exploring the transitions and well-being of young people ... wave 5... · completed secondary...

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Exploringthetransitionsandwell-beingofyoungpeoplewholeaveschoolbeforecompletingsecondaryeducationinSouthAfrica

Branson,N,DeLannoy,AandKahn,ASouthernAfricaLabourandDevelopmentResearchUnit(SALDRU)UniversityofCapeTownSeptember2018

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AbouttheauthorsandAcknowledgementsNicolaBranson:SeniorResearchOfficer,SALDRU,UCT.ArianeDeLannoy:ChiefResearchOfficer,SALDRU,UCT.AmyKahn:Researcher,SALDRU,UCT.Acknowledgements:SupportfromtheDST-NRFCentreofExcellenceinHumanDevelopmentandtheKresgeFoundationtowardsthisresearchactivityisherebyacknowledged.Opinionsexpressedandconclusionsarrivedat,arethoseoftheauthorandarenotnecessarilytobeattributedtotheCoEinHumanDevelopment.

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1. Introduction

InSouthAfrica,youngpeoplewhohavenotcompletedtheirmatricyear,ortheequivalentthereof,aremorelikelytostruggletofindwork,remainunemployedforlongerperiodsoftime,or,iftheydofindwork,arelesslikelytoaccessstable,higherincomejobs(IngleandMlatsheni,2017;MlatsheniandRanchhod,2017;BransonandKahn,2016;Salisbury,2016;VanderBergandVanBroekhuizen,2012).Internationally,agrowingbodyofresearchindicatesadditionalnegativeoutcomesforyouthwhodonotcompletesecondaryeducation,rangingfromhigherlevelsofpoverty,toillhealth(includingmentalhealth),substanceabuse,delinquency,incarceration,andprolongeddependenceonsocialassistance(Bjerk,2012;DeWitteetal.2013;KimberlyandKnight2011;Lundetal.2018).Theseoutcomescreateanobviousconcernforthelossofhumanpotentialfortheindividual.Theyalsoleadtoquestionsaboutcountries’highratesofinvestmentineducationalsystemsandtheeffectivenessofthosesystems,and are at the basis of concerns about the larger societal and economic costs ofincompleteeducation.IntheUnitedStates,forinstance,researchershaveestimatedthat“eachhighschooldropout”accruesacosttothenationaleconomyof“atleast$250000overhisorherlifetime[…]becauseofgreaterrelianceonwelfareandMedicaid,morecriminalactivity,poorerhealthandlowertaxcontributions”(Lansfordetal2016:652).A more nuanced understanding of the longer-term effects of incomplete high schooleducation remains limited in theSouthAfricancontext.Usingdata collectedover fivewavesoftheNationalIncomeDynamicsStudy(NIDS),thispaperaimstocontributetothisliteratureandpresentsafirstexplorationofthetrajectoriesandwell-beingofyoungpeoplewholeaveschoolwithoutcompletingtheirmatricoramatricequivalent.Weaskthe research question “What are the long-term, socio-economic effects of incompletesecondaryschoolingfortheindividualandsocietyatlarge?”Weexaminetheimplicationof incomplete education for labourmarket stability and find that thosewhohavenotcompleted secondary schooling are less connected to the labour market and remainunemployedforlongerperiodsoftime.Thisthenbecomesourlensetoexaminefurtherindividualandsocietaloutcomes.Ourresultsindicatethatthisgroupofyouthwithoutamatric followdifferentpathwaysintermsofmovementintoandoutofemployment–overaten-yearperiod,twothirdsofoursampleexperiencesomedegreeofchurninthelabourmarketwhilesmallerproportionsremaineitherinoroutofemploymentandtheeducationsystempersistently.Thesedifferenttrajectoriesareinlargepartdeterminedby differences in socio-economic background. Thus, the consequences of incompleteeducation vary across individuals and, depending on their connectivity to the labourmarket, they experience different long-termoutcomes. In particular, thosewho comefrompoorerhouseholdsandattendedmoredisadvantaged schoolsaremore likely toremain persistently unemployed which, in the longer term, translates into negativeoutcomes in termsofmentalhealth,subjectivewellbeingandrelianceongovernmentgrants.

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In the following sections, the paper first briefly sketches the landscape on ‘drop outresearch’inSouthAfricaandintroducesthenotionof‘incompletesecondaryeducation’.Itthenpresentsthedataandourmethodofanalysisbeforemovingontothefindings.

2. ThescaleandnatureofincompleteeducationinSouthAfricaCompulsoryeducationinSouthAfricaextendsfromgradeRuntilgrade9,oruntiltheageof15,whicheveroccursfirst(SouthAfricanSchoolsAct,1996).Aftergrade9,learnerscan decide to continue to progress in the educational system, following the HigherEducation and Training (HET) or the Further Education and Training (FET) streams.Thosehighergrades(grade10to12orequivalent,andabove)areexpectedtoprovidetraininginso-calledscarceandcriticalskills,neededforhumanresourcesdevelopmentin general and for “the growthofmodern economies” (Kraak, 2012). From this stageonwards,returnstoeducationinSouthAfricaincrease,butthehighestreturnsonlyreallyaccruefromthecompletionofmatricandhighereducationonwards(BransonandKahn,2016;Salisbury,2016;IngleandMlatsheni,2017;MlatsheniandRanchhod,2017).Itis,however,alsoexactlyinthoseyearsthatlargenumbersofyoungpeoplebegintoleavetheeducationalsystem.Whileenrolmentintheearliestyearsofschoolingishighinthecountry,onlyabout50%ofacohortof learnerswhostartschoolingrade1willeventuallymakeittograde12(Spaull,2015).Therestofthelearnersleavetheschoolingsystem,mainlyingrades10or11,soaftertheendofthecompulsoryschoolingstagebutbeforethecompletionofuppersecondaryeducation.Eventually,onlyabout40%oftheoriginalcohortofchildrengraduates fromgrade12. Inotherwords,60%ofSouthAfrica’syouthhaveeither leftschoolbeforegrade12,orhavefailedtheirmatricexam,andareleftwithoutanykindofrecognisededucationalqualification(Spaull,2015).Whilethetechnicalandvocationaleducation and training system (TVET) should provide these young people withopportunities to continue their schooling, very few of youth access this part of theeducationalsystem(BransonandKahn,2016).Thus,whatismostlyreferredtoas‘drop-out’inthecountry’sliteratureis,infact,leavingschoolbetweentheendofthecompulsorystageofthe‘generaleducationband’(grades1to9)andbeforecompletingtheuppersecondaryeducationyears, i.e.grade12oramatriculationequivalent.

There isabodyof research into the reasonsbehindschool-leavingbeforecompletingsecondaryeducation.Analysesofsurveydatapointattheimpactofvarious,interrelatedsocio-economicfactors.Nationalhouseholdsurveysalsoasktheirparticipantswhatthemainreasonsforleavingschoolare.Thefourmostprominentreasonsgivenbyyoungpeopleare:alackoffinances,seekingemployment,failingagrade,and,forgirls,teenagepregnancy (Gustafsson 2011 also in Spaull, 2015). Timaeus andMoultrie (2015) andMarteletoetal.(2008)findthatprogressthroughschool,innateability,schoolquality,

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andsocioeconomicbackgroundallhavesignificanteffectsonteenpregnancy,whichinturnisamajorcauseofschooldropoutamonggirls.UsingdatacollectedbytheNationalIncomeDynamicsStudy(NIDS),Bransonetal.(2014)foundthat“notkeepingpaceatschoolisafundamentaldeterminantofwhodropsout”;theauthorsfurtherpointoutthatfalling behind at school is strongly correlated with socioeconomic status and schoolqualityinSouthAfrica.Leavingschoolbeforecompletingtheuppersecondaryyearsisthereforeconsideredacumulativeprocessratherthanasingleevent.Thisisimportantasthisunderstandinghasconsequencesfortheexplorationoftheeffectsofincompletesecondaryeducationlateroninlife.RecentanalysesofpooledGeneralHouseholdSurveyDatafortheperiod2010to2016showsthatthegroupoflearnerswholeaveschoolbeforecompletingmatricconstitutesomeofthemostvulnerableinthecountry:65%ofhouseholdsthatcontainyouthinthisgrouparedefinedaspoorusingapovertylineofR1042percapitapermonthin2011Rands(Branson,2017). Inaddition tobeingmorevulnerable topoverty,67%of thisgroupwereNEET(NotinanykindofEducation,EmploymentorTraining)in2011(ibid.).Thisimpliesthatyoungpeoplewholeaveschoolbeforecompletingmatricnotonlyfindit difficult to connect to the labour market, but also to (re)connect to a part of theeducationsystemthatcouldhelpthemprogress.Inthefollowingsections,thepaperfirstprovidesmoredetailonwhatisknownabouttheeffectsofincompleteeducation,beforefocusingbrieflyonyouthwhoare‘NEETs’.

