Women’s Economic Participation in Indonesia

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.


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.


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


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


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


TableA5-3Decompositionofthegenderwagegapacrosstheearningdistribution........................76TableA5-4Decompositionofthegenderwagegapacrosstheearningdistributionforpeopleaged15to29................................................................................................................................................77TableA5-5Decompositionofthegenderwagegapacrosstheearningdistributionforpeopleaged30to44................................................................................................................................................78TableA5-6Decompositionofthegenderwagegapacrosstheearningdistributionforpeopleaged45to64................................................................................................................................................79

Equation1Labourforceparticipation.................................................................................................27Equation2TrendpredictionofdeterminantsofFLFP.........................................................................33Equation3Wageequation...................................................................................................................38Equation4Blinder-OaxacaDecomposition..........................................................................................54Equation5YouthUnemploymentProbitModel..................................................................................61

1


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

2


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

3


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.

4


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

5


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

6


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.

7


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.

8


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

9


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

10


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).

11


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

12


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


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


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


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


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


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


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

19


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

20


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.

21


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

22


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

23


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

24


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.

25


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)).

26


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).

27


• 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.

28


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.

29


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.

30


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

31


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

32


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


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


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


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


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


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


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


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


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


(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


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


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


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


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


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


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.

48


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


• 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


ReferencesADB.(2006).INDONESIA-CountrygenderAssessment(R.a.S.D.Department,Trans.).Manila,

Pillipines:AsianDevelopmentBank.ADB.(2013).GenderToolKit:Transport:AsianDevelopmentBank.ADB.(2015).IndonesiaFactsheet.MandaluyongCity,Philippines:AsianDevelopmentBank.ADB,ILO,&IDB.(2010).Indonesia:criticaldevelopmentconstraints.MandaluyongCity,Philippines:

AsianDevelopmentBank(ADB),InternationalLabourOrganization(ILO)andIslamicDevelopmentBank(IDB).

Ahmed,S.,&Maitra,P.(forthcoming).ADistributionalAnalysisoftheGenderWageGapinBangladesh.TheJournalofDevelopmentStudies.

Ahmed,S.,&McGillivray,M.(2015).HumanCapital,Discrimination,andtheGenderWageGapinBangladesh.WorldDevelopment,67,506-524.doi:http://dx.doi.org/10.1016/j.worlddev.2014.10.017

Alfitri,S.(2012).LegalReformProject,AccesstoJusticeandGenderEquityinIndonesia.IndonesianJournalofInternationalLaw,9(2).

Alisjahbana,A.S.,&Manning,C.(2006).LabourmarketdimensionsofpovertyinIndonesia.BulletinofIndonesianEconomicStudies,42(2),235-261.doi:10.1080/00074910600873674

Aljounaidi,L.(2010).GenderandtransportinMENA:CasestudiesfromWestBankGazaandYemen.Allen,E.R.(2016).AnalysisofTrendsandChallengesintheIndonesianLaborMarket.ADB.AsiaFoundation.(2013).AccesstoTradeandGrowthofWomen’sSMEsinAPECDeveloping

Economies-EvaluatingtheBusinessEnvironmentinIndonesia.AusAid.(2012).EmpoweringIndonesianWomenforPovertyReduction.Jakarta.Azzizah,Y.N.(2014).HumansecurityissueinIndonesia:Focusingongendergapineducation.Bazzi,S.,&Bintoro,M.(2015).ReviewofPolicy-OrientedResearchontheProtectionofIndonesian

OversearMigrantWorkers:MAMPU.Blackwood,E.(2008).Notyouraveragehousewife:MinangkabauwomenricefarmersinWest

Sumatra.InM.Ford&L.Parker(Eds.),WomenandworkinIndonesia(Vol.5,pp.17-40).NewYork:Routledge.

Buchori,C.,&Cameron,L.(2007).GenderequalityanddevelopmentinIndonesia.WorldBank).Availableat:<http://siteresources.worldbank.org/INTINDONESIA/Resources/226271-1155584666848/2847747-1178507773272/GenderBrief.pdf>[4June2012].

