scientific research: latvia: “who is unemployed, inactive ... · facilitate an effective...

The World Bank

INTERNATIONAL BANK FOR RECONSTRUCTION AND DEVELOPMENT INTERNATIONAL FINANCE CORPORATION

MULTILATERAL INVESTMENT GUARANTEE AGENCY

Scientific research: Latvia: “Who is Unemployed, Inactive or Needy? Assessing Post-Crisis Policy Options”

PROFILING OF PEOPLE WITH NO OR LIMITED LABOR-MARKET ATTACHMENT AND OF LOW INCOME

European Social Fund Activity

INVESTING IN YOUR FUTURE!

European Social Fund Activity „ Complex support measures” No. 1DP//1.4.1.1.1./09/IPIA/NVA/001

WhoisunemployedorreceivingwelfarebenefitsinLatvia?Anatomyofjoblessness,marginalemploymentandbenefitdependency1

1. Introduction

A common insight from policy evaluations of both employment and social support measures is thatcareful targeting is crucial for the success of activation and poverty reduction strategies. This isespecially the case when the group of potential policy “clients” is growing and becoming moreheterogeneous,asisthecaseinLatviatoday.

UnemploymentinLatviahasincreaseddramaticallysincetheonsetoftheeconomiccrisisin2008and,while down significantly from its peak in early 2010, remainsmore than twice as high as before thecrisis(Figure1).2With job lossesmountingduringtheearlyphaseof therecession, theshareof long‐termunemployeddeclinedinitiallybeforereaching50percentandmorein2010.However,asistypicalfor the aftermath of deep recessions, long‐term unemployment is now falling only very slowly andremainsataveryhighlevelevenasunemploymentratesdecline.Inaddition,unemploymenttrendsdonot capture the full extent of labor market detachment. As more and more job seekers queue foravailable employment vacancies in a labor‐market downturn, a growing number of jobless becomediscouraged,stopactively looking forwork,andarethereforeno longercountedasunemployed.Asaresult,inactivityratescanremainhighorkeepgrowing,evenasunemploymentratesdecline.InLatvia,the share of labor‐market inactivity remained close to 40 percent throughout 2010, despiteunemploymentratesdroppingbyasmuchasonefifth.

Monitoring and responding to the greater diversity of labor market difficulties is one of the keychallengesofsocialandemploymentpolicyduringandafterarecession.Forinstance,whilerecentjoblosersarelikelytoberelativelywellplacedtoengageinself‐directedjobsearchandmayinitiallyonlyrequire limited“active”employmentsupportservices, long‐termunemployedordiscouragedworkerstypically facemore formidableemploymentbarrierscalling formorecomprehensive interventionandsupport.

Foranumberofreasons,careful targetingandcustomizingof“passive” incomesupportalsobecomesmore importantwhen labormarketsareweak.First, thereareagreaternumberofhouseholds facingsevereeconomicchallenges;makingincometransfersavailabletothosewhoneedthemmostthereforebecomesmorepressing.Second,prolongedlabor‐marketweaknesscanheightenconcernsabout long‐termbenefitdependency.Tailoringfinancialsupporttoindividualandfamilycircumstancescanthenbean important part of strategies tomaintain beneficiaries’ incentives to regain self‐sufficiency. Finally,fiscalpressuresintensifythesearchforwaystotargetandrefocussocialspending.

This paper aims to strengthen the empirical basis for designing and targeting income support andactivationpolicies. Itwillusesurveydata to identify relevantgroupingsandcharacteristicsofbenefitrecipientsandof individualswithnoor limited labor‐marketattachment.Recognizingthat thosewithlimitedornoattachmenttothe labormarketareahighlyheterogeneousgroup, itseekstocontributepolicy‐relevant informationon the typesofsocialandeconomicrisks thatdifferentgroupsare facing, 1 The note was written by Céline Ferré and Herwig Immervoll with inputs from Emily Sinnott. 2 See also Vanags (2012).

and on the barriers that hold back their labor‐market integration. The resulting information canfacilitateaneffectivetargetingofpoliciesthatseektoalleviatethesebarriers.Forinstance,informationonthecharacteristicsofbenefitrecipientsoroflabor‐marketinactiveworking‐ageindividualscanbeabasisforidentifyingwhichgroupsareandarenotservedeffectivelybyexistingactivationandsupportpolicies,andforchannelingpolicyeffortstowardsspecificprioritygroups.

Figure1:Afterarecession,labormarketdifficultiesaremorewidespreadandprobablymorevaried

Source:Eurostat(EuropeanLaborForceSurvey).

Section2ofthepapersetsouttheextentandtypesoflabormarketdifficultiesinLatvia.Theaimistomovebeyond analyzing the situation of the registeredunemployed, and to cover the entire potentialworkforce that suffers from limited labormarket attachment. Those outside or on themargin of thelabor market are frequently moving between non‐employment and different states of “precarious”employment.Asaresult, lookingonlyatsomeofthesestates(e.g.,unemployment)wouldnotcapturethe true extent of labor‐market difficulties or the need for policy intervention. To provide acomprehensiveviewonrecentlabormarketchallenges,thepaperthereforedevelopsabroadconceptof“weaklabormarketattachment”,includingunemployment,inactivity,informalworkaswellassporadicor low‐paidwork. The section gives the definitions used and sources of data.Section3 outlines thecharacteristicsofgroupswhoarefacingseverelabormarketdifficulties.Ituseshouseholdsurveydatatocharacterizegroupsofpeopleshowingweaklabor‐marketattachmentoverextendedperiodsoftime.Section 4 identifies the main groups with persistent labor‐market difficulties. The aim is to grouptogetherindividualswithsimilarcharacteristicsusingawiderangeofdemographic, family,socialandlabor‐marketcharacteristics.Thisprovidespolicymakersandcaseworkersinformationonthedifferingneeds of the various groups experiencing labormarket difficulties in Latvia. In section5, the paperconcludeswithadiscussionofthetargetingofemploymentandincomesupporttotheidentifiedgroups.

ThepaperislinkedtoWorldBank(2013a),whichfocusesonrecipientsofwelfarebenefits(guaranteedminimumincome(GMI)andotherbenefits).Usingadministrativedata forselectedcities,WorldBank(2013a)documents therise inbenefit recipiencyand the incidenceofbenefit receiptamongdifferentpopulation groups. The data is then used to reconstruct benefit spells for each recipient in order to

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investigate whether welfare benefit receipt has been largely a temporary phenomenon or whetherrecipientshavetypicallybeenlong‐termdependentonthesestatebenefits.

ThispaperalsocomplementstheexistingreportonbarrierstoemploymentcommissionedbytheStateEmploymentAgency(SEA).3Theresultsdisplayedheremaydifferfromtheformer,asthemainanalysishereuses longitudinaldata, thedatasetsandyearsofreferencearedifferent fromtheSEAreport, thetypeof informationcollected(andthusthetypeof informationthatcanbeused) ismorequantitativethanintheSEAsurvey,andthepopulationofinterestisnotthesame:WhilethetheSEAreportfocuseson unemployed individuals at a given point of time, the present study comprises a larger group ofindividualswithlonger‐lastingorrepeatedlabormarketproblems,includingtheinactiveandinformal.

2. Joblessness and weak labor-market attachment

Extentandtypesofemploymentdifficulties

Spellsofunemploymentareanecessaryelementofamarket‐basedgrowthprocess.Ifjobreallocationisefficient, inthesensethat jobsmovefromlessproductivetomoreproductivefirmsorsectors, thenitleadstoamoreproductiveeconomyandtohigherincomes.Thisisespeciallytrueafterdeeprecessions,whichtendtobeassociatedwithsignificantrestructuringandchangesinthesectoralcompositionofaneconomy.

Box1:Dataanddefinitions

Data:twopaneldatasets:EU‐SILC2006/9andEU‐SILC2007/10.

Definition of vulnerability: an individual is considered vulnerable (V5) ifhe is experiencing the followingsituationduringatleasthalfoftheobservedyears:

‐“Notworking”(V1):notreportedtohaveworkedduringanymonthoftheyear,ornolaborincome

‐“Lowworkintensity”(V2):employedorself‐employedatleastone,butnomorethansixmonthsduringtheyear

‐“Lowearnings”(V3):laborincomelessthan80percentofthefull‐time,full‐yearminimumwage

‐“Informal”(V4):positivelaborincomebutno(employer)socialsecuritycontributions,orlaborincomeismainlyearnedinkind,orthepersonreportsbeinganunpaidfamilyworker

Thesefourcategoriesrenotmutuallyexclusive,andanindividualcouldforinstancebeengagedininformalwork(V4)andreceivinglowearnings(V3).

Population of interest: vulnerable working‐age individuals (aged 18 to 61) in each model constitute thepopulationofinterest.

Different barriers can, however, prevent or slow an efficient job reallocation, creating significanteconomicandsocialcosts.4Forsomegroups,acombinationofpolicy‐relatedbarriersandinsufficient

3 “Development of method for classification/profiling of unemployed” prepared by Consortium Ltd “Projektu un

kvalitātes vadība,” Ltd. E-Synergy under ESF project “Improvement of Management Capacity of SEA,” No. 1DP/1.3.1.4.0/08/IPIA/NVA/001.