2.1. TheeffectsofincompleteeducationArangeofstudieshaveinvestigatedtheconsequencesofincompleteschoolingintermsoflabourmarketoutcomes.Firstly,researchhasshownthatthereisastrongrelationshipbetweencompletingmatricandlabourforceattachment.Failuretocompletesecondaryschoolisassociatedwithadecreasedprobabilityoffindingstableemploymentaswellasprolonged periods of unemployment (Ingle and Mlatsheni, 2016). Mlatsheni andRanchhod(2017)findthatforyouthtransitioningfromschoolintothelabourmarket,thosewithamatricareapproximately9%morelikelytobecomeemployedwithinatwoyear period compared to those who drop out before completing matric. Long-termunemployment aswell as prolonged andunsuccesful job searchmay, in turn, lead todiscouragement and depression (Lund et al., 2018; Mlatehsni, 2012; Mlatsheni andRanchhod,2017).While robust evidenceon the linkbetweenemployment status andmentalhealthexistsintheinternationalliterature(Lundetal.,2018),thisrelationshipisnotwellunderstoodinSouthAfrica(Mlatsheni,2012).Somequalitativeevidenceinthecountrydoesindicatetheseverestrainthatunemploymentandunsuccessfuljobsearchplace on young people, but such studies do not measure levels of depression incomparablemanners(NewmanandDeLannoy,2014;Pateletal.,2016).Secondly,studiessuchasFinnetal.(2016)andPiraino(2014)findthatthereisahighdegreeof intergenerational transferof economicwellbeing in SouthAfrica and that a

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largepartofearningsinequalityisexplainedbyeducationalattainmentbeingpassedonfromparentstotheirchildren.Thirdly,Ardingtonetal.(2013)lookattherelationshipbetweenincompleteschooling,migrationandgrantrelianceandfindthatcomparedtothosewithoutamatric,thosewhohaveamatricaremorelikelytoremainamigrantorbecomeamigrantafterapensionlossorgaininthehouseholdrespectively.Finally,therehasbeensomeinvestigationintotheinteractionsbetweenmentalhealthandsocioeconomicstatus.ArdingtonandCase(2010)findthatsocioeconomicstatusandeducational attainment are negatively associatedwith depression. Their findings alsosuggestthateducationisprotectiveofphysicalhealthandsocioeconomicstatus,whichareinturnprotectiveofmentalwellbeing.Thus, there is a fair amount of knowledge surrounding the relationship betweeneducationalattainmentandlabourforceoutcomes,andtherearesomeindicationsofarelationshipbetweenlevelsofeducationandgrantreliance,anddepression.Thispaperaimstoextentthisknowledgebydevelopingamoreholisticunderstandingofhowthesefactorsfittogether;specifically,itfocusesonthelongtermeffectsofschoolcompletiononsubjectiveandmentalwellbeingaswellasgrantreliance,withtransitionsintoandoutofthelabourmarketortheeducationsystemactingasachannelthroughwhichtheseeffectsplayout.

2.2. YoungpeoplewhoareNEETAfocusoftheanalysisisyoungpeoplewhowereNEETinthefirstwaveofNIDS:notineducation,employmentortraining.WedistinguishbetweenthosewhowereNEETwithat least a matric, and those who were NEET without a matric. South African policydocumentsdisplay‘graveconcern’oversituationofyoungNEETs,andconsiderthem‘tobe disengaged from bothwork and education’ (Department of Higher Education andTraining, 2017:2).However, thepolicy environmentmakes littledistinctionbetweendifferent‘types’ofNEET(Holteetal.2018),andshowslittleengagementwithquestionsconcerning transitions into and out of NEET state – aspects that could neverthelessdeterminepolicyresponsestosupporttheseyoungpeople.Internationally, a multidisciplinary body of evidence indicates that being NEET, andespecially remaining NEET for an extended period of time, is associated withdeteriorating physical and mental health, substance abuse, precarious job prospects,discouragementintermsofparticipatinginthelabourmarketoreducationsector,socialexclusion,andincreasedriskbehaviour(Mannet.Al,2014;O’Higgins,2015;Henderson,etal.,2017;Bălan,2014;Franzen&Kassman2005;Chen,2011;Graham,2002;Bäckman&Nilsson,2016).There isalsoevidenceof reinforcingrelationshipsbetweensomeoftheseoutcomesandbeingNEET(Baggio,2015;Harambatetal.,2013).Atthesocietallevel, the economic consequences include lost output, lost government revenue and

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increasedpublic spending, for exampleon the justice andpolicing system (O’Higgins,2015).SimilaranalysesarenotasreadilyavailableforSouthAfrica.Thus,thispaperaimstocontributetotheliteratureontheconsequencesofincompletesecondaryeducationbyinvestigatingthetransitionsofacohortofyoungSouthAfricanswholeaveschoolbeforecompletingmatric,andwhomayormaynotremainNEET,overan extendedperiodof time. It also contributesby examiningmental healthoutcomesspecific to youth.Unpacking the life trajectories of these youngpeople, and gaining abetter understanding of their emotional well-being, is important for the design ofinterventionsthatcouldsupportthosewhohaveleftschooltoreturntotheeducationsystem or to transition to the labour force, and thus, to prevent an array of socio-economic‘costs’lateroninlife.

3. Data,sampleandmethodsThe research question “what are the long-term, socio-economic effects of incompletesecondaryschoolingfortheindividualandsocietyatlarge”naturallylendsitselftodatathatfollowthesameindividualovertimei.e.longitudinaldata.Ataminimum,werequiredatathatallowustoinvestigatetheprogressionofyouth(i.e.15to25(35)1yearolds)foranumberofyearsastheytransitionfromschooltopost-schooleducation,thelabourmarketand/orparentingorotheradulttrajectories.Ofparticularinterest,istoexaminethedifferencesinlifetrajectoriesbetweenthosewhodidnotcompletesecondaryschoolversusthosewhodocompletesecondaryschool.The National IncomeDynamics Study (NIDS) iswell-designed to tackle this researchquestion. NIDS is South Africa’s national longitudinal study and has been providingempirical data on the changing lives of South Africans since 2008. The study is aninitiative of the Department of Planning, Monitoring and Evaluation (DPME) and isimplementedbytheSouthernAfricaLabourandDevelopmentResearchUnit(SALDRU)attheUniversityofCapeTown(UCT).Fivewavesofdatafrom2008,2010/2011,2013,2014/2015and2017arenowpubliclyavailable.Theinitialsampleincludedabout28000households. Each 2008 household member became part of the panel and has beentracked since. The survey covers a wide range of topics including individual andhouseholdeducation,labourmarketengagement,income,health,wealthandwell-being.Withthereleaseofwave5,thesedatathereforeprovideapanelofyouthfollowedfortenyearswithrichbiennialinformationontheirtrajectoriesinmultipleaspectsoftheirlives.TheanalysisusesallfivewavesoftheNIDSdata.Ineachwave,alladults(aged15yearsand above) who are currently residing in the household are administered an adultquestionnaire,andachildquestionnaireisadministeredtothemaincaregiver(s)ofall

1SouthAfrica'sNationalYouthCommissionAct,1996,definesyouthasthosebetweentheagesof14to35years.TheInternationalLabourOrganisationuses15-24asthegroupdefinedasyouth.

