PolicyResearchWorkingPaper10544MissingSDGGenderIndicatorsKathleenBeegleUmarSerajuddinBrianStacyDivyanshiWadhwaDevelopmentDataGroup&DevelopmentResearchGroupAugust2023PublicDisclosureAuthorizedPublicDisclosureAuthorizedPublicDisclosureAuthorizedPublicDisclosureAuthorizedProducedbytheResearchSupportTeamAbstractThePolicyResearchWorkingPaperSeriesdisseminatesthefindingsofworkinprogresstoencouragetheexchangeofideasaboutdevelopmentissues.Anobjectiveoftheseriesistogetthefindingsoutquickly,evenifthepresentationsarelessthanfullypolished.Thepaperscarrythenamesoftheauthorsandshouldbecitedaccordingly.Thefindings,interpretations,andconclusionsexpressedinthispaperareentirelythoseoftheauthors.TheydonotnecessarilyrepresenttheviewsoftheInternationalBankforReconstructionandDevelopment/WorldBankanditsaffiliatedorganizations,orthoseoftheExecutiveDirectorsoftheWorldBankorthegovernmentstheyrepresent.PolicyResearchWorkingPaper10544TheSustainableDevelopmentGoalagendalaysoutanambitioussetof231indicatorstotrackprogress.Coun-triescontinuetofallshortintermsofreportingontheindicatorsingeneral,andthisisparticularlythecaseforthesubsetof50gender-relatedindicators,wherecountriesreportedonaverageon31percentoftheseindicatorsinatleastoneyearfrom2016to2020.Acloserlookatthislowcoveragerevealsfoursalientfundings.First,thisisnotjustaproblemofmissingdata;lackofreportingonexistingdataisdetectedtobeaproblem.Forexample,ofthe32gen-der-relatedindicatorsthataresexdisaggregated,ifcountriesthathadapopulationestimatealsohadasex-disaggregatedestimate(whichisalmostalwaysfeasible),theSustainableDevelopmentGoalgendercoverageratewouldbe43per-centinsteadof31percent.Second,betterstatisticalsystemsareamajorpartofthesolution,asstatisticalsystemstrengthiscorrelatedwithhighercoverage.Third,poorercountriesaredoingnoworseinreportingongender-relatedSus-tainableDevelopmentGoalindicatorsthanhigh-incomecountries,despiteweakerstatisticalsystems.Lastly,sizableover(andunder)performanceinreporting,conditionalonstatisticalstrength,suggeststhatcountry-leveladvocacyandfocuscanyieldwinsinSustainableDevelopmentGoalgenderindicatorcoverage.ThispaperisaproductoftheDevelopmentDataGroupandDevelopmentResearchGroup,DevelopmentEconomics.ItispartofalargereffortbytheWorldBanktoprovideopenaccesstoitsresearchandmakeacontributiontodevelopmentpolicydiscussionsaroundtheworld.PolicyResearchWorkingPapersarealsopostedontheWebathttp://www.worldbank.org/prwp.Theauthorsmaybecontactedatkbeegle@worldbank.org.MissingSDGGenderIndicatorsKathleenBeegle,UmarSerajuddin,BrianStacy,DivyanshiWadhwaKeywords:statisticalindicators,gender,nationalstatisticalsystemJEL:C8,J16,I00,O1AllauthorsarewiththeWorldBank.Correspondingauthor:KathleenBeeglekbeegle@worldbank.org.TheauthorsaregratefultocommentsfromHai-AnhDang,AnnaFruttero,andLaurenHarrison.Thefindings,interpretations,andconclusionsexpressedinthispaperareentirelythoseoftheauthors.TheydonotnecessarilyrepresenttheviewsoftheWorldBankanditsaffiliatedorganizations,orthoseoftheExecutiveDirectorsoftheWorldBankorthegovernmentstheyrepresent.21IntroductionTheSustainableDevelopmentGoals(SDGs)layoutanambitiousagendaincludingthatofachievinggenderequalityby2030.Thisagendaispairedwithasetofgoalsandtargetsmeasuredbyconcreteindicatorsandisadoptedbynearlyallcountries.SDG5focusesongenderequalityandsets9measurabletargets(with14indicators)onissuesthatespeciallyaffectwomenandgirls(UnitedNations,2022).ButgendercutsacrossafarwiderrangeoftheSDGsthanjusttheindicatorsunderGoal5.Forexample,SDG3onensuringgoodhealthandwell-beingincludesatargetonreducingmaternalmortality(target3.1).TheSDGagendaalsocallsforsexdisaggregateddataacrossseveralgoalswheremonitoringofgenderdisparitiesisessentialforeffectivepolicy.Forexample,SDG8onpromotingdecentworkandeconomicgrowthsetsatargetofachievingfullemploymentandequalpayforallwomenandmen(target8.5).Thesegenderdataarepromotedaskeytounderstandingifandhowpatternsofprogressdifferbetweenwomenandmenorgirlsandboys(UNWomen,2022).Countriesare,however,fallingshortonreportingongender-relatedindicatorsoftheSDGs.Thispaperanalyzesthepatternsunderlyingthesedatagaps.Theobjectiveofthispaperistolookatthisgloballyagreed-uponsetofgenderdataindicatorsandidentifykeycountrypatternsrelatedtotheexistence,orlackof,suchdata.WefocusontheavailabilityofdatareportingontheSDGssincetheyrepresentaninternationallyagreed-uponsetofgoalstomeetandforcountriestoreporton(UNSD,2022).Missinggenderdataisnotanewconcern.Therearedifferentapproachestodiagnosingthecausesofthelackofgenderdata.OneapproachputforthbyBonfertetal(2022)emphasizesfourobstaclestomoregenderdata(Figure1):(i)lackofdatasourcessuchascoreand/orspecializedsurveys,censusesorrelevantadministrativedata(thatis,thedatasimplyarenotcollected);(ii)methodologicalflawsindatacollection(e.g.,collectinglandholdingsofhouseholdsbutnot3identifyingwhichhouseholdmemberhastherights/ownershiptothisland);(iii)insufficientprocessingofexistingdata;and(iv)lackofdisseminationevenwhendataareavailableandprocessed.Figure1:SourcesofgenderdatagapsSource:Bonfertetal.2022Related,butnotidentical,BuvinicFurst-Nichols,andKoolwal(2014)discussgenderdatagapsasdrivenbyfourgaps:(i)lackofregularproductionatthecountrylevel;(ii)lackofinternationalstandards;(iii)lackofinformationacrossdomains;(iv)lackofgranularity,i.e.,lackoflarge,detaileddatasetsmakingpossibledisaggregation.InthispaperwelookattheproductionandreportingofSDGindicatorsongender,clearlylayingouttheavailability(alternativelythelackof)dataalongindicatortypes–uniquelygenderfocusedversuscross-cutting.Wethenfocusonthechallengesposedbyinsufficientprocessingofavailabledataorthelackofdisseminationevenwhenprocesseddataandconstructedindicatorsareavailable.Whilefocusingonimprovingthestatisticalsystemsisanimportantpartoftheagendatofulfillthegoalofreportingongender-relatedSDGs,somerapidimprovementscanbemadefromexistingdata.42GenderindicatorsfortheSDGsAlthoughnearlyallcountrieshaveagreedtoreportontheSDGindicators,majorgapsexistinindicatoravailabilitysincetheSDGagenda’sinceptionin2015(DangandSerajuddin2020).Gender-relatedSDGindicatorsarenoexception.Thereare231uniqueSDGindicators.Manyoftheindicators,evenifnotobviouslyrelatedtogender,nonethelesshavesub-indicators,suchas,indicatorsbysex,age,ordisabilitystatus.TheUNglobalSDGindicatorsdatabaseprovidesaccesstothedatacompiledfortrackingprogresstowardfulfillingtheSDGs.Weusethisdatasource,ratherthanindividualNSOwebsites,becausedatasubmittedtotheUNGlobalSDGmonitoringdatabasegoesthroughastandardizedprocessincludingacertainlevelofqualitycontrolanddocumentationreview.Weexplorethecoverageofthe50gender-relatedSDGindicatorsoutofthe231uniqueindicators.1Asnotedearlier,gender-relatedSDGindicatorsarenotlimitedtoSDG5ongenderequality,butratherspanindicatorsacross10outof17oftheSDGgoals.All50SDG-genderindicatorsareTier1or2SDGindicators.2Whilemostarerelatedtosexdisaggregationofdata1These50indicatorscloselymatchtheUNWomenminimumsetof52quantitativegenderindicatorsfromtheSDGs(UnitedNationsEconomicandSocialCouncil2012),subsequentlyrevisedtobe51quantitativeindicators,withafewexceptions.Theseexceptionsare:(i)indicators4.7.1,4.a.1,and13.3.1areintheUNWomenminimumsetbutnothereasinourviewtheyarenotgender-relatedorsex-disaggregatedmeasures.(ii)indicator1.1.1includesthe"workingpoor"(employedpopulationbelowinternationalpovertyline)bysexcomponentwhichisinTable1butnotintheUNWomenlist.Relatedly,OpenDataWatch(2019)refersto32SDGgenderindicatorsandanother36“additional”SDGgenderindicators.Thedifferencebetweentheir68andour50SDGgenderindicatorsisthatsomeoftheirsare,inourassessment,genderneutralintermsofthepresentdraftingoftheindicator(suchas1.5.1Numberofdeaths,missingpersonsanddirectlyaffectedpersonsattributedtodisastersper100,000population).2Tier1indicators,accordingtotheUN,areindicatorsthatareconceptuallyclear,haveaninternationallyestablishedmethodologyandstandardsavailable,anddataareregularlyproducedbycountriesforatleast50%ofcountriesandofthepopulationineveryregionwhererelevant.Tier2indicatorsareconceptuallyclearandhaveaninternationallyestablishedmethodologyandsetofstandards,butarenotregularlyproducedbycountries.Therearenogenderequalityindicatorsinthethirdcategory,Tier3,whichisdefinedasanindicatorwithnointernationallyestablishedmethodologyorstandardsestablished,and,thus,theseindicatorsarelikelytohavethelowestrateofcoverage.(UNSD,2022).5(32ofthe50),the18othersarerelatedtogoalsspecifictofemales–highlightingthatgender-relatedSDGindicatorsarenotonlyaboutsexdisaggregation.Table1showstheshareofcountriesforwhichthereisatleastoneannualdatapointinthefive-yearperiodfrom2016to2020foreachofthe50gender-relatedindicators.3,4Theaveragecoveragerateofindicatorsisaround34%for181countries;thatis,anaveragecountrywillhavedatareportedintheSDGwebsiteforabout17outof50indicators.Over90%oftheworld’spopulationlivesinacountrywherelessthanhalfofthe50SDGgenderindicatorsareavailableforanyyearinthis5-yearperiod.Indeed,thegender-relatedSDGindicatorsaremorelikelytobeunreportedthanotherindicators.Fortheoverall181SDGindicators,theaveragereportingrateis65%forthissameperiod.5Next,weunpackseveralnotableaspectsoftheavailability(orlackof)SDGgenderindicators.ForTier1SDGindicators(18outof50),arguablythosethatwillorshouldhavethegreatestavailability,availabilityismuchhigher;countrieshavearecentvalueforonlyabouthalf(51%)oftheindicators.ForTier2indicators(32outof50),theaveragecountryhasarecentvalue3Countrieswithpopulationsoflessthan200,000(34outof215countriesintheUNSDGDatabase)wereexcludedfromthisanalysis.Theseare:AmericanSamoa,Andorra,AntiguaandBarbuda,Aruba,Bermuda,BritishVirginIslands,CaymanIslands,ChannelIslands,Curacao,Dominica,FaroeIslands,Gibraltar,Greenland,Grenada,Guam,IsleofMan,Kiribati,Liechtenstein,MarshallIslands,Micronesia,Fed.Sts.,Monaco,Nauru,NorthernMarianaIslands,Palau,SanMarino,Seychelles,SintMaarten(Dutchpart),St.KittsandNevis,St.Lucia,St.Martin(Frenchpart),St.VincentandtheGrenadines,Tonga,TurksandCaicosIslands,Tuvalu,VirginIslands(U.S.).Encarnacionetal(2022)notethatthepoorestperformersintermsoflowestSDG-genderindicatorsaresmallislandsandnations.Thesecountrieshave,onaverage,verylowreportingratesforSDGs,including,butnotonly,thoserelatedtogender.Ingeneral,smallislandsandnationsareunder-performersintermsofstatisticalperformanceconditionalontheirincomeandhumancaptialindexlevel(Dangetal2021).4TheSDGdatabaseincludesactual(survey/censusorotherprimarydatasourceestimates)aswellasadditionalmodeledestimatesforindicatorswhenprimarysourcesarenotavailableforthecountry.Wedonotusemodeledestimates.5Thisisourowncalculation.DangandSerajuddin(2020)reportlowerratesofSDGindicatorreportinginpartbecausetheyfocusonanearlierperiod(2012-2016)andbecausetheyincludesmallislandsandnations.6foronlyaquarter(24%)oftheindicators.Annex1presentstheavailabilityofTier1andTier2SDGgender-relatedindicatorsbyregionandbycountryincomegrouping.Forthe14indicatorsunderGoal5,thecountryaverageavailabilityis37%,onlymarginallyhigheravailabilitycomparedwiththeaverageavailabilityforallgender-relatedindicators.Figure2showsthisdistribution.Nocountryhasmorethan10ofthese14indicatorsinthe5-yearperiod.Forty-onecountriesreportthreeorfewerindicators.Annex1presentstheavailabilityofSDG5gender-relatedindicatorsbyregionandcountryincomegrouping.Figure2.AvailabilityofSDG5gender-relatedindicators(N=181countries)Note:Thefigureshowsthecoverageofthe14SDG5indicatorswherecoverageisdefinedashavingatleastoneannualdatapointinthefive-yearperiodfrom2016to2020foranindicatorascompiledbytheUN.Amongindicatorsthatrequiresexdisaggregation(32outofthe50),boththepopulationdataandthesex-disaggregateddataarenotreportedforanycountryforfiveindicators(suchasforindicator10.2.1).Forfourofthese32indicators,thesex-disaggregatedandpopulationcoverageratesmatch,aswewouldexpectiftheunderlyingdataidentifiedindividualsex,was0510152025303540012345678910PercentofcountriesNumberofSGD5genderindicators7collectedforbothmalesandfemales,andwasprocessedaccordingly.Wewouldnotexpectthesex-disaggregatedcoverageratetoexceedthepopulationcoverage,anditneverdoes.Moreover,ifthecountryhasasex-disaggregateddatapointtheyalsohaveapopulationestimate.Butthereverseisnotthecase.Forsixofthese32indicators(19%),whilethereissomereporteddataforthepopulationindicator,thereisnosex-disaggregateddatareported.Asanexample,56%ofcountriesreportapopulationrateforSDGindicator10.2.1(theproportionofpeoplelivingbelow50percentofmedianincome),yetnocountryreportsthisstatisticbysex.Thesemissingdataarenottheresultofmissingsexinunderlyingdatasource(inthiscase,householdsurveys).Themeasureitself(livingbelowanincomethreshold)isdefinedatthehouseholdlevelandsoonecanproduceasex-disaggregatedestimatebasedonthehouseholdsinwhichindividualsreside.Forexample,MunozBoudetetal.(2021)reportpovertyratesbysex.6Intheremaining17cases(outof32),wherethereissomereporteddataforboththepopulationandbysex-disaggregation,inahandfulofcasestherearelargegapsbetweenthepercentageofcountrieswitharecentvaluebysexandthosereportingapopulationestimate(i.e.comparingthelasttwocolumnsinTable1whenbothcolumnsarenon-zero).ForSDGindicator1.3.1,ontheproportionofpopulationcoveredbysocialprotectionfloors/systems,79%ofcountrieshavearecentvalueforthepopulation,butonly8%ofcountrieshaveasexdisaggregateddatapoint.AlessdrasticexampleisSDGindicator4.1.1,relatedtoearlychildhoodeducation:64percentofcountrieshaveapopulationestimateforthisindicatorbutonly53percenthaveanestimatebysex.6Thisisnotrelatedtotheissueofmeasuringincomeorpovertyattheindividuallevelversusahouseholdmeasure.Itissimplythepointthatifapopulationestimateisproducedbasedonahousehold-levelmeasure,thenthereisnomethodologicalargumentagainstproducingadditionalestimatesforpopulationsub-categories(beiturban/rural,male/female,forchildren,etc).8TheseresultsshowthattheproblemofmissingSDGgendermeasuresis,inpart,aproblemwithprocessingexistingdataratherthanthelackoftheprimarydatacollection,sincetheunderlyingsources(typicallyhouseholdsurveys)almostalways(ifnotalways)collectthesexofhouseholdmembers.Ifcountrieswithapopulationestimatealsoreporteddatabysex,theSDGgendercoverageratewouldrisefrom31%to43%.Evidencefromothersourcesunderscoretheproblemofavailabledatanotbeingreported.Inareviewofnationalstatisticsfor12countriesrelatedtosex-disaggregateddataonassetownership,employment,andentrepreneurship,Bonfertetal(2023)findthatsuchdataexistbutarenotmadeavailableonaveragefor9outof24indicators.ExamininggenderstatisticsfromtheUK,whencomparingtheOfficeofNationalStatistics(ONS)websiteforgenderdatatothereportingintheUNSDGsite,wefindthatabout31%ofgender-relatedSDGindicators(16outof50)areontheONSwebsitebutnotintheUNSDGsite.Ontheotherhand,only4ofthe50areintheUNSDGsitebutnotontheONSwebsite.Theremaining30SDGgenderindicatorsareinboth(21)orneither(9).Turningtoregionalpatternsincoverage,SouthAsiahasthehighestcoveragerateofSDGgenderindicatorsat36%availability,about5%higherthantheglobalaverage(31%).Therearenoclearpatternsintermsofwhichgroupsortypesofindicatorshavehigherorlowerindicatorcoveragebyregion.Notably,thehigh-incomecountriesdonothavehighercoverageofgender-relatedSDGindicators(Figure4).GDPpercapitaisnotassociatedwithbettercoverageofgenderstatisticsintheUNSDGdatabase(Figure5).ThisisalsothecaseforSDGindicatorsoverall:highincomecountriesdonothavehigherratesofreportingofall181SDGindicators(ratesarehighincome64%,uppermiddleincome72%,lowermiddleincome70%,andlowincome65%).Yet,high9incomecountriesperformnotablybetterontheStatisticalPerformanceIndicatorsandIndex(SPI)–theWorldBank’snewofficialtooltomeasurecountrystatisticalcapacityandisbeingaddedtotheSDGindicatorsunderSDG17(Dangetal.2023).OneexplanationforthisparadoxonreportingSDGsandstatisticalstrengthoverallisthatrichercountriesmayhavebeenslowtoreportSDGscomparedwithlow-andmiddle-incomecountriesthathaveexperienceinengagingwiththeMillenniumDevelopmentGoals(MDGs)(MacFeely2018).AsecondexplanationliesinthespecificfocusofsomeSDGs.ThisdifferenceintheoverallperformanceofnationalstatisticalsystemsandthereportingongenderSDGsmightbeexplainedbypresenceinthelatterofindicatorswhichrelatetophenomenathatarearguablyinfrequentorrareforhighincomecountries(orperceivedassuch).Forexample,dataonchildmarriageandonfemalegenitalmutilation(coveredinSDGtarget5.3)arerarelycollectedinOECDcountries.OECD(2022)describestheextralengthsneededtogetsuchdatafromalternatesourcesinordertobeabletoreportonthisSDG.Athirdexplanation,relatedtothesecondoneabove,isthepresenceofsystematicandlarge-scaledatacollectionundertheDemographicandHealthSurvey(DHS)andtheMultipleIndicatorClusterSurvey(MICS)programs,whicharefocusedonlow-incomecountries(andoftenfinancedwithnon-nationalresources).Thesesurveysareoftenthesourceofgender-relateddata,especiallyinthedomainsoffemalehealthandempowerment.Toassessthis,weexaminethemaindatasourcesforthe50SDGgenderindicators.TheDHSorMICsisthesourceforatleastonecountrydatapointfor13outof50indicatorsbutonlyextensively(welloverhalfofthedatapoints)for5indicators.7WefindveryslightevidencethattheDHS/MICsdatasourceexplainslower7Forthe13indicatorswheretheDHS/MICsiseverthesource,theshareofdatapointswhicharefromDHS/MICsare:2%(indicator4.1.1),4%(indicator5.b.1),14%(indicator5.a.1),16%(indicator3.7.2),24%(indicator3.1.2),10coverageofgender-relatedSDGsinhigh-incomecountriesrelativetolowerincomecountries.Whenexcludingthese5DHS/MICs-dominantindicators,high-incomecountrieshavebasicallythesamecoverage(31%)aslow(22%)andlowermiddle(31%)countries.Andtheycontinuetolagbehinduppermiddleincomecountries(35.4%).Figure3.AvailabilityofSDGgenderindicatorsbyregion(N=181countries)36%(indicator1.4.2),36%(indicator4.5.1),41%(indicator3.7.1),86%(indicator16.2.3),93%(indicator5.3.1),97%(indicator5.6.1),97%(indicator4.2.1),and98%(indicator5.3.2).11Figure4.AvailabilityofSDGgenderindicatorsbyincomegroup(N=181countries)Figure5.GDPpercapitaandSDGgenderindicators(n=181economies)12Table1.SDGindicatorsrelatedtogender(N=181countries)GoalIndicatorTierDescriptionSub-Indicator(ifany)SexDisaggregationAvailableAnyDataAvailable11.1.11Proportionofthepopulationlivingbelowtheinternationalpovertylinebysex,age,employmentstatusandgeographiclocation(urban/rural)--0.0%61.3%1.1.11Proportionofthepopulationlivingbelowtheinternationalpovertylinebysex,age,employmentstatusandgeographiclocation(urban/rural)Employedpopulationbelowinternationalpovertyline,bysexandage(%)0.0%14.9%1.2.11Proportionofpopulationlivingbelowthenationalpovertyline,bysexandage--0.0%53.6%1.2.22Proportionofmen,womenandchildrenofallageslivinginpovertyinallitsdimensionsaccordingtonationaldefinitions(multidimensionalpoverty)--21.5%28.2%1.3.11Proportionofpopulationcoveredbysocialprotectionfloors/systems,bysex,distinguishingchildren,unemployedpersons,olderpersons,personswithdisabilities,pregnantwomen,newborns,work-injuryvictimsandthepoorandthevulnerableProportionofpopulationcoveredbyatleastonesocialprotectionbenefit,bysex(%)9.4%86.7%13Table1.SDGindicatorsrelatedtogender(N=181countries)GoalIndicatorTierDescriptionSub-Indicator(ifany)SexDisaggregationAvailableAnyDataAvailable1.4.22Proportionoftotaladultpopulationwithsecuretenurerightstoland,(a)withlegallyrecognizeddocumentation,and(b)whoperceivetheirrightstolandassecure,bysexandtypeoftenureAnydata:Proportionofpeoplewithlegallyrecognizeddocumentationoftheirrightstolandoutoftotaladultpopulation,bysex(%);AND/ORProportionofpeoplewhoperceivetheirrightstolandassecureoutoftotaladultpopulation,bysex(%);AND/ORProportionofpeoplewithsecuretenurerightstolandoutoftotaladultpopulation,bysex(%)12.7%13.3%22.2.31Prevalenceofanaemiainwomenaged15to49years,bypregnancystatusNA65.2%2.3.22Averageincomeofsmall-scalefoodproducers,bysexandindigenousstatus5.0%5.0%33.1.11MaternalmortalityNA97.8%3.1.21ProportionofbirthsattendedbyskilledhealthpersonnelNA81.2%3.3.11NewHIVinfectionsinuninfectedpopulation,bysex,ageandkeypopulations63.5%78.5%3.7.11Proportionofwomenofreproductiveage(aged15-49years)whohavetheirneedforfamilyplanningsatisfiedwithmodernmethodsNA46.4%3.7.21Adolescentbirthrate(aged10-14years;aged15-19years)NA82.9%14Table1.SDGindicatorsrelatedtogender(N=181countries)GoalIndicatorTierDescriptionSub-Indicator(ifany)SexDisaggregationAvailableAnyDataAvailable44.1.11Proportionofchildrenandyoungpeople(a)ingrades2/3;(b)attheendofprimary;and(c)attheendoflowersecondaryachievingatleastaminimumproficiencylevelin(i)readingand(ii)mathematics,bysexProportionofchildrenandyoungpeopleachievingaminimumproficiencylevelinreadingandmathematics(%)62.4%75.1%4.2.12Proportionofchildrenaged24-59monthswhoaredevelopmentallyontrackinhealth,learningandpsychosocialwell-being,bysexProportionofchildrenaged36−59monthswhoaredevelopmentallyontrackinatleastthreeofthefollowingdomains:literacy-numeracy,physicaldevelopment,social-emotionaldevelopment,andlearning28.7%29.8%4.2.21Participationrateinorganizedlearning(oneyearbeforetheofficialprimaryentryage),bysex77.9%79.0%4.3.12Participationrateofyouthandadultsinformalandnon-formaleducationandtrainingintheprevious12months,bysex38.1%39.8%15Table1.SDGindicatorsrelatedtogender(N=181countries)GoalIndicatorTierDescriptionSub-Indicator(ifany)SexDisaggregationAvailableAnyDataAvailable4.5.12Parityindices(female/male,rural/urban,bottom/topwealthquintileandotherssuchasdisabilitystatus,indigenouspeoplesandconflict-affected,asdatabecomeavailable)foralleducationindicatorsonthislistthatcanbedisaggregatedAnydata:Adjustedgenderparityindexforparticipationrateinorganizedlearning(oneyearbeforetheofficialprimaryentryage),(ratio);AND/ORAdjustedgenderparityindexfortheproportionofteacherswiththeminimumrequiredqualifications,byeducationlevel(ratio);AND/ORAdjustedgenderparityindexforparticipationrateinformalandnon-formaleducationandtraining(ratio);AND/ORGenderparityindexforyouth/adultswithinformationandcommunicationstechnology(ICT)skills,bytypeofskill(ratio);AND/ORAdjustedgenderparityindexforachievingaminimumproficiencylevelinreadingandmathematics(ratio);AND/ORAdjustedgenderparityindexforcompletionrate,bylocation,wealthquintileandeducationlevel;AND/ORAdjustedgenderparityindexforachievingatleastafixedlevelofproficiencyinfunctionalskills,bynumeracy/literacyskills(ratio)NA95.0%4.6.12Proportionofpopulationinagivenagegroupachievingatleastafixedlevelofproficiencyinfunctional(a)literacyand(b)numeracyskills,bysex7.7%8.8%16Table1.SDGindicatorsrelatedtogender(N=181countries)GoalIndicatorTierDescriptionSub-Indicator(ifany)SexDisaggregationAvailableAnyDataAvailable55.1.12Whetherornotlegalframeworksareinplacetopromote,enforceandmonitorequalityandnon‑discriminationonthebasisofsexAnydata:Legalframeworksthatpromote,enforceandmonitorgenderequality(percentageofachievement,0–100)–Area1:overarchinglegalframeworksandpubliclife;AND/ORLegalframeworksthatpromote,enforceandmonitorgenderequality(percentageofachievement,0–100)–Area2:violenceagainstwomen;AND/ORLegalframeworksthatpromote,enforceandmonitorgenderequality(percentageofachievement,0–100)–Area3:AND/ORemploymentandeconomicbenefits;AND/ORLegalframeworksthatpromote,enforceandmonitorgenderequality(percentageofachievement,0–100)–Area4:marriageandfamilyNA52.5%5.2.11Proportionofever-partneredwomenandgirlsaged15yearsandoldersubjectedtophysical,sexualorpsychologicalviolencebyacurrentorformerintimatepartnerintheprevious12months,byformofviolenceandbyageNA81.8%5.2.225.2.2Proportionofwomenandgirlsaged15yearsandoldersubjectedtosexualviolencebypersonsotherthananintimatepartnerintheprevious12months,byageandplaceofoccurrenceNA0.0%5.3.11Proportionofwomenaged20-24yearswhoweremarriedorinaunionbeforeage15andbeforeage18Anydata:Proportionofwomenaged20-24yearswhoweremarriedorinaunionbeforeage18(%);AND/ORProportionofwomenaged20-24yearswhoweremarriedorinaunionbeforeage15(%)NA47.0%17Table1.SDGindicatorsrelatedtogender(N=181countries)GoalIndicatorTierDescriptionSub-Indicator(ifany)SexDisaggregationAvailableAnyDataAvailable5.3.21Proportionofgirlsandwomenaged15-49yearswhohaveundergonefemalegenitalmutilation/cutting,byageNA11.0%5.4.12Proportionoftimespentonunpaiddomesticandcarework,bysex,ageandlocation17.1%17.7%5.5.11Proportionofseatsheldbywomenin(a)nationalparliamentsand(b)localgovernmentsAnydata:Proportionofseatsheldbywomeninnationalparliaments(%oftotalnumberofseats);AND/ORProportionofelectedseatsheldbywomenindeliberativebodiesoflocalgovernment(%)NA98.3%5.5.21ProportionofwomeninmanagerialpositionsProportionofwomeninseniorandmiddlemanagementpositions(%)NA50.3%5.6.12Proportionofwomenaged15-49yearswhomaketheirowninformeddecisionsregardingsexualrelations,contraceptiveuseandreproductivehealthcareNA21.0%5.6.22Numberofcountrieswithlawsandregulationsthatguaranteefullandequalaccesstowomenandmenaged15yearsandoldertosexualandreproductivehealthcare,informationandeducationNA40.9%5.a.12(a)Proportionoftotalagriculturalpopulationwithownershiporsecurerightsoveragriculturalland,bysex;and(b)shareofwomenamongownersorrights-bearersofagriculturalland,bytypeoftenureAnydata:Proportionofpeoplewithownershiporsecurerightsoveragriculturalland(outoftotalagriculturalpopulation),bysex(%);AND/ORShareofwomenamongownersorrights-bearersofagriculturalland,bytypeoftenure(%)14.9%15.5%18Table1.SDGindicatorsrelatedtogender(N=181countries)GoalIndicatorTierDescriptionSub-Indicator(ifany)SexDisaggregationAvailableAnyDataAvailable5.a.22Proportionofcountrieswherethelegalframework(includingcustomarylaw)guaranteeswomen’sequalrightstolandownershipand/orcontrolNA19.9%5.b.12Proportionofindividualswhoownamobiletelephone,bysex48.1%61.3%5.c.12Proportionofcountrieswithsystemstotrackandmakepublicallocationsforgenderequalityandwomen’sempowermentNA32.6%88.3.12Proportionofinformalemploymentintotalemployment,bysectorandsex37.6%37.6%8.5.12Averagehourlyearningsofemployees,bysex,age,occupationandpersonswithdisabilities44.8%45.3%8.5.21Unemploymentrate,bysex,ageandpersonswithdisabilities74.0%75.1%8.7.12Proportionandnumberofchildrenaged5-17yearsengagedinchildlabour,bysexandageProportionofchildrenengagedineconomicactivity,bysexandage(%)30.4%30.4%8.8.12Fatalandnon-fataloccupationalinjuriesper100,000workers,bysexandmigrantstatusAnydata:Fataloccupationalinjuriesamongemployees,bysexandmigrantstatus(per100,000employees);AND/ORNon-fataloccupationalinjuriesamongemployees,bysexandmigrantstatus(per100,000employees)30.9%38.7%19Table1.SDGindicatorsrelatedtogender(N=181countries)GoalIndicatorTierDescriptionSub-Indicator(ifany)SexDisaggregationAvailableAnyDataAvailable8.8.22Levelofnationalcompliancewithlabourrights(freedomofassociationandcollectivebargaining)basedonInternationalLabourOrganization(ILO)textualsourcesandnationallegislation,bysexandmigrantstatus0.0%70.2%1010.2.12Proportionofpeoplelivingbelow50percentofmedianincome,bysex,ageandpersonswithdisabilities0.0%56.4%1111.2.