4 For instance, inadequately resourced or poorly targeted re-employment services reduce the quality of matches between job seekers and job vacancies. Ineffective income support for jobseekers can have a similar effect. On the one hand, insufficient support can prevent jobseekers from engaging in a thorough search for suitable vacancies, or force them to accept low-productivity or informal jobs that do not adequately use or remunerate their skills. On the other hand, overly

skillsorworkexperiencemeansthattheyremain“stuck”withoutajoborinmarginalemploymentforextended periods of time. Long‐term labor‐marketmarginalization or detachment is known to erodehumancapitalandreducebothcurrentincomesandfutureearningsprospects.Addressingthecausesoflong‐termlabor‐marketdifficulties,andalleviatingtheirconsequences, is thereforeacrucialchallengeforlabor‐marketandsocialpolicy.

With long‐termunemployment rising,onewouldexpecta largenumberof individuals inLatvia tobe“stuck”withoutwork.Aweaklabormarketmayalsobeexpectedtopushupthenumberofpeoplewithsporadic,low‐paidorinformalwork.Butinthislatterrespect,theimpactoftherecessionisinfactnotentirelyclear‐cut.Ontheonehand,amuchweakerlabormarketislikelytomakepeoplemorewillingtoengageinlow‐paid,temporaryornon‐declaredemploymentactivitiesasa“secondbest”incomesource.Employerswho are under pressure to reduce costswill also seek tomake greater use of lower‐costalternativestoregularemployees.However,ontheotherhand,informalworkersandotherswithnoorlittleemploymentprotectionfrequentlyassumea“buffer”functionthathelpsfirmstoincreasecapacityduring a boom,without increasing fixed costs. Typically, these jobs are then the first ones to go in adownturn.

Figure2:Fourdifferenttypesoflabor‐marketdifficulties

Using the Survey of Income and Living Conditions (SILC) that follows individuals over a four‐yearperiod,itispossibletoexaminethesetrends,andthehistoriesofpeople’slabor‐marketexperiences,insomedetail.Wedistinguishbetweenfourdifferenttypesoflabor‐marketdifficulties:notworkingatall(“vulnerability I (V1)”, which includes both the unemployed and the inactive), working only a fewmonthsduringtheyear‐“lowworkintensity”‐(“vulnerabilityII(V2)”),lowearnings(“vulnerabilityIII(V3)”),and informalwork(“vulnerability IV(V4)”).Thedefinitionsaresummarized inBox1.Someof

generous or unconditional out-of-work support can delay or weaken job search activities. The relative importance of the different barriers is likely to vary with economic conditions. For instance, evidence summarized in a companion paper shows that when labor markets are weak, adverse work incentives are a less relevant determinant of employment outcomes (World Bank, 2013c, and Immervoll, 2012).

Notworkingatall

Informalwork

Lowearnings

Lowworkintensity

VulnerabilityIV(V4)

VulnerabilityII(V2)

VulnerabilityI(V1) VulnerabilityIII(V3)

thosecategoriescanoverlap(e.g.,thoseworkinginformallyorsporadicallywilltypicallyhaveahigherriskoflowearnings).ThegroupingsareshownschematicallyinFigure2.

Figure3:Broadcategoriesofpersistentlabormarketdifficulties

Between2007and2010,asmanyasonethirdofworking‐ageindividualsareeitheroutofworkorinmarginalemploymentinatleasthalfoftheobservedyears(Figure3,panelb).Onlyaminorityofthem(10percentofworking‐ageindividuals)arepersistentlyjobless.Almostasmanyhaveaninformal,low‐paying or unstable job. And about the same number again move between no job and marginal orsporadic work or are inmore than one of these categories at the same time (e.g., informal and lowearnings). This pattern indicates that persistent labor‐market difficulties are indeed far from one‐dimensional. In particular, they go beyond long‐term unemployment and therefore require anassessmentofabroadrangeofout‐of‐workandin‐worksituations.

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Notes:Persistence is defined as experiencing the relevant status during at least onehalf of theobservedyearsusingthefollowingcategories.“Notworking”(V1):notreportedtohaveworkedduringanymonthoftheyear,ornolaborincome.“Lowworkintensity”(V2):employedorself‐employedatleastone,butnomorethansixmonthsduringtheyear.“Lowearnings”(V3):laborincome less than80percentof the full‐time, full‐yearminimumwage. “Informal” (V4):positivelaborincomebutno(employer)socialsecuritycontributions,orlaborincomeismainlyearnedinkind,orthepersonreportsbeinganunpaidfamilyworker.“Cyclers”:thosewhoareinmorethanoneofthesecategoriesduringtheperiod.Inallcases,thosewhoareineducationorinmilitaryserviceduringmostof the year arenot categorizedas facing labormarketdifficulties (andarethereforenotineitheroftheabovecategories).

Informality isnot capturedby the longitudinaldata spanning2006/2009as it doesnot recordemployers'contributiontosocialsecurityforeveryyear,sowedidnotusethe"informalwork"variable. The longitudinal data spanning 2007/10 does record employer's social securitycontributionforeveryyear,andweusethisinformationtoidentifyinformality(seeBox1).

Source:WorldBankstaffcalculationsbasedontheLatvianversionofEU‐SILC,waves2007‐2010and 2008‐2011 (with 2006 to 2009 and 2007 to 2010 as the reference year for incomes andactivitycalendars).

Asonewouldexpect,persistentlabormarketdifficultieshavebecomemorecommonafterthestartofthedownturn:while21percentofthepopulationwerepersistentlyvulnerablebetween2006and2009,thesamefigurefor2007‐2010risesto30percentofthepopulationofworkingage(seeFigure3).Theproportionofindividualsnotworkingorcyclingbetweenlowpayandnopayhavebothincreasedwiththecrisis,fromrespectively9to10percentand6to10percent.Ontheotherhand,lowworkintensityhasgonedownfrom4to2percent. Onemustbecautious though,aspartof therise invulnerabilityfrom2006/9 to2007/10 isdue to the inclusion in the laterpanelof informality (whichmechanicallyincreasesthenumberofvulnerableindividuals).

3. What characteristics are associated with persistent labor-market difficulties?

This section takes a closer look at individuals categorized as experiencing persistent labor‐marketdifficulties (PLD) above. The information presented complements commonly used labor‐marketstatisticsinseveralways:

Itaccounts for labor‐marketexperienceovera longerperiod,ratherthanataspecificpoint intime;

It is multi‐dimensional, meaning that it accounts for a whole range of potentially relevantcharacteristics (e.g., age, gender, number of children, education andwork experience), ratherthanonlyonedimensionat a time.Thisallows foramoredetailedexaminationof the factorsthat are positively or negatively associatedwith risks of persistent joblessness or precariousemployment;

It considers both individual and family characteristics. Family circumstances are central fordesigning and targeting employment and income support measures. Understanding them isarguably especially important during and after a severe downturn as families can provideessentialincomestabilizationfollowingearningslossesofonefamilymember.

Table 1 reports the main results of a simple statistical model, which relates PLD status during a four-year period to a broad range of potentially relevant individual and family characteristics. This format is convenient for investigating which of a large number of factors are associated with a higher risk of PLD. The regression approach is particularly helpful for disentangling the importance of different factors that are typically correlated (such as sex, family status and work experience), which cannot be done using simple cross tabulations. AnumberofindividualandhouseholdcharacteristicsclearlyincreasetheprobabilityofbeingatriskofPLD (positive coefficient): age, illness, being a single parent, having 3 children ormore, sharing thehouseholdwithasickperson, livinginaruralarea,anylaborstatusotherthanworkingfull‐time, lowqualified jobs. By contrast, more work experience, education, as well as having an occupation thatrequireshigherqualificationlevelsreducestheriskofPLD(negativecoefficient).HigherpartnerincomealsoappearstoreducetheriskofPLD. Thismaybesurprising,asindividualswhoshareresourcesinthesamehouseholdmaybeexpectedtoworklesswhenhouseholdincomegoesup.Theresultindicatesa significant degree of “assortative matching” (those with higher incomes partner with high‐incomeindividualsandviceversa),andthatpersistentlabor‐marketdifficultiesmayfrequentlyaffectmorethanonehouseholdmemberatthesametime.

Table1:FactorsassociatedwithahigherorlowerPLDrisk(2007‐10Panel)

Logisticregression Numberofobservations=4,908Dependentvariable:Persistentvulnerability(V5) LR2(39)=1,035,606 Prob>2=0Loglikelihood=‐967,467 PseudoR2=0.3486

Vulnerabilitystatus Coefficient Std.Err. z P>z [95%C.I.]Constant ‐5.517 0.094 ‐58.73 0.0 ‐5.70 ‐5.33Age 0.173 0.007 23.58 0.0 0.16 0.19Agesquared 0.068 0.019 3.69 0.0 0.03 0.10Agecube ‐0.016 0.002 ‐10.87 0.0 ‐0.02 ‐0.01Experience ‐0.184 0.001 ‐149.91 0.0 ‐0.19 ‐0.18Experiencesquared 0.088 0.003 32.54 0.0 0.08 0.09Dummy(female) ‐0.776 0.007 ‐116.24 0.0 ‐0.79 ‐0.76Dummy(ill/sick) 0.273 0.005 58.42 0.0 0.26 0.28Dummy(inarelationship) 0.150 0.007 21.67 0.0 0.14 0.16Dummy(singleparent) 0.316 0.068 4.65 0.0 0.18 0.45Dummy(children<6yearsold) ‐0.382 0.008 ‐49.17 0.0 ‐0.40 ‐0.37Dummy(3+children) 0.060 0.003 18.51 0.0 0.05 0.07Dummy(>59yearsold) ‐0.035 0.005 ‐7.36 0.0 ‐0.04 ‐0.03Dummy(partnervulnerable) 0.000 0.000 ‐49.12 0.0 0.00 0.00Dummy(otherfamilymemberill) 0.111 0.006 18.76 0.0 0.10 0.12Log(partnerincome) ‐0.028 0.001 ‐25.95 0.0 ‐0.03 ‐0.03Female*Loweducation 0.469 0.007 71.17 0.0 0.46 0.48Female*Higheducation ‐0.510 0.007 ‐68.78 0.0 ‐0.52 ‐0.50Female*Inarelationship 0.003 0.008 0.35 0.7 ‐0.01 0.02Female*SingleParent 0.341 0.070 4.89 0.0 0.20 0.48Female*Children<6y.o. 1.072 0.010 104.89 0.0 1.05 1.09Female*3+children 0.276 0.008 35.64 0.0 0.26 0.29Female*Otherfamilymemberill ‐0.235 0.008 ‐28.68 0.0 ‐0.25 ‐0.22Rural 0.012 0.004 3.11 0.0 0.00 0.02