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residentchildren.Theadultquestionnairecollectsinformationonawiderangeoftopicsat the individual level; the analysis will use a fair portion of these data includingdemographics,labourmarketparticipation,income,education,parentaleducation,andhealth.When an adult is not available, a proxy interview is administered to anotherhouseholdmemberontheirbehalf.Inaddition,ineachwaveofthesurvey,ahouseholdlevelquestionnaireisadministeredtothehouseholdhead.Dataatthehouseholdlevelwhichwillbeusedintheanalysisincludeshouseholdincome,employment,grants,andgeographic location. The NIDS data has also been linked to external administrativedatasets including the Ordinary School’s Master List published by the South AfricanDepartmentofBasicEducation.Someofthisschoolleveldata,includingquintile,pupil-teacher ratio, and the ex-department of education for the respondents’ last schoolattended,willbeusedintheanalysis.Oursampleofinterestusyouthaged15-35yearsinwave1.Theaimofouranalysisistotracktheprogressionofthisgroupintoandoutofthelabourmarketand/oreducationsystemoverthefollowingfourwaves,accordingtowhetherornottheyhaveamatric.Assuch,werestrictouranalysissamplefurthertothoseindividualswhohaveacompleteinterviewinallwavessothatwehaveabalancedpanelofyouthwithinformationovertheentiretenyearsofthesurvey.Weusebothadultandproxydatainordertomaximizeour sample size. The disadvantage to using the proxy data is that because the proxyquestionnaireisnotasextensiveastheadultquestionnaire,someinformation(suchasmentalhealthandsubjectivewellbeing)willbemissingforrespondentsinthewavesinwhichtheyhadaproxyinterviewadministeredontheirbehalf.Toaccountforattritionbias,weconstructbalancedpanelweightsforthesampleofinterest(seeAppendixAfordetails).Theanalysisstartswithadescriptivesummaryofoursample,comparingthosewhohadamatricinwave1tothosewhodidnothaveamatricintermsoflabourforcestatus.Wethenfocusexclusivelyonoursampleofnon-matriculantswhowerenotenrolledinwave1andtracktheirtransitionsintoandoutofNEETstateacrossthefollowingfourwavesto identifythedifferent labourmarketandeducationaltrajectoriestheymaytake.Wepresenttheresultsintheformoftransitionmatricesandtransitiontrees.Finally,weuseourfullanalysissampletocomparetheoutcomesbetweenthosewhohaveandhavenotcompletedmatric,aswellasthreedifferentpathwaysthroughNEETstateacrossthe10-yearperiod,inamultivariateanalysis.Wefirstpresentmeancharacteristicsacrossthedifferentgroupsfollowedbyaseriesofpooledandfixedeffectsregressions.

4. Descriptivestatistics

Westartbyexaminingthelabourforceandenrolmentstatusofyouthin2008bywhethertheyhavecompleteorincompletesecondaryschool.Table1presentsabreakdownofthebalanced panel of youth by employment and enrolment status in wave 1, for thosewithoutandwithmatricrespectively.Thesampleincludesall15-35yearoldsinWave1

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whohadsuccessfulinterviewsinallfivewaves,totallingto4,749respondents.Fromthefirstpanelweseethatoutoftheserespondents,alargeshare(3,449respondents)havenot completed secondary education.While this group includes those still enrolled inschool,panel2showsthat58%ofyouthwhohaveincompletesecondaryeducationarenotenrolledinanykindofeducation.Indeed,youthwhohavenotcompletedsecondaryeducationandarenotenrolledaccountfor41%2ofallyouthandwillbethefocusofouranalysisgoingforward.Thethirdpanelshowscomparativeinformationforyouthwithmatric.Weseethatthemajority (992 or 77%), were not enrolled in education. Only 291 respondents werecontinuinginsomeformofpost-secondaryeducation.3

2AcomparativeestimatefromtheQLFS2017q3is37%.3 Note that the sum of the totals in the second and third panels (4640) falls short of the sum of totals in the first panel (4749) due to missing wave 1 enrolment status for 109 respondents.

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Table1:Summaryofemploymentandenrolmentforthebalancedpanelofyouthinwave1,bycompletionofmatric Balancedpanelofyouth

Nomatric MatricNomatric Matric

Notenrolled Enrolled Notenrolled Enrolled 58% 42% 77% 23% Mean N Mean N Mean N Mean N Mean N Mean NAge(Years) 23 3449 25 1300 28 1944 17 1413 27 992 19 291Male 39% 3449 35% 1300 29% 1944 51% 1413 31% 992 48% 291Female 61% 3449 65% 1300 71% 1944 49% 1413 69% 992 52% 291Employed-fulltime 9% 3449 22% 1300 17% 1944 0% 1413 28% 992 3% 291Employed-parttime 8% 3449 11% 1300 12% 1944 4% 1413 13% 992 6% 291Unemployed-strict 16% 3449 24% 1300 27% 1944 2% 1413 28% 992 12% 291Unemployed-disc 7% 3449 5% 1300 12% 1944 1% 1413 6% 992 3% 291NEA 52% 3449 28% 1300 23% 1944 88% 1413 15% 992 69% 291 %Neet 61% 49% NotestoTable1:Sampleincludesall15-35yearoldsinwave1whohadsuccessfulinterviewsinallfivewaves.Unemployed-discisunemployedrespondentswhoarediscouragedi.e.havenotactivelysoughtworkinthelast4weeks.NEAisnoteconomicallyactiveandNEETisnotinemployment,educationortraining.Meanvaluesweightedusingweightsconstructedtoaccountforattritioninthepanel.

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Ourgroupofinterest–thosewithincompletesecondaryeducationwhoarenotenrolled– look more vulnerable in terms of labour force participation than those who havecompletedmatric.Ahigherproportionofmatriculantswereemployedin2008comparedtothosewithoutmatric.Focusingonlyonthosewhoarenotenrolled,weseethat28%ofmatriculants were employed full time compared to only 17% of non-matriculants,reflecting thepay-off tohaving amatric in the labourmarket.Whilewe find that theproportion of respondents who were strictly unemployed is similar between thosewithoutandwithmatricat27%and28%respectively,thosewithincompletesecondaryeducationaremorelikelytobediscouragedornoteconomicallyactive(NEA)thanthosewithmatric;12%ofyouthwithoutmatric(double thesharewithinthematricgroup)wanttowork,buthavenotsoughtworkinthelast4weeks,suggestinghighcostsandlowrewardstoseekingworkforthisgroup.Afurther23%ofyouthwithoutmatricwhoarenotenrolledareNEA,indicatingthattheyarenolongerseekingoravailabletowork.Of this group, 81% are female andwhen asked to give theirmain reasons for beingunavailable for work, the majority (52%) indicated that domestic and childresponsibilities(includingpregnancy)weretheprimaryreasons. Theothertwomostcommonreasonsprovidedincludedsickness/disabilityandthehighcostsoflookingforwork. The remaining reasons provided included “I do not like the available jobs andwouldrathernotwork”,“Idonotlikeworking”,“Thewagesaretoolow,itisnotworthmy timeworking”, and “Still looking forwork”. Formales, themost common reasonsprovidedwere“Iamsick/disabled”,“Idonotlikeworking”,“Itcoststoomuchtolookforwork”and“Stilllookingforwork”.Similarly,intermsofeducation,21%ofNEAfemalesinoursampleofinterestprovidedpregnancyorhavingababyasthemainreasonsfornotbeingenrolled.Thus, thevulnerabilityof femaleswithin thisgroupofNEETs,andwithoutamatric,isevident.Theothermainreasonsfornotbeingenrolledamongstbothmalesandfemalesincludedwantingtolookforemploymentandnotbeingabletoaffordthecostsofschooling.Asaconsequence,theproportionofyouthinourgroupofinterestwhoareNEET–notinemployment,educationandtraining-ishigh,closetotwooutofeverythreeyouth.YouthwhohavematriculatedarealsovulnerabletobeingNEET(49%areNEET)butthetableshowsthattheyhaveastrongerconnectiontothelabourmarketwith41%employedand28%activelyseekingwork.The wave 1 data provides a snapshot of the circumstances of youth in 2008. TheconsequencesofbeingNEETwillhoweveraccumulateasthetimeinthisstateincreases.InFigure1wethereforeexaminethedurationofbeingNEETpriortowave1separatelyforthosewhowereNEETinwave1(61%oftheincompletesecondarygroup,49%ofthematricgroup)fornon-matriculantsandmatriculants.WeseethatthosewithoutamatrichadbeenintheNEETstateforlongeronaveragethanthosewhohadamatric.Infact,halfoftheyouthwhohadnotcompletedsecondaryeducationhadbeenNEETformorethan5yearswhenwesawthemin2008.Thegraphalsohighlightsthathavingamatric

