UNDP Inequalities RHDR zero draft (02.02.16)

Transcription

UNDPREGIONALHUMANDEVELOPMENTREPORT
ProgressatRisk:InequalitiesandHuman
DevelopmentinEuropeandCentralAsia1
DraftFORCOMMENTS(2February2016)
Abstract:ManyofthedevelopingandtransitioneconomiesofEurope,Turkey,andCentralAsiahaveenjoyed
relativelyhighlevelsofsocio-economicequalities.Since2000incomeinequalitieshavegenerallybeenlowor
falling,whichhashelpedtoreducepovertyandallowedthemiddleclasstostageacomeback.Relatively
comprehensivepre-1990socialprotectionsystemsandhighlevelsofgenderequalityhaveensuredthatthe
benefitsofeconomicgrowthhavebeenfairlyevenlyspread.However,theexpansionofinformal,vulnerable,
andprecariousemploymentiscombiningwithgrowinggapsinsocialprotectionsystemsand(intheregion’s
lesswealthycountries)newpressuresonhouseholdfoodandenergysecuritytoputtheseaccomplishments
atrisk.ThisisparticularlythecaseforthosecountriesintheCommonwealthofIndependentStatesthathave
madesomeofthebestprogressinreducinginequalities—andwhichnowfacegrowingsocio-economic
pressures.Thisreportexaminesthehumandevelopmentaspectsofthesechallenges,withinthecontextof
theSustainableDevelopmentGoalsandthepromiseoftheglobalsustainabledevelopmentagenda2030to
“leavenoonebehind”.
Tableofcontents
Subject
Page
Executivesummary------------------------------------------------------------------------------------------------------
2
Chapter1—Measuringincomeandnon-incomeinequalities--------------------------------------------------
5
Chapter2—Inequalities,employment,andsocialprotection-------------------------------------------------
26
Chapter3—Theeconomicdimensionsofgenderinequalities--------------------------------------------------
57
Chapter4—Inequalities,health,andHIV/AIDS-------------------------------------------------------------------- 77
Chapter5—NaturalCapital,Inequalities,andSustainableHumanDevelopment------------------------
86
Chapter6—Inequalitiesandinclusivegovernance---------------------------------------------------------------
97
References------------------------------------------------------------------------------------------------------------------ 134
1
ThispaperdoesnotnecessarilyreflecttheviewsoftheUnitedNationsDevelopmentProgramme,theUnitedNations,oritsMember
States.
1
Executivesummary
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Significantreductionsinincomeinequalitieshavebeenreportedinmuchoftheregion2sincetheturn
ofthemillennium—particularlyBelarus,Kazakhstan,Moldova,andUkraine,andpossiblyalsoAlbania,
Kosovo,3andtheKyrgyzRepublic.Bycontrast,incomeinequalitiesseemtohaveincreasedinGeorgia,
Turkey,andpossiblytheformerYugoslavRepublicofMacedonia(fYRoM).
Loworfallingincomeinequalitieshavehelpedeconomicgrowthreducepoverty—particularlyinthis
firstgroupofcountries.InGeorgiaand fYRoM,bycontrast,highorrisinglevelsofincomeinequality
haveslowedorfrustratedprogressinpovertyreduction.Thisunderscoreshow—inadditiontobeing
desirable in and of themselves—low or falling inequalities are central to prospects for poverty
reduction,inclusivegrowth,andsustainabledevelopmentintheregion.
Thenumbersofpeopleintheregionlivinginpovertyfellfromatleast46millionin2001toabout5
millionin2013.Thenumbersofpeoplelivinginextremepovertydroppedbelow1millionduringthis
time.Likewise,thenumbersofpeoplevulnerabletopovertydroppedfromabout115millionin2003
tosome70millionin2013.Bycontrast,thesizeofthemiddleclassgrewfromabout33millionin2001
to90millionin2013.Thenumbersofrelatively“wealthy”individuals(livingonmorethanPPP$50/day)
hadrisentosome32millionin2013—mostofwhomwerelivinginTurkeyandKazakhstan.
Theregion’smiddleclasseshavemadeacomebacksincetheturnofthemillennium,followingboth
absoluteandrelativedeclinesduringthe1990s.Inmuchoftheregion,middleclasseshavegrownas
the shares of national income claimed by wealthy households have declined. As of 2013, at least 80
millionpeopleintheregionhadachievedlivingstandardsthatarebroadlyconsistentwiththebounds
ofthe“globalmiddleclass”.
Progress in reducing income inequalities is now being put to the test in much of the region. The
combination of low commodity prices, falling remittances, and slow or negative growth on key
EuropeanandRussianexportmarketsisputtingpressuresonvulnerablehouseholdincomesthathave
notbeenseensincetheturnofthemillennium.Thisposesnewchallengesastheimplementationof
theglobalsustainabledevelopmentagenda2030beginsintheregion—particularlyforlabourmarkets
and social protection systems, but also in light of the growing pressures on natural capital and
ecosystemsinsomeoftheregion’slesswealthycountries.
Labourmarketinequalitiesandexclusionlieattheheartoftheregion’sinequalitychallenges.Thisis
thecasebothintermsoflabourmarketsperse,andbecauseaccesstosocialprotectionisoftenlinked
toformallabourmarketparticipation.Peoplewithoutdecentjobsfacemuchhigherrisksofpoverty,
vulnerability, and exclusion from social services and social protection. The share of workers in
vulnerableinAlbania,Azerbaijan,Georgia,theKyrgyzRepublic,andTajikistanisestimatedataround
50%.
Employment does not necessarily offer much protection against poverty and vulnerability, because
informal, precarious, migratory, and vulnerable employment is widespread throughout the region.
Women,youngworkers,migrants,thelong-termunemployed,peoplewithdisabilities,andotherswith
unequal labour market positions are particularly vulnerable. While trends are improving in some
countriesandforsomegroups,inothers,labourmarketinequalitiesareincreasing.
2
Unlessotherwisenoted,referenceinthispublicationistoAlbania,Armenia,Azerbaijan,Belarus,BosniaandHerzegovina,Georgia,
Kazakhstan,Kosovo(asperUNSCR1244(1999)),theKyrgyzRepublic,theFormerYugoslavRepublicofMacedonia,Moldova,
Montenegro,Serbia,Tajikistan,Turkey,Turkmenistan,Ukraine,andUzbekistan.
3
AllreferencestoKosovointhispublicationarewithintheframeworkofUNSCR1244(1999).
2
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The region faces important challenges in better measuring progress in reducing inequalities and
promotingsustainabledevelopment.Thisisapparentinthedataonincomeinequalities,whichtendto
report on disparities in consumption spending rather than income. It is apparent in the
“employment”/”unemployment” statistical dichotomy, and in the infrequency with which publicly
availablelabour-marketdataaredisaggregatedbyage,gender,ethnicity,andothercriteria.Anditis
apparent in the paucity of indicators to measure the depletion of the region’s natural capital and
environmentalsustainability.
Long-termeffortstoformalizeemploymentarecrucial.Threedirectionsareparticularlyimportant:(i)
effortstoboosttheinstitutionalcapacityoftheinstitutionschargedwithlabourmarketregulation,in
order to better enforce legal protections for workers’ rights in the formal sector; (ii) the abolition of
those labour market regulations that cannot be credibly enforced by state agencies and drive
employment into the informal sector; and (iii) increased investment in active labour market policies,
vocationaleducation,andothermeasurestoboostworkerproductivity.
Policylinkagesbetweenlabourmarketsandsocialprotectionneedtobestrengthened.Whilepoorly
aligned social policies can reduce incentives for labour market participation and hiring, this is not a
reasonforreducingsocialprotectionspendingandcoverage.Instead,whereverpossible,thetaxation
of labour to fund social benefits needs to be reduced in favour of other funding sources. These may
include:(i)highertaxesonenvironmentallyunsustainableactivities;(ii)reductionsinbudgetsubsidies
thataccruetothewealthy;(iii)moreaggressivemeasurestoreducethediversionofbudgetrevenues
totaxhavens;and(iv)morerobustdirectionofbudgetaryprocurementandcontractingresourcesto
companies(e.g.,socialenterprises)thatexplicitlypromotesocialinclusion.
Socialprotectionisalsoaboutsocialservicesandthecareeconomy.Increasedinvestmentsinsocial
service provision—particularly terms of care for children, the elderly, and persons with disabilities—
canboostparticipationinlabourmarketsandvocationaltrainingprogrammes,particularlyforwomen.
InTurkey,forexample,adecisiontobringstatebudgetspendingonsocialcareservicesuptoOECD
levelswouldgenerateanestimated719,000socialcarejobs—morethan2.5timesthetotalnumberof
jobs that would be created by devoting the same amount of budget funds to
construction/infrastructureprojects.Anestimated84%oftheworkershiredintothesesocialcarejobs
wouldhavepermanentcontractsofunlimitedduration(versus25%inconstruction);85%wouldhave
socialsecuritycoverage(comparedto30%inconstruction).
While the region compares favourably to many other developing countries in terms of gender
equality, it also lags behind global best practices in many areas. Moreover, pre-1990s progress in
genderequalitythathadbeenattainedinmanycountries—manyofwhichfeaturedrelativeequality
betweenmenandwomen—hascomeundergrowingthreat.
Gender-based inequalities tend to intersect with, and magnify the impact of, other forms and
dimensions of inequalities, based on class, race, age, ethnicity, disability, occupation and income.
Unequal labour market outcomes in particular can have major implications for broader gender
inequalities and the exclusion of women. Women’s unequal access to social capital or their inferior
positioninthenetworksthatconstitutesocialcapital(whicharemoremarkedinsomecountriesinthe
regionthaninothers)isbothacauseandamanifestationofinequality.
Adjustable net savings, the ecological footprint, and the sustainable human development index
suggest that the depletion of natural capital, and environmental sustainability concerns more
broadly, are relatively pronounced in the region’s lower-middle income countries—which are
concentratedintheCaspianBasin.Inadditiontobeingthesiteofoneoftheworld’slargestman-made
ecologicaldisasters(theAralSeatragedy),developmentmodelsinmanyofthesecountriesarebased
on the extraction and processing of non-renewable fossil fuels, minerals, and non-ferrous metals. In
3
•
some of these countries, this is accompanied by significant household food and energy insecurities.
Thispointstoacertaingeographicinequity—environmentalriskstosustainabledevelopmenttendto
beconcentratedintheeasternpartsoftheregion.
Governancereformsmustbeattheheartofpolicyandprogrammaticresponsestothesechallenges.
Efforts to improve labour market performance, strengthen social protection systems, and better
address the region’s HIV/AIDS challenges require investments in the institutional capacity of labour
inspectorates, public employment and public health services, the local authorities, and NGOs.
Reductionsingender-basedandotherformsofdiscriminationrequireinvestmentsininstitutionsthat
protect human rights, as well as judicial reforms and access to justice. Better quality and more
extensiveandtimelydataonsocialexclusionandenvironmentalsustainabilityrequireinvestmentsin
the institutional capacity of national statistical institutions—particularly in light of the reporting
obligations associated with Agenda 2030 and the SDGs. Improvements in all of these areas require
investments in public administrations and civil services, at both the central and local government
levels.
4
Chapter1—Measuringincomeandnon-incomeinequalities4
Keymessages
• Significantreductionsinincomeinequalitieshavebeenreportedinmuchoftheregionsincetheturn
ofthemillennium—particularlyBelarus,Kazakhstan,Moldova,andUkraine,andpossiblyalsoAlbania,
Kosovo,5andtheKyrgyzRepublic.Bycontrast,incomeinequalitiesseemtohaveincreasedinGeorgia,
Turkey,andpossiblytheformerYugoslavRepublicofMacedonia(fYRoM).
• Loworfallingincomeinequalitieshavehelpedeconomicgrowthreducepoverty—particularlyinthis
firstgroupofcountries.InGeorgiaandfYRoM,bycontrast,highorrisinglevelsofincomeinequality
haveslowedorfrustratedprogressinpovertyreduction.Thisunderscoreshow—inadditiontobeing
desirable in and of themselves—low or falling inequalities are central to prospects for poverty
reduction,inclusivegrowth,andsustainabledevelopmentintheregion.
• Thenumbersofpeopleintheregionlivinginpovertyfellfromatleast46millionin2001toabout5
millionin2013.Thenumbersofpeoplelivinginextremepovertydroppedbelow1millionduringthis
time.Likewise,thenumbersofpeoplevulnerabletopovertydroppedfromabout115millionin2003
tosome70millionin2013.Bycontrast,thesizeofthemiddleclassgrewfromabout33millionin2001
to90millionin2013.Thenumbersofrelatively“wealthy”individuals(livingonmorethanPPP$50/day)
hadrisentosome32millionin2013—mostofwhomwerelivinginTurkeyandKazakhstan.
• Theregion’smiddleclasseshavemadeacomebacksincetheturnofthemillennium,followingboth
absoluteandrelativedeclinesduringthe1990s.Inmuchoftheregion,middleclasseshavegrownas
the shares of national income claimed by wealthy households have declined. As of 2013, at least 80
millionpeopleintheregionhadachievedlivingstandardsthatarebroadlyconsistentwiththebounds
ofthe“globalmiddleclass”.
• Progress in reducing income inequalities is now being put to the test in much of the region. The
combination of low commodity prices, falling remittances, and slow or negative growth on key
EuropeanandRussianexportmarketsisputtingpressuresonvulnerablehouseholdincomesthathave
notbeenseensincetheturnofthemillennium.Thisposesnewchallengesastheimplementationof
theglobalsustainabledevelopmentagenda2030beginsintheregion.
Introduction
Quantitativedataconcerninginequalitiescanbedividedintothreeclasses:(1)incomeinequalities;(2)
non-incomeinequalities;and(3)subjectiveperceptionsofinequalities(intheformofsurveydatagatheredvia
representativesamples).Thischapterfocuseson(1)andelementsof(2)—particularlyasconcernsinequalities
inthedistributionofwealth,butalsointermsofinequalitiesinaccesstobasicservices.(Manyotheraspectsof
non-income inequalities are taken up in the subsequent chapters—particularly as concerns labour markets,
gender,health,andsocialprotection).Bycontrast,subjectiveperceptionsofinequalitiesarenotamajorfocus
of this chapter, or report—although, for reasons explained below, they almost certainly merit additional
researchandanalysis.
Analyses concerning inequalities and programming to respond to them are often hindered by the
paucity of quantitative data—particularly once these discussions go beyond income inequalities, and
particularly in the context the transition and developing economies of Europe and Central Asia.Despite this,
4
PleasesendcommentsonthischaptertoElenaDanilova-Cross(elena.danilova-cross@undp.org)andBenSlay(ben.slay@undp.org).
AllreferencestoKosovointhispublicationarewithintheframeworkofUNSCR1244(1999).
5
5
evenintheregion’slesswealthycountries,policymakersareincreasinglyfocusingoninequalities,exclusion,
andvulnerability,ratherthanonextremeincomepoverty.
This growing interest is accompanied by an emphasis within the Sustainable Development Goals
(SDGs),whichunderpintheglobalAgenda2030forsustainabledevelopmentoninequalities,bothintermsof
SDGs 10 (“reduce inequality within and among countries”) and 5 (“achieve gender equality and empower all
womenandgirls”),andintermsofnumerousotherSDGtargetsand(prospective)indicators.Itisalsomatched
by a renewed commitment on the part of the UN system to support national efforts to improve the quality,
quantity,andavailabilityofsustainabledevelopmentdata—includingdatapertainingtoinequalities.The2014
publicationofAWorldThatCountsreportbytheUNSecretaryGeneral’sIndependentExpertAdvisoryGroup
onaDataRevolutionforSustainableDevelopmentcalledfora“datarevolution”inordertosupporttheSDG
indicatorsthatwillbeusedtomeasureandmonitortosustainabledevelopment.
Incomeinequality
Any assessment of the data on income inequality in the developing and transition economies of
Europe,Turkey,andCentralAsiamustbeginbycallingattentiontotensionsbetweenmultipleandsometimes
confusing data presented for the same country(s) on the one hand, versus the absence of publicly available,
comparabledataforothercountriesintheregionontheother.Furthercomplicationsresultfromthefactthat
the most common international data bases that show income distribution data for the countries of the
region—such as POVCALNET or SWIID—often present data that differ from what can be found on the public
websitesofthenationalstatisticalofficesintheregion.
