasedonsocialexpectations,discriminatoryhiringandotherpractices.Understandingthemostimportantdifferencesdrivinggenderwagegapsisvitalforpolicydesign.
Themostwidelyusedmethodofdecomposingthewagegapintocomponentsthatreflectdifferencesintheunderlyingproductivityofwomenvis-a-vismen(forexample,differenteducationalattainment)anddifferenceinthereturnstothesecharacteristicsthatremainunexplained(andareoftenreferredto as discrimination) is the Blinder-Oaxaca decomposition. 41 Existing studies that attempt todeterminetheexplainedandunexplainedproportionofthegenderwagegap in Indonesiasuggestthat although the raw wage gap has been decreasing over time, the proportion attributed todiscriminationhasbeenincreasing.Thosestudiesweredescribedinthesection2.2.5.Alimitationofthesestudiesisthattheyfocusonlyonthedecompositionofthemean,andonlyintheformalsector.It is likelyhoweverthattheextentofthewagegapandtheextenttowhichitcanbeexplainedbydifferencesinproductivecharacteristicsvariesalongthewagedistribution–forlowerandhigherpaidwomen.Womenatthebottomofthewagedistributionhavedifferentcharacteristicsthanwomenatthetopandalsoeachofthemmayfacedifferentinstitutionalchallenges.Therearealsolikelytobedifferences between the formal sector and the informal sector, where more than 80% of femaleworkersworkinIndonesia.
Inthissectionwepresentagenderwagegapdecompositionalongthewagedistributionfortheformalandtheinformalsector.Weexplorethemaincomponentsoftheexplainedwagegapandpresentevidenceofchangesovertime.
4.2 DataandMethodThedatausedinthissectioncomesfromthe2011NationalSocioeconomicSurvey(Susenas),whichwas described in some detail in Section 3 above. This survey provides information on 285,186householdsacrossIndonesia.Asabove,weusetheSusenasasitprovidesinformationonfertilityandyearsofeducationwhichallowsustoconstructamoreaccurateproxyforyearsofexperiencewhichisanimportantdeterminantofwages42.
Wedefineformalityaccordingtojobemploymentstatus.Aworkerisconsideredformalifheorshereportsbeingi)anemployerassistedbypermanentandpaidemployeesorii)anemployee.Aworkeris considered informal ifheor she reportsbeing i) self-employed; ii) anemployerwithcasualandunpaidworkers;iii)acasualworker;oriv)anunpaidworker.Weexcludeunpaidworkersfromtheanalysis.
37
Women’seconomicparticipationinIndonesia
Oursampleismadeof332,718individualsagedbetween15and64thatreportedwagesandworkedintheweekprevioustothesurveyfor16to84hours.Fromthatsample,161,040individualsworkinthe formalsectorand171,678 individualswork in the informalsector.Figure21shows thehourlywagedistribution (in logs) formalesandfemales in the formaland informalsectors. It showsthatwomen are concentrated amongst low earners in both sectors.We calculate that in the informalsector thegendergap ishigher -onaveragewomen’shourlywagesare67%ofanaverageman’shourlywage,comparedto77%intheformalsector.Thegenderwagegapdecreasesalongthewagedistributionintheformalsectorwhileintheinformalsectoritisrelativelystable.
Figure21LogarithmoftheHourlywagesofmaleandfemaleworkers
Figure22Histogramoftheyearsofexperienceandeducationattainmentbygender
Differencesinwagesbetweenmenandwomencanreflectdifferencesinproductivecharacteristicsbetweengenders.Figure22presentgenderdifferencesoftheyearsofexperience43andeducationattainment.Thesetwovariablesareveryimportantindicatorsoflabourproductivity.Whilewefindthatonaveragemenhavehigheryearsofexperiencethanwomen,forexampleintheformalsectorwecalculatethatmenhaveanaverageofapproximately4.5moreyearsofexperience,wedonotobservebiggenderdifferencesineducationattainment,exceptintertiaryeducation(33%ofwomenversus 17% ofmen). Table 10 presents the differences for other potential determinant of labourproductivity such as health status, vocational training, Internet usage in the last 3 months as anindicatorofcomputerskills,geographicindicators,industrytype,statusofemploymentandmaritalstatus. In the informal sectormorewomen reportbeing self-employed thanmen,withmenmorelikelytobeemployersassistedbytemporaryandunpaidworkers. Intheformalsectorfemalesareover-representedintheservicessector,whichischaracterizedbylongshifts,makingthehourlywageverylow.Menareover-representedinminingandagriculture.
0.1
.2.3
.4.5
0 5 10 15ln(Hourly wage)
Density DensityFemales Males
Formal SectorWage Density Susenas 2011
0.1
.2.3
.4.5
0 5 10 15ln(Hourly wage)
Density DensityFemales Males
Informal SectorWage Density Susenas 2011
0.0
1.0
2.0
3
0 20 40 60 0 20 40 60
Male Female
Den
sity
Years of experienceGraphs by Female
Histogram of the years of experience by gender
38
Women’seconomicparticipationinIndonesia
Weestimatethereturnstotheproductivecharacteristicsshownaboveforeachgenderbyestimatingthefollowingwageequation:
Equation3Wageequation
.",0 = '",01 !0 + -",0,
3 -",0 = 0, 5 = 6789, :96789, where.",0 is the log of the hourly wage for individual; of gender5 , and Xi are the productivecharacteristics – for example, education, experience, industry of employment etc. The estimatecoefficientoneachproductivecharacteristicisanestimateofthereturntothatcharacteristic.
Table11showsthatthereturnstosomecharacteristicsdifferbygender,andthattherearequitelargedifferences between the formal and informal sectors. In the formal sector men and women arerewardedverysimilarly foryearsofexperienceandmostothervariables.Thebiggestdifference isfoundinreturnstoeducation,withwomenreceivingmuchhigherreturnstoeducation.Comparedtoa person with no schooling, having completed senior high school is associated with 58% higherearningsonaverageformen,and104%higherearningonaverageforwomen.Womenalsoreceiveahigher return tovocational training,earningonaverage5%more thanwomenwithoutvocationaltraining,whilemenwithvocationaleducationdonotreceiveawagepremium.
Thepictureissomewhatdifferentintheinformalsector.Returnstoeducationareagainhigherforwomen, although the gender difference is much smaller. Status of employment plays a moreimportantroleintheinformalsector.Thepenaltyforcasualemploymentisgreaterforfemaleworkers.Menworkingincasualjobsearnonaverage13%lessthanmenwhoareemployersassistedbycasualandunpaidemployees,whilefemalesincasualjobsearn22%lessthanwomenwhoareemployersassistedbycasualandunpaidemployees.Marriagealsopenaliseswomenintheinformalsector.Onaveragemarriedmales get an averageearningsbonusof 17% compared tounmarriedmenwhilewomenarepenalizedonaverageby6%comparedtounmarriedwomen.
39
Women’seconomicparticipationinIndonesia
Table10Summarystatisticsofproductivitycharacteristics Formal Informal Allworkers
Male Female Male Female Male Female
VariableMea
nStd.Dev.
Mean
Std.Dev. Mean
Std.Dev. Mean
Std.Dev. Mean
Std.Dev. Mean
Std.Dev.
Noschool 0.08 0.27 0.07 0.25 0.22 0.41 0.26 0.44 0.15 0.36 0.16 0.37Primary 0.2 0.4 0.15 0.36 0.39 0.49 0.37 0.48 0.3 0.46 0.26 0.44JuniorHS 0.17 0.38 0.12 0.33 0.19 0.39 0.18 0.38 0.18 0.39 0.15 0.36
SeniorHS 0.38 0.49 0.32 0.47 0.19 0.39 0.17 0.38 0.28 0.45 0.25 0.43Vocationaltraininginhighschool 0.11 0.31 0.09 0.29 0.04 0.2 0.04 0.2 0.07 0.26 0.07 0.25
DiplomaI/II 0.02 0.12 0.05 0.22 0 0.05 0 0.07 0.01 0.09 0.03 0.17DiplomaIII/IV/S1 0.14 0.35 0.27 0.44 0.02 0.12 0.02 0.13 0.07 0.26 0.15 0.35Postgraduate 0.01 0.11 0.01 0.11 0 0.02 0 0.02 0.01 0.08 0.01 0.08Usedinternetinthelast3months 0.21 0.4 0.25 0.44 0.03 0.17 0.02 0.14 0.11 0.32 0.14 0.35
Yearsofexperience20.8
411.5
316.2
510.6
7 27.8612.6
3 26.0711.3
7 24.5712.6
2 20.95 12.05
Married 0.75 0.43 0.63 0.48 0.86 0.34 0.75 0.43 0.81 0.39 0.69 0.46Numberofchildrenborn 1.58 1.67 2.97 2.15 2.25 2.03Employerassistedbypermanentpaid 0.1 0.3 0.04 0.2 0.05 0.21 0.02 0.15Paidworker/Employee 0.9 0.3 0.96 0.2 0.42 0.49 0.5 0.5
Self-employed 0.4 0.49 0.53 0.5 0.21 0.41 0.25 0.43Employerassistedbytemporary/unpaid 0.38 0.49 0.29 0.45 0.2 0.4 0.14 0.34Casualworker 0.22 0.41 0.19 0.39 0.12 0.32 0.09 0.29
Industry:Agriculture 0.16 0.37 0.09 0.28 0.56 0.5 0.33 0.47 0.37 0.48 0.2 0.4Industry:Mine 0.16 0.37 0.01 0.11 0.11 0.31 0.01 0.09 0.13 0.34 0.01 0.1
Industry:Manufacture 0.14 0.35 0.18 0.38 0.04 0.19 0.09 0.28 0.09 0.28 0.13 0.34Industry:Trade 0.11 0.31 0.13 0.33 0.15 0.36 0.45 0.5 0.13 0.34 0.28 0.45Industry:Service 0.43 0.49 0.6 0.49 0.15 0.36 0.12 0.32 0.28 0.45 0.37 0.48Anyhealthcomplaintlastmonth 0.25 0.43 0.24 0.43 0.29 0.45 0.33 0.47 0.27 0.44 0.28 0.45Urban 0.59 0.49 0.67 0.47 0.31 0.46 0.42 0.49 0.44 0.5 0.55 0.5
Jakarta 0.03 0.17 0.04 0.2 0.01 0.1 0.01 0.11 0.02 0.14 0.03 0.16Sumatra 0.3 0.46 0.28 0.45 0.3 0.46 0.28 0.45 0.3 0.46 0.28 0.45Java-Bali 0.37 0.48 0.43 0.49 0.32 0.47 0.38 0.49 0.34 0.47 0.4 0.49
NTB-NTT 0.03 0.18 0.04 0.19 0.05 0.22 0.06 0.24 0.04 0.2 0.05 0.21Kalimantan 0.12 0.33 0.09 0.29 0.1 0.3 0.09 0.29 0.11 0.31 0.09 0.29
Sulawesi 0.12 0.32 0.12 0.32 0.14 0.35 0.12 0.32 0.13 0.34 0.12 0.32Maluku 0.03 0.16 0.02 0.16 0.04 0.2 0.03 0.17 0.03 0.18 0.03 0.16Papua 0.04 0.19 0.03 0.16 0.05 0.21 0.03 0.18 0.04 0.2 0.03 0.17
N 109882 51158 124791 46887 234673 98045Notes:Yearsofexperienceiscalculatedusingage-yearsofeducation-5.
Inbothsectorswecanclearlyseethedifferencesinreturnsbyindustry.Womenintradeorservicesonaverageearnlessthansimilarwomenworkinginfarmingactivitieswhileonaveragemenintheseindustriesearnmore.Wealsoobserve forboth sectors thatwomenhavea lowerbasewage (theconstant).Intheformalsector,Rp1,247(7.12logwage)istheaveragehourlywageforwomenwithnoexperience,single,livinginruralareasoutsideJakarta,withnohealthcomplaints,novocational
40
Women’seconomicparticipationinIndonesia
training,nouseofinternetinthelastthreemonths,workingasapaidworker/employeeinafarmingjob,withnoeducation;while formenwith thesamecharacteristics theaveragewageperhour isRp1,658(7.41logwage).
Table11OLSestimatesofWagebygenderandsectorofemployment
Formal InformalVARIABLES Male Female Male FemaleYearsofexperience 0.0455*** 0.0506*** 0.0216*** 0.0325***
(0.0001) (0.0001) (0.0001) (0.0001)
Yearsofexperience2/100 -0.0612*** -0.0673*** -0.0322*** -0.0472*** (0.0001) (0.0002) (0.0001) (0.0002)
Married 0.1801*** 0.0863*** 0.1717*** -0.0587*** (0.0004) (0.0005) (0.0005) (0.0006)
Urban 0.1207*** 0.1538*** 0.1039*** 0.1481*** (0.0003) (0.0005) (0.0004) (0.0006)
Jakarta 0.3561*** 0.3128*** 0.4193*** 0.4896*** (0.0005) (0.0007) (0.0011) (0.0016)
Anyhealthcomplaintlastmonth -0.0243*** -0.0051*** -0.0105*** -0.0399*** (0.0003) (0.0005) (0.0003) (0.0006)
Vocationaltraininginhighschool -0.0006 0.0514*** -0.0615*** -0.0904*** (0.0005) (0.0008) (0.0008) (0.0015)
Usedinternetinthelast3months 0.2799*** 0.2455*** 0.2621*** 0.4437*** (0.0004) (0.0006) (0.0009) (0.0019)
Self-employed -0.0337*** -0.0790*** (0.0004) (0.0006)
Employerassistedbypermanentpaid 0.4925*** 0.4826*** (0.0005) (0.0011)
Casualworker -0.1355*** -0.2238*** (0.0005) (0.0009)
Primary 0.1004*** 0.2256*** 0.0622*** 0.0608*** (0.0006) (0.0010) (0.0004) (0.0007)
JuniorHS 0.2706*** 0.5834*** 0.1504*** 0.1773*** (0.0006) (0.0010) (0.0005) (0.0009)
SeniorHS 0.5813*** 1.0441*** 0.2691*** 0.3329*** (0.0006) (0.0010) (0.0006) (0.0011)
DiplomaI/II 0.9030*** 1.4655*** 0.3475*** 0.4513*** (0.0014) (0.0015) (0.0033) (0.0040)
DiplomaIII/IV/S1 1.1321*** 1.6852*** 0.6276*** 0.7032*** (0.0007) (0.0011) (0.0014) (0.0022)
Postgraduate 1.5784*** 2.1995*** 1.0392*** 1.6440*** (0.0014) (0.0021) (0.0061) (0.0104)
Agriculture Horticulture 0.1129*** -0.0494*** 0.0405*** -0.0234***
(0.0021) (0.0034) (0.0010) (0.0018)Plantation 0.5046*** 0.3473*** 0.4054*** 0.3602***
41
Women’seconomicparticipationinIndonesia
(0.0010) (0.0018) (0.0006) (0.0013)Fishery 0.2473*** -0.0028 0.2552*** -0.0224***
(0.0013) (0.0048) (0.0009) (0.0035)Livestock 0.1289*** -0.1232*** -0.1874*** -0.3221***
(0.0017) (0.0041) (0.0011) (0.0025)Forestryandotheragricultureact. 0.2929*** -0.0626*** 0.1069*** -0.1572***
(0.0018) (0.0051) (0.0015) (0.0043)Mining
Mining 0.6564*** 0.4040*** 0.3088*** 0.0125*** (0.0011) (0.0038) (0.0013) (0.0042)
Electricityandgas 0.5755*** 0.3046*** 0.6732*** -0.1151*** (0.0016) (0.0049) (0.0048) (0.0183)
Construction 0.3673*** 0.2727*** 0.3399*** 0.4053*** (0.0009) (0.0028) (0.0006) (0.0044)
Manufacturing 0.4029*** 0.1882*** 0.1676*** -0.3493*** (0.0009) (0.0015) (0.0008) (0.0011)
Trade 0.2343*** -0.1029*** 0.3128*** -0.0632*** (0.0009) (0.0015) (0.0005) (0.0009)
Services Hotelsandrestaurants 0.2263*** -0.1226*** 0.2880*** 0.0800***
(0.0012) (0.0018) (0.0015) (0.0016)Transportation 0.3738*** -0.0204*** 0.0990*** 0.3162***
(0.0010) (0.0027) (0.0007) (0.0062)Communication 0.3155*** 0.0418*** 0.1310*** -0.1674***
(0.0015) (0.0026) (0.0036) (0.0062)FinanceInsurance 0.5239*** 0.2665*** 0.5642*** 0.9245***
(0.0012) (0.0019) (0.0067) (0.0098)Educationservices 0.1425*** -0.1952*** 0.1588*** -0.0807***
(0.0010) (0.0016) (0.0042) (0.0044)Healthservices 0.3183*** -0.0226*** 0.5172*** 0.1019***
(0.0015) (0.0018) (0.0041) (0.0049)Socialservices 0.3778*** -0.1814*** 0.2056*** -0.0514***
(0.0009) (0.0015) (0.0007) (0.0012)Other 0.3317*** -0.1289*** 0.1328*** -0.1440***
(0.0016) (0.0025) (0.0016) (0.0025)Constant 7.4132*** 7.1287*** 8.1436*** 7.9706***
(0.0011) (0.0017) (0.0010) (0.0018)Observations 27,127,539 12,872,858 26,707,731 10,389,466R-squared 0.3771 0.4404 0.1747 0.1409
Notes:Yearsofexperienceiscalculatedusingage-yearsofeducation-Numberofcareerinterruptions-5.Inthe formal sector equation the reference category is Paid worker/Employee. In the informal sector thereferencesisEmployerassistedbytemporary/unpaid.IneducationNoschoolingisthereferencecategory.InindustryFarmingisthereferencecategory.Weincluderegionalfixedeffects.Standarderrorsinparentheses.Significancelevels***p<0.01,**p<0.05,*p<0.1.