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does not however guarantee security in the labour market in that almost 60% ofmatriculantsfoundtobeNEETinwave1hadbeeninthisstatefor3yearsormore.Figure1:NumberofyearsNEETinwave1bymatricattainment

Wenowturnourfocusexclusivelytoourbalancedpanelofyouthwhodidnotcompletesecondaryeducationandwerenotenrolledinanykindofeducationinwave1.Table2presents the number of times (waves) youthwere observed as NEET, employed andenrolledfromwaves1-5,notnecessarilyconsecutively.Weseethat12%werenotNEETinall fivewaves,while19%wereNEET inallwaves, representingbeingNEET for10consecutive years. A similar share is observedNEET three times, four times and fivetimes,withtheshareobservedNEETonceortwiceovertheperiodslightlysmaller.Fromthelasttwocolumnsitisevidentthatfewinoursamplere-entertheeducationsystem,asonly3%of thepanelwereenrolled inoneor twowavesacrosswaves1-5.This isimportanttobearinmindinwhatfollowswherewelookattransitionsintoandoutofNEETstate.Table 2: Number of waves observed as NEET, employed and enrolled for non-matriculants:#Times NEET Employed Enrolled

0 12% 20% 97%1 14% 20% 2%2 16% 19% 1%3 19% 16% 0%4 20% 13% 0%5 19% 12% 0%

0.2

.4.6

.81

Prop

ortio

n of

NEE

TS

No matric Matric

Number of Years NEET by Matric in Wave 1

Less than 1 year 1 year - less than 3 years3 years - less than 5 years More than 5 years

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NotestoTable2:Sampleincludesall15-35yearoldswhodidnothavematricandwerenotenrolledinwave1andwhohadsuccessful interviews inall fivewaves. NEET isnot inemployment,educationortraining.Sharesweightedusingweightsconstructedtoaccountforattritioninthepanel.InTable3awepresenttransitionmatricesintoandoutoftheNEETstateovertimeforoursub-sampleofnon-matriculantswhowerenotenrolledinwave1.AsperTable1,thisincludes1,944respondentsofwhich61%areNEETinwave1.Thefivepanelsofthetableshowtransitionsfromwave1to2,2to3,3to4,4to5,and1to5respectively,witheachrowinapanelsummingto100%.Referringtothelastpanel,weseethatbywave546%of thesampleof thosewhowereNEET inwave1hadtransitionedoutofNEETstate;however,onethirdof thosewerenotNEET inwave1 fell intoNEETstatebywave5.Panels2-4showthat,onaverage,onethirdofthosewhowereNEETinthepreviouswavemanagetotransitionoutofNEETstatebythenextwave;however,similartotheoveralltrend,(onaverage)28%ofthosewhowerenotNEETinthepreviouswavefallintoNEETstatebythenextwave.Thus,weseemovementbothintoandoutofNEETstateacrosswaves.Thatbeingsaid,mostyouthinthispanelofnon-matriculantstendedtoremainintheirstateaseitherNEETornotNEETacrosswaves.Moreimportantly,theprobabilityoftheseyouthremainingintheNEETstatefromonewavetothenextwasmuchhigher(byapproximately40percentagepoints)thantheprobabilityofmovingoutoftheNEETstate and into a non-NEET state. As the NEET state is determined primarily byemployment,or rathera lack thereof (as seen inTables2and1above), these resultssuggest that it is easier for the employed to remain in employment compared to theunemployedfindingemployment.Table3a:TransitionsintoandoutofNEETstateacrosswavesfornon-matriculants–rowpercentages Wave2 Wave3 NEET NotNEET NEET NotNEET

Wave1 NEET 74% 26% Wave2 NEET 72% 28%NotNEET 48% 52% NotNEET 32% 68%

Wave4 Wave5 NEET NotNEET NEET NotNEET


Wave5 NEET NotNEET

Wave1 NEET 54% 46% NotNEET 33% 67%

InTable3b,thecellsineachpanelsumto100%(asopposedtotherowsinTable3a)therebyshowingtheproportionoftheentirebalancedsampleineachtransitionstate.The first4panels indicate thatsamplemembersweremore likely toremain ineitherNEETornon-NEETstateacrosswavesthantotransitionintooroutoftheNEETstate.Inaddition,the4panelsshowthattheproportionofthesampleremainingasnotNEETishigherthantheproportiontransitioningfromNEETtonotNEETfromonewavetothenext,onceagainhighlightingtherelativedifficultyofmovingintoemployment(forthe

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unemployed)comparedtostayinginemployment(fortheemployed).Ontheotherhand,thefinalpanelindicatesthatoverallfromwave1to5,ahigherproportionofrespondentsmovedoutoftheNEETstatecomparedtotheproportionremainingasnotNEET.Thepanelalsoshowsthat,overthe10-yearperiod,thosewhoareNEETinbothwaves1and5constitutethehighestshareofthesample(overonethird);however,someofthemwillhavemovedbetweenstatesduringtheperiod.Table3b:TransitionsintoandoutofNEETstateacrosswavesfornon-matriculants–cellpercentages Wave2 Wave3 NEET NotNEET NEET NotNEET


Wave4 Wave5 NEET NotNEET NEET NotNEET


Wave5 NEET NotNEET

Wave1 NEET 35% 29% NotNEET 12% 24%

WenowlookatallthepossibletransitionpathsacrosstheNEETstateforoursampleofinterestacrossthewaves.Thereare32possiblepathwayswhichhavebeenpresentedintheformoftwotransitiontrees(Figure2aandb).Figure2apresentsthedifferentpathsforthosestartingoutasNEETinwave1,whileFigure2bpresentthedifferentpathsforthosestartingoutasnotNEETinwave1.Thetreesforthesetwogroups(NEETandnotNEET)inwave1haveonlybeenpresentedseparatelyforgreatervisualease.InthetoprowofFigure2awestartwithoursampleofNEETsinwave1,whichincludes64%ofoursampleofinterest.Notethatthisislessthanthe61%wesawinTable1aswenowonlyincluderespondentswhoseNEETstatusisknownineverywave.Inwave2theNEET sample branches off into either NEET (N) or non-NEET (NN) state and thiscontinuesuntilwave5suchthatweendupwith16uniquepathsthroughthedifferentstates(forthosestartingoutasNEET).Ateachnodeineachtree,thepercentageoftheentirebalancedpanelisdisplayed.Forexample,inFigure2a,wave2,17%ofthesamplemembersareinnon-NEETstateand47%areintheNEETstate,havingstartedoutasNEETinwave1.Movingdowntowave3ontheright-hand-side,weseethat11%ofthepanelisnownotNEETand36%arestillNEET(havingbeenNEETinwaves1and2).Theterminalnodesofthetreesinwave5showthepercentagesofthepanelthattookeach of the 32 different paths down the trees. The highest percentage, or the mostcommonpathwayoverthe10-yearperiod,canbefoundonthefarrightofthetreeinFigure2awhereweseethat19%ofthepanelwereinNEETstateinallfivewaves.Notethat this correspondswith Table 2 above. The secondmost common combination of

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statescanbefoundonthefarleftofthetreeinFigure2bwhereweseethat12%ofthepanelwerenotNEETinallfivewaves(againcorrespondingtoTable2).Thus,intotal,31%(oralmostonethird)ofthepanelfoundthemselvespersistentlyineitherNEETornon-NEETstate,whiletheremaining69%foundthemselvestransitioningintoandoutofNEETstateindifferentcombinationsacrossthewaves.AswesawinTables3aand3bearlier,acommonpatternfoundthroughoutthetreesisthatthoseinNEETstateinonewave were more likely to remain in NEET state in the following wave rather thantransitioningoutand,similarly,thoseinnon-NEETstateinonewavearemorelikelytoremainnon-NEETinthefollowingwaveratherthanfallingintoNEETstate.