Cameron,L.,&Contreras-Suarez,D.(2015).GenderWageGapinIndonesia-adistributionalanalysisoftheformalandinformalsector.WorkingPaper.

Cameron,L.,Contreras-Suarez,D.,&Rowel,W.(2015).StagnationoffemalelabourforceparticipationinIndonesia:AnageandcohortanalysisWorkingPaper.

Cameron,L.A.,&Worswick,C.(2001).EducationexpenditureresponsestocroplossinIndonesia:Agenderbias.EconomicDevelopmentandCulturalChange,49(2),351-363.

Centre,W.P.A.(2015).TheFullParticipationReport:ClintonFoundation,Bill&MellindaGatesFoundation.

Cepeda,L.(2013).GenderWageinequalityinIndonesia.InT.W.Bank(Ed.),Presentation.Chi,W.,&Li,B.(2008).Glassceilingorstickyfloor?Examiningthegenderearningsdifferential

acrosstheearningsdistributioninurbanChina,1987–2004.JournalofComparativeEconomics,36(2),243-263.

Chowdhury,A.,Islam,I.,&Tadjoeddin,M.Z.(2009).Indonesia’sEmploymentChallenges:Growth,StructuralChangeandLabourMarketRigidity.EuropeanJournalofEastAsianStudies,8(1),31-59.

Comola,M.,&deMello,L.(2012).SalariedemploymentandearningsinIndonesia:newevidenceontheselectionbias.AppliedEconomics,45(19),2808-2816.doi:10.1080/00036846.2012.667551

52


Dames,C.P.(2012).Gender,ethnicity,infrastructure,andtheuseoffinancialinstitutionsinKalimantanBarat,Indonesia.UniversityofMissouri--Columbia.

DrucillaK.Barker.(2005).BeyondWomenandEconomics:Rereading“Women’sWork”.Signs,30(4),2189-2209.doi:10.1086/429261

Elias,S.,&Noone,C.(2011,DecemberQuarter).ThegrowthanddevelopmentoftheIndonesianeconomy.[Bulletin].

Elliott,J.(1994).LabourlegislationandgenderinIndonesia.AsianStudiesReview,17(3),33-42.doi:10.1080/03147539408712948

Euwals,R.,Knoef,M.,&VanVuuren,D.(2011).Thetrendinfemalelabourforceparticipation:whatcanbeexpectedforthefuture?EmpiricalEconomics,40(3),729-753.

Feridhanusetyawan,T.,Aswicahyono,H.,&Perdana,A.A.(2001).Themale-femalewagedifferentialsinIndonesia.EconomicsWorkingPaperSeries,59.

Ford,M.,&Parker,L.(2008).WomenandworkinIndonesia(Vol.5):Routledge.Fortin,N.,Lemieux,T.,&Firpo,S.(2011).Decompositionmethodsineconomics.Handbookoflabor

economics,4,1-102.Forum,W.E.(2014).TheGlobalGenderGapReport.Switzerland.Friedman,J.,&Thomas,D.(2009).Psychologicalhealthbefore,during,andafteraneconomiccrisis:

ResultsfromIndonesia,1993–2000.TheWorldBankEconomicReview,23(1),57-76.Gallaway,J.H.,&Bernasek,A.(2004).Literacyandwomen'sempowermentinIndonesia:

implicationsforpolicy.JournalofEconomicIssues,519-525.Haidi,N.(2004).CountryInstitutionalReport:Indonesia.InP.Bergstrom(Ed.),Women’s/gender

studiesinAsia-Pacific(pp.273-279).Bangkok:UNESCO.Idrus,N.I.(2008).Makkunraipassimokolo’:BugismigrantwomenworkersinMalaysia.InM.Ford&

L.Parker(Eds.),WomenandworkinIndonesia(Vol.5,pp.154-172).NewYork:Routledge.ILO.(2012).LabourandSocialTrendsinIndonesia2012:ILO.Indonesia,S.(2013).IndonesiaDHS,2012NationalPopulationandFamilyPlanningBoard,Ministry

ofHealth,Jakarta,IndonesiaandMEASUREDHS,ICFInternational,Calverton,Maryland,USA-Seemoreat:http://www.dhsprogram.com/publications/publication-FR275-DHS-Final-Reports.cfm#sthash.GpvNRKn8.dpuf.