Table1—Ginicoefficientsforincomedistributionavailableonnationalstatisticalofficewebsites
Year
Country
Albania
Armenia
Azerbaijan
Belarus
BiH
Georgia
Kazakhstan
Kosovo
KyrgyzRepublic
fYRoM
Moldova
Montenegro
Serbia
Tajikistan
Turkey
Turkmenistan
Ukraine
Uzbekistan
2002
2005
2007
2008
2009 2010
Nodata
2011
2012
2013
2014
I
0.53
0.40
0.37
0.37
0.34 0.36
Nodata
0.36
0.37
0.37
0.37
-
0.26
-
0.27
0.33
0.33
0.30
-
-
-
-
0.27
-
0.41
0.45
0.29
-
0.27
0.27
-
0.42
0.46
0.27
0.30
0.25
0.27
-
0.43
0.46
0.28
0.29
0.25
0.29
-
0.41
0.43
0.28
-
0.23
0.28
-
0.4
0.42
0.28
-
0.22
0.28
-
0.4
0.41
0.28
-
0.22
-
-
-
-
-
-
-
-
-
-
0.37
-
0.30
0.37
0.37
0.41
0.30
0.35
0.42
0.39
0.27
0.34
0.46
0.37
0.27
0.33
0.26
-
-
0.41
0.36
-
0.32
0.37
0.25
-
-
0.41
0.28
-
0.42
0.46
0.29
0.28
0.25
0.38
0.39
0.29
0.34
0.26
-
-
0.40
0.27
0.38
-
0.40
0.26
0.38
-
0.40
0.43
-
0.25
0.32
-
-
0.29
-
0.25
0.26
0.23
0.24
0.23
0.29
0.23
I
I
C
I
I
C
C
I
I
C
I
C
I
C
I
I
C
0.30
0.41
0.46
0.31
-
0.32
0.42
-
0.33
0.37
0.26 0.24
-
-
-
-
0.42 0.40
Nodata
0.26
0.25
0.3
6
I—income-baseddata;C—consumption-baseddata.
Ginicoefficients.AlthoughitisnotlistedasprospectiveSDGindicator,theGinicoefficientremainsthe
mostcommonlyused(andmostavailable)indicatortomeasureincomeinequalityintheregion—asreported
bynationalstatisticaloffices(basedonhouseholdbudgetsurveydata).However,asthedatainTable1show,
whileArmenia,Belarus,BosniaandHerzegovina(BiH),Kazakhstan,theformerYugoslavRepublicofMacedonia
(fYRoM),Serbia,Turkey,andUkrainereportGinismeasuredintermsofincomeperse,Kosovo,Montenegro,
Tajikistan,andUzbekistanreportGinismeasuredintermsofconsumptionspending—whileGeorgia,theKyrgyz
Republic,andMoldovareportbothincome-andconsumption-basedGinisforincomedistribution.Asthedata
for the Kyrgyz Republic show, differences in these series can be quite dramatic—suggesting very different
conclusionsabouttheextentofincomeinequalityinagivencountry.Anevengreaterconcernisthefactthat
these data seem to be publicly available to very limited degrees (or not at all) in Albania, Azerbaijan, Bosnia
and Herzegovina, Tajikistan, Turkmenistan, and Uzbekistan. That is: judging from publicly available national
sources,onlyArmenia,Belarus,Georgia,Kazakhstan,theKyrgyzRepublic,Moldova,Montenegro,Turkey,and
Ukrainereportsignificanttimeseriesdataonincomeinequalities.
AsecondperspectiveisofferedbytheGinicoefficientsforincomedistributionavailableintheWorld
Bank’sPOVCALNETdatabase,whichareshowninTable2below.Thesedata,whichareallconsumption-based,
indicate that reasonably complete and current time series are only available for Armenia, Belarus, Georgia,
Kazakhstan,theKyrgyzRepublic,Moldova,Montenegro,Turkey,Ukraine,andpossiblyKosovo.
AcomparisonofthedatainTables1and2suggestthat,intermsofincomeinequalitiesasmeasured
bytheGinicoefficient,thecountriesoftheregioncanbeplacedinfourgroups:
1) Countries in which the available data on balance point to low or falling income inequality. This
groupincludesBelarus,Kazakhstan,Moldova,andUkraine.Althoughthedataareshakier,Kosovo
andpossiblyAlbaniacouldbeplacedinthisgroupaswell.
2) Countries in which the available data on balance point to high or rising income inequality. This
group includes Georgia and Turkey, and possibly the former Yugoslav Republic of Macedonia
(althoughthedataareshakier).
3) Countries for which data are available, but do not lend themselves to a clear judgement in this
respect.ThisgroupincludesArmenia,theKyrgyzRepublic,andMontenegro.
4) Countriesforwhichtheavailabilityofnationalandinternationaldata(combined)isnotsufficient
forsuchanassessment.ThisgroupincludesAzerbaijan,BosniaandHerzegovina,Serbia,Tajikistan,
Turkmenistan,andUzbekistan.
Table2—GinicoefficientsforincomedistributionfromthePOVCALNETdatabase
Albania
Armenia
Azerbaijan
Belarus
BiH
Georgia
Kazakhstan
Kosovo
2002
0.32
0.35
0.17
0.30
-
0.40
0.34
-
2003
-
0.33
0.19
0.30
-
0.40
0.33
0.29
2004
-
0.38
0.16
0.27
0.34
0.40
0.31
-
2005
0.31
0.36
0.17
0.28
-
0.40
0.30
0.31
2006
-
0.32
-
0.28
-
0.40
0.30
0.30
2007
-
0.30
-
0.29
0.33
0.41
0.29
-
2008
0.30
0.31
-
0.27
-
0.41
0.29
-
2009
-
0.30
-
0.28
-
0.42
0.29
0.32
2010 2011 2012
-
-
0.29
0.31 0.31 0.30
-
-
-
0.28 0.26 0.26
-
-
-
0.42 0.42 0.41
0.29 0.27 0.27
0.33 0.28 0.29
2013
-
0.32
-
-
0.40
0.26
0.27
7
KyrgyzRep.
fYRoM
Moldova
Montenegro
Serbia
Tajikistan
Turkey
Turkmenistan
Ukraine
Uzbekistan**
0.30
0.39
0.36
-
0.33
0.41
-
0.29
0.33
0.29
0.39
0.35
-
0.33
0.33
0.42
-
0.29
0.35
0.35
0.39
0.35
-
0.33
0.33*
0.41
-
0.29
-
0.38
0.39
0.36
0.30
0.33
-
0.42
-
0.29
-
0.38
0.43
0.35
0.29
0.30
-
0.40
-
0.30
-
0.33
-
0.34
0.31
0.29
0.32
0.38
-
0.29
-
0.32
0.44
0.35
0.30
0.28
-
0.38
-
0.27
-
0.30
-
0.33
0.30
0.29
-
0.39
-
0.25
-
0.30
-
0.32
0.29
0.30
-
0.39
-
0.25
-
0.28
-
0.31
0.31
-
-
0.40
-
0.25
-
0.27
-
0.29
0.32
-
-
0.40
-
0.25
-
-
-
0.29
0.33
-
-
0.00
-
0.25
-
*Averageoftwodifferentvalues.
**Basedoneconometricanalysis.
Thesedifferencesnotwithstanding,acommonpatternacrosstheregioncanbeseenwhentheincome
distributiondataareconsideredoveralongerperiodoftime—particularlyforthosetransitioneconomies(not
countingTurkey)forwhichlongertimeseriesareavailable.Thatis,incomeinequalitiesjumpedsharplyduring
the 1990s with the “transition recession”, as real incomes fell for the vast majorities of households, labour
marketsloosened,andsocialprotectionsystemsbegantoencounterincreasingstrains.Theonsetof“recovery
growth” in the new millennium then saw reductions in income inequalities, as the income growth that was
recordedformiddle-classandlow-incomefamiliesapparentlyexceededthatreportedforwealthyhouseholds.
Moreover,despitetheimpactoftheglobalfinancialcrisisof2008-2009andtheEurozonecrisisof2010-2012—
both of which slowed growth in the region substantially—income inequality does not appear to have
deteriorated(Figure1).
Thus, for those countries for which longer time series are available, the data indicate that income
inequalities have generally returned to “pre-transition levels”. A similar pattern is apparent (over a shorter
time span) is apparent for Albania and Kosovo as well. Obvious exceptions to this pattern include Turkey
(which is not a transition economy, and whose development since 1990 has followed a different logic) and
Georgia—whichreportssimilarly(toTurkey)highlevelsofincomeinequality.
Figure1—TrendsinGinicoefficientsforincomeinequalityinselectcountriesoftheregion
0.50
Belarus
Kazakhstan
Moldova
Ukraine
KyrgyzRep.
0.40
0.30
0.20
1988
1998
POVCALNETdata.
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
8
Figure2—Albania:Ratioofincomeofpooresttwo
quintilestonationalincome(1996=100)
Figure3—Armenia:Ratioofincomeofpooresttwo
100
150
98
140
96
130
94
120
110
1996
1999
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
92
90
100
UNDPcalculations,basedonPOVCALNETdata.
110
110
105
105
100
100
95
95
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
1995
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
1996
2002
2005
2008
2012
Othermeasuresofincomeinequality.SDGtarget10.1callsfor“incomegrowthofthebottom
40%ofthepopulationataratehigherthanthenationalaverage”.Asthebelowfiguresshow,mostcountriesin
the region perform well against this “bottom 40” indicator. A number (Armenia, Kazakhstan, the Kyrgyz
Republic,Moldova,Ukraine)scorequitewell;others(Albania,Georgia,Montenegro)lessso.
Figure4—Belarus:Ratioofincomeofpooresttwo
Figure5—Georgia:Ratioofincomeofpooresttwo
9
Figure6—Kazakhstan:Ratioofincomeofpooresttwo
Figure7—Kosovo:Ratioofincomeofpoorest
twoquintilestonationalincome(2003=100)
110
130
125
105
120
115
100
110
95
105
100
90
2003 2005 2006 2009 2010 2011 2012 2013
Other indicators (e.g., Palma ratios, or other ratios of the richest deciles to the poorest) of income
inequality may be calculated on the basis of the quintile/decile income distribution data available on some
nationalstatisticalofficewebsitesorPOVCALNET.However,theydonotshowdramaticallydifferentpictures
fromwhathasbeenpresentedabove.
Figure8—KyrgyzRep.:Ratioofincomeofpoorest
Figure9—Moldova:Ratioofincomeofpooresttwo
250
140
130
200
120
150
2013
2011
2012
2009
2010
2008
2007
2006
2005
2003
2004
2001
2002
1999
100
2000
1993
1998
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
110
100
10
Figure10—Montenegro:Ratioofincomeofpoorest
105
Figure11—Turkey:Ratioofincomeofpooresttwo
110
100
105
95
2011
2012
2009
2010
2008
2007
2006
2005
2003
2004
2005 2006 2007 2008 2009 2010 2011 2012 2013
2002
1987
90
1994
100
95
Poverty,inequality,andinclusivegrowth
Mostofthecountriesoftheregionenjoyedstrongeconomicgrowthduringthefirstdecadeofthenew
millennium. While the global financial crisis pushed many of these economies into recession, as a rule they
experiencedarecoveryduring2010-2013.Thiseconomicgrowthclearlyhelpedreduceincomepovertyinthe
region:themostrecentWorldBankinternationallycomparabledata(basedon2011globalpurchasing-powerparity exchange rates) indicate that poverty rates (measured at the PPP$3.10/day threshold) have fallen in
most countries. In this sense, growth in the region has been “pro-poor”. Moreover, in a number of
economies—suchasAlbania,Belarus,Kazakhstan,Kosovo,theKyrgyzRepublic,Moldova,andUkraine(Figures
13-19)—decliningpovertyrateshavebeenaccompaniedbylowfallingGinicoefficientsforincomeinequality.
IncountriessuchasBelarusandUkraine,thesecontinuingdeclinesinpovertyandinequalityoccurredinspite
ofsloweconomicgrowth,currencycrises,andothermacroeconomicchallenges.
Figure12—Ukraine:Ratioofincomeofpoorest
Figure13—Incomepovertyandinequality(Gini
coefficient)trendsinAlbania(1996-2012)
150
0.35
0.30
140
0.25
130
Incomepoverty
0.20
120
Incomeinequality
0.15
0.10
110
1995
1996
1999
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
0.05
100
0.00
1996
2002
2005
2008
2012
11
UNDPcalculations,basedonPOVCALNETdata. POVCALNETdata.Note—povertyratepercentagesare:
• RelativetothePPP$3.10/daythreshold;and
• Asarulegreaterthan1(i.e.,a.50valueimpliesa
povertyrateof50%,not0.5%).
coefficient)trendsinBelarus(1998-2012)
coefficient)trendsinKazakhstan(1996-2013)
0.35
0.40
0.30
0.35
0.30
0.25
0.25
0.20
Incomepoverty
0.15
0.20
Incomeinequality
0.15
0.10
0.10
0.05
0.05
Incomepoverty
Incomeinequality
0.00
0.00
POVCALNETdata.Note—povertyratepercentagesare:
• Asarulegreaterthan1(i.e.,a.50valueimpliesapovertyrateof50%,not0.5%).
coefficient)trendsinKosovo2003-2013)
coefficient)trendsintheKyrgyzRep.(1993-2012)
0.35
0.80
0.30
0.70
0.25
0.60
0.20
0.15
0.50
Incomepoverty
Incomeinequality
0.40
Incomeinequality
0.10
0.30
0.05
0.20
0.00
Incomepoverty
0.10
2003 2005 2006 2009 2010 2011 2012 2013
12
coefficient)trendsinMoldova1997-2013)
coefficient)trendsinUkraine(1995-2013)
0.70
0.40
Incomepoverty
0.60
0.35
Incomeinequality
0.50
0.30
2013
2012
2011
2010
2009
2008
0.05
2007
0.00
2006
0.10
2005
0.10
2004
Incomeinequality
2003
0.15
2002
0.20
2001
Incomepoverty
2000
0.20
1999
0.30
1998
0.25
1997
0.40
0.00
19951996200220032004200520062007200820092010201120122013
coefficient)trendsinfYRoM1998-2008)
coefficient)trendsinMontenegro(2005-2013)
0.45
0.35
0.40
0.30
0.35
0.30
0.25
0.25
Incomepoverty
Incomeinequality
0.20
0.15
0.15
0.10
0.10
0.05
0.05
0.00
Incomepoverty
0.20
Incomeinequality
1998 2000 2002 2003 2004 2005 2006 2008
0.00
2005 2006 2007 2008 2009 2010 2011 2012 2013
Bycontrast,incountriesliketheformerYugoslavRepublicofMacedoniaandMontenegro(Figures2021),prospectsforfurtherpovertyreductionseemtohavebeenfrustratedbyrelativelyhighorrisinglevelsof
incomeinequality.Thesetrendsconfirmthat,inadditiontobeing“pro-poor”,economicgrowthintheregion
hasalsobeen“inclusive”—inthesenseofreducingincomeinequalitiesaswellaspoverty.Theexperienceof
countrieslikeBelarusandUkrainealsosuggestthatsloweconomicgrowthneednotmeanmorepovertyand
inequality.
13
Non-incomeinequalitymeasures
The Multiple Indicator Cluster Surveys (MICS) tool designed by UNICEF, to assess the well-being of
women and children, has been used in more than 100 countries over the past twenty years, in five rounds.
Mostofthecountriesoftheregionhavebeencovered.TheMICSdatabasecontainsdataforalargenumberof
indicators that have been disaggregated by gender, age, education level, ethnicity, and other vulnerability
criteria.
Forpurposesofthisreport,theMICSdatabasewasexaminedforthecountriesoftheregioninterms
ofdatacorrespondingto12proposedSDGindicators(Table3).
Table3—MICSdataandproposedSDGindicators
ProposedSDGindicator
MICSdatabase
3.1.1Maternaldeathsper100,000livebirths
Dataforthese
indicators
5.b.1Proportionofindividualswhoownamobiletelephone,bysex
wereonly
7.1.1Percentageofpopulationwithaccesstoelectricity
1.3.1Percentageofpopulationcoveredbysocialprotectionfloors/systems,disaggregated collectedfrom
oneroundof
bysex,anddistinguishingchildren,theunemployed,theelderly,peoplewithdisabilities,
MICSsurvey
pregnantwomen/newborns,workinjuryvictims,thepoorandvulnerable
3.2.1Under-fivemortalityrate(deathsper1,000livebirths)
3.2.2Neonatalmortalityrate(deathsper1,000livebirths)
3.7.2Adolescentbirthrate(10-14;15-19)per1,000womeninthatagegroup
4.2.1Percentageofchildrenunder5yearsofagewhoaredevelopmentallyontrackin
health,learningandpsychosocialwell-being(disaggregatedbysex,location,wealth,and
othercriteria,wherepossible).