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Figure2a:Transitiontreeforwave1non-matriculantNEETs

Figure2b:Transitiontreeforwave1non-matriculantnon-NEETs

Wave1

Wave2 NN N

Wave3 NN N NN N

Wave4 NN N NN N NN N NN N

Wave5 NN N NN N NN N NN N NN N NN N NN N NN N7% 1% 1% 1% 2% 1% 2% 3% 5% 3% 2% 2% 5% 6% 5% 19%

N

2% 3% 4% 8%

17%

7% 11%

47%

8%

10% 36%

11% 25%4%

64%

Wave1

Wave2 NN N

Wave3 NN N NN N

Wave4 NN N NN N NN N NN N

Wave5 NN N NN N NN N NN N NN N NN N NN N NN N12% 1% 1% 1% 2% 1% 1% 1% 4% 1% 1% 1% 2% 2% 2% 4%

3%

4% 7%

18%

NN36%

14%

2%

19%

1% 5%

11%

4% 7%13% 2%

17

Therefore,ourpanelofnon-matricscanbethoughtofintermsofthreegroups:thosewho

arepersistentlyinNEETstate(19%)‘alwaysNEET’,thosewhoremainoutofNEETstate

(12%)‘neverNEET’,andthosewhomoveintoandoutofNEETstate(69%)‘sometimes

NEET’acrosswaves1-5.Intheanalysisthatfollows,wewillcomparecharacteristicsand

outcomesofindividualsaccordingtothesethreegroups.

AppendixBshowscomparabletreesforthematriculantgroupwhowerenotenrolledin

wave1.We see that thepathwaysofmatriculants over the ten yearperiod arequite

different fromthosewhodonotcompletematric:8%arepersistentlyNEET,25%are

never NEET and 67% are sometimes NEET. In addition,while the ‘sometimes NEET’

groupisasimilarsizebetweenthematricandnon-matricgroups(67%and69%),most

(63%)of thematriculants in thisgroupspendoneor twoperiodsasNEET,while the

majority(58%)withinthenon-matriculantspend3or4periodsasNEET.Alargershare

ofmatriculants (13%compared toonly3% in thenon-matriculantgroup)also spend

timeenrolledovertheperiod.

Beforeour regressionanalysis,we summarise themeansof various characteristics at

wave1andbetweenwave1andwave5anddiscussouroutcomevariables.InTable4

wecompareindividualandschoolbackgroundvariablesatwave1acrossthethreeNEET

groupsdefined for thosewithandwithoutmatric.At the individual levelwesee that,

overall,themajorityofoursamplearefemale.Furthermore,the‘alwaysNEET’grouphas

thehighestproportionof femalesat86%and91%for thosewithoutandwithmatric

respectively,whilethe‘neverNEETgroup’hasthelowestproportionoffemalesat60%

(61%).Asdiscussedearlier,theseresultshighlightthevulnerabilityoffemalescompared

tomales, inboththe labourmarketandschoolsystem. Intermsofpopulationgroup,

Africansmakeupahigherproportionofthe‘alwaysNEET’and‘sometimesNEET’groups

comparedtothe‘neverNEET’group;inotherwords,theyareslightlymorevulnerableto

beingNEETcomparedtotheirColouredandAsiancounterparts.ThereareonlyWhite

respondentswithinthe‘neverNEET’samplewithmatric.

Notably,weobservethattheaverageyearsofparentaleducationisextremely lowfor

bothmothersandfathers,ataroundthreeyearsacrossallthreegroupswithinthesample

ofyouthwithoutmatric.Thisisduetothehighproportionofparentswithinoursample

ofnon-matricswithnoschoolingatall–specifically,45%ofmothersand56%offathers.

Averageyearsofparentaleducationarehigherat6yearsinthe‘never’and‘sometimes’

NEETgroupsforthosewhohavecompletedamatric.Thesenumbersreflectthehighlevel

ofintergenerationaltransferofeducation(FinnandLeibbrandt,2016)inthecountryin

that incomplete secondary schooling among youth is associated with lower levels of

parentaleducation.

The school-level variablespertain to the last school the respondent attendedandare

intendedtoreflectgeneralschoolquality.Itisevidentthatthoseinthe‘alwaysNEET’and

‘sometimes NEET’ groups are more likely to have attended schools in poorer

18

communities (quintiles 1-3), in the former homelands, andwith higher pupil teacher

ratios.Inotherwords,thosewhoareinNEETstate(alwaysorsometimes)arelikelyto

haveattendedpoorerschoolscomparedtothosewhoareneverNEET.Thisappliesfor

boththosewhohavecompletedmatricandthosewhohavenotcompletedmatric.

Table4:Background(Wave1)characteristicsbyNEETgroup

In Table 5 we compare certain time-variant characteristics at wave 1 and 5, again

accordingtoourthreeNEETgroupsandforthosewithandwithoutmatric.Thegroup

withmatricaremoresocioeconomicallyadvantagedacrossalldimensions.

Withinthe‘alwaysNEET’and‘sometimesNEET’groups,respondentsaremorelikelyto

be NEA or strictly unemployed compared to discouraged in the labour market.

Comparing the with and without matric group in wave 1, we see that while the

composition across employment state is similar for the never and sometimes NEET

groups,youthwhoarealwaysNEETwithoutmatricaremorelikelytobeNEA(41%)than

thosewithmatric(24%).Thosewithmatricalsohaveahighershareofyouthdefinedas

strictlyunemployed(63%).

Withinthe‘alwaysNEETgroup’weseeafallintheproportionofunemployed,whilethe

proportionthatisNEArisesby18percentagepointsforthosewithoutmatricandby29

AlwaysNEET

NeverNEET

SometimesNEET

AlwaysNEET

NeverNEET

SometimesNEET

287 211 1087 62 212 57919% 12% 69% 7% 25% 68%

Female 86% 60% 74% 92% 65% 71%

African 96% 90% 95% 95% 93% 97%

Coloured 3% 9% 5% 0% 5% 3%

Asian 0% 1% 0% 5% 1% 0%

White 0% 0% 0% 0% 0% 0%

Age(years) 28 29 28 27 29 26

Yearsofeducation 9 10 9 12 13 12

Numberofyearsrepeatedagrade 0,8 0,5 0,8 0,4 0,3 0,7

Mother'seducation(years) 3 4 3 4 6 5

Mother'seducationmissing 1% 4% 3% 0% 2% 2%

Father'seducation(years) 3 3 3 4 6 5

Father'seducationmissing 7% 18% 12% 4% 9% 11%

Quintile1 27% 24% 25% 34% 25% 27%

Quintile2 23% 15% 22% 9% 15% 17%

Quintile3 34% 30% 34% 45% 40% 38%

Quintile4 15% 22% 15% 10% 14% 15%

Quintile5 2% 9% 4% 1% 6% 4%

IndependentHomelands 13% 16% 17% 7% 27% 14%

Self-governingterritories 37% 30% 41% 51% 23% 44%

DET 38% 38% 31% 11% 31% 29%

HOA 0% 0% 1% 0% 2% 1%

HOR 2% 10% 5% 0% 5% 2%

WCED,TED,CED,FED 0% 2% 0% 0% 1% 0%

New 10% 2% 6% 31% 9% 8%

Independent 0 0 0 0% 0% 0%

Nomatric Matric

Schoolcharacteristics

Parentaleducation

Personal

N%

19

percentagepoints for thosewithmatric bywave5. This suggests that thosewho are

persistentlyNEETmaystartoffaswantingtoworkbuteventuallyfalloutofthelabour

market altogether. Thismaybe due to increasing levels of despondency over time in

terms of employment prospects (Chen, 2011), or an increase in child and domestic

responsibilitiesinthecaseoffemales.

