WhataccountsforchangesinthechancesofbeingNEETintheUK?

SKOPEWorkingPaperNo.128,July2019

CraigHolmes,EmilyMurphyandKenMayhew

BlavatnikSchoolofGovernment,UniversityofOxford

SKOPE,DepartmentofEducation,UniversityofOxford

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Abstract

Thenumberofyoungpeoplewhoarenotinemployment,educationandtraining–NEET,asthis grouphasbecomewidely knownas– is a key indicatoron the stateof youth labourmarkets and opportunities for young peoplemore generally. The persistence and, in theyears followingtheglobal financialcrisis,growthof thisgroupofyoungpeople isamajorconcern forpolicymakers inEurope forboth its short termand long termconsequences.However, it is a diverse group as people can be NEET for many reasons, and sounderstandingtheimportanceofthesereasonsiscrucialfortargetingpolicyresponses.Thispaper looksatdataonyoungpeoplebetweentheageof16and29intheUK,trackshowthe incidence or chance of being NEET has changed over the period between 1985 and2015,andhighlightswhichcharacteristicsareassociatedwithahigherchanceofbeingNEETandhowtheimportanceofthesedrivershaschangedovertime.WeshowthattheoverallNEETratehasfallenconsiderablysincethe1980s,buthasremainedlargelythesamesince2000.Overthepastthirtyyears,thereductioninyoungpeopleleavingschoolwithfewornoqualifications,feweryoungpeoplehavingchildrenandimprovementsinthechancesforyoungwomentoworkalongsidechildcareresponsibilitieshaveallputadownwardpressureonNEETrates,althoughpenalties forwomenwithchildcarearestill large.Since2000,anincreaseintheincidenceofmentalillhealthhasactedintheoppositeway,whileaslowerrateofimprovededucationalattainmentandsomescarringfromthe2008-9recessionhasalsocontributedtothepersistenceofNEETs.WearguethatthecurrentpolicyfocusonskillsandworkincentivesforreducingthenumberofNEETsintheUKmissestwokeyobstacles–mentalillhealthandchildcare.Policiestoeithertackleincreasingmentalillhealthratesorfacilitate(wherepossibleanddesirable)someformof labourmarketparticipationspecifictosufferersofmentalillhealthcouldbeexpectedtohavesomelargeeffectsontheirown.Moreover, thegovernmentcouldalso looktoreducechildcarecostsandfacilitate flexibleworkingforthosewhowanttobalancecarewithwork.Thatsaid,wefindnorelationshipbetweentheavailabilityofpart-timeworkinaregionandNEETrates,sothesolutionhereisunlikely to be as simple as creatingmore jobs – of any type and quality –which offer asmallernumberofhours.

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1.IntroductionThenumberofyoungpeoplewhoarenotinemployment,educationandtraining–NEET,asthis grouphasbecomewidely knownas– is a key indicatoron the stateof youth labourmarketsandopportunitiesforyoungpeoplemoregenerally.ThetermNEETcanbeseenascapturing vulnerability among youth, referring to both those who are unemployed andseekingwork, aswell as thosewhoareeconomically inactive andhiddenby anexclusivefocus on unemployment statistics (Furlong, 2006). The persistence and, in the yearsfollowingtheglobalfinancialcrisis,growthofthisgroupofyoungpeopleisamajorconcernforpolicymakersinEurope(O’Reillyetal.,2015),notleastbecauseoffearsthatperiodsofunemploymentorinactivityearlyintheworkinglifeincurlongertermeconomicandsocialconsequences. These consequences include lower future earnings (Gregg and Tominey,2005;Ralstonetal.,2016),higherchancesofbeingunemployedanddependentonwelfarebenefitsinthefuture(KellyandMcGuinness,2015;Gregg,2001;Arulampalametal.,2000;ACEVO, 2012), and other well-being and health outcomes (Bell and Blanchflower, 2011;Gutiérrez,Garcíaetal.,2017).

Thispaper looksatdataonyoungpeoplebetweentheageof16and29 intheUK,trackshowtheincidenceorchanceofbeingNEEThaschangedovertheperiodbetween1985and2015,andhighlightswhichcharacteristicsareassociatedwithahigherchanceofbeingNEETandhowtheimportanceofthesedrivershaschangedovertime.OurdatashowthatoverallNEET rateshave fallen considerably,but thatmostof this changehappened in the1980sand1990s,withlittleprogresssincethen.Tounderstandwhythefall inNEETratesacrossthe UK stalled since 2000, we first examine shifts in the composition of individualcharacteristics of young men and women. Prior studies of NEETs have concentrated ondifferent characteristics which affect youngmen andwomen’s participation in educationandthelabourmarket,buttworecurringindividualdriversofheightenedNEETincidenceintheUKandEuropeareyoungpeople’seducationalattainment(theroleoflowqualificationattainment) and family formation processes (the role of care responsibility) (Eurofound,2016).

Ourresultsshowthatsincethe1980s,therehasbeenamarkedreductioninthenumberofmenandwomenwith lowornoqualifications.Howeverthistrenddidnotoccurbetween2000and2007,hencewewouldhaveexpectedthefallinNEETratesduringthisperiodtobemuted.Inaddition,wefindthatwhiletherearepositiveeffectsofareducednumberofyoungpeoplewithnoor lowqualifications, there is littleevidence that theattainmentofdegree-level qualifications has affected the chances of being NEET over the past threedecades.ItisalsothecasethatfeweryoungpeopleintheUKarehavingchildrentodaythanin previous years, which has, in part, been linked to the success of England’s TeenagePregnancy Strategy thatwas implemented between 1999 and 2010 (Skinner andMarino,

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2016).ThisdropinthenumberofyoungwomenwithchildrenhasledtoafallinNEETrates,ashavingcaring responsibilitieshasbeen (andcontinues tobe)an importantpredictorofeconomicinactivity.

Astrikingfindingofourstudyistherecentevolutionoftherolethatyoungpeople’smentalhealth plays in determining the chances of being NEET across the UK. Since 2007, theexpected fall in NEETs due to the resumption of the trend towards more qualifiedindividuals has (at least in part) been offset by the effect of an increase in self-reportedmentalillhealth.ThisfindingrelatestotrendsobservedinotherEuropeancountries,whichhaveseenariseinhealthrelatedrisksparticularlyamongyoungermalecohortsborninthe1980s(BäckmanandNilsson,2016).Aboveotherreportedhealthordisabilityissues,mentalhealthhasthelargesteffectonthechanceofbeingNEETintheUK;andthisisespeciallythecaseformales.

WeshowthatwhenNEETrateshavefallensignificantly(forexample,duringthe1990s),ithasbeenatatimewheregenerallabourmarketconditionswereimproving,reducingNEETrates for all young people regardless of their personal characteristics. For women withchildren, the risk of being NEET has fallen as more young people are able to be inemploymentoreducationalongsidecaringforchildren.Since2000,wehaveseenregionalconditionsalsoplayakeyrole–wefindevidenceofsignificantdifferingoutcomesforthoseliving in London and outside London, although these trends are different for men andwomen.Finally, in theperiod following theglobal financial crisis, those in their teensandearly20sin2015aredoingbetterthanoldercohorts,pointingtoapossiblescarringeffectfrom entering the labourmarket during an economic recession. Overall, the relationshipbetween different individual characteristics and the chance of being NEET has had littleoverallimpactonNEETratessince2000,witheffectslargelycancellingeachotherout.Thishas,however,meantthatthedistributionofwhoisintheNEETgrouphaschangedeveniftheoverallnumberhasnot.Ingeneral,peoplelivinginLondon,thosewithchildren,thosenot livingwithparentsandthoseunder21makeupasmallerportionoftheoverallNEETpopulationthanwewouldhaveexpectedatthestartoftheyear2000.

Thispapercontributestotheliteratureinanumberofways.Firstly,ourdatacoveralong-time span and awide age-range.HeadlineUKNEET rates have been calculated from the1980s(seeDfE,2013,forexample),butthesehavefocusedontherelativelynarrow16-18agerange.Secondly,weanalysehowdifferentfactorsareassociatedwiththeriskofbeingNEET in combination, and how these relations change over time, if at all. Data is oftenpresentedshowingthedifferencesinNEETratesintheUKbyoneparticularcharacteristicata single point in time (SEU, 1999a; Audit Commission, 20101; DCSF/ONS 2009, 2011) but

1ThereportoftheAuditCommissionisnotclearonhowitestimateseffectsizeforeachriskfactor–itisnotobviouslythecasethatthedifferentfactoreffectsizeshavebeenestimatedjointly.Eveniftheyhad,however,ouranalysislooksatalongertimeframe,fromanationallyrepresentativesurvey(ratherthancareersservicedatafrom11localcouncils)anddefinesNEETbeyondtheageof18.

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given the correlation between factors such as education, health and family, these areunabletotellusmorepreciselywhysomepeoplearemorelikelytobeNEETthanothers.Therearealsoanumberof studieswhichdescribe the typical characteristicsof theNEETpopulation in order to demonstrate the heterogeneity of the group (Houseof Commons,2018;DfE, 2018a; Thompson,2011). In comparison, thereare surprisingly fewUK studieswhich lookatmultiple risk factors in combination.BynnerandParsons (2002) lookat thechanceofbeingNEETaged21foracohortofindividualsbornin1970,andhowthatrelatestosocio-economicbackgroundandeducationaloutcomesat16.Fengetal.(2015)usetheScottishLongitudinalStudytoestimateNEETrisksfortwocohortsofScottishyoungpeoplebetweentheagesof16and19.They findeducationalqualifications, schoolabsencesandexclusions, teenage pregnancy and care responsibilities, alongside childhood socio-economic factors,allhavestatistically significant relationshipswithbeingNEET in the lateteenageyears.Finally, inarareexampleofaUKstudyfocusingonNEETsuptotheageof29, Zuccotti and O’Reilly (2019) analyse the intersections of educational attainment,parental employment and ethnicity as factors predicting the chance of being NEET,specificallyconsideringhowtheyactincombination.