42
Women’seconomicparticipationinIndonesia
4.3 DecompositionResultsIn this sectionwe discuss our results of the Blinder-Oaxaca decomposition of themean and thenpresenttheresultsalongthedistributionfocusingonthe10th,30th,70thand90thquantiles.ForthedecompositionalongthedistributionweuseanUnconditionalQuantileRegression.Fordetailsseeappendix1orrefertoouroriginalpaper.Wethenpresentresults forseparateagegroups(15-29,30-44and45-64years)soastogetasenseofhowthegenderwagegapanditsdeterminantsarechangingovertime.
We find thatonaverage the rawgap is 34% in the formal sector and50% in the informal sector.However,aspresentedinfigure23,someofthisgapisduetodifferencesinproductivecharacteristicsamonggenders.Oncewecontrolforthesedifferencestheremainingunexplainedgapdecreasesto20%and36%intheformalandinformalsectorsrespectively.Thismeansthatintheformalsector38%ofthetotalwagegapisexplainedbydifferencesincharacteristicswhile62%ofthetotalwagegapisduetodiscrimination.Intheinformalsectortheproportionexplainedislower25%,implyingthat75%ofthewagedifferentialisduetodiscrimination.
Figure23Genderwagegapdecompositionatthemeanbysectorofemployment
Table 12 presents the decomposition of the explained component which identifies the keycharacteristicswherewomenaredifferentfrommenandthatexplainsomeofthewagedifferences.Variableswithapositivesignrepresentthosecharacteristicswherethemeanforwomenislower(lessproductive)thanthemeanformen,soincreasingthemeanvaluesforwomenwillleadtoareductioninthewagegap.Characteristicswithnegativesignsarethosewherethemeanforwomenishigher(moreproductive)thanthemeanformenandifwomenhadthesamecharacteristicsasmenthewagegapwouldbeevenbigger.
43
Women’seconomicparticipationinIndonesia
Table12Characteristicscontributiontothetotalwagegapatthemeanbysectorofemployment
Formal InformalExperience 36%*** 0%***Married 8%*** 3%***Skills -4%*** 1%***Education -30%*** 2%***Region 0%*** 1%***Statusofemployment 8%*** -1%***Industry 19%*** 18%***Notes:Resultsaregroupedasexperience(experienceandexperience/1002),skills(vocationaltrainingandhealthstatus),andregion(regionaldummies, Jakartadummyandurbandummy).Significancelevels***p<0.01,**p<0.05,*p<0.1.
Fortheformalsectorwefindthatthenumberofyearsofexperienceexplainsthelargestcomponentofthewagegap(36%)44.Theindustrycomponentshowsthatmenworkinmorehighlyremuneratedindustriesandthisexplains19%ofthegap.Similarly,beingmarriedandstatusofemploymenthaveapositivecontributionof8%.Health,vocationaltraining,internetusagehaveonlysmalleffects,theyareshownundertheskillscategory.Educationmakesalargecontributiontothewagegapbutservestoreduceitby30%aswomenaremoreeducatedthanmenintheformalsector.
Intheinformalsectorwefindthattheindustryofemploymentexplainsthelargestcomponentofthewagegap(18%).Humancapitalcharacteristicslikeexperience,education,healthstatus,specializedskillsplayonlyaverysmallrole.
4.3.1 DecompositionacrosstheWageDistributionIntheformalsectorwefindclearevidenceofstickyfloors.Thisisthatwomenatthebottomofthewagedistributionexperienceahigherwagegap.Thewagegapatthe10thquantileis63%andthendecreasesto13%atthe90thquantile.45Inthe informalsectorwefindonlymildevidenceofstickyfloors.Thewagegapdecreasesfrom63%atthe10thquantileto46%atthe90thquantile.
Figure 24 presents the distribution of the total wage gap and the explained and unexplainedproportions. In the formal sector the magnitude of the explained component remains relativelyconstantalongthedistributionandincreasesatthetopend.Atthe10thquantile39%ofthewagegapcanbeexplainedbydifferencesincharacteristicswhile50%ofthegapisexplainedatthetopend.This implies that even when the gap decreases along the distribution, most of the gap is stillunexplained.Thesituationissimilarintheinformalsector(onlyatalowerlevel)wheretheproportionoftheexplainedgapisalmostconstantat23%exceptinthe90thquantilewhere32%ofthegapisexplained.Consequently,theunexplainedpartisrelativelyconstantaswellalongthedistribution.
44
Women’seconomicparticipationinIndonesia
Figure24Genderwagegapacrossthewagedistributionbystatusofemployment
Figure25presentstherelativecontributionofeachcharacteristictotheexplainedgapalongthewagedistribution. The black line,whose axis is on the right, is the percentage of the gap explained bydifferencesincharacteristics. Intheformalsectorwefindthatexperience,education, industryandstatus of employment are the characteristics that explain most of the differences in wages. Themagnitudeoftheircontributionschangeaswemovealongthewagedistribution,andtheirrelativeimportancechangesaswell.Forexample,industryofemploymentexplains23%ofwagedifferentialsinthe10thquantileandisthemostimportantfactorwhileatthe70thquantileistheleastimportantfactor explaining only 4% of the wage gap. Years of experience and education explain a largerproportionofthewagegapatallquantiles.Whiledifferencesinexperiencebetweengendersexplainthegap,differencesineducationhelpreducethegap.Intheinformalsector,industryofemploymentexplainsmostofthewagegap.Thenextmostimportantvariables,butwithfarlessexplanatorypower,aremarital statusandeducation.Themagnitudeand relative contributionof thevariablesdonotchangealongthedistribution.Thetablesthatcontainthemagnitudesarepresentedinappendix5.
Figure25Decompositionoftheexplainedcomponentofthegenderwagegapacrossthewagedistributionbystatusofemployment
45
Women’seconomicparticipationinIndonesia
4.3.2 CohortAnalysisIndonesiahasbeenchanging in recent years. It is likely that the conditionsolderwomen facearedifferentthantheconditionsyoungerwomenface.Insection2wepresentedevidenceofeducationincreases for women. This suggests that the gap in productive characteristics between men andwomenhasbeendecreasingovertime,andthelabourmarketconditionstheyfacemaybechangingaswell.Althoughwearenotabletoobservethesamewomenovertimetoaccountforthesechanges,wecangetanideaofchangesovertimebydoingacohortanalysis.Wedivideoutsampleintothreegroups,peopleaged15to29;30to44and45to65.46
Figure26andfigure27presentstheresultsfortheformalsector.47Wefindevidenceofstickyfloorsforallagegroups.However,thetotalwagegapappearstobedecreasingovertime.Forexample,ifwecomparewomenatthe10thquantileofthewagegapintheolderagegroup(45to64)withtheyoungergroup(15to29)weseethatthegaphasdecreasedfrom88%to43%.Thesamepatternholdsformostoftheotherquantiles.Althoughtherawgapincreaseswithage(orreducesovertime),theproportionofthegapexplaineddecreaseswithage(hasbeenincreasingovertime).Thismeansthatyoungerwomenfacethegreatestproportionofdiscrimination,althoughnotethatthemagnitudeoftheunexplainedcomponentissmallestforthisgroup.Whenlookingatthecharacteristicsthatexplainthewagegapwefindtheeffectofmaritalstatushasdecreasedovertime(isgreaterforoldercohorts).Thisresultmayreflectculturalchangeintermsofhowwomen’straditionalroleasawifeandmotherisviewed.Theroleofeducation inreducingthewagegaphasbecomemore importantovertime.Youngerwomenaremorehighlyeducated(relativetomen)thanolderwomenandthisexplains(inpart)whyolderwomenfacehigherwagegaps.Forexampleinthe10thquantile,whileforyoungerwomenwefindthateducationexplains-15%ofthewagegap(thatis,itreducesthegapby15%),forwomen45to64theeducationaldifferencebetweenmenandwomencontributes2%tothegap.
Figure26Genderwagegapacrossthewagedistributionintheformalsectorbyagecohort
46
Women’seconomicparticipationinIndonesia
Figure27Decompositionoftheexplainedcomponentofthegenderwagegapacrossthewagedistributionintheformalsectorbyagecohort
Intheinformalsectorthetrendsarelesspositive.Theresultsarepresentedinfigures28and29.Thesituation forwomenat the topof thedistribution seems tohavebeen improving for theyoungercohortsbutatthebottomofthedistributionhasbeenworsening.Fortheyoungeragegroupwagegapsarethehighest(80%)atthe10thquantileandthelowestatthe90thquantileat38%.Fortheoldergroupitremainsalmostconstantoverthequantilesat45%acrossthedistribution.Wealsofindthatdiscrimination(magnitudeandproportion)ishigheramongtheyoungercohortsintheinformalsectoranddecreasesaswagesincrease.Increasesineducationamongyoungerwomenworktoreducethewagegapintheinformalsectorbutyoungwomenwhoworkinthissectorfacethehighestproportionofdiscriminationofanyagegrouponanysalary.Maritalstatusexplainsalargeproportionofthewagegap,particularlyamongolderwomen.Thislikelyreflectscareerinterruptionsduetochildbearing.Thattheeffectissmallerforyoungerwomenagainsuggeststhatculturalnormswithregardtomarriagemaybechanginginfavourofwomen.
Figure28Genderwagegapacrossthewagedistributionintheinformalsectorbyagecohort
47
Women’seconomicparticipationinIndonesia
Figure29Decompositionoftheexplainedcomponentofthegenderwagegapacrossthewagedistributionintheinformalsectorbyagecohort
4.4 ConclusionsThereissignificantwagediscriminationagainstwomeninIndonesia.Therawwagegapaverages41%andonlyasmallproportioncanbeexplainedbydifferencesinproductivecharacteristics.Thisistruein both the formal and informal sectors and at most points across the distribution of wages.Differencesinyearsofexperiencebetweenmenandwomenexplainsomeofthedifferenceinwagesaswomenhavelessexperienceonaverageduetocareerinterruptionsassociatedwithchild-rearing.Industrial segregation by gender explains a large portion of the wage gap – women workpredominantlyinfemale-dominatedindustrieslikeservicesandtradewithmenbeingconcentratedin“male” industries likeagricultureandmining. Women’shighereducationalattainmentworkstoreducethewagegap.Overall,theexplainedproportionofthegapisnotlargerthan40%,leavingmostofthewagegapunexplainedandmostlikelyduetodiscriminatorypracticesinthelabourmarket.
Womeninlowerwagejobsfacedifferentchallengesthanwomeninthetoppaidjobs.Thereisstrongevidenceof sticky floors in Indonesia in the formal sector–womenat the lowerendof thewagedistribution faceamuchbiggergenderwagegap thanwomen inhigherwage jobs.There is someevidenceofthisimprovingovertime.Intheinformalsectorthegenderwagegapisrelativelyconstantalongthewagedistribution,althoughyoungerwomenintheinformalsectorwerefoundtofacestickyfloorsthattheiroldercounterpartdonot.
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Women’seconomicparticipationinIndonesia
5. ConclusionsandFutureResearchAgendaDespiteanexpansionofemploymentopportunitiesoverthepastdecades,andsignificantgainsingirls’access to andparticipation in education, Indonesianwomen still do not participate equally in thelabourmarketandtheirlabourforceparticipationhasnotincreasedoverthepasttwodecades.Oncewecontrolforcharacteristicsonthesupplyanddemandsideofthelabourmarketwefindthattheunderlyingpropensityforwomentoparticipateinthelabourforcehasbeenincreasing,especiallyinurbanareas.Nevertheless,evenourmostoptimisticprojectionsthroughto2025showIndonesianotmeeting itsG20goalsof increasingwomen’s labour forceparticipationby25%(to62.5percentofwomenworking)by2025.
Our research also shows that women who do work find it harder to get a job than their malecounterparts,moredifficulttoaccessmorelucrativeandsecureworksectors,workonaveragefarfewerhoursandreceiveonaverageabout70%oftheequivalentmale’swageintheformalsectorand50%intheinformal.Genderwageinequalityisdrivenbydifferencesintraining,theindustrieswomentraditionallyworkin,theoccupationstheycanaccess,andformalityofemploymentstatus.However,evenaftercontrollingforobservablecharacteristics,wefindthatmostofthewagedifferentialsareduetoahighdegreeofwagediscriminationagainstwomen.
Asdescribed in the literature review,genderequality ishampered inmany instancesby lawsandinstitutions that denywomen equal property rights, acknowledgement as a household head, andaccesstowork.Womenareunder-representedpolitically,makingitharderfortheirvoicestobeheard.
Theresultsoftheliteraturereviewandtheanalyticalworkprovidesaframeworkforafutureresearchagendafocusingonthemainbarrierswomenfacewhenenteringthelabourmarket,Table12belowsynthesises the findings presented above. Different facets of inequality are listed in the columnheadingsacrossthetopofthetable.Theseareintheroughorderinwhichtheyaffectwomenoverthe life-cycle. The rows list potential contributing factors to gender inequality. The colour codingindicates the estimated importance of each of the contributing factor to each of the facets ofinequality,fromgreen(notsuchanimportantfactor)toyellow(ofintermediateimportance)tored(ofseriousimportanceandworthyofstudyandpolicyintervention).Itidentifiesanumberofareasforaction.Theroleofwomen’sautonomyandculturalnormsanditsimpactonlabourmarketchoicesisonesuchareaworthyofattention,asistheroleofcaringresponsibilities(andchildcare)andlabourmarketdiscriminationonlabourmarketoutcomes.Physicalaccesstojobsandmarketsisafurtherpotentiallyimportantbarrierwhichhasn’treceivedmuchattention.
Theareasweproposeforfutureresearchare:
• Women’slife-cycleemploymentpatternsacloseexaminationofwomen’sentryandre-entrydecisionsintothelabourmarketandtheirinteractionwithcaringresponsibilitieswouldgeneratenewunderstandingsofbarrierstowomen’sparticipation.Thisshouldincludeananalysisofmovementsbetweensectorsofemployment,occupationsandindustries,andhowthesepatternshavechangedover time. Child care is an essential component of this. Our findings show that reproductiveresponsibilities prevent women from participating in the labour market. Child care arrangementsinside and outside the household have the potential to smooth entry and re-entry to the labourmarketsbeforeandafterchild-rearing.Theyalsopotentiallyinfluencewomen’sdecisionsonsectorofemploymentandindustry.