Forboththosewithandwithoutmatric,weseethatthe‘alwaysNEET’and‘sometimes

NEET’groupsaremorelikelytocomefrompoorerhouseholdswhicharecharacterised

bylowerlevelsofincomeandhigherdependencyratios(numberofnon-workingageto

workingagehouseholdmembers),andaremorelikelytobelocatedinruralareas.

Table5:Comparisonoftime-variantcharacteristicsbetweenwave1and5byNEETgroup

Asweareinterestedingatheringamorecomprehensiveunderstandingoftheeffectsof

incompleteeducation,wewillrunaregressionanalysiswithoutcomevariablesrelated

tomentalhealth,subjectivewellbeingandtheshareofhouseholdincomederivedfrom

grants.Table6summariseschangesintheseoutcomevariablesbyNEETgroupandover

time.

Thefirstoutcomevariableistherespondent’sdepressionscore,whichisacontinuous

measure with range 1-30 whereby a higher score indicates a higher likelihood of

depression. It has been calculated based on the 10-item Centre for Epidemiological

Studies Depression Scale (CES-D-10), which is a depression screening tool and

constitutes the mental health module in the NIDS adult questionnaire. The second

outcomevariableisadepressiondummywhereavalueof1indicatesdepressed(ora

Wave1 Wave5 Wave1 Wave5 Wave1 Wave5 Wave1 Wave5Noteconomicallyactive 22% 27% 41% 59% 0% 0% 21% 22%Unemployed-discouraged 12% 3% 19% 6% 0% 0% 12% 2%Unemployed-strict 30% 17% 41% 35% 0% 0% 31% 15%Employed 36% 54% 0% 0% 100% 100% 35% 60%PChouseholdincome 593 1605 474 1032 937 2475 560 1625Dependencyratio 0,95 0,78 1,10 1,00 0,80 0,80 1,00 0,80Urban 44% 48% 40% 41% 58% 62% 43% 47%Traditional 50% 46% 57% 56% 28% 29% 52% 47%Farms 6% 6% 3% 3% 13% 9% 5% 6%

Wave1 Wave5 Wave1 Wave5 Wave1 Wave5 Wave1 Wave5Noteconomicallyactive 17% 17% 24% 53% 0% 0% 21% 19%Unemployed-discouraged 6% 1% 12% 6% 0% 0% 7% 1%Unemployed-strict 30% 14% 63% 41% 0% 0% 37% 16%Employed 47% 68% 0% 0% 100% 100% 34% 64%PChouseholdincome 1111 3933 687 1574 2096 5879 822 3455Dependencyratio 0,72 0,68 0,70 0,78 0,65 0,58 0,75 0,71Urban 46% 53% 26% 26% 63% 61% 43% 52%Traditional 51% 44% 68% 73% 35% 34% 55% 45%Farms 3% 3% 6% 1% 2% 4% 3% 3%

All

AllMatric

NoMatric

AlwaysNEET NeverNEET SometimesNEET

AlwaysNEET NeverNEET SometimesNEET

20

depression score higher than 12) and 0 otherwise4. The third outcome variable is a

‘happiness’indicatorwhichtakesonavalueof1iftherespondentreportedthattheyare

lesshappythantheywere10yearsago,andavalueof0iftheyreportedthattheyhad

the same level of happiness or are happier than theywere 10 years ago. The fourth

outcomevariableistheshareofhouseholdincomethatisconstitutedbysocialgrants.

For thewell-beingmeasures, higher values indicate lowerwell-being. It is clear that

acrossalloutcomemeasuresandNEETgroupings,thematricgrouphavehigherlevelsof

well-beingandliveinhouseholdswithlowergrantreliance.

Interestingly,thedepressionscoresandratesofdepressiondecreasequitenotablyfrom

wave1to5.Infact,theincidenceofdepressiondeclinesthemostforthe‘alwaysNEET’

groupsuchthatinwave5itislowerthanthe‘sometimesNEET’groupforboththematric

and no matric respondents. When looking at the wave 1 and 3 NIDS data cross-

sectionally,ArdingtonandCase(2010)andEyaletal.(2018)findthatratesofdepression

risewithage.Utilisingthepanel,ourfindingsindicateadecreaseindepressionratesas

thecohortages,suggestingthattheresultsofthesecross-sectionalstudiesareduetoa

birth-cohort-effect rather than an age-effect. These trends in depression levels with

respect to age do warrant further investigation, but falls outside of the scope of the

currentpaper.

Whilethetrendsindepressionbetweenwave1and5seemtobecounterintuitivewhen

comparing acrossNEET groups, Figure 3 reveals a slightly different story. The graph

comparesthedepressedandnon-depressedgroupsinwave1accordingtohowlongthey

hadbeeninNEETstatepriortowave1.WeseethattheproportionthathadbeenNEET

formorethanfiveyearsishigherforthosewhoweredepressedcomparedtothosewho

were not categorised as depressed, suggesting that there may indeed be a positive

relationshipbetweenbeinginNEETstateanddepression.

4Baronetal. (2017) recommend thata cut-off scoreof12 ismostappropriate in termsof indicatinghigh riskofdepressionintheSouthAfricancontext.

21

Figure3:NumberofyearsinNEETstatebydepressioninWave1forthosewithoutmatric

Thelesshappyindicatorshowsthatthe‘alwaysNEET’grouphasthehighestproportion

whoreportedthattheywerelesshappythan10yearsagoinwave1.However,itisthe

‘sometimes NEET’ group that has the highest share of respondents moving from

indicatingtheywerehappyorthesameas10yearspreviouslytoreportingthattheywere

lesshappyinwave5.ThosealwaysNEETaremostlikelytobelivinginahouseholdthat

hasahighershareofincomecomingfromgrantsinwave5,yetthe‘sometimesNEET’

grouphasthehighestincidenceofdepression.Itisnotentirelyclearwhatcausesthese

differences in the emotional well-being outcomes but it is likely that the ‘sometimes

NEET’groupcapturesagroupofyoungpeoplewhoaspireto,andthereforecontinueto

search for, entry into the labour market or (re)connection to the education system.

Repeated failure to fulfil thedesire toaccessbetteror long-termemploymentoptions

maybeatthebasisofthehigherlevelsofdepression.Thisconnectionhasbeenrefered

tointheinternationalliterature(Lundetal.2018).InSouthAfrica,indicationsofsucha

relationship were found in long-term qualitative studies with African young people

looking forworkor for opportunities to continue studying (NewmanandDeLannoy,

2014;Swartzetal.2012).

Whileonsomeof themeasures thematricgroupshowsahigher shareof individuals

worseningtheirstatus,themeanvaluesremainloweronallaccountatwave5compared

tothegroupwithoutmatric.Themultivariateanalysiswhichfollowswillshedmorelight

onthedepressionandsubjectivewellbeingtrendsacrossthethreeNEETgroups.

0.2

.4.6

.81

Prop

ortio

n of

NEE

TS

Not depressed Depressed

Number of Years NEET by Depression in Wave 1

Less than 1 year 1 year - less than 3 years3 years - less than 5 years More than 5 years

22

Table6:OutcomevariablesbyNEETgroup

5. Regressionanalysis

We wish to address the research question “what are the long-term, socio-economic

effectsofincompletesecondaryschoolingfortheindividualandsocietyatlarge”.When

examiningdifferencesinlifetrajectoriesbetweenthosewhodidnotcompletesecondary

schoolversusthosewhodocompletesecondaryschool,thereisanempiricalchallenge

of how to disentanglewhether the observedoutcome is a result of the youth leaving

school before completing matric, or a consequence of other social and economic

disadvantages that may have existed prior to dropping out of school. From our

descriptiveanalysisweknowthatyouthwhodonotcompletematriccomefromlower

socioeconomichouseholds and families.Weobserve somebackground characteristics

(reported in Table 4) that may have impacted the decision to leave school before

completing matric. However, there are other factors, for example household income

duringschool,parentalsupport,motivationandalternative,out-of-schooloptions,that

arenotobserved.Inordertoanswertheresearchquestionofwhethertheyouthwould

have fared better on themeasured well-being outcomes if they had completed their

schooling,weneedtoaccountforthesefactors.