Economicstatus(omitted=workingfull‐time) Workingpart‐time 1.276 0.008 155.54 0.0 1.26 1.29Unemployed 2.440 0.007 326.47 0.0 2.42 2.45Pupil,student,training 0.441 0.014 31.62 0.0 0.41 0.47Retirement 3.450 0.016 220.56 0.0 3.42 3.48Disabled/unfitforwork 3.805 0.016 232.73 0.0 3.77 3.84Domestictasks 3.347 0.014 241.63 0.0 3.32 3.37Otherinactive 2.516 0.016 159.77 0.0 2.49 2.55

Occupationstatus(omitted=craftsworkers) Legislators/seniorofficials/managers ‐0.241 0.013 ‐18.79 0.0 ‐0.27 ‐0.22Professionals ‐0.262 0.011 ‐23.65 0.0 ‐0.28 ‐0.24Technicians/associateprofessionals 0.258 0.010 25.78 0.0 0.24 0.28Clerks 0.042 0.014 2.96 0.0 0.01 0.07Serviceworkers/shop/marketworkers 0.721 0.010 73.45 0.0 0.70 0.74Skilledagriculture/fisheries 1.940 0.014 138.49 0.0 1.91 1.97Craftsworkers 0.678 0.009 73.89 0.0 0.66 0.70Elementaryoccupations 1.307 0.009 141.73 0.0 1.29 1.33Notworking 0.255 0.010 25.82 0.0 0.24 0.27Note:seeAnnexforcompletedefinitionofvariables.Source:WorldBankstaff calculationsbasedon theLatvianversionofEU‐SILC,waves2008‐2011(with2007 to2010as thereferenceyearforincomesandactivitycalendars).

Interestingly,whencontrollingforothercharacteristics,suchaseducationlevels,womenarelesslikelytoexperiencePLD(negativecoefficient forvariableDummy(Female)). However,womenwithgreaterfamilyresponsibilities,suchascaringforayoungchild(Female*Children<6y.o.)aremuchmorelikelytohavepersistentlyweaklabor‐marketattachment.Partneredindividuals(Dummy(inarelationship))(asopposedtothosewhoaresingle,divorcedorwidowed)areatagreaterrisk.

4. What are the main groups with persistent labor-market difficulties?

The results in Section 3 provide pointers for understanding specific individual risk factors that areassociated with a higher probability of persistent joblessness or marginal employment. However,designingandtargetingemploymentandincomesupportmeasuresalsorequiresknowledgeaboutonthecombinedcharacteristicsofpeopleaffectedbyPLD.For instance,caseworkersat theemploymentoffice or the benefit administration need to have as full a picture as possible about their clients’education, income, family situation, health status and work experience. This is something that theregressionapproach,whichfocusesononefactoratatime,cannotprovide.

Tofillthisgap,thissectionidentifiesthesizeandcharacteristicsofdifferentPLDgroups.Thisisdoneusingavariantofastatisticalclusteringapproach.Thebasic ideabehind this is toclusterpeople intogroupsthatarebothmeaningfulstatisticallyandusefulforpolicypurposes.Thismeans

thatgroupmembersshouldbesimilartoeachother,

thatmembersofdifferentgroupsshouldbedissimilar,and

thatthecharacteristicsusedtodefinegroupmembershipshouldbeobservablebypolicymakers,administratorsorcaseworkers.

Box 2 providesmore details on the statistical approach used to search for suitable groupings over awiderangeofdemographic,family,socialandlabor‐marketcharacteristics.Theresultofthisexerciseisa setof groupscharacterizedbysimilarcharacteristicsofmemberswithineachgroup,anddissimilarcharacteristicsbetweengroups.

Box2:Approachestoidentifyingthemeaningfulsubgroups

Latent Class Analysis (LCA) enables a characterization of categorical latent (unobserved) variables from ananalysis of the structure of the relationships among several categorical observed variables. LCA is thus "theclassification of similar objects into groups,where the number of groups, aswell as their forms are unknown"(KaufmanandRousseeuw,1990).Themethodwasoriginallyconceivedofasananalyticmethodforsurveydata.Asanexploratorytechnique,LCAcanbeusedtoreduceasetofseveralcategoricallyscoredvariablesintoasinglelatentvariablewithasetofunderlyingtypesor“classes”.Asaconfirmatorymethod,thelatentclassmodelcanbeused to test hypotheses regarding the researchers’ a priori assertions about the structure of the relationshipamong the observed variables. In this paper, LCA was used as an exploratory technique to find an “optimal”numberofgroupsofindividualsatriskofpovertywiththemostsimilarcharacteristics.

The LCA model can be seen as a probabilistic or model‐based variant of traditional non‐hierarchical clusteranalysis procedures such as theK‐meansmethod. Contrary to traditional ad‐hoc clustering approaches, the LCapproachtoclusteringismodel‐based.ThefundamentalassumptionunderlyingLCAisthatoflocalindependence,whichstatesthatobservations(AROPE(AtRiskOfPovertyorsocialExclusion)individualsinourcase)inthesamelatentclassshareacommonjointprobabilitydistributionamongtheobservedvariables.Sincepersonsinthesamelatentclass(cluster)cannotbedistinguishedfromeachotherbasedontheirobservedresponses,theyaresimilar

toeachother (homogeneous)with respect to theseobservedvariables. Individualsarehenceclassified into theclassforwhichtheyhavethehighestposteriorprobabilityofbelonging,giventheirobservedcharacteristics.

LCAisthusmostsimilartotheK‐Meansapproachtoclusteranalysis inwhichcasesthatare"close"tooneofKcentersaregroupedtogether. In fact,LCAcanbeviewedasaprobabilisticvariantofK‐Meansclusteringwhereprobabilitiesareusedtodefine"closeness"toeachcenter.Assuch,LCAprovidesawaynotonlytoformalizetheK‐Means approach in termsof a statisticalmodel, but also to extend theK‐Means approach in several directions:flexible distance to the center of the cluster, determination of the optimal number of clusters, inclusion ofcategoricalandcontinuousvariables, and inclusionofexogenousvariables.First,whileK‐Meansusesanad‐hocdistancemeasureforclassification,theLCAapproachallowscasestobeclassifiedintoclustersusingmodelbasedposterior membership probabilities estimated by maximum likelihood (ML) methods. Second, LCA providesvariousdiagnosticssuchastheBICstatistic,whichhelpdeterminethe“optimal”numberofclusters.Third,whileK‐Meansclusteringis limitedto interval‐scalequantitativevariables, forwhichEuclideandistancemeasurescanbecalculated,LCAcanbeperformedonvariablesofmixedmetrics(continuous,categorical(nominalorordinal),or counts or any combination of these). Fourth, the LCA model can be easily extended to include exogenousvariables(covariates).

TheLCAmodelwithcovariatescanbewrittenas:

f(yi|zi)=Σkp(x=k|zi)f(yi|x=k)

where yi is a vector of dependent (endogenous) indicators for individual i, zi is a vector of independent(exogenous)covariatesforindividuali,xisanominallatentvariable(andkdenotesaclass,k=1,2,...,K),andf(yi|x=k)denotesthejointdistributionspecifiedforyigivenlatentclassx=k.

Forcontinuousyi,themultivariatenormaldistributionisusedwithclass‐specificmeans.Inaddition,thewithin‐class covariance matrices can be assumed to be equal or unequal across classes, and the local independenceassumptioncanberelaxedbyapplyingvariousstructurestothewithin‐classcovariancematrices.Forvariablesofother/mixedscaletypes,localindependenceamongthevariablesimposesrestrictionsonsecond‐orderaswellastohigher‐ordermoments.

Forthisnote,vulnerableindividualsineachmodelconstitutethepopulationofinterest.Analyseswerecarriedoutusingpersistentvulnerability:populationofindividualswhowerevulnerableinatleastonehalfoftheobservedfourconsecutiveyears.Thepopulationwasthenrestrictedtoworking‐agepopulation(individualsaged18to61).Agecategories,gender,civilstatus,education,experience,economicstatus,householdcomposition,chronicillness,type of vulnerability, partner’s vulnerability status and log‐income, urban/rural breakdown, represent theobserved covariates and were used to predict the vulnerability status. In addition to these active covariates,exogenous variables (inactive covariates) were included in the model, such as vulnerability index, mean laborincome,difficulties topay(heating,arrears),housingownership,whether the individual isactively lookingforajob,andsizeofsocialprotectiontransfers.Theinactivecovariatesdonotinfluencethedivisionofthepopulationofinterestintoclusters:theyareaddedforthedescriptivestatisticsandhelpthereaderunderstandthecompositionofthegroups.