49
Women’seconomicparticipationinIndonesia
• Youth unemployment is an increasing phenomenon that disproportionately affects youngwomen.Whyyoungwomen,particularlybettereducatedwomen,aremorelikelytobeunemployedthanyoungmen,isnotwellunderstoodandisworthyoffurtherattention.• Barriers to entrepreneurship and to expanding women’s businesses need to be betterunderstood.Thiswouldinvolveacloseexaminationofgendergapsinbusinessaspirationsandalsoinlifecyclefactorsandaccesstofinanceandmarkets.• TransportInfrastructuremaybeparticularlyimportantforwomen’slabourforceparticipation.Women’shouseholdresponsibilitiesoftenmakeitdifficultforthemtoworkatadistancefromhome.Transport infrastructure reduces travel times and canmake it feasible for awoman towork at agreaterphysicaldistancefromhome–henceopeningupaccesstojobsandmarketsforproducts.Thisistrueofpublictransportinurbansettingsandroadconstructioninruralenvironments.Theroleofinfrastructureinreducinglabourmarketgendergapsislittleunderstoodandpotentiallyimportant.• Laws and changes of laws is an area that requires further analysis. In recent years theIndonesian government has put in place different laws to promote gender equality in the labourmarketlikeminimumwagesandtheEqualEmploymentOpportunityStrategy,itishowevernotcleartowhatextentsuch initiativeshaveassisted inreducinggender inequality inthe labourmarket, inparticularwhenenforcementremainsachallenge.
50
Wom
en’secon
omicparticipationinIn
done
sia
Table13
AnAn
alysisofFactorsDeterminingLabo
urM
arketG
ende
rIne
quality
inIn
done
sia
Caus
alCha
in:
Child
hea
lth/c
og.
deve
lopm
ent
Educ
ation
Early
mar
riage
/fe
rtility
Health
Labo
urfo
rce
partici
p.M
igra
tion
info
rmal/
form
alOcc
upat
.ch
oice
Wag
esand
co
nditi
ons
Pote
ntialC
ontribut
ingFa
ctor
s:ho
useh
oldincome
mothe
r'shealth
nutrition
healthse
rvice
saccess/qualityofsc
hools
childlabo
urcultu
ralnorms
wom
en'sautono
my
marria
gelaws
childca
re/elderlyca
relabo
urm
arketd
iscrim
ination
labo
urlawsa
ndenforcemen
tsectoralpolicy
physica
laccesstojobs
accessto
finance/prop
ertyrights
alesserfactor
amod
eratefactor
amoreserio
usfactor
AnA
nalysis
ofF
acto
rsD
eter
miningLa
bour
Mar
ketG
ende
rIne
quality
inIn
done
sia
51
Women’seconomicparticipationinIndonesia
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Appendix1:Blinder-OaxacaMethodology
TheBlinder-Oaxacamethodologydecomposestheaveragedifferenceinwagesreceivedbymenandwomen into two components: 1) The Explained Component – which reflects differences incharacteristics between men and women e.g. education, age, industry, occupation, experience,number of hours worked, rural/urban etc. when men and women are rewarded for thesecharacteristicsequally;and2)theUnexplainedComponent-whichreflectsdifferencesinthewayinwhichmen andwomen are rewarded for these characteristics and is often considered to reflectdiscrimination.
Inordertodecomposetherawwagegapintothesecomponents,aregressionmodelofwagesofthetypeshownbelowisestimated:
! = # + %& + '
Where W is a measure of wages, X is a vector of observed characteristics, usually consisting ofvariablesofthetypelistedabove.αandβarecoefficientstobeestimatedandεisanerrorterm.Thisequationisestimatedseparatelyformenandwomen:
!( = #( + %(&( + '
!) = #) + %)&) + '
Formally,thedifferencesinmeanwagescanbewrittenas
Equation4Blinder-OaxacaDecomposition
!( −!) = &( − &) %( + %( − %) &) + (#( − #))
Thefirstterminthisdecompositionistheexplainedpart.Thisistheproportionofwagedifferentialsthatareexplainedbydifferencesincharacteristicsofthegendersifallindividualswereremuneratedas men are. The second term is the unexplained part. This represents the differentials in theremunerationof characteristicsbygenderor thegender coefficientdifferences–the sharedue todiscrimination.
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Women’seconomicparticipationinIndonesia
Appendix2:ProbitestimationofFemaleLabourForceParticipation
VARIABLESAllyears
(1996-2013)Since2007
(2007,2011&2013)Householdhead 0.4568*** 0.4730***
(0.0071) (0.0117)Maritalstatus:Married -0.4728*** -0.4484***
(0.0049) (0.0082)Maritalstatus:Divorced -0.0273*** -0.0520***
(0.0095) (0.0161)Maritalstatus:Widowed -0.4452*** -0.4882***
(0.0087) (0.0142)Urban -0.2341*** -0.2543***
(0.0036) (0.0060)Education:Primary -0.1086*** -0.1052***
(0.0034) (0.0061)Education:Lowersecondary -0.1550*** -0.1496***
(0.0033) (0.0054)Education:Uppersecondary 0.3889*** 0.2511***
(0.0054) (0.0089)Education:Tertiary 0.5739*** 0.6426***
(0.0078) (0.0121)Householdsize -0.0120*** -0.0074***
(0.0011) (0.0019)Numberofelderlyfemales 0.0505*** 0.0457***
(0.0048) (0.0081)Numberofelderlymales 0.0513*** 0.0488***
(0.0048) (0.0080)Babysitter 0.0386*** 0.0226***
(0.0037) (0.0063)Numberofchildren:0to2yearsold -0.2077*** -0.1993***
(0.0031) (0.0053)Numberofchildren:3to6yearsold -0.0229*** -0.0317***
(0.0026) (0.0045)Numberofchildren:7to11yearsold 0.0274*** 0.0166***
(0.0022) (0.0039)Numberofchildren:12to17yearsold 0.0305*** 0.0297***
(0.0021) (0.0036)Distancetonearestdistrictoffice('100km) 0.0150*** 0.0133***
(0.0020) (0.0036)Mainincome:Mining/quarrying -0.2383*** -0.2709***
(0.0157) (0.0289)Mainincome:Processing/industry 0.0199*** 0.0250**
(0.0068) (0.0117)Mainincome:Largetrading/retail -0.0746*** -0.0765***
(0.0050) (0.0084)Mainincome:Servicesotherthantrade -0.1406*** -0.1388***
(0.0045) (0.0076)Unemployment# -0.0150*** -0.0132*
(0.0017) (0.0070)Constant -0.4712*** -0.4235*** (0.0249) (0.0412)Observations 1,173,031 415,669Notes:Standarderrorsinparentheses,***p<0.01,**p<0.05,*p<0.1.Estimationsincludeprovince,ageanddateofbirthfixedeffects.#Unemploymentratebyregion.
56
Wom
en’secon
omicparticipationinIn
done
sia
Appendix3:Projectionsofthedeterminantsoffem
alelabourforceparticipation
0.2.4.6.81
percent
2015
2020
2025
time
Fem
ale
Hou
seho
ld h
eads
0.2.4.6.81
percent
2015
2020
2025
time
Mal
e H
ouse
hold
hea
ds
0.2.4.6.81
percent
2015
2020
2025
time
Fem
ale
Mar
ried
0.2.4.6.81
percent
2015
2020
2025
time
Mal
e M
arrie
d
0.2.4.6.81
percent
2015
2020
2025
time
Fem
ale
Div
orce
d
0.2.4.6.81
percent
2015
2020
2025
time
Mal
e D
ivor
ced
0.2.4.6.81
percent
2015
2020
2025
time
Fem
ale
Wid
owed
0.2.4.6.81
percent
2015
2020
2025
time
Mal
e W
idow
ed0.2.4.6.81
percent
2015
2020
2025
time
Fem
ale
at le
ast p
rimar
y
0.2.4.6.81
percent
2015
2020
2025
time
Mal
e at
leas
t prim
ary
0.2.4.6.81
percent
2015
2020
2025
time
Fem
ale
at le
ast l
ower
sec
onda
ry
0.2.4.6.81
percent
2015
2020
2025
time
Mal
e at
leas
t low
er s
econ
dary
0.2.4.6.81percent
2015
2020
2025
time
Fem
ale
at le
ast u
pper
sec
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ry
0.2.4.6.81
percent
2015
2020
2025
time
Mal
e at
leas
t upp
er s
econ
dary
0.2.4.6.81
percent
2015
2020
2025
time
Fem
ale
at le
ast t
ertia
ry
0.2.4.6.81
percent
2015
2020
2025
time
Mal
e at
leas
t ter
tiary
57
Wom
en’secon
omicparticipationinIn
done
sia
012345
percent
2015
2020
2025
time
Fem
ale
aver
age
hous
ehol
d si
ze
0.2.4.6.81
percent
2015
2020
2025
time
Baby
sitte
rs
0.2.4.6.81
percent
2015
2020
2025
time
Elde
rly fe
mal
e
0.2.4.6.81
percent
2015
2020
2025
time
Elde
rly m
ale
0.2.4.6.81
percent
2015
2020
2025
time
HH
ave
rage
chi
ldre
n 0-
2
0.2.4.6.81
percent
2015
2020
2025
time
HH
ave
rage
chi
ldre
n 3-
6
0.2.4.6.81
percent
2015
2020
2025
time
HH
ave
rage
chi
ldre
n 7-
11
0.2.4.6.81
percent
2015
2020
2025
time
HH
ave
rage
chi
ldre
n 12
-17
58
Wom
en’secon
omicparticipationinIn
done
sia
0.2.4.6.81
proportion
2015
2020
2025
time
Livi
ng in
urb
an
0.2.4.6.81
percent
2015
2020
2025
time
Dis
tanc
e to
mar
ket
0.2.4.6.81
percent
2015
2020
2025
time
Min
ing
indu
stry
0.2.4.6.81
percent
2015
2020
2025
time
Man
ufac
ture
indu
stry
0.2.4.6.81
percent
2015
2020
2025
time
trade
/reta
il in
dust
ry
0.2.4.6.81
percent
2015
2020
2025
time
serv
ices
indu
stry
59
Women’seconomicparticipationinIndonesia
Appendix4:GenderinequalityinunemploymentratesGiven that we found that women from younger cohorts have been increasing their labour forceparticipationcomparedtotheiroldercounterparts,inthisappendixweinvestigateifyoungwomenfacemorechallenges thanyoungmen in terms findingemployment.Higheryouthunemploymentrelativetothetotalunemploymentisachallengefacedbymanydevelopedanddevelopingeconomies,particularlyduringperiodsofcrisis(Scarpetta,Sonnet,&Manfredi,2010).Indonesiaisnoexception(Allen,2016).FigureA4-1showstheIndonesianunemploymentratesince1991.Thegreenlineshowsthetotalunemploymentrateasaproportionofthetotallabourforce.Theredandbluelinesshowtheyouthunemploymentrateforfemalesandmalesrespectively(whereyouthisdefinedasbeingbetween the ages of aged 15 to 24). Youth unemployment is significantly higher that totalunemployment, with young women appearing more vulnerable than young men in periods ofincreasedunemployment.
FigureA4-1UnemploymentRateinIndonesia(ModeledILOestimate)
In this appendix, we estimate the gender differences in unemployment for youth controlling forobserved characteristics. Using a similar method and the same data source as in Section 3.2 weexaminehowfemaleyouthunemploymenthaschangedrelativetothatofsimilarlyagedmales.Thismethodallowsustoextractthetrendandlifecyclepatternsonceaccountingforchangesinotherimportantfactorslikeincreasesinfemaleeducationalattainmentrelativetomenandchangesintheindustrialstructurebyregion.Thisisimportantastheproductivecharacteristicsofthewomenhavechangedrapidlyinthelast20years.
60
Wom
en’secono
micparticipatio
ninIndo
nesia
Tabl
eA4
-1
Unem
ploy
men
tDes
crip
tive
Stat
istics
To
tal
Ur
ban
Ru
ral
Varia
bles
M
ale
Fem
ale
M
ale
Fem
ale
M
ale
Fem
ale
Indi
vidu
alch
arac
teris
tics:
Labo
urfo
rceparticipation
0.58
0.37
0.49
0.35
0.66
0.38
Unemployed
0.11
0.14
0.19
0.19
0.07
0.11
Hou
seho
ldhead
0.09
0.02
0.09
0.03
0.09
0.01
Maritalstatus:Single
0.84
0.70
0.87
0.82
0.83
0.62
Maritalstatus:M
arried
0.15
0.28
0.13
0.17
0.16
0.35
Maritalstatus:Divorced
0.00
0.02
0.00
0.01
0.00
0.02
Maritalstatus:W
idow
ed
0.00
0.00
0.00
0.00
0.00
0.00
Education:Primary
0.32
0.30
0.20
0.18
0.38
0.38
Education:Low
ersecon
dary
0.43
0.42
0.51
0.51
0.38
0.36
Education:Upp
ersecon
dary
0.10
0.10
0.18
0.17
0.06
0.06
Education:Tertiary
0.02
0.05
0.04
0.09
0.01
0.03
Hous
ehol
dch
arac
teris
tics:
Hou
seho
ldsize
5.20
5.30
5.14
Babysitter
0.47
0.49
0.46
Num
berofelderlyfemales
0.08
0.07
0.08
Num
berofelderlymales
0.08
0.07
0.08
Num
berofchildren:0to2yearsold
0.21
0.19
0.22
Num
berofchildren:3to6yearsold
0.25
0.21
0.27
Num
berofchildren:7to11yearsold
0.39
0.33
0.42
Num
berofchildren:12to17yearsold
0.85
0.77
0.89
Villa
gech
arac
teris
tics:
Distancetonearestdistrictoffice('100km
)0.67
0.45
0.81
Mainincome:Agriculture
0.71
0.29
0.96
Mainincome:M
ining/qu
arrying
0.01
0.01
0.00
Mainincome:Processing/indu
stry
0.04
0.10
0.01
Mainincome:Largetrading/retail
0.10
0.25
0.01
Mainincome:Servicesotherthantrade
0.14
0.34
0.02
Unemployment#
3.62
4.01
3.38
Observation
s186,548
117,220
64,770
48,074
121,778
69,146
61
Women’seconomicparticipationinIndonesia
A4-1. DataandmethodsWe use data from Susenas for years 1996, 2000, 2007, 2011 and 2013. This data set comprisesobservations on 303,768 youth who are economically active and allows us to calculate youthunemployment trends for cohorts born between 1974 and 1998 and to look at changes inunemploymentoverthelifecyclefromage15to24.
48TableA4-1showsthemaincharacteristicsattheindividual,householdandvillagelevels.Wefindthat in the total sample 14%of thewomen are unemployedwhile 11%ofmen are unemployed.However,inurbanareasthereislittlegendergapinyouthunemployment.Thewomendifferinsomerespectsfromthemen.Agreaterproportionofwomenaged15to24aremarriedthanmen,thisistrueinurbanandruralareasbutthedifferenceismorepronouncedinruralareas.Womenonaveragehavehighereducationattainment,particularlyinurbanareas.TableA4-5(attheendofthisappendix)shows the descriptive statistics for different years. Themost important feature to highlight is theincreaseineducationalattainmentovertime.Wealsoobservedareductioninhouseholdsizeandinthenumberofchildrenwithinhouseholds,reflectingfertilitydeclines.
Usingapooledsample,weestimatetheprobitmodelpresentedinequation5.
Equation5YouthUnemploymentProbitModel
!"#$%& = () + +&,( + -./.& + 0&12.345
where!"#$%& isanindicatorvariablewithvalueof1iftheperson6isunemployedor0otherwise.+& isasetofindividual,householdandvillagecharacteristics,includingprovincefixedeffects;/.& isanageindicatorvariableand0& isarandomdisturbanceterm.PleasenotethatthevariablesincludedinXaretheoneslistedintableA4-1.
A4-2. ResultsTableA4-2presentsthemarginaleffectofeachvariableontheprobabilityofbeingunemployed.Thefirstcolumnshowstheresultofestimatingequation5overthepooledsampleofmenandwomenaged 15 to 24 years. It shows that women are 2.4 percentage points (21%) more likely to beunemployedthanotherwisesimilarmen.Wethenestimateequation5separately for femalesandmales.Theresultsarepresented inColumns2and3respectively.Wefindthatbothgendershavehigherprobabilityofunemploymentinurbanareascomparedtorural,slightlymoresoformen.Theeffectofeducationalattainmentonunemploymentalsolookssimilarforbothgenders,withhigherlevelsofeducationbeingassociatedwithhigherprobabilitiesofbeingunemployed.Thislikelyreflectsthesocio-economicstatusofindividualsaspoorer(andlesseducatedindividuals)areunabletoaffordto be unemployed and instead are forced to generate employment, often through very smallenterprisesintheinformalmarket.