6.1.1Percentageofpopulationusingsafelymanageddrinkingwaterservices
Dataforthese
indicators
werecollected
from2-3
roundsof
MICSsurvey
Dataforthese
indicators
werecollected
from4rounds
ofMICSsurvey
6.2.1Percentageofpopulationusingsafelymanagedsanitationservices
IntermsofproposedSDGindicator3.7.2(adolescentbirthratesper1,000womeninthe10-14,15-19
agegroups,disaggregatedbyrural/urbanresidence—Figure22):Ingeneral,fertilityratesamongwomen15-19
yearsinSoutheastEuropeislowerthaninCIScountries.Inaddition,thesefiguresarealmosttwiceashighin
ruralareasoftheCIScountriesastheyareinothercountries.Thesehighbirthratesmeanthatyoungwomen
haveearlychildbearingandrearingresponsibilities,whiletheymayinfactstillbechildrenthemselves.Such
circumstancescanturnlimittheirabilitytofullyparticipateinsocial,economic,andprofessionallife.
Early childbearing may also increase the risks of infant and child mortality, which corresponds to
proposed SDG indicators 3.2.1 (under-five mortality rate, deaths per 1,000 live births) and 3.2.2 (neonatal
mortality rate, deaths per 1,000 live births). An important role is often played by maternal education levels:
infant mortality rates are often higher among less well educated women. As the data from the available
countries,theaverageinfantmortalityrateofmotherswithprimaryorincompletesecondaryeducationis50
(per 1,000 live births), versus 28 for women having secondary, vocational or higher education. Moreover, in
almost all countries studied, infant and child mortality rates among the poorest 60% of the population were
abovethoseforthewealthiest40%(Figures23-24).
14
Figure22—Fertilityratesamongyoungwomen15-19years(numberofbirthsper1,000womenofthesame
age)inurbanandruralareas
60
Rural
50
48
Urban
44
40
31.5
29
30
21
22 22
20
18
20
24
19
17.1
9
10
2
2
0
BosniaandHerzegovina2012
Kosovo2014
Macedonia2011
Serbia2014
Kazakhstan2012
Moldova2012
Ukraine2012
Montenegro2013
IndicatorsmeasuringaccesstoeducationandindirectindicatorscoveringtheSDGindicatorPercentage
of youth (15-24) not in education, employment or training (NEET), SDG target 8.6.1, it is an indicator of the
proportion of eligible children attending secondary school. The data show that coverage is not universal. A
significant role in the expansion of enrolment in secondary school is the level of household income. So, for
bothboysandgirls,withanincreaseinthelevelofincomeisanincreaseintheproportionofeligiblechildren
attending secondary school (Figures 25-26). Children in poor families are forced to abandon their education
andgotoworktohelpsupportthehousehold.
Figure23—Childmortalityrates(forchildrenunder5yearsofage)per1,000livebirths
54
Tajikistan2005
Turkmenistan2006
Uzbekistan2006
Georgia2005
Serbia2006
Kazakhstan2011
Albania2005
Macedonia2005
Moldova2012
Ukraine2012
64
46
22
26 33
24
6
100
62
44
35
26
11
5 10
0
73
25
19
20
40
Rischest40%
60
80
100
120
Poorest60%
15
Figure24—Infantmortalityratesper1,000livebirths
46
Tajikistan2005
Turkmenistan2006
79
61
54
39.5
Uzbekistan2006
20
Georgia2005
23
Serbia2006
30
21
Kazakhstan2011
6
Albania2005
52
38
30
23
Macedonia2005
22
11
Moldova2012
Ukraine2012
5
0
18
10
10
20
30
Rischest40%
40
50
60
70
80
90
Poorest60%
Figure25—Percentageofchildrenofsecondaryschoolageattendingsecondaryschoolorhigher(adjusted
netattendanceratio)bywealthindexquintiles,male
120.0
100.0
%
80.0
60.0
40.0
20.0
0.0
1
2
3
4
5
wealthindexquinmle
Figure26—Percentageofchildrenofsecondaryschoolageattendingsecondaryschoolorhigher(adjusted
netattendanceratio)bywealthindexquintiles,female
16
120.0
100.0
%
80.0
60.0
40.0
20.0
0.0
1
2
3
4
5
wealthindexquinmle
MICS indicators concerning access to safe drinking water and sanitation services correspond to SDG
targets6.1.1and6.2.1.MICSdataalsoshowthataccesstoimprovedsanitationanddrinkingriseswithincome
levels(Figures27-28).
Figure27—Usersofimprovedsanitationfacilities(4th,5throunddata)andusersofsanitarymeansofexcreta
disposal(third-rounddata),%ofpopulationbywealthindexquintiles
110.0
100.0
90.0
%
80.0
70.0
60.0
50.0
40.0
1
2
3
4
5
wealthindexquinmle
Figure28—Usersofimprovedwatersource,%populationbywealthindexquintiles
17
110.0
100.0
%
90.0
80.0
70.0
60.0
50.0
40.0
1
2
3
4
5
wealthindexquinmle
Middleclassesintheregion
Manystudiesofinequalitiesnaturallyfocusonthe“mostunequal”—therichestandthepoorest,how
manyofthemthereare,whatmakesthemthisway,andhowdifferenttheyreallyarefromtherestofus.But
analyses of the “tails” of the income distribution are implicitly also concerned with the “middle” of the
distribution,sinceasmallermiddlemakesforbiggertails(andviceversa).Studiesofinequalitiescantherefore
also be studies of the middle class—particularly since concerns about greater inequalities are often
accompaniedbyconcernsaboutmiddleclasses.
Such issues are particularly relevant among the developing and transition economies of Europe,
Turkey, and Central Asia. Prior to the 1990s virtually all of the region’s transition economies had “socialist”
middleclasses,consistingofwelleducatedblue-andwhite-collarworkers,engineers,andothermembersof
the technical, creative, and administrative intelligentsia. While not necessarily commanding incomes or
possessing wealth that corresponded to middle-class societies in OECD countries, these middle classes were
forces of stability, and progress prior to the advent of transition. They certainly thought of themselves as
possessingmiddle-classstatus.
Moreover,sincethe1990s,manyofthesecountries—aswellasTurkey—haveexperiencedsignificant
increasesinper-capitaincome.Theirrelativelylowincomeinequalitylevelsimplythatmillionsofpeopleinthe
region’s upper middle-income countries (Albania, Azerbaijan, Belarus, BiH, Kazakhstan, Kosovo, fYRoM,
Montenegro, Serbia, Turkey, and Turkmenistan) could today be considered members of the “global middle
class”—possiblywithaspirationsandworldviewstomatch.
How large are the region’s middle classes? How are they best defined and measured? Three
approachestoansweringthesequestionsmaybeidentified:
• Materialwell-being,asreflectedinsuchcriteriaasper-capitaincomeandwealth/propertyownership
(e.g.,car(s),housing)andthecorrespondingabilitytoaccesscertainservices(e.g.,education,health,
travel);
• Subjective perceptions, concerning such issues as education, family background, and the associated
socialimplications—basedonindividualself-identification;and
• “Neitherrichnorpoor”.Tobeausefulcategoryofsocialanalysis,themiddleclass(thoseinthemiddle
ofthesocio-economicdistribution)mustbequalitativelyandquantitivelydifferentfromthoseinthe
tails.
18
Manydifferentapproachestodefiningandmeasuringthemiddleclasscanbefoundintheliterature
(for a subset of these, see Box 1). A key question that must be faced is whether the middle class is to be
defined in terms of absolute criteria (e.g., “members of the middle class earn between ‘X’ and ‘Y’ per
day/month/year”);orrelativecriteria(e.g.,“ifthericharethetop10%andthepoorarethebottom20%,then
themiddleclassisthemiddle70%”).
Box1—Methodologiesfordefiningandmeasuringthemiddleclass
• ILO:MembersofthemiddleclasshaveaveragedailypercapitaincomesinthePPP$4-13rangein
developingcountries,andabovePPP$13/dayindevelopedcountries.
• AfricanDevelopmentBank:Membersofthemiddleclasshaveaveragedailypercapitaincomesin
thePPP$10-20range.
• OECD:MembersofthemiddleclasshaveaveragedailypercapitaincomesinthePPP$10-100range.
• Atkinson/Brandolini:Membersofthemiddleclasshaveaveragedailypercapitaincomesintherange
of75-125%ofthemedianincome.
Inthisreport,wepresenttheresultsoftheapplicationoftwosuchapproaches,bothofwhichembody
twokeyelements:(i)theyarebasedonquantitativeindicatorsthataremethodologicallycompatiblewiththe
income equality data presented above; and (ii) they reflect both the “material well being” and “neither rich,
norpoor”logicdescribedabove.Theseare:
• Arelativeapproach,whichdefinesthe:
o Bottom two deciles of national household income distribution data as “lower-income” (i.e.,
relativelypoorerthanthemiddleclass);
o Middlesixincomedecilesas“middleclass”;and
o Toptwoincomedecilesas“upper-income”(i.e.,relativelyricherthanthemiddleclass);and
• Anabsoluteapproach,whichdefinesthe:
o Poor as those living below the World Bank’s new global poverty threshold of PPP$3.10/day
(withtheextremepoorlivingbelowthePPP$1.90/daythreshold);
o VulnerableasthoselivingbelowthePPP$10/daythreshold,butonmorethanPPP$3.10/day;
o MiddleclassasthoselivingbelowthePPP$50/day,butonmorethanPPP$10/day;and
o UpperclassasthoselivingonmorethanPPP$50/day.
Results of the “relative” approach. Trends in the evolution of the middle classes in the region’s
transition economies generally show similar pattern: their share of the national income fell in the 1900s
(during transition recessions) and then recovered after the new millennium. In most of these countries, the
middleclasses’sharesofnationalincomearenowat,orabove,pre-transitionlevels.
Virtuallyallofthevariationinmiddleclasses’sharesofnationalincomecanbeexplainedbyoffsetting
changes in upper-income classes’ shares of national income. The shares of national income received by the
bottom two deciles have remained surprisingly constant over time (at around 8-10% of national income) in
mostoftheregion.
Inallbuttwocountriesintheregion(GeorgiaandTurkey—Figures29-30),themiddleclasses’sharesof
nationalincomehavegenerallybeensignificantlylargerthantheupper-incomeclasses’share.InGeorgiaand
Turkey,bycontrastthesetwosharesareroughlyconstant(at45-50%).Thesharesofnationalincomereceived
bythebottomtwodecilesinthesecountrieshavebeenthesmallestintheregion(fluctuatingaround5%).
19
Figure29—Sharesofnationalincomereceivedby
middle,otherclasses—Georgia(1996-2013)
middle,otherclasses—Turkey(1987-2012)
60%
60%
50%
50%
40%
40%
Lowerincome
Lowerincome
Middleclass
Upperincome
30%
30%
Middleclass
20%
Upperincome
20%
10%
10%
199
199
199
199
200
200
200
200
200
200
200
200
200
200
201
201
201
201
0%
0%
UNDPcalculations,basedonPOVCALNETdata.The“middleclass”isdefinedasthemiddlesixdeciles(“middle60%”)ofthe
nationalincomedistribution.Theotherclassesaredefinedintermsofthetopandbottomtwodeciles(upperandlower
20%),respectively.
middle,otherclasses—Belarus(1988-2012)
60%
60%
50%
50%
40%
40%
Lowerincome
30%
20%
20%
Upperincome
2013
2012
2011
2010
2009
2008
2007
2005
2004
2003
2002
0%
2001
0%
1996
10%
1993
10%
1988
198
199
199
199
199
200
200
200
200
200
200
200
200
200
200
201
201
201
Upperincome
30%
Middleclass
Middleclass
2006
Lowerincome
middle,otherclasses—Kazakhstan(1988-2013)
UNDPcalculations,basedonPOVCALNETdata.The“middleclass”isdefinedasthemiddlesixdeciles(“middle60%”)ofthe
nationalincomedistribution.Theotherclassesaredefinedintermsofthetopandbottomtwodeciles(upperandlower
20%),respectively.
20
Figure33—Sharesofnationalincomereceived
bymiddle,otherclasses—Kosovo(2003-2013)
Figure34—Sharesofnationalincomereceivedbymiddle,
otherclasses—Ukraine(1988-2013)
60%
60%
50%
50%
40%
40%
30%
Lowerincome
30%
Lowerincome
Middleclass
Middleclass
20%
20%
Upperincome
Upperincome
10%
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
1999
1996
1995
0%
1992
2003 2005 2006 2009 2010 2011 2012 2013
1988
10%
0%
Economieswiththelargestmiddleclasses(e.g.,Belarus,Kazakhstan,Kosovo,Ukraine—Figures31-34)
alsotendtohavethelargestsharesofnationalincomereceivedbythebottomtwodeciles,andthesmallest
sharesofnationalincomereceivedbytherichest20%.
Figure35—Changesinabsolutenumbersofmiddle,otherclassesintheregion(2000-2013)
10
7
28
103
8
7
26
100
25
14
31
2000
9
9
34
35
114
115
12
43
111
2
14
52
103
2
16
56
102
1
20
60
98
15
21
9
20
9
16
5
13
6
11
4
10
3
2001
2002
2003
2004
2005
2006
2007
Extremepoverty
Poverty
Vulnerable
6
20
8
18
61
65
92
96
8
2
8
2
2008
16
21
25
32
22
25
25
64
65
66
86
78
75
70
6
2
2010
2011
64
2009
Middleclass--lowermer
24
6
5
2012
Middleclass--uppermer
5
2013
Wealthy
UNDPcalculations,basedonPOVCALNETdata.Figuresareinmillions.TurkmenistanandUzbekistanarenotincluded.
Onthewhole,theincomedistributiondatadonotdescribearegionwhosemiddleclasseshavebeen
decimated by transition or development. They instead broadly suggest a return to pre-transition income
shares. In light of the region’s generally low Gini coefficients, this conclusion should not come as a surprise.
Still,itstandsincontrastwithmanyofthenarratives.Itmaybethatthetrulyrelevantchangesareoccurring
withinthedeciles(especiallythebottomtwo)ratherthanacrossthem—orthatquantitativedataareunableto
21
accurately capture the truly wrenching social changes that these countries have experienced in the past 25
years.Still,theseresultsprovidefoodforthought.
Resultsofthe“absoluteapproach”.Comparedtotheaboveanalysis,thisapproachhasanumberof
advantages.Theseincludeinteralia:(i)explicitlinkstoglobalpovertythresholds—therebylinkingabsoluteand
relative poverty (i.e., inequality) measures; (ii) an extension of the previous approach’s three-tiered social
stratification,toincludealsothosevulnerabletopoverty(i.e.,livingabovethepovertylinebutnotnecessarily
in the middle class)—and also (if we so chose) those living in extreme poverty (i.e., below the World Bank’s
new PPP$1.90/day threshold), as well as different tiers within the middle class (i.e., those living between
PPP$10/dayandPPP$20/day,versusthoselivingbetweenPPP$20/dayandPPP$50/day);and(iii)answersto
suchquestionsas“howmanypeopleincountryXhaveincomesabove$20/day?”
Thisanalysissuggeststhat,during2000-2013,thenumbersofpeopleintheregionlivinginpovertyfell
from 46 million in 2001 to about 5 million in 2013 (Figure 35).6 (The numbers of people living in extreme
poverty,aspertheWorldBank’sPPP$1.90/daycriterion,droppedbelow1million.)Likewise,thenumbersof
peoplevulnerabletopoverty(i.e.,inthePPP$3.10/day–PPP$10/dayrange)droppedfromabout115millionin
2003tosome70millionin2013.Bycontrast,thesizeofthemiddleclassgrewfromabout33millionin2001to
90 million in 2013. Interesting, after nearly disappearing 2002-2004, the numbers of “wealthy” individuals
(livingonmorethanPPP$50/day)hadrisentosome32millionin2013—mostofwhomwerelivinginTurkey
and Kazakhstan. Adding the 25 million individuals estimated to be living on between PPP$20/day and
PPP$50/daytothisfiguresuggeststhatnearly80millionpeopleintheregionhaveachievedlivingstandards
thatarebroadlyconsistentwiththeboundsofthe“globalmiddleclass”.