Twolongitudinalanalyseswereconducted.Foreachofthem,theLCAmodelwasrunwithonetofifteenclasses,thus leading to fifteen different estimations. To determine the optimal number of clusters into which thepopulationofinterestshouldbedivided,weusedtwocriteriathatmaximizethecohesionwithinclustersandthedistancebetweenclusters: theAkaikeandBayesianInformationCriterion(AICandBIC).Choosingthe“optimal”numberofclustersisnotalwaysscientificasoften,neithertheAICnortheBICreachaminimum.Whenthatisthecaseandthereseemtobeseveral“potential”optimums,thefinalnumberofclusterschosenwasdeterminedbythesteepnessoftheAICandBICcurvesincombinationwiththesizeoftheclustersandresearchers’experience.

Asan illustration,Table2showstwoexamplesof theresultinggroupsofPLD individuals. Inorder tofocusonthemainvariablesthatcharacterizethesegroups,theexampleshowsonlythosecharacteristics

thatturnouttobehelpfulfordistinguishingthegroupfromothers(thesubsetofcharacteristicsshownthereforemainlydiffersbetweenthetwogroups).Theillustrationsshowthattheclusteringmethodisable to separate groups quite sharply along some of the characteristics. For instance, almost allindividualsinGroupAwerewomenbutjustoveronequarterinGroupB.Likewise,GroupAmembershadabove‐averageeducationlevels(justunder30percenthadcompletedatertiaryeducation),whilenearlyhalfofGroupBhadnotcompletedsecondaryeducation.OnlyveryfewindividualsinGroupA,butalargemajorityinGroupB,haveaspouseorpartnerwhoisalsofacingPLD.Membersofbothgroupslivewithaspouseorpartnerandhavechildren (under6yearsofage), so this isnotadistinguishingfeature.Butaswillbeshownbelow, it sharplyseparatesbothgroups fromanumberofothergroupsthataremadeupmostlyofunmarriedorchildlessindividuals.

Other characteristics vary more widely within groups. While most women in Group A were notpersistently without a job, a sizeable majority of them are. And although there is, relative to othergroupsofsimilarage,anabove‐averageincidenceofchronicillnessinGroupB,themajorityofthegroupdoes not report long‐lasting health problems. In these and in other cases, individuals who differ incertain respects are nonetheless grouped together if they are, in a statistical sense, otherwise“sufficientlysimilar”.

Table2:Groupswithpersistentlabor‐marketdifficulties‐Anillustration

Panel A Panel B

The statistical clustering tool provides probabilities for characteristics of groupmembers but not, ofcourse,specificlabelsforthesegroups.Resultscanneverthelessbeusefulasabasisforthinkingaboutgroup labels. There are a large number of characteristics so care must be taken to resistoversimplifications,andtokeeparbitraryjudgmentstoaminimum.Withthisinmind,carefullabelingcanbeusefulasabasisfordiscussingsuitablepoliciesforeachofthegroups.ThelabelsshowninTable2illustrateanattempttofindsuitablelabelsthatarecapturerelevantgroupcharacteristics.

The full set of groups is visualized in Figure 4 showing, again, only those characteristics that areparticularlyrelevantfordistinguishingeachofthegroupsfromtheothers.(Annex1TableA.1providesa list of all characteristics that were used as an input into the clustering analysis, along with theirdefinition,whileAnnex2TablesA.2throughA.3showdetailedresultsforallgroupsandtheentiresetofcharacteristics).Labelshavebeenderivedusing the sameprocedureas illustrated inTable2and themostsizablegroupisshownfirst.

Figure4:Groupswithpersistentlabor‐marketdifficultiesCompletegroupingsfortheperiod2007‐10

Group1“singleolderunemployed/disabled”

Group2“singleyoungmaleswithloweducation”

Group3“olderunemployed,fitforwork”

.Old/Middle‐aged45‐61y.o..Single.10+yrs.experience.Loweducation.Manydisabled/unemployed.Chronicillness

.Young 20‐29y.o..Men.Nevermarried.Veryloweducation.Unemployed.Nochildren.Rural

.Older50+

.Married

.10+yrs.experience.Loweducation.Unemployed/Lowearnings/Infrequentwork

Group4“stay‐at‐homemumswithsmallchild”

Group5“poorlyeducated,ruralmalebreadwinner”

Group6“self‐employedoldermen”

.Youngerwomen25‐39y.o..Married/union.Highereducation.Child<6y.o..Rural.Workingpartner

.30‐44 y.o.men.Married/union.VeryLoweducation.10+yrs.experience.Child<6y.o..Rural.Partnernotworking

.Oldermen 40‐54y.o..Married.10+yrs.experience.Self‐employed.Nochildinhousehold.Informal

Group7“disabledolderwomenwithworkingpartner”

Group8“highlyeducatedstay‐at‐homemums”

Group9“disabledolderwomen,partnernotworking”

.Olderwomen50+

.Married

.Lowereducation

.10+yrs.experience

.Highdisability(mostinsample),inactive.Chronicillness.Workingpartner

.30‐39y.o.women.Married.Highereducation(most).10+yrs.experience.Children.Urban .Workingpartner

.Olderwomen50+

.Married

.Lowereducation

.10+yrs.experience

.Unfitforwork,inactive

.Largeshareretiredearly

.Chronicillness

.Partnernotworking

Notes:GroupsizesaregivenforeachgroupandshowthepercentageofallPLDindividualsinagroup.SeeAnnex2TableA.3forfullresultsshowingprobabilities/incidenceforthefullsetofcharacteristics.

Source:World Bank staff calculations based on the Latvian version of EU‐SILC, waves 2008‐2011(with2007to2010asthereferenceyearforincomesandactivitycalendars).

22% 18%

11% 11% 9%

6% 6% 4%

14%

TheclusteringapproachhaspartitionedthePLDpopulationintonineseparategroupsofvaryingsizes:4to22percentofthetotalPLDpopulation(seeFigure4).Outoftheseninegroups,emergepopulationsthat may commonly be associated with labor‐market vulnerability. But there are also a number ofgroupsthatonemaynottypicallyseeasvulnerable,orthatmaynotbeafocusofthepolicydebateatall.

‐ As expected, older individuals with chronic illnesses represent a large chunk of the PLDpopulation:threeofthegroupsconsistofamajorityofchronicallyill,oldindividualswhoareoutof the labor force (unemployed, unfit forwork or at home)whoworkedmore than 10 years(Groups1,7and9).Thelargestgroup(Group1,22percentofallPLD)consistsofdivorcedornever married unemployed and disabled, the second group (Group 7, 6 percent of all PLD)consistsofmarriedolderwomenwithaworkingpartnerwiththehighestlevelofdisabilityoutof all groups, and the smallestgroup (Group9,4percent) ismainlymadeofmarriedwomen,manyofwhomareunfitforworkorhaveretiredearly,whosepartnerisalsonotworking.

‐ Similarly, young or “prime-age” less-educated individuals are well represented within the PLD population. The largest group is made of young married men, many of whom are 20 to 29 years old, with very low levels of education (many have not completed secondary education), and constitutes the second largest PLD group (Group 2, 18 percent). A smaller group (Group 5, 11 percent) is made up of married men, 30 years and older, with again very low levels of education with children and a non-working partner. In both cases, men are divided between individuals who worked full-time in the past and those who mainly remained unemployed during the four years of the study.

‐ Finally,twogroupsemergewhichwouldnothavebeensuspectedtobevulnerable:womenwitharelativelyhigh levelofeducationandself‐employedoldermen.The firstgroup(Group4,11percent)includesyoungmarried(orcivilunion)women,whohaveinthepastworkedfull‐timeandwithyoungchildren(lessthan6yearsold).Thesecondgroup(Group8,6percent)consistsof30‐39year‐oldurbanwomen,workingfull‐time,unemployedorathome,withchildren.Thelastgroup(Group7,9percent)consistsofoldermenmorelikelytobeself‐employed.Informalemployment (V4) is the principal reason why these older men are included in the PLDpopulation.

Looking across groups, it is notable that four groups consist of unemployed individuals and full‐timeworkers:thereisnogroupwherePLDisasoleresultofpersistentjoblessness.Thusalargemajorityofindividuals are not persistently unemployed or inactive but do engage in some formal or informalmarket work during most of the years in our sample. This indicates that limited labor marketattachmentisfrequentlynotaresultoflackingmotivationoraninabilitytowork.Someofthepossiblepolicy implications of these patterns will be discussed in the ALMP and social benefits notes beingprepared as part of this project,whichwill askwhether some groups are, or should be, a particularpriorityforemploymentandincomesupport.

Before looking at the individual groupsmore closely, it is useful to examine the consequences of theeconomiccrisisforthecompositionandtheheterogeneityofthePLDgroups.Figure3abovehasalreadyshown that the size of the PLD group has increased after 2009. The introduction of this report hasargued that thegreaternumberofnewlyunemployed ina recession,and thecontinuallyweak labor‐market conditions in its aftermath, is likely to result in a significantlymore diverse group of peopleexperiencing labor‐market difficulties and, hence, requiring policy support. When applying theclusteringapproachtoanearlierpanel(2006‐2009),andcomparingagainsttheresultsfor2007‐2010,we indeed findevidence thatPLDafter therecessionaffectsnotonlyagreaternumberofpeople,butalsoasignificantlydifferentrangeofpopulationgroups.