Intermsofhouseholdcomposition,wefindthateitherincreasingthenumberofpeopleover64orunder17slightlydecreasestheprobabilityofbeingunemployed.Forexample,foreachextrachildaged0to2theprobabilityofbeingunemployeddecreasesby0.7%forwomenand1.3%formen.Finally,thedistancetothenearestdistrictcapitaldecreasestheunemploymentprobabilityformen
62
Women’seconomicparticipationinIndonesia
TableA4-2UnemploymentMarginalEffects-Total
TotalVARIABLES Female Male Female 0.0240***
(0.0011) Urban 0.0190*** 0.0444***
(0.0028) (0.0020)Householdhead -0.0575*** -0.0570*** -0.0498***
(0.0020) (0.0048) (0.0023)Maritalstatus:Married -0.0637*** -0.0768*** -0.0577***
(0.0013) (0.0023) (0.0016)Maritalstatus:Divorced -0.0188*** -0.0280*** -0.0127
(0.0052) (0.0067) (0.0097)Maritalstatus:Widowed -0.0339*** -0.0421** -0.0295
(0.0127) (0.0178) (0.0188)Education:Primary 0.0252*** 0.0350*** 0.0188***
(0.0024) (0.0044) (0.0027)Education:Lowersecondary 0.1075*** 0.1340*** 0.0848***
(0.0024) (0.0044) (0.0027)Education:Uppersecondary 0.1808*** 0.2012*** 0.1487***
(0.0046) (0.0079) (0.0054)Education:Tertiary 0.2696*** 0.2740*** 0.2672***
(0.0072) (0.0104) (0.0106)Householdsize 0.0064*** 0.0066*** 0.0067***
(0.0004) (0.0007) (0.0005)Babysitter 0.0037*** 0.0056*** 0.0026**
(0.0011) (0.0020) (0.0013)Numberofelderlyfemales -0.0090*** -0.0132*** -0.0066***
(0.0020) (0.0035) (0.0023)Numberofelderlymales -0.0057*** -0.0039 -0.0054**
(0.0020) (0.0035) (0.0023)Numberofchildren:0to2yearsold -0.0099*** -0.0071*** -0.0128***
(0.0015) (0.0025) (0.0018)Numberofchildren:3to6yearsold -0.0101*** -0.0114*** -0.0104***
(0.0012) (0.0021) (0.0015)Numberofchildren:7to11yearsold -0.0076*** -0.0054*** -0.0087***
(0.0010) (0.0017) (0.0011)Numberofchildren:12to17yearsold -0.0062*** -0.0050*** -0.0071***
(0.0008) (0.0014) (0.0009)Distancetonearestdistrictoffice('100km) -0.0058*** -0.0024 -0.0059***
(0.0009) (0.0016) (0.0011)Mainincome:Mining/quarrying 0.0523*** 0.0125 0.0440***
(0.0092) (0.0138) (0.0104)Mainincome:Processing/industry 0.0140*** -0.0217*** 0.0045
(0.0027) (0.0040) (0.0032)Mainincome:Largetrading/retail 0.0492*** 0.0069* 0.0283***
(0.0021) (0.0036) (0.0027)Mainincome:Servicesotherthantrade 0.0568*** 0.0133*** 0.0378***
(0.0019) (0.0033) (0.0026)Unemployment# 0.0221*** 0.0298*** 0.0184*** (0.0005) (0.0008) (0.0005)Observations 303,768 117,220 186,548
63
Women’seconomicparticipationinIndonesia
Notes:Standarderrorsinparentheses,***p<0.01,**p<0.05,*p<0.1.Estimationsincludeprovinceandagefixedeffects.#Unemploymentratebyregion.
TableA4-3UnemploymentMarginalEffectsRuralandUrban
Rural UrbanVARIABLES Female Male Female Male Householdhead -0.0373*** -0.0317*** -0.0797*** -0.0868***
(0.0072) (0.0023) (0.0083) (0.0059)Maritalstatus:Married -0.0728*** -0.0381*** -0.0750*** -0.1084***
(0.0024) (0.0015) (0.0048) (0.0042)Maritalstatus:Divorced -0.0272*** -0.0078 -0.0406*** -0.0436*
(0.0056) (0.0081) (0.0153) (0.0259)Maritalstatus:Widowed -0.0226 -0.0163 -0.1122*** -0.0710
(0.0170) (0.0173) (0.0315) (0.0478)Education:Primary 0.0261*** 0.0105*** 0.0135 0.0311***
(0.0039) (0.0022) (0.0108) (0.0081)Education:Lowersecondary 0.1051*** 0.0627*** 0.1379*** 0.1219***
(0.0046) (0.0026) (0.0096) (0.0069)Education:Uppersecondary 0.1988*** 0.1506*** 0.1775*** 0.1734***
(0.0105) (0.0071) (0.0140) (0.0102)Education:Tertiary 0.2151*** 0.2831*** 0.2820*** 0.3121***
(0.0143) (0.0177) (0.0172) (0.0158)Householdsize 0.0089*** 0.0055*** 0.0038*** 0.0089***
(0.0009) (0.0005) (0.0012) (0.0012)Babysitter -0.0029 -0.0023* 0.0207*** 0.0173***
(0.0021) (0.0012) (0.0037) (0.0031)Numberofelderlyfemales -0.0146*** -0.0089*** -0.0092 -0.0015
(0.0037) (0.0022) (0.0066) (0.0055)Numberofelderlymales -0.0051 -0.0050** -0.0022 -0.0055
(0.0037) (0.0021) (0.0069) (0.0057)Numberofchildren:0to2yearsold -0.0045* -0.0088*** -0.0158*** -0.0233***
(0.0026) (0.0018) (0.0048) (0.0044)Numberofchildren:3to6yearsold -0.0120*** -0.0071*** -0.0116*** -0.0190***
(0.0022) (0.0014) (0.0041) (0.0036)Numberofchildren:7to11yearsold -0.0092*** -0.0066*** 0.0005 -0.0132***
(0.0019) (0.0011) (0.0033) (0.0028)Numberofchildren:12to17yearsold -0.0116*** -0.0071*** 0.0044* -0.0063***
(0.0016) (0.0009) (0.0026) (0.0022)Distancetonearestdistrictoffice('100km) -0.0002 -0.0025*** -0.0050 -0.0132***
(0.0015) (0.0010) (0.0036) (0.0031)Mainincome:Mining/quarrying 0.0489** 0.0425*** -0.0293 0.0486***
(0.0239) (0.0145) (0.0187) (0.0182)Mainincome:Processing/industry -0.0129* 0.0078 -0.0239*** 0.0145**
(0.0076) (0.0061) (0.0063) (0.0060)Mainincome:Largetrading/retail 0.0533*** 0.0388*** -0.0051 0.0357***
(0.0136) (0.0083) (0.0049) (0.0044)Mainincome:Servicesotherthantrade 0.0490*** 0.0519*** 0.0010 0.0487***
(0.0089) (0.0063) (0.0047) (0.0041)Unemployment# 0.0195*** 0.0114*** 0.0438*** 0.0339*** (0.0009) (0.0005) (0.0015) (0.0013)Observations 69,146 121,778 48,074 64,770Notes:Standarderrorsinparentheses,***p<0.01,**p<0.05,*p<0.1.Estimationsincludeprovinceandagefixedeffects.#Unemploymentratebyregion.
64
Women’seconomicparticipationinIndonesia
butnotforwomen.Intermsofindustrialstructure,villageswithagricultureasthemainindustryareassociatedwiththelowestunemploymentformen.Forwomenhowever,manufacturingisassociatedwith a 2.2 percentage point lower probability of unemployment than if agriculturewas themainindustry.
TableA4-3showstheresultswhenweseparateurbanfromrural.49Similarpatternsareobservedinbothurbanandruralareas.Theresultssuggestthatwhileyoutharegenerallyfindingithardtofindemployment,inparticularthosewhoaremoreeducated,menandwomenfacesimilarchallenges.
A4-3. AgeandCohortEffectsFigureA4-1showedhigheryouthunemploymentforwomenthanformen.Inthissectionwereporttheresultsofestimatingageandcohorteffects(aswedidforlabourforceparticipationinthemainbodyofthereport).Thecohorteffectsidentifytrendsinyouthunemployment,independentofotherindividual,householdandvillagecharacteristicsandarepresentedinFigureA4-2.Thefigureshowsthatyouthunemploymenthasbeendecreasingovertime,ceterisparibus,andthatthetrendissimilarforwomenandmen.Theageeffectpanelpresentedontherightshowsthattheprobabilityofbeingunemployedincreasesfromage15to18andthendecreaseswithage.Thepeakatage18coincideswiththeageatwhichschoolingceases.Byage24theprobabilityofbeingunemployedhasdecreasedtobehalfofthatatage15.
FigureA4-2PredictedprobabilityofyouthunemploymentinIndonesia
FigureA4-3andA4-4showtheyouthunemploymenttrendandlifecyclepatterndisaggregatedbyrural/urban.Weobservethatinruralareasyouthunemploymenthasremainedroughlyconstantovertheperiodunderanalysisandtheprobabilityofunemploymentinslightlyhigherforwomenthanformen,holdingotherfactorsconstant.Thisisverydifferentthaninurbanareaswhereweobservelittledifferencebetweenmenandwomenintermsofyouthunemploymentandlargedeclines inyouthunemploymentforbothmenandwomen.Whiletheprobabilityofbeingunemployedinurbanareasforpeopleborn inthe late70swasaround23%, forpeopleborn inthe late90stheprobabilityofunemploymentisaround10%(controllingforageandotherfactors).Thelifecyclepatternissimilarinurbanandruralareas,followingthepatterndescribedabove.
65
Women’seconomicparticipationinIndonesia
FigureA4-3Predictedprobabilityofyouthunemploymentforruralareas
FigureA4-4Predictedprobabilityofyouthunemploymentforurbanareas
A4-4. ConclusionOurexaminationofgenderdifferences inyouthunemployment finds thatyouthunemployment ishigherthanforthegeneralpopulationandparticularlyhighamongthebettereducated.However,thereislittleinthewayofagendergap.Asmallgapisevidentonlyinruralareas.Thisimpliesthatbothyoungmenandwomenfacesimilarchallenges in termsof findingemploymentearly in theirworkinglives.Thisisconcerningintermsoftheabilityofthelabourmarkettoabsorbnewworkers,particularlymoreskilledandproductiveworkers.Thatbothyoungmenandyoungwomenarefacingthe same challenges is however consistent with our earlier finding that cultural change over isreducing the gap between men and women’s labour force participation rates. In terms ofunemployment,attheyoungerendofthejobmarketitseemsthatmenandwomen’sexperiencesdonotdivergeasconsiderablyastheydidinthepast.
66
Women’seconomicparticipationinIndonesia
A4-5. MethodologicalNoteonthereliabilityoftheSusenasunemploymentrates
InIndonesiathenationallabourmarketindicatorsarecalculatedusinginformationfromtheSakernassurvey.ThequestionsusedtocalculatetheunemploymentratechangeacrosssurveyyearsanddifferbetweentheSusenasandtheSakernas.ForthisstudyweusetheinformationcollectedintheSusenassurveyasitallowsustocontrolforhouseholdcomposition,particularlytheexistenceofchildren.Thedefinitionforlabourforceparticipation(LFP)andUnemploymentusedforthisstudyisasfollows–wewillcallthislaterthesimpledefinition:
LabourForceParticipationiscalculatedasthetotalnumberofpeopleaged15ormorewhoare:
• Workinginthereferenceweek• Notworkingbuthaveajobinthereferenceweek• Lookingforajoboropeningabusinessinthereferenceweek• Workedforatleastonehourinthereferenceweek(thisinformationisonlyavailablein
1996and2000).
Unemploymentiscalculatedasthenumberofpeopleaged15ormorewho:
• Arenotworkingordidnothaveajobinthereferenceweek(ordidnotworkforatleastonehourin1996and2000)
• AND,arelookingforajoboropenabusinessinthereferenceweek
After 2001 there was a change in the unemployment definition in Indonesia. Since then, (i)discouraged workers, (ii) people who have a job but have not started working, and (iii) peoplepreparing a business are classified as unemployed and included in the labour force (Suryadarma,Suryahadi,&Sumarto,2005).ThequestionsrequiredtoimplementthischangeinthedefinitionarenotavailableintheSusenasquestionnaire.SeetableA4–4foraquestioncomparison.
NotethatthedefinitionuseddiffersfromthemostrecentdefinitionusedbytheIndonesianStatisticalAgency(BPS)inwhichbeingunemployedisdefined50toinclude:
• Personwithoutworkbutlookingforwork.• Personwithoutworkwhoisestablishinganewbusiness/firm.51• Personwithoutworkwhowasnotlookingforwork,becausetheydonotexpecttofindwork.• Personwhohasmadearrangementstostartworkonadatesubsequenttothereferenceperiod
(futurestarts).
Differencesinthemeasurementofunemploymentratesinthisstudycomparedtothenationalofficialratesreflectdifferencesinthewaythequestionsareworded,samplingdifferencesandthelackofquestionsrequiredtoimplementtheofficialdefinitionbyBPS.Inordertoestimatetheimpactontheestimationsdue to theuseof the Susenas insteadof the Sakernas,we first replicate thenationalofficialratesusingSakernasandthencalculatetheunemploymentrateusingSakernasandthesimpledefinition.FigureA4–5showstheBPSofficialratewithablueline.TheredtrianglesshowtheresultofcalculatingtheunemploymentrateusingtheBPSdefinitionandtheSakernassurvey.Noticethatthedefinitionbefore2001wasthesimpledefinition.ThebluesquaresaretheresultofcalculatingtheunemploymentrateusingSakernasandthesimpledefinition.Finally,theorangedotsaretheresultof calculating theunemployment rateusing theSusenasand the simpledefinition. Thedifference
67
Women’seconomicparticipationinIndonesia
betweentheSakernasandSakernasBPSdefinitionrepresentsthedifferencesinmeasurementduetochangesinthedefinition.WhilethedifferencebetweenSusenasandSakernasistheresultofusingthedifferentsurveys(differentsamplesandmethodologies).
FigureA4-5TotalUnemploymentRate(%oftotallabourforce)
68
Wom
en’s
eco
nom
icp
artic
ipat
ion
inIn
done
sia
TableA4-4Labou
rForceQuestionsinSusenasand
Sakernas
1996
2000
2007
2011
2013
1996
2000
2007
2010
2011
2013
Wor
king
Q20
aQ19
a1Q24
a1Q24
a1Q24
a1Q2a
1Q2a
1Q2a
1Q2a
1Q2a
1Sc
hool
Q20
bQ19
a2Q24
a2Q24
a2Q24
a2Q2a
2Q2a
2Q2a
2Q2a
2Q2a
2Hou
seQ20
cQ19
a3Q24
a3Q24
a3Q24
a3Q2a
3Q2a
3Q2a
3Q2a
3Q2a
3Oth
erQ20
dQ19
a4Q24
a4Q24
a4Q24
a4Q2a
4Q2a
4Q2a
4Q2a
4Q2a
4Q19
bQ24
bQ24
bQ24
bQ3
Q2b
Q2b
Q2b
Q2b
Q2b
Q21
Q20
Q4
Q3
Q22
Q21
jo
b/bu
sine
ss
Q25
jo
b/bu
sine
ss
Q25
jo
b/bu
sine
ss
Q25
jo
b/bu
sine
ssQ5
Q4
Q3
job/
busi
ness
Q3
job/
busi
ness
Q3
job/
busi
ness
Q3
job/
busi
ness
Q27
Q22
Q26
jo
b/bu
sine
ss
Q26
jo
b/bu
sine
ss
Q26
jo
b/bu
sine
ss
Q14
Q5
Q4
Q4
Q4
Q4
Q5
Q5
Q5
Q5
Mainre
ason
forl
ooking
ajo
bQ15
Q19
Q18
Q19
Q19
effo
rtsbe
enm
adeto
find
job/
bus
Q17
Q16
&Q
17Q20
Q19
Q20
Q20
Forh
owlo
nghav
eyo
ube
en
look
ing?