Thecurrentanalysismakesuseofcross-sectionaldata fromtheUKLabourForceSurveys,andaccordinglyhasanumberof limitations.Twomain implicationsareas follows: firstly,we look at snapshots of the NEET population at particular moments in time, and relatethese to particular characteristics of young people. How people’s life histories oraccumulated disadvantages contribute to being NEET is not covered here. Secondly, ouranalysis isprimarilyan individualisticone in that it relates individualcharacteristics to thelikelihood of being NEET, and does not examine broader factors such as class, familybackgroundorneighbourhooddeprivation(KarydaandJenkins,2018).Thisisanimportantpartof theoverallpictureofNEETs in theUK(seee.g.Thompson,2011;Murphy,HolmesandMayhew,forthcoming)butonewhichwecannotaddresswiththelabourforcedata.

The paper is set out as follows. In section 2 we describe our data and demonstrate theoverall changes in NEET rates for male and female young people separately over time.Section3presentsourapproachfordecomposingtheseoverallchangesinNEETrates,theresultsofwhicharepresentedinSection4.Section5concludeswithsomeimplicationsforpolicy.

2. Who are the NEETs?

2.1 Defining NEETs ThetermNEET(or‘statuszero’asthegroupwasinitiallylabelled)wasfirstappliedintheUKtothosebetweentheageof16and18whowerenotclassifiedasunemployedduetothewithdrawal in1988ofentitlementtounemploymentbenefit foryoungpeopleofthatage

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(Furlong 2006). This age group still receives particular attention – for example, the UKDepartmentforEducation(UKDfE)compilesameasureof16-18yearoldsinEnglandwhoareNEETusingadministrativedataonparticipationinemployment,educationandtraining(see, for example, DfE, 2018b). However, in recent years, the term has come to cover amuch wider age range. For instance, the Office for National Statistics’ (ONS) quarterlymeasureofNEETscoversthosebetweentheagesof16and24,usingLabourForceSurveydata rather than less timely administrative data (seeONS, 2019).Outside of theUK, it iscommon to see discussions cover an even wider age group (Eurofound, 2012) – the EUprovidesstatisticsonNEETsuptotheageof29,andasimilarindicatorisnowreportedintheOECD’sEmploymentOutlook.Wefollowthispractice inourresearchand indoingso,we show that problems around participation in the labour force or access to furthereducation and training do not disappear in the latter half of an individual’s twenties – ifanything,theyareconsiderablymorecommonplace.

Individuals in this age range in our data are either EET (in employment, education andtraining)orNEET–theNEETratefiguredescribed in latersections isthenumberofNEETindividuals as a percentage of the total number of young people in the same age range.FigureA1intheDataAppendixshowstheiterativecriteriausedfordeterminingifsomeoneis NEET or EET.Within the NEET category, we distinguish between the active and theinactive,wheretheformerareallNEETswhoarecurrentlysearchingforajob(i.e.theyarethe ILO definition of an unemployed individual). This distinction is important as previousresearch has shown that active and inactive NEETs represent two significantly differentgroups (Maguire, 2014; Eurofound, 2016; Maguire 2018), which we demonstrate in ouranalysis.Moreover,thelonger-termconsequencesofbeingactiveNEETcanbesignificantlydifferent to those of being inactive NEET (Dorsett and Lucchino, 2018), which makesidentifyingbothseparatelyimportantforfuturepolicymaking.

2.2 The dataset Aswellas includingawideragegroupthanthatfoundinofficialUKstatistics,ourdatasetalso covers a longer time period than inUK government data series, going back to 1975before the termNEEThadbeendevised.Wedo this in order toput recent focuson thisgroupinanhistoricalcontext.Toourknowledge,adatasetcoveringsuchalongtimeperiodforsuchanextendedagedemographichasnotbeenproducedoranalysedpreviouslyintheUK.TheUKDfEhas in thepast reporteda longerhistorical timeseries,but thishasbeenrestrictedto16-18yearolds(see,forexample,DfE,2013).

Weusedata fromtheUKLabourForceSurvey,whichwereproducedannually from1975until 1992 and then quarterly thereafter. Table A1 in theData Appendix summarises thevariablesusedineachyear.Forthequarterlysurveys,weusethesecondquarter(ApriltoJune) data as this is closest to the period when the annual survey data fieldwork was

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conducted,whichisimportantbecausethereisacyclicalelementtoNEETmeasurement,asshowninFigureA2intheDataAppendix.

Figure1 shows theproportionof16and29-yearolds thatwereNEETbetween1975and2015. As explained in the Data Appendix, the variables available prior to 1985 are lessconsistent in termsofwhatquestionsareasked–webelievewehaveproducedthebestpossibledefinitionusingwhatwasavailabletousintheLFS,butforclaritywedepictthisasadifferenttimeseries.Ingeneral,NEETrateshavetrendeddownwardssincethe1970sandreachedalowpointinthe2000sbeforelevellingout.Aroundthistrend,theNEETratehasunsurprisinglyfollowedeconomiccycles,withincreasesintheearly1980sandearly1990sandthenagainafterthe2008-9globalfinancialcrisis.Puttingthosecyclestooneside,theoverall change in NEET rates over forty years seems to be from around 19-20% toapproximately14%.

Figure1:TheNEETrate,1975-2015

Source:LFS,owncalculations.

Figure2showsthebreakdownoftheheadlinefigurebygenderandintoactiveandinactiveNEETs(i.e.thosethataresearchingforworkandthosethatarenot).

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1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

NEETrate(inconsistentmethod) NEETrate

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Figure2:ActiveandinactiveNEETratesbygender,1975-2015

(a) Male

(b) Female

Source:LFS,owncalculations.

Overthewholetimeperiod,agreaterproportionofwomenthanmenwereNEETs.Priorto1985, the femaleNEET rate typically exceeded 30% andwas still around 25% in themid

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NEETactive(inconsistent) NEETinactive(inconsistent) NEETactive NEETinactive

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1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

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1990s.However, it declined almost continuously since then and by 2015, it had fallen tounder17%,whichislowerthanwhatitwasdirectlypriortotheGreatRecession.Formen,thelowpointofjustunder10%camein1990.Aftertheearly1990srecession,itgraduallyreturnedtothislevelbytheyear2000,beforeincreasingmarginallyuntil2008.FollowingonfromtheGreatRecession,theproportionofmaleNEETshadnotreturnedtothelevelitwasin2007by2015, letalonethe lowpointat theturnof thecentury.Asa result,while theNEET population comprises more women than men, the gender gap has narrowedsignificantly.In1985,thereweretwiceasmanyfemaleNEETsasthereweremales(31.3%ascomparedto16.2%),whereasin2015,thatratiohadfallentoapointwheretherewereapproximatelythreefemaleNEETstoeverytwomaleNEETs.

Formales,activeNEETsarealargercomponentofthetotalthantheinactive.Forfemales,themajorityofNEETsareinactive.Forboth,activeNEETrateshavefallenovertime,albeitwithaplateauafterthemid2000s.ThemaleactiveNEETrateisfarmorevolatilearoundthis trend however, having jumped almost 5 percentage points between 1990 and 1993.Similarly,between2008and2009theactiveNEETrateincreasedbyalmost3.5percentagepointsformen,butonly1.5percentagepointsforwomen.

Foryoung females,NEET inactivityhasbeensteadilydecliningover the timeperiod, from18.3%in1990,14.7%in2000andunder13%in2015.Incontrasttothis,inactivityhasbeenrising formales – around2%of youngmaleswere inactiveNEET in 1990,whichdoubledoverthe1990sandcontinuedtoincreasegraduallyafter2000.Theconsequenceofthis isthat compared to the 1980s and early 1990s, where male NEETs were almost alwayssearching for work, today male NEETs are almost evenly split between the active andinactivecategory.WhatismoststrikingaboutthisincreaseininactivemaleNEETsisthatithasoccurredduringatimenotonlyofstronglabourmarketperformance,butalsoatatimewhen educational opportunities expanded in terms of further and higher educationalprogrammes,andshorter-termtrainingprogrammesintheworkplace.

It isworthpointingoutthatthesetrendsdonotvarymuchbyage.Thecurrentnear-evensplitbetweenactiveandinactiveNEETsformalesisobservedforteenagersaswellasthosein their late 20s. The decline in female active and inactive rates can be seen across agegroups,althoughitisfarmorepronounced,particularlyintermsofinactivityforthoseovertheageof21.Forwomenunder21,activeNEETrates fell from10.5% in1985to3.4% in2015,whileinactiveNEETratesonlydroppedfrom10.4%to7.8%duringthesametime.Forthoseover25,thefallinactiveNEETrateswassimilar,butforinactiveNEETratesfellfromover35%in1985to18%in2015.Laterinthepaper,wereportthefallintheproportionofwomenwithchildcareresponsibilities(whichweremuchmorecommonforwomenintheirmid to late 20s), as well as weakening of the predicted relationship between thoseresponsibleforchildcareandinactivity–bothfactorsarekeytoexplainingthesetrends.