Figure36—Changesinsharesofmiddle,otherclassesintheregion(2000-2013)
6%
4%
15%
55%
4%
4%
14%
54%
14%
17%
7%
2000
5%
5%
18%
19%
61%
61%
1%
6%
23%
59%
1%
7%
28%
54%
1%
8%
29%
54%
1%
10%
31%
51%
3%
10%
4%
10%
32%
34%
49%
48%
8%
11%
33%
44%
12%
13%
11%
13%
33%
33%
40%
38%
16%
12%
33%
35%
8%
11%
5%
11%
5%
9%
3%
7%
3%
6%
2%
5%
2%
4%
1%
4%
1%
3%
1%
3%
1%
2%
1%
2%
1%
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Extremepoverty
Poverty
Vulnerable
Middleclass--lowermer
Middleclass--uppermer
UNDPcalculations,basedonPOVCALNETdata.TurkmenistanandUzbekistanarenotincluded.
Wealthy
Considerationofthesetrendsintermsofchangesintherelativesizeofthevariousclassesshowsthat,
whereasmorethanthreequartersoftheregionwaslivinginpovertyorvulnerabletoitduring2000-2003,by
2013 this share had dropped to under 40%. While the middle classes were the chief beneficiaries of these
improvements in living standards, it is interesting to note that the share of those living on more than
PPP$50/dayhadrisento16%in2013(fromclosetozeroin2002-2003).
6
ThesedatadonotincludeTurkmenistanandUzbekistan.
22
In broad brush strokes, these results are quite consistent with those suggested by the “relative”
approach to defining the region’s middle classes described above. They also do not describe a region whose
middle classes have been decimated by transition or development. An important difference lies in the two
approaches’ treatment of the wealthy, however. Whereas the relative approach shows the upper classes’
sharesofnationalincomeremainingroughlyconstantorshrinkinginmostoftheregion,theabsoluteapproach
pointstotherapidgrowthinthisgroup’sshareoftotalincomefromvirtuallynothingin2003to16%adecade
later.Thismaybeabletoexplainthewidespreadconcernsaboutgrowinginequalitiesintheregion—evenif
thedistributionoftotalhouseholdincomes(asmeasuredindeciles)hasnotchangedsodramatically.
Howricharetheregion’srich?
Global household wealth is unequally distributed: there are an estimated 31 million millionaires and
more than a thousand billionaires (in US dollar terms) in the world. As one authoritative source notes: “The
bottom half of the global population together possess less than 1% of global wealth. In sharp contrast, the
richest10%own86%oftheworld’swealth,withthetop1%aloneaccountingfor46%ofglobalassets”(Credit
SuisseGlobalWealthDatabook,2013).
Chart1--Theregion'sbillionaires(inbillionUS$)
3.9
topranking
secondtothetop
botomranking
5.3
2.3
1
2.2 1.9 1.4
5.2
1.4 1
Turkey
3.9
Kazakhstan
2.2
Ukraine
5.3
2.3
1.9
1.4
1
1.4
1
topranking
secondtothetop
Georgia
5.2
botomranking
UNDPcalculationsbasedonForbes’slistofreal-timebillionaires.
AquickbrowsethroughForbes’mostrecentbillionaireslist7showsthatonly33(2%)comefromthe
developing and transition economies of Europe, Turkey, and Central Asia.8 Turkey is responsible for 22 of
these,followedbyUkraineandKazakhstan(fiveeach)andGeorgia(withone).Fiveofthesearewomen(four
from Turkey, one from Kazakhstan). Interestingly, large differences in wealth are apparent between these
billionaires:therichestbillionaireinTurkeyissomefourtimesricherthanthe“poorest”billionaire;inUkraine
thegapisfive-fold.Wealthispredominantlyandaccumulatedviathenaturalresourcesandbankingsectorsof
economy; construction and pharmaceuticals are specific to Turkey. Interestingly, one billionaire in Ukraine
makesmoneyintheagriculturalsector.
7
http://www.forbes.com/billionaires/list/#version:realtime(lastconsultedon16January2016)Ofcourse,richlistsarejustestimates.
Theyarepopularpreciselybecausethey’rewillingtoputaharddollarnumberonthepersonalwealthofthesuper-rich.Yetintruth,no
onereallyknowswhatmanyofthesuper-richareworthatanygivenmoment(includingthesuper-richthemselves).Manyofthemown
privatecompanies,whicharehardtovalueuntilthey’resold.Andtheyoftenhavedebts,otheraccounts,financialobligationsand
investmentsthatdon’tshowuptothepublic.
8
Referenceistotheprogrammecountries/territorieswhosedevelopmentaspirationsaresupportedbyUNDP’sRegionalBureaufor
EuropeandCIS.Theseare:Albania,Armenia,Azerbaijan,Belarus,BosniaandHerzegovina,Georgia,Kazakhstan,Kosovo(asperUNSCR
1244(1999)),Kyrgyzstan,theFormerYugoslavRepublicofMacedonia,Moldova,Montenegro,Serbia,Tajikistan,Turkey,Turkmenistan,
Ukraine,andUzbekistan
23
According to the Global Wealth Report, the numbers of millionaires in the region dropped by some
95% during 2010-2015 (Figure 37). Perhaps more usefully, the Global Wealth Report also estimates Ginis
coefficientsforthedistributionofwealth,intheregionaswellasglobally(Table3).Anumberofconclusions
aresuggestedbytheseestimates.First:withtheexceptionsofKazakhstan,Turkey,andUkraine,inequalitiesin
the distribution of wealth generally remained the same or declined during this time. Second, inequalities in
wealthinmostoftheregionaregenerallybelowworldaverages.Thisalsocanbeseenasalegacyfromthe
region’s socialist past, when significant private holdings of wealth as such did not exist. (In light of the large
shareofstatepropertythatremainsinstatehandsinmuchoftheregion,theroleofthestatemaynotbea
legacy.)
Figure37--Theregion'smillionaires
2259
1903
2010
2011
114
116
113
59
2012
2013
2014
2015
Table3—Ginicoefficientsforthedistributionofwealthintheregion(2010-2015)
Albania
Armenia
Azerbaijan
Belarus
BiH
Georgia
Kazakhstan
KyrgyzRepublic
fYRoM
Moldova
Montenegro
Serbia
Tajikistan
Turkey
Turkmenistan
Ukraine
2010
0.68
0.668
0.612
0.648
0.678
0.703
0.658
0.673
0.727
0.688
0.652
0.645
0.669
0.704
-
0.64
2011
0.654
0.644
0.595
0.637
0.665
0.684
0.863
0.659
0.694
0.671
0.669
0.635
0.657
0.844
-
0.889
2012
0.657
0.639
0.652
0.624
0.659
0.79
0.838
0.66
0.689
0.648
0.635
0.626
0.638
0.842
-
0.892
2013
0.656
0.639
0.651
0.622
0.658
0.68
0.867
0.659
0.688
0.647
0.634
0.625
0.638
0.837
0.68
0.9
2014
0.668
0.668
0.646
0.646
0.663
0.68
0.873
0.646
0.69
0.68
0.657
0.654
0.629
0.843
0.667
0.919
2015
0.658
0.628
0.591
0.65
0.67
0.666
0.874
0.633
0.693
0.674
0.658
0.661
0.624
0.821
0.673
0.916
Africa
Asia-Pacific
China
0.849
0.869
0.69
0.872
0.881
0.697
0.865
0.889
0.689
0.846
0.887
0.695
0.856
0.895
0.719
0.856
0.892
0.733
24
Europe
India
LatinAmerica
NorthAmerica
World
0.799
0.778
0.785
0.799
0.881
0.829
0.804
0.793
0.816
0.893
0.831
0.813
0.797
0.842
0.902
0.83
0.813
0.806
0.841
0.905
0.827
0.814
0.809
0.837
0.911
0.834
0.831
0.809
0.842
0.915
Conclusions
Forthosewhoareconcernedabouttheglobaleffectsofincreasinginequalities,theaboveanalysisof
the quantitative data on the distribution of income and wealth in the region suggests a reassuring picture.
ManyofthedevelopingandtransitioneconomiesofEurope,Turkey,andCentralAsia(withafewexceptions)
reportlow,ordeclining,levelsofincomeinequality;estimatesofthedistributionofwealththatarebasedon
internationallycomparablemethodologiesproposethesameresults.However,suchapictureisatoddswith
manycommonlyacceptednarrativesabouttheregion—whichtendtoreferencelargeandgrowinginequalities
inincome,wealth,accesstobasicservices,andotherimportantaspectsofhumandevelopment.
This raises the question: what’s wrong—the data, or the perceptions? To be sure, the quality of the
dataonincomeandwealthinequalitiesintheregionisnotbeyondreproach.Forexample,thatthehousehold
budgetsurveydatafromwhichtheincomeinequalityindicatorsthatpopulatebothnationalandinternational
data bases are drawn are widely recognized as missing both the very poor (who typically slip between the
cracks of national surveying activities) and at least a portion of the incomes of the very rich. It is telling, for
example, that the POVCALNET database reports that virtually no one in the region earns more than
PPP$100/day—millionairesandbillionaires(asreportedbyForbes)notwithstanding.Partofthismaybedueto
the reliance on consumption-based surveys that underpin internationally comparable databases like
POVCALNET.Suchsurveysdonotreflectincomesearnedbutnotspentonconsumption—which,inthecaseof
wealthy households (with high average propensitiestosave)—mayfurtherunderstatethesharesofnational
incomes distributed to wealthy households. All this underscores the need for more investment in national
statisticaloffices’capacitytoconductregularhouseholdbudgetsurveysthataccuratelycapture(accordingto
internationallycomparablemethodologies)theentiretyofhouseholdincomes—includingthosesharesthatare
notconsumed.
Still, these data should not be dismissed out of hand. Declines in income inequalities in many Latin
American countries during the past decade have been well documented; there’s no reason that other
developingregionscannotreportsimilartendencies.Perhapsmoreseriousquestionsconcernwhetherthose
economiesintheregionthatseemtohavemadethemostprogressinreducingincomeinequalities—Albania,
Belarus,Kazakhstan,Kosovo,Moldova,Ukraine—willbeabletomaintaintheseaccomplishmentsinthefaceof
thesocio-economictensionsthatarenowpresentintheregion.
25
Chapter2—Inequalities,employment,andsocialprotection9
Keymessages
• Labourmarketinequalitiesandexclusionlieattheheartoftheregion’s10inequalitychallenges.This
is the case both in terms of labour markets per se, and because access to social protection is often
linked to formal labour market participation. People without decent jobs face much higher risks of
poverty,vulnerability,andexclusionfromsocialservicesandsocialprotection.
• While labour market inequalities exist in many dimensions, they are particularly important when it
comes to access to formal employment. Because informal, precarious, migratory, and vulnerable
employment is widespread throughout the region, employment does not necessarily offer much
protection against poverty and vulnerability. Women, young workers, migrants, the long-term
unemployed,peoplewithdisabilities,andotherswithunequallabourmarketpositionsareparticularly
vulnerabletobroaderrisksofpovertyandexclusion.Whiletrendsareimprovinginsomecountriesand
forsomegroups,inothers,labourmarketinequalitiesareincreasing.
• Many commonly used labour market indicators offer only limited insights into labour market
performance and equalities. This is apparent in the “employment”/”unemployment” statistical
dichotomy,andintheinfrequencywithwhichpubliclyavailablelabour-marketdataaredisaggregated
by gender, ethnicity, and other vulnerability criteria. Different labour market statuses—inactivity,
unemployment, underemployment, informal employment, formal employment, migrant work, etc.—
should be understood as representing points along multi-dimensional continua of labour market
positions, with much overlap and fluidity between the categories. Inequalities among the employed
canbeasgreat,orgreater,thanthosebetweentheemployedandunemployed.
• Long-termeffortstoformalizeemploymentarecrucial.Threedirectionsareparticularlyimportant:(i)
effortstoboosttheinstitutionalcapacityoftheinstitutionschargedwithlabourmarketregulation,in
order to better enforce legal protections for workers’ rights in the formal sector; (ii) the abolition of
those labour market regulations that can not be credibly enforced by state agencies and drive
employment into the informal sector; and (iii) increased investment in active labour market policies,
vocationaleducation,andothermeasurestoboostworkerproductivity.
• Policylinkagesbetweenlabourmarketsandsocialprotectionneedtobestrengthened.Whilepoorly
aligned social policies can reduce incentives for labour market participation and hiring, this is not a
reasonforreducingsocialprotectionspendingandcoverage.Instead,whereverpossible,thetaxation
of labour to fund social benefits needs to be reduced in favour of other funding sources. These may
include:(i)highertaxesonenvironmentallyunsustainableactivities;(ii)reductionsinbudgetsubsidies
thataccruetothewealthy;(iii)moreaggressivemeasurestoreducethediversionofbudgetrevenues
totaxhavens;and(iv)morerobustdirectionofbudgetaryprocurementandcontractingresourcesto
companies (e.g., social enterprises) that explicitly promote social inclusion. The region’s prevailing
demographictrendsindicatethatneedstofindnon-laboursourcesofbudgetrevenueswillsharpenin
thefuture.
• Socialprotectionisalsoaboutsocialservicesandthecareeconomy.Increasedinvestmentsinsocial
service provision—particularly terms of care for children, the elderly, and persons with disabilities—
canboostparticipationinlabourmarketsandvocationaltrainingprogrammes,particularlyforwomen.
InTurkey,forexample,adecisiontobringstatebudgetspendingonsocialcareservicesuptoOECD
9
PleasesendcommentsonthischaptertoSheilaMarnie(sheila.marnie@undp.org)andBenSlay(ben.slay@undp.org).
Unlessotherwisenoted,referenceinthispublicationistoAlbania,Armenia,Azerbaijan,Belarus,BosniaandHerzegovina,Georgia,
Kazakhstan,Kosovo(asperUNSCR1244(1999)),theKyrgyzRepublic,theFormerYugoslavRepublicofMacedonia,Moldova,
Montenegro,Serbia,Tajikistan,Turkey,Turkmenistan,Ukraine,andUzbekistan.
10
26
•
•
levelswouldgenerateanestimated719,000socialcarejobs—morethan2.5timesthetotalnumberof
jobs that would be created by devoting the same amount of budget funds to
construction/infrastructureprojects.Anestimated84%oftheworkershiredintothesesocialcarejobs
wouldhavepermanentcontractsofunlimitedduration(versus25%inconstruction);85%wouldhave
socialsecuritycoverage(comparedto30%inconstruction).
Inmanycountries,gapsbetweendejuresocialprotectionguaranteesanddefactoaccesstosocial
benefitsandservicesaresignificantandgrowing.Addressingthesegapsbalancingcentralizedsocial
protection and employment schemes with more scope for locally provided, more flexible and
individual-focusedmodalitiesofinclusion.
Manyofthoseexcludedfromthelabourmarketarenotreachedbytraditionalactive-labourmarket
programmes. This is due in part to weaknesses in outreach to vulnerable communities (e.g., ethnic
minorities,low-skilledworkersinruralcommunities),butalsotochronicunder-funding.
Overview
Labourmarketinequalitiesconcernnotonlydifferencesbetweenthosewhoareemployedandthose
who are not, but also among those who are employed. In most countries of the region, those who are in
precarious,informal,lowwage,lowproductivityjobscaneasilysufferthesame(orworse)risksofpovertyand
exclusion as those who are without jobs. Inequalities among the employed can be as great, or greater, than
thosebetweentheemployedandunemployed.
However,thesedisparitiesarenoteasytounravelusingstandardlabourmarketindicatorsanddata.
While these may help ensure international comparability, they often fail to capture critical dimensions of
labour market and broader social inequalities. As such, they may provide a poor basis for policy design and
implementation. Low labour force participation rates and (in some countries) high unemployment figures
underscore problems of labour market exclusion for significant sections of the working age population. But
evenwithintheemployedpopulationthereareclearlyinequalitiesaffectingindividualandhouseholdwelfare,
asevidencedbythedataonthe“workingpoor”,andoninformalandvulnerableemployment.Manyofthose
atthebottomoftheincomescalecannotaffordtobe“idle”;theymayhavelittlechoicebuttoengageinlow
qualityorvulnerableemployment.Giventherestrictivecriteriafordefiningtheunemployed,andthelowlevel
andlimiteddurationofsupportforregisteredunemployed,manyworkerswithoutjobsdonotregister.They
eitherwithdrawfromthelabourforce,acceptlow-qualityjobs,orjointhearmyoflabourmigrants.