Q18
Q21
Q20
Q21
Q21
Type
ofw
orkyo
uar
elo
okin
gfo
r?Q18
Q22
Q21
Q22
Q22
Mainre
ason
forn
otlo
okin
gajo
bQ15
Q19
Q23
Q22
Q6
Q6
Ifoffer
edajo
bwou
ldyou
acc
ept
Q16
Q20
Q24
Q23
aQ7
Q7
Are
you
will
ingto
wor
kab
road
Q23
b
Did
you
sta
blishe
dane
wbus
ines
slast
wee
k? Ifworking
orno
working
but
Ifno
tworking
andno
tNo
tes
SUSENA
SSA
KERN
AS
Refe
renc
epe
riod
:Las
twee
kRe
fere
ncepe
riod
:wee
kag
o
Mainly
activ
ity
lastwee
k
Wha
twas
them
ainac
tivity
lastw
eek
Did
you
wor
kat
leas
t1hou
rdur
ingth
epr
evio
usw
eek
Ifnot
wor
king
lastw
eek,doyo
uha
vea
perm
anen
tjob
sbu
twer
ete
mpo
rary
not
wor
king
?
Wer
eyo
ulo
okin
gfo
rajo
blastw
eek?
69
Wom
en’s
eco
nom
icp
artic
ipat
ion
inIn
done
sia
TableA4-5You
thDescriptiveStatisticsbyYear
1996
2000
2007
2011
2013
Variables
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Individu
alcharacteristics:
Labo
urfo
rce
part
icip
atio
n0.
60
0.40
0.56
0.
34
0.
61
0.39
0.57
0.
35
0.
54
0.33
U
nem
ploy
ed
0.14
0.
19
0.
13
0.14
0.10
0.
13
0.
09
0.11
0.10
0.
13
Hous
ehol
dhe
ad
0.08
0.
01
0.
10
0.02
0.09
0.
02
0.
09
0.02
0.07
0.
02
Mar
itals
tatu
s:S
ingl
e0.
87
0.75
0.84
0.
72
0.
84
0.69
0.83
0.
67
0.
84
0.71
M
arita
lsta
tus:
Mar
ried
0.12
0.
23
0.
16
0.26
0.15
0.
29
0.
17
0.31
0.15
0.
27
Mar
itals
tatu
s:D
ivor
ced
0.00
0.
02
0.
00
0.02
0.00
0.
02
0.
00
0.02
0.00
0.
02
Mar
itals
tatu
s:W
idow
ed
0.00
0.
00
0.
00
0.00
0.00
0.
00
0.
00
0.00
0.00
0.
00
Educ
atio
n:A
tlea
stp
rimar
y
0.41
0.
41
0.
37
0.37
0.29
0.
27
0.
26
0.21
0.23
0.
16
Educ
atio
n:A
tlea
stlo
wer
seco
ndar
y
0.34
0.
32
0.
40
0.39
0.47
0.
47
0.
46
0.48
0.48
0.
50
Educ
atio
n:A
tlea
stu
pper
seco
ndar
y
0.07
0.
08
0.
08
0.09
0.11
0.
11
0.
12
0.12
0.15
0.
16
Educ
atio
n:A
tlea
stte
rtia
ry
0.01
0.
01
0.
01
0.02
0.02
0.
07
0.
03
0.09
0.04
0.
11
Hou
seho
ldcharacteristics:
Hous
ehol
dsiz
e5.
54
5.
16
5.
17
5.
01
4.
93
Baby
sitte
r0.
46
0.
45
0.
46
0.
48
0.
51
Num
bero
feld
erly
fem
ales
0.
08
0.
08
0.
08
0.
07
0.
07
Num
bero
feld
erly
mal
es
0.09
0.09
0.07
0.07
0.07
N
umbe
rofc
hild
ren:
0to
2y
ears
old
0.
20
0.
18
0.
22
0.
21
0.
20
Num
bero
fchi
ldre
n:3
to6
yea
rso
ld
0.27
0.23
0.25
0.25
0.22
N
umbe
rofc
hild
ren:
7to
11
year
sold
0.
47
0.
34
0.
36
0.
37
0.
35
Num
bero
fchi
ldre
n:1
2to
17
year
sold
1.
08
0.
86
0.
79
0.
72
0.
71
Villagecharacteristics:
Dist
ance
ton
eare
std
istric
toffi
ce('
100k
m)
0.67
0.54
0.68
0.76
0.74
M
ain
inco
me:
Agr
icul
ture
0.
72
0.
71
0.
70
0.
71
0.
83
Mai
nin
com
e:M
inin
g/qu
arry
ing
0.00
0.00
0.01
0.01
0.00
M
ain
inco
me:
Pro
cess
ing/
indu
stry
0.
03
0.
04
0.
05
0.
05
0.
02
Mai
nin
com
e:L
arge
trad
ing/
reta
il0.
09
0.
11
0.
11
0.
09
0.
04
Mai
nin
com
e:S
ervi
ceso
ther
than
trad
e0.
16
0.
13
0.
13
0.
13
0.
11
Une
mpl
oym
ent#
5.00
3.96
3.33
2.66
2.73
O
bser
vatio
ns
41,0
24
28,5
38
31
,486
19
,119
54,3
21
34,1
38
48
,643
28
,985
11,0
74
6,44
0
70
Wom
en’s
eco
nom
icp
artic
ipat
ion
inIn
done
sia
TableA4-6You
thUnemploymentM
arginalEffectsbyYear
19
96
2000
20
07
2011
20
13
VARI
ABLE
SFe
mal
eM
ale
Fem
ale
Mal
eFe
mal
eM
ale
Fem
ale
Mal
eFe
mal
eM
ale
Urb
an
0.04
13**
*0.
0707
***
0.02
67**
*0.
0574
***
0.02
14**
*0.
0431
***
0.00
64
0.03
36**
*-0
.002
40.
0365
***
(0
.008
2)
(0.0
062)
(0
.006
5)
(0.0
050)
(0
.005
4)
(0.0
036)
(0
.004
7)
(0.0
032)
(0
.008
9)
(0.0
058)
Hous
ehol
dhe
ad
-0.
0938
***
-0.0
778*
**
-0.0
662*
**
-0.0
694*
**
-0.0
637*
**
-0.0
447*
**
-0.0
394*
**
-0.0
419*
**
-0.0
234
-0.0
502*
**
(0
.011
5)
(0.0
055)
(0
.009
4)
(0.0
054)
(0
.007
8)
(0.0
038)
(0
.008
2)
(0.0
037)
(0
.025
9)
(0.0
082)
Mar
itals
tatu
s:M
arrie
d-
0.08
36**
*-0
.075
1***
-0
.099
0***
-0
.064
3***
-0
.058
1***
-0
.051
3***
-0
.072
2***
-0
.045
7***
-0
.097
0***
-0
.048
9***
(0
.005
7)
(0.0
045)
(0
.005
1)
(0.0
048)
(0
.004
3)
(0.0
028)
(0
.004
0)
(0.0
028)
(0
.008
6)
(0.0
060)
M
arita
lsta
tus:
Div
orce
d-0
.035
2**
0.00
49
-0.0
446*
**
0.00
63
-0.0
148
-0.0
267*
-0
.017
5-0
.002
7-0
.004
4-0
.037
2
(0
.016
4)
(0.0
285)
(0
.014
3)
(0.0
309)
(0
.014
2)
(0.0
147)
(0
.011
9)
(0.0
171)
(0
.027
8)
(0.0
333)
M
arita
lsta
tus:
Wid
owed
-0
.043
9-0
.082
8***
-0
.067
1*
0.01
22
-0.0
116
0.00
29
-0.0
605*
**
-0.0
455*
*-0
.000
60.
0133
(0
.041
4)
(0.0
317)
(0
.037
7)
(0.0
756)
(0
.042
4)
(0.0
427)
(0
.020
4)
(0.0
196)
(0
.097
2)
(0.0
892)
Ed
ucat
ion:
Prim
ary
0.
0247
***
0.02
64**
*0.
0422
***
0.03
50**
*0.
0622
***
0.01
99**
*0.
0530
***
0.02
71**
*0.
0407
0.
0006
(0
.007
6)
(0.0
054)
(0
.010
4)
(0.0
072)
(0
.010
9)
(0.0
056)
(0
.011
2)
(0.0
056)
(0
.028
4)
(0.0
112)
Ed
ucat
ion:
Low
erse
cond
ary
0.
1802
***
0.11
71**
*0.
1426
***
0.11
17**
*0.
1610
***
0.08
30**
*0.
1140
***
0.06
75**
*0.
1066
***
0.05
79**
*
(0
.009
1)
(0.0
065)
(0
.011
2)
(0.0
076)
(0
.009
4)
(0.0
052)
(0
.008
8)
(0.0
048)
(0
.022
0)
(0.0
103)
Ed
ucat
ion:
Upp
erse
cond
ary
0.
2415
***
0.17
25**
*0.
2202
***
0.21
90**
*0.
2609
***
0.17
21**
*0.
1693
***
0.11
53**
*0.
1466
***
0.09
24**
*
(0
.014
9)
(0.0
121)
(0
.020
2)
(0.0
153)
(0
.018
0)
(0.0
114)
(0
.016
8)
(0.0
095)
(0
.036
5)
(0.0
178)
Ed
ucat
ion:
Ter
tiary
0.
3451
***
0.28
02**
*0.
3870
***
0.42
71**
*0.
2907
***
0.24
27**
*0.
2795
***
0.24
14**
*0.
3134
***
0.31
03**
*
(0
.028
4)
(0.0
335)
(0
.034
1)
(0.0
334)
(0
.021
0)
(0.0
190)
(0
.021
2)
(0.0
184)
(0
.048
1)
(0.0
384)
Ho
useh
old
size
0.00
67**
*0.
0063
***
0.00
92**
*0.
0051
***
0.00
05
0.00
42**
*0.
0027
**
0.00
44**
*0.
0106
***
0.00
36
(0
.001
8)
(0.0
013)
(0
.001
8)
(0.0
014)
(0
.001
1)
(0.0
009)
(0
.001
2)
(0.0
009)
(0
.003
3)
(0.0
022)
Ba
bysit
ter
0.00
29
0.00
37
0.00
72
0.00
14
0.01
04**
*0.
0046
**
0.00
84**
0.
0021
0.
0007
0.
0050
(0
.004
7)
(0.0
032)
(0
.004
8)
(0.0
034)
(0
.003
5)
(0.0
022)
(0
.003
4)
(0.0
022)
(0
.007
9)
(0.0
049)
N
umbe
rofe
lder
lyfe
mal
es
-0.0
167*
*-0
.019
4***
-0
.018
2**
-0.0
100*
-0
.016
9***
0.
0004
-0
.003
70.
0036
0.
0061
-0
.007
0
(0
.008
1)
(0.0
058)
(0
.007
9)
(0.0
059)
(0
.006
5)
(0.0
038)
(0
.006
5)
(0.0
041)
(0
.014
3)
(0.0
092)
N
umbe
rofe
lder
lym
ales
-0
.008
5-0
.010
9**
-0.0
130
-0.0
007
0.00
81
-0.0
040
-0.0
057
-0.0
053
-0.0
054
-0.0
149
(0
.007
9)
(0.0
055)
(0
.008
0)
(0.0
058)
(0
.006
6)
(0.0
040)
(0
.006
7)
(0.0
043)
(0
.015
6)
(0.0
098)
71
Wom
en’s
eco
nom
icp
artic
ipat
ion
inIn
done
sia
Num
bero
fchi
ldre
n:0
to2
yea
rso
ld
-0.
0195
***
-0.0
160*
**
-0.0
076
-0.0
135*
**
-0.0
055
-0.0
086*
**
0.00
94**
-0
.007
8**
-0.0
190*
-0
.009
7
(0
.005
9)
(0.0
045)
(0
.006
5)
(0.0
052)
(0
.004
3)
(0.0
031)
(0
.004
3)
(0.0
032)
(0
.011
1)
(0.0
074)
N
umbe
rofc
hild
ren:
3to
6y
ears
old
-0
.011
5**
-0.0
129*
**
-0.0
154*
**
-0.0
087*
*-0
.009
7**
-0.0
081*
**
-0.0
025
-0.0
071*
**
-0.0
085
0.00
64
(0
.004
8)
(0.0
035)
(0
.005
3)
(0.0
041)
(0
.003
8)
(0.0
026)
(0
.003
7)
(0.0
027)
(0
.009
2)
(0.0
060)
N
umbe
rofc
hild
ren:
7to
11
year
sold
-0
.004
2-0
.008
7***
-0
.005
8-0
.014
8***
-0
.000
7-0
.003
4*
-0.0
002
-0.0
069*
**
-0.0
086
-0.0
069
(0
.003
9)
(0.0
026)
(0
.004
3)
(0.0
032)
(0
.003
2)
(0.0
020)
(0
.003
1)
(0.0
020)
(0
.007
5)
(0.0
047)
N
umbe
rofc
hild
ren:
12
to1
7ye
arso
ld
-0.0
049
-0.0
097*
**
-0.0
056
-0.0
066*
**
-0.0
002
-0.0
055*
**
-0.0
020
-0.0
081*
**
-0.0
149*
*-0
.006
9*
(0
.003
2)
(0.0
022)
(0
.003
4)
(0.0
025)
(0
.002
6)
(0.0
016)
(0
.002
6)
(0.0
017)
(0
.006
3)
(0.0
039)
Di
stan
ceto
nea
rest
dist
ricto
ffice
('1
00km
)-0
.007
0**
-0.0
058*
*-0
.003
7-0
.008
8**
-0.0
029
-0.0
067*
**
0.00
32*
-0.0
083*
**
0.00
57
-0.0
066*
*
(0
.003
0)
(0.0
023)
(0
.005
0)
(0.0
035)
(0
.002
8)
(0.0
019)
(0
.001
9)
(0.0
014)
(0
.004
8)
(0.0
032)
M
ain
inco
me:
Min
ing/
quar
ryin
g0.
1151
**
0.02
70
-0.0
189
0.02
94
0.01
43
0.01
38
0.00
20
0.03
16**
0.04
43
(0
.057
2)
(0.0
337)
(0
.037
0)
(0.0
279)
(0
.024
1)
(0.0
137)
(0
.016
1)
(0.0
124)
(0.0
783)
Mai
nin
com
e:P
roce
ssin
g/in
dust
ry
-0.
0488
***
-0.0
038
-0.0
305*
**
0.00
19
-0.0
186*
**
0.02
10**
*-0
.030
7***
0.
0027
-0
.009
7-0
.002
2
(0
.010
4)
(0.0
093)
(0
.008
6)
(0.0
084)
(0
.007
1)
(0.0
060)
(0
.005
6)
(0.0
049)
(0
.019
9)
(0.0
138)
M
ain
inco
me:
Lar
getr
adin
g/re
tail
-0.0
081
0.03
72**
*-0
.007
50.
0193
***
-0.0
019
0.01
93**
*-0
.015
8***
0.
0082
*-0
.028
0*
-0.0
086
(0
.009
3)
(0.0
077)
(0
.007
4)
(0.0
064)
(0
.006
3)
(0.0
044)
(0
.005
4)
(0.0
043)
(0
.014
8)
(0.0
108)
M
ain
inco
me:
Ser
vice
soth
erth
antr
ade
0.02
25**
0.
0635
***
-0.0
019
0.02
51**
*0.
0004
0.
0298
***
-0.0
117*
*0.
0132
***
-0.0
072
-0.0
077
(0
.008
9)
(0.0
072)
(0
.007
1)
(0.0
061)
(0
.006
0)
(0.0
045)
(0
.005
1)
(0.0
039)
(0
.011
0)
(0.0
072)
U
nem
ploy
men
t#
0.01
78**
*0.
0108
***
0.01
01**
*0.
0147
***
0.01
45**
*0.
0181
***
0.02
26**
*0.
0218
***
0.01
77**
*0.