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Cruciallyforthekeyquestionthispaperseekstoaddress,theplateauingofNEETratessince2000 is common to all age groups, for both genders. Forwomen, there is essentially nochange inNEETratesforthoseunder22between2000and2015,andonlysmalldeclinesforthoseover21.Formen,allagegroupshavehigherNEETratesin2015thanin2000,andthis is not just due to the consequences of the 2008 financial crisis – these rates wereincreasingbetween2000and2007acrosstheboard,andmostnotablyfortheunder25s.

2.3 Individual drivers of the chances of being NEET in the UK This subsection shows that while the NEET population is quite diverse, there are certaincharacteristicswhich are strongly associatedwith the incidence of being NEET for youngpeopleineachdecade.

Success in the labourmarket is stronglyandpositively correlatedwith formaleducationalattainment–this isshownincountlessempiricalstudiesonthereturnstoeducation,andmaybeexplainedintheorybyrelatingeducationtothedemandforsought-afterskills,orbythe ideathatqualificationsallowemployers toscreenandsortprospectiveemployees forthe jobs theyoffer. In theanalysis,wedistinguish four categoriesofqualification– thosewith a university degree (or higher), those with some form of post-162 secondaryqualifications (which includes A-Levels, Scottish Highers, level 3 vocational qualificationsincluding apprenticeships, and professional qualifications such as nursing and teaching),GCSEs (including Scottish Standards and level 2 vocational qualifications), and thosewithlower level qualifications than that (or no qualifications). These distinctions are broad inorder to capture the main trends as people progress through different levels of theeducationsystem.

Wealsodistinguishthreeagegroups–thosebetween16and21,thosebetween22to25and those aged 26 to 29. The period of time examined has coincided with a massiveexpansionofeducationprovisionatthepost-16secondaryandtertiarylevel,whichshouldhaveabearingonNEETfiguresforthe16-21groupunlessthisexpansionweretoonlyaffectthose who would have otherwise been in employment. The second age group capturesthosewhohavelargelyleftformaleducationandaretransitioningintowork,whilethethirdage group captures those who are typically expected to have moved towards stableemployment.ThisthirdgroupcoversthosewhoarenotconsideredNEETinUKpolicyterms,butareincludedheretodemonstratethatproblemsofinactivityandunemploymentdonotsimplydisappearattheageof25.

Severalstudieshavepreviouslyshownthatcareresponsibilities,specificallytimeawayfromthe labourmarket to have a family, impact the chance of beingNEET, and this impact is

2Thispaperusestheterm‘post-16’torefertoeducationalqualificationsthathavetypicallybeenundertakenaftertheageof16,whichuntilrecentlywasthecompulsoryschoolleavingage.

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particularly pronounced forwomen (e.g. Fenget al., 2015; Audit Commission, 2010).Weuse LFS household composition responses (available from1996 onwards) to examine theimpactsofhavingachild(includingstep-children)ontheincidenceofbeingNEET.

Using those samedata,wealsoexamine the impactofbeingover16and still livingwithparents (including step-parents or parents-in-law). There has been significant mediacoverage of the ‘failure to launch’ phenomenon, where increasing numbers of themostrecentgenerationsofyoungpeoplehaveremainedintheirparentalhome,beenunabletofindajobanddelayedthetransitionfromchildhoodtoadulthood.Thatsaid,thetheoreticalrelationshipbetweencontinuingtolivewithparentsandNEETratesisambiguous–livinginthe parental home reduces living costs thatmightmake the need to find an income lesspressing; for those with care responsibilities, the presence of grandparentsmaymake iteasier to continue to work alongside having a child. Moreover, a positive correlationbetween living at home and being NEET might have the causal effect running in bothdirections,forexampleifaneventsuchasjoblosstriggersayoungperson’sreturntothefamilyhome(Berringtonetal.,2010).Hence,weincludelivingwithparentsinananalysistosee if it relates to NEET status when combinedwith other variables, but stress that anyeffectscannotbeinterpretedascausal.

One final factor that has been repeatedly identified as a driver of being NEET is healthstatus,witharecentemphasisonmentalillhealth(Scottetal.,2013;Gutiérrez-Garcíaetal,2017).WeuseLFSresponsesonself-reportedhealthconditionstoexaminethedifferencesbetween those suffering from ill healthand thosewhoarenot.Wecategorise conditionsintofivegroups:mentalillhealth3;physicalmobilitylimitations4;hearing,sightandspeechimpediments;learningdifficultiesandotherhealthconditions(whichlargelycoversdiseasesand chronic conditions from allergies, respiratory conditions, cardiovascular conditions,digestivesconditions,diabetesandothersthatarenotclassifiedelsewhere)Wecreateanindicator variable for each of these categories, which each take a value of one if therespondentmentionssufferingfromthatcondition.

Alongside these individual drivers of being NEET, there are also external factors that arelikely tomatter.Employmentprospectsaredrivenbythenationaleconomicclimate, thuswhentheeconomyisdoingwellandlotsofnewjobsarebeingcreated,activeNEETratescouldbeexpectedtofall.Figure2showsspikesinNEETratesintheearly1990sandafter2008,bothofwhichcorrespondtoa recessionaryperiod. Inaddition,beinganew labourmarket entrant during an economic recession can have longer term consequences onemployment rates (RaaumandRøed,2006;Rothstein,2019),althoughsomestudieshave3TheLFShealthvariableshavetworelevantcategories–wedefinesomeonewhosuffersfrommentalillhealthasanyonereporting“depression,badnervesoranxiety”or“mentalillness,orsufferfromphobia,panicsorothernervousdisorders”.4Wedefinesomeoneashavingphysicalmobilitylimitationsiftheyreporthealthproblemsrelatingtothe‘armsorhands’,‘legsorfeetor‘backsornecks’.

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found that the probability of employment has not been affected, while pay and type ofoccupation have been (Kahn, 2010, Van den Berge and Brouwers, 2017). If there is ascarringeffectofenteringduringarecession,wewouldexpecttoseeincreasedNEETratesforolderindividualsintheyearsafterthedownturn.Finally,aroundnationaltrendsthereisregional variation, both in terms of themagnitude of economic shocks, as well as otherstructuralchangestothelocallabourmarket–forexample,growingordecliningindustriesconcentratedheavilyinoneregion.Inouranalysis,weindicatecontrolsforEnglishregionsas well as Scotland, Wales and Northern Ireland. In section 4, we try to relate regionaldifferencestoparticularregionallabourmarketmeasures.

Toestablishtheoverallroleplayedbyallofthesefactors,weestimatealinearprobabilitymodeloftheforminequation(1):

𝑁𝐸𝐸𝑇!" = 𝛼 + 𝛽𝑋!" + 𝛾𝑍! + 𝜀!" (1)

whereNEETitisanindicatorvariablethattakesthevalueofoneifindividualiisNEETattimet and zero otherwise, Xit is the set of explanatory variables which predict NEET status,includingage,qualifications,familyandhealthvariablesaswellasregionaldummies,Zt,isasetofyeardummiesandα,βandδarethecoefficientswhichrelatethesecharacteristicstotheprobabilityofbeingNEET.Weestimatethisequationformalesandfemaleseparately.Tables1and2showstheresultsoftheseregressionsformalesandfemalesrespectively.

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Table1:FactorsassociatedwithbeingNEETamongmen,1985-2015

Male 1985-2015 1992-2015 1992-2015 1996-2015 1996-2015Lowqualifications 0.1566 0.1581 0.1583 0.1490 0.1270

(0.002) (0.002) (0.002) (0.002) (0.002)GCSEs 0.0375 0.0457 0.0471 0.0486 0.0424

(0.002) (0.002) (0.002) (0.002) (0.002)Degree -0.0224 -0.0200 -0.0195 -0.0120 -0.0040

(0.002) (0.003) (0.003) (0.003) (0.003)

Age:22-25 0.0413 0.0449 0.0446 0.0483 0.0398 (0.002) (0.002) (0.002) (0.002) (0.002)

Age:26-29 0.0183 0.0217 0.0220 0.0272 0.0160 (0.002) (0.002) (0.002) (0.003) (0.003)

Haschild 0.0390 0.0426 (0.003) (0.003)

Liveswithparent 0.0230 0.0215 (0.002) (0.002)

HaschildxLiveswithparent -0.0222 -0.0241 (0.013) (0.013)

Mentalillhealth 0.3820 (0.006)Physicalmobilitylimitations 0.1189

(0.005)Hearing,sightorspeechimpediment 0.0467 (0.007)Otherhealthconditions 0.0390 (0.003)Learningdifficulties 0.0568 (0.006) Constant 0.0800 0.0830 0.0863 0.0439 0.0472

(0.003) (0.003) (0.004) (0.004) (0.005)Yearcontrol Yes Yes Yes Yes YesRegioncontrols No No Yes Yes Yes Observations 243,001 180,649 180,649 125,332 125,332R2 4.6% 4.4% 4.8% 4.5% 9.1%Notes:Dependent variable ineachmodel is indicator forbeingNEET. Standarderrors reported inparentheses.Effectsstatisticallysignificantatthe1%levelindicateinbold.