Jayachandran,S.(2014).Therootsofgenderinequalityindevelopingcountries:NationalBureauofEconomicResearch.

JICA.(2011).CountryGenderProfile:Indonesia:JapanInternationalCooperationAgency(JICA).Kevane,M.,&Levine,D.I.(2000).ThechangingstatusofdaughtersinIndonesia.Institutefor

ResearchonLaborandEmployment.Levy,C.(2013).Travelchoicereframed:“deepdistribution”andgenderinurbantransport.

EnvironmentandUrbanization.doi:10.1177/0956247813477810Lockley,A.,Tobias,J.,&Bah,A.(2013).GenderanalysisoftheunifiedDatabase:TNP2K.Mahy,P.K.(2012).GenderEqualityandCorporateSocialResponsibilityinMining:AnInvestigationof

thePotentialforChangeatKaltimPrimaCoal,Indonesia.(DoctorofPhilosophyDissertation),AustralianNationalUniversity.

Pinagara,F.A.,&Bleijenbergh,I.(2010).UnderstandingtheLowProportionofWomeninEmploymentinIndonesia:ASystemDynamicsApproach.

Pirmana,V.(2006).EarningsDifferentialbetweenMale-FemaleinIndonesia:EvidencefromSakernasData.Bandung,Indonesia:CenterforEconomicsandDevelopmentStudies,PadjadjaranUniversity.

Priebe,J.,Howell,F.,&Sari,V.(2014).PovertyandtheLabourMarketinIndonesia:EmploymentTrendsAcrosstheWealthDistribution.AvailableatSSRN.

Rammohan,A.,&Robertson,P.(2012).Dokinshipnormsinfluencefemaleeducation?evidencefromindonesia.OxfordDevelopmentStudies,40(3),283-304.

Scarpetta,S.,Sonnet,A.,&Manfredi,T.(2010).Risingyouthunemploymentduringthecrisis.

53


Siahaan,A.Y.(2003).ThepoliticsofgenderanddecentralizationinIndonesia.CenterforPolicyStudies,CentralEuropeanUniversity.OpenSocietyInstitute.<http://pdc.ceu.hu/archive/00002592/>(AccessedinMarch2010).

Siegmann,K.A.(2003).ForeigncapitaleffectsongenderwagedifferentialsinIndonesia:Springer.Siegmann,K.A.(2007).Globalisation,gender,andequity:effectsofforeigndirectinvestmenton

labourmarketsinruralIndonesia.PaperpresentedattheProceedingsoftheGermanDevelopmentEconomicsConference.

Silvey,R.(2004).TransnationalMigrationandtheGenderPoliticsofScale:IndonesianDomesticWorkersinSaudiArabia.SingaporeJournalofTropicalGeography,25(2),141-155.doi:10.1111/j.0129-7619.2004.00179.x

Sugiyarto,G.,Oey-Gardiner,M.,&Triaswati,N.(2006).LabormarketsinIndonesia:keychallengesandpolicyissues.LabormarketsinAsia:issuesandperspectives,301-366.

Suisse,C.(2014).TheCSGender3000:WomeninSeniorManagement:CreditSuisse-ResearchInstitute.

Suryadarma,D.,Suryahadi,A.,&Sumarto,S.(2005).ThemeasurementandtrendsofunemploymentinIndonesia:Theissueofdiscouragedworkers.AvailableatSSRN861464.