Itisdifficulttocharacterizeorgeneralizeabouttheseinequalitieswithintheemployedpopulation.The
ILO’s“decentwork”paradigm—asopposedtoprecarious,vulnerable,orinformalsectorwork—cancertainly
help. But quantifying the share of the workforce enjoying decent work is extremely complex. Some authors
refertoduallabourmarkets,betweenthoseinformalandthoseininformalemployment;orbetweenthosein
decent jobs and those in non-decent jobs. But even here, reality is often more complex than dichotomous,
black-and-white characterizations. For example, public sector employees may have more job security and
betteraccesstosocialprotection—buttheirwagesmaybesolowastomakethempartofthe“workingpoor”.
Informal sector workers may not enjoy labour rights or social protection, but they may be able generate
incomes that are sufficient to keep themselves, and their families, out of poverty. Moreover, even workers
who are formally employed may receive significant shares of their wages in the form of unregistered (and
thereforeuntaxed)cashunderthetable.
Unequalemploymentopportunitieshavealsoledtolargeinternalandexternalmigrationflows—many
of which exhibit high degrees of irregularity/informality. While these movements can raise income and
development opportunities for migrants and their families, they are also associated with many risks and
insecurities.Theymayalsocontributetonewformsofinequalities,mostnotablybetweenthosehouseholds
withmigrantmembersandaccesstoremittances,andthosewithout.
27
UNDP’s2011RegionalHumanDevelopmentReportshowedthat,whilejoblessnessheightenstherisk
of economic exclusion, but it also tends to be associated with other forms of deprivation which together
heighten individual risks of exclusion. Exclusion from employment opportunities were found to be a major
driver of exclusion from economic life, which in turn contributed to exclusion from social and political
processes.11
Driversoflabourmarketexclusionincludethecapital-andresource-(asopposedtolabour-)intensive
economicgrowthpatternswhichhavetakenroot.Theseoftenresultinthepaucityofdecent,formal,private
sectorjobs.Thiscanbeattributedtoalackofstructuralreformstostrengtheninstitutionalcapacityinboththe
privateandstatesectors.Butitalsoreflectsthelowprioritiesoftenascribedtoemploymentgoals,reflecting
the(oftenmistaken)beliefthateconomicgrowthwouldautomaticallyleadtomoreandbetterjobs.Butwhile
it is now widely understood that this link is not automatic, governments have been slow to put in place the
institutionalframeworksneededtodesignandimplementcomprehensivenationalemploymentpolicies.The
successofeffortstoaddresstheconsiderableskillsmismatchesthatstandbehindmanycasesoflabourmarket
exclusion(particularlyforyoungworkers)hasnotbeenespeciallynoteworthy.
Helping economic growth to boost decent job opportunities requires holistic, whole-of-government
approaches—particularly in terms of the links between employment and social protection policies. Most
countriesintheregioninheritedsocialprotectionsystemsthatweredesignedtocomplementfullornear-tofullemploymentsituations.Incircumstancesofentrenchedjoblessness,however,proposalstocompensatefor
the lack of formal employment opportunities by providing minimum income floors have often encountered
opposition, in the form of concerns about excessive fiscal burdens and disincentives for labour market
participation.Manyworkershavethereforehadtoseekinformalemployment—therebylosingaccesstosocial
insurance(e.g.,healthandpensioninsurance)aswellasotherbenefits(e.g.,maternityleave)andprotections
nominallyguaranteedbylaw.
Effortstopromotedecentjobsandstrengthensocialprotectionintheregionmustthereforefocuson
addressingdriversofinformality.Threedirectionsseemparticularlyimportantinthisrespect:
• Effortstoboosttheinstitutionalcapacityoftheinstitutionschargedwithlabourmarketregulation,
inordertobetterenforcelegalprotectionsforworkers’rightsintheformalsector.Intoomanycases,
inspectionsthatidentifyviolationsofcommercial,labour,migration,orsocialprotectionlegislationare
dealt with through payment of bribes—which are seen as necessary to provide a living wage for the
(not always fully trained) civil servants working in these inspectorates. Civil service and public
administration reforms to raise public-sector salaries and reduce other drivers of corruption and
malfeasancethatdistortlabourmarketregulation.
• Thereconsiderationoftaxesandregulationsthatcannotbecrediblycollectedorenforcedbystate
agenciesanddriveemploymentintotheinformalsector.Regulationsandtaxesthatplaceinordinate
burdensonSMEs,ormigrantsandothervulnerableworkers,needtobereconsideredorabolished.
• Increased investment in active labour market policies, vocational education, and other measures to
boostworkerproductivity.
Labourmarketinequalities
11
SeeforexampleRHDR2011,pp18-19.
28
Inequalitiesinemploymentoutcomesandopportunitiesareinpracticedifficulttoseparate.Bothare
reflectedin low employment rates, as well as in high rates of long term unemployment,as well as informal,
vulnerable,andmigratoryemployment.
Labour force participation rates in the region vary from relatively high (70-80% of the working age
population)tobelow50%inothers(
Figure),whiletheaverageregionalunemploymentratesofthosewhoareparticipatinginthelabour
forcewas9.6percentin2014.12EmploymentandparticipationratestendtobeparticularlylowintheWestern
Balkans,andmuchhigherinCentralAsia.Andwhereasemploymentratesdeclinedinmuchoftheregionafter
theearly1990s,theyhavegenerallyreturnedto“pre-transition”levelsinCentralAsiaandtheCaucasus.
Figure1.LabourMarketStatusofWorkingAgePopulationin2014
90
80
70
Unemploymentas%ofpopulamon
Employmentrate
Regionalemploymentrate(weightedaverage)**
60
50
40
30
20
10
0
KAZ AZE KGZ TJK GEO TKM UZB UKR ARM BLR ALB TUR SRB MNE MKD MDA BIH KOS*
Source:ILO,2015,KILM9thed.*2012figure;**notincludingKosovounderUNSCR1244.
Figure2.Labourforceparticipation(left)andemploymentrate(right),workingage
12
ILO,2015,WorldEmploymentandSocialOutlook.Figurefor2014.
29
70
65
CentralAsia
CentralAsia
65
60
60
SouthCaucasusandWesternCIS
55
55
[SERIES
NAME]
50
[SERIES
NAME]
[SERIES
NAME]
50
[SERIES
NAME]
45
45
40
Source:CalculationsbasedonILO,2015,KILM9thed.*exceptfor2012and2013,averagesdonotinclude
KosovounderUNSCR1244.
Figure1.Unemploymentbysub-regionforworking-agepopulation,overtime(left)andin2014(right)
16
[SERIES
NAME]
80%
Acmve
14
100%
60%
12
[SERIES
NAME]
CentralAsia
Jobless
10
40%
20%
0%
CentralAsia
8
6
South
South
Caucasus
Eastern
and
Europe*
WesternCIS
ECIS*
Employmentrate
Inacmve
Left:Regionalandsub-regionalunemploymentrates(weightedaverages).Right:Sub-regionalbreakdownof
labourmarketforworkingagepopulation,2014.Source:CalculationsbasedonILO,2015,KILM9thed.*except
for2012and2013,averagesdonotincludeKosovounderUNSCR1244.
Insomecountries,andinalltheSoutheastEuropeaneconomies,employmentratesarebelow50%of
theworkingagepopulation.Theeconomiccrisisof2008-2009resultedinthelossofmanyjobs,whichisclearly
reflected in the unemployment trends (Figure 3). Although in most countries, economic growth recovered
relatively quickly, in some countries employment rates have been slower to recover. The impact on youth
participationandemploymentrateshasbeenparticularlystark(discussedfurtherbelow).
30
Long-term unemployment (LTU) is usually defined as unemployment lasting for over 12 months.
Available data indicate high LTU incidence in the Western Balkans, where 70-90% of the unemployed have
been searching for employment for longer than 12 months (Figure 4). Considering the overall high
unemploymentratesinthesecountries,thismakesforasubstantialsectionoftheworkingagepopulation.A
study using different data13 shows a high incidence of LTU in Azerbaijan, wherever 50% of the unemployed
were found to have been searching for jobs for over 12 months, and 75% were not officially registered. (Of
thoseregistered,lessthan5%werereceivingsocialbenefits.)Long-termunemploymentmanifestsitselfmore
stronglyamongcertainsocialgroups,suchasRoma(discussedfurtherbelow).
Figure2.UnemploymentandLTUforworking-agepopulation,latestavailabledata
35
30
25
20
15
10
5
0
Unemploymentratein2014
LTU
Source:ILO,2015,KILM9 .Unemploymentdatafor2014*unemploymentdatafor2012;**averagedoes
notincludeKosovounderUNSCR1244.LatestavailableLTUdataforvariousyears2009-2014(seeStatistical
Annex).
Labour market gender differences are significant throughout the region. These differences are
particularly visible in the Caucasus and Central Asia, but also in some Southeast European countries such as
BosniaandHerzegovinaandTurkey.Worryingly,inmostcountries,genderinequalitiesonthelabourmarket,
as measured by the inactivity rate are increasing. Long-term unemployment has particularly strong gender
dimensionsinCentralAsia:inallthecountriesofthissub-regionexceptforKazakhstan,gendergapsinlabourforceparticipationrateshavebeenincreasing.
Figure3.Adultlabourforceparticipationrategendergap,maleminusfemale
th
13
UNDP/MartinaLubyova,2013,TowardsDecentEmploymentthroughAcceleratedStructuralReforminAzerbaijan.
31
50
40
30
20
10
0
-10
TUR KOS* TKM UZB MKD BIH KGZ ALB GEO TJK ARM SRB MNE UKR BLR KAZ AZE MDA
2014-2005
2014
Gendergapinlabourforceparticipationfor2014,andchangeinthegendergapover2014-2005period.
Source:CalculationsbasedonILO,2015,KILM9thed.
Overall, standard labour market indicators point to worrying disparities in employment outcomes,
which in turn suggest considerable inequalities in employment opportunities. They also point to significant
differencesinemploymentoutcomesbysub-region,withSoutheastEuropeancountrieshavingmoretroubling
indicatorsthanCentralAsia,theSouthCaucasusandWesternCIScountries.However,asdiscussedbelow,the
standardindicatorsmaybeunabletofullycapturelabourmarketdisparitiesintheregion,largelybecausethey
do not capture the quality of employment. Central Asia may have higher participation rates and lower
unemployment rates than the Southeast European countries, but few would argue that the quality of
employmentisbetter,orthatthereislessvulnerability.
Limitationsofstandardemploymentindicators
Suchstandardlabourmarketindicatorsaslabourforceparticipation,employment,andunemployment
ratescannotinthemselvescapturethefullextentofinequalitiesinthelabourmarketintheregion,forseveral
reasons.
First: the employment rate shows the share of the working-age population that is engaged in a
productiveactivity—irrespectiveofwhetherthisactivitycorrespondstofulltime,regular,formalanddecent
employment.Thisindicatordoesnotdistinguishbetweenthosewhowork“normal”orregularworkhourson
regular contracts, versus those on shorter and unstable work schedules. Nor does it indicate whether the
activity is in the formal or informal sector, and therefore whether the individuals in question have rights to
protection, a safe working environment, and to social insurance coverage. This indicator thus gives no
indicationofthequalityoftheemploymentenjoyedbydifferentsectionsoftheworkforce,andtheextentof
under-employment and low quality, low wage employment.14 A person working a 40-hour week as an
employeeofaformalsectorenterprisewillbecountedasemployed,inthesamewayassomebodyworkingin
atemporaryjobfor1-2hoursaday,orasaself-employedfarmer,workinginformallyonasmallplot.Togive
someexamples:
14
seeILO’sstandarddefinition,usedtoderiveemploymentindicatorsfromLabourForceSurveys.
32
•
•
Kazakhstan has the highest participation rate in the region (at just under 80% of the working age
population),butsome30%oftheemployedworkforceisself-employed,withthemajorityengagedin
smallscalelow-productivityagriculturalactivities.15
In Azerbaijan (the country with the second highest participation rate in the region), 37% of the
workforce(and44%ofthefemaleworkforce)isemployedinagriculture,whichaccountsforjustover
5% of GDP.16 This disparity results in low rates of labour productivity, and therefore low agricultural
incomes.
KyrgyzstanandTajikistanhavehighparticipationrates,butalsosomeofthelargestsharesofworking
poor and vulnerable employment (see below), as well as labour migrants in the region—suggesting
thatthenumberandqualityofemploymentopportunitiesareinsufficient.
Second:theunemploymentrateisusuallyseenasameasureofthelackofemploymentopportunities:
the proportion of people who do not have a job but are “actively” looking for one. This definition is also
problematic, particularly in those countries with low labour force participation rates. As one analyst puts it:
“most‘potentially’unemployedpersonseitherdonot‘actively’searchforemployment,fallinginthecategory
of‘discouragedworkers’,orseekoutalivingintheovercrowdedinformaleconomy,inastateoftendescribed
as‘disguisedunemployment’”.17Variationsintheeligibilitycriteriausedforregisteringthenatureandduration
of unemployed status may also affect incentives for job-seekers to register as unemployed or engage in job
search activities (i.e., stay in the labour force)—thereby influencing reported labour-market trends. In some
countries these criteria are more restrictive, in order to limit entitlements to unemployment and other
benefits;whereasinothercountriestheyaremoregenerous.(Insomecountriesoftheregion,lessthanone
percentoftheunemployedreceivebenefits.18)Seeninthislight,thequalityoftheunemploymentrateasan
indicatorforcapturingthoseaffectedbylackofemploymentopportunitiesisproblematic.
Third: assessments of labour market performance in the region are sometimes confused by data
quality questions for those indicators that are reported. Differences in reported unemployment rates
sometimes reflecting differing methodologies used to collect the underlying data (e.g., labour force surveys
versus registration data reported by employment offices). The region’s large circular and irregular migration
flowstendtodepressreportedlabourforceparticipationrates,asmigrantsworkingabroadmaybeincludedin
domestic populations (as per national census data) but not counted by labour force surveys as labour force
participantsoramongtheemployed.WhilenotuniquetothetransitionanddevelopingeconomiesofEurope,
Turkey,andCentralAsia,theselacunaemayfurthercomplicatetheinterpretationoflabourmarketdatainthe
region.
Measures to improve labour market statistics are crucial for more effective policy making. So are
efforts to ensure the regularity of published data on employment and/or migration flows, which allow
disaggregation by socio-economic (gender, age, rural/urban location, conflict impact) vulnerability criteria.
Innovative ways of combining quantitative and qualitative data collection and analytical methods are also
required in order to understand the inequalities within the employed, and the barriers facing those not
participatinginthelabourmarket.
Employmentqualityandthedisadvantagedwithinthelabourforce
While the ILO manual on Decent Work Concepts and Indicatorssets out 10 different types of work,
these can be generally be treated in terms of: (i) productive work delivering a fair income; (2) safety in the
•
15
ЭкономическаяактивностьнаселенияКазахстана2010-2014,KazakhstanStateStatisticalCommittee2015
http://www.worldbank.org/en/news/feature/2015/01/06/jobs-challenge-in-the-south-caucasus-azerbaijan;Lyubova/UNDP
Ghai,D.,2003,DecentWork:ConceptsandIndicators.InternationalLabourReview,142(2),113-145.
18
ILO,2014,WorldSocialProtectionReport2014/15.
16
17
33
workplace;and(3)accesstosocialprotectionforworkersandtheirfamilies.Lowqualityemploymentcanbe
“precarious” if it entails unfavourable or short-term contracts. Informal employment (much of which is
precarious) falls into two main categories: work in informal (unregistered) enterprises, and paid work in the
formalsector(registeredenterprise)butunderinformalconditions(withoutcorebenefits,workers’rights,ora
writtencontract).Whiletheformerismorecommoninruralareas(whereagriculturalworkisprominent),the
latterismorecommonlyfoundinurbanareas.
Figure4.Shareofinformalemployment,2013
70
60
Total
Outsideagriculture
Inagriculture
50
40
30
20
10
0
ALB
ARM
TUR
MDA
KAZ
MKD
SRB
Source:ILOSTAT,2015,countrieswithavailabledata.