ThisisshowninFigure5,whichvisualizesthesameclusteringanalysisdoneonthetwodifferentpanelwaves:2006/9and2007/10.Clustersthatrepresentsimilarpopulationsarerepresentedwiththesamecolors.Thetwographsrecordaverysimilarnumberofclusters(eightandnine),sodespiteanincreaseinPLD individuals, thenumberof subgroupshasnot increasedbymuch.Neitherhas the intensityofvulnerabilitymuchchanged,withmostclustersrecordinghighproportionsofhighriskindividualsandlowproportionsoflowriskindividuals.However,onecanspotseveralimportantchangesbetweenthetwowaves.

Figure5:PLDgroupsbyintensityofvulnerability:Compare2006/9and2007/10panel

Notes:Intensityofvulnerabilitywascalculatedusingthestatisticalmodelofpersistent labor‐marketdifficultiespresentedinSectionB.2,Table1.TheestimatedcoefficientswereusedtocalculateaprobabilityofexperiencingPLD.Thosewithascoreinthe bottom/top third of the whole working‐age population (i.e., the 33 percent with the lowest/highest risk) were thenclassifiedas“lowrisk”/”highrisk”.

Source:WorldBankstaffcalculationsbasedontheLatvianversionofEU‐SILC,waves2007‐2010and2008‐2011.

First, of the groups that are present in both panels,most have changed relative size and intensity ofvulnerability.Theelderlyandchronically‐illweresplitintotwolargegroupsintheearlierround(darkandlightorange),andcombinedintothreesmallergroupsinthelaterround(darkandlightorange).Inboth2006/9and2007/10thesegroupsrepresentedaround240thousandindividuals,butbecauseofthegrowingnumberofpeopleexperiencingPLD,theyareamuchsmallerproportionofthePLDgroupin 2007/10. In addition to shrinking in terms of their relative size as a proportion of the overallpopulationexperiencingworkdifficulties,thesegroupsofelderlyandchronicallyillbecamealsolessatriskwithalargerproportionofthesegroupshavinghighintensityofvulnerability.

Second, somegroups existed in the first roundbut thendisappeared in the secondone: the groupofmoreeducatedself‐employedmen(darkblue)whoarevulnerableduetothe informalnatureof theirwork shrunk by half in the 2007/10, and stand out as the groupwith the highest proportion of lowvulnerabilityindividuals.Similarly,thegroupmadeupofoldsingleself‐employedindividuals,workingfull‐timeorunemployed(lightblue)appearonlyinthe2006/09periodwheretheyrepresentthethirdlargestshareofthePLDpopulation.

Third, all groups shift away from lower intensities of vulnerability towards a larger share of highintensityofvulnerability.

Fourthly,employmentstatushadagoodexplanatorypowerinthelaterround,splittinggroupsratherwell, while in the 2006/09, in four groups out of eight, full‐time employment is mixed withunemployment.

5. Conclusions: Targeting employment and income support

Long‐term labor‐market difficulties can lead to economic hardship for the individuals and familiesconcerned. With labor income being the primary income source for working‐age people and theirfamilies,extendedspellswithoutadequatelypaidemploymentleavefamiliesfinanciallyvulnerableandwithahighriskofpoverty.Somegroupsare,however,betterablethanotherstocopewithlowornolaborincome.Theymayhaveaccesstootherincomesources(includingstatebenefits),theymayreceivesupport fromotherhouseholdmembersor fromextended family, or theymayhave savings they candrawupon.

Alargemajorityofhouseholdswithvulnerableindividualsisreceivingsocialtransfers:inthe2007/10EU‐SILCpanel,between76and100percentofthehouseholdsineachclusterreceiveonesocialtransferormore(seeTable3).Inaddition,socialtransfersseemtobegenerallyquitewelltargeted:theclusterswith the highest proportion of unemployed (groups 1 through 5 with respective unemploymentprobabilities of 34, 35, 42, 15, and 43 percent) also appear to be the clusters that have the greatestaccesstounemploymentbenefits(respectively16,13,25,24and23percent).Asexpected,householdswithahighprobabilityofhavingchildren(groups4,5,and8),havethelargestprobabilitiesofreceivingfamilybenefits(respectively99,85and62percent).Thesamestorygoesfordisability,wheregroups1,7,and9haverespectiveprobabilitiesofreceivingthedisabilitytransferof27,32,and31percent.Olderhouseholds (groups 1, 3, 7, and 9), also have the highest probability of receiving an old‐age pension(respectively9,8,7and9percent).

Table3:AccesstoSocialTransfersbyGroup(2007‐2010)

Group1

Group2

Group3

Group4

Group5

Group6

Group7

Group8

Group9

Old,nevermarried/divorced,10+yrs

exp,unemployed,nochild,ill

Youngmen,nevermarried,low

education,FT/unemployed,nochild,

rural

Old,m

arried,10+yrsexp,unemployed,

nochild,V2,V3

Youngerwom

en,m

arried,high

education,FT,youngchild,rural

30‐40y.o.men,m

arried/union,low

education,10+yrsexp,unemployed/FT,

children,partnervulnerablerural

Oldermen,m

arried,10+yrsexp,FT,

self‐em

ployed,nochild,V4

Oldwom

en,m

arried,10+yrsexp,unfit

forwork/athom

e,nochild,ill,V1

30‐40y.o.wom

en,m

arried,high

education,10+yrsexp,

FT/unemployed/athom

e,children,

urban

Oldwom

en,m

arried,low

education,10+

yrsexp,unfitforwork/athom

e/

unem

ployed,nochild,ill,V1,partner

vulnerable

SOCIALBENEFITS

(participation)

Anybenefit 78.9 84.3 84.0 100.0 92.3 76.1 86.6 83.3 93.3Family 19.3 37.5 32.0 99.3 84.7 58.3 37.2 62.1 55.2SocialExclusion 9.0 6.8 5.6 12.6 15.6 2.9 7.5 7.0 2.7Housing 8.8 7.5 4.3 6.5 9.6 0.7 1.8 0.3 4.8Unemployment 16.1 13.3 26.4 24.0 23.0 1.0 3.3 17.4 6.0Sickness 8.8 9.9 13.1 19.8 3.5 5.8 0.6 14.1 2.6Disability 27.2 8.2 14.3 0.2 10.3 1.5 32.6 2.2 31.0Old‐age 8.5 0.2 8.0 0.0 0.3 1.7 6.9 1.9 9.4Survivor 3.8 2.5 0.5 0.8 0.3 0.0 0.1 0.4 1.4

SOCIALBENEFITS(shareof

HHbenefitinHH

disposableincome,HH

receivingthebenefit)


Notes:Intensityofvulnerabilitywascalculatedusingthestatisticalmodelofpersistent labor‐marketdifficultiespresentedinSectionB.2,Table1.TheestimatedcoefficientswereusedtocalculateaprobabilityofexperiencingPLD.Thosewithascoreinthe bottom/top third of the whole working‐age population (i.e., the 33 percent with the lowest/highest risk) were thenclassifiedas“lowrisk”/”highrisk”.FTdenotesafull‐timeworker,OArepresentsold‐agepension.

Source:WorldBankstaffcalculationsbasedontheLatvianversionofEU‐SILC,wave2008‐2011.

The proportion of income provided by social benefits however varies considerably across the ninegroups. They are plotted on the vertical axis in Figure 6 and include family, housing andminimum‐income support, as well as social insurance benefits such as unemployment or sickness/disabilitybenefits and old‐age pensions. Benefits have provided only a limited “top up” of family incomes,

accounting for atmost one third of family incomes in all groups except the groups of older womenchronically‐illandwithloweducation(group9)andoldchronically‐illunemployedindividuals(group1)wheretransfersrepresenthalfandtwo‐thirdsoftotalincome.Transfersremainedquitelowforthemoreeducated(self‐employedand30‐40year‐oldwomen).

Figure6:Targetingincomesupport:Somegroupshavemuchgreaterneedforsupport(Resultsfor2010)

Fromatargetingperspective,itisdesirabletodirectsupporttothosewhoneeditmost.Totheextentthatlowbenefitgenerosityresultsfromalackofneediness,itissimplyareflectionofeffectivetargeting.Indicatorsofhighvulnerability5 inFigure6 suggest thathigher riskhouseholdsare those receivingahigher shareofbenefits in total family income. Indeed, groupswith the largest sharesofpeoplewithhigh risk of vulnerability (older, chronically‐ill) appear to receive themost generous income supportpaymentsrelativetotheirtotalincome.Theresultsareconsistentwiththeinterpretationthatbenefits

5Thosewithascoreinthebottom/topthirdofthewholeworking‐agepopulation(i.e.,the33percentwiththelowest/highestrisk)werethenclassifiedas“lowrisk”/”highrisk”.

Notes: Intensity of vulnerability was calculated using the statistical model of persistent labor‐market difficultiespresentedinSectionB.2,Table1.TheestimatedcoefficientswereusedtocalculateaprobabilityofexperiencingPLD.Those with a score in the bottom/top third of the whole working‐age population (i.e., the 33 percent with thelowest/highestrisk)werethenclassifiedas“lowrisk”/”highrisk”.FTdenotesafull‐timeworker,OArepresentsold‐agepension.