Suryahadi,A.,Widyanti,W.,Perwira,D.,&Sumarto,S.(2003).Minimumwagepolicyanditsimpactonemploymentintheurbanformalsector.BulletinofIndonesianEconomicStudies,39(1),29-50.doi:10.1080/00074910302007

Tambunan,T.(2009).Export-orientedsmallandmediumindustryclustersinIndonesia.JournalofEnterprisingCommunities:PeopleandPlacesintheGlobalEconomy,3(1),25-58.

Tambunan,T.(2014).DevelopmentofWomenEntrepreneursinIndonesia:AreTheyBeing'Pushed'or'Pulled'?Jakarta,Indonesia.

Taniguchi,K.,&Tuwo,A.(2014).NewEvidenceontheGenderWageGapinIndonesia.AsianDevelopmentBankEconomicsWorkingPaperSeries(408).

Tran,H.A.,&Schlyter,A.(2010).Genderandclassinurbantransport:thecasesofXianandHanoi.EnvironmentandUrbanization,22(1),139-155.

UN.(2003).Indonesia:Overviewofachievementsandchallengesinpromotinggenderequalityandwomen'sempowerment:UnitedNations.

UNICEF.(2010).ChildPovertyandDisparitiesinIndonesiaChallengesforInclusiveGrowth:SMERU,BAPENAS,UNICEF.

VanKlaveren,M.,Tijdens,K.,Hughie-Williams,M.,&Martin,N.R.(2010).Anoverviewofwomen'sworkandemploymentinIndonesia.

Vong,J.,Insu,S.,Salian,R.D.,Xu,R.,Kariath,R.,&Bunyong,K.(2013).ImpactofmicrofinanceongenderequalityinIndonesia.PaperpresentedattheProceedingsoftheInternationalConferenceonManagingtheAsianCentury.

Vong,J.,&Song,I.(2015).MicrofinanceandGenderEqualityinIndonesiaEmergingTechnologiesforEmergingMarkets(pp.35-53):Springer.

Weichselbaumer,D.,&Winter Ebmer,R.(2005).AMeta AnalysisoftheInternationalGenderWageGap.JournalofEconomicSurveys,19(3),479-511.

WorldBank.(2010a).EnhancingaccesstofinanceformigrantworkersinIndonesia:evidencefromasurveyofthreeprovinces.WashingtonD.C.:WorldBank.

WorldBank.(2010b).ImprovingaccesstofinancialservicesinIndonesiaWorldBank.WorldBank.(2011).IncreasingAccesstoJusticeforWomen,thePoor,andThoseLivinginRemote

Areas:AnIndonesianCaseStudy.Washington,DC.WorldBank.(2014).Ablessingoracurse?Anewinitiativetounderstandindonesianinternational

migrationPresentation.WorldBank.(2015)WomenBusinessandtheLaw2016.WashingtonD.C.Zhao,H.(2006).TheExpansionofNine-YearCompulsoryEducationinIndonesia:Effectsonthe

EducationMobility,1970-1997.ThePennsylvaniaStateUniversity.

54


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.

55


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


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

onda

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


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


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


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


0.25

0.21

0.27

Num


0.39

0.33

0.42

Num


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


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


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***




(0.0008) (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


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


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


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


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


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


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


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%

1%

1%

Re

gion

0.

0049

***

0.00

63**

*0.

0090

***

0.00

08**

*

-0.0

005*

**

0.00

33**

*0.

0081

***

0.01

00**

*

(0.0

001)

(0

.000

1)

(0.0

001)

(0

.000

1)

(0

.000

1)

(0.0

001)

(0

.000

1)

(0.0

002)

1%

1%

7%

1%

0%

1%

2%

3%

St

atus

ofe

mpl

oym

ent

0.01

37**

*0.

0242

***

0.03

75**

*0.

0441

***

0.

0009

***

-0.0

035*

**

-0.0

052*

**

-0.0

032*

**

(0

.000

1)

(0.0

001)

(0

.000

1)

(0.0

001)

(0.0

001)

(0

.000

1)

(0.0

001)

(0

.000

1)

2%

6%

31

%

42%

0%

-1%

-1

%

-1%

In

dust

ry

0.10

00**

*0.