0229
***
(0
.001
1)
(0.0
008)
(0
.001
2)
(0.0
009)
(0
.001
2)
(0.0
007)
(0
.001
4)
(0.0
009)
(0
.003
5)
(0.0
022)
Obs
erva
tions
28
,538
41
,024
19
,119
31
,486
34
,138
54
,321
28
,985
48
,643
6,
432
11,0
74
Not
es:S
tand
ard
erro
rsin
par
enth
eses
,***
p<0
.01,
**
p<0.
05,*
p<0
.1.E
stim
atio
nsin
clud
eag
efix
ede
ffect
s.#
Une
mpl
oym
entr
ate
byre
gion
.
72
Wom
en’s
eco
nom
icp
artic
ipat
ion
inIn
done
sia
Appendix5:Genderwagegapalongthedistributionbystatusofemployment
TableA5-1Uncon
dition
alQuantileRegressionCo
efficientsbyGenderinth
eForm
alSector
Q
10
Q30
Q
70
Q90
VA
RIAB
LES
Male
Female
Male
Female
Male
Female
Male
Female
Year
sofe
xper
ienc
e0.
0362
***
0.03
61**
*0.
0287
***
0.04
61**
*0.
0572
***
0.07
29**
*0.
0517
***
0.06
86**
*
(0.0
001)
(0
.000
2)
(0.0
001)
(0
.000
1)
(0.0
001)
(0
.000
1)
(0.0
001)
(0
.000
1)
Year
sofe
xper
ienc
e2 /100
-0
.053
7***
-0
.048
4***
-0
.039
5***
-0
.066
9***
-0
.075
1***
-0
.098
5***
-0
.063
0***
-0
.078
0***
(0.0
002)
(0
.000
5)
(0.0
001)
(0
.000
3)
(0.0
001)
(0
.000
2)
(0.0
002)
(0
.000
3)
Mar
ried
0.29
97**
*0.
1695
***
0.21
14**
*0.
1470
***
0.16
40**
*0.
0742
***
0.02
22**
*0.
0297
***
(0
.000
8)
(0.0
011)
(0
.000
5)
(0.0
008)
(0
.000
6)
(0.0
007)
(0
.000
6)
(0.0
009)
U
rban
0.
1708
***
0.20
44**
*0.
1277
***
0.26
96**
*0.
0872
***
0.09
08**
*0.
0835
***
0.04
15**
*
(0.0
007)
(0
.001
3)
(0.0
004)
(0
.000
9)
(0.0
005)
(0
.000
7)
(0.0
005)
(0
.000
9)
Jaka
rta
0.28
93**
*0.
2126
***
0.31
38**
*0.
3397
***
0.31
29**
*0.
3273
***
0.28
86**
*0.
3119
***
(0
.000
7)
(0.0
014)
(0
.000
5)
(0.0
010)
(0
.000
8)
(0.0
011)
(0
.001
1)
(0.0
015)
An
yhe
alth
com
plai
nt
-0.0
582*
**
-0.0
009
-0.0
283*
**
-0.0
369*
**
-0.0
313*
**
-0.0
222*
**
0.01
05**
*0.
0425
***
(0
.000
6)
(0.0
011)
(0
.000
3)
(0.0
008)
(0
.000
5)
(0.0
007)
(0
.000
6)
(0.0
009)
Vo
catio
nalt
rain
ing
inH
S0.
0553
***
0.09
83**
*0.
0129
***
0.03
97**
*-0
.042
9***
0.
0134
***
-0.0
108*
**
0.09
04**
*
(0.0
007)
(0
.001
3)
(0.0
005)
(0
.001
3)
(0.0
007)
(0
.001
3)
(0.0
008)
(0
.001
3)
Use
din
tern
etin
the
last
3m
onth
s0.
1332
***
0.23
15**
*0.
1498
***
0.26
21**
*0.
3753
***
0.33
59**
*0.
3460
***
0.21
77**
*
(0.0
006)
(0
.000
9)
(0.0
004)
(0
.000
9)
(0.0
006)
(0
.001
0)
(0.0
009)
(0
.001
3)
Empl
oyer
ass
isted
by
perm
anen
tpa
id
0.19
67**
*0.
2555
***
0.27
33**
* 0.
4920
***
0.62
02**
* 0.
6421
***
0.67
42**
* 0.
4848
***
(0
.000
8)
(0.0
020)
(0
.000
5)
(0.0
019)
(0
.000
9)
(0.0
022)
(0
.001
3)
(0.0
030)
Pr
imar
y0.
1524
***
0.34
73**
*0.
0808
***
0.27
27**
*0.
0818
***
0.19
37**
*0.
0769
***
0.24
17**
*
(0.0
014)
(0
.003
0)
(0.0
007)
(0
.001
9)
(0.0
008)
(0
.001
1)
(0.0
008)
(0
.001
1)
Juni
orH
S0.
3503
***
0.72
97**
* 0.
2638
***
0.81
29**
* 0.
2564
***
0.55
84**
* 0.
1953
***
0.56
23**
*
(0.0
015)
(0
.003
2)
(0.0
008)
(0
.002
1)
(0.0
008)
(0
.001
2)
(0.0
009)
(0
.001
3)
Seni
orH
S0.
6332
***
1.27
75**
*0.
5445
***
1.53
22**
*0.
6554
***
1.06
20**
*0.
3834
***
0.76
44**
*
(0.0
014)
(0
.003
0)
(0.0
008)
(0
.002
0)
(0.0
009)
(0
.001
4)
(0.0
010)
(0
.001
6)
73
Wom
en’s
eco
nom
icp
artic
ipat
ion
inIn
done
sia
Dipl
oma
I/II
0.72
02**
*1.
5070
***
0.69
79**
*1.
8719
***
1.19
71**
*1.
7280
***
0.87
58**
*1.
3126
***
(0
.002
6)
(0.0
035)
(0
.001
4)
(0.0
026)
(0
.002
3)
(0.0
024)
(0
.003
3)
(0.0
034)
Di
p.III
/IV/
S1
0.80
71**
*1.
6201
***
0.75
34**
*2.
0236
***
1.46
87**
*2.
1004
***
1.33
53**
*1.
6316
***
(0
.001
5)
(0.0
031)
(0
.000
8)
(0.0
020)
(0
.001
1)
(0.0
015)
(0
.001
5)
(0.0
021)
Po
stgr
adua
te
0.84
62**
*1.
6346
***
0.76
02**
*2.
1022
***
1.80
85**
*2.
5506
***
2.61
87**
*3.
1969
***
(0
.001
7)
(0.0
033)
(0
.001
0)
(0.0
024)
(0
.001
6)
(0.0
025)
(0
.004
2)
(0.0
065)
Co
nsta
nt
6.43
03**
*6.
3200
***
7.34
86**
*6.
5062
***
7.58
33**
*7.
1437
***
8.59
62**
*8.
1199
***
(0
.002
7)
(0.0
048)
(0
.001
3)
(0.0
035)
(0
.001
4)
(0.0
023)
(0
.001
6)
(0.0
026)
O
bser
vatio
ns
27,1
27,5
39
12,8
72,8
58
27,1
27,5
39
12,8
72,8
58
27,1
27,5
39
12,8
72,8
58
27,1
27,5
39
12,8
72,8
58
R-sq
uare
d0.
1028
0.
1300
0.
2054
0.
2771
0.
3112
0.
3967
0.
2002
0.
2231
N
otes
:Ye
ars
ofe
xper
ienc
eis
calc
ulat
edu
sing
age-
yea
rso
fedu
catio
n-N
umbe
rofc
aree
rint
erru
ptio
ns-5
.In
sect
orth
ere
fere
nce
cate
gory
isp
aid
wor
ker/
empl
oyee
.In
educ
atio
nno
scho
olin
gis
the
refe
renc
eca
tego
ry.W
ein
clud
ere
gion
ala
ndin
dust
ryfi
xed
effe
cts.
Sta
ndar
der
rors
inp
aren
thes
es.S
igni
fican
cele
vels
***
p<0.
01,*
*p<
0.05
,*
p<0.
1.
74
Wom
en’s
eco
nom
icp
artic
ipat
ion
inIn
done
sia
TableA5-2Uncon
dition
alQuantileRegressionCo
efficientsbyGenderinth
eInform
alSector
Q
10
Q30
Q
70
Q90
VA
RIAB
LES
Male
Female
Male
Female
Male
Female
Male
Female
Year
sofe
xper
ienc
e0.
0188
***
0.02
94**
*0.
0179
***
0.02
61**
*0.
0189
***
0.03
36**
*0.
0191
***
0.03
87**
*
(0.0
001)
(0
.000
2)
(0.0
001)
(0
.000
1)
(0.0
001)
(0
.000
1)
(0.0
001)
(0
.000
2)
Year
sofe
xper
ienc
e2 /100
-0
.031
9***
-0
.042
6***
-0
.028
1***
-0
.037
1***
-0
.026
4***
-0
.049
8***
-0
.025
7***
-0
.057
6***
(0.0
002)
(0
.000
3)
(0.0
001)
(0
.000
2)
(0.0
001)
(0
.000
2)
(0.0
002)
(0
.000
4)
Mar
ried
0.21
55**
*-0
.058
4***
0.
1563
***
-0.0
495*
**
0.13
59**
*-0
.065
1***
0.
1380
***
-0.0
804*
**
(0
.001
0)
(0.0
010)
(0
.000
6)
(0.0
007)
(0
.000
6)
(0.0
008)
(0
.000
7)
(0.0
014)
U
rban
0.
1247
***
0.19
55**
*0.
1178
***
0.16
84**
*0.
0863
***
0.13
08**
*0.
0487
***
0.10
70**
*
(0.0
006)
(0
.001
0)
(0.0
004)
(0
.000
7)
(0.0
004)
(0
.000
7)
(0.0
005)
(0
.001
1)
Jaka
rta
0.19
34**
*0.
3356
***
0.30
00**
*0.
4274
***
0.44
13**
*0.
4991
***
0.40
13**
*0.
5247
***
(0
.001
1)
(0.0
013)
(0
.000
9)
(0.0
013)
(0
.001
3)
(0.0
022)
(0
.002
2)
(0.0
041)
An
yhe
alth
com
plai
nt
-0.0
639*
**
-0.0
794*
**
-0.0
308*
**
-0.0
599*
**
0.01
31**
*-0
.024
9***
0.
0238
***
-0.0
073*
**
(0
.000
6)
(0.0
010)
(0
.000
4)
(0.0
007)
(0
.000
4)
(0.0
007)
(0
.000
6)
(0.0
011)
Vo
catio
nalt
rain
ing
inH
S-0
.006
2***
-0
.058
7***
-0
.050
2***
-0
.077
1***
-0
.096
7***
-0
.082
9***
-0
.081
6***
-0
.126
2***
(0.0
011)
(0
.002
3)
(0.0
009)
(0
.001
7)
(0.0
010)
(0
.002
0)
(0.0
015)
(0
.003
6)
Use
din
tern
etin
the
last
3m
onth
s0.
0951
***
0.18
74**
*0.
0933
***
0.21
82**
*0.
2764
***
0.38
21**
*0.
4490
***
0.86
64**
*
(0.0
012)
(0
.002
3)
(0.0
009)
(0
.001
8)
(0.0
011)
(0
.002
4)
(0.0
020)
(0
.005
5)
Self-
empl
oyed
0.
0387
***
-0.1
008*
**
-0.0
140*
**
-0.0
402*
**
-0.0
486*
**
-0.0
628*
**
-0.0
756*
**
-0.1
137*
**
(0
.000
7)
(0.0
010)
(0
.000
4)
(0.0
007)
(0
.000
5)
(0.0
008)
(0
.000
8)
(0.0
014)
Ca
sual
wor
ker
0.02
05**
*-0
.028
8***
-0
.104
6***
-0
.130
1***
-0
.189
0***
-0
.287
4***
-0
.199
7***
-0
.419
6***
(0.0
009)
(0
.001
5)
(0.0
006)
(0
.001
0)
(0.0
005)
(0
.001
0)
(0.0
007)
(0
.001
7)
Prim
ary
0.08
05**
*0.
0731
***
0.06
41**
*0.
0450
***
0.04
35**
*0.
0443
***
0.03
94**
*0.
0326
***
(0
.000
8)
(0.0
012)
(0
.000
5)
(0.0
008)
(0
.000
5)
(0.0
008)
(0
.000
6)
(0.0
013)
Ju
nior
HS
0.14
59**
*0.
1419
***
0.12
53**
*0.
1182
***
0.14
24**
*0.
1816
***
0.12
85**
*0.
2288
***
(0
.000
9)
(0.0
016)
(0
.000
6)
(0.0
011)
(0
.000
6)
(0.0
011)
(0
.000
8)
(0.0
019)
Se
nior
HS
0.20
06**
*0.
2272
***
0.21
41**
*0.
2142
***
0.28
63**
*0.
3683
***
0.27
79**
*0.
4810
***
(0
.001
0)
(0.0
017)
(0
.000
7)
(0.0
012)
(0
.000
7)
(0.0
014)
(0
.001
1)
(0.0
025)
75
Wom
en’s
eco
nom
icp
artic
ipat
ion
inIn
done
sia
Dipl
oma
I/II
0.25
05**
*0.
3121
***
0.18
78**
*0.
2337
***
0.35
24**
*0.
5134
***
0.30
75**
*0.
5853
***
(0
.004
3)
(0.0
050)
(0
.003
5)
(0.0
042)
(0
.004
2)
(0.0
055)
(0
.006
9)
(0.0
106)
Di
plom
aIII
/IV/
S1
0.24
15**
*0.
3430
***
0.32
08**
*0.
3785
***
0.62
76**
*0.
7839
***
0.94
04**
*1.
3525
***
(0
.001
6)
(0.0
028)
(0
.001
2)
(0.0
021)
(0
.001
7)
(0.0
029)
(0
.003
3)
(0.0
067)
Po
stgr
adua
te
0.05
42**
*0.
3968
***
0.31
36**
*0.
5404
***
0.90
75**
*1.
2245
***
2.12
19**
*3.
5187
***
(0
.008
8)
(0.0
034)
(0
.005
0)
(0.0
030)
(0
.006
4)
(0.0
092)
(0
.016
4)
(0.0
286)
Co
nsta
nt
7.07
88**
*6.
9333
***
7.80
43**
*7.
6335
***
8.65
01**
*8.
3729
***
9.30
32**
*8.
9971
***
(0
.001
8)
(0.0
032)
(0
.001
1)
(0.0
022)
(0
.001
1)
(0.0
023)
(0
.001
5)
(0.0
039)
O
bser
vatio
ns
26,7
07,7
31
10,3
89,4
66
26,7
07,7
31
10,3
89,4
66
26,7
07,7
31
10,3
89,4
66
26,7
07,7
31
10,3
89,4
66
R-sq
uare
d0.
0532
0.
0417
0.
1104
0.
0721
0.
1468
0.
1054
0.
0899
0.
0831
N
otes
:Ye
ars
ofe
xper
ienc
eis
calc
ulat
edu
sing
age-
yea
rso
fedu
catio
n-N
umbe
rof
car
eer
inte
rrup
tions
-5.I
nse
ctor
the
ref
eren
ces
isem
ploy
era
ssist
edb
yte
mpo
rary
/unp
aid.
In
educ
atio
nno
scho
olin
gis
the
refe
renc
eca
tego
ry.W
ein
clud
ere
gion
ala
ndin
dust
ryfi
xed
effe
cts.
Sta
ndar
der
rors
inp
aren
thes
es.S
igni
fican
cele
vels
***
p<0.
01,*
*p<
0.05
,*p
<0.1
.
76
Wom
en’s
eco
nom
icp
artic
ipat
ion
inIn
done
sia
TableA5-3Decom
position
ofthegenderwagegapacrossth
eearningdistribu
tion
Form
al
Inform
al
VARI
ABLE
S
Q10
Q
30
Q70
Q
90
Q
10
Q30
Q
70
Q90
Ra
wd
iffer
ence
0.
4860
***
0.38
23**
*0.
1875
***
0.12
45**
*
0.48
73**
*0.
4160
***
0.37
55**
*0.
3772
***
(0
.000
5)
(0.0
004)
(0
.000
5)
(0.0
005)
(0.0
005)
(0
.000
4)
(0.0
004)
(0
.000
6)
62
.6%
46
.6%
20
.6%
13
.3%
62.8
%
51.6
%
45.6
%
45.8
%
Tota
lExp
lain
ed
0.19
01**
*0.