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Table2:FactorsassociatedwithbeingNEETamongwomen,1985-2015

Female 1985-2015 1992-2015 1992-2015 1996-2015 1996-2015Lowqualifications 0.2792 0.2807 0.2806 0.2070 0.1939 (0.002) (0.002) (0.002) (0.003) (0.003)GCSEs 0.0905 0.1027 0.1036 0.0809 0.0681 (0.002) (0.002) (0.002) (0.003) (0.002)Degree -0.1059 -0.0989 -0.0979 -0.0017 0.0117 (0.003) (0.003) (0.003) (0.003) (0.003) Age:22-25 0.1523 0.1519 0.1514 0.0303 0.0135 (0.002) (0.002) (0.002) (0.003) (0.003)Age:26-29 0.1833 0.1707 0.1705 -0.0270 -0.0471 (0.002) (0.002) (0.002) (0.003) (0.003) Haschild 0.3428 0.3632 (0.003) (0.003)Liveswithparent -0.0354 -0.0398 (0.003) (0.003)HaschildxLiveswithparent 0.0096 0.0166 (0.009) (0.007) Mentalillhealth 0.2402 (0.006)Physicalmobilitylimitations 0.0662 (0.005)Hearing,sightorspeechimpediment 0.0450 (0.009)Otherhealthconditions 0.0370 (0.003)Learningdifficulties 0.1025 (0.009) Constant 0.0845 0.0467 0.0456 0.0525 0.0535 (0.003) (0.003) (0.004) (0.005) (0.005)Yearcontrol Yes Yes Yes Yes YesRegioncontrols No No Yes Yes Yes Observations 258,230 193,663 193,663 134,765 134,765R2 12.4% 11.9% 12.1% 25.3% 27.1%Notes:Dependent variable ineachmodel is indicator forbeingNEET. Standarderrors reported inparentheses.Effectsstatisticallysignificantatthe1%levelindicateinbold.

The table confirms that during the past 30 years, the incidence of NEET is far higher forthose with few qualifications, especially for females. Specifically, and depending on thespecification,lowornoqualificationsisassociatedwithNEETratesapproximately20-28pphigherforwomenand13-16pphigherformen.ThosewithhigherqualificationsthanGCSEshavelowerNEETrates,butthereislittledifferencebetweenmaleswithA-Levelsandmaleswith a degree.Having a degree appears to have a stronger relationshipwith lowerNEETratesforwomen,butthisdisappearsoncewecontrolforhavingachild,whichsuggeststhatqualification effects have a lot to do with the differences by educational level in thepropensitytostartafamilybeforetheageof30.

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Inthispaperwedonotdistinguishbetweenqualificationsbeingacademicorvocationalateach level, as subject information isnot available in the LFS.However, the LFSdatadoeshave information of whether an individual reports that they had completed a tradeapprenticeship, includingadvancedand foundationmodernapprenticeships–onaverage,approximately 12% ofmales and 4% of females included in our analysis have completedone, although for males this figure has dropped from 16% in 1985 to 7% today. Werecalculated the final estimates in Table 1 and 2 including a variable for completion of atrade apprenticeship alongside the other educational measures, which showed thatbetween1996and2015,havingatradeapprenticeshipwasassociatedwitha1.0ppfallinthe chanceof beingNEET formales and2.3pp increase in the chanceof beingNEET forfemales–thelatterpointstothefactthatunlesswehavetheunlikelysituationthatatradeapprenticeshipisactivelytakingawayskills,thereisanelementofselectioninthedecisiontofocusonawork-basedtrainingroute intothe labourmarketthat isbeingpickedupbythisvariable.

NEET rates increased among men and women after the age of 22, which holds truethroughouttheir20s.However,oncecareandlivingarrangementsareincluded,weseethatthemassivelyhigherNEETratesforwomenintheirmidtolate20sarealmostentirelyduetohavingachild.Indeed,NEETratesarelowerintheirlate20sforwomenascomparedtolateteenageyearsoncehavingachildiscontrolledfor.Formales,thoselivingwithparentshavehigherNEETrates,butonlyiftheydon’thavechildren.Forwomen,thisrelationshipisreversed–thoselivingwithparentshavelowerrisksNEETratesthanthoselivingelsewhere.Whatisn’texaminedhereiswhetherthosewomenwhoarenotlivingwiththeirparentsareinsteadlivingwithapartner,andifsowhetherthesewomenaremorelikelytobeNEETduetakingonmoredomesticandhouseholdwork(whichmaybe lessnecessaryforthosestilllivingwithparents).

Thisanalysiswould seemto suggest that twopopular conceptionsof theNEETgrouparenot correct – that there is a large effect caused by teenagemothers5 and young peopledelayingadulthoodby remaining in theirparentalhomewell into their20s.6Firstly,whilebeing a parent increases NEET risks forwomen of all ages, this effect seems to bemostrelevantforthoseover21.Since1996,theproportionoffemalesunder22whohaveachildhas fallen from 8.2% to 5.6% in 2015. By comparison, in 2015, the proportion of thosebetween22and25whohadachildwas28.3%andforthosebetween26and29,itis45.4%.Secondly,whilelivingwithparentsisassociatedwithhigherNEETrisksforyoungmen,theopposite is true for young women. Moreover, the effect is small – an expected 2.3

5The1999SEUreportonteenagepregnancydescribestheUK’spoorrecordonteenagepregnancyandmakesexplicitlinksbetweenteenageparentsandsocialdisadvantageandeconomicinactivity,leadingtotwomainobjectivesforpolicy–areductionofteenpregnancyrates,andanincreaseinteenparentemployment.Strikingly,nosuchequivalentpolicyobjectivesexistsforyoungpeoplewhopregnantintheir20s.6See,forexample,theNewYorkTimesMagazine,June20,2014:https://www.nytimes.com/2014/06/22/magazine/its-official-the-boomerang-kids-wont-leave.html

14

percentage points holding everything else constant - as compared to other variables likehavinglessthanA-Levelqualifications.

ThetablesconfirmthatmentalillhealthhasasubstantialeffectontheriskofbeingNEET.ItisassociatedwithanincreaseinNEETratesof38.2ppformalesand24.0ppforfemales.Akeypointtonote isthattheeffectsofmental illhealthonthechancesofbeingNEETaremuchhigherthanthoseassociatedwithotherhealthconditions,forbothmenandwomen.Inthenextsection,wherewelookatthereasonsNEETrateshavechangedovertime,wefocusonlyonmental illhealthandomittheotherhealthvariables.Wedothistosimplifytheanalysis, andbecause therehasbeen little change in the incidenceof thesevariablesover the timeperiod (unlike formental illhealthwhichhasbecomemuchmorecommonsince2000,seeTable3).

Finally, the introductionof regional controlshas someexplanatorypower inandof itself,butdoesnotaffecttherelationshipbetweenindividualcharacteristicsandNEETvariables.Figure3showsthepredictedeffectsoflivingindifferentpartsoftheUK(relativetoLondon)–ingeneral,womenwithsimilarcharacteristicshavelowerNEETratesinallpartsoftheUKascomparedtoLondon,whileformenthisisonlytrueintheeastandsouthofEngland.Theeffectsizesvary,butarenon-negligibleandallexcepttwoarestatisticallysignificantatthe5%level.

Figure3:PredictedregionaleffectsonNEETrates,1998-2015

Source:LFS,owncalculations.

-5.0%

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

Male Female

15

ThisanalysisshowsoverallpredictorsofbeingNEET,andconfirmsthattherearedifferencesinthechancesofbeingNEETassociatedwithqualificationlevel,familystructure,healthandregion.Inthenextsection,weturnourattentiontoexplainingwhyNEETratesfelloverthe1980sand1990s,andwhytheyhaveremainedrelativelyunchangedsincearound2000.

3. Accounting for changes in NEET rates One explanation for this evolution in NEET rates would be that until 2000, there wereincreasing numbers of young peoplewith the sorts of characteristics that are associatedwith a lower chance of being NEET, but that after 2000 this stopped being the case. Asecondexplanation is that the relationshipbetween those characteristics andbeingNEEThas changed in such a way that we would now expect someone with one of thosecharacteristics to be more likely to be NEET today than in the past. The third potentialexplanationisthattherearestructuralfactorsoutsideofanindividual’scontrolwhichhaveaneffect– forexample, the impactofdeindustrialisation,or the tightnessof local labourmarkets.Inordertoevaluatethesedifferentexplanations,were-estimateequation(1)foreachyear,t:

𝑁𝐸𝐸𝑇!" = 𝛼! + 𝛽!𝑋!" + 𝜀!" (2)

TakingmeanaveragesofthedependentandexplanatoryvariablesgivesthattheNEETrate(themeanoftheNEETvariableatanypointintime)canbeexpressedasafunctionofthemeanaveragecharacteristicsofthepopulationatthatpoint intime.Weare interested inthechangeintheNEETratebetweentimetandtimet+1,sobysubtractingonefromtheotherandrearrangingtermswegetthat:

𝑁𝐸𝐸𝑇!!! − 𝑁𝐸𝐸𝑇! = 𝛼!!! + 𝛽!!!𝑋!!! − 𝛼! + 𝛽!𝑋!

= 𝛽! 𝑋!!! − 𝑋! + 𝛼!!!−𝛼! + 𝑋!!! 𝛽!!! − 𝛽! (3)

ThechangeintheNEETratebetweentwoperiodscanthereforebebrokendownintotwoparts, which we refer to as the composition effect and the coefficient effect.7 Thecompositioneffect isgivenbythefirstterminequation(3),whichcapturesthechange intheNEETratethatwouldbeexpectedifthecharacteristicsofthepopulationbetweenthetwotimeperiodschangedbutthewaythosecharacteristicsrelatedtothechanceofbeingNEET stayed the same. Table 3 shows how the individual characteristics of the youngpeople in our dataset have changed over time. It demonstrates that this age group isconsiderablymoreeducated,withthegrowthinhighereducationalattainmentparticularlyhigh forwomen,whichwewouldexpect to lead to fewerNEETs. Similarly, the fall in theproportionofyoungpeoplewithachildwouldsuggestlesspeoplebeingNEETin2015thanin 1985. The number who are living with parents has increased, but our initial analysis7ThisisthestandardBlinder-Oaxacadecompositionmethod.