Precariousandinformalemploymentintheregionisparticularlyprominentinagriculture:inmany
countries agriculture accounts for more than a third of the employed population (Figure 7). In Ukraine, for
example, two-thirds of informal employment take place in agriculture;19 in Armenia, this share has been
reportedascloseto100%.20Suchemploymentoftenconsistsoflow-productivityagriculturalself-employment
on small plots. Incomes from such work are highly unstable, due to poor harvests or fluctuating farm gate
prices. For these reasons, agriculture is often considered to be a buffer between employment and
unemployment or inactivity, or as “hidden unemployment”. For example, following the 2008-20099 financial
crisisinArmenia,agriculturewastheonlysectortoreportemploymentgrowth.21Employmentinagriculture
thereforeoftenmeetsthecriteriafornon-decentwork:lowandunstableincome,andnoorinsufficientsocial
protectioncoverage.
Ontheotherhand,formalsectoremploymentisnotalways“decent”,andcanalsobeassociatedwith
low wages and poverty risk. For example, while public sector workers in Kazakhstan may enjoy regular
contractsandaccesstosocialprotection,2009householdbudgetsurveydataindicatedthatupto50%ofthe
poorinsomeregionsofthecountrylivedinhouseholdsthatwereheadedbyapublicsectoremployee.22
Figure7.Employmentbysector
19
ILO,2013,DecentWorkCountryProfile:Ukraine.
ILO,2011,DecentWorkCountryProfile:Armenia.
ILO,2011,DecentWorkCountryProfile:Armenia.
22
ADB/UNDPPovertyAssessment,Astana2012,p19(basedonhouseholdbudgetsurveydatafor2009)
20
21
34
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
GEO TJK ALB AZE ARM KGZ MDA KAZ SRB BIH TUR MKD UKR BLR MNE KOS ECIS
2007 2009 2009 2014 2013 2013 2013 2013 2013 2012 2014 2014 2014 2013 2014 2012 2012
Agriculture
Industry
Service
Source:ILO,2015,KILM9thed;HDRO,2015.
TheILOdefines“vulnerableemployment”astheshareofcontributing(non-paid)familyworkersand
own-account(self-employed)workers.Theshareoftheworkingpoor,definedasthosewhoareemployedwith
per-capita incomes below international poverty lines can be another indicator of vulnerable and low quality
employment.AsshowninFigure8(left),overhalfoftheemployedintheSouthernCaucasusandCentralAsia
liveonincomesbelowtheILOupperthresholdusedtomeasuretheshareofworkingpoor(i.e.,thosewithper
capita incomes below PPP$4/day). Figure 8 (right) shows the share of the workforce who are self-employed
(own-accountworkers)andcontributingfamilyworkers.
Figure8.WorkingPoorandVulnerableEmployment
80
Workingpoor
70
70
AtPPP$2aday(2003–2012)
60
AtPPP$4aday(2008–2012)
50
Vulnerableemployment
(2008-2013)
60
50
40
40
30
30
20
20
0
TJK
KGZ
GEO
ARM
ALB
AZE
MDA
MKD
KAZ
TUR
BLR
MNE
SRB
UKR
BIH
ECIS
0
GEO
ALB
AZE
KGZ
TJK
TUR
MDA
ARM
KAZ
SRB
BIH
MKD
UKR
BLR
ECIS
10
10
35
Left:ShareofworkingpoorataPPPUS$2andPPPUS$4thresholds,forcountrieswheredataisavailable,
latestavailableyear.Sources:HDRO,2015;ILO,2015,KILM9thEd.Right:Shareofvulnerableemployment(sum
ofcontributingfamilyworkersandown-accountworkers).Source:HDRO,2015.
TheILOworkingpoorandvulnerableemploymentdatamayimplyareconsiderationoftheconclusions
suggested by the above-mentioned employment and labour force participation data. Whereas workers in
SoutheastEuropemayfacegreaterdifficultiesinfindingajobthanworkersintheSouthCaucasusandCentral
Asia,jobsinSoutheastEuropearemorelikelytobedecent,andlesslikelytobeprecarious,thaninthesesubregions.(ItshouldalsobenotedthatbothBelarusandUkraineperformquitewellintermsofthese“working
poor”and“vulnerableemployment”indicators.)Thecountrieswiththelargestsharesofemploymentinlowproductivityagriculturemayfacethemostproblemswiththequalityofemployment.
Labour productivity, inernational comparison 2012
Output per worker (constant 2005 international USD)
200 000
180 000
160 000
140 000
120 000
100 000
80 000
60 000
40 000
20 000
Lu
xe
m
bo
ur
N g
o
Sw rw
itz ay
D
ev
er
U
ni
el
te land
op
d
ed
St
Ec
a
G tes
on
om erm
an
ie
s
an y
C
M dE
en
id
dl U
tr.
e
&S
Ea
ou
s
Sl
th
ov t
-E
en
as
tE
Sl ia
C
ze
ov
ur
a
o p ch
e
R kia
e
(n
on pu
La
-E blic
tin
U
)&
Am
C
I
er
Es S
ic
a
to
an
ni
a
d
Tu
th
e
C rke
ar
y
ib
be
W an
O
R
Li LD
th
ua
ni
a
N Lat
v
or
th ia
A
R
fri
us
c
si Eas a
an
t
Fe Asi
de a
ra
t
R ion
om
an
FY
Bu ia
R
l
M gar
So
ia
ac
ed
ut
hon
Ea
ia
B
st
e
As
Ka lar
ia
u
an zak s
h
d
th sta
e
n
Tu Pa
c
rk
m ific
en
So ista
n
ut
h
Su
A
b- Aze sia
Sa
rb
ha
a
ra ijan
n
Af
ri
Ar ca
m
en
G ia
e
U org
zb
i
ek a
is
Ta tan
jik
is
ta
n
0
Source:ILOGlobalEmploymentTrends2014
However, alternate measures of labour market vulnerability may produce different results. For
example, data produced by the Eurostat-compatible Statistics on Income and Living Conditions (EU-SILC)
surveysthathavebeenadministeredinSerbiaandtheformerYugoslavRepublicofMacedoniahavefoundthat
those“atriskofpovertyorsocialexclusion”were28%and35%%oftheworkingpopulation,respectively.23
Whilethesefiguresarehigherthanthesharesofworkingpoorreportedforthesecountriesinfigure8,they
areclosetoILOestimatesofvulnerableemployment.
Public opinion surveys that gather information on individual perceptions of their employment status
may also provide insights into the quality and precariousness of employment. For example, Caucasus
Barometer data24 indicate that fewer people report having a job than what would be suggested by the
employment rates generated from national labour force survey data. In Armenia, these figures were 44%
(Caucasus Barometer) compared to 53% (labour force survey); in Azerbaijan they were 41% (Caucasus
Barometer)comparedto63%(labourforcesurvey);inGeorgiatheywere40%(CaucasusBarometer)compared
23
EUROSTAT,figuresfor2013.
CaucasusBarometer,2013.
24
36
to 56% (labour force survey). These disparities may reflect popular beliefs that informal engagement in
agricultureismoreacopingmechanismthanaformofemployment.
Figure9.BalkanBarometer2015
Howconﬁdentareyouinyourabilityto
keepyourjobinthecoming12months?
Howconﬁdentareyouinhavingajobin
2years'lme?
SRB
SRB
MNE
MNE
MKD
MKD
KOS
KOS
BIH
BIH
ALB
ALB
0%
25%
50%
75%
100%
0%
25%
50%
75%
100%
AsimilarsurveyintheWestBalkansalsofoundsome(albeitsmaller)disparitiesbetweenofficialand
self-reportedemploymentrates.25Manyrespondentsalsoreportedhighlevelsofuncertaintyregardingtheir
futureemploymentstatus(Figure9right).Thisstudyconcludedthatthecategoriesofemployment,informal
employment, unemployment, discouraged worker status, and inactivity should be viewed as points on a
continuumratherthanasdiscretecategories.Significantmovementbetweenthesecategoriesmayexist,and
theboundariesbetweenthemmaybeveryfluid.Still,therecanbelittledoubtthatmanyworkersintheregion
labourinconditionsofinformal,precarious,andvulnerableemployment.
These results are consistent with previous estimates of those vulnerable to falling into poverty, (i.e.,
those who are located not far above poverty thresholds). Slay et al (2015) use poverty thresholds of
PPP$5.40/day and PPP$10/day and POLCALNET data to show that the vast majority of the population in
Tajikistan, Kyrgyzstan, Georgia, Armenia and Moldova has been either poor or vulnerable to income poverty
over the last two decades. They also find that, despite improvements in poverty (measured using the
PPP$4.30/day threshold), there has been little progress in reducing the numbers of people vulnerable to
poverty (measured using the PPP$10/day threshold). Approximately 67 million people in the 15 countries
examined were found to be living in poverty, or vulnerable to poverty, using the PPP$4.30/day and the
PPP$5.40/day thresholds, respectively. (Slay et al, 2015, pp29-39). Assuming that labour income accounts
significant shares of vulnerable household incomes, low quality employment can be assumed to be affecting
thevulnerabilityriskofquitesizeablesharesofthepopulation.
Labourmigrants
25
WorldBank,2015,PromotingLaborMarketParticipationandSocialInclusioninEuropeandCentralAsia’sPoorestCountries.
37
Labour migration is another response to unequal access to employment opportunities in the region.
Migration varies in character (formal, informal), nature (seasonal, circular, permanent), and vis-à-vis state
borders(internalversusexternalmigration).Butformanyvulnerablehouseholds—especiallyinruralareas—
externalmigrationhasbecomeaprimarycopingstrategyinresponsetothelackofdecentworkopportunities.
Likewise,remittanceshavebecomeasubstituteforsocialprotectionsystemsformigrants’families.26
FigureXX—Sharesofpopulationoutsideofthecountryoforigin(2013)
45% 43%
40%
26% 25% 22%
21%
18%
17% 15%
13%
12%
12%
7%
7%
5%
4%
3%
UNDPcalculations,basedonUNDESAmigration(countryoforigin)andpopulationdata.
MigrationandremittanceflowsareparticularlyimportantinCentralAsia:asofmid-2015citizensfrom
Kyrgyzstan, Tajikistan, and Uzbekistan accounted for around one third of registered foreigners in Russia
(despite the absence of common borders) and for nearly three quarters of registered foreigners in
Kazakhstan.27 Whereas Russia is the primary destination for migrant workers from the Caucasus and Central
Asia, EU countries are the primary destination for migrants from the Western Balkans—more than 100,000
temporary residence permits are issued annually in EU countries for citizens from the Western Balkans.
Migration flows from Ukraine and Moldova are more evenly split. Remittance flows from Russia to these
countries are likewise substantial: four CIS countries (Tajikistan, Kyrgyzstan, Moldova, and Armenia) are
typicallyamongtheworld’slargestrecipientsofremittances(relativetoGDP);dataindicatemorethan90%of
theseflowscomefromRussia.28
Figure10.RemittancesinflowasshareofGDPin2013
26
InternationalOrganizationforMigration,2015,MigrationFactsandTrends:South-EasternEurope,EasternEuropeandCentralAsia.
Asaresultofhighlabourmigration,someoftheregion’seconomieshavebecomehighlyreliantonremittanceincomes:remittance
flowsareamongthehighestintheworld-Armenia,Kyrgyzstan,MoldovaandTajikistanareallamongtop10countriesforremittances
asaproportionofGDP
27
UNDP,2015,LabourMigration,Remittances,andHumanDevelopmentinCentralAsia.
28
UNDP,2015.Georgia,Kosovo,andsometimesUzbekistanareoftenintheworld’stop20bythisindicator.
38
60%
50%
Globalcountry
rankofshareof
remi_ancesinGDP
[CELLRANGE]
40%
[CELLRANGE]
30%
[CELLRANGE]
[CELLRANGE]
20%
[CELLRANGE]
[CELLRANGE]
10%
[CELLRANGE]
[CELLRANGE]
[CELLRANGE]
[CELLRANGE]
[CELLRANGE]
[CELLRANGE]
[CELLRANGE]
[CELLRANGE]
[CELLRANGE]
[CELLRANGE]
[CELLRANGE]
[CELLRANGE]
0%
ALB ARM AZE BLR BIH GEO KAZ KOS KGZ MKD MDA MNE SRB TJK TUR TKM UKR UZB
Source:WorldBankstaffcalculationshttp://go.worldbank.org/092X1CHHD0
Thesemigrationandremittanceflowscanclearlyhelptoreducepoverty.InKyrgyzstan,forexample,
householdbudgetsurveydataindicatethatremittancesyearlyreducethenumbersofpeoplelivingbelowthe
poverty line by 250,000-300,000. One study finds that labour migration is absorbing a third to one half of
Kosovo’snewlabourmarketentrantseveryyear.Kosovarmigrantsreportthatspendingtimeabroadimproves
theirprospectsforfindingdecentemploymentuponreturninghome.29However,theseflowsalsohavetheir
drawbacks. For one thing, they are strongly pro-cyclical, meaning that an economic downturn or tighter
migrationrulesinRussiacanleadtodramaticdeclinesinremittanceflows.Becausemuchofthismigrationis
highly irregular, workers often have no choice but to engage in more precarious forms of migratory
employment,withoutsocialprotection.Migrationmayalsoincreasedisparitiesbetweenremittance-receiving
and other households.30 And because migrants workers rarely contribute to social insurance funds in either
countriesoforiginordestination,migrationflowsarelikelytomeangrowingfuturepressuresonthefinancial
sustainabilityofold-agepensionsystems.
Youthemployment
Youngpeopleoftenhaveparticularlyunequallabourmarketstatusintheregion.Onlyonethirdofthe
youthpopulationisemployed,andarecentILOstudyfundsthatyouthunemploymentisexpectedtoincrease
inthenextfiveyears.31Labourforceparticipationratesamongtheyouthareoftenwellbelow50%;insome
countries, such as Moldova, it is as low as 20% (Figure 11 below). While low participation rates among the
youth do not necessarily imply poor labour market performance (many young people may choose to study
longer), low youth employment rates in the region are often accompanied by high rates of youth not in
education,employmentortraining(NEET).32Thelabourmarketimpactofthe2008-2009globalfinancialcrisis
29
UNDP,2014,KosovoHumanDevelopmentReport2014:Migrationasaforcefordevelopment.
SeeFalzoni,A.andSoldano,K.,2014,RemittancesandinequalityinEasternEuropeancountries;andPeterski,M.andJovanovic,B.,
2013,“DoRemittancesReducePovertyandInequalityintheWesternBalkans?EvidencefromMacedonia”.
31
ILO,2015,Globalemploymenttrendsforyouth2015:scalingupinvestmentsindecentjobsforyouth.
32
Mauro,J.A.andMitra,S.,2015,Understandingout-of-workandout-of-schoolyouthinEuropeandCentralAsia.Figuresfor15-24age
group,post-2009.
30
39
withintheregionwasmuchgreaterforyouththanforotherworkers.33Youthemploymentratescontinueto
declineorarestagnant.
The highest rates of youth unemployment are reported in Southeast Europe: in Bosnia and
Herzegovina, Serbia, and the former Yugoslav Republic of Macedonia more than half of young people are
unemployed. These are some of the highest youth unemployment rates reported globally. The youth labour
market situation is particularly severe in the former Yugoslav Republic of Macedonia, where a recent study
foundthatonlyaboutathirdofyouthsareeconomicallyactive(ofwhichmorethanone-halfisunemployed)—
and only a sixth are in regular employment.34 Three quarters of those seeking a job have been doing so for
more than a year. While many youth who are not in employment are studying, one-quarter of the youth
populationisbothout-of-schoolandout-of-work.
LabourmarketexclusionconcernsarealsoapparentintheSouthCaucasusandWesternCIScountries,
wherethelabourparticipationrateforyouthfellfrom55%in2000to37%in2013.Youthunemploymentrates
likewise range from around 30% (in Armenia and Georgia) to 14% (in Moldova)—slightly above the global
averageof13%.InallofthesecountiesexceptforMoldova,overhalfofthisyouthunemploymenthasalongtermcharacter(oneyearorlonger).35Migrationisalsoacommoncopingstrategy—particularlyinArmeniaand
Moldova (which typically rank in the world’s top 10 countries in terms of the ratio of remittance inflows to
GDP). Youth unemployment rates tend to be particularly high among the less-educated: in Ukraine, for
example,closeto70%ofyouthwithonlyprimaryeducationareunemployed.36
Figure5.Youthlabourforceparticipation(left),employmentrate(right)
50
Youthparmcipamon
40
Youthemployment
CentralAsia
CentralAsia
45
[SERIES
NAME]
40
35
30
[SERIES
NAME]
[SERIES
NAME]
35
30
South
Caucasus
and
Western
CIS
[SERIES
NAME]
25
33
ILO,2014,GlobalEmploymentTrends.Figurefor2013.ILOregionalestimatesincludeCroatiaandtheRussianFederation.