InTable7wepresentaseriesofregressionsforourfouroutcomevariables.Oneofthe

keyfindingsfromthetransitiontrees,isthatyouthwhodonotcompleteschoolarefar

more likelytoexperiencesustainedor intermittentperiods intheNEETstate.Weare

thereforeinterestedbothindifferencesinwell-beingamongthosewhocompletematric

versus thosewhodo not, and the different experience of being either persistently or

sometimesNEETversusalwaysemployedorenrolledi.e.‘neverNEET’.Thevariablesof

interestaretherefore:firstly,aNEETdummywhereavalueof1indicates‘alwaysNEET’

and a value of 0 indicates ‘never NEET’ or ‘sometimes NEET’. Secondly, a ‘NEET

sometimes’ dummy so that we can differentiate between the ‘never NEET’ and

Wave1 Wave5 Wave1 Wave5Mean Mean Increase Descrease Same Mean Mean Increase Descrease Same

Depressionscore(outof30) 8,81 7,11 35% 57% 7% 7,85 6,80 43% 49% 7%Depressed(depressionscore>12) 22% 10% 6% 18% 76% 11% 3% 2% 11% 87%Lesshappiycomparedto10yearsago 36% 21% 8% 24% 69% 32% 13% 7% 29% 64%Grants-shareofHHincome 0,45 0,39 43% 52% 5% 0,35 0,25 28% 66% 6%



SometimesNEET SometimesNEET

NoMatric MatricWave5-Wave1 Wave5-Wave1

AlwaysNEETAlwaysNEET

NeverNEET NeverNEET

23

‘sometimesNEET’groups.Notethatweareinterestedin ‘longterm’changesinNEET,

thereforewhiletheNEETvalueatwave1 isequaltotheoriginalwave1NEETstatus

variable,inwave5itiscreatedaccordingtotheNEETgroup,asdescribedabove.Third,

anindicatorforbeingpartofthematricgroup,i.e.Matricis1and0forthosewhodonot

completematric. Four, interaction terms between the ‘always NEET’ and ‘sometimes

NEET’indicatorsandthematricindicator,toallowthosewhohavecompletedmatricto

haveadifferentrelationshipbetweenwell-beingandexperienceofbeingNEET.

Twospecificationsareused.Wefirstrunpooledregressions,wherebyeachrespondent

hastwoobservationsinthedata(oneatwave1andoneatwave5),andthisisaccounted

forbyclusteringattheindividuallevel.Theseregressionsarepresentedforcomparison

purposesandeachsubsequentregressionaddsfurthercontrols,firstindividualandthen

householdlevel.Themainregressionsofinterestarethefixedeffectsregressions.Once

again,weusewave1andwave5datasuchthateachindividualhastwoobservations

overtime.Usingfixedeffects,wecancontrolforunobservedtimeinvariantindividual

andhouseholdcharacteristicsthatmayimpactonouroutcomesofinterestandalsohave

impactedthedecisionoftherespondenttoleaveschoolwhentheydid.Onlyexplanatory

variablesthatvaryovertimeareincludedintheregressions,hencetheomissionofthe

indicatorforcompletingmatric.

24

Table7:Regressionsanalysis

Ref:(NeverNEET)NEETalways 0.05** 0.03 0.03 0.06* 0.87*** 0.45 0.27 0.84**

(0.02) (0.03) (0.03) (0.03) (0.28) (0.30) (0.31) (0.41)NEETsometimew2-w5 -0.01 -0.02 -0.01 0.04 -0.43 -0.62** -0.43 0.58

(0.02) (0.02) (0.02) (0.03) (0.27) (0.28) (0.28) (0.39)NEETalwaysXmatric -0.03 -0.03 -0.04 0.01 0.24 0.31 0.01 0.43

(0.03) (0.03) (0.03) (0.05) (0.43) (0.43) (0.43) (0.65)NEETsometimesXmatric 0.07** 0.07** 0.06* 0.11*** 0.93** 0.94** 0.81* 1.58***

(0.03) (0.03) (0.03) (0.04) (0.45) (0.44) (0.44) (0.53)Matric -0.06** -0.02 0.00 -1.19*** -0.64* -0.31

(0.02) (0.03) (0.03) (0.31) (0.33) (0.34)

Individualcontrols X X X X X XHouseholdcontrols X X X X

Observations 5,256 5,238 5,237 5,237 5,256 5,238 5,237 5,237NumberofPIDs 2,855 2,854 2,854 2,854 2,855 2,854 2,854 2,854

Ref:(NeverNEET)NEETalways 0.08*** 0.06* 0.04 0.08** 0.19*** 0.12*** 0.12*** 0.06***

(0.03) (0.03) (0.03) (0.04) (0.02) (0.02) (0.02) (0.02)NEETsometimew2-w5 -0.05* -0.05* -0.03 0.04 0.02 -0.01 0.00 -0.03

(0.03) (0.03) (0.03) (0.04) (0.02) (0.02) (0.02) (0.02)NEETalwaysXmatric 0.06 0.05 0.02 -0.04 -0.00 0.01 -0.01 0.03

(0.05) (0.04) (0.04) (0.06) (0.03) (0.03) (0.03) (0.03)NEETsometimesXmatric 0.06 0.06 0.04 0.02 0.03 0.03 0.02 0.05**

(0.04) (0.04) (0.04) (0.05) (0.02) (0.02) (0.02) (0.03)Matric -0.08*** -0.02 0.01 -0.11*** -0.05*** -0.03

(0.03) (0.03) (0.03) (0.02) (0.02) (0.02)

Individualcontrols X X X X X XHouseholdcontrols X X X X

Observations 5,228 5,21 5,209 5,209 5,734 5,434 5,434 5,434NumberofPIDs 2,854 2,853 2,853 2,853 2,908 2,893 2,893 2,893

POLS FEDepressed DepessionScore

POLS FE

LessHapppyPOLS FE

ShareofhouseholdincomefromGrantsPOLS FE

25

Table7presentsonlythecoefficientsonourkeyvariablesofinterest.FocusingontheresultsfromthefirstcolumnofthePOLSregressionforeachoutcome,threethingsstandout.First,thematriccoefficientisnegativeandsignificantforeachoutcome,indicatingthatthosewhohavecompletedmatrichavelowerdepressionscores,arelesslikelytobedepressed,arelesslikelytoratetheircurrenthappinesslowerthan10yearspreviously,andliveinhouseholdslessreliantonsocialgrantsasashareoftheirincome.Second,the‘alwaysNEET’coefficientispositiveandsignificantineachspecification,indicatinglowerwell-beingwithinthisgroup.Finally,onthedepressionmeasures,the‘sometimesNEETxmatric’coefficientispositiveandsignificant,againsignallinglowerwell-being.When individual and household characteristics are controlled for in the subsequentcolumns of the POLS regression, these coefficients reduce in size and becomeinsignificant(withtheexceptionofthe‘sometimesNEETxmatric’whichpersistsinsizeandsignificance).The fourth column for each outcome shows the FE regressions. Interestingly, thedepression and self-reportedwell-being regressionswhich account for time invariantunobservedcharacteristicsinadditiontotheindividualandhouseholdcontrolsincludedinthePOLSregression,have‘alwaysNEET’coefficientsverysimilarorlargerinsizeandsignificancetothefirstPOLSestimationwhichdidnotincludecontrols.TheregressionsshowastrongrelationshipbetweensustainedNEEThoodandlowerlevelsofwell-being.BeingpersistentlyintheNEETstateoverthe10-yearperiodisassociatedwithhigherlevelsofdepression(a6percentagepointincreaseinlikelihoodofbeingdepressed)andlowerlevelsofself-reportedhappiness(an8percentagepointincreaseinthelikelihoodofreportingalowerlevelofhappinesscomparedto10yearsago).Whilethe‘NEETsometimes’coefficientispositiveinthethreewell-beingregressions,itissmallerinsizethanthe‘alwaysNEET’coefficientandnotsignificant.The‘sometimesNEETxmatric’coefficientremainslargeandsignificantintheFEdepressionregressions,withindividualsinthisgroupbeing11percentagepointsmorelikelytobedepressed.Again,the‘sometimesNEET’statereflectsadegreeof‘churning’betweenemployment,employmentsearchandeducationstatesandmaythusimplyacertainaspirationtofindaccesstomorestableworkortore-gainentryintotheeducationalsystem.Forthosewithamatric, itcanbeexpectedthat thataspiration ishigher thanamongthosewithoutamatric.Prolongedperiodsofchurninginandoutofthelabourmarketortheeducationsystem could lead to a diminished sense of self-efficacy and lead to higher levels ofdisappointmentordepression in this group (Lundet al. 2018),but these connectionswarrantfurtherinvestigation.ThefinalpanelinvestigatestherelationshipbetweenthethreegroupsofNEETstateandcompletionofmatricon socialwelfare reliance.The results indicate thatbeing in theNEETstatepersistently is associatedwitha larger shareof grant incomewithin total