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suggestedthiswouldhaveasmalleffect,anditisnotclearinwhichdirection.Ontheotherhand,mental ill healthproblems (which arenot recorded in 1985)were reportedby lessthan2%ofbothmenandwomen in2000and increasedalmost four-foldby2015,whichwouldbeexpectedtoincreasethenumberofNEETs.

Table3:Descriptivestatistics

1985 2000 2015Male Age 22.3 22.5 22.3

Lowqualifications 39.6% 25.0% 17.3%

GCSEs 24.5% 29.4% 28.3%

A-Levels 29.6% 32.9% 36.6%

Degree(andhigher) 6.3% 12.8% 17.9%

Haschild

10.8% 9.9%

Liveswithparents

57.0% 61.2%

Hasmentalillhealth

1.2% 4.6%

Mentalillhealthismainhealthproblem

0.7% 3.1%

Female Age 22.4 22.7 22.7

Lowqualifications 40.6% 22.8% 13.6%

GCSEs 33.6% 34.3% 27.1%

A-Levels 20.8% 30.2% 36.1%

Degree(andhigher) 5.0% 12.8% 23.2%

Haschild

26.6% 24.5%

Liveswithparents

42.9% 50.5%

Hasmentalillhealth

1.8% 7.1%

Mentalillhealthismainhealthproblem

1.0% 5.3%

Source:LabourForceSurvey,owncalculations.Meanvaluesareshownforeachyear.

Thesecondterminequation(3)givestheshift intheinterceptvariable,αt–whenallthevariables X are defined relative to a reference characteristic (for example, the effect ofhaving a degree compared to someone with post-16 secondary schooling qualifications),what ishappeningtopeoplewithall thereferencecharacteristics (thereferencegroup) iscapturedinthisshift.Werefertothisasareferencegroupeffectthroughouttherestofthispaper. The final part of equation (3) captures the change in the way each characteristicrelatestothechanceofbeingNEET–forexample,ifthepenaltytohavinglowqualifications(intermsofbeingmorelikelytobeNEET)comparedtohavingpost-16secondaryschoolingqualifications getsworse, thenwewould expect theNEET rate to go up, everything elsebeingequal,becausethepeoplethathavethatcharacteristicattheendoftimeperiodaremorelikelytobeNEETthanthatsamegroupwouldhavebeenatthebeginningofthetimeperiod.

Generaleffects–suchasincreasesordecreasesintheoveralldemandforlabourduringaneconomic upturn or recession, or systemic changes in the incentives to participate inemploymentoreducation,forexample,throughreforminthewelfarestate–wouldshowup if the reference group effect explained most of the change. This means that thelikelihoodofbeingNEEThaschangedforthereferencegroup,whiletherelativebenefitofhavingcertaincharacteristics(likequalifications)hasnotchangedcomparedtothisgroup;consequently, the absolute chance of being NEET has shifted for everyone. We include

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regionalvariablesinouranalysis,whereregionaleffectsaregivenrelativetothoselivinginLondon.ThechoiceofLondonasreferencegroupisdeliberatehere-ifvariabilityofregionaleffectsislowbutthetotaleffectofregionsonthenationalNEETrateishigh,thisindicatesadivergencebetweenLondonandelsewhere,whichisrelevanttoongoingdebatesabouttheextentofLondon’seconomicandpoliticaldominanceintheUK.

WeconductouranalysisontheoverallNEETratesseparatelyformenandwomen,andthenmakeafurtherdistinctionbetweenactiveandinactiveNEETS–theseresultsarepresentedin Appendix B. We do this primarily to illustrate how these two groups are not all thatsimilar and that policy to reduceNEET risks needs to treat these subgroups of theNEETpopulation as distinct. The next section presents the results formale and female overallNEETrates.

4. Results

4.1 Qualif ications and age Westartwithestimatingasimpleversionofequation(3),whichjustincludesqualifications,age and regional variables, for four time-periods: 1985-1993, 1993-2000, 2000-2007 and2007-2015.Wedothistolookatthelonger-termtrendsasvariablesonmentalhealthandhouseholdstructurearenotavailable intheseearliertimeperiods–thoseareconsideredforthefinaltwotimeperiodsinsection4.3.

Figure 4 shows that for both males and females, there was an increase in educationalattainment in all of the timeperiods except 2000-2007, particularly the fall in thosewhohavenoorlowqualifications.ThiswouldhavepushedNEETratesdownifnothingelsehadchangedabouttherelationshipbetweenqualificationsandtheriskofbeingNEET.

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Figure4:CompositionaleffectsonNEETratesbygender,1985-2015

(a) Male

(b) Female

Source:LFS,owncalculations.

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

1985-1993 1993-2000 2000-2007 2007-2015

Age:22-25 Age:26-29 Lowquals GCSEs Degree Regions Total

-8.0%

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

1985-1993 1993-2000 2000-2007 2007-2015

Age:22-25 Age:26-29 Lowquals GCSEs Degree Regions Total

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Between2000and2007, therewas little change in thenumberofpeoplewith lowornoqualifications,andgiventhatyoungpeopleinthisgroupcontinuetohaveamuchhigherriskofbeingNEET, this isakeyreasonwhyNEETratesdidnot fallasmuch in theyearsafter2000. It is not immediately obvious why this should have been the case – the share ofpeoplewith lowqualifications fell again in theperiodafter this.However,one reason forthisisduetomigration–inparticular,anincreaseinnewmigrantswithfewqualifications(or,potentially,qualificationsthatarenotrecognisedinthequalificationframeworkusedtogaininformationofeducationintheLabourForceSurvey).

Table4:Migrantsandlowqualifiedyoungpeople

1993 2000 2007 2015

Foreignborn 6.6% 8.3% 13.2% 13.9%

Shareofforeignbornwithlowqualifications 46.7% 47.6% 50.3% 28.8%ShareofUK-bornwithlowqualifications 32.4% 21.7% 18.9% 13.2%

Foreignbornshareoflowqualified 9.2% 16.6% 28.7% 26.2%

Table 4 shows that the share of foreign-born individuals between the age of 16 and 29jumpedfrom8%to13%between2000and2007.Oftheseindividuals,theproportionwithlowornoqualificationsincreasedslightlyduringthattimeperiod,whileforUKbornyoungpeople,thosewithnoqualificationscontinuedtofall.TheresultofboththeincreaseinthenumberofmigrantsandtherelativefallofthosewhowerebornintheUKwhohadnoorlow qualifications was that between 2000 and 2007, the proportion of foreign-bornindividualsinthelow-qualifiedgroupalmostdoubled,from16.6%to28.7%.

Giventhatmigrants,particularrecentmigrants,arelikelytohaveagreaterneedtoworkonaccountofvisaconditions(fornon-EUmigrants)or lessaccesstootherincomesourcestosupport themselves, then we might expect the effect of having low qualifications to beunderstatedbythechangingcompositionofthatgroup.Tocheckthis,weincludeavariablefor being foreign-born as well as a variable for being a recent migrant (which refers toforeign born individuals who arrived in the UK within the previous five years) into ourpreviouslyestimateregressions.Wefindthatformen,allelseequal,theprobabilitythatarecentmigrantwasNEETwas4.5percentagepointslowerthananotherwiseidenticalUK-bornyoungmalein2000.Thisincreasedto6.6percentagepointsby2007,andthenfelltoless than 1 percentage point in 2015. For women, the migration variables were notsignificant (at the 5% level) in 2000 and 2007, but in 2015, recent migrants were 6.1percentagepointsmorelikelytobeNEETthananotheridenticalUK-bornyoungfemale.Inall the cases, the other coefficients on individual characteristics and regional dummiesremainedessentiallythesameasintheearlieranalysis.Astheseeffectsarenothuge,andthe group itself is small, the overall impact of migrants on NEET rates in the UK is notimportant.

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The compositional effects shown in Figure 4 only give us a counterfactual,which tells uswhat we would have expected to have happened if nothing changed in the relationshipbetweenkeyindividualcharacteristicsandthelikelihoodofbeingNEET.Figure5showstheeffect of changes in the relationship between these characteristics and the likelihood ofbeing NEET. It shows that before 2000, the chance of being NEET fell even once thecompositional effects were taken into account – that is, the relationship between thelikelihood of being NEET and a person’s qualifications, age and region improved even asmore people had the characteristics associatedwith a lowerNEET incidence. After 2000,thishasnotbeenthecase.

Figure5showsthatbefore2000,thereferencegroupeffect is large,suggestingthere isageneral trend towards lower risk of being NEET for all young people. In the first period,femalelabourforceparticipationincreased,aneffectwhichwasevenstrongerforthoseintheirmid and late20s. This ageeffect is largely for inactiveNEETs (seeAppendixB), andseemlikelytoberelatedtochanginggenderrolesaroundworkandfamily(althoughthisisspeculativeaswecannotcontrolforhavingachildintheseearlierperiods).Thereferencegroupeffectformeninthefirstperiodissmallerandpositive–1993wasattheendoftheearly1990srecession,sothelabourmarketsituationforeveryonewasdifficult.Theremayalsobesomeeffectoftheendof1980sprogrammesliketheYouthTrainingScheme,whichmovedunemployedmales intotrainingschemesandoutoftheNEETstatistics.Thefigurealsoshowsthatthepenaltytolowqualificationsin1993fellascomparedto1985,soNEETriskforthisgroupdoesn’tworsenasmuchasforthosewithhigherqualifications.