Elder,S.,Novkovska,B.,Krsteva,V.,2013,LabourmarkettransitionsofyoungwomenandmenintheformerYugoslavRepublicof
Macedonia.SeealsoMojsovska,S.andJaneska,V.,2014,EconomicandSocialImplicationsoftheYouthUnemploymentintheRepublic
ofMacedonia.JournalofSocialPolicy,7(11),11-44.
35
Elder,S.,Barcucci,V,Gurbuzer,Y.,Perardel,Y.andPrincipi,M.,2015,LabourmarkettransitionsofyoungwomenandmeninEastern
EuropeandCentralAsia.Basedonasurveyoffivecountriesoftheregion(Armenia,Moldova,FYRMacedonia,Ukraine,Kyrgyzstan),
th
andRussia.FigureforGeorgiaisobtainedfromILOKILM8 ed.
36
Ibid.
34
40
20
Youthunemployment
[SERIES
NAME]
15
[SERIES
NAME]
10
CentralAsia
5
Source:CalculationsbasedonILO,2015,KILM9thed.*exceptfor2012,averagesdonotincludeKosovounder
UNSCR1244.
Figure6.Youthlabourforcein2014
100%
100%
90%
90%
80%
80%
70%
70%
60%
50%
60%
40%
50%
30%
40%
20%
30%
10%
20%
CentralAsia
South
South
Eastern
Caucasus
andWestern Europe*
CIS
ECIS*
Employmentrate
Inacmve
10%
0%
ALB
ARM
AZE
BLR
BIH
GEO
KAZ
KOS*
KGZ
MKD
MDA
MNE
SRB
TJK
TUR
TKM
UKR
UZB
0%
NEET2009
NEET2013
NEET2014
Source:CalculationsbasedonILO,2015,KILM9thed.*AveragesdonotincludeKosovounderUNSCR1244;
Kosovodatafor2012.
Youthlabourforceparticipationdatamaybemisleadinginthatyoungpeoplemayimprovetheirjob
market prospects by remaining in school rather than actively seeking employment. However, in most of the
region, between a quarter and a third of youth populations are “not in employment, education, or training”
41
(NEET). NEET rates reported in the region range from around 40% (Armenia, 2009) to around 12% (Belarus),
withmanycountriesinthe25-30%range(Figure12).37
Inmostoftheregion,youngwomenaremorelikelytobeout-of-workandout-of-schoolthanyoung
men.ThisdifferenceisparticularlypronouncedinCentralAsia,wherethesub-regionalNEETaverageis37%for
womencomparedto19%formen.38AsimilarpatternisapparentinTurkey:whereasfemaleNEETratesreach
35%,foryoungmentheyarearound15%.Youngmenaremorelikelytobeunemployednon-students,while
youngwomentendtobeinactivenon-students:youngmenarelookingfor(regular)jobswhileyoungwomen
arenot.
While unemployment and NEET rates among youth in the region are worrisome, the quality of
employmentofthosewhoareworkingmaybeofequalconcern.Thedatathatareavailablesuggestthatyouth
are more likely to work informally, without written employment contracts (in Armenia this is the case for
almostoneinfouryouthemployees39),orascontributingworkersintheirfamilies’businesses—oftenwithout
social protection or regular remuneration. In Armenia and the former Yugoslav Republic of Macedonia, this
form of employment has been assessed at 17% and 22% of total youth employment, respectively.40 In
Kyrgyzstan, one study found that more than a third of young people in rural areas were working on family
farms(orinfamilybusinesses)sixyearsafterleavingeducation.41
Groupsatparticularriskoflabourmarketexclusion
UNDP’s 2011 Regional Human Development Report found that individuals living with disabilities, or
who are members of an ethnic minority, are at greater risk of economic and social exclusion. However,
whether these risks are translated into actual exclusion depends on how they interact with risk drivers (i.e.,
institutions,norms,policies)andcontexts(i.e.,suchlocalfactorsasresidenceinruralorurbanareas,aswellas
inmono-companytowns;accesstopublictransportandeconomicinfrastructure).Whenpoliciestopromote
inclusion(ortheinstitutionalcapacityneededtoimplementsuchpolicies)areabsent,labourmarketexclusion
can trigger vicious circles of social and economic exclusion—further depressing prospects for labour market
inclusion.42
Drivers of labour market exclusion may differ between countries. Barriers to employment can arise
fromsystemicexclusionbasedongender,ethnicity,orgeography.InMontenegro,geographyisanimportant
factoraslong-termunemploymentratesinthecountry’sruggednortherninterioraremorethandoublethose
reported in the southern coastal regions.43 The position of Roma—one of the region’s largest ethnic
minorities—isanillustrativeexampleofagroupfacinganelevatedrisksoflabourmarketexclusion,andfrom
there exclusion more broadly. Survey data indicate that Roma unemployment rates in 2011 in much of the
WesternBalkansreached50%—wellabovenotonlynationalunemploymentrates,butalsotheunemployment
ratesreportedfornon-RomacommunitieslocatedincloseproximitytoRomaneighbourhoodsorsettlements
(Figure 13). These survey data also show that joblessness rates (reflecting both the unemployed and
discouraged workers) reported for Roma women were well above those for Roma men—as well as for nonRomawomen(Figure14).44
37
Mauro,J.A.andMitra,S.,2015.Tajikistandatafrom2007and2009.
Ibid.Sub-regionalaveragesuseavailabledata;somecountrydataismissing.
39
YouthStudiesInstitute,Armenia,2013.
40
Elder,S.etal.2015.SWTSsurveys2012-2013.
41
ETF(2013),TransitionfromschooltoworkinKyrgyzstan.Resultsofthe2011/12transitionsurvey,Turin.
42
UNDP,2011,BeyondTransition:TowardsInclusiveSocieties.
43
ETF,2011,Long-TermUnemploymentinNorthernMontenegro:FromAnalysistoAction.
44
TheUNDP/WB/ECRegionalRomaSurvey,2011comparedsocio-economicpositionofRomacommunitieswithnon-Roma
communitieslivingincloseproximityinAlbania,BiH,Kosovo,Macedonia,Moldova,MontenegroandSerbia(andNewEUMember
States)
38
42
Figure13.RomaandNon-RomaUnemployment,andnationalunemploymentrate,2011
60%
54%
53%
49%
50%
44%
37%
40%
30%
30%
30%
27%
23%
20%
18%
20%
27%
10%
0%
ALB
BIH
MKD
Romaunemploymentrate
MDA
Non-Romaunemployment
MNE
SRB
Namonalunemploymentrate15+
Source:UNDP/WB/ECRegionalRomaSurvey2011.
UnequalemploymentoutcomesarealsoapparentinthewagesearnedbythoseRomawhodomanage
to find jobs—which in 2011 were found to be 45%-80% of the wages earned by non-Roma. Roma women’s
wageswerefoundtoamounttoonly45%ofthoseearnedbynon-Romamen,and54%ofthewagesearnedby
non-Romawomen.RomayoutharelikewiseatgreaterriskofexperiencinglabourmarketexclusionthannonRomayouth:the2011surveydataindicatethatunemploymentratesforRomayoutharewellabovenational
andnon-Romaunemploymentrates(Figure16).
Figure14.Romajoblessnessrates,maleandfemale
JoblessratesofmaleRomaandnonRoma(%,2011)
JoblessratesoffemaleRomaand
non-Roma(%,2011)
90
63
42
69
59
49
38
36
Roma
55
34
38
63
85
67
89
83
73
59
56
57
81
50
41
26
Non-Roma
Roma
Non-Roma
43
The2011surveydataalsosuggestthatdiscriminationcontributestoRomalabour-marketexclusion.As
thedatainFigure15show,differencesinjoblessnessbetweenRomaandnon-Romalivingincloseproximityto
Romasettlementsareminimalforpersonswithnoformaleducation.However,whilejoblessnessratesdecline
aseducationlevelsriseforbothRomaandnon-Roma,thesedeclinesaremuchsteeperfornon-Roma.Since
neithereducationlevelsnorlocationcanexplainthesedifferences,employerreticencetohire“theother”may
offeratleastapartialexplanation.
Figure15.JoblessnessratesbyeducationlevelsinSoutheast(andCentral)Europe,2011
Post-secondary
40
24
Uppersecondary
52
40
Lowersecondary
66
51
Primaryeducamon
69
61
Noformaleducamon
76
0
10
20
30
Roma
40
50
60
70
80
80
90
Non-Roma
Figure16.YouthRomaandNon-RomaUnemployment,andnationalyouthunemploymentrate,2011
80%
69%
70%
71%
56%
60%
56%
47%
43%
50%
40%
65%
61%
39%
37%
49%
50%
30%
20%
10%
0%
ALB
BIH
Romaunemploymentrate
MKD
MDA
Non-Romaunemployment
MNE
SRB
Namonalunemploymentrate15+
44
ThesesurveydataalsopointtoextensiveRomaengagementintheinformaleconomy.InAlbania,87%
ofworkingRomamen,and79%ofRomawomen,wereemployedinformallyin2011.Theintensityofinformal
employment was particularly strong in Bosnia and Herzegovina, as well as in Montenegro (Figure 17). As
formal-sector employment is a precondition for eligibility for many forms of social protection, Roma
households’extensiveengagementintheinformalsectormayalsoimplytheirexclusionfromsocialprotection
and social services. More than one half of Roma respondents participating in the 2011 survey reported
difficultiesinacquiringmedicines,comparedtooneinfournon-Romasurveyrespondents.45
Figure17.RatioofRomatonon-Romaprevalenceofinformalemployment
7
4.6
5.1
4.8
2.6
4.3
3.1
3.9
2.8
2.6
1.3 1.5
BiH
Macedonia Montenegro
Male
Serbia
Albania
Moldova
Female
Source:UNDP/WB/ECRegionalRomaSurvey2011.Involvementininformalemploymentisdefinedasthe
percentageofworkers(15-64)whoarenotpayinghealthorpensioncontributions.
The above analysis strongly suggests that significant numbers of workers in the region are facing
significant risks of labour market exclusion, which can easily translate into risks of social exclusion more
broadly. The problem is not just lack of jobs—it is a lack of decent jobs. These numbers correspond to
estimatesofthenumbersofindividualswhohaveincomesclosetopovertylines,andwhocouldeasilyslipinto
poverty—which,giventhepredominanceofprecariousandvulnerableemploymentintheregion,couldeasily
happen.Sincewagesarethemainsourceofincomeforpoorandvulnerablehouseholds,shiftingthepatternof
growthsothatthebenefitsaccruemostrobustlytolow-incomehouseholdsrequires,firstandforemost,the
acceleratedcreationofwell-payingincome-andemployment-generationopportunities.
Policiesandprogrammingforlabourmarketinclusion
Insomerespects,prospectsforgeneratingsignificantnumbersofdecentjobsintheregiondependon
factorsbeyondgovernmentcontrol.Theseincludeinparticulargrowthratesinkeyglobalandregionalexport
markets,thepricesofkeyexports,andthe like.However,governmentscan undertakemeasurestoincrease
employers’willingnesstohigherworkersintheformalsector.Theseinclude:
• Reducingsocial-securityandothertaxesonlabour.Theserevenuelossescanbeoffsetby:(i)higher
taxes on carbon-intensive and other environmentally unsustainable activities; (ii) reductions in tax
45
UNDP/Ivanov,A.,Kagin,J.,2014,Romapovertyfromahumandevelopmentperspective.
45
•
•
•
breaks or budget subsidies that accrue primarily to wealthy households; and (iii) more aggressive
measurestoreducethediversionofbudgetrevenuestotaxhavens.
Raisingtheprofileofemploymentpolicieswithinoverallpolicyframeworks.While“gettingtheoverall
growth framework right”, and improvements in business and commercial environments are clearly
necessaryconditionforemploymentgrowth,theyarenotsufficientconditions.Whole-of-government
approaches, in which responsibilities for implementing national employment strategies are clearly
assigned to all relevant government bodies needed instead. In practice, however, employment is
typically“embracedbyeveryone,butownedbynoone”.
Investmentsinstatecapacityareoftenneededinsuchareasaspublicemploymentservices,national
labour policy coordination structures, and platforms for dialogue and employment partnerships
between the private sector, government, and civil society partners (including labour unions). Public
employmentservicesinparticularhaveparticularlyimportantrolestoplayinaddressinglabourmarket
exclusion. Stronger regional- and local-level presences, better use of employment-relevant egovernanceplatforms,andstrongerabilitiestocoordinateandpartnerwithotherinstitutions—inthe
state (e.g., social service offices, vocational training centres), private sector (employers) and in civil
society(e.g.,NGOsrepresentingvulnerablegroups)—allthis(andmore)isneeded.
Be willing to address labour-market discrimination, particularly as concerns ethnic minorities (e.g.,
Roma) but also women. In some cases, improvements in institutional capacity among labour offices
and NGOs may be enough to redress deep-seated labour market exclusion. In other cases, however,
publicawarenesscampaignsandother,legalmeasurestotacklediscriminationmaybeneeded.
Socialprotectionandsocialinclusion
Inequalities in employment opportunities, and their links to social exclusion, have important
implications for social protection systems. The emergence of extensive informal employment has put
considerablefinancialstrainonpoorly-financedsocialprotectionsystems.Atthesametime,employmentand
social protection systems that were designed to work in near-full employment conditions have not been
flexibleenoughtomeettheneedsofthosemostatriskoflabourmarketorsocialexclusion.Thiscombination
hascommonlyledtothedesignandimplementationofsocialpolicyreformsthathavefocusedonreducingthe
sizeandcoverageofsocialassistancebenefits—despitethepaucityofdecentjobs.
When combined with labour market policies, social protection has the double role of: (i) promoting
decent employment (with social insurance coverage); and (ii) providing income support to those who find
themselves without employment. When social protection systems are ineffective, the loss or lack of
employmentcancreateviciouscyclesofexclusion.Theseinturncanbeaggravatedbythelackofreformsin,
and inadequate coordination between, public employment services and social protection agencies—with the
latter focusing on mediation, and the former on administration of benefits. Vocational education, labour
market, and social protection policies are often fragmented across different sectors, with inadequate
instrumentsforinter-departmentalcoordination.Thesignificanceoftheseproblemscanbefurthermagnified
bythefactthatlabourmarketdataandindicatorsarenotalways“fitforpurpose”(asexplainedabove).
Social protection can be understood in different ways. Here we refer to social insurance (pensions,
maternityleave,sickleave,invaliditypension)basedoncontributoryschemes;(ii)socialassistancebasedon
tax-financed support to the poor or vulnerable; (iii) locally provided social support services to households
(including for the elderly living alone, families with members who have disabilities); and (iv) active labour
marketmeasuresaimedathelpingtheunemployedpopulationfindajob.
46
In all the countries of the region except Turkey, broad social protection systems featuring both
contributory social insurance46 components and non-contributory47 components were in place prior to the
1990s.Combinedwithgenerallytightlabourmarketconditions,thesubsidiesforbasicgoodsandservicesthat
wereavailablepriortothe1990s,andextensivepublicinvestmentintheprovisionofhealth,education,and
othersocialandcommunalservices(someofwhichwereprovidedbythestate-orsociallyownedenterprises,
orpublicadministration,inwhichmostpeopleworked),thesesystemsprovidedhouseholdswithhighdegrees
ofeconomicsecurity—muchofwhichwascodifiedinlegal/constitutional“rights”to“free”health,education
andotherservices.However,thesesystemswerealsoquitebureaucraticinnature,andwerelesseffectivein
resolving problems of social exclusion that required local solutions, or could not otherwise be addressed by
regionaldevelopmentorpublicworksprogramming.