1325
***
0.05
70**
*0.
0624
***
0.
1144
***
0.09
68**
*0.
0795
***
0.11
92**
*
(0.0
002)
(0
.000
2)
(0.0
003)
(0
.000
3)
(0
.000
2)
(0.0
002)
(0
.000
2)
(0.0
003)
39%
35
%
30%
50
%
23
%
23%
21
%
32%
To
talU
nexp
lain
ed
0.29
59**
*0.
2498
***
0.13
06**
*0.
0621
***
0.
3729
***
0.31
92**
*0.
2960
***
0.25
80**
*
(0.0
005)
(0
.000
3)
(0.0
003)
(0
.000
4)
(0
.000
4)
(0.0
003)
(0
.000
3)
(0.0
005)
61%
65
%
70%
50
%
77
%
77%
79
%
68%
O
bser
vatio
ns
161,
040
161,
040
161,
040
161,
040
17
1,67
817
1,67
817
1,67
817
1,67
8Co
ntribu
tion
stoth
eExplainedGap:
Expe
rienc
e0.
0820
***
0.07
54**
*0.
1393
***
0.13
91**
*
-0.0
016*
**
0.00
04**
*0.
0028
***
0.00
36**
*
(0.0
001)
(0
.000
1)
(0.0
001)
(0
.000
2)
(0
.000
1)
(0.0
000)
(0
.000
0)
(0.0
001)
17%
20
%
74%
11
2%
0%
0%
1%
1%
M
arrie
d0.
0399
***
0.03
14**
*0.
0194
***
0.00
26**
*
0.01
32**
*0.
0102
***
0.00
89**
*0.
0077
***
(0
.000
1)
(0.0
001)
(0
.000
1)
(0.0
001)
(0.0
001)
(0
.000
0)
(0.0
000)
(0
.000
1)
8%
8%
10
%
2%
3%
2%
2%
2%
Sk
ills
-0.0
057*
**
-0.0
076*
**
-0.0
148*
**
-0.0
105*
**
0.
0051
***
0.00
37**
*0.
0036
***
0.00
54**
*
(0.0
000)
(0
.000
0)
(0.0
001)
(0
.000
1)
(0
.000
0)
(0.0
000)
(0
.000
0)
(0.0
000)
-1%
-2
%
-8%
-8
%
1%
1%
1%
1%
Ed
ucat
ion
-0.0
499*
**
-0.0
646*
**
-0.1
299*
**
-0.1
174*
**
0.
0096
***
0.00
85**
*0.
0093
***
0.00
87**
*
(0.0
001)
(0
.000
1)
(0.0
002)
(0
.000
2)
(0
.000
1)
(0.0
000)
(0
.000
1)
(0.0
001)
-10%
-1
7%
-69%
-9
4%
2%
2%
2%
2%
Re
gion
0.
0019
***
0.00
03**
*0.
0033
***
-0.0
014*
**
-0
.000
1*
0.00
25**
*0.
0064
***
0.00
63**
*
(0.0
001)
(0
.000
1)
(0.0
001)
(0
.000
1)
(0
.000
1)
(0.0
001)
(0
.000
1)
(0.0
001)
0%
0%
2%
-1%
0%
1%
2%
2%
Stat
uso
fem
ploy
men
t0.
0106
***
0.01
59**
*0.
0317
***
0.03
24**
*
0.00
27**
*-0
.002
7***
-0
.005
5***
-0
.004
3***
(0.0
000)
(0
.000
0)
(0.0
001)
(0
.000
1)
(0
.000
1)
(0.0
000)
(0
.000
1)
(0.0
001)
2%
4%
17%
26
%
1%
-1
%
-1%
-1
%
Indu
stry
0.
1114
***
0.08
17**
*0.
0079
***
0.01
75**
*
0.08
54**
*0.
0742
***
0.05
40**
*0.
0917
***
(0
.000
2)
(0.0
001)
(0
.000
1)
(0.0
002)
(0.0
002)
(0
.000
2)
(0.0
002)
(0
.000
2)
23
%
21%
4%
14
%
18
%
18%
14
%
24%
N
otes
:The
raw
diff
eren
cein
per
cent
age
isca
lcul
ated
as(
eraw
diff
eren
ce-1
)×10
0.T
hep
erce
ntag
essh
own
are
the
cont
ribut
ion
toth
eto
talw
age
gap.
Res
ults
are
gro
uped
as
expe
rienc
e(e
xper
ienc
ean
dex
perie
nce/
1002 ),
skill
s(vo
catio
nalt
rain
ing
and
heal
thst
atus
),an
dre
gion
(reg
iona
ldum
mie
s,Ja
kart
adu
mm
yan
dur
ban
dum
my)
.Sta
ndar
der
rors
inp
aren
thes
es.S
igni
fican
cele
vels
***
p<0.
01,*
*p<
0.05
,*p
<0.1
77
Wom
en’s
eco
nom
icp
artic
ipat
ion
inIn
done
sia
TableA5-4Decom
position
ofthegenderwagegapacrossth
eearningdistribu
tion
forpeop
leaged15to
29
Form
al
Inform
al
VARI
ABLE
SQ
10
Q30
Q
70
Q90
Q10
Q
30
Q70
Q
90
Raw
diff
eren
ce
0.35
69**
*0.
2261
***
0.10
56**
*0.
0820
***
0.
5876
***
0.52
10**
*0.
3602
***
0.31
99**
*
(0.0
004)
(0
.000
5)
(0.0
005)
(0
.000
8)
(0
.001
2)
(0.0
008)
(0
.000
9)
(0.0
015)
42.9
%
25.4
%
11.1
%
8.5%
80.0
%
68.4
%
43.4
%
37.7
%
Tota
lExp
lain
ed
0.10
17**
*0.
0718
***
0.00
91**
*-0
.032
6***
0.18
78**
*0.
1467
***
0.06
78**
*0.
0941
***
(0
.000
2)
(0.0
003)
(0
.000
3)
(0.0
005)
(0.0
005)
(0
.000
4)
(0.0
005)
(0
.000
7)
28
%
32%
9%
-4
0%
32
%
28%
19
%
29%
To
talU
nexp
lain
ed
0.25
52**
*0.
1542
***
0.09
65**
*0.
1146
***
0.
3998
***
0.37
43**
*0.
2924
***
0.22
59**
*
(0.0
004)
(0
.000
4)
(0.0
004)
(0
.000
6)
(0
.001
0)
(0.0
007)
(0
.000
8)
(0.0
013)
72%
68
%
91%
14
0%
68
%
72%
81
%
71%
O
bser
vatio
ns
55,4
73
55,4
73
55,4
73
55,4
73
31
,161
31
,161
31
,161
31
,161
Co
ntribu
tion
stoth
eExplainedGap:
Expe
rienc
e0.
0699
***
0.06
61**
*0.
0467
***
0.04
60**
*
0.03
39**
*0.
0295
***
0.02
31**
*0.
0144
***
(0
.000
2)
(0.0
002)
(0
.000
1)
(0.0
002)
(0.0
002)
(0
.000
2)
(0.0
002)
(0
.000
2)
20
%
29%
44
%
56%
6%
6%
6%
5%
Mar
ried
0.00
14**
*0.
0023
***
0.00
21**
*0.
0035
***
-0
.001
1***
0.
0024
***
-0.0
075*
**
-0.0
123*
**
(0
.000
0)
(0.0
000)
(0
.000
0)
(0.0
001)
(0.0
002)
(0
.000
1)
(0.0
002)
(0
.000
2)
0%
1%
2%
4%
0%
0%
-2%
-4
%
Skill
s-0
.004
3***
-0
.008
8***
-0
.014
6***
-0
.026
3***
0.00
35**
*0.
0029
***
0.00
23**
*0.
0028
***
(0
.000
0)
(0.0
001)
(0
.000
1)
(0.0
001)
(0.0
001)
(0
.000
0)
(0.0
001)
(0
.000
1)
-1
%
-4%
-1
4%
-32%
1%
1%
1%
1%
Educ
atio
n-0
.054
2***
-0
.084
0***
-0
.091
2***
-0
.158
3***
-0.0
162*
**
-0.0
144*
**
-0.0
227*
**
-0.0
245*
**
(0
.000
2)
(0.0
002)
(0
.000
2)
(0.0
004)
(0.0
001)
(0
.000
1)
(0.0
002)
(0
.000
2)
-1
5%
-37%
-8
6%
-193
%
-3
%
-3%
-6
%
-8%
Re
gion
-0
.001
3***
-0
.005
4***
-0
.008
7***
-0
.009
1***
0.00
67**
*0.
0073
***
0.00
93**
*0.
0097
***
(0
.000
1)
(0.0
001)
(0
.000
1)
(0.0
001)
(0.0
002)
(0
.000
2)
(0.0
002)
(0
.000
3)
0%
-2
%
-8%
-1
1%
1%
1%
3%
3%
St
atus
ofe
mpl
oym
ent
0.00
23**
*0.
0053
***
0.00
83**
*0.
0205
***
-0
.000
9***
-0
.009
3***
-0
.025
7***
-0
.034
6***
(0.0
000)
(0
.000
0)
(0.0
000)
(0
.000
1)
(0
.000
2)
(0.0
001)
(0
.000
2)
(0.0
002)
1%
2%
8%
25%
0%
-2%
-7
%
-11%
In
dust
ry
0.08
79**
*0.
0964
***
0.06
66**
*0.
0910
***
0.
1618
***
0.12
83**
*0.
0889
***
0.13
85**
*
(0.0
002)
(0
.000
2)
(0.0
002)
(0
.000
3)
(0
.000
5)
(0.0
003)
(0
.000
4)
(0.0
007)
25%
43
%
63%
11
1%
28
%
25%
25
%
43%
N
otes
:The
raw
diff
eren
cein
per
cent
age
isca
lcul
ated
as(
eraw
diff
eren
ce-1
)×10
0.T
hep
erce
ntag
essh
own
are
the
cont
ribut
ion
toth
eto
talw
age
gap.
Res
ults
are
gro
uped
ase
xper
ienc
e(e
xper
ienc
ean
dex
perie
nce/
1002 ),
skill
s(vo
catio
nalt
rain
ing
and
heal
thst
atus
),an
dre
gion
(reg
iona
ldum
mie
s,Ja
kart
adu
mm
yan
dur
ban
dum
my)
.Sta
ndar
der
rors
inp
aren
thes
es.S
igni
fican
cele
vels
***
p<0.
01,*
*p<
0.05
,*p
<0.1
78
Wom
en’s
eco
nom
icp
artic
ipat
ion
inIn
done
sia
TableA5-5Decom
position
ofthegenderwagegapacrossth
eearningdistribu
tion
forpeop
leaged30to
44
Form
al
Inform
al
VARI
ABLE
SQ
10
Q30
Q
70
Q90
Q10
Q
30
Q70
Q
90
Raw
diff
eren
ce
0.56
25**
*0.
4253
***
0.12
04**
*0.
1051
***
0.
5211
***
0.45
52**
*0.
4005
***
0.39
51**
*
(0.0
007)
(0
.000
7)
(0.0
008)
(0
.000
8)
(0
.000
7)
(0.0
005)
(0
.000
6)
(0.0
010)
75.5
%
53.0
%
12.8
%
11.1
%
68
.4%
57
.6%
49
.3%
48
.5%
To
talE
xpla
ined
0.
1692
***
0.13
76**
*0.
0225
***
0.07
94**
*
0.14
19**
*0.
1184
***
0.11
01**
*0.
1536
***
(0
.000
3)
(0.0
004)
(0
.000
5)
(0.0
005)
(0.0
004)
(0
.000
3)
(0.0
003)
(0
.000
5)
30
%
32%
19
%
76%
27%
26
%
27%
39
%
Tota
lUne
xpla
ined
0.
3933
***
0.28
78**
*0.
0979
***
0.02
57**
*
0.37
91**
*0.
3367
***
0.29
05**
*0.
2415
***
(0
.000
6)
(0.0
005)
(0
.000
6)
(0.0
007)
(0.0
006)
(0
.000
4)
(0.0
005)
(0
.000
9)
70
%
68%
81
%
24%
73%
74
%
73%
61
%
Obs
erva
tions
68
,681
68
,681
68
,681
68
,681
75,5
47
75,5
47
75,5
47
75,5
47
Contribu
tion
stoth
eExplainedGap:
Expe
rienc
e0.
0427
***
0.04
80**
*0.
0938
***
0.09
85**
*
0.03
31**
*0.
0295
***
0.03
27**
*0.
0346
***
(0
.000
2)
(0.0
002)
(0
.000
2)
(0.0
003)
(0.0
002)
(0
.000
1)
(0.0
001)
(0
.000
2)
8%
11
%
78%
94
%
6%
6%
8%
9%
M
arrie
d0.
0249
***
0.02
92**
*0.
0300
***
0.02
35**
*
0.01
29**
*0.
0092
***
0.00
70**
*0.
0072
***
(0
.000
1)
(0.0
001)
(0
.000
1)
(0.0
001)
(0.0
001)
(0
.000
1)
(0.0
001)
(0
.000
1)
4%
7%
25
%
22%
2%
2%
2%
2%
Skill
s-0
.001
3***
-0
.004
5***
-0
.007
3***
-0
.006
3***
0.00
37**
*0.
0027
***
0.00
39**
*0.
0067
***
(0
.000
1)
(0.0
001)
(0
.000
1)
(0.0
001)
(0.0
000)
(0
.000
0)
(0.0
000)
(0
.000
1)
0%
-1
%
-6%
-6
%
1%
1%
1%
2%
Ed
ucat
ion
-0.0
157*
**
-0.0
408*
**
-0.1
379*
**
-0.1
364*
**
0.
0061
***
0.00
46**
*0.
0043
***
0.00
33**
*
(0.0
002)
(0
.000
3)
(0.0
004)
(0
.000
4)
(0
.000
1)
(0.0
001)
(0
.000
1)
(0.0
001)
-3%
-1
0%
-115
%
-130
%
1%
1%
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Re
gion
0.
0049
***
0.00
63**
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001)
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1)
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.000
1)
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002)
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St
atus
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oym
ent
0.01
37**
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***
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**
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001)
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6%
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%
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%
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ry
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***
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***
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93**
*0.
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***
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003)
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.000
2)
(0.0
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(0
.000
4)
18
%
18%
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%
53%
16%
16
%
15%
24
%
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es:T
hera
wd
iffer
ence
inp
erce
ntag
eis
calc
ulat
eda
s(e
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eren
ce-1
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hep
erce
ntag
ess
how
nar
eth
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ntrib
utio
nto
the
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lwag
ega
p.R
esul
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eda
sex
perie
nce
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erie
nce
and
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rienc
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ills
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rain
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and
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ths
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s),a
ndre
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fican
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0.05
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79
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icp
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done
sia
TableA5-6Decom
position
ofthegenderwagegapacrossth
eearningdistribu
tion
forpeop
leaged45to
64
Form
al
Inform
al
VARI
ABLE
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10
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ce
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0.37
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***
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***
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1)
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9)
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009)
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.000
6)
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007)
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0)
88
.3%
61
.5%
-4
.7%
11
.7%
45.3
%
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%
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%
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lExp
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ed
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***
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***
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1812
***
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.000
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008)
(0
.000
8)
(0.0
007)
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005)
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.000
4)
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005)
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.000
7)
41
%
41%
11
9%
91%
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38
%
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%
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lUne
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ined
0.
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***
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***
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***
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***
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.000
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008)
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.000
7)
(0.0
007)
(0.0
006)
(0
.000
5)
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005)
(0
.000
7)
59
%
59%
-1
9%
9%
64
%
62%
60
%
52%
O
bser
vatio
ns
36,8
86
36,8
86
36,8
86
36,8
86
64
,970
64
,970
64
,970
64
,970
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ntribu
tion
stoth
eExplainedGap:
Expe
rienc
e0.
0621
***
0.05
11**
*0.
0730
***
0.07
76**
*
0.02
08**
*0.
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***
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05**
*0.