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Figure5:CoefficienteffectsonNEETratesbygender,1985-2015.

(a) Male

(b) Female

Source:LFS,owncalculations.

-6.0%

-5.0%

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

1985-1993 1993-2000 2000-2007 2007-2015

Age:22-25 Age:26-29 Lowquals GCSEs Degree Regions Reference Total

-8.0%

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

1985-1993 1993-2000 2000-2007 2007-2015

Age:22-25 Age:26-29 Lowquals GCSEs Degree Regions Reference Total

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In the1990suntil theyear2000, thereferencegroupeffect iseven larger forbothmalesand females and accounts for almost all of the overall coefficient effect. NEET ratescontinued to fall during this period. This effect is particularly associatedwith adecline inactive NEETs rates for both men and women, which points to improvements in labourmarketconditionsaftertheearly1990srecessionasthemaindriverhere.

After 2000, coefficient effects largely offset any compositional changes that wouldotherwisehavebeenexpectedtoreduceNEETrates.Inparticular,regionalvariationplaysalargerole.Fortheperiodbetween2000and2007,whereingeneraltheeconomycontinuedtoperformaswellasithadinthepreviousperiod,theseregionaleffectsrelatedtoinactiveNEETratherthanactiveNEETs–forwomen,therewasincreaseinthechanceofbeingNEETand inactive in London compared to elsewhere and formen a decrease in the chance ofbeingNEETforthoseinLondonascomparedtomostotherregionsaroundtheUK.

The period following the global financial crisis and recession in 2008-9, and subsequentimpositionofausteritymeasuresbytheCoalitiongovernmentsawtwomaineffects.Firstly,therewereregionaleffects formenandwomenandthesewerenearly identicaloverall–outsideofLondon,NEETrates increasedcompared tobeing inLondon.This related toanincreaseininactiveNEETsoutsideofLondonratherthananincreaseinNEETsseekingwork.The second effect we see relates to age – specifically, the position of NEETs over 22worsened during this period as compared to those under 22. Thismostly affected activeNEET rates, which is consistentwith the notion that thosewho enter the labourmarketaftercompletingeducationduringadownturnfacesomelonger-termscarringeffectsthatcan still be observed in their mid and late 20s. However, it is worth remembering thatduring this timeperiod,anumberofpolicieswere introduced to increaseparticipation ineducationamongst16-17yearolds,notleastanincreaseintheparticipationage(HouseofCommons, 2018). The age effects could therefore simply be reflecting an increase in thenumber of young people staying in education while they are 16 and 17 (who wouldotherwisehavebeen looking forwork), rather thanworseningemploymentoutcomes forolderworkers.Totestthis,weestimatedthesamedecompositionasabove,butexcludedthosebetween16and17,sonowageeffectswouldbecomparingthoseaged18-21(whoareoutsideoftheeffectsoftheseparticipationpolicies)tothosewhoareolder.Theresultsshowamixedpicture.Formen,theageeffectdisappears,suggestingthatmalesintheirlate20s (who entered the labourmarket during the recession) are in noworse a position intermsofemploymentthanthosewhoenteredsomeyearslaterwhentheeconomybegantorecover,andthatthepreviousageeffectsrelatetoeducationalparticipation.Forwomen,however,theageeffectremains,suggestingthattherearegenuineworseningemploymentopportunitiesforwomenintheirmidtolate20sascomparedtowomenintheirteens.

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4.2 The role of labour market characteristics Section4.1pointstotheimportanceofregionaleffectsasakeydriveroftheriskofbeingNEET.Inthissubsection,welooktoseeifthereareanylabourmarketcharacteristicswhichmighthelpusunderstandthisfurther.

Using the LFS, we collect four regional level variables which are related to the labourmarket: the unemployment rate for those between 35 and 60 (to avoid endogeneitybetweenthisvariableandNEETrates),theshareofworkersbetweentheageof16and59inpart-timejobs,theshareofworkersbetweentheageof16and59inlowerskilledservicejobs (specifically SOC groups 6 and 7, which covers personal care occupations and retailserviceoccupations)andtheshareofworkersbetweentheageof16and59inprofessionaljobs(specificallySOCgroup2,whichcoversthetraditionalprofessionaloccupationssuchasdoctor,lawyer,teacher,andsoon).AstheywerebeingusedtoexplainregionaldifferencesascomparedtoLondon,weincludedthemintheanalysisintermsoftheirpercentagepointdifferencefromtheequivalentLondonfigure.Themeasureofpart-timeworkisincludedasa proxy for more flexible, less secure working arrangements. More flexible workingarrangementsmayreducebarrierstoenteringthelabourmarketamongyouthpopulations(O’Reillyetal.2015).Thoughtheevidenceonrelationsbetweenlow-quality‘flexible’workandgender inequalities intheeaseofworking intheUK isopentoquestion(WarrenandLyonette,2018),theabilitytoworkpart-timegivensocietalnormsaroundresponsibilityforchildcaremayhelpyoungwomeninparticularleaveNEETstatus.Ontheotherhand,lesssecureemploymentmaybeseenastooriskytobeworthdisruptingthecertaintyofwelfarepaymentsfor(Maguire,2018)aswellaslowerpaidandhencelessattractivegiventhecostsof find alternative child care arrangements. The overall expected effect is thereforetheoretically ambiguous and could vary by gender as well as by active or inactive NEETstatus.Thetwomeasureswhichcapturethetypeofjobsavailablecouldpotentiallypickupa number of effects, and the direction of the effect is theoretically ambiguous. Having alarger shareof low-skilled service jobswith fewerentry requirements in the region couldpotentiallymakeiteasierforlessqualifiedindividuals(whohavehigherNEETriskstypically)to findemployment.Ontheotherhand, relativesizeofoccupationsmightbeaproxy forsomecurrentlyunobservableindividualcharacteristicsrelatedtobeingNEET–forexample,individualswithahigherattachmenttopursuingacareermayself-selectintoregionswithahigher share of professional occupations. The inclusion of a measure of generalunemploymentconditionsshouldneednoexplanation.

Table4showshowthesevariablesevolvedovertheperiod2000to2015.AswellasbeingapoliticallysalientcomparisongivenconcernsaboutregionaldivergencewithLondon,usingthe capital as a reference point is also analytically convenient in our case because it isalmostalwaystheoutlieralongallfourmetricslisted–Londonhasahigherunemploymentrate,ahighershareofhigherskilledoccupations,alowershareoflowerskilledoccupationsandalowershareofpart-timeemployment.

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Table4: Meanregionaldifferencesinkeylabourmarketindicators

2000 2007 2015

Unemploymentrate(relativetoLondon) -1.0% -2.1% -1.2%Part-timeworkshare(relativetoLondon) 3.4% 3.6% 3.2%Serviceoccupationshare(relativetoLondon) 2.4% 2.4% 2.3%Professionaloccupationsshare(relativetoLondon) -1.5% -1.8% -1.3% Source:LabourForceSurvey,owncalculations

Were-estimateregression(1)fortheperiod2000-2015includingregionaldummiesaswellastheseregional labourmarketvariables.Table5showsthecoefficientsoneachofthese(forspace,weomitreportingtheotherexplanatoryvariablesincludedinTables1and2,butthey are essentially unchanged by the inclusion of regional labourmarket variables). Thetableshowsthatformales,regionalvariation inunemploymenthasasignificanteffectonNEETrates,throughareductioninactiveNEETs,whichiswhatwewouldexpect.Wecouldnot reject the hypothesis that the coefficient is one – implying a one percentage pointincreaseinunemploymentratescorrespondstoanidenticallysizedincreaseinNEETrates.Thereissomesuggestion(althoughtheeffectisonlysignificantatthe10%level)thatareaswithahighershareofhigherskilledprofessionaljobshavelowerNEETratesformen,butnosucheffectforwomen.

Table5:EffectsofregionallabourmarketvariablesonprobabilityofbeingNEET

Male Female

AllNEET Unemploymentrate 0.742*** -0.128

(0.205) (0.217)

Parttimeshare 0.070 0.213

(0.160) (0.167)

Serviceoccupationshare 0.045 -0.491**

(0.222) (0.235)

Professionaloccupationshare -0.357* -0.188

(0.211) (0.223)

Active Unemploymentrate 0.859*** 0.116

(0.172) (0.135)

Parttimeshare 0.245* 0.146

(0.134) (0.104)

Serviceoccupationshare 0.234 0.141

(0.186) (0.146)

Professionaloccupationshare -0.241 0.003

(0.177) (0.139)

Inactive Unemploymentrate -0.116 -0.245

(0.128) (0.191)

Parttimeshare -0.175* 0.067

(0.100) (0.147)

Serviceoccupationshare -0.189 -0.633***

(0.138) (0.208)

Professionaloccupationshare -0.117 -0.191

(0.131) (0.197)

25

Notes:DependentvariableineachmodelisindicatorforbeingNEET,activeNEETorinactiveNEET.Explanatory variables are measured in decimal form, so on the same scale as the dependentvariable. Full regression includes all variables from Tables 1 and 2, as well as year and regiondummies,coefficientsareomittedhereforspace.Standarderrorsreportedbeloweachcoefficientinparentheses.*p<0.1;**p<0.05;***p<0.01

However,areaswithahighershareoflowerskilledserviceoccupationshavelowerfemaleNEETratesforwomen(whichisachievedbyareductionininactivefemaleNEETratherthanactive female NEETs), but there is no relationship between the number of lower skilledservice occupations and male NEET rates. Finally, there is weaker evidence about therelationship with part-time work – specifically, more part-time work has no bearing onfemaleNEETrates,andnooveralleffect formen (althoughthereare lower inactiveNEETratesandhigheractiveNEETratesinareaswithalargerpart-timeshare).