Source:WorldBankstaffcalculationsbasedontheLatvianversionofEU‐SILC,waves2008‐2011(with2007to2010asthereferenceyearforincomesandactivitycalendars).

areeffectiveatreducingvulnerabilityamongthosewhoreceivethem(groups7and9,andtoa lesserextent1),butthatcoverageand/orgenerositycanremainlowforsomeofthegroupswhoappeartobeinparticularneedofincomesupport(group9forinstance).Targeting issues also arisewith activationmeasures, employment services, and other types of activelabormarketpolicies,suchastraining.Spendingonactivelabormarketpoliciesperunemployedpersontypically falls very substantially during recessions (see companion paper World Bank, 2013b, andImmervollandScarpetta,2012).There isastrongcase forsomeautomaticadjustmentofactive labormarket spending as unemployment goes up in order to maintain PES service quality and theaccessibility of labor‐marketprograms.However, recessions leavepolicymakerswithdifficult choicesaboutspendingpriorities.Evenwithastrongcommitmenttoactivelabor‐marketpolicy,theaftermathofrecessionswillresultinincreasedpressurestochannelresourcestothosewhoneedthemmost,ortogroupswherepolicyinterventionislikelytohavethegreatestprobabilityofsuccess.

Those two criteria need not, and generally do not, provide the same answers about the desirabletargetingmechanisms.ThisisillustratedinFigure5above,whichshowsonthehorizontalandverticalaxesthesharesofpeoplewithpredicted“low”and“high”riskofPLDineachgroup(seefigurenotesforadescriptionofhowthesewerecalculated).Thisisusefulbecause,althoughmembersofeachgroupareall facing PLD and share many other characteristics, they are not identical. Some of them will facegreater labor‐market difficulties than others. It is therefore useful to ask how likely it is for eachmembertoexperiencePLDgivenhisorhercharacteristics.

Iftheobjectiveistofocusactivationpolicyeffortsonthosewhoare,inasense,furthestfromfindingandholdingastableandadequatelypaidjob,thenpolicyshouldfocusonthosewithahighriskofPLD.Onecanthinkofthesegroupmembersasthosewithmultipleorrelativelymajoremploymentbarriers.Usingsuch a criterion, groups 1, 3, 7, and 9 (old and retired individuals with chronic illnesses) should beprioritizedforactivationmeasures.

A very different set of priorities would result if the objective is to focus efforts on groups where asignificantnumbersofindividualshaverelativelylowrisksofPLD.Group6wouldneedtobeprioritizedinthiscase,i.e.theself‐employedpopulation.Suchastrategymaybeattractiveaspolicieswouldhaveto“bridge”asmallergapsincepeoplemayalreadyberelativelywellequippedforfindingagood‐qualityjob.Theprobabilityofsuccessfulactivationwouldthereforelikelybehigher.Butatthesametime,someof those“lowrisk” individualsmaywellhavesucceeded inovercoming theirPLDevenwithoutactivepolicysupport.

In practice, it is useful to consider awide range of informationwhen deciding on policy design andtargeting. The information in Figure 5 and Figure 6 should arguably be read in combination as anintegratedpolicy approachwould seek to tackle both employment barriers andPLD risks, aswell asindicators of economic hardship. The different perspective on group characteristics and employmentbarriersalsohighlightstheneedtolinkbenefitdesignandactivationpolicies.Aspartofanemployment‐orientedpolicy framework, benefits provide aprincipal instrument for linkingunemployedpeople toemployment services and active labor market programs. Low benefit coverage among those withpersistentlabor‐marketdifficultiesisnotonlyaconcernfromanequalityandpoverty‐reductionpointof view. It also makes it harder to implement and deliver effective activation strategies andemployment services, as those outside the scope of benefits tend to find accessing these servicessignificantlymoredifficult.

References

Hazans,M.(2011),“WhatExplainsPrevalenceofInformalEmploymentinEuropeanCountries:TheRoleof Labor Institutions, Governance, Immigrants, and Growth”, IZA Discussion Paper No. 5872.InstitutefortheStudyofLabor,Bonn.

Immervoll,H. (2012), “Reforming theBenefit System to 'MakeWorkPay':Options andPriorities in aWeakLaborMarket”,IZAPolicyPaperNo.50,InstitutefortheStudyofLabor,Bonn.

Immervoll,H.andS.Scarpetta.(2012),“ActivationandemploymentsupportpoliciesinOECDcountries.Anoverviewofcurrentapproaches”,IZAJournalofLaborPolicy2012,1:9.

Kaufman,L.andP.Rousseeuw(1990),Findinggroupsindata.NewYork,Wiley.

Vanags, A. (2012), “Latvia. EEO Review: Long‐term unemployment, 2012”, European EmploymentObservatory.

WorldBank(2013a),ExpenditureandPerformanceofWelfareBenefitsandEmploymentProgramsinLatvia.

World Bank (2013b), Unemployment, Joblessness and Social Safety Nets in Latvia: What Does theAdministrativeDataTellus?

WorldBank(2013c),FinancialIncentivesoftheTaxandBenefitSysteminLatvia.

Annex1

TableA.1:VariablesusedforLatentClassAnalyses

ActiveCovariates

AGE

Agegroup(15‐19y.o.)Agegroup(20‐24y.o.)Agegroup(25‐29y.o.)Agegroup(30‐34y.o.)Agegroup(35‐39y.o.)Agegroup(40‐44y.o.)Agegroup(45‐49y.o.)Agegroup(50‐54y.o.)Agegroup(55‐59y.o.)Agegroup(60‐61y.o.)

GENDERFemaleMale

CIVIL

ConsensualunionDivorcedMarriedNevermarriedSeparatedWidowed

EDUCATION

Pre‐primaryPrimaryLowerSecondary(UpperSecondaryPost‐secondary1ststagetertiary

EXPERIENCE

None1year2‐3years4‐5years6‐10years>10years

ECONOMICSTATUS

Disabled/unfitforworkDomestictasksOtherinactivePupil,student,traineeRetirementUnemployedWorkingfull‐timeWorkingpart‐time

SELF‐EMPLOYED DummyCHILDREN<6y.o. Dummy

NUMBEROFCHILDREN<16 None

ActiveCovariatesy.o. 1child

2children>3children

CHRONICILLNESS DummyV1P PersistentV1=NotworkingatallV2P PersistentV2=Lowworkintensity(workinglessthan50%ofyear)V3P PersistentV3=LowearningsV4P PersistentV4=Informaljob(employernotpayingSocialSecuritycontributions)

PartnerV5XP PartnerpersistentV1orV2orV3PartnerV4P PartnerpersistentV4

Partnerincome (Monthly,0ifnopartner)

GEORuralUrban

Inactivecovariates

LOWINTENSITYNoYesN/A

HIGHINTENSITYNoYesN/A

Meanlaborincome

ARREARSNoYesN/A

HEATINGNoYes

HARDSHIPNoYes

OWNHOUSENoYes

ACTIVELYLOOKFORJOBNoYesN/A

SOCIALBENEFITS(%totalhouseholdincomefor

everybody)

FamilySocialExclusionHousingUnemploymentOld‐AgeSicknessDisability

SOCIALBENEFITS(%totalhouseholdincomeforthose

gettingtransfer)

FamilySocialExclusionHousingUnemploymentOld‐Age

ActiveCovariates

SicknessDisability

SOCIALBENEFITS(participation)

Family

SocialExclusion

Housing

Unemployment

Old‐Age

Survivor

Sickness

Annex2

ThefollowingtablespresentdetailedstatisticsontheclustersobtainedwithLatentClassAnalysis.Twotablesusinglongitudinaldataaredisplayed:theseshowtheresultsfortheEU‐SILC2006‐2009andEU‐SILC2007‐2010(seeTableA.2andTableA.3,respectively).

Withineachtable,eachcolumnrepresentsadifferentclusterorgrouping.Thefirstline(sizeofcluster)displays the relativesizeof eachgroupwithrespect to the totalpopulationofvulnerable individuals.Thefirstclusterisalwaysthelargestone,andthentherestofclustersareorganizedindecreasingorderofsize.

Within each table are displayed the different variables used to construct the groups: age categories,gender,civilstatus,educationalattainment,experience,economicstatus,dummiesforself‐employment,children under 6 years old, number of children, chronic illness, type of vulnerability, partner’svulnerabilitystatusandincome,andgeographicindicator.Withineachcolumn(orcluster),thenumberassociatedwitheachoccurrence,istheprobabilitythatoneindividualclassifiedinthatgroupbelongtothatcategory.Forinstance,andindividualinthefirstclusterofTableA.2hasaprobabilityequalto19.2percenttobe60to61year‐old.

The second half of each table displays descriptive statistics of some variables thatwere not used tocreatethegroupings—i.e.inactivecovariates—butthatwereconsideredtobeimportanttounderstandtheclusters inmoredetail.Thus,an individualbelonging to the firstclusterofTableA.2willhaveanaverageannualincomeof€233andaprobabilityofhavingarrearsof31.6percent.

Thecellswithboldednumbersinthetablerepresentthecategorywiththehighestoccurrenceineachcluster/group(i.e.theonethathelpsdistinguishonecluster/groupfromtheotherones).