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***
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.000
3)
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003)
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.000
4)
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005)
(0.0
003)
(0
.000
2)
(0.0
002)
(0
.000
3)
10
%
11%
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51%
70
%
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6%
6%
5%
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arrie
d0.
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***
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***
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*
0.05
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***
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26**
*0.
0414
***
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.000
4)
(0.0
004)
(0
.000
2)
(0.0
003)
(0.0
003)
(0
.000
2)
(0.0
003)
(0
.000
4)
14
%
21%
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7%
10%
15%
15
%
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11
%
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s0.
0003
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***
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25**
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***
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.000
0)
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.000
1)
(0.0
002)
(0.0
001)
(0
.000
0)
(0.0
001)
(0
.000
1)
0%
0%
-1
6%
7%
1%
1%
1%
2%
Ed
ucat
ion
0.01
07**
*-0
.010
6***
-0
.114
5***
-0
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0247
***
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***
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.000
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005)
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.000
6)
(0.0
005)
(0.0
001)
(0
.000
1)
(0.0
001)
(0
.000
2)
2%
-2
%
236%
-7
2%
6%
8%
9%
9%
Re
gion
-0
.001
5***
0.
0014
***
0.00
05**
*-0
.003
5***
-0.0
032*
**
-0.0
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**
0.00
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***
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.000
1)
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002)
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.000
1)
(0.0
001)
(0.0
001)
(0
.000
1)
(0.0
001)
(0
.000
2)
0%
0%
-1
%
-3%
-1%
-1
%
1%
1%
Stat
uso
fem
ploy
men
t0.
0134
***
0.02
94**
*0.
0272
***
0.02
80**
*
0.00
92**
*0.
0098
***
0.01
22**
*0.
0145
***
(0
.000
1)
(0.0
002)
(0
.000
1)
(0.0
002)
(0.0
001)
(0
.000
1)
(0.0
001)
(0
.000
2)
2%
6%
-5
6%
25%
2%
3%
3%
4%
Indu
stry
0.
0895
***
0.02
15**
*-0
.074
7***
0.
0582
***
0.
0225
***
0.01
74**
*0.
0227
***
0.06
02**
*
(0.0
003)
(0
.000
3)
(0.0
004)
(0
.000
4)
(0
.000
3)
(0.0
003)
(0
.000
3)
(0.0
004)
14%
4%
15
4%
52%
6%
5%
6%
16%
N
otes
:The
raw
diff
eren
cein
per
cent
age
isca
lcul
ated
as(
eraw
diff
eren
ce-1
)×10
0.T
hep
erce
ntag
essh
own
are
the
cont
ribut
ion
toth
eto
talw
age
gap.
Res
ults
are
gro
uped
as
expe
rienc
e(e
xper
ienc
ean
dex
perie
nce/
1002 ),
skill
s(vo
catio
nalt
rain
ing
and
heal
thst
atus
),an
dre
gion
(reg
iona
ldum
mie
s,Ja
kart
adu
mm
yan
dur
ban
dum
my)
.Sta
ndar
der
rors
inp
aren
thes
es.S
igni
fican
cele
vels
***
p<0.
01,*
*p<
0.05
,*p
<0.1
80
Women’seconomicparticipationinIndonesia
Endnotes
1TheauthorsofthereportareLisaCameron(Monash)andDianaContrerasSuarez(Monash).EmilySandilands(DFAT)contributedtoChapter2andWilliamRowell(DFAT)toChapter3.2Themainaimofthereport istoquantifythemagnitudeofgenderdisparitiesandtracktheirprogressovertimeinawaythatiscomparablebetweencountriesandacrossfourdifferentareas:health,education,economyandpolitics.3 Women’s work has of course been extensively examined by sociologists, anthropologists and feministresearchers.Thesedisciplinesoftendifferentiatebetweenreproductiveandproductiveworkwhileemphasisingtheirinterdependence.Theyemphasisethatwomen’straditionalrolesandactivitiesthatsupportthefamilyareoftenundervaluedorignored,forexampleworktobuildandmaintainsocialandcommunitynetworks.Thisisundoubtedlytrue,andtheroleofwomenasmothers,caregiversandtheircommunityengagementshapestheirinteractionswiththelabourmarket,FordandParker(2008).DrucillaK.Barker(2005)andIdrus(2008)arguethatthewaywork iscommonlyunderstood leadstounderreportingofwomen’seconomiccontribution.Forexample,womenwhofarmathomeforself-consumptionorforsmallscaletradeareoftennotviewedasbeingengagedinincome-generatingworkandtherefore,thisworkisnotreportedasunpaidwork.Thiswouldsuggestthat female labour force participation may be under-reported in official statistics, and concomitantly thatestimates of average female earnings may be biased upwards. This study relies on the readily-availabletraditionally-measuredlabourmarketoutcomes,whileacknowledgingtheaforementionedlimitations.4TheaverageOECDscoreis494.5Aswewillshowlaterinthereportwhatisunexpectedisthatlabourforceparticipationisnotincreasingeventhoughfertilityratesaredecreasingandageoffirstmarriageisincreasing.6Theyusedatafromthe2002IndonesianNationalSocio-EconomicSurvey(Susenas2002).7By200642.4%ofwomenagedover15yearswereemployed,comparedto79.2%formen.Source:WorldBankIndicatorscalculatedusingdatafromtheIndonesianNationalLabourForceSurvey(Sakernas),8WorldDevelopmentIndicatorsin20149 Underemployment is defined for these calculations as working less than 35 hours per week and severeunderemploymentasworkinglessthan15hours.10Informalsectorgrowthhasremainedstableat0.4%growth,exceptintheperiodfollowingthecrisis(1997-2000)when itgrewaround6.9%,absorbinga largerproportionofnewentrants intothe labourmarketandthosewhohadlostformalsectorjobs(Alisjahbana&Manning,2006;Chowdhuryetal.,2009).11Calculatingtheinformalsectorsharefromofficialdatasourcesisnotstraightforwardasnocleardefinitionofinformalityexists.Thecalculationspresentedherearebasedonadefinitionoftheinformalsectorconsistingofworkers who are self-employed, self-employed with temporary or unpaid workers, casual or freelance, andfamilyorunpaidworkers.Thisleavestheformalsectorconsistingofemployeesandtheself-employedwhohaveregularpaidworkers.1276.8%(67.3%)forwomenversus67.1%(60.3%)formenin1990(2006).ThesefiguresarefromChowdhuryetal.,2009whodefine informalityasown-accountworkersandcontributing familyworkers.TheircalculationsusedtheSakernasdata.Intheperiodfollowingthecrisismaleinformalitywashigherthanfemaleinformality(66.5%versus61.6%).Duringthisperiodthefemalelabourforceincreasedtocompensatefortheeffectsofthecrisisonhouseholdincome.Womenweremorewillingtogetpaidlowerwagesthanmenandsowereabletoholdontoformalsectorjobs(Pirmana,2006;Siegmann,2007).Aftertheeffectsofthecrisiswereovercomethetrendsshiftedbacktopreviouslevels(Chowdhuryetal.,2009).13 Siegmann (2007) examines the role of foreign direct investment and finds that it decreases femaleemploymentrelativetomalesinmanufacturingandthehotelsector,butincreasesitintheagriculturalsector.ThisisbecauseFDIincreasesthedemandforlabourwhichputsupwardspressureonwagesacrosstheeconomy.Thisencourageswomentoenterthelabourmarket,howeveraswomenhavefamilycommitmentsandrelativelylowreservationwages,theyremainconcentratedinlowwagesectors.TheincreaseddemandforlabourinthemanufacturingsectorassociatedwithFDIthusismostlysuppliedbymen.14Datasource:BPS(2010)andVanKlaverenetal.(2010).15Thefemaleshareoftotalearnedincomeismuchlowerthanthis,calculatedasaround30%fromthe2002Susenasdata(ADB,2006).Thisreflectsthatwomenarelesslikelytobeworkingthanmenandworkfewerhoursonaverage.
81
Women’seconomicparticipationinIndonesia
16 The unexplained component captures the impact of unobserved factors, measurement error, modelmisspecificationanddiscrimination.17InparticularaftertheAsianfinancialcrisisin1997.18Thewagevariableused is thenatural logarithmof thehourlywage.Theycontrol forexperience,yearsofeducation,householdcharacteristics,industryandregion.19Theestimatesappear(asitisnotspecifiedinthepaper)torepresentanaveragewagegapoverthefouryearsunderanalysis.Surveyyearisincludedasadummyvariable.20ThewagevariabletheyuseismonthlyrealworkerswageandtheinformationsourceisSakernas2010.21TheonlyotherstudyofwhichweareawarethatdecomposesthegenderwagegapisSiegmann(2003)whichusesthe2001Susenas.Shefindsarawwagegapof43%(similartoPirmana,2006)andthatthediscriminationcomponentaccounts for91%ofthewagedifferential.Thisestimate isnot included inFigure14as it isverydifferentfromtheresultsobtainedbyotherauthors.Thedifferencemaybeexplainedbytheuseofthedifferentdatasourceanddifferentcontrolvariables(thefocusofthisstudyistheeffectofforeigndirectinvestmentonthewagegap).Humancapitalcharacteristics,sectoralvariablesandforeigndirectinvestment(FDI)intensitybyprovince are found to only explains about 9% of the total difference. ILO (2012) reports findings from adecomposition(althoughnotthedecompositionresultsthemselves).Theycalculateagenderwagegapof26%in2012andfindthatobservablecharacteristicsexplainonly41%ofthegap.22Theyestimatethedifferenceinmonthlywageincomecontrollingforpotentialexperience,numberofhoursatwork,occupation,status,region,educationalattainmentandsector.23WeichselbaumerandWinter-Ebmer(2005)inametadataanalysisofstudiesthatexaminethedeterminantsofgenderwagediscriminationfindthatrestrictingtheanalysistoacertainsub-sampleofthepopulation(e.g.formalsectorworkers)limitscomparabilitywithotherstudiesascomparabilitytothewholepopulationislow.Similarly,missingorinaccurateinformationonhumancapitalcharacteristics(e.g.workexperience)canseriouslybiasthecalculationsofthediscriminationcomponent. Incontrastthechoiceofeconometricmethodforthedecompositionorthemeasureofwages(hourlyormonthly)islessimportant.24Fromaround10,000femalemigrantstoSaudiArabiain1980,thenumberincreasedto380,000in1998(Silvey,2004).25For example, some employers retain passports and other travel documents, restrict communication withfamilybackhome,expectverylongworkinghoursbeyondthetermsofthecontractanddonotallowdaysoff.TheSaudiArabiamoratoriumandmoratoriainothercountriesreflectsthegovernmentofIndonesia’sconcernoverthehighlevelsofabuse.26Todate,virtuallynoworkhasbeendoneonthesociological,economic,andpsychologicalimpactsofoverseasmigrationonthefamiliesthatareleftbehind(AusAid,2012).Infactthereisstillnotmuchinformationontheconditions of international workers. The World Bank has started a project to fill the gap in policy-relevantevidenceoninternationalmigrationandremittances.Thisworkhoweverdoesnothaveagenderfocus.27Thisstudyreliesonasmallsampleoffieldinterviews.28BRI’s2003MASSSurveyandBankIndonesiaSMESurvey2005.29Thisisconstantacrossdifferentlevelsofeducation,exceptfortertiaryeducationwhereonly46%ofwomenand55%ofthemenwerenotinterested.30TheIndonesianUrbanTransportKnowledgePortalprovideslinkstoseveralinternationalandIndonesianstudies.Seehttp://transkot.com/themepage.php&themepgid=354.31Some examples of case studies in other countries are: (ADB, 2013; Aljounaidi, 2010; Levy, 2013; Tran &Schlyter,2010)).Thesestudiesdescribehowtransportinfrastructureandservicesarefacilitatingorconstrainingwomen’saccesstoresources,markets,training,information,andemploymentandunderlinetheimportanceofidentifying priority areas for public intervention to improve women’s mobility and enhance their access toeconomicopportunities.32Source:WorldBank Indicators2013.Using theDHSdata the ratewas359per100,000 livebirths in2012(Indonesia,2013).33UsingSUSENASinformation.UsingtheDHS,therateis73%.34Thisisanabbreviatedsummaryofourapproachandfindings.ForamoredetailedaccountoftheresearchseeLisaCameron,Contreras-Suarez,andRowel(2015).35Weuse49agedummiescoveringfrom15to64yearsofage(theomittedcategoryis15yearsofage)and49cohortdummies–oneforeachyearofbirthfrom1943to1992(theomittedcategoryissomeonebornin1943).SeeEuwals,Knoef,andVanVuuren(2011)forasimilarapproachinthecontextoftheNetherlands.36Refertosection3.2.
82
Women’seconomicparticipationinIndonesia
37TocalculateanationalFLFPweestimatethemodeloverbothurbanandruralsamples,includingacontrolforurbanareas.Resultsarepresentedinappendix2.38WecomparedourprojectedfiguresforthepercentageofpopulationbyagegroupagainstUNforecasts.Theyarebroadlysimilar,particularlyforwomenagedover40yearswhoconstitutethemajorityofworkingwomen.39NotethatbothofthepredictionsindicateanincreaseovertheofficialBPSFLFPestimatefor2015.TheBPSusesSakernasinformationtocalculateFLFP.RememberweareusingSusenasinformationforthisanalysis.AsensitivityanalysisexaminingdifferencesbetweenthetwosurveyscanbefoundinLisaCameronandContreras-Suarez(2015)andTableA4-4inappendix4.40Thisisasummaryoftheresearchresults.ForfurtherdetailseeLisaCameronandContreras-Suarez(2015).41Theunexplainedcomponent isanestimateoftheextentofdiscriminationinthelabourmarketbut italsocaptures the effects of unobserved characteristics. Fortin, Lemieux, and Firpo (2011) provide a review ofdifferentdecompositionmethods.422011isalsothefirstyearthattheSusenasprovidesinformationonearningsininformaljobs.43Theproxyforyearsofpotentiallabourmarketexperienceiscalculatedasafunctionofage,yearsofeducationand number of children born ( !"#$%'(")*"$+",-". = 01". − !"#$%'("34-#5+',. −#'(-ℎ+83$",9'$,. − 5 ). We discount experience by one year for each child born. This approach likelyunderstatestheimpactofchildbearingandchildrearingonexperienceforwomen.44Percentagesinthetablesrepresenttheproportionofthecontributionofaspecificcharacteristictotherawwagegap.45Glassceilingsandstickyfloorsaretwodifferentpatternsidentifiedintheliteratureofwagegaps.Ifwomenatthetopofthewagedistributionexperienceahigherwagegap,thisisreferredtoasevidenceofaglassceiling.Incontrast,ifwomenatthebottomofthedistributionexperienceahigherwagegap,itisreferredasastickyfloor.Stickyfloorshavebeenfoundinmanydevelopingcountries.Forexample,ChiandLi(2008)findevidenceofstickyfloorsinurbanjobsinChinawherewomenproductionworkerswithrelativelyloweducationworkinginnon-stateownedenterpriseswerefoundtobeparticularlylowlypaidrelativetoequivalentmen.SimilarlyAhmedandMaitra(forthcoming)andAhmedandMcGillivray(2015)findthatlowearningfemalesinfull-timejobsfacegreaterdiscriminationthanfemalesinhigherearningjobsinBangladesh.46NotethatunlikeinSection3.Inacross-sectionalanalysiswecannotseparatelyidentifytheeffectsofageandcohortsotheresultsreportedherearejustsuggestiveofchangesovertime.47AllthequantitativeresultsarepresentedinAppendix5.48We use the Susenas data (the National Socio-economic Survey). Section A4-5 in this appendix examinesdifferencesinunemploymentratescalculatedusingtheSusenasandSakernas(NationalLabourForceSurvey).49TableA4-6showstheresultsforeachofthesurveyyears.50TakenfromtheBPSwebsite.Accessedon15April2016.51Forexample,collectingcapital,preparingequipment, lookingforabusiness location,applyingforbusinesspermits.Thisdoesnotincludepersonswhojusthaveplanstodoso,orwhoareattendingacourse/trainingtoprepareabusiness/firm.
Women’s Economic Participation in IndonesiaA study of gender inequality in employment, entrepreneurship, and key
enablers for change
Australia Indonesia Partnershipfor Economic Governance