1211.2.1Proportionofpopulationthathasconvenientaccesstopublictransport,bysex,ageandpersonswithdisabilities0.0%0.0%11.7.1211.7.1Averageshareofthebuilt-upareaofcitiesthatisopenspaceforpublicuseforall,bysex,ageandpersonswithdisabilities0.0%0.0%11.7.2211.7.2Proportionofpersonsvictimofphysicalorsexualharassment,bysex,age,disabilitystatusandplaceofoccurrence,intheprevious12months0.0%0.0%1616.1.12Numberofvictimsofintentionalhomicideper100,000population,bysexandage55.8%57.5%16.1.22Conflict-relateddeathsper100,000population,bysex,ageandcause0.0%0.0%20Table1.SDGindicatorsrelatedtogender(N=181countries)GoalIndicatorTierDescriptionSub-Indicator(ifany)SexDisaggregationAvailableAnyDataAvailable16.1.32Proportionofpopulationsubjectedto(a)physicalviolence,(b)psychologicalviolenceand(c)sexualviolenceintheprevious12monthsAnydata:Proportionofpopulationsubjectedtophysicalviolenceintheprevious12months,bysex(%);AND/ORProportionofpopulationsubjectedtorobberyintheprevious12months,bysex(%);AND/ORProportionofpopulationsubjectedtosexualviolenceintheprevious12months,bysex(%)9.4%19.9%16.2.22Numberofvictimsofhumantraffickingper100,000population,bysex,ageandformofexploitation0.0%59.1%16.2.32Proportionofyoungwomenandmenaged18-29yearswhoexperiencedsexualviolencebyage1819.9%19.9%16.7.12Proportionsofpositionsinnationalandlocalinstitutions,including(a)thelegislatures;(b)thepublicservice;and(c)thejudiciary,comparedtonationaldistributions,bysex,age,personswithdisabilitiesandpopulationgroupsAnydata:Ratioforfemalemembersofparliaments(Ratiooftheproportionofwomeninparliamentintheproportionofwomeninthenationalpopulationwiththeageofeligibilityasalowerboundboundary),LowerChamberorUnicameral;AND/ORRatioforfemalemembersofparliaments(Ratiooftheproportionofwomeninparliamentintheproportionofwomeninthenationalpopulationwiththeageofeligibilityasalowerboundboundary),UpperChamberNA0.0%16.7.2216.7.2Proportionofpopulationwhobelievedecision-makingisinclusiveandresponsive,bysex,age,disabilityandpopulationgroup0.0%0.0%Note:Percentofcountrieswithanyreportingontheindicatorinthefiveyears(2016-2020).NAindicatesthattheindicatorisnotrelevantinregardstosexdisaggregation.Source:UNSDGGlobalDatabase.https://unstats.un.org/sdgs/dataportal213.SDGgenderindicatoravailabilityandcountrystatisticalperformanceNextweassesshowacountry’soverallstatisticalperformancerelatestotheavailabilityofgenderdata,andidentifycountriesthatmayhavestrongsystemsoverallbutareunderperformingongenderstatistics.Todoso,wecomparetheavailabilityofgenderstatisticstoscoresontheWorldBank’sStatisticalPerformanceIndicators(SPI)(Dangetal2023).TheWorldBank’sStatisticalPerformanceIndicators(SPI)measurestatisticalperformancefor174countriescoveringover99%oftheworldpopulation.Theindicatorsaregroupedintofivepillars:(1)datause,whichcapturesthedemandsideofthestatisticalsystem;(2)dataservices,whichlooksattheinteractionbetweendatasupplyanddemandsuchastheopennessofdataandqualityofdatareleases;(3)dataproducts,whichreviewswhethercountriesreportonglobalindicators;8(4)datasources,whichassesseswhethercensuses,surveys,andotherdatasourcesarecreated;and(5)datainfrastructure,whichcaptureswhetherfoundationssuchasfinancing,skills,andgovernanceneededforastrongstatisticalsystemareinplace.Withineachpillarisasetofdimensions,andundereachdimensionisasetofindicatorstomeasureperformance.Theindicatorsprovideatimeseriesextendingatleastfrom2016to2020inallcases,withsomeindicatorsgoingbackto2004.9Theindicatorsaresummarizedasanindex,termedtheSPIoverallscore,withscoresrangingfromalowof0toahighof100.WeusetheSPIdatafor2019.8ThedataproductspillarmeasureswhethercountrieshaverecentSDGindicatorsacrossthe17goalsavailableintheUNGlobalSDGIndicatorsdatabase.9Thedatafortheindicatorsarefromavarietyofsources,includingdatabasesproducedbytheWorldBank,InternationalMonetaryFund(IMF),UnitedNations(UN),PartnershipinStatisticsforDevelopmentinthe21stCentury(PARIS21),andOpenDataWatch—andinsomecases,directlyfromnationalstatisticalofficewebsites.22Thereisapositiverelationshipbetweencountries’SPIoverallscoresandtheavailabilityofSDGgenderindicators(Figure6).Forpillar3,whichisoverallSDGcoverage,likewisethereisapositiverelationshipwithSDGgenderindicators(Figure7).Thisisnotsurprisingsincepillar3encompassestheSDGgenderindicatorsitself.Interestingly,althoughSPIandtheSDGgenderindicatorcoveragearepositivelycorrelatedandSPIispositivelycorrelatedwithcountryincomelevel(notshown),asnotedearlier,SDGgenderindicatorcoverageisslightlynegativelycorrelatedwithcountryincome.AbreakdownofcorrelationsisreportedinAnnex2.Itisamongthecountriesinthepoorestquintileofstatisticalsystemscoringwherethegapingenderdataavailabilityislargest.Amongthecountrieswithin2nd,3rd,4th,ortopquintileoftheSPIscore,themeangender-relatedSDGindicatorsavailabilityisbetween17and20.However,forthecountriesinthebottomquintileoftheSPIscore,only12genderindicatorsoutof50areavailableonaverage.Figure6.SDGgenderindicatorsandSPIscore(n=161economies)23Figure7.SDGgenderindicatorsandallSDGindicators(n=161economies)Next,weexplorepatternsofover-andunder-performingcountriesingenderdataavailabilityascomparedtowhatisexpectedgivenacountry’sSPIscore.Specifically,weregressSDGgenderindicatoravailabilityonSPIscore.Wetaketheresidualfrompredictedvaluesasameasureofoverandunderperformance,conditionalonSPI.Weconverttheresidualbymultiplyingitby50(thenumberofSDGgenderindicators)togetanestimateoftheadditional(orfewer)SDGgenderindicatorsacountryproducesascomparedtothepredictednumber.Wealsoreportthedifferencefromthepredictedvalueasshareofthepredictedvalue(whichcanbenegativeorpositive).Figure8showsthecountandFigure9showsthepercentagechangefortopandbottom15countries.Forexample,Serbiareportedon58%oftheSDGgenderindicators.BasedontheSPIoverallscoreofthecountry,itwasexpectedtoproduceonly40%oftheSDGgenderindicators.24Whenwemultiplethis18percentagepointdifferencebythetotalnumberofSDGgenderindicators(50indicators),wegettheadditionalSDGindicatorsthatwerereportedfromwhatwouldbepredicted(around9indicators).Figure8:Top15over/underperformersonavailabilityofgenderSDGindicatorsintermsofnumberofindicators25Figure9:Top15over/underperformersonavailabilityofgenderSDGindicatorsintermsofpercentagechangeAsdiscussedabove,asimpleOLSregressionoftheavailabilityofgenderSDGindicatorsontheSPIoverallscoreindicatesthataround40percentofthevariationintheavailabilityofgenderSDGindicatorsisexplainedbythestatisticalperformanceofacountry,leavingaround60percentunexplained.ManyfactorsbeyondSPImightbecorrelatedwiththeavailabilityofgenderSDGindicators,includingacountry’sregion,itsincomelevel,populationsize,ortheleveloffemaleempowerment.Table2reportsonregressionscontrollingforsomeadditionalcountry-leveltraits.Theseincludethreemeasuresofgenderequality:WorldBankWomen,BusinessandtheLawindex(WorldBank2019),OECD’sSocialInstitutionsandGenderIndex(SIGI)(OECD2019),andtheGenderInequalityIndex(GII)(UN2020).26Controllingforregion,thecorrelationsbetweenSDGgenderindicatorsandbothSPIandGDPpercapitastillhold.ThereissomeindicationthatcountrieswithworsegenderinequalitymeasuresfarebetterintermsofSDGgenderindicatorreporting.Table2.RegressionofavailabilityofSDGgenderindicatorsoncountrytraits(1)(2)(3)(5)(6)(7)SPIOverallScore0.200.430.500.570.60(0.04)(0.06)(0.06)(0.06)(0.08)LogGDPpercapita-5.40-5.05-4.35-4.19(0.64)(0.75)(1.08)(1.26)Popunder1.5million(0/1)-2.95-3.22-0.60-1.23(2.10)(2.08)(2.06)(2.92)WBL0.040.02-0.02-0.17+(0.04)(0.05)(0.06)(0.10)UNDPGII15.54(7.33)OECDSIGI0.10(0.09)Region(0/1):MiddleEast/NorthAfrica-5.590.951.52-0.63(2.76)(2.28)(2.26)(3.34)NorthAmerica-0.86-4.96-2.82-4.69(4.80)(5.33)(4.65)(4.99)SouthAsia7.148.266.18+3.04(3.33)(3.25)(3.38)(3.88)Sub-SaharanAfrica3.523.331.584.08(2.56)(2.40)(2.47)(3.31)N163163163163140101RSq.0.130.150.420.510.510.41Notes:indicatesstatisticalsignificanceat1%,at5%and+at10%.Constanttermincluded.TheWBLhasarangeof1-100,withahighscoreindicatingmoregenderequallawsandregulations.TheOECDSIGISIGImeasurestheextentofgenderdiscriminatorylegislationandrestrictivesocialnormsandpractices,whereahighscoreindicatesgreatergenderinequality.Likewise,theGIImeasurespooroutcomesforwomeninregardstoreproductivehealth,empowerment,andthelabormarket,andahighscoreindicatesgreatergenderinequality.4.DiscussionTheworld’sSustainableDevelopmentGoal(SDG)agendalaysoutanambitioussetofindicatorstotrackprogress.Whiletheoverallcoverageofthe231indicatorscertainlyneeds27improvement,thecoverageforthe50gender-relatedindicatorsisespeciallylow.Onaverage,countrieshave30%oftheseindicatorsforatleastoneyearbetween2016and2020,comparedtoarateof65%forall181SDGindicators.Moreover,thesubsetof14indicatorsunderthespecificGoal5ongenderequalityfareonlyslightlybetter.Thislowcoverageisnotaproblemofill-definedindictors.These50indicatorsareclassifiedaseitherTier1(18indicators)orTier2(32indicators)intermsofstatisticalcomplexity;sothemethodologytocollectsuchdataisestablished.Clearly,theworldneedsmorereportingongender-relevantindicators,buthowmuchofthisproblemisoneoflackofdata(i.e.nosurveyexists)versusafailureinthereportingprocess?For23indicators,alackofdatareportingseemstobeacauseofmissingSDGgenderindicators.Intheseinstances,populationestimatesarebeingreported,butthesex-disaggregatedcounterpartisnotreportedtothesamedegreethoughthisdisaggregation,ifnotalways,isnearlyalwaysfeasible.ThisgapinSDGgenderindicatorreportingseemstobelow-hangingfruit,addressedbyensuringthatsex-disaggregatedinformationisprocessedandreported.Fortheotherindicators,wecannotaseasilydisentangleifdatareportingisthesourceoftheproblemor,rather,thelackofrelevantsurveys/administrativedata.Forsevenindicators,therearenocountrieswithanydatapointinthefive-yearperiod.StatisticalsystemstrengthasmeasuredbytheSPIscoreispositivelycorrelatedwithSDGgenderdataavailability;betterstatisticalsystemsareanimportantpartofthesolution.Still,whenassessingtheperformanceofgenderdataavailabilitybyacountryincomelevel,poorcountriesarenotdoingworsedespiteoftenweakerstatisticalsystems.TheSDGagendawassetinawaythatallcountries,irrespectiveoftheirdevelopmentstatusorincomelevel,weretoreporttheirprogressonalltargets,whichisincontrastwiththeMDGsera,whichwaslargelyfocusedonlow-andmiddle-incomecountries.High-incomecountriesmayhavebeenslowtoadjusttothisshiftintheagendaandhave,therefore,28underreportedstatistics(MacFeely,2018).Certainlytheremayalsobecaseswheretheydonotcollectcertainstatisticsbecauseofthelackofrelevancetotheircountrycontexts,asnotedinthecaseofchildmarriageandfemalegenitalmutilation,butwedonotfindevidencetosupportthisasadrivingfactoroftheresultthatpoorercountriesdoaswellashigh-incomecountriesinreportingSDGgender-relatedindicators.Beyondthesefactors,weareleftwithunexplainedvariationingenderdataavailabilityacrosscountries.Thisispartlycapturedinthenotableover(andunder)performanceinreportinggender-relatedSDGsrelativetostatisticalsystemstrength.Onecantakeasomewhatoptimisticperspectiveincombiningthiswiththetwopreviousfindings–thatsomeportionofunder-reportingisnotdrivenbylackofdatabutbyunder-reporting,andthatcountryincomeisnotdrivinghigherratesofreporting.Evenwithoutmajorinvestmentsinstatisticalsystemsortheyearsitmaytakeforsuchinvestmentstoyieldresults,withsomeconcertedeffort,itispossibletoachievebigwinsinSDGgenderindicatorcoverage.Meanwhile,itisimportanttonotethatwhiletheSDGframeworkofferstheworldaconsensussetofindicatorsselectedaspartofaglobalconsultativeprocess,thereareotherimportantcountry-levelgender-relatedindicatorsavailableoutsidetheSDGsystem.SourcessuchastheUNWomenDataHubandtheWorldBank’sGenderDataPortaloffercompilationsofnationalstatisticsproducedbycountriesandcuratedbyinternationalagencies.29Andlastly,thispapermightlaysomefoundationtodevelopasystematicandcomprehensiveapproachtotrackingcountrystatisticalperformanceregardinggenderdata.TherecentlydevelopedStatisticalPerformanceIndicators(SPI)providesaconceptualframeworktotracktheprogressofcountrydataandstatisticalsystems,with5pillarsondatause,dataservices,dataproducts,datasources,anddatainfrastructure(Dangetal.2023).A“GenderSPI”couldbuildfromtheSPItofocusonspecificareasrelatedtogenderdataproductionanddissemination,andserveasameansofidentifyingprogressorstallsinnationalstatisticalsystemswithregardtogenderdata.Suchanindexwouldaidintrackingandprioritizationofinvestmentsbycountriesanddevelopmentpartnerstoclosegenderdatagaps.30ReferencesBonfert,AnnaTabitha,SarahBunker,KiranCorea,HeatherMoylan,andKolobadiaNayihouba.2023.“Howtoassessgenderdatagapsintheeconomicdomain.”WorldBankDataBlog(March1,2023)Bonfert,AnnaTabitha,TalipKilic,HeatherMoylan,andMiriamMuller.2022."Threewaystotacklegenderdatagaps–and12countriesembracingthechallenge."WorldBankDataBlog,February7,2022.Buvinic,Mayra,RebeccaFurst-Nichols,andGayatriKoolwal.2014."MappingGenderDataGaps."Dang,Hai-AnhH.,JohnPullinger,UmarSerajuddin,andBrianStacy.2023.“StatisticalPerformanceIndicatorsandIndex:ANewTooltoMeasureCountryStatisticalCapacity.”ScientificData10:146.Dang,Hai-AnhH,andUmarSerajuddin.2020.“TrackingtheSustainableDevelopmentGoals:EmergingMeasurementChallengesandFurtherReflections.”WorldDevelopment127:104570.Devarajan,S.2013.Africa'sStatisticalTragedy.ReviewofIncomeandWealth,Series59,SpecialIssue.Encarnacion,Jessamyn,RamyaEmandi,andPapaSeck.2022.“Itwilltake22yearstocloseSDGgenderdatagaps.”UNWomenResearchHighlight(September2022).Inter-agencyandExpertGrouponGenderStatistics(IAEG-GS).2017.TheUnitedNationsMinimumSetofGenderIndicators.UnitedNationsStatisticsDivision,6June2017.https://genderstats.un.org/files/Minimum%20Set%20indicators%20web.pdf.MacFeely,S.2018.The2030Agenda:AnUnprecedentedStatisticalChallenge.FriedrichEbertStiftungInternationalPolicyAnalysis.MunozBoudet,AnaMaria,AntraBhatt,GinetteAzcona,JayneYoo,andKathleenBeegle,Kathleen.2021.“AGlobalViewofPoverty,Gender,andHouseholdComposition.”WorldBankPolicyResearchWorkingPaper9553.WorldBank,Washington,DC.OECD2019.SocialInstitutionsandGenderIndex,SIGI2019GlobalReport:TransformingChallengesintoOpportunities.OECD.2022.TheShortandWindingRoadto2030:MeasuringDistancetotheSDGTargets.OECDPublishing,Paris.OpenDataWatch.2019.BridgingtheGap:MappingGenderDataAvailabilityinAfrica.Serajuddin,U.,Uematsu,H.,Wieser,C.,Yoshida,N.,&Dabalen,A.2015.“DataDeprivation:AnotherDeprivationtoEnd.”WorldBankPolicyResearchWorkingPaperNo7252.UnitedNations.2020.HumanDevelopmentReport2020.UnitedNations.2021.TheSustainableDevelopmentGoalsReport2020.31UnitedNations.2022.Achievegenderequalityandempowerallwomenandgirls.UnitedNationsEconomicandSocialCouncil.2012.“Genderstatistics:ReportoftheSecretary-General.”UnitedNationsStatisticsDivision(UNSD),SustainableDevelopmentGoals.https://unstats.un.org/sdgs/dataportalUNSD.2018.Gender-relevantSDGindicators(80indicators).UNSD.2022.Inter-agencyandExpertGrouponSDGIndicators.UNSD.2022.TierClassificationofGlobalSDGIndicators.UNWomen.2022.SDGMonitoring.WorldBank.2022.Women,Business,andtheLaw.32Annex1FigureA1.1.AvailabilityofTier1SDGgenderindicatorsbyregion(N=181countries)FigureA1.2.AvailabilityofTier1SDGgenderindicatorsbyincomegroup(N=181countries)33FigureA1.3.AvailabilityofTier2SDGgenderindicatorsbyregion(N=181countries)FigureA1.4.AvailabilityofTier2SDGgenderindicatorsbyincome(N=181countries)34FigureA1.5.AvailabilityofSDG5genderindicatorsbyregion(N=181countries)FigureA1.6.AvailabilityofSDG5genderindicatorsbyincome(N=181countries)35Annex2GDPpercapitacorrelationwith:•GenderSDGAvailability:-0.17•SPIOverallScore:0.67•SPIPillar1(Datause)Score:0.42•SPIPillar2(Dataservices)Score:0.53•SPIPillar3(Dataproducts[OverallSDGIndicatoravailability])Score:0.05•SPIPillar4(Datasources)Score:0.74•SPIPillar5(Datainfrastructure)Score:0.7GenderSDGAvailabilitycorrelationwith:•SPIOverallScore:0.48•SPIPillar1(Datause)Score:0.53•SPIPillar2(Dataservices)Score:0.44•SPIPillar3(Dataproducts[OverallSDGIndicatoravailability])Score:0.69•SPIPillar4(Datasources)Score:0.34•SPIPillar5(Datainfrastructure)Score:0.25Note:+indicatesstatisticalsignificanceat10%levelandatthe.1%level.
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