TableA.2:Panel2006‐2009

Cluster

1Cluster

2Cluster

3Cluster

4Cluster

5Cluster

6Cluster

7Cluster

8

SizeofCluster (%tototalPLDpopulation) 16.5 15.8 15.5 14.1 13.9 11.5 8.7 4.0

ACTIVECOVARIATES

AGE

Agegroup(15‐19y.o.) 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0Agegroup(20‐24y.o.) 0.0 4.6 0.0 39.6 0.0 0.0 3.1 4.7Agegroup(25‐29y.o.) 0.0 42.9 0.0 34.5 0.0 0.0 14.4 0.0Agegroup(30‐34y.o.) 0.0 24.5 5.6 12.7 0.0 0.0 25.3 32.9Agegroup(35‐39y.o.) 4.6 16.3 10.2 9.4 5.0 2.3 34.5 42.1Agegroup(40‐44y.o.) 12.7 8.8 17.0 1.7 11.7 9.2 14.0 0.0Agegroup(45‐49y.o.) 21.7 1.1 11.0 0.0 20.9 16.5 8.5 10.6Agegroup(50‐54y.o.) 18.4 1.4 18.4 0.0 25.0 22.7 0.0 4.7Agegroup(55‐59y.o.) 23.2 0.4 27.9 0.0 26.0 32.0 0.0 0.3Agegroup(60‐61y.o.) 19.2 0.0 9.8 0.0 11.4 17.3 0.0 4.6

GENDER Female 55.6 94.8 47.9 50.5 49.2 67.9 62.9 89.3 Male 44.4 5.2 52.1 49.5 50.8 32.1 37.1 10.7

CIVIL

Consensualunion 4.5 20.4 4.3 0.0 2.9 3.3 25.7 20.6Divorced 34.7 3.3 29.3 9.2 3.6 7.3 7.3 0.2Married 16.3 76.2 22.6 0.0 89.4 86.3 64.8 79.1Nevermarried 23.2 0.0 16.6 83.1 0.0 0.0 0.0 0.1Separated 10.4 0.0 10.2 7.3 2.3 0.5 1.8 0.0Widowed 11.0 0.0 17.0 0.4 1.8 2.7 0.3 0.0

EDUCATION

Pre‐primary 0.0 0.0 0.3 0.6 0.0 0.0 0.0 0.0Primary 0.3 0.0 0.8 1.4 0.0 0.0 4.5 0.0LowerSecondary 28.1 14.7 21.4 35.4 10.9 19.6 42.9 0.1(Upper)Secondary 49.7 39.8 60.4 49.9 71.6 66.5 47.2 67.4Post‐secondary 7.7 3.4 3.3 2.6 5.0 4.0 5.3 1.71ststagetertiary 14.2 42.1 13.8 10.1 12.5 10.0 0.1 30.9

Cluster

1Cluster

2Cluster

3Cluster

4Cluster

5Cluster

6Cluster

7Cluster

8

EXPERIENCE

None 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.01year 0.0 3.3 0.0 29.3 0.5 0.0 4.1 9.02‐3years 1.1 23.1 0.0 30.5 0.0 0.0 10.1 0.04‐5years 3.9 22.6 0.1 19.2 0.0 1.9 9.3 13.76‐10years 11.9 22.1 7.9 20.4 2.4 7.2 48.6 36.7>10years 83.1 28.8 91.9 0.6 97.0 90.9 27.9 40.6

ECONOMICSTATUS

Disabled/unfitforwork 44.9 0.0 7.8 2.4 9.1 26.5 1.5 7.4Domestictasks 11.0 15.3 2.8 3.8 4.8 29.7 17.2 13.3Otherinactive 4.7 11.3 2.5 7.4 0.6 5.0 8.5 0.0Pupil,student,trainee 0.0 0.7 0.0 2.4 0.0 0.0 0.0 3.4Retirement 11.9 0.0 5.9 0.0 14.3 12.9 0.0 1.7Unemployed 22.9 14.6 32.8 40.8 35.5 23.3 38.3 51.7Workingfull‐time 2.0 53.6 37.4 34.8 28.3 1.1 26.5 22.5Workingpart‐time 2.7 4.6 10.8 8.3 7.5 1.6 8.0 0.0

SELF‐EMPLOYEDNo 99.8 99.3 86.7 96.1 85.6 100.0 96.9 100.0Yes 0.2 0.8 13.4 3.9 14.4 0.0 3.2 0.0

CHILDREN<6y.o. No 85.2 1.0 86.3 69.8 93.1 88.0 40.5 77.5Yes 14.8 99.0 13.7 30.2 6.9 12.0 59.5 22.6

NUMBEROFCHILDREN<16y.o.

None 80.8 0.5 65.5 49.6 76.5 75.2 16.3 43.01child 7.6 61.8 25.6 29.4 17.7 19.6 27.1 33.02children 10.4 26.4 5.8 18.7 3.8 1.9 36.6 24.0>3children 1.3 11.4 3.1 2.4 2.0 3.3 20.0 0.0

CHRONICILLNESS No 15.1 80.8 49.0 67.3 43.3 31.1 62.8 72.1Yes 84.9 19.2 51.0 32.7 56.7 69.0 37.2 27.9

V1No 0.5 71.1 83.0 74.3 83.3 3.6 57.8 39.2Yes 99.5 28.9 17.0 25.7 16.7 96.5 42.2 60.8

V2No 98.3 36.1 33.9 37.2 29.1 99.8 49.0 75.6Yes 1.7 64.0 66.1 62.8 70.9 0.2 51.1 24.4

V3 No 99.2 66.4 34.6 62.9 41.3 99.5 61.9 92.8Yes 0.9 33.6 65.4 37.1 58.7 0.5 38.1 7.2

Cluster

1Cluster

2Cluster

3Cluster

4Cluster

5Cluster

6Cluster

7Cluster

8

PARTNERV5XN/A 75.6 0.0 69.1 100.0 0.0 2.8 2.3 0.1No 0.0 96.6 0.1 0.0 81.6 70.7 54.7 99.8Yes 24.4 3.4 30.9 0.0 18.4 26.5 43.1 0.1

GEOUrban 46.9 42.5 38.7 51.2 43.0 46.6 28.8 90.2Rural 53.1 57.5 61.3 48.8 57.0 53.4 71.2 9.8

INACTIVECOVARIATES 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

LOWINTENSITYNo 100.0 98.2 94.8 97.9 87.6 100.0 98.3 96.5Yes 0.0 1.8 5.0 1.3 12.4 0.1 1.7 3.5N/A 0.0 0.0 0.2 0.8 0.0 0.0 0.0 0.0

HIGHINTENSITYNo 0.0 20.4 22.5 19.1 26.0 1.0 9.3 17.4Yes 100.0 79.6 77.3 80.1 74.0 99.0 90.7 82.7N/A 0.0 0.0 0.2 0.8 0.0 0.0 0.0 0.0

Meanlaborincome 233 2497 1917 1943 2108 370 1456 3269

ARREARSNo 1.4 11.1 0.9 1.4 1.8 4.0 3.9 7.2Yes 31.6 30.3 38.2 33.6 15.5 24.2 43.9 24.5N/A 66.9 58.6 61.0 65.1 82.7 71.9 52.2 68.3

HEATING No 32.7 13.2 33.9 23.1 14.0 14.1 27.9 5.2Yes 67.3 86.8 66.1 76.9 86.0 86.0 72.1 94.8

HARDSHIPNo 47.6 9.2 39.4 34.4 17.9 26.3 39.2 9.2Yes 52.4 90.8 60.6 65.6 82.1 73.7 60.8 90.9

OWNHOUSENo 17.7 14.0 16.5 11.5 8.1 10.4 26.0 8.7Yes 82.4 86.0 83.5 88.5 91.9 89.6 74.0 91.3

ACTIVELYLOOKFORJOB

No 0.2 2.4 1.3 0.0 0.0 0.5 0.1 0.0Yes 20.4 12.3 28.9 39.0 25.7 26.2 32.5 46.3N/A 79.4 85.3 69.9 61.0 74.2 73.3 67.5 53.7

Cluster

1Cluster

2Cluster

3Cluster

4Cluster

5Cluster

6Cluster

7Cluster

8

SOCIALBENEFITS(participation)

Anybenefit 90.3 99.3 83.3 84.7 84.0 90.4 96.4 78.5Family 21.2 98.3 37.6 48.6 41.0 37.1 86.4 61.7SocialExclusion 13.6 4.4 7.5 10.5 3.6 8.4 21.8 3.8Housing 10.5 0.1 2.7 3.0 1.3 1.3 1.3 0.0Unemployment 1.8 18.3 14.1 13.2 20.2 6.3 16.9 7.7Sickness 11.7 16.6 9.6 9.5 9.9 5.8 13.5 0.6Disability 51.5 0.6 14.2 9.0 13.9 34.0 6.6 5.2Old‐age 6.9 0.0 7.0 0.0 7.4 9.0 0.0 1.6Survivor 1.4 0.0 5.6 2.0 0.0 0.4 0.2 0.0

SOCIALBENEFITS(shareofHHbenefitinHHdisposableincome,

allHH)


SOCIALBENEFITS(shareofHHbenefitinHHdisposableincome,

HHreceivingthebenefit)


TableA.3:Panel2007‐2010

Cluster

1Cluster

2Cluster

3Cluster

4Cluster

5Cluster

6Cluster

7Cluster

8Cluster

9SizeofCluster 21.6 17.5 13.6 11.3 11.2 8.8 6.4 6.1 3.6ACTIVECOVARIATES

AGE

Agegroup(15‐19y.o.) 0.0 1.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0Agegroup(20‐24y.o.) 0.0 31.6 0.0 7.4 1.9 0.0 0.0 4.5 0.0Agegroup(25‐29y.o.) 0.0 40.5 0.0 37.7 12.3 2.6 0.0 8.3 0.0Agegroup(30‐34y.o.) 0.5 19.1 0.0 31.1 28.1 0.3 0.1 21.8 0.0Agegroup(35‐39y.o.) 9.4 6.5 7.3 17.4 20.7 11.6 1.0 24.1 0.1Agegroup(40‐44y.o.) 13.8 0.7 20.5 5.7 12.4 30.1 15.9 5.6 7.7Agegroup(45‐49y.o.) 18.1 0.0 17.4 0.7 8.5 23.2 12.8 7.7 18.9Agegroup(50‐54y.o.) 24.1 0.0 22.0 0.0 9.5 24.1 27.4 23.0 21.4Agegroup(55‐59y.o.) 19.9 0.0 22.8 0.1 6.6 6.3 30.5 2.7 30.0Agegroup(60‐61y.o.) 14.2 0.0 9.9 0.0 0.0 1.8 12.4 2.3 22.0