26

household income by 6 percentage points compared to those who are never orsometimesNEET.Inaddition,thosewhoaresometimesNEETinthematricgrouphaveagreatershareofgrantincomecomparedtothosewhoareneverNEETandhavematric.

6. SummaryandconclusionThewave 1 NIDS data indicates that 41% of all youth had not completed secondaryschoolingandwerenotenrolledin2008.Wehaveseenthattheseyouthlookdifferentfromthosewhohavecompletedmatricinthattheytendtocomefrompoorerhouseholdsand have attended lower quality schools. In addition, they are more likely to beunemployedornoteconomicallyactive.Almosttwooutofthreenon-matriculantswereNEETin2008comparedtojustunderhalfofthematricgroupindicatingthatthosewhohave not completed secondary schooling are less connected to the labour market.Furthermore,averysmallpercentageoftheseyouthreturntosomeformofeducationoverthefollowing10years.Withinoursampleofyouthwithoutmatricandwhoarenotenrolled,thereisevidenceofafairamountofmovementbothintoandoutofNEETstateoverthe10-yearperiodfromwave1towave5.However,thereisahighertendencytoremainintheNEETstatefromonewavetothenextcomparedtomovingoutoftheNEETstate,highlightingthedifficultyof moving into employment amongst the unemployed. We identified three mainpathwaysofNEETstateoverthe10-yearperiod:overtwothirdsofthesampleofinterestmovedintoandoutofNEETstateacrossthe10years,onefifthwerepersistentlyinNEETstate,andthesmallestproportionwereneverNEET.Thesegroupsdifferintermsoftheircharacteristicsattheindividualandhouseholdlevel.Mostnotably,thevastmajorityofthe ‘always NEET’ group are female, and many are NEA due to child and domesticresponsibilities.The‘alwaysNEET’and‘sometimesNEET’groupsarealsomorelikelytocomefrompoorerhouseholdswhicharecharacterisedbylowerlevelsofincomeandhigherdependencyratios,andaremorelikelytobelocatedinruralareas.Wealsofindthatnon-matriculantsaremorelikelytobeinNEETstateforlongerperiodsoftimecomparedtothosewhocompletedsecondaryschool.Almostafifth(19%)ofnon-matriculantsareinNEETstatepersistentlyovertheten-yearperiodcomparedtoonly7%ofmatriculants.OurdescriptivestatisticsrevealedthatthosewhoarepersistentlyNEETaremore likely tobe female,have lowerparentaleducation,andhaveattendedlowerqualityschoolscomparedtothosewhoaresometimesorneverNEET.OurmultivariateanalysisfurthershowedthatbeinginNEETstatehasitsconsequencesat the individual and societal level. Taking unobservable time invariant individualcharacteristics intoaccount, the fixedeffectsregressionanalysisshowedthatbeing inNEETstatepersistentlyisassociatedwithhigherratesofdepression,lowerlevelsofself-reportedhappinessrelativeto10yearspreviously,andgreaterrelianceongrantincomecomparedtoothersourcesofincomeinthehousehold.

27

Thus, our analysis has shown that our sample of youth who have not completedsecondary schooling constitute themost vulnerable in the country. Notonlydonon-matriculants lookdifferent in termsof their characteristicsbut theyalso followquitedifferenttrajectories inthe labourmarketcomparedtomatriculants.Specifically, theytendtoremainintheNEETstateforlongperiodsoftime.Inturn,therearesignificantnegativeconsequencesofbeingNEETintermsofmentalhealthandsubjectivewell-beingas well as increased reliance on social assistance, indicating substantial social andeconomiccostsofincompletesecondaryschoolingtothecountry.Intermsofthedesignofpolicy interventions, it is importanttoconsiderthesedifferentpathsthroughNEETstate,duetothevariationintheircharacteristicsaswellastheirassociatedindividualandsocietalconsequences.Thosewhodonotcompletematricareinfactaheterogenousgroupintermsoftheirlabourmarkettrajectoriesanditisimperativethatinterventionstosupport thembedesignedwiththis inmind. Inparticular,non-matriculant femaleswhocomefrompoorersocio-economicbackgroundsarethemostvulnerableintermsoftheirtendencytoremainNEETforlongerperiodsoftimeandarethereforemorelikelytosuffertheconsequencesintermsofwell-being.Further research into the job market differences between the NEET groups may bebeneficial;specifically,whatare the typesof jobsandsectors thatareassociatedwithremainingoutofNEETstateforsignificantperiodsoftime.Thismayhelpguidepolicymakers in terms of skills development for thosewho have not completed secondaryeducation.

28

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AppendixA:ConstructingweightsthataccountforattritionwithinthesampleofinterestTableA1belowsummarizesmeancharacteristicsatwave1forthoseyouthwhodidnotrespondat eachpoint betweenwave1 and5 (and thereforewere excluded from thesampleofinterest),Attritors,comparedtothosewhowereinterviewedinallfivewaves(andthereforewereincludedaspartofthebalancedpanel),Nonattritors.Weseesomevariationincharacteristicsbetweenthetwogroups.Thus,thesevariableswereusedintheconstructionofthebalancedpanelweightstoaccountforpossibleattritionbias.Thiswasdonebyrunningresponseprobitswiththesevariablesaspredictorvariables.TheNIDSdesignweightswerethenadjustedbytheseprobabilitiesofresponseandrescaledtopopulationestimatein2017.TableA1:Comparisonofwave1characteristicsforattritorsversusnon-attritors

Note:Sampleisthoserespondentswhowere15to35inWave1.Attritorsincludeanyrespondentsthatdidnotanswerallfivesurveys.

n= 4124 n= 4757MaleFemaleAfricanColouredAsianWhiteAgeMarriedYearsofeducationUrbanTraditionalFarmsHouseholdsizeWCECNCFSKZNNWGPMPLP

7%31%7%10%8%11%

49%8%69%11%7%

1%1%2316%9,743%

5%26%7%13%7%7%

36%10%5

15%12%8%

2%6%2421%9,754%

Attritors Nonattritors

49%51%77%15%

43%57%86%12%

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AppendixB:TransiationtreesforthematriculantgroupFigureB1:Transitiontreeforwave1NEETs

N 53% NN N 18% 34% NN N NN N 12% 6% 13% 21% NN N NN N NN N NN N 10% 2% 3% 2% 9% 3% 10% 11% NN N NN N NN N NN N NN N NN N NN N NN N 9% 1% 1% 1% 3% 1% 1% 2% 8% 1% 2% 2% 5% 5% 4% 8%

FigureB2:Transitiontreeforwave1non-NEETs NN 47% NN N

32% 16% NN N NN N

28% 4% 9% 7%NN N NN N NN N NN N26% 2% 3% 1% 8% 1% 2% 5%

NN N NN N NN N NN N NN N NN N NN N NN N25% 1% 1% 2% 1% 1% 1% 0% 6% 1% 1% 1% 1% 1% 1% 3%

exploring the transitions and well-being of young people ... wave 5... · completed secondary...

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