However,theseeffectsonlyhelpunderstandasmallamountofregionaldivergenceinNEETratesbetween2000and2015.AsshowninTable4,regionsoutsideofLondononaveragesawfallingunemploymentrelativetoLondon,whichshouldimplyfallingNEETratesintheseregions relative to London – in reality,weobserve greaterNEET risks outside of London,everythingelsebeingequal.Part-timeshareofemploymentdidnotchangeallthatmuch,andassuch,cannotexplainwhyNEETratesfelloutsideofLondonbetween2000and2007,norwhytheyincreasedcomparedtoLondonafter2007.Theendresult isthatsubstantialregionalvariationstillremainsevenafterincludingindicatorsforthestateofregionallabourmarkets.

4.3 The ful l specif ication after 2000 Asnotedatthebeginningofthissection,theanalysisinsection4.1usesonlytheeducation,age and region variables as these are available over the entire time period examined.However,mental ill health, having a child and/or living with parents are correlatedwithqualifications and age, so in this simple analysis, changes in the number of people thesecharacteristicsapplytoisnotentirelydetectedinthecompositionaleffects(inFigure4)andwouldendupenteringtheanalysisaspartofthecoefficienteffects(inFigure5).8

Whenwe includementalhealthand family structurevariables, a slightlydifferentpictureemerges,asshowninFigures6and7(whichrepeatFigures4and5,butforthefullsetofvariables from 2000 onwards). Figure 6 shows that for males, the increase in reportedmental ill healthwould have pushed upNEET rates, everything else being equal, in bothperiods,butparticularlyafter2007.Theimplicationofthisisthatintermsofcompositional

8Forexample,ifhavingachildhasbeenassociatedwithhavinglowqualifications,andthenumberofpeoplewithlowqualificationsfalls(butfertilitypatternsdonotchange),thismeanstherewouldnowbemorepeoplewithhigherqualificationswhohaveachildthanwewouldhaveexpectedinthepast.Thecoefficientonhigherqualificationswouldthenchangeduetoachangeinthecompositionofpeoplewiththosequalifications,ratherthanachangeinanycausalrelationshipbetweenhavingaparticularqualificationandNEETrisk.

26

effects,Figure4overstateshowmuchNEETrateswereexpectedtofallbecauseitdoesn’taccountformental illhealth,whichhaslargelyoffsetanycompositionaleffectsrelatingtobettereducatedindividuals.Moreover,forwomen,theeffectofeducationonthechanceofbeingNEETissmallerwhenhavingachildiscontrolledfor–hencethecompositionaleffectsforwomenaremuchmoreabout the fall in theshareofyoungwomenwhohaveachildthanaboutchangesineducationalattainment.

Figure6:CompositionaleffectsonNEETratesbygender,2000-2015

a) Male

b) Female

Source:LFS,owncalculations.

-7.0%

-2.0%

3.0%

2000-2007 2007-2015Lowqualifications GCSEs DegreeAge:22-25 Age:26-29 HaschildLiveswithparent HaschildxLiveswithparent Mentalhealth-mainMentalhealth-all Region Total

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

2000-2007 2007-2015

Lowqualifications GCSEs DegreeAge:22-25 Age:26-29 HaschildLiveswithparent HaschildxLiveswithparent Mentalhealth-mainMentalhealth-all Region Total

27

Figure7:CoefficienteffectsonNEETratesbygender,2000-2015

a) Male

b) Female

Source:LFS,owncalculations.

-7.0%

-5.0%

-3.0%

-1.0%

1.0%

3.0%

5.0%

7.0%

2000-2007 2007-2015

Lowqualifications GCSEs DegreeAge:22-25 Age:26-29 HaschildLiveswithparent HaschildxLiveswithparent Mentalhealth-mainMentalhealth-all Region ReferenceTotal

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

2000-2007 2007-2015

Lowqualifications GCSEs Degree

Age:22-25 Age:26-29 Haschild

Liveswithparent HaschildxLiveswithparent Mentalhealth-main

Mentalhealth-all Region Reference

Total

28

When family structureandmental ill health variables are included, the coefficienteffectsshown inFigure7 implya smallerpush towardshigherNEET rates thanwasestimated inFigure5.9Westillobserveanageeffectforthoseover21after2007.Wealsocontinuetoseeregionaleffects,asbefore.Inaddition,theriskofbeingNEETamongstthoselivingwithparents,ascomparedtothosenotlivingwithparents,increased.

5. Conclusion Themainquestion this paper seeks to address iswhy so little progress on the chanceofbeingNEEThasbeenmadesince2000–despiteacontinuedpolicypush towardsgreatereducationalattainmentandotherinterventionsspecificallytargetingtheNEETgroupsincethe late1990s.Ouranalysis shows thatakey reason for this is thecompositionofyoungNEETs over this time period. The proportion of individuals with low or no qualificationschanged little in the first part of the 2000s, whereas it had decreased sizeably in theprevious15years.YetwedonotinterpretfallingNEETratesduringthe1980sand1990sastherebeingacausaleffectofyoungpeoplehavingmorequalifications;supplyordemandsidemechanismscouldberesponsible–forexample,itmighthavebeenthecasethatmorepeoplehavinghigherqualificationsdroveopportunitiestoleaveNEETstatus,butequallyonthedemandside therecouldhavebeenmoreopportunities in the labourmarketanywaywiththosehavinghigherqualificationsjustbetterplacedtotakeupthoseopportunities.WedohowevershowthattherelativeadvantageofhavingatleastGCSElevelqualificationsandaboveonthechancesofbeingNEETintheUKhaslargelyheldupoverthistimeperiod–theexception being for women during the years 2000-2007. After 2007, qualification levelsagainincreased,butatthesametimesodidself-reportedmentalillhealth,whichweshowoffsetanyoftheexpectedreductioninNEETratesfromhavingbetterqualifications.

Akeycontributionthisstudymakestotheliteratureistoillustratetherelativeimportanceof a varied set of NEET predictors over time. Our analysis clearly demonstrates thatcoefficient effects – which capture how changes in the relationship between individualcharacteristics and the chanceof beingNEET affect overallNEET rates – are large. TheserelatedfactorsmostlyloweredoverallNEETratesintheyearspriorto2000,butsince2000have essentially cancelled each other out. As such, what we find is a change in thedistributionofwhobecomesNEETtodayascomparedto20yearsago. Ingeneral,peopleliving inLondon, thosewithchildrenand thosenot livingwithparentsmakeupa smallerpartoftheoverallNEETpopulationthanwewouldhaveexpectedatthestartoftheyear2000 (and before).Once the effect of having children is accounted for,we find evidencethatthoseundertheageof21nowmakeupasmallershareoftheNEETgroupthanthey9Thisisrelatedtotheeffectsofincreasedself-reportsofmentalillhealthnotbeingpickedupinthesimpleranalysisinSection4.1.Specifically,thereferencegroupeffectismorenegative,asinFigure5thiswaspickingupmostoftheeffectofworseningmentalhealth(i.e.itwaspickingupthefactthatasmentalillhealthwasmoreprevalent,theNEETrisksacrossallqualificationandagegroupsshouldbeincreasing).Aroundthis,however,thecoefficientsforothervariableschangedinsimilarwaysascomparedtoFigure5.

29

didbefore2007.We can interpret this as signalling that this group’sNEET risks havenotworsenedasmuchascomparedtotheoldercohorts.

ThestrengthoftheUKlabourmarketasawholehasbeenakeyfactor–weshowthatNEETrisks fell in the 1990s, which accompanied falling rates of unemployment and increasedrates of labour market participation across the entire economy. Greater labour marketopportunities have aidedwomen’s employment,with those in theirmid to late 20swhohavechildreninparticularbeinglesslikelytobeeconomicallyinactiveinrecentyearsthantheywerehistorically.Moreover,differencesinregionalunemploymentconditionsexplainalittleofmaleNEET ratedifferencesacross thecountry,butnot forwomen. Inaddition tothis, being a youngpersonentering the labourmarketduring adownturn canhave long-termconsequences–wefindforwomen,thoseintheirmidtolate20swereinarelativelyworsepositionin2015ascomparedtothoseenteringthelabourmarketseveralyearsafterthe downturn. Finally, the type of jobs available is potentially relevant: we find theproportionoflowerskilledserviceoccupationsinaregionisalsocorrelatedwithlowerNEETrates,althoughthisshouldnotbeimmediatelyinterpretedasacausaleffect–itispossiblethatanotherfactorwhichpushedinactiveNEETsbackintoworkwouldcauseanincreaseinthisformofemployment,ratherthanthesejobsbeingthedriverofNEETratesthemselves.