GENDER Female 48.0 33.9 51.0 94.2 24.0 31.6 68.5 79.5 61.3 Male 52.0 66.1 49.0 5.8 76.0 68.4 31.5 20.5 38.8

CIVIL

Consensualunion 0.0 0.3 3.9 22.4 34.5 2.7 7.7 21.5 10.1Divorced 36.8 4.2 10.0 9.6 7.4 3.4 6.9 0.0 1.9Married 0.0 0.0 82.2 63.7 57.3 90.9 83.5 72.4 81.8Nevermarried 32.8 90.4 0.3 0.6 0.0 0.0 0.0 1.1 0.0Separated 12.7 4.9 1.5 3.8 0.6 3.0 0.0 5.1 0.0Widowed 17.8 0.2 2.2 0.0 0.2 0.0 1.9 0.0 6.1

EDUCATION

Pre‐primary 0.3 0.2 0.0 0.0 1.3 0.0 0.0 0.0 0.0Primary 0.2 2.8 0.1 0.2 2.3 0.0 1.7 0.0 0.0LowerSecondary 19.0 46.7 8.4 18.5 45.9 1.4 8.0 7.2 30.4(Upper)Secondary 60.8 35.7 70.7 52.4 46.0 70.5 74.0 46.5 61.5Post‐secondary 8.1 1.5 8.8 0.0 0.0 11.9 5.2 10.7 6.21ststagetertiary 11.6 13.1 12.0 28.9 4.5 16.2 11.1 35.7 1.9

Cluster

1Cluster

2Cluster

3Cluster

4Cluster

5Cluster

6Cluster

7Cluster

8Cluster

9

EXPERIENCE

None 0.0 6.8 0.0 2.9 0.2 0.0 0.0 0.1 0.01year 0.0 16.9 0.0 9.8 1.5 0.0 0.0 0.9 0.02‐3years 0.2 30.2 0.0 23.4 7.1 0.0 0.4 5.0 0.14‐5years 1.2 16.3 0.1 16.9 10.0 0.3 1.6 8.2 0.56‐10years 12.0 17.0 3.7 23.4 27.2 6.4 13.5 25.9 7.8>10years 86.5 12.9 96.2 23.7 54.0 93.2 84.5 59.9 91.7

ECONOMICSTATUS

Disabled/unfitforwork 22.4 3.4 7.6 0.0 1.4 0.0 30.9 2.3 23.3Domestictasks 7.5 2.4 2.0 25.2 1.8 0.0 31.6 23.8 29.1Otherinactive 3.4 6.1 0.0 10.4 0.0 0.0 2.1 2.5 6.3Pupil,student,trainee 0.0 5.0 0.0 0.6 0.0 0.0 0.0 2.6 0.0Retirement 7.0 0.0 9.6 0.0 0.0 0.0 10.4 1.9 18.9Unemployed 33.6 34.9 42.3 14.6 43.3 2.2 20.5 22.1 21.4Workingfull‐time 21.7 42.9 28.0 46.2 48.6 93.8 4.3 35.0 1.1Workingpart‐time 4.4 5.3 10.6 3.0 4.9 4.0 0.2 9.7 0.0

SELF‐EMPLOYED

No 89.8 93.4 98.2 100.0 91.9 54.9 100.0 87.5 100.0Yes 10.2 6.6 1.8 0.0 8.1 45.1 0.0 12.5 0.0

CHILDREN<6y.o.

No 93.6 81.8 98.1 6.0 47.1 92.1 93.7 65.7 60.2Yes 6.4 18.2 1.9 94.0 52.9 8.0 6.3 34.3 39.8

NUMBEROFCHILDREN<16y.o.

None 84.4 67.6 83.3 0.1 16.3 55.7 79.0 43.6 52.61child 11.4 22.9 13.6 49.4 34.9 31.0 15.9 28.2 38.02children 3.2 5.4 3.1 33.4 30.6 13.3 1.4 22.0 3.1>3children 1.0 4.0 0.1 17.2 18.2 0.0 3.8 6.3 6.3

CHRONICILLNESS

No 42.8 81.2 53.0 92.2 60.2 81.6 36.8 81.2 16.7Yes 57.2 18.8 47.0 7.8 39.8 18.4 63.2 18.8 83.3

V1No 55.2 75.5 89.4 58.9 80.5 98.7 0.6 72.8 1.4Yes 44.8 24.5 10.6 41.1 19.5 1.3 99.4 27.2 98.6

V2 No 75.0 70.8 48.0 53.4 67.5 99.3 100.0 58.1 99.9Yes 25.0 29.2 52.0 46.6 32.5 0.7 0.0 41.9 0.1

V3No 63.2 59.9 38.4 61.6 54.9 82.3 100.0 52.5 99.4Yes 36.9 40.1 61.6 38.4 45.1 17.7 0.0 47.5 0.6

V4No 74.8 64.5 88.1 98.4 60.0 1.5 97.7 67.6 98.0Yes 25.2 35.5 11.9 1.6 40.0 98.5 2.3 32.4 2.0

Cluster

1Cluster

2Cluster

3Cluster

4Cluster

5Cluster

6Cluster

7Cluster

8Cluster

9

PARTNERV5X

N/A 98.4 100.0 2.4 7.9 0.1 1.8 2.6 2.7 0.1No 0.0 0.0 61.3 81.1 26.5 72.7 89.0 96.9 0.1Yes 1.6 0.0 36.3 11.0 73.5 25.5 8.4 0.4 99.8

GEOUrban 46.5 40.5 47.8 43.7 29.2 53.5 50.3 55.9 41.5Rural 53.5 59.5 52.2 56.3 70.8 46.5 49.7 44.1 58.5

INACTIVECOVARIATES 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

LOWINTENSITY

No 95.3 93.0 92.2 97.3 97.7 78.7 99.9 86.2 100.0Yes 4.7 6.5 7.2 1.8 2.2 21.3 0.1 13.6 0.0N/A 0.0 0.5 0.6 0.8 0.1 0.0 0.0 0.2 0.0

HIGHINTENSITY

No 16.3 35.9 22.1 25.0 24.3 72.0 4.2 34.8 0.0Yes 83.7 63.7 77.4 74.2 75.6 28.0 95.8 65.0 100.0N/A 0.0 0.5 0.6 0.8 0.1 0.0 0.0 0.2 0.0

Meanlaborincome 1271.1 1600.4 1901.1 1414.6 2063.9 4608.6 361.3 2100.0 167.1

ARREARSNo 0.3 1.2 1.5 4.9 2.0 0.9 0.4 7.7 0.7Yes 30.9 32.0 30.0 43.0 46.6 25.2 23.0 19.0 44.2N/A 68.8 66.8 68.6 52.2 51.5 73.9 76.5 73.3 55.2

HEATING No 35.6 23.9 19.3 13.7 25.4 13.6 14.2 7.6 24.9Yes 64.4 76.1 80.7 86.3 74.6 86.4 85.8 92.5 75.1

HARDSHIPNo 44.7 28.0 26.6 18.1 29.7 10.1 23.5 6.7 40.8Yes 55.3 72.0 73.4 81.9 70.3 89.9 76.5 93.3 59.2

OWNHOUSENo 21.5 14.6 10.8 17.9 20.5 4.2 4.8 11.1 20.6Yes 78.6 85.4 89.3 82.1 79.5 95.8 95.3 88.9 79.4

ACTIVELYLOOKFOR

JOB

No 1.3 0.5 0.3 1.5 2.0 0.0 3.9 0.3 0.0Yes 28.5 34.4 34.4 14.4 34.3 4.5 15.7 23.3 23.3N/A 70.2 65.0 65.3 84.1 63.7 95.6 80.5 76.4 76.7

Cluster

1Cluster

2Cluster

3Cluster

4Cluster

5Cluster

6Cluster

7Cluster

8Cluster

9

SOCIALBENEFITS

(participation)


SOCIALBENEFITS(shareofHHbenefitinHHdisposableincome,all

HH)


SOCIALBENEFITS(shareofHHbenefitinHHdisposableincome,HHreceivingthebenefit)

Anybenefit 67.6 34.5 35.2 27.7 39.7 19.9 33.2 16.3 71.4Family 11.9 10.8 7.7 14.0 16.2 7.8 8.4 9.7 21.6SocialExclusion 15.6 11.9 10.4 9.7 15.1 18.8 8.8 1.9 37.4Housing 9.7 8.4 9.3 8.8 6.6 26.1 12.5 20.2 6.5Unemployment 28.5 9.3 12.9 12.6 14.4 14.1 17.6 6.7 22.6Sickness 13.8 3.8 8.3 3.3 4.4 8.7 8.7 2.7 1.8Disability 57.5 27.0 20.4 19.8 26.0 ‐3.2 24.7 6.6 46.2Old‐age 57.1 22.9 35.0 20.2 21.3 5.4 34.0 8.2 30.5Survivor 33.3 21.5 18.9 13.1 9.3 14.1 53.1 9.0 37.4

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