The economic transitions that took hold in the 1990s presented huge challenges for these social
protectionsystems.Theemergenceofextensivelabour-marketinformalityandirregularmigrationflows,often
combinedwithdemographictrendsthathaveincreasedthenumbersofpensionersrelativetotheworkforce,
have threatened the financial sustainability of contributory pension schemes and left growing numbers of
workersuncoveredbysocialinsurancesystems.Socialassistanceprogrammeshavebeenexpanded,inorderto
both address these challenges and compensate for reductions in labour market security and in subsidies for
basic goods and services. However, fiscal considerations, concerns about further weakening incentives for
labourforceparticipation,andtechnicaldifficultiesinsettingappropriateeligibilitycriteria(minimizingerrors
of inclusion and exclusion) have limited the scope and effectiveness of these programmes. Moreover, some
countrieshaveemphasizedtheprovisionofsocialassistanceto“deserving”socialgroups(e.g.,warveterans)
whosemembersmaynotnecessarilybeamongthemostpoororvulnerable.
TheWorldBank’sASPIREdatabasecanbeusedtoaddressquestionsaboutthesizeandnatureofthe
resulting gaps in social protection coverage. Data in Figure 18 indicate that both social insurance and social
assistanceprogrammesreducepovertyandinequality(Gini)indicatorsacrosstheregion.However(apartfrom
in Azerbaijan), social insurance (presumably pensions) has the greater impact—reflecting relatively large
benefitsizesandnumbersofbeneficiaries.However,thedatainFigure19indicatethatinmostcountries,less
thanquarterofallsocialprotectionbeneficiariesareinthepoorestquintile—suggestinglargecoveragegaps
amongthemostvulnerable.Moreover,non-contributorysocialassistancecoversonlyasmallproportionofthe
poorest 20%. Social protection systems in Kazakhstan, Kyrgyzstan, and Tajikistan seem to be particularly
lacking:incoverageofthepoorest,spending,andinpovertyandinequalityimpact.Bycontrast,Ukraineand
Belarus appear to achieve impressive reductions in poverty and inequality thanks to higher-than-average
spendingandgoodcoverageofthepoorestquintile.
Social protection in Central Asia. Coverage of both social insurance and social assistance in
Kazakhstan,Kyrgyzstan,andTajikistanisthemostlimitedintheregion—particularlyforthepoorestsectionof
thepopulation(Figure19).48Theimportanceofformallabourmarketstatusindeterminingeligibilityformany
forms of social insurance excludes significant portions of the labour force in the latter two countries, who
engage in irregular migration. Moreover, support for the so-called working poor (i.e., households with
employed adults) is almost total absent. Remittance inflows therefore serve as a substitute for formal social
protectionsystems(atleastinKyrgyzstanandTajikistan).However,thelabourmigrationthatgeneratesthese
inflowsalsoreducescontributionstosocialinsurancesystems,whichbothreducestheirfinancialsustainability
anddeprivesmigrantsofaccesstofuturepensionsandotherbenefits.SocialprotectionsystemsinTajikistan
and Kazakhstan appear to be quite ineffective in relieving poverty among the poorest, while Kyrgyzstan can
reportmoderatesuccessinthisrespect(Figure19).Tajikistaninparticularisfacingurgentchallengesofbetter
targetingsocialassistancetopoorfamiliesthatdonotreceiveremittances.49
46
Insuranceagainstrisksofunemployment,employmentinjury,disability,sickness,maternity,andoldage.
Socialassistanceoflastresort;socialpensions,somedisabilityrelatedallowance,childandbirthallowance,housingandutility
support.
48
ComparabledataforTurkmenistanandUzbekistanarenotavailable.
49
Amir,O.andBerry,A.,2013,ChallengesofTransitionEconomies:EconomicReforms,EmigrationandEmploymentinTajikistan.In:
UNDP,SocialProtection,GrowthandEmployment:EvidencefromIndia,Kenya,Malawi,MexicoandTajikistan,2013.
47
47
SocialprotectioninSoutheastEurope.WiththeexceptionofKosovo,socialprotectionsystemsinthis
sub-regionarepoorlytargeted,withthepoorestquintileoftenreceivinglessthan20%oftotalbenefits(Figure
19topleft).Thisresultsinpartfromthepresenceofcategoricalbenefitsthatarenotlinkedtoincomestatus.
InBosniaandHerzegovina,forexample,overhalfofallsocialprotectionspendingisallocatedtowarveterans;
highspendingoncategoricalbenefitsisalsopresentinAlbaniaandSerbia.
48
Figure18.ImpactofSocialProtectiononInequalityandPoverty50
50
Inthesescatterplots,countrypointsarecolor-codedbysub-region:SEE–orange;CA–green;SCandWCIS–blue.
49
Figure19.Socialprotectionforthepoorest20percentError!Bookmarknotdefined.
Source:WorldBankASPIREdatabase.NodataforMKD,UZBandTKM.ALB2012;ARM2013;AZE2008;BLR
2012;BIH2007;GEO2011;KAZ2010;KSV2011;KGZ2011;MDA2013;MNE2011SRB2010;TJK2011;TUR
2012;UKR2013.Adequacyisdefinedastheamountoftransfersreceivedbythepoorestquintiledividedby
thetotalincomeorconsumptionofthebeneficiariesinthatquintile.Beneficiaryincidenceisdefinedasthe
numberofbeneficiariesinthepoorestquintilerelativetothetotalnumberofbeneficiariesinthepopulation.
Bottom-left:AZEexcludedfromtrendlinecalculation
Throughout the Western Balkans, large numbers of elderly are not covered by any kind of pension
(Albania—whereold-agepensioncoverageisquasi-universal—isanotableexception51).InSerbia,uptoonethirdoftheover-65populationelderlymaynotbecoveredbyanykindofpension.52InKosovo,bycontrast,a
completere-designofthesocialprotectionsystemseemstohavecontributedtoimprovementsinpovertyand
inequality(Figure19Error!Referencesourcenotfound.top)—thanksinparttotheintroductionofasimple,
universalold-agepension.Eligibilityforunemploymentbenefitstendstobequitebroadinthissub-region,and
registering
and
receiving
unemployment
status
makes
further
benefits
available
51
EuropeanCommission,2009,SocialProtectionandSocialInclusionintheWesternBalkans:ASynthesisReport.
EuropeanCommission,2008,SocialProtectionandSocialInclusionintheRepublicofSerbia:ExecutiveSummary.
52
50
(child/maternity/welfare/veteran benefits, as well as health insurance and pensions). This is not the case in
Turkey, where only unemployment benefits are available for the registered unemployed. In the former
YugoslavRepublicofMacedonia,however,accesstounemploymentbenefitsisveryrestricted.InAlbania,as
well as in the former Yugoslav Republic of Macedonia, individual farmers cannot register for unemployment
benefits53—aparticularissueinAlbania,wheresome40%oftheworkforceisengagedinagriculture.Onthe
other hand, recipients of unemployment benefits in other countries may be involved in informal economic
activities. However, those unemployed for long periods of time are often ineligible for adequate support. A
recent European Commission study which included fYR Macedonia, Serbia, and Turkey found that most
servicesaddressingthelong-termunemployedarenotparticularlyeffective.54
Social protection in the South Caucasus and Western CIS. Wide variance in social protection
performanceisapparentinthesecountries.Ontheonehand,inUkraineandBelarusbothsocialassistanceand
socialinsuranceprogrammesenjoyrelativelygoodcoverageofthosemostinneed(Figure19bottom),andare
overall quite successful in both reducing poverty and inequality (Figure 19 top). However, the replacement
ratesofsocialinsurance(contributory)programmes,andinparticularofunemploymentinsurance,arelow—
reducing their effectiveness. (This is particularly the case in Moldova, where the entitlement period is very
short and where contributions are limited by high rates of labour emigration.) While poverty in Belarus and
Ukraine is far less widespread than in other parts of the region, it is more concentrated in certain social
groups—suchasthosewithoutformalincomes,orpeoplelivingwithdisability.
IntheSouthCaucasuscountries,bycontrast,socialinsurancesystems(i.e.,pensions)appeartohave
generous coverage among the poorest—but social assistance is much less effective. Oil-rich Azerbaijan is an
exception,whereover90%ofthepoorestreceivesomesortofbenefit(Figure19topright),andwheresocial
protection makes up over 80% of the poorest quintile beneficiaries’ consumption (Figure 19 top left). Many
structuralshortcomingsareapparentinthesesystems—inGeorgia,forexample,unemploymentbenefitsare
non-existent.WhileunemploymentbenefitsexistintheotherSouthCaucasuscountries,theirlevelsarequite
low.Moreover,becausethesearefinancedonacontributorybasis,workersintheinformalsectorwhodonot
contributetotheunemploymentcompensationfundareunabletoclaimbenefitsfromit.
Whilesocialprotectionsystemsacrosstheregionhavegenerallyretainedtheircoverageandscopeon
paper(seeAnnex1),largegapsincoverageandqualityhaveemergedinpractice.Inequalitiesinlabourmarket
access are in this way extended and amplified as social exclusion. Limitations on state abilities to deliver on
social protection obligations are often offset via such coping mechanisms such as remittances, precarious
employment, and family support. This “informalized” social protection55 can also lead to large out-of-pocket
spendingforrequiredservices,furtherdiminishingequalityofopportunitiesandoutcomesandincreasingrisk
ofsocialexclusion.
Socialprotectionandlabourmarketpolicies—Gettingthelinksright
Extensivelabourmarketinformalityintheregionexcludessignificantnumbersofworkersfromsocial
protection systems and threatens their financial sustainability. Many governments have sought to address
thesechallengesby(further)raisingalreadyhighsocialsecuritytaxes:arecentWorldBankreport56notesthat,
intheWesternBalkans,socialsecurityleviesandothertaxesinlabouraccountforover30%ofwages.Thistax
wedgeisevenlargerforlow-wageandpart-timeworkers.However,thesehightaxesthemselvesplayamajor
53
ETF,2011,ActivatingtheUnemployed:OptimisingActivationPoliciesintheWesternBalkansandTurkey.EuropeanTraining
Foundation.
54
Bouget,D.,Frazer,H.,andMarlier,E.withPeña-Casas,R.andVanhercke,B.,2015,Integratedsupportforthelong-termunemployed:
Astudyofnationalpolicies.EuropeanCommission.
55
Drahokoupil,J.andMyant,M.(2009)VarietiesofCapitalism,VarietiesofVulnerabilities:financialcrisisanditsimpactonwelfare
statesinEasternEuroeandtheCommonwealthofIndependentStates.HistoricalSocialResearch35,266-298.
56
Arias,O.,andSánchez-Pármo,C.,2014.
51
roleindrivingemployment,andeconomicactivity,intotheinformalsector.Effortstobreakthisviciouscycle
mustthereforefocusonreducingthistaxburdenonlabour.
Box3.DoesitpaytoformalizeinformalemploymentinSerbia?
InSerbia,theformalizationofinformalemploymentmeansthatsocialsecuritycontributions(e.g.,tothe
pensionandhealthfunds,unemploymentinsurancefund)willhavetobepaidbybothworkersand
employers.Itmeansthatincometaxwillbewithheldfromworker’spaychecks.Giventhehightaxwedge
andlowprogressivityoflabourtaxationinSerbia,theeffectivetaxrateishigh.Furthermore,unlikeinthe
formerYugoslavRepublicofMacedoniaandBosniaandHerzegovina,socialsecuritycontributionsinSerbia
donotvarywithhoursworked,sopart-timeworkersaredisproportionatelyaffected.Thiscantheoretically
resultinnegativenetwagesforpart-timeworkers.Ifitleadstothereportingofincomesthatwould
otherwisebehidden,formalizationcanalsomeanlosingsocialassistancepaymentsthatarelinkedtoformal
reportedincomelevels.57
Asaresult,aSerbianworkerwhotakesalow-wageformaljobandloseseligibilityforunemployment
benefitscansee90%ofthenominalincreaseinincomeduetoengaginginformalemploymentbelost,due
tohighertaxesandunemploymentbenefitsforegone.58FormanyworkingpeopleinSerbia,formal
employmentsimplydoesnotpay.Onestudyfoundthatmorethan40%ofinformalworkersinSerbiawere
earninglessthanthelegalminimumwagein2008.59Despitethelowearningsofinformalwork,theirnet
incomewasstillhigherthanwhatwasavailableinformaljobs.
Forthesereasons,manyworkersinSerbiachoosetoremaininprecariousinformalemployment.Facedwith
similarcircumstancesandincentives,thefactthatmanyworkersinothercountriesoftheregionbehaveina
similarmannershouldnotcomeasasurprise.
This vicious cycle also suggests that concerns that social assistance may reduce incentives for labour
forceparticipationmaybeexaggerated.Instead,itistheanticipatedlossofunemployment(orother)benefits,
combinedwiththehightaxationoflabourintheformalsector,thatreducesincentivesforworkerstoabandon
informallabour(seeBox3).Anumberofstudiesfromtheregionbearthisout.InArmenia,forexample,the
receipt of social assistance seems not to have an impact on participation in the formal labour force.60 In
Tajikistan, social assistance has been found to have a positive effect on employment rates in female-headed
households—suggesting that such transfer payments can actually promote social inclusion by strengthening
incentivesforformallabourforceparticipation.61
Whenalignedwithwelldesignedactivelabourmarketpolicies,socialprotectionsystemscanfurther
strengthen incentives for formal labour force participation, reducing social exclusion.62 Other labour marketrelated social services that can promote social inclusion include the care of children, the elderly, and others
whoareunabletofullytakecareofthemselves.Unfortunately,publicprovisionofchildcareisnotavailablein
some countries of the region, namely Azerbaijan, Kazakhstan, Tajikistan and Turkey.63 Support for part-time
57
Koettl,J.,2013,DoesFormalWorkPayinSerbia?TheRoleofLaborTaxesandSocialbenefitDesigninProvidingDisincentivesfor
FormalWork.In:C.RuggeriLaderchiandS.Savastano(eds.)PovertyandExclusionintheWesternBalkans.
58
Arias,O.,andSánchez-Pármo,C.,2014.
59
Comparisonof2008LFSandHBSinKoettl,J.,2013.
60
WorldBank,2011,Armenia:SocialAssistanceProgramsandWorkDisincentives.
61
WorldBank,forthcoming,Ethnicity,ConflictandDevelopmentOutcomesintheECARegion.CitedinArias,O.andSánchez-Pármo,C.,
2014
62
SeeKuddo,A.,2009,EmploymentServicesandActiveLaborMarketProgramsinEasternEuropeanandCentralAsianCountries.
WorldBank,SocialProtectionDiscussionPaperNo.0918.SeealsoLehmann,H.andMurayev,A.,2010,LaborMarketInstitutionsand
LaborMarketPerformance:WhatCanWeLearnfromTransitionCountries?WorkingPaper714,DipartimentoScienzeEconomiche,
UniversitádiBologna.
63
WorldBank,2012,WomenBusinessandtheLawDatabasehttp://wbl.worldbank.org/.
52
formal employment is other means through which formal employment can be increased, particularly for
women and youth whose other obligations do not allow for full-time labour force participation. This means
adjustingthetaxburdenorsocialcontributionstohoursworked,andallowingforaflexiblerangeofpart-time
contractsintermsofduration.
Ofcourse,manyofthesemeasureshavefiscalimplications.However,alternativesourcesforfunding
social services and labour market policies that can reduce social exclusion, and which can offset revenues
lossesfromreductionsintaxesonlabour,exist.Thesemayinclude:
• higher taxes on environmentally unsustainable activities (e.g., the use of high-sulphur coal to
generateelectricity);
• reductionsinbudgetsubsidiesthatsupportenvironmentallyunsustainableactivities(e.g.,fossilfuel
productionandconsumptionsubsidies),orwhichaccrueprimarilytothemiddleclass(e.g.,categorical
benefitsforwarveterans)orthewealthy;
• moreaggressivemeasurestoreducethediversionofbudgetrevenuestotaxhavens;64and
• morerobustdirectionofbudgetaryprocurementandcontractingresourcestocompanies(e.g.,social
enterprises)thatexplicitlypromotesocialinclusion.
64
ThemostrecentreportoftheGlobalFinancialInstituteonIllicitFinancialFlowsfromDevelopingCountries:2004-2013findsthatthe
countriesintheregiononaveragelose$65billionannuallyinillicitfinancialflows.Iffivepercentoftheseflowscouldbecapturedas
taxes,thiswouldgenerateanadditional$3.2billioninbudgetrevenues.(Formore,seeDevKarandJosephSpanjers,IllicitFinancial
FlowsfromDevelopingCountries:2004-2013,GlobalFinancialInstitute,December2015.)
53

UNDP Inequalities RHDR zero draft (02.02.16)

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