Ouranalysispointstoanumberofpolicyrecommendations.Thefirstpoint isthatfurtherreductionsinthosewithoutGCSElevelqualificationsmayhelpreduceNEETnumbers10butwe have little evidence that increased education beyond that point is associated withdramatically lowerNEETrates.Oneareawedonotexplore ingreatdetail in thispaper isaround the balance between general and vocational education, due to data limitations.Dolphin (2014)argues that for thosenotgoing throughauniversity route into the labourforce,vocationaloptionsintheUKarelowerquality,lessconnectedtoemployerneedsandcouple with insufficient careers advice and guidance than in neighbouring Europeancountrieswhereyouthunemployment ratesare smaller.Althoughvocationaleducation isbroaderthanrecognisedtradeapprenticeships,wedidshowinsection2.3thattheexistingsystem of apprenticeships has offered young people little additional advantage over thepasttwodecadesintermsofreducingtheirchancesofbeingNEET.Itispossiblethatthereare reforms to vocational education in theUKwhich couldenablemore youngpeople tofind employment, although the UK has a long history of attempting to do this (City andGuilds,2014)andyettheseproblemspersist(Wolf,2011).

In the context of a UK policy narrative entrenched in solving a simplified vision of skillformation and vocational learning (Keep, 2009) however, policy recommendations whichtarget skills and the employability of young people, or which seek to encourageemploymentorfurthertrainingthroughincentivesinthewelfaresystemandguaranteesof

10Presuming,asalwayswiththesesortofinferences,thattheeffectofgainingqualificationonthechancesofbeingNEETratesstayssimilartowhatishasbeeninrecenthistory.

30

workplacements (e.g. Cooke, 2013), aremissing two keyobstacles:mental ill health andchildcare.Ratherthanfocusingonworkincentivesandskilldeficits,policiestoeithertackleincreasingmental illhealthratesorfacilitate(wherepossibleanddesirable)someformoflabourmarketparticipation specific to sufferersofmental ill health couldbeexpected tohave some large effects on their own.Our analysis showed thatmental health problemshave a far larger effect on the chance of being NEET than any other category of healthproblem. These results align with recent research in Switzerland a causal relationshipbetweenmenwithmentalhealthissuesandNEET(Baggioetal.,2015).

Intermsofcare,NEETrateshavebeenpusheddownoverthistimeperiodbythereductioninthenumberofwomenwhohavechildren intheir teensand20s.Previousresearchhaslinkedthis tothesuccessofEngland’sTeenagePregnancyStrategythatwas implementedbetween 1999 and 2010 (Skinner and Marino, 2016), and renewing this approach tominimise unplanned pregnancies could reduce NEET rates further. Moreover, while therelationshipbetweenhavingachildandbeingNEEThasbecomelessstrongoverthetimeperiod, theeffect isstill large.Thiswouldseemtopoint topolicies that reducechildcarecosts and facilitate flexibleworking for thosewhowant to balance carewithwork. Thatsaid,wefindnorelationshipbetweentheavailabilityofpart-timeworkinaregionandNEETrates,sothesolutionhereisperhapsnotassimpleascreatingjobswithlesshours.Perhapsonereasonforthisisthatsuchjobstendtobelesssecureandattractlowerwages,whichshifts the trade-off between work and care responsibilities significantly in favour of thelatter.

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AppendixA:Dataappendix

FigureA1showsaflowdiagramwhichcapturestheprocessfordefinedEETs(i.e.notNEET)andNEETs.

FigureA1:NEETdefinitionflowchart

IneachyearoftheLFSsurveyusedbetween1975and2015,thechallengewastofindthebestvariablestooperationalisethedefinitionofNEET inFigureA1 insuchawaythattheresultingmeasuresareconsistent,despitenumerouschangestotheformatandcontentofthesurvey fromyear toyear. In recentyears, this is relativelystraightforwardasmanyofthe key variables are now standard and easily comparable over time – some are revisedevery few years to include different responses as the world has changed, but they areessentiallyaskingthesamequestions.From1985onwards,webelievewehaveaconsistentmeasureofbeingNEETandinaddition,theLFSprovidespopulationweightswhichwemakeuseof.Wealsosplit thedata intoactiveand inactiveonthebasisofthe ILOdefinitionofunemployment,which involves being available forwork and actively looking for it in thefourweekspreviously.From1992onwards,thisisgivenintheLFS.Before1992,weincludeintheactiveNEETcategoryall thosewhosemaineconomicactivity isreportedasseekingwork,orthosewhohaveanothermaineconomicactivity(apartfromemployment)andwhosaytheyhavelookedforworkinthepreviousfourweeks.

Prior to 1985, the data and variables are less consistent in terms of what questions areasked–webelievewehaveproducedthebestpossibledefinitionusingwhatwasavailable

36

tousintheLFS,butforclaritywedefinethisasadifferenttimeseries.Thepre-1985dataareshowninthetimeseriesgraphs inSection2for interest,butwedonot includethesedata intheanalysis inSection3.Weextendthistimeseriesto1989toshowtheeffectoftwo differences between the two time series – the introduction of the ILO definition ofunemployment(whichaffectsthesplitbetweenactiveandinactiveNEETs),andtheuseofpopulationweights(whichaffectstheoverallNEETrate).

TableA1summarisesthevariablesusedintheLFSdatasetstooperationalisethedefinitionofNEETsetoutinFigureA1

TableA1:LFSvariablesused

Criteriadescription

LFSvariableused(nameinbrackets)withyearscovered

Inemploymentorunemployed(ILOdefinition)

2002-2015:Economicactivity(ILODEFR)

1992-2000:Economicactivity(INECACR)

1989-1991: Economic activity (ECAR) and looking for work in past four weeks(LOOK4WK)

1985: Economic activity (ECONACRG) and looking for work in past four weeks(LOOK4WK)

1981: Economically active in employment (V90), Economically active and seekingwork(V81)andlookingforworkinlastweek(SEEKEMP)

1979: Economic position and status (VAR91) and looking for work in last week(VAR104)

1975:Economicposition(VAR37)

In anapprenticeship

2012-2015:Recognisedtradeapprenticeship(APPRCURR)

2008-2011:Recognisedtradeapprenticeship(APPR8)

2004-2007:Recognisedtradeapprenticeship(APPR4)

1992-2002:Recognisedtradeapprenticeship(APPREN)

1985-1991:Recognisedtradeapprenticeship(APPRENT)

1981:Recognisedtradeapprenticeship(APPRENT)

37

1979:Recognisedtradeapprenticeship(VAR63)

1975:Typeofcourseorganiser(VAR113)

In agovernmenttrainingscheme

20012-2015:Ongovernmentscheme(SCHM12)

2008-2011:Ongovernmentscheme(SCHM08)

2004-2007:Ongovernmentscheme(SCHM04)

2000-2002:Ongovernmentscheme(SCHM99)

1998:Ongovernmentscheme(SCHM98AandSCHM98b)

1989-1996:Ongovernmentscheme(SCHEME)

1985:Ongovernmentscheme(SCHEMES)andOnYouthTrainingScheme(YTS)

1981:Usualeconomicactivity(USITEMP)

1979:Usualeconomicactivity(VAR32)

1975: No information, possibly included in full-time student from usual economicactivity(VAR33)

On a trainingcourse

2002-2015:Stillattendingacourse(ATTEND)

1989-2000:Currenteducationreceived(CURED)

1985:Currenteducationreceived(CUREDG)

1981:Qualifications(QUALONE,QUALTWOandQUALTH)

1979:Qualifications(VAR65,VAR66andVAR67)andTypeofeducationandtrainingreceived(VAR150)

1975:Typeofcourseorganiser(VAR113)

Stillinfulltimeeducation

1992-2015:Ageleftfulltimeeducation(EDAGE)

1985-1991:Ageleftfulltimeeducation(FTEDAGE)

1981: Terminal education age (TEREDAG), Usual economic activity (USITEMP) andEconomicallyinactiveandstudent(V91)

1979: Terminal education age (VAR130), Usual economic activity (VAR32) and

38

Economicallyinactiveandstudent(V87)

1975: Age left full time education (VAR112), Usual economic activity (VAR33) andEconomicallyinactiveandstudent(VAR37)

For the quarterly surveys from 1992 onwards,we use the second quarter (April to June)dataasthisisclosesttotheperiodwhentheannualsurveydatafieldworkwasconducted.Morevoer, looking across the years, data from April to June would seem to provide ameasureofthenumberofNEETindividualsthatavoidsseasonaleffects–inparticular,NEETrates are high immediately after September as individuals finish some form of formaleducationandare searching for theirnextmove,andare lower inOctober toDecember,whichisimpacteduponbyhigherseasonalemploymentprospects.

FigureA3–NEETratesbyquarter,1994-2013

Source:LFS,owncalculations.

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

Allages Allages Allages

1994 2007 2013

Jan-Mar Apr-Jun Jul-Sep Oct-Dec

39

AppendixB

FigureB1:Maleactiveandinactivecoefficienteffects

-7.0%

-6.0%

-5.0%

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

1985-1993 1993-2000 2000-2007 2007-2015

22-25 26-29 Lowquals GCSEs Degree Regions Reference Total

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

1985-1993 1993-2000 2000-2007 2007-2015

22-25 26-29 Lowquals GCSEs Degree Regions Reference Total

40

FigureB2:Femaleactiveandinactivecoefficienteffects

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

1985-1993 1993-2000 2000-2007 2007-2015

22-25 26-29 Lowquals GCSEs Degree Regions Reference Total

-5.0%

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

1985-1993 1993-2000 2000-2007 2007-2015

22-25 26-29 Lowquals GCSEs Degree Regions Reference Total