0752

***

-0.0

027*

**

0.05

53**

*

0.08

59**

*0.

0727

***

0.05

93**

*0.

0950

***

(0

.000

2)

(0.0

002)

(0

.000

2)

(0.0

003)

(0.0

003)

(0

.000

2)

(0.0

002)

(0

.000

4)

18

%

18%

-2

%

53%

16%

16

%

15%

24

%

Not

es:T

hera

wd

iffer

ence

inp

erce

ntag

eis

calc

ulat

eda

s(e

raw

diff

eren

ce-1

)×10

0.T

hep

erce

ntag

ess

how

nar

eth

eco

ntrib

utio

nto

the

tota

lwag

ega

p.R

esul

tsa

reg

roup

eda

sex

perie

nce

(exp

erie

nce

and

expe

rienc

e/10

02 ),sk

ills

(voc

atio

nalt

rain

ing

and

heal

ths

tatu

s),a

ndre

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

79

Wom

en’s

eco

nom

icp

artic

ipat

ion

inIn

done

sia

TableA5-6Decom

position


eearningdistribu

tion

forpeop

leaged45to

64

Form

al

Inform

al

VARI

ABLE

SQ

10

Q30

Q

70

Q90

Q10

Q

30

Q70

Q

90

Raw

diff

eren

ce

0.63

27**

*0.

4795

***

-0.0

485*

**

0.11

09**

*

0.37

35**

*0.

3246

***

0.35

21**

*0.

3737

***

(0

.001

1)

(0.0

012)

(0

.000

9)

(0.0

009)

(0.0

008)

(0

.000

6)

(0.0

007)

(0

.001

0)

88

.3%

61

.5%

-4

.7%

11

.7%

45.3

%

38.3

%

42.2

%

45.3

%

Tota

lExp

lain

ed

0.26

03**

*0.

1951

***

-0.0

578*

**

0.10

05**

*

0.13

28**

*0.

1221

***

0.14

23**

*0.

1812

***

(0

.000

7)

(0.0

008)

(0

.000

8)

(0.0

007)

(0.0

005)

(0

.000

4)

(0.0

005)

(0

.000

7)

41

%

41%

11

9%

91%

36%

38

%

40%

48

%

Tota

lUne

xpla

ined

0.

3724

***

0.28

44**

*0.

0094

***

0.01

04**

*

0.24

07**

*0.

2026

***

0.20

98**

*0.

1925

***

(0

.000

8)

(0.0

008)

(0

.000

7)

(0.0

007)

(0.0

006)

(0

.000

5)

(0.0

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

Co

ntribu

tion

stoth

eExplainedGap:

Expe

rienc

e0.

0621

***

0.05

11**

*0.

0730

***

0.07

76**

*

0.02

08**

*0.

0204

***

0.02

05**

*0.

0205

***

(0

.000

3)

(0.0

003)

(0

.000

4)

(0.0

005)

(0.0

003)

(0

.000

2)

(0.0

002)

(0

.000

3)

10

%

11%

-1

51%

70

%

6%

6%

6%

5%

M

arrie

d0.

0856

***

0.10

01**

*0.

0230

***

0.01

15**

*

0.05

75**

*0.

0499

***

0.05

26**

*0.

0414

***

(0

.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%

-4

7%

10%

15%

15

%

15%

11

%

Skill

s0.

0003

***

0.00

21**

*0.

0077

***

0.00

82**

*

0.00

30**

*0.

0024

***

0.00

25**

*0.

0072

***

(0

.000

0)

(0.0

001)

(0

.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

.079

4***

0.02

31**

*0.

0247

***

0.03

01**

*0.

0333

***

(0

.000

4)

(0.0

005)

(0

.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

026*

**

0.00

18**

*0.

0040

***

(0

.000

1)

(0.0

002)

(0

.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


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


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


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

Date post:	16-Jan-2022
Category:	Documents
Upload:	others
View:	1 times
Download:	0 times